Cluster Analysis of Potomac RiverSurvey Stations Based on

Protozoan Presence-Absence Databy

JOHN CAIRNS, Jr. & ROGER L. KAESLERBiology Department Virginia Polytechnic Institute

Blacksburg, Virginia andDepartment of Geology The University of Kansas

Lawrence, Kansas

ABSTRACT

Four high-water and six low-water limnological surveys of a por-tion of the Potomac River were made from 1956 to 1965 ; samples werecollected at three stations on each survey to determine the effects ofoperation of the PEPCO Dickerson Power Station on the aquaticbiota. Cluster analyses were made of various combinations of Jaccardcoefficients relating 46 aggregations of 647 protozoan species .

Similarities of aggregations of species within a survey were nearlyalways greater than similarities among aggregations from differentsurveys, indicating linear or along-stream environmental influences .Within-survey similarities for the early and late surveys were usuallyhigher than similarities within middle-year surveys, a possible indi-cation of environmental change at all stations, including the control,and subsequent biotic readjustment . Clustering of the 1956 aggrega-tions, taken under high-water conditions before plant operationsbegan, with aggregations from other surveys for any one station in-dicate that 1956 may have been different from other survey years . Oneexplanation is that increased urbanization upstream from the powerstation after 1956 caused some environmental change . No changes inaquatic biota could be attributed to thermal pollution as a directresult of operation of the electric power generating station .

Received December 9th, 1968 .

4 1 4

The clustering method used provides a convenient means of quan-titative analysis of limnological survey data .

INTRODUCTION

In order to determine the condition of the Potomac River in thevicinity of the Dickerson Plant of the Potomac Electric PowerCompany, the Limnology Department of the Academy of NaturalSciences of Philadelphia has made a series of biological, chemical,bacteriological, and physical studies during both high and low flowconditions. The first pair of studies (i .e ., high and low water) wasmade in 1956 and 1957 before plant operations had begun . The firstpower unit of the PEPCO Plant at Dickerson began operation in thespring of 1959 and the second unit in the spring of 1960 . A secondpair of surveys was made in 1960 and a third pair in 1961 to evaluatethe effects of these two units . A third power unit was put in opera-tion in 1962, and a fourth pair of surveys was made that year to evalu-ate the effects of this unit . In 1963 a single survey was made duringthe period of low flow and warm water conditions to further evaluatethe effects of this additional unit . Finally, in 1965 a single survey wasmade, also during the period of low flow and warm water conditions .In addition, cursory low-water surveys were made in 1964* and1966*, but organisms studied did not include Protozoa .

In general the surveys were paired, and each pair consisted of onesurvey in June during the period of comparatively high flow andmoderate water temperature and one in August or September duringthe time of relatively low flow and warm water conditions . In thisway it was possible to evaluate the effects of plant operations on theaquatic organisms under two different ecological situations . Al-though the biological surveys consisted of evaluation of all majorgroups of stream organisms from Protozoa through fish, this paperwill deal exclusively with protozoans because one of us (CAIRNS) hasbeen involved in studies of protozoan communities and speciesinteractions for a number of years and because it seemed worthwhileto test the applicability of the methodology with a single group beforeattempting to study the entire aquatic community. However, workof broader scope is planned, and data are already prepared for com-putation .

Most of the data upon which this paper is based have already beenreported by CAIRNS (1966), and the data upon which the 1966 paper

*Reports to the Potomac Electric Power Company from the LimnologyDepartment, Academy of Natural Sciences of Philadelphia .

41 5

was based have been deposited as document No . 8902 with the APIAuxiliary Publications Project, Photo Duplication Service, Libraryof Congress, Washington, D .C. 20540*. Other papers containingrelevant information on protozoan populations are : CAIRNS (1964,1965, 1968), MAGUIRE (1967), MOHR (1952), PATRICK (1949, 1961),and NOLAND & GoJDICS (1967) .

Data from the 1965 survey were not yet available when CAIRNSwrote the 1966 paper . These data have now been deposited with theNational Auxiliary Publications Service . **

Conclusions in this study have been drawn from cluster analysis ofJaccard coefficients relating stations, the coefficients as usual beingbased on presence or absence of species at stations rather than onabsolute numbers found or on relative abundances. Throughout thispaper the term aggregation is used to refer to all protozoans found at astation or substation during any one survey . Data for 647 protozoanspecies in 46 aggregations were analyzed .

In recent years, methods of cluster analysis have found increasedapplication as availability of high-speed digital computers has per-mitted handling of large data matrices . This is particularly truewhere data or sampling are such that assumptions of rigorousstatistical methods are not satisfied .

