oEPA
United States
Environmental Protection
Agency
Office of Research and
Development
Washington DC 20460
EPA/630/R-94/004A
September 1993
External Review Draft
Draft Ecological
Risk Assessment
Issue Papers
RISK ASSESSMENT FORUM
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CONTENTS
ECOLOGICAL SIGNIFICANCE 2-1
CONCEPTUAL MODEL DEVELOPMENT 3-1
CHARACTERIZATION OF EXPOSURE 4-1
EFFECTS CHARACTERIZATION 5-1
BIOLOGICAL STRESSORS 6-1
ECOLOGICAL RECOVERY 7-1
UNCERTAINTY IN ECOLOGICAL RISK ASSESSMENT 8-1
RISK INTEGRATION METHODS 9-1
Printed on Recycled Paper
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Peer Review
DRAFT
September 1993
Issue Paper
on
ECOLOGICAL SIGNIFICANCE
Mark Harwell
Rosenstiel School of Marine and Atmospheric Science
University of Miami
Miami, FL
Bryan Norton
School of Public Policy
Georgia Institute of Technology
Atlanta, GA
William Cooper
Institute for Environmental Toxicology
Michigan State University
East Lansing, MI
John Gentile
Risk Assessment Forum
Environmental Research Laboratory
U.S. Environmental Protection Agency
Narragansett, RI
Prepared for
Risk Assessment Forum
U.S. Environmental Protection Agency
U.S. Environmental Protection Agency
Region 5, Library (PL-12J)
77 West Jackson Boulevard, 12th Floor
Chicago, IL 60604-3590
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CONTENTS
1. INTRODUCTION 2-6
2. PROBLEM FORMULATION 2-11
2.1. Interaction with the Risk Manager 2-11
2.1.1. Science and Public Interaction: A Process 2-12
2.2. The Valuation of Natural Systems 2-13
2.2.1. Ecological Criteria and Values 2-14
2.2.2. Ecological Sustainability 2-15
2.3. Criteria for Evaluating the Significance of Ecological Change 2-17
2.4. The Role of "Significance" Criteria in Problem Formulation 2-20
3. ANALYSIS 2-22
3.1 Characteristics of the Stress Regime 2-22
3.1.1. Dynamics and Variability of Natural Stress 2-22
3.1.2. Anthropogenic Stress 2-23
3.13. Characteristics of Stress 2-25
3.1.3.1. Extent, Intensity, Frequency, and Duration 2-25
3.1.3.2. Relationship to Recovery 2-27
3.1.3.3. Indirect Effects ' 2-27
3.2. Characterization of Ecological Effects 2-28
3.2.1. The Nature of Ecological Change 2-28
3.2.1.1. Ecological Components of Change and Ecological Endpoints .... 2-29
3.2.1.2. Magnitude of Change 2-33
3.2.1.3. Spatial Extent of Change 2-34
3.2.1.4. Timeframe for Change 2-34
3.2.1.5. Redundancy 2-35
3.2.2. Recovery .' 2-36
3.2.3. Natural Variability 2-37
4. RISK CHARACTERIZATION 2-39
4.1. Societal Values 2-39
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4.2. Risk Integration 2-41
4.2.1. Assessment and Measurement Endpoints 2-42
4.2.2. Uncertainty 2-42
4.2.3. Cumulative Effects 2-44
4.2.4. Recovery 2-44
5. ECOLOGICAL SIGNIFICANCE INPUTS TO DECISION-MAKING 2-46
5.1. Weight of Evidence in Decision-Making 2-46
5.2. Decision-Making in the Presence of Uncertainties 2-48
5.3. Adaptive Management 2-48
5.4. Research in Support of Decision-Making 2-49
6. REFERENCES 2-51
APPENDIX ASCERTAINING PUBLIC VALUES AFFECTING ECOLOGICAL
SIGNIFICANCE 2-54
Introduction 2-54
Techniques of Mainstream Economic Analysis 2-55
Measures of Public Values 2-56
Toward a More "Ecological" Economics 2-58
Resolution of Conflicting Values 2-64
Lessons Learned from the Endangered Species Act 2-65
Toward a More Dynamic and Comprehensive Valuation Process 2-70
Risk and Uncertainty in Protecting Public Values 2-74
Appendix References 2-76
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LIST OF FIGURES
Figure 1. The Environmental Policy Process 2-78
Figure 2. Role of Ecology in Natural and "Modern" Decision-Making Hierarchies 2-79
Figure 3. Risk Typology: Severity and Reversibility 2-80
Figure 4. The Application of "Significance" Principles and Criteria
in Problem Formulation 2-81
Figure 5. Relationship among Net Reproductive Rate (Ro), Its Components (Ix and dx),
and the Intensity of Stress 2-82
Figure 6. Relationships of Maturation and Fecundity on Ro 2-83
Figure 7. Dose-Response Curves Resulting from Combining Delayed Maturation
and Reduced Fecundity 2-84
Figure 8. Successional Stages of Probable Seres for Each Habitat Type Commonly
Found on Loamy Soils 2-85
Figure 9. Stream Classification and Community Structure 2-86
Figure 10. Major Features of Biogeochemical Cycles for Calcium and Other Elements .... 2-87
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LIST OF TABLES
Table 1. The Direct and Indirect Benefits from Wetlands That Must Be Replaced
in Volume, Space, and Kind for Economic Valuation of Natural Systems 2-88
Table 2. Equivalent Concepts in Ecology and Economics for Use
in the Valuation of Natural Systems 2-89
Table 3. Issues Relevant to Integrating Economics, Ecology, and Values 2-90
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1. INTRODUCTION
The Framework for Ecological Risk Assessment (EPA, 1992) provides a construct in which
the risks to the environment from a human activity can be assessed. The framework is
appropriate for assessing potential or anticipated environmental risks (e.g., for comparing the
environmental risks from alternate management policies) as well as for existing environmental
problems (e.g, distinguishing alternate causes of observed conditions). The central objective of
ecological risk assessment is to gain an improved understanding of the magnitude and likelihood
of adverse environmental responses to human activities—explicitly taking into consideration
uncertainties and recovery potential—in order to make informed decisions based on the best
available scientific data and knowledge. A critical element in the risk assessment process calls
for distinguishing environmental responses that matter from those that do not; that is, making a
determination of the ecological significance of the risk.
There is no intrinsic ecological threshold for establishment of ecological significance,
although many ecological issues are germane. Further, what is significant and what is acceptable
can only be determined through decision-making that takes place in the context of human values.
By addressing a variety of ecological and societal issues that relate to the determination of
ecological significance, this chapter provides the societal context in which ecological risk
assessments must be performed. Because there are no simple formulas to follow, guidelines for
risk assessors must present the types of issues to be addressed as well as a road map showing
how to traverse these complex issues to reach a scientifically defensible and environmentally
responsible decision.
The issue of ecological significance is fundamental to the entire ecological risk assessment
process. When gauging significance, the assessor must identify risks that merit attention from all
possible ecological changes that could be associated with some human activity. The basis for the
emphasis on significance is apparent, even if the specific elements are quite complicated:
• virtually all components and processes in the environment exhibit natural
variability continuously and on many time scales;
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• virtually any human activity will result in a change to some component or process
in the environment although only anthropogenic changes that can be distinguished
from natural variability are appropriate for consideration;
• only a small subset of detectable anthropogenic changes actually matter to the
structure, functioning, or overall health of a particular ecosystem;
• defining the criteria for such a subset (i.e., determining what matters) is in part an
ecological question involving basic issues relating to stress ecology and in part a
societal question involving values and perceptions.
Defining what is ecologically significant partially involves the judgment of society-at-large
expressed through risk management, legislation/regulation, or some other mechanism. Consider,
for instance, that human-managed or -dominated ecosystems may range from near-natural or
pristine conditions (essentially as they existed in prehuman times) to the conditions in traditional
zoos, where small segments of habitat or populations are preserved for public viewing (Norton,
1991). Between these two extremes lies a continuum of possible ecological states that could exist
under particular management regimes; for example, modem zoological parks with a diversity
comparable to the natural environment; a monoculture crop or tree plantation; a forest
maintained for periodic clear-cutting; a national park of biome remnants, such as tall grass
prairie; and a wilderness area with rigid and exclusive management policies. When society
assigns an appropriate use for a given landscape, it is highly determinative for defining ecological
significance, even though the societal decision may not be explicit. The decision may imply many
preferences concerning, for example, uniqueness of the habitat or species, recreational or
aesthetic value, economic utility, or cost of restoration and management. Other aspects may
relate to the legal and institutional framework in which such societal decisions are made and
implemented, including the ownership and historical usage or preservation of the system,
previous experiences with environmental catastrophes or successes, and even the personalities
and priorities of individuals leading the dialogue from various perspectives. Overlaid on this is
the potential for distinct shifts in societal preferences; for example, when a swamp that had been
deliberately drained for agriculture or flood-control objectives is subsequently recognized as a
valuable natural wetland (table 1).
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The second major aspect of ecological significance is ecological, requiring that
determinations about some issues are made based on the nature of the ecosystem and its
response/recovery characteristics with respect to particular stresses. Ecological issues include the
nature of the stress and the ecosystem's experience with the stress. For instance, although fire
itself is not a stress to a fire-adapted ecosystem, such as a grassland, the absence of fire can
become a stress. In contrast, fire could be catastrophic for an ecosystem that is not adapted to
such a stressor because the system only recently became exposed to it through human activities
(e.g., tropical rain forests burned for land clearing and later abandoned). It is important for the
risk assessor to recognize that each ecosystem type has a different set of stresses to which it is
vulnerable or to which it is indifferent. Similarly, each stress type may affect particular
ecosystems differently. Thus the approach taken in determining ecological significance depends
on a variety of factors, including:
• the type of ecosystem;
• the characteristics used to assess the health of the ecosystem;
• time and space scales operating simultaneously on the ecosystem;
» natural and anthropogenic stresses in the ecosystem; and
• ways a stress or combination of stresses may be imposed on the ecosystem.
Moreover, because data bases are limited concerning natural variability, ecosystem stress
responses, and recovery processes, risk assessment cannot be made simple, only simplistic.
Nonetheless, many aspects of stress ecology can provide guidance that facilitates reasonable
judgment in the ecological risk assessment and management process.
The third aspect to consider is the distinction between ecological and statistical
significance. In scientific discourse, the term significance typically is assumed to mean the
statistical confidence level at which a hypothesis is rejected. This statistical significance is based
on the number of data points, the nature of their distribution, and the variability in the data.
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Statistical significance is not necessarily the same as ecological significance, which we
define as changes in an ecosystem that are significant in terms of its structure, functioning, or
health. A statistically significant change may not be ecologically important. For example, while
tests of the species composition of the microbial communities of two ecosystems might show
statistically significant differences, if redundancy in the functions performed by the species results
in precisely the same ecological condition, then the differences are not ecologically significant.
Conversely, a determination that something is ecologically significant may not necessarily require
that statistical significance be demonstrated.
The ecological risk assessment process is multilayered, with many different types of
considerations incorporated into the decision-making process; these include non-ecological
factors, such as economics, and decisions such as whether to use a weight-of-evidence approach.
Relying on stringent statistical tests is not compatible with this flexible paradigm. Moreover,
decisions often will have to be made when the data are few or too noisy for a specified statistical
criterion of confidence to be met. For example, based on a wealth of information, it is clear that
major changes in the global climate will result in ecological consequences that are highly
significant; yet, given natural climatic and environmental variability, it would be unreasonable to
wait for statistical proof at the 95 percent confidence level before making policy decisions.
In this chapter, ecological significance often does not necessarily equate with statistical
significance. Rather, the term ecological significance is generally used to involve the distinctions
that must be made for determining (1) whether a change detected or projected in the ecological
system of concern is a change of importance to the structure, function, or health of the system;
and (2) whether such a change in the ecological system is of sufficient type, intensity, extent, or
duration to be important to society. Only if both conditions are met should potential changes be
regarded as ecologically significant.
The issues in this chapter are presented initially as a risk assessor would encounter them
in the problem formulation phase. Since the ecological properties of concern and the criteria
and standards for defining ecological health are not universal and obvious, the risk assessor must
determine ecological assessment endpoints against which to judge the health of ecosystems and
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the acceptability of change. That is, the risk assessor must decide what is an ecologically
significant departure from the desired ecological state.
The central themes addressed in this chapter include:
the importance of temporal, spatial, and organizational scales (e.g., population
versus ecosystem or landscape levels);
the importance of selecting the appropriate suite of ecological endpoints to
evaluate an ecologically significant change;
the concept of relative risk, in which the intensity, spatial extent, recovery
potential, and time to recovery are folded into a determination of the relative
importance of an ecological risk; and
the concept of ecological sustainability, in which a risk assessment is performed in
the context of the long-terra survival of the ecological system.
An assessment of ecological significance must explicitly take into consideration the
potential for the ecosystem at issue to be irreversibly altered causing a reduction in future
biological diversity. Because ecological timeframes go well beyond the life span of a single
generation in human terms, the intergenerational aspect of ecological significance cannot
be ignored.
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2. PROBLEM FORMULATION
2.1. Interaction with the Risk Manager
The problem of ecological significance must be addressed at an early stage of the risk
assessment/risk management process, beginning in the problem formulation stage. Unlike
problems in human health risk assessment, assessment of ecological risk is not guided by widely
accepted and easily identifiable societal values, such as maximizing individual welfare or reducing
risk of disease and mortality. Identifying ecological values must be a part of the problem
formulation component itself because observable changes in ecological systems often are not
immediately classifiable as positive or negative. As a result, judgments must be made about
whether change is significant (i.e., part of a trend, rather than a stage of a cycle) and whether the
change will affect some socially valued commodity or state. For example, when it was noticed in
the 1960s that the waters of Chesapeake Bay were becoming more turbid, there was an
immediate reaction from some individuals claiming an aesthetic loss and a few others claiming an
economic loss (e.g., crab dippers); later it became apparent that the gradual changes were of
much less consequence to recreational boaters. Only after years of discussion, scientific research,
and experimental modeling was it determined that the trend toward turbidity was an ecologically
significant change in the organization of the bay's ecosystem, probably caused by nutrient loading
and the decline of filter-feeding oyster populations. It also took years for the change in water
clarity to constitute a public problem of sufficient magnitude to justify a major policy initiative,
titled "Save the Bay." As a result of this complex interaction of science, public debate, and value
articulation, the Chesapeake Bay Regional Council established a goal for reducing nutrient
loading into the bay's ecosystem.
General agreement that the environmental problem should be a high priority did not
emerge separately from the determination of management goals or from the modeling of the
system under management. Science identifies the issues we face, but values determine whether
we think the issues are a problem. To say that a certain change in an ecosystem has significance
is to say that it warrants attention, and even this is a value judgment based in the common sense
rule of "better safe than sorry.11 This approach recommends that data be gathered and
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interpreted on any important changes in the environment and that due caution be used in
generating risks of irreversible change that may have strong negative impacts on our lives or the
lives of future generations. Throughout the problem formulation process, someone must decide
what changes are sufficiently important to monitor, when to recommend caution, and when to
allow changes to run their course. The process requires that we determine whether changes are
"healthy" or consistent with the integrity of the system.
Ecological management is only vaguely analogous to health management since there is no
clearly defined "patient" that experiences symptoms and seeks care. The description of some
changes as insignificant and of trends across a landscape as healthy or maintaining integrity is
guided by particularly diffuse values that are experienced and expressed on quite different scales.
Some of these changes are experienced as affecting individual welfare, but others (e.g., protecting
biological diversity for future generations) are expressed on an intergenerational scale and have a
communal aspect. Assessing the scale at which to address a problem and whether trends are
sufficiently expansive to threaten the health or integrity of an ecological system is a difficult
undertaking. Yet, for the concept of risk assessment to encompass ecosystems, significance must
be considered an important criterion in determining which trends to monitor and where to
encourage development.
2.1.1. Science and Public Interaction: A Process
Environmental decisions involve both societal values and science, much as do a
physician's judgments regarding whether a particular patient is healthy or ill. This analogy
between environmental management and health management can be instructive. For example,
nobody would suggest that a definition of public health could be articulated without the general
participation of scientists, physicians, and the public. Judgments about ecological significance
and health call for the same degree of public and professional interaction. While the concepts of
health and integrity are both normative and scientific, they are primarily public policy terms
(Costanza et al., 1992). Likewise, by publicly debating the concept of ecosystem health, society
defines what and how much environmental change is considered a problem. This conceptualizing
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process requires a blend of science, value articulation, and analysis. While concepts should be
based on accepted scientific theories whenever possible, it must be remembered that such
theories are anything but pure science.
Figure 1 illustrates the interaction of science, public discourse, and values identification in
discussions of the scale at which to formulate a problem and measure success (Norton and
Ulanowicz, 1992). Decisions about the scale at which to model and describe a problem, to
define human impacts, and to formulate goals operate as a point of convergence for societal
values, policy, and science. In the case of Chesapeake Bay, formulation of the problem as one
concerning water quality and identification of the stressor as nutrient loading from point and
nonpoint sources were inseparable from a recognition that the ecosystem must be modeled at the
scale of the entire watershed (Horton, 1987). Thus policy had to be established at the
multistate/regional level. This case illustrates the importance of temporal and spatial scale as
elements in decisions regarding ecological significance.
Since nature can be modeled at many scales and on many levels, the interaction among
scientists, the public, and managers must inform the choice of scales at which ecological
problems should be modeled. A prerequisite for decisive and effective policy-making is
consensus among scientists and managers—with input from the public—regarding problem
formulation and management goals. Indeed policy-makers must ask such questions as, Is
ecological significance an issue in all risk assessments? and, What criteria can be used to make
such a determination?
2.2. The Valuation of Natural Systems
All societies value nature for the goods and services it provides. Most societies also find
religious or spiritual value in the natural world; indeed, all of the world's major religious
traditions, and many minor ones, explicitly recognize that the right to use natural resources
carries with it an obligation to protect those resources for the future (Brown-Weiss, 1989). Such
values can be difficult to separate into distinct categories. Thus, for example, when early
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environmentalists advocated setting aside land as national parks and other types of preserves,
they did not distinguish between "use" and "nonuse" values and then assign them societal values
as do modern economists. Rather, they assumed that protecting certain lands would be the
"right thing" to do and proceeded to act on available opportunities. Concerning natural systems
then, it is best to think of societal values in terms of a shared continuum of emphasis—
sometimes weighted toward use, sometimes toward longer-term spiritual and communal
values—with different points on the continuum emphasized in different situations
(Norton, 1991).
2.2.1. Ecological Criteria and Values
The integration of ecological criteria with economic criteria and societal values is fairly
straightforward if one assumes that sustainability and survivorship are synonymous (table 2). For
this to be true, however, one must assume a time domain of many generations (i.e.,
intergenerational equity) and a social structure that does not elevate the individual's rights and
privileges above those of society. The relevant issues (table 3) are conceptually well understood,
and the fundamental problems involve the temporal and spatial domains of the control systems
and the institutional perception of the role of the human species in the ecosystem.
Ecological systems are shaped by evolution and managed by a process of natural
selection. The constraints that impinge on individuals can be arranged in a dominance hierarchy
(figure 2), with the physical characteristics of the environment constituting the most restrictive
constraints in the "natural hierarchy," since modifications of local patterns of geochemical cycles
or weather patterns are made with difficulty and are energy intensive. The ecological constraints
involve the direct and indirect interactions (e.g., predation, competition) of the biological
components coexisting within the habitat. Many animal species have developed social structures
that constrain individual behavior through social dominance (e.g., caste-like systems). Also, a
number of human societies have not "modernized" and still barter with the excess production of
their ecological landscapes. In these societies, individual behavior (e.g., concerning rights,
privileges, ownerships) and individual survivorship are constrained from the top down. Though
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not stated explicitly, survivorship of these societies ultimately depends on sustainability. In
contrast, industrialized societies that are energy- and material-intensive have redefined the
control hierarchy socially so that it effectively operates in the opposite direction. Such concepts
as ownership of property, declarations of rights, and the championing of democratic governments
all contribute to the sanctity of the individual. The emphasis of this social philosophy has
resulted in the prevalent notion of "being at war with nature," which has resulted in the
permanent and widespread alteration of many aspects of the physical dimensions of our
ecosystems in the name of fulfilling individual expectations.
The outcome of the individual-based control strategy is the evolution of societies and
interest groups within societies that differ significantly in the emphasis they place on different
points on a continuum of shared values. If we are to avoid conflict between economic
growth/development and environmental protection, we must understand their inherently different
temporal and spatial scales. Maintaining biodiversity requires protecting critical landscape-scale
dynamics over long timeframes. The challenge is to integrate multiple uses within the landscape
by encouraging diverse uses at the smaller, individual-user spatial scales. The maintenance of
landscapes that support critical ecosystem structures and functions will require the harmonization
of short-term individual goals with the long-term, intergenerational needs of society.
2.2.2. Ecological Sustainability
Sustainability has become a popular term, in part because it can convey important
principles for managing resources, but also because the term can mean different things to
different people. The term sustainable development, which is even more commonly used, also
can vary in its meaning, from utilizing only renewable resources at rates compatible with long-
term ecological health to exploiting resources extensively (i.e., even just short of causing system
collapse). In risk assessment, we are concerned with all types of ecosystems, from extensively
human-altered systems to ecosystems selected for intensive care and stewardship. Consequently,
the focus of this discussion is on ecological sustainability as it calls for maintaining the ecological
system at a defined level of quality and health (Lubchenco et al., 1991; Edwards and Reiger,
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1990). In general, human-affected ecosystems could be established at any of a variety of points
along a broad continuum; from, for instance, an area totally covered with cultivated plants and
caged animals on display, to an ecosystem that resembles its state in prehuman history in all
respects. Although neither extreme is usually a realistic nor appropriate possibility, they indicate
the scope of the continuum. Exactly where along the continuum a target is selected for
ecological sustainability must be decided consciously, with attention to both ecological
considerations and the realities of human systems.
The ecosystem's place on the continuum is particularly important for determining
ecological significance. For example, stress-induced change in an already highly altered system,
which in effect has been societally chosen for human utilization and alteration, is quite different
from a projected change in the health of an otherwise pristine, protected ecosystem, such as a
national park or biosphere reserve. Similarly, maintaining a planting-harvesting rotation for a
logging forest in which a limited number of species of trees are planted (e.g., ponderosa pine and
Douglas fir plantings throughout the Pacific Northwest) would be considered ecologically not as
significant as converting an old-growth, highly diverse forest into a deforestation-replanting cycle.
Thus ecological significance assumes much greater importance for endangered, threatened, or
otherwise especially valuable natural ecosystems for which ecological sustainability would be at
risk from human activity.
Ecological risk assessment should not serve to preclude all future human activities or
adverse impacts to the environment. Rather, when appropriately carried out through
characterization of significance, it should be used to identify situations in which ecological
sustainability is threatened. Defining ecological sustainability levels, however, must be consistent
with ecological health principles (section 3). For instance, there must be a recognition that
ecosystems constantly change, with natural variability occurring in relation to some average state
as systems undergo succession or respond to long-term changes in the physical environment.
Thus sustaining an ecosystem at some static condition is both ecologically inappropriate and
impractical or impossible on a large scale. Similarly, just as ecological health must be evaluated
in the context of particular ecological endpoints that are defined in ecosystem-specific cases,
ecological sustainability also must be defined in terms of specific ecological endpoints.
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While these aspects of sustainability may seem simple enough, they often encompass
conflicts related to biases that have not been resolved for most ecosystems. For instance, when
management goals are defined ecologically (e.g., population levels of important species) rather
than economically (e.g., yields of fish), they often are focused on maintaining particular
population levels of selected species (e.g., a population of an aesthetically important wading
bird). Separating natural fluctuations in species populations (e.g., caused by interannual
variability in weather) from human-induced changes is not easily accomplished, however.
2 J. Criteria for Evaluating the Significance of Ecological Change
Risk assessors often may determine that no issues of ecological significance are involved
in a given decision. The challenge, of course, is to formulate criteria—based on sound ecological
principles—for quickly determining when this significance is an issue. Thus general principles are
needed to serve as the basis for such criteria, despite the fact that ecological systems vary greatly
in their ability to absorb impacts and in the types of impacts that threaten their integrity. For
example, systems that have evolved in more arid regions seem less robust when subjected to
exploitation than regions of plentiful rainfall. Further, the robustness of a system is generally
thought to be associated with the amount of redundancy built into the system. Thus every
assessment of risk should take organizational complexity and specific adaptations into account.
Moreover, each assessment should consider the potential vulnerabilities of the particular system.
Once the system's sensitivities and vulnerabilities are established, a decision on the
potential significance of an ecological risk will depend on the following related factors:
• the spatial scale over which the impact will be distributed;
• the time scale over which it will be manifested; and
• the potential for recovery as well as the recovery time required to return the system
to either its state prior to disturbance or to some intermediate agreed-upon state.
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These three factors (and, by implication, functional redundancy) are represented in figure
3, which plots the recovery time (as a measure of reversibility) of the impact of a decision against
the spatial scale over which the change occurs. This matrix, which can be called an "ecological
risk decision square," can be useful for analyzing decisions that have long-term, difficult-to-
reverse, and spatially pervasive impacts. Decisions that carry risks of outcomes that have all of
these characteristics will cluster in the upper-left region (i.e., the "red region"). These decisions
will require careful ecological study and thoughtful analysis of the social impacts of likely
ecological changes and trends.
The concepts and rationale embodied in figure 3 recognize the inherent 'complexity
associated with the analysis of environmental problems. In this respect, the figure goes beyond
the one-dimensional analysis provided by mainstream economics, which holds that all resources
have adequate substitutes (i.e., redundancy) and places minimal emphasis on irreversibility. If
one assumes that all ecological impacts can be understood as gradual and measurable "at the
margins," it may seem that this complication of the decision-making process is unjustified. This
position of mainstream economists can be represented relative to the decision square by denying
there are any "red region" decisions and assuming that the risk decision space is continuous.
The proposed decision model, on the other hand, introduces the concept of temporal and
spatial scale into the decision process. In so doing, the model recognizes an initial need to
establish the possibility that outcomes of a decision may present an irreversible threat to
important ecological resources and to intergenerational values. This determination must be
made prior to applying standard economic analyses that treat all costs and benefits (and
increments and decrements of risks to humans) as expressible in monetary terms and as
interchangeable/fungible. This two-tiered approach to environmental decision analysis introduces
complexity into the decision process. At the beginning, determining ecological significance based
on the three criteria identifies decisions involving moral questions of intergenerational equity
(because such decisions may greatly narrow options for future generations). The approach also
introduces complexity at the decision stage, because one must apply criteria appropriate to the
specific problem.
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The definition of ecosystem significance sought in this chapter is based on a set of
criteria—quantified where possible—but involving professional judgments on the part of scientists
and managers. The definition would facilitate reaching informed decisions in the formative task
of deciding what environmental changes are problems that require management attention. While
the goal of this chapter is to give more specificity to this criteria, we recognize that many
particulars only can be filled in with ecosystem-specific information. It would be naive to think
that any specifications for the criteria could be stated with both precision and generality
(Levins, 1966).
The criteria proposed are consistent with the Report of the Ecology and Welfare
Subcommittee of the EPA Relative Risk Reduction Project (EPA/SAB, 1991). Indeed, the decision
square in figure 3 can be thought of as a formalization and simplification of an important
recommendation of that subcommittee (EPA/SAB 1991; Harwell et al., 1992). In that report,
potential environmental problems were disaggregated and then classified into groups based on
five considerations:
• the spatial extent of the area subjected to the stress;
• the importance of the ecosystem that is actually affected within the stressed area;
• the potential for the problem to cause ecological effects with a likely ecological
response;
• the intensity of exposure or disturbance; and
• the temporal dimension both of effects and the potential ecological recovery.
These criteria should be ranked in the problem formulation stage to decide whether a
given risk is in the "red region." Notably, the criteria include social as well as scientific
considerations and regard the "importance" of the ecosystem at risk as one element of the
multiple criteria. In addition, since these criteria emphasize various aspects of irreversible
impacts and place considerable weight on spatial scale, they could provide data points for
assigning greater specificity to the idea that some public decisions are ecologically significant.
Since problems that rank high for all or most of these criteria would be located in the "red
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region," they would justify scientific monitoring, public discussion, and decision-making regarding
the long-term, social impacts.
Scientific study, both historical and ecological, will dominate the determination of
whether an observed or expected change is part of a natural cycle or represents an
anthropogenically induced trend. A determination about the social impacts of ecological change,
however, inevitably will involve values that go far beyond the expertise of science. It may be
possible to decide, on mainly scientific grounds, that some decisions carry no significant
ecological risk because the impacts will be mitigated by natural variability. Or, it may turn out
that changes in land use associated with certain agents will be canceled out by other agents, as
when one farmer clears a woodlot and plants wheat at the same time another farmer lets a
comparable field lie fallow and enter old-field succession. Once it is determined, however, that
human impacts are causing changes that are highly unlikely to occur naturally or at rates that
preclude adaptation of natural systems, it becomes necessary to decide whether the changes will
significantly encroach on societal values.
2.4. The Role of "Significance" Criteria in Problem Formulation
'The decision model discussed in section 2.3 provides a means to begin integrating social
and ecological values into the selection of endpoints. If reliable determinations of ecological
significance can be made (i.e., if we can intelligently define the "red region" in figure 3) by
applying criteria based on the spatial scale and reversibility of impacts on ecological systems, then
many ecosystem-specific decisions can be left to individuals (e.g., consumers acting within a free-
market economy). Conversely, determinations that certain policies and activities will have
irreversible impacts (i.e., at the ecosystem, landscape, regional, or global level) may require
imposing constraints on individual action—not to limit individual freedom but to define the
conditions of choice. If these conditions are appropriately formulated to match ecosystem-based
constraints, individuals will have incentives to make choices that have positive or neutral
significance for the diversity and sustainability of landscapes.
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One approach to integrating such concepts into problem formulation is illustrated in
figure 4. In this example, the concepts of reversibility, space, and time scale were used along
with knowledge of the stressor and the effects to develop a conceptual model. Thus spatial scale
is directly related to the bounds of the particular risk assessment; that is, the spatial extent of the
ecological systems potentially at risk is generally defined by the spatial extent and co-occurrence
of the stressor(s) and ecological systems. The concept of reversibility has both
disturbance/exposure and effects components integrated by time. For example, the type,
intensity, frequency, and duration of disturbance/exposure is intimately coupled with the type,
spatial extent, duration, and reversibility of anthropogenically induced ecological change.
Applying these concepts at the initial stage of problem formulation provides the risk
assessor with a context and criteria for recognizing the role of significance in the specific
assessment. If significance is an issue, then the concepts of space, time, and reversibility as well
as the ability to discriminate anthropogenic-stress-induced changes from natural variability should
be considered in the selection of assessment and measurement endpoints. The use of space,
time, and reversibility as criteria for judging the significance of ecological change leads logically
to the intergenerational maintenance of landscape structures and functions that are essential for
the sustainability of ecological systems. This is not a trivial issue given that much of the world's
current landscape's structures and functions have been modified by human activity during the
past few decades. Nevertheless, ecological sustainability becomes the environmental benchmark
to which the assessment and measurement endpoints are linked in risk assessment.
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3. ANALYSIS
3.1. Characteristics of the Stress Regime
3.1.1. Dynamics and Variability of Natural Stress
One of the ecological considerations in determining significance relates to the nature of
the stress regime itself. All ecosystems have developed in the context of a particular
environmental stress/ecological response regime, operating on many widely divergent time
scales, including:
• the time for physiological and behavioral response of constituent organisms;
• the life-cycle times of the organisms;
• the population-level time scales of, for example, reproduction, recruitment, and
maximum age;
• the community-level time scales for replacement of species through succession;
• the time for species to undergo adaptation and evolution; and
• the time for the larger physicocheraical environment to be altered (e.g., through
climate cycles, geological and hydrologjc changes).
At each of these time scales, natural variability occurs in the physical conditions that the biota
experience, again ranging from the very rapid (e.g., raicroclimatic changes when a cloud passes
by), through diumal and seasonal changes, to longer-term variability (e.g., from El Nino events
or from hurricanes). This variability regime shapes the nature of the ecosystem. Examples are
everywhere: The dominance of conifers in the Pacific Northwest relates to seasonal moisture
stress that the deciduous forests of the eastern United States do not typically experience. The
tall-grass prairie developed where annual precipitation was insufficient to maintain a forest but
greater than the levels at which desert-adapted organisms could compete. Hardwood hammocks
developed in the Everglades because substrate depressions there reduced the frequency of fires
experienced by the surrounding sawgrass communities. Oligotrophic coastal waters allow the
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development of macrophytes in areas where dense phytoplankton populations are absent. The
frequency of tidal inundation determines the biological and physical structure of the intertidal
community along a gradient from sea to shore. Long periods of environmental constancy allow
development of highly diverse tropical and temperate rain forests. The complete and
interminable darkness of abyssal ecosystems precludes primary production but encourages
development of complex trophic structures with highly adapted life forms.
In a fundamental sense, ecosystems are adapted to their physical and chemical
environment, including their natural variability. Often physical variability is required for the
ecosystem to remain the same. For example, reducing the frequency of fire can alter the species
composition of a forest or subject an area of grassland to invasion by shrubs and trees. At the
regional and global scales, the distribution of biomes is primarily determined by the distribution
of climatic conditions, over which is superimposed geological and other more local-scale, edaphic
conditions. Indeed, this physical and chemical regime is integral to the nature of the biological
communities, and the heterogeneities in the regime in large part allow and necessitate the
diversity of Earth's biota. Nonetheless, what is a nurturing environment for one set of species or
one community type would constitute extreme stress for another. For example, freezing events
kill trees and other organisms in Florida, but not in Maine; a heavy snowfall on deciduous trees
before leaf-fall can have completely different effects than the same event a few weeks later.
This brings us to the first major conclusion about stress: Natural stress, in essence, is the
divergence from the normal physical and chemical regime of an ecosystem; ecological significance
results from such divergences.
3.1.2. Anthropogenic Stress
Notwithstanding the recognition that ecosystems function within the context of natural
variability, the concern for ecological risk assessment is anthropogenic stress. Since the nature of
the stress is one component in determining ecological significance, a particular concern is
whether the stress is a modification of the physicochemical environment or is novel to the
i
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ecosystem. Either type of stress might result in highly significant ecological consequences. For
example, human-induced global climate change may cause tremendous ecological consequences,
although the stresses are merely variations of effects on the natural environment (e.g., changes in
the frequency or intensity of precipitation, increased occurrence of tropical storms, or
combinations of high temperatures and severe drought). Similarly, one of the most ubiquitous
anthropogenic effects on the environment relates to increased nutrient inputs to surface waters,
often resulting in eutrophication, species compositional changes, and sometimes even anoxia of
the water through overproduction of biological matter.
Other anthropogenic stresses are novel to the ecosystem, though involving natural factors.
For example, introduced exotic species can out-compete native species in ecosystems that are
vulnerable because of an availability of niches for organisms that have not evolved along with the
ecosystem. Other examples include the flooding of land through the damming of rivers; the
replanting of harvested forests with only one or two species of trees, often of very limited
genotypes; the alteration of a hydroperiod resulting from the draining of wetlands or from flood-
control measures; and the conversion of grassland or forest to agricultural cropland.
Additionally, anthropogenic stresses may be truly xenobiotic (i.e., involving stresses that
are not a part of the natural environment). Examples include the use of biocides for agriculture,
pest control, and military activities; releases of high levels of air pollutants from industrial
processes; development of novel organisms genetically engineered for release into an
environment; creation of transuranic radionuclides; deployment of kilometers-long drift nets in
the open oceans; and destruction of the stratospheric ozone layer through use of
chlorofluorocarbons.
One might be tempted to derive generalities from consideration of these types of
anthropogenic stresses. Since U.S. Environmental Protection Agency (EPA) regulations over the
past few decades have focused on human health rather than ecological significance concerns, the
emphasis for environmental issues has been on controlling anthropogenically introduced
chemicals in the environment. Yet the EPA Science Advisory Board's Reducing Risk Project
identified the greatest environmental risks for the United States as the stresses associated with
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global climate change, habitat alteration, reduction in biodiversity, and stratospheric ozone
depletion. Only the last of these results from xenobiotic agents, but even here the stresses
themselves are changes in the physical environment. Thus xenobiotics might seem inherently of
greater concern because the system has no experience with the stress. The reality, however, of
present relative risks to the environment is that changes in the physical environment pose the
greatest potential consequences since they involve alterations in the variability of natural physical
and chemical conditions. The need to address nonchemical stresses contrasts with the historical
obsession with toxic chemicals and is one of the driving forces behind development of a new
paradigm for ecological risk assessment.
What stress regime characteristics matter, then, if the differences among stresses are
based on their enhancement of natural variability and their novelty in regard to the particular
ecosystem, especially since a truly xenobiotic stress does not provide obvious criteria for
evaluating ecological significance? The key characteristics of a stress regime are the extent,
intensity, frequency, duration, and, as discussed above, the nature of the stress.
3.1 J. Characteristics of Stress
3.13.1. Extent, Intensity, Frequency, and Duration
Ecosystems often are adapted to cope with many types of natural disturbances, especially
those that are predictable (e.g., intertidal ecosystems adapted to diurnal and monthly cycles) or
periodic (e.g., seagrass communities adapted to a certain frequency of hurricanes). Often when
the spatial extent of stresses is small relative to the spatial scale of the ecosystem and when the
stresses have occurred commonly during the development of the ecosystem, the disturbance can
be absorbed within the system structure, permitting functioning to continue without significant
changes, and perhaps adding to heterogeneity but not altering the ecosystem's health. For
example, wind-induced treefalls may lead to the mosaic of landscape patterns in a deciduous
forest (Bormann and Likens, 1979), in which patches of successional stages of community
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composition may be found at various locations across the landscape but the overall landscape
pattern is constant and characteristic of the physical environment.
Just as there are spatial nodes of high importance in ecological landscapes, there are also
temporal nodes that are critical. Most poikilothermic (cold-blooded) organisms function on
physiological time (e.g., integrated thermal time, such as degree days). This means that ambient
temperature in large part controls the rate of the organism's biological clock. Homeothermic
(warm-blooded) animals operate with temporal organizers, such as seasonality (photoperiods),
circadian rhythms (a biological clock), and tidal fluxes (lunar cycles). The organizing influences
of these external signals result in group activities occurring synchronously. This aggregates
individuals as they go through critical aspects of their life history (e.g., spawning, migration,
hibernation). These aggregations will amplify the magnitude of the impact if some deleterious
event takes place. For acute stresses, on the other hand, ecological significance may relate more
to the intensity of the stress. Severe acute stress has been shown to result in large, abrupt
changes in some characteristic of the ecosystem, often resulting in substantial removal of living
biomass (Grime, 1979) or total biotic material (Reiners, 1983). Such high-intensity stresses often
involve alterations of species composition (e.g., as in the spraying of defoliants in Vietnam
[Tschirley, 1969]), elimination of sensitive species, reduction of organic matter pools, and
reduced biodiversity (cf. Woodwell, 1970; Weinstein and Bunce, 1981; Freedman and
Hutchinson, 1980; Gordon and Gorham, 1963). Unfortunately, extreme stress studies that result
in collapse or fundamental alteration of the ecosystem yield little insight into the more common
environmental risks, which are often of lower-level intensity—perhaps chronic conditions
occurring as simultaneous multiple stresses—or are modifications of natural variability regimes.
Chronic physical disturbances that do not mimic the frequency of natural disturbances, however,
may alter the ability of the ecosystem to absorb damage and consequently alter the ecosystem's
basic structure or functioning (i.e., constitute an ecologically significant change).
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3.1-3.2. Relationship to Recovery
Ecological succession is not a deterministic process. The initial conditions (e.g., the
availability of seed sources and colonizers) can have a significant influence on the trajectory of
the recovery phase. Consequently, stresses that alter initial conditions for recovery can have a
significant effect on the ecological consequences. For example, the white pine forests of
northern Michigan were clear-cut and burned between 1890 and 1910. The intense fires
destroyed much of the organic matter in the soils, and interactions with seedling browsing
resulted in forests that are now restored, although no longer with white pine. From an ecological
perspective, some ecosystems may recover a structure and function similar to the preperturbed
system; in other cases, alterations are irreversible. Determining the significance of that situation
is thus largely socioeconomic rather than ecological.
3.1.3.3. Indirect Effects
Another issue involves indirect effects—effects on one or more ecological endpoint that
result not from the stressor directly, but from effects on other components. For example, the
pesticide Sevin used for gypsy moth suppression was banned in Michigan because of deleterious
effects on native bees that adversely affected the native plants that are bee pollinated. Processes
like reproduction are highly sensitive nodes in demographic models, and interspecific
dependencies for such processes are issues that require special consideration.
Other biologically mediated indirect effects involve basic ecological relationships such as
mutualism, commensalism, selective herbivory, and keystone predation. For example, the
introduction of rabbits to islands devoid of herbivores created plant communities radically
different from those observed before the introductions. Conversely, the removal of starfish from
the rocky intertidal zone of the Pacific Northwest caused a rapid restructuring of the bottom
community (Paine and Levin, 1981).
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3.2. Characterization of Ecological Effects
Among the factors to consider in determining significance are many that relate
specifically to ecological characteristics, including:
• the nature of the ecological change induced by the stress;
• the prospects for ecological recovery following removal of the stress;
• the natural ecological variability experienced by the ecosystem; and
• the direct, indirect, and cumulative impacts as they relate to ecoldgical
sustainability.
Because the specific considerations of ecological significance are situation- and ecosystem-
dependent, no simple, universally applicable rules can be proscribed. This section discusses the
issues that should be considered systematically when making a determination about ecological
significance.
3.2.1. The Nature of Ecological Change
Defining ecosystem health is by no means simple or straightforward. In contrast to
organisms, ecological systems are less robustly defined, their dynamics are inherently less
tractable, and their state is not so easily fully characterized. Further, ecosystems are constantly
subject to stress, from both natural and anthropogenic sources.
Three concerns are fundamental: stress regimes (discussed in section 3.1), ecosystem
responses to stress, and adaptation/recovery processes. Our present understanding of stress,
ecological response, and ecological recovery for most environmental stresses and for most
ecosystems is incomplete and replete with uncertainties. In practice, we also lack a firm
understanding of variability within and across ecosystems, which is significant because ecosystems
are continuously in transition. Moreover, it is difficult to establish appropriate measures of
complex ecosystems for evaluating changes. These uncertainties notwithstanding, we do have a
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base of knowledge about ecological systems and how they are affected by human activities; thus
we are not without the means to make appropriate environmental decisions. Lessons can be
derived from natural stress-response relationships and from experiences with anthropogenic
changes to the environment. The key is to identify factors that need to be systematically
examined and criteria that need to be explicitly considered when making a determination of the
ecological significance of an anthropogenic change.
Stress-induced change in an ecosystem can be characterized by:
• the ecological components or targets of change;
• the amount or intensity of the change;
• the spatial extent of the change;
• the timeframe under which change occurs; and
• the degree of redundancy in the ecological system for the changed components.
9.2.1.1 Ecological Components of Change and Ecological Endpoints
An almost unlimited number of properties of ecosystems could be measured to
characterize a system's health. To reduce all possible ecosystem responses to a manageable level,
however, we must choose a subset of potential ecological assessment endpoints (Limberg et al.,
1986; Harwell and Harwell, 1989; Kelly and Harwell, 1989; Harwell et al., 1986). One of the
critical considerations for determining ecological significance is the correspondence between the
ecological (or assessment) endpoints for the ecosystem and the targets of stress-induced change.
If none of the stress-induced changes (direct or indirect) in an ecological system involve the
selected ecological endpoints, then the change has ecological significance. If some endpoints are
affected, however, then other considerations must apply for determining ecological significance.
Some ecological systems may be protected from significant stress-induced changes by
internal processes of compensation or adaptation. For example, microorganisms may be capable
of converting toxics to nontoxic substances by performing biochemical degradation. Also, toxic
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materials can be removed from sites of biological activity by the organisms themselves, which
uptake compounds and sequester them in storage tissues. Yet ecological systems tend not to be
adapted to rare, extreme events (e.g., freezing events affecting mangrove ecosystems) and may
not be adapted to accommodate most anthropogenic stresses (e.g., inputs of toxic xenobiotic
chemicals). Consequently, anthropogenic, chronic, or acute disturbances that do not mimic the
frequency or nature of natural disturbances can alter the ability of a system to absorb damage
and in turn alter basic properties of the ecological system. Qualitatively different responses by
ecological systems to a disturbance will occur depending on the frequency and novelty of the
disturbance in the evolutionary history of the ecological system. Thus the same disturbance can
have dramatically different consequences depending on the particular ecological system—for
example, the effect of fire on grassland ecosystems versus tropical rainforests. Similarly, a
particular ecological system will likely respond differently to different disturbances. For example,
the grassland may do well in the presence of fire, but be devastated by overgrazing. The
responses of ecological systems vary widely across systems and stresses. Insofar as an
anthropogenic stress mimics a natural stress, stress-response relationships are likely to be
established by the ecological system's adaptations to that type of stress. For novel stresses,
however, such as those caused by human activities, stress-response relationships are quite
unpredictable.
Population level. In addition to the considerations mentioned above, some endpoints can
be highlighted as valuable ecological endpoints. For example, species-level endpoints are
characterized by population parameters, such as age distributions, genetic variation, and
generation times. Life-table parameters may be the most relevant and integrative endpoints. For
example, population responses are reflected in a single parameter, Ro (the net reproductive rate)
(Euler, 1970), or rate of population increase ("r" or L). Ro ("r" and L) is the integral of the
product of the age-specific survivorship (Ix) and the age-specific birth rate (bx). This single
parameter includes the age-weighted influences of all the various endpoints of ecological stress at
the population level (e.g., reproductive failure, mortality, and developmental retardation). When
Ro = 1, the population is exactly replacing itself every generation, and the population is not at
risk even with observed reductions in birth rates and/or increases in mortality rates. When Ro >
1, the population is growing, and when Ro < 1, the population is declining. This approach has
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been applied successfully to the analysis of lexicological data (Daniels and Allan, 1981; Gentile
et al., 1982; Meyer et al., 1986; Caswell, 1978, 1989). Net reproductive value and long-term
population growth rates ("r" and L) will reflect the impacts of stresses long before there are
measurable changes in field population densities (figures 5, 6, and 7).
Community level. Biological communities have a development trajectory that is a function
of their environment's characteristics and initial conditions. In terrestrial communities, the
vegetation component significantly affects the faunal diversity, and the vegetation composition is
generally determined by the soil type and the hydrologic conditions. Certain communities (e.g.,
grasslands) have additional determinants such as fire and grazing. In addition to edaphic factors,
the initial conditions of recovery after a serious disturbance can have a strong influence on the
successional sequence (figure 8). For example, the expected dominant floral species will depend
on which succession sequence is being observed. The temporal dimensions of community
dynamics are usually measured in years and decades.
The animal species associated with a plant community can be highly specialized (e.g., sap-
sucking insects) or very generalized (e.g., white-tail deer). Species that have evolved tight
biochemical interspecific accommodations will tend to be present or absent as a single cluster.
These interspecific associations are usually determined by interspecific processes such as
competition, predation, parasitism, and mutualism. The community structure and function are
important to selecting an animal species as an assessment endpoint in terrestrial ecosystems.
In aquatic communities, successional patterns are not as easily generalized. For example,
freshwater systems may accumulate organic detritus and fill in over long time spans, but these
changes are not generally observable in time periods of years and decades. Changes may be
observed in water quality (e.g., eutrophication, toxics), dominance of primary producers (e.g.,
macrophytes vs. phytoplankton), and the characteristics of the fish community (e.g., detritus-
food-chain carp vs. grazing-food-chain pike).
In lake ecosystems, the biologic populations modulate the nutrient levels that in turn
determine the succession of primary producers. The silicon:phosphorous ratios in lake water
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determine which species of diatoms will dominate. The nitrogen-.phosphorous ratios determine
whether the lake has green algae or blue-green algae as the dominant phytoplankton. This, in
turn, determines whether the lake has a grazing food chain (e.g., green-algae pike) or a detritus
food chain (e.g., blue-green-algae carp). These nutrient ratios also are affected by input from
the watershed.
In riverine ecosystems, the sequence of community change is associated with stream size
(figure 9). The theory of stream continuum appears to hold for those riverine systems where
insects constitute the majority of species in the shredder, collector, and grazer guilds. In tropical
streams, where fish and crustaceans replace the insects, the associations are not as clear.
In both lake and riverine systems, an endpoint for assessment at the community level is
the vitality of the guild structure, with the presence or absence of a given species being generally
of minor importance. Although the presence or absence of a keystone species (e.g., bluegill
sunfish in ponds) will determine the array of dominant species (e.g., small vs. large zooplankton),
the zooplankton/grazer guild will still be present and functioning. Again there are exceptions; for
example, in tropical lakes, where the zooplankton species are all small in body size and the fish
constitute the grazing guild on large phytoplankton.
Communities are variable in structure over both temporal and spatial gradients. The
assessment endpoints should be structural characteristics (e.g., guilds) that reflect the stage of
succession (terrestrial), the size of the system (rivers), and the nutrient ratios (lakes). Individual
species as bioindicators will be relevant if they are tightly associated with a guild of particular
concern.
Ecosystem level. The ecosystem concept links the biotic community to the physical
environment through the transport and fate of energy (trophic structure) and materials
(biogeochemistry). For this level of organization, the shift in focus is from biotic structure
(species associations) to ecosystem function (figure 10). The degree of biotic influence on the
fate and transport of materials depends on the element. For example, phosphorus and calcium
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in the terrestrial system are largely controlled by physical processes, whereas nitrogen fate and
transport are strongly modified by biologic processes.
Since the vast majority of the materials and energy that are sequestered by the
ecosystem's primary producers are directly cycled to the detritus component, primary producers
and detritivores are responsible for the most important ecosystem functions. This is true in both
terrestrial and lake ecosystems; it is not true, however, for riverine ecosystems where the flood
plain constitutes the detritus preprocessor. The storage capacity of the primary producers (e.g.,
woody structures in trees, root systems in prairie grasses) constitutes a buffer (i.e., a capacitor)
from the uncertainty of the physical environments. Chaotic patterns of precipitation, episodic
events like fires and floods, and the strong patterns of seasonality in northern latitudes all
directly influence the intensity of energy fixation and nutrient uptake. In a similar fashion, the
storage capacity of the detritus component (soils and sediments) provides an import stabilizing
(buffer) capacity for ecosystem functions.
The endpoints for assessment at the ecosystem level should be associated with critical
functions rather than structural characteristics, unless they are uniquely associated with each
other (i.e., no redundancy). The primary producer and the detritivore components are the most
important aspects and should be given top priority.
3.2.1.2. Magnitude of Change
The magnitude of stress-induced ecological change involves its intensity and spatial
extent. The degree to which ecosystem endpoints change in response to a given stress is an
inverse measure of the resistance of the ecosystem to that type of disturbance. Thus a highly
resistant ecosystem would change only slightly in response to the same stress that would cause
major displacement in a low-resistance ecosystem. Note that all evaluations of an ecosystem's
resistance (or other measures of stress-induced response) must be made with respect to a
particular stress or combination of stresses, since different stresses may elicit different responses.
For this reason, one cannot accurately characterize a type of ecosystem as being intrinsically
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resistant, and resistance may be seen in one indicator of the ecosystem but not in another. How
an
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ecosystem responds to perturbation and how readily it recovers (i.e., its resilience) concerns
: stability of an ecosystem; like the ecosystem itself, stability can only be defined operationally.
3.2.1 J. Spatial Extent of Change
A factor that relates to the intensity of a response is the spatial extent affected by the
stress. Quite simply, ecological significance increases with the area affected. This is true for
several reasons:
A larger affected area includes a greater fraction of the total area of that
ecosystem type, depending on the relative size of the affected area versus the size
of the habitat type. For example, an activity that would adversely affect the last
remaining undeveloped area of the barrier islands off the Atlantic Coast is more
ecologically significant than an impact covering the same area but of a much more
extensive ecosystem, such as the deciduous forests in the eastern United States.
A larger affected area is likely to be subject to a greater number of other stresses,
increasing the complications from stress interactions.
A larger affected area is more likely to contain specific components of concern,
such as a habitat for endangered species.
The larger the impacted area, the more difficult the recovery.
A larger affected area may involve landscape-level changes, since many ecosystems
may be altered by the stress.
3.2.1.4. Timeframe for Change
A potentially important factor in ecological significance is the rate of change in the
ecological system induced by a stress. If the stress-response is sufficiently slow, the ecological
effect may be compensated by changes in other ecological system components that ameliorate the
consequences of the stress. Significantly, an ecological system typically consists of some
processes occurring over short timeframes simultaneously with other, often controlling processes
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that occur at rates that are orders-of-magnitude slower. This wide divergence in time constants
means that ecological significance for a particular ecological component is in part a function of
the rate of the change and the intrinsic time for the component. For example, a rapid change in
a slow-time component is likely to have greater impact than a change occurring at a rate
consistent with the natural time dynamics of the affected component. Conversely, an ecosystem
is more likely to adapt to a slow change in a rapid-time component, with concomitant reduction
in ecological significance.
Further, ecological systems have intrinsic time lags such that adverse responses from a
stress may be delayed. This is important when distinguishing the long-term impacts of a stress
from the immediately visible effects. Thus caution must be used in ecological risk assessments to
ensure that important but time-lagged, adverse effects are not discounted or not identified.
3.2.1.5. Redundancy
Ecological redundancy means that one ecological component can perform a similar
function to another component. Redundancy can allow the ecological system to adapt or
compensate to a stress-induced change such that no functional changes occur in the system. As a
result, determining ecological significance relates only to the importance of the structural
changes, without concerning any functional considerations. Thus, if a redundant species has no
other particular value (e.g., one of many fungi involved in decomposition), then its loss would not
be considered ecologically significant. Conversely, loss of a species for which there is no
functionally redundant counterpart would constitute ecologically significant change, irrespective
of the direct value of the species to humans. In general, changes in an ecological system's biotic
populations may or may not be of direct ecological importance, since redundancy or other
compensatory mechanisms may mitigate adverse effects on ecological system processes; however,
because changes in ecological system processes invariably result in changes in biological
constituents, ecological significance is more likely to result.
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3.2.2. Recovery
Recovery is the rate and extent that an ecosystem changes in response to the removal of
a stress. Again, there are two components: one related to how rapidly the ecosystem recovers,
the other to how effectively the ecosystem recovers. The temporal aspect is characterized as the
ecological system's resilience, which is defined as the inverse of the length of time required for
an ecosystem to return to its near-normal state. One cannot reasonably define this as a complete
return to a preperturbed state because natural heterogeneity might preclude ever attaining that
precise state. Moreover, it is possible that prior to the disturbance the ecosystem was not at
steady state, even in the absence of human interferences, since properties of an ecological system
may change over time. For example, diversity of a forest ecosystem will increase during the early
stages of ecosystem development, then will decline in the middle stages of succession and
increase again during the later stages. Thus comparisons should be made not to a single set of
steady-state values for the ecological system, but to a mutable set of characteristics describing the
trajectory of the undisturbed ecological system.
Along with resilience issues, recovery concerns questions about whether the ecosystem
will ever return effectively to its preperturbation state or trajectory. It is possible that a complex
ecological system, when subjected to particular disturbances, will become irreversibly transformed
into another system with different components, steady states, and dynamics. This is a well-known
characteristic of many ecological systems. For example, deforestation in the coastal hills of
Venezuela has changed soil structure, seed sources, and the local physical environment such that
forests cannot grow again, even after the areas are abandoned by humans. This phenomenon
also was seen in the irreversible loss of the great forests in Britain during the Neolithic period,
when humans cleared land for agricultural production and energy resources. Perhaps these
examples merely reflect an exceedingly long period of recovery for an ecological system; yet for
the practical purposes of human interest, these examples of system change are permanent.
Recovery of ecosystems is in part dependent on the characteristics of the stress (i.e., the
disturbance regime, including the nature of the stress, its frequency, duration, and intensity) and
in part on the history of the ecological system (e.g., the level of preadaptation to disturbance,
past history of disturbance, and susceptibility of organisms within the ecosystem). An ecological
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system that has been subjected to repeated disturbances may tend to deteriorate over time
because of loss of nutrient reserves or substrate. In other cases, recovery from repeated stress
may be rapid if the important species complete their life cycles during the interim between
disturbance events.
Characterizing the recovery of an ecological system presents the same challenges as
characterizing an ecological system's response to stress—namely, identifying which endpoints to
examine. For example, Is an ecosystem recovered when its pools of nutrients are back to the
prestressed state? When a specific species has reestablished its population at a particular
density? Or when the residues of a toxic chemical in sediments or in biological tissues have
decreased to below some threshold? Just as an ecological system functions and responds to
stress at widely differing rates, hierarchical levels, and spatial extents, it also recovers
differentially. Selecting the appropriate suite of endpoints is not a trivial task, and there are
substantial difficulties added in establishing an appropriate baseline for comparing the stressed
ecological system. Also, natural heterogeneity and fluctuations again raise the issue of detecting
signals from amid the noise of natural variations.
3.23. Natural Variability
Ecological systems vary over time and space naturally, with or without human
interference. That variability is differential over different components of the ecological system,
different spatial scales, and different timefrarnes. For example, a major change from one
ecosystem type to another at a small scale may be highly significant ecologically, yet if such
patch-level changes occur throughout the landscape and over time, a mosaic at the landscape-
level may develop that is in itself quite constant over time and without significant ecological
changes. As another example, natural fluctuations in marine fish populations are often quite
large, with intra- and interannual variability covering orders of magnitudes in population levels.
This is natural, however, and a change in the population caused by some stress may merely be
noise within the natural variability. By contrast, some populations are notably long-lived and
constant in size over time, and a change in comparison with the natural low variability would be
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very significant ecologically. For example, a transient 50 percent reduction in striped bass may
not be ecologically significant, but a 50 percent reduction in the population of redwoods
would be.
On what scale then should ecological system response to stress be measured? Clearly, no
single scale can be selected exclusively. For example, photosynthesis functions at the cellular
level on a time scale of minutes to hours, whereas the life cycle for the leaves is measured in
months or even years, and that of the tree itself is measured in decades to centuries. Similarly,
the population dynamics of the soil bacteria are extremely rapid and fluctuate wildly in time and
space. By contrast, the population dynamics of the forest's bears are long term and cover a large
spatial extent. Responses of these populations must be measured on distinctly different time
scales. If they are not, critical characteristics of population dynamics occurring out of synchrony
with measurement intervals may be missed and the response of the population misinterpreted.
Changes within the forest ecosystem can occur gradually over long time periods and may be
affected by slowly changing external factors such as climate. These factors can operate over
centuries to millennia.
Interactions between the spatial and temporal components of variability also need to be
considered in assessing significance. For example, an extreme weather event may cause damage
from freezing to some particular location but not to a nearby area because of microclimate
differences (e.g., proximity to water, elevation, local wind conditions). Yet a synoptic event may
lead to major impacts affecting all local areas, increasing the ecological significance and reducing
recovery prospects. This is an important distinction in global climate change analyses: Any given
weather station might experience an extreme cold or warm event, but if essentially all stations
experience prolonged or frequent extreme events, the consequences are dramatically increased.
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4. RISK CHARACTERIZATION
The risk assessment process is designed to determine the probability of a particular
change in ecological state or condition occurring as the result of a change in a stress regime.
Although the statement of risk measures the probability of change, it does not necessarily assume
discussion about the significance of that change has taken place (i.e., by explicitly addressing the
question "So what?"). Because such a discussion should be the basis of the description of risk
communicated by the risk assessor to the risk manager and the public, it should be part of the
assessment. The discussion should synthesize information on:
• the characteristics of the stressor;
• the spatial and temporal patterns of exposure;
• the nature, magnitude, and severity of ecological effects;
• recovery; and
• uncertainty.
The risk assessment process also should characterize risks within a broader ecological
context and juxtapose them with societal values. This section addresses the role that science
(e.g., determining endpoints, uncertainty, recovery) and societal values play in measuring,
interpreting, and communicating the significance of risk.
4.1. Societal Values
To characterize risk adequately, public values that may be threatened by a proposed
policy or action must be ascertained. In the best case, an assessment of risk and a determination
about ecological significance would be initiated only after the risk manager has provided a
complete list of the public values at issue. In most cases, however:
the potential impacts of a policy or action are not obvious, at least not until a
preliminary conceptual model is completed;
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public values are not articulated clearly enough to determine what ecological
processes and dynamics present a cause for concern;
even when values are articulated, it is difficult to measure societal values
sufficiently for resolving conflicts and setting priorities; and
public values are evolving as new information is disseminated.
All of these factors imply that the risk assessment/risk management process—and
especially the determination of ecological significance—must be iterative. Although it is clear
that ecological risk-based decision-making requires input regarding societal values, it is less clear
how to best determine and measure those values.
No accepted method exists for ascertaining societal values. Mainstream economists, who
argue that individuals have relatively stable preferences indicated by their willingness to pay for
various goods and services, offer a theoretically comprehensive methodology for evaluating
outcomes of policies in terms of units of individual benefit. Economists recognize that many
commodities and resources, especially ones like ecological system health, are difficult to quantify
because they provide benefits indirectly. Representatives of other disciplines also argue that
economists overemphasize the individual basis of decisions and ignore actions that support values
of a more communal nature (Sagoff, 1988).
A method is needed that would facilitate distinguishing economic-oriented issues from
public-value issues. The risk decision typology (see figure 3) represents a step in the right
direction (see section 2.3).
Even if considerable objectivity can be achieved in determining that a given decision may
have widespread and long-term impacts, societal values will ultimately determine which of these
decisions will require constraint of individual choices. It is unlikely that a truly objective or
algorithmic method for resolving these value issues will ever be developed. In the absence of
such a methodology, risk assessors should involve risk managers, elected officials, and the public-
at-large in an ongoing dialogue that focuses on identifying ecological dynamics that threaten
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public values. (For a detailed discussion of the formation and measurement of public values
affecting risk assessment and management, see the appendix.)
4.2. Risk Integration
The integration phase of the risk assessment process combines information on the stress
regime and stress-response relationships in a probabilistic statement of risk. A variety of
approaches accomplishing this step have been proposed:
empirical models (e.g., distributional and regression analysis, extreme event
analysis);
process (mechanistic) models (e.g., individual, population, community, ecosystem);
and
experimental models (e.g., microcosms, field experiments) (see chapter 9, on risk
characterization).
Each of these approaches utilizes a quantitative expression describing the incremental
change in response relative to changes in the stress regime as the basis for estimating the
probability of risk from measured or modeled environmental patterns of the stressor. While this
statement of risk predicts the probability of a certain magnitude of change in a response as a
function of a change in a stressor, it does not explicitly discuss, even for cases of high probability,
whether those risks pose a significant threat to the ecological system. For example, there may be
a substantial risk (e.g., high probability of impaired reproduction) to an important fisheries
resource population, both in the short term and at a local scale, from a particular stressor.
Nonetheless, the ecological significance to the resource population as a whole may be relatively
minor given the natural variability in recruitment for the particular species and the remaining
large portion of the stock population. Thus both measuring risks and interpreting the
significance of the risks are an essential part of the risk assessment process
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4.2.1. Assessment and Measurement Endpoints
Determining the significance of the ecological changes highlighted by a risk assessment
requires careful examination of the type and intensity of the response in regard to the endpoints
used in the assessment. For example:
Are the observed changes in measurement endpoints distinguishable from similar
measurements at reference sites or from what is known regarding the natural
variability of the response being measured?
Is there a robust, quantitative relationship between the observed changes in
measurement endpoints and the assessment endpoints that are the benchmarks for
the assessment?
Are the changes in the assessment endpoints distinguishable from reference sites
or what is known regarding the natural variability of the response? How much
change in the assessment endpoint is "normal"? What are those boundaries?
How do we know when the bounds have been exceeded and what is within the
bounds of natural variability?
Finally, how is the significance of changes in assessment endpoints interpreted
within the "ecosystem context"; that is, can a "systems" perspective be used to
interpret the significance of changes observed in measurement and assessment
endpoints? For example, the significance of a large change in an endpoint that
represents a highly redundant ecosystem function may be considerably less
important than a small change in the population density of a keystone species.
These relationships (i.e., the connectiveness of the measurement and assessment
endpoints to the system as a whole) could be illustrated using network diagrams
that clearly describe the importance and value of specific pathways.
4.22. Uncertainty
Estimating the significance of a detected or projected risk requires an explicit discussion
of all the uncertainties in the assessment process. In particular, these uncertainties include those
associated with:
• the structure of the conceptual model for the risk assessment;
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• the structure of analytical models used in the assessment;
• the parameterization of the models; and
• stochasticity.
Cothern (1988) presented a useful summary of uncertainties and their importance in the
risk assessment process; also, full treatment of the role and importance of uncertainties in
ecological risk assessments is presented in chapter 8. Most important for the risk manager is a
clear and comprehensive presentation of all the assumptions in the assessment.
Uncertainty provides the issue of significance with a context in which to interpret risks
that can be envisioned as a matrix of uncertainty values and risk values (e.g., severity).
Generally, decisions are straightforward for cases with low-severity risks that include either low
or high uncertainty as well as high-severity risks that include low uncertainty. In contrast,
decisions related to potentially severe risks with high uncertainty are likely to be problematic.
A third variable that must be factored into any determination of significance is natural
variability, or stochasticity. For example, assuming that the aggregate uncertainty associated with
the risk of 50 percent impairment of recruitment to an important commercial fisheries stock is
considerable, the risk may be indistinguishable from the interannual natural variability of
recruitment patterns for the species. This would suggest to the risk manager that the significance
of the projected risk (e.g., 50 percent impairment in recruitment to the population) is negligible.
Similarly, the degree of uncertainty associated with the estimates of risk from stratospheric ozone
depletion also is quite high, as is the variability in susceptibility. Yet, because of the severity of
the potential consequences (i.e., human health risk of cancer as well as global ecological effects),
the risks have been deemed highly significant, resulting in regulatory action. These two examples
illustrate the need to balance uncertainty with information on severity of the risk and natural
variability. This balancing calls for the type of professional judgment required for determining
the significance of the assessed risks.
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4.2.3. Cumulative Effects
Risk assessments tend to focus on risks from a single stressor. To make a determination
about the significance of stressor-specific risks, however, the risk manager must have access to
current scientific information about incremental risks from other stressors (e.g., secondary and
tertiary anthropogenic and natural stressors). Knowledge about the full spectrum of stressors
and associated risks provides a larger context for the decision-maker.
Thus the risk manager must consider cumulative effects that can result from both the
direct effects of multiple stressors and the indirect effects of secondary or tertiary stressors. The
extent to which cumulative effects and subsequent risks from multiple stressors are important will
be specific to the stressor and its temporal and spatial scale, as well as to problem-specific
ecological considerations. In addition to the cumulative action of multiple stressors, the risk
assessor must be aware of the potential for the cascading of indirect effects from secondary and
tertiary stressors, which often are biological in nature. For example, in the case of aquatic
overenrichment the direct risks from the primary stressors (e.g., nitrogen and phosphorous) are
the excess production of carbon and the alteration of phytoplankton community structure.
Although both risks can be considered secondary stressors, the carbon flux to the benthos can be
directly responsible for an alteration of benthic community structure. That is, if the threshold for
the infaunal communities' processing capabilities is exceeded, excess carbon will accumulate. This
will create an oxygen demand that often results in lowered dissolved oxygen. Similarly, changes
in the phytoplankton community structure can alter the plankton's trophic structure, resulting in
impacts to socially valued commercial or recreational resource populations. Thus it is
particularly beneficial to use a systems context for evaluating the significance of results from
ecological risk assessments.
4.2.4. Recovery
Recovery is a primary topic of consideration for the risk manager when interpreting the
significance of the findings of an ecological risk assessment. Time of recovery, spatial scale, and
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temporal scale effects have been suggested as criteria that can be used in the problem
formulation phase of the risk assessment process to guide both the selection of endpoints and the
interpretation of changes in the response of the endpoints. When reviewing the results of the
risk assessment, the risk manager must ask whether the types of risks and their severity are
reversible if management action is taken to mitigate effects. To make an informed decision, the
risk manager must consider the following topics.
Nature of the stressor. What is the nature of the stressor (e.g., physical, chemical,
biological) and properties (e.g., duration, intensity, frequency) that are relevant to recovery? For
example, the potential recovery of an ecosystem from a labile chemical stressor will be quite
different than recovery from a cyclical biological stressor, such as the gypsy moth, or the physical
alteration of habitat.
Structure of the ecological system. The structure of the ecological system or component at
risk will influence both the degree and rate of recovery. For example, the time needed for a
fisheries stock to recover might be a decade or more; the recovery of a benthic infaunal
community could require from 1 to 3 years; and a planktonic community might completely
recover within weeks to months. The common ecological factor in these examples is the
temporal scale of the life history for the populations, given that the stressor has been sufficiently
mitigated.
Spatial scale. The spatial scale of the risks is the third factor that must be considered by
the risk manager when evaluating recovery. For example, what percentage of the landscape is at
risk and how does that relate to the territorial range of the critical populations of concern? Is
there an adequate stock available within the landscape for recruitment, or is there a satisfactory
linkage between one or more landscapes that provides refugia for impacted populations? Are
there adequate corridors, be they terrestrial or aquatic, for successful migration? Often
overlooked in aquatic systems is the secondary risk to migratory species (e.g., anadromous fish,
mammals) that can be posed by acceptable usages of certain portions of water bodies.
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S. ECOLOGICAL SIGNIFICANCE INPUTS TO DECISION-MAKING
5.1. Weight of Evidence in Decision-Making
Although a discussion of ecological risk management and risk communication is beyond
the scope of this chapter, this section briefly addresses certain aspects of these areas as points of
connectivity within the overall process.
Defining ecological significance involves addressing ecological issues concerning the
importance of change in regard to one or more ecological endpoints and societal issues
concerning the explicit or implicit importance of the ecological system in its altered state. Since
there are no simple formulas to follow, in our view the decision-making process involving risk
management and communication should rely on a weight-of-evidence approach. In this way, the
judgment of the decision-maker, supported by appropriate ecological and social science expertise,
can be made on a range of factors.
The first aspect to consider is the specific finding that an anthropogenic change is
ecologically significant. Ultimately, this must be determined by the risk manager and decision-
maker, not by the scientists alone. All ecological, societal, and values issues should be factored
into the determination, along with such considerations as intensity of the stress, magnitude and
spatial extent of the response, time for ecological recovery, importance to society of the changed
components of the ecological system, and economic and other societal costs of management
options.
Consider, for example, the ecological and agricultural consequences of global climate
change. If this possible anthropogenic stress were realized, the effects would be experienced by a
broadly diverse array of plants and animals in virtually all types of habitats, at spatial scales from
minute to global and at temporal scales from days to centuries. Thus there is no simple data
base or single model that can be used to define likely ecological consequences; rather, a suite of
appropriate methodologies would have to be compiled, including for instance:
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historical analogs of extreme climate events and their consequences (e.g., the
effects of the extreme heat and drought in the summer of 1988 on the
environment and agriculture of the United States);
statistical models, also based on historical records but with an emphasis on the
distribution of stress-response correlations rather than extreme events only (e.g.,
the yields of rice productivity in different weather conditions across years of
records);
physiological data bases (e.g., from laboratory experiments on the temperature or
soil moisture tolerances of different individuals from various test species);
whole-ecosystem manipulations (e.g., the use of microcosms to examine how
community and ecosystem dynamics respond to changes in the physical
environment);
paleo-analogs of pollen and other fossil records relating the distribution of plant
and animal species under various climatic conditions;
life-zone classification schemes (e.g., the Holdridge life zones or newer linkage of
geographical information systems with ordination analyses, which relate the
distribution of existing biomes with physical conditions);
process-based ecological models (e.g., the forest stand simulators that predict
community structure, productivity, and sustainability through explicit modeling of
species-competitive interactions and species-specific physiological limitations); and
expert judgment in which all available information and methodologies are
considered in reaching a conclusion about potential effects and their implications.
To adequately assess the potential consequences of global climate change on ecological
systems from population to biome levels, the full suite of such methodologies would be needed,
since any single methodology has limitations and inherent uncertainties. The array of
methodologies also would allow for the derivation of conclusions without the constraints imposed
by individual approaches. The diversity of data bases and analyses would be incorporated into
the weight-of-evidence determination. Similar multiple-methodology approaches would be
appropriate for many other anthropogenic stresses, especially for those involving nonchemical
stresses, multiple stresses and/or multiple target ecological systems, and incomplete data bases.
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5.2. Decision-Making in the Presence of Uncertainties
In reaching determinations about ecological significance and weighing this with economic,
societal, or other factors in risk management, the decision-maker must accommodate the
presence of uncertainties. For any environmental problem facing society today, there are
uncertainties concerning, for example, how ecological systems function and respond to stress and
how recovery processes operate; the availability of data at the various scales of interest;
requirements for extrapolating from laboratory or analog data to real-world ecosystems; and the
continual presence of natural variability in both the physicochemical environment and in
biological organisms and processes. Uncertainty will always be a factor in risk assessment,
regardless of how much research is conducted or how extensive a data base is established. Thus
environmental decisions must be made despite the uncertainties, otherwise they would literally
never be made. Using the weight-of-evidence approach, a 95 percent confidence level is not
necessary or in many cases even feasible. Rather, the risk manager and the decision-maker must
make determinations based on information from analytical techniques and available data bases.
5.3. Adaptive Management
Along with relying on multiple analytical tools and assessments, decision-making in the
presence of uncertainties can be facilitated by using adaptive management. This technique
calls for:
establishing appropriate monitoring for ecological endpoints judged to be
responsive to management decisions;
following the state and trends regarding endpoints and evaluating new information
in the context of anticipated responses or control conditions; and
modifying management decisions for appropriateness based on evidence of the
success, failure, or other more subtle indicators of the efficacy of the management
policy.
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As presented by Rolling (1978), adaptive management reduces the need for certainty,
allows the adjustment of policies as societal values change, and accommodates natural variability
in important ecological endpoints. Refining interim decisions after careful monitoring of
endpoints can permit early reduction or mitigation of stress and can limit the consequences of a
misjudgment.
There are limitations to this approach relating particularly to time lags regarding
responses (e.g., controls on the emission of chlorofluorocarbons may take several decades to be
manifested in reduced stratospheric ozone concentrations) or relating to societal and institutional
factors (e.g., public and political perceptions that an environmental problem is "solved" and thus
no longer of concern). Nevertheless, given the uncertainties and complexities, ecological risk
management and environmental decision-making must be adaptive if it is to be successful. This
concept has been incorporated into the risk assessment framework in the form of feedback loops
that lead from decisions back to the problem formulation and analysis steps. Nonetheless, as the
guidelines are further developed, the adaptive management concept should be incorporated more
extensively.
5.4. Research in Support of Decision-Making
Clearly the focus of environmental research should be the minimization of uncertainties
and the reduction of risk. Thus there are two basic aspects of research needs:
development and improvement of assessment tools (e.g., new models of ecological
systems or experimental manipulations on more complex and larger-scale
systems); and
expansion of our understanding of stress-response relationships and the
consequences of anthropogenic activities (e.g., improved dose-response data bases
on chemical mixtures affecting communities, or improved physiologically based
models to describe the biochemical, individual, and pollution-level responses of a
particular species).
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Also, a great need exists for research on the recovery and ecosystem-shift aspects of stress
ecology (i.e., on the rates and completeness of recovery once an anthropogenic stress is reduced
or eliminated). Similarly, research is needed" on mitigation actions a risk manager could
implement that might alter the effects of a stressor.
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APPENDIX
ASCERTAINING PUBLIC VALUES AFFECTING ECOLOGICAL SIGNIFICANCE
Introduction
Judgments of ecological significance, we have argued, cannot be based on purely
descriptive criteria; judgments regarding which ecological elements, structures, and processes are
most important for society to protect inevitably rest upon value judgments, so the ecological
significance of a decision cannot be assessed except by reference to societal values. The decision
of the U.S. Environmental Protection Agency (EPA) to focus on ecological as well as human
health risk embodies, in itself, a decision to examine the impacts of human activities on systems,
thereby implying a value in the protection of those systems. But systems are many-scaled and
the evaluation of ecological risk will inevitably require evaluations of which levels and scales of
nature are of primary importance to humans.
While it is clear that ecological risk-based decision-making requires input regarding public
values (as is argued in the chapter on ecological significance), it is less clear how those values are
to be determined, measured, and factored into decision-making (Sagoff, 1988). Economists,
ecologists, and philosophers pose value questions so differently that achievement of a
consensually accepted methodology for determining societal values is unlikely at this time. The
purpose of this Appendix is to survey available methodologies for identifying and measuring
societal values that may be at risk in decision-making and to suggest practical guidance to those
who must determine whether a decision is ecologically significant and likely to affect societal
values. Given heated disagreement among practitioners of different disciplines regarding
methods for ascertaining environmental values, the risk manager must use a balanced approach,
drawing information from multiple sources and wisely choosing which value criterion and which
value information to emphasize in particular situations.
Because mainstream economists have proposed what purports to be a comprehensive
approach to measuring societal values, our strategy will be, first, to state market analysis
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techniques espoused by mainstream economists and the rationale behind them. Then we will,
appealing to criticisms by noneconomists and "ecological" economists, show ways in which the
economists' calculations, while perhaps providing initial guidance, must be supplemented by other
methods and by the good judgment of risk managers.
Techniques of Mainstream Economic Analysis
Mainstream economists have generally attempted to determine public values associated
with environmental goods by market techniques. They determine the value of a good or service
by devising means to test how much consumers are willing to pay for it, given the competition of
other wants and needs for their economic resources. Since all resources are assumed to have
adequate substitutes (in the sense that it is assumed that citizens will accept some amount of
compensation for any particular loss of a means to satisfy some preference), economists can
evaluate welfare enjoyed by individuals as a result of outputs of ecological systems and then
aggregate these values toward a total. Economic methods, therefore, treat the problem of
valuation as fully addressed in the processes of individual choice; decisions of individuals seeking
their own welfare, they believe, provide in principle complete data regarding the value of any
environmental good. Since many environmental goods and services are not traded in markets,
the challenge is to gather this data from individual behavior. Means must therefore be devised
to infer these values indirectly.
Economists divide values into two categories: use values and nonuse values (Mitchell and
Carson, 1989). Some authors have also mentioned a separate category of "option" values
(protecting something in case it is needed as a substitute for some currently used resource that
may become scarce) (Randall, 1986; Fisher and Hanemann, 1985), but current thinking treats
option values not as a separate category, but as correction factors in the calculation of use values
(Mitchell and Carson, 1989). Nonuse values are sometimes called "existence values" because they
are values for protecting an object that is expressed by respondents who have no intention of
using the object in question—they value the object for its existence alone, regardless of the use
to which it can be put. For example, a respondent may pay to protect wolves in Alaska, even
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though he or she has no intention of visiting Alaska. The value of an object, in this scheme, is
the sum of its use values and its nonuse values.
Measures of Public Values
Economists' methods for studying individual preferences can be categorized as follows:
(1) direct market observation, which can be applied only to those objects that are actually traded
in real markets; (2) extrapolation from market observation, which includes (a) direct observation
plus adjustment for confounding variables such as government subsidies, and (b) hedonic pricing,
which infers the value of objects that are not traded in markets from differentials in prices of
other objects that are so traded (e.g., valuing the amenity of a beautiful view by observing real
estate transactions and noting the price differential between otherwise similar properties with and
without views); and (3) "contingent" valuation, which is designed to assess how much a person
would be willing to pay for a nonmarket good or service if there were a market for it.
Contingent valuation studies, in turn, may involve questionnaires, bidding games, and mock
markets such as auctions; alternatively, it is possible to construct binding or nonbinding referenda
regarding the imposition of a tax to accomplish the environmental goal.
Alternatively, hypothetical markets could assess how much a person would accept as just
compensation for some good or amenity he or she currently possesses but may lose because of a
public action or policy. But advocates of contingent valuation have been reluctant to use figures
derived from willingness-to-accept studies because they have not fully explained why willingness-
to-accept loss of a commodity consistently turns out to be significantly larger than the
willingness-to-pay for the same commodity (Mitchell and Carson, 1989).
When contingent valuation methods are used to discover market values for nonuse values
(and there is usually no alternative to contingent valuation if dollar values are to be attached to
nonuse values), then many further questions are raised. The willingness-to-pay assigned a nonuse
"commodity" exists only theoretically. There exist, in the nature of the case, no actual,
measurable market behaviors that can serve as a check on bids offered during a contingent
valuation study of a nonuse value. This theoretical point is important because it means that,
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since the markets in which they are expressed are only the hypothetical (i.e., non-existent)
markets constructed in questionnaires and the counter-to-fact scenarios they create, preferences
elicited in contingent valuation studies are a figment of the tests used to create them. This is not
to say that such figures are not useful, but only to say that they can be expected to be very
context dependent. One cannot test their validity against a pattern of real behaviors of
individuals, but only against alternative tests with alternative scenarios.
A recent study by a blue-ribbon panel assembled by National Oceanic and Atmospheric
Administration (NOAA) cautiously endorsed contingent valuation as a method of estimating
dollar figures for nonuse values, provided the studies follow prescribed guidelines. This
conclusion, however, represented a qualified endorsement of contingent valuation for the specific
purpose of proposing damage estimates to be used in court proceedings; it is not obvious that the
NOAA panel's endorsement would also support use of contingent valuation data in a policy
setting process (NOAA, 1993). At least it must be recognized that the commodities evaluated in
contingent valuation studies are narrowly defined and involve only one of perhaps many public
values affected in decisions facing policy-makers. Further, the question of how much validity to
accord hypothetical market valuations of environmental goods remains highly controversial and
many philosophers, ecologists, and dissenting economists question this entire theoretical
framework for interpreting environmental values (see, e.g., Rolston, 1985; Kelman, 1981; Sagoff,
1988; Norton, 1988; Blarney and Common, 1992).
While there exists no shortage of arguments as to why we must seek more
comprehensive, systematic management objectives, there is a foundational disagreement among
policy analysts regarding how those objectives should be discussed and analyzed. Even when
environmental managers within EPA or other agencies of government wish to act to protect
these whole-system characteristics, they are stymied because there exist no accepted means by
which to measure benefits derived from whole ecosystems, and they have little hope of justifying
programs directed to this purpose in terms acceptable to auditors at the Office of Management
and Budget. For example, in a recent National Academy of Sciences study, which attempted to
weigh the relative costs of prevention versus accommodation to global climate change, zero value
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was assigned to the damages to natural systems. The panel justified this exclusion by stating that
no adequate methods for ecosystem valuation exist (NAS, 1992).
<5
Ecologjsts and environmentalists therefore often complain that their most important
points are ignored by economists; economists argue that ecologists have failed to show how
ecological "risks" can be measured or how the goals of avoiding such risks should be assigned
value and importance among the many competing social goals. Economists, on the one hand,
and ecologists and environmental activists on the other hand, therefore tend to talk past each
other, because there exists no comprehensive language of policy analysis that can express these
various positions impartially. In the next section we will suggest some new concepts that may
help to bridge the gulf between the two approaches and show how these concepts direct us
toward new approaches to valuing environmental goods.
Toward a More "Ecological" Economics
It is helpful to envision the intellectual terrain as represented in figure 3 (in the chapter
on ecological significance), which attempts to formulate issues separating ecological and
mainstream economists in neutral terms. This "environmental risk decision square" plots the
variables of reversibility against the magnitude of impacts, defining a decision space on which
various policy decisions can be located, depending on types of possible risks that are incurred in
choosing among possible policies. The risk decision square was shown in the chapter to
introduce spatial and temporal dimensions of risks that are important to ecologists. The decision
square therefore aims to provide a formal representation of the logical space in which ecological
risk assessments are made.1
'It should be noted that the model, as here presented, is incomplete as a decision model, because it does not
incorporate a consideration of uncertainty/probability. Presumably, the obligation to act to avoid a risk to the future
depends also on the degree of probability attached to the likelihood of the risk, given current knowledge, that a
negative outcome will occur. That is, if the present faces two risks, the negative outcomes of which would be
equally harmful to an equal number of people, we assume that there is a stronger obligation to act to avert a risk
that has a 40 percent chance of negative outcome than to avert a risk that has a .05 likelihood of occurring. But
work in this area is too speculative to guide policy at this time. For simplicity, we will use only versions that plot
decisions in two dimensions. In effect, that means we are assuming that, in comparing two risks, the risks carry
equal levels of likelihood of negative outcomes. If this assumption does not hold, risk managers should modify their
judgments accordingly.
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Having introduced a formalized decision space that relates the differing positions outlined
above, it is possible to add further assumptions and principles, giving more structure to the
decision space, which may provide more specific and substantive guidance in situations in which
essential and irreplaceable resources are apparently at risk. First we can note that ecological
economists, purely by virtue of their emphasis on ecological systems and processes, will focus on
the temporal aspects of change; for them, irreversibility is not just interpreted as an abstract
concept of substitutability of one resource for another, as measured against units of welfare
available to consumers—it will include the more concrete parameter of reversal of impacts in
ecological time and space. The horizontal axis can therefore be calibrated as a measure of how
long, given a particular impact or disturbance, would be required for natural processes to reverse
that impact. The horizontal axis therefore locates decisions and policies that incur certain risks
according to the restoration time necessary to repair damage if negative impacts occur as a result
of that decision or policy.
From the ecological viewpoint, according to hierarchical principles formalized within
ecology as "hierarchy theory," this larger scale reflects an awareness that larger ecological and
physical systems normally change more slowly than do smaller systems that form their parts
(Allen and Starr, 1982; O'Neil, et al., 1986; Norton, 1991). The hierarchical approach has a
profound impact on the way ecological economists view the relationship of economics to ecology
because individual economic decisions are relatively short-term responses to evolving conditions
in a hierarchy of larger and larger social systems (e.g., markets) that are, in turn, embedded in
larger ecological systems (e.g., whole ecosystems) and in even larger physical systems (e.g., the
atmosphere). Ecological economics is therefore comfortable with the assumption that humans
decide and act within a multilevel, complex system; they therefore do not seek reduction of all
values to a single measure so that all values can be aggregated together.
We can now represent the claim, often heard in policy circles, that economists and
ecologists employ a different "paradigm" (Daly and Cobb, 1989; Norton, 1992); that is, by
recognizing that ecological economists would, at the expense of being unable to aggregate fully
across the whole space, draw distinctions in types of risks, breaking the risk decision space into
distinct regions where different considerations and criteria apply. They therefore differ from
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mainstream economists who assume that all types of risks can be expressed as dollar values that
vary gradually and at the margins, representing their decision to treat all risks and benefits in
"fungible" terms. Mainstream economists, ecological economists, and ecologists all agree that
preservation of options for the future is a good thing. But because mainstream economists
believe there to be a suitable substitute for every resource, they do not necessarily see
irreversibility as sufficiently important or as sufficiently discontinuous across various policy
outcomes to warrant special categories of irreversible decisions. Ecological processes of
restoration are not relevant to classifying risks because reversibility is not considered the reversal
of specific damage to a particular resource; reversibility is fully achieved by compensation in
dollars sufficient to make the consumer willing to accept the damage.
Ecologists, on the other hand, argue that ecological systems change discontinuously and
that some changes, such as the extinction of a species or the destruction of a rainforest,
introduce crucial thresholds into the decision process. In questioning whether there are "suitable
substitutes" for functioning rainforests or for grizzly bears in Yellowstone Park, many ecologists
urge—in strongly moral terms—that extraordinary efforts to protect such resources are morally
mandatory. They are not, therefore, fungible as against other possible losses or risks.
Given these contrasts we can begin to characterize a "two-tier," or "hybrid," approach to
environmental decision-making, which incorporates aspects of mainstream and ecological
approaches into a more comprehensive system of valuation (Page, 1991; Norton, 1991). This
approach would see the decision space faced by environmental managers as split into regions.
with the corner of the square that is characterized by major negative outcomes that are
irreversible as representing an area of risk where values do not vary continuously with
consumptive values and where moral strictures apply. But the two-tier approach does not
therefore ignore cost-benefit analyses expressed in dollar figures; these analyses provide a useful
method in those decision areas where reversibility is high and scale of impact is relatively small,
or both. In these regions, we will be inclined to accept the usefulness of economic methods on
the assumption that, in these decisions, the future cannot fault us if we compensate destructive
impacts on natural systems with, for instance, increased technological know-how or monetary
capital. In these regions, in other words, decisions are regarded as fungible. Economists would
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therefore accept the same "geography" of the decision space provided there exist any decisions
where moral limitations override and make irrelevant a computation of costs and benefits. This
conceptual geography focuses attention on what has been called a "criterion of ecological
significance"—a criterion based on physical parameters rather than simply economic
consequences.
We have not, in so dividing our decision space, however, violated our policy of conceptual
neutrality because the doctrinaire economist who says that all decisions will be decided on the
basis of costs and benefits as measured in markets can represent his or her position by asserting
that the "red region" is empty. No decisions have a special ecological, in addition to economic,
significance. The position that all resources have substitutes therefore represents the limiting
case of not taking irreversibility or magnitude of impacts as having any nonfungible value.
Ecologjsts differ from economists in believing that scalar differences can be so great as to
introduce discontinuities into the analysis; economists rely entirely on a marginal, incrementalist
style of analysis.
Now, relying on hierarchy theory and its principle that larger, supersystems change more
slowly than do smaller subsystems, we can correlate the vertical axis with spatial scale, creating a
grid that will represent decisions as located on the space. Risks that threaten harm that is
prevalent over a large geographical area and irreversible for a very long time will be clustered in
the upper left. We expect, given our model, that decisions in this area are ecologically significant
and may be governed by moral constraints. Decisions that risk small-scale and quickly reversible
impacts are not significant on the ecological scale and fall in the area governed by economic
reasoning. Applying reasoning such as this, Norton and Ulanowicz (1992), for example, have
argued that, since a commitment to sustain biodiversity is consensually understood to be a
commitment to do so for many generations of humans (at least for 150 years), we can conclude
that the focus of biodiversity policy should be landscape-level ecosystems that normally change
on a different temporal scale, an ecological scale that is slow relative to changes in economic
behavior. If we can isolate the dynamics driving local economic opportunities from the dynamic
supporting biodiversity, it may be possible to encourage both and avoid policy gridlock such as
has occurred over the Northern spotted owl in the Pacific Northwest.
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The central principle of hierarchical organization deeply affects the valuation process as it
is envisaged by ecological economists. Rather than aggregating values that are expressed on
these multiple levels (which would require that the values be measured in a single metric, such as
present dollars), ecological economists recognize that subsystems existing at different scales are
driven by relatively distinct dynamics. They therefore expect humans to express and pursue many
values (which are not reducible to a single common denominator such as the current willingness
to pay), and they see their task as one of integrating multiple values, such as maintaining high
levels of individual welfare and protecting resources for the future, rather than as one of
reducing all values to a common metric and then aggregating toward a bottom-line ratio of costs
and benefits as a guide to policy decisions.
Since crucial variables driving change at different levels of the system can vary effectively,
independent of each other, it is in principle possible that important economic goals can be
achieved (by manipulating variables affecting individual productivity and welfare) while
environmental goods are also protected or even enhanced (by paying attention to distinct
variables affecting the environment). This can be accomplished by modeling the different
changes on different scales and seeking to influence dynamics that are relatively independent
across scales. To the extent that ecological economists can identify dynamics driving economic
opportunity (dynamics that are independent of the larger dynamic of ecosystem health), it may
be possible to avoid choices between growth and the environment by providing economic
incentives that will encourage new, cleaner, and more efficient technology. The problems of
environmental management, therefore, become ones involving separating dynamics associated
with various public purposes, creating economically efficient means to encourage socially
important dynamics (e.g., energy flows), and devising means to discourage damaging technologies
by encouraging efficiency without ecological damage.
