ORNL-5708
EPA-560/11-80-026
Contract No. W-7405-eng-26
METHODS FOR ECOLOGICAL TOXICOLOGY
A CRITICAL REVIEW OF LABORATORY MULTISPECIES TESTS
Edited by
Anna S. Hammons
Contributors
J. M. Giddings
G. W. Suter, II
L. W. Barnthouse
Environmental Sciences Division
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37830
Interagency Agreement No. 78-D-X0387
Environmental Sciences Division Publication No. 1710
Date Published: February 1981
Project Officer
J. Vincent Nabholz
Health and Environmental Review Division
Office of Toxic Substances
Washington, D.C. 20460
Prepared for
Office of Toxic Substances
U.S. Environmental Protection Agency
Washington, D.C. 20460
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37830
operated by
UNION CARBIDE CORPORATION
for the
DEPARTMENT OF ENERGY
::;;..L PROTECTION AGENCY
08317
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DISCLAIMER
This document has been reviewed and approved for publication
by the Office of Toxic Substances, U.S. Environmental
Protection Agency. Approval does not signify that the
contents necessarily reflect the views and policies of the
Environmental Protection Agency, nor does the mention of
trade names or commercial products constitute endorsement or
recommendation for use.
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FOREWORD
The scientific disciplines of ecology and environmental
toxicology have not been communicating adequately with each
other, to the detriment of both. Ecologists are often
falling short when it comes to applying the theory and
findings of their relatively young science in useful practice
to meet society's needs for assessment of the environmental
impacts of toxic pollutants. Environmental toxicologists
are increasingly having difficulty in trying to convince
society's decision makers what the results of their test
methodologies in simple systems really mean in a complex,
highly interactive ecological world.
This report takes a step toward marrying some of the
concepts of these two scientific disciplines. At the request
of the Environmental Protection Agency's Office of Toxic
Substances, the Environmental Sciences Division of Oak Ridge
National Laboratory has reviewed and evaluated potential
techniques for studying ecological effects of toxic chemicals
in systems that transcend the practicable but oversimplified
conditions of most currently used toxicological test systems.
EPA intends to use this study, and companion efforts,
to help guide our future attempts to bring about better
synergy between ecology and environmental toxicology in our
implementation of the Toxic Substances Control Act.
/ /
James J. Reisa, Ph.D.
Associate Deputy Assistant Administrator
for Toxic Substances
U.S. Environmental Protection Agency
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PREFACE
This report was prepared by the Environmental Sciences Division,
Oak Ridge National Laboratory, under an Interagency Agreement between
the Department of Energy and the Environmental Protection Agency.
The study was undertaken because of the need to examine the po-
tential for development and standardization of tests for effects of
chemical substances on selected ecological parameters that are
indicative of interspecific interactions, community dynamics, and
ecosystem functions.
Aquatic and terrestrial laboratory methods for measuring the ef-
fects of chemicals on population interactions and ecosystem properties
are discussed and evaluated for use in ecological hazard and risk
assessment processes. The report is not intended to provide detailed
descriptions of all suitable tests. Instead, it is intended to
provide a critical review of useful or potentially useful ecological
tests (i.e., those most amenable for laboratory test development) for
consideration by various technical and administrative personnel
responsible for implementing the Toxic Substances Control Act.
Although an extensive review of mathematical models was not in-
cluded in the scope of this study, a general discussion of the roles
of broad categories of models in ecotoxicology is provided. The
document is a useful resource for ecologists, environmental
toxicologists, and scientists interested in the application of
mathematical models to environmental hazard and risk assessments.
IV
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ABSTRACT
This report critically evaluates selected laboratory methods for
measuring ecological effects and recommends tests considered most
suitable for research and development for use in predicting the
effects of chemical substances on interspecific interactions and
ecosystem properties. The role of mathematical models in chemical
hazard assessment is also discussed. About 450 references are cited.
A bibliography of more than 700 references is provided.
The Office of Toxic Substances, U.S. Environmental Protection Agency
(EPA) is responsible for implementing the Toxic Substances Control Act
(TSCA). TSCA, promulgated in 1976, is comprehensive legislation
designed to broadly protect human health and the environment from
unreasonable risks resulting from the manufacture, processing,
distribution, use, and disposal of a chemical substance.
Under TSCA, EPA is responsible for identifying and prescribing test
standards to be used in developing the data necessary to predict the
risks associated with chemical releases into the environment. To aid
EPA in this endeavor, laboratory methods for measuring the effects of
chemical substances on aquatic and terrestrial interspecific
interactions and ecosystem processes were reviewed and evaluated for
their potential for standardization for use in environmental hazard
and risk assessment processes. The criteria used for these
evaluations include whether or not the tests are: rapid,
reproducible, relatively inexpensive, unequivocal, sensitive,
socially relevant, predictive, generalizable, and well-developed.
This report was submitted in partial fulfillment of Interagency
Agreement No. EPA 78-D-X0387 between the Department of Energy and the
U.S. Environmental Protection Agency.
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CONTENTS
Foreword iii
Preface iv
Abstract v
Tables xi
Figures xii
Acknowledgments xiii
1. Introduction 1
1.1 Purpose 1
1.2 Scope and Organization 3
1.3 Constraints 4
1.4 Criteria to Be Met for a Standardized Test 5
1.5 References 7
2. Conclusions and Recommendations
2.1 Aquatic Test Systems 9
2.1.1 Available in the Near Future 9
(1) Algal Competition 9
(2) Predation by Fish 10
(3) Mixed Flask Cultures 10
(4) Periphyton Communities 10
(5) Sediment Cores 11
(6) Pond Microcosms 11
2.1.2 Recommended for Research and Development .... 11
(1) Zooplankton-Zooplankton Predation Tests . . 11
(2) Fish-Zooplankton Predation Tests 12
(3) Parasitism 12
(4) Zooplankton-Algae Grazing Tests 12
(5) Pelagic Microcosms 13
(6) Model Streams 13
2.2 Terrestrial Test Systems 13
2.2.1 Available in the Near Future 14
(1) Soil 14
(2) Legume-Rhizobia 15
(3) Mycorrhizae 15
2.2.2 Recommended for Research and Development .... 15
(1) Population Interactions 15
(2) Ecosystems 16
2.3 Mathematical Models 16
2.3.1 Available in the Near Future 16
(1) Ecosystem Simulation Models 16
(2) Generalized Multipopulation Models .... 17
(3) Loop Analysis and Time-Averaging 17
(4) Input-Output Analysis 17
(5) Population Genetics Models 17
2.3.2 Recommended for Research and Development .... 17
(1) Ecosystem Parameter Handbook 17
(2) Model Validation Methods 18
vn
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(3) Theoretical Studies 18
(4) Strategy for Model Selection and
Application 18
2.4 References 19
3. Laboratory Tests for Chemical Effects on Aquatic
Population Interactions and Ecosystem Properties,
J. M. Giddings 23
3.1 Competition 26
3.1.1 Algal Competition Experiments 26
3.1.2 Conclusions and Recommendations 28
3.2 Predation 28
3.2.1 Protozoa-Protozoa 31
(1) Population Dynamics Experiments 31
(2) Mechanistic Studies 32
(3) Evaluation 34
3.2.2 Zooplankton-Zooplankton 34
(1) High-Speed Photography Studies 35
(2) Population Experiments 35
(3) Evaluation 36
3.2.3 Fish-Zooplankton 37
(1) Reactive Distance 37
(2) Prey Selection 39
(3) Capture Success 40
(4) Handling Time 41
(5) Population Experiments 41
(6) Evaluation 42
3.2.4 Fish-Macroinvertebrates 43
(1) Predation on Grass Shrimp 43
(2) Predation on Crayfish 45
(3) Evaluation 46
3.2.5 Fish-Fish 46
(1) Examples of Recent Research 46
(2) Methodological Details 48
(3) Evaluation 50
3.2.6 Conclusions and Recommendations 51
3.3 Parasitism 53
3.4 Plant-Herbivore Interactions 54
3.5 Symbiosis 55
3.6 Ecosystem Properties 55
3.6.1 Properties of Aquatic Ecosystems 55
3.6.2 Realism and Generality 58
3.6.3 Potentially Useful Model Ecosystems 61
(1) Mixed Flask Cultures 61
(2) Periphyton Communities 63
(3) Sediment Cores 65
(4) Pelagic Microcosms 67
(5) Pond Microcosms 70
(6) Model Streams 71
3.6.4 Conclusions and Recommendations 72
3.7 References 75
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4. Laboratory Tests for Chemical Effects on Terrestrial
Population Interactions and Ecosystem Properties,
G. W. Suter, II 93
4.1 Population Interactions 97
4.1.1 Competition 98
(1) Microbial Competition 99
(2) Plant Competition 101
(3) Arthropod Competition 102
(a) Drosophila 102
(b) Other Flies 103
(c) Tribolium 104
(d) Other Grain Insects 104
(e) Soil Arthropods 105
(4) Other Animals 105
4.1.2 Herbivore-Plant 105
(1) Sucking Insect-Plant 107
(a) Aphid-Alfalfa 107
(b) Aphid-Grain 107
(c) Whitefly-Plant 107
(d) Scale-Plant 108
(2) Chewing Insect-Plant 108
4.1.3 Predator-Prey 109
(1) Microbe-Microbe 109
(2) Arthropod-Predators 110
(a) Parasitoid-Gall Midge 110
(b) Parasitoid-Whitefly Ill
(c) Parasitoid-Aphid Ill
(d) Predator-Aphid 112
(e) Parasitoid-Grain Moth 112
(f) Parasitoid-Bean Weevil 113
(g) Parasitoid-Fly 113
(h) Ground-Dwelling Beetle-Prey 113
(i) Spider-Prey 114
(j) Mite-Mite 114
(3) Vertebrate Predators 116
4.1.4 Host-Parasite 116
4.1.5 Symbiosis 117
(1) Lichens 117
(2) Rhizobium-Legume 117
(3) Mycorrhizae 118
4.1.6 Community Composition 121
4.1.7 Summary 122
4.2 Ecosystem Properties 123
4.2.1 Parameters 123
(1) Primary Productivity 123
(2) Nutrient Cycling 124
(3) Community Metabolism 126
(4) Summary 127
4.2.2 Test Components 128
4.2.3 Soil Type 131
4.2.4 Size 132
IX
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4.2.5 Synthetic Systems 132
(1) Soil Systems 132
(2) Litter 133
(3) Soil-Litter 133
(4) Gnotobiotic Soil 134
(5) Soil-Plant 134
(a) Pot 134
(b) Lichtenstein 134
(c) Agroecosystem Chamber 134
(d) Summary 134
(6) Soil, Litter, Plant, and Animal 135
(a) Odum 135
(b) Witkamp 135
(c) Metcalf 135
(d) Terrestrial Microcosm Chamber (TMC) . 136
4.2.6 Excised System 136
(1) Soil Core 136
(2) Grassland Core 137
(3) Sod 137
(4) Treecosm 137
(5) Outcrops 138
4.2.7 Summary 139
4.3 References 140
5. Mathematical Models Useful In Chemical Hazard
Assessment, L. W. Barnthouse 155
5.1 Available Models and Modeling Methodology 158
5.1.1 Ecosystem Simulation Models 158
(1) Terrestrial Simulation Models 159
(2) Aquatic Simulation Models 160
5.1.2 Generalized Multipopulation Models 160
5.1.3 Alternative Methodologies 161
(1) Loop Analysis 161
(2) Time-Averaging 162
(3) Input-Output Analysis 162
(4) Population Genetics Models 162
5.2 Criteria for Evaluating and Selecting Models 163
5.3 References 166
APPENDIXES
A. Summary Table of Aquatic Test Systems 169
B. Summary Table of Terrestrial Test Systems 179
C. Alphabetical Bibliography 189
D. Bibliography Arranged by Sections 249
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TABLES
3.1 Characteristics of model ecosystems 73
4.1 Laboratory studies of mite-mite predation 115
4.2 Relative frequency of significant responses by parameters
of the rhizobium-legume symbiosis to toxic chemicals . . . ng
4.3 Relative frequency of significant responses by ecosystem
process parameters to toxic chemicals in laboratory
systems 129
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FIGURES
4.1 Ratio diagram: Ii/l2 = the ratio of the input
frequencies of species 1 and 2 and O-j/Op = the
ratio of output frequencies
5.1 Scheme for selecting appropriate models for use in
hazard assessments
100
165
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ACKNOWLEDGMENTS
We wish to express our appreciation to the following scientists in the
Environmental Sciences Divison (ESD), Oak Ridge National Laboratory
(ORNL), who reviewed sections of this report and offered valuable
comments and suggestions: B. G. Blaylock, R. B Craig, C. W. Gehrs,
S. B. Gough, F. W. Harris, H. H. Shugart, B. P. Spalding,
W. Van Winkle, B. T. Walton, J. B. Waide, and J. W. Webb. We also
appreciate the comments provided by the National Academy of Sciences
Committee to Review Methods for Ecotoxicology.
In addition, gratitude is expressed to J. Vincent Nabholz, Project
Officer, Environmental Protection Agency (EPA), James J. Reisa,
Associate Deputy Assistant Administrator for the EPA Office of Toxic
Substances, and David E. Reichle, ESD Associate Director, ORNL, for
their advice and continuing support throughout the preparation of this
report.
We also wish to thank members of the Information Division, ORNL, for
obtaining the references reviewed for this report and members of the
Technical Information Department, Science Applications, Inc., Oak
Ridge for preparing the manuscript for publication. The services of
Betty Cornett (ORNL) and Bonnie Winsbro and Judy Mason (SAI) are
especially acknowledged.
xm
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1
SECTION 1
INTRODUCTION
1.1 Purpose
The voluminous production of chemicals since World War II has
significantly increased the potential for exposing the general public
to toxic substances. More than 44,000 chemicals have been listed in
the Toxic Substances Control Act Chemical Substances Inventory:
Initial Inventory, published in May 1979 by the Environmental
Protection Agency (EPA 1979a), and new chemicals are added to the
market at the rate of several hundred per year. Sources of exposure
range from foods and other consumer products to waste disposal sites
and polluted air and water. Increasing concern about the effects of
such exposure led to the development of deliberate and comprehensive
legislation, the Toxic Substances Control Act (TSCA), which was
promulagated in 1976. The Office of Toxic Substances, EPA, is
responsible for implementing TSCA.
Other laws have been enacted that give the federal government
authority to regulate chemical substances. Some agencies responsible
for such regulation include the Food and Drug Administration, Consumer
Products Safety Commission, Occupational Safety and Health
Administration, U.S. Department of Agriculture, and the U.S.
Department of Transportation. For the first time, TSCA subjects the
entire chemical industry in the United States to federal regulation
that broadly protects human health and the environment from
unreasonable risks resulting from the manufacture, processing,
distribution, use, and disposal of a chemical substance. Requirements
under this law include testing of chemicals identified as possible
risks and controlling chemicals proven to present a risk. The most
significant aspect of TSCA is that regulatory action can be taken
before widespread exposure and possible serious damage have occurred.
Therefore, justification for such action must be based on the
predicted effects of specific chemicals on human health and the
environment.
Under TSCA, EPA is responsible for identifying and prescribing
test standards to be used in developing data necessary to predict the
human health and ecological risks associated with releases of chemical
substances into the environment. EPA has recognized a set of standard
toxicity testing procedures for assessing the environmental hazards of
chemicals (U.S. EPA 1979). These procedures are simple, rapid,
inexpensive, and easily applied to large numbers of chemicals in
laboratories throughout the country. Each test measures a direct
toxic response (usually death) of an organism or group of organisms of
a single species. The primary objective of such tests is to screen or
compare chemicals and to rank them according to their relative
toxicity. Chemicals ranking low in toxicity are presumed to pose no
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ecological hazard; chemicals ranking high in toxicity are subjected to
further testing. The success of a hazard assessment program depends
(1) on the ability of the screening tests to correctly identify
potentially hazardous chemicals and (2) on the availability of
advanced test methods to confirm and refine the results of screening
tests and to define the suspected environmental hazards more
precisely.
There is substantial evidence that some chemicals can produce
effects on organisms that do not result in death in single-species
toxicity tests, but that nevertheless impair the ability of the
organism to survive under actual ecological conditions. For example,
polychlorinated biphenyls (PCBs) in concentrations well below the
lethal levels alter the behavior of grass shrimp to such an extent
that the shrimp become more vulnerable to predation by fish; this
effect is not readily detectable unless the fish are present (Tagatz
1976; Farr 1977). The same compounds impair the nutrient uptake
capability of some marine diatoms, an effect that becomes apparent
only when the diatoms are competing with other algal species for
nutrients (Fisher et al. 1974). Effects such as these, which depend
on interactions between populations for their manifestation, can be
just as significant in a realistic ecological context as the more
easily measured direct toxic effects.
A suitable scheme for identifying and evaluating hazards to
environmental systems should include tests for predicting effects on
events and processes occurring above the single-species level.
Therefore, EPA is investigating the potential for developing test
protocols which predict the effects of chemical substances on selected
ecological parameters, indicative of interspecific interactions,
community dynamics, and ecosystem functions. Streamlined protocols
are necessary if consistent results are to be expected among different
laboratories. Unfortunately, the state of the art of ecotoxicology
does not allow the choice of appropriate tests to be made easily. As
a result, EPA has enlisted the aid of the Environmental Sciences
Division (ESD), Oak Ridge National Laboratory (ORNL); the Council on
Environmental Quality (CEQ); and the National Research Council (NRC)
in its effort to determine the importance of including such tests in
hazard assessment processes and to identify suitable extant tests and
those most amenable to laboratory test development.
Three major efforts comprise the investigation initiated by EPA:
(1) a review of laboratory test methods that predict ecological
effects on interspecific interactions and ecosystem properties and of
ecological parameters most amenable for laboratory test development;
(2) an evaluation of their potential utility to the hazard
identification and risk assessment processes of TSCA; and (3)
development of recommendations and criteria that might be used to
advance the state of applied ecological science in toxicological
assessment. The CEQ contracted the NRC to establish a National
Academy of Sciences (NAS) Committee of experts to perform the last
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task. The first two tasks have been performed by ESD, ORNL, under an
Interagency Agreement between EPA and the Department of Energy (DOE).
Results of the ORNL review and evaluation are contained in this
report. The MAS report (NAS 1981) will be available in early 1981.
1.2 Scope and Organization
This report provides a review of tests for measuring aquatic and
terrestrial population interactions and ecosystem properties in
laboratory systems. Little information is available on techniques
developed or used specifically to predict the effects of chemicals on
ecological systems. Nevertheless, tests that might be considered are
discussed in terms of their potential for use in this area. The
criteria used to evaluate this potential include whether or not the
tests are simple, rapid, reproducible, relatively inexpensive, un-
equivocal, sensitive, socially and economically relevant, and
predictive. The extent of experience with and development of each
test as well as the general izabil ity of test results were also
considered. These criteria, which are necessary considerations for
effective implementation of TSCA testing requirements, are defined in
Section 1.4.
The general problems encountered in toxicology testing processes
(i.e., selecting the appropriate dose, interpreting dose response, or
choosing the best test species) intentionally are not discussed in
this report. These problems are not unique to multispecies test
procedures. Choices will depend to some extent on the environmental
characteristics of each chemical, the expected release to the environ-
ment, and the potential for exposure. Criteria for evaluating these
issues must be determined and established while tests are being
developed and standardized.
Many resources were used to gather information, including the
ORNL Ecological Sciences Information Center, workshops, and ESD staff
scientists. The review of testing protocols was initiated by machine
and manual searching for information published in scientific
literature on (1) procedures used to measure changes in population
dynamics such as competition, predation, parasitism, herbivory, and
symbiosis and (2) ecosystem processes such as primary production,
nutrient cycling, community metabolism, and litter decomposition. In
addition, a series of six workshops on ecotoxicological test systems
was conducted by the ESD staff to bring together investigators
presently working with aquatic or terrestrial laboratory test systems.
The intent of these workshops was to ensure that every available test
potentially usable in a standardized ecological effects testing scheme
would be identified and considered. The topics of the workshops were:
Assessment and Policy Requirements of Ecological Toxicity Testing
Protocols, Mathematical Models Useful in Toxicity Assessment, Methods
for Measuring Effects of Chemicals on Terrestrial Ecosystem
Properties, Methods for Measuring Effects of Chemicals on Aquatic
Ecosystem Properties, Methods for Measuring Effects of Chemicals on
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Terrestrial Population Interactions, and Methods for Measuring Effects
of Chemicals on Aquatic Population Interactions. The results of these
workshops will be published as a single ORNL/EPA report (Mammons,
1981). Other valuable resources were the many scientists at ORNL who
were available for consultation, document review, and workshop
participation and whose data files were made available for our
perusal.
This report is organized into three sections: (1) aquatic
population interactions and ecosystem properties, (2) terrestrial
population interactions and ecosystem properties, and (3) mathematical
models. A brief discussion of categories of models is included
because models are recognized as potential tools for identifying and
assessing environmental hazards.
Many published documents describing laboratory test systems were
reviewed by the authors, and many investigators were contacted
personally, but to minimize the time required to complete this
project, no attempt was made to provide detailed methodologies or
discussions of the results of all the tests considered. Nevertheless,
examples of the different types of tests discussed in this report are
cited throughout the text, and a complete bibliography is attached
(Appendixes C and D) for the reader who is interested in obtaining
more detailed information. Summary tables (Appendixes A and B) are
also used to present additional details about the most significant
aspects of specific tests.
1.3 Constraints
As expected, relatively few laboratory tests for predicting the
effects of chemicals on interspecific interactions, community
dynamics, or ecosystem properties exist. In addition, the
understanding of community and ecosystem responses to perturbations is
limited. This limited knowledge in basic ecology makes it impossible
at present to recommend with certainty tests useful for successfully
predicting adverse ecological effects resulting from exposure to
chemical substances. It is important for the development of adequate
hazard assessment tools to establish by continued research into the
mechanisms of communities and ecosystems: (1) the limits to which
these systems can be taken before recovery is no longer possible, (2)
the measurable parameters or "symptoms" indicative of adverse effects,
and (3) the generality of these symptoms among other communities and
ecosystems.
The tests recommended in this report are considered to have the
best potential for use under the TSCA based on the present state of
the knowledge of ecotoxicological testing. As indicated throughout
this report, more information is needed in many areas of ecological
science before unequivocal conclusions can be reached concerning
appropriate laboratory tests for predicting the ecological effects of
chemical substances.
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1.4 Criteria to Be Met for a Standardized Test
This report was prepared in the context of a general, tiered
testing scheme for hazard assessment. Such a scheme provides for
different levels or stages of testing which progressively become more
complex and more definitive as positive results from one level trigger
decisions to proceed to the next higher level.
Several criteria were determined by EPA and ESD to be important
in selecting ecotoxicological tests for development and standard-
ization for use in a hazard assessment scheme. These criteria were
applied to the test systems reviewed for this report in a qualitative
manner based on the scientific judgment of the authors and the input
received from the many researchers who participated in the workshop
series. Several of the criteria were applied differently, depending
on the level of testing that was considered. For example, although
cost should always be minimized, it would be expected to increase with
increasing complexity of the test system used. Sufficient information
was not always available to apply all of the criteria to all of the
tests.
The following list provides definitions of the criteria as they
were used in evaluating the tests selected for inclusion in this
report:
Cost per Test - The total cost of completing a test for a single
chemical assuming that the facilities are already available.
Documentation - The extent to which the behavior of a laboratory
system (not necessarily toxicological) has been investigated and
reported.
Generality - The usefulness of the test in predicting the responses
of a variety of interspecific interactions or ecosystems and
their major components.
Rapidity - The total amount of time required to complete a test
assuming that facilities already exist.
Realism - The ability to unambiguously interpret the response of the
test system in terms of responses of real ecosystems.
Rejection Standards - Defined criteria for rejecting test results—
ranging from informal or common-sense criteria (e.g., many
controls die) to a complete and well-defined set of criteria
(e.g., more than 10% of controls fail to achieve a weight of
20 g).
Replicability - The variance in response within an experiment among
individual units of a test system.
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Reproducibility - The ability of a test to produce common results in
different laboratories.
Sensitivity - The ability of a test to produce measurable responses
at low doses of test chemicals.
Social Relevance - The value to society, direct or indirect, of the
response measured. The value may be economic, aesthetic, or
indirectly related to human health and welfare.
Standardization - The definition of conditions and components of a
test system to allow different laboratories to obtain similar
results from a test.
Statistical Basis - Accepted statistical criteria for detecting and
interpreting responses of the test system.
Training-Expertise Requirements - The extent to which use of a test
may be limited by requirements for higher education, specialized
training, or expertise.
Validity - The extent to which the responses of a test system are
known to reflect responses in the field.
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1.5 References
Farr, J. A. 1977. Impairment of antipredator behavior in
Palaemonetes pugio by exposure to sublethal doses of parathion.
Trans Am. Fish. Soc. 106:287-290.
Fisher, N. S. , E. J. Carpenter, C. C. Remsen, and C. F. Wurster.
1974. Effects of PCB on interspecific competition in natural and
gnotobiotic phytoplankton communities in continuous and batch
cultures. Microbial Ecol. 1:39-50.
Mammons, Anna S. 1981. Ecotoxicological Test Systems: Proceedings
of a Series of Workshops, ORNL-5709; EPA 560/6-81-004, Oak Ridge
National Laboratory, Oak Ridge, Tennessee.
National Academy of Sciences. 1981. Testing effects of chemicals on
ecosystems. A report by the Committee to Review Methods for
Ecotoxicology. National Academy of Sciences, Washington, D.C.
Tagatz, M. E. 1976. Effects of mirex on predator-prey interaction in
an experimental estuarine ecosystem. Trans. Am. Fish. Soc.
105:546-549.
U.S. Environmental Protection Agency. 1979a. Toxic Substances Control
Act chemical substances inventory: Initial inventory. Office of
Toxic Substances.
U.S. Environmental Protection Agency. 1979b. Toxic Substances
Control Act premanufacture testing of new chemical substances
(OTS-050003; FRL-1069-1), Fed. Regist. 44(53): 16240-16292.
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SECTION 2
CONCLUSIONS AND RECOMMENDATIONS
2.1 Aquatic Test Systems
We have surveyed the recent ecological and toxicological
literature for reports of laboratory techniques for measuring the
effects of chemicals on interactions between aquatic organisms.
Interactions considered in this survey included interspecific
competition, predation, parasitism, grazing (herbivory), and
symbiosis. We found few relevant studies pertaining to parasitism,
grazing, or symbiosis. However, a variety of techniques are available
for testing chemical effects on competition and predation. A few of
these techniques appear to be quite amenable for standardization and
routine use—that is, they are relatively simple, rapid, economical,
and reproducible.
We have also surveyed test methods for chemical effects on whole
ecosystems. Ecosystem-level phenomena, such as energy flow, nutrient
cycling, and homeostasis, result from interactions among ecosystem
components, but the mechanisms involved are not completely understood.
Effects of chemicals on ecosystem properties are therefore not pre-
dictable from results of single-species toxicity tests. Very little
is known about the sensitivity of ecosystem properties to toxic
chemicals. Furthermore, the complex network of interactions occurring
in an ecosystem can cause chemical effects on one species to affect
other ecosystem components in unpredictable ways. Because all popu-
lations in nature are parts of whole ecosystems, there is a clear need
for methods of testing chemicals for ecosystem-level effects.
Very few aquatic multispecies test systems have been developed
specifically for chemical hazard assessment, but several have been
refined to the point that protocols could be formulated and tested
with a variety of chemical types (Sect. 2.1.1). Other aquatic test
systems are potentially usable for chemical hazard assessment, but
require further research before standard procedures can be specified
(Sect. 2.1.2). The true merits, if any, of all of these systems will
be revealed only through practical experience. Moreover, effective
use of laboratory test systems to predict chemical effects on aquatic
population interactions and ecosystem properties will depend on
advances in our basic understanding of the structure and function of
aquatic ecosystems. Until such advances are forthcoming, no hazard
assessment protocol at any level of biological organization can be
considered truly "validated."
2.1.1 Available in the Near Future
(1) Algal competition. Algae are more sensitive to toxic
chemicals when competitors are present than in pure culture (Fielding
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10
and Russell 1976; Fisher et al. 1974; Kindig 1979; Mosser et al.
1972). Algal competition tests such as those of Mosser et al. (1972)
and Fisher et al. (1974) are simple, inexpensive, rapid (1 to 2
weeks), easily standardized, and ecologically meaningful.
Developmental needs include selection of appropriate species pairs,
comparison of batch vs. continuous culture techniques, and
standardization of experimental conditions. The ecological
significance of alterations in phytoplankton community structure must
be documented. Algal competition experiments are discussed in Section
3.1.1.
(2) Predation by fish. Predator-prey systems incorporating fish
as predators and either fish or shrimp as prey are ready for standard-
ization as hazard assessment protocols. Various options for the
design of fish predation tests are discussed in Sections 3.2.4 and
3.2.5. Several experimental approaches have been used for measuring
chemical effects, but without comparative data on specific compounds
in different test systems, it is impossible to recommend any
particular system for further development. Rather, the effects of
major design options on the sensitivity, reproducibility, and
efficiency of chemical effects tests should be investigated.
(3) Mixed flask cultures. Mixed cultures of bacteria, algae,
protozoa, and zooplankton have been found to exhibit certain
characteristics common to all ecosystems and could be used as
ecosystem-level "white rats" for screening purposes. These abstract
model ecosystems are small, easily replicated, and technically simple
to operate. The major questions remaining to be resolved are: (a)
are ecosystem-level properties more sensitive to chemicals than
conventional bioassay organisms, and (b) are rankings generated by
these systems different from rankings produced by conventional tests?
If the answer to either question is affirmative, then mixed flask
cultures should be included early in the chemical hazard assessment
testing sequence. Factors to be considered in the design of these
systems are discussed in Section 3.6.3 (1).
(4) Periphyton communities. Periphyton communities, which are
found in nearly every aquatic habitat, exhibit all the major ecosystem
functions. These communities grow well in laboratory systems; they
are stable, replicable, biologically complex, and easily handled.
Periphyton community structure has been widely used as an indicator of
aquatic pollution, and chemical effects on periphyton community
function have been observed in chronic experiments (Rodgers et al.
1980). Unlike the other test systems recommended for development in
the near future, standardization of periphyton systems for chemical
hazard assessment has not been attempted. However, the reviewer sees
no serious methodological obstacles to the development of a periphyton
community assay and recommends that research be initiated towards that
objective [Section 3.6.3 (2)].
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11
(5) Sediment cores. The technique of extracting sediment cores
overlaid with water for study in the laboratory has been widely used
by ecologists. If cores are maintained at ambient temperatures, with
aeration and mixing of the water to simulate natural conditions,
ecological processes and effects of chemicals can be examined over
extended periods of time. The approach is essentially identical for
studies in hypolimnetic, littoral, or coastal marine environments. The
sediment core technique could be applied at almost any level of a
hazard assessment scheme. Simple static systems are amenable to
short-term tests of chemical effects, whereas more complex
semi-continuous flow systems are suitable for long-term studies. An
outline for a chemical testing protocol using sediment cores was
formulated at the Workshop on Methods for Measuring Effects of
Chemicals on Aquatic Ecosystem Properties held in conjunction with
this project (Giddings 1981). This protocol, or one like it, should be
refined and tested with a variety of chemicals. Relevant features of
sediment core systems are discussed in Section 3.6.3(3).
(6) Pond microcosms. Naturally derived pond microcosms are
structurally and functionally realistic representations of natural
ponds. These model ecosystems are quite simple to assemble and to use
for chemical effects studies, and a proposed pond microcosm protocol
has been published (Harris et al. 1980). The next step in the
development of these systems for chemical testing should be
identification of the most sensitive and informative responses to be
measured. The best use of pond microcosms in hazard assessment would
be for confirmation and refinement of predictions based on simpler
laboratory tests. At least one major chemical manufacturer (Monsanto)
includes pond microcosms in the advanced stages of its hazard
assessment program (Gledhill and Saeger 1979). Pond microcosm
research is reviewed in Section 3.6.3(5).
2.1.2 Recommended for Research and Development
(1) Zooplankton-zooplankton predation tests. Most predator-prey
studies with zooplankton have used the population approach in which
groups of prey animals are exposed to a predator for a specified
period of time, and the survivors of the prey population are counted.
These experiments are simple and rapid and could easily be adapted to
toxicity testing. Many zooplankton species are easily cultured, and
large reproductive populations can be maintained in static aquaria.
Predation tests can be conducted in small, static systems.
Experiments can be completed in 8 h or less, and the surviving prey
can be preserved to be enumerated later. Because zooplankton are
nonvisual predators, lighting is not a critical factor, and
experiments can be conducted in darkness. Learning, social
interactions, and disturbances caused by observers are much less
important in zooplankton-zooplankton systems than in fish systems.
The sensitivity of zooplankton predation to chemicals is unknown.
Replicability of zooplankton-zooplankton systems is probably good.
These systems are discussed in Section 3.2.2.
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12
(2) Fi'sh-zooplankton predation tests. Many fish are obligate or
facultative planktivores during at least part of their lives. The
quality and quantity of available prey and the ability of fish to
locate and capture food organisms are important factors in controlling
fish productivity and in determining which fish species will succeed
in a particular environment. Field studies have shown that selective
predation by planktivorous fish can dramatically alter the species
composition of the zooplankton community.
Fish-zooplankton predation tests are more complicated than tests
with zooplankton predators. Fish cultures require more space than
zooplankton cultures, and continuous flow systems are necessary for
most species. Likewise, predation studies involving fish generally
require large volumes and/or continuous flow. Lighting conditions and
background must be carefully controlled to ensure repeatable results
with these visual predators. Effects of learning, social behavior,
and unintentional disturbances are more likely to occur with fish than
with zooplankton predators. All of these factors imply that
fish-zooplankton systems would be less amenable to chemical hazard
assessment than zooplankton-zooplankton systems. However, experiments
with fish might be faster than zooplankton predation tests since fish
consume more prey in a given time than do zooplankton.
Because of the social and economic importance of many
planktivorous fish, an attempt should be made to develop an efficient
fish-zooplankton test system. The problems discussed above and in
Section 3.2.3 indicate that test procedures would have to be specified
in considerable detail, but the problems are not insurmountable in
developing a protocol.
(3) Parasitism. It is widely recognized that the incidence of
parasitism or disease in a population is determined partially by the
physiological state of the host organism and that various environ-
mental stressing agents can reduce the host's resistance to infection
(Snieszko 1974; Wedemeyer 1970). However, only one example was found
of an experiment specifically designed to measure chemically induced
susceptibility to parasitism (Couch and Courtney 1977). Since the
effects of chemicals (in this case, drugs) on parasitism and disease
are the subjects of clinical parasitology, it is recommended that the
literature of this field be surveyed to evaluate the possibility of
developing a hazard assessment protocol.
(4) Zooplankton-algae grazing tests. Grazing by zooplankton on
phytoplankton is recognized as an important component of ecosystem
energy flow and nutrient cycling and as a possible determinant of
plankton community structure, but it has received little attention in
environmental toxicology. One reason for this is that methods for
measuring plankton grazing rates, either in situ or in the laboratory,
are still poorly developed. A phytoplankton-zooplankton hazard
assessment test would be essentially a single-species bioassay, with
zooplankton grazing rate as the measured response. Inert particles
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could be (and often are) substituted for algae in this type of test
without changing the nature of the experiment significantly. The
sensitivity of zooplankton grazing to chemical stress is not known and
should be investigated.
(5) Pelagic microcosms. Simulation of marine and freshwater
pelagic (open-water) ecosystems in laboratory microcosms has been
attempted at the EPA Environmental Research Laboratory at
Narragansett, Rhode Island (Perez et al. 1977) and at the Lawrence
Berkeley Laboratory (Harte et al. 1978, 1980). Pelagic ecosystems are
dominated by physical processes such as turbulence and advection that
are difficult to scale down to a laboratory system. However, by
directing careful attention to simulation of natural physical
conditions, it is possible to reproduce many features of pelagic
ecosystems in the laboratory. In their current state of development,
pelagic microcosms are useful tools for basic research and some
special applications, but they are not yet ready for standardization
as TSCA hazard assessment protocols. Further research should
concentrate on measurements of ecosystem properties rather than
taxonomic structure of pelagic systems. Given several more years of
research, it is possible that a streamlined protocol will emerge for
chemical hazard assessment. Pelagic microcosms are discussed in
detail in Section 3.6.3 (4).
(6) Model streams. Streams are, in the opinion of Warren and
Davis (1971),"among the most difficult freshwater systems to model."
Participants in the Workshop on Methods for Measuring Effects of
Chemicals on Aquatic Ecosystem Properties (Giddings 1981) concluded
that simple laboratory recirculating streams come closest to
satisfying the operational criteria (simplicity, rapidity,
reproducibi1ity, low expense) for a TSCA hazard assessment tool.
However, the same systems that are most amenable for routine chemical
hazard assessment may be the least generalizable to natural
ecosystems. Small recirculating model streams lack the openness that
is the distinctive feature of stream ecosystems; only larger, open
systems are enough like natural streams to permit reliable
predictions. Even with larger model streams, doubts about ecological
realism were expressed by the participants in the Workshop (Giddings
1980). While potentially useful in many areas of applied and basic
ecological research, model streams are not yet suitable for chemical
hazard assessment under TSCA. With further refinement, they might be
used in advanced stages of testing when transport and fate have been
fully characterized and probable ecological effects have been
carefully defined. Model streams are discussed in Section 3.6.3 (6).
2.2 Terrestrial Test Systems
Multispecies test systems are needed to test effects on system
properties that are not present in single species systems because (1)
emergent and collective properties of ecosystems cannot be tested in
single species systems, (2) single organisms and populations do not
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14
necessarily respond realistically in isolation, and (3) the properties
of chemicals can be changed by various ecosystem components. However,
terrestrial ecotoxicology has been largely concerned with the
transport, accumulation, and degradation of toxicants; this activity
generates estimates of environmental concentrations, the results of
which are interpreted according to the responses of single species.
Ecosystem-level responses have been studied much less commonly, and
most of this work has been done with systems that only include soil
and associated microbiota. Only these systems are sufficiently
developed for use in testing effects on ecosystem properties. The
responses of more complex "microcosms" are not yet interpretable in
terms of either their internal responses or their relevance to field
responses, but results are sufficiently promising to justify further
research and development.
Little work has been done on the toxicology of population inter-
actions. It is not clear whether (1) species associations respond to
chemicals as a unit, (2) the effects of chemicals on a species are
qualitatively affected in any regular way by the presence of a second
species, or (3) the presence of a second species simply has a quanti-
tative effect on the response of the first species. A second major
issue is generality-for example, which responses, if any, of a test
system using predation by the parasitoid Encarsia formosa on the
whitefly Trialeurodes vaporariorum are generally applicable to
hymenopteran predators and homopteran prey, insect predators and prey,
or to all predation. Answers to these types of questions are central
to the design of a test program for population interactions because
they indicate what parameters should be measured and which and how
many species associations must be tested.
2.2.1 Available in the Near Future
Because terrestrial ecological toxicology has been a relatively
neglected field, only a few potential test systems are available for
use in the near future. In addition to the problems identified for
each test, there are some common developmental problems. First, a set
of standard reference test chemicals must be identified and used in
test development and as positive controls for test use. Second, the
responses of a test protocol must be validated by field experiments.
Third, the ability of a test protocol to give consistent results must
be confirmed by use in several laboratories.
(1) Soil. The best developed multispecies test system is a
simple test for C02 production and nitrogen mineralization by natural
soil microbial communities [Sect. 4.2.5(1)]. This type of test is
relatively rapid, inexpensive, and easily performed. A tentative
protocol for this test, similar to the one developed by the ORNL
workshop participants (Suter 1981b), should undergo confirmatory
testing to determine the effects of soil type and substrate amendments
on standard reference chemicals. Studies to determine the optimum
number of replicates, amount of soil per replicate, and sampling
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15
schedule could be conducted concurrently. Because this system, as
proposed, requires 2 weeks and is not apparently sensitive [Sect.
4.2.1(4)], it does not appear to be useful as a screening test, but
it could be used relatively early in the hazard assessment process.
(2) Legume-rhizobia. A test for effects on this symbiotic re-
lationship should be developed using a domestic legume, commercial
innoculum, and greenhouse conditions [Sect. 4.1.5(2) and Suter 1980a].
Test development should include examination of the effects of soil
type, legume and Rhizobium species, and parameters measured on test
performance. This test should be easy to perform, relatively
inexpensive, and require less than a month to complete. While it does
not appear suitable for screening, it could be used early in the
testing scheme.
(3) Mycorrhizae. Tests for effects of chemicals on the
symbiosis of flowering plants with endo-and ectomycorrhizae should be
developed [Sect. 4.1.5(3)]. Test development should include
examination of the effects on test performance of soil type, plant and
fungus species, and parameters measured. While these tests appear to
be reasonably inexpensive and easy to perform, they would probably not
be used early in a testing scheme because they require approximately 3
months for completion.
2.2.2 Recommended for Research and Development
(1) Population interactions. Because of the absence of toxico-
logical experience with population interactions other than the two
already listed (Sect. 2.2.1), there is no strong basis for selecting
specific systems or even for prescribing the necessary number of cate-
gories of tests. However, on the basis of perceived importance, feasi-
bility, and ability to represent real systems, we consider the
following potential test systems to be good candidates:
Grass-legume competition [Sect. 4.1.1(2)]
Homopteran-plant herbivory [Sect. 4.1.2(1)]
Lepidopteran-plant herbivory [Sect. 4.1.2(2)]
Parasitoid-homopteran predation [Sect. 4.1.3(2)]
Ladybird-homopteran predation [Sect. 4.1.3(2)]
Mite-mite predation [Sect. 4.1.3(2)]
Other systems are highly developed and easily implemented, but
are not felt to be realistic or representative. These systems can aid
in the development of population interaction tests by providing
relatively quick and inexpensive checks of the generality of responses
observed in the more realistic test systems. This category includes:
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Drosophila competition [4.1.1(3)]
Tribolium competition [4.1.1(3)]
Housefly-blowfly competition [4.1.1(3)]
Parasitoid-grain moth predation [4.1.3(2)]
Parasitoid-fly predation [4.1.3(2)]
Because there is no empirical or theoretical basis for ranking
systems within these groups, ranking should be conducted on the basis
of the interests and qualifications of responding researchers.
(2) Ecosystems. More research and development should be
performed on medium-sized soil core microcosms with soil covers of
litter, herbaceous vegetation, and seedling trees (Sect. 4.2.5 and
4.2.6). These studies are needed to elucidate the importance of the
different physical and biotic components to system response (Sect.
4.2.2) and the importance and representativeness of parameters
measured in microcosms relative to whole-ecosystem responses (Sect.
4.2.1).
2.3 Mathematical Models
A variety of mathematical models and modeling methodologies
appear potentially useful in hazard assessments conducted under TSCA.
Possible uses include both predicting the effects of chemical
substances on multipopulation systems and ecosystems and interpreting
the results of microcosm experiments in terms of causal pathways.
Most of these models and methodologies were developed as research
tools and have never had practical applications. All require
substantial development and testing before they can be reliably used
in hazard assessments. Additional research above and beyond the
development of specific models is required because of the fundamental
differences between mathematical models and laboratory test systems.
The number and identity of components included in a model, as well as
the detail with which each component is modeled, can be designed to
fit the specific needs of the problem at hand. Strategies for
efficiently utilizing this versatility in hazard assessments need to
be developed. Similar, and equally plausible, models of the same
system can yield radically different predictions about the effects of
chemical substances. For this reason, it is essential that efficient
methods for evaluating the validity of model predictions and for
selecting between alternative models be developed.
2.3.1 Available in the Near Future
(1) Ecosystem simulation models. A variety of ecosystem
simulation models exist that could, with varying degrees of
modification, be used to make predictions about the effects of
chemical substances on ecosystems. Because of their relatively
realistic representations of ecological processes, forest succession
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17
models (Botkin et al. 1972; Shugart and West 1977), IBP biome models
(e.g., Innis 1972; Park et al. 1975), and pesticide fate-and-effects
models (e.g., Falco and Mulkey 1976) appear to be especially
appropriate candidates.
(2) Generalized multipopulation models. These are simple,
highly generalized models that can be rapidly and inexpensively
tailored to fit any system of interacting populations, aquatic or
terrestrial. Because physical, chemical, and biological processes are
not represented in realistic detail, these models are thought to be
more appropriate for screening of substances for potential effects
than for detailed toxicant- or site-specific assessments (as might be
required in connection with regulatory actions).
(3) Loop analysis and time-averaging. Loop analysis (Levins
1974; Lane and Levins 1977) and time-averaging (Levins 1979) are
methods of analyzing the qualitative behavior of systems of coupled
differential equations such as those employed in generalized
multipopulation models. In addition to predicting responses of
multipopulation systems to chemical substances, these methods can be
used (a) to identify critical parameters that should be measured, (b)
to identify system properties that enhance or reduce impacts, and (c)
to analyze data obtained from microcosm experiments.
