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Perspectives on the
Chesapeake Bay, 1990
Advances in Estuarine Sciences
Chesapeake Research Consortium
P.O. Box 1120
Gloucester Point, Virginia 23062
Compiled and edited by
Michael Haire and Elizabeth C. Krome
U.S. Environmental Protection Agency
Region III Information Resource
Center (3PM52)
841 Chestnut Street
Philadelphia, PA 19107
April 1990
Printed by the United States Environmental Protection Agency
for the
Chesapeake Bay Program
Printed on recycled paper
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ACKNOWLEDGEMENTS
Members of the Steering Committee selected topics, authors, and
reviewers for this document. The reviewers contributed helpful and
thorough comments on the draft chapters. Marilyn Lewis of the VIMS
Library performed literature searches for the authors. Susan Myers
assisted in reference work, Cynthia Corlett made helpful editorial
suggestions, and Alan Krome helped in proofreading. Pamela Owens
gave capable and dedicated assistance in word-processing and produc-
tion. Gail Mackiernan, Richard Batiuk, Maurice Lynch, and Robert
Summers contributed knowledgeable guidance on specific chapters.
Throughout the project Richard Reynolds and Steven Nelson provided
advice and support.
STEERING COMMITTEE
Mr. Michael Haire
Dept. of the Environmental
Projects Program
2500 Broening Highway
Baltimore, Maryland 21224
Dr. Joseph Mihursky
Chesapeake Research Consortium
1 Williams Street
Solomons, Maryland 20688
Mr. Wayne Sullivan
3600 Edinger Road
Richmond, Virginia 23234
Dr. Charles C. Van Sickle
South East Forest Experiment Station
200 Weaver Blvd.
Asheville, North Carolina 22802
Ms. Gail Mackiernan
Maryland Sea Grant College
H. J. Patterson Hall
University of Maryland
College Park, Maryland 20742
Ms. Laura Lower
Council on the Environment
Ninth Street Office Building
Richmond, Virginia 23219
Dr. Maurice P. Lynch
Virginia Institute of Marine Science
Gloucester Point, Virginia 23062
Dr. L. Eugene Cronin
12 Mayo Avenue
Bay Ridge
Annapolis, Maryland 21403
Dr. Thomas Osborn
Chesapeake Bay Institute
The Rotunda
711 West 40th Street, Suite 340
Baltimore, Maryland 21221
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TABLE OF CONTENTS
Preface
Joseph A. Mihursky and Michael Haire vii
Introduction
Maurice P. Lynch 1
Chapter 1. Coastal Ecosystem Models and the Chesapeake Bay Program:
Philosophy, Background, and Status
Richard L. Wetzel and Charles S. Hopkinson, Jr 7
Chapter 2. The Functional Role of Estuarine Benthos
Robert J. Diaz and Linda C. Schaffner 25
Chapter 3. Role of Best Management Practices in Restoring the Health of the
Chesapeake Bay: Assessments of Effectiveness
Theo A. Dillaha 57
Chapter 4. Developing an Ecological Risk Assessment Strategy for the Chesapeake Bay
John Cairns, Jr. and David R. Orvos 83
DISCLAIMER
Mention of trade names or commercial products
does not constitute endorsement
or recommendation for use.
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Preface
The Chesapeake Bay is an extremely complex and variable estuarine ecosystem, influ-
enced by diverse factors. Within the Bay's natural boundaries, a spectrum of aquatic
environments—ranging from freshwater to nearly full-strength seawater—supports
diverse organisms and allows many chemical reactions to take place. Characterized
by complexities in circulation patterns, nutrient cycles, and food webs, the Chesapeake
Bay is a unique and highly productive natural system.
Historically, the Chesapeake Bay has demonstrated a remarkable resilience to many
natural or man-made perturbations. Unusual events such as hurricanes, droughts, and
seasonal temperature extremes have caused imbalances, but the Bay has gradually
recovered its former state of dynamic equilibrium. Similarly, the Bay remained
relatively unchanged over several centuries of urbanization, shipping, and fishing.
Yet today the Chesapeake Bay appears to be a fragile ecosystem increasingly vulner-
able to the relentless encroachment of man. In fact, most of the problems currently
perceived as causing declines in the health of the Bay have a common denominator—
people. Man has directly influenced the estuary by adding his wastes and by with-
drawing resources from the Bay and its tributaries. In addition, people have acted
indirectly by changing the character of the land, water, and air that surround and
interact with the Bay. In short, man is altering the hydrological and ecological
continuum of the Chesapeake Bay watershed. Today we recognize ecosystem thresh-
olds beyond which resilience or assimilative capacity can be exceeded resulting in
such perceptible changes as low dissolved oxygen concentrations, increasingly turbid
waters, or lowered abundances of fish, shellfish, and other organisms.
On a more subtle level, many researchers point to changes in the pathways of carbon
and energy flow through the Bay food web. Although increased amounts of nutrients
such as phosphorus and nitrogen have stimulated greater production of phytoplankton,
it appears that the carbon energy resulting from photosynthesis is not yielding greater
quantities of useful metazoans such as finfish and shellfish. Indeed, it appears that the
collective effects of water quality changes, habitat losses, recruitment failure, and
fishing mortalities have shifted carbon energy away from the economically productive
metazoan food web and into the trophic "dead end" of microbial production. By
remineralizing excess carbon production in the microbial food web, the ecosystem
consumes precious oxygen and subsequently loses habitat for the more useful meta-
zoan species.
These kinds of Bay-wide impacts result from massive inputs of nutrients and other
chemicals coming from sewage treatment plants or industrial operations (referred to as
point sources) or from the stormwater runoff of rural or urban land (called non-point
sources). These natural materials normally recycle in the environment among plants
and animals, or among land, air, and water. But the large human population in the
Bay watershed has disrupted the balance of the recycling process and has led to severe
problems in some regions of the Chesapeake Bay.
Another type of problem confronting the Bay comes from toxic compounds—man-
made products created by industrial activity, or naturally-occurring chemicals that are
concentrated to levels far exceeding the trace quantities normally found in the environ-
ment. Toxic materials tend to be concentrated in regions of the Bay close to manufac-
turing industries or waste disposal sites. Problems caused by toxic compounds are
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difficult to predict or understand because of their extremely complex chemical
properties. However, these compounds can cause serious human and environmental
health hazards when they enter the Bay.
The complex interactions between pollutants from point and nonpoint sources, toxic
compounds, and ecosystem change are further exacerbated by the diverse cause-and-
effect sequences occurring throughout the Bay watershed. For example, land use
changes in Pennsylvania could begin a sequence of physical, chemical, and biological
events that much later produce oxygen deficiencies in Maryland deep water. Thus,
the observed impact is separated from its cause in both time and space.
These complexities underscore not only the need for research, but also the importance
of presenting research findings to environmental decision-makers. Management
options can be complex and can require years of sustained effort before yielding
significant improvements. Clearly, the Bay system does not necessarily respond to
instant management fixes, nor does it hold to boldly declared target dates for restora-
tion milestones. Why? Because we simply don't know all the answers.
Even with our considerable and growing knowledge base for the Chesapeake system,
there remain many uncertainties and gaps in information. We do not fully understand
all that is wrong with the Bay, or how to repair the system most effectively, or how
long and how intensive our restoration efforts must be. Wise management and
regulatory decision-making depend on our ability to clarify these uncertainties. And
to a large extent, the immediate value of Chesapeake Bay research will be judged by
the contribution it makes to addressing management needs.
Regardless of our degree of commitment to restoring the Bay, we must recognize that
our state, federal, and local agencies work with limited fiscal resources. We must
assess the effectiveness of ongoing management programs and continue to develop
and implement new and better strategies. Chesapeake Bay research will play a key
role in the success of these plans and subsequently the success of our entire restoration
effort. Research results promise to help resource managers understand how pollutants
cycle through and affect the Bay's ecosystem. Moreover, research findings can allow
managers to focus the array of pollution abatement programs and direct limited
financial resources more effectively.
It is for these reasons that the Chesapeake Research Consortium publishes the Per-
spectives series on selected Chesapeake Bay research topics. These volumes provide
the research community with a valuable mechanism for incorporating new research
findings, understanding, and scientific consensus into management actions for
restoring the Bay system. The first Perspectives volume formed the basis for the 1988
Chesapeake Bay research conference, as this volume will for the 1990 conference. By
publishing these synthesis papers and organizing conferences that bring together the
scientific and management communities, CRC works to generate and disseminate the
scientific information that is critical for wise management decisions and a truly effec-
tive restoration of the Chesapeake Bay.
—JOSEPH A. MIHURSKY
—MICHAEL HAIRE
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Perspectives on the Chesapeake Bay, 1990: Introduction
As the Chesapeake Bay Program resolves some of the
issues before it, other problems come to the forefront
for consideration. The 1983 Bay Agreement identified
a small number of critical issues to be addressed. The
selection of these issues was based on a consensus
among citizens, resource managers, and the scientific
and technical community. These groups agreed, first,
that these problems were important, and, second, that
we knew enough about them to develop successful
solutions.
As time has passed, the different parts of the
complex interstate, state-federal, state-local, public-
private, and legislative-executive entities that comprise
the Chesapeake Bay Program have coalesced into an
increasingly effective and efficient apparatus for
dealing with the various parts of the problem. It has
become apparent that the solutions to many of the
problems articulated in 1983 Agreement and its
successor, the 1987 Bay Agreement [1], are not going
to be as simple as was hoped in 1983. The basic
consensus as to the importance of the original problems
still holds, but some newly-identified problems demand
solutions and require integration into the Program.
The Chesapeake Bay Program is moving in un-
charted waters. No other environmental management
effort on this scale has ever been attempted in a system
as complex as the Chesapeake. The effort is compli-
cated by the diversity of approaches and the interrela-
tionship of various program activities. The Compre-
hensive Research Plan approved by the Chesapeake
Bay Program Executive Council in July 1988 [1]
recognizes the need for continuing interdisciplinary
studies of the Bay's estuarine system, subsystems, and
watershed from both basic and applied perspectives.
The Research Plan clearly recognizes that the Bay
Program cannot "stay tied up at pierside waiting for all
of the answers to all of the questions before setting
sail".
This publication presents four reviews of scientific
and technical topics relevant to activities of the Chesa-
peake Bay Program. Like the reviews in the first
publication in this series [2], these topics have broad
implications beyond the immediate scope of the
disciplines involved and promise to make contributions
to other areas of research and management.
For example, modeling has been used in various
ways in the Chesapeake Bay Program since its incep-
tion as a research program funded by the Environmental
Protection Agency. The term modeling, however, is
frequently misused in discussing the ways in which the
Bay's problems should be solved. The distinction
between conceptual and simulation models is not
universally recognized, and the relationship of ecosys-
tem models to water quality, hydrodynamic, and
population (fisheries) models is unclear even to many
participants in the Bay program. The contribution by
Wetzel and Hopkins, COASTAL ECOSYSTEM MODELS AND
THE CHESAPEAKE BAY PROGRAM: PHILOSOPHY, BACK-
GROUND, AND STATUS, clarifies misconceptions about the
use and nonuse of ecosystem models within the
Chesapeake Bay program. For scientists and managers
outside the field of ecosystem modeling, it also offers a
sense of where the effort in the Chesapeake Bay stands
in relation to modeling projects in other coastal
systems.
Efforts to understand problems of living resources
and their response to pollutants initially focused on the
water column and submerged aquatic vegetation
habitats. However, as we have refined our understand-
ing of nutrient and sediment processes, particularly as
they relate to storage and mobilization in the sediments,
it has become apparent that we cannot ignore the role of
the benthos. Diaz and Schaffner, in THE FUNCTIONAL
ROLE OF ESTUARINE BENTHOS, provide a timely and
current review of this subject.
A major strategy of the Bay restoration and protec-
tion involves controlling nonpoint sources of pollution
by applying best management practices (BMPs) to any
land-based activity that might have a deleterious effect
on the Bay's aquatic habitat or living resources.
Dillaha, in ROLE OF BEST MANAGEMENT PRACTICES IN
RESTORING THE HEALTH OF TIE CHESAPEAKE BAY:
ASSESSMENTS OF EFFECTIVENESS reviews the science
behind the assessment of present and developing BMP
technology and outlines the strengths and weaknesses
of some current practices.
As the Chesapeake Bay program begins to address
issues related to toxics, difficult decisions will have to
be made, often without the luxury of complete knowl-
edge of either the problem or the effectiveness of the
proposed solutions. Cairns and Orvos outline a
framework for these choices in their paper, DEVELOPING
AN ECOLOGICAL RISK ASSESSMENT STRATEGY FOR THE
CHESAPEAKE BAY. This term "risk assessment" is one
which should become familiar to all who are interested
in a restored Chesapeake Bay.
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Introduction
The following summaries touch only briefly upon
the material developed by the authors. Readers of this
executive summary are urged to read the full papers.
The authors were requested to write their reviews for a
broad audience including scientists and managers who
are not familiar with the intricacies of the specific
disciplines covered in the reviews.
Ecosystem Modeling and the Chesapeake Bay
Program
The Chesapeake Bay Program is not using the full
potential of ecosystem modeling in the planning and
implementation of restoration and protection strategies.
This under-utilization is not unique to the Chesapeake
Bay. Few attempts have been made to fully incorporate
ecosystem models in the management of other coastal
regions. Some relatively successful conceptual models
of coastal systems and subsystems, including several
for the Chesapeake Bay, have been diagramed and
developed. A few of these conceptual models have
been taken through the steps of computer simulation
validation and sensitivity analysis.
The most successful application of ecosystem
models has been in the planning and guidance of
research programs. Ecologists can confidently use
conceptual and simulation models to identify data
weaknesses and gaps and to form testable hypotheses
about specific systems. However, managers have not
embraced some of the more successful "management-
oriented" ecosystem models, possibly because these
models were not directly commissioned by managers
themselves.
Four cases of management-oriented, ecosystem-level
conceptual and simulation modeling provide a good
description of the interaction between the Chesapeake
Bay Program and the developing state of ecosystem
modeling in the region. At the beginning of the Bay
Program's research phase, the U.S. Environmental
Protection Agency and the U.S. Fish and Wildlife
Service funded the development of a set of conceptual
models that eventually consisted of a whole-system
model with subsystem models of emergent wetlands,
submerged aquatic vegetation, plankton, benthos, and
fish populations structured by feeding types. At the
time these models were developed, they were used to
focus discussions on general directions for the program,
but were not developed further into simulation models
or used in the planning of specific research or manage-
ment activities. (The model developer's departure from
the Bay area may have contributed to the neglect of the
models' further development.)
The other three cases of ecosystem modeling
discussed by Wetzel and Hopkinson involve modeling
conducted by established members of the Bay research
community who have been involved with the federal-
or state- supported research and management activities
of the Chesapeake Bay Program. Kemp and his col-
leagues at the University of Maryland (Horn Point
Environmental Laboratory) used a hierarchical struc-
ture in developing a simulation model consisting of six
submodels—autotrophs, epibiota, plankton and water,
benthos and sediment, mobile invertebrates, and
nekton—which interact through common compart-
ments. This set of models was used extensively to
investigate the decline of submerged aquatic vegeta-
tion. Contemporaneously with these modeling efforts,
Wetzel and his colleagues from the College of William
and Mary (Virginia Institute of Marine Science)
developed conceptual and simulation ecosystem models
dealing with the dominant seagrass species, eelgrass
(Zostera marina) and widgeon grass (Ruppia mari-
tima). Both of these modeling efforts concluded that
the principal explanation for the decline of the Bay's
submerged aquatic vegetation was a reduction in light
intensity caused by increased turbidity largely due to
excess nutrients and suspended sediment.
The final ecosystem modeling effort reviewed is the
network analysis approach currently being used by
Ulanowicz and his colleagues at the University of
Maryland's Chesapeake Biological Laboratory. Wulff
and Ulanowicz have used the network approach to
compare the Chesapeake Bay with the Baltic Sea
ecosystem. Many consider this approach to have great
promise for future work in the Chesapeake.
The major emphasis in the Chesapeake Bay has been
on linking water quality-hydrodynamic models with
models of fisheries populations. Ecosystem modeling
has not been supported to the same extent. Wetzel and
Hopkinson point out that the existence of a state-of-the-
art, three-dimensional, time-variable hydrodynamic-
water quality model may provide the Bay scientific
community with the detail necessary to link physical,
water quality, and ecological processes within a single
modeling framework. They suggest that coupled
ecosystem hydrodynamic-water quality models will be
required to address the large-scale management
decisions facing the Chesapeake Bay Program.
The expertise necessary to move on to this next
generation of management-oriented ecosystem model-
ing already exists within the Chesapeake Bay research
community. What appears to be lacking is a commit-
ment to use ecosystem models to support management
decisions.
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Functional Role of Chesapeake Bay Benthos
The water column, plankton, and dissolved nutrients
(and the interactions among them) have occupied most
of the attention of the scientists and managers of the
Bay Program. Initially, the Bay bottom was treated
merely as a sink or sometimes as a possible source of
nutrients entering the water column. The importance of
the benthic system was brought sharply to the attention
of Bay strategists when the initial sediment submodel in
the water quality model failed totally to respond to
large-scale changes in nutrient reductions. A crash
research program developed sediment nutrient flux
algorithms sufficiently sophisticated to handle the
proposed scenarios for the 1991 reevaluation of nutrient
strategy.
Diaz and Schaffner make a strong case that any
management strategies beyond the present iteration
cannot ignore the functional role of the benthic organ-
isms in the Bay. The general structure and distribution
of the benthic community in the Bay are fairly well
known, and the effects of many geological, physical,
and chemical variables are reasonably well understood.
However, the influence of functional characteristics of
the benthos, such as feeding rates and methods, on
sediment modification are not sufficiently well known
for them to be incorporated into the technical models
being used to evaluate management strategies.
With the benthic environment serving as the major
storage compartment for almost all of the materials that
enter the Bay, any management strategy dealing with
excess nutrients and toxic additions must come to grips
with sediment-organism interactions. Many benthic
species make good indicators of past and current
environmental conditions because of their close
association with sediment through much of their life
history. Not only do benthic organisms serve as
integrators of environmental conditions, but they also
can generate site-specific sediment conditions. Benthic
organisms directly affect the transport of nutrients,
pollutants, and oxygen across the sediment-water
interface in both directions.
In addition to their role in mixing sediments, many
benthic organisms are also capable of increasing
sedimentation. Filter feeders can generate large
quantities of biodeposits. These biodeposits can alter
the s ize distribution of sediments and benthic commu-
nity composition through the ability of filter-feeding
benthos to selectively remove material from the water
column. The chemical characteristics of deposits
resulting from benthic activity may also differ from
those of naturally settling particles, because the
digestive processes of the benthic organisms can both
chemically alter clay mineral structure and add large
amounts of organic material to the deposits. Colonies
of benthic organisms also have been demonstrated to
trap sediments and increase sedimentation rates in the
shallow waters of the Chesapeake Bay.
The benthos are also a key component in the overall
energy flow within the estuarine ecosystem. In the
shallow Chesapeake system, there is a high probability
that much of the phytoplankton and microheterotroph
production moves into the benthos. Secondary produc-
tion in the benthos, in turn, supports many of the fishes
and larger invertebrates that are harvested for human
consumption. Diaz and Schaffner estimate that the Bay
benthos produce about 194,000 metric tons of carbon
each year, or about seven times the production required
to support the maximum combination of fishery yields
taken from the Bay.
The importance of benthic function to the overall
Bay ecosystem highlights the need to increase our
knowledge of the benthos if we are to make wise
choices in the next generation of management issues.
Fortunately, there is extensive literature on the benthos
of the Chesapeake Bay, and much of what we know of
benthic processes in estuarine systems has been learned
in the Chesapeake. Diaz and Schaffner present a series
of key questions about the benthos that are central to
Chesapeake Bay management.
Questions related to interactions between benthic
populations and benthic habitats include the investiga-
tion of organism-environment relationships that can
predict sources and pathways of energy flow within and
among habitats, and the possibility that the spatial
arrangement of various benthic habitats may play an
important role in benthic function.
A second category of questions relates to the role of
benthos in the overall trophic structure of the estuary
and includes more precise definitions of the linkage of
primary organic sources with secondary production and
the linkage of fishery yields with benthic production.
Fundamental issues related to the interaction of the
benthos with the materials of direct concern to the
Chesapeake Bay Program include benthic uptake of
toxic materials and resulting influence on the fate of
these compounds; the magnitude of control that benthic
organisms exert on diagenetic processes and sediment
dynamics; and the relative importance of biological and
physical processes controlling diagenetic processes and
nutrient and toxic dynamics.
Final questions deal with the effects of long-term
climatic changes on benthic function; the presence of
any long-term periodic cycles of benthic population;
and the role of episodic events such as large storms or
dredge material disposal in restructuring benthic
habitats and communities.
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Introduction
Assessment of Best Management Practices in the
Chesapeake Bay Watershed
"To BMP or not to BMP, that is the question." Unfor-
tunately the Bay Program managers dealing with
nonpoint source pollution in the Chesapeake Bay
watershed do not have the luxury of soliloquy. Non-
point source pollution is a major contributor to the
water quality problems throughout the Chesapeake
watershed. In some basins, it is estimated that nonpoint
pollution far exceeds point source pollution. Conse-
quently the restoration and protection of Chesapeake
Bay water quality cannot be accomplished without
significant reduction of nonpoint source pollution. The
only feasible approach known is the use of best
management practices, or BMPs.
What are BMPs? How do they work? How do we
know they work? These three simple questions do not
have simple answers. Dillaha, in his discussion of the
use and effectiveness of BMPs, describes the many
uncertainties in the choice of specific BMPs for specific
sites and situations, and points out that the techniques
available for resolving these uncertainties are not easily
applied. Most BMPs are designed to reduce the
pollutant load in surface water. Recent studies,
however, indicate that some of these practices, although
very effective in reducing surface runoff, may increase
problems of groundwater quality.
BMPs such as conservation tillage, contouring,
terracing, and planting of cover crops are erosion
control practices that reduce the pollutant carrier mass.
The effectiveness of these BMPs in reducing the total
amount of sediment loss is offset to some extent
because the sediment that does erode is enriched with
fine-grained material characterized by a higher adsorp-
tive capacity. Conservation tillage, the most widely
used BMP, has been very effective in reducing erosion
in most applications. The acceptability of conservation
tillage in Maryland and Virginia is enhanced by higher
crop yields and lower production costs relative to
conventional tillage.
BMPs such as vegetative filter strips have been tried
extensively in the Bay watershed, with mixed results.
In hilly regions, vegetative filter strips do not appear to
be very effective. In flatter terrains, this practice is
more effective, but only as long as the strips are
properly maintained to retain sediment-trapping
capability.
Evaluating BMPs is one of the most difficult tasks
facing water quality managers. Each site selected for
BMP evaluation is unique. Detailed monitoring or
measurement of the effectiveness of a BMP at one site
may have little or no relevance to the effectiveness of
the same BMP at a different site. Edge-of-field effects
have been characterized for a number of BMPs, but
those measurements are not easily translated into
impacts on quality of the receiving water. Even if
monitoring programs could be designed to properly
evaluate the effectiveness of specific BMPs, the
problem of response time of the system must be
considered. It has been estimated that the time required
to detect water quality improvements from specific
projects may exceed a decade.
To overcome the limitations of monitoring to
evaluate BMP effectiveness, a number of models have
been developed for nonpoint source problems. Non-
point source models usually focus on the creation of
pollutants and transport of these pollutants across the
land surface to receiving waters or through the soil to
groundwater. Screening models are relatively simple
models used to identify problems within a watershed or
basin or to make some preliminary qualitative evalu-
ations of alternatives. These models have shown that a
few critical areas in a watershed were disproportion-
ately responsible for pollutant loadings.
More complex models, hydrological assessment
models, have been developed to assess current condi-
tions or evaluate alternative management strategies.
These models have been developed for review of both
field-scale and watershed-scale assessments. An
inherent problem with many of the models is the lack of
data for appropriate calibration or verification in
specific basins, watersheds, or other sites. Dillaha
suggests that BMP benefits probably can be best
addressed through use of properly selected simulation
models that account for site-specific conditions.
A number of critical research questions related to
BMP effectiveness must be answered if the full
potential of nonpoint source pollutant reduction is to be
realized in the Chesapeake Bay watershed. We must
obtain a better understanding of the processes transport-
ing pollutants between fields and streams. In this
context, we must determine satisfactory equations for
describing sediment transport in shallow overland flow
and develop nitrogen-accounting models that better
simulate nitrogen transformations and the availability
of nitrogen species in the various microclimates of the
Bay watershed.
If less material is applied to fields, less material is
available for stream or groundwater loading. Develop-
ment of reliable tests for plant-available nitrogen and
refinement of methods for applying agricultural
chemicals below the surface with minimum soil
disturbance will allow farmers to maintain yields while
reducing the amounts of chemicals available for
transport to streams.
Development, testing, and verification of better
models cannot be accomplished without better data
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bases. Long-term intensive monitoring programs
should be established on a number of watersheds within
the Bay region to provide these data bases. While these
data bases are being developed, modeling efforts should
concentrate on physically based, deterministic, distrib-
uted-parameter models that can be used without site-
specific calibration.
Comparison of alternative models must be improved
through the development of standard methods to
evaluate predictive results from the different models.
In addition, targeting or screening models should be
improved, particularly as they relate to cost-effective-
ness of alternative nonpoint source control programs.
The use of geographic information systems is consid-
ered an area for special emphasis.
Certain BMPs are becoming almost standard
practice in agriculture. Specific studies of the design,
installation, and maintenance of these practices should
receive priority consideration. Candidates for this
specific focus are vegetated filter strips (particularly as
regards maintenance), wet ponds enhanced with
wetlands (particularly as regards long-term nutrient
removal), conservation tillage (particularly in regard to
long-range impact on groundwater), and alternative
animal waste containment and treatment facilities
(particularly with regard to nutrient removal and
disinfection).
An Ecological Risk Assessment Strategy for the
Chesapeake Bay
Consider the person who wants to do something in the
Chesapeake Bay watershed that will have what he or
she considers to be a very minimal and acceptable
impact on the Bay's resources. A number of individu-
als oppose the activities, because they believe there is a
possibility of some adverse impact on the Bay, and they
are willing to tolerate no adverse impact of any kind.
How is the situation to be resolved?
Almost all activities of the human species will cause
some perturbations of nature. It is not possible to
predict completely and accurately what the impact of
most activities will be. There is a risk involved in
making any decision. All of us use risk assessment in
our day-to-day activities; we just don't follow formal
protocols in doing so. Therefore, environmental risk
assessment should not be considered a novel or strange
approach to the resolution of the Bay's problems.
Cairns and Orvos point out that protocols for
assessing ecological and human health risk are devel-
oped sufficiently to assess the risk from a particular
event at a specific site. However, comparable proce-
dures have not been developed for conducting risk
assessment of activities that may occur at one point in
the watershed with adverse environmental or ecological
processes elsewhere in the watershed.
A key element in the Cairns and Orvos strategy for
developing a Bay-wide risk assessment program is the
selection of appropriate endpoints (effects). This
selection process must involve scientists, regulators,
municipalities, business interests, agricultural interests,
environmental interests, and any other relevant group or
entity involved in Chesapeake Bay matters. General
agreement about endpoints may not be easy to attain,
and consensus will probably change with time. With-
out agreement on endpoints, however, it will not be
possible to develop a defensible and acceptable
predictive capability.
Once endpoints have been selected, evaluation of
suspected hazards in specific systems or parts of
systems can proceed in a logical framework. The tradi-
tional risk assessment approaches, qualitative vs. quan-
titative, reductionist vs. holistic, and top-down vs.
bottom-up, which have been used in different settings,
must be combined for comprehensive risk assessment
in a system as complex as the Chesapeake Bay water-
shed.
Direct validation of some of the predictive capability
of risk assessment models (particularly those models
that deal with the effects of specific compounds on
specific species) is possible in the laboratory. How-
ever, the more complex models, particularly those
developed to assess risk at the ecosystem level, cannot
be directly validated, primarily because of the potential
for damage in field validation experiments. A surro-
gate for planned experiments may be found in the
inadvertent discharges of hazardous materials or spills
(ecoaccidents). Unfortunately from a research perspec-
tive, the initial reaction to ecoaccidents is to clean them
up or to attempt to mitigate the immediate damage,
with little time or effort spent in trying to determine the
immediate ecological effects of the accident. Cairns
and Orvos clearly state the need for research and
regulatory groups to plan ecoaccident studies well
before any accident occurs.
Since the effectiveness of risk assessment is inti-
mately tied to our understanding of the Chesapeake Bay
ecosytem and the effects of specific materials or
activities on that ecosystem, gaps in our understanding
(or gaps in technology available to help us gain the
neccessary understanding) must be filled. Necessary
research directly applicable to risk assessment includes:
the development of environmentally realistic tests of
acute and chronic toxicity; the incorporation of present
and improved toxicity tests into models developed for
microcosm and mesocosm systems; and the increased
development and use of biomarkers to assess effects.
In addition to these specific research needs, a number
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Introduction
of more general areas need study: energy flow dynam-
ics in the Bay watershed; the resiliency of some
estuarine systems; ecological population theory in
relation to risk assessment; the capabilities of wetlands
areas to remove toxics; the utility of created wetlands
as replacements for natural wetlands; and the impor-
tance of groundwater both as a contributor (nonpoint
pollution) and as a resolver (through water purification
processes functioning in aquifers) of the Bay's water
quality problems.
Risk management and risk assessment must be in-
separable management and scientific partners. Risk
assessment is quintessentially a scientific activity; risk
management, as the synthesis of technical, socioeco-
nomic, and political considerations, is primarily a
management function. However, neither risk assessors
nor risk managers can perform their proper function
without interaction with the other group. Analyzing
and characterizing risk is a scientific pursuit. Deciding
whether a risk is acceptable is a question for society as
whole.
Conclusion
For convenience and manageability, the day-to-day
activities of the Chesapeake Bay Program are coordi-
nated through committees and workgroups such as
Living Resources, Modeling, Toxics, Stock Assess-
ment, etc. In reality, however, these areas are not
isolated from each other, and the best decisions
consider and integrate the deliberations of all these
groups. In an analogous fashion, scientific and
technical studies undertaken in a specific area often
generate information relevant to many other activities.
We believe this to be the situation in the four areas that
are reviewed in this report.
The management community involved in the Bay
Program interacts on an almost continuous basis. This
interaction is critical to the success of a program that
deals across political boundaries with a natural system
as complex as the Chesapeake Bay and its watershed.
In the Bay region, as elsewhere, there is less regular
communication between researchers in different
disciplines or between much of the scientific and
technical community and the day-to-day managers.
Projects such as this document try to improve com-
munications—both within the scientific and technical
community, and between that community and those
responsible for management of the Bay's environment
and resources. We hope that the material contained in
these papers stimulates the incorporation of new and
appropriate ideas into the Chesapeake Bay Program and
contributes to the ultimate success of the most ambi-
tious estuarine management program ever attempted.
—MAURICE P. LYNCH
References
1. 1987 Chesapeake Bay Agreement. In: Comprehensive
research plan, an agreement commitment report from the
Chesapeake Executive Council. Annapolis, MD: Envi-
ronmental Protection Agency; 1988.
2. Lynch, M.P.; Krome, E.G., eds. Perspectives on the
Chesapeake Bay: recent advances in estuarine sciences.
Gloucester Point, VA: Chesapeake Research Consor-
tium; 1987; CRC pub. no. 127; CBP/TRS 16/87.
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Coastal Ecosystem Models and the Chesapeake Bay Program:
Philosophy, Background, and Status
Richard L.Wetzel
College of William and Mary
School of Marine Science
Virginia Institute of Marine Science
Gloucester Point, Virginia 23062
Charles S. Hopkinson, Jr.
Ecosystems Center
Marine. Biological Laboratory
Woods Hole, Massachusetts 02543
Models and Modeling in Ecosystem Research
Models are abstractions or simplifications of systems.
As such they have become useful tools in scientific
analysis of natural systems because the complexity of
natural systems is too overwhelming to be perceived
intuitively [14]. Ecosystem models are defined
operationally as models conceptualized at the hierarchi-
cal level of community organization and above. An
ecosystem model should incorporate sufficient func-
tional attributes of the real system to mimic some
aspects of its behavior, but obviously such models
cannot and should not encompass all attributes.
