United States
Environmental Protection
Agency
Office of Marine
and Estuarine Protection
Washington DC 20460
March 1987
EPA 430/9-86-002
Water
Recommended
Biological Indices for
301 (h) Monitoring Programs
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EPA Contract No. 68-01-6938
TC 3953-03
Final Report
RECOMMENDED BIOLOGICAL INDICES FOR
301(h) MONITORING PROGRAMS
for
U.S. Environmental Protection Agency
March, 1987
by
Tetra Tech, Inc.
11820 Northup Way, Suite 100
Bellevue, Washington 98005
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PREFACE
Recognizing the potential utility of biological indices for 301(h)
monitoring programs, the U.S. EPA Marine Operations Division, Office of
Marine and Estuarine Protection initiated a study with the Office of Research
and Development upon request of EPA Regional Offices to critically evaluate
indices that are currently used by biologists. The members of the 301(h)
Task Force of EPA, which includes representatives for the EPA Regions I, II,
III, IV, IX and X, the Office of Research and Development, and the Office of
Water, are to be commended for their vital role in the development of this
guidance by the technical support contractor, Tetra Tech, Inc.
Biological indices are numerical summaries that are intended to provide
simplified characterizations of complex biological communities. These
indices may prove useful in the interpretation of monitoring data as they
can provide a simple comparison of biological community structure over space
or time. In this manner, indices can be used to assess the effects of
anthropogenic or natural perturbations on indigenous biota. Fifteen biological
indices are reviewed in this document. Nine are recommended for use in
301(h) monitoring programs. The criteria used for evaluation included the
individual indices' biological meaning, its ease of interpretation, and
sensitivity exhibited to community perturbations associated with pollutant
impacts. Although these recommendations are developed specifically for
301(h) monitoring programs, they may also be useful for other marine or
estuarine programs that involve collection of biological data.
This document will be useful to U.S. EPA reviewers of monitoring programs,
permit writers, permittees, and organizations engaged in collection of
environmental monitoring data for U.S. EPA. Utilization of the recommended
indices will enhance both National and Regional consistency in estuarine and
marine coastal monitoring programs.
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ACKNOWLEDGMENTS
This document has been reviewed by the 301(h) Task Force of the Environ-
menta 1 Protecti on Agency, whi ch i ncl udes representati ves from the Water
Management Divisions of u.S. EPA Regions I, II, III, IV, IX, and X; the
Offi ce of Research and Devel opment-Envi ronmenta 1 Research Laboratory -
Narragansett (located in Narragansett, RI and Newport, OR), and the Marine
Operations Division in the Office of Marine and Estuarine Protection, Office
of Water. The assistance of Dr. Richard Swartz, U.S. EPA Envi ronmental
Research Laboratory, Newport, OR is gratefully acknowledged.
This technical guidance document was prepared by Tetra Tech, Inc. for
the U.S. Environmental Protection Agency (Marine Operations Division, Office
of Marine and Estuarine Protection, Office of Water) under the 301(h) post-
decision technical support contract No. 68-01-6938, Allison J. Duryee,
Project Officer. This report was prepared under the direction of Dr. Thomas
C. Ginn, Project Manager. The primary author was Dr. Gordon R. Bilyard.
Ms. Marcy B. Brooks-McAuliffe performed technical editing and supervised
report production.
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RECOMMENDED BIOLOGICAL INDICES FOR 301(h) MONITORING PROGRAMS
The 301(h) regulations require dischargers to conduct_periodic surveys
of those biological communities that are most likely to be affected by
the modified discharge. The data from these surveys are used to compare
biological conditions in the vicinity of the discharge with biological
conditions in reference areas. One approach to making such comparisons
involves the use of biological indices that reduce complex data sets into
simple numerical relationships. There are numerous diversity, biotic,
and similarity indices with which such comparisons may be made. However,
there is little consensus among biologists regarding the suitability of
various indices for describing community properties or for documenting
pollutant impacts, despite extensive, and sometimes lively discussions
(see Goodman 1975; Boesch 1977; Green 1979; Bloom 1981; Tetra Tech 1982;
Washington 1984).
The purpose of this document is to develop recommendations of those
indices that should be used in the interpretation of 301(h) biological
monitoring data. The recommended indices are not intended to fully describe
biological communities. Rather, they are intended to provide one approach
in the overall assessment of compliance with the 301(h) biological criteria.
Other indices may be included in individual monitoring programs to better
characterize community structure, or to provide data relevant to specific
biological conditions of concern. Key issues upon which various indices
are often judged include 1) biological meaning, 2) ease of interpretation,
and 3) sensitivity to community changes caused by pollutant impacts. Each
of these criteria was considered by Tetra Tech and U.S. EPA Office of Research
and Development in developing the recommendations contained herein.
