EPA/600/R-06/078
February 2007
Relationships Among
Exceedances of Chemical
Criteria or Guidelines, the
Results of Ambient Toxicity
Tests, and Community Metrics in
Aquatic Ecosystems
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Cincinnati, OH 45268
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NOTICE
The U.S. Environmental Protection Agency through its Office of Research and
Development funded and managed the research described here. It has been subjected
to the Agency's peer and administrative review and has been approved for publication
as an EPA document. Mention of trade names or commercial products does not
constitute endorsement or recommendation for use.
ABSTRACT
In order to use bioassessments to help to diagnose or identify the specific
environmental stressors affecting aquatic or marine ecosystems, a better understanding
is needed of the relationships among community metrics, ambient chemical criteria or
guidelines and ambient toxicity tests. However, these relationships are not necessarily
simple, because metrics generally assess measurement endpoints at the community
level of biological organization, while ambient criteria or guidelines and ambient toxicity
tests assess measurement endpoints at the organism level. Although a basic
hierarchical relationship exists between the levels of biological organization used as
measurement endpoints by these methods, quantification of this relationship may be
further complicated by the influence of other differences among these methods that
affect their sensitivity and specificity to the stressors present at individual sites.
Since 1990, the U.S. Environmental Protection Agency has conducted
Environmental Monitoring and Assessment Program surveys of both wadeable stream
and estuarine sites. These surveys have collected data on biotic assemblages,
physical and chemical habitat characteristics and, in some cases, water and sediment
chemistry and toxicity. Among these studies is a survey of wadeable streams in the
Southern Rockies ecoregion of Colorado in 1994 and 1995 and a survey of estuaries in
the Virginian Province of the eastern United States from 1990 to 1993. Streams in the
Southern Rockies ecoregion are affected by contamination from hardrock metal mining,
while the estuarine sites may be affected by sediment contamination by polyaromatic
hydrocarbons and metals. We characterized streams as metals-affected based on
exceedance of hardness-adjusted metals criteria for Cd, Cu, Pb and Zn in surface
water; on water column toxicity tests (48-hour Pimephales promelas and Ceriodaphnia
dubia survival); on exceedance of sediment threshold effect levels; or on sediment
toxicity tests (7-day Hyalella azteca survival and growth). Estuarine sites were
characterized as affected by sediment contamination based on exceedance of
sediment guidelines or on sediment toxicity tests (i.e., 10-day Ampelisca abdita
survival). The results of these classifications were contrasted by use of contingency
tables and a measure of association, Y- Then, assemblage metrics were compared
statistically among affected and unaffected sites to identify metrics sensitive to the
contamination. In streams, a number of macroinvertebrate metrics, particularly richness
metrics, were less in groups of sites identified as affected by metals with the criteria or
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ambient toxicity tests, while other metrics were not. Fish metrics were less sensitive to
the metal contamination, but this lack of sensitivity is likely because of the low diversity
of fish assemblages in these Rocky Mountain streams. Similarly at the estuarine sites,
a number of benthic metrics differed between the groups of sites segregated using the
organism-level measure, while other metrics did not. These same metrics also
exhibited relationships with contaminant concentrations in regression analyses. This
variation among metrics depends on the sensitivity of the individual metrics to the
stressor gradients of interest as many metrics may not measure the community
responses characteristic of a specific stressor. The differences between groups for the
more sensitive metrics imply that a relationship exists between the organism-level
effects assessed by ambient chemistry or ambient toxicity tests and the community-
level effects assessed by community metrics. However, the organism-level effects are
only predictive to a limited extent of the community-level effects at individual sites.
Beyond the differences in the levels of biological organization represented by
their measurement endpoints, these methods differ in their specificity and sensitivity to
different stressors. Criteria or guidelines are specific to the contaminants being
measured and assessed and cannot assess contaminants or stressors that are not
measured or that lack guidelines for comparison. Ambient toxicity tests should detect
effects of any toxicants present and bioavailable, but cannot assess other
characteristics of a site that can affect the biotic community. Community metrics are
the least specific of the three methods, because they measure directly community-level
effects in the native assemblages. Metrics may be selected that are sensitive to a
specific stressor, but they also will be sensitive to other stressors, such as alterations in
physical habitat that are not addressed by the other methods.
Other factors also affect the relative sensitivity and predictiveness of these
different methods. Toxicity tests and chemical criteria or benchmarks based on
measurement endpoints that are chronic in duration would be more predictive of
community-level effects. Toxicity tests often use one or two standard species, which
can be more tolerant of specific contaminants than other indigenous species and would
be less predictive of community-level effects than a chemical criterion or benchmark
based on a species sensitivity distribution composed of many species.
Preferred citation:
U.S. EPA. 2006. Relationships Among Exceedances of Chemical Criteria or Guidelines, the Results of
Ambient Toxicity Tests, and Community Metrics in Aquatic Ecosystems. U.S. Environmental Protection
Agency, National Center for Environmental Assessment, Cincinnati, OH. EPA/600/R-06/078.
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TABLE OF CONTENTS
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
LIST OF ABBREVIATIONS viii
PREFACE ix
AUTHORS, CONTRIBUTORS AND REVIEWERS x
1. INTRODUCTION 1
1.1. DATA SETS USED 5
2. WADEABLE STREAMS IN THE SOUTHERN ROCKIES ECOREGION
OF COLORADO 8
2.1. INTRODUCTION 8
2.2. MATERIALS AND METHODS 8
2.2.1. Study Area and Survey Design 8
2.2.2. Water and Sediment Chemistry 10
2.2.3. Invertebrate and Fish Toxicity Tests 10
2.2.4. Macroinvertebrate Collection and Identification 11
2.2.5. Fish Collection and Identification 11
2.2.6. Calculation of Community Metrics 12
2.2.7. Data Handling and Analysis 16
2.3. RESULTS AND DISCUSSION 19
2.3.1. Organism-level Measures 19
2.3.2. Organism-level Measures versus Community Metrics 23
2.3.3. Piecewise Regression Analyses 29
3. ESTUARINE SYSTEMS IN THE VIRGINIAN PROVINCE OF THE
ATLANTIC COAST 34
3.1. INTRODUCTION 34
3.2. MATERIALS AND METHODS 34
IV
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TABLE OF CONTENTS cont.
3.2.1. Study Area and Survey Design 34
3.2.2. Field and Laboratory Methods 35
3.2.3. Sediment Contaminant Concentrations 35
3.2.4. Ambient Toxicity Tests 35
3.2.5. Calculation of Community Metrics 36
3.2.6. Data Handling and Analysis 40
3.3. RESULTS AND DISCUSSION 43
3.3.1. Organism-level Measures 43
3.3.2. Organism-level Measures versus Community Metrics 47
4. CONCLUSIONS 57
5. REFERENCES 60
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LIST OF TABLES
No. Title Page
1 Macroinvertebrate and Fish Metrics that Exhibited Differences Between
the Two Groups Segregated Using at Least One of the Measurement
Endpoints 13
2 Metrics that Did Not Exhibit Differences among the Groups 15
3 Criteria Used to Divide Sites into the Impacted or Unimpacted Groups 17
4 Correspondence of Conclusions of Assessments for Surface Water and
Sediment for Sampling Events 20
5 Correspondence of Conclusions of Assessments Based on Chemical
Criteria and Ambient toxicity tests for Sampling Events 21
6 Enumeration of Sampling Events in Wadeable Streams in the Southern
Rockies Ecoregion of Colorado Where Classification Based on the
Organism-level Measures and that Based on the Community Metric
Disagree 27
7 Benthic Metrics that Exhibited Differences Between the Two Groups
Segregated Using at Least One of the Following Measurement Endpoints ... 37
8 Benthic Metrics that Did Not Exhibit Differences among the Two Groups
Segregated Using at Least One of the Measurement Endpoints 39
9 Criteria Used to Divide Sites into the Impacted or Unimpacted Groups 41
10 Criteria Used to Classify Metrics as Different than Expected 44
11 Correspondence of Conclusions of Assessments Based on Chemical
Criteria and Ambient Toxicity Tests for Sampling Events 45
12 Comparison of Sites where Maximum p from the Logistic Regression
>0.50 for Metals versus for PAHs 48
13 Enumeration of Sampling Events in Estuarine Systems of the Virginian
Province of the Atlantic Coast where Classification Based on the
Organism-level Effects Measures and that Based on the Community
Metric Disagree 54
VI
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LIST OF FIGURES
No. Title Page
1 Map of Colorado, USA, with the Mineralized Region of the Southern
Rockies Ecoregion and Locations of the 1994-1995 Regional
Environmental Monitoring Assessment Program Reaches 9
2 Comparison of Metals Concentrations in Water and in Sediment
Between Groups Identified as Potentially Affected or Unaffected by
the Ambient Toxicity Test of Water and Sediment, Respectively 22
3 Comparison of Macroinvertebrate Metrics Between Groups Identified as
Potentially Affected or Unaffected by Each of the Organism-level Endpoints . 24
4 Comparison of Macroinvertebrate and Fish Metrics Between Groups
Identified as Potentially Affected or Unaffected by Each of the
Organism-level Endpoints 25
5 Piecewise Regressions of Taxa Richness Metrics on the Summed
Ratios of the Dissolved Concentrations of Cd, Cu, Pb and Zn to their
Chronic AWQC 31
6 Piecewise Regressions of Taxa Richness Metrics on the Summed
Ratios of the Sediment Concentrations of Cd, Cu, Pb and Zn to
their TELs 32
7 Comparison of Percent Survival of A. abdita Between Sites where
Maximum p < 0.50 from the Logistic Regressions and those where
Maximum p > 0.50 46
8 Regressions of Residuals of Benthic Metrics (Richness and
Composition) on Maximum p from the Logistic Regressions 49
9 Regressions of Residuals of Benthic Metrics (Pollution-Indicator
and Abundance) on Maximum p from the Logistic Regressions 50
10 Regressions of Residuals of Benthic Metrics on Percent Survival
for the Sediment Toxicity Tests with A. ampelisca 51
VII
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LIST OF ABBREVIATIONS
ANCOVA Analysis of Co-variance
ANOVA Analysis of Variance
APHA American Public Health Association
AVS Acid Volitile Sulfide
AWQC Ambient Water Quality Criteria
EMAP Environmental Monitoring and Assessment Program
EPT Ephemeroptera, Plecoptera, and Trichoptera
ER-M Effects Range - Median
LCL Lower Confidence Limit
PAHs Polyaromatic hydrocarbons
PEL Potential Effects Level
R-EMAP Regional Environmental Monitoring and Assessment Program
SEM Simultaneously-Extracted Metals
TEL Threshold-effect Level
UCL Upper Confidence Limit
USGS United States Geological Survey
VIM
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PREFACE
U.S. EPA's Office of Water, Regional Offices, and other program offices use
three general approaches for the ecological assessment of contaminant exposure and
effects in surface waters or sediments: (1) comparisons of chemical concentration data
in water or sediments to chemical criteria or other guidelines, (2) ambient toxicity
assessments of sediment or water, and (3) bioassessments of biotic assemblages,
such as fish, invertebrates, or periphyton. In practice, these methods are used
independently to assess the attainment of aquatic life use in various water bodies.
Chemical criteria and ambient toxicity assessments are indirect approaches, because
they evaluate the suitability of a water body to support a healthy biotic community,
whereas bioassessments directly assess the existing biotic community. Moreover,
these different methods measure effects using differing measurement endpoints that
assess different levels of biological organization. Chemical criteria and ambient toxicity
assessments are based on measures of the responses of organisms and are generally
indicative of organism- or possibly population-level effects. Bioassessments, while
usually working with selected biotic assemblages, are generally indicative of the
community level effects. In addition, chemical criteria and ambient toxicity assessments
differ, because chemical criteria or guidelines can be based on bioassay data from a
broad range of taxa, whereas ambient toxicity assessments use a few standard
bioassay species.
It is not clear whether these three approaches provide similar levels of protection
to aquatic organisms, populations and communities. The two studies presented in this
report begin to address that question. Results of the first study suggest that, for metals
in Colorado streams, chemical criteria combined in a concentration additivity model
approximate the threshold for effects on aquatic communities observed in
bioassessments. Results of the second study are not as clear but suggest that biotic
metrics can be more protective then chemical thresholds or ambient toxicity
assessments.
This report is intended for ecological risk assessors and field biologists in the
Office of Water, Regional Offices, other program offices, and the States interested in
the application of these methods for evaluating the attainment of aquatic life use in
streams and estuaries and for assessing the causes of impairment in affected systems.
This report may also be of interest to research scientists interested in the further
development of these methods.
IX
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AUTHORS, CONTRIBUTORS AND REVIEWERS
AUTHORS
Michael B. Griffith
U.S. Environmental Protection Agency
National Center for Environmental Assessment
Cincinnati, OH 45268
Chapter 2
Alan T. Herlihy
Department of Fisheries and Wildlife
Oregon State University
Corvallis, OR 97333
James M. Lazorchak
U.S. Environmental Protection Agency
National Exposure Research Laboratory
Cincinnati, OH 45268
Chapter 3
Michael Kravitz
U.S. Environmental Protection Agency
National Center for Environmental Assessment
Cincinnati, OH 45268
EXTERNAL PEER REVIEWERS
Jerome Diamond, Ph.D., Director
Tetra Tech, Inc.
Owings Mills, MD21117
Thomas W. La Point, Ph.D., Professor and Director
Institute of Applied Sciences
University of North Texas
Denton, TX 76203
GaryM. Rand, Ph.D., Professor
Southeast Environmental Research Center (SERC)
Department of Environmental Studies
Florida International University
North Miami, FL 33181
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ACKNOWLEDGMENTS
For the Colorado R-EMAP study in Chapter 2, field sampling design and data
collection were funded by U.S. EPA's Office of Research and Development as part of
its Regional Environmental Monitoring and Assessment Programs. P. Johnson (U.S.