Deterministic, numerical ecosystem models synthesize
large amounts of information on individual parts of
systems and objectively explore hypothesized relations
between components. Isolation of successively smaller
parts of systems for detailed measurement and study
under controlled laboratory conditions may not reveal
the role of the part in the larger system. It is in this area
that ecosystem modeling is particularly valuable.
Perhaps through the integration of reductionist data,
models can demonstrate emergent properties of the
larger, whole system.
Simulation modeling of ecosystems has progressed
rapidly over the past 15 years. Advances in computer
hardware and software have played an obvious part in
This review is contribution no. 1591 from the School of
Marine Science and the Virginia Institute of Marine
Science, College of William and Mary.
this progress; a less obvious factor is the maturing of
the modeler and model end-user. There has been
increased demand for integrative syntheses of large data
bases collected from multidisciplinary research pro-
grams, and the use of models has expanded into other
areas as well. Models are now commonly used to plan
and guide research programs from the outset—to guide
program development, to identify data weaknesses and
gaps, to evaluate management-oriented alternatives,
and, perhaps most important, to formulate testable
hypotheses about a system's structure and function.
Numerical models of marine processes have been in
existence since the late 1930s. Early attempts to
examine population phenomena were little changed
from the basic growth equations of Lotka [28] and
Volterra [44], in which population size was a function
of constant birth and death rates. Later workers [11,
34-36] incorporated terms for phytoplankton division
rate, sinking, respiration, zooplankton grazing, light
fields, nutrient limitation, and vertical turbulence. Only
in 1949 did Riley incorporate phytoplankton and
zooplankton equations into a system of equations with
feedback control. Application of these early models
was seriously limited by the lack of adequate computa-
tional equipment. Not until the introduction of analog
and digital computers in the 1960s was there full
development of dynamic, feedback-controlled models
of ecological processes.
During this time modelers interested in ecosystem
analysis and those interested in population dynamics
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Chapter 1: Ecosystem Models
diverged in approach. Even without computer capabil-
ity it was possible to model the behavior of two
populations over time as long as the equations remained
in the simple Lotka-Volterra form. However, in order
to simulate and analyze the controls on processes, such
as primary production, it was necessary that models
include complex equations representing (at least
hypothetically) biological reality. Without advanced
hardware and software for numerical analysis, such
detailed equations could be solved only for steady-state
conditions.
Management agencies must often make decisions
based on the consequences of specific actions in
complex dynamic ecosystems. While intuition fre-
quently fails to provide the correct prediction, so does
population modeling. A population model that de-
scribes the response of an organism under controlled
and limited conditions may fail in the larger context
where an organism may be affected indirectly through
feedback interactions. By contrast, ecosystem model-
ing, because it incorporates abiotic controls and feed-
back interactions, often can provide reliable predictions
to assist managers in assessing environmental impacts.
Construction and Evaluation of Ecosystem
Models
Any modeling effort should begin with a clearly
defined set of objectives and questions. Once model
objectives are described, the general course of events in
developing an ecosystem model is: (1) construction of
a conceptual model, (2) representation as a diagram-
matic model, (3) formulation of interaction equations,
(4) computer simulation, (5) calibration, sensitivity
analysis, and validation, (6) revision and reformulation
if necessary, and (7) generation of new hypotheses and/
or predictions, if appropriate [14], Often this sequence
of events repeats continuously as new data are collected
and the questions the model is to address evolve.
Before a model can be used to generate hypotheses,
evaluate internal dynamics and controls, or make
predictions, it must be validated. The procedures and
criteria used to validate an ecosystem-level simulation
model are not fully developed, but they generally
involve the comparison of model-generated data with
field-derived data. This comparison must use data
other than those used in model construction (with which
a good fit would be expected because of the interde-
pendence of the two data sets). Therefore many
modeling programs include additional independent data
collection, which allows statistical tests of correspon-
dence between the independent and model data sets.
When it is too expensive to initiate large data-collection
efforts for validation purposes, sensitivity analyses can
reveal which parameters most affect model output.
Additional data collection can then be focused on those
critical or sensitive components.
Sensitivity analysis is further useful in identifying
parameters or processes causing interesting model
behaviors (output). Such analyses frequently reveal
that indirect feedbacks and second-order interactions of
no apparent importance are in fact important. When
models identify processes previously thought unimpor-
tant or when results do not adequately follow observed
field data, alternate hypotheses can be suggested to
account for the predicted behavior. Based on these
alternate hypotheses, new research can be proposed to
test predictions using a combination of model simula-
tion analyses, field studies, and laboratory experiments.
Ecosystem Models of Coastal Systems Outside
the Chesapeake Bay Region
Several estuarine ecosystem-level models from outside
the Chesapeake Bay region were chosen to represent
the best of both management- and research-directed
efforts. The modeling efforts described below were all
developed for specific purposes beyond merely
summarizing large and complex data sets. They
illustrate the contribution of modeling in guiding
research programs and assisting managers with deci-
sion-making. The first three models described are
clearly management-oriented, while the last three are
concerned primarily with basic research.
There have been few efforts to fully incorporate
ecosystem-level models in the management process.
Although many models have been conceptualized and
diagramed, few have been simulated and even fewer
have been maintained as ongoing efforts. We have
chosen management-oriented models to represent each
of these stages of development: (1) conceptual/dia-
grammatic, (2) one-time simulation, and (3) ongoing
simulation and expansion of effort. Each of the
research-oriented models was conceptualized early in a
research program and has evolved and become more
complex as the programs developed.
The first of these modeling efforts consists of a
group of both conceptual and simulation models [5,12,
27] of the Mississippi River deltaic plain region in
Louisiana. The models are management-oriented and
attempt to integrate information from several hierarchi-
cal levels, ranging from the salt marsh to the entire
southern Mississippi River drainage basin, including
urban centers. Next in the management-oriented series
are models developed and simulated by Hopkinson and
Day [19, 20] for two purposes: (1) to evaluate effects of
urbanization on nutrient runoff to a coastal swamp
forest and (2) to evaluate drainage options for promot-
ing water runoff from agricultural areas while at the
same time decreasing eutrophication of receiving
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Wetzel and Hopkinson
waters. Last, modeling efforts of Costanza et al. [5-7]
at the spatial scale of the landscape are discussed.
For the examples in the research-oriented category,
three long-term and evolving research-directed model-
ing programs deal with tidal marsh and estuarine
ecosystems [26, 45, 51, 52], Two of the three have
many similarities but have distinctly different philoso-
phies of model control. These models have been
integral in the planning, development, and analysis of
long-term ecological research programs.
Management-Oriented Models
Mississippi River deltaic plain conceptual models.
The U.S. Fish and Wildlife Service solicited a series of
conceptual models of the Mississippi River deltaic plain
region [2, 5] for the purpose of summarizing existing
data in a form useful for scientists and coastal manag-
ers. The deltaic region consists of the broad, topogra-
phically flat portions of Mississippi and Louisiana that
encompass the largest active delta system in North
America. The region's dynamic nature, high biological
productivity, and intense level of economic activity
(including fisheries, transportation, and minerals
extraction) have combined to create enormous problems
for resource management. The models integrate
information on such diverse topics as ecology, hydrol-
ogy, climatology, and economics and are the product of
several decades of research by the Center for Wetland
Resources at Louisiana State University.
Conceptual models were constructed in a hierarchi-
cal manner in order to (1) organize existing information
on relevant temporal and spatial scales, (2) organize
environmental and management problems within this
framework, and (3) target areas in need of additional
research. The region was abstracted hierarchically into
three spatial scales: the entire region, hydrologic units,
habitats;. The conceptual models describe the overall
compartmental and flow structure, interactions, and
forcing functions considered most important.
At the highest spatial scale of the region, the most
important forcing functions were riverine inputs of
water and sediments, and interactions with the Gulf of
Mexico and the atmosphere. The primary issues
addressed at this scale were wetland loss, natural
switching and direct diversion of Mississippi River
waters within the coastal zone, and water and air quality
and chemical waste disposal.
At the intermediate spatial scale of hydrologic units,
which encompass discrete coastal watersheds, the
primary issues addressed by the conceptual modeling
were (1) the role of wetlands in fishery production, (2)
the effects of hydrologic modifications (e.g., canal
construction, spoil and levee construction) on ecosys-
tem function, (3) eutrophication and toxic substance
influences on ecosystem function, and (4) salt water
intrusion and its effect on community structure, land
loss, and municipal water supply.
The smallest scale of model conceptualization was at
the habitat level. Habitats were identified as contiguous
zones having similar vegetational composition (salt,
brackish, and freshwater marshes; swamps; aquatic
zones; and uplands). Questions of interest included (1)
human-induced stresses on habitats, such as eutrophica-
tion and water impoundment, and (2) estimation of the
rates of ecological production from each habitat and its
value to the overall economy of the deltaic region.
The U.S. Fish and Wildlife Service must frequently
render opinions concerning the effects specific activi-
ties have on the vitality of habitats and species popula-
tions in coastal regions. In order to facilitate the deci-
sion-making process and to guide resource management
and coastal planning, the agency has had a number of
research teams around the United States prepare
ecological characterization studies. These studies
describe the important components and processes of
selected ecosystems and provide an understanding of
their relationships through synthesis and integration of
extant physical, biological, and socioeconomic informa-
tion. The rationale is that only through an understand-
ing of ecosystem function will it be possible to effec-
tively manage natural resources and prudently guide
development; and ecosystem models offer a means to
organize such diverse information. The hierarchical
modeling approach exemplified by the deltaic region
study has proved successful in conceptualizing and
qualitatively elucidating the complex relationships
operating at various levels of ecological organization by
minimizing problems associated with differences in
scale and duration between physical and biological
events. This approach has aided decision-makers in
establishing cause-and-effect relations and in systemati-
cally evaluating the effects of specific activities.
Urban runoff and receiving waters eutrophication
models. Hopkinson and Day [19, 20] constructed a
pair of complementary models of a coastal swamp
forest to investigate relationships among urbanization,
hydrology, nutrient loading to water bodies, and
wetland eutrophication. One model investigated the
interactions between changing land use in uplands, and
storm water and nutrient runoff to adjacent coastal
swamps. The second model addressed the effects of
sheet flow vs. channeled stream flow through swamp
forests on (1) rates of stormwater runoff from the
uplands, (2) swamp productivity and nutrient dynamics,
and (3) eutrophication of receiving water bodies.
The area modeled was the des Allemands swamp
forest ecosystem located in the headwaters of a large
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10
Chapter 1: Ecosystem Models
coastal basin bordered by two distributaries of the
Mississippi River and the Gulf of Mexico. Natural
levees along the distributaries direct runoff water into
the interior wetlands and from the headwaters to the
Gulf of Mexico. Several studies conducted prior to the
modeling effort indicated that urbanization and in-
creased agricultural production on the surrounding
uplands were causing fundamental ecological changes
in the swamp forest ecosystem. It had been docu-
mented that lakes that were once clear and coffee-
colored, with abundant game fish populations, had
become characterized by frequent algal blooms,
periodic fish kills, and "trash" fish populations. Other
studies found strong correlations between the density of
drainage canals in the swamp, eutrophication of lakes,
and decreasing productivity of impounded swamp
forests. The Louisiana Office of State Planning
predicted in 1975 that the uplands surrounding the
swamp ecosystem would experience substantial
development in the following 20 years as a result of
secondary growth associated with the construction of an
offshore oil terminal [19, 20]. The modeling study was
a first attempt to integrate several studies that had been
conducted in and around the swamp forest ecosystem.
The objectives of the modeling were: (1) to quantita-
tively predict present and future rates of nutrient and
water runoff from the natural levee uplands as a result
of changing land use during urbanization, (2) to
ascertain the effects of channeled canals and their
associated spoil banks on water flow through and
drainage in the swamp forest, (3) to evaluate the
feasibility of routing upland runoff directly through
backswamp areas rather than through drainage canals,
and (4) to determine the effectiveness of directing
runoff as sheet flow through backswamps in reducing
the nutrient load to receiving bodies of water.
Hopkinson and Day used a hydrologic model
developed by the Environmental Protection Agency
(EPA) to form the nucleus of a larger model that could
address all these issues. The EPA Storm Water
Management Model is a comprehensive mathematical
model capable of simulating urban storm water runoff
and the receiving effects for lakes and rivers; it is
divided into four major blocks. Hopkinson and Day
used portions of the RUNOFF block to simulate runoff
from uplands and the RECEIVE block to simulate
swamp and lake hydrology and nutrient dynamics.
Most values for equation parameters were obtained
from the literature rather than measured in the field.
Model validation did not include rigorous field com-
parisons; rather, sensitivity analyses identified those
parameters that most affected model output. The
authors found that the parameters that most affected
model results were those in which they had the greatest
confidence. Hopkinson and Day strongly suggested
that future scientific effort should be directed toward
validation of the models with special attention directed
to parameters most critical to model results.
Simulation results showed that runoff volumes and
nutrient loading to the swamp forest would increase
greatly by 1995 if projected changes in land use came
about. It was predicted that nutrient runoff to the
swamp would increase substantially as a result of the
increased runoff. Simulations of swamp hydrodynam-
ics showed that spoil banks retard water exchange
between backswamps and streams, causing prolonged
ponding. It was found that discharge of upland runoff
could be increased by removing spoil banks and
introducing water directly to backswamps rather than to
drainage canals. Important secondary benefits of
backswamp introduction of runoff water would be a net
decrease in nutrient loading to receiving lakes and an
increase in swamp productivity.
Perhaps because these models were not solicited by
management agencies, none of the alternatives sug-
gested by these modeling studies was ever implemented
or further evaluated.
Spatial modeling of wetland dynamics. Most ecosys-
tem-level models are designed to predict compartmental
dynamics at a single point, often in homogeneous
space. Costanza, Sklar, and White [6] have extended
this approach to modeling spatial dynamics by arrang-
ing a spatial array of point ecosystem models and
connecting them with fluxes of water, nutrients, and
sediments. The approach sacrifices generality for
greater realism and precision. The payoffs are signifi-
cant: the model can realistically simulate major changes
in land-cover patterns across large geographic regions
resulting from various-site specific management
alternatives as well as natural changes.
The impetus for developing spatial models was a
request by the U.S. Fish and Wildlife Service to eval-
uate the Corps of Engineers' plan to extend a levee
along the east bank of the Atchafalaya River that would
restrict water and sediment flow into the Terrebonne
marshes of coastal Louisiana [7]. The proposed action
represented a unique opportunity to study landscape
dynamics and develop models for this spatial scale of
resolution. Also, the Atchafalaya landscape is changing
rapidly enough to provide the necessary data to test
basic assumptions and hypotheses concerning land-
scape development.
The spatial simulation model—the Coastal Ecologi-
cal Landscape Spatial Simulation Model (CELSS)—
consists of 2,479 interconnected "cells," each represent-
ing 1 km2 [38]. Each cell in the model contains a
dynamic, nonlinear simulation model. Variables
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Wetzel and Hopkinson
11
include water volume and flow, sediment, nutrients
(nitrogen), salt concentration, and plant biomass and
productivity. The model produces weekly maps of all
the state variables and habitat types. The balance
between sediment deposition and erosion is particularly
critical to habitat succession and the productivity of the
area. Habitat succession occurs in a cell in the model
when physical conditions change sufficiently that one
habitat type becomes more appropriate than another.
Primary inputs for the model are: (1) detailed,
digitized maps for several past years; (2) historical
maps of canal and levee construction; (3) weekly
records for climatic variables; and (4) water level and
salinity in the Gulf of Mexico. In addition field
measurements of plant biomass, production, and
nutrient uptake are required. The model was initialized
for 1956 conditions. It was calibrated by starting with
the 1956 conditions and simulating the changes in the
area by weekly time steps to 1978, for which another
ecosystem type map was available. The simulated and
real maps were statistically compared for degree of fit,
and parameters were adjusted iteratively within
predetermined ranges of uncertainty to maximize the fit
[4]. A by-product of this approach was a fairly elabo-
rate sensitivity analysis of the model's response to
changes in the parameters, which gave the modelers
considerable insight into its dynamic behavior. The
model was validated by comparing its predictions for
1983 with the real 1983 data set. These data were not
used in the calibration phase.
The. model has great potential for application to
issues concerning wetland deterioration. It has been
used to investigate natural wetland loss as related to
sediment supply, marsh productivity, water flow, and
sea-level rise (natural or induced by "greenhouse"
warming), and human-induced wetland loss and gain as
caused by Mississippi River levees, canals, dredged
material placement, marsh impoundments, barrier
island stabilization, and controlled diversions of the
Mississippi River.
The simulation of long-term habitat changes in the
coastal marshes of the Atchafalaya River has demon-
strated that ecological and physical processes can be
coupled and realistically modeled. The results of the
model indicated that the current trend of continued
habitat loss will lead to severe wetland degradation.
The modelers have shown that when spatial processes
and cumulative impacts are considered together, the
effects are greatly magnified.
Costanza et al. (personal communication) are
currently running the model to the year 2033 for several
different scenarios of interest to coastal managers.
They are also developing spatial models for two
different types of coastal ecosystems: (1) the Patuxent
River watershed in the mid-Chesapeake Bay region,
and (2) the North Inlet marshes and watershed in South
Carolina. The results of these efforts will provide (1)
increased understanding of the processes controlling
changes in landscapes, (2) principles for adjusting
spatial and temporal scales to optimize predictability in
models, and (3) new methods for examining the
goodness-of-fit between predictions and data that are
appropriate for spatial ecological modeling and that
require a degree of spatial pattern recognition [40].
Research-Oriented Models
Sapelo Island salt marsh models. The Sapelo Island
salt marsh ecosystem has been the focus of an ecosys-
tem-level modeling effort since the early 1970s. A
review of the development of the modeling program
illustrates not only the philosophical and applied nature
of the continuing studies but also the inherent evolu-
tionary pattern of most large-scale modeling programs.
In addition to serving as an analytical tool [47], the
model has generated testable hypotheses relative to the
principal components, fluxes, and controls on carbon
dynamics in ecosystems dominated by Spartina
alterniflora [51-54].
The research program at the University of Georgia
Marine Institute includes modeling as an integral part of
the overall effort. Field research at Sapelo Island dates
back to the early 1950s. The aim of the initial modeling
effort was to summarize and integrate the results of
these studies and to identify profitable avenues for
future research. Specific objectives of each modeling
effort became more sophisticated as basic knowledge of
ecosystem structure and function improved.
The salt marsh model is composed of 14 abiotic and
biotic compartments that represent the principal
components exchanging carbon among salt marsh-tidal
creek sediments, water, and the atmosphere. The model
explicitly identifies above- and below-ground biomass
and production of Spartina and incorporates an anaero-
bic detrital community in the sediments. The model is
process-oriented but has good resolution at the popula-
tion level of ecosystem organization.
The model's unique mathematical structure is based
on a proposed minimum set of laws governing the
growth and interactions of populations as they form
communities and ultimately ecosystems [49, 50].
External or environmental forcing functions (e.g., light,
temperature, sea level, and tides) were not modeled
explicitly; rather, specific rate coefficients for biotic
exchanges were varied seasonally. Revisions of the
model included algorithms for simulating carbon export
due to tidal exchange that were driven internally. Use
of non-linear feedback-controlled equations in the
model partially dictated the type of data collected in
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12
Chapter 1: Ecosystem Models
field studies. The mathematical derivation of the
feedback functions represents a hypothetical statement
regarding control. Because all parameters defining the
feedback functions are theoretically measurable, the
controls are experimentally testable. The adoption of
this approach allowed the modeling effort and the
experimental research effort to be closely coupled from
the initiation of the program.
Over its history of almost two decades, the salt
marsh model has evolved through seven versions.
Although the major goal of modeling within the overall
research program has not varied, each version was
modified to address specific questions raised in earlier
model simulations and to incorporate data from new
experimental work and field observations. The first
version of the model was implemented specifically
(1) to identify where information and data were missing
relative to the conceptual model and to aid in guiding
research, (2) to identify parameters and/or controls that
governed model behavior, and (3) to qualitatively pre-
dict the role of the salt marsh ecosystem as a potential
source or sink of carbon for contiguous systems and to
identify which components were primarily responsible
for the observed behavior. Version 3 was undertaken to
evaluate the importance of tidal export in the carbon
economy of the marsh system. The sixth version was
directed toward increasing the resolution of the mecha-
nisms governing tidal export, as well as revising those
parameter values for which new data were available.
Based on model simulation results of version 6, the
modelers proposed three alternate hypotheses that could
account for carbon found in excess in the earlier ver-
sion: (1) bedload transport in tidal creeks, (2) erosion
from marsh surfaces after intense rain storms, and
(3) biological vectors of movement (i.e., feeding and
migration of large animals). A new version has been
constructed and simulated, the results of which formed
the thematic focus of a research program currently
funded by the National Science Foundation (Wiegert
and Wiebe, personal communication).
This modeling effort illustrates the variety of roles
ecosystem modeling can and often does play in the
organization, direction, and analysis of ecosystem-level
research. The models have provided insight into the
ecology of coastal wetlands that otherwise would have
come much later or not at all. New generations of the
salt marsh model will include information and material
flows for both carbon and nitrogen; to accomplish this a
second generation of simulation models will be needed,
with complete revision of compartment and flow
structures.
North and Parker River ecosystem models. In a col-
laborative field and modeling effort, the University of
New Hampshire Complex Systems Research Center
and the Marine Biological Laboratory Ecosystems Cen-
ter have been investigating nutrient dynamics in tidal
freshwater marshes and rivers in coastal Massachusetts.
The movement of conservative elements and the pro-
cessing of nitrogen and dissolved organic carbon were
studied in relation to the movement of water in fringing
river marshes. A hydrodynamic model was developed
to integrate field and laboratory data. The one-dimen-
sional water-flow model had the capability of account-
ing for material transport and incorporating material
decay coefficients; thus it could describe the behavior
of both conservative and nonconservative constituents.
Modeling was an integral part of the North River
research program during the 1970s. Initially, concep-
tual models were developed to organize and synthesize
literature information and to form a coherent plan for
field and laboratory research. Only after a period of
ambitious field work did the need arise for development
of a simulation model. The initial objectives were to
(1) calculate material budgets for the river systems, (2)
predict the spatial and temporal distribution of constitu-
ents throughout the estuary, (3) determine the nature
and magnitude of resident biotic transformations of
nitrogen and dissolved organic carbon, (4) determine
the influence of water movement alone on the distribu-
tion of nitrogen and carbon within the river, and (5)
evaluate the importance of biotic uptake in controlling
the distribution of nitrogen and carbon in the river.
Vorosmarty et al. [45] used a detailed one-dimen-
sional tide propagation model in which elevation area
and bottom friction coefficients are specified for each
finite element. The model was calibrated and assumed
validated when the simulated distribution of salinity
closely matched field observations for a tidal cycle. In
the North River, field observations had indicated that
inorganic nitrogen concentrations consistently de-
creased with movement downstream. The calibrated
hydrodynamics model predicted that the decrease in
nutrient concentration could be accounted for largely by
water movement alone (i.e., dilution). After incorpora-
tion of biological transformations the model indicated
that benthos and marshes were responsible for 66% and
33% of total uptake, respectively. The calibrated North
River model obviated the necessity for extremely time-
consuming in-situ sampling. Not only did the model
provide important information on the mass flux of
nitrogen within the North River system, but it also
provided information on the relative importance of
dilution and biotic processing on nitrogen dynamics and
predicted the spatial and temporal distribution of
constituents throughout the river.
It is unfortunate that a stable funding source was not
available for continuing research and modeling efforts
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Welzel and Hopkinson
13
in the North River. Considerable field data had been
collected on nitrogen transformations within marsh
sediments, nitrogen nutrition of marsh macrophytes,
and detrital decomposition in the marsh. These data
were never incorporated into the simulation modeling
effort. Fortunately, the model was general enough that
it was easily transferred to the Parker River when
funding became available in the 1980s for research on
this system. For the Parker River ecosystem, the
principal question addressed by the model is the cause
of a convex downstream pattern in dissolved organic
carbon concentration. For both systems, the modeling
efforts have been useful in estimating material budgets.
Narragansett Bay ecosystem model. Kremer and
Nixon [26] developed a deterministic numerical
ecosystem model of Narragansett Bay to guide research
on the Narragansett Bay ecosystem and to aid managers
in decisions concerning power plant sitings, the
potential of oil spill impacts, and estuarine eutrophica-
tion [29]. The model provided the means for synthesiz-
ing more than 20 years of data on individual parts of the
system. To some extent the emergent properties of a
complex system such as Narragansett Bay could be
simulated in a mechanistic fashion by combining data
on individual parts of the system.
Narragansett Bay is a phytoplankton-based ecosys-
tem in coastal Rhode Island. Here, as opposed to the
ecosystems of Sapelo Island, North River, and Parker
River, attached algae and emergent macrophytes
(marshes) are of reduced ecological importance.
Therefore, emphasis was placed on representing the
major elements of the plankton and the nutrient cycle of
the system.
Among the techniques used to organize existing data
were tables, graphs, material budgets, and flow dia-
grams. Conceptual models that initially contained high
levels of biological complexity were reduced to seven
major compartments for digital simulation analysis.
The simulation model represented flows of mass and
energy, as well as mechanisms controlling the interac-
tions among principal components. Hydrodynamic
processes were represented in the ecosystem model by
incorporation of a numerical hydrodynamic model of
the Bay developed at the University of Rhode Island. A
crude relation between tide height and net transfer of
chemical and biological concentrations was used to in-
terconnect eight spatial regions of the Bay, which were
assumed to be structurally similar in a biological sense
and were vertically and horizontally homogeneous.
Considerable attention was given to ecologically
realistic representation of the major factors controlling
phytoplankton and zooplankton population dynamics.
A maximum rate was postulated as a function of one
factor, and the effects of other factors were included as
dimensionless fractions that reduced the maximum rate.
For example, phytoplankton growth was based on
temperature (which varied daily and seasonally),
nutrients (including nitrogen, phosphorus, and silica),
and light (which varied by season, day, self-shading,
water column depth, and cloud cover). All influenced
net growth of phytoplankton as shown by empirically
verifiable data.
Model validation was accomplished in two ways.
First, model output was compared with field data
collected independently on phytoplankton, zooplankton,
and nutrient concentrations at 13 stations in the bay
concurrent with the modeling effort. Second, model
analyses were conducted to determine the sensitivity of
simulation output to specific parameters in the model.
These analyses suggested areas where additional
research was needed.
The simulation results provided valuable information
that would have been prohibitively expensive if not
impossible to collect in a field program. For example,
at the outset of the research program the relative
importance of nutrient limitation and grazing in
controlling phytoplankton abundance was unknown.
When the model was run with zooplankton removed
from the system, the basic bimodal pattern of phyto-
plankton abundance was still observed. The major
differences with zooplankton removed were a slight
increase in the spring phytoplankton bloom, substan-
tially higher biomass of phytoplankton in summer, and
a lack of marked oscillations. Interactions related to
nutrients, benthic grazing, and tidal flushing accounted
for the gradual decline in biomass after blooms. Other
questions evaluated with the model were the impor-
tance of zooplankton excretion and benthic nutrient
fluxes in supplying phytoplankton nutrient require-
ments, and the importance of predation pressure on
zooplankton.
Kremer and Nixon [26] strongly cautioned the
scientific and management communities against direct
application of this or other ecosystem models to
problems of environmental management. The Narra-
gansett Bay model was not designed to respond to such
large-scale perturbations as large increases in organic
inputs, significant changes in salinity distribution, or
anoxic bottom waters. However, the inherent con-
straints are less important when the model is used to
explore responses of the present system to relatively
minor changes in parameters and processes that are
specifically included in its formulation. Examples of
relevant questions that the Narragansett modelers felt
could be evaluated included (1) the effect of phyto-
plankton and zooplankton mortality that might result
from power plant entrainment and (2) the effects of
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14
Chapter 1: Ecosystem Models
nutrient inputs when the volume of tertiary sewage is
halved or doubled.
Present Ecological Problems in the Chesapeake
Bay Region
With the initiation of the Chesapeake Bay Program over
a decade ago, an intensive, Bay-wide effort was begun
to identify, research, and develop management plans for
critical living and non-living resources of the Bay. Five
areas have received much of the attention of both
scientists and managers: (1) losses and declines in
emergent and submerged aquatic vegetation [17, 31, 32,
22,46, 48], (2) deterioration in water quality caused by
increased loading of nutrients (primarily excess nitro-
gen and phosphorus) to the principal tributary estuaries
and the Bay proper [3, 8, 15, 21], (3) apparent increases
in the areal extent and duration of bottom-water hyp-
oxia and anoxia [9, 30, 37, 39, 41], (4) sources, sinks,
and fates of organic and inorganic contaminants as they
affect natural resources and human health [16], and
(5) losses and declines in recreational and commercial
fisheries [1, 25].
Use of ecosystem models to address these problems
in the Chesapeake Bay has been limited. We use the
term "ecosystem model," as discussed in the previous
sections, to include only those conceptual or simulation
models that include the principal components, path-
ways, and controls for the flow of energy or cycling of
materials and nutrients. By definition these include
hierarchical models at or above the community level of
ecological organization. In the following section, we
review some recent ecosystem models applied to the
Chesapeake Bay relative to these environmental issues
and discuss the role they have played in systems
conceptualization, research planning and direction, and
application to management issues.
Chesapeake Bay Ecosystem Models
As for other coastal systems, ecological modeling based
on principal components and/or major subsystems in
the Chesapeake Bay is a fairly new tool for systems
analysis. The most intense ecological modeling activity
in the Chesapeake Bay has occurred since the initiation
of the Chesapeake Bay Program in 1978. However,
development and progress in Chesapeake Bay ecosys-
tem modeling has been slower than for similar pro-
grams in other areas. Ecosystem modeling has been
secondary to other funded activities and has not
received the support necessary to maintain a significant
and sustained level of effort. In comparison, a great
deal of effort and support has been devoted to the
development and application of numerical simulation
models for coupled hydrodynamic-water quality models
[10, 18]. Here we report on four ecosystem-level
conceptual and simulation models that have been
directed at Chesapeake Bay research and management.
They are (1) the conceptual models developed by Green
[13] at the initiation of the Bay Program, (2) the
simulation modeling efforts of Kemp et al. [22, 23] that
deal with both living resources (primarily submerged
aquatics) and the potential socioeconomic impacts of
various management strategies for upper Chesapeake
Bay systems, (3) the whole-system and principal-
component models for submerged aquatics (eelgrass) in
the lower Bay reported by Wetzel and Neckles [48],
and (4) the whole-ecosystem model recently reported
by Wulff and Ulanowicz [55], These models cover the
range from conceptual to management application.
Conceptual Modeling of the Chesapeake
Bay Ecosystem
At the beginning of the Chesapeake Bay Program, the
U.S. Environmental Protection Agency and the U.S.
Fish and Wildlife Service funded the development of
conceptual models to depict the principal living
resources, the interactions among them, and those
external and internal factors influencing systems
behavior. The resulting models indicated the principal
carbon and nutrient pathways and proposed conceptual
models for the principal subsystem components of the
Bay ecosystem.
After discussions with Bay scientists and a review of
the literature, Green [13] proposed six conceptual
models for the Bay ecosystem: a whole-system model
and five subsystem models representing (1) emergent
wetlands, (2) seagrasses or submerged aquatic vegeta-
tion, (3) plankton, (4) benthos, and (5) trophic dynam-
ics of fish aggregated by feeding type. Green's whole-
ecosystem model proposed 41 components or state
variables divided among wetlands, water column,
benthos, and the environment. The subsystem models
were composed of compartments representing the
principal living resources and nutrients. Figure 1 shows
the 41 principal components assigned to each of the
subsystem models, which when coupled make up the
conceptual Chesapeake Bay model.
The extent to which these conceptual models were
used by Bay scientists, program officers, or manage-
ment personnel is unknown. At the time of their
development and reporting, the models served to focus
discussion. To our knowledge none of the models has
been translated into a simulation version or been
employed in directing research activities. However,
these models' primary purpose was to focus attention
on the Bay as an ecosystem, and in this they were in
part successful.
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Wetzel and Hopkinson
15
1.
2.
3.
4.
5.
6.
7.
8.
9.
