The community degradation index: a new method for assessing the deterioration of aquatic habitats

Wat. Res. Vol. 22, No. 3, pp. 293-301, 1988 Printed in Great Britain

0043-1354/88 $3.00+0.00 Pergamon Press pie

THE COMMUNITY DEGRADATION INDEX: A NEW METHOD FOR ASSESSING THE DETERIORATION OF AQUATIC HABITATS ALAN E. RAMM Estuaries and Coastal Processes Division, The National Institute for Water Research, P.O. Box 17001, Congella 4036, Republic of South Africa

(Received August 1986; accepted October 1987) Abstract--An index method is developed which condenses biological community data into a form which can be readily understood and used by the water resource planner. The concept and mathematical expression of this community degradation index (CDI), as proposed here, is new. A major advantage of the CDI methodology is that no subjective decisions have been made regarding the sensitivity of a particular species of biological assemblage to a pollutant or mix of pollutants. The CDI is applied to fisheries data collected from the Cuyahoga River, Ohio. The close agreement between community degradation as expressed by the index and field observations of the river, provides strong justification for the use of the CDI as a measure of habitat degradation.

Key words----degradation index, fish, communities, aquatic, planning, water resources, management

INTRODUCTION

BIOLOGICAL INDEX METHODS

Background

General

A major goal of the United States' Water Pollution Control Act Amendments (Public Law 92-500) has been to provide swimmable and fishable waters. While the determination of whether waters are swimmable is relatively straightforward, the evaluation of the suitability of an aquatic habitat for the maintenance of specific fish communities is considerably more difficult. However, a survey of the fish in an area can aid in the evaluation of its "fishability". The results are generally presented as lists of species and their relative abundances. While the comparison of such lists and the interpretation of their significance is part of the expertise of a fisheries biologist, such lists are not readily useful to the average planner, who may have little knowledge of fish community relationships. Yet it is the responsibility of planners to incorporate all relevant information into the planing process whenever possible. Consequently, a method of condensing fish community data into a more easily used format is essential if this information is to be used in the planning process.

Although stream use classifications in part identity the general types of fish which may be expected to live in a particular segment of stream, the usual method for evaluating the "health" of the segment is based upon chemical monitoring data. While, this is a widely used method, there are several inherent problems associated with its use. Since chemical monitoring must adhere to a collection schedule, there is always the possibility that periods between collection may include significant discharges. Thus the results of the monitoring may overlook occassional but major discharges. Since organisms are continuously exposed to the receiving water, they serve as integrated measures of the discharges. In addition, synergistic effects of exposure to a mixture of constituents are often difficult if not impossible to determine. This becomes an especially serious problem in waters which receive multiple or complex effluents. Again, an organism can serve as an integrated measure of combined effects. As a consequence, biological community composition/structure methods have been used to evaluate stream quality. Because comparison of faunal lists is difficult, especially to the non-biologist, a variety of such indices have been developed. Among the more widely used indices have been: coefficient of community by Jaccard (1912), Shannon-Wiener diversity index by Shannon (1948), 'index of similarity by Sorensen (1948), index of dominance by Simpson (1949), index of species richness by Margelef (1958),

Purpose A standard method of condensing biological community information for use by professionals not trained in biology is through the use of an index. This paper focuses on the development of an index method for evaluation of stream habitats, the procedures and data required for generation of the index, and the potential use of that index by the prospective planner. 293