(4) Input-output analysis. Input-output analysis (Finn 1976;
Hannon 1973; Lettenmaier and Richey 1978) is a method of econometric
analysis that has been modefied for use in analyzing material budgets
in ecosystems. Presently, its primary use is in deriving descriptive
indices that summarize complex data relating to material cycling
patterns. Changes in these indices may indicate system dysfunction
caused by stress. Input-output analysis requires further development
and testing before it can be used for predictive purposes.
(5) Population genetics models. The very large body of theory
on population genetics can be applied to predicting the evolutionary
responses of populations to chemical substances. Such applications
have great potential value because populations in nature frequently
evolve in response to exposure to chemical substances (e.g.,
pesticides and antibiotics). No other kind of model can predict these
effects.
2.3.2 Recommended for Research and Development
(1) Ecosystem parameter handbook. Standard ecosystem simulation
models, specially tailored for predicting the effects of chemical
substances, and standard data sets are needed for representative
terrestrial and aquatic environments. As an aid to model development,
an ecosystem parameter handbook should be compiled. This handbook
would include definitions and standard notations for parameters that
are used in ecosystem models. It would also include a codification of
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18
properties of ecosystems relevant to modeling (e.g., numbers of
trophic levels and functional groups in different ecosystem types,
relationships between primary and secondary production, and average
numbers of prey species fed on by various predators).
(2) Model validation methods. Research on model validation
methods is urgently needed to support the use of mathematical models
in hazard assessments. Clearly, it is necessary to evaluate the
reliability of any model that will be used as part of the basis for
regulatory actions. Equally important, efficient methods for
determining the relative merits of alternative models must be
developed, because decision makers in contested proceedings are likely
to be presented with different models, sponsored by different
contesting parties, that make radically different predictions because
radically different predictions can be made using different models.
The technical basis for recommending the specific research projects
necessary for developing operational model validation protocols does
not presently exist. It is recommended that EPA develop contacts with
researchers actively engaged in model validation studies to enlist
their aid in developing a research program. A national or
international conference on model validation would be a valuable first
step.
(3) Theoretical studies. Theoretical studies using generalized
multipopulation models, loop analysis, input-output analysis, and any
other similar analytical methodologies should be performed to define
the possible responses of systems to chemical substances. Examples of
the kinds of results that could be obtained are the identification of
(a) system properties that confer resilience or vulnerability to
chemical substances and (b) conditions under which sublethal exposures
to chemical substances can cause destabilization of competitive or
predator-prey systems. Results of such studies, which can be
conducted relatively rapidly and inexpensively, would suggest
processes that should be incorporated in more complex models and
hypotheses that should be tested using ecosystem simulation models,
microcosm studies, and field studies.
(4) Strategy for model selection and application. Regardless of
how many and what kinds of models are available, an overall strategy
for selecting and applying models will be required to use models
productively as part of the hazard assessment process. As part of this
strategy, a flowchart decision tree should be developed as an aid in
identifying the best model(s) for any given assessment problem.
Because development of this strategy will require intimate knowledge
of the hazard assessment process and the overall procedures for
implementing TSCA, active participation by the Office of Toxic
Substances will be necessary.
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2.4 REFERENCES
Botkin, D. B., J.
consequences
60:849-872.
F. Janak, and J. R. Wall is. 1972. Some ecological
of a computer model of forest growth. J. Ecol.
Couch, J. A. , and
pollutants and
Annals N.Y. Acad.
L. Courtney. 1977. Interaction of chemical
virus in a crustacean: A novel bioassay system.
Sci. 298:497-504.
Falco, J. W. , and L. A. Mulkey. 1976. Modeling the effect of
pesticide loading on riverine ecosystems. IN Ott, W. R. (ed.),
Environmental Modeling and Simulation. EPA-600/9-76-016/.
Fielding, A. H. , and G. Russell. 1976.
competition between marine algae. J.
The effect of copper on
Ecol. 64:871-876.
Finn, J. T.
derived
1976. Measures of ecosystem structure and function
from analysis of flows. J. Theor. Biol. 56:363-380.
Fisher, N. S. , E. J. Carpenter, C. C. Remsen, and C. F. Wurster.
1974. Effects of PCB on interspecific competition in natural and
gnotobiotic phytoplankton communities in continuous and batch
cultures. Microbial. Ecol. 1:39-50.
Giddings, J. M. 1981. Methods for measuring effects of chemicals on
aquatic ecosystem properties. IN Hammons, Anna S. (ed.),
Ecotoxicological Test Systems: Proceedings of a Series of
Workshops, ORNL 5709; EPA 560/6-81-004, Oak Ridge National
Laboratory, Oak Ridge, Tennessee.
Gledhill, W. E. , and V. W. Saeger. 1979. Microbial degradation in
the environmental hazard evaluation process, pp. 434-442. IN
Bourquin, A. W., and P. H. Pritchard (eds.), Microbial
Degradation of Pollutants in Marine Environments.
EPA-600/9-79-012.
Hannon, B. 1973.
41:535-646.
The structure of ecosystems. J. Theor. Biol.
Harris, W. F., B. S. Ausmus, G. K. Eddlemon, S. J. Draggan,
J. M. Giddings, D. R. Jackson, R. J. Luxmoore, E. G. O'Neill,
R. V. O'Neill, M. Ross-Todd, and P. Van Voris. 1980. Microcosms
as potential screening tools for evaluating transport and effects
of toxic substances. EPA-600/3-80-042.
Harte, J. , D. Levy, E. Lapan, A. Jassby, M. Dudzik, and J. Rees.
1978. Aquatic microcosms for assessment of effluent effects.
Electrical Power Research Institute EA-936.
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Harte, J. , D. Levy, J. Rees, and E. Saegebarth. 1980. Making
microcosms an effective assessment tool. IN Giesy, J. P. (ed.),
Microcosms in Ecological Research (in press).
Innis, G. S. 1972. Simulation models of grassland and grazing lands.
Prep. No. 35, Grassland Biome, Natural Resource Ecology
Laboratory, Colorada State University, Fort Collins.
Kindig, A. 1979. Investigations for streptomycin-induced algal
competitive dominance reversals. Experimental Report ME25, FDA
Contract No. 223-76-8348, University of Washington.
Lane, P. A., and R. Levins. 1977. The dynamics of aquatic ecosystems
2. The effects of nutrient enrichment on model plankton
communities. Limnol. Oceanogr. 22(3):454-471.
Lettenmaier, D. P., and J. E. Richey. 1978. Ecosystem modeling: A
structural approach. J. Environ. Eng. Dive., Proc. Am. Soc.
Civ. Eng. 104:1015-1021.
Levins, R. 1974. The Qualitative analysis of partially specified
systems. Ann. N.Y. Acad. Sci. 231:123-138.
Levins, R. 1979. Coexistence in a variable environment. Am. Nat.
114:765-783.
Mosser, J. L., N. S. Fisher, and C. F. Wurster. 1972.
Polychlorinated biphenyls and DDT alter species composition in
mixed cultures of algae. Science 176:533-535.
Park, R. , et al. 1975. A generalized model for simulating lake
ecosystems. Contribution No. 152, Eastern Deciduous Forest
Biome, U. S. International Biological Program. Simulation
Councils, Inc.
Perez, K. T., G. M. Morrison, N. F. Lackie, C. A. Oviatt, and
S. W. Nixon. 1977. The importance of physical and biotic
scaling to the experimental simulation of a coastal marine
ecosystem. Helgol. Wiss. Meersunters. 30:144-162.
Rodgers, J. H., Jr., J. R. Clark, K. L. Dickson, and J. Cairns, Jr.
1980. Nontaxonomic analyses of structure and function of
aufwuchs communities in lotic microcosms. IN Giesy, J. P.
(ed.), Microcosms in Ecological Research (in press).
Shugart, H. H. , and D. C. West. 1980. Forest succession models.
BioScience. 30:308-313.
Snieszko, S. F. 1974. The effects of environmental stress on
outbreaks of infectious diseases of fishes. J. Fish. Biol.
6:197-208.
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Suter, G. W. 1981a. Methods for measuring effects of chemicals on
terrestrial population interaction. IN Hammons, Anna S. (ed.),
Ecotoxicological Test Systems: Proceedings of a Series of
Workshops, ORNL-5709; EPA 560/6-81-004, Oak Ridge National
Laboratory.
Suter, G. W. 1981b. Methods for measuring effects of chemicals on
terrestrial ecosystem properties. IN Hammons, Anna S. (ed.),
Ecotoxicological Test Systems: Proceedings of a Series of
Workshops, ORNL-5709; EPA 560/6-81-004, Oak Ridge National
Laboratory.
Wedemeyer, G. 1970. The role of stress in disease resistance of
fishes, pp. 30-35. IN Snieszko, S. F. (ed.), A Symposium on
Diseases of Fishes and Shellfishes. Amer. Fish. Soc.,
Washington, D.C.
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LABORATORY TESTS FOR CHEMICAL EFFECTS
ON AQUATIC POPULATION INTERACTIONS
AND ECOSYSTEM PROPERTIES
J. M. Giddings
Environmental Sciences Division
Oak Ridge National Laboratory
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SECTION 3
LABORATORY TESTS FOR CHEMICAL EFFECTS ON AQUATIC
POPULATION INTERACTIONS AND ECOSYSTEM PROPERTIES
This section presents the results of a survey to identify methods
for measuring chemical effects on aquatic population interactions and
whole ecosystems. These methods are evaluated in the context of a
tiered hazard assessment scheme (Cairns 1980; Hushon et al. 1979). In
such a scheme, chemicals are first subjected to a battery of simple,
rapid tests aimed at identifying those chemicals that might be
hazardous to the environment. Chemicals that are indicated to be
potentially hazardous are tested further to better define the effects
that might occur and to establish the concentration ranges likely to
produce those effects. If the concentration that produces adverse
effects is close to the expected environmental concentration, the
chemical is tested under more realistic conditions to confirm the
earlier results and to predict the ecological impacts in as much
detail as possible.
Tests to be used early in the assessment process must be highly
sensitive, since the objective is to produce no false negatives
(Hushon et al. 1979). Because these early tests will be applied to
hundreds or thousands of chemicals, they must also be rapid,
inexpensive, replicable, and readi1y standardized for use by different
laboratories. Tests for confirmation and prediction can be more
expensive and time-consuming, since few chemicals will reach this
stage of the assessment scheme; however, these tests must include as
much ecological realism as possible so that actual effects may be
reliably predicted. Tests used in the intermediate stages of the
assessment scheme are designed to compromise between realism on the
one hand and sensitivity, rapidity, replicability, low cost, and
standardizabi 1 ity on the other. Most of the tests reviewed are most
suitable for the intermediate and advanced stages of hazard
assessment, but a few might be incorporated into the initial battery.
Few aquatic multispecies test systems have yet been developed or
adapted for chemical hazard assessment. Without a great deal more
practical experience with chemical effects testing above the
population level, it will be impossible to determine which types of
tests will be most useful. .Development of chemical hazard assessment
protocols should draw on the entire body of ecological experience
rather than focusing too narrowly on a particular published procedure.
Therefore, this review can only indicate general approaches that
appear to be fruitful, without recommending specific procedures to be
followed. Where alternative strategies exist for conducting a given
type of experiment, major issues are discussed which must be resolved
before a standard method can be selected.
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3.1 Competition
Competition has been defined as an interaction between two
species in which each population adversely affects the other in the
struggle for limiting resources (Odum 1971). Competition is not a
series of discrete events, like predation, but rather is manifested
over generations in the history of a population. Therefore,
laboratory studies of competition are usually conducted with
short-lived organisms such as bacteria, algae, and zooplankton.
Many organisms are more sensitive to toxic chemicals when
competitors are present than in pure cultures (Fielding and Russell
1976; Fisher et al. 1974; Kindig 1979; Mosser et al. 1972). Chemical
effects on competition are generally interpreted as effects on the
abilities of organisms to take up, assimilate, or store a limiting
resource. If competing species are affected by a chemical in
different degrees, the normal competitive dominance under a given set
of conditions may be altered or reversed. On a community level, this
results in changes in the relative abundance of species, with or
without a change in the total biomass or overall activity of the
community (May 1973; O'Neill and Giddings 1979).
The effects of a chemical on a group of competing species depend
on the environmental conditions and on which species are present
(Fielding and Russell 1976). The behavior of any species, including
its abundance and distribution in space and time, can vary
tremendously in the presence of different competitors (O'Neill and
Giddings 1979). Thus, the results of a competition experiment with
two species do not indicate what would have occurred if a third
species had been involved. Competition experiments have been used
primarily to elucidate the mechanisms of competition and to validate
ecological theories, rather than to predict the course of events in
nature.
Because they are (1) extremely sensitive and (2) nonpredictive,
competition experiments are most applicable in the first or
intermediate levels of the hazard assessment sequence. Zooplankton
competition experiments reported in literature range from 6 to 100
weeks in duration, so their utility for testing large numbers of
chemicals is doubtful. Experiments with algae can be completed in as
little as 4 days, and bacterial experiments may be even shorter.
Because only one example of a bacterial competition experiment (Hansen
and Hubbell 1980) was found in our literature review, we have focused
on algae as logical subjects of tests for chemical effects on
competition.
3.1.1 Algal Competition Experiments
Competition can be extremely important in structuring algal
communities (O'Neill and Giddings 1979). Shifts in algal dominance
may have repercussions on the quality and abundance of animal life in
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an aquatic ecosystem because some algal species are not easily
ingested or are of greater nutritional value to consumers than are
others (see references cited in Mosser et al. 1972). Assessment of
the true ecological significance of alterations in algal community
structure should be an objective of future research.
Of all the numerous published studies of algal competition
(Appendix A), those by Mosser et al. (1972) and Fisher et al. (1974)
are perhaps the best demonstrations of the ability of algal systems to
reveal effects at very low chemical concentrations. These experiments
involved the marine diatom Thalassiosira pseudonana and the marine
green alga Dunaliella tertiolecta. The growth of T. pseudonana in
pure batch culture was inhibited by polychlorinated biphenyl (PCB) at
25 ug/L, but not at 10 ug/L or less. D. tertiolecta in pure culture
was unaffected by 25 t^g/L. When grown together in batch culture with
no PCB, T. pseudonana attained densities 8 or 9 times as high as D.
tertiolecta. However, when PCB at 1 ug/L was included in the medium,
the growth of T. pseudonana was slightly reduced and that of D.
tertiolecta was substantially increased, resulting in T. pseudonana to
D. tertiolecta cell ratios of only about 2 to 1 (Mosser et al. 1972).
The authors concluded that the diatom normally stripped the nutrients
from the medium before the green alga could achieve much growth. PCB
impaired the diatom's nutrient uptake capacity and thus permitted the
green alga, which was unaffected, to reach higher population
densities.
In a subsequent study, Fisher et al. (1974) compared the effects
of PCB in batch and continuous cultures of the same two species. PCB
at 0.1 ug/L did not affect the outcome of competition in mixed batch
cultures, nor did it affect either species growing alone in continuous
culture. In mixed continuous culture, PCB reduced the proportion of
T. pseudonana to 50% of the total cells, as compared to control
proportions of over 90%. When natural phytoplankton communities
dominated by T. pseudonana and two other diatom species were tested in
similar continuous culture experiments, the same effect on T.
pseudonana was observed as in the two-species experiments. The PCB
concentration of 0.1 ug/L that produced this effect was at least two
orders of magnitude below the concentration that inhibited pure batch
cultures of T. pseudonana.
Continuous cultures are appropriate for algal competition
experiments for several reasons (Fisher et al. 1974). In a continuous
culture, resources are always limiting; therefore, competition is
always occurring. Batch cultures, however, do not become
nutrient-limited until they reach the senescent phase. Continuous
cultures can be maintained in the active growth phase for longer
periods than batch cultures, thus allowing competitive displacement to
take place. The greater sensitivity of continuous cultures, compared
to batch cultures, derives from these two factors. Continuous
cultures are certainly more representative of most natural growth
situations. On the other hand, batch cultures are technically simpler
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(although analytically more complex) than continuous cultures. An
effort should be made to assess the relative cost, efficiency, and
sensitivity to chemicals of these two kinds of competition tests.
3.1.2 Conclusions and Recommendations
The studies of Fisher et al. (1974) and Mosser et al. (1972) are
examples of how algal competition experiments could be applied in
chemical hazard assessment. Other competition experiments are
described in Appendix A. Because these systems are relatively simple
and extremely sensitive to chemicals,they should be developed into a
TSCA hazard assessment protocol. A systematic search for suitable
species pairs (freshwater and marine) should be undertaken, basing the
final selection on ease of culture, predictability of response,
sensitivity to chemicals, and ecological relevance. Optimal
experimental conditions can then be established, and the system can
undergo the validation and interlaboratory testing sequence necessary
for all standard methods.
3.2 Predation
The principal mechanism by which chemicals (and other types of
stress) have been observed to influence predator-prey interactions is
through behavioral alterations in the prey. These behavioral changes
often make the prey more conspicuous to predators (e.g., increased
activity, erratic movement, failure to seek shelter) or reduce their
ability to avoid capture once detected (e.g., sluggishness, slowed
swimming speed, reduced stamina). Most published experiments on
chemical effects on predator-prey interactions, therefore, have been
essentially behavioral studies.
Behavioral effects of chemicals are generally the most sensitive
type of sublethal response. Furthermore, natural predators are
frequently capable of discerning behavioral abnormalities in their
prey even when the abnormalities are not obvious to a human observer.
Therefore, predator-prey interactions should be affected by chemicals
at lower concentrations than many biological responses measured in
conventional toxicity tests. Indeed, many of the studies reviewed in
this section demonstrated predator-prey effects at concentrations
orders of magnitude below the lethal level. The apparent sensitivity
of predator-prey interactions is the major justification for their
inclusion in a hazard assessment program.
However, because chemical effects on predation derive primarily
from behavioral alterations in the prey, the response of any
particular predator-prey combination may not be readily generalizable
to other species pairs. For example, stress-induced hyperactivity can
make mosquito fish more susceptible to predation by largemouth bass
(Goodyear 1972), but less susceptible to predation by bowfin, which
prefer slow-moving prey (Herting and Witt 1967). Current knowledge of
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critical factors in predator-prey interactions does not allow us to
select a small set of species pairs upon which to base general
conclusions about chemical effects on predation. An effect observed
in one situation serves only to indicate the potential for effects on
other predator-prey interactions, but the magnitude, direction, or
even occurrence of effects on other species pairs cannot be accurately
predicted.
Effects observed in most laboratory predator-prey experiments are
of no more value in predicting actual events in nature than
conventional bioassay results. A multitude of physical, chemical, and
ecological factors other than predation influence the distribution,
abundance, and activities of species in natural ecosystems. As an
obvious example, a population whose density is limited by
intraspecific competition may be totally unaffected by changes in
predation rates. To predict the effect of a chemical on natural
populations from results of a predator-prey experiment, detailed
information on the population dynamics of both species and on the
trophic structure of the ecosystem would be needed at the very least.
High sensitivity, poor generalizability, and poor predictive
power of predator-prey tests imply that they would be most useful in
the early stages of a hazard assessment scheme. Methods used early in
the testing sequence must be simple, inexpensive, and rapid since they
will be applied to a large number of chemicals; they must also be well
standardized so that consistent results can be achieved by different
laboratories. Therefore, the experimental approaches evaluated below
were selected from the many published techniques because of their
efficiency and ease of standardization.
Many studies of predation are designed to measure specific
components of a predator-prey interaction such as reactive distance,
handling time, or capture success. An alternative approach is to
enclose a predator with a population of prey and count the survivors.
These two types of experiments can be labeled the "mechanistic
approach" and the "population approach," respectively, for lack of
better terms. It must be presumed that at least some mechanisms are
more sensitive to chemical stress than the net survivorship of the
population because various factors may compensate for changes in
particular components of the interaction. To choose a hypothetical
example, a chemical that produces hyperactivity in the prey might
reduce the searching time of the predator, but may simultaneously make
the prey more difficult to capture. The net effect on the predation
rate might be small. Effects of chemicals on mechanisms might also be
more generalizable to other species than effects on net population
survival. To extend the above example, a chemical causing
hyperactivity in one species would probably produce the same effects
on related species, but the compensating effect (decreased capture
success) would depend on specific behavioral characteristics of the
predator and the prey; hence, the net outcome might be different with
different species pairs. Focusing on a single component of the
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predator-prey interaction might make a test more sensitive and more
generalizable, but omission of potential compensating effects would
reduce the predictive power of the test compared to a test using the
population approach.
The mechanistic and population approaches impose different
demands on the investigator. Population experiments under reasonably
realistic conditions take at least several hours and sometimes days or
weeks. Mechanistic measurements are often completed in seconds or
minutes. However, mechanistic measurements must be repeated many
times to generate enough data for statistical analysis, while a
carefully controlled population experiment might need to be performed
only once or twice to achieve the same level of statistical
confidence. Most mechanistic approaches require that an observer
monitor the experiment continuously (e.g., to count attacks or
captures or to measure handling time or reactive distance). A
population experiment can be designed in such a way that only one
count of surviving prey is necessary. Depending on the organisms
involved, the survivors may even be preserved to be counted at the
convenience of the experimenter. Therefore, one experimenter can
conduct a number of population experiments at once, but mechanistic
experiments have to be run separately. Because experience is lacking
with either approach to chemical testing, neither is clearly
preferable in every case.
In the population approach, treated and control prey may be
offered to the predator simultaneously or in separate trials. Either
strategy has certain advantages and disadvantages, as discussed in
Sect. 3.2.5(2). With simultaneous exposure of two prey groups to the
predator, some means of differentially marking the groups is
necessary; this may be impossible with zooplankton. The experimental
results are complicated by the continuously changing ratios of the two
prey groups. The possibility of treated prey affecting the
performance of control prey cannot be discounted, especially in
experiments with schooling fish. However, when prey groups are
presented separately, differences in predator performance may obscure
treatment effects. Ideally, the same predator or group of predators
should be tested with both treated and control prey so that variations
among predators do not. influence the results. Even with this
precaution, the order in which prey groups are presented may be
significant; learning in one trial may affect the outcome of the next.
[This is possible even in protozoa (G. W. Salt, personal
communication).] Another disadvantage of separate presentation is
that more trials are required than when prey groups are presented
simultaneously. A systematic investigation of these factors should be
conducted before selecting either experimental design for chemical
hazard assessment. It should be pointed out, however, that mixed
groups of exposed and unexposed prey are probably unusual in nature.
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3.2.1 Protozoa-Protozoa
Protozoa have been popular subjects for predation studies since
the early experiments of Cause (1934; Cause et al. 1936). Salt (1967)
offered two reasons why protozoa are well suited for such research:
(1) "if there are any universal characteristics of predation they
should be present in the simplest animals;" and (2) such
characteristics should be more easily discernable in protozoa than in
animals with sexes, life stages, and other complicating factors.
Protozoa have therefore been used as model predators; the publications
reviewed here did not consider the ecological significance, if any, of
protozoan predation.
(1) Population dynamics experiments. The ciliates Pi dim'urn
nasutum (a predator) and Paramecium aurelia or P. caudatum (prey) were
selected by Cause (1934), and many of those who followed him, in
studying predator-prey interactions among protozoa. Cause found that
mixed cultures of these species were invariably short-lived. Growth
of Paramecium populations allowed Pi dim'urn to increase. Didinium then
drove the prey to extinction and subsequently starved. This simple
predator-prey oscillation leading to extinction of prey was also
observed by Luckinbill (1973, 1974) and by Veilleux (1979). As Salt
(1974) pointed out, this phenomenon is "precisely what does not occur
in nature." A great deal of theoretical and experimental work,
including the studies reviewed below, has been directed towards
identifying the critical factors permitting stable coexistence of
protozoan predators and their prey.
Luckinbill (1973) reasoned that the predator-prey interaction
might be stabilized if the frequency of predator-prey encounters could
be reduced. He cultured Pi dim'urn and Paramecium together in a medium
to which methyl cellulose had been added to slow the movements of both
species. The medium was enriched with Cerophyl, a bacterial growth
medium, inoculated with Aerobacter aerogenes as food for Paramecium.
The cultures were started with 35 predators and 90 prey in 6 ml of
medium. All the animals were removed and placed in fresh medium every
2 days. Without methyl cellulose, these cultures went through a
typical predator-prey oscillation terminating in less than 10 h with
the extinction of Paramecium. Methyl cellulose prolonged the
interation; the cultures persisted through two to three oscillations
over 16 days, and Pi dim'urn was the first to become extinct.
Luckinbill found that the oscillations could be perpetuated by
reducing the food supply to the prey (by reducing the Cerophyl
concentration). With fewer bacteria, the Paramecium were
undernourished at the peaks of their population density. Pi dim'urn
feeding on these undernourished Paramecium reproduced more slowly than
when feeding on healthy prey and were unable to completely eliminate
the prey. These cultures (with methyl cellulose) went through seven
stable oscillations in 32 days and were terminated voluntarily.
Luckinbill concluded that coexistence of predator and prey was
possible if two conditions were met: (1) the prey were able to reach
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low enough densities that the predator could not find them all, while
still maintaining numbers that ensured the survival of the population;
and (2) the prey were restricted in their growth by something other
than predation (in this case, food).
Veilleux's (1979) methods were nearly identical to Luckinbil1's
method, with the following exceptions: (1) rather than transferring
all the animals to fresh medium every 2 days, Veilleux replaced half
the culture volume with fresh medium and did not remove any ciliates;
and (2) the cultures were started with 15 Pi dim'urn and 45 Paramecium.
Without methyl cellulose, these cultures became extinct without
oscillations. With methyl cellulose in the medium, the experimental
outcome depended on the Cerophyl concentration. At high Cerophyl
concentrations, the prey eventually became extinct after a series of
oscillations of increasing amplitude. At slightly lower Cerophyl
concentrations, the predator became extinct. With still lower
Cerophyl levels, the cultures reached stable oscillations. At the
lowest Cerophyl concentrations, the Paramecium did not support the
nutritional requirements of Pi dim'urn, and the latter became extinct.
The conditions resulting in stable oscillations were the same as those
in Luckinbil Ts study (1973).
Luckinbill (1974) attempted to produce stable cultures without
methyl cellulose by increasing the culture volume. He reasoned that
with a relatively large "arena" for the predator-prey interaction, the
prey could reach low enough population densities to avoid capture by
the predator while still maintaining an absolute population size
sufficient to ensure their survival. He established cultures ranging
from 0.1 mL to 1000 ml, each with initial densities of 20 Paramecium
and 10 Pi dim'urn per milliliter. The cultures were observed under a
dissecting microscope at 20-min intervals until no Paramecium could be
found. None of the cultures attained stable oscillations, but their
persistence increased from 2.8 h at 0.1 mL to 82 h at 1000 mL.
Reducing the Cerophyl concentration prolonged the existence of large
cultures, but did not stabilize them. In nature, the almost
infinitely large "arena," coupled with possible food limitation of
prey, may permit the coexistence of protozoan predators and prey
(Luckinbill 1974). In the laboratory, coexistence has been achieved
only in cultures with methyl cellulose.
(2) Mechanistic studies. Salt (1967, 1968, 1969, 1974) and
Veilleux (1979) devised experiments to measure several other aspects
of the predator-prey interaction among protozoa. Unlike the
experiments described above, these mechanistic studies were not
intended to perpetuate a predator-prey system, but rather to measure
various components of the interaction over short time intervals. The
experiments were conducted in 0.1-mL cultures, covered by a layer of
paraffin oil to prevent evaporation (Salt 1967). Salt (1967) devised
an automated system to photograph entire 0.1-mL drops periodically.
The numbers of animals and, in some cases, their metabolic state could
be determined with good accuracy by examining the film record under a
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dissecting microscope. Salt's basic technique was to start a culture
with two predators (Pidim'urn nasutum, Amoeba proteus, or Woodruffia
metabolica) and about 200 prey (Paramecium aurelia). No food was
provided for the prey; prey were added to the cultures as needed to
maintain the desired densities throughout an experiment. Each
experiment was terminated when the predators reached a preselected
density. Based on the counts derived from the film record, Salt
calculated the generation times, feeding rates, and other
characteristics of the predator. His primary objective was to examine
variations in these parameters as a function of predator and prey
densities. Veilleux (1979) used similar methods (generally in shorter
experiments) to investigate the effects of methyl cellulose and
Cerophyl concentrations.
The generation times of Woodruffia (Salt 1967), Amoeba (Salt
1968), and Didinium (Salt 1974; Veilleux 1979) were independent of
predator and prey densities. The generation time of Didinium,
however, was increased when the animals were feeding on undernourished
Paramecium (Veilleux 1979). According to Salt (1969), Woodruffia
cultured in the laboratory for 1000 to 1500 generations had longer
generation times than members of the same species freshly collected
from the field. He inferred that the animals had undergone genetic
changes in the laboratory cultures and cautioned against using data
from laboratory stocks to make quantitative predictions about wild
populations.
The rate of food consumption by Didiniurn was shown to vary with
the density of prey (Salt 1974; Veilleux 1979). Tiie maximum feeding
rate in Salt's experiments was about two prey per predator per hour;
Veilleux (1979) measured up to 12 prey per predator per day. The
discrepancy may reflect differences in Cerophyl concentrations in the
cultures of Paramecium fed to the predators or the Didinium cultures
used by the two investigators may have been genetically different.
Because both authors omitted certain relevant information in their
descriptions of methods, the discrepancy remains unresolved.
Another quantity measured in several of these studies was the
number of prey consumed by one predator before fission occurred. Salt
found this number to decrease with increasing predator density in
cultures of Woodruffia (Salt 1967) and Amoeba (Salt 1968) and later
concluded that the metabolic efficiency of these predators was greater
at high densities (Salt 1979). Veilleux (1979) measured a three-fold
variation in prey consumed per fission in Didinium over a range of
Cerophyl concentrations.
For the most part, the connection between these mechanistic
studies and the population dynamics experiments has not been made. In
particular, the density-dependence of some components of the
predator-prey interaction have yet to be assimilated into mechanistic
population models.
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(3) Evaluation. The social significance of nonpathogenic
protozoa is nil, and the ecological significance of protozoan
predators is not well known. The major advantages of protozoa as
subjects for chemical effects tests are their small size and ease of
culture. Protozoa tests are probably easier to standardize than tests
with higher organisms, and no special equipment or skills are
required. Counting protozoan populations, however, is tedious and
could limit the number of tests that could be run in a given period of
time. The suggestion that protozoa, by virtue of their simplicity,
exhibit the essential features of all predator-prey phenomena (Salt
1967) is not entirely logical, and there is little evidence to support
it. Protozoan predator-prey systems have little utility for chemical
hazard assessment in their present state of development.
3.2.2 Zooplankton-Zooplankton*
The impact of predation on the composition of freshwater
zooplankton communities has been extensively studied over the past two
decades in field observations and laboratory experiments (Hall et al.
1976). The primary emphasis has been on vertebrate predators (see
Sect. 3.2.3). Only recently have studies focused on the effects of
invertebrate predators on zooplankton communities. Brooks and Dodson
(1965) originally hypothesized that when vertebrate predation was low,
the dominance of large zooplankton species was due to their ability to
outcompete smaller species for a limited food supply. However,
efforts to verify this hypothesis were inconclusive (Hall et al.
1976). Dodson (1974a) later proposed that small zooplankton are
selectively reduced by invertebrate predators. Supportive evidence
for this hypothesis has come from numerous field studies (Allan 1973;
Anderson 1970; Confer and Cooley 1977; Dodson 1970, 1972; Lynch 1979;
McQueen 1969; Sprules 1972). Other field studies have suggested that
under the constant stress of invertebrate predation, individuals of
the stressed populations undergo morphologica" changes (Dodson 1974b;
Kerfoot 1975; O'Brien and Vinyard 1978; O'Brien and Schmidt 1979;
O'Brien et al. 1979) or reproductive changes (Kerfoot 1974, 1977a) to
reduce this predation. Laboratory studies that have attempted to test
these hypotheses are the focus of this section. Although these
studies were designed to examine individual predator-prey
interactions, the techniques could be adapted for the testing of
chemical substances for environmental effects.
Invertebrate predators such as the cladocerans Leptodora and
Polyphemus, cyclopoid copepods, and certain calanoid copepods, and the
phantom midge larvae Chaoborus are primarily nonvisual, grasping
predators that depend to some extent on random contact for prey
capture (Zaret 1975). Gerritsen and Strickler (1977) recognized four
*This section was contributed by John D. Cooney, University of
Tennessee.
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progressive stages of interaction for this type of predation:
encounter, attack, capture, and ingestion. However, because of the
small size of the animals, very little detailed information is
available on these various stages.
(1) High-speed photography studies. Through the use of
high-speed photography and constant observation under a dissecting
microscope, Kerfoot (1977b, 1978) was able to document the
predator-prey interaction between cyclopoid copepods of the genus
Cyclops and the cladoceran Bosmina longirostris. Kerfoot found that
cyclopoids can perceive objects at a distance of about 2 to 3 body
lengths and that most attacks on prey occur within a single body
length (about 1 to 2 mm). Zooplankton swimming speeds have also been
measured by high-speed photography (Gerritsen 1978; Strickler 1977).
Different instars and sexes of the same species may swim at different
speeds. This is important because the probability of a planktom'c
animal encountering an invertebrate predator is determined in part by
the animal's swimming speed. Acridine, a nitrogen-containing aromatic
compound, has been observed to reduce the swimming speed of copepods
(J.D. Cooney, unpublished data).
High-speed photography has also revealed that the predator's
hunting strategy is important in determining the probability of
encountering prey. Ambush predators, such as phantom midge larvae
(Chaoborus), rest motionless in the water column and attack passing
prey. For these animals, encounter probability is a function of prey
speed. With predators that swim continuously, such as the calanoid
copepod Epischura, encounter probability is relatively constant
(Gerritsen 1978).
Studies such as these have provided useful information on
predator-prey interactions. However, high-speed photography
techniques are highly specialized and are not readily adaptable to
general toxicity testing.
(2) Population experiments. Most predator-prey studies with
zooplankton have used the population approach in which prey animals
(or groups of prey of different sizes or species) are exposed to a
predator for a specified period of time, and the survivors of the prey
population are counted. Groups of prey without predators are
sometimes included in these experiments as controls. Experiments may
be as short as 6 to 8 h (Mull in 1979), or they may continue for
several days with new prey added daily (Brand! and Fernando 1974;
Confer 1971). The length of the experiments should be shorter than
the reproductive period for the test animals because many predators
eat their own young, which would bias the results. Many species of
predators and prey have been studied. Cyclopoid copepods (e.g.,
Mesocyclops and Cyclops) are the most common predators, and
cladocerans (e.g., Bosmina and Ceriodaphnia) or calanoid copepods
(e.g., Acartia or Diaptomus) are typical experimental prey. A few
studies (Brandl and Fernando 1978; Li and Li 1979) have used natural
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prey communities. Similar techniques have been used to study
predation by insect larvae (Akre and Johnson 1979; Gerritsen 1978;
Thompson 1978).
In most of these studies, the experimental animals were obtained
directly from field collections and then sorted in the laboratory,
either by using a dissecting microscope or by passing the plankton
sample through a series of sieves of various mesh sizes. These
procedures are tedious and may injure the animals. Brand! and
Fernando (1978) used a sieve to remove predators and then used a
plankton splitter to subdivide the prey animals into control and test
groups. Predators were then reintroduced at varying densities, and
prey numbers were compared with control groups after 24 h.
Acclimation periods for experimental animals ranged from 6 h
(Confer 1971) to one week (Kerfoot 1977). Standard acclimation
periods are important to ensure the same nutritional status for
predators in each test. Some investigators recommend starving
predators for 24 h before testing (Akre and Johnson 1979; Gerritsen
1978; Kerfoot 1977; Li and Li 1979).
Containers most frequently used in testing were glass beakers,
ranging in size from 50 to 4000 mL (Brandl and Fernando 1979; Confer
1971; Kerfoot 1977; Landry 1978; Mull in 1978). Li and Li (1979) used
small Petri dishes, which facilitated observations under a dissecting
microscope. Kerfoot (1977) found rectangular 10 L aquaria to be
inadequate because prey animals would remain in the corners, where
predators have difficulty feeding.
Studies that use field collections as a means of obtaining
experimental animals are severely limited by temporal abundance of
suitable predators and prey. Using zooplankton species for which
culture methods have already been determined and life history
parameters measured in the laboratory (e.g., Diaptomus clavipes,
Bosmina longirostris, Cyclops bicuspidatus thomasi, Cyclops versa!is)
would be more efficient and would provide an abundance of experimental
animals of the required sizes throughout the year. The use of
laboratory animals would also reduce the inherent variability of
results obtained using field-collected animals because laboratory
populations could be homogeneous with respect to nutritional status.
(3) Evaluation. Zooplankton predator-prey experiments are
simple and rapid and could be easily adapted to toxicity testing.
Many zooplankton species are easily cultured, and large reproductive
populations can be maintained in static aquaria. Their short
lifespans and small size make it possible for many experiments to be
conducted in limited space and time. Experiments can be completed in
8 h or less, and the surviving prey can be preserved to be enumerated
later. Because zooplankton are nonvisual predators, lighting is not a
critical factor, and experiments can be conducted in darkness.
Learning, social interactions, and disturbances caused by observers
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are much less important in zooplankton predation tests than in tests
with fish (see Sects. 3.2.3, 3.2.4, and 3.2.5). Replicabi 1 ity of
zooplankton-zooplankton systems is probably good. There are no
reports of chemical effects studies on zooplankton predator-prey
interactions.
3.2.3 Fish-Zooplankton
Many fish are obligate or facultative planktivores during at
least part of their lives. The quality and quantity of available prey
and the ability of fish to locate and capture food organisms are
important factors in controlling fish productivity and in determining
which fish species will succeed in a particular environment.
Furthermore, several field studies have shown (Brooks and Dodson 1965;
Dodson 1970; Galbraith 1967; Green 1967; Hall et al. 1970;
Warshaw 1972; Wells 1970) that selective predation by planktivorous
fish can dramatically alter the species composition of the zooplankton
community. Brooks and Dodson (1965) hypothesized that fish alter
zooplankton communities by preferentially consuming larger
individuals. This suggestion prompted many investigations into the
selective feeding habits of planktivorous fish and the mechanisms
responsible for the observed food preferences. Effects of toxic
chemicals or other environmental stresses have not been examined in
this context, but some of the experimental techniques used to study
fish-zooplankton interactions in the laboratory could be adapted for
chemical hazard evaluation.
(1) Reactive distance. In a recent analysis of fish predation
on zooplankton, O'Brien (1979) distinguished four phases of the
interaction: location of prey by fish, followed by pursuit, attack,
and capture. Because prey are small relative to predators, location
of prey is usually the critical step in feeding. Most planktivorous
fish are visual predators, and their ability to locate prey is
influenced by prey size (Confer and Blades 1975a, b; Confer et al.
1978; Eggers 1977; Vinyard and O'Brien 1976; Ware 1972, 1973; Werner
and Hall 1974), prey pigmentation (Confer et al. 1978; Eggers 1977;
Ware 1973; Zaret 1972; Zaret and Kerfoot 1975), prey movement (Confer
and Blades 1975a; Eggers 1977; Ware 1973), predator hunger (Confer et
al. 1978), and light intensity (Confer et al. 1978; Eggers 1977;
Vinyard and O'Brien 1976; Ware 1973).
The ability of a fish to locate zooplankton prey is commonly
expressed in terms of reactive distance (RD)--the distance between
predator and prey when the predator begins pursuit. Reactive
distances have been measured in the laboratory by Confer and Blades
(1975a, b), Confer et al. (1978), Vinyard and O'Brien (1976), Ware
(1972, 1973), and Werner and Hall (1974). The methods used in these
various experiments have much in common. In each case a starved fish
is placed at one end of a long, narrow aquarium, and a prey is
introduced at a distance beyond the fish's visual range. The point at
which the fish begins to pursue the prey is observed, and the distance
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from that point to the prey is measured by a scale along one side of
the aquarium. Distinguishing active pursuit from random searching is
not always possible; Confer and Blades (1975a) reported discarding
one-third of their observations for this reason, and the problem has
undoubtedly occurred with other workers who simply did not report it.
A long, narrow aquarium is necessary so that RD can be accurately
determined from the positions of predator and prey along one
dimension. This introduces some artificiality into the predator-prey
interaction since fish need only search in one direction. Confer et.
al. (1978) used a large aquarium and three observers to determine RD
in three dimensions for lake trout (Salvelinus namaycush). They
discovered that fish searching in three dimensions are not 100%
efficient--that is, they overlook some prey within their visual range.
These authors concluded that the actual volume searched by this fish
is 50 to 70% less than would be estimated from the RD measured in a
long, narrow tank. This factor would not affect comparisons of
relative RD, but it would have to be considered in predicting absolute
predation rates in nature.
The reactive distance of fish decreases as they become satiated
(Confer et al. 1978). To eliminate this variable from experiments,
fish are usually starved for at least 24 h before feeding trials. A
single fish can be used for a number of trials in one experiment
before satiation begins to reduce the RD. Bluegill (Lepomis
macrochirus) 6.5 cm in length can consume more than 25 large Daphnia
magna without affecting RD (Vinyard and O'Brien 1976), and 11-cm lake
trout (Salvelinus namaycush) can eat 65 D. magna before RD begins to
decline (Confer et al. 1978). A fish can be used for more than one
experiment if a starvation period is allowed between experiments.
Not all fish are amenable to laboratory experimentation. Zaret
(1972; Zaret and Kerfoot 1975) found that Melaniris chagresi, a
planktivore from Gatun Lake, Panama, were extremely nervous in aquaria
and could not be held in captivity for more than 10 days. Vinyard and
O'Brien (1975) reported terminating some feeding sessions with
bluegill (Lepomis macrochirus) when the fish became excited or
distressed. Any fish used in predation studies must be conditioned to
find and capture prey under experimental conditions. Introducing the
prey without attracting or disturbing the fish may be difficult.
Vinyard and O'Brien (1976) waited until the fish was facing the
opposite direction before placing the prey into the aquarium. In
other studies (O'Brien et al. 1976; Ware 1973), the aquarium was
partitioned into a holding compartment and a feeding compartment.
After the fish was placed in the holding compartment and activity
normalized, the prey was positioned in the feeding compartment. Then
the fish was released by removing the partition. Pre-experimental
conditioning and isolation of the fish during prey introduction are
useful practices for reducing extraneous factors that could influence
the behavior of the fish.
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Measurements of RD appear to be replicable within and between
experiments. Werner and Hall (1974) reported standard errors
equivalent to 3 to 5% of the mean in experiments with two bluegill
(Lepomis macrochirus). In 9 to 28 trials with two rainbow trout
(Salmo gairdneri) feeding on five size classes of prey, standard
errors were less than 10% of the mean RD (Ware 1972). Confer et al.
(1978) found no significant differences among lake trout (Salvelinus
namaycush) of similar sizes. Among a group of eight pumpkinseed
(Lepomis gibbosus), the responses of six were statistically
indistinguishable (Confer and Blades 1975b). O'Brien (1979) compiled
RD vs prey size data from several sources and found a good agreement
between experiments.
(2) Prey selection. Reactive distance is incorporated into many
mathematical models of fish predation on zooplankton (Confer and
Blades 1975a; Confer et al. 1978; Eggers 1977; O'Brien et al. 1976;
Ware 1973; Werner and Hall 1974). These models consistently indicate
that the probability of encountering prey (a function of RD) is of
primary importance in determining the diet of fish at low prey
densities. At higher densities, a fish may see more than one prey at
once, and the fish's diet will then depend partially on which prey is
selected. This conclusion was supported by the experiments of Werner
and Hall (1974). O'Brien and his co-workers have used two methods to
examine prey selection by bluegill (Lepomis macrochirus). One method
(O'Brien et al. 1976) was an extension of the RD experiments described
above. The fish was held behind a screen while two Daphnia magna of
the same or different sizes were positioned in the aquarium; the fish
was then allowed to swim through an opening in the screen. The
experimenters noted which prey was selected and the distances of both
prey from the fish when pursuit began. They determined that bluegill
select the prey with the largest apparent size, regardless of the
actual size of the individuals offered. Thus, a small D. magna close
to the fish might be selected over a larger individual at a greater
distance. The authors determined that the data of Werner and Hall
(1974) were consistent with the apparent-size-selection hypothesis.
