&EPA
United States
Environmental Protection
Agency .«'
Office of Research and
Development
Washington DC 20460
EPA/600/4-90/030
November 1990
Macro! n vertebrate
Field and Laboratory
Methods for Evaluating the
Biological Integrity of
Surface Waters
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EPA/600/4-90/030
November, 1990
MACROINVERTEBRATE FIELD AND LABORATORY METHODS FOR EVALUATING
THE BIOLOGICAL INTEGRITY OF SURFACE WATERS
by
Donald J. Klemm1, Philip A. Lewis1, Florence Fulk2,
and James M. Lazorchak1
Aquatic Biology Branch1 and Development and Evaluation Branch2,
Quality Assurance Research Division,
Environmental Monitoring Systems Laboratory - Cincinnati
ENVIRONMENTAL MONITORING SYSTEMS LABORATORY - CINCINNATI
OFFICE OF MODELING, MONITORING SYSTEMS, AND QUALITY ASSURANCE
OFFICE OF RESEARCH AND DEVELOPMENT
U. S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
Printed on Recycled Paper
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DISCLAIMER
This document has been reviewed by the Environmental Monitoring Systems
Laboratory - Cincinnati (EMSL-Cincinnati), U.S. Environmental Protection Agency
(USEPA), and approved for publication. The mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
ii
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FOREWORD
Environmental measurements are required to determine the chemical and
biological quality of drinking water, surface waters, ground waters, waste
waters, sediments, sludges, and solid waste. The Environmental Monitoring
Systems Laboratory - Cincinnati (EMSL-Cincinriati) conducts research to:
o Develop and evaluate methods to identify and measure the concentration
of chemical pollutants.
o Identify and quantitate the occurrence of viruses, bacteria, other
human pathogens and indicator organisms.
o Perform ecological assessments and measure the toxicity of pollutants
to representative species of aquatic organisms and determine the
effects of pollution on communities of indigenous freshwater, estuarine,
and marine organisms, including the phytoplankton, zooplankton,
periphyton, macrophyton, macroinvertebrates, and fish.
o Develop and operate a quality assurance program to support achievement
of data quality objectives for environmental measurements.
This manual describes guidelines and standardized procedures for the use
of macroinvertebrates in evaluating the biological integrity of surface waters.
It was developed to provide biomonitoring programs with the most recent benthic
invertebrate methods for measuring the status and trends of environmental
pollution on freshwater, estuarine, and marine macroinvertebrates in field and
laboratory studies. These studies are carried out to assess water quality
criteria for the recognized beneficial uses of water and to monitor surface
water quality.
Thomas A. Clark
Director
Environmental Monitoring Systems
Laboratory - Cincinnati
iii
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PREFACE
The Aquatic Biology Branch, Quality Assurance Research Division,
Environmental Monitoring Systems Laboratory - Cincinnati is responsible for the
development, evaluation and standardization of methods for the collection of
biological field and laboratory data by EPA regional, enforcement, and research
programs engaged in inland, estuarine, and marine water quality and permit
compliance monitoring, and other studies of the effects of impacts on aquatic
organisms, including the phytoplankton, zooplankton, periphyton, macrophyton,
macroinvertebrates, and fish. The program addresses methods for sample
collection; sample preparation; organism identification and enumeration; the
measurement of biomass and metabolic rates; the bioaccumulation and pathology
of toxic substances; bioassay; biomarkers; the computerization, analysis, and
interpretation of biological data; and ecological assessments. Biological
methods recommended for use.in the U.S. Environmental Protection Agency are
included in this manual: "Macroinvertebrate Field and Laboratory Methods for
Evaluating the Biological Integrity of Surface Waters."
This document provides macroinvertebrate methods for evaluating the
biological integrity of fresh, estuarine, and marine waters. The subjects
covered include selection of sample sites, qualitative and quantitative sampling
methods, sample processing, data analysis techniques, quality assurance and
quality control procedures, safety and health recommendations, taxonomic
bibliography, and the pollution tolerance of selected macroinvertebrate species.
The manual is a revision and enlargement of the chapter on
macroinvertebrate methods originally published in the document, "Biological
Field and Laboratory Methods for Measuring the Quality of Surface Waters and
Effluents," Environmental Monitoring Series, USEPA, 1973, EPA-670/4-73-001, and
was developed in the Aquatic Biology Branch, Environmental Monitoring Systems
Laboratory - Cincinnati to provide biomonitoring programs with current methods
for assessing point and non-point sources of impacts, status and trends water
quality monitoring.
IV
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ABSTRACT
This manual describes guidelines and standardized procedures for using
benthic macroinvertebrates in evaluating the biological integrity of surface
waters. Included are sections on quality assurance and quality control
procedures, safety and health recommendations, selection of sampling stations,
sampling methods, sample processing, data evaluation, and an extensive taxonomic
bibliography of the benthic macroinvertebrate groups. Supplementary information
on the pollution tolerance of selected species, examples of macroinvertebrate
bench sheets and macroinvertebrate data summary sheets, and a list of equipment
and supplies for conducting bionionitoring studies are provided in the Appendices.
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CONTENTS
Foreword. ......' ......' ..'-... iii
Preface ........... . . . . . . . . . . . . . . . ..'.. iv
Abstract . . . . . . v
Figures ix
Tables .? ....... *
Acknowledgment xii
1. Introduction 1
Literature Cited . . . . ... 4
2. Quality Assurance and Quality Control . . ... . . ... 7
Introduction ............... 7
Data Quality Objectives .......... . . .... 8
Facilities and Equipment. . . . . . . . . . .... .... . 9
Calibration Documentation and Record Keeping. ..... 9
Qualifications and Training . . . ".-'. . . . ..... . . 10
Standard Operating Procedures ............. 11
Literature Cited , 11
3. Safety and Health . . 13
Introduction. . . . 13
General Precautions . . . . . . . . . . . . . .... . 13
Safety Equipment and Facilities . -.'". . . . . . . . . . 14
Field and Laboratory Operations . . . .. .... -. -; . . 14
Disease Prevention. . . . . ... . ... . . . . . . . 15
Literature Cited 15
4. Selection of Sampling Stations 16
Introduction ."-. . ,. . . . ' 16
Location of Sampling Stations ........ 17
Selecting Control Stations. . 18
Study Design 19
Considerations of Abiotic Factors . . . 26
Literature Cited 30
5. Sampling Methods ... . . . 32
Introduction 32
Qualitative Sampling. . . . . . . . ....... . . . 32
Semi-quantitative Sampling 33
Quantitative Sampling ....... . . . . . -. . . . . 33
Sampling Devices 35
Commonly Used Grabs 40
Stream Net Samplers 48
Drift Nets 53
vii
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Artificial Substrate Samplers . . . ; 59
Coring Devices 66
Frames 71
Rapid Bioassessment Protocols 72
Ohio EPA Invertebrate Community Index Method 73
Standard Qualitative Collection Method 73
Miscellaneous Qualitative Devices . 73
Suction Samplers. 74
Photography 74
Scuba 75
Brails . . 76
Other Mussel Collecting Methods 77
Literature Cited. . 77
6. Sample Processing 93
Sieving . 93
Preservation and Fixation 95
Labelling and Record Keeping 97
Sorting and Subsampling 97
Preparation of Microscope Slide Mounts. . . 100
Drying Methods 104
Organism Identification 104
Biomass 105
Literature Cited 105
7. Data Evaluation 109
Introduction 109
Analyses of Qualitative Data. . 109
Analyses of Semi-quantitative and Quantitative Data . . Ill
Rapid Bioassessment Techniques 117
Community Metrics and Pollution Indicators 117
Statistical Methods 123
Literature Cited 159
8. Taxonomic Bibliography. . . 164
Appendices 207
A. Pollution Tolerance of Selected Macroinvertebrates. . . . 207
B. Hilsenhoff's Family Level Pollution Tolerance Values
for Aquatic Arthropods . ...... 245
C. Examples of Macroinvertebrate Bench Sheets. 247
D. Example of Macroinvertebrate Summary Sheet 250
E. List of Equipment and Supplies 253
vm
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FIGURES
- ( * .- i» _
Number Page
1. Example of transect sampling scheme in rivers
and streams . . .. . .>>... . . . 23
2. Example of transect sampling scheme lakes,
reservoirs, and coastal waters .. .....; . . . ...» 23
3. Illustration of how sighting lines are used to
locate fixed sampling locations in lakes,
reservoirs, or estuaries ..... ... ... . ... ... 24
4. Example of grid sampling scheme in rivers. ......... 25
5. Grab samplers (Ponar, Ekman, Wildco Box Cor^r) .... . . . 41
6. Grab samplers (Petersen) ...... ..... . . . . . . . > 43
7. Grab samplers (Smith-Mclntyre, Van Veen, ,
Orange Peel, Shipek) .................... 46
8. Stream-net samplers 51
9. Artificial substrate samplers ... . . . . 62
10. Core samplers ..-.' . . . . ...... 70
11. Great Lakes sieve bucket . . . ...... . ..... .; . , . ., .; 94
12. Imhoff cone subsampler . . ... . ... . v » . . ... . ... 99
13. Five-segmented palp of a water mite. . . . . . . . . . . .-. 101
14. Water mite showing dorsum separated from venter. . . . . . , 102
15. Top (A) and side (B) views of the double
cover-glass technique for mounting aquatic
water mites. ............ . . .... . 102
ix
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TABLES
Number Page
1. Categories for field evaluation of substrate
characteristics . 27
2. Substrate particle size classification of
sieve analysis ',-. ' . : *. 28
3. Summary criteria for grab samplers 35
4. Summary criteria for stream-net samplers . 48
: 'i;,.*, F ' ' : - ',' ' ' ".',''. C' ' '» 1 ,
5. Summary criteria for artificial substrate
samplers ........ 59
6. Summary criteria for coring devices. ....... 67
7. Typical responses to various types of stress by
parameters of benthic community structure . 113
8. Example of calculation of mean diversity 114
9. Pooled Stenonema data from three riffle stations 131
10. Stenonema data from three riffle stations . 132
11. Occurrence of three species of midges. .......... 133
12. Macroinvertebrate biomass collected at different
times of day from the Little Miami River at
Milford, Ohio 135
13A. Generalized ANOVA 136
13B. Completed ANOVA Table Using Macroinvertebrate
Biomass Data 137
14A. Macroinvertebrate biomass (grams wet. wt.) 139
14B. Log10 transformed data 139
15. Treatment totals for the data of Table 14B 140
16. Analysis of variance table for field study data
of Table 14 142
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Number Page
17. Macroinvertebrate Biomass Collected at Different
Times of Day from the Little Miami River at
Mil ford, Ohio 145
18. Example of Shapiro-Wilk's Test: Centered
Observations 145
19. Example of Shapiro-Wilk's Test: Ordered
Observations 146
20. Coefficients for the Shapiro-WiIks Test . . 146
21. Example of Shapiro-wilk's Test: Table of
Coefficients and Differences. »....' 147
22. Quantiles of the Shapiro-Milks Test Statistic 149
23. Functions for calculating species diversity
and (for perfectly random sampling) its standard
error logarithms are to base 10. Table values
are accurate to within ± 1 in the eighth
significant figure. . ..... 152
24. The diversity of species, d, characteristic of
MacArthur's model for various numbers of
hypothetical species,s'*. . . 158
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ACKNOWLEDGMENTS
John Winter and Cornelius I. Weber, Quality Assurance Research Division,
EMSL-Cincinnati deserve special recognition for their valuable technical reviews
of the document.
The subcommittee for macroinvertebrates, L. Tebo, Chairman, R. Garton, P.A.
Lewis, K. Mackenthun, W.T. Mason, R. Nadeau, D. Phelphs, R. Schneider, and R.
Sinclair; the subcommittee for biometrics, R. Lassiter, Chairman, R. Harkins,
and L. Tebo are recognized as contributors to these chapters in the USEPA, 1973,
"Biological Field and Laboratory Methods for Measuring the Quality of Surface
Waters and Effluents."
Review comments from the following persons are gratefully acknowledged:
Mike Barbour and Sam Stribling, EA Engineering, Science, and Technology, Inc.,
Sparks, MD; Mike C. Beiser, Mississippi Department of Natural Resources, Bureau
of Pollution Control, Jackson, MS; Robert Bode, New York State Department of
Environmental Conservation, Albany, NY; Robert W. Cooner and staff, Alabama
Department of Environmental Management, Field Operations Division, Special
Studies Section, Montgomery, AL; Wayne S. Davis, U.S. Environmental Protection
Agency, Region 5, Environmental Sciences Division, Ambient Monitoring Section,
Chicago, IL; William R. Diamond, U.S. Environmental Protection Agency, Criteria
and Standards Division, Washington, DC; Chris Faulkner, U.S. Environmental
Protection Agency, Assessment and Watershed Protection Division, Washington, DC.;
Jim Harrison, U.S. Environmental Protection Agency, Region 4, Atlanta, GA; Terry
A. Hollister, U.S. Environmental Protection Agency, Region 6, Houston, TX; Hoke
Howard, U.S.Environmental Protection Agency, Region 4, Athens, GA; Jim
Kurtenbach, U.S.Environmental Protection Agency, Region 2, Edison, NJ; William
T. Mason, Jr., National Fisheries Research Center, Fish and Wildlife Service,
Gainesville, FL; Loys Parrish, U.S.Environmental Protection Agency, Region 8,
Denver, CO; Juan Dale Rector and staff, Tennessee Department of Health and
Environment, Laboratory Services, Nashville, TN; Steve W. Tedder and staff, North
Carolina Department of Environment, Health, and Natural Resources, Division of
Environmental Management, Water Quality Section, Raleigh, NC.
We thank Charlie Strobe!, SAIC, USEPA, ERL-Narragansett for providing
information on the SAIC Integrated Navigation and Surveying Systems for
oceanographic and geophysical marine surveys; Fred Holland and Jeffrey Frithsen,
Varsar, Inc., for information on EMAP draft methods for near coastal sampling
and processing.
We thank Kahl Scientific Instrument Corporation, El Cajon (San Diego), CA
and Wildlife Supply Company, Saginaw, MI for providing some of the illustrations
or photographs of sampling devices in Section 5, Sampling Methods.
We also thank the following individuals: Minghua Grisell, Computer
Sciences Corporation (CSC), for doing the formatting and typing of equations in
the data evaluation section; Mary M. Sullivan, Quality Assurance Research
Division, EMSL-Cincinnati for providing valuable secretarial assistance, and
Betty Thomas, Editorial Assistant, Program Operations Staff, EMSL-Cincinnati for
performing a thorough editorial review.
xii
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SECTION 1
INTRODUCTION
1.1 Benthic invertebrates comprise a heterogenous assemblage of animal
groups (taxa) that inhabit the sediment or live on or\in other bottom
substrates in the aquatic environment. They vary in size from forms small
and difficult to see without magnification to other individuals large enough
to see without difficulty.
1.2 The benthic invertebrates that are large enough to be seen by the
unaided eye and which can be retained by a U.S. Standard No. 30 sieve (28
meshes per inch, 0.595 mm openings) and live at least part of their life
cycles within or upon available substrates in a body of water or water
transport system are defined as macroinvertebrates. If a more representative
sample of the benthos such as chironomids and other small forms (e.g., naidid
and tubificid oligochaetes or aquatic worms) is desired, a U.S. Standard No.
60 sieve (60 meshes per inch, 0.250 mm openings) may be used.
1.2.1 Benthos (n.), Benthic (adj.)--the community of organisms living in or
on the bottom or other substrate in an aquatic environment.
1.2.2 Benthic invertebratean invertebrate of the benthos.
1.2.3 Habitatthe place where an organism lives; for example mud, gravel,
rocks, shoreline, vegetation, twigs, leaf packs, riffle/run, pool, etc.
1.2.4 Microhabitata smaller and more restricted area in a habitat; the
immediate environment of the organism.
1.3 The standard opening for estuarine and marine benthic animals is also
U.S. Standard No. 30 sieve (28 meshes per inch, 0.595 mm openings), and new
benthic programs should use the No. 30 sieve for collecting these animals.
To accommodate some historical data bases, a 1.0 mm screen, U.S. Standard No.
18 sieve may be used.
1.4 Any available substrate may provide suitable habitat for benthos,
including bottom sediments, submerged logs, debris, pilings, pipes, conduits,
vascular aquatic plants, root masses, filamentous algae, etc. The major
taxonomic groups of freshwater macroinvertebrates include the insects,
annelids, mbllusks, flatworms, and crustaceans. The major invertebrate
groups in estuarine and marine water are the mollusks, annelids, crustaceans,
roundworms, cnidarians (coelenterates), sponges, bryozoans, and echinoderms.
1.5 The macroinvertebrates are important members of food webs, and their
well-being is reflected in the well-being of the higher forms such as fish.
Many invertebrates, such as the marine and freshwater shellfish (clams and
mussels), are important commercial and recreational species. Some, such as
mosquitoes, black flies, biting midges, leeches, Asiatic clams, and zebra
mussels, are of considerable public health significance or are considered
pests. Many forms are important for digesting organic material and recycling
nutrients.
1
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1.6 Benthic macroinvertebrates are frequently used as environmental
indicators of biological integrity because they are found in most aquatic
habitats. They are of a size that makes them easily collected. They can be
used to describe the water quality conditions or health of the ecosystem
components and to identify causes of impaired conditions.
1.6.1 A community of macroinvertebrates in an aquatic lentic or lotic
ecosystem is very sensitive to stress; and, thus, its characteristics serve
as a useful tool for detecting environmental perturbation resulting from
introduced point and non-point sources of pollution. Because of the limited
mobility of these benthic organisms and because many species have life cycles
of a year or more, their characteristics are a function of conditions during
the recent past, including reactions to infrequently discharged pollutants
that would be difficult to detect by periodic chemical sampling.
i'1'1" " " ' , '"; '. i, ',, j^ , , ,
1.6.2 Macroinvertebrates show responses to a wide array of potential
pollutants (agricultural, domestic, industrial, mining, etc.), including
those with synergistic or antagonistic effects that adversely affect the
physiological, biochemical, and reproductive functions of the species. The
analysis of changes in the makeup of different aquatic communities is one way
to detect water quality problems. Knowledge of changes in the community
structure (abundance and composition) and function (see Section 1.7) of
benthic macroinvertebrates helps to indicate water quality status and trends
in the aquatic environment. Also the regular sampling of macroinvertebrates
can be used to document both spatial and temporal changes in the biological
integrity of surface waters. Different types of environmental stress will
often produce different macroinvertebrate communities.
1.6.3 In addition, because of the phenomenon of "biological magnification"
and relatively long-term retention of toxic substances by benthic organisms,
toxic materials such as metals, pesticides, radioactive materials, which are
only periodically discharged into the environment or which are present at
undetectable levels in the water or sediment, may be detected by chemical
analyses of selected components of the macroinvertebrate community.
1.7 Individuals or groups of macroinvertebrates can be separated into
trophic levels, such as herbivores, omnivores, or carnivores and, in stream
ecosystems, functional feeding relationships (Cummins, 1973, 1974, 1975;
Cummins and Klug, 1979; Cummins et al_., 1984; Cummins and Wilzbach, 1985).
In a well-balanced system, all three types will likely be present. They
include deposit and detritus feeders, collectors, shredders, grazers or
scrapers, parasites, scavengers, and predators.
1.8 In most biomonitoring studies, identification at, or near the species
level will be required to determine water quality conditions (Resh and
Unzicker, 1975). Tolerant species (Appendix A) will usually become dominant
only in polluted waters.
1.9 In pollution-oriented studies of macroinvertebrate communities, there
are basically three sampling approachesqualitative, semi-quantitative, and
quantitativethat may be utilized singly or in combination. These sampling
approaches are used to link ecosystem endpoints to stresses (e.g., physical
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habitat alterations, inert solids, eutrophication, organic enrichment,
thermal disruptions, ambient toxic wastes, and cumulative impacts) measured
by bioindicator methods and techniques. See Section 5, Sampling Methods and
Section 7, Data Evaluation.
1.10 During studies of water quality accommodations should be made for
stream size, geographic location, and seasonality (Lenat, 1983). Also, flow
conditions are related to the relative impact due to point and nonpoint
sources of pollution. High flow usually increases the impact of nonpoint
sources, while it reduces the impact of point sources. In streams with low
flow, the reverse is often true. In addition, the presence, distribution,
and abundance of aquatic macroinvertebrates, especially aquatic insects, may
be subject to wide seasonal variations (Hilsenhoff, 1988). Thus, when
conducting comparative studies, the investigator must be careful to avoid the
confounding effects of these seasonal changes. Seasonal variations are
particularly important in freshwater habitats dominated by aquatic insects
having several life stages, not all of which are aquatic.
1.11 The design of macroinvertebrate studies should be based upon study
goals and data quality objectives (DQOs) (See Section 2, Quality Assurance
and Quality Control). To supplement the material contained in this manual,
a number of basic references should be reviewed or available to ^investigators
of the macroinvertebrate communities, particularly to investigators engaged
in aquatic water quality and pollution studies. These include Armitage
(1978), Benke, Gillespie, and Van Arsdall (1984), Brinkhurst (1974), Cairns
and Dickson (1973), Cummins (1966, 1973, 1974, 1975), Cummins and Klug
(1979), Cummins et §1. (1984) >. Cummins and Wilzbach (1985), Edmondson and
Winberg (1971), Elliott (1977), Goodnight and Whitley (1960), Hart and Fuller
(1974), Hell awe!1 (1978, 1986), Hilsenhoff (1977), Howmiller and Scott
(1977), Hynes (1960, 1970), Holme and Mclntyre (1971), Hulings and Gray
(1971), Lenat (1983), Lind (1974), Merritt and Cummins (1984), Mason (1981),
Metcalfe (1989), "M11brink (1983), Meyer (1990), Neuswanger, Taylor, and
Regnolds (1982), Pennak (1989), Posey (1990), Resh (1979), Resh and Rosenberg
(1984), Resh and Unzicker (1975), Reynoldson et al_. (1989), Ward and Stanford
(1979), Warren (1971), Waters (1977), Welch (1948), Welch (1980), and Winner
et al. (1975).
1.12 This manual was composed to assist biologists and managers in USEPA
and other Federal, state, and private water monitoring organizations in the
use of macroinvertebrates for evaluating the biological integrity of surface
waters. The manual contains laboratory and field methods that will aid in
the monitoring, detection, and bioassessment of surface waters and the
effects of environmental stress on macroinvertebrate communities. It will
also facilitate the expansion of our knowledge of the ecological requirements
of macroinvertebrate species in fresh, estuarine, and marine habitats. The
manual includes sections on quality assurance and quality control, safety and
health, sampling site selection, sampling methods and techniques, sample
processing, data evaluation, and a taxonomic bibliography, containing the
current taxonomy used for identifying the macroinvertebrates of North
America. Information on the pollution tolerance of selected species and
examples of bench and data summary sheets are provided in the Appendices.
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1.12 Literature Cited
Armitage, P.O. 1978. Downstream changes in the composition, numbers and
biomass of bottom fauna in the Tees below Cow Green Reservoir and in
an unregulated tributary Maize Beck, in the first five years after
impoundment. Hydrobiologia 58:145-156.
Benke, A.C., D.M. Gillespie, and T.C. Van Arsdall. 1984. Invertebrate
productivity in a subtropical blackwater river: the importance of
habitat and life history. Ecol. Mono. 545:25-63.
Brinkhurst, R.O. 1974. The benthos of lakes. St. Martin's Press, New
York, NY. 190 pp.
Cairns, 0. Jr. and K.L. Dickson. 1973. Biological methods for the assess-
ment of water quality. Special Technical Publication 528. American
Society for Testing and Materials, Philadelphia, PA. 256 pp.
Cummins, K.W. 1966. A review of stream ecology with special emphasis on
organism-substrate relationships. In: K.W. Cummins, C.A. Tryon and
R.T. Hartman (eds.). Organism-substrate relationships in streams.
University of Pittsburgh Special Publication No. 4, Pittsburgh, PA. pp.
2-51.
Cummins, K.W. 1973. Trophic relations of aquatic insects. Ann. Rev.
Entomol. 18:183-206.
Cummins, K.W. 1974. Structure and function of stream ecosystems. BioScience
24:631-641.
Cummins, K.W. 1975. Macroinvertebrates. IQ: B.A. Whitton (ed.). River
ecology. University of California Press,, Berkeley,and Los Angeles, CA.
or Blackwell Scientific Publications, England, pp. 170-198.
Cummins, K.W. and M.J. Klug. 1979. Feeding ecology of stream invertebrates.
Ann. Rev. Ecol. Syst. 10:147-172.
Cummins, K.W., G.W. Minshall, J.R. Sedell, C.E. Cushing, and R.C. Petersen.
1984. Stream ecosystem theory. Verh. Internat. Verein. Limnol.
22:1818-1827.
Cummins, K.W. and M.A. Wilzbach. 1985. Field procedures for analysis of
functional feeding groups of stream macroinvertebrates. Contribution
1611, Appalachian Environmental Laboratory, University of Maryland,
Frostburg, MD.
Edmondson, W.T. and G.G. Winberg (eds.). 1971. A manual on methods for the
assessment of secondary productivity in fresh water. Blackwell
Scientific Publications, International Biological Programme Handbook 17,
Oxford and Edinburgh. 358 pp.
Elliott, J.M. 1977. Some methods for the statistical analysis of samples of
4
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benthic invertebrates. Freshwater Biological Association, Scientific
Publication No. 25. The Ferry House, Ambleside, Cumbria, England.
166 pp.
Goodnight, C.J. and L.S. Whitley. 1960. Oligochaetes as indicators of
pollution. Proc. 15th Ind. Waste Conf., Purdue Univ., IN., pp. 139-142.
Hart, G.W., Jr. and S.L.H. Fuller. 1974. Pollution ecology of freshwater
invertebrates. Academic Press, New York, NY. 389 pp.
Hellawell, J.M. 1978. Biological surveillance of rivers. Water Research
Center, Stevenage, England. 332 pp.
Hellawell, J.M. 1986. Biological indicators of freshwater pollution and
environmental management. Elsevier Applied Science Publishers, NY.
546 pp.
Hilsenhoff, W.L. 1977. Use of arthropods to evaluate water quality of
streams. Tech. Bull. Wisconsin Dept. Nat. Resources 100. 15 pp.
Holme, N.A. and A.D. Mclntyre (eds.). 1971. Methods for the study of marine
benthos. Blackwell Scientific Publications, International Biological
Programme Handbook 16, Oxford and Edinburgh. 346 pp.
Howmiller, R.P. and M.A. Scott. 1977. An environmental index based on
relative abundance of oligochaete species. J. Wat. Pollut. Control
Fed. 49(5):809-815.
Hulihgs, N.C. and J.S. Gray. 1971. A manual for the study of meiofauna.
Smithsonian Contr. Zool. No. 78. Smithsonian Institution Press,
Washington, DC. 84 pp.
Hynes, H.B.N. 1960. The biology of polluted waters. Liverpool University
Press, Liverpool. 202pp.
Hynes, H.B.N. 1970. The ecology of running waters. University of Toronto
Press, Toronto, Ontario. 555 pp.
Lenat, D.R. 1983. Benthic macroinvertebrates of Cane Creek, Norh Carolina,
and comparisons with other southeastern streams. Brimleyana 9:53-68.
Lind, O.T. 1974. Handbook of common methods in limnology. C.V. Mosby Co.,
St. Louis, MO. 154 pp.
Mason, C. 1981. Biology of freshwater pollution. Longmans, London.
Merritt, R.W. and K.W. Cummins. 1984. An introduction to the aquatic insects
of North America (Second edition). Kendall/Hunt Publishing Company,
Dubuque, IA 52001.
Metcalfe, J.L. 1989. Biological water quality assessment of running waters
based on macroinvertebrate communities: history and present status in
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Europe. Environ. Pollut. 60:101-139.
Meyer, Judy L. 1990. A blackwater perspective on riverine ecosystems.
Bioscience 40(9):643-651.
Milbrink, G. 1983. An improved environmental index based on the relative
abundance of oligochaete species. Hydrobiologia 102:89-97.
Neuswanger, D.J.,W.W. Taylor, and J.B. Reynolds. 1982. Comparison of
macroinvertebrate herptobenthos and haptobenthos in side channel and
slough in the Upper Mississippi River. Freshwat. Invertebr. Biol.
l(3):13-24.
Pennak, R.W. 1989. Fresh-water invertebrates of the United States: Protozoa
to Mollusca. John Wiley & Sons, Inc., New York, NY. 628 pp.
Posey, M.H. 1990. Functional approaches to soft-substrate communities: how
useful are they? Rev. Aquatic Sci. 2(3,4):343-356.
Resh, V.H. 1979. Sampling variability and life history features: basic
considerations in the design of aquatic insect studies. J. Fish. Res.
Bd. Can. 36:290-311.
Resh, V.H. and D.M. Rosenberg. 1984. The ecology of aquatic insects.
Praeger Publishers, New York, NY. 625 pp.
Resh, V.H. and J.D. Unzicker. 1975. Water quality monitoring and aquatic
organisms: the importance of species identification. J. Wat. Pollut.
Control Fed. 47:9-19.
Reynoldson, T.B., D.W. Schloesser, and B.A. Manny. 1989. Development of a
benthic invertebrate objective for mesotrophic great lakes waters. J.
Great Lakes Res. 15(4):669-686.
Ward, J.V. and J.A. Stanford (eds.). 1979. The ecology of regulated streams.
Plenum Publishers, New York, NY. 398 pp.
Warren, C.E. 1971. Biology and water pollution control. W.B. Saunders
Publisher, Philadelphia, PA. 434 pp.
Waters, T.F. 1977. Secondary production in inland waters. Adv. Ecol. Res.
10:1-164.
Welch, P.S. 1948. Limnological Methods. McGraw-Hill Book Company, Inc.,
New York, NY. 381 pp.
Welch, E.B. 1980. Ecological effects of waste water. Cambridge Univ.
Press, Cambridge, England.
1 I1 "' ' li
Winner, R.W.., J.S. Van Dyke, N. Can's, and M.P. Parrel 1. 1975. Response of
the macroinvertebrate fauna to a copper gradient in an experimentally
polluted stream. Verh. Internat. Verein. Limnol. 19:2121-2127.
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SECTION 2
QUALITY ASSURANCE AND QUALITY CONTROL
2.1 Introduction
2.1.1 A strong quality assurance (QA) program and effective quality control
(QC) procedures are needed for operating an adequate macroinvertebrate
bioassessment or monitoring laboratory to ensure that all data produced are
valid and of known quality. The term "quality assurance" refers to the
quality control functions and involves the totally integrated program for
ensuring the reliability of monitoring data; the term "quality control"
refers to the routine application and procedures for obtaining prescribed
standards of performance and for controlling the measurement process (USEPA,
1978). Quality assurance programs have two primary functions in a
macroinvertebrate laboratory. First, the program should continually monitor
the reliability of the data generated to determine the accuracy, precision,
completeness, comparability, and representativeness of the data. The second
function is the control of the quality of the data so as to meet the
requirements for reliability that the program demands. Quality assurance and
control must be a continuous process that includes all aspects of the
program, including field collection and preservation, sample processing, and
data analysis; otherwise the data generated may not be reliable and useful
for decision making and the results will be of little use in establishing the
biological integrity of the water body under study. In order to support the
operation of a consistent plan, the persons responsible for QA should consult
the EPA Quality Assurance manual (USEPA, 1984a). All EPA QA programs should
be based on USEPA order 5360.1 (USEPA, 1984b) which describes the policy,
objectives and responsibilities of all USEPA program and regional offices*.
2.1.2 Components of the QA program (USEPA, 1979) should include the
following:
2.1.2.1 Collection, preservation and analysis of all samples should follow
approved methodology.
2.1.2.2 Sampling equipment, flow measuring devices, and other measuring
instruments such as pH, DO, and conductivity meters should be calibrated
according to manufacturer's instructions, and documented.
2.1.2.3 Assurance that representative samples are collected (See Section 4,
Selection of Sampling Sites).
2.1.2.4 Determination of precision of sampling and analysis procedures.
2.1.2.5 Use of replication in all phases of the sampling and analysis
program.
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2.1.2.6 Participation in interlaboratory investigations and use of quality
control samples.
2.1.2.7 Accurate and timely recording, maintenance, and storage of data in
a log book, computer, or other data storage and retrieval system.
2,2 Data Quality Objectives (DQOs)
2.2.1 A full assessment of the data quality needed to meet the study
objectives should be made prior to preparation and implementation of the QA
plan. Data quality is a measure or description of the type and amount of
error associated with a set of data. Determination of data quality is
accomplished through the development of data quality objectives (DQOs), which
are statements of the level of uncertainty a decision-maker is willing to
accept or the quality of the data needed to support a specific environmental
decision or action. Both qualitative and quantitative descriptors of data
quality must be considered in order to determine whether data are appropriate
for a particular application. Data quality objectives are target values for
data quality and are not necessarily criteria for the acceptance or rejection
of data.
2.2.2 Data quality objectives are developed in three stages. During the
first stage, the decision-maker determines what information is needed,
reasons for the need, how the information will be used, and specifies time
and resource constraints. The second stage involves the technical staff and
decision-maker interacting to establish a detailed and clarified specifica-
tion of the problem, how the information will be used, any constraints
imposed on the data collection, and what limitations of the information will
be acceptable. The third stage involves the analysis of possible approaches
to collection and analysis of the data and a determination of the quality of
the data that can be expected to result from each approach. The best
approach is selected based on the criteria agreed upon in the second stage.
It may be necessary to modify the objectives of the study during the
development of these DQOs. Details for developing DQOs are described in two
U.S. Environmental Protection Agency documents (USEPA, 1984c and 1986)
available from the Quality Assurance Management Staff, Office of Research and
Development, Washington, DC 20460.
2.2.3 After the final DQOs are established, the detailed project QA plan
should be finalized stating specific quantitative and qualitative data
quality goals and QC procedures that will be used to control and characterize
error (USEPA, 1980). The goals based on the DQOs will be the criteria for
measuring the success of the QA program.
2.2.4 The Quality Assurance Management Staff, Office of Modeling, Monitoring
Systems, and Quality Assurance, is responsible for providing guidance for the
inclusion of DQOs in quality assurance program and project plans, and for
providing guidance to the regions on the application of the DQOs development
process. The EPA regional offices are responsible for ensuring that state
QA program and project plans conform with grant requirements specified in 40
CFR Part 30, and for assisting the states in developing DQOs requirements
that meet state needs.
8
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2.2.5 Regional and state laboratories or monitoring personnel in need of
assistance in preparing Quality Assurance Project Plans or development of
DQOs for bioassessment projects can contact personnel of the Aquatic Biology
Branch in the Quality Assurance Research Division, Environmental Monitoring
Systems Laboratory-Cincinnati, for assistance (FTS 684-8114 or COML 513-533-
8114, FAX FTS 684-8181 or COML 513-533-8181).
2.3 Facilities And Equipment
2.3.1 Laboratory and field facilities and utility services must be in place
and operating consistent with their designed purposes so that quality
environmental data may be generated and processed in an efficient and cost-
effective manner. Suitability of the facilities for the execution of both
the technical and QA aspects of the study should be assessed prior to
initiation of the study. Adequate space, lighting, temperature, noise
levels, and humidity should be provided. Satisfactory safety and health
maintenance features must also be provided (see Section 3, Safety and
Health).
2.3.2 Equipment and supplies necessary to adequately collect, preserve and
process biological samples must be available and in good operating condition.
See Appendix E for a list of recommended equipment and supplies.
2.3.2 To ensure data of consistently high quality, a plan of routine
inspection and preventive maintenance should be developed for all facilities,
and equipment. All inspections, calibrations, and maintenance must be
documented in individually bound notebooks. This documentation should
include detailed descriptions of all calibrations performed, adjustments
made, and parts replaced and each entry should be signed and dated.
2.3.3 Taxonomists and aquatic biologists who are capable of identifying
organisms are expected to have at their disposal adequate taxonomic
references to perform the level of identification required. See Section 8,
Taxonomic Bibliography, for a list of selected taxonomic references. Aquatic
biologists should check this list and obtain those references that will be
needed for the identification of specimens to the lowest taxonomic level
possible.
2.3.4 Representative specimens of all taxa identified should be verified by
a specialist who is a recognized authority in that particular taxonomic
group. These specimens should be properly labeled as reference or voucher
specimens, including the name of the verifying authority, permanently
preserved, and stored in the laboratory for future reference.
2.4 Calibration, Documentation, and Record Keeping
2.4.1 Quality assurance plans should contain mechanisms for demonstrating
the reproducibility of each measuring process. Regular calibration of
instruments, proper documentation, and permanent record keeping are essential
aspects of such plans.
2.4.2 Each measuring device must be calibrated before each use according to
9
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the manufacturer's instructions, and routine checks using National Institute
of Standards and Technology, or other standards of known accuracy, should be
made to demonstrate that variables are within predetermined acceptance
limits. Permanent records giving dates and details of these calibrations and
checks must be kept. Documentation is necessary to identify each specific
measuring device, where and when it is used, what maintenance was performed,
and the dates and steps used in instrument calibration. Each sample
collected should also be documented by assigning a unique identification
number and label (See Section 6, Sample Processing). Data should be
documented to allow complete reconstruction, from initial field record
through data storage system retrieval.
2.4.3 Whenever samples are collected to be used as evidence in a court of
law, it is imperative that laboratories and field operations follow written
chain-of-custody procedures for collecting, transferring, storing, analyzing,
and disposing of the samples. The primary objective of chain-of-custody
procedures is to create written record which can be used to trace the
possession of the sample from the moment of collection through the
introduction of the analytical data into evidence. Explicit procedures must
be followed to maintain the documentation necessary to satisfy legal
requirements. All survey participants should receive a copy of the study
plan and be knowledgeable of its contents prior to implementing the field
work. A presurvey briefing should be held to reappraise all participants of
the survey objectives and chain-of-custody procedures. After all chain-of-
custody samples are collected, a debriefing should be held in the field to
check adherence to chain-of-custody procedures. Chain-of-custody procedures
are detailed in three USEPA manuals (USEPA, 1974, 1982, and 1990).
2.4.4 Field and laboratory personnel should keep complete and permanent
records of all conditions and activities that apply to each individually
numbered sample sufficient to satisfy legal requirements for any potential
enforcement or judicial proceedings. All field and laboratory data sheets
should be dated and signed by the sampler and analyst, respectively.
Notebooks, data sheets, and all other records that may be needed to document
the integrity of the data should be kept permanently filed in a safe and
fireproof place.
2.5 Qualifications and Training
2.5.1 All personnel need to have adequate education, training, and
experience in the areas of their technical expertise and in QA to fulfill
their designated responsibilities. Because no formal academic programs in
research QA exist, most QA experience will have to be acquired through on-
the-job training.
2.5^2 At least one professional biologist with training and experience in
biological sampling methods and macroinvertebrate identification should be
on the staff and should be personally involved in the field work as well as
the laboratory analysis of the samples. Statistical expertise should be
readily available and consulted during every phase of the project.
2.5.3 Management should periodically assess the training needs of all
10
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personnel engaged in QA and recommend and support their participation in
appropriate and relevant seminars, training courses, and professional
meetings. Biologists and technicians should be expected to participate
regularly in evaluation and/or certification programs where appropriate.
2.5.4 The laboratory should have on file an up-to-date resume for each
person who is responsible for the analysis, evaluation and reporting of
biological data. :
2.6 Standard Operating Procedures (SOPs)
2.6.1 Each laboratory must define the precise methods to be used during each
step of the sample collection, analysis, and data evaluation process. These
written procedures become the standard operating procedures (SOPs) describing
the operation of the laboratory. Standard operating procedures for a
macroinvertebrate laboratory should describe in stepwise language, easily
understood by the potential user, the sampling methodology, details of
preservation and labeling the samples, use of taxonomic keys, use and
calibration of measuring instruments, replication and QC requirements, sample
custody and handling procedures, and data evaluation and handling. The SOPs
should include a listing of the taxonomic keys and references that should be
used for each level of identification required and for each taxonomic group.
It should provide an outline of the steps to be taken to assure the quality
of the data.
2.6.2 The SOPs should stress the need for the traceability of the samples.
At a minimum it should specify that each sample be assigned a unique
identification number and be properly labeled with the sample number,
sampling location, and name of the collector. It should describe procedures
to ensure that each sample collected, as accurately and precisely as
possible, represents the community sampled.
2.6.3 The SOPs should be approved by the proper authority and should be
easily accessible to personnel for referral.
2.6.4 The laboratory SOPs should be followed as closely as possible. Any
deviations should be documented as to the reason for the deviation and any
possible effect the deviation might have on the resulting data.
2.7 Literature Cited
USEPA. 1990. Manual for the evaluation of laboratories performing aquatic
toxicity tests. EPA/600/4-90/031. Klemm, D.J., L.B. Lobring, and W.H.
Horning, II. U.S. Environmental Protection Agency, Environmental
Monitoring Systems Laboratory, Cincinnati, OH 45268.
USEPA. 1974. Model state monitoring program. EPA-440/9-74-002. U.S.
Environmental Protection Agency, Office of Water and Hazardous
Materials, Monitoring and Data Support Division, Washington, DC 20460.
USEPA. 1978. Quality Assurance Newsletter. Environmental Monitoring and
Support Laboratory - Cincinnati, OH 45268.
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USEPA. 1979. Aquatic Biology, Chapter 13. Jn: Handbook for analytical
quality control in water and wastewater laboratory. EPA-600/4-79-019.
U.S., Environmental Protection Agency, Environmental Monitoring and
Support Laboratory, Cincinnati, OH 45268.
USEPA. 1980. Guidelines and specifications for preparing quality assurance
project plans. QAMS-005/80. U.S. Environmental Protection Agency,
Office of Monitoring,and Quality Assurance, Office of Research and
Development, Washington, DC 20460.
USEPA. 1982. Manual for the certification of laboratories analyzing drinking
water: Criteria and procedures - Quality assurance. EPA-570/9-82-002.
U.S. Environmental Protection Agency, Office of Drinking Water,
Washington, DC 20460.
USEPA. 1984a. Guidance for preparation of combined work/quality assurance
project plans for environmental monitoring. Report No. OWRS QA-1, U.S.
Environmental Protection Agency, Washington, DC 20460.
USEPA. 1984b. Policy and program requirements to implement the quality
assurance program. EPA Order 5360.1, U.S. Environmental Protection
Agency, Washington, DC 20460.
| , ' " " ' , "; _ ' '' i ' "L ' , ','. ' . '
USEPA. 1984c. The development of data quality objectives. Prepared by the
EPA Quality Assurance Management Staff and DQO Workshop. U.S. Environ-
mental Protection Agency, Office of Research and Development,
Washington, DC 20460.
USEPA. 1986. Development of data quality objectives. Descriptions of stages
I and II. Prepared by the Quality Assurance Management Staff. U.S.
Environmental Protection Agency, Office of Research and Development,
Washington, DC 20460.
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SECTION 3
SAFETY AND HEALTH
3.1 Introduction .
3.1.1 Collection and analysis of berithic samples involve significant risks to
personal safety and health. While safety is often not considered an integral
part of berithic sampling routine, the biologist must be aware of unsafe working
conditions, hazards connected with the operation of sampling gear, and other
risks. Management should assign health and safety responsibilities and establish
a program for training in safety, accident reporting, and medical and first aid
treatment. Written safety policies should be available to all persons involved
in the sampling and analysis of macroinvertebrate samples and this should include
a copy of the USEPA (1986) safety manual.
3.2 general Precautions
3.2.1 Basic good housekeeping practice should be followed both in the field
and in the laboratory. These practices should be aimed at protecting the staff
from physical injury, preventing or reducing exposure to hazardous or toxic
substances, avoiding interferences with laboratory operations, and producing
valid data.
3.2.2 Operation of benthic sampling device:; involves hazards that must be
addressed by the person using the equipment. Some grab samplers (e.g., Ekman,
Smith-Mclntyre) have spring loaded cocking devices that can cause serious injury
if not handled and operated carefully. Other grabs (e.g., Ponar) have safety
locking pins that must be put in place to prevent injury. Persons using these
devices should become familiar with the hazards involved and establish
appropriate safety practices prior to using them.
3.2.3 Field personnel should known how to swim. Waders should always be worn
with a belt to prevent them from filling with water in case of a fall. A life
jacket at dangerous wading stations is advisable if one is not a strong swimmer
because of the possibility of sliding into deep holes.
3.2.4 Many hazards lie out of sight in the bottoms of lakes, rivers and streams.
Broken glass or sharp pieces of metal embedded in the substrate can cause serious
injury if care is not exercised when walking or working with the hands in such
environments. Infectious agents and toxic substances that may be absorbed
through the skin or inhaled may also be present in the water or sediment.
3.2.5 Personnel must consider and prepare for hazards associated with the
operation of motor vehicles, boats, winches, tools, and other incidental
equipment. Boat operators should be familiar with U.S. Coast Guard rules and
regulations for safe boating contained in a pamphlet, "Federal Requirements for
Recreational Boats," available from your local U.S. Coast Guard Director or
Auxiliary or State Boating Official (U.S. Coast Guard, 1987).'
3.2.6 Prior to a sampling trip, personnel should determine that all necessary
equipment is in safe working condition and that the operators are properly
; ..''',. . 13 . ''.-.;.
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trained to use the equipment.
i , ".if:
3.2.7 Safety equipment and first aid supplies should be available in the
laboratory and in the field at all times. A snake bite kit should be carried
on all field trips in areas that may be infested with poisonous snakes. All
motor vehicles and boats with motors should have fire extinguishers.
3.3 Safety Equipment and Facilities
3.3.1 Necessary and appropriate safety apparel such as waders, lab coats,
gloves, safety glasses, and hard hats should be available.
3.3.2 First aid kits, fire extinguishers and blankets, safety showers, and
emergency spill kits should be readily available in the laboratory at all times.
3.3.3 A properly installed and operating hood should be provided in the
laboratory for use when working with volatile chemicals that may produce
dangerous fumes.
IP
3.3.4 Communication equipment should be available to field personnel and those
working in mobile labs in remote areas for use in case of an emergency.
3.3.5 Facilities and supplies should be available for cleaning of exposed body
parts that may have been contaminated by pollutants in the water. Soap and an
adequate supply of clean water or ethyl alcohol may be suitable for this purpose.
3.4 Field and Laboratory Operations
L , ' .j .. I > ' i . ' , . , ' , :!- : '
3.4.1 At least two persons should be involved in all field collecting trips
and no one should be left alone while in the field.
3.4.2 All surface waters should be considered potential health hazards due to
toxic substances or pathogens and exposure to them should be minimized as much
as possible. Exposed body parts should be cleaned immediately after contact with
these waters.
3.4.3 All electrical equipment should bear the approval of Underwriters
Laboratories and be properly grounded to protect against electric shock.
3.4.4 Staff training in basic first aid and cardio-pulmonary resuscitation is
strongly recommended.
3.4.5 Before transporting grab sampling devices, be sure all safety lock pins
are in place or transport them in the closed position. Read and follow all
safety instructions provided by the manufacturer.
3.4.6 Use a winch for retrieving samples collected with heavy sampling
devices such as the Ponar grab and use care in lifting heavy items to prevent
back injury.
3.4.7 Heavy gloves should be used when hands are used to agitate the substrate
14
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during collection of square-foot type samples and when turning over rocks during
hand picking.
3.4.8 Persons working in areas where poisonous snakes may be encountered should
check with the local Drug and Poison Control Center for recommendations on what
should be done in case of a bite from a poisonous snake. If local advice is not
available and medical assistance is over an hour away, carry a snake bite kit
and be familiar with its use. Any person allergic to bee stings or other insect
bites should take proper precautions and have any needed medications handy.
3.4.9 Personnel dealing in field activities on a regular or infrequent basis
should be in sound physical condition and have a physical exam annually in
accordance with Regional or State Safety Officer's requirements.
3.4.10 Hypothermia--all field personnel should be familiar with the symptoms
of hypothermia and know what to do in case symptoms should occur. Hypothermia
can kill a person at temperataure much above freezing (up to 50°F) if he or she
is exposed to wind and rain or otherwise becomes wet.
3.5 Disease Prevention -.-'".
3.5.1 Because it is not known what pollutants may be present in surface waters
and sediments, they should be considered potential health hazards and exposure
to them kept to a minimum.
3.5.2 Personnel, who may be exposed to water known or suspected to contain
human wastes, should be immunized against tetanus, hepatitis, typhoid fever,
and polio.
3.6 Literature Cited
US Coast Guard. 1987. Federal requirements for recreational boats. U.S.
Department of Transportation, United States Coast Guard, Washington, DC
20593.
USEPA. 1986. Occupational health and safety manual. Office of Planning and
Management, U.S. Environmental Protection Agency, Washington, DC 20460.
15
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SECTION 4
SELECTION OF SAMPLING STATIONS
4.1 Introduction
4.1.1 The design of monitoring programs is one of the major sources of
error or uncertainty in water quality data (Thornton e_t al., 1982).
Proper selection of sampling sites (overall sampling areas) should be
directed toward minimizing uncertainty or, at least, provide a means by
which variability may be reduced.
4.1.2 If samples are taken at random over the whole stream, river or lake
bottom, the sample sites may differ physically and species counts can be
highly variable. A reasonable sample size would be expected to detect
only a population density difference of more than 200% of the mean between
two sites (Schwenneker and Hellenthal, 1984). If, however, the potential
sampling areas are restricted to those of similar physical nature, this
variability will be reduced so that differences of 50% or better can often
be obtained. Mason et al_. (1973) found that three artificial substrate
replicate samples could be expected to give estimates within 20% of the
true mean at the 95% confidence level.
4.1.3 Chutter and Noble (1966) studied the effects of sample site
selection using the Surber square foot sampler and concluded that the
closer the sampling site is defined the more reliable will be the sampling
data in terms of a single species per square foot. Therefore, selection
of sampling sites with similar substrate types (e.g., particle size),
current velocity, depth and other physical characteristics will aid
greatly in reducing variability.
4.1.4 Most organisms, even in a selected and defined habitat type, are
not evenly distributed over the bottom of a waterbody so replicate
samples will be needed to evaluate this variability (Cairns and Dickson,
1971). The crucial question is how many samples should be taken. The
answer will depend on the purpose of the study, data quality objectives,
physical characteristics of the sampling location, the type of sampler to
be employed, and the time available. Mackey et al. (1984) considered four
replicates in each distinctive environmental zone along the river to be
adequate when using pond nets. A minimum of two replicate samples at each
station are required when using drift nets (Lewis et al- > 1975). Two
(Mason et al., 1973) or three replicate samples are required for
artificial substrate type samplers, and three replicate samples are the
absolute minimum when using Surber and Hess type samplers (Needham and
Usinger, 1956) or grab samplers (Lewis et
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aggregated later after individual samples are analyzed and tabulated, but
potentially important comparisons among habitats are lost if samples are
composited.