EPA, Region VIII, Denver, Colorado) helped coordinate the field work and analysis of
the chemistry and macroinvertebrate samples and, along with W. Schroeder (U.S. EPA,
Region VIII, Denver, Colorado), provided details on the sampling and analyses for
water and sediment chemistry. Comments by M. Kravitz, F. McCormick, G. Suter and
two anonymous reviewers greatly improved the quality of the manuscript on which
Chapter 2 is based. Also, preparation of that manuscript was supported in part by a
U.S. EPA cooperative agreement (CR824682) with Oregon State University.
For the Virginian Estuarine Province EMAP study in Chapter 3, field sampling
design and data collection were funded by U.S. EPA's Office of Research and
Development as part of its Environmental Monitoring and Assessment Program -
Estuaries and managed by D. Keith, C.J. Strobel, J. Martinson, J.B. Frithsen, K.J. Scott,
J. Paul, A.F. Holland, R.W. Latimer and S.C. Schimmel. Comments by J. Paul
improved the quality of Chapter 3.
XI
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1. INTRODUCTION
In general, the U.S. EPA has used three different methods for the ecological
assessment of contaminant exposure and effects in surface waters or sediments.
These methods are (1) comparisons of chemical concentration data in water or
sediments to chemical criteria or other guidelines, (2) ambient toxicity assessments of
sediment or water and (3) bioassessments of selected biotic assemblages, such as
fish, invertebrates or periphyton.
Chemical criteria or other guidelines are generally concentrations of specific
contaminants of interest that are associated with some threshold for biological effects.
These guidelines are derived using numerical methods from compilations of laboratory
bioassay or other effects data, such as species sensitivity distributions (Suter et al.,
2001). The most commonly-used chemical criteria are the national ambient water
quality criteria for the protection of aquatic life that have been derived from laboratory
bioassay data following U.S. EPA guidelines (1985). Procedures have been proposed
for deriving sediment guidelines for non-ionic organic chemicals or metals by applying
the theory of equilibrium-partitioning to water quality criteria to estimate threshold
concentrations of these contaminants in sediment pore water (U.S. EPA, 2003a;
Hansen et al., 1996). This approach has been extended to assess mixtures of
polyaromatic hydrocarbons (PAHs) and divalent metals (Swartz et al., 1995; U.S. EPA,
2003b,c). Other paired chemistry and effects data sets, usually for natural sediments
containing mixtures of contaminants, have been used to derive sediment-effects
concentrations such as Effects Range - Median (ER-M), and Potential Effects Level
(PEL, MacDonald et al., 1996). An ER-M is defined as a sediment chemical
concentration above which effects were frequently observed or predicted for most
species (Long et al., 1995). A PEL is defined as a sediment chemical concentration
above which adverse effects were frequently observed. Paired chemistry and sediment
toxicity test data have been used to derive sediment effect concentrations (U.S. EPA,
1996) or logistic regressions that estimate the probability that a sediment is toxic (Field
et al., 2002). Quantitative chemical data for water or sediments are compared with
these chemical criteria, guidelines or sediment-effects concentrations to determine
whether a contaminant of interest is at a concentration that may have adverse effects
on aquatic organisms.
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In ambient toxicity assessments, samples of sediments or water are tested
directly in laboratory bioassays with standard organisms and protocols. These standard
organisms include Pimephales promelas Rafinesque (fathead minnow) and
Ceriodaphnia dubia (Jurine) (a cladoceran) for testing freshwater (U.S. EPA, 1993),
Hyalella azteca Saussure (an amphipod) and Chironomus tentans Fabricius (a midge)
for testing freshwater sediments (U.S. EPA, 2000a), Mysidopsis bahia (M.) (mysid
shrimp) or Cyprinodon variegatus Lacepede (sheepshead minnow) for testing estuarine
water (U.S. EPA, 1993) or Ampelisca abdita Mills (an amphiod) and Rhepoxynius
abronius (J.L. Barnard) (an amphipod) for testing estuarine sediments (U.S. EPA,
1994a). Acute tests for water are conducted for 24 to 96 hours, while those for
sediments are conducted for 7 to 10 days, and the measurement endpoints are survival
and sometimes growth. Chronic tests may be conducted for 7 to 42 days, and the
measurement endpoints are survival, growth, and usually some measure of
reproductive success. A sample is identified as having adverse effects on aquatic
organisms if a measurement endpoint is significantly reduced compared with
concurrently-run controls.
In bioassessments, samples of a selected biotic assemblage, such as fish or
benthic invertebrates, are collected, and the organisms are identified, counted, and
sometimes weighed. These data are then used to calculate and score metrics that
describe the assemblage. The metric scores are then summed to produce an index of
biotic integrity (Barbour et al., 1999). A broad range of metrics can be calculated
depending on the diversity of the selected biotic assemblage. General classes of
metrics include richness metrics (i.e., counts of the number of specified taxa in the
assemblage), evenness metrics, composition metrics, trophic or habitat guild metrics.
Whether a metric is indicative of adverse effects at a site can be determined by
comparison with its value at sites determined to represent reference conditions
(Barbour et al., 1999). Variation in a metric relative to a known stressor gradient,
particularly in relation to a threshold in a stressor gradient, can also show adverse
effects (Karr and Chu, 1998). We use this second definition in this report.
These different methods assess effects using differing assessment and
measurement endpoints at different levels of biological organization (U.S. EPA, 2003d).
Moreover, assumptions exist about the relationships among the levels of protection
associated with each of these assessment tools. Chemical criteria, guidelines, or
effects-concentrations that are based on laboratory bioassay data and ambient toxicity
assessments that use laboratory bioassays are based on measures of the responses of
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organisms, such as survival, growth and fecundity, and, therefore, are show organism-
level effects. Bioassessments, because they quantify characteristics of selected biotic
assemblages, show community-level effects. In addition, chemical and ambient toxicity
assessments differ, because chemical assessments can be based on laboratory
bioassay or other data from a broad range of taxa, whereas ambient toxicity
assessments use a few standard, bioassay species to test environmental samples.
A premise about the relationships among the measurement endpoints of each
of these assessment tools and the protection for higher levels of biological organization
is that these levels of biological organization are hierarchical (O'Neill et al., 1986).
Laboratory bioassays measure survival, growth, and fecundity, but these organism-level
effects may be extrapolated to population-level effects because rates of mortality and
reproduction affect the number of individuals in a population (Kuhn et al., 2000).
Chemical water quality criteria, as derived by U.S. EPA (1985), are assumed to be
protective of at least 95% of the taxa in aquatic communities because the thresholds
are set at the fifth percentile of the genera sensitivity distribution for a chemical. Other
methods for deriving chemical guidelines may use different thresholds. The level of
protection at the community level for ambient toxicity assessments may be variable
because of variable sensitivity of the bioassay species to different chemicals compared
with the indigenous taxa in communities.
Some of these premises have been previously addressed in studies intended to
validate whole effluent and ambient toxicity tests (Mount et al., 1984, 1985, 1986a,b,c;
Mount and Norberg-King, 1985, 1986; Norberg-King and Mount, 1986; Birge et al.,
1989; Eagleson et al., 1990; Dickson et al., 1992; Clements and Kiffney, 1994;
Diamond and Daley, 2000), but many of those studies predate the full development of
standardized bioassessment protocols and the use of many community-level metrics.
Moreover, these studies were mostly conducted at relatively few individual sites on
single stream systems upstream and downstream of known point-sources.
Mount et al. (1984) and related studies compared the results of chronic 7-day
tests with Ceriodaphnia spp. and P. promelas of serial dilutions of effluents and of
ambient water and the results of community surveys of fish or macroinvertebrates.
Their study reaches included from one to more than ten point sources, which included
publically-owned treatment plants (POTWs), industrial plants, and chemical plants.
Community measurements included the total number of taxa, total density, Shannon-
Weaver species diversity, a community-loss index, and the density and percentage
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composition of individual species and of major taxa, such as Ephemeroptera,
Trichoptera, Chironomidae, and Mollusca.
Birge et al. (1989) compared the results of 8-day embryo-larval tests with P.
promelas of ambient water and the results of community surveys of macroinvertebrates
and fish. Their study reaches were upstream and downstream from a POTW, and
community measurements included Shannon-Weaver species diversity, a coefficient of
dominance, species richness, total density, the percent composition of
macroinvertebrate functional groups, and the presence or absence of fish species.
Eagleson et al. (1990) compared the results of chronic, 7-day tests with C. dubia
of effluents taking into account the site-specific dilution of the effluent in the receiving
stream and the results of community surveys of macroinvertebrates conducted
upstream and downstream of the effluent discharge. The sources of the effluents were
classified as either municipal or industrial. Community measurements were total taxa
richness and the taxa richness of major taxa groups, such as Ephemeroptera,
Plecoptera, Trichoptera, Chironomidae, Oligochaeta, and Crustacea.
Dickson et al. (1992) reanalyzed data from several of the above studies along
with data from the Trinity River collected upstream and downstream six major POTWs.
The Trinity River study compared short-term, chronic tests with C. dubia and P.
promelas of ambient water with the results of community surveys of macroinvertebrates
and fish. Community measurements were fish or macroinvertebrate richness and
evenness, and a fish index of biotic integrity.
Clements and Kiffney (1994) compared the results of chronic, 7-day tests with C.
dubia of ambient water collected along a metals contamination gradient upstream and
downstream of California Gulch, a point source of mine drainage to the Arkansas River,
with the results of community surveys of macroinvertebrates. Community
measurements were taxa richness, total abundance, and the percent abundance of
Ephemeroptera and Orthocladiinae.
Use of these methods in ecological assessment and management of
environmental contaminants can benefit from greater understanding of the relationships
among these levels of biological organization and their protection by the measurement
endpoints assessed by these methods. Although the Office of Water follows a policy of
independent applicability (U.S. EPA, 1991), this policy has been questioned because of
misunderstandings about the relationships among these methods and their relative
limitations.
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The following described research tested the assumptions about the relationships
between the measurement endpoints at the organism level used by chemical criteria or
guidelines and other bioassay-based regulatory tools with assemblage metrics, which
are measurement endpoints at the community level of biological organization. The
objectives of this project were to
(1) assess the availability of data sets from studies that have used two or all
three of the methods to assess sediment or surface water quality at a
number of sites,
(2) compare and contrast statistically the results produced by the different
methods at different sites to determine the relationships among the
measurement endpoints assessed by each method,
(3) assess the extent to which the methods that are based on measurement
of organism-level effects are predictive and protective of effects at the
assemblage or community level as measured by assemblage metrics.
1.1. DATA SETS USED
A limitation to this approach is the availability of data sets from studies that have
used two or all three of the methods to assess sediment or surface water quality at a
number of sites. Several regional data sets were identified from the U.S. EPA's
Environmental Monitoring and Assessment Program (EMAP), and these data sets
encompass studies of both wadeable streams and estuaries. However, these EMAP
data sets have limitations. First, many EMAP studies have not analyzed potentially
toxic contaminants in surface water, either in streams or estuaries. Because of the
random-selection approach of EMAP, only a small proportion of sites are likely to have
surface water concentrations of these contaminants above detectable limits, unless
widespread sources for a contaminant exist across a region. In 1994 and 1995, a
Regional EMAP (R-EMAP) survey of the Southern Rocky Mountains ecoregion
(Omernik, 1987) of Colorado had widespread sources. These sources consisted of
historical and active hard rock, metals mining sites (Lyon et al., 1993), and these
streams were sampled for total and dissolved metals in surface water. For the same
reasons, ambient toxicity tests of surface water have not been conducted in many
EMAP studies, but ambient toxicity tests using Pimephales promelas and Ceriodaphnia
dubia were conducted in this Colorado R-EMAP study. Also for these reasons,
sampling of sediments for chemical analyses or ambient toxicity tests has been
uncommon in EMAP wadeable stream studies. However, again this Colorado R-EMAP
study collected sediment samples that were analyzed for metals and tested with
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ambient toxicity tests using Hyalella azteca. EMAP - Estuaries has routinely collected
sediment samples for chemical analyses and for ambient toxicity tests, often using
Ampelisca abdita. These studies have been conducted in cooperation with the National
Oceanographic and Atmospheric Administration's National Status and Trends Program,
which has routinely collected sediments and bivalves for chemical analysis (O'Connor,
1994). An EMAP - Estuaries study of the Virginian Estuarine Province (Strobel et al.,
1999) conducted from 1990 to 1993 was selected for analysis.
A common thread of most EMAP studies has been the sampling and analysis of
biotic assemblages, particularly benthic invertebrates and fish. Both the Colorado
R-EMAP study and the Virginian Province EMAP study collected benthic invertebrates
and fish. However, because only sediment chemistry and ambient toxicity test data
were available for the Virginian Province EMAP study, we used only the benthic
invertebrate data from that study.
Several limitations are imposed on our assessment by use of these data sets
and by technical aspects of the three methods used for the ecological assessment of
contaminant exposure and effects. These data sets are secondary data, because they
were collected for purposes that were different from those for which they are used in
this report. As a result, some aspects of their study design are not optimal for our
purposes. For example, the ambient toxicity tests conducted in both studies were acute
in duration (U.S. EPA, 1993, 1994a,b), whereas the results of chronic toxicity tests
would have been more comparable to the community metrics, which generally reflect
longer-term effects (Karr and Chu, 1998). Moreover, EMAP generally uses a random-
selection approach to identifying sampling sites (Strobel et al., 1999; Herlihy et al.,
2000), although both studies included some sites where contamination was known or
suspected to occur. While both studies were conducted in regions (i.e., the historical
mining region of the Southern Rockies in Colorado and estuaries of the Virginian
estuarine province of the eastern United States), where widespread contamination of
surface water or sediments is known to occur, the number of sites classified into the
unaffected or affected groups was unbalanced (i.e., the number of sites in the
unaffected groups was larger than the number in the affected group). Many sites were
also potentially affected by other stressors that may not be identifiable by comparisons
of chemistry to available criteria or guidelines or by the ambient toxicity tests but may
affect community metrics.