Ecological economics thus recognizes another level, or scale, of decision-making; this is
the macroscale on which societies and cultures set conditions on market behavior and thereby
influence individual decisions by changing the conditions within which individual decisions are
made. But it would be a mistake to assume ecological economists necessarily favor increased
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regulation. Ecological economists favor experimental programs that are designed to increase
economic opportunities and reverse environmental damage.
The central issue facing the risk manager can now be formulated quite simply: Are there
any, and if so how many, environmental decisions that are located in the "red region" and thus
governed by moral, rather than economic, criteria? If there are any, the goal should be to
determine precisely which decisions fall into these two broad categories. Judgments of ecological
significance are a central component of this decision-making dynamic (see figure 2 in the chapter
on ecological significance). Decisions that must be addressed in moral terms may represent
decisions that have ecological significance because risks of ecologically significant changes will be,
given our two-tier decision process, exactly those that may entail important ecological changes
that affect large ecological systems for long periods of time. It stands to reason that these
decisions should be given more careful scrutiny by risk managers.
It seems likely that the further development of new methods for measuring natural capital
and depletion of it will remain an important preoccupation of ecological economists for two
reasons: first, they believe that a prerequisite to improving the treatment of natural systems is
understanding and quantifying ways in which exploitative and careless treatment of the
environment reduces opportunities for future and sustained growth; and, second, the problem of
devising a new or expanded accounting system provides a forum for discussing the comparative
strengths and weaknesses of economic and ecological methods of valuation. Accordingly, a
central issue in devising alternative accounting methods is whether the new accounts will record
dollar costs and benefits or whether there will be separate physical accounts, counted in terms of
units of energy or other physical parameters (Odura, 1964; Harmon, 1991).2
^ fact, it is useful to note that ecological economists and environmentalists attack the use of GNP as the key
accounting framework on two separable grounds. First, they argue that GNP, which is a measure of economic
activity, does not actually measure individual welfare. They point out, for example, that the Exxon Valdez oil spill
resulted in huge cleanup expenditures that increased economic activity, but that these expenditures were indicative
of losses, not gains, in individual welfare. Second, they note that economic growth as measured by GNP might
indicate an increase in consumer welfare, but these increases might be offset (or partially offset) by associated
declines in standing stocks of, for example, timber, or by degradation of ecological systems. In both of these cases,
GNP is rendered inadequate as a guide to either individual welfare or to economic "growth" in a broader sense that
includes maintenance of natural capital.
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A central methodological innovation proposed by ecological economists would
supplement, or even replace, the use of the gross national product as a measure of economic
well-being (Costanza, 1991). Instead, they propose methods of measuring increases and
decreases in "natural capital" as a more realistic approach to keeping national accounts. Repetto
et al. (1982), for example, recalculated the national accounts of Indonesia, a country that has
achieved unusually high rates of growth in GNP by concentrating on exploitative industries,
especially the export of timber from rainforests. They found that after correcting for depletion
of standing stocks of timber and other resources, growth of GNP was reduced significantly.
These questions of evaluation and accounting are too difficult and contentious to fully
resolve here, though it is clear that a more comprehensive notion of public values than is
currently captured in the economists' vocabulary of individual preferences is needed to address
questions of ecological risk assessment and management. By juxtaposing societal versus
individual needs, intergenerational versus economic time scales, landscape versus local spatial
scales, nonuse (existence) versus use values, and the concept of ecological reversibility versus
non-reversibility, a strategy might be devised for using the data and models of economists in
conjunction with broader, ecologically based approaches to evaluation. This would provide an
initial, if tentative, identification of societal values and incorporate them into judgments of
ecological significance.
Resolution of Conflicting Values
One approach to an effective environmental policy is to reduce value conflicts, whenever
possible, by implementing policies that serve multiple values simultaneously. If, as hypothesized
in the chapter on ecological significance, the smaller scale dynamics affecting individual decisions
at the plot level are somewhat independent of the larger scale dynamics that drive ecosystem
health and integrity, it may be possible to integrate economic development and environmental
protection. For example, development projects for countries of the South, such as planting
woodlots for future fuel needs, can have positive economic, ecological, and long-term global
impacts by reducing population. (Research shows that most families in less industrialized
societies have more children because they need help with household chores such as gathering
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firewood and fetching water.) Here is an opportunity to affect a variety of values expressed at
the level of family welfare; at the level of ecological change, by reducing erosion and increasing
water retention; and at the intergenerational, global level by reducing birth rates.
To the extent that "lever" policies of this type can be devised and implemented,
environmental policy can move beyond conflicts and direct regulations toward integration and
positive programs of investment. In some cases, once problems are properly formulated and
modeled according to scale, and once all the public values in question are clearly articulated and
associated with specific dynamics, policies with win-win outcomes may be possible. This should
be true, for example, in most or all of the cases in which risk assessors are able to determine that
the assessed activity is ecologically insignificant because its impacts are easily reversible or
because the scale of the impacts is small. In cases in which policies may affect ecological
significance, creative thinking may lead to policies that enhance, rather than threaten, socially
valued ecological processes.
In the end, of course, irreducible conflict will remain, at least with respect to emphasis
and priorities. There will be disagreements, of course, among experts themselves and among
interest groups regarding the placement of certain risky activities in the "red region" and
regarding the evidence of the likelihood of negative outcomes once outcomes are designated as
possibly risky. Policy analysts probably will not agree regarding exactly what information is
relevant for deciding whether a given policy is risky.
Lessons Learned from the Endangered Species Act
The Endangered Species Act of 1973 (ESA) and past attempts to interpret and enforce it
provide managers with the best available experience in making public decisions regarding
biological criteria. That Act as eventually amended charges public servants to identify and
protect all species that are "endangered" or "threatened," unless a high-level committee judges
that the values of protecting a species are overridden by important national or regional interests.
Here we have an example of agencies charged to act according to the criteria of the Safe
Minimum Standard of Conservation (Ciriacy-Wantrup, 1952; Bishop, 1978), which requires that,
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when an irreversible loss of a resource is threatened, society should act to protect that resource,
"provided the social costs are bearable." Note that this reasoning provides one example of
decisions that are located in the "red region" on the basis of irreversibility. If one also gives this
event a spatial dimension by showing that the species and its services will be lost over an entire
landscape, protection of endangered species can represent an example of decision processes that
can be located in the "red region" of the risk decision square.
The Act has had some remarkable successes, including, for example, its role in the
recovery of the alligator in Florida and the Gulf Coast region. But, of course, it also has been
the subject of significant controversy, especially when the protection of a species has been
perceived to clash with important development projects.
On the positive side, first, the ESA has clearly called public attention to the problem of
biodiversity loss and therefore has made a huge contribution to the process of educating the
public about the importance, and difficulty, of protecting biological resources. In the process, the
Act has served as a rallying point for environmentalists and also as a unifying target of criticism
for advocates of economic development. Second, the implementation of the Act has put in place
a scientific core of government employees who are responsible for protecting biological diversity
and resources and a set of bureaucratically specified procedures that are triggered when a species
is listed as endangered or threatened. Third, the ESA has led to many successful cooperative
efforts between the federal government and state (and in some cases local) governments. Finally,
research has shown that, for the vast majority of cases, the costs of protecting a species are
remarkably low (Bishop, 1978; Tobin, 1990). On the whole, therefore, the ESA has succeeded to
the extent that it has placed important issues on state and national agendas, raising public
consciousness and stimulating public discussion and debate.
On the other side, the Act has been far from an unqualified success. First, even when
judged only against the internally stated objectives of the program, especially those of developing
recovery programs for each endangered species, the effort has a low rate of success (Tobin,
1990). The low rate of success in developing recovery programs was, for a long time, a result of
difficulties caused in designating critical habitat and the difficulty of providing cost-benefit
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analyses sufficiently comprehensive to fulfill demands at the Office of Management and Budget.
Some of these issues have been resolved and there is reason to believe that a new and aggressive
program will be developed within the Department of the Interior (U.S. Department of the
Interior, no date). Second, while protection of most species has cost little, there have been a
few, highly publicized cases (e.g., involving the Northern spotted owl in the Pacific Northwest and
the snail darter at the Tellico Dam) in which the Act has conflicted with forces favoring
economic development. In those cases, the Act has been at the center of divisive debate as
opponents have decried it as placing more emphasis on birds and fish than on human welfare.
Finally—and we argue below that this third weakness has contributed to the others—the Act has
formulated debates about biological diversity and resources in an unfortunately individual,
atomistic manner in which the target of protection is particular species or particular populations.
Since wild species necessarily exist within complex ecological systems that form their habitats, it
is doubtful that any policy approach that focuses mainly on species protection can provide a fully
adequate approach to biological protection (Norton, 1986,1987; Norton and Ulanowicz, 1992).
Important lessons applicable to the problem of defining ecological significance can be
drawn from management experience with the Endangered Species Act. First, while the focus on
species provides an understandable and communicable formulation of the problem of protecting
biological resources, and while it allows a fairly clear means to state goals for developing
recovery programs for each species judged on biological grounds to be endangered, the single-
species formulation does not always present the issues involved in biological protection in the
most perspicacious manner. In the spotted owl case, for example, invocation of the ESA
reinforced the tendency for opponents of protection to cast the issue simply as a conflict between
an owl species and jobs. In fact, of course, the conflict concerns use of old-growth forests much
more broadly. The narrow, species-vs.-jobs formulation stood in the way of public recognition
that the real public choice was between short-term economic gains and long-term protection of
important ecosystem services and economic opportunities for deriving values from the remaining
areas of old-growth forests. Thus, while the ESA is useful in situations where a species has
become rare because of gradual habitat destruction and where a recovery plan with small
economic consequences can be devised, the Act is less helpful in cases where a species becomes
the crux of a broad debate about public goals and land-use planning over whole regions. Or, to
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put the same point differently, we have learned from the successes and failures of the ESA that,
while loss of a species is one indicator of ecosystem change that is irreversible and therefore
raises questions of the "ecological significance" of those changes, the full consequences of those
changes cannot be represented on only a single level of the ecosystem.
In order to assess the full social and ecological significance of the loss of a single species
or a population of a rare species, it is necessary to understand that species or population in its
larger ecological context. Since ecological systems are best understood as hierarchically ordered,
as noted above, changes in species composition must be characterized as one aspect of constant
change taking place on many levels and scales. It is impossible to determine the ecological
significance of a change in species composition without understanding the impact of that change
on microprocesses, on other species' ability to persist in the area, and on the larger ecological
processes that are manifest in ecological systems and across the larger landscape.
A second important lesson, therefore, emerges from the ESA: It is important to pay
attention not just to the elements that perform ecological processes but, even more importantly,
to the processes themselves. The ESA was in a sense the culmination of a long tradition of
single-species, atomistic management that began with the monitoring and protection of game
species more than a century ago. The rise of ecology and its emphasis on system processes first
modified atomistic management by calling attention to the crucial role of habitat in supporting
species. Even more recently, however, the definition of biological resources has been expanded
to include "biological diversity" that includes not only species diversity and genetic variability
within species, but also variability across habitats. Two populations of a species that exist in
different ecological systems and niches, for example, are exposed to very different selection
processes, which implies that diversity of ecosystem processes are crucial in the protection of
species over multiple generations. More importantly, in the long run these processes are
essential to ecosystem development and to evolutionary processes. If the goal is to perpetuate
diversity, then counting and protecting species is less important in the long run than
understanding and protecting ecological processes within which species reproduce and adapt over
time. Some researchers, for example, have built upon the insight that some species are
"keystones" in their ecosystem (species whose contributions are essential to maintain a stable
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structure over time) and argued that we should be even more concerned about "keystone
processes."
Third, the public and academic discussions of how to make decisions about how to
enforce the Act have apparently pointed up weaknesses in current techniques of evaluation as
means to determine the full value of biological resources. In particular, the reductionistic
methods of evaluating species for their impacts on human preferences (i.e., measuring their
importance in enhancing human welfare) and then aggregating these methods have proved
inadequate, at least in practice. While the concepts of "use value" and "existence value" may, in
theory, point toward testable questions about human preferences that could in principle
represent the "total dollar value of a species," the actual execution of such a benefits analysis has
not been completed for a single species. Two reasons account for this: First, potential uses of a
species are not ascertainable until exhaustive research is done on possible uses; this research,
since it cannot ever claim to have examined every possible use, must somehow estimate the
present value of unknown uses for the species. Second, even if the human uses of a species are
identified and measured, no method has been developed for assessing the "contributory" value of
species (i.e., the value a species has in developing and preserving ecosystem processes and in
supporting other species that have direct and indirect impacts on human welfare through uses
and through the enjoyment of free ecosystem services (Norton, 1988). These discussions of how
to value a species (and then how to factor that value into a cost-benefit decision-making process
regarding whether to build a dam, for example) have therefore clarified the limits of aggregative
valuation processes when applied to ecosystem processes.
Finally, we can draw the lesson that, while a more ecological and less species-by-species
approach requires a more complex formulation of the goals of protection programs, a proper
formulation of the multileveled problem of assessing ecological significance can actually simplify
the problem of protection. In some cases, for example, declining populations of a species in
particular areas can be understood as a natural outcome of ecosystem development and be
judged to be of no ecological significance. This judgment will be easier to make if the processes
are understood and if the species is establishing and maintaining populations in other areas that
are at different stages of ecological development. In certain cases, however, it might even be
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concluded that loss of a species or population from a whole area is of little ecological
significance. These latter conclusions, of course, will require an understanding of ecological
processes, but they will also require a well-developed public consensus about values and priorities
in the protection of natural systems. These decisions require, ultimately, some means to identify
and articulate the social goals that are intimately intertwined with ecological processes. The
progress made in understanding the strengths and weaknesses of the single-species approach as
embodied in the ESA has led, at least indirectly, to the discussions of ecosystem risk and to the
struggle to articulate criteria of ecological significance as exemplified in this paper. One aspect
of that struggle (see figure 1 in the chapter on ecological significance) is the determination of the
correct scale on which to model natural processes and on which to formulate environmental
goals. To implement the goal of protecting ecologically significant elements and processes in
nature, there must be a spiraling effect as scientists, managers, and the public interact to
articulate societal values, determine which dynamics are essential to protect those values, and
formulate goals of management.
Toward a More Dynamic and Comprehensive Valuation Process
By adopting a two-tiered decision model, we have attempted to exhibit the importantly
different roles of mainstream microeconomic analysis and of methods for identifying ecological
risk. Since many environmental problems are problems of scale, we have argued that the basic
structure of decision-making should recognize at least a fuzzy line between those activities and
policies that risk minor and reversible changes to the environment and those that risk changes
that will affect whole ecological systems over long periods of time. This approach recognizes an
important role for economics, in either case. In the former case, economic analysis may be given
largely free rein; social choices, when not ecologically significant, can be governed by normal
economic analysis (corrected, we hope, for considerations of equity). In cases in which there is a
determination that a risk of ecologically significant damage may result from an activity or policy,
the role of economics is better seen as engaging in "cost-effectiveness analysis." Whereas cost-
benefit analysis balances all costs and benefits of a project, cost-effectiveness accepts certain
(politically mandated) constraints on goals and examines the various policy options by which to
achieve those goals, seeking the least-costly method for achieving the politically mandated goal.
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But several features of mainstream economic analysis make it ill-suited to determine
whether an activity or policy carries risk of ecologically significant change. First, as noted above,
economic analyses are seldom comprehensive. While it is useful to know, for example, that
species may have option value, it is almost inconceivable that an "objective" figure could be given
with any confidence to represent a rationally informed dollar figure for the option value of a
species whose biological characteristics are hardly known. Second, as noted above (and in the
chapter on ecological significance), ecological problems often involve discontinuities—activities
we engage in may alter the trajectory of ecological change by passing thresholds beyond which
stress to a system encourages rapid deterioration. Third, ecological significance is manifest not
just on a single level, but on many levels. Often it is ecosystem-level characteristics that govern
the flow of ecological services; the emphasis on short-term individual welfare characteristic of
economics is unlikely to reflect values that emerge on this scale. The second and third
weaknesses of economic analyses have been addressed here by emphasizing the importance of
understanding the temporal and spatial scale of risks encountered in the decision-making process.
Finally, economic analysis is not a very useful tool for deciding ecological significance
because of its "static" nature. Any study of public preferences will be conducted at a particular
time, and will be administered against a particular backdrop of information (which may be
mainly the background knowledge of respondents or may rely more heavily on informational
packets provided by researchers). Having noted above that preferences, especially of nonuse
values, are theoretical entities with no referent in actual human behavior, it follows that
judgments of the willingness to pay are highly relative to the particular context. This
characteristic makes very difficult the transfer of willingness-to-pay data from one context to
another. It also follows that the way questions are framed (i.e., as a political, legal, or economic
question) will affect the values that individuals will express (Sagoff, 1988; Blarney and Common,
1992). This suggests, for example, that citizens might respond quite differently to a contingent
valuation, such as a binding referendum on their willingness-to-pay taxes to protect parks and
reserves, than they will when the question is posed as a matter of economic choices of individual
consumers. Toman (1992), for example, has explored the possibility that some questions of
protection values are best considered by making an analogy to decisions made at a constitutional
convention.
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Four new trends in evaluational studies should be kept in mind by risk managers. These
are: (1) a trend toward more interactive processes of evaluation and management; (2) a tendency
to emphasize locally based values; (3) increasing emphasis on interpretations of the scale of an
activity as an important precursor to determining the values affected; and (4) an increasing
willingness, in cases where human values clearly depend on ongoing ecological processes, to
conceptualize the value of an ecological resource as the cost that would be incurred to replace
that process or function by artificial means. We explain and give a brief example of each of
these trends:
(1) Whereas questionnaires can often provide important base-line data regarding social
preferences, answers by respondents are necessarily dependent on the amount and quality of
information provided them. In areas of uncertainty about both values and science, it is useful to
engage the public in an ongoing process in which their evaluations lead to ecological models and
the models allow the proposal of more precise management goals. The use of focus groups,
town meetings, for instance, as well as pilot projects, in which communities "experiment" to
determine if they like the outcome of management initiatives, may become more important in
environmental value assessment.
(2) More and more authors are emphasizing the importance of particular and place-
oriented values (Ehrenfeld, 1993; Norton, 1991) in the protection of cultural connections with
natural systems. For example, one proposed study will use focus groups formed from citizens
who live and work on Chesapeake Bay to express and discuss the values they place on the bay
and the goals they would adopt for management of the bay's ecosystem.
(3) An emphasis on the scale of economic activities and their impacts on ecological
activities focuses attention on diverse economic opportunities for development that, collectively,
have positive economic impact but, because they are small scale, tend to cancel each other out in
their impacts on whole ecosystems. Suppose it is judged that, in a given area, a trend toward
monocultural agriculture threatens the diversity of the landscape and regional planning results in
a shared desideratum to diversify land use. Once the problem and goal are clearly articulated, it
may be possible to make public investments (e.g., the creation of farmers markets in urban areas
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within the matrix) that will encourage some farmers to choose smaller-scale production of
foodstuffs (e.g., vegetables and honey production for local markets). Integrative environmental
policy, therefore, can be creative and enabling, rather than simply based on regulation.
(4) Ecological properties that provide benefits to biological species are those structural
characteristics (capital) that ensure sufficient survivorship. The structural properties of
ecosystems both reflect and support species diversity, spatial complementarity, functional
redundancy, and adequate proportionalities of functional niches (e.g., predator/prey ratios). If
the structural integrity is maintained (survivorship), then the critical functions are sustainable.
These functions constitute "free goods" for human society and are generally taken for granted
when short-term tradeoffs are decided on. If one assumes that the continuation of these
ecological functions in volume, in place, and in kind are essential for the sustainabih'ty of human
systems, then one can assign a value based on the cost of replacement. These costs must,
however, include both construction costs and long-term management and maintenance costs.
These functions will no longer be free goods, but will reflect the real costs of an engineered
alternative that could well be very energy intensive. For example, consider a typical pothole
wetland with its complement of cattails, bacteria, algae, and animal fauna. The direct benefits
(see table 1 in the chapter on ecological significance) can be defined as those that result in the
transport and fate of materials that are considered to be associated with environmental damages.
Emergent vegetation has the highest productivity; anaerobic sediments store carbon dioxide and
allow microbes to detoxify pesticides. One can calculate the cost of building a "bioreactor" or a
"chemical reactor" that would perform the same function in the same volume at the same
location. The wetland runs on solar energy (free goods) and regulates its own internal structure
and rates of chemical processing. The engineered alternative would most likely run on fossil
fuels and the control system would probably be complex and expensive (i.e., you pay an engineer
more than you do bacteria). The indirect benefits are those for which no easily engineered
alternatives exist (see table 1). The benefits of biodiversity and landscape diversification are
generally recognized and are reflected in real-estate valuation. The high productivity (bioraass
energy) and breeding and nursery areas (fish and game recruitment) produce indirect benefits,
since they could support other, primarily economic, activities.
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Indirect benefits are often not registered in economic analyses that measure individual
preferences because the values are most evident on the level at which the social community
interacts with the larger landscape. Since it is difficult or impossible to assign dollar values to
preferences for these characteristics, replacement value provides a reasonable conceptualization
of the societal values involved, provided one can identify those values that the society will choose
to perpetuate technologically if their natural sources are lost. But this method should not be
thought of as a direct competitor of studies that ascertain the level of consumer preferences for a
given commodity. Indirect benefits emerge on the scale of the ecological system and in the
intergenerational options available to a culture. Replacement value seems a more applicable
concept after a decision of ecological significance has already been reached.
Risk and Uncertainty in Protecting Public Values
What options are available to minimize and mitigate risk in those cases where it is
determined that there is a significant risk that some human activity entails some risk of inflicting
significant impacts on ecological systems and that those impacts may negatively impact human
welfare in the future? The danger, one might argue, is that many activities, including activities
that are necessary to fulfill basic human needs, may have some risk of harming ecological
systems. Policy-makers, that is, may face the following quandary: There is significant risk that
activity A will have negative consequences for ecological functioning. If the policy-maker takes
the risk to be too high, activity A will cease and the values derived from it will be lost to society;
if the decision-maker decides the risk is acceptable and activity A is allowed, then the
government will be blamed if activity A leads to loss of welfare. The decision seems to hinge on
the probability of the risk as well as the decision-maker's attitude toward the risk. Some of this
anxiety can be alleviated if one recognizes that activity A may be modified—that there may be
alternative, lower-risk actions that will achieve the societal values associated with activity A. The
risk manager, nevertheless, must (hopefully with democratic guidance) decide how much risk is
too much.
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It is also possible to spread risk around, directing some risk at the private sector, through
the technique of "assurance bonding" (Costanza and Perrings, 1990).3 According to this
technique, private developers and other users of a resource would be required by a government
agency to post a bond in the amount of a "worst-case" scenario for destruction of societal values.
The burden would then be on the resource user to show (a) that its planned activities carry less
risk, in which case the bond amount would be reduced, or (b) to post the bond and, after work is
completed, demonstrate that the social costs did not occur in order to receive a refund of the
bond. This and other techniques may prove useful in cases where the risks of proposed activities
cannot be estimated accurately.
'This technique would in most cases require enabling legislation.
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Appendix References
Allen, T.F.H.; Starr, T.B. (1982) Hierarchy: perspectives for ecological complexity. Chicago:
University of Chicago Press.
Bishop, R.D. (1978) Endangered species and uncertainty: the economics of the Safe Minimum
Standard. Amer. J. Agricul. Econ. 60 (1):10-18.
Blarney, R.; Common, M. (1992) Sustainability and the limits to pseudo-market valuation.
Discussion paper. Centre for Resource and Environmental Studies. Australian National
University, Canberra.
Ciriacy-Wantrup, S.V. (1952) Resource conservation. Berkeley, CA: University of California
Press.
Costanza, R., ed. (1991) Ecological economics: the science and management of sustainability.
New York: Columbia University Press.
Costanza, R.; Perrings, C. (1990) A flexible assurance bonding system for improved
environmental management. Ecol. Econ. (2):57-75.
Daly, H.; Cobb, J. (1989) For the common good. Boston: Beacon Press.
Ehrenfeld, D. (1993) Beginning again. New York: Oxford University Press.
Fisher, A.C.; Hanemann, W.M. (1985) Option value and the extinction of species. Berkeley, CA:
California Agricultural Experiment Station.
Harmon, B. (1991) Accounting in ecological systems. In: Costanza, R. Ecological economics: the
science and management of sustainability. New York: Columbia University Press.
Kelman, S. (1981) Cost-benefit analysis: an ethical critique. Regulation, Jan./Feb., pp. 74-82.
Mitchell, R.C.; Carson, R.T. (1989) Using surveys to value public goods: the contingent
valuation method. Washington, DC: Resources for the Future.
National Academy of Sciences (NAS). (1992) Policy implications of greenhouse warming:
mitigation, adaptation, and the scientific base. Panel on Policy Implications of
Greenhouse Warming. Washington, DC: National Academy Press.
National Oceanic and Atmospheric Administration (NOAA). (1993) Natural resource damage
assessments under the Oil Pollution Act of 1990. Fed. Reg. 58:4601-4614.
Norton, E.G. (1986) The preservation of species. Princeton, NJ: Princeton University Press.
Norton, B.G. (1987) Why preserve natural variety? Princeton, NJ: Princeton University Press.
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Norton, B.C. (1988) Commodity, amenity, and morality: the limits of quantification in valuing
biodiversity. In: Wilson, E.O. Biodiversity. Washington, DC: National Academy Press.
Norton, B.G. (1991) Toward unity among environmentalists. New York: Oxford University
Press.
Norton, B.G. (1992) A new paradigm for environmental management. In: Costanza, R.; Norton,
B.G.; Haskell, B. Ecosystem health: new goals for environmental management. Covelo,
CA: Island Press.
Norton, B.G.; Ulanowicz, R.E. (1992) Scale and biodiversity policy: a hierarchical approach.
Ambio 21:244-249.
Odum, E. (1964) The new ecology. Bioscience 14.
O'Neil, R.V.; DeAngelis, D.L.; Waide, J.B.; Allen, T.F. (1986) A hierarchical concept of
ecosystems. In: May, R., ed. Monographs in population biology. Princeton, NJ:
Princeton University Press.
Page, T. (1991) Sustainability and the problem of valuation. In: Costanza, R. Ecological
economics: the science and management of sustainability. New York: Columbia
University Press.
Randall, A. (1986) Human preferences, economics, and the preservation of species. In: Norton,
B.G. The preservation of species. Princeton, NJ: Princeton University Press.
Repetto, R.; Magrath; Wells, M.; Beer, C.; Rossini, F. (1982) Waiting assets: national resources
in the National Income Accounts. Washington, DC: World Resources Institute.
Rolston, H., in (1985) Valuing wildlands. Environ. Ethics 7 (l):23-48.
Sagoff, M. (1988) The economy of the Earth: philosophy, law, and the environment.
Cambridge, UK: Cambridge University Press.
Tobin, R. (1990) The expendable future: U.S. politics and the protection of biological diversity.
Chicago: University of Chicago Press.
Toman, M.A. (1992) Economics and "sustainability": balancing tradeoffs and imperatives.
Working Paper ENR91-05 Rev. Washington, DC: Resources for the Future.
U.S. Department of Interior, (no date) National biological survey. In: United States Department
of Interior Budget Justifications, FY 1994.
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An Environmental
Problem Perceived
i
Competing Expressions of the
Problem in Rough Concepts or
Ordinary Language
Environmental
Ethics
Determination of
Scale of System
of Analysis( 1)
Precise
Definition
of Problem
z
Discussion of Public
Values and GoalsO)
Development of Precise Concepts and
Models that embody scalar decisions(2)
Valuation
Standards •
relevant to
policy goals
prescribed
I
Prioritize Goals
T
Performance
Standards to
test progress
in achieving
ecological
goals
Formulate Management Hypotheses
I
(1) "Boundary
Problems"
(2) Physiological
Problems
(3) "PerspecnvaT
Problems
Figure 1. The environmental policy process. Environmental problems are not clearly
formulated when they first emerge in public discourse. Determination of the proper
scale at which a problem should be "modeled" requires an interactive, public process
in which public values guide the scientific development of models. Once the problem
is precisely defined and models developed, the process of experimentation with
solutions can begin (Norton and Ulanowicz, 1992).
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HO
o
Q
o.
z
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u
V
la
ojo
1
e
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2
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IRREVERSIBLE
CATASTROPHE
Red Region"
Extinction
Biological
Impoverish-
ment
HIGH COST BUT
REVERSIBLE
IRREVERSIBLE
INCONVENIENCE
Economic Impacts of Higher
Real Resource Costs, Varying
Degrees of Severity and
Reversibility
REVERSIBLE
INCONVENIENCE
Figure 3. Risk typology: severity and reversibility (Norton, 1992).
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Stressor Characteristics
• Mods of action
• Duration and Intensity
• Timing and frequency
• Variability
• Exposure boundary
Ecosystem s @ Risk
• Structure and function
• Diversity/redundancy
• Keystone species
• Habitat Integrity
• System boundary
Ecological Characteristics
• Magnitude and extant
• Health?
• Enacts boundary
IRREVERSIBLE
CATASTROPHE
HIGH COST BUT
REVERSIBLE
Extinction
Biological
Impoverish-
ment
Economic Impacts of Higher
Reel Resource Costs, Varying
Degrees of Severity and
Reversibility
Endpolnt Selection
"Sustainabillty"
* Assessment endpolnts
• Measurement endpoln*
Conceptual Modal
• Ecosystems 9 risk
• Exposure pathways
• Spatial, temporal and
ecological boundaries
• Direct and indirect effects
exposure relationships
• Causal inferences
• Potential ecc-significance
Figure 4. The application of "significance" principles and criteria in problem formulation
(Norton, 1992).
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a = low dose
b = medium dose
c = high dose
InNl
therefore:
Dose
Example: thresholds exist in the dose response curve
InNl
Age
Dose
Figure 5. Relationship among net reproductive rate (Ro), its components (be and dx), and the
intensity of stress.
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Control: No Stress
bx
bx
Delayed Maturation
Reduced Births
bx
Ase
Age
Figure 6. Relationships of maturation and fecundity on Ro. Impact of delayed maturation on
Ro is much greater than that of reduced birth if the females have a high -dz. Age of
first reproduction is important.
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Ro
Age
a= no threshold
b = threshold
Dose
Figure 7. Dose-response curves resulting from combining delayed maturation and reduced
fecundity.
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(0.5 METERS)
.__ MICROBES
/
(1-2 METERS)
PRODUCERS
UVASCULAR
HYDROPHYTES)
(4-6 METERS)
(10 METERS)
PRODUCERS
(VASCULAR
HYDROPHYTES)
PRODUCERS
(PERIPHYTON)
(50-75 METERS)
• PRODCERS
(PHYTOPLANKTON)
„ COLLECTORS
(ZOOPLANKTON)
ORS
12^700 METERS)
Figure 9. Stream classification and community structure (Cummins, 1977).
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, ,_ . . Surface
InterndGrcuIalion, Weathering
!> L.°.nd / and Erosion
Communities
Atmospheric Grculation
Internal Circulation,
Ocean Water and f Plankton
oil - . * . *
Mr ing * •
. . •
* •
* • .
• • •
•
• •
• •
• •
* *
•
•
• • •
.Regeneration *. ' .
'Internal Grculation, Benthos
UNO
• * • Elevation of
Sedimentary Rocks
. .• ' (81
^Incorporation in Deep •
. ' • Sediments and Rocks -.
Figure 10. Major features of biogeochemical cycles for calcium and other elements. Circulations
within ecosystems, on land and in the sea, are linked into the global circulation
through cycles. A: Shorter term cycles from the ocean surface into the atmosphere by
salt spray, into terrestrial ecosystems by precipitation, and back to the ocean in
stream water. B: Longer-term cycles from ocean waters into deep sediments and
rocks, to exposure on land surfaces after elevation of these rocks, and back to the
ocean by varied routes involving in-soil weathering, dust, stream flow, or atmospheric
circulation and precipitation.
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Table 1. The Direct and Indirect Benefits from Wetlands That Must Be Replaced in Volume,
Space, and Kind for Economic Valuation of Natural Systems
Direct Benefits Indirect Benefits
Denitrify nitrates Biodiversity
Uptake of phosphorus High productivity
Detoxify pesticides Landscape diversification
Carbon dioxide sink Breeding and nursery areas
Sequester eroded soils
Modulate storm impacts
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Table 2. Equivalent Concepts in Ecology and Economics for Use in the Valuation of Natural
Systems
Ecological Criteria
Economic Criteria
Survivorship
Resiliency
Diversity
Adaptivity
Energy/nutrients
Integrated systems
R-selection
K-selection
Extinction
Structure
Function
Indirect effects
Sustainability
Robustness
Decentralization
Creativity
Currency
Life-cycle analysis
Growth economy
Zero sum game
Bankruptcy
Capital
Productivity
Multipliers
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Table 3. Issues Relevant to Integrating Economics, Ecology, and Values
Issues
Intergenerational equity
Discounting
Willingness to pay
Efficiency vs. equity
Stewardship vs. ownership
Total systems analysis
Temporal/spatial extraneous factors
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Peer Review
DRAFT
September 1993
Issue Paper
on
CONCEPTUAL MODEL DEVELOPMENT
Lawrence Barnthouse
Environmental Sciences Division
Oak Ridge National Laboratory
Oak Ridge, TN
Joel Brown
Department of Biological Sciences
University of Illinois at Chicago
Chicago, IL
Prepared for:
Risk Assessment Forum
U.S. Environmental Protection Agency
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CONTENTS
1. INTRODUCTION 3.5
1.1. Case Studies 3.7
1.1.1. Special Review of Granular Carbofuran 3-7
1.1.2. Modeling Future Losses of Louisiana Forest Wetlands 3-9
2. PROBLEM CLASSIFICATION 3-11
3. CHARACTERIZING THE STRESS REGIME 3-14
3.1. Source 3-14
3.2. Particular Stressor Characteristics 3-16
3.2.1. Defining the Characteristics 3-16
3.2.1.1. Intensity 3-16
3.2.1.2. Frequencies 3-17
3.2.1.3. Duration 3-18
3.2.1.4. Timing 3-19
3.2.1.5. Scales 3-19
3.2.1.6. Modes of Action 3-19
3.2.2. Stressor Characteristics of Granular Carbofuran 3-20
3.2.3. Stressor Characteristics of Louisiana Forest Wetlands 3-21
3.3. Types of Stressors and Characterizing the Stress Regime 3-21
3.3.1. Chemical Stressors 3-22
3.3.1.1. Pesticides 3-22
3.3.1.2. Hazardous Contaminants 3-23
3.3.1.3. Stimulatory Contaminants 3-25
3.3.1.4. Radionuclides 3-27
3.3.2. Physical Stressors 3-27
3.32.1. Human Exploitation 3-29
3.3.2.2. Habitat Change 3-30
3.3.2.3. Habitat Destruction and Landscape Effects 3-31
3.3.3. Biological Stressors 3-34
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4. ECOSYSTEM COMPONENTS AT RISK 3-35
4.1. Ecosystems Bounds 3-36
4.2. Level of Biological Organization 3-37
4.3. Ecological Effects : 3-38
5. SELECTION OF ENDPOINTS 3-41
5.1. Ecological Relevance 3-42
5.2. Policy Goals and Societal Values 3-43
5.3. Susceptibility to the Stressor 3-43
5.4. Scale Considerations 3-44
5.5. Measurement and Assessment Endpoints 3-44
5.5.1. Endpoints for the Granular Carbofuran Study 3-45
5.5.2. Endpoints for the Louisiana Forest Wetlands Study 3-46
6. THE CONCEPTUAL MODEL 3-48
6.1. Flow Diagrams 3-48
6.2. Impact Hypotheses and Quantitative Response Relationships 3-50
6.2.1. Scientific Methodology for Developing the Conceptual Model 3-50
6.3. Summary of Measurement Endpoints and Extrapolation Techniques 3-52
6.4. Model/Data Selection Criteria and Quality Assurance Standards 3-53
6.5. Example Conceptual Model: Granular Carbofuran 3-53
6.6. Example Conceptual Model: Louisiana Forest Wetlands 3-54
7. SUMMARY 3-57
8. REFERENCES 3-59
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LIST OF FIGURES
Figure 1. Sample Event-Tree Diagram for Illustrating the Possible Causes of
Recruitment Failure in a Fish Population 3-63
Figure 2. Sample Food Web-Type Conceptual Model 3-64
Figure 3. Mass Balance Diagram for the Fate of Trichloroethylene
in a Six-Compartment System 3-65
Figure 4. Dynamics Contained in FORFLO 3-66
Figure 5. (a) Flow Diagram and Conceptual Model for the Granular Carbofuran
Case Study; (b) Expanded Flow Diagram for the Carbofuran Example 3-67
LIST OF TABLES
Table 1. Selected Processes and Parameters Relevant to the Transport and
Transformation of Chemical Stressors 3-68
Table 2. Conceptual Issues and Modeling Considerations under Habitat
Alteration, Fragmentation, and Destruction 3-69
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1. INTRODUCTION
Conceptual model development is the final step in the problem formulation phase of the
U.S. Environmental Protection Agency's (EPA's) Framework for Ecological Risk Assessment (U.S.
EPA, 1992). The conceptual model "describes how a given stressor might affect the ecological
components in the environment" and "describes the relationships among the assessment and
measurement endpoints, the data required, and the methodologies that will be used to analyze
the data" (page 9). In short, the conceptual model serves as a concrete plan for conducting the
analysis phase of the assessment and defines the types and quantity of information available for
risk characterization.
Although the term conceptual model development may be new to ecological assessment,
the idea itself has been a part of environmental impact assessment at least since the mid 1970s.
Sanders et al. (1978), in describing approaches to development of monitoring programs designed
to measure ecological impacts of new energy production facilities, defined a phase called
"prediction" that involved (1) synthesis of existing information about the characteristics of the
facility, composition and toxicity of expected effluents, and characteristics of potentially exposed
ecosystems; (2) synthesis of a box-and-arrow diagram of causal relationships within the
ecosystem; and (3) generation of a set of "impact hypotheses" that would guide the collection and
interpretation of subsequently collected data. Beanlands and Duinker (1984) defined a
"framework" for project-specific environmental impact assessments that includes description of
causal linkages between project activities and valued ecosystem components and development of
explicit impact hypotheses. Westman (1985) described a five-phase impact assessment sequence
in which the first two phases correspond closely to the problem formulation phase of the EPA
framework and culminate in the identification of specific ecological impacts to be addressed in
subsequent experimental, field, and modeling studies.
Both earlier and more recent discussions of problem formulation have emphasized that
successful completion of a risk assessment requires a logical analytical plan that identifies the
most likely consequences of the action being contemplated and defines the data and analytical
methodologies required to resolve existing uncertainties. Within the EPA framework, the
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conceptual model performs this critical role. The steps in formulation of the problem,
culminating in the development of the conceptual model, include:
1. classifying the problem in terms of the management context, the ecological
context, and the temporal, spatial, and regulatory constraints;
2. summarizing available data concerning the stressors and the ecosystems at risk;
3. determining assessment and measurement endpoints; and
4. assembling the conceptual model: causal pathways, impact or recovery hypotheses,
model/data selection criteria, and measurements or models to be used in the
analysis phase.
Step 1 is primarily policy driven and corresponds more or less directly to the "Discussion
Between the Risk Assessor and the Risk Manager (Planning)" phase in the framework. The
objective of this step is to define the management bounds of the assessment in terms of the kind
of decision to be made, the possible management actions, and the kind of information required
to support the decision. Step 2 is an information acquisition step in which existing physical,
chemical, and biological data relevant to the assessment is identified. Data sources must be
evaluated to determine whether available information is sufficient or whether additional data are
needed. Step 3 involves summarizing the problem in the form of assessment and measurement
endpoints (Suter, 1989). These are formalized ecological equivalents of the standardized cancer
risk endpoints (e.g., 10"6 lifetime risk) and data types (e.g., rodent bioassay) used in human health
risk assessment. Step 4 synthesizes all of the previous steps in a form suitable for guiding the
subsequent collection and analysis of data.
Although it is expected that all risk assessments will require a conceptual model, the way
in which the model is developed will vary greatly depending on circumstances. As part of many
regulatory programs (e.g., EPA pesticide and toxic chemicals programs), relatively standardized
risk assessments are performed for large numbers of individual chemicals; thus some or all
components of the conceptual model may be written directly into program guidance. For other
situations, including most National Environmental Policy Act (NEPA) environmental impact
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assessments and Superfund investigations, conceptual models must be developed separately for
each assessment.
The discussion of conceptual model development in this chapter presupposes that goals
for environmental protection and management (e.g., what we want the ecosystem to look like,
how we balance natural preservation and economic development, how much ecological change is
considered to be acceptable) have already been determined (for a discussion of these value-
driven issues, see chapter 2, on ecological significance). The purpose of this chapter is to discuss
the translation of agreed-upon values and goals into technically credible and cost-effective
risk assessments.
1.1. Case Studies
Throughout this chapter, we use examples to illustrate the diverse aspects of conceptual
model development. The variety of examples helps illuminate the full spectrum of possible
circumstances and scenarios that can arise, although each example provides an incomplete view
of the development of particular models. We continually refer, however, to two specific case
studies in an effort to provide continuity throughout the discussions.
1.1.1. Special Review of Granular Carbofuran
In the late 1980s, EPA's Office of Pesticide Programs (OPP) undertook a special review
of granular formulations of the broad-spectrum insecticide/nematicide carbofuran in light of
evidence that it posed a risk to birds. OPP undertook this review based on the Federal
Insecticide, Fungicide, and Rodenticide Act [FIFRA], which authorizes cancellation of
registration for any pesticide that poses an unreasonable risk to humans or the environment.
The special review process utilized data from laboratory toxicity studies, field studies, and
reported incidents of bird kills to assess the potential for adverse impacts to avian species. Based
on this information, EPA proposed cancellation of registration for the use of granular
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carbofuran, concluding that it "generally poses unreasonable risks to birds through direct and
secondary poisoning" (see Houseknecht, 1993).
Carbofuran (2,3-dihydro-2,2-dimethyl-7-benzofuranyl methylcarbamate) is applied in
granular form, using a variety of techniques (e.g., band, in-furrow, broadcast), to 27 different
types of crops for the control of certain insects and nematodes. Following application at the
beginning of the growing season (e.g., April to June), either the pesticide becomes incorporated
in the soil, where degradation occurs fairly rapidly, or the granules remain intact on the soil
surface, where the pesticide may persist for over 60 days. Birds may ingest the granules
accidentally during normal ground-foraging, insectivorous birds may ingest exposed invertebrates,
and scavenging or predatory birds may ingest exposed birds and other vertebrates.
Suspicions about adverse effects to birds from carbofuran arose from reports about 40
separate die-off events claiming from 1 to more than 2,000 birds. The diversity of poisoned birds
(i.e., waterfowl, robins, songbirds, shorebirds, and raptors) indicated several direct and indirect
avenues of exposure. Exposure studies found carbofuran granules and residue on the soil
surface, within earthworms, and within bird carcasses. In addition, other information linked
carbofuran with adverse effects in birds: (1) carbofuran is acutely toxic to birds—in some cases a
single granule can kill a small bird; (2) whole-body residues of carbofuran within bird carcasses
from purported die-offs frequently have exceeded LD^s; and (3) systematic searches of fields
following carbofuran applications have found bird mortality rates of at least 0.1 to 3.6 birds per
acre (Houseknecht, 1993).
In this case study, avifauna is the component of the environment under protection, with
particular attention given to hawks, eagles, and owls. The regulatory endpoint concerns the
decision of whether to revoke the registration of granular carbofuran.
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1.1.2. Modeling Future Losses of Louisiana Forest Wetlands
The Lake Verret Basin, Atchafalaya floodplain of the Mississippi River delta, in
Louisiana, includes a variety of bottomland forest types, agricultural fields, industrial parks,
urban areas, and a system of levees dating from the 1930s. Activities such as petroleum
pumping, land development, dredging, and shipping may have increased rates of erosion and silt
runoff, while the levees may have reduced the rates of siltation and sedimentation. Increased
erosion and reduced sedimentation could increase subsidence rates and expose the bottomland
forests to increased flooding, elevated water tables, and increased salinity. Moreover, subsidence
could promote a successional process in which the community composition of 16 principal tree
species shifts from dry to wet bottomland hardwood, then to swamp (cypress-tupelo forest), and
eventually to unforested marsh or open water. Such changes in forest structure can have
concomitant effects on a number of bird species (e.g., the downy woodpecker and the wood
duck) and species of mammal (e.g., the gray squirrel, swamp rabbit, mink) that live within these
different habitats.
With wetlands protection as the regulatory endpoint,1 EPA's Office of Policy, Planning,
and Evaluation (OPPE) undertook this case study and focused on predicting changes in the
wetlands of the Lake Verret Basin. To predict changes in forest composition under actual and
hypothetical rates of subsidence and changes in two bird and three mammal species, the risk
assessment parametrized a bottomland succession model (FORFLO [Pearlstine et al, 1985]) and
used the model's output as input for the habitat suitability indices (HSI). The forest model
requires data on the existing vegetation (e.g., relative abundances, size, densities of canopy tree
species), hydrologic data (e.g., average water heights and water-table depths over 24 half-month
periods), and site data (e.g., elevation and soil type). Once parametrized, FORFLO projected
1 "In 1987, at the request of the EPA Administrator, a National Wetlands Policy Forum
convened to suggest ways to improve wetland regulation and management. In its final report,
Protecting America's Wetlands, the Forum recommended '... no overall net loss of the nation's
remaining wetlands base, as defined by acreage and function....' At present, EPA lacks risk
assessment and management approaches for considering physical habitat alteration and biological
diversity." (Brody et al., 1993)
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future changes in the abundance, size, and density of the 16 tree species. For instance, at
present rates of subsidence, the wet bottomland hardwoods will be replaced completely by
cypress-tupelo swamp within 50 years and the succession of dry bottomland hardwoods to swamp
will be complete within 120 years (Brody et al., 1993).
In this case study, the valued components of the environment included the dry
bottomland hardwood forest, the wet bottomland hardwood forest, and the cypress-tupelo
swamp. Changes in subsidence fates constitute the stressor, the sources of which include earlier
levee construction on the Atchafalaya and Mississippi rivers. Despite a regulatory climate that
encourages wetland protection, no person, agency, or entity capable of making a management
decision based on the risk assessment was available. As a result, the risk assessment provided
only a means for making projections based on present conditions in regard to assumptions about
a variety of land-use practices and subsidence rates.
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2. PROBLEM CLASSIFICATION
The technical approach taken in risk assessment must be selected based on the
regulatory/management context. For example, the Toxic Substances Control Act (TSCA)
requires EPA to act on pre-manufacture notifications (PMNs) for new chemicals within 90 days,
based on relatively little information about the structure and activity of the particular chemical.
In contrast, registration of pesticides can require extensive laboratory and field toxicity testing
but limited field investigation. Similarly, Superfund remedial investigations can take several years
and produce substantial quantities of field data, although data are gathered under relatively
uncontrolled conditions. Because these situations involve different types of scientific
uncertainties, each requires a different kind of technical guidance.
Concerning determinations about the objectives and technical boundaries of a risk
assessment, communication between the risk manager and the technical risk assessor is essential.
The Framework Report implies that this communication is conducted with a single risk manager;
however, these determinations can be quite complex, requiring input from many "risk managers."
In some regulatory programs, especially programs involving regulation of pollutant discharge or
chemical/pesticide manufacture, the problem classification step is implicit in the regulations
governing the program and therefore does not need to be performed individually for each risk
assessment. For such programs, time, resource, and problem area constraints are written into
program-level risk assessment guidelines. In the best cases, moreover, applicants know in
advance what information they must provide to risk assessors, just as risk assessors know how
they will analyze and interpret the data. In other programs, especially those dealing with toxic
waste disposal and environmental restoration, even determinations about which law applies may
require agreement between several different federal and state regulatory agencies.
The first, and ultimately perhaps the most important, discussion between risk managers
and technical risk assessors should occur when program-level risk assessment guidelines are
developed. These guidelines should reflect a consensus on criteria for establishing consistency
and credibility applicable to all risk assessments performed for the particular office. The two
types of guidelines that are relevant to the risk assessment process concern: technical content and
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procedural criteria. Guidance on technical content consists of identification of the specific
models and data types that should be used as well as the decision rules that must be followed for
all risk assessments performed for the particular office. For example, OPP's standard evaluation
procedures (Urban and Cook, 1986) specify (1) the kinds of toxicity test data to be used to
summarize the effects on nontarget biota of a pesticide proposed for FIFRA registration, (2) the
methods for quantifying ambient environmental exposures, and (3) a set of "risk criteria" defining
the action OPP may take (i.e., registration, denial of registration, or seek more data) based on
different combinations of exposure and effects data.
Procedural guidance consists of general data-collection criteria that leave specifics about
particular data and methods to be developed on a case-by-case basis. For example, the Office of
Solid Waste and Emergency Response's (OSWER's) Environmental Evaluation Manual (U.S.
EPA, 1989) provides general procedural guidelines for designing ecological risk assessments at
Superfund sites without identifying what types of data to use. Such specifics are left to the
development of "Sampling and Analysis Plans" and "Data Quality Objectives."
The particular office's specificity requirements for risk assessments should strongly
influence the balance between technical and procedural aspects of its risk assessment guidelines.
For example, because the Office of Prevention, Pesticides, and Toxic Substances (OPPTS)
performs standardized assessments for large numbers of chemical substances, detailed program-
level technical guidance has been developed covering exposure routes to be investigated, data to
be collected, and analytical methodologies to be used (Urban and Cook, 1986; Zeeman and
Gilford, 1993). In contrast, guidance for Superfund ecological risk assessments emphasizes
process over technical content, since considerable guidance on sample collection, data quality
assurance, and data analysis is required. For such assessments, critical issues concern data
quality objectives, methods for measuring specific variables (e.g., chemistry, benthic grab
samples), and statistical methodologies. Because the specific pathways and measurements and
the appropriateness of particular models vary greatly among sites, specific guidance for certain
aspects of the assessment cannot be provided in advance.
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The appropriate procedures and technical content for a risk assessment should be
reflected in the conceptual model. For general assessments, a conceptual model that is very
simple in structure may be sufficient, and even explicitly called for in program guidance. For
site-specific assessments, it may be necessary to develop a sophisticated conceptual model that
includes many exposure pathways, species, and measurement approaches. Thus each model will
be unique to the particular assessment, with the empirical foundation of each established by
taking into account the available information on the stress regime and the ecosystems at risk.
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3. CHARACTERIZING THE STRESS REGIME
After classifying the problem, the next step in the development of the conceptual model
involves summarizing available information on sources and agents of stressors and gathering data
on populations, species, communities, and ecosystem functions at risk. This model should be
based on knowledge as well as hypotheses about the stressors and ecosystem components and the
potential interplay between the two. Hypotheses regarding this interplay serve two essential
purposes: (1) they can reduce what may be a considerable number of candidate stressors and
ecosystem components to a manageable few; and (2) they can suggest flow diagrams linking
potential stressors to ecosystem components (see section 6.1). The network of casual pathways
linking stressors to various effects on measurement and assessment endpoints will ultimately
constitute the conceptual model (see section 6.2).
Important information for characterizing the stress regime includes the source; aspects
such as intensity, frequency, duration, timing, scale, and mode of action; and whether the agent
or stressor is chemical, physical, or biological (U.S. EPA, 1992, page 11). Information on a
stressor's source answers the question, "Where does it come from?" A stressor's frequency,
duration, timing, and scale answer the question concerning co-occurrence, "Where is the
stressor?" And, a stressor's intensity, mode of action, and type (i.e., chemical, physical, or
biological) answer the question concerning ecological effects, "What might it do?"
3.1. Source
The process of gathering information on the possible source of a stressor can begin the
dialogue between the risk manager and assessor regarding the stress regime. Identifying the
source may lead the risk assessor to the regulatory endpoints and to the risk manager, who
generally regulates sources rather than stressors. Moreover, most regulations differentiate among
various sources that may involve the same or different stressors (e.g., sport and commercial
fisheries).
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Identifying the source often will allow the risk assessor to determine the scale, duration,
and frequency of the stressor; its potential co-occurrence with particular ecosystem components;
and the likelihood that additional stressors are related to the same or a different source. A
source can be natural (e.g., tar pits) and/or anthropogenic (e.g., oil spills), geographically well
defined (e.g., point-source pollution), or geographically vague and diffuse (e.g., automobile
emissions).
In the case of granular carbofuran (Houseknecht, 1993), the source is the group of
farmers who apply the insecticide/nematicide to 27 types of agricultural crops using a variety of
application techniques. The source can be identified more specifically as relating to the
particular form of application (i.e., band, in furrow, and aerial) and the particular crop species.
Application technique is significant because it determines the level of frequency for exposed
granules (i.e., highest for aerial and lowest for in-furrow application). The significance of crop
species in regard to source is that it determines the timing, dose, and circumstances of
application (e.g., application of carbofuran to rice fields increases the likelihood of exposure to
waterfowl and shorebirds).
The Louisiana forests wetlands example (Brody et al., 1993) shows how the risk assessor
and manager may need to trace the stressor (e.g., subsidence) back to the source(s). In this case,
sources of subsidence can include natural processes of erosion and compaction or a variety of
human activities such as land development, dredging, "propwash" from ships, recreational
activities, and construction of flood-control levees. The risk assessor selected levee construction
as the primary source. Natural processes were ruled out because prior to implementation of
flood-control measures the basin experienced a net accretion of sediments as a result of high
siltation rates. Levees were implicated because they have prevented sedimentation from
occurring across the basin's flood plains, and the levees have reduced suspended sediments in the
Mississippi River.
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3.2. Particular Stressor Characteristics
3.2.1. Defining the Characteristics
Information on the intensity, frequency, duration, timing, scale, and mode of action of a
stressor provides clues about its ecological aspects. When the assessment is predictive, the
stressors are generally known and the resulting conceptual model will be based on these stressors
and their associated characteristics. The task of problem formulation then becomes one of
predicting an array of ecological effects. Other stressors enter the conceptual model only insofar
as they may mitigate, compound, or add to the effects of the target stressors.
In a retrospective or ecological effects-inspired assessment, where the stressor may be
unknown or hypothetical, the ecological effects serve as the basis of the conceptual model.
Using the model, an array of stressors and possible sources can be predicted. Other ecological
effects enter the model only insofar as their occurrence (or lack of occurrence) indicates the
influence of particular stressors. In a retrospective assessment, the magnitude, scale, and type of
ecological effects (and the absence of other effects) may indicate the stressor's characteristics and
suggest its identity. In all cases, information about the following particular characteristics of a
stressor will come from empirical, inferred, or estimated data.
3.2.1.1. Intensity
The expression "the dose makes the poison" is applicable in risk assessment in regard to
the importance of information on a stressor's intensity, since generally the relationship between
the intensity of the stressor and the magnitude of an ecological effect is monotonic. Below some
threshold there may be no detectable ecological effect, or the effect eventually may fall below the
bounds of relevance to the risk manager.
Intensities may be measured as the concentrations or reactivities of chemical stressors, as
the densities or population sizes of biological stressors, or as the magnitude of physical stressors.
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Chemical stressors may be elaborated further as distributions of concentrations in space or time.
When appropriate, the intensity of chemical stressors may be estimated from solubilities,
structure-activity relationships (SAR), binding properties of organic aggregates, and vapor
pressures. For biological stressors, additional considerations may include the organisms
invasiveness, vagility, dispersal rates, pathogenicity, or rates of predation/herbivory on other
components of the biota (see chapter 6, on biological stressors). The magnitude of physical
stressors can relate to the number of, for example, visitors, vehicles, hunters, or mining/logging
permits associated with the exploitation of an area and the distribution of these activities in time
and space. In regard to the Louisiana forest wetlands study, the magnitude of habitat
modification may relate to the density of building sites, the proportion of wetland subjected to
dredging, or changes (or lack thereof) in the level of water tables.
The application of intensity considerations in the conceptual model also comprises aspects
of "dose" in exposure characterization (see chapter 4) and "dose response" in effects
characterization (chapter 5).
3.2.1.2. Frequencies
In conjunction with its intensity, the frequency of a stressor determines its disturbance
regime (see chapter 5, on effects characterization, and chapter 7, on ecological recovery). A
stressor event can be isolated, episodic, or continuous. Thus, for example, events can be
characterized by the influence of periodicity (e.g., daily, lunar, seasonal) or the absence of such
influences (i.e., stochastic or chaotic).
Chemical stressors may be introduced to an ecosystem as a single event (e.g., an oil spill),
but are more likely to be introduced episodically (e.g., regular applications of many agricultural
pesticides) or continuously (i.e., as in the case of many pollutants). Biological stressors may
occur as single events if the introduced organism (e.g., a biological control agent) dies off within
a relatively short time, or may be episodic if the introduced organism is self-sustaining and
experiences flush-crash population dynamics. Physical stressors may be continuous, as in the case
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of gradual alteration of habitat, or episodic, as in the case of habitat destruction and with certain
forms of exploitation by humans (e.g., those dictated by weekend recreation or seasonal hunting).
Infrequent or single-event stressor exposures focus the conceptual model on acute effects
and the potential for ecological recovery (see chapter 7). Seasonal or cyclic frequencies focus the
conceptual model on ecosystem components that are likely to temporally co-occur with the
stressor. Continuous exposures increase the likelihood of chronic effects as well as indirect
effects among ecosystem components mediated through trophic interactions among
coexisting species.
3.2.1J. Duration
A stressor's disturbance regime influences the duration of the stressor's effects. For many
stressors, the duration of indirect effects can be longer than for direct effects.
For chemical stressors, important information to consider includes the substance's
persistence in water, benthic sediments, soil, and air as well as the rate and extent that the
chemical will degrade under particular conditions. The duration of a biological stressor depends
on whether the organism proliferates and becomes established as a self-sustaining population.
The duration of physical disturbances ranges from very short to permanent.
As the duration of an event increases, the focus of the conceptual model will tend to shift
from acute to chronic effects and from direct ecological effects to indirect effects, such as
biomagnification through a food chain. Stressor duration also has direct relevance for ecological
recovery issues (see chapter 7).
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3.2.1.4. Timing
When the frequency of a stressor is episodic, information on the stressor's timing with
regard to seasonality and other biological cycles may be important in determining ecological
effects. For example, agricultural pesticides are generally applied during the growing season,
thus increasing the likelihood of adverse ecological effects and the number of pathways by which
the stressor may enter and influence the food chain. Similarly, because most human recreational
activities are carried out during the day, data on diurnal rather than nocturnal animals may be
more relevant for the conceptual model. Thus the timing of the stressor event can be important
for assessing the stressor's effect on various ecosystem components.
3.2.1.5. Scales
A stressor may operate on spatial scales that range from local to global and from habitat
specific (e.g., the aquatic versus terrestrial component) to all habitats within an exposed area.
Thus it is important to place bounds on the spatial scale of a stressor because the more
expansive the scale or the more diverse the habitats involved, the greater the number of
ecosystems at risk and the larger the set of possible ecological effects. The scale of a
retrospective assessment may be determined by the observed ecological effects, whereas a
predictive risk assessment may have to rely on fate diagrams or models to develop a theory about
the scale. Similarly, it is easier to develop a conceptual model for a site-specific assessment than
for a geographically vague assessment.
3.2.1.6. Modes of Action
Information on a stressor's mode of action may be the most important characteristic for
linking the stressor to the ecosystem components at risk. Mode of action sometimes can be
synonymous with ecological effects, but rarely can it be defined independent of an assessment of
the potential interactions between a stressor's other characteristics and some biotic component.
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Indeed, in one sense the definition of a stressor's mode of action begins at the stressor's entry
point or at co-occurrence with biotic processes, which initiates stimulatory or inhibitory effects
that lead to the ecological consequences of interest.
In its narrow sense, definition of a stressor's mode of action should be reserved to
describe the physiological and biochemical effects of a chemical stressor on an individual
organism (e.g., narcotic, neuroinhibitor/transmitter, hormonal substitute, nutrient). The key to
mode of action, however, may be in identifying critical events that result in ecological effects.
For example, while it may be useful to know that cyanide acts as a cytochrome inhibitor, it might
be more useful if its mode of action is incorporated into a risk assessment as a chemical stressor
that causes mortality in individual organisms.
In its broadest sense, it is important that the stressor's modes of action at various scales
are described. For example, a toxic waste site may serve as a "killjar" for organisms that migrate
into or through the site, or it may act as a reservoir of toxics that seep into surrounding habitats
or waterways. In such a case, not only do the toxics have a chemical mode of action, but the site
itself has a mode of action within the broader ecological landscape.
3.2.2. Stressor Characteristics of Granular Carbofuran
In the granular carbofuran study (Houseknecht, 1993), the intensity of the stressor
includes the application rate (pounds of active ingredient/per acre), the number of exposed
granules (per square foot), the concentration in soil invertebrates (ppm), and concentration in
bird carcasses available to raptors and scavengers. The granular carbofuran example illustrates
that exposure frequencies are related to the characteristics of the source, properties of the
stressor, and interactions of the stressor with the environment.
Application of carbofuran is episodic, occurring at the beginning of each growing season
(e.g., April to June, depending on location and crop). Granules may be persistent and available
to birds on the soil surface for as many as 60 days following application. Contamination of
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invertebrate prey and birds (i.e., secondary exposure to raptors) represents a declining and
continuous source of carbofuran. As a result, stressor duration may be very short in the soil,
somewhat longer on the soil surface, and longer still in invertebrates and birds.
The timing of carbofuran applications corresponds directly with breeding and migration
patterns in birds, which are timed to take advantage of the invertebrate production against which
the pesticide is used. The scale of carbofuran can be defined by the observations of bird die-offs
(i.e., 40 incidents of carbofuran-related bird kills) and the amount of agricultural land under
application (e.g., 4.5 to 5.5 million acres of corn, 0.64 to 2.0 million acres of sorghum). In birds,
carbofuran acts as a neurotoxin and neurostimulant by inhibiting the action of cholinesterase.
Thus the mode of action focuses the conceptual model on acute rather than chronic effects.
3.2 J. Stressor Characteristics of Louisiana Forest Wetlands
In the Lake Verret Basin case study (Brody et al. 1993), the stressor's intensity could be
expressed as rate of subsidence (0.5 to 1.0 cm per year), as frequency of flooding, or as the
height of the water table. Flooding frequency and water-table height were selected as measures
of stressor intensity because they vary seasonally, while the subsidence occurs as a gradual and
continuous process. Duration of subsidence in Louisiana bottomland forest is presumed to be
permanent, and the scale is defined by its present range (about 50,000 hectares of the Lake
Verret Basin). The mode of action of subsidence involves increased submersion of roots in water
and encroachment of salt water into the wetlands. These changing hydrological regimes alter the
growth, survivorship, and establishment of the constituent tree species.
3.3. Types of Stressors and Characterizing the Stress Regime
Often the type of agent or stressor also can be useful in identifying regulatory endpoints,
the stressor's mode of action, and likely ecological effects. This section examines the ways in
which information about the three types of stressors—chemical, physical, and biological—
influence the way in which the stress regime is incorporated into the conceptual model.
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33.1. Chemical Stressors
Chemical stressors can be divided into pesticides, hazardous contaminants, stimulatory
contaminants, and radionuclides that may represent a deliberate or accidental disturbance. Such
distinctions are significant because, for example, pesticides, radionuclides, and pollutants often
vary in respect to applicable law, the appropriateness of the decision-maker or regulatory
authority, and the amount of available information and test data. It is important to be aware,
however, that the same chemical (e.g., carbon dioxide) used as a pesticide to fumigate insects
may turn up as a hazardous pollutant in confined spaces (e.g., a naturally occurring toxin in bat
caves of the southwestern United States) or as a stimulatory pollutant that increases plant growth
and the efficiency of plant water use.
3.3.1.1. Pesticides
Since pesticides are in a class of chemicals that can have inhibitory effects on individual
organisms, they are heavily regulated at the state and federal levels and considerable information
about them is available. Moreover, much of the assessment process for pesticides is well defined
and prescribed by existing guidelines (e.g., the Federal Register).
Although pesticides are used to affect target populations of organisms, the likelihood that
they will affect similar nontarget organisms is high. In forestry and agricultural management,
applications generally are restricted to portions of confined areas to achieve specific objectives.
In contrast, urban and household pesticide use may involve unlimited quantities and unrestricted
applications. While initial applications may be bounded and clearly defined in either commercial
or household use, parameters can change with time.
During and after application, many pesticides can be leached or eroded from terrestrial
areas into waterways. In some uses, such as aquatic-vegetation control, pesticides may be applied
directly into waterways, allowing toxins to spread downstream. Organisms also may spread
pesticides by entering the affected area, ingesting or becoming coated with a toxin, and then
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migrating from the area. In such ways persistent and lipophilic pesticides may accumulate to
extraordinarily high concentrations in soils, aquatic sediments, and top-level predators.
Concerning mode of action, most insecticides, herbicides, and fungicides act directly to
inhibit or stimulate enzyme action by physiologically impairing reproduction, metamorphosis,
and/or development; or by causing behaviors such as aversion. Moreover, pesticides can be
transformed into derivative chemicals with distinct modes of action or bioaccumulate through
food chains. Given that the effects of pesticides often mimic natural agents of historically
notable toxicants (e.g., alkaloids, allelopathic chemicals, antibiotics), information on their role as
stressors draws heavily from the theory and principles of toxicology.
Within the conceptual model, the stress regime might show ways in which the chemical
and its derivatives may contact or enter target and nontarget biota. When ecological effects
include the transfer of chemicals through the food chain, an event-tree diagram may show the
important aspects regarding the species' ecologies that promote or encourage bioaccumulation or
spread (figure 1). Useful information for such a model might include acute and chronic dose-
response relationships (e.g., LC50, LD^, EC^, No Observed Effect Concentration [NOEC],
Lowest Observed Effect Concentration [LOEC]). If the primary ecological effect of the chemical
is to depress the abundance of one component of the ecosystem, then a food web conceptual
model may show the subsequent direct and indirect ecological effects that may be transferred
through the food web as a result of population interactions (figure 2). For pesticides, the stress
regime will generally include dose-response relationships involving acute and chronic effects to
the individual.
33.1.2. Hazardous Contaminants
A much larger class of chemical stressors includes toxic by-products from commercial
operations and hazardous constituents of household products. Because the introduction to the
environment of such toxins may be incidental, information on intensities, frequencies, duration,
and scales may be limited and variable. For example, unplanned or accidental releases of
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pollutants (e.g., leakage from storage tanks, failure of pollution-control devices, transportation
accidents) present unpredictable events in time and space (for information on factoring stochastic
events into risk characterization, see chapter 9), occur continuously or episodically, and may be
of a low or high intensity. Households tend to release mixtures of toxins into the air, landfills,
ground water (septic systems), and sewer systems at low intensities. In aggregate, however,
household releases generally occur at large scales and with varying frequencies and durations.
Although point-source pollution from industrial facilities may be of known intensity, timing, and
duration, the composition of the effluent may include several unexpected chemicals as a result,
for instance, of a malfunction of pollution-control equipment.
The conceptual model for such a stress regime may begin with a transport,
transformation, and fate diagram that postulates how the chemical(s) enters the air, water, and/or
soil (see table 1). For chemicals under consideration, the kinetic or equilibrium constants must
either be measured using standardized protocols (U.S. EPA, 1988) or estimated from empirically
derived SAR or quantitative SAR. At this stage, such a diagram should provide answers to
questions about where the chemicals and their derivatives will go and what their respective initial
or equilibrium concentrations will be in the air, soil, water, or sediments. For example, the
distribution of the pollutant may take the form of a mass balance model (Mackay and Paterson,
1993) in which the continuous release of the pollutant may generate an equilibrium distribution
within various abiotic or biotic "compartments" of the environment (figure 3).
While the transport and fate model may account for some sorption and uptake by the
biota, additional aspects of the chemical should be considered in terms of the biota's
transformation and metabolism (e.g., biodegradation), uptake and storage (e.g., bioaccumulation
and biomagnification), and elimination. Biodegradation may be a particularly important
consideration regarding the duration and intensity of the stressor, while degradation rate
constants will depend on the interaction of the chemical with the ecology of the microorganisms.
Estimates based on laboratory cultures or chemical structure may be biased and thus suspect
(Klecka, 1985), especially since biodegradation may depend heavily on characteristics of the
environment, such as pH, temperature, moisture, oxygen, substrate density and compaction, and
availability of other nutrients (Walker, 1978).
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Toxicokinetic models, which are mathematical simulation models that estimate the
internal dose of toxic chemicals from estimates or measurements of the external dose (Suter
1993), can provide predictions about uptake, absorption, or bioaccumulation pathways of
chemicals from the abiotic and biotic environment. A final consideration regarding the fate and
transport of chemicals concerns the potential for biomagnification through the food chain. If this
potential is established, the focus of the conceptual model may be drawn toward higher trophic
levels and top-level predators. As with pesticides, these toxic effects will generally be formulated
as a dose-response model of chronic and acute effects.
3J.1J. Stimulatory Contaminants
Contaminants such as phosphate, nitrogen, and pH can alter nutrient cycles of an
ecosystem, acting as fertilizers for some organisms in the biotic community. The consequences of
increasing the nutrient load of a system can be profound and highly idiosyncratic (Abrams, 1993),
with measurable concentrations of nutrients increasing or declining. Generally, however, certain
nutrient concentrations will increase, while others decline (Tilman, 1982). As a result, the
abundance of organisms that directly utilize a particular resource may increase or decline
(Hairston et al., 1960; Oksanen et al., 1981) and the relative frequency of constituent species may
change dramatically. Much of the increase in nutrient loading may be manifested at the level of
the predators that prey on organisms utilizing the particular resource. This is likely to lead to
the demonstration of the paradox of enrichment; namely, that feeding the prey may simply result
in more predators (Rosenzweig, 1971).
When a chemical stressor acts as a fertilizer, ecological effects that should be anticipated
include changes in resource concentrations, changes in densities of constituent species, and
changes in community composition as some species become excluded from the community and
others are able to colonize the altered community. Theories pertaining to population
interactions can provide the concepts and empirical data necessary to hypothesize the direction
and magnitude of change in the exposed ecosystem. Important concepts include direct effects,
such as competition and predation, and indirect effects, such as resource competition,
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apparent competition, mutualism, and changes in behavior. Resistance (i.e., How much will
community composition change?) and resilience (i.e., How quickly and reliably will a community
return to its original configuration following a disturbance?) are also important issues (see
chapter 7, on ecological recovery). Consideration of these concepts will yield a conceptual model
capable of predicting effects on keystone species, species compositions, and ecosystem processes.
A dose-response model that successfully characterizes the stress regime of a pesticide or
hazardous contaminant may not be appropriate for characterizing stimulatory contaminants.
Instead, a food web-type conceptual model (see figure 2) or an ecosystem model based on
nutrient cycling (e.g., of carbon, nitrogen, phosphorus, sulfur) may provide a better
characterization. In general, the addition of a nutrient initially will benefit mostly the species
whose population size is largely limited by the resource. As this population increases in number,
the species may alter concentrations of other nutrients, outcompete other species for nutrients,
and benefit its predator. The predator in turn may decimate relatively rare prey species as the
predator population increases in number from consuming the "fertilized" prey (this negative
indirect effect of two prey species on each other via a shared predator is known as apparent
competition [Holt, 1977; Holt and Kotler, 1987]). Such population interactions begin a cascade
of direct and indirect effects throughout the food web. As a result, indices of biotic integrity may
prove more useful in the conceptual model than information on the dose-response relationships
of individual species.
A number of contaminants may have both toxic and stimulatory effects depending on
concentrations, durations, and the organisms under consideration. For example, urea and carbon
dioxide can directly increase primary productivity; urea also can provide a valuable nitrogen
source for plants, and carbon dioxide can increase plant growth rates and water-use efficiencies,
which can change community composition by favoring woody plants over forbs and grasses.
Alternatively, urea can poison and reduce growth rates in secondary consumers (e.g., many
vertebrates) by creating electrolyte imbalances, disrupting protein metabolism, and increasing
water loss through excretion. Taking these alternative poisonous and nutritive effects for the
same chemical stressor into consideration requires rather different scales of perception. The
toxic effects may be formulated as a dose-response relating exposure to chronic and acute effects
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on individuals, while the stimulatory effects may be formulated as effects on productivity,
biomass, population sizes, and the composition of species within a community.
3.3.1.4. Radionuclides
The unique hazard posed by radionuclides is the release of various types of radiation that
may disrupt the integrity and normal functioning of an organism's tissues (i.e., radiation
sickness), result in nonviable or mutated sperm and egg cells, or increase the level of somatic
mutations. Although radionuclides can be assessed using a dose-response model at the level of
the individual, the radioactive ion or element also may be toxic. The combined radiation toxicity
and chemical toxicity of many heavy-metal radionuclides encourages the use of a conceptual
model that presents alternative hypotheses for these two different avenues of toxicity.
Although radiation can be the focus of an ecological risk assessment, in most cases
radiation is more appropriately the subject of human risk assessment since its effects are of more
immediate concern for human health. In terms of the environment, although radiation can have
adverse effects on ecosystems, it can have indirect effects that in sum benefit nature. For
example, by triggering the evacuation of people from an area affected by a radiation release (e.g.,
Chernobyl, radionuclide waste dumps), the species left behind may increase and recover faster
from the stress event.
3.3.2. Physical Stressors
This section provides a more detailed discussion of physical stressors than the above
discussion on chemical stressors because (1) the majority of work already done on ecological risk
assessment has focused on chemicals; (2) the Science Advisory Board (U.S. EPA, 1990a,b) has
identified the need for addressing issues of species extinction, habitat alteration, and habitat
destruction; (3) the types of information available and the types of information needed may
differ substantially for chemicals and physical disturbances; and (4) stress regimes for physical
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stressors generally draw more heavily from a different body of theory (i.e., population ecology)
than chemical stressors (i.e., ecotoxicology).
Physical stressors can be divided into exploitation, habitat change, and habitat destruction
and landscape effects. These distinctions are significant because as one moves along the
continuum from human activities such as recreation or resource harvest to habitat removal and
development, the focus of an assessment may change from concern for the species or impacted
area to concern about ecological effects on adjacent or distant areas.
Natural disturbance regimes contribute to the dynamic stability of "healthy" ecosystems by
promoting species diversity, resetting successional stages, and fostering nutrient cycling and
productivity. Anthropogenic disturbances may either mimic natural disturbance regimes and thus
result in little or no adverse ecological effects or jolt systems into novel successional trajectories
that lead to community compositions deemed less valuable (see chapter 2, on ecological
significance, and chapter 7, on ecological recovery).
Since risk assessments of physical stressors often confront the conflicting land-use goals of
exploitation and preservation, the risk assessor should know in advance what evidence of
potential ecological effects will suffice to permit the risk manager to take action. Thus
regulatory endpoints relevant to the risk manager (e.g., the Endangered Species Act, Wetlands
Protection Act, Marine Mammals Act) must be included in the scientific process when
appropriate. Four ecosystem components are readily identifiable for this purpose:
• the manager may be interested in a target species, subspecies, or ecotype (e.g.,
units of conservation governed by the Endangered Species Act);
• the manager may be interested in maintaining ecosystem functioning, integrity,
and self-sufficiency (e.g., preserving the viability and functioning of wetlands,.or
the watershed functioning of upland habitats);
• the manager may be interested in preserving biodiversity (e.g., restoration and
preservation of prairie plant communities); and
• the regulatory environment may dictate additional constraints and considerations
(e.g., recreation, zoning ordinances, multiple land and water use).
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3J.2.1. Human Exploitation
Even a seemingly innocuous use of an area may result in adverse ecological effects. For
example, tourists pursuing cheetahs on the Serengeti with their cameras prevent them from
hunting for food. The mere presence of humans may represent a set of direct and indirect
physical stressors (e.g., trail damage by hikers and off-road vehicles, plant collecting and animal
poaching, noise pollution).
The ways in which the presence of humans may stress an ecosystem are specific to the
activity, season, and affected species. Often human exploitation of an area will be concurrent
with the effects of other chemical and physical stressors. Because we hesitate to label ourselves a
major ecological stressor, nonhuman stressors may initially receive more attention. Moreover,
human exploitation may be difficult to discern and data on the intensity and types of human
activities within an area may be unavailable, incomplete, or difficult to collect. Yet the rapid
recovery of wildlife at a number of Supertund sites indicates that chemical stressors sometimes
may pose less of an ecological problem for ecosystems than the presence of humans.
Besides human intrusions and the associated ecological effects, harvesting represents a
commercially or recreationally valuable activity that increases the mortality of target and non-
target species alike (e.g., marine mammals and birds caught in oceanic drift nets). Thus agencies
such as the U.S. Forest Service and state fish and wildlife agencies are responsible for preserving
such activities to some degree. In some cases, the physical stressor of harvesting may be
compounded by the introduction of non-native species (see chapter 6, on biological stressors).
Conceptual models of harvesting effects can be based on stock-recruitment models for estimating
the yield and population consequences of different harvesting regimes. Such models incorporate
life-history data on the species' population dynamics along with information on spatial and
temporal uncertainties, density dependence (e.g., compensatory mortality), and population
interactions (e.g., competition from nontarget species). These models are widely used to set
harvesting quotas.
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3.3.2.2. Habitat Change
Even without the influence of anthropogenic stress, ecosystems tend to be in a state of
change. The dynamic nature of ecosystems is attributable to both biotic factors (e.g., population
interactions, non-equilibrium population dynamics, and secondary succession) and abiotic factors
(e.g., climate-induced disturbances, erosion and accretion, fire, temporal edaphic variability).
Anthropogenic disturbance regimes functioning as physical stressors, however, can alter the
frequency, specificity, and intensity of change.
The most apparent effect of human-caused stressors on individual habitats is acceleration
of the rate of change, biasing the distribution and trajectory of habitat dynamics. For example,
although extinction occurs naturally, anthropogenic disturbance greatly accelerates the rate;
erosion is a natural process, but agricultural practices (e.g., clear-cutting of forests) can
accelerate the process by several orders of magnitude; climate change is a natural process that
may be accelerated and biased by the release of greenhouse gases.
Frequently anthropogenic physical stressors reduce habitat heterogeneity by accelerating
successional processes without triggering concomitant increases in the origination of early
successional habitats. Such truncation of normal processes often results in habitat change that is
unidirectional and irreversible. Consider, for example, the threat to Louisiana forest wetlands
posed by increased rates of subsidence, which appear to be associated with petroleum pumping
and the construction of levees. The stressed habitats have become truncated and the lowland
forests made vulnerable to flooding and salt-water encroachment. Areas that would have been
suitable for colonization, as existing forest habitats became flooded, have been developed for
residential and agricultural use and are protected by the same levees that may be affecting
subsidence rates.
Generally the ecological effect of a physical stressor on a habitat is loss of a rare,
desirable, or economically important system or species. Although the stressor may mimic natural
disturbance regimes and successional processes, additional physical stressors may combine to
alter the trajectory of habitat change. For instance, in the Louisiana bottomland area the
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combination of lowland subsidence and the development of land at slightly higher elevations is
likely to result in the loss of cypress-tupelo forests in their present area and limit the trees' ability
to exploit new areas.
Thus a conceptual model should emphasize processes that influence the viability of
populations and species, the maintenance of certain ecosystem functions, and the diversity of
ecological communities. Also, the model should address any stress that threatens the loss of a
keystone species (e.g., natural predators of deer, which are inclined to overgraze) or a keystone
process (e.g., fire suppression) as well as any stress altering the pattern of disturbance regimes.
When ecological effects are anticipated at the level of a target species, habitat suitability indices
should be used; however, when the integrity of an entire community may be affected, indices of
biotic integrity should be used (Karr, 1981; Karr et al., 1986; Poulson, 1992).
3.3.2.3. Habitat Destruction and Landscape Effects
Physical stressors that promote habitat destruction differ significantly from those that
merely cause habitat change. Habitat change involves a level of intrusion that is reversible to
some degree because the basic functioning of the ecosystem is maintained. In contrast, habitat
destruction involves change that is essentially permanent (see chapter 7, on ecological recovery).
When assessing the potential for habitat destruction, a determination must be made
about whether the loss of the habitat will itself act as a stressor on the broader ecosystem. Such
a stress can pose unacceptable risks to regional, local, or adjacent areas. Moreover, organisms
that must migrate through the destroyed habitat may be affected.
In such cases, ecological risk assessment may be in conflict with human risk assessment.
In part, human risk assessment is concerned with whether natural ecosystems pose a threat to
humans in adjacent areas (e.g., from disease vectors). Ecological risk assessment, however,
models the potential stresses imposed by human development on the adjacent natural
ecosystems. The opposite perspectives of ecological and human risk assessments with regard to
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habitat destruction relate to edge effects; that is, boundaries between habitats (human or
otherwise) provide windows through which processes and activities within a habitat can strongly
influence processes within another habitat. Indeed, the boundary itself may function as a distinct
habitat, promoting or inhibiting a variety of functions of adjacent communities. Through such
boundary areas can pass a variety of chemical, physical, and biotic stressors. For example,
habitats adjacent to farmland experience fertilizer and pesticide runoff; habitats near urban
development may be subjected to chemical stressors from sewer systems and biotic stressors from
domestic pets and inadvertently introduced species.
Along with edge effects, habitat destruction can promote fragmentation and insularity
within ecosystems (e.g., creation of a road may divide a species community). Fragmentation and
insularity can be modeled on species-area relationships, which demonstrate that as fragments of a
community become smaller and more isolated they will support less diversity (e.g., chaparral
isolates in the vicinity of San Diego, California, harbor far fewer bird species than areas of the
same approximate size within continuous chaparral habitats [Bolger et al., 1991]). Also, small
isolated populations are more susceptible to demographic and environmental stochasticity
(Shaffer, 1981; Goodman, 1987) and less likely to receive immigrants or disperse successful
emigrants. Further, as the amount of suitable habitat declines, a species' occupation of the
remaining suitable habitat will also decline (Lande, 1987). Moreover, when habitat destruction
pushes the proportion of remaining suitable habitat below a minimum area, extinctions will occur
(Quinn and Hastings, 1987). Theories and models of biogeography (i.e., species-area
relationships, nested subsets, assembly rules, and habitat selection) can provide the basis for
conceptual models dealing with habitat fragmentation and insularity.
Habitat fragmentation and insularity also may decrease the scale, or "grain," of an
environment. Coarse-grained habitats occur on scales that are large relative to the activity scale
of a given individual, whereas fine-grained habitats occur on a small scale relative to the activities
of an individual (Levins, 1968; Brown and Pavlovic, 1992). Fine-grained habitats tend to
promote generalist species to the exclusion of species that are habitat specialists. Thus habitat
fragmentation and insularity may result in the loss of species that are specialized for a particular
coarse-grained habitat. For example, of three woodpecker species in forests of the midwestern
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United States, fragmentation has resulted first in the loss of pileated woodpeckers (the largest),
followed by the decline of hairy woodpeckers (intermediate size), leaving apparently unaffected
only downy woodpeckers (the smallest), which reliably reside within suburbs. In Illinois, gray
squirrels and fox squirrels are associated with large-scale and fragmented tracts of woodlands,
respectively. A steady, state-wide decline in gray squirrels and an increase in fox squirrels has
been linked to deforestation, which took place up until the 1950s. More recently, however,
increased forest growth is reversing these trends. Such changes in species population trends can
provide useful information for developing a conceptual model of habitat change or destruction.
Source-sink habitats represents another important ecological concept pertaining to habitat
destruction (Puliiam, 1988). In many ecological systems, a species may occupy a particularly
favorable habitat that supports surplus individuals available to spread from the hot spot
(Goodman, 1987) into less favorable, sink habitats. Source-sink habitats may exhibit three
important characteristics:
in the absence of the source, the population would become extinct in the sink
habitat;
under certain conditions, the sink habitat may harbor a larger population of the
species than the source habitat; and
the quality of the sink habitat influences population viability in the source habitat
(i.e., a mild sink may increase viability, while a severe sink may reduce viability in
the source habitat).
Thus destruction of source habitats may have serious consequences for a target species.
Also, habitat destruction that transforms mild sinks into severe sink habitats may have equally
negative consequences. For instance, the success of reintroducing wolves into Yellowstone
National Park may hinge critically on the availability of somewhat hospitable habitat outside of
the park's boundaries.
With respect to habitat destruction, any conceptual model must consider the diversity,
scale, fragmentation, and linkages of habitats. The several modeling approaches available for
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such studies include metapopulation analyses (DeAngelis and Waterhouse, 1987; Murphy et al.,
1990), source-sink population processes (Pulliam, 1988), population viability analysis (Dennis et
al., 1991), species-area relationships, and nested species subsets (Patterson and Atraar, 1986).
Rolstad (1991) provides a list of the conceptual issues relevant to habitat destruction and
fragmentation (table 2).
333. Biological Stressors
The introduction of native, exotic, or genetically engineered organisms can constitute
biological stressors on an ecosystem (see chapter 6). Such introductions may be intentional (e.g.,
fish stocks), casual (e.g., "Meadow in a Can" seedings introduced to urban gardens, exotic bird
releases in Hawaii), or unintentional (e.g., sea lampreys and zebra mussels in the Great Lakes).
In regard to biological stressors, conceptual models should focus primarily on the adverse
ecological effects of a species introduction (e.g., What are the ecological effects of the sea
lampreys already present in areas of the Great Lakes?). The risk manager may be interested in
either introducing a chemical to control or eradicate the biotic stressor (e.g., the application of
lampricides) or releasing a genetically engineered organism for control (e.g. Rhizobia containing
additional genes to enhance nitrogen fixation). In the conceptual model, species eradication or
control programs would be the regulatory endpoints, the organism would be considered the
source, and the organism's activities as a competitor, predator, or pathogen would represent
the stressors.
Alternatively, the conceptual model might focus on the risk of invasion or spread of an
organism that might become a stressor. In this case, the risk manager may be evaluating the
advantage of establishing a quarantine to reduce the chances of another invasion in the Great
Lakes by the zebra mussel. In the conceptual model, regulations pertaining to the transport and
quarantine of plants and animals would be the regulatory endpoints, activities such as shipping
would constitute the sources, and activities such as pumping bilge water or transporting
organisms would represent the stressors that may have the ecological effect of introducing an
undesirable exotic species.
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4. ECOSYSTEM COMPONENTS AT RISK
Summarizing available data on ecosystems at risk constitutes the second step of problem
formulation. In evaluating such data, the risk assessor must:
• identify the ecosystem and its components that are at risk;
• select the levels of biological organization at which to anticipate ecological effects,
and;
• identify the possible ecological effects.
Ecosystems involve interactions between organisms and the biotic and abiotic components
of their environment—interactions that can be altered by stressors such as exposure to chemicals,
habitat disturbances, harvesting, and species introductions. Thus the risk assessor must consider
cause-and-effect linkages between agents and components of the ecosystem. Because such
linkages require the co-occurrence of the agent and the ecosystem, an important issue concerns
the spatial and temporal distribution of the agent (see section 3.2.1.5.). The assessor must
determine whether the scale of the agent is geographically vague (e.g., granular carbofuran) or
fixed in time and space (e.g., subsidence of the Mississippi delta).
Once the geographic bounds of an agent have been determined, the habitats within the
geographic range can be specified (e.g., Does the stressor regime involve aquatic or terrestrial
systems?). The habitat specificity of the agent determines the ecosystem components—which can
be characterized as abiotic or biotic—that may be subject to primary effects. Linkages between
agents and ecosystem components also can be defined in terms of primary effects. Biotic
components can be further characterized in terms of the level of biological organization, since
the response of biotic components can occur at the biochemical, physiological, behavioral,
population, and community levels.
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4.1. Ecosystems Bounds
The process of defining an ecosystem at risk involves establishing the discrete bounds for
communities of interacting species while recognizing that some ecosystem linkages occur on a
global scale. Although such definitions of ecosystem components are primarily concerned with
operational characteristics, spatial patterns relating to abiotic conditions of climate and geology
are also relevant. At the global scale, for example, the Earth can be divided between land
masses and the oceans, but the continental shelves and tidal zones are important intermediary
ecosystems.
The concept of zoogeographic realms has been used to describe the tendency of plant
(and animal) species to be specific to particular areas (e.g., this explains why Hawaii is home to a
disproportionate number of the species listed under the Endangered Species Act). In contrast,
the concepts of biomes and life zones have been used to describe distributions of plant
communities at the scale of continents. Temperature and precipitation provide reliable
predictors of vegetation type, structure, and productivity. Thus, for example, increasing
precipitation can cause shifts in plant communities along a continuum of xerophytic scrub,
grassland, savanna, and forests.
At the scale of states, assessment of vegetation types can be further improved by
incorporating the effects of soil type, geology, and hydrology. The predominant materials in soils
and the interactions of these materials with plant communities strongly influence important
abiotic soil conditions such as acidity, alkalinity, nitrogen and phosphorus content, and soil
particle size (e.g., soil properties may either exacerbate or mitigate the effects of acid rain). At
the scale of counties within states, further characterization of vegetation types can be made by
considering past and present human land-use practices (e.g., agricultural, urban development).
At still smaller scales, incorporation of slope and aspect from topographical maps can be used to
assess plant communities.
Moreover, available data can be used to characterize vegetation communities (e.g., from
satellite imagery or geographic information systems [GIS], National Parks surveys, or municipal
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arboretums). The level of detail in classifying possible vegetation communities at risk will be
influenced by available data and the spatial scale of the stress regime.
Because of its relative conspicuousness and direct association with particular abiotic
factors, studies of vegetation (i.e., primary producers) can provide considerable information for
characterizing an ecosystem. Other aspects of the ecosystem community often are tightly
correlated or associated with the primary producers (e.g., soil microbes, fungi, and invertebrates,
as well as vertebrate herbivores and carnivores). Some range maps can provide information on
birds and mammals; many amphibians, reptiles, and fish; and a few insects and other
invertebrates. Besides professional data bases, natural history surveys and surveys performed by
groups such as the Audubon Society can provide valuable information on species distributions.
Within a particular ecosystem, it may be possible to further focus the sites of primary
effects by making distinctions between aquatic and terrestrial systems; the air, soil surface, and
rhizosphere; and habitats or microhabitats. For example, chemical releases into air, soil, water,
or sediments may be inferred from fate and transport models.
4.2. Level of Biological Organization
The source and characteristics of the stressor can provide indications about ecosystem
components at risk and the spatial and temporal scale of potential effects. Thus the next step in
developing the conceptual model involves selecting the specific components of the ecosystem that
are likely to be sensitive to the stressor's primary or secondary effects. Stressors can affect an
organism in terms of biochemistry (intracellular processes), physiology (homeostatic processes
occurring among cells and organ systems within multicellular organisms), behavior (feeding,
mating, and vigilance responses of an individual), population (density, age/stage-structure, and
growth rate among individuals within a species), community (assemblages or food webs of
interacting populations of different species), ecosystem (abiotic and biotic processes that
determine nutrient cycling and energy flow), or landscape (processes occurring among tenuously
coupled populations or communities).
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In general, the selection of ecosystem components integrates three considerations:
• ecological significance (see chapter 2) and regulatory aspects;
• amount and types of existing information;
• potential for ecological effects based on current scientific theory.