294

ALANE. RAMM

and evenness index by Pielou (1966). All of these indices give some information concerning community structure, and all but the coefficient of community and index of similarity make use of abundance data. Karr (1981) developed an index which is designed to assess the biotic integrity directly through 12 attributes of fish communities in streams. These attributes fall into several categories, including species composition and richness, trophic composition, and fish abundance and condition. Considerably detailed community information is thus required to derive and apply this index. More recently, a series of studies have been conducted by the Freshwater Biological Association, River Laboratory, England (Furse et al., 1984; Wright et al., 1984; Armitage et al., 1983). These studies describe analyses of the macroinvertebrate collections from 268 running-water sites in Great Britain. Ordination and classification of this data set was conducted using detrended correspondence analysis and two-way indicator species analysis (Hill, 1979a, b; Hill and Gauch, 1980; Gauch, 1984). These analyses resulted in the classification of the sites into sixteen groups, and the production of a species "key" which facilitates subsequent classification of new sites. Multiple discriminant analysis was also used to relate groups to a variety of other environmental variables. Both presence-absence and abundance information are considered by ecologists to be important community structure components. The presence of species contributes to the species richness and "information" content of a community and appears to be related to community stability. The relative abundance of species within a community dictates the evenness of distribution of individuals among those species present. Pielou (1975) has shown that this evenness characteristic is also apparently related to the stability of a biological community. Of the many indices that have been used, perhaps the most widely applied has been the ShannonWeiner Index of Diversity, calculated as

u ' : - E {p, logp,}. This expression, derived from information theory, provides a measure of entropy per species. In many instances, the index appears to be related to community stability. But taken alone, diversity does not necessarily provide any direct information on the quality or degree of degradation of the environment from which the sample was taken. Some studies have indicated a relationship between water quality and species diversity. Although it has been found that diversity generally ranges from zero to one in polluted streams, this generalization appears to have many exceptions according to Wilhrn (1967, 1968,. 1970). The community degradation value concept A method which compares the present biological community to the community that would exist in the

absence of (or prior to) degradation would provide a direct measure of the present degree of degradation. Such a method would be of considerable practical value to the water resource planner, for it would facilitate comparison of aquatic habitats. Several general considerations are necessary, however, prior to establishing such an index: (1) It must be assumed that the major differences between the historical (or potential) community and the present assemblage are due to habitat degradation. (2) Historical information on community composition should be available in order to reconstruct the "potential" assemblage. The more sparse the existing documentation, the greater the degree of judgement necessary for determining the potential composition. (3) Using an historical basis for reference to the present assemblage almost universally precludes the use of species abundance data for two reasons: (a) while the historical presence of biota may be well documented in some instances, abundance data are generally absent; and (b) abundances of biota are known to vary significantly on a seasonal and annual basis. A valid comparison of historical vs present abundances requires knowledge of average or typical abundances. This is rarely the case for any historical records, and only under unusual circumstances is it known for the most recent collections. This restriction implies that diversity measures, which rely on relative abundance information, are not suitable in this context. However, some of the theoretical considerations inherent in the ShannonWiener index have been incorporated in the following development. (4) It is generally not practical, due to manpower and economic limitations, to utilize a broad variety of faunal groups to develop an index. While strong arguments favor the use of macroinvertebrates for an index, very little historical information is generally available for this group of organisms. In addition, species identification within as diverse a group as "invertebrata" is extremely time-consuming. As a consequence, the fish community has been of the following development. DEVELOPMENT OF THE COMMUNITY DEGRADATION INDEX

The biological segment concept The biological segment concept proposed here is analogous to the stream segment concept commonly used in waste-load allocation modeling (Thomann, 1972). Unlike the stream segment concept, however, man-induced pollution is considered as ! an impact parameter only if its effects are largely irreversible. A biological segment is defined as a portion of a stream in which the fish community remains generally homogeneous due to the relatively uniform nature of the physical habitat. Physical habitat includes gross

Community degradation index stream morphological characteristics such as bottom topography, substrata composition, flow characteristics, gradient, canopy, and bank cover. Depending upon the local conditions and size of the stream, the size of a segment can vary considerably. Nevertheless, the transition from one segment to another is often marked by obvious physical discontinuities. Examples of these would be entry of a tributary to a main stem, sudden change in stream gradient, or transition in substrate character, or fringing vegetation. Departures from a relatively homogeneous faunal assemblage within a biological segment would only be expected if changes in other environmental conditions, such as water quality, were occurring. The biological segment is an artificial construct, a simplification of the real world, which provides a more manageable model for analysis. While it is clear that many variations do in fact occur within a segment, the among-segments variability will be much greater than the within-segment variability. Development of the Community Degradation Index (1) Comparison among communities. In order to establish the magnitude of degradation at a given site, the "observed" and "potential" species composition must be compared. An accepted ecological method for making comparisons is the use of a similarity index. While many similarity indices have been developed and used by ecologists, one simple and commonly used index, developed by Jaccard, has been in use for over 50 years (Mueller-Dombois and Ellenberg, 1974). It is expressed as