Proper survey design and execution are essential for the recommended
indices to be accurate, statistically testable descriptors of community
structure. However, discussions of appropriate survey designs, field methods,
1
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and laboratory methods are beyond the scope of this document. For further
information the reader is referred to the following publications:
Genera 1
Benthos
Unesco
Unesco
Jacobs
Stofan
(1974)
(1976 )
and Grant
and Grant
Ell i ott (1971)
Gonor and-Kemp (1978)
Swartz (1978)
Reed (1980)
Eleftheriou and Holme (1984)
Gamb 1 e (1984)
Holme and Wi11erton (1984)
McIntyre et a1. (1984)
Tetra Tech (1982)
Tetra Tech (1985)
Tetra Tech (1986)
Plankton
(1978)
(1978 )
Fishes
Savi 11 e (1977)
Mearns and Allen (1978)
Hayes (1983)
Goodman (1975) and Washington (1984) criticize most diversity indices
for their lack of biological meaning. Their criticisms appear well-founded.
The two diversity indices that they conclude are most biologically meaningful
are Simpson's D (per Washington) and Hurlbert's PIE (per Goodman and Washing-
ton). Simpson's D and Hurlbert's PIE describe complementary properties
of communities. The former index estimates the probability that two randomly
chosen individuals will be of the same species. The latter index estimates
the probability that an encounter between two randomly chosen individual s
will be interspecific (i .e., will be between individuals of different species).
Simpson's D and Hurlbert's PIE estimate the probability of "sameness"
or "differentness," respectively, associated with two randomly chosen
individuals from a community. This concept appears to have some intuitive
biological meaning, but its relevance to assessing pollutant impacts has
not been demonstrated. Moreover, the types of responses which may be expected
of Simpson's D and Hurlbert's PIE under various types of stresses are
essentially undocumented. Only the response of Simpson's D in an experimental
freshwater benthic stream community has been investigated. In that study,
the value of Simpson's D changed little over the range of copper concentrations
2
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tested (Perkins 1983). Washington (1984) states that Hurlbert's PIE "has
not been evaluated for aquatic ecosystems or indeed by many ecologists
in gener~l for any ecosystems." He recommends its use only for estimating
the probability of interspecific encounters. Green (1979) levels two additional
criticisms at diversity indices, including Simpson's D and Hurlbert's PIE.
First, simpler indices (such as numbers of species) are less ambiguous
and are often as informative as diversity indices (see also Hurlbert 1971).
Second, other analytical techniques are able to reduce biological data
to a useful and ecologically meaningful form, while retaining more information
than do diversity indices.
The two diversity indices that have been used most often in the 301(h)
program are Margalef's species richness (SR) and Shannon-Wiener diversity
(H'). Neither is recommended herein for routine inclusion in 301(h) monitoring
programs. In the SR index, the number of species is normalized by the
natural logarithm of the number of individuals, such that SR is actually
a measure of dominance. Its applicability to biological data is determined
by the correctness of the assumption that individuals are lognormally
distributed among the species in a given sample. Because this assumption
may not be valid for all communities, SR is not recommended herein for
inclusion in 301(h) monitoring programs. The assumption of a lognormal
distribution may be tested for each benthic community sampled. However,
this would require considerable additional work, and would, at best, yield
data of questionable value.
The Shannon-Wiener index (H') is the diversity index most commonly
encountered in the 301(h) applications. It is based on information theory,
wherein diversity is equated with the uncertainty attached to encountering
a given event (i.e., organism) within a set of events (see Washington 1984).
It has the advantages of being normally distributed, being reasonably inde-
pendent of sample size, and being statistically testable (Hutcheson 1970;
Odum 1971). Wi1hm and Dorris (1968) first used Shannon-Wiener diversity
to assess pollutant impacts in freshwater stream benthic communities.
In that study, values of H' decreased in response to pollutant stresses.
Other investigators quickly incorporated H' into their studies of pollutant
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impacts, since it appeared that values of H' provided an unbiased, mathematical
estimation of stress.
