GEOLOGICAL SURVEY CIRCULAR 848-B 

Biota and Biological Parameters 

as Environmental Indicators 

Biota and Biological Parameters 

as Environmental Indicators 

Edited by Phillip E. Greeson 

Briefing Papers on Water Quality 

GEOLOGICAL SURVEY CIRCULAR 848-B 

1981 
United States Department of the Interior 

JAMES G. WATT, Secretary 

Geological Survey 

Doyle G. Frederick, Acting Director 

Library of Congress Cataloging in Publication Data 

Main entry under title: 

Biota and biological parameters as environmental indicators. 

(Briefing papers on water quality) (Geological Survey circular ; 848-B) 

Bibliography: p. 

Supt. of Docs. no.: I 19.4'2:848-A 

1. Water quality bioassay. I. Greeson, Phillip E. II. Series. III. Series: Geological Survey circular ; 848-B. 

QE75.C5 no. 848-B [QH96.8.B5] 557.3s 81-607891 [628.1'61] AACR2 

Free on application to Distribution Branch, Text Products Section, 

U. S. Geological Survey, 604 South Pickett Street, Alexandria, VA 22304 
FOREWORD 

In August 1974, the Water Resources Division of the U.S. Geological Survey introduced the first of 

a series of briefing papers that were designed to increase the understanding of its employees of the 

significance of various aspects of water quality. Numerous briefing papers have been prepared by the 

Quality of Water Branch. Others will be prepared as the need arises. Each paper addresses a 

separate topic and is written in a nontechnical, easy-to-understand manner for distribution within the 

organization. 

Because of the favorable reception that the papers have received and their apparent effectiveness 

in accomplishing the objective stated above, it would appear that their wider distribution would serve 

a useful purpose. It is hoped that a wide range of persons, including those interested in the quality of 

our Nation's water resources, but who have little or no technical training, will find value in reading 

the papers. Furthermore, it is hoped that the papers will be suitable for supplemental reading in 

secondary education programs and in beginning college-level courses. 

The U.S. Geological Survey plans to publish several U.S. Geological Survey Circulars that contain 

compilations of briefing papers on particular aspects of water quality. This second Circular contains 

four papers describing selected biota and biological parameters as enviromental indicators. 

III 

Philip Cohen 

Chief Hydrologist 

CONTENTS 

Page 

Foreword -------------------------------------------------------------------- III 

Abstract --------------------------------------------------------------------- B1 

Species diversity and its measurement, by Robert C. Averett--------------------------- 3 

Algal growth potential,by W. Thomas Shoaf----------------------------------------- 7 

Estimation of microbial biomass by measurement of adenosine triphosphate (ATP), 

by W. Thomas Shoaf -------------------------------------------------------- 11 

Lake classification- Is there method to this madness?,by Dennis A. Wentz________________ 15 

ILLUSTRATIONS 

Page 

FIGURE 1. Characteristic growth curves of a unicellular alga in a culture of limited volume___ B7 

2. Summary of the major trophic types of lakes------------------------------- 18 

3. Hypothetical curve of the course of natural eutrophication of lakes _____________ 18 

4. Effect of lake depth on the process of eutrophication------------------------- 18 

v 

BRIEFING PAPERS ON WATER QUALITY 

Biota and Biological Parameters 

as Environmental Indicators 

Edited by Philip E. Greeson 

ABSTRACT 

This is the second of several compilations of briefing papers 

on water quality prepared by the U.S. Geological Survey. Each 

briefing paper is prepared in a simple, nontechnical, easy-tounderstand manner. This U.S. Geological Survey Circular conBl 

tains papers on selected biota and biological parameters as enviromental indicators. Briefing papers are included on "Species 

diversity and its measurement," "Algal growth potential," 

"Estimation of microbial biomass by measurement of adenosine 

triphosphate (ATP)," and "Lake classification-Is there method 

to this madness?" 

Species Diversity and its Measurement 

By Robert C. Averett 

ABSTRACT 

Wit? the decrease in popularity of the single-species indicatoro:gam.sm concept, there arose an understanding in the natural 

diversity of organisms. This, combined with the science of informa~ion theory, led to the development of formulae for com-

~u~ng. a diversity index. Interpretation of an index value has 

hmitatlons; but, when used properly, it is a valuable tool for the 

assessment of water quality. 

INTRODUCTION 

Man has always marveled at the number of different types of organisms on Earth. Sketches on 

cave walls are testimony that early man not only 

knew about many types of animals, but also knew 

someth~ng about their roles in nature. Today we 

recognize about 37,000 species of microorganisms, 341,000 species of plants, and 

1.'358,000 species of animals. Taxonomists (scien-

~Ists that classify organisms) continually are addI~g to these numbers as new organisms are 

discovered. The insects are the largest single 

grou~. of org~nisms and have been only partially 

classified. It IS estimated that if all the different 

kinds of insects on Earth were classified at this 

time, they would exceed 2 million species- a figure 

that exceeds the known types of all other animals. 

The biological definition of a species is a group of 

actually or potentially interbreeding organisms 

repro~uctively isolated from other such groups of 

orgamsms. A true species must meet this criterion. 

It is this concept of a species that man uses in his 

husbandry of domestic animals, and in his use of 

organisms in defining a particular environment. 

CONCEPT OF SINGLE·SPECIES INDICATORS 

The use of organisms to indicate certain environmental conditions stemmed from the work of 

botanists of the 18th and 19th centuries. Plants, 

because they responded to temperature and other 

climatic vari~b~es, as well as to soil types, were 

used as an aid m the classification of the physical 

B3 

environment. In a similar manner, the. presence or 

absence of specific types of animals was used to 

delineate certain climatic conditions. The use of 

these biological indices became more popular as 

more knowledge was gained about the distribution 

and abundance of organisms. 

The first comprehensive work on using aquatic 

organisms to define water-quality conditions was 

by two German biologists, Kolkwitz and Marsson 

(1908, 1909). They published findings on a stream 

that received domestic wastewater. From the 

standpoint of organism types and diversity, they 

were able to divide the stream into four distinct 

reaches, which they called zones: the clean water 

zone, the septic zone where the sewage is first introduced, the zone of active sewage decomposition, 

and the zone of recovery. 

Other workers followed this classification procedure, or modified it slightly to fit their own findings. Each zone of the stream was inhabited by 

distinct groups of organisms. Moreover, in those 

zones where the waste load was heaviest and 

where the stream bottom was covered by waste 

solids, the number of organisms per unit area was 

very high, but the number of different kinds of 

organisms was very low. In other words, there 

were many organisms, but the diversity (number of 

species) was low. Further, nymphs and larvae of 

some stream insects (particularly the mayflies, 

stoneflies, and caddisflies) were frequently absent 

from areas receiving heavy loads of organic waste 

but were present in clean water reaches. Much of 

this absence resulted from the alteration of the 

physical stream bottom by waste sludge. In addition, low oxygen concentrations and an altered 

food suppy prevented them from living in the 

heavily polluted section of the stream. In contrast, 

some trueflies (for example, midges, gnats), that 

were adapted to living in the sludge of a stream 

bottom and in water that had a low oxygen concentration, thrived, and in the absence of competition, 
their numbers per unit areas was very high. These 

findings led to the single-species indicatororganism concept whereby specific groups of 

organisms were unadapted to pollution, but others 

were able to tolerate significant changes in the environment. 

The single-species indicator-organism concept 

had several failings. First, it was based only upon 

organic (oxygen-demanding) waste loadings. 

Sublethal levels of toxic materials (some industrial 

and agricultural wastes) were ignored, yet they 

also may be preferential as to which organisms 

they kill. All organisms, including man, tolerate 

varying concentrations of potentially toxic 

materials, and a concentration that is toxic to one 

organism, or a group of physiologically similar 

organisms, may not be toxic to others. Consequently, there may be great variability in the effects of toxic materials on the distribution and 

abundance of aquatic life. 

The second failing was that organisms that make 

up the groups found in the various stream reaches 

have had to come from somewhere. That is, they 

are all natural members of the stream fauna, but, 

when the stream is altered, some find the habitat 

more suitable and their numbers increase. Those 

that find the habitat less suitable die or leave the 

area; for example, the presence of truefly larvae in 

a sample does not indicate pollution or waterquality degradation. The single-species indicatororganism concept, therefore, is not sound unless 

practiced by someone thoroughly knowledgeable in 

the habitat requirement and physiology of the 

organisms. As a general biological technique for 

assessing water quality, it has severe limitations. 

