EPA-670/4-73-001
July 1973
BIOLOGICAL FIELD AND LABORATORY METHODS
FOR MEASURING THE QUALITY OF SURFACE WATERS AND EFFLUENTS
Edited by
Cornelius I. Weber, Ph.D.
Chief, Biological Methods
Analytical Quality Control Laboratory
National Environmental Research Center-Cincinnati
Program Element 1BA027
NATIONAL ENVIRONMENTAL RESEARCH CENTER
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
CINCINNATI, OHIO 45268
-------
Review Notice
This report has been reviewed by the National Environmental Research
Center, Cincinnati, and approved for publication. Mention of trade names or
commercial products does not constitute endorsement or recommendation
for use.
-------
FOREWORD
Man and his environment must be protected from the adverse effects of
pesticides, radiation, noise and other forms of pollution, and the unwise
management of solid waste. Efforts to protect the environment require a
focus that recognizes the interplay between the components of our physical
environment — air, water, and land. The National Environmental Research
Centers provide this multidisciplinary focus through programs engaged in
• studies on the effects of environmental contaminants on man and the
biosphere, and
• a search for ways to prevent contamination and to recycle valuable
resources.
This manual was developed within the National Environmental Research
Center — Cincinnati to provide pollution biologists with the most recent
methods for measuring the effects of environmental contaminants on fresh-
water and marine organisms in field and laboratory studies which are carried
out to establish water quality criteria for the recognized beneficial uses of
water and to monitor surface water quality.
Andrew W. Breidenbach, Ph.D.
Director
National Environmental
Research Center, Cincinnati, Ohio
in
-------
PREFACE
This manual was published under Research Objective Achievement Plan
1BA027-05AEF, "Methods for Determining Biological Parameters of all
Waters," as part of the National Analytical Methods Development Research
Program. The manual was prepared largely by a standing committee of senior
Agency biologists organized in 1970 to assist the Biological Methods Branch
in the selection of methods for use in routine field and laboratory work in
fresh and marine waters arising during short-term enforcement studies, water
quality trend monitoring, effluent testing and research projects.
The methods contained in this manual are considered by the Committee
to be the best available at this time. The manual will be revised and new
methods will be recommended as the need arises.
The Committee attempted to avoid duplicating field and laboratory
methods already adequately described for Agency use in Standard Methods
for the Examination of Water and Wastewater, 13th edition, and frequent
reference is made to this source throughout the manual.
Questions and comments regarding the contents of this manual should be
directed to:
Cornelius I. Weber, Ph.D.
Chief, Biological Methods Branch
Analytical Quality Control Laboratory
National Environmental Research Center
U.S. Environmental Protection Agency
Cincinnati, Ohio 45268
-------
BIOLOGICAL ADVISORY COMMITTEE
January 1, 1973
CHAIRMAN: Cornelius I. Weber, Ph. D.
Name
Anderson, Max
Arthur, John W.
Bugbee, Stephen L.
DeBen, Wally
Duffer, Dr. William R.
Gakstatter, Dr. Jack H.
Harkins, Dr. Ralph
Horning, William
Ischinger, Lee
Jackson, Dr. Herbert W.
Karvelis, Ernest
Kerr, Pat
Keup, Lowell E.
Kleveno, Conrad
LaBuy, James
Lassiter, Dr. Ray
Program
Indiana Office, Region V, Evansville,
IN
Natl. Water Quality Lab, Duluth, MN
Region VII, Kansas City, MO
Natl. Coastal Pollution Research
Program, Corvallis, OR
Natl. Water Quality Control Research
Program, Ada, OK
Natl. Eutrophication Survey Pro-
gram, Corvallis, OR
Region VI, Ada, OK
Newtown Fish Toxicology Lab, New-
town, OH
Natl. Field Investigations Center,
Cincinnati, OH
Natl. Training Center, Cincinnati, OH
Natl. Field Investigations Center,
Cincinnati, OH
Natl. Fate of Pollutants Research
Program, Athens, GA
Office of Air & Water Programs,
Washington, DC
Region V, Chicago, IL
Region HI, Charlottesville, VA
Region IV, Southeast Water Lab,
Athens, GA
Name
Maloney, Thomas
Mathews, John
Murray, Thomas
Nadeau, Dr. Royal
Nebeker.Dr. Alan V.
Oldaker, Warren
Parrish, Loys
Phelps, Dr. Donald K.
Prager, Dr. Jan C.
Preston, Ronald
Sainsbury, John
Tebo, Lee
Thomas, Nelson A.
Tunzi, Dr. Milton
Wagner, Richard A.
Warner, Richard W.
Program
Natl. Eutrophication Research Pro-
gram, Corvallis, OR
Region VI, Dallas, TX
Office of Air & Water Programs,
Washington, DC
Oil Spill Research Program, Edison,
NJ
Western Fish Toxicology Lab, Cor-
vallis, OR
Region I, Needham Heights, MA
Region VIII, Denver, CO
Natl. Marine Water Quality Labora-
tory, Narragansett, RI
Natl. Marine Water Quality Labora-
tory, Narragansett, RI
Wheeling Office, Region III,
Wheeling, WV
Region X, Seattle, WA
Region IV, Athens, GA
Large Lakes Research Program,
Grosse He, MI
Region IX, Alameda, CA
Region X, Seattle, W A
Natl. Field Investigations Center,
Denver, CO
Other personnel who were former members of the Advisory Committee or assisted in the preparation
of the manual:
Austin, R. Ted
Boyd, Claude E.
Collins, Dr. Gary
Garton, Dr. Ronald
Hegre, Dr. Stanley
Katko, Albert
McFar land, Ben
Natl.Eutrophication Survey, Corvallis,
OR
Savannah River Ecology Lab, Aiken,
SC
Analytical Quality Control Lab,
Cincinnati, OH
Western Fish Toxicology Lab, Cor-
vallis, OR
Natl. Marine Water Quality Lab,
Narragansett, RI
Natl. Eutrophication Survey, Cor-
vallis, OR
Analytical Quality Control Labora-
tory, Cincinnati, OH
McKim, Dr. James
Mackenthun, Kenneth
Mason, William T. Jr.
Lewis, Philip
Schneider, Robert
Seeley, Charles
Sinclair, Ralph
Stephan, Charles
Natl. Water Quality Lab, Duluth, MN
Office of Air and Water Programs,
EPA, Washington, DC
Analytical Quality Control Lab,
Cincinnati, OH
Analytical Quality Control Lab,
Cincinnati, OH
Natl. Field Investigations Center,
Denver, CO
Region IX, San Francisco, CA
Natl. Training Center, Cincinnati,
OH
Newtown Fish Toxicology Lab, New-
town, OH
VI
-------
PERSONNEL CONTRIBUTING TO THE
BIOLOGICAL METHODS MANUAL
SUBCOMMITTEES:
Biometrics
Lassiter, Dr. Ray — Chairman
Harkins, Dr. Ralph
Tebo, Lee
Plankton
Maloney, Thomas — Chairman
Collins, Dr. Gary
DeBen, Wally
Duffer, Dr. William
Katko, Albert
Kerr, Pat
McFarland, Ben
Prager, Dr. Jan
Seeley, Charles
Warner, Richard
Periphyton-Macrophyton
Anderson, Max - Chairman
Boyd, Dr. Claude E.
Bugbee, Stephen L.
Keup, Lowell
Kleveno, Conrad
Macroinvertebrates
Tebo, Lee — Chairman
Carton, Dr. Ronald
Lewis, Philip A.
Mackenthun, Kenneth
Mason, William T., Jr.
Nadeau, Dr. Royal
Phelphs, Dr. Donald
Schneider, Robert
Sinclair, Ralph
Fish
LaBuy, James — Chairman
Karvelis, Ernest
Preston, Ronald
Wagner, Richard
Bioassay
Arthur, John — Chairman
Hegre, Dr. Stanley
Ischinger, Lee
Jackson, Dr. Herbert
Maloney, Thomas
McKim, Dr. James
Nebeker, Dr. Allan
Stephan, Charles
Thomas, Nelson
Vll
-------
INTRODUCTION
The role of aquatic biology in the water
pollution control program of the U. S.
Environmental Protection Agency includes field
and laboratory studies carried out to establish
water quality criteria for the recognized
beneficial uses of water resources and to
monitor water quality.
Field studies are employed to: measure the
toxicity of specific pollutants or effluents to
individual speciqs or communities of aquatic
organisms under natural conditions; detect
violations of water quality standards; evaluate
the trophic status of waters; and determine
long-term trends in water quality.
Laboratory studies are employed to: measure
the effects of known or potentially deleterious
substances on aquatic organisms to estimate
"safe" concentrations; and determine environ-
mental requirements (such as temperature, pH,
dissolved oxygen, etc.) of the more important
and sensitive species of aquatic organisms. Field
surveys and water quality monitoring are
conducted principally by the regional
surveillance and analysis and national enforce-
ment programs. Laboratory studies of water
quality requirements, toxicity testing, and
methods development are conducted principally
by the national research programs.
The effects of pollutants are reflected in the
population density, species composition and
diversity, physiological condition and metabolic
rates of natural aquatic communities. Methods
for field surveys and long-term water quality
monitoring d-- oribed in this manual, therefore,
are directed marily toward sample collection
and processing, organism identification, and the
measurement cf biomass and metabolic rates.
Guidelines are also provided for data evaluation
and interpretation.
There are three basic types of biological field
studies; reconnaissance surveys, synoptic
surveys, and comparative evaluations. Although
there is a considerable amount of overlap, each
of the above types has specific requirements in
terms of study design.
Reconnaissance suiveys may range from a
brief perusal of the stuay area by boat, plane, or
car, to an actual field study in which samples are
collected for the purpose of characterizing the
physical boundaries of the various habitat types
(substrate, current, depth, etc.) and obtaining
cursory^ information on the flora and fauna.
Although they may be an end in themselves,
reconnaissance surveys are generally conducted
with a view to obtaining information adequate
to design more comprehensive studies. They
may be quantitative or qualitative in approach.
As discussed in the biometrics section, quantita-
tive reconnaissance samples are very useful for
evaluating the amount of sampling effort
required to obtain the desired level of precision
in more detailed studies.
Synoptic surveys generally involve an attempt
to determine the kinds and relative abundance
of organisms present in the environment being
studied. This type of study may be expanded to
include quantitative estimates of standing crop
or production of biomass, but is generally more
qualitative in approach. Systematic sampling, in
which a deliberate attempt is made to collect
specimens from all recognizable habitats, is
generally utilized in synoptic surveys. Synoptic
surveys provide useful background data, are
valuable for evaluating seasonal changes in
species present, and provide useful information
for long-term surveillance programs.
The more usual type of field studies involve
comparative evaluations, which may take various
forms including: comparisons of the flora and
fauna in different areas of the same body of
water, such as conventional "upstream-
downstream" studies; comparisons of the flora
and fauna at a given location in a body of water
over time, such as is the case in trend
monitoring; and comparisons of the flora and
fauna in different bodies of water.
Comparative studies frequently involve both
quantitative and qualitative approaches. How-
ever, as previously pointed out, the choice is
often dependent upon such factors as available
resources, time limitations, and characteristics of
the habitat to be studied. The latter factor may
be quite important because the habitat to be
studied may not be amenable to the use of quan-
IX
-------
titative sampling devices.
A special field method that warrants a brief
notation is scuba (Self Contained Underwater
Breathing Apparatus). Scuba enables the biolo-
gist to observe, first hand, conditions that other-
wise could be described only from sediment,
chemical, physical, and biological samples taken
with various surface-operated equipment. Equip-
ment modified from standard sampling equip-
ment or prefabricated, installed, and/or operated
by scuba divers has proven very valuable in as-
sessing the environmental conditions where sur-
face sampling gear was inadequate. Underwater
photography presents visual evidence of existing
conditions and permits the monitoring of long-
term changes in an aquatic environment.*
By utilizing such underwater habitats as
Tektite and Sublimnos, biologists can observe,
collect, and analyze samples without leaving the
aquatic environment. Scuba is a very effective
tool available to the aquatic biologist, and
methods incorporating scuba should be con-
sidered for use in situations where equipment
operated at the surface does not provide suffi-
cient information.
*Braidech, T.E., P.E. Gehring, and C.O. Kleveno. Biological
studies related to oxygen depletion and nutrient regeneration
processes in the Lake Erie Basin. Project Hypo-Canada Centre
for Inland Waters, Paper No. 6, U. S. Environmental Protection
Agency Technical Report TS05-71-208-24, February 1972.
SAFETY
The hazards associated with work on or near
water require special consideration. Personnel
should not be assigned to duty alone in boats,
and should be competent in the use of boating
equipment (courses are offered by the U. S.
Coast Guard). Field training should also include
instructions on the proper rigging and handling
of biological sampling gear.
Life preservers (jacket type work vests) should
be worn at all times when on or near deep water.
Boats should have air-tight o,r foam-filled com-
partments for flotation and be equipped with
fire extinguishers, running lights, oars, and
anchor. The use of inflatable plastic or rubber
boats is discouraged.
All boat trailers should have two rear running
and stop lights and turn signals and a license
plate illuminator. Trailers 80 inches (wheel to
wheel) or more wide should be equipped with
amber marker lights on the front and rear of the
frame on both sides.
Laboratories should be provided with fire
extinguishers, fume hoods, and eye fountains.
Safety glasses should be worn when mixing
dangerous chemicals and preservatives.
A copy of the EPA Safety Manual is available
from the Office of Administration, Washington,
D.C.
-------
CONTENTS
FOREWORD !
PREFACE
BIOLOGICAL ADVISORY COMMITTEE
PERSONNEL CONTRIBUTING TO THE
BIOLOGICAL METHODS MANUAL
INTRODUCTION
BIOMETRICS
PLANKTON
PERIPHYTON
MACROPHYTON
MACROINVERTEBRATES
FISH
BIOASSAY
APPENDIX
XI
-------
BIOMETRICS
-------
BIOMETRICS
Page
1.0 INTRODUCTION 1
1.1 Terminology 1
2.0 STUDY DESIGN 2
2.1 Randomization 2
2.2 Sample Size 4
2.3 Subsampling 6
3.0 GRAPHIC EXAMINATION OF DATA 6
3.1 Raw Data 6
3.2 Frequency Histograms 6
3.3 Frequency Polygon 7
3.4 Cumulative Frequency 7
3.5 Two-dimensional Graphs 8
4.0 SAMPLE MEAN AND VARIANCE 9
4.1 General Application 9
4.2 Statistics for Stratified Random Samples 10
4.3 Statistics for Subsamples 10
4.4 Rounding 10
5.0 TESTS OF HYPOTHESES 11
5.1 T-test 11
5.2 Chi Square Test 13
5.3 F-test 15
5.4 Analysis of Variance 15
6.0 CONFIDENCE INTERVALS FOR MEANS
AND VARIANCES 18
7.0 LINEAR REGRESSION AND CORRELATION 19
7.1 Basic Concepts 19
7.2 Basic Computations 20
7.3 Tests of Hypotheses 24
7.4 Regression for Bivariate Data 26
7.5 Linear Correlation 27
8.0 BIBLIOGRAPHY 27
-------
BIOMETRICS
1.0 INTRODUCTION
Field and laboratory studies should be well-
planned in advance to assure the collection of
unbiased and precise data which are technically
defensible and amenable to statistical evaluation.
The purpose of this chapter is to present some
of the basic concepts and techniques of sampling
design and data evaluation that can be easily
applied by biologists.
An attempt has been made to present the
material in a format comfortable to the non-
statistician, and examples are used to illustrate
most of the techniques.
1.1 Terminology
To avoid ambiguity in the following discus-
sions, the basic terms must be defined. Most of
the terms are widely used in everyday language,
but in biometry may be used in a very restricted
sense.
1.1.1 Experiment
An experiment is often considered to be a
rigidly controlled laboratory investigation, but
in this chapter the terms experiment, study, and
field study are used interchangeably as the
context seems to require. A general definition
which will usually fit either of these terms is
"any scientific endeavor where observations or
measurements are made in order to draw
inferences about the real world."
1.1.2 Observation
This term is used here in much the same
manner as it is in everyday language. Often the
context will suggest using the term "measure-
ment" in place of "observation." This will imply
a quantified observation. For statistical
purposes, an observation is a record representing
some property or characteristic of a real-world
object.
This may be a numeric value representing the
weight of a fish, a check mark indicating the
presence of some species in a bottom quadrat -
in short, any type of observation.
1.1.3 Characteristics of in terest
In any experiment or sampling study, many
types of observations or measurements could be
made. Usually, however, there are few types of
measurements that are related to the purpose of
the study. The measurement of chlorophyll or
ATP in a plankton haul may be of interest,
whereas the cell count or detritus content may
not be of interest. Thus, the characteristic of
interest is the characteristic to be observed or
measured, the measurements recorded, analyzed
and interpreted in order to draw an inference
about the real world.
1.1.4 Universe and experimental unit
The experimental unit is the object upon
which an observation is made. The characteristic
of interest to the study is observed and recorded
for each unit. The experimental unit may be
referred to in some cases as the sampling unit.
For example, a fish, an entire catch, a liter of
pond water, or a square meter of bottom may
each be an experimental unit. The experimental
unit must b^ clearly defined so as to restrict
measurements to only those units of interest to
the study. The set of all experimental units of
interest to the study is termed the "universe."
1.1.5 Population and sample
In biology, a population is considered to be a
group of individuals of the same species. The
statistical use of the term population, however,
refers to the set of values for the characteristic
of interest for the entire group of experimental
units about which the inferences are to be made
(universe).
When studies are made, observations are not
usually taken for all possible experimental units.
Only a sample is taken. A sample is a set of obser-
vations, usually only a small fraction of the total
number of observations that conceivably could
be taken, and is a subset of the population. The
term sample is often used in everyday language
to mean a portion of the real world which has
been selected for measurement, such as a water
-------
BIOLOGICAL METHODS
sample or a plankton haul. However, in this
section the term "sample" will be used to
denote "a set of observations" — the written
records themselves.
1.1.6 Parameter and statistic
When we attempt to characterize a popula-
tion, we realize that we can never obtain a per-
fect answer, so we settle for whatever accuracy
and precision that is required. We try to take an
adequately-sized sample and compute a number
from our sample that is representative of the
population. For example, if we are interested in
the population mean, we take a sample and com-
pute the sample mean. The sample mean is
referred to as a statistic, whereas the population
mean is referred to as a parameter. In general,
the statistic is related to the parameter in much
the same way as the sample is related to the pop-
ulation. Hence, we speak of population param-
eters and sample statistics.
Obviously many samples may be selected
from most populations. If there is variability in
the population, a statistic computed from one
sample will differ somewhat from the same
statistic computed from another sample. Hence,
whereas a parameter such as the population
mean is fixed, the statistic or sample mean is a
variable, and there is uncertainty associated with
it as an estimator of the population parameter
which derives from the variation among samples.
2.0 STUDY DESIGN
2.1 Randomization
In biological studies, the experimental units
(sampling units or sampling points) must be
selected with known probability. Usually,
random selection is the only feasible means of
satisfying the "known probability" criterion.
The question of why known probability is re-
quired is a valid one. The answer is that only by
knowing the probability of selection of a sample
can we extrapolate from the sample to the
population in an objective way. The probability
allows us to place a weight upon an observation
in making our extrapolation to the population.
There is no other quantifiable measure of "how
well" the selected sample represents the
population.
Thus our efforts to select a "good" sample
should include an appropriate effort to define
the problem in such a way as to allow us to
estimate the parameter of interest using a sample
of known probability; i.e., a random sample.
The preceding discussion should leave little
doubt that 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 tne
randomization is planned, and therefore bias is
planned out of the study. This is usually accom-
plished 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.
Reference to the definition of the term,
sample, at the beginning of the chapter will
remind us that a sample consists of a set of
observations, each made upon an experimental
or sampling unit. To sample randomly, the
entire set of sampling units (population) must be
identifiable and enumerated. Sometimes the task
of enumeration may be considerable, but often
it may be minimized by such conveniences as
maps, that allow easier access to adequate
representation of the entity to be sampled.
The comment has frequently been made that
random sampling causes effort to be put into
drawing samples of little meaning or utility to
the study. This need not be the case. Sampling
units should be defined by the investigator so as
to eliminate those units which are potentially of
no interest. Stratification can be used to place
less emphasis on those units which are of less
interest.
Much of the work done in biological field
studies is aimed at explaining spatial distri-
butions 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
-------
BIOMETRICS - RANDOM SAMPLING
in such a manner that a unit of space may be
used, so that random sampling may be more
easily carried out.
For example, suppose the problem is to
estimate the chlorophyll content of algae in a
pond at a particular time of year. The measure-
ment is upon algae, yet the sample consists of a
volume of water. We could use our knowledge of
the way the algae are spatially distributed or
make some reasonable assumptions, tnen
construct a random sampling scheme based upon
a unit of volume (liter) as the basic sampling
unit.
It is not always a simple or straightforward
matter to define sampling units, because of the
dynamic nature of 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 consider-
able.
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 sampling units have been
defined. At this point, random sampling pro-
vides an objective means of obtaining informa-
tion to achieve the objectives of the study.
One satisfactory method of random sample
selection is described. First, number the universe
or entire set of sampling units from which the
sample will be selected. This number is N. Then
from a table of random numbers select as many
random numbers, n, as there will be sampling
units selected for the sample. Random numbers
tables are available in most applied statistics
texts or books of mathematical tables. Select a
starting point in the table and read the numbers
consecutively in any direction (across, diagonal,
down, up). The number of observations, n
(sample size), must be determined prior to
sampling. For example, if n is a two-digit
number, select two-digit numbers ignoring any
number greater than n or any number that has
already been selected. These numbers will be the
numbers of the sampling units to be selected.
To obtain reliable data, information about the
statistical population is needed in advance of the
full scale study. This information may be
obtained from prior related studies, gained by
pre-study reconnaissance, or if no direct in-
formation is available, professional opinion
about the characteristics of the population may
be relied upon.
2.1.1 Simple random sampling
Simple (or unrestricted) 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 has an equal chance of being
selected. This may be accomplished by using the
random selection scheme already described.
2.1.2 Stratified random sampling
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 into subpopulations (strata) with a
resulting increase in efficiency of estimation.
Perhaps the most profitable means of obtaining
information for stratification is through a pre-
study reconnaissance (a pilot study). The pilot
study planning should be done carefully,
perhaps stratifying based upon suspected varia-
bility. 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 set up a more realistic study and
avoid costly and needless expenditures. To maxi-
mize precision, strata should be constructed
such that the observations are most alike within
strata and most different among strata, i.e.,
minimum variance within strata and maximum
variance among strata. In practice, the informa-
tion used to form strata will usually be from
previously obtained data, or information about
characteristics correlated with the characteristic
of interest. In aquatic field situations, stratifica-
tion may be based upon depth, bottom type,
isotherms, and numerous other variables sus-
pected of being correlated with the character-
-------
BIOLOGICAL METHODS
istic of interest. Stratification is often done on
other bases such as convenience or administra-
tive imperative, but except where these cor-
respond with criteria which minimize the
variation within strata, no gain in precision may
be expected.
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 feature may
be the obvious choice for the strata divisions).
Where a gradient is suspected and where stratifi-
cation 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 as many strata
are selected as may be handled in the study. In
practice, gains in efficiency due to stratification
usually become negligible after only a few divi-
sions unless the characteristic used as the basis
of stratification is very highly correlated with
the characteristic of interest.
2.1.3 Systematic random sampling
In field studies, the biologist frequently
wishes to use some sort of transect, perhaps to
be assured of including an adequate cross section
while maintaining relative ease of sampling. The
use of transects is an example of systematic
sampling. However, a random starting point is
chosen along the transect to introduce the
randomness needed to guarantee freedom from
bias and allow statistical inference.
The method of placement of the transect
should be given a great deal of thought. Often
transects are set up arbitrarily, but they should
not be. To avoid arbitrariness, randomization
should be employed in transect placement.
2.2 Sample Size
2.2.1 Simple random sampling
In any study, one important early question is
that of the size of the sample. The question is
important because if, on the one hand, a sample
is too large, the effort is wasteful, and if, on the
other hand, a sample is too small, the question
of importance to the study may not be properly
answered.
Case 1 — Estimation of a Binomial Proportion
An estimate of the proportion of occurrence
of the two categories must be available. If the
categories are presence and absence, let the
probability of observing a presence be P (0 < P
< 1) and the probability of observing an absence
be Q (0 < Q < 1, P + Q = 1). The second type of
information which is needed is an acceptable
magnitude of error, d, in estimating P (and
hence Q). With this information, together with
the size, n, of the population, the formula for n
as an initial approximation (n0), is:
(D
The value for t is obtained from tables of
"Student's t" distribution, but for the initial
computation the value 2 may be used to obtain
a sample size, n0, that will ensure with a .95
probability, that P is within d of its true value. If
n0 is less than 30, use a second calculation
where t is obtained from a table of "Student's t"
with n0- 1 degrees of freedom. If the calculation
results in an n0, where j£ < .05, no further
calculation is warranted. Use n0 as the sample
size. If -TT- > .05, make the following computa-
tion:
(2)
Case 2 — Estimation of a Population Mean for
Measurement Data
In this case an estimate of the variance, s2,
must be obtained from some source, and a state-
ment of the margin of error, d, must be ex-
pressed in the same units as are the sample
observations. To calculate an initial sample size:
d2
(3)
-------
BIOMETRICS - RANDOM SAMPLING
If n0 < 30, recalculate using t from the tables,
and if -Q°> .05, a further calculation is in order:
rio ,.,
n=^r (4)
After a sample of size, n, is obtained from the
population, the basic sample statistics may be
calculated. The calculations are the same as for
equations (11) through (15) unless the sample
size, n, is greater than 5 percent of the popula-
tion N. If ^% > .05, a correction factor is used so
that the calculation for the sample variance is:
_ /N-n\
~UTV
(5)
n-l
The other calculations make use of, s2, as
calculated above, wherever s2 appears in the
formulas.
2. 2. 2 Stra tified random sampling
To compute the sample size required to
obtain an estimate of the mean within a
specified acceptable error, computations can be
made similar to those for simple random
sampling: a probability level must be specified;
an estimate of the variance within each stratum
must be available; and the number of sampling
units in each stratum must be known. Although
this involves a good deal of work, it illustrates
the need for a pilot study and indicates that we
must know something about the phenomena we
are studying if we are to plan an effective
sampling program.
If the pilot study or other sources of informa-
tion have resulted in what are considered to be
reliable estimates of the variance within strata,
the sampling can be optimally allocated to
strata. Otherwise proportional allocation should
be used. Optimal allocation, properly used, will
result in more precise estimates for a given
sample size.
For proportional allocation the calculation for
sample size is:
(6)
Nd2
1 +
where t - the entry for the desired probability
level from a table of "Student's t" (use 2 for a
rough estimate); Nk = the number of sampling
units in stratum k; sk2 = the variance of stratum
k; N = the total number of sampling units in all
strata; and d = the acceptable error expressed in
the same units as the observations.
For optimal allocation, the calculation is:
t2(SNkSk)2
n = _|^l_ (7)
i + —. !fk
where the symbols are the same as above and
of
where sk =~\J sk2, the standard deviation
stratum k [see Equations ( 1 6) to (19)] .
Having established sample size, it remains to
determine the portion of the sample to be
allocated to each stratum.
For proportional allocation :
(8)
where nk = the number of observations to be
made in stratum k.
For optimal allocation:
nk =
(9)
N2d2
Sample selection within each stratum is
performed in the same manner as for simple
random sampling.
2.2.3 Systerna tic random sampling
After the location of a transect line is
selected, the number of experimental units (the
number of possible sampling points) along this
line must be determined. This may be done in
many ways depending upon the particular situa-
tion. Possible examples are the number of square
meter plots of bottom centered along a 100-
meter transect (N = 100); or the meters of
distance along a 400-meter transect as points of
departure for making a plankton haul of some
predetermined duration perpendicular to the
transect. (In the second example, a question of
subsampling or some assumption about local,
homogeneous distribution might arise since the
plankton net has a radius less than one meter).
The interval of sampling, C, determines sample
-------
BIOLOGICAL METHODS
size: n - N/C. The mean is estimated.as usual;
the variance as for a simple random sample if
there are no trends, periodicities, or other non-
random effects.
2.3 Subsampling
Situations often arise where it is natural or
imperative that the sampling units are defined in
a two-step manner. For example: colonies of
benthic organisms might be the first step, and
the measurement of some characteristic on the
individuals within the colony might be the
second step; or streams might be the first
(primary) step, and reaches, riffles or pools as
the second step (or element) within the unit.
When a sample of primary units is selected, and
then for each primary unit a sample is selected
by observing some element of the primary unit,
the sampling scheme is known as subsampling or
two-stage sampling. The computations are
straight forward, but somewhat more involved.
The method of selection of the primary units
must be established. It may be a simple random
sample (equal probabilities), a stratified random
sample (equal probabilities within strata), or
other scheme such as probability proportional to
size (or estimated size) of primary unit. In any
case, let us call the probability of selection of
the i— primary unit, Z,. For simple random
sampling, Zl = -^, where N is the number of
primary units in the universe. For stratified
random sampling, Zk i = ^T-> where k signifies the
™k
k— stratum. For selection in which the primary
units are selected with probability proportional
to their size, the probability of selection of the
;th
j— primary unit is
n
S Li
00)
where L equals the number of elements in the
primary unit indicated by its subscript. If
stratification is used with the latter scheme,
merely apply the rule to each stratum. Other
methods of assigning probability of selection
may be used. The important thing is to establish
the probability of selection for each primary
unit.
3.0 GRAPHIC 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. Cell counts (algal cells per milliliter) will
serve as the numeric example (Table 1).
3.1 Raw Data
As brought out in other chapters of this
manual, it is of utmost importance that raw data
be recorded in a careful, logical, interpretable
manner together with appropriate, but not super-
fluous, 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.
3.2 Frequency Histograms
To construct a frequency histogram from the
data of Table 1, examine the raw data to deter-
mine the range, then establish intervals. Choose
the intervals with care so they will be optimally
integrative and differentiative. 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 exist as to which
interval an observation belongs, i.e., the end of
one interval must not be the same number as the
beginning of the next.
The algal count data in Tables 2 and 3 were
grouped by two interval sizes (10,000 cells/ml
and 20,000 cells/ml). It is>easyto,see that the data
are grouped largely in the range 0 to 6 x 104
cells/ml and that the frequency of occurrence is
-------
BIOMETRICS - GRAPHIC EXAMINATION
TABLE 1. RAW DATA ON PLANKTON
COUNTS
Date
June
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Count
23,077
36,538
26,923
23,077
13,462
19,231
21,154
61,538
96,154
23,077
46,154
48,077
51,923
50,000
292,308
165,385
42,308
Date
June
25
26
27
28
29
30
July
1
2
3
4
5
6
7
8
9
10
Count
7,692
23,077
134,615
32,692
25,000
146,154
107,692
13,462
9,615
148,077
53,846
103,846
78,846
132,692
228,846
307,692
Date
July
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
Count
44,231
50,000
26,923
44,231
46,154
55,768
9,615
13,462
3,846
3,846
11,538
7,692
13,462
21,154
17,308
TABLE 2. FREQUENCY TABLE FOR DATA
IN TABLE 1 GROUPED AT AN INTERVAL
WIDTH OF 10,000 CELLS/ML
lesser, the larger the value. Closer inspection will
reveal that with the finer interval width (Table
2), the frequency of occurrence does not in-
crease monotonically as cell count decreases.
Rather, the frequency peak is found in the
interval 20,000 to 30,000 cells/ml. This observa-
tion was not possible using the coarser interval
width; the frequencies were "overintegrated"
and did not reveal this part of the pattern. Finer
interval widths could further change the picture
presented by each of these groupings.
Although a frequency table contains all the
information that a comparable histogram con-
tains, the graphical value of a histogram is
usually worth the small effort required for its
construction. Figures 1 and 2 are frequency
histograms corresponding to Tables 2 and 3,
respectively. It can be seen that the histograms
are more immediately interpretable. The height
of each bar is the frequency of the interval; the
width is the interval width.
3.3 Frequency Polygon
Another way to present essentially the same
informatiqn 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. The data of Table 3 are used to
Interval
0- 10
10- 20
20- 30
30- 40
40- 50
50- 60
60- 70
70- 80
80- 90
90-100
100-110
110-120
120-130
130-140
140-150
150-160
160-170
170-180
180-190
190 - 200
Frequency
6
7
9
2
6
5
1
1
0
1
2
0
0
2
2
0
1
0
0
0
Interval
200-210
210-220
220-230
230 - 240
240 - 250
250 - 260
260-270
270-280
280 - 290
290-300
300-310
310-320
320-330
330 - 340
340 - 350
350 - 360
360 - 370
370 - 380
380 - 390
390-400
Frequency
0
0
1
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
illustrate the frequency polygon in Figure 3.
3.4 Cumulative Frequency
Cumulative frequency plots are often useful in
data interpretation. As an example, a cumulative
frequency histogram (Figure 4) was constructed
using the frequency table (Table 2 or 3). 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.
6-
2-
—
-,
-,
"n HI ITI n n m
0 40 80 120 160 200 240 280 320
ALGAL CELLS/ML, THOUSANDS
Figure 1. Frequency histogram; interval width is
10,000 cells/ml.
-------
BIOLOGICAL METHODS
TABLE 3. FREQUENCY TABLE FOR DATA
IN TABLE 1 GROUPED AT AN INTERVAL
WIDTH OF 20,000 CELLS/ML
Interval
0- 20
20- 40
40- 60
60- 80
80 - 100
100 - 120
120- 140
140-160
160- 180
180-200
Frequency
13
11
11
2
1
2
2
2
1
0
Interval
200 - 220
220 - 240
240 - 260
260 - 280
280 - 300
300 - 320
320-340
340-360
360-380
380-400
Frequency
0
1
0
0
1
1
0
0
0
0
Closely related to the cumulative frequency
histogram is the cumulative frequency distribu-
tion 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 (in this case the number of
observations or 48).
The value of the cumulative frequency distri-
bution 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
14
10-
i
i e-
2-
-PR-
0 40 80 120 160 200 240 280 320
ALGAL CELLS/ML, THOUSANDS
Figure 2. Frequency histogram; interval width is
20,000 cells/ml.
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 reading and extend these results
beyond their range of applicability.
14-,
10-
>-
C_3
3=
UJ
i 6
2-
0 40 80 120 160 200 240 280 320
ALGAL CELLS/ML, THOUSANDS
Figure 3. Frequency polygon; interval width is
20,000 cells/ml.
120 160 200 240 280 320 360 400
ALGAL CELLS/ML, THOUSANDS
Figure 4. Cumulative frequency histogram; in-
terval width is 10,000 cells/ml.
3.5 Two-dimensional Graphs
Often data are taken where the observations
are recorded as a pair (cell count and time),
(biomass and nutrient concentration). Here a
quick plot of the set of pairs will usually be of
value. Figure 5 is such a graph of data taken
from Table 1. Each point is plotted at a height
-------
BIOMETRICS SAMPLE MEAN AND VARIANCE
corresponding to cell count and at a distance
from the ordinate axis corresponding to the
number of days since the beginning observation.
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.
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.
300-
200-
100
10
20
30
40
DAYS
Figure 5. An example of a two-dimensional
graph plotted from algal-count data in Table 1.
4.0 SAMPLE MEAN AND VARIANCE
4.1 General Application
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 Xl,
the second X2, etc., and the last 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
Xj, 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 computa-
tions by capital sigma, 2. Associated with 2 are
an operand (here, X;), a subscript (here, i = 1),
and a superscript (here, n), I, Xt The sub-
1= i
script i = 1 indicates that the value of the
operand X is to be the number labeled Xt in our
data set and that this is to be the first observa-
tion 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.
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 compu-
tations for a sample of n observations, i.e., they
are statistics.
Mean(X):
X =
(ID
Variance (s2):
(£*)'
(12)
n-l
Note: The Xj's are squared, then the summation
is performed in the first term 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 parentheses.
Standard deviation (s):
(13)
(14)
Variance of the mean (s|):
A.
-------
BIOLOGICAL METHODS
Standard deviation of the mean or standard
error (s-):
sampling) :
For the sample mean:
(15)
(20)
4.2 Statistics for Stratified Random Samples
The calculations of the sample statistics for
stratified random sampling are as follows (see
2.2.2 Stratified random samples):
For the mean of stratum k:
nk
%
_ 1=1
y = •
Vki
(16)
i.e., simply compute an arithmetic average for
the measurements of stratum k.
For the variance of stratum k:
r>k-l
i.e., simply Equation 12 applied to the data of
the kth stratum.
For the mean of the stratified sample:
m
Xst=
(18)
for either type allocation or alternatively for
proportional allocation:
Yst ='
m
k-ljfl
n
(19)
Note that Equations (18) and (19) are
identical only for proportional allocation.
4.3 Statistics for Subsamples
If simple random sampling is used to select a
subsample, the following formulas are used to
calculate the sample statistics (see 2.3 Sub-
where y is the average, computed over sub-
samples as well as for the sample
Li
j=i yij (21)
Yi= n
where y; j equals the observation for the j*—
element in the i'— primary unit, and Ls is the
number of observations upon elements for
primary unit i.
For the variance of the sample mean:
(y)=-
i
n A A
-S (Yi-Yn)2
n(n- 1) ( I, Li)2
(17) where Y, is computed as
zT
where Yn is pomputed as
Yn=-2 Yj =
n =
or alternatively
s2 (y) =-
5V.
SY,
n(n-l)(S LO2
(22)
(23)
(24)
(25)
4.4 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
10
-------
BIOMETRICS - TESTS OF HYPOTHESES
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^
1.239
28.5849
Rounded
1.24
28.58
• 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
13.251
13.25001
Rounded
13.3
13.3
• If the number is recorded to only one place
to the right of the last place to be kept, and that
digit is 0, or if the significant digits two or more
places beyond the last place to be kept are all 0,
a special rule (odd-even rule) is followed to en-
sure 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, or if all following
significant digits are 0, 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
13.2500
13.3500
Rounded
13.2
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 to 6.
Retaining Significant Figures
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.
5.0 TESTS OF HYPOTHESES
Often in biological field studies some aspect
of the study is directed to answering a hypothet-
ical question about a population. If the hy-
pothesis is quantifiable, such as: "At the time of
sampling, the standing crop of plankton biomass
per liter in lake A was the same as the standing
crop per liter in lake B," 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 dis-
cussed in the section on sampling (2.0).
Three standard types of tests of hypotheses
will be described: the "t-test," the "x2-test,"
and the "F-test."
5.1 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 popula-
tion 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
0-0
(26)
t =
where d = some sample statistic; S# = the
standard deviation of the sample statistic; and
0 = 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, ex., of rejecting the null hypothesis
when it is true, and the row headings being the
degrees of freedom. Entry of the table at the
11
-------
BIOLOGICAL METHODS
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, n, of some population
equals 10. Then we would write the null
hypothesis (symbolized HO ) as
H0:ju=10
Here 10 is the value of 0 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 investi-
gator merely wants to answer whether the
sample indicates that n = 10 or not, then the
alternate hypothesis, Ha, is
Ha:ju^= 10
If it is known, for example, that fj. cannot be less
than 10, then Ha is
Ha:^>10
and by similar reasoning the other possible Ha is
Ha:/n<10
Hence, there are two types of alternate hy-
potheses: one where the alternative is simply
that the null hypothesis is false (Ha :^L^ 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: ju (> or <) 10]. In the case of Ha : ju
^ 10, the test is called a two-tailed test; in the
case of 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 is .025
and the table is for one-tailed tests, use this same
column for .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 HO : ju = M (the population mean
equals some value M):
x- M
(27)
where X is given by equation (11) or other
appropriate equation; M = the hypothesized
population mean; and s% is given by equation
(15). The t-table is entered at the chosen proba-
bility level (often .05) and n- 1 degrees of free-
dom, where n is the number of observations in
the sample.
When the computed t-statistic exceeds the
tabular value there is said to be a 1 - a proba-
bility that H0 is false.
Testing HQ : Hi - ju2 (the mean of the popula-
tion from which sample 1 was taken equals the
mean of the population from which sample 2
was taken):
- X2
- X2
(28)
where sXl - x2 ~ the pooled standard error
obtained by adding the corrected sums of
squares for sample 1 to the corrected sums of
squares for sample 2, and dividing by the sum of
the degrees of freedom for each times the sum
of the numbers of observations, i.e.,
+n2)
An alternative and frequently useful form is
(30)
- x2
(n2 -
n2)
n2 - 2)
where Si2 and s22 are each computed according
to equation (12).
For all conditions to be met where the t-test is
applicable, the sample should have been selected
*£ sign, when unsubscripted, will indicate summation for all
observations, hence £Xj means sum of all observations in
sample 1.
12
-------
BIOMETRICS - CHI SQUARE TEST
from a population distributed as a normal distri-
bution. 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. One method of checking
whether the data are normally distributed is to
plot the observations on normal probability
graph paper. If the plot approximates a straight
line, using the t-test is acceptable.
The t-test is used in certain cases where it is
known that the parent distribution is not
normal. One case commonly encountered in
field studies is the binomial. The binomial may
describe presence or absence, dead or alive, male
or female, etc.
Testing H,, : P = K (the population proportion
equals some value K):
t =
P- K
pq_
n
(31)
where P = the symbol for the population propor-
tion (e.g., proportion of males in the popula-
tion); K = a constant positive fraction as the
hypothesized proportion; p = the proportion
observed in the sample; q = the complementary
proportion (e.g., the proportion of females in
the sample or 1 - p); and n = the number of
observations in the sample. Note that since p is
computed as (number of males in the sample) /
(total number of individuals in the sample), it
will always be a positive number less than one,
and hence, so will q. Again a must be chosen; Ha
can be any of the types previously discussed;
and the degrees of freedom are n -1.
Count data, where the objects counted are
distributed randomly, follow a Poisson distribu-
tion. If the Poisson can be used as an adequate
description of the distribution of the popula-
tion, an approximate t may be computed.
Testing HQ : n = M for the Poisson (the mean
of the population distributed as a Poisson equals
some hypothesized value M):
X-M
(32)
Note that X = a2 for the Poisson, thus"
standard deviation of the mean, s^ .
is the
5.2 Chi Square Test (X2 -test)
Like t, X2 values may be found in mathe-
matical and statistical tables tabulated in a two-
way arrangement. Usually, as with t, the column
headings are probabilities of obtaining a larger
X2 value when HO is true, and the row headings
are degrees of freedom. If the calculated X2 ex-
ceeds 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:
2 = (n-l)s2 G3)
A ^ \~>~>)
is useful in tests regarding hypotheses about o2.
• The other form:
X2 = S^ (34)
where 0 = an observed value, and E = an ex-
pected (hypothesized) value, is especially useful
in sampling from binomial and multinomial
distribution, i.e., where the data may be classi-
fied into two or more categories.
Consider first a binomial situation. Suppose
the data from fish collections from three lakes
are to be pooled and the hypothesis of an equal
sex ratio tested (Table 4).
TABLE 4. POOLED FISH SEX
DATA FROM 3 LAKES
No. males
892*(919)f
No. females
946 (919)
Total
1838
"Observed values.
fExpected, or hypothesized, values.
To compute the hypothesized values (919
above), it is necessary to have formulated a null
hypothesis. In this case, it was
Ho : No. males = No. females = (.5) (total)
13
-------
BIOLOGICAL METHODS
Expected values are always computed based
upon the null hypothesis. The computation for
X2
is
Y2 _(892-919)2 + (946-919)2 59 _ , *
x __ i.syn.s.
*n.s. = not significant
There is one degree of freedom for this test.
Since computed X is not greater than tabulated
X2 (3.84), 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 test 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 lake from which the sample
was obtained (Table 5).
TABLE 5. FISH SEX DATA FROM 3 LAKES
Lake
1
2
3
Total
No Males
346* (354)t
302 (288)
244 (277)
892 (919)
No Females
362 (354)
274 (288)
310(277)
946(919)
Total
708
576
554
1838
X2
.36 n.s.
l.SOn.s.
7.88
P = .005
1.59 n.s.
*Observed values.
t Expected, or hypothesized values.
With the use of the same null hypothesis, the
following results are obtained.
The individual X2's were computed in the
same manner as equation (34), in separate tests
of the hypothesis for each lake. Note that the
first two are not significant whereas the third is
significant. This points to probable ecological
differences among lakes, a possibility that would
not have been discerned by pooling the data.
The test for interaction (dependence) is made
by summing the individual X2 's and subtracting
the X 2 obtained using totals, i.e.,
X2 (interactions) = 2X2 (individuals) - X2 (total)
= .36 + 1.30 + 7.88- 1.59 = 7.95
The degrees of freedom for the interaction X2
are the number of individual X2 's minus one; in
this case, two. This interaction X2 is significant
(P > .025), which indicates that the sex ratio is
indeed dependent upon the lake.
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 6).
TABLE 6. OCCURRENCE OF THREE
SPECIES OF FISH
Stream
Number of organisms
Species 1 Species 2 Species 3
Frequency
1
2
3
4
Total
Expected
ratio
24* (21.7)t
15 (18.5)
28 (27.4)
20 (19.4)
87
87/264
12(12.5)
14(10.6)
15 (15.7)
9(11.2)
50
50/264
30(31.7)
27 (26.9)
40 (39.9)
30 (28.4)
127
127/264
66
56
83
59
264
* Ob served values.
•(•Expected, or hypothesized
To discuss the table above, O( j = the observa-
tion for the i1— stream and the j*— species.
Hence, O2 3 is the observation for stream two
and species three, or 27. A similar indexing
scheme applies to the expected values, E; j. For
the totals, a subscript replaced by a dot (.)
symbolizes that summation has occurred for the
observations indicated by that subscript. Hence,
O.2 is the total for species two (50); O3. is the
total for stream three (93); and O 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 -r^, the ratio of the sum
for species j to the total of all species. This ratio
multiplied by the total for stream i gives the
expected number of organisms of species j in
stream i:
O.
(Oi.)
(35)
For example,
Ei2= Tf*- (00
= 12.5
14
-------
BIOMETRICS - F-TEST
/•f
X is computed as
x2 =
-= 2.69(n.s.)
For this type of hypothesis, there are (rows - 1)
(colums - 1) degrees of freedom, in this case
(3) (2) = 6
In the example, x2 is nonsignificant. Thus, there
is no evidence that the ratios among the organ-
isms 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 males to females 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. Because the ratios are derived from
the data in the later case, a better fit to these
ratios (smaller X2) is expected. This is compen-
sated for by loss of degrees of freedom. Thus,
smaller computed X2's may be judged signifi-
cant than would be in the case where the ratios
are hypothesized independently of the data.
5.3 F-test
The F distribution is used for testing equality
of variance. Values of F are found in books of
mathematical and statistical tables as well as in
most statistics texts. Computation of the F
statistic involves the ratio of two variances, each
with associated degrees of freedom. Both of
these are used to enter the table. At any entry of
the F tables for (ni - 1) and (n2 - 1) degrees of
freedom, there are usually two or more entries.
These entries are for various levels of probability
of rejection of the null hypothesis when in fact
it is true.
The simplest F may be computed by forming
the ratio of two variances. The null hypothesis is
HO : Oj2 = o2 2. The F statistic is
F =
(36)
where Sj2 is computed from nj observations
and s2 2 from n2. For simple variances, the
degrees of freedom, f, will be fj = nt - 1 and
f2 = n2 - 1. The table is entered at the chosen
probability level, a, and if F exceeds the tabu-
lated value, it is said that there is a 1 - a
probability that al 2 exceeds o2 2.
5.4 Analysis of Variance
Two simple but potentially useful examples
of the analysis of variance are presented to
illustrate the use of this technique. The analysis
of variance is a powerful and general technique
applicable to data from virtually any experimen-
tal 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 cor-
relations 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 assump-
tions are rarely completely fulfilled, but the
analysis of variance can be used unless signifi-
cant departures from normality, or 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 appli-
cability of the test before issuing final reports or
publications.
5.4.1 Rando mized 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 treat-
ments 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 attaching to
15
-------
BIOLOGICAL METHODS
slides suspended in streams varies from stream to
stream. A simple analysis such as this could
precede a more in-depth biological study of the
comparative productivity of the streams. Data
from such a study are presented in Table 7.
TABLE?. PERIPHYTON
PRODUCTIVITY DATA
Stream
1
2
3
Slide
1
2
3
4
1
2
3
4
1
2
3
4
Biomass
(mg dry wt.)
26
20
14
25
34
28
Lost
23
31
35
40
28
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:
HO : There are no differences in the
biomass of organisms attached to the
slides that may be attributed to differ-
ences among streams.
In utilizing the analysis of variance, the test
for whether there are differences among streams
is made by comparing two types of variances,
most often called "mean squares" in this con-
text. Two mean squares are computed: one
based upon the means for streams; and one that
is free of the effect of the means. In our
example, a mean square for streams is computed
with the use of the averages (or totals) from the
streams. The magnitude of this mean square is
affected both by differences among the means
and by differences among slides of the same
stream. The mean square for slides is computed
that has no contribution due to stream differ-
ences. If the null hypothesis is true, then differ-
ences among streams do not exist and, therefore,
they make no contribution to the mean square
for streams. Thus, both mean squares (for
streams and for slides) 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 HQ is
not true, i.e., if there are real differences due to
the effect of streams, then the mean square for
streams 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
8.
The computations are:
(85 + 85+ 134)2
C = Yi - = 8401.45
2 Xj j2 = 262 + 20 2 + • • • + 402 + 282 = 8936
i j
Total SS = 8936 - 8401.45 = 534.55
v Xj 2 852 852 1342
S (~} = ~4~ + ~+ T~
= 8703-58
Streams SS = 8703.58 - 8401.45 = 302.13
Slides w/i streams SS = Total SS - Streams SS
= 534.55- 302.13
= 232.42
The mean squares (MS column) are computed
by dividing the sums of squares (SS column) by
its corresponding degrees of freedom (df
column). (Nothing is usually learned in this
context by computing a total MS.) The F-test is
TAB LE 8 . F-TEST USING PERIPHTON DATA
Source
Total
Streams
df
N-l*
t-1
S :
ij
s
SS
*ij2 -c
Xj- -C
Slides w/i streams
Total SS - Stream SS
*The symbols are defined as: N = total number of observations
(slides); t = number of streams; ri = number of slides in stream i;
Xjj = an observation (biomass of a slide); Xi. = sum of the
observations for stream i; and C = correction for mean =
ij
N
Source
Total
Streams
Slides w/i
streams
df
10
2
8
SS
534.55
302.13
232.42
MS
151.065
29.055
F
5.20*
*Significant at the 0.05 probability level.
16
-------
BIOMETRICS - ANALYSIS OF VARIANCE
performed by computing the ratio, (mean square
for streams)!(mean square for slides'), in this
151.065 con
case'"2^055 = 5'20-
When the calculated F value (5.20) is com-
pared with the F values in the table (tabular F
values) where df = 2 for the numerator and df =
8 for the denominator, we find that the calcu-
lated F exceeds the value of the tabular F for
probability .05. Thus, the experiment indicates a
high probability (greater than 0.95) of there
being a difference in biomass attached to the
slides, a difference attributable to differences in
streams.
Note that this analysis presumes good biologi-
cal procedure and obviously cannot discriminate
differences in streams from differences arising,
for example, from the slides having been placed
in a riffle in one stream and a pool in the next.
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 two
factors affecting a biomass measurement —
streams and slides within streams. If the model
had included other factors, a more complicated
analysis of variance would have resulted.
5.4.2 Factorial design
Another application of a simple analysis of
variance may be made where the factors are
arranged factorially. Suppose a field study where
the effect of a suspected toxic effluent upon the
fish fauna of a river was in question (Tables 9
and 10). Five samples were taken about one-
quarter mile upstream and five, one-quarter mile
downstream in August of the summer before the
plant began operation, and the sampling scheme
was repeated in August of the summer after
operations began.
Standard statistical terminology refers to each
of the combinations PiT1; P2Tj, PiT2, and
P2T2 as treatments or treatment combinations.
Of use in the analysis is a table of treatment
totals.
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:
HO : The toxic effluent has no effect upon
the weight of fish caught
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 interest. Of
interest to this study is whether the upstream-
downstream difference varies with times. This
type of contrast is termed a positions-times inter-
action. Thus, our null hypothesis is, in statistical
TABLE 9. POUNDS OF FISH CAUGHT
PER 10 HOURS OVERNIGHT SET OF A
125-FOOT, 1 ^-INCH-MESH GILL NET
„. Positions
limes
Before
(Tj)
After
(T2)
Upstream (Pi)
28.3
33.7
38.2
41.1
17.6
15.9
29.5
22.1
37.6
26.7
Downstream (P2)
29.0
28.9
20.3
36.5
29.4
19.2
22.8
24.4
16.7
11.3
TABLE 10. TREATMENT TOTALS FOR
THE DATA OF TABLE 9
Positions
Before
After
Positions
totals
Upstream
158.9
131.8
290.7
Downstream
144.1
94.4
238.5
303.0
226.2
Grand total
529.2
17
-------
BIOLOGICAL METHODS
terminology.
Ho : There is no significant interaction effect
Computations for testing this hypothesis with
the use of an analysis of variance table are
presented below.
Symbolically, an observation must have three
indices specified to be completely identified:
position, time, and sample number. Thus there
are three subscripts: X; jk is an observation at
position i, time j, and from sample k. A value of
1 for i is upstream; 2, downstream; 1 for j is
before; 2, after. A particular example is X^s,
the third sample upstream after the plant began
operation, or 22.1 pounds. A total (Table 10) is
specified by using the dot notation. For the
value of Xj j , 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 Xl l. is 158.9, and the value of Xt..,
where samplings and times are both totaled to
give the total for upstream, is 290.7.
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 samples (in this case p = 2);
s = number of samples per treatment combina-
tion; and n = the total number of observations.
The computations are:
Correction for mean (CT):
(SXjjk)2_ (5292)2
= 14002.63
Treatment Sum of Squares (SSTMT):
- 14002.63 = 456.69
Times Sum of Squares (SST):
Y Y -2
(158.9)2 (131 8)2 (144.1)2 (94. 4)2
'
(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.)
Positions Sum of Squares (SSP):
- CT
sp
--CT
- 294.91
Interaction of Positions and Times Sum of
Squares (SSPT):
SSTMT - SSP - SST
456.69- 136.24- 294.91 = 25.54
Error Sums of Squares:
SXijk2- SSTMT- CT
15308.24 - 456.69 - 14002.63 = 848.92
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 posi-
tions effect is not significant at any probability
usually employed. The times effect is significant
with probability greater than .95. The inter-
action effect is not significant, and we, there-
fore, conclude that no effect of the suspected
toxic effluent can be distinguished in this data.
Had the F value for interaction been large
enough, we would have rejected the null hy-
pothesis, and concluded that the effluent had a
significant effect (Table 11).
TABLE 11. ANALYSIS OF VARIANCE
TABLE FOR FIELD STUDY DATA
OF TABLE 9
Source
Treatments
Positions
Times
Positions
X times
Error
df
3
1
1
1
16
SS
456.69
136.24
294.91
25.54
848.92
MS
136.24
294.91
25.54
53.05
F
2.56
5.55*
<1
= 136.24
6.0 CONFIDENCE INTERVALS FOR MEANS
AND VARIANCES
When means are computed in field studies, the
desire often is to report them as intervals rather
than as fixed numbers. This is entirely reason-
able because computed means are virtually
always derived from samples and are subject to
the same uncertainty that is associated with the
sample.
18
-------
BIOMETRICS - CONFIDENCE INTERVALS
The correct computation of confidence
intervals requires that the distribution of the
observations be known. But very often approxi-
mations are close enough to correctness to be of
use, and often are, or may be made to be, con-
servative. For computation of confidence inter-
vals 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 avail-
able to allow a valid application.
The confidence interval for a mean is an inter-
val 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 . 1 , .05, .01 or any probability
value), then the statement may be written
This is read, "The probability that the lower con-
fidence limit (CLt ) is less than the true mean (ju)
and that the upper confidence limit (CL2) is
greater than the true mean, equals 1 - a." How-
ever, we never know whether or not the true
mean is actually included in the interval. So the
confidence interval statement is really a state-
ment about our procedure rather than about ju.
It says that if we follow the procedure for re-
peated experiments, a proportion of those ex-
periments equal to a will, by chance alone, fail
to include the true mean between our limits. For
example, if a = .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) n- j value
from tables of Student's t is obtained corre-
sponding to n-1 degrees of freedom and
probability a. The computation is
CL, =X-(ta)(SxO
CL2 = X + (ta) (SxO
Other confidence limits may be computed,
and one additional confidence limit is given in
this section - the confidence limits for the true
variance, a2. The information needed here is
similar to that needed for the mean, namely, the
estimated variance, s2 ; the degrees of freedom,
n-1; and values from X2 tables. The values from
X2 depend upon the degrees of freedom and
upon the probability level, a. The confidence
interval is
= 1-CV
This will be illustrated for a = .05; (n- 1) = 30;
and s2 = 5. Since a = .05; 1 - | = 0.975; the
associated X2 975 = 16.8 and the X20 025 =
47.25. Thus, the probability statement for the
variance in this case is
P(3.19b a2 ^16.8) = .95
7.0 LINEAR REGRESSION AND CORRE-
LATION
7.1 Basic Concepts
It is often desired to investigate relationships
between variables, i.e., rate of change of biomass
and concentration of some nutrient; mortality
per unit of time and concentration of some
toxic substance; chlorophyll and biomass; or
growth rate and temperature. As biologists, we
appreciate the incredible complexity of the real-
world relationships between such variables, but,
simultaneously, we may wish to investigate the
desirability of approximating these relationships
with a straight line. Such an approximation may
prove invaluable if used judiciously within the
limits of the conditions where the relation holds.
It is important to recognize that no matter how
well the straight line describes the data, a causal
relationship between the variables is never
implied. Causality is much more difficult to
establish than mere description by a statistical
relation.
When studying the relationship between two
variables, the data may be taken in one of two
ways. One way is to measure two variables, e.g.,
measure dry weight biomass and an associated
chlorophyll measurement. Where two variables
19
-------
BIOLOGICAL METHODS
are measured, the data are termed bivariate. The
other way is to choose the level of one variable
and measure the associated magnitude of the
other variable.
Straight line equations may be obtained for
each of these situations by the technique of
linear regression analysis, and if the object is to
predict one variable from the other, it is
desirable to obtain such a relation. When the
degree of (linear) association is to be examined,
no straight line need be derived - only a
measure of the strength of the relationship. This
measure is the correlation coefficient, and the
analysis is termed correlation analysis.
Thus, linear regression analysis and linear cor-
relation analysis are two ways in which linear
relationships between two variables may be
examined.
7.2 Basic Computations
7.2.1 R egression equa tion
The regression equation is the equation for a
straight line,
Y = a + bx
A graphic representation of this function is a
straight line plotted on a two-axis graph. The
line intercepts the y-axis a distance, a, away
from the origin and has a slope whose value is b.
Both a and b can be negative, zero, or positive.
Figure 6 illustrates various possible graphs of a
regression equation.
The regression equation is obtained by "least-
squares," a technique ensuring that a "best" line
will be objectively obtained. The application of
least-squares to the simple case of a straight line
relation between two variables is extremely
simple.
In Table 12 is a set of data that are used to
illustrate the use of regression analysis. Figure 7
is a plot of these data along with fitted line and
confidence bands.
In fitting the regression equation, it is con-
venient to compute at least the following quan-
tities:
(1) n = the number of pairs of observation of X
andY,
(2) ZX = the total for X,
(3) 2Y = the total for Y,
(4)
(5)
(6)
(7)
(8)
(9)
TABLE 12. PERCENT SURVIVAL
TO FRY STAGE OF EGGS OF
GOGGLE-EYED WYKE VERSUS
CONCENTRATION OF
SUPERCHLOROKILL IN
PARENTS' AQUARIUM WATER
Percent survival (Y)
74.
82.
68.
65.
60.
72.
64.
60.
57.
51.
50.
55.
24.
28.
36.
0.
10.
4.
Concentration, ppb (X)
1.
1.
1.
2.
2.
2.
3.
3.
3.
4.
4.
4.
6.
6.
6.
10.
10.
10.
2X2 = the total of the squared X's,
ZY2 = the total of the squared Y's,
ZXY = the total of the products of the X,Y
pairs,
(SX)2 = the square of quantity (2),
(2Y)2 = the square of quantity (3),
(2X)(2Y) = the product of quantities (2)
and (3),
(10) CTX = quantity (7) divided by quantity (1),
(11) CTy = quantity (8) divided by quantity (1),
(12)CTxy = quantity (9) divided by quantity
(1).
With the calculation of these quantities, most
of the work associated with using linear regres-
sion is complete. Often calculating machine
characteristics may be so utilized that when one
quantity is calculated the calculation of another
is partly accomplished. Modern calculators and
computers greatly simplify this task.
In Table 13 are the computed values of
quantities (1) through (12) for the data of Table
12.
The estimated value for the slope of the line,
b, is computed using
_ £XY - CTxv = (6)-(12)
ZX2 -CTX (4)-(10)
(37)
20
-------
BIOMETRICS - LINEAR REGRESSION
For the example, this is
2453 - 3726.67
498 - 338
b =
rounded to the nearest whole number.
Computation of the estimated intercept, a, is
as follows:
a = y-bx (38)
(3) , (2)
"(1) (1)
which for the example
- 86°
- ~n
= 82
78
—
rounded to the nearest whole number.
Thus, the regression equation for this data is
Y = 82 - 8X
A
where Y = the percent survival, and X = con-
centration of pesticide.
Figure 7 shows the regression line, plotted
along with the data points. Note that this line
appears to be a good fit but that an eye fit might
have been slightly different and still appear to be
a "good fit." This indicates that some uncer-
tainty is associated with the line. If a value for y
is obtained with the use of the regression equa-
tion with a given x, another experiment, how-
ever well controlled, could easily produce a dif-
ferent value. The predicted values for y are
TABLE 13. COMPUTED VALUES
OF QUANTITIES (1) THROUGH
(12) FOR THE DATA OF TABLE 1 2
Quantity
Value
( 1) n
( 2) SX
( 3) 2Y
( 4) 2X2
( 5) EY2
( 6) 2XY
( 7) (2X)2
( 8) (2^Y)2
( 9) (2;-'
(10) CTX
(ID CTy
(12) CTxy
18
78
860
498
51,676
2,453
6,084
739,600
67,080
338
41,088.89
3,726.67
subject to some uncertainty, and a statement of
that uncertainty should invariably accompany
the use of the predicted y.
7.2.2 Confidence intervals
The proper statement of the uncertainty is an
interval estimate, the same type as those
previously discussed for means and variances.
The probability statement for a predicted y
depends upon the type of prediction being
made. The regression equation is perhaps most
often used to predict the mean y to be expected
when x is some value, but it may also be used to
predict the value of a particular observation of y
when x is some value. These two types of predic-
tions differ only in the width of the confidence
intervals. A confidence interval for a predicted
observation will be the wider of the two types
because of uncertainty associated with variations
among observations of y for a given x.
To compute the confidence intervals, first
compute a variance estimate. This is the variance
due to deviations of the observed values from
the regression line. This computation is:
Sy.x =
CT
CI
(£XY-CTxy.)2
- CTX)
(39)
For this example:
51,676-
Sy.X ='
(2.453-3,727)2
(498-338)2
18-2
= 28
This statistic is useful in other computations as
will become apparent.