Although widely used in pollution impact studies since 1968, Shannon-Wiener
diversity has two major problems. The first is aptly stated by Goodman (1975):
1I...the Shannon-Weaver measure of species diversity is the negative
logarithm of the geometric mean of the probability per individual
of correctly guessing, in sequence, the species identity of each
individual in a random ordering of an assortment of individual s
whose relative species frequencies are given by (Pi), when the
IIguessll is carried out by picking some arbitrary ordering of
this assortment of individuals. There does not seem to be an
ecological process that corresponds in any obvious way to this
imaginary ordering of individuals, nor am I aware of any more
sensible process that exactly reduces to the Shannon-Wiener diversity
statistic.1I
The second problem is that values of H' are determined primarily by
the equitability of individuals among the species, and secondarily by species
richness. H' may actually increase under conditions of slight to moderate
stress if equitability increases as the number of species decreases. Swartz
et al. (1980) documented this phenomenon at a dredge site in Yaquina Bay,
Oregon, and Perkins (1983) documented it in laboratory tests for the effects
of different concentrations of dissolved copper on freshwater benthic
organisms. Values of H' may be e~uivocal under conditions of slight to
moderate stress, but are often greatly reduced under conditions of severe
stress. Under severe stress, the "peak of opportuni stsll [sensu Pearson and
Rosenberg (1978)J may be reached, wherein a few opportunistic species become
very abundant and other species are excluded or greatly reduced in abundance.
At or near the IIpeak of opportunists,1I both species richness and the equita-
bility of individuals among the species are reduced, so values of H' decline
dramatically. Values of H' are simple scalar numbers which alone cannot
demonstrate that the IIpeak of opportunistsll has been reached. Such a
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determination requires examination of the species abundance data, since
low values of H' are sometimes attributable to other causes.
Because of the possi bi 1 i ty that H' may give IIfa1 se positivesll under
conditions of slight or moderate stress, and because low values of H' may
not be due to anthropogenic stresses, it will be necessary to examine critically
the values of other, simpler community variables each time H' is calculated.
The simpler community variables recommended below are much more biologically
meaningful than H', so the inclusion of H' in 301(h) monitoring programs
wi 11 not confer any advantages over the recommended variables. H' is not
recommended as a required primary variable in 301(h) monitoring programs.
It is available in the Ocean Data Evaluation System (ODES), however, and
may be used as an additional indicator of community structure. If values
of H' are calculated as part of an applicant's monitoring program, evenness
(J; see Pie10u 1966) should also be calculated so that the importance of
the equitabi1ity component may be assessed. (J is also available in ODES.)
We concur with Green (1979) that some of the simplest measurements
of community structure are the most informative, and satisfy the three
aforementioned criteria: biological meaning, ease of interpretation, and
sensitivity to changes caused by pollutant impacts. Consistent use of
the following six measures of community structure is recommended in 301(h)
monitoring programs: (1) numbers of species per unit area, (2) numbers
o fin d i v i d u a 1 s per u nit are a, ( 3) d om i n a n c e , (4) the I n fa una 1 I n d e x (II),
(5) abundances of pollution sensitive species, and (6) abundances of oppor-
tunistic and pOllution tolerant species. (Note: The II may not be applicable
to all receiving water environments. See discussion below.) Values of
these six variables may be determined from the list of species abundances
generated during the taxonomic analysis of the collections. Moreover,
values of these six variables may be tested statistically using parametric
or nonparametric techniques. The selection of appropriate statistical
tests, the design of monitoring programs to collect statistically testable
data,. and determinations of the degree of change in values of biological
variables which could be considered indicative of impact are beyond the
scope of this document. The reader is referred to Sokal and Rohlf (1969),
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Elliott (1971), Winer (1971), Zar (1974), Green (1979), and Tetra Tech (1982)
for information on these topics.
Numbers of species and numbers of individuals have been used extensively
in evaluating effects of marine sewage discharges, and are good indicators
of organic enri~hment and other stresses (Pearson and Rosenberg 1978; Boesch
and Rosenberg 1981; Gray 1982). In most cases, species distributions are
aggregated such that numbers of species collected at a station are not
linearly related to numbers of individuals (see Sanders 1968). Therefore,
a sufficiently large area must be sampled to characterize the complement
of species present at a given station. Appropriate samp1 e si zes wi 11 vary
among biological groups and habitats. A discussion of methods which may
be used to determine appropriate sample sizes is beyond the scope of tbis
document. However, guidance may be found in Elliott (1971), Gonor and
Kemp (1978), Jacobs and Grant (1978), Stofan and Grant (1978), Mearns and
Allen (1978), Green (1979), Gray (1981), and Tetra Tech (1982).
The dominance index recommended herein is defined as the minimum number
of species required to account for 75 percent of the total number of individuals
in a samp1 e (see Swartz et a 1. 1985). Although thi s measure has not been
used extensively in the 301(h) program, Swartz et al. (1985) demonstrated
that it is useful for describing community structure, and that it is statis-
tically testable. Moreover, it is easily calculated and does not assume
an underlying distribution of individuals among species (e.g., a lognormal
or geometric distribution). A similar dominance measure (using a criterion
of 60 percent of the organisms) was used successfully by Word and Mearns
(1979) in the Southern California Bight.