4.1.5 A sound sampling design requires substantial understanding of the
organisms being sampled and the types and limitations of the sampling
devices to be employed. Data reduction techniques also, should be
included in the study plans. Knowledge of locations of possible sources
of pollution as well as insight into the intensity of the expected effects
of the environmental changes that may be occurring at the site are also
of great value. Other factors that will need to be involved in proper
selection of the sampling sites include objectives of the study,
accessibility, flow and mixing characteristics of effluents, personnel
and facilities available to conduct the study, and historical data from
previous studies. The primary concern in designing a sampling scheme is
to gain an accurate measurement with high precision with the least effort
possible to optimize productivity of available person-hours (Downing,
1979).
4.2 Location of Sampling Stations (Sampling Locations Within Each Site)
4.2.1 After determining the specific data quality objectives of the study
and defining clearly what information is needed, it is necessary to select
specific reaches of the stream or areas of the lake to use as sampling
sites. Reconnaissance of the waterway (pilot study) at this time, using
the Rapid Bipassessment Protocol I (Plafkin et a].., 1989) or similar
techniques, is important. Note possible sources of pollution, access
points, bottom types, flow characteristics, and other physical
characteristics that will need to be considered in selecting the sampling
sites. The results of the pilot study may be used to obtain estimates of
variances needed to establish sample size. Other advantages of the pilot
study are that it accomplishes a detailed reconnaissance and it provides
the opportunity to obtain experience in the actual field situation where
the final study will be made. Information obtained and difficulties
encountered may often be used to avoid costly and needless expenditures
during the full scale study. Although the number and location of sampling
stations will vary with each individual study, the following basic rules
modified from Cairns and Dickson (1971), if carefully followed, should
result in a sound survey design.
4.2.1.1 Always have at least one reference station (control station) away
from all possible discharge points to provide a basis for comparison
between areas above and below the point of discharge. This station should
be directly above the effluent discharge in streams or just outside the
zone influenced by the discharge in lakes and estuaries. It is advisable
to add a second reference station well above or outside the zone of
influence. See Section 4.3, Selecting Control Stations.
4.2.1.2 Establish a station directly below the source of pollution in
streams or at the point of discharge into lakes. If the discharge does
not mix completely immediately on entering a stream, left-bank,
midchannel, and right bank substations should be established.
17
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4.2.1.3 Establish stations at various distances downstream from the
discharge in streams or away from the discharge point in lakes. Effort
should be made to space the collecting stations approximately
exponentially farther apart going down stream from the pollution source
to determine the extent of the recovery zone.
4.2.1.4 All sampling stations should be as ecologically similar as
possible in order to compare the benthic fauna collected at these sites.
Decreasing station similarity with regard to habitat parameters generally
indicate decreasing station comparability. The ability to control or
measure inherent natural variability will enhance the overall assessment
of benthic community structure and function. Bottom substrate, depth,
temperature, flow velocity, bank cover, and salinity, etc. should be
similar at each site. Where stations cannot be located in areas of
similar habitats it may be necessary to use artificial substrate samplers
to collect the samples.
4.2.1.5 Sampling stations for macroinvertebrates should be close to the
sites where sampling for chemical and physical analyses will be located.
4.2.1.6 Sampling stations should be located in areas where benthos is
not influenced by atypical conditions, such as those created by bridges
or dams unless effects of atypical conditions make up part of the study
objectives. For instance, urbanized areas include these structures as
typical, and, in some cases, may provide the best suitable habitat that
is available.
4.2.1.7 Sampling stations should be located so that samples can be
collected from all the stations in a study on approximately the same day.
If samples are collected on different days, emergence of adults may occur
at a later collection site resulting in erroneous conclusions.
4.2.1.8 The sampling stations should be in places that are easily
accessible. Long hiking distances and steep banks should be avoided if
at all possible. If a boat will be needed for sample collection, the
station should be located near a boat dock or launch ramp. In some
habitats, such as a large lake, estuary, or ocean, sampling stations will,
of necessity, often be miles from the boat launch ramp. If artificial
substrate samplers are being used, the possibility of vandalism should be
taken into account when selecting stations for installing these sampling
devices.
4.2.2 Sampling to assess the effects of non-point sources of pollution
requires a number of stations along the stream in the impacted area.
Samples should also be collected in the unimpacted upstream.area and the
downstream recovery zone of the impacted stream.
4.3 Selecting Control Stations
4.3.1 Selecting appropriate control stations is a critical step because
the control condition is the best estimate of integrity available to the
investigator. The control station must be at a representative site at
18
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which conditions adequately reflect or approximate the conditions of the
water body being investigated. Four basic approaches available as
estimates of control conditions are: (1) consult historical records, (2)
use pristine or least disturbed areas, (3) use ecoregion reference sites,
and (4) use computer simulation techniques to create a hypothetical
benthic community as a reference station.
4.3.1.1 Historical records may be incomplete or nonexistent but, if
available, can often provide valuable information on the status of
previous conditions at the site. Usefulness of computer simulation
techniques will depend on the quality and quantity of data available on
the site in question. The most viable option most of the time is the use
of least disturbed areas as controls in combination with the other three
approaches.
4.3.1.2 The investigator, therefore, must look for the least impacted
areas as close to the impacted area as possible or to an ecoregion
reference station as the control site. The ecoregion reference station
represents the best attainable conditions for all streams (or other water
bodies) with similar physical characteristics for a given ecoregion
(Plafkin et al_., 1989). Ecoregions are geographic patterns of similarity
among ecosystems, grouped on the basis of environmental variables such as
climate, soil type, physiography and vegetation. From the data base that
has been generated at the ecoregion reference station it would be
theoretically possible to determine the expected aquatic community
structure that would exist in the study area if not impacted (or in its
pristine condition). If the ecoregion reference station or a station in
an adjacent area is used as the control site, a second 'control station
should be sampled in the least impacted area of the water body under study
for comparison. Care must be taken because most navigable waterways have
been altered by channelization, dredging, bridge building, etc.
4.4 Study Design
4.4.1 Once the sampling stations are chosen, the investigator will need
to determine exactly where the samples will be collected at each station
in order to determine the biological integrity of the aquatic community.
Two types of sampling plans are discussed: 1) random sampling is used
when quantitative data is needed, and 2) non-random sampling may be used
to generate qualitative data or semi-quantitative data.
4.4.2 Random Sampling
4.4.2.1 In biological studies using the quantitative sampling approach,
the exact location of sample collection (sampling units) and number of
samples to be collected at each station must be selected with some known
probability that a certain measure of precision will be obtained.
Usually, random selection is the only feasible means of satisfying this
criterion. Only by knowing the probability of selecting a specific sample
can one extrapolate from the sample to the population in an objective way.
The probability allows one to place a weight upon an observation in making
an extrapolation to the population. There is no other quantifiable
19
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measure of how well the selected sample represents the population. Thus
the study plan should include an appropriate effort to define the problem
in such a way as to allow a person to estimate the parameter of interest
using a sample of known probability called a random sample.
4.4.2.2 There is a fundamental distinction between a "haphazardly-
selected" sample and a "randomly-selected" sample. The distinction is
that a haphazardly-selected sample is one where there is no conscious
bias, whereas a randomly-selected sample is one where there is consciously
no bias. There is consciously no bias because the randomization is
planned and, therefore, bias is planned out of the study. This is usually
accomplished with the aid of a table of random numbers. A sample selected
according to a plan that includes random selection of experimental units
is the only sample validly called a random sample.
4.4.2.3 Quantitative sampling in biological field studies is most often
aimed at explaining spatial distributions of population densities or of
some parameter related to population densities and the measurement of
rates of change which permit prediction of some future course of a
biologically-related parameter. In these cases the sampling unit is a
unit of space (volume, area). Even in cases where the sampling unit is
not a unit of space, the problem may often be stated in such a manner
that a unit of space may be used, so that random sampling may be more
easily carried out.
4.4.2.4 It is not always a simple or straightforward matter to define
sampling units, because of the dynamic nature of the hydrology of streams
and living populations. Many aquatic organisms are mobile, and even
rooted or sessile forms change with time, so that changes occurring during
the study often make data interpretation difficult. Thus, the benefit to
be derived from any attempt to consider such factors in the planning stage
will be considerable.
4.4.2.5 Random sample selection is a subject apart from the selection of
the study site. It is of use only after the study objectives have been
defined, the type of measurements have been selected, and the number of
samples has been determined. At this point, random sampling provides an
objective means of obtaining information to achieve the objectives of the
study.
4.4.2.6 One satisfactory method of random sample selection is to number
the universe, or entire set of sampling units available, from which the
sample will be selected. This could be accomplished by marking off equal
distances on a line transect across the stream and numbering each mark
consecutively or by dividing a section of a water body (the sampling
station) into grids as in figure 4 and numbering each intercept. The
total nymber of marks or intercepts is "N". Then from a table of random
numbers select as many random numbers, n, as there are sampling units
selected for the sample. Select a starting point in the table and read
the numbers consecutively in any direction (across, diagonal, down, up).
For example, if "N" is twenty, .select only numbers less than or equal to
20, ignoring any number greater than "N" or any number that has already
20
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been selected. These numbers will be the numbers of the sampling units
to be selected (Cummins, 1962).
4.4.2.7 If a random starting point is chosen along the transect to
introduce randomness needed to guarantee freedom from bias and allow
statistical inference and the samples are collected at points chosen
systematically along the transect, the data collected could be considered
quantitative. To avoid arbitrariness, randomization should also be
employed in transect placement.
4.4.2.8 Simple Random Sampling is used when there is no reason to
subdivide the population from which the sample is drawn. The sample is
drawn such that every unit of the population (numbered section or grid)
has an equal chance of being selected. This may be accomplished by using
the random selection scheme already described. Because the spacial
distribution of benthic communities is so closely related to physical
factors such as substrate type, current velocity, depth, and salinity, a
design using simple random sampling is seldom meaningful. Therefore, it
is usually best to stratify the habitat on the basis of known physical
habitat differences and select sampling units by an appropriate
randomization procedure in each habitat type; a procedure known as
stratified random sampling.
4.4.2.9 Stratified Random Sampling is usually the preferred sampling
design because of a resulting increase in precision. If any knowledge
of the expected size or variation of the observations is available, it
can often be used as a guide in subdividing the population (potential
sampling points or un-its) into subpopulations (strata) (Gaufin et al_.,
1956). Information obtained during the pilot study will be useful in
determining what strata to sample. The pilot study planning should be
done carefully, perhaps stratifying based upon suspected variability in
community structure. To maximize precision, strata should be constructed
such that the observations are alike within strata and different among
strata. In practice, the information used to form strata will usually be
from previously obtained data or the pilot study. In aquatic field
situations, stratification may be based upon bottom type, depth,
isotherms, and numerous other variables suspected of being correlated with
the characteristic of interest.
4.4.2.10 Stratification may also be done on other bases such as
convenience or administrative imperative, but except where these
correspond with criteria which minimize the variation within strata, no
gain in precision may be expected.
4.4.2.11 Number of strata - In aquatic biological field studies, the use
of knowledge of biological cause-and-effect may help define reasonable
strata (e.g., thermoclines, sediment types, etc., may markedly affect the
organisms so that the environmental features may be the obvious choice for
the strata divisions). Where a, gradient is suspected and where
stratification is based on a factor correlated to an unknown degree with
the characteristic of interest, the answer to the question of how many
strata to form and where to locate their boundaries is not clear. Usually
21
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as many strata are selected as may be needed to meet the data quality
objectives of the study. In practice, gains in efficiency with increasing
stratification usually become negligible after only a few divisions unless
the characteristic used as the basis of stratification is very highly
correlated with the characteristic of interest.
4.4.2.12 For many quantitative studies, it is often necessary in the
interest of economy and efficiency and within the limitations of the
available gear, to sample primarily at sites having substrates which
normally support the most abundant and varied fauna, and devote a minimum
effort to those substrates supporting little or no life. For instance,
in many large, swiftly flowing rivers of the midwest and southeast, the
areas of "scour" with a substrate of shifting sand or hardpan may be
almost devoid of macroinvertebrates; sampling effort may be reduced there
in favor of the more productive areas of "deposition" on the inside of
bends or in the vicinity of obstructions. Just the opposite situation may
occur in many of the swiftly-flowing upland streams, where most of the
effort may be devoted to sampling the productive rubble and gravel riffle
areas instead of the pools.
4.4.2.13 When the location of sampling stations and placement of the
samplers at these stations are done in a non-random manner, as is often
done in practice, the sample is best considered a semi-quantitative sample
even though a quantitative sampling device is used in the study.
4.4.3 Systematic Sampling
4.4.3.1 If quantitative data are not needed, some type of systematic
sampling is generally employed for synoptic surveys and reconnaissance
studies. Line transects established at discrete intervals across a river
or stream and sampled at quarter points, or more frequent intervals, are
a form of systematic sampling (Fig. 1). Use of this type of sampling
assures an adequate cross section while maintaining relative ease of
sampling. In lakes, reservoirs, wetlands, and estuaries, transects may
be established along the short or long axis or may radiate out from a
source of pollution (Fig. 2). The method of placement of the transect
should be given a great deal of thought so that sampling stations will be
as representative as possible. The confounding effects of changes in
physical characteristics of the environment along the transect must be
fully recognized and accounted for. A topographical map with fixed bench
marks, a surveyor's sighting instrument mounted on a tripod, and surveying
stakes marked off in centimeters are useful for establishing a line
transect. The sampling points should be marked so that the fixed stations
can be visited during each sampling visit. These fixed stations can be
marked on a rope extended between poles on each side of a stream or buoys
can be attached to weights on the bottom.
4.4.3.2 In lakes, reservoirs and estuaries the stations may be marked by
use of sighting stakes or dabs of paint on rocks established on the shore.
Two sighting lines should be established for each station so that they
intersect at the fixed site (Fig. 3).
22
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Point Source
» Control Station
Line Transect
Figure 1. Example of transect sampling scheme in rivers and streams.
Control
Point
Shoreline
Control
Point
Figure 2. Example of transect sampling scheme in lakes, reservoirs, and
coastal waters.
23
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LAKE
Station 2
v Station 3
Station I
Stations 1 and 3 are
used as sighting points
to help locate station 2
/\
Sighting poles
or paint dots
V
\ > v*1
Figure 3. Illustration of how sighting lines are used to locate fixed
sampling locations in lakes, reservoirs, or estuaries.
4.4.3.3 Two other methods of locating stations on large water bodies are
the Loran-C and Navstar/GPS methods of sighting longitude and latitude
(USEPA, 1987).
4.4.3.4 Loran is an acronym for long range navigation. It is a pulsed
low-frequency electronic navigation system that operates at 90 to 110 KHz
in the hyperbolic mode. Loran-C has a nominal absolute accuracy of 185-
460 meters over short distances using ground waves, whereas repeatable
accuracy varies from 15-90 meters. Loran-C is frequently used for coastal
monitoring programs, however, it can be used up to 160 Km inland if
overland transmission of signals is used. User capability is unlimited.
4.4.3.5 Navstar/GPS is an acronym for Naystar Global Positioning System
(GPS). It is a second generation satellite navigation system currently
under development by the U.S. Department of Defense. Its purpose is to
provide precise, continuous, worldwide, all-weather, three-dimensional
navigation for land, sea, and air applications. More information on these
and other systems can be found in "Evaluation of Survey Positioning
24
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Methods for Nearshore Marine and Estuarine Waters" (USEPA, 1987).
4.4.3.6 Instead of line transects, the investigator may employ the grid
sampling scheme in rivers lakes, reservoirs, wetlands, and estuaries as
another type of systematic sampling (Fig. 4). Grid sampling may be either
random or non-random depending on the method of choosing the sampling
points within the grid as discussed above for transect sampling (See
section 4.4.2.6).
Point
Source
Figure 4. Example of grid sampling scheme in rivers.
4.4.3.7 In another form of systematic sampling, the investigator, using
a variety of gear, consciously selects and intensively samples all
recognizable habitat types. Such a non-random sampling plan may be used
for collecting qualitative data. Non-random sampling is often employed
during the reconnaissance phase of the study to gain a general idea of
the type of benthic organisms that will be sampled during the main phase
of the study. Use of kick nets in riffle areas and hand picking from
rocks in pool areas are typical collection methods employed during this
phase of the sampling program. These non-random sampling methods are also
commonly used in rapid bioassessment studies (Plafkin et a]_.,- 1989).
4.4.3.8 In conducting synoptic surveys or other qualitative studies and
taking into account the limitations of available sampling devices,
sampling stations should be selected to include all substrate types. If
these qualitative samples are to be used for determining the effects of
pollutants where the pollutant does not have a direct effect on the
substrate, the investigator must bear in mind that only the fauna from
sites having similar substrates in terms of organic content, particle
size, vegetative cover, and detritus will provide valid data for
comparison.
25
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4.5 Consideration of Abiotic Factors
4.5.1 Regardless of the method used to select the sampling unit, the
biologist must consider and account for those natural environmental
variations that may affect the distribution of organisms in the waterbody
under investigation. Among the more important environmental variables in
freshwater habitats are substrate type and stability, gradient, current
velocity, flow rate, water depth (spates and drought in lotic waters),
light and temperature regimes, and water quality characteristics such as
dissolved oxygen, turbidity, acidity, hardness, alkalinity, sulfates, and
nutrient concentration. In mountain ranges the elevation is an important
consideration because it affects water temperature and other stream
characteristics. In estuaries, additional variables that must be
accounted for are the salinity gradient and tidal cycles.
4.5.2 Substrate Type is one of the most important factors for controlling
the characteristics of the community of macroinvertebrates found at a
given location in a body of water (Scott, 1958). Over a period of time,
the natural substrates may be greatly altered by the discharge of
particulate mineral or organic matter, and the location and expanse of
various substrate types (silt, sand, gravel, etc.) may change because of
normal variations in hydrologic factors such as current velocity and
stream flow. The biologist, therefore, must be cognizant of changes in
the nature and properties of the substrate which may provide clues on the
quality and quantity of pollutants and other factors which affect the
normal distribution of the benthic fauna.
4.5.2.1 Where the pollutant has a direct effect on the characteristics
of the substrate, the effects of these changes may be inseparable from
the effects of changes in water quality. Where substrate has
deteriorated, faunal effects may be so obvious that extensive sampling
may not be required and special attention should be given to the physical
and/or chemical characterization of the deposits.
4.5.2.2 Because of the importance of substrate (in terms of both organic
content and particle size) in macroinvertebrate studies, it is suggested
that one or more unsieved substrate samples be collected from each station
for use in conducting an analysis of substrate characteristics.
4.5.2.3 The mineral and organic matter content of the stream, lake, or
estuary bottom at each sampling station should be classified and recorded
on suitable forms, on a percentage basis, using the categories shown in
Table 1, which should be applicable to most situations with only slight
modification.
4.5.2.4 It is often desirable to further evaluate the inorganic
components of the substrate by conducting a wet and dry particle size
analysis in the laboratory. This analysis should be conducted on
replicate samples from each sampling site with the use of standard sieves
following the modified Wentworth classification shown in Table 2. Methods
for separating the coarse fractions are given in Welch (1948). The silt-
clay fraction may be considered "silt" if it is of a fine, loose
26
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consistency upon drying, and "clay" if it is of a sticky consistency
forming hard clumps on drying (Lewis et a].., 1982). If it is desirable
to further separate the silt-clay fraction, a Coulter counter as described
by Walker et al_. (1974) is recommended.
TABLE 1. CATEGORIES FOR FIELD EVALUATION OF SUBSTRATE CHARACTERISTICS*
Tvoe
Size or characteristic
Inorganic Components
Bed rock or solid rock
Boulders
Rubble/cobble
. Gravel
Sand
Silt
Clay-Marl/hard pan
Organic Components
Detritus
Peat
Muck
>256 mm (10 in.) in diameter
64 to 256 mm in diameter
2 to 64 mm in diameter
0.06 to 2.0 mm in diameter
<0.06 mm in diameter, of a loose
consistency easily disturbed
<0.004 mm in diameter, of a sticky
consistency not easily disturbed,
slick feeling when rubbed between
fingers
Wood, sticks, and other undecayed
coarse plant materials
Variously decomposed, green to brown,
plant remains
Completely decomposed, black, fine
organic matter
*Modified from Roelofs, 1944.
4.5.2.5 Analysis of Organic Content - The organic content may be
determined by drying and ashing a weighed amount of a representative
sample of the sediment.
4.5.2.6 Dry weight is determined by weighing the sample in a tared
porcelain crucible, drying in an oven at 105 degrees C to a constant
weight (24 hours), and weighing.
4.5.2.7 Ash-free weight is determined after the dry weight is done.
Place the same crucible with the dried sample in a muffle furnace at 500
degrees C for one hour. Cool, rewet the ash with distilled water, and
bring to constant weight-(about 24 hours) at 105 degrees C. The ash is
wetted to reintroduce the water of hydration of the clay and other
minerals that, though not driven off at 105 degrees C, is lost at 500
degrees C. This water loss often amounts to ten percent of the weight
lost during ignition and, if not corrected for, will be interpreted as
organic matter. Subtract the weight of the crucible from the dry weight
to obtain ash-free weight.
27
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4.5.3 Gradient is the percent of slope of the stream bed which affects
velocity and the ability of the stream to maintain substrate quality.
Gradient is particularly important in streams and rivers where it
influences siltation and scouring.
TABLE 2. SUBSTRATE PARTICLE SIZE CLASSIFICATION FOR SIEVE ANALYSIS*
Name
Boulder
Rubble
Coarse Gravel
Medium Gravel
Fine Gravel
Coarse Sand
Medium Sand
Fine Sand
Very Fine Sand
Silt
Clay
Particle Size (mm)
>256
64 to 256
32 to 64
8 to 32
2 to 8
0.5 to 2
0.25 to 0.5
0.125 to 0.25
0.0625 to 0.125
0.0039 to 0.0625
<0.0039
U.S. Standard Sieve Number
Available but not U.S. Standard
10
35
60
120
230
See Text
See Text
*Modified from Wentworth, 1922; see Cummins, 1962.
4.5.4 Current velocity affects the distribution of organisms in lotic
environments and along the windswept shores of lentic environments, both
directly because of differing species requirements and indirectly by
sorting of bottom sediments. Therefore, it is of critical importance that
velocity be considered when sampling stations are selected, and when data
are analyzed. Only stations with similar velocity should be compared.
In addition, windswept and protected shores of lakes may not be
comparable. At the actual time of sampling, velocity should be determined
at each sampling point. Relatively inexpensive current meters are
commercially available (See equipment list in Appendix E). Current
velocity may also be determined by use of a home-made velocity head tube
described by Ciborowski (1989).
4.5.4.1 At depths greater than three feet, use the two-point method; take
readings at two points, 0.2 and 0.8 of the depth below the surface. The
average of these two observations is taken as the velocity.
4.5.4.2 At depths less than three feet, take one reading at 0.6 of the
depth. Where artificial substrate samplers or drift nets are being
utilized, take the reading directly upstream of the sampler and at the
same depth as the sampler.
4.5.5 Flow rate may be a factor in the distribution of benthic organisms
in that it indirectly effects other factors such as current velocity and
water depth. Also, flow rate is a factor in the dilution of toxic
substances in the water. During periods of low flow a toxic material will
cause greater stress on the organisms present because of higher
concentration of the substance. For this reason it is often desirable to
28
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sample areas of suspected problems during low flow conditions in order to
determine if an effluent is causing a stress on the aquatic community.
Comparison of a sampling station during the sample period from year to
year may not be valid if there is a large difference in flow rate between
the two years.
4.5.6 Depth indirectly affects the distribution of aquatic macro-
invertebrates as a result of its influence on the availability of light
for plant growth, water temperature, the zonation of bottom deposits,
water chemistry (particularly oxygen), and on phototactic responses of
organisms. In selecting sampling stations for both qualitative and
quantitative studies, depth must be measured and included as an
independent variable in the study design.
4.5.7 Turbidity is defined as a cloudy condition in water due to the
suspension of silt or finely divided organic matter. It is an important
factor in that it directly effects light penetration and indirectly
effects the productivity of algae and aquatic plants. The settling out
of solids can also eliminate all life from a stream or river, or reduce
its amount without greatly altering its composition simply by shading out
all or some of the plant life, smothering out all algal growth, and
altering the nature of the substratum.
4.5.8 Salinity is an important factor in marine and estuarine
environments. The salinity of freshwater is generally a few parts per
million compared to approximately 35 parts per thousand for sea water.
Where sea water and fresh water meet in estuaries, there may be wide
fluctuations of salinity due to variations in tides and river discharge,
and a salt wedge may extend upstream under the fresh water layer for a
significant distance. This area may be inhabited to some extent by both
freshwater and saltwater forms, but the number of species is usually less
than that under more stable conditions of salinity (Macan, 1963). Since
movement and location of many species is governed by tides and salinity,
these must be taken into account in determining sampling location as well
as time of sampling.
4.5.8.1 Because of the extreme spatial and temporal fluctuations of
salinity in the estuaries, simple, rapid instrumental measurements are
more desirable than slower, more precise chemical methods (Mangelsdorf,
1967). Wide range, temperature-compensated conductivity salinometers are
recommended for determining both horizontal and vertical salinity profiles
at high-slack and low-slack tide levels in the area of estuary or reach
of river being studied.
4.5.9 Tidal inundation (the amount of time that a particular stratum is
inundated in marine intertidal zones) affects the kinds of organisms that
can live within the substrate. Organisms that can resist desiccation and
temperature change are able to colonize the intertidal zone. Organisms
that cannot, will be restricted to the sublittoral zone or area below the
tidal reach.
4.5.10 Chemical factors such as alkalinity, pH, hardness and sulfates are
29
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also important factors to .consider. They affect the numbers and
composition of macroinvertebrates in the stream. Alkalinity is closely
related to primary productivity. An increase in sulfates causes
deterioration in water quality and adversely affects the macroinvertebrate
community.
4.6 Literature Cited
Cairns, J., Jr. and K.L. Dickson. 1971. A simple method for the
biological assessment of the effects of waste discharges on aquatic
bottom dwelling organisms. J. Wat. Pollut. Control Fed. 43(5):755-
772.
Cibordwski, J.J.H. 1989. Velocity head tube: A new field instrument for
measuring current (Abstract only). Bull. N. Amer. Benthol. Soc.
6(1):83.
Chutter, P.M. and R.fi. Noble. 1966. The reliability of a method of
sampling stream invertebrates. Arch. Hydrobiol. 62(1):95-103.
Cummins, K.A. 1962.. An evaluation of some techniques for the collection
and analysis of benthic samples with special emphasis on lotic
waters. Am. Midi. Nat. 67:477-504.
Downing, J.A. '1979. Aggregation, transformation and the design of benthic
sampling programs. Can, J. Zoo!. 36:1454-1463.
t''; '". ' ' in, "
. -" . . ' , "' J'i' ; . '
Gaufin, A.R., E.K. Harris, and H.J. Walter. 1956. A statistical
evaluation of stream bottom sampling data obtained from three
standard samplers. Ecology 37:643-648.
Lewis, P.A., W.T. Mason, Jr., and C.I. Weber. 1982. Evaluation of three
bottom grab samplers for collecting river benthos. Ohio J. Sci.
82(3):107-113.
,...,,.' , .
Lewis, P.A., D.J. Klemm, and H.M. Savage. 1975. The use of
macroinvertebrate drift in water quality investigations. Presented
at the 23rd Annual North American Benthological Society Meeting,
Springfield^ IL^ March 26-28, 1975.
Macan, t.T. 1963. Freshwater ecology. Camelot Press Ltd., London and
Southampton,. England. 338 pp.
Mackey, A.P.,-D.A. Cooling, and A.D. Berrie. 1984. An evaluation of
sampling 'strategies for qualitative surveys of macroinvertebrates in
rivers .using pond nets,. J. Appl. Ecol. 21:515-534.
Mangelsdorf, D.C. 1967. Salinity measurements in estuaries. Estuaries
Publ. No. 83, American Association for the Advancement of Sci.
pp. 71-79.
Mason, W.T., C.I. Weber, P.A. Lewis, and E.C. Julian. 1973. Factors
" '. ' '" / 30
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affecting the performance of basket and multiplate
macroinvertebrate samplers. Freshwat. Biol. 3:409-436.
Needham, P.R. and R.L. Usinger. 1956. Variability in the macrofauna of
a single riffle in Prosser Creek, California, as indicated by the
Surber sampler. Hilgardia 24:383-409.
Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K. Gross, and R.M. Hughes.
1989. Rapid bioassessment protocols for use in streams and rivers:
Benthic macroinvertebrates and fish. EPA/440/4-89/001. U.S.
Environmental Protection Agency, Office of Water Regulation and
Standards, Washington, DC 20460.
Roelofs, E.W. 1944. Water soils in relation to lake productivity. Tech.
Bull. No. 190, Agr. Exp. Stat., State College, Lansing, Mich.
Schwenneker, B.W. and R.A. Hellenthal. 1984. Sampling considerations in
using stream insects for monitoring water quality. Environ. Entomol.
13:741-750.
Scott, D.C. 1958. Biological balance in streams. Sewage Ind. Wastes
30:1169-1173. ,
Thornton, K.W., R.H. Kennedy, A.D. Magoun, and G.E. Saul. 1982. Reservoir
water quality sampling design. Wat. Resources Bull. 18(3):471-480.
USEPA. 1987. Evaluation of survey positioning methods for nearshore
marine and estuarine waters. EPA-430/9-86-003. U.S. Environmental
Protection Agency, Office of Marine and Estuarine Protection,
Washington, DC.
Walker, P.M., K.V. Woodyer, and T. Hitka. 1974. Particle size
measurements by Coulter Counter of very small deposits and low
suspended sediment concentrations in streams. Sedimentary Petrol.
44(3):673-679.
Welch, P.S. 1948. Limnological methods. McGraw-Hill Book Co., New York,
NY. 381 pp.
Wentworth, C.K. 1922. A scale of grade and class terms for clastic
sediments. J. Geol. 30:377-392.
31
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SECTION 5
SAMPLING METHODS
5.1 Introduction
5.1.1 Aquatic macroinvertebrates are good indicators of environmental
water quality in fresh, estuarine, and marine waters. The analysis of
faunal assemblages is an excellent way to detect water quality problems.
Different kinds of stress will often produce different "communities of
benthic macroinvertebrates. The sampling equipment and methods
discussed can be used to study and analyze macroinvertebrate communities
for ambient or special studies, and the resulting data and information
can be used to document both spatial and temporal changes in water
quality. The sampling devices and methods of this section relate to
qualitative, semi-quantitative, and quantitative sampling.
5.1.1.1 Qualitative and semi-quantitative sampling of macroinverte-
brates are relatively easy. The current methodology discussed here is
well developed, and the equipment needed for sampling is not elaborate.
Many effective methods of data analysis, including pollution indices and
diversity indices, have been developed for use with macroinvertebrates
(also, see Section 7, Data Evaluation).
5.1.1.2 Quantitative sampling is more difficult. Random sampling and
the patchy distribution of macroinvertebrates within the substrate often
means larger numbers of samples are needed in order to be able to make
reasonable estimates of community structure and population densities.
However, this is not a problem confined only to macroinvertebrates, but
to other aquatic animals as well. Also, see Section 4, Selection of
Sampling Sites and Section 7, Data Evaluation.
5.1.2 The sampling methods employed should depend on the data quality
objectives (DQOs) (see Section 2, Quality Assurance and Quality Control)
of the study determined by interaction of the decision making authority
and biomonitoring expertise of qualified aquatic biologists.
5.1.3 A list of equipment, supplies, and companies that can provide
sampling gear for collecting benthic macroinvertebrates can be found in
Appendix E.
5.2 Qualitative Sampling
5.2.1 The objective of qualitative studies is to make within or between
site comparisons to determine the presence or absence of benthic
macroinvertebrates having varying degrees of tolerance to pollution and
to obtain information on the richness of taxa, at or near the species
level (taxa present and relative abundance). Samples are obtained with
the use of a wide variety of collecting methods and gear, many of which
are not amenable to quantification on a unit-area or volume basis. Any
collecting device (e.g., dip or hand nets, kick nets, screens, dredges,
32
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grab samplers, stream-net samplers, and artificial substrate samplers)
can be used for qualitative collections of macroinvertebrates. The use
of several methods of collection at each station can increase the total
number of taxa collected. When conducting qualitative studies, an
attempt is usually made to collect as many taxa as possible in the time
available by exhaustive sampling in all available habitat types. No
habitat should be overlooked at the site if the objective of the study
is to obtain a representative collection of the macroinvertebrates.
5.2.2 Experience and skill are required in selecting suitable
collecting techniques and recognizing and locating various types of
habitats where qualitative samples can be collected.
5.2.3 When conducting comparative studies of the macrobenthos, a major
drawback is the confounding effect of the differences in physical
habitat among the different stations being studied. This problem is
particularly inherent in qualitative studies when an attempt is made to
systematically collect as many species as possible at the sampling
stations or reaches of streams being compared. Unfortunately,
differences in habitat unrelated to the effects of pollution may render
such comparisons meaningless. To minimize this drawback, the
investigator should carefully record, in the field, the habitats from
which specimens are collected (a habitat assessment) and then base
comparisons only on stations with like habitats in which the same amount
of collecting effort has been expended. Appropriate sampling methods,
such as the use of artificial substrates, should be utilized to
eliminate the problem of comparing different physical habitats among
stations being studied.
5.2.4 Advantages of qualitative sampling are the wide latitude in
collecting methods, the types of habitats that can be sampled are
relatively unrestricted, and the processing of the samples is usually
less time consuming.
5.2.5 Limitations of qualitative sampling include collecting techniques
that are subjective and depend on the skill and experience of the sample
collector, sampling results of one investigator can be difficult to
compare with those of another, and no information on standing crop or
biomass can be generated from a qualitative study.
5.3 Semi-quantitative Sampling
5.3.1 Semi-quantitative sampling data can be generated based on methods
that measure the collection of benthic macroinvertebrates by level of
effort (e.g., time expended per habitat) or when quantitative sampling
devices are used to collect samples in a non-random manner. Examples
of some semi-quantitative methods include the 10 rock method (Lewis,
personal communication), traveling kick method (Hornig and Pollard,
1978; Pollard, 1981), and Rapid Bioassessnient Protocols II and III
(Plafkin et a].., 1989). See Section 7, Data Evaluation.
5.4 Quantitative Sampling
33
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5.4.1 Quantitative methods essentially provide an estimation of the
numbers or biomass (standing crop) of the various components of the
macroinvertebrate community per unit area, volume, or sampling unit.
The method also provides information on the species composition,
richness of species, and distribution of individuals among the species.
The high variability often associated with some macroinvertebrate
populations makes them difficult to study quantitatively (Schwenneker
and Hellenthal, 1984), but multi-metric assessment endpoints are used
to avoid the difficulty of utilizing only population-based measurement
endpoints. Section 7, Data Evaluation and Elliott (1971) should provide
statistical principles for sampling and data analyses of benthic
macroi nvertebrates.
5.4.2 Quantitative estimates are obtained by using devices that sample
a unit area or volume of the habitat. The major considerations are the
size of the sampling units, the number of sampling units in each sample,
and the location of sampling units in the sampling area. Grab samplers,
stream-net samplers (e.g., Surber and related type samplers, Hess and
related type samplers, and drift nets), and artificial substrate type
samplers, are examples of devices that are used to collect samples
quantitatively.
5.4.3 Sampling precision in the study of macroinvertebrate populations
is affected by the substrate area encompassed by the sampling device and
the patchiness in distribution of the organisms. The smaller the
substrate surface area encompassed by a sampling device, the larger the
number of sampling units required to obtain a desired level of precision
(Elliott, 1971). Precision can be increased by collecting larger
sampling units or by increasing the numbers of sampling units collected.
A quantitative approach necessitates that a measure of the precision be
obtained by replicate sampling. Replicate sampling in each habitat
(habitat niche, microhabitat, or strata) selected for study is an
absolute requirement.
5.4.3.1 For measurement of precision, three replicate random sampling
units at each sampling station are an absolute minimum. Five replicates
at each station would increase the statistical precision and accuracy.
A series of single sampling units taken at discrete points along a
transect do not represent replicate samples of benthic organisms unless
it can be demonstrated that the physical characteristics of the habitat
do not change along the transect.
5.4.4 The total number of samples depends on the degree of precision
required, which will depend on the type of study and data quality
objectives (DQOs). A reconnaissance or pilot study of the station may
be necessary to help determine the sample size. Southwood (1966) gives
a formula for determining the number of sampling units required for a
specific level of precision.
5.4.5 The data from properly designed quantitative studies are amenable
to the use of simple but powerful statistical tools that aid in
maintaining the objectivity of the data evaluation process (see Section
34
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7, Data Evaluation). The measures of precision and probability
statements that can be attached to quantitative data reduce ;the
possibilities of bias in the data evaluation process and make the
results of different investigators more readily comparable. The
advantages of quantitative methods are that they provide a measure of
invertebrate diversity, biomass, and productivity,1 and their associated
precision, thereby providing objective comparisons within, between^ and
among studies or intra- and interstudy comparisons.
5.4.6 No one sampling device is completely adequate to sample all types
of habitat. When either qualitative, semi-quarititativeyor quantitative
devices are used, only selected portions of the environment are usually
sampled. Also, because of the potential use of these data, experienced
and skilled biologists are needed for sample collections,,
5.5 Sampling Devices
5.5.1 Grab Samplers (Grabs)
5.5.2 Grabs are devices designed to penetrate the substrate by virtue
of their own weight and leverage and have spring- or gravity-activated
closing mechanisms. The jaws of grabs are forced shut by weights, lever
arms, springs, or cables. All grabs are designed to take discrete
"bites" or "scoops" of a defined area into the bottom sediment of a
lake, stream, estuary, ocean, or similar habitats to sample the benthos.
Scoops are grab samplers that scoop sediment with a rotating container;
In shallow waters, some of these devices may be rigged on poles or rods
and physically pushed into the substrate to a predetermined depth,
5.5.2.1 The number and kinds of macroinvertebrates collected by a
particular grab may be affected by the habitat sampled, substrate, type
sampled, depth of penetration, angle of closure, completeness of closure
of the jaws and loss of sample material during retrieval, creation of
a "shock" wave and consequent "washout" of organisms at the surface of
the substrate, and the effect of the high-flow velocities often
encountered in rivers and wave action in large lakes and ;:oceans on 6t
stability of the sampler. ; ^ ^ ;:,-
5.5.2 Selecting Grab Sampling Devices -' , r v,
5.5.2.1 Table 3 summarizes criteria for select thg^grJabs/ , ' ';:"Vy
TABLE 3. SUMMARY CRITERIA FOR GRAB; SAMPLERS ; ;
i ' ,' "-].-, , '' ' .-'-*,'
1. Ponar Grab (Standard)
A. Habitats and Substrates Sampled: Freshwater Takes, rivers,
estuaries, and reservoirs with hard and soft sediments such asl
clay, hard pan, sand, gravel and muck; somewhat less efficient'
in softer sediments. : ;; > ^" , /
35 '' ''".'^."',:' "''' >"-'V. ":: ' .-:; '.(
O V -,,-.'".- ' ' - . ' = ../,.' ;
-------
TABLE 3. SUMMARY CRITERIA FOR GRAB SAMPLERS (Continued)
B. Effectiveness of the Device: Not entirely adequate for deep
burrowing organisms in soft sediments; very efficient for hard
sediments; collects both qualitative and quantitative samples.
C. Advantages: Better penetration than other grabs; side plates and
screens reduce washout, shock waves and substrate disturbance;
best quantitative grab sampler for freshwater use.
D. Limitations: A very heavy grab that requires use of a boat with
winch and cable; stones, pebbles and other debris can hold jaws
open causing loss of sample.
2. Petite Ponar Grab
A. Habitats and Substrates Sampled: Freshwater lakes, rivers and
reservoirs and estuaries with moderately hard sediments such
as sand, silt and mud; will not penetrate clay; somewhat less
efficient in soft sediments and coarse gravel.
B. Effectiveness of the Device: Not entirely adequate for deep
burrowing organisms in soft sediments; not useful in clay.
C. Advantages: Good penetration for such a small grab; side plates
and screens reduce washout, shock waves and substrate
disturbance; can be operated by hand without boat or winch.
D. Limitations: Jaws can be blocked by stones, sticks and other
debris causing loss of part of the sample; not efficient in
swiftly flowing water of over one meter per second velocity.
Selected Literature: APHA, 1989; ASTM, 1990; Brinkhurst, 1967, 1974;
Elliott ei al., 1978, 1980, 1981b; Flannagan, 1970; Howmiller, 1971;
Hudson, 1970; Lewis et al., 1982; Powers and Robertson, 1967; USEPA,
1973.
3. Ekman Grab (Standard, Tall, Large, and Extra-large)
A. Habitats and Substrates Sampled: Freshwater rivers, lakes and
reservoirs where there is little current; soft sediments such
as muck and silt.
B. Effectiveness of the Device: Efficient only in soft sediments
but weights can be added for deeper penetration in fine sand;
collects both qualitative and quantitative samples.
C. Advantages: Easy to operate by hand without winch, can be pushed
into substrate in shallow water; hinged doors at top reduce
washout, shock waves and disturbance of the substrate; comes
in a range of sizes.
36
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TABLE 3. SUMMARY CRITERIA FOR GRAB SAMPLERS (Continued)
D. Limitations: Light weight so that jaw will not penetrate hard
substrates; jaws often do not close completely due to blocking
of jaws or failure of closing mechanism; inefficient in
deep water or where there is even moderate current.
Wildco box corer resembles a heavy duty Ekman grab that has been
designed to penetrate harder substrates with the addition of a frame
and weights. The device can be used to collect infauna of lakes and
estuaries. The box corer may also be used to sample finely divided
muck, clays, mud, ooze, submerged marl, or fine peaty bottoms. The
sampler weighs about 14 kg, but a maximum of 49 kg (12 removable
weights) may be used. The sample area is 150 x 150 x 225 mm; a
removable acrylic liner is included.
Selected Literature: APHA, 1989; ASTM, 1990; Beattie, 1979; Burton and
Flannagan, 1973; Ekman, 1911, 1947; Flannagan, 1970; Howmiller, 1971;
Hudson, 1970; Lanz, 1931; Lewis et aj.., 1982; Lind, 1974; Milbrink and
Wiederholm, 1973; Paterson and Fernando, 1971; Rowe and Clifford, 1973;
Rawson, 1947; Schwoerbel, 1970; Welch, 1948; USEPA. 1973.
4. Petersen Grab (Standard and Baby)
A. Habitats and Substrates Sampled: Freshwater lakes, reservoirs
and rivers.and estuaries with sand, gravel,.clay and hard pan
substrates.
B. Effectiveness of the Device: Less effective in most substrates
than the Ponar, Baby Petersen effective in moderately soft
sediments.
C. Advantages: Can give quantitative samples if used properly;
range of sizes available.
D. Limitations: Standard grab is heavy and requires boat with
winch; can cause washout if dropped rapidly to the bottom;
shallow bite by jaws so that deeper burrowing organisms are not
sampled; jaws are easily blocked by debris causing loss of
sample; hard to use in adverse weather; of questionable value
as a quantitative sampler.
Selected Literature: APHA, 1989; ASTM, 1990; Barnes, 1959; Birkett,
1958; Brinkhurst, 1974; Davis, 1925; Edmondson and Winberg, 1971;
Elliott and Tullett, 1978; Holme and Mclntyre, 1971; Hudson, 1970;
Howmiller, 1971; Jensen, 1981; Lewis et al., 1982; Petersen, 1918;
Petersen and Tensen, 1911.
5. Smith-Mclntyre Grab
A. Habitats and Substrates Sampled: Marine and estuaries; adaptable
37
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TABLE 3. SUMMARY CRITERIA FOR GRAB SAMPLERS (Continued)
to large rivers> lakes and reservoirs with sand, gravel, clay
and similar substrates.
B. Effectiveness of the Device: Limited penetration; has been
widely used for sampling in marine and estuarine habitats.
C. Advantages: Provides reasonably quantitative samples; trigger
plates help penetrate the substrate.
D. Limitations: Very heavy, needs boat and power winch; spring
loaded jaws could be hazardous; inefficient for collecting deep
burrowing organisms; jaws can be blocked by debris.
Selected Literature: APHA, 1989; ASTM, 1990; Carey and Heyamoto, 1972;
Carey and Paul, 1968; Elliott and Tullett, 1978; Holme, 1964; Hopkins,
1964; Hunter and Simpson, 1976; Mclntyre, 1971; Smith and Mclntyre,
1954; Tyler and Shackley, 1978; Wigley, 1967; Word, 1976, 1977; Word
St al., 1976.
6. Van Veen Grab
A. Habitats and Substrates Sampled: Marine and estuaries with sand,
graVel, mud, clay and similar substrates; could be adapted to
freshwater.
B. Effectiveness of the Device: Penetrates to a depth of 5 to 7 cm.
C. Advantages: Jaws close better than the Petersen Grab; samples
most types of sediments; comes in a range of sizes.
D. Limitations: A very heavy grab that requires a large boat and
power winch; jaws may become blocked by debris such as rocks and
sticks; not useful for deep burrowing organisms.
Selected Literature: APHA, 1989; ASTM, 1990; Barnes, 1959; Beukema,
1974; Birkett, 1958; Elliott and Drake, 1981b; Elliott and Tullett,
1978; Holme, 1971; Lassig, 1965; Longhurst, 1959; Mclntyre, 1956;
Nichols and Ellison, 1966; Schwoerbel, 1970; Ursin, 1954; Wigley, 1967;
Word, 1976a, 1976b; Word et a]_., 1976.
7. Orange-Peel Grab
A. Habitats and Substrates Sampled: Marine waters and deep
lakes with sandy substrates containing cobble, rubble and coarse
gravel.
B. Effectiveness of the Device: For qualitative use only; sampling
area not constant.
38
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TABLE 3. SUMMARY CRITERIA FOR GRAB SAMPLERS (Continued)
C. Advantages: Comes in a range of sizes; works well in deep water;
closes relatively well to prevent loss of sample; good for
reconnaissance.
D. Limitations: Very heavy so that large boat with power winch and
cable lines is required; does not sample constant area and
depth.
Selected Literature: APHA, 1989; ASTM, 1990; Briba and Reys, 1966;
Elliott and Tullett, 1978; Hartman, 1955; Hopkins, 1964; Merna, 1962;
Packard, 1918; Reish, 1959; Thorson, 1957, Word, 1976, 1977.
8. Shipek Grab
A. Habitats and Substrates Sampled: Estuaries and large deep lakes
with sand, gravel, mud and clay substrates..
B. Effectiveness of the Device: A relatively good quantitative
sampler.
C. Advantages: Good for collecting a small sample in deep Water.
D. Limitations: A heavy grab that requires the use of a boat with
a power winch; must be lowed vertically so is not effective in
moving water; inefficient for collecting deep burrowing
organisms; samples small area.
Selected Literature: APHA, 1989; ASTM, 1990; Barnes, 1959; Elliott and
Tullett, 1978; Flannagan, 1970; Holme, 1964, 1971; Holme and Mclntyre,
1971.
5.5.3 Precautions
5.5.3.1 Always inspect grabs for mechanical defects prior to use.
5.5.3.2 Exercise caution at all times when handling grabsv
5.5.4 Significance and Use of Grabs
5.5.4.1 Qualitative and ^quantitative samples of macroinvertebrates
inhabiting sediments or substrates are may be taken by grabs. Grab
samplers, if used correctly, are devices that sample a unit area of the
habitat. In view of the advantages and limitations regarding the
penetration of the sediment by many grabs and their closing mechanisms,
it is not possible to recommend any single instrument as suitable for
general use. However, the Petersen grab is considered the least
effective bottom grab sampler and, therefore, has limited application.
The type and size of the grab sampler or device selected for use will
depend on such factors as the size of boat, hoisting gear available, the
type of substrate or sediment to be sampled, depth of water, current
39
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velocity, and whether sampling is in sheltered areas or in open waters
of large rivers, reservoirs, lakes, or oceans. The choice of grab will
depend largely on what is available, what is suitable for the sampling
area, and what can be used with the least difficulty.
5.6 Commonly Used Grabs
5,6.1 The ponar grab sampler (Fig. 5A,B) is most commonly used for
sampling macroinvertebrates from sediments in lakes, rivers, reservoirs,
estuaries, and oceans with coarse and hard substrates, such as coarse
sand, gravel, and similar substrates, rather than soft sediments, such
as mud, fine sand, or sludge. The sampler can be used in moderate
currents and deep waters.
5.6.1.1 The Ponar grab sampler has paired jaws that must penetrate
beneath the surface of the substrate without disturbing the water
surface boundary layer, close when positioned properly on the bottom,
and retain discrete samples of sediment while it is brought to the
surface for processing. The device has side plates and a screen on the
top of the sample compartment to prevent loss of the sample during
closure. With one set of weights, this heavy steel sampler can weigh
20 Kg. Word et a]_. (1976a) reports that the large amount of surface
disturbance associated with Ponar grabs can be greatly reduced by simply
installing hinges rather than fixed screen tops, which will reduce the
pressure wave associated with the sampler's descent into the sediment.
The standard Ponar takes a sample area of 523 cm2. A small version, the
petite Ponar grab, takes a sample area of 232 cm and can be used in
habitats where there may be an unusual abundance of macroinvertebrates,
thus eliminating the need to subsample.
5.6.1.2 When not in use, a safety pin lock attached to the lever bar
prevents closing of the sampler until the pin is removed.
5.6.1.3 The weight of the standard Ponar grab makes it necessary to use
a winch and cable or portable crane for retrieving the sample, and
ideally the samples should be taken from a stationary boat or platform.
The smaller version, petite Ponar grab, is designed for hand-line
operation, but it may be used with a winch and cable.
5.6.2 The Ekman grab sampler (Fig. 5C) is used to obtain samples of
macroinvertebrates from soft sediments, such as very fine sand, mud,
silt, and sludge, in lakes, reservoirs, estuaries, and similar habitats
where there is little current. This grab is inefficient in deep waters,
under adverse weather conditions, and in waters of moderate to strong
currents or wave action. The Wildco box corer (Fig. 5D) is like a heavy
duty Ekman with a frame and weights and is used to collect
macroinvertebrates in lakes and estuaries. Because of its weight a
winch is necessary for retrieving the sample from a stationary boat or
platform.
5.6.2.1 The Ekman grab sampler is a box-shaped device with two scoop-
like jaws that must penetrate the intended substrate without disturbing
40
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Figure 5. Grab Samplers. (A) Standard Ponar; (B) Petite Ponar; (C)
Large, tall, and standard Ekman grabs; (D) Wildco box corer
41
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the water surface boundary layer, close when positioned properly on the
bottom, and retain a discrete sample of sediment while it is brought to
the surface for processing. Hinged doors on the top of the grab
prevents washout during sample lowering and retrieval. The grab is made
of 12 to 20 gauge brass or stainless steel and weighs approximately 3.2
Kg. The box-like part holding the sample has spring-operated jaws on
the bottom that must be manually set. The sampler is available in
several sizes; however, in very soft substrates only a tall model should
be used, either a 23 cm or a 30.5 cm model. Ekman is not used with a
winch very often but can be operated from a boat with a winch and cable.