For the confidence interval, the square root of
the above statistic, or the standard error of
deviations from regression is required, i.e.,
The confidence limits are computed as follows
for a predicted mean:
•bXp±(ta)(Sy.x)V-
where ta is chosen from a table of t values using
n- 2 degrees of freedom and probability level a;
A
Y = the computed Y for which the confidence
«
interval is sought, a mean Y predicted to be
21
-------
BIOLOGICAL METHODS
POSITIVE INTERCEPT
POSITIVE SLOPE
POSITIVE INTERCEPT
NEGATIVE SLOPE
ZERO INTERCEPT
POSITIVE SLOPE
NEGATIVE INTERCEPT
POSITIVE SLOPE
NEGATIVE INTERCEPT
NEGATIVE SLOPE
NOTE: A SLOPE OF ZERO IMPLIES
NO RELATIONSHIP.
Figure 6. Examples of straight-line graphs illustrating regression equations.
22
-------
BIOMETRICS - LINEAR REGRESSION
80-,
95% CONFIDENCE BANDS
(PREDICTED MEAN)
60-
95% CONFIDENCE BANDS
(PREDICTED VALUE]
40-
20-
PREDICTED SINGLE
X VALUE AND CL
FOR Y=40
2 4 6
CONCENTRATION OF SUPERCHLOROKILL (PPB)
Figure 7. Regression analysis of data in Table 12.
23
-------
BIOLOGICAL METHODS
observed on the average when the X value is Xp;
Xp = the particular X value used to compute Y;
X = the mean of the X's used in these computa-
2X (2)
tions; = j^-; 2X2 = relation (4) in the
computations; and CTX = relation (10) in the
computations. Note that in using Equation (41)
where the signs (±) are shown, the minus (-) sign
is used when computing the lower confidence
limit and the plus (+) for the upper.
If a confidence interval for a particular Y
A
(given a particular X, i.e., Y) is desired, the
confidence limits are computed using
bXp±(ta)(Sy,x
(Xp - X)2
-CTX)
(42)
Note that Equation (42) differs from Equation
(41) only by the addition of 1 under the radical.
All the symbols are the same as for Equation
(41). Again these confidence intervals will be
A
wider than those for Y.
If a graphical representation of the confi-
* A
dence interval for Y or Y over a range of X is
desired, merely compute the confidence interval
for several (usually about 5) values of X, plot
them on the same graph as the regression line,
and draw a smooth curve through them. The
intervals at the extremes of the data will be
wider than the intervals near the mean values.
This is because the uncertainty in the estimated
slope is greater for the extreme values than for
the central ones.
With such a plot, the predicted value of Y and
its associated confidence interval for a given X
can be read (see Figure 7, vertical line corre-
sponding to X = 3 and notation).
7.2.3 Calibration curve
Often with data such as that given in Table
12, a calibration curve is needed from which to
predict X when Y is given. That is, the linear
relation is established from the data where
values of X (say pesticide) are fixed and then Y
(survival of eggs) is observed, where this relation
predicts Y given X; then unknown concentra-
tions of the pesticide are used, egg survival
measured, and the relation is worked backwards
to obtain pesticide concentration from egg
survival. This may be done graphically from a
plot such as that illustrated in Figure 7.
Predicted X's and associated contidence intervals
may be read from the plot (see horizontal line
corresponding to y = 40 and notation).
Calibration curves and confidence intervals
may also be worked algebraically. Where the
problem has fixed X's, as in the example, the
equation for X should be obtained algebraically,
i.e.,
(43)
for a predicted X (X) given a mean value Ym
from a sample of m observations, the confidence
limits may be computed as follows:
compute the quantity
. = b2-
(2X2 - CTx)
compute the confidence limits as
(44)
A
-CTX)
where Ym = the average of m newly observed Y
values; X, b, Y, sy.x, 2X2, CTX, and n = values
obtained from the original set of data and whose
meanings are^unchanged. Note that m may equal
one, and Ym would therefore be a single
observation.
7.3 Tests of Hypotheses
If it is not clear that a relationship exists
between Y and X, a test should be made to
determine whether the slope differs from zero.
The test is a t-test with n-2 degrees of freedom.
The t value is computed as
t = -
Sy.x
(45)
where
- CTX
Since the null hypothesis is
H0:ft, = 0
set j30 = 0 in the t-test and it becomes
t-i
24
-------
BIOMETRICS - LINEAR REGRESSION
If the computed t exceeds the tabular t, then the
null hypothesis is rejected and the estimated
slope, b, is tentatively accepted. Other values of
j30 may be tested in the null hypothesis and in
the t-test statistic.
With data such as those in Table 12, another
hypothesis may be tested — that of lack of fit of
the model to the data, or bias. This idea must be
distinguished from random deviations from the
straight line. Lack of fit implies a nonlinear
trend as the true model, whereas random devia-
tions from the model imply that the model
adequately represents the trend. If more than
one Y observation is available for each X (3 in
the example Table 12), random fluctuations can
be separated from deviations from the model,
i.e., a random error may be computed at each
point so that deviations from regression may be
partitioned into random error and lack of fit.
The test is in the form of an analysis of vari-
ance and is illustrated in brief form symbolically
in Table 14. Here, the F ratio MSL/MSE tests
linearity, i.e., whether a linear model is suffi-
cient; the ratio MSR/MSD tests whether the
slope is significantly different from zero.
TABLE 14. ILLUSTRATION OF ANALYSIS
OF VARIANCE TESTING LINEARITY OF
REGRESSION AND SIGNIFICANCE OF
REGRESSION
Source
Total
Regression
Deviations from
regression
Lack of fit
Error
df
n-1
1
n-2
m-2
n-m
MS
MSR
MSD
MSL
MSE
F
MSR/MSD
MSL/MSE
level of X; in this case always 3. For the
example,
T-2
E —= 51341
kj
With this, the analysis of variance table (Table
15) may be constructed. In the first part of
Table 15, the sums of squares and degrees of
freedom are given symbolically to relate to the
computations of Table 13 and to the above
computations. The mean squares (MS) are always
obtained by dividing SS by df.
When the data for Table 12 are analyzed
(second part of Table 15), there is a very
unusual coincidence in the values of MS for
deviations from regression, lack of fit, and error.
Note that this is coincidence and they must
always be computed separately.
As already known from the graph, t-test, etc.,
the regression is highly significant. A negative
result from the test for nonlinearity (lack of fit)
was also suspected from the visually-satisfactory
fit of Figure 7. Therefore, for this range of data,
we can conclude that a linear (straight line) rela-
TABLE 15. ANALYSIS OF VARIANCE OF
THE DATA OF TABLE 12; TESTS FOR
LINEARITY AND SIGNIFICANCE OF
REGRESSION*
Source
Total
Regression
Deviations from
regression
Lack of fit
df
n-1
1
n-2
m-2
SS
2Y2-CTy
(SXY-CTxy)2
(2X2-CTX)
Total SS - Regression SS
Deviation SS - Error SS
Error
2Y2
*Symbols refer to quantities of Table 13 or to symbols de-
fined in the text immediately preceding this table.
For the data of Table 12:
To use this ^nalys.-s, one set of computations
must be made in addition to those of Table 13.
The computation is the same as that for treat-
ment sums of squares in the analysis of variance
previously discussed; in this case, levels of X are
comparable to treatments. First compute the
sum of the Y's, Tt, for each level of X. For
X = 1, T, = 224, etc. Then compute:
where kj = the number of observations for the **significant at the o.oi probability level.
ki
Source
Total
Regression
Deviations from
regression
Lack of fit
Error
df
17
1
16
4
12
SS
10,587
10,139
448
113
335
MS
10,139
28
28
28
F
362**
1 n.s.
25
-------
BIOLOGICAL METHODS
tionship exists, with estimated slope and inter-
cept as computed.
7.4 Regression for Bivariate Data
As mentioned, where two associated measure-
ments are taken without restrictions on either,
the data are called bivariate. Linear regression is
sometimes used to predict one of the variables
by using a value from the other. Because no
attempt is usually made to test bivariate data for
lack of fit, a test for deviation from regression is
as far as an analysis of variance table is taken.
Linearity is assumed. Large deviations from
linearity will appear in deviations from regres-
sion and cause the F values that are used to test
for the significance of regression to appear to be
nonsignificant.
Computations for the bivariate case exactly
follow those for the univariate case [quantities
(1) to (12) and as illustrated for the univariate
case, Table 13]."The major operating difference
is that, for bivariate data, the dependent variable
is chosen as the variable to be predicted, whereas
for univariate data, the dependent variable is
fixed in advance. For example, if the bivariate
data are pairs of observations on algal biomass
and chlorophyll, either could be considered the
dependent variable. If biomass is being
predicted, then it is dependent. For the uni-
variate case, such as for the data of Table 12,
percent survival is the dependent variable by
virtue of the nature of the experiment.
In the preceding section, it was seen that X
and its confidence interval could be predicted
from Y for univariate data (Equations 43, 44,
and 45). But note that Equation (43) is merely
TABLE 16. TYPES OF
COMPUTATIONS ACCORDING
TO VARIABLE PREDICTED AND
DATATYPE*
Predicted
variable
Y
X
Bivariate
data
y = RI (X)
x = R2 (Y)
Univariate data
(fixed X's)
y = RI (X)
X = Rf1 (y)
*Ri symbolizes the regression using Y as
dependent variable, R2 a regression computed
using X as dependent variable, R^1 is a alge-
braic rearrangement solving for X when the
regression was Rj.
an algebraic rearrangement of the regression of
Y on X. For the bivariate case, this approach is
not appropriate. If a regression of Y on X is
fitted for bivariate data, and subsequently a pre-
diction of X rather than Y is desired, a new
regression must be computed. This is a simple
task, and all the basic quantities are contained in
a set of computations similar to computations in
Table 13. A summary of the types of computa-
tions for univariate and bivariate data is given in
Table 16.
Since the computations for the bivariate
regression of Y on X are the* same as those for
the univariate case, they will not be repeated.
Where X is to be predicted, all computations
proceed simply by interchanging X and Y in the
notation. The computations for b and a are:
for the slope:
bx.y =
xy
- CT
(6) - (12)
(5) - (11)
(46)
for the intercept:
"x.y '
(Sx)
(i)
-b
'x.y '
x'y
(47)
(i)
7.5 Linear Correlation
If a linear relationship is known to exist or
can be assumed, the degree of association of two
variables can be examined by linear correlation
analysis. The data must be bivariate.
The correlation coefficient, r, is computed by
the following:
2XY - CTXy
V(2X2 -
CTX)
- CTy)
(48)
A perfect correlation (all points falling on a
straight line with a nonzero slope) is indicated
by a correlation coefficient of, r = 1, or r = - 1.
The negative value implies a decrease in one of
the variables with an increase in the other.
Correlation coefficients of r = 0 implies no linear
relationship between the variables. Any real data
will result in correlation coefficients between
the extremes.
26
-------
BIOMETRICS - LINEAR CORRELATION
If a correlation coefficient is computed and is
of low magnitude, test it to determine whether
it is significantly different from zero. The test, a
t-test, is computed as follows:
(49)
The computed t is compared with the tabular t
with n- 2 degrees of freedom and chosen proba-
bility level. If the computed t exceeds the
tabular t, the null hypothesis that the true corre-
lation coeffiqient equals zero is rejected, and the
computed r may be used.
8.0 BIBLIOGRAPHY
Cochran, W. G. 1959. Sampling techniques. John Wiley and Sons, New York. 330 pp.
Li, }. C. R. 1957. Introduction to statistical inference. Edwaid Brothers, Inc., Ann Arbor. 553 pp.
Natrella, M. G. 1963. Experimental statistics. National Bureau of Standards Handbook No. 91, U.S. Govt. Printing Office.
Snedecor, G. W., and W. G. Cochran. 1967. Statistical methods, 6th edition. Iowa State Univ. Press, Ames.
Southwood, R. T. E. 1966. Ecological methods with particular reference to the study of insect populations. Chapman and Hall, Ltd.,
London. 391 pp.
Steele, R. G. D., and J. H. Torrie. 1960. Principles and procedures of statistics with special reference to the biological sciences. McGraw
Hill, New York. 481 pp.
Stuart, A. 1962. Basic ideas of scientific sampling. Hafner, New York. 99 pp.
27
-------
PLANKTON
-------
PLANKTON
1.0 INTRODUCTION
2.0 SAMPLE COLLECTION AND PRESERVATION 1
2.1 General Considerations 1
2.1.1 Influential factors 1
2.1.2 Sampling frequency 2
2.1.3 Sampling locations 2
2.1.4 Sampling depth 2
2.1.5 Field notes 3
2.1.6 Sample labelling 3
2.2 Phytoplankton 3
2.2.1 Sampling equipment 3
2.2.2 Sample volume 4
2.2.3 Sample preservation 4
2.3 Zooplankton 4
2.3.1 Sampling equipment 4
2.3.2 Sample volume 5
2.3.3 Sample preservation 5
3.0 SAMPLE PREPARATION 5
3.1 Phytoplankton 5
3.1.1 Sedimentation 6
3.1.2 Centrifugation 6
3.1.3 Filtration 6
3.2 Zooplankton 6
4.0 SAMPLE ANALYSIS 6
4.1 Phytoplankton 6
4.1.1 Qualitative analysis of phytoplankton 6
4.1.2 Quantitative analysis of phytoplankton 8
4.2 Zooplankton 12
4.2.1 Qualitative analysis of zooplankton 12
4.2.2 Quantitative analysis of zooplankton 12
5.0 BIOMASS DETERMINATION 13
5.1 Dry and Ash-Free Weight 13
5.1.1 Dry weight 14
5.1.2 Ash-free weight 14
5.2 Chlorophyll 14
5.2.1 In vitro measurements 14
5.2.2 In vivo measurement 15
5.2.3 Pheophytin correction 15
5.3 Cell Volume 16
5.3.1 Microscopic (algae and bacteria) 16
5.3.2 Displacement (zooplankton) 16
5.4 Cell Surface Area of Phytoplankton 16
6.0 PHYTOPLANKTON PRODUCTIVITY 16
6.1 Oxygen Method 16
6.2 Carbon-14 Method 17
7.0 REFERENCES 17
-------
PLANKTON
1.0 INTRODUCTION
Plankton are defined here as organisms sus-
pended in a body of water and because of their
physical characteristics or size, are incapable of
sustained mobility in directions counter to the
water currents. Most of the plankton are micro-
scopic and of essentially neutral buoyancy. All
of them drift with the currents.
Plankton consists of both plants (phytoplank-
ton) and animals (zooplankton), and complex
interrelationships exist among the various com-
ponents of these groups. Chlorophyll-bearing
plants such as algae usually constitute the
greatest portion of the biomass of the plankton.
Phytoplankton use the energy of sunlight to
metabolize inorganic nutrients and convert them
to complex organic materials. Zooplankton and
other herbivores graze upon the phytoplankton
and, in turn, are preyed upon by other organ-
isms, thus passing the stored energy along to
larger and usually more complex organisms. In
this manner nutrients become available to large
organisms such as macroinvertebrates and fish.
Organic materials excreted by plankton, and
products of plankton decomposition, provide
nutrients for heterotrophic microorganisms
(many of which are also members of the plank-
ton assemblage). The heterotrophs break down
organic matter and release inorganic nutrients
which become available again for use by the
"primary producers." In waters severely pol-
luted by organic matter, such as sewage, hetero-
trophs may be extremely abundant, sometimes
having a mass exceeding that of the algae. As a
result of heterotrophic metabolism, high con-
centrations of inorganic nutrients become avail-
able and massive algal blooms may develop.
Plankton may form the base of the food
pyramid and drift with the pollutants; therefore,
data concerning them may be particularly signif-
icant to the pollution biologist. Plankton blooms
often cause extreme fluctuations of the dis-
solved oxygen content of the water, may be one
of the causes of tastes and odors in the water
and, if present in large numbers, are aesthetically
objectionable. In some cases, plankton may be
of limited value as indicator organisms because
the plankton move with the water currents;
thus, the origin of the plankton may be obscure
and the duration of exposure to pollutants may
be unknown.
The quantity of phytoplankton occurring at a
particular station depends upon many factors
including sampling depth, time of day, season of
year, nutrient content of water, and the pres-
ence of toxic materials.
2.0 SAMPLE COLLECTION AND PRES-
ERVATION
2.1 General Considerations
Before plankton samples are collected, a study
design must be formulated. The objectives must
be clearly defined, and the scope of the study
must remain within the limitations of available
manpower, time, and money. Historical, biolo-
gical, chemical, and physical (especially hydro-
logical) data should be examined when planning
a study. Examination of biological and chemical
data often reveals areas that warrant intensive
sampling an*l other areas where periodic or
seasonal sampling will suffice.
Physical data are extremely useful in the
design of plankton studies; of particular impor-
tance are data concerning volume of flow, cur-
rents, prevailing wind direction, temperature,
turbidity (light penetration), depths of reservoir
penstock releases, and estuarine salinity
"wedges."
After historical data have been examined, the
study site should be visited for reconnaissance
and preliminary sampling. Based on the results
of this reconnaissance and on the preliminary
plankton data, the survey plan can be modified
to better fulfill study objectives and to facilitate
efficient sampling.
2.1.1 Influential factors
In planning and conducting a plankton survey,
a number of factors influence decisions and
often alter collection routines. Since water cur-
rents determine the directions of plankton
movements, knowing the directions, intensity,
and complexity of currents in the sampling area
is important. Some factors that influence cur-
-------
BIOLOGICAL METHODS
rents are winds, flow, solar heating, and tides.
Sunlight influences both the movements of
plankton and primary production. Daily vertical
plankton migrations are common in many
waters. Cloud cover, turbidity, and shading (e.g.,
from ice cover and dense growths of vegetation)
influence the amount of light available to plank-
ton.
Chemical factors, such as salinity, nutrients,
and toxic agents, can profoundly affect plank-
ton production and survival.
The nutrients most frequently mentioned in
the literature as stimulators of algal growth are
nitrogen and phosphorus; however, a paucity of
any vital nutrient can limit algal production. The
third category of chemical factors, toxic agents,
is almost limitless in its components and com-
binations of effects. Toxic compounds may be
synergistic or antagonistic to one another and
may either kill planktonic organisms or alter
their life cycles. Many chemicals discharged in
industrial effluents are toxic to plankton.
2.1.2 Sampling frequency
The objectives of the study and time and man-
power limitations dictate the frequency at which
plankton samples are taken. If it is necessary to
know the year-round plankton population in a
body of water, it is necessary to sample weekly
through spring and summer and monthly
through fall and winter. However, more frequent
sampling is often necessary. Because numerous
plankton samples are usually needed to char-
acterize the plankton, take daily samples when-
ever possible. Ideally, collections include one or
two subsurface samples per day at each river
sampling station and additional samples at
various depths in lakes, estuaries, and oceans.
2.1.3 Sampling locations
In long-term programs, such as ambient trend
monitoring, sampling should be sufficiently fre-
quent and widespread to define the nature and
quantity of all plankton in the body of water
being studied. In short-term studies designed to
show the effects of specific pollution sources on
the plankton, sampling station locations and
sampling depths may be more restricted because
of limitations in time and manpower.
The physical nature of the water greatly
influences the selection of sampling sites. On
small streams, a great deal of planning is not
usually required; here, locate the stations up-
stream from a suspected pollution source and as
far downstream as pollutional effects are ex-
pected. Take great care, however, in interpreting
plankton data from small streams, where much
of the "plankton" may be derived from the
scouring of periphyton from the stream bed.
These attached organisms may have been ex-
posed to pollution at fixed points for unknown
time periods. On rivers, locate sampling stations,
both upstream and downstream from pollution
sources and, because lateral mixing often does
not occur for great distances downstream,
sample on both sides of the river. In both rivers
and streams, care should be taken to account for
confusing interferences such as contribuiions of
plankton from lakes, reservoirs, and backwater
areas. Plankton sampling stations in lakes, reser-
voirs, estuaries, and the oceans are generally
located in grid networks or along longitudinal
transects.
The location, magnitude, and temperature of
pollutional discharges affect their dispersal,
dilution, and effects on the plankton. Pollutants
discharged from various sources may be antag-
onistic, synergistic, or additive in their effects on
plankton. If possible, locate sampling stations in
such a manner as to separate these effects.
In choosing sampling station locations,
include areas from which plankton have been
collected in the past. Contemporary plankton
data can then be compared with historical data,
thus documenting long-term pollutional effects.
2.1.4 Sampling dep th
The waters of streams and rivers are generally
well mixed, and subsurface sampling is suffi-
cient. Sample in the main channel and avoid
backwater areas. In lakes and reservoirs where
plankton composition and density may vary
with depth, take samples from several depths.
The depth at the station and the depth of the
thermocline (or sometimes the euphotic zone)
generally determines sampling depths. In shallow
areas (2 to 3 meters, 5 to 10 feet), subsurface
-------
PLANKTON COLLECTION
sampling is usually sufficient. In deeper areas,
take samples at regular intervals with depth. If
only phytoplankton are to be examined, samples
may be taken at three depths, evenly spaced
from the surface to the thermocline. When col-
lecting zooplankton, however, sample at 1-
meter intervals from the surface to the lake
bottom.
Because many factors influence the nature
and distribution of plankton in estuaries, in-
tensive sampling is necessary. Here, marine and
freshwater plankton may be found along with
brackish-water organisms that are neither strictly
marine nor strictly freshwater inhabitants. In
addition to the influences of the thermocline
and light penetration on plankton depth distri-
bution, the layering of waters of different sa-
linities may inhibit the complete mixing of
freshwater plankton with marine forms. In
estuaries with extreme tides, the dimensions of
these layers may change considerably during the
course of the tidal cycle. However, the natural
buoyancy of the plankton generally facilitates
the mixing of forms. Estuarine plankton should
be sampled at regular intervals from the surface
to the bottom three or four times during one or
more tidal cycles.
In deep marine waters or lakes, collect plank-
ton samples at 3- to 6-meter intervals through-
out the euphotic zone (it is neither practical nor
profitable to sample the entire water column in
very deep waters). The limits of sampling depth
in these waters may be an arbitrary depth below
the thermocline or the euphotic zone, or both.
Perform tow or net sampling at 90° to the wind
direction.
2.1.5 Field notes
Keep a record book containing all information
written on the sample label, plus pertinent
additional notes. These additional notes may
include, but need not be restricted to:
• Weather information — especially di-
rection and intensity of wind
• Cloud cover
• Water surface condition — smooth? Is
plankton clumping at surface?
• Water color and turbidity
• Total depth at station
• A list of all types of samples taken at
station.
• General descriptive information (e.g.,
direction, distance, and description of
effluents in the vicinity). Sampling
stations should be plotted on a map.
2.1.6 Sample labelling
Both labels and marker should be water proof
(a soft-lead pencil is recommended). Insert the
labels into sample containers immediately as
plankton samples are collected. Record the
following information on all labels:
Location
name of river, lake, etc.
distance and direction to nearest city
state and county
river mile, latitude, and longitude, or
other description
Date and time
Depth
Type of sample (e.g., grab, vertical plank-
ton net haul, etc.)
Sample volume, tow length
Preservatives used and concentration
Name of collector
2.2 Phytoplankton
2.2.1 Sampling equipment
The type of samping equipment used is highly
dependent upon where and how the sample is
being taken (i.e., from a small lake, large deep
lake, small stream, large stream, from the shore,
from a bridge, from a small boat, or from a large
boat) and how it is to be used.
The cylindrical type of sarrtpler with stoppers
that leave the ends open to allow free passage of
water through the cylinder while it's being
lowered is recommended. A messenger is re-
leased at the desired depth to close the stoppers
in the ends. The Kemmerer, Juday, and Van
Dorn samplers have such a design and can be
obtained in a variety of sizes and materials. Use
only nonmetallic samplers when metal analysis,
algal assays, or primary productivity measure-
ments are being performed. In shallow waters
and when surface samples are desired, the
-------
BIOLOGICAL METHODS
sampler can be held in a horizontal position and
operated manually. For sampling in deep waters,
the Nansen reversing water bottle is often used
and a boat equipped with a winch is desirable.
Take caution when sampling from bridges with a
Kemmerer type water bottle; if the messenger is
dropped from the height of a bridge, it can
batter and destroy the triggering device. To
avoid this, support a messenger a few feet above
the sampler by an attached string and drop it
when the sampler is in place.
Net collection of phytoplankton is not
recommended for quantitative work. Nanno-
plankton and even larger algae, such as some
pennate diatoms, are thin enough to pass
through the meshes of the net if oriented
properly. Using a pump also presents problems:
when the water is stratified, the tubing must be
flushed between samplings and delicate algae
may be harmed.
2.2.2 Sample volume
No fixed rule can be followed concerning the
volume of sample to be taken — sampling per-
sonnel must use their own judgment. The vol-
ume of the sample needed depends on the
numbers and kinds of analyses to be carried out,
e.g., cell counts, chlorophyll, dry weight. When
phytoplankton densities are less than 500 per
ml, approximately 6 liters of sample are required
for Sedgwick-Rafter and diatom species pro-
portional counts. In most cases, a 1- to 2-liter
sample will suffice for more productive waters.
2.2.3 Sample preservation
Biologists use a variety of preservatives, and
each has advantages. If samples are to be stored
for more than 1 year, the preferred preservative
is formalin (40 percent formaldehyde = 100 per-
cent formalin), which has been neutralized with
sodium tetraborate (pH 7.0 to 7.3). Five milli-
liters of the neutralized formalin are added for
each 100 ml of sample. This preservative will
cause many flagellated forms to lose flagella.
Adding saturated cupric sulfate solution to the
preserved samples maintains the green color of
phytoplankton samples and aids in distin-
guishing phytoplankton from detritus. One milli-
Hter of the saturated solution per liter of sample
is adequate. Adding detergent solution prevents
clumping of settled organisms. One part of
surgical detergent to five parts of water makes a
convenient stock solution. Add 5 ml of stock
solution per liter of sample. Do not use deter-
gent when diatom slides are to be made.
Merthiolate is less desirable as a preservative,
but offers the advantage of staining cell parts
and simplifying identification. It also causes
some of the algae, such as blue-greens, to lose
gas from their vacuoles and, therefore, enhances
settling. Samples preserved with merthiolate are
not sterile, and should not be stored for more
than 1 year. After that time formalin should be
used. Merthiolate solution is prepared by dis-
solving the following in 1 liter of distilled water.
• 1.0 gram of merthiolate (sodium ethyl-
mercury thiosalicylate).
• 1.0 ml of aqueous saturated iodine-
potassium iodide solution prepared by
dissolving 40 grams of iodine and 60
grams of potassium iodide in 1 liter of
distilled water.
• 1.5 gram of Borax (sodium bo rate)
Dissolve each of the components separately in
approximately 300 ml of distilled water, com-
bine, and make up to 1 liter with distilled water.
Add the resulting stock solution to samples to
give a final concentration (V/V) of 36 mg/liter
(i.e., 37.3 ml added to 1 liter of sample).
2.3 Zooplankton
2.3.1 Sampling equipment
Zooplankton analyses require larger samples
than those needed for phytoplankton analyses.
Collect quantitative samples with a messenger-
operated water bottle, plankton trap, or metered
plankton net. Obtain semi-quantitative samples
by filtering surface water samples through nylon
netting or by towing an unmetered plankton net
through the water. In moderately and highly
productive waters, a 6-liter water sample is
usually sufficient. In oligotrophic, estuarine, and
coastal waters, remove zooplankters from several
hundred liters of the waters being sampled with
the use of towed nets. Take duplicate samples if
chemical analyses are desired.
-------
PLANKTON PRESERVATION
Several sampling methods can be used.
Towing
An outboard motor boat fitted with a small
davit, meter wheel, wire-angle indicator, and
hand-operated winch is desirable. A 3- to 5-kg
weight attached to the line is used to sink the
net. Maintain speed to ensure a wire angle near
60° for easy calculation of the actual sampling
depth of the net. The actual sampling depth
equals the amount of wire extended times the
cosine of the wire angle.
Oblique tow-Make an 8-minute tow at four
levels in the water column (2 minutes at each
level: just above the bottom, 1/3 total depth,
2/3 total depth, and just below the surface) to
estimate zooplankton abundance.
Horizontal tow-Take samples for estimating
zooplankton distribution and abundance within
a particular layer of water with a messenger-
operated net equipped with a flow-through
measuring vane (such as the Clarke-Bumpus
sampler). Each tow lasts from 5 to 8 minutes.
Vertical two-Lower a weighted net to the
desired depth, record the amount of line ex-
tended, and retrieve at a rate of 0.5 to 1.0
meters per second. The volume of water strained
can be estimated. Duplicate vertical tows are
suggested at each station.
To sample most sizes of zooplankters, two
nets of different mesh size can be attached a
short distance apart on the same line.
Net casting
Zooplankton can also be sampled from shore
by casting a weighted net as far as possible,
allowing the net to reach depth, and hauling to
shore at the rate of 0.5 to 1.0 meters per second.
Take several samples to obtain a qualitative
estimate of relative abundance and species
present.
Suggested net sizes are: No. 6 (0.239 mm
aperture) for adult copepods in estuarine and
coastal waters; No. 10 (0.158 mm) for cope-
podites in saline water or microcrustacea in fresh
water; and No. 20 (0.076 mm) for rotifers and
nauplii. The No. 20 net clogs easily with phy-
toplankton because of its small aperture size.
Rinse messenger-operated samplers with clean
water, allow to dry, and lubricate all moving
parts with light machine oil. Clean nylon netting
material thoroughly, rinse with clean water, and
allow to dry (out of direct sunlight) before
storing.
2.3.2 Sample volume
The sample volume varies with the specific
purpose of the study. Twenty-liter surface
samples obtained by bucket and filtered through
a No. 20 net are large enough to obtain an
estimate of zooplankton present in flowing
streams and ponds. In lakes, large rivers, estu-
aries and coastal waters, filter 1.5 m3 (horizon-
tal tow) to 5 m3 (oblique tow) of water through
nets for adequate representation of species pres-
ent.
2.3.3 Sample preservation
For identification and enumeration, preserve
grab samples in a final concentration of 5 per-
cent neutral (add sodium tetraborate to obtain a
pH of 7.0 to 7.3) formalin. Adding either 70
percent ethanol or 5 percent neutral formalin,
each with 5 percent glycern (glycerol) added, to
preserve the concentrated net samples. Formalin
is usually used for preserving samples obtained
from coastal waters. In detritus-laden samples,
add 0.04 percent Rose Bengal stain to help
differentiate zooplankters from plant material.
For chemical analysis (taken, in part, from
Recommended Procedures for Measuring the
Productivity of Plankton Standing Stock and
Related Oceanic Properties, National Academy
of Sciences, Washington, D.C. 1960), the con-
centrated sample is placed in a fine-meshed
(bolting silk or nylon) bag, drained of excess
water, placed in a plastic bag, and frozen for
laboratory processing. If the sample is taken
from an estuarine or coastal station, the nylon
bag is dipped several times in distilled water to
remove the chloride from interstitial seawater
which can interfere with carbon analysis.
3.0 SAMPLE PREPARATION
3.1 Phy to plankton
As the phytoplankton density decreases, the
amount of concentration must be increased and,
accordingly, larger sample volumes are required.
-------
BIOLOGICAL METHODS
As a rule of thumb, concentrate samples when
phytoplankton densities are below 500 per ml;
approximately 6 liters of sample are required at
that cell concentration. Generally, 1 liter is an
adequate routine sample volume.
The following three methods may be used for
concentrating preserved phytoplankton, but
sedimentation is preferred.
3.1.1 Sedimentation
Preserved phytoplankton samples can often be
settled in the original storage containers. Settling
time is directly related to the depth of the
sample in the bottle or settling tube. On the
average, allow 4 hours per 10 mm of depth.
After settling, siphon off the supernatant
(Figure 1) or decant through a side drain. The
use of a detergent aids in settling. Exercise
caution because of the different sedimentation
rates of the diverse sizes and shapes of phyto-
plankton.
3.1.2 Centrifugation
During centrifugation, some of the more
fragile forms may be destroyed or flagella may
become detached. In using plankton centrifuges,
many of the cells may be lost; modern
continuous-flow centrifuges avoid this.
3.1.3 Filtration
To concentrate samples by filtration, pass
through a membrane filter. A special filter
apparatus and a vacuum source are required.
Samples containing large amounts of suspended
material (other than phytoplankton) are
difficult to enumerate by this method, because
the suspended matter tends to crush the phyto-
plankters or obscure them from view. The
vacuum should not exceed 0.5 atmospheres.
Concentration by filtration is particularly useful
for samples low in plankton and silt content.
3.2 Zooplankton
The zooplankton in grab samples are con-
centrated prior to counting by allowing them to
settle for 24 hours in laboratory cylinders of
appropriate size or in specially constructed
settling tubes (Figure 1).
50.8 CM
I.D,
Figure 1. Plexiglas plankton settling tube with
side drain and detachable cup. Not
drawn to scale.
Take care to recover organisms (especially the
Cladocera) that cling to the surface of the water
in the settling tube.
4.0 SAMPLE ANALYSIS
4.1 Phytoplankton
4.1.1 Qualitative analysis of phytoplankton
The optical equipment needed includes a good
quality compound binocular microscope with a
mechanical stage. For high magnification, a sub-
stage condenser is required. The ocular lens
should be 8X to 12X. Binocular eyepieces are
generally preferred over a monocular eyepiece
because of reduced fatigue. Four turret-mounted
objective lenses should be provided with mag-
nifications of approximately 10, 20, 45, and
-------
PLANKTON COUNTING
100X. When combined with the oculars, the
following characteristics are approximately
correct.
Objective
lens
10X
20X
45X
100X
Ocular
lens
10X
10X
10X
10X
Subject
magnification
100X
200X
450X
1000X
Maximum working
distance between
objective and
cover slip, mm
7
1.3
0.5-0.7
0.2
Depth of
focus, H
8
2
1
0.4
An initial examination is needed because most
phytoplankton samples will contain a diverse
assemblage of organisms. Carry out the identi-
fication to species whenever possible. Because
the size range of the individual organisms may
extend over several orders of magnitude, no
single magnification is completely satisfactory
for identification. For the initial examination,
place one or two drops of a concentrate on a
glass slide and cover with a No. 1 or No. 1-1/2
cover slip. Use the 1 OX objective to examine the
entire area under the cover slip and record all
identifiable organisms. Then examine with the
20 and 45X objectives. Some very small or-
ganisms may require the use of the 100X
objective (oil immersion) for identification. The
initial examination helps to obtain an estimate
of population density and may indicate the need
for subsequent dilution or concentration of the
sample, to recognize characteristics of small
forms not obvious during the routine counting
procedure, and to decide if more than one type
of counting procedure must be used.
When identifying phytoplankton, it is useful
to examine fresh, unpreserved samples. Pres-
ervation may cause some forms to become dis-
torted, lose flagella, or be lost together. These
can be determined by a comparison between
fresh and preserved samples.
As the sample is examined under the micro-
scope, identify the phytoplankton and tally
under the following categories: coccoid blue-
green, filamentous blue-green, coccoid green,
filamentous green, green flagellates, other pig-
mented flagellates, centric diatoms, and pennate
diatoms. In tallying diatoms, distinguish be-
tween "live" cells, i.e., those that contain any
part of a protoplast, and empty frustules or
shells.
The availability of taxonomic bench refer-
ences and the skill of the biologist will govern
the sophistication of identification efforts. No
single reference is completely adequate for all
phytoplankton. Some general references that
should be available are listed below. Those
marked with an asterisk are considered essential.
American Public Health Association, 1971. Standard methods
for the examination of water and wastewater. 13th edition.
Washington, D.C.
Bourrelly, P. 1966-1968. Les algues d'eau douce. 1966. Tome
Mil, Boubee & Cie, Paris.
Fott, B. 1959. Algenkunde. Gustav Fischer, Jena. (2nd revised
edition, 1970.)
Prescott, G. W. 1954. How to know the fresh-water algae. W. C.
Brown Company, Dubuque. (2nd edition, 1964.)
*Prescott, G. W. 1962. Algae of the Western Great Lakes Area.
(2nd edition), W. C. Brown, Dubuque.
*Smith, G. M. 1950. The freshwater algae of the United States.
(2nd edition), McGraw-Hill Book Co., New York.
Ward, H. B., and G. C. Whipple. 1965. Fresh-water biology. 2nd
edition edited by W. T. Edmonson. John Wiley and Sons, New
York.
*Weber, C. I. 1966. A guide to the common diatoms at water
pollution surveillance system stations. USDI, FWPCA, Cin-
cinnati.
West, G. S., and F. E. Fritsch. 1927. A treatise on the British
freshwater algae. Cambridge Univ. Press. (Reprinted 1967; J.
Cramer, Lehre; Wheldon & Wesley, Ltd.; and Stecherthafner,
Inc., New York.)
Specialized references that may be required
for exact identification within certain taxo-
nomic groups include:
Brant, K., and C. Apstem. 1964. Nordisches Plankton. A. Asher
& Co., Amsterdam. (Reprint of the 1908 publication published
by Verlag von Lipsius & Tischer, Kiel and Leipzig.)
Cleve-Euler, A. 1968. Die diatomeen von Schweden und Finn-
land, I-V. Bibliotheca Phycologica, Band 5, J. Cramer, Lehre,
Germany.
Cupp, E. 1943. Marine plankton diatoms of the west coast of
North America. Bull. Scripps Inst. Oceanogr., Univ. Calif.,
5:1-238.
Curl, H 1959. The phytoplankton of Apalachee Bay and the
Northwestern Gulf of Mexico. Univ. Texas Inst. Marine Sci.,
Vol. 6, 277-320.
*Drouet, F. 1968. Revision of the classification of the Oscilla-
toriaceae. Acad. Natural Sci., Philadelphia.
*Drouet, F., and W. A. Daily. 1956. Revision of the coccoid
Myxophyceae. Butler Univ. Bot. Stud. XII., Indianapolis.
Fott, B. 1969. Studies in phycology. E. Schweizerbart'sche
Verlagsbuchhandlung (Nagele u. Obermiller), Stuttgart, Ger-
many.
-------
BIOLOGICAL METHODS
*Fritsch, F. E. 1956. The structure and reproduction of the
algae. Volumes I and II. Cambridge University Press.
Geitler, L. 1932. Cyanophyceae. In: Rabehnorst's Kryptoga-
men-Flora, 14:1-1096. Akademische Verlagsgesellschaft
m.b.H., Leipzig. (Available from Johnson Reprint Corp., New
York.)
Glezer, Z. I. 1966. Cryptogamic plants of the U.S.S.R., volume
VII: Sihoflagellatophyceae. Moscow. (English Transl. Jerusa-
lem, 1970) (Available from A. Asher & Co., Amsterdam.)
Gran, H. H., and E. C. Angst. 1930. Plankton diatoms of Puget
Sound. Univ. Washington, Seattle.
Hendey, N. I. 1964. An introductory account of the smaller
algae of British coastal waters. Part V: Baccilariophyceae (Dia-
toms). Fisheries Invest. (London), Series IV.
Huber-Pestalozzi, G., and F. Hustedt. 1942. Die Kieselalgen. In:
A. Thienemann (ed.), Das Phytoplankton des Susswassers, Die
Binnengewasser, Band XVI, Teil II, Halfte II. E. Schweizer-
bart'sche Verlagsbuch-handlung, Stuttgart. (Stechert, New
York, reprinted 1962.)
*Hustedt, F. 1930. Die Kieselalgen. In: L. Rabenhorst (ed.),
Kryptogamen-Flora von Deutschland, Osterreich, und der
Schweiz. Band Vii. Akademische Verlagsgesellschaft m.b.h.,
Leipzig. (Johnson Reprint Co., New York.)
*Hustedt, F. 1930. Bacillariophyta. In: A Pascher (ed.), Die
Suswasser-Flora Mitteliuropas, Heft 10. Gustav Fischer, Jena.
(University Microfilms, Ann Arbor, Xerox.)
Hustedt, F. 1955. Marine littoral diatoms of Beaufort, North
Carolina. Duke Univ. Mar. Sta. Bull. No. 6. Duke Univ. Press,
Durham, N. C., 67 pp.
Irenee-Marie, F. 1938. Flore Desmidiale de la region de Mon-
treal. Lapraine, Canada.
*Patnck, R., and C. W. Reimer. 1966. The diatoms of the United
States. Vol. I, Academy of Natural Sciences, Philadelphia.
Tiffany, L. H., and M. E. Britton. 1952. The algae of Illinois.
Reprinted in 1971 by Hafner Publishing Co., New York.
Tilden, J. 1910. Minnesota algae, Vol. 1. The Myxophyceae of
North America and adjacent regions including Central America,
Greenland, Bermuda, the West Indies and Hawaii. Univ.
Minnesota. (First and unique volume) (Reprinted, 1969, in
Bibliotheca Phycologica, 4, J. Cramer, Lehre, Germany.)
4.1.2 Quantitative analysis ofphytoplankton
To calibrate the microscope, the ocular must
be equipped with a Whipple grid-type micro-
meter. The exact magnification with any set of
oculars varies, and therefore, each combination
of oculars and objectives must be calibrated by
matching the ocular micrometer against a stage
micrometer. Details of the procedure are given
in Standard Methods, 13th Edition.
When counting and identifying phyto-
plankton, analysts will find that samples from
most natural waters seldom need dilution or
concentration and that they can be enumerated
directly. In those samples where algal concen-
trations are extreme, or where silt or detritus
may interfere, carefully dilute a 10-ml portion
of the sample 5 to 10 times with distilled water.
In samples with very low populations, it may be
necessary to concentrate organisms to minimize
statistical counting errors. The analyst should
recognize, however, that manipulations involved
in dilution and concentration may introduce
error.
Among the various taxa are forms that live as
solitary cells, as components of natural groups
or aggregates (colonies), or as both. Although
every cell, whether solitary or in a group, can be
individually tallied, this procedure is difficult,
time consuming, and seldom worth the effort.
The unit or clump count is easier and faster and
is the system used commonly within this
Agency. In this procedure, all unicellular or
colonial (multi-cellular) organisms are tallied as
single units and have equal numerical weight on
the bench sheet.
The apparatus and techniques used in
counting phytoplankton are described here.
Sedgwick-Rafter (S-R) Counting Chamber
The S-R cell is 50 mm long by 20 mm wide by
1 mm deep; thus, the total area of the bottom of
the cell is 1000 mm2 and the total volume is
1000 mm3 or one ml. Check the volume of each
counting chamber with a vernier caliper and
micrometer. Because the depth of the chamber
normally precludes the use of the 45X or 100X
objectives, the 20 X objective is generally used.
However, special long-working-distance, higher-
power objectives can be obtained.
For the procedure, see Standard Methods,
13th Edition. Place a 24 by 60 mm, No. 1 cover-
glass diagonally across the cell, and with a large-
bore pipet or eyedropper, quickly transfer a 1-ml
aliquot of well-mixed sample into the open
corner of the chamber. The sample should be di-
rected diagonally across the bottom of the cell.
Usually, the cover slip will rotate into place as
the cell is filled. Allow the S-R cell to stand for
at least 15 minutes to permit settling. Because
some organisms, notably blue-green algae, may
8
-------
PLANKTON COUNTING
float, examine the underside of the cover slip and
add these organisms to the total count. Lower
the objective lens carefully into position with
the coarse focus adjustment to ensure that the
cover slip will not be broken. Fine focus should
always be up from the cover slip.
When making the strip count, examine two to
four "strips" the length of the cell, depending
upon the density of organisms. Enumerate all
forms that are totally or partially covered by the
image of the Whipple grid.
When making the field count, examine a
minimum of 10 random Whipple fields in at
least two identically prepared S-R cells. Be sure
to adopt a consistent system of counting organ-
isms that lie only partially within the grid or
that touch one of the edges.
To calculate the concentration of organisms
with the S-R cell, for the strip count:
, CXlOOOmnP
Palmer-Maloney (P-M) Nannoplankton Cell
The P-M cell was especially designed for
enumerating nannoplankton with a high-dry
objective (45X). It has a circular chamber 17.9
mm in diameter and 0.4 mm deep, with a
volume of 0.1 ml. Although useful for exam-
ining samples containing a high percentage of
nannoplankton, more counts may be required to
obtain a valid estimate of the larger, but less
numerous, organisms present. Do not use this
cell for routine counting unless the samples have
high counts.
Pipet an aliquot of well-mixed sample into
one of the 2X5 mm channels on either side of
the circular chamber with the cover slip in place.
After 10 minutes, examine the sample under the
high-dry objective and count at least 20 Whipple
fields.
To calculate the concentration of organisms:
No. per ml =
C X 1000mm3
A X D X F
where:
C = number of organisms counted (tally)
L = length of each strip (S-R cell length), mm
D = depth of a strip (S-R cell depth), mm
W = width of a strip (Whipple grid image
width), mm
S = number of strips counted
To calculate the concentration of organisms
with the field count:
,
No.perml=
CX 1000 mm3
A x p x F •
where :
C = actual count of organisms (tally)
A = area of a field (Whipple grid image area),
2
mm
F =
depth of a field (S-R cell depth), mm
number of fields counted
Multiply or divide the number of cells per
milliliter by a correction factor for dilution
(including that resulting from the preservative)
or for concentration.
where:
C = number of organisms counted (tally)
A = area of a field (Whipple grid image), mm2
D = depth of a field (P-M cell depth), mm
F = number of fields counted
Bacterial Counting Cells and Hemocytometers
The counting cells in this group are precisely-
machined glass slides with a finely ruled grid on
a counting plate and specially-fitted ground
cover slip. The counting plate proper is sepa-
rated from the cover slip mounts by parallel
trenches on opposite sides. The grid is ruled such
that squares as small as 1/20 mm (50 M) to a side
are formed within a larger 1-mm square. With
the cover slip in place, the depth in a Petroff-
Hausser cell is 1/50 mm (20 n) and in the
hemocytometer 1/10 mm (lOO/u). An optical
micrometer is not used.
With a pipet or medicine dropper, introduce a
sample to the cell and at high magnification
identify and count all the forms that fall within
the gridded area of the cell.
To calculate the number of organisms per
milliliter, multiply all the organisms found in the
gridded area of the cell by the appropriate
factor. For example, the multiplication factor
-------
BIOLOGICAL METHODS
for the Petroff-Hausser bacterial counting cell is
based on the volume over the entire grid. The
dimensions are 1 mm X 1 mm X 1/50 mm.
which gives a volume of 1/50 mm3 and a factor
of 50,000.
Carefully follow the manufacturer's instruc-
tions that come with the chamber when
purchased. Do not attempt routine counts until
experienced in its use and the statistical validity
of the results are satisfactory. The primary
disadvantage of this type of counting cell is the
extremely limited capacity, which results in a
large multiplication factor. Densities as high as
50,000 cells/ml are seldom found in natural
waters except during blooms. Such populations
may be found in sewage stabilization ponds or in
laboratory cultures.
For statistical purposes, a normal sample must
be either concentrated or a large number of
mounts per sample should be examined.
Membrane Filter
A special filtration apparatus and vacuum
source are required, and a 1-inch, 0.45ju mem-
brane filter is used.
Pass a known volume of the water sample
through the membrane filter under a vacuum of
0.5 atmospheres. (Note: in coastal and marine
waters, rinse with distilled water to remove salt.)
Allow the filter to dry at room temperature for
5 minutes, and place it on top of two drops of
immersion oil on a microscope slide. Place two
drops of oil on top of the filter and allow it to
dry clear (approximately 48 hours) at room
temperature, cover with a cover slip, and
enumerate the organisms. The occurrence of
each species in 30 random fields is recorded.
Experience is required to determine the
proper amount of water to be filtered. Signifi-
cant amounts of suspended matter may obscure
or crush the organisms.
Calculate the original concentration in the
sample as a function of a conversion factor
obtained from a prepared table, the number of
quadrates or fields per filter, the amount of
sample filtered, and the dilution factor. (See
Standard Methods, 13th Edition.)
Inverted Microscope
This instrument differs from the conventional
microscope in that the objectives are mounted
below the stage and the illumination comes from
above. This design allows cylindrical counting
chambers (which may also be sedimentation
tubes) with thin clear glass bottoms to be placed
on the stage and sedimented plankton to be
examined from below, and it permits the use of
short focus, high-magnification objectives
including oil immersion. A wide range of con-
centrations is automatically obtained by merely
altering the height of the chamber. Chambers
can be easily and inexpensively made: use tubu-
lar Plexiglas for large capacity chambers, and
flat, plastic plates of various thicknesses, which
have been carefully bored out to the desired
dimension, for smaller chambers; then cement a
No. 1 or No. 1-1/2 cover slip to form the cell
bottom. Precision-made, all-glass counting
chambers in a wide variety of dimensions are
also available. The counting technique differs
little from the S-R procedure, and either the
strip or separate field counts can be used. The
Whipple eyepiece micrometer is also used.
Transfer a sample into the desired counting
chamber (pour with the large chambers, or pipet
with 2-ml or smaller chambers), fill to the point
of overflow, and apply a glass cover slip. Set the
chamber aside and keep at room temperature
until sedimentation is complete. On the average,
allow 4 hours per 10 mm of height. After a suit-
able period of settling, place the chamber on the
microscope stage and examine with the use of
the 20X, 45 X, or 100X oil immersion lens.
Count at least two strips perpendicular to each
other over the bottom of the chamber and aver-
age the values. Alternatively, random field
counts can be made; the number depends on the
density of organisms found. As a general rule,
count a minimum of 100 of the most abundant
species. At higher magnification, count more
fields than under lower power.
When a 25.2 mm diameter counting chamber
is used (the most convenient size), the conversion
10
-------
PLANKTON COUNTING
of counts to numbers per ml is quite simple:
C X 1000 mm3
No. per ml (strip count) =
L XW XD X S
where:
C = number of organisms counted (tally)
L = length of a strip, mm
W= width of a strip (Whipple grid image
width), mm
D = depth of chamber, mm
S = number of strips counted
xi , ,r tJ ,
No. per ml (field count) =
CX 1000
t\ A U A r
where:
C = number of organisms counted (tally)
A = area of a field (Whipple grid image area),
mm2
D = depth of chamber, mm
F = number of fields counted
Diatom Analysis
Study objectives often require specific identi-
fication of diatoms and information about the
relative abundance of each species. Since the
taxonomy of this group is based on frustule
characteristics, low-power magnification is
seldom sufficient, and permanent diatom
mounts are prepared and examined under oil
immersion.
To concentrate the diatoms, centrifuge 100
ml of thoroughly mixed sample for 20 minutes
at 1000 X g and decant the supernatant with a
suction tube. Pour the concentrated sample into
a disposable vial, and allow to stand at least 24
hours before further processing. Remove the
supernatant water from the vial with a suction
tube. If the water contains more than 1 gm of
dissolved solids per liter (as in the case of
brackish water or marine samples), salt crystals
form when the sample dries and obscure the
diatoms on the finished slides. In this case,
reduce the concentration of salts by refilling the
vial with distilled water, resuspending the plank-
ton, and allowing the vial to stand 24 hours
before removing the supernatant liquid. Repeat
the dilution several times if necessary.
If the plankton counts are less than 1000 per
ml, concentrate the diatoms from a larger
volume of sample (1 to 5 liters) by allowing
them to settle out. Exercise caution in using this
method, however, to ensure quantitative
removal of cells smaller than 10 microns in
diameter.
Thoroughly mix the plankton concentrate in a
vial with a disposable pipet, and deliver several
drops to a No. 1, circular 18-mm coverglass. Dry
the samples on a hotplate at 95°C. (Caution:
overheating may cause splattering and cross-
contamination of samples.) When dry, examine
the coverglasses to determine if there is suffi-
cient material for a diatom count, if not, repeat
the previous steps one or two more times,
depending upon the density of the sedimented
sample. Then heat the samples on a heavy-duty
hotplate 30 minutes at approximately 570°C to
drive off all organic matter. Remove grains of
sand or other large objects on the cover glass
with a dissection needle. The oil immersion
objective has a very small working distance, and
the slide may be unusable if this is not done.
Label the frosted end of a 25- X 75-mm
microscope slide with the sample identification.
Place the labelled slide on a moderately warm
hotplate (157°C), put a drop of Hyrax or
Aroclor 5442 (melt and use at about 138°C)
mounting medium (Index of Refraction
1.66-1.82) at the center, and heat the slide until
the solvent (xylene or toluene) has evaporated
(the solvent is gone when the Hyrax becomes
hard and brittle upon cooling).
While the coverglass and slide are still hot,
grasp the coverglass with a tweezer, invert, and
place on the drop of Hyrax on a slide. It may be
necessary to add Hyrax at the margin of the
coverglass. Some additional bubbles of solvent
vapor may appear under the coverglass when it is
placed on the slide. When the bubbling ceases,
remove the slide from the hotplate and place on
a firm, flat surface. Immediately apply slight
pressure to the coverglass with a pencil eraser (or
similar object), and maintain until the Hyrax
cools and hardens (about 5 seconds). Spray a
protective coating of clear lacquer on the frosted
end of the slide, and scrape the excess Hyrax
from around the coverglass.
Identify and count the diatoms at high
magnification under oil. Examine random lateral
strips the width of the Whipple grid, and iden-
tify and count all diatoms within the borders of
the grid until 250 cells (500 halves) are tallied.
11
-------
BIOLOGICAL METHODS
Ignore small cell fragments. If the slide has very
few diatoms, limit the analysis to the number of
cells encountered in 45 minutes of scanning.
When the count is completed, total the tallies
and calculate the percentages of the individual
species.
4.2 Zooplankton
4.2.1 Qualitative analysis of zooplankton
In the initial examination, remove excess
preservative from the sample with the use of an
aspirator bulb attached to a small piece of glass
tubing whose orifice is covered with a piece of
No. 20 mesh netting. Swirl the sample, and with
a large-bore pipet, remove a portion of the
suspension and place 2 ml into each section of a
four-compartment glass culture dish (100 X 15
mm). Examine a total of 8 ml for adult
Copepoda, Cladocera, and other large forms
with the use of a binocular dissecting micro-
scope at a magnification of 20 to 40 X. Count
and identify rotifers at a higher magnification
(100X). All animals should be identified to
species if possible. For qualitative analysis of
relative frequency, the following classification is
suggested:
Species in
fields, %
60 - 100
30 - 60
5-30
1 - 5
Relative
frequency
abundant
very common
common
occasional
rare
The following taxonomic bench references are
recommended:
Caiman, W. T. 1912. The Crustacea of the order Cumacea in the
collection of the United States National Museum. No.
1876-Proc. U. S. Natl. Mus. 41: 603-676.
Chien, S. M. 1970. Alonella fitzpatricki sp. n. and A. leei sp. n:
new Cladocera from Mississippi. Trans. Amer. Microsc. Soc.
89(4): 532-538.
Conseil Permanent International Pour L'ExplorationDe LaMer.
1970. Fiches DTdentification du Zooplankton. Sheets No.'s
1-133.
Davis, C. 1949. The pelagic Copepoda of the northeastern
Pacific Ocean. Univ. Wash. Publ. in Biol. 14:1-188. Univ. Wash.
Press, Seattle.
Davis, C. 1955. The marine and freshwater Plankton, Mich. State
Univ. Press, East Lansing.
Edmondson, W. T. (ed.). Ward, H. B. and G. C. Whipple. 1959.
Fresh-water biology. John Wiley and Sons, New York, 1248
pp.
Faber, D. J. and E. J. Jermolajev. 1966. A new copepod genus in
the plankton of the Great Lakes. Limnol. Oceanogr. 11(2):
301-303.
Ferguson, E., Jr. 1967. New ostracods from the Playa lakes of
eastern New Mexico and western Texas. Trans. Amer. Microsc.
Soc. 86(3)-.244-250.
Hyman, L. H. 1951. The Invertebrates: Acanthocephala,
Aschelminthes, and Ectoprocta. The pseudocoelomate
Bilateria. Vol. III. McGraw-Hill, New York, 572 pp.
Light, S. F. 1938. New subgenera and species of diaptomid
copepods from the inland waters of California and Nevada.
Univ. Calif. Publ. in Zool. 43(3): 67-78. Univ. Calif. Press,
Berkeley.
Marsh, C. C. 1933. Synopsis of the calanoid crustaceans, exclu-
sive of the Diaptomidae, found in fresh and brackish waters,
chiefly of North America. No. 2959, Proc. U. S. Nat. Mus. 82
(Art. 18): 1-58.
Pennak, R. W. 1953. Fresh-water invertebrates of the United
States. The Roland Press Co., New York. 369 pp.
Ruber, E. 1968. Description of a salt marsh copepod cyclops
(Apocyclops) spartinus n. sp. and a comparison with closely
related species. Trans. Amer. Microsc. Soc. 87(3):368-375.
Wilson, M. S. 1956. North American Harpacticoid copepods.
1. Comments on the known fresh water species of the
Canthocamptidae.
2. Canthocamptm oregonensis n. sp. from Oregon and
California. Trans. Amer. Microsc. Soc. 75 (3): 290-307.
Wilson, M. S. 1958. The copepod genus Halicyclops in North
America, with a description of a new species from Lake Pont-
chartrain, Louisiana, and the Texas coast. Tulane Studies ZooL
6(4): 176-189.
Zimmer, C. 1936. California Crustacea of the order Cumacea.
No. 2992. Proc. U. S. Natl. Mus. 83:423-439.
4.2.2 Quan titative analysis of zooplankton
Pipet Method
Remove excess liquid using a screened (No. 20
mesh net) suction device until a 125- to 250-ml
sample volume remains. Pour the sample into a
conical container graduated in milliliters, and
allow the zooplankton to settle for 5 minutes.
Read the settled volume of zooplankton;
multiply the settled volume by a factor of five
to obtain the total diluted volume; and add
enough water to obtain this volume. Insert a
1-ml Stempel pipet into the water-plankton
mixture, and stir rapidly with the pipet. While
the mixture is still agitated, withdraw a 1-ml
subsample from the center of the water mass.
Transfer the subsample to a gridded culture dish
(110 X 15mm) with 5-mm squares. Rinse the
12
-------
PLANKTON BIOMASS
pipet with distilled water into a culture dish to
remove any adherent organisms. Enumerate
(about 200 zooplankters) and identify under a
dissecting microscope.
To calculate the number of plankton with an
unmetered collecting device:
Total no. =
To calculate the number of plankton with a
metered collecting device:
No. per m3 of water =
TN XDV
SV
where:
DV= total diluted volume, ml
SV = total subsample volume, ml
TN = total no. zooplankters in sample
Q = quantity of water strained, m3
Counting Chamber
Bring the entire concentrate (or an appro-
priate aliquot) to a volume of 8 ml, mix well,
and transfer to a counting chamber 80 X 50 X 2
mm (8-ml capacity). To fill, use the technique
previously described for the Sedgwick-Rafter
cell. The proper degree of sample concentration
can be determined only by experience.
Using a compound microscope equipped with
an ocular Whipple grid, enumerate and identify
the rotifers (to species if possible) in 10 strips
scanned at a magnification of 100X (one-fifth of
the chamber volume). Enumerate the nauplii
also during the rotifer count. Count the adult
microscrustacea under a binocular dissecting
microscope at a magnification of 20 to 40 X by
scanning the entire chamber. Species identi-
fication of rotifers and microcrustacea often
require dissection and examination under a
compound microscope (see Pennak, 1953).
When calculating the number of plankton,
determine the volume of the counting chamber
from its inside dimensions. Convert the tallies to
organisms per liter with the use of the following
relationships:
Rotifers per liter =
T XC
P X V
... ,. TXC
Microcrustacea per liter =
where:
T =
total tally
C = total volume of sample concentrate, ml
P = volume of 10 strips in the counting
chamber, ml
V = volume of netted or grab sample, liters
S = volume of counting chamber, ml
5.0 BIOMASS DETERMINATION
Because natural plankton populations are
composed of many types of organisms (i.e.,
plant, animal, and bacterial), it is difficult to
obtain quantitative values for each of the com-
ponent populations. Currently-used indices
include dry and ash-free weight, cell volume, cell
surface area, total carbon, total nitrogen, and
chlorophyll content. The dry and ash-free
weight methods yield data that include the par-
ticulate inorganic materials as well as the plank-
ton. Cell volume and cell surface area determi-
nations can be made on individual components
of the population and thus yield data on the
plant, the animal, or the bacterial volume, or
surface area, or both. Chlorophyll determi-
nations yield data on the phytoplankton.
5.1 Dry and Ash-Free Weight
To reduce the amount of contamination by
dissolved solids, wash the sample with several
volumes of distilled water by centrifugation or
settling. After washing, concentrate the sample
by centrifugation or settling. If possible, take
sufficient sample to provide several aliquots each
having at least 10 mg dry weight. Process at
least two replicate aliquots for each sample.
(Generally, 10 mg dry weight is equivalent to
100 mg wet weight.)
13
-------
BIOLOGICAL METHODS
5.1.1 Dry weight
Place the aliquot of concentrated sample in a
tared porcelain crucible, and dry to a constant
weight at 105°C (24 hours is usually sufficient).
Subtract the weight of crucible to obtain the dry
weight.
5.7.2 Ash-free weight
After the dry weight is determined, place the
crucible in a muffle furnace at 500°C for 1 hour.
Cool, rewet the ash with distilled water, and
bring to constant weight at 105°C. The ash is
wetted to reintroduce the water of hydration of
the clay and other minerals that, though not
driven off at 105°C, is lost at 500°C. This water
loss often amounts to 10 percent of the weight
lost during ignition and, if not corrected for, will
be interpreted as organic matter. Subtract the
weight of crucible and ash from the dry weight
to obtain ash-free weight.
5.2 Chlorophyll
All algae contain chlorophyll a, and measuring
this pigment can yield some insight into the
relative amount of algal standing crop. Certain
algae also contain chlorophyll b and c. Since the
chlorophyll concentration varies with species and
with environmental and nutritional factors that
do not necessarily affect the standing crop,
biomass estimates based on chlorophyll measure-
ments are relatively imprecise. Chlorophyll can
be measured in vivo fluorometrically or in ace-
tone extracts (in vitro) by fluorometry or
spectrophotometry.
5.2.1 In vitro measurement
The algae differ considerably in the ease of
pigment extraction. The diatoms extract easily,
whereas the coccoid greens extract with diffi-
culty. Complete extraction of pigments from all
taxonomic groups, therefore, requires disruption
of the cells with a tissue grinder or blender, or
by freezing or drying. Generally, pigment is
more difficult to extract from old cells than
from young cells.
Concentrate the algae with a laboratory cen-
trifuge, or collect on a membrane filter (0.45-/U
porosity) or a glass fiber filter (0.45-^ effective
pore size). If the analysis will be delayed, dry
the concentrate and store frozen in a desiccator.
Keep the stored samples in the dark to avoid
photochemical breakdown of the chlorophyll.
Place the sample in a tissue grinder, cover with
2 to 3 milliliters of 90 percent aqueous acetone
(use reagent grade acetone), add a small amount
(0.2 ml) of saturated aqueous solution of magne-
sium carbonate and macerate.
Transfer the sample to a screw-capped cen-
trifuge tube, add sufficient 90 percent aqueous
acetone to bring the volume to 5 ml, and steep
at 4°C for 24 hours in the dark. Use the solvent
sparingly, avoiding unnecessary pigment dilu-
tion. Agitate midway during the extraction
period and again before clarifying.
To clarify the extract, centrifuge 20 minutes
at 500 g. Decant the supernatant into a clean,
calibrated vessel (15-ml, screw-capped, cali-
brated centrifuge tube) and determine the vol-
ume. Minimize evaporation by keeping the tube
capped.