CONCEPT OF SPECIES DIVERSITY 

At about the time that the single-species 

indicator-organism concept was losing its popularity (not because it was completely erroneous, but 

because it was too often misused), the speciesdiversity concept was becoming popular. This concept considered the entire community of aquatic 

organisms and, thus, was sound from a biological 

standpoint. Initially, the concept consisted of simply counting the number and types of organisms in · 

a given sample. Later, the ratio of the number of 

organisms to the number of species was used as a 

simplified species-diversity index. While this 

technique also provided a quantitative approach, it 

was difficult to interpret the results because the 

ratios often changed greatly as the number of 

organisms in the sample increase. 

Later, the science of information theory was applied to biological sampling problems. Briefly, the 

objective of information theory is to measure the 

amount of order or disorder in a system. In the 

biological community, we may wish to measure a 

number of things, but for our purposes the most 

important would be the number of species and the 

number of individuals in each species. That is, we 

may want to look at the richness or variety of the 

community structure and ask the question, "How 

difficult (or easy) will it be to correctly predict the 

species of the next individual in the sample?" This 

is similar to a message receiver predicting the next 

symbol, for example, dot or dash, in a Morse code 

message. Because it is dot-dash, on-off, or yes-no, 

relations can be represented by 0-1 digits, or 

binary digits. They often are measured in terms of 

bits (for number of binary digits). 

The information theory diversity index IS 

calculated using the function: 

s 

o=- E p;lOgzp;= - E (niln)logz(niln) (1) 1=1 

where: o = species diversity or information content of the sample (bits/individual) .. 

The term o often is denoted by H. 

s =number of species in the sample 

p;=niln=proportion of the total sample belonging to the ith species. 

This function, developed simultaneously but independently, by Wiener (1948) and Shannon and 

Weaver (1949), is a measure of the amount of 

uncertainty. The larger the amount of uncertainty 

and, hence, the greater the diversity of the species, 

the greater the diversity or the value of o. For example, a message such as aaaa has no uncertainty, 

and, thus, no diversity and, therefore, o = 0. In a 

sample where all organisms are of the same species 

(a rare event in nature), the diversity would be 

zero. In other words, in a single-species sample, 

one individual would tell as much about the whole 

sample as would any other individual, or all of the 

individuals combined. In a sample of many species, 

we would have to identify more organisms, if we 

wished to exhaust all of the information from the 

sample. Thus, a sample in which each organism 

belonged to a different species (again, a rare event 

in nature), would have a high diversity and o would 

be a positive number. Refer again to our example 

where the message aaaa has no uncertainty. In 

contrast, the message {3acf.>'Y has a high uncertainty, and predicting the next letter would be much 

more difficult, and o would be some positive 

number. Note that logarithms to the base 2 are 

used so as to obtain bits per individual. 

The diversity index, o, is influenced by the 

number of species in the sample as well as by the 

B4 
evenness of the distribution of the species in the 

sample. The measure of evenness is attained first 

by calculating the maximum diversity, o max, as: 

s 

o max=- E Glog2!)= log2s (2) 

1=1 

The highest value of o max is attained when the 

number of individuals per species is distributed 

equally -that is, when all the species are present 

in the same proportion!. In two samples in which 

the species are distributed evenly, the sample with 

the largest number of species will have the 

greatest diversity (Pielou, 1969). From equations 1 

and 2, a measure of the evenness of the species in 

the sample (J) can be calculated as: 

quality changes. Rather, it is a tool, and, like a tool, 

it must be used correctly to be effective. There are 

precautions that must be observed. First, the level 

of taxonomic level above species is valid. While 

desirable, it is not necessary to classify all 

organisms to the species level. Second, the physical 

habitat, such as velocity of flow and stream bottom 

material must be considered and reported. 

Streams that have various sizes of bottom material 

will have a greater diversity of organisms than 

streams that have material all of a similar size. 

Third, the season of the year and life history of the 

organisms must be considered. Some organisms 

have annual periods of dominance-for example, 

immediately after egg hatching, a particular group 

0 J=-- 0 max 

The diversity index, o, has obvious advantages 

over the single-species indicator-organism concept, or the simple method of counting the number 

of species in a sample. First, it is a dimensionless 

equation, and numbers or biomass (weight per 

volume or area) can be used. Second, the importance of each species in the community is expressed as a ratio representing the contribution of 

each species to the total diversity or biomass. 

Third, and of great importance, diversity indices 

derived from information theory are virtually independent of sample size. The occurrence of rare 

species, as additional samples are collected, does 

not greatly change the value of o. 

of organisms may be abundant, but shortly 

(

3

) thereafter, natural mortality factors take their toll 

and reduce the number of individuals. Fourth, the 

method of collection and the mesh size of the 

screen used to separate the organisms is of utmost 

importance. It is invalid to compare diversity indices where different collecting gear and screen 

sizes have been used. Consequently, comparisons 

of diversity indices must be between similar taxonomic categories, similar substrates, and at 

similar times of the year, using similar collecting 

and separating techniques. 

In a group of 10 samples from four habitats (one 

of which was a sample board), Wilhm and Dorris 

(1968) found that the diversity reached 95 percent 

of the asymptotic diversity (that value of o that 

essentially does not change with the addition of 

new samples) before five samples were collected. 

In a subsequent paper, Wilhm (1970) reported that 

the diversity index ranged from 0 to 10, but usually 

was less than 5. In streams that do not receive 

waste material (clean water zones), it was between 

3 and 4, while, in streams or stream reaches receiving organic waste, the diversity-index was usually 

less than 1. Wilhm (1970) further suggested that 

the asymptotic diversity value should be used. 

Generally, as mentioned, this occurs between two 

and five samples. 

LIMITATIONS OF DIVERSITY INDEX 

VALUES 

While the diversity index has a number of 

positive attributes, it also has its limitations. It is 

not a panacea for the problem of measuring waterB5 

Regardless of these limitations, the diversity index (d) is a valuable tool, and with it a more detailed quantitative assessment of water quality can 

be made. It does not, however, explain the role of 

the individual organism in the community, the 

physiological stress that water-quality changes 

place upon the organisms, or the physiological 

stress that the use of the water may place upon 

man. These processes will never be explained with 

something as simple as the diversity index. Happily, continued research is providing information on 

the habitat, behavorial, and physiological requirements of aquatic organisms and ultimately 

will lead to a greater understanding and use of 

species diversity and the diversity index as one tool 

for assessing water-quality changes. 

REFERENCES 

Kolkwitz, R., and Marsson, M., 1908, Okologie der 

pflanzlichen Saprobien: Gerichte der Deutschen 

Botanischen Gesellschaft 26a, p. 505-519. (Translated 

1967, Ecology of plant saprobia, p. 47-52, in Keup, L. E., 

Ingram, W. M., and Mackenthun, K. M., eds., Biology of 

water pollution: Federal Water Pollution Control Administration, U.S. Department of the Interior, 290 p.). 

--, 1909, Okologie der tierischen Saprobien: Beitrage 

zur Lehre von der biologischen Gewasserbeurteilung. Internationale Revue der Gesamten Hydrobiologie and 
Hydrogeographie 2, p. 126-152. (Translated 1967, Ecology 

of animal saprobia, p. 85-95, in Keup, L. E., Ingram, W. M., 

and Mackenthun, K. M., eds., Biology of water pollution: 

Federal Water Pollution Control Administration, U.S. 

Department of the Interior, 290 p.). 

Margalef, Ramon, 1968, Perspectives in ecological theory: 

Chicago, Ill., University of Chicago Press, 111 p. 

Pielou, E. C., 1969, An introduction to mathematical 

ecology: New York, John Wiley and Sons, 268 p. 

Shannon, C. E., and Weaver, WarrenJ 1949, The mathematical theory of communication: Urbana, Ill., University of 

Illinois Press, 177 p. 

B6 

Warren, C. E., 1971, Biology and water pollution control: 

Philadelphia, Pa., W. B. Saunders Co., 434 p. 

Wiener, Norbert, 1948, Cybernetics: Cambridge, Massachusetts Institute of Technology Press, 194 p. 

Wilhm, J. L., 1970, Range of diversity index in benthic 

macro-invertebrate populations: Journal of the Water Pollution Control Federation, v. 42, no. 5, pt. 2, p. R221, R224. 

Wilhm, J. L., and Dorris, T. C., 1968, Biological parameters for water quality criteria: Bioscience, v. 18, no. 6, p. 

477-481. 
Algal Growth Potential 

By W. Thomas Shoaf 

ABSTRACT 

Algal growth potential (AGP) is defined as the maximum algal 

mass that can be produced in a natural water sample under 

standardized laboratory conditions. Measurements of AGP can 

be used for a variety of purposes; however, an understanding of 

the principles of the test and the factors that affect its expression is critical to proper data interpretation. 