WWtowis
Plants and epiphytes
Benthic algae
Small consumers
Birds
Other wildlife
Detritus
Decomposers
Organic N, P, and C
Inorganic N, P, and C
Benthos
25. Sea grasses
26. Epiphytes
27. Benthic algae
28. Epifauna
29. Infauna
30. Oysters and clams
31. Blue crabs
32. Crustaceans, etc.
33. Detritus
Water column
10. Net phytoplankton
11. Nanoplankton
12. Net zooplankton
13. Microzooplankton
14. Ctenophores and jellyfish
15. Waterbirds
16. Menhaden, larval fish, etc.
17. Killifish, etc.
18. Croaker, etc.
19. Bluefish, etc.
20. Detritus
21. Bacteria and protozoa
22. Organic N, P, and C
23. Inorganic N, P, and C
24. Dissolved oxygen
Environment
35. Pollutants
36. River drainage
37. Land runoff
38. Atmosphere
39. Atlantic croaker
40. Deep sediments
41. Man
Figure 1. Compartments! structures (41) and submodels (4) included in the conceptual ecosystem model of the Chesapeake
Bay [13].
Hierarchical Modeling of Complex Systems
A particularly interesting approach to ecosystem
modeling was adopted by University of Maryland
scientists in their Bay Program research on submerged
aquatics. Kemp et al. [22] have summarized their
initial efforts. The hierarchical structure of their model
is composed of six principal submodels: autotrophs,
epibiota, plankton and water, benthos and sediments,
mobile invertebrates, and nekton. Each submodel is
developed as a simulation version and the interaction
among submodels is via common compartments. The
output from one submodel serves as input to one or
more other submodels. The interesting aspect of this
approach is that the modeler per se becomes part of the
interactive modeling process by controlling the output
and connectivity among the submodels. The rationale
behind the approach is that each submodel is tractable
and serves as an analog to the experimental research
program.
Simulation studies with the autotroph and nekton
submodels were used to evaluate aquatic primary
production by the principal autotrophic components
(phytoplankton, submerged macrophytes, macrophyte
epiflora, and benthic algae), the principal factors
influencing the timing and magnitude of primary
production, and the potential effects of changes in
primary production on the nekton (resident, predatory,
and schooling fish) (Figures 2 and 3). The models were
validated by comparison of predicted standing stocks
(biomass and/or numbers) with field-derived estimates.
In general, the model predictions agreed with field
estimates, so the models were further used to investi-
gate causal relations for explicitly modeled parameters
(light, sediments, nutrients, and herbicides). The results
suggested that the primary control on aquatic macro-
phyte biomass and productivity was submarine light as
influenced by nutrient concentrations and suspended
sediment loads. Herbicides, which had been considered
initially as a potential cause for the loss of submerged
aquatics throughout the Bay, had little effect because
their ambient concentrations were low [23]. On the
basis of these modeling results, supported by both
direct observation and experimentation, Kemp and
colleagues suggested that management concerns should
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16
Chapter 1: Ecosystem Models
PHYTO-
PLANKTON
EPIFLORA
PLANT
SHOOTS
SHADE / BE NTH 1C
ALGAE
Figure 2. The compartmental structure and flow diagram used to simulate the autotroph submodel for studies of submerged
aquatic vegetation in the upper Chesapeake Bay. For clarity of the diagram, the flows indicating losses from compartments due
to respiration, excretion, or mortality have been omitted (redrawn from Kemp et al. [23]). T = temperature; F = turbulence; DIN =
dissolved inorganic nitrogen; sed. org. = sediment organics.
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Wetzel and Hopkinson
17
B
COMMER
DIAL
FISHER-
IES
EXTERNAL
PREDATORS
EXTERNAL STOCKS
A: EPIPHYTES
B: MOBILE INVERTEBRATES
C: SESTON
D: BENTHOS
Figure 3. The compartmental structure and flow diagram used to simulate the nekton submodel for studies of submerged aquatic
vegetation in the upper Chesapeake Bay. As in Figure 2, some flows have been omitted for clarity (redrawn from Kemp et al.
[23]). T = temperature; org. seds. = organic sediments.
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18
Chapter 1: Ecosystem Models
focus on controlling nutrient and sediment inputs to the
Bay ecosystem.
The approach exemplified by the Maryland studies
illustrates the necessary strong coupling that must exist
between conceptual-simulation ecosystem modeling,
empirical research, and management to insure that both
ecological and socioeconomic needs are considered
within the ecosystems context. The framework
proposed by Kemp et al. [22] should be considered a
first-generation "model" for guiding the efforts of Bay
scientists, managers, and users to resolve current
problems in the Chesapeake Bay.
Seagrass Ecosystem Models
While Kemp et al. were beginning studies on sub-
merged aquatics in the upper Bay, comparable research
with ecosystem modeling as a major component was
begun in the lower Bay on the two dominant species of
seagrasses, Zostera marina (eelgrass) and Ruppia
maritima (widgeon grass) [46, 48]. The initial model-
ing efforts in the program were directed at development
of a realistic conceptual model that (1) depicted the
principal structural and functional components of these
ecosystems, (2) summarized the extant data and
identified where information or data were lacking,
(3) could, on mathematical translation and computer
simulation, simulate carbon flow and trophic interac-
tions that implicitly included the effects of altered or
variable submarine light environments, dissolved
inorganic nutrient regimes, and the effects of declining
seagrass habitat, and (4) would guide a multi-discipli-
nary research effort over its projected five-year dura-
tion. Results of this modeling activity are not reported
in the literature because its primary purpose was to be a
research management tool [Wetzel, unpublished data].
Continuing work with the seagrass ecosystem model
for the lower Chesapeake Bay resulted in the develop-
ment of a detailed subsystem model for the dominant
macrophyte, Z. marina (Figure 4). Driving the devel-
opment and implementation of this model was a clear
need to better address the relationships among subma-
rine light, macrophyte photosynthesis, epiphytic
fouling, and epiphyte-grazer interactions [42], The
results of previous research demonstrated empirically
the close coupling among these factors and principal
system components, and suggested that rather small
changes in submarine light available for macrophyte
photosynthesis might have great impact on community
stability and might explain the dramatic declines in
seagrass distribution and abundance observed in the
lower Bay [32,46].
On the basis of simulation studies with this model,
Wetzel and Heckles [48] concluded that submarine
light intensity was the principal factor governing
eelgrass community stability and long-term survival
(10-year simulation studies). Factors that affect light
available to the macrophyte include suspended particles
(primarily fine inorganic sediments) and the degree of
epiphytic fouling. As a result of these modeling
exercises, further empirical studies have been under-
taken to understand the principal controls on epiphyte
growth (light and nutrients) and the role of resident
epiphytic grazers. The model in its present version can
evaluate the effects of altered light regimes as influ-
enced by suspended particles on the depth distribution
and productivity of eelgrass. Current revisions to the
model are intended to address water quality alterations
resulting from proposed management plans.
From a management standpoint, these models and
their simulation results support the conclusions offered
by Kemp et al. [23]: Nutrient and sediment inputs to
the subestuaries and Bay mainstem must be reduced if
existing submerged aquatic macrophytes are to be
conserved or enhanced either naturally or artificially.
Network Analysis and the
Chesapeake Bay Ecosystem
Wulff and Ulanowicz [55] developed ecosystem-level
models of the Chesapeake Bay and Baltic Sea for com-
parative purposes (Figure 5). They developed a com-
mon compartmental and interactive flow structure and
addressed ecosystem-level characteristics by applying
network analysis techniques. The result of these
modeling studies was the rather surprising conclusion
that the Chesapeake appears to be operating under
greater stress than the Baltic Sea ecosystem. The inter-
esting aspect of this work is not the actual conceptuali-
zation and construction of the network (although these
are important considerations in the development phase)
but the analysis techniques per se. This approach (i.e.,
network analysis) appears to have great promise for the
analysis of ecosystem-level phenomena. The potential
for application of these ecosystem analysis techniques
to management issues, particularly for issues that
address whole ecosystems, requires further study and
support.
Conclusions
These ecosystem-level simulation models for coastal
ecosystems and the Chesapeake Bay illustrate the
varied roles for modeling at this level of hierarchical
organization. Models have provided conceptual
schemes for viewing the system as a whole, summa-
rized large data sets to identify both sensitive processes
and data gaps, guided research into profitable areas for
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Wetzel and Hopkinson
19
ATMOSPHERE
^TGRAZERS
ZOSTERA
LEAVES
SEDIMENTS . * •
1.0-1.55 m
Figure 4. The compartmental structure and flow diagram used to simulate Zostera marina (eelgrass) photosynthesis and growth
in the lower Chesapeake Bay under various conditions of environmental stress. Solid lines represent material transfers and
dashed lines represent negative feedback controls (from Wetzel and Neckles [48]). PARz = photosynthetically active radiation at
depth z.
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20
Chapter 1: Ecosystem Models
MICROZOO-
PLANKTON
PELAQIC
PRODUCERS
PELAQIC
BACTERIA
CARNI-
VOROUS
FISH
DISSOLVED
ORGANIC
CARBON
INVERTE-
BRATE
CARNIVORE
PLANKTI
VOROUS
FISH
SUSPENDED
PARTICIPATE
ORGANIC
CARBON
BENTHIC
USPENSION
FEEDERS
BENTHIC
INVERTE-
BRATE
CARNIVORES
SEDIMENT
^•ARTICULATE
ORGANIC
CARBON
BENTHIC
PRODUCERS
DEPOSIT
FEEDERS
Figure S. The compartmental structure and flow diagram for the Chesapeake Bay ecosystem used by Wulff and Ulanowicz [55]
for network analysis and comparison with the Baltic Sea ecosystem. As in figures 2 and 3, some flows have been omitted for
clarity.
addressing both basic scientific questions and contem-
porary management needs, and provided results that
have guided management decisions. It is also apparent
that ecosystem-level modeling and simulation analysis
for Chesapeake Bay resources has been an under-
utilized tool whose potential is far from being realized.
We have not considered in this review two other
major classes of models that are widely known and
have been employed far longer in the management
process: water quality models and fisheries models.
Both have been reviewed elsewhere [43]. By our
definition, these models are not ecosystem-level tools
of analysis. However, the current efforts to develop
and validate a three-dimensional, time-variable hydro-
dynamic-water quality model for the Chesapeake Bay
[18] may provide the detail necessary for coupling
physical, water quality, and ecological processes within
a modeling framework. These models will incorporate
much greater detail relative to biologically controlled
processes (e.g., plankton and benthic processes)
coupled to hydrodynamics and water quality. It may
prove feasible in the near future to couple these models
with large-scale ecosystem models by incorporating
output of the hydrodynamic-water quality models as
forcing/control input for ecosystem simulation studies,
and similarly by using results of mechanistic ecosystem
models to control feedback information to the hydro-
dynamic model. Ultimately, coupled models of this
type and scale will be required for addressing large-
scale management decisions.
For the present, ecosystem models for submerged
aquatic vegetation, particularly those developed for the
upper Bay, best exemplify modeling strengths and
potentials in basic ecological research coupled with
management needs for the Bay. Management of this
largest of U.S. estuaries is a complex issue, and con-
flicting uses and demands on its resources will undoubt-
edly increase. Employing ecosystem-level models as a
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Wetzel and Hopkinson
21
tool in research and management will help insure that
the functioning of the whole system or principal
subsystems is not compromised as a result of actions
directed at lower levels of ecological organization.
We suggest that the best way to approach this effort
is by dedicating resources, both financial and scientific,
for ecological modeling of Chesapeake Bay living
resources. The technology and scientific expertise
exists today within the Bay community. The level of
effort and commitment of funds should be comparable
to that of the present hydrodynamic-water quality
modeling efforts. Effective, long-term management of
Chesapeake Bay living resources depends to a large
extent on this commitment and coupling with physical-
water quality models. Development of these "hybrid"
models will better address both fundamental and
management-oriented questions related to living
resources and environmental quality.
Acknowledgements
Developing and writing this review required the time
and assistance of many colleagues and friends. Particu-
lar among these were Marilyn Lewis of the VIMS
Library, who conducted an online literature search, and
Rick Reynolds of CRC, who suffered through delays
and weak excuses. Our special thanks are extended to
Hilary Neckles, who provided us many helpful com-
ments and editorial changes, and to Bruce Neilson and
Carl Hershner for reviews of the draft manuscript.
Also, thanks to three anonymous reviewers for sugges-
tions and corrections that have been incorporated. To
our modeling colleagues Bob Costanza, John Day,
Michael Kemp, Scott Nixon, Charlie Vorosmarty, and
Dick Wiegert, thanks for the conversations (debates)
over the years that have helped focus the ideas and
conclusions presented here.
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Wetzel and Hopkinson
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-------
The Functional Role of Estuarine Benthos
Robert J. Diaz and Linda C. Schaffner
Virginia Institute of Marine Science
College of William and Mary
Gloucester Point, Virginia 23062
25
Introduction
The benthic environs of the Chesapeake Bay range
from intertidal flats to deep channels. Each habitat
within this range is characterized by a myriad of
organisms and processes, the majority of which are
cryptic and not easily observed or understood. Yet it
has been suggested that most physical, chemical,
geological, and biological processes in estuaries are
regulated or modified by interactions with the benthic
system [84]. An increasing body of evidence demon-
strates that benthic organisms are involved in basic
functional processes influencing energy transfer,
nutrient dynamics, and cycling and fate of toxicants.
This review evaluates functional relationships of
benthic organisms in this complex system. We limit
our coverage to subtidal areas; reviews for vegetated
and intertidal habitats are presented elsewhere [91, 264,
282].
Specific questions regarding the functional impor-
tance of subtidal benthos that we will address are:
• Given present information, can we identify major
benthic habitats in Chesapeake Bay? Can we
predict organism abundance, biomass, and
functional group composition and scales of
variability in these parameters on the basis of
known animal-habitat relationships?
• For distinct habitat types what are the characteris-
tic mechanisms and rates of benthic processes
that affect function? Can we predict the impor-
tance of biotic processes relative to physical
processes in nutrient dynamics and in the move-
ment of sediments or their associated toxicants?
This review is contribution no. 1595 from the College
of William and Mary, Virginia Institute of Marine
Science.
• What factors control benthic production? How
does benthic production contribute to the overall
energy budget for the Bay?
Animal-Habitat Relationships
Early studies focusing on the ecology of macrobenthic
organisms (>500 |J.m) in the Chesapeake Bay estuarine
system identified strong patterns of association between
individual organism abundance or community structure
and gradients of physical-chemical parameters [33-37,
80, 81, 87,103, 108, 138,152, 165, 168, 178, 203, 211,
245, 252, 271, 280, 315, 339, 343, 350, 352]. These
studies demonstrated that salinity is the major factor
governing organism distribution and patterns of species
diversity in the Chesapeake Bay, as it is in estuaries
worldwide [83, 270, 360]. At moderate to high
salinities within the estuary, patterns of organism
distribution, abundance, and diversity are further corre-
lated with sediment type [34, 81, 210]. Distribution and
abundance patterns for dominant benthic macrofaunal
invertebrates in the Chesapeake Bay estuarine system
reflect these relationships (Table 1).
Other major physical gradients within the estuary
also influence the distribution of benthic communities
and abundances of component organisms. Oxygen
availability in the water column exerts an important
influence on benthic community structure and function,
both directly by affecting benthic organisms' metabolic
processes and indirectly by affecting water column
processes, particularly in the upper Chesapeake Bay
[137,151, 166, 221, 226, 260]. Energy gradients and
frequency of bottom disturbance are important in
limiting species distribution patterns in the lower Bay
[210, 311, 313] and also influence how biological and
physical processes interact in the benthos [314, 315].
Organism response to energy gradients seems espe-
cially important in determining where large infaunal
-------
26
Chapter 2: Benthos
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Diaz and Schaffner
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species and associated biogenic structuring of sedi-
ments will be found in the estuary [311, 312, 315].
Gradients in allochthonous and autochthonous carbon
input are likely to be important in structuring communi-
ties [269], but have generally not been studied directly
in the Chesapeake Bay estuarine system [185].
Estuaries are characteristically variable, and the
scales of spatial and temporal variability are important
for understanding and predicting benthic community
structure. It seems reasonable to expect that elucidating
these relationships will help to identify patterns of
community function as well. Studies of temporal
variability in estuarine benthic communities have
demonstrated strong seasonal trends as well as variabil-
ity on shorter and longer time scales [37,70,86, 152,
163, 166, 268, 359]. Many non-seasonal trends are
difficult to explain [37, 86, 359]; others can be attrib-
uted to changes in physical conditions, especially
salinity and oxygen [163, 166, 325].
Characterizing major estuarine habitat types on the
basis of a full range of physical variables facilitates
understanding of animal-habitat relationships and
benthic response to important physical variables both
within and among habitat types [311,315]. This
approach seems especially important in the Chesapeake
Bay, where many physical factors are highly correlated
and spurious correlations between biotic and physical
processes are likely to be overlooked. Given the
existing biological and physical data base for the
Chesapeake Bay, we present our view of major benthic
habitat characteristics in Table 2.
Effects of Benthic Organisms on Physical
Dynamics and Chemical Processes of
Estuarine Sediments
The influence of benthic organisms on estuarine
function, particularly with respect to parameters and
processes that affect toxicant and nutrient dynamics,
Notes for Table 1 (facing page). Habitat abbreviations: for
salinities, TF = tidal freshwater, OL = oligohaline, LM = low
mesohaline, HM = high mesohaline, P = polyhaline, PE =
poly-euhaline; for sediments, M = mud, MX = mixed, S =
sand. A = no. individuals/m2, categories are: (-) <100, (+)
100 -1000, (*) >1000; B = ash-free dry weight/in2, categories
are: (-) < 1, (+) 1-20, (*) >20. Taxonomic categories are: A =
Annelida, Am = Amphipoda, B = Bivalvia, C = Cumacea, G
= Gastropoda, In = Insecta, Ph = Phoronida, Is = Isopoda, Ur
= Urochordata. Data compiled from [80, 88,158,168, 187,
210,252,280,311,312].
* Locally abundant only
depends in part on the physical-chemical behavior of
these reactants. Some substances remain as solutes in
the water mass, while others have affinities for particles
[263]. Toxicants tend to bind with small particles that
are rich in organics [263, 308]. Therefore, we center
our discussion on current knowledge of organism-
sediment-fluid interactions, with particular reference to
the Chesapeake Bay. Mechanisms and rates of organ-
ism interactions with the sedimentary environment are
first summarized so that the relative magnitudes of
processes are clear and so that predictions can be made
regarding the likely importance of different processes
throughout the estuary. Effects on sediment physical
dynamics, chemical diagenesis, and nutrient exchange
across the interface are reviewed. A final section
considers how these basic interactions are likely to
influence toxicant transport and fate within the Chesa-
peake Bay and other estuarine or shallow coastal
systems.
Functional Characteristics of Chesapeake
Bay Benthos
Biogenic alteration of sediment and pore-water charac-
teristics, transport, and mixing is primarily a function of
two things: organism densities, and their ways of living
and moving within sedimentary deposits. Yet most
benthic environments encompass a myriad of processes
and interactions that reflect the inevitable variety of
living habits exhibited by resident fauna. Classification
into broader functional categories can be useful for
identifying how larger groups of organisms influence
benthic processes [114, 205, 235, 287, 288]. Although
simple classification schemes may not allow adequate
prediction of organism effects on some processes,
especially sediment transport [179, 256], the approach
greatly facilitates estimates of the magnitudes of
different processes, thereby lending insight into the
potential outcome of many interactions [127, 140].
Feeding methods and rates are among the most
important variables influencing sediment modification
by benthic organisms. Feeding methods determine
where benthic organisms obtain particles or other food
items (i.e., the water column, surface, or subsurface).
Rates of feeding determine, in part, the rates at which
organisms move and mix sediments. Living positions
and motility patterns determine the potential limit of
influence by a given organism within a sedimentary
deposit. Motility is also strongly correlated with the
types of structures benthic organisms build or create.
The types of structures organisms produce (e.g. tubes,
fecal pellets, tracks) determine, in part, which benthic
processes they are likely to influence. Information on
functional characteristics, including feeding type,
-------
28
Chapter 2: Benthos
Table 2. Characteristics of major benthic habitats in the Chesapeake Bay estuarine system.
Habitat type
Physical
characteristics
Macrobenthic community
characteristics
Macrofauna
density
Macrofauna
biomass
Tidal freshwater
Shoals
Channels
Oligohaline
Shoals
Channels
Shallow depths
Mud to sand sediments
Wave-and tide-dominated
High turbidity
allochthonous carbon)
Low to moderate light
penetration
Intermediate depths
Mud to sand sediments
Fluid mud possible
Tide-dominated
High turbidity
(allochthonous carbon)
No light penetration
Occasional low oxygen
Shallow depths
Mud to sand sediments
Wave- and tide-dominated
High deposition
(allochthonous carbon)
Low to moderate light
penetration
Moderate depths
Mud sediments
Fluid mud possible
Tide-dominated
High deposition
(allochthonous carbon)
No light penetration
Occasional low oxygen
Stenohaline, otherwise Low
eurytopic fauna
Deposit and suspension feeders
Moderate diversity
Bivalves high
Others low
Stenohaline, otherwise
eurytopic fauna
Deposit and suspension feeders
Moderate diversity
Low
Bivalves high
Others low
Euryhaline, eurytopic fauna Low to high
Deposit and suspension feeders
Low diversity
Bivalves high
Others low
Euryhaline, eurytopic fauna
Deposit and suspension
feeders
Low diversity
Low to high
Bivalves high
Others low
Mesohaline
Shoals
Channels
Shallow depths
Sand sediments
Wave- and tide-dominated
Low to moderate turbidity
Moderate light penetration
Occasional low oxygen
Intermediate to deep depths
Mud sediments
Fluid mud possible
Tide-dominated
High turbidity
No light penetration
Seasonal low oxygen
Euryhaline, eurytopic fauna Moderate to Bivalves high
All feeding types high* Others moderate
Moderate diversity*
Euryhaline, eurytopic fauna Moderate to Bivalves high*
All feeding types high* Others moderate*
Moderate diversity*
Polyhaline
Shoals
Shallow depths
Sand sediments
Wave- and tide-dominated
Low turbidity
High light penetration
Stenohaline, eury- to
stenotopic fauna
All feeding types
Moderate diversity
Low to moderate Low to moderate
-------
Diaz and Schaffner
29
Basin
Channels
Intermediate depths
Silt and fine sand sediments
Tide-dominated
Low turbidity
Seasonal light penetration
Occasional low oxygen
Moderate to deep depths
Mud to sand sediments
Tide-dominated
Moderate turbidity
No light penetration
Occasional low oxygen
Stenohaline, eury- to Moderate Moderate to high
stenotopic fauna
All feeding types
High diversity
Stenohaline, eury- to Moderate Low to high
stenotopic fauna
All feeding types
Moderate to high diversity
Poly-euhaline
Shoals
Basin
Channels
Shallow depths
Sand sediments
Wave- and tide-dominated
Low turbidity
High light penetration
Intermediate depths
Silt and fine sand
sediments
Tide-dominated
Low turbidity
Seasonal light penetration
Moderate to deep depths
Mud to sand sediments
Tide-dominated
Low turbidity
Seasonal light penetration
Stenohaline, stenotopic
fauna
All feeding types
Moderate to high diversity
Stenotopic fauna
All feeding types
High diversity
Stenotopic fauna
All feeding types
High diversity
Low to moderate Low to moderate
Moderate
Moderate to high
Moderate
Low to high
; Except when low oxygen conditions prevail
mobility mode, defecation mode, and type of dwelling
produced are summarized along with depth range and
sediment reworking rates for common Chesapeake Bay
organisms in Table 3.
Oxygen distribution into the sediments is also
strongly influenced by functional characteristics of
resident fauna. In most benthic environments, oxygen
penetration into the sediment by molecular diffusion is
limited to a few millimeters; transport into deeper
layers is strongly mediated by macrofaunal ventilation
[283, 284]. Organisms ventilate sediments by feeding,
respiring, and burrowing [200, 227, 236]. Ventilation
rates reported for marine and estuarine benthic species
vary considerably. Mangum [224] reported low venti-
lation rates (<4 ml/hr) for maldanid polychaetes, but
rates as high as 480 ml/hr have been reported for the
suspension-feeding polychaete Chaetopterus vario-
pedatus [74]. Others have reported rates for polychae-
tes ranging from 1.5 ml/hr to about 180 ml/hr, with
most rates ranging between about 50 and 180 ml/hr [8,
11,74, 75,197, 227]. Measured rates for bivalves
range from about 5-90 ml/hr for Mulinia lateralis [324]
up to 344 ± 32 ml/hr for Mya arenaria [225]. Low
rates reported for amphipods [118] contrast with a wide
range of rates reported for burrowing decapods
(29-3,204 ml/hr) [101, 136, 191].
Patterns of ventilation exhibited by tube and burrow
dwellers are generally complex, but all species studied
are apparently intermittent irrigators [8, 74, 101, 117,
136, 224]. This means that nearly all tube and burrow
structures are likely to undergo periodic hypoxia, which
may be important for the dynamics of some important
biogeochemical processes such as the coupling of
nitrification and denitrification (see below).
Through excretion benthic organisms add reactive
materials to the sediment that may alter diagenetic
processes. Nearly all invertebrates excrete urine that
contains sugars and amino acids as well as ammonia
[64, 354]. Average ammonium excretion rates for
small infaunal invertebrates range from <0.01 io.mol/hr
for the small amphipod Corophium volutator to > 1.0
(imol/hr for the polychaete Nereis virens [45, 157, 191,
198, 202, 364]. Mucous exopolymers are produced in
great quantities by bacteria and many other benthic or-
ganisms including macrobenthos [69,162, 179, 298].
Mucopolysaccharides and associated compounds alter
the "stickiness" of sediments and can be a source of
reactive organic substances. Production of these sub-
-------
30
Chapter 2: Benthos
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32
Chapter 2: Benthos
stances is of potential importance for physical sediment
dynamics and chemical processes [4, 162,179, 256,
298]. Macrobenthic tube and burrow linings are typ-
ically composed of mucopolysaccharides, which may
be rich in sulfides or phosphates [8, 202, 246, 366].
Effects of Benthic Organisms on Physical
Sediment Dynamics
The effects of benthic organisms on physical sediment
dynamics are well documented for both marine and
freshwater environments [179, 193, 205, 235, 238, 287,
288]. It has been shown that organism activities may
significantly influence rates of sediment deposition,
types of particulate materials deposited, sediment strati-
graphy, mass properties, and transport probabilities.
Faunal effects on deposition processes. Enhanced
deposition of particulate materials due to the feeding
activities of benthic suspension feeders (including most
major taxonomic groups) is a common and probably
important process promoting benthic-pelagic coupling
in estuarine and shallow coastal systems [97, 98, 121,
145-148, 275, 276, 299, 346]. Chesapeake Bay oysters
on 0.405 ha of bottom may produce up to 981 kg dry
weight of biodeposits weekly [145]. Calculations sug-
gest that suspension feeders can daily filter >100% of
the volume of water in shallow-water habitats, includ-
ing parts of the Chesapeake Bay [63, 78, 125, 168].
The physical characteristics of particles transferred
from the water column to the benthos by suspension
feeders are different from those exhibited by naturally
sedimenting particles [276, 299]. Digestive processes
may alter clay mineral structure [15, 276], and this may
Notes for Table 3 (previous pages). Feeding types are: BD =
subsurface deposit, HD = head down subsurface deposit, SD =
surface deposit, SF = suspension, C = carnivore, O = omnivore.
Mobility modes are: S = sedentary, LM = limited mobility,
M = mobile. Defecation modes are: S = material deposited on
surface, I = injected into water column, P = pellet, ovoid or
ellipsoid, R = rod, RI = ribbon, C = coil, U = unconsolidated.
Dwellings produced are: B = burrow, T = tube, V = void.
Sediment reworking rates are expressed as mg dry weight
sediment individual'1 day1), values reported for egestion (feces
and pseudofeces, if present) unless otherwise indicated.
* Underlining indicates that species feeds at the sediment
surface; o+ indicates depth range above sediment surface.
+ Calculated from data presented by author.
* First value is rate without suspended particles; second is rate
with suspended particles, originally reported as mg individual'1
h"1 (D. Dauer, personal communication)
§ Assuming 1 cm3 = 2.08 grams dry wt.
** For Peloscolex multisetosus.
++ For Amphitrite ornata.
influence particle-toxicant relationships [293]. When
particles retained by four species of suspension feeders
were studied by Haven and Morales-Alamo [148], 82-
93% by volume were smaller than 4 ju.m; 95% were
smaller than 9 (im. Fecal pellets produced by suspen-
sion feeders are typically in the size range of sand
particles [192,276] and have settling velocities much
higher than those calculated for fine sediment particles
[146, 233, 275, 276, 331]. Such biodeposition proc-
esses may lead to the formation of organic-rich muds in
areas where muds would not be predicted to settle on
the basis of hydrodynamic regimes [276].
Benthic organisms may enhance sediment deposition
by other mechanisms as well. Lynch and Harrison
[219] showed that a dense colony of the small tube-
building amphipod, Ampelisca abdita, enhanced
sediment deposition at a shallow subtidal (3.6 m) site in
the York River, Virginia, presumably by sediment
trapping. Sediment trapping by tube-building organ-
isms may increase the deposition of fine sediments in
areas where they would not typically be deposited [22].
Bioturbation: effects on sediment mixing and mass
properties. Effects of benthic organisms on sediment
mixing vary as a function of physical processes
(especially lateral advection and sediment accumula-
tion) and biological feeding, burrowing, and tube-
building processes [180, 243]. Interactions between
these processes are stochastic and can have complex
effects on particle distributions [180, 300].
Diffusional models of sediment mixing processes
[230] predict that when DB (the particle biodiffusion
coefficient) is high relative to the sedimentation rate,
bioturbation homogenizes near-surface sediments [135]
and obscures the effects of other processes on strati-
graphic signals [316]. When the sedimentation rate is
high relative to bioturbation, organisms have little
effect on particle redistribution [262]. Schaffner et al.
[315] suggested that even relatively rapid bioturbation,
limited to a very narrow mixing zone at the sediment
surface, had little effect on mixing in high-sedimenta-
tion areas of the James River.
Non-random bioadvective mixing by head-down
deposit feeders may move sediment large distances
quickly [292]. In the absence of diffusional mixing,
marker peaks (introduced at the sediment surface, as a
toxicant might be added) subjected to advective mixing
move down within the sediment with little or no broad-
ening [292]. Diffusional and advective mixing act to-
gether to disperse materials introduced at the sediment
surface uniformly throughout feeding depths [300,
301]. Rapid turnover and hornogenization of the sedi-
ment column to feeding depths typifies areas dominated
by head-down deposit feeders [94, 224, 286, 314].
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Diaz and Schaffner
33
Feeding, burrowing, and tube-building may have dif-
fering effects on particle distributions. Selective inges-
tion of fine particles, incorporation of these particles
into fecal pellets, and subsequent deposition of fecal
pellets at the sediment surface will tend to keep fine
particles in an active surface mixed layer [180, 338].
Burrowing may produce the opposite results by selec-
tively "pumping" coarse sediment fractions towards the
surface [239]. The activities of some deposit feeders
result in graded bedding, typically with a coarse lag
layer forming below feeding depths [289, 300, 338].
Most species studied also exhibit some selectivity for
finer particles in tube construction [8, 132, 202, 318].
Alterations in particle characteristics and mass
properties result from bioturbation activities. Selective
ingestion of fine or organically encrusted particles by
many deposit feeders means that fecal pellets are often
organically enriched relative to ambient sediments
[213,214]. Fecal pellet production increases particle
size by packaging small silt- and clay-sized particles
into sand-sized particles. Schaffner et al. [315] noted
that pelletization of surface sediments (0-5 cm) result-
ing from the feeding activities of Macoma balthica was
nearly complete for a site in the James River during the
early summer. Fecal pellets and remnants of fecal
pellets are an important component of Chesapeake Bay
sediments [148, 192, 195, 252]. In muddy sediments,
feeding and burrowing activities tend to increase sedi-
ment water content and decrease compactness [205,
290].
Erosion and sediment transport. The complexities of
processes governing organism effects on sediment
erosion and transport have been discussed by Jumars
and Nowell [179]. Benthic organisms affect erodibility
and sediment transport via alteration of: (1) fluid mo-
mentum impinging on the bed [102, 319], (2) particle
exposure to the flow [256], (3) adhesion among par-
ticles [128-130, 164], and (4) particle momentum [174,
179, 256, 302]. Because the net effects of a species on
erosion and transport are highly dependent on flow
conditions, bed configuration, and community composi-
tion, general predictions of stabilization vs. destabiliza-
tion cannot be made given existing data [179, 216].
Interactions are likely to be especially complex in
estuaries since both physical and biological processes
are characteristically dynamic.