295

(2) Dissimilarity calculation. Jaccard's index provides a measure of similarity between the above assemblages. What is really required, however, is a measure of dissimilarity. This is readily accomplished by subtracting the above result from unity, a procedure commonly employed in community ordination technqiues. Thus, dissimilarity (D) is given as D = 1- J

(3)

and for the previous example D = l - 0.25 = 0.75. This may be interpreted within the context of stream degradation as implying that the assemblage at the site is 75% degraded relative to the potential assemblage for its location. (3) The degradation measure. Suppose fish species lists have been determined for the two following sites: Observed species (O)

Potential species (P)

l0 5

40 20

Site A Site B

Both of the above sites reflect the same relative degree of degradation (D = 0.75), calculated as JA=10/40=0"25 JB=5/20=0.25

and and

D^=1-0.25=0.75 DB = 1 = 0 . 2 5 = 0 . 7 5 .

This provides a measure of similarity between the observed and potential assemblages. By way of exmaple, consider a hypothetical site with an observed assemblage (O) of l0 species, which lies within a biological segment having a potential assemblage (P) of 40 species. Applying Jaccard's index gives

D can thus be considered as a measure of degradation. From a broader perspective, however, the degree of degradation of each of the above cannot be considered equal. This is because the loss of richness is different. Site A has lost 30 species, whereas Site B has lost only 15 species. Thus the site with the greater potential species richness has lost twice as many discrete "kinds" of organisms. It has experienced a more severe loss of "biological information". Ecologically, the biological information content of a system is a measure of significance (Margalef, 1968; Pielou, 1975). Since a major purpose of a degradation index is to differentiate among communities, those which have suffered a greater information loss must rank as more degraded. Consequently a factor must be incorporated in the degradation measure (D) which relates the potential richness at a site (P), to the maximum potential richness of any site within the area of study (Pm~). In practice, the potential richness (P) of each biological segment is determined, and P , ~ is the highest individual value of P available from all the biological segments within the area of study. The most straightforward manner of incorporating this richness factor into the index is

J = O/P = I0/40 = 0.25.

P / Pm~.

This'is equivalent to stating that the observed site assemblage is 25% similar to the potential assemblage for the location.

However, this simple relationship does not correctly express the appropriate relationship between community structure and species richness. From a theor-

. J = C/T

(l)

where C is the number of species common to two assemblages and T is the total number of different species contained within the two. The value of J will range between 0 and l, where I represents identical species lists, and 0 indicates no species in common. In this application, the observed species assemblage will always be a subset of the potential assemblage. If the number of species in the observed assemblage is defined as "O", and the number in the potential list is P, then O = C and P = T, and equation (1) becomes J = 0/P.

(2)

296

ALAN E. IL~M

etical basis, information content of a community is instead directly related to the logarithm of species richness. This is most easily visualized by considering the widely used Shannon-Wiener diversity index (information measure) given as

n'

=

-E {p, logp,}.

This i n f o r m a t i o n measure incorporates b o t h richness

and evenness. For completely even communities, however, the above can be rewritten as H" = log s where s is the richness as measured by the number of species present and log is the natural logarithm (Pielou, 1975). Thus, information content is directly related to the logarithm of richness, and the factor can better be represented as log P/log Pm~"

(4)

The relationship described in (4) can now be incorporated into (3) to give O = (l - J) {log P/log P~.~ }.