DEFINITION 

Algal growth potential (AGP) is defined as the 

maximum algal mass (dry weight) that can be produced in a natural water sample under standardized laboratory conditions. The characteristic 

growth pattern for unicellular algae in a culture of 

limited volume is shown in figure 1. 

AGP is the algal mass present at stationary 

phase and is expressed as milligrams per liter dry 

weight of algae produced. Dry weight was chosen 

as a measure of algal growth because it is the most 

reliable parameter for all sample sources. 

STANDARDIZATION OF ANALYSIS 

Determination of the AGP of a water sample using the natural algae present is perhaps most 

Stationary phase 

AGE OF CULTURE 

FIGURE 1.-Characteristic growth curve of a unicellar alga in a 

culture of limited volume. 

B7 

desirable. The presence of these algae and 

associated micro-organisms suggests that they are 

suited to survive the environment in which they 

live, and their potential for accelerated growth is 

of primary interest in many situations. The 

primary difficulty in using indigenous algae is the 

time required for measuring growth of diverse 

forms of algae such as colonial, filamentous, or 

univellular forms. In some instances, water 

samples contain few algae, and the introduction of 

foreign algae would be needed to insure a growth 

response. 

The AGP of a number of samples can be compared only when variables which affect algal 

growth, such as light and temperature, are standardized and controlled. The need for standardization of techniques for measuring AGP for routine 

analysis was recognized by the Joint Industry/Government Task Force on Eutrophication 

(Bartsch, 1969). A cooperative developmental effort by the Soap and Detergent Association and 

the U.S. Environmental Protection Agency to produce a test for determining AGP in natural water 

samples was the result (Bartsch, 1971). 

Selenastrum capricornutum was selected as a test 

organism for the following reasons: 

1. It is a unicellular alga and its growth can be 

monitored accurately and rapidly with an electronic particle counter. 

2. Selenastrum has been shown to tolerate acidic 

and alkaline waters as well as oligotrophic 

(nutrient-poor water) and eutrophic (nutrientrich water) conditions in aquatic environments 

(Forsberg, 1972). 

3. It is listed among the most pollution-tolerant 

algae by Palmer (1969). 

4. The AGP determined with S. capricornutum 

and the potential determined with the species 

indigenous to the water sample correlate well 

(Maloney and others, 1972). 

5. The AGP for various freshwater environments 

using S. capricornutum, as measured by the 
standard test (Bartsch, 1971), varies widely 

from 0.1 to 248 milligrams dry weight of algae 

per liter (National Lake Survey Program, 

1973). 

APPLICATION 

The design of a project is developed to meet a 

specific objective. All pertinent factors must be 

considered in planning if valid results and conclusions are to be obtained. It should be possible to 

evaluate AGP data more effectively in light of 

other biological, chemical, and physical 

measurements obtained by sampling at common 

sites. Various collection methods for streams and 

lakes have been described by Guy and Norman 

(1970), Greeson and others (1977), and Greeson 

(1979). 

The objectives of AGP tests might include the 

establishment of baseline data or determination of 

the growth-controlling nutrient(s), the influence of 

a nutrient on algal growth, or the source of a 

nutrient when several inflows are involved. One of 

the first items that should be determined when 

measuring AGP is an understanding of what constitutes a significant change. A certain degree of 

variability can be expected with any assay or 

survey; however, once the baseline data are 

established, experimentation can be conducted. 

The principle of the AGP determination is based 

on Liebig's Law of the Minimum, which recognizes 

that the development of a population is essentially 

regulated by the substance occurring in minimal 

quantity relative to the requirements of the population.AGP measurements have been used frequently 

to derive information on the required nutrients 

which are limiting algal growth (Leischman and 

others, 1979). This type of measurement usually is 

designed to examine in detail only a few nutrients 

which preliminary testing indicates may be 

limiting or in short supply. The approach is to 

observe the effect that certain growth promoting 

substances (nutrients) have upon the algae. These 

substances may be of precise known composition 

(Bartsch, 1971; Miller and Maloney, 1971; Maloney 

and others, 1972) or their composition may be 

somewhat less known, such as that of an effluent 

which may flow into a lake or stream from a 

suspected nutrient source (Brown and others, 

1969). The practice of adding such substances to a 

water sample is called spiking. In the case of 

chemical spikes, it is obvious that if increasing 

amounts of a limiting nutrient are added to a water 

sample, eventually another factor becomes 

limiting. The determination of how much and what 

to spike may be made easier when the concentration of the nutrient(s) of interest is known. Because 

algal growth in natural water samples can be 

limited by one or several nutrients in close succession, it might be necessary to test spikes of combinations of nutrients that could be limiting. 

When only one nutrient is limiting algal growth, 

it is likely that a correlation will be observed between the concentration of that nutrient and the 

AGP. Some AGP studies have shown correlations 

between orthophosphate and algal growth (Wang 

and others, 1972), while correlations also are likely 

for other nutrients in other aquatic environments. 

INTERPRETATION OF RESULTS 

AGP data which are derived in the laboratory 

under controlled conditions of light and 

temperature do not necessarily reflect conditions 

in the natural aquatic environment from which the 

sample was taken. Only the potential at a given 

time can be measured. In nature, daily and 

seasonal variations in the intensity and duration of 

light occur, which can effect algal growth. Changes 

in other environmental factors can be important as 

well. For example, the rate of algal growth roughly 

doubles with every 11 oc (20°F) rise in water 

temperature between 0°C (32°F) and 32°C (90°F) 

(Ferguson, 1968). Growth can be inhibited by any 

suspended material, living or nonliving, which interferes with the effective penetration of light 

essential to the algae. Grazing by invertebrates 

and fish, toxic materials entering the water, plant 

or animal excretions, or decay products from the 

algae themselves are all additional examples of factors that may inhibit growth potential. 

In the standard procedure described by Bartsch 

(1971), Miller and others (1978), and Greeson 

(1979), membrane filtration of the water sample to 

remove indigenous algae, bacteria, fungi, and any 

other particles which might interfere with the procedure is required. These micro-organisms contain 

nutrients, which are not available to other algae 

while these organisms are living, but later become 

a source of nutrients as a result of decay after 

death. Thus, it is possible to measure a high concentration of algae during a "bloom," but observe a 

low AGP. After cell death and decay, the AGP 

probably will increase. This, as well as the other 

factors mentioned above, must be taken into consideration when attempting to make predictions 

about future algal growth. 

B8 
SUMMARY 

Algal growth potential is defined as the maximum algal mass (dry weight) that can be produced 

in a natural water sample under standardized 

laboratory conditions. AGP measurements are 

designed to establish baseline data, growth 

limiting factors (nutrients), and the influence and 

source of various growth promoting nutrients, so 

as to provide improved means for predicting and 

controlling excessive algal growth in aquatic 

habitats. Data can be compared only when the 

variables that control algal growth are standardized. Algal growth potentials derived in the 

laboratory may not reflect natural conditions 

because of insufficient light or temperature, grazing by invertebrates or fish, or the presence of toxic materials. An understanding of the principle of 

the test and the factors that affect the expresion of 

AGP is critical to proper data interpretation. 

REFERENCES CITED 

Bartsch, A. F., 1969, Provisional algal assay procedure: Corvallis, Oreg., U.S. Environmental Protection Agency, 61 p. 

--1971, Algal assay procedure: Bottle test: Corvallis, 

Oreg., U.S. Environmental Protection Agency, 82 p. 

Brown, R. L., Bain, R. C., and Tunzi, M. G., 1969, Effects of 

nitrogen removal on the algal growth potential of San Joaquin valley agricultural tile drainage effluents: Paper 

presented at the American Geophysical Union National 

Meeting, San Francisco, 15 p. 

Ferguson, F. A., 1968, A nonmyopic approach to the problem of 

excessive algal growth: Environmental Science and 

Technology, v. 2, p. 188-193. 

Forsberg, C. G., 1972, Algal assay procedure: Journal of the 

Water Pollution Control Federation, v. 44, p. 1623-1628. 

Greeson, P. E., ed., 1979, A supplement to- Methods for 

collection and analysis of aquatic biological and 

microbiological samples (U.S. Geological Survey Techniques 

of Water-Resources Investigations, Book 5, Chapter A4): 

U.S. Geological Survey Open-File Report 79-1279, 92 p. 

Greeson, P. E., Ehlke, T. A., Irwin, G. A., Lium, B. W., and 

Slack, K. V., eds., 1977, Methods for collection and analysis 

of aquatic biological and microbiological samples: U.S. 