Three procedures for analysis and concen-
tration calculations are described.
Trichromatic Method
Determine the optical density (OD) of the
extract at 750, 663, 645, and 630 nanometers
(nm) using a 90 percent aqueous acetone blank.
Dilute the extract or shorten the light path if
necessary, to bring the OD66 3 between 0.20 and
0.50. The 750 nm reading is used to correct for
turbidity. Spectrophotometers having a reso-
lution of 1 nm or less are preferred. Stopper the
cuvettes to minimize evaporation during the
time the readings are being made.
The chlorophyll concentrations in the extract
are determined by inserting the corrected 1-cm
OD's in the following equations. (UNESCO
1966).
Ca=11.64D663- 2.16D645 + 0.10D630
q, = -3.94D663+20.97D64S- 3.66D630
Cc = ~5.53D663- 14.81D64S + 54.22D630
where Cfl, Q>, Cc are the concentrations, in
milligrams per liter, of chlorophyll a, b, and c,
respectively, in the extract; and D663, D645,
and D630 are the 1-cm OD's at the respective
wavelengths, after subtracting the 750-nm blank.
14
-------
PLANKTON PIGMENTS
The concentration of pigment in the phyto-
plankton grab sample is expressed as mg/m3 or
Mg/m3 or jug/liter and is calculated as follows:
mg chlorophyll a/m3 =
Ca X volume of extract (liters)
volume of grab sample (m3)
Fluorometric (for chlorophylls)
The fluorometric method is much more sensi-
tive than the photometric method and permits
accurate determination of much lower con-
centrations of pigment and the use of smaller
sample volumes. Optimum sensitivity is obtained
at excitation and emission wavelengths of 430
and 663 nm, respectively, using a R-136 photo-
multiplier tube. Fluorometers employing filters
should be equipped with Corning CS-5-60
excitation and CS-2-64 emission filters, or their
equivalents. Calibrate the fluorometer with a
chlorophyll solution of known concentration.
Prepare a chlorophyll extract and determine
the concentration of chlorophyll a by the
spectrophotometric method as previously de-
scribed.
Prepare serial dilutions of the extract to
provide concentrations of approximately 0.002,
0.006, 0.02 and 0.06 mg chlorophyll a per liter
of extract, so that a minimum of two readings
are obtained in each sensitivity range of the
fluorometer (1/3 and 2/3 of full scale). With the
use of these values, derive factors to convert the
fluorometer readings in each sensitivity range to
milligrams of chlorophyll a per liter of extract.
P _ Cone, chlorophylls (mg/1)
s fluorometer reading
where Fs is the fluorometric conversion factor
and 5 is the sensitivity range (door).
5.2.2 In vivo measurement
Using fluorescence to determine chlorophyll a
in vivo is much less cumbersome than methods
involving extraction; however, it is reportedly
considerably less efficient than the extraction
method and yields about one-tenth as much
fluorescence per unit weight as the same amount
in solution. The fluorometer should be cali-
brated with a chlorophyll extract that has been
analyzed with a spectrofluorometer.
To determine the chlorophyll a, zero the
fluorometer with a distilled water blank before
taking the first sample reading at each sensitivity
level.
Mix the phytoplankton sample thoroughly to
ensure a homogenous suspension of algal cells.
Pour an aliquot of the well-mixed sample into a
cuvette, and read the fluorescence. If the reading
(scale deflection) is over 90 units, use a lower
sensitivity setting, e.g., 30X > 10X >3X> IX.
Conversely, if the reading is less than 15 units,
increase the sensitivity setting. If the samples fail
to fall in range, dilute accordingly. Record the
fluorescent units based on a common sensitivity
factor, e.g., a reading 50 at IX equals 1500 at
30X.
5.2.3 Pheophytin Correction
Pheophytin is a natural degradation product
of chlorophll and often occurs in significant
quantities in phytoplankton. Pheophytin a,
although physiologically inactive, has an absorp-
tion peak in the same region of the visible
spectrum as chlorophyll a and can be a source of
error in chlorophyll determinations. In nature,
chlorophyll is converted to pheophytin upon the
loss of magnesium from the porphyrin ring. This
conversion can be accomplished in the labora-
tory by adding acid to the pigment extract. The
amount of pheophytin a in the extract can be
determined by reading the OD663 before and
after acidification. Acidification of a solution of
pure chlorophyll a results in a 40 percent re-
duction in the OD663, yielding a "before:after"
OD ratio (663b/663a) of 1.70. Samples with
663b/663a ratios of 1.70 are considered free of
pheophytin a, and contain algal populations
consisting mostly of intact, nondecaying organ-
isms.
Conversely, samples containing pheophytin a
but not chlorophyll a show no reduction in
OD663 upon acidification, and have a
663t>/663a ratio of 1.0. Samples containing both
pigments will have ratios between 1.0 and 1.7.
To determine the concentration of pheophy-
tin a, prepare the extract as previously described
and determine the OD663. Add one drop of
1 N HC1 to the cuvette, mix well, and reread the
OD7 5 o and OD6 6 3 after 30 seconds.
15
-------
BIOLOGICAL METHODS
Calculate the chlorophyll a and pheophytin a
as follows:
Chlorophyll a (mg/m3) = 26.7 (663fr~663a) X E
VX L
Pheophytin a (mg/m3) = 26.7 (1.7 X 663a- 663fr) X E
V X L
where 663^ is the 1-cm corrected OD663 before
acidification; 663a is the OD663 after acidifi-
cation; E the volume of acetone used for the
extraction (ml); V the volume of water filtered
(liters); and L the path length of the cuvette
(cm).
5.3 Cell Volume
5.3.1 Microscopic (algae and bacteria)
Concentrate an aliquot of sample by settling
or centrifugation, and examine wet at a 1000X
magnification with a microscope equipped with
a calibrated ocular micrometer. Higher magnifi-
cation may be necessary for small algae and the
bacteria. Make optical measurements and
determine the volume of 20 representative
individuals of each major species. Determine the
average volume (cubic microns), and multiply by
number of organisms per milliliter.
5.3.2 Displacement (zooplankton)
Separate sample from preservative by pouring
through a piece of No. 20 mesh nylon bolting
cloth placed in the bottom of a small glass
funnel. To hasten evaporation, wash sample with
a small amount of 50 percent ethanol to remove
excess interstitial fluid and place on a piece of
filter or blotting paper. Place the drained plank-
ton in a 25-, 50-, or 100-ml (depending on
sample size) graduated cylinder, and add a
known volume of water from a burette. Read
the water level in the graduated cylinder. The
difference between the volume of the zooplank-
ton plus the added water and the volume of the
water alone is the displacement volume and,
therefore, the volume of the total amount of
zooplankton in the sample.
5.4 Cell Surface Area of Phytoplankton
Measure the dimensions of several represen-
tative individuals of each major species with a
microscope. Assume the cells to be spherical
cylindrical, rectangular, etc., and from the linear
dimensions, compute the average surface area
(ju2) per species. Multiply by the number of
organisms per milliliter (Welch, 1948, lists
mathematical formulas for computing surface
area).
6.0 PHYTOPLANKTON PRODUCTIVITY
Phytoplankton productivity measurements
indicate the rate of uptake of inorganic carbon
by phytoplankton during photosynthesis and are
useful in determining the effects of pollutants
and nutrients on the aquatic community.
Several different methods have been used to
measure phytoplankton productivity. Diurnal
curve techniques, involving pH and dissolved
oxygen measurements, have been used in natural
aquatic communities by a number of investi-
gators. Westlake, Owens, and Tailing (1969)
present an excellent discussion concerning the
limitations, advantages, and disadvantages of
diurnal curve techniques as applied to non-
isolated natural communities. The oxygen
method of Gaarder and Gran (1927) and the
carbon-14 method of Steeman-Neilson (1952)
are techniques for measuring in situ phyto-
plankton productivity. Tailing and Fogg (1959)
discussed the relationship between the oxygen
and carbon-14 methods, and the limitations of
both methods. A number of physiological
factors must be considered in the interpretation
of the carbon-14 method for measurement of
phytoplankton productivity. Specialized appli-
cations of the carbon-14 method include bio-
assay of nutrient limiting factors and measure-
ment of the potential for algal growth.
The carbon-14 method and the oxygen
method have the widest use, and the following
procedures are presented for the in situ field
measurement of inorganic carbon uptake by
these methods.
6.1 Oxygen Method
General directions for the oxygen method are
found in: Standard Methods for the Exami-
nation of Water and Wastewater, 13th Edition,
pp. 738-739 and 750-751.
16
-------
PLANKTON PRODUCTIVITY
Specific modifications and additions for
apparatus, procedures, and calculations are:
Apparatus - Rinse the acid-cleaned sample
bottles with the water being tested prior to use.
Procedure - Obtain a profile of the input of
solar radiation for the photoperiod with a
pyroheliometer. Incubate the samples at least 2
hours, but never longer than to that point where
oxygen-gas bubbles are formed in the clear
bottles or dissolved oxygen is depleted in the
dark bottles.
Calculations - Using solar radiation profile
and photosynthetic rate during the incubation
period, adjust the data to represent phyto-
plankton productivity for the entire photo-
period.
6.2 Carbon-14 Method
General directions for the carbon-14 method
are found in Standard Methods for the Exami-
nation of Water and Wastewater, 13th Edition,
pp. 739-741 and 751-752.
Specific modifications and additions for
apparatus, procedures, and calculations are listed
below:
7.0 REFERENCES
7.1 Sample Collection and Preservation
7.1.1 General considerations
Hutchinson, G. E. 1957. A treatise on limnology, Vol. 1. Geography, Physics, and Chemistry. John Wiley and Sons, Inc., New York.
Hutchinson, G. E. 1967. A treatise on limnology, Vol. 2, Introduction to lake biology and the limnoplankton. John Wiley and Sons,
Inc., New York.
Reid, G. K. 1961. Ecology of inland waters and estuaries. Reinhold Publishing Co., New York.
Ruttner, F. 1953. Fundamentals of limnology (transl. by D. G. Frey and F. E. J. Fry), University of Toronto Press, Toronto, Canada.
7.7.2 Phytoplankton
Ingram, W. M., and C. M. Palmer. 1952. Simplified procedures for collecting, examining, and recording plankton. JAWWA. 44:617.
Lackey, J. B. 1938. The manipulation and counting of river plankton and changes in some organisms due to formalin preservation.
Pub. Health Rep. 53:2080.
Weber, C. I. 1968. The preservation of phytoplankton grab samples. Trans. Amer. Microscop. Soc. 87:70.
Welch, P. S. 1948. Limnological methods. Blakiston Co., Philadelphia.
7.1.3 Zooplankton
Arnon, W., et al. 1965. Towing characteristics of plankton sampling gear. Limnol. Oceanogr. 10(3):333-340.
Barlow, J. P. 1955. Physical and biological processes determining the distribution of zooplankton in a tidal estuary. Biological Bull.
109(2):211-225.
Barnes, H., and D. J. Tranter. 1964. A statistical examination of the catches, numbers, and biomass taken by three commonly used
plankton nets. Aust. J. Mar. Freshwater Res. 16(3):293-306.
Apparatus - A fuming chamber is not re-
quired. Use the methods of Strickland and
Parsons (1968) to prepare ampoules containing a
carbonate solution of the activity desired.
Procedure - The carbon-14 concentration in
the filtered sample should yield the number of
counts required for statistical significance;
Strickland and Parsons suggest a minimum of
1,000 counts per minute. Obtain a profile of the
input of solar radiation for the photoperiod with
a pyroheliometer. Incubate up to 4 hours; if
measurements are required for the entire photo-
period, overlap 4-hour periods from dawn until
dusk (e.g., 0600-1000, 0800-1200, ,
1400-1800, 1600-2000). A 4-hour incubation
period may be sufficient, however, provided
energy input is used as the basis for integrating
the incubation period into the entire photo-
period. To dry and store the filters, place the
membranes in a desiccator for 12 hours following
filtration. Fuming with HC1 is not required, and
dried filters may be stored indefinitely.
Calculations - Using solar radiation profile and
photosynthetic rates during the incubation
period, adjust data to represent phytoplankton
productivity for the entire photoperiod.
17
-------
BIOLOGICAL METHODS
Bayly, I. A. E. 1962. Ecological studies on New Zealand lacustrine zooplankton with special reference to Boeckella propinqua Sars
(Copepoda: Calanoida). Aust. J. Mar. Freshwater Res. 13(2): 143-197.
Brooks, J. L. 1957. The systematics of North AmericaDaphnia. Yale Univ. Press, New Haven.
Culver, D. A., and G. J. Brunskill. 1969. Fayetteville Green Lake, New York. V. Studies of primary production and zooplankton in a
meromictic marl lake. Limnol. Oceanogr. 14(6):862-873.
Curl, H., Jr. 1962. Analysis of carbon in marine plankton organisms. J. Mar. Res. 20(3):181-188.
Dovel, W. L. 1964. An approach to sampling estuarine macroplankton. Chesapeake Sci. 5(1-2): 77-90.
Faber, K. J. 1966. Free-swimming copepod nauplii of Narragansett Bay with a key to their identification. J. Fish. Res. Bd. Canada,
23(2): 189-205.
Faber, K. J. 1966. Seasonal occurrence and abundance of free-swimming copepod nauplii in Narragansett Bay. J. Fish. Res. Bd.Canada,
23(3):415-422.
Frolander, H. F. 1957. A plankton volume indicator. J. Cons. Perm. int. explor. Mer. 22(3):278-283.
Frolander, H. F. 1968. Statistical variation in zooplankton numbers from subsampling with a Stempel pipette. JWPCF, 40(2), Pt. 2: R
82-R 88.
Galbraith, M. G., Jr. 1967. Size-selective predation onDaphnia by rainbow trout and yellow perch. Trans. Amer. Fish. Soc. 96(1):1-10.
Hall, D. J. 1964. An experimental approach to the dynamics of a natural population of Daphnia galeata mendotae. Ecology,
45(1):94-112.
Hazelwood, D. H., and R. A. Parker. 1961. Population dynamics of some freshwater zooplankton. Ecology, 42(2):266-274.
Herman, S. S., J. A. Mihursky, and A. J. McErlean. 1968. Zooplankton and environmental characteristics of the Patuxent River
Estuary. Chesapeake Sci. 9(2): 67-82.
Johnson, W. E. 1964. Quantitative aspects of the pelagic entomostracan zooplankton of a multibasin lake system over a 6-year period.
Verh. Internat. Verein. Limnol. 15:727-734.
Jossi, J. W. 1970. Annotated bibliography of zooplankton sampling devices. U. S. Fish. Wildl. Serv., Special Scientific Report.
Fisheries. No. 609.
Likens, G. E., and J. J. Gilbert. 1970. Notes on quantitative sampling of natural populations of planktonic rotifers. Limnol. Oceanogr.
15(5):816-820.
McGowan, J. A., and V. J. Fraundorf. 1966. The relationship between size of net used and estimates of zooplankton diversity. Limnol.
Oceanogr. 11(4): 456-469.
National Academy of Sci. 1969. Recommended procedures for measuring the productivity of plankton standing stock and related
oceanic properties. Washington, D. C., 59 pp.
Paquette, R. G., and H. F. Frolander. 1967. Improvements in the Clarke-Bumpus plankton sampler. J. Cons. Perm. int. exploi. Mer.
22(3)284-288.
Paquette, R. G., E. L. Scott, and P. N. Sund. 1961. An enlarged Clarke-Bumpus sampler. Limnol. Oceanogr. 6(2):230-233.
Pennak, R. W. 1957. Species composition of limnetic zooplankton communities. Limnol. Oceanogr. 2(3):222-232.
Smith, M. W. 1961. A limnological reconnaissance of a Nova Scotian brown-water lake. J. Fish. Res. Bd. Canada, 18(3):463-478.
Smith, P. E., R. C. Counts, and R. I. Clutter. 1968. Changes in filtering efficiency of plankton nets due to clogging under tow. J. Cons.
Perm. int. explor. Mer. 32(2):232-248.
Smyly, W. J. P. 1968. Some observations on the effect of sampling technique under different conditions on numbers of some
fresh-water planktonic Entomostraca and Rotifera caught by a water-bottle. J. Nat. Hist. 2:569-575.
Stress, R. G., J. C. Neess, and A. D. Hasler. 1961. Turnover time and production of planktonic crustacea in limed and reference portion
of a bog lake. Ecology, 42(2): 237-245.
Tranter, D. J., J. D. Kerr, and A. C. Heron. 1968. Effects of hauling speed on zooplankton catches. Aust. J. Mar. Freshwater Res.
19(l):65-75.
Ward, J. 1955. A description of a new zooplankton counter. Quart. J. Microscopical Sci. 96:371-373.
Yentsch, C. S., and A. C. Duxbury. 1956. Some factors affecting the calibration number of the Clarke-Bumpus quantitative plankton
sampler. Limnol. Oceanogr. l(4):268-273.
Yentsch, C. S., and F. J. Hebard. 1957. A gauge for determining plankton volume by the mercury immersion method. J. Cons. Perm.
int. explor. Mer. 32(2):184-190.
7.2 Sample preparation and analysis
7.2.7 Sample analysis — phytoplankton
Hasle, G. R., and G. A. Fryxell. 1970. Diatoms: cleaning and mounting for light and electron microscopy. Trans.Amer. Microscop.
Soc., 89(4):469-474.
18
-------
PLANKTON REFERENCES
Holmes, R. W. 1962. The preparation of marine phytoplankton for microscopic examination and enumeration on molecular filters. U.
S. Fish and Wildlife Serv., Special Scientific Report. Fisheries No. 433, 1-6.
Jackson, H W., and L. G. Williams. 1962. Calibration and use of certain plankton counting equipment. Trans. Amer. Microscop. Soc.
81:96.
Lackey, J. B. 1938. The manipulation and counting of river plankton and changes in some organisms due to formalin preservation.
Publ. Health Repts. 53(47):2080-93.
Levinson, S. A., R. P. MacFate. 1956. Clinical laboratory diagnosis. Lea and Febiger, Philadelphia.
Lund, J. W. G., C. Kipling, and E. D. LeCren. 1958. The inverted microscope method of estimating algae numbers and the statistical
basis of estimations by counting. Hydrobiologia, 11(2):143-70.
McCrone, W. C., R. G. Draftz, and J. G. Delly. 1967. The particle atlas. Ann Arbor Science Publishers, Inc., Ann Arbor.
McNabb, C. D. 1960. Enumeration of freshwater phytoplankton concentrated on the membrane filter. Limnol. Oceanogr. 5:57-61.
National Academy of Sciences. 1969. Recommended procedures for measuring the productivity of plankton standing stock and related
oceanographic properties. NAS, Washington, D. C. 59 pp.
Palmer, C. M., and T. E. Maloney. 1954. A new counting slide for nannoplankton. Amer. Soc. Limnol. Oceanog. Spec. Publ. No. 21,
pp. 1-6.
Prescott, G. W. 1951. The ecology of Panama Canal algae. Trans. Amer. Microscop. Soc. 70:1-24.
Schwoerbel, J. 1970. Methods of hydrobiology (freshwater biology). Pergamon Press, Hungary, 200 pp.
Utermohl, H. 1958. Zur Vervollkommnung der quantitativen Phytoplankton-Methodek. Mill. Intern. Ver. Limnol. 9:1-38.
7.2.2 Biomass determination
Chlorophyll
Lorenzen, C. J. 1966. A method for the continuous measurement of in vivo chlorophyll concentration. Deep Sea Res. 13:223-227.
Lorenzen, C. J. 1967. Determination of chlorophyll and pheopigments: spectrophotometric equations. Limnol. Oceanogr.
12(2):343-346.
Moss, B. 1967. A spectrophotometric method for the estimation of percentage degradation of chlorophylls to pheo-pigments in
extracts of algae. Limnol. Oceanogr. 12(2):335-340.
Strickland, J. D. H., and T. R. Parsons. 1968. A practical handbook of seawater analysis. Fisheries Res. Board of Canada, Bulletin No.
167,311 pp.
United Nations Educational, Scientific, and Cultural Organization. 1966. Monographs on oceanographic methodology. 1. Determi-
nation of photosynthetic pigments in sea water. UNESCO, Paris. 69 pp.
Yentsch, C. S., and D. W. Menzel. 1963. A method for the determination of phytoplankton chlorophyll and phaeophytin by
fluorescence. Deep Sea Res. 10:221-231.
Cell Surface Area
Mackenthun, K. M. 1969. The practice of water pollution biology. U.S. Dept. Interior, FWPCA. 281 pp.
Mullin, M. M., P. R. Sloan, and R. W. Eppley. 1966. Relationship between carbon content, cell volume, and area in phytoplankton.
Limnol. Oceanogr. 11(2):307-311.
Welch, P. S. 1948. Limnological methods. Blakiston Co., Philadelphia. 344 pp.
7.3 Phytoplankton productivity
American Public Health Association. 1970. Standard Methods for the Examination of Water and Wastewater, 13th Edition, APHA,
Washington, D. C.
Beyers, R. J., J. L. Larimer, H. T. Odum, R. A. Parker, and N. E. Armstrong. 1963. Directions for the determination of changes in
carbon dioxide concentration from changes in pH. Publ. Inst. Mar. Sci., Univ. Texas, 9:454-489.
Beyers, R. J., and H. T. Odum. 1959. The use of carbon dioxide to construct pH curves for the measurement of productivity. Limnol.
Oceanogr. 4(4):499-502.
Bransome, Edwin D., Jr. (ed.) 1970. The current status of liquid scintillation counting. Grune and Stratton, Inc., New York. 394 pp.
Chase, G. D., and J. L. Rabinowitz. 1967. Principles of radioisotope methodology. 3rd edition. Burgess Publ. Co., Minneapolis. 633 pp.
Edwards, R. W., and M. Owens. 1962. The effects of plants on river conditions IV. The oxygen balance of a chalk stream. J. Ecol.
50:207-220.
Fee, E. J. 1969. Numerical model for the estimation of photosynthetic production, integrated over time and depth in natural waters.
Limnol. Oceanogr. 14(6):906-911.
Gaarder, T., and H. H. Gran. 1927. Investigations of the production of plankton in the Oslo Fjord. Rapp. et Proc Verb., Cons.
Internatl. Explor. Mer. 42:1-48.
19
-------
BIOLOGICAL METHODS
Goldman, C. R., and R. C. Carter. 1965. An investigation by rapid Carbon-14 bioassay of factors affecting the cultural eutrophication
of Lake Tahoe, California-Nevada. J. WPCF, 37(7): 1044-1059.
Goldman, C. R. 1969. Measurements (in situ) on isolated samples of natural communities, bioassay technique for nutrient limiting
factors. In: A manual on methods for measuring primary production in aquatic environments (R. A. Vollenweider, ed.) IBP
Handbook, No. 12. F. A. Davis, Philadelphia, pp. 79-81.
Goldman, C. R. 1963. Measurement of primary productivity and limiting factors in freshwater with C-14. In: Proc. conf. on primary
productivity measurement, marine and freshwater (M. S. Doty, ed.) Univ. of Hawaii, Aug.-Sept. 1961. U. S. Atomic Energy
Commission, Div. Tech. Inf. T.I.D. 7633, 103-113.
Goldman, C. R. 1968. The use of absolute activity for eliminating serious errors in the measurement of primary productivity with
C-14. J. Cons. Int. Explor. Mer. 32:172-179.
Jenkins, D. 1965. Determination of primary productivity of turbid waters with carbon-14. J. WPCF, 37:1281-1288.
Jitts, H. R., and B. D. Scott. 1961. The determination of zero-thickness activity in geiger counting of C14 solutions used in marine
productivity studies. Limnol. Oceanogr. 6:116-123.
Jitts, H. R. 1963. The standardization and comparison of measurements of primary production by the carbon-14 technique. In: Proc.
Conf. on Primary Productivity Measurement, Marine and Freshwater (M. S. Doty,ed.) Univ. of Hawaii, Aug.-Sept. 1961. U. S. Atomic
Energy Commission, Div. Tech. Inf. T.I.D. 7633,103-113.
Joint Industry/Government Task Force of Eutrophication. 1969. Provisional algal assay procedure, pp.16-29.
McAllister, C. D. 1961. Decontamination of filters in the C14 method of measuring marine photosynthesis. Limnol. Oceanogr.
6(3):447-450.
Odum, H. T. 1956. Primary production in flowing water. Limnol. Oceanogr. 1(2):102-117.
Odum, H. T. 1957. Primary production measurements in eleven Florida springs and a marine turtle grass community. Limnol.
Oceanogr. 2(2):85-97.
Odum, H. T., and C. M. Hoskin. 1958. Comparative studies on the metabolism of marine waters. Publ. Inst. Mar. Sci., Univ. of Texas,
5:16-46.
Owens, M., and R. W. Edwards. 1963. Some oxygen studies in the River Lark. Proc. Soc. for Water Treatment and Examination,
12:126-145.
Park, K., D. W. Hood, and H. T. Odum. 1958. Diurnal pH variation in Texas bays and its application to primary production estimation.
Publ. Inst. Mar. Sci., Univ. Texas, 5:47-64.
Rodhe, W., R. A. Vollenweider, and A. Nauwerck. 1958. The primary production and standing crop of phytoplankton. In: Perspectives
in Marine Biology (A. A. Buzzati-Traverso, ed.), Univ. of California Press, pp. 299-322.
Saijo, Y., and S. Ichimura. 1963. A review of recent development of techniques measuring primary production. In: Proc. conf. on
primary productivity measurement, marine and freshwater (S. Doty, ed.) Univ. Hawaii, Aug.-Sept. 1961. U. S. Atomic Energy
Commission, Div. Tech. Inf. T.I.D. 7633, 91-96.
Steeman-Neilson, E. 1952. The use of radioactive carbon (C-14) for measuring organic production in the sea. J. Cons. Int. Explor. Mer.
18:117-140.
Strickland, J. D. H., and T. R. Parsons. 1968. A practical handbook of seawater analysis. Fish. Res. Bd. Canada, Bull. No. 167, 311 pp.
Tailing, J. F., and G. E. Fogg. 1959. Measurements (in situ) on isolated samples on natural communities, possible limitations and
artificial modifications. In: A manual of methods for measuring primary production in aquatic environments (R. A. Vollenweider,
ed.) IBP Handbook, No. 12, F. A. Davis, Philadelphia, pp. 73-78.
Thomas, W. H. 1963. Physiological factors affecting the interpretation of phytoplankton production measurements. In: Proc. conf. on
primary productivity measurement, marine and freshwater (M. S. Doty, ed.) Univ. Hawaii, Aug.-Sept. 1961. U. S. Atomic Energy
Commission, Div. Tech. Inf. T.I.D. 7633, 147-162.
Verduin, J. 1952. Photosynthesis and growth rates of two diatom communities in western Lake Erie. Ecology, 33(2): 163-168.
Westlake, D. F., M. Owens, and J. F. Tailing. 1969. Measurements on non-isolated natural communities. In: A manual on methods for
measuring primary production in aquatic environments (R. A. Vollenweider, ed.) IBP Handbook, No. 12. F. A. Davis, Philadelphia.
pp. 90-100.
20
-------
PEBIPHYTON
-------
PERIPHYTON
Page
1.0 INTRODUCTION 1
2.0 SAMPLE COLLECTION AND PRESERVATION 2
2.1 Qualitative Sampling 2
2.2 Quantitative Sampling 2
3.0 SAMPLE PREPARATION AND ANALYSIS 3
3.1 Sample Preparation 3
3.2 Sample Analysis 3
4.0 BIBLIOGRAPHY 5
-------
PERIPHYTON
1.0 INTRODUCTION
Periphyton is an assemblage of a wide variety
of organisms that grow on underwater substrates
and includes but is not limited to, bacteria,
yeasts and molds, algae, protozoa, and forms
that may develop large colonies such as sponges
and corals. All organisms within the community
are not necessarily attached but some may bur-
row or live within the community structure of
the attached forms.
Literally translated, periphyton means
"around plants," such as organisms overgrowing
pond weeds, but through widespread usage, the
term has become associated with communities
of microorganisms growing on substrates of any
nature. Aufwuchs (Seligo, 1905), the German
noun for this community, does not have an
equivalent English translation, but essentially
means growing on and around things. Other
terms that are essentially synonymous with
periphyton or describe important or predomi-
nant components of the periphytic community
are: nereiden, bewuchs, laison, belag, besatz,
attached, sessile, sessile-attached, sedentary,
seeded-on, attached materials, slimes, slime
growths, and coatings. Some of these terms are
rarely encountered in the literature. Terminology
based on the nature of the substrate is as
follows:
Substrate
Adjective
various epiholitic, nereiditic, sessile
plants epiphytic
animals epizooic
wood epidendritic, epixylonic
rock epilithic
Most above-listed Latin-root adjectives are
derivatives of nouns such as epihola, epiphyton,
epizoa, etc. (After Srameck-Husek, 1946 and
Sladeckova, 1962).
Periphyton was recognized as an important
component of aquatic communities before the
beginning of the 20th century, and the study of
periphyton was initiated in Europe in the early
1900's. Kolkwitz and Marsson in two articles
(1908 and 1909) made wide use of components
in this community in the development of the
saprobic system of water quality classification.
This system has been continued and developed
in Middle and Eastern Europe (Srameck-Husek,
1946; Butcher, 1932, 1940, 1946; Sladeckova,
1962; Sladecek and Sladeckova, 1964; Fjerding-
stad, 1950, 1964, 1965).
The study of periphyton was introduced in
the United States in the 1920's and expanded in
the 1930's. The use of the community has
grown steadily and rapidly in water quality in-
vestigations (Blum, 1956; Cooke, 1956; Patrick,
1957; Cairns, et al., 1968).
The periphyton and plankton are the principal
primary producers in waterways — they convert
nutrients to organic living materials and store
light energy through the processes of photo-
synthesis. In extensive deep waters, the plankton
are probably the predominant primary pro-
ducers. In shallow lakes, ponds, and rivers, the
periphyton are the predominant primary pro-
ducers.
Periphyton is the basis of the trickling filter
system form of secondary sewage treatment. It
is the film of growths covering the substrate in
the filter that consumes nutrients, micro-solids,
and bacteria from the primary treated sewage
passing through the filter. As these growths ac-
cumulate, they eventually slough from the sub-
strate, pass through the filter, and are captured
in the final clarifier; thus, they change chemical
and biological materials to a solid that can
be removed with the physical process of
settling. Excellent studies and reports on this
process have been published by Wisniewski
(1948), Cooke (1959), and Holtje (1943).
The periphyton community is an excellent
indicator of water quality. Changes may range
from subtle alteration of species composition to
extremely dramatic results, such as when the
addition of organic wastes to waters supporting
a community of predominately diatom growths
result in their replacement by extensive slime
colonies composed predominately of bacteria
such as Sphaerotilus or Leptomitus and vorticel-
lid protozoans.
1
-------
BIOLOGICAL METHODS
Excessive growth stimulated by increased
nutrients can result in large, filamentous
streamers that are esthetically unpleasing and
interfere with such water uses as swimming,
wading, fishing, and boating, and can also affect
the quality of the overlying water. Photo-
synthesis and respiration can affect alkalinity
(U. S. FWPCA, 1967) and dissolved oxygen con-
centrations (O'Connell and Thomas, 1965) of
lakes and streams. Metabolic byproducts
released to the overlying water may impart
tastes and odors to drinking waters drawn from
the stream or lake, a widespread problem
throughout the United States (Lackey, 1950;
Silvey, 1966; Safferman, et al, 1967). Large
clumps of growth may break from the site of
attachment and eventually settle to form accu-
mulations of decomposing, organic, sludge-like
materials.
Periphyton have proven useful in, reconnais-
sance surveys, water quality monitoring studies,
short-term investigations, research and develop-
ment, and enforcement studies., The investiga-
tion objectives dictate the nature, approach, and
methodology of sampling the periphyton com-
munity. Factors to be considered are the time
and duration of the study and the characteristics
of the waterway.
Sladeckova (1962) published an extensive
review of methodology used in investigating this
community. <
2.0 SAMPLE COLLECTION AND PRESER-
VATION
2.1 Qualitative Sampling
Time limitations often prohibit the use of
artificial substrate samplers for quantitative col-
lection, and thus necessitate qualitative sampling
from natural substrates. Periphyton usually
appear as brown, brownish-green, or green
growths on the substrate. In standing or flowing
water, periphyton may be qualitatively collected
by scraping the surfaces of several different
rocks and logs with a pocket knife or some otlier
sharp object. This manner of collecting may also
be used as a quantitative method if accurate
measurements are made of the sampled areas.
When sampling this way, limit collections to
littoral areas in lakes and shallow or riffle areas
in flowing water where the greatest number and
variety of organisms are found. Combine the
scrapings to a volume of 5 to 10 ml for a suf-
ficient sample. In lakes and streams 'where long
strands of filamentous algae occur, weigh the
sample.
After scraping has been completed, store the
material in bottles containing 5 percent forma-
lin. If the material is for chlorophyll analysis, do
not preserve. Store at 4°C in the dark in 100 ml
of 90 percent aqueous acetone. Use bottle caps
with a cone-shaped polyethylene seal to prevent
evaporation.
2.2 Quantitative Sampling
The standard (plain, 25 X 75 mm) glass micro-
scope slide is the most suitable artificial sub-
strate for quantitative sampling. If less fragile
material is preferred, strips of Plexiglas may be
used in place of glass slides.
Devices for exposing the substrates can be
modified to suit;a particular situation, keeping
in mind that the depth of exposure must be con-
sistant for all sampling sites. In large rivers or
lakes, a floating sampler (APHA, 1971) is
advantageous when turbidities are high and the
substrates must be exposed near the surface. In
small, shallow streams or littoral areas of lakes
where turbidity is not a critical factor, substrates
may be exposed in several ways. Two possible
methods are: (a) attach the substrates with
PLASTIC TAK adhesive to bricks or flat rocks
in the stream bed, or (b) anchor Plexiglas racks
to the bottom to hold the substrates. In areas
where siltation is a problem, hold the substrates
in a vertical position to avoid a covering of silt.
If desired, another set of horizontally-exposed
substrates could be used to demonstrate the
effects of siltation on the periphyton com-
munity.
The number of substrates to be exposed at
each sampling site depends on the type and
number of analyses to be performed. Because of
unexpected fluctuations in water levels, cur-
rents, wave action, and the threat of vandalism,
duplicate samplers should be used. A minimum
of four replicate substrates should be taken for
each type of analysis.
-------
PERIPHYTON
The length of exposure depends upon many
factors, including the survey time schedule,
growth patterns, which are seasonal, and pre-
vailing hydrologic conditions. On the assump-
tion that periphyton growth rate on clean sub-
strates proceeds exponentially for 1 or 2 weeks
and then gradually declines, the optimum ex*
posure period is 2 to 4 weeks.
3.0 SAMPLE PREPARATION AND ANALYSIS
3.1 Sample Preparation
Sample preparation varies according to the
method of analysis; see the 13th edition of
Standard Methods, Section 602-3 (APHA,
1971).
3.2 Sample Analysis
3.2.1 Identification
In addition to the taxonomic references listed
in the Plankton Section, the following bench
references are essential for day-to-day periphy-
ton identification.
Algae
Desikachaiy, T. W. 1956. Cyanophyta. Indian Counc. Agric.
Res., New Delhi.
Fairdi, M. 1961. A monograph of the .fresh water species of
Cladophora and Rhizoctonium. Ph.D. Thesis, Univ. Kansas
(available in Xerox from University Microfilms, Ann Arbor).
Islan, A. K., and M. Nurul. 1963. Revision of the genus Sttgeo-
clonium. Nova Hedwigia, Suppl. 10. J. Cramer, Weinheim,
Germany.
Rananthan, K. R. 1964. Ulotrichales. Indian Counc. Agric. Res.,
New Delhi.
Randhawa, M. S. 1959. Zygn^maceae. Indian Counc. Agric. Res.,
New Delhi.
Tiffany, L. H. 1937. Oedogoniales, Oedogoniaceae. In: North
American Flora, ll(l):l-102. N. Y. Bot. Garden, Hafner Publ.
New York.
Fungi
Cooke, W. Bridge. 1963. A laboratory guide to fungi in polluted
waters, sewage, and sewage treatment systems. USDHEW,
USPHS, DWSPC, Cincinnati.
Protozoa
Bick, H. 1967-69. An illustrated guide to ciliated protozoa
(used as biological indicators in freshwater ecology). Parts 1-9.
World Hlth. Organ., Geneva, Switzerland.
Kudo, R. R. 1963, Protozoology. Charles Thomas, Publ., Spring-
field, 111..
Rotifers
Donner, J. 1966. Rotifers. Butler and Tanner, Ltd., London.
Edmundson, W. T. 1959. Freshwater biology. John Wiley and
Sons, New York.
Pennak, R. W. 1953. Freshwater invertebrates of the United
States. Ronald Press, New York.
Microcrustacea
Edmondson, W. T. (see above).
Pennak, R. W. (see above).
3.2.2 Counts and enumeration
Sedgwick-Rafter Method
Shake vigorously to mix the sample, transfer 1
ml to a Sedgwick-Rafter cell, and make strip
counts, as described in the Plankton Section,
except that a cell count is made of all organisms.
If the material is too concentrated for a direct
count, dilute a 1-ml aliquot with 4 ml of dis-
tilled water; further dilution may be necessary.
Even after vigorous shaking, the scrapings may
contain large clumps of cells. These clumps can
result in an uneven distribution of material in
the counting chamber that could seriously affect
the accuracy of the count. Should this condition
occur, stir 50 ml of the sample (or a proper
dilution) in a blender for 1 minute and reex-
amine. Repeat if necessary. Caution: Some
colonial organisms cannot be identified in a frag-
mented condition. Therefore, the sample must
be examined before being blended.
The quantitative determination of organisms
on a substrate can then be expressed as:
where:
C =
V —
DF =
L =
W =
D *
S =
A " =
., „ . 2 C X 1000 mm3 X V X DF
No. cells/mm - LXWXDXSXA
number of cells counted (tally)
sample volume, ml
dilution factor
length of a strip, mm
width of a strip (Whipple grid image
width), mm :
depth of a strip (S-R cell depth), mm
number of strips counted
area of substrate scraped, mm2
-------
BIOLOGICAL METHODS
Diatom Species Proportional Count
Before preparing the diatom slides, use an
oxidizing agent to digest the gelatinous stalks
and other extracellular organic materials causing
cell clumping. Before the oxidant is added,
however, centrifuge or settle the sample to re-
move the formalin.
If centrifugation is preferred, transfer the
sample to a conical tube and centrifuge 10
minutes at 1000 X G. Decant the formalin, re-
suspend the sample in 10 ml of distilled water,
and recentrifuge. Decant, take up the sample in
8 ml of 5 percent potassium (or ammonium)
persulfate, and transfer back to the (rinsed)
sample vial.
If the settling method is preferred, follow the
instructions given in the Plankton Section for
removing salt from the diatom concentrate, but
add persulfate or hydrogen peroxide instead of
distilled water. After the formalin is replaced by
the oxidant, heat the sample to 95°C for 30
minutes (do not boil). Cool, remove the oxidant
by centrifugation or settling, and take up the
diatoms in 2 to 3 ml of distilled water. Proceed
with the preparation of the permanent diatom
mount as described in the Plankton Section.
Label the slide with the station location and
inclusive sample dates. Carry out the diatom
strip count as described in the Plankton Section,
except that separated, individual valves (half cell
walls) are tallied as such, and the tally is divided
by two to obtain cell numbers.
3.2.3 Biomass
Cell Volume
See the Plankton Section.
Dry and Ash-free Weight
See the Plankton Section.
Centrifugation, Sedimentation and Displacement
Centrifugation. Place sample in graduated
centrifuge tube and centrifuge for 20 minutes at
1000 X G. Relate the volume in milliliters to the
area sampled.
Sedimentation. Place sample in graduated
cylinder and allow sample to settle at least 24
hours. Relate the volume in milliliters to the
area sampled.
Displacement. Use displacement for large
growths of periphyton when excess water can be
readily removed. Once the excess water is re-
moved, proceed as per Plankton Section; how-
ever, do not pour sample through a No. 20
mesh, nylon bolting cloth.
Chlorophyll
The chlorophyll content of the periphyton is
used to estimate the algal biomass and as an
indicator of the nutrient content (or trophic
status) or toxicity of the water and the taxo-
nomic composition of the community. Periphy-
ton growing in surface water relatively free of
organic pollution consists largely of algae, which
contain approximately 1 to 2 percent chloro-
phyll a by dry weight. If dissolved or particulate
organic matter is present in high concentrations,
large populations of filamentous bacteria,
stalked protozoa, and other nonchlorophyll
bearing microorganisms develop and the percent-
age of chlorophyll a is then reduced. If the
biomass—chlorophyll a relationship is expressed
as a ratio (the autotrophic index), values greater
than 100 may result from organic pollution
(Weber and McFarland, 1969; Weber, 1973).
Ash-free Wgt(mg/m2)
Autotrophic Index = ;=-; —-—; ;—T,
Chlorophyll a (mg/m2)
To obtain information on the physiological
condition (or health) of the algal periphyton,
measure the amount of pheophytin a, a physio-
logically inactive degradation product of chloro-
phyll a. This degradation product has an absorp-
tion peak at nearly the same wavelength as chlo-
rophyll a and, under severe environmental condi-
tions, may be responsible for most if not all of
the OD6 6 3 in the acetone extract. The presence
of relatively large amounts of pheophytin a is an
abnormal condition indicating water quality
degradation. (See the Plankton Section.)
To extract chlorophyll, grind and steep the
periphyton in 90 percent aqueous acetone (see
Plankton Section). Because of the normal sea-
sonal succession of the algae, the taxonomic
composition and the efficiency of extraction by
steeping change continually during the year.
Although mechanical or other cell disruption
may not increase the recovery of pigment from
-------
PERIPHYTON
every sample, routine grinding will significantly tremely sensitive to photodecomposition and
increase (10 percent or more) the average re- lose more than 50 percent of their optical
covery of chlorophyll from samples collected activity if exposed to direct sunlight for only 5
over a period of several months. Where glass minutes. Therefore, samples placed in acetone in
slides are used as substrates, place the individual the field must be protected from more than
slides bearing the periphyton directly in separate momentary exposure to direct sunlight and
small bottles (containing 100 ml) of acetone should be Placed immediately in the dark
when removed from the sampler. Similarly, Samples not placed m acetone in the field
, -ix j r- 4.1. t-f • i should be iced until processed. If samples are
place penphyton removed from other artificial nQt tQ be processed on the day coilected, now.
or natural substrates in the field immediately in eve^ thgy should be frozen and hdd at _2()oc
90 percent aqueous acetone. (Samples should be For the chlorophyii analysis, see the Plankton
macerated, however, when returned to the lab.) Section.
Acetone solutions of chlorophyll are ex-
4.0 BIBLIOGRAPHY
American Public Health Association. 1971. Standard methods for the examination of water and wastewater, 13th ed., APHA,
New York.
Blum, J. L. 1956. The ecology of river algae. Bot. Rev. 22(5): 291.
Butcher, R. W. 1932. Studies in the ecology of rivers. II. The microflora of rivers with special reference to the algae on the river bed.
Ann. Bot. 46:813-861.
Butcher, R. W. 1940. Studies in the ecology of rivers. IV. Observations on the growth and distribution of sessile algae in the River Hull,
Yorkshire. J. Ecology, 28:210-223.
Butcher, R. W. 1946. Studies in the ecology of rivers. VII. The algae of organically enriched waters. J. Ecology, 35:186-191.
Butcher, R. W. 1959. Biological assessment of river pollution. Proceedings Linnean Society, 170:159-165; Abstract in: J. Sci. Food
Agn. 10:(11):104.
Cairns, J., Jr., D. W. Albough, F. Busey, and M. D. Chanay. 1968. The sequential comparison index - A simplified method for
nonbiologists to estimate relative differences in biological diversity in stream pollution studies. JWPCFJ40(9):1607-1613.
Cooke, W. B. 1956. Colonization of artificial bare areas by microorganisms. Bot. Rev. 22(9):613-638.
Cooke, W. B. 1959. Fungi in polluted water and sewage. IV. The occurrence of fungi in a trickling filter-type sewage treatment plant.
In: Proceeding, 13th Industrial Waste Conference, Purdue University, Series No. 96, 43(3):26-45.
Cummins, K. W., C. A. Tyron, Jr., and R. T. Hartman (Editors). 1966. Organism-substrate relationships in streams. Spec. Publ. No. 4,
Pymatumng Lab. of Ecol., Univ. Pittsburgh. 145 pp.
Fjerdingstad, E. 1950. The microflora of the River Molleaa with special reference to the relation of the benthal algae to pollution.
Folia Limnol. Scand. No. 5, Kobenhaven. 123 pp.
Fjerdingstad, E. 1964. Pollution of stream estimated by benthal phytomicroorganisms. I. A saprobic system based on communities
organisms and ecological factors. Hydrobiol. 49(1):63-131.
Fjerdingstad, E. 1 965. Taxonomy and saprobic valency of benthic phytomicroorganisms. Hydrobiol. 50(4):475-604.
Hawkes, H. A. 1963. Effects of domestic and industrial discharge on the ecology of riffles in midland streams. Intern. J. Air Water Poll.
7(6/7):565-586.
Holtje, R. H. 1943. The biology of sewage sprinkling filters. Sewage Works J. 15(1): 14-29.
Keup, L. E. 1966. Stream biology for assessing sewage treatment plant efficiency. Water and Sewage Works, 113:11-411.
Kolkwitz, R., and M. Marsson. 1908. Oekologie der pflanzlichen Saprobien. Berichte Deutschen Botamschen Gesellschaft
26a:505-519.
Kolkwitz, R., and M. Marsson. 1909. Oekologie der Tierischen Saprobien. Internationale Revue Gesamten Hydrobiologie
Hydrographie, 2:126-152.
Lackey, J. B. 1950. Aquatic biology and the water works engineer. Public Works. 81:30-41,64.
Mackenthun, K. M. 1969. The practice of water pollution biology. U. S, FWPCA, Washington, D.C. 281 pp.
Mackenthun, K. M., and L. E. Keup. 1970. Biological problems encountered in water supplies. JAWWA, 62(8):520-526.
O'Connell, J. M., and N. A. Thomas. 1965. Effect of benthic algae on stream dissolved oxygen. Proc. ASCE, J. Sanit. Eng. Div.
91:1-16.
Odum, H. T. 1957. Trophic structure and productivity of Silver Springs, Florida. Ecol. Monogr. 27:55-112.
Parrish, L. P., and A. M. Lucas. 1970. The effects of waste waters on periphyton growths in the Missouri River. (Manuscript). U. S.
FWPCA Nat'l. Field Investigations Center, Cincinnati.
-------
BIOLOGICAL METHODS
Patrick, R. 1957. Diatoms as indicators of changes in environmental conditions. In: Biological Problems in Water Pollution-
Transactions of the 1956 Seminar, Robert A. Taft Sanitary Engineering Center, U. S. Public Health Service, Cincinnati, Ohio. pp.
71-83. W57-36.
Rohlich, G. A., and W. B. Sarles. 1949. Chemical composition of algae and its relationship to taste and odor. Taste Odor Control J.
18:1-6.
Safferman, R. S., A. A. Rosen, C. I. Mashni, and M. E. Morris. 1967. Earthy - smelling substance from a blue-green alga. Environ. Sci.
Technol. 1:429-430.
Seligo, A. 1905. Uber den Ursprung der Fischnahrung. Mitt.d. Westpr. Fisch. 17(4):52.
Silvey, J. K. G. 1966. Taste and odors - Joint discussion effects of organisms. JAWWA, 58(6):706-715.
Sladecek, V., and A. Sladeckova. 1964. Determination of the periphyton production by means of the glass slide method. Hydrobiol.
23:125-158.
Sladeckova, A. 1962. Limnological investigation methods for the periphyton ("Aufwuchs") community. Bot. Rev. 28:286-350.
Srameck-Husek. 1946. (On the uniform classification of animal and plant communities in our waters) Sbornik MAP, 20(3):213 Orig. in
Czech.
Strickland, J. D. H. 1960. Measuring the production of marine phytoplankton. Bull. No. 122. Fish. Res. Bd. Canada, Ottawa, 172 pp.
(Review of methods of primary production measurement, many applicable to periphyton analyses.)
Thomas, N. A. 1968. Methods for slide attachment in periphyton studies. (Manuscript). U. S. FWPCA, Natl. Field Investigations
Center, Cincinnati.
U. S. Federal Water Pollution Control Administration. 1967. Effects of pollution on aquatic life resources of the South Platte River in
Colorado. Vol. 2. Technical Appendix. USFWPCA, Cincinnati. 85pp.
Warner, R. W., R. K. Ballentine, and L. E. Keup. 1969. Black-water impoundment investigations. U. S. FWQA, Cincinnati, Ohio. 95 pp.
Weber, C. 1973. Recent developments in the measurement of the response of plankton and periphyton to changes in their environ-
ment. In: Bioassay Techniques and Environmental Chemistry. G. Glass, ed. Ann Arbor Science Publishers, Inc., p 119-138.
Weber, C. I., and B. McFarland. 1969. Periphyton biomass-chlorophyll ratio as an index of water quality. Presented at the 17th Annual
Meeting, Midwest Benthological Society, Gilbertsville, Ky., April, 1969.
Weber, C. I., and R. L. Raschke. 1966. Use of a floating periphyton sample for water pollution surveillance. U. S. FWPCA, Cincinnati,
Ohio.
Weston, R. S., and C. E. Turner. 1917. Studies on the digestion of a sewage filter effluent by a small and otherwise unpolluted stream.
Mass. Inst. Technol., Sank. Res. Lab. Sewage Exper. Sta. 10:1-43.
Wisniewski, T. F. 1948. The chemistry and biology of milk waste disposal. J. Milk Food Technol. 11:293-300.
Young, O. W. 1945. A limnological investigation of periphyton in Douglas Lake, Michigan. Trans. Amer. Microscop. Soc. 64:1.
-------
MACBQPHYTON
-------
MACROPHYTON
Page
1.0 INTRODUCTION 1
2.0 SAMPLE COLLECTION AND ANALYSIS 1
2.1 Qualitative Sampling 2
2.2 Quantitative Sampling 2
3.0 REFERENCES 3
-------
MACROPHYTON
1.0 INTRODUCTION
Macrophytes are all aquatic plants possessing a
multi-cellular structure with cells differentiated
into specialized tissues. Included are the mosses,
liverworts, and flowering plants. Their sizes
range from the near microscopic watermeal to
massive cypress trees. The most commonly dealt
with forms are the herbaceous water plants.
Macrophyton may be conveniently divided
into three major growth types:
Floating. These plants have true leaves and
roots and float on the water surface (duckweed,
watermeal, water hyacinth).
Submerged. These plants are anchored to the
substratum by roots and may be entirely sub-
mersed or have floating leaves and aerial repro-
ductive structures (water milfoil, eel grass, pond-
weeds, bladderwort).
Emersed. These plants are rooted in shallow
water and some species occur along moist shore
lines. The two major groups are:
Floating leafed plants (water lilies and water
shields).
Plants with upright shoots (cattails, sedges,
woody shrubs, rice and trees.
The use of macrophytes in water quality
investigations has been sorely neglected.
Kolkwitz and Marsson (1908) used some species
in their saprobic system of water quality classifi-
cation, but they are rarely mentioned in most
literature. A number of pollutants have dramatic
effects on macrophyte growth:
Turbidity restricting light penetration can
prevent the growth of submerged weeds.
Nutrients can stimulate overproduction of
macrophytes in numbers sufficient to create
nuisances or can stimulate excessive plankton
growths that effect an increase in turbidities,
thus eliminating macrophyte growths.
Herbicidal compounds, if present at sublethal
concentrations, can stimulate excessive growths
or they can, at higher concentrations, destroy
plant growths.
Organic or inorganic nutrients, or both, can
support periphytic algal and slime growths
sufficient to smother and thus destroy sub-
mersed forms.
Sludge deposits, especially those undergoing
rapid decomposition, usually are too unstable or
toxic to permit the growth of rooted plants.
The rampant growth of some macrophytes has
caused concern over recent years (Holm et al.
1969). Millions of dollars are spent each year in
controlling macrophytes that interfere with
irrigation operation, navigation, and related
recreational uses. Mechanical cutting, applica-
tion of herbicides, and habitat alteration are the
primary control methods. Mackenthun and
Ingram (1967) and Mackenthun (1969) have re-
viewed and summarized control techniques.
Yount and Grossman (1970) and Boyd (1970)
discussed schemes for using macrophytes to re-
move nutrients from effluents and natural
waters.
Aquatic macrophytes are a natural component
of most aquatic ecosystems, and are present in
those areas suitable for macrophyte growth,
unless the habitat is altered. Furthermore, the
proper proportions of macrophytes are ecologi-
cally desirable (Wilson, 1939; Hotchkiss, 1941;
Penfound, 1956; Boyd, 1971). Boyd (1970,
1971) introduced concepts of macrophyte
management opposed to the current idea of
eradicating aquatic macrophytes from many
aquatic ecosystems. Much additional research is
needed on the role of macrophytes in aquatic
ecosystems.
The objective of an investigation dictates the
nature and methodology of sampling macro-
phytes. Critical factors are the time available,
how critical the information is, expertise avail-
able, duration of the study, and characteristics
of the waterway.
Techniques are few, and the investigator's best
asset is his capability for innovating sound
procedures.
2.0 SAMPLE COLLECTION AND ANALYSIS
Collecting representative genera from the
macrophyton community is generally not diffi-
cult because of their large size and littoral habi-
tats. Macrophytes may be readily identified to
genera and some to species in the field, or they
may be dried in a plant press and mounted for
1
-------
BIOLOGICAL METHODS
further identification. Small, delicate species
may be preserved in buffered 4 percent formalin
solution. Some of the more useful taxonomic
works for identification are Muenscher (1944),
Eyles and Robertson (1944), Fassett (1960) and
Winterringer and Lopinot (1966).
2.1 Qualitative Sampling
Qualitative sampling includes visual observa-
tion and collection of representative types from
the study area. Report the extent of growth as
dense when coverage is continuous, moderate
when growths are common, and sparse when the
growth is rarely encountered. The crop of plants
may be comprised of just one genus or may be a
mixture; if a mixture, estimate the percentage of
individual types.
Sampling gear is varied and the choice of tools
usually depends on water depth. In shallow
water, a garden rake or similar device is very
effective for collecting macrophytes. In deeper
water, employ grabs, such as the Ekman, to
collect submersed types. In recent years, scuba
diving has gained popularity with many investi-
gators in extensive plant surveys. Phillips (1959)
provides detailed information on qualitative
sampling.
2.2 Quantitative Sampling
Quantitative sampling for macrophytes is
usually to determine the extent or rate of
growth or weight of growth per unit of area. The
study objectives determine whether measure-
ments will involve a single species or several.
Before beginning a quantitative investigation,
develop a statistical design to assist in deter-
mining the best sampling procedure, sampling
area size, and number of samples. Often proce-
dures adapted from terrestrial plant surveys are
applicable in the aquatic environment. The
following references will be helpful in adopting a
suitable technique: Penfound, 1956; Westlake,
1966; Boyd, 1969; Forsberg, 1959, 1960;
Edwards and Owens, 1960; Jervis, 1969; Black-
burn, et al., 1968.
Standing crop. Sampling should be limited to
small, defined subareas (quadrates) with conspic-
uous borders. Use a square framework with the
poles anchored on the bottom and floating line
for the sides. Collect the plants from within the
frame by hand or by using a long-handled garden
rake. Forsberg (1959) has described other
methods such as laying out long, narrow
transects.
Obtain the wet weight of material after the
plants have drained for a standard period of
time, determined by the investigator. Dry the
samples (or subsamples for large species) for 24
hours at 105°C and reweigh. Calculate the dry
weight of vegetation per unit area.
Planimeter accurate maps to determine the
total area of investigation. If additional boat or
air reconnaissance (using photographs) is done
to determine type and extent of coverage, data
collected from the subareas can then be ex-
panded for the total study area. Boyd (1969)
describes a technique for obtaining surface
coverage by macrophytes in a small body of
water.
Productivity. Estimate standing crops at pre-
determined intervals to relate growth rates to
pollution, such as nutrient stimulation, retarda-
tion, or toxicity from heavy metals and thermal
effects. Wetzel (1964) and Davies (1970)
describe a more accurate method with the use of
a carbon-14 procedure to estimate daily produc-
tivity rates of macrophytes.
-------
MACROPHYTON
3.0 REFERENCES
Blackburn, R. D., P. F. White, and L. W. Weldon. 1968. Ecology of submersed aquatic weeds in south Florida canals. Weed Sci.
16:261-266.
Boyd, C. E. 1969. Production, mineral nutrient absorption, and biochemical assimilation by Justicia americana and Alternanthera
phUoxeroides. Archiv. Hydrobiol. 66:139-160.
Boyd, C. E. 1970. Vascular aquatic plants for mineral nutrient removal from polluted waters. Econ. Bot. 24:95-103.
Boyd, C. E. 1971. The limnological role of aquatic macrophytes and their relationship to reservoir management. In: Reservoir Fisheries
and Limnology, G. E. Hall (ed.), Spec. Publ. No. 8. Amer. Fish. Soc., Washington, D.C. pp. 153-166.
Davies, G. S. 1970. Productivity of macrophytes in Marion Lake, British Columbia. J. Fish. Res. Bd. Canada, 27:71-81.
Edwards, R. W., and M. Owens. 1960. The effects of plants on liver conditions. I. Summer crops and estimates of net productivity of
macrophytes in a chalk stream. J. Ecol. 48:151-160.
Eyles, D. E., and J. L. Robertson, Jr. 1944. A guide and key to the aquatic plants of the southeastern United States. Public Health
Bull. No. 286. U.S.Gov. Printing Office, Washington, D.C. 151 pp.
Fassett, N. C. 1960. A manual of aquatic plants. Univ. Wisconsin Press, Madison. 405 pp.
Forsberg, C. 1959. Quantitative sampling of subaquatic vegetation. Oikos, 10:233-240.
Forsberg, C. 1960. Subaquatic macrovegetation in Osbysjon, Djurholm. Oikos, 11:183-199.
Holm, L. G., L. W. Weldon, and R. D. Blackburn. 1969. Aquatic weeds. Science, 166:699-709.
Hotchkiss, N. 1941. The limnological role of the higher plants. In: A symposium of hydrobiology, Univ. Wisconsin Press, Madison, pp.
152-162.
Jervis, R. A. 1969. Primary production in the freshwater marsh ecosystems of Troy Meadows, New Jersey. Bull. Torrey Bot. Club,
96:209-231.
Kolkwitz, R., and M. Marsson. 1908. Oekologie der pflanzlichen Saprobien. Berichte deutschen botanischen Gesellschaft, 26a:505-5l9.
Mackenthun, K. M. 1969. The practice of water pollution biology. U. S. FWPCA, Cincinnati. 281 pp.
Mackenthun, K. M., and W. M. Ingram. 1967. Biological associated problems in freshwater environments. U. S. FWPCA, Cincinnati.
287 pp.
Muenscher, W. C. 1944. Aquatic plants of the United States. Comstock Pub. Co., Ithaca. 374 pp.
Penfound, W. T. 1956. An outline for ecological life histories of herbaceous vascular hydrophytes. Ecology, 33:123-128.
Phillips, E. A. 1959. Methods of vegetation study. Henry Holt & Co., New York, 107 pp.
Westlake, D. F. 1966. The biomass and productivity of Gylceria maxima. \. Seasonal changes in biomass. J. Ecol. 54:745-753.
Wetzel, R. G. 1964. A comparative study of the primary productivity of higher aquatic plants, periphyton, and phytoplankton in a
large shallow lake. Int. Rev. ges. Hydrobiol. 49' 1-61.
Wilson, L. R. 1939. Rooted aquatic plants and their relation to the limnology of fresh-water lakes. In: Problems of Lake Biology. Publ.
No. 10, Amer. Assoc. Adv. Sci. pp. 107-122.
Winterringer, G. S., and A. C. Lopinot. 1966. Aquatic plants of Illinois. II). State Museum Popular Ser. Vol. VI, 111. State Museum
Division. 142 pp.
Yount, J. L., and R. A. Grossman, Jr. 1970. Eutrophication control by plant harvesting. JWPCF, 42:173-183.
-------
MACRlllllfERTEBRATES
-------
MACROINVERTEBRATES
I'age
1.0 INTRODUCTION 1
2.0 SELECTION OF SAMPLE SITES 2
2.1 Systematic Sampling 2
2.2 Random Sampling 2
2.3 Measurement of Abiotic Factors 2
2.3.1 Substrate 2
2.3.2 Depth 4
2.3.3 Current Velocity 4
2.3.4 Salinity 4
3.0 SAMPLING METHODS 5
3.1 Quantitative 5
3.1.1 Definitions and Purpose 5
3.1.2 Requirements 5
3.1.3 Advantages 5
3.1.4 Limitations 6
3.2 Qualitative 6
3.2.1 Definitions and Purpose 6
3.2.2 Requirements 6
3.2.3 Advantages 6
3.2.4 Limitations 6
3.3 Devices 7
3.3.1 Grabs 7
3.3.2 Sieving Devices 9
3.3.3 Coring Devices 9
3.3.4 Artificial Substrates 10
3.3.5 Drift Nets 11
3.3.6 Photography 12
3.3.7 Qualitative Devices 12
4.0 SAMPLE PROCESSING 12
4.1 Sieving 12
4.2 Preservation 13
4.3 Labelling 13
4.4 Sorting and Subsampling 13
4.5 Identification 14
4.6 Biomass 15
5.0 DATA EVALUATION 15
5.1 Quantitative Data 15
5.1.1 Reporting Units 15
5.1.2 Standing Crop and Taxonomic Composition .... 15
5.1.3 Diversity 16
5.2 Qualitative Data 18
5.2.1 Indicator Organism Schemes 18
5.2.2 Reference Station Methods 18
6.0 LITERATURE CITED 32
7.0 TAXONOMIC BIBLIOGRAPHY 33
7.1 Coleoptera 33
-------
Page
7.2 Crustacea 34
7.3 Diptera 34
7.4 Ephemeroptera 35
7.5 Hemiptera 36
7.6 Hirudinea 36
7.7 Hydracarina 36
7.8 Lepidoptera 36
7.9 Megaloptera 36
7.10 Mollusca 36
7.11 Odonata 37
7.12 Oligochaeta 37
7.13 Plecoptera 37
7.14 Trichoptera 37
7.15 Marine 38
-------
MACROINVERTEB RATES
1.0 INTRODUCTION
The aquatic macroinvertebrates, as discussed
in this section, are animals that are large enough
to be seen by the unaided eye and 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.
Any available substrate may provide suitable
habitat including bottom sediments, submerged
logs, debris, pilings, pipes, conduits, vascular
aquatic plants, filamentous algae, etc.
The major taxonomic groups included in fresh
water are the insects, annelids, molluscs, flat-
worms, roundworms, and crustaceans. The
major groups in salt water are the molluscs,
annelids, crustaceans, coelenterates, porifera,
and bryozoans.
Benthic macroinvertebrates can be defined by
location and size but not by position in the
trophic structure since they occupy virtually all
levels. They may be omnivores, carnivores, or
herbivores; and in a well-balanced system, all
three types will likely be present. They include
deposit and detritus feeders, parasites,
scavengers, grazers, and predators.