4.3. Ecological Effects
A wide range of possible ecological effects can result from any given stressor. At one
extreme are ecosystems that are precariously balanced and highly susceptible to even slight
perturbations. At the other are ecosystems that are highly resilient and predetermined. For
many ecosystems, however, only certain components are susceptible to change in response to a
given disturbance, while other components are quite resilient.
Although most assessment endpoints will concern ecological effects that have
consequences at the level of populations or communities, in most cases ecological effects at or
below the level of individuals should have less relevance in the conceptual model except as
measurement endpoints. Of particular significance are ecological effects that directly influence
mortality or fecundity within a community because such effects can have consequences at the
population scale. Effects that increase mortality include those that interfere with an individual's
developmental or homeostatic processes (sometimes referred to as chronic effects) and those that
remove individuals from the population (i.e., acute effects). Life-history models of population
growth can be used to determine which changes in age- or stage-specific birth and death rates
will have significance for population growth. When appropriate, consideration of density
dependence can help the risk assessor evaluate whether changes in population growth rates will
be mitigated by compensatory mortality or fecundity. Population growth dynamics can be
particularly relevant when the stressor is a chemical substance that may have direct inhibitory or
stimulatory effects on the ecosystem or the stressor is a physical disturbance involving
exploitation and harvesting. In the study of granular carbofuran, it was found that consumption
of a single granule could be fatal to a small bird, since 5-day subacute LCJ0s indicated high
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toxicity among bird species despite variability. In the Louisiana forest wetlands example, the
FORFLO model of forest dynamics provided a formal life-history presentation of mortality and
fecundity effects.
Another view of ecological effects recognizes the energy and resource needs of organisms.
For example, dams can restrict the access of salmon to spawning grounds. Similarly, subsidence
in the Louisiana forest wetlands restricts the access of adult trees' root systems to oxygen and
deprives seeds and seedlings of suitable germination and growth sites. Thus change in access to
resources can be expected to have population consequences. Resource-based models of
population dynamics are useful for evaluating the effects of factors such as light, temperature,
carbon dioxide, and moisture on plant life; the effects of forage quantity and quality on
herbivores; and the effects of predators on fishery stocks. The resource-based approach also may
be appropriate for modeling the effects of chemical stressors that act as fertilizers or physical
disturbances that result in the removal of resources (e.g., How might Arctic whales be influenced
by the harvesting of krill?) or restrict access to resources (e.g., How is the cheetah's hunting
success influenced by the presence of tourists?).
The risk assessor also must recognize that effects on one component of an ecosystem can
have cascading effects on other components. For example, the connection between food webs
can result in indirect effects. Thus a predator of a prey that has been exposed to inhibitory
chemicals may suffer in two ways: (1) the predator may become exposed to the chemical via prey
consumption (e.g., biomagnification), and (2) the availability of prey may decline if the prey
begins to die off because of the chemical exposure.
More generally, several important indirect effects are associated with population
interactions:
• resource competition can occur when two predators share the same prey;
• apparent competition can occur when two prey share a common predator;
• indirect mutualism can occur when two predators have different prey that are
themselves competitors; and
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trophic cascades can occur when an organism preys on the predator of another
prey species.
Thus, for example, indirect effects can provide a partial explanation for why coyotes of
the southwestern United States were not more significantly affected by predator-control
programs. Although such programs often were quite successful in reducing the populations of
foxes, wolves, and birds of prey, the coyotes benefitted from the removal of other predators,
especially wolves.
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5. SELECTION OF ENDPOINTS
As noted in the Framework Report, endpoint selection is a critical component of the
problem formulation stage of a risk assessment. The Framework Report as well as the literature
on risk assessment (e.g., Suter, 1989; Suter and Bamthouse, 1993) define assessment endpoints as
formal expressions of the environmental attributes protected by management actions and
addressed in risk assessments. In contrast, measurement endpoints are the specific laboratory
and field observations that provide the empirical basis for the risk assessment.
Defining assessment and measurement endpoints for particular ecosystems involves
three steps:
identifying the valued attributes of the environment that are considered to be at
risk;
defining these attributes in operational terms; and
selecting the types of data required to assess the status and potential changes in
those attributes (Suter and Bamthouse, 1993).
As noted in chapter 6, on ecological significance, the identification of "valued attributes"
is a complex process involving sociopolitical considerations. The principal concern of the
discussion in this section is the translation of values into endpoints, rather than definition of the
values themselves.
Valued attributes are derived from regulations, which in some cases define the process
for identifying the attributes. For example, the NEPA "scoping process" is used to identify public
and agency concerns for evaluation in environmental impact statements. In contrast, the Clean
Water Act (section 316b) stipulates that a "balanced indigenous population" must be maintained
in water bodies receiving thermal plumes from power plants.
In most cases, agencies have relatively wide latitude for translating statutorily defined
values into assessment endpoints. Even under the Endangered Species Act, the U.S. Fish and
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Wildlife Service has substantial flexibility in defining the meaning of the term "species" and in
prioritizing the lengthy list of potential candidate species (see chapter 2, on ecological
significance).
Since there are many possible assessment endpoints for most ecological risk assessments,
the framework specifies three factors to be considered in making a selection: ecological
relevance, policy goals and societal values, and susceptibility to the stressor.
5.1 Ecological Relevance
Ecologically relevant endpoints "reflect important characteristics of the system and are
functionally related to other endpoints" (U.S. EPA, 1992, page 13). Since determining ecological
relevance in specific cases requires a great deal of expert judgment, clearer definitions of
"important characteristics" and "functionally related" are needed if this particular criterion is to
have any practical value. Suter and Barnthouse (1993) have cast some light on the definition of
the relevance by elaborating on the concept of significance:
The biological significance of a property is determined by its importance to a
higher level of the biological hierarchy. For example, a physiological change is
biologically significant if it affects a property of the whole organism such as survival or
fecundity, a change in fecundity of individuals is biologically significant if it affects the
size, productivity, or other property of the population, and a decrease in the size of a
population is biologically significant if it affects the number of species, the productivity,
or some other property of the ecosystem [page 23].
Certain major categories of organisms (e.g., principal primary producers, forage species,
keystone predators) and ecosystem processes (e.g., primary production, nutrient cycling) can be
generally defined as biologically significant according to the above definition. For generic risk
assessments, however, no more specificity in the definition may be required. Yet for (certain)
risk assessments, determinations of which population or ecosystem characteristics are ecologically
significant must be made on a site-specific basis from expert judgment and preliminary
site surveys.
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5.2. Policy Goals and Societal Values
Because risk assessments are performed to support management decisions and managers
must be sensitive to societal concerns, organisms that are considered endangered or of
commercial or recreational value are obvious choices as assessment endpoints regardless of their
biological significance. Indeed, because they are rare, endangered species would not necessarily
be considered biologically significant according to the Suter and Bamthouse (1993) definition.
Thus, although the definition of relevance is important in ecological risk assessment, it is equally
important for ecological concepts to be communicated to managers and the public-at-large. That
is, assessments of risks to species diversity or ecosystem function can influence decisions only if
the value of these characteristics can be effectively communicated. It is essential that guidance
be provided on how these values can be communicated through conceptual models—guidance
that should be established at the agency level as well as the program level.
5.3. Susceptibility to the Stressor
Determining susceptibility to the stressor involves consideration of both real and potential
exposure to a stressor as well as the types of effects that can result. For chemical stressors,
properties can be used to make predictions about the environmental partitioning and degradation
effects and the ecosystem components that will be exposed. Also, toxicity tests can be used to
predict which of the potentially exposed organisms are likely to be most sensitive. For other
kinds of stressors, susceptibility may be more difficult to determine. Based on general life-history
considerations, it is known that populations with long life cycles and low reproductive rates are
more vulnerable to extinction from increases in mortality than are species with short life cycles
and high reproductive rates (Mertz, 1971; Bamthouse, 1993). Susceptibilities of species to
extinction caused by landscape fragmentation are often related to their home range and
minimum habitat size. Guidance on determining susceptibility to nonchemical stressors is needed
both at the program level and at the assessment level.
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5.4. Scale Considerations
Additionally, specific ecosystem types and taxonomic groups influence the selection of
assessment endpoints m terms of scale considerations. Because all organisms are destined to die,
individual organisms are valid assessment endpoints only if they belong to species protected by
statute. Otherwise, effects must occur over a large enough area to have an adverse impact on
biological hierarchy or have a noticeable impact on populations or ecosystems of interest to the
public. For this reason, most assessment endpoints should be defined at the population or
ecosystem level, rather than at the individual level. At the higher levels of organization, the
time-space scales addressed are likely to be suited to observations and experiments performed to
support risk assessments (Suter and Barnthouse, 1993).
5.5. Measurement and Assessment Endpoints
Measurement endpoints, as defined both in the Framework Report and by Suter (1993),
are quantities such as LC50s or diversity indices measured by toxicologists or field biologists.
Some of these values may be genetically defined (e.g., water-quality criteria) for use in
standardized assessments; others are defined and measured on a assessment-specific basis.
Since endpoints generally refer to characteristics of populations and ecosystems defined
over fairly large spatial scales and long time periods, it is usually impractical to directly measure
changes in these characteristics as part of an assessment. In many cases (e.g., pesticide
registration and toxic chemicals review), assessments must be made before any large-scale release
can be allowed to occur. Thus some form of extrapolation based on expert judgment, statistical
methods, or simulation models is usually required to link the measurement endpoints to
assessment endpoints. Because assessment endpoints will usually be keyed to levels of biological
organization above the individual organism and measurement endpoints frequently are set at the
level of individuals, effects on individuals usually need to be aggregated or extrapolated to infer
effects on population or ecosystem-level assessment endpoints.
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5.5.1. Endpoints for the Granular Carbofuran Study
While guidance for endpoint selection can make the process seem fairly straightforward,
in practice the process can be complex. Consider, for example, the case study for granular
carbofuran (Houseknecht, 1993). FIFRA requires simply that pesticides pose no "unreasonable
risk to man or the environment, taking into account the economic, social, and environmental
costs, and benefits of the use of the pesticide" (FIFRA, section 2[bb]). Specific definitions of
"unreasonable risk" were left for EPA to develop when implementing regulations. Risks to birds
through direct mortality, indirect mortality (i.e., secondary poisoning resulting from consumption
of contaminated prey), and reduced reproduction are identified as factors that should be
considered in the pesticide registration process. Guidance is not provided, however, on whether
the assessment should address risks to individual birds or bird populations, or whether risks to
some non-endangered species (e.g., starlings, introduced agricultural pest) are more acceptable
than risks to others (e.g., the wood thrush, which is a migratory species native to North America
that is experiencing population declines over most of its range).
No rationale for the approach to endpoint selection is provided in the granular
carbofuran case study. The assessment departed significantly from the routine because it was a
"special review"—a retrospective assessment conducted following observations of bird mortality
associated with applications of granular carbofuran. Several different types of measurement
endpoints were employed in the carbofuran special review: observations of bird mortality
following routine applications of granular carbofuran, measurements of carbofuran residues or
acetylcholinesterase inhibition in carcasses of dead raptors, experimentally determined oral LDjgS,
and field studies. Extrapolation from measurements to the assessment endpoint—"risk to bird
populations"—was accomplished through simple decision rules and professional judgment.
Observations of carbofuran residues or acetylcholinesterase inhibition in dead birds were
assumed to indicate carbofuran poisoning. LD^ estimates (mg/kg) were compared to application
rates (mg/ft2) under the assumption that the presence of carbofuran in a concentration of more
than a few LDjoS per square foot indicated a significant risk to ground-foraging birds.
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No attempt was made to draw inferences concerning potential reductions in abundance or
an increase in the risk of extinction in local bird populations. Also, no case was made that
mortality to individual birds is inherently unacceptable. The real, although unstated, assessment
endpoints for this study appear to have been (1) the obvious mortality of birds in and around
fields following application of the pesticide, and (2) the secondary poisoning of raptors, especially
bald eagles. The measurement endpoints for the first assessment endpoint were (1) the bird kill
incidents reported to EPA, which provided an association between granular carbofuran and bird
kills, and (2) the confirmatory toxicity tests and field tests, which showed that birds foraging over
even very small areas (i.e., 1 square foot or less) could easily ingest lethal doses of the pesticide.
Qualitative findings of granular carbofuran residues and cholinesterase inhibition in raptor
corpses accounted for the only measurement endpoints used to address the second
assessment endpoint.
5.5.2. Endpoints for the Louisiana Forest Wetlands Study
A different approach to endpoint definition was taken in the Louisiana forest wetlands
case study (Brody et al., 1993). In this case, the assessment and measurement endpoints appear
to have been influenced strongly by the availability of two quantitative assessment tools: the
FORFLO model, which is a forest succession model (Pearlstine et al., 1985; Shugart, 1984) that
simulates the influence of a hydrologic regime on the growth and reproduction of wetland forest
tree species (i.e., the model considers competition among the trees and the tolerances of the
various tree species to waterlogging); and the U.S. Fish and Wildlife Service HSIs, which relate
the physical and biological characteristics of an ecosystem to the habitat requirements of specific
wildlife species.
The case study paper identifies "physical alteration or change in the forest community and
associated habitat value" as the assessment endpoint. Yet only forest trees and vertebrate species
for which habitat requirements are well defined were included in the assessment. Vegetation
types in the study area were characterized as "dry bottomland," "wet bottomland," and
"swamp"—each with its own characteristic assemblage of tree species. The FORFLO model was
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used to simulate successional changes and transitions in these assemblages produced by different
rates of subsidence (leading to increased flood duration and frequency). "Habitat value" was
operationally defined as the habitat requirements of five representative vertebrate species: gray
squirrel (Sciurus carolinensis), swamp rabbit (Sylvilagus aquaticus), mink (Mustela visori), downy
woodpecker (Picoides pubescens), and wood duck (Aix sponsa).
Measurement endpoints were defined simply to be the input data required by FORFLO
and the HSIs. For FORFLO, these data included (1) baseline field data on the community
composition of representative forest stands, (2) species-specific growth and physiological
tolerance parameters, and (3) a time series of environmental data (in particular, data on
hydrologic regimes). For the HSIs, the required data included various measures of species-
specific habitat variables (e.g., vegetation composition, mast production, availability of
herbaceous cover, standing water area).
The assessment itself proceeded in three steps: (1) scenarios for future subsidence rates
were developed; (2) future changes in vegetation states were calculated based on the scenarios
for three representative tree communities (dry bottomland hardwood, wet bottomland hardwood,
and swamp forest) found in the area; and (3) changes in community structure (as predicted by
FORFLO) were used to calculate changes in habitat suitability for the five representative species.
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6. THE CONCEPTUAL MODEL
As noted in Section 1, the conceptual model serves as a summary of the risk assessment's
problem formulation phase and as a plan for the analysis and risk characterization phases. We
suggest including the following elements, at a minimum, in a conceptual model:
• a flow chart linking the stressor(s) being evaluated to the assessment endpoints;
• a set of discrete hypotheses concerning the possible effects of the stressor(s) on
the endpoints (the hypotheses should highlight the specific measurements or
model outcomes needed to distinguish among alternatives);
• the specific measurements and extrapolation techniques to be used in the analysis
phase of the assessment; and
• model/data selection criteria and quality assurance standards.
Whether this information is codified in program guidance or developed separately for
each assessment depends on the specifics of the assessment being performed.
6.1. Flow Diagrams
Figures 1 and 2 present two possible forms of a flow chart linking stressor(s) to endpoints
of concern. The event-tree diagram in figure 1 depicts various mechanisms, both anthropogenic
and natural, that could contribute to recruitment failure in a fish population. This particular
example of an event-tree diagram has been used by EPA's Office of Toxic Substances to
illustrate its approach to ecological risk assessments (discussed in Suter, 1993). The advantage of
the event tree is that it facilitates consideration of alternative mechanisms leading to the same
ecological consequence. Moreover, it readily accommodates information on behavioral, physical,
and toxicological effects. Although convenient for conceptualization, the event-tree diagram can
be difficult to relate to suitable quantitative assessment approaches.
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Figure 2 is a more conventional flow chart based on the physical movement of a toxic
contaminant from a source, through environmental media, to direct and indirect effects on a fish
population (i.e., food-chain effects). This type of flow chart has the advantage of corresponding
more directly to the quantitative environmental fate models that are often used in risk
assessment. It is only applicable, however, for chemical stressors. Also, it does not lead as
directly to consideration of alternatives.
The flow chart shown in figure 4 depicts the influence of hydrology on the structure and
function of a bottomland forest ecosystem, drawn in "energy circuit language" (Odum, 1971). In
this chart, the symbols represent energy sources and transformation processes, environmental
influences, and internal regulatory mechanisms. Energy circuit notation is quite general, allowing
representation of chemical, physical, and biological processes. Such diagrams are often quite
complex, however, and not readily comprehensible to nonexperts.
Regardless of the approach chosen, the problem of aggregation is the most significant
technical issue to be considered in developing a flow diagram. Indeed, an infinite array of
possible conceptual models could be developed for any assessment problem. For example,
species could be grouped according to trophic levels or identified individually. Similarly, all
possible environmental pathways could be included in the model or only those few believed to be
ecologically significant based on an expert evaluation of existing information. In general,
however, only pathways thought to be sufficiently important for collection of data should be
included. The degree of resolution of the compartments should likewise be geared toward the
data that will be collected or that is already available.
For the granular carbofuran study a highly simplified flow diagram was used (figure 5a).
Although the diagram leaves out most of the complex ecological processes that occur in
agricultural ecosystems, it provides a reasonable representation of the conceptual model actually
used in EPA's special review of this pesticide. The only assessment endpoints identified are
ground-foraging birds and the raptors that prey on them. The exposure pathways of concern are
(1) ingestion of granules by ground-foraging birds, (2) ingestion of contaminated invertebrates,
and (3) secondary poisoning of raptors feeding on poisoned prey. The only relevant
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environmental fate data are pesticide application rates. The only biological data employed are
bird kill data (i.e., incident reports and experimental field trials), laboratory toxicity studies, and
results from raptor autopsies. Figure 5b shows a slightly more complex flow diagram that
includes ecological processes that could have been investigated but that were deemed irrelevant
to the special review. This diagram, which includes a more detailed representation of the
exposure process and considers raptor population dynamics, would be appropriate if the
assessment had required quantitative estimates of the impacts of granular carbofuran on
raptor populations.
6.2. Impact Hypotheses and Quantitative Response Relationships
The development of impact hypotheses prior to conducting field studies has been
recommended as a means of increasing study efficiency (Sanders et al., 1978; Beanlands and
Duinker, 1984). An imaginative ecologist can list dozens—or even thousands—of potential
ecological effects from any stressor. A manageable set of the most likely exposure pathways and
effects must be selected for investigation during the problem formulation phase of the risk
assessment. For example, figure 1 indicates that recruitment failure in a fish population may
have many causes, some of which may be related to the regulatory action being contemplated
and some of which are unrelated. The conceptual model should identify which of these causes
will be investigated.
6.2.1. Scientific Methodology for Developing the Conceptual Model
The conceptual model should result in the development of explicit hypotheses, which will
improve the efficiency of data collection and force the risk assessor to establish a plan for the
assessment before getting started. Moreover, the assessor should justify the selection of
particular exposure pathways and ecological effects, explaining why they are deemed of greater
concern than other possible pathways and effects. Also, the conceptual model should include
specific criteria for determining the significance of results (i.e., the rejection or acceptance of the
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null hypothesis). By being explicit about the various components of the conceptual model, the
risk assessor facilitates communication among interested parties.
To develop the model, the risk assessor should use available information on stressors.
ecosystem components, and ecological effects to generate hypotheses. Even when only
incomplete data is available, it is important to develop theories about connections between
possible stressors and ecological effects. Nonetheless, the amount of information should strongly
influence one's confidence in conclusions drawn from the conceptual model. For example,
conclusions based on sparse information should be reconsidered if countervailing evidence
becomes available. As available information increases, the assessor's confidence in the new or
modified model should also increase.
When sufficient information is available, however, hypotheses should be explicit. In the
granular carbofuran study, for example, one of the endpoints of concern was mass kills of
ground-foraging birds that ingest pesticide particles. Alternative hypotheses could be stated as
follows:
HO: Granular carbofuran is hazardous to ground-foraging birds
HI: Granular carbofuran is not hazardous to ground-foraging birds
The hypotheses are stated in the reverse of the usual form (i.e., a null hypothesis of no impact)
because FIFRA regulations specify that a pesticide must be assumed hazardous unless risk
assessments show that it is safe. HO would be rejected and HI accepted if: (1) few or no
incidents of bird kills associated with granular carbofuran application were reported to EPA, (2)
laboratory studies demonstrated that granular carbofuran has low toxicity, or (3) field tests
indicated that exposure and mortality are low under actual field conditions. Subsequently,
numerous bird kill incidents were reported to EPA, laboratory studies showed that very small
doses of granular carbofuran (i.e., as small as a single granule) are sufficient to kill small birds,
and birds were killed in significant numbers during field tests. The null hypothesis was accepted,
and granular carbofuran was declared hazardous to birds.
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While in principle it should be possible to state every risk assessment in the form of
explicit hypotheses and tests, in practice this approach is not always practical for decision-making.
Consider, for example, the Louisiana forest wetlands case study. Rather than a regulatory
assessment of the acceptability or unacceptability of a specific product or proposed action, this
assessment involved quantitative estimation of potential ecosystem effects associated with likely
changes in the rates of subsidence. Further development of this study would presumably involve
identification of quantitative relationships between specific watershed management strategies and
future subsidence rates. Agencies responsible for watershed management would use this
information to make more informed decisions about future land and water use in the lower
Mississippi floodplain. Assessments in which managers are presented with a quantitative
relationship rather than a pass/fail criterion are quite common in resource management and in
NEPA assessments. Rather than specifying null and alternative hypotheses, conceptual models
for these kinds of assessments should specify (1) the specific relationships to be included (e.g.,
between subsidence and tree community structure), and (2) the data required to develop
those relationships.
6J. Summary of Measurement Endpoints and Extrapolation Techniques
The measurement endpoints and extrapolation methods for testing impact hypotheses or
developing relationships should be listed, referenced by hypothesis, and identified with the
appropriate compartments and links in the flow diagram. While these steps may appear trivial,
they are important for reviewers of assessment plans because they encourage the risk assessor to
be explicit (i.e., avoiding such rationales for data collection as "it's always done this way").
In some regulatory programs, specific measurements (e.g., toxicity test protocols) or
extrapolation equations are established in program guidelines. In other situations, these are
developed on a case-by-case basis. In Superfund investigations, for example, compilation of a list
of data requirements is the initial stage in the establishment of data quality objectives (DQOs);
this list is (or should be) referenced to a flow diagram. Once the types of data are identified,
specific measurement techniques can be selected and statistical confidence limits established.
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The development of procedures for establishing DQOs (an ongoing effort within the Superfund
program) provides a means of bridging the gap between conceptual model development as
described in the framework and the specific sampling plans required for field investigations.
6.4. Model/Data Selection Criteria and Quality Assurance Standards
As noted above, specific models and data types can be specified in program guidance only
for standardized assessments. For programs in which most or all risk assessments are site
specific, criteria for selecting models and ensuring data quality should be established in program
guidance. For commonly used environmental fate models and standard environmental data
types, such as contamination concentrations and population abundance surveys, these criteria can
be readily specified. For more complex studies of problems, such as introduced organisms or
habitat alteration, which will require novel models and field studies, existing standards for model
selection and data quality are unlikely to be adequate. When these standards exist, however,
criteria for determining the adequacy of models and sampling schemes proposed for individual
assessments should be included in program guidance.
6.5. Example Conceptual Model: Granular Carboniran
As discussed by Houseknecht (1993), granular carbofuran is "a broad-spectrum insecticide
and nematicide registered for control of pests on 27 agricultural crops and for certain forest and
pineseed orchard uses." The assessment endpoints addressed in the risk assessment included
lethality to ground-foraging birds and raptors preying or scavenging on ground-foraging birds.
Flow diagram: A flow diagram is depicted in figure 5a. Granular carbofuran is applied to
the soil to kill pests that attack plant roots. Ground-foraging birds ingest pesticide particles,
receiving a dose that is dependent on the application rate, the quantity of active ingredient in
each particle, and the rate of degradation of the pesticide in typical soil conditions. Depending
on the size of the bird and the number of particles ingested, birds may die following ingestion of
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one or several particles. Because carbofuran is a neurotoxin, death, if it occurs, happens shortly
after exposure; if a bird does not die, it is expected to recover. Dead or behaviorally impaired
birds may be eaten by raptors, which can be poisoned by eating the tainted prey.
Impact hypotheses: The null hypothesis for this assessment, defined by statute, is that
granular carbofuran is hazardous. The hypothesis can be rejected if bird kills are not reported,
or if laboratory studies show that carbofuran has low toxicity (i.e., kills are from some other
cause), or if field tests show that exposure and mortality should be rare under actual
field conditions.
Measurement endpoints and extrapolation techniques: Measurement endpoints for this
assessment include (1) reports of bird kill incidents associated with carbofuran applications, (2)
laboratory toxicity tests using several bird species, and (3) experimental applications of granular
carbofuran under conditions meant to duplicate actual agricultural applications. A hazard index
(LD50s/ft2) was used as a descriptor of ecological risks associated with carbofuran applications.
Model/data selection criteria and quality assurance standards: Selection criteria and
quality assurance standards were not described in the case study report.
6.6. Example Conceptual Model: Louisiana Forest Wetlands
In this assessment (Brody et al, 1993), the stressor is changes in water-level elevations
(0-5 cm/yr) due to subsidence, reduced sedimentation, salt-water intrusion, and reduced drainage
gradients within the Lake Verret Basin. The sources of these changes in water level include
natural processes (i.e., delta formation and tectonic subsidence) and anthropogenic processes
(i.e., levee construction, dredging, land development, shipping, and petroleum pumping). The
amount of suspended sediments in the Mississippi River may have declined by 50 percent since
the 1950s. The assessment does not purport to establish a direct link between a particular source
and the stressor. Rather, it is noted that prior to levee construction, the Lake Verret Basin was
experiencing a net accretion of sediments in which bottomland forests were actually replacing
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swamp forests. The shift from net accretion to net subsidence implicates prior levee construction
as the source of these changes. The assessment endpoints are (1) the area of dry bottomland
forest, wet bottomland forest, and cypress-tupelo swamp forest; (2) the relative abundance, sizes,
and densities of sixteen canopy tree species; and (3) two species of bird (the downy woodpecker
and the wood duck) and three mammal species (the gray squirrel, swamp rabbit, and mink).
Flow diagram: The FORFLO model predicts changes in forest compositions along with
changes in hydrologic parameters, and it provides the flow diagram for the risk assessment
(figure 4). Several abiotic factors (i.e., subsidence, river flow, and rainfall) influence water tables,
flood frequencies, and flood durations. The water-table and flood characteristics influence the
mortality and growth of the different tree species. The composition of the forest is further
elaborated as a life-history model that takes the current size structure of the forest and projects
seed production, seedling growth, and the future size and species composition of the forest.
Continual change in flood characteristics prevents the model from achieving a stable forest
composition. The flow diagrams leading to the HSIs are not given in the case study. »
Impact hypotheses: The null hypothesis for this assessment is that the present forest
types and compositions within the Lake Verret Basin will remain unchanged into the future.
This hypothesis will be rejected if the output of the FORFLO model shows a regular succession
of forest types from bottomland hardwoods to swamp forest. In one sense the null hypothesis is
a "straw man" since increased water levels will inevitably lead to swamp and open-water habitat.
In the absence of a dialogue between risk manager and assessor, however, it is impossible to
structure the impact hypotheses in the form of upper and lower bounds of acceptable habitat
change. More generally, the output from FORFLO and the HSIs represent a set of.
testable hypotheses.
Measurement endpoints and extrapolation techniques: For the abiotic components,
measurement endpoints include height of the water table, flood frequency, and flood duration
(from the U.S. Geological Survey). For the species composition of the forest, measurement
endpoints include tree species frequency, dominance, density, size, and replacement sequences
(measured over a 2-year period). Temperature and precipitation estimates were taken from
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cliraatological records. Other parameters for the tree species (e.g., maximum growth rate and
optimal water level) were taken from estimates found in the published literature. The necessary
parameters for the bird and mammal HSIs were derived from FORFLO, measured directly in
the field, or estimated from published values.
Model/data selection criteria and quality assurance standards: Selection criteria and
quality assurance standards were not described in the case study report.
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7. SUMMARY
The principal themes of this chapter can be summed up as completeness, efficiency, and
scientific rigor. Task assessments should be complete in terras of addressing all relevant
management goals and considering all ecological knowledge necessary to support informed
ecological risk management decisions. The discussion of management contexts in section 2 is
intended both to emphasize that the definition of "complete" differs for different kinds of
management problems and to identify the types of program-level risk assessment guidance
appropriate for different regulatory situations. The discussions of stressor characteristics and
ecosystems at risk in sections 3 and 4 are intended to summarize the range of stressor-specific
and ecosystem-specific information of potential value for ecological risk assessments and to
provide criteria for identifying the information required for any given assessment.
Risk assessments should be efficient in terms of collecting only relevant information and
avoiding information that is redundant. Necessary information should not neglected. Further,
the time required for the assessment should be commensurate with the time available for making
risk management decisions. Section 2 discusses the influence of available time on decisions
concerning the balance between modeling and site-specific data collection in risk assessments.
Sections 3 and 4 discuss how knowledge of the stress regime and ecosystem components can
bring focus to the broad range of potential ecological effects. The selection of endpoints (section
5), the development of flow diagrams (section 6.1), and the summary of measurement endpoints
and extrapolation techniques (section 6.3) are all intended to facilitate efficiency.
Risk assessments obviously should be scientifically as rigorous as possible, given the
constraints imposed by the needs of risk managers for timely decisions. Risk assessment cannot
be expected to be as rigorous as basic scientific research, however, because research is generally
not constrained by timetables and research scientists can (and should) be extremely conservative
in determining the amount of evidence required to accept or reject hypotheses. Although
assessment scientists have no such luxury, they still should be expected to establish and meet
appropriate standards of scientific rigor. The discussion of impact hypotheses (section 6.2) and
data/model selection criteria (section 6.4) are directed at facilitating the establishment of such standards.
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With reference to the Framework Report (U.S. EPA, 1992), a properly developed
conceptual model should ensure completeness, efficiency, and rigor by summarizing all of the
technical elements of the assessment in a form that enables risk managers to determine whether
management objectives will be addressed and that permits scientific peer reviewers to determine
whether standards for technical adequacy will be satisfied. While the development of a complete
and credible conceptual model does not guarantee a successful risk assessment, the absence of
such a model virtually ensures a risk assessment with useless or indefensible results. We have
tried to identify the range of issues that must be addressed and the process required to develop
good conceptual models. It is still up to each agency or program office to use these principles to
develop guidelines and quality standards appropriate to its specific mission.
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8. REFERENCES
Abrams, P.A. (1993) Effect of increased productivity on the abundances of trophic levels. Amer.
Natur. 141:351-371.
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Beanlands, G.E.; Duinker, P.N. (1984) An ecological framework for environmental impact
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Bolger, D.T.; Alberts, A.C; Soule, M.E. (1991) Occurrence patterns of bird species in habitat
fragments: sampling extinction and nested species subsets. Amer. Natur. 137:155-166.
Brody, M.S.; Valette, Y.; Troyer, M.E. (1993) Ecological risk assessment case study: modeling
future losses of bottomland wetland forests and changes in wildlife habitat within a
Louisiana basin. In: A review of ecological assessment case studies from a risk assessment
perspective, section 12. EPA/630/R-92/005.
Brown, J.S.; Pavlovic, N.B. (1992) Evolution in heterogeneous environments: effects of migration
on habitat specialization. Evol. Ecol. 6:360-382.
DeAngelis, D.L.; Waterhouse, J.C. (1987) Equilibrium and nonequilibrium concepts in
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Dennis, D.; Munholland, PX.; Scott, MJ. (1991) Estimation of growth and extinction
parameters for endangered species. Ecol. Monogr. 61:115-143.
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Hairston, N.G.; Smith, FJE.; Slobodkin, LJB. (1960) Community structure, population control,
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Houseknecht, C.R. (1993) Ecological risk assessment case study: special review of the granular
formulations of carbofuran based on adverse effects on birds. In: A review of ecological
assessment case studies from a risk assessment perspective, section 3. EPA/630/R-92/005.
Karr, J.R. (1981) Assessment of biotic integrity using fish communities. Fisheries 6:21-27.
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Karr, J.R.; Fausch, K.D.; Angenneier, P.L.; Yant, P.R; Schlosser, IJ. (1986) Assessing biological
integrity in running waters: a method and its rationale. Illinois National History Survey
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from chemicals, vol. 1. Boca Raton, FL: CRC Press.
Lande, R. (1987) Extinction thresholds in demographic models of territorial populations.
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Levins, R. (1968) Evolution in changing environments. Princeton, NJ: Princeton University
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Mackay, D. (1987) The holistic assessment of toxic organic chemicals in Canadian waters. Can.
Water Res. J. 12:14-20.
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Ecological risk assessment, ch. 5. Boca Raton, FL: Lewis Publishers, pp. 129-152.
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105:437-453.
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Boca Raton, FL: Lewis Publishers, pp. 91-127.
Murphy, D.D.; Freas, K.E.; Weiss, S.B. (1990) An environment-metapopulation approach to
population viability analysis for a threatened invertebrate. Conserv. Biol. 4:41-53.
Odum, E.P. (1971) Fundamentals of ecology. Philadelphia: W.B. Saunders Company.
Oksanen, L.; Fretwell, S.D.; Arruda, J.; Niemela, P. (1981) Exploitation ecosystems in gradients
of primary productivity. Amer. Natur. 118:240-261.
Patterson, B.D.; Atmar, W. (1986) Nested subsets and the structure of insular mammalian
faunas and archipelagos. In: Heaney, L.R.; Patterson, B.D., eds. Island biogeography of
mammals. London: Academic Press, pp. 65-82.
Pearlstine, L; McKellar, H.; Kitchens, W. (1985) Modelling the impacts of a river diversion on
bottomland forest communities in the Santee River Floodplain, South Carolina. Ecol.
Model. 29:281-302.
Poulson, T.L. (1992) Assessing groundwater quality in caves using indices of biological integrity.
In: Proceedings of the 3rd Conference on Hydrology, Ecology and Monitoring, and
Management of Groundwater in Karst Terranes. National Groundwater Association,
Dublin, OH.
Pulliam, H.R. (1988) Sources, sinks, and population regulation. Amer. Natur. 132:652-661.
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Quinn, J.F.; Hastings, A. (1987) Extinction in subdivided habitats. Conserv. Biol. 1:198-208.
Rolstad, J. (1991) Consequences of forest fragmentation for the dynamics of bird populations:
conceptual issues and the evidence. Biol. J. Linn. Soc. 42:149-163.
Rosenzweig, M.L. (1971) Paradox of enrichment: destabilizing of exploitation ecosystems in
ecological time. Science 171:385-387.
Sanders, F.S.; Adams, S.M.; Barnthouse, L.W.; Giddings, M.; Huber, E.E.; Kumar, K.K.; Lee, D.;
Murphy, B.; Suter, G.W.; Van Winkle, W. (1978) Strategies for ecological effects
assessment at DOE energy activity sites. Oak Ridge National Laboratory, Oak Ridge, TN.
ORNI/TM-6783.
Shaffer, MX. (1981) Minimum viable population sizes for conservation. Bioscience 31:131-134.
Shugart, H.H. (1984) A theory of forest dynamics. New York: Springer-Verlag.
Suter, G.W., II. (1989) Ecological endpoints. In: Warren-Hicks, W.; Parkhurst, B.R.; Baker, S.S.
Jr., eds. Ecological risk assessment of hazardous waste sites: a field and laboratory
reference document. Corvallis Environmental Research Laboratory, OR, pp. 2:1-2:28.
EPA/600/3-89/013.
Suter, G.W., II. (1993) Exposure. In: Suter, G.W., ed. Ecological risk assessment. Boca Raton,
FL: Lewis Publishers, pp. 153-172.
Suter, G.W., II; Barnthouse, L.W. (1993) Assessment Concepts. In: Suter, G.W., ed. Ecological
risk assessment. Boca Raton, FL: Lewis Publishers, pp. 21-48.
Tilman, D. (1982) Resource competition and community structure. Princeton, NJ: Princeton
University Press.
Urban, D.L.; Cook, NJ. (1986) Hazard evaluation, standard evaluation procedure, ecological
risk assessment. U.S. Environmental Protection Agency, Washington, DC.
EPA/540/9-85-001.
U.S. Environmental Protection Agency. (1988) Toxic Substance Control Act test guidelines,
amendments. 40 CFR part 798. Fed. Reg., Sept. 7, pp. 34522-34530.
U.S. Environmental Protection Agency. (1989) Ecological assessment of hazardous waste sites.
Office of Research and Development, Corvallis, OR. EPA/600/3-89/013.
U.S. Environmental Protection Agency. (1990a) Reducing risk: setting priorities and strategies
for environmental protection. SAB-EC-90-021. September.
U.S. Environmental Protection Agency. (19905) Relative risk reduction project: reducing risk,
appendix A: The report of the ecology and welfare subcommittee. EPA/SAB-EC-90-
021 A. September.
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U.S. Environmental Protection Agency. (1992) Framework for ecological risk assessment. Risk
Assessment Forum, Washington, DC. EPA/630/R-92/001.
Walker, A. (1978) Simulation of the persistence of eight soil-applied herbicides. Weed Res.
18:305.
Westman, W.E. (1985) Ecology impact assessment, and environmental planning. New York:
John Wiley & Sons.
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Toxic Substances Control Act (TSCA): an introduction. In: Landis, W.G.; Hughes, J.S.;
Lewis, MA., eds. Environmental toxicity and risk assessment. American Society for
Testing and Materials, Philadelphia, PA. ASTM-STP-1179.
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Water Column
Phyto-
plankton
Zoo-
plankton
Contaminant
Discharge
Contaminant exposure pathways
Figure 2. Sample food web-type conceptual model.
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EMISSION
REACTION
46
AOVECTION
53
REACTION
0 I
ADVECTION
0 2
METABOLISM
BIOUPTAKE if
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Figure 4. Dynamics contained in FORFLO (Pearlstine et at, 1985).
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5a
Bird kill Incident Raptor mortality
1
t
Pesticide
application
to field
^^
Ingestion of
particles by
birds
i
Raptor
predation and
scavenging
Bird kill Incident
raptor
population
dynamics
i
Raptor population
reduction
application rale
physical form
degradation rale
foraging rale
mode of acton
subtotal effects
threshold
LD50
Figure 5. (a) Flow diagram and conceptual model for the granular carbofiiran case study;
(b) expanded flow diagram for the carbofiiran example.
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Table 1. Selected Processes and Parameters Relevant to the Transport and Transformation of
Chemical Stressors (after Mill, 1993)
Process Parameter
(rate constant) (properties of environment)
Atmosphere
Photolysis Light intensity
Oxidation Oxidant concentrations
Rain out Precipitation rate
Sticking coefficient
Transport Wind velocity
Surface waters
Volatilization Henry's Law constant
Surface roughness
Sorption/bio-uptake Organic/lipid content of
sediments and/or organisms
Hydrolysis pH, temperature
Photolysis Light intensity
Biotransforraations Organism population, nutrient
level, temperature, pH
Note: Processes and their associated parameters may be incorporated into the conceptual model
depending on the system and the available data. The resulting transport and fate model may
provide the basis for analyzing exposure (see chapter 4, on characterizing exposures).
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Table 2. Conceptual Issues and Modeling Considerations under Habitat Alteration,
Fragmentation, and Destruction (adapted from Rolstad, 1991)
Habitat Change
Consequences
Population-level considerations
Reduced connectivity,
insularization, increased
intrafragment distance
Reduced fragment size,
reduced total area
Reduced or less-effective
dispersal, reduced immigration,
less vagueness about space,
increased environmental
stochasticity, reduced
population sizes, disruption
of source-sink process
Increased demographic
stochasticity, increased
extinction rates
Landscape or community-level effects
Reduced interior-edge
ratio, reduced fragment
size or frequency
Reduced heterogeneity
within fragments
Loss of keystone species
from the habitat
Favor generalist over
specialist species;
increased competition,
predation, and parasitism
from species in surrounding
areas; increased extinction
rates
Loss of species diversity,
increased dominance by a few
species, reduced population
sizes for most species
Loss of species or change
in community composition
via indirect effects such as
apparent competition,
resource competition, and
indirect mutualism
Note: These are typical habitat disturbances resulting from human land-use patterns. Each of
these sorts of disturbances (habitat change) can be modeled using theories from population
ecology that alternatively (or in combination) include spatial and temporal variability, habitat
selection, life-history parameters, density-dependence, environmental and demographic
stochasticity, and population interactions such as competition and predation.
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Peer Review
DRAFT
September 1993
Issue Paper
on
CHARACTERIZATION OF EXPOSURE
Glenn W. Suter II
Environmental Sciences Division
Oak Ridge National Laboratory
Oak Ridge, TN
James W. Gillett
Department of Natural Resources
Cornell University
Ithaca, NY
Sue Norton
Office of Health & Environmental Assessment
U.S. Environmental Protection Agency
Washington, DC
Prepared for
Risk Assessment Forum
U.S. Environmental Protection Agency
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CONTENTS
1. INTRODUCTION 4-5
1.1. Scope 4.5
1.1.1. Sources and Agents 4-6
1.1.2. Types of Exposures 4-7
1.1.3. Predictive and Retrospective Assessments 4-9
1.2. Presentation of Results: The Exposure Profile 4-9
1.2.1. Dimensions of Exposure 4-10
1.2.2. Uncertainty 4-11
1.2.3. Relationship to Risk Management 4-12
2. PROBLEM FORMULATION 4-13
2.1. Defining the Source and Agent 4-14
2.1.1. Source-Driven versus Effects-Driven Assessments 4-14
2.1.2. Multiple Agents 4-15
2.2. Defining the Spatial and Temporal Extent 4-15
2.2.1. Extent Based on Agent/Source 4-16
2.22. Extent Based on Effects 4-17
2.2.3. Extent Based on System Processes 4-17
2.3. Defining the Receiving Environment 4-18
2.4. Assessment Endpoints 4-18
2.5. Causal Pathways in Conceptual Models 4-20
3. ANALYSIS PHASE: CHARACTERIZING EXPOSURE FROM ADDITIONS OF
PHYSICAL AND CHEMICAL AGENTS 4-22
3.1. Further Source and Agent Characterization 4-22
3.2. Detailed Pathway Analysis 4-24
3.2.1. Fate and Transport Processes 4-24
3.2.1.1. Advective Transport 4-25
3.2.1.2. Transfers among Media 4-26
3.2.2. Formation of Secondary Agents 4-29
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3.2.2.1. Transformation 4-29
3.22.2. Interactions with Ecological Processes 4-32
3.3. Evaluation and Quantification of the Exposure Process 4-32
3.3.1. Behavioral Attributes 4-33
3.32. Routes of Contact 4-34
3.33. Parameterization of Components 4-35
3.33.1. Receptors 4-36
333.2. Environment 4-37
3.333. Chemodynamic Processes 4-37
3.3.4. Quantification Methods 4-38
3.3.4.1. Estimating Effective Concentration or Dose ... 4-38
3.3.4.2. Bioassays and Biomarkers 4-40
3.4. Implementation Issues 4-41
3.4.1. Selection of Models 4-41
3.4.1.1. Validation 4-41
3.4.1.2. Suitability 4-42
3.42. Acquisition of Data 4-43
3.42.1. Data Quality Objectives 4-43
3.42.2. Statistical Analysis 4-44
3.5. Summary 4-44
4. THE ANALYSIS PHASE: CHARACTERIZING DISTURBANCES 4-46
4.1. Direct Disturbances 4-46
4.2. Secondary or Indirect Exposures from Disturbance 4-47
5. GAPS INKNOWLEDGE 4-49
6. REFERENCES 4-51
APPENDIX GLOSSARY OF TERMS 4-55
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LIST OF FIGURES
Figure 1. The relationship of exposure to sources of agents, activities causing
disturbances, and effects 4-63
Figure 2. An example of a simple ecological risk assessment involving exposure
of birds to a pesticide application (the risk-of-effect box represents
the assessment endpoint) 4-64
Figure 3. An example of an ecological risk assessment involving a multistage
exposure (i.e., an ultimate agent [UV radiation] generated by a multistage
exposure process from an initial agent [chlorofluorocarbons] released by a
source [air conditioners]) but a single, direct ecological effect
(the risk-of-effect box represents the assessment endpoint) 4-65
Figure 4. An example of an ecological risk assessment involving a causal network
of multiple exposures and effects 4-66
Figure 5. Example 1 of an ecological risk assessment involving exposure to a
disturbance generated by a multistage exposure process and resulting
in a single, direct ecological effect (the risk-of-effect box represents
the assessment endpoint) 4-67
Figure 6. Example 2 of an ecological risk assessment involving exposure to a
disturbance generated by a multistage exposure process resulting
in a single direct ecological effect 4-68
Figure 7. A simple conceptual model for ecological risk assessment of a waste
burial ground 4-69
Figure 8. A conceptual model for ecological risk assessment of the effects of
logging on salmon production in a forest stream 4-70
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1. INTRODUCTION
EPA's Framework for Ecological Risk Assessment (U.S. EPA, 1992a) and all other risk
assessment paradigms require characterization of exposure. The critical concept in
characterizing exposure is that an organism or other system must be in contact with or co-occur
with an agent or a disturbance (i.e., it must be exposed) before there is a risk (EPA, 1992a,b).
The risk arises from this exposure and from the relationship between exposure and response
(figure 1). An aspect of ecological risk assessment not covered by standard terminology concerns
chains of causation. For this concept, which involves cascades of exposures and effects, we use
the term stress regime.1
1.1. Scope
The scope of this chapter starts with the release of an agent from a source and extends to
its uptake or interaction with the ecosystem or its components that constitute the assessment
endpoint—the process termed exposure. By analogy, this concept can be applied to various
human activities that physically disturb the environment. Thus the purpose of this discussion is
to provide an exposure profile as an input to the risk characterization.
This introductory section defines the bounds of exposure characterization and discusses
how the products of this activity must be structured to allow integration with the characterization
of effects in the risk characterization (section 1.2). Section 2 discusses the role of exposure in
problem formulation. Sections 3 and 4 consider characterization of exposure in traditional
exposure assessments and in less traditional assessments that involve predictive or retrospective
analysis. Section 5 addresses some information gaps.
'Terms used in this chapter are defined in the appendix, which also addresses ambiguities and
controversies concerning terras. See especially the appendix note on "stress regime."
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Sources produce or release agents as physical, chemical, or biological entities. Although
biological agents have a number of characteristics that conceptually parallel those of physical and
chemical agents, assessing them presents particular difficulties. Thus biological agents are
discussed elsewhere (see chapter 6).
1.1.1. Sources and Agents
This chapter draws extensively from experiences with chemical substances as pollutants
and various physical agents (e.g., heat and ionizing radiation) as health and ecological hazards.
The addition of such agents to the environment is the usual subject of risk assessments. Except
for introductions of biological agents, exposure assessments for additions are conceptually
straightforward. The characterization of exposure is intended to apply to these as well as to
many types of disturbances generated by human activities.
Physical disturbances can be divided into three classes: system eliminations, system
modifications, and component deletions. System eliminations completely destroy an ecosystem
and replace it with an anthropogenic system, sucrras a parking lot or soy bean field. For these,
characterization of exposure can be a simple matter of describing the area destroyed and its
former suitability for the endpoint species, community, or other attribute. System modifications
do not completely destroy an ecosystem but modify its character (e.g., changes in the hydrologic
regime of a stream by diversion or hydroelectric generation). In general, characterization of
exposure for such an agent involves defining an area and its change in suitability. Component
deletions remove a particular component of the ecosystem without causing an extensive physical
change (e.g., resource harvesting and entrainment in power plant cooling systems). For such
disturbances the expression of exposure is the number of organisms or other units removed per
unit area. For system eliminations, time is usually irrelevant because the exposure is essentially
infinite; but for system modifications and component deletions, duration and frequency must
be specified.
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This chapter is deliberately unbalanced in the sense that it is largely devoted to exposure
to chemicals. Introductions of biological agents are treated in a separate paper, and exposures to
physical agents are usually treated as specialized assessments (i.e., heat, ionizing radiation, and
increased ultraviolet [UV] radiation from stratospheric ozone depletion). The different types of
physical disturbances are both significant and common, but they have not been treated as
subjects of ecological risk assessment. Thus, in contrast to chemicals, relatively little guidance is
available on characterization of physical disturbances of ecosystems.
1.12. Types of Exposures
The ultimate objective of exposure characterization is to demonstrate a logical
connection between the subject agent or disturbance and the vulnerable and valuable receptors in
such a manner as to permit evaluation of the relationship. Figure 2 shows a typical, simple
exposure process that extends from a pesticide application to exposure of birds. Although the
exposure-response relationship and ultimate effect (the assessment endpoint is the reduced
fecundity in birds) are included in this diagram for completeness, typically they would be
analyzed as part of the ecological effects assessment and risk integration steps, respectively.
Such a simple structure can quickly become complex when the primary agent is
transformed to secondary chemical or physical agents through physical or biological processes.
Examples include the increasing UV radiation brought on by the release of chlorofluorocarbons
(CFCs) (figure 3), the lowering of dissolved oxygen (DO) after increased nutrient loads (figure
4), and the generation of methyl mercury from inorganic mercury by anaerobic microbial
processes.
Secondary disturbances attributable to physical deletions and modifications of the
environment can be handled similarly. Examples include the increase in stream temperature
from the removal of riparian vegetation (figure 5) and the change in frequency from the flooding
of riparian communities downstream of a levee (figure 6).
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These secondary exposures and disturbances logically fall within the purview of exposure
characterization, which requires expertise. For example, in the case of nutrient loads to an
estuary, estimation DO levels probably would be performed by the same chemist or engineer who
models dispersion of the nutrients. Indirect effects, however, have traditionally been treated in
the ecological effects characterization component of an ecosystem effects model (O'Neill et al.,
1982; Suter and Bartell, 1993) or more commonly are deferred for the risk characterization
component (e.g., the chemical is toxic to crustacean zooplankton at estimated exposure levels and
they are eaten by fish).
Combined exposure and effects models have been proposed (Bartell et al., 1988) and
used in specific situations. For example, Brody et al. (1989) modeled the combined changes in
plant communities and associated wildlife populations resulting from an increase in flooding
frequencies following the construction of river levees. These combined models, however, are not
widely used and are required only in assessments where there is significant feedback between
exposure and effects. For example, if the biota are a major sink for the contaminant and toxic
effects are sufficient to significantly decrease standing biomass, available concentrations will
increase. Similarly, toxic effects may modify behavior, thereby changing exposure.
Although it is not necessary to separate the exposure characterization from the effects
characterization, it is critically important that the interface between the two is clearly defined in
the assessment to ensure completeness. Consider, for example, assessments of chronic exposure
of aquatic organisms to chemicals. While exposure assessors typically have seen their task as
estimating the equilibrium total concentration of chemical in water, effects assessors have seen
their task as estimating the continuous concentration causing effects in clean laboratory water.
The issues of bioavailability and temporal dynamics often "fall through the crack" between the
two analyses. In this regard, the emphasis on an interaction between exposure and effects
analyses in the Framework Report represents an advance over previous risk assessment paradigms.
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1.1 J. Predictive and Retrospective Assessments
In this chapter we recognize two types of assessments. The first is predictive assessments,
which estimate exposures and effects of proposed future contaminant releases or other hazardous
actions. Predictive assessment techniques also can be applied to disturbances that occurred in
the past but for which the measurement of exposure and effects was not possible. Predictive
assessments begin with an estimate of the source and proceed to model the processes that result
in exposure or disturbance.
The second class of assessments is retrospective assessments. Since observations or
measurements of exposure and effects are available for such assessments, epideraiologjcal
-techniques can be employed (Suter, 1993). Indeed, the methods that can be used for these
assessments are much more diverse than those that are appropriate for predictive assessments.
Such critical issues as reproductive failure in certain birds and winter kill of certain salmonids
have been analyzed using retrospective assessments.
1.2. Presentation of Results: The Exposure Profile
The product of the characterization of exposure is an exposure profile that is important
for the risk characterization phase of the risk assessment. Although the exposure profile will be
written as the last step of the exposure characterization, we discuss it first in this chapter because
the success of the characterization of exposure depends on exposition of its results in a form that
meets the needs of risk characterization and ultimately of risk management. This section
discusses the need to express exposure in appropriate dimensions, to acknowledge and quantify
uncertainty, to describe the methods and assumptions used, and to note how this step relates to
broader aspects of risk management.
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1.2.1. Dimensions of Exposure
For the results of an assessment of exposure or disturbance to be useful, they must be
expressed in units that correspond to the exposure or disturbance dimensions that determine the
risk. In particular, the units of exposure must be commensurate with the units of the exposure-
response relationship generated by the effects assessment if they are to be integrated in the
risk characterization.
In general, exposure has three dimensions: intensity, time, and extent. Intensity is
expressed as the concentration or dose for chemicals, the dose for ionizing radiation, and the
level of nonionizing radiation. Analogous measures of intensity can be identified for other
agents. For changes in hydrology, level or stage is an appropriate measure of the intensity of
riparian flooding (Brody et al., 1989; Pearlstein et al., 1985), wetland status (Lyon, 1993), or
aquatic habitat (Orth, 1987). Harvesting intensity is the rate of removal or the number or mass
removed. For habitat destruction or modification, intensity may be expressed as changes in the
levels of habitat characteristics (e.g., primary production or foliage height diversity) that
determine density or productivity of the endpoint populations or communities (Bovee and Zuboy,
1988).
The temporal dimension of exposure is usually expressed as duration (Suter, 1993). It
may be expressed simply as the time over which exposure occurs or exceeds some threshold
intensity or as a variable over which intensity is integrated. If exposure occurs as repeated,
discrete events without significant variation in duration (e.g., logging or discrete chemical spills),
the frequency of recurrence is the important temporal dimension of exposure. If the repeated
events have significant and variable durations, then both types of temporal dimensions must be
considered. In cases where the seasonal periodicity of an exposure influences the extent and
magnitude of effects (e.g., influx of hydrogen ions and aluminum during snow melt), this factor
should be described in addition to the duration and frequency.
Seldom has extent been incorporated explicitly in ecological risk assessments, perhaps
because the assessments tend to focus on the estimation of effects at the point of maximum
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exposure. When it has been considered, it usually has been defined simply as the area exposed
or disturbed. It also might be the area over which exposure exceeds some threshold intensity or
duration, the areas within which particular categories of intensity or duration occur, or a variable
over which intensity and duration are integrated. At larger spatial scales, however, area alone
may not be an adequate descriptor of extent of exposure. The effective extent may be a function
of the pattern of exposure, including the number of areas and their shape and arrangement. A
general solution to the problem of incorporating pattern into ecological assessments has yet to be
developed, but landscape ecology provides concepts that may be applied in individual cases.
The dimensions of exposure may be treated in various ways in an assessment: as
variables, as constants (i.e., by collapsing to a single point), as factors to be ignored, or as new
variables created through combining (e.g., the use of the product of concentration and time as a
dose variable) (Suter, 1993). In any case, guidance for ecological risk assessment should insist
that the assessor states how each of the three general dimensions is being treated and why that
treatment is necessary or appropriate.
1.2.2. Uncertainty
Characterization of exposure, more than characterization of effects, lends itself to formal
quantitative uncertainty analysis. Monte Carlo simulation and similar error propagation
techniques are applied easily to the mathematical models used to predict or describe transport
and fate of contaminants. Many of the environmental parameters that force the use of these
models (e.g., wind speed and direction, flow rates, precipitation rates) are stochastic and their
distributions can be specified from weather records, hydrologic records, and similar data sets.
Many other parameters are known without error (e.g., molecular weight, activity of
radionuclides) or their uncertainties can be reasonably bounded (e.g., K^, solubility). Therefore,
assessors who model exposures to additions of chemical and physical agents should be taking the
lead in applying quantitative uncertainty analysis to ecological risk assessment. The fact that
exposure assessment is applicable to uncertainty analysis suggests that it be appropriate to
present exposure uncertainties in terms of the probabilities of exceeding exposure levels
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estimated to be thresholds for significant effects. Nonetheless, certain other sources and
expressions of uncertainty also would need to be presented (Funtowicz and Ravetz, 1991). (For
more on uncertainty, see chapter 8.)
1.2.3. Relationship to Risk Management
Ecological risk assessments are usually driven by legal, economic, or other management
goals and perceptions (e.g., such as the requirements of the Endangered Species Act [ESA] and
the Federal Insecticide, Fungicide and Rodenticide Act [FIFRA], as described in chapter 10).
The product of exposure characterization must fit the needs of such management decisions.
Indeed, it is often the exposure—rather than some mitigation or remediation of effects—that is
eliminated or altered as a result of the assessment.
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2. PROBLEM FORMULATION
Problem formulation is the initial step in ecological risk assessment that defines the
threat to be assessed and thereby determines the scope and content of the assessment (U.S.
EPA, 1992a). The exposure assessor must collaborate with the effects assessor to define the
problem in such a way that both the potentially significant ecological threats and the concerns of
the risk managers are incorporated.
The principal product of problem formulation is a conceptual model that describes the
sources or actions, the agents or disturbances, the exposure pathways, the causal pathways for
secondary effects, and the assessment endpoints.
The exposure analyst is primarily responsible for definition/characterization of the
following:
• Agent and source. For predictive assessments, the agent and source are provided
as an input. Nonetheless, the exposure assessor may need to better characterize
the source for purposes of the exposure assessment (e.g., by asking for
information on temporal variability of the source or by converting expected sales
of a detergent builder such as phosphate into rate and distribution of releases).
In retrospective assessments, the source and even the agent may be unknown and
the exposure assessor may need to help define the potential sources and agents
based on potential exposure pathways.
• Assessment endpoint. Of the criteria for selection of assessment endpoints (U.S.
EPA, 1992a; Suter 1989,1990), the exposure assessor is most concerned with
estimating susceptibility. Thus, although inherent sensitivity must be considered,
the assessor should be particularly concerned with populations or communities
that are the most exposed and therefore likely to be susceptible (see appendix).
• Ecosystem at risk. The exposure assessor has a major role in this component of
conceptual model development that includes defining the locations of the sources,
the extent of distribution of the agent, and the media contaminated or the
communities exposed or disturbed. The exposure assessor also must ensure that
the ecosystems at risk and the abiotic and biotic factors that influence the
exposure are defined sufficiently for the analysis phase to be performed.
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2.1. Defining the Source and Agent
The first link in the causal chain portrayed in the conceptual model is the introduction of
a chemical, physical, or biological agent by a source, or an equivalent action that initiates a
disturbance (e.g., timber sales that initiate logging). The processes for defining the source and
the agent are different if the assessment is initiated by concern about a prescribed source or by
concern about observed effects.
2.1.1. Source-Driven versus Effects-Driven Assessments
Source-driven assessments are conducted to determine the risks posed by a particular
source or set of sources. Such assessments usually concern the regulation of new chemicals and
effluents, establishment of criteria and standards, or remediation of spills or other past releases.
Because the source serves as the impetus for the assessment, it must be identified at the outset
of problem formulation as specifically and quantitatively as possible using available information.
Such information might include locations, composition of the released agent, rates of release,
temporal dynamics of release, mode of release, and carrier media. For source-driven
retrospective assessments, such as remedial investigations of hazardous waste sites, measurements
of contaminants in ambient media (including biota) may supplement source definition.
Effects-driven assessments are conducted to determine the cause of observed effects and
to estimate their magnitude and extent. Exposure characterization for such assessments calls for
hypothesizing causal pathways and tracing them back to sources of agents. This component of
problem formulation can be based on four types of information:
• observation of symptoms (i.e., behaviors such as ataxia, overt pathologies such as
lesions, and internal physiological or histological indications) and the distribution
of effects among species, trophic groups, or habitats (if differential responses of
organisms are observed, they may be compared to knowledge of the relative
sensitivity of the taxa, sexes, or life stages in controlled exposures; if the potential
agents have different modes of exposure, the habitats or trophic groups that are
most affected may provide clues to the likely cause);
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evidence of exposure from the affected organisms, including body burdens of
contaminants and bioraarkers of exposure such as induction of metabolic enzymes;
knowledge of the various sources and disturbances that are potentially responsible;
and
data on the spatial and temporal relationship between the effects and the
potentially causal sources.
Application of these approaches to determining potential causes of fish kills is discussed by
Meyer and Barklay (1990).
2.12. Multiple Agents
Multiple agents can result in a common exposure or disturbance (e.g., increased flooding
from upstream devegetation and channelization), a common effect (e.g., fish population
reduction attributable to toxicity, habitat loss, or overfishing), or multiple effects. In general,
assessment of risks posed by multiple chemical and physical agents requires a separate exposure
analysis for each agent as well as analysis of the nature and degree of interaction (see chapter 5
on effects characterization).
2.2. Defining the Spatial and Temporal Extent
Problem formulation also calls for specifying the spatial and temporal extent of the
assessment to facilitate appropriate selection or development of exposure models. This facilitates
proper distribution of sampling and sufficient consideration of all significant sources and effects
(see chapter 3, on conceptual model development). Ultimately, the exposure assessor must
ensure that the spatial and temporal scale of the assessment is sufficient to include all significant
sources and exposure or disturbance processes. Moreover, the assessor must ensure that the
characterization of exposure encompasses all significant direct and indirect effects.
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2.2.1. Extent Based on Agent/Source
The spatial and temporal extent of the assessment can be defined in terms of the
distribution in space of sources, the duration of release from sources, and the properties of the
agent that control its movement and persistence in the environment. The role of source
distribution in space and time is obvious and is usually easy to specify or estimate. For individual
chemicals, appropriate considerations are the distributions of production, use, and disposal.
Once an agent is released from a source, its inherent properties determine its ability to
spread and persist. Any property of the agent that makes it less site specific, more capable of
moving between media, and more persistent, extends the temporal and spatial basis for the
assessment. Gillett (1983) suggested that a combination of the log of the half-life for
biodegradation (log t^, log of Henry's Law constant (log Hc), and log of the octanol-water
partition coefficient (log K^, or log P) projects separation of the chemicals that are likely to be
teachable or to present atmospheric exposure, to be sediment/soil bound, and/or to be
bioaccumulable. That screening function for ecotoxicologic concerns is valid and useful for
*
neutral, nonionized, nonpolyraeric organic molecules; yet it has limited applicability to
organometallics and charged or ionized organic molecules. Nevertheless, the ability to determine
the pervasiveness and persistence of any agent seems to be of high priority. Once vulnerable,
valuable receptors are identified in the iterative processes of problem formulation, then the
assessment effort can be focused on the temporal and spatial dynamics of effects in relation to
the agent's properties. Neely (1985) has used this approach in a global model of chemical
dispersion that offers a crude scoping of any chemical in conjunction with large safety factors.
The spatial and temporal extent of actions that destroy or modify ecosystems or their
components are usually easily defined in terms of proximate disturbances (i.e., the area to be
flooded by a dam, to be paved for a parking lot, or to be logged can be readily specified). .
Typically the duration of the initial disturbance is essentially instantaneous and the main
temporal variable is the time to recovery (see chapter 7). Still, some disturbances, such as
implementation of a fishery management plan, may have a finite duration.
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The formation of secondary agents and disturbances may expand both the temporal and
spatial extent of the assessment. For example, assessments of DDT must take into account its
transformation to DDD and DDE, which have different and sometimes more powerful effects on
species distant from the source of the DDT. Similarly, assessments of logging practices must
take into account erosion and sedimentation of downgradient streams.
2.22. Extent Based on Effects
Specifying the distribution of observed effects, of exposed endpoint populations and
ecosystems, and of secondarily affected populations and ecosystems also helps to determine the
spatial and temporal extent of the assessment (see chapter 7, on conceptual models, and chapter
5, on effects characterization). Although the area in which exposure or disturbance occurs may
be relatively small and the duration of the disturbance brief, the consequences may extend to the
entire range of an endpoint population or ecosystem and the duration may extend through
recovery or induction of secondary effects.
2.2.3. Extent Based on System Processes
System processes provide the mechanisms for transport, transformation, and degradation
of contaminants; for transport and fate of introduced organisms; and for secondary disturbances.
Thus in problem formulation the environmental processes and properties that are important to
the exposure must be specified. For example, in assessments of the aquatic ecological risks of
the release of an organic chemical from a point source, the important processes are dilution,
sorption, and degradation. Thus the flow regime of the receiving system, its suspended and
dissolved organic matter content, and any limitations on the degradation rate must be specified
and incorporated into the exposure or disturbance model. Some system processes may expand
the spatial and temporal extent of the assessment beyond the range of the proximate exposure
and effects (e.g., acid rain and increases in UV radiation attributable to depletion of the
stratospheric ozone layer by CFC compounds).
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2.3. Defining the Receiving Environment
The receiving environment may be an actual place where effects have occurred or a
source is located, or it may be one or more generic reference environments that typify the types
of environments likely to be exposed or disturbed. If the receiving environment is a place, then
the primary role of the exposure assessor is to ensure that it is sufficiently defined to allow
characterization of the exposure. The assessor accomplishes this by using existing information,
contributing to development of the conceptual model, and ensuring that sufficient information is
gathered in the analysis phase to characterize the exposure.
For source-driven assessments concerning the addition of chemical and physical agents,
the conceptual model must include, at a minimum, qualitative judgments concerning the
significant pathways from the source to the various media in which organisms or communities
would be exposed. For source-driven assessments of actions that may delete or modify
ecosystems or their components, the conceptual model must include both the proximate
disturbance and the causal pathways likely to transmit the disturbance to other environmental
components. For example, logging can increase the silt load, nutrient concentrations, and light
levels in streams; thus streams as well as forests are disturbed by logging.
For effects-driven assessments, the conceptual model must include the potential routes of
exposure for the affected organisms as well as the routes of transport or the causal pathways by
which the contamination has occurred. This process relies heavily on experience and judgment.
For chemical and physical agents, however, simple screening models of dilution and partitioning
can be used to estimate the pattern of contamination or the pathways from effects to sources.
2.4. Assessment Endpoints
Of the criteria for choosing assessment endpoints, only the estimation of susceptibility
requires input from the exposure assessor. Susceptibility is a function of the inherent sensitivity
of receptors to the agent and of the magnitude of exposure or disturbance. Given that the
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contaminated or disturbed environments will have been defined, the relative exposure or
disturbance of a receptor is a function of the way in which the receptor uses the environment.
For chemical contaminants, the following considerations are relevant:
• Organisms that occur in or feed on the most contaminated media are more
exposed.
• Organisms that are exposed to a medium by multiple routes are more exposed
(e.g., fish that respire and feed from water are more exposed to aqueous
contaminants than turtles, which only feed from water).
• Organisms that are less capable of avoiding contaminated media are more
exposed.
• Organisms that have small ranges relative to the extent of contamination will have
the greatest exposure of those individuals in the contaminated areas.
• Populations that have small ranges relative to the extent of contamination will
have the greatest exposure of those populations in the contaminated areas.
• Organisms contacting contaminated media during sensitive life stages (e.g.,
migration, reproduction) are more susceptible.
For example, if a contaminant is spilled on soil, earthworms are in the most contaminated
medium since they feed on the soil, are in dermal contact with it, and respire the air in soil
pores; also they have relatively slow avoidance capabilities and the range of both an individual
earthworm and an interbreeding population is likely to be encompassed by a single spill. In
contrast, robins are exposed to the more contaminated medium only if the contaminant is
biomagnified by worms relative to the soil, since robins feed largely from the soil. Robins have
relatively little dermal contact with the soil, however, and it may be fairly easy for them to avoid
contaminated areas since spills are likely to be smaller than the range of an individual robin and
nearly all spills will be smaller than the range of a robin population. When making a
determination about susceptibility, such exposure-related considerations should be combined with
information on the sensitivity of the particular species. Susceptibility then should be considered
along with other criteria to determine which species properties would serve as the most
appropriate assessment endpoints (Suter, 1989; U.S. EPA, 1992a).
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Similar considerations should be taken into account on a case-by-case basis when
determining the susceptibility of potential endpoint species or communities to agents other than
chemicals. For example, neotropical warblers that breed in large tracts of undisturbed forest
would be a logical focus for an assessment of effects of forest fragmentation, since the particular
species of bird is likely to co-occur with the disturbance during a sensitive life stage.
2.5. Causal Pathways in Conceptual Models
Typical conceptual models for ecological risk assessments of chemicals or physical agents
resemble diagrams of transport and fate models. Indeed, the major conceptual problem in the
problem formulation phase usually concerns determining the contaminant's pathways and the
ecosystem components that will be significantly exposed. A simple example for a generic upland
waste burial ground is presented in figure 7. In this case, a judgment has been made that
atmospheric routes are negligible and that carnivores would not consume enough prey from the
site to be significantly exposed. Judgments also have been made about the appropriate degree of
aggregation. For example, all plants are aggregated because there is insufficient phytotoxicity
data or plant uptake data to perform more taxon-specific assessments. Different chemical classes
or types of waste might have similar or radically different pathways, depending on their
physicocheraical properties and modes of action. Finally, indirect effects of the proximate toxic
effects are judged to be negligible in this case.
If indirect effects were judged to be potentially significant, then the conceptual model
would need to include effects pathways as well as exposure pathways (see figure 4 in chapter 3,
on conceptual model development). Regardless of the conceptual model's structure, developing
such information into a diagram is particularly useful for communicating with risk managers and
with members of the assessment team.
For physical disturbances, no general concept such as contaminant fate is appropriate as
an organizing focus of the conceptual model. For example, a conceptual model for risks from
logging posed to salmon reproduction (figure 8) includes a mixture of indirect disturbances and
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effects that are merely related to the term stress regime. For effects-driven assessments, the
conceptual model must be based on information about exposures and disturbances and any other
environmental factors that could contribute to the observed effect (see, e.g., figure 1 in chapter 3,
on conceptual model development).
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3. ANALYSIS PHASE: CHARACTERIZING EXPOSURE FROM ADDITIONS OF
PHYSICAL AND CHEMICAL AGENTS
During the analysis phase, the primary duties of the exposure assessor are to (1) further
characterize the source and agent, (2) perform a detailed quantitative pathway analysis that
includes the formation of secondary agents, and (3) evaluate the exposure process. These three
steps are discussed below, followed by a discussion of implementation issues.
Exposure analysis can be quite simple, involving, for example, quantification of the
release, transport, and partitioning of a discrete chemical and its contact with a local population
of fish. More complex analyses may involve the transformation of agents and the formation of
secondary agents resulting in a variety of interactions with different ecological systems. For
example, the release of nutrients in an estuary can increase primary productivity, resulting in
increased organic decomposition and eventually decreased DO to the detriment of fish and
benthos contacting low-oxygen areas. The characterization of exposure encompasses this entire
array of agents, secondary agents, and their contact with ecological components (see figure 4).
3.1. Further Source and Agent Characterization
Because detailed knowledge of a source is often essential for an assessment and for any
subsequent decision-making, it contributes significantly to the ability of the assessor and the
manager to address any problems. Sources may be characterized and assessed prior to operation,
either through a permitting process or as a part of liability protection, product stewardship, or
life-cycle analysis. Under several laws (e.g., the Clean Air Act, the Clean Water Act, and the
Federal Insecticide, Fungicide and Rodenticide Act) new or existing sources are the designated
targets of assessment. In retrospective assessments, the objective may be to link a known
exposure or effect back to a source so that source management options can be considered.
Sources are commonly classified as mobile (auto) or stationary (sewage treatment plant);
point (pipeline or smokestack) or nonpoint (wheat field, contaminated sediments); deliberate
(pesticide application), adventitious (brake fluid leaks), or accidental (spill). In addition, sources
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are classified by regulations as fully permissible (homeowner's compost pile), specifically
permitted by an agency (National Pollutant Discharge Elimination System [NPDES]), or
prohibited (use of a banned pesticide).
A complete source characterization includes the specific content, timing, duration,
location, and intensity of any releases. The content of the source includes the composition and
physical form, the complexity or degree of contamination with known or unknown pollutants or
toxicants, and the media into which releases may occur. The timing and duration of a source
may be continuous, intermittent, seasonal, or random, or may vary with a prescribed distribution.
Location concerns the geographic position of sources as well as its location with respect to
transport media (e.g., streams and aquifers) and ecological receptors (e.g., nesting colonies and
wetlands). The intensity of the source is the set of dimensions, such as rate of release of a
chemical or radiation flux, that determine the magnitude of exposures given a duration and
receiving environment.
The definition of the source depends on the scope of the assessment. For example,
consider the UV spectrum, characterized by intensity at specific wavelengths (as an input
spectrum) summed over time, as a typical physical agent. If the effective receptor is known, the
exposure (i.e., quantal uptake) could be expressed as the sum over time of incident light energy
times the absorption efficiency. The source for an ecological assessment program might be
defined as incident radiation given some hypothesized state of the stratosphere; yet for the
assessment to lead to actions, it must ultimately address the sources of CFCs. In this case the
exposure assessment would include the transport of CFCs to the stratosphere, subsequent ozone
depletion, and modification of the UV radiation as well as radiation absorption (see figure 3).
A somewhat more typical agent would be a chemical used as a pesticide. The receptor
could be a target organism (pest), a nontarget receptor organism (a beneficial or adventitiously
exposed organism), or any other system. The assessment could be linked to a specific or generic
site for a particular period (an instance, a program, or the product life) defined by an assessment
scenario. Usually product label recommendations specify concentration, formulation, application
methods, rate and timing, and crop/target of an application. Through a combination of
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monitoring, laboratory experimentation, and modeling, the assessor would obtain data on spray
deposition to the crop, to soil, and to nontarget organisms as well as concentrations in media
attributable to subsequent transport and transformation.
3.2. Detailed Pathway Analysis
A pathway analysis describes the course of an agent as it interacts with the environment.
The agent may be transported within a medium, be transferred between media, or be
transformed through biotic or abiotic processes, potentially generating secondary agents. The
objective is to characterize the spatial and temporal distribution of primary and secondary agents
resulting from the addition of agents into the environment.
3.2.1. Fate and Transport Processes
Much of exposure assessment for chemical contaminants is concerned with estimating the
results of processes occurring within the environment. These can be divided into advective
transport and partitioning between media. The other processes that control the fate of a
chemical are abiotic and biotic transformations, which often result in formation of secondary
contaminants.
The necessary physicochemical and microbiologic parameters for chemodynamic
processes have been brought together in a number of data bases. Most of these are computer-
accessible and include various data quality factors incorporated into their assembly, review, and
updating (Howard et al., 1986,1988). In spite of this apparent wealth of data, only a fraction of
the values for the estimated 70,000 chemicals in commerce at the time of enactment of the Toxic
Substances Control Act (TSCA) in 1979 have been acquired. Experience counsels against
extrapolating among chemical classes to fill these data gaps. For example, the bioaccumulation
of various substituted aminonitrophenols is unrelated to structure or log K^,, whereas
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bioaccuraulation of the pyrethrins and pyrethroids may vary by more than a factor >105,
although log K^, varies by <1.2.
3.2.1.1. Advective Transport
The advective transport of an agent with the movement of a medium is dominated
primarily by the forces acting on the medium: water moving by gravity, soil blowing in a storm,
body residues moving with a bird in migration. Other relevant forces include such processes as
solubilization, diffusion, and volatilization that keep the agent in the medium or remove it. Thus
a sorbed chemical will move primarily with the soil particle as the particle is eroded and becomes
sediment to be deposited in a slow-moving reach of a stream. If the particle were then picked
up by a filter-feeding invertebrate or otherwise partitioned out of one medium to another,
advection by that pathway would be terminated.
Movement within a medium is accompanied by diffusion and suspension/sedimentation
(by gravity, convection, and turbulence). Intermedia transport—soil erosion, bioturbation,
washout of dust from air—also can be a common advective process, but the transfer is mitigated
by features different than in the transport process or by forces acting on the agent in relation to
the medium. Thus, whether a soil particle is suspended in air to be blown away by a
thunderstorm will depend on the angle and velocity of the impinging drop, the forces binding the
particle to the soil matrix (including degree of compaction, soil moisture, and organic matter
content), the wind velocity, and the temperature affecting density and viscosity of water and air.
Similar issues develop in the processes of leaching, atmospheric rainout, and biomigration.
Apparently deterministic processes that are nonlinear and dependent on initial conditions may be
deemed chaotic and predictable only by empirical means. Thus prediction of sediment and soil
erosion/deposition, leaching, and bioaccumulation within food webs apparently is limited.
Advective transport can span especially long distances. Consider, for example, the
transcontinental dispersal of sulfur oxides leading to acid deposition and the contamination of
Antarctica and oceanic islands by persistent pesticides. Since clearance of materials from large
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lakes or aquifers can take centuries, we will need to account for advective transport of bioactive
substances for decades at least, even though these materials may no longer be in use. By the
same token, an apparently negligible contribution to risk in a site-specific assessment may
contribute to a significant risk at a more distant site.
In retrospective assessments, pathways may be constructed from a known source and
employ the methods described above. Alternatively, the objective may be to trace the pathways
back from a particular location to the original source or sources. Gordon (1988) describes
receptor models that combine transport modeling with characteristics of the contaminant mixture
at air sampling locations to trace contaminants back through transport pathways to suspected
atmospheric sources.
3.2.1.2. Transfers among Media
Transfers of chemicals and radionuclides between media greatly influence their ultimate
distributions. The objective of most exposure analyses is to estimate the concentrations of
chemicals in different media at equilibrium. In some cases, such as bioconcentration of methyl
mercury, equilibrium may never be reached and the analysis would be based on kinetic
parameters.
The relative equilibrium concentrations of a chemical in two media or phases within a
medium is represented as the partition coefficient for water and oil-like substances; the sorption
coefficient for soils and sediments in contact with water; and, for such processes as
solution/precipitation (solids and liquids) and volatilization/condensation (gases), the solubility
product and vapor pressure, respectively. For the last two phenomena, the partitioning only
pertains to layers at the interface between media, since mixing of the more distant layers is
advective (diffusion and mass flow or circulation). The volatility of a chemical from a wetted
surface into air is proportional to its Henry's Law constant (i.e., the ratio of the vapor pressure
to the water solubility at a given temperature and atmospheric pressure).
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As noted above, movement of a chemical by partitioning takes place during transport and
intermedia transfers. If the equilibrium is very large or very small, such transfers themselves do
not displace the chemical, since fully water-soluble chemicals will stay in water and strongly
sorbed agents will remain sorbed. In many instances, however, the dynamics of the several
interacting processes result in redistribution between media, dispersal and dilution in some
instances, and slow and invidious exposure of parts of the environment in others. The rates of
the forward and reverse reactions composing the equilibrium constant are assumed to be high
enough that interphase transfers take place more quickly than intramedia and intermedia
transport.
Partitioning processes are the most thoroughly studied of all chemodynamic processes,
and the methods for estimating the equilibrium parameters are well developed, as demonstrated
in Lyman et al. (1990). Lipophilicity, as represented by the octanol-water partition coefficient
(K^,), is a major factor in environmental chemodynamic processes and is used to estimate water
solubility, bioconcentration from water, and soil sorption for nonionized neutral molecules. Thus
considerable effort has gone into obtaining sound measurements and robust quantitative
structure-activity relationships (QSAR) for K^,. As a result, the relationships of parameters to
K^,, provide a means of crosschecking measured or estimated values employed in chemodynamic
models. Failure of these to check out implies that measurement must be given priority. For
example, the natural pyrethrin insecticides and many of their synthetic substitutes have especially
high log K^ values but extremely rapid rates of hydrolysis and bio-oxidation; moreover, they are
not bioconcentrated as predicted. If the log K^ were relied on as the sole measure of
environmental chemodynamics, the bioconcentration of unstable compounds would be
overestimated, whereas the uptake and sorption of newly synthesized, more stable pyrethroids
would be underestimated.
Partitioning is particularly sensitive to some environmental conditions, especially if the
compound has a weakly ionized group that may be affected by pH. Solubility and vapor pressure
are particularly sensitive to temperature. For Henry's Law constant it is critical that both vapor
pressure and water solubility be measured at the same temperature (because these features
change at different rates as temperatures change). K^ is less sensitive, however, because
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solubilities in the two solvents usually change at similar rates in the same direction.
Computation of the nonionized forms by the Henderson-Hasselbach equation can adjust the
distribution for pH but may not properly estimate ion exchange and ionic bonding.
For ionic chemicals, the ion types (i.e., forms with a given valence and degree of
substitution, such as Hg°, Hg+2, CH3-Hg+1) constitute a species into which the chemical is
apportioned. Estimating the equilibrium speciation—which is dependent on temperature, pH,
Eh, and ionic strength—may involve the simultaneous solution of up to 50 equations for as many
as 20 species. Empirical knowledge of the binding capacities of different clay minerals and soil
and sediment organic matter is required for speciation to be made site- and situation-specific.
The regularity of reactions of solubility products and complexes, on the other hand, extends
speciation as a chemodynamic process in all environments.
The complexity of speciation can be used to illustrate the sorts of practical strategies that
are used to estimate ecological exposures. In most cases, assessors begin by assuming that the
chemical exists entirely in its most toxic form. If there is no significant risk under this exposure,
the complexities are avoided. If significant risks are estimated and if speciation could make a
difference in the result, then a speciation study must be performed focusing on the species that
are predominant or contribute significantly to the risk. For some chemicals, such as ammonia,
simple and effective speciation models exist; for retrospective assessments, however, it is often
possible but expensive to measure species. One simply needs to determine that knowledge of
speciation is needed, since often specific knowledge of the contaminant is useful. For example,
because methyl mercury in fish flesh is always at least 85 percent methyl, it constitutes the
species in the diet of piscivores. Similarly, chromium in soil is trivalent except in unusual
circumstances.
Bioaccumulation can be the most important process involving partitioning. Yet because
bioaccumulation represents the net effect of uptake, metabolism, storage and excretion, it is
primarily an outcome of partitioning only for "conservative" or metabolically stable chemicals.
For fat soluble chemicals the bioaccumulation rate is adjusted by the lipid composition of the
receiving species. Also, the rate may be very sensitive to species-specific rates of metabolism
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and/or excretion. Although there are demonstrable instances in which some chemicals increase
in concentration for each trophic level within food webs, this phenomenon of biomagnification
does not occur with most chemicals.
3.2.2. Formation of Secondary Agents
After its release into the environment, an agent can interact with a wide variety of abiotic
and biotic processes, such as chemical oxidation, metabolism, and primary production. The
consequences of these interactions range from complete detoxification of a chemical to chains of
events, such as eutrophication, that can completely change an ecosystem's form.
3.2.2.1. Transformation
Since changes in a chemical's structure may activate, inactivate, or detoxify it, information
about, for example, biotic and abiotic reactions, the effects of conditions, and requirements for
reactants is critical for characterizing exposure.
The role of biotic and abiotic reactions in chemodynamics of environmental pollutants is
not always clear. For example, the evaluation of microbial biodegradation is complicated by the
role of sorption in preventing degradation (Alexander, 1986). In contrast, surface binding may
provide a mechanism for abiotic catalysis of photolysis, hydrolysis, oxidation, or reduction.
Counterintuitively, water may inhibit some reactions in which it participates (e.g., soil catalysis of
hydrolysis) by out-competing the chemical for binding sites (e.g., manganese or magnesium).
Thus it is usually necessary to clarify the contributions of both biotic and abiotic transformations
to a pollutant's fate.
Abiotic reactions. Abiotic reactions are those in which no organism is involved. The
nature of many reactions is well known and the rate may be described by various SAR.
Oxidation/reduction reactions may be generated by a variety of materials, although the rates are
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usually 10* to 103 faster in biological systems. In saturated soils, the creation of anaerobic
conditions and a very low Eh determine the biota available for reactions as well as the abiotic
consequences. Photolysis is less predictable in terms of rates and products than either hydrolysis
or oxidation, but the mechanics may be more accurately described when the quantum yield and
light absorption spectrum are known for a situation in which the incident light can be predicted
(e.g., location within the geosphere, time of year). Since a number of products from photolysis
are identical or similar to those generated biotically, active agents (e.g., dieldrin from aldrin) may
be created by this type of reaction. In some instances the phototransformation products are
unique to that process (e.g., photodieldrin) and may be even more toxic than other abiotic and
biotic transformation products. Finally, there is the fairly well characterizedJntracellular
interaction of photolysis with toxic action (Gala and Giesy, 1992) in which an abiotic
transformation process occurs in the receptor organism.
Biotic reactions. Because microbiota make up the majority of life forms and, importantly,
are resident in soils and sediments, their ability to transform chemicals is critical to chemical fate.
The detailed information controlling microbial action may be in chromosomes or plasmids or
may be shared within communities, and it is subject to enhancement or induction under some
circumstances. Conversely, microbial transformation may be limited by the naivete of the
organisms with respect to either transport into the cell or enzymatic transformations thereafter.
Thus the chemical history of an area may create an accommodated or acclimated community
with respect to a given agent, whereas another microbial community will be relatively naive. In
the latter, a period of induction of up to 2 weeks may be required to achieve competence, after
which the biodegradation may proceed quickly (i.e., in a few days). As a result, expressions of
biotic stability (or ready degradation) may be misleading when they are based on simplistic
calculations of when half of the initial concentration has disappeared.
Microbial communities carry out an amazing variety of reactions. If the chemical can be
converted completely to inorganic substances (e.g., carbon dioxide, water, ammonia), the process
is termed mineralization. If only one or so steps of degradation are accomplished by a given
organism or community—perhaps coincidentally because of substrate similarities between the
pollutant and naturally occurring compounds, and without population growth on the
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pollutant—then the process is termed co-metabolism. Thus this process may be the source of a
more persistent moiety or the compound may require transport to a new set of conditions and
organisms (e.g., aerobic—> anaerobic) for mineralization to proceed.
Some chemicals that are not mineralized disappear (at least temporarily) from an
environment because they are converted to "bound residues"—so-called because the chemical
cannot be extracted therefrom, although radiolabel studies strongly imply its presence (or that of
a metabolite). On the other hand, such labels can result from assimilation of normal metabolites
(e.g., conversion of 14CO2 to 14C-labeled cell walls) or mechanical entrapment of sorbed materials
(e.g., as in a pH shift that closes a clay lattice). Some bound residues may provide a toxicant to
biota as the matrix of recalcitrant organic matter to which the binding or covalent bonding has
occurred is metabolized. The critical issue regarding bound residues for exposure assessment is
to assure the manager that a mass balance study accounts for the activity and bioavailability of
parent and transformation products over the period of assessment.
Transformations by higher organisms decrease in significance relative to fate and
transport of the agent the further one moves from microbiota. Invertebrates and rooted plants
may play key roles in the food web and sometimes are particularly important for the mobilization
and bioavailability of chemicals; yet transformations may not be critical. Plants do store some
toxicants as conjugates (reacted with simple sugars, amino acids, peptides, and anions such as
phosphate or sulfate) that become available to herbivores upon digestion. At the upper levels of
the food web, biotransformations are usually important largely in terms of toxicodynamics.
Omnivores (e.g., human beings, swine) tend to have broader competence in the range of
chemicals handled than herbivores, which tend to have much broader competence than
carnivores. In any group, however, biotransformation may be a critical element in selective
toxicity (see chapter 5, on effects characterization).
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3.2.2.2. Interactions with Ecological Processes
Interactions of agents with ecological processes can result in a stress regime that is
challenging to trace and difficult or impractical to predict. The complexity of some of these
interactions is illustrated by the cascade of exposures and effects that resulted from increasing
nutrient inputs into Chesapeake Bay. Complexity increases with the length of the pathway (i.e.,
the farther in time, space, or transformation steps that the active agent is from the original
source or human activity).
A key component of any exposure assessment is the assurance from the'assessor that the
appropriate agent is targeted. For example, the sag in DO associated with the chronic release of
biodegradable organic carbon into a waterway can cause mercury in sediments to be methylated
under the more anaerobic conditions accompanying that DO sag. Tubificid worms may then
proliferate, even as the diversity of mesofauna declines, and acquire tolerated burdens of methyl
mercury from the surrounding sediments. Thus these worms mobilize previously unavailable,
perhaps even buried, deposits of mercury in the stream. In this case, the exposure assessor
would identify mobilized mercury as an active agent to which fish are exposed.
The knowledge of natural processes and the consequences of changes in conditions is
clearly a vital part of being able to make such assessments. This aspect underscores the
importance of soil and aquatic sciences, biogeochemistry, hydrology, and meteorology in
providing a basic background for exposure assessments.
3.3. Evaluation and Quantification of the Exposure Process
The exposure process defines the effective contact between the receptors (organism or
system) and the contaminated or modified environment. The attributes or behaviors of the
receptors that will influence the extent of exposure are evaluated to identify routes of contact
with affected media. Exposure is quantified through the use of a model, by chemical/physical
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analyses, or by bioassay. As final steps, the assessor evaluates the role of exposure-based
behaviors and other feedback mechanisms in controlling subsequent outcomes.
3 J.I. Behavioral Attributes
The time an organism spends in a particular habitat or activity often determines the
extent and route of exposure. For this reason, the evaluation of behavior is an explicit part of
the exposure analysis of individuals and populations and parallels time-motion studies for
occupational health/exposure. Some behaviors such as stomata opening are physiologically
controlled such that a particular pathway (vapor phase and paniculate intake by plants, in this
example) is predictably affected by physical conditions such as drought, heat, and humidity.
Animal behaviors, such as feeding, grooming, and digging, determine the dermal, ingestion, and
inhalation exposures.
Prey or food selection may include behavioral and physiological mechanisms, but also will
be influenced by ecological attributes such as prey abundance and competition. Since animal
feeding studies can reveal behavioral facets as well as body residues and bioaccumulation
patterns, exposure assessors should be reluctant to forego them in favor of methods based solely
on chemical structure.
As the receptor system becomes more complex, so does the role of behavior in exposure.
Exposure may or may not be a quality of the environment sensed by an animal. In studying the
effects of various pesticides on the interactions of crickets (Acheata domestica) and voles
(Microtus canicaudus) in a terrestrial model ecosystem, Gillett et al. (1983) noted that pregnant
or lactating female voles (normally herbivorous) ingested significant quantities of crickets, but
only if there was foliage present. In open field trials on bare ground, voles and crickets barely
seemed to acknowledge each other's existence: Crickets made no effort to avoid voles, and voles
never pursued crickets. In the typical ryegrass-alfalfa system used in the studies, however, up to
30 percent of the voles' food intake was crickets.
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Where behaviors result in hierarchical ordering of a population (e.g., pecking order,
feeding location for a predator), exposures could be graded between individuals because of, for
instance, differential access to food or oxygenated water. Failure to obtain enough of an
essential resource might subject a receptor organism to disease, predation, or some other adverse
consequence lumped into morbidity and mortality.
In addition to directly influencing exposure, behavior in response to exposure can
mitigate or enhance exposure. For example, prey that respond more slowly to threats because of
toxicity can be subjected to increased exposure to predators (Bildstein and Forsyth, 1979). On
the other hand, avoidance of a food item because of an organoleptic response to a toxicant can
decrease exposure. These exposure-related behaviors are best considered as effects per se,
except that they will increase or decrease the presumed direct or indirect exposure of the
receptor species. Moreover, although these responses often can be demonstrated in isolation in
the laboratory (even as a dose-response relationship), there are few examples of extrapolation to
field populations. Should they be known or detected, the assessor must consider ad hoc
evaluation of body burden or other appropriate measures of exposure.