Geological Survey Techniques of Water-Resources Investigations, Book 5, Chapter A4, 332 p. 

Guy, H. P., and Norman, V. W., 1970, Field methods for the 

measurement of fluvial sediments: U.S. Geological Survey 

Techniques of Water-Resources Investigations, Book 3, 

Chapter C2, 59 p. 

Leischman, A. A., Greene, J. C., and Miller, W. E., 1979, 

Bibliography of literature pertaining to the Genus 

Selenastrum: Corvallis, U.S. Environmental Protection 

Agency, EPA-60017-79-021, 191 p. 

Maloney, T. E., Miller, W. E., and Shiroyama, Tamotsu, 

1972, Algal responses to nutrient addition in natural 

waters. I. Laboratory assays: Nutrients and eutrophication, 

Special Symposium, American Society of Limnology and 

Oceanography, v. 1,p. 155. 

Miller, W. E., and Maloney, T. E., 1971, Effects of secondary 

and tertiary wastewater effluents on algal growth in a lakeriver system: Journal of the Water Pollution Control 

Federation, v. 43, p. 2361-2365. 

Miller, W. E., Green, J. C., and Shiroyama, Tamotsu, 1978, The 

Selenastrum capricornutum Printz algal assay bottle 

test- Experimental design, application, and data interpretation protocol: Corvallis, U.S. Environmental Protection Agency, EPA-600/9-78-018, 125 p. 

National Lake Survey Program, 1973, Highlights: Corvallis, 

Environmental Protection Agency, 5 p. 

Palmer, C. M., 1969, A composite rating of algae tolerating 

organic pollution: Journal of Phycology, v. 5, no. 1, p. 

78-82. 

Wang, W. C., Sullivan, W. T., and Evans, R. L., 1972, A 

technique for evaluating algal growth potential in Illinios 

surface water: Urbana, Illinois State Water Survey, Report 

of Investigation 72, 16 p. 

B9 

Estimation of Microbial Biomass by 

Measurement of Adenosine Triphosphate (ATP) 

By W. Thomas Shoaf 

ABSTRACT 

Andenosine triphosohate is present in all living cells and is the 

primary energy donor in celluar processes. The measurement of 

the amount of andenosine triphosohate present in a water sample, therefore, is a measurement of the total biomass in the sample. The firefly luciferase reaction with luciferin is the commonly used method for determining the presence and amount of 

adenosine triphosphate in a sample. The method is very sensitive and rapid. 

INTRODUCTION 

For many ecological studies, it is important to 

know the total amount of living material or 

biomass present in an ecosystem. When dealing 

with larger animals and plants, it is relatively easy 

to measure biomass by conventional methods, such 

as collecting and weighing them. For the microorganisms, however, it can become very difficult. 

Both phytoplankton and zooplankton biomasses 

can be estimated by direct microscopic determination of volume and number. Bacteria can be. 

estimated by various plating techniques (colony 

counting) and extinction dilution techniques. The 

techniques are often time-consuming, expensive, 

and subject to inherent sources of error. Some of 

the major problems include the following: Direct 

microscopic counting of bacteria may yield inaccurate biomass estimates because of the difficulty 

in distinguishing bacteria from bacteria-sized inert 

particles. There is, also, the inability to differentiate between living and nonliving cells, and the 

problems of cell aggregation characteristic of 

bacteria and algae. Low estimates may be produced by plating and extinction techniques which 

are selective because of the chemical composition 

of the media and variations in physical parameters 

such as temperature and pressure. The extinction 

dilution technique also may be biologically selective 

in that one form might overgrow the other. 

For a total biomass determination, it would be 

desirable to measure some substance (for example, 

a biochemical compound) that is present in all living cells but is not associated with nonliving particulate material. This cellular substance should 

have a short survival time after the death of a cell, 

so that it would be specific for viable (living) 

biomass. It should be present in proportion to some 

measure of the total biomass for all microorganisms: algae, bacteria, fungi, and protozoans. 

Sensitive and accurate methods for its detection 

should be available. 

The biochemical compound which seems to meet 

these requirements best is adenosine triphosphate 

(ATP). ATP is the primary energy donor in cellular 

life processes. Its central role in living cells and its 

biological and chemical stability make it an excellent indicator of the presence of living material. 

The level of endogenous ATP (that is, the amount 

of ATP per unit biomass) in bacteria (Allen, 1973), 

algae (Holm-Hansen, 1970), and zooplankton 

(Holm-Hansen, 1973) is relatively constant when 

compared to the cellular organic carbon content in 

several species of organisms. Furthermore, its concentration in all phases of a growth cycle remains 

relatively constant. In studies where cell viability 

was determined (Hamilton and Holm-Hansen, 

1967; Dawes and Large, 1970), the concentration 

of ATP per viable cell remained relatively constant 

during periods of starvation. The quantity of ATP, 

therefore, can be used to estimate total living 

biomass. 

ANALYTICAL DETERMINATION OF ATP 

Very sensitive methods of ATP determination 

have been developed from the finding that 

luminescence in fireflies has an absolute requirement for ATP (McElroy, 1947). The ATP is determined by measuring the amount of light produced 

when ATP reacts with reduced luciferin (LH2) and 

oxygen (02) in the presence of firefly enzyme 

luciferase and magnesium, producing adenosine 

Bll 
monophosphate (AMP), inorganic pyrophosphate 

(PPi), oxidized luciferin (L), water (H20), carbon 

dioxide (C02), and light (hv), by the following reaction: 

ATP + 0 2 + LH2 luciferase AMP+ PPi + L 

Mg+2 

+H20+C02+hv. 

The bioluminescent reaction is specific for ATP 

and the reaction rate is proportional to the ATP 

concentration; one photon of light is emitted for 

each molecule of ATP hydrolyzed. When ATP is introduced to suitably buffered (with respect to pH) 

enzymes and substrates (reduced luciferin and oxygen), a light flash follows; it decays in an exponential fashion. Either the peak height of the light 

flash or integration of the area under the decay 

curve can be used to form standard curves. Quantities of ATP as low as 10-12 to 10-13 grams of ATP 

can be measured accurately. 

APPLICATION 

ATP analysis has been used to estimate the 

biomass at varying depths in the ocean (HolmHansen and Booth, 1966). The ATP levels found at 

various depths in the ocean indicate bacterial 

populations between 50 and 2,000 times those 

found by plating techniques. This may reflect the 

inadequacies of plating techniques referred to 

above. 

The effect of chemical or thermal pollution on a 

system has been determined by passage of water 

through plant cooling systems and by monitoring 

the before and after levels of ATP. In such a study 

on the Chesapeake Bay, ATP appeared to be a 

reliable measure of biomass viability (Dupont Corporation, 1970). The effects of industrial plants and 

cities on streams can be evaluated in a similar 

fashion. Likewise, the effects of suspected (or 

known) biocides on micro-organisms also could be 

measured immediately, without the usual delay encountered with plate counts (for bacteria) or 

phytoplankton anaylysis (for algae). 

Biomass determination by ATP measurement 

can be correlated with measurement of chlorophyll 

where the dominant life form is phytoplankton 

(Holm-Hansen, 1969). If bacteria or protozoans are 

present .in large numbers and not evenly 

distributed with the phytoplankton, then a correlation with chlorophyll would not be expected (HolmHansen and Paerl, 1972). A ratio of ATP (biomass) 

to chlorophyll can be useful for determining 

whether the living matter in a sample is 

predominantly autotrophic (mainly algal) or contains a large number of heterotrophic organisms 

(bacteria, protozoans). Although the ranges of 

values are yet unestablished, low values of the 

ATP to chlorophyll ratio should be indicative of 

autotrophic conditions, whereas high values imply 

a more heterotrophic community. 

Since ATP can be correlated directly with living 

carbon (Holm-Hansen, 1973), another tool for accessing biological water quality is available in the 

ratio of living carbon (determined by ATP) to total 

organic carbon. This ratio may be a useful measure 

of the degree of eutrophication. In an oligotrophic 

situation, a high percentage of the total organic 

carbon is in living cells and, thus, the ratio of living 

carbon (determined by ATP) to total organic carbon will be at its highest. As conditions move 

toward the eutrophic, more nonliving organic carbon accumulates and the percent of living carbon 

(to the total) decreases. Determination of this ratio 

may, therefore, be a good method for describing 

the overall biological condition. 