The Infauna1 Index (II) is a mathematical index based on the abundances
of indicator species (see Word 1978, 1980). Although it was first developed
to describe the proportions of various feeding types within benthic communities,
it was later used successfully to assess impacts on benthic communities
near sewage outfalls in the Southern California Bight. The II has proven
to be a good empirical tool for assessing the spatial extent and magnitudes
of such impacts. Because its application to impact assesment is independent
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of the trophic relationships of the indicator species (Swartz et al. 1985),
it is referred to in this document as the Infaunal Index, rather than the
Infaunal Trophic Index (ITI), as originally proposed. To date, the II
has been applied successfully in the Southern California Bight (Word 1978,
1980). The ITI has recently been applied in Puget Sound as well (Word,
J.Q., 29 May 1985, personal communication). (A report describing application
of the II to benthic communities in Puget sound is being prepared by J.Q. Word.)
It could be further developed in other biogeographic regions by studying
the response patterns of infaunal species near sewage discharges.
Examination of abundances of individual indicator species per unit
area are often informative. Such analyses may be especially useful in
those regions where the II has not been developed. Abundances of pollution
sensitive species (e.g., the amphipods Rhepoxynius abronius and Ampelisca
spp., the ophiuroid Amphiodia urtica) or taxonomic groups (e.g., Phylum
Echinodermata) are often depressed in impacted areas compared with reference
areas. Conversely, abundances of opportunistic or pollution tolerant species
(e.g., the polychaetes Polydora ligni, Streblospio benedicti, and Capitella
capitata, the bivalve mollusc Parvilucina tenuisculpta) or taxonomic groups
(e.g., Class Oligochaeta) may be enhanced. [See Word et al. (1977) and
Pearson and Rosenberg (1978) for lists of opportunistic and pollution tolerant
taxa.] Used in conjunction with other information on the structure of
benthic communities, the absence of pollution sensitive species and the
enhancement of populations of opportunistic and pollution tolerant species
may help define the spatial extent and magnitude of impacts.
Existing compilations of pollution sensitive, opportunistic, and pollution
tolerant species (e.g., Word et al. 1977; Pearson and Rosenberg 1978) are
incomplete. They are heavily weighted in favor of Europe, northeastern
North America, and southern California, where most pollution studies have
been conducted. Other indicator species undoubtedly exist, but have not
yet been identified. Species abundances should be carefully scrutinized
during the execution of 301(h) monitoring programs for the purpose of
identifying additional indicator species, and documenting their patterns
of response to municipal wastewaters.
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Changes in the abundances of pollution sensitive, opportunistic, and
pollution tolerant species are best detected by statistical testing.
Statistical testing requires the collection of an adequate number of replicate
samples at each station to reduce within-station variance to an acceptable
level. Without statistical testing it is difficult to determine (in all
but the most obvious cases) whether differences in the abundances of indicator
species are due to natural variation or to anthropogenic stresses. This
is particularly true for opportunistic and pOllution tolerant species,
which are often present in unimpacted areas. These species may exhibit
slightly enhanced or greatly enhanced abundances, depending on the degree
and type of anthropogenic stress.
In some instances the inclusion of biological variables in addition
to the six recommended above may be warranted. Such additional variables
might include the abundances of common species known to be important prey
organisms of vertebrates, determinations of feeding guild affinities, abundances
of major taxonomic groups (i .e., phyla, classes, orders, families), or
abundances of numerically dominant species. Additional variables are likely
to be most informative in cases where the discharge is large, or where
the receiving environment is heterogeneous. They should supplement the
six basic variables, and should be chosen to collect those data most appropriate
for the size of the discharge, the characteristics of the receiving environment,
and the commercial and recreational uses of the environment and biota.
It is not possible to determine a priori which of these or other variables
might be informative for a particular receiving environment. The selection
of additional biological variables must be made after a preliminary examination
of the data. Moreover, they may change over the course of the monitoring
program as more is learned about the receiving environment and biota.
Biomass is a variable that has been used in 301(h) surveys. It has
been shown to vary as organic content of the sediment increases (see Pearson
and Rosenberg 1978), and can provide an estimate of the degree of organic
enrichment which is occurring near a given outfall. The inclusion of biomass
as a required variable in 301(h) monitoring programs is not recommended
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for benthos or fishes, however, because of problems inherent in the collection
of biomass data. Biomass may be included in monitoring programs as an
additional variable, provided its limitations are understood. Among the
problems associated with biomass data are that some taxa lose weight when
immersed in preservative fluids, while others gain weight (Howmiller 1972;
Lappalainen and Kangas 1975; Wiederholm and Ericksson 1977; Mills et al.