3.3.2. Routes of Contact
The physical contact of an organism with its environment is limited by one or more
membranes. Nonetheless, contact with any one medium may involve multiple routes, which can
include inhalation or respiration, ingestion, and dermal transfer. Thus a trout may ingest some
water bearing a pollutant, but the majority of contact will be via respiratory transfer (gills) and
some dermal transfer, with secondary exposure attributable to ingestion of food that is in
equilibrium with the water.
With each route of contact there are factors affecting the efficiency of transfer. If that
efficiency is low enough, that route of exposure will be trivial, regardless of the concentration in
the medium. Some of these factors for a chemical include its exact form or species, the rate of
presentation at the contact surface with respect to the rate of uptake across the surface, and the
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rate of mixing of the solution or material directly in contact with the organism with the bulk
phase of the medium. On the part of the organism, the contact may be determined by the
architecture of the tissue in contact with the medium (which may be a function of age, size,
strain, gender, nutritional or physiologic status, or disease condition, as well as temperature), the
rate of circulation away from the contact surface and distribution in the organism (which may be
controlled by activity or physiologic status), and species- or individually selective features of
biotransformation and uptake.
Models of plant uptake are still controversial. Boersma et al. (1991) have proposed that
evapotranspiration flux can move chemicals from soil into foliage in a predictable manner within
a narrow range of log K,^ but that compounds with higher K^, values are tied up in the roots.
Foliar'penetration and uptake of gaseous pollutants and particulates via the stomata are also
important for some chemicals. Because of the difficulties, assessments of plant contamination
(and of transfer to parts consumed by animals) are usually evaluated empirically.
Exposure assessment at higher levels of organization (e.g., communities, ecosystems) can
be accomplished by estimating the exposure of the component part or by establishing an
operational boundary around the entire unit of interest (e.g., the perimeter of a lake including 30
cm of sediment). For the component-part approach, exposure routes can be evaluated as
described above, but attention must be paid to the full distribution of exposure to each
component. For the operational-boundary approach, exposure routes can be evaluated as fluxes
across the boundary (e.g., atmospheric deposition, inflow, burial in deep sediments, and outflow).
3.3.3. Parameterization of Components
Once the important routes of exposure have been identified, the parameters influencing
exposure must be quantified. These parameters can include description of the temporal and
spatial distribution of the agent, the receptor, the receiving environment, and chemodynamic
processes.
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Parameters can be measured directly or estimated from empirical relationships. In either
case variability among parameter values in a population should be distinguished from uncertainly
in the true value of the parameters. Three measures are generally used to select parameters: (1)
a single value representing a mean or conservative estimate (e.g., upper 95th percentile), (2) a
range or distribution of values, or (3) a value specified for a particular scenario. The first
approach is commonly used, even though it may yield results that are realistic for only a small
fraction of the time and will either underestimate maximum exposures or overestimate
population exposures. The third has come to be employed in a handbook-type approach in
which an assessor merely fills in some information and calculates a value by rote, which may be
appropriate for health protection but which creates a host of difficulties for an ecological
exposure assessment. The second approach uses simulations or measurements to generate a
range and frequency of values, taking into account the statistical properties of the processes and
their parameters as a part of evaluation of uncertainties (see chapter 8, on uncertainty in
ecological risk assessment).
3.33.1. Receptors
Values for many of the receptor parameters of interest are available in the literature for
a few birds and mammals. Values for amphibians and reptiles, however, are rare. Allometric
equations can be used to estimate values on the basis of body weight when species-specific values
are unavailable. Allometric equations are available for food ingestion rates (e.g., Nagy, 1987),
water intake rates (e.g., Calder and Braun, 1983), and inhalation rates (e.g., Lasiewski and
Calder, 1971; Stahl, 1967).
For mobile organisms, an estimate of the amount of time spent in a given area may be
needed. Data on home range size can provide a general sense of how far an animal may roam;
however, such data provide an incomplete picture because the area in which an animal moves
varies with several factors, including reproductive status, season, and habitat quality. Moreover,
most animals do not roam or feed randomly within their home range. Further, the term home
range has been used inconsistently in the literature and estimates of home range can vary
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substantially with the measurement technique used. Data on range sizes are generally lacking for
fish, although trout have been shown to consistently return to a single rock (Bachman, 1984).
Seasonal movements (e.g., anadromous and catadromous species) and even daily migrations
(vertical movement of zooplankton) can be important determinants of exposure for
aquatic species.
3.3.3.2. Environment
Often tabulated historical data is available on, for instance, stream flows or
sedimentation rates or for documenting the geologic, hydrologjc, oceanographic, meteorologic,
and atmospheric character of the environment. Such measurements permit the creation of
subsidiary data in the form of "canonical" or "reference" environments (e.g., a warm, "blackwater"
southeastern U.S. stream; a western mountain lake; a southwestern irrigation canal) for which
average values have been computed, for example, for the daily insolation spectrum, organic and
sediment particle content, hardness, pH, and salinity. Soil maps are available with data on
characteristics of both surface and subsurface soils regarding bulk density, organic matter, cation-
exchange capacity, pH, water-holding capacity, and crop patterns. These data are of the type
characterizing the environment and its conditions affecting chemodynamics.
3 J3 J. Chemodyuamic Processes
A number of empirical methods have been developed and brought together as an
alternative to measurement, an aid to planning appropriate measurements, or a check on
measurements that have already been made (Lyman et al., 1990). These usually rely on structure
or other simple or readily derived properties; empirical statistical correlations may have serious
errors, putting results in the category of a "good guess." Nonetheless, their use as point values
with error terms or as distributions can create a viable picture of the chemodynamics or at least
provide a picture that can be verified by simple hypothesis testing experiments or even
microcosm studies.
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Since often data on receptors and their properties relative to chemodynaraics and uptake
are particularly sparse, a hierarchy of data selection and estimation techniques is required. A
measured value of high quality is preferable to a soundly estimated value, which is preferable to
data of questionable character. A measured or characterized distribution is preferable to, for
instance, a presumed normal, log-normal, arc-sine distribution. Data within a taxon may provide
the mean or range of values for unmeasured members of the taxon, but data beyond measured
taxa must include all data from taxa at the same level as the range of values. If the distribution
of the range of values is unmeasured, it is presumed to be uniform over that range.
3J.4. Quantification Methods
The necessity for the dimensions of exposure to be comparable to those of the effects
limits meaningful quantification to three basic sets of techniques: (1) estimation of effective
concentrations or doses in media, (2) measurement of residues or biomarkers in the receptors,
and (3) bioassays using organisms for which the response-exposure relationship is known.
33.4.1. Estimating Effective Concentration or Dose
The calculation of an effective concentration or dose is one of the most common
methods used to estimate exposure. Its simplicity is both a strength and a limitation in that,
although exposure estimates can be quickly and consistently generated, they can be unrealistic
and indifferent to indirect effects (although integrated aquatic exposure effects models address
the latter concern). The approach normally combines modeled or measured concentrations of a
contaminant with assumptions or parameters describing contact. For example, exposure to
respired media (e.g., water for aquatic organisms, air for terrestrial) is commonly quantified by
assuming that contaminants are well mixed and that the organism contacts a representative
concentration. For solid media (e.g., food, soil) and ingestion of water, ingestion rates are
combined with estimated concentrations in dose equations similar to those used for human
health assessments.
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A simple and effective means of representing individuals or even classes of organisms is
via a simple one-compartment model, such as the one employed by Thomann (1981,1989)
Simple One-Compartment Aquatic Exposure Model
A,F
*i
Cw <==> cb ;
where C,,, Cj, and Q, are the chemical concentrations in water, prey organisms,
and the receptor; k, is the uptake rate; It, is the loss rate; A is the absorption
efficiency; F is the feeding rate; k, is the combined elimination rate for
metabolism, assimilation, and excretion; and Xj represents the various products,
each of which may require a model.
The ratio kj/kj is directly proportional to K^, and ke tends to be inversely
proportional to K^, but empirical relationships are available for various
organisms and classes of chemicals. For terrestrial animals, a similar
representation usually ignores inhalation as insignificant (Ram and GiUett, 1992).
Ram and Gillett (1992) were able to assemble a food web of over 250 vertebrate species
and 40 invertebrate "dietary item" guilds, each with a physiologically based pharmacokinetic
(PBPK) model (as illustrated in the equation box) such that 90 percent of measured
polychlorinated biphenyl (PCB) values for 46 species involved in the site-specific assessment of
the model were within the 95 percent confidence interval of the PBPK model outputs. Because
diets were assembled for winter and summer seasons—effectively reassembling guilds and dietary
items—the number of parameters needed was >104. By using a clustering technique tcrreduce
the number of food item guilds and by estimating parameters according to a preset hierarchy,
assembly and computation of the models were effectively reduced in scale and scope but not in
the accuracy of the outputs.
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The exposure assessor must consult with the ecological effects assessor to determine the
timeframe over which exposure will be averaged (e.g., shorter times for acute and reproductive
effects, longer times for chronic effects). As concentrations become more episodic or variable,
the more the use of averaging becomes problematic. In extreme cases, averaging may not be
appropriate at all and a pharmacodynamic approach may need to be used.
3 J.4.2 Bioassays and Biomarkers
Historically, trie discovery of an effect led to investigations seeking a chemical, biological,
or physical agent responsible for the adverse effect. Such investigations were accomplished by
using responses of organisms (btoassay technique) to standard dilutions or similar fractionations.
The basis for the response was uncovered iteratively. For example, "chick edema factor" was
replaced by measured polychlorinated dioxins and later still by co-planar tetrachlorodioxins and
benzofurans. In the case of the several avian heart teratomorphs, we now are virtually certain of
the causative agents as measured by highly trained specialists in mass spectrornetry.
The foregoing process took about four decades, which is an intermediate period between
the decade that it took to identify egg-shell thinning or delayed-type neurotoxicity and the
decades spent on the as-yet unidentified causes of conifer decline in Western Europe. It implies
that bioassay is still a useful approach that chemical measurement cannot entirely replace.
Empirical quantification of exposure has often taken the form of biomarkers (Huggett et
al., 1992), which can be either an accumulation of biochemical or physical damage or of the
chemical itself. The Irving organisms (e.g., tethered or caged fish; tested, marked, released and
recaptured field mice for further testing; growing ryegrass) can have nonliving substitutes in some
cases, such as moss bags (Huckabee, 1973) for heavy metal accumulation from the air and
dialysis bags filled with hexane or some other organic solvent for water. All of these permit
some integration of the exposure regime intensity over time.
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3.4. Implementation Issues
The discussion below focuses on two implementation issues: the selection and use of
models (with particular attention given to validity for the intended purpose) and data acquisition
and analysis.
3.4.1. Selection of Models
A large number of fate and transport models are available for each medium (for a recent
review, see Calamari, 1993) with outputs typically in the form of concentration over time for a
particular location. They vary in sophistication, ease of use, applicability to a range of
assessment types and approaches, and degree of validation.
3.4.1.1. Validation
The validity of a model may be in the mind of the user and the user's audience (Suter,
1993), but it also must assure peers that a reasonable degree of precision and accuracy is
achieved within an adequately comprehensive and complete scope in an efficient manner. At
present, most models in use are computer programs that have been verified as to their accuracy
and dependability in a set of cases. Yet the embodied assumptions may or may not be
appropriate, necessary, or sufficient for every application. Indeed, most models are limited to a
single medium (or to media assumed to be in equilibrium with that medium) in terms of
predictions. Thus there are air and water plume models that can represent the dispersion from a
stack or outfall into an air-shed or water body, respectively; there are runoff and leaching models
for terrestrial systems; there are multimedia food web models (Ram and Gillett, 1992) and .
multicompartment models in which the pharmacodynamics within a vertebrate permit modeling
of specific organ ingestion (Lindstrom et al., 1974); and there are plant uptake models (Boersrna
et al., 1991).
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With few exceptions, environmental models must have one or more parameters
determined from actual data. If those data are from the site or subject area, most would
consider the process one involving model calibration, which implies limited applicability or
transportability of the outcome to other, even similar, sites. For example, biodegradation rates
can vary on the basis of the extent to which a site is untainted with respect to previous releases.
The value measured may be useful elsewhere with respect to chemical history.
Models are usually validated by a reserved data set not used in calibration or otherwise,
with validation criteria set forth in advance and justified as to their applicability and
reasonableness. Models purporting to accomplish the same task can be compared by the same
type of criteria, allowing the user, if desired, to construct a quantitative measure of validity for
that task.
Whereas achieving validation of exposure models at the level of environmental
organization for a watershed or pond is quite feasible (e.g., Paterson and Schnoor, 1992), we are
much less sure of riverine and estuarine systems and of regions or landscapes. Heterogeneity of
ecosystems, conditions, and pertinent processes stretches the credibility of "average" results and
places excess uncertainty on simulations. There is greater confidence in stream-reach and air-
shed models than in watershed and regional models because users are more likely to have
obtained data on a consistent basis, perhaps even from one source.
3.4.1.2. Suitability
The exposure pathway analysis is the chief factor in determining the suitability of any
given model for a particular assessment. Once preliminary evaluation has determined the degree
of mass balance or accountability available and calculations have estimated the relative
proportions to be allocated to each pathway, the assessor must examine the models for these
media in the pathway(s) to ascertain whether assumptions and available data for
parameterization are suitable.
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A model also must be compatible with the ecosystem and with the temporal and spatial
dimensions of the assessment. For example, advective transport of soil and soil-associated
chemicals is most commonly estimated using the Universal Soil Loss Equation (USLE), which
was intended to represent long-term impacts of average rainfall with agronomic use with a view
to selection of soil conservation and management techniques (Wischmeier and Smith, 1978).
Attempts to use this model to represent transfer of sorbed material from unmanaged soils will
likely not yield an accurate appraisal if empirical data are used within too narrow a span of time
(Risse et al., 1993). Alternatives include SWRRB (Arnold et al., 1990) and AGNAPS (Young et
al., 1987).
3,4-2. Acquisition of Data
3.4.2.1. Data Quality Objectives
Most ecological risk assessments have more than their share of uncertainties. In the past,
no small pan of the uncertainty could be attributed to data that were inadequately obtained,
managed, and evaluated, then unceremoniously assembled or passed on to the manager. With
the advent of Good Laboratory Practices and establishment of quality assurance/quality control
(QA/QC) plans as standard operating procedures in testing and analysis, the means to construct
and attain sound data-quality objectives became inculcated for exposure assessment professionals.
Although not every instance or assessment demonstrates these principles, they are at least firmly
established in theory and practice.
Simply put, the data must be robust enough to support the decisions to be made by
management. Background and controlled exposure data should permit causality to be
investigated in the field. Data acquisition must be carried out by approved and tested methods,
with trained personnel, and using well-maintained and standardized equipment. Data
management must provide for identification of where, when, by whom, and how each sample was
made or taken—for the surety of data storage and sample archives; for the verily of the data
itself; and for correction, should errors occur.
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For predictive assessments, although the data quality objectives mainly pertain to the
validation study, they also are relevant to the parameterization of models and calibration of field
sites. Testing data and data characterizing chemicals and environmental media are frequently
covered by the detailed needs of QA/QC programs.
3.4.2.2. Statistical Analysis
Suter (1993) notes that statistical models of exposure data tend to be most suited for
description, extrapolation, and hypothesis testing (i.e., whether exposure at one site is
significantly greater than at another site where effects may or may not have been observed).
Predictive exposure assessment of chemical or physical additions uses the first two extensively;
evaluative or retrospective assessments will rely on the first and third. Descriptive statistics,
including the nature of any distributions and the scale of error terms—especially as a coefficient
of variation (coefficient of variation, standard error/mean)—are typically obtained.
Examination of measurement error along with analysis of sampling frequency will
ascertain the robustness of any particular set of measurements (Gilbert, 1987). There is a
constant battle, however, between the sufficiency of sampling and costs of analysis. Such
problems can be especially acute when moved into three dimensions, as in a Superfund site
adjacent to a river. Thus a receptor may be down stream, down gradient, and down wind or just
above or below a hazardous source, depending on the depth and medium being evaluated.
Guidance in sampling needs to take cost effectiveness into account via a parsimonious approach
(e.g., Boomer et al., 1985).
3.5. Summary
The objective of the exposure characterization is to provide a profile in time and space of
the> distribution or intensity of exposure to the agent and its products. This section describes an
approach in which the pathway from a source to a valuable and vulnerable receptor was
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examined critically and, to the extent feasible, quantitatively in terms that are consistent with
establishment of co-occurrence, contact, and, ultimately, uptake by that receptor.
Broadly, analytical implementation requires two major inputs from those responsible for
characterizing the ecosystem and those with the responsibility for integrating exposure and
response assessments: (1) the time factor of the response with respect to the rate of change or
ft-
variation of the agent, and (2) the time to decision. Clearly, if the manager must reach a
decision in the near term, sampling intensity and analytical methodology have different forces
working on them than if the decision is not time sensitive. This time-to-decision factor may be
determined by, for example, the interval between breeding seasons for an endangered species,
the time it takes for a critical concentration to build up in estuarine sediments, or a budgetary
imperative; in other words, anything theoretical or pragmatical that can limit or force a decision,
no matter how prosaic.
As noted above, any analysis will require the time to create a background record and
establish appropriate control of unexposed areas. Training of staff and contractors adds further
to this lead time. Whenever too short a tirneframe is used for sample collection, there is a
corresponding danger of sampling and/or extrapolation error.
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4. THE ANALYSIS PHASE: CHARACTERIZING DISTURBANCES
4.1. Direct Disturbances
In general, analysis of direct exposure to disturbances such as logging activity or the
construction of dams or parking lots is a relatively simple process once the problem formulation
stage of the assessment has specified the appropriate units to be used for measuring intensity,
time, and extent of exposure (section 2). Unlike exposure to chemical, biological, and physical
agents, for assessments of these types of disturbances there are no exposure pathways to be
modeled and no contaminant concentrations, biomarkers, or radiation levels to be measured—
the wetland is filled, the fish are harvested, the valley is flooded.
Thus the analytical phase for such assessments consists of specifying the particular
attributes of the disturbance that are harmful and then quantifying the effective deletion or
modification. Often the process involves relating the area modified to the areas occupied or
used in particular ways by the endpoint populations or communities. For retrospective
assessments, such refinements can be based on observations and measurements of both resources
and disturbances. For predictive assessments, the location of resources are specified in relation
to the proposed disturbances. This task has been simplified by the availability of technologies
such as global positioning systems, optical scanners, image analyzers, geographic information
systems, and relatively inexpensive computers.
The process of overlaying the distribution of the disturbance onto the distribution of the
valued resources to establish the assessment endpoints can be considerably improved by defining
the patterns of disturbance for more recent as well as all prior disturbances within the range of
the resource. For example, the habitat of a species may be defined as a set of habitat patches
that supports populations via corridors that permit genetic exchange and repopulation. An .
individual disturbance that removes a relatively small portion of the total habitat may sever a
corridor or render a habitat patch too small to sustain a population. Analogous large-scale
spatial considerations also could enter into assessments of the functional properties of ecosystems
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and regions. Such an approach borrows from the relatively new fields of landscape ecology and
conservation biology.
The temporal scale of the disturbance also may be easily defined. If the disturbance is
transient (e.g., logging a site or clearing it by burning the vegetation), the temporal scale of the
risk is determined by the recovery process, which is logically part of the effects assessment, and
by the recurrence frequency. If the disturbance continues, the duration of the disturbance and of
recovery must be considered. For example, a reservoir disturbs a stream and riparian ecosystems
until it "silts-in" or is removed. In some cases, however, recovery is not an issue; for example,
ecosystems affected by the construction of highways or industrial sites are seldom allowed to
recover. Similarly, implementation of a resource management plan removes fish, wildlife, timber,
or forage for a finite period, after which it is replaced by another plan.
For these direct disturbances of the environment, the assessment burden is largely on the
effects assessor, who must translate harvesting or exposure to the modified environment into
effects on the assessment endpoints (see chapter 5, on effects characterization).
4.2. Secondary or Indirect Exposures from Disturbance
Although most effects of disturbances are experienced by the ecosystems that are directly
disturbed (e.g., the forest logged or the woodland developed for industry), indirect disturbances
also may be significant (e.g., the siltation of streams by agricultural tillage, logging, strip mining,
and construction). Thus indirect disturbances should be considered in the problem formulation
phase of the assessment.
A sometimes challenging part of the analysis phase, however, involves identifying the
specific consequences of the disturbance that will affect the assessment endpoint. For example,
the removal of riparian vegetation can generate multiple secondary disturbances; yet it is the
resulting increase in stream temperature that appears to be the primary cause of adult salmon
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mortality. Different secondary disturbances influence other endpoints, such as decreased
spawning and egg mortality.
Methods appropriate for analysis of indirect disturbances are specific to the type of
disturbance. Often, potentially useful empirical and theoretical/mechanistic approaches are
followed. For example, estimates of erosion and subsequent siltation may be based on
experience with a particular type of disturbance (e.g., strip mining in the region) or on models
(e.g., Arnold et al, 1990; Young et al., 1987). In general, if the process by which indirect
disturbances occur are complex and if there have been similar disturbances in the past that have
been studied, empirical data will provide the best evidence for the assessment of proposed
disturbances (Goodman, 1976).
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5. GAPS IN KNOWLEDGE
The following data gaps are believed by the authors to be of particular importance for
characterization of exposure in ecological risk assessment.
1. With respect to exposure to chemicals, additional data are primarily needed on
bioavailability. EPA is addressing issues concerning the bioavailability of chemicals in sediments
and is beginning to address issues with respect to metals in water, where the problems are most
conspicuous. Yet, bioavailability needs to be considered for all contaminants and routes of
exposure, and numerous questions need to be addressed. For example, How available are
organic chemicals that are sorbed to suspended particles and dissolved organic matter? How
available are compounds in various soils to plants and animals? Moreover, research needs to be
performed to allow prediction and measurement of bioavailable forms.
Bioavailability issues encompass more than merely sorption/desorption considerations
(which is still an issue in non-aqueous phase liquids [NAPLs]). For example, the route of uptake
in relation to residue stability (bioconcentration vs. bioaccumulation) is a vexing problem;
covalent bond formation to soil and sediment organic matter followed by ingestion or
biodegradation may release the agent or a transformation product; enterohepatic cycling exposes
chemical agents to microbial and organismic modification. Some of these are steps to complete
mineralization and detoxication; in other circumstances, the result is activation.
A consequence of the uncertainty surrounding bioavailability in the terrestrial
environment is the current inability to reliably predict chemical concentrations for the lowest
trophic levels. Research is needed to improve accuracy and precision in estimates of
concentrations in plants, amphibians and reptiles, insects and small mammals, and birds.
2. Decision rules or other direction is needed to determine what chemical characteristics
and what environmental circumstances need to be considered for various modes of exposure.
For example, some uptake routes such as inhalation and dermal absorption are seldom
considered in wildlife exposure assessments, although any one could exceed the conventionally
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estimated dietary exposures in particular cases. Similarly, recent investigations at the Milltown
Superfund site that found dietary uptake to be the primary route of exposure for trout to arsenic,
cadmium, copper, lead, and zinc suggest that dietary exposure to chemicals in aquatic systems
needs to be reconsidered. Finally, when nitroaromatics were investigated, there was an
implication that they or their metabolites were transpired from the leaves, thus laying munition
residues open to mobilization by a route that has not been thoroughly explored.
3. Although the modeling of transport and fate of chemicals is generally the best-
developed component of ecological risk assessment, the estimation of biodegradation is an
exception. Better ways to estimate the biodegradation of chemicals in various media and
conditions are needed. Whether the transformation results in increased or decreased toxicity, for
example, is of less importance to the exposure assessment per se than whether it changes the
exposure pathway and critical exposure concentrations at various points with respect to receptors.
4. The translation of areas physically disturbed by an individual action into appropriate
units of exposure for assessment, of the viability of populations requires a better understanding of
regional population ecology. Currently, the favored paradigm for assessing terrestrial animals
uses habitat units connected by habitat corridors. The utility of this paradigm is unknown,
however, and no generally accepted approach for determining whether a particular disturbance
will effectively eliminate a habitat unit or corridor has been developed.
5. Better data and methods are needed for developing average conventions for aquatic
and terrestrial organisms and populations. While range size currently is used to establish areas
for developing spatially averaged chemical concentrations, the method is ineffective for animals
that are especially mobile or for those that are particularly selective in their use of raicrohabitats.
The issue is aggravated in situations where concentrations are highly variable, such as with soils
at hazardous waste sites.
6. A collective examination of disturbances as a class might lead to useful generalizations
about how exposures to disturbances can be most effectively described.
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APPENDIX
GLOSSARY OF TERMS
Note: Most of the exposure-related terms defined in this glossary are not defined in the
Framework Report (U.S. EPA, 1992a). Others propose expansions or refinements to those in
Framework Report. The exception is the term agent, which is preferable to the term stressor,
which implies a prejudgment of the results of the assessment and which is inconsistent with other
EPA risk assessment guidance. The term "stress regime" was coined by a participant at the issue
paper interim meeting held June 16-17,1993.
Agent. An entity that results from a release from a source or from a human action and is
potentially hazardous. Agents may include chemicals, dams, exotic organisms, or sediments. The
term is used in EPA's Guidelines for Exposure Assessment (U.S. EPA, 1992b) (i.e., with exposure
defined as "contact of a chemical, physical, or biological agent"), is defined there for human
health assessments, and is applicable for ecological risk assessment with the following
modification:
An agent is a physical, chemical, or biological entity or an action that is released to
or imposed upon the environment from a source.
This definition covers the range from individual chemicals to flooding.
Bioavailable. Chemicals in a form such that if organisms were in contact with it,
absorption would occur. It is specific to the type of organism, chemical, and route of exposure.
An appropriate definition is:
An agent is bioavauable if it is capable of readily being taken up by an exposed
receptor.
Chemodynamics. In parallel with the term pharmacodynamics, long used to refer to the
movement of chemicals in the body, Chemodynamics can be defined as:
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The set of physicochemical, advective, biotic and mechanical processes operating on a
chemical or other agent that determine its spatial and temporal distribution in the
environment.
Chemicals flowing through an organism or moved in an abiotic medium (air, water, soil) may
have effects even in that process, and those effects may or may not be the subject of the direct
assessment. Thus bioaccumulation may involve any number of organisms in several tiers of the
food web before it may be involved with the object of the effects assessment. All of those steps
are part of the chemodynamics as far as that receptor would be concerned. Yet the
pharmacodynamics in each organism may be important to the overall effects assessment as well
as the concentration of the agent resulting within a given scenario. The ambiguous nature of this
perspective can be confusing to assessors and managers.
Contact. EPA defines exposure in terms of "contact. . . with the outer boundary of an
organism" (U.S. EPA, 1992b). This is appropriate for assessments performed at the organism
level and the population level, because assessments of populations are performed on the basis of
the average exposure or some other integral of the exposures of the component individuals. Yet
it is often appropriate to speak of the exposure of an ecosystem as a unit rather than in terms of
the component exposures. Examples include exposure of a terrestrial ecosystem to atmospheric
deposition or of a lake or river to inputs from its tributaries. Thus an appropriate definition is:
Contact is the occurrence of an agent at an interface with a receptor.
Co-occurrence. The term co-occurrence in the definition of exposure is added to account
for effects that are not a result of direct contact, including competition by an introduced
organism and reduced dissolved oxygen due to organic pollution. Thus an appropriate
definition is:
Co-occurrence is the occurrence of an agent in sufficient spatial and temporal
proximity to a receptor to result in a response if the magnitude of exposure is
sufficient.
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The concept of sufficient proximity depends on the dynamics of the agent, the receiving
environment, and the endpoint entity. For example, proximity of fish to an organic pollution
source is a function of flow, degradation rate of the pollutants, oxygenation rates, and tolerance
of the fish for low dissolved oxygen.
Disturbance. This term is equivalent to agent, but is applied to physical deletions or
modifications. Examples include logging, dredging, and flooding. Thus an appropriate
definition is:
A human activity or consequence thereof that physically deletes or modifies all or part of an
ecological system.
Dose. Definitions of dose are legion. EPA uses approximately 140 words to define the
term with regard to human exposures to chemicals (U.S. EPA, 1992b). EPA provides a general
definition and then defines 5 varieties of dose (some of which do not fit the general definition),
but does not cover radiological dose to humans or ecological uses of the term. For example, air
pollution phytopathologists commonly define dose as the product of concentration and time
(ppmh) (McLaughlin and Taylor, 1985). A general definition, adapted from Suter (1993) is:
Dose is an integral measure of effective exposure.
Any attempt to define dose more narrowly for ecological risk assessment is likely to fail because
diverse uses are deeply embedded in the sciences involved in exposure assessment. When the
term dose is used, the user must be careful to define it in context. Dose is used for chemicals,
radiation, and pathogens; there is no need to extend the concept to other agents.
Exposure. The term exposure is as appropriate to ecological risk assessment as to human
health risk assessment. Fish and trees can be exposed as well as humans, and populations or
ecosystems can be exposed as well as individuals. The term does not apply only to chemicals.
Organisms are commonly said to be exposed to radiation, pathogens, or heat. The term can also
be applied to other hazards such as exposure of a benthic community to dredging, exposure of an
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owl population to habitat modification, or exposure of a wildlife population to hunting. If these
uses seem stilted, the alternative term disturbance may be used,
Although the operational definition of exposure and particularly the units of measure
i
depend on the hazard, the following general definition can be used:
Exposure is the intensity, distributed over space and time, of contact or co-occurrence
of an agent with a receptor.
This definition is equivalent to EPA's definitions: "contact of a chemical, physical or biological
agent with the outer boundary of an organism" (U.S. EPA, 1992b) and "co-occurrence of or
contact between a stressor and an ecological component" (U.S. EPA, 1992a), except for (1)
emphasis on the intensity of the agent (e.g., concentration, dose, abundance), (2) inclusion of
spatial and temporal dynamics, and (3) the idea (absent in the first definition) that for some
agents, such as an introduced competitor species, co-occurrence is a more appropriate concept
than contact.
Note that exposure also may be defined as a process rather than an event. The
definition in Suter (1993) can be expanded as follows:
Exposure is the process by which an agent comes into contact with or co-occurs with
a receptor.
Exposurt pathway. EPA's definition (U.S. EPA, 1992b) can be adapted as follows to
apply to chemical, physical, and biological agents that act directly on a receptor:
The exposure pathway is the physical course a chemical, physical, or biological agent
takes from the source to the receptor.
The term must be adapted further to be applicable to environmental deletions and modifications:
The exposure pathway is the physical course a chemical, physical, or biological agent
takes from the source to the receptor or is the causal pathway by which an agent
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directly modifies the receiving environment, produces any secondary modifications,
and exposes a receptor to an ultimately modified environment.
The term is not applicable to the processes by which indirect effects are induced; the terms
causal pathway or influence pathway should be used for such processes or for the combination of
the exposure pathway and the pathway by which indirect effects are induced.
Incremental exposure. An appropriate definition is:
Incremental exposure is that component of exposure that is attributable to the source
being assessed; it is in addition to background exposure and exposure attributable to
other definable sources.
Intensity. An appropriate definition is:
Intensity is that dimension of exposure that serves to indicate the capacity of a
particular agent to affect a receptor given a constant duration and extent of exposure.
Examples include concentration, dose, and harvesting rate.
Intervallfrequency. The recurrence of episodic exposures can be defined as the frequency
of occurrence or the length of the interval between recurrences. It may be defined as the mean,
minimum, maximum, or planned exposure, or any other expression of a temporal variable may
be used.
Receptor. An appropriate definition is:
The receptor is the biological system that is exposed to the agent.
Receptor is a convenient, general term that is clear in most contexts and is applicable
generally to tissues, organisms, populations, and ecosystems. "Ecological component" is the term
in the framework for the things that are exposed; Cohrssen and Covello (1989) use "biological
system," which is more general. Either of these is acceptable and may be clearer in some
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contexts; however, "receptor" is usually clearer in discussions of exposure where the emphasis is
on the source-receptor relationship.
Source, This term is conventionally defined to include agents that are entities but not
actions; however, an appropriate expanded definition is:
A source is an entity or action that releases to the environment or imposes on the
environment a chemical, physical, or biological agent.
Thus sources may include a waste treatment plant, a pesticide application, or a dredging project.
Stressor. This term has been redefined by the Risk Assessment Forum to indicate that it
is a broad term that includes both agents and the primary effects of an agent that may cause
secondary effects. The new definition is:
Any physical, chemical, or biological entity that can induce an adverse response. The stressor
may be an agent or may be derived from interactions between an agent and the ecological
system.
Stress regime. This term was developed and hastily approved as an umbrella term to
cover exposure and disturbance (see text box). We believe, however, that defining the term as
the causal network of exposures and effects that can occur in complex ecological risk assessments
is more in keeping with the proposer's original concept. In addition, it interferes less with
clearer and generally accepted terminology. Thus an appropriate definition is:
The causal network of interactions of exposures and effects resulting in secondary exposures,
secondary effects, and, finally, ultimate effects.
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Alternative Definitions for Stress Regime
The term stress regime was developed and approved rather hastily without careful
consideration of its meaning or utility. Having attempted to use it in this report, we do not
believe that it is a useful substitute for the terms exposure or disturbance. We also do not
believe that defining it in that way captures the idea of the individual who proposed the term.
The example he used was ecological effects of "greenhouse gases" and ranged from climatic
effects through, for example, changes in local weather, plant physiology, competition, and
reproductive success to changes in continental vegetation patterns. He specifically excluded
consideration of the greenhouse gases themselves; thus he did not mean for it to encompass
the text box in the Framework Report that was labeled "characterization of exposure" and is
now labeled "characterization of stress regime." His example, however, includes induction of
effects on the assessment endpoint through a chain or network of secondary effects. That is,
it includes the primary exposure and any secondary exposures and effects that lead to effects
on the assessment endpoint. Therefore, if the term stress regime must be used, it is a
synonym for causal chain, pathway, or network (Andrewartha and Birch, 1984) and refers to
possible interactions between the two halves of the analysis phase. Lipton et al. (1993)
referred to this concept as the "risk cascade."
Susceptibility. Susceptibility is commonly used to indicate the characteristics of an
organism that determine its magnitude of response to a particular exposure (i.e., as an antonym
to resistance). EPA (1992a) has used it to indicate the characteristics of an organism that
determine its magnitude of response to particular ambient contamination levels and distributions
(U.S. EPA, 1992a). In other words, EPA includes characteristics of an organism that determine
its exposure as well as its sensitivity to the exposure. Although this use is somewhat
unconventional, we adopt it here because there is no generally accepted alternative term for this
important concept (one possible alternative is vulnerability). Thus an appropriate definition is:
The characteristic of an organism or other system that determines the magnitude of its
response to a contamination or disturbance of the environment.
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Total exposure. An appropriate definition is:
Total exposure is exposure to an agent resulting from all sources and pathways; total
exposure equals incremental exposure plus background exposure plus exposure to
sources or pathways outside the scope of the assessment.
This definition can be applied to actions as well as entities (e.g., total exposure of wetlands in a
region to dredging and filling, where each permitted action is a source), or the term total
disturbance may be substituted. The definition also could be used for a class of agents having
the same mode of action (e.g., entrainment of larval fish in hydroelectric turbines, cooling
systems, and intakes of water treatment plants).
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Physical modifications
or deletions
Chemical or physical
additions
Human Activity
Disturbance
Exposure
Exposure-Response Curve
(from Ecological
Effects Characterization)
1
Risk of Effect
Figure 1. The relationship of exposure to sources of agents, activities causing disturbances, and
effects.
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Source
pesticide application
Agent
pesticide
Exposure
pesticide x birds
Exposure-response
Curve
pesticide - fecundity
Risk of Effect
reduced fecundity in birds
Figure 2. An example of a simple ecological risk assessment involving exposure of birds to a
pesticide application (the risk-of-effect box represents the assessment endpoint).
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Source
Air conditioners
Agent 1
CFCs
Agent 2
decreased ozone
Agent 3
Increased UV-B
i
Exposure
zooplankton x
increased UV-B
Exposure-response
UV-B levels -
zooplankton mortality
Risk of Effect
zooplankton mortality
Figure 3. An example of an ecological risk assessment involving a multistage exposure (Le., an
ultimate agent [UV radiation] generated by a multistage exposure process from an
initial agent [chlorofluorocarbons] released by a source [air conditioners]) but a
single, direct ecological effect (tbe risk-of-effect box represents the assessment
endpoint).
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Source
Ag nonpoint runoff
Agent 1
nitrogen
Exposure 1
nitrogen x algae
Exposure-Response 1
nitrogen - productivity
Effect 1 (Agent 2)
Increased detritus
Exposure 2
detritus x detrivores
Exposure-Response 2
detritus - decomposition rates
decomposition - oxygen use
Effect 2 (Agent 3)
low DO
Exposure 3
low DO x fish
Exposure-Response 3
low DO - mortality
Risk of Effect 3
increased fish mortality
Figure 4. An example of an ecological risk assessment involving a causal network of multiple
exposures and effects. This example, however, can be treated as a multistage
exposure (e.g., like the risks from increased UV exposure in figure 3) because the
intermediate ecological exposure-response relationships are commonly incorporated
into the exposure model (the risk-of-effect box represents the assessment endpoint).
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Human activity
removal of riparian vegetation
Disturbance 1
decreased shading
destabilization of banks
T
Disturbance 2
increased solar radiation
stream becomes wide and shallow
Disturbance 3
stream temperature rises
Exposure
salmon x temperature
Exposure-response
temperature -
salmon mortality
Risk of Effect
salmon mortality
Figure 5. Example 1 of an ecological risk assessment involving exposure to a disturbance
generated by a multistage exposure process and resulting in a single, direct ecological
effect (the risk-of-effect box represents the assessment endpoint).
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Human activity
upstream levee construction
Disturbance 1
increased frequency and severity
of downstream flooding
I
Disturbance 2
high soil moisture increases in
duration, frequency and extent
I
Exposure
plant seeds and seedlings
x increased soil moisture
Exposure-response
soil moisture -
germination and survival
Effect (likelihoodrrisk)
plant community changes
Figure 6. Example 2 of an ecological risk assessment involving exposure to a disturbance
generated by a multistage exposure process resulting in a single direct ecological
effect.
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Discharges to
Drainage
Ditches
Aquatic
Community
in Ditches
Ground Water,
Runoff Water,
and Eroded
Soil
Clinch River,
Poplar Creek
Surface Soil
Sitewide
Terrestrial
Ecosystem
1
Soil
Invertebrates
Figure 7. A simple conceptual model for ecological risk assessment of a waste burial ground.
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Figure 8. A conceptual model for ecological risk assessment of the effects of logging on salmon
production in a forest stream. The assessment includes a series of exposures and
responses.
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Issue Paper
on
EFFECTS CHARACTERIZATION
Patrick J. Sheehan
ChemRisk Division
McLaren/Hart
Alameda, CA
One L. Loucks
Department of Zoology
Miami University
Oxford, OH
Prepared fon
Risk Assessment Forum
U.S. Environmental Protection Agency
Peer Review
DRAFT
September 1999
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CONTENTS
1. INTRODUCTION 5.6
2: CONCEPTS AND TERMINOLOGY 5-8
2.1. General Considerations 5-8
2.1.1. Direct and Indirect Effects 5-8
2.12. Distinguishing Ecological Effects of Chemical and Physical Stressors 5-9
2.1.3. The Concept of Threshold 5-10
2.1.4. Scale Issues Associated with the Level of Biological Organization 5-11
2.2. Linking Stressors to Effects on Individuals and Populations 5-13
2.3. Linking Stressors to Effects on Ecosystem Structure 5-14
2.4. Linking Stressors to Effects on Ecosystem Function 5-16
2.5. Stressor Effects on Ecological Pulsing and Stability 5-16
3. EFFECTS CHARACTERIZATION 5-18
3.1. Endpoint Context 5-18
3.2. Type of Effects Data and Study Methods 5-20
3.2.1. Individuals 5-21
3.2.2. Populations 5-24
3.23. Ecosystem Structure 5-25
3.2.4. Ecosystem Function and Regulation 5-27
3.3. Evaluation Methods 5-29
3.3.1. Methods to Evaluate Data Quality 5-29
3.32. Matching Data and Measurement Endpoint to the Assessment
Endpoint 5-29
3.33. Representative Data 5-30
3.3.4. Methods to Evaluate Data Sufficiently for Statistical Models 5-31
33.5. Methods for Evaluating Large-Scale and Long-Term
(Regional) Effects 5-32
33.6. Balancing Local-Scale/Small-Risk Problems with Good Data in
the Same Assessment with Large-Scale, Potentially Large-Risk
Problems Having Poor and Insufficient Data 5-33
4. ECOLOGICAL RESPONSE ANALYSIS: STRESSOR RESPONSES 5-34
4.1. Chemical Stressors 5-34
4.1.1. Direct Effects of Chemical Stressors 5-34
4.1.2. Indirect Effects of Chemical Stressors 5-35
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CONTENTS (cent)
4.2. Physical Stressors 5-37
4.3. Multiple Stressors: Aquatic Ecosystem Examples 5-38
4J.1. Multiple Chemicals in Aquatic Systems 5-39
4.32. Chemical and Physical Stressors 5-40
4.3.3. Chemicals and Human Exploitation of Aquatic Resources 5-41
4.4. Multiple Stressors: Terrestrial Ecosystem Case Study 5-41
5. ECOLOGICAL RESPONSE ANALYSIS: THE ROLE OF
VARIOUS TYPES OF MODELS 5-44
5.1. Cheraical-Concentration/Dose-Response Models 5-46
5.1.1. Concentration/Dose-Response Function 5-47
5.12. Time-Response and Time-Concentration Functions 5-47
5.13. Concentration-Time-Response Functions 5-47
5.1.4. Probabilistic Models to Handle Uncertainty 5-48
5.2. Approaches to Stressor-Response Measurement and Modeling for Ecosystems ... 5-48
5.2.1. Early Concentration-Response Models for Ecosystems 5-48
5.2.2. Models of Cascading Effects in Aquatic Ecosystems 5-49
, 5.3. Lake and Forest Ecosystem Component Models Developed in NAPAP 5-50
5.4. Hierarchical Approaches Integrating Individuals, Populations, Subsystems
and Ecosystems .*... 5-51
5.5. Evaluation of Causality 5-52
6. ECOLOGICAL RESPONSE ANALYSIS: RELATING MEASUREMENT
AND ASSESSMENT ENDPOINTS 5-54
6.1. Extrapolations among Different Taxa 5-54
6.2. Extrapolations from Laboratory to Field Conditions 5-55
6.3. Extrapolations across Ecological Hierarchy or Organization Levels 5-57
6.4. Extrapolations across Spatial and Temporal Scales 5-58
7. STRESS REGIME-RESPONSE PROFILES 5-59
7.1. Stress Regime-Response Relationships for Chemicals or Physical Stressors
Acting on Individuals and Populations, Short- to Mid-Term 5-59
7.2. Stressor-Response Relationships Attributable to Chemicals and Physical and
Habitat Alterations Acting on Ecosystem Function and Stability Characteristics,
for Large Areas, Long-Term 5-60
8. REFERENCES 5-62
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LIST OF FIGURES
Figure 1. Spatial and Temporal Scales within which Individuals, Populations, Ecosystems,
and Regions Respond to Physical and Chemical Stressors, as Shown by the
Overlapping Ellipses 5.74
Figure 2. Mortality in a Fish Population Exposed to a Range of Concentrations of a
Chemical in Water 5-75
Figure 3. Toxic Effects as a Function of Concentration, Duration, and
Proportion Responding 5-76
Figure 4. Time-Course of Ecosystem Response to a Strong Piscivore Year Class
(Solid Line) and a Partial Winter Kill of Piscivores (Dashed Line) 5-77
Figure 5. A Schematic Representation of Individual-Based Population Modeling
X,, Y., and Z, Showing Characteristics of an Individual Organism n Such
as Size and Leaf Area. 5-78
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LIST OF TABLES
Table 1. Examples of Possible Assessment and Measurement Endpoints for
Evaluation of the Effects of Insecticide Spraying for
Spruce Budworm Control 5-79
Table 2. Types of Responses to Stressors Characterized in Ecological Risk
Assessment 5-80
Table 3. Simplified Conceptual Model of the Apparent Time Scales of
Physiological and Ecological Processes Associated with Plant
Community Responses to Chronic Air Pollution 5-84
Table 4. Scales of Observation and Management in Case Studies Evaluated by
the NRC/NAS Committee 5-85
Table 5. Interaction of Air Pollution and Temperate Forest Ecosystems under
Conditions of Intermediate Air Contaminant Load—Designated
Class n Interactions 5-86
Table 6. Total Soil and Litter Ca2* Held in the Litter Layer and the Surface
2.5-cm Soil Layer, Expressed as a Percentage of the Total Ca in the Soil
and Litter to a 50-cm Depth, at Three Sites along an Acid-Deposition
Gradient from Southern Illinois to Southern Ohio 5-87
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1. INTRODUCTION
Characterization of the direct and indirect effects likely to be induced by exposure of an
ecological system to chemicals or to treatments relating to resource harvests—or the level of
resolution desired from other ecological manipulations—is one of the larger components of risk
assessment (see chapter 1). How such characterizations are approached depends substantially on
how the framework for ecological risk assessment is structured. In this paper, we seek to
complement the treatments of exposure characterization (chapter 4) and of biological stressors
(chapter 6), while providing a basis for the discussion of uncertainty and risk characterization
(chapters 8 and 9).
The particular goal of this chapter is to explore principles and data bases by which to
characterize the diverse effects dealt with in ecological risk assessment. We propose to
summarize—and quantify, in so far as that may be possible—information on the following seven
elements of effects characterization:
• theoretical and conceptual foundations;
• common methods and approaches available;
• stressor(s) and assessment endpoint relationships, including evaluation of available
ecological effects data;
• patterns of responses to .one or more stressors;
• tools for ecological response analysis;
• relationships of measurement endpoints to assessment endpoints, including
extrapolations and methods for considering causality, and
• development of approaches to stressor-response profiles.
As outlined in the Framework Report (U.S. EPA, 1992a), the elements of effects
characterization must be thought of as a set of measurements or computations that are
interactive with all the other components of risk assessment, including significance determination,
problem formulation, and, ultimately, risk characterization. Thus the tools and approaches
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discussed in other chapters are relevant here, but need not be repeated. Especially relevant is
the chapter on biological stressors (chapter 6), a particular class of effects that will be noted only
occasionally in this chapter.
Much of the rationale and methodology for ecological risk assessment has come from
health risk assessment methods (NRC/NAS, 1993) where "effects characterization" is not a major
issue in its own right. Advanced approaches suitable for ecological systems have been developed
recently, partly because of improvements in data and predictive tools (Suter, 1993), but also
because of the recognition of fundamental differences between human and ecological systems.
Legislation such as the National Environmental Policy Act (NEPA) of 1970, the Endangered
Species Act of 1973, and the National Forest Management Act of 1976 have all directed that
effects of human intervention in natural resource systems be considered (Loucks, 1992), which
has led to the creation of new data bases and assessment methods. In this discussion,
consideration also is given to the greatly improved theoretical and empirical foundations now
available for considering indirect as well as direct effects over long periods of time (Sheehan,
1984a,b,c; Shriner et al., 1990; Suter, 1993; NRC/NAS, 1993). Thus, this chapter is organized
around conceptual foundations that serve as the basis for consideration of approaches available,
patterns of responses to one or more stressors, and relationships to measurement and
assessment endpoints.
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2. CONCEPTS AND TERMINOLOGY
Conceptual issues and definitions of terms must be considered together in characterizing
the ecological effects of chemical and physical stressors. Terms relating to the framework have
been considered in chapter 1 (see also U.S. EPA, 1992a), but in this discussion we need to be
more explicit about direct versus indirect effects, scale considerations in space and time, and
thresholds.
2.1. General Considerations
Consensus is needed on the understanding of many new terms and concepts concerned
with evaluating both stressor effects induced directly and indirect or ecologically mediated
effects. These include problems in quantitatively distinguishing effects against a background of
natural biological variability, the influences of spatial and temporal scales on assessments at
various levels of biological organization; and specific theoretical, conceptual, and measurement
considerations involved in characterizing stressor-effect relationships for individuals, populations,
ecosystems, and regions.
2.1.1. Direct and Indinct Effects
An effect is a change in the state or dynamics of an organism, population, or ecological
system resulting from exposure to a chemical, physical, or biological stressor. The effects of
chemical and physical stress regimes on biota and ecological systems may be either direct or
indirect. Direct effects are those that can be related causally to exposure of the stressor. They
are illustrated by many of the outcomes commonly assessed in ecotoxicological studies. These
include, for example, the mortality of individuals and reductions in population abundance
associated with exposure to a chemical at a chronic-effects concentration, or the reductions in
reproductive success and young age-class recruitment of species in a physically disturbed habitat.
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In contrast, indirect effects occur as a result of the changes induced in a species or
ecosystem by a stressor acting on the physical or chemical environment or on habitat quality,
rather than as a direct response to the stressor. For example, the effects of acid deposition
(SO*, NO*') on forest trees are indirect, since they are related to a chain of interactions initiated
by the acid stressors. The end products of this series of indirect effects include reduced tree
growth, increased mortality in some species, and probably insect and disease infestations related
indirectly to the magnitude of the stress regime. A second example of indirect effects is
associated with aerial insecticide spraying in the Canadian prairies, which produces direct effects
on exposed aquatic macroinvertebrates in ponds and indirect effects at higher trophic levels
(Sheehan et al., 1987,1993a). The secondary effects from wind-spread aerial insecticide
applications include reduced recruitment of ducklings when macroinvertebrate prey abundance (a
key food resource) is reduced substantially by the insecticide during post-hatch period, when
ducklings are obligate feeders on these organisms. Thus both direct and indirect effects can be
ecologically significant and need to be considered in ecological risk assessments.
2.12. Distinguishing Ecological Effects of Oumical and Physical Stressors
Fundamental difficulties for quantitative examination of ecosystems arise from the
inherent biological variability of natural systems related to cyclic, stochastic, or successional
changes in populations. Our ability to describe many biological phenomena is limited by
practical constraints of sampling time and area. We are at times unable to apply measurement
strategies appropriate to resolving natural versus induced population trend patterns in
experimental and monitoring studies.
One problem is in deciding whether an observed change in a system endpoint is a
deviation caused by the presence of a toxic chemical, physical disturbance, or biological
perturbation or is part of the natural fluctuation or disturbance-recovery transient inherent in the
ecosystem dynamics. The question of distinguishing ecological effects attributable to chemical,
physical, or biological stressors from inherent variability is central to characterizing effects and
estimating risks to ecological systems. Still, our understanding of the key ecological and
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statistical issues often has been inadequate. Hurlbert (1984) described some of the experimental
design problems in ecological studies and presented data on the problems arising from misuse of
inferential statistics. Adequate baseline measurements are not available in many situations, at
least not measurements sufficient to allow definition of the types and sizes of inherent
fluctuations in species and process rates. Notwithstanding the significance of this problem, an
equally important issue for design of laboratory and field experiments involves the testability of
hypotheses involving fluctuating data.
2.1 J. The Concept of Threshold
The concept that there is an exposure threshold below which no measurable effects or no
biologically significant effects occur is the basis for much of the regulation of toxic substances in
the United States. Although the concept of threshold in ecotoxicology has been challenged,
possibly because it appears permissive of some level of a chemical or physical stress regime, it is
implicit in much ecological literature as well as in the regulation of chemicals (Woodwell, 1974;
Caims, 1977,1992; Odura et al., 1979). To characterize the ecological effects associated with
exposures to various stressors, scientists have to address the theoretical and practical problems of
identifying thresholds. Indeed, there is probably no single threshold applicable to all stressors,
species, assessment endpoints, or biological levels of organization. Rather, appropriate response
curves, sometimes including thresholds, specific to the assessment objectives, stressor, and
assessment endpoints of interest will need to be identified.
A second issue is whether to select a threshold based on our ability to measure it (e.g., a
statistically significant change in the individual, population, or ecosystem) or based on the
biological significance of the effect (e.g., a change that affects a population's potential to survive
or function within the ecosystem). The discussion of significance in chapter 2 is relevant here as
well. As noted above, distinguishing a change is a serious sampling and statistical challenge in
fluctuating and dynamic systems. Distinguishing a biologically significant effect has the added
complication of assigning a value to the assessment endpoint (see chapter 2). A small but
statistically significant decline in species richness in a stream ecosystem might be considered
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insignificant socially, while a dramatic decline in species richness would be regarded as highly
unfavorable, especially if such a decline affected the functioning or biological integrity of the
exposed ecosystem. Cairns (1992) provides a further discussion of the use of thresholds
in ecotoxicology.
2.1.4. Scale Issues Associated with the Level of Biological Organization
The effects of chemical and physical stressors may be assessed in terms of measurements
applicable to individuals, populations, ecosystems, or regions, or a combination of these levels of
biological organization. The lowest organization level warranting consideration in ecological risk
assessment—and this only occasionally—is the individual organism. Although stressor effects on
molecules, cells, tissues, and organs can be measured, they have little practical significance in
ecological risk assessment except in helping to interpret effects on whole organisms and
populations. On a human time scale, a population of individuals of a species is the smallest
persistent biological unit of interest. Ecosystems can be viewed, to some degree, as aggregates of
populations functioning together; however, ecosystems also have unique structural features (e.g.,
spatial structure, diversity) and functions (e.g., nutrient cycling, food web relationships) that have
very long-term significance. Although in the context of risk assessment, regions are spatial
aggregates of contiguous ecosystems, data on populations also are important at this scale.
Regions also have both the properties of their constituent ecosystems and unique attributes
associated with patterns of ecosystem interactions at a regional scale.
Although both measurement and assessment endpoints can be defined at various levels of
biological organization, the endpoints are not equally important over the range of spatial and
temporal scales (Suter, 1993), as is shown in figure 1. At short temporal scales (days to months)
and small spatial scales (micrometers to meters), effects of chemicals can be assessed on
microorganism populations. Little can be predicted about the ultimate effects of chemical and
physical stressors, however, on long-lived organisms at a scale of ecosystems, all of which
function primarily at large spatial and long temporal scales. Nevertheless, since ecosystem
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functions are carried out by individuals that make up populations, the distinctions between levels
of organization are not as real as they seem.
Individual macroorganisrns operate over a range of spatial and temporal scales. Small
animals and plants in both terrestrial and aquatic systems operate spatially within millimeters to
meters and generally within a temporal scale of days to months. Large individual animals and
plants (e.g., birds and trees) operate within spatial scales of meters to tens of kilometers and a
temporal scale of months to tens of years or centuries. The effects of chemicals on populations
of organisms usually can be assessed only within a temporal scale of months to years and a
spatial scale of meters to kilometers. The populations do not live in an environmental or
biological vacuum, however. Direct chemical effects on one or more populations may, in turn,
affect other populations in the exposed ecosystem. Such effects may occur indirectly through
changes in habitat availability or in predator-prey or competitive interactions with other species.
There is, therefore, a significant overlap in spatial and temporal scales for the assessment of
chemical effects on population dynamics and on ecosystem dynamics. Yet, because additional
secondary or indirect effects may occur over longer time scales and therefore may spread to
larger areas, the range of spatial and temporal scales for evaluating chemical effects on
ecosystems is broader than for the assessment of effects on populations alone (i.e., years to
hundreds of years and meters to hundreds of kilometers). The spatial scale of regional responses
to chemical exposure overlaps with but extends beyond that of individual exposed ecosystems
(hundreds of kilometers to thousands of kilometers). The temporal scale of regional dynamics
also overlaps that of the component ecosystems but may be longer than that for any one
ecosystem in the region (tens of years to hundreds of years). Spatial and temporal scales
associated with ecological effects at the various levels of biological organization are also
discussed by Sheehan (1984a,b,c, 1991) and Suter (1993).
As noted in chapter 4, exposures to chemical and physical stressors also occur over a
range of spatial and temporal scales. Clearly, the scales of exposure need to be related to the
scales of biological organization in order to assess the risks of ecological effects. Single local
applications of pesticides often occur on a spatial scale of meters in homes and gardens to
perhaps a kilometer in agricultural settings over a temporal scale of hours to a few days.
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Chemical and petrochemical spill events occur on a somewhat larger spatial scale, but the
residual contamination from these spills may pose exposure on a temporal scale of days to years.
Chemical releases from hazardous waste sites and aqueous effluent discharges occur on a spatial
scale similar to spill events, but exposures from these sources occur over longer temporal scales
of months to tens of years. In contrast to these small-scale chemical releases, broad-scale
applications of pesticides, such as aerial spraying to control grasshoppers in the prairie regions of
the United States and Canada (Sheehan et al., 1987) and the spruce budworm in the forests of
New Brunswick (Mitchell and Roberts, 1984), covered hundreds to thousands of kilometers in
space and occurred intermittently over years to tens of years. At the extreme, widespread aerial
transport of photochemical oridants and acids has led to stresses in large geographical regions
(thousands to tens of thousands of kilometers) as reported by Shriner et al. (1990). Cumulative
exposures to airborne chemicals (and aqueous effluent discharges within watersheds) also take
place over long time periods (tens to hundreds of years). A more detailed discussion of the scale
of chemical hazards can be found in Suter (1993) and Sheehan (1993).
2.2. Linking Stressors to Effects on Individuals and Populations
Most methods for characterizing the ecological effects of chemical and physical stressors
and most applications of these methods for assessing ecological risks have focused on
populations, while recognizing that causal processes operate through individuals. Although a
focus on the individual in ecological systems is analogous to the focus on the individual in human
health assessments, the utility of individual-based assessments is more limited in ecotoxicology.
The status of individuals is clearly of importance in the case of threatened and endangered
species. In most other situations, however, it is the population that is the persistent biological
unit and the focus of environmental protection.
The effects of stressors are often first manifested in individual organisms, or in an
individual's ability to reproduce successfully. Thus effects on populations are an integration of
the stressor effects on the performance of individuals. The significance of the effect on one level
of biological organization is typically assessed at the next higher level. For example, effects on'
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individuals are biologically significant if they are lethal, or if they reduce the reproductive success
of the organisms in replacing themselves in the population. Also, effects on populations are
biologically significant if they substantially reduce the natural abundance and/or alter the
distribution of the population so as to reduce its functional role within ecosystems.
Laboratory tests for characterizing the effects of stressors on macroorganisras are tests in
which the effects are expressed through the actions of individuals of a sampled population, even
though the outcome is expressed (e.g., as an LC^) as a population effect. Only large-scale
laboratory tests on microorganisms are likely to be considered direct population-level tests.
Therefore, a key issue in effects assessment is the extrapolation of test data for individuals to
predict effects at the population and ecosystem level. The importance of field studies in
characterizing the effects of stressors on higher levels of biological organization is reinforced by
our inability and lack of resources for conducting population-scale studies in the laboratory.
2.3. Linking Stressors to Effects on Ecosystem Structure
The structure of an ecosystem is defined by the abundance and biomass of populations
and their spatial, taxonomic, and trophic organization. Integrated responses of the populations
in an ecosystem to a stressor will be reflected in alterations of ecosystem structure. These
alterations may include reduction in population size or eventual extinction, reduction in species
richness, alteration in species dominance and diversity, and alterations in spatial structure.
Although such changes in ecosystem structure may reflect exposure to a particular stressor, they
do not necessarily forecast the internal mechanisms by which the change is expressed.
The induced changes in structural properties are the changes of primary interest in
characterizing effects on ecosystems, rather than on the specific structure of the ecosystem itself, •
although, obviously, this is ecologically important as well. To assess change, an appropriate set
of structural references is essential. Baseline data on an ecosystem prior to stressor exposure can
provide an ideal control for comparisons. Such data are seldom available, however. A time
series of changes in structural indices after introduction of a stressor also would reflect effects on
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ecosystem structure. Reductions in numbers, biomass, and taxonomic and trophic diversity
indicate a short-term disruption in equilibrium conditions. Conversely, increases in these indices
with time would suggest at least partial recovery of the system. If baseline data are unavailable
(as is frequently the case), changes still can be monitored over time. Obviously, however, no set
of values would be available through which to quantify an undisturbed ecosystem reference case.
Still, this approach can be used to monitor changes in conjunction with stressor abatement.
Some ecologists have argued that structural indices best meet statistical criteria for the
monitoring of ecosystem response to toxic substances. These indices do not have the daily
periodicity of primary productivity or the short-term variability associated with respiration.
Ecosystem structure, however, is only loosely related to ecosystem function. Ecosystem structure
(e.g., diversity and dominance patterns) may change significantly under stress with no
accompanying disruption of functions (e.g., productivity), or, inversely, function may be altered
without significant changes in composition and diversity (Matthews et al., 1982). The lack of a
predictable relationship between structural and functional responses to stress suggests the need
for a balanced approach considering both structure and function in assessing stressor effects at
the ecosystem level.
Structural characteristics and the numerical indices dependent on them provide a range
of information differing in ecological value. For instance, it is more informative to know about
changes in the taxonomic or trophic composition of an exposed ecosystem than merely the
changes in biomass or the general abundance of organisms. Also, structural indices do not
necessarily follow similar patterns of change under different types of induced stress. Hellawell
(1977) described several possible alterations in an ecosystem that would be reflected in biomass
only; in biomass and relative dominance; or in biomass, dominance, and composition. Because
of the differences in (1) the value of information provided by these indices, (2) the ease with
which they are measured or calculated, and (3) the sensitivity of their response to stress, the
selection of an index of ecosystem structure to assess ecological risks should be conducted
thoughtfully and be based on the objectives of the assessment and the sensitivity, general
applicability, ease of measurement, and ecological meaningfulness of the index.
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2.4. Linking Stressors to Effects on Ecosystem Function
During the 1970s and 1980s, a series of papers began to show that ecosystem regulation,
like cellular and organismal regulation, is achieved often through the activity of small but critical
constituents in ecosystems functioning hierarchically (Shugart et al., 1976; Loucks, 1985; O'Neill
et al., 1986; Vanni et al., 1992). External stressors that subtly alter these regulatory agents and
rates magnify the linkage and the effects of the stressor on system function (Sheehan, 1984c).
The series of case studies reviewed in the report Issues in Risk Assessment (NRC/NAS, 1993)
shows the prospect of using our preliminary understanding of linkages within ecosystems to
assess risk attributable to the alteration of feedback rates in ecosystems.
The complexity and incompleteness, to date, in theoretical and empirical foundations for
predicting stressor linkages is especially evident in the population and ecosystem-level effects
literature reviewed by Suter (1993). The species providing specific ecosystem functions as well as
their sensitivity to stressors (e.g., soil fauna functioning in nutrient and organic matter cycling)
are largely unknown and are only now beginning to be investigated quantitatively. A kind of
experimental "sensitivity" analysis may be needed in large-scale field studies, however, to validate
the patterns of stressor effects on ecosystem processes and function.
15. Stressor Effects on Ecological Pulsing and Stability
Ecological systems are understood now as usually having some yearly, shorter, or longer-
term pattern of fluctuation or pulse. While the yearly pulses are often satisfactorily understood
in aquatic ecosystems, they are not the same each year and the significance of natural deviations
from the average pattern is not understood. Each yearly or longer pulse induces a transient
biological response that over short periods or in local areas may be viewed as a temporary
instability in which resilience operates to restate the system. Over the long term, or as a large-
area average, many such transients can be incorporated as part of the functioning of a regional
ecosystem with appropriate properties of stability. This average will have certain characteristics
of equilibrium conditions, but in the presence of external or anthropogenic stressors the
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processes controlling fine-scale perturbations and transients can be changed and the risk of
destabilizing the entire system may be high. Transients outside the range of resilience for the
system can be induced, creating a new, potentially degraded and irreversible equilibrium. The
introduction of undesirable alternate stable states, and possibly technically chaotic responses, is
probable under some circumstances. Our understanding of these risks, however, is still too
incomplete to prescribe a standard methodology.
Pulsing and stability characteristics need to be considered in terms of a probability
distribution of some form. Since the transition to a new equilibrium may run for several
decades, the transition period (i.e., during which the ecosystem is not in equilibrium, a period
that the risk manager may be confronting now) should be viewed as a time of destabilization for
which the present science can provide little or no predictive capability. Since so many of our
terrestrial wetland and aquatic resources are already in a transient response to environmental
changes, research on theory and models for characterizing risks to non-equilibrium systems
should be given a high priority.
Before trying to detail the role of anthropogenic stressors in these systems, however, the
full range of natural stressors that induce fluctuations and long-term, natural trends must be
quantifiable first. Although this is beginning to be feasible for freshwater environments, it may
be a long way off for most terrestrial systems considering that interactions between drought,
insect, and disease outbreaks for the various age classes of forest are not well understood even
for natural unstressed systems. The effects of stressors, while likely to change these natural
interactions in some direction, may be beyond quantification for risk assessment purposes—at
least at this time.
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3. EFFECTS CHARACTERIZATION
Effects characterization is the qualitative and quantitative description of the relationship
between the stressor and response (effects) in the exposed individuals, populations, or
ecosystems. This section address endpoints, types of effects data, and evaluation methods.
3.1. Endpoint Context
Endpoints are formal expressions of the environmental values to be protected (Suter,
1989), and these endpoints must be explicitly defined in assessing ecological effects. The
response of the ecosystem to the stressor is assessed in terms of these endpoints. Establishing
endpoints requires identifying valued attributes of the ecosystem that are considered to be at risk
and then defining these attributes in operational terms (Suter, 1993). Since generally regulatory
statutes broadly define the environmental attributes to be protected, more specific operational
definitions of endpoints are essential to a useful risk assessment. Without clear definitions,
endpoints provide no clear direction for testing and modeling. For example, a general goal such
as "ecosystem health" is inadequate for risk assessment.
Suter (1993) has identified five criteria that any endpoint should satisfy:
• societal relevance,
• biological relevance,
• unambiguous definitions,
• accessibility to prediction and measurement, and
• susceptibility to hazardous agents.
Societal relevant indicates that the ecosystem attribute should be understood and valued
by the public and by decision-makers. Since we cannot practically study all species or ecosystem.
properties in exposed ecosystems, focusing on endpoints with societal relevance makes good
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sense. This approach also best serves the managers of environmental quality and biological
resources, who require a similar focus to be successful.
As stated previously, the biological significance of effects on an endpoint at one level of
biological organization often are measured by subsequent impacts propagated at higher levels.
For example, a change in the survival or reproductive success of individual organisms is
significant if it affects the abundance or distribution of the population, and a decrease in
population abundance is significant if it affects the structural or functional relationships in the
ecosystem. Because of functional redundancy and other compensatory relationships in
ecosystems, not all changes to populations necessarily affect ecosystem function. Therefore,
endpoints may be identified that have societal relevance but little biological significance (e.g., the
loss of a small number of redwood trees). Conversely, endpoints with biological relevance may
not be perceived as important to society (e.g., the loss of a keystone species such as the star fish
in intertidal habitats). Clearly, endpoints with both biological and societal significance should
receive the utmost consideration for characterizing ecological effects. An expanded discussion of
the ecological significance of effects of stressors is presented in chapter 2.
Finally, if the response of the endpoint to a stressor cannot be measured or estimated
from the measurement of related responses, or if the endpoint is insensitive to the stressor, then
the endpoint cannot be assessed. The best endpoints are those that are sensitive to the stressor
and for which there are well-developed test models and field-measurement techniques.
There are two types of endpoints: assessment endpoints and measurement endpoints.
Assessment endpoints are the characteristics of the ecosystem to be protected. They are often
defined at relatively large scales and therefore are not easily measured directly (e.g., tree
production in forests or fish production in the Great Lakes). Measurement endpoints are the
laboratory and field data that are extrapolated to characterize the assessment endpoints that
cannot be measured directly (e.g., LC^ data for a fish species, or growth rate data for a specific
tree species under well-described greenhouse or field-exposure conditions). Examples of
assessment and measurement endpoints are presented in table 1.
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Characterizing the effects of stressors requires the identification of explicit assessment
and measurement endpoints and methods to extrapolate from measurement to assessment
endpoints. To facilitate the comparison of effects from different stressors, or to integrate the
effects of multiple stressors, comparable endpoints must be used. For example, the effects of
exposures to physicochemical and biological stressors can all be assessed in terms of changes in
population recruitment or abundance, but cannot be assessed in terms of chemical LCjoS since
there is no equivalent measure for physical or biological stressors.
32. Type of Effects Data and Study Methods
The types of responses that can be used to characterize the effects of stressors on
individuals, populations, and ecosystems are reviewed in Moriarty (1983), Sheehan (1984a,b,c),
U.S. EPA (1988a), and Suter (1993), and summarized in table 2. The projected change (i.e.,
either an increase or a decrease) in response associated with the magnitude of the stress regime
is what interests the risk assessor. A detectable change in an endpoint that indicates an
r alteration in the ecosystem is an "effect"; however, the biological and social significance of the
. change (i.e., whether it is an "adverse" effect) can only be judged with respect to the viability of
the exposed individuals or success of the populations or functioning of the ecosystem and the
value society attaches to the amenities changed.
Data with which to assess the effects of stressors come from three sources: controlled
laboratory and field studies, studies in stressed and matched reference (unstressed) ecosystems,
and studies associated with environmental accidents (e.g., oil spills). Methods for measuring the
t
effects of chemicals on aquatic organisms (Persoone et al., 1984; Waldichuk, 1985), terrestrial
animals (Dobson, 1985), and aquatic and terrestrial plants (Calamari et al., 1985; Kozlowski,
1985) were reviewed in the 1980s. A variety of raultispecies tests also were reviewed during, this
same period (Hammons, 1981; Cairns 1985,1986).
Change in the exposed system is measured with respect to a baseline or some reference,
such as the untreated control in a toxicity test or an uncontarainated but similar stream, lake,
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forest, or watershed. That an effect can only be demonstrated by comparison with a control is
one of the principles of effects assessment (Green, 1979).
Several types of measurements may be collected to assess a single assessment endpoint.
For example, to assess the effects of a chemical on the reproductive success of a species, one can
measure the time to sexual maturity, the number of young per reproductive event, the number of
reproductive events per time interval (or lifetime), or the viability of the young produced.
Although data on any one of these measurement endpoints may indicate a reduction in the
reproductive success of the organism as a result of exposure to the chemical, concordance among
data for several measurement endpoints is important for reducing uncertainty in characterizing
the stress regime-response relationship.
3.2.1. Individuals
Attributes that reflect individual performance and are the most easily related to
population fitness are growth, survival to normal life span (vs. early mortality), and reproductive
success. These individual response endpoints are shown in table 2. A review of the effects of
chemicals on individual performance was provided by Sheehan (1984a). Tests to evaluate the
toxicity of chemicals to individual organisms were recently reviewed by Suter (1993).
Growth is the net result of many essential processes, such as consumption and
respiration. As a summation of many factors, it is a useful integrated index of physiological
status, applicable to multicellular organisms that have not yet reached their maximum biomass.
Cell division rate in unicellular organisms is a useful measure of both growth and reproduction.
A reduced rate of growth has been correlated with a reduced rate of survival in young birds
(Street, 1978) as well as the inability to reach sexual maturity and reduced reproductive success
in a number of species (Buikema et al., 1980; Krapu, 1974,1979; Donaldson and Scherer, 1983).
Growth of individuals is measured in toxicity tests for larval fish (U.S. EPA, 1988b, 1989a), some
algae (U.S. EPA, 1990), and aquatic vascular plants (U.S. EPA, 1982). Although growth is also
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an important index of performance for terrestrial vertebrates, standard testing procedures
emphasize mortality and reproductive effects over growth and developmental responses.
Reproduction is the single most important function in the life cycle of an organism.
Successful reproduction is essential to population recruitment and the continuation of the
species. Therefore, the real test of long-term impact of sublethal stress on exposed individuals is
whether the individual is capable of reproducing successfully. As a means of ensuring
perpetuation, certain species have evolved a strategy of shunting a larger than normal proportion
of available energy into reproduction under stressful environmental conditions (Bayne, 1975).
The importance of reproductive damage to species survival has stimulated much current
research. An evaluation of methods to assess the effects of chemicals on the reproduction
function of a number of wild mammalian and nonmammalian taxa recently was prepared (Vouk
and Sheehan, 1983) and should be of value in improving the assessment of reproductive damage.
Reproductive failure for sexual species can occur during a number of processes: courtship,
development of gametes, fertilization, embryo development, hatching (birth), and early growth.
Since the reproductive process encompasses all life stages, its successful completion is a basic
individual goal. The inability of an organism to successfully complete any one stage of the
reproductive process would indicate a reduced reproductive fitness of the population.
As an endpoint, mortality (i.e., shortened life span) can be quantified readily in
laboratory studies and in situations of gross field exposures. The value of recognizing what
concentration/dose of toxic substances or magnitude of physical stress regime can cause a lethal
response in an individual organism is obvious, since shortening the normal life span of individuals
(particularly a large number of individuals) can affect population abundance and reproduction
potential. Mortality is an endpoint that can be measured for all life stages and species that can
be tested. LCj,, and LD^ data are the most commonly reported toxicity values (for populations).
While they provide a substantial base of effects information for ecotoxicologists, these effects
data are based on short-term laboratory tests with a limited number of species and chemicals.
There is considerable uncertainty associated with extrapolations from toxic effect to "no effects"
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concentrations, from surrogate to native species, and from laboratory to field conditions. These
extrapolation issues are discussed further in section 6 of this chapter.
Chronic toxicify tests for aquatic organisms include full life-cycle tests (egg to egg), partial
life-cycle tests (adult to juvenile), early life-stage tests (egg to juvenile), and "short-term chronic"
tests (eggs and larvae or just larvae). Several reviews of full life-cycle and other partial life-cycle
tests have suggested that both provide similar estimates of "safe" concentrations if growth or
reproductive success are assessed along with mortality (McKira, 1977; Macek and Sleight, 1977;
Weltering, 1984). The standard endpoint of chronic tests on fish has been the maximum
acceptable toxicant concentration (MATC), more recently termed the chronic value by the U.S.
Environmental Protection Agency (U.S. EPA, 1989a). The MATC is derived by hypothesis
testing and has been criticized for not providing a consistent level of protection (Stephan and
Rogers, 1985; Suter, 1993).
Full life-cycle and partial life-cycle (sensitive life stage) test data are available for few
wildlife species or surrogates. For terrestrial mammals, data are largely limited to that generated
for domestic mice and rats as a result of carcinogenicity bioassays. For birds, data exist for
embryo toxicity (Hoffinan and Albers, 1984) and reproductive toxicity (U.S. EPA, 1982; ASTM,
1991), but for very few species. Chronic tests to establish chemical dose-response relationships
for birds are largely limited to ducks and quail. Almost no dose-response data exist for raptors
and piscivorous birds that are the focus of many current food web risk assessments. The
threshold for significant effects on avian reproduction is calculated using hypothesis testing
statistics in a manner similar to the MATC in fish.
A number of ecotoxicologists have suggested that a global index rather than a single
endpoint is the preferred expression of toxicity test results for individuals (McKira et al., 1976;
Eaton et al., 1978; Javitz, 1982; Suter, 1993). Such an index is the weight of young per female,
which is a function of survivorship in each life stage, fecundity, and growth.
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3.22. Populations
The population is the persistent biological unit and is the focus of most ecological risk
assessments. Population endpoints include changes in abundance, distribution, recruitment, age
structure (size distribution), and genetic composition. The population endpoints and methods for
their measurement are shown in table 2.
The premature death and reduced reproductive success of individuals are ultimately
reflected in lower recruitment and abundance and altered distribution of the exposed
populations. Abundance is the most commonly assessed population endpoint; however, it is not
the easiest to interpret. Abundance is a dynamic property that may change dramatically during
annual cycles or time cycles of other duration. Most point estimates of abundance provide only a
"snapshot" of population size that can be compared to a pre-stress baseline or reference
abundance level. Characterization of abundance changes attributable to stress regimes also must
take into account other factors that influence abundance but are not related to the stressor(s) of
interest. These may include immigration rate, emigration rate, predation rate, harvesting rate
(hunting or fishing mortality), and food availability.
Population abundance estimates are generally based on field counts of population
numbers. Because only in rare cases can all members of a population be counted, accuracy and
precision of abundance estimates depend on the sampling design used to collect these data. For
organisms with multiple life stages, sampling all of the life stages is essential to estimating
population abundance accurately.
Recruitment success is a clear indicator of the ability of the population to replace itself.
Recruitment is a particularly important endpoint for assessing stressor effects on birds and
mammals, where the number of young produced per female is small in comparison to other
species. The number of juveniles reaching sexual maturity per parent is a measure of
recruitment in avian and mammalian populations. Again, recruitment success in stressed
populations is generally assessed with field studies of the number of individuals reaching
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maturity. An analysis of age/size distribution in macroinvertebrate and fish populations provides
data on the size of the juvenile population approaching sexual maturity.
Stressor alterations of the population gene pool have been reported but are more difficult
to quantify than other population endpoints. In addition, the significance of changes hi generic
composition are difficult to judge within a short term.
3.2.3. Ecosystem Structure
Several existing reviews have documented the principal features of ecosystem structure
required in risk assessment (Smith, 1981; Sheehan, 1984b; Waide, 1988; Bartell et al., 1992).
Others (e.g. Kitchell, 1992) have described these subjects in terms of community structure,
including such concepts as top-down control and "cascading." Although communities and
ecosystems are fundamentally rather different concepts, multispecies population ecology and
species-sensitive ecosystem function research have lessened the need for explicit consideration of
community-level effects. To this end we will consider community and ecosystem structure as
equivalent concepts insofar as they serve as elements in understanding effects of stressors on
larger-scale systems.
A number of the endpoints used to assess effects on ecosystem structure are presented in
table 2. These include loss of populations, alterations in diversity and dominance relationships,
changes in abundances or biomass, alterations in spatial structure, and changes in stability or
fluctuation patterns.
To understand what might be a significant, induced departure from "normal" ecosystem
(or community) structure, one has to have considered the relationship between the means of
measurement (and variability over short periods) and their relationship to spatial and temporal
scale (see Suter, 1993). Let us say it is sufficient for now to note that small-scale variability in
responses (and in predictive capability) can be incorporated in larger-area and longer-term
averaging. The goal in risk assessment is to estimate how a Stressor may have altered local or
short-period responses (e.g., fruiting, fires, or flooding), or the spatial distribution of these
events, so as to bring about a change in the dynamics or mean endpoint expression in the larger-
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scale system. The remarkable range in the time scale of important responses is summarized in
table 3.
Another component of ecosystem structure that is readily affected by anthropogenic
stressors is change in the demographics, life span, and pattern of senescence in long-lived species
such as trout in large lakes and certain forest trees (Beeton, 1969; NRC/NAS/RSC, 1985; Smith,
1981; Pedersen and McCune, 1990). These effects can lead to changes in population abundances
(or in community composition) that represent a significant alteration in ecosystem functioning.
Examples include changes in water clarity and nutrient cycling, in leaf production and leaf-litter
quality, and in decomposition or other critical rates that affect mineral cycling and growth
(Smith, 1981). Thus, because over long periods of time these anthropogenically induced changes
in relative species dominance not only alter the structure of ecosystems but also their
functioning, they are capable of inducing a new, potentially irreversible equilibrium in nutrient
processing and related food chain productivity.
The effects of external stressors and their implications for biological diversity of
ecosystems is a special problem (Barker and Tingey, 1992). Conventional measures of change in
diversity (concentration of dominance or equitability measures) are not as useful as is often
assumed (Armentano and Bennett, 1992). Winner and Bewley (1978) and others have shown
that a "diversity index" is insensitive for air pollution effects on species except under the most
severe stress, while relative cover and the number of species per quadrat are reasonably sensitive.
New approaches to the measurement of biological diversity that help to characterize ecosystems
are needed. Local species extinctions are relatively common and probably significant in inducing
changes in ecosystem productivity or function. "Missing species" are difficult to quantify,
however, and means for observing their effects on ecosystem processes are only now
being developed.
The effects of chemical stressors on ecosystem structure have been assessed with
multispecies microcosm and mesocosm tests (reviewed by Cairns, 1985,1986) and field studies of
structure in exposed and reference systems (reviewed by Herricks and Cairns, 1982).
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3.2.4. Ecosystem Function and Regulation
As previously suggested, the larger-scale and longer-term responses of ecosystem
endpoints must—if causal mechanisms and predictive capability are to be recognizable—
incorporate the altering of processes at the population and raicrobial level. Ultimately, the fine-
scale changes must be reflected in projections of effects on endpoints such as net primary
production (NPP) or net ecosystem production (NEP). For example, ozone (Oj) effects on
forests are probably important for their altering of certain productivity endpoints. Yet these
effects can be estimated only partially from the known relationships between Oj concentration
and tree growth, by species and age class. Similarly, the productivity of rivers with a high
sediment load is affected by the shading effects of sediment on photosynthetic rates, affecting
each part of the food chain differently. The means for making computations of effects at the
functional or system regulatory (feedback) level and for aggregating them to the level of
significant large-scale endpoints are developing slowly. The approaches must be expanded
greatly and tested, however, before guidelines for their use in risk assessment can become
operational.
In addition to NPP and NEP, another important component of ecosystem functioning
.that is altered in many species is formation of secondary metabolites such as phenol glycosides.
Such compounds are critical in affording resistance to disease and insect attack; many stressors,
however, such as O, and acid gases taken up in foliage, substantially alter the formation of
secondary metabolites (Coleman et al., 1992). The outcomes of these interactions are not yet
fully predictable, partly due to the very large number of insect and disease agents for which
various resistance mechanisms exist. The likelihood of major change in ecosystem response
patterns operates through such mechanisms, however. Further work also is needed on how
agents such as the ammonium ion in rain water may affect resistance by vertebrate groups (such
as amphibians) to bacterial infections. Changes in the availability of carbon (C) relative to
nitrogen, either by the altering of net photosynthesis rates (e.g., through O, effects and carbon
dioxide [CO2J enrichment) or by increasing the availability of nitrogen (N) in the nutrient cycle
(rainfall inputs) can affect the C:N ratio of primary metabolites (Coleman et al., 1992). This
affects production of the largely carbon-based secondary metabolites and, therefore, insect or
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disease resistance (especially following droughts). These processes may account for the
emergence of the new concept of "opportunistic pathogens" among forest trees (Sinclair et al.,
1987). The prospect that even a few native diseases can take advantage of some stress-induced
I
reduction in disease resistance, and thereby cause extensive damage to resources such as forests,
introduces a significant new dimension of risk assessment for which research is only beginning.
Although abundant data are available on nutrient cycles in ecosystems, the risk assessor
should be concerned primarily with the likelihood of changes being induced in nutrient cycling.
Changes in the cycling of N, phosphorus (P), potassium (K), calcium (Ca), or magnesium (Mg)
can result from apparently small shifts in ecosystem function. Some of these may be the result of
selective effects by the stressors on key species (e.g., on soil fauna or fungi), as well on the
physicochemical soil system. The potential for changes in the magnitude of the nutrient pools
(or in their turnover times) needs to be given special attention, as was done in the work of Finn
(1976) and Watson and Loucks (1979). These papers show highly characteristic patterns of N
and P turnover in ecosystems. Many of the nutrient pools (e.g., dissolved inorganic P) have short
turnover times (minutes) and are highly susceptible to stressors that then have a radical effect on
the whole system. Risk assessment must focus on the likelihood of changes in these rates and
turnover times (which are poorly known in terrestrial systems), rather than on the gross stocks
and fluxes that have been the focus of most nutrient cycling studies.
Finally, an increasingly common measure of "system function" status for ecosystems is the
Index of Biological Integrity (IBI), a measure of both food web connectedness and diversity
(Miller et al., 1988). Despite several major efforts, however, IBI has not proven to be consistent
enough in its definition for broad application across very different types of ecosystems, or even
for comparing degraded systems where it is uncertain that some of the desired linkages ever
existed. IBI is in the nature of a potentially useful, highly integrative endpoint, but in its present
form it falls short of having most of the features desired in an endpoint, at least for site-focused
assessments.
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3.3. Evaluation Methods
Effects data can be evaluated on several levels to address specific questions. First, are
the data of good quality? Second, are the data appropriate for the endpoint being assessed?
Third, are the data representative of the individuals, populations, or ecosystems of interest?
Fourth, are the data sufficient to meet statistical model requirements? Each of these data
evaluation concerns is addressed in the following sections.
3 J.I. Methods to Evaluate Data Quality
Laboratory stressor-response studies and field effects characterization studies should
incorporate quality assurance procedures to assess data quality. These may include culture
controls to evaluate test organism health, reference controls to evaluate organism response to an
environment without the stressor of interest but with other important environmental factors, and
positive controls to evaluate response of the test organism to a single stressor in comparison with
historic test results with the same stressor. The quality of effects data can only be judged with
respect to study controls. Therefore, the use and comprehensive characterization of laboratory
and field control samples is key to the quality assurance of effects data.
3-3.2. Matching Data and Measurement Endpoint to the Assessment Endpoint
The more closely related the measurement endpoint is to the assessment endpoint, the
less the uncertainty in extrapolation of the effects data in the risk assessment In contrast, the
more remote the effects data from the assessment endpoint of interest, the more uncertainty in
the risk assessment. This is a problem evident in the greater use of acute exposure LQ,, or LD,,
data rather than EC10> a Lowest Observed Effect Level (LOEL), or a No Observed Effect Level
(NOEL) for predicting no effect concentrations for species of interest in risk assessment.
Barnthouse et al. (1990) showed that for fish, effective concentrations for chronic responses can
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be predicted to no greater than two orders of magnitude precision in the absence of life-cycle or
partial life-cycle data for the species of interest.
Similarly, the more closely related the test species to the species of interest, the less the
uncertainty in the risk assessment. This is based on the assumption that a species can represent
the taxon to which it belongs, but that the uncertainty associated with prediction from species to
species increases with increasing taxonomic distance. This generalization is supported for aquatic
animals by the work of Suter et al. (1983), Sloof et al. (1986), and Mayer and Ellersieck (1986).
The generalization that similarity in toxic response is related to taxonomic similarity will not
likely hold if there is not a gradient of the traits controlling sensitivity to the stressor. This would
be true either when all species evolved a similar adaptation to a stressor or when all species were
equally unprepared to resist a stressor.
3JJ. Representative Data
Although a substantial amount of data may be available for an area of concern, the data
may have been collected for objectives other than risk assessment and thus its usefulness in a risk
assessment may be limited. This is true of the qualitative biological survey data collected for
environmental impact reports and one-time monitoring programs that lack the quantitative
properties to be useful in an assessment of stress regime-response relationships.
Similarly, toxicity test data where the exposure methods bear no relationship to potential
environmental exposures are of limited usefulness in effects assessment. Included are data from
tests based on chemical exposures via injection and other unnatural routes of uptake, which may
be useful in determining the mechanism of toxicity but not for determining a relevant dose-
response relationship. Equally concerning is the use of data from laboratory or field studies on
species unrelated to those of concern in the risk assessment, given the high degree of variability
in stressor response among species, particularly species that are not taxonomically and
functionally related as previously described. A related problem are data collected from improper
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sampling designs. Biased sampling, or collection of samples of improper type or size, will
provide unrepresentative estimates of population parameters.
3 J.4. Methods to Evaluate Data Sufficiently for Statistical Models
Two types of statistical models are used to characterize stress regime-response
relationships: (1) hypothesis test models and (2) curve-fitting models. In the hypothesis test
model, responses at the exposure concentrations are compared with control responses to test the
null hypothesis that they are the same. In the curve-fitting model, a function is fitted to a set of
points relating the measured effects to measurements of exposure based on the hypothesis that
the slope of the relationship is not zero. These two models are used for different objectives: the
hypothesis test model to assess differences in response between treatment and control, and the
curve-fitting model to prescribe a level of effect within the range of exposure measured. Neither
model is designed to make predictions outside of the range of test data. To extrapolate
accurately to effects below the stressor levels tested requires a mechanistic model based on the
mechanism of action rather than a statistical model.
The discriminatory power of both statistical models is dependent on the number of
samples available for inclusion in the analysis. For the hypothesis test model, the power of the
test to avoid false positives (i.e., identifying an effect when no effect exists) and false negatives
(i.e., not identifying an effect when an effect exists) is related to the variability in test (sample)
data, the minimum level of discrimination desirable (e.g., 10 percent difference between
treatment and control means), and the allowable level of false positives and negatives.
The sufficiency of data to support a test of selected power and confidence, or the
evaluation of the power of a hypothesis test based on collected data, can be determined from
various power law models (Green, 1979). Power analysis can be used to evaluate the robustness
of the test data to detect effects at one point in time or to distinguish trends and directions in a
time series of data.
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3J.5. Methods for Evaluating Large-Scale and Lang-Term (Regional) Affects
Because of the incomplete understanding of how to aggregate from species and local-
scale effects to long-term regional effects, information and guidance is still developing and, for
*
the time being, must be used provisionally through case studies. Probably the best documented
case, reflecting progress in methods but also problems in the assessment of causal mechanisms, is
the work done on understanding changes in Great Lakes fisheries (Eshenroder et al., 1991). A
key issue is whether the decline in traditional, self-reproducing fisheries is attributable to
overexploitation of these top predators, to enrichment (and related food chain effects), or to the
regional toxic burdens acting on sensitive stages in reproduction. All of these alternatives are
explored in the 1985 review of the Great Lakes Water Quality Agreement (NRC/NAS/RSC,
1985), and all are judged to be contributors in different degrees in each of the major lakes.
Determining the stressor magnitude was difficult in each case, as was documenting either a
theoretical or empirical causal mechanism for the effects: impairment of stock replacement by
overexploitation; impairment of reproduction through toxic effects; or change in aeration of the
cold bottom-water habitat attributable to increased decomposition demand following enrichment
(NRC/NAS/RSC, 1985). Risk assessment is obviously hampered when even detailed
retrospective studies have difficulty attributing outcomes to one or another causal mechanism
operating on a large scale.
Another example that is still being debated is the region-wide effect of acid rain and
oxidants on U.S. forests, especially in the Northeast, the Southeast, and in southern California
(Shriner et al., 1990). The problem is affected by the multiplicity of pollutants involved,
differences in dose, great differences in species sensitivities, the evidence of previous declines
(which may or may not be due to a quite different "cause"), and the great difficulty in
extrapolating from seedling tests to long-term whole-tree effects. Region-wide effects
characterization also is limited by the absence of a consensus on methods for landscape-scale
effects (Sheehan, 1993).
One final example of an evaluative approach at the large scale is that developed through
the U.S. Environmental Protection Agency (EPA)/Science Advisory Board (SAB) study of
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priority problems, the most serious apparently being large-scale, large-impact, high-uncertainty
problems that are not readily quantified (Harwell et al., 1992).
3.3.6. Balancing Local-Scale/Small-Risk Problems with Good Data in the Same Assessment with
Large-Scale, Potentially Large-Risk Problems Having Poor and Insufficient Data
The approaches described in previous sections indicate a methodology in which we have
some confidence when the stressor is a single, known chemical acting on defined life stages of a
known species or population. The tributyltin case study is a useful example (NRC/NAS, 1993).
This and five other case studies at larger scales are illustrated in table 4. The challenge in large-
system risk assessment, however, frequently requires that we consider multiple chemicals (as in
the Great Lakes) circulating over a large area and potentially affecting many species in a wide
array of poorly known ecosystem functions. Changes in systems at these scales obviously affect
many more people and their commerce than most single-species effects, although large-system
effects mostly begin with single-species effects.
When the multispecies system is poorly understood, we do not know whether single-
species information can be combined satisfactorily through large-area or long-term averaging.
One obvious option is to consider refraining from performing a risk assessment when the
uncertainties inherent in large systems are apparent. Does one then treat that part of the system
for which data are available as the major component of the effects characterization problem?
Probably not if the risk assessment is to have credibility. It is possible that the mechanistic
approach is inappropriate, for reasons discussed by Bowlatabadi and Morgan (1993) in relation
to assessments of the risks from climate change.