Effects of Benthic Organisms on Chemical
Diagenesis and Nutrient Flux across the
Sediment-Water Interface
Benthic metazoans influence sediment diagenesis and
nutrient flux both directly (by modifying sediment
diffusion geometry, mixing sediments, pumping fluids,
and adding reactive substances such as mucus) and
indirectly (by influencing microbial activities and
growth rates). Basic metabolic processes contribute to
the consumption of oxygen, the degradation of organic
matter, and the release of carbon dioxide and limiting
nutrients.
Structural effects introduced by macrobenthos
strongly influence sediment diffusion geometries by
altering the pathways along which fluids flow and
solutes are exchanged [4, 6]. At typical natural
densities, burrow construction by populations of single
macroinfaunal species can increase oxic sediment
volume by at least 30-150% [175, 198]. Ventilation
enhances the daily exchange rate of solutes across the
sediment-water interface in shallow marine and
freshwater environments [104, 114, 232]. Changes in
the sediment fabric may also enhance exchange by in-
creasing sediment porosity and decreasing tortuosity
[73, 114, 234]; these changes may enhance passive
sediment ventilation [349]. Physical processes may en-
hance ventilation of abandoned biogenic structures [5].
Active tube and burrow ventilation by macroinfaunal
invertebrates appears to be an important process gov-
erning oxic sediment volume and the distribution of in-
faunal organisms in the Chesapeake Bay [92, 311, 312].
Sediment ventilation by macrobenthic organisms
increases the apparent diffusion within the sediments
over that observed by molecular diffusion [2]. Empiri-
cally determined "apparent diffusion coefficients"
range from about 10'5 to 10'4 cm2 sec'1, i.e., 10-100
times higher than molecular diffusion coefficients for
bulk sediments [4]. Aller's "average diffusion geome-
try" model, which accounts for the three-dimensionality
of macrofaunal effects on diagenetic reactions, shows
that: (1) pore-water solute concentration is a function of
infaunal size and abundance, (2) reaction rate kinetics
determine reactant response to animal activities, and
(3) vertical profiles of solutes depend on depth variation
in reaction rates as well as faunal attributes. In general,
burrow creation and irrigation by macrofauna prevent
solute buildup away from the sediment surface. Given
organisms may, however, have varying effects on
solute flux as a function of both organism character-
istics (e.g., density or size) and the kinetics and depth
dependence of reaction rates.
Macrobenthos may directly influence the concentra-
tion of reactants and the environment in which they are
transformed. When coupled with lateral advection,
biogenic sediment reworking will tend to increase
inventories of organic carbon and other reactants in
areas of high biological activity [4, 7]. The combined
effects of sediment mixing and ventilation tend to move
the redoxcline down into the sediment (or out from
burrow and tube walls) by moving electron acceptors
-------
34
Chapter 2: Benthos
down into the sediment [4, 114]. Therefore, macroben-
thos effectively increase the depth at which more
energetic diagenetic reactions can occur.
The distribution and rates of microbial activities
within the sediment are generally determined by the
availability of electron acceptors for respiration and the
supply of metabolizable organic substances [56].
Infauna stimulate rates of microbially mediated organic
decomposition and remineralization. Feeding by
consumers increases the surface area of organic detritus
available for microbial growth, rearranges particles and
microniches or reexposes new surfaces, and may
maintain microbial populations in a high-productivity
growth phase [10, 12,46, 68, 110, 112, 141, 144, 181,
215, 337]. Ventilation and mixing decrease metabolite
buildup and increase electron acceptor supply [29, 198,
201, 202]. Organisms also add reactive substances [8,
298] or increase subduction or capture of reactive
organic matter [4, 7].
Examples: effects on carbon, phosphorus and nitro-
gen cycling. Biodeposition by infaunal and epifaunal
suspension feeders in the Chesapeake Bay and other
coastal systems enhances organic matter deposition [98,
145, 183, 248]. Partly because of microbial stimula-
tion, carbon mineralization rates are also enhanced in
the presence of infaunal organisms [14, 46, 98, 113,
114, 201]. Two routes of remineralization—through
metabolic processes and through production of animal
tissues (see Benthic Energy Flow)—are the dominant
pathways controlling carbon flux out of active surface
sediments. Most benthic communities apparently lose
relatively little carbon via burial [9, 28, 344].
Benthic organisms are likely to influence the
distribution of phosphorus in sediments by enhancing
deposition of organic and inorganic phophorus, altering
the position of the redoxcline, and redistributing
particulates relative to the redoxcline. Studies on direct
infaunal effects on phosphorus behavior and the impor-
tance of these processes for exchange in estuarine and
marine habitats are generally lacking. In freshwater
habitats, chironomids either enhance the release of
phosphorus as a positive function of organism abun-
dance and temperature [122, 123] or have no apparent
effect on phosphorus flux [231]. Bioturbation by fresh-
water tubificids apparently slows the release of phos-
phorus during subsequent periods of anoxia because a
ferric hydroxide-orthophosphate complex is prevented
from forming [114]. Phosphate may be trapped in the
burrow walls of marine and estuarine macroinfaunal
organisms because of the formation of insoluble mixed
iron-manganese complexes and phosphate sorption [3,
139, 190, 198]. Flux across burrow walls shows sensi-
tivity to concentrations in the water column, as would
be expected given a functioning phosphate buffer
system [198]. Bacterial processes may also be impor-
tant in regulating net flux [99]. Animal excretion of
phosphorus may be far greater than measured fluxes, a
possibility suggesting that interacting physical or
biological processes must limit flux [3, 40,99, 198,
255].
Benthic nitrogen recycling involves numerous
transformations mediated by both microbes and larger
metazoans. Important mechanisms by which benthic
organisms influence nitrogen transformations and flux
are the same as those influencing the degradation of
organic matter. These processes have been reviewed in
detail [2, 4, 6, 114, 155,200].
For a wide variety of marine, estuarine, and fresh-
water habitats, macroinfaunal invertebrates have been
found to increase efflux of ammonium or total dis-
solved inorganic nitrogen and influx of oxygen across
the sediment-water interface [2, 14, 96, 156, 157, 186,
199, 200, 231, 364]. Animals and associated burrows
generally account for a high percentage of bulk ammon-
ium or total inorganic nitrogen flux (17-90% and 34-
80%, respectively), but these values are highly depen-
dent on sediment organic content, nitrification activity,
and animal species and density [200, 364]. Flux of am-
monium across the sediment-water interface is influ-
enced by depth-dependent reactions. Either uptake by
benthic microalgae or processes causing rapid oxidation
at the sediment-water interface can inhibit release from
the sediment [10, 156]. Excretion of ammonium by
macrofauna is important and can account for 20-60% of
ammonium flux from the sediments in marine and
freshwater habitats [29, 202, 237, 248, 364]. Tempera-
ture strongly influences aerobic respiration rates and
therefore rates of ammonium flux [253, 254]. Variable
uptake and release rates have been reported for nitrate
flux across the sediment-water interface [54, 220]; rates
may be sensitive to water column concentrations [198].
High values for potential nitrification are associated
with tube and burrow structures and fecal pellets
produced by macrofauna [29, 157, 202]. Macrobenthos
increase total denitrifcation and are likely to increase
the ratio of denitrification to total nitrification [6]. In
some cases, macroinfauna stimulate nitrification-deni-
trification coupling, which results in loss of inorganic
nitrogen [106,200]. Tight coupling of nitrification-
denitrification processes in coastal and estuarine sedi-
ments has been attributed to macrofaunal burrow
construction and sediment pelletization, both of which
provide increased juxtaposition of anoxic and oxic
microenvironments [176, 200, 282, 310].
Effects on Toxicants
Interactions between toxicants and benthic organisms
-------
Diaz and Schaffner
35
are relatively poorly studied [285]. Our overall under-
standing of how benthic organisms influence the
transport and fate of toxicants is generally limited by
the fact that toxicants themselves are a diverse group,
characterized by complex chemical reactions. Chemi-
cal and physical factors affecting input, distribution,
and availability of metals and organics in estuaries have
been summarized by Olsen et al. [263], Fowler [119],
Rice and Whitlow [293, 294], and Sanders and Riedel
[308]. As outlined by Rice and Whitlow [293], benthic
organisms affect the distribution and behavior of metals
in the environment in five ways: (1) by mechanical
bioturbation, they alter the quantity of reactive surface
area and redistribute particles relative to the redoxcline;
(2) they increase the depth of the redoxcline and
depress the zone of iron and manganese reduction, such
that transition metals associated with iron and manga-
nese or sulfides have different distributions; (3) they
influence microbial processes that affect speciation and
cycling; (4) by direct ingestion and egestion, they alter
the chemical environment; and (5) they act as reser-
voirs. Similar relationships are likely to apply to
relationships between organisms and organic toxicants.
Accumulation of toxicants in sediments is often
enhanced by biotic processes, especially suspension
feeding [119]. Metals and organic toxicants may be
enriched in fecal pellets and burrows of benthic organ-
isms [1,8, 38, 170]. Bioturbation activities are likely to
increase the inventories of particulate-sorbed toxicants
as they increase the inventories of organic material [4,
7]. The creation of areas of intense biogeochemical
activity by infaunal organisms influences the flux and
behavior of metals. Some metals are mobilized under
anoxic conditions, especially in areas of intense
decomposition along outer walls of some burrows [8].
Following mobilization, iron, manganese, and zinc are
concentrated by precipitation on oxidized burrow walls;
metals may also be scavenged from the ventilation
stream [6, 8].
Bioturbation has been reported to increase the flux
of metals from the sediment [31, 161, 173, 194, 296,
297]. Oscillations in pore-water concentrations related
to the combined effects of microbial activity and
bioturbation may enhance metal flux across the
sediment-water interface [161], Bioturbation also
appears to enhance the transport of hydrophobic
organic pollutants out of sediments [182]. The pres-
ence of macrofauna may enhance microbial degradation
of organic toxicants [26]. Changes in faunal density or
behavior as a function of toxicity [48, 57] will likely
also influence rates of cycling. Distribution of toxi-
cants in sediments is also influenced by bioaccumula-
tion, trophic transfer, biodegradation, and migration, as
discussed by Swartz and Lee [330] and Fowler [119].
Understanding organism effects on toxicant distribu-
tion and behavior is complicated by the apparent
importance of density-dependent processes [293-295].
In general, dense populations of macrofauna reduce the
levels of potential toxicants in both the environment
and the individuals comprising the population [293].
Interactions between biological and physical processes
are also important. For example, rates of metal flux
across the interface may be enhanced by coupling
physical particle resuspension and bioturbation [309].
Relative importance of biological vs. physical
processes. The relative importance of biologically
mediated processes in different regions of the estuary
varies as a function of the relative intensities of
biological and physical processes and non-linear
interactions [314, 315]. Studies of animal-sediment
interactions conducted as part of the EPA-sponsored
Chesapeake Bay Program [252, 280, 342] suggested
that, on an areal basis, biotic sediment mixing processes
dominate physical processes throughout much of the
main-stem Chesapeake Bay bottom. Based on patterns
of primary sediment structure, bioturbation intensity,
and types of biogenic structures preserved in sediment
cores from the mainstem bay and the Virginia tributar-
ies, Schaffner et al. [314, 315] were able to identify
distinct patterns in bioturbation processes. They found
that time-averaged sediment structuring and mixing in
low-salinity areas (i.e., tidal freshwater to low mesohal-
ine) are dominated by physical processes but that
bioturbation may be important in areas of very low
energy and low sediment accumulation. Bioturbation
predominates at high salinities (poly- to euhaline) even
where tidal currents are moderately strong and sedi-
ments may accumulate, but is less important where
physical reworking due to oceanic or wind waves is
intense. Within major habitat types, bioturbation
intensity was found to vary as a function of salinity,
reflecting gradients in faunal characteristics and
abundance. Bioturbation is severely limited in Bay
areas characterized by anoxia [280, 281, 314].
Trends in other biologically mediated processes are
also likely. For example, in low-salinity habitats (tidal
freshwater to mesohaline) benthic feeding processes are
likely to greatly enhance the deposition of carbon,
nutrients, and other substances from the water column.
In the lower polyhaline estuary this process may be an
important mechanism for benthic-pelagic coupling in
areas populated by the polychaete annelid Chaetopterus
variopedatus and associated epifaunal communities
[312]. Bioadvective sediment mixing is likely through-
out the estuary, given the distribution of head-down
feeders. The depth of sediment column influenced by
this process will increase with increasing salinity, as
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36
Chapter 2: Benthos
dominant head-down feeders progress from small
oligochaetes and capitellid polychaetes in low-salinity
areas to larger maldanid polychaetes in high-salinity
areas. Biologically enhanced sediment ventilation will
predominate where sediment disturbance and frequency
of oxygen stress are low (i.e., intermediate depths or
protected shallow areas in low-salinity habitats, and
intermediate and greater depths at high salinities), since
these areas generally support the greatest densities of
large or deep-dwelling infaunal species. Reactions me-
diated by available oxic-anoxic interface area should be
enhanced in areas of high fecal pellet production (espe-
cially where suspension and interface feeders exhibit
high biomass [see Benthic Energy Flow section]) and
high sediment ventilation (areas dominated by Macoma
spp. and large tube-builders). Seasonality is likely for
nearly all biologically-mediated processes because or-
ganism feeding, burrowing, and respiring activities are
strongly driven by temperature. Sublethal oxygen
stress, limited to summer months for the Chesapeake
Bay system, will influence the behavior of some ben-
thic organisms and their importance in some processes.
Benthic Energy Flow
Concept and Importance
In general, the annual income and output of energy for
an ecosystem are in balance. What we are most
interested in are the energetic transformations that
occur among and within portions of an ecosystem
during the year. Benthic habitats (or the benthic
boundary layer [362]) are conspicuous sites for the
focusing and transforming of biological energy and are
an integral part of ecosystem function. Lindeman [207]
was one of the first to consider this overall flow and
balance of matter in an energetic sense: if all the
components of an ecosystem could be expressed in
common units of energy, then the functioning of the
system could be more easily understood. Thermody-
namics is then the common denominator that defines
the manner in which energy can be transformed and
describes the ecological usefulness of different forms of
energy [27, 356]. Benthic secondary production is part
of the larger scheme of energy movement through an
ecosystem; it is best thought of as a process within an
ecosystem and not as a distinct entity (see systems and
network models presented elsewhere [24, 27, 363]).
Productivity of an ecosystem refers to its capacity to
produce organic matter either autotrophically (primary
production) or heterotrophically (secondary produc-
tion). By definition secondary production for a period
of time is considered to be the total of growth incre-
ments of all individuals existing at both the start and
the end of the period, the growth of newly born individ-
uals, and the biomass of individuals that do not survive
to be part of the final population biomass [358]. Sec-
ondary production includes yield (in the sense of har-
vest); loss to predators and decomposers, which repre-
sent mortality; and growth and reproduction, which
represent increase in biomass of a population. Typi-
cally secondary production calculations do not consider
metabolic processes and represent net production.
Measurements of biomass, standing stock, or
standing crop, while important in comparing immedi-
ately available energy, are quite inadequate for predict-
ing rates of predator cropping, yield, or growth. To
understand energy flow and production we need to
know the rate of organic matter elaboration (bioenerget-
ics) and the factors (biotic and abiotic) that control the
fate of biomass produced (Table 4). Trophic interac-
tions are the main pathways by which energy is moved
through an ecosystem [258]. Measuring the production
of all trophic levels should elucidate much of dynamics
of an ecosystem. The development of our understand-
ing of these dynamics has consisted of clarification of
concepts of production and factors controlling food
uptake, assimilation, and metabolism.
Estimation of benthic secondary production can be
approached from several directions. It can be estimated
directly either from patterns of population growth and
mortality [71, 100] or from physiology [365]. It can be
estimated indirectly from life history characteristics
(life span) [303], size at maturity [133] or maximum
size [25], or from turnover ratios (production/biomass)
[317]. Because of the diversity of benthic species and
their complex life histories it is impossible to apply a
single method for estimation of secondary production.
Difficulties in quantitatively estimating population size
and following the life histories of organisms, along with
the large amount of time and labor needed to process
data, have limited the number of secondary production
studies in all estuarine systems.
Organic Matter Budgets
The process of benthic secondary production is driven
by two things: the sources of organic matter, which
provide energy for the diverse secondary producers;
and the fate of biomass produced, which either is
consumed by predators or decomposers, or survives as
standing stock. In the Chesapeake Bay, as with many
estuarine systems (see summaries in Boynton et al.
[41]) overall carbon dynamics are dominated by
phytoplankton production in both the water column and
benthos [124, 320, 345]. Not all of the phytoplankton
production directly reaches the benthos. Bacterioplank-
ton form a microbial loop [357] that internally cycles a
portion of organic carbon production in the water
column [223, 320]. The shallowness of the Chesapeake
-------
Diaz and Schaffner
37
Table 4. Factors that affect secondary production (P), with the general direction of effect.
Factor
General effect
Source
Abiotic factors
Temperature
Salinity
Sediment type
Exposure
Tidal elevation
Depth
Water quality
Tories
Habitat complexity (vegetation)
Higher temperature = higher P
Higher salinity = higher P (to a point)
Mixed sediments = higher P
Semi-exposed areas = higher P
Lower elevation = higher P
Intermediate depths = higher P
Increased nutrients = higher P (to a point)
Excess nutrients or more pollutants = lower P
More structure = higher P
[177, 365]
[242]
[18, 32, 242, 273]
[273]
[159]
[278]
[140]
[21, 105, 269]
[140]
Biotic factors
Predation
Competition
Amensalism
Quality of food
Quantity of food
Successional stage
Recruitment success
Life history
Life span
Age
Size
Voltinism
Tendency to increase P
Tendency to increase P*
Competition for space reduces P
Tendency to decrease P*
Higher labial C = higher P
More food = higher P
Pioneering stage = higher P
Equilibrium stage = lower P
Higher recruitment = higher P
Shorter life = higher P
Younger age = higher P
Smaller body = higher P
More generations = higher P
[60, 140, 150]
[265, 266]
[273]
[287]
[208, 336]
[206, 365]
[257]
[291]
[189, 242]
[267, 303]
[250]
[25,111,133]
[353]
* Theoretical effect
Bay, however, closely links the water column with the
bottom so that production (phytoplankton and micro-
heterotroph) in the water column has a high probability
of being transferred to the benthos through turbulent
mixing and subsequent suspension- or filter-feeding
activities of organisms [63, 65, 77, 152, 223, 249], or
through direct sedimentation [82, 143, 320, 327]. A
substantial portion of deposited organic carbon may
pass ihrough the "small food web" made up of micro-
heterotrophs and meiofauna [204].
In spring, when phytoplankton production and
biomass peak [41, 115, 322], about 50% of the carbon
transported to the benthos is from phytoplankton [40].
The other 50% of the carbon comes from allochthonous
and other autochthonous sources [41, 42]. In summer,
there is more consumption of phytoplankton production
in the water column by planktivorous fish [24], zoo-
plankton [154], and microheterotrophs [20, 66], with
about 20-40% of the carbon delivered to the benthos
coming from phytoplankton [41, 320].
Other sources of dissolved and paniculate organic
carbon to the benthos include autochthonous production
from vascular plants, seagrasses, benthic microalgae
and macroalgae, and allochthonous additions from land
runoff and sewage [342]. The contribution of these
sources to the total carbon budget of the Bay varies
seasonally and spatially [28]. Delivery of these carbon
sources to the benthos is patchy and reflects proximity
to sources [153,272]. This is particularly true for
sewage inputs [87, 342]. In tidal freshwater, oligohal-
ine, and lower mesohaline habitats, paniculate organic
carbon from upland drainage is a major portion of the
total supply; however, quantitatively little is known
about its utilization [28, 116]. Processing and cycling
of autochthonous and allochthonous production is done
almost entirely in the benthos through detritus food
webs [23, 61, 228, 244, 335].
Trophic Transfer
Secondary production by benthic organisms is the
major pathway by which organic carbon is recycled out
of the sediment and eventually out of the Chesapeake
Bay system. The majority of fishes that utilize the
Chesapeake Bay are involved in organic carbon export.
As transients, they are only within the Bay for a portion
of their life cycle [19, 209, 323]. Benthic secondary
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38
Chapter 2: Benthos
production may also be a means by which eutrophica-
tion is directly regulated through the accumulation of
carbon and nutrients in living biomass [184, 249, 259,
345]. Much of the obvious productivity in the Bay, in
terms of fishery yield, is directly linked to the secon-
dary production of the benthos through feeding [30,
168,208,209,229,347].
There is, however, no simple connection between
benthic secondary production and fishery species. Not
all benthic production is utilized by or available to
fishery species [172, 218, 242]. In addition to the sto-
chastic element of predation, which allows for a certain
level of prey survival, estuarine organisms avoid
predators by quick escape responses, by burrowing
below the sediment surface, and to a lesser extent
because of large size. Benthic standing stock biomass
at any given time then represents surviving prey. We
can consider the ratio between annual production and
biomass (P/B ratio), the inverse of which constitutes an
estimate of the percentage of production that goes into
maintaining the standing stock. This is not considered
to be energy respired but energy that is needed to
produce biomass. While average biomass is a static
measure of energy over the course of a year, a portion
of the biomass is replaced by new biomass produced
during the year. Approximately 20-50% of the total
benthic secondary production in estuarine systems is
carried over from year to year as standing stock
biomass [23, 168]. The percentage biomass carryover
tends to be higher in "mature" communities that exhibit
successionally advanced characteristics [257, 360, 361],
because in these communities organisms live longer
and are larger, which increases biomass.
A portion of the secondary production is also cycled
within the benthos by infaunal predators [13, 58, 240,
348]. Many infaunal species are predacious (particu-
larly important are nemerteans, many polychaetes, and
some amphipods and gastropods) and influence the
energetics of other infaunal species by preying on adult,
juvenile, or larval stages [13, 67, 261]. The production
of infaunal predators is then potentially available to
epifaunal predators and may actually be more available
than nonpredacious infauna, because of the free-
burrowing and surface-searching habits associated with
a predacious life history [13]. Based on abundance the
ratio of predacious to nonpredacious infauna can be as
high as 0.25 in sand, 1.38 in mixed sediments, and 0.12
in mud [13]. The importance of infaunal predators to
benthic energetics seems to vary with sediment type. In
Port Hacking, Australia, Rainer [278] found that in sand
infaunal predators accounted for 8-40% of the total ben-
thic secondary production, in mixed sediments they ac-
counted for 9-12%, and in mud there were no infaunal
predators. On the basis of the ratio of predacious to
nonpredacious infauna, one would expect similar trends
in predator productivity in the Chesapeake Bay [167].
Estimates of the energy transfer (expressed as a ratio
of prey consumed/total prey production) needed to
sustain epibenthic predators (fish and crabs) are 12-
17% of the annual infaunal production for poly-
euhaline shallow sandy habitats (Gullmar Fjord,
Sweden) [107], about 20% for the Ythan Estuary [23],
4-75% for shallow muddy-sand habitats (Skagerak-
Kattegat, Sweden) [242], 30-50% for Georges Bank
[326], and 39-67% for low mesohaline muddy habitats
(Patuxent River) [168]. These estimates reflect inter-
habitat and inter-ecosystem differences in the magni-
tude of secondary production utilized by predators;
these differences are functions of predation pressure, re-
cruitment-settlement success, and food supply [17,
242]. During years when any combination of these
factors is favorable, consumption efficiency declines.
In the Skagerak-Kattegat, Moller et al. [242] found that
when benthic recruitment was above average, consump-
tion efficiency of epibenthic predators declined to
4-10%. During years of average infaunal recruitment,
consumption efficiency of epibenthic predators was
51-75%. The average consumption efficiency for six
consecutive years in the Skagerak-Kattegat habitats was
43%. While these estimates of utilization span the
20-25% value classically thought to represent energy
transfer to fish species [196], the higher values are
likely most representative of total energy transferred to
all epibenthic predators. In the Chesapeake Bay, if on
average 10% of the annual secondary production is
consumed within the benthos and 30% survives as
standing stock or is not available to epibenthic preda-
tors, then 60% is left for potential consumption by
epibenthic predators (fish and crabs).
If fisheries landing statistics for the Bay are assumed
to be at least representative estimates of minimum fish-
eries species production, and the production or ecolo-
gical efficiency (fish prey consumption/fish production
ratio) of the fishes is assumed to be about 17-22% [134,
222], then the total consumption of benthic inverte-
brates can be estimated. About 21,400-27,500 metric
tons of organic carbon, or 267,500-343,750 metric tons
wet weight, of benthic organisms are needed each year
as fish food to sustain demersal fishery yields (Table 5).
This magnitude of secondary production is easily
supported by benthic organisms and represents a total
Bay average of 1.9-2.4 gC nr-yr1 or 23.4-30.1 g wet
weight m2yr', with 11,427 km2 taken as the area of the
Bay and tributaries [72]. In addition to serving as prey
several benthic herbivores are directly harvested and
contribute about 950 metric tons of organic carbon
annually (Table 4). This slightly increases overall
benthic productivity to about 2.0-2.5 gCm2yr1.
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Diaz and Schaffner
39
Table 5. Summarized fisheries landing statistics (metric tons wet weight), with estimates of benthic biomass
(metric tons organic carbon) needed to support this yield of major bottom-feeding species, or to support maximum
sustainable yield (MSY). Production or ecological efficiency (fish prey consumption/fish production ratio) of the
fish and crabs is assumed to be about 17-22 % [134, 222]. Wet weight was converted to organic carbon by: 1 g wet
wt = 0.08 gC [353].
Annual Annual
recreational average, commercial average
1960-1979 1970-1977
Species [328] [188]
Benthic predators
Blue crab 28,000
Maryland 14,200*
Virginia 7,100+
Total
Spot 1,000
3,500
Total
Croaker 900
1,800
Total
White perch 500
1,430
Total
Flounder 130
Total
Grand total commercial and recreational
Grand total commercial
Grand total recreational
Grand total maximum sustainable yield
Benthic herbivores*
Oyster 10,300
Hard clam 400
Soft clam 1,200
Grand total commercial
Grand total maximum sustainable yield
, Benthic
MSY biomass needed
[340,341] for average year
10,200
29,500
5,200
2,600
18,000
360
1,400
1,300
1,660
320
650
970
180
1,400
520
700
50
1,400
50
21,400
11,100
10,300
12,200
13,600
27,300
- 13,200
- 6,700
- 3,300
- 23,200
470
- 1,600
- 2,070
420
850
- 1,270
230
670
900
60
60
- 27,500
- 14,400
- 13,100
- 15,900
950
3,270
Benthic
biomass needed
for MSY
10,700 - 13,900
500 - 660
500 - 660
500 - 660
Averaged landings, 1983-1984 (Maryland DNR 1989)
+ Assumed to be 50% of Maryland landings.
* Fisheries yield is assumed to be a minimum estimate of annual production.
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40
Chapter 2: Benthos
Table 6. Predicted macrobenthic secondary productivity of major subtidal benthic habitats in the Chesapeake Bay.
(Only areas of the mainstem, James, and Potomac Rivers were used because detailed sediment data were not
available for other tributaries.) Data are summarized from [80, 90,93, 168, 169, 172, 1871. See Table 2 for habitat
characteristics.
Major habitat
Area (km2)
Mean annual productivity (SE),
g C/m2/yr
Average of mud and mixed sediments production.
Average of sand and mixed sediments production.
Assumes remaining tributaries have similar proportion of habitats.
Total habitat productivity
(SE), metric tons C/yr
Tidal freshwater
Mud
Sand
Mixed
Oligohaline
Mud
Sand
Mixed
Low mesohaline
Mud
Sand
Mixed
High mesohaline
Mud
Sand
Mixed
Polyhaline
Mud
Sand
Mixed
Euhaline
Mud
Sand
Mixed
Total
Total Chesapeake Bay system
455
102
10
496
59
76
393
98
36
1,525
1,388
268
509
1,764
512
148
768
29
11,427
1.8
145.5
289.2
14.4
18.0
21.7
14.4
41.0
10.8
8.1
8.8
25.0
9.0
32.0
15.6
17.2
5.7
28.6
8,636
( 0.5)
(143.7)*
(246.5)
(3.4)
(3.6)*
(5.1)
(4.1)
(0.4)
(1.0)
(1.3)
(2.8)
(4.1)
(2.5)
(11.2)
(3.6)
(11.4)*
(0.9)
(1.1)
18,552
819
14,841
2,892
9,853
7,142
1,062
1,649
10,066
5,659
4,018
389
31,266
12,352
12,214
6,700
69,016
4,581
56,448
7,987
7,753
2,546
4,378
829
146,506
193,854'
(228)
(14,657)
(2,465)
(1,686)
(212)
(388)
(1,611)
(39)
(36)
(1,982)
(3,886)
(1,099)
(1,272)
(19,757)
(1,843)
(1,687)
(691)
(32)
Total Bay Secondary Production
The strengths of organismal-environmental relation-
ships are such that it is possible to approximate and pre-
dict macrobenthic secondary production, and hence po-
tential trophic energy, for any given habitat (Table 6).
While the error associated with these broad habitat
predictions is large, the predictions do provide a rela-
tive starting point for considering the magnitude of
energy that flows through the macrobenthos. On a unit
area basis the highest amount of macrobenthic organic
carbon (potential trophic energy) is in the tidal fresh-
water habitats (32.7 metric tons Ckitfyr1) and the least
in the euhaline habitats (8.2 metric tons Ckm2yr').
The bulk of the tidal freshwater production is due to the
introduced clam, Corbiculafluminea, while production
of the euhaline habitat is spread more evenly over
hundreds of species [80,90, 93, 172, 187]. Production
within the other habitats is usually dominated by
several species with many others contributing. An
exception is oligohaline habitats, which are also
dominated by an introduced clam, Rangia cuneata.
On a total area basis, macrobenthic production is
highest in polyhaline habitats (69,016 metric tons C/yr)
and lowest in the euhaline habitats (7,753 metric tons
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Diaz and Schaffner
41
Cyr1). The bulk of the Bay's macrobenthic production,
about 70%, occurs in high mesohaline and polyhaline
habitats (Table 6). Overall, a total of about 194,000
metric tons of organic carbon is produced each year.
This is about seven times the production needed to
support the maximum combination of fishery yields
(27,500 metric tons C). Maximum sustainable yield
models for key commercial species require an even
smaller portion of the total macrobenthic production
(Table 5). While unharvested commercial and non-
commercial demersal fish consume an unknown portion
of benthic production, it does not seem that Allen's
paradox (more predator production than prey produc-
tion [27, 212]) is at work in the Chesapeake Bay.
Importance of Habitat
Secondary production is not spatially uniform. The
magnitude of productivity (both primary and secon-
dary) is regulated through the complexities of physical
and biotic interactions that make up a habitat (Table 2).
These interactions affect the basic aspects of an
organism and population that are critical to the magni-
tude of production (growth, individual weight, life span,
fecundity, and reproductive success). It is therefore
difficult to determine the effect of any one factor on
production. For example, the influence of temperature
on production is not the same as its influence on
metabolism. In metabolism studies there is usually a
doubling of metabolism for every 10° C rise in tempera-
ture (Q10). With secondary production the situation is
more complicated because environmental interactions
are superimposed on the fundamentally physiological
production process. Acartia clausi maturation rates in
the Black Sea increased by a factor of 3.7 between 8°C
and 21° C, but specific production increased by 4.5 over
the same temperature range [365].
Not much detail is known about subtidal softbottom
habitat-production relationships within any ecosystem,
because of the complexity of interactions and the
limited number of production studies. In general, pro-
duction tends to be highest in spatially complex shallow
habitats (such as seagrass beds [120, 247, 273, 278] and
oyster reefs [76]) and lowest in spatially uniform deep
habitats (such as muddy channels [51] and isolated
basins [278]). This habitat-related gradation in produc-
tion is an important aspect of ecosystem energetics that
must be considered in accounting for the trophic im-
portance of the benthos and in assessing the relative
value of habitats. Averaged estimates of total macro-
benthic secondary production, based on direct calcula-
tions of biomass and P/B ratios from a variety of
sources [80, 89, 90, 93, 168, 172, 187] by major
subtidal unvegetated softbottom habitats in the Ches-
apeake Bay clearly demonstrate the variation in
production between habitats (Table 6).