1972). For this reason, the most accurate biomass estimates are performed
on live material. But it is rarely practical to sort and weigh live specimens.
Accurate measurements of biomass are further compromised by evaporation
from the specimens during the weighing process. Because the 70 percent
alcohol in which the specimens are usually stored is volatile, small variations
in drying time (to rid specimens of surface fluids) may increase the errors
associated with the weight measurements. Adherence to a strict time schedule
for blotting and weighing specimens will improve the accuracy of the data,
but will not solve the evaporation problem. Shelled organisms present
further problems. Either the soft tissues must be removed from the shells,
or conversion factors must be used. Removing the organisms from their
shells is time consuming, and may not be 100 percent efficient. Alternatively,
the development of conversion factors is time consuming, and the use of
conversion factors introduces additional error components into the data.
After biomass data have been collected, they must be examined carefully
for anomalous results. The chance occurrence of a single large organism
in a sample (e.g., a large echinoderm) may increase the biomass estimate
for a given sample several fold, and produce anomalous results. When required,
biomass data should be collected for whole plankton samples, major taxonomic
groups of benthic invertebrates (e.g., polychaetes, molluscs, crustaceans),
and individual fish species (e.g., Dover sole, white croaker).
Given the foregoing limitations, biomass is not recommended as a primary
variable for 301(h) monitoring programs. It has been used, however, in
conjunction with species composition and abundance data to demonstrate
the relative effects of enrichment on benthic commmunities (Pearson and
Rosenberg 1978). Where such historical biomass data exist, it may be infor-
mative to include biomass as an additional monitoring variable.
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In lieu of biomass estimates, it is recommended herein that abundances
and other measures of community structure be the primary variables used
for estimating the degree of organic enrichment occurring near a given
outfall. Numerical abundances have also been shown to vary as organic
content of the sediments increases (see Pearson and Rosenberg 1978), and
are subject to fewer problems during the process of data collection than
are biomass data. Other variables may also be used in lieu of biomass
for estimating impacts. For example, Mearns and Word (1982) developed
a numerical relationship between benthic biomass on the continental shelf
off southern California and the mass emission rates of suspended solids
from sewage discharges. This relationship is similar to the numerical
relationships developed for mass emissions rates of suspended solids vs. in-
faunal abundances and II values. Since abundances and II values may be
estimated more accurately than biomass values without adding additional
costs to monitoring programs, it is recommended herein that 301(h) monitoring
programs emphasize measurements such as abundance and II estimates, rather
than biomass estimates for benthos and fishes. (Note: it may be desirab1e
to include biomass as a variable describing plankton populations, since
abundances of pl anktoni c organi sms are often di fficult to estimate accurately.)
Classification analyses are the most commonly used multivariate analyses
in the 301(h) program. Their continued use in 301(h) monitoring programs
is recommended because they generate visual representations of among-site
and among-species relationships by grouping entities with similar character-
istics (e.g., stations with similar benthic assemblages). Normal (Q-:-mode)
analyses that describe relationships among sites have been very useful
for defining the areal extent and intensity of impacts caused by sewage
effluent. Most often, Q-mode analyses have been used only for individual
surveys. It is recommended herein that they also be conducted for multiple
survey dates, so that the amelioration or deterioration of benthic conditions
through time may be visualized.
The Bray-Curtis similarity index is the resemblance measure most often
used in the 301(h) applications. Its continued use is recommended in 301(h)
monitoring programs for several reasons. The mathematical derivation of
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the Bray-Curtis index is easy to understand; the index is widely used in
academic and applied studies (Boesch 1977); and it has been shown to be
superior to three other commonly used resemblance measures, Morisita's
overlap, Horn's information theory, and Canberra metric (Bloom 1981).
The only major criticism of the Bray-Curtis similarity index is that is
may overemphasize the most abundant species. In data sets where dominance
is high, a square root or logarithmic transformation may be applied to
reduce the importance of abundant taxa. This approach is prefer~ble to
using an index that de-emphasizes abundant taxa, but that yields less accurate
estimates of similarity (e.g., the Canberra metric resemblance measure).