The principal institutional response for situations where long-system assessments may not
be possible should be to seek new, appropriate data, often for large areas and/or long periods.
Some assessments may have to be postponed or methods may have to be developed using broad
judgments of potential risks, at least until better information and better tools become available.
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4. ECOLOGICAL RESPONSE ANALYSIS: STRESSOR RESPONSES
A number of the direct and indirect effects of chemical and physical stressors on
individuals, populations, and ecosystems have been quantified. Several examples are presented in
this section.
4.1. Chemical Stressors
Although the effects of chemicals on individual organisms and populations have been
widely studied, chemical effects on ecosystems are less well documented.
4.1.1. Direct Effects of Chemical Stressors
The effects of substantial exposures of a variety of chemicals on sexual maturation and
gamete development have been reported for fish (Donaldson and Scherer, 1983), amphibians
(Martin, 1983), and invertebrate taxa (Dixon, 1983; Davey et al., 1983). Reduced egg
production, reduced hatch success, and the reduced size of brood per female and the viability of
hatchlings have been documented for substantial chemical exposures to birds (e.g., Peakall,
1983), fish (e.g., Birge et al., 1979), and aquatic invertebrates (e.g., Hatakeyama and Yasuno,
1981). In field studies, Schofield (1976) observed a reduction in the reproductive success of
freshwater fish in low pH lakes.
A variety of laboratory toxicity tests and field studies in highly contaminated
environments have documented increased (above normal) mortality rates for a variety of
vertebrate and invertebrate species at specific chemical exposures. These data have been
reviewed on a chemical-specific basis by the EPA for ambient water quality and health
assessment documents and by the U.S. Fish and Wildlife Service (U.S. FWS) for contaminant
hazard reviews.
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A variety of field studies have shown reductions in the abundance of exposed populations
inversely correlated with increasing chemical contaminant concentrations. Selected studies were
reviewed by Sheehan (1984a,b, 1991). Reductions in population abundance and the complete
extinction of sensitive populations resulting from the stress of chemical exposures are the primary
factors altering ecosystem structure. Such effects may not be totally attributable to toxic
mortality, but may be due also to induced reductions in reproductive success or the abilities of
the organisms to function successfully in competitive and trophic interactions. One of the better
documented trends in population extinction was recorded for fish during the period of
industrially stimulated acidification of poorly buffered lakes (e.g., Muniz and Leivestad, 1980).
Within exposed fish and wildlife populations the incidence of various nonspecific disease
symptoms appears to be associated with chemical exposures in degraded aquatic and terrestrial
environments. Sindermann et al. (1980) reported an increased incidence in ulcers and fin rot in
fish from highly contaminated estuarine waters. Hetrick et al. (1979) reported measured
susceptibility of rainbow trout to an infectious virus after exposure to sublethal levels of copper.
A similar reduction in tolerance to disease and insect infestations has been reported for
chemically exposed plants (e.g., Treshow, 1978).
4.1.2. Indirect Affects of Chemical Stresson
Several major studies, but especially the acid-deposition research sponsored by the
National Acid Precipitation Assessment Program (NAPAP), have begun to document the
importance of indirect effects on organism metabolism largely mediated through changes in water
or soil chemistry. An interesting example is induction of the toxic responses of fish to
raonomeric aluminum in lakes receiving acidity from rainfall (NAPAP, 1989a). The higher
hydrogen ion concentration may induce ionic effects on organisms; however, the more profound
effect is through the solubilization of aluminum, an infrequent, often storm-related phenomenon.
Direct and indirect effects of air pollutants on forests are illustrated in table 5. Most of
the direct effects (e.g., altered seed production, reproduction, nutrient cycling) also can be seen
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as a prospective indirect effect at an ecosystem level (i.e., the change in seed production leads to
changes in species composition, and changes in photosynthesis lead to reduced standing biomass
and increased or decreased insect and disease epidemics (depending on the species). These
results illustrate that even the concept of what is direct and what is indirect needs to be
treated carefully.
Another example of a direct effect leading to an indirect effect is evident when the
acidification of soil horizons (through H+ addition) leads to losses of Ca and Mg from the soil
nutrient pools and the immobilization of P and solubilization of aluminum (Al) at low pH's. In
this case the hydrogen ion concentration, or change in H+, is not itself the proximate cause of P
deficiency or Al toxicity as expressed in plants, but the means by which these two new stressors
are induced (Shriner et al., 1990; Loucks et al., 1993). Obviously, the mechanisms by which
indirect effects are expressed are difficult to discern and quantifiable only through intensive
studies.
Alteration of ecosystem properties also can be induced indirectly through changes in the
.chemical environment of organisms functioning in decomposition processes. Food quality for
.these organisms can be changed, inducing changes in the mix of populations making up the
.decomposition community, in both aquatic and terrestrial environments.
Two recent studies indicate that insecticide-induced invertebrate mortality in sprayed
areas may indirectly affect birds by reducing the abundance of available prey organisms and
destabilizing their food web (Sheehan et al., 1987,1993a; Pascual and Peris, 1992). In both
cases, synthetic pyrethroid insecticides that are highly toxic to aquatic and terrestrial
macroinvertebrates, but pose no toxic hazard to birds, were used in insect pest control, resulting
in substantial nontarget invertebrate mortality. The insecticide-induced shortage in prey
availability was shown to be particularly critical when the birds are obligate feeders on
macroinvertebrate prey.
Herbicides also have been shown to affect bird populations indirectly by reducing
available nesting habitat and protective cover. Dwemychuk and Boag (1973) reported a 74
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percent reduction in the nest density of ducks in an area sprayed with 2,4-D. In contrast, Rands
(1985) demonstrated how cereal-grain-field headlands left unsprayed with herbicide had larger
grey partridge broods than sprayed headlands. The success in unsprayed areas may be related to
the improved cover afforded by the vegetation protecting the partridge broods from discovery
by predators.
4.2. Physical Stressors
Experience with physical stressors as agents in risk assessment is more limited than with
chemical stressors. It includes changes in the hydrology of a site, the introduction or absence of
fire, and the physical manipulation of forests, fisheries, or wildlife population through harvesting.
All of these treatments can induce a wide range of effects, both direct and indirect.
Probably the most widespread are the direct effects on organism metabolism in
populations and communities, which can be induced by any change in soil-water availability or
flows in streams and wetlands. Thus the facilitation of runoff events following land conversions,
induction of water-level change in wetlands, and associated erosion and sediment transport are
among the most visible direct effects acting themselves as stressors (Loucks, 1990a). A special
rt~.S *., +*-jr*\ f.~-''J» 0 +
case of hydrologic effects-is constituted by the changes induced by alteipg the normal amplitude
and seasonality of peak water level and drawdown in swamps and marshes (Bedford and Loucks,
1984; Loucks, 1990b).
The periodic combustion of accumulated growth in terrestrial ecosystems also must be
considered a physical stressor, just as floods are a stressor in aquatic ecosystems. Human
activities change the frequency of both events, thus altering characteristics of the ensuing
recovery transient and the status of area-wide equilibria or destabilizations. The direct effects
that ensue from changes in increased or decreased frequency of fire (e.g., in Yellowstone
National Park or in the West Coast chaparral ecosystems) produce a wide range of indirect
effects on species through change in the severity of the fires, change in the potential to maintain
diverse habitats, and alterations of other processes.
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Like fire, the physical removal of forest mass (clear-cutting, chipping) or the physical
altering of habitat or the induced dominance of selected species by chemical treatments must be
viewed as a physical stressor leading to observable ecological effects, both short term and long
term. These direct effects may be more important for the indirect effects they facilitate (e.g.,
erosion and changes in nutrient cycling) than the physical exposure itself. Many of the resulting
indirect effects are well documented for common forest ecosystems (e.g., Bormann and Likens,
1979), but poorly documented for rare ecosystem types containing unusual biological diversity.
The long-term effects of harvesting on the composition of the herbaceous understory (e.g., Duffy
and Meier, 1992) is not well known for any forest ecosystem.
In aquatic ecosystems, physical manipulations include the altering of flow regimes in
rivers, lakes, and reservoirs. A good example of effects that should have received a risk
assessment are those resulting from reservoir management, as documented in the Glen Canyon
Study (NRC, 1987). Here the type of outflow (clear bottom water and cold) induced major
alterations in the Colorado River below the dam until sediment loading and temperature were
restored to normal. Other indirect effects from these physical stressors also are interesting: The
characteristic flushing times of lentic and coastal environments are changed, often affecting the
seasonal reproductive pattern of aquatic species. Hypoliranion aeration, key components of
decomposition, and nutrient cycling all can be affected, inducing new equilibria or system
destabilization in specialized habitats within otherwise lightly impacted systems (NRC/NAS/RSC,
1985).
4.3. Multiple Stressors: Aquatic Ecosystem Examples
In aquatic ecosystems, populations are often exposed to a mixture of chemicals in water
and sediments, to combinations of physical and chemical stressors, and to chemical stressors and
human exploitation pressures. Approaches to characterizing effects of multiple stressors and to
quantifying the relative contribution of individual stressors to the aggregate population response
are being developed.
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4 J.I. Multiple Chemicals in Aquatic Systems
Most effluent released to aquatic systems contains a variety of chemicals and the
sediments in the waterways in industrial areas contain a variety of metals and persistent organic
substances. The occurrence of chemicals in matures influences toxicity in two ways: (1)
chemical mixtures can cause a toxic effect that is qualitatively or quantitatively different from any
component acting alone; and (2) the effects of one chemical may influence the kinetics of uptake
metabolism or excretion of other chemicals. An effect measured for organisms exposed to
multiple chemicals is an index of the response of the organisms to the aggregate mixture. The
most direct way to determine toxicity is to test the mixture directly. Based on whole-effluent,
receiving-water, or sediment toxicity tests alone, however, no conclusions can be drawn as to the
contributions of specific chemicals to the measured toxicity; similarly, single-chemical toxicity test
data alone are insufficient to accurately predict the toxicity of a chemical mixture.
The most commonly used model of joint toxic action is the concentration addition model
(Finney 1971; Marking and Dawson, 1975). This model is based on isobole theory and involves
the use of the toxic unit concept to sum the action of various components of the mixture
according to the formula:
where:
the concentration of chemical i
the LCj,, value for chemical i
The ratio C/UCM is termed the toxic unit. This model is applicable if chemicals act on similar
sites with similar models of action. A second model, the response additive model, assumes that
the site and model of action of the chemical are different and the chemicals do not interact.
Thus the organism responds to the chemical to which it is most sensitive relative to the
chemical's concentration (Kodell and Pounds, 1985). This relationship is expressed as:
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c2) = p(q)
where:
P(C,) = the probability of a response, given concentration GI
P(C2) = the probability of a response, given concentration C2
In retrospective studies of effluent and sediments, the toxicity identification evaluation
(TIE) approach described by EPA is useful in quantifying the contribution of constituents of the
mixture to toxicity (U.S. EPA, 1989b). The TIE approach either separates components from the
mixture or masks their activity (e.g., addition of chelators to bind metals), retests the toxicity of
the amended samples, and compares the results to those for the original mixture.
4.3.2. Chemical and Physical Stresson
In navigable waterways in industrial areas, aquatic organism populations may be exposed
to chemical contamination and physical disturbances associated with such activities as shipping
and dredging. At issue generally is the contribution of the chemical stressors to the effects
measured in the ecological system that is also physically disturbed.
The contributions of chemicals and disturbance to measured effects (low population
abundances) can be evaluated in field studies by matching sites having low chemical
concentration and low disturbance with sites having low chemical concentrations and high
disturbance, and sites having high chemical concentrations and low disturbance with sites having
high chemical concentrations and high disturbance. In addition, toxicity tests with water and
sediments in these areas also may provide insight into the contribution of chemical exposures to
the measured effects.
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4.3.3. Chemicals and Human Exploitation of Aquatic Resources
One topic that has not received much attention is the effect of chemical (or for that
matter, physical) stressors on aquatic populations that are heavily exploited for human use. The
issues are two fold. First, does chemical accumulation limit the fisheries resource for human use.
Second, does the effect of chemical exposure in conjunction with the stress of harvesting (and
other stressors) pose a threat to the populations.
The EPA national study of chemical residues in fish for 106 sites showed that PCBs at 42
of these sites posed an estimated lifetime cancer risk of 1 in 10,000 to human consumers of fish
(U.S. EPA, 1992b). Of equal concern is the effect of such chemical exposures on the
sustainability of a harvested fishery. In theory, since high existing levels of stress reduce the
ability of populations to resist new stress regimes, exposing a heavily harvested fish population to
a chemical at a toxic concentration would further reduce the population's abundance and/or
recruitment and lower its probability of survival. A corollary to this theory is that increasing the
rate of harvesting should increase the sensitivity of a population to a given chemical exposure.
Although this theory is widely held, there are few quantitative studies that have estimated the
relative magnitude of effects of chemical and harvesting regimes on fisheries. The Great Lakes
studies described earlier in this section provide one example. Another is provided by Barnthouse
et al. (1990) who evaluated the interaction between fishing mortality and chemical toxicity on
striped bass and menhaden populations using population models: /Die study confirmed the
increased sensitivity of heavily exploited populations over less-exploited populations to chemical
exposures. The relative effects of chemicals and harvesting also has been examined for duck
populations (Sheehan et al., 1987).
4.4. Multiple Stressors: Terrestrial Ecosystem Case Study
The characterization of effects resulting from combinations of natural and anthropogenic
stressors is a common but difficult problem. The direct and indirect effects often operate on
multiple species and multiple ecosystem processes. The problem may be best illustrated by
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current research on the effects of acid deposition and O3 on poorly buffered oak forests in the
Ohio Valley (Loucks et al., 1993). Earlier studies, with experimental support, had proceeded
under the expectation that a base-titration reaction in the soil would take place over some very
extended period of time, up to a century (Shriner et al., 1990). The result would be a loss of
cations and nutrient depletion, possibly affecting tree growth. This multistressor effect is more
complex, however.
Testing hypotheses as to the patterns of effects on forests from acid deposition must start
with documentation of the dose, both current and cumulative historical ion-deposition amounts,
and the concentration of pollutants such as O,. These data were obtained along a gradient in
sulfate (SO2;) deposition from Illinois to Ohio (the Ohio Corridor Study) with funding from the
U.S. Forest Service from late 1986 to 1990 (Loucks et al., 1993). The results show even steps in
the gradient between each of the four states for cumulative deposition of SO2; during the past 8
decades, ranging from 6.9 to 9.6 eq/rn2 in Ohio between 1900 and 1949. Other results show that
soils differ along the gradient in several ways indicative of acidic deposition effects: The eastern
sites (Indiana and Ohio) with the longest period of high deposition and the greatest cumulative
dose are characterized by significantly lower pH (3.95) and a higher percent of carbon in the
surface mineral (Al) horizon than the lower-dose western sites (pH 4.57). The lower-mineral B
horizon of the high-dose sites in Ohio shows significantly lower pH, lower total bases, and a
lower percent base saturation than the reference site in Illinois (Loucks et al., 1993). The lower
level of bases on the high-dose sites is proportional to estimates of 50- and 86-year cumulative
wet-plus-dry deposition of SO2;, and to the higher level of exchangeable A13+ at those sites.
Among the three states with closely analogous soils, the Ca:Al ratio is significantly lower at the
eastern high-dose sites.
Yet research on the soil fauna, which are short-lived species closely linked to soil
chemistry, show greatly reduced populations in the higher-dose, Indiana and Ohio sites compared
to southern Illinois. Earthworm populations are 1/m2 compared with 30/m2 at the reference site.
An exacerbation of these differences is evident in the year following a severe drought (i.e., 1989)
compared with 1990. Tree growth, evaluated through the individual-tree basal area increment
(BAT) from 1934 to 1960 and 1960 to 1987 (before and after the most serious elevation of
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pollutants), shows decline at sites with low soil Ca:Al ratios, while no white oak and only 5 to 10
percent of the black oak show decline at sites with moderate Ca:Al ratios. Tree mortality rates
have been significantly higher during the decade 1978 to 1987 in comparison to 1968 to 1977,
despite little difference in weather. Many of these measures (e.g., growth or mortality) are
illustrative of effects for which drought, insects, and disease are stressors, along with oxidants and
acid inputs.
Further results (in table 6) illustrate an apparent indirect outcome from the long-term
effects of soil pH change on litter processing. Litter mass has accumulated more that 10-fold at
the high-dose Ohio site, to over 5,000 g/m2, and half that at the intermediate site in Indiana.
The weight of calcium in the litter and surface-soil layer is arrayed similarly, from 113 g/ra2 at
the reference site in Illinois to 84.6 g/m2 in Ohio. Expressed as the percent of all calcium held in
the surface layers, the 73.1 percent in Ohio represents a profound immobilization of this nutrient
compared with the 8.2 percent held in the surface layers at the reference site. These results
indicate that the very low Ca:Al ratios observed at the high-dose site (and cited above) may be
attributable to a biologically mediated indirect effect of the acidity, the result of sensitivity of
certain stages of soil fauna rather than the expected direct effects on soil chemistry. A risk
assessment of the ecosystem-level consequences of acidic deposition must include consideration
of the apparent "cascading" of biological processes in the terrestrial system, as well as the direct
chemical processes.
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5. ECOLOGICAL RESPONSE ANALYSIS: THE ROLE OF VARIOUS TYPES OF MODELS
Models are an important part of the effects characterization process, although many
potential uses of models have not been fully realized. In effects characterization, models may be
used for prediction, explanation, or extrapolation. The principal use of models in effects
characterization, however, is to describe the relationships between the duration and intensity of
the stress regime and the response of the exposed biological system.
There are three types of models used in effects characterization: physical, statistical, and
mechanistic. Physical models are material representations of the system that can be manipulated
or tested. Single- or multi-species toxicity tests are commonly used physical models in effects
characterization. Physical-toxicity test models and field-Study models of biological effects provide
much of the current exposure-response data for effects characterization.
Statistical models attempt to derive generalizations by using regression and other
statistical techniques to summarize experimental stress regime-response data. Concentration
. dose-response models are obtained by statistically fitting a continuous function such as a probit
or logjt to results of toxicity tests with discrete concentrations or doses. The model assumes that
the response of exposed organisms to the chemical exposure can be characterized by a statistical
distribution independent of the mechanism of toxic action. Statistical regression models can be
used to quantify the relative sensitivity of different taxa or life stages to stressors (e.g., Mayer and
Ellersieck, 1986). Multidimensional response surface models have been used to quantify
interactive effects of various stressors such as chemicals and pH on biota (e.g., Mount et al.,
1988). All statistical models are strictly empirical approaches for fitting curves and surfaces to
test data.
Mechanistic models describe the relationship between some phenomenon and its
underlying cause in quantitative terms. Types include pharmacodynamic or toxicodynamic
models that incorporate mechanisms of uptake, distribution, and toxic action. In principle,
mechanistic models can account for characteristics of organisms (e.g., age, nutritional status,
reproductive status) that are known to influence sensitivity to stressor exposure. The proper
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'accounting for individual or population characteristics would provide assistance in extrapolating
test data from species to species and from laboratory to field conditions. The greatest benefit to
improving stress regime-response characterization is expected in the development and application
of mechanistic models.
Four reviews of the models available for assessing the ecological risks of toxic chemical
exposures have been published in recent years (Barnthouse et a!., 1986; Bamthouse, 1992;
Emlen, 1989; Suter, 1993). Organism-level effects models include toxicodynamic models
(Mancini, 1983; Kooijman and Metz, 1984; Lassiter, 1985; Lassiter and Hallara, 1990) that relate
the risk of mortality to the uptake and internal concentration of chemicals. Models of the effects
of stressors on energetics and growth (Sugden and Harris, 1972; Kitchell et al., 1977; Rice et al.,
1983; Bart ell et al., 1986) also are relevant to effects characterization but have not been
extensively applied to date.
The population-level models of current or potential use in risk assessment include the
many models developed for management of fish and wildlife populations. These are primarily
demographic models that predict the effects of physical, chemical, or biological stressors on
population parameters such as abundance or recruitment. Emlen (1989) suggests that the most
relevant endpoints for population modeling are "pseudo extraction"—an abstraction that
measures the probability of a population falling below some predetermined fraction of its
undisturbed value—and alteration of the temporal mean population abundance. Logan (1986)
and Barnthouse et al. (1987,1989,1990) provide examples of the use of fisheries-derived models
in chemical risk assessment. Bamthouse et al. (1987) developed a method for extrapolating
logistic functions and confidence limits for untested fish species. The study used mortality data
from three life stages (egg larvae and juveniles) to encompass the fish life cycle from egg to first
reproduction. The reproductive potential for a one-year-old female recruit then was modeled
using a form of demographic population modeling that accounted for the female's annual
probability of survival at different ages, her expected fecundity at different ages (provided that
she survives), the probability that a spawned egg will hatch, and the probability that a newly
hatched juvenile will survive to age one. There are also a few examples of the application of
population models to characterize the effects of chemicals on wildlife. Grant et al. (1983)
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evaluated the effects of vertebrate pesticides on great horned owls using a Lesie Matrix model
approach. A similar approach was used to predict the recovery of seabird populations from the
stress of an oil spill (Samuals and Ladino, 1983). Tipton et al. (1980) used a different approach,
classifying bobwhite quail into various categories, with respect to their physiological response, to
characterize the effects of contamination by methylparathion.
Community and ecosystem models are the most complex and diverse among ecological
effects models. They may be site specific (Kremer and Nixon, 1978; Andersen and Ursin, 1977)
or generic (O'Neill et al., 1982); generally they are designed to mimic portions of ecosystems.
The driving variables are physical and chemical factors (e.g., temperature, pH, nutrient
concentration, chemical concentrations), and population interactions (e.g., grazing or predation)
are described with mathematical relationships. The effects characterization component of these
models expresses the effect of the stress regime on the parameters of interest in the exposed
ecological system. Large-scale models for regions or landscapes have been proposed to
characterize ecological effects (Dale and Gardner 1987; Graham et al., 1991), but such models
have not yet been widely used in risk assessment.
5.1. Chemical-Concentration/Dose-Response Models
For chemicals, the concentration/dose-response relationship is a graded relationship
between the concentration or dose of the chemical to which the organism is exposed and the
severity of the response elicited. Generally, within certain limits, the greater the concentration or
dose of the chemical, the more severe the response. The curve fitted to represent this
relationship generally will be asymptotic since at all concentrations below some minimum
threshold value no measurable adverse response will be elicited, while at all concentrations above
some maximum value most or all of the organisms will be adversely affected. The steeper the
slope in the central portion of the curve, the more intense the response over a narrow range of
concentration or dose. For the concentration/dose-response relationship to have precise
meaning, the duration of exposure must be specified.
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5.1.1. ConcentrationlDose-'Response Function
The most common two-dimensional model in ecotoxicology is the concentration- or dose-
response function (figure 2). In this model, although time is not included, response data are for
a specified duration of exposure, and either severity or proportion responding is eliminated in
order to have a single response variable. Normally the percent response is plotted against
concentration or dose. The probit, logit, Weibull, or other function is fit to the discrete
concentration/dose-response data from the toxicity test to provide a continuous response model
for the range of exposure. This function is used to generate effect concentrations (e.g., ECW
5.1.2. Time-Response and Time-Concentration Functions
Time-response functions are like concentration-response functions but are used to
generate a LT^. Time-response functions are useful when the concentration is relatively
constant but the duration of exposure is variable.
*• *
A two-dimensional model that includes time is the time-concentration function. This
model is created by collecting data at multiple times during the toxicity test and calculating an
ECj,, or LCjo for each time. These concentrations are then plotted against time and a function is
fit to the data. The resulting curve may indicate when acute lethality has ceased—in places
where the curve is asymptotic to the time axis. The UCX for the time that is in the asymptotic
part of the curve is termed the threshold or incipient
5.1 J. Concentration-Time-Response Functions
Although three-dimensional representations of toxic effects are rare, they provide an
effective way to present toxicity test data as a response surface (figure 3). Such models have the
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obvious advantage of allowing the risk assessor to estimate the level of effects from combinations
of intensity and durations of exposure.
5.1.4. Probabilistic Models to Handle Uncertainty
Human health risk assessment models have used Monte Carlo simulations to evaluate
uncertainties in both exposure and dose-response assessments (e.g., McKone and Bogan, 1991).
Similar models are now being used in ecological risk assessments (Bartell et al., 1992; Sheehan et
al., 1993b). These models incorporate distributions of exposure and effect parameters and use
Monte Carlo simulation to generate a probability distribution of environmental and laboratory
test-based exposures that can be compared in order to characterize risks. The use of
probabilistic modeling to define uncertainty in exposure-response assessment is particularly
important for species for which limited data are available.
5.2. Approaches to Stressor-Response Measurement and Modeling for Ecosystems
Stressor-response modeling for the ecosystem area of ecological risk assessment will
require greater attention if our ability to predict effects at higher levels in the biological
organization are to be realized.
5.2.1. EaHy Concentration-Response Models for Ecosystems
Few attempts have been made to model chemical effects on population and ecosystem
endpoints. The Standard Water Column Model (SWACOM) provides an example of such a
modeling approach (O'Neill et al., 1982). SWACOM presents a Monte Carlo simulation
approach for extrapolating laboratory toxicity data to predict effects such as decreased
productivity and reduction in fish biomass. The translation for toxicity data incorporates the
information on the mechanism of toxic action with population interactions taken into account
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and the uncertainty in laboratory measurements retained. Risk estimates are generated in the
form of probabilities that an effect could occur. A detailed description of ecosystem simulation
modeling is provided in Bartell et al. (1992).
The strengths of this methodology are that a variety of endpoints can be assessed for each
source-exposure regime and the risk algorithm can be applied to a variety of chemicals. Further
application and refinement of such models will depend on refinements in our understanding of
the mode of toxic action of the chemicals of interest and of the ecological interactions within the
system to be simulated.
5.22. Models of Cascading Effects in Aquatic Ecosystems
Some of the most complex and counterintuitive responses of ecosystems to external
manipulation are being modeled successfully in clear, low-nutrient northern lakes (Vanni et al.,
1992). Freshwater plankton communities in these lakes are regulated by a variety of factors,
among which nutrient enrichment and predators are two of the most important. Both are often
manipulated by humans, and can, therefore, be thought of as anthropogenic stressors. An
increase in limiting nutrients can stimulate production and biomass of phytoplankton, which in
turn can stimulate production and biomass of herbivorous zooplankton. Predators such as fish
can influence plankton communities through selective predation on large versus small
zooplankton species. Because large zooplankton have relatively high grazing rates (per
individual) and graze on a wider range of food particles, size-selective predation by fish on large
zooplankton also can have a substantial influence on phytoplankton and nutrient cycling. The
series of opposing interactions illustrated in figure 4 have come to be known as "cascading
effects" because of the way control from above is expressed stepwise down through the system to
primary producers.
The influences and interaction among these factors have been investigated quantitatively
through manipulation experiments and simulation modeling (Vanni et al., 1992). The modeling
has been used to forecast the responses to planned manipulations and to elucidate the
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mechanisms responsible for changes induced in response to altered predator and/or nutrient
conditions. Scavia et al. (1988) had used simulation modeling earlier to investigate the influence
of food web interactions on the magnitude and scale of variability in primary production in Lake
Michigan, with good descriptive results.
The work by Vanni et al. (1992) used simulation modeling to investigate various scenarios
of planktivory and phosphorus-loading rate, and used observed responses in Lake Mendota
photoplankton to test the effects of those scenarios. The model, which has obvious hierarchical
control features and feedbacks, simulates the Lake Mendota plankton food web of the late 1980s
and accurately predicts many consequences of observed food web changes. The results suggest
that food web manipulation represents a viable strategy for reducing the severity of summer algal
blooms. Vanni concludes that simulation models such as the one he tested can be useful tools in
forecasting the consequences of food web manipulations and thus are illustrative of tools
available for use in characterizing ecological effects for risk assessment.
53. Lake and Forest Ecosystem Component Models Developed in NAPAP
One of the largest investments in large-system stressor-response modeling was that
undertaken as part of NAPAP between 1982 and 1990. Two reports summarize those results in
one place (NAPAP, 1989b; NAPAP, 1990), but some of the results are published in many other
papers as well. Among the watershed and aquatic ecosystem models likely to be useful in risk
assessment are the following: Integrated Lake-Watershed Acidification Study (ELWAS), for
northeastern mountain lake ecosystems; Model of Acidification of Groundwater in Catchments
(MAGIC), for soil chemical, ground water, and stream chemistry changes; and the Regional
MAGIC Model for analysis of area-wide changes in stream chemistry. Questions of error
propagation, validation, and appropriate applications have all been discussed at length in the
NAPAP reports.
An even larger number of models were developed and tested for forest responses, but
most were only for components of ecosystems. Important among these are the following:
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Nutrient Cycling Model (NuCM), useful in completing mass balances of nutrients at specific sites
and in simulating long-term trends from anthropogenic deposition on forest vegetation and soils;
and Response of Plants to Interacting Stresses (ROPIS), a useful model for evaluating the
interactions between canopy air pollution effects and soil-water and nutrient uptake and growth.
Both oxidant and acidity effects can be evaluated.
These examples of relatively well-documented, large-scale stressor-response models are
cited to illustrate that although substantial computational capability exists for assessment of risks
from a range of external stressors, the local, species-specific, and site-specific data requirements
are substantial—something that should be recognized. A handbook of species and ecosystem
model parameters drawn from published papers would probably be very helpful in developing a
generalized, but quantitatively precise, "expert-system" approach to ecosystem risk assessment.
5.4. Hierarchical Approaches Integrating Individuals, Populations, Subsystems, and Ecosystems
In situations where the known stressor is operating at the level of individual organisms
(as opposed to the mostly mass balance or physical system-level effects discussed above) but the
endpoint of interest is at the ecosystem level, a somewhat different approach to system
integration across scales may be required. For example, work by Cai et al. (1993) breaks up the
processes of forest liner decomposition into a hierarchy of soil faunal feedbacks and species
demographics. An array of linked models is being developed—from individual-based to
ecosystem level—to incorporate the effects of rain chemistry inputs and soil pH changes. The
individual-based model is used first to incorporate known effects of high H+ concentrations on
the sensitive life stages of earthworms and other raacroinvertebrate groups. Known dose-
response relationships can then be used to compute the changes induced in the populations of
these species, which as noted in section 4.4 have experienced up to a 90 percent decline in
numbers at high-dose sites in the Ohio Valley (Loucks et al., 1993). The consequences of these
changes for the mass action of decomposition (a rate change) can then be computed for the
forest ecosystem as a whole. The model as a whole (still in development) can be validated
against the data shown in table 6.
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5.5. Evaluation of Causality
The difficulty in appropriate attribution of causality outside of controlled-environment
experiments continues to be a problem for ecological risk assessment, particularly at the level of
complex systems, in spite of progress in understanding and defining the problem during the past
10 years. Several important papers have been written, all of which are well summarized by Suter
(1993). The central problem is the so-called ecological fallacy, the assumption that measured
differences between the environments inhabited by subject populations of interest are really the
cause of differences in the frequency of some disease. The differences actually may be caused by
some unconsidered factor, and correlation does not prove causation. Koch's postulates have
been proposed as a "standard" means of testing causality (Woodman and Cowling, 1987), but
they are so specific for diseases as stressors that other authors have sought means for
generalizing more broadly from them. One of these general treatments is that of Hill (1965),
adapted by Suter (1993). Nine "factors" are suggested, a majority of which must be fulfilled:
• Strength—a steep exposure-response curve.
• Consistency—consistent association of an effect with a hypothesized cause.
• Specificity—-the more specific the cause, the more convincing the association with
an effect.
• Temporality—a cause must always precede its effects.
• Biological gradient—effect should increase with increasing exposure.
• Plausibility—underlying theory should make it plausible that the effect resulted
from the cause.
• Coherence—implicit relationships should be consistent with all available evidence.
• Experiment—changes in effects should follow experimental treatments representing
the hypothesized cause.
• Analogy—cause-and-effect relationship should be similar to some well-known
examples.
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The point is that multiple lines of evidence usually are needed to ensure a reasonable
inference of causality. Experimental proof is simple and ideal, but relatively few situations can
be reduced to a controlled-experiraent situation, and thus the need to incorporate uncertainty in
risk assessments. Assembling the multiple lines of evidence for the causality of stressor effects at
single sites or for larger systems such as lakes will usually require evidence from other similar
systems and modeling for the site under direct consideration. The aquatic food web model
described previously (Vanni et al., 1992) is a good example. Field observations of fish, plankton,
and nutrients might never have been seen as having the strength, consistency, specificity,
coherence, and plausibility to attribute one or another as "causal." Yet when the same results are
viewed in the context of linked relationships in a simulation model, they can be seen as causal.
Given the large body of preexisting experimental work and theory for food chain coupling, a
structured model incorporating a "cascade" of linkages is plausible and the need for a very large
uncertainty term is reduced.
Two major questions remain in regard to the causality-uncertainty tradeoff: What is the
range of other physical or biological conditions (e.g., in turbid reservoirs or river reaches) where
one might now conclude these same effects to be causally linked to equivalent manipulations?
And to what extent can causality be inferred in quite different systems where the underlying
theory is less well developed or the experimental field testing is only partial?
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6. ECOLOGICAL RESPONSE ANALYSIS: RELATING MEASUREMENT AND
ASSESSMENT ENDPOINTS
In most ecological risk assessments, the measurement endpoints are not necessarily the
same as the assessment endpoints. To characterize effects associated with stressor exposures,
extrapolations must be made. These include extrapolations among taxa, from laboratory to field
conditions, across the ecological hierarchy, and across spatial and temporal scales. The need for
extrapolation in risk assessment is obvious; the problem is to assess the uncertainty in these
extrapolations.
6.1. Extrapolations among Different Taxa
An important issue to be considered in effects extrapolation is the difference in sensitivity
among species. Suter (1993) describes two types of taxonomic extrapolations. One involves
extrapolating from the test data for one species (e.g., fathead minnow) to predict the response of
a second species (e.g., rainbow trout). The second type of extrapolation is from data for one or
a few species to predict the sensitivity distribution for the whole community exposed to
the stressor.
Species-to-species extrapolations are based largely on regression analyses. Kenaga (1978,
1979) regressed LCjoS and LDjgS for all combinations for eight terrestrial and aquatic species
using results from toxicity tests with 75 pesticides. Similar regression analyses have been
performed for a variety of aquatic species and chemicals (e.g., Maki, 1979; Sloof et al., 1986).
Regression between pairs of species allows one to predict responses for only the few species that
have been frequently tested. Ecological risk assessment often requires assessment of effects on
species that are not generally tested. Suter et al. (1983) devised an approach to address this
shortcoming based on taxonomic relationships between the test species and species of interest.
For aquatic organisms, regressions were performed, for example, on all pairs of species in a
common genus and on all pairs of genera within a common family. Extrapolations are then
made between taxa having the next higher taxonomic level in common. This approach is based
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on the assumption that differences in sensitivity for aquatic organisms increase with increasing
taxonomic distance.
No similar analysis has been performed for terrestrial species, although several
researchers have compared species sensitivities to chemicals. Peakall and Tucker (1985)
compared rats to starlings, starlings to red-wing blackbirds, and mallard ducks to bullfrogs.
These comparisons show that the ratio of LDA values for the bird species fall largely within one
order of magnitude, while the LDX ratio values for starlings and rats, and mallards and bullfrogs,
were more widely dispersed—generally between two and three orders of magnitude. The
differences in sensitivity to chemicals increased as the phylogenetic differences increased. This
finding is similar to that of Suter et al. (1983) for aquatic species. These data also indicate that
there is a moderate level of uncertainty associated with extrapolations to closely related species,
but a great deal of uncertainty with respect to extrapolations between distant taxa or for a wide
variety of unrelated taxa.
6.2. Extrapolations from Laboratoiy to Field Conditions
Laboratory tests measure stressor-response relationships under well-controlled conditions.
Such tests may not mimic the environmental conditions, organism conditions, exposure
conditions, or, more importantly, the biological interactions that occur in the field. Several
approaches have been used to facilitate extrapolations from laboratory effects data to predict
effects in the field.
To address differences in environmental conditions between the laboratory and field for
aquatic organisms, researchers have developed regressions for a few variables such as
temperature and toxitity (Mayer and Ellersieck, 1986), pH and toxicity (Mayer and Ellersieck,
1986), and water hardness and toxicity (U.S. EPA, 1985). At present, no extrapolation models
have been developed for addressing laboratory and field differences in concentrations of organic
and inorganic nutrients, suspended sediments, total biomass, and other factors that are known to
affect the availability of chemicals to aquatic biota. The magnitude of influence these factors
exert on the concentration-response relationship is unknown, but most laboratory test models are
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assumed to be conservative models; that is, the addition of sediments to the test system is
expected to reduce bioavailability of the introduced chemical and its toxicity to test organisms.
Differences in sensitivity between wild and laboratory strains of test organisms have been
evaluated for a few aquatic and terrestrial species. McEwen et al. (1973) found no differences in
sensitivity to chemicals between pen-reared and wild-trapped pheasants. Peakall and Tucker
(1985) reported that wild-caught Daphnia magna and Daphina magna from a long-held
laboratory culture were equally sensitive to chemical exposures. In general, field experiments
suggest that when environmental conditions are equivalent, organisms in the field respond at
about the same exposure level as organisms in the laboratory (Grassland, 1982; Hansen and
Garton, 1982).
Exposure conditions in the laboratory are generally as a single pulse with concentrations
diminishing with time or concentrations. Field exposures are likely to be much more erratic.
Although there have been few attempts to design laboratory tests to mimic exposures under field
conditions, more complex exposure regimes could be incorporated into testing to improve
confidence in this aspect of extrapolation. Aside from this, continuous exposure tests likely
represent an upper bound of field exposure conditions. LCy, values for flow-through aquatic
tests were found to be consistently lower than LCjoS for static tests for the same species by ratios
of 0.12 to 8.3 (Mayer and Ellersieck, 1986).
A more direct approach that should account for environmental and ecological factors is
the use of mesocosms, artificial streams, or controlled field studies that more closely mimic field
conditions. Boyle et al. (1985) used experimental pond systems to evaluate the effects of
fluorene on the ecological structure and function of these aquatic systems and to evaluate the
predictability of laboratory toxicity tests for them. They found phytoplankton, zooplankton, and
aquatic insect communities were much less sensitive in the pond systems than expected based on
laboratory test data. Taking this one step further, Sheehan et al. (1987) used field data on the
toxicity of two insecticides, relative to their respective LCjo values from laboratory tests, to
predict reductions in aquatic macroinvertebrate numbers and biomass for exposed pond
ecosystems. These data were then used as "benchmarks" for extrapolations of chemicals with
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similar LCX values. In situ aquatic enclosures also have been used to reduce uncertainty in
extrapolations from test to natural systems by incorporating natural environmental factors and
ecological functions in the test model (e.g., Kaushik et al., 1985).
6-3. Extrapolations across Ecological Hierarchy or Organization Levels
Extrapolation of expected ecological response patterns from the species and population
level to ecosystems is a new field of inquiry. The approach outlined above—i.e., aggregating
from effects on individuals ("individual-based models"), to effects on populations, to effects on
ecosystem processes (Cai et al., 1993)—is only one approach, and probably applicable in only a
limited number of situations. A comparative test of this simple extrapolation approach using No
Observed Effect Concentrations (NOECs) for single species and ecosystems is reported by Sloof
et al. (1986) and by Suter (1993), with satisfactory results.
A more sophisticated approach will require articulation of the unique properties of each
level of organization, something like the "criterion" characteristics proposed by Allen and
Hoekstra (1992). Following their (and other) proposals, researchers and assessors must
recognize the unique properties afforded each level of organization—properties potentially at risk
from effects expressed at a lower level of organization. For many reasons, effects on these
"criterion" properties are unlikely to be included in the simple "aggregating up" paradigm of Cai
et al. (1993) or Sloof et al. (1986). A possible example is the property of "integrity" that is often
associated with ecosystems. Aggregating the toxicity effects on earthworms and their cumulative
effect in decreased litter decomposition and nutrient cycling, or the correlative approach of Sloof
et al. (1986), still does not provide an estimate of the loss of integrity at the higher level of
organization, if that were an endpoint of interest. A substantial body of new work will be
required if risks to the unique properties of any of the higher levels of organization are to
be addressed.
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6.4. Extrapolations across Spatial and Temporal Scales
As noted above, many problems have been encountered in extrapolation between species
and between rates of exposure. With experience in extrapolations for human health risk
assessments, some of these problems have been addressed. The corresponding problems in field
environments and with exposures that are cumulative over several decades are just now beginning
to be addressed The discussion by Suter (1993) is illustrative. Citing Reckhow (1983), he
suggests that for extrapolation between ecosystems (i.e., spatially), the assessor should first define
an exposure-response model for a measure corresponding to the ecosystem-level assessment
endpoint. The assessor also must define a model that describes the tested system and the
systems to which the assessor wishes to apply the exposure-response model. Multivariate
statistical tests could be used to estimate the probability that the ecosystems of interest belong to
the same state space as the ecosystems used to develop the exposure-response model. If the
assessor decides that the two sets of ecosystems are not the same, the model can be respecified
so that it is applicable to the systems of interest and the assessor can proceed with the
extrapolation. This approach has not yet been applied to effects assessment, however, and much
further testing is needed.
This still leaves the problem of extrapolation between temporal scales, (i.e., the
estimation of risk to ecosystem endpoints after 40 to 80 years of exposure to lead, or to acidic
deposition, based on a 4-year experiment). These extrapolations have been projected in the
course of acid-deposition effects assessments, with some confidence for physicochemical
endpoints in the soil (which appear possibly to have been wrong) but very little in projections of
long-term biological responses. The reason, of course, is that there is still very little
understanding of the mechanisms of aging of long-lived species making up ecosystems, or of how
aging processes are affected by stressors. Without some reasonable idea of the mechanisms that
would be operating over the long term, or even the mechanisms that are operating in the short
term, extrapolations across time scales have little or no foundation at this time.
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7. STRESS REGIME-RESPONSE PROFILES
The results of the characterization of ecological effects can be summarized in a stress
regime-response profile, which is a synthesis of the effects—both direct and indirect, short term
to long terra—that are identified in a characterization analysis. The profile relates quantitatively
the magnitude, duration, frequency, and timing of exposures with the types and magnitude of
direct and indirect effects. To be most informative, an expression of the stress regime-response
profile should include the shape of the relationship (usually some curvelinear form), upper and
lower limits, and uncertainty bands. For primary effects, many elements of a stress regime-
response profile may be shown schematically, such as the concentration-response or dose-
response curves and uncertainty bands for chemical agents. The stress regime-response profile
also may be displayed as a probability distribution for certain clearly understood relationships.
It is more difficult to display graphically or even quantitatively the relationship between
the primary agent and its stress regime—either on populations or ecosystems—and secondary or
tertiary effects. In these situations, the profile will be a written synthesis of the linkages between
the stressor and observed responses in the system. These relationships will be oblique in some
cases, and their characterization will require description of a large number of interactions
intermediate to the cause and secondary effects.
7.1. Stress Regime-Response Relationships for Chemicals or Physical Stressors Acting on
Individuals and Populations, Short- to Mid-Term
Stress regime-response profile diagrams are frequently produced for chemical toxicity
tests but are infrequently described for physical stressors. One can imagine, however, that
physical stress regime-response profiles could be developed in a manner similar to chemical
exposure-response profiles.
Examples of chemical concentration-response profiles are presented in figures 2 and 3,
showing a clear relationship between chemical concentration, exposure duration, and organism
response. The response surface shown in figure 3 integrates exposure duration and
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concentration into the response profile. These profiles do not show the uncertainty in the
concentration-response data; however, the uncertainty band could be added easily to enhance the
value of the profile. Bamthouse et al. (1987) provide an example of a concentration-response
profile with uncertainty bands that provide a clearer characterization of effects and extrapolation
uncertainties.
The synthesis models described above could be applied to more complex systems. As the
system under evaluation becomes more complex, the stress regime-response profile would
integrate a greater number of endpoints at various levels of biological organizations into the
effects characterization. Barnthouse (1993) provides an example of a profile that integrates
exposure-response data and uncertainties for individuals and populations in a stressed ecosystem
(figure 5). Such a profile could be expanded to be more comprehensive by adding dimensions to
account for a larger number of populations of interest in an exposed ecosystem. This profile
model also could be extended to describe ecosystem-level effects—in addition to individual and
population-level effects—if the ecosystem-level stress regime-response relationships are
understood and the requisite ecosystem data are gathered.
7.2. Stressor-Response Relationships Attributable to Chemicals and Physical and Habitat
Alterations Acting on Ecosystem Function and Stability Characteristics, for Large Areas,
Long-Term
In principle, the stressor-response profiles described above for stressors acting on
individuals and populations seem to be a reasonable concept for the same stressors when acting
on ecosystems. For the reasons discussed in earlier sections, a simple "aggregating up" of effects
expressed at the population level can lead to a first approximation of effects on certain ecosystem
functions, at least locally. Where this is the case, essentially the same response profile could be
applied to specific ecosystem functions, changing only the dependent variable.
Although the general stress regime-response model proposed in the previous section for
individuals and populations is theoretically applicable for characterizing structural and functional
effects on ecosystems, such profiles have not yet been generated. First of all, we are able, at
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present, to describe concentration/dose-response relationships for only a few illustrative chemicals
and for only a few of the important ecosystem functions. It is unclear as to whether such profiles
can be generalized to other key functions such as multispecies feeding, net production, and net
migration (e.g., for birds). Similar uncertainties exist for characterizing physical stressors and
their effects on ecosystem function, partly because we do not have metrics of these stressors that
can be satisfactorily aggregated or averaged.
We probably know enough about what is needed of a response profile for ecosystem
function to begin asking the right questions or adapting certain models to the problem, but it is
premature to anticipate how difficult such a synthesis will be. Two questions to be considered
are: How essential is this part of the puzzle in comparison to other gaps in our knowledge noted
earlier? And, now that we see an array of needs, is there a way in which a wide variety of these
needs can be met through one coherent program of experiments and models, as is being done in
the global change research program?
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-0.41
19
13
0*1'
Figure 3. Toxk effects as a function of concentration, duration, and proportion responding
(from Nimmo et aL, 1977).
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PISCIVORE
BIOMASS
VERTEBRATE
PLANKTIVORE
BIOMASS
INVERTEBRATE
PLANKTIVORE
BIOMASS
LARGE
HERBIVORE
BIOMASS
SMALL
HERBIVORE
BIOMASS
CHLOROPHYLL
PRIMARY
PRODUCTION
TIME
Figure 4. Time-course of ecosystem response to a strong piscivore year class (solid line) and a
partial winter kill of piscivores (dashed line) (Carpenter et aL, 1985).
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i Distributions ol parameters of individual organisms
2. Sampling to assign parameter
values to individuals
}
1
±
±
I tndjvidua) ft
3, Distributions of exogenous variables
4 Modeling response of
individuals (Rn)
5. Modeling response of
the population (Rp)
I
;* j
Figure 5. A schematic representation of individual-based population modeling X,,, Yn, and Zn
showing characteristics of individual organism n such as size and leaf area. A,,, and
Bnl are characteristics of the environment experienced by individual n at time t such
as temperature, pollutant concentrations, and prey availability. St.t is the state of the
organism at the previous time step. R,, is response of individuals such as death or
maturation. R. is response of the population such as abundance or harvestable
biomass (from Barnthouse, 1993).
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Table 1. Examples of Possible Assessment and Measurement End points for Evaluation of the
Effects of Insecticide Spraying for Spruce Budworm Control
Problem
Assessment Endpoint
Measurement Endpoint
Possible nontarget effects of
long-term application of
insecticides to regional forests
to control spruce budworm
Probability of >10%
reduction in salmon
populations in streams in the
sprayed area
Significant decrease in tree
canopy bird populations
LCj0 or NOAEL for salmon
or related fish species
Dietary LDa for Japanese
quail egg hatch and fledgling
Success in treated and
reference areas
A 20% decrease in fruit
production from bee-
pollinated plants
Population numbers for
selected bird species in
treated and reference areas
for bees
Abundance and diversity of
natural bees
Significant decrease in forest
litter decomposition
Populations of selected bee
species in treated and
reference areas
Fruit production (e.g.,
blueberries) in treated and
reference areas
Microbial respiration in soils
from treated and reference
areas
Soil arthropod abundance in
leaf litter in treated and
reference areas
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Table 2. Types of Responses to Stressors Characterized in Ecological Risk Assessment (adapted
from Sheehan, 1991)
Response
Measurement
Comments
Impacts on Individual Organisms
Reduced growth Rate of change in size or
Altered development
Reduced reproductive
success
Shortened life span
Impacts on Populations
Reduced abundance
Altered distribution
Changed age structure
Altered gene pool
mass of the organism
Ability to develop to a
mature adult stage or time to
reach sexual maturity
Time to first production of
offspring, number of offspring
per reproductive event,
number of reproductive
events per lifetime
Length of life span as
compared to normal length
Number of individuals or
biomass of population versus
chemical exposure
Presence/absence or
commonness/rareness of a
population versus chemical
exposure
Frequency distribution of age
or size class versus chemical
exposure
Electrophoretic analysis of
genotypic frequencies versus
chemical exposure
Growth is frequently related
to development and the
probability of survival
Delays in reaching sexual
maturity often translated into
reduced fecundity
Reduced reproductive success
may mean a lower rate of
recruitment to the population
Shortened life span reduces
the individual's reproductive
contribution to the
population
Must be compared to
abundance of reference
population in uncontaminated
area or control population in
enclosure study
Analysis must account for life
history and other ecological
and environmental factors
structuring population
distribution
Can be used to assess
recruitment success or
recovery
Opportunistic species more
likely than specialized species
to develop resistant
populations
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Table 2. (cont)
Response
Measurement
Comments
Impacts on Ecosystem Structure and Dynamics
Population extinction
Changed community
composition
Dominance switches
Changed diversity
Changed similarity
Reduced abundance/biomass
Absence of a population
known to have existed prior
to chemical exposure
Species list, indicator species,
or indicator assemblages
versus chemical exposure
Relation of abundance versus
chemical exposure
Margalet, Simpson, or
Shannon diversity indices
versus chemical exposure
Coefficient of similarity,
quotient of similarity, or
percentage similarity versus
chemical exposure
Total number or biomass of
individuals in community
versus chemical exposure
Loss of "key" commercial or
ecological populations is
easiest to interpret
Best to know what taxa are
absent as well as what taxa
are present; 'trophic
organization may provide
insights into effect on feeding
relationships
Abundance under chemical
stress often depends on the
opportunistic life histories of
species
Applicable only to gross
levels of chemical pollution
because conflicting data form
richness and evenness
components and insensitivity
to moderate levels of
pollution; species richness
may be a more consistent
index of chemical stress
Measurements are more
consistent and sensitive than
diversity indices in assessing
chemical effects on
community composition
Measurements do not provide
much information on the
ecological character of the
system, but these are the
least expensive variables to
measure
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Table 2. (cont)
Response
Measurement
Comments
Altered spatial structure
Vertical and horizontal
patterns versus chemical
exposure
Stability fluctuations
Resistance
Elasticity
Amplitude
Hysteresis
Impacts on Ecosystem Functions
Reduced organic
decomposition
Reduced nutrient
conservation
Reduced primary productivity
A 50% change in species
composition or richness
versus chemical exposure
Recovery time to reach 85%
similarity to the original
composition
Model simulation of recovery
threshold
Sperman's rank correlation
coefficient comparing
disappearance and
reappearance of species
Decomposition rate of plant
litter or reference organic
substrates versus chemical
exposure
Net loss of essential elements
in mass balance studies versus
chemical exposure; nutrient
spiraling length in stream
studies
14C assimilation rate or other
methods of measuring plant
growth versus chemical
exposure
Only of value in assessing
pollution effects on non-
mobile communities, such as
forests that exhibit a distinct
spatial structure
Measures the system's
resistance to pollution
Measures the system's ability
to recover from chemical
stress
Measures the maximum
amount of damage from
which a system can recover in
a specific time
Measures the degree to which
an ecosystem's pattern of
recovery is not the reversal of
the pattern of species loss
Effects may not be obvious
until some time after
exposure
Of most obvious importance
in terrestrial ecosystems
Long-term reductions in
primary productivity are the
most obvious index of a
functionally stressed
ecosystem
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Table 2. (cont)
Response Measurement Comments
Reduced ecosystem Net gain in ecosystem Integrates chemical effects on
production production versus chemical primary productivity
exposure (e.g. net oxygen
evolution in an aquatic
system); Odura's index of
power may be appropriate
Altered food web and Changes in predator-prey or The loss of a predator
functional regulation consumer-consumed indirectly through the
interactions versus chemical elimination of prey and the
exposure dramatic increase in a
population after the removal
of a predator or competitor
are examples of chemical
alternation of functional
regulation
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Table 3. Simplified Conceptual Model of the Apparent Time Scales of Physiological and
Ecological Processes Associated with Plant Community Responses to Chronic Air
Pollution* (Armentano and Bennett, 1992)
Response Variable
Time Scale Interval
Pollutant uptake
Reduced photosynthesis; altered membrane permeability
Reduced labile carbohydrate pool
Reduced growth of root tips and new leaves
Decreased leaf area
Differences in species growth performance
Reduced community canopy cover
Reduced reproductive capacity
Shifts in interspecific competitive advantage
Alteration of community composition
Change in species diversity
Change in community structure (physiognomy)
Functional ecosystem changes (e.g., decline in nutrient
cycling efficiency, net productivity)
10'1 to 103 minutes
101 to 103 minutes
10° to 101 days
101 to 102 days
102 to 1025 days
102 to 1025 days
102 to 103 days
102 to 103 days
102 to 10" days
10" to 10* days
103 to 104 days
10" to 104-5 days
10" to 10" days
*Time scales are suggested for a hypothesized forest community exposed to chronic ozone levels
comparable to much of the eastern United States of the 1980s. The time-scale intervals, which
are not verified empirically and are not intended to be associated with a specific site, are
suggested as the ranges within which response symptoms would be clearly detected given current
capabilities in pollution effects research.
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Table 4. Scales of Observation and Management in Case Studies Evaluated by the NRC/NAS
Committee (NRC/NAS, 1993)
Case Study
Tributyltin
Agricultural
chemicals
PCB and TCDD
Spotted owl
Species
introduction
Georges Bank
Observational Scale
Spatial Temporal
< 1m3 < 1 yr
- 1 ha (laboratory)
< 5 yr (field)
~ 1 ha < i yr (field)
< 1 L ~ 1 mo
(laboratory)
~ 300 km2 < 6 yr
< 100 rn2 ~ 1 yr
(greenhouse)
~ 104 km2 last 30 yr
Management Scale
Spatial
Chesapeake Bay
Agricultural
region
Lakes or rivers
Pacific
Northwest
Agricultural
region
- lO'tan2
Temporal
> Syr
>5yr
> 10 yr
>100yr
» lyr
next 5 yr
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Table 5. Interaction of Air Pollution and Temperate Forest Ecosystems under Conditions of
Intermediate Air Contaminant Load—Designated Class II Interactions (Smith, 1981)
Forest Soil and Vegetation:
Activity and Response
Ecosystem Consequence
and Impact
1. Forest tree reproduction,
alteration, or inhibition
2. Forest nutrient cycling, alteration
a. Reduced litter
decomposition
b. Increased plant leaching, soil
leaching, and soil weathering
c. Disturbance of raicrobial
symbioses
3. Forest metabolism, alteration
a. Decreased photosynthesis
b. Increased respiration
4. Forest stress, alteration
a. Phytophagous insects, increased or
decreased activity
b. Microbial pathogens, increased or
decreased activity
c. Foliar damage increased by direct air
pollution influence
1. Altered species composition
2. Reduced growth, less bioraass
3. Reduced growth, less biomass
4. Altered ecosystem stress
a. Increased or decreased insect
infestations
b. Increased or decreased disease
epidemics
c. Reduced growth, less biomass,
altered species composition
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Table 6. Total Soil and Litter Ca2* Held in the Litter Layer and the Surface 2.5-cm Soil Layer,
Expressed as a Percentage of the Total Ca in the Soil and Litter to a 50-cm Depth, at
Three Sites along an Acid-Deposition Gradient from Southern Illinois to Southern
Ohio (Loucks et aL, 1993).
litter (g/ra2)
Weight of Ca in
Surface 2.5 on (g/m2)
Weight of Ca, 23 to
50 cm (g/ra2)
Tonch-of-Nature
(Illinois)
634
127.40
Hoosier National
Forest Edge of Appalachia
(Indiana) (Ohio)
Depth of A° (cm)
Weight of A° (g/ra2)
Weight of Ca in
0.25
350
5.02
1.30
2470
35.4
2.75
5198
74.6
6.87
53.31
9.98
31.06
Total Ca (g/ma)
% of Ca in surface
layers
138.76
8.2
95.58
44.2
115.64
73.1
-
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Peer Review
DRAFT
September 1993
Issue Paper
on
BIOLOGICAL STRESSORS
Daniel Simberloff
Department of Biological Science
Florida State University
Tallahassee, FL
Martin Alexander
Department of Soil, Crop, and Atmospheric Sciences
Cornell University
Ithaca, NY
Prepared for
Risk Assessment Fornm
UJS. Environmental Protection Agency
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CONTENTS
1. INTRODUCTION AND SCOPE 6-3
2. KEY DIFFERENCES BETWEEN BIOLOGICAL STRESSORS AND
CHEMICAL AND PHYSICAL STRESSORS 6-5
3. SURVIVAL, PROLIFERATION, AND DISPERSAL 6-9
3.1. Survival 6-9
3.2. Proliferation 6-13
33. Dispersal 6-18
4. EFFECTS 6-24
4.1. Types of Effects 6-25
42. Stochasticity and Dose Response 6-33
5. DEFINING ENDPOINTS 6-36
6. UNCERTAINTY OF BIOLOGICAL STRESSORS AS OPPOSED TO
CHEMICAL AND PHYSICAL STRESSORS 6-38
6.1. Action in the Face of Uncertainty 6-40
7. RECOVERY , 6-42
8. SUMMARY .' 6-44
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1. INTRODUCTION AND SCOPE
Several factors must be considered in assessing the risk posed by a biological stressor.
The stressor must first invade a particular ecosystem. Yet even if it survives, it need not have a
discernible impact on that ecosystem. The probability of either successful invasion or disruptive
effect is nighty uncertain. This high degree of uncertainty is the basis for some paranoia
concerning the outcome of the release of novel organisms—particularly genetically engineered
organisms—into managed or natural ecosystems. Conversely, despite the uncertainty, some
biologists are quite unconcerned about novel introductions. Both groups muster evidence to
support their contentions, but rarely have members of either camp attempted a formal risk
assessment for such stressors. That some introduced organisms have become established and
have caused major disruptions proves that a risk exists, especially for organisms about which
there is little information—that is, most organisms. On the other hand, the likely failure of
many—and probably most—introduced species to become established or to have a detrimental
impact on an ecosystem (Simberloff, 1981) suggests that the risk is small for most species. Data
do not exist, however, to assess how small is small or to make a determination for most potential
biological stressors about the likelihood of being problematic. Further, some of those relatively
few introduced species that have survived have generated huge costs, leading to the enormous
concern about biological stressors.
Thus the risk assessor concerned with biological stressors probably wiD encounter two
disparate groups of experts. One will emphasize the evidence for problems and will cite
acknowledged instances of major ecological impact The other win downplay such evidence as
idiosyncratic and will present plausible arguments that the-average introduced species wiH be very
unlikely to create an environmental stress. This expert background noise characterizes the
setting for risk assessment of biological stressors.
We begin in section 2 by outlining the key differences between biological and other types
of stressors, including differences that produce the uncertainty mentioned above. In section 3 we
discuss what generalizations are available on survival, proliferation, and dispersal of biological
stressors, with an emphasis on what is not known genetically about these processes. Then, in <
section 4 we consider the myriad kinds of effects that biological stressors can have on
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populations, communities, and ecosystems. In sections 5 and 6 we discuss how the uncertainties
associated with biological stressors, and various characteristics of biological as opposed to
chemical and physical stressors, complicate the choice of assessment endpoints. Finally, in
section 7 we consider ways in which recovery from biological stressors resembles and also differs
from recovery from chemical and physical stressors.
In this paper we conceive of biological stressors as living entities added to an ecosystem.
We do not address other sorts of stress generated by changes in the living organisms of a system,
even though such stresses may b,e extremely important For example, the loss of species to
human harvest can have drastic consequences. Consider the 100-fold decline of the eastern
oyster (Crassostrea virginica), which has had myriad effects. At the turn of the century, oysters
removed particles of 2 to 20 microns in size from Chesapeake Bay by filtering the entire volume
of the bay's water approximately weekly. Today oysters are so scarce that the bay water is
filtered only once a year (Newell, 1988). Because suspended particles can be organisms
themselves and are crucial in transporting nutrients and are a factor in light penetration and
sedimentation, such drastic alteration in their cycling must have substantially affected the entire
ecosystem. Similarly, the loss of various bird species to hunting must have affected plant
dispersal secondarily hi many ecosystems, with subsequent effects on species composition and
other ecosystem features. We view the stressor in these instances, however, s the human activity
that produced the species decline, not the species undergoing the decline.
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2. KEY DIFFERENCES BETWEEN BIOLOGICAL STRESSORS AND CHEMICAL AND
PHYSICAL STRESSORS
The Framework for Ecological Risk Assessment (U.S. EPA, 1992) has many facets that
would be useful in assessing risks imposed by a biological stressor such as an introduced species
or a genetically engineered organism. For example, the Framework separates exposure and
hazard analysis, and the same separation is appropriate for biological agents. Similarly, the
emphasis in the Framework on the scope of the assessment and the breadth of possible risks to
be considered applies equally to biological stressors. Certain characteristics of living organisms,
however, vitiate the utility of a framework written primarily with physical and chemical stressors
in mind.
First, an living organisms reproduce and, in the course of reproduction, may multiply.
With a chemical stressor, simply ceasing to impose the stress inevitably leads to a lessening of the
risk of any ecological effect as the molecule breaks down naturally or as various mitigation
procedures facilitate the breakdown of the molecule and/or its breakdown products or their
transport from the site. With a living organism, arresting its introduction to a site need not
substantially lower the risk of an ecological effect—even if the organism dies—so long as it can
reproduce there.
For some introduced species, a population increases only slightly initially, then remains
stable for many generations as individuals just replace themselves. In 1870, about 12 pairs of the
Old World tree sparrow (Passer montanus) were released in St. Louis, Missouri. They quickly
established a persistent population, estimated at 2^00 to 25,000 individuals, which has been
restricted for over a century to St Louis and adjacent areas of Missouri and Illinois (Lever,
1987). Stragglers are occasionally reported as far away as Wisconsin, but the population never
expands in range or size. The Mediterranean fruit fly (Ceratitis capitate), a major pest associated
with fruit and vegetables, appears to have established a persistent but generally small population
in southern California despite repeated, extensive eradication campaigns carried out by the U.S.
Department of Agriculture. A blue-ribbon committee established by the University of California
(Berkeley) concluded that the sporadic outbreaks hi the Los Angeles area are not likely the
result of repeated introductions, but manifestations of a sparse population that has persisted for
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at least 5 years and possibly much longer (Van Steenwyk, 1990; Carey, 1991). For risk
assessment, the situation may be considerably worse than these metazoan examples indicate.
Microbes can persist for long periods, by various means (see section 3.1), at such low numbers as
to be undetectable, yet can, under certain circumstances, grow rapidly and spread. Chemical and
physical stressors normally would not have such abilities, though a sequestered chemical could be
liberated by some physical and/or chemical perturbation.
Reproduction and low-level, long-term maintenance at one site, however, is probably not
the most common trajectory of a surviving introduced species. Most introduced .species that
survive at all probably increase in numbers and spread, at least initially. Certainly both the tree
sparrow and the Mediterranean fruit fly increased, though the initial increase was limited and
there was almost no geographic spread. A more likely result, if an introduced species survives, is
substantial population increase associated with at least some geographic spread Although rate
of increase is limited by such life-history characteristics as generation length and fecundity, all
species have the capacity for exponential increase in an appropriate environment.
From the standpoint of potential ecological effects, the possibility of geographic spread is
of even greater concern than the likelihood of local increase. Indeed, all species have means of
dispersal. Because the nature of this dispersal process and of range changes generally has been
extensively studied (e.g., Johnson, 1969; Pielou, 1979), some assessment of risk of spread can be
attempted. Yet this process of spread, particularly for species that move of their own volition
rather than through passive means (e.g., by wind, water currents, or phoresy), is qualitatively
different from that of chemical or physical stressors. Indeed, many living organisms are capable
of dispersal over extremely long distances, and such dispersal is nicely to make risk assessment
particularly difficult—a problem discussed in more detail in section 33. It is also worth noting
that even species that depend on either physical processes or other species for transport can
increase their range substantially through increments because they can reproduce and multiply as
each generation reaches a new suitable site.
For example, although the Asian fungus causing Dutch elm disease (Ophiostoma ulmi)
requires two bark beetles for dispersal in North America, the fungus spread rapidly from its
initial site of introduction in the early 20th century to infect most elms throughout the East
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(Elton, 1958; von Broembsen, 1989). Interestingly, one of its beetle vectors, introduced from
Europe at approximately the same time as the fungus itself, spread more rapidly than the fungus
(Elton, 1958); nevertheless, the fungus ultimately reached almost the entire range of its host.
Another characteristic difference between biological stressors and chemical or physical
ones is the range of stressed organisms. For example, pathogens affecting animals, plants, and
microorganisms can be particularly host specific, as can parasitoids of insects and some
phytophagous insects. Such limited ranges of suscepts are not characteristic of chemical or
physical stressors, which typically act on a wide range of species.
A key difference between chemical or physical stressors and biological ones is that the
latter can evolve, and this evolution can either increase or decrease the risk of ecological effects.
For example, the Dutch elm disease fungus was introduced to North America from Europe on
infected logs. In North America, it apparently evolved more pathogenic strains, which have been
implicated in a recent new outbreak of the disease in Europe (von Broembsen, 1989). On the
other hand, numerous introduced pathogens and their hosts have coevohred such that initially
virulent diseases have become relatively benign (Ewald, 1983). For the myxoma virus introduced
to-control rabbits in Australia, the virus has become attenuated and the rabbits resistant (Krebs,
1985; Williamson, 1992). Yet evolution itself has numerous unpredictable aspects. For example,
it may be severely constrained by which mutations happen to arise during a particular period, or
by which chromosomal crossovers occur. Such aleatory components of evolution vastly
complicate risk assessment and differ qualitatively from any process associated with chemical or
physical stressors.
A biological stressor may generate another kind of bizarre problem, a development
completely different from anything encountered with chemical and physical stressors—the
stressor may become a valued ecosystem component For example, although eucalyptus
introduced to Angel Island in San Francisco Bay provide a much less suitable habitat than native
plants for the resident native animal populations, a California State Department of Parks and
Recreation plan to remove them generated a firestorm of protest from people who found the
plant species aesthetically pleasing (Azevedo, 1990). Similarly, the population of mountain goats
(Oreamnos americanus) introduced by hunters to the mountains of Washington state's Olympic
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Peninsula in the 1920s has grown and spread. Now the goats are destroying native plants,
including rare species, in the Olympic National Park. A National Park Service plan to eliminate
almost all of them, however, alarmed not only animal rights groups but hunters (Luoma, 1989).
This scenar'o is j layed out repeatedly with game mammals introduced by hunters that destroy
native plants; feral pigs are of particular concern. The general problem has been exacerbated
recently with the advent of animal rights groups that do not oppose the removal of the animals
per se but object to killing them by any currently practical means.
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3. SURVIVAL, PROLIFERATION, AND DISPERSAL
For a biological stressor present at a particular site, four factors must be considered in a
risk assessment: (1) probability that the organism will survive; (2) probability that the survivor
will multiply to yield a larger population; (3) probability that the species win disperse from the
initial site to another location at which establishment is possible; and (4) probability that the
stressor will be harmful. If probabilities for the first and fourth factors are zero (i.e^ the species
does not survive or has no harmful ecological effects), the probability of a deleterious
environmental impact is zero. If the probability for the third is zero (Le., the organism fails to
spread), the probability of environmental stress resulting other than at the initial-site need not be
zero. One must also consider the probability of genetic information associated with
environmental impact being transferred to another species; this possibility is discussed in
section 4.1. If such a lateral transfer" occurs, the same probabilities of survival, multiplication,
dispersal, and harmful effects must be applied to the organism receiving the genetic information
(Alexander, 1985a). A similar approach to risk analysis is used by the U.S. Department of
Agriculture (Orr and Cohen, 1991).
3.1. Survival
The number of propagules 'of a released biological stressor is likely to be critical to its
initial reproduction and multiplication and thus to the risk of a potential ecological effect Every
species has a "minimum viable population size" (Shaffer, 1981; Simberloff, 1988) such that, when
the population falls below this point, rapid extinction is likely because of a number of fofces, all
of which affect small populations more heavily than large ones. For example, some dioecious
species experience difficulty finding mates at low densities, while other species may require group
stimulation of ovarian development or mating (Simberloff, 1986a, 1988). These problems
concerning breeding and increase at low population size—collectively termed the Alice
effect—have been the target of some modeling efforts (Dennis, 1989). For a number of classes
of introduced species, an increase in the number of propagules contributes to an increased
probability of survival, though many other factors also contribute (SmaDwood, 1990). Pimm
(1991), studying game bird introductions with different numbers of propagules, found probability
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of survival to increase with the number of propagules and even claimed to find a sigmoidal shape
for the curve.
Survival and initial increase also dep nd critically on the environment at the site of
introduction. The percentage of surviving propagules varies with the particular organism and the
environment (Liang et al., 1982). Some species can survive in one environment for many years
with no replication but disappear rapidly in other environments. The critical environmental
differences may be quite subtle, as in the case of microorganisms that persist in one soil but
decline to undetectable levels in another. Nonetheless, every species has a range of habitats
compatible with survival and reproduction; often the range for survival is wider than it is for
reproduction. For example, many plant species' geographic ranges are limited not by the ability
of adults to survive, but by their ability to produce seed at the margins of their range. Thus
individual plants that appear perfectly healthy at the margins may all derive from seeds produced
in more central populations and be unable to produce seed that will expand the population
further (e.g., Neilson and Wullstein, 1983). In fact, many species may exist as metapopulations
(Le., loosely connected sets of populations) in which a few "source" sites with ideal habitat
produce dispersing individuals that colonize lower-quality "sinks" (Pulliam, 1988; Pulliam and
Danielson, 1991). While such a species would not be capable of maintaining itself in the sinks,
these may constitute the majority of the range.
The vast majority of propagules of plants, animals, and microbes almost certainly die
without issue because they end up in inappropriate habitats (e.g., terrestrial seeds land in water,
microbes colonize too acidic or alkaline an environment, parasitic, organisms fafl to find a
suitable host). Determining what exactly constitutes a receptive environment for a species often
is not a trivial matter, but instead one that requires intensive experimental and natural historic
research. Information on the survival of microorganisms comes chiefly from the public health
and agricultural literature. Abundant data exist, but only for a few organisms that are important
in diseases of humans, livestock, and agricultural crops, for a few bacteria used as indicators of
fecal contamination, and for several bacteria of agronomic importance (Alexander, 198&). This
information shows clearly that certain microorganisms are able to persist, often for long periods,
in environments in which they are not indigenous (Liang et al., 1982). While the relative
frequency among microorganisms of species that survive in alien habitats is unknown, we know
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that the rate of death of such introduced microorganisms is affected by a number of physical and
chemical factors, including drying, moisture level, pH, solar radiation, salinity, and the presence
of organic and inorganic toxins.
Moreover, although the survival of many microorganisms is not greatly affected by these
stresses, they do not persist For most potential microbial invaders, it is not the physical and
chemical properties of the environments (at least of soils and waters) that preclude
establishment In laboratory tests, samples of sofl or water that are sterilized (to render them
free of other microorganisms) are readily colonized by species that do not—as well as those that
do—have the capacity to survive or to proliferate in nonsterBe samples of the same sofl or water.
Undoubtedly, the activities of some of the resident populations constitute the basis of this biotic
resistance to the successful establishment of an invader. The activities of concern may be
competition for limited resources between the recent arrival and indigenous populations,
parasitism, or grazing by protozoa or other predators. Although grazing pressure and the degree
of competition and parasitism clearly vary by habitat, it is usually impossible to predict which
community or habitat will be suitable for invasion, except for environments that have such
adverse conditions because of physical or chemical properties that only species tolerant of those
factors wfll be able to flourish.
Thus microbial species are precluded from establishment because they fail to compete
with the indigenous microflora and as a result die from starvation or because they are susceptible
to predation by protozoa and possibly metazoa. As hi metazoans and plants, however,
competition between introduced and native species is extremely difficult to document in the field
(see section 4.1). Although microorganisms have parasites, parasitism is not known to eliminate
introduced microorganisms. Nonetheless, parasites of microorganisms may be important when a
species achieves substantial population size or biomass; an impact of parasites on a low-density
population of a particular host species is unlikely. Again, the survival of a microorganism in one
environment but not in another often is probably the result of differences in the competitive
and/or predatory regimes. Data to support this assertion are sparse, however, and most studies
are not very convincing. Moreover, the view that nutritionally fastidious microorganisms wfll not
persist hi natural environments runs counter to the facts; complex nutrition may affect the
capacity of a species to proliferate, but it does not necessarily deter its survival
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To survive, an introduced microorganism must tolerate abiotic stresses. It must also
avoid, evade, or cope with many competing microorganisms, the grazing activity of predators, and
possibly, attack by parasites. Starvation is a major stress, and a survivor must be able to endure
periods of nutrient deprivation. Some microorganisms overcome these obstacles by forming
resting structures (Le., the endospores of certain bacterial genera; cysts of many protozoa; and
sderotia, chlamydospores, conidia, and other structures of fungi). These resting structures can
persist for many years. Even some bacteria and fungi with no such specialized structures will
persist for months, years, and even decades. Data also suggest that certain bacteria enter a
dormant state in which they appear to be injured in some manner. Injured bacteria that have
been found in natural waters (Roszak and Corwell, 1987) and have undergone some physiological
stress such that they will not multiply in the usual media used for their enumeration can be
recovered in appropriately supplemented media. These "injured" bacteria generally would not be
detected if one used conventional procedures; yet they may endure for long periods and
ultimately give rise to a population that grows and has a deleterious impact In many instances,
the number of surviving microorganisms may be below the level of detection and be deemed to
be absent Given suitable conditions, however, the few survivors will multiply and give rise to
large and possibly harmful populations. A potentially major impact from a stressor initially
below detection limits, however, is not characteristic of abiotic stressors.
/
Some metazoans and plants also have resting stages in which they can remain viable for
long periods, even in a stressful environment The seeds of many species may remain dormant in
seed banks buried in the soil for many years before germination—this is a typical trait of annuals
and other short-lived species. Thus one cannot assume that a plant has disappeared from an
area simply because no seedlings are observed. It is possible to assay sofl for viable seeds but the
procedure can be onerous, especially if seed density is low. Among animals, resistant eggs of
nematodes, fairy shrimp, rotifers, mosquitoes, and other species correspond to the resting stages
of microorganisms and seeds of plants. Similarly, sponge gemmules, bryozoan statocysts, and
other life-history stages constitute persistent resting stages and often are characteristically
produced when the environment becomes harsh.
Therefore, considerable uncertainty exists in predicting the probability of survival of «
microbiological, plant, and metazoan stressors, except for the very few groups that have been
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intensively studied. Methods exist for assessing survival, but in view of the frequent lade of
understanding of the reasons for elimination or endurance and of the contribution of abiotic and
biotic factors to death, only a few generalizations are possible.
3.2. Proliferation
For microorganisms, various terms are used to describe an increase in number or
bioraass. Although the term growth is often used for bacteria and other unicellular
microorganisms, such growth actually refers to multiplication (i.e., increases in cell number or
population size). Filamentous microorganisms (Le., fungi, many algae, and some bacteria) may
increase in biomass without a concomitant increase in countable numbers; thus, they grow
without necessarily multiplying. Some specialists refer to this increase in number or biomass as
colonization or, less commonly, establishment To avoid semantic difficulties in this discussion,
growth, multiplication, and colonization are all considered proliferation. For plants and
metazoans, "population growth* and "multiplication" both mean an increase in number, while
"colonization" refers to the establishment of a propagule as well as a subsequent increase in
number.
After an introduction, a propagule that does not diapause by some means can multiply
rapidly. For an unlimited environment, numerous models of local population growth predict the
initial trajectory (Krebs, 1965). For plants and metazoans, depending on the level of knowledge
available, these models can incorporate such features as age and sex structure. If interactions
with other species do not intervene, this local population growth can often be modeled quite
accurately. Except for highly localized environmental effects, however, the main stresses imposed
by an introduced species would be experienced at a much broader geographic scale as the initial
population spreads to form a metapopulation or completely separate populations.
Metapopulation dynamics are just beginning to be modeled (e.g^ Harold and Gflpin, 1991) and
few empirical data are available to test the models. Moreover, the simple trajectories of
single-species population growth are greatly complicated when interactions come into play. This
is not to say that prediction is not possible in such instances, but that many data are needed and
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detailed observation and, often, experimentation are necessary to establish which interactions are
crucial in limiting a population's growth.
The fact that proliferation of microorganisms occurs is obvious. It is attested to by t' te
outbreak and spread of diseases of humans, other animals, and plants as well as the development
of phytoplankton blooms, the spoilage of foods, and the appearance of large bacterial numbers
on early emerging roots. The issue in risk analysis is not whether proliferation of any
microorganism will take place, but whether the population density or biomass of a particular
species will increase hi a given environment Currently the data base on this issue is very small.
Proliferation is essential for any species to have an environmental effect because the
number of initial propagules is nearly always too small to be of ecological concern. Proliferation
requires that nutrients that can be used by the particular organism be available. For most
microorganisms, the limiting nutrient is carbon (C) because the supply of inorganic nutrients is
generally not limiting. In those instances in which much readily degradable organic matter is
present and that organic matter has a high C:N or C:P ratio, the limiting nutrient may be
nitrogen (N) or phosphorus (F). For algae and photosynthetic bacteria, the limiting nutrient is
inorganic—usually N or P. The episodic increase in the supply of limiting nutrients, however, is
not sufficient to result in an increase in the abum ance of an individual species requiring that
nutrient Many coexisting species may use the same nutrients. Which of these many species are
able to respond and proliferate cannot generally be predicted. The sole exception is the
environment in which a truly unique nutrient exists, and, apart from a few organic materials that
support a limited range of microorganisms, the truly unique nutrients are host organisms. The
host for a parasite constitutes a unique nutrient for a pathogen, although the uniqueness requires
that the parasite overcome the many barriers to infection (e.g., skin, cutin, phagocytes, lignified
tissues, antibodies).
From the viewpoint of exposure analysis, for which microbial proliferation is an essential
component, the magnitude of the increase hi population size or biomass can be enormous. A
few bacterial cells may multiply to yield populations of 10*, 10', 10U, or more cells, and the
exposure rises in parallel with this increase hi organism abundance. Similarly, a biomass of less
than 1.0 ng (10* g) may increase to yield a biomass of 1.0 kg or more, such as can occur in algal
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blooms in aquatic environments. In this fashion, the ultimate exposure—in the worst case—is
the maximum population size or biomass to which susceptible populations or communities
are exposed.
Microbial rates of proliferation vary enormously (Alexander, 1985b). At one extreme are
many soil bacteria; because of the slow turnover of organic material in sofl not receiving recent
additions of plant residues or leaf litter, no more than a few cell divisions may occur each year.
At the other extreme are bacteria, fungi, algae, or protozoa that develop at very h,'gh rates and
for which a doubling in cell numbers or biomass may require less than an hour. Unrestricted
proliferation of a single cell with a doubling time of 1 hour would yield a population 8 million
times larger after one day—a phenomenal increase in exposure. A key word is "unrestricted,*
because rarely do conditions permit unrestricted growth, just as in plants and metazoans
discussed above. Yet few of the restrictions on microbial proliferation are characterized, other
than nutrient limitations and host responses. Even these two have qualifications, moreover, since
an environment in which microorganisms are limited by a particular organic nutrient often
receives episodic inputs of that limiting nutrient Similarly, a host species that typically exhibits
resistance to parasites contains compromised or genetically more susceptible individuals, or it
undergoes modifications because of environmental changes that permit rapid proliferation of a
parasitic microorganism.
Maximum plant and metazoan reproductive rates also span an enormous range; generally
the rate is inversely correlated with body size (Bonner, 1965; Fenchel, 1974). Reproductive rates
are useful in assessing risk from a new species, but other actors often overweigh them. If a
biological stressor is introduced to an ecosystem during or- soon after a disturbance that greatly
reduces potentially competing existing populations, a high reproductive rate may increase the
probability of initial establishment and even dominance. Weedy plants with high reproductive
rates often dominate locally after a hurricane or trail-clearing in a forest Maximum rates
measured under ideal conditions, however, probably bear little relation to realized rates under
almost any field conditions, except perhaps at the outset of an introduction. Also, many species
that are both common in nature and ecologically important have low maximum reproductive
rates, while many species with high reproductive potential are uncommon except in sparsely ,
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distributed disturbed areas. Thus the stress imposed on a system by a quickly reproducing
organism may be temporary.
I
Studies of individual microorganisms in the laboratory provide hints of the traits that may
result in rapid proliferation hi nature—but no more than hints. In the absence of validation in
nature, or at least hi microcosms, they should be taken only as highly tentative suggestions. One
might assume that a species with the fastest multiplication rate would be the one that proliferates
most readily in nature. As with plants and metazoans, however, this simplistic assumption is
tenuous because many of the dominant species in nature do not multiply quickly. In fact, for
plants and metazoans, many simple models of species interactions fail because they, entail this
assumption. The intrinsic multiplication rate of a species in isolation under ideal conditions must
be considered along with the limitations imposed by physical and chemical constraints in the
environment, the concentration and turnover of limiting nutrients, grazing pressure, competition
with other organisms at the same site, and the possible impact of parasites. Given the paucity of
knowledge about the impact of these limitations on proliferation, the unmistakable conclusion is
that rate of multiplication and, indeed, whether a particular introduction will proliferate at all
usually cannot be predicted for most organisms and most environments. Among the few
microorganisms that are exceptions are pathogens of humans and economically important animal
and plant species as well as microorganisms of environments that are so hai h (e.g., solar salt
ponds and hot springs) that few species that reach these sites are able to tolerate the abiotic
stresses.
The main attempt to avoid the intensive research effort needed to fill the lacunae
described above, and thus to produce a shortcut to predicting survival and initial proliferation, is
the hypothesis of "biotic resistance" (Simberloff, 1986b), which states that introduced species are
less likely to survive hi more diverse, complex communities because of the increased "resistance"
of various sorts from resident species. Among possible forces opposing the insertion of an
introduced species into a community are competition, predation, and parasitism. Sometimes the
survival and effects of a biological stressor seem obviously attributable to a release from
biological resistance. For example, the invasion of Lake Huron by the alewife (Alosa
pseudoharengus) along with its subsequent proliferation and the numerous accompanying •
ecological effects depended on the prior introduction of the sea lamprey (Petromyzon marinus),
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which greatly reduced populations of native fishes such as the lake trout (Salvelinus namaycush)
and burbot (Lota lota) that would have competed with the alewife (Smith, 1968).
However, it is unlikely that the simple criteria of size and complexity of the resident
community will greatly help prediction of what constitutes a receptive environment For
example, one avatar of the biotic resistance hypothesis is that disturbed habitats are more easily
invaded than undisturbed ones. Yet a dose examination of records of invasions into various
habitat types casts doubt on this view. The habitats, such as agricultural ones, that appear to be
particularly invasible are generally anthropogenous ones (i.e.» new groups of species in a highly
human-modified physical setting), while disturbance per se does not seem to conduce
automatically to invasibility. For example, naturally disturbed habitats such as fire disdimax
forests or high-energy beaches do not appear to have more introduced spedes than do other
pristine habitats (Simberloff, 1986b). Similarly, another version of the biotic resistance
hypothesis is that prior invasion of a system by one or more spedes hinders successful
subsequent invasion. In the systems in which this effect has been studied, however, it appears
that the spedes introduced earlier were a priori more likely to survive independently of which
other and how many other spedes were present, while the later invaders were poorer colonists
that would in any circumstances have had a low probability of survival (Keller, 1984; Washburn,
1984; Simberloff and Boecklen, 1991). For microbes, apart from some highly stressed or
nutrient-poor environments, there is little evidence that species-poor communities are more
easily invaded than species-rich ones.
_ A complication in assessing risk from a novel biological entity is that, even if a site has
habitat adequate for survival and reproduction of a spedes, colonization has a stochastic element
such that "replicate* introductions do not have the same trajectory. For example, a few pairs of
the Old World house sparrow (Passer domestiau) were released in Brooklyn, New York, in 1851,
never to be seen again. A larger number of propagules was rdeased there in 1852 with the same
result In 1853, at the same time of year and in the same place, a similar-sized propagule was
rdeased. It multiplied enormously, the spedes spread throughout North America, and it is now
one of the most common birds on the continent (Long, 1981), displacing native martins,
swallows, and wrens (Sharpies, 1982). Similar tales abound among insect introductions for
biological control It is often assumed that such differences in the outcomes of "replicate*
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releases are caused by genetic differences among the propagules, but such differences are
probably immeasurable for most propagules of potential biological stressors (Simberioff, 1985).
Thus, at least some fraction of the apparent stochasticity of biological introductions will always
be present.
3-3. Dispersal
Once a species has survived at its site of introduction, its potential spread must be
assessed. Many biological stressors are mobile, and their dispersal often does not follow the
trajectories associated with movement of chemical or physical stressors. The probability of
dispersal or spread of the biological stressor from the point of its first introduction or detection
to other sites is critically important to risk assessment The stressor's impact at the original
location may be negligible or even undetectable. If it is transported to a more hospitable site,
however, it may cause major harm there. Even if its impact in the environment of initial
introduction is substantial, the stress could be highly localized and self-contained unless the
stressor spread to new areas suitable for establishment Moreover, the issue of dispersal is not
merely one of physical dissemination. Because the propagule must reach the new locale alive,
/
dispersal must be assessed together with an evaluation of factors that could cause the death of
the propagule during dispersal.
Organisms can disperse by a bewildering array of means (Mackenzie et aL, 1985; Upper
and Hirano, 1991). Particular microorganisms can use one or more of several modes: (1)
through the air; (2) in association with currents or by mixing in streams, rivers, lakes, or marine
waters; (3) over the soil surface with runoff after precipitation; (4) through the soil with vertical
movement of water; (5) through ground water in aquifers; (6) by splashes or raindrops falling on
foliage or the sofl surface; (7) in connection with animal movement; and (8) as a consequence of
human activity or implements. In addition, microorganisms have the capacity to move by such
means as ballistic discharges as wen as via hyphal growth by fungi, phototaxis by algae in surface
waters, or motility by bacteria. The distances reached by such means, however, are rarely more
than a few centimeters—a few meters at most—and thus such movement is not likely to be ,
consequential hi risk assessment
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Aerial dispersal is often the chief or only dispersal mode of a microorganism (Upper and
Hirano, 1991). Individuals may be lifted into the air when winds dislodge propagules from plant
foliage; by ballistic discharges that allow fungal spores to move away from the plant or soil
surface with small particles of soil carried aloft by the wind; as aerosols from surface irrigation
waters as well as from sewage treatment plants; or as dust from farming, construction, or other
human activities. The rapid spread of some fungal diseases of plants, for instance, provides
evidence of this means of spread. Aerial dispersal depends on the nature of aerial dissemination
of particles, the factors that result in the introduction of microbial propagules into the air, and
the death rate of propagules during such transport
Many microorganisms require water to move (Alexander, 1971). Such movement may be
tied (o lateral currents in both freshwater and marine environments, vertical mixing in many
bodies of water, or the lateral transport of water and suspended soil particles following rainfall or
snowmelt The extent of movement depends on the physical transport of the water, the factors
that place the organisms in the path of flow, and survival of the organisms as they are being
moved. Although bacteria and viruses also pass through soil with percolating waters, few of the
propagules move far because they are sorbed to soil surfaces or retained by physical filtration
associated with small pores in soil. Nonetheless, many microorganisms can enter the underlying
/
aquifer by passing through channels. While lateral dissemination of bacteria and viruses may
occur with the moving stream of ground water, the distances traversed are short
Biological vectors often serve as an efficient means to move microorganisms. The vectors
may be birds, large terrestrial animals, insects, rodents, fish, earthworms, growing roots, and,
probably, zooplankton. The number of microbial propagules borne by such vectors may be small;
however, the dissemination often is remarkably efficient because the vector may carry the
propagule unerringly to a new habitat that the microorganism can exploit, as is common with
insect, bird, and rodent transmission.
Mctazoans and plants use most if not all of the means that microorganisms do to
disperse. They are transported passively by wind and water, carried phoreticalry by animals, and
are often moved by human activities such as agriculture. Movement through sofl is relatively ,
unimportant and, in any event, would be quite local. Even passive movement by animals and
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plants is often initiated, just as in microorganisms, by a behavior or process that places the
propagulc in the vicinity of the transport agent, (e.g., plants explosively release their seeds to
wind and water currents, spideriings assume postures and spin silk that facilitates ballooning).
Virtually al anr tal and plant species have characteristic stages in their life cycles during which
dispersal is especially likely, and these stages are often associated with behavior or structures that
enhance dispersal (Johnson, 1969).
Many animals, however, have more active, sustained behaviors that enhance the
probability of great dispersal. Some are migratory, for example, and others have innate behavior
that causes their departure from the natal area to be prolonged and, often, to terminate far from
it Further, many animals and some plants can time their movements to increase the likelihood
of dispersing effectively and terminating the dispersal in a habitat suitable for existence
and reproduction.
Many of the factors governing the transport of inanimate materials, especially particles,
would likewise govern the transport of microorganisms and small plants and metazoans. Indeed,
much of the modeling of such transport is based on particle transport or related models, such as
smoke-stack diffusion. A critical difference, however, is that living organisms can die (or viruses
can lose infectivity). Particularly for passively transported organisms, dispersal models that do
not account for the decline of viability or infectivity will overestimate the distance likely to be
dispersed or the number of viable propagules that will arrive at a new site. Unfortunately, there
are not substantial data on death during dispersal in various media.
Distances traversed by microorganisms, plants, and metazoans range from a few
centimeters to thousands of kilometers. In particular, active or passive aerial dispersal (including
transport by birds) can move organisms vast distances. The literatures of agricultural and forest
entomology, public health, veterinary medicine, and plant pathology contain many data on
means of spread.
Faced with the bewildering array of dispersal means and their varying efficiencies, Pielou
(1979) distinguished between two rather distinct forms of spread. In the first, which she called
diffusion, a species' spread more or less closely approximates increasing concentric circles for
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which the circumferences become progressively more warped. The Colorado potato beetle
(Leptinotarsa decemlineata) in Europe (Nowak, 1975) and the Japanese beetle (PopUtia japanica)
in the United States are good examples. The rate at which such species' ranges expand is a
function of the dispersal and 'lehavioral biology of the species. Various models, beginning with
simple diffusion models (e.g., Upper and Hirano, 1991; Strauss and Levin, 1991; Anderson and
May, 1991), describe the process. These models typically consider the initial density of organisms
and the rate and efficacy of the means of dissemination. The larger the source and the hardier
the propagule, the greater the probability of a successful dispersal. The most extensively
developed models are epidemiological ones concerned with microbial disease agents. Such
models typically predict where microorganisms will be transported and how many win arrive
alive. Those that relate to pathogens affecting plants are particularly useful for studying the
aerial dispersal of spores that lead to disease in economic crops, while those concerned with
infectious agents affecting humans are primarily concerned with host-to-host transmission and
with the movements of individual affected hosts.