The most productive habitats tend to have mixed and
sandy sediments, with muddy habitats showing the
lowest production. Mean levels (± SE) of production in
mud, sand, and mixed sediments, reported from a
number of Bay studies (Table 6), are 10.7 ± 1.4, 14.9 ±
3.5, and 58.9 ± 35.5 gC-m2yr1, respectively. Similar
patterns of sediment-associated secondary production
were found by Pihl [273] at 22 sites along the coast of
Sweden and by Bodin et al. [32] in the Bay of
Douarnenez, France. The effect of salinity is superim-
posed on sediment type, with highest production in oli-
gohaline and low-mesohaline muds, low-mesohaline
and polyhaline sands, and tidal freshwater and high-
mesohaline mixed sediments.
Periodic low dissolved oxygen (hypoxia, <2 ppm
O2) and anoxia (0 ppm O2) in bottom waters may be a
key factor in affecting secondary productivity, depend-
ing on the concentration of oxygen and the length of
time a habitat is exposed. Habitats that are exposed to
extensive periods of anoxia have low annual production
[168, 278, 279]. The amount of productivity in these
habitats is a function of how quickly benthos can recruit
and grow during periods of "normal" oxygen concen-
trations [251]. In a near-anoxic basin (Port Hacking,
Australia) productivity was lower than at a nearby
station that experienced only hypoxia, by a factor of
almost 16 [278, 279]. Chesapeake Bay areas that expe-
rience anoxia do not seem to have as high a factor dif-
ferentiating productivity of habitats. While some areas
known to be affected by anoxia do have lower produc-
tivity, the trend is not consistent. The inconsistency is
likely due to a combination of duration of exposure to
anoxia and rapid recovery of benthos [166]. Secondary
production in habitats that are known to experience
only hypoxia is of the same magnitude as in habitats
that always have normal oxygen concentrations.
Annual P/B or turnover ratios express the number of
times that the biomass changes (turns over) for the year
studied relative to total biomass produced. When these
ratios are calculated for major taxonomic groups by
habitat, they can show trends in the overall contribution
of taxonomic groups to production. In the Chesapeake
Bay, polychaetes are most productive at mesohaline
salinities in mud and mixed sediments. Mollusks are
most productive in high-mesohaline muds and polyhal-
ine sands. Crustaceans have higher production in
mixed-sediment oligohaline and sandy low-mesohaline
habitats. Nemerteans are most productive in mesohal-
ine sands. Overall, crustaceans have the highest
average annual P/B ratios (5.7 ± 1.3) followed by
polychaetes (4.9 ± 1.4), nemerteans (4.3 ± 1.6), and
mollusks (2.9 ± 1.6). These trends in P/B ratios reflect
the life histories of the organims (Table 4) [353], and
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42
Chapter 2: Benthos
are similar to those found in other systems. However,
the overall magnitude of the P/B ratios for Chesapeake
Bay is higher than in other systems [303, 351], reflect-
ing optimal conditions for high secondary production—
temperate climate, large pool of organic matter, and,
just as important, a large population of predators to
keep benthic populations in a high-growth phase.
Comparison with Other Systems and Trends
The weighted average macrobenthic production for the
Chesapeake Bay is about 17 gCm2yr', with a total
habitat range of about 2-289 gC rn^yr1. This average
production compares well with data from other major
estuarine systems. However, the high range for the
Chesapeake Bay is higher than other systems. The
majority of estuarine and coastal production studies
were conducted in polyhaline and euhaline sandy
habitats, with a variety of methods. The range of
macrobenthic production in these subtidal unvegetated
habitats is about 1-146 gCm2yr1. The range for similar
habitats in Chesapeake Bay is about 6-32 gCnr'yr1.
Long-term variation in benthic energy flow results
from the benthos interacting with temporal (natural and
anthropogenic) trends that affect the secondary produc-
tion process (Table 2). Understanding of these interac-
tions is insufficient to relate their importance to eco-
system function. Few community production studies
have addressed more than single-year time periods and
those that have done so have reported different trends.
Over a four-year period at a muddy site off the North-
umberland coast, Buchanan et al. [50] found that ben-
thic population densities more than doubled, but there
was essentially no change in benthic production. The
total yearly production was differently partitioned be-
tween species of differing P/B ratio each year. In shal-
low sandy habitats of the Skagerak-Kattegat, Moller et
al. [242] found median productivity over a six-year
period was about 10.7 gCm2yr', with one-year pro-
ductivity reaching 145.6 gCm2yr' after a very success-
ful recruitment of benthos. After the Grevelingen estu-
ary was closed from the sea, the productivity of its shal-
low sand habitats increased about twofold over a four-
year period [361]. The increase in productivity was
stepped: productivity in the first two years was about
27 gCm2yr1, and in the second two years, about
53 gCm2yr'. The changes in the Grevelingen could be
explained largely by succession patterns for developing
ecosystems and the physical dynamics of the habitat.
Summary
In this review we have described the important ties of
the benthic subsystem to the entire Chesapeake Bay
ecosystem. In general, benthic community structure
and the distribution of benthic organisms in the
Chesapeake Bay estuarine system are strongly corre-
lated with salinity and sediment type and are further
influenced by patterns of dissolved oxygen and other
sources of physical variability. Long-term trends in
abundance and biomass can often be explained by
coincident changes in the environment.
Most functional groups are well represented
throughout the estuary. Suspension-feeding bivalves
(e.g., Corbicula fluminea andMacoma spp.) dominate
faunal biomass in low-salinity areas. In higher-salinity
areas other suspension-feeding taxa (especially the
polychaete Chaetopterus variopedatus and tunicate
Molgula manhattensis) are important biomass contribu-
tors, along with suspension-feeding bivalves (e.g., Ensis
directus and Mytilus edulis) and a variety of other
feeding types.
Although the mechanisms and rates by which
benthic organisms influence their environment are
known for some of the organisms (or closely related
species) found in the Chesapeake Bay, we still lack a
general understanding of basic feeding processes,
behavior, and living habits for many benthic species. In
addition, the effects of these processes on nutrient flux,
remineralization, and especially toxicant transport and
fate are very poorly understood. We do not have
sufficient information to predict the importance of
temporal variability in these processes, although some
insight into the net results of physical and biological
interactions in the sedimentary environment has been
gained from the stratigraphic record. At some times,
benthic organisms appear to serve as the primary pumps
or conduits for the bidirectional flow of nutrients,
pollutants, and other elements across the sediment-
water interface or benthic boundary layer. They are
also clearly a key link in supporting the high yield of
commercial and recreational fisheries (fish and crabs)
by converting primary organic matter into forage
biomass. Some benthic species (clams and oysters) are
also directly harvested.
The benthic environment functions as the major
storage compartment for virtually all materials that
enter the Bay, from pollutants to primary organic
matter. The close association of benthic organisms
with the sediment and their life histories make them
good integrators of past and current environmental
conditions, and thus ideal sensors of pollutant impacts.
While local problems from pollution are easily recog-
nized in the benthos, the subtle broad-scale effects on
benthic function are unknown. This lack of understand-
ing of system-level effects is due to the inherent
variability of estuarine systems and our lack of knowl-
edge about factors that control benthic function. Such
knowledge is essential if we are ever to distinguish the
effects of pollutants in a holistic context or to enhance
fishery yields.
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Diaz and Schaffner
43
Management Implications for the Benthos and
Benthic Resources
The 1987 Chesapeake Bay Agreement has underscored
the need for sound management of all Bay resources
[59]. If this task is to be accomplished, the entire Bay
must be perceived as a single functioning ecosystem.
Effective management of living resources and their
habitats requires knowledge of what affects these
resources, both positively and negatively, and how they
function within the ecosystem. Because the benthic
subsystem (the entire benthic boundary layer) is the
final focus for pollutants and is integrally connected to
every other Bay subsystem, reliable detection and
interpretation of habitat conditions requires adequate
understanding of benthic function.
Any management plan for Chesapeake Bay living
resources needs to start with the recognition of the Bay
as a single ecosystem. From this perspective a detailed
plan or model can be produced that will allow for
identification of key flows and controls. Preliminary
attempts to model the Chesapeake Bay confirm how
complex and interconnected the system is [24, 131,
363]. This level of complexity requires that managers
more clearly focus their attention, consider ecosystem-
level implications, and set limits to management goals.
This response would improve communication with
scientists, who are asked to provide data necessary to
determine whether management strategies have been or
would be successful.
This review of benthic function points to a lack of
data within the benthic compartment coupling benthos
to other Bay components in terms of energy flow and
other material cycling. This lack of data is not specific
to the Chesapeake Bay but extends to many estuarine
and coastal ecosystems. Although comprehensive data
on specific aspects of benthic coupling (usually energy
flow) are available for relatively simple systems [305,
332, 363], major problems in the management of living
resources arise because of lack of data.
The more important research needs for Chesapeake
Bay that must be addressed in the near future to allow
sound management of living resources are:
• Are there organism-environment relationships
that can predict energetic characteristics (sources
and pathways of flow) within and among habi-
tats?
• Does the spatial arrangement of various benthic
habitats (i.e., bare sand, mud, marshes, seagrass
beds, oyster bars) play an important role in
benthic function?
• How closely coupled are the primary organic
carbon sources (autochthonous and alloch-
thonous) and secondary production?
• How closely coupled are fishery yields and
benthic production?
• What magnitude of control is exerted by the full
range of benthic organisms (microfauna to fish)
on diagenetic processes and nutrient dynamics?
• What are the relationships between toxicants and
benthic organisms that control uptake and fate?
• What is the relative importance of biological vs.
physical processes in controlling diagenetic
processes and nutrient and toxicant dynamics?
• How do long-term changes in climate affect
benthic function, and are there any long-term
periods?
• What role do episodic events (e.g., large storms,
dredge material disposal) play in restructuring
benthic habitats?
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44
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57
Role of Best Management Practices in Restoring the Health of the Chesapeake Bay:
Assessments of Effectiveness
TheoA.Dillaha
Virginia Polytechnic Institute and Scientific University
Department of Agricultural Engineering
Blacksburg, Virginia 24061
Introduction
Nonpoint source (NFS) pollution is a significant
source of water quality problems in the Chesapeake
Bay. Pollution control programs are attempting to
reduce NFS pollutant generation with best manage-
ment practices (BMPs), which are intended to
minimize the negative environmental consequences of
land-use activities while maintaining the productivity
of the land.
The goal of this article is to discuss the role of
BMPs in NPS pollution control and to describe
methods of BMP assessment. Specific objectives are:
• Discuss the factors and/or processes that affect
BMP effectiveness.
• Describe BMPs commonly used in the Chesa-
peake Bay region.
• Discuss why BMP effectiveness varies from site
to site.
• Describe the principal methods—monitoring and
modeling—used for BMP assessment.
• Identify research needed to improve the effective
utilization of BMPs for NPS pollution control.
Factors Influencing BMP Effectiveness
Pollutant loss from land is a function of the concentra-
tion of the pollutant in its carrier (water or sediment)
and the mass of the carrier. Consequently, pollutant
losses can be reduced by decreasing either concentra-
tions or carrier mass. Unfortunately, reducing carrier
mass often increases concentrations, and pollutant
reductions may not be as great as expected [2]. The
principal transport mechanism of a pollutant is often a
function of the degree to which the pollutant adsorbs
to soil. Adsorption is a function of the chemical
properties of the soil and chemical pollutant. Soils
high in clay and organic matter have high adsorptive
capacity, while sandy soils have low adsorptive
capacity. If the adsorptive capacity of a soil for a
chemical can be assessed, the likely transport mode of
the chemical and a BMP appropriate for its control
can be determined [2].
Of equal importance with pollutant loss from the
land surface is pollutant transport from source areas to
downslope waterbodies. During this process, pollut-
ant transport is reduced by deposition, sorption, and
chemical transformations. Coarser sediment is
trapped by terraces, filter strips, riparian zones, ponds,
and other BMPs that reduce flow velocities and
sediment transport capacity. Although BMPs may
reduce sediment yield, reductions in adsorbed
chemical losses often are much lower, because
sediment-bound chemicals are preferentially adsorbed
to fine sediment, which is less susceptible to deposi-
tion than coarser sediment.
The second mechanism reducing downslope
pollutant transport is sorption. Dissolved chemicals in
surface or subsurface flow can be removed by
adsorption to the land surface, vegetation, or sus-
pended sediment.
The third process affecting pollutant losses during
transport is chemical and biological transformation.
This process is usually not significant in surface
runoff in upland areas because transport time from the
source area to upland streams is too short. Significant
transformations may occur, however, if the transport
time from upland streams to receiving waters is long.
Pollutants transported via subsurface drainage, which
have a relatively long transport time, are susceptible
to transformations both in the root zone and in
groundwater. Mineralization of organic nitrogen and
denitrification of nitrate in riparian zones [63] and
degradation of soluble pesticides are important
transformations.
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58
Chapter 3: BMPs
Description and Comparison of
Common BMPs
Currently, only a few BMPs are both technically
feasible and socially and economically acceptable [2].
In selecting BMPs to control a particular pollutant, it
is useful to determine how the pollutant is being
transported to receiving waters. A BMP that inter-
feres with this process can then be identified. With
this in mind, BMPs can be classified as reducing
pollutant carrier mass, pollutant concentrations, or
pollutant delivery from the source area to receiving
waters.
Important BMPs that reduce carrier mass include
conservation tillage, contouring, terraces, and cover
crops. These BMPs are erosion control practices and
consequently are effective in reducing sediment-
bound pollutants. Conservation tillage and other
BMPs that reduce sediment loss often enrich eroded
sediment with soil fines. Since fines have a higher
adsorptive capacity, they transport proportionally
more chemicals than coarser particles, and sediment
loss from conservation tillage systems is often
enriched with sediment-bound chemicals. These
practices also increase infiltration and may increase
groundwater contamination.
BMPs intended to reduce pollutant concentrations
involve the method, rate, timing, and formulation of
chemical applications. The concentration of a
chemical at the land surface (and therefore the amount
available for loss in surface runoff) may be reduced
by removing the chemical, incorporating the chemical,
reducing application rates, or applying the chemical
when it is less susceptible to loss. To control subsur-
face losses, the mass of chemical in the soil must be
reduced by lower rates of application, better timing, or
use of formulations less susceptible to leaching.
Examples of BMPs intended to reduce pollutant
concentrations in surface and subsurface flow include
integrated pest management, improved manure and
fertilizer management, street sweeping, and waste oil
collection and recycling.
Structural practices that reduce pollutant delivery
include terraces, filter strips, ponds, sediment deten-
tion basins, and infiltration trenches. These BMPs
improve surface water quality by increasing sedimen-
tation and infiltration, but they may increase ground-
water contamination.
Whatever the BMP, it is imperative to remember
that BMP effectiveness is extremely site-specific. If a
BMP is effective at one site, there is no guarantee that
it will be equally effective at another site. The effec-
tiveness of BMPs is controlled by characteristics of
the site's soil, topography, cropping, and climatology.
Since no two sites have identical characteristics, BMP
effectiveness will vary from site to site. Conse-
quently, the system of BMPs selected to control NFS
pollution problems at any particular site must be
designed to address the unique characteristics of that
site. The following sections contain brief descriptions
of BMPs commonly used for NPS pollution control in
the Chesapeake Bay region. An assessment of the
relative effectiveness of these BMPs for NPS pollu-
tion control is given in Table 1. Additional informa-
tion on BMPs used for NPS pollution control in the
Chesapeake Bay basin has been reviewed by the
Chesapeake Bay Liaison Office [114].
BMPs that Reduce Carrier Mass
Conservation tillage. Conservation tillage is the
fastest-growing practice in the history of U.S. agricul-
ture [22]. Currently over half the cropland in Virginia
and Maryland is in conservation tillage, because in
this region it results in higher crop yields and lower
production costs than conventional tillage. It is also
presumed to be one of the best available BMPs for
controlling NPS pollution. Conservation tillage is
defined as any tillage or planting system that leaves at
least 30% of the soil surface covered with crop
residue after planting. Major types of conservation
tillage include no-till, ridge-till, strip-till, and mulch-
till. Conservation tillage affects pollutant transport in
surface runoff by decreasing soil erosion and surface
runoff, increasing infiltration, and reducing incorpora-
tion of agricultural chemicals.
Surface residues associated with conservation
tillage reduce soil erosion by protecting the soil from
flowing water and raindrop splash. If raindrops do not
hit the soil surface, soil particles are not detached as
easily from the soil mass and erosion is greatly
reduced. Erosion has been found to be approximately
halved for every 9-16% increase in residue cover [5].
This finding implies that conservation tillage should
reduce erosion by 75-90% compared with conven-
tional tillage. Conservation tillage systems also
increase infiltration and reduce average annual runoff
volumes by about 25% compared with conventional
tillage [2], but these effects are highly variable.
Reduced runoff would be expected to reduce the
transport of dissolved and sediment-bound chemicals.
Unfortunately, concentrations of dissolved and
sediment-bound chemicals in surface runoff often
increase with conservation tillage and may offset a
large part of the reduction in runoff volume.
There is considerable uncertainty as to the effect of
conservation tillage on the movement of pesticides,
nitrates, and other dissolved chemicals to ground-
water. One of the chief unknowns is the effect of
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Table 1. Best management practices and structural practices for nonpoint source pollution control.
Effectiveness in reducing losses
Practice
Type
Sediment
Nutrients
Pesticides
Agricultural
Conservation tillage*
Winter cover crops
Critical area planting
Vegetative filter strip*
Contouring*
Terracing*
Strip cropping
Ponds*
Pasture management
Nutrient management
Integrated pest management
BMP
BMP
BMP
BMP
BMP
BMP
BMP
Structural
BMP
BMP
BMP
Excellent
Good
Excellent
Mixed
Good
Excellent
Good
Good
Good
-
-
Mixed
Good
Fab-
Mixed
Good
Good
Good
Fair
Good
Excellent
-
Mixed
Fair
Fair
Unknown
Good
Good
Good
Fair
-
-
Excellent
Urban
Street sweeping
Infiltration trenches
Porous pavement*
Sediment barriers
Sediment detention basin*
Mulching and temporary cover
BMP
Structural
Structural
Structural
Structural
BMP
Good
Good
Good
Good
Good
Good
Good
Good
Good
-
Fair
-
-
Good
-
-
-
-
* Practices that can potentially increase groundwater quality problems.
preferential flow through soil macropores on chemical
movement through the vadose zone. Preferential flow
allows surface water to flow through macropores in
the soil and bypass the biologically active region of
the root zone responsible for most biological and
chemical transformations. With preferential flow,
dissolved chemicals move 2-20 times faster than
would be predicted by conventional Darcy flow
theory [12]. In addition, preferential flow may allow
supposedly immobile pollutants to reach groundwater.
Research indicates that conservation tillage greatly
increases preferential flow because without tillage,
macropores resulting from earthworm tunnels and
decayed root channels are not destroyed [115, 6].
Most studies have found that pesticide loss is less
with conservation tillage than with conventional
tillage. Generally, 15-40% more pesticides are
applied with conservation than conventional tillage
[57,23], but this increase is usually offset by reduced
sediment yields and runoff volumes. Pesticide loss
reductions are smaller than sediment and runoff
volume reductions, however, because the pesticides
concentrate on the soil surface with conservation
tillage, resulting in higher concentrations of pesticides
in sediment and surface runoff.
The most significant factors affecting nutrient
transport with conservation tillage are the placement,
timing, and rates of fertilizer applications. The
primary goal of conservation tillage is to minimize
disturbance of surface residues. From an agronomic
and water quality viewpoint, however, it is desirable
to incorporate or place fertilizers close to plant roots
and away from the soil surface where they are subject
to loss in surface runoff. Unfortunately, these two
goals may be in conflict because typical fertilizer
incorporation practices also incorporate residue.
When fertilizers are broadcast and not incorpo-
rated, they concentrate near the soil surface and are
susceptible to surface loss. Conservation tillage has
been reported to increase nutrient concentrations at
the soil surface by 600% [2]. In a five-year study
comparing conventional and no-till corn-soybean
rotations, concentrations of phosphorus in the upper
5 cm of the soil profile were 67% higher with no-till
[41]. Similar results are expected with nitrogen
except that increased infiltration with conservation
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Chapter 3: BMP s
tillage may leach nitrate lower into the soil profile.
Concentration of nutrients near the soil surface with
conservation tillage has two consequences: higher
concentrations of nutrients in eroded sediment, and
higher concentrations of dissolved nutrients in surface
runoff. For example, in the corn-soybean rotation
study discussed above, sediment-associated phospho-
rus loss would decrease with no-till only if the 67%
increase in soil phosphorus concentrations were offset
by a 67% reduction in soil loss. Dissolved nutrient
concentrations in runoff are directly proportional to
nutrient levels at the soil surface [4]. Thus losses of
dissolved nutrients with conservation tillage will not
decrease relative to conventional tillage unless the
increased concentrations are offset by larger reduc-
tions in runoff volume.
Vegetative filter strips. Vegetative filter strips (VFSs)
are bands of planted or indigenous vegetation, situated
between pollutant source areas and receiving waters,
which remove sediment and other pollutants from
surface runoff. VFSs are a type of structural practice
designed to remove pollutants from field effluents,
rather than reduce pollutant generation within the
field. The major pollutant removal mechanisms
associated with VFSs involve changes in flow
hydraulics that enhance infiltration, deposition,
filtration, adsorption, and absorption of pollutants.
Currently, there are no standards or accepted methods
for VFS design, and many VFSs are installed in areas
where they are ineffective for pollutant reduction [35].
Research at the University of Kentucky on VFSs
found that sediment-trapping efficiencies were high as
long as flow was shallow and the VFSs were not
inundated with sediment, but trapping efficiency
decreased dramatically at higher runoff rates that
inundated the media [7, 56, 57]. Several short-term
experimental studies have reported on the effective-
ness of VFSs in reducing concentrations of sediment,
nutrient, bacteria, and organics in agricultural runoff
[29-31, 34, 35, 40, 78, 87, 122, 123]. These studies
have reported that over the short term and with
shallow flow, VFSs are very effective for sediment
and sediment-bound pollutant removal, with trapping
efficiencies exceeding 50%. Dissolved pollutants
such as nitrate and orthophosphorus, however, are not
removed as effectively, and several studies reported
that effluents from VFS often had higher concentra-
tions of dissolved nutrients than the influent [34, 78].
This result was attributed to the release of previously
trapped sediment-bound nutrients that were converted
to soluble forms. VFS plots with concentrated flow,
similar to that expected under field conditions, were
reported to be 20-50% less effective than shallow-
flow plots for pollutant removal [34].
VFS performance in the field was evaluated by
observing VFSs on 18 farms in Virginia [29]. Per-
formance was reported to fall into two categories
depending upon site topography. In hilly areas, VFSs
were judged to be ineffective for pollutant removal
because most drainage concentrated in natural
drainageways within the fields before reaching the
VFSs. Flow across these VFSs during the larger
runoff-producing storms (the most significant in terms
of water quality) was primarily concentrated, and the
VFSs were locally inundated and ineffective. This
assessment was confirmed by the fact that little
sediment accumulated in most of the VFSs observed.
In flatter regions, VFSs appeared to be more
effective. Slopes were more uniform, and larger
portions of stormwater runoff entered the VFSs as
shallow flow. This observation was supported by
significant sediment accumulations in many of the
coastal-plain VFSs. Several 1- to 3-year-old VFSs
had trapped so much sediment that they were higher
than the fields they were protecting. In these cases,
runoff flowed parallel to the VFS until it reached a
low point, where it crossed the VFS as concentrated
flow. These VFSs needed maintenance to regain their
sediment-trapping ability, but the landowners, with no
economic incentive, were unlikely to perform the
maintenance.
Recently, researchers have begun investigating the
effectiveness of riparian zones in removing pollutants
from cropland runoff. Riparian zones have been
reported to trap 84-90% of the sediment [18] and 50%
of the phosphorus [17] leaving cultivated fields.
Several models have been developed for VFS design
and evaluation. The Kentucky filter strip model is an
event-based model for designing VFSs with respect to
sediment removal [7, 58, 59]. The model was
evaluated using data from experimental field plots in
Mississippi for multiple storm events, and predictions
were in good agreement with observed sediment
discharge values [60]. Several researchers [46, 118]
have evaluated VFS effectiveness for erosion control
using the CREAMS model [66], CREAMS, like the
Kentucky model, does not consider the long-term
effectiveness of VFSs because it cannot account for
sediment accumulations within a VFS. Consequently,
CREAMS would be expected to overestimate long-
term sediment trapping. The model also cannot
account for concentrated flow effects.
An event-based VFS model, GRAPH [72], was
developed to simulate nutrient transport in VFSs. The
model is based on the GRASSF version of the
Kentucky VFS model [56]. GRAPH considers the
effects of advection, adsorption/desorption processes,
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and changes in sediment size distribution on phospho-
rus transport. GRAPH was verified with data from
VFS field plots and model predictions, and observed
phosphorus transport in VFSs compared favorably.
Grassed waterways. Grassed waterways are structural
practices designed to prevent gully formation by trans-
porting surface runoff and sediment downslope with
as little accumulation of sediment as possible. If gully
erosion is a significant problem, grassed waterways
are an effective practice for their control. During
smaller runoff-producing storms, flow in grassed
waterways is shallow and they may function like
VFSs. No quantitative studies have been reported,
however, which investigated the effectiveness of
grassed waterways for pollutant reduction [84]. One
study reported that grassed waterways that trapped
sediment quickly became inundated with sediment and
were rendered ineffective [71].
Contouring. Contour farming has been found to re-
duce soil loss 0-85% on an average annual basis, de-
pending on the field slope, the height of ridge/furrow
systems, and local rainfall characteristics [105]. Re-
ductions in sediment loss and runoff volumes of >50%
are typical in areas with mild slopes [2]. Contouring
is most effective on permeable soils with mild slopes
[84]. With conventional tillage or ridge-till contour-
ing, surface runoff collects in furrows and there is
little runoff and soil loss from the field unless the
furrows overtop. If the furrows overtop and fail, water
from one furrow flows downslope to the next furrow,
causing it to fail; and the process repeating down the
slope causes severe rill or gully erosion. Soil loss
under these circumstances is as severe as that which
would occur without contouring. Because of this
problem, contouring may not be suitable for every site.
Terraces. Terraces are very effective in reducing NFS
pollution in surface runof; level terraces were reported
to reduce soil loss by 94-95%, nutrient losses by 56-
92%, and runoff by 73-88% [2]. These reductions are
achieved by storing water temporarily and allowing
sediment to deposit and water to infiltrate. Conse-
quently, terraces would be expected to enhance the po-
tential for the movement of dissolved pollutants to
groundwater. Under some circumstances, graded ter-
races can improve field drainage and reduce pollutant
loadings to groundwater. As with contouring, severe
erosion can result if terraces are overtopped. Properly
designed and maintained terraces will last for 10 to 20
years. Terraces are expensive to install, and more
cost-effective pollutant reduction can often be accom-
plished with conservation tillage and contouring [84].
Close-grown crops and winter cover crops. Close-
grown crops such as small grains and grasses are very
effective in reducing erosion, with >70% reductions in
soil loss and 11-96% reductions in runoff volume
compared with conventional tillage [2]. Winter cover
crops are effective for erosion and runoff control
because they provide a protective canopy and root
system to hold the soil in place over the winter. By
spring, when fields in conventional tillage are bare
and most major pollution-producing storms occur,
cover crops are well developed and the soil is pro-
tected from erosion. Cover crops also reduce surface
runoff by minimizing surface sealing and by increas-
ing infiltration and the resistance to overland flow.
Non-legume cover crops have been reported to
decrease nitrate loadings to groundwater due to plant
uptake over the winter and subsequent release to crops
during the late spring and summer [110]. Legume
cover crops were also reported to tie up soil nitrogen
over the winter and provide nitrogen for subsequent
crops, reducing fertilizer requirements [84].
Protective cover. Protective vegetative cover and
mulching of bare soil in urban areas is similar to
conservation tillage in agricultural areas. Covering
exposed soil surfaces with vegetation, straw, gravel,
wood chips, and other available mulches protects the
soil surface from raindrop impact, increases soil
roughness and infiltration, and reduces soil erosion.
Vegetative cover and mulching are more effective for
NPS pollution control in urban areas than other urban
BMPs because they reduce losses of fine sediment,
clay and silt particles, which most other urban BMPs
cannot control. Straw mulch on 12% and 15% slopes
applied at rates of 5 and 10 metric tons/ha, respec-
tively, reduced erosion by 75-80% [88]. Mulching is
only a temporary solution, however, and every effort
should be made to convert bare soil to a permanent
protective cover as soon as possible during the
construction process for permanent erosion control.
BMPs that Reduce Pollutant Concentrations
Nutrient and pesticide management. Improved
management of agricultural chemicals and animal
wastes can greatly reduce NPS pollution. Often, the
formulation of a chemical may influence its suscepti-
bility to loss. For example, some formulations are
highly soluble but others are only slightly soluble.
Use of a less soluble form would be desirable if
groundwater contamination were a problem, whereas
a more soluble form might be desirable if erosion
were high and surface runoff were the pollutant
problem. For example, in runoff events immediately
after surface application of fertilizers, urea (a highly
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Chapter 3: BMP s
soluble form of nitrogen) was found to be less
susceptible to loss in surface runoff than ammonium
nitrate [79]. Presumably, urea's higher solubility and
lower adsorptivity allowed it to infiltrate into the soil
profile where it was less susceptible to loss in surface
runoff. The ammonium nitrate, however, adsorbed to
soil particles, could not move down into the soil, and
was lost with eroded sediment. If runoff had occurred
several days after fertilizer application, rather than
immediately, these results could have been reversed.
The ammonium nitrate would probably have been
converted to soluble nitrate, a form that would have
leached down into the soil profile, whereas the urea
might have been converted to ammonium, a form less
susceptible to leaching. These differences demon-
strate the importance of the timing of chemical
applications with respect to runoff events.
Nitrification inhibitors can be used to block the
conversion of ammonia (which is plant-available but
not readily leachable) to nitrate, which is highly
mobile. In cooler regions of the U.S. these inhibitors
have been found to be effective in reducing ammonia
volatilization as well as nitrate loadings to ground-
water after applications of nitrogen fertilizer and
animal wastes [49]. Nitrification inhibitors have not
been reported to be very effective in the temperate
Chesapeake Bay region. Slow-release nitrogen
fertilizers were reported to reduce nitrogen losses by
as much as 40-85% [83].
Losses of dissolved nutrients and pesticides are
highly correlated with the timing of chemical applica-
tions. Losses increase as the time between application
and the first runoff-producing event decreases. The
best way to minimize these losses is to apply only the
amount of chemical needed by the crop, close to the
time it is needed by the crop. For example, fall
fertilization is undesirable if crops will not be planted
until spring because the fertilizer may be lost before
the crops can use it. Similarly, manure applications
are not recommended in the winter because the
nutrients will not be utilized until spring. For nitro-
gen, split applications are highly recommended
because nitrogen is very mobile. With this method,
nutrients meeting a portion of the crop's needs are
applied at planting and the rest are applied later in the
growing season when a better estimate of crop
nutrient needs can be made. This practice can greatly
reduce fertilizer applications while maintaining crop
yields. Split application of nitrogen can reduce
nitrogen losses by up to 30% in comparison with
single applications [83]. Another alternative is to
plant a cover crop in the fall to scavenge residual
nitrate in the root zone over the winter. The cover
crop is then incorporated in the spring to release
nitrogen for the spring crop. This reduces nitrate
leaching in the fall and winter when most nitrate
leaching occurs in temperate climates [12]. Soil
testing and application of the minimum amount of
fertilizer recommended to meet plant needs is the
most important BMP for nutrient management [83].
Placement of chemicals is also important. Furrow-
band applications were found to reduce losses of
carbofuran in surface runoff by about 50% compared
with surface broadcasting [14]. Herbicide application
under corn residue had little effect on herbicide losses
in surface runoff compared with application to the
residue itself [4]. Chemical incorporation can also
reduce chemical losses. Subsurface application
reduced picloram concentrations in surface runoff by
72% compared with surface application [13]. Herbi-
cide application followed by disking reduced losses
by approximately two-thirds compared with surface
application.
Similar trends have been found with nutrients.
Disking reduced losses of soluble fertilizers by 50-
75% compared with surface application, and plow-
down incorporation reduced losses almost to those of
unfertilized control plots [2]. Similarly, point injec-
tion of fertilizer to a depth of 5 cm did not result in
additional fertilizer loss compared with unfertilized
control plots. A study of band incorporation of phos-
phorus fertilizer found that there were no significant
differences in dissolved phosphorus concentrations in
runoff from conventional, no-till, and conservation
tillage plots [81]. Soluble phosphorus losses were
found to be correlated with runoff volume reductions.