.11 ',''', '» ' . 'I'l . ,!: , , '"
5.6.2.2 Exercise caution at all times once the grab is loaded or cocked
because a safety lock is not part of the standard design.
5.6.3 The Petersen grab sampler (Fig. 6A,B) is designed to obtain
samples of macroinvertebrates from sediments in lakes, reservoirs, and
similar habitats and is adaptable to rivers, estuaries, and oceans.
This grab sampler has limited application, and is not recommended for
quantitative benthic work and must be used with due consideration of its
defects when quantitative estimates are attempted. It is useful for
sampling sand, gravel, marl, and clay in moderate currents and deep
waters, the sampler cannot be used under adverse weather conditions.
This sampler is available in a range of sizes that will sample an area
from 0.06 to 0.099 m2. A consensus of aquatic biologists consider the
use of this device the least preferable grab sampler and would use it
only in limited applications.
5.6.3.1 The Petersen grab sampler has paired jaws that must penetrate
the intended substrate without disturbing the water surface boundary
layer, close when positioned properly on the bottom, and retain the
sample of sediment while it is brought to the surface for processing.
This heavy steel device can weigh 13.7 Kg, but may weigh as much as 31.8
Kg when auxiliary weights are bolted to its side. The extra weights are
to make the grab stable in swift current and to give additional cutting
force in firm bottom sediments. It has been suggested that users of
this device modify it by the addition of end plates and by cutting large
strips out at the top of each side and adding hinged 30 mesh screen as
in the Ponar grab. It is necessary to use a winch and cable to lower
and raise the sampler.
5.6.3.2 Newer versions of the Petersen grab sampler may have a screened
window at the top of each jaw to allow water to escape while the grab
is descending and closing. While some modifications may close or
function better, the sampling characteristics remain the same. Most of
the modified versions are intended for use in estuarine and marine
waters,.
5.6.3.3 Ideally a stationary boat or platform should be used when
taking samples. The modified Petersen devices are designed to be quite
heavy and require heavy gear and a large vessel for efficient operation.
A small version can be hauled aboard by hand and held with one hand for
washing procedures.
42
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Figure 6. Grab Samplers: (A) Original Petersen; (B) Modified Petersen
43
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5.6.4 The Smith-Mclntyre grab sampler (Fig. 7A) is designed to obtain
samples of macroinvertebrates from sediments in rough weather and deep
water in lakes, rivers, estuaries, and oceans. This device samples a
surface area of 0.1 m2 and is useful for sampling macroinvertebrates
from sand, gravel, mud, clay, and similar substrates.
5.6.4.1 The Smith-Mclntyre grab sampler has paired jaws that are forced
to penetrate into the intended substrate by two "loaded" strong coiled
springs, must close when positioned properly on the bottom, and retain
discrete samples of sediment while it is brought to the surface for
processing. The device is heavy and can weigh 45.4 Kg or more. The
chief advantages of the sampler are its stability and easier control in
deep and rough waters. The spring-loaded jaws of the Smith-Mclntyre
grab must be considered a hazard and caution should be exercised when
using the device. Due to the weight and size, this device must be used
from a vessel with boom and lifting capabilities.
5.6.4.2 The Smith-Mclntyre grab sampler is fitted with gauze panels or
free swinging panels on the top to reduce the shock wave during descent.
:-' ' i'V . .. Mi ''
5.6.4.3 Larger Smith-Mclntyre grabs can be constructed depending on the
type of bottom to be sampled and additional weights can be fitted to the
frame of the grab sampler for additional penetration into the sediment.
5.6.5 The Van Veen grab sampler (Fig. 7B) is used to obtain samples of
macroinvertebrates from sediments in estuaries and other marine
habitats, and is adaptable to freshwater areas. It can also be used for
qualitative sampling. This device is useful for sampling sand, gravel,
mud, clay and similar substrates and is available in two sizes, 0.1 m
and 0.2 m2. Larger and double versions of this grab are available, and
their use is dependent upon the type of bottom to be sampled, and the
type of vessel available to deploy this sampler.
5.6.5.1 The Van Veen grab sampler has paired jaws that must penetrate
the intended substrate without disturbing the water surface boundary
layer of the substrate, close by pincher-like action of two long arms
when positioned properly on the bottom, and retain discrete samples of
sediment while it is brought to the surface for processing. The long
arms give added leverage for penetrating hard sediments. The advantage
of using the twin Van Veen is that with a single lowering, two separate
bottom sediment sampling units can be collected from the same station.
5.6.5.2 The Van Veen is basically an improved version of the Petersen
grab in that long arms have been attached to the jaws to stabilize the
grab on the bottom in the open sea just prior to or during closure of
the device. Additional weights can be applied to the jaws to effect
greater penetration in sediments.
5.6.6 The Orange-Peel grab sampler (Fig. 7C) is used primarily in
marine waters and deep lakes where it has advantages over other grabs
when sandy substrates are sampled, but it cannot be used under adverse
44
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weather conditions. This grab should not be used in critical
quantitative work that is to be compared with results of other areas and
is recommended as a reconnaissance sampler only. The sampler is
available in a range of sizes but the 1600 cm3 is generally used,
although larger sizes are available.
5.6.6.1 The Orange-Peel grab sampler has four curved jaws that close
to encircle a hemisphere of sediment. It must penetrate the intended
substrate without disturbing the water surface boundary layer, close
when positioned properly on the bottom, and retain discrete samples of
sediment while it is brought to the surface for processing. The top of
the sampler is enclosed by a canvas bag, serving as a portion of the
sample compartment. When taking samples, a stationary boat or platform
should be used.
5.6.6.2 A recent modification of the Orange-Peel, described by Reish
(1959) has a new trigger mechanism and more efficient closing jaws, and
the volume of sample to surface-area sampled relationship has been
worked out.
5.6.6.3 The surface area sampled by this device varies with penetration
depth or volume sampled. The device penetrates to a maximum depth of
18 cm, but depth of penetration will vary.
5.6.7 The Shipek (scoop) grab sampler (Fig. 7D) is designed to obtain
samples of macroinvertebrates from sediments in marine waters and large
inland bodies of water. This device is useful for sampling macro-
invertebrates from s'and, gravel, mud, clay, and similar substrates. It
is designed to take a sediment sample with a surface area of 20 cm to
approximately 10 cm deep at the center.
5.6.7.1 The Shipek (scoop) grab sampler consists of a semi-cylindrical
scoop that must be positioned properly on the bottom to take a scoop and
retain discrete samples of sediment through 180°. Holmes and Mclntyre
(1971) report that this device is usually used by geologists to collect
small samples rather than by biologists. However, it can be used in
marine waters and large inland lakes, reservoirs, and rivers. Unlike
many other types of samplers, closure of the device is made at the side,
rather than at the bottom. This sampler cannot be used under adverse
wind and wave conditions. The sampler requires a vessel with a winch
and cable.
5.6.8 General Operating Procedures
5.6.8.1 Most grabs are heavy sampling devices that should be operated
using a hand or powered winch and cable from a boat. In large bodies
of water ships are used for this operation.
5.6.8.2 Grabs must be lowered slowly because free-fall may airplane the
device, causing the device to land improperly or causing a pressure wave
and blowout of the surface layer of sediment when the grab reaches the
45
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Figure 7. Grab Samplers:
Peel; (D) Shipek
(A) Smith-Mclntyre; (B) Van Veen; (C) Orange-
46
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bottom. In order for the device to operate effectively, it must bite
vertically.
5.6.8.3 When most grabs reach the bottom, their weight will cause them
to penetrate the substrate, and the slack-off on the cable allows the
locking lever to release, therefore permitting the movement that allows
the horizontal locking bar to drop out of the locking notch and allow
the jaws to close as the device is raised. Other grabs are closed by
spring action or some other mechanical device after penetrating the
substrate.
5.6.8.4 In the Ekman grab the jaws are cocked by raising them upward
into the cocked position using the attached cable and securing the cable
to the catch pin located at the top of the sampler. Once on the bottom,
indicated by a slack line, a messenger is sent down the line tripping
the catch mechanism, causing the spring loaded jaws to close the bottom
of the sampler and contain the sediment.
5.6.8.5 The Smith-Mclntyre grab is "loaded" by compressing the large
coil springs mounted on the instrument with the loading bar. As soon
as the spring is loaded, the safety pin is inserted to prevent the
accidental triggering of the bottom plates. Once the device is
overboard, just prior to lowering to the bottom, the safety pins are
removed. When the trigger plates contact the bottom, pressure on these
plates releases the two coiled springs that drive the buckets (jaws)
into the sediment. Closure of the sampler is made at the side, rather
than at the bottom. After closure the sample is given optimum
protection from washout during the return trip to the surface by the
cylindrical configuration of the sampler. Once on deck, the sampler is
placed on a stand; the sample buckets can be disengaged from the rest
of the device by releasing two retaining latches at each end of the
upper semi cylinder, and the sample is dumped into a large basin or
washtub and prepared for processing. After the sample has been removed,
the springs may then be loaded and the safety pins installed.
5.6.8.6 The chains from the jaws of the Van Veen are attached to the
counter balance mechanism, as are the slackened wires from the long
arms. Tension is carefully applied to the trigger mechanisms as the
sampler is winched off its platform, and once the tension is firmly
changed from the jaws, the grab is relatively stable in the cocked
position. Care should be exercised in lowering the Van Veen through the
surface of the water as occasionally contact will produce slack in the
chain that will trip the counter balance mechanism. The grab is lowered
slowly to the bottom, and once it makes contact with the bottom, the
grab is winched in initially closing the jaws containing the sediment.
Retrieve the grab slowly to prevent washout.
5.6.8.7 The Shipek grab is composed of two concentric half cylinders,
the inner semi cylinder is rotated at high torque by two spirally wound
external springs. Upon contact with the bottom, the two external
springs are automatically released by the inertia of a self-contained
weight upon a sear mechanism which trips the catch and the scoop rotates
47
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upward. At the end of its 180° travel, the sample bucket is stopped and
held at the closed position by residual spring torque. After closure
the sample is given optimum protection from washout. The scoop is
disengaged from the upper semi cylinder by releasing the two retaining
latches at each end of the upper semi cylinder.
5.6.8.8 Once on board, the sample is placed into either a suitable
container or a sieving device directly for processing (see Section 6).
Thoroughly wash or hose the grab with water, so that all sediment
materials are included in the sample before a replicate sample is taken.
5.7 Stream-Net Samplers
5.7.1 Stream-net samplers are lotic collecting devices, fitted with a
net of various mesh sizes that collect organisms from flowing water
passing through the sampler.
5.7.2 Selecting Stream-Net Sampling Devices
5.7.2.1 Table 4 summarizes criteria for selecting stream-net sampling
devices.
TABLE 4. SUMMARY CRITERIA FOR STREAM-NET SAMPLERS
1. Surber Sampler
A. Habitats and Substrates Sampled: Shallow, flowing
streams, less than 32 cm in depth with good current;
rubble substrate, mud, sand, gravel.
B. Effectiveness of Device: Relatively quantitative when
used by experienced biologist; performance depends on
current and substrate.
a
C. Advantages: Encloses area sampled; easily transported
or constructed; -samples a unit area.
''hi!'!'1' ' ' '
D. Limitations: Difficult to set in some substrate types,
that is, large rubble; cannot be used efficiently in
still, slow moving streams.
2. Portable Invertebrate Box Sampler, Hess Sampler, Hess Stream
Bottom Sampler, and Stream-Bed Fauna Sampler
A. Habitats and Substrates Sampled: Same as Surber.
B. Effectiveness of Device: Same as Surber.
C. Advantages: Same as above except completely enclosed
with stable platform; can be used in weed beds.
48
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TABLE 4. SUMMARY CRITERIA FOR STREAM-NET SAMPLERS (continued)
D. Limitations: Same as Surber.
Selected Literature: APHA, 1989; ASTM, 1990; Canton and Chadwick, 1984;
Elliott and Tullett, 1978; Ellis and Rutter, 1973; Hess, 1941; Kroger,
1972; Lane, 1974; Merritt et a!., 1984; Needham and Usinger, 1956;
Pollard and Kinney, 1979; Rutte.r and Ellis, 1977; Rutter and Poe, 1978;
Rutter and Ettinger, 1977; Resh, 1979; Resh e_t al., 1984; Schwenneker
and Hellenthal, 1984; Surber, 1937, 1970; Usinger, 1963; Waters and
Knapp, 1961; Welch, 1948; Winner el a!., 1980.
3. Drift Nets
A. Habitats and Substrates Sampled: Flowing rivers and
streams; all substrate types.
B. Effectiveness: Relatively quantitative and effective in
collecting all taxa which drift in the water column;
performance depends on current velocity and sampling
period.
C. Advantages: Low sampling error; less time, money,
effort; collects macroinvertebrates from all substrates,
usually collects more taxa.
D. Limitations: Unknown where organisms come from; terrestrial
species may make up a large part of sample in summer and
periods of wind and rain; does not collect non-drifting
organisms.
Selected Literature: Allan, 1984; Allan and Russek, 1985; APHA, 1989,
ASTM, 1990; Bailey, 1964; Berner, 1951; Brittain and Eikeland, 1988;
Chasten, 1969; Clifford, 1972a,b; Coutant, 1964; Gushing, 1963, 1964;
Dimond, 1967; Edington, 1965; Elliott, 1965, 1967; 1969, 1970; 1971;
Elliott and Minshall, 1968; Ferrington, 1984; Hales and Gaufin, 1969;
Hemsen, 1956; Hildebrand, 1974; Holt and Waters, 1967; Hynes, 1970;
Keefer and Maughan, 1985; Larimore, 1972, 1974; Larkin and McKone, 1985;
Lehmkuhl and Anderson, 1972; McLay, 1970; Merritt et al., 1984; Minshall
and Winger, 1968, Modde and Schulmbach, 1973, Muller, 1965, 1974,
Mullican et al., 1967; Mundie, 1959, 1964; Pearson and Franklin, 1968;
Pearson and Kramer, 1969, 1972; Pearson et al., 1968; Pfitzer, 1954;
Radford and Hart!and-Rowe, 1971; Reisen and Prins, 1972; Resh, 1979;
Resh et al., 1984; Spence and Hynes, 1971; Tanaka, 1960; Tranter and
Smith 1968; USEPA, 1973; Waters, 1961, 1962, 1964, 1965; 1966; 1968,
1969a,b, 1972; Wilson and Bright, 1973; Winner et al., 1980; Wojtalik
and Waters, 1970.
5.7.3 The Surber, portable invertebrate box, Hess, Hess stream bottom,
and stream-bed fauna samplers (Fig. 8A-E) were designed as quantitative
samplers when carefully used by an experienced biologist; however, they
49
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are more often used to collect qualitative samples or semi-quantitative
samples because of the large number of samples needed for an acceptable
level of precision (Needham and Usinger, 1956). They outline a definite
unit-area for collecting the macroinvertebrates within the area. They
are designed to be placed by hand onto or in some cases into sand,
gravel, or rubble substrate types (usually in riffle/run areas) in
shallow streams, or shallow areas of rivers. The drift net sampler
(Fig. 8F) is a qualitative and quantitative collecting device used to
capture drifting organisms in flowing waters. It differs from the other
net type samplers in that it collects from a unit volume of water rather
than from a unit area of bottom.
5.7.4 Significance and Use of Stream-Net Samplers
5.7.4.1 The significance of using stream-net samplers is to collect
macrobenthos inhabiting a wide range of habitat types from shallow
flowing streams or shallow areas in rivers. The stream-net devices
(Surber, portable invertebrate box, Hess, Hess stream bottom, and
stream-bed fauna samplers) are unit area samplers used for collecting
benthic organisms in certain types of substrates. They may be used to
obtain estimates of the standing crop, for example, biomass, number of
individuals and number of taxa of benthic macroinvertebrates per unit
area of stream bottom. Efficiency of the sampler depends on the
experience and ability of the user. Drift net samplers are designed to
collect emigrating or dislodged benthic macroinvertebrates inhabiting
all substrate types that either actively or passively enter the water
column in flowing streams and rivers and is used to determine drift
density and drift rate.
5.7.5 Description of Surber Type Samplers
5.7.5.1 The Surber sampler consists of two 30.5-cm frames, hinged
together; one frame rests on the substrate, the other remains upright
and holds the nylon net. The sampler is positioned with its net mouth
open, facing upstream. When in use, the two frames are locked at right
angles, one frame marking off the area of substrate to be sampled and
the other frame supporting a net to strain out organisms washed into it
from the sample area.
5.7.5.2 Modification of the Surber sampler to overcome some of the
limitations of its use (for example, loss of organisms due to backwash)
has resulted in the design and construction of a number of related
sampling devices, such as the four-sided (enclosed) portable
invertebrate box sampler, the cylindrical Hess sampler, the cylindrical
Hess stream bottom sampler, and the cylindrical stream-bed fauna
sampler. These devices sample 0.1 m2.
5.7.5.3 Operation of the portable invertebrate box, Hess, Hess stream
bottom, and stream-bed fauna samplers are similar to the Surber sampler.
5.7.5.4 The net used to collect macroinvertebrates can vary in mesh
size, length, taper, and material, for example, canvas, taffeta, or
50
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Figure 8. Stream-Net Samplers: (A) Surber sampler; (B) Portable
invertebrate box sampler; (C) Hess sampler; (D) Hess stream bottom
sampler; (E) Stream-bed fauna sampler (F) Drift net
51
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nylon monofilament. It is usually made of nylon, and a variety of mesh
sizes is available. The mesh size used will depend on the objectives
of the study. A mesh size of 0.35 mm, for example, will retain most
instars of aquatic insects.
5.7.5/6 While a smaller mesh size might increase the number of smaller
invertebrates and young instars collected, it will clog more easily and
exert more resistance to the current than a larger mesh, possibly
resulting in a loss of organisms due to backwashing from the sample net.
5.7.5.7 The polyester foam base of the portable invertebrate box
sampler conforms to a variety of substrates to prevent the loss of
organisms from beneath the sampler. The Hess, Hess stream bottom, and
stream-bed fauna samplers can be "turned" into most sediment types to
a depth of several centimeters. The Surber sampler rests on the surface
of most sediments.
5.7.5.8 When sampling is completed, the net of the portable
invertebrate box sampler slides out for cleaning or exchange with a
different net. Hess-type samplers may have a mason jar ring and an
adapter with a fixed or removable cloth net bucket. Some of the stream-
net samplers have fixed nets.
5.7.5.9 These samplers cannot be used as efficiently in still or deep
water of more than 30.48 cm (1-ft) depth. If the water depth is greater
than 30.48 cm (1-ft), benthic organisms may wash over the top of the net
rather than into it.
5.7.5.10 While there can be large sampling errors associated with their
use by an inexperienced operator, these samplers can provide data which
are precise and comparable if they are used consistently by one
experienced person in similar habitats.
5.7.5.11 If the water velocity is very great, resistance provided by
the small mesh of the net or debris washed into it, or both, may result
in a backwashing effect that washes benthic organisms out of the sample
area of the Surber sampler or over the top of the other samplers.
5.7.6 General Operating Procedures
5.7.6.1 Position these samplers securely on the substrate, parallel to
the flow of the water, with the net pointing downstream.
5.7.6.2 The samplers are brought down quickly to reduce the escape of
rapidly moving organisms.
5.7.6.3 There should be no gaps under the edges of the frame that would
allow for washing of water under the net and loss of benthic organisms.
Eliminate gaps that may occur along the edge of the Surber sampler frame
by careful shifting of rocks and gravel along the outside edge of the
sampler. This is also true of the cylindrical-type samplers if they are
on rubble substrate that makes turning into the bottom difficult. The
52
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portable invertebrate box sampler polyester foam pad can conform to a
relief of 7.6 cm (3 in.).
5.7.6.4 Take care not to disturb the substrate upstream from the
sampler, to avoid excessive drift into the sampler from outside the
sample area.
5.7.6.5 Once the sampler is positioned on the stream bottom, it should
be maintained in position during sampling so that the area delineated
remains constant.
5.7.6.6 Hold the Surber sampler with one hand or brace with the knees
from behind. The Hess, Hess stream bottom, and stream-bed fauna
samplers, and the portable invertebrate box samplers can be held with
one hand or braced with the knees from the sides. The portable
invertebrate box sampler also can be sat upon for convenience while
sampling; this provides the collector with a stable sampling platform
that allows maximum manipulation of the substrate with little sampler
movement.
5.7.6.7 Heavy gloves should be required when handling dangerous debris;
for example, glass or other sharp objects present in the sediment.
5.7.6.8 Turn over and examine carefully all rocks and large stones and
rub carefully in front of the net with the hands or a soft brush to
dislodge the organisms and pupal cases, etc. clinging to them before
discarding. Scrape attached algae, insect cases, etc., from the stones
into the sample net.
5.7.6.9 Wash larger components of the substrate within the enclosure
with stream water; water flowing through the sampler should carry
dislodged organisms into the net.
5.7.6.10 Stir the remaining gravel and sand vigorously with the hands
to a depth of 10 cm (4.0 in.) where applicable, depending upon the
substrate, to dislodge bottom-dwelling organisms.
5.7.6.11 It may be necessary to hand pick some of the heavier mussels
and snails that are not carried into the net by the current.
5.7.6.12 Remove the sample by inverting the net (or, washing out sample
bucket, if applicable) into the sample container (wide-mouthed jar) with
10% buffered formalin fixative or 70-80% ethanol.
5.7.6.13 Examine the net carefully for small organisms clinging to the
mesh, and remove them (preferably with forceps to avoid damage) for
inclusion in the sample.
5.7.6.14 Rinse the sampler net after each use.
5.8 Drift Nets
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5.8.1 Significance and Use of Drift Nets
5.8.1.1 Macroinvertebrate drift is a normal feature of flowing waters
(Brittain and Eikeland, 1988). Drift of organisms may be used to assess
environmental stress or pollution in some situations. Stress,
fluctuations in water level, changes in light intensity, and changes in
temperature are the basic factors that influence the extent of
macroinvertebrate drift.
5.8.1.2 One source of drifting macroinvertebrates is the immature
insects in the final stages of metamorphosis that actively seek to reach
the water surface where emergence to the adult stage occurs. Regular
periodic downstream drift rate of immature insects and other
macroinvertebrate fauna in slow-moving streams or rivers is markedly
reduced in comparison to lotic habitats with rapidly flowing water.
5.8.2.3 Drift insects are about evenly distributed at all levels in a
stream, but in large rivers drift is more abundant near the bottom in
the shore-line zone.
5.8.2.4 It is generally found that there are pulses of drift organisms
that move from top to bottom of the water column, at least during
periods of low flow.
5.8.2.5 Drift collections can be used to determine drift density, rate,
and periodicity of drift organisms, and interesting aspects of the
organisms' life histories, for example, period of transformation.
5.8.2.6 Drift nets are useful for collecting macroinvertebrates that
actively or passively enter the water column or that are dislodged from
the substrate; naturally or by stress. They are particularly well-
suited for synoptic surveys because they are light weight and easily
transported.
5.8.2.7 The first step in interpreting drift data is to determine the
respective contributions of constant, behavioral, and catastrophic drift
to the samples being analyzed.
, ll,,:!i, i. ,." , i .
5.8.2.8 Only constant and behavioral drift are usually utilized in a
synoptic survey, but catastrophic drift is extremely important in
testing for recent discharges of toxic materials.
5.8.2.9 Bear in mind that the drift density may not be a function of
the total bottom population density or of production; however, species
composition of the drift is useful as an index of species composition
of the benthos.
5.8.2.10 Density and composition of invertebrate drift are influenced
by many factors that also must be considered when interpreting the data,
including stage of life cycle, weather, time of day, light intensity,
population density, temperature, turbidity, water level fluctuation,
season, current velocity, growth rate, photoperiod, and proximity to
54
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tributary streams.
5.8.2.11 In an enriched stream there is usually a marked increase in
total numbers and biomass of drifting organisms as the stream becomes
more polluted. Intolerant forms decrease and pollution tolerant forms
increase proportional to changing water quality.
5.8.2.12 Thousands of organisms, including larvae of stoneflies,
mayflies, caddisflies, and midges and other Diptera, may be collected
in a sampling period of only a few hours.
5.8.2.13 The drift net efficiently collects organisms originating from
all types of substrates upstream and a wide spectrum of microhabitats
in lotic (flowing) waters.
5.8.2.14 The device is restricted to flowing rivers or streams with a
current velocity of more than 0.05 m/s.
5.8.3 Advantages of Using Drift Nets
5.8.3.1 A benthic sample shows only which taxa were existing in the
particular area (usually some fraction of a square meter, etc.) that was
sampled. The great variation among benthic samples, even in a limited
area, illustrates the necessity of several samples and the influence of
selecting the collecting stations. One drift sample might be adequate
for collecting the majority of invertebrate taxa in a stream reach,
whereas a large number of benthic samples would be needed to cover the
variety of bottom habitats even in an uniform reach of the stream.
s
5.8.3.2 Quantitative benthic sampling is seldom extended to include
stream banks, organic substrates (logs, etc.), and areas of dense
vegetation. The drift net collects organisms from all these areas.
5.8.3.3 Drift net collections often require much less sorting work than
a series of grab samples. Drift samples do not require the laborious,
time-consuming job of washing out silts, clays, and other materials and
of sorting and picking through much of the debris for the organisms in
the samples.
5.8.3.4 Nets are light-weight and easy to set up in a stream and
usually yield a light-weight sample free from most debris. Benthic
sampling in flowing water often .procures .samples heavy with inorganic
materials.
5.8.3.6 A drift net is inexpensive to construct, whereas bottom
samplers are often costly and more than one kind may be required to
adequately sample the multiple habitat types present in a stream or
river.
5.8.4 Limitations of Use of Drift Nets
5.8.4.1 Certain aquatic organisms enter the drift only sporadically and
55
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might be missed even though common in the benthos.
5.8.4.2 The relative abundance of macroinvertebrates in a drift sample
often differs significantly from their "relative" abundance on the
stream bottom.
5.8.4.3 A slight current is necessary if a drift collection is to be
taken (greater than 0.05 m/s).
5.8.4.4 Most species drift more abundantly at night, so that the best
collections are usually taken in the dark. Time of sampling depends on
the purpose of the study. Day samples are usually adequate for showing
effects of pollution on the stream reach.
5.8.4.5 There is a waiting period while the drifting organisms
accumulate In the net, but not as long as with using artificial
substrates.
5.8.4.6 Tree leaves in the autumn, floating and anchor ice in the
winter, and heavy debris (logs) during floods may interfere with drift
net collecting and make processing difficult.
5.8.4.7 The abundance and composition of drift changes, daily, hourly,
or seasonally and might prevent direct comparison of collections taken
at different times. At times certain life stages of an organism might
not be fairly represented in the drift. The same holds true for other
types of sampling.
5.8.4.8 Drift collections give little precise habitat information for
individual organisms, since the exact source of the individual is not
known.
5.8.4.9 Collections of drift, with the organisms originating an
indefinite distance above the collecting site, may not show local or
temporary deleterious effects imposed on an aquatic community, whereas
bottom samples might reveal the destruction or reduction of benthos in
a small area. Studies have shown that most drift organisms originate
from only several meters upstream from the nets (Elliott, 1967).
5.8.5 Description of Drift Nets
5.8.5.1 The typical drift net consists of a bag of nylon or nylon
raonofilament. The drift net generally preferred is the simple
rectangular net which is light-weight, easy to install, and gives an
adequate sample of the drifting macroinvertebrates. The U.S. Standard
No. 30 (0.595-min mesh openings) net is often used for collecting
macroinvertebrates.
5.8.5.2 Drift nets vary in size, but the type recommended for use in
water pollution surveys or other ecological assessments has an upstream
opening of 15 by 30 cm, and the collection bag is 1.3 m long. A variety
of mesh sizes is available, and mesh size should be selected based on
56
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the objectives of the study; the finer the mesh, the more organisms
(instars) will be collected.
5.8.5.3 The frame typically consists of a 0.045-m2 (15 by 30-cm) brass
rod structure anchored into the stream bed by a pair of steel rods.
5.8.5.4 Drift nets are anchored in the stream by driving 1/2-in. steel
rods into the stream bottom or mounting the rods in concrete slabs that
are weighted down with stones. Use cable clamps to secure the nets to
the rods.
5.8.5.5 The drift net frame can be fitted anteriorly with a mouth
reducing rectangular plexiglass enclosure (Rutter and Ettinger 1977) to
increase filtration efficiency and volume of water passing through the
net.
5.8.5.6 Alternatives to the typical drift net include the waterwheel
drift sampler (Pearson and Kramer, 1969) which might be useful in large
rivers or streams with slow flow which can be reached by automobile.
5.8.5.7 An automatic drift sampler (Muller, 1965) can be constructed
that eliminates the need of an attendant at the sampling site during
collection of as many as eight consecutive samples.
5.8.5.8 A modified emergence-trap drift sampler (Mundie, 1964; Gushing,
1964) is useful in streams with extremely high drift, where water is
very turbid, or where a long sampling period is desired without
clogging.
5.8.5.9 The average volume of water passing through the net is
determined by measuring the water velocity at the mouth of the drift net
with a current meter at the beginning of the sampling period and at the
end of the sampling period using the average, and recording the total
time the drift net is set in the water column. Results are expressed
as numbers per cm3 of water passing through the net.
5.8.5.10 The efficiency of the net is determined by the simultaneous
measurement of the water velocity passing by the set drift net.
5.8.6 General Operating Procedures
5.8.6.1 Because the performance and sampling efficiency of a drift net
sampler varies with local stream conditions, seasonal changes, and water
level, make a preliminary test before the start of regular drift
sampling in order to determine the best sampling stations, best sampling
interval, number of nets needed, mesh size, and best sampling depth.
5.8.6.2 For synoptic surveys, one net set above each of the major areas
of population concentrations is usually adequate; but for definitive
studies a minimum of two drift nets should be set at each station so
that drift from above a pollution source, drift from the polluted reach,
57
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and drift from the zone of clean water downstream from the recovery zone
can be compared.
5.8.6.3 Take into consideration the fact that the drift net will
collect drifting organisms that may have entered the drift from an
indefinite distance upstream or a tributary stream. Nets located 80 to
100 m below the effluent will generally sample the polluted reach
efficiently. A drift net below a riffle collects more animals than one
below a pool.
5.8.6.4 For definitive studies, install four nets at each station - two
about 25 cm from the bottom and two about 10 cm below the surface in
water not exceeding 3 m in depth.
5.8.6.5 If the objective of the study is to relate pupal exuviae to
pollution, or to collect terrestrial organisms that may float on the
surface, then extend one net slightly above the surface.
5.8.6.6 Ideally, collect 24-h drift samples; but this is usually not
practicable unless one resorts to the use of a water-wheel, automatic
drift sampler, or a modified drift sampler with a restricted opening to
solve the clogging problem or by changing the nets at regular intervals.
5.8.6.7 Although the sampling interval will vary with time of day,
current velocity, density of drift organisms, and floating debris,
collect 1-3 hours daytime drift samples when either a 24-h or overnight
sampling period is not prudent.
5.8.6.8 Drift nets have also been used from small boats in large rivers
(Rutter and Ettinger, 1977).
5.8.6.9 Because the size of the catch varies as the flow of water
through the net varies, it is necessary to measure the current velocity
at the entrance of each net at the beginning and end of each sampling
period so that the catch can be converted into number of organisms per
volume of water flowing through the net.
5.8.6.10 At the end of the specified sampling period, remove the net
from the water by loosening the cable clamps and raising the net over
the top of the steel rods, taking care not to disturb the bottom
upstream of the net.
5.8.6.11 Concentrate the material in the net in one corner by swishing
up and down in the water and then wash into a bucket half-filled with
water. Then sieve and handle the sample in the regular manner.
.
5.8.6.12 Subdividing the sample substantially reduces analysis time
with large samples (Waters, 1969a and USEPA, 1973).
5.8.6.13 Reporting data as numbers of individuals per net is
meaningless because no two drift net samples are collected under exactly
the same conditions of current velocity, stream discharge, and sampling
58
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interval. Conversion equations and other statistical aspects of drift
sampling are given by Elliott (1970). An equation for converting the
data to number per 100 m3 of water flow is:
X = lOOa/bdc
where:
X = number of organisms per 100 m3,
a = number of organisms in the net (density)
b = number of minutes of the sampling interval,
c = current velocity, m/min, and
d = area of the net opening in m2.
5.9 Artificial Substrate Samplers
5.9.1 Artificial substrate samplers are devices made of natural or
artificial materials of various composition and configuration that are
placed in water for a predetermined period of exposure and depth for the
colonization of indigenous macroinvertebrate communities. They are used
in obtaining qualitative and quantitative samples of macroinvertebrates
in rivers, streams, lakes, and reservoirs.
5.9.2 Artificial substrate sampling can effectively augment bottom
substrate sampling because many of the physical variables encountered
in bottom sampling are minimized (e.g., variable depth and light
penetration, temperature differences, and substrate types).
5.9.3 Samples usually contain negligible ' amounts of extraneous
material, permitting quick laboratory processing.
5.9.4 Selecting Artificial Substrate Samplers
5.9.4.1 Table 5 summarizes criteria for selecting artificial substrate
samplers.
TABLE 5. SUMMARY CRITERIA FOR ARTIFICIAL SUBSTRATE SAMPLERS
1. Multiplate (Modified Hester-Dendy) Sampler
A. Habitats and Substrates Sampled: All types of habitats in
rivers, streams, lakes and reservoirs; not efficient in
wetlands; uses hardboard or porcelain substrate.
B. Effectiveness of the Device: Colonization depends on type of
substrate; selective for certain types of organisms; three
replicates considered adequate.
C. Advantages: Excellent for water quality monitoring; uniform
substrate type; high level of precision; samples contain
negligible amount of debris; provides habitats of known area for
a known time at a known depth.
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TABLE 5. SUMMARY CRITERIA FOR ARTIFICIAL SUBSTRATE SAMPLERS (Continued)
D. Limitations: Requires trip for installation and trip for
'collection; subject to vandalism; biased for aquatic insects;
need to use caution in reuse of plates that may have been
contaminated with toxicants, oil, etc.; may require additional
weight for stability; up to eight weeks wait for results.
Selected Literature: APHA, 1989; Beck et al., 1973; Beckett and Miller,
1982; Cairns, 1982; Flannagan and Rosenberg, 1982; Fullner, 1971;
Gr.eeson et al., 1977; Hall, 1982; Harrold, 1978; Hester and Dendy, 1962;
Hellawell, 1978; Jacobi, 1971; Mason et a]_., 1973; McConville, 1975;
McDaniel, 1974; Merritt and Cummins, 1984; Ohio EPA, 1987; Rosenberg and
Resh, 1982; USEPA, 1973; Wefring and Teed, 1980.
2. Basket Sampler
A. Habitats and Substrates Sampled: All types of habitats in
rivers, streams, lakes and reservoirs; may be used in areas
where other methods are not feasible; not efficient for sampling
in wetlands.
B. Effectiveness of the Device: Colonization depends on type of
artificial substrate used in the basket (rocks, 3M Conservation
Webbing, etc.); selective of certain types of fauna; three
replicates considered adequate.
C. Advantages: Excellent for water quality monitoring; uniform
substrate type at each station for better comparison and high
level of precision; gives quantitatively comparable data;
samples contain negligible amounts of debris; does not require
additional weight for stability; samples a known area at a known
depth for a known exposure time.
D. Limitations: Require trip for installation and another for
collection; biased for insects; samplers and floats often
difficult to anchor; may be navigation hazard; susceptible to
vandalism; records only biotic community present during exposure
period; no measure of past conditions; size and texture of
limestone substrates may vary from study to study; up to eight
weeks wait for results.
Selected Literature: Anderson and Mason, 1968; APHA, 1989; Benfield et
al., 1974; Bergensen and Galat, 1975; Bull, 1968; Cairns, 1982;
Flannagan and Rosenberg, 1982; Hall, 1982; Hanson, 1965; Hellawell,
1978; Leopold, 1970; Lium, 1974; Mason et al., 1967, 1973; Merritt and
Cummins, 1984; Newlon and Rabe, 1977; Rabeni and Gibbs, 1978; Rabeni et
al., 1985; Rosenberg and Resh, 1982; USEPA, 1973; Voshell and Simmons,
1977; Zillich, 1967.
5.9.5 Significance and Use of Artificial Substrate Samplers
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5.9.5.1 Multiple-plate and basket samplers (Figure 9A-F) are usually
colonized by a wide variety of invertebrates which have some means of
mobility (active or passive) that are borne in the current. The
organisms that colonize the artificial substrates are primarily aquatic
insects, aquatic oligochaetes, crustaceans, cnidarians, turbellarians,
bryozoans, and mollusks. The colonization of these organisms should be
relatively equal in similar habitats and reflect the capacity of the
water to support aquatic life. Although these samplers may exclude
certain mollusks or worms, they collect a sufficient diversity of
benthic fauna to be useful in assessing water quality.
5.9.5.2 Recovery techniques are critical for insuring collection of
all organisms retained on the sampler.
5.9.5.3 Uniform substrate type reduces the effects of substrate
differences.
5.9.5.4 Optimum time for substrate colonization is 6 weeks for most
water in the United States.
5.9.5.5 Quantitatively comparable data can be obtained in environments
from which it is virtually impossible to obtain samples with
conventional devices.
5.9.6 Description of Multiple-Plate Samplers
5.9.6.1 Multiple-plate samplers consist of standardized, reproducible
artificial substrate surfaces for colonization by.aquatic organisms.
Their uniform shape and texture compared to natural substrates greatly
simplifies the problem of sampling. The sampler is constructed from
readily available materials.
5.9.6.2 The modified multiple-plate sampler (Fig. 9A,B) is constructed
of 0.125 in (0.3 cm) tempered hardboard or ceramic material with 3 in
(7.6 cm) round or square plates and 1 in (2.5 cm) round spacers that
have 5/8 in holes drilled in the center (Fullner, 1971). The plates are
separated by spacers on a 0.25 in (0.63 cm) diameter eyebolt, held in
place by a nut at the top and bottom. A total of 14 large plates and
24 spacers are used in each sampler. The top nine plates are each
separated by a single spacer, plates 9 and 10 are separated by two
spacers, plates 11 and 12 are separated by three spacers, and plates 13
and 14 are separated by four spacers. The hardboard sampler is about
5.5 in (14 cm) long, 3 in (7.6 cm) diameter, exposes approximately 1,160
cm (.116 m ) of surface area for the attachment of organisms, and
weighs about 1 Ib (0.45 kg). The ceramic sampler is 6.5 in. long and
weighs 2.2 Ibs (1 kg). The ceramic plates can be chemically cleaned,
oven dried and reused indefinitely as they are stable and unaffected by
long-term immersion in water. The sampler will not warp with time;
therefore, the spacings between plates do not change, assuring replicate
and efficient sampling. Each sampler is supplied with a 6 m (20') long
nylon suspension rope. The total weight is 1 Kg (2.2 Ibs.). Sturdy
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3 in. x 3 in
plates
1 in. x 1 in
spacers
1/4 in. nut
1/4 in.
eye bolt
Plate numbers
D
Figure 9. Artificial Substrate Samplers: (A) Schematic drawing of
multiplate Sampler; (B) Typical round multiplate type; (C) Original
Hester-Dendy multiplate, square design; (D) Jumbo and standard hardboard
and porcelain multiplate designs
62
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Figure 9. Artificial Substrate Samplers: (E) Barbecue basket; (F)
Basket samplers, cylindrical and square types
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wire stakes for holding the sampler above the riverbed are recommended
accessories.
5.9.6.3 When the samplers are suspended from the eyebolt, whether in
strong currents or not, a 5 Ib weight, such as a brick, is attached by
.6 ra wire to a 1/4 in turnbuckle. The turnbuckle is screwed tightly
onto the shank of the multiplate eyebolt. The weight serves to
stabilize the sampler and to lessen undue disturbance to the organisms.
Upon retrieval, the weight is gently cut free before the sampler is
bagged. Care should be taken not to reuse samplers exposed to oils and
chemicals that may inhibit colonization during the next sampling period.
Due to its cylindrical configuration, the sampler fits a wide mouth
container for shipping and storage purposes. The sampler is
inexpensive, compact, and light weight which are valuable attributes in
water quality surveys.
5.9.7 Description of a Basket Sampler
5.9.7.1 The typical type of basket sampler (Fig. 9E) used is the one
described by Mason et al_. (1967). It is a cylindrical "barbecue" basket
11 in (28 cm) long and 7 in (17.8 cm) in diameter and is filled with
approximately 17 Ibs (7.7 kg) of natural rocks that vary from 1 to 3 in
(2.5 to 7.6 cm) in diameter. A hinged door on the side allows access
to the contents. An estimated 3.2 square ft (0.3 sq. m) of surface area
is provided for colonization by macroinvertebrates. A 1/8 inch wire
cable is passed through the long axis of the basket; one end is fastened
with a cable clamp, and the other end is attached to a 5 gallon metal
container filled with polyurethane foam used as a float. A 3/8 inch
steel rod that is threaded at each end is passed through the long axis
of the float and fastened at each end by nuts. Three inch long 1-1/8
by 1/8 inch strap iron secured on the rods by nuts serves as swivels at
each end. The wire cable used to suspend the basket is attached to the
swivels by holes drilled for that purpose. The float can be attached
to a stationary structure or the basket can be anchored to the bottom
in shallow water. The rugged construction of this particular basket
sampler is heavy enough to resist movement by most water currents. In
using the basket as a method of collecting macroinvertebrates, special
consideration should be given to the types of substrates placed within
the basket. Substrates tested have varied from limestone, tin cans,
concrete cones, #200 3M Conservation Webbing (3M Corporation, St.
Paul, MM), and porcelain spheres. Since each type of substrate will
result in a different species diversity, the type of substrate used
should be determined by the study objectives, weighing the advantages
and disadvantages of each substrate type. For most investigations, a
basket filled with 30 5-8 cm diameter rocks or rock-like material is
recommended.
5.9.8 Precautions
5.9.8.1 Physical factors such as stream velocity and installation depth
may variably affect degree of colonization.
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5.9.8.2 The sampling method is selective for drifting organisms and
for those which preferentially attach to hard surfaces.
5.9.8.3 Recovery techniques are critical for insuring collection of
all organisms retained on the sampler.
5.9.8.4 Samplers are vulnerable to vandalism and often lost.
5.9.8.5 Caution should be exercised in reuse of samplers that may be
subjected to contamination by toxicants, oils, etc.
5.9.8.6 The sampler provides no measure of the biota and the condition
of the natural substrate at a station or of the effect of pollution on
that substrate.
5.9.8.7 Sampler and floats must be anchored or fixed in place. This
is sometimes difficult, and they may present a navigation hazard.
5.9.8.8 The sampler only records the community that develops during
the sampling period, thus reducing the value of the collected fauna as
indicators of prior conditions.
5.9.9 General Operating Procedures
5.9.9.1 Artificial substrate samplers are usually positioned in the
euphotic zone of good light penetration (one to three feet, or .3-.9
m) for maximum abundance and diversity of macroinvertebrates (Mason, et
aj..' 1973). Optimum time for substrate colonization is six weeks for
most types of water in the United States. For uniformity of depth,
suspend sampler from floats on 1/8 in. or 3.2 mm steel cable. If water
fluctuation is not expected during sampling period, the samplers may be
suspended from stationary objects. If vandalism is a problem, use
subsurface floats or place sampler on supports placed on the bottom.
Regardless of installation technique, use uniform procedures (e.g., same
exposure period, sunlight, current velocity and habitat type). At
shallow water stations (less than 1.2 m deep), install samplers so that
the exposure occurs midway in the water column at low flow. If the
samplers are installed in July when the water depth is about four feet
and the August average .low flow is two feet, the correct installation
depth in July is one foot above the bottom. The sampler will receive
sunlight at optimum depth (one foot) and will not be exposed to air
anytime during the sampling period. Care should be exercised not to
allow the samplers to touch bottom which may permit siltation, thereby,
increasing the sampling error. In shallow streams with sheet rock
bottoms, artificial substrate samplers are secured to 3/8 in. (.95 cm)
steel rods that are driven into the substrate or secured to rods that
are mounted on low, flat rectangular blocks (Hilsenhoff, 1969). These
must, however, be securely anchored to the rock bottom to avoid loss
during floods.
5.9.9.2 Artificial substrate samplers can be attached to floats, cement
structures, a weight, or a rod driven into the stream-bed or lake-bed.
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At least two or three samplers should be installed at each collecting
site. Leave the samplers in place for at least 6 weeks to allow for
organism colonization. The exposure time should be consistent among
sites during the study. If study time limitations reduce this period,
the data must be evaluated with caution, and in no case should data be
compared from samplers exposed for different time periods.
5.9.9.3 The samplers may be installed in pools or riffles/runs
suspended below the water surface. Make the collections as
representative of the reach a.s possible by insuring that the samplers
are not to close to the bank. In streams up to a few meters in width,
install the devices about midstream. In larger streams install the
devices at about one-quarter of the total width from the nearest bank.
5.9.9.4 To minimize losses of animals when retrieving multiplate and
basket samplers, approach from downstream, lift the sampler quickly and
place the entire sampler in a polyethylene jug or bag containing 10%
formalin or 70-80% ethanol. Once the sampler is touched it must be
removed from the water at once or many of the animals will leave the
sampler. If the sampler must be disturbed during the recovery process
so that it cannot be lifted straight up out of the water, a net should
be used to enclose the sampler before it is disturbed.
5.9.9.5 The organisms can be removed in the field by disassembling the
sampler in a tub or bucket partially filled with water and scrubbing
the rocks or plates with a soft-bristle brush to remove clinging
organisms. Pour the contents of the bucket through a No. 30 or 60 sieve
and wash the contents of the sieve into a jar and preserve with 10%
formalin or 70-80% ethanol. If the organisms are not removed in the
field, place the sampler and the detached portion of sample into a wide-
mouth container or sturdy plastic bag containing preservative for
transporting to the laboratory. Label the sample with the location,
habitat, date, and time of collection. The exposed multiplate sampler
can be taken to the laboratory where the plates are removed from the
bolt and cleaned with a soft-bristled brush. The basket samplers are
usually disassembled in the field; however, they can be taken to the
laboratory and disassembled if placed in preservative in a water-tight
container.
5.9.9.6 Cleaned samplers can be reused unless there is reason to
believe that contamination by toxicants (e.g., chemicals or oils) has
occurred. These substances may be toxic to the macroinvertebrates or
may inhibit colonization. Do not reuse hardboard, porcelain plates, or
any other substrate that have been exposed to preservatives. Clean the
multiple-plates before reassembly and use.
5.10 Coring Devices
5.10.1 Included in this category are single and multiple-head coring
devices, tubular inverting devices, and open-ended stovepipe devices.
5.10.2 Selecting Coring Devices
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5.10.2.1 Table 6 summarizes criteria for selecting coring devices
TABLE 6. SUMMARY CRITERIA OF CORING DEVICES
1. KB Core Sampler
A. Habitats and Substrates Sampled: Freshwater rivers,lakes,
estuaries; soft sediments only, 40% silty clay.
B. Effectiveness of the Device: Permits analysis of
stratification in quantitative and qualitative samples;
uses 5.08 cm (2 inch) pipe core tube; used in shallow to
medium shallow water up to 30.5 m (100 feet) or deeper.
C. Advantages: Samples a variety of substrates up to
harder types; sampling tube can be modified for various
diameters up to 100 cm2 substrate surface; least
disturbance to water/bottom interface; standard and
heavy models available; wide variety of core tubes, liner
tubes, core catchers, and nosepieces.
D. Limitations: Gravity operated; samples limited surface
area; standard KB core sampler head, without core tube weights
approximately 8 kg (18 pounds), but additional weight can be
added to sampler; requires boat and powered winch.
2. Ballchek Single and Multiple Tube Core Sampler
A. Habitats and Substrates Sampled: Same as KB Core Sampler.
B. Effectiveness of the Device: Samples deep burrowing
organisms in soft sediment, particularly effective for
sampling oligochaetes; uses 5.08 cm (2 inch) or 7.62 cm (3
inch) pipe core tube; used in shallow or deep waters, 3 m to
183 m (10-600 feet); multiple core sampler weight approximately
38 kg (84 pounds); check valves work automatically, prevent loss
of sample.
C. Advantages: Good penetration in soft sediments; small
volume of sample allows for greater number of replicates
to be analyzed in a short period of time; single or multiple
(four) core tube sampler available; three inch pipe for larger
cores and/or deep water lakes and oceans available; wide variety
of core tubes, liner tubes, core catchers, and nosepieces.
D. Limitations: Heavy device, approximately 38 kg,
requires boat and winch; gravity operated; does not
retain sand unless bronze core retainers are used which
require additional weight to insure penetration.
3. Phelger Core Sampler
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TABLE 6. SUMMARY CRITERIA OF CORING DEVICES (Continued)
A. Habitats and Substrates Sampled: Same as above core samplers.
B. Effectiveness of the Device: Similar to KB core sampler.
C. Advantages: Similar to KB core sampler.
D. Limitations: Gravity operated or can be messenger
operated with a suspension-release device; styles and
weights vary among manufacturers, some use
interchangeable weights, between 7-35 kg, others use
fixed weights up to 41 kg; length core taken varies with
substrate texture.
4. Box Core Sampler
A. Habitats and Substrates Sampled: Same as above core
samplers, also oceans.
B. Effectiveness of the Device: Same as above core
samplers; samples a surface area of 100 cm2 and a
sediment depth of 20 cm.
C. Advantages: Same as above core samplers.
D. Limitations: Same as above core samplers; also deployed
from ships or other platforms; diver collected cores are
preferred.
5. Hand-Operated Core Samplers
A. Habitats and Substrates Sampled: Same as above core
samplers.
B. Effectiveness of the Device: Sampled by hand or by diver.
C. Advantages: Can be used in shallow water. In deep water can be
used with a diver, usually a trained biologist, who can collect
and recognize substrate and bottom changes to stratify sampling;
can be used with extension handles of 5, 10, or 15 feet; used
with pipe fitting for driving from a pontoon boat, dock, or
bridge.
D. Limitations: Limited area sampled.
Selected Literature: APHA, 1989; Brinkhurst, 1967, 1974; Burton, 1974;
Coler and Haynes, 1966; Edmondson and Winberg, 1971; Flannagan, 1970;
Gale, 1977; Hamilton et a]_., 1972; Holme, 1964; Holme and Mclntyre,
1971; Miller and Bingham, 1987; Poole, 1974; Schwoerbel, 1970.