(5)

Applying this factor to the previous example, with P=,~ = 100 now gives: DA = (1 - JA) {log PA/1og P ~ } = 0.75 (log 40/log 100) = 0.66 D s = (1 -- ,1"8) {log PE /log /)max}

While the degradation measure (D), defined in (5) above, always lies in the range 0 < D < 1, we have in practice defined the community degradation index (CDI) as 10, D. The CDI thus ranges from 0 (no degradation) to 10 (totally degraded site). The relationship between J, P and CDI is shown in Fig. 1. It is clear from the figure that the CDI is not linear. Consequently, it is inappropriate to conclude that a site with a CDI of 4 is twice as degraded as a site with a CDI of 2. This figure also indicates that the sensitivity of the CDI (ability to discriminate between sites), decreases rapidly at lower values of P/P~,x. A

1.o

CDI

0.9~----~--~

I-

t

2-

o.., -I

3-

o.,-I 0:2 0:3 o:, o:s 0:6 P I Pm,',x

Fig. 1

APPLICATION OF T H E C O M M U N I T Y DEGRADATION INDEX

Background During the period 1977-1978 the Northeast Ohio Areawide Coordinating Agency (NOACA) supported an investigation which resulted in the collection of representative fish assemblages from 188 sites within five major river basins in Northeast Ohio (Ramm et al., 1978). The data resulting from this study has been used to develop CDIs for each of the sites based upon the protocol in Fig. l. It is not the purpose of this paper to describe the results of this investigation, but rather to use selected portions of the results to illustrate the application of the CDI. A thorough description of criteria used for delineation of biological segments within each river basin, lists of potential fish species for each segment, fish species lists for each site, and details of sampling methodology and effort is contained within the above referenced report. Consequently, the details of the survey methods and results will only be briefly discussed.

Biological segmentation

= 0.75 (log 20/log 100) = 0.53.

o.o o:,

flow chart of the methodology for determining the CDI for a selected site is shown in Fig. 2. The following will provide an example of the application of this index method for determining the relative degree of degradation in a major river basin in Northern Ohio, U.S.A.

o'., o.,

Biological segments were established for each of the river basins. The delineation of each segment was based upon a set of physical habitat criteria which included (a) width and depth of the stream, (b) substrata composition, (c) stream gradient, (d) lake effect, (e) degree of development of riffles, runs, and pools and (f) other features including stream canopy, undercutting of banks and aquatic vegetation (Ramm et al., 1978). Due to concern regarding the somewhat subjective nature of this approach, coordination of fish assemblages taken from 16 sites with known high water quality were conducted for one of the basins (Grand River) using D E C O R A N A (Hill, 1979). Given uniformly high water quality, one would expect the differences and similarities among sites to reflect physical habitat differences. Clusters resulting from this ordination procedure were compred with the seven biological segments resulting from the physical habitat criteria previously listed. Strong agreement was found between the two methods which lends credence to the somewhat subjective approach used to define segments throughout the five fiver basins (Ramm et al., 1978, p. 50). Figure 3 shows the segments delineated for the Cuyahoga River basin, the system which will be used as an example of the application of the CDI procedure for areawide planning.

Community degradation index

297

Table I. Species richness by site and segment SEGMENT 3

4

5

6

7

8

?

~0

11

SITE 1 3 4 5 8 9 11 13

CDI 11 1

12 12 8

2 23 20

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 51 32 33 34 35 36 37 58 59 40 41 42 45 44 45 46 47 48 50 51 52

20 7 26 13 13 19 14 11 19 14 10

2 14 11 12 11

9 4 10 10 14

6 10 8

5.0 5.8 7.1 7.9 7.1 7.0 5.2

11' 8 16 59

36

43

45

3.3 5.5 6.5 7.1 5.5 6.5 7.3 7.5 7.0 8.7 7.7 8.2 8.6 5.9 6.0 5.7 6.3 6.8 6.4 6.6 6.6 3.0 8.1 7.3

7.7 7.3

21 17 11 7

49

7.4 8.7 7.3 6.1 8.6 9.3 8.2 4.3 4.9 4.4 5.3 3.9 6.6

46

38

40

35

39

23

Example: refering to the above table, consider site 47, belonging to segment 5. Applying the equation for calculating CDI: CDI = 10 * (1 - J)(Iog P/log Pmx) where J = O/P = 11/45 and P.~x= 59 gives: CDI = 10 * (I - 0.24)(log 45/log 59) = 7.1.