Also, technical differences among the three methods go beyond the methods'
differences in the levels of biological organization used as their measurement
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endpoints. For example, differences are related to laboratory testing versus field
sampling and the selection of test species that are amenable to their use in a laboratory
setting. The intent of this report is to address the relationships among the
measurement endpoints used by the three methods. However, these aspects of study
design and technical differences among the methods are discussed in the following
chapters to clarify how they affect the observed relationships among the measurement
endpoints.
The following chapters outline our comparisons of the results of the three
methods for assessment of contaminant exposure and effects at sites sampled by
(1) the R-EMAP study conducted in 1994 and 1995 of wadeable streams in
the Southern Rockies ecoregion of Colorado and
(2) the EMAP study conducted from 1990 to 1993 of poly-euhaline estuarine
sites in the Virginian Province of the eastern United States.
The chapter on the R-EMAP study of wadeable streams in the Southern Rockies
ecoregion of Colorado has already been published in a slightly different form in the
journal, Environmental Toxicology and Chemistry (Griffith et al., 2004). Similarly, the
chapter on the EMAP study of poly-euhaline estuarine sites in the Virginian Province
was written to be published soon in a scientific journal. The final chapter summarizes
our conclusions based on these two comparisons.
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2. WADEABLE STREAMS IN THE SOUTHERN ROCKIES ECOREGION OF
COLORADO
2.1. INTRODUCTION
In this chapter, we compare and contrast statistically the results of three different
methods used by the U.S. EPA for the ecological assessment of contaminant exposure
and effects in surface water and sediments of freshwater ecosystems: (1) chemical
criteria for the protection of aquatic life such as ambient water quality criteria (AWQC)
or sediment-effects concentrations, (2) ambient toxicity assessments of water or
sediments, and (3) bioassessments offish or macroinvertebrate assemblages to
determine the relationships among the levels of biological organization assessed by
each method. We also assess the extent to which organism-level effects predict effects
at the community level. This approach is applied to the effects of metals contamination
in streams associated with hard rock, metal mining in the mineralized belt of the
Southern Rockies ecoregion of Colorado. This region is characterized by historical and
active mining for base metals, and discharges from approximately 23,000 abandoned
mines affect more than 2000 km of streams in Colorado (Lyon et al., 1993; Colorado
Division of Minerals and Geology, 2003).
2.2. MATERIALS AND METHODS
2.2.1. Study Area and Survey Design. The mineralized belt of the Southern Rockies
ecoregion includes headwater drainages of the South Platte, Arkansas, Rio Grande,
and Colorado Rivers (Figure 1). We present data compiled from R-EMAP surveys
conducted in 1994 and 1995. As part of these surveys, 73 sampling sites were
selected using a randomization method with a spatial systematic component (Herlihy et
al., 2000). The stream network on the digitized version of the 1:100,000 scale USGS
topographic map was used as the sample frame. The surveys were restricted to 2nd, 3rd
and 4th order (Strahler, 1957) on the 1:100,000 scale map. Sample probabilities were
set so that roughly equal numbers of 2nd-, 3rd- and 4th-order streams appeared in the
sample. Besides the 73 random sites, 13 other sites were selected that were variable
distances either upstream (i.e., six sites) or downstream (i.e., seven sites) of known
mining sites. Subsets of sites were revisited either within a year or during the second
year to assess variability between visits, but data from only the first visit to a site were
considered in these analyses. Nevertheless, some sites lacked data for one or more of
the measurements, such as chemistry, toxicity tests, macroinvertebrates or fish.
8
-------
mineralized region
random-selection reaches
upstream reaches
downstream reaches
FIGURE 1
Map of Colorado, USA, with the Mineralized Region of the Southern Rockies Ecoregion
and Locations of the 1994-1995 Regional Environmental Monitoring Assessment
Program (R-EMAP) Reaches
-------
Streams were sampled from late July to late September each year. This period
of the water year is when stable base flows occur in these Rocky Mountain streams.
Sampling was conducted to avoid episodic events when biological and chemical
conditions were likely different from those during baseflow (Herlihy et al., 2000). A
length of stream equal to 40 times the mean low-flow, wetted width (minimum of 150 m
and maximum of 500 m) was delineated around each randomly chosen sampling point.
The reach length was based on EMAP pilot studies that suggested this reach length
was necessary to characterize the physical habitats in the stream (Herlihy et al., 2000).
Eleven cross-section transects were established at equal intervals along the length of
the reach.
2.2.2. Water and Sediment Chemistry. Stream water samples were collected in a
flowing portion near the middle of each stream reach in low-density polyethylene
containers (Lazorchak et al., 1998). Samples for dissolved cations and metals were
filtered (0.45-|jm filter) in the field, and samples for dissolved and total metals were
preserved with 2 ml of concentrated HN03 (U.S. EPA, 1987). All samples were placed
on ice and sent to the analytical laboratory (Lazorchak et al., 1998). Base cations and
metals were determined by atomic absorption (U.S. EPA, 1987). Hardness was
calculated from dissolved Ca and Mg (APHA, 1995). The detection limits achieved for
Cd, Cu, Pb, and Zn were 0.3, 0.5, 2.0, and 2.0 |jg/L, respectively.
Sediments for metal analysis were collected from depositional areas near each
of the nine interior cross-section transects along a reach and placed in resealable
plastic bags, placed on ice and sent to the analytical laboratory (Lazorchak et al., 1998).
Samples were digested with HN03 and HCI, and metals were measured by atomic
absorption (U.S. EPA, 1994b). The detection limits achieved for Cd and Pb were 0.025
and 1.08 mg/kg dry weight of sediment, respectively. Cu and Zn were detected in all
tested samples.
2.2.3. Invertebrate and Fish Toxicity Tests. Subsamples of the water and sediments
were also used in ambient toxicity tests. Water toxicity tests were conducted with <24-
hour-old Ceriodaphnia dubia and 3- to 7-day-old Pimephales promelas using standard
water column toxicity testing procedures (U.S. EPA, 1993). The bioassays were 48-
hour, static-renewal tests, conducted at 20°C. Moderately-hard reconstituted water was
used for the control water. Negative controls with moderately-hard reconstituted water
were run with each set of field samples, and 90% survival in the negative control was
required for a test to be valid. Also, tests with a reference toxicant, KCI, were used to
evaluate the condition of the C. dubia and P. promelas. The measurement endpoint for
10
-------
these bioassays was percent survival. Preliminary comparisons showed that survival in
the test bioassays where survival was 80% or less was significantly less than survival in
the control bioassays.
Sediment toxicity tests were conducted with 7-day-old Hyalella azteca using
sediment toxicity testing procedures (U.S. EPA, 1994b). The tests were conducted in
several sets, with 10 to 14 sediments tested in each set. The bioassays were 7-day,
static-renewal tests, conducted at 25°C. Reformulated, moderately-hard, reconstituted
water was used as the overlying water (Smith et al., 1997), and potting soil sediment
was used as the control sediment. Animals were fed and the temperature of the
overlying water was recorded daily. At the end of the test, the sediments were sieved
through a U.S. standard #60 screen (250-|jm mesh), and the live animals were
collected and counted. Animals were euthanized with 70% ethanol, dried for 2 hours at
100°C, and placed in a desiccator until weighed. Negative controls with a potting soil
sediment were run with each set of field samples, and 80% survival in the negative
control was required for a test to be valid. Also, a water-only test with a reference
toxicant, KCI, was used to evaluate the condition of the amphipods. The measurement
endpoints for this bioassay were percent survival and percent growth. Preliminary
comparisons indicated that survival and growth in the test bioassays where survival was
85% or less (Minimum significant difference [MSD] = 4.93%, Thursby et al., 1997) or
growth was 90% or less (MSD = 8.93%), were significantly less than survival and
growth in the control bioassays.
2.2.4. Macroinvertebrate Collection and Identification. Semi-quantitative
macroinvertebrate samples were collected from riffles or pools at each of the nine
interior cross-section transects along a reach with a kick net (Lazorchak et al., 1998).
The samples from each transect were combined into separate composite riffle and pool
samples for each reach. Because of the preponderance of riffle habitats at all sites
(i.e., a pool composite sample was collected at only 11 of 86 sites), only data from
composite riffle samples were used in these analyses. A 300-organism subsample was
counted for each composite sample. Abundance per m2 was estimated based on the
number of grids sorted, subsamples and transects in a composite sample.
2.2.5. Fish Collection and Identification. Fish were collected from the entire stream
reach according to time and distance criteria using pulsed direct-current backpack
electrofishing equipment supplemented by seining (Lazorchak et al., 1998). Total
collection time was not less than 45 minutes and not longer than 3 hours within the
defined sampling reach and was divided in proportion to the area of the stream reach
11
-------
within each of the ten intervals between the eleven cross-section transects. Seining
was used in conjunction with electrofishing to ensure sampling of species that may
otherwise have been under-represented by an electrofishing survey alone or when a
stream was too deep for electrofishing to be conducted safely. The objective was to
collect a representative sample of the fish assemblage by methods designed to collect
all except very rare species, and provide a robust measure of proportional abundances
of species. Sport fish and easily recognized species were identified and released.
Voucher specimens (up to 25) of smaller individuals of each species and unidentified
specimens were retained for museum verification.
2.2.6. Calculation of Community Metrics. We used the macroinvertebrate data to
calculate various community metrics (Tables 1 and 2) proposed in the literature
(Barbour et al., 1999). Richness metrics are the number of taxa identified in a sample
within the specified group (e.g., total taxa richness, Plecoptera taxa richness).
Abundances metrics are the number of individuals found in a sample within the
specified group (e.g., total abundance). Composition metrics are the abundance of
individuals in the specified taxonomic group divided by total abundance or by the
specified larger group (e.g., Chironomidae) and expressed as a percentage (%
individuals that were Ephemeroptera, % Tanytarsini of Chironomidae). Evenness
metrics are either total abundance divided by total taxa richness (e.g., abundance per
taxon) or the abundance of the most common taxon or five most common taxa divided
by total abundance and expressed as a percentage (e.g., % individuals that were the
most common taxon) Trophic or habitat guild metrics can quantify taxa richness of a
particular trophic or habitat guild (e.g., collector-gatherer taxa richness), or the
abundance of individuals in the trophic or habitat guild divided by total abundance and
expressed as a percentage (e.g., % individuals that were collector-gatherers).
Pollution-indicator metrics can quantify taxa richness of a group of indicator taxa (e.g.,
intolerant taxa richness), or the abundance of individuals in the group of indicator taxa
divided by total abundance and expressed as a percentage (e.g., % individuals that
were tolerant taxa). Similarly, we calculated community metrics for fish (Tables 1 and
2), but these were limited by the low natural diversity of fish assemblages in these
coldwater systems (McCormick et al., 1994). The maximum total fish species or
subspecies richness observed was six, while maximum native fish species or
subspecies richness observed was four. Of those sites with fish, the mean proportion
of fish that were trout was 82.7%, and a mean 97.4% of the trout were not native.
12
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TABLE 1
Macroinvertebrate and Fish Metrics that Exhibited Differences Between the Two Groups Segregated
Using at Least One of the Measurement Endpoints. The values are F for the one-way analysis-of-
variance (ANOVA) comparing the metric between the unaffected and affected groups segregated
based on the measure endpoints: D, the hardness-adjusted dissolved chronic criteria for Cd, Cu, Pb, or
Zn; WT, the results of 48-hour, water toxicity tests with C. dubia or P. promelas', S, sediment threshold-
effects-levels for Cd, Cu, Pb, or Zn based on 28-day H. azteca tests; and ST, results of 7-day,
sediment toxicity tests with H. azteca. The p associated with F is in parentheses
Community Metrics
D
WT
S
ST
Macroinvertebrate Metrics
Total taxa richness
Total abundance
Abundance pertaxon
Intolerant taxa richness
Ephemeroptera taxa richness
Plecoptera richness
Trichoptera taxa richness
EPT taxa richness
Chironomidae taxa richness
% lnd.b, tolerant taxa
Orthocladinae taxa richness
Tanytarsini taxa richness
Coleoptera taxa richness
% Ind., Ephemeroptera
% Orthocladinae of
Chironomidae
% Tanytarsini of Chironomidae
% Ind., Coleoptera
% Ind., Diptera and noninsects
% Ind., Most common taxon
% Ind., Five most common taxa
Collector-filterer taxa richness
Collector-gatherer taxa
richness
21.36 (<0.001)a
11.99 (<0.001)a
9.11 (0.003)a
10.81 (0.002)a
7.82 (0.006)a
5.04 (0.027)
6.36 (0.014)
10.74 (0.002)a
5.81 (0.018)
0.56 (0.46)
3.84 (0.053)
6.14 (0.015)
2.71 (0.10)
2.55 (0.11)
2.10 (0.16)
1.95 (0.17)
3.20 (0.078)
0.01 (0.93)
6.90 (0.010)
6.02 (0.016)
2.94 (0.090)
11.94 (<0.001)a
39.67 (<0.001)a
6.90 (0.010)
2.98 (0.088)
23.12 (<0.001)a
15.55 (<0.001)a
10.55 (0.002)a
15.15 (<0.001)a
24.41 (<0.001)a
12.07 (<0.001)a
4.68 (0.033)
11.23 (0.001)a
13.02 (<0.001)a
5.14 (0.026)
4.24 (0.043)
5.35 (0.023)
7.62 (0.007)
3.88 (0.052)
2.77 (0.10)
4.21 (0.043)
5.83 (0.018)
4.30 (0.041)
19.46 (<0.001)a
10.08 (0.002)a
1.21 (0.27)
0.68 (0.41)
7.24 (0.009)a
8.48 (0.005)a
0.88 (0.35)
3.42 (0.068)
6.31 (0.014)
1.69 (0.20)
0.43 (0.51)
0.42 (0.52)
5.57 (0.021)
4.98 (0.028)
0.39 (0.54)
0.01 (0.94)
3.53 (0.064)
7.27 (0.009)a
4.54 (0.036)
0.21 (0.65)
0.77 (0.38)
2.70 (0.10)
5.10 (0.027)
11.42 (0.001)a
3.10 (0.082)
1.65 (0.20)
11.71 (0.001)a
6.65 (0.012)
1.83 (0.18)
3.42 (0.068)
6.31 (0.014)
3.97 (0.050)
0.54 (0.47)
0.92 (0.34)
10.77 (0.002)a
0.55 (0.46)
1.70 (0.20)
0.92 (0.34)
9.71 (0.003)a
2.96 (0.089)
0.04 (0.84)
0.55 (0.46)
2.38 (0.13)
0.51 (0.48)
8.49 (0.005)a
13
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TABLE 1 cont.