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5.10.2.2 Coring devices can be used at various depths In any substrate
that is sufficiently compacted so that an undisturbed sample is
retained; however, they are best suited for sampling the relatively
homogenous soft sediments, such as clay, silt, or sand of .the deeper
portions of lakes, reservoirs, and oceans. Because of the small area
sampled, data from coring devices are likely to provide very imprecise
estimates of the standing crop of macrobenthos.
5.10.2.3 KB type, Ballchek, and Phleger corers (Fig. 10A,B,C) are
examples of devices used in shallow and deep water; they depend on
gravity to drive them into the sediment. The cores are designed so that
they retain the sample as it is withdrawn from the sediment and returned
to the surface. Hand corers (Fig. 10D) designed for manual operation
are used in shallow water. Sections of the core can be extruded and
preserved separately or the entire core can be retained in the tube and
processed in the field or laboratory. Intact cores can also be
preserved by freezing and processed later.
5.10.2.4 Additional replication with corers is feasible because of the
small amount of material per sample that must be handled in the
laboratory. Multiple-head corers have been used in an attempt to reduce
the field sampling effort that must be expended to collect large series
of core samples (Flannagan, 1970).
5.10.2.5 The Dendy inverting sampler (Welch, 1948) is a highly
efficient coring-type device used for sampling at depths to 2 or 3
meters in nonvegetated substrates ranging from soft muds through coarse
sand. Because of-the small surface area sampled, data obtained by this
sampler suffer from the same lack of precision (Kajak, 1963) as the
coring devices described above. Since the per-sample processing time
is reduced, as with the corers, large series of replicates can be
collected. The Dendy sampler is highly recommended for use in habitats
for which it is suitable.
5.10.2.6 Stovepipe-type devices include the Wilding sampler (Wilding,
1940; APHA, 1989) and any tubular material such as 60-to-75 cm sections
of standard 17-cm-diameter stovepipe (Kajak, 1963) or 75-cm sections of
30-cm-diameter aluminum irrigation pipe fitted with handles. In use,
the irrigation pipe or commercial stovepipe is manually forced into the
substrate, after which the contained vegetation and coarse substrate
materials are removed by hand. The remaining materials are repeatedly
stirred into suspension, removed with a long-handled dipper, and poured
through a wooden-framed floating sieve. Because of the laborious and
repetitive process of stirring, dipping, and sieving large volumes of
material, the collection of a sample often requires 20 to 30 minutes.
5.10.2.7 The use of stovepipe samplers is limited to standing or slowly
moving waters having a maximum depth of less than 60 cm. Since problems
relating to depth of sediment penetration, changes in cross-sectional
area with depth of penetration, and escapement of organisms are
circumvented by stovepipe samplers, they are recommended for quanti-
tative sampling in all shallow-water benthic habitats. They probably
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D
Figure 10. Core Samplers: (A) KB corer, standard and heavy duty; (B)
Ballchek corer, single and multiple types; (C) Phleger corer; (D) Hand-
operated corer
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represent the only quantitative device suitable for sampling shallow-
water habitats containing stands of rooted vascular plants and they will
collect organisms inhabiting the vegetative substrates as well as those
living in sediments.
5.10.2.8 In marine waters benthic macrofauna are generally collected
using various box cores deployed from ships or other platforms, or diver
collected cores. A box coring device consisting of a rectangular corer
having a cutting arm which can seal the sample prior to retraction from
the bottom should be used. In order to sample a sufficient number of
individuals and species, and to integrate the patchy distribution of
fauna, each sample should have a surface area of no less than 100 cm2
and a sediment depth of at least 20 cm. In sediments having deep,
burrowing fauna, a box corer capable of sampling deeper sediment may be
needed. In sandier sediments, it may be necessary to substitute a grab
sampler for the box corer in order to achieve adequate sediment
penetration. Sufficient replicates (usually 3 to 10) should be taken
to produce an asymptotic cumulative species curve. Visual inspection
of each sample is necessary to insure an undisturbed and adequate amount
of sample is collected.
5.11 Frames
5.11.1 For estimating the populations of attached marine organisms on
a rocky shore, 0.1 m2 or 1 m square-shaped metal frames can be used for
delineating percent coverage of the colonial forms. At least ten frames
should be counted for characterizing the distribution statistically.
Samples of the algae and macroinvertebrates should be removed from a
measured area for species identification and weighed for biomass
determination. It is important to note the attitude of the sampling
frame relative to the horizontal and vertical axis in order to relate
the data with the zonation patterns. A vertical plane is apt to have
a dramatically different species array compared to a horizontal plane
even with both being at the same level with the intertidal zone.
5.11.2 ^Attaching a 35 mm SLR camera to a sampling frame so that the
focal distance is fixed is an excellent method for documenting the
population present at each sampling site. Species enumeration and
percent cover can be estimated from the developed photographs. This
method is especially useful for documenting temporal changes at a
particular sampling site.
5.11.3 For sampling the infauna of beaches, a 0.1 m2 square metal frame
with a 15 cm lip is useful. The frame can be deliberately thrown near
a fixed position (see Section 4.4.3, Systematic Sampling). Stovepipe
or large coffee can work very well in most sandy, sandy-mud beaches but
have limited use in cobble beaches. All of the substrate is removed and
screened in fine-meshed screens. The animals retained are washed or
picked from the screens and preserved for later identification and
enumeration.
5.11.4 Edged frames (.1 m2) or corers can be utilized for
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systematically sampling the substrates around fixed positions on the
flats. At least five replicate samples should be collected at each site
for statistically delineating the distribution patterns of the infauna
populations. The substrate is then washed through fine meshed screens.
The invertebrates can be washed or picked from the screens and
preserved. Flats represent areas of quiet, low velocity waters with
the settling of suspended materials. Flats near pollution sources are
good sites to observe the impact of all settled materials, non-toxic and
toxic. Some flats are so poorly drained as to require snowshoes or
similar devices for walking out to the sampling area. In such areas,
it may be easier to sample at high tide from a boat using a conventional
benthic grab.
5.12 Rapid Bioassessment Protocols (RBPs) for Macroinvertebrates (see
Plafkin et al_., 1989 and Section 7, Data Evaluation.)
5.12.1 The methods describe three different protocols (I,II, and III)
for use in wadable streams and rivers to determine water quality. The
RBPs are considered qualitative and semi-quantitative sampling
techniques for assessing the health of benthic macroinvertebrate
communities. The protocols consist of three basic components--water
quality and physical characteristics, habitat assessment, and biosurvey.
The biological assessment involves integrated data analyses of both
functional and structural components of the macroinvertebrate
communities through the use of metrics. The protocols describe
guidelines for a rapid means of detecting water quality and aquatic life
impairments and assessing their relative severity. The RBPs are not
intended to replace traditional biomonitoring methods but provide an
option which may be cost effective. These RBPs work very well as a
surveillance tool to prioritize sites for more intensive evaluations
(quantitative biological surveys) but are not always comparable to the
results obtained with more traditional methods such as artificial
substrate samplers or drift nets. The same metrics (RBPs) may be used
with these more traditional methods of collection and give qualitative
or quantitative results.
5.12.1.1 Protocol I provides for basic qualitative information for a
subjective judgment of macroinvertebrate abundance and presence. The
method consists of habitat assessment and the collection of macro-
invertebrates from all possible habitats. The specimens are identified
to orders and counted in the field. The data are used to make a
subjective assessment of stream water quality or impairment.
5.12.1.2 Protocol II provides a reasonably reproducible assessment of
biological impact and consists of habitat assessment and collecting
macroinvertebrates from all available habitats. The specimens are
identified to families, and the list of families in a 100-organisms
subsample is used in the evaluation. The study is based on established
guidelines in scoring parameters, and the stream site would be
classified as to water quality or degree of impact and possible cause.
5.12.1.3 The objectives of Protocol III are to assess the biological
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impact and to establish the basis for trend monitoring of pollution
effects over a period of time. The method consists also of specific
guidelines for evaluating the habitat assessment parameters and
collecting macroinvertebrates from all available habitats. The protocol
is similar to Protocol II except that the specimens are identified to
the lowest possible taxonomic level (genus, species). The data are
categorized into parameters based on taxa richness, biotic index,
percent composition, and functional group designations. The
classification of stream sites is dependent on established guidelines.
5.13 Ohio EPA Invertebrate Community Index method (ICI) (see Ohio EPA,
1987, 1989)
5.13.1 The ICI semi-quantitative method uses 10 metrics to determine
if wadable streams or rivers are polluted using benthic
macroinvertebrates. Nine of the 10 metrics are based on multiple plate
artificial substrate samples, and one is based on dip net sampling
(Ohio EPA, 1987 and 1989). Also, see Section 7, Data Evaluation.
5.14 Standard Qualitative Collection Method (see Lenat, 1988; Eagleson,
it a!., 1990, and NC DEN, 1990 and Section 7, Data Evaluation)
5.14.1 The method emphasizes multiple-habitat sampling, field-picking
of samples, and the use of both coarse- and fine-mesh samplers. This
standard qualitative method consists of collecting macroinvertebrates
in shallow streams, usually less than 1.5 m deep using two kick net
samples, three dip net samples (sweeps), one leaf-pack sample, three
aufwuchs samples, one sand sample, and visual search collections. The
data resulting from this method, especially taxa richness, can be used
to assign water quality ratings. The method is applicable for most
between-site and/or between-date comparisons. Also, a secondary
abbreviated qualitative method (EPT survey) can be used to quickly
determine between-site differences in water quality. The number of
collections is decreased from 10 samples in the standard quality
collections to only four samples: one kick, one sweep, one leaf-pack and
visual searches in the abbreviated method.
5.15 Miscellaneous Qualitative Devices
5.15.1 The investigator has an unlimited choice of gear for collecting
qualitative samples. Any of the quantitative devices discussed
previously, plus hand-held screens, dip nets, sweep nets, kick nets,
rakes, tongs, post-hole diggers, bare hands, and forceps can be used for
collecting benthic macroinvertebrates from freshwater, estuarine, and
marine environments. For deep-water collecting, some of the
conventional grabs described earlier and dredges are normally required.
In water less than 2 meters deep, a variety of gear may be used for
sampling the sediments including long-handled dip nets and post-hole
diggers. Collections from vascular plants and filamentous algae may be
made with a dip net, common garden rake, potato fork, or oyster tongs.
Collections from floating debris and rocks may be made by hand, using
forceps to catch the smaller organisms. In shallow streams, short
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sections of common window screen may be fastened between two poles and
held In place at right angles to the water flow to collect organisms
dislodged from upstream materials that have been agitated.
5.15.2 Dip, hand, sweep, kick nets and screens are rapid devices for
collecting macroinvertebrates in wadable streams and rivers or at low
tide in the inter-tidal zone of tidal sites^ Two approaches are
generally used, one in which the investigator sweeps the dip or hand net
through aquatic habitats (Slack, et a],., 1976; Armitage, et al_., 1981)
and one in which the kick net or hand held screen is held stationary
against the streambed, facing upstream, and the investigator physically
disturbs the stream bottom just upstream from the net or screen. The
investigator vigorously kicks with the feet four or five times into the
streambed to disturb the habitat in an upstream direction (Hynes, 1961;
Morgan and Egglishaw, 1965; Frost, et aj.., 1971; Armitage, e_t t]_., 1974;
Armitage, 1978; Hornig and Pollard, 1978; Pollard, 1981; and Plafkin,
£t al«> 1989). The kicks disturb the substrate, dislodging the
macroinvertebrates and some detritus, and cause the benthos to be swept
by the current into the net. The debris and organisms in the kick net
are then washed down into a sieve bucket and larger leaves and debris
are removed.
5.15.3 Dredges are devices that are usually pulled by hand or power
boat across or through the bottom sediment of a lake or stream to sample
the benthos and prevent loss of active macroinvertebrates. the forward
motion of the dredge carries macroinvertebrates into the net.
5.15.3.1 Elliott and Drake (1981a,b) compared four light-weight dredges
for sampling in rivers. They indicated that the dredges are not
suitable for quantitative sampling. Also, considerable variation
existed in their effectiveness as qualitative samplers for estimating
the total number of taxa per sample.
5.15.3.2 Dredges should be emptied after collection into a shallow
tray, bucket, or sieving device if the sample is sorted on-site. The
sample can be placed directly in labeled wide-mouth containers with
preservative and transported back to the lab for processing.
5.16 Suction Samplers
5.16.1 Suction samplers have been used widely in sampling macro-
invertebrates in fresh, estuarine, and marine waters (Brett, 1964;
Larsen, 1974; Gale and Thompson, 1975). They can be placed directly on
the sampling station and can be operated by hand in shallow water or by
a scuba diver fn deep water (see 5.18).
5.17 Photography
5.17.1 The use of photography is mainly limited to environments that
have suitably clear water and are inhabited by sessile animals and
rooted plants. Many estuarine habitats, such as those containing
corals, sponges, and attached algal forms, fall in this category and can
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be photographed before, during and after the introduction of stress.
The technique has been used with success in south Florida to evaluate
changes brought about by the introduction of heated effluents.
5.17.2 The technique for horizontal underwater photos using scuba gear
involves placing a photographically identifiable 1.0 m2 area frame or
marker in the habitat to be photographed and an additional nearby marker
on which the camera is placed each time a photograph is taken. By this
means, identical areas can be photographed repeatedly over a period of
time to evaluate on-site changes in sessile forms at both affected and
control stations. Vertical, overhead photos may be taken under suitable
conditions.
5.17.3 Photographs are also useful in documenting a habitat or
alterations in a station over time (e.g., increase in canopy cover,
changes in channelization of a stream, and effects of flooding, etc.).
5.18 Scuba
5.18.1 This equipment can be used in freshwater sampling of mollusks
in large riverine systems or with diver collected cores.
5.18.2 The reader is referred to Simmons (1977), Sommers (1972), U.S.
Department of the Navy, U.S. Navy diving manual (latest edition), and
Gale and Thompson (1974) for much additional information on this
subject. All USEPA diving operations should be conducted in accordance
with standards set forth in the U.S. EPA Occupational Health and Safety
Manual-1440, 1986, entitled Chapter 10, EPA Diving Safety Policy.
Therefore, if the need for diving capability exists, approval must be
obtained through an USEPA regional laboratory diving officer. Scuba
gear can be used to improve aquatic sampling; in particular sampling of
mussels, other benthos, and fish. Isom, et al_.,. (1979) reported
utilizing scuba in rediscovery of snails, which were thought to be
extinct. Various investigators had sampled the same areas previously
on numerous occasions.
5.18.3 Gale (1977) notes the numerous applications of scuba to sampling
benthos including placement and retrieval of artificial substrate; use
of suction samplers (Larsen, 1974; Gale and Thompson, 1975); sampling
with a quadrate frame; and, perhaps most importantly, identifying and
delineating substrate types for purpose of determining sampling effort
(stratified sampling) and choice of samplers.
5.18.4 If pelecypods (freshwater mussels) are to be sampled with brails
in areas which historically contained them and/or it is desired to
sample quantitatively, scuba can be used effectively in taking
quadrates. In large rivers, which have mussel beds with homogenous
substrate, it is desirable to take at least 10 square meter quadrates
(10,000 square cm each). In small rivers where the mussels' niche may
be between rocks and it is generally difficult to place a square meter
frame, then a 0.5 square meter frame (2500 square cm) should be utilized
with no less than 3 square meters, or twelve 0.5 square meter samples
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taken. Samples should be taken randomly In all cases, which In the
latter instance, will result in collection of good representative
diversity (see Section 2, Quality Assurance and Quality Control).
5.18.5 -Scuba diving is safe if conducted by rigid safety standards,
some of which are mandatory for scientific/educational diving (See
Federal Register. July 22, 1977; 42, 41: pp. 37650-37673). Conformance
with theseand subsequent standards is costly but essential for safe
conduct of scuba sampling. See references listed above for more in
depth discussion of safety, the buddy system, etc. The need for
observance of safety rules cannot be overemphasized.
5.19 Brails
5.19.1 This device is primarily limited to sampling of bivalve mussels
in large (non-wadable) rivers.
5.19.2 The use of brails for commercial harvest of mussels has been the
common practice since before 1900; however, this practice and scuba have
been used by investigators to study mussel populations on a limited
basis.
5.19.3 The reader is referred to Coker (1919), Van der Schalie (1941),
Scruggs (1960), Lopinot (1967), Isom (1969), Bates (1970), Starrett
(1971), and Buchanan (1980) for more information on collecting mussels,
brails, and brailing. Coker (1919) describes how to make a brail.
5.19.5 Once the site to be sampled has been identified, reference
should be made to historical literature for determination of species
that may be encountered.
5.19.6 Quantitative sampling is accomplished with a crowfoot brail
to determine the rate of catch per drag from a given area. All
equipment can be made or rented from and fished by a commercial
fisherman. Each brail sample consists of dragging a measured distance
of 100 m, then sorting and counting the catch. The area sampled is
calculated in square yards by multiplying the length of brail by 100
m. Catch success is expressed in terms of the average catch of mussels
per square per drag. Brail sampling is randomized within fishing area
and by time periods during two complete harvest seasons (March through
August).
5.19.7 Brailing is also an effective qualitative sampling device,
especially in large, deep rivers. Where possible, the services of a
commercial mussel fisherman should be utilized. The experienced mussel
fisherman is adept at using brails and only extensive experience would
make an investigator's results equivalent to the general mussel
fisherman. Maximum legal brail length is 16 feet (approximately 5 m)
in some states; diameter of wire used for hooks is also controlled.
These points can be worked out with the state permitting agency.
5.19.8 A minimum of six 100 m long hauls (drags) should be accomplished
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where a single brail is used. Most commercial fisherman use two brails
simultaneously; thus, only three hauls would be required. Record the
time for each haul; however, take about 20 minutes to make each haul
since a very slow speed is best for catching mussels. If the hauls are
made too fast, the catch will be small. If a significant mussel
population is found, then qualitative or quantitative scuba (see 3.18,
Scuba) samples should be taken. A minimum of 10 m samples should be
taken by scuba at each station. All specimens should be identified to
species, growth cessation rings counted, and measured for determination
of population age structure.
5.19.9 Mussel fishing with brails is highly dependent on experience of
the user; however, they are very efficient in the hands of experienced
users as attested to by almost 100 years of continuous use.
5.19.10 Availability of brail ing equipment may be a deterrent to its
use; however, if the method is adopted more widely by the scientific
community, suppliers may develop to meet the need.
5.20 Other Mussel Collecting Methods
5.20.1 Mussels found in small or medium sized streams and rivers that
can be waded are often found most numerous on bars where the pools break
off into shoals. Sometimes, there are constrictions in streams at these
points where weed beds can be found. Sample into the lower end of
pools, around the weed beds, and in riffles/runs and fast-flowing water.
A long-handled rake modified with a rectangular collection basket of
one-quarter inch Wire mesh, dredge dip net, or using the hands are the
best method for sampling mussels from these habitats (Starrett, 1971).
It is advisable to wear gloves and place a net below the area being
sampled to catch small mussels that might otherwise not be collected.
5.20.2 Other collection techniques and procedures can be found in the
1941 Annual Report of the American Malacological Union. Information on
collecting snails can be found in the same publication.
5.20.3 If rare or endangered species are collected, they should be
returned to their habitat since it is illegal to take such species.
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-''.-- >
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SECTION 6
SAMPLE PROCESSING
6.1 Sieving
6.1.1 Samples collected with grabs, coring devices, and artificial
substrates contain varying amounts of finely divided materials such as
decomposed organic material, silts, clays, and fine sand. To reduce sample
volume and expedite sample processing in the laboratory, these fines should
be removed in the field by passing the sample through a U.S. Standard No. 30
sieve. Sieves may be commercial models or homemade sieves framed with wood
or metal. Floating sieves with wooden frames reduce the danger of accidental
loss of both sieve and sample when working over the side of a boat in deep
waters. A sieve should contain no cracks or crevices in which small
organisms can become lodged.
6.1.2 Sampling efficiency is increased by using sieves with smaller mesh
openings (Mason et al_., 1975; Barber and Kevern, 1974; and Zelt and Clifford,
1972). However, use of the smaller mesh size does not have an appreciable
effect on the eutrophic classification based on common biotic indices.
Precision based on coefficient of variation (CV) increased with smaller mesh
size (Mason et a]_., 1975). Usually the increased length of time required to
use the smaller mesh sieve sizes is not compensated for by the increased
accuracy of results (Hummon, 1981). Also, organisms passing through the U.S.
Standard No. 30 sieve are not macroinvertebrates by definition. (See
Section 1, Introduction).
6.1.3 If at all possible, sieving should be done in the field immediately
after the sample is collected and the captured organisms are still alive,
but time can often be saved by returning to the laboratory with the samples
unsieved and doing the sieving with a mechanical device such as the
elutriation apparatus described by Worswick and Barbour (1974). If the
sample is likely to include tubificid worms, leeches, or Turbellaria, a few
representative specimens of each should be picked out before sieving and
fixed in 10% buffered formalin or transported live to the laboratory for
fixing or immediate identification. Once preserved, many organisms become
quite fragile and if subjected to sieving will be broken up, lost, or
rendered unidentifiable. Great care should be taken in sieving preserved
samples containing mayflies, stoneflies and worms to reduce breaking the
specimens or otherwise damaging body parts necessary for identification.
6.1.4 Sieving may be accomplished by one of several techniques depending
upon the preference of the biologist. In one method, the sample is placed
directly into a sieve and the sieve is then partially submerged in water and
agitated until all fine materials have passed through. The sieve is
agitated, preferably in a large tub of water but sieving may be done over
the side of the boat if care is taken not to spill the sample. A variation
of this technique is to place the original sample in a tub or bucket, add
screened water, stir, and pour the resulting slurry through a U.S. Standard
No. 30 sieve. Only a moderate amount of agitation is required to completely
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clean the sample. Since this method requires considerably less effort, most
biologists may prefer it. A sieve bucket (Fig.11) described by Hiltunen
(1983) for use in the Great Lakes works well under most conditions and allows
the sample to be sieved while the boat is under way to the next sampling
site, the cycle sieve described by Mason (1976) works well in calm weather
from a small boat but is cumbersome and impractical for use from large boats,
bridges or other such structures. In all of the above methods, remove,
carefully clean, and discard all the larger pieces of debris and rocks from
the sample before stirring or agitating.
Vent
Level of bottom mesh
Great Lakes sieve bucket (From Hiltunen, 1983).
6.1.5 Artificial substrate samplers are placed intact into a bucket or tub
of screened water and dismantled. Each individual piece of substrate is
rinsed, gently but thoroughly cleaned under water with a soft brush such as
a soft bristled toothbrush, examined visually, and laid aside. The water in
the bucket or tub is then poured through a U.S. Standard No. 30 sieve to
remove the fines. After most of the fines are washed from the sample, the
organisms are left scattered over the surface of the screen. These organisms
can be picked from the screen with forceps and placed in the sample
container. A faster method is to concentrate them at one edge of the sieve
by gently swirling thesieve in a little water, then tilting the sieve over
a wide-mouth jar and gently backflush the organisms into the jar with water
from a wash bottle directed through the screen.
6.1.6 Another way to separate the organisms from the detritus is the
flotation method in which a concentrated aqueous solution of sugar, salt, or
other chemical is poured over the sample in the tub or bucket causing the
animals to float up out of the detritus due to the difference in the specific
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gravity of the animals and solution. The organisms can then be poured or
scooped into the sample container with a sieve spoon. Some organisms, such
as clams and snails, must still be hand picked from the debris because they
are too heavy to float. Two or three Ibs. of sugar per gallon of water makes
a good flotation solution (Anderson, 1959).
6.1.7 When drift net or Surber-type samplers are being used, it is usually
possible to empty the bag directly into a white bottom enamel pan or small
bucket and hand pick the organisms into a sample container filled three-
fourths full of preservative.
6.1.8 Although the U.S. Standard No. 30 (600 /zm) sieve is also commonly
used in marine studies, some investigators (Grassle et a]_. 1985) have chosen
to use a 300 jum sieve in order to more efficiently sample smaller and
juvenile macrofauna. This practice requires more time and taxonomic
expertise. The 600 /urn sieve is usually adequate since the vast majority of
macrofaunal biomass and production is associated with larger forms.
6.1.8.1 For marine work the use of more than one sieve in series, one on top
of the other, allows benthic communities to be fractionated by size allowing
comparisons of community size distributions between stations and over time.
Commonly used sieve sizes are 300 Aim, 500 /im, 600 Mm, 1 mm, and 2 mm.
6.1.8.2 Sieving marine samples should be done by rinsing organisms with a
gentle spray of water to minimize mechanical damage to the organisms. Direct
heavy jets of water should not be used and an elutriate procedure that
ensures that the major source of water is from the bottom of the sieves is
recommended. Water used in sieving should be obtained from the sample site
whenever possible. Fresh water should never be used to sieve unpreserved
marine fauna because of osmotic effects that cause cell bursting.
6.2 Preservation and Fixation
6.2.1 All samples collected in the field should be preserved in 70-80% ethyl
alcohol (ethanol), but ideally, and for ease in identification,
representative specimens of leeches, aquatic oligochaetes, and other soft
bodied organisms, if time permits, should first be fixed in 10% formalin to
fix the tissue. After fixation (about 10 minutes), depending on size and
number of organisms, or after returning to the laboratory, they may be
preserved in 70-80% ethanol. This process should aid in their identification
(see Section 6.5.4. and 6.5.5). Because wash water is contained in the
sieved material, the stock preservative solution added to the sample should
be over-strength (90%) so that the final solution will be sufficient to
preserve the organisms. Grab samples collected from lakes, the muddy bottoms
of large rivers, estuaries and oceans are often fixed and preserved in ten
percent buffered formalin because they contain many worms which are difficult
to identify after being preserved in ethanol. Formalin should be buffered
to a neutral or slightly alkaline level with borax.
6.2.2 Since leeches dropped alive into preservatives such as 70-80% ethanol
or 10% formalin solution contract strongly, some diagnostic features used
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ilV.'j :i"i '"'iirii I1': Hi ,.
for species identification may be difficult to determine by the inexperience.
Ideally, specimens should first be narcotized by direct placement into
carbonated water, fixed in 10% formalin, and preserved in 70-80% ethanol.
If this procedure is inconvenient in the field, the specimens should be
preserved directly in 70-80% ethanol. Most specimens still can be identified
to species but might take a little longer than usual. Additional collecting,
narcotizing, and processing techniques can be found in Klemm (1982, 1985).
6.2.3 Turbellarians that require identification to species should be
transported to the laboratory alive in a small amount of water (Pennak, 1978,
1989).
6.2.4 Although not always necessary, species identifications are easier and
morphemetrie analyses are facilitated if marine organisms are relaxed after
sieving and prior to fixation and preservation. Organisms to be relaxed are
transferred from sieves to a fine mesh (approximately 100 pm) bag and placed
in a solution of magnesium chloride (approximately 75 g/1) for about 10
minutes. The organisms may then be fixed and preserved.
6.2.4.1 A 10% (by weight) formalin solution is most commonly used to fix and
preserve marine samples. The solution is buffered to keep the dissolution
of molluskan shells to a minimum.
6.2.4.2 Because formaldehyde is a carcinogen, and because some individuals
develop severe sensitivities to formaldehyde over time, some researchers
prefer to transfer samples from formalin to ethanol for preservation. This
is acceptable if samples are only to be used to do taxonomic studies.
However, biomass measurements should not be done on samples preserved in
ethanol. Although weight loss due to preservation in formalin is significant
(10-20%) (MilIs gt al_., 1982; Schram et al_., 1981; Williams and Robins 1982),
weight loss due to preservation in ethanol is greater.
6.2.5 Sample containers used for holding preserved samples should be large
enough so that they are not over one-half full of the washed sample before
the preservative is added. Quart or liter sized jars are adequate for most
samples collected with artificial substrate, drift net, or square-foot type
samplers, but two or more jars may be needed for a grab sample depending on
the amount of detrital material mixed with the sample. Hand picked specimens
are usually preserved by placing, them directly into small screw-cap vials
filled with 70-80% ethanol.
6.2.6 If the samples are not sorted within two or three weeks after
collecting, the preservative should be poured off and replaced with fresh
preservative for permanent storage (Cairns and Dickson, 1971).
6.2.7 After sorting and/or identification most macrbinvertebrates should be
stored in a solution of 70-80% ethanol and 5% glycerine in vials sealed with
tightly fitting rubber stoppers. If screw-cap vials are used, they should
be sybmerged in 70-80% ethanol in a larger container and should be checked
yearly to replace alcohol lost because of evaporation or Teflon tape can be
used to secure the screw-caps to prevent evaporation.
.'96 "
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6.3 Labelling and Record Keeping
6.3.1 All sample containers must be labeled in the field immediately upon
collection. Sample labels made of water-resistant paper should be placed
inside each sample container. Write all information on the label with a
soft-lead pencil or waterproof ink. Where the volume of sample is so great
that several containers are needed, additional external labels with sample
number and notations such as 1 of 2, 2 of 2, etc. are helpful for identifying
the sample containers when the samples are logged in at the laboratory. All
labels must include a sample identification number which corresponds to the
number entered in the field notebook for that sample, the sampling date,
water body and location from which the sample was collected, and the name of
the collector. In addition to the information on the label, the field
notebook should include the sampling method, weather, substrate
characteristics, depth, and any other physical or environmental conditions
noted.
6.3.2 Marine sample data sheets should include date of collection, time of
day, station number, geographic coordinates, replicate number, core
penetration depth, and the identification number and final storage location
of each sample. These data sheets should also include space for comments on
the visual appearance of each sample (e.g., obvious tubes or burrows,
presence or absence of a surface flocculent layer, sediment color, apparent
depth of the redox-potential discontinuity, etc.); ancillary data such as
water temperature, salinity, secchi disk visibility, vertical profiles of
dissolved oxygen; and other data potentially useful in the interpretations
of benthic community data.
6.3.3 As soon as possible after returning to the laboratory, each sample
should be assigned an ID number in sequence. This number identifies the
sample in a bound ledger where all the information from the field label and
field notebook are recorded for permanent record. The sample ID number must
also be placed prominently on the sample container before storing so that it
can be identified when needed. This sample ID number should be placed on all
specimen vials, microscope slides, and other items connected with the sample.
6.4 Sorting and Subsampling
6.4.1 Sorting
6.4.1.1 Sort through the samples by hand in the laboratory using a low power
(2X) scanning lens or a stereomicroscope. Place one or two tablespoonfuls
of the. sample in a white enamel pan (size 25 X 40 X 5 cm) filled about one-
third full of water. Usually small insects and worms will float free of most
of the debris when ethanol-preserved samples are transferred to the pan.
These floating organisms should be removed before they soak up water and
sink. They can be skimmed off with a sieve spoon or poured off. Addition
of about one tablespoon full of sugar and stirring the sample will cause most
of the other organisms to float free. Flotation in formal in-preserved
samples is accomplished by adding sugar slowly to raise the specific gravity
to 1.12 (Pask and Costa, 1971). Numerous other techniques have been proposed
to aid recovery of the organisms from the sample debris, including solutions
97
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i'"1 -.i I" ,'.'.."' I ' ",..,'.. " ,!'","", "i ',',.'""' >p "1.1! .:",.:', ji,.« ;:"'»'. ' ..'.'.Ml ill' TI'
1 '' ' ''"' »' rV' ". ' . ' ''',i' ' ""'', '.I . ''''. 'i'V, ! ] , ',;
of magnesium sulphate, D-mannitol, calcium chloride or sodium chloride;
electricity; bubbling air through samples in a tube, etc. The efficacy of
these techniques is affected both by the characteristics of the substrate
material and the types of organisms present (Flannagan, 1973). Regardless
of the sorting method used, heavy organisms such as clams and snails will not
float and will have to be picked out with forceps.
6.4.1.2 Various staining methods have been devised to help speed the sorting
process (Williams and Williams, 1974). Staining samples in the field with
either rose bengal or phloxine B at a concentration of 100 g/L of ethanol or
formalin significantly reduces sorting time for benthic samples (Mason and
Yevich, 1967). Should the stain interfere with identifications where color
patterns or internal organs must be examined, the stain can be removed by
placing the organisms in 95% ethanol over night.
6.4.1.3 As soon as the sample is sorted, make note in the log book,
including the date and the initials of the person who sorted the sample. It
is often advisable to ask a co-worker to check the sample debris before
discarding to be certain no organisms were overlooked. The organisms may be
sorted and transferred to watch glasses or petri dishes for immediate
identification and counting, or stored in vials for future identification.
6.4.2 Subsampling
6.4.2.1 Analysis time for samples containing large numbers of organisms can
be substantially reduced if the samples are subdivided before sorting. There
are several methods for subdividing the samples and each method has its
advantages and disadvantages.
6.4.2.2 Welch (1948) described a method that has been used successfully for
many years, the sample is thoroughly mixed and distributed evenly over the
bottom of a shallow white-bottom pan. A divider, delineating one-quarter
sections, is placed in the tray and one quarter or two opposite quarters are
sorted.
6.4.2.3 An air driven subsampler (Figure 12) was described by Wrona et al.
(1982) and modified by the State of Maine Department of Environmental
Protection (Susan Davies, Personal communication). The sample is placed in
a Imhoff-type settling cone that is filled with water to a total volume of
one liter. The sample is gently agitated for two to five minutes by use of
an air stone sealed into the bottom and connected to an air supply. One-
quarter of the sample is removed with a wide-mouth 50 ml dipper or test tube
in five aliquots and combined in a white-bottom pan for hand sorting. If
less than 100 organisms are present in the one-quarter subsample, additional
one-quarter subsamples are removed until the subsample contains at least 100
organisms. Large or heavy organisms that cannot be suspended by agitating
the water are sorted and counted separately.
6.4.2.4 The Rapid Bioassessment Protocols II and III (Plafkin et a].., 1989)
use a modification of a subsampling method described by Hilsenhoff (1987).
All large detrital material (leaves, twigs, etc.) are rinsed, visually
98
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inspected for organisms, and discarded. The sample is then poured into a
white-bottom pan that has been marked with a grid pattern of 5-cm squares.
Grids are randomly selected and all the organisms in the selected grids are
picked in succession until approximately 100 organisms have been removed from
the sample. All,the organisms in the grid that contains the 100th organism
are picked once that grid is started. Before using this method, live
organisms should be narcotized with club soda or nicotine before sorting so
they will not move from square to square.
IMHOFF CONE
AIR STONE
RUBBER SEAL
AIR SUPPLY
Figure 12*. Imhoff cone subsampler (From Wrona e_t al_., 1982).
6.4.2.5 Regardless of the method used for subsampling, the sorted sample
should be labelled to reflect the portion sorted (e.g., 2X if half sorted,
4X if one-quarter sorted, 100 C if 100 count method was used, etc.) with the
sample ID number. The unsorted portions of the sample should be combined,
preserved, labeled and stored for future reference. It should be discarded
only if there is no possible future need.
6.4.2.6 Experience has shown that, if less than one-quarter of the original
sample is sorted, considerable error may result in estimating the total
numbers of worms and other organisms that tend to clump. If the sample
contains large numbers of a single taxonomic group (such as oligochaete worms
99
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or midges) but few other organisms, it may be advisable to subsample the
abundant taxa and pick all of the other organisms.
6.5 Preparation of Microscope Slide Mounts
6.5.1 To identify certain taxa of macroinvertebrates, it is often necessary
to make slide mounts of all or parts of the organisms for examination under
a compound microscope. Generally, if the organism is over 10 mm in length,
it is best to carefully remove the important diagnostic structures (such as
mouthparts or genitalia) with fine pointed forceps and mount them on
microscope slides. Some large chironomids and tubificid worms that are too
long to be mounted whole are cut in half and mounted under two separate cover
glasses on the same slide.
6.5.2 Because most of the slides made for diagnostic purposes will be
discarded after the organisms have been identified, we recommend mounting
directly from the preservative using a water miscible mounting medium
consisting of a mixture of two-thirds CMCP-9 and one-third CMCP-9AF (Beckett
and Lewis, 1982). This mixture stains the organism a light red and contains
a clearing agent providing optimum contrast for easy viewing of taxonomically
important structures after about 12 hours clearing time. Because CMCP-9/9AF
is a low viscosity medium, the specimen can be easily manipulated after the
cover glass is in place by using pressure from forceps on the cover glass,
rolling the specimen while viewing with a dissecting microscope until the
best viewing position is obtained. The slides may be made permanent by
ringing the cover glass with additional CMCP-9/9AF followed 24 hours later
with polyurethane spar varnish or fingernail polish. Round 12 mm or 15mm
cover glasses are recommended because they are less likely to trap air
bubbles, are easier to manipulate, and less likely to break with pressure
than the square ones. This method has proven very successful for making
semi-permanent slides of whole chironomids and oligochaetes and parts of
mayflies, caddisflies, and other macroinvertebrates.
6.5.3 Other slide-making techniques have been recommended for specific
groups of organisms (Mason, 1973; Beck, 1975; Britton and Greeson, 1988).
Although these methods are more time consuming and require more effort than
the above method, they are thought to produce superior results by some
taxonomists and are considered more permanent.
6.5.3.1 Many chironomid taxonomists use KOH to clear the midges before
mounting them in Euparal (Mason, 1973) or CMCP-10 (Beck, 1975). The US
Geological Survey (Britton and Greeson, 1988) has adopted a slightly modified
version of this method for mounting midges and blackflies as follows:
1. Place the specimens in distilled water for 10 minutes to remove the
preservative.
2. Transfer to crucibles containing 10% KOH and heat for 10 to 15 minutes
to digest opaque tissue, taking care not to digest exoskeleton also.
3. Soak in distilled water for at least 3 minutes to remove KOH.
4. Soak in 95% ethyl alcohol for three to five minutes.
5. Mount in a drop of Euparal or CMCP-10.
6. Place specimen ventral side up and cover with a 12 mm cover glass.
100
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7. Working under a stereoscopic microscope, apply pressure from a pencil
eraser to roll ventral side up and flatten the head capsule.
8. Allow the slide to dry for about a week before storing on edge.
6.5.3.2 Water mites mounted using either of the above methods are nearly
impossible to identify beyond family level. If identification to genus or
species is needed, the mites should be dissected first to speed the clearing
process and make it possible to examine sclerotized plates and other
structures on both dorsal and ventral surfaces of the abdomen. First, using
a dissecting microscope, forceps and a needle, separate one palp or the
entire gnathostoma with palps from the body and mount the palps in the
position shown in Figure 13. Next, separate the dorsum of the abdomen from
the venter leaving a small section of the posterior body wall intact as shown
in Figure 14, and mount with the venter and dorsum upward. Rather than
dissect the very small specimens, pierce the body wall in the posterior-
lateral areas to facilitate the clearing process and mount with the ventral
surface upward (Britton and Greeson, 1988).
Claw
Spine
Figure 13. Five-segmented palp of a water mite (From Britton and Greeson,
1988).
6.5.3.3 When permanent slides are needed for the water mites, the double
cover-glass glycerine method described by Mitchell and Cook (1952), modified
by Britton and Greeson (1988), and illustrated in Figure 15 should be used.
6.5.4 Aquatic oligochaete wormsTo identify oligochaete worms the specimens
must be go through a clearing process and be side mounted. The identification
of species requires a compound light microscope and some specimens require
oil immersion (1000X). Some worm specialists make temporary mounts by
placing oligochaete specimens on sides in Amman's lactophenol (100 g phenol,
100 ml lactic acid, 200 ml glycerine, 100 ml water), a medium which clears
tissues and eliminates the risk of specimen desiccation if a more permanent
101
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Dorsum
Venter
Intact section
of body wall
Dissection line
\
Figure 14. A water mite showing the dorsum separated from the venter,
leaving a small section of the posterior body wall intact (From Britton and
Greeson, 1988).
Glycerin jelly
Canada balsam
12-millimeter
circular cover glass -Ji
B
Figure 15. Top (A) and side (B) views of the double cover-glass technique
for mounting aquatic water mites (From Britton and Greeson, 1988).
102
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mount cannot be prepared immediately following extraction from the sample
(Brinkhurst, 1986; Hiltunen and Klemm, 1980, Stimpson, et a].., 1982; or
Klemm, 1985). The clearing process usually takes a few hours to a few days
depending on the size of the specimens. Gentle application of heat will
speed the clearing process. If the specimens are preserved in 70-80% ethyl
alcohol, they should be placed in 30% ethyl alcohol and then in water for a
short time to leach out the alcohol before clearing. The alcohol retards
the clearing process of Amman's lactophenol (Hiltunen and Klemm, 1980,
Stimpson et al., 1982; Klemm, 1985). Do not leave specimens in the water too
long (not more than two hours) because the worms will begin to deteriorate.
Naidids and tubificids can be held indefinitely in Amman's lactophenol or 10%
buffered formalin for later processing and mounting.
6.5.4.1 Non-resinous media are recommended for rapid processing of large
numbers of specimens. For extremely important reference specimens, a
permanent resinous mounting medium is best.
6.5.4.2 The non-resinous semi-permanent mounting media (CMCP-9 or 9AF, CMCP-
10, or aquamount), which also contain clearing agents, are the simplest to
use, allow for rapid processing of specimens, and are usually adequate for
species identification. If Fuschin dye is added to the colorless mounting
media (CMCP-9 or CMCP-10), only enough of the dye should be used (avoid
overstaining) to slightly or partially stain the specimens. The specimens
can be mounted directly on the slide using these media. However, the
clearing process of these media takes approximately 24 hours. If the slides
are to be semi-permanent, the edge of the cover slip should be sealed with
finger nail lacquer to prevent the mounting medium from shrinking and forming
bubbles under the cover slip. An 18 mm diameter, No. 0 or 1 round cover
glass is appropriate because it will adequately accommodate the size range
of the worms and the shape allows for maneuvering the specimen to rest in the
most desired position by gentle rotation of the cover glass.
6.5.4.3 Place naidids or tubificids on their sides so that both dorsal and
ventral fascicles of chaetae can be examined (Hiltunen and Klemm, 1980;
Stimpson et al_., 1982; Klemm, 1985). A variation from this is followed with
specimens of Dero which must be viewed from the dorsal aspect, revealing the
arrangement of the branchial apparatus (Hiltunen and Klemm, 1980, Klemm,
1985). The methods sections found in Hiltunen and Klemm (1980)and Klemm
(1985) should be consulted for more specific information on identification
of specimens.
6.5.4.4 Optimal resolution and longevity of mounted materials are achieved
only in resinous media (e.g., Canada Balsam, Harleco's Xylene Coverbond,
etc.). These mounting media require dehydration of the specimens through the
alcohol series and clearing before mounting in Canada balsam or other
resinous medium, but they produce the best permanent mounts (Knudsen, 1966;
Klemm, 1985).
6.5.5 Leeches species identification of most specimens do not require
mounting on slides. A stereozoom microscope of 500X is needed for species
identification. However, specialized slide-making techniques must be used
for species identification of some leeches (See Klemm, 1982, 1985, 1990).
103
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6.5.6 Regardless of the mounting method used or the permanence of the
slides, proper labelling is a must. The label should include the date the
slide was made, the sample ID number, and the initials of the person who made
the slide. Labels on permanent slides should also include the location of
the collecting site and name of the collector.
6.6 Drying Methods
6.6.1 Occasionally, alcohol-preserved specimens may require dry mounting on
points or minutens for identification. .The critical point drying method is
recommended because the pigments colors are preserved, specimens do not
collapse, and they are not brittle. Specimens to be dried are taken from 80%
ethanol and passed through the alcohol series of washes in a small mesh
screen basket with a lid, ending with two washes in 100% ethanol. After
removal from the alcohol wash, the specimens, with the basket, are placed in
the chamber of the critical point drier and processed according to dryer
instructions (Gordh and Hall, 1979).
6.7 Organism Identification
6.7.1 The taxonomic level to which animals are identified depends on the
needs, experience, and available resources. However, species level
identification is very important in determining water quality and
environmental pollution (Resh and Unzicker, 1975). The rapid bioassessment
protocol II calls for organism identification only to the family level for
use with Hilsenhoff's (1988) Family Biotic Index, whereas protocol III calls
for identification to genus or species if possible (Plafkin et i]_., 1989).
Many state programs carry most organism identifications to the genus level,
while others (e.g., State of Maine) carry identification of certain taxa,
such as stoneflies and mayflies, to species. Although the selective
sensitivity of a family-level identification effort is often sufficient for
differentiating non-impaired, moderately impaired, and severely impaired
conditions, subtle differences in biological impairment will not be discerned
except by species-level identification (Plafkin et aJL, 1989). In general,
identifications should be carried to the lowest taxonomic level readily
possible, and the taxonomic level to which identifications are carried in
each major group should be constant throughout a given study.
" ' r . ' "" , ' '" .«l» i ,!, ! " ' , , '^111
6.7.1.1 Since the accuracy of identification depends on the availability of
up-to-date taxonomic literature, A library of the basic taxonomic literature
1s essential for benthic laboratories. Basic references that should be
available in a macroinvertebrate identification laboratory are listed in
Section 8, Taxonomic Bibliography.
6.7.2 For comparative purposes and quality control checks, a reference
collection of identified specimens should be established in each laboratory.
6.7.3 Most identifications to order and family can be made using a hand lens
or a stereoscopic microscope with up to 50X magnification. Identification
to genus and species often requires a compound microscope with phase contrast
capable of 1000X magnification. Preparation of specimens for microscopic
viewing is discussed in Section 6.5.
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6.7.4 Insect larvae often comprise the majority of macroinvertebrates
collected with artificial substrate samplers, drift nets, and other net type
devices. In certain cases, identifications are facilitated if exuviae,
pupae, and adults are available.
6.7.5 The life history stages of an insect can be positively associated only
if specimens are reared individually. Small insect larvae can be reared
individually in 6 to 12 dram vials half filled with stream water and aerated
by use of a fine-drawn glass tubing. Mass rearing can be carried out by
placing rocks and sticks containing the larvae in an aerated aquarium.
Current can be provided in the aquarium by use of a magnetic stirrer (Mason
and Lewis, 1970).
6.7.6 As organisms are identified, the individuals in each taxonomic
category are counted and the numbers recorded on bench sheets (see Appendix
C). Samples are compared by use of a summary sheet (see Appendix D) which
provides room for comparing eight samples from the same sampling site.
6.8 Biomass
6.8.1 Macroinvertebrate biomass (weight of organisms per unit area) is a
useful quantitative estimation of standing crop-and is useful in assessing
the biological integrity of surface waters. One study shows that biological
assessments of water quality status using biomass estimates of wet, dry, and
ash-free dry weights provide essentially similar results concerning impact
of a sewage treatment plant discharge as did counts of individual organisms
using a variety of commonly utilized biotic indices of water quality (Mason
et aj_., 1983, 1985). To determine wet weights, soak the organisms in
distilled or deionized water for 30 minutes, centrifuge for one minute at 140
g in wire mesh cones, and weigh to the nearest 0.1 mg. To obtain dry weight,
dry the organisms to a constant weight at 105 degrees C for 4 hours or vacuum
dry at 105 degrees C for 15 to 30 minutes at one-half atmosphere. Cool to
room temperature for 15 minutes and weigh to nearest 0.1 mg. Freeze drying
(-55 degrees C, 10 to 30 microns pressure) can be used. It has advantages
over oven drying because the organisms remain intact for identification and
reference, preservatives are not needed, and cooling the material in
desiccators after drying is not required. The main disadvantage of freeze
drying is the time (usually 24 hours) required for drying to a constant
weight. To obtain ash-free dry weight, ash the dried organisms at 500
degrees C for one hour. Cool the ash to ambient temperature in a desiccator
and weigh to the nearest 0.1 mg. Express the biomass as ash-free dry weight.
6.8 Literature Cited
Anderson, R.O. 1959. A modified floatation technique for sorting bottom
fauna samples. Limnol. Oceanogr. 4:223-225.
Barber, W.E. and N.R. Kevern. 1974. Seasonal variation of sieving efficiency
in a lotic habitat. Freshwat. Biol. 4:293-300.
Beck, W.M., Jr. 1975. Chironomidae. In: F.K. Parrish (ed.). Keys to the
water quality indicative organisms of the southeastern United States.
105
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U.S. Environmental Protection Agency, Environmental Monitoring and
Support Laboratory, Cincinnati, OH 45268. pp.159-180.
Beckett, D.C. and P.A. Lewis. 1982. An efficient procedure for slide
mounting of larval chironomids. Trans. Am. Microsc. Soc. 10(l):96-99.
Brinkhurst, R.O. 1986. Guide to the freshwater aquatic microdrile
oligochaetes of North America. Canadian Special Publication Fisheries
and Aquataic Sciences 84, Department of Fisheries and Oceans, Ottawa,
Ontario, Canada. 259 pp.
Britton, L.J. and P.E. Greeson. 1988. Methods for collection aridanalysis
of aquatic biological and microbiological samples. Jn: U.S. Geological
Survey. Techniques of water-resources investigations of the United
States Geological Survey, Book 5, Chapter A4." Open File Report 88-190,
USGS, Federal Center, Box 25425, Denver, CO 80225-0425.
Cairns, J., Jr. and K.L. Dickson. 1971. A simple method for the biological
assessment of the effects of waste discharges on aquatic bottom-dwelling
organisms. J. Wat. Pollut. Control Fed. 43(5):757-772.
Flannagan, J.F. 1973. Sorting benthos using floatation media. Techn. Rpt.
No. 354, Fisheries Research Board of Canada, Freshwater Institute,
Winnipeg, Man., Canada, pp. 1-14.
Gordh, G. and J.C. Hall. 1979. A critical point drier used as a method of
mounting insects from alcohol. Entomol. News 90(l):57-59.
«", «" 'i iI",,,* ..' , MII' .:,'"' 'in .a ,!,,! ' ' ; ' i
Grassle, J.F., J.P. Grassle, L.S. Brown-Leger, R.F. Petrecca, and N.J.
Copley. 1985. Subtidal macrobenthos of Narragansett Bay. Field and
mesocosm studies of the effects of eutrophication and organic input on
benthic populations. Jn: J.S. Gray and M.E. Christiansen (eds.). Marine
biology of polar regions and effects of stress on marine organisms. John
Wiley and Sons, Inc., New York, NY. pp. 421-434.
Hilsenhoff, W.L. 1987. An improved biotic index of organic stream pollution.
Great Lakes Entomol. 20:31-39.
Hilsenhoff, W.L. 1988. Rapid field assessment of organic pollution with a
family level biotic index. J. N. Am. Benthol. Soc. 7(l):65-68.
Hiltunen, J.K. 1983. Versatile bucket for sieving benthos samples. Progr.
Fish-Cult. 45(4):229-231.
Hiltunen, J.K. "and D.J. Klemm. 1980. A guide to the Naididae (Annelida:
Clitellata: Oligochaeta) of North America. EPA-600/4-80-031. U.S.