Two clustering algorithms have,been used most often in the 301(h)
program, the unweighted pair-group method using arithmetic averages'(i .e.,
group average method), and the flexible sorting strategy. The group average
method is recommended herein for inclusion in 301(h) monitoring programs
because the dendrograms it generates are distorted very little from the
original similarity matrix (Boesch 1977). The flexible sorting strategy
may also produce little distortion when the cluster intensity coefficient,
S, equals zero (Boesch 1977). This algorithm has the advantage of being
able to contract space when a>o, and to dilate space when a-
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by projecting all of the data points onto the linear functions which capture
the greatest proportion of the variance in the data set. Among the most
commonly used ordination procedures are principal components analysis,
discriminant analysis, reciprocal averaging, canonical correlation, and
detrended correspondence analysis. The assumptions, objectives, and compu-
tational procedures for each of these and other ordination procedures differ
considerably. Some of these procedures have been shown to be better than
others, but at present no single ordination technique nas been shown to
be clearly superior for the analysis of biological data. For this reason,
no ordination technique is recommended herein for routine inclusion in
301(h) monitoring programs. It may be desirable, however, for one or more
ordination techniques to be tested for possible inclusion as an additional
analytical tool. Interested parties should consult Cooley and Lohnes (1971),
Gauch and Whittaker (1972), Gauch et al. (1977), Green (1979), and Gauch (1982).
Based on the foregoing considerations, biological indices and methods
recommended for inclusion in 301(h) monitoring programs are listed in Table 1.
Qualifications concerning their use are footnoted as appropriate. The
biological groups to which each of the indices and methods may be applied
are also given. Also listed are other indices and methods that were discussed
above, but are not recommended.
The Ocean Data Evaluation System (ODES) i~ a computerized system used
to store and analyze data from 301(h) monitoring programs. The availability
of each index and method in ODES is also presented in Table 1.
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TABLE 1.
BIOLOGICAL INDICES DISCUSSED IN THIS REVIEW
Index/Method
Biological Recommended
Characteristic for 301(h) Available
Measured Monitoring?a on ODES?
Dissimilarity Yes:P,B,F Yes
Clustering algorithm Yes:P,B,F Yes
for Bray-Curtis
Clustering algorithm Yes:P,B,F Yes
for Bray-Curtis
Community structure Yes:P,B,F Yes
Community structure Yes:Bc Yes
Total abundance Yes:P,B,F Yes
Total taxa Yes:P,B,F Yes
Community structure Yes:P,B No
Bray-Curtis
Flexible sorting
Group average
sorting
Dominanceb
Infaunal index
No. individual s
No. speci es
Opportunistic and
pollution tolerant
species
Pollution-sensitive
species
Biomass
Hurlbert's PIE
Margalef's SR
Pielou's J
Sh~nnon-Wiener H'
Simpson's 0
Community structure Yes:P,B No
Standing crop NO:P,B,F Yes
Diversity No:P,B,F No
Diversity No:P,B,F No
Evenness No:P,B,Fd Yes
Di vers ity No:P,B,Fd Yes
Di vers ity NO:P,B,F No
a P (plankton), B (benthos), and F (fishes) indicate those biological groups
to which a given index may be applied.
b Defined as the minimum number of species required to account for 75 percent
of the individuals in a sample (see Swartz et al. 1985).
c Where developed.
d May be used together as additional indices of community structure.
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REFERENCES
Bloom, S.A. 1981. Similarity indices in community studies:
pitfalls. Mar. Eco1. Prog. Ser. 5:125-128.
potential
Boesch, D.F. 1977. Application of numerical classification in ecological
investigations of water pollution. EPA-600/3-77-033. U.S. EPA, Corvallis,
OR. 115 pp. .
Boesch, D.F.,
communities.
G.W. Barrett
NY.
and R. Rosenberg. 1981. Response to stress in marine benthic
pp. 179-200. In: Stress Effects on Natural Ecosystems.
and R. Rosenberg (eds). John Wiley & Sons, Inc., New York,
Cooley, W.W., and P.R. Lohnes.
Wiley & Sons, Inc., New York, NY.
1971. Multivariate data analysis.
364 pp.
John
Eleftheriou, A., and
In: Methods for the
(eds) . IBP Handbook
Oxford, U.K.
Elliott, J.M. 1971. Some methods for the statistical analysis of samples
of benthic invertebrates. Scientific Publication No. 25. Freshwater Biological
Association, Ferry House, U.K. 148 pp.
N.A. Holme. 1984. Macrofauna techniques. pp. 140-216.
Study of Marine Benthos. N.A. Holme and A.D. McIntyre
No. 16, second edition. Blackwell Scientific Publications,
Gauch, H.G. 1982. Multivariate analysis in community ecology. Cambridge
Studies in Eco10gy:1. Cambridge Univeristy Press, Cambridge, U.K. 298
pp.