While some of the more recent models (e.g., Hengeveld, 1989) seem to simulate some
past, observed gradual spreads of introduced species strikingly closely, it is too early to tell if
their predictions of current spreads will be accurate. The warping of the range circumference as
diffusion proceeds is probably caused by heterogeneities in the physical environment (Simberloff,
1986b), and it is quite possible that, if one knew enough about the habitat requirements of a
particular species, a diffusion-type model could be modified to reflect these heterogeneities. For
example, the Atlantic Ocean and Chesapeake Bay seem to have prevented the Japanese beetle
from spreading evenly in all directions from its point of introduction in New Jersey. The
unsuitability of ocean as a habitat for a terrestrial beetle is easy to deduce, but the influence of
other habitat gradients on diffusion dispersal will be subtler.
Models for aerial dispersal of microorganisms are probably useful in risk assessments of
nonpathogenic microorganisms. The paucity of information on death, however, win likely affect
their utility because different species have vastly different rates of death owing to the irradiation,
desiccation, and temperature stresses encountered during aerial transmission. Models for
watcrborne dispersal have received less attention for microorganisms, plants, and metazoans. <
Among aquatic-introduced plants and animals, the spread of many of the most problematic
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species (c.g., white amur [Ctenopharyngodon idella], European carp [Cyprinus caipio], zebra
mussel \Dreissenapofymorpha], purple loosestrife [Lythrum saUcaria], water hyacinth [Eichhomia
arassipes\ Eurasian water mflfoil [Myriophyllum spicatum]) has not been modeled in more than
cursory fashion despite extensive empirical nformation. It may seem reasonable to use models
that were developed to simulate the transport of chemicals in water to study microbial or small
plant or metazoan dissemination; however, the attachment of microorganisms to particles, the
likely different death rates of different species of organisms, and differences in death rates
between sorbed and free cells suggest great caution should be used in applying transport models
of nonliving materials to organisms. Thus, predicting even the gradual, diffusive spread of living
organisms is difficult despite a number of possible models.
By contrast to more or less regular diffusion, some introduced species have spread
irregularly from the outset or after a short period of circular range expansion. Often several foci
arise simultaneously by a long-distance "jump" (Pielou, 1979), each subsequently serving as a base
for slower circular growth or yet another long-distance leap. The aphid Hydaphis tatarica was
restricted to a small area of southern Russia, apparently by the limited range of its host, Tatarian \
honeysuckle (Lonicera tatarica). It was in the process of spreading gradually westward and had
just reached the Moscow region when it was scientifically described in 1935. As the host
.honeysuckle was planted as an ornamental throughout much of central and southern Europe,
however, the aphid's range increased greatly and irregularly, often to areas not contiguous with
the original range.
Modeling jump-dispersal to allow prediction seems a far more formidable task than
modeling diffusion dispersal. Probably many more propagules actually undergo jump-dispersal
than are recorded. Most of them, however, never establish ongoing populations because they
either M in unsuitable habitat or M to increase for the various reasons outlined above (e.g.,
insufficient number of propagules). Yet it is dear that many suitable sites are not reached by
adequate propagules of the myriad introduced species that have survived and often increased
dramatically in many areas of the globe. Because physical forces such as upper air currents can
sometimes be identified as likely agents of jump-dispersal, at least direction may be predictable.
For other modes, such as the archetypicalry improbable event of the transport of a seed on a
bird's foot (Simpson, 1952), generating useful probabilities for prediction would be much more
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difficult Nevertheless, long-distance transport by birds was certainly important in establishing
the ranges of many invertebrates and freshwater algae as well as flowering plants (Caiiquist,
1974; Pielou, 1979) and so must be a fairly common event
Human movement of living organisms (anthropochory), both deliberate and inadvertent,
usually constitutes jump-dispersal. Recreational vehicles have transported gypsy moth (Lymantria
dispar) egg masses on land and zebra mussel (Dreissena potymorpha) juveniles in freshwater.
Innumerable introductions of terrestrial, freshwater, and marine species have occurred in ship's
ballast The ornamental plant industry spread many important species, such as purple loosestrife
(Lythrum salicaria), while individuals seeking attractive flowers dispersed others (e.g., water
hyacinth [Eichhomia crassipes]), and forestry activities yet others (e.g., MeJaleuca quinquenervia).
Fish and game departments as well as individual fisherman have completely changed the
ichthyofaunas of many areas, including the American West, where in several drainages, most of
the native species are threatened (Moyle, 1986). Many biological stressors have been introduced
through deliberate or inadvertent release of pets. For certain taxa, introduction societies have
changed entire faunas. In the Hawaiian islands, at least 70 species of passeriform and
columbiform birds have been introduced; many survived and these completely dominate lowland
areas, while native birds are now almost restricted to upland native forests (Simberloff and
Boeckle., 1991). Where an introduction is deliberate, an initial jump-dispersal can be predicted.
For many inadvertent cases of anthropochory, detailed prediction will be impossible, although
heavy use of certain transportation routes will likely generate correspondingly large numbers of
jump-dispersals along those routes.
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4. EFFECTS
A novel biological entity, such as an introduced species or a genetically engineered
organism, can affect an ecosystem in numerous ways. Often, de'ibera s introductions of game
animals are said to be desirable on the grounds that they are "filling an empty niche" (Ebenhard,
1988), and inadvertently introduced species that establish especially large populations also are
occasionally said to have "occupied a vacant niche." It is notoriously difficult, however, to show
that a niche truly is empty (Herbold and Moyle, 1986), and in one sense it seems impossible that
there is such an entity. Because ultimately resources are metabolized in some way, if only by
bacteria, it is difficult to imagine that a surviving introduced species would not at least affect the
topology of energy-flow webs and nutrient cycles (Simberloff, 1991). In any event, even a species
that fills a classically empty niche can have an enormous impact on an ecosystem. In 1788, the
first English settlers to Australia brought 7 cattle, 7 horses, and 44 sheep. By 1974, cattle alone
numbered 30 million and were producing 300 million cowpats daily, which are not removed by
native dung beetles and modify existing plant communities, provide breeding habitat for
numerous insect species, and in some areas dry and blanket the ground. Three beetle species
introduced in 1967 filled this "empty niche" by quickly establishing over large areas and rapidly
removing vast numbers of cowpats, working the dung into the soil. Their full impact has yet to
be calculated but mu. t be enormous.
Attempts to predict the ecological consequences of biological stressors are confounded by
the enormous number of possible effects, whether the effects are on particular species or on the
function of entire communities. A biological stressor can have a major impact on a single
population (e.g., if it is a host-specific pathogen or parasite), or it may harm several species (e.g.,
if it has a wide host range). It may upset entire communities, or it may alter one or more
processes important to ecosystem function. Although biologists and environmental scientists
often have favorite species or processes they feel should be examined in any assessment of
ecological effects of biological stressors, many of the favored species or processes do not seem
always to have a role so critical as to warrant particular concern. Unfortunately, there is no
consensus on which species or processes should be singled out for assessment, nor is there
research designed to facilitate such decisions. It is almost platitudinous to say that the perceived
importance of the ecological consequences of a new species depends on the perceived
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importance of the species or processes affected. Yet the problem often is lack of agreement on
which are the important species or processes.
4.1. Types of Effects
One can begin to enumerate the effects that introduced species may have by dividing
them into direct and indirect effects. A species can afl'ect another directly by, for example,
killing it, eating it, or displacing it On the other hand, the effect can be indirect—for example, a
species can modify the habitat of another species or reduce its prey. Although such indirect
effects often are subtle and difficult to elucidate, they are increasingly recognized as extremely
important (Strauss, 1991). They are treated further in chapter 5, on effects characterization.
An introduced species can directly affect another as a pathogen or parasite (Anderson
and May, 1982). Myxoma virus introduced from South America into Australia in 1951 initially
killed over 99 percent of the huge European rabbit population, though subsequent evolution of
both virus and rabbit has allowed some recovery (Krebs, 1985; Williamson, 1992). Frequently,
various epidemiological models, often of the diffusion type, can predict more or less accurately
the geography and time-course of i le initial spread of a pathogen (Anderson and May, 1982;
Dobson and May, 1986). The evolution of benignity and resistance, however, can severely limit
the longer-term predictive power of these models. Further, jump-dispersal of infected hosts can
be a more important mode of spread than diffusion.
The impact of a pathogenic stressor can range from insignificant to devastating on the
plants or animals it invades. A particular microorganism may multiply on or within a host plant
and cause little harm, or it may cause overt damage to a small or large percentage of individuals
of a susceptible host species. If that host fills a "keystone* role (Simberloff, 1991) in the
community, the microbial stressor could markedly disturb the entire community. Such major
disruptions are wen documented for several fungi (e,g^ those causing chestnut blight or Dutch
elm disease). The diseases caused by these fungi have had major impacts on community
structure and ecosystem function, even though they each acted on a single host species. The -
affected host was a critical component of plant communities and the effects rippled through
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populations that interacted with the hosts in various ways. For example, the American chestnut
(Castanea dentate) comprised 25 percent of the individual trees of many forests (Elton, 1958) and
more than 40 percent of overstory trees and 53 percent of basal area (Krebs, 1985). Several
insect species that are host specific to the American chestnut are either endangered or exti ict
because of the destruction of their host by chestnut blight (Oplcr, 1978). The subsequent effects
of the loss of these species on their predators and parasites is unstudied. The oak wflt disease
(Ceratocystis fagaceanan) has increased on many native oak species because the population of red
oak (Quercus mbra), which is particularly susceptible, increased greatly when the chestnut
disappeared (Quimby, 1982). It seems inconceivable that the loss of such a dominant tree could
not have affected many other aspects of ecosystem structure and function, although no
substantial studies have been performed. Whether the consequences of the disappearance of the
chestnut qualify as important effects depends on how one assesses significance (see chapter 2, on
ecological significance).
Such impacts will be especially devastating if the host population has had no contact with
the pathogen or parasite and thus has no resistance. Indeed, the literature of human and
veterinary medicine attests to the marked decline in host populations following exposure to novel
viruses or bacteria. The impact also is affected by the genetic heterogeneity of the potential host
population, its density, and the stage of its life c cle at which exposure to the stressor occurs.
The severity of disease is greater if plants in a community are not diverse and if genotypes in a
population are few, as is the case with the devastation caused by fungi in the monospetific
cropping systems common in agriculture. Furthermore, if the population of susceptible
individuals is dense, the effect of a parasitic microorganism is likely to be far greater than if the
host population is sparse (Pimentel, 1985; Levin and Harwell, 1986). The stage in the life cycle
of host plants or the age of animal hosts also may alter the impact of the pathogen.
Many dramatic examples illustrate damage to natural ecosystems as a result of the
introduction of pathogenic microorganisms. In less than 50 years following its introduction from
Asian ornamental nursery material, the fungus Endothia parasitica became established on 91
million hectares of US. forestland and almost totally eliminated the American chestnut (as noted
above). Introduction of the fungus Ophiostoma ulmi had a devastating impact on forests
containing large populations of American elms. Phytophthora dnnamomi, a fungus pathogenic to
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nearly a thousand plant species, has similarly affected large wooded areas following its
introduction into regions of Australia and the United States in which it was not previously found
(von Broembsen, 1989). The introduction of the rinderpest virus into parts of Africa resulted in
massive death of cattle and wild ungulates (Holmes, 1982) with subsequent ripple effects on
other components of the community such as vegetation and predators (Barbault, 1992). Similar
effects of myxoraa virus and avian disease organisms are discussed elsewhere in this section.
Competition generally is notoriously difficult to document in the field, and declines of
native species said to be due to competition from introduced ones often can be ascribed also to
other forces. Without experiment, it is impossible to establish the causes with certainty. For
example, the decline of the otter (Lutro kttra) in Britain and Sweden in the 1950s was for a long
time believed to be caused by competition for space with the introduced American mink (Mustek
vison), which spread rapidly at approximately this time. More recently, however, the decline of
the otter has been shown conclusively to be due to organochlorine pesticides; the otter is more
detrimental to the mink than the mink is to the otter (Chanin and Jefferies, 1978).
Nevertheless, sometimes the observational evidence convincingly implicates competition
from an introduced species hi the decline of a native one. For example, the introduction of bam
owls (Tyto alba) to the Seychelles islands has coincided with t .e decline of the endemic
Seychelles kestrel (Falco araea), and this is probably because of nest site competition (Penny,
1974). The limiting resource does not even have to be used hi the same way by the native and
introduced species for the native to be harmed. For example, because nest boxes on poles for
eastern bluebirds (Sialia statis) in Bermuda are used as perches by the introduced great Iriskadee
(Pitangus sulpuratus), the bluebirds cannot nest in them (Samuel, 1975). In addition to sifch
"resource competition," in which a species detrimentally affects another by preempting a limiting
resource, an introduced species can engage in "interference competition* by aggressive behavior.
On the island of Oshima, in Japan, the introduced grey-bellied squirrel (CaUosdurus camceps)
chases the native oriental white-eye (Zosterops paJpebrosa) from flowers of camellia
(Temstroemiaceae). This interference affects both the bird and the plant because the white-eye
pollinates it (deVos et aL, 1956).
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The prevalence of interspecific competition in nature and the nature of the evidence
required to demonstrate it have been controversial subjects in ecology for over a decade (e.g.,
Lewin, 1983). So it is not surprising that it is difficult to estimate quantitatively the fraction of
introductions that result in interspecific competition and the importance of that competition.
Ebenhard (1988), admitting that the evidence is often sketchy, found reports of potential
competition in a selection of the literature for 9 percent of mammal introductions and 18 percent
of bird introductions. One might imagine, however, that literature reports would be particularly
likely where there is a possibility of competition. Thus these figures may exaggerate the
prevalence of competition among introductions as a whole.
An introduced species can prey on a native animal or be an herbivore of a native plant.
Bird species have been eliminated all over the world by introduced rats, mustelids, and feral
dogs, cats, and pigs (King, 1984; Atkinson, 1989). The most famous case is that of the lighthouse
keeper's cat on Stephen Island, New Zealand. The cat arrived m 1894 and eliminated the entire
population of the Stephen Island wren (Xenicus tyaQi) within a year (Greenway, 1967).
Numerous introductions of insect predators and parasitoids for biological control of insect
pests and weeds associated with agriculture have lowered the populations of the latter to stable,
insignificant levels (Krebs, 1985). Although such an approach is widely touted as
"environmentally friendly," particularly in comparison to chemical control, numerous cases have
been documented in which introduced biological control agents harmed or even eliminated
nontarget native species (Howarth, 1991; Simberloff, 1992). Sometimes the resulting impact is
far frqm the point of introduction. For example, the cactus moth (Cactoblastis cactorum) was
introduced to the island of Nevis in the West Indies in 1957 to control various species of prickly
pear (Opuntid). It island-hopped by its own flight throughout the West Indies, reaching the
Florida Keys by 1990 and destroying the entire remaining wild population of the endemic
semaphore cactus (Opuntia spinosissima), a candidate endangered species (Simberloff, 1992).
Because of the mobility of living organisms, it will be difficult to establish a reasonable scope for
assessing the risk of potential ecological effects. Clearly, the host range of a parasite or predator
is a key component in this assessment
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Given the difficulty of establishing even the status of species of plants and animals that
do not stand out in any way (the vast majority), much less the controls on their populations, and
the remarkable sets of circumstances that revealed the existence of a few local extinctions caused
by biological control introductions, there is every reason to think that predators and parasitoids
introduced for biological control have caused numerous local extinctions and possibly global ones
as well (Howarth, 1991; Simberloff, 1992). Similarly, it is not possible to estimate accurately how
many previous introductions have inimically affected native species by predation. Ebenhard
(1988) found the possibility of such an effect for about one third of 290 species of mammals that
have been introduced worldwide—but this can be only the roughest of estimates. Moreover, the
effects of mammals are far more likely to be noticed than those of most other taxa.
An introduced plant can affect native plants by allelopathy. For example, the African ice
plant (Mesembryanthemum aystattinum) has been introduced into California with devastating
impact on native vegetation (Vivrette and Muller, 1977; Macdonald et al., 1989). It is an annual
that accumulates salt throughout its life. When it dies, rain and fog leach the salt into the soil,
where it suppresses growth and germination of native species. An effect similar to allelopathy
can be induced by introduced nitrogen-fixing plants. For example, the Atlantic shrub Myricafaya
was introduced to young, nitrogen-poor volcanic regions of the island of Hawaii where there are
no native nitrogen-fixers. The shrub forms near monocultures, to the detriment of native plants
adapted to nitrogen-poor soils and to the benefit of other exotic plants (Vitousek, 1986).
Introduced species can be vectors or reservoirs of disease to which they are more or less
resistant but native species are susceptible. The major reason that so many native bird species of
Hawaii have gone extinct and that so many of the remainder are threatened is habitat destruction
(discussed below). A key contributing factor, however, is avian pox and malaria vectored by
birds introduced from Asia in the late 19th and early 20th centuries (van Riper et aL, 1986).
These diseases may prevent native species from colonizing otherwise suitable native upland forest
in which densities of introduced species are high. An introduced species may even serve as a
reservoir for a disease that was not introduced with it On Puerto Rico, for example, the
introduced small Indian mongoose (Hapestes auropunctatus) carries rabies, but did not introduce
it (Sflverstein and Sflverstein, 1974). It is well known that human diseases brought by Europeans
to North and South America, Australia and New Zealand, and various small islands around the
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world were devastating to many native peoples (e.g., Crosby, 1986). There is every reason to
think that other animals were equally affected by species introduced by Europeans, and the same
may be true of plants as well. Establishing that a native species is limited in range or numbers
lr a disease is extremely difficult, and determining the origin and reservoir of that disease
even more so.
Probably the most important ecological effect of introduced species is modification of the
habitat, because such modification can affect entire communities of species and frequently whole
ecosystems. In the 18th and 19th centuries, much of the northeastern North American coast
consisted of mud flats and salt marshes, not the current rocky beaches. This change was wrought
by the European periwinkle snail (Uttorina Bttorea) (Bertness, 1984; Dean, 1988), which was
introduced to Nova Scotia about 1840 and it has slowly expanded southward. The periwinkle
eats algae on rocks and rhizomes of marsh grasses. When it is experimentally excluded, algae
and then mud quickly cover the rocks, after which grasses invade the mud. Thus the physical
nature of the entire intertidal zone of a large region has changed, with the consequent change of
the entire community. Introduced feral pigs (Sus scrofa) have similarly modified entire
ecosystems by rooting and selectively feeding on plant species with starchy bulbs, tubers, and
rhizomes (references in Simberloff, 1991). Further, they have greatly modified soil characteristics
by thinning the forest litter, mixing organic and mineral layers, and creating bare ground. In
turn, these changes increased concentrations of nitrogen and potassium hi soil solution and
accelerated the leaching of many minerals (Singer et aL, 1984). In some areas, the changes have
greatly aided the invasion of exotic plants (Loopc and Scowcroft, 1985).
An introduced plant can modify the entire plant community by various means. We
discuss above such impacts by a nitrogen-fixer in nitrogen-poor soil and by allelopathy. Fire
enhancement can have equally great effects. Around 1900, Melaleuca quinquenavia was
introduced from Australia to south Florida, where it has displaced less fire-resistant species, such
as cypress, over thousands of hectares (Ewel, 1986). Several introduced plants act similarly as
fire-enhancers in Hawaii (Vitousek, 1986). Because the fire regime can critically determine the
composition of a plant community, and the plant community in turn constitutes the habitat for
the animal community, such spedes that effect great changes in the fire regime can have
enormous impacts.
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Introduced plant species can modify the habitat by constituting forests where none had
previously existed. Along rivers in the arid southwestern United States, salt-cedar (Tamarix spp.)
and Russian olive (Eleagnus angustifoUa) have had far-reaching effects because they are new
forests (K lopf - nd Olson, 1984; Vitousek, 1986). Salt-cedars were introduced in the 19th
century, and their deep roots allow them to maintain themselves in situations where other plants
cannot, such as on the flood plain of the Colorado River. These forests provide habitat for
animal species. Also, salt-cedars transpire so heavily around desert springs that they have
replaced entire marsh communities with a monoculture forest The effects of Russian olive are
similar, and it is found further north. While its devastating impact on some bird guilds has been
studied, its full effect can only be guessed at Although mangroves cover intertidal soft
substrates in sheltered tropical bays and estuaries in most of the world, they were unknown in
Hawaii, where such sites were unforcsted. In 1902, seedlings of red mangrove (Rhizophora
mangle) were planted on Molokai. This mangrove has since spread to other islands by natural
dispersal and perhaps deliberate plantings and now forms frequent forests up to 20 meters high.
Although the consequences of this new habitat have not been studied, the effect of this change
must be enormous. For example, healthy mangrove swamps drop about 4,000 kg of leaves
annually per hectare, and the roots form critical habitat for fishes and shrimp (Carey, 1982) and
accumulate sediment (Holdridge, 1940).
Finally, introduced species can hybridize with native ones, potentially modifying the native
species in some undesirable fashion or even changing it so much that it would not be regarded as
the same species. Introgression from cultivated sorghum (Hordenum vulgarwn) has rendered
shattercane (Sorghum bicolor) and Johnson grass (Sorghum halepense) more serious pests
(Harian, 1982). Introduction of the fish Gambusia affinis and G. hoJbrooJd for mosquito control
has led to their hybridization with a restricted endemic (G. heterochu) and threatens the
existence of the latter as a separate species (Courtenay and Meffe, 1989). In the Seychelles
islands, the local subspecies of the Madagascar turtle dove (Streptopelia picturata mstrata) has
been destroyed by hybridization with the nominate subspecies (S.p. ptcturata) (Penny, 1974).
Similarly, in Japan the native subspecies of the Siberian weasel (Mustela abirica itatsi) has been
genetically changed by massive hybridization with the introduced Korean subspecies (Mi
coreana).
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For plants and metazoans, one can predict the circumstances under which such
hybridization is most likely. Of course subspecies of the same species can exchange genes.,
Species within groups with mainly behavioral means of reproductive isolation are probably more
likely to be subject to ?uch 8 process, since the hybrids are at least fertile and there would not
have been prior selection for a behavioral avoidance (Ebenhard, 1988). Wild congeners of feral
domestic species are likely to hybridize; for example, few "pure" polecats (Mustela putorius)
probably remain in Britain because of widespread release of the domestic ferret, M. *furd"
(Ebenhard, 1988). It would be difficult to generate quantitative predictions, however, about the
risk of such hybridization or its effects on the species concerned. Many species that are closely
related do not exchange genes, either because of a post-zygotic isolation mechanism, such as
chromosomal incompatibility, or because of a subtle pre-zygotic one. Moreover, some species
that are not closely related have formed hybrids hi nature, particularly among plants. The
particular genes exchanged can have such varied and unpredictable effects as to defy prediction.
"Lateral transfer" of genes also is possible for microorganisms (Stotzky et al., 1991). It
may occur even if the original biological stressor fails to survive. Thus the possibility of hazard
remains in the form of genetic information in the absence of the original biological stressor. The
species now bearing genetic information coding for the deleterious traits may survive, it may be
widely dispersed, and it may multiply. Indeed, the recipient microorganism may be more
ecologically fit than the originally introduced individual and thus constitute a higher risk. Most
of the information on gene transfer among bacteria comes from laboratory studies using highly
artificial test conditions or environmental samples treated in fashions that make them unreal
simulants of natural conditions. Many of these studies suggest that bacteria are engaging hi a
wild orgy, exchanging genes with a frequency that would result in the loss of individuality'of the
particular species involved. Granted some of the enthusiasm of microbial geneticists is excessive;
nevertheless, it is likely that gene exchange does occur in nature. The frequency of transfer of
such genetic information, however, particularly of genes important in causing ecological harm,
remains totally unknown (references in Regal, 1986).
Most of the potential effects we have discussed are direct effects. As noted above,
myriad types of indirect effects are possible and could be quite difficult to recognize, much less
predict For example, the large blue butterfly (Maculina arion) was inadvertentty extinguished hi
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Britain by an apparently unrelated biological control introduction (Ratcliffe, 1979). Caterpillars
of the blue must develop in underground nests of the ant Myrmica sabuleti. The ant, in turn,
cannot nest in overgrown areas. Changing land use patterns and reduced livestock grazing left
primarily rabbits maintaining the habitat Then an ad hoc attempt to control rabbits with the
myxoma virus reduced their populations and, consequently, reduced ant populations to the point
that the butterfly went extinct. Effects may be.even more byzantine. For example, a mite
(Pedicitloides ventricosus), was accidently introduced into Fiji and attacked larvae and pupae (but
not eggs and adults) of the coconut leafinining beetle Pmmecotheca reichd. The mite locally
destroyed all the larvae and pupae during the dry season. The adult beetles then oviposited and
died, converting the beetle population to one with synchronous, non-overlapping generations.
The consequent absence of larvae and pupae during certain periods caused the mite population
to crash, as did the populations of two native parasitoids that had previously controlled the
beetle. Mite and parasitoids did not live long enough to persist during the intervals between
occurrences of the host stages they required for opposition. So the beetle population exploded
(DeBach, 1974). Who could have forecast such an event? Determining that the cause of the
beetle explosion was the mite must have been very difficult, given that the mite population was
now very low. Had coconuts not been an economically important crop, this case would still be a
mystery.
t
Since species can interact through shared prey or hosts, shared predators, parasites and
pathogens, many types of habitat modification, and a variety of tritrophic interactions, the
possible sorts of indirect effects are enormous, and there seems no way to do more than to list
some obvious possibilities in each case. Certainly a quantitative estimate of the probability of
various effects is currently impossible.
4.2. StochasticHy and Dose Response
As noted in section 32, there appears to be a stochastic element to whether a propagule
survives and reproduces at a site. Similarly, there is variation hi the effect a new species has on
a community once it has successfully dispersed For example, different invasions of influenza in
the same human populations have very different effects (references in Regal, 1986). Some
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fraction of this variation may be explained by even minor genetic differences among the surviving
individuals in the different propagules. Data on such differences are not usually available.
For microorganisms, another source of variation also is quite unpredictable at present.
The relationship between the dose of a toxicant and the response of a test population is
important for risk assessments of chemicals. A simple dose-response relationship is not evident
in available information on biological stressors. It is evident, however, that large populations of
some species will produce an effect, whereas small populations cause no demonstrable change.
Dose for a biological stressor is number of organisms, population size, or biomass. A simple test
system would allow the establishment of a mathematical relationship between population size of
the stressor and the response of a susceptible species, permitting, for example, an analog of an
LDjo. Despite the many parasites, pathogens, and hosts that have been investigated, either such
simple dose-response curves do not characterize biological stressors, or, possibly, there are
unstudied heterogeneities (e.g., genetic ones) among the propagules. Nevertheless, the outbreak
of plant disease to economically significant extents sometimes can be predicted from knowledge
of the number of fungal propagules present at a site (Shrum, 1978). Complicating the
establishment of quantitative relationships between exposure and response is the fact that many
of the potentially harmful propagules can be viable but not active. The density of viable
propafe ties is not necessarily the concentration that poses a'threat, because the percentage of
these organisms that are active—or will be active in the near future—depends highly on the
particular environment
The successful establishment of a microorganism in an environment or the creation of an
undesirable effect often requires large populations, and a small introduction will not remain
extant for long or will have no impact In effect, a threshold exists for many microorganisms
below which the population is not maintained and a deleterious impact is not observed.
Although few microorganisms have been studied in this regard, it appears that the threshold
varies greatly with the particular organism. The dearest evidence comes from studies of the
number of bacteria needed for infection of humans: For ingested bacteria, 180 cells of ShigeQa
flexneri, 10s cells of Salmonella typhi, but 10* cells of Vibrio cholerae and Escheridua coU are
needed (Collins, cited by Levy, 1986). The reasons for these vastly different thresholds are not
generally known; in some environments, they may result from predation that eliminates a small
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population but leaves sufficient survivors of a large population to cause an effect (Ramadan et
aL, 1990). In other words, whether the stressor has an impact boils down to whether it survives
long enough in large enough numbers.
On the other hand, an organism that is frequently introduced into an environment in
small numbers may cause an effect, whereas the same population size introduced infrequently
will cause no harm. This difference may be the result of changes in the suitability of the
environment and the propitiously timed proliferation of the small numbers. In short, there may
be stochastic elements in this phenomenon similar to those already discussed with respect
to survival
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5. DEFINING ENDPOINTS
Endpoints for some potential effects of biological stressors are straightforward and do not
differ in kind from those associated with some chemical and/or physical strc sors. For example,
an introduced species that preys on a native one might be expected to reduce the population of
that species, and an appropriate assessment endpoiht would be this population decline. The
measurement endpoint would then be identical to the assessment endpoint—one would monitor
the population. If the assessment endpoint were construed in this instance as some change in
community structure or function, again the measurement endpoint might well be population size,
or it might be some nutrient or energy flow believed to be associated with the prey species.
Many of the ecological effects discussed in section 4.1 similarly would not present unique
problems in defining cndpoints.
The evolution of both the introduced and native species, however, and the possibility of
•
gene exchange between them (discussed in sections 2 and 4.1), complicate greatly the definition
of justifiable endpoints. The evolution of resistance, virulence, and benignity can be very quick,
particularly if the species involved have short life cycles. At other times, however, such processes
can be lengthy with an end effect that is important nonetheless. Even the coevolution in the
myxoma-rabbit system in Australia, which seems especially rapid from a biological control
standpoint, would be viewed as quite slow in the context of a typical risk assessment. There were
about six epizootics in the first quarter century after viral introduction, and the rabbit mortality
rate did not fall substantially until the third one (Krebs, 1985).
The acquisition of novel hosts by introduced phytophagous and parasitic insects will
bedevil risk analyses. For example, the American tephritid fruit fly Rhagctetis pomonetla
originally was almost wholly a pest of hawthorn, although apples, introduced from Europe, had
been within its range and habitat for almost two centuries. In 1865 it was first recorded as
attacking apples in the Hudson River Valley, and this "apple race" spread rapidly to southern
New England and beyond (Bush, 1975). Currently, it is an economically important pest of apples
in much of the eastern and midwestera United States. Many successful biological control
programs entail new associations between parasitoid or phytophage and host, and these
associations include species that had been thought to be monophagous or oligophagous
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(Hokkanen and Pimcntel, 1984,1989). Indeed, the very conditions in which most introduced
species find themselves upon introduction (e.g., small population size, novel environment) might
be expected to lead to rapid evolution, which may include new host range (cf. Roderick, 1992);
moreover, a single gene mutation can change host specificity (Williamson, 1992). Such hf st
range expansions occur in nature, as with the Australian gall wasp (Dennill et aL, 1993), and can
be produced by genetic engineering, as has been done in certain fungi (Schaefer et aL, 1989).
Similarly, species can evolve an expanded tolerance of physical factors that would greatly
increase the probability of important ecological effects—but this might take a long time. Hie
evolution of resistance to insecticides and herbicides is a well-known phenomenon (references in
Begon et al., 1990), sometimes occurring quickly and other times much more slowly. Similarly,
plants evolve tolerance of novel soil contaminants at varying rates (e.g., Walley et al., 1974).
Although the cause of sudden outbreaks in which available resources seem not to have changed
is uncertain, genetic change such as a mutation or a recombinant event is often likely (Siraberloff
and CorwelL 1984). Because both mutations and chromosomal crossover are random events, it is
not possible to predict their nature and very difficult to specify even the probability of a
detectable change of this sort Further, these events can happen at any time, including many
generations after the introduction.
/
Sudden dramatic increases in geographic range also are documented, such as that for the
collared dove (Streptopdia decaocto) in Europe (Simberloff and Corwefl, 1984). While none of
these has been linked conclusively to genetic change as opposed to habitat change, it is not
impossible that such changes have occurred.
Perhaps the key aspect of possible genetic and evolutionary change that bears on choice
of endpoints is that much of it is not incremental, but would arise abruptly and might begin to
generate effects almost instantaneously. Thus, whereas the addition of a chemical stressor might
be expected gradually to lower the size of one or more populations or gradually to affect a
population statistic such as fertility, a biological stressor might have little or no effect for a
period, until a specific recombinant event or mutation produces a different sort of organism with
different effects.
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6. UNCERTAINTY OF BIOLOGICAL STRESSORS AS OPPOSED TO CHEMICAL AND
PHYSICAL STRESSORS
Biological stressors, then, present a series of unique challenges to risk assessment. These
are largely associated with the complexity of communities of interacting organisms and the
likelihood of some degree of evolutionary change, which is at least partially aleatory rather than
deterministic. Such complexity forces a risk assessor to consider a myriad of possible effects-
many of them probably important to community function—as assessment endpoints.
Additionally, the complexity of community organization means there will be some uncertainty in
associating measurement endpoints with assessment endpoints, particularly assessment endpoints
at the community and ecosystem levels. Even if a set of measurement endpoints can be agreed
upon as relevant, sufficient monitoring will likely entail a heavy economic and work burden since
there are so many factors to monitor. The evolutionary change complicates endpoint choice in
two ways: It makes temporal limits questionable, and it means that change need not
be incremental.
Moreover, quantitative risk assessment models are more difficult to devise for biological
than for chemical and physical stressors because living organisms are quirky and communities are
complex. Characteristics of living organisms such as evolutiro, behavior associated with
dispersal, and dormancy are an difficult to model. Even static community models are-highly
idiosyncratic and their predictions not well borne out; accurate dynamic community ecological
models simply do not exist These are the reasons for our emphasis on kinds of effects and the
heavy use of examples—an approach that is probably the best that risk assessment can achieve
for biological stressors.
For both genetically engineered organisms and introduced species, forecasts have been
attempted on the basis of the ecological effects of similar genotypes or species. One might
reasonably doubt, however, that such predictions can be useful as anything more than a
"shopping list' of potential effects to anticipate. For example, the distinctly different trajectories
of closely related introduced species suggest that the experience of one could not have been
particularly helpful in predicting the effects of the other. Consider the tree sparrow, which was
restricted for over a century to the vicinity of St. Louis, and the house sparrow, which spread
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over almost all of North America and is now one of our most common birds (see sections 2 and
32). These congeners are extremely similar in both morphology and habits in their native Old
World ranges. There is currently no convincing explanation for the dramatic difference in their
effects. More puzzling still is that, in parts of their native ranges, the tree sparrow is more
common, as it is in Australia, where both species are introduced (Long, 1981).
Similarly, four congeneric mongoose species have been introduced for rat control in
various parts of the world. One, the small Indian mongoose Herpestes auropunctatus, has
survived in most regions to which it was introduced and had a devastating impact on native birds
and reptiles (Lever, 1985). The others all failed to establish populations (Ebenhard, 1988).
Numerous aphelinid wasps of the genus Aphytis have been introduced to California to control the
citrus pcstAnnidiella aurantii (the California red scale), with widely differing results (Simberloff,
1986b). Some have quickly disappeared, others became established but were severely limited in
range and/or density; a few established substantial populations over wide areas but had distinctly
different effects on the scale. In this rare instance, intensive research was performed to explain
some of the differences in these trajectories (Luck et al., 1982), and the results indicate that no
superficial effort would have predicted the different outcomes.
Further, the literature on introductions is highly biased. Almost t rtainly most
introductions fail to establish ongoing populations; of these, almost certainly most have rather
small effects on the target community or ecosystem (Simberloff, 1991). Introduced species that
have survived and have had major impacts, however, are much more likely to have been noticed.
Probably the more substantial the impacts, the greater probability that the species will be studied
and the study published.
It is often claimed (e.g., Brill, 1985; Davis, 1987) that the impact of the introduction of a
genetically engineered organism on a target community wfll be easier to assess than the impact of
an introduced species, because the genetic change can be characterized quite precisely. One
should view this assurance skeptically, however, on two main grounds: First, the differences
between similar species or even similar genotypes that distinguish innocuous from tremendously
important components of a community are often very subtle and would not easily have been
deduced merely from a knowledge of each species or genotype in isolation. Intensive field
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research may be needed. For example, of two closely related fire ants in the southeastern
United States, the native Solenopsis geminata is a relatively minor, unobtrusive species, while the
introduced Solenopsis invicta is one of the major regional insect pests, affecting plant
communities, native insects, and quite possibly vertebrates like gopher tortoises (Gopherus
pofyphemus). Yet the biological distinction that has caused the radical difference in these
ecological impacts is probably a very subtle behavioral one that determines the size of the
mounds and thus production df alates (Simberloff, 1985). The rice brown planthopper
(Nilaparvata lugens) demonstrates that even a single gene change can greatly affect ecological
impact Until recently a minor pest of rice, it has become a major pest over much of Asia since
the 1970s, apparently because of single gene changes that affect the efficacy of resistance in rice
(Sogawa, 1982). Further, the limited research on the precise determinants of species'
biogeographic ranges (e.g., Neilson and WuUstein, 1983) suggests that these determinants cannot
be determined other than by intensive field research, and that they are generally very subtle traits
that could be modified easily by a single mutation.
A second reason for skepticism about the predictability of ecological effects of genetically
engineered organisms is the possibility of lateral transfer of genes from one species to another
(see section 4.1). Though it seems possible through extraordinary measures to prevent such an
occurrence in a small-scale field test of plants (Gfllett, 1987), such events would not be
controllable in nature. Too little is known about such genetic exchange hi both plants and
bacteria to assume that it will not happen occasionally or that its effects will be minimal (Tiedjc
etaL,1989).
6.1. Action in the Face of Uncertainty
Decisions will be made even in the absence of good science and adequate predictive
capacity. Thus, guidance must be provided to the risk assessor, but it should be used only after
the enormous uncertainties involved are noted. As pointed out in section 3, predictive ability or
knowledge of four major factors is needed to perform a risk assessment: probabilities that the
biological stressor will survive, that the survivors will multiply, that propagules win disperse, and
that the organism win be harmful. With some introduced organisms, information about one or
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more of these factors will be extensive; more often, such information will be scanty. It may be
possible to draw on analogies with closely related organisms. For example, the U.S. Department
of Agriculture and the U.S. Fish and Wildlife Service rely on such analogies in their risk
assessments of introduced species and genetically engineered organisms. Such information
should be used, however, only with an explicit acknowledgment that related organisms often
behave in vastly different fashions with respect to one or more of the four factors.
Often a Delphic process is used to provide essential information on biological stressors.
This approach calls upon committees of experts on related organisms in an attempt to assess the
nature of risks from a new species (e.g., the blue-ribbon committee at University of California,
discussed in section 2, which was convened to assess the risks posed by the Mediterranean fruit
fly in California; the National Institutes of Health Recombinant DNA Advisory Committee
[RAC]). Though the members of such committees generally are "experts" in the sense of
extensive professional knowledge about related organisms, they are not able to muster evidence
beyond what is available in the scientific literature. While the Delphic approach is useful
because an expert's "feel" for a group of organisms is valuable for purposes of a risk assessment,
"feel" cannot substitute for experimentally tested and scientifically derived knowledge about
a system.
When asked to deal with applied environmental problems, ecologists typically claim that
further basic research is needed. While this need is perhaps greater in regard to risks from
biological stressors than for most other problems, it is worth noting that a vast literature already
exists on several factors that are important for risk assessment. Unfortunately, much of that
information does not deal directly with the organisms of likely concern to risk assessors, except in
cases where the stressor is a well-known pathogen of humans, livestock, or crops. Further,
almost all potentially relevant cross-genus or cross-family generalizations are riddled with
exceptions. What is needed to assess risk from biological stressors is information on the specific
morphological or physiological characteristics that are the basis for survival, multiplication,
dispersal, and ecological effects. Currently available information on a number of species wfll
have to serve for now as a generic basis for predictions of risk. Thus, until the amount of such
information increases significantly, risk assessment for biological stressors wfll be speculative ,
at best
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7. RECOVERY
A major issue in assessing ecosystem recovery (see chapter 7) is the "moving target"
problem (Simberloff, 1990a). Communities, and the ecosystems in which they are embedded, are
dynamic entities, so they change to some extent even in the absence of any stressors. Indeed,
even climax communities are never truly unchanging. Therefore, hi order to determine whether
complete recovery has occurred, one would have to know the trajectory that an ecosystem would
have taken in the absence of the stressor. Because such knowledge is imperfect, the best one can
hope for is that the system's structure and function are not outside the bounds established by
normal, dynamic ecosystem and community processes. Another issue concerns the level at which
one assesses recovery. If one weighs various ecosystem properties, such as those related to
energy flow and nutrient cycling, recovery analysis can be performed much more easily (aside
from the moving target problem) than if one demands population-by-population matching of the
prestressor ecosystem and the stressed one.
Recovery from a biological stressor can differ significantly from recovery from chemical
and physical stressors because of several irrevocable changes that can be brought about by a new
organism (Simberloff, 1990b). This will be particularly true if one analyzes recovery on a
spedes-by-species basis, rather than recovery of ecosystem function. Of course, complete
recovery of structure is impossible if the stressor causes global extinction of a resident species, or
it may take a very long time if local extinction must be redressed by immigration from a distant
population. The opposite effect is probably even more problematic—it is tremendously difficult
to eliminate an introduced species once it has become established (Simberloff, 1994). Even
where a sustained, regional effort has succeeded hi eradicating an introduced species, as with the
coypu in Great Britain (references hi Simberioff, 1990b), recovery has been slow. In some cases,
it will be impossible. Virtually all the endemic forest birds of Guam have been eliminated by the
Australian brown tree snake (Boiga imgularis) introduced hi the late 1940s or early 1950s
(Savidge, 1987). Even if the snake were removed, which seems a distant dream today, in what
sense could one ever say that the island community has •recovered"? It wfll never again have the
species of birds that it once contained.
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A genetic change in resident species caused by biological stressors, even stressors that do
not persist, might further complicate recovery. Moreover, hybridization, lateral transfer, and
even 'ordinary* evolution, such as resistance to a new disease or parasite, wfll change resident
species i ermauently.
Non-evolutionary effects of biological stressors, such as habitat modification or various
forms of population suppression (e.&, predation, parasitism, competition), resemble effects of
chemical and physical stressors. Yet because introductions are usually irreversible—at least given
current technologies (Simberioff, 1994)—assessment of recovery from biological stressors usually
differs from assessment of recovery from chemical and physical stressors. Thus this type of
recovery assessment wfll have to account for the predicted continued density of the
biological stressor.
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8. SUMMARY
Four factors—survival, multiplication, dispersal, and ecological effects—determine the
risk posed by a biolog cal suessor. For particular classes of biological stressors some information
exists for one or more of these factors, but little or no relevant information exists for many
potential biological stressors. Thus risk assessors often are drawn to examine species that are
closely related to the stressor, an exercise that requires recognition of the significantly different
ways in which closely related species behave.
The fact that biological stressors reproduce and may multiply renders risk assessment for
such stressors more difficult than for chemical or physical stressors. The assessment can be
further complicated by the myriad ways that biological stressors disperse, both actively and
passively. Particularly noteworthy is the frequency of jump-dispersal in comparison to simple
diffusion. Though the exact trajectory of such discontinuous dispersal is extremely difficult to
predict, it may be possible to assess the probability that it will occur and to assign greater
probabilities to certain geographic routes than to others. One factor to consider is that jump-
dispersal is characteristic of but not restricted to transport of living organisms by humans.
Once a biological stressor reaches a site, its potential effects on the target ecosystem are
numerous. Since many types of effects have been extensively studied by ecologists, the ecological
literature can be useful for beginning to assess potential risks. Studies may indicate, for example,
that one should be particularly vigilant about anticipating certain types of effects. Nonetheless,
for most species of potential biological stressors, the literature is deficient concerning effects.
* *
Perhaps the key difference between biological stressors and chemical or physical
ones—the factor that most affects risk assessment of a biological stressor—is that biological
entities evolve. Because evolution can play a role in survival, multiplication, dispersal, and
effects, it can have a profound influence on the risk posed by a particular stressor. Because it is
a stochastic process, however, and because many aspects of evolution remain quite unpredictable,
performing a risk assessment for a biological stressor can be quite complicated. At present a
Delphic process remains the most reasonable starting point, once the uncertainties involved are
dearly stated.
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Peer Review
DRAFT
September 1993
Issue Paper
on
ECOLOGICAL RECOVERY
Stuart G. Fisher
Department of Zoology
Arizona State University
Tempe, AZ
Robert Woodmansee
Department of Range Science
Colorado State University
Fort Collins, CO
Prepared for
Risk Assessment Forum
U.S. Environmental Protection Agency
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CONTENTS
1. INTRODUCTION 7.5
1.1. A Complex of Considerations 7.5
1.2. A Protocol for Recovery Analysis 7.7
2. PROBLEM DEFINITION AND ESTABLISHMENT OF
THE SPATIAL AND TEMPORAL SETTING 7-9
2.1. Who Defines the Problem and the Recovery Endpoints? 7-9
2.2. What Ancillary Disturbances or Changes Are Occurring? 7-12
3. THE CONCEPTUAL MODEL: BASIC FACTORS INFLUENCING
ECOLOGICAL RECOVERY 7-14
3.1. Physical and Biological Factors 7-14
3.1.1. Weather and Climate 7-14
3.1.2. Water 7-14
3.1.3. Soil Properties 7-15
3.1.4. Assemblages of Organisms 7-15
3.2. Economic, Social, and Organizational Factors 7-16
3.2.1. Energy 7-17
3.2.2. Economic Viability 7-17
3.2.3. Cultural Influences 7-18
3.2.4. Organizational Viability 7-18
3.2.5. Politics, Policy, Laws, and Regulation 7-19
4. THE CONCEPTUAL MODEL: ECOSYSTEM STABILITY AND FLEXIBILITY . . . 7-20
4.1. Stability Concepts and Definitions 7-20
4.1.1. Stability 7-20
4.1.2. Disturbance and Stress 7-20
4.1.3. Resistance 7-22
4.1.4. Resilience/Recovery 7-23
4.2. Factors Influencing the Recovery of Ecosystems 7-24
4.2.1. Resistance 7-25
t
4.2.1.1. Mechanisms of Resistance 7-25
4.2.1.2. Limitations to Resistance 7-29
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4.2.2. Disturbance Type 7-30
4.2.2.1. Various Disturbances 7-30
4.2.2.2. Natural versus Anthropogenic Stress 7-33
4.2.3. The Spatial and Temporal Nature of Disturbance 7-34
4.2.4. Disturbance Scale 7-35
4.2.5. Ecosystem Type 7-37
4.2.6. Ecosystem Linkage 7-37
4.2.7. Biological Characteristics 7-38
4.2.7.1. Individuals and Populations 7-38
4.2.7.2. Communities 7-40
4.2.7.3. Ecosystems 7-41
5. REFERENCE SITE SELECTION, UNCERTAINTY, AND
ECOLOGICAL SIGNIFICANCE 7-43
5.1. Selection of Reference Sites 7-43
5.2. Sources of Uncertainty in Endpoint Selection 7-44
5.2.1. Species Substitution 7-44
5.2.2. Redundancy 7-45
5.2.3. Ecological Equivalence 7-46
5.2.4. Successional Determinism 7,46
5.3. The Ecological Significance of Recovery 7-47
6. REFERENCES 7-49
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LIST OF FIGURES
Figure 1. Spatial and Temporal Considerations in Describing Ecosystems 7-56
Figure 2. Recovery Analysis Protocol 7-57
Figure 3. Generic River Basin 7-58
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1. INTRODUCTION
1.1. A Complex of Considerations
Ecosystems, whether "natural," extensively managed (e.g., replacement of a few of the
native species), or intensively managed (e.g., massive replacement of species and disruption of
soils and sediments), have been described in terms of many biological and physical variables (e.g.,
biodiversity, energetics, patterns of nutrient cycling). Recent descriptions include aspects such as
physical, chemical, and biological services; economic and social values and perceptions (i.e.,
productivity, profitability, sustainability, and equitability); and potentiality (i.e., values of future
and former states). While all of these characteristics represent legitimate viewpoints and must be
considered in ecosystem management, protection, and rehabilitation, the ability of ecosystems to
respond to stress and disturbance whether natural or human caused is often overlooked. If
anticipated and desirable, the response process is termed recovery; if the response is deemed
undesirable, it is termed degradation or decline. As a property of ecosystems, recovery or the
potential for recovery should be evaluated during risk assessment.
*
While the Framework for Ecological Risk Assessment (U.S. EPA, 1992) does not explicitly
incorporate an analysis of ecosystem recovery, failure to consider recovery can lead to faulty
assessments by either overestimating or underestimating the risk involved in any ecological
situation. For example, although restoration of a forest to climax (or stability) stage may appear
to be complete, damage to seed banks might impair the capacity of the ecosystem to recover
from future disturbance events. Loss of refugia or interruption of linkages that may not be
evident in normal ecosystem- functioning, may be central to homeostatic response to stress. Thus
the capacity of an ecosystem to respond to disturbance must be considered an important
assessment endpoint.
Ecosystems are always in a state of change, responding to environmental stresses and
disturbances at short and long time scales. These stresses may be predictable (e.g., diel and
seasonal), unpredictable (e.g., volcanic eruption, landslides), or somewhere in between (e.g.,
flooding, drought, fire). They may be natural or human caused, or they may reflect the
interactions of factors. Stresses can cause the existing ecosystem to change permanently to an
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other state (i.e., into a different ecosystem) that may not be prescribed or desirable
(degradation). Or, they can cause an ecosystem to be altered temporarily, before returning tcp its
previous state (recovery). Yet another outcome of change can be the development of a
prescribed and desirable ecosystem state that is distinct from the previous state.
Recovery, or succession, is an ecosystem property that can be utilized to hasten the
return of an ecosystem to a selected endpoint, providing an ecological service that mitigates
damage. By understanding the factors involved in successional change and using them in the
assessment, the manager may facilitate return to the desired endpoint (e.g., by reseeding with
pioneer species or delaying fish stocking plans). The ecological risk assessment should factor in
the ecosystem's natural capacity for recovery.
The potential for an ecosystem to recover depends on its current state, its current stressor
and disturbance regimes, its successional history, its history of disturbance, the desirable future
state, the intensity of management applied, and chance (figure 1). Establishing desired outcomes
of recovery (i.e., endpoints) requires consideration of the current state, some desired historical
state, or, in some cases, a concept for a more desirable state than currently exists or previously
existed. All of these factors require careful evaluation given uncertainties about ecosystem
properties (see chapter 8, on uncertainty in ecological risk assessment). For example,
susceptibility to a stressor may be a function of successional stage, in terms of the species that is
dominant when stress occurs, the total biomass of the system, or community seasonal phenology.
Moreover, the state of recovery can influence patterns of exposure and differential contact with
components of the ecosystem. For example, toxicants released shortly after flooding in a lowland
stream may be routed quickly through the system; some months later, however, when
macrophytes dominate the benthos, the residence time of toxicants may be greatly increased.
Thus the successional (recovery) status of the target ecosystem must be considered when
assessing the ecological effect of stressors.
Disturbance events that occur in sequence can have varying effects because recovery from
an earlier disturbance may not be complete before the onset of subsequent events. Thus risk to
the ecosystem can be quite different if natural disturbances have been frequent and severe as
opposed to infrequent and benign. Moreover, simultaneous agents (e.g., anthropogenic and
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natural) can exacerbate or mitigate the effect of individual stressors, depending on the
successional status. Because sequential stressors can be natural or anthropogenic, their analysis
must take all agents into consideration. Thus stressor effects must be considered in regard to
regimes of disturbance and the related recovery state of the ecosystem.
Because typically ecosystems are in the process of recovering from natural disturbance
events, a range of states is available for endpoint selection by the risk assessor. Since a risk
assessment requiring restoration of a climax state for an ecosystem that was far from this state
when the stress occurred would be unrealistic, a better restoration goal might be an achievable
state on the recovery trajectory. Thus an appropriate alternate goal might be a somewhat earlier
or later successional state than the one exhibited by the ecosystem when the stress event
occurred. To chose the best endpoint, it is necessary to know the course of the ecosystem's
natural trajectory (i.e., to understand successional pathways exhibited by the particular ecosystem
under study). Ultimately, the goal should be to get the ecosystem back on track.
Choosing the most desirable ecosystem outcome also involves establishing a social
contract based on management goals, policies, laws, and regulations. While directing ecosystems.
along desirable pathways is the responsibility of, for instance, land and water managers, assessing
which of these pathways is the most probable is the considerable task left to the risk assessor.
This chapter focuses on the ecological implications of ecosystem recovery following disturbance
that the risk assessor must consider. Social, institutional, political, and economic factors are
discussed briefly because most, if not all, ecosystems are directly or indirectly managed or
significantly influenced by humans. In our view, these human factors cannot be ignored.
1.2. A Protocol for Recovery Analysis
This chapter is organized in part around a suggested protocol for assessing the potential
for ecosystem recovery and raises some essential ecological and social considerations. This road
i
map (figure 2) is adapted from the Decision Analysis Methodology being developed by the
Terrestrial Ecosystem Regional Research and Analysis (TERRA) Laboratory (DeCoursey et al.,
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1993; Woodmansee and Riebsame, 1993), which represents emerging structured analysis schemes
intended to facilitate knowledge management and group-to-group interaction.
Developing a clear problem statement that describes the potential for recovery of an
ecosystem is essential for performing a realistic assessment (section 2). Because the effects of
recovery might be felt by many sectors of society, the problem definition phase should not be
exclusive to risk assessors and scientists. We emphasize the need for agreement among people
("stakeholders") with differing viewpoints (figure 3) about the nature of the anticipated impacts
and the potential recovery; the policy and management implications associated with the recovery;
issues concerning who will define policy and management goals, make and implement the
choices, bear the costs, and realize benefits; and the biological, physical, economic, and social
constraints that are operable. Along with developing the fundamental problem statement, the
spatial and temporal scales of the problem must be established. Next, the basic assessment, or
conceptual modeling phase of the analysis, can be accomplished (sections 3 and 4; related
considerations are taken up in section 5). The assessment must be based on strong theoretical
foundations in the natural and social sciences as well as the most complete information available
on the specific ecosystem relevant to the problem statement. Further, available information fcom
the most reliable ecological, economic, and social/cultural data bases and the most relevant
expertise must be included.
The final steps in the protocol are beyond the scope of this paper. Upon completion of
the conceptual modeling phase, a formal mathematical or expert systems modeling effort should
be established—if resources permit. Next, empirical studies, intuition, and common sense can be
used in analyzing the integration of the preceding steps. Finally, scientists, managers, policy-
makers, and representatives of the public need to debate and discuss issues and then achieve a
consensus on recommended management strategies for recovery.
We recognize that implementation of this protocol is dependent on available human and
financial resources, the urgency of the problem, political interest, and institutional mandates.
)
Yet, if ecological risk assessment is to include considerations of ecosystem recovery, then such a
structured analysis is necessary, even if the formal modeling and intensive data analysis phases
need to be neglected.
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2. PROBLEM DEFINITION AND ESTABLISHMENT OF THE SPATIAL
AND TEMPORAL SETTING
Analysis of the effects of any disturbance on an ecosystem requires rigorous description
of its current state, its history, the nature of the proposed or existing stress, and the nature of the
subsequent management system. To establish a clear definition of the problem, the risk assessor,
risk managers, and principal stakeholders must agree on the problem's fundamental nature. In
particular, the geographic and time-related boundaries and dimensions of the problem must be
specified (i.e., Will recovery be expressed at the regional, landscape, or patch scales over periods
of seasons, years, decades, or centuries?). Further, as the spatial dimensions of change are
specified, the appropriate sector hierarchies (i.e., biological, physical, economic, and social) must
be established (Woodmansee, 1989; Rosswall et al., 1988) along with description and
quantification of important factors within each sector where possible. Assumptions about
important but poorly understood factors must be clearly stated.
After describing its current state, the analysis must consider the ecosystem in the terms of
its successional status (e.g., Does its state represent a major departure from "natural" serai
stages? Is it a completely exotic system? Has natural disturbance history or management
significantly altered its successional trajectory?). Clear description of these historical
considerations are essential for evaluating the recovery potential of any ecosystem.
Finally, the probable effects of the disturbance or stress from which recovery is desirable
must be described (e.g., Is the disturbance or stress equivalent to naturally occurring analogs? Is
it an exotic stress?). Since such issues are discussed in other chapters, this chapter emphasizes
the importance of the stressor's nature in assessing the potential for the recovery of an
ecosystem.
2.1. Who Defines the Problem and the Recovery Endpoints?
i
Since contemporary ecosystem management and regulation schemes emphasize
hierarchical decision-making processes, many systems are managed by a centralized authority,
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such as an owner/operator, a governing board, or a legally appointed administrator. Similar to
this type of management approach is decision-making by institutional mandate. For all such
organizations, the lines of authority are direct and decisions are absolute. Explicit and implicit
regulations and policies, which are derived and interpreted in a myriad of ways, influence
management decision-making by organizations. Decisions usually are consistent with the mission
and mandate of the organization responsible for regulation or management of the specific
ecosystem. Such contemporary organizational mandates tend to be narrow, singling out
commodities or species, for example, or they emphasize service. The same can be said about
environmental impact assessments—and will be said about ecological risk assessment—if old
patterns are maintained.
Increasingly, these conventional approaches to decision-making are being challenged by
various sectors of society. Trends are indicating clearly that authoritarian management is no
longer acceptable, as individuals and "stakeholder" organizations seek greater participation in the
regulation and management of the environment (Naisbett, 1982; Naisbett and Aburdene, 1990;
Peters and Waterman, 1982; and Kessler et al., 1992). As a result, society has placed a great
deal of decision-making and regulatory responsibility in the hands of judges. The courts,
however, are adversarial by design and oriented to unambiguous resolutions, but few
environmental issues are that simple.
A far more sensible approach to establishing regulations concerning recovering
ecosystems is to bring interested individuals and organizations into the risk assessment process.
In an inclusive approach, conflicts can be resolved and a consensus can be developed on recovery
endpoints and the management plan—before anyone feels the need to sue. This approach,
currently being used by U.S. Environmental Protection Agency's (EPA's) Office of Water
Watershed Protection, is essential where management for recovery is a key goal, since the
recovery plan will need to accommodate many viewpoints over the long term. Moreover, once a
consensus is established, coalitions must be formed to protect the plan from future interventions.
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Determining who should be involved in this collaborative process involves answering the
following questions:
• Who makes policy?
• Who defines policy options?
• Who chooses options?
• Who sets management goals?
• Who implements goals?
• Who pays and in what currency?
• Who benefits and in what ways?
Adequately answering these questions requires that the risk assessor confer with risk managers
and the public.
Once interest groups are identified and their viewpoints are represented, the risk assessor
can begin resolving issues concerning, for example:
• the starting point and where it fits within appropriate spatial and temporal scales;
• the characteristics or indicators and possible effects of disturbance; and
• the goals for recovery and ecological endpoints (e.g., What ecosystem would be
desirable following recovery? Is the current ecosystem the desirable system, or is
some historical ecosystem sought, or is an entirely new ecosystem most
desirable?).
Such issues must be evaluated in the context of natural and human-caused disturbance.
While some anthropogenic disturbances (e.g., erosion, flooding, fire, and pest outbreaks) have
natural analogs that can be instructive as models for recovery, others (e.g., introduction of exotic
chemicals, massive destruction of soils or sediments) may have no natural analogs and thus
provide models for recovery that are speculative at best. To thoroughly evaluate these questions
and choose applicable models, the risk assessor must have access to the best scientific
knowledge available.
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2.2. What Ancillary Disturbances or Changes Are Occurring?
The risk assessor must be certain to focus on the disturbance that is the subject of the
analysis, since recovery from that stress can easily take on prominence. The danger of this
approach is that it can lead to the age-old scientific mindset that advises "hold all other variables
constant." Unfortunately, the real world rarely holds all variables but one constant. Thus when
risk analyses are performed to determine the effects of specific stresses, the analyses also must
take into account other changes occurring in the ecosystem. These can include climatic
influences, biological invasions, chemical alterations, recreational pressures, and commercial
impacts. The risk assessor must not ignore these ancillary changes because they can significantly
influence the desired recovery.
Thus the spatial boundaries of the ecosystem must be clearly described for purposes of
risk assessment, because generalizations and abstractions are of heuristic value only. Careful
matching of hierarchical levels is essential for avoiding conflicts among scales. This type of
approach calls for analysis of 10 basic sectors: (1) weather, (2) water, (3) soil properties, (4)
assemblages of organisms, (5) energy, (6) economic viability, (7) individual behavior, (8) cultural
and community viability, (9) organizational viability, and (10) political, policy-making, legal, and
regulatory influences (described further in section 3). All are interactive and interdependent,
and none should be factored out of a recovery analysis without thoughtful consideration.
Although the sectors are not of equal importance in all systems, many scientists, educators, land
managers, policy-makers, and members of the public tend to overlook their interactions, failing
to recognize that managing lands and water to ensure desirable recovery depends on
sector integration.
The risk assessor's analysis also must include an evaluation of the efficacy of
implementing regulations that are intended to ensure a reasonable chance of recovery.
Questions that must be raised as an explicit part of an analysis include, Are there significant
political, financial, or social impediments to implementing policies, regulations, or management
goals? Are there significant natural or human-caused changes in the biophysical controls of the
system that might interfere with the recovery of the ecosystem? Similarly, are there significant
economic, social, or institutional factors that might interfere with recovery?
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Finally, the risk assessor should make every attempt to develop a formal conceptual
model of the ecosystem at issue. The model should explicitly account for all pertinent sectors
that can influence recovery, with linkages between sectors clearly described. Driving variables
and their temporal and spatial characteristics also must be included. Once the sector-level
conceptual model is established, submodels should be developed that emphasize internal controls
and feedbacks within each component.
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3. THE CONCEPTUAL MODEL: BASIC FACTORS INFLUENCING
ECOLOGICAL RECOVERY
3.1. Physical and Biological Factors
3.1.1. Weather and Climate
Weather and climate are particularly important factors in ecosystem modeling because
they govern recovery. The growing consensus that global climate patterns are being altered by
human activity poses a special concern to terrestrial ecosystem functioning and management.
The critical aspects of weather and climate in regard to ecosystem functioning include
precipitation amounts, pattern, and seasonality.
Similarly, daytime and nighttime temperature norms and extremes, seasonal temperatures,
and the length of the growing season regulate ecosystems and determine their biological and
physical productivity. Additionally, the frequency and severity of extreme events (e.g., hail,
frosts, flood) is important. If any of these parameters change dramatically, it is almost certain
that system functions and components will also change.
Less well understood but potentially important weather and climate characteristics
include the atmosphere's chemistry (e.g., trace gases, carbon dioxide, paniculate) and radiation
transmissivity (i.e., ultraviolet radiation, quality, and intensity) as well as average cloud cover.
The direct and indirect effects on ecosystem composition and functioning of these characteristics
are the subject of intense research and debate.
3.1.2. Water
Since water is a critical element of all ecosystems, particularly in the arid and semiarid
regions of the world, it is profoundly important in determining ecosystem management success.
Changes in the amount and quality of water, often related to its seasonal availability and its
distribution, can completely alter the viability of ecosystems. Because water is intimately related
to both climate and human activity, it demands special attention in any recovery analysis.
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Humans demand water for domestic, industrial, and agricultural uses and satisfy this
demand by impounding and diverting surface water, regulating stream flows, pumping ground
water, and designing sophisticated water allocation and distribution schemes. In some areas,
water has come to be the chief point of tension between agricultural and urban sectors. The
effects of chemical use and soil erosion, for instance, take on greater significance when translated
into "off-farm" effects related to surface and ground water. Indeed, in many areas, urban and
recreational needs have come to outweigh agricultural uses of water.
3.1.3. Soil Properties
Many soil properties have a profound influence on the physical and biological integrity
and the sustainability of ecosystems. Thus analysis of the potential for recovery of a disturbed
ecosystem is critically dependent on the response and behavior of the system's soils. Prominent
characteristics of soil that should be considered in developing the conceptual model are texture,
structure, and volume; organic matter and forms; nutrient availability; credibility and
sedimentation; the functioning of soil organisms; and the degree of contamination from
pollution. The nature of land management practices used at a site also can profoundly influence
soil properties.
Knowing how much change soil properties can tolerate without jeopardizing the
functioning of the entire ecosystem is often important information. Indeed, if there is one
inviolate rule regarding sustainability of ecosystems, it is that basic soil resources must be
safeguarded to maintain desirable terrestrial ecosystems.
3.1.4. Assemblages of Organisms
Defining the nature of assemblages of biological organisms involves identifying desirable
i
organisms for the recovering ecosystem as well as characteristics required for the assemblage's
maintenance, both above and below ground and in streams, rivers, lakes, and reservoirs. For
example, the risk assessor must ask, Is the ecosystem currently close to its "natural" or "native"
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state and can that state be maintained with minimal human intervention? If the desired
ecosystem is not based on native species, will it be self maintaining (i.e., will it perpetuate itself
under current and future climate, water, and soil conditions)? Is the ecosystem capable of
producing crops and supporting animals that meet current and potential future needs, and what
kinds and degree of human intervention are necessary to ensure successful and profitable
production? Are game or nongame species a desirable goal of ecosystem management? Is the
diversity of genetic stock (i.e., biodiversity) an important issue in managing the ecosystem? What
is the status and significance of weeds, pests, and diseases associated with the system? Is the
current or proposed management scheme compatible with maintaining aesthetically valuable
organisms and other environmental goals imposed by society?
Answers to such questions with reference to specific ecosystems is essential for wise
management during recovery. Risk analysis, assessment of the impacts of stress and disturbance,
and consideration of the prospects for recovery must be based on the best understanding of
ecological principles as applied to the particular ecosystem. This understanding relies on the
integration of knowledge concerning the ecosystem's physical and biological factors (addressed
generally in section 4).
3.2. Economic, Social, and Organizational Factors
Establishing the goals and criteria for the desirable recovery and sustainability of an
ecosystem is a social construct because human activity causes many disturbances and influences
the subsequent recovery processes. Moreover, people assess and evaluate the state and progress
of the effected ecosystem. Thus forecasting the recovery of ecosystems from specific stress
regimes presents an assessment challenge that calls for an integrated consideration of natural and
social systems. This approach is especially relevant for agriculture, forestry, and fisheries
systems, where the traditional paradigm has been to subvert natural processes with technology to
achieve social goals. Creating, recreating, and approximating ecological processes ultimately
•
requires a change in the social management and assessment of ecosystems.
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3.2.1. Energy
Energy is often both an input and output associated with ecosystems. Many managed
ecosystems that are the subject of risk assessments produce timber and fuel wood, agricultural
crops, or fish. Such ecosystems usually require machines, fertilizers, and/or pesticides for
development and maintenance. The interrelationship of energy and agriculture, for example, is
particularly important to our understanding of ecosystem management (CAST, 1992). Thus the
potential for recovery cannot be assessed without considering the availability, efficiency, and
sustainabiliry of energy.
3.2.2. Economic Viability
Most managed ecosystems are expected by society to produce valued commodities or
products well into the future. Indeed, while an extensive body of literature on agricultural (e.g.,
Hazell and Norton, 1986; Ikerd, 1990), forestry, and fisheries economics (Costanza, 1991)
addresses issues concerning the commercial sustainabiliry of ecosystems, much less is known f
about the economics of recreation, aesthetics, and other nonmonetary values of ecosystems. The
challenge for the risk assessor is to account for the real economic value of the ecosystem and
determine how the associated stress regimes will affect recovery. Further, the assessor must
evaluate important trends in recovery criteria and forecasts of future conditions.
Economics may not be a direct or principal issue in all cases, and ecosystem impacts on
"off-site" environmental and social values will need to be more closely scrutinized in the future
(Costanza, 1991). For example, the response of farmers and ranchers, foresters, recreational
resource managers, and fisheries managers to climatic change may be influenced by subsidies that
require ecosystem recovery and sustainability. Moreover, despite the importance of economic
considerations, increasingly people are giving nonmonetary value importance in assessing the
environments in which they live. Such priorities, which seem to have particular appeal in urban
communities, can conflict with the priorities of people who depend on timber, fishing, and
agriculture for their livelihood. If such aesthetic and spiritual values gain primacy, society will
need to account for the incomes lost from hampering those enterprises.
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3.2-3. Cultured Influences
Recovering ecosystems have a strong interplay with the cultural communities in which
they are embedded (Hart, 1991). Indeed, the success of any overall management practice
intended to enhance recovery is dependent on the ecosystem's cultural viability. Thus the risk
assessor must take into account the cultural setting, its relationship to existing ecosystems and
stress regimes, and cultural trends.
3.2.4. Organizational Viability
Recovery analysis must take into account organizational bodies involved in research,
management, and policy-setting concerning the ecosystem at issue. An organization's funding
priorities and stability provide strong indications of how it integrates its mission and mandate
with its goals, objectives, and political orientation. Thus an organization that accepts the concept
of sustaining ecological recovery as part of its mission must set its priorities to manage for that
purpose. Because sustainability is a concept with long-term implications, funding and human
resources must be available on a stable basis. As a result the risk assessors must address such
questions as, Is the relevant organization sufficiently established such that long-term missions can
be accomplished, or is recovery likely to be hindered by future changes in the economic, social,
or political climate?
All organizations develop their own peculiar culture, history, myths, and dogma. Public
attitudes about the role of an organization are shaped both by how the organization is viewed in
the social context and how it views itself. Thus to implement policies and management practices
that encourage sustainable ecosystem recovery, an organization must have suitable organizational
dynamics and political viability. Understanding the functioning of key organizations and
governmental bodies, as well as the interactions of organizations with overlapping jurisdictions, is
essential in recovery analysis. Among the main organizational characteristics to be evaluated are
mission or mandate; goals and objectives; human resource structure; funding priorities and
stability; and ability to cope with change. Because organizational goals usually reflect short time
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horizons, a major challenge in recovery analysis involves determining whether the organizational
goals and objectives aid or impede ecological recovery.
3.2.5. Politics, Policy, Laws, and Regulation
Politics, governmental policies, laws, and regulations are clearly related to the
effectiveness of any ecosystem recovery scheme. Government policies and regulation provide a
broad and important context for ecosystems, and affect the role of other variables. Thus
different policy scenarios should be developed and factored into the recovery analysis with an eye
to future developments and factor interactions.
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4. THE CONCEPTUAL MODEL: ECOSYSTEM STABILITY AND FLEXIBILITY
4.1. Stability Concepts and Definitions
4.1.1. Stability
An ecosystem's stability characteristics compose the larger conceptual framework for
recovery. There is some confusion, however, about the definition of stability among ecologists of
various disciplines. For example, Rolling (1973) approached stability largely from the population
perspective, defining resilience as persistence and stability as return to equilibrium after
disturbance. These definitions are at variance with the understanding of stability generally
adopted in the ecological literature since a definition was developed by Webster et al. (1975).
Stability—as used in this discussion—is defined as the nature of an ecosystem's response
to a small, disturbance-related displacement from its normal trajectory (Webster et al., 1975).
Stability consists of two components: resistance, which concerns the capacity of the system to
avoid deflection in state despite a disturbance, and resilience, which is the ability of the system to
regain its initial state once deflected.
4.1.2. Disturbance and Stress
Disturbance is generally accepted to be a discrete, punctuated perturbation resulting in
the loss of resident organisms and providing an opportunity for non-native species to become
established (Sousa, 1984). As such, disturbance changes the ratio of organisms to resources in a
given population, community, or ecosystem (Pickett and White, 1985). Disturbance can be
caused by a host of agents, either biological or physical. Thus wind, flooding, fire, and drought
are considered disturbances, as are insect (pest) outbreaks, bioturbation, and localized, intense
predation or grazing. Pickett et al. (1989) further define disturbance as an agent that is external
to the system of interest; thus an agent of disturbance from within an ecosystem (e.g., a stream)
is a disturbance at the scale of the subsystem that experiences the effect (e.g., algae on a rock),
but is not a disturbance of the system as a whole. Similarly, a significant spate in a stream or
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river is a disturbance at the temporal scale in regard to the weeks or months over which effects
are manifested. Further, over the longer term (years), floods are not considered disturbances;
rather, the absence of floods may be regarded as a disturbance.
Because disturbances are complex they must be thoroughly described in any ecological
analysis. Pickett and White (1985) and Sousa (1984) provide lists of disturbance attributes to
consider when performing an analysis that include areal extent, magnitude, frequency, return
time, and the predictability of disturbance events. Disturbance is a variable influence requiring
careful description and recognition that ecosystem response in terms of resistance and resilience
will depend on the nature of the disturbance. Unfortunately, too little is known about ecosystem
response to variable disturbances, to sequential events (i.e., disturbance regimes), and to
simultaneous disturbances. Because disturbance seldom occurs as a discrete, suprathreshold
event, it is difficult to adequately predict disturbance effects in advance of the event. Thus more
research is needed in this area.
Resh et al. (1988) define disturbance as an unpredictable event (i.e., outside the range of
events normally experienced by an ecosystem). This definition distinguishes events that are pa,rt
of the cycle of an ecosystem from those outside the system's trajectory, leaving undefined various
events that cause devastating mortality but may occur predictably (e.g., flood and drought, fire,
and pest outbreaks). Rykiel (1985) provides a mechanism for avoiding this problem by defining
disturbance as an agent apart from its effect (i.e., a potential cause) and perturbation (and stress)
as the effects of the event. Rykiel differentiates stress from perturbation by its level of action,
measuring stress as a physiological or functional change in organisms and perturbation as the
influence on ecological components or processes.
Barrett et al. (1976) define stress as a perturbation that is either foreign or natural to a
system but applied at an excessive level. Kelly and Harwell (1990) define perturbation, stress,
and disturbance synonymously. Because stress is defined by Niemi et al. (1990) as a component
of a disturbance syndrome associated with an identifiable agent (the stressor), stress and
disturbance could be understood as being synonymous terms when a single stressor is involved.
According to these definitions, disturbance and stress move the ecosystem—or a component of
it—outside of the range of states it "normally" experiences. While it may be clear that such
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stressors as heavy metals and toxic organic compounds are outside the ecosystem's normal
experience, making such a determination about natural events such as flood and fire requires
imposition of an arbitrary time scale. Duration of disturbance presents a conceptual difficulty
that was addressed by Bender et al. (1984) by differentiating between pulse disturbances and
press disturbances, which involve a longer timeframe. Thus a flash flood may be considered a
pulse disturbance, while drought is thought of as a press disturbance (Stanley and Fisher, 1991).
We propose that disturbance be defined as an event that potentially displaces the system
of interest (e.g., population, community, ecosystem) from its normal trajectory (i.e., the one it
otherwise would have taken). This definition focuses on the event rather than the effect, since
events that are disturbances in one type of system may not be significant in another and a range
of effects may result from the same event experienced by a range of ecosystems, communities,
or populations.
Thus the first step in the analysis of the ecological risk associated with any disturbance is
to fully describe the disturbance event (e.g., Pickett and White, 1985) and its probable effects.
This description must include consideration of disturbance attributes that in the past had beea
dealt with in an arbitrary and often conflicting manner, such as perturbation, pulse, press, stress,
and predictability. Not only does this approach make ecological sense, it also is consistent with
ecological risk assessment schemes that include an analysis of exposure and biological effects of
anthropogenic stressors.
4.1 J. Resistance
Resistance is measured as the inertia of state variables across a disturbance. In essence,
assessing resistance involves a before-and-after analysis of the effect of the event. Units can
reflect absolute or percent change; several variables should be measured, depending on the
assessment endpoints (e.g., population size and the structure of the dominant and keystone
j
species, exemplary taxa representing the resident ecological guilds, and species of special interest
such as endangered, commercial, or sport species). At the community level, species richness,
equitability, spatial pattern, and niche partitioning should be considered. Primary productivity,
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photosynthesis:respiration (P:R) ratio, ecosystem-level nutrient retention, and decomposition
rates could be included when considering the response of an entire ecosystem.
4.1.4. Resilience I Recovery
Resilience refers to the capacity of the system to regain its previous state once the
disturbance is removed. Measurement endpoints should be assessed with the same variables used
to describe resistance, even though in resilience analysis several variant expressions are available.
For instance, the time required to reach the predisturbance state may be used or, in the case of
asymptotic recovery trajectories, half-saturation times may be used. Also, the rate of change in
the measurement endpoint may be used to reflect resilience, indicating the rate of initial change
without assuming that the "before" state will be reached (Grimm and Fisher, 1989).
Because populations, communities, and ecosystems are constantly changing in response
both to the most recent disturbance and seasonal and long-term climatic pressures, risk
assessment presents significant complexity. Often it is unrealistic to expect recovery to the . .
predisturbance state, rather than to some point on the predisturbance trajectory. For example, a
grassland burned in spring will likely be populated in summer by predictable insect species.
Similarly, stream insect communities of the Southwest devastated by flash floods recover to
trajectories determined by underlying seasonal change (Boulton et al., 1992a). Thus selection of
measurement endpoints for recovery should be realistically selected after considering (1) the
predisturbance state at the study site, (2) nearby undisturbed sites, and (3) the literature
describing recovery patterns in replicate ecosystems of the type in question.
Resilience, recovery, succession, and ecosystem development are somewhat synonymous
and often are used interchangeably to describe post-disturbance change. Resilience is a neutral
value but was used by Rolling (1973) to refer to what most ecologists now call resistance; thus
some confusion may persist. Recovery implies a state of response to stress from disturbance.
i
Succession, a classic concept in ecology, describes the sequence and causes of change in
populations and communities following a disturbance; however, because the conceptual bounds
and implications of succession are not agreed upon by the broader ecological community, this
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term is best avoided (Fisher, 1990). Ecosystem development is a term used by Odum (1969) to
describe succession in ecosystems such that a variety of ecosystem-level properties of dubious
value and uncertain origin are incorporated; the concept is historically related to the Clementsian
supraorganism concept (Clements, 1916), which has been largely discredited. Odum considers
succession in ecosystems to follow an ordered, predictable process characterized by regular
changes in several holistic properties over time. Similar models were presented by Margalef
(1963, 1968), who described succession in terms of changes in pigment ratios,
photosynthesis.-biomass (P:B) ratio, P:R ratio, and information (or its inverse, entropy).
Margalef s uniquely ecosystem-oriented view implies a certain degree of control over the process
of succession described in terms of collective properties (e.g., P/R, P/B, tightness of nutrient
cycling, entropy). This concept is useful because it focuses on ecosystem services, but it is
tangential to traditional biologic succession, which focuses on organisms and communities.
Moreover, this supraorganism view is held in some disfavor by evolutionary ecologists, since it
presupposes a cybernetic function .of ecosystems for which there is little evidence but no shortage
of debate (Engleberg and Boyarsky, 1979; Knight and Swaney, 1981; McNaughton and
Coughenour, 1981). Many of the ecosystem-level trends hypothesized by Odum were drawn
from and influenced by studies of autotrophic ecosystems such as forests and aquarium-style '
microcosms in autotrophic succession. Unfortunately, many ecosystems are heterotrophic,
depending on energy derived outside their boundaries, and deviate from the developmental
trends proposed by Odum (see Fisher et al., 1982).
4.2. Factors Influencing the Recovery of Ecosystems
In this section, recovery specifically refers to post-disturbance changes in biological
systems. This use of the term is not intended to imply that systems in a state of recovery are
unusual, undesirable, or ecologically deficient apart from such a judgment established in
reference to assessment endpoints. If the problem formulation step in ecological risk assessment
identifies and assigns values to various assessment endpoints (e.g., species diversity, primary (
productivity, population size), then these endpoints will serve to differentiate ecosystem states in
terms of human-assigned values. Such values are imposed by the assessor and are not otherwise
ecologically inherent in the disturbed system.
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4.2.1. Resistance
In general, resistance as well as resilience are negatively correlated in ecosystems (Sousa,
1984; Webster et al., 1983). Thus ecosystems with a high capacity to resist or stress tend to have
a limited ability to recover from disturbance events, and vice versa. As a result, it would be
incorrect to assume that a badly damaged ecosystem would experience a slow or difficult
recovery period; indeed, the opposite is more likely to be true. Also, an ecosystem that is only
slightly altered by a disturbance may recover quite slowly. In assessing recovery potential, it is
important to understand the overall stability characteristics of an ecosystem to make realistic
predictions about the capacity for recovery.
Resistance is a particularly important component of conceptual modeling because it
establishes the initial conditions of the recovery process. The starting point for recovery may
characterize an ecosystem as one that has been changed considerably by a short-lived disturbance
event (e.g., by a fire, a hurricane, a chemical spill) or one that has been subjected to a prolonged
period of moderate stress. In either case, the most important variable for predicting recovery
capacity is the system's state once the stress is removed so that recovery can begin. This state* .
can be described in individual, population, community, or ecosystem terms and may be weighted
for assessment endpoints selected by the risk assessor.
4.2.1.1. Mechanisms of Resistance
Physical factors. Physical factors are an important component of resistance in any
ecosystem. For example, flood effects can be moderated by streambed slope, extent of lateral
wetlands, floodplain size, and the position and extent of levees. Flood effects also depend on
substrate size and distribution. For example, a boulder-cobble bottom streambed will resist
scouring to a greater degree than a sand bottom streambed, which experiences a longer period of
disturbance since the streambed is mobile for a longer period during flood recession. Although
•
clay substrates exhibit greater resistance than sandy substrates, once entrained they settle
extremely slowly (Morisawa, 1968) and the disturbance event is prolonged (Wood et al., 1992).
i
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Resistance of lake ecosystems to disturbance is a function of the relationship between
depth and area (i.e., the hypsographic curve), circulation patterns, and degree of stratification.
These properties influence exposure of the biota to toxic substances that enter through
hydrologic pathways. The interaction of the hypsographic and stratification characteristics '
determines the extent to which toxic materials come in contact with the benthic community.
Considering transparency, this interaction also determines the extent to which autotrophic and
heterotrophic ecosystem components are influenced by the event. For example, a lake in which a
large volume of water overlies—and therefore may interact with—the littoral zone may respond
differently than one in which contact with the littoral zone is limited (e.g., by a steep shoreline).
Although terrestrial ecosystem resistance is largely a function of the biotic state, physical
factors play a role as well. For example, fire propagation is a function of the moisture content of
fuels, but it is also related to weather (e.g., precipitation, relative humidity). Similarly, landscape
patchiness, which is a physical property, may influence disturbance size and shape (e.g., as a
function of patch size, percolation, and the size, shape, and position of firebreaks such as roads).
Successional age. Successional age (i.e., time elapsed since the last disturbance) influences
the extent to which subsequent disturbances change target ecosystems. For example, early
Successional ecosystems may not be significantly affected by disturbance events that occur in
rapid succession for the obvious reason that little remains to be further damaged. In some
streams, however, recolonization occurs rapidly and pioneer communities quickly establish
themselves. Thus disturbance events separated by weeks, or even days, can add substantially to
cumulative damage. Successive floods in alluvial streams may add to disturbance effects since
the saturation of previously dry channel sediments can result in increased runoff compared to the
first disturbance (i.e., lower resistance is associated with the second event). While this effect can
extend throughout a watershed in mesic regions, it is largely confined to channels in arid lands
that are characterized by minimal soil abstraction from rainfall and sheetflow runoff patterns
(Fisher and Grimm 1985; Fogel and Duckstein, 1970). In forests, however, because fuel buildup
is a slow process that depends on the mortality of large trees or tree parts, recently burned
>
forests are somewhat immune to disturbance since they exhibit higher resistance as a function of
the disturbance history.
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Resistance of riparian vegetation in stream channels is also a function of successional age.
Although early seedling stages show minimal resistance to annual floods, large riparian trees tend
to be highly resistant. Baker (1990) demonstrated that recruitment of cottonwood seedlings is
associated with hydrologic events recurring every 4 years; however, conditions resulting in stands
of saplings that are the same age occur only every 11 years (i.e., the seedling stage vulnerable to
floods of a magnitude that has minimal effect on saplings). When this 11-year opportunity arises,
resistance of the riparian component of such ecosystems increases markedly. A similar situation
exists with macrophytes in streams, where algal communities showing minimal resistance to
spates typify the first 2 to 3 years of post-flood succession in sandy rivers but eventually give way
to macrophytes that are more firmly rooted in the stream substrate. Resistance of the
macrophyte community is substantially higher than that of the early, algal phase (Wood et al.,
1992).
Biogeochemical mitigation. When the affected ecosystem is described in terms of intact
biogeochemical cycling, resistance to disturbance is assessed in terms of the extent to which these
cycles are altered by a particular disturbance event. In ecosystems where nutrient cycling
involves a spatial separation of processes, certain components may be differentially influenced-by
stress. Underground processes in forests, for instance, may not be significantly affected by fire
and hyporheic processes may be immune to flooding. Similarly, although nitrification occurs in
stream sediments and vertically transported nitrate supports the high rates of primary production
of benthic plants (Valett, 1991), floods remove the benthic half-cycle in such ecosystems. Since
flood water is high in both nitrate and ammonium, however, a flood can enhance rates of
nitrification below the sediment surface. Thus assessment of ecosystem resistance must take
spatial aspects of system function into account.
Refugia. Refugia are extremely important in assessing the immediate and prolonged
effect of disturbance. Indeed, any ecological assessment must be built on an understanding of
the type, distribution, and capacity of refugia (Sedell et al., 1990) since their effectiveness
determines the extent to which an ecosystem is altered by stress and concerns the starting point
*
for subsequent recovery.
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Refugia effectiveness is specific to the system under consideration and the organisms
involved. For example, for spates in streams and rivers, substrate materials may provide a refuge
for macroinvertebrates, insulating the community from all but the largest events, as is the case in
Rocky Mountain and eastern U.S. trout streams (Peckarsky, 1986; Skinner and Arnold, 1988). In
sand bottom streambeds, however, sediments provide no refuge for surface-dwelling
macroinvertebrates; instead, refugia are found in the airspace of the riparian corridor (Gray and
Fisher, 1981), in backwaters, in nearby undisturbed tributary streams, or in unflooded upstream
reaches. The effectiveness of such refugia will determine not only the species and abundance of
survivors, but the pathways of recolonization and the nature of pioneer communities.
Interestingly, however, hyporheic organisms show relatively high resistance to floods and probably
occupy refugia deep in sandy alluvial deposits (e.g., desert streams [Boulton et al., 1992b]) or in
debris accumulations associated with the stream surface (e.g., streams in Virginia [Palmer, 1990]).
The concept of refugia is based on the understanding that disturbance effects are not
uniform in space. Thus it is necessary to understand the mode of disturbance to properly assess
resistance and the potential for an ecosystem's recovery.
t
Individual adaptations. An array of adaptations enables individual organisms to resist
disturbance as a result of natural selection operating over long periods. These selective forces
are associated with the nature of the disturbance and are likely to be well developed where the
type and nature of disturbance events have been predictable over an evolutionary period.
Adaptations can involve morphological, physiological, or behavioral features of the organisms.
For example, jack pine (Pinus banksiana) produces a cone that opens at the high temperatures
associated with forest fires, utilizing a mechanism of resistance at the level of the genome as a .
facilitator of recovery. Similarly, many invertebrates of temporary ponds possess resting stages
that facilitate survival during long dry periods. Mollusks of the coastal, rocky intertidal zone
attach themselves firmly to substrate by byssal threads so they can maintain their position during
storms and resist assaults by debris (e.g., logs) entrained in the crashing surf; during low tide
mollusks close their shells to avoid desiccation, a response that also may be used to avoid the
i
effects of toxic chemical spills. Moreover, fish of the southwestern deserts of the United States
have been shown to respond behaviorally to impending flash floods, swimming laterally away
from the stream center and the full force of the disturbance. Interestingly, Poeciliopsis
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occidentalis, a native to the Southwest and a species with evolutionary experience with floods,
exhibits this behavior, while the closely related Gambusia affinis, from the hydrologically benign
Mississippi drainage, does not (Meffe, 1984).
4.2.1.2. Limitations to Resistance
Because resistance is an essential component of stability, it greatly influences recovery
and must be carefully considered in any assessment of ecological risk. An important
consideration is that several factors may limit "natural" resistance in many ecosystems. In
particular, since attributes associated with resistance often are keyed to the organism level of an
ecosystem rather than to populations and communities, it is more important for genetic traits to
be passed on despite the disturbance event than for ecosystem functioning to remain intact.
Nonetheless, adequate capacity for ecosystem resistance depends on the maintenance of
reasonably intact, structurally complex functioning and a full set of refugia. Often simplified
systems are vulnerable to disturbance because they do not possess the array of resistance features
available in "natural" ecosystems. Also, communities or ecosystems that have undergone species.
substitution may lose their capacity to resist the stress of disturbance (Meffe, 1984).
Resistance to disturbance is in large part a characteristic of organisms that have been
shaped by natural selection in an environment characterized by a limited set of disturbance types
and magnitudes, regardless of severity. In ecosystems with such organisms, subtle changes in the
intensity, timing, or magnitude of disturbance beyond the range experienced by resident species
can have devastating effects. Additionally, such ecosystems can be eradicated by novel
disturbances with which the community has no evolutionary experience (e.g., radioactivity,
organic solvents, outbreaks of exotic pests).
Unfortunately, our ecological understanding of the complex of factors that confer
resistance characteristics—at both the ecosystem and organism levels—is-lacking, presenting a
/
challenge for ecological risk assessment until more research is conducted.
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4.2.2. Disturbance Type
4.2.2.1. Various Disturbances
Many types of disturbances occur in the wide variety of ecosystems that exist, since
disturbance is the rule rather than the exception. Indeed, most ecosystems consist of
communities of individuals that are perturbation dependent. Yet alteration in disturbance type,
magnitude, or frequency can cause greater changes than those associated with the established
disturbance regime.
Rain and flooding. Flood disturbance is typical for many stream ecosystems, especially in
arid regions (Fisher et al., 1982; Siegfried and Knight, 1977), and even essential for the proper
ecological functioning of large flood-plain rivers (Sparks et al., 1990). Similarly, although
riparian ecosystems may experience substantial mortality during extensive flooding, the continued
existence of such complex systems depends on periodic disturbance (Campbell and Green, 1968).
In contrast, excessive rainfall may favor the growth of annuals in terrestrial ecosystems, which
then increases their susceptibility to fire (Vogl, 1980). , .
Wind. Hurricanes and cyclones represent formidable agents of disturbance, especially in
such areas as south Florida and the Gulf Coast, but often far beyond these areas as well. In
1938, for instance, hurricane damage to hardwood forests in New England was extensive.
Although such disturbance events can be damaging to habitat and wildlife alike, diversity is often
maintained by, for example, the rejuvenation of dunes, the opening of new habitat, the dispersal
of seeds, and the moderation of competitive exclusion.
Fire. Fire is a catastrophe in forests and grasslands that can significantly reduce biomass
and initiate lengthy successional recovery sequences. Nonetheless, many ecosystems are fire
dependent. While some plants resist fire by relying on thick bark or underground meristems,
others (e.g., the Pin cherry) depend on fire and sprout readily from seeds in fire-damaged areas
i
(Marks, 1974). Fire intensity ranges widely from rapidly moving ground fires with minimal effect
on canopy trees to crown fires, which are devastating to mature vegetation. On a landscape
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scale, fires maintain a patchy horizontal structure that supports a diverse, larger community
consisting of several local assemblages in various states of succession.