Community Metrics
Predator taxa richness
Shredder taxa richness
Scraper taxa richness
D
4.30 (0.041)
6.87 (0.010)
5.52 (0.021)
WT
5.01 (0.028)
16.41 (<0.001)a
7.25 (0.009)
S
1.98 (0.16)
7.43 (0.008)a
4.54 (0.036)
ST
2.84 (0.10)
0.91 (0.34)
4.61 (0.035)
Fish Metrics
Total species richness
Salmonidae species richness
Total abundance
Adult abundance
Salmonidae abundance
% Ind., native species
% Ind., Salmonidae
% Ind., native Salmonidae
% Oncorhynchus of
Salmonidae
4.61 (0.030)
5.40 (0.023)
3.21 (0.077)
3.10 (0.082)
5.83 (0.018)
0.00 (0.98)
3.99 (0.049)
0.65 (0.42)
0.42 (0.52)
8.36 (0.005)
7.08 (0.010)
4.36 (0.040)
4.50 (0.037)
3.45 (0.067)
2.32 (0.13)
12.18 (<0.001)a
1.84 (0.18)
3.35 (0.071)
5.85 (0.018)
3.69 (0.059)
3.93 (0.051)
3.85 (0.054)
0.75 (0.39)
7.86 (0.006)a
0.06 (0.81)
6.14 (0.015)
5.60 (0.021)
0.93 (0.34)
0.51 (0.48)
1.88 (0.18)
1.72 (0.19)
3.12 (0.081)
0.20 (0.66)
1.31 (0.26)
0.86 (0.36)
0.04 (0.85)
a statistically significant when p was corrected with the sequential Bonferroni technique
b % Ind. = Percentage of individuals
14
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TABLE 2
Metrics that Did Not Exhibit Differences among the Groups
Macroinvertebrate Metrics
Fish Metrics
% Ind.
Ind.*, Plecoptera
Ind., Trichoptera
EPT taxa
Ratio, EPT to EPT + Chironomidae
% Ind., Chironomidae
% Ind., Diptera
Crustacea and Mollusca taxa richness
% Ind., Oligochaeta and Hirundea
Hilsenhoff s biotic index
Collector-filterers
Col lector-gatherers
Predators
Shredders
Grazers
Native species richness
Native species abundance
Native, non-Salmonidae species richness
Native, non-Salmonidae abundance
% Ind., native, non-Salmonidae
% Ind.
% Ind.
% Ind.
% Ind.
% Ind.
Ind. = Percentage of individuals
15
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2.2.7. Data Handling and Analysis. We classified sampling events into two groups:
those sites potentially affected and those sites unaffected by metals in surface water or
sediment. We repeated this segregation four times, each based on one of the four
different organism-level measures (Table 3). We classified the sites based on the
chemistry data using chronic AWQCs from U.S. EPA (1999, 2001) and the sediment
threshold-effect levels (TELs) from U.S. EPA (1996). Because the water quality criteria
for Cd, Cu, Pb and Zn are hardness-dependent, the exact values of these criteria varied
among sites. The TELs are based on a compilation of data from 28-day H. azteca
sediment toxicity tests and were total concentrations of 0.583, 28.0, 37.2 and 98.1
mg/kg dryweight of sediment for Cd, Cu, Pb and Zn, respectively (U.S. EPA, 1996).
Because contamination associated with metal mining generally consists of a mixture of
metals, a site was included in the potentially affected groups based on water or
sediment chemistry if the concentration of at least one metal exceeded its criterion.
Classifications of sites to the two groups were compared between surface water
and sediments and between the ambient criteria and ambient toxicity tests with
contingency tables. We calculated the index \ (Goodman and Kruskal, 1972) to assess
the association between the groups. The index y is a measure of association in the
assignment of sites to groups that ranges from -1, if there was no agreement in the
assignment of sites to groups by the two methods, to +1, if there was complete
agreement. We used PROC FREQ (SAS, 1999) in these analyses.
Selected macroinvertebrate and fish metrics were individually compared
between each pair of groups using a one-way analysis of variance (ANOVA) to answer
the question, "Was the mean value of the metric different between the groups identified
as affected or unaffected by metals based on the organism-level measures?" Statistical
significance was set at a = 0.05, and the probabilities for simultaneous tests were
corrected with the sequential Bonferroni technique (Rice, 1989). We used PROC GLM
in this analysis.
These methods are often used concurrently to make decisions about adverse
effects at individual sites. Therefore, we quantified the frequency of disagreement
between an assessment of sites based on organism-level effects and that based on the
significant community metric. If a community metric decreases as a stressor increases,
an assessment based on that metric would differ if the metric was "greater than
expected" at a site identified as affected based on organism-level effects or if the metric
was "less than expected" at a site identified as unaffected based on organism-level
effects. In this study, all the statistically significant metrics decreased in the affected
16
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TABLE 3
Criteria Used to Divide Sites into the Impacted or Unimpacted Groups
Variable
Dissolved concentrations of Cd, Cu, Pb,
orZn*
Survival of C. dubia or P. promelas* in a
48-hour toxicity test
Sediment concentrations of Cd, Cu, Pb,
orZn*
Survival or growth* of H. azteca in 7-day
toxicity test
Organism-level Measure
> hardness-adjusted dissolved chronic
criteria (U.S. EPA, 1999, 2001)
< 80% survival
> TEL for the 28-day H. azteca sediment
toxicity test (U.S. EPA, 1996)
< 85% survival or < 90% growth
* At least one of
17
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group, and we defined community metrics as "greater than expected" when the metrics
were greater than the 95% upper confidence limit (UCL) of an affected group and as
"less than expected" when the metrics were less than the 95% lower confidence limit
(LCL) of the unaffected group as calculated in the one-way ANOVA. We used PROC
MEANS (SAS, 1999) to calculate the 95% UCL and LCL.
We used piecewise or segmented regression (Toms and Lesperance, 2003)
further to explore the relationships between the significant metrics and the
concentrations of Cd, Cu, Pb and Zn in surface water or sediments relative to the
organism-level-based criteria. Piecewise regression is an approach to modeling data
where the regression changes at one or more points, called join points, along the range
of the independent variable (Bellman and Roth, 1969). If the criteria or effects-level
values (i.e., the chronic AWQC for surface water or the TEL for sediments) represent
threshold concentrations for effects at the community level as measured by the metrics,
then a1 or p7 should be significantly less than 0 in the piecewise regression model,
y=ce0+ oc,x, + @0logex2 + fiXitogex2 (Eq. 1)
where:
x1 = a dummy variable with a value of 1 if at least one metal exceeded its
criterion or sediment-effects concentration and a value of 0 otherwise
x2 = the summation of the ratios of the concentration of each metal to its
criterion or sediment-effects concentration
y = the metric value.
By designing the analysis in this way, the model is reduced to
y = ao +£0fog6x2 (Eq. 2)
when no metals exceed their criteria or sediment-effects concentration because a1 x1 =
0 and p7 x1 Iogex2 = 0. The coefficients, o^ and p7, then are the changes in the intercept
and slope of the regression when at least one metal exceeds its criterion or sediment-
effects concentration. We used PROC GLM (SAS, 1999) in these regression analyses.
This approach, using the summed ratios of the concentration of each metal to its
criterion or sediment-effects concentration as the continuous independent variable,
assumed that the effects of the four metals were concentration additive and that the
criteria or sediment-effects concentrations represent their common mechanism and
threshold level of effect. The criteria do not account for possible synergistic or
antagonistic effects among these metals (U.S. EPA, 2000b).
18
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2.3. RESULTS AND DISCUSSION
Because data were not complete for some sites (i.e., some sites lacked fish
data, chemistry data or toxicity data), macroinvertebrate metrics could be compared for
83 to 85 sites depending on the organism-level measurement endpoint. Fish metrics
could be compared for 76 to 78 sites.
2.3.1. Organism-level Measures. Using either metal concentrations or ambient
toxicity tests, we identified more sites as affected by sediment contamination than by
surface water contamination because there were more sites where metal
concentrations or ambient toxicity tests indicated sediments were toxic whereas surface
water was not than sites showing the reverse (Table 4). The association among
groups, Y, was +0.89 between assessments based on water or sediment metal
concentrations and +0.83 for those based on water or sediment toxicity tests.
As described in the literature on the hydrogeochemistry of the mine drainage that
results in this metal contamination (Chapman et al., 1983; Filipek et al., 1987), metal
concentrations in water are greatest closer to the mine source, but decrease as metal
solubility changes in relation to pH and other factors. Metal concentrations in
sediments increase downstream of the mine source within the zone where the metals
are deposited. Although pH data for these sites were considered invalid, dissolved
organic carbon ranged from less than a detection limit of 1.0 mg/L to 10.8 mg/L.
Therefore, we would expect some sites to have elevated concentrations of these metals
in sediment but not water. Also, the tests of sediment measure incrementally more
sensitive endpoints than those for water (i.e., survival and growth versus just survival).
Comparing metal concentrations versus ambient toxicity tests, more sites were
identified as affected based on metal concentrations than on ambient toxicity tests
(Table 5), because metal concentrations indicated surface water or sediments were
toxic whereas ambient toxicity tests did not indicate toxicity at more sites than in the
reverse where ambient toxicity tests indicated toxicity although criteria did not. The
association among groups, \, was greater for the assessments based on water (\ =
+0.98) than those based on sediment (\ = +0.73). The mean summed ratios of the
dissolved concentrations of the four metals to their chronic AWQCs and the mean
summed ratios of the sediment concentrations of the four metals to their TELs were
greater at sites classified as affected by the ambient toxicity tests for water and
sediment, respectively (Figure 2). However, these two measures agreed in their
classification of a site at only 53% of the 19 sites identified as affected by at least one
19
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TABLE 4
Correspondence of Conclusions of Assessments for Surface Water and Sediment for
Sampling Events
("Vitaria l\i — J-H RCA
^iiLciid ^Y ~~ ^u.oy^
Were sediment TELs exceeded?
No
Yes
Total
Ambient toxicity tests (y = +0.83)
Did sediment ambient toxicity tests
show effects?
No
Yes
Total
Were water criteria exceeded?
No
53
15
68
Yes
3
15
18
Total
56
30
n = 86
Did water ambient toxicity tests show
effects?
No
63
10
73
Yes
4
7
11
Total
67
17
n = 84
20
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TABLE 5
Correspondence of Conclusions of Assessments Based on Chemical Criteria and
Ambient Toxicity Tests for Sampling Events
XA/otor l\i — -4-O QR^
VVdlcl ^Y ~~ ^U.yo^
Did water toxicity tests show
effects?
No
Yes
Total
Sediment (y = +0.73)
Did sediment toxicity tests show
effects?
No
Yes
Totals
Were metal AWQC exceeded?
No
65
1
66
Yes
8
10
18
Total
73
11
n = 84
Were metal sediment TELs
exceeded?
No
49
5
54
Yes
18
12
30
Totals
67
17
n = 84
21
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b •
0* 5 •
a
1 4'-
U
c 3 •
o
F"
o 2 -
C -
J^
1
^
rti
t %
c -1 •
o
p
w -2 •
^3>
* -3-
j(
F = 68.53 (p< 0.001)
n
n
n
i
;
H
:
H I
n g
H -
i
B
n
F = 13.67 (p <
n
a
i
1
0.0001)
n
|
n
s
p-.
1
' — '
a
b
• ^
'^"r
.4 p
"c
- 3 o
- 2 -^
c
. o
is
i_
- 0 ^
u o>
(J
c
- -1 o
o
' '2 ^J
D)
-^ -
J(
Unaffected Affected
Water toxi city
Unaffected Affected
Sediment toxi city
Ambient Toxi city Tests
G = the raw data
The boxes show the mean and 95% confidence limits.
FIGURE 2
Comparison of Metals Concentrations in Water [loge(I Concentration / Chronic AWQC)]
and in Sediment [loge(I Concentration / TEL)] Between Groups Identified as Potentially
Affected or Unaffected by the Ambient Toxicity Tests of Water and Sediment,
Respectively
22
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measure for water and only 34% of the 35 sites identified as affected by at least one
measure for sediment.