Environmental Protection Agency, Environmental Monitoring and Support
Laboratory, Cincinnati, OH 45268. 58 pp.
Hummon, W.D. 1981. Extraction by sieving: A biased procedure in studies of
stream meiobenthos. Trans. Am. Microsc. Soc. 100(3):278-284.
106
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Klemm, D.J. 1982. Leeches (Annelida: Hirudinea) of North America. EPA
600/3-82-025. U.S. Environmental Protection Agency, Environmental
Monitoring and Support Laboratory, Cincinnati, OH 45268. pp. 6-10.
Klemm, D.J. 1985. A guide to the freshwater Annelida (Polychaeta, naidid
and tubificid Oligochaeta, and Hirudinea) of North America.
Kendall/Hunt Pub!. Co., Dubuque, IA. 198 pp.
Klemm, D.J. 1990. Hirudinea. In: Peckarsky, P.R. Fraissinet, M.A. Penton,
and D.J. Conklin, Jr. (eds.). Freshwater macroinvertebrates of
northeastern North America. Cornell University Press. Cornell, NY.
pp 398-415.
Knudsen, J.W. 1966. Biological Techniques. Harper and Row, Publishers, New
York, NY. 525 pp.
Leggett, W.C. 1989. Changes in size and weight of hydromedusae during
formalin preservation. Bull. Mar. Sci. 44(3):1129-1137.
Mason, W.T., Jr. 1973. An introduction to the identification of chironpmid
larvae. Analytical Quality Control Laboratory, U.S. Environmental
Protection Agency, Cincinnati, OH 45268.
Mason, W.T., Jr. 1976. Portable, hand-operated cycle sieve for washing
macroinvertebrate samples. Progr. Fish-Cult. 38(l):30-32.
Mason, W.T., Jr. and P.A. Lewis. 1970. Rearing devices for stream insect
larvae. Progr. Fish-Cult. 32(l):61-62.
Mason, W.T., Jr., P.A. Lewis, and P.L. Hudson. 1975. The influence of sieve
mesh size selectivity on benthic invertebrate indices of eutrophication.
Verh. Internat. Verein. Limnol. 19:1550-1561.
Mason, W.T., Jr., P.A. Lewis, and C.I. Weber. 1983. An evaluation of benthic
macroinvertebrate biomass methodology. Part 1. Laboratory analytical
methods. Environ. Monitor. Assessment 3:29-44.
Mason, W.T., Jr., P.A. Lewis and C.I. Weber. 1985. An evaluation of benthic
macroinvertebrate biomass methodology. Part 2. Field assessment and
data evaluation. Environ. Monitor. Assessment 5:399-422.
Mason, W.T., Jr. and P.P. Yevich. 1967. The use of phloxine B and rose
bengal stains to facilitate sorting benthic samples. Trans. Am.
Microsc. Soc. 86(2):221-223.,
Mills, E.L., K. Pittman and B. Munroe. 1982. Effect of preservation on the
weight of marine benthic invertebrates. Can. J. Fish. Aquat. Sci.
39:221-224.
i
Mitchell, R.D. and D.R. Cook. 1952. The preservation and mounting of water
mites. Turtox News 30:169-172.
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Pask, W.M. and R.R. Costa. 1971. Efficiency of sucrose flotation in
recovering insect larvae from stream benthic samples. Can. Entomol.
103:1649-1652.
Pennak, RlW. 1978. Fresh-water invertebrates of the United States. (Second
Edition). John Wiley and Sons,Inc., New York, NY. 803 pp.
Pennak, R.W. 1989. Fresh-water invertebrates of the United States: Protozoa
to Mollusca. (Third edition). John Wiley and Sons, Inc., New York,
NY. 628 pp.
Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K. Gross, and R.M. Hughes. 1989.
Rapid bioassessment protocols for use in streams and rivers: Benthic
macroinvertebrates and fish. EPA/440/4-89-001. U.S. Environmental
Protection Agency, Office of Water, Washington, DC 20460.
Resh, V.H. and J.D. Unzicker. 1975. Water quality and aquatic organisms: the
importance of species identification. J. Wat. Pollut. Control Fed.
47:9-19.
Schram, M.D., G.R. Ploskey, and E.H. Schmitz. 1981. Dry weight loss in
Ceriodaphnia lacustris (Crustacea: Cladocera) following formalin
preservation. Trans. Am. Micros. Soc. 100:326-329.
Stimpson, K.S., D.J. Klemm, and O.K. Hiltunen. 1982. A guide to the
freshwater Tubificidae (Annelida: Clitellata: Oligochaeta) of North
America. EPA-600/3-82-033. U.S. Environmental Protection Agency,
Environmental Monitoring and Support Laboratory, Cincinnati, OH 45268.
61 pp.
Welch, P.S. 1948. Limnological methods. McGraw-Hill Book Co., New York,
NY. 381 pp.
Williams, D.D. and N.E. Williams. 1974. A counterstaining technique for use
in sorting benthic samples. Limnol. Oceanogr. 19(1):152-154.
Williams, R. and D.B. Robins. 1982. Effects of preservation on wet weight,
dry weight, nitrogen and carbon contents of Calanus helgolandicus
(Crustacea: Copepoda). Mar. Biol. 71:271-281.
Worswick, J.M., Jr. and M.T. Barbour. 1974. An elutriation apparatus for
macroinvertebrates. Limnol. Oceanogr. 19(3):538-540.
Wrona, F.J., J.M. Culp, and R.W. Davies. 1982. Macroinvertebrate
subsampling: a simplified apparatus and approach. Can. J. Fish. Aquat.
Sci. 39:1051-1054.
Zelt, K.A. and H.F. Clifford. 1972. Assessment of two mesh sizes for
interpreting life cycles, standing crop, and percent composition of
stream insects. Freshwat. Biol. 2:259-269.
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SECTION 7
DATA EVALUATION
7.1 Introduction
7.1.1 One of the major concerns of USEPA, other federal, state and private
agencies is to describe water quality and habitat quality in terms which are
easily understood by the non-biologist. The purpose of this section is not
to recommend one particular data evaluation method, but to point out a number
of more common methods. Some of these methods may not be applicable to every
stream or water body in the United States.
7.1.2 Water quality and habitat quality are reflected in the species
composition and diversity, population density and biomass, and physiological
condition of indigenous communities of aquatic organisms. A number of data
interpretation methods have been developed based on these community
characteristics to indicate the water quality and the degree of habitat
degradation, and also to simplify communication problems regarding management
decisions.
7.2 Analyses of Qualitative Data
7.2.1 As previously defined, qualitative data result from samples collected
in such a manner that no estimates of numerical abundance or biomass can be
calculated. The principle output is a list of taxa collected in the various
habitats of the environment studied. The numerous schemes advanced for the
analysis of qualitative data may be grouped under two categories; the
indicator organism scheme and reference station methods.
7.2.2 Indicator Organism Scheme
7.2.2.1 For this technique, individual taxa are classified on the basis of
their tolerance or intolerance to various levels of domestic wastes (Beck,
1954; Lewis, 1974; Chutter, 1972; Hilsenhoff, 1977; Howmiller and Scott,
1977; Milbrink, 1983; Reynoldson et al_. 1989). Taxa are classified as
tolerant or intolerant according to their presence or absence in different
environments as determined by field studies. Beck (1955), reduced data,
based on the presence or absence of indicator organisms, to a simple
numerical form for ease in presentation. Clean water taxa are given twice
the weight as tolerant organisms in the formula:
2 (n Class I) + (n Class II) - Biotic Index
where "n" is the number of taxa in that class. Values less than 10 are
considered to indicate a polluted stream.
7.2.3 Reference Station Methods
7.2.3.1 Reference station methods (Ohio EPA, 1989) compare the
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characteristics of the fauna in clean water habitats with those of fauna in
habitats subject to stress. Patrick (1950) compared stations on the basis
of richness of species, and Wurtz (1955) ,used indicator organisms in
comparing stations.
7.2.4 If adequate background data are available to an experienced
investigator, both of these techniques can prove quite useful; particularly
for demonstrating the effects of gross to moderate organic contamination on
the macroinvertebrate community. To detect more subtle changes in the
raacroinvertebrate community, quantitative data on numbers or biomass of
organisms are needed. Data on the presence of tolerant and intolerant taxa
and richness of species may be effectively summarized for evaluation and
presentation by means of line graphs, bar graphs, pie diagrams, histograms,
or pictorial diagrams (Ingram and Bartsch, 1960).
7.2.5 Classification of representative macroinvertebrates according to their
tolerance of organic wastes is presented in Appendix A. Hilsenhoffs (1977)
original tolerance classification with a numerical range of 0 to 5 is
followed in Appendix A. Later, Hilsenhoff (1987) modified his biotic index
for Wisconsin taxa to include more intermediale values with a numerical
ranged of 0-10. However, similar results can be obtained using index values
of either 0-5 or 0-10, and adequate information is not available for many
species that would allow use of the more definitive 0-10 tolerance range
(Hilsenhoff, 1990, personal communication). In most cases, the taxonomic
nomenclature used is that of the original authors listed at the end of
Appendix A. The pollutional classifications were arbitrarily placed in three
categoriestolerant, facultative, and intolerantdefined as follows:
Tolerant: Organisms frequently associated with gross organic
contamination, that are generally capable of thriving under
anaerobic conditions. Tolerance values 4 and 5.
Facultative: Organisms having a wide range of tolerance that
frequently are associated with moderate levels of organic
contamination. Tolerance values 2 and 3,
Intolerant: Organisms that are usually notfound associated with
organic contaminants and are generally intolerant of even moderate
reductions in dissolved oxygen. Tolerance values 0 and 1.
When evaluating qualitative data in terms of material such as that contained
in Appendix A, the investigator should keep in mind the pitfalls mentioned
earlier, as well as the following:
7.'2.5.1 Since tolerant species may be found in both clean and degraded
habitats, a simple record of their presence or absence is not of
significance. However, the presence of intolerant organisms provides
evidence of only one conditionclean water. But the fact that sensitive
(intolerant) species may be totally absent, because of the discharge of toxic
substances or thermal pollution, would indicate that absence of intolerant
species may not be a reflection of the presence of organic wastes. The
presence of tolerant organisms is a significant indicator of organic
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pollution only when they are dominant in the sample.
7.2.5.2 The presence or absence of particular taxa may depend more on
characteristics of the environment, such as velocity and substrate, than on
the level of degradation by organic wastes. This affects both the original
placement of the taxa in the classificatory scheme and its presence in study
samples.
7.2.5.3 Because indicator species evaluations are based on the presence or
absence of organisms, a single specimen has as much weight as a large
population. Therefore, studies may be biased by the drift of organisms into
the study area. The technique is totally subjective and dependent upon the
skill and experience of the individual who makes the field collections.
Therefore, results of one investigator are difficult to compare with those
of another, particularly where data are summarized in an index such as that
proposed by Beck (1955).
7.2.6 Biotic Index
7.2.6.1 Many of the problems discussed above can be overcome by use of the
biotic index proposed by Chutter (1972) and modified by Hilsenhoff (1977) for
use with the index values given in Appendix A. Any organisms not listed in
Appendix A should be given an index of three (3) unless available information
would suggest a different value. This same formula is used with the family
level biotic index of Hilsenhoff (1988a) and the Rapid Bioassessment metric
2 of Protocol III (Plafkin et al_., 1989) where pollution tolerance values of
0-10 are used. Appendix B gives the family level index values (Hilsenhoff,
1988a) for use with the family level biotic index. Results are comparable
between stations in the same and nearby streams if similar habitats were
sampled using similar methods and sampling effort (Hilsenhoff, 1988a,b).
The formula to use is:
ni ar
HBI=S
N
Where "n,-" is the number of individuals in the "ith" taxa, "a,-" is the index
value of that taxa, and "N" is the total number of individuals in the sample.
Biotic index values below 1.75 indicate excellent water quality, 1.76-2.50
indicate good water quality, 2.51-3.75 indicate fair water quality, 3.76-4.00
indicate poor water quality, and over 4.00 would indicate serious water
quality problems.
7.2.6;2 The following are water quality values for Hilsenhoff's (1988a)
family level biotic index: 0.00-3.75 (excellent), 3.76-4.25 (very good),
4.25-5.00 (good), 5.01-5.75 (fair), 5.76-6.50 (fairly poor), 6.51-7.25
(poor), and 7.26-10.00 (very poor).
7.3 Analyses of Semi-quantitative and Quantitative Data
7.3.1 The high variability usually associated with benthic macroinvertebate
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populations makes them difficult to study quantitatively because of the large
number of samples needed to obtain normal levels of precision. For most
benthic studies, it is generally impractical, due to large number of samples
needed, to detect population changes of less than 100% of the mean. Many
benthic populations exhibit such high variability (see Section 4.5.) that any
reasonable number of replicate samples would be too small to detect a
population density difference of more than 200% of the mean between two sites
(Schwenneker and Hellenthal, 1984). It is important to keep this limitation
in mind as one considers the methods to use in evaluating the data.
7.3.2 Data from quantitative samples may be used to obtain total standing
crop of individuals, or biomass, or both and numbers or biomass, or both, of
individual taxa per unit area or unit volume or sample unit. Data from
quantitative samples may also be evaluated in the same manner as discussed
for qualitative samples but results will be qualitative. In order to reduce
the amount of time spent in field sampling, there has been a recent trend to
collect databased on level of effort or other not strictly quantitative
methods and treat the data as semi-quantitative. These data are then
analyzed using the quantitative methods described in this section.
7.3.3 For purposes of comparison and to provide data useful for determining
production, a uniform convention must be established for the units of data
reported. For this purpose, USEPA biologists should adhere to the following
units:
Data from devices sampling a unit area of bottom are reported in
grams dry weight or ash-free dry weight per square meter (gm/m2),
or numbers of individuals per square meter, or both.
Data from multiplate samplers are reported in terms of the total
surface area of the plates, as grams dry weight or ash-free dry
weight or numbers of individuals per square meter, or both.
. , i1, '»::; K ^ ,il',:: , , " '!",''
Data from rock-filled basket samplers are reported as grams dry
weight, ash-free dry weight, or numbers of individuals per sampler,
or both.
7.3.4 Three informative parameters of benthic community structure which may
be obtained from quantitative grab or artificial substrate sample data are
standing crop (biomass or numbers), species richness, and species
composition. Standing crop and species richness in a community are highly
sensitive to natural environmental conditions and to anthropogenic
perturbations resulting from the introduction of contaminants. These
parameters, particularly standing crop, may vary considerably in unpolluted
habitats, where they may range from the typically high standing crop of
littoral zones of glacial lakes to the sparse fauna of torrential soft-water
streams. Thus, it is important that comparisons be made only between truly
comparable habitats. Typical responses of standing crop or species richness
to various types of stress are shown in Table 7 below:
7.3.5 Organic enrichment and sludge deposits are frequently associated. The
responses shown are by no means simple or fixed and may vary depending on a
112
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number of factors Including a combination of stresses acting together or in
opposition, indirect effects (such as the destruction of highly productive
vegetative substrate by temperature alterations, sludge deposits, turbidity,
or chemical weed control) and the physical characteristics of the stressed
environment; particularly in relation to substrate and current velocity.
Table 7. TYPICAL RESPONSES TO VARIOUS TYPES OF STRESS BY
PARAMETERS OF BENTHIC COMMUNITY STRUCTURE
Standing crop
Stress (Numbers or Biomass) Number of Taxa
Toxic substance Reduces Reduces
Severe temperature changes Variable Reduces
Silt Reduces Reduces
Low pH Reduces Reduces
Inorganic nutrients Increases Variable
Organic enrichment (Low DO) Increases Reduces
Sludge deposits (Non toxic) Increases Reduces
7.3.6 Data on standing crop and species richness may be presented in simple
tabular form or pictorially with bar and line graphs, pie diagrams, and
histograms. Whatever the method of presentation, the number of replicates
and the sampling variability must be shown in the tables or .graphs. Sampling
variability may be shown as a range of values or as a calculated standard
deviation, as discussed in Section 7.6.
7.3.7 Data on standing crop and species richness are amenable to simple but
powerful statistical techniques of evaluation. Under grossly stressed
situations, such analyses may be unnecessary; however, in some cases, the
effects of environmental perturbations may be so subtle in comparison with
sampling variation that statistical comparisons are a helpful and necessary
tool for the evaluation process. For this purpose, biologists engaged in
studies of macroinvertebrates should familiarize themselves with the simple
statistical tools discussed in Section 7.6.
7.3.8 The usefulness of species composition as a parameter of environmental
quality is based on the generally observed phenomenon that relatively
undisturbed environments support communities having large numbers of species
with no individual species present in overwhelming abundance. If the species
found in a random sample from such a community are ranked on the basis of
their numerical abundance, there will be relatively few species with large
numbers of individuals and large numbers of species represented by only a few
individuals. Many forms of stress alter species composition by making the
environment unsuitable for some species or by giving other species a
competitive advantage.
7.3.9 It is important for the investigator to keep in mind that there are
naturally occurring severely stressed environments supporting communities
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dominated by one or more species adapted to rigorous conditions. Examples
include the profundal fauna of deep lakes and the black fly dominated
communities of the high gradient, bedrock section of a torrential stream.
Furthermore, because colonization is by chance, both species richness and
species composition may be highly variable in a successional community; for
this reason, data summarized from artificial substrate samples must be
evaluated with caution. These confounding factors can be reduced by
comparing data from similar environments and by exposing artificial substrate
samplers long enough for a relatively stable communityto develop.
7.3.10 Data on species composition may be summarized and evaluated using
percent species composition tables, frequency distribution tables and/or
graphs; however, for any appreciable number of samples, such methods of
presentation are so voluminous that they are virtually impossible to compare
and interpret. Fortunately, single numerical values which provide a measure
of species composition can be extracted from indices of diversity as proposed
by Margalef (1957) and subsequently utilized by numerous workers (Mclntosh.,
1967; Cairns and Dickson, 1971; Wilhm and Dorris, 1968). Mean diversity (d)
may be calculated using the machine formula presented by Lloyd, Zar, and Karr
(1968) and better known as the Shannon-Weaver mean diversity (Shannon and
Weaver, 1963).
d=C (N Iog10 N - sn,- Iog10 nf)
N
where C=3.321928 (converts base 10 log to base 2); N = total number of
Individuals; and nf - total number of individuals in the i species. When
their table (see Table 23) is used, the calculations are simple and
straightforward, as shown in Table 8.
Table 8 EXAMPLE OF CALCULATION OF MEAN DIVERSITY
Taxa
Number
1
2
' 3
'- 4
5
6
7
8
9 "
10
Totals 10
Number of Individual
in each Taxon (n.-)
41
5
18
3
1
22
1
2
12
4
109
s n- log n-
(From Table 23)
66.1241
3.4949
22:5949
.4314
.000
29.5333
. 0000
.6021
j2 9502
2.4082
139.1391
N log N -(109) = 222.0795 (From Table 23)
sn{ Iog10 n,- - 139.1391 (From Column 3 above)
d - 3.321928 (222.0795-139.1391)
; 109
114
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d = 0.030476 X 82.9404
d = 2.5
7.3.10.1 Mean diversity as calculated above is affected both by richness of
species and by the distribution of individuals among the species (species
composition) and may range from zero to 3.321928 log N. Since the calculated
value of mean diversity is a result of the interaction of two parameters
which may vary independently, it is often insensitive to subtle changes in
community structure. Therefore, unless the environment has been grossly
modified, mean diversity (d) often has limited value in detecting alterations
in community structure and serves mainly as an intermediate step in the
calculation of a single numerical value for species composition.
7.3.11 To evaluate the component of diversity due to the distribution of
individuals among the species (species composition), the calculated d must
be compared with a hypothetical maximum d based on an arbitrarily selected
distribution. The meas lire of redundancy proposed by Margalef (1957) is based
on the ratio between d and a hypothetical maximum computed as though all
species were equally abundant. In nature, equality of species is quite
unlikely, so Lloyd and Ghelardi (1964), proposed the term "equitability11 and
compared d with a maximum based on the distribution obtained from
MacArthur's (1957) broken stick model. The MacArthur model results in a
distribution quite frequently observed in nature; one with a few relatively
abundant species and increasing numbers of species represented by only a few
individuals. Sample data are not expected to conform to the MacArthur model,
since it is only being used as a yardstick against which the distribution of
abundances is being compared. Lloyd and Ghelardi (1964) devised a table for
determining equitability by comparing the number of species (s) in the sample
with the number of species expected (s') from a community that conforms to
the MacArthur model. In the table (reproduced as Table 24) the proposed
measure of equitability is:
s'
where s = the number of taxa in the sample and s' = the tabulated value.
7.3.11.1 For the example given above:
s' 8
e - - = - = 0.8
s 10
where "s"' is found from Table 24 using "d of 2.5. Equitability "e", as
calculated, may range from 0 to 1 except in the unusual situation where the
distribution in the sample is more equitable than the distribution resulting
from the MacArthur model. Such an eventuality will result in values of "e"
greater than 1, and this occasionally occurs in samples containing only a few
specimens with several taxa represented. The value of "e" is not entirely
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sample size independent and should not be used for samples containing fewer
than five taxa. , , . ,. ,,,, , , , ,,
7.3.11.2 Equitability ("e") is very sensitive to slight changes in community
structure. Since the sample is a representation of the community sampled,
a usable index must be sensitive to sample differences and within station
variability must be handled by proper study design and adequate replication.
Equitability above 0.5 is indicative of waters not affected by oxygen demand
wastes. Even slight levels of degradation have been found to reduce
equitability below 0.5, generally below 0.3.
7.3.12 Quantitative data can also be produced using the biotic index
described in 7.2.6 as long as quantitative methods were used in sample
collection and analysis, and proper assumptions are made concerning the
subjective nature of the pollution tolerance values.
7.3.13 A rather simple technique for evaluating quantitative data is the
sequential comparison index (SCI) which estimates relative differences in
biological diversity (Cairns and Dickson, 1971). The method requires no
taxonoraic expertise on the part of the investigator and is based on
differences in the shape, color, and size of the organisms. It should be
stressed that the method is useful only as a technique to evaluate the
diversity of the bottom community rapidly producing numerical data which can
be interpreted statistically. However, it should not be used to replace
other more exact techniques providing information on the identity and
pollution tolerance of the organisms and requiring persons trained in
aquatic ecology.
7.3.14 Wilhm's Species Diversity Index (Wilhm and Dorris, 11968) is based
upon information theory and is an attempt to give a numerical value to the
environmental changes caused by waste dischargers. This index takes into
account not only the number of species encountered, but also the relative
abundances of the different species and is very similar to that described in
section 7.3.10. Results from this system indicate that values of "d" less
than one are indicative of heavy pollution, values from one to three indicate
moderate pollution and values above three are found in clean water areas.
7.3.15 Harkins and Austin (1973) have also developed a" method that appears
to be universal in scope and has worked well in diverse situations. This
method is based on average diversity per individual and redundancy which are
reduced to a single index value per sample utilizing a nonparametric
discrimination technique which then gives a unique distance value from a
predefined "biological desert" condition (control values). This condition
exists as the case of no organisms present or only one species containing "n"
organisms.
'- ), ..' . , . . ,, ,,v !-. ' - , :< !, ' ' .i'1 ''
7.3.15.1 Computer programs have been written to perform the needed
calculations as well as the analysis of variance which can be used with this
method. Harkin and Austin's method then is essentially an objective method
for reducing several biological indexes to a single meaningful value that
will reflect subtle changes in the structure of aquatic communities. The
resulting sets of standardized distance values can be compared subjectively
116
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or can be subjected to statistical evaluation and probability level of
differences assessed. With this method any changes of quality will be
detected and can be plotted for long-term trend analysis.
7.4 Rapid Bioassessment Techniques
7.4.1 Rapid Bioassessment Techniques (Plafkin fit il.» 1989) are generally
considered both qualitative and semi-quantitative. The protocols were
established as a rapid means of detecting aquatic life impairments and
assessing their relative severity and are not intended to replace traditional
biomonitoring methods. The three protocols each consist of three basic
components: water quality/physical characteristics, habitat assessment, and
a biosurvey. The biological assessment in each protocol involves an
integrated analysis of both functional and structural components of the
aquatic communities through use of metrics for benthic macro invertebrates and
fish.
7.4.1.1 Rapid Bioassessment Protocol I consists of an estimation of the
level of diversity of the aquatic biota; an estimation of the relative
abundance of major macrobenthic taxa, using a qualitative sampling process
to include as many habitats as possible; observations of the presence of
fish, plants and physical structures; observations on habitat alterations;
and observation on possible sources of impact.
7.4.1.2 Rapid Bioassessment Protocol II consists of an in the field
estimation of the abundance level of the major aquatic biota, a list of
families found in a 100-organisms subsample based on field identification,
the number of individuals in each family, and separation of these into
scraper and filtering collector functional feeding groups, collection of a
course particulate organic material (CPOM) sample, and observations as in
Protocol I.
7.4.1.3 Rapid bioassessment Protocol III is similar to Protocol II except
that the subsampling and identifications are done in the laboratory and the
organisms are identified to genus or species.
7.4.1.4 Rapid Bioassessment Protocols IV and V are based on fish surveys
conducted by fishery personnel usually with assistance from the aquatic
biologist involved with Protocols I to III.
7.5 Community Metrics and Pollution Indicators
7.5.1 Biological impairment of the benthic community may be assessed by use
of metrics including community, population and functional parameters.
Metrics measure different components of the community structure and have
different ranges of sensitivity to stress. It is advisable, therefore, to
use several metrics because an integrated approach provides more assurance
of a valid assessment. A few of the more useful metrics are briefly
described.
7.5.2 Species (or Taxa) Richness reflects the health of the community
through a measurement of the variety of taxa (total number of families and/or
117
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"»< v /"ip1 "jff s f;,;1,.;: j,'"' "; |:;;;']?»; ^ [ t .; f.;,, fg^m r; | ; ,i|;
genera and/or species) present. Richness generally increases with increasing
water quality, habitat diversity, and/or habitat suitability. Sampling of
highly similar habitats will reduce the variability in this metric
attributable to factors such as current speed and substrate type. Some
pristine headwater streams may be naturally unproductive, supporting only a
very limited number of taxa. In these situations, organic enrichment may
result in an increase in number of taxa.
7.5.3 The modified Hilsenhoff Biotic Index fHBH (Plafkin et aj_. 1989) was
developed to summarize overall pollution tolerance of the benthic arthropod
community with a single value. This index was developed as a means of
detecting organic pollution in communities inhabiting rock or gravel
riffles/runs. Although Hilsenhoff's (1977) biotic index using tolerance
values of 0-5 was originally developed for use in Wisconsin, it is
successfully used by several states and should prove reliable for extensive
use, perhaps requiring regional modification in some instances. Based on an
ip depth study of 53 Wisconsin streams Hilsenhoff (1988a) expanded the scale
for tolerance values to 0-10. The 0-10 scale was adopted for use with the
Rapid Bioassessment Protocol III and was modified to include non-arthropod
species.
7.5.3.1 Although it may be applicable for other types of pollutants, use of
the HBI in detecting non-organic pollution effects has not been thoroughly
evaluated. The state of Wisconsin is conducting a study to evaluate the
ability of Hilsenhoff's index to detect non-organic effects. Winget and
Mangum (1979) have developed a tolerance classification system applicable to
the assessment of nonpoint source impact.
7.5.3.2 Invertebrate Community Index (ICI)--Ohio EPA (1989) measures the
condition of the macroinvertebrate community by use of the Invertebrate
Community Index (ICI). This index is a modification of the Indexof Biotic
Integrity (1BI) used for fish (Karr, 1981) consisting of ten community
metrics. Scoring of each metric varies with drainage area and ecoregion
(Ohio EPA, 1987), and all but one metric is generated from artificial
substrate (multiplate) samplers. Metric 10 is based solely on qualitative
sample data.
7.5.4 Ratio of Scraper and Filtering Collector Functional Feeding Groups
reflect the riffle/run community food base and provides insight into the
nature of potential disturbance factors. The proportion of the two feeding
groups is important because predominance of a particular feeding type may
indicate an unbalanced community responding to an overabundance of a
particular food source. The predominant feeding strategy reflects the type
of impact detected.
7.5.4.1 A description of the functional feeding group concept can be found
in Cummins (1973). Genus-level functional feeding group designations for
most aquatic insects can be found in Merritt and Cummins (1984). Within a
functional feeding group individual taxa may be either specialists which are
restricted to the utilization of a specific food resource or be facultative
and thus be able to exploit a broader range of food resources. The trophic
generalists (see Merritt and Cummins, 1984) are expected to be better able
' '»' i'!l!,l': " '"'i. . i,, ' '
118
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to tolerate disturbance to aquatic habitats and thus become numerically
dominant because of their more flexible ability to utilize available
resources.
7.5.4,2 The relative abundance of scrapers and filtering collectors in the
riffle/run habitat provides an indication of the periphyton community
composition and availability of suspended fine particulate organic material
(FROM) associated with organic enrichment. Scrapers increase with increased
abundance of diatoms and decrease as filamentous algae and aquatic mosses
(which cannot be efficiently harvested by scrapers) increase. However,
filamentous algae and aquatic mosses provide good attachment sites for
filtering collectors, and the organic enrichment often responsible for over
abundance of filamentous algae provide FROM utilized by the filterers.
7.5.4.3 Filtering collectors are also sensitive to toxicants bound to fine
particles and may decrease in abundance when exposed to sources of such bound
toxicants. The scraper-to-filtering-collector ratio may not be a good
indication of organic enrichment if adsorbing toxicants are present. This
situation is often associated with point source discharges where certain
toxicants adsorb readily to dissolved organic matter forming FROM during
flocculation. Toxicants thus become available to filterers vtia FROM.
7.5.5 Ratio of Shredder Functional Feeding Group and Total Number of
Individuals collected in a coarse particulate organic material (CPOM) sample
is also based on the functional feeding group concept. The abundance of the
shredder functional group relative to the abundance of all other functional
groups allows evaluation of potential impairment as indicated by the CPOM-
based shredder community. Shredders are sensitive to riparian zone impacts
and are particularly good indicators of toxic effects when the toxicants
involved are readily adsorbed to the CPOM and either affect the microbial
communities colonizing the CPOM or the shredders directly (Plafkin et al_.
1989).
7.5.5.1 the degree a toxicant effects shredders versus filterers depends on
the nature of the toxicant and the organic particle adsorption efficiency.
Generally, as the size of the particle decreases, the adsorption efficiency
increases as a function of the increased surface to volume ratio (Hargrove
1972). Toxicants of a terrestrial source (pesticides and herbicides)
accumulate on CPOM prior to leaf fall thus haying a substantial effect on
shredders. The focus of this approach is on a comparison to the reference
community, which should have .an abundance and diversity of shredders
representative of the particular area under study. This allows for an
examination of shredder or collector "relative" abundance as indicators of
toxicity.
7.5.6 Ratio of Epheitieroptera-Plecoptera-Trichoptera (EPT) and Chironomidae
abundance uses relative abundance of these indicator groups as a measure of
community balance. Good biotic condition is reflected in communities having
a fairly even distribution among all four major groups and with substantial-
representation in the sensitive groups Ephemeroptera, Plecoptera, and
Trichoptera. Skewed populations having a disproportionate number of the
generally tolerant Chironomidae relative to the more sensitive insect groups
119
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may indicate environmental stress (Ferrington 1987). Certain species of some
genera such as Cricotopus are highly tolerant (Lenat, 1983; Mount ej. al.,
1984), opportunistic,and may become numerically dominant in habitats exposed
to metal discharges where EPT taxa are not abundant, thereby providing a good
indicator of toxicant stress (Winner gt al., 1980; Clements et al., 1988).
7.5,6.1 Chironomids tend to become increasingly dominant in terms of percent
taxonomic composition and relative abundance along a gradient of increasing
enrichment or heavy metals concentration (Ferrington 1987).
'-; \ | ,n IL ,' : ,, '',''. , ' " i '" ' ,, ,;|I'I||1'1 iJ',,;!"* ijfl ' «. 'I.,,,, " ,' '"' , , ' ,'" 11 , , ,' ; i " '"I! il'",'i
7.5.7 The EPT Index (the total number of distinct taxa within the orders
Ephemeroptera, Plecoptera, and Trichoptera) compared to total taxa present
generally increases with increasing water quality. This value summarizes
taxa richness within the insect orders that are generally considered to be
pollution sensitive. Headwater streams which are naturally unproductive may
experience an increase in taxa (including EPT taxa) in response to organic
enrichment.
7.5.8 An alternative to the ratio of EPT and Chironomidae abundance metric
is tf|e Indicator Assemblage Index (IAI) developed by Shackleford (1988). The
IAI integrates the relative abundances of the EPT taxonomic groups and the
relative abundances of chironomids and annelids upstream and downstream of
a pollution source to evaluate impairment. The IAI may be a valuable metric
in areas where the annelid community may fluctuate substantially in response
to pollutant stress.
7.5.9 Percent Contribution of Dominant Taxon to the total number of
organisms is an indication of community balance at the lowest possible
taxonomic level. (The lowest positive taxonomic level is assumed to be genus
or species in most instances). A community dominated by relatively few
species would indicate environmental stress. Shackleford (1988) has modified
this metric to reflect "dominants in common" (DIC) utilizing the dominant
five taxa at the stations of comparison. The DIC will provide a measure of
replacement or substitution between the reference community and the
downstream station.
7.5.10 Community Similarity Indices are used in situations where reference
communities exist. The reference community can be derived through sampling
an upstream station or prediction for a region using a reference data base.
Data sources or ecological data files may be available to establish a
reference community for comparison. Several of the many similarity indices
available are discussed below:
. ii" " ; , : '.; J .' ' (.' ' , -. , ,:, ' '' !>,'?,'' .;:'"' ;;,:. '"'}' ,: ..; <
7.5.10.1 Community Loss Index measures the loss of benthic species between
a reference station and the station of comparison. The community loss index
was developed by Courtemanch and Davies (1987) and is an index of
dissimilarity with values increasing as the degree of dissimilarity from the
reference station increases. Values range from zero (0) to "infinity."
Based on preliminary data analysis, this index provides greater
discrimination than the following two community similarity indices. The
formula for determining community loss index is:
120" ."" : " '" './'
-------
a - c
I =
b
where I = Coefficient of Community Loss, "a" is the number of taxa at the
unimpacted site, "b" is the number of taxa at the study site, and "c" is the
taxa common to "a" and "b". The result is a ratio of the number of taxa
assumed lost due to the pollution source (a-c) to the number of taxa
remaining including any new taxa.
.7.5.10.2 Jaccard Coefficient of Community measures the degree of similarity
in taxonomic composition between two stations in terms of taxa presence or
absence and discriminates between highly similar collections (Jaccard, 1912).
Coefficient values, ranging from 0 to 1.0, increase as the degree of
similarity with the reference station increases. See Boesch (1977), and
USEPA (1983) for more detail. The formula for the Jaccard Coefficient is:
Jaccard Coefficient - a
a + b + c
where
a = number of species common to both samples
b = number of species present in Sample B but not A
c = number of species present in Sample A but not B
Sample A = reference station
Sample B = station of comparison
7.5.10.3 The Index of Similarity (S) Between Two Samples has been used to
determine whether shifts in community assemblages have occurred along a
stream gradient or above and below a pollutional impact. The Index of
Similarity can also be used as a quality assurance tool when evaluating
variance in community assemblages between two control or reference sites. The
inverse of the Index of Similarity is known as the Index of Dissimilarity.
Both are reported as percentages and the formula is ( Odum, 1971):
2 C
5 _
A + B
Where A = Number of Species in Sample 1
B = Number of Species in Sample 2
C = Number of Species Common to both Species
1 - S = Index of Dissimilarity
7.5.10.4 The Pinkham and Pearson Community Similarity Index measures the
degree of similarity in taxonomic composition in terms of taxa abundances and
can be calculated with either percentages or numbers. A weighting factor can
be added that assigns more significance to dominant species. See Pinkham and
Pearson (1976) and USEPA (1983) for more detail. The formula is:
121
-------
S.I.
ab
Min (X1a, X1b)
Max (Xia, Xib)
ia. __JP/ 2
Weighting factor
where X1a, X1b
number of individuals in the ith species in sample A or B.
7.5.10.5 A Percent Similarity Method described by Gauch and Whittaker (1972)
matches the benthic community structure ofthe site under study with an
unimpacted site (control). It is a calculation of the degree to which the
distribution of individuals within specific taxa in one site is similar to
the distribution in another matched site. The value may range from zero (0)
for sites with no taxa in common, to one (1) for identical communities.
P.S.
2 S min. (Pij} Pfk)
where P.S. - Percent similarity, P1d - Percentage of taxa "i" in
community "j", and P1k - Percentage of organisms of taxa "i" in
community Hk".
"I " ' ' , ,»,' ': .' ' ' '' "' ' , . !i, ' ,'
7.5.10.6 Other Community Similarity Indices include Spearman's Rank
Correlation (Snedecor and Cochran, 1980); Mori seta's Index (Mori seta, 1959);
Biotic Condition Index (Winget and Mangum, 1979); and Bray-Curtis Index (Bray
and Curtis, 1957; Whittaker, 1952). Calculation of a chi-square "goodness
of fit" (Cochran, 1952) may also be appropriate.
7.5.11 Presence and/or Absence of Specific Indicator Organisms is usually
based upon a classification of organisms as either pollution sensitive
(intolerant), facultative (variable), or tolerant (see paragraph 7.2.5). For
example, usually stoneflies, mayflies, and caddisflies are considered
sensitive or facultative and, therefore, are usually the first to suffer in
a polluted environment. Sludgeworms and bloodworms, on the other hand, can
tolerate very heavy pollutional loads.
7.5.11.1 The method differs from the biotic index of Hilsenhoff (1977, 1987)
in that only selected indicator species are used to make decisions, whereas
his biotic index used all the organisms in the samples.
7.5.11.2 A classic example of a system using the presence/absence criteria,
is the Saprobien system (Kolkwitz and Marsson, 1908) which recognizes three
basic zones of pollution ranging from a zone of heavy pollution
(polysaprobic) characterized by a lack of dissolved oxygen, an abundance of
bacteria, and the presence of a few tolerant species, to a zone of recovery
(oligosaprobic) characterized by relatively pure water with a somewhat stable
species diversity and dissolved oxygen concentration. This system was
developed for use in Europe. ' Its usefulness is limited to organic pollutants
in slow moving streams and is not always applicable to rivers and streams of
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the United States. A modification of the method was used in studies of the
Illinois River (Richardson, 1928) and of a stream in southern Ohio (Gaufin and
Tarzwell, 1956). A further modification of this method in combination with the
biotic index was recently used by Rabeni et al. (1985) in the study of a Maine
river. The results appear to be encouraging for wide use in this country. This
approach is highly subjective and would naturally vary from one stream to
another. It is also restricted to organic-type wastes.
7.5.12 Mean Number of Individuals per Sample is a simple means of comparing
biological data. All of the individuals in all the replicate samples from one
station are counted and divided by the number of replicates to yield the number
of individuals per sample.
7.6 Statistical Methods
7.6.1 Graphical Examination of Data
Often the most elementary techniques are of the greatest use in data
interpretation. Visual examination of data can point the way for more
discriminatory analyses, or on the other hand, interpretations may become so
obvious that further analysis is superfluous. In either case, graphical
examination of data is often the most effortless way to obtain an initial
examination of data and affords the chance to organize the data. Therefore, it
is often done as a first step. Some commonly used techniques are presented
below.
7.6.1.1 Raw Data
It is of utmost importance that raw data be recorded in a careful, logical,
interpretable manner together with appropriate, but not superfluous, annotations.
Note that although some annotations may be considered superfluous to the
immediate intent of the data, they may not be so for other purposes. Any note
that might aid in determining whether the data are comparable to other similar
data, etc., should be recorded if possible.
7.6.1.2 Frequency Histograms
To construct a frequency histogram (see Freund, 1986) from the data,
examine the raw data to determine the range, then establish intervals. Choose
the intervals with care so they will be optimally integrative and differentiate.
If the intervals are too wide, too many observations will be integrated into one
interval and the picture will be hidden; if too narrow, too few will fall into
one interval and a confusing overdifferentiation or overspreading of the data
will result. It is often enlightening if the same data are plotted with the use
of several interval sizes. Construct the intervals so that no doubt exists as
to which interval an observation belongs, i.e., the end of one interval must not
123
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be the same number as the beginning of the next.
Although a frequency table contains all the information that a comparable
histogram contains, the graphical value of a histogram is usually worth the small
effort required for its construction. Histograms are more immediately
interpretable. The height of each bar is the frequency of the interval; the
width is the interval width.
7.6.1.3 Frequency Polygon
Another way to present essentially the same information as that in a
frequency histogram is the use of a frequency polygon. Plot points at the height
of the frequency and at the midpoint of the interval, and connect the points with
straight lines.
7.6.1.4 Cumulative Frequency
Cumulative frequency plots are often useful in data interpretation. The
height of a bar (frequency) is the sum of all frequencies up to and including the
one being plotted. Thus, the first bar will be the same as the frequency
histogram, the second bar equals the sum of the first and second bars of the
frequency histogram, etc., and the last bar is the sum of all frequencies.
Closely related to the cumulative frequency histogram is the cumulative
frequency distribution graph, a graph of relative frequencies. To obtain the
cumulative graph, merely change the scale of the .frequency axis on the cumulative
frequency histogram. The scale change is made by dividing all values on the
scale by the highest value on the scale.
,: i1 !'..: vfi;." '" , " !..:.. " '.!"
The value of the cumulative frequency distribution graph is to allow
relative frequency to be read, i.e., the fraction of observations less than or
equal to some chosen value. Exercise caution in extrapolating from a cumulative
frequency distribution to other situations. Always bear in mind that in spite
of a planned lack of bias, each sample, or restricted set of samples, is subject
to influences not accounted for and is therefore unique. This caution is all the
more pertinent for cumulative frequency plots because they tend to smooth out
some of the variation noticed in the frequency histogram. In addition, the phrase
"fraction of observations less than or equal to some chosen value" can easily be
read "fraction of time the observation is less than or equal to some chosen
value." It is tempting to generalize from this readingand extend these results
beyond their range of applicability.
1 , " " ",,"! "' i'i, ' 5 IN " '", ;, ; " i 'I'fli'1
7.6.1.5 Two-dimensional Graphs
Often data are taken where the 'observations are recorded as a pair (biomass
and nutrient concentration). Here a quick plot of the set of pairs will usually
be of value. The peaks and troughs, their frequency, together with intimate
knowledge of the conditions of the study, might suggest something of biological
interest, further statistical analysis, or further field or laboratory work.
-124 ' ' " ' ' '
-------
7.6.1.6 In summary, carefully prepared tables and graphs may be important and
informative steps in data analysis. The added effort is usually small, whereas
gains in interpretive insight may be large. Therefore, graphic examination of
data is a recommended procedure in the course of most investigations.
7.6.2 Sample Mean and Variance
7.6.2.1 Notation
Knowledge of certain computations and computational notations is essential
to the use of statistical techniques. Some of the more basic of these will be
briefly reviewed here.
To illustrate the computations, let us assume we have a set of data, i.e.,
a list of numeric values written down. Each of these values can be labeled by
a set of numerals beginning with 1. Thus, the first of these values can be
called Xp the second X2, etc., and the 7ast one we call Xn,. The data values
are labeled with consecutive numbers (recall from the definitions that these
numeric values are observations), and there are n values in the set of data. A
typical observation is X., where i may take any value between 1 and n, inclusive,
and the subscript indicates which X is being referenced.
The sum of the numbers in a data set, such as our sample, is indicated in
statistical computations by capital sigma, 2. Associated with 2 are an operand
(here, X,.), a subscript (here, i = 1), and a superscript (here, n).
n
The subscript i= 1 indicates that the value of the operand X is to be the number
labeled Xi in our data set and that this is to be the first observation of the
sum. The superscript n indicates that the last number of the summation is to be
the value of Xn the last X in our data set.
7.6.2.2 Calculation of the Sample Mean and Variance
Computations for the mean, variance, standard deviation, variance of the
mean, and standard deviation of the mean (standard error) are presented below.
Note that these are computations for a sample of n observations, i.e., they are
statistics.
Note: The X.'s are squared, then the summation is performed in the first term
125
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Mean
n
n
j 2
n
Variance (s2) : s2=-=^
12-1
of the numerator; in the second term, the sum of the X^s is first formed, then
the sum is squared, as indicated by the parenthesis.
, -. , : .. ' :"!; ..-.;'',» !.;.;,: , .;£ , ,, ^
Standard deviation (s) : s
j.
Variance of the mean (s%) : s%=
" "" '
Standard devi a ti on of the mean ( s-) : s% = ^/sf
7.6.3 Rounding
The questions of rounding and the number of digits to carry through the
calculations always arise in making statistical computations. Measurement data
are approximations, since they are rounded when the measurements were taken;
count data and binomial data are not subject to this type of approximation.
Observe the following rules when working with measurement or continuous
data.
* When rounding numbers to some number of decimal places, first look at the
digit to the right of the last place to be retained. If this number is
greater than 5, the last place to be retained is rounded up by 1; if it is
less than 5, do not change the last place - merely drop the extra places.
To round to 2 decimal places:
Unrounded Rounded
< in-1
"'' "L ' . i I,,,
1.239 1.24
28.5849 28.58
126
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* If the digit to the right of the last place to be retained is 5,then look
at the second digit to the right of the last place to be kept, provided
that the unrounded number is recorded with that digit as a significant
digit. If the second digit to the right is greater than 0, then round the
number up by 1 in the last place to be kept; if the second digit is 0,
then look at the third digit, etc. To round to 1 place:
Unrounded Rounded
13.251 13.3
13.25001 13.3
* If the number is recorded to only one place to the right of the last place
to be kept, a special rule (odd-even rule) is followed to ensure that
upward rounding occurs as frequently as downward rounding. The rule is:
if the digit to the right of the last place to be kept is 5, and is the
last digit of significance, round up when the last digit to be retained is
odd and drop the 5 when the last digit to be retained is even. To round
to 1 place:
Unrounded Rounded
13.25 13.2
13.3500 13.4
Caution: all rounding must be made in 1 step to avoid introducing bias. For
example the number 5.451 rounded to a whole number is clearly 5, but if the
rounding were done in two steps it would first be rounded to 5.5 then 6.
Retention of significant figures in statistical computations can be
summarized in three rules:
* Never use more significance for a raw data value than is warranted.
* During intermediate computations keep all significant figures for each
data value, and carry the computations out in full.
* Round the final result to the accuracy set by the least accurate data
value.
7.6.4 Tests of Hypotheses
7.6.4.1 Introduction
Often in biological field studies some aspect of the study is directed to
answering a hypothetical question about a population (Allan, 1984). If the
hypothesis is quantifiable, such as: "At the time of sampling, the standing crop
127
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of macroinvertebrates per basket at station 1 was the same as at station 2", then
the hypothesis can be tested statistically. The question of drawing a sample in
such a way that there is freedom from bias, so that such a test may be made, was
discussed in Section 4, Selection of Sampling Stations.
There are many different types of hypothesis tests. Two basic categories
of hypothesis tests are parametric tests, those based on the data following a
specific distribution, and nonparametric tests, thosebased on relative rankings
of the data. Three standard parametric tests of hypotheses will be presented
here: the t-test, the x2 test, and the F-test. For information concerning
nonparametric tests see Conover, 1980.
7.6.4.2 T-test
The t-test is used to compare a sample statistic (such as the mean) with
some value for the purpose of making a judgment about the population as indicated
by the sample. The comparison value may be the mean of another sample (in which
case we are using the two samples to judge whether the two populations are the
same). The form of the t-statistic is
s*
where 0 » some sample statistic; S^ = the standard deviation of the sample
statistic; and 9 « the value to which the sample statistic is compared (the value
of the null hypothesis).
The use of the t-test requires the use of t-tables. The t-table is a two-
way table usually arranged with the column headings being the probability, a, of
rejecting the null hypothesis when it is true, and the row headings being the
degrees of freedom. Entry of the table at the correct probability level requires
a discussion of two types of hypotheses testable using the t-statistic.
The null hypothesis is a hypothesis of no difference between a population
parameter and another value. Suppose the hypothesis to be tested is that the
mean, /*, of some population equals 10. Then we would write the null hypothesis
(symbolized H0) as
H0 : p, = 10
Here 10 is the value of 8 in the general form for the t-statistic. An
alternative to the null hypothesis is now required. The investigator, viewing
the experimental situation, determines the way in which this is stated. If the
investigator merely wants to answer whether the sample indicates that n = 10 or
128
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not, then the alternate hypothesis, Ha, is
Ha : n * 10
If it is known, for example, that n cannot be less than 10, the Ha is
H» ' H > 1°
and by similar reasoning the other possible Ha is
Ha : n < 10
Hence, there are two types of alternate hypotheses: one where the
alternative is simply that the null hypothesis is false Ha: fi f 10; the other,
that the null hypothesis is false and, in addition, that the population parameter
lies to one side or the other of the hypothesized value [Ha : n (> or <) 10].
In the case of H : u ? 10, the test is called a two-tailed test; in the case of
o
either of the second types of alternate hypotheses,, the t-test is called a one-
tailed test.
To use a t-table, it must be determined whether the column headings
(probability of a larger value, or percentage points, or other means of
expressing a) are set for one-tailed or two-tailed tests. Some tables are
presented with both headings, and the terms "sign ignored" and "sign considered"
are used. "Sign ignored" implies a two-tailed test, and "sign considered" implies
a one-tailed test. Where tables are given for one-tailed tests, the column for
any probability (or percentage) is the column appropriate to twice the
probability for a two-tailed test. Hence, if a column heading 0.025 and the
table is for one tailed tests, use this same column for 0.05 in a two tailed test
(double any one-tailed test heading to get the proper two-tailed test heading;
or conversely, halve the two-tailed test heading to obtain proper headings for
one-tailed tests).
Testing HQ : n = M (the population mean equals some value M) :
t = ~*~M
where X is given by the sample mean; M = the hypothesized population mean; and
s^ is given by the standard deviation (standard error) of the mean. The t-table
is entered at the chosen probability level (often 0.05) and n-1 degrees of
freedom, where n is the number of observations in the sample.
When the computed t-statistic exceeds the tabular value there is said to
129
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be a 1-ft probability that H0 is false.
Testing H0: ^ - nz (the mean of the population from which sample 1 was
taken equals the mean of the population from which sample 2 was taken):
t=
S-y ~~ &Y
where Xi and X2 are the means from sample 1 and sample 2 respectively and
8Xi " SJ^ is the standard error for the difference X1-X2 calculated as
follows:
A
(n1+n2-2)
.(fi^fi)
where Sj and sz are variances of samples one and two respectively, and nx and
n2 are the number of observations for each sample.
For all conditions to be met where the t-test is applicable, the sample
should have been selected from a population distributed as a normal distribution.