The other method used for determining prey preference was the
"tilt box" (Vinyard and O'Brien 1975). This technique was based on
the following aspects of bluegill behavior: (a) bluegill will orient
their dorsal surface toward light; (b) they will orient their ventral
surface toward gravity; and (c) the actual position of the fish is a
compromise between the light response and the gravity response, with
the light response taking on greater importance when the fish sees a
prey of interest. The tilt box was a 50- by 15- by 15-cm plexiglas
chamber illuminated from the side by a reflector flood lamp. Water
was passed through the box with a current speed of 2 to 6 cm/s, which
ensured that the fish faced the appropriate direction. A
2- by 1- by 14-cm presentation chamber was located 10 cm in front of
the fish. Test fish were placed in the chamber for 1 to 2 h per day
for a week to familiarize them with the environment. In each
experiment, a starved fish was placed in the box in dim light for 1/2
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to 1 h. The light was then turned on full, and the tilt of the fish
was measured against a protractor on the rear wall of the chamber. A
prey was placed in the presentation chamber, and the change in tilt of
the fish was measured. The change in tilt was found to be
proportional to the length of the prey, ranging from about 1° for
small prey to 7° for large prey. No change in tilt occurred when prey
were not presented. Small bluegill (which are entirely planktivorous)
responded more than large bluegill (which eat other prey besides
zooplankton).
Recently, Fisher et al. (1980) used the tilt box to measure the
effect of hydrazine on bluegill (Lepomis macrochirus). Artificial
prey (a piece of commercial fish food glued to a microscope slide) was
used instead of live zooplankton. Individual fish were placed in the
box, acclimated for 5 min in the dark, and then illuminated from the
side. After 1 to 6 min the tilt was measured. A screen in front of
the prey was then removed and the tilt was measured again. Each fish
was used in only one experiment and was exposed to the hydrazine only
during the time that it was in the tilt box (10 to 15 min). Hydrazine
had no effect on the tilt before the prey was exposed, but it
significantly reduced the change in tilt when the screen was removed.
The chemical effect in the tilt box occurred at 0.1 mg/L; the static
96-h LC50 for this species was determined to be 1.08 mg/L. The
authors cautioned that "drawing ecological implications from this
study would be inappropriate because both the prey used and lateral
light sources are not natural aspects of the bluegill's habitat. Yet,
as a sensitive technique to assess toxicant stress, the dorsal light
response offers a new approach for behavioral bioassay studies." As
the authors point out, more information on the natural predatory
behavior of the bluegill is needed before the biological significance
of the dorsal light response can be determined.
(3) Capture success. Planktivorous fish are very successful in
capturing most prey they pursue. Pumpkinseed (Lepomis gibbosus) were
100% successful at capturing Daphnia magna and D_- pulex in RD
experiments (Confer and Blades 1975a), and rainbow trout (Salmo
gairdneri) were 84 to 91% successful at capturing amphipods (Ware
1972). The capture success of L gibbosus for copepods averaged 80%,
with daily variances possibly due to learning by the fish. The
copepods became sluggish after 36 h in the laboratory, which added to
the variability in capture success (Confer and Blades 1975a).
Copepods are stronger, faster swimmers than cladocerans; they are also
negatively rheotactic and, therefore, swim away from the suction
currents produced by planktivorous fish (Janssen 1976). Drenner et
al. (1978) constructed an artificial suction device to test the
avoidance capabilities of various zooplankton species. The capture
frequency for Ceriodaphnia reticulata and Daphnia galeata mendotae was
the same as for neutrally buoyant bubbles and heat-killed Daphnia; D.
pulex escaped somewhat more successfully, and Cyclops sp., Mesocyclops
sp. , Diaptomus pal 1idus, and Chaoborus sp. avoided the suction
strongly. Janssen (1976) used a similar device to demonstrate that
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suction currents capture more Daphnia retrocurva than Diaptomus
oregonensls. Brooks and Dodson (1965) suggested that the escape
capabilities of Cyclops bicuspidatus thomasi were responsible for that
species remaining in Crystal Lake, Connecticut, in the face of
predation by the alewife (Alosa pseudoharengus), which had eliminated
all other zooplankton of the same size. Evasion is a function of
temperature; arctic grayling capture copepods more successfully at 5°C
than at 15°C (O'Brien 1979). As noted by Drenner et al. (1978), the
ability of zooplankton to avoid capture by fish has drawn little
attention in predation studies despite the fact that this phenomenon
is fairly easy to measure in the laboratory.
(4) Handling time. The interval between seizure of prey and
swallowing is known as handling time. Werner (1974) measured handling
times for bluegill (Lepomis macrochirus) and green sunfish (L
cyanellus) feeding on various types of prey. His method was
exceedingly simple: a fish was fed prey one at a time while an
observer with a stopwatch measured the time between seizure and
swallowing. Handling time was relatively constant, approximately 1 s,
for small prey and rose steeply for prey nearly as large as the mouth
of the fish. Handling time for a given prey size increased gradually
as the fish continued eating because satiated fish swallowed prey 2h
to 3 times more slowly than hungry fish. Ware (1972) observed a
similar effect with rainbow trout (Salmo gairdneri), noting that
partially satiated fish often rejected a prey several times before
swallowing. Handling time sets an upper limit to feeding rates at
high prey densities (Ware 1972) and may restrict small fish to small
prey under these conditions (Werner 1974), but this is not likely to
be significant in most natural situations.
(5) Population experiments. All of the experiments just
described involved close observation of mechanisms involved in
individual predation events. The results of these studies were used
to identify critical factors in the predator-prey interaction and
formed the basis for many mechanistic models of fish predation on
zooplankton. To test the predictions and implications of these models
and, in some cases, to derive values for model parameters, a different
experimental approach has been used in which fish are allowed to feed
on a zooplankton population or community rather than one individual at
a time. The outcome of such an experiment is determined by comparing
the surviving prey population with the initial population or by
analyzing the stomach contents of the fish. The objective is to
assess the feeding selectivity of the predator without necessarily
distinguishing the mechanisms of selection.
The work of Drenner et al. (1978) is typical of this approach.
Experiments were conducted in plastic swimming pools containing 120 to
150 L of water. Gizzard shad (Dorosoma cepedianum) were placed in the
pools (31 to 38 fish per pool), and a freshly collected zooplankton
community was mixed into the water. The zooplankton were sampled
periodically, and experiments lasted from 1 to 13.5 h. Cladocerans
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(Daphnia galeata mendotae and Ceriodaphm'a retlculata) were consumed
most rapidly, cyclopoids (Cyclops sp. and Mesocyclops sp.) less
rapidly, and the calanoid Diaptomus pallidus least rapidly. These
results were consistent with conclusions reached in experiments with
artificial suction feeders (Drenner et al. 1978; Janssen 1976).
Werner and Hall (1974) adopted a similar approach for experiments
with bluegill (Lepomis macrochirus). Ten fish were acclimated in
pools (1.3 to 1.7 m in diameter, 15 to 28 cm deep) for 24 h, and then
Daphnia magna were added at various densities and size class
proportions. To avoid significant changes in the prey populations,
the fish were allowed to feed for only 0.5 to 5 min and were then
removed from the pool and their stomach contents analyzed. The
results indicated that large prey were consumed in greater proportions
than their proportions in the prey population. The authors analyzed
the data in terms of a model based on foraging energetics. O'Brien et
al. (1976) later demonstrated that the same data could be explained by
an apparent-size-selection model (see above).
Zaret (1972) examined the relative preference of Melaniris
chagresi (a tropical planktivore) for two forms of Ceriodaphnia
cornutum by allowing two fish to feed for approximately 1 h on a
mixture of the two forms and then analyzing the fish stomach contents.
The feeding time was selected to permit the fish to consume 10 to 30%
of the prey. As noted above, this fish was difficult to handle in the
laboratory. Two to three fish were added to each 38-L aquarium the
day before the experiment, and one fish had usually died by the time
the experiment began. These experiments confirmed the preference of
M. chagresi for the more visible (larger eye pigmentation area) form
of C. cornutum.
Ware (1972) measured the consumption rate of rainbow trout (Salmo
gairdneri) on the amphipods Crangonyx richmondensis and Hyalella
azteca at different prey densities and in the presence of different
litter substrates. The amphipods were placed in the
90- by 45- by 45-cm aquarium 1 h before the experiment began to allow
them to disperse and find cover. One fish was then added and observed
for 50 min. Attacks and captures were recorded, and the number of
surviving prey was determined at the end of the feeding period.
(6) Evaluation. Predation by fish on zooplankton is an
important phenomenon in aquatic ecosystems. Interference with
fish-zooplankton interactions could have significant economic
consequences as well since the diet of many commercial and game fish
consists mainly of zooplankton or planktivorous fish. Because
hundreds of laboratories throughout the country are presently equipped
to culture fish and zooplankton for single-species bioassays, the
incorporation of predation tests into chemical hazard assessments
would not require facilities or skills not already available.
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The sensitivity of fish-zooplankton interactions to toxic
chemicals is unknown. Effects on the visual acuity, swimming speed,
agility, and behavior of the fish would probably have a greater
influence on predation than any physiological or behavioral impairment
of the zooplankton. Exposing the predator, instead of (or in addition
to) the prey, to a test chemical would be a logical experimental
approach. Of the various parameters measurable in mechanistic
studies, reactive distance is most likely to be influenced by
toxicants. The reactive distance of an individual fish for a given
class of prey can be measured in a few hours, but the measurement is
labor-intensive and replicates would have to be run sequentially
rather than simultaneously. The population approach described above
is a more efficient means of measuring effects on fish-zooplankton
systems since experiments can be set up with many replicates at once,
and surviving prey can be preserved and counted when convenient. The
studies by Drenner et al. (1978) are good examples of the population
approach to fish-zooplankton interactions.
Because light intensity, turbidity, and background all have
significant effects on the ability of fish to locate zooplankton,
experimental conditions must be carefully controlled in
fish-zooplankton studies. Hunger and feeding experience of the fish
are critical in any predation experiment. The age and size of both
predators and prey must be specified, and other factors (configuration
of the test chamber, timing of the experiments, and potential
interference by the observer) can also influence the results. All
these variables should be rigidly standardized among tests and among
laboratories if consistent results are to be achieved.
The ecological and economic significance of fish predation on
zooplankton and the widespread familiarity with these animals as
subjects of bioassays justify incorporation of fish-zooplankton
predation tests in the battery of hazard assessment methods. Studies
should be undertaken to determine the sensitivity of fish-zooplankton
interactions to toxic chemicals and to optimize the experimental
procedure for routine testing.
3.2.4 Fish-Macroinvertebrates
A survey of the literature indicated that little laboratory
research has been done on predation by fish upon macroinvertebrates.
Two groups of studies are reviewed here: (1) a series of experiments
on predation by estuarine fish on grass shrimp conducted at EPA's Gulf
Breeze Environmental Research Laboratory (ERL) and (2) a group of
studies concerning the interactions between small mouth bass and
crayfish. Grass shrimp and crayfish, both detritivores, are important
components of the food webs of coastal ecosystems and lakes,
respectively.
(1) Predation on grass shrimp. Tagatz (1976) reported the first
of a series of predation experiments involving the grass shrimp,
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Palaemonetes pugio. His experiments were performed in model
ecosystems similar in concept to the terrestrial-freshwater microcosms
of Metcalf et al. (1971). The model ecosystems consisted of 4 cm of
sand and 160 L of artificial seawater in 180-L aquaria. Turtle grass
(Thalassia testudinum) was planted over two-fifths of the bottom
surface, and 75 grass shrimp were added. The systems were allowed to
equilibrate for 4 to 6 days, and mi rex was then added. There was no
significant mortality of shrimp for 13 days in these systems compared
to controls. After 13 days, two pinfish (Lagodon rhomboides) were
introduced into each tank, and the numbers of surviving shrimp were
determined after 1 to 3 days of predation. Predation was
significantly higher in systems treated with mirex than in the
controls. The author recognized that the results might have reflected
effects of mirex on either the predator or the prey, but concluded on
the basis of previous toxicity tests that only shrimp were affected.
He stated that the concentration of mirex found to alter the
predator-prey interaction (0.025 ug/L) was "the lowest, concentration
of mirex in water that has been reported to cause death of an
estuarine animal." Death in this case was an indirect result of
exposure to the toxicant.
Tagatz (1976) believed that the effects of mirex were caused by
alterations in the behavior of the grass shrimp, but he reported no
observations that would support this contention. Farr (1977)
conducted experiments specifically designed to reveal behavioral
alterations in shrimp exposed to toxicants. He conditioned Gulf
killifish (Fundulus grandis) to feed on grass shrimp introduced into
the aquarium with a dip net. Ten shrimp were presented to each fish
daily, and the survivors were removed after a 3-h feeding period.
When the fish had become accustomed to this procedure, Farr exposed
groups of shrimp to methyl or ethyl parathion for 24 to 72 h and then
fed them to the killifish as usual. He measured the time between the
consumption of the first shrimp and the capture of the third and
counted the survivors after 15 min and again after 3 h, when the
remaining shrimp were removed. A single run consisted of one fish,
which was fed control shrimp one day and treated shrimp the next;
thus, each run included its own control. Farr found that parathion
significantly reduced the time needed for the fish to capture the
second and third shrimp and increased the number of shrimp consumed in
15 min. There were no effects on the total number of shrimp captured
in 3 h (probably because there were few survivors even among
controls). Treated shrimp were more active than controls and
therefore presumably more conspicuous to the fish. Since parathion
also decreased their physical endurance, the shrimp were easier for
the fish to catch.
In a subsequent study, Farr (1978) examined prey selection by
killifish which were offered grass shrimp and sheepshead minnows
(Cyprinodon variegatus) simultaneously. Equal numbers of shrimp and
minnows were placed in aquaria, and some were exposed to methyl
parathion for 24 h. One killifish was then added to each tank, and
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prey survival was monitored for 5 days. In tanks without parathion,
minnows were consumed more rapidly than shrimp. Parathion caused
increased predation on both species, but shrimp were affected more
strongly than minnows, and selection by the predator was apparently
reversed for a time. This effect was more pronounced at higher
parathion concentrations. Farr presented the results in three ways:
(a) as percent survival of each species; (b) as the ratio of surviving
shrimp to surviving minnows; and (c) as a capture coefficient equal to
the ratio of prey species consumed, divided by the ratio of prey
species available to the predator. Presentations (b) and (c) both
indicated that parathion erased the predator's preference for minnows,
but only (a) revealed that the survival of treated minnows averaged
61% of controls. Farr did not mention this latter result and omitted
statistical treatment of the data in (a); therefore, the significance
of the effect on minnows is unknown. The two-prey system (Farr 1978)
may be an improvement over the one-prey experiments (Farr 1977), but
inconsistencies in the 1978 paper make an objective evaluation
impossible.
Experiments on predation on grass shrimp continued for a time
after Farr's departure from the Gulf Breeze ERL, but have now been
suspended; further research has been directed towards single-species
behavioral bioassays (C. R. Cripe, personal communication).
(2) Predation on crayfish. Factors affecting predation on
crayfish (Orconectes propinquus) by smallmouth bass (Micropterus
dolomieui) were investigated by Stein and Magnuson (1976) and by Stein
(1977). Experiments were conducted in flow-through aquaria with sand,
pebble, or gravel substrates. In a typical experiment, equal numbers
of four size classes of crayfish were placed in tanks with one bass;
surviving crayfish were removed, counted, and returned to the tanks
every 2 h (Stein 1977). (In other experiments, survivors were counted
daily.) Variations on this experimental design were used to measure
predation as a function of the sex, reproductive condition, and
molting stage of the crayfish (Stein 1977) and as a function of
substrate type (Stein and Magnuson 1976). These experiments lasted
from 10 h to 7 days.
The handling time for bass feeding on crayfish was measured in
another series of experiments (Stein 1977). An opaque tube was placed
vertically in the water, and a crayfish was added. When the crayfish
settled to the bottom of the tube, the tube was removed; the bass were
trained to eat crayfish presented in this way. The time from capture
to swallowing (the handling time) was measured in each encounter.
Handling time varied with prey size and molting stage. Using an
approach similar to that of Werner and Hall (1974) [Sect. 3.2.3(5)],
Stein used the data to predict the prey size that would optimize the
predation efficiency of the bass (pursuit plus handling time divided
by energy gain).
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The presence of a predator was found to influence the behavior of
crayfish (Stein and Magnuson 1976). Various activity patterns were
quantified for 3 days without fish. Bass were then introduced into
some aquaria, and crayfish behavior was monitored for 3 more days. In
the presence of the predator, active behavior patterns (such as
walking, climbing, grooming, and feeding) were reduced, and the
crayfish spent more time hiding in the substrate. Crayfish in tanks
with bass also preferred pebble to sand because the former substrate
offered greater opportunity for hiding; this preference was not seen
when no fish were added. In all cases, behavioral effects were most
noticeable among prey groups most susceptible to predation (juveniles
and nonreproductive adults).
(3) Evaluation. The evaluation of fish-macroinvertebrate tests
as hazard assessment tools is included with the evaluation of
fish-fish systems in Sect. 3.2.5(3).
3.2.5 Fish-Fish
Predator-prey interactions among fish have been the subject of
numerous laboratory investigations during the past 10 years. Most
experiments have been designed to compare the vulnerability of two or
more groups of prey. The groups may be different species (Coble 1973;
Herting and Witt 1967; Mauck and Coble 1971) or members of the same
species differing in size, color, form (Coble 1973; Mauck and Coble
1971), physiological condition (Coble 1970; Herting and Witt 1967;
Vaughan 1979), or previous exposure to chemical or physical stress
(Baker and Modde 1977; Coutant 1973; Coutant et al. 1974; Deacutis
1978; Goodyear 1972; Kania and O'Hara 1974; Sullivan et al. 1978;
Sylvester 1972, 1973; Weltering et al. 1978; Wolters and Countant
1976; Yocum and Edsall 1974). Examples of recent research and a
discussion of methodological details are presented in this section.
(1) Examples of recent research. The focus of many
predator-prey studies with fish has been the effects of toxicants or
thermal stress on the susceptibility of prey to predation. Kania and
O'Hara (1974) exposed groups of mosquito fish (Gambusia affinis) to
0.005 to 0.1 mg/L of mercury and offered each group, along with equal
numbers of untreated mosquito fish, to largemouth bass (Micropterus
sal mo ides). After 60 h, all the remaining mosquito fish were
collected and counted. It was found that short exposure to low levels
of mercury impaired the normal escape behavior of the prey, and
predation was heavier on the treated group than on the controls. The
effect was a function of mercury concentration and was seen as low as
0.01 mg/L, which is well below the lethal concentration for this fish.
Weltering et al. (1978) studied the effects of ammonia on the
interaction between largemouth bass and mosquito fish. The approach
differed from that of Kania and O'Hara (1974); predator and prey were
both exposed to the toxicant continuously throughout the experiment.
Ammonia concentrations above 0.34 mg/L caused physiological and
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behavioral changes in the predator, resulting in a lowered predation
rate. The effect was greatest at high prey densities, where the
predator was actively harassed by the prey. Like Kania and O'Hara
(1974), Woltering et al. (1978) observed changes in the predator-prey
interaction at toxicant concentrations below the lethal level.
The effects of acute and chronic exposure to cadmium on the
vulnerability of fathead minnow (Pimephales promelas) to predation by
largemouth bass were examined by Sullivan et al. (1978). Subtle
behavioral changes in the prey increased their vulnerability at
cadmium concentrations less than one-hundredth of the reported maximum
allowable toxicant concentration (MATC) for this species. These
changes are described in detail by Sullivan and Atchison (1978).
Increased predation on thermally stressed fish was reported in a
series of papers by Coutant and co-workers (Coutant 1973; Coutant et
al. 1974; Coutant et al. 1979; Welters and Coutant 1976). In one
study (Coutant 1973), juvenile rainbow trout (Salmo gairdneri) and
chinook salmon (Oncorhynchus tshawytscha) were exposed to elevated
temperatures for varying lengths of time and then placed in a tank
with adult rainbow trout. When about 50% of the prey had been
consumed, the survivors were removed and counted. The stressed fish
exhibited disorientation, erratic swimming, unnatural posture, and
reduced escape abilities; consequently, they suffered higher predation
than unstressed prey. The effects were related to the exposure
temperature and exposure time and were significant at 11% of the
median lethal time or 2.5°C below the median lethal temperature. The
experiments were intended to simulate the actual experience of
juvenile fish near the thermal discharge of the Hanford, Washington,
nuclear reactor.
In a subsequent study, Coutant et al. (1974) acclimated juvenile
channel catfish (Ictalurus punctatus) and largemouth bass to several
above-normal temperatures and then placed them with adult largemouth
bass at 16°C. When the acclimation temperature was 7 to 9°C higher
than the predation temperature, the prey were "benumbed" and rested on
the bottom rather than seeking refuge. The predators recognized and
preferentially selected the shocked fish. A much greater thermal
shock is necessary to kill these fish. Welters and Coutant (1976)
observed similar effects with cold-shocked bluegill (Lepomis
macrochirus). Other studies on thermal effects include those by
Deacutis (1978) with killifish (Fundulus majalis) feeding on larvae of
Atlantic silverside (Menidia menidia) and flounder (Paralichthys
dentatus); by Sylvester (1972, 1973) with coho salmon (Oncorhynchus
kisutch) feeding on sockeye salmon fry (0. nerka); and by Yocum and
Edsall (1974) with yellow perch (Perca flavescens) feeding on fry of
lake whitefish (Coregonus clupeaformis).
Goodyear (1972) demonstrated increased predation by largemouth
bass on mosquito fish that had been exposed to gamma radiation. In
this experiment, the prey were provided with refuge from the predator,
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and nonirradiated fish could survive for 20 days with only 5% losses.
However, irradiated fish tended to wander out of the refuge, and 60%
were consumed in 20 days. Goodyear proposed the method as a simple
screening test for toxicants.
Several investigators (Coble 1970; Herting and Witt 1967; Vaughan
1979) have studied the influence of disease, parasitism, and viral
infection on the predator-prey interaction. Herting and Witt (1967)
presented bowfin (Amia calva) with pairs of prey species including
golden shiner (Notegonus chrysoleucas), bluegill, green sunfish
(Lepomis cyanellus), and largemouth bass. The preference of bowfin
for one prey species over the other could be reversed if one of the
prey species was diseased, parasitized, or suffered from handling
stress. For example, normal bluegill were less vulnerable than green
sunfish when both were offered together to the predator, but bluegill
suffering from columaris disease were more vulnerable than green
sunfish. A similar reversal was seen when largemouth bass parasitized
by trematodes were offered together with healthy golden shiners. The
authors concluded that the changes in relative vulnerability were due
to sluggish behavior, which drew the attention of the predator (bowfin
prefer slow-moving or stationary prey) and reduced agility and stamina
of the prey. Vaughan (1979) as well as Coble (1970) observed no
increased vulnerability in bluegill infected with lymphocystis virus
or in fathead minnows infected with yellow grub (Clinostomum
marginatum) respectively. Vaughan (1979) suggested that these
negative results were due to the absence of noticeable behavioral
changes in the infected prey.
(2) Methodological details. Most experiments on fish
predator-prey interactions have been conducted in flow-through aquaria
containing 100 to 750 L of water or in pools holding up to 3600 L of
water. Deacutis (1978) studied predation by small killifish in 9-L
tubs; at the other end of the size range, Mauck and Coble (1971)
performed experiments in 0.04-ha ponds. Ginetz and Larkin (1975)
constructed experimental troughs in a salmon spawning channel for
studies of rainbow trout feeding on sockeye salmon fry. Most workers,
however, have used conventional fish tanks.
In many cases, cover or refuge was provided for prey and/or
predators. Cover has consisted of artificial vegetation (Coble 1973;
Sullivan et al. 1978; Vaughan 1979), tree limbs (Mauck and Coble
1971), or bricks (Coble 1973). In Goodyear1s (1972) studies of
largemouth bass predation on mosquito fish, a shallow refuge area was
provided for the prey, separated from the main portion of the aquarium
by a coarse screen. The screen was necessary because some bass would
pursue the prey into the shallow area, whereas others would not,
creating variability in the experimental results. Shallow refuge
areas were used by Kania and O'Hara (1974) and Weltering et al. (1978)
in experiments with the same two species. Provision of refuge or
cover for the prey increases their chances of survival and creates a
more realistic environment for the predator-prey interaction. When no
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cover is present, the prey are usually consumed within minutes.
Wolters and Coutant (1976) did not provide cover and reported
difficulty in terminating some of their experiments before 50% of the
prey were consumed since this sometimes occurred in less than 1 min.
With cover, and especially with a refuge, experiments can be continued
for several weeks if desired (Goodyear 1972).
Experiments have been conducted using fish from laboratory stocks
as well as fish from the field. In either case, the fish must be
preconditioned to the experimental situation. Acclimation to a
particular temperature or light intensity can affect the performance
of predator and prey (Coutant et al. 1974; Ginetz and Larkin 1976;
Sylvester 1972; Wolters and Coutant 1976; Yocum and Edsall 1974).
Learned behavior on the part of both animals also plays an important
role in predation studies and can be a source of unexpected
variability in the results. For instance, Baker and Modde (1977)
reported that bluegills were timid in their first two encounters with
blacktail shiners (Notropis venustus), but beginning with the third
trial, they became more aggressive and actively searched for prey.
Most investigators have trained the predators to feed under
experimental conditions; Goodyear (1972) and Weltering et al. (1978)
conditioned the prey to the predator as well.
In comparisons of predation on different groups of prey, the
different groups may be offered to the predator simultaneously or in
separate trials. When more than one prey type is present in one
aquarium, differential marking is sometimes necessary to distinguish
the groups. Many workers (e.g., Coutant 1973; Sullivan et al. 1978)
used cold branding to identify treated and control prey. Kania and
O'Hara (1974) used a radioisotope (197Hg) to tag mosquito fish exposed
to mercury; FitzGerald and Keenleyside (1978) suggested 131I for the
same purpose. Some marking techniques may affect the vulnerability of
the prey. For example, Baker and Modde (1977) demonstrated that
blacktail shiners marked with a particular stain were selected by
largemouth bass and bluegill over unmarked shiners. Fin clipping is
another marking technique that can affect the predator-prey
interaction (Mauck and Coble 1971).
When alternative prey are presented to the predator
simultaneously, the ratio of prey abundances can influence selection
by the predator (Coutant 1973; Coutant et al. 1979). Results o.f an
experiment may then depend on the proportions of prey added initially
and on changes in those proportions during the test. To minimize this
factor, experiments are often terminated before half of the prey are
consumed (Coutant 1973; Coutant et al. 1974; Mauck and Coble 1971;
Vaughan 1979; Wolters and Coutant 1976).
When different groups of prey are presented to the predator in
separate trials, the problems of differential marking and prey
proportions are avoided, but identical conditions must be carefully
maintained from one trial to the next. The size, experience, and
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physiological condition of the animals are important factors in
predation experiments. Predators are usually starved for 24 h or more
before each experiment to achieve a uniform degree of hunger. Yocum
and Edsall (1974) exposed the same predators to stressed and
nonstressed prey alternately, with each group of predators serving as
its own control. (A similar approach is often used in studies of fish
predation on zooplankton--see Sect. 3.2.3).
In a few instances, predators and prey have been exposed to
stress together in the same experimental chamber instead of exposing
the prey separately and then adding them to the tank. The studies of
Tagatz (1976) and Farr (1978) are discussed in Sect. 3.2.4(1), and
those of Weltering et al. (1978) are described above. We found no
reports of experiments in which only predators were exposed to a
toxicant or other stress.
The outcome of a fish predator-prey experiment is usually
determined by counting the surviving prey. When different prey groups
are presented to predators in separate trials, analysis of variance is
used to test for treatment effects (e.g., Ginetz and Larkin 1976;
Sylvester 1973). When different prey groups are presented
simultaneously, the results are often expressed as some type of
selection index (e.g., Baker and Modde 1977; Coutant 1973; Coutant et
al. 1974; Herting and Witt 1967; Mauck and Coble 1971; Wolters and
Coutant 1976). Alternatively, a chi-square test may be used to
compare the proportions of prey consumed with the proportions
initially present (e.g., Coble 1973; Kania and O'Hara 1974). Sullivan
et al. (1978) developed a special statistical technique for analyzing
predation results.
A few investigators have measured the results of predator-prey
experiments in ways other than counting survivors. Yocum and Edsall
(1974) and Deacutis (1978) counted the number of attacks, captures,
and escapes during experiments. This approach made it possible to
differentiate effects on prey attractiveness (as indicated by
frequency of attacks) from effects on escape abilities (as indicated
by the ratio of captures or escapes to attacks). In both these
studies, heat-stressed prey were attacked less frequently, but
captured more successfully, than controls. Sylvester (1972) recorded
the time of capture of each prey and expressed the results as the mean
survival time of the prey. Yocum and Edsall (1974) found this
approach unsatisfactory with yellow perch feeding on whitefish fry
because individual predators differed greatly in the time taken to
discover the prey. Weltering et al. (1978) measured the growth of
predators during 10-day experiments; the results reflected the same
trends as numbers of prey consumed.
(3) Evaluation. The studies of Tagatz (1976) arid Farr (1977
1978) on predation by fish on grass shrimp are well-known examples of
chemically induced alterations in prey behavior leading to increased
susceptibility to predation. The predator's role in these
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experimental systems is to detect the behavioral alterations. If a
human observer were equally perceptive, the tests could be simplified
to single-species behavioral bioassays. The same is true of most of
the fish-fish studies discussed in this section. As stated above, the
major justification for employing this type of predator-prey system in
hazard assessment is its extreme sensitivity to chemical stress.
In most other respects, what has been said in Sect. 3.2.3(6)
about fish-zooplankton interactions applies here as well. Fish-shrimp
and fish-fish systems are somewhat more complex than fish-zooplankton
systems, since a refuge or cover should be provided for the prey to
permit ecologically significant behavioral effects to be revealed.
Another difference between these experiments and those with
zooplankton is that shrimp or fish prey may have equal or greater
economic importance than the predator.
There are no serious obstacles to the development and standardi-
zation of predator-prey test procedures with shrimp or fish as prey.
A predation experiment could be a convenient sequel to a
single-species acute bioassay. For example, shrimp could be exposed
to a range of chemical concentrations for determination of an acute
LC50. The animals from the sublethal treatments could then be
presented to a predator to determine whether their survival abilities
had been impaired. An integrated testing sequence such as this would
provide a more ecologically meaningful indication of the potential
hazards of a chemical than conventional bioassays alone, with no
serious increase in cost.
3.2.6 Conclusions and Recommendations
All the predator-prey interactions discussed, except for
protozoan predation, are of known ecological significance. Many have
been shown to be highly sensitive to chemicals and other types of
disturbance. Tests for chemical effects on the interaction between
any two species are not likely to provide reliable information about
interactions between other species pairs or to permit accurate
predictions of effects that would occur in the context of a whole
community or ecosystem. Therefore, the most suitable position for
predator-prey tests in a chemical hazard assessment sequence is
immediately after screening tests.
Laboratory systems with zooplankton predators and prey are
probably the most efficient for chemical testing. Many zooplankton
species are easily cultured, and large reproductive populations can be
maintained in static aquaria. Predation tests can be conducted in
small, static systems, Experiments can be completed in 8 h or less,
and the surviving prey can be preserved to be enumerated later.
Because zooplankton are nonvisual predators, lighting is not a
critical factor, and experiments can be conducted in darkness.
Learning, social interactions, and disturbances caused by observers
are much less important in zooplankton-zooplankton systems than in
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fish systems. The most likely mechanisms for chemical effects on
zooplankton predation are: (1) reduced swimming speed of predators or
prey, (2) reduced capture success of predators, or (3) reduced escape
success of prey. The sensitivity of these mechanisms to chemicals is
unknown. Replicability of zooplankton-zooplankton systems is probably
good. Species that might be suitable predators in chemical test
systems include Mesocyclops edax and Cyclops spp. , while Diaptomus
spp., Bosmina longirostris, and Ceriodaphnia spp. would be appropriate
prey.
Fish-zooplankton predation tests are somewhat more complicated
than tests with zooplankton predators. Fish cultures require more
space than zooplankton cultures, and continuous flow systems are
necessary for most species. Likewise, predation studies involving
fish generally require large volumes and/or continuous flow. Lighting
conditions and background must be carefully controlled to ensure
repeatable results with these visual predators. Effects of learning,
social behavior, and unintentional disturbances are more likely to
occur with fish than with zooplankton predators. All these factors
imply that fish-zooplankton systems would be less amenable to chemical
hazard assessment than zooplankton-zooplankton systems. However,
experiments with fish might be faster than zooplankton predation tests
since fish consume more prey in a given time than do zoop'lankton.
Possible mechanisms for chemical effects on fish-zooplankton
interactions include: (1) impaired vision of the fish; (2) reduced
swimming speed of predator or prey; and (3) reduced avoidance ability
of prey. The sensitivity of fish-zooplankton systems to chemicals is
unknown, but might be enhanced if zooplankton with well-developed
escape abilities (such as Diaptomus spp.) were used as the prey.
Replicability may be a problem with these systems because so many
experimental variables can affect the results.
Because of the social and economic importance of many plankti-
vorous fish, an attempt should be made to develop an efficient fish-
zooplankton test system. The problems discussed above indicate that
test procedures would have to be specified in considerable detail, but
the problems are not insurmountable in developing a protocol. Common
bioassay organisms such as rainbow trout, bluegill, and Daphnia could
be readily applied to predator-prey experiments.
Predation experiments with fish as predators and
macroinvertebrates or fish as prey have the same technical
complications as fish-zooplankton experiments, but to a greater
degree. Nevertheless, relatively simple fish-fish systems have been
successfully used to test for effects of stress. The sensitivity of
fish-shrimp and fish-fish systems to chemicals has been demonstrated;
indeed, these are the only predator-prey systems for which we have
information on chemical effects. The largemouth bass-mosquito fish
systems of Goodyear (1972), Kania and O'Hara (1974), and Weltering et
al. (1978) have proved quite amenable to effects testing, as have many
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other systems described in Sects. 3.2.4 and 3.2.5. The background of
experience with chemical effects tests in such systems may offset the
inherent difficulty of devising suitable test protocols. It is
recommended, therefore, that a tentative protocol be developed for
fish-fish (or fish-shrimp) experiments and that they be compared with
zooplankton-zooplankton and fish-zooplankton systems before a final
decision is reached on the best system for hazard assessment. It is
also recommended that research be conducted to devise sensitive,
objective indicators of subtle behavioral effects, with the ultimate
objective of replacing fish-fish and fish-shrimp tests with simple,
single-species behavioral assays since alteration of prey behavior is
the most likely mechanism of chemical effect on these interactions.
To summarize the recommendations in this section, tests with fish
as predators and either fish or shrimp as prey are well-known and
could be standardized for chemical hazard assessment in the near
future. Tests with zooplankton as predators or prey are potentially
easier to use than fish predation tests, but further research must be
conducted before zooplankton-zooplankton or fish-zooplankton systems
can be adapted to chemical testing. Protozoa-protozoa predation tests
are not recommended for development in this context.
3.3 Parasitism
It is widely recognized that the incidence of parasitism or
disease in a population is determined partially by the physiological
state of the host organism and that various environmental stressing
agents can reduce the host's resistance to infection (Snieszko 1974;
Wedemeyer 1970). Snieszko (1974) cited several instances of increases
in parasitic infections in fish exposed to pesticides. Draggan (1977)
reported indirect evidence of effects of chromium on the interaction
between carp eggs and a fungal parasite. However, these observations
were incidental to studies conducted for other purposes. Effects of
drugs on parasitism and disease are, of course, the subject of
clinical parasitology, which is outside the scope of this review.
The only example found of an experiment specifically designed to
measure chemically induced susceptibility to parasitism was that of
Couch and Courtney (1977). These authors examined penaeid shrimp from
the Gulf of Mexico and found a high incidence of Baculovirus infection
in the population. Infected shrimp were identified by microscopic
examination of hepatopancreatic cell nuclei. A group of 925 shrimp
was exposed to 0.7 ug/L Aroclor® (a polychlorinated biphenyl) for 35
days, and the incidence of parasitic infection in the population was
compared with a control group held under similar conditions. Infected
shrimp initially comprised 23.3% of the population. After 35 days,
45.7% of the control group were parasitized, compared with 75% of the
shrimp exposed to PCB. Mortality was 13% in controls and 50% in
treated shrimp. It was impossible to separate direct PCB toxicity
from mortality resulting from increased parasitism without a parallel
experiment using noninfected animals. The authors recognized the need
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for such an experimental design, but found it to be impossible due to
an inability to raise shrimp xenobiotically and to detect latent viral
infections in apparently healthy shrimp. Possible mechanisms for the
observed effects of PCB on this host-parasite system were: (1) loss
of resistance of shrimp to new viral infections; (2) enhancement of
latent infections; (3) increased virulence of the virus; or (4)
increased cannibalism on intoxicated individuals (cannibalism being
one mechanism by which the virus is transmitted through the
population). PCB was found to accumulate in the site of infection
(the hepatopancreas), but not in tail muscle, which was uninfected.
The large numbers of animals involved in this study and the
number of histopathological examinations required to determine the
effect of one chemical at one concentration in one treatment group
lead to questions about the practicality of this system for routine
chemical hazard assessment. An earlier attempt to demonstrate the
same effect using fewer individuals and shorter exposure times was
inconclusive (Couch 1976). Moreover, the effect of PCB on the
shrimp-Baculovirus system is probably not generalizable to any other
host-parasite interaction. A chemical that failed to produce an
effect in the shrimp-Baculovirus test would not necessarily be
innocuous in other situations. We conclude that there are no
host-parasite systems amenable to development as hazard assessment
tests at this time. It is recommended that the parasitological
literature be surveyed to evaluate the possiblity of developing a
hazard assessment protocol.
3.4 Plant-Herbivore Interactions
The major plant communities in aquatic ecosystems are
phytoplankton and macrophytes. Grazing on macrophytes has been
studied very little by ecologists, and no relevant laboratory studies
were found in our review of the literature. Grazing by zooplankton on
phytoplankton is recognized as an important component of ecosystem
energy flow and nutrient cycling and as a possible determinant of
plankton community structure, but it too has received little
attention. One reason for this is that methods for measuring plankton
grazing rates, either in situ or in the laboratory, are still poorly
developed. The sensitivity of zooplankton grazing to chemical stress
is not known and should be investigated.
A phytoplankton-zooplankton hazard assessment test would be
essentially a single-species bioassay, with zooplankton grazing rate
as the measured response. Inert particles could be (and often are)
substituted for algae in this type of test without changing the nature
of the experiment significantly. The literature was not searched
thoroughly for laboratory phytoplankton-zooplankton systems because
our attention was directed towards areas with more promise for
chemical hazard assessment.
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It should be noted that grazing is one of the important processes
in mixed flask culture model ecosystems [Sect. 3.6.3(1)]. It is,
however, difficult to separate grazing from other processes occurring
simultaneously in these systems.
3.5 Symbiosis
No published reports of chemical effects on symbiotic
interactions among aquatic organisms were found. Because symbiosis
represents a high degree of specialization on the part of the
interacting species, chemical effects on one species pair would
probably not be relevant to other pairs. With the possible exception
of zooxanthellae in coral polyps, symbiosis is less important in
aquatic ecosystems than any of the other interactions reviewed in this
report. Symbiosis does not seem to be a logical subject for inclusion
in a chemical hazard assessment program.
3.6 Ecosystem Properties
All organisms in nature live in ecosystems. The structural and
functional properties of ecosystems determine the context in which
organisms, populations, and communities develop, persist, and
interact. Therefore, chemical effects on ecosystem properties have
the potential to influence all the components of the ecosystem. In
some situations, effects on ecosystem properties may be direct
consequences of easily observed effects on dominant organisms, and
knowledge of the responses of those organisms may be sufficient to
infer hazards to ecosystems. In other instances, the mechanisms of
ecosystem effects may be obscure. In either case, the ramifications
of ecosystem-level effects on all components of an ecosystem can be
unpredictable and far-reaching. This is the major justification for
the development of methods to assess the hazards of chemicals to
ecosystems.
This section reviews the properties of aquatic ecosystems and
discusses the central issue of laboratory studies at the ecosystem
level—the problem of predicting effects on natural ecosystems from
responses measured in simplified laboratory systems. Finally, some
general types of laboratory model ecosystems, or microcosms, that
might be adaptable for chemical hazard assessment under TSCA are
described.
3.6.1 Properties of Aquatic Ecosystems
An ecosystem is essentially an energy processing unit. Incoming
solar energy is converted first to chemical energy and finally to
heat. Because the energy processing capacity of an ecosystem depends
on a steady supply of inorganic nutrients, the ecosystem expends a
certain fraction of the energy it processes to ensure that nutrients
are retained and recycled. Cycling of essential elements is
accomplished through interactions among components of the ecosystem.
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These interactions confer a degree of homeostatic control, which
permits the maintenance of maximum persistent biomass in the face of
environmental fluctuations (Whittaker and Woodwell 1972; Reichle et
al. 1975). The existence of ecosystem homeostasis is implied by the
persistence of complex natural systems through time. Elucidation of
homeostatic mechanisms is a primary objective of ecosystem analysis.
Ecosystem function may be conceived in terms of superimposed
flows of energy and matter. Conversion of solar energy in
photosynthesis is accompanied by production of organic matter from
inorganic elements. Chemical energy is released as heat by
respiration, and the elements in organic matter are returned to
inorganic form. In a mature ecosystem, the two portions of the
matter-energy conversion are approximately in balance, at least over
an annual cycle.
Thus, ecosystem metabolism consists of two basic: processes, an
anabolic or productive process and a catabolic or regenerative
process. The productive process is mediated almost entirely by green
plants; the rate of this process is termed gross primary productivity
(GPP). The regenerative process is a function of both autotrophs and
heterotrophs and represents the total energetic cost of operating the
ecosystem. The difference between GPP and total ecosystem respiration
(Rp) is the net ecosystem productivity (NEP), which represents storage
of energy in biomass or detritus (Reichle et al. 1975). The ratio of
GPP to Rr, usually referred to as P/R, is one index of ecosystem
metabolism that has been measured in several aquatic ecosystems. Odum
(1956) proposed the use of P/R for classifying ecosystems as
autotrophic (P/R > 1) or heterotrophic (P/R < 1) and noted that either
type of system tends to approach P/R = 1 over time. Odum (1969)
listed P/R = 1 as an attribute of mature ecosystems, concluding that
P/R could be used as an index of relative maturity. P/R ratios
approximating 1 have been found in many laboratory microcosms (Beyers
1962, 1963; Copeland 1965; Gorden et al. 1969; Giddings and Eddlemon
1978; Harris et al. 1980) and natural systems (Riley 1956; Odum 1957;
Odum and Hoskin 1958; Jordan and Likens 1975).
Microcosm studies consistently demonstrate that P/R departs from
1 when a system is disturbed. Microcosms grown at 23°C had a P/R of
1.09 at that temperature. When the temperature was lowered to 13°C,
P/R rose to 1.27; and at 33°C, P/R was 0.81 (Beyers 1962). Microcosms
dominated by turtle grass growing at 1500 foot candles (fc) had a P/R
approximating 1. When the light was reduced to 230 fc, both P and R
declined immediately, and P/R fell below 1. After 90 days, P and R
had returned to their initial level; P/R was about 1; and the turtle
grass community had been replaced by blue-green algae (Copeland 1965).
Increased grazing pressure has the same effect as decreased light
intensity: a decrease in both P and R, with P/R falling below 1
(McConnell 1962; Beyers 1963). In pond microcosms, P/R fell from 1.0
to 1.4 at steady state to 0 or below (i.e., negative net production)
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when arsenic was added and returned to 1.0 after 3 weeks (Giddings and
Eddlemon 1978). Various toxic substances added to large experimental
pools produced the same result (Whitworth and Lane 1969). Thus, P/R
appears to be a reliable indicator of stress-induced changes in
ecosystem metabolism.
Nutrient cycling is more difficult to measure than ecosystem
metabolism. The easiest and most common approach to monitoring
nutrient conditions in aquatic ecosystems is to measure the
concentrations of dissolved inorganic nutrients. The extremely low
concentrations of dissolved inorganic phosphorus and nitrogen in most
lakes and ponds are evidence of the close coupling between rates of
supply and rates of uptake by aquatic plants. Because of this
coupling, changes in nutrient regimes may not be reflected in ambient
nutrient concentrations (Schindler et al. 1971). Nutrient
concentrations in sediment interstitial water may be more sensitive
indicators of altered nutrient cycling than open-water nutrient
concentrations (Harris et al. 1980). In a system with well-defined
boundaries, the balance between nutrient inputs and outputs is a
measure of the ability of the system to retain nutrients; retention of
nutrients is a characteristic of mature, undisturbed ecosystems
(Likens et al. 1977; Odum 1969).
Aquatic autotrophs, especially phytoplankton, respond rapidly to
changes in nutrient regimes. The physiological state of autotrophs is
very dependent on their nutrient status. The nutrient status of
autotrophs can be assessed by measuring their response to nutrient
enrichment, by determining nutrient concentrations in plant tissues,
or by means of various physiological indicators such as alkaline
phosphatase activity and enhancement of dark C02 fixation by ammonium.