2.3.2. Organism-level Measures versus Community Metrics. When each metric
was compared between pairs of groups segregated using the organism-level measures
using a one-way ANOVA, a number of macroinvertebrate metrics exhibited significant
differences between at least one pair of groups segregated using the organism-level
measures (Table 1), whereas other metrics did not exhibit significant differences
between any pairs of groups (Table 2). To be conservative, we will concentrate on
those metrics for which F was statistically significant when p was corrected with the
sequential Bonferroni technique. The metrics listed in Table 1 with the greatest F
values from the one-way ANOVA are generally richness metrics: total taxa richness
[AWQC - F = 21.36 (p<0.001 < adjusted p=0.050), water toxicity test - F = 39.67
(p<0.001 < adjusted p=0.050), sediment TEL - F = 10.08 ( p=0.002 < adjusted
p=0.050), sediment toxicity test - F = 11.42 (p=0.001 < adjusted p=0.050)],
Ephemeroptera, Plecoptera and Trichoptera (EPT) taxa richness [AWQC - F = 10.74
(p=0.002 < adjusted p=0.010), water toxicity test - F = 24.41 (p<0.001 < adjusted
p=0.025)], Tanytarsini taxa richness [water toxicity tests - F = 13.02 (p<0.001 < adjusted
p=0.006), sediment toxicity tests - F = 10.77 (p=0.002 < adjusted p=0.017)], intolerant
taxa richness [AWQC - F = 10.81 (p=0.002 < adjusted p=0.013), water toxicity test - F =
23.12 (p<0.001 < adjusted p=0.016), sediment toxicity test - F = 11.71 (p=0.001 <
adjusted p=0.050)], and collector-gatherer richness [AWQC - F = 11.94 (p<0.001 <
adjusted p=0.017), water toxicity test - F = 19.46 (p<0.001 < adjusted p=0.013),
sediment toxicity test - F = 8.49 (p=0.005 < adjusted p=0.010)], for macroinvertebrates
(Figures 3 and 4). An exception is the total number of individuals [AWQC - F = 11.99
(p=0.001 < adjusted p = 0.025)] for macroinvertebrates (Figure 4), which is an
abundance metric. The metrics that exhibited significant differences between pairs of
groups and are listed in Table 1 are relatively sensitive to the stressor gradient
represented by metals contamination, whereas the metrics listed in Table 2 are
insensitive to this gradient. Similar metrics were identified for being sensitive to this
gradient by multivariate analyses in Griffith et al. (2001).
This sensitivity of richness metrics to metal contamination is consistent with an
assumption that effects at the organism and population levels are the basis of effects
observed at the community level. Persistent toxicants, such as metals, increase
mortality and decrease growth and reproduction of individuals within an exposed
population. These are organism-level effects that result in reduced abundances at the
23
-------
n=67 18 73 11 55 30 67 17
1* •
If
Vi
1 12'
I
3 Q
3 * '
3 •
CoUe ctor- gather or richness
M M K» K»
5 Cfl «
-------
18 73 11 55 30 67 17
l= 67 18 73 11 55 30 67 17
Totil tax a richness - nacncdiiTCitHlirjitss
= S B 8 S '& i
E
*
El
*
E
*
El
*
E
**
3
c.
E
**
3
|
I
E
m
**
**
U A
Sediment
bioassay
U A
Dissolved
criteria
U A
Water
bioas:say
U A
Dissolved
criteria
U A U A
Water Sediment
bioassay TEL
U A
Sediment
bioassay
n = number of sites classified in each group
U = unaffected group
A = affected group
ns = not significant
* = p < 0.05
** = significant when probabilities for simultaneous tests
were corrected with a sequential Bonferroni technique
FIGURE 4
Comparison of Macroinvertebrate and Fish Metrics Between Groups Identified as
Potentially Affected or Unaffected by Each of the Organism-level Endpoints. The boxes
show the mean and 95% confidence limits of each metric for each group, while the
whiskers show the range.
25
-------
population level (Kuhn et al., 2000). At some threshold, population recruitment fails,
and more sensitive species will be eliminated from the community (Sheehan, 1984).
Because the threshold concentrations at which different species are affected vary, more
of the species in a community would be affected with increasing toxicant
concentrations, and taxa richness would decrease (Barnthouse et al., 1986). The
insensitivity of various composition metrics suggests no concomitant increase in more
tolerant species, which could adapt or acclimatize themselves to these toxicants,
occurred in compensation for the eliminated species (Vinebrooke et al., 2003). Such
population effects would also be the basis of the observed decrease in the total number
of individuals collected. We did not test other abundance metrics for
macroinvertebrates because such metrics are not normally used in bioassessments.
Abundance metrics require quantitative samples, and many states and other entities
collect only qualitative samples as part of bioassessments (Barbour et al., 1999).
However, this R-EMAP study collected semi-quantitative samples.
Fish metrics were less sensitive to the metal contamination. Only two
composition metrics were significantly different between one pair of groups (Table 1,
Figure 4): % individuals that were native species [sediment TEL - F = 7.86 (p=0.006 <
adjusted p=0.017) and % individuals that were Salmonidae [water toxicity test - F =
12.18 (p<0.001 < adjusted p=0.006)]. However, this lack of sensitivity by the fish
metrics might be a result of the low diversity of the fish assemblage in these cold-water
streams. Maximum total fish species or subspecies richness in these streams was six,
and maximum native fish species or subspecies richness was four. In streams with
fish, a mean of 83% of the fish were Salmonidae, and a mean of 97% of the
Salmonidae were not native species or subspecies.
When classification of sites to the affected and unaffected groups based on
organism-level effects is compared with individual metric values, the methods differ in
their assessment of adverse effects at some sites (Table 6). For example, the total
taxa richness metric for macroinvertebrates was greater than the 95% upper confidence
limit of the mean of the affected group for 6 of the 18 sites classified as affected based
on exceedance of the dissolved metals criteria and was less than the 95% lower
confidence limit of the mean of the unaffected group for 28 of the 67 sites classified as
unaffected.
Sites in the unaffected group where metrics are less than the expected range
may be affected by other stressors. Previous analyses also identified increased
nutrients and fine sediments and decreased canopy cover associated with livestock
26
-------
TABLE 6
Enumeration of Sampling Events in Wadeable Streams in the Southern Rockies
Ecoregion of Colorado Where Classification Based on the Organism-level Measures
and that Based on the Community Metric Disagree
Metric
Number of Sampling Events*
Classified
as
Unaffected
Metric <95%
LCL for
Unaffected
Group
Classified
as
Affected
Metric
>95% UCL
for Affected
Group
Dissolved Chronic Criteria
Total taxa richness
(macroinvertebrates)
Total number of individuals
Number, Individuals per taxon
Intolerant taxa richness
Ephemeroptera taxa richness
EPT taxa richness
Collector-gatherer taxa richness
67
67
67
67
67
67
67
28
36
32
23
22
20
30
18
18
18
18
18
18
18
6
1
3
5
7
4
6
Water Toxicity Tests
Total taxa richness
(macroinvertebrates)
Intolerant taxa richness
Ephemeroptera taxa richness
Plecoptera taxa richness
Trichoptera taxa richness
EPT taxa richness
Chironomidae taxa richness
Orthocladinae taxa richness
73
73
73
73
73
73
73
73
29
25
24
28
29
25
32
31
11
11
11
11
11
11
11
11
3
2
2
3
4
4
3
3
27
-------
TABLE 6 cont.
Metric
Tanytarsini taxa richness
Collector-gatherer taxa richness
Shredder taxa richness
% Individuals, Salmonidae
Number of Sampling Events
Classified
as
Unaffected
73
73
73
67
Metric <95%
LCL for
Unaffected
Group
27
33
40
25
Classified
as
Affected
11
11
11
11
Metric
>95% UCL
for Affected
Group
3
4
1
3
Sediment Threshold Effects Levels
Total taxa richness
(macroinvertebrates)
Ephemeroptera taxa richness
% Coleoptera
Shredder taxa richness
% Individuals, native species
55
55
55
55
49
21
25
28
30
39
30
30
30
30
29
13
9
9
8
0
Sediment Toxicity Tests
Total taxa richness
(macroinvertebrates)
Intolerant taxa richness
Tanytarsini taxa richness
% Tanytarsini of Chironomidae
Collector-gatherer taxa richness
67
67
67
67
67
26
22
23
33
33
17
17
17
17
17
7
6
4
2
5
* The total number sampling events is the sum of the columns labeled "Classified as
unaffected" and "Classified as affected."
28
-------
grazing in riparian zones as another stressor gradient in these Rocky Mountain streams
(Griffith et al., 2001). Also, because most sites were only sampled once, we do not
know the temporal variability of metal concentrations in these streams, and these single
measurements may underestimate exposure offish or macroinvertebrates to metals in
some streams.
At sites in the affected group where metrics were greater than the expected
range, exposure to metals in surface water and sediments may differ from that
measured, in part because of unaccounted for effects on metal bioavailability. In
surface water, factors, such as dissolved organic carbon, pH, or other cations besides
water hardness, may also affect metal bioavailability (Di Toro et al., 2001), but U.S.
EPA water quality criteria are currently only adjusted for water hardness. The TELs
were derived from analyses of laboratory bioassay data (U.S. EPA, 1996) that did not
consider possible factors affecting metal bioavailability in sediments (Chapman et al.,
1999). Acid volatile sulfide (AVS) can affect the bioavailability of metals in sediments
(Liber et al., 1997). However, AVS was not measured in this study, and significant
concentrations of AVS are unlikely to occur in the coarse, well-aerated sediments of
these shallow, high-gradient streams. Including these additional factors that affect
metal bioavailability in models used to adjust the criteria or other guidelines may be
appropriate.
The differences in assignment of sites to affected and unaffected groups based
on criteria or sediment-effects concentrations versus ambient toxicity tests likely also
result from the direct assessment of bioavailability by the ambient toxicity tests.
However, there is also a difference in duration between the organism-level endpoints
for the chemical criteria and ambient toxicity tests. The criteria we used for surface
water are chronic criteria, whereas the ambient toxicity tests would be considered acute
in duration. Chronic effects are expected at lower concentrations of toxicants than
acute effects, and chronic effects would be reflected by the community metrics.
2.3.3. Piecewise Regression Analyses. Metal contamination associated with hard-
rock metal mining is a complex impact on streams. In the mineralized zone of the
Southern Rockies Ecoregion, the contamination is a mixture of primarily four metals,
Cd, Cu, Pb and Zn, that changes as surface water chemistry changes downstream from
the mine source (Chapman et al., 1983). To simplify our analyses, we assumed a
potential impact if one or more of the concentrations of these four metals in surface
water exceeded their hardness-adjusted criteria or in sediments exceeded their TEL.
Therefore, the affected group includes a continuum of sites from those in which one
29
-------
metal minimally exceeded its criterion to those in which all four metals greatly exceeded
their criteria. Moreover, the criteria may not necessarily represent actual threshold
concentrations for adverse effects at the community level. For surface water, the slope
of the piecewise regression of the four macroinvertebrate metrics; total taxa richness,
intolerant taxa richness, collector-gatherer richness and EPT taxa richness; on the
summed ratios of the dissolved concentrations of the four metals to their chronic
AWQCs was positive or not significantly different from 0 when the metal concentrations
were all less than the AWQCs (Figure 5). When at least one metal exceeded its
AWQC, the piecewise regressions for the summed ratios were negative and
significantly different from 0. This suggests that the chronic criteria for water
approximate threshold levels for adverse effects the for macroinvertebrate
assemblages in these streams. Conversely, for sediments, the slope of the piecewise
regression of these same four metrics on the summed ratios of the sediment
concentrations of the four metals to their TELs was negative and significantly different
from 0 when the metal concentrations were all less than the TELs (Figure 6). When at
least one metal exceeded its TEL, the slope was less negative, but this change in slope
was significant only for EPT taxa richness. This suggests that the TELs do not
approximate threshold levels for adverse effects for macroinvertebrate assemblages in
these streams, because taxa richness decreased with increasing metals although
sediment concentrations of the four metals were less than the TELs.
Besides assessing measurement endpoints at different levels of biological
organization, chemical criteria, ambient toxicity tests and community metrics differ in
their specificity to different stressor gradients (Karr and Chu, 1998). Ambient criteria
are very specific to whatever contaminants are being measured and assessed and
ignore any unmeasured contaminants or stressors that lack criteria. Ambient toxicity
tests detect toxicity associated with any bioavailable contaminant in the tested surface
water or sediments but do not assess other characteristics of the stream. Community
metrics are not generally designed to be stressor specific. Therefore, while community
metrics may be sensitive to specific stressors (Norton et al., 2000; Griffith et al., 2001;
Ofenbock et al., 2004), those metrics also will be sensitive to other concurrent
alterations of the stream that affect the structure of the biotic assemblages. This
includes alterations of physical habitat that are not addressed by chemical criteria.
We used a simple approach in classifying the sites into unaffected and affected
groups. This was done, recognizing that only recently have models been constructed to
extrapolate accurately between the organism- and population-level effects (Kuhn et al.,
30
-------
0 -"-r
r2-O.H
**
* *****
.E-lQ.OIxrOJtt^]
1
L
•** I
I * » • '
I • • * * * •
-3 -2 -1 I
4 5
-3-2-10
30-
-3-2-1*1
y = the metric value
x, (dummy variable) = 1 if at least one metal exceeds its chronic AWQC (open circles), or x1 = 0 otherwise (solid
circles)
X2 = E (ratios of the dissolved concentrations of Cd, Cu, Pb, and Zn to their chronic AWQC)
* = coefficient significantly different from 0 at p < 0.05
The solid lines are the predicted regression lines for each segment.
FIGURES
Piecewise Regressions of Taxa Richness Metrics on the Summed Ratios of the
Dissolved Concentrations of Cd, Cu, Pb and Zn to their Chronic AWQC
31
-------
-1 0
log (Eoonjoentrafajon/IEL)
log
y = the metric value
x1 (dummy variable) =1 if at least one metal exceeds its TEL (open circles), orx, = 0 otherwise (solid circles)
X2 = £ (ratios of the sediment concentrations of Cd, Cu, Pb, and Zn to their TELs)
* = coefficient significantly different from 0 at p < 0.05
The solid lines are the predicted regression lines for each segment.