Preparation of segment lists Potential segment lists were developed for each biological segment defined above ( N O A C A , 1978). These lists represented those species of fish which were present, or would be expected to be present, prior to stream degradation. Table 1 summarizes the potential species richness determined for the eleven biological segments illustrated in Fig. 3. With relaw.a. 22D---c

tively few exceptions, these lists were supported by historical documentation of species occurrence ( N O A C A , 1978). Historical information is well documented for the Northern Ohio rivers. The absence of historical data in other areas would make the establishment of potential segment lists more subjective. However, the use of consensus techniques, such as the Analytical Hierarchy Process (Saaty, 1986), by a panel of local biologists, might prove to be an

298

ALAN E. l~MM

~

observed| species /

lists

e)eiop

2.

1.

JDefermine "1 potentict[ [ segment / tists

EstQbtish

biological segments

"~1= ~i similarity [ indices for / sites

~

site J

Fig. 2

alternative approach where good historical records are scarce. Site collections Lists of species collected at each survey site were prepared (NOACA, 1978). Each site collection represented the composite of collections taken within all habitats (rime, run, pool) present at the site. A standard sampling effort, adjusted for habitat size,

was used in order to make the results comparable (NOACA, 1978). The survey data revealed that the fish assemblage composition of individual segments was rather distinct. Cross-comparisons of similar stream orders among drainages, or even of segments and order within the same drainage and order, indicated a lack of similarity among them. Such results demonstrate that each drainage and segment contained an individualistic fauna. Collection sites for

_.-=~-----:7.-.--z~-~ 7----- ~'--.-~'.---- _--3...: .--

~~.-~.ake Er.ie~-:-

_--_---_~ _----.__

----5_-'-'-- _----:"7------. ---.-_-- ___. ......... -

- --=-C

N

SCALE I 6.000 metres

Fig. 3

Community degradation index

299

._-_ - . -.---._-- = . - __-~ :'~

--- ~.-Loke Erie--:_.-_.:

m_.:

:_---::_

31

~2

27

h40

3~

11

N SCALE I

6.000 metres

Collection Site Fig. 4 the Cuyahoga River Basin are shown in Fig. 4. Table 1 further shows the variation in species richness at sites within each biological segment.

Calculation of CDIs Calculations of CDIs for each site were made and are summarized in Table 1. An example of the calculation procedure applied to site 47 is appended to Table 1 to enhance clarity. The results for all sites are depicted in Fig. 5, with shaded circles indicating the relative degree of degradation at each site. The shaded portion of each circle represents the relative degradation for the site, with a completely shaded circle representing a CDI of 10. Thus, a half-shaded circle indicates a CDI of 5, a quarter-shaded circle a CDI of 2.5, etc.

Interpretation The 46 sites in the Cuyahoga River basin had a mean CDI of 6.0, which is the highest average CDI for the five basins studied. (In comparison, the mean CDI for the Grand River, 30 miles to the east, and in a generally rural area, was 3.4). This was not

surprising, as the Cuyahoga River has been known nationally for its relatively polluted status. The CDIs ranged from a maximum value of 9.3 (site 9) in the main stem of the river, to a minimum of 2.1 in the upper reaches of Furnace Run, a tributary (site 40). Site 9 is directly downstream of extensive industrial and municipal development, in an area in which very poor water quality, as indicated by chemical monitoting, is well documented through extensive Environmental Protection Agency (EPA) records. Site 40 is in a suburb, Broadview Heights, at the upper reaches of a tributary, and in a well developed residential area, but with no visible signs of municipal or industrial discharge. It is interesting to note the impact of discharges in both the main stem of the river and in tributaries, as reflected in the .corresponding CDI values. The section of the river from downstream of site 11 to site 16 flows through an area proposed at one time as potential National Park land. It is one of the most visibly degraded portions of the river, due largely to upstream industrial and municipal discharges. Ohio EPA records collected over the past 15 years also