Techniques exist for measurement of specific microbial processes
contributing to the cycling of nutrients, including nitrogen fixation,
nitrification, denitrification, sulfate reduction, and methanogenesis.
Other components of the nutrient cycle, such as uptake by plants and
regeneration from detritus, can be measured by isolating these
processes from competing processes. However, determination of
nutrient flux in whole ecosystems generally requires isotopic tracers
such as 32P and 15N.
Very little is known about the sensitivity of nutrient cycling to
toxic chemicals in aquatic ecosystems. It is possible that the
structural and functional redundancy of most ecosystems would
compensate for chemical effects on individual components of the
nutrient cycle. Indeed, such stabilizing redundancy is one aspect of
the homeostatic character of mature ecosystems. However, if a
chemical were to disrupt nutrient cycling significantly, the effects
on the ecosystem would be serious and unpredictable.
Techniques for measuring or predicting effects of chemicals on
aquatic ecosystem properties are in an early developmental stage.
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There is very little information by which to compare the sensitivity
of ecosystem properties to chemicals with the sensitivity of
conventional bioassay organisms. Neither do we know the degree to
which responses of one ecosystem are likely to occur in other
ecosystems. Research is needed on the whole gamut of potential
ecosystem-level effects in a variety of ecosystems so that general
answers to these questions may begin to emerge. Such research must be
supported by conceptual advances in ecosystem analysis and by the
development of practical techniques for measuring ecosystem
properties. Thus, the search for tools for hazard assessment at the
ecosystem level is inseparable from basic research into the ecology of
whole ecosystems.
3.6.2 Realism and Generality
In discussing the applications of model ecosystems to chemical
hazard evaluation, a distinction is often made between "generic"
systems, which exhibit properties common to all ecosystems without
mimicking any natural ecosystem in particular, and systems that
simulate some specific ecosystem in greater or lesser detail. Such a
distinction is necessary because two of the criteria for an
ecosystem-level test protocol are not wholly compatible—namely, the
requirements of realism and generality. Realistic simulation of any
single ecosystem is achieved at the expense of generality; yet a test
cannot provide information relevant to a range of ecosystem types
without sacrificing some ability to represent a particular ecosystem
in detail. These conflicting demands are frequently lumped together
and termed "extrapolation," which refers to the general problem of
using laboratory experiments to make inferences about natural
phenomena. Such lumping of concepts is dangerous. The confusion
arising from misunderstanding the dual nature of extrapolation has
fueled much controversy about the utility and role of model ecosystems
in hazard assessment.
Realistic simulation of some ecosystems is inherently more
difficult than others. In terrestrial ecosystems, the size of the
dominant vegetation may be the critical factor limiting the degree of
simulation possible in the laboratory. In contrast, aquatic model
ecosystems are constrained mainly by the dimensions of the dominant
physical processes (mixing, turbulence, flow). The physical features
of ponds, for example, are much easier to incorporate into laboratory
systems than those of rivers, streams, or pelagic environments. Years
of experience with one type of aquatic microcosm may lead
investigators to make sweeping statements about the degree of realism
that microcosms can achieve without appreciating that realism is a
function of the ecosystem being modeled.
Likewise, some aspects of aquatic ecosystems are more readily
reproduced in the laboratory than others. Realistic simulation of
higher trophic levels is typically not possible in small laboratory
systems. However, decomposer communities can be easily incorporated
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into model ecosystems. A major goal of research with any type of
model ecosystem should be to identify those aspects of the system that
most accurately represent the natural prototype.
It is important here to distinguish between structural and
functional similarity. Exact duplication of the absolute abundances
of all species is not necessary for reasonable simulation of the
important processes occurring in an ecosystem. Unless a particular
species has some economic, social, or aesthetic importance, its
abundance may be of little concern to us. We are more concerned with
the continued well-being of the system as a whole than with its
structural details. Because of the functional redundancy of most
ecosystems, some species can be entirely replaced by others without
altering the overall productivity or persistence of the ecosystem.
Conversely, research with gnotobiotic microcosms has shown that
assemblages of the same species can be quite different in their
functional characteristics. This is not to deny the value of good
simulation of ecosystem structure in a laboratory system, but rather
to emphasize that species abundance is not the only, or the best,
measure of the success of simulation.
These thoughts lead quite naturally to a consideration of the
other criterion for a hazard assessment tool — namely, generality. If
a model ecosystem and a natural ecosystem may be functionally similar
in spite of structural differences, then the same comparison might be
made between natural ecosystems. That is, we may be able to distin-
guish certain universal ecosystem properties measurable in all systems
and, by studying these properties and their response to toxic
chemicals, make inferences that would be meaningful in any ecosystem.
This concept is the basis for the abstract model ecosystems originated
by Beyers (described by Gorden et al. 1969) and since adapted and
modified by many theoretical and applied ecologists. Such model
ecosystems, consisting of a few species of bacteria, algae, and
invertebrates, have no natural counterparts; in a strict structural
sense, they are totally unrealistic, and yet they exhibit features
such as succession, metabolic balance, and homeostasis that are
characteristic of all terrestrial and aquatic ecosystems. Most people
who use these experimental systems consider them to be fully valid
ecosystems, to be studied just as one studies lakes, streams, and
other naturally occurring ecosystems. Abstract model ecosystems have
often been suggested as ecosystem-level "white rats," implying that
they might be used to deduce general ecosystem properties in the same
way as laboratory rats have been used to investigate the principles of
mammalian physiology.
Unfortunately, the universal ecosystem properties of which we are
currently aware are of little recognized social or economic relevance
in themselves. The causal connections between population-level
phenomena and ecosystem properties have yet to be elucidated. Thus, a
chemical effect observed in an abstract model ecosystem might indicate
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a potential for disruption of processes in natural ecosystems, but the
nature of those disruptions cannot at present be predicted.
The problem of generalizing from model ecosystem results to
different natural ecosystems will remain an obstacle in system-level
hazard assessment until more comparative data are available for
natural systems. There are a number of substances (e.g., certain
trace elements and pesticides, and petroleum products) for which
dose-response observations have been made in many natural ecosystems.
Such data could be compiled to provide frequency distribution curves
of ecosystem sensitivity against which the sensitivity of particular
laboratory ecosystem tests could be compared. Construction of such
data bases represents an empirical approach to "calibrating"
laboratory systems for general predictions of safe exposure levels in
nature.
As a chemical progresses through the hazard assessment testing
sequence, the need for general indicators of potential effects
diminishes, and the need for realism in the testing situation
increases. At the initial screening level, information about the
relative hazards of chemicals helps determine the need for more
extensive testing. A general, or abstract, model ecosystem may be
useful at this stage for ranking chemicals in order of potential
effects on ecosystem processes. The rankings would be expected to be
more consistent among different ecosystems, and hence more
generalizable, than would qualitative or quantitative predictions of
effects. Thus, any laboratory system exhibiting ecosystem properties
could be used to identify those chemicals with the greatest potential
for affecting ecosystems. The major criterion for such a laboratory
system is its ability to generate rankings that are consistent with
the actual hazard potential of the chemicals in nature, rather than
its ability to simulate specific ecosystem effects. Test chemicals
could be compared with selected standard reference chemicals to
identify those with the greatest potential for environmental effects.
Once a chemical has been indicated to be hazardous and the types
of ecosystems likely to be exposed are known (through the exposure
assessment process), realistic simulation becomes the major objective
of ecosystem-level tests. The realism of model ecosystems is
sometimes evaluated in terms of how well they "track" their natural
prototypes through time. The question might be raised, how well does
any ecosystem track another ecosystem? If a model ecosystem were
perfected to the extent that it was identical in every measurable
aspect to its natural prototype, it would be imperfect with respect to
every other natural ecosystem. Since chemical hazard assessments
under TSCA will usually be concerned with protecting more than a
single ecosystem (although, especially in the later stages of the
assessments, concern might be limited to one type of ecosystem),
perfect tracking does not seem to be a reasonable criterion for
realistic simulation. Rather, the "validity" of a model ecosystem
could be assessed by comparing its behavior with the range of natural
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ecosystems. A laboratory ecosystem designed with realistic simulation
as the major objective should be typical, but not necessarily
identical to any particular example, of its ecosystem type.
A model ecosystem that satisfies this criterion becomes a
reasonable substitute for a field experiment. When a chemical is
tested in a realistic microcosm or in the field, the experiment
results are scrutinized to determine which observed effects might be
expected to occur in other ecosystems and which are
situation-specific. Direct toxic effects on components of the test
system are probably generalizable in that the same effects would occur
in other situations where the same organisms receive the same exposure
to the chemical. The difficulty arises in distinguishing direct toxic
effects from indirect effects caused by interactions among ecosystem
components. An intimate knowledge of the ecology of the test system
is necessary if this distinction is to be made. Likewise, prediction
of indirect effects in other ecosystems requires an understanding of
the structure and function of these ecosystems as well. At present,
our ability to predict indirect chemical effects in whole ecosystems
is rudimentary (see Sect. 5). Results of a model ecosystem experiment
are best viewed as examples of what could occur in a typical
ecosystem. The predictive power of model ecosystems will depend on the
growth of our basic understanding of ecosystem dynamics.
3.6.3 Potentially Useful Model Ecosystems
The number and diversity of aquatic model ecosystems is
staggering. For the purposes of this review, six general categories
have been selected for detailed discussion. Large, outdoor systems
(e.g., Pilson et al. 1977) have been omitted as have the more
complicated laboratory devices (e.g., Cooper and Copeland 1973),
because construction of large numbers of replicate systems would be
impractical. Other systems (e.g., Metcalf et al. 1971) have been
omitted because, in the reviewer's opinion, they do not adequately
represent ecosystem processes and are, therefore, unsuitable for
testing chemicals for ecosystem-level effects. The six categories
reviewed below range from nonrepresentational flask ecosystems to
realistic simulations of natural ecosystems. Many of these systems
have been used to test chemical effects, but none are so developed
that a standardized test procedure has been specified. Few have been
extensively compared with natural ecosystems. Therefore, "what has
been done" is given less attention than "what can be done." No
attempt has been made to document specific details of construction or
operation of these systems; the reader is referred to the examples
listed in the bibliography and to the general reviews of aquatic
microcosm technique that have appeared in recent years (Warren and
Davis 1971; Cooke 1977; Giddings 1980b; see also the papers contained
in Giesy 1980).
(1) Mixed flask cultures. To many people, the word "microcosm"
refers to a flask containing a mixed culture of bacteria, algae, and
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microinvertebrates. In terms of sheer numbers of publications, mixed
flask cultures are the most commonly used type of aquatic model
ecosystem. Beyers was perhaps the first to use these systems for
ecological research (Gorden et al. 1969). He inoculated an artificial
growth medium with microorganisms from a sewage oxidation pond and
maintained the cultures until a stable biotic composition was
achieved. These cultures are still in existence, and the original
species are still present. The organisms include several species of
algae, Paramecium, a flagellate, rotifers, an ostracod, and 11 species
of bacteria (Gorden et al. 1969). The strategy of inoculating
artificial media with organisms collected from lakes, ponds, streams,
aquaria, horse troughs (Ollason 1977), cemetery urns (Leffler 1977),
and other sources appears to be consistently successful in producing
simple, relatively stable model ecosystems (Bryfogle and McDiffett
1979; Cooper 1973; Kelly 1971; Kurihara 1978a,b; McConnell 1962, 1965;
Neill 1972; Reed 1976; Thomas 1978; Waide et al. 1980). Gorden et al.
(1969) demonstrated that these simple systems exhibit many of the
properties common to all terrestrial and aquatic ecosystems (Odum
1969). They have also been used to study population- and
community-level phenomena, and in a few instances, the effects of
toxicants have been examined.
Because of their simplicity and small scale (usually less than
1 L), mixed flask cultures are relatively easy to mass produce for
experiments with large numbers of replicates. The variablity among
replicates can be minimized by cross-inoculating periodically during
the first few weeks of growth. This ensures that random extinctions
do not affect the composition of the community that eventually
develops. Gorden (1967) noted the importance of including at least a
few individuals of the larger species (particularly ostracods) in the
inoculum of each culture since the presence or absence of these
organisms has a disproportionate effect on the rest of the community.
With these precautions, the coefficients of variation (CVs) of most
measurements of ecosystem structure and function can be held below 50%
(Kelly 1971; Leffler 1977). Even these values may be misleadingly
high since oscillations occurring in some parameters may be identical,
but out of phase among replicates, which results in high CVs at any
single point in time. Waide et al. (1980) and Taub (personal
communication) have attempted to overcome this problem by plotting
microcosm behavior in a two-dimensional phase space with, for example,
pH and dissolved oxygen levels as the two axes; identical, but
out-of-phase, replicates will have identical trajectories in such a
phase space.
Reproducing the same ecological characteristics from one
experiment to the next is more difficult than producing good
replicates within one experiment. Of course, natural sources of
inocula will change between experiments. An alternative is the
gnotobiotic approach (Taub 1969a,b,c; Nixon 1969), which establishes
experimental communities by adding known numbers of organisms from
stock monocultures. This method has the added advantage that initial
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population sizes may be manipulated by the experimenter. A major
disadvantage is that pure stock cultures of all members of the
community must be maintained; therefore, the cost in time and money of
conducting an experiment is substantially increased. Another drawback
of the gnotobiotic approach is that the organisms brought together in
these artificial communities may not be representative of natural,
co-adapted species assemblages. For this reason, gnotobiotic
communities are probably not reliable for studies of ecosystem-level
properties; most of Taub's research (1969a,b,c; Taub 1976; Taub and
Crow 1980; Taub et al. 1980) focuses instead on population
interactions.
Leffler's approach to the problem of achieving consistent results
from one experiment to the next is to examine properties of mixed
flask cultures that are insensitive to changes in community
composition (Leffler, personal communication). Leffler is currently
evaluating mixed flask cultures as screening tools for chemical hazard
assessment. His strategy is to measure the effects of chemicals on a
few easily measured integrative properties of the model ecosystems and
to rank chemicals in order of the concentrations required to produce
an observable effect. Leffler hypothesizes that these rankings will
be consistent among mixed cultures with differing species composition
even if the absolute values of the measured parameters are not
consistent. As discussed in Sect. 3.6.2, the rankings, not the
observed effects, constitute the output of this experimental design.
The model ecosystems are used to identify chemicals capable of
disrupting ecosystem processes, but do not specify which processes are
disrupted or how these effects might be manifested in natural systems.
Since many single-species bioassays have the same objective (ranking
of chemicals by potential hazard), model ecosystems would be valuable
primarily if they were more sensitive than conventional bioassay
organisms or if they generated different rankings than those of
conventional tests. If ecosystem-level screening tests merely echoed
the results of simpler, more easily standardized bioassays, their use
for screening chemicals would be questionable.
(2) Periphyton communities. Periphyton (also known as aufwuchs)
is the community of organisms attached to or associated with benthic
substrates or the submerged surfaces of macrophytes. The periphyton
community includes bacteria, algae, and many kinds of invertebrates
(Odum 1971). Periphyton are found in nearly all aquatic habitats. In
stream ecosystems, periphyton are usually the major primary producers.
They are invariably present in laboratory streams and can be a
nuisance in pelagic model ecosystems (Harte et al. 1978). Although
the periphyton community is only one part of an aquatic ecosystem, its
functions include all the major ecosystem processes such as primary
production, respiration, decomposition, and nutrient uptake,
transformation, and regeneration (Rodgers et al. 1980). Periphyton
communities have been used as indicators of ecosystem stress (Patrick
1973; Rodgers et al. 1980).
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Periphyton communities are easily produced in laboratory systems.
Typically, water from a natural stream, lake, or marine coastal
ecosystem is circulated over glass slides, porcelain plates, or other
artificial substrates, and organisms in the water colonize the
substrates within a few days. Alternatively, an artificial medium may
be used, with periphyton-covered rocks as an inoculum.
The ecology of laboratory periphyton communities has been
thoroughly studied by Mclntire (Mclntire et al. 1964; Phinney and
Mclntire 1965; Mclntire 1968a,b; Mclntire 1973). In these systems,
the species composition of the community became uniform over the
substrate (gravel) within 1 month, and biomas.s was fairly constant
after 2 to 3 months. Mclntire noted that the plant communities
"remained surprisingly constant" for at least 2 years, varying only in
the relative abundance of species. He stated that "a well-developed
periphyton community as a unit has a characteristic growth form and
responds metabolically to external environmental factors [light,
temperature, C02, dissolved oxygen, current] in a predictable way"
(Mclntire 1968a).
Laboratory systems for periphyton studies are usually designed in
such a way that samples of the substrate can be removed for
measurements of biomass, pigments, metabolism, or species abundance
without disturbing the rest of the system (Bott et al. 1977; Gerhart
et al. 1977; Kehde and Wilhm 1972; Kevern and Ball 1965; Mclntire et
al. 1964; Rodgers et al. 1980; Wulff 1971). Phinney and Mclntire
(1965) placed trays of substrate from the laboratory stream into
chambers for measurement of photosynthesis and respiration at
different temperatures and light intensities. Effects of toxicants
could be studied in the same way. Replicate samples from a laboratory
stream could be placed in chambers with test solutions, and effects on
metabolism (Phinney and Mclntire 1965; Rodgers et al. 1980) or rates
of degradation of organic matter (Bott et al. 1977) could be measured
over short periods of time. One stream system could provide enough
replicate samples of the community for many toxicity tests, and if the
community remains stable as Mclntire et al. (1964) indicate that it
should, experiments performed at different times would be comparable.
Few other experimental systems offer the combination of stability,
replicability, biotic complexity, and ease of handling found in
laboratory periphyton communities.
Chronic effects of chemicals on laboratory periphyton communities
have been studied by Gerhart et al. (1977) and by Rodgers et al.
(1980). Whereas a single laboratory stream can supply material for
many short-term tests, long-term experiments require that each stream
be used for only one treatment regime. Obviously, the number of tests
that can be performed by a single laboratory is severely limited.
However, the stability of laboratory periphyton communities makes them
ideal for chronic effects studies, providing a smooth baseline against
which treatment effects can be measured. Rodgers et al. (1980)
compared the variability and sensitivity of several structural and
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functional parameters, including dry weight, ATP, chlorophyll-a, C02
assimilation in the light and dark, and S04 assimilation in the light
and dark. They found that functional measurements were much more
consistent than structural measurements, and consequently, significant
treatment effects were more readily detected with the functional
parameters. Gerhart et al. (1977) also detected no effects on biomass
or chlorophyll in their experiments, but they did observe minor (not
statistically significant) changes in species abundance in communities
exposed to coal leachate. They reported "excellent replicability of
diatom communities" among their three control systems. No functional
parameters were measured.
Results of laboratory periphyton studies, in the opinion of
Kevern and Ball (1965), are consistent with ecological theory and with
observations on natural systems. If light, temperature, and water
flow are realistically reproduced, these laboratory systems are
probably representative of natural periphyton communities. The major
artificiality in laboratory systems may be the absence (in most
studies) of grazers. Studies of grazer effects (Admiraal 1977; Kehde
and Wilhm 1972; Mclntire 1968a) have produced conflicting results, and
further research in this area is warranted.
(3) Sediment cores. The sediment is the site of many important
processes in aquatic ecosystems including decomposition of organic
matter, nutrient regeneration, and degradation of contaminants. Ex-
changes between the sediment and the overlying water play a major role
in nutrient cycles and in controlling chemical conditions in lakes and
marine environments (Golterman 1976; Hutchinson 1975; Mortimer 1941,
1942; Pomeroy et al. 1965). Because processes occurring in the
sediment and at the sediment-water interface are difficult to measure
in situ, the technique of extracting sediment cores with overlying
water for study in the laboratory has been widely used by ecologists.
If cores are maintained at ambient temperatures, with aeration and
mixing of the water to simulate natural conditions, ecological
processes and effects of chemicals can be examined over extended
periods of time. The methodological approach is essentially identical
for studies in hypolimnetic, littoral, or coastal marine environments.
Sediment cores, unlike terrestrial soil cores (Sect. 4.2), have
not been used extensively in research on chemical contaminants. The
following discussion is based on work performed at EPA's Gulf Breeze
Environmental Research Laboratory (Pritchard et al. 1979) and at the
Utah Water Research Laboratory at Utah State University (Porcella et
al. 1976). Much of the information presented here comes from personal
communications with H. P. Pritchard (Gulf Breeze ERL) and Allen Medine
(formerly of Utah State University, presently at the University of
Connecticut). An outline for a chemical testing protocol using
sediment cores was formulated by these two scientists at the Workshop
on Methods for Measuring Effects of Chemicals on Aquatic Ecosystem
Properties held in conjunction with this project (Giddings 1981).
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The Gulf Breeze cores are extracted intact from an estuarine salt
marsh. They are used primarily in short-term (up to 21 days) studies
of microbial degradation of organic contaminants. Because of the
relatively short duration of the experiments, semicontinuous
replacement of the overlying water is not necessary. Like Medine's
microcosms, the Gulf Breeze cores are sealed, and various chemical
measurements can be made on the air leaving the systems. Although
these microcosms are designed for degradation experiments, Pritchard
believes that they could be used to study the effects of chemicals on
microbial communities and possibly (if larger cores were used) on
benthic invertebrates.
Medine and Porcella's systems consist of homogenized lake
sediment, artificial medium, and a gas phase. The sediment is
homogenized to promote uniformity among replicate systems. An
artificial medium is used to facilitate mass balance calculations; 10%
of the water volume is replaced each day. The systems are completely
sealed so that gas production and consumption in the microcosms can be
measured. Cores are illuminated to simulate shallow littoral habitats
or darkened to simulate hypolimnetic regions. These cores can be used
to measure the effects of chemicals on major biogeochemical cycles (C,
N, S) including denitrification and N-fixation, microbial respiration,
organic matter decomposition, primary production (in illuminated
systems), and species diversity. Both Medine and Pritchard report
good agreement among replicate cores.
A number of experimental factors have been found to influence the
behavior of sediment cores. Medine emphasizes the importance of sedi-
ment and water characteristics on measured variables, especially
nutrient exchange across the sediment-water interface. Pritchard
notes that the microbial activity in his systems is affected by the
dimensions of the core, the water:sediment ratio, and the sediment
surface area. The Gulf Breeze researchers have also investigated the
effects of homogenizing the sediment and observed that cores with
homogenized sediments degrade some organic chemicals faster than
intact cores, at least over 8 to 15 days. Medine's experiments run
for several months, and it is conceivable that the stimulation of
microbial activity caused by mixing the sediment disappears once the
initial flush of nutrients is exhausted.
The sediment core technique could be applied at almost any level
of a hazard assessment scheme. Simple static systems like the Gulf
Breeze cores are amenable to short-term tests of chemical effects,
whereas Medine's complex semicontinuous flow cores are suitable for
long-term studies. The Gulf Breeze researchers have also experimented
with continuous-flow, sediment-water systems for long-term degradation
experiments. Intact cores with natural water should provide realistic
sitespecific simulation for short experiments. Realistic simulation
is probably not possible over long periods (Pritchard), but even
homogenized cores with artificial medium can reproduce the general
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features of natural sediments (Medine). Sediment cores definitely
merit further development as hazard assessment tools.
(4) Pelagic microcosms. The pelagic zones of lakes and coastal
ecosystems present serious problems for microcosm simulation. The
structure and function of pelagic ecosystems are strongly influenced
by water movements, which carry planktonic organisms up and down in
the water column (thereby exposing them to a range of nutrient and
light regimes) and resupply the surface water with nutrients from the
bottom water and the littoral zone (Nixon et al. 1980). Currents also
transport plankton communities horizontally, bringing them into
contact with other patches containing different organisms and
different nutrient conditions (Margalef 1968). Enclosure of a pelagic
system alters the vertical distribution of organisms and dissolved
substances, cuts off nutrient inputs, and creates homogeneity in the
place of patchiness. Primary production per unit volume of a
phytoplankton community is usually low in pelagic microcosms; thus,
fish are difficult to maintain without seriously altering community
structure (Jassby et al. 1977b). Pelagic ecosystems are inherently
variable and unpredictable, and pelagic microcosms are no less so
(Giddings 1980).
Many aquatic ecosystems can be satisfactorily reproduced in the
laboratory if natural physical conditions are simulated
(Giddings 1980). Perez et al. (1977) attempted to duplicate the
physical conditions of lower Narragensett Bay in 150-L pelagic
microcosms. The microcosms were stirred with paddles to create
turbulence equal to that of the bay, as measured by dissolution rates
of hard sugar or gypsum. Microcosm water was replaced with bay water
semicontinuously at a turnover rate approximately equal to that of the
bay. The natural temperature regime was maintained by pumping bay
water through a water bath around the microcosms. The natural
photoperiod was reproduced. Experiments with light intensity are
discussed later. A small benthic component was included in each
microcosm, consisting of an intact sediment core in an opaque box
through which microcosm water was circulated at a rate approximating
that estimated for the bay. The ratio of sediment surface area to
water volume was equal to the overall surface/volmume ratio of the
bay. In short, an effort was made to establish conditions in the
laboratory as close as possible to estimates or measurements from the
natural ecosystem.
In their first experiment, Perez et al. (1977) found that, when
the average light intensity in the microcosm water column was equal to
the estimated average light intensity in the bay water column, an
algal bloom occurred. Reasons for this bloom are still unknown
(Perez, personal communication). Because grazers were more abundant
in the microcosms than in the bay, the bloom probably did not result
from reduced grazing. Release from nutrient limitation is a
possibility; nitrogen concentrations in the bay water were quite high,
but phosphorus (which was not measured) may have limited algal growth
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in the bay. Whatever the cause, Perez found that the bloom could be
prevented by reducing the light intensity to 15% of that in the bay
water column. Although imposing light limitation succeeded in holding
algal growth in check, the fact that light levels were many times
higher in the bay implies that the natural algal community was not
light-limited. Consequently, subsequent experiments on the effects of
turbulence, water turnover time, and sediment surface area are
difficult to interpret. This research demonstrated the difficulty of
simulating pelagic conditions in laboratory microcosms.
Because exchange rates of nutrients, organisms, and suspended
material between the pelagic and benthic components were a major
uncertainty in the original design, Perez is now developing a
modification of the benthic-pelagic coupling in these microcosms. The
modified systems will include a sediment core and a 1-m water column
set up alongside the pelagic tank; turbulence near the sediment
surface and exchanges with the pelagic portion will be under direct
experimental control (Perez, personal communication). For chemical
testing, Perez has constructed pelagic microcosms entirely of glass.
The fate, transport, and effects of radio-labelled
2-ethyl-hexylphthalate were studied in a series of experiments lasting
30 to 90 days each. Perez reports good repl icability in these
experiments for measurements related to the fate and transport of the
chemical, with more variability in measurements of phytoplankton,
zooplankton, and bacteria (Perez, personal communication). He
concludes that pelagic microcosms are useful for intensive studies of
chemicals of particular interest, but are impractical for screening.
Researchers at Lawrence Berkeley Laboratory (LBL) have
experimented with freshwater pelagic microcosms for several years
(Dudzik et al. 1979; Jassby et al. 1977a,b; Harte et al. 1978, 1980).
These microcosms are 50- or 700-L tanks containing natural water or
artificial medium and a naturally derived lake plankton community.
Turbulence is created by gentle aeration. There is no water
replacement and no benthic component (the latter will be included in
future experiments; J. Harte, personal communication).
A serious problem encountered in the early work with these
microcosms was the growth of periphyton on the walls of the tanks.
After several months of operation, the chemistry and biology of the
systems were dominated by the periphyton, making realistic simulation
of pelagic conditions impossible. Attempts at mechanical and
biological control of side growth were ineffectual. The researchers
concluded that the microcosms were most useful in the early stages of
community development (before periphyton growth became significant),
which were likened to the seasonal blooms observed in most temperate
lakes (Dudzik et al. 1979; Harte et al. 1980; Jassby et al. 1977a,b).
Eventually, a strategy of periodic transfer of the cultures to clean
vessels proved successful in avoiding the periphyton problem (Harte et
al. 1980). Perez (personal communication) eliminates wall growth in
his pelagic marine microcosms by scraping the walls daily.
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The LBL microcosms were used to test a technique for predicting
the sensitivity of lakes to stress. The sensitivity prediction was
based on the response of a sample of the pelagic community to organic
enrichment. Microcosms with different initial nutrient levels were
constructed and monitored for 2 months. The sensitivity of the
microcosms was assessed by using the organic enrichment method, and
the systems were then treated with ammonium, iron, or phenol and
monitored for another 2 months. The predicted sensitivity of each
type of system was compared with the actual response of the system to
perturbation. The features measured in the microcosms included
phytoplankton and zooplankton populations, nutrient concentrations,
and (in some experiments) diurnal pH changes for estimation of primary
productivity. The authors concluded that taxonomic enumeration was
best able to characterize the response of pelagic microcosms to stress
(Harte et al. 1978, 1980). Nutrient concentrations were insensitive
to the chemical perturbations, possibly because of the
disproportionate influence of periphyton on the water chemistry.
Productivity estimates were sometimes difficult because the small pH
changes could be detected only by measurements too precise for most
instruments (Harte et al. 1978).
Microcosm research at LBL is progressing in three areas. First,
tracking studies have been undertaken to compare pelagic microcosms
with the natural lake ecosystems from which they were derived. It has
been found -that the phytoplankton community dynamics of the microcosms
can be made to approximate those of the lake for up to 2 months if
(1) the natural temperature regime is reproduced and (2) microcosm
wall growth is controlled. A second area of research is the extension
of Perez's benthic-pelagic coupling to freshwater systems. Finally,
experiments on chemical effects are continuing, with the emphasis on
interactions between chemicals and organic enrichment, and on the
resulting alterations in decomposition rates and nutrient cycling
(Harte, personal communication).
Considerable work remains before the applicability of pelagic
microcosms to chemical hazard assessment can be determined. If
detailed plankton counts are necessary for evaluating the response to
chemicals, then these systems are not practical for testing large
numbers of chemicals. The labor required for species enumeration is
excessive, and special training in plankton identification is
required. The replicability of plankton counts is generally poor
(Harte et al. 1978). Reproducing species dynamics from one experiment
to the next may be difficult. In addition, the true significance of
population changes is not apparent, since major shifts in plankton
communities can occur without altering community functions (Harte et
al. 1980; O'Neill and Giddings 1979).
Attention must be given to measurements of ecosystem properties
in pelagic microcosms. Production and respiration should be fairly
easy to monitor in these systems by measuring diurnal fluctuations in
dissolved oxygen. Various approaches to detecting chemical effects on
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nutrient cycling were described in Section 3.6.1. Further development
of pelagic microcosms for chemical assessment is proceeding along
these lines at LBL. Comparisons between pelagic microcosms and
natural pelagic ecosystems should address these ecosystem-level
properties instead of, or in addition to, the taxonomic structure of
the plankton community. Strategies for improving the ability of
microcosms to simulate natural pelagic ecosystems must be devised.
In their current state of development, pelagic microcosms are useful
tools for basic research and some special applications, but they are
not yet ready for standardization as TSCA hazard assessment protocols.
(5) Pond microcosms. The development and characterization of
pond microcosms has been the objective of a research program initiated
at Oak Ridge National Laboratory (ORNL) in 1975. Very similar model
ecosystems have been under study at EPA's Athens Environmental
Research Laboratory (Brockway et al. 1979) and have been included in
the chemical environmental assessment program of the Monsanto Company
(Eggert et al. 1979; Gledhill and Saeger 1979). The evaluation
presented in this section is based primarily on the results of the
ORNL study (Harris et al. 1980).
Of all natural aquatic ecosystems, shallow ponds are the least
distorted by encapsulation under laboratory conditions. Mature pond
microcosms are ecologically quite similar to temperate ponds in mid-
summer. The dominant pond plants and animals (except fish, in most
cases) thrive in pond microcosms. Microcosm periphyton and sediment
communities contain the same taxonomic groups in roughly the same
proportions as natural ponds. Water chemistry in microcosms is often
similar to the parent ecosystem even after months in the laboratory.
Most importantly, effects of chemical perturbations in ponds appear to
be reproduced accurately in pond microcosms. One reason for this
realism is that the physical conditions characteristic of ponds
(shallow depth, lack of turbulence) are easily reproduced in aquaria.
Another is that virtually all of the important ecological components
and processes of whole pond ecosystems can be included in microcosms.
This is not true for other aquatic ecosystem types, which must be
broken down into subsystems (such as periphyton, sediment, or
plankton) for study. Consequently, results of pond microcosm studies
can be applied to natural systems with fewer assumptions and
extrapolations than results derived from other experimental systems.
The pond microcosms developed at ORNL, Athens, and Monsanto are
all derived by placing natural sediment, water, and samples of natural
pond communities into aquaria and allowing the systems to evolve. The
communities undergo a succession exhibiting many universal features of
ecosystem development (Odum 1969) and culminating in a well-regulated
system in which chemical and biological meaurements fluctuate within
narrow limits. Although the exact course of succession may differ
among replicate microcosms and between experiments, the mature
communities are usually very similar. Coefficients of variation among
mature replicates are below 20% for most measurements, particularly
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71
production, respiration, and the P/R ratio (Brockway et al. 1979;
Giddings and Eddlemon 1979; and references cited therein). Pond
microcosms can remain in this stable, reproducible condition for many
months and are thus ideal for studies of chronic effects of chemicals
on whole ecosystems.
Pond microcosms are extremely simple to construct and operate.
The fact that three laboratories have independently established
similar experimental protocols for microcosm experiments is evidence
of this. The microcosms designed by these laboratories are
ecologically very similar despite different source materials. Thus,
we conclude that the technique could be used successfully in any
laboratory, with the quality of results dependent mainly on accurate
measurements and analyses rather than on system design.
The research groups at Athens and Monsanto have used pond micro-
cosms primarily for studies of chemical transport and degradation.
The ORNL program originally had similar objectives, but it was
realized that the microcosms could also be used to measure ecosystem-
level effects. Experiments have been conducted on the effects of
arsenic (Giddings and Eddlemon 1978, 1979; Harris et al. 1980) and a
coal-derived oil (Giddings 1979). A second, more comprehensive
experiment with a synthetic oil began in August 1980 and will be
followed by an experiment with the same material in outdoor ponds;
this combination of studies should permit a thorough evaluation of the
utility of pond microcosms for predicting effects in larger systems.
Based on results obtained so far, our tentative conclusion is that
effects can be realistically simulated in the laboratory systems.
The principal question that remains is not, "Do pond microcosms
accurately represent ponds?", but rather, "To what extent are ponds
representative of other aquatic ecosystems of interest?"
Pond microcosms would not be convenient for screening large
numbers of chemicals--experiments require too much time (about 2
months to reach maturity) and space. They could be extremely useful
at intermediate and upper levels of a hazard assessment program. The
hazard evaluation process at Monsanto incorporates pond microcosms for
predictive and confirmative studies after initial screening with
simpler systems (Gledhill and Saeger 1979).
(6) Model streams. To a much greater degree than other
ecosystems discussed above, streams are open systems in which
processes occurring at a given point influence conditions downstream.
Energy and nutrient fluxes in streams may be more "spiral" than cyclic
(Webster 1978). Therefore, the ecosystem really includes the entire
length of the stream from headwater to mouth. For this reason streams
are, in the opinion of Warren and Davis (1971), "among the most
difficult freshwater systems to model." Critical parameters in the
design of model streams include inflowing water quality (especially
nutrient levels and organic content), bottom type, depth, current
velocity, temperature, and light (Warren and Davis 1971).
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72
Participants in the Workshop on Methods for Measuring Effects of
Chemicals on Aquatic Ecosystem Properties (Giddings 1981) recognized
three major classes of model streams: closed (completely
recirculating) systems, partially recirculating systems, and open
(once-through flow) systems. These system types generally fall along
a gradient from small, completely recirculating laboratory devices to
large-scale outdoor streams. The smaller, recirculating model streams
are easier to construct and operate, are less expensive, and require
less laboratory space than the larger systems. The methodology of
smaller systems is also more easily transferred to other laboratories
than larger systems. In the opinion of the workshop participants
(Giddings 1981), statistical analysis of results is easiest with small
model streams. The inherent variability of larger models means that
more samples are needed to achieve a given level of confidence in the
measurements and that temporal trends are more difficult to detect.
Finally, responses to chemicals are more easily interpreted in small
systems, where cause and effect are more easily distinguished than in
complex systems. Because of these factors, simple laboratory
recirculating streams come closest to satisfying the operational
criteria for a TSCA hazard assessment tool.
However, the same systems that are most amenable for routine
chemical hazard assessment may be the least generalizable to natural
ecosystems. Small recirculating model streams lack the openness that
is the distinctive feature of stream ecosystems; only larger, open
systems are enough like natural streams to permit reliable
predictions. Even with larger model streams, doubts about ecological
realism were expressed by participants in the workshop (Giddings
1981). Because of the difficulty of reproducing the structure and
function of stream ecosystems, model streams may be most useful for
studies at the organism or population level. Warren and Davis (1971)
mention many potential research applications, including studies of
animal behavior, habitat selection, food selection, territoriality,
predation, and competition. Studies of community structure, ecosystem
metabolism, diversity, and stability are not recommended since factors
controlling these properties may or may not be included in the model
system (Giddings 1980).
Our conclusion is that model streams, while potentially useful in
many areas of applied and basic ecological research, are not promising
for chemical hazard assessment under TSCA. At best, they might be
employed in advanced stages of testing when transport and fate have
been fully characterized and probable ecological effects have been
carefully defined. In such cases, the model ecosystems must be
specifically designed to incorporate the processes and components
relevant to the questions being asked.
3.6.4 Conclusions and Recommendations
The relevant characteristics of the model ecosystem types
discussed above are summarized in Table 3.1. The second column
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74
heading in this table, "Ability to Replicate," refers to all aspects
of constructing and using many replicate test units. Variability
among replicates (largely a function of the response being measured)
is one factor included here. "Realism" implies the ability of the
model ecosystem to simulate a particular natural ecosystem, and
"generality" was discussed in Sect. 3.6.2. Without a great deal more
comparative data on aquatic ecosystem functions and responses to
chemicals, generality is difficult to evaluate for any model
ecosystem; the entries under this heading are highly subjective and
likely to change as our knowledge improves.
The only model ecosystem potentially efficient enough for rou-
tinely testing large numbers of chemicals is the naturally derived
mixed flask culture. If these systems are found to be more sensitive
to chemicals than conventional assays, the ecosystem tests could
replace certain less sensitive and less efficient single-species tests
such as the algal growth test. If the model ecosystem tests rank
chemicals differently from conventional tests (that is, if
ecosystem-level hazards are not predictable from single-species
bioassays), mixed flask cultures could be used in conjunction with the
existing battery of tests. Either of these possibilities is
contingent on the outcome of the ecosystem tests being relatively
independent of the system's species composition since any particular
taxonomic structure may be difficult to repeat exactly in successive
experiments.
Sediment cores, periphyton communities, and model ponds are all
potentially useful in intermediate or advanced stages of hazard
assessment. Model ponds require more time and space than the other
two systems and are, therefore, somewhat less efficient for routine
testing. Sediment and periphyton systems also have the advantage that
they can be applied to almost any aquatic ecosystem. Model ponds, on
the other hand, are whole ecosystems, whereas the sediment and
periphyton systems represent only parts of whole ecosystems. Model
ponds are the most realistic type of model ecosystem. All three of
these laboratory systems merit further development. Strategies need
to be developed for making these systems as widely representative as
possible.
Pelagic microcosms and model streams are still too unwieldy and
unpredictable for use as TSCA testing tools. They are neither as
efficient nor as realistic as model ponds, but they have been quite
useful for basic ecological research and could be of value for special
applications in chemical testing.
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75
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LABORATORY TESTS FOR CHEMICAL EFFECTS ON
TERRESTRIAL POPULATION INTERACTIONS
AND ECOSYSTEM PROPERTIES
G. W. Suter, II
Environmental Sciences Division
Oak Ridge National Laboratory
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SECTION 4
LABORATORY TESTS FOR CHEMICAL EFFECTS ON TERRESTRIAL
POPULATION INTERACTIONS AND ECOSYSTEM PROPERTIES
The potential multispecies laboratory test systems discussed in
this section were selected on the basis of a literature review and
workshops on population interactions and ecosystem properties (Suter
1980a, b). Highest priority was given to systems that had been used
for testing effects of chemicals. Somewhat lower priority was given
to systems that were well studied and documented, but that were
designed for such uses as pure research or studies of chemical
transport. Lowest priority was given to systems that had (1) been
little studied or (2) had not been studied at all as complete
laboratory systems, but that had been suggested by one of the workshop
panels.
Potential test systems are identified and evaluated in the text
of this section and in Appendix B. The criteria used for test
evaluation include (1) the state of development of a system,
(2) sensitivity of the system, (3) ability of the system to simulate
responses in the real world, (4) the ecological and economic
importance of the organisms and processes included, (5) cost,
(6) technical difficulty, (7) the availability of system components,
(8) the range of responses displayed by the system, and (9) the time
to response.
Multispecies test systems should be included in a chemical hazard
assessment scheme because of (1) the effects of ecological systems on
the activity of test chemicals, (2) the effects of the system context
on the responses of the individual components, and (3) the effects of
chemicals on holistic properties of systems.
Ecological systems may affect the activity of a test chemical by
chemically or physically transforming it, by concentrating or diluting
it, or by changing its availability. The soil microflora may degrade
or detoxify a chemical or may even increase toxicity through partial
oxidation. The soil itself may affect the availability and toxic
properties of a chemical by sorption and abiotic oxidation and
reduction. Higher organisms may take up chemicals and partially or
completely metabolize them, sequester them in relatively inactive
tissues such as the cuticle, or pass them to exploiters in a
concentrated form.
The response of an individual organism or population to a
chemical may be modified by its interactions with other system
components. For example, chemicals may affect the ability to avoid
predation, find prey, compete, or subsist on toxic or marginally
nourishing hosts. Because interactions between organisms often result
in stress or increased energy expenditure, traditional response
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parameters may be more sensitive to chemicals in multispecies systems.
Because interactions of organisms or populations require behavioral
and physiological responses which are not displayed in isolation, the
range of measurable responses to a chemical is greater in multispecies
than in single species systems. Therefore, the responses of the
individual components in a multispecies test system may be both more
realistic and more sensitive than if that component were tested alone.
Holistic properties, those which are characteristic of an entire
hierarchical level of organization, can measurably respond to test
chemicals. These include collective properties which are
summarizations of the properties of system components and emergent
properties which are not summarizations of the properties of
components (Salt 1979). Collective properties such as diversity,
foodweb connectivity, and community production and respiration provide
indices of the state of the system. In many cases, the responses of
these collective properties have greater practical importance than the
responses of the individual component organisms or populations (e.g.,
soil respiration is more important than the respiration of any
individual microbial population). Collective properties can be no
more sensitive than the most sensitive component, but they can be
considerably less sensitive. Functional or numerical replacement of
sensitive species by insensitive analog species can result in the
masking of toxic effects when collective properties are measured
(O'Neill and Giddings 1979). This structural and functional
redundancy is, however, a property of natural systems and does not
invalidate the use of collective properties as indicators of the
effects of chemical on communities.
Emergent properties are often attributed to communities and
ecosystems on the basis of loosely supported teleological arguments or
loose definitions of emergence. Emergent properties are probably
uncommon in communities and ecosystems because selection has
relatively little opportunity to act on these higher organizational
levels (Salt 1979). The replacement rate of communities is very low
relative to those of populations within a community and individuals
within a population so that selective pressure is less intense at
higher organizational levels. In addition, community-level selective
pressure must act in the face of gene flow to the constituent
populations from other communities. Recent successes in predicting
the properties of communities with models based on the properties of
populations (O'Neill and Giddings 1979; Shugart and West 1980) suggest
that emergent properties need not be invoked at the community and
ecosystem level.* Therefore, the only emergent properties which
*A less restrictive definition of emergent properties is used in
systems theory. The components of such systems (e.g., transitors or
plant populations) are treated as having properties which are
independent of the system into which they are assembled. The system
merely reduces the range of behavior of the components. The emergent
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appear to be testable in multispecies systems are those associated
with interactions of pairs of coevolved species such as the formation
of nodules by legumes and rhizobia and of lichen thalli by algae and
fungi.
4.1 Population Interactions
This section is organized according to competition, predation,
and the other conventional classes of population interactions. This
organization is not meant to imply that tests can be developed to
represent these interactions in the same sense that rats serve to
represent mammal species of varying sensitivity. The class Mammalia
is composed of organisms that share a large number of physiological
processes, but the class of interactions called competition, for
example, has no mechanistic commonality. Exploitation competition
consists of division of a limiting resource (Park 1954), which can
occur by a contest or scramble (Nicholson 1954). Interference
competition (Park 1954) consists of the many other mechanisms by which
one organism reduces another organism's use of a limiting resource
including allelopathy, interspecific territoriality, predation, and
physical contact. The large number of distinct mechanisms of inter-
action, which are called competition because they share a common
outcome, are unlikely to respond in a qualitatively similar manner to
chemical substances. Similar arguments can be made concerning
predation, symbiosis, herbivory, and parasitism.