FIGURES
Piecewise Regressions of Taxa Richness Metrics on the Summed Ratios of the
Sediment Concentrations of Cd, Cu, Pb and Zn to their TELs
32
-------
2000), and we still cannot accurately model or extrapolate between population and
community effects because of the difficulties of incorporating variation in exposure and
response across the hierarchical levels of time, space and organization (de Kruijf, 1991;
Karr and Chu, 1998). Considering this simple classification, one might expect few, if
any, of the metrics would have exhibited differences in their means between the two
groups. However, a number of metrics, particularly richness metrics, exhibited
differences between the groups although the conclusions based on the organism-level
measures and on community metrics disagreed at some sites. This would suggest that
a relationship exists between the organism-level effects assessed by ambient criteria or
guidelines or ambient toxicity tests and the community-level effects assessed by
community metrics. However, the organism-level effects are only predictive to a limited
extent of the community-level effects at individual sites, because this predictability is
affected by differences among the methods that go beyond the hierarchical levels of
biological organization used as their measurement endpoints. We need to assess the
generality of these relationships for other contaminants besides metals.
33
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3. ESTUARINE SYSTEMS IN THE VIRGINIAN PROVINCE OF THE
ATLANTIC COAST
3.1. INTRODUCTION
In this chapter, we compare and contrast statistically the results of three different
methods used by the U.S. EPA for the ecological assessment of contaminant exposure
and effects in sediments in estuarine ecosystems: (1) chemical guidelines, (2) ambient
toxicity assessments, and (3) bioassessments of benthic invertebrates to determine the
relationships among the levels of biological organization assessed by each method.
We also assess the extent to which organism-level effects predict effects at the
community level. Through these comparisons, we expected to assess the relationships
among the levels of biological organization protected by the different methods and
assess the extent to which organism-level effects are predictive of effects at the
community level. In this paper, this approach is applied to the effects of sediment
contamination in estuaries of the Virginian Province of the Atlantic coast of the United
States. Contaminants in these sediments were expected to be metals, polyaromatic
hydrocarbons (PAHs), some pesticides and polychlorinated biphenyl (PCB) congeners.
3.2. MATERIALS AND METHODS
3.2.1. Study Area and Survey Design. The Virginian Province of the United States
includes estuarine habitats along the Atlantic coast extending from Cape Henry, Virginia
to Cape Cod, Massachusetts. In the following tables, we present data compiled from
U.S. EPA's EMAP surveys conducted from 1990 to 1993. As part of these surveys,
sampling sites were selected in a stratified, random manner within each of three
classes of estuaries based on size: large estuaries, large tidal rivers and small estuaries
or tidal rivers (Strobel et al., 1999). In the Virginian Province, this sampling approach
identified 12 large estuaries, five large tidal rivers and 144 small estuaries or tidal rivers.
Additional sites were selected non-randomly in areas for which there was prior
knowledge of ambient environmental conditions that represent areas with likely
anthropogenic disturbance. Some sites were revisited during a subsequent year to
assess variability among years, but data from only one visit to a site were considered in
these analyses. Nevertheless, some sites lacked data for one or more of the
measurements, such as chemistry, toxicity tests or benthic invertebrates.
Sites were sampled from July to September each year. This index period was
selected as the period of the year when biotic responses to potential anthropogenic and
natural stressors were anticipated to be most pronounced (Strobel et al., 1995).
34
-------
3.2.2. Field and Laboratory Methods. Field methods for the Virginian Province
surveys are fully documented in Reifsteck et al. (1993), and laboratory methods are
documented in U.S. EPA (1995). These methods are summarized briefly below.
At each station, salinity (%o), temperature (°C), and dissolved oxygen (DO, mg/L)
were recorded with a model SBE-25 Sealogger conductivity-temperature-depth profiler
(Sea-Bird Electronics, Inc., Bellevue, WA).
At each station, generally three replicate grab samples were collected with a
0.044-m2 Young-modified Van Veen grab (Theodore E. Young, Sandwich, MA) and
processed for benthos (i.e., at two sites, only two replicate grab samples were collected
and processed). Samples were sieved in the field with a 0.5-mm mesh screen.
Material retained on the screen was preserved in 10% buffered formalin with rose
bengal. In the laboratory, samples were sorted. Organisms were counted, weighed,
and identified to the lowest possible taxonomic level, usually species (Strobel et al.,
1995).
Additional grab samples were collected at each site, and the top two cm of
sediment was composited for analysis of percent silt-clay, contaminant concentrations
and sediment toxicity (Strobel et al., 1995). Percent silt-clay was the portion of
sediment passing through a 63-|jm screen.
3.2.3. Sediment Contaminant Concentrations. Subsamples of the composited
sediments were analyzed for organic and metal contaminants. Analysis of organics
involved Soxhlet extraction and extract drying with NaS04, concentration with a
Kuderna-Danish apparatus and cleanup with activated Cu for elemental S and gel
permeation chromatograph or alumina for organic interferents (Paul et al., 1999). PAHs
were analyzed with gas chromatography/mass spectrometry. Pesticides and PCB
congeners were analyzed with gas chromatography/electron capture detection
confirmed by a second column. For Ag, Al, Cr, Cu, Fe, Mn, Ni, Pb and Zn, sediments
were digested with HF and HN03 on a hot plate followed by analysis with inductively-
coupled plasma, atomic emission spectrometry. For As, Cd, Sb, Se and Sn, sediments
were digested with HN03 and HCI in a microwave oven followed by analysis with a
Zeeman-corrected, stabilized-temperature graphite furnace atomic absorption
spectrometry (Paul et al., 1999). Hg was analyzed by cold-vapor atomic absorption
spectrometry.
3.2.4. Ambient Toxicity Tests. Other subsamples of the composited sediments were
used in ambient toxicity tests. Standard, acute, 10-day static tests (U.S. EPA, 1995;
Strobel et al., 1999) were conducted with juvenile Ampelisca abdita. Prior to testing,
35
-------
the amphipods were acclimated at 20°C for at least 48 hours. During testing, the
amphipods were not fed. For each sediment tested, five glass test chambers were
filled with 200 ml of sediment and 600 ml of seawater with salinity of 30 %o. The
chambers were illuminated constantly to inhibit amphipod emergence from the
sediment and maximize exposure. The water was aerated to maintain dissolved
oxygen concentrations >90% saturation. Temperature of the overlying water was
maintained at 20+1 °C. Dead animals were counted and removed daily, and at the end
of the test, the sediments were sieved through a 0.5-mm screen and live amphipods
were collected and counted. Any amphipods, which were not accounted for, were
presumed to have died during the test. Negative controls with an uncontaminated
sediment were run with each set of field samples, and 85% survival in the negative
control was required for a test to be valid. Also, a water-only test with a reference
toxicant, CdCI2 or C12H25S04Na (sodium dodecyl sulfate), was used to evaluate the
condition of the amphipods. The measurement endpoint for these bioassays was
percent survival. These test bioassays indicated toxicity if survival was statistically
different from (a = 0.05) and <80% of survival the corresponding negative control
bioassays (Thursby et al., 1997; Strobel et al., 1999).
3.2.5. Calculation of Community Metrics. We used the benthos data to calculate
various community metrics (Tables 7 and 8), identified as indicative of community
integrity in the literature (Fauchald and Jumars, 1979; Engle et al., 1994; Weisberg et
al., 1997; van Dolah et al., 1999; Olsgard et al., 2003). Richness metrics are the
number of taxa identified in a sample within the specified group (e.g., total taxa
richness, Polychaeta species richness). Abundance metrics are the number of
individuals found in a sample within the specified group (e.g., total abundance, Spionida
abundance), while total biomass in the dry weight of organisms in a sample.
Composition metrics are the abundance of individuals in the specified taxonomic group
divided by total abundance or by the specified larger group (e.g., Polychaeta) and
expressed as a percentage (e.g., % individuals that were Mollusca, % Polychaeta that
were Spionida). Evenness metrics are either total abundance, the abundance of the
specified group, or biomass divided by total taxa richness (e.g., abundance per taxon,
biomass per taxon) or the abundance of the two most common taxa divided by total
abundance and expressed as a percentage (e.g., % individuals in the two most
common taxa). Trophic or habitat guild metrics can quantify taxa richness of a
particular trophic or habitat guild (e.g., Polychaeta omnivore species richness, Infaunal
taxa richness)or the abundance of individuals in the trophic or habitat guild divided by
36
-------
37
TABLE 7
Benthic Metrics that Exhibited Differences Between the Two Groups Segregated Using at Least One of the Measurement Endpoints.3 The values
from the analysis of covariance (ANCOVA) are a, the intercept; b.,, the slope of the regression of the metric on percent silt and clay; b2, the slope
of the regression of the metric on percent total organic carbon, and F (b3), the F value for comparison of the regression of the metric between the
unaffected and affected groups segregated based on the measurement endpoint. For b1 and b2, NS indicates p>0.05, * indicates p < 0.05 and **
indicates p<0.01 for a t test that the slope was significantly different from 0. The p associated with F (b3) is in parentheses and * indicates that the
regression slopes were statistically significant different between the two groups when p was corrected with the sequential Bonferroni technique.
Community Metrics
Total taxa richness
Phyllodocida species richness
Capitellida species richness
Polychaeta omnivore richness
Crustacea species richness
Pollution-indicative taxa richness
Number of individuals pertaxon
Number of infaunal individuals per taxon
Biomass per taxon
% Individuals0'"1, Polychaeta
% Polychaeta8, Phyllodocida
% Polychaeta, Spionida
% Polychaeta, predators
Sediment Chemistry
a
34
5.7
3.0
2.3
13
1.6
7.3
13
013
0.71
0.20
0.25
0.40
bi
NS
NS
NS
NS
-0.073**
NS
NS
NS
NS
NS
0.0010**
NS
NS
b2
-4.2**
-0.64**
NS
NS
NS
NS
3.1**
5.9**
NS
NS
-0.31**
NS
-0.030*
F(b3)
3.97 (0.048)
5.24 (0.023)
11.10 (0.001)*
6.11 (0.014)
7.67 (0.006)
5.06 (0.026)
0.09 (0.77)
0.05 (0.83)
2.91 (0.090)
6.15 (0.014)
6.68 (0.011)
33.04 (<0.001)*
10.62 (0.001)*
Sediment Toxicity Test
a
34
5.8
3.3
2.2
13
1.7
10
19
0.012
0.72
0.21
0.22
0.40
bi
NS
NS
NS
NS
-0.082**
NS
NS
NS
NS
NS
0.00087*
NS
NS
b2
-4.8**
-0.78**
-0.28**
NS
NS
NS
NS
NS
NS
NS
-0.033**
0.043**
-0.042**
F(b3)
0.53 (0.47)
1.00 (0.32)
1.05 (0.31)
0.61(0.44)b
0.30 (0.58)
0.00 (0.98)b
5.15 (0.024)
6.17 (0.014)
9.48 (0.002)*
0.15 (0.70)b
9.538 (0.003)*
2.47 (0.12)
0.59 (0.44)
-------
TABLE 7 cont.
Community Metrics
% Individuals, Gastropoda
% Individuals, Crustacea
% Individuals, Pollution-indicative taxa
% individuals, Pollution-sensitive taxa
% Individuals, Streblospio benedicti
% Individuals, Mulinia lateralis
% Individuals, Paraprionospio pinnata
% Individuals, Acteocina canaliculata
Phyllodocida abundance'
Spionida abundance
Gastropoda abundance
Decapoda abundance
Survival
Sediment Chemistry
a
0.25
0.44
0.085
0.43
0.017
0.011
0.042
0.081
3.4
3.6
3.5
1.6
94
b1
NS
-0.0047**
0.0028**
NS
NS
0.0014**
0.0017**
0.00086**
0.011**
NS
0.012*
NS
NS
b2
NS
0.13**
NS
NS
0.047**
NS
-0.027*
NS
-0.42**
NS
-0.46**
NS
NS
F(b3)
9.60 (0.002)*
4.93 (0.028)
29.98 (<0.001)*
5.89 (0.016)
32.95 (<0.001)*
1.91 (0.17)
7.02 (0.009)*
7.99 (0.005)
14.33 (<0.001)*
5.30 (0.022)
5.99 (0.015)
9.95 (0.002)*
19.42 (<0.001)
Sediment Bioassay
a
0.24
0.45
0.066
0.43
0.012
0.0051
0.044
011
3.5
3.7
3.4
1.7
))
b1
NS
-0.0051**
0.0021*
NS
NS
0.0015**
0.0018**
NS
0.011**
NS
NS
NS
))
b2
NS
0.12**
0.054*
NS
0.075**
NS
-0.035**
NS
-0.50**
NS
NS
-0.18**
))
F(b3)
2.08 (0.15)b
0.04 (0.84)
10.87 (0.001)*
4.38 (0.038)
3.37 (0.068)
6.54 (0.011)
0.90 (0.34)
0.51 (0.48)b
7.21 (0.008)*
0.36 (0.55)b
3.13 (0.08)b
0.50 (0.48)
))
38
The measurement endpoints were: Sediment Chemistry = maximum p from logistic regressions of Field et al. (2002) and Sediment Bioassay :
results of acute, 10-day, sediment toxicity tests with juvenile Ampelisca abdita.
b The p for F value for the overall equation was >0.05.
c % Individuals = Percentage of total individuals that were the specified subgroup.
d Percent metrics were transformed as arcsin Jy/ioo) •
e % Polychaeta = Percentage of Polychaeta individuals that were the specified subgroup.
f Abundance metrics were transformed by loge(y+1).