Even if the population is not distributed normally, however, as sample size
increases, the t-test approaches to applicability. If it is suspected that the
population deviates too drastically from the normal, exercise care in the use of
the t-test. Another assumption of the t-test is that the variances of the two
populations are equal. Both the normality assumption and the equal variance
assumption should be formally tested prior to using the t-test.
7.6.4.3 Chi-Square Test (x2)
The chi-square test is useful for statistically testing a hypothesis.
Like t, x values may be found in mathematical and statistical tables tabulated
in a " ' ................... ..............
two-way arrangement. Usually, the column headings are probabilities of obtaining
a larger x2 value when H0 is true, and the row headings are degrees of freedom.
If the calculated x2 exceeds the tabular value, then the null hypothesis is
rejected. The chi square test is often used with the assumption of approximate
normality in the population.
Chi-Square appears in two forms that differ not only in appearance, but
that provide formats for different applications.
One form is useful in tests regarding hypotheses about a2:
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X2= (j3"1o)g2 where H0:o2=o20
The other form:
where 0 = an observed value, and E = an expected (hypothesized) value, is
especially useful in sampling from binomial and multinomial distributions, i.e.,
where the data may be classified into two or more categories (k).
Consider first a binomial situation. Suppose the Stenonema mayflies (2
species) from three stream riffle stations are pooled and the hypothesis of an
equal ratio of the two species is tested based on the hypothetical data in Table
9. . .
Table 9. POOLED STENONEMA DATA FROM THREE RIFFLE STATIONS
Stenonema sp. 1 Stenonema sp. 2 Total
892* (919**) 946* (919**) 1838
* Observed value.
** Expected or hypothesized value.
To compute the hypothesized values (919 above) it is necessary to have formulated
a null hypothesis. In this case it was HQ:No. Sp. 1 = No. Sp. 2 = (0.5) (Total).
Expected values are always computed based upon the null hypothesis. The
computation for x2 is
X2_ (892-919)2+ (946-919)2 _1>59 ^^
n.s. = not significant at a = 0.05
There is one degree of freedom for this test. Since the computed x2 is not
131
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greater than the tabulated x2 (3.84) for a =0.05, the null hypothesis is not
rejected. This test, of course, applies equally well to data that has not been
pooled, i.e., where the values are from two unpooled categories.
The information contained in each of the collections is partially
obliterated by pooling. If the identity of the collections is maintained, two
types of tests may be made; a test of the null hypothesis for each collection
separately; and a test of interaction, i.e., whether the,.ratio depends upon the
riffle from which the sample was obtained (Table 10).
With the use of the same null hypothesis, the following results are
obtained. All tests were made at the a =0.01 level of significance. (Note:
A significance level of 0.01 is used, instead of 0.05, to allow for the fact that
multiple tests are being made within one experiment)
The individual X2's were computed, using the second form of chi square
above, in separate tests of the hypothesis for each riffle. Note that the first
two are not significant whereas the third is significant. This points to
probable ecological differences among riffles, a possibility that would not have
been discerned by pooling the data.
Table 10. STENONEMA DATA FROM THREE RIFFLE STATIONS
Riffle So. 1 So. 2 Total
1
2
3
Total
346*
302
244
892
(354)+
(288)
(277)
(919)
362
274
310
946
(354)
(288)
(277)
(919)
708
576
554
1838
0
1
7
1
.36
.30
.88
.59
n.
n.
n.
s.
s.
s.
* Observed values.
+ Expected, or hypothesized values.
The test for interaction (dependence) is made by summing the individual
fM i.-|:Jiw v ' ,(
X2's and subtracting the x2 obtained using totals, i.e.,
X2 (interactions) = 2 x2 (individuals) -x2 (total)
= 0.36 + 1.30 +7.88 - 1.59
= 7.95
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The degrees of freedom for the interaction x are the number of individual x s
rt
minus one; in this case, two. This interaction x is significant, which
indicates that the dominant species is indeed dependent upon the riffle.
Another x2 test may be illustrated by the following example. Suppose that
comparable techniques were used to collect from four streams. With the use of
three species common to all streams, it is desired to test the hypothesis that
the three species occur in the same ratio regardless of stream, i.e., that their
ratio is independent of stream (Table 11).
TABLE 11. OCCURRENCE OF THREE SPECIES OF MIDGES
Number
Stream
Species 1
1 24* (21.7)+
2 15 (18.5)
3 28 (27.4)
4 20 (19.4)
Total 87
Expected
ratio 87/264
* Observed values.
+ Expected or hypothesized.
of organisms
Species 2
12 (12.5)
14 (10.6)
15 (15.7)
9 (11.2)
50
50/264
Species 3
30 (31.7)
27 (26.9)
40 (39.9)
30 (28.4)
127
127/264
Frequency
66
56
83
59
264
To discuss the table above, 0.. = the observation for the ith stream and the
,-th
j species. Hence, 023 is the observation for stream two and species three. A
similar indexing scheme applies to the expected values, E...- For the totals, a
subscript replaced by a dot Ei symbolizes that summation has occurred for the
observations indicated by that subscript. Hence, 0 2 is the total for species
two (50); 0, is the total for stream three (83); and 0.. is the grand total
(264).
Computations of expected values .make use of the null hypothesis that the
ratios are the same regardless of stream. The best estimate of this ratio for
any species is 0 ./O , the ratio of the sum for species j to the total of all
* J *
species. This ratio multiplied by the total for stream i gives the expected
133
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number of organisms of species j in stream i:
For example,
12.5
X2 is computed as
For this type of hypothesis, there are (rows - 1) (columns - 1) degrees of
freedom, in this case
(4-1) (3-1) = 6
In the example, since the computed x is not greater than the tabulated
X (12.59) for a=0.05 the null hypothesis cannot be rejected. Thus, there is no
evidence that the ratios among the organisms are different for different streams.
Tests of two types of hypotheses by x2 have been illustrated. The first
type of hypothesis was one where there was a theoretical ratio, i.e., the ratio
of sp.l to sp.2 is 1:1. The second type of hypothesis was one where equal ratios
were hypothesized, but the values of the ratios themselves were computed from the
data. To draw the proper inference, it is important to make a distinction
between these two types of hypotheses.
7.6.4.4 Analysis of Variance
Another form of hypothesis testing is the analysis of variance (ANOVA).
The ANOVA is a powerful and general technique applicable to data from virtually
any experimental or field study. There are restrictions, however, in the use of
the technique. Experimental errors are assumed to be normally (or approximately
normally) distributed about a mean of zero and have a common variance; they are
also assumed to be independent (i.e., there should be no correlations among
responses that are unaccounted for by the identifiable factors of the study or
by the model). The effects tested must be assumed to be linearly additive. In
practice these assumptions are rarely completely fulfilled, but the analysis of
variance can be used unless significant departures from normality, or
134
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correlations among adjacent observations, or other types of measurement bias are
suspected. It would be prudent, however, to check with a statistician regarding
any uncertainties about the applicability of the test before issuing final
reports or publications. Two simple but potentially useful examples of the
analysis of variance are presented to illustrate the use of this technique.
7.6.4.4.1 Randomized Design
The analysis of variance for completely randomized designs provides a
technique often useful in field studies. This test is commonly used for data
derived from highly-controlled laboratory or field experiments where treatments
are applied randomly to all experimental units, and the interest lies in whether
or not the treatments significantly affected the response of the experimental
units. This case may be of use in water quality studies, but in these studies
the treatments are the conditions found, or are classifications based upon
ecological criteria. Here the desire is to detect any differences in some type
of measurement that might exist in conjunction with the field situation or the
classifications or criteria.
For example, suppose it is desired to test whether the biomass of organisms
in drift nets in a stream varies due to sampling time. Data from such a study
are presented in Table 12.
In testing with the analysis of variance, as with other methods, a null
hypothesis should be formulated. In this case the null hypothesis could be:
H : There are no differences in the biomass of organisms that may be
o
attributed to time of sampling.
TABLE 12. MACROINVERTEBRATE BIOMASS COLLECTED AT DIFFERENT TIMES OF DAY FROM
; THE LITTLE MIAMI RIVER AT MILFORD, OHIO
^Sampling Time Replicate Biomass
(Time) number (mg dry wt.)
9:OOAM - IrOOPM 1 1678
2 1211
3 1644
4 1137
1:OOAM - 4:OOPM 1 1604
2 1639
3 2077
4 2581
4:OOPM - 7:OOPM 1 4276
2 2400
3 3183
4 3451
135
-------
In utilizing the analysis of variance, the test for whether there are
differences across time is made by comparing two types of variances, most often
called "mean squares" in this context. Two mean squares are computed: one based
upon the means for times; and one that is free of the effect of the means. In
our example, a mean square for times is computed with the use of the averages (or
totals) from the sampling time. The magnitude of this mean square is affected
both by differences among the means and by differences among nets of the same
time. The mean square within time is computed that has no contribution due to
time differences. If the null hypothesis is true, then differences among
sampling time do not exist and, therefore, they make no contribution to the mean
square for times. Thus, both mean squares (between times and within times) are
estimates of the same variance, and with repeated sampling, they would be
expected to average to the same value. If the null hypothesis (H0) is true, the
ratio of these values is expected to equal one. If H is not true, i.e., if
o
there are real differences due to the effect of times, then the mean square
between times is affected by these differences and is expected to be the larger.
The ratio in the second case is expected to be greater than one. The ratio of
these two variances forms an F-test.
The analysis of variance is presented in Table ISA.
TABLE 13A. Generalized ANOVA Table
Source df SS
Total N-i * 2-sZ-fXli-c
i j ij
Between Times t-1 [ £, xf.) /rj - C
Within Times A- (r.,-1) Total SS - Stream SS
2 2
*The symbols are defined as N=total number of observations (nets); t=number of
sampling times; renumber of nets for sample time i; Xi;j=an observation (biomass
of net j at sampling time i); X^sum of the observations for sampling time i; and
136
-------
C=correction for mean =
N
TABLE 13B. Completed ANOVA Table Using Macroinvertebrate Biomass Data
Source df SS MS F
Total 11 10,381,723
Between Times 2 7,717,020 3,858,510 13.03**
Within Times 9 2,664,703 296,078
** Significant at the 0.05 probability level.
The computations are:
70+7
J. £
= (5670+7901+13310)3 =
+. . . + (3451)2=70,597,403
Total SS =70,597,403 - 60,215,680 = 10,381,723
*£, . (5670)2_(7901)2+(13310)2 = 67/932/700
1 r, 4 4 4
Between Times SS = 67,932,700-60,215,680 = 7,717,020
Within Times SS = Total SS - Between Times SS
= 10,381,723-7,717,020=2,664,703
The mean squares (MS column) are computed by dividing the sums of squares
(SS column) by its corresponding degrees of freedom (df column). The F-test is
performed by computing the ratio, (Between Times MS)/(Within Times MS), in this
case:
137
-------
3,858,510.,, ft,
296,078
When the calculated F value (13.03) is compared with the F values in the
table (tabular F values) where df = 2 for the numerator and df = 9 for the
denominator, we find that the calculated F exceeds the value of the tabular F for
probability greater than 0.95. Thus the conclusion is that there are significant
differences in biomass due to time of sampling.
Note that this analysis presumes good biological procedure and obviously
cannot discriminate differences in sampling time from differences arising, for
example, from the net having been placed in riffles with different current
velocity. In general, the form of any analysis of variance derives from a model
describing an observation .in the experiment. In the example, the model, although
not stated explicitly, assumed only one factor affecting a biomass measurement -
- sampling time. If the model had included other factors, a more complicated
analysis of variance would have resulted.
7.6.4.4.2 Factorial Design
Another application of a simple analysis of variance may be made where the
factors are arranged factorially. Suppose a field study was conducted where the
effect of a suspected toxic effluent upon the macroinvertebrate fauna of a river
above and below a sewage treatment plant (STP) was in question (Tables 14A and
14B). Five samples were taken about one-quarter mile upstream and five one-
quarter mile downstream in the spring, and the sampling scheme was repeated again
in the summer. Standard statistical terminology refers to each of the
combinations PJit Pz'1fi) PJ^, and P2T2 as treatments or treatment combinations.
;" "': .. '. . '. '' . ''.'.'" .,.**,: .'' ' , :, .». ;.''. i ,.
In planning for this field study, a null and alternate hypothesis should
have been formed. In fact, whether stated explicitly or not, the null hypothesis
was: " ' ""' ' "" '"' '" '"'
H0: The toxic effluent has no effect upon the macroinvertebrate biomass
collected.
This hypothesis is not stated in statistical terms and, therefore, only
implicitly tells us what test to make. Let us look further at the analysis
before attempting to state a null hypothesis in statistical terms.
In this study two factors are identifiable: times and positions. A study
could have been done on each of the two factors separately, i.e., an attempt
could have been made to distinguish whether there was a difference associated
with times, assuming all other factors insignificant, and likewise with the
positions. The example, used here, however, includes both factors
simultaneously. Data are given for times and for positions but with the
complication that we cannot assume that one is insignificant when studying the
other. For the purpose of this study, whether there is a significant difference
with times or on the other hand with positions, are questions that are of little
138
-------
interest. Of interest to this study is whether the above-below the STP
difference varies with times. This type of contrast is termed a positions-times
interaction. Thus, our null hypothesis is, in statistical terminology:
H : There is no significant interaction effect
0
An analysis of variance may be used to test this hypothesis. In order to
meet the normality and homogeneity of variance assumptions of the analysis, the
raw data were Iog10 transformed (Table 14B). All calculations are on the
transformed data.
TABLE 14A. MACROINVERTEBRATE BIOMASS (GRAMS WET WT.)
Time Collected Collected above STP Collected below STP
Spring 437
343
337
635
373
Summer 888
1778
4332
1078
859
193
86
- 119
505
171
28
18
117
26
78
TABLE 14B. LOG10 TRANSFORMED DATA
Time Collected Collected above STP Collected below STP
Spring 2.64 2.28
2.54 1.93
2.53 2.08
2.80 2.70
2.57 2.23
Summer 2.95 1.45
3.25 1.26
3.64 2.07
3.03 1.41
2.93 1.89
139
-------
TABLE 15. TREATMENT TOTALS FOR THE DATA OF TABLE 14B
Total Positions Times totals
Above Bel ow
Spring 13.08 11.22 24.3
Summer 15.8 8.08 ?3.88
Positions Grand
totals 28.88 19.3 48.18
Symbolically, an observation must have three indices specified to be
completely identified: position, time, and sample number. Thus there are three
subscripts: X1jk is an observation at position i, time j, and from sample k. A
value of 1 for i is above the STP; 2, below the STP; 1 for j is spring; 2,
summer. A particular example is X123, the third sample above the STP for the
summer, or 3.64. A total (Table 15) is specified by using the dot notation. For
the value of X^., then the individually sampled values for position i, time j
are totaled. It is a total for a treatment combination. For example, the value
of Xn., is 13.08, and the value of X^., where sampling and times are both
totaled to give the total for above the STP ,is28.88. Treatment totals are
presented in Table 15.
For a slight advantage in generality, let the following additional symbols
apply: t - number of times of sampling (in this case t = 2); p = number of
positions sample (in this case p = 2); s = number of samples per treatment
combination; and n « the total number of observations.
The computations are:
Correction for the mean (CT):
rr_ ^l-Jt"*"** - (48.18)2
" 5~ ~^o
= (2.64)2+ (2.54)2+ + (1. 89) 2 - 116 . 06 = 7 . 54
140
-------
Note that the divisor (5) may be factored out here, if desired, but where
a different number of samples is taken for each treatment combination it should
be left as above.
Position Sum of Squares (SSP):
/ / .
_
SSP = ^ ^ - -CT= (28.88)2 + (19. 3)2 _116-06 =4.59
st 10 10
Times Sum of Squares (SST):
SST= «2 -CT- ^-*-3) + (23.88) _116p06 =0.01
sp 10 10
Interaction of Positions and Times of Sums Squares (SSPT):
SSPT = ±J- - SPS - SST - CT
(13.08)% (11.22)*, (15.80)3+ (8 . 08) 2 _4 _
Error Sums of Squares (SSE):
SSE = TSS - SSP - SST - SSPT = 7.54-4.59-0.01-1.72=1.22
The completed ANOVA, including F tests, is given in Table 16. Although not
important to this example, the main effects, positions and times, are tested for
significance. The F table is entered with df = 1 for effect tested, and df = 16
for error. The positions effect is significant and the times effect is not
significant, both tested at a=0.05. The interaction effect is significant, and
we, therefore, conclude that there is a significant effect of the effluent
changes across time on biomass.
141
-------
TABLE 16. ANALYSIS OF VARIANCE TABLE FOR FIELD STUDY DATA OF TABLE 14
Source df SS MS
Positions
Times
Positions X Times
Error
1"
1
1
16
4.59
0.01
1.72
1.22
'
4.59
0.01
1.72
0.08
57.38 **
0.125
21.51 **
"!, ""ir ',
Total 19 7.54
** Significant at the 0.05 probability level.
7.6.5 Confidence Interval for Means
When means are computed in field studies, the desire often is to report
them as intervals rather than as fixed numbers. This is entirely reasonable
because computed means are virtually always derived from samples and are subject
to the same uncertainty that is associated with the sample.
The correct computation of confidence intervals requires that the
distribution of the observations be known. But very often approximations are
close enough to correctness to be of use, and often are, or may be made to be,
conservative. For computation of confidence intervals for the mean, the normal
distribution is usually assumed to apply for several reasons: the central limit
theorem assures us that with large samples the mean is likely to be approximately
normally distributed; the required computations are well known and are easily
applied; and when the normal distribution is known not to apply, suitable
transformation of the data often is available to allow a valid application.
The confidence interval for a mean is an interval within which the true
mean is said to have some stated probability of being found. If the probability
of the mean not being in the interval is a (a could equal 0.1, 0.05, 0.01, or any
probability value), then the statement may be written:
'i» ' T " ' ' : , ,,H. ;, iir HI ..,,. - , ,., . j, , 1,1 :,,
'if ' , ;, .,"'; "I ' 'i " ' '"i, ,'ji'iiiii'"".1" ,,,/,'Lir i*l'l*'5ll1 !v,;';,is ;i S :, . , ni,, ',,'". -, i1'1'1,!,,'') f"'1 ' * ,L^ !»
P (CL1<\i< CL2) =1-a
This is read, "The probability that the lower confidence limit (CLj) is
less than the true mean (/*) and that the upper confidence limit (CL2) is greater
than the true mean, equals 1-a." However, we never know whether or not the true
142
-------
mean is actually included in the interval. So the confidence interval statement
is really a statement about our procedure rather than about ft. It says that if
we follow the procedure for repeated experiments, a proportion of those
experiments equal to a will, by chance alone, fail to include the true mean
between our limits. For example, if a=0.05, we can expect 5 of 100 confidence
intervals to fail to include the true mean.
To compute the limits, the sample mean, X,; the standard error, sx; and the
degrees of freedom, n-1; must be known. A ta/2 n-1 value from tables of Student's
t is obtained corresponding to n-1 degrees of freedom and probability a. The
computation is:
CL1=X- (tu/2)-(Sz)
CL2 =X+ (ta/2)'(s^)
7.6.6 Validating Normality and Homogeneity of Variance Assumptions1
7.6.6.1 Introduction
The t-test and the analysis of variance are parametric procedures based
on the assumptions that the observations within treatments are independent and
normally distributed, and that the variance of the observations is homogeneous
across all groups of observations. These assumptions should be checked prior to
using these tests, to determine if they have been met. Tests for validating the
assumptions are provided in the following discussion. If the tests fail (if the
data do not meet the assumptions), a non-parametric procedure such as Friedman's
Test or Wilcoxon's Rank Sum Test may be more appropriate. However, the decision
on whether to use parametric or non-parametric tests may be a judgment call, and
a statistician should be consulted.in selecting the analysis.
7.6.6.2 Test for Normal Distribution of Data
A formal test for normality is the Shapiro-Wilk's Test. The test
statistic is obtained by dividing the square of an appropriate linear combination
of the sample order statistics by the usual symmetric estimate of variance. The
calculated W must be greater than zero and less than or equal to one. This test
is recommended for a sample size of 50 or less. If the sample size is greater
than 50, the Kolomogorov "D" statistic is recommended. An example of the Shapiro-
Wilk's,test is provided below.
-. The example uses macroinvertebrate biomass data. The same data are used
1Adapted and modified from USEPA, 1989
143
-------
in the discussion of the homogeneity of variance determination and the one-way
analysis of variance example. The data and the mean and standard deviation of
the observations at each time are listed in Table 17.
The first step of the test for normality is to center the observations
by subtracting the mean of all the observations within "a concentration from each
observation in that concentration. The centered observations are listed in Table
18.
Calculate the denominator, D, of the test statistic:
D =
i n
2,664,705 - ( 3=2,664,704
Where: X1 - The ith centered observations.
i "!, ' "ifi,: '
n - The total number of observations.
Order the centered observations from smallest to largest.
X(D _ x(2) f x(n)
Where X^ denotes the ith ordered observation. The ordered observations
are listed in Table 19.
From Table 21, for the number of observations, n, obtain the coefficients
alf a2, ...., ak, where k is approximately n/2. For the data in this example,
n « 12, k - 6. The a. values are listed in Table 20.
Compute the test statistic, W, as follows:
W= [ V a±
k
V
1=1
2,664,704
The differences, X{n"1+1) - X(i), are listed in Table 20.
144
-------
The decision rule for this test is to compare the critical value from
Table 22 to the computed W. If the computed value is less than the critical
value, conclude that the data are not normally distributed. For this example,
the critical value at a significance level of 0.01 and 12 observations (n) is
0.805. The calculated value, 0.973, is not less than the critical value. Thus,
the conclusion of the test is that the data are normally distributed.
TABLE 17. MACROINVERTEBRATE BIOMASS COLLECTED AT DIFFERENT TIMES OF DAY FROM
THE LITTLE MIAMI RIVER AT MILFORD, OHIO
Sampling Time
Replicate
number
Biomass
(mg dry wt.)
S2
X
9: 00AM - 1:OOPM 1
2
3
4
1:OOAM - 4:OOPM 1
2
3
4
4: 00PM - 7: 00PM 1
2
3
4
1678
1211
1644
1137
1604
1639
2077
2581
4276
2400
3183
3451
80,161 1418
209,392 1975
598,680 3328
TABLE 18. EXAMPLE OF SHAPIRO-WILK'S TEST: CENTERED OBSERVATIONS
Sampling Time Replicate ; -
1 2 3 4
9:00AM - 1:OOPM 260 -207 226 -281
1:OOPM - 4:00PM -371 -336 102 606
4:00PM - 7:00PM 948 -928 -145 123
145
-------
TABLE 19. EXAMPLE OF SHAPIRO-MILK'S TEST; ORDERED OBSERVATIONS
i
1
2
3
4
5
6
X(D
-928
-371
-336
-280
-207
-145
i
7
8..; , :' ..
9
10
11
12
X(i)
1Q2
123
"226'
260
606
948
TABLE 20. EXAMPLE
i
1
2
3
4
5
6
OF SHAPIRO-WILK'S TEST
DIFFERENCES
a, ' ;: "
.5475
.3325
.2347
.1586
.0922
.0303
: TABLE
X(n-i-i)
1876
977
596
507
330
247
OF COEFFICIENTS AND
:xfi) ", V .. "'
X(12)_x(l)
v(ll) v(2)
v(10) y(3)
v(9) v(4)
x(8) x(5)
x(7) -x{6)
146
-------
TABLE 21. COEFFICIENTS FOR THE SHAPIRO-WILKS TEST1
\"
K
i
2
3
4
5
2
\
0.7071
3
0.7071
0.0000
4
0.6872
0.1667
5
0.6646
0.2413
0.0000
6
0.6431
0.2806
0.0875
-
r
0.6233
0.3031
0.1401
0.0000
8
0.6052
0.3164
0.1743
0.0561
9
0.5888
0.3244
0.1976
0.0947
0.0000
10
0.5739
0.3291
0.2141
0.1224
0.0399
\"
1
2
3
4
5
6
7
8
9
10
n
\
0.5601
0.3315
0.2260
0.1429
0.0695
0.0000
'
12
0.5475
0.3325
0.2347
0.1586
0.0922
0.0303
13
0.5359
0.3325
0.2412
0.1707
0.1099
0.0539
0.0000
14
0.5251
0.3318
0.2460
0.1802
0.1240
0.0727
0.0240
15
0.5150
0.3306
0.2495
0.1878
0.1353
0.0880
0.0433
0.0000
16
0.5056
0.3290
0.2521
0.1939
0.1447
0.1005
0.0593
0.0196
n
0.4968
0.3273
0.2540
0.1988
0.1524
0.1109
0.0725
0.0359
0.0000
18.
0.4886
0.3253
0.2553,
0.2027
0.1587
0.1197
0.0837.
0.0496
0.0163
'
19
0.4808
0.3232
0.2561
0.2059
0.1641
0.1271
0.0932
0.0612
0.0303
0.0000
20
0.4734
0.3211
0.2565
0.2085 .
0.1686
0.1334
0.1013 .
0.0711
0.0422
0.0140
\."
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
21
\
0.4643
0.3185
0.2578
0.2119
0.1736
0.1399
0.1092
0.0804
0.0530
0.0263
0.0000
...
22
0.4590
0.3156
0.2571
0.2131
0.1764
0.1443
0.1150
0.0878
0.0618
0.0368
0.0122
23
0.4542
0.3126
0.2563
0.2139
0.1787
0.1480
0.1201
0.0941
0.0696
0.0459
0.0228
0.0000
24
0.4493
0.3098
0.2554
0.2145
0.1807
0.1512
0.1245
0.0997
0.0764
0.0539
0.0321
0.0107
_
25
0.4450
0.3069
0.2543
0.2148
0.1822
0.1539
0.1283
0.1046
0.0823
0.0610
0.0403
0.0200
0.0000
26
0.4407
0.3043
0.2533
0.2151
0.1836
0.1563
0.1316
0.1089
0.0876
0.0672
0.0476
0.0284
0.0094
27
0.4366
0.3018
0.2522
0.2152
0.1848
0.1584
0.1346
0.1128
0.0923
0.07.28
0.0540
0.0358
0.0178
0.0000
28
0.4328
0.2992
0.2510
0.2151
0.1857
0.1601
0.1372
0.1162
0.0965
0.0778
0.0598
0.0424
0,0253
0.0084
29
0.4291
0.2968
0.2499
0.2150
0.1864
0.1616
0.1395
0.1192
0.1002
0.0822
0.0650
0.0483
0.0320
0.0159
0.0000
30
0.4254
0.2944
0.2487
0.2148
0.1870
0.1630
0.1415
0.1219
0.1036
0.0862
0.0697
0.0537
0.0381
0.0227'
0.0076
1Taken from Conover, 1980.
147
-------
TABLE 21. COEFFICIENT FOR THE SHAPIRO-WILKS TEST (Continued)
V
N
i
2
3
4
S
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
\
t\
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
\31
0.4220
0.2921
0.2475
0.2145
0.1874
6;i641
0.1433
0.1243
0,1066
0.0899
0.0739
0.0585
0.0435
0.0289
0.0144
0.0000
\41
0.3940
0.2719
0.2357
0.2091
0.1876
0.1693
0.1531
0.1384
0.1249
0,1123
0.1004
0.0891
0.0782
O.Q677
0.0575
0.0476
0.0379
0.0283
0.0188
0.0094
0.0000
32
0.4188
0.2898
0.2462
0.2141
0.1878
0.1651
0.1449
0.1265
0.1093
0.0931
0.0777
0.0629
0.0485
0.0344
0.0206
0.0068
42
0.3917
O."2701
0.2345
0.2085
0.1874
0.1694
0.1535
0.1392
0.1259
0.1136
0.1020
0.0909
0.0804
0.0701
0.0602
0.0506
0.0411
0.0318
0.0227
0.0136
0.0045
33
0.4156
0.2876
0.2451
0.2137
0.1880
0.1660
0.1463
0.1284
0.1118
0.0961
0.0812
0.0669
0.0530
0.0395
0.0262
0.0131
0.0000
43
0.3894'
0.2684
0.2334
0.2078
0.1871
0.1695
0.1539
6.1398
0.1269
0.1149
0.1035
0.0927
0.0824
0.0724
0.0628
0.0534
0.0442
0.0352
0.0263
0.0175
0.0087
0.0000
34
0.4127
0.2854
0.2439
0.2132
0.1882
0.1667
0.1475
0.1301
0.1140
0.0988
0.0844
0.0706
0.0572
0.0441
0.0314
0.0187
0.0062
44
0.3872
0~2667
0.2323
0.2072
0.1868
0.1695
0.1542
0.1405
0.1278
0.1160
O.i049
0.0943
6.6842
0.0745
0.0651
0.0560
0.0471
0.0383
0.0296
0.0211
0.0126
0.0042
35
6^4096
0.2834
0.2427
0.2127
0.1883
0.1673
0,1487
6.1317
0.1160
0.1013
0.0873
0.0739
0.0610
0.0484
0.0361
0.0239
0.0119
0.0000
45
6.3850
0.2651
0.2313
0.2065
0.1865
0.1695
0,1545
0.1410
6: 1286
0.1170
6.1062
0.0959
0.0860
6,0765
6.0673
6.0584
0.0497
6.0412
0.0328
0.0245
0.0163
0.0081
0.0000
36
0.4068
0.2813
0.2415
0.2121
0.1883
0.1678
0.14%
9,1331
6.1179
0.1036
0.0900
0.0770
0.0645
0.0523
6.6404
0.0287
0.0172
0.0057
46
0.3830
0.2635"
0.2302
0.205?
0.1862
0.1695
0.1548
6.1415
0.1293
0.1180
0.1073
0.0972
P.0876
O.Q783
0.0694
0.0607
0.0522
0.0439
0.0357
0.0277
0.0197
0.0118
0.0039
.
37
0.4040
0.2794
6.2403
0.2116
0.18S3
0.1683
0.1505
0.1344
Q-1196
0.1056
0.0924
0.0798
0.0677
0.055?
0.0444
0.0331
0.0220
0.01 10
0.0000
,
47
0.3808
0.2620
0.2291
0,2052
0.1859
0.1695
0=1550
0.1420
0.1300
0.1189
0.1085
0-Q986
0.0892
0.0801
0.0713
0.0628
0.0546
0.0465
0.0385
0.0307
0.0229
0.0153
0.0076
0.0000
, "
38
' .. '' !» ill'1'!',' !
0.4015
0.2774
0.2391
0.2 110
0.1881
6.1686
0,1513
JU356
0.1211
0.1075
0.0947
0.0824
0,0706
0,0592
0.048~r
0.0372
0.0264
0.0158
0.0053
48
,0:37§,9"
0.2604
0.2281
0.2045
0.1855
0.1693
0.1551
6.1423
0.1306
97?1?,7,
0.1095
6.0998
6r09§6
6.0817
0.0731
0.0648
0.0568
0.0489
0.0411
0.0335
0.0259
6.0185
0.0111
0.0037
i
39
0.3989
0.2755
0.2380
0.2104
0.1880
6.1689
0.1520
0.1366
63225
0.1092
0.0967
0.0848
0.0733
0.0622
6.0515
0.0409
0.0305
0.0203
0.0101
0.0000
49
0,3770
0.2589
6.2271
0.2038
0.1851
0.1692
P,1553
0.1427
0.1312
0.1205
0.1105
o.ioio
M9\9]
0.0832
6.0748
0.0667
0.0588
0.0511
0.0436
6.0361
0.0288
0.02\5
0.0143
0,0071
Q-Oo6p
40
0.3964
0.2737
0.2368
0.2098
0.1878
0.1691
0.1526
Q-1376
6,1237
0.1108
0.0986
0.0870
0.0759
0.0651
6.0546
0.0444
0.0343
0.0244
0.0146
0.0049
50
0-3751
0.2574
0.2260
0.2032
0.1847
0.1691
6.1554
6.1430
0.1317
0.1212
0.1113
6.1020
6.0932
0.0846
0.0764
0.0685
0.0608
0.0532
0.0459
0.0386
0.0314
6.0244
0.0174
0.0104
0.0035
148
-------
TABLE 22. QUANTILES OF THE SHAPIRO-WILKS TEST STATISTIC1
n
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
0.01
0.753
0.687
0.686
0.713
0.730
0.749
0.764
0.781
0.792
0.805
0.814
0.825
0.835
0.844
0.851
0.858
0.863
6.868
0.873
0.878
0.881
0.884
0.888
0.891
0.894
0.896
0.898
0.900
0.902
0.904
0.906
0.908
0.910
0.912
0.914
0.916
0.917
0.919
0.920
0.922
0.923
0.924
0.926
0.927
0.928
0.929
0.929
0.930
0.02
0.756
0.707
0.715
0.743
0.760
0.778
0.791
0.806
0.817
0.828
0.837
0.846
0.855
0.863
0.869
0.874
0.879
0.884
0^888
0.892
0;895
0.898
0.901
0.904
0.906
0.908
0.910
0.912
0.914
0.915
0.917
0.919
0.920
0.922
0.924
0.925
0.927
0.928
0.929
0.930
0.932
0.933
0.934
0.935
0.936
0.937
0.937
0.938
0.05
0.767
0.748
0.762
0.788
0.803
0.818
0.829
0.842
0.850
'" 0.859
0.866
0.874
0.881.
0.887
0.892
0.897
0.901
0.905
0.908
0.911
0.914
0.916
0.918
0.920
0.923
0.924
0.926
0.927
0,929
0.930
0.931
0.933
0.934
0.935
0.936
0.938
0.939
0.940
0.941
0.942
0.943
0.944
0.945
0.945
0.946
0.947
0.947
0.947
0.10
0.789
0.792
0.806
0.826
0.838
0.851
0.859
0,869
0.876
0.883
0.889
0.895 .
0.901
0.906
0.910
0.914
0.917
0.920
0.923
0.926
0.928
0.930
0.931
0.933
0.935
0.936
0.937
0.939
0.940
0.941
0.942
0.943
0.944
0.945
0.946
0.947
0.948
0.949
0.950
0.951
0.951
0.952
0.953
0.953
0.954
0.954
0.955
0.955
0.50
0.959
0.935
0.927
0.927
0.928
0.932
0.935
0.938
0,940
0.943
.0.945
0.947
0.950
0.952
0.954
0.956
0.957
0.959
0.960
0.961
0.962
0.963
0.964
0.965
0.965
0.966
0.966
0.967
0.967
0.968
0.968
0.969
0.969
0.970
0.970
0.971
0.971
0-972
0.972
0.972
0.973
0.973
0.973
0.974
0.974
0.974
0.974
0.974
0.90
0.998
0.987
0.979
0.974
0.972
0.972
0.972
0.972
0.973
0.973
0.974
0.975
0.975
0.976
0.977
0.978
0.978
0.979
0.980
0.980
0.981
0.981
0.981
0.982
0.982
0.982
0.982
0.983
0.983
0.983
0.983
0.983
0.984
0.984
0.984
0.984
0.984
0.985
0.985
0.985
0.985
0.985
0.985
0.985
0.985
0.985
0.985
0.985
0.95
0.999
0.992
0.986
0.981
0.979
0.978
0.978
0,978
0.979
0.979
0.979
0.980
0.980
0.981
0.981
0.982
0.982
0.983
0.983
0.984
0.984
0.984
0.985
0.985
0.985
0.985
0.985
0.985
0.986
0.986
0.986
0.986
0.986
0.986
0.987
0.987
0.987
0.987
0.987
0.987
0.987
0.987
0.988
0.988
0.988
0.988
0.988
0.988
0.98
1.000
0.996
0.991
0.986
0.985
0.984
0.984
0.983
0.984
0.984
0.984
0.984
0.984
0.985
0.985
0.986
0.986
0.986
0.987
0.987
0.987
0.987
0.988
0.988
0.988
0.988
0.988
0.988
0.988
0.988
0.989
0.989
0.989
0.989
0.989
0.989
0.989
0.989
0.989
0.989
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.99
1.000
0.997
0.993
0.989
0.988
0.987
0.986
0.986
0.986
0.986
0.986
0.986
0.987
0.987
0.987
0.988
0.988
0.988
0.989
0.989
0.989
0.989
0.989
0.989
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.990
0.991
0.991.
0.991
0.991
0.991
0.991
0.991
0.991
0.991
0.991
0.991
0.991
1Taken from Conover, 1980.
149
-------
7.6.6.3 Test for Homogeneity of Variance
For the analysis of variance, the variances of the data obtained for each
group of observations are assumed to be equal. Bartlett's Test is a formal test
of this assumption. In using this test, it is assumed that the data are normally
distributed.
The data used in this example are biomass data from the one-way analysis
of variance example and the Shapiro-Wilk's Test example. These data are listed
in Table 17, together with the calculated sample variance for each group of
observations.
The test statistic for Bartlett's Test (Snedecor and Cochran, 1980) is as
follows:
C
B =
1=1
P
£
1=1
Where: V
In
Degrees of freedom for each time
Number of levels of times
The average of the individual variances.
Loge
1 +'[.
Since B is approximately distributed as chi-square with p - 1 degrees of
freedom when the variances are equal, the appropriate critical value is obtained
from a table of the chi-square distribution for p - 1 degrees of freedom and a
significance level of a. If B is less than the critical value then the variances
are assumed to be equal.
For the data in this example, V.- 4-1=3, p
1.148. The calculated value is:
3, S2 = 296,078, and
B =
3 3
[V 3) In^2-3V (Infl?)]
i=l i=l
1.148
B = 9 (12.598) -3 (36.846) = 2 .477
150
-------
Since B is approximately distributed as chi-square with 2 degrees of
freedom when the variances are equal, the appropriate critical value for the test
is 9.210 (see a x2 table) for a significance level of 0.01. Since B = 2.477 is
less than the critical value of 9.210, conclude that the variances are not
different.
7.6.6.4 Transformations of the Data
When the assumptions of normality and/or homogeneity of variance are not
met, transformations of the data may remedy the problem, so that the data can be
analyzed by parametric procedures, rather than a non-parametric technique such
as Friedman's Test or Wilcoxon's Rank Sum Test. Examples of transformations
include log, square root, arc sine square root, and reciprocals. After the data
have been transformed, Shapiro-Milk's and Bartlett's test should be performed on
the transformed observations to determine whether the assumptions of normality
and/or homogeneity of variance are met.
151
-------
Table 23 is reproduced here with permission from Lloyd, Zar, and Karr (1968) for
use in calculating mean diversity (d) (see 7.3.10, page 114). To use the table,
find the number of individuals (n) in column 1 and read the log of that number
in column 3 (n log n).
ii '''!'»i.
?; :='",
152
-------
01
TABLE
23 FUNCTIONS FOR CALCULATING
SPECIES DIVERSITY AND (FOR PER-
FECTLY RANDOM SAMPLING) ITS STAND-
ARD ERROR LOGARITHMS ARE TO BASE
10. TABLE VALUES ARE ACCURATE TO
WITHIN ±1 IN THE
EIGHTH SIGNIFICANT
FIGURE.
n
r
2
3
4
5
6
7
8
9
10
11
12
13
logn!
.0000
.3010
.7782
1.3802
2.0792
2.8573
3.7024
. 4.6055
5.5598
6.5598
7.6012
8.6803
9.7943
n logn nlog2 n
.0000 .0000
.6021 .1812
1.4314 .6829
2.4082 1.4499
3.4949 2.4428
4.6689 3.6331
5.9157 4.9993
7.2247 6.5246
8.5882 8.1952
10.0000 10.0000
11.4553 11.9295
12.9502 13.9756
14.4813 16.1313
n log n ! n log n n log2 n
14 10.9404 16.0458 18.3905
15 12.1165 17.6414 20.7479
16 13.3206 19.2659 23.1985
17 14.5511 20.9176 25.7381
18 15.8063 22.5949 28.3628
19* 17.0851 24.2963 31.0690
20 18.3861 26.0206 ' 33.8536
21 1917083 27.7666 36.7135
22 21.0508 29.5333 39.6462
23 22.4125 31.3197 42.6490
24 23.7927 33.1251 45.7196
25 25.1906 34.9485 48.8559
26 26.6056 36.7893 52.0559
n
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
logn!
28.0370
29.4841
30.9465
32.4237
33.9150
35.4202
36.9387
38.4702
40.0142
41.5705
43.1387
44.7185
46.3096
47.9116
49.5244
51.1477
52.7811
54.4246
56.0778
57.7406
59.4127
61.0939
62.7841
64.4831
66.1906
67.9066
69.6309
71.3633
73.1037
74.8519
76.6077
78.3712
80.1420
81.9202
83.7055
85.4979
87.2972
89.1034
90.9163
92.7359
94.5619
96.3945
98.2333
100.0784
101.9297
103.7870
105.6503
107.5196
109.3946
111.2754
113.1619
115.0540
116.9516
118.8547
120.7632
122.6770
124.5961
n logn
38.6468
40.5204
42.4095
44.3136
46.2322
48.1648
50.1110
52.0703
54.0424
56.0269
58.0235
60.0318
6210515
64.0824
66.1241
68.1765
70.2391
72.3119
74.3946
76.4869
78.5886
80.6996
82.8196
84.9485
87.0861
89.2322
91.3866
93.5493
95.7199
97.8985
100.0849
102.2788
104.4803
106.6891
108.9051
111.1283
113.3585
115.5955
117.8394
120.0899
122.3470
124.6106
126.8806
129.1569
131.4393
133.7279
136.0226
138.3231
140.6296
142.9418
143.2598
147.5834
149.9125
152.2472
154.5873
156.9327
159.2835
n log2 n
55.3177
58.6395
62.0196
65.4566
68.9490
72.4952
76.0942
79.7445
83.4451
87.1948
90.9925
94.8372
98.7280
102.6638
106.6439
110.6674
114.7334
118.8412
122.9900
127.1791
131.4078
135.6755
139.9814
144.3250
148.7056
153.1227
157.5757
162.0642
166.5874
171.1450
175.7365
180.3613
185.0191
189.7093
194.4316
199.1854
203.9705
208.7863
213.6326
218.5088
223.4148
228.3500
233 3143
238,3071
243.3282
248.3772
253.4540
258.5580
263.6891
268.8469
274.0312
279.2417
284.4781
289.7401
295.0275
300.3400
305.6774
n
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
. 127
128
129
130
131
132
133
134
135
136
137
138
139
140
log n!
126.5204
128.4498
130.3843
132.3238
134.2683
136.2177
138.1719
140.1310
142.0948
144.0632
146.0364
148.0141
149.9964
151.9831
153.9744
155.9700
157.9700
159.9743
161.9829
' 163.9958
166.0128
168.0340
170.0593
172.0887
174.1221
176.1595
178.2009
180.2462
182.2955
184.3485
186.4054
188.4661
190.5306
192.5988
194.6707
196.7462
198.8254
200.9082
202.9945
205.0844
207.1779
209.2748
211.3751
213.4790
215.5862
217.6967
219.8107
221.9280
224.0485
226.1724
228.2995
230.4298
232.5634
234.7001
236.8400
238.9830
241.1291
n logn
161.6395
164.0006
166.3669
168.7382
171.1145
173.4957
175.8818
178.2728
180.6685
183.0689
185.4740
187.8837
190.2980
192.7169
195.1402
197.5679
200.0000
202.4365
204.8772
207.3222
209.7715
212.2249
214.6824
217.1441
219.6098
222.0795
224.5532
227.0309
229.5124
231.9979
234.4872
236.9803
239.4771
241.9777
244.4821
246.9901
249.5017
252.0170
254.5359
257.0583
259.5843
262:1138
264.6467
267.1831
269:7229
272.2661
274.8126
277.3625
279.9158
282,4723
285.0320
287.5951
290.1613
292.7307
295.3033
297.8791
300.4579
n log2 n
311.0395
316.4259
321.8364
327.2709
332.7291
338.2108
343.7157
349.2437
354.7946
360.3680
365.9640
371.5821
377.2223
382.8844
388.5682
394.2734
400.0000
405.7477
411.5164
417.3059
423.1160
428.9466
434.7976
440.6686
. 446.5597
452.4706
458.4013
464.3514
470.3210
476.3098
482.3178
488.3447
494.3905
500.4550
506.5380
512.6395
518.7594
524.8974
531.0535
537.2275
543.4194
549.6290
555:8561
562.1007
568.3627
574.6420
580.9383
587.2517
593.5821
599.9292
606.2930
612.6735
619.0704
625.4837
631.9134
638.3592
644.8212
-------
TABLE 23. (Continued)
n loj?n!
141 243.2783
142 245.4306
143 2474860
144 249.7443
145 2515057
146 254,0700
147 256.2374
148 258,4076
149 2604808
150 262.7569
151 264.9359
152 267.1177
153 269.3024
154 271.4899
155 273.6803
156 275.8734
157 278.0693
158 280.2679
159 282.4693
160 284.6735
161 286,8803
162 289.0898
163 2913020
164 2934168
165 295.7343
166 297.9544
167 300.1771
168 302.4024
169 304.6303
170 306.8608
171 309.0938
172 3113293
173 3134674
174 315.8079
175 318.0509
176 320.2965
' 177 322.5444
178 .324.7948
179 327.0477
180 329.3030
181 331.5606
182 333.8207
183 336.0832
184 338.3480
185 340.6152
186 342.8847
187 345.1565
188 347.4307
189 349.7071
190 351.9859
191 354.2669
192 356.5502
193 358.8358
194 361.1236
195 363.4136
196 365.7059
197 368.0003
nloKn
303.0399
305.6249
308.2131
310,8042
3133994
315.9955
318.5956
321.1987
323,8048
326.4137
329.0255
331.6402
334.2578
336.8782
339.5014
342.1274
344.7562
347.3878
350.0221
352.6592
355.2990
357.9414
360.5866
363.2344
365.8849
368.5379
371.1936
373.8520
3764129
379.1763
381.8423
3844109
387,1820
389.8556
3924317
395.2102
397.8913
4004748
403.2607
405.9491
408.6398
411.3330
414.0285
416.7265
419.4268
422.1294
424.8344
427.5417
430.2513
432.9632
435.6774
438.3938
441.1126
443.8335
446.5567
449.2822
452.0098
n log1 n
651.2991
657.7930
6643027
670.8281
6773692
683.9258
690.4979
697,0853
703,6880
710.3060
716.9390
7234871
730.2501
736.9280
743.6207
750.3281
757.0501
763.7867
770.5377
777.3032
784.0830
7903770
797.6852
804.5075
811.3438
818.1941
825.0582
831.9362
838.8280
845.7334
852.6524
859.5850
8664311
873.4906
880.4634
887.4496
894.4489
901.4615
908.4871
9154257
922.5774
929.6419
936.7193
943.8096
950.9125
958.0282
965.1564
972.2973
979.4506
986.6164
993,7946
1000.9852
1008.1880
1015.4031
1022.6304
1029.8698
1037.1213
n
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
logn!
370,2970
3724959
374.8969
377.2001
3794054
381,8129
384.1226
386.4343
388.7482
391.0642
3933822
395.7024
398.0246
400.3489
402.6752
405.0036
407.3340
409,6664
412,0009
4143373
416.6758
419.0162
4213587
423.7031
426.0494
428.3977
430.7480
433.1002
435.4543
437.8103
440,1682
442.5281-
444,8898
447.2534
449.6189
451.9862
454,3555
456.7265
459.0994
461.4742
463.8508
466.2292
468.6094
470.9914
473.3752
475,7608
478.1482
480.5374
482.9283
485.3210
487:7154
490.1116
4924096
494.9093
4973107
499.7138
502,1186
niogn nk>|*n.
454.7397 10443W9
457.4718 1051.6604
460,2060 10585478
4625424 1066.2471
465.6810 10734513
468.4217 1060.8812
471.1646 1088.2159
473,9095 10934622
476.6566 11025202
479.4059 1110,2897 .
482.1572 1117.6709
484.9106 1125.0635
487.6661 1132.4675
490.4236 11393829
493.1832 1147.3098
495.9449 1154.7479
498.7085 1162,1973
501/4743 1169,6579
504.2420 1177.1296
507.0118 1184.6126
509.7835 1192.1066
512.5573 1199.6116
5153330 1207.1277
518.1107 1214.6547
520.8904 1222.1926
523.6720 1229.7415
526.4556 1237.3011
529.2411 1244.8716
532.0285 1252.4528
534.8179 1260.0447
537.6091 1267.6473
540.4023 1275,2606
543.1974 1282.8844
.545.9944 12904188
548.7932 1298.1637
5514939 1305.8192
5543965 1313.4850
557.2009 1321.1613
560,0072 1328,8479
562.8154 1336.5448
565.6253 1344.2521
568.4371 1351.9696
571.2507 1359.6973
574,0661 1367.4352
576.8833 1375.1833
579.7023 1382.9415
582.5231 1390.7098
585.3457 1398.4881
588.1700 1406.2764
590.9961 1414.0747
593.8240 14213829 '
596.6536 1429.7010
599:4850 1437.5291
602.3181 1445:3669
605.1529 1453.2146
607.9895 1461.0720
6103278 1468.9392
n
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280'
281
282
283
284
285
286
* 287
288
289
290-
291
292
293
. 294
295
296
297 .
298
299
300
301
302
303
304
305
306
307
308
309
310
311
logn!
5044252
5065334
5033433
511.7549
514.1632
5164832
518.9999
521.4182
5233381
526.2597
528.6830
531.1078
533.5344
535.9625
5383922
5403236
543.2566
545.6912
548.1273
550.5651
553.0044
555.4453
557.8878
5603318
562.7774
565.2246
567.6733
570.1235
5724753
575.0287
577,4835
579.9399
5823977
584.8571
587.3180
589.7804
592,2443
594.7097
597.1766
599,6449
602.1147
6044860
607.0588
6094330
612.0087
614.4858
616.9644
619.4444
621.9258
624.4087
626.8930
629.3787
6313659
6343544
636.8444
639.3357
641.8285
n IOK n n log1 n
613,6677 14763161
6164094 14M.7026
6193528 14924988
622.1979 15004047
625.0446 1508.4201
627.8931 15163450
630.7432 1524.2795
6334949 1532.2234
636.4484 1540.1769
639.3034 1548.1397
642.1602 1556.1119
645,0185 1564.0936
6473785 1572.0845
650.7401 1580.0847
653,6034 1588,0943
656.4682 1596.1130
659.3347 1604U410
662.2027 1612.1782
665.0724 1620.2245
667.9437 1628.2800
6703165 1636.3446
673.6909 1644.4182
6764669 1652.5009
679.4445 16604927
6823236 1668,6934
685:2042 1676J8031
688,0865 1684.9217
690,9702 1693.0492
693.8556 1701'.1856
696.7424 1709.3309
699.6308 1717,4850
7024207 1725:6479
705.4121 1733.8196
708.3050 1742.0001
711.1995 1750:.1893
714.0954 1758.3871
716.9929 17664937
719.8918 1774.8089
722.7922 1783.0327
725.6941 1791.2651
7284975 17994061
7314023 1807.7557
734.4087 1816:0138
737.3164 1824.2803
740.2257 18324554
743.1364- 184018389
746.0485 1849.1308
748.9621 1857.4312
751.8771 1865.7399
754.7936 1874.0570
757.7115 1882.3824
760,6308 1890.7162
763.5515 1899.0582
766.4736 1907.4085
7693972 1915.7670 .