Species present, distribution, and abundance
of aquatic macroinvertebrates may be subject to
wide seasonal variations. Thus, when conducting
comparative studies, the investigator must be
quite careful to avoid the confounding effects of
these seasonal changes. Seasonal variations are
particularly important in fresh-water habitats
dominated by aquatic insects having several life
stages, not all of which are aquatic.
The macroinvertebrates are important
members of the food web, and their well-being is
reflected in the well-being of the higher forms
such as fish. Many invertebrates, such as the
marine and fresh-water shellfish, are important
commercial and recreational species. Some, such
as mosquitos, black flies, biting midges, and
Asiatic clams, are of considerable public health
significance or are simple pests;and many forms
are important for digesting organic material and
recycling nutrients.
A community of macroinvertebrates in an
aquatic ecosystem is very sensitive to stress, and
thus its characteristics serve as a useful tool for
detecting environmental perturbations resulting
from introduced contaminants. Because of the
limited mobility of benthic organisms and their
relatively long life span, their characteristics are
a function of conditions during the recent past,
including reactions to infrequently discharged
wastes that would be difficult to detect by
periodic chemical sampling.
Also, because of the phenomenon of
"biological magnification" and relatively long-
term retention of contaminants by benthic
organisms, contaminants such as pesticides,
radioactive materials, and metals, which are only
periodically discharged or which are present at
undetectable levels in the water, may be
detected by chemical analyses of selected com-
ponents of the macroinvertebrate fauna.
In pollution-oriented studies of macroinverte-
brate communities, there are basically two
approaches—quantitative and qualitative—that
may be utilized singly or in combination.
Because of the basic nature of this decision, the
section of this manual relating to sampling
methods and data evaluation of macroinverte-
brates is arranged on the basis of whether a
quantitative or qualitative approach is used.
Ideally, the design of macroinvertebrate
studies should be based upon study goals or
objectives; however, the ideal must frequently
be tempered by the realities of available
resources, time limitations imposed on the
study, and the characteristics of the habitat to
be studied. To aid in selecting the most
advantageous sampling method, sample sites,
and data evaluation, the reader of this section
should be familiar with the material in the
"Introduction" of this manual, particularly
those portions outlining and discussing require-
ments of the various types of field studies in
which an investigator may become involved.
To supplement the material contained in this
manual, a number of basic references should be
available to investigators of the benthic com-
munity, particularly to those engaged in water
-------
BIOLOGICAL METHODS
pollution studies. These include Standard
Methods (2), Welch (57), Mackenthun (37),
Kittrell (29), Hynes (26), and Buchanan and
Sommers (9).
2.0 SELECTION OF SAMPLE SITES
As discussed and defined more fully in the
section on biometrics, sample sites may be
selected systematically or by various randomiza-
tion procedures.
2.1 Systematic Sampling
Unless the data are to be utilized for quantita-
tive evaluations, 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 and serve as an excellent means of
delimiting and mapping the habitat types. In
lakes, reservoirs, and estuaries, transects may be
established along the short or long axis or may
radiate out from a pollution source. If a random
start point is used for selecting sampling sites
along the transects, the data may be amenable to
quantitative evaluation (see Biometrics Section).
As will be discussed, however, the confounding
effects of changes in physical characteristics of
the environment along the transect must be fully
recognized and accounted for.
In another form of systematic sampling, the
investigator, using a variety of gear, consciously
selects and intensively samples all recognizable
habitat types. As previously mentioned, this
form of sample site selection is useful for
synoptic surveys and for comparative studies
where qualitative comparisons are being made.
2.2 Random Sampling
For conducting quantitative studies, where a
measure of precision must be obtained, some
type of randomization procedure must be
employed in selecting sampling sites. This selec-
tion may be carried out on the whole of the area
under study (simple random sampling), or the
randomization procedure may be conducted
independently on selected strata (stratified
random sampling). Because the characteristics of
macroinvertebrate communities are 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, the investigator should
stratify the habitat on the basis of known
physical habitat differences and collect samples
by the random grid technique within each
habitat type.
As alluded to above, and regardless of the
method of sample site selection, the biologist
must consider and account for those natural
environmental variations that may affect the
distribution of organisms. Among the more
important natural environmental variables in
fresh-water habitats are substrate type, current
velocity, and depth. In estuaries, the salinity
gradient is an additional variable that must be
accounted for.
2.3 Measurement of Abiotic Factors
2.3.1 Substrate
Substrate is one of the most important factors
controlling the characteristics of the community
of aquatic macroinvertebrates found at a given
location in a body of water (49). 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
hydrolic 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 consider factors which affect the normal
distribution of the benthic fauna.
Where the pollutant has a direct effect on the
characteristics of the substrate, the effects of
changes in water quality may be inseparable
from the effects of changes in the substrate. In
cases where substrate deterioration has occurred,
faunal effects may be so obvious that extensive
sampling may not be required, and special atten-
tion should be given to the physical and/or
chemical characterization of the deposits.
In conducting synoptic surveys or other types
of qualitative studies and taking into account
-------
MACROINVERTEBRATE SAMPLING
the limitations of available sampling devices,
sampling sites should be selected to include all
available substrates. If these qualitative samples
are to be used for determining the effects of
pollutants where the pollutant does not have a
direct affect 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.
For quantitative studies, it is sometimes
necessary in the interest of economy and
efficiency and within the limitations of the avail-
able gear, to sample primarily at sites having
substrates which normally support the most
abundant and varied fauna, and devote a mini-
mum 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 macroinver-
tebrates; sampling effort may be reduced there in
favor of the more productive areas of "deposi-
tion" 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.
Because of the importance of substrate (in
terms of both organic content and particle size)
in macroinvertebrate studies, it is suggested that
sufficient samples be collected to conduct the
following minimal analyses and evaluations:
• In the field, classify and record, on suitable
forms, the mineral and organic matter
content of the stream, lake, or estuary
bottom at each sample site on a percentage
basis with the use of the categories shown
in Table 1. Although the categories given in
Table 1 may not apply universally, they
should be applicable to most situations with
only slight modification.
TABLE 1. CATEGORIES FOR FIELD EVALUATION OF SOIL CHARACTERISTICS*
Type
Size or characteristic
Inorganic Components
Bed rock or solid rock
Boulders
Rubble
Gravel
Sand
Silt
Clay
Marl
Organic Components
Detritus
Fibrous peat
Pulpy peat
Muck
>256 mm (10 in.) in diameter
64 to 256 mm (2V4 to 10 in.) in diameter
2 to 64 mm (1/12 to 2'/2 in.) in diameter
0.06 to 2.0 mm in diameter; gritty texture when rubbed between fingers.
0.004 to 0.06 mm in diameter
-------
BIOLOGICAL METHODS
• In the laboratory, evaluate the inorganic
components by conducting a wet and dry
particle size analysis on one or more
samples and preferably on replicate samples
from each sampling site with the use of
standard sieves and following the modified
Wentworth classification shown in Table 2.
Detailed procedures for sediment analysis
are found in IBP handbook No. 16.*
TABLE 2. SOIL PARTICLE SIZE
CLASSIFICATION*
Name
Particle size
(mm)
U.S. standard sieve
series #
Boulder >256
Rubble 64-256
Coarse gravel 32-64
Medium gravel 8-32 f
Fine gravel 2-8 10
Coarse sand 0.5-2 35
Medium sand 0.25-0.5 120
Fine sand 0.125-0.25 230
Very fine sand 0.0625-0.125
Silt 0.0039-0.0625 Centrifuge (750 rpm, 3 min)J
Clay
-------
MACROINVERTEBRATE SAMPLING
Because of the extreme spatial and temporal
fluctuations of salinity in estuaries, simple, rapid
instrumental methods of measurement are more
desirable than slower, more precise chemical
methods (38).
Wide-range, temperature-compensated con-
ductivity 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.
3.0 SAMPLING METHODS
3.1 QUANTITATIVE
3.1.1 Definitions and purpose
Although the data may be evaluated in various
ways, a quantitative method essentially involves
an estimation of the numbers or biomass
(standing crop) of the various components of
the macroinvertebrate community per unit area
in all or a portion of the available habitats
(including artificially introduced habitats) in the
ecosystem being studied, and provides informa-
tion on the species composition, richness of
species and distribution of individuals among the
species.
3.1.2 Requirements
Obtain quantitative estimates by using devices
that sample a unit area or volume of habitat,
such as a Surber square-foot sampler, which in
use presumably collects all organisms enclosed
within the frame of the sampler, or an artificial
substrate sampler having a fixed volume or
exposing a fixed amount of surface.
In the study of macroinvertebrate popula-
tions, the sampling precision is affected by a
number of factors, including: size, weight, and
construction of the sampling device, the type of
substrate, and the distribution of organisms in
and on the substrate. For example, it is expected
that the estimates of standing crop drawn from a
series of samples will be more precise (have a
lower coefficient of variation) when the
community consists of a few species represented
by a large number of individuals, evenly distri-
buted in the substrate. Conversely, a large coef-
ficient of variation would be expected if the
fauna consists of a large number cf species with
a patchy distribution of individuals. To obtain
the same level of precision at a given level of
probability, a larger number of replicates would
be required in the latter case than in the former.
In general, the smaller the surface area
encompassed by a sampling device, the larger the
number of samples required to obtain a desired
level of precision. Thus, precision can be
increased by collecting larger samples, or by
increasing the numbers of samples collected.
An objective, quantitative approach neces-
sitates that a measure of the precision of the
estimates be obtained — thus, replicate sampling
in each habitat or stratum selected for study is
an absolute requirement. For measurement of
precision, three replicates are an absolute
minimum. (A series of single samples taken at
discrete points along a transect do not represent
replicate samples of benthic organisms unless it
can be demonstrated that the physical character-
istics of the habitat do not change along the
transect.)
It is preferable, if data are available (or can be
obtained by reconniassance or exploratory
studies), to determine the number of replicates
on the basis of the desired level of precision as
discussed in the Biometrics Section.
3.1.3 Advantages
In addition to providing the same data
obtained from a qualitative study, the standing
crop data generated by a quantitative study pro-
vide a means of comparing the productivity of
different environments; and if a measure of
turnover is available, the actual production can
be computed.
The use of quantitative sampling devices in
carefully chosen habitats is recommended
because they reduce sampling bias resulting from
differences in expertise of the sample collector.
The data from properly designed quantitative
studies are amenable to the use of simple but
-------
BIOLOGICAL METHODS
powerful statistical tools that aid in maintaining
the objectivity of the data evaluation process.
The measures of precision and probability state-
ments that can be attached to quantitative data
reduce the possibilities of bias in the data evalu-
ation process and make the results of different
investigators more readily comparable.
The advantages, then, of quantitative methods
are:
• They provide a measure of productivity.
• The investigator can measure precision of
estimates and attach probability statements,
thus providing objective comparisons.
• The data of different investigators may be
compared.
3.1.4 L imi ta tio ns
Presently, no sampling devices are adequate to
sample all types of habitat; so when quantitative
devices are used, only selected portions of the
environment may be sampled.
Sampling precision is frequently so low that
prohibitive numbers of replicate samples may be
required to obtain meaningful estimates. Sample
processing and analysis are slow and time-
consuming. In some cases, therefore, time limi-
tations placed on a study may prohibit the use
of quantitative techniques.
3.2 Qualitative
3.2.1 Definitions and purpose
The objective of qualitative studies is to deter-
mine the presence or absence of forms having
varying degrees of tolerance to contaminants
and to obtain information on "richness of
species." Samples are obtained with the use of a
wide variety of collecting methods and gear,
many of which are not amenable to quantitation
on a unit-area basis. When conducting qualitative
studies, an attempt is usually made to collect all
species present by exhaustive sampling in all
available habitat types.
3.2.2 Requirements
Recognizing and locating various types of
habitats where qualitative samples can be
collected and selecting suitable collecting
techniques require experience and a high level of
expertise.
When conducting comparative studies of the
macrobenthos, a major pitfall is the confounding
effect of the differences in physical habitat
among the different stations being studied. This
danger is particularly inherent in qualitative
studies when an attempt is made to systemati-
cally collect representative specimens of all
species present at the sampling stations or
reaches of river being compared. Unfortunately,
differences in habitat unrelated to the effects of
introduced contaminants may render such com-
parisons meaningless. Minimize this pitfall by
carefully recording, in the field, the habitats
from which specimens are collected and then
basing comparisons only on stations with like
habitats in which the same amount of collecting
effort has been expended.
3.2.3 Advantages
Because of wide latitude in collecting tech-
niques, the types of habitat that can be sampled
are relatively unrestricted. Assuming taxonomic
expertise is available, the processing of qualita-
tive samples is often considerably faster than
that required for quantitative samples.
3.2.4 Limitations
Collecting techniques are subjective and
depend on 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.
As discussed elsewhere, the drift of organisms
into the sample area may bias the evaluation of
qualitative data and render comparisons
meaningless.
No information on standing crop or produc-
tion can be generated from a qualitative study.
3.3 Devices
3.3.1 Grabs
Grabs are devices designed to penetrate the
substrate by virtue of their own weight and
leverage, and have spring- or gravity-activated
closing mechanisms. In shallow waters, some of
these devices may be rigged on poles or rods and
physically pushed into the substrate to a
-------
MACROINVERTEBRATE GRABS
predetermined depth. Grabs with spring-
activated closing devices include the Ekman,
Shipek, and Smith-Mclntyre; gravity-closing
grabs include the Petersen,* Ponar, and Orange
Peel. Excellent descriptions of these devices are
given in Standard Methods (2) Welch (57). Grabs
are useful for sampling at all depths in lakes,
estuaries, and rivers in substrates ranging from
soft muds through gravel.
In addition to the previously discussed
problems related to the patchy distribution of
organisms in nature, the number and kinds of
organisms collected by a particular grab may be
affected by:
• 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
"wash-out" of near-surface organisms
• stability of sampler at the high-flow
velocities often encountered in rivers.
Depth of penetration is a very serious problem
and depends on the weight of sampler as
opposed to the particle size and degree of
compaction of the bottom sediments. The
Ekman grab is light in weight and most useful
for sampling soft, finely divided substrates
composed of varying proportions of fine sand,
clay, slit, pulpy peat, and muck. For clay
hardpan and coarse substrates, such as coarse
sands and gravels, the heavier grabs such as the
orange peel or clam shell types (Ponar, Petersen,
Smith-Mclntyre) are more satisfactory.
Auxiliary weights may be added to aid penetra-
tion of the substrate and to add stability in
heavy currents and rough waters.
Because of differences in the depth of pene-
tration and the angle of "bite" upon closure,
data from the different grabs are not compar-
able. The Ekman essentially encloses a square,
which is equal in area from the surface to
*Forest Modification of the Petersen grab described in Welch
(57).
maximum depth of penetration before closure.
In soft substrates, for which this grab is best
suited, the penetration is quite deep and the
angular closure of the spring-loaded jaws has
very little effect on the volume of sample
collected. In essence this means that if the depth
of penetration is 15 cm, the organisms lying at
that depth have the same opportunity to be
sampled as those lying near the surface.
In clam-shell type grabs, such as the Petersen,
Ponar, Shipek, and Smith-Mclntyre, the original
penetration is often quite shallow: because of
the sharp angle of "bite" upon closure, the area
enclosed by the jaws decreases at increasing
depths of substrate penetration. Therefore,
within the enclosed area, organisms found at
greater depths do not have an equal opportunity
to be sampled as in the case of the Ekman grab
and other sampling methods described in the
next section. This problem is particularly true of
the Shipek sampler — the jaws do not penetrate
the substrate before closure and, in profile, the
sample is essentially one-half of a cylinder.
Probably one of the most frustrating aspects
of sampling macroinvertebrates with various
types of grabs relates to the problem of incom-
plete closure of the jaws. Any object — such as
clumps of vegetation, woody debris, and gravel
— that cannot be sheared by the closing action
of the jaws often prevents complete closure. In
the order of their decreasing ability to shear
obstructing materials, the common grabs may be
ranked: Shipek, Smith-Mclntyre, Orange Peel,
Ponar, Petersen, and Ekman. If the Ekman is
filled to within more than 5 cm of the top,
there may be loss of substrate material on
retrieval (16). An advantage of the Ekman grab
is that the surface of the sediment can be
examined upon retrieval, and only those samples
in which the sediment surface is undisturbed
should be retained.
All grabs and corers produce a "shock" wave
as they descend. This disturbance can affect the
efficiency of a sampler by causing an outward
wash (blow-out) of flocculent materials near the
mud —water interface that may result in
-------
BIOLOGICAL METHODS
inadequate sampling of near-surface organisms
such as phantom midge larvae, and some
chironomid midges. The shock wave of the
Ekman grab is minimized by the use of hinged,
freely-opening top flaps. The Ponar grab is a
modified Petersen with side curtains and a
screen on the top. The screen allows water to
pass and undoubtedly reduces the shock wave;
however, divers have observed blow-out with
this device (16).
Grab-collected samples provide a very
imprecise estimate of the numbers of individuals
and numbers of taxa of aquatic macroinverte-
brates. A summary of data from various sources
shows that the mean coefficient of variation (C)
for numbers of individuals collected by Ponar,
Petersen, and Ekman grabs was 46, 48, and 50
percent, respectively (Table 3). In most of the
studies on which the calculations in Table 3 are
based, the level of replication ranged from three
to six samples. Estimations of number of taxa
are more precise: for Ponar, Petersen, and
Ekman grabs, the mean calculated C was 28, 36,
and 46 percent respectively (Table 3).
On the basis of the calculations in Table 4,
there appear to be no consistent differences in
the precision of estimates collected by Ekman,
Ponar, and Petersen grabs in mud or sand sub-
strates. The poor closure ability of the Ekman in
coarse substrates such as gravel is demonstrated
by the large C values for the Ekman as compared
with values for the Petersen and Ponar in gravel
substrates.
Another way of demonstrating the reliability
of grab sample estimates of macrobenthos
standing crop is to calculate, at a given proba-
bility level, the range of values around the
sample mean in which the true mean should lie
if a given number of replicate samples were
collected. From the data shown in Table 3 for
the Petersen, Ponar, and Ekman grabs in various
types of substrate, coefficients of variation near
50 percent for numbers of individuals and 35
percent for numbers of taxa should be expected
with 3 to 6 replicates. With the use of these
expected values, the true mean for numbers of
individuals and number of taxa of macroinverte-
brates should lie within plus or minus 36 percent
TABLE 3. MEAN AND MODAL VALUES FOR COEFFICIENTS OF VARIATION*
(EXPRESSED AS PERCENTAGE) FOR NUMBERS OF INDIVIDUALS AND NUMBERS
OF TAXA OF MACROINVERTEBRATES COLLECTED BY VARIOUS DEVICES
Sampling
device
Rock-filled
barbeque
basket
Ponar
Petersen
Ekman
Surber
Corert
Stovepipe
Individuals
Mean
32
46
48
50
50
50
56
Modef
21-30
41-50
51-60
41-50
41-50
31-40
Taxa
Mean
20
28
36
46
38
Modef
11-20
11-20
21-30
31-40
21-30
Remarks
22 sets of samples with 4-6 reps, per set (52) and
2 sets of samples having 15 and 16 reps. (13).
12 sets of samples with 3-12reps. per set(16, 31).
21 sets of samples with 3-6 reps, per set (31, 53,
54).
27 sets of samples with 3-12 reps, per set (8, 16, 31,
45, 53).
60 sets of samples having 6 reps, per set (20).
7 sets of samples having 10 reps, per set (8).
32 sets of samples having 3-4 reps, per set (53).
*Coefficient of variation = (standard deviation x 100)/mean.
t Frequency distribution based on 10% increments.
JQligochaetes only.
-------
MACROINVERTEBRATE SIEVING AND CORING DEVICES
and 25 percent, respectively, of the sample mean
at a 95 percent probability level, if 10 replicates
were collected. (See Biometrics Section.)
Precision would, of course, be increased if
additional samples were collected, or if the
sampling method were more precise.
Since the assumptions necessary for the
statistical calculations shown in Tables 3 and 4
are not likely met in the data of different
investigators collected from different habitats,
the above calculations only provide a gross
approximation of the precision to be expected.
They do, however, serve to emphasize the very
imprecise nature of grab sample data and the
resultant need for careful stratification of the
type of the habitat sampled and sample repli-
cation.
TABLE 4. MEAN COEFFICIENTS OF
VARIATION (EXPRESSED AS PERCENTAGE)
FOR NUMBERS OF INDIVIDUALS AND
NUMBERS OF TAXA OF MACROINVERTE-
BRATES COLLECTED IN DIFFERENT
SUBSTRATES BY GRAB-TYPE DEVICES
AND A CORER DEVICE*
Sampling
device
Ekman
Petersen
Ponar
Corerf
Substrate
Mud
Ind.
49
41
46
50
Taxa
40
29
25
Sand
Ind.
41
50
38
Taxa
21
41
33
Gravel
Ind.
106
49
48
Taxa
74
20
19
Calculated from data in references (8, 16, 31, 45, 53, 54).
fOligochaetes only.
3.3.2 Sieving devices
For quantitative sampling, the well-known
Surber square-foot sampler (2, 57) is the most
commonly used sieving device. This device can
be used only in flowing water having depths not
greater than 18 inches and preferably less than
12 inches. It is commonly used for sampling the
rubble and gravel riffles of small streams and
may be used in pools where the water depth is
not too great.
When using a sieving-type device for quantita-
tive estimates, reliability may be affected by:
• adequacy of seating of the frame on the
substrate
• backwash resulting from resistance of the
net to water flow - at high velocity of flow
this may be significant
• care used in recovering the organisms from
the substrate materials
• depth to which the substrate is worked
• drift of organisms from areas upstream of
the sample site
To reduce the possibility of bias resulting
from upstream disturbance of the substrate,
always stand on the downstream side of a sieving
device and take replicates in an upstream or
lateral direction. Never start in the upstream
portion of a pool or riffle and work in a down-
stream direction.
The precision of estimates of standing crops
of macrobenthos obtained with Surber-type
sieving devices varies widely and depends on a
number of factors including the uniformity of
substrate and distribution of organisms therein,
the care used in collecting samples, and level of
sample reph'cation.
For a large series of Surber samples from
southeastern U. S. trout streams, the coefficient
of variation (C) ranged from 11 percent to
greater than 100 percent (Table 3). The mean
value of C was near 50 percent, and more than
one-half of the C values fell between 30 and 50
percent. These values are similar to the 20 to 50
percent reported by Allen (1) and for those
discussed above for grab sample data.
3.3.3 Coring devices
Included in this category are single- and
multiple-head coring devices, tubular inverting
devices, and open-ended stovepipe-type devices.
Coring devices are described in Standard
Methods (2) and Welch (57). Corers can be used
at various depths in any substrate that is
sufficiently compacted so that the sample is
retained; however, they are best suited for
sampling the relatively homogeneous soft
sediments of the deeper portions of lakes.
-------
BIOLOGICAL METHODS
Because of the small area sampled, data from
coring devices are likely to provide very
imprecise estimates of the standing crop of
macrobenthos. As the data in Table 3 illustrate,
the variability in numbers of oligochaetes (a
dominant component of the fauna studied)
collected in corers is similar to that for grab-type
devices; however, the corer data were calculated
from two to three times as many replicate
samples and were collected from a relatively
homogeneous substrate.
Such 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 (19).
The Dendy inverting sampler (57) is a highly
efficient coring-type device used for sampling at
depths to 2 or 3 meters in nonvegetated sub-
strates 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 (51) 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.
Stovepipe-type devices include the Wilding
sampler (2, 57) and any tubular material such as
60 to 75 cm sections of standard 17-cm-
diameter stovepipe (51) or 75 cm sections of
30-cm-diameter aluminum irrigation pipe fitted
with handles. In use, the irrigation pipe or com-
mercial 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.
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 penetra-
tion, changes in cross-sectional area with depth
of penetration, and escapement of organisms are
circumvented by stovepipe samplers, they are
recommended for quantitative sampling in all
shallow water benthic habitats. They probably
represent the only quantitative device suitable
for sampling shallow-water habitats containing
stands of rooted vascular plants and will collect
organisms inhabiting the vegetative substrates as
well as those living in sediments. The coef-
ficients of variation for the stovepipe samples in
Table 3 are comparable to the coefficients for
grab samples, although the stovepipe samples
were collected in heavily vegetated and conse-
quently highly variable habitats.
3.3.4 Artificial substrates
The basic multiple-plate sampler (23) and
rock-filled basket sampler (21) have been
modified by numerous workers (17, 40) and are
widely used for investigating the macroinverte-
brate community. Both samplers may be
suspended from a surface float or may be
modified for use in shallow streams by placing
them on a rod that is driven into the stream
bottom or anchored in a piece of concrete (24).
A multiple-plate sampler similar to that
described by Fullner (17), except with circular
plates and spacers, is recommended for use by
EPA biologists. This sampler is constructed of
0.3-cm tempered hardboard cut into 7.5-cm
diameter circular plates and 2.5-cm circular
spacers. A total of 14 plates and 24 spacers are
required for each sampler. The hardboard plates
and spacers are placed on a Mi-inch (0.625 cm)
eyebolt so that ihere are eight single spaces, one
double space, two triple spaces, and two
quadruple spaces between the plates. This
sampler has an effective surface area (excluding
the bolt) of 0.13 square meter and conveniently
fits into a wide-mouth glass or plastic jar for
shipment and storage. Caution should be
exercised in the reuse of samplers that may have
been subjected to contamination by toxicants,
oils, etc.
The rock basket sampler is a highly effective
device for studying the macroinvertebrate
community. A cylindrical, chromeplated basket
10
-------
MACROINVERTEBRATE ARTIFICIAL SUBSTRATES AND DRIFT NETS
(2) or comparable enclosure filled with 30, 5 to
8-cm-diameter rocks or rock-like material is
recommended for use by EPA biologists.
To reduce the number of organisms that
escape when the samplers are retrieved, the
multiple-plate sampler and the rock-filled basket
sampler should be enclosed by a dip net con-
structed of 30-mesh or finer grit bolting cloth.
Artificial substrate samplers, to a great extent,
depend on chance colonization by drifting or
swimming organisms; and, thus, the time of
exposure may be critical to the development of
a relatively abundant and diverse community of
organisms. Adequate data are currently unavail-
able to determine the optimum exposure period,
which is likely to differ in different bodies of
water and at different times of the year. Until
more data become available, adoption of a
6-week exposure period (2) is provisionally
recommended as standard. If study time limita-
tions 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 (43).
In deeper waters, artificial substrate samplers
should be suspended from floats and should be
well up in the photic zone so that periphytic
growths can develop and provide food for
grazing forms of macroinvertebrates. Unless the
water is exceptionally turbid, a 1.2-meter
(4-foot) depth is recommended as standard. If
the water is less than 2.5 meters deep, the
sampler should be suspended from a float half-
way between the water surface and the stream
bed.
In some situations, artificial substrate
methods are the best means of conducting
quantitative studies of the ability of an aquatic
environment to support a diverse assemblage of
macroinvertebrate organisms. Advantages of the
method are:
• The confounding effects of substrate differ-
ences are reduced.
• A higher level of precision is obtained than
with other sampling devices (Table 3).
• Quantitatively comparable data can be
obtained in environments from which it is
virtually impossible to obtain samples with
conventional devices.
• Samples usually contain negligible amounts
of extraneous material, permitting quick
laboratory processing.
Limitations of artificial substrate samplers are:
• The need for a long exposure period makes
the samplers unsuited for short-term survey
studies.
• Samplers and floats are sometimes difficult
to anchor in place and may present a
navigation hazard.
• Samplers are vulnerable to vandalism and
are often lost.
• Samplers provide no measure of the
condition of the natural substrate at a
station or of the effect of pollution on that
substrate, including settled solids.
• Samplers only record the community that
develops during the sampling period, thus
reducing the value of the collected fauna as
indicators of prior conditions.
Two other objections often made to the use
of artificial substrate samplers are that they are
selective to certain types of fauna and the data
obtained do not provide a valid measure of the
productivity of a particular environment. The
validity of the latter objection depends on study
objectives and may be of minor consequence in
many pollution-oriented studies. The selectivity
of artificial substrate samplers is a trival objec-
tion, since all currently available devices are
selective. The selectivity of conventional
sampling devices other than artificial substrates
is directed toward those organisms that inhabit
the types of substrate or substrates for which a
particular type of sampler is designed.
3.3.5 Drift nets
Nets having a 1 5 by 30-cm upstream opening
and a bag length of 1.3 m (No. 40 mesh
netting) are recommended for small, swift
streams. In large, deep rivers with a current of
approximately 0.03 meters per second (mps),
nets having an opening of 0.093 m2 are recom-
mended (2). Anchor the nets in flowing water
(current not less than 0.015 mps) for from 1 to
24 hours, depending on the density of bottom
11
-------
BIOLOGICAL METHODS
fauna and hydrologic conditions. Place the top
of the nets just below the surface of the water to
permit calculation of the flow through the nets
and to lessen the chance for collection of
floating terrestrial insects. Do not permit the
nets to touch bottom. In large rivers, maximum
catches are obtained 0.3 to 0.6 meter above the
bottom in the shoreline zone at depths not
exceeding 3 meters.
Drift nets are useful for collecting macro-
invertebrates that migrate or are dislodged from
the substrate; they are particularly well-suited
for synoptic surveys because they are light-
weight and easily transported. 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. Maximum drift intensity
occurs between sunset and midnight (55). Elliot
(14) presents an excellent synopsis of drift net
methodology.
i
3.3.6 Pho tography
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 be photo-
graphed before, during, and after the introduc-
tion of stress. The technique has been used with
success in south Florida to evaluate changes
brought about by the introduction of heated
effluents.
The technique for horizontal underwater
photos using scuba gear involves placing a photo-
graphically identifiable 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 also be
taken under suitable conditions.
3.3.7 Qualitative devices
The investigator has an unlimited choice of
gear for collecting qualitative samples. Any of
the qualitative devices discussed previously, plus
hand-held screens, dip nets, rakes, tongs, post
hole diggers, bare hands, and forceps can be
used. For deep-water collecting, some of the
conventional grabs described earlier 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 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.
4.0 SAMPLE PROCESSING
4.1 Sieving
Samples collected with grabs, tubular devices,
and artificial substrates contain varying amounts
of finely divided materials such as completely
decomposed organic material, silts, clays, and
fine sand. To reduce sample volume and
expedite sample processing in the laboratory,
these fines should be removed by passing the
sample through a U. S. Standard No. 30 sieve.
Sieves may range from commercially con-
structed models to 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 deeper waters. A good sieve
contains no cracks or crevices in which small
organisms can become lodged.
If at all possible, sieving should be done in the
field immediately after sample collection and
while the captured organisms are alive. Once
preserved, many organisms become quite fragile
and if subjected to sieving will be broken up and
lost or rendered unidentifiable.
Sieving may be accomplished by one of
several techniques depending upon the reference
of the individual biologist. In one technique, the
sample is placed directly into a sieve and the
12
-------
MACROINVERTEBRATE SAMPLE PROCESSING
sieve is then partially submerged in water and
agitated until all fine materials have passed
through. The sieve is agitated preferably in a tub
of water.
A variation of this technique is to place the
original sample in a bucket or tub, add screened
water, stir, and pour the slurry through a U. S.
Standard No. 30 sieve. Only a moderate amount
of agitation is then required to completely clean
the sample. Since this method requires consider-
ably less effort, most biologists probably prefer
it.
In both of the above methods, remove all the
larger pieces of debris and rocks from samples
collected, clean carefuly, and discard before the
sample is stirred or agitated.
The artificial substrate samplers are placed in
a bucket or tub of screened water and are
dismantled. Each individual piece of substrate
material is shaken and then cleaned gently under
water with a soft brush (a soft grade of tooth-
brush is excellent), 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.
4.2 Preservation
Fill sample containers no more than one-half
full of sample material (exclusive of the preserv-
ative). Supplemental sample containers are used
for samples with large volumes of material.
Obtain ample numbers and kinds of sample
containers before the collection trip: allow two
or three 1-liter containers per grab sample, a
1-liter container for most artificial substrate
samples, and 16-dram screw-cap vials for miscel-
laneous collections.
Preserve the sample in 70 percent ethanol. A
70 percent ethanol solution is approximated by
filling the one-half-full bottle, containing the
sample and a small amount of rinse water, with
95 percent ethanol. Do not use formalin.
4.3 Labelling
Make sample labels of water-resistant paper
and place inside the sample container. Write all
information on the label with a soft-lead pencil.
Where the volume of sample is so great that
several containers are needed, additional
external labels with the log number and
notations such as 1 of 2, 2 of 2, are helpful for
identifying sample containers in the laboratory.
Minimum information required on the sample
label is a sample identification (log) number.
The log number identifies the sample in a bound
ledger where the name of water body, station
number, date, sampling device used, name of
sample collector, substrate characteristics,
depth, and other environmental information are
placed.
4.4 Sorting and Subsampling
For quantitative studies, sort and pick all
samples by hand in the laboratory using a low-
power scanning lens. To pick organisms
efficiently and accurately, add only very small
amounts of detritus (no more than a heaping
tablespoon full) to standard-sized (25 X 40 X 5
cm), white enamel pans filled approximately
one-third full of water. Small insects and worms
will float free of most debris when ethanol-
-preserved samples are transferred to the water-
filled pan.
Analysis time for samples containing
excessively large numbers of organisms can be
substantially reduced if the samples are sub-
divided before sorting. The sample is thoroughly
mixed and distributed evenly over the bottom of
a shallow tray. A divider, delineating one-quarter
sections, is placed in a tray, and two opposite
quarters are sorted. The two remaining quarters
are combined and sorted for future reference or
discarded (57). The aliquot to be sorted must be
no smaller than one-quarter of the original
sample; otherwise considerable error may result
in estimating the total numbers of oligochaetes
or other organisms that tend to clump. The same
procedure may be followed for individual
taxonomic groups, such as midges and worms,
that may be present in large numbers.
Numerous techniques other than hand-picking
have been proposed to recover organisms from
the sample, including sugar solutions, salt solu-
tions, stains, electricity for unpreserved samples
in the field, bubbling air through sample in a
tube, etc. The efficacy of these techniques is
affected both by the characteristics of the sub-
strate material and the types of organisms. No
13
-------
BIOLOGICAL METHODS
technique, or combination of techniques, will
completely sort out or make more readily
discernible all types of organisms from all types
of substrate material. In the end, the total
sample must be examined. If technicians are
routinely conducting the picking operation,
these techniques may lead to overconfidence
and careless examination of the remainder of the
sample. If used with proper care, such aids are
not objectionable; however, they are not recom-
mended as standard techniques.
As organisms are picked from the debris, they
should be sorted into major categories (i.e.,
insect orders, molluscs, worms, etc.) and placed
into vials containing 70 percent ethanol. All vials
from a sample should be labeled internally with
the picker's name and the lot number and kept
as a unit in a suitable container until the
organisms are identified and enumerated, and
the data are recorded on the bench sheets. A
typical laboratory bench sheet for fresh-water
samples is shown in the Appendix.
4.5 Identification
The taxonomic level to which animals are
identified depends on the needs, experience, and
available resources. However, the taxonomic
level to which identifications are carried in each
major group should be constant throughout a
given study. The accuracy of identification will
depend greatly on the availability of taxonomic
literature. A laboratory library of basic
taxonomic references is essential. Many of the
basic references that should be available in a
tenthos laboratory are listed at the end of the
chapter.
For comparative purposes and quality control
checks, store identified specimens in a reference
collection. Most identifications to order and
family can be made under a stereoscopic
microscope (up to SOX magnification). Identifi-
cation to genus and species often requires a com-
pound microscope, preferably equipped with
phase contrast (10, 45, and 100X objectives) or
Nomarski (interference phase) optics.
To make species identifications, it is often
necessary to mount the entire organism or parts
thereof on glass slides for examination at high
magnification. Small whole insects or parts
thereof may be slide-mounted directly from
water or 70 percent ethanol preservative if CMC
mounting media is used. Label the slides
immediately with the sample log number and
the name of the structure mounted. Euparol
mounting medium may be preferable to CMC
for mounts to be kept in a reference collection.
Place specimens to be mounted in Euparol in
95 percent ethanol before mounting.
To clear opaque tissue, heat (do not boil) in a
small crucible (5-ml capacity) containing 5 to 10
percent KOH solution (by weight) until it
becomes transparent. The tissue can be checked
periodically under a stereoscopic microscope to
determine if it is sufficiently cleared. Then trans-
fer the tissue stepwise to distilled water and 95
percent ethanol for 1 minute each and mount
with CMC or Euparol. Several different
structures can be heated simultaneously, but do
not reuse the KOH solution.
The above methods work well for clearing and
mounting midges, parts of caddisflies, mayflies,
stoneflies, other insects, crustaceans, and
molluscs; however, worms, leeches, and turbel-
larians require more specialized treatment before
mounting (10, 47).
Larval insects often comprise the majority of
macroinvertebrates collected in artificial
substrate samplers and bottom samples. In
certain cases, identifications are facilitated if
exuviae, pupae, and adults are available. Collect
exuviae of insects with drift nets or by skimming
the water's surface with a small dip net near the
shore. Obtain adults with sweep nets and tent
traps in the field or rear larvae to maturity in the
laboratory.
The life history stages of an insect can be
positively associated only if specimens are reared
individually. Rear small larvae individually in 6-
to 12-dram vials half filled with stream water
and aerated with the use of a fine-drawn glass
tubing. Mass rearing can be carried out by
placing the larvae with sticks and rocks in an
aerated aquarium. Use a magnetic stirrer inside
of the aquarium (41) to provide a current.
14
-------
MACROINVERTEBRATE DATA EVALUATION
4.6 Biomass
Macroinvertebrate biomass (weight of
organisms per unit area) is a useful quantitative
estimation of standing crop. To determine wet
weights, soak the organisms in distilled water for
30 minutes, centrifuge for 1 minute at 140 gin
wire mesh cones, and weigh to the nearest 0.1
mg. Wet weight, however, is not recommended
as a useful parameter unless, by a determination
of suitable conversion factors, it can be equated
to dry weight.
To obtain dry weight, oven dry the organisms
to a constant wfight at 105°C for 4 hours or
vacuum dry at 105°C for 15 to 30 minutes at
1/2 atmosphere. Cool to room temperature in a
desiccator and weigh. Freeze drying (-55°C, 10
to 30 microns pressure) has advantages ovei oven
drying because the organisms remain intact for
further 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 length
of time (usually 24 hours) required for drying to
a constant weight.
To completely incinerate the organic material,
ash at 550°C for 1 hour. Cool the ash to
ambient temperature in a desiccator and weigh.
Express the biomass as ash-free dry weight.
5.0 DATA EVALUATION
5.1 Quantitative Data
5.1.1 Reporting units
Data from quantitative samples may be used to
obtain:
• total sunding crop of individuals, or
biomass, or both per unit area or unit
volume or sample unit, 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 s mples in part 5.2.
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, EPA
biologists should adhere to the following units:
• Data from devices sampling a unit area of
bottom will be 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 will be
reported in terms of the total surface area
of the plates in grams dry weight or ash-free
dry weight or numbers of individuals per
square meter, or both.
• Data from rock-filled basket samplers will
be reported as grams dry weight or numbers
of individuals per sampler, or both.
5.7.2 Standing crop and taxonomic composi-
tion
Standing crop and numbers of taxa in a com-
munity are highly sensitive to environmental
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 are made only between truly com-
parable environments. Typical responses of
standing crop or taxa to various types of stress
are:
Stress
Standing crop
(numbers or
biomass)
Number of
taxa
Toxic substance Reduce .
Severe temperature
alterations Variable
Silt Reduce .
Inorganic nutrients Increase .
Organic nutrients
(high O2 demand) Increase
Sludge deposits
(non-toxic) Increase
Reduce
Reduce
Reduce
Variable -
often no
detect-
able
change
Reduce
Reduce
15
-------
BIOLOGICAL METHODS
Organic nutrients and sludge deposits are fre-
quently associated. The responses shown are by
no means simple or fixed and may vary depend-
ing on a number of factors including:
• a combination of stresses acting together or
in opposition,
• indirect effects, such as for example the
destruction of highly productive vegetative
substrate by temperature alterations, sludge
deposits, turbidity, chemical weed control,
• the physical characteristics of the stressed
environment, particularly in relation to sub-
strate and current velocity.
Data on standing crop and numbers of taxa
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 the
Biometrics Section of this manual.
Data on standing crop and number of taxa 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 environ-
mental perturbations may be so subtle in com-
parison with sampling variation that statistical
comparisons are a helpful and necessary tool for
the evaluative process. For this purpose,
biologists engaged in studies of macroinverte-
brates should familiarize themselves with the
simple statistical tools discussed in the
Biometrics Section of this manual.
5.1.3 Diversity
Diversity indices are an additional tool for
measuring the quality of the environment and
the effect of induced stress on the structure of a
community of macroinvertebrates. Their use 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 in 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
tend to reduce diversity by making the environ-
ment unsuitable for some species or by giving
other species a competitive advantage.
The investigator must be aware that there are
naturally occurring extreme environments in
which the diversity of macroinvertebrate
communities may be low, as for example the
profundal fauna of a deep lake or the black
fly-dominated communities of the high gradient,
bed rock section of a torrential stream. Further-
more, because colonization is by chance,
diversity may be highly variable in a successional
community; for this reason, diversity indices
calculated from the fauna of artificial substrate
samplers must be evaluated with caution. These
confounding factors can be reduced by compar-
ing diversity in similar habitats and by exposing
artificial substrate samplers long enough for a
relatively stable, climax community to develop.
T A- U S S
Indices, such as -r?>
J S-l ,
-XT- T XT' and -T x7 where
N Log N Log N
S = number of taxa and N = total number of
individuals, are merely additional means of sum-
marizing data on total numbers and total taxa in
a single numerical form for evaluation and
summarization. They add no new dimension to
the methods of data presentation and analyses
discussed above and, in addition, are highly
influenced by sample size. Sample size in this
context relates to the total number of organisms
collected (an uncontrollable variable in most
macroinvertebrate sampling), not to the area or
volume of habitat sampled. Do not use such
indices for summarizing and evaluating data on
aquatic macroinvertebrate communities.
There are two components of species
diversity:
• richness of species
• distribution of individuals among the
species.
It is immediately obvious that the second
component adds a new dimension that was not
considered in the methods for evaluating data
16
-------
MACROINVERTEBRATE SPECIES DIVERSITY
discussed above. The distribution of individuals
among the species may be readily presented in
frequency distribution tables or graphs; but for
any appreciable number of samples, such
methods of presentation are so voluminous that
they are virtually impossible to compare and
interpret.
Indices of diversity based on information
theory, as originally proposed by Margalef (39)
and subsequently utilized by numerous workers,
include both components of species diversity as
enumerated above. Additionally, a measure of
the component of diversity due to the distribu-
tion of individuals among the species can readily
be extracted from the overall index. For
purposes of uniformity, the Shannon-Weaver
function is provisionally^ recommended for
calculating mean diversity d.
The machine formula presented by Lloyd,
Zar, and Karr (34) is:
_
d = - (N log! o N - 2 ni log! 0 nj)
where C = 3.321928 (converts base 10 log to
base 2 [bits]); N = total number of individuals;
and ni = total number of individuals in the jth
species. When their tables (reproduced in Table
5) are used, the calculations are simple and
straightforward, as shown by the following
example:
Number of individuals
in each taxa (nj's)
41
5
18
3
1
22
1
2
12
4
nj Iog10 nj
(from Table 5)
66.1241
3.4949
22.5949
1.4314
.0000
29.5333
.0000
.6021
12.9502
2.4082
Total 109
139.1391
N log, o N = 222.0795 (from Table 5)
I, ni log! o nj = 139.1391
(2220795 _ 139.1391)
= 0.030476 X 82.9404
= 2.5
Mean diversity, d, as calculated above is affected
both by richness of species and by the distribu-
tion of individuals among the species and may
range from zero to 3.321928 log N.
To evaluate the component of diversity due to
the distribution of individuals among the
species, compare the_calculated d with a
hypothetical maximum d based on an arbitrarily
selected distribution. The measure of
redundancy proposed byJVIargalef (39) 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 (33)
proposed the term "equitability" and compared
d with a maximum based on the distribution
obtained from MacArthur's (36) broken stick
model. The MacArthur model results in a distri-
bution 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 (33) 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 com-
munity that conforms to the MacArthur model.
In the table (reproduced as Table 6 of this
Section), the proposed measure of equitability
is:
where s = number of taxa in the sample, and s' =
the tabulated value. For the example given
above (without interpolation in the table):
Total number of taxa, s = 10
Total number of individuals, N = 109
a u <-*
=~ ~ Tn = 0.
s 10
17
-------
BIOLOGICAL METHODS
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 estimate of d and e improves with increased
sample size, and samples containing less than
100 specimens should be evaluated with caution,
if at all.
When Wilhm (59) evaluated values calculated
from data that numerous authors had collected
from a variety of polluted and unpolluted
waters, he found that in unpolluted waters d was
generally between 3 and 4, whereas in polluted
water, d was generally less than 1. However,
collected data from southeastern U. S. waters by
EPA biologists has shown that where degrada-
tion is at slight to moderate levels, d lacks the
sensitivity to demonstrate differences. Equit-
ability e, on the contrary, has been found to be
very sensitive to even slight levels of degrada-
tion. Equitability levels below 0.5 have not been
encountered in southeastern streams known to
be unaffected by oxygen-demanding wastes, and
in such streams, e generally ranges between 0.6
and 0.8. Even slight levels of degradation have
been found to reduce equitability below 0.5 and
generally to a range of 0.0 to 0.3.
Agency biologists are_encouraged to calculate
both mean diversity d and equitability e for
samples collected in the course of macroinverte-
brate studies. (If the mean and range of values
found by different sampling methods and under
varying levels and types of pollution are
reported to the Biological Methods Branch,
these data will be included in tabular form in
future revisions of this Section.)
5.2 Qualitative Data
As previously defined, qualitative data result
from samples collected in such a manner that no
estimate of numerical abundance or biomass can
be calculated. The output consists of a list of
taxa collected in the various habitats of the
environment being studied. The numerous
schemes advanced for the analysis of qualitative
data may be grouped in two categories:
5.2.1 Indicator-organism scheme
For this technique, individual taxa are
classified on the basis of their tolerance or
intolerance to various levels of putrescible
wastes (4, 5, 30, 42, 48). Taxa are classified
according to their presence or absence in dif-
ferent environments as determined by field
studies. Beck (6) reduced data based on the
presence or absence of indicator organisms to a
simple numerical form for ease in presentation.
5.2.2 Reference station methods
Comparative or control station methods
compare the qualitative characteristics of the
fauna in clean water habitats with those of
fauna in habitats subject to stress. Patrick (46)
compared stations on the basis of richness of
species and Wurtz (61) used indicator organisms
in comparing stations.
If adequate background data are available to
an experienced investigator, both of these tech-
niques can prove quite useful-particularly for
the purpose of demonstrating the effects of
gross to moderate organic contamination on the
macroinvertebrate community. To detect more
subtle changes in the macroinvertebrate com-
munity, collect quantitative data on numbers or
biomass of organisms. Data on the presence of
tolerant and intolerant taxa and richness of
species may be effectively summarized for evalu-
ation and presentation by means of line graphs,
bar graphs, pie diagrams, histograms, or pictoral
diagrams (27).
The calssification by various authors of repre-
sentative macroinvertebrates according to their
tolerance of organic wastes is presented in Table
7. In most cases, the taxonomic nomenclature
used in the table is that of the original authors.
The pollutional classifications of the authors
were arbitrarily placed in three categories —
tolerant, facultative, and intolerant — defined as
follows:
• Tolerant: Organisms frequently associated
with gross organic contamination and are
generally capable of thriving under
anaerobic conditions.
18
-------
MACROINVERTEBRATE INDICATOR ORGANISMS
• Facultative: Organisms having a wide range
of tolerance and frequently are associated
with moderate levels of organic contamina-
tion.
• Intolerant: Organisms that are not found
associated with even moderate levels of
organic contaminants and are generally
intolerant of even moderate reductions in
dissolved oxygen.
When evaluating qualitative data in terms of
material such as that contained in Table 7, the
investigator should keep in mind the pitfalls
mentioned earlier, as well as the following:
• Since tolerant species may be found in both
clean and degraded habitats, a simple record
of their presence or absence is of no signifi-
cance. Therefore, the indicator-organism
technique can provide positive evidence of
only one condition—clean water—and this
only if taxa classified as intolerant are
collected. An exception to this rule would
occur where sensitive species may be totally
absent because of the discharge of toxic
substances or waste heat.
Because evaluations are based on the mere
presence or absence or organisms, a single
specimen has as much weight as a large
population. Therefore, data for the original
classification and from field studies may be
biassed by the drift of organisms into the
study area.
The presence or absence of a 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 place-
ment of the taxa in the classificatory
scheme and its presence in study samples.
Technique is totally subjective and quite
dependent upon the skill and experience of
the individual who makes the field collec-
tions. Therefore, results of one investigator
are difficult to compare with those of
another, particularly where data are sum-
marized in an index such as that proposed
by Beck (6).
19
-------
BIOLOGICAL METHODS
«c^inr^c^^^
iT^eoeneneOcn^m
i en cd
i?.S
tncoencocncoencO'*Oi'*aimo«r)
— — ' ififtin^oiSr^t^oooooiOQ
- • - • --csjcMe;n
i ••" c^i ig co 01 in 01 <
j.— tpcoeo^'OCMeor^i
lenai — 5iencMr-r^cM(
p?Si
S?S!
• >-• * r- o CM «
|g|§
CQWui?
O5 fcg
Ho1g|
PL) tO O W
Pi <-> W ^
WH<
W
1 w
Om ^
^rn^P^CQv-^Hij
pq«l_jw<:525
CQ tig (j Q * 3 O
HonpL,
-------
MACROINVERTEBRATE SPECIES DIVERSITY
>r--cooi5">P-"CNen*t'iniO'£r--(
>CT>CiCJiOlOOOOOOOOC><
>--iOCMt--j-C'.«CMi'*J-OOCncy)<
ICMCMCMCMCMCMCJCMCMCMCsll
> — CO CTi en p p en <
i en r*- en —« o o « <
tOCM — •* — CMtO-a-iO
CMcomenenTf-'^om
wcocowcoco«cococbc«eocococococScooococ3c5c5to5oco
«™S2!t««2:=*PSM«:*±£S:2&!S~?S?>S«»98r5. aSffi^^JtllP^SaffE:
i lO CM O O •— •*
igssssss
eSi:=:^:^:^^5KNe>;ft%iftK$c$^mKi^aM&£&^3Ro9^$9ecj
^M^co^m^^moo^A^ao^^^^^^^G^^^^^^^^^^m^^^^^^^^^^fO^t^^^^^t^^xm^'n^
* "^ 4E
en .-t •
* i~- co i
"CMcn *f'intoi^.coff>O'-^cs4e'i'j'iotp'~^<
) CM ff> t~~ ^ -H I
-
in 10 "O 'O 10 10 10 >o 10 10 ^S ^o '-o "
) •* CM o CTI r-^ .n •* i
) co co oo co en m 01 c
i en O en o r* P (
i 10 to in irt >o i
'----^
CQ
Si— en CM cj> CM CM i
r^ eg -^ >o ex o t
> ^ ^* 10 co «-i to CM <
> ffi tN trj 55 CM in 01 <
«-"^
*«eii?)n^oi^eoo^
ii'''-'
-i-~'-
' 5P fB M C: = * '
lOOCM-t-^focpcM^-enO^to
'OOiCT> — loocor^co— e?j p —< CM en ^- 10 i
! ^H CM CM CM CM CM CN (
i
iCT>cr>cj}O
ii
vOtoSlCMCMeNCMCMeNCMeNeMCMCNCMeNeNeNCMCM.CMeNeNCM<
i*^CMOO^CMP^^^c^eTi^Coienenc^O(Or^cMcncpt^^cop^ineo-*'i'r~eneN'*>coiOint^eMco
i--'r^tor^.cM!C3i2i^-«tO'«'^*r^cncMicNtDcjo-ojin—HO^"^rOCj)Cr>?)cO'j5t5CTi'T'*^""cnr-.^cMentOCMff>
ScMiioeoeMiOC7i3'ooenoocnCT)'O«—'r^^-^ooioen'-- C7ir^tOi'*'eneneNeNCMeNen*'iOtDt^cn-«cnincop
^^r^cnptocMOTinc^coin^Mirj^M^wMincNMinc^e^^enoi^
~ CM •* r^ p CM tn r^ o en >n co «-• en to Ci « ^f r«^ 01 CM in r-~' o en 10 e»* -H' •* to Oi CM •*' r* O CM uS co —' en tfi er> CM'
^^^^o^oOtoto3r>-r^t^r^aOOTcoopc^eT)cripoop'-">--'--^^cMCMCMenencnp^^^'^f^*'in
enenenencnenenenenen^enencnenenencnencnenen^^Tf-^Th^Tt''^'^Tt''^-^Tt'^-^>4-4-i*''r'*1
jenencncncnencnenenenenenenenenenenenencnen
m S en ifj o
en CM — O P
21
-------
BIOLOGICAL METHODS
to Is- to e'l co -^ i
- - CTi Is- tO if) f~ '
- CM gj O'-|CMc
^'Tii3"1^^^
i-i i-* CM m ""i 3- o •
^i
• •**• ^- ^- «*• «*• ••$• ^j1 10 in 'O 'O in in «") io in >n «o in in
)CMtpO^OCMOCOtO'-pScpoScMtOO«OMCO'Otn
)in»niO'7iioio»o»oioioi
j to to to to to co
) CO 00 *-• CM (
) c?> m eji ^^ O Is- -- i
CMe^lc^
'^w^c^cnt*«^
i -
i eo en en eo co i
i eO eo eO en en eo <
Ol CM "O 00 <
^" tO «O iO I
s en en en en <
i co co ob co co co i
^fi?^01*10^*'1'
enc6ifcn>nStocMi
CM CM r~i — —. £
leoenencneneoeoeneneoeoeoeoi
i o o S »H ^-i —
cnenenenencne
iiooOj-«^'oo>-'^t'is-qenh-o*otoc?»cM>neJ>cM
• «^HtMCMCMeoencn^^'3*io«O'O>otC'CpocM<
ien"tCC7)-H'4-r^oCMm'c6o*'i(*9^^^t^<
ichcnoiooo — "—-^—"CMeMCMCMencnen-
i^r-,^CMCMCMCMCMCMCMCMCMCMCMCMCMCM<
pa
r*>r*>c£o}*™^ri-»->iXQtP(
~ ^•t33o«-ieOkrtcoOen"O<
? «T3 * o * n !
u-j -«j- en CM CM -i <
-..
jeocoeneocnenencnenenencneoenen
coeneoenencneneneneneocni
cooo«
ICMCMCMCMCMCMCMC^JCMCMCMCMCMI
it^^r^oentoccn
fcMCMCMCMCMCMCMCMCM
>CMiOCOCM»t-r»O1>'-i
»*j«cM™cMen'nfeeo
ienCT)tDCMCT>'?'CMOO
CPQOOQ—«>-<^--iCMCMCMCMen<
to CT) CM ^ r~- eft
eO en 3- * 3- g^
CM to r- (
iri 'n *° '
Ol CT) CJ") (
300 •- en to 01 — •
a r^ r» r- i-* co i
i O en to CO — •
1 10 >o to >n yp i
i CM *«*• i~* <
) r« t^> i~* (
) O O O <
^iM^
i-^r^
en-He^
CM CO iT) l-> I
CMCMCMCMCMC^eMCMCMCMCMCMCMCMCMCMCMCMI
i«S?3i?
J oS CM »n co ^i
.'
ICNCMe>IC^CMe>ICMeNC>ICMCNe>ICMCMeMeNCJeNCMCNCMCMCMC^
cnenc-itoenenenencnen<
i *^ CM eo ^t> in to r^> <
22
-------
MACROINVERTEBRATE SPECIES DIVERSITY
co to io en — ocor-§?eocMOC7>cor~-t3in'1
— ~« to r-
• co o 01 in to t
o~)incMO'ji--in^^''
« ^ oo A 3r CO « to co ~ i"ri cci CMi «n cK CM in oi CM -^ ch en to O en t
i co co en
i en CM -^s
icMincftCM'ricncMioaicMinaicMirio^CMioo^CMinoScM'i^cjicM
lirjOCMinO^CM'l^CJiCMJPCnCM
llHcMCMCM'cMC^CMCMCMWCMcVicMCMCMcMCMCMCMCMCMCMMFic^
ICMC>ICMCNCMCMCMCMCMCV|CMCMCMeNCMCMCMCMCMeM
— r-x co O <
to •**• en CM <
CM •-> o CT> <
) CO CT) S eO O CO I
! o ff) en CM
-
^—
.
i -H CM en ^* 'O t
iO-^CMen^-intor-*oooiO"'CMcn'
, — «„—• — — MM«nCMCMCMCM<
icooococococococoeococococoooi
lOto^^tooen^finioeno
! o '*• —• CM co i-~
)totor^ccocM^toocnr^^S«^c^cc^^co2^-tn«--'OOCJ™CM'i*'C6coQ
ir^^.^*ent£encDt---ioc'fCTir-*rCM
«n 10 m tn m i
-
I CM CM CM CM (
Tif-vtO^cvpaor-.incn — oeot£>'
>^CM — ffjr~-to-*cM—• o^i^-(5vcM<— oiooto-»;en — pcotOincncMpcnr-.vOTf-en—«pi
i o ™ CM en *$• in (
c g2!
)iT)iOiO^P\O^*Or^'^-*^'f^cOt
• $$^>$^.^.i«.^.$^>^t.^f.
| « eo 10 i
i O en U> (
» r^ r-^ t» i
JOicjiog — CMcn^-iniSt^-eoa^ocMeoi
;eo»-oco^Dn'CMC)ao(O^'CMOCTii^iO«
> ^-" CM en * in '
> Q Q Q Q o <
SO ^N CM en ^- in to i
co co co eo co en en <
jgiO'—'CMcn^'intpi^-i
23
-------
BIOLOGICAL METHODS
St^
o
~
)"fencocMOC7iincocT)inc;--fcy)--'<
> —t CM cn m <-o i
)Of^tnCMCT)f'---rCMQ^
- CT> O —•i CM CO -f S r- CO
O en —i co 01 <
co CM to — in i
CM CM CM CM I
) CM Ol —' *r
' en tO O
^i^CMOjqp^r^cOinoo^oJ'ncMeocOCC^O^cMooaiTfeor^^uDCMe^r^incoini^rM^incoin—'f:
_., — -,«coOWto^cooicvJCMco^inr^o^c'jincooJi^'^r^CMcoinc'jCTir^iOT^cooio4CMrO'«*'iocoOcotoo-
too^t^ — >ncQoi^no^QOCM^oo^ccCMi^wir>O''*'CT}COeocir--~CM^C'--O'—• ^o-^o—• '£>•—ix£) — i-~CMr^co<
cocor^^tqo^crieocqcM^O«inGn-coeof^cMy3«incn^cqeor^cM'^«inc^
i o O p o i— — »~* j
-f co en i
e-i g co (
; ii*i •**• r-- -f
; ^o co o co
) >n i-o t-- i~~
I'i'cococO'f-iof^-cT)'—"Jr^-^ino
i o ^ c-i eo «r in <-D t^. cn CD •—i en •+• ',0
'~2«>t::r;i>£:i=$
"
oiCT)CT)OOOO-H---— cjoicvicncocoen
I C^l C-J Cl Cl M C-l CM Ol C*> OJ Cs| C^l C~l C<1 M C^J CM
i CJ •*• 1O CO O CM -f
> o o o o —• —< —•
^ 1
O
-
; s sssss
Is;
•a
a>
3
c
'-*-»
o
o
'
COCOCOOOCOCOCOCOCOCOCOCOl
OCMcccninin — o-teoto-**-r-- -fin
coC'~^'O*t'cococn*OTfinr--O">oiin
rfco---£cQ^incocMincdcM
^-r-COCOCOCOCOCOCOCOCOCO
' (O O CO
' Tf Tf f
> CO CO CO
.O^OC-lOcOCOCCCTiC-J'fCC—lUDC-ICOTfO.
1-11'-)-^—H^j-aicoo"nin'f-tc -t-Tj-in^cOCT)-— -«t
oir--C'ir^-<—i ^p i—i to -H to r-ito^H^o—«',£;o(r--
co O —i en -+• to i-
~ -" 10 <-D -
. cn cn — eo i
j CO r^ r- r--
> 'f tO —i m — ,
) cn CM •-£> tn •* co to co to cri
i r- co co eo cri -t- o m -- --c oj co co cn
—-cotocOfMinco —
OJcoiotOcocn — oi-fmr-cot
CiO"—•ojeOTf'tor~.cOcTiO—• '
if m m m m in m m m o ^o ••& (
CM >n en IM to
1 -rf •"+• •* '
; co co co c
• 5 r- 4 CM o cfi r- i
> co CTI — co in Op co <
1 CO CO CO CO CO CO CO I
I Ol C-l Ol CM C-l CM CM (
cn O oj co --.
BCT\ —• OJ, CO ^t- ' . _. ....
^or~.r^r^r^t-.[-~t^r^cocococo<
COCOCOCOCOCOCOCOCOCOCCCOCOCOCOCO'
i o eo in — — t
;§'
i i— co O
i o —* eo
) CT> CTi CTi
) CO CO CO
-*
O^CTin cn (~~ 't' r^ cn
inr^oeor^.»-ito^H^ocMOO'n •«}• eo « cricotoincoc-joaicor^
oocncriCTia^cncocococococccor-.r-r-.
oen^OCTi^H'^-r^Oco^cJiCMin co-H^-i^Oe1^
eo eo eo to ^f ^r tf 10 >n in in to up '-p t-~. r~> i"- co co
Tf- 2 '
ICMCMCMCMCMCMCMCM'
¥?S
CM 01 CM
in co — -jr-oen'^oai
CTiCTiOOO-*—1 — —•
^ ^f in >n in o *n u~i in
3 o m co t
iissll
i co -H 3- r^- o
J CM CM CM CM CM
to' to O^
SSS
• — CM en •* a*s '* r~* i
W
_-»
Sr^^^t
_CM^
jcocnmcpigin^CTi't'CMOjeni
icMincocMypoooin—«r^eo(
i o en t
) r- ; ~
• O (
• co oj i
I CM CM CM CM tM OJ CM OJ C"J OJ CM CM CM OJ CM Ol CM CM Ol CM OJ CM CM CM .
SI
I
. t"- l~«. l>" I
" — ^-—« — —i — -«^-)CMOJO|
cocooococococococococococoeocococoaj
CM r- —; co — Tf r^ o ^ jO ?^ ^
-,.-, "-c^cocoeoeocneocoeocococo
Ol OJ CM oi OJ Ol OJ CM Ol OJ CM C-J Ol CM OJ
-cocTiO — CMen-f'^'-c^co
•eMcnoenoeriQenoeoocMcncMccc^oi-ocM
O CM
(O iO
'
^^
iO — ojeniT)ij3i^.cocrio-H
-'''-'i"''
icM-^cMincnto^coiOQf
ico^^coiO^encMCMenf
CM tD CT) .
jooooSSoooooSooSsS---
ICMCMCMOJCMCMCMCMCMCMCMCMOIOICMC-ICMOJC
u~jio>^eoCT>Q — CMen-*inu5«^coO)p—.CM'
cMCMCMeMCMenenenenTncncncneneoiv ^j-1^-1
COCOCOOOCOCOCOCOCOCOCOCOCOCOCOCOCOCOl
)tnoi'icnoSenot^^'^^oiiOenocoincnoccineo«-«cO|5ircM
•CO"— Lno^cO'Sp'^r^^HincOCM^ocni^-'—iincocM'-OOcoi^-^^in
icor— O"«hi>''c^f~~OICMCMC-JCMC-JCMCMCMCMCMCMCMC^CMOIC^
coSoDco«»oocccococosococccocococooococococo££co£cocococctM
24
-------
MACR01NVERTEBRATE SPECIES EQUITABILITY
TABLE 6. THE DIVERSITY OF SPECIES, ^CHARACTERISTIC OF MacARTHUR'S
MODEL FOR VARIOUS NUMBERS 6p HYPOTHETICAL SPECIES, s'*
s"
1
2
3
4
5
6
7
8
9
10
11
12
13
14
IS
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
d"
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
6.0510
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
d
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
"The data in this table are reproduced, with permission, from Lloyd and Ghelardi, Reference 33.