SUMMARY 

The determination of biomass (total living 

material) has been a difficult task for investigators 

in the field of aquatic microbiology. Measurement 

of ATP provides a very sensitive and rapid 

estimate of biomass. While useful as an estimate of 

biomass alone, it also may be related to determinations of chlorophyll and total organic carbon to 

provide ratios which give more information about 

biological water quality. 

REFERENCES CITED 

Allen, P. D., 1973, Development of the luminescence biometer 

for microbial detection: Developments in Industrial 

Microbiology, v. 14, p. 67-73. 

Dawes, E. A., and Large, P. J., 1970, Effect of starvation ·on 

the viability and cellular constituents of Zymomonas 

anaerobia and Zymomonas mobilis: Journal of General 

Microbiology, v. 60, p. 31-40. 

Dupont Corporation, 1970, Water ecology/waste water treatment: Wilmington, Del., Dupont Applications Brief no. 

760BF1 (A 72069), 2 p. 

Hamilton, R. D., and Holm-Hansen, 0., 1967, Adenosine 

triphosphate content of marine bacteria: Limnology and 

Oceanography, v. 12, p. 319-324. 

Holm-Hansen, 0., 1969, Determination of microbial biomass 

in ocean profiles: Limnology and Oceanography, v. 14, p. 

740-747. 

. --1970, ATP levels in algal cells as influenced by 

environmental conditions: Plant Cell Physiology, v. 11, p. 

689-700. 

--1973, Determination of total microbial biomass by 

measurement of adenosine triphosphate, in Stevenson, L. 

H., and Colwell, R. R., eds., Estuarine microbial ecology: 

Columbia, University of South Carolina Press, p. 73-89. 

B12 
Holm-Hansen, 0., and Booth, C. R., 1966, The measurement of 

adenosine triphosphate and its ecological significance: Limnology and Oceanography, v. 11, p. 510-519. 

Holm-Hansen, 0., and Paerl, H. W., 1972, The applicability 

of ATP determination for estimation of microbial biomass 

and metabolic activity: Memoirs of 1st Italia Idrobioliae, 

Supplement 29, p. 149-168. 

McElroy, W. D., 1947, The energy source for bioluminescence 

in an isolated system: Proceeding of National Academy 

Sciences, v. 33, p. 342-346. 

Bl3 

Lake Classification-Is There Method 

to this Madness? 

By Dennis A. Wentz 

ABSTRACT 

The number of scientific publications discussing lake 

classification schemes has increased considerably since the early 

1970's. This has resulted primarily from the enactment of the 

Federal Water Pollution Control Act Amendments of 1972. 

This paper discusses lake classifications based upon geologic, 

hydrologic, and trophic schemes. Many of the schemes have 

limited applicability, while others are adaptable to a variety of 

situations. 

INTRODUCTION 

The study of lakes has developed into a popular 

hydrological pursuit in recent years, and there are 

those who would encourage even greater expansion in this area. Perhaps first, however, we would 

be well advised to pause and reflect on some of the 

more timely developments of current limnological 

research. One of these is in the area of lake 

classification. 

The number of scientific publications addressing 

lake classification has increased considerably since 

the early 1970's. "To what end are these classifications dedicated?," you might ask. Or, "Why has 

there been a sudden interest in classifying lakes?" 

The purpose of this paper is to answer these questions and to provide some insight into the various 

lake-classification schemes that have been proposed. Although it has not been possible to review 

all lake classifications that have been suggested, 

the bibliographies included herein should provide an 

adequate base for anyone interested in further pursuing the topic. In particular, those trophic 

classifications based on the nutrient loading concepts introduced by Vollenweider (1968) have been 

specifically excluded, as their consideration would 

have made the paper excessively long. 

Perhaps the first order of business is to define 

exactly what is meant by the term classification. 

According to Sokal (197 4), classification is 

" * * * the ordering or arrangement of objects into 

groups or sets on the basis of their relationships." 

(It should be pointed out that the relationships 

sometimes are presumed.) Thus, classification is 

the process of defining the groups into which objects are placed. The process of assigning an object 

to a previously defined group, on the other hand, 

can be described as categorization or identification. 

The next question that logically arises is "Why 

classify?" In general, there seem to be two basic 

reasons. The first is that the data base is too large 

to consider conveniently. Classification results in a 

decreased number of objects that, in turn, are 

more comprehensible and manageable. In this 

case, the classification process becomes an end in 

itself. A typical example might be the taxonomic 

classification of living things. 

A second reason for classifying is that the subject matter is not well understood. It is hoped that 

the classification system will reflect the processes 

that have led to the observed arrangement, thus 

revealing a better understanding of natural laws. 

In this case, classification becomes a means to an 

end- a tool. It is the second reason that seems to 

have given impetus to many of the attempts at lake 

classification, particularly the geologically and 

hydrologically oriented systems. 

Recently, considerations of lake management 

and protection strategies-especially those designed to abate cultural (accelerated) eutrophication-have provided additional, more pragmatic, 

incentives for devising lake-trophic classifications. 

This is particularly true in light of Section 314(a) of 

the Federal Water Pollution Control Act Amendments of 1972 (Public Law 92-500), which dictates: 

"Each State shall prepare or establish, and submit 

to the Administrator for his approval * * * an 

identification and classification according to 

eutrophic condition of all publicly owned freshwater lakes in such State * * * ·" 

B15 
Such trophic-classification schemes are to be 

used to assess trends in lake quality and to aid administrators in environmental decisionmaking. 

Properly designed and employed, these schemes 

will allow for optimum management and protection of the lake resource. 

CHARACTERISTICS OF 

CLASSIFICATION SCHEMES 

Perhaps the most important distinction to make 

in regard to classification schemes is between 

monothetic and polythetic systems (Sokal, 197 4). 

Monothetic systems require that classes differ 

from one another by a single property that is 

uniformly different among classes. Thus, lakes can 

be classified according to their stratification properties, as measured by the number of times the 

water column circulates annually. For example, 

the following scheme was proposed by Hutchinson 

(1957) and summarized by Wetzel (1975): 

Class designation 

Amictic 

Cold monomictic 

Dimictic 

Warm monomictic 

Oligomictic 

Polymictic 

Description 

Noncirculating; perennially frozen; Antarctic and some high-altitude lakes 

One circulation period, in summer; temperatures 

never greater than 4 oc; Arctic and high-altitude 

lakes 

Two circulation periods, in spring and fall; temperate 

lakes and high-altitude lakes in subtropics 

One circulation period, in winter; temperatures never 

less than 4 °C; subtropical lakes 

Circulating rarely and at irregular intervals; 

temperatures well above 4 oc; tropical lakes 

Circulating frequently or continuously; lakes with 

large surface areas in windy regions of low humidity 

or high altitude; tropical lakes. 

The above classes are subdivisions of either 

holomictic lakes, wherein the entire water column 

can circulate, or meromictic lakes, wherein a lower 

layer remains permanently stratified because of increased density from high dissolved-solids concentrations. 

In polythetic classification systems, classes are 

defined on the basis of sharing a large proportion 

of similarities, but not necessarily having any property in common (Sokal, 1974). An excellent example of such a system is the classical scheme that has 

developed from the terms oligotrophic and 

eutrophic, as applied to lakewater types by 

Naumann (1919). Brief histories of the development of this scheme have been presented by 

Hutchinson (1969) and Rodhe (1969). Identification 

of lakes as eutrophic conjures up visions of (1) large 

concentrations or rates of input of nutrients, particularly phosphorus; (2) high rates of primary productivity, with associated large standing crops of 

algae (often blue-greens) and small Secchi-disc 

transparencies; (3) high rates of organic sedimentation; and ( 4) hypolimnia devoid of oxygen. It is 

not necessary that a lake possess all these 

characteristics to be considered eutrophic, nor is it 

necessary that all eutrophic lakes possess any one 

of these characteristics. The nonuniformity of the 

trophic-classification scheme makes it particularly 

difficult to apply, although this is not its only disadvantage, as is explained below. 

Uttormark and Wall (1975) discussed several 

characteristics of classification systems that also 

are worth summarizing. In post-set systems, 

classes are defined based on an analysis of the data 

base. Such systems are termed relative -lakes are 

classified with respect to one another, and the 

structure of the system may change when new 

lakes are added. This introduces an element of 

complexity when trying to apply such systems to 

data different from that used to derive the 

classification. 

Preset systems, on the other hand, are composed 

of the predefined classes that do not depend on the 

data base. These systems are simpler to use than 

postset systems; a new lake ean be added and 

categorized without changing the structure of the 

classification. 

Uttormark and Wall (1975) described analytic 

systems- classes defined by a statistical or other 

numerical analysis of the data base- and empirical 

systems, whose class boundaries were defined arbitrarily. 