The method of chemical incorporation is also affected
by the type of crop residue. Shallow tillage with
knives or disks may be acceptable with corn residue,
but a single disking for ammonia application with soy-
bean residue may significantly reduce surface cover
and increase soil erosion [5]. If subsurface applica-
tion equipment is unavailable or unacceptable from an
erosion standpoint, dribble banding of liquid and solid
fertilizers is recommended to minimize nutrient losses
in surface runoff [80], Animal wastes should either be
applied via liquid injection or be incorporated
immediately after surface application to minimize
volatilization and losses in surface runoff [82],
Morrison [80] gives an excellent review of
machinery for improved fertilizer application with
conservation tillage. Slot injectors for liquid and dry
fertilizers are described which greatly increase the
efficiency of fertilizer use and minimize losses in
surface runoff. Coulter/nozzle, v-wheel-and-sweep,
high-pressure nozzle slot injectors, and injectors on
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paraplow blades are described along with an innova-
tive spoked-wheel point injector [3].
Integrated pest management. Integrated pest
management (IPM) can be defined as the use of
management practices for pest control that eliminate
unnecessary applications of pesticides and replace
pesticide use with biological and cultural controls
whenever possible. A basic premise of IPM from a
fanner's viewpoint is that pesticides should never be
applied unless the cost of not applying pesticides (in
terms of reduced crop yield and quality) exceeds the
cost of applying pesticides. This is a radical departure
from conventional farming, where pesticides are often
applied whether they are needed or not as a routine
prophylactic practice. Important IPM practices
include: biological controls, regulatory procedures
(training of applicators), cultural methods, pest-
resistant crops and livestock, scouting by IPM
specialists, and genetic manipulation of crops to
increase their resistance or of pests to reduce their
viability. Significant reductions in pesticide usage
have been achieved in most IPM programs without
decreasing agricultural profitability. The most
significant factor retarding adoption of IPM is lack of
education of potential users. An overview of IPM
principles and practices is given by the Council of
Agricultural Science and Technology [19].
In one study, 80% of New York apple producers
were reported to use some IPM practices [68].
Producers that employed comprehensive IPM prac-
tices used 30%, 47%, and 10% less insecticides,
miticides, and fungicides, respectively, and saved an
average of $95.80/ha-yr over an 11-year period
without significantly affecting fruit quality. The study
also found that IPM users were younger and better
educated than non-users. Another New York study
found that increased use of IPM with onions led to a
32% reduction in pesticide use [42]. In Indonesia,
IPM techniques reduced pesticide usage by 60% and
increased rice yields by 25% [12]; apparently, the
amount of pesticides required to control the pesticide-
resistant pests was so high that the pesticides had a
toxic effect on the rice crop. Evidence that education
can reduce pesticide use is also supported by data
from Nebraska suggesting that more-educated or
better-trained pesticide users have fewer application
errors than less-educated users [53]. Additional
information on BMPs for controlling pesticide losses
is reported by the National Water Quality Evaluation
Project [85].
Street-cleaning. Street cleaning and collection of
leaves and grass clippings can have a significant
impact on storm-water quality [44]. Street cleaning
once or twice a day was required to reduce losses of
sediment and heavy metals [93]. Typical street-
sweeping programs clean streets only once or twice
per month with an efficiency of approximately 50%,
but efficiency decreases as average particle size
decreases. Street-sweeping alone is usually ineffec-
tive in reducing urban pollutant losses to acceptable
levels [88]. A significant portion of the organic
matter in storm-water runoff in urban areas is due to
leaves and grass clippings deposited on street sur-
faces. A rigorous program of grass and leaf removal
can significantly reduce these losses [88].
Chemical use control and hazardous waste
collection. Regulatory or voluntary programs such as
waste oil recycling and elimination of road salts can
reduce urban water quality problems. Use of road
salts is estimated to result in $3 billion in environ-
mental damage in the United States each year [44].
Replacement of road salts with hydrophobic de-icers
has been found to reduce these problems [69]. One
liter of waste oil can contaminate a million liters of
clean water. Problems with waste oil and other
hazardous wastes can be reduced if convenient
collection and recycling centers are provided.
Structural Practices
Sediment traps and barriers. Sediment traps are
small temporary structures (straw bales, stone or pre-
fabricated check dams, silt fences, excavated ditches,
and small pits) used during construction projects to
trap coarse sediment particles by reducing flow veloc-
ities and promoting deposition. Their effectiveness
for removing dissolved pollutants, fine sediment, and
pollutants associated with fine sediment is minimal
[88]. Sediment traps and barriers require frequent
maintenance to maintain their effectiveness. They
should be inspected after every runoff event, and
accumulated sediment should be removed whenever
it exceeds 50% of the device's sediment storage
capacity.
Sediment detention basins and ponds. Sediment
detention basins are large structures designed to
reduce peak runoff rates and to remove a certain
percentage of the sediment in stormwater runoff.
There are three general types of detention ponds: dry
ponds, wet ponds, and extended wet ponds [70]. Dry
ponds are not very important in reducing pollutant
losses because of their short detention times. Their
primary purpose is to reduce peak runoff rates, but
they will also trap coarse sediments if cleaned out
regularly. Wet ponds have a permanent pool level,
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Chapter 3: BMPs
and during runoff events additional water is temporar-
ily stored to reduce peak runoff rates. Wet ponds
have longer detention times than dry ponds and are
more effective in removing sediment and sediment-
bound pollutants. Sediment removal efficiencies of
wet ponds are typically 50-75% [51]. Detention
ponds are most effective in watersheds with coarse
soils and least effective in watersheds with fine soils.
Extended wet ponds have sufficiently long detention
times to allow all but the most colloidal sediment to
settle. These ponds are very effective for the removal
of sediment and sediment-bound pollutants, but they
have limited impact on dissolved pollutants unless
they have very long detention times that allow
sufficient time for biological assimilation. Sediment
detention basins may need to be cleaned as often as
twice per year to maintain their efficiency.
Infiltration trenches. Infiltration trenches are
subsurface trenches filled with coarse aggregate that
are used to store surface runoff until it can infiltrate
into the soil. They are particularly effective in
intercepting the first flush of runoff from impervious
areas. This interception is important because the first
flush usually has the highest pollutant concentrations
and may be responsible for most of the pollutant load
from a storm. The surfaces of infiltration trenches are
usually covered with grass, stone, or other porous
material to allow water to enter the trench rapidly. All
sediments and sediment-bound pollutants entering
infiltration trenches are trapped, but dissolved
pollutants may be transported to groundwater.
Infiltration trenches require a high degree of mainte-
nance because the walls of the trenches tend to clog
with fine sediment [88].
Porous pavement. Porous pavement is low-density
permeable asphalt with a thick aggregate reservoir
base course for water storage [70]. Precipitation
entering porous pavement can be stored until it
infiltrates into the soil, or it can be released slowly to
reduce peak runoff rates. An investigation of porous
pavement in New York found that it reduced peak
runoff rates by as much as 83% [44]. Porous pave-
ment has frequently been reported to have problems
with clogging. This problem indicates that porous
pavement is effective in trapping sediment and would
also be effective for removal of sediment-bound
pollutants until it clogs. Monthly cleaning with
conventional vacuum trucks is recommended to
minimize clogging. If porous pavement systems are
designed for infiltration of all of the influent, they
could become a source of groundwater contamination.
Cost-Effectiveness of BMPs
The cost-effectiveness of different BMPs for reducing
sediment loadings to Lake Springfield in Illinois was
found to vary greatly [102]. The costs of conservation
tillage, structural BMPs (sediment basins, terraces,
grass waterways, etc.), and lake dredging were $1.05,
$6.73, and $5.16 per metric ton of sediment; thus con-
servation tillage was by far the most economic prac-
tice. The structural practices were not cost-effective
because they were not effective during the infrequent
larger storms that were responsible for most of the
sediment loadings to the lake. The Rural Clean Water
Program (RCWP) at Rock Creek had similar findings.
Structural practices were found to be very effective
for sediment reduction (55% reduction), but their
initial capital costs were high and their annual main-
tenance costs were estimated to range from $22-37/ha
(for sediment retention structures) to $49-$119/ha (for
improved irrigation structures) [75]. Because of these
high costs and fears that the structures would not be
maintained after cost-sharing ended, the project
started encouraging conservation tillage and discov-
ered that sediment losses were reduced up to 90%. In
addition, productivity generally increased with con-
servation tillage. Farmers switching to conservation
tillage saved an average of $82/ha/yr. Thus conserva-
tion tillage was a better practice than conventional
tillage agronomically as well as environmentally.
Methods Used to Evaluate BMPs
With the increased use of BMPs for NFS pollution
control, there is an obvious need for methods of
assessing BMP effectiveness. Proper management of
any system requires reasonable estimates of the
expected impacts of alternatives being considered.
This is particularly true with NPS pollution control, as
planners face the often conflicting goals of minimiz-
ing pollution and maximizing economic return to the
landowner. An effective plan can be developed only
from good data. Estimates of BMP effectiveness are
essential for (1) selecting the most appropriate BMP
for a particular problem and site; (2) estimating the
benefits of BMP implementation; (3) ranking BMP
alternatives in terms of cost-effectiveness; and (4)
determining an optimum BMP program based upon
program objectives. Program objectives include both
pollution control objectives, such as meeting desired
water quality goals, and financial objectives, such as
maximizing the cost-effectiveness of cost-share
monies, minimizing costs to the landowner, maintain-
ing long-term productivity, or maximizing return
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65
to the landowner while meeting water quality ob-
jectives [107].
Approaches
Two approaches have been used to evaluate BMP
effectiveness: monitoring and modeling. Water quality
monitoring can be defined as any effort to obtain an
understanding of the physical, chemical, and biological
characteristics of water via statistical sampling.
Monitoring is the most direct way to assess BMP
effectiveness, but it has several significant drawbacks.
First, there is tremendous variability in the soil, land
use, topography, and weather factors influencing BMP
effectiveness. Consequently, a BMP may be highly
effective for pollutant control at one site but ineffective
at another site. Second, monitoring is time-consuming
and expensive. If the particular BMPs selected for a
site are found to be ineffective, considerable time and
resources will have been wasted and it may take many
more years to develop an effective program.
The second approach to BMP assessment uses
mathematical models. Physically-based models
attempt to describe the physical, chemical, and
biological processes affecting the natural system being
modeled. This allows consideration of site-specific
soil, land use, topographic, and weather factors that are
critical to BMP effectiveness. Models of another class,
empirical models, are less suitable for BMP evaluation
because they do not describe the physical processes
controlling the system being simulated. They are
simply statistically derived equations and should not be
used beyond the range of data used in their develop-
ment. Consequently they are not good for simulating
the site-specific nature of BMPs [2],
Monitoring Approaches
The level of monitoring required to evaluate BMP
effectiveness should be a function of the objectives of
the monitoring program. If the only objective is to
determine whether BMP implementation is effective in
reducing pollutant losses from a watershed, then a
weekly grab-sampling program at the watershed outlet,
with streamflow monitoring, may be adequate. At the
other extreme, if the data are to be used for model
development and evaluation, then monitoring may
have to include: weather parameters; land use changes;
chemical applications, rates, and timing; chemical
levels in soils; cultivation, planting, and harvesting
methods and dates; and streamflow. In addition, edge-
of-field monitoring may be necessary to assess the
effectiveness of specific BMPs in reducing in-field
pollutant losses, and monitoring of ephemeral drain-
ageways may be necessary to assess pollutant transport
from fields to streams. A major problem with edge-
of-field monitoring is that it measures what is leaving
the field and not what is reaching downslope water-
bodies. Unless losses between the field and receiving
waters are accounted for, pollutant loadings and con-
centrations may be greatly overestimated.
The study objectives will also determine the size of
the area to be studied and whether natural or simulated
rainfall will be used. Monitoring studies on smaller
areas (<0.5 ha) and short-term studies can often
benefit from the use of simulated rainfall that allows
control of the timing, amount, and intensity of the rain.
Using simulated rainfall can reduce both the time and
cost of data collection by allowing BMPs to be
evaluated during critical crop-growth stages and
chemical application periods.
One of the earliest and most popular approaches
for evaluating BMPs involves the use of paired plots
or watersheds. With this approach, two or more areas
with similar size, soils, topography, and weather are
treated the same and monitored during a pre-BMP
phase for two years or more. An empirical statement
can then be made about the behaviors of the water-
sheds relative to each other. Then, after BMPs are
implemented in one or more (but not all) of the
watersheds, monitored watershed responses are
compared with those predicted by the regression
equations to determine if the BMPs have had an effect.
Replicated plots and watersheds are another
traditional method for evaluating BMPs. With this
method, each treatment is replicated two or more
times and all the plots are monitored simultaneously.
Replicated plots are usually small because it is often
difficult to find large areas with the desired uniform
soils, slope, and cropping history and because repli-
cated plot studies require the analysis of very large
numbers of samples.
Another approach involves the use of upstream and
downstream monitoring. With this method, a pair of
monitoring stations are established upstream and
downstream of a subwatershed in which BMPs have
been installed. The upstream and downstream
concentrations are then compared to assess the impact
of BMP implementation. For this method to succeed,
it is critical that the flow volume from the drainage
area above the BMP be similar to the flow volume of
the BMP drainage area. Otherwise, the upstream flow
will dilute the discharge from the BMP area so much
that there will be no detectable differences in the
concentrations.
Spooner et al. [107, 109] give an excellent over-
view of monitoring system designs for evaluating the
effectiveness of agricultural NPS pollution control
programs. They point out that monitoring systems
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Chapter 3: BMP s
often lack a sound experimental design, with the result
that data collection efforts and results are inconclu-
sive. Four approaches for design and analysis of
monitoring systems are presented: (1) before-and-after
time trend analysis uncorrected for meteorological
variables, (2) before-and-after time trend analysis
corrected for meteorological variables, (3) monitoring
upstream and downstream of a NFS implementation
area, and (4) paired watershed designs. They suggest
appropriate hypotheses and statistical techniques for
each experimental design. The paired watershed de-
sign is recommended as the best monitoring method
for documenting water quality improvements in the
shortest period of time. If the quantity of NFS pollu-
tion prior to implementing BMPs is of interest, the
upstream-downstream approach is recommended.
The before-and-after design is reported to be the
easiest design to follow, but it is not recommended
because it may be difficult to attribute water quality
improvements to BMP installation. The time required
to detect the desired change will be a function of the
noise in the system and how large a change is actually
being made.
A generalized framework for integrating agricul-
tural NFS water quality problem identification, data
collection, data management, and project assessment
has been developed [67]. It suggests logical and
efficient ways to combine data evaluation and
assessment with problem definition and data collec-
tion. This report also provides comprehensive lists of
the types of data that must be collected to address
different NFS pollution problems.
Another study developed a five-step conceptual
model to optimize the design of NFS pollution control
programs and monitoring systems [94]: (1) definition
of monitoring objectives to guide data collection ef-
forts; (2) choice of level of detail required for moni-
toring system alternatives; (3) watershed analysis to
identify critical areas for BMP implementation and
monitoring; (4) development of a monitoring program
to detect and verify statistically the sources of NFS
pollution; and (5) prioritization of monitoring tasks
with reference to program objectives. Whitfield [119]
also provides a good review of monitoring project
designs.
Guidelines for measuring water quality impacts of
NFS control projects were presented by a USEPA-
USDA interagency task force [96]. The procedures
presented are minimum recommended techniques for
detecting water quality changes due to BMP implem-
entation in waters impaired by NFS pollution.
Specific evaluation alternatives are presented for
streams, lakes, and groundwater. The report notes
that improvements in water quality impaired by
eutrophication and biological degradation may not be
measurable within 3- to 5-year project periods
because natural systems are highly variable and are
slow to show response to subtle changes.
A symposium on the design of water quality
information systems was held in Fort Collins, Colo-
rado in 1989 to assess the quality and utility of water
quality data and to emphasize a systems approach to
water quality monitoring design [106]. A key point
made at the conference was that collecting data is easy
compared with analyzing it and that monitoring proj-
ects must have quantifiable information goals identi-
fied at their outset that will result in quantitative
information. Proposed statistical methods need to be
evaluated in light of expected data limitations such as
serial correlations, seasonality, missing data, non-
detections, and multiple observations in one sampling
period. The sampling frequencies, variables moni-
tored, and monitoring locations can then be deter-
mined from the information goals and supporting
statistical analysis. The conference also stressed that
data collection is worthless without good information
objectives, decision technology, quality assurance,
and data management. Quality assurance was
reported to be the most critical component of data
collection.
The same general techniques described for
monitoring of surface water quality also apply to
groundwater monitoring. Groundwater monitoring is
more complex, however, because it is more difficult to
measure and allow for covariates such as flow rates,
recharge area, dilution from the recharge area outside
the study area, loadings to groundwater, and the
quality of upstream or upgradient water. It is also
difficult to track the movement of pollutants through
the soil profile to groundwater. In addition, ground-
water is often very slow to respond to reductions in
pollutant loadings. Depending on the aquifer and the
pollutant of concern, it may take anywhere from a
month to centuries for groundwater quality to im-
prove. On the other hand, because pollutant concen-
trations in groundwater are much less variable than
those in surface waters, smaller changes in pollutant
concentrations are necessary to demonstrate signifi-
cant impacts of BMPs on groundwater quality.
Numerous publications give more details on ground-
water and groundwater monitoring [20, 43, 65, 113].
Rural Clean Water Program
Monitoring Projects
The 10-year Rural Clean Water Program (RCWP) was
initiated in 1980 to demonstrate how agricultural NFS
pollution could be controlled. The program is admini-
stered by the USDA and USEPA. Each project had
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Dillaha
67
four principal activities: cost-sharing of BMPs,
technical assistance to farmers, educational programs
for farmers, and water quality monitoring. Cost-
sharing of up to 75% of the cost of practices with a
maximum of $50,000 per farm was allowed. BMP
cost and effectiveness in improving water quality was
found to be highly site-specific. For example, manure
storage resulted in substantial water quality improve-
ments in the Vermont RCWP where manure could be
used to replace commercial fertilizer, but there were
few water quality benefits from manure storage in the
Pennsylvania RCWP because so much manure was
available that even if no commercial fertilizer was
applied, there was still gross over-application of
nutrients [92].
A review of the first seven years of the RCWP
reported that targeting of BMPs was "the key to NFS
pollution control" and essential for cost-effective
water quality improvements [76]. Considerations
used to target resources to critical areas contributing
the most to water quality impairment usually included
erosion rates, proximity to streams, and livestock
densities. Provision of technical services such as
nutrient analysis of manure, plant tissue, and soil
(Pennsylvania and Virginia RCWPs) was very
effective in inducing farmers to improve nutrient man-
agement practices. Several projects demonstrated that
conservation tillage and management of nutrients and
pesticides are the most cost-effective BMPs for NFS
pollution control.
There appears to be a direct correlation between
watershed size and the time required to detect im-
provements in water quality [76]. Water resource
systems that are managed closely or that flush quickly
show the fastest improvements. Consequently,
irrigation ditches and small streams would be ex-
pected to respond much more quickly than rivers,
lakes, and estuaries. In the first six to seven years of
the RCWP, none of the RCWPs achieved significant
water quality improvements in lakes [76]. Estimated
response times required to achieve measurable
improvements in water quality after BMP implemen-
tation are given in Table 2.
The RCWP monitoring project budgets for the five
comprehensive monitoring projects have ranged from
$700,000 to $2,000,000 over a 10-year period [76].
Most of these projects are not evaluating specific
BMPs but rather evaluate overall watershed response.
Of the first four RCWPs to show significant water
quality improvement, three were not comprehensive
monitoring projects but had well-designed grab-
sampling programs. This result demonstrates that
appropriate grab sampling can document water quality
improvements in 5-10 years. To detect these changes,
however, will require a 20-40% change in geometric
mean pollutant concentrations because of the random
variability in aquatic systems. It was suggested that
information gained from the RCWP may not establish
cause-and-effect relationships between BMPs and
water quality, nor can the success of one project
necessarily indicate the likely success of another proj-
ect because of the site-specific nature of BMPs [76].
The five RCWP projects with comprehensive
monitoring programs were studied to assess their cost-
effectiveness with respect to water quality improve-
ments [121]. The results of these five projects sug-
gested the following considerations for improving the
efficiency of future agricultural NPS programs:
• Target funds and projects only towards water-
bodies with water quality problems causing
substantial economic damage.
• The relative costs and effectiveness of BMPs can
greatly influence project effectiveness. Use
BMPs appropriate for the area, and target them
to the areas responsible for the water quality
problems.
• The effectiveness of a particular BMP can vary
Table 2. Estimated number of years required to achieve and detect water quality improvements.
Water resource
system
Irrigation canal
Stream
Estuary
Lake
Groundwater aquifer
Response time
(years)
0-1
1-5
0-5
2-10
Unknown
Time for significant
response (years)
3-8
5-13
5-12
6-14
Unknown
Adapted from [76].
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Chapter 3: BMP s
greatly from one site to the next because of
differences in soils, cropping, topography, and
other factors.
• Some projects may not be economically justifi-
able unless on-site benefits such as long-term
productivity are considered in addition to off-site
water quality benefits.
Below are additional details on three of the
RCWPs. Details are sketchy on most of the RCWP
projects because little has been published.
St.AlbansRCWP was initiated in 1981 in the 13,500-
ha St. Albans Bay watershed in Vermont [16]. Water
quality impairment in the bay was attributed to
excessive nutrient loss from cropland and dairies. An
extensive monitoring network was established to
measure the impacts of BMP implementation. There
were four types of monitoring: (1) grab sampling at
bay stations, (2) instream sampling of four tributaries
and the St. Albans wastewater treatment plant with
automatic samplers, (3) a pair of small watersheds
with automatic samplers and stage recorders to
evaluate improved manure management practices, and
(4) grab sampling at four other stream locations at 20-
day intervals to monitor additional subwatersheds.
Flow was measured continuously at each station, and
three recording raingages monitored precipitation.
Four years of monitoring was insufficient to evaluate
water quality trends in the bay and its tributaries.
Only in the paired watersheds was it possible to
document significant improvements in water quality.
Rock Creek RCWP was initiated in 1980 to investi-
gate the effectiveness of structural BMPs such as
sediment detention basins, vegetative filter strips, and
improved irrigation structures in reducing water
quality impairment caused by irrigation return flows
[86]. The project's monitoring program was success-
ful in demonstrating improvements in water quality.
The BMPs resulted in significant reductions in
sediment, total phosphorus, orthophosphorus, and
total Kjeldahl nitrogen. Reductions in inorganic
nitrogen losses were not conclusively demonstrated.
This RCWP was somewhat unusual in that it was
conducted in an arid region (20-25 cm of annual
precipitation) where almost all runoff was produced
by surface irrigation.
The original purpose of the Rock Creek RCWP
was to show that water quality would respond to
structural improvements to the irrigation system: lined
channels, gated pipe, vegetative filter strips, sediment
basins, and buried pipe runoff control systems. These
structural BMPs were successful in the short term, but
the project officials concluded that they would be un-
successful in the long run because these practices re-
quire a high degree of maintenance, which would not
continue after cost-sharing ended. Economic analysis
suggested that it would be more effective to use less
costly field management practices such as irrigation
water management and conservation tillage, which are
designed to keep soil and nutrients in the field.
Nansemond River-Chuckatuck Creek RCWP. Re-
sults of the first five years of the 10-year Nansemond-
Chuckatuck RCWP in southeast Virginia are de-
scribed by Fisher [45]. Over $2 million in cost-share
funds have been contracted, and significant reductions
in agriculturally generated pollutants from cropland
and livestock have been reported. Agricultural BMPs
being implemented include: permanent vegetative
cover; animal waste control systems; diversions;
grazing land protection systems; waterway systems;
cropland protective systems; critical area plantings
(vegetative filter strips); conservation tillage; stream
protection; sediment detention, erosion, and water
control structures; fertilizer management; and nutrient
management. The project reports that it has achieved
substantial water quality improvements, but states that
these improvements may be masked by declining
water quality due to rapid urbanization in the area.
Statistical Design Criteria for
Effective Monitoring
Numerous researchers have investigated the role of
statistical design in the development and assessment
of monitoring programs [11, 61,95, 108, 109, 116,
117]. One study reviewed methods for determining
statistically significant changes in water quality due to
NFS control programs and presented a method for
determining the magnitude of water quality changes
needed to document significant differences over time
[108]. Adjustments were presented that could reduce
the estimate of variability and decrease the water
quality change required for statistical significance.
These adjustments included accounting for changes in
precipitation and runoff, changing sampling fre-
quency, increasing the monitoring period, and
performing statistical trend analysis. For the data
from the Rock Creek (Idaho) RCWP, a 30-60%
reduction in the unadjusted geometric mean concen-
tration was required for documenting a significant
change in water quality. However, if adjustments
were made to reduce the estimate of variability, the
required change fell to 20-40%. In a similar study, it
was found that NFS phosphorus loadings to Lake Erie
would have to be reduced by about 35% before the
changes could be detected. If the loads were adjusted
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Dillaha
69
for changes in discharge, the required change would
drop to 20%. At present rates of adoption of conser-
vation tillage and associated decreases in phosphorus
loadings, a 35% change would require more than 25
years to detect, but the 20% change would require
only eight years [95]. Obviously, in this case a
monitoring design that incorporates flow measure-
ments would be desirable. Monitoring at the Taylor
Creek-Nubbin Slough RCWP was reviewed to
determine what levels of detected changes in total and
orthophosphorus concentrations were required to
indicate real changes in water quality. The minimum
level (after adjustment for covariates such as precipi-
tation and seasonally) ranged from 10% to 59% over
nine years [109].
Limitations of Monitoring
Data obtained in many monitoring studies are often so
compromised in one or more respects that they are of
little value [27]. Routine water quality monitoring
programs have generated mountains of data in the
past, but little data analysis has been done and
reported. Monitoring often has a poorly defined
purpose and results in a "data-rich but information-
poor' syndrome [117]. To overcome these problems,
study designers must address not only the what,
where, when, and how of sampling, but also the why.
These are the critical questions of quality assurance.
Thus it is essential that a quality assurance plan be
prepared for all monitoring projects to insure that data
supply the needed information [33]. The major
problem with monitoring is that it is so demanding
that often it becomes a goal in itself and its original
purpose is forgotten [27].
Modeling Approaches
Nonpoint source models used to evaluate the effec-
tiveness of BMPs for NFS pollution control range in
complexity from simple empirical models like the
USLE [120] to complex physically-based watershed-
scale models like ANSWERS [9]. Some models have
also been developed for evaluating specific BMPs
such as filter strips [56, 72]. NFS models generally
concentrate on the generation of pollutants and their
transport across the land surface to waterways or
through the soil profile to groundwater. Water quality
models, on the other hand, are more concerned with
the transport and fate of pollutants during concen-
trated flow in streams, rivers, lakes, and other large
waterbodies. Because of these differences, NPS
models are often called loading models, while water
quality models are called receiving water models.
Only a few models, such as HSPF [64] and SWAM
[25] (see following section), combine both loading
and receiving water aspects into one overall model.
Types of Models
NPS pollution models can generally be classified as
either screening or hydrologic assessment models
[89]. Screening models are usually relatively simple
and are intended to identify problem areas within
large drainage basins or to make preliminary qualita-
tive evaluations of BMP alternatives. One of the most
important uses of screening models is the identifica-
tion of potentially critical sources of NPS pollution
within watersheds. Numerous studies have indicated
that, for many watersheds, a few critical areas are
responsible for a disproportionate amount of the
pollutant yield. Consequently, concentration of
pollution control activities in these critical areas can
maximize the improvement in downstream water
quality achievable with limited funds [111]. Because
of the simple nature of screening models, their
predictions are expected to be accurate only within an
order of magnitude or so. Examples of NPS screening
models include the USLE, GAMES [77], and VirGIS
[101] (see following section).
Hydrologic assessment models are much more
complex than screening models and are intended for
assessing current conditions or alternative manage-
ment scenarios. The predictions of good hydrologic
assessment models should be within a factor of 2 of
observed values if model parameters are measured on
site or if the model is calibrated. Otherwise, predic-
tions should be within an order of magnitude of
observed values. Hydrologic assessment models can
also be subdivided into field-scale and watershed-
scale models. Field-scale models attempt to describe
hydrologic processes within a single field or land
resource unit with uniform soils, cropping, topogra-
phy, and weather. They do not attempt to describe
pollutant transport and fate beyond the boundaries of
the field. Examples of field-scale hydrologic models
include: CREAMS [66]; CNS and CPM [54];
GLEAMS [73]; NTRM [100]; and PRZM [15] (see
following section).
Watershed-scale hydrologic assessment models
attempt to describe pollutant transport in the field and
between fields and receiving waters. Some are event-
oriented (single storm predictions) while others are
continuous simulation models. Several can be used to
identify critical sources of NPS pollution in water-
sheds and to target BMPs. Most can be used to
evaluate the cost-effectiveness of alternative BMP
implementation scenarios. Important NPS watershed-
scale hydrologic models include: AGNPS [125],
ANSWERS [9], ARM [38], HSPF [64], NPS [36],
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Chapter 3: BMPs
STORM [112], SWAM [25], SWMM [62], and
WEPP [50] (see following section).
Available NFS Models
Available NPS hydrologic assessment models are
shown in Table 3, with indications as to what parame-
ters are simulated.
Chemicals Runoff and Erosion from Agricultural
Management Systems (CREAMS) [66] is a physi-
cally-based, field-scale model developed for compar-
ing pollutant loads from alternate management
practices. CREAMS does not require parameter
calibration with observed data, but calibration
improves its accuracy [74]. One of the most attractive
features of CREAMS is its comprehensive user's
manual, which documents the model's development
and facilitates parameter selection. The model has
been tested in many areas of the world and is the
state-of-the-art field-scale model for BMP assessment.
Although it is intended for use as a continuous
simulation model, it can also be used as an event-
oriented model. The model estimates runoff volume,
peak runoff, infiltration, evapotranspiration, soil
moisture, percolation, sediment yield, particle-size
distribution of eroded sediment, and losses of dis-
solved and adsorbed nitrogen, phosphorus, and
pesticides in surface runoff and percolate. The
primary limitation of the model is that as a field-scale
model, it is limited to areas with uniform soils and
cropping and does not consider pollutant transport to
receiving waters. CREAMS has also been found to
underestimate runoff volumes. It is more accurate in
representing average annual runoff volumes than daily
or monthly runoff volumes [103]. It does not work
well in cold climates [16].
Cornell Nutrient Simulation (CNS) and Cornell
Pesticide Simulation (CPS) Models [54] are field-
scale models developed to predict nutrient and pesti-
cide losses from agricultural fields. Both models use
the SCS curve number equations to predict surface
runoff (CPS also uses the Green- Ampt [52] infiltra-
tion equation) and a modification of the Universal Soil
Loss Equation (USLE) to predict soil erosion [90].
Some problems were encountered in comparing the
CNS model simulations and observed data in Georgia
and New York [89]. The CPS pesticides model con-
siders pesticide degradation, volatilization, percolation
through the root zone, and loss in surface runoff.
Groundwater Loading Effects of Agricultural
Management Systems (GLEAMS) Model [73] uses
the basic foundation of CREAMS and adds compo-
nents to simulate the movement of water and chemi-
cals within the crop root zone. At present, GLEAMS
simulates subsurface movement only of pesticides, but
a nitrogen model is being developed. GLEAMS does
not consider movement between the root zone and the
water table. GLEAMS divides the root zone into 3 to
12 layers, and pesticide transport within the root zone
is by advection. Diffusion and volatilization are not
considered. Pesticide application can be partitioned
between the soil and foliage and can be incorporated
to any depth. Pesticide degradation rates can vary by
soil zone. GLEAMS can simultaneously model the
transport of 10 chemicals and their degradation prod-
ucts, and multiple applications of pesticides are al-
lowed each year. In an independent evaluation,
GLEAMS was found to predict peak pesticide con-
centrations within an order of magnitude and often
within a factor of 2 or 3 of observed values [104].
Nitrogen-Tillage-Residue Management (NTRM)
Model [98,99, 100] is a field-scale, continuous
simulation model developed to evaluate existing and
proposed soil management practices with respect to
erosion, soil fertility, tillage, crop yield, crop residues,
and irrigation. The model simulates carbon and nitro-
gen transformations including nitrification, denitrifi-
cation, mineralization, immobilization, urea hydroly-
sis, and non-symbiotic nitrogen fixation as a function
of soil moisture and temperature, with use of zero-
and first-order process equations. The model is more
sophisticated than other field-scale models in describ-
ing the physical processes affecting transport and
transformations, but computing requirements are high.