25
-------
BIOLOGICAL METHODS
TABLE 7. CLASSIFICATION, BY VARIOUS AUTHORS, OF THE TOLERANCE OF
VARIOUS MACROINVERTEBRATE TAXA TO DECOMPOSABLE ORGANIC WASTES;
TOLERANT (T), FACULTATIVE (F), AND INTOLERANT (I)
Macroinvertebrate T
Porifera
Demospongiae
Monaxonida
Spongillidae
Spongi/la fragilis
Bryozoa
F.ctoprocta
Phylactolaemata
Plumatellidae
Plumatella repens
P. princeps var. mucosa 48
P. p. var. mucosa spongiosa
P. p. var. fruticosa 48
P. polymorpha var. repens
Cristatellidae
Cristatella mucedo
Lophopodidae
Lophopodella carteri
Pectinatella magnified
Endoprocta
Urnatelhdae
Urnatella gracilis
Gymnolaemata
Ctcnostomata
Paludicellidae
Paludicella ehrenbergi
Coelenterata
Hydrozoa
Hydroida
Hydridae
Hydra
Clavidae
Cordylophora lacustris
Platyhclminthes
Tutbellaria
Tricladida
Planariidae
Ptonaria
Nematoda
Nematomorpha
Gordioida
Gordiidae
Annelida
Oligochaeta 5,4
Plesiopora
Naididae
Nais
Dero
Ophidonais 60
Stylaria
Tubificidae
Tubifex tubifex 48,42
Tubifex 48,18,60
Limnodrilus hoffmeisteri 48,3,42
L. claparedianus 48
Limnodrilus 48,18,60
Branchiura sowerbyi 42
F
48
51
48
51
48,42
48
42
42
42
48
42
48
48
48
42
48
42
I
42*
48
42
48,42
Macroinvertebrate T
Prosopora
Lumbriculidae 60
Hirudinea
Rhynchobdelhda
Glossiphoniidae
Glossiphonia complanata 48
Helobdella stagnalis 48,42
H. nepheloidea 48
Placobdella montifera 60
P. rugosa
Placobdella
Piscicolidae
Piscicola punctata
Gnathobdellida
Hirudidae
Macrobdella 28
Pharyngobdellida
Erpobdellidae
Erpobdella punctata 48
Dina parva 48
D. microstoma 48
Dina
Mooreobdella microstoma 42
Hydracarina
Arthropoda
Crustacea
Isopoda
Asellidae
Asellus intermedium
Asellus 60
Lirceus
Amphipoda
Talitridae
Hyallela azteca
H. knickerbockeri 48
Gammandae
Gammarus
Crangonyx pseudogracilis
Decapoda
Palaemonidae
Palaemonetes paludosus
P. exilipes 48
Astacidae
Cambarus striatus 25
C. fodiens 1
C. bartoni bartoni
C. b. cavatus
C. conasaugaensis
C. asperimanus
C. latimanus
C. acuminatus
C. hiwassensis
C. extraneus
C. diogenes diogenes 1
C. cryptodytes^
F
48
42
60
42
48
42
42
4
5,3,
4,42
42
42
5,3,
4
1
1
1
I
5
5,4
1
1
1
1
1
1
1
*Numbers refer to references enumerated in the "Literature"
section immediately following this table.
t Albinistic
26
-------
MACROINVERTEBRATE POLLUTION TOLERANCE
TABLE 7. (Continued)
Macroinvertebrate T
C. floridanus
C. carolinus\ 1
C. longulus longirostris
Procambarus raneyi
P. acutus acutus 1
P. paenmsulanus
P. spicu lifer
P. versutus
P. pubescens
P. litosternum
P. enoplosternum
P. angustatus
P. seminolae
P. truculentus% 1
P. advena\ 1
P. pygmaeus% 1
P. pubischelae
P. barbatus
P. howellae
P. troglodytes 1
P. epicyrtus
P. fallax 1
P. chacei
P. lunzi
Orconectes propinquus
O. rusticus
O. juvenilis
O. erichsonianus
Faxonella clypeata
Insecta
Diptera
Chironomidae
Pentaneura inculta
P. carneosa
P. flavifrons 5
P. melanops 44,12
P. americana
Pentaneura
A blabesmyia junta
A. americana
A. illinoense 12
A. mallochi
A. ornata
A. aspera
A. peleensis
A. auriensis
A. rhamphe
A blabesmyia
Procladius culiciformis 60
P. denticulatus 42
Procladius 12
Labrundinia floridana
L. pilosella
L. virescens
Guttipelopia
Conchapelopia
Coelotanypus scapularis
C. concinnus 42
F
1
1
1
1
1
1
1
1
1
1
1
1
1
42
42
1
1
60
60,44
3,4,
42
48,60
44
42
4
42
44,12
4,44,
12
42
42
42
48,60,
44,12
I
1
1
1
1
1
3,4
60,12
44,12
42,44
5
4
4
4
4
42
4
42
4
44
Macroinvertebrate T
Psilotanypus bellus 42
Tanypus stellatus 44,12
T. carinatus
T. punctipennis
Tanypus
Psectrotanypus dyari 44,12
Psectrotanypus
Larsia lurida
Clinotanypus caliginosus
Clinotanypus
Orthocladius obumbratus
Orthocladius
Nanocladius
Psectrocladius niger
P. julia
Psectrocladius
Metriocnemus lundbecki
Cricotopus bicinctus
C. bicinctus group 42
C. exilis
C. exilis group
C. trifasciatus
C. trifasciatus group
C. politus
C. tricinctus
C. absurdus
Cricotopus
Corynoneura taris
C. scutellata
Corynoneura
Thienemanniella xena
Thienemanniella
Trichocladius robacki
Brillia par
Diamesa nivoriunda
Diamesa
Prodiamesa olivacea
Chironomus attenuatus group 5,4,
42,12
C.riparius 18,44,
12
C. riparius group 42
C. tentans
C. tentans-plumosus 60
C.plumosus 48,18,
60
C. plumosus group 42
C. carus 4
C. crassicaudatus 4
C. stigmaterus 4
C. flavus
C. equisitus
C. fulvipilus 4
C. anthracinus
C. paganus
C. staegeri
F
18,60
42
44,12
44,12
48
44
4
I
5
44,12
4
5,48
42
42
44
42
44
42
44
60
60
12
60
60,42,
44,12
4,42
4,44
4
3,4,
44,12
12
12
44,12
12
18,44,
12
44
4
44,12
5,42,
12
4,42
4,44
3,4
4
18,42,
44
60
12
44
12
48,12
12
12
jNot usually inhabitant of open water; are burrowers.
27
-------
BIOLOGICAL METHODS
TABLE 7. (Continued)
Macroinvertebrate T
Chironomus 5
Kiefferullus dux 4
Cryptochironomus fulvus 3,4
C. fulvus group
C. digitatus
C. sp. B (Joh.)
C. blarina
C. psittacinus
C. nais
Cryptochironomus 5
Chactolabis atroviridis
C. ochreatus
Endochironomus nigricans
Stenochironomus macateei
S. hilaris
Stictochironomus devinctus
S. varius
Xenochironomus xenolabis
X. rogersi
X. scapula
Pseudochironomus richardson
Pseudochironomus
Parachironomus abortivus group
P. pectinatellae
Cryptotendipes emorsus
Microtendipes pedellus
Microtendipes
Paratendipes albimanus
Tribelos jucundus
T. fuscicornis
Harnischia collator
H. tenuicaudata
Phaenopsectra
Dicrotendipes modestus
D. neomodestus
D. nervosus
D. incurvus 42
D. fumidus
Glyptotendipes senilis
G. paripes 4
G. meridionalis
G. lobiferus 48,4,
42
G. barbipes 42
G. amplus
Glyptotendipes 12
Polypedilum halterale
P. fallax
P. scalaenum 4
P. illinoense
P. tritum
P. simulans
P nubeculosum
P. vibex
Polypedilum
Tanytarsus neoflavellus
T. gracilentus
T. dissimilis
Rheotanytarsus exiguus 5
Rheotanytarsus
F
60
42
48
42
42
4,42
42
42
42
42
42
42
44
42
42
42
42
5,44,
12
42
3,4,
42,44
42
42
48,44
44,12
42
I
44,12
44,12
12
5
12
60
12
12
44,12
42,44
3,4
4,12
44
42
44,12
44,12
12
44,12
12
44,12
12
42
44
42
42,12
12
42,12
42
12
44,12
4,12
4
44,12
12
12
44
12
18
12
42
3,4
Macromvertebrate j
Cladotanytarsus
Micropsectra dives
M deflecta
M. nigripula
Calopsectra gregarius S
Calopsectra
Stempellina johannseni
Culieidae 4
Culex pipiens 1 8,44
A nopheles punctipennis
Chaobondae
Chaoborus punctipennis
Ccratopogonidae 5,4
Palpcmyia tibialis
Palpomyia
Bezzia glabra 44
Stilobezzia antenalis 44
Tipulidac 4
Tipula caloptera
T. abdominalis
Pseudolimnophila luteipennis
Hexatoma
Eriocera
Psychodidae 4
Psychoda alternata 44
P. schizura 44
Psychoda 42
Telmatoscopus albipunctatus 60
Telmatoscopus
Simuhdac 42
Simulium vittatum
S. venustrum
Simulium
Prosimuhum johannseni
Cnephia pecuarum
Stratiomyndac 4
Stratiomys discalis 44
S. meigeni 44
Odontomyia cincta
Tabanidac 4
Tabanus atratus 18
T. stygius
T. benedictus 44
T. giganteus
T. lineola 44
T. variegatus
Tabanus
Syrphidae 4
Syrphus americanus 44
Eristalis bastardi 18,44
E. aenaus 44
E. brousi 44
Eristalis 44
Empididae
Ephydridac
Brachydeutera argentata 44
Anthomyiidac
Lepidoptera
Pyrahdidae
Trichoptera
Hydropsychidae
Hydropsyche orris
F
42
60
44
60,42
42
60
48,60
42
60
44
18,44
44
44
44
42
42
5,4
42
I
12
42
44,12
44,12
12
44
44
44
44
44
44
44
5,4
44
3
44
44
44
44
44
28
-------
MACROINVERTEBRATE POLLUTION TOLERANCE
TABLE 7. (Continued)
Macromvertebrate T
H. bifida group
H. simulans
H. frisoni
H. incommoda
Hydropsyche
Cheu ma topsyche
Macronemum Carolina
Macronemum
Potamyia flava
Psychomyndae
Psychomyia
Neureclipsis crepuseularis
Polycentropus
Cyrnellus fraternus
Oxyethira
Rhyacophilidae
Rhyacophila
Hydroptihdae
Hydroptila waubesiana
Hydroptila
Ochrotrichia
Agraylea
Leptocendae
Leptocella
A thripsodes
Oecetis
Philopotamidae
Chimarra perigua
Chimarra
Brachycentndae
Brachycentrus
Molannidae
Ephemeroptera
Heptagenudae
Stenonema integrum
S. rubromaculatum
S. fuscum
S. pulchellum
S. ares
S. scitulum
S. femoratum
S. termination
S. interpunctatum
S. i. ohioense
S. i. canadense
S. i. hetero tar sale
S. exiguum
S. smithae
S. proximum
S. tripunctatum
Stenonema
Hexagenndae
ffexagenia limbata
H. billneata
Pentagenia vittgera
Bactidae
Baetis vagans
Callibaetis floridanus 4
Callibaetis
F
42
48
5,18,
3,4,
42
42
42
42
5,4
5,4
32,42
32
32
42
18,42
32
60
18
I
42
42
5,3,4
5,4
5,3,4
42
42
42
5,48,
4
5,4
48
42
5,3,4
42
42
48
42
42
3,4
5,4
4
48
32
32
32
42
32,42
32
32
5,3,4
5,3,4
3
32
32
42
48
42
42
Macromvertebrate T
Caenidae
Caen is dimmuta 4
Caenis
Tricorythidae
Siphlonundae
Isonychia
Plecoptera
Per lid ae
Perlesta placida
A croneuria abnormis
A. arida
Nemouridae
Taeniopteryx mvahs
Allocapnia viviparia
Perlodidae
Isoperla bilineata
Neuroptera
Sisyridae
Climacia areolaris
Megaloptera
Corydalidae
Corydalis cornutus
Sialidae
Sialis inj'urnata
Siatis
Odonata
Calopterygidae
Hetaerina titia
Agnonidae
Argia apicalis
A. translata
Argia
Ischnura verticahs 48
Enallagrna antennatum
E. signatum
Aeshnidae
Anax junius
Gomphidae
Gomphus pallidus
G. plagiatus
G. externm
G. spmiceps
G. vastus
Gomphus
Progomphus
Dromogomphus
Erpetogomphus
Libellulidac
Libellula lydia
Neurocordulia moesta
Plathemis
Macromia
Hemiptera 4
Corixidae
Corixa 1 8
Hesperocorixa 1 8
Gerridae
Gerris 1 8
Belostomatidae
Belostorna 18,3
Hydrometridae
Hydrometra martini 3
F
42
42
18
42
18
42
42
42
42
42
42
42
5,3,4
42
42
5,4
42
42
18
42
42
5,42
42
I
48
42
5,4
3
42
42
42
42
5,3,4
48
4
5,4
48
48
48
48
5,4
4
29
-------
BIOLOGICAL METHODS
TABLE?. (Continued)
Macroinvertebrate T
Coleoptera 4§
Elmidae
Stenelmis crenata
S. sexlineata
S. decorata 50
Dubiraphia
Promoresia
Optioservus
Macronychus glabratus
Anacyronyx variegatus
Microcylloepus pusillus
Gonielmis dietrichi
Hydrophilidae
Berosus 42
Tropisternus natator 1 8
T. lateralis 3
T. dorsalis
Dytiscidae
Laccophilus maculosus 1 8
Gyrinidae
Gyrinus floridanus 3
Dineutus americanus 18
Dineutus
Mollusca
Gastropoda
Mesogastropoda
Valvatidae
Valvata tricarinata
V. piscinalis
V. bicarinata
V. b. var. normalis
Viviparidae
Vivaparus contectoides
V. subpurpurea
Campeloma integrum
C. rufum
C. contectus
C. fasciatus
C. dec/sum
C. subsolidum
Campeloma
Lioplax subcarinatus
Pleuroceridae
Pleurocera acuta
P. elevatum
P. e. lewisi
Pleurocera
Goniobasis livescens
G. virginica 28
Goniobasis
Anculosa
Bulimidae
Bulimus tentaculatus
A mnicola emarginata
A. limosa
Somatogyrus subglobosus
Basommatophora
Physidae
Physa Integra 18,28
P. heterostropha 28
F
42,50
42,50
50
50
42
28
28
28
28
28
28
48,28
60
48,28
28
28
28
48,28
28
28
28
28
28
I
18,50
18
50
50
50
50
48
48,28
48
48
48
48
28
48
5,4
48
48
48
Macroinvertebrate T
P. gyrina
P. acuta
P. fontinalis
P. anatina 28
P. halei 28
P. cubensis 28
P. pumilia 3
Physa 5,4
Aplexa hypnorum
Lymnaeidae
Lymnaea ovata 28
L. peregra
L. caperata
L. humilis
L. obrussa
L. polustris
L. auricularia
L. stagnalis
L. s. appressa
Lymnaea 4
Pseudosuccinea columella
Galba catascopium 28
Fossaria modicella 28
Planorbidae
Planorbis carinatus
P. trivolvis 28
P. panus 28
P. corneus
P. marginatus
Planorbis
Segmentina armigera 28
Helisoma anceps
H, trivolvis
Helisoma 3,4
Gyraulus arcticus
Gyraulus
Ancylidae
Ancylus lacustris
A. fluviatilis
Ferrissia fusca
F. tarda
F. rivularis
Ferrissia 5,3,4
Bivalvia
Eulamellibranchia
Margaritiferidae
Margaritifera margaritifera
Unionidae
Unio complanata 28
U. gibbosus 28
U. batavus
U. pictorum
U. tumidus
Lampsilis luteola
L. alata
L. anadontoides
L. gracilis
L. parvus
Lampsilis
Quadrula pustulosa
F
28
28
28
28
28
28
28
28
28
28
28
42
28
28
28
28
28
28
28
28
28
28
28
42
28
28
28
28
28
48
48,42
28,42
I
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
48
§Except riffle bettles
30
-------
MACROINVERTEBRATE POLLUTION TOLERANCE
TABLE 7. (Continued)
Macroinvertebrate T
Q. undulata
Q. rubiginosa
Q. lachrymosa
Q. plicate
Truncilla donadformis
T. elegans
Tritigonia tuberculata
Symphynota costata
Strophitus edentulus
Anodonta grandis
A. imbecillis
A. mutabilis
Alasmodonta costata
Proptera alata
Leptodea fragilis
Amblema undulata
Lasmigona complanata
Obliquaria reflexa
Heterodonta
Corbiculidae
Corbicula manilensis
Sphaeriidae 5,4
Sphaeriu mnotatum 28
S. corneum
S. rhomboideum
S. stria tinum
S. s. var. corpulentum
F
28
28
28
28
28
28
28
28,42
48,28
28
28
28
28
28
28
48
I
48
48
28
42
42
60
42
Macroinvertebrate T
S. s. var. ttlycashense
S. sulcatum
S. stamineum
S. moenanum
S. vivicolum
S. solidulum
Sphaerium
Musculium securis
M. transversum 48,28
M. truncation 48
Musculium 60
Pisidium abditum 28
P. fossarinum
P. pauperculum crystalense
P. amnicum
P. casertanum
P. compressum 48
P. fallax
P. henslorvanum
P. idahoensis 28
P. complanatum 48,28
P. subtruncatum
Pisidium
Dresisseniidae
Mytilopsis leucophaeatus
Mactridae
Rangia cuneata
F
48
28
48,28
28
28
42
28
28
28
48,28
28
28
28
28
48,28
28
48
28
28
I
28
28
28
28
28
31
-------
BIOLOGICAL METHODS
6.0 LITERATURE CITED
1. Allen, K. R. 1951. The Horokiwi Stream - a study of a trout population. New Zealand Marine Dept. Fish Bull. #10. 231 pp.
2. American Public Health Association. 1971. Standard methods for the examination of water and wastewater, 13th edition.
American Public Health Association, New York. 874 pp.
3. Beck, W. M., Jr., Biological parameters in streams. Florida State Board of Health, Gainesville. 13 pp. (Unpublished)
4. Beck, W. M., Jr., Indicator organism classification. Florida State Board of Health, Gainesville. Mimeo. Kept. 8 pp. (Unpublished)
5. Beck, W. M., Jr. 1954. Studies in stream pollution biology: I. A simplified ecological classification of organisms. J. Fia. Acad.
Sciences, 17(4):211-227.
6. Beck, W. J., Jr. 1955. Suggested method for reporting biotic data. Sewage Ind. Wastes, 27(10):! 193-1197.
7. Brmkhurst, R. O. 1963. Taxanomical studies on the Tubificidae (Annelida, Ohgochaeta). Intematl. Rev. Hydrobiol.,
(Systematische Beihefle) 2: 1-89.
8. Brinkhurst, R. O., K. E. Chua, and K. Batoosingh. 1969. Modifications in sampling procedures as applied to studies on the bacteria
and tubificid oligochaetes inhabiting aquatic sediments. J. Fish. Res. Bd. Canada, 26(10):2581-1593.
9. Buchanan, T. J., and W. P. Sommers. 1969. Techniques of water investigations of the United States. Geological Survey. Chapter
8A, Discharge Measurements at Gaging Stations, Book 3, Applications of Hydraulics.
10. Carpenter, J. H. 1969. A new killing and fixing technique for small animals. Trans. Amer. Microscop.Soc. 88-450-45 1.
ll.Chutter, F. M. and R. G. Noble. 1966. The reliability of a method of sampling stream invertebrates. Arch. Hydrobiol.,
62(1):95-103.
12. Curry, L. L. 1962. A survey of environmental requirements for the midge (Diptera: Tendipedidae). In: Biological Problems in
Water Pollution. Transactions of Third Seminar, C. M. Tarzwell, ed., USDHEW, PHS, Robert A. Taft Sanitary Engineering
Center, Cincinnati.
13. Dickson, K. L., J. Cairns, Jr., and J. C. Arnold. 1971. An evaluation of the use of a basket-type artificial substrate for sampling
macromvertebrate organisms. Trans. Am. Fish. Soc. 100(3):553-559.
14. Elliott, J. M. 1970. Methods of sampling invertebrate drift in running water. Ann. Limnol. 6(2): 133-159.
15. Elliot, J. M. 1971. Some methods for the statistical analysis of samples of benthic invertebrates. Freshwater Biological Association,
U.K. Ferry House, Ambleside, Westmorland, England. 144 pp.
16. Flannagan, J. F. 1970. Efficiencies of various grabs and corers in sampling freshwater benthos. J. Fish. Res. Bd. Canada,
27(10):169M700.
17. Fullner, R. W. 1971. A comparison of macroinvertebrates collected by basket and modified multiple-plate samplers. JWPCF,
43(3):494-499.
18. Gaufin, A. R., and C. M. Tarzwell. 1956. Aquatic macromvertebrate communities as indicators of organic pollution in Lytle Creek.
Sewage & Ind. Wastes, 28(7)-906-924.
19. Hamilton, A. L., W. Burton, and J. Flannagan. 1970. A multiple corer for sampling profundal benthos. J. Pish Res, Bd. Canada,
27(10):1867-1869.
20. Hassler, W. W., and L. B. Tebo, Jr. 1958. Fish management investigations on trout streams. Fed. Aid Proj. F4-R Comp. Report.
Fish. Div., N.C. Wildl. Resour. Comm., Raleigh, N.C.
21. Henson, E. B. 1965. A cage sampler for collecting aquatic fauna. Turtox News, 43(12):298-299.
22. Henson, E. B. 1958. Description of a bottom fauna concentrating bag. Turtox News, 36(1): 34-36.
23. Hester, F. E., and J. S. Dendy. 1962. A multiple-plate sampler for aquatic macroinvertebrates. Trans. Amer. Fish. Soc.
91(4): 420-421.
24. Hilsenhoff, W. L. 1969. An artificial substrate device for sampling benthic stream invertebrates. Limnol. Oceanogr. 14(3):465-471.
25. Hubbs, H. H., Jr. 1965. List of Georgia crayfishes with their probable reactions to wastes (lethal chemicals not taken into
consideration). Mimeo. Rept. 1 p. (Unpublished)
26. Hynes, H. B. N. 1970. The ecology of running waters. Liverpool Univ. Press.
27. Ingram, W. M., and A. F. Bartsch. 1960. Graphic expression of biological data in water pollution reports. JWPCF, 32(3):297-310.
28. Ingram, W. M. 1957. Use and value of biological indicators of pollution: Fresh water clams and snails. In: Biological Problems in
Water Pollution, C. M. Tarzwell, ed. USDHEW, PHS, R. A. Taft Sanitary Engineering Center, Cincinnati.
29. Kittrell, F. W. 1969. A practical guide to water quality studies of streams. USDI, FWPCA, Washington, D. C.
30. Kolkwitz, R., and M. Marsson, 1909. Ecology of animal saprobia. Int. Rev. of Hydrobiology and Hydrogeography, 2.126-152.
Translation In: Biology of Water Pollution, USDI, FWPCA, Cincinnati. 1967.
31. Lewis, P. A., W. T. Mason, Jr., and C. I. Weber. A comparison of Peterson, Ekman, and Ponar grab samples from river substrates. U.
S. Environmental Protection Agency, Cincinnati. In preparation.
32. Lewis, P. A. 1969. Mayflies of the gemisStenonema as indicators of water quality. Presented at: Seventeenth Annual Meeting of
the Mid. Benth. Soc., Kentucky Dam Village State Park, Gilbertsville, Ky. 10 pp.
32
-------
MACROINVERTEBRATE REFERENCES
33. Lloyd, M., and R. J. Ghclardi. 1964. A table for calculating the "equitabihty" component of species diversity J. Amm. Ecol.
33:217-225.
34. Lloyd, M., J. H. Zar, and J. R. Karr. 1968. On the calculation of information — theoretical measures of diversity. Am. Mid. Nat.
79(2):257-272.
35. Macan, T. T. 1963. Freshwater ecology. Camelot Press Ltd., London and Southampton, England. 338 pp.
36. MacArthur, R. H. 1957. On the relative abundance of bird species. Proc. Nat. Acad. Sci., Washington, 43:293-295.
37. Mackenthun, K. M. 1969. The practice of water pollution biology. USDI, FWPCA, Washington, D. C.
38. Mangelsdorf, D. C. 1967. Salinity measurements in estuaries. Estuaries. Publication $83, American Association for the
Advancement of Science, pp. 71-79,
39. Margalef, D. R. 1957. Information theory in ecology. General systems 3:36-71. (English translation by W. Hall.)
40. Mason, W. T., Jr., J. B. Anderson, and G. E. Morrison. 1967. A limestone-filled artificial substrate sampler-float unit for collecting
macromvertebrates from large streams. Prog. Fish-Cult. 29(2):74.
41. Mason, W. T., Jr. and P. A. Lewis. 1970. Rearing devices for stream insect larvae. Prog. Fish.-Cult. 32(l):61-62.
42. Mason, W. T., Jr., P. A. Lewis, and J. B. Anderson. 1971. Macromvertebrate collections and water quality monitoring in the Ohio
River Basin, 1963-1967. Cooperative Report, Office Tech. Programs. Ohio Basin Region and Analytical Quality Control
Laboratory, WQO, USEPA, NERC-Cincinnati.
43. Mason, W. T., Jr., C. I. Weber, P. A. Lewis, and E. C. Julian. 1973. Factors affecting the performance of basket and multiplate
macromvertebrate samplers. Freshwater Biol. (U.K.) 3:In press.
44. Paine, G. H., Jr. and A. R. Gaufm. 1956. Aquatic diptera as indicators of pollution in a midwestern stream. Ohio J. Sci. 56(51:291.
45. Paterson, C. G., and C H. Fernando. 1971. A comparison of a simple corer and an Ekman grab for sampling shallow-water benthos.
J. Fish. Res. Bd. Canada, 28(3):365-368.
46. Patrick, R. 1950. Biological measure of stream conditions. Sewage Ind. Wastes, 22(7):926-938.
47. Pennak, R. W. 1953. Freshwater invertebrates of the United States. Ronald Press Co., New York. 769 pp.
48. Richardson, R. E. 1928. The bottom fauna of the middle Illinois River, 1913-1925: Its distribution, abundance, valuation, and
index value in the study of stream pollution. Bull. 111. Nat. Hist. Surv. XVII(XII):387-475.
49. Scott, D. C. 1958. Biological balance in streams. Sewage Ind. Wastes, 30:1169-1173.
50. Sinclair, R. M. 1964. Water quality requirements of the family Elmidae (Coleoptera). Tenn. Stream Poll. Cont. Bd., Dept. Public
Health. Nashville.
51. Tebo, L. B., Jr. 1955. Bottom fauna of a shallow euthrophic lake, Lizard Lake, Pocahontas County, Iowa. Amer. Mid. Nat.
54(1):89-103.
52. U.S. Environmental Protection Agency. Data collected from the Coosa, Chattahoochee, Escambia and Savannah Rivers by the
Aquatic Biology Branch, Region IV, Surveillance and Analysis Division, Athens, Georgia. (Unpublished)
53. U.S. Environmental Protection Agency. Data collected from the vicinity of Big Cypress Swamp jetport, south Florida, by the
Aquatic Biology Branch, Region IV, Surveillance and Analysis Division, Athens, Georgia. (Unpublished)
54. U.S. Public Health Service. 1963. Data collected from the Ohio, Wabash and Allegheny Rivers by the Biology Section, National
Water Quality Network, Cincinnati, Ohio. (Unpublished)
55. Waters, T. F. 1962. Diurnal periodicity in the drift of stream invertebrates. Ecology, 43(2):316-320.
56. Waters, T. F. 1969. Invertebrate drift-ecology and significance to stream fishes. In: Symposium Salmon and Trout in Streams, T. G.
Northcote, ed. H. R. MacMillan Lectures in Fisheries. Univ. British Columbia, Vancouver, pp. 121-134.
57. Welch, P. S. 1948. Limnological methods. The Blakiston Co., Philadelphia, Pa. 381 pp.
58. Wentworth, C. K. 1922. A scale of grade and class terms for elastic sediments. J. Geology, 30:377-392.
59. Wilhm, J. L. 1970. Range of diversity index in benthic macroinvertebrate populations. JWPCF, 42(5):R221-R224.
60. Wimmer, G. R., and E. W. Surber. 1952. Bottom fauna studies in pollution surveys and interpretation of the data. Presented at:
Fourteenth Mid. Wildl. Conf., Des Moines, Iowa. 13 pp.
61. Wurtz, C. B. 1955. Stream biota and stream pollution. Sewage Ind. Wastes, 27(11): 1270-1278.
7.0 TAXONOMIC BIBLIOGRAPHY
7.1 Coleoptera
Brown, H. P. 1970. A key to the dryopoid genera of the new world (Coleoptera, Dryoidea). Ent. News, 81:171-175.
Hinton, H. E. 1940. New genera and species of Elmidae (Coleoptera). Trans. Royal Entomol. Soc. 91(3):65-104.
Leech, H. B. 1948. Contributions toward a knowledge of the insect fauna of Lower California. No. 11, Coleoptera:Haliplidae,
Dytiscidae, Byrinidae, Hydrophilidae, Limnebiidae. Proc. Calif. Acad. Sci. 24:375-484, 2 pi.
33
-------
BIOLOGICAL METHODS
Sanderson, M. W. 1938. A monographic revision of the North American species ofStenelmis (Dryopidae:Coleoptera). Kansas Univ.
Dept. Eng. 25(22):635-717.
Sanderson, M. W. 1953. A revision of the nearctic genera of Elmidae (Coleoptera). J. Kansas Ent. Soc. 26(4): 148-163.
Sanderson, M. W. 1954. A revision of the nearctic genera of Elmidae (Coleoptera). J. Kansas Ent. Soc. 27(1): 1-13.
Sinclair, R. M. 1964. Water quality requirements of the family Elmidae (Coleoptera). Tenn. Stream Pollution Control Board, Nashville.
14pp.
Wilson, C. B. 1923. Water beetles in relation to pondfish culture, with life histories of those found in fishponds at Fairport, Iowa. Bull.
U.S. Bur. Fish. XXXIX:231-345.
Wooldridge, D. P. 1967. The aquatic Hydrophilidae of Illinois. 111. State Acad. Sci. 60(4):422-431.
Young, F. N. 1954. The water beetles of Florida. Univ Fla. Press. Biol. Science Series, V(l): 1-238.
7.2 Crustacea
Bousfield, E. L. 1958. Fresh-water amphipod crustaceans of glaciated North America. Canad. Field Nat. 72:55-113.
Crocker, D. W. 1957. The crayfishes of New York State. N.Y. State Mus. and Sci. Service Bull. 355. pp. 13-89.
Hobbs, H. H., Jr. 1942. The crayfishes of Florida. Univ. Fla. Biol. Sci. Series, III(2):1-179.
Hobbs, H. H., Jr. and C. W. Hart, Jr. 1959. The freshwater decapod crustaceans of the Appalachicola drainage system in Florida,
southein Alabama, and Georgia. Bull. Fla. State Mus., BioL Sci. 4(5):145-191.
Holsinger, J. R. 1967. Systematics, speciation, and distribution of the subterranean amphipod Stygonectes. US Nat. Mus. Bull.
32:1-827.
Francois, D. D. 1959. The crayfishes of New Jersey. Ohio J. Sci. 59(2):108-127.
Ortmann, A. E. 1931. Crayfishes of the southern Appalachians and Cumberland Plateau. Ann. Carnegie Mus. 20:61-160.
Rhoades, R. 1944. Crayfishes of Kentucky, with notes on variations, distributions, and descriptions of new species and subspecies.
Amer. Midi. Nat. 31:111-149.
Riegel, J. A. 1959. The systematics and distribution of crayfishes in California. Calif. Fish Game, 45(1):29-50.
Stansbery, D. H. 1962. A revised checkbst of the crayfish of Ohio (Decapoda:Astacidae). Ohio State Univ. Dept. Zool. Ent.,
Columbus. 5 pp.
Turner, C. L. 1926. The crayfish of Ohio. Ohio Biol. Surv. Bull. No. 13, 3(3):145-195.
Williams, A. B. 1954. Speciation and distribution of the crayfishes of the Ozark Plateau and Ouachita Provinces. Kans. Univ. Sci. Bull.
36(12):803-918.
Williams, A. B. 1965. Marine decapod crustaceans of the Carolmas. USDI, Fish Wildl. Serv., Bur. Comm. Fish. 65(l):l-298.
7.3 Diptera
Bath, J. L., and L. D. Anderson. 1969. Larvae of seventeen species of chironomid midges from southern California. J. Kans. Entomol.
Soc. 42(2):154-176.
Beck, E. C., and W. M. Beck, Jr. 1959. A checklist of the Chironomidae (Insecta) of Florida (Diptera:Chironomidae). Bull. Fla. State
Mus. 4(3):85-96.
Beck, E. C., and W. M. Beck, Jr. 1969. The Chiionomidae of Florida. Fla. Ent. 52(1): 1-11.
Beck, W. M., Jr. and E. C. Beck. 1964. New Chironomidae from Florida. Fla. Ent. 47(3):201-207.
Beck, W. M., Jr. and E. C. Beck. 1966. Chironomidae (Diptera) of Florida. Part I. Pentaneurini (Tanypodinae). Bull. Fla. State Mus.
10(8):305-379.
Beck, W. M., Jr. and E. C. Beck. 1969. Chironomidae (Diptera) of Florida. Part HI. The Harnischia complex. Bull. Fla. State Mus.
13(5):277-313.
Cook, E. F. 1956. The nearctic Chaoborinae (Diptera:Culicidae). Minn. Agr. Exp. Sta. Tech. Bull. 218:1-102.
Curran, H. C. 1965. The families and genera of North American Diptera. H. TrippCo., Woodhaven, N.Y. 515 pp.
Curry, L. L. 1958. Larvae and pupae of the species of Cryptochironomus (Diptera) in Michigan. ASLO, 3(4):427-442.
Curry, L. L. 1962. A key for the larval forms of aquatic midges (Tendipedidae:Diptera) found in Michigan. NIH Rept. No. 2, Contract
No. RG-6429. Dept. Biol. Central Mich. Univ., Mt. Pleasant. 149 pp.
Darby, R. E. 1962. Midges associated with California rice fields, with special reference to their ecology (Diptera:Chironomidae).
Hilgardia, 32(1): 1-206.
Dcndy, J. S., and J. E. Sublette. 1959. The Chironomidae (=Tendipedidae:Diptera) of Alabama, with Descriptions of Six New Species.
Ann. Ent. Soc. Amer. 52(5):506-519.
Frommer, S. 1967. Review of the anatomy of adult Chironomidae. Calif. Mosquito Contr. Assoc., Tech. Series Bull. No. 1. 40 pp.
Hamilton, A. L., O. A. Saether, and D. R. Oliver. 1969. A classification of the nearctic Chironomidae. Fish. Research Bd. Can., Tech.
Rept. 129.42pp.
34
-------
MACROINVERTEBRATE REFERENCES
Haubei, U. A. 1947. The Tendipedinae of Iowa. Amer. Mid. Nat. 38(2):456-465.
*Johannsen, O. A. 1934. Aquatic Diptera. Part I. Nemocera, exclusive of Chironomidae and Ceratopogonidae. Mem. Cornell Univ. Agr.
Exp. Sta. 164:1-70.
*Johannsen, O. A. 1935. Aquatic Diptera. Part II. Orthorrhapha-Brachycera and Cyclorrhapha. Mem. Cornell Univ. Agr. Exp. Sta.
177:1-62.
*Johannsen, O. A. 1937. Aquatic Diptera. Part III. Chironomidae: subfamilies Tanypodinae, Diamesinae, and Orthocladiinae. Mem.
Cornell Univ. Agr. Exp. Sta. 205:1-84.
*Johannsen, O. A. 1934-37. "Aquatic Diptera" may be purchased from Entomological Reprint Specialists, East Lansing, Mich.
48823.
Johannsen, O. A. 1937a. Aquatic Diptera. Part IV. Chironomidae: subfamily Chironominae. Mem. Cornell Univ. Agr. Exp. Sta.
210:1-56.
Johannsen, O. A. 1964. Revision of the North American species of the genus Pentaneura (Tendipedidae:Diptera). J. New York Ent.
Soc. 54.
Johannsen, O. A. H. K. Townes, F. R. Shaw, and E. Fisher. 1952. Guide to the insects of Connecticut. Part VI. The Diptera of true
flies. Bull. Conn. Geol. and Nat. Hist. Surv. 80:1-255.
Malloch, J. R. 1915. The Chironomidae or midges of Illinois, with particular reference to the species occurring in the Illinois River.
Bull. 111. State Lab. Nat. Hist. 10:273-543.
Mason, W. T., Jr. 1968. An introduction to the identification of Chironomid larvae. Division of Pollution Surveillance, FWPCA, USDI,
Cincinnati. 90 pp. (Revised 1973).
Roback, S. S. 1953. Savannah River tendipedid larvae. Acad. Nat. Sci., Philadelphia, 105:91-132.
Roback, S. S. 1957. The immature tendipedids of the Philadelphia area. Acad. Nat. Sci., Philadelphia Mono. No. 9. 148 pp.
Roback, S. S. 1963. The genus Xenochironomus (Diptera:Tendipedidae) Kieffer, taxonomy and immature stages. Trans. Amer. Ent.
Soc. 88:235-245.
Roback, S. S. 1969. The immature stages of the genus Tanypus Meigen. Trans. Amer. Ent. Soc. 94:407-428.
Saeter, O. A. 1970. Nearctic and palaearctic Chaoborus (Diptera:Chaoboridae). Bull. Fish. Res. Bd. Can., No. 174. 57 pp.
Stone, A., C. W. Sabrasky, W. W. Wirth, R. H. Foote, and J. R. Coulson, eds. A catalog of the Diptera of America north of Mexico.
USDA Handbook No. 276.
Stone, A., and E. R. Snoddy. 1969. The blackflies of Alabama (Diptera:Simuliidae). Auburn Univ. Agr. Exp. Sta. Bull. No. 390. 93 pp.
Sublette, J. E. 1960. Chironomid midges of California. Part I. Chironominae, exclusive of Tanytarsini (Calopsectrini). Proc. U.S. Natl.
Museum, 112:197-226.
Sublette, J. E. 1964. Chironomid midges of California. Part II. Tanypodinae, Podonominae, and Diamesinae. Proc. U.S. Natl. Museum,
115(3481):85-136.
Sublette, J. E. 1964. Chironomidae (Diptera) of Louisiana. Part I. Systematics and immature stages of some lentic chironomids of west
central Louisiana. Tulane Studies Zool. 11(4): 109-150.
Thomsen, L. C. 1937. Aquatic Diptera. Part V. Ceratopogonidae. Mem. Cornell Univ. Ga. Exp. Sta. 210:57-80.
Townes, H. K. 1945. The nearctic species of Tendipedini. Amer. Mid. Nat. 34(1):1-206.
Wood, D. M., B. I. Peterson, D. M. Davies, and H. Gyorhos. 1963. The black flies (Diptera:Simuliidae) of Ontario. Part II. Larval
identification, with descriptions and illustrations. Proc. Ent Soc. Ontario, 93:99-129.
7.4 Ephemeroptera
Berner, L. 1950. The mayflies of Florida. Univ. Fla. Press, Gainesville. 267 pp.
Berner, L. 1959. A tabular summary of the biology of North American mayfly nymphs (Ephemeroptera). Bull. Fla. State Mus.
4(1):1-5 8.
Burks, B. D. 1953. The mayflies, or Ephemeroptera, of Illinois. Bull. 111. Nat. Hist Surv. 26:1-216.
Edmunds, G. F., Jr., R. K. Allen, and W. L. Peters. 1963. An annotated key to the nymphs of the families and subfamilies of mayflies
(Ephemeroptera). Univ. Utah Biol. Series XII(l):l-55.
Leonard, J. W., and F. A. Leonard. 1962. Mayflies of Michigan trout streams. Cranbrook Institute Sci., Michigan. 139 pp.
Needham, J. G., J. R. Traver, and Yin-Chi Hsu. 1935. The biology of mayflies. Entomological Reprint Specialists, Inc., East Lansing,
Mich.
Needham, J., and H. E. Murphy. 1924. Neotropical mayflies. Bull. Lloyd Libr. No. 24, Entom. Series No. 4, pp. 5-79, Cincinnati.
Spieth, H. T. 1947. Taxonomic studies on the Ephemeroptera: Part IV. The genus Stenonema. Ann. Entomol. Soc. Am. XL.-1-162.
35
-------
BIOLOGICAL METHODS
7.5 Hemiptera
Brooks, G. T. 1951. A revision of the genusAnisops (Notonectidae, Hemiptera). Univ. Kans. Sci. Bull. XXXIV(8):301-519.
Cummings, C. 1933. The giant water bugs (Belostomatidae, Hemiptera). Univ. Kans. Sci. Bull. XXI(2): 197-219.
Hungerford, H. B. 1933. The genusNotonecta of the world. Univ. Kans. Sci. Bull. XXI(9):5-195.
Hungerford, H. B. 1948. The Corixidae of the western hemisphere. Kans. Univ. Sci. Bull. 32:1-827.
Hungerford, H. B., and R. Matsuda. 1960. Keys to subfamilies, tribes, genera, and subgenera of the Gerridae of the world. Univ. Kans.
Sci. Bull. XLI(l):3-23.
Schaefer, K. F., andW. A. Drew. 1968. The aquatic and semiaquatic Hemiptera of Oklahoma. Proc. Okla. Acad. Sci. 47:125-134.
Schaefer, K. F., and W. A. Drew. 1969. The aquatic and semiaquatic Hemiptera (Belostomatidae and Saldidae) of Oklahoma. Proc.
Okla. Acad. Sci. 48:79-83.
7.6 Hirudinea
Meyer, M. C., and J. P. Moore. 1954. Notes on Canadian leeches (Hirudinea), with the description of a new species. Wasmann J.
Biology, 12(l):63-95.
Sawyer, R. T. 1967. The leeches of Louisiana, with notes on some North American species (Hirudinea, Annelida). Proc. La. Acad. Sci.
XXX: 32-38.
7.7 Hydracarina
Crowell, R. Mi 1960. The taxonomy, distribution, and developmental stages of Ohio water mites. Bull. Ohio Biol. Surv. 1(2): 1-77.
7.8 Lepidoptera
Lange, W. H. 1956. A generic revision of the aquatic moths of North America (Lepidoptera:Pyralidae Nymphulinae). Wasmann J.
Biology, 14(1):59-114.
7.9 Megaloptera
Baker, J. R., and H. H. Neunzig. 1968. The egg masses, eggs, and first instar larvae of eastern North American Corydalidae. Ann. Ent.
Soc. Amer. 61(5):1181-1187.
Davis, K. C. 1903. Aquatic insects in New York State. Part 7, Sialididae of North and South America. N. Y. State Mus. Bull.
68:442-486.
Needham, J. G., and C. Betten. 1901. Aquatic insects in the Adirondacks. N. Y. State Mus. Bull. 47:383-612.
Ross, H. H., and T. H. Prison. 1937. Studies of nearctic aquatic insects. Bull. 111. Nat. Hist. Surv. 21:57-78.
7.10 Mollusca
Amos, M. H. 1966. Commercial clams of the North American Pacific Coast. US Bur. Comm. Fish. Circular, 237:1-18.
Baker, F. C. 1928. The fresh-water mollusca of Wisconsin. Wise. Acad. Sci. Bull. Part I. Gastropoda, 70:1-505. Part II. Pelecypoda,
70:1-495.
Branson, B. A. (No date). Checklist and distribution of Kentucky aquatic gastropods. Ky. Dept. Fish and Wildl., Res. Fish. Bull. No.
54. pp. 1-20.
Call, R. E. 1899. Mollusca of Indiana. Ind. Dept. Geol. Nat. Res., 24th Ann. Rept. pp. 337-535.
Clarke, A. H., Jr. and C. O. Berg. 1959. The freshwater mussels of central New York with an illustrated key to the species of
northeastern North America. Mem. Cornell Univ. Agr. Exp. Sta. 367:1-79.
Clench, W. J., and R. D. Turner. 1956. Freshwater mollusks of Alabama, Georgia, and Florida from the Escambia to the Suwannee
River. Fla. State Mus. Bull. l(3):97-239.
Goodrich, C. 1932. The Mollusca of Michigan. Univ. Mich. Handbook Series No. 5, pp. 1-120.
Heard, W. H., and J. Burch. 1966. Key to the genera of freshwater pelecypods of Michigan. Mich. Mus. Zool., Univ. Mich., Circ. No. 4
Ann Arbor.
Ingram, W. M. 1948. The larger freshwater clams of California, Oregon, and Washington. J. Ent. Zool. 40(4):72-92.
Leonard, A. B. 1959. Gastropods in Kansas. Kans. Univ. Dept. Zool., State Biol. Surv. 224 pp.
Murry, H. D., and A. B. Leonard. 1962. Unionid mussels in Kansas. Kans. Univ. Dept. Zool., State Biol. Surv. No. 28. 104 pp.
Ortmann, A. E. 1919. A monograph of the naiades of Pennsylvania. Part III, Systematic account of the Genera and species. Carnegie
Inst. Mus. 8(1): 1-378.
36
-------
MACROINVERTEBRATE REFERENCES
Robertson, I. C. S., and C. L. Blakeslee. 1948. The Mollusca of the Niagara frontier region. Bull. Buffalo Soc. Nat. Sci. 19(3):1-191.
Sinclair, R. M., and B. G. Isom. 1963. Further studies on the introduced asiatic clam Corbicula in Tennessee. Tenn. Stream Poll.
Control Bd., Tenn. Dept. Public Health. 75 pp.
Stein, C. B. 1962. Key to the fresh-water mussels (family Unionidae) of western Lake Erie. Ohio State Univ., Stone Lab. 5 pp.
Taft, C. 1961. The shell-bearing land snails of Ohio. Bull. Ohio Biol. Surv. 1(3):1-108.
Thompson, F. G. 1968. The aquatic snails of the family Hydrobiidae of peninsular Florida. Univ. Fla. Press. 268 pp.
7.11 Odonata
Byers, C. F. 1930. A contribution to the knowledge of Florida Odonata. Univ. Fla. Biol. Sci. Ser. 1(1): 1-327.
Gorman, P. 1927. Guide to the insects of Connecticut. Part V, The Odonata or dragonflies of Connecticut. Conn. Geol. Nat. Hist. Surv.
39:1-331.
Kennedy, C. H. 1915. Notes on the life history and ecology of the dragonflies (Odonata) of Washington and Oregon. Proc. US Nat.
Mus. 49:259-345.
Kennedy, C. H. 1917. Notes on the life history and ecology of the dragonflies (Odonata) of central California and Nevada. Proc. US
Nat. Mus. 52:483-635.
Needham, J. G., and M. J. Westfall, Jr. 1954. Dragonflies of North America. Univ. Calif. Press, Berkeley and Los Angeles. 615 pp.
Walker, E. M. 1958. The Odonata of Canada and Alaska. Vol. 1 and 2. Univ. Toronto Press, Toronto.
Williamson, E. B. 1899. The dragonflies of Indiana. Ind. Dept. Geol. Nat. Res., 24th Annual Rept. pp. 229-333.
Wright, M., and A. Peterson. 1944. A key to the genera of Anisopterous dragonfly nymphs of the United States and Canada (Odonata,
suborder Anisoptera). Ohio J. Sci. 44:151-166.
7.12 Oligochaeta
Brinkhurst, R. O. .1964. Studies on the North American aquatic Oligochaeta. Part I. Proc. Acad. Nat. Sci., Philadelphia,
116(5):195-230.
Brinkhurst, R. O. 1965. Studies on the North American aquatic Oligochaeta. Part II. Proc. Acad. Sci., Philadelphia, 117(4): 117-172.
Brinkhuist, R. O. 1969. Oligochaeta. In: Keys to Water Quality Indicative Organisms (Southeast United States). FWPCA, Athens, Ga.
Galloway, T. W. 1911. The common fresh-water Oligochaeta of the United States. Trans. Amer. Micros. Soc. 30:285-317.
7.13 Plecoptera
Claasen, P. W. 1931. Plecoptera nymphs of America (north of Mexico). Published as Volume III of the Thomas Say Foundation, Ent.
Soc. Amer. Charles C. Thomas, Springfield, 111.
Claasen, P. W. 1940. A catalogue of the Plecoptera of the world. Mem. Cornell Univ. Agr. Exp. Sta. 232-:l-235.
Prison, T. H. 1935. The stoneflies, or Plecoptera, of Illinois. Bull. 111. Nat. Hist. Surv. 20:281-371.
Prison, T. H. 1942. Studies of North American Plecoptera. Bull. 111. Nat. Hist. Surv. 22:235-355.
Gaufin, A. R., A. V. Nebeker, and J. Sessions. 1966. The stoneflies (Piecoptera) of Utah. Univ. Utah Biol. Series, 14(1): 1-93.
Harden,?. H., and C. E. Mickel. 1952. The stoneflies of Minnesota (Plecoptera). Univ. Minn. Agr. Exp. Sta. 201:1-84.
Hilsenhoff, W. L. 1970. Key to genera of Wisconsin Plecoptera (stoneflies) nymphs, Ephemeroptera (mayfly) nymphs, Trichoptera
(caddisfly) larvae. Res. Rept. No. 67, Wis. Dept. Nat. Res., Madison.
Jewett, S. G., Jr. 1955. Notes and descnptions concerning western stoneflies (Plecoptera). Wasmann J. Biol. 91(l):l-543.
Jewett, S. G., Jr. 1959. The stoneflies (Plecoptera) of the Pacific Northwest. Ore. State Coll. Press. 95 pp.
Jewett, S. G., Jr. 1960. The stoneflies (Plecoptera) of California. Bull. Calif. Insect Surv. 6(6):125-177.
Nebeker, A. V., and A. R. Gaufin. 1966. The Capnia Columbiana complex of North America (Capniidae:Plecoptera). Trans. Amer.
Ent. Soc. 91:467-487.
Needham, J. G., and P. W. Classen. 1925. A monograph of the Plecoptera or stoneflies of America north of Mexico. Published as
Volume II of the Thomas Say Foundation, Ent. Soc. Am. Charles C. Thomas, Springfield, 111.
Ricker, W. E., and H. H. Ross. 1968. North American species of Taeniopteryx (Plecpptera:Insecta). J. Fish. Res. Bd. Can.
25:1423-1439.
7.14 Trichoptera
Betten, C. 1934. The caddisflies or Trichoptera of New York State. N. Y. State Mus. Bull. 292:1-576.
Edwards, S. W. 1966. An annotated list of the Trichoptera of middle and west Tennessee. J. Tenn. Acad. Sci. 41:116-127.
Flint, O. S. 1960. Taxonomy and biology of nearctic limnephelid larvae (Trichoptera), with special reference to species in eastern
United States. Entomologica Americana, 40:1-120.
37
-------
BIOLOGICAL METHODS
Flint, O. S. 1961. The immature stages of the Aictopsychinae occurring in eastern North America (Trichoptera:Hydropsychidae). Ann.
Ent.Soc.Amer. 54(1):5-11.
Flint, O. S. 1962. Larvae of the caddisfly Genus Rhyacophila in eastern North America (Trichoptera:Rhyacophilidae). Proc. US Natl.
Mus. 113:465-493.
Flint, O. S. 1963. Studies of neotropical caddisflies. Part I, Rhyacophilidae and Glossosomatidae (Trichoptera). Proc. US Natl. Mus.
114:453-478.
Flint, O. S. 1964. The caddisflies (Trichoptera) of Puerto Rico. Univ. Puerto Rico Agr. Exp. Sta. Tech. Paper No. 40. 80 pp.
Flint, O. S. 1964a. Notes on some nearctic Psychomyiidae with special reference to their larvae (Trichoptera). Proc. US Nat. Mus. Publ.
No. 3491, 115:467-481.
Hickin, N. E. 1968. Caddis larvae, larvae of the British Trichoptera. Associated Univ. Presses, Inc., Cranbury, N. J. pp. 1-480.
Leonard, J. W., and F. A. Leonard. 1949. An annotated list of Michigan Tiichoptera. Mich. Mus. Zool. Occ. Paper No. 522. pp. 1-35.
Lloyd, J. T. 1921. North American caddisfly larvae. Bull. Lloyd Libr. No. 21, Entom. Series No. 1. pp. 16-119.
Ross, H. H. 1941. Descriptions and records of North American Trichoptera. Trans. Amer. Entom. Soc. 67:35-129.
Ross, H. H. 1944. The caddisflies, or Trichoptera, of Illinois. Bull. 111. Nat. Hist. Surv. 23:1-326.
Wiggins, G. B. 1960. A preliminary systematic study of the North American larvae of the caddisflies, family Phryganeidae
(Trichoptera). Can. J. Zool. 38:1153-1170.
Wiggins, G. B. 1962. A new subfamily of phiyganeid caddisflies from western North America (Trichoptera:Phryganeidae). Can. J.
Zool. 40:879-891.
Wiggins, G. B. 1963. Larvae and pupae of two North American limnephilid caddisfly genera (Trichoptera:Limnephilidae). Bull.
Brooklyn Ent. Soc. 57(4): 103-112.
Wiggins, G. B. 1965. Additions and revisions to the genera of North American caddisflies of the family Brachycentridae with special
reference to the larval stages (Trichoptera). Can. Ent. 97:1089-1106.
Wiggins, G. B., and N. H. Anderson. 1968. Contributions to the systematics of the caddisfly genera Pseudostenophylax andPhilocasca
with special reference to the immature stages (Trichoptera:Limnephilidae). Can. J. Zool. 46:61-75.
Yamamoto, T., and G. B. Wiggins. 1964. A comparative study of the North American species in the caddisfly genus Mystacides
(Trichoptera:Leptoceridae). Can. J. Zool. 42:1105-1126.
7.15 Marine
Hartman, O. 1961. Polychaetous annelids from California. Allan Hancock Pacific Expeditions. 25:1-226.
Hartman, O., and D. J. Reish. 1950. The marine annelids of Oregon. Ore. State Coll. Press, Corvallis, Ore.
Miner, R. W. 1950. Field book of seashore life. G. P. Putnam's Sons, New York.
Smith, R. I. 1964. Keys to the marine invertebrates of the Woods Hole region. Woods Hole Marine Biol. Lab., Cont. No. 11.
Smith, R., F. A. Pitelha, D. P. Abbott, and F. M. Weesner. 1967. Intertidal invertebrates of the central California coast. Univ. Calif.
Press. Berkeley.
38
-------
FISH
-------
FISH
Page
1.0 INTRODUCTION 1
2.0 SAMPLE COLLECTION 1
2.1 General Considerations 1
2.2 Active Sampling Techniques 2
2.2.1 Seines 2
2.2.2 Trawls 3
2.2.3 Electrofishing 5
2.2.4 Chemical Fishing 5
2.2.5 Hook and Line 6
2.3 Passive Sampling Techniques 7
2.3.1 Entanglement Nets 7
2.3.2 Entrapment Devices 7
3.0 SAMPLE PRESERVATION 10
4.0 SAMPLE ANALYSIS 1
4.1 Data Recording 1
4.2 Identification 1
4.3 Age, Growth, and Condition 1
5.0 SPECIAL TECHNIQUES 1
5.1 Flesh Tainting 1
5.2 Fish Kill Investigations 12
6.0 REFERENCES 13
7.0 BIBLIOGRAPHY 14
7.1 General References 14
7.2 Electrofishing 14
7.3 Chemical Fishing 15
7.4 Fish Identification 16
7.5 Fish Kills 18
-------
FISH
1.0 INTRODUCTION
To the public, the condition of the fishery is
the most meaningful index of water quality.
Fish occupy the upper levels of the aquatic food
web and are directly and indirectly affected by
chemical and physical changes in the environ-
ment. Water quality conditions that significantly
affect the lower levels of the food web will
affect the abundance, species composition, and
condition of the fish population. In some cases,
however, the fish are more sensitive to the pol-
lutant(s) than are the lower animals and plants;
they may be adversely affected even when the
lower levels of the food web are relatively
unharmed.
Many species of fish have stringent dissolved
oxygen and temperature requirements and are
intolerant of chemical and physical contami-
nants resulting from agricultural, industrial, and
mining operations. The discharge of moderate
amounts of degradable organic wastes may in-
crease the nutrient levels in the habitat and
result in an increase in the standing crop of fish.
This increase, however, usually occurs in, only
one or a few species and results in an imbalance
in the population. The effects of toxic wastes
may range from the elimination of all fish to a
slight reduction in reproductive capacity,
growth, or resistance to disease and parasitism.
Massive and complete fish kills are dramatic
signs of abrupt, adverse changes in environ-
mental conditions. Fish, however, can repopu-
late an area rapidly if the niche is not destroyed,
and the cause of the kill may be difficult to
detect by examination of the fish community
after it has recovered from the effects of the
pollutant. Chronic pollution, on the other hand,
is more selective in its effects and exerts its in-
fluence over a long period of time and causes
recognizable changes in the species composition
and relative abundance of the fish.
The principal characteristics of interest in
field studies of fish populations include: (1)
species present, (2) relative and absolute abun-
dance of each species, (3) size distribution, (4)
growth rate, (5) condition, (6) success of repro-
duction, (7) incidence of disease and parasitism,
and (8) palatability. Observations of fish
behavior can also be valuable in detecting en-
vironmental problems; e.g. ventilation rates,
position in the current, and erratic movement.
Fish may also be collected for use in laboratory
bioassays, for tissue analyses to measure the con-
centrations of metals and pesticides, and for
histopathologic examination.
Fisheries data have some serious limitations.
Even if the species composition of the fish in a
specific area were known before and after the
discharge of pollutants, the real significance of
changes in the catch could not be properly
interpreted unless the life histories of the
affected species were understood, especially the
spawning, seasonal migration, temperature
gradient and stream-flow responses, diurnal
movements, habitat preferences, and activity
patterns. Without this knowledge, fish presence
or absence cannot be correlated with water
quality. Of course, any existing data on the
water quality requirements of fish would be of
great value in interpreting field data.
Fisheries data have been found useful in en-
forcement cases and in long-term water quality
monitoring (Tebo, 1965). Fishery surveys are
costly, however, and a careful and exhaustive
search should be conducted for existing informa-
tion on the fisheries of the area in question
before initiating a field study. State and Federal
fishery agencies and universities are potential
sources of information which, if available, may
save time and expense. Most states require a col-
lecting permit, and the state fishery agency must
usually be contacted before fish can be taken in
a field study. If data are not available and a field
study must be conducted, other Federal and
State agencies will often join the survey and
pool their resources because they have an
interest in the data and have found that a joint
effort is more economical and efficient.
2.0 SAMPLE COLLECTION
2.1 General Considerations
Fish can be collected actively or passively.
Active sampling methods include the use of
seines, trawls, electrofishing, chemicals, and
-------
BIOLOGICAL METHODS
hook and line. Passive methods involve entangle-
ment (gill nets and trammel nets) and entrap-
ment (hoop nets, traps, etc.) devices. The chief
limitations in obtaining qualitative and quantita-
tive data on a fish population are gear selectivity
and the mobility and rapid recruitment of the
fish. Gear selectivity refers to the greater success
of a particular type of gear in collecting certain
species, or sizes of fish, or both. All sampling
gear is selective to some extent. Two factors that
affect gear selectivity are: (1) the habitat or
portion of habitat (niches) sampled and (2) the
actual efficiency of the gear. A further problem
is that the efficiency of gear for a particular
species in one area does not necessarily apply to
the same species in another area. Even if non-
selective gear could be developed, the problem
of adequately sampling an area is difficult
because of the nonrandom distribution of fish
populations.
Temporal changes in the relative abundance of
a single species can be assessed under a given set
of conditions if that species is readily taken with
a particular kind of gear, but the data are not
likely to reflect the true abundance of the
species occurring in nature.
Passive collection methods are very selective
and do not obtain representative samples of the
total population. Active methods are less selec-
tive and more efficient, but usually require more
equipment and manpower. Although the choice
of method depends on the objectives of the
particular fishery investigation, active methods
are generally preferred.
2.2 Active Sampling Techniques
2.2.1 Seines
A haul seine is essentially a strip of strong
netting hung between a stout cork or float line at
the top and a strong, heavily-weighted lead line
at the bottom (Figure 1). The wings of the net
are often of larger mesh than the middle
portion, and the wings may taper so that they
are shallower on the ends. The center portion of
the net may be formed into a bag to aid in con-
fining the fish. At the ends of the wings, the
cork and lead lines are often fastened to a short
stout pole or brail. The hauling lines are then
attached to the top and bottom of the brail by a
short bridle.
Figure 1. The common haul seine. (From Dumont and Sundstrom, 1961.)
2
-------
FISH SAMPLING
Deepwater seining usually requires a boat.
One end of one of the hauling lines is anchored
on shore and the boat pays out the line until it
reaches the end. The boat then changes direction
and lays out the net parallel to the beach. When
all of the net is in the water, the boat brings the
end of the second hauling line ashore. The net is
then beached rapidly.
The straight seines (without bags), such as the
common-sense minnow seines, can usually be
handled quite easily by two people. The method
of paying out the seine and bringing it in is
similar to the haul seine, except the straight
seine is generally used in shallow water where
one member of the party can wade offshore
with lines.
Bag and straight seines vary considerably in
dimensions and mesh size. The length varies
from 3 to 70 meters, and mesh size and net
width vary with the size of the fish and the
depth of the water to be sampled.
Nylon seines are recommended because of the
ease of maintenance. Cotton seines should be
treated with a fungicide to prevent decay.
Seining is not effective in deep water because
the fish can escape over the floats and under the
lead line. Nor is it effective in areas that have
snags and sunken debris. Although the results
are expressed as number of fish captured per
unit area seined, quantitative seining is very
difficult. The method is more useful in deter-
mining the variety rather than the number of
fish inhabiting the water.
2.2.2 Trawls
Trawls are specialized submarine seines used
in large, open-water areas of reservoirs, lakes,
large rivers, estuaries, and in the oceans. They
may be of considerable size and are towed by
boats at speeds sufficient to overtake and en-
close the fish. Three basic types are: (1) the
beam trawl used to capture bottom fish (Figure
2), (2) the otter trawl used to capture near-
bottom and bottom fish (Figure 3), and (3) the
mid-water trawl used to collect schooling fish at
various depths.