In general, preset systems are empirical, and 

postset systems are analytic. 

Finally, one should be aware of the differences 

between single-purpose and multi-purpose 

classification systems. Multi-purpose systems are 

more difficult to develop and generally become 

quite generalized. Such systems sacrifice utility for 

applicability to a broad spectrum of situations. On 

the other hand, the usefulness of lake classification 

often can be enhanced, if it is allowed to become 

dynamic, whereby single-purpose schemes are 

developed for specific applications. One must 

beware, however, of expending great amounts of 

resources trying to design an ad hoc scheme for 

each new situation. In reality, a compromise approach probably is best. 

DESCRIPTIONS OF SOME 

LAKE-CLASSIFICATION SCHEMES 

Most of the lake-classification schemes that have 

been developed probably can be described best as 

geologic, hydrologic, or trophic. Examples of each 

are summarized below. 

B16 
GEOLOGIC SCHEMES 

One of the most widely accepted lake classifications is that of lake-basin origin proposed by 

Hutchinson (1957). By considering the geologic 

processes responsible for lake-basin origin, the 

classification has been somewhat regionalized, as 

certain processes act predominantly on certain 

areas of the earth's surface. Hutchinson (1957) 

described 76 major basin types formed by tectonic 

activity, volcanic activity, landslides, glacial processes, rock solution, stream processes, wind, 

shoreline processes, organic accumulation, activities of higher organisms (including man), and 

meteoric impact. Twenty of the major basin types 

are of glacial origin, and Hutchinson (1957) noted 

that, "No lake-producing agent can compare in importance with the effects of the Pleistocene glaciations." 

A second geologically oriented scheme, that may 

be useful in some areas, is based on altitude 

(Pennak, 1966). Four limnological zones were 

recognized in northern Colorado: (1) the plains 

zone, below 1, 700 m (5,600 ft); (2) the foothills zone, 

1, 700-2,500 m (5,600-8,200 ft); (3) the montane 

zone, 2,500-3,200 m (8,200-10,500 ft); and (4) the 

alpine zone, above 3,200 m (10,500 ft). Generally 

speaking, lakes at higher altitudes are colder, less 

productive, and contain lower concentrations of 

dissolved solids than lakes at lower altitudes. 

HYDROLOGIC SCHEMES 

Born and others (197 4) devised a hydrologic 

classification based on 63 glacial lakes located 

mostly in the north-central United States and 

south-central Canada. The incentive for the 

classification was based on the premise that certain hydrologic data are required to adequately 

define the nature and causes of lake-quality problems, and, thus, to outline appropriate steps for 

lake management and protection. 

Lakes were classified on the basis of three 

descriptors: (1) regime dominance, that is, whether 

a lake's water budget is surface-water dominated 

(drainage lakes) or ground-water dominated 

(seepage lakes); (2) system efficiency, or flushing 

rate of water through the lake; and (3) position of 

the lake in the regional ground-water flow system. 

The latter determination is important for seepage 

lakes; it describes whether ground-water flows 

primarily from the aquifer to the lake (discharge 

lake), from the lake to the aquifer (recharge lake), 

or into the lake on one side and from the lake on 

the opposite side (flow-through lake). 

In developing their classification, Born and 

others (1974) allowed for input of data or, because 

actual measurements often were not available, for 

input of descriptive information. The latter is 

basically an inference or best guess as to the functioning of the hydrologic system based on proxy 

criteria established by Born and others (1974). The 

available hydrologic data base was judged too 

sparse to adequately assess the performance of the 

proxy criteria, and it was concluded that " * * * a 

useful and workable classification cannot be 

adopted at this time." System efficiency seemed to 

be the most difficult of the descriptors to evaluate 

on the basis of the proxy criteria. 

In Wisconsin, extreme lake levels leave boat 

docks high and dry in some years and cause flooded 

shorelines in others. In order to arrive at a better 

understanding of these stage fluctuations, 

Novitzki and Devaul (1978) devised a hydrologic 

classification similar to, but simpler than, that of 

Born and others (1974). Lakes were designated as 

surface-water flow-through (major surface inlet 

and outlet), ground-water discharge (surface outlet 

only), and ground-water flow-through or landlocked (no surface inlet or outlet). Landlocked 

lakes have the largest water-level fluctuations, and 

ground-water discharge lakes have the smallest 

fluctuations. The implications of using such a 

scheme, based on simple observations, for management purposes are obvious and illustrate the practical utility of lake classification. 

Both of the above schemes employ a somewhat 

arbitrary determination as to which hydrologic 

variables should be used to classify lakes: the 

classifications are preset. Winter (1977) used a 

more general, postset, analytic approach to determine which variables are important. His study of 

150 randomly selected natural lakes in northcentral United States assumed that there existed 

no established variables upon which to base a 

hydrological classification. Winter (1977) selected 

all the variables " * * * believed to control the atmospheric, surface-, and ground-water interchange with a lake." His list of 13 variables included 1 relating to atmospheric-water interchange, 3 

relating to surface-water interchange, 6 relating to 

ground-water interchange, and 3 relating directly 

to water quality and indirectly to ground-water interchange. Thus, the input data were weighted 

heavily toward the ground-water component of the 

lake's hydrologic budget. 

Numeric data were available for some of the 

variables, the rest were subdivided based on 

qualitative descriptions, and numeric values were 

B17 
assigned to each subgroup. The entire data set was 

subjected to principal-component analysis- a 

multivariate statistical technique (see, for example, Rummel, 1970) designed to disclose the important independent variables controlling the 

variance in a large complex data matrix. It was 

concluded (Winter, 1977) that precipitationevaporation balance, ground-water dissolvedsolids concentration, stream outflow, relation of 

drainage-basin area to lake area, position (altitude) 

in the regional ground-water flow system, regional 

slope, and local relief of the lake's drainage basin 

all should be considered in a general hydrologic 

classification of lakes. Stream inflow and drift texture (a measure of hydraulic conductivity) might be 

considered important, depending on how the 

results of the principal-component analysis are interpreted. Thus, the number of variables required 

was reduced from 13 to 7 or 9. 

TROPHIC SCHEMES 

Before discussing trophic classification schemes, 

it would be appropriate to examine briefly the lake 

trophic-state concept, as traditionally perceived, 

and some of its implications. 

First, a useful distinction to be made is that between eutrophication and dystrophication. 

Reviews of the details of these processes have been 

presented by Rodhe (1969) and Wetzel (1975). It is 

important to note that the two processes follow 

separate pathways originating from a similar 

oligotrophic beginning (fig. 2); one process is not 

an extension of the other. When the rate of supply 

of organic matter to a lake comes predominantly 

from the watershed (allotrophy), the resulting 

process is dystrophication. When the rate of 

organic-matter supply results primarily from 

primary productivity of phytoplankton in the lake 

(autotrophy), eutrophication occurs. 

Dystrophy 

> 

::t: 

0.. 

0 

a:: 

t0 

...J 

...J 

<( 

01 igotrophy Eutropby 

AUTOTROPHY 

FIGURE 2. -Summary of the major trophic types of lakes 

(modified from Rodhe, 1969, and Wetzel, 1975). 

A second important point is that both 

eutrophication and dystrophication are continuums. Note, for example, the graphical 

representation of the eutrophication process offered by Hasler (1947) (fig. 3). The result of this 

continuum realization has been an expansion of the 

terminology of Naumann (1919) to include 

mesotrophy, for those lakes having characteristics 

of both oligotrophy and eutrophy, and, more 

recently, ultraoligotrophy, hypereutrophy 

(presumably describing the effects of cultural or 

accelerated eutrophication), and other variants. 

AGE OF LAKE 

FIGURE 3.-Hypothetical curve of the course of natural 

eutrophication of lakes (modified from Hasler, 1947). 

Finally, one must be aware of the controls that 

can be imposed on the eutrophication process by 

morphometry, particularly lake depth. Odum 

(1971) has presented the conception outlined in 

figure 4. Thus, for example, a lake can receive 

large quantities of nutrients; but if the lake is deep, 

the volume of water in the lake will keep the concentrations low enough so problems do not 

develop. 

All the above points are reflected in current lake 

trophic classifications. Any such scheme ideally 

should accommodate both eutrophication and 

dystrophication; however, as the latter process is 

rarer, produces fewer esthetically displeasing 

results, and, most importantly, is not accelerated 

SMALL LARGE 

CONCENTRATIONS CONCENTRATIONS 

OF NUTRIENTS OF NUTRIENTS 

SHAL- Morphometric Eutrophy LOW eutrophy ')I 

t t 

01 igotrophy Morphometric 

DEEP ol igotrophy 

FIGURE 4.-Effect of lake depth on the process of eutrophication 

(modified from Odum, 1971). 