300

ALAN E. R.AMM

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-=----I.(ike E r-ie--~~ = _------~_:.---~ :

-

- - °

.

.

.

.

.

.

-~_.~._--..- . : - - +_..-..~.- ._-.....-

.~.-~-.: .-:--_--- .r--------:---

N SCALE I

6,000 metres

Fig. indicate very poor water quality in this reach of the river. CDIs range from 8.0 to 9.3 within this reach. The CDIs also increase downstream within all major tributaries, indicating progressive degradation. Site 4, at the mouth of Tinker's Creek showed a very high CDI (8.2). Tinker's Creek has numerous waste discharges into it as indicated by records available through the Ohio EPA's water quality data base. The most undisturbed continuous stretch of the river occurs downstream of Hiram Rapids (site 45) to above Lake Rockwell (site 13). The CDIs here average about 3.5 indicating relatively low degradation. This is an area commonly sought after by outdoor recreation enthusiasts, particularly for canoeing and nature viewing. SUMMARY

AND

RECOMMENDATIONS

The concept of a community degradation index, as proposed here is a relatively simple one, namely that one can assess the deterioration of a habitat by comparing it with a theoretical (ideal) situation. However, the mathematical expression of this concept in a relatively simple and understandable format, and on an ecologically sound basis is new. A major advantage of the CDI methodology is that no subjective decisions have to be made regarding the sensitivity of a particular species to a pollutant or mix of

pollutants. The CDI has thus far been applied to a somewhat limited set of data. However, the close agreement between the habitat degradation as expressed by the index and the field observations made in the five river basins studied, provide initial justification for its use as a measure of habitat degradation. As indicated in the example of its application to the Cuyahoga River, the CDI can be used to rank aquatic sites relative to their degree of habitat degradation. This provides the planner with a useful tool for incorporating biological information into the planning process. For example, a high CDI reflects a meager assemblage relative to what couM be present if the habitat quality were improved. Improvement here generally represents a return of the aquatic habitat to its former "unspoiled" condition. In most cases, the aesthetics associated with this former condition are desirable as well. Thus the potential for improvement at a site with a high CDI is greater than that at a low CDI site, and a ranking of sites by magnitude of CDI is similar to a ranking by potential for improvement. Where limited funds for improvements must be allocated within a planning area, the CDI can aid the water resource planner in optimizing the net benefits of the expenditures. Certain cautions should be exercised when using this index for planning purposes. It is often the case

Community degradation index that some locations must be singled out for special consideration. If, for example, endangered species habitat or scenic river designation is appropriate for portions of an area, then these criteria must be used in conjunction with the CDI for evaluating the relative "value" of the area. As with all index methods, some information is lost in the process of preparing the index. Whenever condensation of information occurs, "detail" is sacrificed for "perspective". This tradeoff is concommitant with the development of any model of the real world. It is not possible to simultaneously have both high detail and broad perspective in any model. Thus the ideal use of the CDI is in conjunction with other information such as species lists, which sacrifice perspective for detail. In the absence of experts, who can interpret such lists, however, the CDI alone can still be a useful tool for the knowledgeable water resource planner. Because the concept is new, the CDI developed here is a first attempt at formulating a mathematical expression (model) of the concept. As with all such models, it is earnestly hoped that the concept will be extended, further refined, and applied to a broader set of data. Two aspects of the method, in particular, should be closely examined in future refinements, namely delineating biological segments, and establishing segment lists. As precise a method as possible should be used to delineate biological segments. It may be possible to establish a firmer set of criteria for delineation purposes and accomplish this in a more objective m a n n e r than was possible in this instance. Due to the nature of the concept, however, it is likely that its determination will always remain somewhat subjective. It is also suggested that future investigations could be conducted to determined the sensitivity of the C D I to errors associated with creating the list of potential species for the segments. The sensitivity of the CDI to inappropriate inclusion or omission of species on a potential list should be examined. From such a sensitivity analysis the relative effect of stream size, degree of pollution, and other such factors could also be determined. With sufficient data it may also be possible to associate a relative error measure with the CDI.