-------
TABLE 8
Benthic Metrics that Did Not Exhibit Differences among the Two Groups
Segregated Using at Least One of the Measurement Endpoints3
Invertebrate Metrics
Infaunaltaxa richness
Polychaeta species richness
Spionida species richness
Terebellida species richness
Polychaeta sessile richness
Pollution-sensitive taxa richness
% Individuals, two most common taxa
Pielou's evenness index
% Polychaeta9, Terebellida
% Polychaeta, Hesionidae
% Polychaeta, Capitellidae
% Polychaeta, Orbiniidae
% Polychaeta, Cirratulidae
% Polychaeta, Nereididae
% Polychaeta, sessile or discretely motile
individuals
% Polychaeta, surface deposit feeders
%Polychaeta, subsurface deposit feeders
% Individuals, Decapoda
% Individuals'3, Mollusca
% Individuals, Bivalvia
% Bivalviac, Tellinidae
% Bivalvia, Lucinidae
% Individuals, Crustacead
% Amphipoda8, Ampeliscidae + Haustoriidae
% Individuals, Pollution-sensitive taxa
% Individuals, pollution-sensitive Group Af
% Individuals, Mediomastus spp.
Total abundance
Infaunal abundance
Polychaeta abundance
Capitellidae abundance
Terebellida abundance
Mollusca abundance
Amphipoda abundance
Total biomass
aSediment Chemistry = maximum p from logistic regressions of Field et al. (2002); Sediment Bioassay
results of acute, 10-day, sediment toxicity tests with juvenile Ampelisca abdita
b Percentage of individuals that were the specified subgroup
c Percentage of Bivalvia that were the specified family
d excluding Pycnogonida and Thoracica
e Percentage of Amphipoda that were the specified families
f pollution sensitive Group A = Ampeliscidae, Tellinidae, Hesionidae, Cirratulidae, C. polita, and C.
burbancki (van Dolah et al., 1999)
g Percentage of Polychaeta that were the specified subgroup
39
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total abundance or abundance of the specified larger group and expressed as a
percentage (% Polychaeta that were predators). Pollution-indicator metrics are the
abundance of one or more pollution-indicator taxa divided by total abundance or by the
abundance of a larger taxonomic group (e.g., % individuals that were pollution-
indicative taxa, % individuals that Streblospio benedicti).
3.2.6. Data Handling and Analysis. The sites ranged in salinity from freshwater tidal
(<0.5 %o) to poly-euhaline (>18 %o), and many community metrics were correlated with
this gradient, particularly because some metrics were often 0 either at the freshwater
tidal or poly-euhaline sites. Therefore, to reduce this source of variation, we only used
data from the poly-euhaline sites. To focus on effects associated with contaminants in
sediments, we also excluded sites where the measured concentration of dissolved
oxygen was less than 2.0 mg/L. As a result, data from 201 sites were used in these
analyses.
We classified sampling events into two groups, those sites potentially affected
and those sites unaffected by contaminants in sediment. This segregation was
performed twice using the two different organism-level measures (Table 9). We used
the logistic regression models from Field et al. (2002) to classify the chemistry data.
The logistic regression models are for 10 metals, 22 PAHs, total PCBs and 4
organochlorine pesticides and are based on a compilation of matching data for
sediment chemistry and 10-day sediment toxicity tests with the amphipods, R. abronius
or A. abdita from a wide-range of estuarine habitats on the Atlantic, Gulf and Pacific
coasts of North America. It should be noted that a subset of the data used by Field et
al. (2002) was taken from these Virginian Province surveys. The logistic regression
models estimate the probability that sediments from a site will exhibit toxicity based on
individual chemical concentrations, though sediments may be contaminated with a
mixture of chemicals. Field et al. (2002) warn that these logistic regression models are
not dose-response relationships but can be considered indicators of toxicity. A site was
included in the potentially affected group based on sediment chemistry if the predicted
probability that the sediment was toxic exceeded 0.5 for at least one chemical (Field et
al., 2002).
Classifications of sites to groups was compared between sediment chemistry
and ambient toxicity tests with contingency tables, and the index \ (Goodman and
Kruskal, 1972) was calculated to assess the association between the groups. The
index y is a measure of association in the assignment of sites to groups that ranges
from -1, if there was no agreement in the assignment of sites to groups by the two
40
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TABLE 9
Criteria Used to Divide Sites into the Impacted or Unimpacted Groups
Variable
Sediment concentrations of measured
metals, polyaromatic hydrocarbons, total
polychlorinated biphenyls, or pesticides
Survival of A. abdita in a 10-day toxicity
test
Organism-level Measure
Maximum p from logistic regression
models (Field et al., 2002) >0.50
<80% of and significantly different from
survival in controls
41
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methods, to +1, if there was complete agreement. We used PROC FREQ (SAS, 1999)
in these analyses. As the focus of this research is the relationships between
classifications of sites with these two methods, sediment chemistry and ambient toxicity
tests, and community metrics, this analysis was done to contrast how these two
methods classify the sites.
Because many benthic metrics also varied with the silt and clay content or the
organic carbon content of the sediment, we compared each benthic invertebrate metric
between each pair of groups using analysis of covariance (ANCOVA). The question
answered was, "Was the regression of the metric on percent silt and clay and percent
organic carbon in the sediments different between the groups identified as affected or
unaffected by contaminants based on the organism-level measures?" The data were
fitted to the model:
y=a+b1x1 + h2x2+b3x3 (Eq. 3)
where:
x3 = a dummy variable with a value of 1 if at least one metal exceeded its
criterion or sediment-effects concentration and a value of 0 otherwise
x1 = % silt and clay content of the sediment
x2 = % organic carbon content of the sediment
y = the metric value.
By designing the analysis in this way, the model reduces to a two-way ANCOVA, if
either b., or b2 is not significantly different from 0, and reduces to a one-way ANOVA, if
both b1 and b2 are not significantly different from 0. To homogenize the variance,
abundance metrics were transformed by loge(y+1) and percentage metrics were
transformed by ^(y/100) arcsine. Statistical significance was set at a = 0.05, and the
probabilities for simultaneous tests were corrected with the sequential Bonferroni
technique (Rice, 1989). We used PROC GLM (SAS, 1999) in this ANCOVA.
To explore further the relationships between the significant metrics and the
organism-level measures, we examined the residual of each metric,
Residual=Qbserved-[a+biXi + b2X2} (Eq. 4)
where a, b1; and b2 are the estimated intercept and significant slopes from the
regression in Equation 3 (Draper and Smith, 1981).
42
-------
This approach removes the variation in the metric variables resulting from the silt
and clay content and the organic carbon content of the sediments. Then, we regressed
the residuals of the significant metrics either against maximum p from the logistic
regressions or percent survival of A. abdita in the ambient toxicity tests.
These methods may be used concurrently to make decisions about whether
adverse effects are occurring or are likely at individual sites. Therefore, we quantified
the frequency of disagreement between assessments of sites based on organism-level
effects and those based on the significant community metrics. An assessment based
on a community metric would differ if the metric was "different from expected" at sites
identified as affected or unaffected based on organism-level effects. However, whether
a metric was "different from expected" changed depending on whether a metric
increased or decreased at affected sites. We defined community metrics as "different
from expected" using the 95% confidence limits as outlined in Table 10. We used
PROC REG, PROC UNIVARIATE, and PROC GLM to calculate the parameters
necessary to estimate the 95% confidence limits.
3.3. RESULTS AND DISCUSSION
Because data were not complete for some sites (i.e., some sites lacked
invertebrate data, chemistry data particularly for PCBs or pesticides, or ambient toxicity
test data), comparisons were made for 152 to 201 sites depending on the variables
being compared.
3.3.1. Organism-level Measures. A few more sites were identified as affected based
on chemistry than on ambient toxicity tests (Table 11) because chemistry indicated
sediments were toxic whereas the ambient toxicity tests did not at more sites than did
the reverse where ambient toxicity tests indicated toxicity whereas chemistry did not.
The association between groups, \, was +0.724 for the assessments using ambient
toxicity tests versus chemistry, and mean percent survival of A. abdita in the toxicity
tests was less among the sites where maximum p was greater than 0.5 (Figure 7).
However, these two measures agreed in their classification of a site at only 25% of the
43 sites where sediments were identified as affected by least one measure.
This inconsistency in classification of sediments as affected between ambient
toxicity tests and chemistry has been identified previously (O'Connnor and Paul, 2000;
O'Connor et al., 1998) for other benchmarks. Although A. abdita has been a standard
species for testing of estuarine sediments (U.S. EPA, 1994a), it may be more tolerant of
many contaminants compared with other indigenous estuarine species (Hyland et al.,
1996). The logistic equations we used were based on an analysis of compiled data on
43
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TABLE 10
Criteria Used to Classify Metrics as Different than Expected
Metric
Increases at affected sites
Decreases at affected sites
Unaffected sites
Metric residual for individual site > Upper
95% confidence limit of mean metric
residual for unaffected sites
Metric residual for individual site < Lower
95% confidence limit of mean metric
residual for unaffected sites
Affected sites
Metric residual for individual site < Lower
95% confidence limit of mean metric
residual for affected sites
Metric residual for individual site > Upper
95% confidence limit of mean metric
residual for affected sites
44
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TABLE 11
Correspondence of Conclusions of Assessments Based on Chemcial Criteria and
Ambient Toxicity Tests for Sampling Events
Y = +0.724
No
Sediment toxicity tests show Y
effects?
Totals
Maximum p from logistic regression
models >0.50?
No
143
14
157
Yes
18
11
29
Totals
161
25
n = 186
45
-------
120 -,
100 -
80
•a
1-
5?
4D -
30 -
0 -
F = 2Q24 (p< 0.001)
B
D
B
D
Maximum p < 0.50
Maximum p > 0.50
G = the raw data
The boxes show the mean and 95% confidence limits.
The dashed line is 80%, the percent survival used to classify sites based on the ambient toxicity tests.
FIGURE/
Comparison of Percent Survival of A. abdita Between Sites where Maximum p < 0.50
from the Logistic Regressions and those where Maximum p > 0.50
46
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sediment chemistry and 10-day sediment toxicity tests with the amphipod R. abronius in
addition to A. abdita (Field et al., 2002). Moreover, only mortality was used as the
measurement endpoint in these data, instead of the multiple endpoints used by Long et
al. (1995) to derive ER-Ms. Also, Field et al. (2002) used 90% survival in the test
bioassay to classify sediments as toxic, whereas we used 80% survival relative to the
negative control to classify sediments as toxic in the ambient toxicity tests.
A p from the logistic regression models for a least one measured constituent
exceeded 0.5 at 32 of 211 sites. For each of these sites, a p exceeded 0.5 for one or
more metals, one or more PAHs or both (Table 12). The p from the logistic regression
for total PCBs exceeded 0.5 at only 1 site and p for pesticides exceeded 0.5 at 8 of the
152 sites where PCBs or pesticides data were available. However, all these sites also
were contaminated by metals or PAHs.
3.3.2. Organism-level Measures versus Community Metrics. A number of benthic
metrics exhibited significant differences between at least one pair of groups segregated
using the organism-level measures (Table 7). Other metrics did not exhibit significant
differences between any pairs of groups (Table 8). However, these differences among
metrics appear to depend on the sensitivity of the benthic metrics to the stressor
gradient being examined (Griffith et al., 2001). The metrics with the greatest F statistics
for the comparison between the two groups identified based on sediment chemistry in
Table 7 included a richness metric, Capitellida species richness (Figure 8); composition
metrics, percent Polychaeta that were Spionida and percent individuals that were
Gastropoda (Figure 8); a trophic metric, percent Polychaeta that were predators;
pollution-indicator metrics, percent individuals that were pollution-indicative taxa and
percent individuals that were Streblospio benedicti (Figure 9); and abundance metrics,
Phyllodocida abundance and Decapoda abundance (Figure 9). However, the
comparisons of metrics between the groups identified based on the ambient toxicity
tests showed fewer significant differences (Table 7), and the statistically significant
metrics were percent Polychaeta that were Phyllodocida, percent individuals that were
pollution-indicative taxa and Phyllodocida abundance and the evenness metric,
biomass pertaxon (Figure 10).
Percent silt and clay content of the sediments ranged from 0.1% to 99.4%, while
the % organic carbon content of the sediments ranged from 0.01% to 7.0% and was
correlated with the % silt/clay content (i.e., r = 0.77). Of the metrics that also showed
significant differences between the groups classified as affected and unaffected based
on sediment chemistry, % Polychaeta that were predators exhibited a negative
47
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TABLE 12
Comparison of Sites where Maximum p from the Logistic Regression
>0.50 for Metals versus for PAHs
Maximum p from logistic regression
models for metals >0.50?
No
Yes
Totals
Maximum pfrom logistic
regression models for
PAHs >0.50?
No
179
188
Yes
17
23
Totals
185
26
n =
48
-------
Residual" 0.415-1J81 (Maximum p)
Residual • -0.0769 + 0.362 jMaKimum p)
Ma
-------
JS 1.3 -
1 1.0 -
5 '•*"
I
* o.S-
'w 0.0 -
0.0
Residual -0.0839 + 0721 [Maximum p)
I O
Residud- 4),0556 *
-------
1.20
l = 0.044B - 0X100466 ('A Surciual)
1.2 -i
Residual = 0.550 - 0.00482 ("/, Survival)
g
111
1C 51
%E wine!
ID
Residual --0.0943*0 00130 (% Suivivd)
Res id ud- 4(515 < OJ30946 (% Survivd)
D
110
The solid lines are the predicted regression lines.
The dashed lines are the 95% confidence limits.