B18 
by man's activities, it normally has not been considered. Also, most recent trophic classifications 

have taken a quantitative approach, in an attempt 

to avoid some of the subjectivity involved in determining when a lake changes from oligotrophic to 

eutrophic. Morphometric oligotrophy (fig. 4) 

creates particular problems when trying to apply 

some of the trophic-classification schemes. 

Classifications of lake trophic-state might be 

based on physical, chemical, or biological 

measurements, or on various combinations of 

these. In one scheme requiring only chemical 

measurements, Zafar (1959) proposed a differentiation of trophic states using Pearsall's (1922) cation ratio, ([Na] + [K])/([Ca] + [Mg]), where the concentrations are expressed on an equivalent basis: 

Trophic state 

Basically 

oligotrophic 

Basically 

mesotrophic 

Basically eutrophic 

Pearsall's 

cation ratio 

>2.0 

1.2-2.0 

< 1.2 

This classification depends on Pearsall's (1922) 

conclusion that waters with high ratios normally 

contain small concentrations of nitrogen, silica, 

carbonate, and phosphorus, whereas, waters having low ratios usually are rich in these nutrients. 

Shannon and Brezonik (1972) used several 

multivariate statistical procedures to classify 55 

lakes in north and central Florida. An expression 

for a trophic-state index (TSI) was derived using 

principal-component analysis: 

TSI=0.919 s1J+0.800COND+0.896TON 

+ 0. 738TP + 0.942PP + 0.862CHA 

+0.634 ci+5.19, 

where: SD = Secchi-disc transparency, in meters, 

COND =specific conductance, in micro mhos 

per centimeter at 25°C, 

TON= total organic-nitrogen concentration, 

in milligrams per liter of nitrogen, 

TP =total phosphorus concentration, in 

milligrams per liter of phosphorus, 

PP =primary productivity, in milligrams 

per cubic meters per hour of carbon, 

CHA= chlorophyll a concentration, in 

milligrams per cubic meter, and 

CR =Pearsall's (1922) cation ratio. 

TSI's ranged from 1.3 to 22.1; they were divided 

into five groups and labeled as follows: 

Trophic state TSI 

Ultraoligotrophic TSI < 2 

Oligotrophic 3 > TSI;;::: 2 

Mesotrophic 7 > TSI;;::: 3 

Eutrophic 10 > TSI;;::: 7 

Hypereutrophic TSI;;::: 10 

The classification of Shannon and Brezonik 

(1972) is a postset system: adding more lakes to the 

data base could change the classification structure. 

Thus, this system cannot be applied directly to 

other areas. One also should appreciate the fact 

that the same variables used by Shannon and 

Brezonik (1972) may not be appropiate trophicstate indicators in other regions; only the approach 

may be applicable. 

Bortleson (1978) used a similar technique to 

classify lakes in the State of Washington. He 

calculated a trophic-state index, called a 

characteristic value (CV), based on principalcomponent analysis of 617 lakes: 

CV =59.4 (si-0.661) 

+74.9(UON -0.697) 

+ 126(UP- 0.124)+ 104.1, 

where: SD = Secchi-disc transparency, in meters, 

UON =total organic-nitrogen concentration 

in the upper waters, in milligrams per 

liter of nitrogen, and 

UP= total phosphorus concentration in the 

upper waters, in milligrams per liter 

of phosphorus. 

Values of CV ranged from 1 to 1,259. Bortleson 

(1978) did not label CV ranges according to traditional trophic-state designations; however, lakes 

with larger values of CV normally would be considered more eutrophic. Bortleson's (1978) 

classification is a postset scheme. 

A somewhat simpler approach to the trophic 

classification of lakes has been presented by the 

U.S. Environmental Protection Agency (1974) and 

was based on 209 lakes sampled by the National 

Eutrophication Survey staff in 1972. A trophic index number (TIN) was derived from annual median 

concentrations of total phosphorus · (mg P/L ), 

dissolved phosphorus (mg P/L), and inorganic 

nitrogen (mg N/L), 500 minus the annual mean 

Secchi-disc transparency (inches), 15 minus the 

minimum hypolimnetic dissolved-oxygen concenB19 
tration (mg/L), and the annual mean chlorophyll a 

concentration (Jtg/L). For each lake, each variable 

was ranked according to the percentage of lakes 

having a greater value. Thus, for example, a lake 

having a low total phosphorus concentration would 

have a high percentile rank for that variable. The 

TIN is the sum of the percentile ranks for each of 

the six variables; the more eutrophic lakes have 

lower TIN's. 

Using a data set with 209 observations, the TIN 

. can vary from 594 (six variables x 99 percent) to 0. 

Actual values were in the range 573-41 and were 

labeled as follows: 

Trophic state TIN 

Oligotrophic > 500 

Mesotrophic 499-420 

Eutrophic < 420 

The U.S. Environmental Protection Agency (1974) 

emphasized: " * * * the [trophic] index number 

ranges * * * are only valid when used with the 

data base of 209 lakes and reservoirs considered in 

this report. If a different data base were 

used, * * * the resulting index number might be interpreted quite differently * * * ." This classification is also a postset system. 

An illustration of a preset system is provided by 

Bartleson and others (1974) in an early attempt at 

classifying Washington lakes according to trophic 

condition. Each of 24 separate variables was rated 

from 1 to 5, based on actual measurements, higher 

values indicating more eutrophic conditions. The 

variables were separated into: (1) natural physical 

factors, predominantly measures of lake morphometry; (2) culturally related factors, which 

represent nutrient-supply rates from various 

sources; and (3) trophic-state indicators, which are 

measures of chemical, physical, and biological conditions in the lake. A summed score was determined for each of the three groups of factors, and 

each score was interpreted separately. 

Because of the number of variables involved, application of the system requires an exhaustive 

data-collection effort. Bartleson and others (197 4) 

applied the scheme to seven Washington lakes to 

demonstrate the technique; however, they did not 

critically evaluate its effectiveness or its 

transferability to other regions. 

The classification of 1, 14 7 Wisconsin lakes was 

accomplished by Uttormark and Wall (1975) using 

an approach similar to that of Bartleson and others 

(197 4). The variables considered (with their rating 

scales in parentheses) are: (a) hypolimnetic 

dissolved-oxygen concentration (0-6); (b) Secchidisc transparency (0-4); (c) history of fishkills 

(0-4); and (d) assessment of recreational use impairment by algal blooms or emergent vegetation 

(0-9). A larger rating implies a more eutrophic condition. The ratings from each of the four variables 

are summed to yield a lake-condition index (LCI). 

The technique of Uttormark and Wall (1975) is 

much less data-intensive than that of Bartleson 

and others (197 4). In addition, the LCI technique 

was designed to allow for substitution, when 

necessary, of subjective information for actual 

data. This characteristic permitted a considerably 

larger number of Wisconsin lakes to be classified 

than would have been possible otherwise. 

According to Uttormark and Wall (1975) 

" * * * LCI values are not synonymous with 

trophic status * * *;"however, the following comparisons were offered: 

Trophic state 

Very oligotrophic 

Oligotrophic 

Meso trophic 

Eutrophic 

Very eutrophic 

LCI 

0-1 

2-4 

5-9 

10-12 

>13 

LCI's for Wisconsin lakes ranged from 0 to 22; 

and, for 42 lakes classified both by U ttormark and 

Wall (1975) and by the U.S. Environmental Protection Agency (1974), the following results were 

noted: 

Trophic state 

Oligotrophic ______ _ 

Mesotrophic ______ _ 

Eutrophic ________ _ 

Total ________ _ 

Number of Lakes 

LCI method 

(Uttormark and 

Wall, 1975) 

5 

16 

21 

42 

TIN method 

(U.S. Environmental 

Protection 

Agency, 1974) 

2 

5 

35 

42 

Thus, the LCI method seems to be more lenient 

for assessing trophic state than the TIN method. 

Uttormark and Wall (1975) also evaluated the 

possibility of using the LCI technique in other 

parts of the United States. They determined that 

the method would have direct applicability in the 

northeast and northern midwest, and would least 

likely be useable in the south and southwest. They 

indicated that the method might be useable, with 

some modification, throughout the rest of the 

country. 