Pesticide Root Zone Model (PRZM) [15] is a field-
scale, continuous simulation model developed by the
USEPA to simulate the effects of agricultural manage-
ment practices on pesticide fate and transport. Runoff
is predicted by the SCS curve number equation; soil
loss, by a modification of the USLE. The model
simulates the vadose zone from the soil surface to
groundwater. The vadose zone is divided into several
layers with various properties and degradation rates.
Pesticide processes considered include advective and
dispersive flux, sorption, degradation in the soil, and
plant uptake. Volatilization is not considered.
Applications can be partitioned between foliage and
the soil surface. Surface applications can be incorpo-
rated by tillage. The model permits only one applica-
tion of pesticides per year. In a study in south
Georgia, PRZM was used as a screening model (no
calibration), and predictions were similar to those of
GLEAMS. Most predictions of peak pesticide
concentrations were within a factor of 2 or 3 of
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Dillaha
71
observed values, and almost all were within an order
of magnitude [104].
Agricultural Nonpoint Source Pollution (AGNPS)
Model [125] is an event-based, watershed-scale model
developed to simulate runoff, sediment, chemical
oxygen demand, and nutrient transport in surface
runoff from ungaged agricultural watersheds. Subsur-
face transport processes are not considered at present,
but a groundwater loading version of the model is
planned. Nutrients considered include nitrogen and
phosphorus. The model operates on a square cell
basis, which facilitates data base creation. All model
inputs are defined on the cell level. Pollutants are
routed from the source cell through intervening cells
to the watershed outlet. Model output may be viewed
at any cell, a capability that allows identification of
critical source areas and evaluation of targeting
alternatives. Runoff volume is simulated with use of
the SCS curve number method and the peak runoff
rate equation used in CREAMS. Erosion and sedi-
ment transport are calculated with modified forms of
the USLE and Bagnold's stream power equation [1].
Nutrient yield in the sediment-bound phase is calcu-
lated as a function of the nutrient content of the field
soil, the sediment yield, and an enrichment ratio that
is a function of soil texture and sediment yield. Sol-
uble nutrient loss is a simple function of the soil nu-
trient level and an extraction coefficient. The model
considers only losses of total nitrogen and phosphorus
Table 3. Principal models used for nonpoint source pollution assessment.
Model*
Field-scale
CREAMS
CNS
CPM
GLEAMS
NTRM
PRZM
WEPP
Watershed-scale
AGNPS
ANSWERS
ARM
HSPF
NPS
STORM
SWAM
SWMM
WEPP
Time scale
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Event
Event
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
Watershed
characterization
-
-
-
-
-
-
-
Distributed
Distributed
Lumped
Lumped
Lumped
Lumped
Distributed
Distributed
Distributed
Groundwater
loading
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
No
Yes
Yes
No
Parameters
simulated*
S.N.P
S,N
S,P
S,N,P
S,P
S,P
S
S,N,B,COD
S,N
S,N,P
S,N,P,B,DO,O
S,N,DO
S,N,BOD
S,N,P
S,N,B,COD,O
S
*CREAMS = Chemicals Runoff and Erosion from Agricultural Management Systems; CNS = Cornell Nutrient
Simulation; CPM = Cornell Pesticide Simulation; GLEAMS = Groundwater Loading Effects of Agricultural
Management System; NTRM = Nitrogen-Tillage-Residue Management; PRZM = Pesticide Root Zone Model;
WEPP = Water Erosion Prediction Project Model; AGNPS = Agricultural Nonpoint Source Pollution; AN-
SWERS = Areal Nonpoint Source Watershed Environment Response Simulation; ARM = Agricultural Runoff
Management; HSPF = Hydrologic Simulation Program-Fortran; NPS = Nonpoint Source Pollution Loading;
STORM = Storage, Treatment, and Overflow Model; SWAM = Small Watershed Model; SWMM = Storm Water
Management Model; WEPP = Water Erosion Prediction Project Model.
+ S = sediment, N = nutrients, P = pesticides, COD = chemical oxygen demand, B = bacteria, DO = dissolved
oxygen, BOD = biochemical oxygen demand, O = others.
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Chapter 3: BMP s
and does not consider nutrient transformations. The
model also allows for inputs from feedlots, sewage
treatment plants, and other point sources. Data file
creation is time-consuming, since 22 parameters must
be specified for each cell; however, a model user's
guide is available [124] and the CREAMS handbook
[66] is a useful source of parameter values. The mod-
el has been tested for runoff estimations on 20 water-
sheds in the north central United States. Peak runoff
rates were approximately 1.6% less than observed
values with a coefficient of determination of 0.81.
Sediment yield predictions compared well with ob-
served sediment yields from two watersheds in Iowa
and Nebraska [125]. The nutrient model has not been
adequately tested yet, but limited testing on Minnesota
watersheds indicated that the model provides realistic
estimations of nutrient concentrations in runoff [125].
The model's effectiveness in predicting total runoff
volume was not reported, so it is difficult to assess the
ability of the model to predict total yields.
Areal Nonpoint Source Watershed Environment
Response Simulation (ANSWERS) Model [9] is an
event-oriented, watershed-scale model developed to
describe the impact of existing and proposed agricul-
tural management practices on water quality in
ungaged watersheds. Recent versions of ANSWERS
include an extended sediment detachment/transport
model allowing prediction of sediment yield and
concentrations for mixed particle-size distributions
[28], a phosphorus transport model [111], and a
nitrogen transport model [32]. ANSWERS subdivides
the watershed into a uniform grid of square cells.
Land use, slopes, soils, and management practices are
assumed to be uniform within each cell. Typical cell
sizes range from 0.4 to 4 ha, with smaller cells
providing more accurate simulations. Eight to 10
parameter values must be provided for each cell. The
extended sediment model uses a modification of
Yalin's equation similar to that in CREAMS [47].
A non-equilibrium desorption equation is used to
account for the desorption of soluble phosphorus from
the soil surface to surface runoff. Sediment-bound
phosphorus is modeled as a function of the specific
surface area of the eroded sediment. The equilibrium
between soluble and sediment-bound phosphorus is
modeled with a Langmuir isotherm [111]. The
nitrogen transport version of the model simulates
nitrogen transformations of applied fertilizer and soil
nitrogen between the time of fertilizer applications
and runoff events. Soluble nitrogen transport in
surface runoff is modeled with the assumption of
complete mixing of the soil surface and surface
runoff. Sediment-bound nitrogen is modeled as a
function of the clay content of transported sediment.
The nutrient transport versions of ANSWERS received
preliminary verification using water quality data
collected from rainfall simulator plot studies. Model
predictions of dissolved orthophosphorus, nitrate,
ammonia, and sediment-bound total nitrogen and
phosphorus were generally within a factor of 3 of
observed values. The phosphorus transport version has
been used to demonstrate how targeting of BMPs can
be used to increase the cost-effectiveness of cost-share
monies [111]. Targeting of cost-share funds to critical
areas in the Nomini Creek, Virginia, watershed (10%
of the cropland area) was shown to cut the cost of
reducing phosphorus losses by approximately 80%.
In an independent evaluation of ANSWERS, the
Wisconsin Department of Planning found that AN-
SWERS was inaccurate and impractical for their land
use planning purposes [8]. A review of the Depart-
ment's report, however, shows that ANSWERS was
not designed for the Department's intended applica-
tion. This mismatch demonstrates a common model-
ing problem; attempts to make the model fit the
situation rather than finding a model suitable for the
situation are seldom successful.
Agricultural Runoff Management (ARM) Model [38]
is a continuous simulation model developed to estimate
runoff, sediment, nutrient, and pesticide loadings to
surface waters from surface and subsurface flow. The
model is an overland flow version of the Stanford
Watershed Model [21]. Small watersheds of 200 to
500 ha in size can be simulated. Land use, cropping,
and management practices are assumed to be uniform
throughout the watershed, so it is not possible to
identify critical source areas or evaluate targeting
strategies. ARM is poorly suited to NPS planning on
ungaged watersheds because it requires long-term
historical runoff and water quality records for calibra-
tion. Data requirements are extensive and data are
difficult to obtain in most cases because many parame-
ters have little physical significance. Calibration,
testing, and verification are suggested for each
application of the model [24].
Hydrologic Simulation Program-Fortran (HSPF)
Model [64] is an improved version of the ARM model
and is probably the most extensively used NPS model
[26]. HSPF is a continuous, watershed-scale model
developed to simulate the movement of dissolved
oxygen, organic matter, temperature, pesticides,
nutrients, salts, bacteria, sediment, pH, and plankton
from the land surface through streams, reservoirs, and
groundwater. Both point and NPS inputs can be
simulated. This capability allows comparisons
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Dillaha
73
between the relative magnitudes of point and NFS
pollution during water quality planning. HSPF is
better than ARM at simulating watershed diversity
because it allows the watershed to be subdivided into
land segments with relatively uniform meteorologic
characteristics, soils, crops, and management prac-
tices. Runoff from the land segments drains to
channel reaches with uniform hydrologic properties
and to larger receiving waters if they exist. It is
difficult to include many land segments in the model
because increasing their numbers greatly increases
requirements for calibration and input data. HSPF is
a large model that requires several years of historical
hydrologic records for calibration, extensive data
bases, and large computer resources. A publication is
available to help with selecting model parameters for
simulating agricultural BMPs [39]. The calibration
required to run the model and its lumped parameter
approach make it difficult to evaluate changing water-
shed conditions caused by BMP implementation. The
model is calibrated to existing conditions, and modify-
ing parameters for future conditions is difficult [88].
HSPF has received much less independent verification
than other NPS models and should be used with
caution. Formal training is recommended before
attempting to use HSPF [24].
Nonpoint Source Pollution Loading (NPS) Model
[36] is an earlier version of the ARM model devel-
oped to estimate nutrient losses in surface runoff from
urban and agricultural areas. Like ARM and HSPF,
NPS requires historical hydrologic records for
calibration. In addition to nutrients, the model
simulates runoff, sediment, water temperature, and
dissolved oxygen. Data requirements are not as
extensive as those of ARM, and the model can
simulate runoff from up to five conceptual land
segments in a single run. The model was reported to
adequately simulate total nitrogen and phosphorus
loadings and concentrations from agricultural water-
sheds where nutrients were primarily sediment-bound
[37]. Where runoff is low and pollutants are primarily
in dissolved forms, the model does not predict well
because it does not consider dissolved pollutant
transport. All pollutant losses other than sediment are
estimated by multiplying sediment losses by a potency
factor. Thus the model would probably be poor in
evaluating BMPs like conservation tillage that greatly
reduce sediment yields but often increase pollutant
concentrations. Because of these shortcomings, the
model has limited value for evaluating BMPs [24]. A
modified version of NPS was used as the NPS
pollution loading submodel in the Chesapeake Bay
Program's Chesapeake Basin Model [55]. The model
received a limited amount of calibration on 11 test
watersheds within the basin. Few details on the
calibration and testing of the model have been
reported, and it is unclear whether the model was
appropriate or well-calibrated.
Storage, Treatment, and Overflow Model (STORM)
[112] is either an event-based or continuous simula-
tion model developed for urban storm-water manage-
ment. The program is intended for simulating the
quantity and quality of runoff from small, primarily
urban, watersheds, but rural areas can also be simu-
lated. Modeled parameters include total and volatile
suspended solids, biochemical oxygen demand, total
nitrogen, and orthophosphorus. As with NPS, the
water quality parameters are assumed to be related to
suspended solids. The model does not route surface
runoff, and because of the questionable use of the
rational method for estimating runoff, runoff volumes
can be highly inaccurate even with calibration [88].
Soil loss from pervious areas is estimated by using the
USLE and user-supplied delivery ratios. Wash-off
from impervious areas is estimated by using a form of
the Sartor wash-off equation [97]. The model
considers storage and treatment of storm-water and
can consider urban BMPs such as sediment detention
basins and ponds (using a trap efficiency parameter)
and street sweeping.
Small Watershed Model (SWAM) [25] is a continu-
ous simulation, watershed-scale model currently
being developed to simulate the response of small
agricultural watersheds (less than 2500 ha) to land
management practices. SWAM is essentially a water-
shed version of CREAMS and can represent fields,
channel segments, ponds or small reservoirs, and
groundwater flow. Like AGNPS and ANSWERS,
SWAM uses a grid of square cells to represent over-
land flow source areas. Runoff from the source areas
is routed to downslope receiving waters. The model
has an internal weather generator to simulate weather
if historical time series are not available. Water
quality processes considered include nutrient cycling
and transport; decay of plant residue; pesticide
application, degradation, and transport; and soil heat
flux. Management practices that can be simulated
include irrigation, tillage, fertilizer and pesticide
application, terraces, buffer strips, animal grazing,
crop rotations, and contour farming [25]. Validation
studies that have been reported deal only with
individual components of the model, and it is unclear
whether the entire model has ever been assembled.
The developers of SWAM report that validation and
testing of the complete model is just beginning [25].
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74
Chapter 3: BMP s
Storm Water Management Model (SWMM) [62] is
the most sophisticated and widely used model devel-
oped for urban storm-water management. SWMM is
a continuous simulation, watershed-scale (5 to 2000
ha) model that simulates runoff quantity and quality
from pervious and impervious areas, erosion, scour,
sediment transport, dry weather flow and pollutant
routing in sewers, storm-water storage and treatment,
and receiving water quality. SWMM divides a
watershed into small homogeneous subcatchments (a
maximum of 200) and routes runoff from these
catchments to the drainage system. SWMM simulates
wash-off from impervious surfaces in the same
manner as STORM. Loadings of pollutants other than
sediment are generated from sediment yields using
user-supplied potency factors. The program is large
and requires extensive input data, but calibration is
not required. A comprehensive user's manual is
available [62], and the USEPA supports regular user's
conferences and workshops on the model. The
model's use for simulating NFS pollution processes
and problems was reported to be limited [88).
Water Erosion Prediction Project Model (WEPP)
[48, 50] is one of the most significant developments
that will affect NFS pollution control efforts in the
future. This model is being developed by the U.S.
Department of Agriculture to replace the USLE. The
USLE was developed over 20 years ago and has been
an integral part of virtually all NFS and erosion-
control planning efforts. The WEPP model is
intended to correct some of the deficiencies of the
USLE, such as poor estimation of erosion with
contouring and steep slopes. In addition, WEPP will
be able to predict soil losses for individual storms
using readily available input data. WEPP will be
computer-based and will contain soil, crop, and
weather databases to facilitate model use. The model
will simulate the effects of climate, soils, topography,
and cropping-management conditions on erosion,
deposition, and sediment transport. The model will
consist of three basic versions: (1) representative
overland flow profile, (2) watershed, and (3) grid.
The initial version of the WEPP technology was
delivered during the summer of 1989 and the final
model version intended for public use is expected to
be available in 1992.
Geographic Information Systems (GISs) are data
bases/models that can be used for a wide variety of
land-use planning purposes. For NFS pollution
control planning, they have been shown to be very
effective in targeting and prioritizing NFS pollution
control resources. Virginia's program for agricultural
NFS pollution control is built around the Virginia
Geographic Information System (VirGIS) [101].
Other states and the U.S. Environmental Protection
Agency in the Chesapeake Bay region have developed
similar GISs for their NFS programs. VirGIS con-
tains seven layers of base data: soils, land use, surface
water, elevations, watershed boundaries, political
boundaries, and locations of livestock facilities. The
base data are stored in a cellular digital map form.
Cell sizes range from 1/9 to 1 ha depending upon the
data type. Additional data layers derived from the
base data layers include cell slope, slope length,
length-slope factor, credibility factor, soil loss toler-
ance factor, delivery ratio, water quality index, and
erosion index. VirGIS data are currently being used
in the Virginia NFS program to identify and prioritize
agricultural land areas needing improved NFS man-
agement. Once areas have been prioritized, cost-share
and technical resources are targeted to improve the
cost-effectiveness of the program. VirGIS data has
also been used to identify highly erodible lands for the
Soil Conservation Service. VirGIS currently contains
over 36 million cells, or an area of 4 million ha. The
system is expected to expand until the entire Chesa-
peake Bay drainage in Virginia is mapped.
Advantages and Limitations of Modeling
Modeling was found to be an important element of the
RCWP program [76]. Models used for targeting and
BMP assessment in the RCWP included: CREAMS
[66], AGNPS [124], ANSWERS [10], and the USLE
[120]. RCWP modeling experiences suggested that
all models must be carefully calibrated for site-
specific conditions even if it is claimed that a model
requires no calibration. For example, CREAMS was
found to be inaccurate in the northern climate of the
Vermont RCWP, but minor modifications of the
model greatly improved its accuracy and utility there
[16]. A method for determining whether model pre-
dictions are within a prescribed factor of true values
was developed and demonstrated on PRZM [91].
An important obstacle in using models for BMP
assessment is that the greatest modeling needs are in
rural ungaged watersheds. Lumped parameter models
requiring calibration such as ARM, HSPF, and NFS
are of limited value since there are few if any histori-
cal data available for calibration. Models such as
AGNPS, ANSWERS, and SWAM that do not require
calibration, however, require extensive amounts of
information on watershed characteristics, which may
or may not be readily available. In general, physi-
cally-based, deterministic models are better able to
simulate the effects of BMPs and are therefore
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Dillaha
75
recommended for evaluation of BMP effectiveness.
At best, however, they and all other NPS models are
accurate only to within a factor of 2 or 3, and their
predictions should be used with full consideration of
these uncertainties.
Summary and Implications
Considerable progress has been made in quantifying
edge-of-field pollutant losses for most NFS pollutants
for site-specific conditions. In addition, a theoretical
understanding of many of the processes affecting
pollutant transport has been developed. This knowl-
edge has allowed the development of a variety of
mathematical models for simulating pollutant fate
and transport. Unfortunately, many of the models are
highly site-specific, and the data required for use of
the models are not readily available. Also, because
most models are research-oriented, very few (if any)
are capable of accurately simulating more than one or
two different BMPs; thus they are not generally
useful for planning purposes.
Conservation tillage is probably the most univer-
sally accepted BMP for reducing agricultural NPS
pollution in surface runoff. Conservation tillage and
other BMPs, however, are unlikely to achieve sig-
nificant reductions in nutrient and pesticide delivery
to waterways unless nutrient and pesticide levels in
surface soils can be reduced. Surface application of
fertilizers is the most popular method of fertilization
for conservation tillage, but it is inappropriate from
both agronomic and water quality viewpoints. New
chemical application methods are needed that incor-
porate agricultural chemicals into the soil with min-
imal disturbance of surface residue. The effects of
conservation tillage on pollutant loadings to ground-
water is still open to considerable debate. Since
conservation tillage increases infiltration and prefer-
ential flow, it is reasonable to assume that pollutant
loadings to groundwater will also increase.
The effectiveness of BMPs for NPS pollution
control was repeatedly reported to be highly site-
specific. Variability in site conditions (soils, land
use, topography, and weather) makes it difficult to
show statistically significant differences between
management practices. Consequently, it is difficult
to use information obtained from RCWP and other
monitoring projects to establish direct cause-and
effect-relationships between BMPs and water quality,
and the success of one project does not indicate the
likely success of another project. Field- or plot-scale
and paired watershed-scale studies in areas with
uniform site conditions are often successful in finding
statistically significant differences in BMPs, but
transferring these results to areas with different site
conditions is questionable. Watershed-scale monitor-
ing programs require a minimum of 5 years to detect
water quality improvements. Because of time and
economic constraints and because of the difficulty of
extrapolating experimental field data from one area to
another, evaluation of future BMP benefits can
probably best be accomplished with properly selected
simulation models that account for the effects of site-
specific soil, crop, topographic, and weather condi-
tions.
Research Needs for Effective Utilization of
BMPS
Research needs to improve the effectiveness of BMPs
for NPS pollution control include:
• Improved understanding of the processes
transporting pollutants between fields and
streams. Particular emphasis should be placed
on the role of filter strips and riparian zones.
• Development of satisfactory equations for de-
scribing sediment transport in shallow overland
flow. Currently-used equations were developed
for channel flow conditions, which differ signif-
icantly from shallow overland flow conditions.
• Development of a nitrogen accounting model for
temperate humid regions to allow better simula-
tions of nitrogen transformations and availability
of various nitrogen species.
• Development of a reliable test for plant-available
nitrogen to reduce over-application of nitrogen
fertilizers.
• Development of methods for applying agricul-
tural chemicals below the soil surface while
minimizing disturbance of surface residue.
• Development of a VFS design model that con-
siders the effects of concentrated flow and long-
term sediment and nutrient accumulation in
VFSs.
• Building of comprehensive data bases for model
development, testing, and verification. A series
of intensively monitored (long-term) watersheds
should be established that collect detailed data
on weather; land use; cropping; chemical appli-
cations; runoff rates, quantity, and quality; and
soil chemistry and pollutant transport. Without
these data, models will continue to be evaluated
with inadequate data bases, and modeling
limitations will be blamed on data rather than on
the models themselves.
• Concentration of model development efforts for
BMP assessment on physically-based, determin-
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76
Chapter 3: BMPs
istic, distributed-parameter models. These
models are designed for use without calibration
and have significant theoretical advantages over
other types of models in evaluating BMP
effectiveness.
Development of standard methods to determine
whether model predictions fall within a pre-
scribed range of true values. Without this, it is
difficult to compare alternative models or
determine the adequacy of model predictions.
Improved screening/targeting models to identify
potentially critical sources of NFS pollution to
improve the cost-effectiveness of NFS pollution
control programs. Particular emphasis should be
placed on the development of geographic
information systems for NFS management.
Evaluation of the long-term nutrient removal ca-
pabilities of wet ponds enhanced with wetlands.
Improved understanding of the long-term effects
of conservation tillage practices, particularly no-
till, on groundwater quality.
Research into the effectiveness of alternative
animal waste containment and treatment
facilities on nutrient removal and disinfection.
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83
Developing an Ecological Risk Assessment Strategy for the Chesapeake Bay
John Cairns, Jr., and David R. Orvos
University Center for Environmental and Hazardous Materials Studies
Virginia Polytechnic Institute and State University
Blacksburg, Virginia 24061
Introduction
The world's population growth, along with increased
individual expectations for improved quality of life,
have vastly increased the pressures upon natural
resources. As a consequence, those resources, includ-
ing the Chesapeake Bay, may eventually be unsuitable
for use. An estimated population growth of 20% [83]
by the year 2020 in and around the Bay will place
additional burdens on this watershed, which is simulta-
neously used for a variety of purposes, with frequent
conflict or competition between uses [34].
Protocols for assessing ecological and human health
risk have recently been developed to the point that an
accurate assessment of risk from a particular event in a
local area can often be made. However, development
of protocols for use on larger, regional ecosystems has
not proceeded as rapidly. Effective management of
regions such as the Chesapeake Bay cannot be carried
out in the current fragmented fashion. Relevant
research directives, rational risk assessment, and
integrated resource management are essential. The
following discussion reviews the components and
strategies of risk assessment for regional areas, exam-
ines strategy development for the Bay, identifies areas
needing additional research, and analyzes the impor-
tance of risk assessment to management practices.
Describing risk assessment methodologies requires
definition of some terminology. Stress is used in this
discussion to describe any factor that may cause
damage to an ecosystem, even though in the past stress
has been viewed as an individual physiological
reaction. Risk is defined here as the probability of harm
from a natural or anthropogenic stress in the environ-
ment [16]. Prior to 1977, potential environmental
damage was assessed by considering only effects [75].
The coupling of effects assessment with exposure gave
rise to exposure assessment, a process that has found its
way into many federal regulations [75]. Environmental
risk assessment is still a developing field and has been
defined in different ways. A National Research
Council Committee [63] defines environmental risk
assessment as "the characterization of the potential
adverse health effects of human exposure to environ-
mental hazards." An Environmental Protection Agency
(EPA) review of assessment methods [35] defines eco-
logical risk assessment as any "assessment related to
actual or potential ecological effects resulting from
human activities."
Risk assessment should be a scientific endeavor,
depending on scientific data and evaluations that
provide information to scientists as well as the public,
commercial, and regulatory sectors [75]. Risk manage-
ment, however, as the process of determining how to
deal with the risk, by definition includes scientific,
political, and socioeconomic facets [16, 63]. Although
adverse environmental effects upon human activities
are obviously important, this review concentrates solely
on ecological risk assessment using the EPA definition,
with the understanding that both human and non-human
activities affect the Bay's integrity [10].
While the objectives of particular ecological risk
assessments may vary, any such assessment should (1)
evaluate actual or potential risk from an environmental
impact, (2) determine the probability that the impact
may, in fact, adversely affect the environment, and (3)
predict potential risk prior to the actual impact. These
goals are feasible when the stressor and its affected area
are well defined, but may be more difficult to achieve
when the area is larger and the stressor is combined
with other natural and anthropogenic factors, as is the
case with the Chesapeake Bay.
The concept of localized risk assessment, the
qualitative and/or quantitative evaluation of actual or
potential harm to the environment, has been well
documented and refined in recent years [8, 15, 33, 37-
39,42, 73]. The use of environmental impact assess-
ments to predict and assess environmental and human
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Chapter 4: Risk Assessment
health risks has been mandated by federal, state, and
local statutes for some time. These procedures,
although they may be open to subjective bias because
of legal ambiguity, have been useful for predicting
localized impact from specific sources.
By contrast, the success of regional risk assessment
has not been convincingly demonstrated. Such assess-
ments examine risk to an entire region, such as the
Chesapeake Bay, rather than just a particular locality,
such as the area immediately around an industrial plant.
Few studies have adequately addressed the regional
concept, and additional research to examine what fac-
tors are important in regional assessment as well as the
uncertainty involved is warranted [53, 75]. A good
approach to regional risk assessment to date is offered
by Hunsaker et al. [46]. The authors portray regional
risk assessment as having two distinct phases: (1) a def-
inition phase in which source terms (qualitative and
quantitative descriptions of the disturbance source),
endpoints, and a reference environment (geographic lo-
cation and temporal period of the region at risk) are de-
fined, and (2) a solution phase that combines exposure
and effects to produce a risk probability. The inter-
dependence of source terms, endpoints, and reference
environment is emphasized, as is the importance of
using functionally defined regions. For example, high
ozone levels in the Adirondack region of New York
caused subsequent insect outbreaks that affected water
quality and wildlife habitat. A regional risk assessment
resulted in the conclusion that high levels of ozone did
have a regional effect, particularly on landscape pattern.
Quantitative risk assessment, or the assignment of a
probability to a risk [41, 63], is difficult even in ideal
situations because of the inherent variability of both the
environment and the testing procedures used to evaluate
the hazard. This task is further complicated when the
region affected is large and diverse. An excellent
review of several quantitative methods is found in
Barnthouse et al. [4].
Conceptual Review of Risk Assessment
Hazard and risk assessment methodologies have been
refined primarily because their use is required by
federal, state, and local statutes. These statutes are
reviewed elsewhere [2, 16, 54].
Risk assessment traditionally has several compo-
nents [63]: (1) hazard identification, (2) dose-response
assessment (usually for a particular substance), (3)
exposure assessment, and (4) risk characterization.
Hazard identification is the determination of whether a
particular substance is either a human or ecological
hazard [16]. It must be used with hazard control and
assessment to provide effective hazard management
(Figure 1). While identifying a hazard may seem
simple, legal statutes may require a preponderance of
scientific data to support the labeling of a substance as a
hazard [71].
The dose-response assessment is one of the funda-
mental principles of aquatic toxicology [55, 70].
Characterizing the relationship between the applied
dose of a substance and the response allows projections
of what response a particular dose may elicit. This step
will be difficult to apply to sources of pollution that
contain many substances.
Exposure assessment is an important component of
the risk assessment process since exposure must be
accurately known before risk from a particular impact
can be predicted or modeled. Exposure is the substance
concentration that is in immediate contact with an
organism [36]. Exposure is not the same as absorbed
dose, a measurement that requires additional physical
and physiological data. In its complete form, exposure
assessment examines the route, duration, and magnitude
of the exposure as well as describing the types of
organisms, populations, and habitats that are exposed
[63]. Recently, the term receptor characterization has
been used to describe the process of determining which
biota are subject to risk at a species, community,
population, or ecosystem level [44].
Risk assessment combines hazard identification,
dose-response assessment, and exposure assessment to
produce an estimate of the impact a particular substance
has when administered at a particular concentration.
These components may be well understood for specific
releases of well-characterized substances in a thor-
oughly studied ecosystem, but may be more ambiguous
in a regional risk assessment. Even in localized areas,
uncertainty plays a major role; this is amplified in
regional areas such as the Bay.
Endpoint Selection
The risk assessment process involves many judgments,
such as determining which impacts are important to
assess, defining what is to be. protected, and deciding
how to measure impact on those parameters selected as
important. The process of ascertaining which impacts
are important may be of research interest only, but more
likely will be of regulatory concern as well. Impacts
applicable to the Chesapeake Bay include sediment
loading, nutrient loading, and low-level toxic chemicals
in water, sediment, and adjacent wetlands [34, 56, 82].
Defining what is to be protected is an important
aspect of the strategy process. This subjective process
incorporates political and socioeconomic factors, and
priorities may change with time and administrations.
Yet, without a definition of what resources to protect,
subsequent development of strategy, testing procedures,
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Cairns and Orvos
85
Hazard Management
Zone of Endless Curiosity
Hazard
Identification
Hazard
Assessment
Zone of
Unknown
Cost /Benefit
Zone of
Limited Control
Figure 1. Components and interrelationships of hazard management.
and model development may be worthless. Although
prioritizing resources is beyond the scope of this
review, considerations should include protection of
species diversity and ecosystem functioning as well as
preservation of submerged aquatic vegetation (SAV),
aquatic biota habitat, and adjacent wetlands.
Once areas of importance are defined, then end-
points for measuring the effect of stresses upon these
important areas may be selected. Regardless of what
endpoints are chosen, they should possess common
characteristics (Table 1). Several groups of endpoints
have been proposed, including assessment and mea-
surement endpoints [46] and chronic and acute end-
points [33]. Generalized agreement about endpoints
must be reached by all parties concerned about Bay risk
assessment, including regulators, municipalities, re-
searchers, and environmental groups. While such a con-
sensus may be difficult to achieve, we must strive to
agree on what is and what is not important to monitor in
the Bay before risk assessment strategies can be further
developed. Relevant endpoints for the Bay are dis-
cussed in a subsequent section (Endpoint Selection
Criteria).
Information Uses and Transfer
Once defined, risks must then be communicated to the
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86
Chapter 4: Risk Assessment
Table 1. Ideal endpoint characteristics for Chesapeake Bay risk assessment.
Characteristic
Example
Socioeconomic relevance
Biological relevance
Susceptibility to hazard
Unambiguous operational definition
Predictive capability
Finfish yield
Wetlands acreage/distribution
Distribution of submerged aquatic vegetation
Percentage loss of oyster production
LC50 of zinc for particular species under standard conditions
managers, regulators, and politicians who will establish
legal mandates [1]. Although characterizing and
analyzing risk is a scientific pursuit, deciding whether
that risk is acceptable to society is not [22]. Such
decisions are made by politicians and managers using
cost-benefit analyses and integrated management with a
highly subjective, nonquantifiable component. While
these individuals use scientific data, they also incorpo-
rate various political and socioeconomic components.
Scientists in the past have often failed to realize this and
have not entered the decision-making process; however,
this process is a vital component of risk management.
Scientists must ensure that scientific data are properly
used by public officials. This is especially true when
regulators call for additional data and data-reviews in
order to sway a decision. Many examples exist of
additional studies that have been funded for the purpose
of delaying a controversial decision. Unfortunately, the
Chesapeake Bay cannot wait for additional multitudes
of data to be collected and analyzed before fundamental
decisions are made concerning its ultimate fate.
Reviews on decision-making processes and their
associated socioeconomic and political components
have been published elsewhere [63].
Types of Risk Assessment Methods
Even though many risk assessment methods exist [4,
35, 37, 52, 58], most assess potential for risk or
perceived risk in a particular ecosystem. Methods can
be grouped in several ways: qualitative vs. quantitative,
top-down vs. bottom-up, or reductionist vs. holistic
methods [16, 35]. Of the methods reviewed in the EPA
[35] report, most have one of three objectives: (1) to set
priorities, (2) to support the establishment of guidelines
or standards, or (3) to serve as an input to risk manage-
ment decision-making. Specific federal risk assessment
methods have been in use for some time and vary
depending upon the intended use. Designed for use
with specific statutes as previously discussed, they are
numerous; they have been reviewed elsewhere [35].