The vertical line is the percent survival of 80% used to classify the two groups,
! = sites classified as unaffected
" = sites classified as affected
FIGURE 10
Regressions of Residuals (i.e., after variation due to the percent silt & clay content and
the percent organic carbon content of the sediment were removed) of Benthic Metrics
on Percent Survival for the Sediment Toxicity tests with A. ampelisca: A. Biomass per
taxon, B. percent individuals that were pollution-indicative taxa, C. percent Polychaeta
that were Phyllodocida, and D. Phyllodocida abundance. Note that % survival
decreases along the X axis. Therefore, the slope of the regression equation estimates
the change in the residual from right to left on the graph.
51
-------
relationship with % organic carbon, % individuals that were Streblospio bendicti
exhibited a positive relationship with % organic carbon, % individuals that were
pollution-indicative taxa exhibited a positive relation with % silt and clay, and
Phyllodocida abundance exhibited a positive relationship with % silt and clay and a
negative relationship with % organic carbon (Table 7). Of the metrics that also showed
significant differences between the groups classified as affected and unaffected based
on the sediment toxicity tests, % individuals that were pollution-indicative taxa showed
positive relationships with both % silt and clay and % organic carbon, and both %
Polychaeta that were Phyllodocida and Phyllodocida abundance showed a positive
relationship with % silt and clay and a negative relationship with % organic carbon.
The sensitivity of these richness, composition, trophic guild, pollution-indicator
and abundance metrics to the identified sediment contamination is consistent with an
assumption that effects at the organism and population levels are the basis of effects
observed at the community level. Toxicants, such as PAHs and metals, may increase
mortality and decrease reproduction of organisms within exposed populations that are
less tolerant to the toxicants. In turn, more tolerant organisms in exposed populations
may experience less of an increase in mortality and less of a decrease in reproduction,
and these populations may increase, in part, because of reduced species interactions
(Vinebrooke et al., 2003). These are organism-level effects that result in altered
relative abundances at the population level (Kuhn et al., 2000). Such population effects
would also be the basis of the observed changes in the absolute abundances of
different taxa. If at some threshold population recruitment fails, less tolerant species
will be eliminated from the community (Sheehan, 1984). Because the threshold
concentrations at which different species are affected vary, more of the species in a
community would be affected with increasing toxicant concentrations, and taxa richness
would decrease (Barnthouse et al., 1986). However, single species toxicity tests may
not be a very sensitive indicator for such community changes if the test organism is
more tolerant than other indigenous taxa. This sensitivity may be further reduced
because of the acute duration of the toxicity tests. The metrics measure chronic
effects, which occur at lower concentrations of toxicants than acute effects. This may
explain the fewer metrics that distinguished between sites classified based on the
ambient toxicity tests.
While the assessments using toxicity tests and biotic metrics may have been
more comparable if the duration of the toxicity tests were chronic, this is a limitation of
our use of secondary data, which was collected for another purpose. We used EMAP
data, and because of decisions made by the EMAP researchers, only data from toxicity
52
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tests of acute duration were available. Moreover, the random site-selection approach
of EMAP results in sampling of uncontaminated and contaminated sites in proportion to
their occurance across a region. This resulted in the unbalanced distribution of sites
between the unaffected and affected groups as identified by sediment chemistry or the
ambient bioassays. The advantage of this data set is includes data from a large
number of sites that do not exhibit spacial correlations.
When classification of sites to the affected and unaffected groups based on
organism-level effects is compared with individual metric values, the methods differ in
their assessment of adverse effects at some sites (Table 13). For example,
Phyllodocida species richness was less than the 95% lower confidence limit of the
mean of the unaffected group for 88 (51.5%) of the 171 the sites classified as
unaffected. Moreover, Phyllodocida species richness was greater than the 95% upper
confidence limit of the mean of the affected group for 8 (26.7%) of the 30 sites
classified as affected by metals or PAHs based on the logistic equations. Sites in the
unaffected group where metrics are different from expected are probably affected by
other stressors. Previous analyses have identified other contaminants in sediments,
such as some pesticides, butyltins, or selenium (Kiddon et al., 2003), that could not be
assessed with the logistic equations (Field et al., 2002). Other stressors may include
excess nutrients, with their effect on light penetration, on dissolved oxygen in the water
column and on total organic carbon in the sediments; the presence of marine debris or
other habitat alterations (Strobel et al., 1999; Kiddon et al., 2003). We only excluded
sites where low dissolved oxygen was an obvious additional stressor.
At sites in the affected group where metrics were different from expected,
exposure to contaminants in sediments may differ from that measured, in part because
of unaccounted for effects on bioavailability. The logistic regressions were derived from
analyses of bioassay data that did not consider possible site-specific factors affecting
the bioavailability of the contaminants in sediments (Field et al., 2002). Alternate
approaches to assessing sediment chemistry have measured AVS or the fraction of
organic carbon, which may affect the bioavailability of metals and organics such as
PAHs, respectively (Liber et al., 1997; U.S. EPA, 2003b). While these methods attempt
to assess the bioavailability of these contaminants, there are limitations to these
approaches, particularly with the assumption that equilibrium conditions exist within the
sediments for metals and AVS or PAHs and organic carbon (O'Connor and Paul, 2000).
In preliminary analyses, sediments from only 9 of 201 sites could be classified as
potentially toxic based on the equilibrium partitioning model for chronic PAH effects
(U.S. EPA, 2003b). Five of those sites had a maximum p > 0.5 and five sites exhibited
53
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54
TABLE 13
Enumeration of Sampling Events in Estuarine Systems of the Virginian Province of the Atlantic Coast where
Classification Based on the Organism-level Effects Measures and that Based on the Community Metric Disagree
Metric
Number of Sampling Events*
Classified as
Unaffected
Metric Different from
Expected for
Unaffected Group
Classified as
Affected
Metric Different from
Expected for
Affected Group
Classification Based on Sediment Chemical Concentrations
Capitellida species richness
% Polychaeta, Spionida
% Polychaeta, predators
% Individuals, Gastropoda
% Individuals, Pollution-indicative taxa
% Individuals, Streblospio benedicti
Phyllodocida abundance
Decapoda abundance
171
171
171
171
171
171
171
171
70
54
86
92
54
27
75
81
30
30
30
30
30
30
30
30
8
16
6
7
15
17
11
9
-------
TABLE 13cont.
Metric
Number of Sites
Classified as
Unaffected
Metric Different from
Expected for
Unaffected Group
Classified as
Affected
Metric Different from
Expected for
Affected Group
Classification Based on Sediment Toxicity Tests
Biomass pertaxon
% Individuals, Pollution-indicative taxa
% Polychaeta, Phyllodocida
Phyllodocida abundance
159
159
159
159
33
46
83
69
27
27
27
27
21
15
10
11
55
* For each metric, the total number of sampling events is the sum of the columns labeled "Classified as unaffected" and
"Classified as affected."
-------
toxicity in the ambient toxicity tests. The Simultaneously Extracted Metals/Acid Volatile
Sulfide (SEM/AVS) ratio exceeded one for sediments from 27 of the 133 sites where
AVS data were available. However, this means only that the metals may be
bioavailable and not that their bioavailable concentrations are sufficient to cause toxicity
(Hansen et al., 1996). This may be why only four of those sites exhibited toxicity in the
ambient toxicity tests, and only three sites had a maximum p > 0.5.
Besides assessing measurement endpoints at different levels of biological
organization, chemical guidelines, ambient toxicity tests and community metrics differ in
their specificity to different stressor gradients (Karr and Chu, 1998). Chemical
guidelines are very specific to the contaminants being measured and assessed and
ignore any unmeasured contaminants or stressors that lack guidelines for comparison.
Ambient toxicity tests detect toxicity associated with the entire milieu of bioavailable
contaminants in the tested sediments but do not assess other characteristics of the
estuarine site. Community metrics are not generally stressor specific. Therefore, while
community metrics may be sensitive to specific stressors (Griffith et al., 2001), they also
will be sensitive to other concurrent alterations of the ecosystem that affect the
structure of the biotic assemblages, including alterations of physical habitat that are not
addressed by chemical benchmarks.
We used a simple approach in classifying the sites into the unaffected and
affected groups. This was done, recognizing that only recently have models been
constructed to extrapolate accurately between the organism- and population-level
effects (Kuhn et al., 2000), and we still cannot accurately model or extrapolate between
population and community effects because of the difficulties of incorporating variation in
exposure and response across the hierarchical levels of time, space and organization
(de Kruijf, 1991; Landis, 2002). Considering this simple classification, one might expect
few, if any, of the metrics would have exhibited differences in their means between the
two groups. However, a number of metrics exhibited differences between the groups
although the conclusions based on the organism-level measures and on community
metrics disagreed at some sites. This would suggest that a relationship exists between
the organism-level effects assessed by chemistry or ambient toxicity tests and the
community-level effects assessed by community metrics. However, organism-level
effects are only predictive to a limited extent of the community-level effects at individual
sites. This also suggests benthic metrics may be used to confirm adverse effects at
sites identified for further analysis based on chemical data as has been done with
ambient toxicity tests (O'Connor et al., 1998). However, care is needed in the selection
of appropriate metrics because metrics differ in their sensitivity to different stressors.
56
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4. CONCLUSIONS
At least for the stressors identified, metals in stream water and sediments or
metals and PAHs in estuarine sediments, these two studies show relationships between
effects at the organism level, as identified by criteria or other benchmarks for surface
water or sediments, or by ambient toxicity tests of surface water or sediments and
effects at the community level, as assessed with community metrics for
macroinvertebrates or fish that are sensitive to the effects of these toxicants. Although
effects at the organism level observed in toxicity tests can be linked conceptually to the
effects measured by community metrics at the community level, these relationships are
not necessarily simple. Furthermore, these relationships are obscured by technical
differences among the methods beyond the differences in the levels of biological
organization represented by their measurement endpoints. These technical differences
affect the methods' specificity and sensitivity to the stressors being assessed. This is
why the organism-level effects are only predictive to a limited extent of the community-
level effects at individual sites and why these methods frequently differ in their
assessment of individual sites. The value of our assessment is that we were able to
use much larger data sets to show the statistical relationships among these methods,
as opposed to the comparisons of relatively few individual sites in previous studies.
Criteria or guidelines are specific to the contaminants of interest in each
ecosystem and environmental medium. However, criteria or guidelines cannot assess
contaminants or stressors that are not measured or that lack guidelines for comparison.
Ambient toxicity tests are less specific to individual contaminants because they should
detect effects of any toxicants present and bioavailable in either surface water and
sediments. However, ambient toxicity tests do not assess other characteristics of a site
that can affect the biotic community.
Community metrics are the least specific of the three methods, because they
directly measure community-level effects in the native assemblages. Although metrics
may be selected that are sensitive to a specific stressor (Norton et al., 2000; Ofenbock
et al., 2004), those metrics will not be necessarily sensitive only to that stressor and will
respond to other stressors, such as alterations in physical habitat. Other community
metrics will be insensitive to the specific stressors of interest, because they may not
measure alterations in assemblage structure characteristic of the stressor of interest.
Therefore, metrics alone probably cannot be used to establish stressor-specific
causality but might be used to indicate likely stressors at particular sites. Moreover,
data sets similar to those analyzed in this study that include both measurements of
57
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biological assemblages and of stressors might be used to assess stressor-specific,
response relationships and identify thresholds for effects associated with specific
stressors. The segmented regression technique used in the analysis of the Colorado
REMAP data could be used to identify such thresholds for effects.
Other factors also affect the relative sensitivity of these different methods.
Toxicity tests that are designed to measure endpoints that are chronic in duration and
chemical criteria or benchmarks that are based on chronic measurement endpoints
should be more predictive of community-level effects than those based on acute
measurement endpoints, because community metrics reflect longer-term changes in
communities (Karr and Chu, 1998). Toxicity tests often use one or two standard
species, which can be more tolerant of specific contaminants than other indigenous
species. In such cases, toxicity tests would be less predictive of community-level
effects. A chemical benchmark based on a species sensitivity distribution composed of
many species is likely to be more predictive of community-level effects. Because of
these limitations and because these methods are complementary, the policy of
independent application remains appropriate.
These differences in specificity that make these methods complementary might
be used in a strength of evidence analysis (U.S. EPA, 2000c). Low values of metrics
known to be sensitive to particular stressors could be used to suggest that those
stressors have influenced the community at a site. Subsequently, ambient toxicity tests
of site media may be used to verify whether these stressors are toxic contaminants
present water or sediments. Chemical analyses would verify whether such media
contained toxic concentrations of the contaminants.
Because of the technical differences between these methods, their relative
protectiveness, even when considering specific contaminants, such as metals in
freshwater or sediments or metals and persistent organics in estuarine sediments, is
variable and difficult to quantify with certainty. In some cases, such as the AWQCs for
metals in freshwater and the thresholds identified by piecewise regression for various
metrics, the protectiveness may be similar. However, in other cases, such as the TELs
for metals in freshwater sediments, the guidelines may not estimate values that are
related to distinct changes in the biotic assemblages as quantified by the metrics.
Moreover, this protectiveness is dependent on how the point where adverse effects are
considered significant is estimated. This point can be based on acute effects or chronic
effects. A point can also be based on a statistically significant change relative to control
tests or reference conditions or based on a specified percent change relative to a
control tests or reference conditions. Field et al. (2002) state that maximum p from their
58
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logistic regressions may be selected by a user to "match the level of protectiveness
appropriate for the objectives of their assessment." Techniques, like piecewise
regression, may be used to identify true thresholds, which represent levels of
contaminants or other stressors above which biotic assemblages exhibit significant
changes. However, a threshold model may not be appropriate in cases where both the
contaminant and response change in a more linear fashion.
59
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5. REFERENCES
APHA (American Public Health Association). 1995. Standard Methods for the
Examination of Water and Wastewater, 19th ed, A.D. Eaton, L.S. Clescer, A.E.
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