The beam trawls have a rigid opening and are
difficult to operate from a small boat. Otter
trawls have vanes or "otter boards," which are
attached to the forward end of each wing and
are used to keep the mouth of the net open
while it is being towed. The otter boards are
approximately rectangular and usually made of
wood, with steel strapping. The lower edge is
shod with a steel runner to protect the wood
when the otter slides along the bottom. The
leading edge of the otter is rounded near the
bottom to aid in riding over obstructions.
The towing bridle or warp is attached to the
board by four heavy chains or short heavy metal
rods. The two forward rods are shorter so that,
when towed, the board sheers to the outside and
down. Thus, the two otters sheer in opposite
directions and keep the mouth of the trawl open
and on the bottom. Floats or corks along the
headrope keep the net from sagging, and the
weights on the lead-line keep the net on the
bottom. The entrapped fish are funneled back
into the bag of the trawl (cod end).
A popular small trawl consists of a 16- to
20-foot (5- to 6-m) headrope, semiballoon
modified shrimp (otter) trawl with 3/4-inch (1.9
cm) bar mesh in the wings and cod end. A 1/4-
inch (0.6 cm) bar mesh liner may be installed in
the cod end if smaller fish are desired. This small
trawl uses otter boards, the dimensions of which,
in inches, are approximately 24 to 30 (61 to
76 cm) X 12 to 18 (30 to 46 cm) X 3/4 to 1-1/4
inches (0.9 to 3.2 cm), and the trawl can be
operated out of a medium-sized boat.
The midwater trawl resembles an otter trawl
with modified boards and vanes for controlling
the trawling depth. Such trawls are cumbersome
for freshwater and inshore areas.
Trawling data are usually expressed in weight
of catch per unit of time.
The use of trawls requires experienced person-
nel. Boats deploying large trawls must be
equipped with power winches and large motors.
Also, trawls can not be used effectively if the
bottom is irregular or harbors snags or other
debris. Trawls are best used to gain information
on a particular species of fish rather than to esti-
mate the overall fish population. See Rounsefell
and Everhart (1953), Massman, Ladd and
McCutcheon (1952) and Trent (1967) for
further information on trawls.
-------
BIOLOGICAL METHODS
Figure 2. Thebeam trawl. (From Dumont and Sundstrom, 1961.)
Figure 3. The otter trawl. (From Dumont and Sundstrom, 1961.)
4
-------
FISH SAMPLING
2.2.3 Electro fish ing
Electrofishing is a sampling method in which
alternating (AC) or direct (DC) electrical current
is applied to water that has a resistance different
from that of fish. The difference in the resist-
ance of the water and the fish to pulsating DC
stimulates the swimming muscles for short
periods of time, causing the fish to orient
towards and be attracted to the positive elec-
trode. An electrical field of sufficient potential
to immobilize the fish is present near the posi-
tive electrode.
The electrofishing unit may consist of a
110-volt, 60-cycle, heavy-duty generator, an
electrical control section consisting of a
modified, commercially-sold, variable-voltage
pulsator, and electrodes. The electrical control
section permits the selection of any AC voltage
between 50 to 700 and any DC voltage between
25 to 350 and permits control of the size of the
electrical field required by various types of
water. The alternating current serves as a stand-
by for the direct current and is used in cases of
extremely low water resistance.
Decisions on the use of AC, DC, pulsed DC, or
alternate polarity forms of electricity and the
selection of the electrode shape, electrode
spacing, amount of voltage, and proper equip-
ment depend on the resistance, temperature, and
total dissolved solids of the water. Light-weight
conductivity meters are recommended for field
use. Lennon (1959) provides a comprehensive
table and describes the system or combination
of systems that worked best for him.
Rollefson (1958, 1961) thoroughly tested and
evaluated AC, DC, and pulsating DC, and dis-
cussed basic electrofishing principles, wave
forms, voltage -- current relationships, electrode
types and designs, and differences between AC
and DC and their effects in hard and soft waters.
He concluded that pulsating DC was best in
terms of power economy and fishing ability
when correctly used. Haskell and Adelman
(1955) found that slowly pulsating DC worked
best in leading fish to the anode. Pratt (1951)
also found the DC shocker to be more effective
than the AC shocker.
Fisher (1950) found that brackish water re-
quires much more power (amps) than fresh-
water, even though the voltage drops may be
identical. Seehorn (1968) recommended the use
of an electrolyte (salt blocks) when sampling in
some soft waters to produce a large enough field
with the electric shocker. Frankenberger
(1960), Larimore, Durham and Bennett (1950)
and Latta and Meyers (1961) have excellent
papers on boat shockers. Frankenberger and
Latta and Meyers used a DC shocker and Lari-
more et al. an AC shocker. Stubbs (1966),
used DC or pulsed DC, and has his (aluminum)
boat wired as the negative pole. In his paper, he
also shows the design and gives safety pre-
cautions that emphasize the use of the treadle
switch or "deadman switch" in case a worker
falls overboard.
Backpack shockers that are quite useful for
small, wadeable streams have been described by
Blair (1958) and McCrimmon and Berst(1963),
as has a backpack shocker for use by one man
(Seehorn, 1968). Most of these papers give dia-
grams for wiring and parts needed.
There are descriptions of electric trawls (AC)
(Haskell, Geduliz, and Snolk, 1955, and Loeg,
1955); electric seines (Funk, 1947; Holton,
1954; and Larimore, 1961); and a fly-rod elec-
trofishing device employing alternating polarity
current (Lennon, 1961).
The user must decide which design is most
adaptable to his particular needs. Before
deciding which design to use, the biologist
should carefully review the literature. The crew
should wear rubber boots and electrician's gloves
and adhere strictly to safety precautions.
Night sampling was found to be much more
effective than day sampling. Break sampling
efforts into time units so that unit effort data
are available for comparison purposes.
2.2.4 Chemical fishing
Chemicals used in fish sampling include
rotenone, toxaphene, cresol, copper sulfate, and
sodium cyanide. Rotenone has generally been
the most acceptable because of its high degrad-
ability; freedom from such problems as precipi-
tation (as with copper sulfate) and persistant
toxicity (as with toxaphene); and relative safety
for the user.
Rotenone, obtained from the derris root
(Deguelia elliptica, East Indies) and cube root
-------
BIOLOGICAL METHODS
(Lonchocarpus nicour, South America), has
been used extensively in fisheries work through-
out the United States and Canada since 1934
(Krumholz, 1948). Although toxic to man and
warm-blooded animals (132 mg/kg), rotenone
has not been considered hazardous in the con-
centrations used for fish eradication (0.025 to
0.050 ppm active ingredient) (Hooper, 1960),
and has been employed in waters used for
bathing and in some instances in drinking water
supplies (Cohen et al., 1960, 1961). Adding acti-
vated carbon not only effectively removes
rotenone, but it also removes the solvents,
odors, and emulsifiers present in all commercial
rotenone formulations.
Rotenone obtained as an emulsion containing
approximately 5 percent active ingredient, is
recommended because of the ease of handling. It
is a relatively fast-acting toxicant. In most cases,
the fish will die within 1 to 2 hours after expo-
sure. Rotenone decomposes rapidly in most
lakes and ponds and is quickly dispersed in
streams. At summer water temperatures,
toxicity lasts 24 hours or less. Detoxification is
brought about by five principal factors: dis-
solved oxygen, light, alkalinity, heat, and turbid-
ity. Of these, light and oxygen are the most im-
portant factors.
Although the toxicity threshold for rotenone
differs slighly among fish species, it has not been
widely used as a selective toxicant. It has, how-
ever, been used at a concentration of 0.1 ppm of
the 5 percent emulsion to control the gizzard
shad (Bowers, 1955).
Chemical sampling is usually employed on a
spot basis, e.g. a short reach of river or an em-
bayment of a lake. A concentration of 0.5 ppm
active ingredient will provide good recovery of
most species of fish in acidic or slightly alkaline
waters. If bullheads and carp are suspected of
being present, however, a concentration of 0.7
ppm active ingredient is recommended. If the
water is turbid and strongly alkaline, and resist-
ant species (i.e., carp and bullheads) are present,
use 1-2 ppm. To obtain a rapid kill, local con-
centrations of 2 ppm can be used; however, cau-
tion is advised because rotenone dispersed into
peripheral water areas may kill fish as long as the
concentration is above 0.1 ppm.
A very efficient method of applying emulsion
products is to pump the emulsion from a drum
mounted in the bottom of a boat. The emulsion
is suctioned by a venturi pump (Amundson boat
bailer) clamped on the outboard motor. The
flow can be metered by a valve at the drum hose
connection. This method gives good dispersion
of the chemical and greater boat handling safety,
since the heavy drum can be mounted in the
bottom of a boat rather than above the gun-
wales, as required for gravity flow.
Spraying equipment needed to apply a
rotenone emulsion efficiently varies according to
the size of the job. For small areas of not more
than a few acres, a portable hand pump ordinar-
ily used for garden spraying or fire fighting is
sufficient. The same size pump is also ideal for
sampling the population of a small area.
A power-driven pump is recommended for a
large-scale or long-term sampling program. A
detailed description of spraying equipment can
be found in Mackenthun and Ingram (1967).
The capacity of the pump need not be greater
than 200 liters per minute. Generally speaking, a
1-1/2 H.P. engine is adequate.
The power application of rotenone emulsives
requires a pressure nozzle, or a spray boom, or
both, and sufficient plumbing and hose to con-
nect with the pump. The suction line of the
pump should be split by a "Y" to attach two
intake lines. One line is used to supply the
toxicant from the drum, and the other line, to
supply water from the lake or embayment. The
valves are adjusted so the water and toxicant are
drawn into the pumping system in the desired
proportion and mixed.
In sampling a stream, select a 30- to 100-
meter reach depending on the depth and width
of the stream; measure the depth of the section
selected, calculate the area, and determine the
amount of chemical required. Block off the area
upstream and downstream with seines. To
detoxify the area downstream from the rote-
none, use potassium permanganate. Care must
be exercised, however, because potassium
permanganate is toxic to fish at about 3 ppm.
2.2.5 Hook and line
Fish collection by hook and line can be as
simple as using a hand-held rod or trolling baited
-------
FISH SAMPLING
hooks or other lures, or it may take the form of
long trot lines or set lines with many baited
hooks. Generally speaking, the hook and line
method is not acceptable for conducting a
fishery survey, because it is too highly selective
in the size and species captured and the catch
per unit of effort is too low. Although it can
only be used as a supporting technique, it may
be the best method to obtain a few adult speci-
mens for heavy metal analysis, etc., where
sampling with other gear is impossible.
2.3 Passive Sampling Techniques
2.3.1 Entanglement nets
Gill and trammel nets are used extensively to
sample fish populations in estuaries, lakes, reser-
voirs, and larger rivers.
A gill net is usually set as an upright fence of
netting and has a uniform mesh size. Fish
attempt to swim through the net and are caught
in the mesh (Figure 4). Because the size of the
mesh determines the species and size of the fish
to be caught, gill nets are considered selective.
The most versatile type is an experimental gill
net consisting of five different mesh size sec-
tions. Gill nets can be set at the surface, in mid-
water, or at the bottom, and they can be
operated as stationary or movable gear. Gill nets
made of multifilament or monofilament nylon
are recommended. Multifilament nets cost less
and are easier to use, but monofilament nets
generally capture more fish. The floats and leads
usually supplied with the nets can cause net en-
tanglement. To reduce this problem, replace the
individual floats with a float line made with a
core of expanded foam and use a lead-core
leadline instead of individual lead weights.
The trammel net (Figure 5) has a layer of
large mesh netting on each side of loosely-hung,
smaller gill netting. Small fish are captured in
the gill netting and large fish are captured in a
"bag" of the gill netting that is formed as the
smaller-mesh gill netting is pushed through an
opening in the larger-mesh netting. Trammel
nets are not used as extensively as are gill nets
in sampling fish.
Results for both nets are expressed as the
number or weight of fish taken per length of net
per day.
Stationary gill and trammel nets are fished at
right angles to suspected fish movements and at
any depth from the surface to the bottom. They
may be held in place by poles or anchors. The
anchoring method must hold the net in position
against any unexpected water movements such
as, runoff, tides, or seiches.
Drifting gill or trammel nets are also set and
fished the same as stationary gear, except that
they are not held in place but are allowed to
drift with the currents. This method requires
constant surveillance when fishing. They are
generally set for a short period of time, and if
currents are too great, stationary gear is used.
The use of gill nets in the estuaries may
present special problems, and consideration
should be given to tidal currents, predation, and
optimum fishing time, and to anchors, floats,
and line.
The gunnels of any boat used in a net fishing
operation should be free of rivets, cleats, etc., on
which the net can catch.
2.3.2 Entrapment devices
With entrapment devices, the fish enter an en-
closed area (which may be baited) through a
series of one or more funnels and cannot escape.
The hoop net and trap net are the most com-
mon types of entrapment devices used in fishery
surveys. These traps are small enough to be de-
ployed from a small open boat and are relatively
simple to set. They are held in place with
anchors or poles and are used in water deep
enough to cover the nets, or to a depth up to 4
meters.
The hoop net (Figure 6) is constructed by
covering hoops or frames with netting. It has
one or more internal funnels and does not have
wings or a lead. The first two sections can be
made square to prevent the net from rolling in
the currents.
The fyke net (Figure 7) is a hoop net with
wings, or a lead, or both attached to the first
frame. The second and third frames can each
hold funnel throats, which prevent fish from
escaping as they enter each section. The oppo-
site (closed) end of the net may be tied with a
slip cord to facilitate fish removal.
-------
BIOLOGICAL METHODS
Figure 4. Gill net. (From Dumont and Sundstrom, 1961.)
Figure 5. Trammel net. (From Dumont and Sundstrom, 1961.)
8
-------
FISH SAMPLING
Figure 6. Hoop net. (From Dumont and Sundstrom, 1961.)
Figure 7. Fyke net. (From Dumont and Sundstrom, 1961.)
9
-------
BIOLOGICAL METHODS
Hoop nets are fished in rivers and other waters
where fish move in predictable directions,
whereas the fyke net is used when fish move-
ment is more random such as in lakes, impound-
ments, and estuaries. Hoop and fyke nets can be
obtained with hoops from 2 to 6 feet (0.6 to 1.8
meters) in diameter, but any net over 4 feet (1.2
meters) in diameter is too large to be used in a
fishery survey.
Trap nets use the same principle as hoop nets
for capturing fish, but their construction is more
complex. Floats and weights instead of hoops
give the net its shape. The devices are expensive,
require considerable experience, and are fished
in waters deep enough to cover them.
One of the most simple types is the minnow
trap, usually made of wire mesh or glass, with a
single inverted funnel. The bait is suspended in a
porous bag. A modification of this type is the
slat trap; this employs long wooden slats in a
cylindrical trap, and when baited with cheese
bait, cottonseed cake, etc., it is used very suc-
cessfully in sampling catfish in large rivers
(Figure 8).
Most fish can be sampled by setting trap and
hoop nets of varying mesh sizes in a variety of
habitats. Hoop and trap nets are made of cotton
or nylon, but nets made of nylon have a longer
life and are lighter when wet. Protect cotton
nets from decay by treatment. Catch is recorded
as numbers or weight per unit of effort, usually
fish per net day.
3.0 SAMPLE PRESERVATION
Preserve fish in the field in 10 percent forma-
lin. Add 3 grams borax and 50 ml glycerin per
liter of formalin. Specimens larger than 7.5 cm
should be slit on the side at least one-third of
the length of the body cavity to permit the
preservative to bathe the internal organs. Slit the
fish on the right side, because the left side is
generally used for measurements, scale sampling,
and photographic records.
Fixation may take from a few hours with
small specimens to a week or more with large
forms. After fixation, the fish may be washed in
running water or by several changes of water for
at least 24 hours and placed in 40 percent
isopropyl alcohol. One change of alcohol is
necessary to remove the last traces of formalin.
Thereafter, they may be permanently preserved
in the 40 percent isopropyl alcohol.
Figure 8. Slat trap. (From Dumont and Sundstrom, 1961.)
10
-------
FISH IDENTIFICATION
4.0 SAMPLE ANALYSIS
4.1 Data Recording
The sample records should include collection
number, name of water body, date, locality, and
other pertinent information associated with the
sample. Make adequate field notes for each col-
lection. Write with water-proof ink and paper to
ensure a permanent record. Place the label inside
the container with the specimens and have the
label bear the same number or designation as the
field notes, including the locality, date, and col-
lector's name. Place a numbered tag on the out-
side of the container to make it easier to find a
particular collection. Place any detailed observa-
tions about a collection on the field data sheet.
Record fishery catch data in standard units such
as number or weight per area or unit of effort.
Use the metric system for length and weight
measurements.
4.2 Identification
Proper identification of fishes to species is im-
portant in analysis of the data for water quality
interpretation. A list of regional and national
references for fish identification is located at the
end of this chapter. Assistance in confirming
questionable identification is available from
State, Federal, and university fishery scientists.
4.3 Age, Growth, and Condition
Changes in water quality can be detected by
studying the growth rate of fishes. Basic
methods used to determine the age and growth
offish include:
• Study of fish length-frequencies, and
• Study of seasonal ring formations in hard
bony parts such as scales and bones.
The length-frequency method of age deter-
mination depends on the fact that fish size varies
with age. When the number of fish per length
interval is plotted on graph paper, peaks gen-
erally appear for each age group. This method
works best for young fish.
The seasonal ring-formation method depends
on the fact that fish are cold-blooded animals
and the rates of their body processes are affected
by the temperature of the water in which they
live. Growth is rapid during the warm season and
slows greatly or stops in winter. This seasonal
change in growth rate of fishes is often reflected
in zones or bands (annual rings) in hard bony
structures, such as scales, otoliths (ear stone),
and vertebrae. The scales of fish may indicate
exposure to adverse conditions such as injury,
poor food supply, disease, and possibly water
quality.
Note the general well being of the fish — do
they appear emaciated? diseased from fungus?
have open sores, ulcers, or fin rot? parasitized?
Check the gill condition, also. Healthy fish will
be active when handled, reasonably plump, and
not diseased. Dissect a few specimens and check
the internal organs for disease or parasites. The
stomachs can be checked at this time to deter-
mine if the fish are actively feeding.
5.0 SPECIAL TECHNIQUES
5.1 Flesh Tainting
Sublethal concentrations of chemicals, such as
phenols, benzene, oil, 2, 4-D, are often respon-
sible for imparting an unpleasant taste to fish
flesh, even when present in very low concentra-
tions. Flesh tainting is nearly as detrimental to
the fisheries as a complete kill.
A method has been developed (Thomas,
1969) in which untainted fish are placed in cages
upstream and downstream from suspected waste
sources. This procedure will successfully relate
the unacceptable flavor produced in native fish
if exposed to a particular waste source.
To ensure uniform taste quality before expo-
sure, all fish are held in pollution-free water for
a 10-day period. After this period, a minimum
of three fish are cleaned and frozen with dry ice
as control fish. Test fish are then transferred to
the test sites, and a minimum of three fish are
placed in each portable cage. The cages are sus-
pended at a depth of 0.6 meter for 48 to 96
hours.
After exposure, the fish are dressed, frozen on
dry ice, and stored to 0°F until tested. The con-
trol and exposed samples are shipped to a fish-
tasting panel, such as is available at the food
science and technology departments in many of
11
-------
BIOLOGICAL METHODS
the major universities, and treated as follows: (a)
The fish are washed, wrapped in aluminum foil,
placed on slotted, broiler-type pans, and cooked
in a gas oven at 400°F for 23 to 45 minutes
depending on the size of the fish, (b) Each
sample is boned and the flesh is flaked and
mixed to ensure a uniform sample, (c) The
samples are served in coded cups to judges.
Known and coded references or control samples
are included in each test. The judges score the
flavor and desirability of each sample on a point
scale. The tasting agency will establish a point
on the scale designated as the acceptable and
desirable level.
5.2 Fish Kill Investigations
Fish mortalities result from a variety of
causes, some natural and some man-induced.
Natural fish kills are caused by phenomena such
as acute temperature change, storms, ice and
snow cover, decomposition of natural organic
materials, salinity changes, spawning mortali-
ties, and bacterial, parasitic, and viral epidemics.
Man-induced fish kills may be attributed to
municipal or industrial wastes, agricultural
activities, and water manipulations. Winter kills
occur in northern areas where ice on shallow
lakes and ponds becomes covered with snow,
and the resulting opaqueness stops photo-
synthesis. The algae and vascular plants die
because of insufficient light, and their decompo-
sition results in oxygen depletion. Oxygen deple-
tion and extreme pH variation can be caused
also by the respiration or decay of algae and
higher plants during summer months in very
warm weather. Kills resulting from such causes
are often associated with a series of cloudy days
that follow a period of hot, dry, sunny days.
Occasionally fish may be killed by toxins
released from certain species of living or de-
caying algae that reached high population
densities because of the increased fertility re-
sulting from organic pollution.
Temperature changes, either natural or the
result of a heated water discharge, will often
result in fish kills. Long periods of very warm,
dry weather may raise water temperatures above
lethal levels for particular species. A wind-
induced seiche may be hazardous to certain
temperature-sensitive, deep-lake, cold-water fish,
or fish of shallow coastal waters.
Disease, a dense infestation of parasites, or
natural death of weakened fish at spawning time
must always be suspected as contributory
factors in fish mortalities.
Explosions, abrupt water level fluctuations,
hurricanes, extreme turbidity or siltation, dis-
charges of toxic chemicals, certain insecticides,
algicides, and herbicides may each cause fish
kills.
Recent investigations in Tennessee have
shown that the leaking of small amounts of very
toxic chemicals from spent pesticide-containing
barrels used as floats for piers and diving rafts in
lakes and reservoirs can produce extensive fish
kills.
Fish die of old age, but the number so af-
flicted at any one time is usually small.
All possible speed must be exercised in con-
ducting the initial phases of any fish kill investi-
gation because fish disintegrate rapidly in hot
weather and the cause of death may disappear or
become unidentifiable within minutes. Success
in solving a fish kill problem is usually related to
the speed with which investigators can arrive at
the scene after a fish kill begins. The speed of
response in the initial investigation is enhanced
through the training of qualified personnel who
will report immediately the location of observed
kills, the time that the kill was first observed,
the general kinds of organisms affected, an esti-
mate of the number of dead fish involved, and
any unusual phenomena associated with the kill.
Because there is always the possibility of legal
liability associated with a fish kill, lawyers,
judges, and juries may scrutinize the investiga-
tion report. The investigation, therefore, must
be made with great care.
12
-------
FISH KILLS
6.0 REFERENCES
Blair, A. A. 1958. Back-pack shocker. Canad. Fish Cult. No. 23, pp. 33-37.
Bowers, C. C. 1955. Selective poisoning of gizzard shad with rotenone. Prog. Fish-Cult. 17(3):134-135.
Cohen, J. M., Q. H. Pickering, R. L. Woodward, and W. Van Heruvelen. 1960. The effect of fish poisons on water supplies. JAWWA,
52(12):1551-1566.
Cohen, J. M., Q. H. Pickering, R. L. Woodward, and W. Van Heruvelen. 1961. The effect of fish poisons on water supplies. JAWWA,
53(12)Pt. 2:49-62.
Dumont, W. H., and G. T. Sundstrom. 1961. Commercial fishing gear of the United States. U.S. Fish and Wildlife Circular No. 109.
U.S. Government Printing Office, Washington, D.C., 61 pp.
Fisher, K. C. 1950. Physiological considerations involved in electrical methods of fishing. Canad. Fish Cult. No. 9, pp. 26-34.
Frankenberger, L. 1960. Applications of a boat-rigged direct-current shocker on lakes and streams in west-central Wisconsin. Prog.
Fish-Cult. 22(3): 124-128.
Funk, J. L. 1947. Wider application of electrical fishing method of collecting fish. Trans. Amer. Fish. Soc. 77:49-64.
Haskell, D. C., and W. F. Adelman, Jr. 1955. Effects of rapid direct current pulsations on fish. New York Fish Game J. 2(1):95-105.
Haskell, D. C., D. Geduldiz, and E. Snolk. 1955. An electric trawl. New York Fish Game J. 2(1): 120-125.
Holton, G. D. 1954. West Virginia's electrical fish collecting methods. Prog. Fish-Cult. 16(1): 10-18.
Hooper, F. 1960. Pollution control by chemicals and some resulting problems. Trans. Second Seminar on Biol. Problems in Water
Pollution, April 20-24, USPHS, Robert A. Taft San. Engr. Ctr., Cincinnati, p241-246.
Krumholz, L. A. 1948. The Use of Rotenone in Fisheries Research J. Wildl. Mgmt. 12(3):305-317.
Larimore, R. W. 1961. Fish population and electrofishing success in a warm water stream. J. Wildl. Mgmt. 25(1):1-12.
Larimore, R. W., L. Durham, and G. W. Bennett. 1950. A modification of the electric fish shocke.- for Lake Work. J. Wildl. Mgmt.
14(3):320-323.
Latta, W. C., and G. F. Meyers. 1961. Night use of a D C electric shocker to collect trout in lakes. Trans. Amer. Fish.Soc. 90(l):81-83.
Lennon, R. E. 1959. The electrical resistivity in fishing investigations. U.S. Fish Wildl. Serv., Spec. Sci, Rept. Fish. No. 287, pp. 1-13.
Lennon, R. E. 1961. A fly-rod electrode system for electrofishing. Prog. Fish-Cult. 23(2):92-93.
Loeb, H. A. 1955. An electrical surface device for crop control and fish collection in lakes. New York Fish Game J. 2(2):220-221.
McCrimmon, H. R., and A. H. Berst. 1963. A portable A C - D C backpack fish shocker designed for operation in Ontario streams.
Prog. Fish-Cult. 25(3): 159-162.
Mackenthun, K. M. 1969. The practice of water pollution biology. USDI, FWPCA, 281 pp.
Mackenthun, K. M., and W. M. Ingram. 1967. Biological associated problems in freshwater environments, their identification, investiga-
tion and control. USDI, FWPCA, 287 pp.
Massman, W. H., E. C. Ladd, and H. N. McCutcheon. 1952. A surface trawl for sampling young fishes in tidal rivers. Trans. North
Amer. Wildl. Conf. 17:386-392.
Pratt, V. S. 1951. A measure of the efficiency of alternating and direct current fish shockers. Trans. Amer. Fish.Soc. 81(l):63-68.
Rollefson, M. D. 1958. The development and evaluation of interrupted direct current electrofishing equipment. Wyo. Game Fish Dept.
Coop. Proj. No. 1. pp. 1-123.
Rollefson, M. D. 1961. The development of improved electrofishing equipment. In: Proc. Forty-first Ann. Conf. West.Assoc. St. Game
and Fish Comm. pp.218-228.
Rounsefell, G. A., W. H. Everhart. 1953. Fishery science: Its methods and applications. John Wiley and Sons, New York.
Seehorn, M. E. 1968. An inexpensive backpack shocker for one man use. In: Proc. 21st. Ann. Conf. Southeastern Assoc. Game and
Fish Comm. pp. 516-524.
Stubbs, J. M. 1966. Electrofishing, using a boat as the negative. In: Proc. 19th Ann. Conf. Southeastern Assoc. Game and Fish Comm.
pp. 236-245.
Tebo, Jr., L. B. 1965. Fish population sampling studies at water pollution surveillance system stations on the Ohio, Tennessee, Clinch
and Cumberland Rivers. Applications and development Report No. 15, Div. Water Supply and Pollution Control, USPHS. Cincinnati.
79pp.
Thomas, N. 1969. Flavor of Ohio River channel catfish (Ictalarus punctatus Raf.).USEPA. Cincinnati. 19 pp.
Trent, W. L. 1967. Attachment of hydrofoils to otter boards for taking surface samples of juvenile fish and shrimp. Ches. Sci.
8(2):130-133.
13
-------
BIOLOGICAL METHODS
7.0 BIBLIOGRAPHY
7.1 General References
Allen, G. H., A. C. Delacy, and D. W. Gotshall. 1960. Quantitative sampling of marine fishes — A problem in fish behavior and fish
gear. In: Waste Disposal in the Marine Environment. Pergamon Press, pp 448-511.
American Public Health Association et al. 1971. Standard methods for the examination of water and wastewater. 13th ed. APHA,
New York. pp. 771-779.
Breder, C. M., and D. E. Rosen. Modes of reproduction in fishes. Amer. Mus. Natural History, Natural History Press, New York. 941
PP-
Calhoun, A., ed. 1966. Inland fisheries management. Calif. Dept. Fish and Game, Sacramento. 546 pp.
Carlander, K. D. 1969. Handbook of freshwater fishery; Life history data on freshwater of the U.S. and Canada, exclusive of the
Perciformes, 3rd ed. Iowa State Univ. Press, Ames. 752 pp.
Curtis, B. 1948. The Life Story of the Fish. Harcourt, Brace and Company, New York. 284 pp.
Gushing, D. H. 1968. Fisheries biology. A study in population dynamics. Univ. Wis. Press, Madison. 200 pp.
FAO. 1964. Modern fishing gear of the world: 2. Fishing News (Books) Ltd., London. 603 pp.
Green, I. 1968. The biology of estuarme animals. Univ. Washington, Seattle. 401 pp.
Hardy, A. 1965. The open sea. Houghton Mifflin Company, Boston. 657 pp.
Hynes, H. B. N. 1960. The biology of polluted water. Liverpool Univ. Press, Liverpool. 202 pp.
Hynes, H. B. N. 1970. The ecology of running waters. Univ. Toronto Press. 555 pp.
Jones, J. R. E. 1964. Fish and river pollution. Butterworths, London. 203 pp.
Klein, L. 1962. River pollution 2: causes and effects. Butterworths, London. 456 pp.
Lagler, K, F. 1966. Freshwater fisheries biology. William C. Brown Co., Dubuque. 421 pp.
Lagler, K. F., J. E. Bardach, and R. R. Miller. 1962. Ichthyology. The study of fishes. John Wiley and Sons Inc., New York and
London. 545 pp.
Macan, T. T. 1963. Freshwater ecology. John Wiley and Sons, New York. 338 pp.
Marshall, N. B. 1966. Life of fishes. The World Publ. Co., Cleveland and New York. 402 pp.
Moore, H. B. 1965. Marine ecology. John Wiley and Sons, Inc., New York. 493 pp.
Reid, G. K. 1961. Ecology of inland waters and estuaries. Reinhold Publ. Corp., New York. 375 pp.
Ricker, W. E. 1958. Handbook of computations for biological statistics of fish populations. Fish. Res. Bd. Can. Bull. 119. 300 pp.
Ricker, W. E. 1968. Methods for the assessment of fish production in fresh water. International Biological Program Handbook No. 3.
Blackwell Scientific Publications, Oxford and Edinburgh 326 pp.
Rounsefell, G. A., and W. H. Everhart 1953. Fishery science, its methods and applications. John Wiley & Son, New York. 444 pp.
Ruttner, F. 1953. Fundamentals of limnology. Univ. Toronto Press, Toronto. 242 pp.
Warren, C. E. 1971. Biology and water pollution control. W. B. Saunders Co., Philadelphia. 434 pp.
Welch, P. S. 1948. Limnological methods. McGraw-Hill, New York. 381 pp.
7.2 Electrofishing
Applegate, V. C. 1954. Selected bibliography on applications of electricity in fishery science. U.S. Fish and Wildl. Serv., Spec. Sci.
Rept. Fish. No. 127. pp. 1-55.
Bailey, J. E., et al. 1955. A direct current fish shocking technique. Prog. Fish-Cult. 17(2):75.
Burnet, A. M. R. 1959. Electric fishing with pulsatory electric current. New Zeal. J. Sci. 2(1)'48-56.
Burnet, A. M. R. 1961. An electric fishing machine with pulsatory direct current. New Zeal. J. Sci. 4(1):151-161.
Dale, H. B. 1959. Electronic fishing with underwater pulses. Electronics, 52(l):l-3.
Elson, P. F. 1950. Usefulness of electrofishing methods. Canad. Fish Cult. No. 9, pp. 3-12.
Halsband, E. 1955. Untersuchungen uber die Betaubungsgrenzimpulzaheln vor schiedener suswasser Fische. Archiv. fur Fishereiwis-
senschaft, 6(l-2):45-53.
Haskell, D. C. 1939. An electrical method of collecting fish. Trans. Amer. Fish.Soc. 69:210-215.
Haskell, D. C. 1954. Electrical fields as applied to the operation of electric fish shockers. New York Fish Game J. 1(2): 130-170.
Haskell, D. C., and R. G. Zilliox. 1940. Further developments of the electrical methods of collecting fish. Trans. Amer. Fish. Soc.
70:404-409.
14
-------
FISH REFERENCES
Jones, R. A. 1959. Modifications of an alternate-polarity electrode. Prog. Fish-Cult. 21 (1):3942.
Larkins, P. A. 1950. Use of electrical shocking devices. Canad. Fish. Cult., No. 9, pp. 21-25.
Lennon, R. E., and P. S. Parker. 1955. Electric shocker developments on southeastern trout waters. Trans. Amer. Fish. Soc.
85:234-240.
Lennon, R. E., arid P. S. Parker. 1957. Night collection of fish with electricity. New York Fish Game J. 4(1):109-118.
Lennon, R. E., and P. S. Parker. 1958. Applications of salt in electrofishing. Spec. Sci. Rept., U.S. Fish Wildl. Serv. No. 280.
Ming, A. 1964a. Boom type electrofishing device for sampling fish populations in Oklahoma waters. Okla. Fish. Res. Lab., D-J Federal
Aid Proj. FL-6, Semiann. Rept. (Jan-June, 1964). pp. 22-23.
Ming, A. I964b. Contributions to a bibliography on the construction, development, use and effect', of electrofishing devices. Okla.
Fish. Res. Lab., D-J Federal Aid Proj. FL-6, Semiann. Rept. (Jan.-June, 1964). pp. 33-46.
Mo nan, G. E., and D. E. Engstrom. 1962. Development of a mathematical relationship betweei, electn-field parameters and the
electrical characteristics offish. U.S. Fish Wildl. Serv., Fish. Bull. 63(1): 123-136.
Murray, A. R. 1958. A direct current electrofishing apparatus using separate excitation. Canad. Fish Cult., No. 23, pp. 27-32.
Northrop, R. B. 1962. Design of a pulsed DC-AC shocker. Conn. Bd. Fish and Game, D-J Federal Aid Proj. F-25-R, Job No. 1.
Omand, D. N. 1950. Electrical methods of fish collection. Canad. Fish Cult. No. 9, pp. 13-20.
Petty, A.C. 1955. An alternate-polarity electrode. New York Fish Game J. 2(1): 114-119.
Ruhr, C. E. 1953. The electric shocker in Tennessee. Tenn. Game Fish Comm. (mimeo). 12 pp.
Saunders, J. W., and M. W. Smith. 1954. The effective use of a direct current fish shocker in a Prince Edward Island stream. Canad.
Fish. Cult., No. 16, pp. 42-49.
Schwartz, F. J. 1961. Effects of external forces on aquatic organisms. Maryland Dept. Res. Edu., ' ;\sapeake Biol. Lab.,Contr. No.
168, pp. 3-26.
Smith, G. F. M., and P. F. Elson. 1950. A-D.C. electrical fishing apparatus. Canad. Fish Cult., No. 9, pp. 3446.
Sullivan, C. 1956. Importance of size grouping in population estimates employing electric shockers. Pro;;. Fish-Cult. 18(4):188-PO.
Taylor, G. N. 1957. Galvanotaxic response offish to pulsating D.C. J. Wildl. Mgmt. 21(2)-201-213.
Thompson, R. B. 1959. The use of the transistorized pulsed direct current fish shocker in assessing populations of resident fishes. In:
Proc. Thirty-ninth Ann. Conf. West. Assoc. St. Fish and Game Comm. pp. 291-294.
Thompson, R. B. 1960. Capturing tagged red salmon with pulsed direct current. U.S. Fish Wildl. Serv., Spec. Sci. Rept. - Fish, No.
355, 10pp.
Vibert, R., ed. 1967. Fishing with electricity - Its applications to biology and management. European Inland Fish Adv. Comm. FAO,
United Nations, Fishing News (Books) Ltd. London, 276 pp.
Webster, D. A., J. L. Forney, R. H. Gibbs, Jr., J. H. Severns, and W. F. Van Woert. 1955. A comparison of alternating and direct electric
currents in fishery work. New York Fish Game J. 2(1) 106-113.
Whitney, L. V., and R. L. Pierce. 1957. Factors controlling the input of electrical energy into a fish in an electrical field. Limnol,
Oceanogr. 2(2):55-61.
7.3 Chemical Fishing
Hester, F. E. 1959. The tolerance of eight species of warm-water fishes to certain rotenone formulations. In: Proc. 13th Ann. Conf.
Southeastern Assoc. Game and Fish Comm.
Krumholz, L. A. 1950. Some practical considerations in the use of rotenone in fisheries research. J. Wildl. Mgmt., vol. 14.
Lawrence, J. M. 1956. Preliminary results on the use of potassium permanganate to counteract the el1 cts of rotenone on fish. Prog.
Fish-Cult. 18(1):15-21.
McKee, J. E., and H. W. Wolf, eds. 1963. Water quality criteria. 2nd ed. Calif. Water Quality Control Be :rd Publ. 3A.
Ohio River Valley Water Sanitation Commission. 1962. Aquatic life resources of the Ohio River, pp. 7 "',-84.
Post, G. 1955. A simple chemical test for rotenone in water.Prog. Fish-Cult. 17(4) 190-19'.
Post, G. 1958. Time vs. water temperature in rotenone dissipation. In: Proc. 38th Ann. Conf. Game and Fish Comm. pp. 279-284.
Solman, V. E. F. 1949. History and use of fish poisons in the United States. Dominion Wildlife Service. O" _,. 15 pp.
Sowards, C. L. 1961. Safety as related to the use of chemicals and electricity in fishery managp'-' U.S. Fish and Wildl. Serv., Bur.
Sport Fish and Wildl., Branch Fish Mgt., Spearfish. South Dakota. 33 pp.
Tanner, H. A., and M. L. Hayes. 1955. Evaluation of toxaphene as a fish poison. Colo. Coop. Fish. Res. Unit, Quar. Rep. 1(3-4): 31-39.
Turner, W. R. 1959. Effectiveness of various rotenone-containing preparations in eradicating farm pond fish populations. Kentucky
Dept. Fish and Wildl. Res., Fish. Bull. No. 25, 22 pp.
Wilkins, L. P. 1955. Observations on the field use of cresol as a stream-survey method. Prog. Fish-Cult. 17:85-86.
15
-------
BIOLOGICAL METHODS
7.4 Fish Identification
General:
Bailey, R. M., et al. 1970. A list of common and scientific names of fishes from the United States and Canada. 3rd ed. Spec. Publ.
Amer. Fish. Soc. No. 6. 149 pp.
Blair, W. F., and G. A. Moore. 1968. Vertebrates of the United States. McGraw Hill, New York. pp. 22-165.
Eddy, S. 1957. How to know the fresh-water fishes. Wm. C. Brown Co., Dubuque. 253 pp.
Jordan, D. S., B. W. E\ermann, and H. W. Clark. 1955. Check list of the fishes and fish like vertebrates of North and Middle America
north of the northern boundary of Venezuela and Colombia. U.S. Fish Wildl. Ser., Washington, D.C. 670 pp.
LaMonte, F. 1958. North American game fishes. Doubleday, Garden City, N.Y. 202 pp.
Morita, C. M. 1953, Freshwater fishing in Hawaii. Div. Fish Game. Dept. Land Nat. Res., Honolulu. 22 pp.
Perlmutter, A. 1961. Guide to marine fishes. New York Univ. Press, New York. 431 pp.
Scott, W. B., and E. J. Crossman. 1969. Checklist of Canadian freshwater fishes with keys of identification. Misc. Publ. Life Sci. Div.
Ontario Mus. 104 pp.
Thompson, J. R., and S. Springer. 1961. Sharks, skates, rays, and chimaeras. Bur. Comm. Fish., Fish Wildl. USDI Circ. No. 119, 19 pp.
Marine: Coastal Pacific
Baxter, J.L.I 966. Inshore fishes of California. 3rd rev. Calif. Dept. Fish Game, Sacramento. 80 pp.
Clemens, W. A., and G. V.Wilby. 1961. Fishes of the Pacific coast of Canada. 2nd ed. Bull. Fish. Res. Bd. Can. No. 68. 443 pp.
McAllister, D. E. 1960. List of the marine fishes of Canada. Bull. Nat. Mus. Canada No. 168; Biol. Ser. Nat. Mus. Can. No. 62. 76 pp.
McHugh, J. L. and J. E. Fitch. 1951. Annotated list of the clupeoid fishes of the Pacific Coast from Alaska to Cape San Lucas, Baja,
California. Calif. Fish Game, 37:491-95.
Rass, T. S., ed. 1966. Fishes of the Pacific and Indian Oceans;Biology and distribution. (Translated from Russian). Israel Prog, for Sci.
Translat., IPST Cat. 1411; TT65-50120;Trans Frud. Inst. Okeaual. 73. 266 pp.
Roedel, P. M. 1948. Common marine fishes of California. Calif. Div. Fish Game Fish Bull. No. 68. 150 pp.
Wolford, L. A. 1937. Marine game fishes of the Pacific Coast from Alaska to the Equator. Univ. Calif. Press, Berkeley. 205 pp.
Marine: Atlantic and Gulf of Mexico
Ackerman, B. 1951. Handbook of fishes of the Atlantic seaboard. American Publ. Co., Washington, D.C.
Bearden, C. M. 1961. Common marine fishes of South Carolina. Bears Bluff Lab. No. 34, Wadmalaw Island, South Carolina.
Bigelow,H.B.,andV/. C. Schroeder. 1953. Fishes of the gulf of Maine. Fish. Bull. No. 74. Fish Wildl. Serv. 53:577 pp.
Bigelow, H. B. and W. C. Schroeder. 1954. Deep water elasmobranchs and chimaeroids from the northwestern slope. Bull. Mus. Comp.
Zool. Harvard College, 112:37-87.
Bohlke, J. E.,and C. G. Chaplin. 1968. Fishes of the Bahamas and adjacent tropical waters. Acad. Nat. Sci. Philadelphia. Livingston
Publishing Co., Wynnewood, Pa.
Breder, C. M., Jr. 1948. Field book of marine fishes of the Atlantic Coast from Labrador to Texas. G. P. Putnam and Sons, New York.
332pp.
Casey, J. G. 1964. Angler's guide to sharks of the northeastern United States, Maine to Chesapeake Bay. Bur. Sport Fish. Wildl. Cir.
No. 179, Washington, D.C.
Fishes of the western North Atlantic. 1,1948-Mem. Sears Fdn., Mar. Res. 1.
Hildebrand, S. R.,andW. C. Schroeder. 1928. Fishes of Chesapeake Bay. U.S. Bur. Fish. Bull. 43:1-366.
Leim, A. H., and W. B. Scott. 1966. Fishes of the Atlantic Coast of Canada. Bull. Fish. Res. Bd. Canada. No. 155.485 pp.
McAllister, D. E. 1960. List of the marine fishes of Canada. Bull. Nat. Mus. Canada No. 168;Biol. Ser. Nat. Mus. Can. No. 62. 76 pp.
Pew, P. 1954. Food and game fishes of the Texas Coast. Texas Game Fish Comm. Bull. 33.68 pp.
Randall, J. E. 1968. Caribbean reef fishes. T. F. H. Publications, Inc.,Jersey City.
Robins, C. R. 1958. Check list of the Florida game and commercial marine fishes, including those of the Gulf of Mexico and the West
Indies, with approved common names. Fla. State Bd. Conserv. Educ. Ser 12. 46 pp.
Schwartz, F. J. 1970. Marine fishes common to North Carolina. North Car. Dept. Cons. Develop., Div. Comm. Sport Fish. 32 pp.
Taylor, H. F. 1951. Survey of marine fisheries of North Carolina. Univ. North Car. Press, Chapel Hill.
16
-------
FISH REFERENCES
Freshwater: Northeast
Bailey, R. M. 1938. Key to the fresh-water fishes of New Hampshire. In: The fishes of the Merrimack Watershed. Biol. Surv. of the
Merrimack Watershed. N. H. Fish Game Dept., Biol. Surv. Kept. 3. pp. 149-185.
Bean, T. H. 1903. Catalogue of the fishes of New York. N. Y. State Mus. Bull. 60. 784 pp.
Carpenter, R. G., and H. R. Siegler. 1947. Fishes of New Hampshire. N.H. Fish Game Dept. 87 pp.
Elser, H. J. 1950. The common fishes of Maryland - How to tell them apart. Publ. Maryland Dept. Res. Educ. No. 88. 45 pp.
Everhart, W. H. 1950. Fishes of Maine. Me. Dept. Inland Fish Game. (ii). 53 pp.
Greeley, J. R., et al. 1926-1940. (Various papers on the fishes of New York.) In: Biol. Surv. Repts, Suppl. Ann. Rept., N.Y. St Cons.
Dept.
McCabe, B. C. 1945. Fishes. In: Fish. Sur. Rept. 1942. Mass. Dept. Cons7pp. 30-68.
Van Meter, H. 1950. Identifying fifty prominent fishes of West Virginia. W. Va. Cons. Comm. Div. Fish Mgt. No. 3. 45 pp.
Whiteworth, W. R., R. L. Berrieu, and W. T. Keller. 1968. Freshwater fishes of Connecticut. Conn. State Geol. Nat. Hist. Surv. Bull.
No. 101. 134pp.
Freshwater: Southeast
Black, J. D, 1940. The distribution of the fishes of Arkansas. Univ. Mich. Ph.D. Thesis. 243 pp.
Briggs, J. C. 1958. A list of Florida fishes and their distribution. Bull. Fla. State Mus. Biol. Sci. 2:224-318.
Can, A. F., Jr. 1937. A key to the freshwater fishes of Florida. Proc. Fla. Acad. Sci. (1936):72-86.
Clay, W. M. 1962. A field manual of Kentucky fishes. Ky. Dept. Fish Wildl. Res., Frankfort, Ky. 147 pp.
Fowler, H. W. 1945. A study of the fishes of the southern Piedmont and coastal plain. Acad. Nat. Sci., Philadelphia Monogr. No. 7.
408 pp.
Gowanlock, J. N. 1933. Fishes and fishing in Louisiana. Bull. La. Dept. Cons. No. 23. 638 pp.
Heemstra, P. C. 1965. A field key to the Florida sharks. Tech. Ser. No. 45. Fla. Bd. Cons., Div. Salt Water Fisheries.
King, W. 1947. Important food and game fishes of North Carolina. N.C. Dept. Cons, and Dev. 54 pp.
Kuhne, E. R. 1939. A guide to the fishes of Tennessee and the mid-South. Tenn. Dept. Cons., Knoxville. 124 pp.
Smith, H. 1970. The fishes of North Carolina. N.C. Geol. Econ. Surv. 2:xl; 453 pp.
Smith-Vaniz, W. F. 1968. Freshwater fishes of Alabama. Auburn Univ. Agr. Exper. Sta. Paragon Press, Montgomery, Ala. 211 pp
Freshwater: Midwest
Bailey, R. M., and M. O. Allum. 1962. Fishes of South Dakota. Misc. Publ. Mus. Zool. Univ. Mich. No. 119.131 pp.
Cross, F. B. 1967. Handbook of fishes of Kansas. Misc. Publ. Mus. Nat. Hist. Univ. Kansas No. 45. 357 pp.
Eddy, S,and T. Surber. 1961. Northern fishes with special reference to the Upper Mississippi Valley. Univ. Minn. Press, Minneapolis.
276 pp.
Evermann, B. W., and H. W. Clark. 1920. Lake Maxinkuckee, a physical and biological survey. Ind. St. Dept Cons., 660 pp. (Fishes,
pp. 238-451).
Forbes, S. A,and R. E. Richardson. 1920. The fishes of Illinois. 111. Nat. Hist. Surv. 3: CXXXI. 357 pp.
Gerking, S. D. 1945. The distribution of the fishes of Indiana. Invest. Ind. Lakes and Streams, 3(1):1-137.
Greene, C. W. 1935. The distribution of Wisconsin Fishes. Wis. Cons. Comm. 235 pp.
Harlan, J. R., and E. B. Speaker. 1956. Iowa fishes and fishing. 3rd ed. Iowa State Cons. Comm., Des Moines, 337 pp.
Hubbs, C. L., and G. P. Cooper. 1936. Minnows of Michigan. Cranbrook Inst. Sci., Bull 8. 95 pp.
Hubbs, C. L., and K. F. Lagler. 1964. Fishes of the Great Lakes Region. Univ. Mich. Press, Ann Arbor. 213 pp.
Johnson, R. E. 1942. The distribution of Nebraska fishes. Univ. Mich. (Ph.D. Thesis). 145 pp.
Trautman, M. B. 1957. The fishes of Ohio. Ohio State Univ. Press, Columbus. 683 pp.
Van Ooosten, J. 1957. Great Lakes fauna, flora, and their environment. Great Lakes Comm., Ann Arbor, Mich. 86 pp.
Freshwater: Southwest
Beckman, W. C. 1952. Guide to the fishes of Colorado. Univ. Colo. Mus. Leafl. 11. 110 pp.
Burr, J. G. 1932. Fishes of Texas; Handbook of the more important game and commercial types. Bull Tex. Game, Fish, and Oyster
Comm. No. 5,41 pp.
Dill, W. A. 1944. The fishery of the Lower Colorado River. Calif. Fish Game, 30:109-211.
LaRivers, I.,andT. J. Trelease. 1952. An annotated check list of the fishes of Nevada. Calif. Fish Game, 38(1): 113-123.
17
-------
BIOLOGICAL METHODS
Miller, R. R. 1952. Bait fishes of the Lower Colorado River from Lake Mead, Nevada, to Yuma, Arizona, with a key for their
identification. Calif. Fish Game. 38(1):742.
Sigler, W. F., and R. R. Miller, 1963. Fishes of Utah. Utah St. Dept. Fish Game. Salt Lake City. 203 pp.
Walford, L. A. 1931. Handbook of common commercial and game fishes of California. Calif. Div. Fish Game Fish Bull. No. 28. 181 pp.
Ward, H. C. 1953. Know your Oklahoma fishes. Okla. Game Fish Dept, Oklahoma City. 40 pp.
Freshwater: Northwest
Baxter, G. T., and J. R. Simon. 1970. Wyoming fishes. Bull. Wyo. Game Fish Dept. No. 4.168 pp.
Bond, C. E. 1961. Keys to Oregon freshwater fishes. Tech. Bull. Ore. Agr. Exp. Sta. No. 58. 42 pp.
Hankinson, T. L. 1929. Fishes of North Dakota. Pop. Mich. Acad. Sci. Arts, and Lett. 10(1928):439460.
McPhail, J. D., and C. C. Lindsey. 1970. Freshwater fishes of Northwestern Canada and Alaska. Fish. Res. Bd. Canada, Ottawa. Bull.
No. 173. 381pp.
Schultz, L. P. 1936. Keys to the fishes of Washington, Oregon and closely adjoining regions. Univ. Wash. Publ. Biol. 2(4): 103-228.
Schultz, L. P. 1941. Fishes of Glacier National Park, Montana. USDI, Cons. Bull. No. 22. 42 pp.
Wilimovsky, N. J. 1954. List of the fishes of Alaska. Stanford Ichthyol. Bull. 4:279-294.
7.5 Fish Kills
Alexander, W. B., B. A. Southgate, and R. Bassindale. 1935. Survey of the River Tees, Pt. II. The Estuary, Chemical and Biological.
Tech. Pop. Wat. Pol. Res., London, No. 5.
Anon., 1961. Effects of Pollution on Fish. Mechanism of the Toxic Action of Salts of Zinc, Lead and Copper. Water Pollution
Research, 1960:83.
Burdick, G. E. 1965. Some problems in the determination of the cause of fish kills. In: Biological Problems in Water Pollution. USPHS
Pub. No.999-WP-25.
Carpenter, K. E. 1930. Further researches on the action of soluble metallic salts on fishes. J. Exp. Biol. 56:407-422.
Easterday, R. L., and R. F. Miller. 1963. The acute toxicity of molybdenum to the bluegill. Va. J. Sci. 14(4):199-200. Abstr.
Ellis, M. M. 1937. Detection and measurement of stream pollution. Bull. U.S. Bur. Fish. 48:365-437.
Extrom, J. A.,andD.S. Farner. 1943. Effect of sulfate mill wastes on fish life. Paper Trade J. 117(5): 27-32.
Fromm, P. O., and R. H. Schiffman. 1958. Toxic action of hexavalent chromium on largemouth bass. J. Wildlife Mgt. 22:4044.
Fujiya, M. 1961. Effects of kraft pulp mill wastes on fish. JWPCF, 33(9):968-977.
Havelka, J., and M. Effenberger. 1957. Symptoms of phenol poisoning of fish. Ann. Czech. Acad. Agric. Sci., U. Serv. Animal Prod.
2(5): 421.
Henderson, C., Q. H. Pickering, and C. M. Tarzwell. 1959. Relative toxicity of ten chlorinated hydrocarbon insecticides to four species
of fish. Trans. Amer. Fish. Soc. 88:23-32.
Ingram, W., and G. W. Prescott. 1954. Toxic freshwater algae. Amer. Mid. Nat. 52:75.
Jones, J. R. E. 1948. A further study of the reaction of fish to toxic solutions. J. Exp. Biol. 25:22-34.
Kuhn, O., and H. W. Koecke. 1956. Histologische und cytologische Veranderungen der fishkierne nach Einwirkung im wasser
enthaltener schadigender Substanzen. Ztschr. F. Zellforsch. 43:611-643. (Cited in Fromm and Schiffman, 1958.)
Mathur, D. S. 1962a. Histopathological changes in the liver of certain fishes as induced by BHC and hndane. Proc. Natl. Acad. Sci.
India, Sec. B, 32(4):429434.
Mathur, D. S. 1962b. Studies on the histopathological changes induced by DDT in the liver, kidney and intestine of certain fishes.
Experientia, 18:506.
Rounsefell, G. A., and W. R. Nelson. 1966. Red-tide research summarized to 1964, including an annotated bibliography. USDI Special
Sci. Kept., Fisheries No. 535.
Schmid, O. J.,and H. Mann. 1961. Action of a detergent (dodecyl benzene sulfonate) on the gills of the trout. Nature, 192(4803):675.
Shelford, U. E. 1917. An experimental study of the effects of gas wastes upon fishes, with special reference to stream pollution. Bull.
111. Lab. Nat. Hist. 11:381412.
Skrapek, K. 1963. Toxicity of phenols and their detection in fish. Pub. Health Eng. Absts. XLIV(8):Abst. No. 1385.
Smith, L. L., Jr. et al. 1956. Procedures for investigation of fish kills. A guide for field reconnaissance and data collection. Ohio River
Valley Water Sanitation Comm., Cincinnati.
Stansby, M. E. 1963. Industrial fishery technology. Reinhold Publ. Co.,New York.
Stundle, K. 1955. The effects of waste waters from the iron industry and mining on Styrian waters. Osterrich Wasserw. (Austria). 7:75.
Water Poll. Abs. 29:105. 1956.
18
-------
FISH REFERENCES
U.S. Department of the Interior. 1968a. Pollution caused fish kills 1967. FWPCA Publ. No. CWA-7.
U.S. Department of the Interior. 1968b. Report of the National Technical Advisory Commission. FWPCA, Washington, D.C.
U.S. Department of the Interior. 1970. Investigating fish mortalities. FWPCA Publ. No. CWT-5. Also available from USGPO as No.
0-380-257.
Wallen, I. E. 1951. The direct effect of turbidity on fishes. Bull. Okla. Agr. Mech. Coll. 48(2): 1-27.
Wood, E. M. 1960. Definitive diagnosis of fish mortalities. JWPCF, 32(9):994-999.
19
-------
BIOASSAY
-------
BIOASSAY
Page
1.0 GENERAL CONSIDERATIONS 1
2.0 PHYTOPLANKTON - ALGAL ASSAY 2
2.1 Principle 2
2.2 Planning Algal Assays 2
2.3 Apparatus and Test Conditions 3
2.3.1 Glassware 3
2.3.2 Illumination 3
2.3.3 PH 3
2.4 Sample Preparation 3
2.5 Inoculum 3
2.6 Growth Response Measurements 3
2.7 Data Evaluation 4
2.8 Additions (Spikes) 5
2.9 Data Analysis and Interpretation 5
2.10 Assays to Determine Toxicity 5
3.0 PERIPHYTON 5
3.1 Static 5
3.2 Continuous Flow 6
4.0 MACROINVERTEBRATES 8
5.0 FISH 8
6.0 REFERENCES 8
6.1 General 8
6.2 Phytoplankton — Algal Assay 9
6.3 Periphyton 10
6.4 Macroinvertebrates 10
6.5 Fish 11
FATHEAD MINNOW CHRONIC TEST 15
BROOK TROUT CHRONIC TEST 25
-------
BIOASSAY
1.0 GENERAL CONSIDERATIONS
The term BIOASSAY includes any test in
which organisms are used to detect or measure
the presence or effect of one or more substances
or conditions. The organism respohses measured
in these tests include: mortality, growth rate,
standing crop (biomass), reproduction, stimu-
lation or inhibition of metabolic or enzyme
systems, changes in behavior, histopathology,
and flesh tainting (in shellfish and fish). The
ultimate purpose of bioassays is to predict the
response of native populations of aquatic organ-
isms to specific changes within the natural
environment. Whenever possible, therefore, tests
should be carried out with species that are native
(indigenous) to the receiving water used as the
diluent for the bioassay. Bioassays are important
because in most cases the success of a water
pollution control program must be judged in
terms of the effects of water quality on the con-
dition of the indigenous communities of aquatic
organisms. Also, in many cases, bioassays are
more sensitive than chemical analyses.
Two general kinds of bioassays are recog-
nized :
• laboratory tests conducted to determine the
effects of a substance on a species; more or
less arbitrary conditions are employed;
• in situ tests conducted to determine the
effects of a specific natural environment;
the test organisms are held in "containers"
through which the water circulates freely.
The general principles and methods of con-
ducting laboratory bioassays presented in
Standard Methods for the Examination of Water
and Waste Water, 13th edition (APHA, 1971)
apply to most bioassays, and the described
methods can be used with many types of aquatic
organisms with only slight modification.
The following are suggested improvements to
the methods given in Standard Methods, 13th
edition (APHA, 1971).
• The 48- and 96-hour LC50 values are
presently important for determining com-
pliance with water quality standards as
established by various pollution control
authorities. Short-term threshold infor-
mation can be derived by reporting LC50
values at 24-hour intervals to demonstrate
the shape of the toxicity curve.
• Reports of LCSO's should state the method
of calculation used and the statistical con-
fidence limits when possible.
• Rubber or plastic materials should be used
in bioassay equipment only after consider-
ation has been given to the possibility of
the leaching of substances such as plas-
ticizers or sorption of toxicants.
• Test materials should be administered in
such a way that their physical and chemical
behavior approximates that in natural
systems.
Biological tests can be conducted in any kind
of water with proper precautions, and although
most tests have been conducted in freshwater,
the same general principles apply to brackish
and salt waters. The literature contains a great
many formulations for artificial seawater. Of
these, a modification of the Kester et al. (1967)
formulation (LaRoche et al., 1970; Zaroogian et
al., 1969) seems to support the greatest variety
of marine organisms. When metal-containing
wastes are to be bioassayed, omitting EDTA and
controlling trace metals, as described by Davey
et al. (1970), is recommended.
Using a standard toxicant and a parallel series
in a standard medium is recommended to help
assess variations due to experimental technique
and the condition of the organisms. Such tests
are also useful in distinguishing effects due to an
altered character of the effluent from changes
in the sensitivity of the organism, or from
changes in the quality of the receiving water.
1
-------
BIOLOGICAL METHODS
When making waste management decisions, it
is important to consider and tentatively define
the persistence of a pollutant. Materials that
have half lives less than 48 hours can be termed
as rapidly decaying compounds; those with half
lives greater than 48 hours but less than 6
months, as slowly decaying; and those com-
pounds in natural waters with half lives longer
than 6 months, as long-lived persistent materials.
Bioassays can be conducted over almost any
interval of time, but the test duration must be
appropriate to the life stage or life cycle of the
test organisms and the objectives of the investi-
gation. The purpose of short-term tests, such as
acute mortality tests, is to determine toxicant
concentrations lethal to a given fraction (usually
50 percent) of the organisms during a short
period of their life cycle. Acute mortality tests
with fish generally last about 4 to 7 days. Most
toxicants, however, cause adverse effects at
levels below those that cause mortality. To meet
this need, long-term (chronic) tests are designed
to expose test organisms to the toxicant over
their entire life cycle and measure the effects of
the toxicant on survival, growth, and reproduc-
tion. Sometimes only a portion of the life cycle
is tested, such as studies involving growth or
emergence of aquatic insects. With fish, such
tests usually last for 30, 60, or 90 days and are
often termed subacute.
Laboratory bioassays may be conducted on a
"static" or "continuous flow" basis. The specific
needs of the investigator and available test facil-
ities determine which technique should be used.
The advantages and applications of each have
been described in Standard Methods, (APHA,
1971) and by the National Technical Advisory
Committee (1968). Generally, the continuous-
flow technique should be used where possible.
Apparatus advantageous for conducting flow-
through tests includes diluters (Mount and
Warner, 1965; Mount and Brungs, 1967), valve
controlling systems (Jackson and Brungs, 1966)
and chemical metering pumps (Symons, 1963).
The biological effects of many industrial
wastes are best evaluated in the field; trans-
porting large volumes of industrial wastes to a
laboratory for bioassay purposes can be imprac-
tical. Testing facilities are best located at the site
of the waste discharge. A bioassay trailer
(Zillich, 1969) has proven useful for this pur-
pose. In situ bioassay procedures are also a good
method for defining the impact to aquatic life
below the source of industrial waste discharges
(Basch, 1971).
Biomonitoring, a special application of biolo-
gical tests, is the use of organisms to provide
information about a surface water, effluent, or
mixtures thereof on a periodic or continuing
basis. For the best results, biomonitoring should
maintain continuous surveillance with the use of
indigenous species in a flow-through system
under conditions that approximate the natural
environment.
2.0 PHYTOPLANKTON - ALGAL ASSAY
The Algal Assay Procedure: Bottle Test was
published by the National Eutrophication Re-
search Program (USEPA, 1971) after 2 years of
intensive evaluation, during which excellent
agreement of the data was obtained among the 8
participating laboratories. This test is the only
algal bioassay that has undergone sufficient eval-
uation and refinement to be considered reliable.
The following material represents only a brief
outline of the test. For more explicit details, see
the references.
2.1 Principle
An algal assay is based on the principle that
growth is limited by the nutrient that is present
in shortest supply with respect to the needs of
the organism. The test can be used to identify
algal growth-limiting nutrients, to determine
biologically the availability of algal growth-
limiting nutrients, to quantify the biological
response (algal growth response) to changes in
concentrations of algal growth-limiting nutri-
ents, and to determine whether or not various
compounds or water samples are toxic or inhib-
itory to algae.
2.2 Planning Algal Assays
The specific experimental design of each algal
assay is dictated by the particular problem to be
solved. All pertinent ecological factors must be
considered in planning a given assay to ensure
that valid results and conclusions are obtained.
-------
ALGAL ASSAY
Water quality may vary greatly with time and
location in lakes, impoundments and streams. If
meaningful data are to be obtained, therefore,
the sampling program must take these variations
into account.