Reductionist ecological approaches to assessment
compartmentalize the myriad ecological processes that
may be affected into understandable units [16, 72]. The
effect of the hazard upon these ecological compart-
ments is then evaluated. Such approaches do not
adequately evaluate synergistic effects and may be low
in environmental realism. A relevant example for the
Bay would be the study of nutrient addition. Compart-
mentalizing the problem allows the effects of sewage
treatment plants, agriculture, and industry to be studied
independently of each other; but the total effect of all of
these sources upon an entire region may be ignored.
Holistic approaches consider the ecosystem, with all of
its interactions, as an entity for evaluation; however,
environmental uncertainty may be unacceptable, and
deciding what parameters should be evaluated is indeed
difficult. The entire Bay cannot yet be studied to
understand the complexities of interactions; therefore, a
combination of these procedures must be utilized.
Another differentiation is between top-down and
bottom-up methods [35]. Top-down methods evaluate
structural and functional changes at both the commu-
nity and ecosystem level directly and require ecosystem
data; however, few data exist for actually predicting the
effects of chemicals on such ecosystem properties [18,
35]. EPA concludes that top-down methods are useful
for setting priorities but not for quantitative risk assess-
ment. Bottom-up methods approximate community-
level effects using models primarily based on labora-
tory data from population and individual responses,
such as mortality [35]. These methods address ecosys-
tem processes, especially transfer processes, but require
large amounts of specific chemical and site data; there-
fore, they are most useful in supporting decisions for
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Cairns and Orvos
87
site-specific locations. Another criticism [35] of
bottom-up methods is that there is no accepted defini-
tion of "ecosystem health."
Local risk assessment strategies, such as bottom-up
methods [35], often utilize large amounts of site-
specific data for predictive and modeling capabilities.
Therefore, applying them to regional settings, such as
the Bay, will be difficult because of the expense of data
collection. Although extensive data bases already exist
for the Bay [47], they may not be useful if the chosen
endpoint was not represented in the initial data collec-
tion. However, risk assessments of regional ecosystems
may adopt endpoints such as landscape cover, commu-
nity composition, biotic diversity, population shifts, and
primary productivity. More uncertainty is likely in
regional assessments than in small, localized assess-
ments because current methodologies, geared primarily
toward acute effects, may not be applicable to a large
watershed that also contains a multitude of chronic and
nonpoint pollution sources.
Quantitative risk analysis methods — i.e., those that
quantify the probability of risk as well as the degree of
uncertainty — include analysis of extrapolation error,
fault tree analysis, ecosystem uncertainty analysis,
analytic hierarchy method, and the quotient method.
The ecological theory required in such system analysis
is still in its infancy [35]. Research and refinement of
these techniques must be continued since ultimately
they will provide us with a quantified estimate of Bay
risk and its associated uncertainty as well as giving
regulators the capability of predicting risk. All of these
quantitative methods, as well as their applications,
advantages, and limitations, are described in an
excellent review by Barnthouse et al. [4].
Another factor for consideration in strategy develop-
ment is the extremes of acute vs. chronic releases of
stressful inputs. Acute spills are infrequent, arouse
negative public opinion [43], and may result in subse-
quent legislation, such as that following the Bhopal
accident and the Alaskan oil spill. Chronic releases,
often far more damaging, are less likely to attract public
attention, funding, and ensuing regulation. Politicians
and funding agencies must be made cognizant of these
acute vs. chronic conflicts so that adequate attention
and funding are directed to chronic input problems,
including sediment influx, toxic chemicals, and
nutrients. Both of these extremes require creation and/
or modification of risk assessment schemes and
different management approaches for their resolution.
Uncertainty Assessment
Estimates of uncertainty in risk assessment may be
large and confusing. Several reviews exist [35, 38, 42,
46], so only a brief discussion will be presented here.
Table 2. Tiered system for determining hazard effects
at different levels of biological organization.
Tier 1 - Screening or range finding tests
Tier 2 - Predictive tests
Tier 3 - Validating Tests
Tier 4 - Monitoring
From [23].
Uncertainty is inherent in the risk assessment process.
Application or safety factors are often used in assess-
ment approaches to deal with this uncertainty; these are
sometimes, but not always, based on scientific data.
Some other techniques used to include uncertainty in
the actual assessment and modeling are statistical
confidence limits, Monte Carlo simulation, sensitivity
analysis, and field validation [35].
Significant uncertainty exists when laboratory
bioassays are extrapolated to actual environmental
effects, as is necessary in bottom-up schemes [20, 35].
Also, uncertainty is present in top-down approaches
when ecosystem parameters are examined directly with
ill-suited endpoints [35]. In conclusion, the EPA report
states: "As models become more complex and represen-
tative of real-world processes, other difficulties arise."
It also states: "The more realistic the model, the less
likely it is that adequate... data are available." There-
fore, endpoints must be relevant to the Bay, testing
procedures must be well documented, and experimental
design and statistical analysis procedures must be
adequate. It is recommended that a tiered system of
tests (Table 2) be adopted and that prescriptive legisla-
tion be more ecosystem-specific [23]. Potential and
real errors must be quantified and their effect upon risk
assessment ascertained.
Ultimately, improved predictive models will be
developed so that potential risk may be quantified. It is
imperative that such models consider spatial and tem-
poral scaling in the strategy development process [50]
since both of these will influence any strategy devel-
oped for the Bay. For example, toxics and other envi-
ronmental stressors may have a cumulative or aggregate
impact through time that is not readily evident over a
short period [24]. One of the serious problems in
estimating risk on the basis of short-term toxicity tests
on a site-by-site basis is the possibility of missing either
cumulative impacts or aggregate impacts in the system
as a whole. As a consequence, feedback of information
taken directly from the Bay is essential to control error,
at least until the predictive models of risk become more
useful than they now are. In short, we need to continue
and even improve the Bay monitoring program.
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88
Chapter 4: Risk Assessment
Table 3. Evaluation of recovery characteristics of selected ecosystems as rated by a panel of experts.
Ecosystems
Information
presently
available
Potential for
man-influenced
restoration
Water ecosystems
Freshwater flowing
Freshwater impounded
Estuary
Marsh
Shoreline
4
5
3
6
7
6
7
4
Terrestrial ecosystems
Arctic
Temperate
Tropic
8
10
2
5
10
1
Note: A rating of 10 represents the most information or highest potential; a rating of 1, the least information or
lowest potential. From [27].
Field Validation Considerations
Many models and assessments are never field-validated
[20, 26], and others may have poor predictive capability
because of confounding environmental variables. Since
the objective is to prevent damage to ecosystems, most
predictive risk models are based on evidence in the
literature, laboratory experiments, or extrapolations
from accidents in somewhat similar ecosystems. Direct
validation of these predictive models in the Bay itself is
rarely possible because of the damage such a study
would cause; therefore, the use of ecoaccidents to
validate predictive models should provide information
not readily gathered in any other way. Regulatory and
research groups must plan ecoaccident studies now so
that when an accident occurs, the logistics of studying it
have already been addressed. An exchange of both
planning and post-accident information with other
groups facing similar problems can advance the
development of a more robust array of predictive
models. Although accidental releases may be useful in
validating models that use toxic substances, it is urged
that validation of models using sediment influx and
nutrient addition should be considered now. Use of
existing research facilities on the Bay as well as
development of additional ones in remote areas would
contribute to our understanding of how these inputs
affect Bay function on a broader scale. We urge that
models developed for microcosm and mesocosm use be
tested in the Bay.
Risk to the Chesapeake Bay ecosystem might be
divided into two components: (1) risk due to displace-
ment or degradation of normal ecological conditions
and (2) risk of an inordinate recovery time from such
displacement. Indeed, estuaries have among the slow-
est recovery characteristics (4 on a scale of 10) of water
ecosystems, while marshes and freshwater systems rank
higher (Table 3). However, in some instances, recovery
might be quite rapid from certain types of displace-
ments in estuaries since estuarine organisms are more
resilient. Fisher [40] demonstrated that estuarine phyto-
plankton were more resistant to chemical stress than,
for example, organisms in open ocean, because they
were more tolerant of variable salinity and temperature.
In this case, the risk would be less than if severe dis-
placement were accompanied by an extended recovery
time. Both of these components can be estimated from
information generated outside the Chesapeake Bay
ecosystem itself but can be rigorously validated only by
information obtained directly from the Bay.
Pertinent Ecological Theory
Examining the various forms and components of risk
assessment requires a brief review of a few ecological
principles. Obviously, a thorough examination of
ecology and other disciplines essential to risk assess-
ment is beyond the scope of this review, and readers are
urged to consult other references [6, 37, 38, 72],
The biogeochemical cycling of nutrients is one of
the most crucial elements for consideration, especially
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Cairns and Orvos
89
for the Bay. Primary production in aquatic ecosystems
is chiefly limited by nutrients, and any alteration may
have significant ramifications [72, 82]. Increased
nutrient inputs into the Bay, primarily nitrogen and
phosphorus, have resulted in phytoplankton prolifera-
tion to such an extent that light penetration to SAV is
reduced. This eutrophication appears to be the main
element in SAV community changes since 1930 [32]
and must be incorporated into a risk assessment
scheme.
Organisms in the Bay may take up toxic materials
either directly from the water or indirectly by ingesting
food. Bioaccumulation, the process by which sub-
stances are taken up by aquatic organisms, is often
confused with bioconcentration and biomagnification.
Bioconcentration is a process of net accumulation of a
substance directly from the water into the organism.
Biomagnification, a consequence of bioaccumulation
and bioconcentration, results in a net increase in tissue
toxicant concentrations as the chemical passes through
trophic levels [70]. Although the amount of a particular
toxicant or other stress may be accurately known, the
actual amount to which organisms are exposed is often
not. The bioavailability of a substance is dependent
upon many factors, including temperature, pH, hard-
ness, and salinity [19, 37,77]. These factors are
important because they serve as modifiers of the toxic
response and, therefore, must be considered in risk
evaluation and resolution.
Ecological population theory describes relationships
in populations, including birth, growth, density,
regulation, and death. Such factors are important
because determining toxicant effects on populations is
far more feasible and realistic than determining effects
on individuals. Barnthouse et al. [6] related population
theory to ecological risk assessment and concluded that
neither population nor ecosystem theory alone provides
suitable models that could predict long-term impacts
from the release of hazardous chemicals. However,
such theory must be considered in research and
assessment design and will become even more impor-
tant in the risk evaluation and regulatory process as
present methods are refined and new ones developed.
Assimilative capacity is the ability of an ecosystem
to receive an impact without significant alteration in
structural and/or functional parameters of the indige-
nous community [12, 13]. For most deleterious
materials, there appear to be three ecological response
zones: (1) one in which no adverse dose-related effects
are noted, (2) one in which a change in concentration is
accompanied by a change in response, and (3) one in
which the system is incapable of further sublethal
response [13]. Thus, exceeding this assimilative
capacity will produce biologically significant environ-
mental impact. Assimilative capacity depends upon
many factors, including prior ecosystem stresses, nature
and duration of stresses (using the definition of stress
described earlier), system inertia and resiliency, and
residual effects [12]. Critics of this concept (e.g.,
Campbell [30]) assert that some alteration, detectable or
not, follows the introduction of a substance, man-made
or not. Cairns [17] defends the concept with a thorough
examination of both the strengths and weaknesses of
assimilative capacity. Whether or not ecosystem
assimilative capacity in fact exists, society is acting as
though it does [13]; at present levels of population and
individual expectations, it is difficult politically or
technologically to act otherwise. Assimilative capacity
may be measurable in the Bay with use of a scoring
system that evaluates the "health" of an area and the
stress to which it is being subjected [12]. This assimila-
tive capacity theory may explain why many Bay
problems have seemingly become more serious in
recent years as the estuary encounters more toxic
substances, nutrients, sediments, and wetlands degrada-
tion while lacking the ecological resources to compen-
sate for them.
Biological integrity is defined as the maintenance of
community structure and function of a particular locale
[14]. It is often used as an all-encompassing term but
may be quantified for some biota, such as fish, in a
particular area [49]. Biological integrity is closely
linked with two other concepts: species diversity and
richness, and assimilative capacity. If the assimilative
capacity of an area is exceeded, for example by a
toxicant, biological integrity may be impaired as
species richness and diversity decline. Although it is
difficult to relate assimilative capacity to a regional
watershed like the Chesapeake Bay because of the
natural uncertainty in observations, it is unfortunate that
more attention is not given to this crucial issue in
determining ecological risk to the Chesapeake Bay and
other major ecosystems.
Ecological Risk Assessment Strategy
for the Chesapeake Bay
The main factors that became apparent as this discus-
sion was being prepared were the complexity, diversity,
and economic importance of the Chesapeake Bay. No
one ideal scheme can be developed and applied to
formulate a risk assessment scheme for this region.
While dozens of plans could be considered, a frame-
work of four plans could effectively address the
following areas: (1) chronic point source impacts,
(2) nonpoint source discharges, (3) cumulative and
aggregate impacts, and (4) acute accidental releases of
hazardous substances. A skeleton plan is shown
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90
Chapter 4: Risk Assessment
STEP 1: Hazard Identification
1) Identify actual or suspected hazard(s)
2) Locate hazard source
3) Review literature on potential risks
STEP 2: Dose-Response Assessment
1) Establish relationship between concentration
and chosen endpoint
- May be difficult with poorly
character! zedhazard
- Laboratory assays may not be
environmentally realistic
2) Select endpoint/biomarker — a critical step
STEP 3: Exposure Assessment
1) Predict/measure biota exposure to hazards
2) Characterize uncertainty
3) Consider physical/weather influences
STEP 4: Risk Characterization
1) Estimate risk to selected biota and habitat
2) Develop/validate model
3) Characterize uncertainty
Figure 2. A skeleton risk assessment strategy for the Chesa-
peake Bay. This strategy will have to be modified for specific
objectives as noted in the text. Dose-response assessment,
step 2, may not be feasible for many regional assessments.
in Figure 2. While the strategy presented includes all of
these, refinements will clearly be needed as new data
and technology become available.
Estuarine Considerations
Many characteristics of the Bay contribute to the
difficulty of developing a risk assessment strategy for
it. Estuaries are often dominated by physical forces
[54] and offer the greatest diversity in water composi-
tion [34, 64, 82]. Other unique features of the Bay also
contribute to difficulties, including depth and width,
salinity variation, flow characteristics, and nutrient
inputs. Excellent reviews of these topics can be found
elsewhere [64], but some major points are presented
here. Although the depth of the Bay may reach 171
feet, the average depth is about 26 feet [82]. At this
depth, the Bay is far more vulnerable to the effects of
wind than bodies of deeper water, resulting in greater
temperature fluctuations, higher waves, and decreased
settling of suspended solids [64]. The Bay's deepest
regions are in channels in its midstream. Width varies
greatly, from 4 miles to 30 miles, and salinity also
varies depending upon the season, precipitation, tidal
cycle, and tributary flow [82]. The Bay drains a
watershed area of some 64,000 square miles that is very
diverse: 7% urban, 36% agriculture, 3% wetlands, and
54% forests. These features and diversity contribute to
the Bay's environmental problems [10, 34, 82].
Factors adversely affecting the Bay that must be
considered in the assessment process include sediment
influx and transfer, nutrient input, wetlands degrada-
tion, and low-level toxicants [9, 10, 82]. The main
difficulty presently facing the Bay is an increase in its
primary production rate, already acknowledged to be
among the highest of estuarine systems [82]. The
presence of these stresses and their subsequent effects
upon SAV and other components of the food chain
have caused losses in habitat for the Bay, its tributaries,
and adjacent wetlands.
Other large bodies of water, such as the Hudson
River, the Great Lakes, and the Baltic Sea, have
environmental problems resembling those of the Bay.
Limburg et al. [54] examined the Hudson River, an
estuarine system in New York, that has been used for
power generation and suffered from toxic chemical
release and dredging for highway construction. These
activities affect that estuary in different and often un-
predictable ways. Limburg and colleagues note that, of
all the biota monitored in the Hudson, only fish seemed
to be important to the public. They also stress the need
for ecosystem testing using many species, along with
careful assessments of the benthos, submerged aquatic
vegetation, and the adjacent wetlands. These criteria
apply to the Chesapeake Bay as well.
The Great Lakes also suffer from many of the same
problems as the Bay, but to a large extent these have
been overcome by international cooperation and
intensive study. The environmental problems of the
Lakes have been addressed by the International Joint
Commission on the Great Lakes [28]. Problems are
defined and solved by remedial action plans for the
many regions that comprise the Lakes. Questions
posed by the Science Advisory Board of the Interna-
tional Joint Commission for risk assessment, adapted
for the Bay, are in Table 4. As is the case for the Bay,
regional risk assessment for the Lakes has gaps in
endpoint selection and uncertainty. A recent report
from the National Wildlife Federation (NWF) [65] has
stressed the problems and uncertainties of Lake
environmental concerns. The report concluded that
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Cairns and Orvos
91
"the problem of toxic pollution of Lake Michigan sport
fish is more serious than has previously been reported."
The report states that eating 11 meals of 30-inch lake
trout in a lifetime exposes an individual to a 1-in-
10,000 chance of developing cancer. The authors state
the uncertainty of their prediction and the report
appears not to have been peer-reviewed, but it does
raise a point that has its parallel in the Bay, namely that
human activities may come at a high cost.
Initial Strategy Development
Strategy development must define what impacts are to
be examined, identify what resources are at risk as well
as their relative values, determine what endpoints are
scientifically and socially acceptable, and stipulate
which testing procedures will be used to ascertain and
predict environmental impact.
As previously noted, the Bay offers a complex
environment for regional risk assessment. If such a
regional assessment is to be successful, then several
impacts must be studied concurrently. While the
relati ve importance of these impacts is subject to
debate, we have chosen the following for inclusion in
this scheme: excess nutrients; sediment influx and
transfer; and low-level toxicants, including herbicides
and pesticides. These stresses have adversely affected
SAV, finfish and shellfish, habitat, and water quality.
Adverse effects on these resources have already
resulted from eutrophication, primary production
increases, changes in species distribution, habitat loss,
and overharvesting. To quantify these impacts and
ascertain their risk, proper endpoint selection is crucial.
Endpoint Selection Criteria
Obviously, the choice of endpoints and biomarkers
must be relevant to the environment being assessed [5,
51, 74, 79] but will be under the influence of regulators,
politicians, and other groups. Stress will have varied
impacts on different ecosystems [7], but the majority of
state-of-the-art biological tests for hazard assessment
use single species as stress indicators. However, there
are questions about the adequacy of this approach when
such tests are used as predictive tools [18, 51].
The characteristics of the ideal endpoints for the Bay
are described in Table 1. Potential structural endpoints
for localized use in the Bay include species diversity,
richness, range, recruitment, biomass, mortality, trophic
structure, and fecundity. Extreme care must be used
when selecting species for examination since spatial
distribution, stress susceptibility, and economic or
ecological relevance must be considered [54, 79, 81].
Biota such as SAV, oysters and other shellfish, plank-
ton, benthic communities, and finfish (especially
gamefish) should be used in the Bay and adjacent
wetlands. Fish have often been used because of their
economic and recreational importance [54], even
though there are other organisms of comparable
ecological importance. While examining the effects on
fish of chronic exposure to toxics, Suter et al. [79]
found the most sensitive effect was a reduction in
fecundity, rather than effects on early life stages as is
now being proposed by some regulators. Monitoring of
birds associated with adjacent wetlands has been
suggested for the Great Lakes and should serve well for
the Bay also [3]. Bandurski suggests that amateur
birdwatching groups can assist in the studies. Other
structural endpoints, such as rates of loss for various
wetland communities, are also in need of assessment.
While functional responses, such as enzyme inhibi-
tion, productivity, and nutrient cycling, have been
somewhat useful in acute, localized assessments [29],
they may not be especially useful in regional risk
assessment since no consistent endpoints have been
developed [80] and variability is a problem. Potential
functional endpoints of use in the Bay include microbial
and serum enzyme activities, behavioral assessments,
net production, substrate utilization, and literally dozens
of others that are reviewed elsewhere [25, 59, 60, 62,
69,81].
Endpoints are not available that delineate selected
parameter impacts when two or more stresses are
present. While exceptions exist, such as metal accumu-
lation in particular organs, organismal lesions, some
enzymes, etc., these are difficult to apply in regional
assessments. However, biomarker development should
eventually provide the technology to improve the
understanding of stress effects and delineation.
Large data bases exist for the Bay [57] that may aid
in endpoint selection. While our choices of regional
endpoints are not exhaustive, they should promote
discussion. Our recommendations include assessing
primary production via satellite [68]; using computer-
ized geographical data bases to predict effects from
agricultural and commercial activity in adjacent
terrestrial areas [45]; monitoring SAV [10, 76]; deter-
mining wetland-associated bird populations; using fish
communities as bioindicators [48]; delineating adjacent
wetlands acreage to finer scales; determining the range
and associated habitat quality for important species,
such as shellfish, finfish, and SAV; and continuing to
monitor water quality. Other methods, such as infrared
monitoring and use of DNA, antibodies, and other
biomarkers, may eventually be applicable to the Bay
[36, 38, 61, 66], as will the use of computer-based risk
assessment models [67].
Development of Risk Assessment Criteria
Once we have agreed on which resources are important
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92 Chapter 4: Risk Assessment
Table 4. Questions to consider in developing an environmental risk assessment strategy for the Chesapeake Bay.
1. How does the Chesapeake Bay compare to other regions with regard to toxics in the environment?
2. What is known about the fate and persistence of toxic chemicals in the Chesapeake Bay?
3. What are the most notable barriers that have prevented an ecosystem perspective in managing toxics in the
Chespeake Bay?
4. To what degree can the toxic impacts observed in fish and other wildlife be used as an "early warning
system" to protect human health and the ecosystem as a whole?
5. What general categories of toxics are of most concern in the Chesapeake Bay, and what are the relative
toxicities of these substances at various levels of biological organization (i.e., populations, communities,
ecosystem)?
6. Are there substantive differences between the levels of potential toxicants measured in the environment, e.g.,
what is the significance of 0.01 mg zinc, 0.1 mg, 1.0 mg, 10 mg, etc.?
7. Do we really have the methodologies and data bases to evaluate realistically the risk encountered by target
populations through multiple and cumulative exposures in the Chesapeake Bay?
8. What are the effects of prolonged ingestion of fish and water containing trace levels of toxic chemicals on
humans and other species?
9. Are there any examples of known injury to human health from toxic contaminants in the Chesapeake Bay?
10. What research methods are available to quantify the different patterns of toxic exposure risk and to identify
potential interactive effects from combined chemical insults to the Chesapeake Bay ecosystem?
11. What are appropriate endpoints for monitoring ecosystem integrity and/or biological integrity?
12. What are the demographics of human populations consuming fish, shellfish, and other wildlife in the Ches-
apeake Bay ecosystem?
13. How does one identify critical subpopulations subject to the effect of toxic exposure under the assumption of
known average populations?
14. How does one convert reactive interest in toxic chemicals (i.e., "not in my backyard") into proactive preven-
tive efforts?
15. What are the benefits in cost, including concealed costs, in ignoring long-term burdens to society resulting
from ecological damage for the sake of short-term gains with respect to economic exploitation of use re-
sources?
16. How much are people willing to sacrifice (i.e., how much money will they pay) for good environmental
quality and the prospect of long-term sustainable use?
17. Are present statutory frameworks reasonable and effective in protecting the Chesapeake Bay in view of the
large data requirements and the frequent impossibility of meeting these requirements?
18. Are existing institutional frameworks adequate for development and appropriate interpretation of toxicity data
for the Chesapeake Bay and for the management of biological, physical, and social dimensions of toxic risks?
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Cairns and Orvos
93
19. Are there major differences between how risk is communicated by regulatory agencies and how it is per-
ceived by the general public?
20. How can we do a better job of communicating risk, particularly in view of the "mixed messages" that the
public gets from inconsistencies in guidelines and regulations?
21. How can we do a better job of lessening risks associated with contaminants in the environment; and how can
preventive strategies be put in place that have as a basis a presumption of harm to the aquatic environment
and to human health?
22. How can we learn to live with a system in which reduction of risk to even acceptable levels appears economi-
cally, technically, and politically unobtainable?
23. What implications for risk management are there in considering people (especially local populations) as parts
of the impacted ecosystem?
24. How can we develop a better citizen understanding of ecological effects in order to encourage responsible
individual behavior and generate political support for legislative action?
Source: John Cairns, Jr., is a member of the Great Lakes Science Advisory Board of the International Joint Com-
mission and received permission from Dr. Peter C. Boyer, Secretary, to modify information from a report as it
applies to the Chesapeake Bay.
to protect and have selected endpoints on which our
decisions are to be based, we must quantify the amounts
of sediment, nutrients, and toxic substances that our
resources are being exposed to and determine how
much exposure we will allow in the future. We must
also seek to attain the policy of no net loss of wetland
acreage and function. Fortunately, the Bay already has
an extensive monitoring program. Ideally, a federal
agency, such as EPA, should oversee the development
and implementation of risk assessment so that interstate
control can be maintained and political interference
minimized.
Establishing acceptable limits for the various
impacts that the Bay faces is not the purpose of this
review. Currently, because of fiscal limitations, this is
often done by using structure-activity relationships,
single-species toxicity tests, and literature reviews. If
we are to protect and restore the Bay, we must direct
additional funds to agencies that will then be respon-
sible for determining cause-and-effect relationships
based upon scientific data from realistic experimental
scenarios, including an increased use of sediment in
toxicity tests. This will be an expensive, formidable
task.
Once a model assessment scheme has been devel-
oped, and selected important resources and relevant
endpoints have been be agreed upon, then monitoring
of the Bay should allow assessment of its overall health
and a prediction of future risk. Large amounts of data
that are not assimilated into a comprehensive frame-
work followed by definitive decisions and actions are
useless. Such a coordinated framework must include
components that will address data compilation, criteria
formulation, and technology development. Obviously,
new technology needs to be developed. Scientists, not
politicians or managers, must decide what constitutes a
healthy Bay; it will then be up to the politicians and
managers to decide if such a goal is attainable based
upon the collection of scientific, socioeconomic, and
political considerations. The authors differ from others
in believing scientists must be a part of the decision
process so that the scientific component is not mini-
mized in the final decision, as is now often the case.
Present procedures for regulating inputs into the Bay
and assessing risk are primarily administered by the
States of Maryland, Virginia, and Pennsylvania, and the
District of Columbia under mandates from federal and
state law and with the assistance of the EPA Chesa-
peake Bay Program [10, 11]. These management
procedures have been criticized, especially those
concerning future growth and development [83]. If the
needs of the Bay, and not just the individual jurisdic-
tions, are to be identified and addressed, then all parties
must reevaluate their motives and goals.
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Chapter 4: Risk Assessment
Research Directives
If we are to go from a damage assessment philosophy to
a risk assessment strategy for the Bay, crucial gaps in
available technology must be identified and addressed.
Currently, we are not able to delineate the effects of a
particular stress when it is found in conjunction with
several others; we are unsure of the long-term ramifica-
tions of low-level toxicant concentrations; we are
ignorant of how toxicity exposure is affected by Bay
sediments and biota; we need additional studies of the
factors controlling bioavailability; we need to examine
groundwater as a source of nutrients and pollutants to
the Bay; and we need to know if replacement wetlands
perform the same functions as natural wetlands.
We suggest that a long-term research plan be devel-
oped that identifies weaknesses in present knowledge
[78] and prescribes what research is needed to correct
those deficiencies. The following is presented as a
listing of research needs over the next several years.
• Development of environmentally realistic toxicity
tests, both acute and chronic, to deal with toxics
and herbicides. Whether these take the form of
single or multispecies tests, provisions for varied
trophic levels and sediment inclusion must be
developed.
• Determination of the feasibility of using present
or improved toxicity tests as predictive models
in microcosm or mesocosm systems.
• Increased development and use of biomarkers
[35, 38,44]. The utility of these tests appears
promising but needs additional exploration and
development.
• Further quantification of the energy flow
dynamics in the Bay to increase understanding
of the Bay watershed as an entity in itself.
• A better understanding of why some ecosys-
tems are relatively resilient to impacts while
others are severely affected.
• Refinements in and adaptation of ecological
population theory as it pertains to risk assess-
ment. The Oak Ridge National Laboratory is at
the forefront of this area and should continue to
be included in Bay ecological assessment
development.
• Increased research into the quantity and quality of
adjacent wetland areas, their capabilities in
toxicant removal and as species habitat, as well
as the utility of created wetlands as replace-
ments for those destroyed by human activities.
• An enhanced understanding of the importance
of groundwater in both contributing to (non-
point pollution) and resolving (water purifica-
tion in aquifers) some of the Bay's problems.
STEP1: Identify:
1) all organizations intending to use the
resource
2) uses, including episodic, by the general
public
3) potential impacts of proposed uses outside
the resource management area
STEP 2: Inform all interested parties, including
the general public, of the entire spectrum of
organizations wishing to use the resource
STEP 3: Require each organization proposing to
use the resource to indicate how the proposed use
would affect the resource
STEP 4: Send this information to all resource
users; identify conflicting or damaging uses
STEP 5: Use decision analysis or comparable
technique to resolve conflict situations including
activities not compatible with long-range sustain-
able use
STEP 6: Establish quality control conditions to
ensure that resource is not damaged by proposed
uses
STEP 7: Implement monitoring program to
ensure that predetermined quality control
conditions are being met
Figure 3. An integrated resource management protocol for
the Chesapeake Bay. Such a protocol will allow an integrated
approach to environmental problems facing the Bay.
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Cairns and Orvos
95
Integrated Resource Management
If we are to succeed at predictive risk assessment, we
must use integrated approaches incorporating all of
these techniques, as well as those yet to be developed
[21,31].
Inevitably, management decisions involve an
interaction of science, politics, and economics. In fact,
risk assessment reflects a tension between the two basic
objectives of regulation: factual accuracy and result
orientation [71].
Integrated resource management has many benefits,
including cost-effectiveness, long-term resource
protection, enhanced potential for multiple use of re-
sources, more rapid and effective restoration of dam-
aged resources, and a reduction of conflicts between the
various sectors involved in decision-making [20].
Integrated resource management should follow a
stepwise process (Figure 3) with the primary objective
being the protection of the ecological integrity of the
Chesapeake Bay so that it will be suitable for sustained
use in a variety of ways. To do this, adequate assess-
ment of realized and potential risks must be executed.
Legislators need to be provided with specific environ-
mental quality objectives rather than general goals.
Summary
To develop an ideal risk assessment scheme for the Bay
will require additional research and development of
technologies, as described, that will allow hazard and
exposure assessment beyond what is presently avail-
able. However, with available technology, we can
establish a framework for regional Bay risk assessment.
This will require collaboration and compromise. We
must move from damage assessment to risk assessment
so we can strive to predict the fate of the Bay. The Bay
is a complex system, but by identifying the problems
facing the Bay (habitat loss, species distribution shifts,
changes in energy flow dynamics, nutrient enrichment,
low-level toxics, wetlands loss, overharvesting),
deciding how to measure effects (endpoints), establish-
ing what are "acceptable" effects (criteria), and using
risk assessment methodologies to forecast how selected
resources will be affected, we can begin to control the
ultimate environmental fate of the Bay.
Central issues in risk assessment remain: whether
risk is significant, who is responsible for proving that
significance, how to eliminate tension between compo-
nent groups in the risk assessment process, and how
much risk is acceptable [71]. Additional problems also
remain for Bay risk assessment: no agreement as to
what a healthy ecosystem is, minimal consensus as to
what constitutes a relevant endpoint, the need to
identify and quantify uncertainty, and a lack of method-
ologies applicable to a large watershed. Finally, large
gaps exist in our best available technology, and relevant
research must be initiated to address these.
It is hoped that this review will increase the aware-
ness of the reader and stimulate discussion as to the
methods and limitations of risk assessment. While we
lack technology to achieve "ideal" risk assessment, we
must not wait until that technology is developed to
proceed. We must crawl before we walk, and walk
before we can run. With regard to regional ecological
risk assessment for the Chesapeake Bay, we are just
starting to stand. Only through direction, cooperation,
and compromise will the "ideal" ecological assessment
be attainable.
Acknowledgements
The authors are indebted to Rich Batiuk, Barbara
Niederlehner, Robert Atkinson, and two anonymous
reviewers for their editorial suggestions. The coopera-
tion of Rick Reynolds of CRC is also appreciated, as is
the editorial assistance of Darla Donald.
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