Erosion. Catastrophic erosion is associated with precipitation events and typically occurs
on steep slopes covered with a mantle of unconsolidated soils. Soil slumps, landslides, and debris
flows (Swanson et al., 1987) are relatively rare events that alter geomorphology, soil dynamics,
and, in the case of stream channels, fluvial geomorphology (Lamberti et al., 1991). For example,
the extensive arroyos cut into the desert landscape of the southwestern United States in the late
1800s were caused by a combination of overgrazing and vegetation changes associated with
climate shifts (Hastings and Turner, 1965). This alteration in the landscape led to lowered water
tables and the loss of riparian plants, as well as changes in the geomorphology of stream
channels and the draining of cienegas (i.e., wetlands), which had been extensive in the region.
As with debris flows in the Pacific Northwest, recovery from arroyo cutting episodes is slow
(Hendrickson and Minckley, 1984).
Drought. Periods of low water availability represents a stress for both terrestrial and
aquatic ecosystems. Although droughts tend to be prolonged rather than recurrent disturbances,.
both their onset and termination often are easily recognized. In lakes and ponds, drought
influences the residence time of water and dissolved and suspended substances and, in extreme
cases, can lead to salinization of output water, which occurs largely by evaporation rather than
seepage. Generally droughts have a more severe effect on reservoirs, which are usually
impoundments of water from large rivers, than natural lakes because turnover time (i.e., response
time) is short.
Streams are influenced by droughts in several ways. For example, in permanent streams
limited flow can restrict available habitat, ultimately yielding a discontinuous series of stream
reaches. In desert streams, droughts tend to have a greater effect on habitat loss than flash
flooding in terms of mortality and productivity (Stanley, 1993).
I
Recovery after a drought can be impaired if populations of potential colonizers of the
newly inundated stream channel are reduced. Depending on the pattern of drought, available
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colonizers may relocate in significant numbers from the main channel of a stream to tributaries
of local springs. Such shifts of colonizers render prediction of recovery patterns uncertain.
Since fish show little or no resistance to drought in bodies of water in North America,
such disturbance events can cause high mortality. Thus, in most cases, recovery is dependent on
a continuous aquatic connection with a colonization source—usually downstream areas that
remain permanent. Biomass recovery is usually rapid following rewatering; however, the species
composition of the new community is likely to recover more slowly (Larimore et al., 1959).
Riparian areas and wetlands can be lost when a receding water table drops below the
rooting zone. Reestablishment of vegetation in such areas requires restoration of the ground-
water table to its previous level during the time of year when seedlings are established
(Stromberg and Patten, 1990).
While droughts are among the most common disturbance events in nature, little is known
about their effect on streams and rivers because fluvial ecologists are not adept at dealing with
ecosystems that expand and contract. Needed research on drought effects at a variety of spatial.
scales is likely to be increasingly important, given impending global climate changes and
accelerated regional desertification.
Animal-caused perturbations. Although biotic effects are endogenously generated,
disturbance attributable to animal activities can be quite pronounced in ecosystems. In aquatic
systems, grazing by invertebrates or fish can markedly influence the abundance and structure of
the algal community (Power et al., 1985), an occurrence often related to predation transmitted
through complex food chains. For example, abundant predators reduce grazer populations,
releasing-algae from grazer control and resulting in luxuriant growths (Power, 1990).
In addition to animals, pathogens represent endogenous agents of disturbance. In an
Arizona stream, epidemics of a bacterial pathogen infecting diatoms can cause a loss of biomass
and productivity, similar to that caused by a flash flood, without directly influencing the rest of
the trophic structure of the system (Peterson et al., 1993).
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In terrestrial systems, overgrazing by cattle can significantly affect plant community
structure and productivity (Ferguson and Ferguson, 1983). The concentrated browsing of elk and
moose, however, maintains openings in the boreal forest. In the grasslands of the western
United States, openings are similarly maintained by the grazing of large mammals. Similarly, sea
and marsh birds may kill vegetation in nesting areas by depositing large quantities of guano
(Vogl, 1980). Also, acacia-associated ants have been found to clear vegetation and debris from
below host trees. Although this is itself a disturbance, it reduces the probability of disturbance
from fire (Sousa, 1984).
4.2.2.2. Natural versus Anthropogenic Stress
Anthropogenic disturbance can differ from natural disturbance in several ways. It can:
• increase or decrease disturbance intensity;
• alter disturbance regimes, frequency, or timing;
*
• be induced by unique stressors (e.g., toxic chemicals, exotic organisms);
• be applied in a unique spatial pattern; and
• be superimposed on ecosystems that are already stressed by an array of natural
agents.
In short, anthropogenic stress tends to influence organisms in a manner that is well outside their
evolutionary experience, often altering communities and ecosystems in dramatic and
unpredictable ways.
Human activity also can ameliorate natural disturbances by influencing their frequency,
magnitude, or expansiveness. Thus fire-control policy can result in less-frequent fires, and flood-
control efforts can minimize disturbance by spates. In contrast, hydropower generation can
»
produce intertidal zones in freshwaters that have a periodicity to which organisms are not
adapted (Fisher and LaVoy, 1972). Similarly, the clear-cutting of forests can remove ecosystem
nutrients along with forest products and disturb soils. This combination of anthropogenic effects
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has no natural analog. Superimposed on these ecosystems are community changes resulting
either from altered disturbance regimes or introduced, exotic species. ,
In addition to altering natural disturbance regimes, human activity can introduce stressors
that are novel in terms of the evolutionary history of the resident organisms (e.g., oil spills,
organic solvents, radioactive substances, heavy metals). Indeed, anthropogenic stresses trigger
responses from the organism to the ecosystem level of organization that are difficult to predict
from research on natural systems. Once stresses are alleviated, recovery can be better assessed
from general studies that have been modified to account for an altered starting point. Thus
there is no reason to think that a flood-damaged stream and one influenced by a petrochemical
spill will not recover by similar mechanisms, assuming colonization sources remain intact. On the
other hand, recovery of large, fire-damaged areas may be slower than recovery for smaller areas
as a result of greater colonization distances.
4.2 J. The Spatial and Temporal Nature of Disturbance
£
Because recovery is likely to be sensitive to the spatial nature of disturbance and the time
(at several scales) at which it occurs, ecological risk assessment must include an accurate
description of the disturbance event relative to space and time. Although space is relatively easy
to describe as the area affected (Sousa, 1984), a more useful description must address the area's
relationship to geographic, topographic, environmental, and community gradients (White and
Pickett, 1985). This more comprehensive approach to defining the spatial nature of a
disturbance is critical if the stressor does not encompass the entire ecosystem (e.g., the
disturbance may differentially affect sites that are important as rerugia). Although all
disturbances affecting a given area should be described separately, it also may be appropriate in
some cases to describe the spatial nature of disturbance in terms of its recurrence within a
particular timeframe.
»
Recovery can be affected by altered colonization distances, especially in cases where
refugia are destroyed. Thus the availability of colonizers or their propagules may depend on the
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spatial extent of the disturbance. For this reason, the affected area should be expressed as a
percentage of the ecosystem's total area.
The timing of a disturbance can significantly influence both resistance and resilience and
should be considered at several scales (e.g., time of day, season, successional time). For instance,
seasonal phenology will determine differential exposure of ecosystem components to the
disturbance event (e.g., spring fires will differentially affect spring herbs); also, time of day can be
important when disturbances are recurrent. Similarly, in desert streams, where flash floods last
only a few hours, many species of emergent insects in the aerial corridor above the stream, which
represent the primary source of colonists, live less than a day as adults. In such environments,
the adult populations of mayflies and chironomids may flourish as a result of their emergence
from early evening through early morning hours, while late afternoon populations are likely to be
greatly reduced by daytime mortality. Thus late afternoon floods can have a more significant
impact on these insect populations than floods that occur after dark when emergence is well
under way and can influence the rate of post-flood recovery (Jackson and Fisher, 1986). Also,
because some mayfly species emerge only in early morning hours, their contribution to the
colonization pool is increased if storms generate late-night flash floods.
A temporal description of disturbance should include a measure of the disturbance
frequency and return interval as well as the rotation period (i.e., time required to disturb an area
equivalent to the study area [White and Pickett, 1985]). Such measures gauge the availability
and successional age of nearby systems that might contribute colonizers to the disturbed site
along with facilitating assessment of the effect of sequential events on the system. For example,
flash floods in rapid succession in a desert stream would have a greater effect on the
development time of the dominant taxa than the same number of floods separated by
several weeks.
4.2.4. Disturbance Scale
t
Along with contributing to the definition of a disturbance's spatial nature, scale effects
are important in ecological risk assessment because assessment and measurement endpoints must
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be keyed to the same scales. For example, disturbances such as water drawdown and drought
that alter ecosystem size tend to affect a large, heterogeneous area. Thus, for ecosystems subject
to such disturbances, recovery of biological components (e.g., macroinvertebrates, fish) should be
measured in a stratified manner to reflect this heterogeneity. Otherwise, biotic recovery at sites
inundated with water, for example, will give a false measure of the recovery rate.
Disturbance scale also influences the variables that emerge as important for controlling a
given recovery process. For example, post-flood recovery of algal communities in streams is best
explained in relation to the time that has elapsed—at the temporal scale of years—since the
disturbance event; in the short term, however, rate of recovery is a function of nutrient
availability (Grimm, 1993). Thus, in post-flood management, an assessment of the recovery
potential must specify the scale to facilitate selecting between alternatives involving nutrient
enhancement or flow regulation. Similarly, Duarte (1991) showed that macrophyte abundance
(and presumably recovery potential) is a function of lake morphometry and transparency at a
regional scale as well as a function of slope, exposure, and depth within a given lake. Moreover,
at the scale of a given macrophyte bed, biotic interactions and sediment characteristics are the
primary controlling factors. Thus, if endpoints involve macrophyte abundance in a particular ,
littoral zone, it would be unproductive to assess recovery potential in terms of either
transparency or exposure, since neither is relevant at this scale. Instead, the risk assessor should
focus on restoration of sediments and "normal" biotic interactions (i.e., factors that directly
influence macrophyte biomass in a lake).
Even though natural disturbances may devastate communities on one scale, they may be
necessary to maintain diversity and normal ecosystem functioning at the larger scale. For
example, ecologists have come to appreciate the role of fire in the maintenance of large-scale
ecosystems, accepting that fire prevention as a management approach intended to hasten
"recovery" of disturbed patches can be counterproductive. Deciding when it is appropriate to
control natural disturbances requires an understanding of landscape dynamics. In this area of
study, aquatic systems in particular are poorly understood in terms of spatial heterogeneity and
•
scale effects. Expansion of landscape tools, techniques, and concepts into the aquatic arena is
needed (Fisher, 1993).
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4.2.5. Ecosystem Type
Although ecosystem type has a significant effect on recovery, it is unlikely that any single
recovery theory is sufficiently broad to embrace all types. Nonetheless, major differences among
ecosystem types can be divided into four characteristics relevant to recovery:
• status of existing standing biomass;
• time required to regrow standing biomass;
• mobility of the medium relative to vectors imposing agents of disturbance; and
• connection with adjacent systems.
For example, terrestrial ecosystems disturbed by airborne agents will begin recovery
immediately after the source of disturbance is eliminated. Lakes, however, may show prolonged
effects, depending on hydrologic residence time and turnover of involved intrasystem storage
pools (e.g., sediments). Streams and rivers lie somewhere in between these extremes, since
toxicants usually are rapidly removed by flowing water. (Other examples of ecosystem-specific*
differences are discussed throughout this chapter.)
4.2.6. Ecosystem Linkage
Although recovery can result from either regrowth of ecosystem survivors or colonization
from outside the system, it usually involves both mechanisms. Indeed, intact adjacent systems are
often essential to significant, timely recovery following disturbance. Thus assessment of
ecological risk must be based on a knowledge of recovery mechanisms typifying the subject
ecosystem—information that often must come from studies of similar systems.
Along with serving as a source of recolonizers, adjacent systems may be linked also in
terms of long-term recovery. For example, when a stream as well as an adjacent forest are
affected by a disturbance event, the recovery pattern may be altered. Thus at New Hampshire's
Hubbard Brook Experimental Forest, where deforestation was accompanied by increased runoff
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flow and transport of dissolved and suspended materials, algal communities in adjacent aquatic
areas responded to increased nutrients with additional blooms. As a result, the rate of recovery
was enhanced, but to a different, more autotrophic endpoint (Bormann et al., 1968). Webster et
al. (1983) reported significantly delayed recovery of stream communities in a deforested
watershed in North Carolina as a result of altered organic inputs from the adjacent terrestrial
forest and suggested that stream recovery would depend on the much slower process of forest
recovery. Indeed, it has been shown that food webs in forested streams are heavily dependent on
organic inputs from adjacent forests (Fisher and Likens, 1973).
Thus adequate risk assessment must be based on a thorough understanding of ecosystem
linkages with respect to organisms, energy, and nutrients. Since strong interactions among
ecosystems are the rule rather than the exception, recovery is likely to be strongly influenced by
linkage.
4.2.7. Biological Characteristics
*
4.2.7.1. Individuals and Populations
The types of organisms in a community will influence the rate of recovery after
disturbance, since recovery depends on the regrowth of survivors and recolonization from refugja
within the system or from nearby systems. The basic biological and life history features of
survivor and colonizer organisms, however, may be quite different. Although survivors
demonstrate high resistance attributable to a variety of mechanisms, they are often larger, slower;
and less vagile than colonizers. Colonizers are early successional species that are generally
vagile, fecund, prolific, rapidly growing, and often small (Vogl, 1980). Populations of colonizer
species may experience high mortality during disturbance but then recover rapidly.
As with whole ecosystems, resistance and resilience show certain inverse correlationstat
the species level. This is particularly true for organisms in stream ecosystems that are subjected
to flood disturbance. In such environments, small aquatic insects experience high mortality but
recolpnize rapidly; larger insects such as coleopterans and hemipterans are much more resistant
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to flooding and have substantially longer life cycles (Gray and Fisher, 1981). This principle is
probably most applicable when disturbance is associated with some physical process to which
body size confers a degree of immunity. It might not apply when disturbance is induced by
anthropogenic agents such as toxins, for which dose response and susceptibility may be
independent of body size. Samuels and Ladino (1983) found that a population of long-lived,
slowly reproducing terns required 20 years to recover to predisturbance levels after an oil spill,
while the more rapidly reproducing herring gull recovered in just 5 years.
Life history or "vital attributes" of populations can vary widely and significantly influence
the rate of recovery. Such attributes relate to the method of arrival at the disturbed site, the
ability to grow in the post-disturbance environment, and the time needed to reach critical life
stages (Noble and Slatyer, 1980). Recovery of a set of populations will depend on when such
vital attributes are manifested relative to the likelihood of disturbance recurrence.
Life history characteristics also can vary spatially. For example, since attributes of fish
species in a multispecies community may vary longitudinally, recolonizers that are small and
developmentally fast tend to be more abundant in headwaters (Schlosser, 1990). Other factors
being equal, the recovery of fish populations depends on the location of the disturbance in the
drainage basin.
Moreover, the ability of ecosystem populations to interact can influence the successful
recovery of the entire complex. For example, exotic grasses used to revegetate disturbed arctic
sites were found to grow rapidly, but they were unable to establish permanently unless
continuously fertilized. Although native grasses established themselves more slowly, they
persisted without fertilization (Chapin and Chapin, 1980). In contrast, alders fix nitrogen and
serve as nurse trees for slower-growing Douglas fir trees on sites where available nitrogen is
scarce (Finegan, 1984). Thus successful recovery of individual species populations is a function
of physiology (e.g., nutrient uptake) as well as life history.
i
One of the greatest challenges in recovery management is to maintain conditions that
encourage growth of the longest-lived species in a community. Since such species are often the
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slowest growing members of the community (Schlosser, 1990), management practices that focus
on them are likely to protect other ecosystem populations as well (Gore et al., 1990).
4.2.7.2. Communities
Recovery of communities is assessed largely as a description of the temporal change in
resident species. For some time ecologists have argued about the "rules" that govern the
assembly of communities on the temporal spectrum. Concerning terrestrial systems, there is little
agreement as to whether assembly rules are universal; however, it is most likely that they are
specific to the site (i.e., the community type) (Lawton, 1987).
Disturbance-generated change in abiotic conditions plays an important role in the initial
assembly of communities. For example, clear-cut or herbicide-treated forests are initially wetter
and rich with nitrogen compared with nearby reference sites (Bormann et al., 1968; Sollins et al.,
1981). Such conditions hasten recovery and determine the success of potential colonizers,
allowing assembly rules to be significantly influenced by biotic interactions (e.g., competition, '
predator-prey activity, pathogenesis, and mutualism [Lawton, 1987]). These interactions can
operate to varying degrees in individual ecosystems. Four models describe the typical sequence
of community change: facilitation, tolerance, inhibition, and random colonization (Connell and
Slatyer, 1977). The models differ primarily in the degree to which biological interactions
influence the rate of recovery.
Three points relevant to environmental risk assessment emerge from a consideration of
assembly rules:
• There is no one set of rules applicable to ecosystems generally since similar
ecosystems (e.g., forests, grasslands) may operate quite differently.
• Assembly rules may vary within a given recovery sequence and among components
of a given community (e.g., early successional vegetation may demonstrate
facilitation, while early successional birds may demonstrate development that is
more consistent with inhibition models). Also, control may reverse in later
successional communities at the same site.
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There is a strong stochastic component to community assembly such that
stochastic early stages may "lock in" later stages. Indeed, alternate deterministic
endpoints are dependent on random initial conditions (Lawton, 1987; Horn,
1981).
In ecological risk assessment, there is no adequate substitute for a thorough
understanding of the ecology of the community type under consideration. Unfortunately,
ecological research cannot always provide especially detailed information.
4.2.7.3. Ecosystems
Recovery at the level of the ecosystem involves reestablishment of biomass and ecosystem
functioning measured as energy flow and nutrient cycling. For regrowth to occur, energy in the
form of sunlight or organic matter must be available to support accretive processes. In terrestrial
systems, sunlight is seldom limiting during initial stages of recovery. In lakes, however, increased
turbidity or altered depth structure may change the distribution of light energy below the lake
surface. Because in the aphotic zone of lakes, in underground environments of terrestrial
ecosystems, and in streams and rivers, much of the available energy is allochthonous, restoration
of normal inputs is a requisite to ecosystem recovery. For example, the recovery of streams
disturbed by deforestation is limited until the forest watershed (especially the riparian zone)
recovers to the point where allochthonous inputs are restored in quantity and quality (Webster et
al., 1983; Golladay et al., 1989). In contrast, when disturbance of streams is caused by the
release of caustic, acidic, or toxic materials, rapid recovery of ecosystem function can be expected
(Cairns et al., 1971) once residual toxicity is removed because energy supplies remain largely
unaffected. Release of sequestered toxics, however, can trigger a relapse (e.g., resuspension of
river or stream sediments) (Suter, 1993).
Nutrient cycling at the ecosystem level can be measured as the extent to which the system
retains nutrients (Odum, 1969). Since disturbance causes "leakiness," recovery can be measured
in terms of this output-input ratio. Thus, since retention capacity is largely vested in biomass,
full recovery of an ecosystem depends on regrowth of vegetation, which may take hundreds of
years in temperate forests and grasslands (Likens et al., 1978). Recovery is often more complex
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than this, however, because late-stage successional ecosystems display a reduced net biomass
increment and have the potential to become leaky, as has been demonstrated in forests (Vitousek
and Reiners, 1975) and streams (Grimm, 1987), and logically applies to all ecosystems.
For nitrogen—and probably for many other elements—the processes involved in retention
or export are especially complex. In terrestrial ecosystems, for example, nitrogen leakiness is a
function of 8 processes that vary regionally among 19 forest sites analyzed in the United States
(Vitousek et al., 1979). The analysis found that while many processes result in nitrogen loss,
vegetation regrowth was requisite in all cases to restore normal nutrient retention patterns.
At the scale of a heterogeneous landscape that consists of patches representing various
stages of disturbance, retention of nutrients overall will reflect some mathematical average of the
functioning of individual patches. A landscape comprising a mix of successional states will be
more retentive than either a widely disturbed landscape or one uniformly covered with old-
growth forest. Thus recovery at this level involves maintenance of an optimal mix of landscape
patches (i.e., states of recovery) rather than prevention of disturbance, since stress may enhance
the nutrient retention of the larger landscape rather than decrease it (DeAngelis et al., 1985)..
Unfortunately, no routine assay has been developed to address changes in whole
ecosystem processes such as P/R ratios and nutrient retention (Bartell et al., 1992). As a result,
monitoring of the recovery process at the ecosystem level must be carried out in partnership
between managers and research ecologists.
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5. REFERENCE SITE SELECTION, UNCERTAINTY, AND ECOLOGICAL SIGNIFICANCE
5.1. Selection of Reference Sites
The selection of reference sites for recovery analysis must involve consideration of
assessment endpoints. In cases where disturbance-related effects are minor, reference sites can
be drawn from nearby sites; data on conditions before the disturbance, if available; or similar but
spatially distant sites (e.g., chaparral sites in Italy as reference sites for recovery of disturbed sites
in California). Data on the prior state of the study site is probably the best source of reference
sites. Unfortunately, the availability of data depends on the extent and distribution of suitable
monitoring programs. Similar sites in close proximity are probably the second best source of
reference sites. Distant sites can be useful when the affected site is a rare ecosystem locally;
however, comparisons must be based on collective properties rather than specifics (e.g., species
lists), since taxononomic overlap is likely to be limited.
When disturbance is significant for the particular ecosystem, reference site selection is
more complex. Under the best circumstances, knowledge of the natural recovery sequences for
the ecosystem should be used to assess potential endpoints. Because successional trajectories are
stochastic, however, recovery of disturbed sites also can be assessed statistically, if knowledge of
state variables over successional time can be estimated (Loehle and Smith, 1990). This approach
can be taken even if comparable control sites do not exist (Loehle et al., 1990).
Acceptability of recovery can be more heavily weighed if recovery is based on return to
some point on a natural recovery trajectory rather than a return to a state identical to that
present before the disturbance event. Determinations about acceptability, therefore, must
include consideration of the frequency distribution of various states in the larger landscape. In
some cases, a disturbance is capable of returning a system to an earlier or different stage without
risk. Thus remediation may be unnecessary if the stress mimics natural processes and the
disturbance is incorporated in the larger landscape (Urban et al., 1987).
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5.2. Sources of Uncertainty in Endpoint Selection
Several aspects of uncertainty relate to statistical uncertainty or the inability to predict
recovery trajectories or endpoints precisely because of a lack of knowledge about biological
functioning. Another source of uncertainty is based on ecological theory, which holds that
because communities and ecosystem are flexible they are not totally predictable. Several factors
that contribute to uncertainty in recovery analysis are briefly described in this section.
5.2.1. Species Substitution
Species membership in an ecological community is determined by many factors, generally
resulting in a wide variety over time and space. Thus recovery criteria that specify exact
duplication of community composition (in relation to a reference site) may be unrealistically
stringent, of doubtful ecological necessity, and unachievable. An alternative, more realistic
approach might be to consider community structure in a hierarchical manner based on ecological
function. Recovery criteria might then specify that some subset of an ecological guild (e.g.,
insect pollinators) be reestablished, but not all species that are characteristic of the reference
site. This approach assumes substantial knowledge about the ecosystem and the appropriate
resolution of guild structure. In communities with many mutualisms and a complex guild
structure, criteria for successful recovery may be particularly demanding. Conversely, in recently
assembled communities (e.g., deciduous forests in the eastern United States [Davis, 1981]), guild
structure may be more simple and recovery more easily achieved. Regardless, measures of
species diversity, however sophisticated, will not adequately reflect the guild structure or recovery
to the reference condition.
Given our current level of knowledge about the uniqueness of niches of individual species
and uncertainty about the role of functional redundancy in communities and ecosystems, some
ecologists would argue that anything short of exact restoration of the original species complex is
unacceptable. The contention holds that some species—based on their ecological function—may
be more important than others and that some ecosystems may require a more complete
restoration of the original community in order to restore ecological functioning.
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Introductions of exotic species seem to support the contention that some species are
more important than others in particular ecosystems. In most cases, introductions have resulted
in marked changes in community structure and ecosystem functioning. For instance, the
introduction of exotic fish in Florida and the Southwest has resulted in dramatic ecological
change (Courtney et al., 1974; Minckley, 1973; Moyle, 1976), despite the fact that introduced
species may have comfortably fit into existing guilds.
Because emergent characteristics of introduced species cannot be predicted from
biological information alone, recovery of communities should be assessed and managed using
native species alone. Substitution of exotics for native species should be avoided as both a
management tool or an acceptable endpoint of successful recovery. While this principle has been
widely accepted as valid for animal species, managers have been curiously slow to accept it for
plants, despite extensive supporting evidence (e.g., studies concerning introductions of kudzu,
melaleuca, brome, and prickly pear).
Managed ecosystems are often replete with human engineered species (e.g., in
agriculture) or introduced exotics (e.g., pine plantations, reservoirs). In the Southwest, for „ .
example, although large lakes are rare, reservoirs are common and of recent origin, with fish
communities that consist almost totally of exotics from the Mississippi River drainage. These
recently assembled communities fluctuate widely. Moreover, the community and trophic
structure is simplified and system-to-systern variation is considerable.
Although establishing an assessment endpoint with reference to natural systems is
difficult, substitutions that involve exotics should be avoided.
5.2.2. Redundancy
Redundancy concerns the concept that ecological function is often vested in several
•
species. Based on this concept, reference systems should be selected that have a similar guild
structure to the system at risk, suggesting that species membership in these guilds is a lesser
concern. There are pitfalls to this approach, however. If the assessment endpoint is established
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with reference to ecological function (e.g., nutrient retention, erosion resistance), recovery may
be achieved long before community structure is restored. If, on the other hand, the assessment
endpoint is cast in terms of community composition, measurement endpoints must be developed
along the same lines.
5.2 J. Ecological Equivalence
Ecological equivalence is a measure of redundancy that indicates the potential for species
substitutions. The concept of ecological equivalence is based on the observation that similar
ecological roles are played by quite different species in spatially distinct, nonoverlapping
communities. This does not imply, however, that equivalent species can be interchanged (i.e.,
they may not be equivalents in a different ecosystem). Along with supporting the argument
against introductions, equivalence also dictates that reference systems must be selected from a
broad range of possibilities, if assessment endpoints focus on ecosystem—rather than
community—characteristics. For example, streams in the Sonoran desert of the southwestern
United States may be suitable reference systems for streams in southern Spain, for which there"
are no intact reference sites. Although species composition may show no overlap, recovery can
be assessed in reference to guild structure and ecosystem function across these disparate sites.
5.2.4. Successional Determinism
Successional determinism refers to the predictability of successional trajectories and
endpoints of apparently identical areas. For example, can a disturbed system be expected to
regain its former structure and function? Is it likely to recover to a state similar to nearby but
undisturbed sites?
The predictability and repeatability of endpoints is implicit in early studies of succession
(Clements, 1916) and is now an aspect of ecosystem development concepts (Odum, 1969). In
terms of certain collective properties, the predictability and repeatability models that have been
developed are adequate. For example, they show that biomass increases and P/R rises, then
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declines, during succession. A more realistic view, however, is that succession is flexible and
stochastic, since trajectories of recovery are predictable only within a broad range and many
alternative edaphic climaxes exist. Thus, rather than selecting a single site as a reference, the
reference should account for a probability distribution of alternatives. Such a state-frequency
expression should be derived from a knowledge of the recovery process at similar sites and study
of the various seres typical in the particular region.
5 J. The Ecological Significance of Recovery
The ecological significance of recovery has three aspects, and establishment of assessment
endpoints should treat each of these thoroughly. The first is the value of the affected system as
a habitat, requiring that the risk assessor consider recovery in terms of community structure.
The level of detail at which this analysis should occur and the selection of endpoints will depend
on whether system values call for high general diversity, the existence of endangered or
threatened species, commercially valuable species, or keystone species. If general diversity is
considered a value, for instance, then species diversity (i.e., richness and equitability componeijts)
should be monitored. When one or a few species are of special importance, individual
populations must be monitored closely. Indicator species are likely to be of limited value in
either case, since communities are notoriously flexible.
Recovery also is significant in terms of ecosystem services, which the recovery analysis
must identify and then link with a suitable measurement endpoint. For example, wetlands are
often valued as nutrient filters because they can store material of interest (e.g, sediments or
phosphorus) or shunt certain materials to the atmosphere (e.g., nitrogen, sulfur, carbon).
Recovery of a wetland's retention capacity can be monitored as storage (e.g., sediment or
phosphorus); upstream-downstream transport difference; or, assuming inputs are unchanged,
output rate (e.g., flux or concentration). Relay filtration (the wetland's shunting capacity) cannot
be satisfactorily deduced from input-output differences since these reflect storage as well. Thus
measurement of microbial processes such as denitrification, sulfate reduction, and
methanogenesis are required. In some cases, short-cut measures such as redox may be correlated
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with these microbial processes and can be incorporated in the array of measurement endpoints
adopted, but guidance from an experienced microbial ecologist is essential.
Finally, recovery analysis must include consideration of the ecosystem's habitat and
services in the larger context of the landscape. To do this, the risk assessor must know the
extent to which the system of interest is linked with nearby or adjacent systems. For example,
clear-cutting disturbs not only forests, but also streams and downstream systems such as lakes,
reservoirs, and estuaries. No recovery is complete until linkage systems also return to reference
conditions. Thus measurement endpoints must either be extensive (i.e., total ecological
monitoring of linked systems) or focus on the linkages themselves (i.e., transport rates linking
systems), regardless of whether an ecosystem or a community approach to recovery is followed.
For all three aspects of significance, assessments must be made at several scales. At the
landscape scale, for instance, disturbance is a desirable, natural process that contributes to the
structural and functional diversity of the whole. Part of wise management involves ensuring that
disturbance and recovery continue to occur at the appropriate spatial and temporal scales of the
landscape. Thus measurement endpoints at the larger scale must incorporate the concepts and
tools of landscape ecology (Turner and Gardner, 1991).
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Peer Review
DRAFT
September 1993
Issue Paper
on
UNCERTAINTY IN ECOLOGICAL RISK ASSESSMENT
Eric P. Smith
Department of Statistics
Virginia Polytechnic Institute and State University
Blacksbnrg, VA
HJL Shugart
Department of Environmental Sciences
University of Virginia
Charlottesvilfe, VA
Prepared fon
Risk Assessment Forum
U.S. Environmental Protection Agency
Draft document. Do not cite, quote, or distribute. g-i
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CONTENTS
1. INTRODUCTION
2. JROBiJEM FORMULATION 8-8
2.1. Structural Uncertainty g-10
2.2. Ecosystem Characterization g_10
23. Stressor Characterization 8-11
2.4. Conceptual Model Formulation 8-13
2.5. Implementation of Models 8-15
2.6. Summary 8-16
3. ANALYSIS-PHASE UNCERTAINTY 8-18
3.1. Uncertainties in Designed Experiments 8-20
3.2. Uncertainties Attributable to Extrapolations 8-24
33. Field Studies 8-24
3.4. Computer Simulation Models 8-26
3.5. Credibility (Model Validation) 8-27
4. RISK CHARACTERIZATION 8-29
4.1. Qualitative Characterization of Uncertainty 8-30
42. Quantitative Assessments of Uncertainty §-31
4.2.1. Uncertainty in Structure 8-31
4.23. Uncertainty in Statistical Models ." 8-32
4.23. Uncertainty Factors/Extrapolation Models 8-33
4.2.4. Uncertainty in Computer Models 8-35
4^5. Sensitivity Analysis 8-36
4.2.6. Monte Carlo or Error Analysis 8-36
4.2.7. Stochasticity in Models 8-38
43. Reducing Uncertainty 8-40
5. DESCRIBING UNCERTAINTY 8-42
6. REFERENCES 8-44
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LIST OF FIGURES
Figure 1. Links Among Various Atmosphere-Biosphere Modeling Efforts 8-50
Figure 2. Most I'mitm^ Factor in a Simulated Successional Sequence as a Function
of Tree Height and Time 8-51
LIST OF TABLES
Table 1. Uncertainties and Their Importance in Ecological Risk Assessment 8-52
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1. INTRODUCTION
Any scientific inquiry involves knowledge and lack of knowledge. The process of
discovering what we know often leads to a better • nderstanding of what we do not know. This
general lack of knowledge or lack of certainty is what we refer to as uncertainty. In any
ecological risk assessment, we try to obtain as much knowledge as possible about the relationship
between exposure to the stressor and the system that may be affected by the exposure. Because
of lack of knowledge about how the particular system functions, how the stressor relates to the
system, and how the system affects the stressor, uncertainty is generated. As described in the
Framework for Ecological Risk Assessment (U.S. EPA, 1992) "...the uncertainty analysis identifies
and, to the extent possible, quantifies the uncertainty in problem formulation, analysis and
risk characterization."
Uncertainty in an ecological risk assessment refers to anything in the assessment that
causes prediction to err, leading to doubts in the development or the results of the assessment.
Lack of certainty in ecological risk often is associated with variability. Variability represents only
one component of uncertainty, however. It is the difference between what is expected and what
*
actually occurs. As it is typically used in statistics, variability is measured and interpreted
& aiming that the model and assumptions in the analysis are correct. Uncertainty is more
significant since it also is associated with lack of knowledge; for example, in the choice of the
model of the relationship between a stressor and an organism or lack of knowledge of parameter
values in a model
O'Neal and Gardner (1979) focus on three sources of uncertainty: model structure,
parameterization, and stochastidty. Model structure uncertainty includes all unknowns in the
process of developing a model Choice of endpoints, determination of effects and relationships
between the stressor and the ecosystem, and the selection of the model (if possible) are all
included in this component of uncertainty.
Parameter uncertainty refers to uncertainty associated with the parameters of the model,
given that one can be developed. Parameters must be estimated from laboratory, field, or other
• studies. Thus uncertainty is introduced by using an estimated value, which represents insufficient
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or unreliable information (data) for the parameter of interest Further uncertainty may be
introduced by recognizing that the value of the parameter may not be fixed but may vary (e.g.,
spatially or temporally).
By stochasticity is meant the natural variation in a system or response that is attributable
to uncontrolled factors other than the stressor. Such factors are considered random in nature—
examples include site-to-site differences, weather effects, changes in physical and chemical
conditions, and variation in the responses to the stressor (e.g., uptake of a toxicant)—and may or
may not be included in the assessment.
Recognizing the three components of uncertainty is important since they provide an
approach for characterizing uncertainty. Further, including stochasticity as a component implies
a limitation to the risk assessment; that is, there is a point at which errors attributable to the
other components become unimportant given the stochasticity. One may have excellent
information about how a toxicant affects an aquatic organism, but the information will yield
limited predictions because the natural variability in a stream has a strong effect on the exposure \
of and response to the toxicant. Stochasticity also allows for the occurrence of surprise events.
Thus, despite a good knowledge base, the chance occurrence of certain factors may result hi
unpredictable t 'ents.
These three components of uncertainty in ecological risk are not, of course, the only
views of components of uncertainty. For example, Suter (1990) also considers uncertainty
attributable to mistakes, such as errors hi sampling, data entry, and computer coding. Surprises
and mutations may form another component One value in thinking about the three components
above, however, is that they provide a quantifiable view of uncertainty, and methods described hi
this paper are available for assessing the effects of these uncertainties on estimates of risk.
Rather than concentrating on the various components of uncertainty, however, this paper
addresses how uncertainty is involved in the risk assessment process.
Uncertainties are present at all phases of the risk analysis process. Table 1 lists some of
the uncertainties in ecological risk assessment and their potential influence on assessment of risk.
For example, the problem formulation stage of risk assessment has uncertainties that relate to
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the conceptual model and choice of endpoints. While in some problems it is quite clear what the
endpoints are, what the stressor is, and what is at risk in the ecosystem, it is more common that
some or all of the relationships are not known completely. For most risk assessments, uncer-
tainty is associated with the selection of the model and even the model''ng pr jcess. In systems
with great knowledge gaps, the use of quantitative models may not be possible and simple
qualitative models are the best that can be used.
Uncertainty is also present in the analysis phase of the assessment Analysis of the
exposure and the ecological effects are based on empirical evidence that may come from
laboratory studies, simulation models, and field studies. Hie studies, however, may be from
different locations, may have different species or communities, and may simplify the
actual relationships.
Uncertainties present in the analysis phase of risk assessment lead to vagueness in the
characterization of risk. In this phase of the analysis, exposure and stressor response
characterization are combined and interpreted in the context of the ecosystem. Thus, lack of
certainty in analysis will lead to uncertainties in interpretation of risk. Additional doubt may be
>
introduced in the interpretation of results by changing societal values. For example, loss of
certain species 20 years ago vas not as important as it is today.
An uncertainty analysis is an attempt to assess the doubts or reservations in the risk
assessment process and ascertain the influence they have on the decision-making process. The
analysis may be qualitative or quantitative. It may consist of exploring different models of
stressor response to investigate the effect of the model's uncertainty on the response. Or it may
consist of parameter alterations to assess the effects of uncertainty in parameters used to make
predictions. The results of the analysis of uncertainty may lead to a distribution of estimated
risk, estimation of risk under different scenarios, or simply the listing of assumptions and
reservations along with their possible consequences.
We feel that the assessment of uncertainty should occur at all phases of the risk '
assessment, since knowledge and lack of knowledge are not separate components of the
assessment While studies often try to express confidence in the assessment approach taken as
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the best one, it is also important to express any lack of confidence so that a better assessment
can be made given the state of knowledge. Because uncertainty is a part of risk, understanding
where it occurs and why will lead to risk assessments that are more accurate.
This paper examines the various phases of risk assessment in relation to uncertainty.
Section 2 describes how uncertainty is involved in the process of problem formulation, addressing
problems associated with model structure, scale, ecosystem characterization, and stressor
characterization. In section 3, aspects of uncertainty in the analysis phase of assessment are
discussed, particularly concerns arising from the analysis of statistical experiments and field
studies, from extrapolation over different ecological and environmental scales, and from the
implementation and parameterization of models. Section 4 describes how knowledge
deficiencies enter into the characterization of risk and how effects of uncertainty are evaluated
quantitatively and qualitatively. Several methods for dealing with doubts in model structure,
statistical models, extrapolations, and computer models are presented. The integration of
uncertainty into the decision-making process, another important consideration, also is described
in section 5, along with some suggested approaches. Our purpose is not to present an exhaustive \
review of all sources of uncertainty and methods for its assessment Rather, we have attempted
to present some of the important aspects of uncertainty as they relate to ecological risk
assessment and the Framework, while offr. ring suggestions for dealing with lack of knowledge.
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2. PROBLEM FORMULATION
Problem formulation can constitute a major source of uncertainty in the fundamental
predictions used as a basis to assess risk (table 1). This uncertainty can arise in the mathematical
structures used in developing predictive models and from the implementation of models once
they have been developed. Model development is often a surprisingly ad hoc procedure, and the
consequences of selecting one formulation over another are not always given a particularly deep
analysis. Frequently, anticipated parameter estimation problems influence decisions in
model formulation.
For example, a typical model for demographic analyses would be in the form of ordinary
differential equations for each population's numbers. Such models are formulated for
demographic problems because there is a tradition for estimating their parameters (natality,
mortality) and because the more complex age- or size-structured integro-differential equations
are difficult to parameterize and solve. If the population response of interest is dependent on
the age-specific changes in the mortality rate, then the simple model will not have a structure
that allows a realistic assessment of the consequences. One could develop equivalent examples
*
involving tradeoffs in model development between model simplicity and model complexity or
between spatial heterogeneity or homogeneity. Biologj and ecology involve the relationship
between geometrical structures of living things and attendant processes.
The formulation of a problem almost always involves the identification of the parts of the
ecosystem involved and the function of these parts. This fundamental theme in problem
formulation in the biological sciences recurs in cellular biology, genetics, morphology, and
population structures as the relationship between form and function. The organization of tissues
is intimately related to their physiological function. Moreover, it is important to the function of
the DNA molecule that it is a spiral helix. The adaptive implications of the morphology of
plants and animals is central to both taxonomy and evolutionary biology.
In biology, these themes are variously described as 'structure and function* or "pattern
and process." Depending upon the examples chosen, there may be an emphasis on the manner
in which processes influence pattern. For example, what changes in the morphology of the
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vertebrate limb develop in the evolution of flight? Or, how do the seeds of plants vary between
arid and moist environments? In other examples, the emphasis is on the pattern or structure
modifying the processes. Pattern and process are linked in a biological yin and yang in which
each causes and is caused by the other.
The relationship between form and function, or pattern and process, is also a classic
ecological theme (lindeman, 1942; Watt, 1947; Whittaker and Levin, 1977). Bormann and
Likens (1979) pointed out the effects of changes in forest structure on processes such as
productivity and nutrient cycling. Many Geologists recognize that pattern and process are
mutually causal, with changes in ecosystem processes causing changes in pattern and
modifications in ecosystem pattern changing processes. Nonetheless, it is difficult to investigate
directly the feedback between pattern and process.
In many ecological studies, a knowledge of the degree of dominance of particular causal
factors at particular scales is especially important. The knowledge of which factors are important
at a given scale also is involved in the determination of the "rules* for deciding what should be
included in the formulation of a given model That different phenomena may be invoked inf
developing models of analogous phenomena at different scales is responsible to a degree for
what is categorized as the "art"—as opposed to the science—of ecological modeling. While a
determination of the significance of processes at a given time or space scale is important in
model formulation and evaluation, it is a consideration neither trivial nor unique to the
developers of computer models. For example, in the case of "hierarchy theory" (Allen and Starr,
1982; Allen and Hoekstra, 1984; O'Neill et aL, 1986; Urban et al., 1987), one finds a focus on
expressing relevant mathematical developments in a manner that can provide insight into the
ways ecosystems are structured at different scales.
No hard and fast rules for the "proper" method of model formulation exist. At the
fundamental level, science is a creative endeavor, and model formulation is not immune to this
need for creativity. Nevertheless, certain considerations and procedures have proven efficacious
in developing models and for identifying uncertainties, as discussed below.
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2.1. Structural Uncertainty
Discussions of uncertainty in models often are restricted to problems of parameter
estimation from observations of a given quality. Important sources of uncertainty arise in the
formulation of a particular problem. Currently a dichotomy between process and structure is an
important limitation to a comprehensive synthesis of the behavior of ecosystems (O'Neill et al.,
1986). Are ecosystems an interdigitation of processes accounting for the dynamics of water,
carbon, and mineral elements, or are they a structured collection of organisms interacting with
their environment? While this distinction is never quite this clear hi ecosystem research, it tends
to be so in an important class of synthesis tools—simulation models. Current ecosystem
simulation models can be categorized as either process or structure models. Process models
attribute cause in ecosystem responses almost entirely to processes, while little explicit
importance is attributed to system structure (Parton et al., 1988). Models of ecosystem structure
tend to represent the opposite conceptualization (Shugart, 1984).
Terrestrial ecosystem structure, provided primarily by the vegetation, is the result of
responses of individual plants to environmental constraints of resource availability, temperature,
and disturbance. Ecosystem structure provides a context for other processes and, in turn, is
modified by them. The response times of structural and process dynamics can be juite different,
with processes typically having faster dynamics than structure. This temporal mismatch makes it
difficult to reconcile the structural and process aspects of ecosystems and it creates a dichotomy
in the thinking of ecologists that is a consequence of differences in embedded scales of the
phenomena they consider.
Ecosystem Characterization
In a classic paper by Tansley in 1935, the term "ecosystem" was first defined as an
arbitrary system with respect to both its spatial extent and the phenomena considered:
i
The more fundamental conception is, as it seems to me, the whole system (in the sense of
physics), including not only the organism-complex, but also the whole complex of physical
factors forming what we call the environment—the habitat factors in the widest sense.
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Though the organisms may claim our primary interest, when we are trying to think
fundamentally we cannot separate them from their special environment, with which they
form one physical system.
It is the systems so formed which, from the point of view of the ccologist, are the basic
units of nature on the earth. These ecosystems, as we may call them, are of the most
various kinds and sizes. They form one category of the multitudinous physical systems of
the universe, which range from the universe as a whole down to the atom.
In this first use of "ecosystem" in the English language, Tansley stressed that ecosystems
are of "various lands and sizes." This relative arbitrariness and abstraction was viewed by
Tansley as a necessary step in the formulation of an ecological science on a par with physics and
other, more established sciences. The usefulness of the ecosystem concept has been proven in
the 50 years since Tansley coined the term.
As more fully defined by Tansley, an ecosystem can be depicted as the set of interacting
external variables, ecological processes, and patterns all with equivalent space-time domains. An
ecosystem may be large or small with respect to either temporal or spatial scales, but there
should be an equivalency in the time and space domains in the patterns and processes
*
considered.
12. Stressor Characterization
The categorization of controlling factors important at different space and time scales in
*
particular ecosystems has been the topic of several reviews (Delcourt et aL, 1983; Pickett and
White, 1985). Historically, this interest is evident in AS. Watf s early work (1925) on beech
forests, which he elaborated on hi his now classic paper on pattern and process hi plant
communities (Watt, 1947). These themes have been reiterated by several subsequent ecologists
(Whittaker, 1953; Bormann and Likens, 1979).
The factors governing structure and processes vary considerably within and among •
biomes. In mesic forests, a frequent constraint is the availability of light As a forest
environment tends from mesic to xeric, or nutrient-poor conditions, the effective constraint shifts
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from above- to below-ground factors (Webb et al., 1978; Tilraan, 1988; Smith and Huston, 1989).
Under still drier conditions, forest changes to grassland in which the principal constraint is below
ground, suggesting patterns in the influence of environmental constraints in structuring
ecosystems across broad environmental gradients.
The importance and nature of ecological disturbance has been a determinant of ecology
research for the past 20 years. Disturbance is an ambiguous term in the vocabulary of Geologists
(Sousa, 1984; Pickett and White, 1985); in practice it is necessary to define the spatial
distribution, frequency, predictability, size or area, intensity or severity, and interactions with
other disturbances to sufficiently specify a particular disturbance regime (Pickett and White,
1985). A disturbance event is usually thought of as a discontinuity in an exogenous variable(s)
that can alter system structure and the balance among process rates, potentially leading to system
reorganization. This definition is directly analogous to the stressor terminology used in the
Framework. Disturbance has been variously viewed as an external catastrophe, a rejuvenating
force, a source of diversification, and an active principle counterbalancing the tendency for
competitive interactions to lead to the eventual extinction of species. In these roles, disturbance
is characterized as external (i.e., as a force from without that alters the ecological system in some
way) or, in some cases, internal (i.e., a force that is at least in part mediated in its frequency or
effect by factors inside a particular ecosystem). Of course, disturbance is all of these depending
on point of view and the disturbance in question.
Temporal patterns in disturbances may have profound effects on ecosystem structure as
well as process rates. For vegetation structure, the interplay between disturbance features and
plant life-history traits is the important feature (Denslow, 1980; Sousa, 1984), while for system
processes, the critical features are those that are rate mediating (Odum, 1969; Vitousek and
Reiners, 1975; Bormann and Likens, 1979; Vitousek et al, 1989). Disturbance frequency may
resonate with natural frequencies of plant growth forms (i.e., phenology, time to first
reproduction, longevity) to amplify environmental patterns (Neilson, 1986). Thus, there may be
an interaction between abiotic environmental constraints and biotic natural-history phenomena
that results in system-specific spatial and dynamical patterns (Allen and Wyleto, 1984; O'Neill et
aL, 1986; Urban et al., 1987; Shugart and Urban, 1989).
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2.4. Conceptual Model Formulation
Hie interweaving of models of differing fundamental scales is a problem of considerable
difficulty In many fields using dynamic models, so-called stiff system problems, in which the
time constants for important processes span several orders of magnitude, can exceed the capacity
of modem digital computers. Interestingly, both production and decomposition modeling can
lead to stiff systems of equations if fast processes that influence production and decomposition
(e.g., biophysical responses of the leaf surfaces with responses of sec-1 or min-1; microbial
growth or soil chemical kinetics with similarly fast responsivities) are coupled with slow processes
(e.g., tree mortality, soil organic matter dynamics, or soil genesis). As a numerical problem, stiff
systems are sometimes solved by separating the system into one system of "fast" variables and .
another of "slow" variables that can be evaluated separately. In ecological modeling, this same
procedure is applied in something of an ad hoc manner in the assumptions regarding which
processes can be included or excluded from a given model formulation.
One would hope that the relatively fast-response models can be interfaced to some
degree with relatively slow-response models. For example, figure 1 is a diagram developed at a
National Aeronautics and Space Administration (NASA) conference on the topic of scale
considerations in the interfacing of climate models and ecological models. It was developed in
part in response to climate modelers interested in having dynamic models on the response of
vegetative canopies at a space scale (about 100 x 100 ion2) that exceeds that of physiologically
based models (and, indeed, is on the outer fringe of the spatial domains of even the larger scale
ecological models) but is on the time scale of many physiological models (approximate minutes
and hours). While it is clearly important that ecologists in developing vegetation models have an
initial interest in understanding the time and space scales of ecological phenomena, we must also
realize that scientists in other fields (e.g., atmospheric sciences, oceanography) are increasingly
posing modeling problems for ecologists that are in unfamiliar parts of the spacc-and-time
domain.
In many cases, one would like to use a model in "scaling-up"—communicating in some
quantitative and relatively direct fashion the outcome of a model at a relatively fine space or '
time scale to a coarser scale. In many physically based systems, scaling-up is can be done in a
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relatively straightforward and, in some cases, elegant fashion because one can assume (or
demonstrate analytically) that certain terms of the equations can be ignored at larger scales of
time or space. Campbell's text (1977) on environmental biophysics provides a discussion of the
relation among 'he PC jman-Monteith equation for evapotranspiration and several
radiant-energy-driven, coarser-scale evapotranspiration models (e.g., the Priestly-Taylor equation
[1972]) that serves as an excellent example of this sort of scaling-up. Unfortunately, the problem
of scaling-up from detailed models can be considerable in some cases. Three important classes
of problems include:
1. Numerical problems in scaling-up. In the case of the scaling-up of a detailed
model, such as the type of models that were classified as "physiologically based
models," one is faced with problems such as propagation of error, nonconstancy of
conditions assumed to be constant over relatively short (or small) measurement
intervals, or the computational cost of solving equations with small time constants
over long intervals. This last problem can, in extreme cases, lead to numerical
solution problems as well. Of course, physiological models are constructed at
space-and-time scales appropriate to benchtop experimentation and thus are
potentially valuable for interpreting experimental data on novel conditions such as
alterations in the carbon dioxide concentrations.
2. Initial condition sensitivity and chaotic behavior. One important development in
mathematics that has implications in the scaling-up and development of detailed
models has come from the analysis deterministic systems that are chaotic or
unpredictable. For example, starting with two sets of conditions describing the
state of the atmosphere that are so similar as to be identical if one attempted to
measure them, the equations describing the fluid motion of the atmosphere have
the property that the predicted atmospheric dynamics will diverge over time. This
has made the long-term prediction of weather using detailed physical models
appear to be impossible except in a statistical sense. Thus it is reasonable to
expect such dynamics to prevail in natural ecosystems (Whittaker and Levin,
1977).
3. Transmutation across scales. O'Neill et al. (1986) used the term "transmutation
across scale" to describe the tendency for representations of processes to be
transmuted or changed when viewed from a different point of reference. As an
example, figure 2 shows the proportion of trees in different strata and in stands of
different ages limited in their growth by light, water, temperature, and nutrients.
The result was obtained by running an individual tree-based simulation model
from an open (bare ground) condition to a relatively mature forest The ,
interesting feature of this result is with respect to the so-called Liebig's Law of the
Minimum when applied across scales. Liebig's law states that the most limiting
factor will control growth. Pastor and Post (1986) used the LINKAGES model to
evaluate the principal constraints in forests through time and among trees of
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different stature. The principal constraint changed from below ground to above
ground as trees grew and the canopy dosed. The principal constraint also varied
with tree size and time in the simulated succession. The dominant individuals
were limited by below-ground constraints, while understory individuals shifted
from below- to above-ground constraints as they were overtopped. This result
cautions againtt oversimplifying ecosystems hi terms of "the primary constraint"
and suggests parallels in patterns of constraint through time and over spatial
gradients.
The point here is not to defend Liebig's law or to speculate at what level (if any) it
should be applied Rather, the point is that the "law* does not work at both the level of the
individual and the canopy of a forest simultaneously. Indeed, any number of rules for allocation
or optimization have this property. An optimal solution for the shape, function, or cost of a part
of a system rarely can be expected to conform to the shape, function, or cost of the part when
the entire system is optimized by the same criteria.
25. Implementation of Models
^
Several studies have attended the natural scales of ecosystems in a qualitative or
semiquantitative manner (Delcourt et al., 1983; Urban et al., 1987). By "natural scales," we refer
to the temporal frequencies or patterns of spatial variability to which a particular system is
responsive. Obversely, this implies that the smaller scales to which the ecosystem cannot respond
(i.e., higher frequencies and finer grained spatial patterns) are witnessed only as average values
and gives an indication of the larger scales that the ecosystem witnesses as effectively constant
values. The structure of a given model implementation reflects fundamental decisions as to what
state variables and parameters should be included in a given model.
The natural frequency of a system can be determined through spectral analysis (Shugart,
1978). Emanuel et aL (1978) and Shugart et al. (1981) computed power spectra for the FORE!
model and found a dominant peak at a periodicity of about 250 years, which roughly corresponds
to the life span of trees and reflects the pulsing of forest systems by the death of large trees (ie.,
gap dynamics). The use of these sorts of analytical techniques and the insights that they provide
into fundamental system dynamics have been proposed as a theoretical alternative to ecological
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modeling (Pielou, 1981). Little direct work has been performed on the spectral performance of
actual ecosystems, however—something that is largely attributable to the long, regularly sampled
data sets that are needed to perform the analyses. Some model analyses (Emanuel et aL, 1978)
and analyses of data sets from microcosms [van T 'oris et aL, 1980) have indicated a relationship
between the spectral content and the time response of a system and the response of the system
to stress. If this is the case, one might then have an a priori prediction of the relative stress
resistance of ecosystems.
An additional consideration concerns the spatial expression of system dynamics. One
technique recently presented by Levin (Levin and Buttel, 1986) is based on a regression of
sample variance versus scale (as aggregate sample size). In this analysis, a temporal variance
term is computed for some system attribute and recomputed for a geometric series of sample
quadrants from a grid or transect. If both the variance term and sample size are log
transformed, a linear relationship obtains, and if there is no spatial autocorrelation in the system
(no scale-dependent interactions), the slope of this line is -1.0. Spatial autocorrelation
(scale-dependency) deflects the regression slope from -1.0. The important feature of this analysis \
is that there is an expected "null model* for cases in which there are no spatial effects.
2.6. Summary
The conceptual phase of ecological risk assessment is associated with summarizing
information and making choices. In the summarization process, we discover how much we
know—and often how little we know—about the stressor and ecosystem response. Uncertainties
occur resulting from choice of endpoint, choice of model and modeling approach, choice of scale,
and availability of information. Choice of endpoint, both for the stressor and ecosystem, are
frequently governed by available information. Because ecosystems are dynamic in time and
space, the issue of scale is especially relevant to the understanding of uncertainty.
A fundamental part of ecological risk assessment is the documentation of knowledge and
justification of the model Part of this documentation requires a discussion of the uncertainties
related to endpoint, scale, process, and structure as well as other factors important to the
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modeling process. Ranking model components in terms of uncertainties, model and information
flow charts, and description of information needs is valuable at this stage for making
management decisions about research necessary to reduce uncertainty.
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3. ANALYSIS-PHASE UNCERTAINTY
In the analysis phase of the risk assessment, uncertainty arises from the process of
transforming a general model(s) chosen in the model development ph AC in.o a specific model(s)
and attempting to implement the model Recognizing that different risk assessments may use
different types of models and possibly multiple models, we describe the three main classes of
models that we expect to be used in a risk analysis and address the associated uncertainties.
These models are derived from experimental designs, observational studies, and biological
principles. The first two types of models tend to be simple and empirical, while models derived
from biological principles tend to be more complicated. Additional information on models is
provided in other issue papers (see chapters 4,5, and 9).
In ecological risk assessment, there are two main components to the analysis of risk: the
exposure and the modeling of exposure, and the modeling of ecosystem effects given an exposure
or exposure profile. Expected results from this phase of the assessment are profiles of exposure
and effects. The role of uncertainty analysis in this phase involves the description of doubts in
the assessment of their effects. We begin by discussing the two components of the analysis phase
(see table 1), focusing on uncertainty. Then we describe unknowns associated with experimental
designs, field studies, extrapolations, and computer models. Quantification of uncertainty in the
profiles is described in section 4, on risk characterization.
The analysis of exposure is an area where there may be a great deal of knowledge
uncertainty. With pesticides or chemicals, uncertainty may arise because of interactions with
other chemicals, variation attributable to peak versus averages, and spatial variability.
Differences may be attributable to the frequency of exposure, duration of exposure, and intensity
of exposure as well as the path of exposure. Interactions with nonstressors or with other
stressors may enhance or inhibit effects. For example, surfactants bind to soil, thus reducing the
exposure concentration in the water column. Increased total dissolved solids could produce a
direct threat to aquatic biota, but also reduce concentrations of metals and organic contaminants.
Some adjustment needs to be made for these secondary effects, since the measured concentration
of material that enters a system may not reflect the exposure.
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A significant source of uncertainty may be attributable to the inability to measure the
stressor or exposure to the stressor. Chemical levels predicted to affect organisms may be below
detection levels. Lack of knowledge about when the stressor is important may lead to poor
exposure information. For example, an important challenge to researchers sampling sitet, with
toxic wastes is the problem of spatial variability. Moreover, knowing the location of "hot spots" is
critical to obtaining information about the magnitude of potential exposure.
Models that predict exposure may range from simple regression models to complex
computer models. With computer models, additional uncertainties may arise because of the
difficulty in using available data to estimate parameters in the computer model Unlike a
statistical or empirical model, the parameters often are not directly estimated from observations.
Rather, estimates are frequently taken from other studies, possibly on other organisms, from
other sites, or under other conditions, for example. Parameter estimates may be "fine tuned" to
produce models that reasonably mimic actual data on expectations. The "tuning* is done by
changing the parameter estimates until the model is close to observed data. This process is not
necessarily unsatisfactory since the model (and its associated set of parameters) may be used as a \
"testable hypothesis" when validation data are available.
A similar set of uncertainties exii s that is associated with models of ecological effects.
Often there is a lack of knowledge on which organisms are affected and how the organisms are
affected by the stressor. While primary effects on organisms often are reasonably predicted,
subtle effects are rarely predicted with reliable accuracy. For example, available knowledge may
indicate which species are likely to be harmed by a stressor, but the analyst may not have
information on the effects of reduced abundance on species not harmed by the stressor (e.g.,
competition and predation effects).
Models of ecological effects range from simple to complex (see chapter 5, concerning
characterization. Although simple models often are easy to develop and parameterize,
extrapolations may be required to relate a model to an ecological problem. Difficulties also may
arise that relate to variables not included in the model More complex models may incorporate
greater ecological realism at the expense of predictive ability. Difficulties in developing more
realistic models include knowledge gaps associated with components of complex models (or an
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entire model), lack of data for parameterization of the model or components of the model,
problems associated with incorrect scales, and problems associated with choice of model
endpoints.
Even adequate knowledge of ecological effects and exposure does not necessarily
guarantee a successful model Complete knowledge rarely occurs in ecological risk assessment,
and any lack of knowledge in the exposure profile and the response profile may lead to surprises.
In general, surprises are viewed as events that occur but are not predicted by the model, or
events that have a low probability of occurrence. Sometimes these events are not even
considered in the formulation of the models. For example, one of the most thoroughly
investigated ecological problems is the outbreak of the spruce budworm in Canada (Walters and
Rolling, 1990). Despite being well studied and modeled, the models and risk assessors railed to
predict outbreaks in Newfoundland in the 1960s and increased frequencies of insects in stands of
young trees.
Another example of surprise concerns ecological nonlinearities and capacities of soils.
Stigliani (1988) discusses three surprises associated with shrinkage of capacities of soils: the ,
release of toxics following cessation of liming; the release of toxics attributable to changing redox
conditions in overlying water bodies; and the release c'.' sulfates in wetlands under dry conditions.
It seems that little can be done to predict surprises. Recognizing the possibility of such
events suggests that approaches to ecological risk assessors must adapt to new knowledge, make
attempts to plan for surprises by recognizing knowledge uncertainties, and be willing to alter
strategies as surprises occur.
3.1. Uncertainties in Designed Experiments
Models often are derived from the results of experimental studies that include bioassay
studies, field experiments, and microcosm and mesocosm studies. These studies often investigate
a relationship between a stressor and the ecosystem or a surrogate for the ecosystem. Because
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these are designed experiments, uncertainties arise from the statistical design, implementation of
the design, and analysis of the resulting data.
Uncertainties may be introduced by choice of statistical procedure and design. Designs
of experiments involve choice of hypotheses to be evaluated, choice of responses to measure,
choice of stressor and magnitude of stress, and methods of analysis. Additional components
include laboratory standards, measurement process, and quality control associated with the
experiment Improper control of the quality of the study may even lead to erroneous results.
An important aspect of experimental studies is the control of uncertainty through the control of
variation, which involves proper choice of sample size, proper choice of levels, and control of
variation attributable to extraneous factors that may influence results. Careful attention must be
paid to the design of the experiment to ensure the validity of the results.
Some uncertainties may arise from statistical procedures and the interpretation of
statistical data. A particularly important concern is the proper interpretation of statistical
methods, such as hypothesis tests (Parkhurst, 1990), which are used to evaluate a statement
associated with a stressor. For example: the system has recovered from stress, a dose of 0.5^
mg/L has no effect on organisms, the stressor has no effect In scientific studies, hypotheses are
usually stated to be rejected. The hypothesis of no change or effec, is called the null hypothesis
(Ho), while the hypothesis of effect or change is called the alternate hypothesis (Ht). The
decision process is given below.
Truth
Decision
Do not reject HO
Reject HO
Errors can be made in rejecting or not rejecting the null hypothesis. For example, if the
null hypothesis is that a particular stressor has no effect, the null hypothesis is not rejected if the
variation in data is large relative to the signal in the data. The hypothesis is not assumed to be
true simply because it has not been rejected. The study may not have been designed well enough
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HO
No error
Type I error
HI
Type H error
No error
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to detect the signal that would show the hypothesis to be incorrect. The usual approach for
assisting in the design of experiments involves the power of the test, which is the probability that
the hypothesis is rejected when in fact the hypothesis is raise. The proper statistical design of a
study will focus on the power of the test and choose a sample size to adequately detect important
signals. Peterman (1990) emphasized this point in evaluating the design of impact assessment
programs for power plants. In many studies, Peterman found the sample sizes used were
insufficient to assess anything but a gross change hi ecological conditions. Hence, some of the
studies may have indicated no effect attributable to the power plant when, in fact, one occurs.
Even with adequate test power, detection of change or differences may be hindered by improper
sampling, natural and unnatural influences, and confounding factors. For example, an effluent
that is discharged into a rocky substrate may have no effect on the biota present because there
are little biota present to affect Factors such as floods may alter habitats in control and impact
sites differentially, making them no longer comparable.
Hypotheses about recovery (see chapter 7), no effects, and the safety of stressors are
difficult to evaluate from a purely hypothesis-testing approach. In such studies, it is desirable
that the null hypothesis not be rejected. The above truth table may be represented in terms of
ecological effect:
Truth
Decision
No ecological effect
Ecological effect
No ecological effect
No error
Type I error
Ecological effect
Type H error
No error
The difficulty arises because the rejection of a hypothesis is a strong statement, while
evidence that favors the null hypothesis is regarded as confirmatory evidence and not proof. We
do not, for example, prove safety in a statistical study. Thus the decision of no ecological effect,
or "the system has recovered," really means that there is no evidence to indicate otherwise.
j
This consideration is important, for example, when estimating safe levels of toxicants or
other stressors. One approach is to set up an experiment hi which groups of organisms are
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exposed to different concentrations of a toxicant. Tests would then be conducted comparing the
control (zero dose) to concentrations. If the effect of the concentration were not statistically
different from the control, then the concentration could be said to be safe. Only when the null
hypothesis of no difference between the control and the toxicant concentration is rejected
statistically could the concentration be viewed as unsafe. As pointed out by Parkhurst (1990),
the burden of proof is placed on the toxicologist to show that a concentration is more toxic than
the control, rather than being placed on those who would use the toxicant to show that a dose
does not cause an effect Human risk assessment concerning lead suggests that as more
endpoints are measured, and as the ability to measure effects improves, estimates of safe levels
become lower. Thus estimates of safe levels from simple studies must be viewed with
some suspicion.
It is important, therefore, that gross uncertainties about hypothesis tests be controlled by
proper statistical control of studies. Small sample sizes, poorly designed experiments, ignorance
of the proper variables to measure or proper times to sample may lead to acceptance of a no
effect assessment when in fact there is an effect and high uncertainty.
Although the above discussion focuses on hypothesis testing, many of the same comments
and concerns pertain to the creation of models from experimental studies and the estimation »f
exposures and effects. Many of the difficulties with dose-response models, which are perhaps the
most commonly used model hi risk assessment, are discussed in the health literature (Cothem,
1988, Park and Snee, 1983). Although statistical models produce estimates of uncertainty in the
form of variance of predicted values and parameters, these estimates also have uncertainties.
For example, different laboratories may produce different models and estimates. Effects
associated with laboratory methods, technician effects, experimental conditions, and organism
variation (table 1) may lead to significant doubts about the validity of these estimates. Other
sources of concern about models that can arise are related to extrapolation and the use of
information from field (observational) studies.
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3.2. Uncertainties Attributable to Extrapolations
It is difficult (and in some cases impossible) to measure directly the effects of a stressor
on an ecosystem. Just as we cannot allow a nuclear power plant to discharge radioactive
material in order to see what happens, we cannot wait 20 yean to observe the effect of low levels
of pesticides on duck populations. Thus, given the need to assess effects and to model the
response of stressors, surrogate studies and models must be used. For example, a model of the
tenacity of a chemical may be developed based on the chemical structure and activity of the
chemical Similarly, the toxicity of a chemical to one species may be used to estimate the effect
the chemical has on the growth of the species or on the growth of other species. When models
are used in this manner, uncertainty arises that is attributable to the need to extrapolate across
endpoints, species, and even systems. For example:
• Results may be based on small-scale studies and applied over a larger scale. (For
instance, since a toxicity test often spans a short time period [e.g., 7 days], effects
of low dose may be misleading because a long-term low dose may give a different
effect than a short-term one.)
• The endpoint of interest may be replaced by a measurable endpoint. (For
instance, laboratory studies often involve a mortality or growth in a single species,
while the endpoint of interest may be a measure of ecosystem function.)
• Uncertainties may occur as a result of the genetic composition of laboratory
species relative to field species.
• Uncertainties may occur as a result of laboratory conditions not reflecting field
conditions or may relate to factors not considered in laboratory studies (e.g.,
hardness of the water).
For additional discussion of this problem, see Suter (1993). An approach for estimating the
effects of extrapolation is discussed in section 4.
3.3. Field Studies
I
Reid studies often are more complicated than laboratory studies and have additional
sources of uncertainty. One of the important components of the laboratory study is the ability to
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control the variation and bias through randomization and replication: Randomization of levels of
the stressor to the units being studied can eliminate potential sources of bias; replication of
experimental units is a useful tool for reducing uncertainty attributable to variation.
Typically field studies are not controlled experiments. More commonly they are
observational studies or at best partially controlled experiments. The stressor is not applied at
random to the field; rather, more often it exists in the field. For example, in the case of the
Northern spotted owl, where the stressor is the cutting of old-growth forest, sections of old-
growth forest containing spotted owls are not cut at random. Other examples of what are
actually observational studies include environmental accidents such as oil spills.
The difficulty with observational studies is that measured effects represent possible effects
from the stressor as well as effects from any other stressors or agents that affect the system.
While the different effects may be confounded in an observational study, confounding is less
important in controlled experiments because of the randomization. In assessing the effects of
cutting old-growth forest on spotted owls, it becomes difficult to assign a quantitative effect since
tree cutting is confounded with other factors such as local climate changes, other changes in the
forest that are specific to the cutting area or home range of the owl, and other potential stressors
(e.g., other human interventions), as well as unseen influences.
Uncertainty in the results of field studies can be especially important when replication of
the stressor is not possible. The most common of such situations is when the stressor is unique
to the site. Consider, for example, assessment of the effect of a power or chemical plant on a
river ecosystem. One approach to assessing an effect is to collect samples before and after the
plant starts operation, above and below the site of the plant on the river. Suppose that a study
finds that there are some minor differences in ecosystem organisms and that these differences
change after the plant is started. Several problems arise in trying to assign the cause of change
to the plant, since the change also may be attributable to alterations in the habitat of the site
from natural events (e.g., flooding) or to construction of the plant (e.&, from soil deposition
caused by erosion), not operation. The change also might be associated with factors such as
increased fishing in the vicinity of the plant or differences in the sampling protocol (Smith et al.,
in press). Additional uncertainty associated with field studies is attributable to their
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observational nature (Hurlbert, 1984). Thus the results of a field study have to be viewed as
associations between a stressor and the system and not linked to a cause. Complementing field
studies with laboratory studies is an effective way to reduce some of this uncertainty.
Another concern associated with field studies, especially monitoring studies, is the lack of
statistical rigor in the design and collection of the data (Rose and Smith, 1992). Often data are
collected without an explicit statement of purpose (hypothesis) and with little regard for
principles of statistical or sampling design. Mismatches between the monitoring design and
hypotheses of interest result in loss of efficiency and power. Hypotheses may be poorly stated or
may change over time, and data collected for one purpose may be used for other purposes (i.e.,
to address hypotheses they were not intended to address). Along with loss of test power, this
mismatch between hypotheses results in increased uncertainty and potentially misleading
conclusions.
Besides problems in interpreting stressor effects in field studies, there also may be
problems associated with establishing the exposure profile in the field. An important problem in
risk assessment involves establishing the existence and a means of measuring the exposure, since
exposure in an ecosystem is often patchy, making the integration of effects difficult. For
example, toxicants in an aquatic system may be diluted, and the concentration depends on flow,
location, and the time interval after the toxicant was introduced. Approaches have been
developed for dealing with these difficulties. Models of exposure and effects can incorporate
some of these effects. When information is available, models can be designed to focus on
average exposure and effects. Also, field studies can be designed to detect exposures greater
than a given magnitude with some certainty when averaging over time or space. A more difficult
problem is designing studies that account for multiple effects and exposures.
3.4. Computer Simulation Models
Because of the complexity of ecosystems, computer models are often used to characterize
the effects of stressors or to predict exposure profiles. Computer models have an advantage over
statistical models in their ability to accommodate greater complexity; also, they are designed
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using biological principles rather than empirical relationships. The additional complexity,
however, may result in different—and possibly greater—uncertainties that arise from the need to
parameterize and calibrate the models and to deal with stochasticity in the system.
Computer models often are made up of components, each one dealing with a different
part of the system. For example, a model of watershed acidification may involve as many as 30
processes or subprocesses over one or more spatial scales. Each component is a small model
that requires parameterization. 'When using these models to assess effects in an untested region,
the parameterization may involve different soil layers and the assumption of steady-state
conditions. Although such parameterization can be quite complex—involving hundreds of
parameters—rarely is enough information available for estimating each parameter individually.
Frequently in such situations, models are calibrated to available data; that is, the parameters are
altered until model predictions approximate to the information on the variables being modeled.
As pointed out by Rose et al. (1991), the approach used for configuring and calibrating
the models can yield input values with unaccounted uncertainties and biases that can affect
model predictions. The calibration process requires skill, especially with complex models, and
different investigators may derive quite different model parameterizations. Indeed, it may not be
possible to "separate the modelers and their decisions from the models and their formulations"
(Rose et al., 1991). One method to assess a model is called model validation, or credibility.
3.5. Credibility (Model Validation)
• *
Models designed to mimic exposure or responses of systems to stress require an
assessment of the credibility or validity of the model Mankin et aL (1977) and Shugart (1984)
divide model testing into two basic types of procedures (Le., verification and validation) and view
model application as a measure of a model's usefulness. A model is verified by testing whether it
can be made consistent with some set of observations. In validation procedures, a model is
tested on its agreement with a set of observations independent of those used to estimate '
its parameters.
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Often when testing a simulation model (or other model), it is important to ensure that
the model can simulate or mimic the pattern of the system under the constraint that all
parameters in the model are realistic (Shugart, 1984). The model should be able to duplicate,
for example, the pattern observed in fores' plan, ations with a fair degree of accuracy using
reasonable parameters. Successful verifications, even though they do not use data that are
strictly independent of the model, still represent reasonable tests of the model. It should be
dear from the definitions that model verification and validation can be the same sorts of tests.
Indeed, most models are tested in the verification mode because validation data are so difficult
to obtain. The drawback to model verification is that the "goodness of fit" of the model is often
inflated and represents a best-case scenario. Because the same data are used to fit and assess
the model, the goodness of the fit has been maximized. Thus one would not expect the same
level of accuracy if the model were applied to a new system or at a later time. Model validation
procedures call for independent data and estimates of how well the model will perform under
more typical situations. A useful discussion of the aspects of validation are given in OTCeefe
et al. (1987).
The analysis of a model's credibility is useful not only for computer simulation models
but for other models as well, such as the use of surrogate test systems (e.g., single-species tcxricity
;est, mesocosm) for ecosystems. How well the test system mimics the ecosystem should be
evaluated using validation methods. Most evaluations of test surrogates use verification rather
than validation data in assessment of the test performance; however, the results from such
evaluations are often misinterpreted in terms of the test system's ability to predict the effects of
stress on the ecosystem (Cairns and Smith, 1989).
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4. RISK CHARACTERIZATION
Models of risk and its characterization may be qualitative or quantitative. While risk
models are typically quantitative, qualitative models may arise as a result r f the complexity of the
assessment and the lack of knowledge. While qualitative models often can provide road maps
for assessing the importance of various components and pathways of a system and how a stressor
affects these pathways, quantitative models attempt to numerically evaluate risk. Regardless of
the model, risk is typically viewed in terms of probability of effect given exposure or probability
times consequence of exposure (Suter, 1993). For example, Suter et al. (1986) define the risk
associated with toricity to chemicals as the probability that the expected environmental
concentration of a chemical exceeds the surrogate endpoint concentration (usually determined
from a lexicological study). The probabilistic interpretation results from the recognition of
stochasticity in the estimation of exposure and/or effect concentrations. Although estimated as a
probability, risk may be represented as a distribution or a cumulative distribution function, or as
a function of the exposure or the effect When uncertainty is taken into account, the risk is no
longer viewed as a single number or distribution but rather as a series of distributions (Helton,
1993). How these distributions are estimated depends on the model used to evaluate risk and
the amount of information available to assess uncertainty.
The assessment of uncertainly may be qualitative or quantitative, just as the models of
risk may be qualitative or quantitative. Uncertainty in the characterization of risk has to be
evaluated relative to the goals of the risk assessment and the information available. Most
assessment problems are not solved by a quantitative or qualitative model Rather, it is the
model that is solved and the solution is related to the problem. Thus the results of the model
should be interpreted relative to the uncertainty of the model and assessment goals. The value
of uncertainty analysis is realized in terms of questions such as: Can we do better given the
understanding that we have of the system? What are the major sources of uncertainty and can
these uncertainties be reduced? Under what situations will estimates of risk be good or bad?
What are the implications that a given policy will have on management goals? Such questions
can be addressed through the qualitative and quantitative assessment of uncertainty. '
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A selection of methods for assessing uncertainty are described below. These include the
qualitative assessment of uncertainty and a variety of methods for quantitative uncertainty
estimation. The methods addressed estimate properties of the distribution of risk (e.g., 95th
percentile, safety factors) or the distribution function (e.g., Monte Carlo methods).
4.L Qualitative Characterization of Uncertainty
Qualitative characterization of uncertainty employs subjective methods to assess
uncertainty. These methods may be as simple as lists of the uncertainties involved in the risk
assessment. The lists need not numerically evaluate the uncertainties but may provide a rough
assessment of the magnitude of uncertainty (as in table 1). Expert opinion also provides a
qualitative assessment of uncertainty. Although expert opinion is a necessary component of
assessment, since experts do not agree, their opinions should be used in conjunction with data.
Studies in engineering indicate a wide variation in expert opinion (Morgan et al., 1985). One
would expect an even wider range of opinion in ecological assessments where problems are less
wen investigated (Suter, 1993, page 41).
Qualitative assessment of uncertainty may be addressed through the use of qualitative
models. Simple models address only the cause-and-effect relationships, importance of factors,
and the effects of possible actions. Methods for building these models include the Leopold
matrix and various simulation programs (see, e.g., Rolling, 1978). Qualitative models can be
quite useful for sizing problems and solutions, which involves finding the right variables and
factors to consider, the proper indicators to investigate, and the choice of actions to consider.
Qualitative models are useful for getting "quick looks" at the problem, for investigating possible
scenarios quickly, and for indicating where the knowledge uncertainties are. Although it is
possible to get a rough indication of the magnitude of uncertainty with a qualitative model,
qualitative models suffer from the lack of a measure of belief (certainty or uncertainty) in the
results since uncertainties related to parameterization and stochasticity are hard to include hi the
models (Rolling, 1978).
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42. Quantitative Assessments of Uncertainty
Since in many risk analyses a model is chosen and then used to produce an estimate of
risk or effects associated with a stressor, it is possible to produce quantitative estimates of
uncertainty. Numerical estimates of uncertainty are primarily available for statistical and
computer models. With statistical models, the measure of uncertainty is usually associated with
the estimate of variance. With computer models, the quantitative analysis of uncertainty has
several names (e.g., uncertainty analysis, error analysis, Monte Carlo analysis, sensitivity analysis)
and may produce a single number with which to estimate uncertainty or a distribution of output
that provides information on the range and magnitude of uncertainty.
From a modeling perspective, three sources of uncertainty are of interest: structural,
parameter, and natural variation. Structural uncertainty analysis focuses on how different
mathematical formulations of the model alter the output and decision; parameter uncertainty
analysis addresses how uncertainty in model constants change the output; and analysis of the
effects of natural variation indicates how stochastic uncertainty alters the output and decisions.
4.2.1. Unctrtainty in Structure
Uncertainty in the modeling process arises from the selection or development of the
model. In an ideal application, it would be possible to have enough information to develop a
model and then evaluate the ability of the model to predict ecological effects. It is more often
the case, however, that the risk evaluation is complex and information is sparse relative to the
complexity. This leads to a large number of possible models; hence, comparison of the models is
important. One approach is to compare models by varying the components of the model, which
will give rise to an estimation of uncertainty attributable to changing the structure of the model
Components that greatly alter the model output require care in evaluation and calibration.
In applying models to ecological risk assessments, a researcher may have to choose •
among competing models that involve different assumptions, parameter sets, spatial and temporal
scales, and inputs. Typically, extensive data sets are not available to choose from among the
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different models based on calibration and testing. A recent technique, called input mapping,
establishes a set of rules for comparing models (Rose et al., 1991). Given a common set of data,
input mapping is a method for transforming this common set into input sets for the different
models. Hie approach removes some of the bias arising from the need to calibrate the models
for specific applications.
Uncertainty in Statistical Models
Uncertainty in statistical models is usually represented by prediction or
parameter/endpoint variance. The variance is a measure of the uncertainty introduced by using a
sample of observations and a model to estimate the quantity of interest. Variance is a measure
of how far apart (i.e., how spread apart) repeated values would be if sampling could be repeated.
The variance may be attributable to particular factors, such as sampling errors, the sampling
design, and errors in handling and processing measurements, and basically represents any
differences in observations or predictions that cannot be explained by the model. Another
measure of uncertainty in risk assessment is the confidence interval on an estimated parameter or
*
prediction interval on a model prediction. These intervals represent ranges of values that are
plausible given the variation in the data used to estimate the parai icter or prediction. Parameter
estimates or model predictions with greater uncertainty wiH have larger variances and wider
confidence intervals. In complex models, prediction variance may be too complicated to
estimate, thus requiring advanced techniques or computer simulation to estimate the uncertainty
(see, e.g., PeUetier and Gros, 1991).
* •
In a statistical analysis of information, power analysis is a method for quantifying
uncertainty in a statistical test In this context, the term power is used to mean the chance that a
false statement is rejected. For example, if one is using data to determine if a potential stressor
affects an ecosystem and it actually does, then the power of the test is a measure of how likely it
is that we will decide that the stressor actually has an effect The power of a test is a number
between zero and one. Values of power near one indicate that the test is extremely sensitive,
while values near zero indicate little sensitivity. Power is limited by the number of observations
used in the test (more is better), the variability in the system (less is better), the probability of
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falsely rejecting the null hypothesis when it is true (higher probability results hi higher power),
and the size of the suspected effect (a greater effect is more likely to be detected).
In some cases, variance represents all or most of the uncertainty hi the risk analysis
process. However, hi other cases (e.g., when data are available at one site but the assessment is
carried out at another site), additional sources of uncertainty are likely to be present and the
variance may result hi overconfidence hi results. For such cases, some understanding of the
other sources of uncertainties (e.g., differences between sites) is necessary. The additional
uncertainty is sometimes represented as model "bias," which refers to the deviation of the
predicted or estimated quantity from the true value. Note that one difference between variance
and bias is that variance typically is calculated assuming the model is true when calculating error,
while the bias is calculated using the true model. Bias (squared) and variance are sometimes
combined to form an overall measure of uncertainty called mean squared error. Bias, variance,
and mean squared error are Often used to evaluate the prediction capability of models (Bartell
et ah, 1986).
4.23. Uncertainty FactorslExtrapolation Modtts
One approach to incorporating uncertainty into a risk analysis of toxicants is through an
uncertainty factor. This approach is most often applied when the result of the risk analysis is a
single number, such as the amount of a pesticide or chemical that is allowed into a system. The
approach may be quite simple. For example, one uses the best available information to pick a
number, then the number is divided by a safety or extrapolation factor (e.g.» 10,100), which
indicates the degree of magnitude of uncertainty in extrapolation from laboratory or test results
to the environment Perhaps the most common example of an uncertainty factor involves the
quotient method (Bamthouse et al, 1982), which computes the ratio of the environmental
concentration that is expected by the test endpoint concentration (e.g., LCg,). The ratio is then
multiplied by a risk factor (or an assessment factor) and compared to 1.0. Values less than 1.0
indicate safety. Bioaccumulation factors and acute/chronic ratios also may be viewed as
uncertainty factors.
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A more recent approach developed to account for uncertainty in choosing a safe level of
a stressor is called the extrapolation method. In this approach, the unit to extrapolate over is
defined. For example, to set a level of safety for a chemical, tests on single species may be used
with an interest in protecting all the species in an ecosystem. Thus, since one would use single-
species results to extrapolate across species, the unit is the species. Each unit is tested separately
and the test result (e.g., an LCX value) is viewed as a number representing a sample from the
population of units. By relating the population of units to a distribution of values, statistical
theory can be used to estimate a number that is smaller than most of the values in the
population (e.g., smaller than 95 percent of the values) for most sets of test units of the same
sample size (e.g., 95 percent). Theoretically the method is better than the simple approach of
choosing an uncertainty factor, since the uncertainty factor applied is based on the amount of
data used to assess the stressor effects and the variation in response. The method is being used
currently to set safety levels for new chemicals in the European Community (van Leeuwen,
1990), and a similar model is being used by the U.S. Environmental Protection Agency (EPA)
(Stephan et al., 1985). The approach does not include all potential sources of uncertainty,
however, and may be subject to biases (for a more extensive summary along with references, see
Suter, 1993).
Uncertainty factors can be made more precise when data relating different scales or
endpoints are available. For example, if a chemical model is used to estimate the toxicity of a
new chemical, the relationship between other chemicals and toxicity can be used to predict the
toxicity of the new compound. Uncertainty then is estimated based on the variance of the
prediction (Sloof et aL, 1986). This method is useful for the evaluation of uncertainty in the
toxicity of chemicals, requires a reasonably large data base, and works best for chemicals of
simflar characteristics (Lindgren et aL, 1991). An example in which this approach is used to
estimate uncertainty attributable to extrapolation across different levels of complexity is described
by Bamthouse et aL (1990) and is called the Analysis of Extrapolation Error (AEE). The
method uses information on a well-studied endpoint (e.g., rainbow trout LC,o) to predict the
distribution of a desired endpoint (e.g., production of juvenile brook trout), using data available
on different stressors (usually chemicals). The prediction is usually carried out using regression
models and may extend over several levels of extrapolation, based on available data. The
analysis ends by estimating the probability of risk by computing the probability that exposure
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exceeds the effect cndpoint. Although the approach usually assumes that the endpoints follow a
normal or lognormal distribution, other statistical models may be used. Details and examples are
given hi Suter (1993).
4.2.4. Unctrtainty in Computer Models
Uncertainty in computer models is usually approached through the analysis of errors in
parameters and analysis of the effects of stochasticity. Uncertainty assessment through error
analysis yields estimates of model output variance as related to uncertainty hi parameter
estimates. An additional uncertainty factor may be added to account for variance related to the
stochasticity of the system, and although it is not a common practice, one also can estimate the
uncertainty or bias attributable to the incorrect model. Most estimates of uncertainty are
obtained by using computer simulation methods (Le., Monte Carlo analysis), although the
variance of the model also can be estimated using "first-order" analysis or sensitivity analysis.
This method, which uses small perturbations or partial derivatives to estimate model variance
attributable to parameter imprecision, is best suited to simple models, linear models, and models
with small variances (Gardner et al., 1981; Pelletier and Gros, 1991).
The techniques used to distinguish how model responses are conditioned by inputs and by
model parameter values are generally referred to as "uncertainty analyses" (for a review, see
Beck, 1987). The technique used to conduct a sensitivity analysis of linear models is one of the
classical approaches to the problem (Tomovic, 1964). Because of nonlinearities and stochastic
elements, most of the techniques that are widely used hi ecological sciences employ Monte Carlo
simulations, although first-order approximations quite often are useful. O'Neill and Gardner and
their colleagues have been particularly productive in the investigation of ecosystem models using
these methods (e.g., O'Neal and Gardner, 1980; Gardner and O'Neill, 1981,1983).
One also can examine variation about a nominal system trajectory, which reflects
i
uncertainty in model parameters and hi the specification of driving variables. Such analysis is
similar to what has been referred to as "error propagation" (e.g., Garen and Burges, 1981;
Guyraon et al., 1981). For a suite of model trajectories representing a spatial environmental
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gradient, appropriate error propagation statistics can be computed (Gardner and O'Neill, 1983).
These methods allow a direct assessment of transient error variance, which may be of
considerable importance when the models are run in a forecast mode, as would be the case, for
le, in an environmental assessment
4.15. Sensitivity Analysis
Sensitivity analysis is a mathematical technique in which the partial derivatives of the
model output variable(s) are taken with respect to the parameters to measure how much the
model output changes when the parameter is changed by a small amount Parameters with high
sensitivity are important in the model. The value of a sensitivity analysis is that sensitivities of
the parameters can be used to rank the parameters in terms of importance. This information can
be used to quantify uncertainty (using a first-order variance approximation), to indicate where
additional research is required (identifying which parameters need to be well estimated), or to
develop a reduced set of parameters for further study.
>
Although the technique sounds simple, a sensitivity analysis can be complicated by a
number of factors. For example, the approach, as described above, is of limited use when the
model is highly nonlinear or if there are strong relationships between parameters. Also,
sensitivities may not be constant over time or space. Both of these problems hinder the ranking
of variables in terms of importance. Methods to deal with nonlinearities and relationships
between parameters are generally complicated (Downing et aL, 1985); some of these are
described below. Bartell et al. (1992, ch. 6) gives an example of a sensitivity analysis
involving time.
4.2.6. Mont* Carh or Error Analysis
Monte Carlo uncertainty analysis, or error analysis, (Gardner et aL, 1980) is another
method that attempts to assess the importance of parameter uncertainty. Parameters are viewed
as random quantities, and a distribution is assumed for each parameter or a multivariate
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distribution is assumed for the set of parameters. Random values are drawn from the
distribution(s) and the values are substituted for the model parameters. The values of the input
parameters and model output are then recorded. The process is repeated numerous times (more
than the number of parameters; preferably enough times to get a stable estimate of effects) to
produce a large set of model parameters and output Correlation methods then are used to
relate changes in the parameter with the output of the model High (positive or negative)
correlations indicate that a parameter is important (see, e.g., Bartell et al., 1992). Variations on
this approach depend on the complexity of the model, the number of parameters that need to be
assessed, the approach to assessing correlation, and the method by which the potential parameter
values are constrained. This same process may be applied to assess sensitivity, by restricting the
variance so that it is small (1 to 2 percent of the mean).
The current view is that the Monte Carlo analysis is the better approach for assessing
parameter uncertainty. Sensitivity analysis focuses on the effects of small changes in parameter
values, and (reasonably) a linear relationship between the model variable and the parameter is
needed for accurate results. When the relationship has higher order effects, the method will
produce results quite different from the Monte Carlo method. Since most models have
nonlinear components, the Monte Carlo approach is preferred.
Difficulties can arise in using the Monte Carlo method when the number of parameters is
large and little is known about the variation and distribution of the estimated parameters. When
the computation burden is large (as with a large number of model parameters or an expensive,
time-consuming model) computational schemes are required to produce a good analysis. An
adaptation of the above procedure developed by Iman and Conover (1979,1982) can be used to
gauge model response over a wide range of variation. The method has been successfully used by
Jaffe and Parker (1984) with a stream pollution model and by Wolock et al (1986) with a
hydrologjcal model The latter adaptation of the procedure consists of two major steps (Jaffe
and Ferrara, 1984). First, the models are exercised in a Monte Carlo mode using a Latin
hypercube sampling design (a design to pick values from parameter sets of equal probability).
i
The model parameters and the parameters describing the input are ranked, as are the model
responses of interest Second, the ranks of the responses are regressed on the ranks of the
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parameters and the usual statistics of multiple regression are used to infer the relative
importance of each of the parameters on the responses.
Another approach, v hich *j applied when there is a large number of parameters, involves
approximating the model with a polynomial model or response surface. First, the important
parameters are selected using sensitivity analysis. Then they are perturbed and a response model
is fitted as a function of the parameters and the model output The response surface is then
analyzed (Cox and Baybutt, 1981).
There are a number of unresolved difficulties with the analysis of uncertainties in
computer models. With complex computer models, it may not be possible to estimate accurately
the distribution or variance of estimated parameters or to estimate the covariance between two
or more estimated parameters. The use of a complex scheme (e.g., Latin hypercube sampling,
response surface analysis) may add other uncertainties and modeler bias. Complex models can
require a long time to run, even on high-speed computers, and shortcuts to assessments may miss
information associated with certain combinations of parameters. Models may not converge for
all sets of parameters or may converge to absurd solutions.
Uncertainty in computer models attributable to parameter uncertainty can be expressed
in terms of effects on variances of output or lead to a rank ordering of parameters. The former
provides a quantitative estimate of the effect of the parameter, while the latter provides an
indication of the relative effect The effects of uncertainty can be combined to assess the effects,
for example, of subsets of parameters, components of the model, and direct versus indirect
effects (Gardner et aL, 1980).
4.2.7. Stochasticity in Models
Stochasticity in models can be incorporated through the use of a stochastic model of the
ecological endpoints or by adding Stochasticity to the simulation model. Stochastic models, which
are based on stochastic differential equations rather than deterministic differential equations
(Tiwari, 1979; Tiwari and Hobbie, 1976), treat the endpoint as a random variable. The difficulty
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with stochastic differential equation models is the analytical complexity. With computer models,
uncertainty from natural variation can be incorporated into the model to make a stochastic
simulation model. Distributions are assumed or estimated from available data for input variables
(e.g., weather, temperature). Random sa.npler are then selected from these distributions and
used to generate one model output By repeating the process, a scenario of possible output is
generated. This scenario is usually summarized by a distribution of output values or by the mean
or variance of the output
Stochastic simulation models can be quite useful for assessing the importance of the
stochastic component of uncertainty and assigning limits to the sensitivity of the risk estimates.