301

Research Group) for their invaluable ideas, advice and criticism. REFERENCES

Armitage P. D , Moss D., Wright J. F. and Furse M. T. (1983) The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. Wat. Res. 17, 333-347. Gauch H. G. (1984) Multivariate Analysis in Community Ecology. Cambridge University Press, Cambridge. Hill M. O. (1979a) A FORTRAN program for arranging multivariate data in an ordered two-way table by classification of the individuals and attributes. Ecology & Systematics Division, Cornell University, Ithaca, N.Y. Hill M. O. (1979b) A FORTRAN program for detrended correspondence analysis and reciprocal averaging. Ecology & Systematics Division, Cornell University, Ithaca, N.Y. Hill M. O. and Gauch H. G. (1980) Detrended correspondence analysis. An improved ordination technique. Vegetatio 42, 47-58. Jaccard P. (1912) The distribution of the flora in the alpine zone. New Phytol. 11, 37-51. Karr J. R. (1981) Assessment of biotic integrity using fish communities. Fisheries 6, No. 6, 21-27. Margalef D. R. (1958) Information theory in ecology. Gen. Syst. 3, 36-47. Margalef D. R. (1968) Perspectives in Ecological Theory. University of Chicago Press, Chicago, Ill. Mueller-Dombois D. and Ellenberg H. (1974) Aims and Methods of Vegetation Ecology. Wiley, New York. Pielou E. C. (1966) The measurement of diversity on different types of biological collections. J. theor. Biol. 13, 131-138. Pielou E. C. (1975) Ecological Diversity. Wiley, New York. Ramm A. E., White A. M. and Alldridge N. (1978) River basin habitat study. Northeast Ohio Areawide Coordinating Agency. Report No. 63, Cleveland, Ohio. Saaty T. L. (1986) Decision Making for Leaders. University of Pittsburgh Press, Pittsburgh, Pa. Shannon C. E. (1948) A mathematical theory of communication. Bell Syst. Tech. J. 27, 379-423. Simpson E. H. (1949) Measurement of diversity. Nature 163, 688--690. Sorenson T. (1948) A method of establishing groups of causal amplitude in plant societies based upon similarity of species content. K. Danske Videnskrift. Selsk. 5, 1-34. Thomann R. V. (1972) Systems analysis and water quality management. Environmental Science Services Division, New York. Wilhm J. L. (1967) Comparison of some diversity indices applied to populations of benthic macroinvertebrates in a stream receiving organic wastes. J. Wat. Pollut. Control Fed. 39, 1673-1683. Wilhm J. L. (1968) Use of biomass units in Shannon's formula. Ecology 49, 153-156. Wilhm J. L. (1970) Range of diversity index in benthic macroinvertebrate populations. J. Wat. Pollut. Control Acknowledgements--I would like to express may appreFed. 42, R221-224. ciation to the Northeast Ohio Areawide Coordinating Agency (NOACA), Cleveland, Ohio for financially support- Wright J. F., Moss D., Armitage P. D. and Furse M. T. (1984) A preliminary classification of running-water sites ing this work as a portion of a Section 208 study. Many in Great Britain based on macroinvertebrate species and people were supportive to me during the development of the prediction of community type using environmental the concepts. Special thanks are due to Dr E. B. Long data. Freshwat. Biol. 4, 221-256. (NOACA), and Dr A. M. White (Cleveland Environmental