This problem is not serious for tests that are used only for
screening chemicals and not for predicting specific effects.
Screening tests only need to be sensitive to a wide range of chemicals
and to produce a representative relative ranking of toxicity. The
outcome of many population interactions is highly sensitive to normal
ecological variables, and it seems likely that they would also be more
sensitive to chemicals than a single-species bioassay. This
supposition has rarely been tested, however, and is not always
supported by the evidence (e.g., Kochhar et al. 1980). This use of
population interaction tests would, like the use of second stressor in
bioassays, simply be a method of increasing or broadening sensitivity
properties of systems theory (e.g., signal amplification or community
biomass) are simply a result of the topology of the system (Caswell et
al. 1972). If this general model is as correct for ecosystems as for
electrical circuits, ecosystems have no emergent properties in Salt's
(1979) sense. Predictions of effects of toxicants on ecosystem
properties are made on the basis of individual responses of individual
organisms which are assumed to be independent of the system context
(e.g., West et al. 1980). Emergent properties according to systems
theory require better models rather than better tests (see Sect. 5).
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over that of a standard, single-species test. Development of
multispecies test systems simply for their sensitivity is not
recommended.
Predictive tests (those that actually predict responses in the
field) are necessary to establish the significance of responses
observed in screening tests. For this purpose, tests must be
representative of classes of interactions that are economically or
ecologically important and yet are so narrowly defined as to encompass
a generally uniform set of response mechanisms. Examples of these
tests might include predation by hymenopteran parasitoids and
herbivory by homopterans.
Another general problem concerns the definition of a population
interaction test system. Because the results of population
interactions are defined in terms of changes in population size and
composition, the test systems must allow completion of multiple life
cycles by each component species. This requirement might be
circumvented in many instances by developing predictive indicators of
response. One strategy is to use experimental designs and
mathematical models that permit the prediction of outcome from data on
a single generation, such as those developed for competition by DeWit
(Sect. 4.1.1). Another strategy is to isolate components of the
interaction that are both sensitive to toxicants and important to the
outcome of the interaction, such as (possibly) predator searching
efficiency or photosynthesis rates of competing plants. Finally,
stress symptoms such as reduced larval size in competing Drosophila
may provide early indicators of the ultimate outcome of the system.
Test systems developed using these strategies would only be indi-
cative of effects on population interactions and not truly predictive
because they inevitably ignore some components of the interaction.
Tests of predator searching, for example, typically treat the prey as
passive fodder. All test systems that do not include numerous
generations exclude the possibility of evolutionary responses. It
will be important to determine the magnitude of error induced by
simplifying the interactions relative to errors induced by
extrapolating between different groups of interacting species and by
extrapolating from the laboratory to the field.
4.1.1 Competition
"Competition occurs when a number of animals [or plants] (of the
same or different species) utilize common resources the supply of
which is short; or if the resources are not in short supply,
competition occurs when animals [or plants] seeking that resource
nevertheless harm one or the other in the process" (Birch 1957). As
indicated in Sect. 4.1, this widely quoted definition of competition
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includes a broad variety of mechanisms of interaction, more than one
of which is often involved in a particular two-species interaction.
Operationally, competition is said to occur when the fitness of one
population is reduced by the presence of another population that uses
a common resource.
Different approaches to the analysis of competition have
developed. Because Park's Triboleum competition system [Sect. 4.1.1
(2)] invariably results in extinction of one species, the response of
this system is expressed in terms of time to extinction and is modeled
by a stochastic version of the Lotka-Voltera competition equations
(Leslie 1958). Results from competition systems that are stable (i.e.,
do not lead to extinction) or that cannot be carried to termination
because of the long generation times of the organisms involved have
results expressed as changes in relative frequency. These are
analyzed in terms of DeWit's (1960) ratio diagrams (Fig. 4.1). Data
are fit to the model:
log (Oi/Og) = log a + p log (1{/12^,
where Ql/Q2 is the ratio of the output frequencies of the two species,
and Ii/I2 is the ratio of the input frequencies. The intercept
(log a) provides a measure of the fitness differential when the input
ratio equals 1, whereas p measures the change in relative fitness with
varying input frequency. A line with a slope of 1 [Fig. 4.1 (line a)]
indicates that fitness is independent of the relative frequency, and
one species will become extinct. A line with a slope <1 [Fig. 4.1
(line b)] indicates that the less frequent species is favored, and a
stable equilibrium frequency exists at the intersection of the fitted
line with the diagonal. A slope >1 [Fig. 4.1 (line c)] indicates that
the more frequent species is favored, and the equilibrium is unstable.
Maximum likelihood methods provide a more efficient analysis of this
model than the traditional least squares regression (Adams and Duncan
1979). The experimental design used with this analysis is the
replacement series. The total input density is kept constant, and the
ratio of the two species is varied (e.g., 0:5, 1:4, 2:3, 3:2, 4:1,
5:0).
(1) Microbial competition. Microbial competition has received
considerable attention. However, nearly all such work has been
performed using liquid culture (Alexander 1971; Fredrickson 1977;
Meers 1973) because the use of soil greatly inhibits the extraction,
identification, and enumeration of microorganisms. Studies that
realistically address competition in the soil (e.g., Rennie and
Schmidt 1977) require elaborate techniques such as the fluorescent
antibody technique. Because of this problem, tests for effects
on microbial competition should be limited to liquid cultures that
simulate aquatic systems. Further, interest in soil microorganisms
primarily concerns the processes that they perform rather than the
species performing them. Microbial processes are discussed in Sect.
4.2.
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10
CM
o
(3
O
0.1
X
0.1
10
LOG (Ml,)
FIGURE 4.1 • RATIO DIAGRAM: '1 /12 = THE RATIO OF THE INPUT FREQUENCIES OF
SPECIES 1 AND 2 AND °i /O, = THE RATIO OF OUTPUT FREQUENCIES.
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(2) Plant competition. Because autotrophic plants lack the
diverse resource base and behavioral repertoire of heterotrophic
organisms, plant competition is both intense and mechanistically
limited. Plants engage in exploitation competition for space (light),
water, and mineral nutrients. Interference competition between plants
primarily involves allelochemicals released as leachates from living
or dead tissues or as root exudates. The hypothesis that plant
competition is a relatively uniform process is supported by White and
Harper's (1970) determination that a wide variety of combinations of
plant species in the field and laboratory give a good fit to the
equation of Yoda et al. (1973) for the relationship of weight (w) to
density (p) in self-thinning communities: w = cp -3/2, where c is a
constant.
Socially important plant competitors include weeds and crops,
more and less commercially desirable species of trees, and components
of mixed-species crop and pasture systems. Some pairs of plant
species such as mixed barley and oat crops (DeWit 1960) engage in pure
exploitation competition [Fig. 4.1 (line a)]. Apparent stable
equilibria due to rare species advantage include species of Avena
(Jain 1969) and Papaver (Harper and McNaughton 1962). Interference
competition could be demonstrated using any allelopathic plant (Rice
1974 and 1979). Allelopathy could result in an advantage to the more
common species [Fig. 4.1 (line c)].
Competition between pasture grasses and legumes is a relatively
well-studied system that is also commercially important. Clover-grass
mixtures are frequently used in seeded pastures to maximize yield and
nutritional quality of the pasture. Bennett and Runeckles (1977)
found that 0.09 ppm ozone changed the crimson clover-annual ryegrass
competitive balance from favoring clover to favoring ryegrass.
Kochhar et al. (1980) found that ladino clover growth was reduced by
fescue competition and by 0.03 ppm ozone, but the combination of ozone
and fescue produced no greater growth decrement than either factor
produced alone. However, leachate from ozone-exposed fescue, but not
control fescue, inhibited clover nodulation. While the differences in
the results of these two studies may be attributable in part to
differences in experimental design and techniques, they suggest that
generalization may be difficult even between closely related systems.
A tentative protocol for a clover-grass competition test is presented
in Suter (1981a).
Alternative candidates for plant competition exist in profusion.
Competitors could be chosen to represent taxonomic groups (i.e.,
monocot-dicot) life forms (i.e., tree-herb or annual-perennial) or
community types (i.e., tilled agriculture or old field). Which of
these organizational schemes would provide the strongest basis for
predictive generalization is not clear.
Plant competition tests should be designed as replacement series
with at least three ratios (each species alone and an equal mix).
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Plants would ideally be grown to maturity because of differences in
responses to chemicals in different phenological stages and the
importance of effects on production of propagules. Nevertheless,
shorter tests have some applicability because vegetative biomass of
immature plants is the parameter of interest in many managed systems.
Competitive outcomes measured by harvesting vegetation can be analyzed
in terms of relative yield (r = the yield of a species in the
mixture/monoculture yield), total relative yield (RYT = rj + r2), and
the crowding coefficient (kx'2 12'
(3) Arthropod competition. The arthropod competition systems
discussed below represent over 90% of the laboratory studies of
terrestrial arthropod competition. They are all saprophytic systems.
Competitive interactions between herbivores have received relatively
little attention. Any herbivorous arthropod that is a significant
competitor of a pest species is likely to be a pest itself. Although
damage may be somewhat reduced by interference competition between
pests, there are no positive outcomes from such competition, and
therefore it has little appeal to management-oriented entomologists.
Competition between predators, and particularly among parasitoids, has
important effects on the success of biocontrol. Therefore, these
interactions have been somewhat better studied. Arthropod herbivore,
predator, and parasitoid competition are discussed in Sects. 4.1.2 and
4.1.3(2).
(a) Drosophila. The members of the genus Drosophila are
among the most studied organisms in biology. Hundreds of papers have
been published on competition among more than a dozen species of
Drosophila over a period of 45 years (beginning with L'Heritier and
Teissier 1937). Because this work has been dominated by population
geneticists, emphasis has been placed on the evolution of fitness
under competition. The response of Drosophila competition to
chemicals has not been studied.
Depending on the pair of Drosophila species and physical
conditions chosen, a particular species may become extinct. This
species may be indeterminant (Barker and Rodger 1970; Miller 1964), or
both species may coexist indefinitely even though they occupy the same
niche by the criterion of the Lotka-Voltera competition equations
(Ayala 1970, 1971). Coexistence can be explained by an increase in
fitness with decreasing frequency [Fig. 4.1 (line a)] or by evolved
shifts in competitive advantage. Complete shifts in dominance have
been observed in competition between [). melanogaster and D. simulans
(Moore 1952) and between D. serrata and both D. pseudoobscura and D.
melanogaster (Ayala 1966), supporting the evolutionary model.
Relatively stable frequency ratios that support the DeWit model of
frequency dependence have been obtained using the following pairs: D.
pseudoobscura and D. willistoni (Ayala 1971); D. pseudoobscura and D.
serrata; and D. nebulosa and D. serrata (Ayala 1969). These stable
frequencies are achieved with varying input frequencies.
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Drosophila experiments have traditionally been conducted in
250-mL or smaller bottles or vials with a yeast-containing medium
covering the bottom. Various alternative container designs that offer
some advantages in manipulation have been used, but none of them are
widely accepted. Media, vials, anesthetizing equipment, and some
Drosophila species and a large variety of mutant types are
commercially available. The use of flies with conspicuous genetic
markers makes sorting relatively easy.
Although the number of adult flies of each species is the
standard parameter in Drosophila competition experiments, a variety of
other parameters, including stage-specific viability, length of
stages, weight of adults, wing length, and the ratios of numbers,
weights, and development times of males and females, have been used.
All these parameters have been shown to respond to the effects of
competition, but their response to chemicals is unknown.
The utility of Drosophila competition as a screening test is
suggested not only by the sensitivity of the outcome to temperature,
light, and other physical parameters, but also by its response to
radiation. Moth and Barker (1977) found that viability of flies was
significantly reduced by 35 (jCi of 32P in 30 ml of medium. Blaylock
and Shugart (1972) found that treatments of 250 and 500 rads, but not
1000 rads, increased the fitness of inbred D. simulans in competition
with inbred D. melanogaster. They concluded from this and previous
studies that low levels of radiation in a largely homozygous
population results in heterosis, but at high levels the effects of
deleterious genes predominate. Because Drosophila species have been
shown to coexist in the field in fruit, oak fluxes, and fungi
(Atkinson 1979; Budnik and Brncic 1974), this laboratory system
represents a natural phenomenon. The outcome of competition among
Drosophila or other saprophagic flies is not, however, of such
importance that a predictive test system is desirable.
A Drosophila competition test might be simply based on
changes in relative frequency after one generation at one frequency.
This test would only require a few small vials, and by using D.
melanogaster and D. simulans (the best-studied species pair), it could
be completed in 2 weeks. The sensitivity of the test could probably
be increased by using three input frequencies so that the parameters
of the DeWit competition model could be estimated. While Drosophila
competition may play an important role in the development of a theory
of ecotoxicology, it does not appear to be sufficiently representative
of important interactions in the field to warrant its use as a test
protocol.
(b) Other flies. Although the great preponderance of
literature on competition between flies is concerned with Drosophila,
significant work has been done on other species. These include
species of blowfly (Ullyett 1950), housefly and blowfly (Pimentel,
Feinberg et al. 1965), and varieties of housefly (Boggild and Keiding
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1958; Sokal and Sullivan 1963). In addition to being less well
studied than Drosophila, these species require more space and
therefore would be more expensive to maintain. Therefore, they are
not recommended.
(c) Tribolium. Competition between flour beetles of the
genus Tribolium has been studied at least as much as Drosophi la
competition. More than 100 papers have been published on Tribolium
competition since Park's (1948) seminal monograph. These studies have
been concerned with the ways in which competition leads to extinction
of one or the other member of the pair T. confusum (cf) and T.
castaneum (cs). The characteristic of this system that has attracted
the most attention is the indeterminacy of outcome. At certain
initial frequencies of specified populations and at specified
temperatures and humidity, the surviving species cannot be predicted.
This indeterminacy indicates a fine competitive balance. Therefore,
Tribolium competition, like Drosophila competition, may be highly
sensitive to a wide range of chemicals.
The Tribolium system is easily initiated by placing the
desired proportions of the two species in shell vials containing 8 g
of whole wheat flour with 5% yeast. All life stages are removed by
sieving at monthly intervals and placing in fresh medium. A tentative
testing protocol proposed for this system calls for operating the
system under conditions that produce an indeterminant outcome (Suter
1980a). The primary response criterion proposed is determinacy of
outcome. That is, an effect has been demonstrated if one species
becomes extinct in all chemically treated replicates. Time to
extinction would be a secondary response criterion. The difficulty
with this proposal is that the indeterminate systems require about 2
years for completion. This large time requirement results from the
longevity of the adult beetles [323 days for cf and 213 for cs (Mertz
1972)] and their relatively long generation time (30 days). If, as
was conjectured in the proposed protocol, the determinacy of the
outcome could be predicted with 90% accuracy after 150 days, the
system is still not as rapid as other screening tests. A single
generation test for Tribolium competition would require a month, and
there is no basis for predicting outcome from the results of a single
generation as there is for Drosophila.
Because these species exist almost entirely as pests of
stored grain products, the laboratory system is the "natural" system.
The outcome of competition in these "natural" systems is, however,
immaterial to the grain products owner. The particular combination
of exploitation competition with predation and cannibalism that
characterizes this system is unlikely to respond in a manner that
is predictive of interaction in any important group of organisms.
(d) Other grain insects. Park was preceded in the study of
competition among grain insects by Crombie (1945, 1946), who studied
the beetles T. confusum, Rhizopertha dominica, and Oryzaephilus
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surlnamensis and the moth Sitotroga cerealella. These studies, like
those of Park, were initiated to investigate problems in population
biology. Crombie's systems were supplanted by Park's because of the
greater theoretical appeal of competition between sibling species.
More recently, LeCato (1975a, 1975b, and 1978) reexamined systems of
taxonomically diverse graminivorous insects with the idea of reducing
grain losses by encouraging predation. If this idea ever proves
useful (the research has been at least temporarily discontinued), the
effects of fumigants and residues of pesticides or other chemicals in
the grain would be critical.
(e) Soil arthropods. Detritivorous soil and litter
arthropods show a remarkable combination of high species diversity and
low feeding specificity (Anderson 1962). This "enigma" gives the
problem of soil arthropod competition theoretical importance. These
organisms can be maintained as competitors on a totally artificial
system of plaster of Paris and charcoal (Culver 1974; Longstaff 1976)
or in soil-litter microcosms (Anderson 1978). The former system is
too artificial to represent effects of chemicals in the field and is
too poorly understood and developed to be appealing as a screening
test. On the other hand, microarthropods in soil and litter are
relatively difficult to extract quantitatively and census. These
organisms are important primarily because of their collaborative role
in decomposition and nutrient cycling. Tests for these processes are
discussed in Sect. 4.2.
(4) Other animals. No competition tests are recommended for
competition among nonarthropod animals. Nematodes approach the
arthropods in ecological importance, but they are difficult to
identify and are therefore poor candidates for a population test.
Vertebrates are obviously important, but testing for effects on
competition between populations of even the smallest species (as
opposed to simple behavioral interactions) would require an
excessively large area and long time period.
4.1.2 Herbivore-Plant
This section considers herbivorous insects feeding on flowering
plants. These two groups dominate the earth's biota, accounting for
more than 60% of procaryotic species (Gilbert 1979). Insects account
for the great preponderance of herbivory, rivaled only by ungulates in
semiarid grasslands. Ungulate herbivory, for obvious reasons, is not
considered for laboratory test systems.
While herbivores may act as predators (by killing individual
plants) or as overtly mutualistic symbionts, most herbivores are
functionally analogous to parasites, consuming the tissues or fluids
of the host plant without directly killing it (Gilbert 1979). It has
been hypothesized that consumption of plant parts by herbivores
generally increases the overall fitness of the host plants (Owen and
Wiegert 1976); the success of programs to control exotic weeds by
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importing herbivorous insects from the weed's area of origin suggests
that such cryptomutualism is not the rule. Certainly, intensive
agricultural and sylvicultural practices tend to uncouple such
mutualistic relationships, resulting in highly "virulent" herbivores.
Nevertheless, the fact that herbivorous insects can modify the
allocation of plant resources in ways that are not always detrimental
(Harris 1973) suggests that net plant production must be measured by
tissue type and age to understand herbivore effects.
Herbivores that feed on vegetation can be divided into chewing
and sucking types. Sucking insects have several advantages as test
organisms: (1) they are typically small, and many of them can be
crowded on a single plant; (2) many of them are either immobile or
relatively inactive except during dispersal phases; (3) they are
highly sensitive to changes in plant physiology as reflected in sap
chemistry; (4) many of them produce several generations per year; and
(5) most economically important species have several known predators
that may be added to the system (Sect. 4.1.3). While these insects
may be highly sensitive to chemicals that are taken up by the plant
(witness the efficiency of systemic pesticides), they would be
insensitive to chemicals deposited on the leaf surface. For such
chemicals, an external chewing insect test system would be required.
Because of the relatively long life cycles of flowering plants,
herbivore-plant population interaction tests would probably be limited
to growing the plant through seed set. Even with this reduction in
scope, there are no apparent existing laboratory systems for this
interaction (effects of herbivory over one full life cycle of the
plant). Population ecologists have avoided the problems of
maintaining live plants by using insects that can be raised on inert
media (e.g., Drosophila and Tribolium). Agricultural and ecosystem
entomologists typically raise insects on stems or individual leaves of
plants when determining consumption rates or pesticide response.
Whole-plant cages (Adams and Van Emden 1972) are seldom used, in part
because of effects of the cage on light, humidity, and other
environmental conditions. Large (>0.5 m2) cages that contain several
potted plants probably provide better conditions, but if each plant is
to be treated as a replicate, nonflying insects must be used, and
mobile forms such as apterous aphids must be constrained by barriers
(Adams and Van Emden 1972). Because the great majority of studies of
plant-herbivore interactions are conducted in the field, there is
little experience with these laboratory systems. Test systems would
need to be largely developed from scratch, but there do not appear to
be major technical problems.
The life cycles of many herbivorous insects are sufficiently long
and complex that most insects and the plants could not practically be
raised through multiple life cycles in routine tests. Indicator tests
that only include the activities of certain life stages might be
developed for those population interactions. These tests must be
chosen to include stages in development that are likely to be sensi-
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tive to a variety of chemicals (ecdysis may be an example) and those
stages that are sensitive to the resistance mechanisms of the host
plants. Antibiotic plant defensive mechanisms act at various stages
in the life cycle of the insect to inhibit growth, reduce survival,
disrupt development, or reduce reproduction (Waiss et al. 1977). Test
chemicals may reduce or enhance host plant resistance.
This section emphasizes herbivores using domestic plants because
(1) these species are well known, (2) the plants are easily
cultivated, (3) many of the insects can be obtained from culture
(Dickerson et al. 1980) and maintained on defined artificial diets,
and (4) their social relevance i's obvious. Highly coevolved
herbivore-plant species pairs from natural communities may, however,
prove to be more sensitive and more representative of the majority of
the earth's biota. This possibility should be considered during
development of advanced test systems.
(1) Sucking Insect-Plant. While some hemipterans are important
herbivores, the majority of sucking herbivorous insects are
homopterans. As previously mentioned, these insects have several
advantages as test organisms. The herbivore-plant species pairs
discussed in this section were selected primarily on the basis of a
recent workshop held at ORNL (Suter 1981a).
(a) Aphid-alfalfa. The spotted alfalfa aphid (Therioaphis
trifolli) is an important pest of alfalfa in California and other
western states. It is a good candidate for a test system to
represent this class of interactions because it involves an econ-
omically important host plant that can be easily and rapidly grown.
The system could be readily extended to include predators [Sect.
4.1.3(2)], and it might be possible to create an aphid competition
test by adding the pea aphid (Acyrthosiphon pi sum), which is also a
pest of alfalfa in California. While no suitable experimental or
testing system has been demonstrated for these species, it should be
relatively easy to adapt the techniques of mass rearing aphids on
potted alfalfa seedlings (Finney et al. 1960) to testing by using
whole-plant cages.
(b) Aphid-grain. Individual, whole-plant cages were used
by Windle and Franz (1979) in a study of the effects of greenbugs
(Schizaphis graminum) on competition between barley varieties.
Greenbugs, a chronic pest of small grains, caused a reversal in compe-
titive dominance as measured by the crowding coefficient
[Sect. 4.1.1(2)] in aphid-resistant and susceptible varieties.
Effects of aphids on plant production were demonstrated within 2
weeks, but the effect changed from positive to negative between weeks
2 and 6.
(c) Whitefly-plant. The greenhouse whitefly (Trialeurodes
vaporariorum) is an important pest of greenhouse crops with over 200
host plants. The relevance of this system to greenhouse culture is
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both its chief advantage and its chief disadvantage. The test and
real world conditions are identical, but whiteflies are insignificant
in the United States outside of greenhouses and citrus groves. The
greenhouse whitefly can be raised on potted beans, cotton, tomatoes,
or any of its many other host plants. It has a generation time of 21
days at 20°C, which includes a crawler stage of the first instar,
scale-like second and third instars, a "pupa" and winged adult.
Although whole greenhouses have been used as experimental units in
whitefly control studies, a test system would use whole-plant cages
(Nechols and Tauber 1977).
(d) Scale-plant. Scale insects (Coccoidea) present
considerable advantages for determining life table data because of
their sessile nature and the record of mortality provided by the
shells. The brown soft scale (Coccus hesperidum) uses citrus and
other tropical and subtropical trees and a large variety of greenhouse
plants. Its development may be completed in 26 days at 27°C, and the
primarily parthenogenic females may produce over 200 progeny
(Saakyan-Baranova 1964). This scale is easily maintained in the
laboratory on Coleus or Begonia.
(2) Chewing insect-plant. Chewing herbivores, primarily
Coleoptera, Lepidoptera, and Orthoptera, are ecologically and economi-
cally important and represent a distinct mode of plant-insect-chemical
interaction. Most of them are difficult to maintain in the laboratory
over multiple generations because the adults are relatively large and
highly mobile and have different requirements from the larvae.
Because many of them are also voracious, they would require relatively
large plants to moderate herbivory to a level at which plant responses
could be measured.
No clearly preferable insect-plant species pair for this test
system exists. A recent workshop recommended that tests for chewing
herbivore-plant interactions should utilize the corn earworm (cotton
bollworm, tomato fruit worm, Heliothis zea) and possibly the corn
rootworm, Japanese beetle, Cactoblastis, gypsy moth, and a grasshopper
because they are well studied, economically important, and have docu-
mented exploiters (Suter 1981a). The corn earworm could be easily
cultured because it is hearty and euryphagous, but it is fairly large
and is probably insensitive to pesticides and other chemicals. Some
other species such as the gypsy moth and Cactoblastis are relatively
unsuitable because they use slowly growing hosts. The alfalfa
caterpillar (Colias eurytheme) and green cloverworm (Plathypena
scabra) are somewhat smaller important species which consume alfalfa,
an easily and rapidly grown herb.
The fact that the young and the adults share the same habitat
gives grasshoppers and other orthoptera an advantage over lepidoptera.
Some beetles, such as the Mexican bean beetle (Epilachna varivestis),
share this advantage even though they are homometabolous and small.
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The physical test, system for this relationship in most cases must
be some sort of whole-plant cage. Dyer and Bokhari (1976) maintained
individual grasshoppers (Melanoplus sanguinipes) for 18 days in single
plant cages containing hydroponic blue grama grass (Bouteloua
graci1 is). Larger cages containing several plants will be required for
true studies of population interactions. Soil-dwelling herbivores
such as the corn rootworm can be maintained in pots or even in plastic
pouches (Ortman and Branson 1976). Nonflying insects may be isolated
by placing a sticky trap around each plant (Robinson et al. 1978).
This technique provides a measure of emigration which could indicate a
change in herbivore preference.
4.1.3 Predator-Prey
Predation is often defined functionally as all forms of exploita-
tion that regularly result in death of the exploited species. That
definition is used here except for herbivory because herbivory is
predominately nonlethal. The definition includes parasitoids and
microbial "parasites" such as Bdellovibrio, but not pathogens and true
parasites, which typically do not kill or consume a large fraction of
an individual host.
Predator effectiveness is the product of the predation rate and
the population growth rate of the herbivore and behavioral response to
predation. Predation rates are the product of changes in predator
density (numerical responses) and the predation rate per individual
(functional responses) (Solomon 1949). Most laboratory studies of
terrestrial predation are concerned with the components of the
functional response, searching rate, capture rate, handling time, and
satiation. Numerical responses are relatively neglected because of
the difficulty of maintaining predators and prey together in the
laboratory for multiple generations. Prey species are typically
presented to the predator under circumstances that do not permit an
appropriate behavioral response by the prey; they very seldom
reproduce in the experimental system, and they may even be replaced by
artificial prey (e.g., Moiling 1966; Gardner 1966).
(1) Microbe-microbe. While most studies of microbes that kill
and consume other microbes are concerned with protozoan predators, the
predatory habit is also practiced by a variety of bacteria and fungi.
Microbial predation may be considered beneficial if the prey is a
plant pathogen (e.g., Habte and Alexander 1975) or detrimental if it
is a beneficial species such as Rhizobium (e.g., Danso et al. 1975).
Because of the relative difficulty of quantitatively extracting
and enumerating microbes in soil, it is recommended that any tests
involving enumeration of microbial predators and prey be conducted
in aquatic systems. Predation on plant pathogens can be evaluated in
terms of the presence of plant pathology. The best example of this
type of system is the control of Rhizoctonia solani through
destruction of its sclerotia by Tricoderma harzisnum. A test protocol
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110
for this system using damping-off of radishes as the measured response
has been tentatively proposed (Suter 1981a). Respiration and
transformation of mineral nutrients by prey organisms have also been
used as indicators of the effects of microbial predators (e.g.,
Telegdy-Kovats 1932). These responses are discussed as ecosystem
processes [Sect. 4.2.5 (4)].
(2) Arthropod-predators. Traditionally defined predators kill
their prey before consuming them or kill them by consuming them rather
rapidly. Parasitoids differ in that they kill their host (prey) by
consuming them over a relatively long time. It has been argued on
theoretical grounds that parasitoids are better adapted than most
other predators to control the populations of herbivorous insects
(Doutt and DeBach 1964). This argument is borne out by the
predominance of parasitoids in successful insect biocontrol programs.
Therefore, parasitoids are emphasized in this section.
The relatively high sensitivity of arthropod predators to pesti-
cides suggests that they may provide sensitive toxico'logical tests.
Pesticide applications commonly eliminate arthropod predators, often
resulting in the creation of secondary pests and the resurgence of
primary pests to greater abundance than before treatment. The
effectiveness of a predator as a biocontrol agent can be verified in
the field by applying pesticides at concentrations that eliminate the
predator without damaging the prey populations (DeBach and Huffaker
1971). The effectiveness of predation may be even more sensitive than
predator mortality.
Laboratory studies of predators as potential biocontrol agents
generally are not concerned with the population biology of the
predator and prey species. Population interactions are studied in the
field. Laboratory studies of the relative toxicity of pesticides to
predators and prey generally measure mortality rather than effects on
the predation process. Therefore, laboratory test systems for
arthropod predator-prey population interactions cannot readily be
adapted from existing experimental systems for biocontrol agents. As
a rule, ecological experiments use easily manipulated, interesting, or
unusual species (Sects, (a), (e), and (f) below) rather than important
species.
Searching capacity has been found to be the most important
indicator of the ability of predators in biocontrol programs to
maintain pest populations below an economic threshold (Huffaker et al.
1971). Therefore, some basis for using predation rate as an indicator
of predator-prey population interactions exists. Nevertheless, tests
that only use predation rate or its components should be supported by
studies of true population interactions.
(a) Parasitoid-gall midge. The California endemic midge
Rhopalomyia californica (Cecidomyiidae) that forms galls on Baccharis
pilularis is attacked by 12 species of hymenopteran parasitoids.
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Ill
Force (1970, 1974) has performed field and laboratory investigations
of this "community" to elucidate the means by which this diversity of
parasitoids is maintained and its effect on the midge. The community
experiments are performed in 48- by 38- by 40-cm screened cages with
40 Bacharis seedlings in small pots. The cages are kept in a
greenhouse. The midge and the six parasitoid species used in these
experiments are not available from culture, but they are readily
obtained by collecting galls and are easily maintained in the
laboratory. The life cycles of three parasitoids investigated are
27, 38, and 46 days.
Six species of parasitoids can be maintained together in a
cage for at least 100 days. The outcome is determined by details of
the biology of the parasitoids, including restraint from and success
in superparasitism, multiparasitism, and hyperparasitism. This system
is particularly well developed for studying population interactions
between parasitoid competitors and between parasitoids and their host.
It does not represent an economically important species association,
and none of the biological constituents are commercially available.
The physical system of large, whole-plant cages could serve as a model
for test systems using other species.
(b) Parasitoid-whitefly. Since the 1920s the parasitoid
Encarsia formosa has been used as a biocontrol agent for the
greenhouse whitefly (Helgesen and Tauber 1974). At 18°C the fecundity
of the whitefly is 10 times as great as that of Encarsia although the
rate of development is equal; at 26°C the fecundity is equal, and the
rate of development of the parasitoid is twice that of the whitefly
(Hussey and Bravenboer 1971). Encarsia attacks the scale larvae of
the whitefly, and parasitized scales are blackened and therefore
readily recognized. Encarsia completes its life cycle in 2 to 4
weeks. This species pair is well studied; its dynamics in the
greenhouse are relatively predictable (Burnett 1967), and the
parasitoid is commercially available.
(c) Parasitoid-aphid. Three parasitoids of the spotted
alfalfa aphid, Praon exsoletum (P. palitans), Trioxys complanatus (T.
utilis), and Aphelinus asychis (A. semiflavus), have been the subjects
of intensive laboratory study. Force and Messenger's (1964a and b,
1965) system of alfalfa stem "bouquets" in 3.5- by 15-cm glass tubes
was designed to study the effects of physical conditions on the life
history parameters of the parasitoids and on larval parasitoid
competition. This system permits examination of parasitoid
development and hunting efficiency of the adults although searching is
minimized by the small chambers. A larger system with whole plants
would permit examination of true population interactions and would
permit studies of searching. Chemicals might affect the outcome of
competition resulting from multiple parasitism, or they might diminish
the ability of A. asychis to discriminate parasitized hosts.
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112
(d) Predator-aphid. The best-studied predators of aphids
are coccinellid (ladybird) beetles. Populations of the spotted
alfalfa aphid are reduced by native coccinellid predators of the
genera Hyspodamia and Coccinella. These predators have been the
subject of considerable field investigation (Hagen, van den Bosch, and
Dahlsten 1971) and would contribute to the completeness of a test
system based on alfalfa and the spotted alfalfa aphid.
Laboratory studies of predation by coccinellids on aphids
have been conducted using C. septempunctata on Acyrithasiphon pi sum
and Aphis fabae (Murdoch and Marks 1973) and Adalia bipunctata on
Drepanosiphon platanoides (Dixon 1970), but these studies do not
include full life cycles. Several species of coccinellids are
commercially available.
Other aphid predators that could be used in a test system
include green lacewings (Chrysopa), syrphid flies (Syrphus,
Metasyrphus, etc.), and damsel bugs (Nabis). These predators are not
as well studied or as readily available as coccinellids.
(e) Parasitoid-grain moth. Species of Lepidoptera from
five families infest stored grains, pulses, nuts, and their products
(Benson 1973). They are attacked by parasitoids from five families of
Hymenoptera and one species of Diptera. Because of the economic
importance of grain insects and their ease of manipulation in the
laboratory, they have been the subjects of many laboratory studies.
Parasitoid-grain moth experimental systems consist of sets
of replicate chambers, ranging in size from 0.005 to 13.8 m3. Several
containers of grain or other substrate are placed in the chambers with
moths and parasitoids. The life cycles of a typical moth Ephesta
(Anagasta) kuhniella and parasitoid Exidechthis canescens are 41 to
106 days and 21 to 33 days, respectively, at 27°(T The system could
be elaborated by incorporating multiple prey and parasitoid species or
the oophagous mite Blattisocius.
Because the parasitoids discover prey by probing the
substrate with the ovipositers, searching efficiency is the key factor
in the parasitoid population even in small (0.61-m3) chambers (Benson
1973). By providing refuges for the moth larvae, the system can be
made to persist for 2 years or more in 0.13-m3 chambers (Flanders and
Badgley 1963). Thus, the system lends itself to tests of both
predation rate and true population interactions. This system has
considerable advantages because of extensive previous laboratory study
and ease of manipulation resulting from the use of grain rather than
whole plants to support the herbivore. The chief disadvantage of the
system is that it is only directly relevant to grain storage. While
the mechanisms of parasitoid-host interaction may be sufficiently
uniform to permit generalization from this system, chemically treated
grain would not be directly analagous to any important mode of
ecosystem contamination.
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113
(f) Parasltold-bean weevil. Another set of important and
rather extensive laboratory studies of predation are those conducted
by Syunro Utida on the parasitoids of the azuki bean weevil
(Callosobruchus chinensis) (summarized in Utida 1957). Bean weevils
were raised on azuki beans in petri dishes and exposed to the braconid
parasitoid Heterospilus prosopidis, alone or in competition with the
chalcid parasitoid, Neocatolaccus namezophagus. To study population
fluctuations, Utida counted the populations at 7- to 10-day intervals
and ran the experiments for several months. Generation time for the
bean weevils is three weeks. As a potential test system, this
experimental system shares the advantages and disadvantages of the
parasitoid grain moth system already described. While the azuki bean
weevil is not readily available in the United States, a similar system
has recently been developed utilizing another bean weevil (Zabrotes
subfasciatus) with the parasitoids H. prosopidis and Anisopteromalus
calandrae (Kistler 1980).
(g) Parasitoid-fly. This system was developed by David
Pimentel to investigate the mechanisms of predator-prey coexistence.
The system consists of an array of 1, 16, or 30 plastic boxes
connected by plastic tubes (Pimentel et al. 1963). The boxes contain
vials of medium on which houseflies (Musca domestica), blowflies
(Phaenicia sericata), bluebottle flies ( Phormia regina), or
greenbottle flies (Phaenicia sericata) are raised. These serve as
prey for the hymenopteran parasitoid Nasonia vitripennis. The
predation rate of another fly pupa parasitoid (Muscidifurax raptor)
has been studied relative to N. vitripennis (DeBach and Smith 1941)
and could be used as a competitor in this system. The housefly,
blowfly, and Nasonia have life cycles of 13, 14, and 14 days
respectively. These three species are commercially available.
This system is similar to the parasitoid-grain moth system
because its population ecology is relatively well known, and it is
based on a medium that is convenient, but not directly relevant to
field conditions. Searching efficiency of the parasitoid is not an
important factor in the system as constituted so it is not useful for
tests on predation rate. This system emphasizes the coevolution of
fecundity of the parasitoid and resistance of the fly. It would be
difficult, however, to demonstrate that coevolution was reduced by a
chemical.
Such a system could be developed using Drosophi1 a and the
parasitoid Pseudeucoila bochei. Use of these species should allow
some miniaturization of the system. In addition, there has been far
more experience with Drosophila than with houseflies or blowflies.
(h) Ground-dwelling beetle-prey. Staphylinid and carabid
beetles are common predators of ground-dwelling arthropods and
molluscs. While these beetles have been shown in the field to be
important predators of a variety of insect pests, few laboratory
studies have been performed on them.
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114
Harris and Oliver (1979) examined predation by the
staphylinid Philonthus creunatus on the eggs and larvae of the hornfly
Haematobia irritans. Hornfly eggs were placed on manure pats, which
were placed on either a soil-vermiculite mixture or a section of sod.
Beetles were added in varying densities, and the emerging flies were
counted. While this system appears to realistically simulate the
field situation, it was sustained for less than a full generation of
either the predator or prey. Because the beetles primarily consume
the egg stage of the fly, behavior of the prey is not an important
component of the system, and chemical exposure of the prey should
begin before the predation test.
Small carabids such as Notiophi1 us can consume collembola in
simple arenas (Eijsackers 1978). Because collembola can be raised on
plaster, charcoal, and yeast, a test of predation behavior could be
easily developed. A population test would require soil for the
immature carabids, which would considerably complicate enumeration of
both prey and predator.
Because neither of these systems for predator-prey inter-
actions using ground-dwelling beetles appears promising, development
is not recommended. It should be possible to introduce these
predators into Pimentel's fly system [Sects. 4.1.1(3) and 4.1.3(2)] to
test the generality of the responses observed.
(i) Spider-prey. Although spiders are major predators in
many natural ecosystems, interest in their role as predators has been
limited because they have not been shown to control outbreaks of
insect pests. Laboratory studies of spider predation have been con-
cerned with spider behavior; those that study the functional response
to prey density most closely approximate a population interaction test
(Haynes and Sisojevic 1966; Gardner 1966; Hardman and Turnbull 1974).
Drosophila, which were used as prey in these studies, are easily
obtained and cultured, but spiders are not commercially available, and
techniques for rearing spiders are only now being developed.
(j) Mite-mite. Unlike insect predators and prey,
herbivorous and predatory mites have been well studied as interacting
populations in realistic laboratory conditions (Table 4.1). This is
probably due, in large part, to Huffaker and Kennett's (1956)
demonstration that the dynamics of mite predatory-prey interactions in
strawberry fields are adequately simulated by laboratory studies.
Most of these experimental systems consist of mites on arrays of
potted plants in a greenhouse or environmental chamber, with water or
grease barriers used to isolate treatments or individual plants within
treatments. Although the mites are counted in sample leaves or
plants, the outcome is typically described in terms of control (the
herbivore population reaches levels that damage the plant). The
control of a herbivore by a predator depends not only on the pair of
species used but also on physical conditions, the characteristics of
the host plant, and the input ratio of the predator and prey. Systems
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that are near the balance point between control and escape of the prey
might be highly sensitive to chemical perturbations. The test might
be scored on the basis of the presence or absence of large numbers of
mites. For most of the systems listed in Table 4.1, this outcome
would be reached in less than a month.
Huffaker's system of predator and prey mites on oranges
(Huffaker et al. 1963) had considerable heuristic value in the
development of ecology. This system is, however, much more difficult
to relate to the real world than a system on plants, and its elaborate
array of 252 partially covered oranges was difficult to establish and
maintain.
(3) Vertebrate predators. Hoi ling's (1959) laboratory studies
of the functional response of deer mice (Peromyscus maniculatus)
hunting pine sawfly (Neodiprion sertifler) cocoons were important to
the development of the theory of predation. Similar studies of
predatory behavior in enclosed arenas have since been conducted using
a variety of other vertebrates (e.g., Craig 1978). This type of
system is not a good candidate for protocol development because
relatively large arenas are required (3 by 1.2 by 1.8 m in Holling's
case), population responses are not included, and behavioral effects
of chemicals on vertebrates are already being tested in relation to
human health effects.
4.1.4 Host-Parasite
The experimental determination of effects of chemicals on
host-parasite interaction has been treated as a rather complex,
single-species problem. In one view, the parasite is considered as a
second stress that, like thermal shock, modifies the intensity of the
host's response to the chemical. Alternatively, chemicals are treated
as potential drugs that may rid the host of the parasite. In neither
case are the host and parasite treated as a system of interacting
populations. This situation partly reflects a general lack of
interest by experimental ecologists in parasitism relative to other
types of population interactions as a result of the apparent absence
of an experimentally tractable conceptual scale. The appropriate
scale for laboratory population experiments lies somewhere between the
microscale of medical physiology, described above, and the macroscale
at which epidemiologists model or monitor the spread of infection and
the evolution of virulence and resistance.
Even if a laboratory host-parasite population system were found
or developed, it would not necessarily be a good test system. Because
host-parasite relations are highly intimate and coevolved, their
dynamics are dominated by peculiarities of structure and physiology
that are not readily generalized.
For these reasons, host-parasite population interactions are not
considered further in this document. Parasites of insects and plants
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117
that might be developed as traditional tests for effects on virulence
are discussed in the report of a recent ORNL workshop (Suter 1981a).
4.1.5 Symbiosis
Symbiotic relationships are defined as those which benefit at
least one of the partner species without harming either; these include
Odum's (1971) commensal ism, mutualism, and protocooperation. Because
of the great diversity of ways in which one species may benefit
another, the mechanisms of symbiosis are probably less uniform than
those of most other classes of population interactions. They range
from very intimate obligate relationships such as the termite-
intestinal flagellate relationship to the rather loose commensal
relationships such as phoresy.
This section deals primarily with the symbiotic relationships
between higher plants and mycorrhizal and nitrogen-fixing
microorganisms. Because these relationships are ubiquitous and
important to primary production, they have the broadest relevance and
greatest ecological and social importance of any symbiotic
relationship. Lichens are considerably less important, but are
obvious candidates for a testing protocol because of their use in air
pollution monitoring.
(1) Lichens. Although many of the algal and fungal symbionts
that form lichens are capable of independent existence, the symbiotic
unit is functionally and reproductively distinct from its
constituents. The existence of an independent taxonomic nomenclature
for lichens reflects the proto-organismal character of lichens.
Because lichen tests are performed by collecting whole lichens rather
than by bringing together the constituent symbionts (a difficult and
seldom successful procedure), lichen tests are procedurally identical
to single-species tests. Therefore, it can be argued that lichens do
not constitute a multispecies test system.
Lichens are highly sensitive to gaseous air pollutants,
particularly S02 (Ferry et al. 1973). They may also be sensitive to
organic vapors and aerosols. Lichen tests are performed by exposing a
piece of thai!us that has been activated by wetting to the chemical
vapor or aerosol. Potential response parameters include respiration,
photosynthesis growth, pigmentation, potassium loss, and death.
(2) Rhizobiurn-legume. Although nitrogen fixation is carried out
by a variety of free-living microbes and microbes living symbiotically
with higher plants (Alexander 1971), the Rhizobiurn-legume symbiosis is
the predominate source of fixed nitrogen in terrestrial ecosystems.
Because of the agricultural importance of legumes, numerous tests have
been conducted to determine the effects of agricultural chemicals on
Rhizobiurn-legume symbiosis. Because the sensitivity of in vitro
rhizobia is poorly correlated with sensitivity of the whole
plant-microbe system (Lin et al. 1972, Fisher 1976, and Fisher et al.
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118
1978), only whole-system tests should be considered. Rhizobia-
inoculated seeds or sprouts can be grown on agar slants or in soil or
artificial media (vermiculite, sand, etc.). The growth medium can
significantly influence response to a chemical. Because simple media
such as vermiculite may decrease rather than increase the sensitivity
of the test without reducing variability (Smith et al. 1978), soil
should be used as the growth medium for the sake of realism.
The ultimate socially relevant response of this system is
productivity of the legume partner. Parameters that may be measured
include weight of plant parts, number and weight of propagules,
frequency of flowering, stem elongation, and damage symptoms.