2.3 Apparatus and Test Conditions
2.3.1 Glassware
Use good-quality borosilicate glassware. When
studing trace nutrients, use special glassware
such as Vycor or polycarbonate containers.
Although container size is not critical, the sur-
face to volume ratios are critical because of
possible carbon limitation. The recommended
sample volumes for use in Erlenmeyer flasks are:
40 ml in a 125 ml flask; 60 ml in a 250 ml flask;
and 100 ml in a 500 ml flask. Use culture
closures such as loose-fitting aluminum foil or
inverted beakers to permit good gas exchange
and prevent contamination.
2.3.2 Illumination
After inoculation, incubate the flasks at 24 ±
2°C under cool-white fluorescent lighting: 200
ft-c (2152 lux) ± 10 percent for blue-green algae
and diatom test species, and 400 ft-c (4304 lux)
± 10 percent for green algae test species. Meas-
ure the light intensity adjacent to the flask at
the liquid level.
2.3.3 pH
To ensure the availability of carbon dioxide,
maintain the pH of the incubating cultures
below 8.5 by using the sample volumes men-
tioned above and shaking the cultures at 100
oscillations per minute. In samples containing
high concentrations of nutrients, such as highly-
productive surface waters or domestic waste
effluents, it may be necessary to bubble air or an
air/carbon dioxide mixture through the culture
to maintain the pH below 8.5.
2.4 Sample Preparation
Two alternate methods of sample preparation
are recommended, depending upon the type of
information to be obtained from the sample:
• membrane filtration (0.45 pore diameter) —
remove the indigenous algae by filtration if
you wish to determine the growth response
to growth-limiting nutrients which have not
been taken up by filterable organisms, or if
you wish to predict the effect of adding
nutrients to a test water at a specific time.
• autoclaving — autoclave samples if you wish
to determine the amount of algal biomass
that can be grown from all nutrients in the
water, including those in the plankton.
Autoclaving solubilizes the nutrients in the
indigenous filterable organisms and releases
them for use by the test organisms.
2.5 Inoculum
The algal test species may be one of those
recommended in the Bottle Test or another that
has been obtained in unialgal culture. Grow the
test species in a culture medium that minimizes
the intracellular carryover of nutrients in the
test species when transferred from the stock
culture to the test water (Table I.) When taken
from the stock culture, centrifuge the test cells
and discard the supernatant. Resuspend the
sedimented cells in an appropriate volume of
glass-distilled water containing 15 mg sodium
bicarbonate per liter and recentrifuge. Decant
the supernatant, resuspend the algae in fresh
bicarbonate solution, and use as the inoculum.
The amount of inoculum depends upon the algal
test species used. The following initial cell con-
centrations are recommended:
Test organism
Selenastrum capricornutum
Anabaena flos-aquae
Microcystis aeruginosa
Prepare test flasks in triplicate.
Initial cell count/ml
1000/ml
50000/ml
50000/ml
2.6 Growth Response Measurements
The method used to determine growth re-
sponse during incubation depends on the
equipment available. Cells may be counted with
a microscope, using a hemacytometer or a
Palmer-Maloney or Sedgwick-Rafter plankton
counting chamber. The amount of algal biomass
may be determined by measuring the optical
density of the culture at 600 -750 nm with a
colorimeter or spectrophotometer. The amount
of chlorophyll contained in the algae may be
-------
BIOLOGICAL METHODS
TABLE 1. STOCK CULTURE AND CONTROL NUTRIENT MEDIUM
MACROELEMENTS:
Compound
NaNO3
K2HP04
MgCl2
MgSO4-7H2O
CaCl2-2H2O
NaHCO3
Final
concentration
(mg/1)
25.500
1.044
5.700
14.700
4.410
15.000
Element
furnished
N
P
K
Mg
Mg
S
Ca
Na
Element
concentration
(mg/'i)
4.200
0.186
0.468
1.456
1.450
1.911
1.203
11.004
(If the medium is to be filtered, add the following trace-element-iron-EDTA solution from a single
combination stock solution after filtration. With no filtration, K2HK)4 should be added last to avoid
iron precipitation. Stock solutions of individual salts may be made up in 1000 x's final concentration
or less.)
MICROELEMENTS:
H3B03
MnCl2
ZnCl2
CoCl2
CuCl2
Na2MoO4-2H2O
FeCl3
Na2EDTA-2H2O
(Mg/D
185.64
264.27
32.70
0.78
0.009
7.26
96
333
B
Mn
Zn
Co
Cu
Mo
Fe
(Mg/D
33
114
15
0.35
0.003
2.88
33
measured either directly (in vivo) by fluoro-
metry or after extraction by fluorometry or
spectrophotometry. If available, an electronic
particle counter will provide an accurate and
rapid count of the cells. All methods used for
determining the algal biomass should be related
to a dry weight measurement (mg/1) determined
gravimetrically. (See the Plankton Section of the
manual for analytical details.)
2.7 Data Evaluation
Two parameters are used to describe the
growth of a test alga: maximum specific growth
rate and maximum standing crop. The maximum
specific growth rate (Mmax) f°r an individual
flask is the largest specific growth rate (/LI)
occurring at any time during incubation. The
Mmax for a set of replicates is determined by
averaging the Mmax °f the individual flasks. The
specific growth rate,M,is defined by:
where:
In
= log to the base "e"
X2 = biomass concentration at the end of the
selected time interval
Xj = biomass concentration at the beginning
of the selected time interval
t2 - tj = elapsed time (days) between selected
determinations of biomass
Because the maximum specific growth rate
(Mmax) occurs during the logarithmic phase of
growth (usually between day 3 and day 5), the
biomass must be measured at least daily during
the first 5 days of incubation.
-------
ALGAL ASSAY
The maximum standing crop in any flask is
defined as the maximum algal biomass achieved
during incubation. For practical purposes, the
maximum standing crop is assumed to have been
achieved when the rate of increase in biomass
has declined to less than 5 percent per day.
2.8 Additions (Spikes)
The quantity of cells produced in a given
medium is limited by the nutrient present in the
lowest relative quantity with respect to the
needs of the organism. If a quantity of the
limiting substance were added to the test flasks,
cell production would increase until this addi-
tional supply was depleted or until some other
substance became limiting to the organism.
Adding substances other than the limiting sub-
stance would not increase algal growth. Nutrient
additions may be made singly or in combination,
and the growth response can be compared with
that of unspiked controls to identify those sub-
stances that limit growth rate or cell production.
In all instances, the volume of a spike should
be as small as possible. The concentration of
spikes will vary and must be matched to the
waters being tested. When selecting the spike
concentration, keep in mind that (1) the con-
centration should be kept small to minimize
alterations of the sample, but at the same time,
be sufficiently large to yield a potentially
measureable response; and (2) the concentration
should be related to the fertility of the sample.
2.9 Data Analysis and Interpretation
Present the results of spiking assays together
with the results from two types of reference
samples: the assay reference medium and un-
spiked samples of the water under consideration.
Preferably, the entire growth curves should be
presented for each of the two types of reference
samples. Present the results of individual assays
in the form of the maximum specific growth
rate (with time of occurrence) and maximum
standing crop (with time at which it was
reached), both with the confidence interval
indicated.
Growth rate limiting nutrients can be deter-
mined by spiking a number of replicate flasks
with single nutrients, determining the maximum
specific growth rate for each flask, and com-
paring the averages by a Students' t-test or other
appropriate statistical tests.
Data analysis for multiple nutrient spiking can
be performed by analysis of variance calcu-
lations. In multiple nutrient spiking, accounting
for the possible interaction between different
nutrients is important and can readily be done
by factorial analysis. The same methods de-
scribed above can be used to determine the
nutrient limiting growth of the maximum
standing crop.
2.10 Assays to Determine Toxicity
As previously pointed out, the assay may be
used to determine whether or not various com-
pounds or water samples are either toxic or
inhibitory to algal growth. In this case the sub-
stance to be tested for toxicity is added to the
standard algal culture medium in varying con-
centrations, the algal test species is added, and
either the maximum standing crop or maximum
specific growth rate (or both) determined. These
are then compared to those obtained in the
standard culture medium without the additions
(controls). The LC50, or that concentration at
which either 50% of the maximum standing crop
or maximum specific growth rate is obtained, as
compared with the controls, is then calculated.
3.0 PERIPHYTON
Uniform methods for conducting bioassays
with periphyton have not been developed, and
their environmental requirements and tox-
icology are still relatively unknown. Many of the
common species have not been successfully
cultured, and the bioassays that have been
carried out with the algae and other micro-
organisms occurring in this community were
conducted principally to screen potential
algicides, fungicides, and other control agents.
Two kinds of tests can be conducted with
periphyton: static and continuous flow.
3.1 Static
Because the techniques currently employed in
the Algal Assay Procedure: Bottle Test (USEPA,
1971) have been more rigorously tested than
any procedure previously used for periphyton,
-------
BIOLOGICAL METHODS
this method is recommended for static bioassays
with the periphyton.
3.2 Continuous Flow
Many periphyton grow well only in flowing
water and can be studied only in situ or in arti-
ficial streams (Whitford, 1960; Whitford et al,
1964). The following procedure, which is similar
to the method described by Mclntire et al.
(1964), is tentatively recommended at this time.
• Test Chamber — Twin, inter-connected
channels, each approximately 4" X 4" X
36", with two inches of water circulated by
a paddle wheel. Duplicate chambers should
be provided for each condition tested
(Figures 1 and 2).
Current velocity - 30 cm/sec.
Temperature - 20° C
Light - 400 fc, cool-white (daylight)
fluorescent lamps
Culture medium — Optional
a. Algal Assay Medium (Table 1).
b. Natural surface water supply
Where direct flow-through is not pro-
vided, the water exchange rate should
ensure a complete change at least six
times daily.
WATER SOURCE
FLOWMETER
WATER SUPPLY
'CONTROL VALVE
I cm
RUBBER
TUBING
2.5cm I.D. RUBBER
TUBING
SCREW
CLAMP
PADDLE WHEEL
50cm DIA.
ELECTRIC VARIABLE
SPEED MOTOR
>
OVERFLOW
DRAIN FLUME
TROUGH-DIMENSIONS
INSIDE WIDTH = 25cm
INSIDE LENGTH= 3m
INSIDE DEPTH = 20cm
INCH CLAMPS
NPUT AND OUTPUT
SAMPLE BOTTLES
Figure 1. Diagram of laboratory stream, showing the paddle wheel for circulating the water between the two
interconnected troughs and the exchange water system. (From Mclntire et al, 1964).
-------
PERIPHYTON BIO ASSAY
FILTERED WATER SUPPLY LINE
SCREW CLAMP
27cm C- CLAMP
5cm ANGLE IRON
WOOD STRIP
6mm LUCITE TOP
ADJUSTABLE HEIGHT '
1500 WATT
INCANDESCENT LAMP
I D POLYETHYLENE '""*
cm HARD RUBBER GASKET
—LIP OF CHAMBER
WOOD STRIP
2cm X 3mm IRON STRIP
OVERFLOW HEAD
JAR
15 mm Y- SHAPED
CONNECTING
TUBES
TWO-STAGE
PRESSURE
REDUCING VALVE
GLASS
RASCHIG
RINGS
UBBER' STOPPERS
CHAMBER-DIMENSIONS
INSIDE WIDTH =50c
INSIDE LENGTH = 60c
INSIDE DEPTH m 17 c
2cm MARINE PLYWOOD WATER JACKET
GAGE
COLD ROLLED
STEEL
PORCELAIN
COATED
WATER JACKET
INLET AND OUTLET
OXYGEN
STRIPPING
COLUMN
150 mm
I.D X | 5m
LONG
ADJUSTABLE
HEAD CONTROL
INPUT
SAMPLE
BOTTLE
PORCELAIN
COATED
STEEL TRAY
CENTRIFUGAL
PUMPS
CARBON DIOXIDE GAS
CYLINDER
NITROGEN
DIFFUSER
AMBER BY-PASS
SCREW CLAMPS
TYGON TUBING
NITROGEN GAS'
CYLINDER
6mm RUBBER TUBIN6
CHAMBER DRAIN
/STAINLESS STEEL
CURRENT DIFFUSION
CHAMBER
Figure 2. Diagram of photosynthesis-respiration chamber, showing the chamber with its circulating and ex-
change water systems, the water jacket for temperature control, the nutrient and gas concentration control system,
and the light source.
Test organism(s) — Optional; filamentous
blue-green or green algae or diatoms.
a. Unialgal culture — No standard test
organisms are available
b. Periphyton community — Use "seed" of
periphyton from the water resource for
which the data are being developed.
Acclimatization period — The culture (or
community) should be allowed to develop
in the test chambers for a minimum of two
weeks before introducing the test condi-
tion.
Maintaining test conditions - Chemicals are
added to the water supply prior to flow
into the test chamber. Temperature control
may be maintained by placing thermostat-
ically controlled heating (or cooling)
elements in the channel.
Substrate — A minimum of eight 1" X 3"
plain glass slides should be placed on the
bottom of each channel.
Test duration — Two weeks
Evaluation — The effects of the test
condition are evaluated at the end of the
test period by comparing the biomass and
community structure in the test chambers
with that of the control chambers. (See
Periphyton Section for methodology.)
a. Biomass — Use four of the eight slides;
analyze individually.
(1) Chlorophyll a (mg/m2)
(2) Organic matter (Ash-free weight,
g/m2)
b. Cell count and identification — Use four
pooled slides.
(1) Cell density (cells/mm2 )
(2) Species proportional count
(3) Community diversity (Diversity
Index)
Toxicity - The toxicity of a chemical or
effluent is expressed as the LC50, which is
-------
BIOLOGICAL METHODS
the concentration of toxicant resulting in a
50% reduction in the biomass or cell count.
Community diversity is not affected in the
same manner as biomass and cell counts,
and would yield a much different value.
4.0 MACROINVERTEBRATES
In general, most of the considerations covered
by Standard Methods (APHA, 1971) apply
equally well to macroinvertebrate tests in fresh
and marine waters. Recent refinements in acute
and chronic methodology for aquatic insects,
amphipods, mussels, and Daphnia have been
described by Gaufm (1971), Bell and Nebeker
(1969), Arthur and Leonard (1970), Dimick and
Breese (1965), Woelke (1967), and Biesinger and
Christensen (1971), respectively.
5.0 FISH
The general principles and methods for acute
and chronic laboratory fish toxicity tests are
presented in Standard Methods (APHA, 1971)
and in the report of the National Technical
Advisory Committee (1968). Sprague (1969,
1970) has recently reviewed many of the prob-
lems and the terminology associated with fish
toxicity tests.
Chronic tests are becoming increasingly
important as sublethal adverse effects of more
and more toxic agents are found to be signifi-
cant. At present, a chronic fish bioassay test is a
relatively sophisticated research procedure and
entails large allocations of manpower, time, and
expense. Important contributions in this area
include those by Mount and Stephan (1969),
Brungs (1969), Eaton (1970), and McKim et al.
(1971).
Two procedures for chronic toxicity tests
using the fathead minnow, Pimephales promelas
Rafinesque, and the brook trout, Salvelinus
fontinales (Mitchell),developed by the staff of
the National Water Quality Laboratory, U.S.
Environmental Protection Agency, Duluth,
Minn., are presented following the references in
this section.
6.0 REFERENCES
6.1 General
American Public Health Association. 1971. Standard methods for the examination of water and wastewater. 13th ed. Amer. Public
Health Assoc., New York. 874 pp.
Basch, R. 1971. Chlorinated municipal waste toxicities of rainbow trout and fathead minnows. Mich. Bur. Water Mgmt., Dept. Nat.
Res., Lansing, Mich. Final Report of Grant Number 18050GZZ for the U.S. Environmental Protection Agency.
Davey, E. W., J. H. Gentile, S. J. Erickson, and P. Betzer. 1970. Removal of trace metals from marine culture media. Limnol.
Oceanogr. 15:486-488.
Jackson, H. W., and W. A. Brungs. 1966. Biomonitoring of industrial effluents. Proc. 21st Ind. Waste Conf., Purdue Univ., Eng. Ext.
Bull. No. 121. pp. 117.
Kester, D. R., I. W. Duedall, D. N. Connors, and R. M. Pytkowicz. 1967. Preparation of artificial seawater. Limnol. Oceanogr.
12(1):176-179.
LaRoche, G., R. Eisler, and C. M. Tarzwell. 1970. Bioassay procedures for oil and oil dispersant toxicity evaluation. JWPCF,
42:1982-1989.
Mount, D. I., and R. E. Warner. 1965. A serial-diluter apparatus for continuous delivery of various concentrations in water. PHS Publ.
No. 99-WP-23. 16pp.
Mount, D. I., and W. A. Brungs. 1967. A simplified dosing apparatus for fish toxicology studies. Water Res. 1:21-29.
National Technical Advisory Committee. 1968. Water quality criteria. Report of the National Technical Advisory Committee on Water
Quality Criteria to the Secretary of the Interior. USDI, FWPCA, Washington, D. C. 234 pp.
Symons, J. M. 1963. Simple continuous flow, low and variable rate pump. JWPCF, 35:1480-1485.
Zaroogian, G. E., G. Pesch, and G. Morrison. 1969. Formulation of an artificial medium suitable for oyster larvae development. Amer.
Zool. 9:1144.
Zillich, J. 1969. The simultaneous use of continuous flow bioassays and automatic water quality monitoring equipment to evaluate the
toxicity of waste water discharges. Presented at: 44th Annual Conf. of Mich. Water Poll. Cont. Assoc., Boyne Falls, Mich., June 16,
1969. 3 pp.
-------
BIOASSAY REFERENCES
6.2 Phytoplankton — Algal Assay
Berge, G. 1969. Predicted effects of fertilizers upon the algae production in Fern Lake. FiskDiv. Ski. Ser. HavUnders. 15:339-355.
Davis, C. C. 1964. Evidence for the eutrophication of Lake Erie from phytoplankton records. Limnol. Oceanogr. 9:275-283.
Edmondson, W. T., and G. C. Anderson. 1956. Artificial eutrophication of Lake Washington. Limnol. Oceanogr. l(l):47-53.
Francisco, D. E. and C. M. Weiss. 1973. Algal response to detergent phosphate levels. JWPCF, 45(3}:480-489.
Fruh, E. G., K. M. Stewart, G. F. Lee, and G. A. Rohlich. 1966. Measurements of Eutrophication and Trends. JWPCF,
38(8): 1237-1258.
Goldman, C. R., and R. C. Carter. 1965. An investigation by rapid C14bioassay of factors affecting the cultural eutrophication of Lake
Tahoe, California. JWPCF, 37:1044-1063.
Hasler, A. D. 1947. Eutrophication of lakes by domestic drainage. Ecology, 28(4):383-395.
Johnson, J. M., T. O. Odlaug, T. A. Olson and O. R. Ruschmeyer. 1970. The potential productivity of fresh water environments as
determined by an algal bioassay technique. Water Resc. Res. Ctr., Univ. Minn. Bull. No. 20. 77 pp.
Joint Industry/Government Task Force on Eutrophication. 1969. Provisional Algal Assay Procedure. P. O. Box 3011, Grand Central
Station, New York, N.Y., 10017. 62 pp.
Lake Tahoe Area Council. 1970. Eutrophication of Surface Waters - Indian Creek Reservoir, First Progress Report, FWQA Grant No.
16010 DNY.
Maloney, T. E., W. E. Miller, and T. Shiroyama. 1972. Algal Responses to Nutrient Additions in Natural Waters. I. Laboratory Assays.
In: Nutrients and Eutrophication, Special Symposia, Vol. I, Amer. Soc. Limnol. Oceanogr., Lawrence, Kansas, p 134-140.
Maloney, T. E., W. E. Miller, and N. L. Blind. 1973. Use of Algal Assay in Studying Eutrophication Problems. Proc. Internat. Assoc.
Water Poll. Res., Sixth Conference, Jerusalem, 1972.Pergamon Press.
Middlebrooks, E. J., E. A. Pearson, M. Tunzi, A.Admarayana, P. H. McGauhey, and G. A. Rohlich. 1971. Eutrophication of surface
water - Lake Tahoe. JWPCF, 43:242-251.
Miller, W. E. and T. E. Maloney. 1971. Effects of Secondary and Tertiary Waste Effluents on Algal Growth in a Lake River System.
JWPCF, 43(12):2361-2365.
Murray, S., J. Schertig, and P. S. Dixon. 1971. Evaluation of algal assay procedures - PAAP batch test. JWPCF, 43(10): 1991-2003.
Oglesby, R. T., and W. T. Edmondson. 1966. Control of Eutrophication. JWPCF, 38(9):1452-1460.
Potash, M. 1956. A biological test for determining the potential productivity of water. Ecology, 37(4):631-639.
Rawson, D. S. 1956. Algal indicators of lake types. Limnol. Oceanogr. 1:18-25.
Schreiber, W. 1927. Der Reinkultur von marinem Phytoplankton und deren Bedeutung fur die Erforschung der Produktions-fahigkeit
des Meerwassers. Wissensch. Meeresunters. N.F., 16:1-34.
Shapiro, J. and R. Ribeiro. 1965. Algal growth and sewage effluent in the Potomac estuary. JWPCF, 37(7): 1034-1043.
Shelef, G., and R. Halperin. 1970. Wastewater nutrients and algae growth potential. In: H. I. Shuval, ed., Developments in Water
Quality Research, Proc. Jerusalem Internat'l. Conf. on Water Quality and Poll. Res., June, 1969. Ann Arbor-Humphrey Science Publ.
p. 211-228.
Skulberg, O. M. 1964. Algal problems related to the eutrophication of European water supplies, and a bioassay method to assess
fertilizing influences of pollution on inland waters. In: D. F. Jackson, ed., Algae and Man, Plenum Press, N.Y. p. 262-299.
Skulberg, O. M. 1967. Algal cultures as a means to assess the fertilizing influence of pollution. In: Advances in Water Pollution
Research, Vol. 1, Pergamon Press, Washington, D. C.
Strom, K. M. 1933. Nutrition of algae. Experiments upon the feasibility of the Schreiber method in fresh waters; the relative
importance of iron and manganese in the nutritive medium; the nutritive substance given off by lake bottom muds. Arch. Hydrobiol.
25:38-47.
Toerien, D. F., C. H. Huang, J. Radimsky, E. A. Pearson, and J. Scherfig. 1971. Final report.provisional algal assay procedures. Report
No. 71-6, Sanit. Eng. Res. Lab., Coll. Eng. Sch. Pub. Hlth., Univ. Cahf., Berkeley. 211 pp.
U. S. Environmental Protection Agency. 1971. Algal assay procedure: bottle test. National Eutrophication Research Program, USEPA,
Corvalhs, Oregon.
Wang, W., W. T. Sullivan, and R. L. Evans. 1973. A technique for evaluating algal growth potential in Illinois surface waters. 111. St.
Water Sur., Urbana, Rept. of Investigation 72, 16 pp.
Weiss, C. M. and R. W. Helms. 1971. Interlaboratory precision test - An eight-laboratory evaluation of the Provisional Algal Assay
Procedure: Bottle Test. National Eutrophication Research Program, U. S. Environmental Protection Agency. Corvallis, Oregon. 70
pp.
-------
BIOLOGICAL METHODS
6.3 Periphyton
Burbank, W. D., and D. M. Spoon. 1967. The use of sessile ciliates collected in plastic petri dishes for rapid assessment of water
pollution. J. Protozool. 14(4):739-744.
Cairns, J., Jr. 1968. The effects of dieldrin on diatoms. Mosq. News,28(2):177-179.
Cairns, J., Jr. 1969. Rate of species diversity restoration following stress in freshwater protozoan communities. Univ. Kansas, Sci. Bull.
48:209-224.
Cairns, J., Jr., and K. L. Dickson. 1970. Reduction and restoration of the number of fresh-water protozoan species following acute
exposure to copper and zinc. Trans. Kansas Acad. Sci. 73(1): 1-10.
Cairns, J., Jr., A. Scheier, and N. E. Hess. 1964. The effects of alkyl benzene sulfonate on aquatic organisms. Ind. Water Wastes,
1(9):1-7.
Fitzgerald, G. P. 1964. Factors in the testing and application of algicides. Appl. Microbiol. 12(3):247-253.
Jackson, H. W., and W. A. Brungs. 1966. Biomonitoring industrial effluents. Ind. Water Eng. 45:14-18.
Mclntire, C. D., R. L. Garrison, H. K. Phinney, andC. E. Warren. 1964. Primary production in laboratory streams. Limnol. Oceanogr.
9(1):92-102.
Mclntire, C. D., and H. K. Phinney. 1965. Laboratory studies of periphyton production and community metabolism in lotic environ-
ments. Ecol. Monogr. 35:237-258.
Mclntire, C. D. 1966a. Some effects of current velocity on periphyton communities in laboratory streams. Hydrobiol. 27:559-570.
Mclntire, C. D. 1966b. Some factors affecting respiration of periphyton communities in lotic environments. Ecology, 47:918-930.
Mclntire, C. D. 1968a. Structural characteristics of benthic algal communities in laboratory streams. Ecology, 49(3):5 20-537.
Mclntire, C. D. 1968b. Physiological-ecological studies of benthic algae in laboratory streams. JWPCF, 40(11) Part 1:1940-1952.
Otto, N. E. 1968. Algaecidal evaluation methods using the filamentous green alga, Cladophora. Rep. No. WC-40, Div. Res., Bur.
Reclam., USDI, Denver.
Patrick, R. 1964. Tentative method of test for evaluating inhibitory toxicity of industrial waste waters. ASTM Standards, Part 23,
pp. 517-525, American Society for Testing and Materials, Philadelphia, Pa.
Patrick, R. 1966. The effect of varying amounts and ratios of nitrogen and phosphate on algae blooms. Proc. Ind. Waste Conf. (Purdue)
21:41-51.
Patrick, R. 1968. The structure of diatom communities in similar ecological conditions. Amer. Nat. 102(924): 173-183.
Patrick, R., J. Cairns, Jr., and A. Scheier. 1968a. The relative sensitivity of diatoms, snails, and fish to twenty common constituents of
industrial wastes. Prog. Fish-Cult. 30(3):137-140.
Patrick, R., B. Crum, and J. Coles. 1969. Temperature and manganese as determining factors in the presence of diatom or blue-green
algal floras in streams. Proc. Nat. Acad. Sci. Phil. 64(2):472-478.
Patrick, R., N. A. Roberts, and B. Davis. 1968b. The effect of changes in pH on the structure of diatom communities. Not. Natur.
416:1-16.
Phaup, J. D., and J. Gannon. 1967. Ecology of Sphaerotilus in an experimental outdoor channel. Water Res. 1:523-541.
Phinney, H. K., and C. D. Mclntire. 1965. Effect of temperature on metabolism of periphyton communities developed in laboratory
streams. Limnol. Oceanogr. 10(3):341-344.
Whitford, L. A. 1960. The current effect and growth of fresh-water algae. Trans. Amer. Microsc. Soc. 79(3):302-309.
Whitford, L. A., G. E. Dillard, and F. J. Schumacher. 1964. An artificial stream apparatus for the study of lotic organisms. Limnol.
Oceanogr. 9(4):598-600.
Whitton, B. A. 1967. Studies on the growth of riverain Cladophora in culture. Arch. Mikrobiol. 58:21-29.
Whitton, B. A. 1970. Toxicity of zinc, copper, and lead to Chlorophyta from flowing waters. Arch. Mikrobiol. 72:353-360.
Williams, L. G., and D. I. Mount. 1965. Influence of zinc on periphytic communities. Amer. J. Bot. 52(l):26-34.
Wuhrmann, K. 1964. River bacteriology and the role of bacteria in self-purification of rivers. In: Principles and Applications in Aquatic
Microbiology. John Wiley, NY.
Zimmermann. P. 1961. Experimentelle Untersuchungen uber die okologische Wirkung der Stromungsgeschwindigkeit auf die
Lebensgemeinschaften des fliessenden Wassers. Schweiz. z. Hydrol. 23:1-81.
6.4 Macroinvertebrates
Arthur, J. W., and E. N. Leonard. 1970. Effects of copper on Gammarus pseudolimnaeus, Physa Integra, and Campeloma decisum in
soft water. J. Fish. Res. Bd. Canada, 27:1277-1283.
Bell, H. L., and A. V. Nebeker. 1969. Preliminary studies on the tolerance of aquatic insects to low pH. J. Kansas Entomol. Soc.
42:230-236.
Biesinger, K. E., and G. M. Christensen. 1971. Metal effects on survival, growth, and reproduction and metabolism ofDaphnia magna.
National Water Quality Laboratory, Duluth, Minnesota, 43 pp.
10
-------
BIOLOGICAL METHODS
Dimick, R. E., and W. P. Breese. 1965. Bay mussel embryo bioassay. Proc. 12th Pacific Northwest Ind. Conf., College of Engineering,
Univ. of Wash. pp. 165-175.
Gaufin, A. R. 1971. Water quality requirements of aquatic insects. Department of Biology, University of Utah. Contract No.
14-12-438, USDI, FWPCA, National Water Quality Laboratory, Duluth. 65 pp.
Woelke, C. E. 1967. Measurement of water quality with the Pacific oyster embryo bioassay. In: Water Quality Criteria, Amer. Soc. for
Testing and Materials, Special Tech. Pub. No. 416. pp. 112-120.
6.5 Fish
Brungs, W. A. 1969. Chronic toxicity of zinc of the fathead minnow, Pimephales promelas Rafinesque. Trans. Amer. Fish. Soc.
98:272-279.
Eaton, J. G. 1970. Chronic malathion toxicity to the bluegill (Lepomis macrochirus Rafinesque). Water Res. 4:673-684.
McKim, J. M., and D. A. Benoit. 1971. Effects of long-term exposures to copper on survival, growth, and reproduction of brook trout
Salvelinus fontinalis (Mitchill). J. Fish. Res. Bd. Canada, 28:655-662.
Mount, D. I., and C. E. Stephan. 1969. Chronic toxicity of copper to the fathead minnow (Pimephales promelas, Rafinesque) in soft
water. J. Fish. Res. Bd. Canada, 26:2449-2457.
Sprague, J. B. 1969. Measurement of pollutant toxicity to fish. I. Bioassay methods for acute toxicity. Water Res. 3:793-821.
Sprague, J. B. 1970. Measurement of pollutant toxicity to fish. II. Utilizing and applying bioassay results. Water Res. 4:3-32.
11
-------
RECOMMENDED BIOASSAY PROCEDURES
NATIONAL WATER QUALITY LABORATORY
DULUTH, MINNESOTA
Recommended Bioassay Procedures are estab-
lished by the approval of both the Committee
on Aquatic Bioassays and the Director of the
National Water Quality Laboratory. The main
reasons for establishing them are: (1) to permit
direct comparison of test results, (2) to en-
courage the use of the best procedures available,
and (,3) to encourage uniformity. These proce-
dures should be used by National Water Quality
Laboratory personnel whenever possible, unless
there is a good reason for using some other
procedure.
Recommended Bioassay Procedures consider the
basic elements that are believed to be important
in obtaining reliable and reproducible results in
laboratory bioassays. An attempt has been made
to adopt the best acceptable procedures based
on current evidence and opinion, although it is
recognized that alternative procedures may be
adequate. Improvements in the procedures are
being considered and tested, and revisions will
be made when necessary. Comments and
suggestions are encouraged.
Director, National Water Quality Lab (NWQL)
Committee on Aquatic Bioassays, NWQL
13
-------
Fathead Minnow Pimephales promelas
Rafinesque Chronic Tests
April, 1971
(Revised January, 1972)
1.0 PHYSICAL SYSTEM
1.1 Diluter
Proportional diluters (Mount and Brungs,
1967) should be employed for all long-term
exposures. Check the operation of the diluter
daily, either directly or through measurement of
toxicant concentrations. A minimum of five
toxicant concentrations and one control should
be used for each test with a dilution factor of
not less than 0.30. An automatically triggered
emergency aeration and alarm system must be
installed to alert staff in case of diluter, tempera-
ture control or water supply failure.
1.2 Toxicant Mixing
A container to promote mixing of toxicant-
bearing and w-cell water should be used between
diluter and tanks for each concentration.
Separate delivery tubes should run from this
container to each duplicate tank. Check at least
once every month to see that the intended
amounts of water are going to each duplicate
tank or chamber.
1.3 Tank
Two arrangements of test tanks (glass, or
stainless steel with glass ends) can be utilized:
a. Duplicate spawning tanks measuring 1X1
X 3 ft. long with a one sq. ft. portion at
one end screened off and divided in half for
the progeny. Test water is to be delivered
separately to the larval and spawning
chambers of each tank, with about one-
third the water volume going to the former
chamber as to the latter.
b. Duplicate spawning tanks measuring 1 X 1
X 2 ft. long with a separate duplicate
progeny tank for each spawning tank. The
larval tank for each spawning tank should
be a minimum of 1 cu. ft. dimensionally
and divided to form two separate larval
chambers with separate standpipes, or
separate 1/2 sq. ft. tanks may be used. Test
water is to be supplied by delivery tubes
from the mixing cells described in Step 2
above.
Test water depth in tanks and chambers for
both a and b above should be 6 inches.
1.4 Flow Rate
The flow rate to each chamber (larval or
adult) should be equal to 6 to 10 tank
volumes/24 hr.
1.5 Aeration
Total dissolved oxygen levels should never be
allowed to drop below 60% of saturation, and
flow rates must be increased if oxygen levels do
drop below 60%. As a first alternative, flow rates
can be increased above those specified in 1.4.
Only aerate (with oil free air) if testing a non-
volatile toxic agent, and then as a last resort to
maintain dissolved oxygen at 60% of saturation.
1.6 Cleaning
All adult tanks, and larvae tanks and chambers
after larvae swim-up, must be siphoned a mini-
mum of 2 times weekly and brushed or scraped
when algal or fungus growth becomes excessive.
1.7 Spawning Substrate
Use spawning substrates made from inverted
cement and asbestos halved, 3-inch ID drain tile,
or the equivalent, each of these being 3 inches
long.
1.8 Egg Cup
Egg incubation cups are made from either
3-inch sections of 2-inch OD (1 1/2-inch ID)
polyethylene water hose or 4-oz., 2-inch OD
round glass jars with the bottoms cut off. One
end of the jar or hose sections is covered with
15
-------
BIOLOGICAL METHODS
stainless steel or nylon screen (with a minimum
of 40 meshes per inch). Cups are oscillated in
the test water by means of a rocker arm appara-
tus driven by a 2 r.p.m. electric motor (Mount,
1968). The vertical-travel distance of the cups
should be 1 to 1 1/2 inches.
1.9 Light
The lights used should simulate sunlight as
nearly as possible. A combination of Duro-Test
(Optima FS)1 -2 and wide spectrum Grow-lux3
fluorescent tubes has proved satisfactory at the
NWQL.
1.10 Photoperiod
The photoperiods to be used (Appendix A)
simulate the dawn to dusk times of Evansville,
Indiana. Adjustments in day-length are to be
made on the first and fifteenth day of every
Evansville test month. The table is arranged so
that adjustments need be made only in the dusk
times. Regardless of the actual date that the
experiment is started, the Evansville test photo-
period should be adjusted so that the mean or
estimated hatching date of the fish used to start
the experiment corresponds to the Evansville
test day-length for December first. Also, the
dawn and dusk times listed in the table need not
correspond to the actual times where the experi-
ment is being conducted. To illustrate these
points, an experiment started with 5-day-old
larvae in Duluth, Minnesota, on August 28
(actual date), would require use of a December 5
Evansville test photoperiod, and the lights could
go on anytime on that day just so long as they
remained on for 10 hours and 45 minutes. Ten
days later (Sept. 7 actual date, Dec. 15 Evans-
ville test date) the day-length would be changed
to 10 hours and 30 minutes. Gradual changes in
light intensity at dawn and dusk (Drummond
and Dawson, 1970), if desired, should be in-
cluded within the day-lengths shown, and should
not last for more than 1/2 hour from full on to
full off and vice versa.
1 Mention of trade names does not constitute endorsement.
Duro-Test, Inc., Hammond, Ind.
Sylvania, Inc., New York, N. Y.
1.11 Temperature
Temperature should not deviate instanta-
neously from 25°C by more than 2°C and
should not remain outside the range of 24 to
26°C for more than 48 hours at a time. Temper-
ature should be recorded continuously.
1.12 Disturbance
Adults and larvae should be shielded from
disturbances such as people continually walking
past the chambers, or from extraneous lights
that might alter the intended photoperiod.
1.13 Construction Materials
Construction materials which contact the
diluent water should not contain leachable sub-
stances and should not sorb significant amounts
of substances from the water. Stainless steel is
probably the preferred construction material.
Glass absorbs some trace organics significantly.
Rubber should not be used. Plastic containing
fillers, additives, stabilizers, plasticizers, etc.,
should not be used. Teflon, nylon, and their
equivalents should not contain leachable
materials and should not sorb significant
amounts of most substances. Unplasticized poly-
ethylene and polypropylene should not contain
leachable substances, but may sorb very signifi-
cant amounts of trace organic compounds.
1.14 Water
The water used should be from a well or
spring if at all possible, or alternatively from a
surface water source. Only as a last resort should
water from a chlorinated municipal water supply
be used. If it is thought that the water supply
could be conceivably contaminated with fish
pathogens, the water should be passed through
an ultraviolet or similar sterilizer immediately
before it enters the test system.
2.0 BIOLOGICAL SYSTEM
2.1 Test Animals
If possible, use stocks of fathead minnows
from the National Water Quality Laboratory in
Duluth, Minnesota or the Fish Toxicology
16
-------
FATHEAD MINNOW BIO ASSAY
Laboratory in Newtown, Ohio. Groups of
starting fish should contain a mixture of
approximately equal number of eggs or larvae
from at least three different females. Set aside
enough eggs or larvae at the start of the test to
supply an adequate number of fish for the acute
mortality bioassays used in determining appli-
cation factors.
2.2 Beginning Test
In beginning the test, distribute 40 to 50 eggs
or 1-to 5-day-old larvae per duplicate tank using
a stratified random assignment (see 4.3). All
acute mortality tests should be conducted when
the fish are 2 to 3 months old. If eggs or 1-to
5-day-old larvae are not available, fish up to 30
days of age may be used to start the test. If fish
between 20 and 60 days old are used, the
exposure should be designated a partial chronic
test. Extra test animals may be added at the
beginning so that fish can be removed periodi-
cally for special examinations (see 2.12.) or for
residue analysis (see 3.4).
2.3 Food
Feed the fish a frozen trout food (e.g., Oregon
Moist). A minimum of once daily, fish should be
fed ad libitum the largest pellet they will take.
Diets should be supplemented weekly with live
or frozen-live food (e.g., Daphnia, chopped
earthworms, fresh or frozen brine shrimp, etc.).
Larvae should be fed a fine trout starter a
minimum of 2 times daily, ad libitum; one
feeding each day of live young zoo plankton
from mixed cultures of small copepods, rotifers,
and protozoans is highly recommended. Live
food is especially important when larvae are just
beginning to feed, or about 8 to 10 days after
egg deposition. Each batch of food should be
checked for pesticides (including DDT, TDE,
dieldrin, lindane, methoxychlor, endrin, aldrin,
BHC, chlordane, toxaphene, 2,4-D, and PCBs),
and the kinds and amounts should be reported
to the project officer or recorded.
2.4 Disease
Handle disease outbreaks according to their
nature, with all tanks receiving the same treat-
ment whether there seems to be sick fish in all
of them or not. The frequency of treatment
should be held to a minimum.
2.5 Measuring Fish
Measure total lengths of all starting fish at 30
and 60 days by the photographic method used
by McKim and Benoit (1971). Larvae or juve-
niles are transferred to a glass box containing 1
inch of test water. Fish should be moved to and
from this box in a water-filled container, rather
than by netting them. The glass box is placed on
a translucent millimeter grid over a fluorescent
light platform to provide background illumi-
nation. Photos are then taken of the fish over
the millimeter grid and are enlarged into 8 by 10
inch prints. The length of each fish is sub-
sequently determined by comparing it to the
grid. Keep lengths of discarded fish separate
from those of fish that are to be kept.
2.6 Thinning
When the starting fish are sixty (± 1 or 2) days
old, impartially reduce the number of surviving
fish in each tank to 15. Obviously injured or
crippled individuals may be discarded before the
selection so long as the number is not reduced
below 15; be sure to record the number of
deformed fish discarded from each tank. As a
last resort in obtaining 15 fish per tank, 1 or 2
fish may be selected for transfer from one
duplicate to the other. Place five spawning tiles
in each duplicate tank, separated fairly widely to
reduce interactions between male fish guarding
them. One should also be able to look under
tiles from the end of the tanks. During the
spawning period, sexually maturing males must
be removed at weekly intervals so there are no
more than four per tank. An effort should be
made not to remove those males having well
established territories under tiles where recent
spawnings have occurred.
2.7 Removing Eggs
Remove eggs from spawning tiles starting at
12:00 noon Evansville test time (Appendix A)
each day. As indicated in Step 1.10, the test
time need not correspond to the actual time
where the test is being conducted. Eggs are
loosened from the spawning tiles and at the
17
-------
BIOLOGICAL METHODS
same time separated from one another by lightly
placing a finger on the egg mass and moving it in
a circular pattern with increasing pressure until
the eggs begin to roll. The groups of eggs should
then be washed into separate, appropriately
marked containers and subsequently handled
(counted, selected for incubation, or discarded)
as soon as possible after all eggs have been re-
moved and the spawning tiles put back into the
test tanks. All egg batches must be checked
initially for different stages of development. If it
is determined that there is more than one
distinct stage of development present, then each
stage must be considered as one spawning and
handled separately as described in Step 2.8.
2.8 Egg Incubation and Larval Selection
Impartially select 50 unbroken eggs from
spawnings of 50 eggs or more and place them in
an egg incubator cup for determining viability
and hatchability. Count the remaining eggs and
discard them. Viability and hatchability deter-
minations must be made on each spawning (>49
eggs) until the number of spawnings (>49 eggs)
in each duplicate tank equals the number of
females in that tank. Subsequently, only eggs
from every third spawning (>49 eggs) and none
of those obtained on weekends need be set up to
determine hatchability; however, weekend
spawns must still be removed from tiles and the
eggs counted. If unforeseen problems are encoun-
tered in determining egg viability and hatch-
ability, additional spawnings should be sampled
before switching to the setting up of eggs from
every third spawning. Every day, record the live
and dead eggs in the incubator cups, remove the
dead ones, and clean the cup screens. Total
numbers of eggs accounted for should always
add up to within two of 50 or the entire batch is
to be discarded. When larvae begin to hatch,
generally after 4 to 6 days, they should not be
handled again or removed from the egg-cups
until all have hatched. Then, if enough are still
alive, 40 of these are eligible to be transferred
immediately to a larval test chamber. Those
individuals selected out to bring the number
kept to 40 should be chosen impartially. Entire
egg-cup-groups not used for survival and growth
studies should be counted and discarded.
2.9 Progeny Transfer
Additional important information on hatch-
ability and larval survival is to be gained by
transferring control eggs immediately after
spawning to concentrations where spawning is
reduced or absent, or to where an affect is seen
on survival of eggs or larvae, and by transferring
eggs from these concentrations to the control
tanks. One larval chamber in, or corresponding
to, each adult tank should always be reserved for
eggs produced in that tank.
2.10 Larval Exposure
From early spawnings in each duplicate tank,
use the larvae hatched in the egg incubator cups
(Step 2.8. above) for 30 or 60 day growth and
survival exposures in the larval chambers. Plan
ahead in setting up eggs for hatchability so that
a new group of larvae is ready to be tested for
30 or 60 days as soon as possible after the
previously tested group comes out of the larval
chambers. Record mortalities, and measure total
lengths of larvae at 30 and, if they are kept, 60
days posthatch. At the time the larval test is
terminated they should also be weighed. No fish
(larvae, juveniles, or adults) should be fed within
24 hr's. of when they are to be weighed.
2.11 Parental Termination
Parental fish testing should be terminated
when, during the receding day-length photo-
period, a one week period passes in which no
spawning occurs in any of the tanks. Measure
total lengths and weights of parental fish; check
sex and condition of gonads. The gonads of
most parental fish will have begun to regress
from the spawning condition, and thus the dif-
ferences between the sexes will be less distinct
now than previously. Males and females that are
readily distinguishable from one another because
of their external characteristics should be
selected initially for determining how to
differentiate between testes and ovaries. One of
the more obvious external characteristics of
females that have spawned is an extended, trans-
parent anal canal (urogenital papilla). The
gonads of both sexes will be located just ventral
to the kidneys. The ovaries of the females at this
time will appear transparent, but perhaps con-
18
-------
FATHEAD MINNOW BIOASSAY
taining some yellow pigment, coarsely granular,
and larger than testes. The testes of males will
appear as slender, slightly milky, and very finely
granular strands. Fish must not be frozen before
making these examinations.
2.12 Special Examinations
Fish and eggs obtained from the test should
be considered for physiological, biochemical,
histological and other examinations which may
indicate certain toxicant-related effects.
2.13 Necessary Data
Data that must be reported for each tank of a
chronic test are:
a. Number and individual total length of
normal and deformed fish at 30 and 60
days; total length, weight and number of
either sex, both normal and deformed, at
end of test.
b. Mortality during the test.
c. Number of spawns and eggs.
d. Hatchability.
e. Fry survival, growth, and deformities.
3.0 CHEMICAL SYSTEM
3.1 Preparing a Stock Solution
If a toxicant cannot be introduced into the
test water as is, a stock solution should be pre-
pared by dissolving the toxicant in water or an
organic solvent. Acetone has been the most
widely used solvent, but dimethylformanide
(DMF) and triethylene glycol may be preferred
in many cases. If none of these solvents are
acceptable, other water-miscible solvents such as
methanol, ethanol, isopropanol, acetonitrile,
dimethylacetamide (DMAC), 2-ethoxyethanol,
glyme (dimethylether of ethylene glycol,
diglyme (dimethyl ether of diethylene glycol)
and propylene glycol should be considered.
However, dimethyl sulfoxide (DMSO) should
not be used if at all possible because of its
biological properties.
Problems of rate of solubilization or solubility
limit should be solved by mechanical means if at
all possible. Solvents, or as a last resort, sur-
factants, can be used for this purpose, pnly after
they have been proven to be necessary in the
actual test system. The suggested surfactant is
p-tert-octylphenoxynonaethoxy-ethanol (p-1, 1,
3, 3-tetramethylbutylphenoxynonaethoxy-
ethanol, OPE10) (Triton X-100, a product of
the Rohm and Haas Company, or equivalent).
The use of solvents, surfactants, or other
additives should be avoided whenever possible.
If an additive is necessary, reagent grade or
better should be used. The amount of an
additive used should be kept to a minimum, but
the calculated concentration of a solvent to
which any test organisms are exposed must
never exceed one one-thousandth of the 96-hr.
LC50 for test species under the test conditions
and must never exceed one gram per liter of
water. The calculated concentration of sur-
factant or other additive to which any test
organisms are exposed must never exceed one-
twentieth of the concentration of the toxicant
and must never exceed one-tenth gram per liter
of water. If any additive is used, two sets of
controls must be used, one exposed to no addi-
tives and one exposed to the highest level of
additives to which any other organisms in the
test are exposed.
3.2 Measurement of Toxicant Concentration
As a minimum, the concentration of toxicant
must be measured in one tank at each toxicant
concentration every week for each set of dupli-
cate tanks, alternating tanks at each concen-
tration from week to week. Water samples
should be taken about midway between the top
and bottom and the sides of the tank and should
not include any surface scum or material stirred
up from the bottom or sides of the tank.
Equivolume daily grab samples can be com-
posited for a week if it has been shown that the
results of the analysis are not affected by storage
of the sample.
Enough grouped grab samples should be
analyzed periodically throughout the test to
determine whether or not the concentration of
toxicant is reasonably constant from day to day
in one tank and from one tank to its duplicate.
If not, enough samples must be analyzed
weekly throughout the test to show the vari-
ability of the toxicant concentration.
19
-------
BIOLOGICAL METHODS
3.3 Measurement of Other Variables
Temperature must be recorded continuously
(see 1.11.).
Dissolved oxygen must be measured in the
tanks daily, at least five days a week on an alter-
nating basis, so that each tank is analyzed once
each week. However, if the toxicant or an
additive causes a depression in dissolved oxygen,
the toxicant concentration with the lowest dis-
solved oxygen concentration must be analyzed
daily in addition to the above requirement.
A control and one test concentration must be
analyzed weekly for pH, alkalinity, hardness,
acidity, and conductance, or more often, if
necessary, to show the variability in the test
water. However, if any of these characteristics
are affected by the toxicant,the tanks must be
analyzed for that characteristic daily, at least
five days a week, on an alternating basis so that
each tank is analyzed once every other week.
At a minimum, the test water must be ana-
lyzed at the beginning and near the middle of
the test for calcium, magnesium, sodium, po-
tassium, chloride, sulfate, total solids, and total
dissolved solids.
3.4 Residue Analysis
When possible and deemed necessary, mature
fish, and possibly eggs, larvae, and juveniles,
obtained from the test, should be analyzed for
toxicant residues. For fish, muscle should be
analyzed, and gill, blood, brain, liver, bone,
kidney, GI tract, gonad, and skin should be con-
sidered for analysis. Analyses of whole organ-
isms may be done in addition to, but should not
be done in place of, analyses of individual
tissues, especially muscle.
3.5 Methods
When they will provide the desired infor-
mation with acceptable precision and accuracy,
methods described in Methods for Chemical
Analysis of Water and Wastes (EPA, 1971)
should be used unless there is another method
which requires much less time and can provide
the desired information with the same or better
precision and accuracy. At a minimum, accuracy
should be measured using the method of known
additions for all analytical methods for tox-
icants. If available, reference samples should be
analyzed periodically for each analytical
method.
4.0 STATISTICS
4.1 Duplicates
Use true duplicates for each level of toxic
agent, i.e., no water connections between dupli-
cate tanks.
4.2 Distribution of Tanks
The tanks should be assigned to locations by
stratified random assignment (random assign-
ment of one tank for each level of toxic agent in
a row followed by random assignment of the
second tank for each level of toxic agent in
another or an extension of the same row).
4.3 Distribution of Test Organisms
The test organisms should be assigned to tanks
by stratified random assignment (random assign-
ment of one test organism to each tank, random
assignment of a second test organism to each
tank, etc.).
5.0 MISCELLANEOUS
5.1 Additional Information
All routine bioassay flow-through methods
not covered in this procedure (e.g., physical and
chemical determinations, handling of fish)
should be followed as described in Standard
Methods for the Examination of Water and
Waste water, (American Public Health Associ-
ation, 1971), or information requested from
appropriate persons at Duluth or Newtown.
5.2 Acknowledgments
These procedures for the fathead minnow
were compiled by John Eaton for the Commit-
tee on Aquatic Bioassays. The participating
members of this committee are: Robert Andrew,
John Arthur, Duane Benoit, Gerald Bouck,
William Brungs, Gary Chapman, John Eaton,
John Hale, Kenneth Hokanson, James McKim,
Quentin Pickering, Wesley Smith, Charles
Stephan, and James Tucker.
20
-------
FATHEAD MINNOW BIOASSAY
6.0 REFERENCES
For additional information concerning flow through bioassays with fathead minnows, the following references are listed:
American Public Health Association. 1971. Standard methods for the examination of water and wastewater. 13th ed. APHA. New
York.
Brungs, William A. 1969. Chronic toxicity of zinc to the fathead minnow, Pimephales promelas Rafinesque. Trans. Amer. Fish. Soc.
98(2): 272-279.
Brungs, William A. 1971. Chronic effects of low dissolved oxygen concentrations on the fathead minnow (Pimephales promelas ), J.
Fish. Res. Bd. Canada, 28(8): 1119-1123.
Brungs, William A. 1971. Chronic effects of constant elevated temperature on the fathead minnow (Pimephales promelas). Trans.
Amer. Fish. Soc. 100(4): 659-664.
Carlson, Dale R. 1967. Fathead minnow, Pimephales promelas Rafinesque, in the Des Moines River, Boone County, Iowa, and the
Skunk River drainage, Hamilton and Story Counties, Iowa. Iowa State J. Sci. 41(3): 363-374.
Drummond, Robert A., and Walter F. Dawson. 1970. An inexpensive method for simulating Diel patterns of lighting in the laboratory.
Trans. Amer. Fish. Soc. 99(2):434-435.
Isaak, Daniel. 1961. The ecological life history of the fathead minnow, Pimephales promelas (Rafinesque ).Ph.D. Thesis, Library, Univ.
of Minnesota.
Markus, Henry C. 1934. Life history of the fathead minnow (Pimephales promelas j, Copeia, (3): 116-122.
McKim, J. M., and D. A. Benoit. 1971. Effect of long-term exposures to copper on survival, reproduction, and growth of brook trout
Salvelinus fontinalis (Mitchill). J. Fish. Res. Bd. Canada, 28: 655-662.
Mount, Donald I. 1968. Chronic toxicity of copper to fathead minnows (Pimephales promelas, Rafinesque). Water Res. 2: 215-223.
Mount, Donald I., and William Brungs. 1967. A simplified dosing apparatus for fish toxicology studies. Water Res. 1: 21-29.
Mount, Donald I., and Charles E. Stephan. 1967. A method for establishing acceptable toxicant limits for fish — malathion and the
butoxyethanol ester of 2,4-D. Trans. Amer. Fish. Soc. 96(2): 185-193.
Mount, Donald I., and Charles E. Stephan. 1969. Chronic toxicity of copper to the fathead minnow (Pimephales promelas) in soft
water. J. Fish. Res. Bd. Canada, 26(9): 2449-2457.
Mount, Donald I., and Richard E. Warner. 1965. A serial-dilution apparatus for continuous delivery of various concentrations of
materials in water. PHS Publ. No. 999-WP-23. 16 pp.
Pickering, Quentin H., and Thomas O. Thatcher. 1970. The chronic toxicity of linear alkylate sulfonate (LAS) to Pimephales promelas
Rafinesque. JWPCF, 42(2): 243-254.
Pickering, Quentin H., and William N. Vigor. 1965. The acute toxicity of zinc to eggs and fry of the fathead minnow. Progr. Fish-Cult.
27(3): 153-157.
Verma, Prabha. 1969. Normal stages in the development of Cyprinus carpio var. communis L. Acta biol. Acad. Sci. Hung. 21(2):
207-218.
21
-------
FATHEAD MINNOW BIOASSAY
Appendix A
Test (Evansville, Indiana) Photoperiod
For Fathead Minnow Chronic
Dawn to Dusk
Time
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
6:00
-4:45)
-4:30)
-4:30)
-4:45)
-5:15)
-5:45)
-6:15)
-7:00)
-7:30)
-8:15)
-8:45)
-9:15)
-9:30)
-9:45)
-9:45)
-9:30)
-9:00)
-8:30)
Date
DEC. 1
15
JAN. 1
15
FEB. 1
15
MAR. 1
15
APR. 1
15
MAY 1
15
JUNE 1
15
JULY 1
15
AUG. 1
15
Day-length (hour and minute)
10:45)
10:30)
10:30)
10:45)
11:15)
11:45)
12:15)
13:00)
13:30)
14:15)
14:45)
15:15)
15:30)
15:45)
15:45)
15:30)
15:00)
14:30)
5-month pre-spawning
growth period
4-month spawning
period
6:00-8:00) SEPT. 1 14:00)
6:00-7:30) 15 13:30)
6:00-6:45) OCT. 1 12:45) post spawning period
6:00-6:15) 15 12:15)
6:00-5:30) NOV. 1 11:30)
6:00-5:00) 15 11:00)
23
-------
Brook Trout Salvelinus fon finales
(Mitchill) Partial Chronic Tests
April, 1971
(Revised January, 1972)
1.0 PHYSICAL SYSTEM
1.1 Diluter
Proportional diluters (Mount and Brungs,
1967) should be employed for all long-term
exposures. Check the operation of the diluter
daily, either directly or through the measure-
ment of toxicant concentrations. A minimum of
five toxicant concentrations and one control
should be used for each test with a dilution
factor of not less than 0.30. An automatically
triggered emergency aeration and alarm system
must be installed to alert staff in case of diluter,
temperature control or water supply failure.
1.2 Toxicant Mixing
A container to promote mixing of toxicant-
bearing and w-cell water should be used between
diluter and tanks for each concentration.
Separate delivery tubes should run from this
container to each duplicate tank. Check to see
that the same amount of water goes to duplicate
tanks and that the toxicant concentration is the
same in both.
1.3 Tank
Each duplicate spawning tank (preferably
stainless steel) should measure 1.3 X 3 X 1 ft.
wide with a water depth of 1 foot and alevin-
juvenile growth chambers (glass or stainless steel
with glass bottom) 7 X 15 X 5 in. wide with a
water depth of 5 inches. Growth chambers can
be supplied test water by either separate delivery
tubes from the mixing cells described in Step 2
above or from test water delivered from the
mixing cell to each duplicate spawning tank. In
the second choice, test water must always flow
through growth chambers before entering the
spawning tank. Each growth chamber should be
designed so that the test water can be drained
down to 1 inch and the chamber transferred
over a fluorescent light box for photographing
the fish (see 2.10).
1.4 Flow Rate
Flow rates for each duplicate spawning tank
and growth chamber should be 6-10 tank
volumes/24 hr.
1.5 Aeration
Brook trout tanks and growth chamtvr« must
be aerated with oil free air unless the-,- are no
flow limitations and 60% of saturatir -i uin be
maintained. Total dissolved oxygen levels should
never be allowed to drop below 60% of satu-
ration.
1.6 Cleaning
All tanks and chambers must be siphoned
daily and brushed at least once per week. When
spawning commences, gravel baskets must be re-
moved and cleaned daily.
1.7 Spawning Substrates
Use two spawning substrates per duplicate
made of plastic or stainless steel which measure
at least 6 X 10 X 12 in. with 2 inches of .25 to
.50 inch stream gravel covering the bottom and
20 mesh stainless steel or nylon screen attached
to the ends for circulation of water.
1.8 Egg Cup
Egg incubation cups are made from 4-oz.
2-inch OD round glass jars with the bottoms cut
off and replaced with stainless steel or nylon
screen (40 meshes per inch). Cups are oscillated
in the test water by means of a rocker arm
apparatus driven by a 2 r.p.m. electric motor
(Mount, 1968).
1.9 Light
The lights used should simulate sunlight as
nearly as possible. A combination of Duro-Test
(Optima FS)1'2 and wide spectrum Gro-lux3
fluorescent tubes has proved satisfactory at the
NWQL.
1 Mention of trade names does not constitute endorsement.
2Duro-Test, Inc., Hammond, Ind.
3Sylvania, Inc., New York, N. Y.
25
-------
BIOLOGICAL METHODS
1.10 Photoperiod
The photoperiods to be used (Appendix A)
simulate the dawn to dusk times of Evansville,
Indiana. Evansville dates must correspond to
actual dates in order to avoid putting natural
reproductive cycles out of phase. Adjustments in
photoperiod are to be made on the first and
fifteenth of every Evansville test month. The
table is arranged so that adjustments need be
made only in the dusk times. The dawn and
dusk times listed in the table (Evansville test
time) need not correspond to the actual test
times where the test is being conducted. To
illustrate this point, a test started on March first
would require the use of the photoperiod for
Evansville test date March first, and the lights
could go on any time on that day just so long as
they remained on for twelve hours and fifteen
minutes. Fifteen days later the photoperiod
would be changed to thirteen hours. Gradual
changes in light intensity at dawn and dusk
(Drummond and Dawson, 1970), may be in-
cluded within the photoperiods shown, and
should not last for more than 1 /2 hour from full
on to full off and vice versa.
1.11 Temperature
Utilize the attached temperature regime (see
Appendix B). Temperatures should not deviate
instantaneously from the specified test tempera-
ture by more than 2°C and should not remain
outside the specified temperature ±1°C for more
than 48 hours at a time.
1.12 Disturbance
Spawning tanks and growth chambers must be
covered with a screen to confine the fish and
concealed in such a way that the fish will not be
disturbed by persons continually walking past
the system. Tanks and chambers must also be
shielded from extraneous light which can affect
the intended photoperiod or damage light-sensi-
tive eggs and alevins.
1.13 Construction Materials
Construction materials which contact the
diluent water should not contain leachable sub-
stances and should not sorb significant amounts
of substances from the water. Stainless steel is
probably the preferred construction material.
Glass absorbs some trace organics significantly.
Rubber should not be used. Plastic containing
fillers, additives, stabilizers, plasticizers, etc.,
should not be used. Teflon, nylon, and their
equivalents should not contain leachable
materials and should not sorb significant
amounts of most substances. Unplasticized pol-
yethylene and polypropylene should not contain
leachable substances, but may sorb very signifi-
cant amounts of trace organic compounds.
1.14 Water
The water used should be from a well or
spring if at all possible, or alternatively from a
surface water source. Only as a last resort should
water from a chlorinated municipal water supply
be used. If it is thought that the water supply
could be conceivably contaminated with fish
pathogens, the water should be passed through
an ultraviolet or similar sterilizer immediately
before it enters the test system.
2.0 BIOLOGICAL SYSTEM
2.1 Test Animals
Yearling fish should be collected no later than
March 1 and acclimated in the laboratory to test
temperature and water quality for at least one
month before the test is initiated. Suitability of
fish for testing should be judged on the basis of
acceptance of food, apparent lack of diseases,
and 2% or less mortality during acclimation with
no mortality two weeks prior to test. Set aside
enough fish to supply an adequate number for
short-term bioassay exposures used in deter-
mining application factors.
2.2 Beginning Test
Begin exposure no later than April 1 by dis-
tributing 12 acclimated yearling brook trout per
duplicate using a stratified random assignment
(see 4.3). This allows about a four-month
exposure to the toxicant before the onset of
secondary or rapid growth phase of the gonads.
Extra test animals may be added at the begin-
ning so that fish can be removed periodically for
special examinations (see 2.13), or for residue
analysis (see 3.4).
26
-------
BROOK TROUT BIO ASSAY
2.3 Food
Use a good frozen trout food (e.g., Oregon
Moist). Fish should be fed the largest pellet they
will take a minimum of two times daily. The
amount should be based on a reliable hatchery
feeding schedule. Alevins and early juveniles
should be fed trout starter a minimum of five
times daily. Each batch of prepared food should
be checked for pesticides (including DDT, TDE,
dieldrin, endrin, aldrin, BHC, chlordane, toxa-
phene, 2,4-D, and PCBs), and the kinds and
amounts should be reported to the project
officer or recorded.
2.4 Disease
Handle disease outbreaks according to their
nature, with all tanks receiving the same treat-
ment whether there seems to be sick fish in all
of them or not. The frequency of treatment
should be held to a minimum.
2.5 Measuring Fish
Record mortalities daily, and measure fish
directly at initiation of test, after three months
and at thinning (see 2.6) (total length and
weight). Fish should not be fed 24 hours before
weighing and lightly anesthetized with MS-222
to facilitate measuring (100 nig MS-222/liter
water).
2.6 Thinning
When secondary sexual characteristics are well
developed (approximately two weeks prior to
expected spawning), separate males, females and
undeveloped fish in each duplicate and ran-
domly reduce sexually mature fish (see 4.4) to
the desired number of 2 males and 4 females,
and discard undeveloped fish after exami-
nation. Place two spawning substrates (described
earlier) in each duplicate. Record the number of
mature, immature, deformed and injured males
and females in each tank and the number from
each category discarded. Measure total length
and weight of all fish in each category before
any are discarded and note which ones were dis-
carded.