Gauch, H.G., and R.H. Whittaker.
Ecology 53:868-875.
1972.
Comparison of ordination techniques.
Gauch, H.G., R.H. Whittaker, and T.R. Wentworth. 1977. A comparative
study of reciprocal averaging and other ordination techniques. J. Ecol.
65:157-174.
Gamble, J.C. 1984. Diving. pp. 99-139. In: Methods for the Study of
Marine Benthos. N.A. Holme and A.D. McIntyre (eds). IBP Handbook No. 16,
second edition. Blackwell Scientific Publications, Oxford, U.K.
Gonor, J.J., and P.F. Kemp. 1978. Procedures for quantitative ecological
assessments in intertidal environments. EPA-600/3-78-078. U.S. EPA, Corvallis,
OR. 104 pp.
Goodman, D. 1975. The theory of diversity-stability relationships in
ecology. Q. Rev. Bio1. 50:237-266.
14
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Gray, J.S. 1981. The ecology of marine sediments.
Press, Cambridge, U.K. 185 pp.
Cambridge University
Gray, J.S. 1982.
Res. 16:424-443.
Effects of pollutants on marine ecosystems.
Neth J. Sea
Green, R.H.
biologists.
1979. Sampling design and statistical methods for environmental
John Wiley & Sons, Inc., New York, NY. 257 pp.
Hayes, M.L. 1983. Active fish capture methods.
Techniques~ L.A. Nielson and D.L. Johnson (eds).
Bethesda, MD.
pp. 123-145. In: Fisheries
American Fisheries Society,
Holme, N.A., and P.F. Willerton. 1984. Position fixing of ship and gear.
pp.27-40. In: Methods fo'r the Study of Marine Benthos. N.A. Holme and
A.D. McIntyre (eds). IBP Handbook No. 16, second edition. Blackwell Scientific
Publications, Oxford, U.K.
Howmil1er, R.P. 1972. Effects of preservatives on weights of some common
macrobenthic invertebrates. Trans. Am. Fish. Soc. 4:743-746.
Hulbert, S.H. 1971. The nonconcept of species diversity:
and alternative parameters. Ecology 52:577-586.
a critique
Hutcheson, K. 1970. A test for comparing diversities based on the Shannon
formula. J. Theoret. Bio1. 29:151-154.
Jacobs, F., and G.C. Grant. 1978. Guidelines for zooplankton sampling
in quantitative baseline and monitoring programs. EPA-600/3-78-026. U.S. EPA,
Corvallis, OR. 52 pp.
Lappalainen, A., and P. Kangas. 1975.
Baltic Sea. II. Interrelationships of
of macroinfauna in the Tvarminne area.
60:297-312.
Littoral benthos of the northern
wet, dry, and ash-free weights
Int. Rev. Gesamten Hydrobiol.
McIntyre, A.D., J.M. Elliott, and D.V. Ellis. 1984. Design of sampling
programmers.' pp. 1-26. In: Methods for the Study of Marine Benthos.
N.A. Holme and A.D. McIntyre (eds). IBP Handbook No. 16, second edition.
Blackwell Scientific Publications, Oxford, U.K.
Mearns, A.J., and J.M. Allen. 1978.
biological surveys. EPA-600/3-78-083.
Use of small otter trawls in coastal
U.S. EPA, Corvallis, OR. 33 pp.
Mearns, A.J., and J.Q. Word. 1982. Forecasting effects of sewage solids
on marine benthic communities. pp. 495-512. In: Ecological Stress and
the New York Bight: Science and Management. G.F. Mayer (ed). Estuarine
Research Foundation, Columbia, SC.
Mills, E.L., K. Pittman, and B. Munroe. 1982. Effects of preservation
on the weight of marine benthic invertebrates. Can. J. Fish. Aquat. Sci.
39:221-224.
15
-------
Odum, E.P. 1971. Fundamentals of ecology.
Co., Philadelphia, PA. 574 pp.
(Third Edition).
W.B. Saunders
Pearson, T.H., and R. Rosenberg. 1978. Macrobenthic succession in relation
to organic enrichment and pollution of the marine environment. Oceanogr.
Mar. Biol. Annu. Rev. 16:229-311.
Perkins, J.L. 1983. Bioassay evaluation of diversity and community comparison
,indices. J. Water Pollute Control Fed. 55:522-530.
Pielou, E.C. 1966. The measurement of diversity in different types of
biological collections. Theor. Biol. 13:131-144.