Nitrogen content of plant parts provides an integrative measure of
nitrogen fixation and is also an indicator of forage quality. Direct
indicators of the symbiotic relationship include the number, position,
size, and color of nodules; leghaemoglobin content; and nitrogen
fixation rate of the whole system or of excised roots or nodules.
None of these parameters are clearly more sensitive to toxicants than
the others (Table 4.2), and most are easily determined. The N
fixation rate and plant N content determinations require some analytic
sophistication, but at least one of these parameters should be
determined as an indication of the effectiveness of the nodules.
The few time-course studies shown in Table 5.2 indicate that
sensitivity of the system generally diminishes with time. This may be
simply explained by degradation of the test chemical and adaptation of
the symbionts, or it may be the result of reduction in sensitivity of
the symbiont pair with age. Letchworth and Blum (1977) found that
sensitivity of clover top weight and number of nodules to ozone
decreased with the age at which the plants were exposed. Modulation
of the first root (crown nodules) is more variable than nodulation of
lateral roots (Tu 1977) and thus may be more sensitive to toxicants.
Therefore, a short-term test using legume seedlings may be sensitive
and may indicate the potential for interference problems with
establishment and reproduction of legumes. A more realistic test for
pasture legumes and natural legumes would be provided by the
fescue-clover competition system discussed in Sect. 4.1.1(2), but this
system is less well developed.
(3) Mycorrhizae. Most flowering plants form mycorrhizal
associations with fungi. The primary benefit ascribed to mycorrhizae
is enhanced uptake of phosphorus. Mycorrhizae may also enhance uptake
of other nutrients and water and protect the plant from root diseases.
While the mycorrhizal association is generally beneficial to the
higher symbiont, under certain environmental conditions, mycorrhizae
may be neutral or even parasitic. Chemicals may not only deprive
plants of the benefits of mycorrhizal symbiosis, but may modify the
symbiotic nature of the association.
The benefits of this association can be measured directly in
terms of the quantity and quality of plant production. The
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mycorrhizal association itself can be examined according to the extent
of infection. Ectomycorrhizal infection can be readily evaluated in
terms of the amount of root covered with a mycelial mantle.
Endomycorrhizae do not significantly modify the appearance of infected
roots and therefore must be evaluated microscopically. Preparation
techniques have been developed by Ambler and Young (1977) and Kormanik
et al. (1980). These techniques are used to measure percent infection
and the frequency of arbuscules and vesicules. These measures of
infection are likely to respond more rapidly than plant production to
a chemical that affects the association and may be more sensitive than
plant production. The large clamydospores of endomycorrhizae are
readily removed by wet sieving, but clamydospore production is less
sensitive than root infection to pesticides (Menge et al. 1979). Of
39 combinations of crops and pesticides, 24 resulted in reduced
endomycorrhizal infection (Menge et al. 1979).
It may also be possible to develop a test system based on the
ability of mycorrhizae to suppress root diseases. Mycorrhizae may
inhibit pathogens (1) by producing antibiotics, (2) by stimulating the
root to produce antibiotics, (3) by modifying root exudates, or (4) by
forming a physical barrier to infection (Marx 1969). A root disease
that produced a rapid visible response and that was suppressed by
mycorrhizae would form an easily scored and possibly sensitive test
system.
Because of the taxonomic, functional, and structural differences
between endomycorrhizae and ectomycorrhizae, test systems should be
developed for both types of associations. The difference in
sensitivity between the two types is unknown, but endomycorrhizae
recover more slowly because they do not form airborne spores.
Tentative protocols for Pi soli thus tinctorius and loblolly pine
(ectomycorrhizae) and Glomus spp. and a grass (endomycorrhizae) have
been proposed by participants in a recent workshop at ORNL (Suter
1981a). These protocols call for rather long test runs (105 and 84
days), but it may be possible to distinguish effects of chemicals on
infection rates more rapidly. Any test system that includes a plant
can serve as a test for the mycorrhizal association if suitable
inoculum is included. Any phytotoxicity test that uses nonmycorrhizal
plants is likely to give results that are irrelevant to field
conditions.
4.1.6 Community Composition
The properties that are unique to the community level of
organization include species composition, succession, food web
structure, species turnover rate, and diversity. Multicellular plants
and macroinvertebrate and vertebrate animals are too large and
long-lived to display these properties in the laboratory. Microbes
and microinvertebrates, as previously mentioned, are difficult to
extract quantitatively, identify, and enumerate. Because the soil
community's composition is not sufficiently important relative to its
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function to justify a difficult and expensive test, no tests for
terrestrial community properties are recommended.
4.1.7 Summary
While terrestrial ecosystem-level responses to chemicals have
received some attention (Sect. 4.2), population interactions have been
neglected. The only interactions that have received significant toxi-
cological attention and therefore could be adopted in the near term as
TSCA test standards are the legume-rhizobia and mycorrhizal fungus-
plant associations. Development is needed to arrive at acceptable
protocols for these symbiotic associations because there has been no
consistency in the techniques used to date. The test systems
suggested in a recent ORNL workshop (Suter 1981a) would be a good
starting point. In addition, the opinions expressed at that workshop
and in Sect. 4.1.5 concerning appropriate response parameters must be
confirmed by systematic testing with reference chemicals. The
economic and ecological importance of these plant-microbe associations
makes development of these tests highly desirable.
Drosophila and Tribolium competition, Pimentel's fly and wasp
systems, and the parasitoid-grain moth system constitute a second
class of potential test systems. These are highly developed
experimental systems, which could be readily implemented but for which
there is no toxicological experience. These systems may be quite
sensitive, but their ability to generate relevant predictions is
questionable. Because these systems are relatively well understood
and fairly easily operated, they might be examined concurrently with
the developing test protocols to better understand the way in which
chemicals affect general classes of population interactions.
Finally, there is a group of potential test systems that is
neither well studied toxicologically nor well developed as
experimental systems, but that appears worthy of long-term
development. This category includes general interactions: plant
competition, herbivory, and predation. The best candidate for a plant
competition test is clover-grass because of its economic importance,
its seminatural character, and the work done on its response to ozone.
No strong bases for selecting a particular species of host plant and a
sucking or chewing insect for herbivory tests exist. For the reasons
listed in Sect. 4.1.2, small homoptera appear to be good subjects for
a population interaction test. Hymenopteran parasitoids are the best
candidates for the predators in a predator-prey test because they are
small, important as biocontrol agents, and well studied. While the
parasitoid-grain moth and fly systems are relatively well studied and
easily maintained, a system involving parasitoids of homoptera raised
on whole plants should be much more representative of natural and
agronomic systems. A similarly realistic test for conventional
predators would include a coccinellid or neuropteran predator and a
homopteran prey raised on whole plants. Mite predator-prey systems
are relatively well developed, compact, and rapid. They would be
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ideal test systems if they can be shown to be representative of
insects as well as mites.
The potential for combining categories of tests is obvious. One
can easily imagine, for example, a test system involving competition
between clover and grass that is inoculated with Rhizobium and
mycorrhizae and that supports competing herbivores and predators.
Such a system would have considerable appeal as a highly inclusive and
realistic screening test, but simpler systems would be necessary to
explain the cause of the observed responses.
The use of any of these tests for broad predictions of ecological
effects will depend on a considerable increase in our knowledge of the
nature of ecological processes. Some bases for that knowledge will
result from the process of test development.
4.2 Ecosystem Properties
The two basic processes which are characteristic of ecosystems
are the cycling of nutrient elements and the capture and transfer of
energy. While chemical contaminants may modify the physical and
chemical components of these processes by affecting soil pH or by
chelating metal ions, TSCA chemicals are unlikely to occur in the
environment at concentrations sufficient to have significant direct
effects on soil chemistry unless large spills occur. Effects on the
terrestrial biota are likely to be far more significant.
This section discusses the parameters measured in tests for
effects on nutrient dynamics, primary production, and saprophytic
metabolism in terrestrial ecosystems. Nonsaprophytic secondary
production is not considered because it is much less readily measured
as a whole ecosystem characteristic than as a component of specific
population interactions (Sect. 4.1). Problems of selecting the size
and components of test systems are also discussed. Examples of
synthetic and excised test systems are briefly described in terms of
their relative applicability to toxicological testing.
4.2.1 Parameters
(1) Primary productivity. The ecological importance and social
relevance of certain ecosystem processes are evident. For example,
the ecologist's primary production is the forester's and
agriculturalist's yield. This parameter might be a sufficient test
criterion in itself, except that response to chemicals may be very
slow because of the mediation by effects on soil chemistry,
reproductive success, herbivore and pathogen activity, or other
factors. Hazard evaluation procedures involving any system that
contains plants should include primary production, measured in terms
of dry mass yield because of its importance and ease of determination.
Transient effects on primary production from which the plant recovers
can be detected by C02 uptake or 02 release, but if these effects are
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not reflected in yield, their importance is questionable. Other,
easily measured plant characteristics that may aid in the
interpretation of test results include (a) symptoms of damage such as
chlorosis and necrosis, (b) phenological parameters such as time to
flowering, and (c) physiological parameters such as the nutrient
status of the leaves.
(2) Nutrient cycling. Processes that influence soil fertility
(nutrient cycling processes), such as transformations and movement of
nutrient elements and degradation of organic materials, also have
obvious social relevance. Nutrient transformations include fixation,
mineralization, and oxidation-reduction reactions. The most important
transformations in terms of biological production are those that
involve the macronutrients. Of these, the best candidates for
toxicologies'! testing are nitrogen and sulfur, the macronutrients
whose dynamics are dominated by biological processes. (Carbon
dynamics are considered in terms of photosynthesis and respiration.)
Nitrogen is the most important, but not all steps in the N cycle are
important in all systems. Nitrogen fixation makes an insignificant
contribution in most agricultural systems because of fertilization
(fixation by legumes is an exception) and in mature natural systems
because of the dominance of internal cycling. Nitrogen is often
important in natural pastures and immature natural ecosystems, and
nitrogen mineralization (ammonification) is important in natural
systems. Nitrification is considered undesirable in many agricultural
systems because of nitrate leaching and is a minor process in many
natural systems because of rapid immobilization of ammonia.
Biological nitrogen immobilization is important in nearly all systems,
but it is difficult to measure directly; indirect indicators include
plant N content and available N concentrations. Denitrification is
limited under aerobic conditions, and inhibition of this process would
generally not be considered detrimental.
Loss of nutrient elements by leaching can be important to
ecosystem maintenance and productivity if sufficiently large and
sustained. It has also been hypothesized to be a rapid and highly
sensitive indicator of ecosystem stress (O'Neill et al. 1977). The
terrestrial portion of a recent microcosm research program at ORNL was
based on this premise (Harris 1980). The synthesis of this effort
concluded that Ca and N03 leaching would be sufficient parameters for
use in a toxicology screening test (Ross-Todd et al. 1980). Ca
concentrations in leachate had the least variance and the greatest
sensitivity of the nutrients considered [Mg, Ca, dissolved organic
carbon (DOC), K, N03, P, and NH4] followed by Mg, which was highly
correlated with Ca. N03 loss was much more variable than Ca and Mg,
but was highly sensitive.
The mechanisms of nutrient loss in these test systems,
particularly Ca loss, are not understood. The large importance of
cation exchange processes and carbonate chemistry relative to
biological processes in most soils raises important questions of
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interpretation. When Jackson et al. (1979) used Na2S04 as a nontoxic
control salt for Na3As04, Ca leaching was higher in the controls than
in the experimentals. Van Voris et al. (1978) added 45Ca to the
surface of a Cd-contaminated grassland core. They concluded that "the
Ca isotope was totally retained in the top 2.5 cm of the soil
indicating that the Ca loss was not due to cation exchange since the
ion exchange sites were not saturated." The results can be
realistically explained by isotopic dilution, the process of
displacement of native Ca on exchange sites by 45Ca; the experiment,
therefore, did not eliminate exchange processes as a major factor in
Ca loss. The coincidental occurrence of increased Ca loss and
decreased C02 efflux cited by Van Voris et al. (1978) is suggestive,
but it does not establish a biological cause for Ca loss.
Nutrient export becomes even more difficult to interpret when
organic chemicals are tested. Metabolism of an organic chemical leads
to immobilization of nutrients, masking any leakage of nutrients from
stressed biota. This process might explain why hexachlorobenzene
caused greater Ca loss at lower concentrations in a soil core study by
Ausmus et al. (1979). Gile et al. (1979) examined the effects of four
organic agricultural chemicals on nutrient loss. Leaching of most
nutrients was unaffected or reduced by the chemicals, again suggesting
that immobilization was stimulated. Because most TSCA test substances
will be organic, this could be a serious disadvantage to using
nutrient export tests to predict effects.
Other problems with nutrient leaching studies concern
interpretation of results in terms of effects in the field. Leaching
of nutrients from a 5- to 15-cm-deep soil core does not mean that the
nutrients will appear in surface or ground water or that they are lost
to the biotic community. In many, if not most cases, nutrients
leached from the A horizon are retained in lower soil horizons. In
this case the nutrient, is not lost, but rather has been mobilized and
transferred to another relatively immobile pool. This movement would
be advantageous to deeper-rooted plant species. A second problem is
the inability to adequately interpret the seriousness of the observed
response. In soil microcosms, a toxicant-elevated nutrient loss rate
typically returns to control levels within 3 weeks, even though
toxicant and nutrient concentrations in the soil have not appreciably
declined. A parameter that is that resilient will only be useful if
it is indicative of longer-term ecosystem responses.
Another approach to determining the effect of chemicals on
nutrient dynamics is to measure nutrient availability by extraction.
Such extractions are conventionally performed by shaking a soil slurry
formed with dried, screened soil and a chemical extractant. Jackson
and Hall (1978) leached soil cores with extractant solutions, thereby
deriving estimates of available Ca, NH3, N03, and P04 that were lower
than those obtained from slurries, but that were more sensitive to the
effects of heavy metals. Like nutrient leaching, nutrient
availability would respond to effects on mineralization and
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immobilization. It has the advantage over nutrient leaching of
explicitly taking into account ion exchange processes, but its
response is less often significant, and the direction of response is
less regular (Ausmus et al. 1979; Jackson et al. 1979; and Jackson et
al. 1978).
(3) Community metabolism. The most, common measure of soil
metabolism is C02 efflux. Chemicals that are toxic and persistent and
to which the biota cannot adapt will simply depress C02 efflux.
Chemicals that serve as substrates or supply mineral nutrients would
elevate C02 efflux. Chemicals that are toxic but leave no residues
such as fumigants, that are readily degraded, or to which the biota
adapts cause a short depression in C02 efflux, followed by a rebound
to very high levels and a slower return to predisturbance levels.
This pattern also occurs in response to drying or physical disturbance
of the soil, in which case the cycle typically requires 5 to 14 days.
It is one of the major determinants of the equilibration period
required for soil test systems. Elevated C02 efflux during the
rebound period is generally attributed to the degradation of microbes
killed by the disturbance.
If C02 efflux is measured continuously by infrared gas analysis,
cycles in the system's carbon balance can be monitored. The number of
distinguishable cyclic frequencies was used by Van Voris et al. (1978)
as an indicator of the functional complexity of microcosms. This
index, however, was used to predict response to a toxicant rather than
as an indicator of response. The functional significance of these
cycles is unknown.
Carbonaceous substrates are frequently added to the soil to
examine effects of chemicals on a specific degradation process or to
ease the C02 determination by increasing the efflux rate. In the
absence of evidence that degradation of a specific substrate is
particularly sensitive to toxic chemicals, it is probably best to
maximize realism by using no amendment or by using only whole plant
material. In this way, the range of microbes involved in the test is
maximized. If, as Domsch (1970) has hypothesized, autochthonous
organisms and those that degrade resistant substances are most
sensitive to toxic effects, degradation of native soil organic matter
may be a sensitive process. If carbonaceous amendments are used, the
time until peak respiration may be more sensitive than total
respiration (Domsch 1970; Spa!ding 1978).
Other methods of determining soil community metabolism include 02
uptake, heat production, and ATP concentration. Methods of measuring
02 consumption (a) are less precise and therefore less sensitive than
C02 efflux (Lighthart et al. 1977), (b) do not represent microbial
respiration as completely as C02 (Stotzky 1965), and (c) therefore
would only be useful if the respiratory quotient (RQ) was of interest.
Klein (1977) found that RQ was a sensitive indicator of seasonal
changes in the microbial community, but it was not affected by any of
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the 14 salts of heavy metals that significantly reduced C02 efflux
(Lighthart et al. 1977).
ATP assays are difficult and expensive to perform, are not
amenable to time course analysis because of their destructive nature,
and do not seem to offer any particular advantages over other indices
of soil metabolism.
The sensitivity of soil metabolism to toxic chemicals is
questionable. The only good body of evidence for disruption of
decomposition by pollutants is that for heavy metals (Coughtrey et al.
1979; Harris 1980; Jackson and Watson 1977; Lighthart et al. 1977;
Ruhling and Tyler 1973; Spa!ding 1978; Tyler 1976). This effect is
generally attributed to metal toxicity. However, it has been attri-
buted to total salt concentration by Lighthart et al. (1977) because
sodium salts were as effective as heavy metal salts of equivalent
ionic strength. Spalding (1978) concluded that the effect of heavy
metals on soil respiration primarily resulted from the formation of
resistant metal-organic complexes rather than from direct toxicity.
These mechanisms would not contribute to the effects of organic
compounds on decomposition. The effect of most organic compounds
would be to increase metabolism by serving as a microbial substrate.
Enzyme activity determinations are used to indicate the potential
of soils to perform certain chemical transformations. Results of
enzyme assays reflect changes in the character of ecosystems less
directly than the parameters previously discussed. Therefore, enzyme
assays could only be recommended if they were known to be particularly
sensitive to chemicals or particularly rapid and inexpensive. Because
available evidence indicates that neither of these cases is true,
enzyme assays are not recommended.
Transformation of chemical contaminants is also an ecosystem
function. While this process typically results in detoxification,
partial oxidation of chemicals can result in increased toxicity. One
chemical may also decrease the rate of degradation of a second
chemical, leading to undesired toxic effects and contamination of food
or enhancing the effectiveness of agricultural chemicals that degrade
too rapidly (Kaufman 1977). Effects of one pesticide on the
degradation of another have been demonstrated by Kaufman et al. (1970,
1971, 1977). In addition to pesticides, the soil biota degrades toxic
chemicals from the air, chemical spills, and buried or land-farmed
wastes. The soil biota is also responsible for scavenging inorganic
gaseous pollutants from the atmosphere. The extent and potential
importance of interference with this process by chemicals is unknown.
(4) Summary. Table 4.3 summarizes the results of several
studies that have examined the effects of toxic substances on more
than one ecosystem process. Fungal and bacterial counts are included
because they are frequently determined in studies of ecosystem
processes. Few of these studies consider primary production, but the
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results of one study suggest that It is more sensitive to organic
chemicals than microbial processes and populations (Eno and Everett
1977) and should be determined in any system containing plants. C02
efflux is a rapid and sensitive indicator of biotic response, but it
may increase or decrease depending on time since the perturbation, the
degradabi1ity and persistence of the test chemical, the nutrient
status of the system, and other factors. It should be measured over
time to clarify the nature of the response. Mineralization of
nitrogen and other nutrients is an important process that is
relatively sensitive to metal salts (Liang and tabatabi 1977) and
moderately rapid. Nitrification and nitrogen fixation appear to be
somewhat less sensitive and are as likely to increase as to decrease.
The few results from ATP assays do not appear promising, particularly
in light of the relatively high expense and difficulty of this assay.
Enzyme assays, in addition to being difficult to interpret, appear to
be relatively insensitive to perturbations. Nutrient leaching is
sensitive to metal salts and is quite rapid in some cases, but to date
the results with organic compounds are not promising. Nutrient
availability appears to be less sensitive, consistent, and rapid than
nutrient leaching.
There is good evidence that heavy metals disrupt ecosystem
processes at concentrations that do not acutely affect most individual
organisms. However, the studies cited herein and reviews of
insecticide and herbicide effects on terrestrial ecosystems (Brown
1978; Greaves et al. 1976; Cullimore 1971) indicate that soil microbes
and the ecosystem processes that they conduct are typically less
sensitive to organic chemicals than individual organisms and
populations. Because most TSCA-regulated chemicals are organic, they
are more likely to behave like organic agrochemicals than metals.
Nevertheless, effects on ecosystem processes are sufficiently impor-
tant that a simple system to measure C02 efflux and N mineralization
in soil should be included in any testing scheme. Other parameters
such as nutrient leaching are potentially useful but require further
development.
4.2.2 Test Components
While component ecosystem processes such as ammonification can be
conducted by a single bacterial clone in a liquid minimum medium, the
realism of responses measured in that system are highly questionable.
Most microbial ecologists would agree that minimal realism requires a
mixed microbial culture in soil. Some would argue further that
because the presence of litter, plant roots, and soil invertebrates
significantly modify the absolute and relative rates of soil
processes, they must also be included in a test system for any basic
ecosystem process. The importance of these components in determining
responses to chemicals has not been investigated. C02 efflux has been
measured in a wide variety of test systems, but its response shows no
trends with increasing system complexity (Table 4.3). Nutrient
leaching in response to metals is not clearly affected by the presence
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of plants although the treecosm responses may have been slowed by the
trees and litter (Jackson et al. 1978). Extraneous components (those
which do not qualitatively affect system response) would increase the
cost of a test, would complicate interpretation of results, and might
interfere with measurements. No ecosystem components have been shown
to be extraneous in this sense.
Soil structure also can be considered a system component. Ausmus
and O'Neill (1978) found that intact soil cores and homogenized soil
columns did not differ in C02 efflux, but the homogenized soil lost
significantly more DOC in leachate with a larger percent variation.
In another study (Jackson et al. 1979; Jackson and Levine 1979)
arsenic transport and nutrient concentrations in leachate before As
treatment did not differ in 30-cm-diameter intact and homogenized soil
columns; extractable Ca and P04 levels showed inconsistent
differences. Leaching of DOC was higher in the intact columns
(contradicting the result described above), and ATP concentration and
fungal biomass were significantly reduced in the intact columns, but
not in the homogenized columns. Although homogenized soil would seem
intuitively to be less variable than intact cores, there were no
consistent differences in variability in these studies. Therefore,
the choice between intact and homogenized soil structures may be made
on the basis of convenience. Small cores are most easily obtained by
extraction, but larger systems such as the treecosm would probably be
more easily assembled.
4.2.3 Soil Type
The problem of selecting a soil type for use in tests of
terrestrial ecosystem processes is essentially the same as the problem
of choosing species for tests of species interactions. The choice is
critical to the outcome of the test because the responsiveness of
ecosystem processes to chemicals is highly dependent on soil type.
One possible solution to the problem is to simply prescribe
limits on soil texture, organic carbon content, and pH. This is the
simplest solution and is probably the only one that is currently
feasible, but unidentified discrepancies in results would still occur,
and the range of field situations to which the test could apply would
be limited. A second possible solution is to designate a standard
soil or a series of standard soils that are representative of major
regional soil types. This solution would produce relatively
consistent results, but would require that EPA or some other agency be
responsible for distributing certified standard soil. Another
approach is to allow testing laboratories to select their test soil,
but to require the use of standard reference chemicals as positive
controls. This solution is based on the assumption that relative
sensitivity of soils to different chemicals is nearly constant, at
least within broad categories of chemicals and soils. This assumption
will need to be tested before reference chemicals are proposed.
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In some cases, the production, distribution, and use of a
chemical may be so delimited as to allow identification of a small
number of soils which could be affected by the chemical. In these
cases, testing of the potentially affected soils would increase test
validity at the cost of not producing standard results.
4.2.4 Size
The size of a test system is determined by (1) the components and
processes that must be included, (2) the amount of material necessary
for measurement of the response parameters, and (3) the necessity of
reducing variance by increasing the volume of material. If plants or
macroinvertebrates are included in the system, their requirements are
likely to determine the minimum size. Laboratory systems are not
large enough to support vertebrate animals without unrealistically
severe disruption of the system (Gile and Gillett 1979; Metcalf et
al. 1979). The volume of soil or litter in purely microbial systems
is usually determined by the volumes required for chemical analysis or
measurement of gas uptake or efflux.
Studies that consider the effect of size on system response are
rare. Ross-Todd et al. (1980) analyzed the results of two experiments
that considered the effect of size (10 x 10 cm vs 30 x 15 cm and 15 x
25 cm vs 30 x 25 cm) on response of grassland cores to As. The larger
cores produced generally higher concentrations of nutrients in
leachate, but the relative variability of this parameter was
inconsistent. Leachate concentration showed a clearer treatment
effect (was more sensitive) in the larger cores. C02 efflux was less
variable in the larger cores. These results suggest that fewer large
systems would be required to show a statistically significant
treatment effect, but this advantage must be balanced against the
higher cost of preparing and maintaining larger systems.
4.2.5 Synthetic Systems
This section discusses synthetic systems, those that are
assembled or constructed from ecosystem components. The basic
components are soil, plants, animals, and nonliving organic matter.
The applicability of these systems to tilled fields where strong
structural relationships of soil, litter, and plants do not develop is
obvious. Natural ecosystems are probably less well simulated by these
test systems than by excised, intact systems, but the differences in
response have not been demonstrated in work to date.
Only one or a few key references are cited for each test system
in this and the following section. A more complete set of references
is provided in the bibliography (Appendix D).
(1) Soil systems. Most studies of terrestrial ecosystem
processes are performed by microbiologists using natural microflora in
soil. The soil may be dried, sieved, ground, formed into a slurry, or
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amended with substrate materials. Slurries, like liquid cultures and
agar plates, do not realistically represent the soil. Grinding is
unnecessarily destructive, and sieving should be kept to a minimum to
retain crumb structure. Drying is also unnecessary and reduces the
diversity of the microflora. Soil amendments increase the rate and
magnitude of microbial activities, making measurement easier, but they
may qualitatively modify the effects of a test chemical if they are
not representative of common substrates in the field. Glucose greatly
decreases the sensitivity of microbial respiration to pesticides
(Bartha 1967).
Soil systems (test systems consisting of soil and microbiota) can
be used to test effects on any of the ecosystem processes previously
discussed except those that require plants (primary production and
plant uptake of nutrients). Decades of work by agronomic
microbiologists indicate that nutrient dynamics and the effectiveness
of soil fumigants in agricultural systems are adequately represented
by soil systems. Until led, natural systems may not be adequately
represented by these systems because of the importance of
litter-root-soil structure relationships.
Schemes for testing the effects of chemicals on soil processes
have been suggested by Johnen and Drew (1977), Atlas et al. (1978),
the U.S. EPA (1979), and the participants in a recent workshop held by
ORNL (Suter 1981b). None of these schemes have been subject to
validation, standardization, or interlaboratory transfer, but the test
proposed at the ORNL workshop appears to best fit the requirements of
TSCA.
(2) Litter. This system is identical to the soil system except
that litter, rather than soil, is the medium (Spalding 1979). Litter
responses to chemicals have received much less attention because
forests have less economic importance than field crops and have
received less intentional input of chemicals. Litter alone does not
represent forest ecosystems as well as litter and soil and offers no
significant advantage in cost or rapidity of response. Therefore, it
does not appear to be a good candidate for protocol development.
(3) Soil-1itter. This system is essentially a combination of
the previous two, a layer of sifted litter on top of a layer of
homogenized and sieved soil. In the form developed by Bond et al.
(1976), the system is enclosed in an apparatus that permits continuous
and simultaneous measurement of C02 efflux, 02 uptake, and heat
output. It is designed to make possible complete and accurate
measurement of the integrated responses of the forest floor microbial
community to toxicants. This system would be suitable for development
as a test protocol if it was simplified by only measuring respiration
as C02 efflux. Coefficients of variation for C02 efflux from this
system are low (<10%) and comparable to those for intact forest soil
cores (Ausmus and O'Neill 1978).
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(4) Gnotoblotic soil. Rather than using a natural, undefined
community of microflora and fauna in soil test systems, a defined
community can be assembled in sterilized soil (Coleman et al. 1977).
These gnotobiotic systems are useful research tools and are likely to
be sensitive because they lack functional redundancy. However, they
are expensive and difficult to maintain and are unlikely to respond as
realistically to test chemicals as natural soil communities. For
these reasons, gnotobiotic systems are not good candidates for test
protocols.
(5) Soil-plant. These systems are designed to reveal the effect
or fate of agricultural chemicals applied to field crops.
(a) Pot. This is essentially a test for effects of
pesticides on crop plants using a pot of field soil in which effects
on the microbial community and microbial processes are determined (Eno
and Everett 1958). The use of large pots makes it possible to grow
the plants to maturity and examine effects on reproduction and yield.
The simplicity of this system and the large mass of experience with
growing potted plants in greenhouses makes this an appealing test
system for effects of chemicals on agricultural systems.
(b) Lichtenstein. This system consists of corn seedlings
grown in layers of contaminated and uncontaminated homogenized soil
contained in an 86- by 154-mm-high plastic cylinder, resting in a
leachate collector (Lichtenstein et al. 1977). The only validation
provided for this system is comparison of the results for a pesticide
(N-2596) with an independent field study. In that study, far shorter
persistence was found in the soil of field plots planted to rye than
was found in the Lichtenstein system (Lichtenstein et al. 1977). This
system is also essentially a pot test, except that the pots are not
large enough to grow the plants to maturity.
(c) Agroecosystem chamber. This system consists of crop
plants (cotton or tomatoes at five plants/chamber) grown on a 15-cm
layer of sieved soil in a 115 cm high x 150 cm x 50 cm closed glass
box with controlled air flow (Nash et al. 1977). The system is
designed to provide a complete description of pesticide fate by
permitting the measurement of volatilization and residues in soil,
plants, and leachate. No attempts to field validate this system have
been reported. The chief advantage of this system for effects studies
is that the air flow system would permit measurement of whole system
respiration.
(d) Summary. As effects tests, these systems are
essentially plants in different-sized pots, one of which has a cover
to control air flow. This type of system could be adapted to measure
nutrient leaching in agricultural ecosystems, and microbial processes
can be measured if the potting medium is not artificial. With the
deliberate addition of a pathogen, herbivore, or another plant, a more
realistic ecosystem process test and a test for population
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interactions are presumably obtained. The primary technical problem
concerns the size of pot that is necessary to support the processes of
interest.
(6) Soil, litter, plant, and animal. These systems represent
attempts to assemble true microcosms--laboratory systems that contain
all the major components and processes of a selected terrestrial
ecosystem.
(a) Odum. This system consists of natural soil and litter
and small plants of five different taxa in a 16.2-cm-diameter plastic
desiccator (Odum and Lugo 1970). An air flow of 2.5 L/min was
maintained through ports in the lid, and C02 content was measured with
an infrared gas analyzer. Because the soil and litter were not
subjected to harsh treatments and the flora consisted of whole
transplants, a representative invertebrate fauna was included.
The purpose of this system was to supplement a field study
of radiation effects on a tropical forest. The system permitted
greater resolution in metabolic measurements than did the unconfined
ecosystem. Neither respiration nor photosynthesis was found to be
affected in these systems by 25,000 r of gamma radiation. The lack of
effects on respiration was not surprising because respiration is
dominated by the microflora, which are resistant to radiation. The
absence of effects on photosynthesis was somewhat unexpected because
damage to plants outside the microcosms was observed at that radiation
level, but the photosynthetic enzyme system is resistant to radiation
at levels that cause morphological damage to plants. The baseline
respiration rate was two orders of magnitude lower in the microcosms
than in the field.
(b) Witkamp. These systems were designed as research tools
to study the dynamics of fallout isotopes (137Cs) and mineral
nutrients under various physical and biological conditions (Witkamp
1976). They consist of a glass or plastic cylinder 7 to 13 cm in
diameter by 10 to 13 cm deep, with a leachate port to which various
combinations of soil, litter, soil fauna, and seedling trees may be
added. They have not been used for chemical testing, but have the
advantage that their nutrient dynamics have been modeled and are
relatively well understood. The approach of using major system
elements as components in a factorial design would be useful for
determining mechanisms of toxic response. Transfer rates are
generally higher in these systems than in the field, but the
mechanisms and pathways are qualitatively similar (Witkamp 1976).
(c) Metcalf. The original version of this system consisted
of sloping soil in an aquarium with a crop and terrestrial fauna on
the high end and water and aquatic flora and fauna on the low end
(Metcalf et al. 1971). More recently, this system has been supplanted
by a more efficient design. It consists of either 400 g of
vermiculite or 3000 g of soil planted with corn (Metcalf et al. 1979).
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After 10 days, saltmarsh caterpillars, slugs, earthworms, and pi 11 bugs
are added. On day 15 a vole is added, and on day 20 the system is
terminated. The primary purpose of this system is to analyze the fate
of pesticides in agricultural systems. Effects are determined
incidentally by measuring plant growth and noting deaths of animals.
The only validation provided for this system is a general
comparison of pesticide fate in the system and the field. Results
agree "very closely" (Metcalf et al. 1979). If this system were
adapted for use in effects testing, the vole should be deleted. The
voles greatly disrupt the systems by burrowing and typically consume
the entire flora and fauna of the system. This situation is obviously
not typical of the role of voles in ecosystems, and the diet provided
is probably no more realistic than commercially prepared food for
laboratory mice. The use of screw-topped jars provides a cheap and
easily closed system for gas analysis.
(d) Terrestrial microcosm chamber (TMC). The TMC is essen-
tially an enlargement and elaboration of the Metcalf system. It con-
sists of a 1 x 0.75 x 0.6 m glass box with ports for airflow, water
addition as rain or a "spring," and a leachate port (Gile and Gillett
1979). It contains 20 cm of synthetic soil, alfalfa, rye grass, two
species of nematodes, earthworms, enchaetraeid worms, two species of
pillbugs, mealworms, crickets, snails, and a pregnant vole. Like the
Metcalf system, it is used primarily to study the fate of pesticides
and secondarily to determine effects.
The TMC results with Dieldrin were validated by comparison
with published field and laboratory studies. While many results are
comparable, others are not. The concentration of residues in the vole
are more than an order of magnitude higher than would be expected from
field studies. The problems of including a mammal in a microcosm are
reduced but not eliminated in this larger system.
The advantages of this larger, elaborate system over the
Metcalf system have largely to do with studies of fate. If the vole
is deleted, the Metcalf system has no significant relative
disadvantages as a test system and is considerably cheaper and easier
to operate.
4.2.6 Excised Systems
Systems that are excised, intact from the field, are discussed in
this section. These systems were developed out of the belief that the
structural relationships of soil, litter, and plants are critical to
ecosystem dynamics.
(1) Soil core. A 5-cnrdiameter by 5- or 10-cm-deep soil core is
encased in a heat-shrunk polyvinylchloride (PVC) sleeve and supported
on a leachate collector. Aboveground vegetation may be removed or
left in place. The system was designed to serve as a general-purpose
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test system for determining the fate and effects of toxic materials.
This is the only one of the six systems discussed at the 1977 Workshop
on Terrestrial Microcosms for which a testing protocol was proposed
(Gillett and Witt 1979; see also Harris 1980). No attempt has been
made to field validate this specific system, but the nutrient leaching
results have been related to the general body of evidence on nutrient
loss summarized by O'Neill et al. (1977). This system, which was
developed at ORNL, has been used at the Corvallis Environmental
Research Laboratory to test pesticides and herbicides (Gile et al.).
The Corvallis study did not obtain the same regular increase in
nutrient leaching that was obtained at ORNL even though one common
chemical (hexachlorobenzene) was used; it is not clear whether the
disparity is due to differences in the soils used or other factors.
(2) Grassland core. This system consists of intact cores that
are sufficiently large (15- to 30-cm diameter by 10 to 25 cm) to
support a representative portion of a grassland community. The 15-cm
diameter cores of Van Voris et al. (1978) supported averages of 14
individual plants of 6.3 species. The version of this system
recommended by Harris (1980) is supported on a Plexiglass disk with
central port and encased in a heat-shrunk PVC sleeve. This system was
designed to test the fate and effects of chemicals in grassland
communities. Jackson et al. (1979) attempted to validate this system
in the field. While nutrient leaching and soil ATP levels in the
cores were affected by As, no response was measured in the field.
Because the cores were kept in the field and because untreated cores
and plots were comparable, this result implies that enclosure
increases the sensitivity of these parameters.
(3) Sod. This system consists of a 16-cm diameter by 7-cm deep
section of sod contained in a closed 4-L Nalgene jar (Campbell 1973).
Ports are provided in the lid for periodic measurement of C02
production by infrared gas analysis. This system was designed to dis-
play the response of grassland ecosystems to stress. It is similar to
the grassland core, but has no provision for monitoring nutrient loss.
It offers no particular advantage as a test system.
(4) Treecosm. This system consists of an intact 45- x 45- x
25-cm block of forest soil containing an approximately 2-m-tall red
maple sapling and associated ground flora (Jackson et al. 1978). The
primary purpose of this system was to investigate the ability of
microcosms to simulate a specific field perturbation and elucidate the
mechanisms of the observed field response.
Comparison of treecosm results with studies by Jackson and Watson
(1977) of the effects of smelter emissions on Crooked Creek watershed
partially validate the system. While the pattern of uptake of metals
from the smelter dust was similar to that at Crooked Creek,
differences in transfer rates were sufficient to prevent development
of a predictive transport model of Crooked Creek watershed from the
treecosm results (Luxmore and Begovich 1979). This disparity was
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attributed to the physical conditions of the greenhouse in which the
treecosms were kept. While the treecosms were treated to simulate
areas at Crooked Creek receiving high metal deposition rates, the
small increase in macronutrient pools observed in treecosm litter
better simulated areas that received intermediate levels of
deposition. Low macronutrient levels were observed in tree tissues at
Crooked Creek, but not in the treecosm. The increased leaching of
macronutrients from treated treecosms suggests a mechanism for the
decreased macronutrient levels in the soil at Crooked Creek. The
elevated soil respiration rates and ATP concentrations observed in
treated treecosms contrast sharply with the reduced respiration and
elevated litter biomass observed at Crooked Creek. Reduced fungal
lengths in treated treecosms correspond to the reduction in ami no
sugar concentrations observed at Crooked Creek.
The disparities between treecosm and field results may be attri-
buted to the greenhouse environment or to differences in soil and
biota between Crooked Creek, Missouri, and Oak Ridge, Tennessee, the
source of the treecosms. It seems likely, however, that many of the
disparities are attributable to the 20-month period of the treecosm
experiment, which is relatively short in terms of forest dynamics.
The high levels of internal nutrient cycling in trees buffer them
against changes in soil chemistry. This characteristic also delays
any soil responses that depend on changes in characteristics of the
litter fall or root dynamics.
Because trees and their mycorrhizal symbionts dominate the
dynamics of forest ecosystems, the treecosm is the minimum system that
displays all the major forest ecosystem processes. Assembling such a
large system from soil, litter, and a nursery tree would probably be
easier than excising a large block of soil, but might increase the
equilibration period and reduce realism. The size of the system could
be reduced by using a seedling rather than a sapling tree, but the
effects of this change are unknown. It would be highly desirable to
establish that inexpensive and rapidly responding parameters such as
nutrient leaching from soil cores are not only indicative of system
stress, but are predictive of changes in forest production or other
socially valued parameters. In the absence of such an ideal test, the
treecosm should be developed as a confirmatory test for forest
ecosystem responses to stress.
(5) Outcrops. This system consists of excised sections of small
isolated communities that have developed in depressions on rock out-
crops (McCormick and Platt 1962). The excised sections are arranged
in a concrete trough, which is sloped to provide drainage. The major
appeal of this system is that an entire, clearly defined, simple
community is recreated. However, this community type is not suffi-
ciently common to support harvesting for TSCA testing or to be consi-
dered an important community type. In addition, the peculiar hydro-
logy of these systems makes them unrepresentative of most terrestrial
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ecosystems. These considerations preclude the development of this
system as a testing protocol.
4.2.7 Summary
There has been no consistent line of laboratory system
(microcosm) development oriented toward ecosystem processes that would
lead to a clearly useful test system for ecosystem processes. Most
terrestrial microcosms have been developed to suit the needs of a
specific research program rather than as generally applicable testing
tools. In addition, most microcosm research has been concerned with
the transport and fate of chemicals rather than with their effects.
Therefore, only a simple ecosystem-level test system can be recom-
mended for immediate use.
This system would consist of soil with and without a realistic
organic amendment. Parameters measured would include C02 efflux,
nitrogen mineralization, and nitrification. A system of this type
(see Suter 1981b, for a proposed protocol) would be reasonably rapid
and inexpensive while providing a realistic test of ecologically
important and relatively well-understood processes of terrestrial
ecosystems. Because of the considerable experience of microbial
ecologists with this type of system, development would consist
primarily of determining whether soil characteristics can be defined
to give comparable results among different laboratories. This
exercise should include the development of positive controls.
Considerably more development will be required before more
complex microcosms can be used as test systems. Basic questions about
microcosm design, optimum size, and the importance of components such
as litter, plants, and animals remain unresolved. It is still not
clear that microcosms display important responses to chemicals that
are not apparent in or predictable from simpler plant, animal, and
microbe toxicity tests. Parameters such as nutrient leaching rates
and the frequency distribution of community C02 exchange must be
better understood in terms of their mechanisms and responses to
chemicals before they can be used in standardized predictive test
systems.
Development of microcosms as test systems must proceed by an
orderly consideration of component interactions. Physical and
biological components (including the microflora) should be treated as
elements in a factorial design. A few well-studied pesticides or
other chemicals should be used as surrogates for TSCA-regulated
chemicals to maximize the bases for validation and comparison with
standard test systems. Such a program would provide a firm basis for
support of test protocol.
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West, D. C. , S. B. McLaughlin, and H. H. Shugart. 1980. Simulated
forest response to chronic air polluation stress. J. Environ.
Qua!. 9:43-49.
White, J. , and J. L. Harper. 1970. Correlated changes in plant size
and number in plant populations. J. Ecol. 58:467-485.
Windle, P. N. , and E. H. Franz. 1979. The effects of insect
parasitism on plant competition: Greenbugs and barley. Ecology
60(3):521-529.
Witkamp, M. 1976. Microcosm experiments on element transfer. Int. J.
Environ. Stud. 10(1):59-63.
Yoda, K. , T. Kira, H. Ogawa, and H. Hozumi. 1963. Self-thinning in
overcrowded pure stands under cultivated and natural conditions.
J. Biol. Osaka City Univ. 14:107-129.
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MATHEMATICAL MODELS USEFUL IN CHEMICAL HAZARD ASSESSMENT
L. W. Barnthouse
Environmental Sciences Division
Oak Ridge National Laboratory
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SECTION 5
MATHEMATICAL MODELS USEFUL IN
CHEMICAL HAZARD ASSESSMENT
Mathematical models and laboratory test systems are similar in
that both can be viewed, for the purpose of hazard assessment, as ana-
logues of natural ecosystems. However, they are not interchangeable.
Whereas laboratory systems are composed of real organisms,
mathematical models consist solely of mathematical representations of
organisms. Thus, models are more tenuously connected to reality than
are laboratory systems. Many alternative models (in principle, an
infinite number) of any real ecosystem are possible. Moreover,
similar, and equally plausible, models of the same ecosystem can yield
radically different predictions about the response of the system to
chemical stress. The all-important subject of model validation has in
the past received too little attention and is the single greatest
limitation on the use of mathematical models in hazard assessment.
According to Shugart and O'Neill (1979), model validation is the most
important problem remaining in the field of ecological modeling. The
limitless variety of models and modeling methods confers advantages as
well as disadvantages. In comparison to laboratory test systems,
mathematical models are extraordinarily versatile. The number and
identity of components included in a model, the detail with which each
component is modeled, and the method used to analyze the model can be
tailored to the specific needs of each hazard assessment.
This section focuses on general types of models rather than on
specific models. There are two reasons for this emphasis. First, the
number of types of models is far smaller than the number of individual
models. The various types of models differ in applicability and
practicality to a greater extent than do different models within the
same type. Moreover, describing the characteristics, advantages, and
disadvantages of types of models provides insights that can facilitate
the design and evaluation of future models. Second, different types
of models are required for different purposes. Many ways exist in
which models can be used to evaluate hazards, from initial screening
of classes of substances for potential effects to site-specific evalu-
ations of specific substances. Selecting the best models for any
given assessment involves both technical and nontechnical decisions
that can only be made by persons involved in that assessment.
Relatively little work has been done on developing and applying
mathematical models to predict effects of toxic substances on multi-
population systems and ecosystems. Many existing ecosystem simulation
models and environmental fate models could be modified for toxic
effects prediction. In addition to relatively complex simulation
models, broad classes of simpler, more generalized models and modeling
methodologies appear to be potentially useful in toxic effects assess-
ment. Many of these models are not useful for site-specific
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assessments, and their predictions are primarily qualitative rather
than quantitative. However, where applicable (e.g., in preliminary
screening), they can be much more rapidly and inexpensively applied
than can detailed simulation models.