772.3221 1924.1337
775.2485 1932.5087
n
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337-
338
339
340
341
342
343
344
345
346
347'
348
349
350
351
352
353
354-
355
356
357
358
359
360
361 "
362
363
364
365
366
367
368
log n!
644.3226
6463182
6493151
651,8134
6543131
656.8142
6593166
6613204
6643255
6663320
6693399
671.8491
6743596
6763715
679.3847
6813993
684.4152
686.9324
689.4509
6915707
694.4918
697.0143
6994380
702.0631
704.5894
707.1170
709.6460
712,1762
714.7076
717.2404
719v7744
722.3097
724.8463
727.3841
729.9232
732.4635
735.0051
737.5479
740.0920
742.6373
745.1838
747.7316
750.2806
752.8308 -
755.3823
757.9349
760.4888
763.0439
765.6002
768.1577
770.7164
773.2764
775.8375
778.3997
780.9632
783.5279
786.0937
n late n n log3 n
778.1762 1940J9I8
781.1054 1949.2831
784,0359 1957.6825
726,9678 1966.0900
7895011 19744056
792.8358 19825293
795,7718 19913610
798.7092 1999.8007
801.6480 2008,2484
804.5881 2016.7041
8074296 2025.1678
810.4724 2033.6394
813.4166 2042.1189
816,3621 2050.6064
819.3089 2059.1016 -
822.2571 .2067.6048
825.2066 1,2076.1157
828.1575 : 2084.6345
831.10%* 2093.1611
834.0631 2101.6954
837.0178 2110.2375
839.9739 2118.7874
842.9313 2127.3449'
845.8900 2135.9102
848.8500 2144.4831 ;
851.8113 2153.0636 "
854.7738 2161.6518
857.7377 2170.2477
860.7028 21783510-,*
863.6692* 2187,4620"
866.6369 2196.0806:
869.6059 2204:7067 *
872.5761 2213:3403' :
875:5476 2221.9814:
8784203 2230.6299:
881,4943 2239.2860'i 7
884.4696: 2247.9495 ""
887:4461 2256.6204:-' 1
890.4238 2265.2988':,
893.4028 2273.9845
896.3830 2282.6776* =
899.3645 2291.3780' ". .-
902.3472 2300:0858' '
9053311 23083009'
908.3162 23174233 ,
911.3026 2326.2531 -
914:2901 - 2334.9900^
917:2789 2343,7342'
920.2689 2352.4857;
923.2601 2361.2444
926.2525 2370.0102- :
929.2461 2378.7832
932.2409 2387.5634
935.2369 2396.3508
938.2341 24053453
941.2324 2413,9469
944;2320 2422.7556
-------
TABLE 23. (Continued)
tn
01
n
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
log n! n log n
788.6608 947.2327
791.2290 950.2346
793.7983 953.2377
796.3689 956.2420
798.9406 959.2474
801.5135 962.2540
804:0875 965.2617
806.6627 968.2706
809.2390 971.2807
811.8165 974.2919
814.3952 977.3043
816.9749 980.3178
819.5559 983.3324
822.1379 986.3482
824.7211 989.3651
827.3055 992.3832
829.8909 995.4024
832.4775 998.4227
835.0652 1001.4441
837.6540 1004.4667
840.2440 1007.4904
842.8351 1010.5152
845.4272 1013.5411
848.0205 1016.5681
850.6149 1019.5963
853.2104 1022.6255
855.8070 1025.6558
858.4047 1028.6873
861.0035 1031.7198
863.6034 1034.7534
866.2044 1037.7882
868.8064 1040.8240
871.40% 1043.8609
874.0138 1046.8989
876.6191 1049.9379
879.2255 1052.9781
881.8329 1056.0193
884.4415 1059.0616
887.0510 1062.1049
889.6617 1065.1493
892.2734 1068.1948
894.8862 1071.2414
897.5001 1074.2890
900.1150 1077.3376
902.7309 1080.3874
905.3479 1083.4381
907.9660 1086.4900
910.5850 1089.5428
913.2052 1092.5967
915.8264 1095.6517
918.4486 1098.7077
921.0718 1101.7647
923.6961 1104.8228
926.3214 1107.8819
928.9478 1110.9420
931.5751 1114.0031
934.2035 1117.0653
n log2 n
2431.5714
2440.3942
2449.2241
2458.0610
2466.9050
2475.7559
2484.6139
2493.4787
2502.3506
2511.2294
2520.1151
2529.0077
2537.9072
2546.8135
2555.7268
2564.6469
2573.5738
2582.5075
2591.4480
2600.3953
2609.3493
2618.3102
2627.2777
2636.2520
2645.2330
2654.2206
2663.2150
2672.2160
2681.2237
2690.2380
2699.2589
2708.2865
2717.3206
2726.3613
2735.4086
2744.4624
2753.5228
2762.5897
2771.6631
2780.7430
2789.8293
2798.9222
2808.0215
2817.1272
2826.2394
2835.3580
2844.4830
2853.6143
2862.7521
2871.8962
2881.0467
2890.2035
2899.3666
2908.5360
2917.7117
2926.8938
2936.0820
n
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
'448,
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
logn! nlogn
936.8329 1120.1285
939.4633 1123.1927
942.0948 1126.2579
944.7272 1129.3242
947.3607 1132.3914
949.9952 1135.4597
952.6307 1138.5290
955.2672 1141.5993
957.9047 1144.6705
960.5431 1147.7428
963.1826 1150.8161
965.8231 1153.8904
968.4646 1156.9657
971.1071 1160.0419
973.7505 1163.1192
976.3949 1166.1974
979.0404 1169.2766
981.6868 1172.3568
984.3342 1175.4380
986.9825 1178.5202
989.6318 1181.6033
992.2822 1184.6875
994.9334 1187.7725
997.5857 1190.8586
1000.2389 1193.9456
1002.8931 1197.0336
1005.5482 1200.1226
1008.2043 1203.2125
1010.8614 1206.3033
1013.5194 1209.3952
1016.1783 1212.4880
1018.8382 1215.5817
1021.4991 1218.6764
1024.1609 1221.7720
1026.8237 1224.8686
1029.4874 1227.9661
1032.1520 1231.0646
1034.8176 1234.1640
1037.4841 1237.2643
1040.1516 1240.3656
1042.8200 1243.4678
1045.4893 1246.5710
1048.1595 1249.6750
1050.8307 1252.7801
1053.5028 1255.8860
1056.1758 1258.9928
1058.8498 1262.1006
1061.5246 1265.2093
1064.2004 1268.3189
1066.8771 1271.4295
1069.5547 1274.5409
1072.2332 1277.6533
1074.9126 1280.7665
1077.5930 1283.8807
1080.2742 1286.9958
1082.9564 1290.1118
1085.6394 1293.2287
n log2 n
2945.2766
2954.4774
2963.6844
2972.8976
2982.1171
2991.3428
3000.5746
3009.8126
3019.0568
3028.3071
3037.5636
3046.8261
3056.0948
3065.36%
3074.6505
3083.9374
3093.2305
3102.5295
3111.8346
3121.1458
3130.4629
3139.7861
3149.1152
3158.4504
3167.7915
3177.1385
3186.4915
3195.8505
3205.2154
3214.5862
3223.9629
3233.3455
3242.7339
3252.1283
3261.5284
3270.9345
3280.3464
3289.7641
3299.1876
3308.6169
3318.0521
3327.4930
3336.9396
3346.3921
3355.8503
3365.3142
3374.7838
3384.2592
3393.7403
3403.2271
3412.7196
3422.2177
3431.7216
3441.2310
3450.7462
3460.2669
3469.7933
n
, 483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
5W
505
506
507
508
509
510
511
512
513
514
515
516
517
. 518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
.533
534
535
536
537
538
539
log n ! n log n
1088.3234 1296.3465
1091.0082 1299.4651
1093.6940 1302.5847
1096.3806 1305.7052
1099.0681 1308.8266
1101.7565 1311.9489
1104.4458 1315.0720
1107.1360 1318.1961
1109.8271 1321.3210
11.12.5191 1324.4468
1115.2119 1327.5735
1117.9057 1330.7011
1120.6003 1333.8296
1123.2957 1336.9589
1125.9921 1340.0891
1128.6893 1343.2202
1131.3874 1346.3522
1134.0864 1349.4850
1136.7862 1352.6187
1139.4869 1355.7533
1142.1885 1358.8887
1144.8909 1362.0250
1147.5942 1365.1621
1150.2984 1368.3002
1153.0034 1371.4390
1155.7093 1374.5788
1158.4160 1377.7193
1161.1235 1380.8608
1163.8320 1384.0031
1166.5412 1387.1462
1169.2514 1390.2902
1171.9623 1393.4350
1174.6741 1396.5807
1177.3868 1399.7272
1180.1003 1402.8746
1182.8146 1406.0228
1185.5298 1409.1718
1188.2458 1412.3217
1190.9626 1415.4724
1193.6803 1418.6240
1196.3988 1421.7764
1199.1181 1424.9296
1201.8383 1428.0836
1204.5592 1431.2385
1207.2811 1434.3942
1210.0037 1437.5507
1212.7271 1440.7080
1215.4514 1443.8662
' 1218.1765 1447.0252
1220.9024 1450.1850
1223.6292 1453.3456
1226.3567 1456.5070
1229.0851 1459.6693
1231.8142 1462.8323
1234.5442 1465.9962
1237.2750 1469.1609
1240.0066 1472.3263
n log2 n
3479.3253
3488.8630
3498.4062
3507.9550
3517.5094
3527.0693
3536.6349
3546.2059
3555.7825
3565.3646
3574.9523
3584.5454
3594.1441
3603.7482
3613.3578
3622.9730
3632.5935
3642.2195
3651.8510
3661.4879
3671.1302
3680.7779
3690.4310
3700.0896
3709.7535
3719.4228
3729.0974
3738.7775
3748.4629
3758.1536
3767.8496
3777.5510
3787.2577
3796.9697
3806.6870
3816.4095
3826.1374
3835.8705
3845.6089
3855.3526
3865.1015
3874.8556
3884.6150
3894.3795
3904.1493
3913.9243
3923.7045
3933.4899
3943.2804
3953.0761
3962.8770
3972.6830
3982.4942
3992.3105
4002.1319
4011.9584
4021.7901
n
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579.
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596-
log n !
1242.7390
1245.4722
1248.2061
1250.9409
1253.6765
1256.4129
1259.1501
1261.8881
1264.6269
1267.3665
1270.1068
1272.8480
1275.5899
1278.3327
1281.0762
1283.8205
1286.5655
1289.3114
1292.0580
1294.8054
1297.5536
1300.3026
1303.0523
1305.8028
1308.5541
1311.3062
1314.0590
1316.8126
1319.5669
1322.3220
1325.0779
1327.8345
1330.5919
1333.3501
1336.1090
1338.8687
1341.6291
1344.3903
1347.1522
1349.9149
1352.6783
1355.4425
1358.2074
1360.9731
1363.7395
1366.5066
1369.2745
1372.0432
1374.8125
1377.5827
1380.3535
1383.1251
1385.8974
1388.6705
1391.4443
1394.2188
1396.9940
n log n n log2 n
1475.4926 4031.6268
1478.6597 4041.4687
1481.8276 4051.3156
1484.9963 4061.1676
1488.1658 4071.0247
1491.3361 4080.8868
1494.5072 4090.7541
1497.6791 4100.6263
1500.8517 4110.5035
1504.0252 4120.3859
1507.1995 4130.2732
1510.3745 4140.1655
1513.5504 4150.0629
1516.7270 4159.9652
1519.9044 4169.8726
1523.0826 4179.7849
1526.2616 4189.7021
1529.4413 4199.6245
1532.6219 4209.5516
1535.8032 4219.4838
1538.9853 4229.4210
1542.1682 4239.3630
1545.3518 4249.3099
1548.5362 4259.2618
1551.7214 4269.2187
1554.9074 4279.1804
1558.0941 4289.1470
1561.2816 4299.1185
1564.4698 4309.0949
1567.6589 4319.0762
1570.8487 4329.0623
1574.0392 4339.0533
1577.2305 4349.0492
1580.4226 4359.0499
1583.6154 4369.0554
1586.8090 4379.0658
1590.0033 4389.0810
1593.1984 4399.1010
1596.3943 4409.1258
1599.5909 4419.1555
1602.7882 4429.1898
1605.986.3 4439,2291
1609.1852 4449,2731
1612.3848 4459.3218
1615.5851 4469.3754
1618.7862 4479.4337
1621.9880 4489.4967
1625.1906 4499.5645
1628.3939 4509.6370
1631.5979 4519.7143
1634.8027 4529.7963
1638.0082 4539.8830
1641.2144 4549.9744
1644.4214 4560.0706
1647.6291 4570.1714
1650.8376 4580.2769
1654.0468 4590.3871
-------
TABLE 23. (Continued)
n los n!
597 1399.7700
598 H023467
599 14053241
600 1408.1023
601 1410.8811
602 1413,6608
603 1416.4411
604 1419.2221
605 1422.0039
606 1424,7863
607 14273695
608 1430.3534
609 1433.1380
610 1435.9234
611 1438.7094
612 1441.4962
613 1444.2836
614 1447.0718
615 ' 1449.8607
616 1452.6503 -
617 1455,4405
618 145812315
619 1461.0232
620 . 1463.8156
621 1466.6087
622 1469.4025
623 1472.1970
624 1474.9922
625 1477.7880
. 626 14803846
627 1483.3819
628 1486:1798-
629 1488i9785-
630 1491.7778
631 1494,5779
i 632 '- 1497,3786'
633 1500.1800
634 1502.9821
635 1505.7849
636 1508.5883
637 1511.3924
638 1514.1973
639 1517.0028
640 1519.8089
641 1522.6158
642 1525.4233
643 1528,2316
644 1531.0404
645 1533.8500
646 1536.6602
647 1539,4711
648 1542.2827
649 1545.0950
650 1547.9079
651 1550:7215
652 15533357
1 653 1556:3506
n log n n log2 n
1657.2567 46003020
1660,4673 4610,6215
1663.6787 4620,7457
1666,8907 4630.8746
1670,1035 4641.0081
16733171 4651.1463
16763313 4661.2891
1679.7463 4671.4365
1682.9620 4681.3886
1686.1784 4691.7452
16893955 4701.9065
1692,6134 4712,0724
1695.8319 4722.2429
1699.0512 4732.4180
1702.2712 47423977.
1705.4919 4752.7819
1708.7133 4762.9707
1711.9354 4773.1641
1715.1582 4783,3620
1718.3817 47933645
1721.6059 4803.7715
1724.8309 4813.9831
1728.0565 4824.1992
1731.2828 4834.4198
17343099 4844.6450
1737.7376;' 4854.8746
1740.9660' 4865.1088
1744.1952 4875.3475
1747.4250 48853906
1750.6555 4895.8383
:1753,8867 4906.0905
1757.1187 4916.3470'
1760.3512 4926,6082
117633845 4936;8737
1766.8185, 4947.1437
:1770:0532* 4957.4182
1773.2885 4967.6971
17763246: 4977.9804 .
1779,7613; 49883682
1782.9987- 49983604
1786,2368 5008:8571
1789.4756 5019:1581
1792.7150 5029.4636
1795.9552 5039.7734
1799;i960 5050.0877
1802.4375 5060.4064
1805,6796 5070.7294
1808.9225 5081.0568
1812.1660 5091.3886
1815.4102 5101.7248
1818.6551 5112.0653
1821.9006 3122.4102
1825.1468 5132.7594
1828.3937 5143.1130
1831.6412' 5153.4709
1834.8894 5163.8331
;1838.1383 5174,1997
n
634
633
636
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686 :
687
688 :
689 !
690 '
691 '
692
693 ;
694
695
696
697
698
699
700"
701
702 :
703
704
705
706
707
708
709 '
710
loen!
1539.1662
1361.9824
1564,7993
1567.6169
1570,4351
1573.2540
1576.0735
1578.8938
1531.7146
15843361
1587.3583
1590.1811
1593 .0046
1593.8287
1598.6535
1601.4789
1604.3050
1607.1317
1609.9591
1612,7871
1615:6158
1618i4451
1621.2750
1624.1056
162,6,9368
1629.7687
163216012
1635.4344
1638,2681
1641.1026
164319376
164617733
1649.6096
1652,4466
1655.2842
1658.1224
1660.9612
1663,8007
166616408
1669.4816
1672.3229
1675.1649
1678.0075
1680.8507
1683,6946
16863391
1689J3842 '
169212299
1695.0762
1697.9232
1700,7708
170316189
1706.4678
1709O172
171211672
1715.0179
1717.8691
n log n
18413878
1844,6380
1847.8889
1851.1404
1834,3926
1857.6455
1860.8990
1864.1532
1867,4080
1870.6635
1873.9196
1877.1764
1880.4338
1883.6919
1886.9507
1890.2101
1893.4701
1896.7308
1899,9921
1903.2541
19063168
1909.7800
1913.0440
1916.3085
19193737
1922.8396
1926.1060
1929,3732
1932.6409
1935.9093
1939.1784
1942.4480
1945.7183
1948.9893
1952.2608
19553330
1958.8059
1962.0793
1965.3534
1968.6281
1971.9035
1975.1794
1978.4560
1981.7332
1985.0111
1988.2895
19913686
1994.8483
1998.1286
2001.4096
2004.6911
2007.9733
2011.2561
20143395
2017.8235
2021.1082
2024.3934
nlog'n n
5154.5705
51943159
52053254
5215,7092
5226.0973
5236,4897
5246,8865
5257.2875
5267.6927
5278.1022
52883161
5298.9341 "
53093564
5319.7830
5330.2138
5340.6489
5351.0881
53613317
5371.9794
5382.4313
5392.8875
5403.3478
5413,8124
5424.2812
5434.7542
5445.2313
5455.7125
5466.1981
547616877 " i
548711815
5497.6794 " .
5508.1816
5518:6878
5529.1982 , ;
5539.7128
'55502314 Si*
(5560.7542
5571.2811
5581.8122 , '"
5592.3474
1 5602.8866
5613,4299
5623.9774
56343289
5645.0845 1 .
5655.6442 1
5666.2079
5676.7758 "'"
5687.3477
5697.9236
57083037
; 5719.0878 h
5729.6758 I -
'57402680 -: "-
i 5750.8642
',5761.4644
s 5772.0686 :,:;
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752 ,
753
754
755.
756
757
758
759
760
761
762
763
764
765
766
767
loj?n! nloirn
n log2 n
1730.7210 2027.6793 3782.6769
17233735 2030JSS7
1726.4265 2034.2528
1729.2802 " 20373403
1732.1346 WMhBiaS
1734.9893 2044.1177
1737.8450 2047.4072
1740.7011 2030,6973
17433578 2053.9881
17464152 2057.2794
1749,2731 20603713
1752,1316 2063,8638
1754,9908 2067.1570
1757,8505 2070.4507
176017109 2073.7450
17633718 2077.0400
1766.4333 2080.3355
1769.2955 2083,6316
1772.1582 2086,9283
1775.0215 2090.2257
1777.8854 - 20933236
1780.7499 2096,8221
' 1783.6150 2100.1212
1786.4807 2103,4209
1789:3470 . 12106:,7212
17922139 - 2110,0221
1795.0814, 211313235
1797.9494 2116,6256
1800,8181 211919282
1803.6873 2123.2314
1806.5571 2126v5353
1809.4275 : 2129;8397
1812,2985 2133tI447
1815.1701 , 2136U503
1818:0422 2139.7564
1820^9150 2143.0631 ,
1823,7883 2146.3705
182656622 2149)6784
18293367 '. 2152J9869!
1832,4117 21562959
18351.2874 215916056
1838vl636 2162,9158
184t,0404 . 2166,2266
1843S9178 ;2169.5380
1846:17957 1 2172,8499 ,
184916742 2176.1625
18523533 . 2179.4756_
1855.4330 ; 2182.7892
1858:3132 2186,1035
1861.1941 2189.4183
1864.0754 2192.7337
1866.9574 , '2196.0497
1869,8399 2199.3662
1872?7230 2202.6833
1873:6067 2206.0010
187814909 2209.3192
188l!i3757 ; ,2212:6380
3793,1832
5803 J033
58143257
5823.1300 i
3835.7783
3846.4103
3857.0468
5867.6870 ,
58783312
5888,9794 :
5899.6316
5910.2877 ,
5920.9478
593116118 , ,
5942,2797 .
5952..9517 ,
596316275 \
5974,3073
5984.9910
5995,6786 1
60063701 '
6017,0656 :
6027.7650 ., :
6038,4683 i :
6049.1734 ' !
6059.8865 ! '
. 6070.6015
6081.3203
6092,0430
6102.7697
61133002
61,24:2345
6134)9727
6145;7J14G
6156)4608
6167s2105
61775642
6188»7216
6199)4829
6210J248H
622110170
6231 17898
62423664
6253,3469
6264.1311
6274.9191
6285.7110
62963066
6307.3060
6318.1093 ,
6328..9163
6339j7271
635015416
-6361.36CHi
6372.1821
638310079
n
768
769
770
771
i L 772
! 773
774
775
776
: 777
i 778
: ; 779
i 780
: 781
782
783
: 784
' 785
786
i f 787
i i 788
; I 789
790
1 t 791
; . 792
i ; 793
' 794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
log n! n IOR n
1884.2611 2215.9374
1887.HJO 2219.277$
1890JJ335 222J397B
1892J205 2225.9189
1895,8082 2229.2403
1898.6963 22323627
19013831 2235.8855
1904.4744 2239.2088
1907.3642 2242.5327
1910.2547 2245.8571
1913.1456 2249.1821
1916.0372 225215077
1918.9293 2255,8338
1921.8219 2259.1604
1924.7131 2262.4877
1927.6089 2265.8154
19303032 2269.1438
1933,3981 2272.4727
1936,2935 2275,8021
1939,1895 2279,1321
1942i0860 -2282(4626
19449831 "2285.7937
1947:8807 ,2289,1254
1950.7789 2292.4576
1953,6776 !2295i7903
19563769 '2299,1236
195914767 230214575
I962377I . 230517918'
1965,2780 '23091 1268
1968U794 2312,4623
1971,0814 -231517983
1973:9840 !2319ll349
1976,8871 232214720
1979.7907 S2325.8096
1982:6949 ;2329il478
19853996 1233214866-
19883049 233518258
1991,4106 J2339U657
1994,3170 12342:5060
1997:2239 23451,8469
2000.1313 j 23491. 1884
2003,0392 :2352l5303
20055477 2355.8729
20083567 S2359.2159
2011.7663 123623595
2014,6764 ,2365.9036
20173870 ,;23692483
2020.4982 23723934
2023,4099 S23755391
2026:3221 j 23792854
2029.2348 2382.6322-
2032.1481 j2385:9795
2035.0619 !2389.3273
2037.9763 i 2392.6757
2040.8911 1239610246
2043,8065 12399:3741
204617225 J2402.7240
n log* n
6393,8376
MM 6710 .
6415.3031
64263489
6437.193S
6448,0419
6458.8940
6469.7498
6480.6094 ,
6491.4727
6502.3396
6513,2103
6324.0847
65343628
6545.8446
6556.7301
6567.6193
65783122 -
6589.4088 '
66003090
6611,2129
6622.1205
6633.0317
6643.9467
6654,8652 ' -
6665.7875 -
6676,7134 '
6687.6429 -
66983761
67093129
6720:4534 J
6731H975 1-
6742:3452 i T,,
67532965 _s ''
67642515 :
6775v2100 E- t!!
6786.1722 - ~
6797,1380 ,
6808.1074
6819.0804 - "
6830,0569 J
6841,0371 -.
6852.0209 ; "'--
6863,0082 .
6873,9992 < ,
6884,9937
6895,9918 .
6906:9935
691 7.9987 J ,
6929,0074
6940,0198
695110357 ,
6962.0551
6973.0781 ~:
6984. 1D47 ; '_-'
6995U347 ' ,'
7006.1683 : ,"-,
-------
TABLE 23. (Continued)
n logn!
825 2049.6389
826 2052.5559
827 2055.4734
828 2058.3914
829 2061.3100
830 2064.2291
831 2067.1487
832 2070.0688
833 2072.9894
834 2075.9106
835 2078.8323
836 2081.7545
837 2084.6772
838 2087.6005
839 2090.5242
840 2093.4485
841 " 2096.3733
842 2099.2986
843 2102.2244
844 2105.1508
845 ,2108.0776
846 2111.0050
847 2113.9329
848 2116.8613
849 2119.7902
850 2122.7196
851 2125.6495
852 2128.5800
853 2131.5109
854 2134.4424
855 2137.3744
856 2140.3068
857 2143.2398
858 2146.1733
859 2149.1073
860 2152.0418
861 2154.9768
862 2157.9123
863 2160.8483
864 2163.7848
865 2166.7218
866 2169.6594
867 2172,5974
868 2175.5359
869 2178.4749
870 2181.4144
871 2184.3544
872 2187.2950
873 2190.2360
874 2193.1775
875 2196.1195
876 2199.0620
877 2202.0050
878 2204.9485
879 2207.8925
880 2210.8370
881 2213.7820
n log n
2406.0745
2409.4255
2412.7770
2416.1291
2419.4817
2422.8348
2426.1884
2429.5426
2432:8973
2436,2525
2439.6082
2442.9644
2446.3212
2449.6785
2453.0363
2456.3946
2459,7534
2463.1128
2466.4726
2469.8330
2473.1939
2476.5553
2479:9172
2483.2797
2486:6426
2490,0061
2493.3700
2496.7345
2500.0995
2503.4650
2506.8310
2510.1975
2513.5645
2516:9321
2520J001
2523.6686
2527.0377
2530.4073
2533.7773
2537.1479
2540.5189
2543.8905
2547.2625
2550.6351
2554.0082
2557.3817
2560.7558
2564.1304
2567.5054
2570.8810
2574.2570
2577.6336
2581.0106
2584.3882
2587.7662
2591.1447
2594.5238
n log2 n
7017.2054
7028.2462
7039.2903
7050.3380
7061.3893
7072.4440
7083.5023
7094.5640
7105.6293
7116.6980
7127,7703
7138.8460
7149.9252
7161.0079
7172.0942
7183.1838
7194.2770
7205.3736
72(6.4736
7227.5772
7238.6842
7249.7946
7260,9086
7272.0259
7283.1467
7294:2709
7305:3986
7316.5297
7327.6642
7338.8022
7349.9436
7361.0884
7372.2366
7383^3882
7394.5433
7405.7018
7416.8636
7428.0289
7439,1975
7450.3696
7461.5450
7472,7238
7483.9060
7495.0916
7506.2805
7517.4728
7528.6686
7539.8676
7551.0700
7562.2758
7573.4849
7584.6974
7595.9133
7607.1324
7618.3549
7629.5808
7640.8100
n
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
399
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
logn! nlogn
2216.7274 2597.9033
2219.6734 2601.2833
2222.6198 2604.6638
2225.5668 2608.0448
2228.5142 2611.4263
2231.4621 2614.8082
2234.4105 2618.1907
2237.3594 2621.5736
2240.3088 2624.9571
2243.2587 2628.3410
2246.2091 2631.7254
2249.1599 2635.1104
2252.1113 2638.4957
2255.0631 2641.8816
2258.0154 2645.2680
2260.9682 2648.6548
2263.9214 2652.0421
2266.8752 2655.4300
2269.8295 2658.8182
2272.7842 2662.2070
2275:7394 2665.5963
2278.6951 2668.9860
2281.6512 2672.3763
2284.6079 2675.7669
2287.5650 2679.1581
2290.5226 2682.5498
2293.4807 2685.9419
2296.4393 2689.3346
2299.3983 2692.7277
2302.3578 2696.1212
2305.3178 2699.5153
2308.2783 2702.9098
2311.2393 2706.3048
2314.2007 2709.7003
2317.1626 2713.0963
2320.1249 2716.4927
2323.0878 2719.8896
2326.0511 2723,2869
2329.0149 2726.6848
2331.9792 2730.0831
2334.9439 2733.4819
2337.9091 2736.8812
2340.8748 2740.2809
2343.8409 2743.6811
2346.8075 2747.0818
2349.7746 2750.4829
2352.7421 2753.8845
2355.7101 2757.2866
2358.6786 2760.6891
2361.6476 2764.0921
2364.6170 2767.4956
2367.5869 2770.8996
2370.5572 2774.3040
2373.5280 2777.7088
2376.4993 2781.1142
2379.4710 2784.5200
2382.4433 2787.9262
n log2 n
7652.0425
7663.2784
7674.5175
7685.7600
7697.0059
7708.2549
7719.5074
7730.7632
' 7742.0222
7753.2846
7764.5502
7775.8192
7787.0914
7798.3670
7809.6458
7820.9279
7832.2133
7843.5020
7854.7939
7866.0891
7877.3876
7888.6893
7899.9943
7911.3026
7922.6141
7933.9288
7945.2468
7956.5681
7967.8926
7979.2203
7990.5513
8001.8855
8013.2230
8024.5636
8035.9075
8047.2546
8058.6049
8069.9584
8081.3152
8092.6752
8104.0383
8115.4047
8126.7742
8138.1470
8149.5229
8160.9021
8172.2844
8183.6699
8195.0586
8206.4504
8217.8455
8229.2438
8240.6451
8252.0497
8263.4574
8274.8683
8286.2823
n
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
log n ! n log n
2385.4159 2791.3330
2388.3890 2794.7402
2391.3626 2798.1478
2394.3367 2801.5559
2397.3112 2804.9645
2400.2862 2808.3735
2403.2616 2811.7831
2406.2375 2815.1930
2409.2138 2818.6034
2412.1906 2822.0143
2415.1679 2825.4256
2418.1456 2828.8374
2421.1238 2832.2497
2424.1024 2835.6624
2427.0815 2839.0755
2430.0611 2842.4891
2433.0411 2845.9032
2436.0216 2849.3177
2439.0025 2852.7327
2441.9838 2856.1481
2444.9657 2859.5640
2447.9479 2862.9804
2450.9307 2866.3972
2453.9138 2869.8144
2456.8975 2873.2321
2459.8815 2876.6502
2462.8661 2880.0688
2465.8510 2883.4879
2468.8365 2886.9074
2471.8223 2890.3273
2474.8087 2893.7477
2477.7954 2897.1686
2480.7827 2900.5898
2483.7703 2904.0116
2486.7584' 2907.4338
2489.7470 2910.8564
2492.7360 2914.2795
2495.7254 2917.7030
2498.7153 2921.1270
2501.7057 2924.5514
2504.6965 2927.9762
2507.6877 2931.4016
2510.6794 2934.8273
2513.6715 2938.2535
2516.6640 2941.6801
2519.6570 2945.1071
2522.6505 2948,5347
2525.6443 2951.9626
2528.6386 2955.3910
2531.6334 2958.8199
2534.6286 2962.2491
2537.6242 2965.6788
2540.6203 2969.1090
2543.6168 2972.5396
2546.6138 2975.9706
2549.6111 2979.4020
n log2 n
8297.6995
8309.1199
8320.5433
8331.9700
8343.3998
8354.8326
8-366.2687
8377.7079
8389.1503
8400.5957
8412.0442
8423.4960
8434.9507
8446.4087
8457.8698
8469.3339
8480^8011
8492.2715
8503.7450
8515.2216
8526.7012
8538.1840
8549.6698
8561.1588
8572.6508
8584.1459
8595.6442
8607.1454
8618:6498
8630.1571
8641.6676
8653.1812
8664.6973
8676.2174
8687.7402 .
8699:2660
8710.7948
8722.3267
8733.8617
8745.3997
8756.9407
8768.4847
8780.0319
8791.5819
8803.1351
8814.6913
8826.2505
8837,8127
8849.3781
8860.9463
8872.5176
8884.0918
8895.6692
8907.2495
8918.8328
8930.4191
n
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008 -
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
''1024
1025
. 1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
log n !
2552.6090
2555.6072
2558.6059
2561.6051
2564.6046
2567:6046
2570.6051
2573,6059
2576:6072
2579:6090
2582.6111
2585.6137
2588.6168
2591.6202
2594.6241
2597.6284
2600.6332
.2603,6384
2606.6440
2609.6500
2612J6565
2615.6634
. 2618.6707
2621.6784
2624.6866
2627.6952
2630.7043
2633.7137
2636.7236
. 2639.7339
2642.7446
i 2645.7557
2648.7673
2651.7793
2654.7917
2657.8046
2660.8178
2663.8315
. 2666.8456
2669.8601
2672.8751
2675.8904
2678,9062
2681.9224
2684.9390
2687.9560
2690.9735
2693.9914
, 2697.0096
: 2700.0284
2703.0475
2706.0670
2709.0869
2712.1073
2715.1281
2718.1493
n log n n log2 n
2982.8340 8942.0084
2986.2663 8953.6007
2989.6991 8965.1960
2993.1323 8976.7944
2996.5659 8988.3956
3000.0000 9000.0000
3003.4345 9011.6072
3006.8694 9023.2174
3010.3048 9034.8307
3013.7406 9046.4469
3017.1769 9058.0660
3020,6136 9069.6881
3024.0507 9081.3132
3027.4882 9092.9413
3030.9262 9104.5724
3034.3646 9116.2063
3037,8034 9127.8433
3041.2427 9139.4832
3044.6823 9151.1260
3048.1225 9162.7719
3051.5630 9174.4205
3055.0040 9186.0723
3058.4454 9197.7269
3061.8872 9209.3845
3065.3295 9221.0450
, 3068.7722 9232.7084
3072.2153 9244.3749
.3075.6588 9256.0441
3079.1028 9267.7163
3082.5471 9279.3915
3085.9919 9291.0696
3089.4372 9302.7506
3092.8828 9314.4346
3096.3289 9326.1213
3099.7754 9337.8110
3103.2223 9349.5038
3106.6697 9361.1993
3110.1174 9372.8977
3113.5656 9384.5991
3117.0142 9396.3033
3120.4633 9408.0105
3123.9127 9419.7206
3127.3625 9431.4336
3i30.8128 9443.1494
3134.2635 9454.8682
3137.7147 9466.5897
3141.1662 9478,3142
3144.6181 9490.0416
3148.0705 9501.7719
3151.5233 9513.5050
3154.9765 9525.2410
3158.4301 9536.9799
3161.8842 9548.7216
3165.3386 9560.4662
3168.7935 9572.2136
3172.2487 9583.9640
-------
TABLE 24. THE DIVERSITY OF SPECIES, d, CHARACTERISTIC OF MacARTHUR'S
MODEL FOR VARIOUS NUMBERS 6F HYPOTHETICAL SPECIES, s'*
s,'
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
T
0.0000
0.8113
1.2997
1.6556
1.9374
2.1712
2.3714
2.5465
2.7022
2.8425
2.9701
3.0872
3.1954
3.2960
3.3899
3.4780
3.5611
3.6395
3.7139
3.7846
3.8520
3.9163
3.9779
4.0369
4.0937
4.1482
4.2008
4.2515
4.3004
4.3478
4.3936
4.4381
4.4812
4.5230
4.5637
4.6032
4.6417
4.6792
4.7157
4.7513
4.7861
4.8200
4.8532
4.8856
4.9173
4.9483
4.9787
5.0084
5.0375
5.0661
s'
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
d
5.0941
5.1215
5.1485
5.1749
5.2009
5.2264
5.2515
5.2761
5.3004
5.3242
5.3476
5.3707
5.3934
5.4157
5.4378
5.4594
5.4808
5.5018
5.5226
5.5430
5.5632
5.5830
5.6027
5.6220
5.6411
5.6599
5.6785
5.6969
5.7150
5.7329
5.7506
5.7681
5.7853
5.8024
5.8192
5.8359
5.8524
5.8687
5.8848
5.9007
5.9164
5.9320
5.9474
5.9627
5.9778
5.9927
6.0075
6.0221
6.0366
epic-
s'
102
104
106
108
110
112
114
116
118
120
122
124
126
128
130
" 132
134
136
138
140
142
144
146
148
150
152
154
156
158
160
162
164
166
168
170
172
174
176
178
180
182
184
186
188
190
192
194
196
198
200
d
6.0792
6.1069
6.1341
6.1608
6.1870
6.2128
6.2380
6.2629
6.2873
6.3113
6.3350
6.3582
6.3811
6.4036
- 6.4258
6.4476
6.4691
6.4903
6.5112
6.5318
6.5521
6.5721
6.5919
6.6114
6.6306
6.6495
6.6683
6.6867
6.7050
6.7230
6.7408
6.7584
6.7757
6.7929
6.8099
6.8266
6.8432
6.8596
6.8758
6.8918
6.9076
6.9233
6.9388
6.9541
6.9693
6.9843
6.9992
7.0139
7.0284
7.0429
s'
205
210
215
220
225
230
235
240
245
250
255
260
265
270
275
""280
285
290
295
300
310
320
330
340
350
360
370
380
390
400
410
420
430
440
450
460
470
-480
490
500
550
600
650
700
750
800
850
900
950
1000
cf
7.0783
7.1128
7.1466
7.1796
7.2118
7.2434
7.2743
7.3045
7.3341
7.3631
7.3915
7.4194
7.4468
7.4736
7.5000
7.5259
7.5513
7.5763
7.6008
7.6250
7.6721
7.7177
7.7620
7.8049
7.8465
7.8870
7.9264
7.9648
8.0022
8.0386
8.0741
8.1087
8.1426
8.1757
8.2080
8.2396
8.2706
8.3009
8.3305
8.3596
8.4968
8.6220
8.7373
8.8440
8.9434
9.0363
9.1236
9.2060
9.2839
9.3578
*Thc data in this table are reproduced, with permission, from Lloyd and Ghelardi
158
-------
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163
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IR* >'!"*
SECTION 8
TAXONOHIC BIBLIOGRAPHY
8,1 General References
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-------
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8.2 Annelida - Polychaeta
Foster, N. 1972. Freshwater polychaetes (Annelida) of North America.
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8.3 Annelida - Oligochaeta
Brinkhurst, R.O. 1964. Studies on the North American aquatic Oligochaeta,
Part I. Proc. Acad. Nat. Sci. Phila. 116(5):195-230.
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Brinkhurst, R.O. 1982. Oligochaeta. In: S.P. Parker (ed.). Synopsis and
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Brinkhurst, R.O. 1986. Guide to the freshwater aquatic microdrile
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Brinkhurst, R.O. 1988. A taxonomic analysis of the Haplotaxidae
(Annelida: Oligochaeta). Can. J. Zool. 66:2243-2252.
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Brinkhurst, R.O. and D.G. Cook. 1966. Studies on the North American
aquatic Oligochaeta III: Lumbriculidae and additional notes and records of
other families. Proc. Acad. Nat. Sci. Phila, 118:1-33.
Brinkhurst, R.O. and B.G.M. Jamieson. 1971. Aquatic Oligochaeta of the
World. Univ. Toronto Press, Toronto, Ontario, Canada. 860 pp.
Brinkhurst, R.O. and R.D. Kathman. 1983. A contribution to the taxonomy
of the Naididae (Oligochaeta) of North America. Can. J. Zool.
61:2307-2312.
Brinkhurst, R.O. and M. Marchese. 1987. A contribution to the taxonomy
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Brinkhurst, R.O. and M.J. Wetzel. 1984. Aquatic Oligochaeta of the world:
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48 pp.
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Annelida (Polychaeta, naidid and tubificid Oligochaeta, ,and Hirudinea)
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from western North America. .Can. J. Zool. 66: 2304-2311.
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logica. the University of New Brunswick, Fredericton, New Brunswick,
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Reynolds, J.W. and D.G. Cook. 1989. A catalogue of names, descriptions
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New Brunswick, Canada E2K 1E5. 37 pp.
Stimpson, K.S., D.J. Klemm, and J.K. Hiltunen. 1982. A guide to the
freshwater Tubificidae (Annelida:Clitellata:01igochaeta) of North
America. EPA-600/3-82-033. U.S. Environmental Protection Agency,
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61 pp.
Stimpson, K.S., D.J. Klemm, and J.K. Hilturien. 1985. Freshwater Tubificidae
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Wetzel, M.J. 1987. Limnodrilus tortilipenis, a new North American
species of freshwater Tubificidae (Annelida:Clitellata:Oligochaeta).
Proc. Biol. Soc. Wash. 100(1):182-185.
Whitley, L.S. 1982. Aquatic Oligochaeta. In: A.R. Brigham, W.U. Brigham
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8.4 Annelida - Hirudinea
Appy, R.G. and M.J. Dadswell . 1981. Marine and estuarine piscicolid leeches
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Burreson, E.M. 1976. Aestabdella gen, n. (Hirudinea:Piscicolidae) for
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if ".H|!I, , ..... ! mi " ' ..... nijpi'i1 « J' lii '" ' , » i,11,.,, ' i1, ' ""I'*! ...... |ii"i.iiii r m,, i1 ',"'! i, , f. ....... H, niiii i i" ...... ,
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Burreson, E.M. 1977. Oceanobdella pall i da N.sp. (Hirudinea:Piscicolidae)
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Burreson, E.M. 1977. Two new species of Malmiana (Hirudinea:
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Burreson, E.M. 1977. Two new marine leeches (Hirudinea:Piscicolidae)
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' '
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Caballero, C.E. 1932. Heroobdella ochoterenai. nov so. Institute de
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8.5 Coleoptera
Anderson, R.D. 1971. A revision of the nearctic representatives of
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'l" 11'' "
-------
Saether, O.A. 1969. Some nearctic Podonominae, Diamesinae, and Orthocladiinae
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-------
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8.9 Diptera - Other
Alexander, C.P. 1967. The crane flies of California. Bull. Calif. Insect
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Jamnback, H. 1965. The Culicoides of New York State (Diptera:
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Johannsen, O.A. 1952. Heleidae (Ceratopogonidae). In: Guide to the insects of
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183
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Mathis, W.N. 1979. Studies of Notiphilinae/(Diptera:Ephydridae),
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Niplsen, L.T.and D.M. Rees. 1961. An identification guide to the mosquitoes
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:>, " ' ' ; '" .. "i ; ". ';. lf ;'';{ ..... Mjlii i '-: ' , y.'i. ,("-; '>. ;,, . , \, , ,-;, :
Pechuman, L.L., D.W. Webb, and H.J. Teskey. 1983. The Diptera, or true flies
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Peters, T.M. and E.F. Cook. 1966. The nearctic Dixidae (Diptera). Misc. Publ
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_
Peterson, B.V. 1970. The Prosimulium of Canada and Alaska. (Diptera:
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Roth, J.C. 1967. Notes on Chaoborus species from the Douglas Lake Region,
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Saether, O.A. 1970. Nearctic and palaearctic Chaoborus (Diptera-.Chaoboridae) .
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Siverly, R.E. 1972. Mosquitoes of Indiana. Ind. State Board Health, 1330 West
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Snoddy, E.L. and R. Noblet. 1976. Identification of the immature black flies
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Steyskal, G.C., T.W. Fisher, L. Knutson and R.E. Orth. 1978. Taxonomy of North
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Stone, A. and E.R. Snoddy. 1969. The blackf lie's of Alabama (Diptera:
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Teskey, H.J. 1969. Larvae and pupae of some eastern North American Tabanidae
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Turner, W.J. 1974. A revision of the genus Svmphoromvia Frauenfeld Diptera:
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8.10 Ephemeroptera (Mayflies)
Allen, R.K. 1973. Generic revisions of mayfly nymphs. 1. Traverella in North
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Allen, R.K. and G.F. Edmunds, Jr. 1959. A revision of the genus Ephemerella
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Allen, R.K. and G.F. Edmunds, Jr. 1961. A revision of the genus Ephemerella
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Allen, R.K. and G.F. Edmunds, Jr. 1961. A revision of the genus Ephemerella
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Allen, R.K. and 6.F. Edmunds, Jr. 1962. A revision of the genus Ephemerel Ta
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Allen, R.K. and G.F. Edmunds, Jr. 1963. A revision of the genus Ephemerella
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Allen, R.K. and G.F. Edmunds, Jr. 1963. A revision of the genus Ephemerella
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Bergman, E.A. and W.L. Hilsenhoff. 1978. Baetis (Ephemeroptera:Baetidae) of
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8.11 Hemiptera
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8.12 Hydracarina (Acarina) (Water Nites)
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Cook, D.R. 1959. Studies on the Thyasinae of North America (Acarina:
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Cook, D.R. 1960. Water mites of the genus Piona in the United States (Acarina:
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206
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APPENDIX A
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
PORIFERA
Anheteromeyenia
ryderi X Yes 3
Ephydatia
fluviatilis X No 4
muelleri X No 2
Eunapius >
fragilis X Yes 4
Heteromeyenia
tubisperma X No 3
Spongilla
lacustris X No 3
Trochospongilla
horrida X No 3
pennsylvanica X No 2
BRYOZOA
Fredericella
sultana 2
Hyalinella
punctata .X .No 2
lophopodella
carteri 1
Pectinatella
magnifica X 1
Plumatella
casmiana X 3
emarginata X 4
repens X 4
Urnatella
gracilis 3
COELENTERATA
Cordylophora
lacustris .2
Craspedacusta
sowerbyi 2
TURBELLARIA
Cura
foremanni 2
Dugesia
dorotocephala - 4
207
-------
,;'!'! ; " s '.., >;v: ...... a v
...... ; ..... ,! , ...... IIP ill; v i1!" i" « '
POLLUTION TOLERANCE OF SELECTED HACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa To! erant Sens i ti ve To! erant tol erant Facul tati ve Into] erant
tigrina
Phagocata
gracilis
S''V !"'!;:'::i,', '"'.' '."4' ",,:'"" ""':. ' ".'.'"'.
2
NEHERTEA . , , ' .' , , ' ' " ..' ,. ',,,',,
Prostoma
graecense 3
ANNELIDA - POLYCHAETA
SABELLIDAE
Manayunkia
speciosa 3
ANNELIDA - OLIGOCHAETA
NAIDIDAE
Amphichaeta
americana 2
Chaetogaster
diaphanus 2
diastrophus 2
Dero
digitata 2
nivea ' " , , ,, .,.,: ' '' ,,;;," ', '"'3,
obtusa ' ' ' , ' ''.': '. ,,'!..,',. 3"
pectinata 2
Nais
barbata 4
behnlngl 3
bretscheri 3
conmunis 4
elinguis 5
pardalis 4
simplex 3
variabilis 5
Ophidonais
serpentina 4
Pristina
aequiseta 3
Slavina
appendiculata 2
Specaria
josinae 2
Sty!aria
fossularis 3
lacustris 3
Vejdovskyella
comata
intermedia 4
... " 208
"H ' ' ''i'! '.iimii'i'llll1 '
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy MetalsAcidTolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
TUBIFICIDAE~~
Aulodrilus
americanus . 3
limnobius 3 «
pigueti 3
pluriseta 3
Bothrioneurum
vejdovskyanum 2
Branchiura
sowerbyi 4
Ilyodrilus
tempietoni 3
Isochaetides
curvisetosus 2
Limnodrilus
cervix 4
claparedianus 4
hoffmeisteri 5
maumeensis 5
udekemianus 5 :
Potamothrix
moldaviensis 3
vejdovskyi 3 ,
Quistadrilus
multisetosus 4
Spirosperma
carolinensis 3
ferox 3
multisetosus 4
nikolskyi 2
Tubifex
tubifex X 5 :
ANNELIDA - HIRUDINEA
ERPOBDELLIDAE
Erpobdella
parva 4 ,
punctata 4
Mooreobdella
microstoma 4
HAEMOPIDAE
Haemopis
grandis 3
marmorata 3
GLOSSIPHONIIDAE
Alboglossiphonia
heteroclita 3
Gloiobdella
elongata 4
209
-------
."ii*"i7*ffli*'B*ii
POLLUTION TOLERANCE OF SELECTED MACRO INVERTEBRATES (Continued)
,!'"' , ', i , i. ..'-,'. , . N : , ," ' ,', .gin ....... 'Y ........... ,, ...... inn iirn;,,!,! T,, ..... j; ..... ..:, * ....... , * ......
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Helobdella' . ' ' " ' ', ' ......... "" " "'"" ..... , '. " ,'"' ""."' '," '.V^
stagnalis 4
tri serial is 3
Glossiphonia
complanata X 4
PI acobdel 1 a
multilineata , ....... ...... :... ..... . ......... . ...... , .......... , 2
ornata 3
papillifera 3
parasitica 2
PISCICOLIDAE
Hyzobdella
lugubris 3
Piscicola
punctata 3
HYDRACARIA
Albia , , .. , . .,,,, ........... .,,.:. ..... . ,
station 1s 1
Arrenurus .......................... ' ........................................ ..
kenki 2
pi anus 3
serratus 2
Bandakia
anisitsipalpis 0
elongata 0
Euthyas
*; truncata ..... M , , , . , ... ..... , ..,., ...... , , 2
Front ipoda
americana 2
Fore.Ha , , ... , ..... ...... ......... ........ ,, .......... ...... ..... ...... .... ,, , ........ . ., ,,.,, ........ . ......... ...... .... ,,. ..... ,
cookl "' , , ................... 2 ..................... """- -«
Hydrachna
cOnjecta 1
crenulata , , . ..... , ., , .............. ,, ..... ......... .... , ......... , ...... ,,1
magnisculata 1
railiaria 1
rotunda ., ,,, ......... , . , . , ...... ....... ... , ..... ., ......... .............. . ..... ... ,. ...... 1 ............
stipata 1
Hydrodromi a
despiciens 2
Hydryphantes
tenuabilis 3
Hygrobates
fluviatilis 4
longipalpis 4
neodctoporus 2
.. 210 , . , ,, ....................... , ................. ......
.nil ' :,;;! ji, ,, . '. ...... i' ,i,,. ..... . ' ..... .'N , ' j'| , j' i; , ;; " f ' ii Jf .11 j i ' ' ' i' |. ' : i ' iiniiiiu^ ......
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Lebertia
quinquemaculosa
Limnesia
maculata
undulata
Neumania
rotundra
Oxus
connatus
Fiona
carnea
constricta
pugilis
rotunda
Pirata
insularis
Sperchon
crassipalpus
glandulosus
Sperchonopsis
verrucosa
Testudacarus
minimus
Thyas
barbigera
bruzel i i
stolli
Tiphys
americanus
simulans
Unionicola
formosa
2
2
2
2
4
4s
2
2
2
3
3
3
1
,.