B20 
Thus far, one pervading point must be noticed: 

Except for Zarfar's (1959) scheme based on Pearsall's (1922) cation ratio, Secchi-disc transparency 

has been incorporated into all the trophic 

classifications discussed. Therefore, it is not surprising that a system has been developed (Carlson, 

1977) based entirely on this measurement. The use 

of a single variable upon which to base a trophicstate index greatly simplifies data collection, and 

leads to somewhat less ambiguity than indexes 

based on several variables. The choice of Secchidisc transparency is logical for a single-variable 

system, as Carlson (1977) notes that transparency 

has been related to hypolimnetic oxygen deficit 

(Lasenby, 1975) and to chlorophyll a and total 

phosphorus concentrations. All these latter 

variables have been incorporated into one or more 

of the previously discussed indexes. 

Carlson's (1977) trophic-state index is computed 

from the equation TSI(SD)= 10(6-log2SD), (3) 

where: SD = Secchi-disc transparency, in rp.eters. 

By taking the . logarithm. to the base 2 of 

the 8ecchi-disc transparency, each doubling or 

halving of the transparency, which is assumed to 

reflect a corresponding doubling or halving of the 

algal biomass, causes the TSI(SD) to change by one 

unit. Subtracting from 6 ( = log264) assures that the 

index is always positive (a transparency of 64 

meters should never be exceeded) and inversely 

related to transparency. Multiplying by 10 allows 

one to carry 2 significant digits without including a 

decimal point. The index ranges from 0, at a 

Secchi-disc transparency of 64 meters, to a practical limit of about 100 (at a transparency of 62 

mm): 

TSI(SD) 

0 

10 

20 

30 

100 

Secchi-disc transparency, 

in meters 

64 

32 

16 

8 

0.062 

Carlson's (1977) index also can be calculated 

from chlorophyll a or total phosphorus concentration, based on generalized regression equations 

relating each of these variables to Secchi-disc 

transparency; indexes calculated in this manner 

normally are labeled TSI (Chl) and TSI (TP), 

respectively. 

An advantage of computing the trophic-state index from Secchi-disc transparency is that the required data are simply and cheaply collected. The 

total phosphorus index is most stable seasonally; 

however, this advantage must be weighed against 

the probability that the general regression equation presented by Carlson (1977) actually describes 

the Secchi-disc total phosphorus relationship for 

the particular lake in question. Alternatively, the 

actual relationship for any given lake could be 

defined, but this makes the classification effort 

more complex and expensive. 

Although Carlson (1977) did not label ranges of 

his index in terms of traditional trophic-state terminology, these relationships can be inferred as 

follows. Vollenweider (1968) suggested a classification of European lakes into ultraoligotrophic, 

oligomesotrophic, mesoeutrophic, eupolytrophic, 

and polytrophic based on total phosphorus concentrations. His mesoeutrophic class is defined by 

total phosphorus concentrations of 10 to 30 mg 

Pfms and corresponds to the mesotrophic label of 

Sakamoto (1966) for Japanese lakes having 

roughly the same range of total phosphorus concentrations. By converting the total phosphorus 

concentrations to values of TSI(TP) (Carlson, 

1977), one arrives at the following tentative boundaries for trophic states: 

Trophic state 

Oligotrophic 

Mesotrophic 

Eutrophic 

TSI(TP) 

<37 

37-53 

>53 

In his review of lake-classification schemes, 

Shapiro (1975) noted that Carlson's index is 

" * * * applicable to many lakes." Indeed, it may be 

the most widely used system currently employed. 

Applications in Maine (Bailey, 1975), the Great 

Lakes states (Garn and Parrott, 1979), and Colorado (L. J. Britton and D. A. Wentz, written commun., 1980) are known. Many others undoubtedly 

exist. 

Trophic classifications based primarily on 

biological data have been much less rigorous and 

quantitative than those previously discussed. The 

use of algal indicators has been discussed by 

Rawson (1956), who summarized the following 

major differences between trophic states: 

Trophic state 

Algal 

measurement Oligotrophic Eutrophic 

Quantity __________ Poor 

Variety ___________ Many species 

W aterblooms ______ Very rare 

Rich 

Few species 

Frequent 

B21 
Rawson (1956) noted that the few algal species 

often thought to be associated with eutrophic conditions may be a misnomer resulting from rarer 

species being overlooked in the relatively rich 

populations. He pointed out the importance of obtaining algal observations throughout the growing 

season, and provided a list of 18. algal indicators, 

arranged in order from oligotrophy through 

eutrophy based on dominant species in lakes of 

western Canada. Differentiation of trophic-state 

based on these indicators is not straightforward 

and should be approached with caution. 

Tarapchak (1973) classified 68 Minnesota lakes 

as oligotrophic, mesotrophic-eutrophic, salineeutrophic, and hypereutrophic on the basis of 

major solutes and the assumption that " * * * it is 

likely that salinity is in fact a good indirect 

measure of nutrient level." He investigated 

associated algal communities and found that the 

number of phytoplankton species was the only 

biological index sufficient for distinguishing the 

lake groups: diversity, evenness, standing crop, 

and various ratios (quotients) of numbers of individuals in certain taxonomic groups did not give 

satisfactory results. Of the 13 algal species Tarapchak (1973) found to be indicative of oligotrophy 

and the 9 species listed as representing eutrophy, 

only 1 species also was listed by Rawson (1956). 

Primary productivity is a likely algal-associated 

variable for classifying lake trophic condition, 

especially given the concept in figure 2. Wetzel 

(1975) provided the following summary, based approximately on net primary productivity: 

Trophic state 

Ultraoligotrophic 

Oligotrophic 

Mesotrophic 

Eutrophic 

Mean "net"primary 

productivity 

(mgC/m2/day) 

<50 

50-300 

250-1,000 

> 1,000 

The greatest objection to this approach is the implicit assumption that organic matter inputs from 

littoral macrophytes and from sources in the lake's 

watershed are small, relative to those from the 

phytoplankton. 

Relatively little has been published regarding 

trophic characterization of lakes on the basis of 

zooplankton. Ghilarov (1972) was successful in 

separating seven oligotrophic, morphometrically 

oligotrophic, mesotrophic, and morphometrically 

eutrophic lakes north of the Arctic Circle in northwestern Russia, using a plot of zooplankton 

biomass versus diversity. In addition, Brooks 

(1969) summarized evidence indicating changes, 

due to complex competition-predation interrelationships, in copepod populations from the larger 

Bosmina coregoni longispina to the smaller B. 

longirostris during lake eutrophication. Thus, differences in zooplankton populations among lake 

trophic-states have been demonstrated; however, 

interpretation of lake trophic-state based on properties of zooplankton populations is not a welldefined procedure. 

Shapiro (1975) reviewed several trophic-classification systems based on benthic macroinvertebrates and on fish production. He noted that these 

often are difficult to understand and apply, and 

that are not likely to have widespread application. 

Finally, mention should be made of recent attempts to classify lake trophic condition from data 

collected by Landsat satellites. Multispectral scanners aboard Landsat measure intensities of light 

reflected from the earth in four wavelength bands: 

500-600, 600-700, 700-800, and 800-1,100 

nanometers. Boland (1976) developed a trophicstate index based on principal-component analysis 

of ground truth data from 100 Minnesota, Wisconsin, Michigan, and New York lakes sampled by the 

National Eutrophication Survey staff in 1972. He 

then used multiple regression analysis to predict 

his trophic-state index (dependent variable) from 

the intensities of light in the four Landsat 

wavelength bands (independent variables) 

-reflected from within the lakes. In Wisconsin, 

1,380 of 3,100 lakes with surface areas greater 

than 8.1 hectares (20 acres) have been categorized 

according to trophic status using a similar approach (Holmquist, 1977; Holmquist and Scarpace, 

1977; Martin and Holmquist, 1979). 

Both the above studies found that Secchi-disc 

transparencies could be predicted from Landsat 

data. This would indicate that Carlson's (1977) 

trophic-state index also could be used in conjunction with Landsat data to classify lakes. 

CONCLUSIONS 

It is interesting to note that the writing of this 

paper has become, itself, an exercise in classification. Hopefully, the above classification and 

categorization of lake-classification schemes have 

provided enough illumination to penetrate much of 

the murkiness, but not so much as to make the subject totally transparent. Looking back on what has 

been said, perhaps the most obvious conclusion is 

that a large variety of lake-classification schemes 

have been proposed. Some have rather limited apB22 
plicability; others appear to be adaptable to different situations. When the need for a lakeclassification system arises, the most important 

point to remember is not to apply these schemes indiscriminantly. As in all scientific endeavors, the 

results obtained through lake classification are 

porportional only to the thought directed initially 

to the problem. 

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B23 
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99 p. 

,z U.S. GOVERNMENT PRINTING OFFICE: 1981-341-614/224 

B24 



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