In addition to the types of models available, evaluation criteria
are discussed in this section. These criteria are important because
they are needed (1) for judging the usefulness of models proposed for
chemical effects assessment, (2) for designing future models, and (3)
for deciding how specific models can best be utilized. These criteria
relate not only to the properties of the models, but also to the match
between the capabilities of the models and the objectives of hazard
evaluation schemes. Much of the information discussed in this section
is the result of a workshop, Mathematical Models Useful in Toxicity
Assessment, sponsored by ORNL and EPA (Barnthouse 1981).
5.1 Available Models and Modeling Methodologies
During the workshop on mathematical models, three general
categories of potentially useful models were discussed:
1. Ecosystem simulation models.
2. Generalized multipopulation models.
3. Alternative methodologies.
This section contains brief descriptions of the types of models and
methodologies included in each category and of the advantages and dis-
advantages of each type for predicting the effects of chemical
substances.
5.1.1 Ecosystem Simulation Models
Of the various kinds of models that can be used to predict
effects of chemical substances on multipopulation complexes and
ecosystems, ecosystem simulation models are the best known and the
only kind to have had significant practical applications to date.
They incorporate far more detailed representations of abiotic and
biotic processes than do the other models discussed here. The major
advantage of this detail is that the physical and chemical processes
that govern the fate of chemical substances in the environment and the
biological processes that govern the effects of these substances on
organisms can be more realistically modeled. However, the complexity
necessitated by this detail makes these models comparatively difficult
and expensive to use. They frequently require extensive modification
to be implemented on a computing system other than the one for which
they were designed. These models are difficult for persons other than
the original developers to use, unless extensive documentation (which
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is rare) is available. Perhaps more important, large amounts of
relatively costly data are required to calibrate ecosystem simulation
models. Reference data sets that can be used to calibrate models and
to verify predictions made by models will be required before ecosystem
simulation models can be profitably used in risk assessments.
(1) Terrestrial simulation models. Local, regional, and even
global-scale models have been developed to predict the transport and
fate of anthropogenic materials in terrestrial ecosystems. The best
developed of these are the local-scale cycling models that are used to
predict doses to man resulting from radioisotope releases (Hoffman et
al. 1977). Regional-scale models of DDT cycling and bioaccumulation
have been used in legal proceedings related to DDT regulation
(Harrison et al. 1970). Global-scale models are now being developed
to assess and predict changes in atmospheric C02 levels due to fossil
fuel combustion (Emanuel et al. 1980). The local and regional models
can be used to predict the transport and bioaccumulation of chemical
substances, provided that sufficient information on the relevant
chemical properties of the substances is available. A major
disadvantage of all these models is that they assume that the modeled
substance behaves like a tracer and has no effects on the modeled
system. All would require substantial modification and validation to
predict effects.
Nonlinear ecosystem simulation models such as the biome models
developed under the auspices of the International Biological Program
(IBP) (e.g., Innis 1972) can, at least in principle, be used to
predict chemical effects. The most important limitation to their use
is that unusually large quantities of data are required to calibrate
them. Even when calibrated, independent data sets (not usually
available) are required for validation, i.e., to show that they can
accurately predict the effects of stress on ecosystems.
Forest succession models (e.g., Shugart and West 1977) are now
being used to simulate the effects of SO and forest management
practices on the structure and productivity of forests. These models
require minor modifications to predict effects of chemical substances,
and the predictions made (changes in timber yield) are socially
relevant. Data requirements are less severe than for IBP-type models,
but only soil compartments and vegetation are modeled. Although
effects on animals of forest successional changes caused by chemical
substances may be indirectly inferred from model predictions, they
cannot be predicted directly.
Other succession models have been used to evaluate environmental
impacts on naturally occurring forests. Botkin (1973, 1977)
considered the effects of C02 enrichment on plant growth and
subsequent effects on forest dynamics. Mclaughlin et al. (1978) and
West et al. (1980) conducted model experiments on chronic air
pollution stress expressed as a change in growth rates of
pollution-sensitive trees.
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A review of forest succession models by Shugart and West (1980)
concluded that forest succession models can provide a necessary
adjunct to laboratory-based assessments of environmental effects and
that models will become increasingly important, tools for prediction if
human activities alter environmental conditions on a global scale.
(2) Aquatic Simulation Models. Many models have been developed
to simulate the transport and fate of materials in aquatic ecosystems
(e.g., Smith et al. 1977; Mogenson and Jorgensen 1979; Fagerstrom and
Asell 1973). Some of these were constructed specifically to predict
the transport and fate of chemical substances such as pesticides,
PCBs, and heavy metals. Like the corresponding terrestrial fate
models, they cannot be used to predict the effects of chemical
substances on ecosystems. They must be modified or coupled to a model
that can predict effects.
Nonlinear ecosystem simulation models exist for most types of
aquatic ecosystems (e.g., Park et al. 1975; Scavia et al. 1976; Steele
and Frost 1977; Kremer and Nixon 1978). Many of these models are
detailed enough so that effects of chemical substances on organismal
physiology can be extrapolated to population and ecosystem effects.
The lower trophic levels (phytoplankton and zooplankton) are generally
modeled in the greatest detail, and success at validating model
predictions has been greatest at these levels.
A few models are now being developed that incorporate both
sufficient physical and chemical detail to predict the fate of
substances and enough biological detail to predict effects (e.g.,
Falco and Mulkey 1976). None of these models has been applied to
date.
5.1.2 Generalized Multipopulation Models
Ecosystem simulation models are intended to be realistic repre-
sentations of particular ecosystem types. Modifying them to model a
different ecosystem can be time-consuming and expensive.
Alternatively, it is also possible to construct simple, highly
generalized multipopulation models that can be rapidly and
inexpensively tailored to fit any system of interacting populations,
aquatic or terrestrial. Using this modeling strategy, no attempt is
made to model every component of an ecosystem; only those processes
believed to be critically important are modeled. Transport phenomena
are not incorporated in these models. Thus, they can be used to
predict the effects of chemical substances on systems, but not the
fate of those substances. These models are not thought to be
appropriate for detailed chemical- and site-specific hazard
assessments. They can be used in the early stages of an assessment to
rapidly explore the possible effects of toxic substances. Results of
these preliminary studies can aid in determining whether a more
detailed modeling effort is warranted.
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These models can be classified into four groups. In order of
increasing complexity, these are:
1. Functionally simple, not environmentally coupled (e.g.,
DeAngelis et al. 1975; Canale 1970; Levin 1974; Hassell and
Comins 1976).
2. Functionally simple, environmentally coupled (e.g., Emanuel
and Mulholland 1975).
3. Functionally complex, not environmentally coupled (e.g., Hsu
et al. 1977; Travis et al. 1980).
4. Functionally complex, environmentally coupled (e.g., Craig
et al. 1979; Eggers 1975; Anderson and Ursin 1977).
Within each category, models can be either spatially homogeneous or
spatially complex and either age-dependent or not. Although many of
the cited examples were developed with particular systems of
populations in mind, the principles used can be applied to any system.
5.1.3 Alternative methodologies
In additon to ecosystem simulation models and generalized multi-
population models, several less familiar modeling methods appear to be
potentially useful in hazard assessment. Two of these, loop analysis
and time-averaging, are methods of analyzing the qualitative behavior
of systems of coupled differential equations. They could be applied
to many of the generalized multipopulation models discussed in the
previous section. A third method, input-output analysis, is a method
of econometric analysis that has been modified for use in ecology. In
addition to these newly developed methods, the well-developed (but
infrequently applied) theory of population genetics may be useful in
predicting the evolutionary responses of populations exposed to
chemical substances.
(1) Loop analysis. Loop analysis (Levins 1974; Lane and Levins
1977) can be used to analyze partially specified systems of equations
(i.e., systems in which the patterns of interaction among the
component variables are known, but parameter values and functional
forms are not). The definitions of the variables are entirely
arbitrary (e.g., they can be populations, aggregated groups of
populations, life-stages, or even physiological rates). Loop analysis
has been used in theoretical studies of eutrophication (Lane and
Levins 1977), but has not been used to predict effects of chemical
substances. It can be used to predict the response of a
multipopulation system to an applied stress, to identify critical
parameters that should be measured, and to identify system properties
that enhance or reduce impacts.
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(2) Time-averaging. Time-averaging (Levins 1979) was designed
to be complementary to loop analysis. In loop analysis, the system
being modeled is assumed to be at, or at least close to, equilibrium.
If the natural system being modeled is in reality far from
equilibrium, conclusions drawn from loop analysis may not be valid.
In contrast, time-averaging assumes that the system is fluctuating and
is not at equilibrium.
Like loop analysis, time-averaging can be applied to any system
of interacting populations or aggregates of populations. However,
instead of focusing on average population (or aggregate population)
sizes as in loop analysis, time-averaging focuses on the variances and
covariances of the population sizes. In theory, measurements of these
variances and covariances, and changes in variances and covariances in
response to inputs of chemical substances, may be used to distinguish
populations that are directly affected by a chemical substance from
those that are indirectly affected. This application of
time-averaging may be especially useful in interpreting the results of
microcosm experiments.
(3) Input-output analysis. Input-output analysis is an
econometric method that has been adapted for use in ecology (Hannon
1973; Finn 1976; Lettenmaier and Richey 1978). It has been used to
compare material cycling patterns in different ecosystems. The
analysis can be applied either to whole ecosystems or to subsystems
within ecosystems. It has been hypothesized that structure and
cycling indices derived by using input-output analysis may be useful
as indicators of environmental stress. In theory, input-output
analysis can be used to predict changes in material flow patterns in
response to stress, but further development and testing are required
before it is known whether this is feasible in practice.
(4) Population genetics models. Population biologists have used
a variety of models to study the evolution of populations and systems
of interacting populations in response to changes in their
environments (e.g., Kimura and Ohta 1971). All of these models relate
rates of changes in gene or phenotype frequencies to selective
pressure, heritability, and genetic variance within populations. They
can be used to predict adaptive responses of species to toxic
substances and to predict the effects of those responses on population
size, location, behavior, and interactions with other species.
Although population genetics models have not been used to predict
the effects of chemical substances on populations, they are
potentially valuable for this purpose because populations in nature
frequently evolve in response to exposure to chemical substances.
Pesticide tolerance in insects and antibiotic resistance in pathogens
are notorious examples. Practical applications would require
experimental work to measure the genetic variances in tolerance within
and between populations for species of interest and to estimate
selection intensities in the field.
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5.2 Criteria for Evaluating and Selecting Models
No existing models have been demonstrated to be useful for
predicting the effects of toxic substances on ecosystems. Moreover,
no single model or model type can fulfill all regulatory needs. For
this reason, one task at the workshop on Mathematical Models Useful in
Toxicity Assessment was development of criteria that could be used to
evaluate the usefulness of existing models, modified versions of
existing models, and new models. These criteria include not only the
properties of the models themselves, but also the match between the
capabilities (and deficiencies) of the models and the objectives of a
hazard assessment scheme. The criteria selected are defined as
follows:
1. The degree of modification required for handling toxic
material inputs. Can toxic material inputs be modeled
directly? Are the physical and chemical processes that
govern the transport and fate of toxic materials included in
the model? Are the biological processes directly affected
by toxic materials included in the model?
2. Data requirements. Is the amount of data required for para-
meterizing the model consistent with the available resources
(i.e., time and money)?
3. Generality. Can the model be used for only one geographic
region or ecosystem type, or can it be easily applied to
others?
4. Ease of validation. Has the model been validated against
baseline data? Are the output variables (i.e., those that
must be measured to test the model's predictions) easily
measurable? Do modifications required for handling toxic
materials invalidate the model? Can the model be tested
with microcosm systems and with field data?
5. Social relevance. Is the model output relevant to
regulatory needs?
6. Relevance to monitoring. Does the model suggest an environ-
mental monitoring protocol? For example, does it suggest
indicator variables that are easily measurable and that
could be used as early warnings of environmental effects.
7. Spatial/temporal scales. Do the spatial and temporal scales
of the model match the basic impact scale?
8. Ease of use. Is the model documentation comprehensible,
consistent, and complete? Is the computer code readily
available? How much modification is required to implement
the code on a different computer system?
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9. Acceptance by the scientific community, especially the
ecological community. Is the model based on biological
ideas and mathematical procedures accepted by most of the
ecological community?
Figure 5.1 presents a scheme that could be used to identify
specific models for use in such evaluations. The scheme uses aquatic
ecosystem simulation models as 'examples, but it could apply equally to
any type of model. It is exceedingly important to note that the
choice of the best model(s) for any given hazard assessment involves a
number of decisions that require active participation by the Office of
Toxic Substances. These decisions include formulating the specific
legal or social questions that the model will be expected to answer
and specifying whether the purpose of the assessment is the screening
of many substances for potential effects or the detailed evaluation of
particular substances in connection with regulatory actions.
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(IDENTIFICATION)
IDENTIFY SPECIFIC
REQUIREMENTS AND
OBJECTIVES, i.e.,
Law and/or Social
Relevancy
"CONSIDERATIONS"
1. SCREENING: SIMPLER
MODELS ADEQUATE
2. DISPUTED RULINGS:
MORE COMPLEX
3. POLITICAL AND LEGAL
CONSTRAINTS
"ALTERNATIVES"
FATE
MODELS
I
ECOSYSTEM
MODELS
I
CRITERIA FOR IDENTIFICATION
AND DEVELOPMENT
1. INCORPORATE BIOLOGIC PROCESS
LEVELS
2. - ALL TROPHIC LEVELS
3. PORTABLE
4. DOCUMENTED (ESPECIALLY PEER
LITERATURE)
5. CAPABLE OF "TRADITIONAL" +
FATE = EFFECTS
_ E. G. CLEANER
oc
UJ
oc
LL
HYDROCOMP
TETRATECH
EFFECTS
EXAMS
PEST
MODEL SELECTION
1. BASED ON BENCHMARK DATA SET
FROM EITHER "FIELD" OR "MANUFACTURED
TEST" DATA
2. BY AN INDEPENDENT PANEL (EPA SELECTION)
i
IDENTIFICATION OF SPECIFIC MODEL
FOR SPECIFIC TASK
FIGURE 5.1 SCHEME FOR SELECTING APPROPRIATE MODELS FOR USE
IN HAZARD ASSESSMENTS.
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5.3 References
Andersen, K. P., and E. Ursin. 1977. A multispecies extension to the
beverton and holt theory of fishing, with accounts of phosphorus
circulation and primary production. Medd. fra Dan. Fisk.
Havunders. 7:313-345.
Barnthouse, L. W. 1981. Mathematical models useful in toxicity
assessment. IN Hammons, Anna S. (ed.) Ecotoxicological Test
Systems; Oak Ridge National Laboratory, (in press).
Botkin, D. B. 1973. Estimating the effects of carbon fertilization on
forest composition by ecosystem simulation: IN G. M. Woodwell,
and E. V. Pecan (eds.). Carbon and the Biosphere. Proceedings
24th Brookhaven Symposium in Biology. CONF-720510, National
Technical Information Service, Springfield, Va. pp. 328-344.
Botkin, D. B. 1977. Forests, lakes, and the anthropogenic production
of carbon dioxide. BioScience 27:325-331.
Botkin, D. B. , J. F. Janak, and J. R. Wallis. 1972. Some ecological
consequences of a computer model of forest growth. J. Ecol.
60:849-872.
Canale, R. P. 1970. An analysis of models describing predaor-prey
interactions. Biotechnol. Bioeng. 12:353.
Craig, R. B., D. L. DeAngelis, and K. R. Dixon. 1979. Long- and
short-term dynamic optimization models with application to the
feeding strategy of the loggerhead shrike. Am. Nat. 113:31-51.
DeAngelis, D. L., R. A. Goldstein, and R. V. O'Neill. 1975. A model
for trophic interaction. Ecology 56:881-892
Effers, D. M. 1975. A Synthesis of the Feeding Behavior and Growth
of Juvenile Sockeye Salmon in the Limnetic Environment. Ph.D.
dissertation, University of Washington.
Emanuel, W. R. , and R. J. Mulholland. 1975. Energy based dynamic
model for Lago Pond, Georgia. IEEE trans. Autom. Control.
AC-20:98-101.
Emanuel, W. R., J. S. Olson, and G.G. Killough et al. 1980. The
expanded use of fossil fuels by the U.S. and the global carbon
dioxide problem. J. Environ. Manage. 10:37-49.
Fagerstrom, T. , and B. Asell. 1973. Methyl mercury accumulation in
an aquatic food chain. A model and implications for research
planning. Ambio 2:164-171.
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167
Falco, J. W. , and L. A. Mulkey. 1976. Modeling the effect of
pesticide loading on riverine ecosystems. IN Ott, W. R., (ed.),
Environmental Modeling and Simulation. EPA-600/ 9-76-016/.
Finn, J. T. 1976. Measures of ecosystem structure and function
derived from analysis of flows. J. Theor. Biol. 56:363-380.
Hannon, B. 1973. The structure of ecosystems. J. Theor. Biol.
41:535-646.
Harrison, H. L., et al. 1970. Systems studies of DDT transport.
Science 170:503-508.
Hassell, M. P., and H. N. Comins. 1976. Discrete time models for
two-species competition. Theor. Popul. Bio. 9:202-221.
Hoffman, F. 0., C. W. Miller, D. L. Shaeffer, and C. T. Garten, Jr.
1977. Computer codes for the assessment of radionuclides
released to the environment. Nuclear Safety 18:343-354.
Hsu, Hubbel, and Waltman. 1977. A mathematical theory of single
nutrient competition in continuous cultures of microorganisms.
SIAM J. of Appl. Math. 32:366-383.
Innis, G. S. 1972. Simulation models of grassland and grazing lands.
Prep. No. 35, Grassland Biome, Natural Resource Ecology
Laboratory, Colorado State University, Fort Collins.
Kimura, M. , and T. Ohta. 1971. Theoretical aspects of population
genetics. Monog. Popu. Biol. 4. Princeton University Press,
Princeton, N. J.
Kremer, J. N. , and S. W. Nixon. 1978. A Coastal Marine
Ecosystem-Simulation and Analysis. Springer-Verlag.
Lane, P. A., and R. Levins. 1977. The dynamics of aquatic ecosystems
2. The effects of nutrient enrichment on model plankton
communities. Limnol. Oceanogr. 22(3).-454-471.
Lettenmaier, D. P., and J. E. Richey. 1978. Ecosystem modeling: A
structural approach. J. Environ. Eng. Dive., Proc. Am. Soc.
Civ. Eng. 104:1015-1021.
Levin, S. A. 1974. Dispersion and population interaction. Am. Nat.
108:207-228.
Levins, R. 1974. The qualitative analysis of partially specified
systems. Ann. N.Y. Acad. Sci. 231:123-138.
Levins, R. 1979. Coexistence in a variable environment. Am. Nat.
114:765-783.
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168
Mclaughlin, S. B. , D. C. West, H. H. Shugart, and D. S. Shriner.
1978. Air pollution effects on forest growth and succession:
applications of a mathematical model. IN H. B. H. Cooper, (ed),
Proceedings, 71st Meeting of the Air Pollution Control
Association. Document No. 78-24.5 APCA., Houston, TX.
Mogenson, B. and S. E. Jorgensen. 1979. Modelling the distribution
of chromium in a Danish firth. IN S. E. Jorgensen, (ed), Proc.
1st International Conference on the State of the Art in
Ecological Modelling. Copenhangen 1978.
Park, R., et al. 1975. A generalized model for simulating lake
ecosystems. Contribution No. 152, Eastern Deciduous Forest
Biome, U. S. International Biological Program. Simulation
Councils, Inc.
Scavia, D. B. , J. Eadie, and A. Robertson. 1976. An Ecological Model
for Lake Ontario. Model formulation, calibration, and
preliminary evaluation. NOAA Technical Report ERL 371-GLERL 12.
Shugart, H. H. , and R. V. O'Neill (eds.). 1979. Systems Ecology.
Benchmark Papers in Ecology 9. Dowden, Hutchinson, Ross, Inc.
Stroudsburg, Pa.
Shugart, H. H., and D. C. West. 1977. Development of an appalachian
deciduous forest succession model and its application to
assessment of the impact of the chestnut blight. J. Environ.
Managem. 5:161-179.
Shugart, H. H. , and D. C. West. 1980. Forest succession models.
BioScience. 30:308-313.
Smith, J. H. , et al. 1977. Environmental pathways of selected
chemicals in freshwater systems, Part I. EPA 600/7-77-113.
Steele, J. H. , and B. W. Frost. 1977. The structure of Plankton
communities. Philos. Trans. R. Soc. , London, Ser. B.
280:485-534.
Travis, C. C. , W. M. Post, and D. L. DeAngelis. 1980. Analysis of
compensatory Leslie matrix models for competing species. Theor.
Pop. Biol. (in press).
West, D. C. , S. B. McLaughlin, and H. H. Shugart. 1980. Simulated
forest response to chronic air pollution stress. J. Environ.
Qua!. 9:43-49.
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169
APPENDIX A*
SUMMARY TABLE OF
AQUATIC TEST SYSTEMS
^Complete references can be found in Section 3.7.
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171
APPENDIX A
SUMMARY TABLE OF AQUATIC TEST SYSTEMS
Author(s)
A COMPETITION
Fielding and Russel 1
1976
Fisher et al 1974
Frank 1957
Goulden and Horning 1980
Hansen and Hubbell 1980
Kindig 1979
Klotz et al 1976
Lange 1974
Marshall 1969
Mickelson et al 1979
Mosser et al 1972
System components
Algae (Ectocarpus
s i lieu losis , Ulothrix
f lace a , Erythrotrichi a
carnea) in batch
culture
AT gae (Dunal lei la
tertiolecta , Thdlassio-
s i ra pseudonana ) J n
batch and continuous
cul ture
Daphn i a magna ,
D pul icaria
Daphnia galeata men-
dotae, Bosmina
longi rostn s
Bacter i a (Eschenchia
col i , Pseudomonds
aerugi nosa) in continu-
ous culture
Algae (Scenedesmus sp ,
Anabaena sp , Chlorella
sp , Anki strodes-mus sp ,
Selenastrum sp 'i in
batch cultures
Algae (Chlorel Id sp ,
Achnanthes deflexa)
in semiconti nuous
culture
Algae (Microcyst is
aerugi nosa, Most oc
muse or urn, Phormidi urn
foueolarum) in batch
culture
Daphnia magna ,
D pulex
Algae ( Thai ass i os i ra
gravida, SkeJetonema
cos tat urn, Chaetoceros
septentrionalisj in
continuous culture
Al gae ( Thai ass i os i ra
pseudonana , Dunal iel la
tertiolecta) in batch
cultures
Measured responses Duration of expt Expt' 1 variables Validation
Final yield (biomass) 35 days Cu
Population density 7 to 16 days PCB
Population density, 60 days Food source
size classes, sex
rat'os, ft ot ephipp'a,
# of shed parthenogemc
egg5
Population density, 64 to 108 days
age classes, mortality
Population density 60 to 120 h Nutrient concen-
tration , di luti on
rate
Population density, 32 to 58 days Streptomycin
opti ca 1 dens ity
Population density 7 days Sewage treatment
plant effluent
Population density, 31 days
pH, COD
Population density, 100 weeks Gamma radiation
# gravid females,
# males , # shed
ephippia, # eggs in
brood chambers
Population density 10 days Dilution rate
Population density, 4 days PCB, DDT
biomass
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172
Author(s)
Muller and Lee 1977
Russel 1 and Fielding
1974
Tilman 1977,
Titman 1976
B PREDATION
Akre and Johnson 1979
Baker and Modde 1977
Bethel and Holmes 1977
Brandl and Fernando 1974
Confer 1971
Confer and Blades
Confer et al 1978
Cooke 1971
Coutant 1973
Coutant et al 1974
Deacutis 1978
System components
Dilate (Euplotes
vannus) , nematode
(Chrom.idori na germanica),
foramam feran (Al logromia
latico 1 1 an s)
See Fielding ana
Russell 1976
Alqae [Aster i one 1 la
formos a , CycloteTia
meneghi rnana) ir semi-
conti n jous cul ture
Zooplankton
(Anomalaqrion hastatum.
D ap _h n \ a roag_na,
S.|mocep_ha_1_us yet u ]_ u s )
Largemouth bass ,
bl uecji 1 1 , bl ackt ai 1
shiner
Amph ipods , ducks ,
muskrats
Zoopl ankton
(AcanthocycTops
vernalis, Cenodaphnia
reticulata)
vernalis, Mesucyclops
edax, natural
communities)
Zooplankton
(Mesocycjops edax,
Diaptomus f loridanus)
Bluegi 1 1 , Daphnia
natui al~copepods
Lake trout, brook
trout, Daphjija magna,
D pjJjex
Newt, frog tadpole,
gravel
Rainbow trout,
chinodk salmon
Largemouth bass ,
channe 1 catf i sh
Killifish, Atlantic
•%ilve)side, flounder
Measured responses
Population density
Fi nal yi e d (bioma^s 1
Population density
Prey surv i val
Prey survival
Prey surv i val
Prey survival
Prey survival
Reactive distance.
Reacti ve di stance
# Attacks,
# captures
Prey survi val
Prey survival
# Attacks,
# captures
# escapes
Durati on of expt Expt1 1 variables Val idation
42 days Food density
35 days Light, tempera-
ture, sal im'ty
42 days Nutrient concen-
tration , dil uti on
rate
12 h Prey density,
prey species,
predator hunger
15 mm Stain
5 to 15 mi n Para; itism
(ducks) 24 h
(muskral s)
5 days Predator- diet,
prey si £e
2 to 5 days Prey size,
prey density
Seconds Prey size, liqht
Seconds Prey si ze, prey
pigmentation,
predator hunger,
light, aquarium
shape
1 to 5 imn DDT
3 to 10 min Heat shock
30 mm Cold shock
30 mm Heat shock
-------
173
Author(s)
Orenner et al 1978
Eisler 1973
Farr 1977
Farr 1978
Goodyear 1972
Gerritsen 1978
Herling and Witt 1967
Kama and O'Hara 1974
Kerfoot I977a, b
Landry 1978
Li and Li 1979
Luckinbill 1973
Luckinbil! 1974
Mullin 1979
O'Brien et al 1976
System components
Gizzard shad,
natural zoopl ankton
cofnmuni ty
Gastropod drill, mussel
Ki 1 1 i f i sh , gi ass
shrimp, sand
Ki 1 1 i f i sh, grass
shrimp, sheepshead
mi nnow, sand
Larqemouth bass ,
mosquito fish, refuge
Zooplankton I Cyclops
scutifer, Chdoborus sp }
Bowfin, Carious prey
(fish)
Largemouth bass ,
mosquito fish, refuge
Zooplankton (Cyclops
bicuspidatus. C
vernal i s , Bo'>mi na
lonqi rostri s
-or-
Epischura nevadensis,
Bosmina longi rostri s ,
gap h n i a ambigua,
Ceriodaphm a sp )
Zoop lank ton (Labjdocera
tr i spi nosa , Acartia
clausi , A tonsa,
Paracalanus parvis.
Cal anus paci f icus
Zooplankton (Acantho-
cyc lops vernal is,
roti f ers , cl adocerans ,
copepods)
Protozoa (Didimum
nasutum, Parameci um
aurelia)
Protozoa (Did? mum
nasutum, Parameci um
aurel la)
Zooplankton (Tortanus
discaudatus, Acartia
clausi )
Bluegil 1 , Daphnia
Measured responses Duration of expt
Prey survival 1 to 13^ h
Prey survival, 28 to 32 days
# attacks,
predator egg production
Prey survival , 3 h
capture time
Prey survival b days
Prey survival 20 days
Prey survival 4 h
Prey survival 24 h
Prey survi val 60 h
ft Encounters , S h
ft attacks,
ft injuries,
ft i ngestions
Prey survival 3 days
Prey survival 24 h
Prey survival 24 h
Population density 6 to 33 days
Population density, 3 to 80 h
population extinction
Prey survival 6 to 8 h
Prey selection Seconds
Fxpt' 1 variables Val idation
Crude 01 1 ,
01 1 di spersant
Methyl parathion,
ethyl parathion
Methyl parathion
Gamma radiation
Prey size
Disease,
parasi ti sm
handl ing stress
Hg
Prey 'nstar
Prey speci es ,
prey size
Prey species,
prey size,
prey densi ty ,
jar size,
anesthesia
Prey species ,
prey size
Bacterial food,
methyl cellulose
Culture volume
Prey instar,
jar size
Prey size
-------
174
Author(s)
Salt 1967
Salt 1968
Salt 1974
Stein 1977
Stein and Magnuson 1976
Sullivan et al 1978
Sylvester 1972
Sylvester 1973
Tagatz 1976
Thompson 1978
Van den Ende 1973
Vaughan 1979
Veilleux 1979
Vmyard and O'Brien 1976
Ward et al 1976
Ware 1972
Werner 1974
Werner and Hall 1974
System components
Protozoa (Woodruffia
metabolica, Paramecium
aurelia)
Protozoa (Amoeba
proteus , Paramec i urn
aurel ia)
Protozoa (Didinium
nasutum, Paramecium
aurelia)
Smal Imouth bass ,
crayfish, gravel
Smal Imouth bass,
crayf i sh, gravel
Largemouth bass, fat-
head minnow, gravel,
arti f tcaf plants
Coho salmon,
sockeye salmon
Coho salmon,
sockeye salmon
Pinfish, grass shrimp,
seagrass, sand
Damselfly nymph,
Daphnj a magna
Bacteria (Klebsiella
aerogenes) , protozoa
(Tetrahymena pyriformis)
in continuous culture
Largemouth bass,
bluegi 1 1 , refuge
Protozoa (Didinium
nasutum, Paramecium
aurel ia)
Bluegil 1, Daphnia
magna
Marsh fiddler crab
in salt marsh plots
Rainbow trout,
amphipods (Crangonyx
richmondensis, Hyalella
azteca), litter sub-
strates
Bluegil 1 , green sun-
fish, Daphnia magna
Bluegil1, Daphma magna
Measured responses Duration of expt
Population density, -100 h
generation time,
searching rate,
capture rate
Population density, -300 h
generation time,
feeding rate
Population density, 6 to 72 h
generation time,
feeding rate
Prey survival, 10 h to ' days
handling time
Prey behavior 3 days
Prey survival 3 to 7 days
Mean survival time 5 to 10 mm
Prey survival 15 mm
Prey survival 1 to 3 days
Attack coefficient, 24 h
handling time,
prey survival
Population density 1100 h
Prey survival Not specified
Population density, Not specified
f i ssi on rate , feedi rig
rate
Prey selection Seconds
Population density 6 weeks
Prey survival , 50 mm
if attacks, # captures,
handl ing time,
reactive distance
Handling time Minutes
Prey survival, 0 5 to 5 mm
reactive distance
Expt1 1 variables Validation
Prey and predator
densities
Prey <,ex,
moulting stage,
substrate type
Presence of
predator
Cd
Heat shock
Heat shock
Mi rex
Prey density,
Viral i nfection
Prey size
Insecticide
Prey density,
substrate type,
predator hunger
Prey size,
predator hunger
Prey size,
prey density
-------
175
Author(s)
Koltering et a] 1978
Wolters and Coutant 1976
Yocum and Edsall 1974
Zaret 1972
C PARASITISM
Couch and Courtney 1977
0 MIXED FLASK CULTURES
Bryfogle and McDiffett
1979
Cooper 1973
Ferens and Beyers 1972
Fraleigh 1971
Gorden 1967
Kelly 1971
Kurihara 1978a,b
(see also Sugiura
et al 1976a,b)
Leffler 1977
McConnell 1962
NcConnell 1965
Neil] 1972, 1975
Largemouth bass,
mosquito f i sh , refuge
Largemouth bass,
bluegill
Yel low perch, lake
whitefish
Fish (Melani n s
c hag re si ) , zooplankton
(Cej"j_pdaphma cornutum)
Shrimp , vi rus
Flask, water, pond
inocul urn
Water, steri le pond
sediment, pond water
with zooplankton
removed
See Gorden 1967
See Gorden 1967
Arti f i cial medium,
inoculum from Beyers'
original culture
(algae, bacteria ,
f lagel late , ostracod)
Arti f icial medi urn,
i nocul urn from lakes ,
ponds, streams
Artificial medium,
pond inoculum
Arti f ici at medium,
inoculum from aquaria,
Beyers ' cultures , ponds
Tap water, pond
i noculum
Tap water, pond
inoculum, TjJ ap i a
added later
Wei 1 water, algae,
Crustacea
Measured responses Duration of expt Expt'l variables Validation
Prey survival 10 days NH3
predator growth
Prey survival 1 to 30 mm Cold shock
# Attacks, 30 mm Heat shock
# captures
Prey survival 1 h Prey morphology
% Infection, mortality 35 days PCB
P, R, chlorophyll, 48 days Herbicide
biomass , populations
P, R 40 days Herbivorous fish
P, R, chlorophyll, 40 days Gamma radiation
biomass
P, R, chlorophyll, 80 days Phosphorus
P, R, populations, 75 days None
POM, DOM, thiamin,
glyoxylate uptake
P, R, chlorophyll, 59 weeks Temperature
carotenoids , biomass ,
alkalinity, C02 , DOM,
DIM, TOM, TIM, popu-
1 ations
P, R, biomass, popula- 140 days B-BHC, Cu
tions
P, R, populations,^ 18 weeks Temperature
Element Distribution
Index
P, R, organic matter 222 days Nutrients
P, R, fish biomass 1^ months Herbivorous fish
P, populations, >1 year Gambusia
crustacean gut predation
contents , micro-
habitat, survivorship ,
fecundity
-------
176
Author(s)
Ollason 1977
Reed 1976
Thomas 1978
Waide et al 1978
Taub (I969a,b,c, 1976,
Taub and Crow 1980,
Taub, Crow, and Hartman
1980, Crow and Taub 1979)
E PER1PHYTON COMMUNITIES
Admiraal 1977
Bott et al 1977
Cushing and Rose 1970
Gerhart et al 1977
Kedhe and VJi Ihem 1972
Kevern and Ball 1965
Mclntire et al 1964
System components
Arti f icial medium,
inoculum from horse
trough
Water, pond i nocul urn,
various substrates.
Water, inoculum from
various sources
Arti f icial medi urn,
pond inoculum
Bacteria , a! gae ,
Daphma, protozoa,
rotifers, ostracod,
artificial medium,
sediment
Recirculating sea water,
natural periphyton on
natural sediment
Reci rculati ng stream
water, periphyton
colonizers , various
substrates
Recirculating river
water, per iphyton
colonizers, glass tube
substrate
Partially recirculating
stream, per iphyton
substrate:.
Reci rculal i ng stream
water, periphyton
colonizers , snails ,
microscope slide
substrate')
Recirculati ng artifi-
cial medium, periphyton
inoculum on rock sub-
strates
Partially recirculating
stream water in wooden
troughs, qravel , peri-
phyton colonizers
Measured responses Duration of expt
Populations 60 days
Populations 20 weeks
Pigments, POM
DO, pH, temp , con- 70 days
ducti vity , turbidi ty ,
fluorescence, total P,
SRP , algal populations
Population density, 1 to 2 months
pigments, optical
density, biomass ,
productivity
Chlorophyll, 4 to 23 weeks
populations
Carbon flux, litter
decomposition, NTA
decomposition
65Zn uptake
Chlorophyll, popula- 25 days
Biomass, chlorophyll, 92 days
populati ons
Water chemistry, P, R >1 month
P, R, biomass, chloro- 2 years
phyl 1 , popul ations
Expt1 1 variables
Light levels
Nutrient enrich-
ment
Cd, ni trogen
Turbulence, 1 ight
regime, pH,
Daphma grazing
Algicide, in-
secticides ,
organic enr ich-
ment, Hg, Cd, PCB
Source ol
sediment,
nutrient
enrichment
Cu
None
Coal leachate
Grazing
Temp , light, EDTA
None
Val idation
Compared
w/natural
stream
Mclntire 1968a,b
Phinney and Mclntire
1965
As above, plus snails
Light, temp
Temp
-------
177
Author(s)
F OTHER MODEL ECOSYSTEMS
Medine et al 1980,
Porcella et al 1976
Pritchard et al 1979
Dudzik et al 1979,
Harte et al 1978, 1980,
Jassby et al 1977a,b
Hams et al 1980
Brockway et al 1979
Eggert et al 1979
Homogenized sediment,
artificial medium, air,
and sediment biota
Intact sediment core,
natural water, sediment
biota
Arti f icial medium,
natural lake plankton
Natural sediment , pond
water, macrophyte com-
munity, and associated
biota
Sand, natural sediment,
Natural sediment,
pond water, pond biota
Nutrient dynamics,
ecosystem metabolism
(Biodegradation and
contaminant transport)
Population dynamics,
nutrient concentra-
tions
Ecosystem metabolism,
nutrient dynamics,
water chemistry,
taxonomic groups
Ecosystem metabolism,
chemistry
(B7odegradat jon and
contaminant transport)
Duration of expt Expt'l variables Validation
80 to 120 days Heavy metals
8 to 21 days Organic con-
taminants
6 weeks to Phenol , NH4 , Fe
several months
2 to 12 months Arsenic, coal-
derived 01 1
Undetermi ned
2 to 4 months Organic con-
taminants
-------
179
APPENDIX B*
SUMMARY TABLE OF
TERRESTRIAL TEST SYSTEMS
^Complete references can be found in Section 4.3.
-------
181
APPENDIX 8
SUMMARY TABLE OF TERRESTRIAL TEST SYSTEMS3
Test system
Components
Measured
responses
Response
time
(days)
Perturbations
tested
4 1 Population interactions
411 Competition
(1) Microbes
Rennie and
Schmidt 1977
(2) Plants
Bennett and
Runeckles 1977
Natural popula-
tions of two
Nitrobacter
specie's in N02-
enriched soi Is
in screwcap
tubes
Seeds of grass
and clover are
seeded as mono-
cultures and a
mixture in
15 6-cm pots of
a ferti 1 ized
soil mix and
thinned to 12
plants per pot
Number
teria,
rate
of bac-
ox idation
Leaf area,
weight
number
lers,
of parts,
of til-
(3) Arthropods
(a) Drosophila
Ayala 1969
(b) Other flies
Housefly-blowfly
Pimentel et al
1965
Adult flies
added to 0.47-L
bottles with
culture medium
(simplest and
most common
system)
Number of adults,
adult weight,
ratios of weight
and numbers by
sex, wing length,
viability, length
of life stages,
and time to ex-
tinction
Blowfly-blowfly
Ullyett 1950
(c) Tribolium
Park 1957
Houseflies and
blowflies in
9 5x13 3xl9-cm
boxes with vials
of larval medium,
either singly or
in sets of 4 or
16 boxes con-
nected by plastic
tubing
Newly hatched
larvae of 2
blowfly species
are placed on
140 g of beef in
a 45x45xlO-cm
box
Adult beetles
added to 8 g of
f]our and yeast
in a shell vial
in an incubator
Numbers of
adults and time
to extinction
Larva and pupa-
Hum weight and
length, fecund-
ity, sex ratio,
mortality, number
at each life
stage.
Number of adults
and larvae and
tine to extinc-
tion
-------
182
Test system
(d) Other grain
insects
Crombie 1945
(e) Soil arthro-
pods
Longstaff 1976
Anderson 1978
Components
Adult insects or
eggs are added
to 10 g of
cracked wheat
in jars in an
incubator
Collembola are
15 7-cm2 x 3-cm
dish with a
floor of moist
plaster of Pans,
charcoal , and
yeast
Microarthropods
are removed by
drying from
intact 9x9xlO-cm
1 itter-soil
columns in
plastic con-
tainers and re"
placed with the
competing species.
Measured
responses
Number of adults,
fecundity, lon-
gevity, length,
and weight
Number of indi-
class
Number of indi-
vidual s by soi 1
horizon and
microhabitat
Response
time Perturbations
(days) Cost tested Validation
~ 365 Low
(exp)
84 to >168 low
(exp)
14 Moderate Numbers and dis-
(exp) tribution were
compared to the
field
4.1 2 Herbivory
(1) Sucking insects
(a) Aphid-alfalfa No system developed.
(b) Aphid-grain
Windle and Franz
1979
2 Cultivars of
barley planted
together and
singly, 69
seeds/25 5-cm
pot with 100
aphids/pot con-
tained by eel lu-
lose nitrate
col lars
Number of barley
leaves and fil-
lers, height, and
dry weight
Number of aphids
and damage.
(c) Whitefly-plant See 4.1 3(2)(b)
(d) Scale-plant
(2) Chewing
insects
Grasshopper-grass
Dyer and Bokhari
1976
Corn-rootworm-corn
Ortman and Branson
1976
No system devel -
oped.
Blue gramma
grass grown in
a flask of
nutrient solu-
tion with a
grasshopper
contained on the
plant top by a
screen cage.
Newly hatched
rootworms are
added to plastic
pouches of soil
containing corn
seedlings.
Changes in solu-
tion pH, plant
growth, grass-
hopper intake
rate, digestive
efficiency,
growth, and
amount of litter
cut
Growth rate and
% survival of
rootworms
14 to 42
(exp)
18
(exp)
10
(exp)
-------
183
Test system
413 Predation
(1} Microbes
Alexander 1975
Components
1010 Xanthomonas
to 10 g of
sterile and non-
sterile soil in
150 ml dilution
bottles
Measured
responses
Counts of bac-
dators
Response
time Perturbations
(days) Cost tested Val idation
3 Moderate
(2) Arthropods
(a) Parasitoid-
gall midge
Force 1970
(b) Parasitoid-
whitefly
Nechols and
Tauber 1977
McClanahan 1970
40 Baccharis
seed!ings in
small pots are
placed in
groups of 10
at weekly inter-
vals into
48x38x40-cm
screen cages
Eight adult
midges are added
with each group
of plants, and
1 to 4 species
of wasps are
added at ap-
propriate times.
Tobacco plants
were held in
1-m3 screen
cages Indi-
vidual adult
whiteflies were
allowed to
oviposite within
single-leaf cages,
and newly emerged
parasitoids were
added at different
intervals for 8
to 12 h
Eight potted
cucumbers in an
isolated 3 2x5.2-ro
section of
greenhouse were
exposed to 690
adult whiteflies,
and 15 days
later, 210 adult
parasitoids.
Pesticide was
sprayed at in-
tervals
Life table
statistics for
the wasps;
frequency of
parasitism and
multiparasitism.
Parasitization
rate; parasitoid
and whitefly
development and
mortality
Numbers of para-
sitized and ofl-
parasitized
whiteflles over
time and numbers
of adult white-
flies and para-
sitoids at ter-
mination
60 to 200
(exp)
High
75
(exp)
Oxthioquinox
-------
184
Test system
(c) Parasitoid-
aphid
Force and
Messenger 1964,
1965
(d) Predator- aphid
Murdock and Marks
1973
(e) Parasitoid-
grain moth
Benson 1974
bean weevi 1
Utida 1957
(g) Parasitoid-fly
Pimentel et al
1963
Components
Aphids were
raised on
alfalfa stems n
vials of water
within 3 5xl5-cm
glass tubes held
vertical ly in an
environmental
chamber Host
and parasitoid
densities and
number of para-
sitoid species
were varied
Single ladybird
larvae were
placed on
potted bean
plants with dif-
ferent ratios of
two aphid species
Plants were iso-
lated by "Fluon1 -
coated plastic
col lars
Moths and wasps
were raised in
90x90x75 -cm
cages with 81
cardboard trays
containing 50 g
of wheat- feed
each Nine tra>s
were replaced
each week
were raised on
beans in petri
dishes and
or two species
of parasitoids
Single
9 5x13 3xl9-cm
plastic boxes
or arrays of 16
or 30 boxes con-
nected by 0 64-cm
plastic tubes
containing vials
of fly medium,
parasitoid wasps,
and houseflies or
blowflies
Measured
responses
Parasitoid sur-
vival , develop-
ment rate,
fecundity, and
percent parasi-
tization and
super parasiti-
zation of the
host
Prey selection.
predator, and
prey behavior
Numbers of wasps
and moths by
developmental
stage
weevils and
wasps
Numbers of flies
and wasps
Response
time Perturbations
(days) Cost tested Validation
1 (hunting) Moderate
9 to 36
(development)
0 36 Moderate
(hunting)
35 Moderate
(moth life cycle)
59
(exp)
(weevil 1 ife cycle)
1050
(exp)
42 to 132 Moderate
(extinction of
single cell
system)
133 to 224
(16 cell system)
574
(30 cell system)
-------
185
Test system
(h) Ground-
dwel ling beetles
Hams and 01 iver
1979
(i) Spiders
Turnbill 1974
(j) Mites
Huffaker 1956
Huffaker et al
1963
414 Parasitism
415 Symbiosis
(1) Lichens
(2) Rhizobium-
Legume
Pareek and Gaur
1970
Components
Staphyl imd bee-
tles preyed on
horn fly eggs
and larvae on
manure pats
placed on a soi 1-
vermiculite mix-
ture on a
32x28x5-cm piece
of sod covered
with mesh
7 5-cm3
of eight 7 6x7 6x
5 6- cm trays
with "Fluon"-
coated sides con-
nected by 25x24-
cm arched plastic
bridges Preda-
tors and prey were
subadult lycosid
spiders and vesti-
gial-winged
Drosophi la
Two groups of 36
field-infected
potted straw-
berries were
arranged on
greenhouse
benches One
group was sprayed
with parathione
to el muriate the
predator
Mite herbivores
and predators
were placed on
a random subset
of 252 partially
covered oranges,
which rested in
glass coasters
on 3 wire mesh
shelves, which
were connected
by wooden dowels
No systems dis-
cussed
No systems
discussed
Inoculated
seeds sown in
pots of seived
soil that had
been sprayed
with pesticide.