1
1
1
1
1
0
1
1
ARTHROPODA - CRUSTACEA
ISOPODA
Asellus
attenuatus 3
brevicaudatus 4
communis 3
intermedius ' 3
militaris 3
racovitzai 3
Lirceus
fontinalis X 3
lineatus
211
-------
1.1 .
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES(Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant FacultativeIntolerant.
, ' '/ ' ,,, , ,, ' ' , ;':, '!' ' ' 'ii :" , ' i
: . _ ,t . ,_ji__i ;_:___; ;';,;;,,,, i .I"1., . ,'.' I' ,' | I il. I I
AMPHIPODA
Crangonyx
gracilis " 3
obliquus 1
pseudogracilis X 3
serratus 2
Gammarus
fasciatus 2
lacustris 2
minus , , , , , ,. ,., , ,2
pseudolimnaeus X 2
tigrinus 2
Hyallela
azteca , , 2
Synurella
chamber!aini 2
PECAPODA
Cambarus
acuminatus . 3
asperimanus 1
bartonii Yes 3
exraneus " 1
diogenes Yes 4
floridanus 2
longirostris 3
longulus No 1
Orconectes , ,
immunis 2
obscurus Yes 2
propinquus No 2
rusticus 3
virilis ' ' ",.' ... ' 7 '.;'.', ' .. ' ,',' , ... ..'.'.' 1 .'
Palaemonetes
exilipes 4
kadiakensis 2
paludosus 2
Procambarus
acutus 4
clarkii 3
!.'.ji ' '..' '' ,: " , i.':.1 ' ' '.: .(.',.. ' "f .' ! ,. ;/ i'i'" > :; ', Vni ; . " ii
HYSIDACEA
Hysis
relict a , , , , ,., , I
212
,, J,': if, ' L;; i,: « ij '!'bill! " 1 j1,!'":", " '' ^' ,..!'H i',,;',',,Hij, ,J i':, ;''"I.11 Si1''!, 1 :nir"'''i:,,''JAIliiiili1' Ji'1 '.illlllh lite"!''!'
-------
POLLUTION TOLLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant-Tolerant Facultative Intolerant
INSECTA - DIPTERA
CHIRONOHIDAE
Ablabesmyia
aequifasciata 4
americana -:.. ->2 \: . ;: -\
annul ata Yes ,; v 1
aspera X Yes 2
auriensis 1
basal is Yes 2 ,
cinctipes Yes 2
hauberi Yes ^ ;t :..-. 1
illinoensis 1
janta 3
mallochi X No 2 -.-..
monilis X Yes 2
parajanta Yes 3 ;
peleensis 2
philosphagnos Yes 2
rhamphe 2
tarella 3
Arctopelopia
flavifrons Yes 0
Boreochlus
persimilis . Yes 0
Brillia
flavifrons 2
par X 1
parva 0
Calopsectra
confusa Yes : , 1
dendyi , Yes . 2
gregarius 4
neoflavella Yes 2
Cardiocladius
obscura Yes 2
platypus Yes . 0
Chaetocladis
atroviridis 0
ochreatus 0
213
-------
I
POLLUTION TOLLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
1 «, ' |i .,, ,| 'l|ii| || i ,|| t mi |||||'||i "i i ]j i|i ,|i, "' ,!»; .' ,, ' ' / .' '"
Yes
X Yes
1
Yes
Yes
X No
Yes
Yes
No
X Yes
No
Yes
X No
Yes
No
No
No
''' fc^'^-'* t :.''
3
4
4
3
3
3
4
3
5
5
3
3
3,,
if.( ^ 2, ii(_
3
4
3
2
" "2
2
2
3
' ' 2
; " ; 3. '
2
3
3
3
3
' ' '''il v'i '"''V'l'J ' '
1'
1
1
1
1
1
"""' o
1
1
" 1
214
-------
POLLUTION TOLLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Cryptotendipes
casuarius
darbyi
emorsus
Demeijerea
atrimanus
brachialis
Demicryptochironomus
vulneratus
Diamesa
nivoriunda
spinacies
Dicrotendipes
californicus
fumidus
incurvus
leucoscelis
lobus
modestus
neomodestus
nervosus
Einfeldia
austeni
brunneipennis
natch itocheae
Endochironomus
nigricans
Epiococladius
flavens
Eukiefferiella
coerulescens
Glyptotendipes
ampl us
barbipes
lobiferus
meridional is
paripes
senilis
Goeldichironomus
holoprasinus
Guttipelopia
currani
.-
Yes
No
Yes
No
Yes
No
X No
Yes
Yes
Yes
X No
X No
No
No
No
4
No
No 4
No
No 5
Yes
2
2
2
2
2
3
3
2
2
3
3
3
3
3
3
3
1
1
1
0
1
1
1
0
0
1
1
215
-------
f1"!;,1
'"
__ _ _
;; iff:; :'kV'..i..;tvi.<:',;.'vi ''MI!". . . ..v; >; '''"in
TOLLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
, -. ., :..;:: ^T'Oi '^l^:^ ^m !ES^ai;l-TCffll r JM'.ICT^ :!! :i'^ 1 'T^I
POLLUTION
1 ~"' '"''"!"' '' "' ' """"" ' ' "" ' ' ' '"' " "ft,:";~', i
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Harnishia
amachaerus """" Yes '"" "2
boydi " Yes '"3 "
collator , , , 3 :
curtilamellata ' ' '"" "' " 2" ' '" " ""
edwardsi No 2
galeator No 2
tenuicaudata 2
viridulus , , " No ', /" ^"'. ".'/'.I 2.' ' , [ ". "" '"I
Heterotrissocladius ,. . .' "n , . ' '.,','. ',','" " 'r~ "n. '.
marcidus ' ""' ' "" '",' "0
Hydrobaenus
pilipes 2
Kiefferulus
dux 3
Labrundinia
becki Yes 0
floridana 1
johannseni Yes 2
neopilosella Yes 1
pilosella . Yes 2
yirescens i 2
Lars i a " ""'"'
lurida , ; . . , , ; '"" ., 2
Lauterborniella
agrayloides No 0
varipennis Yes 3
Leptochironomus
nigrovittatus 2
Macropelopia
decedens Yes 1
Metriocnemus
abdoraino-flavatus Yes 4
hamatus Yes 0
knabi , ' Yes 4 ;.', ' .".". ,,'" '.." ,"' ".. :.'". '.'.
lundbecki X 1
Micropsectra
deflecta 1
dives Yes 2
dubia No 1
nigripila 3
polita No 0
Microtendipes
pedellus X Yes 1
216
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Monopelopia
boliekae 0
tillandsia Yes 4
Nanocladius
alternantherae No 2
balticus 1
distinctus 3
minimus 1
parvulus 1
Natarsia
fastuosa No 0
Nilodorum
devineyae 2
Nilotanypus
americanus Yes 3
Nilothauma
babyi 1
bicornis 2
Odontomesa
fulva No 0
Omisus
pica Yes 1
Orthocladius
annectens Yes 2
obumbratus Yes 1
Pagastiella
orophila Yes 3
ostansa 2
Parachironomus
abortivus 3
alatus Yes 2
carinatus No 3
directus No 2
hirtalatus 3
loganae Yes 1
monochromus No 3
pectinatellae 3
potamogeti 1
schneideri Yes 3
. sublettei 2
tenuicaudatus 2
Paracladopelma
nais 3
undine Yes 1
217
-------
Hi;,11!
POLLUTION TOLERANCE OF SELECTED MACRO INVERTEBRATES (Continued)
; ....... : ", ' "' ......... ;,:': ::' f ....... ''! ...... ' :' J"1::1!"! : If i !!'-''»' i1 V' '. ,..: ......... "'t ....... I/WC* MMFas"!'*' ...... I*:* t-u. .............. M ....... ....... , ...... .,/,-! ...... ,,s-:'!;
Heavy Metals
Taxa Tolerant Sensitive
Paral auterborni el 1 a
elachista
nigrohalteralis
subcincta
Paramerina
anomal a
sroithae
Parametriocnemus
lundbeckii
Paratendipes
albimanus X
subaequalis
thermophilus
Parochlus
kiefferi
Pedionomus
beckae
Pentaneura
americana
carneosa
comosa
flavifrons
inconspicua
inculta
melanops
ornata
Phaenopsectra
profusa
Polypedilum
angulum
apicatum
aviceps
faraseniae
convictum
digitifer
fall ax X
halterale X
illinoense X
labeculosum
1 aetum
riubeculosum
obtusum
scalaenum X
Acid
Tolerant
'* '" >''"
No
No
Yes
No
No
No
No
No
Yes
Yes
No
Yes
No
Yes
No
Yes
No
Yes
Yes
Tolerance to Organic
.Tolerant Facultative
'^^ r ' ^:;;i: ^' ***
'" ' 3
3
2
' " ' 2 '
i " , ''i< in ' i,iiiriiii ' i in J '' ,
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
simulans No 3
sordens 1
trigonum No 2
tritum 3
vibex 1
Procladius
adumbratus No 2
bell us X No 3
culiciformis Yes 2
denticulatus No 4
riparius No 2
Prodiamesa
olivacea Yes 0
Psectrocladius
elatus 2
Julia 3
niger 3
vernal is Yes 2
Psectrotanypus
dyari 4
venustus No 1
Pseudochironomus
fulviventris 2
julia 2
richardsoni No 2
Psilotanypus
bell us 4
Rheocricotopus
robacki 3
Rheotanytarsus
exiguus X Yes 3
Robackia
claviger 3
demeijerei 3
Sergentia
coracina No 0
Smittia
aterrima Yes 1
Stempellina
johannseni 2
Stenochironomus
hilaris No 1
macateei No 1
Stictochironomus
devinctus X Yes 0
varius No 2
219
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals f Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant tolerant FacultativeIntolerant
Tanypus
carinatus
clavatus
grodhausi
neopunctipennis
parastellatus
punctipennis
stellatus
Tanytarsus
buck! eyi
dissimilis
gracilentus
neoflavellus
quadratus
recens
Thalassomyia
bureni
Thienemanniella
xena
Thienemannimyia
barber i
senata
Tribe! os
fuscicornis
jucundus
Trichocladius
robacki
Xenochironomus
rogersi
scopula
taenionotus
xenolabis
Zavrelimyia
carneosa
'
No
No
No
X No
X No
No
No
No
No
No
Yes
Yes
X Yes
Yes
X No
X No
: ", I ., : , ,, ,::;::,' , i1:
,.: ". '. ;;, 3," ; " .;. .
2
3
3
4
; ". , ' 3 ; ..
2
3
2
2
2
2
2
2
1
1
1
o ',
" o ;; ".".'"
"o
i
i
i
V
o
OTHER DIPTERA
Anopheles
crucians 3
punctipennis 2
Antocha
saxicola No
Atherix
variegata X 2
Bezzia
glabra 4
220
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
~ ~~Heavy Metals Add Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Blepharicera
tenuipes 0
Brachydeutera
argentata 4
Cnephia
dacotensis 0
mutata 2
pecuarutn 2
Chaoborus
albatus 3
americanus 2
flavicans 2
punctipennis 3
Culex
attratus 3
erraticus 3
pipiens 4
restuans 3
Eristalis
aeneus 5
bastardii 5
brousii 5
Mansonia .
titillans 3
Metasyrphus
americanus 4
Odontomyia
cincta 3
Palpomyia
tibialis 3
Prosimuliurn
fuscum 3
gibsoni 3
johannseni 1
magnum 1
mixtum 3
mysticum 1
rhizophorum 1
Protoplasa
fitchii 3
Pseudolimnophila
luteipennis ,1
Psychoda
alternata Yes 5
221
-------
i: ::'; ""Is- !:J ' *>,,
" ' Ji' Si' ' ' > V ':"' ','"1 ininilii , i'in" ; ,
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
-* - » ' ';: '", "i, , , || ini | i |
Siraulium
aurium . ' ,..''.', , ,4' ','. ...... "
clarkei " ' ' " ' ' ' ............... ..... ' ........ ....... ..... '' "" "2 ......... ..... .............
corbis 1
croxtoni 1
decorum 2
eufyadminiculum 1
fibrinflatum 3
jenningsi 2
Johannseni 0
latipes 1
luggeri 3
meridional e 1
pictipes 2
rugglesi 3
tuberosum 2
venustum 2
verecundum 3
vittatum 3
Sphaeromais ........ ........... '" ...... '' "'' ::i ...... "'" ............ " ..... "" ........ " ' ..... ...... " .......... ..... llFl
longipennis 3
Stegopterna
mutata 3
Stilobezzia
antenna! is 4
Strati otnys
discalis 4
meigenii 4
Tabanus
atratus 3
bened ictus 4
giganteus 1
lineola 4
stygius 2
variegatus 1
Telmatoscopus
albipunctatus 4
Tipula
abdominal is X Yes 1
, caloptera ' ' ' ..... ' ' " .................. " ......... ' ............ ' 1
Toxorhynchites
rutilus 3
222
-------
POLLUTION TOLERANCE OF SELECTED MACF10INVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
INSECTA - TRICOPTERA
Agarodes
distinctum 2
Agrypnia
vestita 1
Amiocentrus
aspilus No 0
Anisocentropus
pyraloides Yes 0
Apatania
incerta 0
Aphropsyche
doringa 0
Arctopsyche
grandis X 1
irrorata Yes 0
ladogensis 0
Asynarchus
montanus 3
Brachycentrus
americanus No 1
incanus 0
lateral is No 0
numerosus 1
occidental is . 1
Ceraclea
ancylus 2
cancel!ata 0
diluta 1
flava No 1
maculata 1
neffi 1
nepha 2
punctata 0
slossonae 2
tarsipunctata No 1
transversa X 1
Ceratopsyche
alhedra 2
alternans 2
bronta 3
bifida 3
morosa 1
slossonae 2
sparna 1
walkeri 1
vexa 2
223
-------
"' .'|1!; I1 !."'':l: ""ill!1'i'I1; I "Win,,1 /,!!, ;i|j.i" !'h .'I ', , I'^iiniiiitfiii'iillllT' 'f? IV "'I',:;'«' ''"'Jill';.'1 ' ,,[. I'lGii'sillli'V''''''!.!!^!''!!'!!!!.)!.!!!!.!!!''".Jiiii:;'],,]!!!!;
11 ^'' H iii» i ',. i ''. 'Hi1'1 T i, f. I:,-" ' !',!; ' ' i,',"' : ,. " , '""!, j,., it:! ii,. ' '!:, l,ii!,: '*:' l wan n!''",
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy
Taxa Tolerant
Chlmarra
atterriraa
feria
obscura
perigua
socia
CulophiTa
tnoracica
Cyrnel 1 us
fraternus
Diplectrona
metaqui
modesta
Do! ophi lodes
distinctus
Fattigia
pele
Frenesia
missa
Glyphopsyche
irrorata
Goera
calcarata
fuscula
stylata
Helicopsyche
boreal i s
Hesperbphyl ax
designatus
Heteroplectron
americanum
Hydatophyl ax
argus
Hydropsyche
aerata
arinale
betteni X
bidens
cuanis
demora
depravata
dicantha
frisoni
incommoda
leonardi
Metals Acid Tolerance to Organic Wastes*
Sensitive Tolerant. Tolerant. Facultative Intolerant
; :" ; ; - ';" '-' ' - '" .-.
X Yes
2
""
X No 3
'Yes
Yes
"" ' "' ""
X No ._ _ ( .. , , ,^,2
2
2
3
Yes 3
2
3
" ' ' '" ' 3
3
X No 3
2
/; . ,,
1
1
1
0
1 "
0
0
1
0
1
'"'0
1
0
0
0
.
0
1
1
0
224
!ll, ., . . , "" ; '
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
~^~""~~~Heavy MetalsAcidTolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
orris
phalerata
placoda
scalaris
simulans
X
X
No
No
3
2
2
1
1
venularis 1
Hydroptilia
waubesiana . 2
Ironoquia
punctatissima 2
Leptocerus
amerlcanus 1
Leuchotrichla
plctlpes 1
Limnephilus
rhombicus No 1
submonilifer No 1
Lype
diversa Yes 1
Macronemum
Carolina X 0
zebratum 2
Matrioptila
jeanae 0
Micrasema
kluane 1
rusticum 1
wataga 1
Molanna
blenda Yes 1
Mystacides
* sepulchral is X No 1
Nectopsyche
albida 1
dorsal is 2
exquisita 0
pavida 2
Nemotaulius
hostilis 2
Neureclipsis
crepuscularis X Yes 1
Oligostomis
ocelligera 1
Onocosmoecus
quadrinotatus 1
225
-------
, ' '
.v ,. ,,. ..,.
:'!; % .rVV{,y::j m II I
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Oropsyche
howel1ae
PaTaeagapetus
eelsus
Parapsyche
apical is
Phylocentropus
placidus
Potainyia
f 1 ava
Pseudogoera
singularis
Pseudostenophylax
uniformis
Psilotreta
indecisa
Psychoglypha
subborealis
Psychomyia
flavida
Pycnopsyche
gentilis
guttifer
lepida
Rhyacophila
acutilaba
amicis
atrata
brunnea
Carolina
carpenteri
fuscula
glaberrima
invaria
ledra
lobifera
tnelita
mycta
nigrita
t'orva
vibox
vulphipes
X
X
Yes
Yes
X
X
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
2
3
2
2
2
2
3
2
2
2
0
0
0
0
0
0
0
1
0
0
0
0
0
1
1
1
0
0
ID ill
226
-------
POLLUTION TOLERANCE OF SELECTEDtMACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Symphitopsyche
bifida X No 3
bronta 2
macleodi 0
morosa 2
riola 2
sparna Yes 3 .......
Trentonius
distinctus 2
Wormaldia .
moestus 0
INSECTA - EPHEMEROPTERA
Ameletus
lineatus 0
Ametropus
albrighti 0
Arthroplea
bipunctata No 1
Attenella
attenuata 2
Baetis
austral is 0
bicaudatus No 0
brunneicolor 2
flavistriga 2
frondalis 3
hageni 3
intercalaris 3
longipalpus 3
macdunnoughi 3
propinquus 3
pygmaeus 2
spiethi Yes 0
tricaudatus No 2
vagans No 2
Baetisca
bajkovi No 2
Carolina 0
escambiensis No 0
gibbera Yes 0
lacustris 3
laurentina 2
obesa Yes 1
rogersi Yes 0
227
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
1 t ' , ',.' . ' -. i | Kill 11 il I * ' in
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
* ' ': M-i'M'""';;7-1 '"'. ;;' ' .' i ; -
Brachycercus
lacustris X
macul atus
Caenis
arnica
anceps
diminuta
forcipata
hilaris
latipennis
simulans
Callibaetis
coloradensis
floridanus
pretiosus
Centroptilum
viridocularis
Choroterpes
basal is
hubbelli
Cloeon
al amance
rubropictum
Dannella
lita
simplex
Do! an i a
americana
Drunel 1 a
cornutella ,
Epeorus
al bertae
longimanus
vitreus
Ephemera
bl anda
guttulata
simulans
vari a
Ephemerel 1 a
allegheniensis
aestiva
attenuate
aurivilli
berneri
bicolor X
Yes 2
3
Yes 4
Yes
No
No
Yes 4
No
No
2
X Yes
2
No
No ,
2, ,
X No
No 2
1
0
1
1
0
1
0
0
,. o,. ",'.
1
1
1
0
1
0
0
1
1, ,.
1
1
b
0
o
o
0
0
228
',-.' i . , , i i"
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Taxa
Carolina
catawba
coloradensis
cornuta
coxalis
crenula
deficiens
doddsi
dorothea
excrucians
flavilinea
frisoni
funeral is
grandis
hecuba
inermis
invaria
lita
longicornis
needhami
rotunda
septentrional is
serrata
serratoides
simplex
spiculosa
subvaria
temporal is
teresa
tibialis
trilineata
versimilis
wal keri
wayah
Ephoron
album
Euryl ophella
aestiva
bicolor
funeral is
lutulenta
temporal i s
Habrophlebia
vi brans
Habrophlebiodes
americana
brunneipennis
Heavy Metals Acid
Tolerant Sensitive Tolerant
No
X No
No
No
No
No
X
No
X Yes
No ,
No
No
Yes
No
X
Tolerance to Organic
Tolerant Facultative
2
2
3
2
2
3
2
3
3
3
2
Wastes*
Intolerant
1
1
0
0
1
1
1
0
0
1
0
0
1
0
0
1
1
0
1
0
1
0
1
1
1
1
0
1
1
0
0
1
229
-------
<';;*'';i/M. ';!;V ?*,'' ':'&., V- W:tW.."* ti
POLLUTION TOLERANCE OF ;SEL|CTED MACRQINVERTEBRAIES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant tolerant Facultative Intolerant
Heptagenia
criddlei No 2
diabasia 2
flavescens 2
hebe No 2
lucidipennis 2
maculipennis 2
pull a No 0
Heterocloeon
curiosum 1
Hexagenia
atrocaudata X No 0
bilineata No 2
limbata X No 1
munda No 2
rigida No 2
Homoeoneuria
dolani X C
Isonychia
bicolor 2
pictipes 1
sadleri No 0
Leptophlebia
bradleyi Yes 0
cupida No 0
intermedia Yes 2
nebulosa No 4
nervosa 2
Leucocuta
hebe , ... . .. , , , ,,..... . ,, 1
Litobrancha '.,' ..,.. , : .. ,,,,:l, , ,.....
recurvata 3
Neoephemera
purpurea 0
youngi X Yes 1
Nixe
lucidipennis 1
Paraleptophlebia
bicornuta Yes 2
bradleyi 3
debilis No 0
heteronea No 1
moll is No 1
praepedita No 0
volitans Yes 3
230
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Pentagenia
vittigera X No 2
Potamanthus
distinctus 1
rufous , 1
Pseudiron
central is 3
Pseudocloeon
Carolina No 1
dubium 2
myrsum 2
parvulum No 1
punctiventris 2
Rhithrogenia
hageni No 2
impersonata 0
jejuna 0
pellucida 0
robusta No 1
undulata 0
Serratella
deficiens 1
sordida 1
Siphlonurus
alternatus No 0
Siphloplecton
speciosum Yes 0
Stenacron
candidum 0
Carolina 0
f1 oridense Yes 0
interpunctatum X Yes 2
pallidum No 2
Stenonema
annexum 0
ares No 1
bipunctatum X No 2
carlsoni 0
exiguum Yes 3
femoratum X No 3
fuscura 2
integrum No 3
ithaca 0
luteum 0
mediopunctatum 1
modestum 1
231
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
ii||i!"ii|i VUM' ' .Ml
Heavy Metals
Taxa Tolerant Sensitive
nepotellum
pudicum
pulchellura
quinquespinum
rubromacul atum
rubrum
smithae
terminatum
tripunctatum X
vicariura X
Tortopus
incertus
Tricorythodes
albilineatus
rainutus
i " '' " ', HI"'-"
«i ' , " 7 ' ;
INSECTA - PLECOPTERA
Acroneuria
abnormis X
;, "arida
carol inens is
evoluta X
georgiana
internata X
lycorias
perplexa
rural is X
xanthenes
Agnetina
capitata
Allocapnia
granul ata
nivicola
recta
rickeri
vivipara
Amphinemura
delosa
linda
Atoperl a
ephyre
Attaneuria
rural i s
Brachyptera
fasciata
Acid
Tolerant
No
No
No
No
Yes
Yes
No
No
No
Yes
No
Yes
No
No
No
Yes
No
No
No
No
No
No
Yes
To] erance to Organ i c
Tolerant Facul tati ve
2
2 ;
" ' 2
2
2
3
, ,,, 2
"! .'' :: ,lif,K ,* if ., _'i'"ij,vr ' ' . "
, ' ,iii , , " '"' 'i. " |, ji'iT'i" j, ,;iil, ,,,,,, v
2
2
2 ;
2, ;. '
Wastes*
Intolerant
, -, ' - ,
1
1
' . , 1 .
1
1
1
;: ; " v ,
in, ', '. I1!"',! !' ,
"".".".'.. 1 '. "
1
1
Q
1
l
1
"l
0
0
6
0
i
;. ".. ,"p
0
i
; :: ...,r .
232
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Taxa
Clioperla
clio
Diploperla
duplicata
Eccoptura
xanthenes
Hastaperla
brevis
Hesperoperla
pacifica
Hydroperl a
crosbyi
Isogenoides
frontal is
olivaceus
Isogenus
bilobatus
decisus
subvarians
Isoperla
bilineata
clio
cotta
decepta
dicala
frisoni
fulva
holochlora
lata
marlynia
mohri
namata
nana
orata
richardsoni
signata
similis
slossonae
transmarina
Leuctra
ferruginea
sibleyi
tenella
tenuis
Nemocapnia
Carolina
Heavy Metals Acid
Tolerant Sensitive Tolerant
No
Yes
No
No
No
No
No
Yes
No
No
No
No
No
No
No
Yes
X
Tolerance to Organic
Tolerant Facultative
2
,
2
2
2
2
2
2
2
2
Wastes*
Intolerant
1
0
1
1
0
0
0
1
1
1
0
1
1
1
1
0
1
1
1
1
1
0
0
0
0
0
233
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
~~~^ Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Nemoura
trispinosa
Neoperl a
clymene
stewarti
Oemopteryx
glacial is
Paracapnia
angul ata
Paragnetina
immarginata
media
Perlesta
frisoni
placida
Perlinella
drymo
ephyre
Phasganophora
capitata
Prostoia
completa
similis
Shipsa
rotunda
Soyedina
vail icul aria
Strophopteryx
fasciata
Sweltsa
med i ana
Taeniopteryx
burksi
maura
metequi
nivalis
parvul a
Zapada
cinctipes
Zealeuctra
claasseni
'
X No
X No
X No
No
X
X No
No
Yes
No
Yes
X No
No
No
; '
1
1
1
1
1
1
1
Z" . '. ' :'".' ' '.. I "i
2
1
1
1 , .
1
"l
l",,'"
0
1
o
2
2
2
1
1
1
,.' ,',, ' .. ' ' '. .,'.0." ','
234
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
INSECTA - ODONATA
Aeshna
umbrosa No 2
Amphiagrion
saucium 5
Anax
junius No 3
Argia
apical is No 3
moesta X No 4
trans!ata No 2
Basiaeschna
Janata 3
Boyeria
grafiana 3
vinosa X Yes 2
Calopteryx
aequabilis 3
maculata 3
Cannacri a
gravida 2
Chromagrion
conditum 2
Coenagrion
resolutum 4
Cordulegaster
erroneus 2
fasciatus 1
maculatus 2
sayi 0
Dromogomphus
spinosus X No 2
spoliatus No 1
Didymops
transversa 2
Enallagma
antennatum No 3
civile 4
ebrium 4
hageni 4
si gnaturn Yes 2
Epitheca
cynosura 2
princeps 2
semiaquea 1
235
-------
I1'1!'!' '"'Ilii "nil1 ,!''": II "Xlirirt'll: ,] '"iP! IB'Til"11'1!III1!"1":!!!"; ir! ,'""
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa , (i Tolerant Sensj^ive ^pljB^a^t^T^e^a^F^cyl.t^tiye Iptol erant
Erythrodiplax
berenice
connata
Gomphus
externus
pallidus
plagiatus
spiniceps
vastus
Hagenius
brevistylus
Hetaerina
' " '
No
No
No
No
' ' ' :
2
' 2
2
2
2
2
2 '
1
americana 3
titia No 0
Hylogomphus
brevis 2
Ischnura
posita No 3
vertical is No 4
Lanthus , , ' ,, . " , .' ,",, ,., ,.,,.,
albi sty!us No 1
parvulus 3
Leucorrhinia
intacta 4
Libellula
deplanata 2
lydia 2
pulchella 4
Macroraia ,, , , ,,vi v, ,, , ,,,, i;i,
georgiana 1
illinoiensis 1
taeniolata 2
Neurocordulia
molesta 2
obsoleta No 1
yamaskanensis 0
Pachydiplax
longipennis No 3
Plathemis
lydia 4
Progomphus
obscurus No 3
Stylogomphus
albistylus 0
236
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
INSECTA - NEUROPTERA
Climacia
areolaris X No 1
INSECTA - HEGALOPTERA
Chauliodes
pectinicornis 2
rastricornis 2
Corydalus
cornutus X Yes 3
Nigronia
fasciatus 0
serricornis 1
Sialis
infumata X Yes 4
INSECTA - HENIPTERA
Belostoma
fluminea X Yes 4
Benacus
griseus 2
Callicorixa
audeni Yes 2
Hydrometra
martini 4
Limnogonus
hesione X No 3
Merragata
hebroides 3
Mesovelia
mulsanti 3
Nepa
apiculata 1
Rhagovelia
obesa X 3
INSECTA - COLEOPTERA
Anchytarsus
bicolor 1
Ancyronyx
variegatus Yes 2
Anodocheilus
exiguus 2
Bidessus
fuscatus 2
237
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
~:Heavy MetalsAcidTolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Copelatus
glyphicus 2
Cyfaister
fimbriolatus 3
Deral1 us
altus 2
Dibolocelus
ovatus 3
Dineutus
araericanus 4
Dubiraphia ',
bivittata 4
minima 3
quadrinotata X Yes 3
vittata X 2
Dytlscus
hybridus 2
Ectopria
nervosa X 0
Gonielrais
dietrichi 2
Graphoderus
Hberus 2
Gyrinus
floridensis 4
Haliplus
fasciatus 3
Helichus
lithophilus 3
striatus 3
Helochares
maculicollis 2
Hoperius
planatus 1
Hydrochara
obtusata 1
Hydrophilus
triangularis 1
Hyogrotus
farctus 2
Laccobius
agilis 2
Laccophilus
maculosus 4
Laccornis
difformis 2
238
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Macronychus
glabratus X Yes 2
Matus
ovatus 2
Microcylloepus
pusillus 1
Optioservus
fastiditus 2
oval is No 2
trivittatus 1
Oulimnius
latiusculus 0
Pelonomus
obscurus 2
Peltodytes
muticus 3
sexmaculatus 3
Promoresia
elegans 0
tardella 0
Psephenus
herricki X No 1
Ptilodactyla
augustata 0
serricollis . 0
Sperchopsis
tesselatus 2
Stenelmis
crenata X Yes 1
decorata X No 4
sexlineata X No 3
Tropisternus
dorsal is 3
lateral is 4
natator 4
MOLLUSCA - GASTROPODA
Amnicola y
emarginata 1
limosa No 1
Aplexa
hypnorum No 2
Bithynia
tentaculata 4
239
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
' '"" ''''"' >' ' " ' : s'' 1 ; ' "'
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
. ;;, "' ' '". i' Hi.' ' «ii i, <: { -...VUHNV1'1' :' v ,: ,Vi iJ-'. ;" ''MS
"Ij I!
Campel oma
decisum
integrum
ruf urn
subsolidum
Elirnia
livescens
virginica
Ferrissia
fusca
rivularis
tarda
Fossaria
modi eel la
obrussa
Gyraulus
arcticus
Helisoma
anceps
trivolvis
Lioplax
subcarinata
Lymnaea
appressa
humilis
ovata
peregrina
stagnalis
Neoplanorbis
carinatus
Physa
fontinalis
halei
Physel 1 a
acuta
anatina
cubensis
gyrina ?
heterostropha
Integra
Planorbis
trivolvis
Planobula
arraigera
, |f . 'i.1 .'.' " "V,, t . }! I",:' "',- , f , 1 '": ' '. ; KH"1 Jfci ., '" l!",""!n. ti!
',. r ,..,yes , '.;.,;' 3 ." ', ,
2
........ . 2
3
No 3
No 4
3
No 1
No 3
,
4
3
,, , ' 3' ' ".
,
' '"'" "No' ! "" 3 ' "
No 4
1
1
3
5
3
No 2
"." , ,1
2
4
2
, . ,' ,4'",,,"
, . ..4
4
No 4
No 4
, 4 . ,'- " " ','
4 'i;-, ' '_' t ^ ",.'..".,"
i ......... LtJlfl
246
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Pleurocera
acuta
lewisi
Pseudosuccinea
col umel 1 a
Radix
auricularia
Stagnicola
caperata
catascopium
palustris
Valvata
bicarinata
piscinalis
sincera
tricarinata
Viviparus
contectoides
subpurpureus
No
4
No
No
No
No
No 4
2
2
3
3
3
2
2
3
1
1
1
MOLLUSCA - BIVALVIA
Alasmidonta
triangulata
undulata
Amblema
plicata
Anodonta
cataracta
gibbosus
grandis
imbecillus
implicata
undulata
Corbicula
manilensis
Cyclonaias
tuberculata
Elliptic
complanata
congaraea
icterina
shepardiana
waccamawensis
Yes
2
2
3
2
2
2
3
2
2
1
1
0
0
241
-------
';! ,i ,i, ,i v'l1' : .'I'l'i ,"' " ,;,!'!','
'! .' .,: I"1"!!!1!1!!,' , 'Ih
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES(Continued)
' ' ' , , : t ': ' . , , , ' ' ,;',. 'I1',!,; ,» 1'""! 1 i';:i'i|i"i »;T,"iil : ail" , "I . '. A f"i:i. : '"
Heavy Metals Acid Tolerance to Organic Wastes*
Taxa TpJeranJ; Sensitive Tolerant Tolerant Facultative Intolerant
i ; ji is,''»"
Eupera
cubensis
Lampsilis
cariosa
1 uteol a
ochracea
3
, .2
,::,':: : ;.
0
0
parvus 2
teres 2
Lasmigona
complanata 2
costata 2
Leptpdea
fragilis 0
Hargaritifera
margaritifera 0
Musculium ..,,, ,
partumeium 4
securis 2
transversum 3
Obliquaria
reflexa 0
Pisidiura
abditum 4
amnicum 2
casertanum 4
complanatum 4
compressum 4
crystalense 2
fallax 2
henslowanum 2
idahoense 4
subtruncatura 2
Proptera
alata ,, . , . < - 1
Quadrula
lachrymosa 2
pustulosa 2
rubiginosa 2
Rangia
cuneata 3
I
242
t nil, > il IX,.
' .', . ' ,< Vi ," if*!1 "Ill" : ' '..i1'"! '.. . :
:a;',"t'";l"8 l;:. '*: -'il-liii!1''. iil;i;'!" !..'Jli:,'!s I'tlii'
-------
POLLUTION TOLERANCE OF SELECTED MACROINVERTEBRATES (Continued)
Heavy Metals Acid .Tolerance to Organic Wastes*
Taxa Tolerant Sensitive Tolerant Tolerant Facultative Intolerant
Sphaerium
corneum 3
lilycashense 3
notatum 4
rhomboideum 3
solidula 1
sulcatum 3
stamineum 3
striatinum 3
transversutn 4
Strophitus
edentulus 2
Truncilla
donaciformis 1
Uniomerus
tetralasmus 1
*Ranking from 0 to 5 with 0 being the least tolerant.
References used in determining tolerances
Beck, W.M. 1977. Environmental requirements and pollution tolerance of common
freshwater Chironomidae. EPA-600/4-77-024. U.S. Environmental Protection
Agency, Environmental Monitoring and Support Laboratory, Cincinnati, OH
45268.
Harris, T.K. and T.M. Lawrence. 1978. Environmental requirements and
pollution tolerance of Trichoptera. EPA-600/4-78-063. U.S. Environmental
Protection Agency, Environmental Monitoring and Support Laboratory,
Cincinnati, OH 45268.
Hart, C.W., Jr. and S.L.H. Fuller. 1974. Pollution ecology of freshwater
invertebrates. Academic Press, New York.
Hubbard, M.D. and W.L. Peters. 1978. Environmental requirements and
pollution tolerance of Ephemeroptera. EPA-600/4/78-061. U.S.
Environmental Protection Agency, Environmental Monitoring and Support
Laboratory, Cincinnati, OH 45268.
Hilsenhoff, W.L. 1977. Use of arthropods to evaluate water quality of
streams. Tech. Bull.,Wisconsin Dept. Nat. Resour. 100. 15 pp.
Hilsenhoff, W.L. 1987. An improved biotic index of organic stream
pollution. Great Lakes Entomol. 20(l):31-39.
243
-------
','i.JI Jl'l""
Mason, W.T., Jr., P.A. Lewis, and J.B. Anderson, 1971.
Macroinvertebrate collections and water quality monitoring in the
Ohio River Basin 1963-1967. Office of Technical Programs, Ohio
Basin Region and Analytical Quality Control Laboratory, U.S.
Environmental Protection Agency, Cincinnati, OH 45202.
Penrose, D. 1978. Aquatic macroinvertebrate species list and assigned biotic
index values. Water Quality Operations Branch, North Carolina Department
of Natural Resources and Community Development, Raleigh, NC.
Plafkin, J.L., M.T. Barbour, K.D. Porter, S.K. Gross, and R.M. Hughes.
1989. Rapid bioassessment protocols for use in streams and rivers:
Benthic macroinvertebrates and fish. EPA/444/4-89-001. U.S.
Environmental Protection Agency, Office of Water, Washington, DC
20460. ^ ' ^ , , , , .. ,, . , i.,:,a. ,.,, , , , , ;, , , ,, , ,..,.
. ,, ; ',.' ' , : i* ',; ..." ; iiihr:;"1; ;:il"\:«:;:;,-,-i:;":'' t;f ."'> '- ' «' n.
Rabeni, C.F., S.P. Davies, and K.E. Gibbs. 1985. Benthic invertebrate
response to pollution abatement: Structural changes and functional
implications. Wat. Resources Bull. 21(3):489-497.
Surdick, R.F. and A.R. Gaufin. 1978. Environmental requirements and
pollution tolerance of Plecoptera. EPA-600/4-78-062. U.S.
Environmental Protection Agency, Environmental Monitoring and Support
Laboratory, Cincinnati, OH 45268.
244
-------
APPENDIX B
Hilsenhoffs Family Level Pollution Tolerance Values for Aquatic Arthropods1
Order Family Tolerance Value
PIecoptera
Ephemeroptera
Odonata
Trichoptera
Capniidae
Chloroperlidae
Leuctridae
Nemouridae
Perlidae
Perlodidae
Pteronarcyidae
Taeniopterygidae
Baetidae
Baetiscidae
Caenidae
Ephemerellidae
Ephemeridae
Heptageniidae
Leptophlebiidae
Metretopodidae
Oligoneuriidae
Polymitarcyidae
Potomanthidae
Siphlonuridae
Tricorythidae
Aeshnidae
Calopterygidae
Coenagrionidae
Cordulegastridae
Corduliidae
Gomphidae
Lestidae
Libellulidae
Macromiidae
Brachycentridae
Glossosomatidae
Helicopsychidae
Hydropsychidae
Hydroptilidae
Lepidostomatidae
Leptoceridae
1
1
0
2
1
2
0
2
4
3
7
1
4
4
2
2
2
2
4
7
4
3
5
9
3
5
1
9
9
3
1
0
3
4
4
1
4
Hilsenhoff, 1988. Rapid field assessment of organic pollution with a
family-level biotic index. O.N. Am. Benthol. Soc. 7(l):65-68.
245
-------
Order
Trichoptera (cont.)
Hegaloptera
Lepidoptera
Coleoptera
Dlptera
Amphipoda
Isopoda
Family
Limnephilidae
Molannidae
Odontocefidae
Philopotamidae
Phryganeidae
Polycentropodidae
Psychomyiidae
Rhyacophilidae
Sericostomatidae
Corydalldae
Slalidae
Pyralidae
Dryopidae
Elmidae
Psephenidae
Tolerance Value
4
6
0
3
4
6
2
0
3
0
4
5
4
4
Athericidae 2
Blephariceridae 0
Ceratopogonidae 6
Blood-red Chirpnpmidae(Chironomini) 8
Other (including pink) Chironomidae 6
Dolochopodidae 4
Empididae 6
Ephydridae 6
Psychodidae 10
Simuliidae
Muscidae
Syrphidae
Tabanidae
Tipulidae
Gammaridae
Talitridae
Asellidae
6
6
10
6
3
4
8
8
246
-------
APPENDIX C
EXAMPLES OF MACROINVERTEBRATE BENCH SHEETS
247
-------
Type of Sampler_
Collection Depth.
Sybstrate Type
Remarks
MACROINVERTEBRATE DATA SHEET
Sample No._
Date
Location
: / i\"if*: fii X' i!: : ;! ;«
Identification by_
Station #_
Collector
Enter Family and/or Genus and Species Name on Blank Line.
Oraanfsms
Diotera
Chironomidae
, , ; \
. !:" ii
" ' '1 : ":ll|ll!
,i ' , I1"
1 ' ' ill1
11
-.
Other
Trichootera
.- , '"
n1 ' ' '' :;
.,.
Plecootera
.,.
' '!»
""
Eohemeroptera
,i ' HI!
, ...
Odonata
'''" , "
Henri Dtera
i in" ! i1 " 'i
No.
A.
I.
A - Adult, I - Immature
Total No. Organisms
Coleoptera
Neuroptera and Meqaloptera
Crustacea
Oliqochaeta
Hirudinea
Bivalvia
Gastropoda
Brvozoa
Coelenterata
Other
No.
A.
I.
Total No. Taxa
248
:',! I ',;, , ; ' 'I"
-------
MARINE MACROINVERTEBRATES
Name of water
Collected by_
Sorted by_
body_
Sample No.
Station No.:
Date collected
Identified by_
Group*
Porifera
Hydrozoa
Scyphbzoa
Anthozoa
Ctenophora
Turbellaria
Rhynchocoela
Echiura
Priapulida
Sipuncula
PoqonoDhera
Polvchaeta
Oliqochaeta
Hirudinea
Monoplacophora
Polyplacophora
Aplacophora
Bivalvia
Gastropoda
Scaphopoda
Cephalopoda
Merostomata
Pycnoqonida
Ostracoda
Cirripedia
Leptostraca
Stomatopoda
Cumacea
Tanaidacea
Isoooda
Amphipoda
Decapoda
Phoronida
Bryozoa
Entoprocta
Brachiopoda
Cinoidea
Stelleroidea
Ethinoidea
Holothuroidea
Enteropneusta
Pterobranchia
Chaetoqnatha
Urochordata
Cephalochorsata
Number of
Orders
Number of.
Families
Number of
Genera
Number of
Species
Total
Individuals
*Use separate sheet for taxa names when identified beyond group.
249
-------
Ill I'
I' I
APPENDIX D
EXAMPLE OF MACROINVERTEBRATE SUMMARY SHEET
'Slit, i ' 1
; v1 , ) :!,,,:,
5 "',.,''" ' '.-i, i:
1111 ill;" " "I'l "nlii'J
i!:;111-,, , an1 "'i jpiit.i'i,
,; is,, ".'HI, ,
-------
MACROINVERTEBRATE LABORATORY - Summary of Data
Water Body.
Location
Sampl er
: Bpttom Type
Depth to Sampler,
Organisms:
Diptera
Chironomidae
Other
Trichoptera
Plecoptera
Ephemeroptera
Odonata
Anisoptera
Zvqoptera
Neuroptera
Hemiptera
Coleoptera
Lepidoptera
Crustacea
Amphipoda
Isopoda
Annelida
Oliqochaeta
Hirudinea
Turbellaria
Mollusca
Pelecvpoda
Gastropoda
Bryozoa
Coelenterata
T. Individuals
T. Species
/
X = organisms present, not counted
Species Present:
F - fragmented E - exuvia
251
-------
SUMMARY OF MACROINVERTEBRATE DATA
STATION (LOCATION):
ORGANISM:
Date
Total Individuals
Total Taxa
252
-------
APPENDIX E
LIST OF EQUIPMENT AND SUPPLIES
Listed below are equipment and supplies needed for the collection
and analysis of macroinvertebrate samples. The data quality objectives
and sampling and analysis methods should determine the type of equipment
and supplies needed. The source numbers refer to the companies that are
listed at the end of the table. Mention of these sources or products
does not constitute endorsement by the U.S. Environmental Protection
Agency.
Item Unit Source
Boat, flat bottom, 14-16 ft
snatch-block meter
wheel and trailer, 18 hp
outboard motor, life
jackets, other accessories 1 (7,15)
Boat crane kit and winch , 1 (3,15)
Boat, inflatable with oar set 1 (1»15)
Cable fastening tools (4,15)
Cable clamps, 1/8 " 25
Nicro-press clamps, 1/8 " 100
Nicro-press tool, 1/8 " 1
Wire cutter, Felco 1
Wire thimbles, 1/8 " 25
Cable, 1/8 ", galvanized steel 1000 ft (3,15)
Large capacity metal wash tub 1
Sample wash bucket (sieve) 1 (8,14)
Core sampler, hand held 1 (3,8,14)
Box corer 1 (14)
K-B corer 1 (8)
Wide-barrel gravity corer 1 (14)
Phleger corer 1 (8,14)
Ballchek single or multiple corer 1 (8,14)
Ewing portable piston corer 1 (14)
Hardboard multiplate sampler 10 (3,8)
Ceramic multiplate sampler 10 (14)
Trawl net 1 (8)
Dredge 1 (3,8,14)
Rectangular box sediment sampler 1 (14)
Drift net, stream 6 (8,14)
Triple-net drift sampler 2 (14)
Stream bottom sampler, Surber type 2 (3,8,14)
Portable invertebrate box sampler 2 (13)
Stream-bed fauna sampler, Hess type 2 (14)
Hess stream bottom sampler 2 (8)
Grab sampler, Ponar 1 (3,8,14)
253
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WUdco box corer
Grab sampler, Ekman
Grab sampler, Petersen
Grab sampler, Smith-Mclntyre
Grab sampler, Van Veen
Grab sampler, Orange Peel
Grab sediment sampler, Shipek
Basket, bar B-Q, tumbler (#740-0035)
Sieves, US standard No. 30
Flow meter, mechanical
Mounting media, CMCP-9/9AF with stain
Mounting medium, CMCP-9
Mounting medium, CMCP-10
Fuchsin basic, C.I. dye
Mounting medium, Aquamount
Refrigerated circulator
Water pump, epoxy-coated
Holding tank, constant temp
Balance, top-loading
Counter, 12-unit, 2X6
Counter, hand tally
Waders, with suspenders
Boots, hip
Raincoat
Magni-focuser, 2X
Microscope, field
Magnifier, illuminated + base
Magnifier, pocket, 5X, 10X, and 15X
Microscope, compound, with
phase and bright-field,
trinocular, 10X and 15X
eyepieces, 4X, 10X, 20X,
45X and 100X objectives
Microscope, stereoscopic, with stand
Microscope slide dispenser
Microscope slides and cover
slips, 12 and 15 mm circles
Photographic system, photostar
Camera, photomicrographic,
with 50 mm lens
Stirrer, magnetic
Aquarium, 10 gal., with cover,
air pump and filter
Aquatic dip net, Model 412D
Jars, screw cap, specimen
Bottles, Wide mouth, 32 oz
Specimen jars, wide mouth, 4 oz
Specimen jars, wide mouth, 6 oz
Vials, specimen, 1 oz
Petri dish, ruled grid
Petri dish, compartmented
Watch glasses
1
1
1
1
1
1
1
12
2
1
4 oz
4 oz
4 oz
25 g
4 oz
1
2
1
1
1
2
1 pr
1 pr
1
1
1
1
1
(8)
(3,8,14)
(3,8,14)
(14)
(14)
(14)
(8)
(9,11)
(5)
(3)
No longer available
(6)
(6)
(6)
(12)
(5)
(1)
(10)
(5)
(3)
(3)
(1,15)
(1,15)
(3,15)
(5)
(3)
(3)
(3)
1
1
1
10 gross
1
1
1
1
2
5 dz
1 case
48
48
10 gross
4
1 case
10
(5)
(2)
(1)
(1)
(5)
(1,15)
(5)
(1,15)
(3)
(I)
(1)
(1)
(1)
(1)
(1)
(1)
(1)
254
-------
Vacuum oven
Sounding lead and calibrated line
Forceps, watchman's, stainless
Forceps, microdissection
Dissecting set, basic
Water test kit, limnology
Thermometer, digital
Wash bottle, wide mouth, 500 ml
Wash bottle, polyethylene, 4 oz
Dropper bottle, polystop, 30 ml
Desiccator, polypropylene
Clip board with cover
Calculator, scientific
Marker, permanent, black
Pen set, slim pack, Koh-i-noor
Heavy paper tags with string
Ice chest, insulated, 48 qt
Blue ice, soft pack
Plastic bags
Formalin, 10 percent
Ethyl alcohol
Trays, polypropylene, sorting
1
1
1 pr
2 pr
1
1
1
4
2
2
1
2
1
2
1
1000
2
10
100
4 L
20 L
6
(5)
(3)
(1)
0)
(1)
(1)
(1)
(1)
(1)
(2)
(1)
(3,15)
(3,15)
(3,15)
(3,15)
(1,15)
(3,15)
(3,15)
(3,15)
(2)
(2)
(5)
Sources of equipment and supplies:
1. Carolina Biological Supply Co.
2700 York Rd.
Burlington, NC 27215
2. Fisher Scientific
50 Fadem Rd.
Springfield, NJ 07081
3. Forestry Suppliers, Inc.
205 West Rankin Street
Jackson, MS 39284-8397
4. Industrial Rope Supply
5250 River Rd.
Cincinnati, OH 45233
5. Curtin Matheson Scientific, Inc.
9999 Veterans Memorial Drive
Houston, TX 77038-2499
6. Polyscience
400 Valley Rd.
Warrington, PA 18976
7. MonArk Boat Company
Monticello, AK 71655
255
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8. Wildlife Supply Company
301 Case Street
Saginaw, MI 48602
9. Tenacq
2007 NE 27th Ave.
Gainesville, FL 32609
10. Frigid Units, Inc.
3214 Sylvania Ave.
Toledo, OH 43613
11. W.C. Bradly Enterprises, Inc.
P.O. Box 1240
Columbus, GA 31993
i' i ; ,r in '1|h ,, I. ', i' ]; i '
12. Gallard-ScMesinger Chemical Mfg. Corp.
584 Mineola Avenue
Carle Place, NY 11514
13. Ellis-Rutter Associates
P.Q, Box 401
Punta Gorda, FL 33950
14. Kahl Scientific Instrument Corp.
P.O. Box 1166
El Cajon, CA 92022-1166
15. Locally
- .;:,:'Ms;1'1
",, ' ''il,ill": : ' 'fllhll. In / lif |ii
. ' ' :' Til1,!1 . '"': «
! : lit! .'til-!"
!V -.i"1
256
,11 "!
* U.S. GOVERNKSENT PIU^^'ING OFHCE:l 991 -5"te-187/20553
-------
-------
United States
Environmental Protection
Agency
Center for Environmental Research
Information
Cincinnati OH 45268
BULK RATE
POSTAGE & FEES PAID
EPA -
PERMIT No. G-35
Official Business
Penalty for Private Use, $300
Please make all necessary changes on the above label.
detach or copy, and return to the address in the upper
left-hand corner.
If you do not wish to receive these reports CHECK HERE a;
detach, or copy this cover, and return to the address in the
upper left-hand corner.
EPV600/4-90/030
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