2.7 Removing Eggs
Remove eggs from the redd at a fixed time
each day (preferably after 1:00 p.m. Evansville
time, so the fish are not disturbed during the
morning).
2.8 Egg Incubation and Viability
Impartially select 50 eggs from the first eight
spawnings of 50 eggs or more in each duplicate
and place them in an egg incubator cup for
hatch. The remaining eggs from the first eight
spawnings (>50 eggs) and all subsequent eggs
from spawnings should be counted and placed in
separate egg incubator cups for determining
viability (formation of neural keel after 11-12
days at 9°C). The number of dead eggs from
each spawn removed from the nest should be
recorded and discarded. Never place more than
250 eggs in one egg incubator cup. All eggs
incubated for viability are discarded after 12
days. Discarded eggs can be used for residue
analysis and physiological measurements of
toxicant-related effects.
2.9 Progeny Transfer
Additional important information on hatch-
ability and alevin survival can be gained by trans-
ferring control eggs immediately after spawning
to concentrations where spawning is reduced or
absent, or to where an affect is seen on survival
of eggs or alevin, and by transferring eggs from
these concentrations to the control tanks. Two
growth chambers for each duplicate spawning
tank should always be reserved for eggs pro-
duced in that tank.
2.10 Hatch and Alevin Thinning
Remove dead eggs daily from the hatchability
cups described in Step 2.8 above. When hatching
commences, record the number hatched daily in
each cup. Upon completion of hatch in any cup,
randomly (see 4.4) select 25 alevins from that
cup. Dead or deformed alevins must not be in-
cluded in the random selection but should be
counted as being dead or deformed upon hatch.
Measure total lengths of the 25 selected and
discarded alevins. Total lengths are measured by
the photographic method used by McKim and
Benoit (1971). The fish are transferred to a glass
box containing 1 inch of test water. They should
be moved to and from this box in a water filled
container, rather than by netting them. The glass
box is placed on a translucent millimeter grid
27
-------
BIOLOGICAL METHODS
over a fluorescent light box which provides
background illumination. Photos are then taken
of the fish over the millimeter grid and are
enlarged into 8X10 inch prints. The length of
each fish is subsequently determined by com-
paring it to the grid. Keep lengths of discarded
alevins separate from those which are kept. Place
the 25 selected alevins back into the incubator
cup and preserve the discarded ones for initial
weights.
2.11 Alevin-Juvenile Exposure
Randomly (see 4.4) select from the incuba-
tion cups two groups of 25 alevins each per
duplicate for 90-day growth and survival expo-
sures in the growth chambers. Hatching from
one spawn may be spread out over a 3-to 6-day
period; therefore, the median-hatch date should
be used to establish the 90-day growth and sur-
vival period for each of the two groups of alevins.
If it is determined that the median-hatch dates
for the five groups per duplicate will be more
thai three weeks apart, then the two groups of
25 i, evins must be selected from those which are
less than three weeks old. The remaining groups
in the duplicate which do not hatch during the
three-week period are used only for hatchability
results and then photographed for lengths and
preserved for initial weights. In order to equalize
the effects of the incubation cups on growth, all
groups selected for the 90-day exposure must
remain in the incubation cups three weeks
before they are released into the growth
chambers. Each of the two groups selected per
duplicate must be kept separate during the
90-day period. Record mortalities daily, along
with total lengths 30 and 60 days post-hatch,and
total length and weight at 90 days post-hatch.
Alevins and early juveniles should not be fed 24
hours before weighing. Total lengths are meas-
ured by transferring the growth chambers de-
scribed earlier to a translucent millimeter grid
over a fluorescent light box for photographing as
described in Step 2.10 above. Survival and
growth studies should be terminated after three
moi ths. Terminated fish can. be used for tissue
residue analysis and physiological measurements
of toxicant-related effects.
2.12 Parental Termination
All parental fish should be terminated when a
three-week period passes in which no spawning
occurs in any of the spawning tanks. Record
mortality and weigh and measure total length of
parental fish, check sex and condition of gonads
(e.g., reabsorption, degree of maturation, spent
ovaries, etc.) (see 3.4).
2.13 Special Examinations
Fish and eggs obtained from the test should
be considered for physiological, biochemical,
and histological investigations which may
indicate certain toxicant-related effects.
2.14 Necessary Data
Data that must be reported for each tank of a
chronic test are:
a. Number and individual weights and total
lengths of normal, deformed, and injured
mature and immature males and females at
initiation of test, three months after test
commences, at thinning and at the end of
test.
b. Mortality during the test.
c. Number of spawns and eggs. A mean
incubation time should be calculated using
date of spawning and the median-hatch
dates.
d. Hatchability.
e. Fry survival, growth and deformities.
3.0 CHEMICAL SYSTEM
3.1 Preparing a Stock Solution
If a toxicant cannot be introduced into the
test water as is, a stock solution should be pre-
pared by dissolving the toxicant in water or an
organic solvent. Acetone has been the most
widely used solvent, but dimethylformanide
(DMF) and triethylene glycol may be preferred
in many cases. If none of these solvents are
acceptable, other water-miscible solvents such as
methanol, ethanol, isopropanol, acetonitrile,
dimethylacetamide (DMAC), 2-ethoxyethanol,
glyme (dimethylether of ethylene glycoft
diglyme (dimethyl ether of diethylene glycol)
28
-------
and propylene glycol should be considered.
However, dimethyl sulfoxide (DMSO) should
not be used if at all possible because of its
biological properties.
Problems of rate of solubilization or solubility
limit should be solved by mechanical means if at
all possible. Solvents, or as a last resort, sur-
factants, can be used for this purpose only after
they have been proven to be necessary in the
actual test system. The suggested surfactant is
p-tert-octylphenoxynonaethoxyethanol (p-1, 1,
3, 3-tetramethylbutylphenoxynonaethoxy-
ethanol, OPE10) (Triton X-100, a product of
the Rohm and Haas Company, or equivalent).
The use of solvents, surfactants, or other
additives should be avoided whenever possible.
If an additive is necessary, reagent grade or
better should be used. The amount of an
additive used should be kept to a minimum, but
the calculated concentration of a solvent to
which any test organisms are exposed must
never exceed one one-thousandth of the 96-hr.
LC50 for test species under the test conditions
and must never exceed one gram per liter of
water. The calculated concentration of sur-
factant or other additive to which any test
organisms are exposed must never exceed one-
twentieth of the concentration of the toxicant
and must never exceed one-tenth gram per liter
of water. If any additive is used, two sets of
controls must be used, one exposed to no
additives and one exposed to the highest level of
additives to which any other organisms in the
test are exposed.
3.2 Measurement of Toxicant Concentration
As a minimum,the concentration of toxicant
must be measured in one tank at each toxicant
concentration every week for each set of
duplicate tanks, alternating tanks at each con-
centration from week to week. Water samples
should be taken about midway between the top
and bottom and the sides of the tank and should
not include any surface scum or material stirred
up from the bottom or sides of the tank.
Equivolume daily grab samples can be com-
posite lor a week if it has been shown that the
Jesuits of the analysis are not affected by storage
of the sample.
Enough grouped grab samples should be
analyzed periodically throughout the test to
determine whether or not the concentation of
toxicant is reasonably constant from day to day
in one tank and from one tank to its duplicate.
If not, enough samples must be analyzed weekly
throughout the test to show the variability of
the toxicant concentration.
3.3 Measurement of Other Variables
Temperature must be recorded continuously
(see 1.11).
Dissolved oxygen must be measured in the
tanks daily at least five days a week on an
alternating basis, so that each tank is analyzed
once each week. However, if the toxicant or an
additive causes a depression in dissolved oxygen,
the toxicant concentration with the lowest dis-
solved oxygen concentration must be analyzed
daily in addition to the above requirement.
A control and one test concentration must be
analyzed weekly for pH, alkalinity, hardness,
acidity, and conductance, or more often, if
necessary, to show the variability in the test
water. However, if any of these characteristics
are affected by the toxicant, the tanks must be
analyzed for that characteristic daily, at least
five days a week, on an alternating basis, so that
each tank is analyzed once every other week.
At a minimum, the test water must be
analyzed at the beginning and near the middle of
the chronic test for calcium, magnesium,
sodium, potassium, chloride, sulfate, conduct-
ance, total solid, and total dissolved solids.
3.4 Residue Analysis
When possible and deemed necessary, mature
fish, and possibly eggs, larvae, and juveniles,
obtained from the test, should be analyzed for
toxicant residues. For fish, muscle should be
analyzed, and gill, blood, brain, liver, bone,
kidney, GI tract, gonad, and skin should be
considered for analysis. Analyses of whole
organisms may be done in addition to, but
should not be done in place of, analyses of
individual tissues, especially muscle.
29
-------
BIOLOGICAL METHODS
3.5 Methods
When they will provide the desired infor-
mation with acceptable precision and accuracy,
methods described in Methods for Chemical
Analysis of Water and Wastes (EPA, 1971)
should be used unless there is another method
which requires much less time and can provide
the desired information with the same or better
precision and accuracy. At a minimum, accuracy
should be measured using the method of known
additions for all analytical methods for
toxicants. If available, reference samples should
be analyzed periodically for each jnalytical
method.
4.0 STATISTICS
4.1 Duplicates
Use true duplicates for each level of the toxic
agent, i.e., no water connections between dupli-
cate tanks.
4.2 Distribution of Tanks
The tanks should be assigned to locations by
stratified random assignment (random assign-
ment of one tank for each level of the toxic
agent in a row, followed by random assignment
of the second tank for each level of the toxic
agent in another or an extension of the same
row).
4.3 Distribution of Test Organisms
The test organisms should be assigned to tanks
by stratified random assignment (random assign-
ment of one test organism to each tank, random
assignment of a second test organism to each
tank, etc.).
4.4 Selection and Thinning Test Organisms
At time of selection or thinning of test
organisms the choice must be random (random,
as defined statistically).
5.0 MISCELLANEOUS
5.1 Additional Information
All routine bioassay flow- through methods
not covered in this procedure (e.g., physical and
chemical determinations, handling of fish)
should be followed as described in Standard
Methods for the Examination of Water and
Wastewater (American Public Health Associ-
ation, 1971).
5.2 Acknowledgments
These procedures for the brook trout were
compiled by J. M. McKim and D. A. Benoit for
the Committee on Aquatic Bioassays. The
participating members of this committee are:
Robert Andrew, John Arthur, Duane Benoit,
Gerald Bouck, William Brungs, Gary Chapman,
John Eaton, John Hale, Kenneth Hokanson,
James McKim, Quentin Pickering, Wesley Smith,
Charles Stephan, and James Tucker.
6.0 REFERENCES
For additional information concerning flow-through bioassay tests with brook trout, the following references are listed:
Allison, L. N. 1951. Delay of spawning in eatern brook trout by means of artificially prolonged light intervals. Prog. Fish-Cult. 13:
111-116.
American Public Health Association. 1971. Standard methods for the examination of water and wastewater. 13th ed. APHA, New
York.
Carson, B. W. 1955. Four years progress in the use of artificially controlled light to induce early spawning of brook trout. Prog.
Fish-Cult. 17:99-102.
Drummond, Robert A., and Walter F. Dawson. 1970. An inexpensive method for simulating Diel patterns of lighting in the laboratory.
Trans. Amer. Fis,h. Soc. 99(2): 434-435.
Fabricius, E. 1953. Aquarium observations on the spawning behavior of the chai,Salmo alpinus. Rep. Inst. Freshwater Res., Drotting-
holm, 34: 14-48.
Hale, J. G. 1968. Observations on brook trout, Salvelinus fontinalis spawning in 10-gallon aquaria. Trans. Amer. Fish. Soc. 97:
299-301.
Henderson, N. E. 1962. The annual cycle in the testis of the eastern brook trout, Salvelinus fontinalis (Mitchill).Can. J. Zool. 40:
631-645.
30
-------
BROOK TROUT BIOASSAY
Henderson, N. E. 1963. Influence of light and temperature on the reproductive cycle of the eastern brook trout Salvelinus fontinalis
(Mitchill). J. Fish. Res. Bd. Canada, 20(4): 859-897.
Hoover, E. E.. and H. E. Hubbard, 1937. Modification of the sexual cycle in trout by control of light. Copeia, 4: 206-210.
MacFadden, J. 1961. A population study of the brook trout Salvelinus fontinalis (Mitchill). Wildlife Soc. ?ab. N". 7.
McKim, J. M., and D. A. Benoit. 1971. Effect of long-term exposures to copper on survival, reproduction, and growth of brook trout
Salvelinus fontinalis (Mitchill). J. Fish. Res. Bd. Canada, 28: 655-662.
Mount, Donald I. 1968. Chronic toxicity of copper to fathead minnows (Pimephales promelas, Rafinesque). Water Res. 2: 215-223.
Mount, Donald I., and William Brungs. 1967. A simplified dosing apparatus for fish toxicology studies. Water Res. 1: 21-29.
Pyle, E. A. 1969. The effect of constant light or constant darkness on the growth and sexual maturity of brook trout. Fish. Res. Bull.
No. 31. The nutrition of trout, Cortland Hatchery Report No. 36, p 13-19.
U. S. Environmental Protection Agency. 1971. Methods for Chemical Analysis of Water and Wastes. Analytical Quality Control
Laboratory, Cincinnati, Ohio.
Wydoski, R. S., and E. L. Cooper. 1966. Maturation and fecundity of brook trout from infertile streams. J. Fish. Res. Bd. Canada,
23(5): 623-649.
31
-------
BIOLOGICAL METHODS
Appendix A
Test (Evansville, Indiana) Photoperiod
For Brook Trout Partial Chronic
Dawn to Dusk
Time Date Day-length (hour and minute)
6:00-6:15) MAR. 1 12:15)
6:00-7:00) 15 13:00)
6:00-7:30) APR. 1 13:30)
6:00-8:15) 15 14:15)
6:00-8:45) MAY 1 14:45)
6:00-9:15) 15 15:15)
6:00-9:30) JUNE 1 15:30) Juvenile-adult exposure
6:00-9:45) 15 15:45)
6:00-9:45) JULY 1 15:45)
6:00-9:30) 15 15:30)
6:00-9:00) AUG. 1 15:00)
6:00-8:30) 15 14:30)
6:00-8:00) SEPT. 1 14:00)
6:00-7:30) 15 13:30)
Spawning and egg incubation
6:00-6:45) OCT. 1 12:45)
6:00-6:15) 15 12:15)
6:00-5:30) NOV. 1 11:30)
6:00-5:00) 15 11:00)
6:00-4:45) DEC. 1 10:45)
6:00-4:30) 15 10:30)
6:00-4:30) JAN. 1 10:30) Alevin-juvenile exposure
6:00-4:45) 15 10:45)
6:00-5:15) FEB. 1 11:15)
6:00-5:45) 15 11:45)
32
-------
BROOK TROUT BIO ASSAY
Months
Mar.
Apr.
May
June
July
Aug.
Sept.
Oct.
Nov.
Dec.
Jan.
Feb.
Mar.
Appendix B
Temperature Regime for Brook Trout Partial Chronic
Temperature ° C
Juvenile-adult exposure
Spawning and egg incubation
Alevin-juvenile exposure
9
12
14
15
15
15
12
™» ••*
9
_9
~9
9
9
9
A constant temperature
must be established just
prior to spawning and egg
incubation, and maintained
throughout the 3-month
alevin-juvenile exposure.
33
-------
APPENDIX
-------
APPENDIX
Page
1.0 BENCH SHEETS 1
1.1 Phytoplankton Sedgwick-Rafter Count 1
1.2 Zooplankton Count 2
1.3 Plankton and Periphyton Diatom Analysis 3
1.4 Periphyton Sedgwick-Rafter Count 4
1.5 Plankton and Periphyton Pigment and Biomass 5
1.6 Macroinvertebrates 6
2.0 EQUIPMENT AND SUPPLIES 7
2.1 Plankton and Periphyton 7
2.2 Macroinvertebrates 8
2.3 Fish 11
3.0 UNITS OF MEASUREMENT CONVERSION FACTORS ... 13
-------
1.0 BENCH SHEETS
1.1 Phytoplankton Sedgwick-Rafter Co unt
River or Lake_
Station
State
Gentries
Ph y toplank ton Sedgwick-Rof Ier Count
Date Analyzed Station No.
Analyzed by
Date
Collected
CODE
ORGANISM
Tota
TALLY
C/ML.
1 coccoid blue -green algae per ml. -
Total filamentous
olue-gn
en algae
TOTALS
\
/
Total coccoid green algae-
1
Total filament <
Total gn
3us gre<
en fla
;n algae •<
ellatea-<
Total other pignented flagellates -<
\
e/ml.
Pennates
First check
Recorded
c/ml.
Wash, st
Wash, st
Most
Abundant Centric
Diatoms
Algae Shells
Live
Melos.lOthers
Totals
c/nl
Total live centric aiatomB
^^^^ c/ml'.''
Pennate Shells
Live Pennates
Total live pennate diatoms-
Remarks:
S-R Factor: ,-
TOTAL LIVE ALGAE
(C/ml}1-
-------
1.2 Zooplankton Count
Zooplankton Count
COEB
ORGANISM
ROT HERA
Keratella
Bracaionus
Polyarthra
Synchaeta
Trichocera
TALLY
C/LITER
Total Rotifers per liter-*
./
V
CLADOCERA
Bonlaa
paphnln
Molna
Ceriodaphnia
copKPonA
Nauplii
Cyclopa &
related genera
Dlaptoma
/
Total Crustacea per liter<
HEMATODiS(per liter)
OTHER mVEROKBRATES : (per liter)
\
\
Most
Abundant
Rotifers
Most
Abundant
Crustacea
flactor_
Analyzed l>y_
Date Analyzed
-------
1.3 Plankton and Periphyton Diatom Analysis
PLANKTON AND PEJtIPHTTON
DIATOM ANALYSIS
River
Live Centrlcs_
Live Bennates_
Total Live
3-R Count
Station
State
_Dead Centries_
_Dead Pennates_
Total Dead
Station HuBber_
Date Collected_
Analyzed by _
Date Analyzed _
Counting Tl»e
Species
Coscincxliscus
Cyclotrlla
Meneghiniana
f Meloeira
amb-
grai
Igua
lulata
distans
i Rhizosolenia
f Stephanodiscus
bantzschli
invlsltatus
astrea minutula
/Other cpntrics
I
fAchnanthes
| Amph
ilprora
Amphora
Asterlonella
formoaa
Caloneis
Cocconeis
. Cynatopleura
r Cyiab
ella
; Dlatoma vulg
are
Diploneis smith!!
' Epit
hernia
Eunotia
Code
FIRST
SECOHD
TJi-UU)
Total
FOUKTU
%
Percent
others
Ho. species
Species
Tragilarla crotonenais
construens
f FruBtulia
Gomphonema
I QcnphonelB
GyroaigBB
pfcridion circulare
NavlciUa
pfltzschla
fPinnularia
I
Eleuroslgma
[RholeoBphenla curvata
rstauronels
L
(Rhopalodla
Surlrella
Synedra
ulna
acus
Tabellarla
fenestrata
flocculosa
Total
*
Remarks:
Total count
-------
1.4 Periphyton Sedgwick-Rafter Count
FKRIPHYPON SEDGWICK-RAFTER COUNT
River or Lake Inclusive Dates
Station
Date Analyzed
State Analyzed by
CODE ORGANISM Tally c/mm2
Total coccold blue-green algae \
Total filamentous
blue-gre
en algae
\
,<
Gentries
Pennates
Total coccold green algae
Total filamentous green algae
Total
green flagellates
Other coccold algae
\
<
<
fS
\
\
Other plgmented flagellates
f^mento(iSbacter\a,Mdiifw^^'^
Protozoa
c/mm2
Diatoms
<
Cenrc Ses
Live Gentries
C/mm
Total live centric diatoms
Pennate ells
Live Pennates
<
Preservatlve
No. Slides
Area Scraped '_~
Scrapings diluted to __
First check Recorded
S-R Factor_
Remarks:
Total live pennate diatoms
TOTAL
(cells/mm2)
mis.
-------
1.5 Plankton and Periphyton Pigment and Biomass
PLANKTON AND PERIPHYTON
CHLOROPHYLL AND BIOMASS DATA
I. IDENTIFYING INFORMATION:
A. Station:
B. Date:
C. Method of Sample
Collection and Handling:
II. SPECTROPHOTOMETER DATA:
A. OPTICAL DENSITY MEASUREMENTS:
Instrument used:
Extract Dilution i
Volume Factor 750
Rep.
1.
2.
3.
k.
- Optical Density Readings 1 663
663b* 6^5 630 663a* ' b/a
*(b = before acidification; a = after acidification)
B. CHLOROPHYLL CALCULATIONS:
Concentration of Sample area Chlorophyll content
Chlorophyll in Extract or volume of sample
(i°8/l) (liters; m2) ( ug/1; rog/m2)
Chi a Chi b Chi c
Chi a Chi b Chi c
Rep.
1.
2.
3-
k.
III. FLUOROMETER DATA:
Instrument Used;
Reading Before (b) Reading After (a)
Acidification Acidification
Dilution
Factor
Rep.
1.
Reading
Rb
Sens.
Level (s) Ra
(S) , Rb/Rs
2.
3.
it.
IV. ORGANIC MATTER
Cruc.
No,
Rep.
(ASH- FREE WEIGHT)
Empty
Crucible
Weight
(A)
Weight Weight Sample
with Dry After Dry
Sample Firing Weight
(B) (C) (B-A)
Ash Organic
Free Matter
Weight (gm/m2)
(B-C)
2.
3.
4.
V. REMARKS:
-------
1.6 Macro in vertebrates
MACROINVERTEBRATE LAB BENCH SHEET
Name of water
Collected by
Sorted by
body
Lot No.
Station No.
Date collected
*
DIPTERA
TRICHOPTERA
PLECOPTERA
EPHEMEROPTERA
ODONATA
NEUROPTERA
HEMIPTERA
COLEOPTERA
L(N)1
Pi
TOTAL
#
DRY WGT
(mg)
*
CRUSTACEA
HIRUDINEA
NEMATODA
BIVALVIA
GASTROPODA
OTHER
TOTAL
#
DRY WGT
Total # of organisms
Total # of taxa
* Initials of taxonomists in this column
Total dry weight
Ash-free weight
1
L=larvae, N = nymph, P = pupae
-------
2.0 EQUIPMENT AND SUPPLIES
This section contains an abbreviated list of equipment and supplies used for the collection and
analysis of biological samples. The companies and addresses are listed alphabetically at the end of the
table. Mention of commercial sources or products in this section does not constitute endorsement by
the U. S. Environmental Protection Agency.
Item
2.1 Plankton and Periphyton
Sampling and field equipment
Water sampler, alpha bottle, nonmetallic, transpaient, 6 liter
Plankton sampler, Clarke-Bumpus, 12 inch, with No. 10 and No. 20 nets and buckets
Plankton towing net, No. 20 (173 mesh/inch)
Plankton net with bucket, Wisconsin style, No. 20 net (173 mesh/inch)
Submarine photometer, with deck cell
Laboratory equipment
Balance, analytical, 100 gm capacity, accuracy 0.1 mg.
Balance, Harvard Trip, double beam, (to balance loaded centrifuge tubes)
Centrifuge, clinical, Centricone, 8-place
Centrifuge, IEC, model UV, Refrigerated
Centrifuge head, 8-place, 100 ml
Centrifuge shields, cups
Centrifuge trunnion rings
Centrifuge tubes, plain, round bottom, polypropylene, 100 ml
Blood Cell Calculator (counter), 8-Key
Fluorometer, Turner 111 or equivalent, equipped with:
Red-sensitive photomultiplier tube No. R-136
Turner No. 1 10-853 blue lamp, T-5
Turner No. 1 10-856, lamp adaptor for T-5 lamp
Turner No. 1 10-005, Standard sample holder
Turner No. 110- , High-Sensitivity sample holder
Turner No. 110-871, flow-through cuvette
Corning filter No. CS-5-60 (excitation)
Corning filter No. CS-2-64 (emission)
Disposable vials for fluorometer, 12 X 75 mm, 5 ml, Kahn type
Hot-plate, Thermolyne HP-A1915B, thermostatically controlled (to dry
diatoms on cover glasses), 115 volts, 750 watts.
Hot-plate, Chromalox, 230 volts, 2000 watt, AC, three heat (to incinerate
diatom preparation on cover glasses).
Microscope and accessories (Americal Optical, Series 10T Trinocular Microstar,
or equivalent).
In-base illuminat r ard transformer.
Trinocular body.
Graduated mechanical stage.
Quadruple nose piece.
N.A. 1.25 condenser.
Condenser mount.
Objective, 4X, Achromatic.
Objective, 10X, Achromatic,
Objective, 20X, Achromatic, standard, must have working distance greater than
1 mm for Sedgwick-Rafter counts.
Objective, 45X, Achromatic.
Objective, 100X, Achromatic.
Wk'e field eyepieces, 10X,
Source*
(30)
(30)
(30)
(30)
(7)
(24)
(25)
Cat. No.
*
1160TT
37
2944-B50
Unit
8
8
16
Approx.
Cost (1973)
$ 150.00
400.00
41.00
92.00
500.00
1,000.00
50.00
100.00
850.00
50.00
30.00
20.00
9.00
110.00
2,000.00
30.00
30.00
1,500.00
*See list of suppliers at the end of this table.
-------
Item
Light meter
Muffle furnace, 1635 Temco, Thermolyne, 240 volts
Temperature control for muffle furnace, Amplitrol Proportioning Controller,
0-2400°F, for 240 volt furnace (recommended for use with Temco 1635).
Oven, Thermozone, forced draft, double walled, three shelves, 230 C.
Pipetting machine, automatic, large, BBL. (for dispensing preservative).
*Spectrophotometer, double-beam, recording, resolution 2 nm or better at
663 nm; Coleman-124 or equivalent.
Washer, mechanical, glassware, variable speed, Southern Cross, Model 300-B-2,
Complete.
Supplies
Cubitainer, 1 qt (approx 1 liter)
Cubitainer shipping carton, 1 qt
Bottles, pill, square, DURAGLAS, 3 ounce for periphyton samples. Do not use
caps supplied with bottles.
Caps, Polyseal, black, size 38, G. C.M.I, thread No. 400. Use on Duraglas bottles
above.
Crucibles, Coors, high form, porcelain, size 1, capacity 30 ml
Crucible covers for above, Size G
Desiccator, aluminum, with shelf
Merthiolate, powder No. 20, (Thimerosal, N.F.)
Metal plate, 5 X 10 X 1/8 inches, steel (to transfer cover glasses between
hot-plates).
Micrometer, eye-piece, whipple
Micrometer, stage (American Optical)
Mounting medium, HYRAX 1
Pipettes, disposable, Pasteur type, 5-3/4 inches
Sedgwick -Rafter Counting Chamber, as prescribed by "Standard Methods for the
examination of Water and Wastes."
Tissue grinder, glass, Duall, complete
Vials, Opticlear, Owens-Illinois, 3 drams, snap caps, for diatom preparation.
2.2 Macro invertebrates
Boat, flat bottom, 14-lb teet, Arkansas Traveler or Boston Whaler with winch
and davit, snatch-block meter wheel, and trailer, 18 hp Outboard motor, Life
jackets, other accessories
Cable fastening tools:
Cable clamps, 1/8 inch
Nicro-press sleeves, 1/8 inch
Nicro-press tool, 1/8 inch
Wire cutter, Felco
Wire thimbles, 1/8 inch
Cable, 1 /8 inch, galvanized steel
Large capacity, metal wash tubs
Core sampler, K. B,, multiple, and gravity corers
Hardboard multiplate sampler
Trawl net
Drift net, stream
Grabs
Ponar
Ekman, 6X6 inch
Petersen, 100 square inch
Weights for Petersen
Source
(29)
(24)
(1)
(8)
(8)
(16)
(24)
(24)
(24)
(13)
(16)
(16)
(3)
(23)
(30)
(12)
(21)
(17)
(20)
(30)
(30)
(30)
(30)
(30)
(30)
(30)
(30)
Cat. No.
Model 756
7750-M10
i
clear glass
amber glass
3319-B55
3319-D47
3747-C10
400
P5205-2
1801
sizeC
SK-3
7
2400
15
1725
196B
1750
1751
Unit
1 doz.
Idoz.
]/2 gross
'/2 gross
Vi gross
Case (36)
Case (72)
% ounce
1 ounce
1 pound
1 ounce
2'/2 gross
Gross
25
100
1
1
25
1 000 feet
1
1
1
2
1
1
1
1 pair
Approx.
Cost (1973)
100.00
180.00
230.00
350.00
320.00
330.00
7.00
4.00
8.00
15.00
11.00
25.00
20.00
22.00
2.00
7.00
95.00
18.00
32.00
10.00
8.00
9.00
10.00
11.00
3,000.00
3.00
6.00
32.00
7.00
2.00
89.00
3.00
225.00
7.50
100.00
76.00
200.00
78.00
200.00
25.00
-------
Item
Basket, Bar-B-Q, (RB-75) Tumbler
Sieve, US standard No. 30 (0.595 mm opening) and others as needed
Flow meter, TSK, (propeller type)
Flow meter, electromagnetic, two-axis
Mounting media, CMC-9AF
Mounting media, CMC-S
Low-temp bath
Water pump, epoxy-encapsulated, submersible and open air.
Sounding equipment and specialized gear
Large, constant temperature holding tanks with 1/3 hp water chiller, charcoal
Polyethylene bottles, dark bottles, tubing
Cahn electrobalance
Porcelain balls for baskets (2-inch diameter)
Porcelain multiplates
Counter, differential, 9 unit, Clay-Adams
Counter, hand tally
Magnifier, Dazor, 2X, floating, with illuminator and base.
Microscope, compound, trinocular, equipped for bright-field and phase microscopy
with 10X and 15X wide-field oculars, 4.0 X, 10X, 20X, 45X,and 100X bright-
field objectives, and 45X and 100X phase objectives.
Stereoscopic dissecting Microscope
Tessovar photomacrographic Zoom System
Camera body, 35 mm Zeiss Contarex, for Tessovar
Stirrer, magnetic
Aquaria (of various sizes)
Aquatic dip nets
Microscope Slides and Cover slips, Standard square, 15 mm
Vials, specimen, glass, 1 dram, 15 mm X 45 mm
Petri dish, ruled grid, 150 mm X 15 mm
Freeze dryer with freezing shelf
Vacuum oven
Source
(22)
(26)
(10)
(15)
(6)
(6)
(31)
(14)
(7,9,11)
(5)
(18)
(27)
(4)
(4)
(23)
(24)
(6)
(32)
(32)
(6)
(6)
(6)
(6)
(6)
(2)
(28)
(19)
Cat. No.
1
V 73250 L
313 T.S.
94370
1A-MD
MT-700
DTL
unlapped
B 41 20-4
' 3297-H10
375 A 95
49-65-01
10-2611
375AA4514
320A 10
320A210
315A57
315AA4094
10-800
5831
Unit
12
1 each
4 ounce
4 ounce
1
2
1
1
1 pound
1
1
2
1
1
1
1
1
1
10 gross
1 ounce
10 gross
12
1
1
Approx.
Cost (1973)
25.00
10.00
200.00
2,600.00
2.00
2.00
500.00
50.00
540.00
1,000.00
0.30
7.50
105.00
11.00
50.00
2,000.00
1,000.00
1,779.00
600.00
42.50
31.00
3.50
78.00
24.00
4,000.00
300.00
-------
Sources of equipment and supplies for plankton, periphyton, and macroinvertebrates
2.
Coleman Instruments
42 Madison St.
Maywood,IL 60153
Corning Glass Works
1470 Merchandise Mart
Chicago, IL 60654
3. Custom Research and Development Company, Inc.
Mt. Vernon Rd., Route l,Box 1586
Auburn, CA 95603
4. Ferro Corporation
P. O. Box 20
East Liverpool, OH 43920
5. Frigid Units, Inc.
3214 Sylvania Ave.
Toledo, OH 43613
6. General Biological Inc.
8200 S. Hoyne Ave.
Chicago, IL 60620
7. G-M Manufacturing & Instrument Company
2417 Third Ave.
New York, NY 10451
8. Hedwin Corporation
1209E. Lincolnway
Laporte, IN 46350
9. Hydro Products
11777 Sorrento Valley Rd.
San Diego, CA 92121
10. Inter Ocean, Inc.
3446 Kurtz St.
SanDiego.CA 92110
11. Kahl Scientific Instruments
P.O.Box 1166
ElCajon, CA 92022
12. Kontes Glass Company
Vineland, NJ 08360
13. Eli Lilly Company
307 E. McCarty St.
Indianapolis, IN 46206
14. March Manufacturing Company
Glenview, IL 60025
15. Marsh-McBirney, Inc.
2281 Lewis Ave.
RockviUe, MD 20851
16. Matheson Scientific
1850Greenleaf Ave.
Elk Grove Village, IL 60007
17. MonArk Boat Company
Monticello, AK 71655
18. Nalge Corporation
Rochester, NY 14602
19. National Appliance Company
P. O. Box 23008
Portland, OR 97223
20. National Telephone Supply Company
3100 Superior St.
Cleveland, OH 44114
21. Owens-Illinois
P. 0. Box 1035
Toledo, OH 43666
22. Paramont Wire, Inc.
1035 Westminster Ave.
Alhambra, CA 91803
23. Scientific Products
1210 Leon Place
Evanston, IL 60201
24. Arthur H. Thomas Company
Vine Street at Third
P. O. Box 779
Philadelphia, PA 19105
25. G. K. Turner, Assoc.
2524 Pulgas Ave.
Palo Alto, CA 94303
26. W. S. Tyler Company
Mentor, OH 44060
27. Ventron Instrument Corporation
7500 Jefferson St.
Paramont.CA 90723
28. Virtis Company
Gardiner, NY 12525
29. Weston Instruments, Inc.
614 Frelinghuysen Ave.
Newark, NJ 07114
30. Wildlife Supply Company
301 CassSt.
Saginaw, MI 48602
31. Wilkens-Anderson Company
4525 W. Division St.
Chicago, IL 60651
32. Carl Zeiss, Inc.
444 Fifth Ave.
New York, NY 10018
10
-------
2.3 Fish
Sources of information on fishery sampling equipment.
American Association for the Advancement of Science. Annual guide to scientific instruments (Published in Science).
American Society of Limnology and Oceanography. 1964. Sources of limnological and oceanographic apparatus and supplies. Special
Publ. No.l.IX:i-xxxii.
Oceanology International Yearbook/Directory.
Sinha, E. Z., and C. L. Kuehne. 1963. Bibliography on oceanographic instruments. 1. General. II. Waves, currents, and other
geophysical parameters. Meteorol. Geoastrophys. Abst. Amer. Meterol. Soc. 14:12424298; 1589-1637.
U.S. Fish and Wildlife Service. 1959. Partial list of manufacturers of fishing gear and accessories and vessel equipment. Fishery Leaflet
195.27pp.
Water Pollution Control Federation Yearbook.
11
-------
\ UNITED STATES
EPARTMENT OF
COMMERCE
OBLIGATION
UNITS OF MEASUREMENT
Conversion Factors and Special Tables
\
October, 1972
REPRINTED FROM
Units of Weight and Measure
International (Metric) and U.S. Customary
NBS Miscellaneous Publication 286
May, 1967
WITH THE COURTESY OF:
TECHNICAL INFORMATION OFFICE
NATIONAL ENVIRONMENTAL RESEARCH CENTER, CINCINNATI
U.S. ENVIRONMENTAL PROTECTION AGENCY
-------
CONTENTS
(Reprint includes only those items asterisked) Page
^INTRODUCTION _ _ 1
THE INTERNATIONAL SYSTEM 1
Prefixes 3
HISTORICAL OUTLINE
France 3
The United States 5
WEIGHTS AND MEASURES IN THE WORLD'S INDEPENDENT STATES
Metric 6
Nonmetric 6
IMPORTANT DATES IN U. S. METRIC HISTORY 7
SELECTED BIBLIOGRAPHY.... 8
^DEFINITIONS 9
.£.
Definitions of Units 9
^SPELLING AND SYMBOLS FOR UNITS 10
*
Some Units and Their Symbols 10
* UNITS OF MEASUREMENT—CONVERSION FACTORS
*Length 11
*Mass 12
•^Capacity, or Volume. 13
*Area 17
^SPECIAL TABLES
^Equivalents of Decimal and Binary Fractions of an Inch in Millimeters 18
y
International Nautical Miles and Kilometers 19
UNITS OF MEASUREMENT—TABLES OF EQUIVALENTS
Length 21
Mass .-. 127
Capacity, or Volume 161
Area 219
Chisholm, L.J., Units of Weight and Measure. International
(Metric) and U.S. Customary. (NBS Miscellaneous Publication
286). For sale by Superintendent of Documents, U.S. Government
Printing Office, Washington, D.C. 20402. Price $2.25.
-------
Units of Weight and Measure
International (Metric) and U.S. Customary
L. J. Chisholm
The primary purpose of this publication is to make available the most often
needed weights and measures conversion tables—conversions between the U. S.
Customary System and International (Metric) System. A secondary purpose is
to present a brief historical outline of the International (Metric) System—
following it from its country of origin, France, through its progress in the
United States.
Key Words: Conversion tables, International System (SI), Metric System,
U. S. Customary System, weights and measures, weights and
measures abbreviations, weights and measures systems, weights
and measures units.
Introduction
Two systems of weights and measures exist side by side in the United States today,
with roughly equal but separate legislative sanction: the U. S. Customary System and the
International (Metric) System. Throughout U. S. history, the Customary System (inherited
from, but now different from, the British Imperial System) has been, as its name implies,
customarily used; a plethora of Federal and State legislation has given it, through implica-
tion, standing as our primary weights and measures system. However, the Metric System
(incorporated in the scientists' new SI or Systeme International d'Unites) is the only sys-
tem that has ever received specific legislative sanction by Congress. The "Law of 1866"
reads:
It shall be lawful throughout the United States of America to employ the
weights and measures of the metric system; and no contract or dealing, or pleading
in any court, shall be deemed invalid or liable to objection because the weights or
measures expressed or referred to therein are weights or measures of the metric
system.1
Over the last 100 years, the Metric System has seen slow, steadily increasing use in
the United States and, today, is of importance nearly equal to the Customary System.
The International System *
* For up-to-date information on the international metric system,
see current edition of The International System of Units (Si),
Editors: Chester Page and Paul Vigoureux (NBS Special Publication
330). For sale by Superintendent of Documents, U.S. Government
Printing Office, Washington, D. C. 20402. Price 30 cents. For
NBS policy on the usage of SI, see NBS Technical News Bulletin
Vol. 55 No. 1, pp. 18-20, January 1971.
1 Act of 28 July 1866 (14 Stat. 339)—An Act to authorize the use of the Metric System of Weights and Measures.
-------
Six units have been adopted to serve as the base for the International System: *
Length meter
Mass kilogram
Time second
Electric current ampere
Thermodynamic temperature kelvin
Light intensity candela
Some of the other more frequently used units of the SI and their symbols and, where
applicable, their derivations are listed below.
SUPPLEMENTARY UNITS
Quantity
Plane angle
Solid angle
Unit
radian
steradian
Symbol
rad
sr
Derivation
DERIVED UNITS
Area
Volume
Frequency
Density
Velocity
Angular velocity
Acceleration
Angular acceleration
Force
Pressure
Kinematic viscosity
Dynamic viscosity
Work, energy, quantity of heat
Power
Electric charge
Voltage, potential difference,
electromotive force
Electric field strength
Electric resistance
Electric capacitance
Magnetic flux
Inductance
Magnetic flux density
Magnetic field strength
Magnetomotive force
Flux of light
Luminance
Illumination
square meter
cubic meter
hertz
kilogram per cubic meter
meter per second
radian per second
meter per second squared
radian per second squared
newton
newton per square meter
square meter per second
newton-second per square meter
joule
watt
coulomb
volt
volt per meter
ohm
farad
weber
henry
tesla
ampere per meter
ampere
lumen
candela per square meter
lux
m8
Hz
kg/m3
m/s
rad/s
m/s2
rad/s2
N
N/m2
m2/s
N-s/m2
J
W
c
V
V/m
n
F
Wb
H
T
A/m
A
1m
cd/m2
Ix
(kg-m/s2)
(N-m)
(J/8)
(A-s)
(W/A)
(V/A)
(A-s/V)
(V-s)
(V-s/A)
(Wb/m2)
(cd • sr)
(lm/m2)
* Recent (1971) addition of the mole as the unit for amount of
substance brings the total to seven units. See asterisked foot-
note on page 1.
-------
Definitions
In its original conception, the meter was the fundamental unit of the Metric System,
and all units of length and capacity were to be derived directly from the meter which was
intended to be equal to one ten-millionth of the earth's quadrant. Furthermore, it was
originally planned that the unit of mass, the kilogram, should be identical with the mass of
a cubic decimeter of water at its maximum density. The units of length and mass are now
denned independently of these conceptions.
In October 1960 the Eleventh General (International) Conference on Weights and
Measures redefined the meter as equal to 1 650 763.73 wavelengths of the orange-red radia-
tion in vacuum of krypton 86 corresponding to the unperturbed transition between the
2pio and 5rfs levels.
The kilogram is independently defined as the mass of a particular platinum-iridium
standard, the International Prototype Kilogram, which is kept at the International Bureau
of Weights and Measures in Sevres, France.
The liter has been defined, since October 1964, as being equal to a cubic decimeter.
The meter is thus a unit on which is based all metric standards and measurements of length,
area, and volume.
Definitions of Units
Length
A meter is a unit of length equal to 1 650 763.73 wavelengths in a vacuum of the orange-
red radiation of krypton 86.
A yard is a unit of length equal to 0.914 4 meter.
Mass
A kilogram is a unit of mass equal to the mass of the International Prototype Kilogram.
An avoirdupois pound is a unit of mass equal to 0.453 592 37 kilogram.
Capacity, or Volume
A cubic meter is a unit of volume equal to a cube the edges of which are 1 meter.
A liter is a unit of volume equal to a cubic decimeter.
A cubic yard is a unit of volume equal to a cube the edges of which are 1 yard.
A gallon is a unit of volume equal to 231 cubic inches. It is used for measuring liquids
only.
A bushel is a unit of volume equal to 2 150.42 cubic inches. It is used for measuring dry
commodities only.
Area
A square meter is a unit of area equal to the area of a square the sides of which are 1
meter.
A square yard is a unit of area equal to the area of a square the sides of which are 1 yard.
-------
Spelling and Symbols for Units
The spelling of the names of units as adopted by the National Bureau of Standards
is that given in the list below. The spelling of the metric units is in accordance with that
given in the law of July 28, 1866, legalizing the Metric System in the United States.
Following the name of each unit in the list below is given the symbol that the Bureau
has adopted. Attention is particularly called to the following principles:
1. No period is used with symbols for units. Whenever "in" for inch might be confused
with the preposition "in", "inch" should be spelled out.
2. The exponents "2" and "3" are used to signify "square" and "cubic," respectively,
instead of the symbols "sq" or "cu," which are, however, frequently used in technical
literature for the U. S. Customary units.
3. The same symbol is used for both singular and plural.
Some Units and Their Symbols
Unit
acre
are
barrel
board foot
bushel
carat
Celsius, degree
centare
centigram
centiliter
centimeter
chain
cubic centimeter
cubic decimeter
cubic dekameter
cubic foot
cubic hectometer
cubic inch
cubic kilometer
cubic meter
cubic mile
cubic millimeter
cubic yard
decigram
deciliter
decimeter
dekagram
dekaliter
dekameter
dram, avoirdupois
Symbol
acre
a
bbl
fbm
bu
c
°C
ca
eg
cl
cm
ch
cm'
dm3
dam3
ft3
hm'
in3
km3
m3
mi3
mm3
yd3
dg
dl
dm
dag
dal
dam
dr avdp
Unit
fathom
foot
furlong
gallon
grain
gram
hectare
hectogram
hectoliter
hectometer
hogshead
hundredweight
inch
International
Nautical Mile
kelvin
kilogram
kiloliter
kilometer
link
liquid
liter
meter
microgram
micromch
microliter
mile
milligram
milli liter
Symbol
fath
ft
furlong
gal
grain
g
ha
hg
hi
hm
hhd
cwt
in
IJ4M
K
kg
kl
km
link
liq
liter
m
Mg
pin
Ml
mi
mg
ml
Unit
millimeter
minim
ounce
ounce, avoirdupois
ounce, liquid
ounce, troy
peck
pennyweight
pint, liquid
pound
pound, avoirdupois
pound, troy
quart, liquid
rod
second
square centimeter
square decimeter
square dekameter
square foot
square hectometer
square inch
square kilometer
square meter
square mile
square millimeter
square yard
stere
ton, long
ton, metric
ton, short
yard
Symbol
mm
minim
oz
oz avdp
liq oz
oz tr
peck
dwt
liq pt
Ib
Ib avdp
Ibtr
liq qt
rod
s
cm2
dm2
dam2
ft2
hm2
in2
km2
m2
mi2
mm2
yd2
stere
long ton
t
short ton
yd
10
-------
Units of Measurement—Conversion Factors *
Units of Length
To
To Convert from
Centimeters
Multiply by
Inches 0393 700 8
Feet 0.032 808 40
Yards 0.010 93G 13
Meters 0.01
To
Inches
Feet
Yards
Miles
Millimeters
Centimeters
Kilometers
To Convert from
Meters
Multiply by
39.370 08
3.280 840
1.093 613
0.000 621 37
1 000
100
0.001
To
Feet
Yards
Centimeters
Meters
To Convert from
Inches
Multiply by
0083 333 33
0.027 777 78
2.54
0.025 4
To Convert from
Feet
To
Multiply hv
Inches 12
Yards 0333 333 3
Miles 0000 !•<•> 3°
Centimeters . _ 30.48
Meters 0.304 8
Kilometers 0.000 30-i J
* All boldface figures are exact; the others generally are given to seven significant figures.
In using conversion factors, it is possible to perform division as well as the multiplication process shown
here. Division may be particularly advantageous where more than the significant figures published here are
required. Division may be performed in lieu of multiplication by using the reciprocal of any indicated mul-
tiplier as divisor. For example, to convert from centimeters to inches by division, refer to the table headed
"To Convert from Inches" and use the factor listed at "centimeters" (2.54) as divisor.
To
Inches
Feet ... .
Miles
Centimeters .
Meters
To Convert from
Yards
Multiply
36
- - . 3
0.000 56
. 91.44
0.914 4
by
3 18
To
Inches
Feet
Yards
Centimeters
Meters
Kilometers
To Convert from
Miles
Multiply by
63 360
5 280
1 760
160 934.4
1 609.344
1.609 344
11
-------
Units of Mass
To Convert from
Grams
To
Multiply by
Grains
Avoirdupois Drams.
Avoirdupois Ounces.
Troy Ounces
15.432 36
0.564 383 4
0.035 273 96
0.032 150 75
Troy Pounds 0.002 679 23
Avoirdupois Pounds 0.002 204 62
Milligrams 1 000
Kilograms 0.001
To Convert from
Metric Tons
To
Multiply by
Avoirdupois Pounds 2 204.623
Short Hundredweights 22.046 23
Short Tons 1.102311 3
Long Tons 0.984 206 5
Kilograms 1 000
To Convert from
Grains
To
Multiply by
Avoirdupois Drams 0.036 571 43
Avoirdupois Ounces 0.002 285 71
Troy Ounces 0.002 083 33
Troy Pounds 0.000 173 61
Avoirdupois Pounds _. 0.000 142 86
Milligrams 64.798 91
Grams 0.064 798 91
Kilograms 0.000 064 798 91
To
To Convert from
Avoirdupois Pounds
Multiply by
Grains 7 000
Avoirdupois Drams 256
Avoirdupois Ounces 16
Troy Ounces 14.58333
Troy Pounds 1.215278
Grams 453.592 37
Kilograms
Short Hundredweights..
Short Tons
Long Tons
Metric Tons.
0.453 592 37
0.01
0.000 5
0.000 446 428 6
0.000 453 592 37
To
To Convert from
Kilograms
Multiply by
Grains 15 432.36
Avoirdupois Drams.
Avoirdupois Ounces.
Troy Ounces. _
Troy Pounds
Avoirdupois Pounds.
Grams
Short Hundred weights .
Short Tons
Long Tons.
Metric Tons
564.383 4
35.273 96
32.150 75
2.679 229
2.204 623
000
0.022 046 23
0.001 102 31
0.000 984 2
0.001
To-
To Convert from
Avoirdupois Ounces
Multiply by
Grains 437.5
Avoirdupois Drams 16
TroyOunces - 0.9114583
Troy Pounds 0.075 954 86
Avoirdupois Pounds 0.062 5
Grams 28.349523 125
Kilograms 0.028 349 523 125
To
To Convert from
Short Hundredweights
Multiply by
Avoirdupois Pounds .
Short Tons
Long Tons.
Kilograms
Metric Tons
100
0.05
0.044 642 86
45.359 237
0.045 359 237
12
-------
To Convert from
Short Tons
To
Multiply by
Avoirdupois Pounds 2 000
Short Hundredweights 20
LongTons 0.892 857 1
Kilograms 907.184 74
Metric Tons 0.907 184 74
To Convert from
Troy Ounces
To
Multiply by
Grains 480
Avoirdupois Drams 17.554 29
Avoirdupois Ounces - 1.097 143
Troy Pounds 0.083 333 3
Avoirdupois Pounds 0.068 571 43
Grams 31.103 476 8
To
To Convert from
Long Tons
Multiply by
Avoirdupois Ounces 35 840
Avoirdupois Pounds 2 240
Short Hundredweights_, 22.4
Short Tons 1.12
Kilograms 1 016.046 908 8
Metric Tons.
1.016 046 908 8
To Convert from
Troy Pounds
To
Grains - 5
Avoirdupois Drams
Avoirdupois Ounces -
Troy Ounces .
Avoirdupois Pounds-
Grams .
Multiply by
760
210.651 4
13.165 71
12
0.822 857 1
373.241 721 6
Units of Capacity, or Volume, Liquid Measure
To Convert from
Millillters
To
Minims -_
Liquid Ounces
Gills
Liquid Pints . .
Liquid Quarts
Gallons
Cubic Inches
Liters . --
Multiply by
16.230 73
0.033 814 02
0.008 453 5
0.002 113 4
0.001 056 7
0.000 264 17
0.061 023 74
0.001
To Convert from
Cubic Meters
To
Multiply by
Gallons 264.172 05
Cubic Inches 61 023.74
Cubic Feet 35.314 67
Liters. 1 000
Cubic Yards 1.307 950 6
To
Liquid Ounces
Gills
Liquid Pints
Liquid Quarts
Gallons.
Cubic Inches
Cubic Feet
Milli liters
Cubic Meters
Cubic Yards
To Convert from
Liters
Multiply by
.. -. 33.814 02
.„ _ 8.453 506
2.113 376
1.056 688
0.264 172 05
61.023 74
0.035 314 67
.. 1 000
0.001
0.001 307 95
To Convert from
Minims
To
Liquid Ounces
Gills .-
Cubic Inches
Milliliters -
Multiply by
0.002 083 33
0.000 520 83
0.003 759 77
0.061 611 52
13
-------
To
To Convert from
GUIs
Multiply by
Minims,,
Liquid Ounces.
Liquid Pints, _
Liquid Quarts,
Gallons
Cvibic Inches,
Cubic Feet.,,
Millihters
Liters __
1 920
4
0.25
0.125
0.031 25
7.218 75
0.004 177 517
118.294 118 25
0.118 294 118 25
To
Minims
Gills
Liquid Pints
Liquid Quarts
Gallons
Cubic Inches,
Cubic Feet,.,
Milliliters
Liters . . . . ,
To Convert from
Liquid Ounces
Multiply by
480
0.25
0.062 5
0.031 25
_ 0.007 812 5
, 1.804 687 5
.. 0.001 044 38
29573 53
. 0.029 573 53
To
To Convert from
Cubic Inches
Multiply by
Minims 265.974 0
Liquid Ounces 0.554 112 6
Gills 0.138 528 1
Liquid Pints. __. 0.034 632 03
Liquid Quarts 0.01731602
Gallons 0.0043290
Cubic Feet 0.0005787
Milliliters _. 16.387 064
Liters 0.016 387 064
Cubic Meters 0.000 016 387 064
Cubic Yards 0.00002143
To
To Convert from
Liquid Pints
Multiply by
Minims
Liquid Ounces.
Gills
Liquid Quarts.
Gallons
Cubic Inches.
Cubic Feet...
Milliliters
Liters
680
16
4
0.5
0.125
28.875
0.016 710 07
473.176 473
0.473 176 473
To
To Convert from
Cubic Feet
Multiply by
Liquid Ounces 957.5065
Gills 239.376 6
Liquid Pints 59.84416
Liquid Quarts 29.922 08
Gallons 7.480 519
Cubic Inches,.
Liters
Cubic Meters ,
Cubic Yards,.
1 728
28.316 846 592
0.028 316 846 592
0.037 037 04
To
To Convert from
Cubic Yards
Multiply by
Gallons
Cubic Inches 46
Cubic Feet
Liters
Cubic Meters
201.974 0
656
27
764.554 857 984
0.764 554 857 984
14
-------
To
To Convert from
Liquid Quarts
Minims
Liquid Ounces.
Gills
Liquid Pints, .
Gallons
Cubic Inches.
Cubic Feet...
Milli liters
Liters
Multiply by
15 360
32
8
2
0.25
57.75
0.033 420 14
946.352 946
0.946 352 946
To
To Convert from
Gallons
Multiply by
Minims 61 440
Liquid Ounces 128
Gills. 32
Liquid Pints.. 8
Liquid Quarts 4
Cubic Inches 231
Cubic Feet....
Milliliters
Liters
Cubic Meters.
Cubic Yards..
0.133 680 6
3 785.411 784
3.785 411 784
0.003 785 411 784
0.004 951 13
Units of Capacity, or Volume, Dry Measure
To Convert from
Liters
To
Multiply by
Dry Pints 1.816 166
Dry Quarts 0.908 082 98
Pecks 0.113 510 4
Bushels 0.028 377 59
Dekaliters 0.1
To Convert from
Cubic Meters
To
Multiply by
Pecks 113.510 4
Bushels _ 28.377 59
To
Dry Pinte
Dry Quarts..
Pecks. .. .
Bushels
Cubic Inches .
Cubic Feet
Liters
To Convert from
Dekaliters
Multiply by
. . . 18.161 66
. . .. 9.080 829 8
. . .. 1.135 104
0.283 775 9
610.237 4
0.353 146 7
10
To Convert from
Dry Pints
To
Multiply by
Dry Quarts 0.5
Pecks 0.062 5
Bushels 0.015 625
Cubic Inches 33.600 312 5
Cubic Feet 0.019 444 63
Liters 0.550 610 47
Dekaliters 0.055 061 05
15
-------
To
Dry Pints
Pecks __ _ _.
Bushels
Cubic Inches
Cubic Feet
Liters
Dekaliters .
To Convert from
Dry Quarts
Multiply by
2
_ 0.125
0.031 25
67.200 625
0.038 889 25
1.101 221
0 110 122 1
To Convert from
Pecks
To
Multiply by
Dry Pints 16
Dry Quarts 8
Bushels 0.25
Cubic Inches 537.605
Cubic Feet 0.311
114
Liters 8.809 767 5
Dekaliters 0.880 976 75
Cubic Meters __ 0.008 809 77
Cubic Yards 0.011 52274
To
Dry Pints
Dry Quarts
Pecks
Cubic Inches -
Cubic Feet
Liters
Dekaliters
Cubic Meters
Cubic Yards.
To Convert from
Bushels
Multiply by
64
32
4
2 150.42
. --- 1.244 456
35.239 07
3 523 907
0.035 239 07
0.046 090 96
To
Dry Pints
Dry Quarts
Pecks
Bushels
To Convert from
Cubic Feet
Multiply by
51.428 09
25.714 05
. . ... 3.214 256
0.803 563 95
To
Dry Pints
Dry Quarts
Pecks
Bushels
To Convert from
Cubic Inches
Multiply by
0.029 761 6
0.014 880 8
0.001 860 10
_ . . 0.000 465 025
To
Pecks
Bushels
To Convert from
Cubic Yards
Multiply by
86.784 91
21.696 227
16
-------
Units of Area
To Convert from
Square Centimeters
To
Multiply by
Square Inches 0.155 000 3
Square Feet 0.001 07639
Square Yards 0.000 119 599
Square Meters. 0.000 1
To Convert from
Hectares
To
Multiply by
Square Feet 107639.1
Square Yards 11959.90
Acres..._ 2.471 054
Square Miles 0.003 861 02
Square Meters 10000
To Convert from
Square Meters
To
Multiply by
Square Inches 1
Square Feet
Square Yards
Acres
Square Centimeters 10
Hectares.
550.003
10.763 91
1.195 990
0.000 247 105
000
0.000 1
To Convert from
Square Inches
To
Square Feet
Square Yards
Square Centimeters
Square Meters
Multiply by
0.006 944 44
0.000 771 605
6.451 6
0.000 645 16
To Convert from
Square Feet
To
Multiply by
Square Inches 144
Square Yards 0.111 111 1
Acres 0.000 022 957
Square Centimeters 929.030 4
Square Meters 0.092 903 04
To
To Convert from
Acres
Multiply by
Square Feet.. 43 560
Square Yards 4840
Square Miles 0.0015625
Square Meters 4 046.856 422 4
Hectares 0.404 685 642 24
To
To Convert from
Square Yards
Multiply by
Square Inches 1
Square Feet
Acres
Square Miles
296
9
0.000 206 611 6
0.000 000 322 830 6
Square Centimeters .
Square Meters
Hectares
8 361.273 6
0.836 127 36
0.000 083 612 736
To
To Convert from
Square Miles
Multiply by
Square Feet 27 878 400
Square Yards 3 097 600
Acres 640
Square Meters 2 589 988.110 336
Hectares 258.998 811 033
17
-------
Special Tables
Length—Inches and Millimeters—Equivalents of Decimal and
Binary Fractions of an Inch in Millimeters
From 1/64 to 1 Inch
H's
l
«'•
l
2
Sths
1
2
3
4
leths
i
2
3
4
5
6
7
8
32ds
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
64ths
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
Milli-
meters
= 0.397
= .794
= 1.191
= 1.588
= 1.984
= 2.381
= 2.778
= 3-175
= 3.572
= 3.969
= 4.366
= 4.762
= 5.159
= 5.556
= 5.953
= 6.350
= 6.747
= 7.144
= 7.541
- 7.938
= 8.334
= 8.731
= 9.128
- 9.525
= 9.922
= 10.319
= 10.716
= 11.112
= 11.509
= 11.906
= 12.303
= 12.700
Decimals
of
an inch
0.015625
.03125
.046875
.0625
.078125
.09375
. 109375
.1250
. 140625
. 15625
.171875
.1875
.203125
.21875
.234375
.2500
.265625
.28125
.296875
.3125
.328125
.34375
.359375
.3750
.390625
.40625
.421875
.4375
.453125
.46875
.484375
.5
Inch
1
1A'*
2
w*
3
4
Sths
5
6
7
8
leths
9
10
11
12
13
14
15
16
32ds
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
64tha
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
Milli-
meters
-13.097
-13.494
= 13.891
= 14.288
= 14.684
= 15.081
= 15.478
= 15.875
= 16.272
= 16.669
= 17.066
= 17.462
= 17.859
= 18.256
= 18.653
= 19.050
= 19.447
= 19.844
= 20.241
= 20.638
= 21.034
= 21.431
= 21.828
= 22.225
= 22.622
= 23.019
= 23.416
= 23.812
= 24.209
= 24.606
= 25.003
= 25.400
Decimals
of
an inch
0.515625
.53125
.546875
.5625
.578125
.59375
.609375
.625
.640625
.65625
.671875
.6875
.703125
.71875
.734375
.75
.765625
.78125
.796875
.8125
.828125
.84375
.859375
.875
.890625
.90625
.921875
.9375
.953125
.96875
.984375
1.000
18
-------
Length—International Nautical Miles and Kilometers
Basic relation: International Nautical Mile = 1.852 kilometers.
Int. nautical
miles
0
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
20
1
2
3
4
5
6
7
8
9
30
1
2
3
4
5
6
7
8
9
40
1
2
3
4
5
6
7
8
9
Kilometers
1.852
3.704
5.556
7.408
9.260
11.112
12.964
14.816
16.668
18.520
20.372
22.224
24.076
25.928
27.780
29.632
31.484
33.336
35.188
37.040
38.892
40.744
42.596
44.448
46.300
48.152
50.004
51.856
53.708
55.560
57.412
59.264
61.116
62.968
64.820
66.672
68.524
70.376
72.228
74.080
75.932
77.784
79.636
81.488
83.340
85.192
87.044
88.896
90.748
Int. nautical
miles
50
1
2
3
4
5
6
7
8
9
60
1
2
3
4
5
6
7
8
9
70
1
2
3
4
5
6
7
8
9
80
1
2
3
4
5
6
7
8
9
90
1
2
3
4
5
6
7
'8
9
100
Kilometers
92.600
94.452
96.304
98.156
100.008
101.860
103.712
105.564
107.416
109.268
111.120
112.972
114.824
116.676
118.528
120.380
122.232
124.084
125.936
127.788
129.640
131.492
133.344
135.196
137.048
138.900
140.752
142.604
144.456
146.308
148.160
150.012
151.864
153.716
155.568
157.420
159.272
161.124
162.976
164.828
166.680
168.532
170.384
172.236
174.088
175.940
177.792
179.644
181.496
183.348
185.200
Kilometers
0
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
20
1
2
3
4
5
6
7
8
9
30
1
2
3
4
5
6
7
8
9
40
1
2
3
4
5
6
7
8
9
i
Int. nautical
miles
0.5400
1.0799
1.6199
2.1598
2.6998
3.2397
3.7797
4.3197
4.8596
5.3996
5.9395
6.4795
7.0194
7.5594
8.0994
8.6393
9.1793
9.7192
10.2592
10.7991
11.3391
11.8790
12.4190
12.9590
13.4989
14.0389
14.5788
15.1188
15.6587
16.1987
16.7387
17.2786
17.8186
18.3585
18.8985
19.4384
19.9784
20.5184
21.0583
21.5983
22.1382
22.6782
23.2181
23.7581
24.2981
24.8380
25.3780
25.9179
26.4579
Kilometers
50
1
2
3
4
5
6
7
8
9
60
1
2
3
4
5
6
7
8
9
70
1
2
3
4
5
6
7
8
9
80
1
2
3
4
5
6
7
8
9
90
1
2
3
4
5
6
7
8
9
100
Int. nautical
miles
26.9978
27.5378
28.0778
28.6177
29.1577
29.6976
30.2376
30.7775
31.3175
31.8575
32.3974
32.9374
33.4773
34.0173
34.5572
35.0972
35.6371
36.1771
36.7171
37.2570
37.7970
38 3369
38.8769
39.4168
39.9568
40.4968
41.0367
41.5767
42.1166
42.6566
43.1965
43.7365
44.2765
44.8164
45.3564
45.8963
46.4363
46.9762
47.5162
48.0562
48.5961
49.1361
49.6760
50.2160
50.7559
51.2959
51.8359
52.3758
52.9158
53.4557
53.9957
*O.S. GOVERNMENT PRINTING OFFICE: 1973-757-567/5305 19
------- |