Measured
responses
Number of emerg-
ing flies
Number of flies
ki 1 led
Numbers of pre-
dators and prey
Numbers of pre-
dators and prey
Plant and seed
weight and N
content, number
of nodules, and
leghaemoglobin
content
Response
time Perturbations
(days) Cost tested Validation
14 Moderate
(fly development)
6 Moderate
ing
<9 to 365 Moderate
(predator
effect seen,
length of ex-
periment)
490 Moderate
(maximum length
of experiment)
28 to 91 Moderate >40 pesticides Some field studies
(nodule forma- and ozone have been done, bui
tion-plant not to validate
maturation) laboratory studies
-------
186
Test system
(3) Mycorrhizae
Wilde and
Persidsky
1956
4 2 Ecosystems
Components
Seedlings in
pots of soil
inoculated
with mycorrhizal
fungi in a
greenhouse
Measured
responses
Plant weight,
number of fungal
propagules , % of
root length
infected
Response
time
(days) Cost
240 Moderate
(exp)
Perturbations
tested Val idation
Numerous pesti-
cides
425 Synthetic systems
(1) Soil
Atlas et al
1978
(2) Litter
Spalding 1979
(3) Litter and soil
Bond et al
1976
(4) Gnotobiotic soil
Coleman et al
1977
(5) Plant and soil
(a) Pot
Eno and Everett
1958
(b) Lichtenstein
et al. 1977
Sieved soil in a
flask, bottle,
or other con-
tainer
Sorted litter in
Sieved soil
(150 g) and
seived litter
(15 g) in a
beaker or lined
can in a gas and
temperature con-
trol system
Pseudomonas sp
Acanthamoeba sp ,
and Mesoplogas-
ter sp. in 20 g
of dried, sifted,
and sterilized
soil in a 50-mL
Erlenmeyer
flask.
7.6-L pots
of field soil
with 10 bean
plants
Layers of
toxicant-con-
taminated and
uncontaminated
homogenized soil
with corn plants,
in a 86-mm-dia-
meter, 1-L
plastic cylinder
mounted on a lea-
chate collecter
Microbe numbers,
respiration,
nutrient dyna-
mics, enzyme
assays, ATP
assays
Respiration and
Respiration,
heat output,
microbe numbers
C02 efflux, N
and P minerali-
zation and
immobilization
and numbers of
bacteria, proto-
zoa, and nematodes.
Plant germina-
tion, plant
production, mi-
crobe numbers,
respiration, and
nitrification
Toxicant fate,
(soil , plant,
and leachate),
plant biomass,
plant symptoms
1 to >100 Low
(exp)
1 to 28 Low
20 High
(exp)
14 High
(exp)
17 Moderate
(exp)
22 Low
(exp)
Many Informal , decades
chemicals of experience
indicate general
validity.
7 heavy metals None, related
field studies
20 metal salts, None, related
03, and S02 generally to
field studies
10 insecticides
Phorate, Compared to field
Stauffer N-2596, trial for N-2596
Eptam, and Phorate
-------
187
Test system
(c) Agroecosystem
chamber
Nash et al
1977
(6) Soil, litter,
and animal
(a) Odum -
Odum and Lugo
1970
(b) Witkamp
Witkamp and
Frank
1970
(c) Metcalf
Metcalf et al
1979
microcosm chamber
Gile and Gillett
1979
Response
Measured time
Components responses (days)
Crop plants Toxicant fate 35
grown on 15 cm (air, soil, (exp)
of seived soil leachate and
in a 115-cm-high plant)
x 150 cm x 50 cm
closed glass box
plant
Natural soi 1 , C02 exchange
litter, a
flowering plant,
fern, moss,
lichen, and algae
in a 16 2-cm-
diameter plastic
desiccator
Round containers Mineral nutrient 98
7 to 13-cm- dynamics, C02 (exp)
diameter by 10 to efflux, and litter
13-cm with a weight
leachate port,
soi 1 or sand,
litter, millipedes,
snai Is, and seed-
lings
Corn seedlings Pesticide fate, 20
grown in vermi- plant growth, (exp)
culite or soil and faunal
with earthworms, numbers
i sopods , slugs ,
saltmarsh cater-
pillars, and a
vole in a 19-L
wide-mouth jar
Ports in the lid
and base permit
air and leachate
sampling
glass box with fauna! lumbers, (exp)
20 cm of synthe- and vole
tic soil, alfal- behavior
fa, ryegrass ,
nematodes, earth-
worms, enchytraeid
worms, i sopods,
mealworms, crickets,
snai lst and a
pregnant vole
Ports al low air
and leachate
sampl ing
Cost tested Val idation
Moderate Toxaphene, DDT,
Si 1 vex, Zineb,
Maneb
Moderate Radiation Compared to the
radiated forest
from which the
components were
derived
Moderate
comparable to
that reported
from the field
published field
and laboratory
studies of
dieldnn
-------
188
Test system
426 Excised
(1) Soil core
Jackson et a1
1977
(2) Grassland
Jackson and
Levine 1978,
1978
(3) Sod
Campbell 1973
(4) Treecosm
Jackson et al
1978
(5) Outcrop
McCormick and
Platt 1962
Components
systems
b-cm-diameter x
5- or 10-cm in-
tact soil core
shrunk plastic
and supported on
a leachate
rollecter
core 15 to 30-cm-diani-
eter x 10 to 25-
cm grassland
plastic and
leachate collec-
ter
16-cm diameter x
7-cm-deep sod in
a nalgene jar
An intact block
of forest soil
45x45x25-cm with
one ^2~m sapling
and associated
ground flora
sealed with epoxy
i n wood boxes
Excised 90x90-cm
segments of
granite outcrop
communities
arranged in a
1x6 5-m concrete
trough
Measured
responses
Nutrient loss,
respiration,
toxicant loss,
Microbe biomass,
soil enzymes
Toxicant loss
and uptake,
nutrient loss ,
soi 1 ATP CO
flux, plant bio-
arthropod, nema-
tode, bacteria,
and fungi
Respiration,
photos>nthesis,
plant symptoms,
species abundance
Transport,
nutrient loss,
respiration,
ATP,
microbe density,
primary production
Plant growth,
plant reproduc-
tion, plant
species density,
soi 1 erosion
Response
time
(days) Cost
30 to 59 Lew
(exp)
100 to 175 Moderate
<1 (time to response Moderate
of respiration and
photosynthesis)
45
(plant damage symptoms)
300
(exp)
365 High
(exp)
Perturbations
tested Val idation
As, dieldri n, None
methyl para-
thion, 2, 4,
5-T, and hexa-
chlorobenzene
As, Cd
Radiation None
Pb smelter dust Yes, results are
qualitatively
but not quanti-
tatively con-
firmed by field
studies and
model ing
Radiation In terms of
natural proper-
ties, but not
response to
toxicants
The study cited is representative of the system type described. Measured responses and perturbations are listed for all known experiments
of each type, not just the study cited
Notes in parentheses indicate how response time was determined, (exp) indicates that the length of the experiment, which may be arbitrary,
was used.
-------
189
APPENDIX C
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191
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APPENDIX D
SECTIONAL BIBLIOGRAPHY
AQUATIC AND TERRESTRIAL TEST SYSTEMS
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3. AQUATIC TEST SYSTEMS
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plantings, PCNB, and Trichoderma harzianum on pathogen and
disease. Phytopathology 68:900-907.
Herbert, H. J. 1962. Influence of Typhlodromus (T.) Pyri Scheuten on
the development of Bryobia arborea M. & A. populations in the
greenhouse. The Can. Entomol. 94:870-873.
Moiling, C. S. 1959. The components of predation as revealed by a
study of small-mammal predation of the European pine sawfly.
Can. Entomol. 91:293-320.
Hoi ling, C. S. 1966. The functional response of invertebrate
predators to prey density. Mem. Entomol. Soc. Can. 48:1-88.
Huffaker, C. B. 1958. Experimental studies on predation: Dispersion
factors and predator-prey oscillations. Hilgardia
27(14):343-383.
Huffaker, C. B. 1971. The phenomenon of predation and its roles in
nature, pp. 327-343. IN Den Boer, P. J., and G. R. Cradwell
(eds.), Dynamics of Populations, Proceedings of the Advanced
Study Institute on Dynamics of Numbers in Populations. Centre
for Agricultural Publishing and Documentation, Wageningen.
Huffaker, C. B. , and C. E. Kennett. 1956. Experimental studies on
predation: Predation and cyclamen-mite populations on
strawberries in California. Hilgardia 26(4):191-222.
Huffaker, C. B., C. E. Kennett, B. Matsumoto, and E. G. White. 1973.
Some parameters in the role of enemies in the natural control of
insect abundance, pp. 59-75. IN Southwood, T. R. E. (ed.),
Insect Abundance. Blackwell Scientific Publications, Oxford.
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294
Huffaker, C. B. , P. S. Messenger, and P. De Bach. 1971. The natural
enemy component in natural control and the theory of biological
control, pp. 16-67. Huffaker, C. B. (ed.), Biological Control.
Plenum Press, New York.
Huffaker, C. B. , K. P. Shea, and S. G. Herman. 1963. Experimental
studies on predation: Complex dispersion and levels of food in an
acarine predator-prey interaction. Hilgardia 34(9):305-330.
Hussey, N. W., and W. J. Parr. 1965. Observations on the control of
Tetranychus urticae Koch on cucumbers by the predatory mite
Phytoselulus riegeli Dosse. Entomol. Exp. Appl. 8:271-281.
McClanahan, R. J. 1970. Intergrated control of greenhouse whitefly
on cucumbers. J. Econ. Entomol. 63:599-601.
McMurtry, J. A., and G. T. Scriven. 1968. Studies on predator-prey
interactions between Amblyseius hibisci and Oligonychus punicae:
Effects of host-plant conditioning and limited quantities of an
alternate food. Ann. Entomol. Soc. of Am. 61:393-397.
Messenger, P. S. 1964. Use of life tables in a bioclimatic study of
an experimental aphid-braconid wasp host-parasite system.
Ecology 45: 119-131.
Messenger, P. S. , and D. C. Force. 1963. An experimental
host-parasite system: Therioaphis maculata (Buckton)-Praon
palitans Muesebeck (Homoptera: Aphidae: Hymenoptera: Bracomidae).
Ecology 44:532-540.
Murdoch, W. W. , and J. R. Marks. 1973. Predation by coccinellid
beetles: Experiments on switching. Ecology 54:160-167.
Nechols, J. R. , and M. J. Tauber. 1977. Age-specific interaction
between the greenhouse whitefly and Encarsia formosa: Influence
of host on the parasite's oviposition and development. Environ.
Entomol. 6:143-149.
Nechols, J. R. , and M. J. Tauber. 1977. Age-specific interaction
between the greenhouse whitefly and Encarsia formosa: Influence
of the parasite on host development. Environ. Entomol.
6:207-210.
Olson, D. , and D. Pimentel. 1974. Evolution of resistance in a host
population to attacking parasite. Environ. Entomol. 3:621-624.
Pimentel, D. 1968. Population regulation and genetic feedback.
Science 159:1432-1437.
Pimentel, D. , S. A. Levin, and D. Olson. 1978. Coevolution and the
stability of exploiter-victim systems. Am. Nat. 112:119-125.
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295
Pimentel, D. , W. P. Nagel, and J. L. Madden. 1963. Space-time
structure of the environment and the survival of parasite-host
systems. The Am. Nat. 97:141-167.
Pimentel, D. , and F. A. Stone. 1968. Evolution and population
ecology of parasite-host systems. Can. Entomol. 100:655-662.
Roper, M. M., and K. C. Marshall. 1978. Effects of a clay mineral on
microbial predation and parasitism of Escherichia coli.
Microbiol. Ecology 4:279-289.
Salt, G. 1934. Experimental studies in insect parasitism. II.
Superparasitism. Proc. Royal Soc. London 114:455-476.
Salt, G. 1935. Experimental studies in insect parasitism. III. Host
selection. Proc. Royal Soc. London 117:413-435.
Scherff, R. H. 1973. Control of bacterial blight of soybean by
Bdellovibrio bacteriovorus. Phytopathology 63:400-402.
Schlinger, E. I., and J. C. Hall. 1959. A synopsis of the biologies
of three important parasites of the spotted alfalfa aphid. J.
Econ. Entomol. 52:154-157.
Schlinger, E. I., and J. C. Hall. 1960. The biology, behavior, and
morphology of Praon palitans Muesebeck, an internal parasite of
the spotted alfalfa aphid, Therioaphis maculata (Buckton)
(Hymenoptera: Braconidae, Aphidiinae). Ann. Entomol. Soc. Am.
53:144-160.
Schlinger, E. I., and J. C. Hall. 1961. The biology, behavior, and
morphology of Trioxys utilis, an internal parasite of the spotted
alfalfa aphid, Therioaphis maculata (Hymenoptera: Braconidae,
Aphidiinae). Ann. Entomol. Soc. Am. 54:34-45.
Starr, M. P., and N. L. Baigent. 1966. Parasitic interaction of
Bdellovibris bacteriovorus with other bacteria. J. Bacteriol.
91: 2006-2017.
Tauber, M. J., and R. G. Helgeson. 1974. Biological control of
whiteflies in greenhouse crops. N.Y. Food Life Sci. 7:13-16.
Ullyett, G. C. 1949. Distribution of progemy by Cryptus inornatus
Pratt (Hymenoptera: Ichneumonidae). Can. Entomol. 81:285-296.
Ullyett, G. C. 1949. Distribution of progeny by Chelonus texanus
Cress. (Hymenoptera: Braconidae). Can. Entomol. 81:25-44.
Utida, S. 1950. On the equilibrium state of the interacting
population of an insect and its parasite. Ecology 31:165-175.
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296
Utida, S. 1957. Cyclic fluctuations of population density intrinsic
to the host-parasite system. Ecology 38:442-449.
Utida, S. 1957. Population fluctuation, an experimental and
theoretical approach. Cold Springs Harbor Symposia on
Quantitative Biology 22: 139-151.
Van de Vrie, M. 1962. The influence of spray chemicals on predatory
and phytophagous mites on apple trees in laboratory and field
trials in the Netherlands. Entomophaga 3(3):243-250.
White, E. G. , and C. B. Huffaker. 1969. Regulatory processes and
population cyclicity in laboratory populations of Anagasta
kuhniella (Zeller) (Lepidoptera: Phycitidae). I. Competition for
food and predation. Res. Popul. Ecol. 11:57-83.
White, E. G. , and C. B. Huffaker. 1969. Regulatory processes and
population cyclicity in laboratory populations of Anagasta
kuhm'ella (Zeller) (Lepidoptera: Phycitidae). II. Parasitism,
predation, competition and protective cover. Res. Popul. Ecol.
11:150-185.
4.1.5 SYMBIOSIS
Ambler, J. R., and J. L. Young. 1977. Techniques for determining
root length infected by vesicular-arbuscular mycorrhizae. Soil
Sci. Soc. Am. J. 41:551-555.
Backman, P. A., and E. M. Clark. 1977. Effect of carbofuran and
other pesticides on vesicular-arbuscular mycorrhizae in peanuts.
Nematropica 7:13-18.
Bird, G. W. , J. R. Rich, and S. U. Glover. 1974. Increased
endomycorrhizae of cotton roots in soil treated with nematocides.
Phytopathol. 64:48-51.
Carlyle, R. E., and J. D. Thorpe. 1947. Some effects of ammonium and
sodium 2,4-dichlorophenoxyacetates on legumes and the Rhizobium
bacteria. J. Am. Soc. Agron. pp. 929-936.
Carney, J. L., H. E. Garrett, and H. G. Hedrick. 1978. Influence of
air pollutant gases on oxygen uptake of pine roots with selected
mycorrhizae. Phytopathology 68:1160-1163.
Ferry, B. W. , M. S. Baddeleym, and D. L. Hawksworth. 1973. Air
Pollution and Lichens. The Athlone Press of the University of
London. 389 pp.
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297
Fisher, 0. J. 1976. Effects of some fungicides on Rhizobium trifolii
and its symbiotic relationship with white clover. Pestic. Sci.
7:10-18.
Fisher, D. J. , A. L. Hayes, and C. A. Jones. 1978. Effects of some
surfactant fungicides on Rhizobium trifolii and its symbiotic
relationship with white clover. Ann. of Appl. Biol. 90(l):73-84.
Grossbard, E. 1970. Effect of herbicides on the symbiotic
relationship between Rhizobium trifolii and white clover, pp.
47-59. IN Symposium on White Clover Research, Queens Univ. of
Belfast, 1969.
Guttay, A. J. R. 1976. Impact of deicing salts upon the
endomycorrhizae of roadside sugar maples. Soil Sci. Soc. Amer.
J. 40:952-954.
Halliday, J., and J. S. Pate. 1976. The acetylene reduction assay as
a means of studying nitrogen fixation in white clover under sward
and laboratory conditions. J. Brit. Grassland Soc. 31:29-35.
Hawksworth, D. L., and F. Rose. 1976. Lichens as Pollution Monitors.
Edward Arnold, London. 60 pp.
Iyer, J. G. , and S. A. Wilde. 1965. Effect of vapam biocide on the
growth of red pine seedlings. J. Forestry 63:703-704.
Kochhar, M., U. Blum, and R. A. Reinert. 1980. Effects of O3 and
(or) fescue on ladino clover: Interactions. Can. J. Bot.
58(2):241-249.
Kormanik, P. P., W. C. Bryan, and R. C. Schultz. 1980. Procedures
and equipment for staining large numbers of plant root samples
for endomycorrhizal assay. Can. J. Microbiol. In press.
Kulkarni, J. H. , J. S. Sardeshpande, and D. J. Bagyaraj. 1974.
Effect of four soil-applied insecticides on symbiosis of
Rhizobium with Arachis hypogaea Linn. Plant Soil 40(1):169-172.
Letchworth, M. B. , and U. Blum. 1977. Effects of acute ozone
exposure on growth, nodulation and nitrogen content of ladino
clover. Environ. Pollut. 14:303-312.
Lin, S., B. R. Funke, and J. T. Schulz. 1972. Effects of some
organophosphate and carbamate insecticides on nitrification and
legume growth. Plant Soil 37:489-496.
Manning, W. J. , W. A. Feder, and P. M. Papia. 1972. Influence of
long-term low levels of ozone and benomyl on growth and
nodulation of pinto bean plants. Phytopathology 62(5):497.
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298
Marx, D. H. 1969. Antagonism of mycorrhizal fungi to root pathogenic
fungi and soil bacteria. Phytopathology 59:153-163.
Marx, D. H. , W. G. Morris, and J. G. Mexal. 1978. Growth and
ectomycorrhizal development of loblolly pine seedlings in
fumigated and nonfumigated soil infested with different fungal
symbionts. Forest Sci. 24:193-203.
Menge, J. A., E. L. V. Johnson, and V. Minassian. 1979. Effect of
heat treatment and three pesticides upon the growth and
reproduction of the mycorrhizal fungus Glomus fasciculatus. The
New Phytol. 82(2): 473-480.
O'Bannon, J. H., and S. Nemec. 1978. Influence of soil pesticides on
vesicular-arbuscular mycorrhizae in citrus soil. Nematropica
8:56-61.
Pareek, R. P., and A. C. Gaur. 1970. Effect of dichloro diphenyl
trichloro-ethane (DDT) on symbiosis of Rhizobium sp. with
Phaseolus aureus (Green Gram). Plant Soil 33:297-304,
Selim, K. G. , S. A. Z. Mahmoud, and M. T. El-Mokadem. 1970. Effect
of dieldrin and lindane on the growth and nodulation of V icia
faba. Plant Soil 33:325-329.
Smith, C. R. , B. R. Funke, and J. T. Schulz. 1978. Effects of
insecticides on acetylene reduction by alfalfa, red clover and
sweetclover. Soil Biol. and Biochem. 10(6):463-466.
Tu, C. M. 1977. Effects of pesticide seed treatments on Rhizobium
japonicurn and its symbiotic relationship with soybean. Bull.
Environ. Contam. Toxicol. 18(2):190-199.
4.2 ECOSYSTEMS
Gillett, J. W., and J. M. Witt (eds.). 1979. Terrestrial Microcosms.
Proceedings of the Workshop on Terrestrial Microcosms, Symposium
on Terrestrial Microcosms and Environmental Chemistry. NSF/RA
79-0034. National Science Foundation, Washington, D.C. 35 pp.
Harris, W. F. (ed.). 1980. Microcosms as potential screening tools
for evaluating transport and effects of toxic substances: Final
report. ORNL/EPA-4. Oak Ridge National Laboratory, Oak Ridge,
Tennessee. 382 pp.
Suter, G. W. 1981. Methods for measuring effects of chemicals or
terrestrial ecosystem properties. IN Hammons, Anna S. (ed.),
Ecotoxicological Test Systems: Proceedings of a Series of
Workshops, ORNL-5709; EPA 560/6-81-004, Oak Ridge National
Laboratory.
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299
Witt, J. M. , and J. W. Gillett. 1979. Terrestrial microcosms and
environmental chemistry. NSF/RA 79-0026. National Science
Foundation, Washington, D.C. 147 pp.
4.2.1 PARAMETERS
Coughtrey, P. J., C. H. Jones, M. H. Martin, and S. W. Shales. 1979.
Litter accumulation in woodlands contaminated by Pb, Zn, Cd and
Cu. Oecologia (Berlin) 39:51-60.
Jackson, D. R., and A. P. Watson. 1977. Disruption of nutrient pools
and transport of heavy metals in a forested watershed near a lead
smelter. J. Environ. Qual. 6:331-338.
4.2.2 TEST COMPONENTS
Ausmus, B. S. , and E. G. O'Neill. 1978. Comparison of carbon
dynamics of three microcosm substrates. Soil Biol. Biochem.
10:425-429.
Draggan, S. 1979. Effects of substrate type and arsenic dosage level
on arsenic behavior in grassland microcosms. Part I: Preliminary
results on 74As as transport, pp. 102-110. IN Witt, J. M., and
J. W. Gillett (eds.), Terrestrial Microcosms and Environmental
Chemistry. NSF/RA 79-0026. National Science Foundation,
Washington, D.C.
4.2.4 SIZE
Shirazi, M. A. 1979. Development of scaling criteria for terrestrial
microcosms. EPA-600 13-79-017. U.S. Environmental Protection
Agency, Corvallis, Oregon.
4.2.5 SYNTHETIC SYSTEMS
Anderson, R. V., D. C. Coleman, C. V. Cole, E. T. Elliott, and J. F.
McClellan. 1979. The use of soil microcosms in evaluating
bacteriophogic nematode response to other organisms and effects
on nutrient cycling. Int. J. Environ. Studies 13:175-182.
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300
Anderson, R. V., E. T. Elliott, J. F. McClellan, D. C. Coleman, C. V.
Cole, and H. W. Hunt. 1978. Trophic interactions in soils as
they affect energy and nutrient dynamics. III. Biotic
interactions of bacteria, amoebae, and nematodes. Microbiol.
Ecol. 4:361-371.
Atlas, R. M. , D. Pramer, and R. Bartha. 1978. Assessment of
pesticide effects on non-target soil microorganisms. Soil Biol.
Biochem. 10: 231-239.
Bartha, R. , R. P. Lanzilotta, and D. Pramer. 1967. Stability and
effects of some pesticides in soil. Applied Microbiol. 15:67-75.
Bartha, R. , and D. Pramer. 1965. Features of a flask and method of
measuring the persistence and biological effects of pesticides in
soil. Soil Sci. 100:68-70.
Beall, M. L. , Jr., R. G. Nash, and P. C. Kearney. 1976.
Agroecosystem--A laboratory model ecosystem to simulate field
conditions for monitoring pesticides, pp. 790-793. Proc. of EPA
Conf. on Environ. Modeling and Simulation. April 19-22,
Cincinnati, Oh.
Bond, H. , B. Lighthart, R. Shimabuku, and L. Russell. 1976. Some
effects of cadmium on coniferous forest soil and litter
microcosms. Soil Sci. 121(5):278-287.
Bond, H. , B. Lighthart, and R. Volk. 1979. The use of soil/litter
microcosms with and without added pollutants to study certain
components of the decomposer community, pp. 111-123. Witt, J.
M. , and J. W. Gillett (eds.), Terrestrial Microcosms and
Environmental Chemistry. NSF/RA 79-0026. National Science
Foundation, Washington, D.C.
Cole, C. V., E. T. Elliott, H. W. Hunt, and D. C. Coleman. 1978.
Trophic interactions in soils as they affect energy and nutrient
dynamics. V. Phosphorus transformations. Microbiol. Ecol.
4:381-387.
Cole, L. K. , and R. L. Metcalf. 1979. Predictive environmental
toxicology of pesticides in the air, soil, water and biota of
terrestrial model ecosystems, pp. 57-73. IN Witt, J. M., and J.
W. Gillett (eds.), Terrestrial Microcosms and Environmental
Chemistry. NSF/RA 79-0026 National Science Foundation,
Washington, D.C.
Cole, L. K. , R. L. Metcalf, and J. R. Sanborn. 1976. Environmental
fate of insecticides in terrestrial model ecosystem. Int. J.
Environ. Stud. 10:7-14.
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301
Cole, L. K. , J. R. Sanborn, and R. L. Metcalf. 1976. Inhibition of
corn growth by aldrin and the insecticides fate in the soil, air,
crop and wildlife of a terrestrial model ecosystem. Environ.
Entomol. 5: 583-589.
Coleman, D. C. , R. V. Anderson, C. V. Cole, E. T. Elliott, L. Woods,
and M. K. Campion. 1978. Trophic interactions in soils as they
affect energy and nutrient dynamics. IV. Flows of metabolic and
biomass carbon. Microbiol. Ecol. 4:373-380.
Coleman, D. C. , C. V. Cole, R. V. Anderson, M. Blaha, M. K. Campion,
M. Clarholm, E. T. Elliott, H. W. Hunt, B. Shaefer, and J.
Sinclair. 1977. An analysis of rhizosphere-saprophage
interactions in terrestrial ecosystems. Ecol. Bull. (Stockholm)
25:299-309.
Coleman, D. C. , C. V. Cole, H. W. Hunt, and D. A. Klein. 1978.
Trophic interactions in soils as they affect energy and nutrient
dynamics. I. Introduction. Microbiol. Ecol. 4:345-349.
Cullimore, D. R. 1971. Interaction between herbicides and soil
micro-organisms. Residue Rev. 35:65-80.
Domsch, K. H. 1970. Effects of fungicides on microbial populations
in soil. pp. 42-46. IN Pesticides in the Soil: Ecology,
Degradation and Movement. Int. Symp. on Pesticides in the Soil.
Michigan State University, East Lansing.
Domsch, K. H. , and W. Paul. 1974. Simulation and experimental
analysis of the influence of herbicides on soil nitrification.
Arch. Microbiol. 97:283-301.
Elliott, E. T., C. V. Cole, D. C. Coleman, R. V. Anderson, H. W. Hunt,
J. F. McClellan. 1979. Amoebal growth in soil microcosms: A
model system of C,N, and P trophic dynamics. Int. J. Environ.
Stud. 13:169-174.
Eno, C. F., and P. H. Everett. 1977. Effects of soil applications of
10 chlorinated hydrocarbon insecticides on soil microorganisms
and the growth of stringless black valentine beans. J. Environ.
Qual. 6(l):235-238.
Ghiorse, W. C. , and M. Alexander. 1977. Effect of nitrogen dioxide
on nitrate oxidation and nitrate-oxidizing populations in soil.
Soil Biol. Biochem. 9:353-355.
Gile, J. D., and J. W. Gillett. 1979. Fate of 14C-dieldrin in a
simulated terrestrial ecosystem. Arch. Environ. Contam. Toxicol.
8:107-124.
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302
Gile, J. D., and J. W. Gillett. 1979. Fate of selected fungicides in
a terrestrial laboratory ecosystem. J. Agric. Food Chem.
27:1159-1164.
Gile, J. D., and J. W. Gillett. 1979. Terrestrial microcosm chamber
evaluations of substitute chemicals, pp. 75-85. IN Witt, J. M.,
and J. W. Gillett (eds.), Terrestrial Microcosms and
Environmental Chemistry. NSF/RA 79-0026. National Science
Foundation, Washington, D.C.
Gillett, J. W., and J. D. Gile. 1976. Pesticide fate in terrestrial
laboratory ecosystems. Int. J. Environ. Stud. 10:15-22.
Greaves, M. P., H. A. Davies, J. A. P. Marsh, and G. I. Wingfield.
1976. Herbicides and soil microorganisms. Crit. Rev. Microbiol.
5(1): 1-38.
Herzberg, M. A., D. A. Klein, and D. C. Coleman. 1978. Trophic
interactions in soils as they affect energy and nutrient
dynamics. II. Physiological responses of selected rhizosphere
bacteria. Microbiol. Ecol. 4:351-359.
Jenkinson, D. S. , and D. S. Powlson. 1976. The effects of biocidal
treatments on metabolism in soil. I. Fumigation with chloroform.
Soil Biol. Biochem. 8:167-177.
Johnen, B. G. , and E. A. Drew. 1977. Ecological effects of
pesticides on soil microorganisms. Soil Sci. 123(5):319-324.
Klein, D. A. 1977. Seasonal carbon flow and decomposer parameter
relationships in a semi arid grassland soil. Ecology
58(1):184-190.
Klein, D. A., and E. M. Molise. 1975. Ecological ramifications of
silver iodide nucleating agent accumulation in a semi-arid
grassland environment. J. Appl. Meteorol. 14:673-680.
Kudeyarov, V. N., and D. S. Jenkinson. 1975. The effects of biocidal
treatments on metabolism in soil. VI. Fumigation with carbon
disulphide. Soil Biol. Biochem. 8:375-378.
Labeda, D. P., and M. Alexander. 1978. Effects of S02 and N02 on
nitrification in soil. J. Environ. Qua!. 7:523-526.
Lichtenstein, E. P. 1979. Fate of pesticides in a soil-plant
microcosm, pp. 95-101. Witt, J. M. , and J. W. Gillett (eds.),
Terrestrial Microcosms and Environmental Chemistry. NSF/RA
79-0026. National Science Foundation, Washington, D.C.
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303
Lichtenstein, E. P. , T. W. Fuhremann, and K. R. Schulz. 1974.
Translocation and metabolism of [14C] phorate as affected by
percolating water in a model soil-plant ecosystem. J. Agric.
Food Chem. 22:991-996.
Lichtenstein, E. P., T. W. Fuhreman, K. R. Schulz, and R. F. Skrentny.
1967. Effect of detergents and inorganic salts in water on the
persistence and movement of insecticides in soils. J. Econ.
Entomol. 60:1714-1721.
Lichtenstein, E. P., K. R. Schulz, and T. T. Liang. 1977. Fate of
fresh and aged soil residues of the insecticide (14C)-N-2596 in a
soil-corn-water ecosystem. J. Econ. Entomol. 70:169-175.
Lighthart, B. , and H. Bond. 1976. Design and preliminary results
from soil/litter microcosms. Int. J. Environ. Stud. 10:51-58.
Lighthart, B. , H. Bond, and M. Ricard. 1977. Trace element research
using coniferous forest soil/litter microcosms.
EPA-600/3-77-091. U.S. Environmental Protection Agency,
Corvallis, Ore. 51 pp.
Lu, Po-Yung, R. L. Metcalf, and E. M. Carlson. 1978. Environmental
fate of five radiolabled coal conversion by-products evaluated in
a laboratory model ecosystem. Environ. Health Perspect.
24:201-208.
Lu, Po-Yung, R. L. Metcalf, and L. K. Cole. 1978. The environmental
fate of 14C-pentachlorophenol in laboratory model ecosystems.
pp. 53-63. IN Rango-Rao, K. (ed.), Pentachlorophenol: Chemistry,
Pharmacology and Environmental Toxicology, Plenum Press, N.Y.
Lu, Po-Yung, R. L. Metcalf, A. S. Hirwe, and J. W. Williams. 1975.
Evaluation of environmental distribution and fate of hexachloro-
cyclopentadiene, chlordene, heptachlor, and heptachlor epoxide in
a laboratory model ecosystem. J. Agric. Food Chem. 23:967-973.
Metcalf, R. L. 1977. Model ecosystem approach to insecticide
degradation: A critique. Ann. Rev. Entomol. 22:241-261.
Metcalf, R. L. 1977. Model ecosystem studies of bioconcentration and
biodegradation of pesticides. Environ. Sci. Res. 10:127-144.
Metcalf, R. L. , L. K. Cole, S. G. Wood, D. J. Mandel, and M. L.
Milbrath. 1979. Design and evaluation of a terrestrial model
ecosystem for evaluation of substitute pesticide chemicals.
EPA-600/3-79-004. U.S. Environmental Protection Agency,
Corvallis, Ore. 20 pp.
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304
Metcalf, R. L., I. P. Kapoor, P.-Y. Lu, C. K. Schuth, and P. Sherman.
1973. Model ecosystem studies of the environmental fate of six
organochloride pesticides. Environ. Health Perspect. 4:35-44.
Metcalf, R. L. , G. K. Sangha, and I. P. Kapoor. 1971. Model
ecosystem for the evaluation of pesticide biodegradability and
ecological magnification. Environ. Sci. Tech. 5(8): 709-713.
Nash, R. G. , and M. L. Beall, Jr. 1979. A microagroecosystem to
monitor the environmental fate of pesticides, pp. 86-94. IN
Witt, J. M., and J. W. Gillett (eds.), Terrestrial Microcosms and
Environmental Chemistry. NSF/RA 79-0026. National Science
Foundation, Washington, D.C.
Nash, R. G., M. L. Beall, Jr., and W. G. Harris. 1977. Toxaphene and
1 ,1 ,l-trichloro-2,2-bis(p-chlorophenyl) ethane (DDT) losses from
cotton in an agroecosystem chamber. J. Agric. Food Chem. 25(2):
336-341.
Odum, H. T., and A. Lugo. 1970. Metabolism of forest-floor
microcosms, pp. 135-156. IN Odum, H. T. (ed.), Tropical Rain
Forest. U.S. Atomic Energy Commission, Washington, D.C.
Parr, J. F. 1974. Effects of pesticides on micro-organisms in soil
and water, pp. 315-340. IN Guenzi, W. D. (ed), Pesticides in
Soil and Water. Soil Science Society of America, Inc., Madison,
Wise.
Patton, B. C. , and M. Witkamp. 1967. Systems analysis of 134cesium
kinetics in terrestrial microcosms. Ecology 48:813-824.
Powlson, D. S. , and D. S. Jenkinson. 1976. The effects of biocidal
treatments on metabolism in soil. II. Gamma irradiation,
autoclaving, air-drying and fumigation. Soil Biol. Biochem.
8:179-188.
Ruhling, A., and G. Tyler. 1973. Heavy metals pollution and
decomposition of spruce needle litter. Oikos 24:402-416.
Sanborn, J. R. , and C.-C. Yu. 1973. The fate of dieldrin in a model
ecosystem. Bull. Environ. Contam. Toxicol. 10:340-346.
Schulz, K. R., T. W. Fuhreman, and E. P. Lichtenstein. 1976.
Interactions of pesticide chemicals. Effect of eptam and its
antidote on the uptake and metabolism of [14C]phorate in corn
plants. J. Agric. Food Chem. 24:269-299.
Spa!ding, B. P. 1977. Enzymatic activities related to the
decomposition of coniferous leaf litter. Soil Sci. Soc. Amer. J.
41:622-627,
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305
Spalding, B. P. 1978. The effect of biocidal treatments on
respiration and enzymatic activities of douglas-fir needle
litter. Soil Biol. Biochem. 10:537-543.
Spalding, B. P. 1979. Effects of divalent metal chlorides on
respiration and extractable enzymatic activities of douglas-fir
needle litter. J. Environ. Qual. 8:105-109.
Tu, C. M. 1970. Effect of four organophosphorus insecticides on
microbial activities in soil. Appl. Microbiol. 19:479-484.
Tu, C. M. 1978. Effect of pesticides on acetylene reduction and
microorganisms in a sandy loam. Soil Biol. Biochem. 10:451-456.
Witkamp, M. 1969. Environmental effects on microbial turnover of
some mineral elements. Soil Biol. Biochem. 1:167-184.
Witkamp, M. 1976. Microcosm experiments on element transfer. Int.
J. Environ. Stud. 10(l):59-63.
Witkamp, M. , and B. Ausmus. 1975. Effects of tree species,
temperature, and soil on transfer of manganese-54 from litter to
roots in a microcosm. pp. 694-699. IN Howe 11 , F. G. , J. B.
Gentry, and M. H. Smith (eds.), Mineral Cycling in Southeastern
Ecosystems. CONF-740513. National Technical Information Service,
Springfield, VA.
Witkamp, M. , and B. Baryansky. 1968. Microbial immobilization of
137Cs in forest litter. Oikos 19:392-395.
Witkamp, M., and M. Frank. 1967. Cesium-137 kinetics in terrestrial
microcosms. pp. 635-643. IN Nelson, D. J. , and F. C. Evans
(eds.), Symposium on Radioecology. USAEC Document Conf. 670503.
Witkamp, M., and M. L. Frank. 1970. Effects of temperature, rainfall
and fauna on transfer of 137Cs, K, Mg, and mass in
consumer-decomposer microcosms. Ecology 51:465-474.
Yu, C. C., G. M. Booth, D. J. Hanson, and J. R. Larsen. 1974. Fate
of bux insecticide in a model ecosystem. Environ. Entomol.
3:975-977.
Yu, C. C. , G. M. Booth, D. J. Hanson, and J. R. Larsen. 1974. Fate
of carbofuran in a model ecosystem. J. Agric. Food Chem.
22:431-434.
Yu, C. C. , G. M. Booth, D. J. Hanson, and J. R. Larsen. 1975. Fate
of alachlor and propachlor in a model ecosystem. J. Agric. Food
Chem. 23:877-879.
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306
Yu, C. C. , G. M. Booth, D. J. Hanson, and J. R. Larsen. 1975. Fate
of pyrazon in a model ecosystem. J. Agric. Food Chem.
23:300-311.
Yu, C. C. , G. M. Booth, and J. R. Larsen. 1975. Fate of triazine
herbicide cyanazine in a model ecosystem. J. Agric, Food Chem.
23: 1014-1015.
4.2.6 EXCISED SYSTEMS
Ausmus, B. S. , G. J. Dodson, and D. R. Jackson. 1978. Behavior of
heavy metals in forest microcosms. III. Effects on litter-soil
carbon metabolism. Water Air Soil Pollut. 10:19-26.
Ausmus, B. S. , S. Kimbrough, D. R. Jackson, and S. Lindberg. 1979.
The behaviour of hexachlorobenzene in pine forest microcosms:
Transport and effects on soil processes. Environ. Pollut.
13:103-111.
Campbell, S. D. 1973. The Effect of Cobalt-60 Gamma-Rays on
Terrestrial Microcosm Metabolism. Ph.D. dissertation, University
of Michigan, Ann Arbor, Mich. 144 pp.
Gile, J. D. , J. C. Collins, and J. W. Gillett. 1979. The soil core
microcosm—A potential screening tool. EPA-600/3-79-089. U.S.
Environmental Protection Agency, Corvallis, Oregon. 41 pp.
Jackson, D. R. , B. S. Ausmus, and M. Levine. 1979. Effects of
arsenic on nutrient dynamics of grassland microcosms and field
plots. Water Air Soil Pollut. 11:13-21.
Jackson, D. R. , and J. M. Hall. 1978. Extraction of nutrients from
intact soil cores to assess the impact of chemical toxicants on
soil. Pedobiologica 18:272-278.
Jackson, D. R. , and M. Levine. 1979. Transport of arsenic in
grassland microcosms and field plots. Water Air Soil Pollut.
11:3-12.
Jackson, D. R. , J. J. Selvidge, and B. S. Ausmus. 1978. Behavior of
heavy metals in forest microcosms. I. Transport and distribution
among components. Water Air Soil Pollut. 10:3-11.
Jackson, D. R. , W. J. Selvidge, and B. S. Ausmus. 1978. Behavior of
heavy metals in forest microcosms. II. Effects on nutrient
cycling processes. Water Air Soil Pollut. 10:13-18.
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307
Jackson, D. R. , C. D. Washburne, and B. S. Ausmus. 1977. Loss of Ca
and N03-N (calcium, nitrate-nitrogen) from terrestrial microcosms
as an indicator for assessing contaminants of soil pollution.
Water Air Soil Pollut. 8(3):279-284.
McCormick, J. F. , and R. B. Platt. 1962. Effects of ionizing
radiation on a natural plant community. Radiat. Bot. 2:161-188.
McCormick, J. F. , and R. B. Platt. 1964. Ecotypic differentiation in
Diamorpha cymosa. Bot. Gaz. 125:271-279.
Murphy, P. G. , and J. F. McCormick. 1971. Ecological effects of
acute beta irradiation from simulated fallout particles on a
natural plant community, pp. 454-481. Bensen, D. W. , and A. H.
Sparrow (eds.), Survival of Food Crops and Livestock in the Event
of Nuclear War. Atomic Energy Commission Symposium Series, No.
24.
Ross-Todd, M., E. G. O'Neill, and R. V. O'Neill. 1980. Synthesis of
terrestrial microcosm results, pp. 242-264. IN Harris, W. F.
(ed.), Microcosms as Potential Screening Tools for Evaluating
Transport and Effects of Toxic Substances: Final Report.
ORNL/EPA-4, Oak Ridge National Laboratory, Oak Ridge, Tenn.
Van Voris, P., R. V. O'Neill, H. J. Shugart, and W. R. Emanual. 1978.
Functional complexity and ecosystem stability: An experimental
approach. ORNL/TM-6199. Oak Ridge National Laboratory, Oak
Ridge, Tenn. 120 pp.
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TECHNICAL REPORT DATA
(Please read instructions on the reverse before completing)
i. REPORT NO.
EPA-560/11-80-026
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Methods for Ecological Toxicology. A Critical
Review of Laboratory Multispecies Tests.
5. REPORT DATE
February 1981
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
A. S. Hammons (editor)
8. PERFORMING ORGANIZATION REPORT NO.
ORNL-5708
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Environmental Sciences Division
Oak Ridge National Laboratory
Oak Ridge, Tennessee 37830
10. PROGRAM ELEMENT NO.
B2BL2S
11 CONTRACT/GRANT NO,
IAG No. EPA-78-D-X0387
12. SPONSORING AGENCY NAME AND ADDRESS
Office of Toxic Substances
US Environmental Protection Agency
Washington, D.C. 20460
13 TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This document provides a review and evaluation of laboratory methods for measuring
the effects of chemicals on aquatic and terrestrial population interaction and
ecosystem properties. The use of mathematical models in ecotoxicological assess-
ment is also addressed. More than 450 references are cited and a bibliography of
700 references is included.
Laboratory tests are evaluated for their potential for standardization for use
in the ecological hazard and risk assessment processes under the Toxic Substances
Control Act. The criteria used for these evaluations include whether or not the
tests are: rapid, reproducible, relatively inexpensive, unequivocal, sensitive,
socially and economically relevant, predictive, generalizable, and well-developed.
i;.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
Aquatic ecology
Aquatic orginiims
Assessment
Biological systems
Ecology
Environmental tests
Environments
Hazards
Invertebrates
Laboratory tests
Me thododogy
Plants(Botany)
Terrestrial Ecology
Test methods
Tox i co1ogy
Vertebrates
Water
b.IDENTIFIERS/OPEN ENDED TERMS
Aquatic microcosms
Community structure
Ecosystem function
Ecotoxicology
Interspecific
inte ract ion
Model ecosystem
Terrestrial microcosms
Tea c ing protocols
COSATl Held/Group
06/T
18. DISTRIBUTION STATEMENT
Release unlimited
19, SECURITY CLASS /Tins Report)
L _ __Uncl assified
21 NO. OF PAGES
307
•?0 SECURITY CLASS (This page)
Unclassif i e d
"t22. PRICE
EPA Form i2?0_i (Rev. 4-77)
GOVERNMENT PRINTING OFFICE 1981-740-062/507
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