Given information on natural variability, the severity of an ecological insult can be assessed as a
function of the risk. When the natural variation is great, the estimate of risk is also likely to be
highly variable and the variability of the risk estimate will only improve with increased stress.
Natural variability limits our ability to separate the stressor's effects from naturally occurring
effects. Understanding natural variation, however, may lead to a better understanding of when
and where the stressor is most likely to have the greatest impact
t
Some knowledge of the effects of stochasticity is of value in planning and interpreting risk
assessments. In systems with greater natural variability, the amount of variability may determine
if additional studies are worth undertaking. Knowledge of the variability of endpoints may be
quite useful for choosing among various endpoints. For instance, endpoints with a high "signal to
noise ratio" are valuable for assessing effects. Thus criteria may not be only whether the
particular Midpoint represents something of value but also whether it is robust to the
environment but sensitive to the stressor.
The information from the analysis of stochasticity and/or parameter uncertainty also can
be used to describe risk as a distribution rather than a single number. Since the expected effects
of a toxicant or other stressor are influenced by stochasticity, site selection, and physical and
chemical factors, a distribution of risk may better represent the expected range of risk that may
be encountered in the field.
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Other approaches to quantifying uncertainty are available but have not been extensively
applied to ecological risk assessment problems. These include "belief functions," fuzzy
mathematics, and Bayesian methodologies (see, e.g, the papers hi von Furstenberg, 1990).
4.3. Reducing Uncertainty
In some ecological risk assessments it may not be possible to reduce uncertainty enough
to allow reasonable risk estimates to be made. Nonetheless, in many risk assessments, good
scientific practices will reduce uncertainty. Some general views are provided in this section.
When data are to be collected it is important to use statistical principles to reduce
uncertainty. Statistical approaches to data collection are based on replication, randomization,
and control of variation (Le., blocking or stratification). Replication suggests that repetition of
observations are made on the stressor. Care must be taken that the repetition represents as
dose a new measurement as possible and not a "pseudofeplicate." Randomization implies that
the measurements are taken in a probabilistic fashion to avoid bias induced by order effects.'
Finally, part of statistical methodology is concerned with methods for controlling variation in
studies. SoiiiC methods include forming blocks or strata of units before applying treatments or
taking measurements, creating composites of material before taking measurements or accounting
for variation attributable to factors that are influential but not of interest (covariates).
. When the model involves different spatial units, it may be of value to subdivide spatially
or temporally or use additional information to improve prediction. Hettelingh, Gardner, and
Hordijk (1992) show that when parameter values in an acidification model are split according to
the sensitivity of the region that the model is applied over, explanation of the variance in
predictions by model parameters can be unproved. Thus it is important to identify important
strata in ecosystems and see how the model varies over these strata.
i
Another approach to reducing uncertainty is to use multiple lines of evidence.
Combining a field study with a laboratory study can be quite powerful for eliminating
uncertainty. The field study may be useful for indicating an association between the stressor and
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the ecosystem, while the laboratory study can establish a casual relationship. Little work seems
to have been done on assessing uncertainties for such combined studies.
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5. DESCRIBING UNCERTAINTY
An important element of uncertainty in risk analysis is the communication between the
risk assessor and the risk manager. While it is the job of the risk assessor to pro- ucc ue best
estimate of risk and to defend that estimate, it is also important that the uncertainties associated
with that estimate be explained. It is the role of the risk manager to insist that uncertainties
be explained.
The explanation of uncertainty may involve qualitative or quantitative components. The
qualitative components may involve other pathways to models, assumptions about the important
endpoints to measure, or assumptions about exposure. Other important elements would include
information about the importance of various components. For example, rank orderings of
parameters may be quite useful for indicating not only which parameters have the highest
uncertainty but also which parameters have the highest certainty. The list can affect future
studies intended to reduce the uncertainty of the estimated parameters. We recommend that this
list or set of explanations and concerns be included in a separate section in the risk document or '•
in subsections associated with the different phases of the Framework. Some, but not all, of the
issues that must be addressed include: What led to the choice of endpoints and what are the
consequences of this choice? What limitations are there in existing data and how does this affect
the assessment? Why were particular models chosen? Which components of models are well
studied and which are not? What are the effects and limitations of extrapolations? The issues in
the model conceptualization process and in the building of models for exposure and effects
assessments need to be described hi the risk document It is advisable to have the document
reviewed both internally and externally for possibilities not considered by the risk assessor.
Quantitative aspects of uncertainty need to be described in terms of variability concerning
the risk estimate or in descriptions of effects of parameter uncertainty and stochasticity on
ecological models. We recommend that risk and uncertainty be represented as distributions and
families of distributions when possible, rather than as a single risk estimate. A distribution is
useful because it integrates many of the components of risk and provides a range of risk as well
as a central measure of risk. Additionally, the properties of this distribution can be summarized.
Since it is often worthwhile to consider a variety of scenarios in evaluation of risk, the influence
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of weather might be included in a study by considering effects of high stochasticity versus low
stochasticity. The range produced might indicate when certain policies will be most or least
effective. There is no "best" approach to producing these distributions since methods vary based
on the complexity of the model and information that is available. We further recommend that
sensitivity analysis be used where possible to assess important components of the model(s) and
advise that error analysis be used in the assessment of effects of uncertainties.
Understanding the relationship between environmental variation or stochasticity and risk
evaluation is important because it indicates the limits of the predictive ability of the procedure.
With extreme natural variability, a simple numerical risk estimate cannot be produced with high
confidence of correctness. The risk manager has to be made aware that the predictive effects of
a stressor have to be evaluated relative to natural variability and that as the background
variability increases, the predictive ability decreases. Natural variability is especially important in
selection of endpoints, since endpoints with high response ability also may be responsive to
natural changes in the system. In the best case, the risk assessor will be able to indicate whether
the process can be improved and will set limits for that improvement.
*
The possibility of high variability in ecosystem risk studies suggests that the approach to
risk management must be able to adapt to changes u certainty. As knowledge increases about a
stressor, hypotheses about causes of effects can be eliminated and models of risk and the risk
process improved. This adaptive approach (Rolling, 1978) to assessment and management of
risk is necessary in the face of the high uncertainty of many ecological assessments.
In the evaluation of uncertainty, one must keep in mind the words of George Box (1979):
"all models are wrong but some are useful/ Uncertainty analysis is the process of determining
the usefulness of models, of assessing the limitations, and of reporting on those limitations. The
analysis forms an important part of risk assessment of ecosystems and needs to be a component
of the process.
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Climate Change
Vegetation Models
fast
log (t)
One Year
slow
Figure 1. Links among various atmosphere-biosphere modeling efforts (Shugart, 1986).
Draft document. Do not cite, quote, or distribute.
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0-10
0-10
10-20
0-10
10-20
20-30
Year
MOR limiting factor
I [Light
^^1 Soil moisture
{;;;!;:;:;] Nitrogen
[ ] Temperature
0-50
100-150
200-250
Figure 2. Most limiting factor in a simulated successions! sequence as a function of tree height
and time. The simulated successional sequence is based on results from an individual
tree-based-model of a northern hardwood forest The simulation is initiated at year 0.
The sections of each circle are allocated according to the proportion of individual
trees having the indicated factor as the most limiting factor to tree growth (adapted
from Pastor and Post, 1986).
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Table 1. Uncertainties and Their Importance in Ecological Risk Assessment (adapted from
Cothera, 1988)
Source of Uncertainty
Importance
Magnitude of Effect
Poor knowledge of system
Extreme variation, incorrect
scales
Wrong model, endpoints,
exposure routes
Surprises
Data collection practices
Design of laboratory
experiments and quality
control
Without any knowledge of the Many orders of
system, it is not possible to build a magnitude
useful model
Great variation in weather, for
example, may cause a large change
in the importance of the stressor.
Modeling large-scale phenomenon
using a small-scale model may lead
to great uncertainty
Measuring the wrong endpoint may
lead to missed effects. Lack of
knowledge of the exposure or model
may lead to large errors
Unexpected effects may occur
caused either by important gaps in
knowledge or by random effects.
Despite low probability of
occurrence, effects can have great
consequences
Errors in data collection and entry
may lead to mistakes in interpreting
statistical analyses
Adherence to laboratory standards
is necessary to avoid errors induced
by lack of care
Order(s) of magnitude
Variability in mesocosms or Mesocosm studies have higher
other ecosystem surrogates variability than laboratory studies
and need to be carefully designed
Extraneous variables Physical conditions may have a
strong effect on laboratory results
Mistakes in statistical analysis Outliers, wrong statistical model
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Table L (cont)
Source of Uncertainty
Importance
Magnitude of Effect
Interactions
Uncertainty may be introduced by
failing to account for interactions
among species or combined effects
of chemicals or stressors
Parameterization of computer
model
Parameter estimates are taken from
the literature, not from a fit to
actual observations
Mistakes in computer code of Errors in code may lead to gross
simulation models prediction errors
Extrapolations across one
species to another species to
community laboratory to field
spatial (local to regional)
Spatial and temporal
variability
Variability in laborato-v
conditions
test
Minor mistakes in choice of
statistical model
Statistical design of
Using a model developed for a
simple endpoint may lead to errors
when applied to estimate a more
complex endpoint
It is difficult to predict with
precision either over long time
periods or over space
Variation in test organisms or
concentrations of chemicals, for
example, may cause under* or over-
estimation of effects.
Including variables that are not
necessary in the model may lead to
increased variance; missing variables
may add a bias
Proper statistical design is important
Up to one order of
magnitude
manipulative studies (choice in laboratory and field studies
of stressor levels,
randomization, number of
experimental units, number of
units per treatment)
Design of field study
especially when sample sizes are
limiting. Estimates of quantities,
such as no-observed-effect levels,
may be greatly affected by sample
size and other factors
Haphazard design of field studies
may lead to incorrect decisions
regarding effects
Potentially of great
importance
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Peer Review
DRAFT
September 1993
Issue Paper
on
RISK INTEGRATION METHODS
Richard G. Wiegert
Institute of Ecology
University of Georgia
Athens, GA
Steven M. Bartell
Center for Risk Analysis
SENES Oak Ridge, Inc.
Oak Ridge, TN
Prepared fan
Risk Assessment Forum
U.S. Environmental Protection Agency
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CONTENTS
1. INTRODUCTION 9-5
2. PREVIOUS CONSIDERATIONS OF RISK CHARACTERIZATION •-?
3. RISK CHARACTERIZATION AND THE FRAMEWORK 9-8
3.1. Problem Formulation 9-8
3.2. Analysis 9-9
4. RISK CHARACTERIZATION 9-10
4.1. Empirical Models 9-10
4.1.1. Single-Value Comparisons 9-10
4.1.2. Joint Distributions 9-11
4.13. Regression Models 9-13
4.1.4. Fuzzy Sets and Fuzzy Arithmetic 9-14
4.1.5. Extreme Event Analysis 9-15
4.1.6. Advantages and Disadvantages 9-17
42. Process (Mechanistic) Models 9-18
4.2.1. Models of Individuals 9-19
4.22. Population Models 9-20
4.23. Community Models .' 9-25
4.2.4. Ecosystem Models 9-26
4.2.5. Landscape and Regional Models 9-29
4.2.6. Aggregation and Disaggregation 9-31
42.7. Implementation 9-33
43. Physical and Experimental Models 9-34
4.3.1. Cosras 9-34
4.32. Field-Scale Experiments 9-35
4.4. Example Applications 9-36
4.4.1. Phosphorus-Loading Models 9-36
4.4.2. Toxicity Risk Models in the Ecosystem Context 9-37
4.43. Ecosystem Models as Population Predictors 9-37
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CONTENTS (cont)
4.5. Uncertainties 9-38
4.5.1. Model Sensitivity Analysis 9-38
4.5.2. Model Verification 9-39
4.53. Model Validation (Evaluation) ... 9-40
4.6. Natural Variability 9-40
4.7. Multiple Stressors 9-41
4.8. System Resistance and Resilience 9-42
4.9. Monitoring Variability, EMAP, and Ecological Risk Assessment 9-43
4.9.1. Monitoring Variability 9-44
4.92. EPA's EMAP and Ecological Risk Assessment 9-45
4.10. Recovery 9-45
4.10.1. Criteria for Assessing Recovery 9-46
4.102. Exposure-Recovery Functions 9-46
4.10.3. Probability of Recovery 9-47
5. RISK SUMMARY 9-48
5.1. Qualitative versus Quantitative Assessment 9-48
5.2. Degree of Confidence 9-49
5.2.1. Evaluation of Exposure Analysis 9-49
5.22. Evaluation of the Exposure-Response Relationships 9-50
5.2.3. Applicability of Methods and Models 9-51
52.4. Assumptions and Uncertainties 9-52
53. Contribution to Weight of Evidence 9-53
6, REFERENCES 9-54
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LIST OF FIGURES
Figure 1. Identification of Structure and Function in a System Diagram 9-62
LIST OF TABLES
Table 1. Improving the Technical Content of Risk Characterization 9-63
Table 2. Methods for Error Propagation in Mathematical Models 9-64
Table 3. Application Factors for Extrapolating to Concentrations of Cnccm 9-65
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Risk characterization is the interactive process of extracting and integrating
decision-relevant information from hazard, dose-response, and exposure evaluations and
rendering it comprehensible to a diversity of users.
AIHC, 1992
1. INTRODUCTION
This chapter discusses characterization of ecological risks in relation to the Framework for
Ecological Risk Assessment ((U.S. EPA, 1991). In doing so, the paper evaluates the range of
approaches available to the risk assessor for integrating data on exposure and stressor-response
profiles, and it discusses the methodology available to implement these approaches. The
exposure profile summarizes the magnitude and spatial-temporal patterns of exposure. The
stressor-response profile summarizes data on the effects of the stressor (i.e., its relationship to
the assessment cndpoint).
The Framework Report identifies several approaches that can be used to characterize
ecological risk: (1) comparing single-effect and exposure values; (2) comparing distributions of
different exposures and effects on the same system; and (3) examining different exposures and
effects in different systems or considering multiple effects from single exposures on the same or
on different systems. Methods suited to evaluations using the above approaches involve sc ic
form of physical or mathematical modeling. This paper addresses the applicability of these
methods and approaches, and it discusses their advantages and disadvantages. Where possible,
the critical assumptions underlying the use and interpretation of these methods is stated.
Also, techniques available for translating the exposure profile and the stressor profile into
estimates of ecological risk are presented. The principal methods discussed include the use of
several different empirical models, process (or mechanistic) models, experimental facilities (e.g.,
micro-, mesocosras), and, finally, whole-system manipulations. The importance of followup
monitoring studies is addressed, as is the relevance of ecological risk characterization to the U.S.
Environmental Protection Agency's (EPA's) Environmental Monitoring and Assessment
Program (EMAP).
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This treatment of risk characterization also provides example applications, outlines the
information requirements for implementing the various methods, and points out the strengths
and limitations of these separate approaches. Issues of uncertainty in models and data are
discussed in relation to characterizing risk and recovery. Finally, risk characterization is
considered in the context of the overall ecological risk summary.
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2. PREVIOUS CONSIDERATIONS OF RISK CHARACTERIZATION
Risk characterization involves integrating all information gathered and analyses performed
in t! e course of the assessment and communicating it to risk managers in an understandable
manner. Not surprisingly then, the process of risk characterization continues to be a focal point
for improving risk assessment and risk management (see, e.g., Paustenbach, 1991; AIHC, 1992).
Previous considerations have produced general points of agreement concerning the necessary
components of an effective risk characterization. In particular, important aspects of risk
F(AlHt)
characterization were recognized at the American Industrial Health Council 991 workshop,
titled "Improving Risk Characterization"; these are outlined in table 1. The AIHC emphasizes
two requirements for full characterization of risk: (1) a full description of all the qualitative and
quantitative elements of the risk analysis must be provided; and (2) a candid discussion of the
uncertainties associated with each of the components of the risk analysis and their implications
for the overall assessment is critical to full characterization of risk. A complete summary of risk
estimates, assumptions, limitations, and uncertainties also are recognized by Paustenbach (1991)
as fundamental to the risk characterization process.
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3. RISK CHARACTERIZATION AND THE FRAMEWORK
Because the risk characterization process represents the integration of all aspects of the
framework, each t f the major components is reviewed briefly from the perspective of its potential
contribution to risk characterization.
3.1. Problem Formulation
During this initial phase, the risk assessment must be characterized as to scope, goal(s),
resources, and possible analyses: The scope addresses the level of biological organization (e.g.,
individual endangered species, population, community, landscape, region/watershed/drainage
basin), coupled with the degree to which the physicochemical environment is involved. The
scope of the study may be determined largely by the nature of the stressor. For example, will the
assessment consider risks associated with chemical (toxicants, nutrients), physical (habitat
change), biological (disease), ecological (introduced exotic competitors), or mixed stressors?
The nature of the stressor will identify in large part the relevant scale (temporal or
/
spatial) of the assessment, and, therefore, relevant scales to be considered in choosing methods
for risk integration. Examples of different scales are ponds, streams, lakes in the spatial domain,
and single chemical spills versus periodic (e.g., agricultural pesticide application) or continuous
(e.g., atmospheric acidic deposition) events in the time domain.
The problem formulation phase also includes inquiry concerning the nature of anticipated
ecological effects in relation to the stressor. The scale, level of resolution, and available
information can assist in selecting among the various approaches and tools for estimating and
characterizing risk.
The goal of the risk assessment may require either predictive or retrospective types of
assessments. Point estimates, estimates of ranges, or both may be necessary. Taken together,
the statements of scope and goal(s) will permit evaluation of the decision-makers' requirements
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to (1) discuss how these requirements further constrain the studies; (2) list the degree of advance
knowledge of regulatory options that may be required; and (3) consider the resources, both
financial and personnel related, that may be required.'
The level of reliability in the approach(es), the analyses, and the feasibility of the
requirements must be evaluated. In general, these requirements can be met only by giving
thorough consideration to the statistical techniques that are available and feasible for defining
and characterizing reliability.
3.2. Analysis
The analysis includes evaluation of exposure and effects information in the context of the
endpoints at risk. The data for both exposure and effects need to be compatible with the risk
integration technique to be used. How does the resource at risk (e.g., an endangered species, a
typical farm pond, an entire commercial fishery) or the enduse of a product dictate the
availability and quality of exposure and effects information? What considerations are necessary
to ensure that the exposure and effects data are compatible for use in the appropriate risk
integration techniques? Answers to such questions will help formulate the approaches (i.e.,
methods, models, and data) used to estimate ecological risks and will help determine the
architecture of the risk characterization.
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4. RISK CHARACTERIZATION
This section considers potential methods and approaches for integrating exposure iind
effects profiles and provides estimate? of ecological risk. These qualitative and quantitative
estimates constitute the basis for the subsequent risk characterization. Their strengths,
weaknesses, and uncertainties are added to the overall assessment.
4.1. Empirical Models
If sufficient data on the responses of any ecological system to perturbation by a stressor
are available, it may be possible to empirically characterize risks in relation to the assessment
endpoints. A number of alternative methods exist for estimating risks in this manner. It should
be recognized in further development of the framework, however, that all quantitative methods
are ultimately empirical, requiring numerical data at some point to arrive at an answer. The
term empirical is used in this chapter to refer to direct analysis of available data or use of strictly
statistical procedures and does not include the use of process models or experimental
approaches.
4.1.1. Single-Value Comparisons
Qualitative assessments can be performed by comparing point estimates of exposure to
single measures of toxicity. The quotient method (Barnthouse et al, 1982; Suter, 1992) is based
on a ratio of an exposure concentration to a toxicity benchmark, appropriately adjusted by an
application factor determined by the source of the toxicity benchmark (table 2).
Several assumptions are explicit or implicit in the quotient method when used to assess
risk. First, in routine application, exposure concentrations are assumed invariant in space or
time. To the extent that the particular circumstances violate this assumption about exposure, the
results of the quotient method could provide inaccurate estimates of risk. For example, Breck et
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al. (1988) demonstrated the implications of spatial-temporal variability in exposure toxic effects
for fish populations exposed to aluminum.
Second, and perhaps most important, availab>j effc ;ts data are assumed to be suitable for
extrapolating directly to the field. Quotient estimates are based on assumptions that the species
most sensitive in the laboratory is similar to the most sensitive species in the field. The
application factors in table 3 were derived empirically to adjust for some components of the
uncertainties associated with these kinds of extrapolations, based on the source of the effects
data. The reliability of these values, however, has yet to be rigorously examined. The factors
appear to have some empirical validity (Suter, 1992); however, a process-level understanding that
would suggest these factors has yet to be offered. Additionally, factors of this kind have not
been developed for stressors other than toxic chemicals. Although in principle the quotient
method could be developed for other disturbances, its current applications are mainly for risks
posed by toxic chemicals.
The quotients appear most useful for screening purposes or for assessing comparative
.effects of natural or human-caused disturbances. Assessments based on a comparison of single-
,effects values or point estimates are not consistent with a probabilistic framework and, strictly,
should no. be considered as quantitative estimates of risk. Note that these quotients, while
unitless, do not share the same range (i.e., [0-1J) as probabilities. Thus it remains difficult to
integrate results from quotient methods with any assessment endpoints that are framed in terms
of probability (e.g., the probability of a 20 percent decrease in annual production of a species
of interest).
4.12. Joint Distributions
More realistic assessments can be performed by comparing distributions of exposure to
distributions of toxicity data (e.g., Suter et al., 1983). Comparisons of these distributions
recognize the spatial-temporal variability in exposure and the variability in ecological response.
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There are several sources of variability in exposure. Point sources of pollutants can vary
in time. Nonpoint sources can vary in both space and time; for example, aerial application and
the subsequent runoff of pesticides from agricultural fields will show strongly seasonal and
topographically related heterogeneity. These sources of variability sugg,st that realistic
assessments or estimates of risk require that distributions of exposure be used.
Several factors contribute to variability in ecological response to disturbance. For
example, a population exposed in nature will likely differ from its taxonomically similar
laboratory counterpart with respect to genetics, age (size) structure, ecological context, and
history. These sources of variability suggest that distributions of response should be used in
comparisons of exposure to disturbances for assessments or risk estimates.
In this approach, risk is calculated as the probability that the exposure concentrations and
the concentrations correlated with measurable effects represent the same underlying statistical
distribution; that is, risk is the probability that the two distributions overlap perfectly. Methods
in basic statistics have been developed to measure this degree of overlap. The methods have
been modified to easily determine the contribution of bias and variance of each distribution to
the degree of overlap (i.e., risk) (Bartell et at, 1986b).
In some applications, risk has been characterized as the degree of overlap of a
distribution of exposure concentrations with a point estimate of a concentration associated with
the toxic response (e.g., Suter et al., 1983). In this approach, the implied test is that the effects
concentration is a statistical sample of the distribution of exposure concentrations. Thus it is a
determination of the probability that the distribution of exposure concentrations includes the
effects concentration. These kinds of integration methods are intermediate between single-value
comparisons (i.e., the quotient method) and the comparison of Joint distributions.
The comparison of joint distributions could be performed by generating a distribution of
quotients through randomly sampling from the distribution of exposures and the distribution of
effects concentrations and then performing the division. (The operation is denned as long as
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nonzero concentrations are required to measure the effect of interest, which holds by definition
in a controlled toxicity experiment.)
4.1 J. Regression Models
Regression analysis involves determining the best fit of sets of observed/measured data to
a postulated mathematical model. The models are usually empirical (i.e., their parameters are
chosen to provide the best approximation to the data), but express no causal relationships
explaining the data. This is not required, however, and some (particularly simple) mechanistic
models also can be constructed using regression techniques.
Regression models can be used to establish the best empirical models for estimating
single-value parameters. For example, toxicity data on a range of concentrations can be used to
estimate the LCX for a species. Furthermore, if a species to be exposed has not been tested, its
LCjg may be obtained, as a first approximation, from regressions on related species (e.g.,
taxonoraic interpolations). For example, the LC^ value for certain taxonomic groups of
organisms has been used to estimate acute or chronic toxicity benchmarks for other species
(Suter et al., 1983; Blanck, 1984; Koujraan, 1987; Sloof and Canton, 1983; Sloof et al., 1983;
Sloof et al., 1986).
Regression techniques also are useful in deriving empirical models of parameters that vary
with time (or with some other independent variable). For example, predictive models of the
effects of a stressor on the dynamics of ecological systems require knowledge of the effect of the
stressor, in various concentrations, on parameters such as metabolic rate, growth rate, and
reproductive success. Experimental or observational data can be used to obtain the best fitting
model. Sometimes the dynamic response of entire ecosystems can be reliably predicted by
stressor-response functions derived from regression analyses. Well-known examples include the
phosphorus-loading models of Vollenweidcr (1969,1975,1976) and Reckhow (1979) in which a
few measurements of the physical characteristics of a lake are used to predict the relationship of
phosphorus input and subsequent chlorophyll concentrations (i.e., the level of eutrophication).
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One of the main advantages of a (correct) regression model is that it predicts with
measurable confidence. This is particularly the case for predicting effects at (or near) the mean
exposure concentration when predictive power decreases as exposures approach the lower or
upper limits of the exposure concentrations. The goodness of fit of the model to the data is a
function of both. Assuming linearity in the data simplifies the model. The validity of this
assumption is best determined by visual examination of a scatterplot of the relationship; the
human eye is a sensitive detector of departures from linearity and nonhomogeneity of variance.
Often some transformation of the data may be required (e.g., log-log, semilog) before a linear
regression model can be postulated. If a workable transformation cannot be found, a polynomial
model of an appropriate degree higher than one must be used. In the absence of knowledge
about the causal relationships between the variables, caution must be used in adding terms to the
polynomial model simply to further improve the statistical fit, lest spurious relationships
be incorporated.
4.1.4. Fuzzy Sets and Fuzzy Arithmetic
Fuzzy set theory offers a different perspective concerning variability and uncertainty from
parametric distribution functions (Zadeh, 1965). .n fuzzy set theory, membership functions
replace the probability density functions. A membership function consists of a set of values each
of which is a "member" according to a certain level of confidence or certainty.
Membership functions can be used to more effectively represent subjective uncertainties
associated with model parameters (Person and Kuhn, 1992). Fuzzy sets are particularly
amenable to incorporating subjective uncertainties into risk estimation. These functions provide
an alternative to representing subjectivity by forcing expert opinion to fit parametric distributions.
Manipulation of membership functions, or fuzzy sets, involves the use of a subset of
interval mathematics. The operations of addition, subtraction, multiplication, and division are
defined. Thus it is possible to use a fuzzy approach in the comparison of joint frequency
distributions by substituting the corresponding membership functions. Fuzzy quotients can be '
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used to characterize risk when the exposure concentrations or effects concentrations are not
sufficiently known to employ parametric statistics.
Another method that should be explored for risk characterization is the use of a
combination of fuzzy and parametric methods to provide a hybrid mathematical approach for
estimating risks (Person and Kuhn, 1992). Consider a process-oriented model (e.g., population,
ecosystem model) where some of the input parameters are described by parametric distributions,
while others are represented as membership functions. This hybrid approach might provide the
best (least biased) tool to combine the subjective and nonsubjective uncertainties common to
most risk estimations.
The results of fuzzy calculations can be manipulated to produce analogs to risks, where
the probabilities (i.e., [0.1]) are replaced by confidence values associated with possible levels of
effects. These confidence levels also are enumerated over a range of 0 to 1. Fuzzy arithmetic
also can be used to construct analogs to parametric cumulative distributions. These cumulative
confidence functions can be used analogously to associate a subjective expectation that a
specified level of effect would be realized Thus the results of application of fuzzy methods in
jisk integration are operationally analogous to the methods based in normal statistics discussed
elsewhere in this paper.
4.1.5. Extreme Event Analysis
The analysis of extreme events focuses on estimating the likelihood of the occurrence of
low-frequency-high-consequence events (Asbeck and Haimes, 1984; Karison and Haimes, 1988;
Mitsiopoulos and Haimes, 1989). Extreme ecological endpoints might include, for example, the
probability of local extinction of a species or the complete functional breakdown of an ecosystem.
Therefore, it is imperative that methods for characterizing risks of extreme events be added to
the arsenal of risk estimation tools.
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Statistics based on assumptions of normality might not provide the most effective means
for estimating low-probability events. Normal statistics focus on the mean of the statistical
population. Thus, use of parametric methods is not the best approach for estimating extreme
events; that is, 95 percent confidence estimates (or 99 percent) are not necessarily good estimates
of extreme event because such estimates are really quantitative statements about the mean. One
simple example lies in the nature of the standard normal distribution, where the tails of the
distribution must be truncated (at least to non-negative values) to make sense in most ecological
applications: Detailed analysis of the tails of this distribution may provide inaccurate estimates
of low-probability events. Moreover, by definition, the less likely the event, the fewer the data
points that will be available to accurately characterize these regions of the distribution. Although
prediction intervals come closer to addressing extreme events, they also are highly influenced by
the data that define conditional expectations to the statistics of extremes (e.g., Gumbel, 1958).
Low-probability events can be more accurately assessed by reforming the underlying
model to focus on those ecological and stressor circumstances that increase their potential for
occurring. The probability density function that describes risk can be constructed using different
methods (e.g., statistical, simulation modeling) that are described in this chapter. This function
can be partitioned into several conditional risk subfunctions (Karlson and Haimes, 1988): one
for high-frequency, low-consequence events; another for intermediate range events; and a chird
for extreme and catastrophic events. The statistics of extremes demonstrates that the largest (or
smallest) values of a distribution of random samples also are random variables with probability
distributions that can be derived from the initial distribution (Gumbel, 1958).
The partitioned multiobjective risk method (PMRM) can be used to estimate the
parameters that define the distributions of the extreme events (Karlson and Haimes, 1988;
Mitsiopoulos and Haimes, 1989). The PMRM provides a tool for a more comprehensive
characterization of risk; however, the method is sensitive to the choice of the initial distribution.
In many applications, the distribution best representing the fundamental nature of the problem is
not evident. This limitation can be addressed, however, by examining the implications of
different underlying distributions for the conditional risks of extreme events (Karlson and
Haimes, 1988).
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4.1.6. Advantagts and Disadvantages
Empirical approaches to risk characterization offer several dear advantages: (1) well-
established quantitative relationships between stress and response permit efficient estimates of
risk with associated levels of statistical confidence; (2) the risks are typically easily calculated,
given the empirical model; (3) new data can be readily incorporated to refine, extend, or reject
the model; and (4) the domain of applicability of the model is often quantifiable. For example,
the bioaccumulation of hydrophobic organic chemicals can be predicted using regression models
based on the octanol-water partitioning coefficients. The regression holds for chemicals with log
K,,. less than 4.5 (Mackay and Paterson, 1992). For less-soluble organic compounds (i.e., log K^
greater than 4.5), this relationship provides less-accurate estimates of bioaccumulation because
food web effects and other factors become increasingly important.
The main disadvantage of the empirical model approach lies in the data requirements and
statistical nature of the relationship between exposure and response. For instance, the quality of
the model is strictly data dependent (i.e., no data on system response, no model of this type).
The parameters of the empirical model offer no process-level understanding of the relationship
between exposure and response. Their values are chosen solely to improve the goodness of fit of
the model Thus the validity of applying the model to new situations is constrained by the
ecological and lexicological particulars of the data used to derive the original model and is
always problematic. The addition of each new data point directly changes the behavior of state
variables in the model
A major constraint regarding this approach is that for some disturbances it simply may be
impossible to construct such empirical models because the entire purpose of the approach is to
avoid occasions of the disturbance that could provide the necessary data (e.g., toxic spills,
introduction of genetically engineered microorganisms, nudear winter, ionizing radiation).
Since statistical or empirical relationships imply nothing regarding underlying cause-and-
effect rdationships, risks estimated empirically require faith in the statistical adequacy of the
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model as well as the data, and extrapolation of the predictions outside of the data set is not
justifiable. This also means that the parameters and coefficients in the models (e.g., the betas in
a regression equation) may have little or no meaningful interpretative value that could contribute
to i nderstanding the relationship or developing and evaluating mitigation or remediation
alternatives. Additionally, ecological and lexicological data commonly make for multidimensional
and sparse data sets. Subsequent derivations of empirical relationships without some process-
level understanding can lead to statistical "fishing expeditions," where the constraints and
assumptions underlying the statistical method of choice are relaxed or ignored. Finally, in some
instances (e.g., multiple nonlinear regression methods), the estimation of model parameters is
nontrivial. Final values can be heavily influenced by initial estimates, the complex geometry of
the solution space, local minima, and the nature of the estimation algorithm.
Where possible and appropriate, however, empirical models offer good predictive ability
at a relatively modest cost in terms of data required. If sufficient data are available, empirical
approaches to risk characterization may be the method of choice. Indeed, if risks can be directly
estimated to the desired accuracy and precision using existing data, why collect more data or
build additional models?
Process (Mechanistic) Models
Process models are an attempt to mathematically represent the physical, chemical, and
biological processes that determine the dynamics of ecological systems and to formulate the
lexicological processes that translate stress into response. They are quite literally a priori sets of
hypotheses about the causal mechanisms operating in the system the model represents. As such,
the inductive step from observation/experimentation on the system to the computer program is
followed by computer-generated deductive predictions that are subject to testing to decide on the
value of the model.
Process models will become increasingly important in characterizing ecological risks; the
shear number of assessments will require the increased use of models. In some instances,
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avoiding the "experimental" evaluation of the disturbance is the whole point, and models
represent the only alternative. Process models continue to be developed to address diverse
ecological issues at many levels of measurement. Although interesting and important progress
continues in modeling the risk-related dynamics of biochemical systems (Andersen et al., 1987;
Gcrlowski and Jain, 1983), this chapter focuses on models of at least whole organisms.
The concept of "levels of organization" has been used in an overly simplistic manner in
the development of methods for ecological risk assessment. A tidy, nested, spatial hierarchy has
been developed to dissect natural complexity into regions or landscapes that encompass
watersheds that contain ecosystems; ecosystems consist of communities that in turn comprise
populations of individual organisms. In constructing this categorization of complexity, the
essential conceptual contributions to ecology associated with these terms often are glossed over
or missed altogether. These terms have become convenient ecological shorthand, instead of
stimulating conceptually powerful alternative approaches to the description, study, and
understanding of Nature. We attempt, in this section, to present these alternative modeling
views in a manner that emphasizes their commonality as alternative tools to fashion ecological
risk assessments.
4.2.1. Models of Individuals
' Individual-based models (EBMs) have been developed to describe the growth of individual
organisms (Huston et al., 1989). These models are attractive because the primary-state variable
is conveniently scaled to the observer; organisms represent tangible entities for mathematical
description. Different approaches to modeling individuals have been offered. Some IBMs simply
address growth (e.g., FORET; Shugart and West, 1977). Others attempt to simulate behavior,
life history, growth, and reproduction. Hallam et al. (1990) developed an IBM that evaluates
risks posed to Daphnia populations by hydrophobic organic chemicals.
A major advance offered by the individual modeling approach lies in the abflity to include
aspects of individual biology and ecology that are best described using different units and
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measures. These growth algorithms can include bibenergetics of growth, as well as behavioral
attributes and life history phenomena, that are not readily formulated in a single differential
equation that requires a single conserved unit of measurement (e.g., kcals, carbon, or nitrogen).
Another promise of IBMs lies in their ability to examine the implications of different
modes of disturbance on the growth and survival of the individual. By simulating a large number
of individuals, IBMs can be used to examine some population impacts of disturbance.
The primary limitations in the development of IBMs are (1) determining what constitutes
"individuality" in the basic formalism of the model; and (2) the relative availability of data that
truly measure the characteristics and attributes of individuals (Bartell and Brenkert, 1991). If the
same governing equations are used to describe both the growth dynamics of an individual and
the population, it should not be surprising that the aggregate behavior of the individuals closely
resembles that of the population. Such similarity, however, is not an emergent property of
the IBM.
Individuality might take the form of correlations among values of growth parameters; that
is, individuals characterized by higher growth rates might have higher feeding rates, lower
respiration (or maintenance) rates, and higher assimilation efficiencies. Individuals that grow
more slowly might have the opposite growth characteristics. The main limitation will always be
in corroborating these kinds of assumptions with measurements made on actual individual
organisms. Certainly this approach might be more applicable for organisms scaled similarly to
humans (e.g^ large body size, longevity, and ability to recapture and remeasure).
Population Models
In risk estimation, a population has come to mean a collection of individuals belonging to
the same species and occupying the same local habitat More value could be added to
population-level characterizations by defining the population more specifically. The ecologically
relevant population may be different from a localized statistical population. Ecological risks
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posed to a population defined in terras of its genetics (i.e., reproductive individuals, adaptability)
may be quite different than risks posed to the collective population. The same may be true for
different age (size) classes or life-history stages of a population. Moreover, a local (or statistical)
collection may be the wrong population fc assessing risks of reduction if the species is broadly
distributed throughout the region and is relatively mobile. Thus, risks to the ecologically
important population could be over- or underestimated. The lesson for risk characterization is
that the population must be defined rigorously and meaningfully if population ecology is to
contribute to its full potential in ecological risk estimation. This rigor also will be important in
the evaluation and recovery components of an overall risk assessment
Ecologjsts have been modeling the dynamics of populations since the early decades of this
century (Hutchinson, [1978], provides some fascinating historical insights, despite the lack of the
word model in the index). Papers too numerous to adequately review here have resulted from
the efforts of ecologists to quantify basic population dynamics (see May, 1973). Two primary
approaches to describing population dynamics have been used, one based on changes in the
number of individuals (Lotka, 1924), and another based on conserved mass/energy (e.g.,
Richman, 1958; Slobodkin, 1961; Kitchell et ah, 1974,1977). Steele (1974) combined both
approaches in his model of marine zooplankton population dynamics. Models of changes in
numbers of individuals cannot be joined to produce multicompartmental systems models
representing conserved mass/energy flow. Energy/element-based population models can be easily
converted in this manner and one can recover from their dynamics numbers of individuals.
Effective application of population modeling per se in ecological risk estimation has been
limited mainly by difficulties in deriving disturbance-response functions that can be used to alter
model parameter values in relation to the degree of disturbance. Despite this limitation,
population models have been used to characterize ecological risks in selected applications (e.g.,
Bamthouse, 1992; Person et al., 1991).
Population models of some degree of species or life-history-stage aggregation form the
basis for many, if not all, models of larger-scale ecological systems. Therefore, an adequate
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knowledge of the range of structural complexity and associated dynamical behavior of population
models is essential for constructing models of the larger-scale systems.
An attraction of population models lies in their relative structural and mathematical
simplicity. Population dynamics are characterized as a constrained compound interest problem.
The complexity of nature is aggregated into the estimates of the model parameters (e.g.,
maximum growth or population ingestion rate, resource satiation levels, and density-related
carrying capacity). Analytical solutions of time-varying population size can be obtained for many
of these models. Matrix manipulation or numerical integration methods are routinely used to
solve some of the more-complex, multiple-age (size) class population models. Models of
competing populations or predator-prey populations have been thoroughly studied (May, 1981).
Another advantage of the age (size) class demographic population models lies in the
ability to derive parameter values from repeated observations or measurements of the
population. Detailed process-level understanding is not required to assemble a model that can
extrapolate current transition probabilities or growth rates into estimates of future population
size. This same lack of understanding of the underlying mechanisms regulating population
dynamics also can be a disadvantage; for example, the model fails to alert the user to
/
circumstaices that change the demographic model parameter values (i.e., the effects of
a stressor).
Certain demographic model formulations are inherently unstable, converging to zero or
diverging to infinity unless the parameter values are balanced to greater precision than could
ever be measured for natural populations. Density-dependent formulations must be posited for
numerical stability. It remains nearly impossible to empirically demonstrate the existence of such
mechanisms in real-world populations, although significant efforts continue to be directed at
establishing plausible density dependencies (e.g., compensatory effects in fish [Rose et ah,
in prep.]).
Population models also can exhibit chaotic behavior in certain regions of their parameter
space (May and Oster, 1976; Schaffer, 1985). Chaotic models produce aperiodic results despite
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an underlying mathematical determinism. This model behavior appears as more of a
mathematical artifact of the basic model construct than as a characteristic of natural populations
(O'Neill et al., 1982b). The potential for chaotic behavior must be kept in mind when these
kinds of models are used to project population sizes over time.
The use of such models to estimate ecological risks will undoubtedly continue. As with
process-level models, model verification, validation, and analysis will remain important issues to
be addressed in using population models to characterize ecological risks.
The bioenergetics approach to population modeling is based on a mathematical
description of the energy budget of the population (Kitchell et al., 1974). Growth is modeled as
the integration of rates of energy input minus energy losses for the population. The
bioenergetics population models have been used to (1) project future population size given
estimates of the bioenergetics of growth, food availability, and environmental conditions (e.g.,
Kitchell et al., 1974,1977); and (2) to estimate feeding inputs by fitting the model to measured
growth data (Rice and Cochran, 1984).
The bioenergetics equations used in these models permit the incorporation of stressor
/
effects, particularly su lethal effects, as alterations in the energy budget of the organisms that
make up the population (e.g., Bartell, 1990). The equations describe the physiology of growth,
expressed as the processing of energy (or biomass expressed as carbon). Stressor effects can be
represented as changes in the rates of the physiological processes that determine growth in the
model population (O'Neill et al., 1982a, 1983; Breck and Bartell, 1988; Bartell et al., 1992). A
limited amount of experimental evidence has been collated that supports this approach for a
variety of chemical stressors, physiological processes, and organisms (Bartell et al., 1992). The
lexicological literature needs to be comprehensively surveyed and analyzed to determine the
extent to which this approach usefully applies (see section 4.4.2 for further elaboration of
this point).
As with the demographic representations of population dynamics, bioenergetics models
have limitations that can impede their application for ecological risk estimation. One limitation
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is the incomplete quantification of the bioenergetics of growth for species of interest. To
implement a model for a particular population, data from several species are used to estimate
the values of model parameters. Complete descriptions of the bioenergetics of growth have not
been assembled for many species.
The bioenergetics models are typically sensitive to estimates of energy inputs. For
example, fish growth as predicted by the bioenergetics models (e.g.f Kitchell et al., 1974,1977;
Rice and Cochran, 1984) relys heavily on the estimates of temperature-dependent feeding rates
(Bartell et al., 1986a). Values of fish feeding rates under nonlaboratory conditions are nearly
impossible to obtain. Temporal changes in diet composition (and its caloric value) also can
introduce uncertainty in estimating relations between consumption and growth.
Another limitation in the bioenergetics models is the description of the population as a
unit of biomass, carbon, or its caloric equivalent. Size (age) structure, which characterizes the
demographic models by definition, is typically lacking in the bioenergetics models. This level of
aggregation may limit the applicability of these models, particularly if specific ages or sizes of
organisms are differentially sensitive to the stressor(s) of concern.
One potentially powerful approach to estimating risks to populations lies in the
integration of bioenergetics and demographic methods. The process-based bioenergetics models
might be used to establish stressor-rcsponse relations in terms of physiological understanding.
Despite data limitations, separate bioenergetics models might be developed for different age or
size classes to provide the necessary detail in population descriptions. The results of the
bioenergetics models then could be used to estimate the macroscale parameters required in a
demographic description of the overall stressor effects on the growth dynamics of the population.
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4.2J. Community Models
In ecology, the concept of community has its roots in the early studies of Clements (1916)
and Gleason (1926) on the formation and successional change in plant aggregations. Alth nigh
these two workers, and later adherents, differed widely on the mechanisms of establishment,
change, and ultimate state of communities, they shared an emphasis on biotic interactions. The
physical environment, though crucial as a whole in determining the type of community found,
was disregarded as a temporally varying force that could shape successional change. The
emphasis was placed on the biota, its diversity, and the mechanisms by which species were
packed into niches or displaced those already in place. This emphasis was continued in the
development of island biogeographic theory (MacArthur and Wilson, [1967], who focus on the
probabilities of colonization of isolated patches of habitat by species and on their rate of
extinction). Thus community models would be expected to focus on processes that reflect the
outcomes of the interactions among the biota (e.g., colonization, successional change,
biodiversity, niche breadth). Because these interactions are the result of the attributes of
individuals and populations, and because in the aggregate they form processes important to the
ecosystem and to the landscape, there is quite obviously a very fuzzy line separating models of
community from the other category of models. At best, we may categorize models of community
as containing interactions of three or more speues (to separate them from individual-based
models and two-species population models of competition and predation), with minimal inclusion
of temporally varying parameters representing the physical environment (to separate them from
models of ecosystems). In addition, we may add the constraint that models of community not
explicitly consider two- and three-dimensional spatial heterogeneity (to separate them from
models of entire landscapes).
With the caveats above in mind, what is left? The answer must be, from the standpoint
of methods to assess ecological risk, not much. Perusal of the pages of ecological journals such
as the American Naturalist, Ecology or those published by the British Ecological Society yield
dozens of models of the community processes enumerated above. These models, however, were
designed to propose or assess some theoretical construct of community ecology and were not
expected to include algorithms that would permit assessment of risk from some stressor. All of
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the community properties are properties of ecosystems as well. Furthermore, the nature of most
stressors is that they are either transported via some physical component of the system (e.g., air
or water) or they directly modify the physical environment Thus, although specific models of
community are vital as tools to develop and modify community theory, the risk assessment model
will usually incorporate the community theory in the context of both the biotic community and its
physical environment (i.e., in models of the ecosystem or the landscape).
One group of community models, however, offers some potential in terras of risk
assessment tools—models of food webs (Cohen et al., 1993; Pimm, 1982), which incorporate
many of the attributes of community. For instance, certain impacts of biodiversity are included,
as are elements of species' interactions via the trophic pathways. The models can be constructed
using graph theory (Pimm, 1982) and are largely qualitative, although certain quantitative
variables can be computed (e.g., the mean length of food chains). Because food webs, varying in
degree of detail, are documented for a large number of ecological communities (Cohen et al.,
1993; Pimm, 1982), such models may serve as a first-approximation qualitative predictor of
certain kinds of response to a stressor that can potentially eliminate designated species from a
.community. Because the predictions are based on qualitative data, however, caution must be
used in interpretation.
4.2.4. Ecosystem Models
In risk characterization, "ecosystem" all too often refers to an entity that corresponds
more closely to habitat, as evidenced in the framework (e.g., lake ecosystems, wetland
ecosystems, coastal ecosystems). One important contribution of the ecosystem concept to
ecological theory lies in the understanding that biological (e.g., excretion and egestion) and
ecological (e.g., predation, decomposition, and nutrient cycling) processes can modify the
physical, chemical, and biological environment The role of these feedback mechanisms in
determining productivity and maintaining system functional integrity has been long recognized in
ecology (e.g., Lindeman, 1942; Hutchinson, 1948). Other key contributions of ecosystem theory
include recognition of the scale dependence and hierarchical structure of ecological systems
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(Allen and Starr, 1982; O'Neill et al., 1986), the potential for discontinuous behavior in system
function (e.g., catastrophe [Thora, 1975]) and structure (e.g., strange attractors [Schaffer, 1985]),
and relations between energy flow and nutrient cycling as they influence system recovery from
response (DeAngelis et al., 1989a). These advances in ecosystem understanding should be
considered carefully in the development of ecosystem-level endpoints for risk analysis.
Ecosystem models explicitly address biotic-abiotic constraints, interactions, and feedback.
They are by definition multicompartmental models (Wiegert, 1993). Attempts are made to
represent relevant physicocheroical characteristics of the environment that influence and in turn
are influenced by biological interactions (e.g., predator-prey relations, differential resource use).
This environmental complexity is aggregated into the parameter estimates and assumptions of the
population and community models.
Simulation models of the dynamics of ecosystems are constructed using the relationship—
from systems science—that structure of the system (or model) interacts with function to produce
behavior of the system (or model) as a whole. Because the usage of the words structure and
function in this context follow the definitions from systems science, and not ecology (Hill and
Wiegert, 1980), we briefly reiterate them (figure 1).
Structure is the abstract (conceptual) part of the model; the "box and arrow" diagrams
Illustrate the structural aspects of the model. Setting out the structure of a model is an
extremely important step, for it is in this operation that the capabilities and goals of the model
are incorporated. Structure determines the degree of complexity of the model and largely
determines the number of parameters and thus the informational requirements of the model.
The functional components of the model constitute the specific occupants (e.g., species or
aggregated groups, abiotic storages) of the "boxes," plus physical components (e.g., light,
temperature, precipitation), and finally the functions and specific parameter values.
Dynamic behavior of the model through time can be measured by changes in standing
stock (e.g., number, biomass, energy) and by changes in fluxes between compartments. The /
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bookkeeping unit(s) of choice may enter and leave the system from the surroundings. In
addition to these sources and sinks, models explicitly dealing with colonization and/or
successional change must make provision for the entrance and exit of new structural
(species) compartments.
The primary advantage of the ecosystem models lies in their ability to incorporate
detailed formulation of the processes believed to determine the production dynamics of the
system, as well as the ability to address physicocheraical measures of disturbance (e.g., energy
flow, material cycling, habitat alteration).
The potential for additional detail also can be a potential for misuse in applying
ecosystem models to risk estimation. The data requirements for model development and
implementation can quickly exceed the amount of information commonly available for specific
systems. Values for remaining parameters must be estimated using published data that often are
not directly applicable to the system of interest. Uncertainties introduced by a complex array of
parameter values can produce model results too imprecise to be of use in decision-making.
Thus, as we emphasized above, the initial structural diagram of the model must be carefully
tailored to meet the goals of the model without adding unneeded detail.
Because mechanistic ecosystem models incorporate causal statements into the functional
components, they have the disadvantage that their predictive accuracy is completely dependent
on the correctness of these causal mechanisms. Thus verification and validation (see sections
452 and 4.53) are vital tests. Once verified and validated, however, such models have the
advantage that their predictions are not constrained to an observed range of perturbations on a
real ecosystem, as are those of empirical models. Mechanistic ecosystem models also are more
costly to construct than empirical models; however (as discussed in section 4.1.6), if data suitable
for mechanistic models are not available (or cannot be obtained because of the nature of the
stressor), then there are no alternatives.
These models are categorized on the basis of spatial and temporal scales and the degree
of heterogeneity they are capable of representing. The temporal scale of the model will be fixed
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by the goals of the model and the rates at which processes operate in the target ecosystem.
Spatial heterogeneity can be categorized in three ways: (1) patch boundaries fixed and within
patch—successional change is slow with respect to the temporal scale of the model simulations
(e.g., the tidal creek, creek bank, and high marsh habitats of intertidal marshes of the eastern
United States [Wiegert, 1986]); (2) patch boundaries fixed—successional change within the
patches is part of the simulation goal of the model (e.g., models of forest habitat where
successional age determines suitability as a population source or sink fPulliam et aL, 1992]), or
successional change in a marsh in response to environmental degradation [Costanza et aL, 1988]);
(3) patch boundaries and within patch—succession varying temporally on a small scale with
respect to the temporal scale of the simulations (e.g., disturbance models, where patch creation is
a function of some outside force acting in a systematic or stochastic manner [see, e.g., Wiegert,
1979]).
Because of the increasing need to model the consequences of stressors applied to the
environment, increasingly more process models will be constructed specifically for such
applications (e.g., Bartell et al., 1992). Because most ecosystem process models, however,
already contain explicit formulations of the processes that are potentially affected by stressors,
many can be adapted rather easily to this purpose (e.g., Suter and Bartell, 1992).
. Landscape and Regional Models
Certain disturbances imply ecological risks at spatial scales commensurate with landscape
or regional models. For example, acid deposition, radionuclide transport, and agricultural
application of pesticides and fertilizers may portend ecological responses at the scales of
headwater watersheds, large river systems (e.g., the Mississippi River basin [Thurman et al.,
1992]), or large regions with boundaries determined by prevailing weather (usually wind) patterns
(e.£, acid rain or Chernobyl). Hie methods used to address these larger-scale risks are
essentially those of the spatially explicit ecosystem models. Hie larger size of the spatial scale,
however, invites the use of special technologies (e.g., geographic information systems) and
imposes some constraints.
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These landscape and regional models could be considered to be simply larger, more
spatially diverse ecosystem models. Although they share a property that is found in a few
ecosystem models, it is one that is a requisite in the large system models: the recognition that not
orly must spatial heterogeneity be modeled, but that the location of each spatially distinct type
relative to the others is also important For example, the landscape watershed model containing
agricultural land, floodplain forest, and river must dearly delineate the location of each with
respect to the other. The dynamics of such a model will be quite different if all of the floodplain
is forested as opposed to the system in which some of the floodplain is used for agriculture.
Spatially explicit models of landscape dynamics that might be used to address larger-scale
risks are being developed (e.g., Turner and Gardner, 1991). These models are potentially useful
for examining the propagation of disturbances across landscapes (e.g., fire, insect pests).
Landscape and regional models are potentially useful in risk analysis because of the inherent
spatial nature of ecological disturbances.
Larger-scale models offer advantages and disadvantages. The major advantage lies in
more realistic spatial-temporal description of environmental factors that influence risk. Known
, locations of point sources can be represented in the model. Spatial and temporal variability in
dimate, soils, vegetation type, and disturbance can be specified. Information pertinent to
estimating risks is not ignored, which is in contrast with the implicit simplifying assumptions of
nonspatial or point models. Another advantage specific to these models is the ability to map risk
back to time and place in the system of interest. Finally, spatial models might also be used to
design and evaluate alternative mitigation or restoration activities.
The trade off for these large spatial models concerns the additional data required to
develop and execute the model. As with the ecosystem models, a primary drawback in the
development and application of landscape and regional models is the number of initial values,
parameter values, and boundary values required to implement models of even fairly simple
construct (e.g., Bartell and Brenkert, 1991).
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Another consideration lies in the magnitude and potential difficulty of the computations
required for simulation. Typical model structure is based on a finite element or finite difference
representation of system spatial structure; corresponding formulations often are sets of coupled
nonlinear ->arti?' differential equations. Obtaining accurate, stable, and efficient solutions of
these equations remains an active area of science in and of itself. Correspondingly, the number
of scientists and institutions with these kinds of capabilities is small in comparison to those able
to develop and use empirical approaches or simpler models to estimate ecological risks.
Nevertheless, the ability to model the system in exacting detail is a seductive goal.
43.6. Aggregation and Disaggregation
Because of the need to compromise between the model simplicity and the extreme
complexity (diversity) of even the simplest of real systems, aggregation of species and life-history
stages is necessary in conceptualizing the abstract structure of almost all models of ecosystems,
landscapes, and regions. How to accomplish this aggregation without compromising the goals of
the model is the problem. To a large extent, experience with the targeted ecological system is
invaluable. A group of ecologists familiar with the system (including the modeler, if at all
possible) can often solve many of the aggregation problems for the initial model on an intuitive
basis, much as the same process is used to formulate some of the functional relationships for
which data might be initially absent. For example, if the goals of the model require risk
assessment for specific species of organisms in the system, then the species must be represented
as individual-state variables, perhaps even further disaggregated as separate life-history stages.
State variables (i.e., compartments) that are known (or suspected) to be vital to the goals of the
model or to the dynamics of the system should not be aggregated with variables that have greatly
different specific rates of flux. As a trivially obvious example, one should not aggregate detritus-
feeding fish and aerobic bacterial decomposers if the dynamics of the resulting aggregated-state
variables are important to the goals of the model.
Other problems of aggregation are much more subtle. Wiegert (1975,1977) studied
simple thermal communities with the goal of experimentally testing predictions from various '
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levels of aggregation in the field Five levels of aggregation of a simple cyanobacterial-ephydrid
fly system produced a surprising result. As expected, the best predictor was the most
disaggregated model, where most life-history stages of the fly were in separate compartments,
and a great deal of complex detail was incorporated in the functional control of the spatial
heterogeneity of the cyanobacterial mat and of fly reproduction and growth. Various increases in
aggregation were made, the most extreme being reduction to a simple two-compartment model
of blue-green algae and the consumer fly. When these were tested against the independently
observed behavior of the field system, all intermediate stages of aggregation were found to be
worse predictors than the most aggregated case. The explanation lay in the degree to which
aggregation (simplification) of the structure of the model was paralleled by simplification of the
functional attributes. Complex structural "webs" that were not supported by functional detail
produced worse predictions (often instabilities) than the model using extreme aggregation
of both.
Too much disaggregation in models results in such a great number of compartments,
fluxes, and consequent parameters that falsifying predictions of the model becomes more
difficult; or at least understanding which causal mechanisms may be responsible for an erroneous
prediction may be difficult.
Similarly, Wlosinski and Minshall (1983) studied the behavior of a model of stream
invertebrate production by varying the number of state variables used to describe these
invertebrates. At the finest level of resolution, 37 taxonomic populations described the
invertebrate community. Other levels of aggregation used 15, 8, and 1 state variables to
represent invertebrate production dynamics. In comparisons with field measurements, the
8-variable functional description provided the most accurate model results.
In summary, "rules of thumb" about model aggregation/disaggregation are as follows:
(1) do not aggregate components with greatly disparate rates of fluxes; (2) do not greatly
increase the disaggregation of the structural aspects of the model without a corresponding
increase in the sophistication of the functional relationships and controls; and (3) disaggregate
models only insofar as required by the goals of the model, in order to facilitate testing.
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4.2.7. ImpUnuntation
Construction and implementation of mechanistic or process models may be highly
individualistic and system/stressor orientr 1 Nonetheless, we can specify two general pathways or
strategies: the submodel approach and the approach that begins with a general model and
proceeds to more detailed models. This latter approach is often referred to as a process of going
from the generic model to the site-specific model.
In the first case, the detailed site-specific model is developed by chaining together a series
of models of well-defined (and often relatively loosely coupled) subsystems. As an example,
consider the development of process models of plant productivity, the aboveground plant
biophage-based food web, the surface and underground plant saprophage-based food web, and
the anaerobic microbial decomposer web. Connecting each of these via their appropriate
pathways of interaction and transport/transformation yields a detailed system model. This
approach is most useful when there is sufficient information available about each subsystem to
provide comparable submodels.
When the system is less well known, or at least some of the subsystems are data poor, the
more common approach is to begin with a simple and general model of the system, incorporating
whatever is known and using literature and best estimates for the remainder. Such a model
serves to pinpoint not only areas where further data are needed, but simple sensitivity analyses
(section 4,5.1) can provide information on the parameters that must be evaluated carefully and
those for which an estimate are sufficient, at least initially. With continued research on the
system, such a model becomes not only more detailed, but-also more site specific. The time
constraints on the risk analysis process may often preclude the development and integration of
detailed submodels or the continued refinement of a more general system model • In such cases,
the simple general model can be constructed, rendered site specific with whatever data are
available, and used.
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4J. Physical and Experimental Models
Artificial ecological systems can be used to measure or quantify the response of ecological
systems to disturbance. Thus these tools may be twwc- ful instruments for risk integration.
Additionally, one way of testing predictions of both empirical and mechanistic models is by the
use of physical analogues of the real system (cosms) or a particular expression (usually small) of
the real system itself (field-scale experiments). Cosms of various size permit various degrees of
replication, depending on their size. The difference between the "cosra" and the field-scale
experiment is, however, more than one of size and the degree to which movement across the
boundaries of the system can be controlled.
43.1. Cosms
Certain disturbances of importance in ecological risk assessments lend themselves to study
hi artificial or experimental ecosystems (Suter and Bartell, 1992). Artificial ecosystems of various
size and construction have been used for decades to study the ecological impacts of temperature
alterations, nutrient additions (e.g., Conover and Francis, 1973), or chemical contaminants
(Gies} 1980). These cosms comprise both lentic and lotic systems (in the sense of the presence
or absence of deliberately induced current). If a current is caused by simple stirring, the cosra
may remain more similar to a pond than to a stream. Some studies (e.g., see Giesy, 1980) were
conducted hi outdoor artificial streams.
43.2. Fi*U-ScaJ* Experiments
Whole-system manipulations have contributed to the basic ecological understanding of
ecosystem response to disturbance (e.g., Schindler et aL, 1985; Carpenter et ah, 1985).
The major advantage of this approach is that real-world systems are the object of study; realistic
levels of complexity are represented by definition. The results of manipulative experiments do
not have to be extrapolated to the real world, as hi the case of manufactured ecosystems.
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Nonetheless, extrapolation problems still remain if the systems are considered representative of
those at other locations.
The major drawbacks of whole-system experiments include (1) difficulty in replication
with attendant high variance and low statistical power; (2) expense in manipulation, sampling,
and data processing; (3) duration of the experiments; and (4) uncontrolled conditions that can
confound interpretation of results. In the case of toxic chemicals, whole-system manipulations
involving introduction of the toxicant require expensive waste treatment Some experiments are
strictly regulated or prohibited (e.g., field studies using genetically engineered organisms).
Such experiments may involve manipulation of any combination(s) of abiotic components,
autotrophs, and heterotrophs. In the case of abiotic components, such as organic and inorganic
nutrients, additive experiments are possible but depletions are not. Such additions are often
easier in aquatic systems and human-managed (i.e., agricultural) systems.
More commonly, biotic components are experimentally manipulated by removal
experiments employing undisturbed controls rather than by additions, because the latter are
usually too costly, even if possible.
/
In both cases, however, inadvertent large-scale addition "experiments" can result from the
too hastily widespread commercial introduction of a toxic compound or the accidental or
deliberate introduction of an organism that turns into a pest Much has been learned in the past
from the results of such damaging perturbations.
4.4. Example Applications
Ecological models, both empirical and mechanistic, have been used in many ways to
predict the effects of acute and chronic perturbations. The scope of this chapter does not permit
an in-depth discussion of the range of these applications and, in any case, it is not intended to be
a literature review. We focus on three examples to give some idea of the wide scope of model
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type and usage. One of the models is largely an empirical phosphorous-loading model for lakes,
the second combines a population toxicity simulator with a mechanistic pelagic ecosystem model
to assess risk to fish, and the third uses a mechanistic estuarine model to predict the effect of
manipulating a population.
4.4.1. Phosphorus-Loading Models
Vollenweider (1969) used observed empirical relationships between nutrient availability
and depth, together with outflow losses and sedimentation, to develop an empirical model of
phosphorus-loading that would be adequate to predict levels of eutrophication in unstratified
lakes -for which only a few required physical measurements were available. Later improvements
in the model incorporated additional attributes, such as lake basin morphometry, water residence
times, and phosphorus residence times (Vollenweider, 1975,1976), that permitted the application
of the model to a wider class of lakes. Modifications to adapt such models to stratified lakes or
rapidly flushed reservoirs require only that adequate data bases on such lake types are available.
The conditions for the success of the predictions made with such models depend almost
wholly on the adequacy of the & a base used for fitting and evaluating and the skill with which
the model fitting is done. The drawback, apart from the necessity of having a large existing data
bank, is that such models are not justifiably extrapolated beyond the range of the data with which
they were constructed. By contrast, mechanistic models are valid, at least in theory, over a wide
range of behavior by the variables of state, since the data-fitting is done at the parameter level
rather than at the level of the behavior of variables of state.
4.4.2. Toxicity Risk Models in the Ecosystem Context
Bartell (1990) explored the feasibility of predicting the effects of toxic chemical stressors
on the fish populations of the pelagic zone. Following the methodological schema of O'Neill et
aL (1982a), he employed a population-specific toxic effects model together with a pelagic
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ecosystem model (see section 422). The former was used to generate a toxic effects matrix
from the relation between stressor concentration and population growth rate. The effects matrix
was then coupled to a mechanistic pelagic ecosystem model. In this manner the effects of the
toxic stressor on populations could be evaluated in the context of the entire ecosystem.
The conditions for the adequacy of such models were established in sections 422 and
4.2.4. Essentially, the conditions embody the uncertainties involved in fitting the toricity data to
the population growth model and the verification of the ecosystem model to which it
is connected.
4.4.3. Ecosystem Models as Population Predictors
When the data for an empirical model are not available or cannot be collected (see
section 4.2.4), mechanistic predictors are the only recourse. In Spain's Ria Arosa, the probable
effect of increasing the number of mussel rafts on the mussel harvest per raft was desired.
Unfortunately, only one Ria had been exploited heavily in this manner. Thus no data existed on
the response of the variables of state (productivities) to various levels of stocking. Furthermore,
the consequence of increasing the number of i ifts, with the consequent movement of families
into the region and a possible decline in the mussel harvest per raft, was a socially unacceptable
risk. Although this example deals with the management of an economic resource, it is illustrative
of the conditions under which a mechanistic model might be used in risk assessment
In collaboration with a large number of Spanish and North American scientists, a detailed
mechanistic model of the estuary and the mussel rafts was begun (Wiegert and Penas, 1982), and
increasingly more detailed modifications of the model have been made during the ensuing decade
(Wiegert and Penas, in prep.). The important prediction from this model was that increasing the
number of commercial raft-borne mussels in the pelagic zone of the Ria Arosa by 50 percent
would increase slightly the overall harvest of mussels but would significantly lower the production
of mussels per raft and thus lower the income of the individual families that owned the rafts.
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4.5. Uncertainties
Uncertainties in using purely mechanistic models for prediction stem entirely from the
uncertainty involving the degree to which the structure of the model is correct and the functional
controls and parameter values are real In the present case (example 4.43), the model was
verified (section 452) against the data from the situation in the 1981 to 1986 period during
which data were collected by a team of Spanish and American ecologists. During this period the
estuary supported approximately 2,000 rafts and the model predictions were verified against the
productivity and standing crop data. Evaluation (validation, section 4.53) could only come about
if the rafts were to be increased (or decreased). Since rafts are family-run, such changes in raft
numbers could be accompanied by major perturbations in the social and economic structure of
the region. Even when verified but not validated, however, such predictions from mechanistic
simulation models are still useful as cautionary tales suggesting certain lines of research that may
yield ever better prediction. (Note that an alternative test, involving larger, rather than more
rafts, appears to be taking place and may yet yield validation data for this model.)
4.5.1. Modtl Sensitivity Analysis
Sensitivity analysis measures the partial derivatives of system-state variables with respect
to the model parameter values (Tomovic and Karplus, 1963; Gardner et al, 1981). A sensitivity
analysis of a simulation model is conducted by measuring change in the deterministic model
solution in response to changes in the model parameters. Such analyses also can be used with
models containing stochastic elements by either disabling the random number generator and
using the mean parameter values or choosing the same random sequence for each run in the
analysis. The latter is easily done in most programming languages by making the randomization
"seed" a constant
Depending on the mathematical complexity of the model, (i.e., if the model equations are
especially few and continuous), the partials might be evaluated analytically. Models of greater
complexity (L&, virtually an models useful for risk assessment) necessitate numerical
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approximation of the partial derivatives. Gardner et aL (1981) present a comprehensive
comparison of analytical and numerical sensitivity analyses of a lotic ecosystem model. Bartell et
al. (1986a) compare individual parameter perturbation methods and Monte Carlo (i.e.,
simultaneous stochastic variation of groups of parameters) methods of sensitivity analysis offish
bioenergetics models.
Sensitivity analysis should not be confused with uncertainty analysis. Uncertainty analysis
refers to the quantification of variability in model output when best estimates of realistic
variability in model parameters and external forcing functions are propagated through the model.
The methods of error propagation outlined earlier in this paper have all been used to assess
model uncertainties (see table 2).
4.5.2. Model Verification
Verification is the step in model development where the mathematics and computer
coding are examined to determine if the intentions of the modeless) have been accurately
represented in the model construct This step involves the detailed examination of the model to
/
ascertain whether the functional formulations, external forcing functions, nitial conditions,
parameter values, and other model assumptions and constructs have been faithfully articulated in
the coding of the model The strongest verification is obtained by testing the model predictions
against a data set from the system/site the model was designed to represent (Shugart and West,
1980). Successful verification of the model is a prelude to the basic execution of the model.
4.5J. Model Validation (Evaluation)
Comparisons of model predictions with independent data sets from the same or other
systems (depending on the generality expected of the model) are used to validate the models
(Shugart and West, 1980; Caswell, 1975). In developing the guidelines for ecological risk; we
suggest replacing the term validation with evaluation. Models are simplifications of nature and,
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therefore, invalid in the sense that the model cannot predict more than some small set of the
behavior or the real system. Any model can be pushed to the point of failure. Moreover, the
model builder, intimate with the strengths and limitations of the model, can design any number
of experiments that wfll support or refute the model A model cannot be "validated* anymore
than a statistical hypothesis can be proved. Model utility, in the terminology of Manldn et aL
(1975), should replace validation in method and concept Hie emphasis should be placed on
determining the conditions where model results are likely to be sufficiently accurate for useful
risk estimation; that is, accurate enough to produce information conducive to coherent decision-
making. The degree of accuracy undoubtedly will be influenced by the severity of the
consequences of an incorrect decision. Thus criteria concerning utility will vary by model and
model application.
Model evaluation should proceed to determine its domain of applicability. The values of
initial conditions, model parameters, and external forcing functions that prove conducive to
accurate results should be determined for the model Similarly, conditions that suggest likely
inaccuracies in model results should be described. Refinements, guided by the results of
sensitivity analysis, can be directed at increasing the domain of model applicability.
4.6, Natural Variability
Ecological risks wfll have to be evaluated in the context of natural variation in the
endpoint of interest, since ecological systems are dynamic in space and time. Fluctuations in
system state (e.g* species richness, population size, primary productivity, mineral cycling
efficiencies, decomposition rates) occur in the absence of human disturbance. Therefore the
accurate assessment of ecological risks in dynamic systems win depend on the characteristic
variability of the selected system endpoints. Simply stated, the greater the natural fluctuations in
an ecological endpoint, the greater the difficulty in measuring a response to disturbance; hence
the greater the difficulty in assessing ecological risk.
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Quantitative understanding of the characteristic fluctuations in ecological systems can be
used to judiciously select endpoints for assessment First, endpoints should be selected in terms
of their dynamics and referenced to the decision-making or regulatory environment For
sample, selection of changes in the composition of forest tree species as an endpoint in risk
assessment is simply not practical in the context of regulatory frameworks with dominant 2- and
4-year political cycles, even though the ecological consequences could be highly significant At
the other end of the spectrum, for example, selection of changes in adenylate phosphate pools as
an endpoint could require sampling at a scale of milliseconds—again impractical. The lesson to
be learned: Choose endpoints that permit measurement of sufficient grain and extent (Allen and
Starr, 1982) to conveniently characterize natural fluctuation and departures from natural
fluctuations.
Knowledge of characteristic system dynamics also can be used to speculate where in an
ecological system a disturbance might produce its greatest effect or highest risk. System
components that fluctuate naturally on scales similar to that of the disturbance are at risk. If the
disturbance is extremely "slow," or low frequency, the system can adapt; an extremely high
frequency disturbance simply may not be "seen" by the system (Allen and Starr, 1982). Thus
known natural variability might be used to advantage in selecting endpoints for assessing a
disturbance of known frequency or recurrence.
4.7. Multiple Stressors
A critical challenge to developing capabilities in risk characterization lies in recognizing
that single, isolated stressors are seldom the focus of real-world ecological risks. Except for
isolated spills (e.g., petroleum hydrocarbons) or intentional broad-scale applications (e.g.,
agricultural herbicides and biocides) of a single compound or narrow class of chemical congeners
(e.g., PCBs), most environments are variously contaminated by a diverse assemblage of organic
contaminants, fertilizers, trace metals, pesticides, and radionuclides. The ultimate ecological risks
are determined by complex, interacting chemical mixtures. In the case of natural stresses,
systems suffer at larger scales in space and time from fire, flood, drought, and pest outbreaks.'
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Assessments of multiple stressors will require establishing a scientific basis for choosing
among alternative models for combined effects. In the case of toxic chemicals, the properties of
additivity, antagonism, synergism, or domination by the most toxic chemical (DiToro et aL, 1988)
are altemativ models that may apply for various mixtures. Current capabilities and experience
in choosing a priori from these models for particular assessment is extremely limited.
Experimental approaches to chemical mixtures have consisted of simple factorial designs using
two 01 three compounds or the use of the mixture as a 'chemical* in performing toxicity assays
and conducting experiments in the laboratory (Franco et aL, 1984) or field (Giddings et al.,
1984).
Modeling approaches have emphasized the application of chemical spetiation models
(e.&, MINEQUL), assuming conditions of thermodynamic equilibrium, to calculate the degree of
expected freely dissolved form, complexed compound, and different valence states (i.e., metals).
These models are of unknown reliability when making calculations for chemical systems that
depart from controlled laboratory situations.
4.8. System Resistance and Resilience
/
Measures of both the degree to which a system is sensitive to a stressor regime and the
degree to which it might compensate or recover are covered hi detail by other chapters (see
chapters 5 and 7). Yet an understanding of the major components of this sensitivity is necessary
in any discussion of risk methodology. Compensatory effects within the system are responsible
for the degree of both resistance and resilience exhibited by the system in response to the
perturbation induced by a stressor. Although the terms resistance and resilience sometimes have
been used in conflicting ways in the ecological literature, the useful and now commonly accepted
definitions follow the dictionary: Resistance is the inverse of the degree to which a system
changes in response to a perturbation (i.e.» the inverse of sensitivity), whereas resilience is a
measure of the rate at which a system returns to the original state following the removal of the
disturbance (Webster et aL, 1975; Wade and Webster, 1976; Carpenter et aL, 1992). Resistance
also has been referred to as inertia (Westman, 1978). Although the terms resistance and
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resilience generally are measured only in systems (or models) that exhibit locally stable steady
states, there is no theoretical reason why they cannot be used in the evaluation of systems and
models exhibiting stable limit cycles. In such uses, however, the evaluation of resistance and
resilience must be conduct d in a more subjective and less formal manner.
Formal evaluation of both resistance and resilience is illustrated in Carpenter et al. (1992)
using the methods found hi Harrison and Fekete (1980) and DeAngelis et aL (1989b). These
calculations can be made on data representing either measured flows in experiments on real
systems or on models consisting of sets of linear differential equations. For models with
nonlinear and often discontinuous differential equations, direct application of these techniques is
not possible. In such cases, however, the model simulations are run and the resulting flow data
from any given simulation can be analyzed with respect to model resistance and resilience.
These attributes of systems provide important information about the potential response(s)
of a system to perturbations. They are themselves dependent, however, on the type of stressor
and its intensity, duration, and frequency. These are all characteristics that must be explicitly
specified at the outset of model development if the goals of the model include predicting the
resistance and resilience of the system.
4.9. Monitoring Variability, EMAP, and Ecological Risk Assessment
Spatial/temporal variability in systems is handled by standard sampling and statistical
techniques. Although spatial variability may be treated hi either a time-dependent or time-
independent manner, temporal variability in ecosystems requires time-related data. For certain
attributes of both populations and ecosystems, historical records of variability are available for
physical environmental attributes (e.g^ gas content of glacial vacuoles and oxygen isotopes in
carbonate), as well as for some physiological processes (e.g, tree ring width). In most cases,
however, contemporary long-term monitoring must be conducted.
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4.9.1. Monitoring Variability
The degree to which the target system varies during the natural course of time is clearly
an important component of risk ar.essn? mt If the degree of variability of the unstressed system
is not known, the evaluation of predictions of the response to a stressor will be compromised.
Indeed, many of the predicted responses to stressors may take years, decades, or even longer to
manifest themselves. Yet once apparent, responses may be extremely serious. Consider the
current situation with respect to atmospheric contaminants and the ozone layer. Predictions of
such relatively slow (or at least long-lag) phenomena must be evaluated in the context of some
framework that defines the normal variation around which a constant mean may be assumed. It
is even more difficult to make predictions about the effects of a stressor on systems in which the
unstressed condition is one of slow change upward or downward (e.g., sea level rise or
warming/cooling trends).
Although any model can be simulated to provide a predicted rate of change hi the mean
of any given attribute, the evaluation of these predictions can only be performed through
comparison with a set of long-term data. Yet long-term data sets are problematic, for two
reasons: (1) there is resistance on the pan of foundations and government agencies that fund
scientific research to commit funds on a permanent or even semipermanent basis, and, in any
case, most government agencies have funding "windows" that make it difficult to secure long-term
support; and (2) long-term monitoring does not often result in interesting or counterintuitive
breakthroughs, thus making its justification more difficult
432. EPA'sEMAP and Ecological Risk Assessmtnt
The two difficulties listed above are gradually being overcome. On the one hand, there is
growing recognition of the need not only to monitor the normal variability in the ecosystems of
which we are embedded, but also the need to be able to provide an early warning when change
in a critical state variable or parameter begins to exceed this "normal* rate of change (e.g, note
the usage of the Mauna Loa carbon dioxide measurements).
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A specific response to the need for long-term monitoring was conceived and implemented
by EPA with EMAP (Olsen, 1992). The goal of this program is to identity environmental
variables that will repay long-term monitoring, throughout the United States, by enabling
detection of relatively subtle departures from their nr rraal means and standard deviations. Thus
EMAP is intended to serve as a tool for assessing the relative "health* of the environment and
pinpointing areas and/or species that need attention—perhaps remedial, perhaps initially a more
detailed assessment.
Although EMAP is not designed to predict risk; the data from its monitoring efforts
should prove valuable for parameterizing some risk models and for testing model predictions
about the dynamics of systems exhibiting "normal" behavior. We advise that the ecological risk
assessment protocol not only plan to take advantage of the data bases that win be provided by
the EMAP initiative, but that means be found to inform the managers of EMAP about the
general and specific needs of the risk assessment guidelines. In this way, as EMAP evolves and
reassesses its own needs and goals, it can, wherever possible, incorporate the needs of this and
other initiatives within EPA
4.10. Recovery
The complement to ecological risk is recovery, which is defined as the system's response
once the disturbance is removed. In a classical sense, recovery measures the return of the system
to conditions that pertained prior to the disturbance.
Recovery is conceptually related to system stability, particularly to resilience (see section
4.8 and referenced chapter 7).
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4.10.1. Criteria for Asstssing Rtcovery
Recovery can be assessed according to various criteria. Following the general spirit of the
guidelines, however, recovery should be assessed in terms of the re ponse variables that were
used to estimate or characterize ecological risk. If these variables return to their predisturbance
values after removal of the disturbance, the system can be judged to have recovered.
Alternatively, other criteria can be chosen to monitor recovery. Similar to risk estimation,
recovery can be assessed using population, community, or ecosystem measures. There is no
recipe for selecting recovery criteria; recovery win be defined in relation to the risk endpoints
and the disturbance. The important point is that criteria must be chosen and their
predisturbance values must be known.
4J0.2. Exposun-Rtcavery Functions
Exposure-recovery functions define relations between the degree of the disturbance (i.e.,
magnitude, frequency, and duration) and the time required for recovery. In essence, predicting
recovery is the rev rse of predicting a response to the stressor in the first place. All models of
succession, for example, are models of recovery from the effects of some stress, whether acute or
long term. Thus the same rules apply when constructing recovery models as when constructing
risk models: If data on recoveries from differing magnitudes, frequencies, and durations of stress
exist, then empirical models can be constructed; otherwise, mechanistic models must suffice as
predictive tools.
4103. Probability ofRgcovtry
Realistic assessment of ecological recovery requires a probabilistic framework (Bartell et
at, 1992). Ecological systems are characteristically dynamic The system-state varies in space
and time. A single system driven by seasonal cycles will demonstrate similar, but not identical,
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patterns of system state from year to year. A set of similar systems will exhibit similar, but not
identical, behavior during the same period.
Long-term, systematic changes in state follow from the biotic-abioti interactions that
alter habitats and the physicochemical context Natural variability and successional trends argue
that recovery must be assessed against a backdrop of variance in the criteria used to measure
recovery.
Variance also will characterize the post-disturbance system response. Spatial-temporal
variations characteristic of the undisturbed system may be modified by the disturbance, or the
disturbance might add another source of variance to the system.
The combination of natural system variability and variability in the disturbed system
suggest that, at any point in space or time, meaningful comparisons of system states require the
comparison of distributions. Recovery is an evaluation of the null hypothesis that the
distributions of system state are identical over time or space. Thus recovery can never be
assessed with complete certainty; rather, it can be best represented as a probability. Note that if
the system stabilizes in a new configuration (i.e., alternative stable state), the probability of
recovery according to the predis urbance system state is zero; yet, according to criteria developed
from stability theory, the system can be described as "recovered.*
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5. RISK SUMMARY
The selection and application of methods for risk characterization should take into
consideration the form in which the risk analysis will be communicated to all concerned partiej
and, equally important, how the risk results will be used by risk managers.
The risk characterization is communicated through the risk summarization, which should
facilitate the presentation of both qualitative assessments and quantitative risk estimates. The
summary also should address the degree of confidence to be placed in the analysis, as well as
identify new information that could improve the assessment. The summary should finally assess
the contribution of the risk characterization to the overall weight of evidence in the risk
assessment. The following discussion identifies and discusses several important topics in each of
the components of an effective risk summary.
5.1. Qualitative versus Quantitative Assessment
Qualitative assessments of risk at their simplest are yes/no alternatives to the question of
whether a stressor will prove to be a problem. On a more graduated scale, as we noted above,
certain kinds of analyses can be considered qualitative (e.g., food web analysis) because
sometimes they can give an ordered answer to the question.
Instead of considering the alternatives to qualitative versus quantitative assessment, we
might find it more profitable to consider what level of measurement we can achieve with the
chosen method for risk assessment. From the standpoint of the available statistical tests, the
data from our analyses fall into the categories of nominal, ordinal, interval, and ratio (Siegel,
1956). Nominal data are purely dassificatory (e.g., effect versus no effect). Note, however, that
even here the yes or no probably will be based on some predicted attribute(s) of the system
being "higher" or "lower* than "usual." Thus nominal data are generally of little interest in risk
assessment, and the pure qualitative classification is also of relatively little interest Ordinal data
are what most classifiers have in mind when they talk of "qualitative" assessment (i.e., the effect
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is greater than or less than, but the amount of difference cannot be specified). The remaining
two categories of data are the stuff of quantitative science. The interval scale of measurement
requires that a measure of the difference between two ordered items be available, and the ratio
level of categorization requires a true zero point. The vast majority of the quantitative data
resulting from risk assessment will be of the latter type, since negative value for flows and
standing stocks, for example, are of no relevance in ecology.
Thus most predictive methodologies of ecological risk assessment will be quantitative, with
even the simplest initial question of yes or no generally being answered with data of at least the
ordinal level.
5.2. Degree of Confidence
Confidence placed in the overall characterization of ecological risks for any application
will be a function of the reliability of the information entering into the analysis and the
competence of the integration and translation of this information into estimates of risk.
Evaluation of the accuracy and utility of the risk characterization will require a thorough
understanding of the quality of the exposure analysis and th' exposure-response assessment.
Additionally, an evaluation of the accuracy of the methods and models used to integrate this
information, including an assessment of the assumptions and uncertainties that entered each
stage of the overall risk analysis, must also be performed.
5.2.1. Evaluation of the Exposure Analysis
Evaluation of the exposure analysis should include an assessment of the scaling of the
stress of concern, including its relevant frequency, magnitude, duration, and spatial extent Was
the exposure assessment based on site-specific measurements, differently scaled monitoring data,
statistical extrapolations, analogy to other similar situations, or an educated guess? If a constant
exposure was used in the analysis, was this justified in relation to what is generally known about
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the stressor of interest? If the stressor is a chemical, was the correct chemical species used to
estimate exposure (e.g., PCB congener, metal valence state, polar or nonpolar aromatic
molecule)? Answers to at least these questions are necessary to evaluate the accuracy and utility
of the overall exposure assessment.
The exposure assessment is undoubtedly the central component of the risk
characterization. The focus of the risk analysis is to translate the exposure estimate into
estimates of its probable impacts on ecological resources. Imprecision in the estimated exposure
is tolerable for extremely low or extremely high exposures; that is, in the "flatter" portions of the
dose-response functions. Fundamentally, the steeper the slope in this function, the greater the
implications of imprecision in exposure on the expected response. For situations where the best
estimate of exposure lies in this region of the exposure-response function, risks should be
estimated over the range of possible exposures, using either deterministic best- and worst-case
exposures or by applying some error propagation method over an appropriate distribution
of exposures.
5*2.2. Evaluation of the Exposure-Response Relationships
The quality of the exposure-response relationships used in the assessment should be
summarized as pan of the overall risk characterization. Several aspects of these functions can be
addressed as part of the summary. First, it should be stated whether the stressor used to develop
the functions was the same as the stressor of concern in the original problem formulation. For
example, if a similar, but not identical, chemical species was* used to develop the exposure-'
response functions in the assessment, this should be clearly noted. Substitution of analogous
stressors, for whatever reason, can clearly introduce bias and uncertainty into the assessment.
Second, it will be unlikely that effects data will be routinely available for the same species
of concern identified in the problem formulation. Thus the summary should address the nature
and extent of species substitutions used to develop the exposure-response functions. The
implications of using substitute species in terms of bias and uncertainty introduced to the
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assessment also should be discussed. The same argument applies in cases where the endpoint
effect is not a species, but a different taxonomic level of organization, or a community, or an
ecosystem property (e.g., rate of decomposition or nutrient cycling).
Third, in assessments where exposure-response functions have been established between
the exact stressor and endpoints of concern, the residual variance concerning these functions
should be included in the risk characterization and presented in the risk summary. Graphical
presentation of the functions and the supporting data may prove useful in this regard.
Alternatively, confidence limits about the exposure-effects functions could be listed. The range
of exposures over which the functions are expected to validly apply should be included in the
risk summary.
Fourth, the nature of the extrapolation using the exposure-response functions should be
outlined in the summary. Typically, the functions will have been developed from data collected
in the laboratory or in other controlled conditions. In the assessment, these functions will
necessarily be extrapolated to field circumstances defined in the problem formulation phase. The
degree of departure between the scale of the assessment problem and the source of the
exposure-response functions (i.e., somewhat analogous to the application-factor approach, see
table 3) should be presented as part of the risk summary.
5.2 J. Applicability of Methods and Models
The methods of ecological risk analysis encompass a number of different kinds of models,
and within each model category are a number of ways the model may be structured and
employed. At a first approximation, we differentiate three classes of model: (1) empirical
models, which require an a prior data base on stressor/system responses; (2) process or
mechanistic models, which are constructed on causal relationships; and (3) physical/experimental
models, which are actual representations, at some scale, of the target system.
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Empirical models may be constructed and manipulated using a variety of mathematical
and statistical techniques; among these are single-value comparisons, joint distributions,
regression models (of several types), fuzzy sets, and extreme event analysis. Empirical models
are applicable for any stressor-responsc relationship that is within the range of the data set used
to construct the model.
Process (mechanistic) models are generally classified by scale (i.e., as models focusing on
the individual, population, community, ecosystem, and landscape or region). They may be used
for prediction in situations where prior data on a range of stressor-response relationships are not
available and where the nature of the stressor and/or the rarity or value of the target system
precludes large-scale experimentation on examples of the target system. They are more
data-demanding (and thus more expensive and difficult to construct) than are empirical models.
Once verified, however, their predictions are less restricted than empirical models with respect to
the range of stressor magnitude, frequency, and duration that may be simulated.
Physical models are generally classified in terms of scale as well, usually as either
laboratory cosms of various sizes or large-scale field experiments. Where cosms are physically
bounded by the experimenter, field experiments usually are bounded by natural ecotones,
although some of the large-bounded cosms are located in the natural environment. The results
from experiments on physical models may be used to predict risk directly, or they may be used to
implement empirical models. Data from physical models often are used to parameterize causal
process models as well.
5.14. Assumptions and Uncertainties
The risk summary should detail the assumptions and uncertainties introduced in the
course of overall assessment. As indicated in the previous sections, uncertainties can take the
form of inaccuracy and imprecision in the estimates of exposure and the development of the
exposure-response functions. Assumptions will necessarily be made in the implementation of
models used to translate the exposure profile and the exposure-response functions into estimates
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of ecological risk. These assumptions should be explicitly stated in the risk summary.
Additionally, uncertainties in the form of gaps in information or scientific understanding (i.e.,
model uncertainty) can enter the assessment. These uncertainties should be summarized as part
<-f an effective risk characterization (AIHC, 1992).
5J. Contribution to Weight of Evidence
The risk characterization and the risk summary convey the results of the overall
assessment to the risk managers. The ecological risks, however, represent only one component
of an often complex decision-making process dictated by the particular legislation that mandated
the assessment. Ecological risks must be of a form and content that can be used with other
kinds of information (e.g., worker or human health risks, economic cost-benefit analyses, social
implications, political considerations) in a comprehensive risk management or regulatory
decision-making process. This concept of risks being part of an overall weight-of-evidence
approach to decision-making resulted to some degree from the concern that a single piece of
"negative data" could dominate an analysis, while other "positive" aspects of a problem were
ignored (Paustenbach, 1991). To be effective, therefore, the risk characterization need not be
perfect. It should provide sufficient information, however, to allow the risk managers and other
users of the assessment results to choose intelligently among the decision options (AIHC, 1992).
Toward this end, the risk summary must accurately and concisely convey the ecological risks and,
equally as important, address how reliable the risk estimates are in relation to the current
scientific understanding and to the data, models, assumptions, and uncertainties that are part of
the overall assessment.
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ECOSYSTEM MODELING
F(X.Y,T,P)
SYSTEM STRUCTURE
F(X.Y,T,P)
SYSTEM FUNCTION
(b)
ECOLOGICAL STRUCTURE
F(X.Y.T.P)
ECOLOGICAL FUNCTION
(c)
Figure 1. Identification of structure and function in a system diagram (redrawn from Hill and
Wiegert, 1980).
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Table 1. Improving the Technical Content of Risk Characterization (AIHC, 1992)
Relevance
Clarity
Balance
Consistency
Detail
Ensure compatibility between the needs of the
assessment and the nature of the technical
results
Highlight the important results, remove
extraneous information, make ample use of tables
and graphs
Realistically portray the range of scientific
views, provide the basis for assumptions and
judgments, acknowledge the uncertainties
Be consistent in terminology, definitions,
and the processing of information from one
characterization to another
Provide the necessary level of detail
appropriate for the endusers of the risk
characterization
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Table 2. Methods for Error Propagation in Mathematical Models
Method
Application
Reference
Parameter perturbation
First-order variance
propagation
Probability density
function/moment method
Fourier amplitude
sensitivity test (FAST)
Deterministic
uncertainty analysis
Monte Carlo methods
Lakes and reservoirs
Biochemical oxygen demand
(BOD) in rivers
Eutrophication model
of Saginaw Bay, MI
Eutrophication model
ofLakeMorey, VT
Piezometric heads in
an aquifer
BOD in the Sacramento River
An autocatatytic
chemical reaction
Waste flow through
an aquifer
A stream ecosystem
Anthracene in ponds
Biocncrgetics of fish
growth
Eutrophication hi
Saginaw Bay, MI
Recknagel, 1984
Rinaldi and
Soncini-Sessa,
1978
Scavia et al.,
1981
Walker, 1982
Derringer and
Wilson, 1981
Tumeo and Orlob,
1989
McRae et al., 1982
Worley, 1987
Gardner et al., 1981
Bartell et al., 1983
Bartcll et al., 1986
Scavia et al., 1981
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Table 3. Application Factors for Extrapolating to Concentrations of Concern (U.S. EPA, 1984)
Data Source Application Factor
A single measured or estimated 1,000
acute LCj» ECa
Lowest of five acute LC^ or EC* 100
values for invertebrates and fish
Lowest chronic No Observed Effects Concentration (NOEC) 10
value for most sensitive species in acute tests
Concentration of concern determined 1
from field measurements
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