Reed, S.A. 1980. Sampling and transecting techniques on tropical reef
substrates. pp. 71-89. In: Environmental Survey Techniques for Coastal
Water Assessment-Conference Proceedings. Sea Grant Cooperative Report
UNIHI-SEAGRANT-CR-80-01. Water Resources Research Center, University of
Hawaii, Manoa, HI.
Sanders, H.L. 1968.
Amer. Nat. 102:243-283.
Marine benthic diversity:
a comparative study.
Sa vi 11 e, A. (ed). 1977. Survey methods for appra is i ng fi shery resources.
FAO Fish Tech. Pap. No. 171. 76 pp.
Sokal, R.R., and F.J. Rohlf.
Francisco, CA. 776 pp.
Stofan, P.E., and G.C. Grant. 1978. Phytoplankton sampling in quantitative
baseline and monitoring programs. EPA-600/3-78-025. U.S. EPA, Corvallis, OR.
27 pp.
1969.
Biometry.
W.H. Freeman & Co., San
Swartz, R.C.
macrobenthos.
1978. Techniques for sampling and analyzing the marine
EPA-600/3-78-030. U.S. EPA, Corvallis, OR. 27 pp.
Swartz, R.C., W.A. DeBen, F.A. Cole, and L.C. Bentsen. 1980. Recovery
of the macrobenthos at a dredge site in Yaquina Bay, Oregon. pp. 391-408.
In: Contaminants and Sediments. Volume 2: Analysis, Chemistry, Biology.
R.A. Baker (ed). Ann Arbor Science Publishers, Inc., Ann Arbor, MI.
Swartz, R.C., D.W. Schultz, G.R. Ditsworth, W.A. DeBen, and F.A. Cole.
1985. Sediment toxicity, contamination, and macrobenthic communities near
a large sewage outfall. pp. 152-175. In: Validation and Predictability
of Laboratory Methods for Assessing the Fate and Effects of Contaminants
in Aquatic Ecosystems. T.T. Boyle (ed). American Society for Testing
and Materials. STP 865. Philadelphia, PA.
Tetra Tech. 1982. Design of 301(h) monitoring programs for municipal
wastewater discharges to marine waters. EPA 430/9-82-010. U.S. EPA, Office
of Water Program Operations, Washington, DC. 135 pp.
16
-------
Tetra Tech. 1985. Quality assurance and quality control (QA/QC) for
monitoring programs: guidance on field and laboratory methods.
report in preparation for the U.S. Environmental Protection Agency.
Tech, Inc., Bellevue, WA. 224 pp. .
301(h)
Draft
Tetra
Tetra Tech, Inc. 1986. Evaluation of coastal survey positioning methods
for section 301(h) monitoring programs. Draft report in preparation for
the U.S. Environmental Protection Agency. Tetra Tech, Inc., Bellevue,
WA. 103 pp.
Unesco. 1974. Zooplankton sampling.. Monographs on Oceanographic Methodology
2. The Unesco Press, Paris. 174 pp.
Unesco. 1976. Zooplankton fixation and preservation. Monographs on
Oceanographic Methodology 4. The Unesco Press, Paris. 350 pp.
Washington, H.G. 1984. Diversity, biotic and similarity indices. A review
with special relevance to aquatic ecosystems. Water Res. 18:653-694.
Wiederholm, T., and L. Eriksson. 1977. Effects of alcohol preservation
on the weights of some benthic invertebrates. Zoon 5:29-31.
Winer, B.J. 1971. Statistical principles in experimental design.
Hi 11 Book Co., New York, NY. 907 pp.
McGraw-
Word, J.Q. 1978. The infaunal trophic index.
Water Research Project Annual Report, W. Bascom (ed).
pp. 19-39. In: Coastal
SCCWRP, El Segundo, CA.
Word, J.Q. 1980. Classification of benthic invertebrates into infaunal
trophic index feeding groups. pp. 103-121. In: Coastal Water Research -
Project. Biannual Report of the years 1979-1980, W. Bascom (ed). SCCWRP,
Long Beach, CA.
Word, J.Q. 29 May 1985. Personal Communication (phone by Dr. Gordon R.
Bilyard). Evans-Hamilton, Inc., Seattle, WA.'
Word, J.Q., and A.J. Mearns. 1979. 60-meter control survey off southern
California. TM 229. SCCWRP, El Segundo, CA. 58 pp.
Word, J.Q., B.L. Myers, and A.J. Mearns. 1977. Animals that are indicators
of marine pollution. pp. 199-206. In: Coastal Water Research Project
Annual Report, W. Bascom (ed.). SCCWRP, El Segundo, CA.
Zar, J.H. 1974. Biostatistical analysis.
Cliffs, NJ. 620 pp.
Prentice-Hall, Inc., Englewood
17
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