One of the major recent developments responsible for rapidincrease in the use of cluster analysis has been numerical taxonomy,and it is in the numerical taxonomic literature that one finds the mostthorough discussion of clustering methods (SNEATH & SOKAL, 1962 ;SOKAL & SNEATH, 1963). The techniques have also been appliedsuccessfully in marine ecology, particularly by geologically orientedecologists (KAESLER, 1966; MADDOCKS, 1966 ; RUCKER, 1966 ; VALEN-TINE, 1966). A recent paper by BONHAM-CARTER (1967) reviewednon-ecological applications of cluster analysis in the geologicalsciences, and Moss (1967, 1968) has examined and compared existingcluster analytical techniques and developed a new method for dis-playing clusters graphically .

SELECTION AND DESCRIPTION OF STATIONS

CAIRNS (1966) described the stations from which data for this studywere collected . The reader is referred to that paper for details and for

*A copy of these approximately 42 pages of data may be obtained by citingthe document number and remitting $ 6.25 for photo prints or $ 2.50 for 35mm microfilm. Advanced payment is required and check or money ordershould be made payable to: Chief, Photo Duplication Service, Library ofCongress .** See Addendum.

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maps of each station . Figure 1, modified from CAIRNS' map, showsthe location of the study area, of the three stations within it, and ofthe site of the power plant. The stations were selected so that allwould include presumably comparable habitats, thus hopefullyinsuring a comparable opportunity for a species to become estab-lished at any station . The intent of the surveys and the originalanalysis of data they produced were to determine species diversityaccording to the method proposed by PATRICK (1949) . Numbers ofindividuals per species were of secondary importance in the analysis .For such a study, having stations of equal area is much less importantthan having stations that include all types of habitats. The samecriterion applies to this study in which abundance of individuals iscompletely disregarded . CAIRNS (1966) listed the following generalecological conditions considered in selecting stations : (1) structureof the river bed, (2) current, (3) contour, stability, and compositionof the substrate, (4) sedimentation, (5) vegetation in the surroundingdrainage area, (6) quality and quantity of debris, and (7) collectabilityof the study area .

Station 1, located upstream from the power station, is a controlstation .

Station 2, located just downstream from the power plant outfall,is also a short distance below the confluence of the Potomac andMonocacy Rivers . It is close enough to the power plant to reflect anydamage to aquatic life by thermal pollution from the plant, but it isnot far enough away to allow complete mixing of (1) the outfall withthe rest of the Potomac River water or (2) the water of the MonocacyRiver with the water of the Potomac. To determine the extent andeffect of mixing, the right and left halves of the river were regardedas distinct substations of station 2, and protozoan occurrence datawere kept separate in all but the 1960 surveys. The two subsampleswere analyzed both separately and pooled in this study .

Station 3 was located far enough downstream from the power plantand the mouth of the Monocacy River for nearly complete mixing tohave occurred . Its function was that of a delimiting station in casestation 2, particularly substation 2L (left bank facing downstream),showed noticeable deterioration as a result of thermal pollution fromthe power plant .

Inflow of water from the Monocacy River into the middle of thestudy area is a complicating factor that was unavoidable . It is virtu-ally impossible to determine which changes in biota at substation 2Lare the effects of the Monocacy River and which ones occurred as adirect result of installation of the power station. Ideally, for purposesof this study, another control station would have been located on theMonocacy River; but the additional expense could not be justified

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418

I

01

Scale - Miles1 i

5

Fig. 1 . Map of locality showing power plant site and three stations .

at the time the program was designed, since determination of thenumber of species for pollution assessment alone did not require it .

COLLECTION OF PROTOZOAN SAMPLES

Details of collecting, sample analysis, and related informationwere given by CAIRNS' (1966) . However, it maybe well to repeat someof the critical background information and to expand upon and em-phasize some of the problems related to the characterization ofprotozoan populations which might not be entirely clear to non-protozoologists . The hypothesis proposed by PATRICK (1949) andextended in this paper is based upon data collected for an entirelydifferent purpose - the biological assessment of water quality .Sampling areas or stations were chosen in accordance with themethods originally proposed by PATRICK (1949) which are based onthe assumption that environmental stress or pollution will cause areduction in number of species of aquatic organisms inhabiting theexposed area. Thus, one or more stations are set up below the sourceof pollution and the number of established species found in each ofthese is compared to the number of species in one or more controlstations above the source of pollution . Many industries now surveythe major groups of organisms in the aquatic community (includingalgae, protozoans, other invertebrates, and fish) at three or fourstations as well as making chemical and physical determinations ofwater quality before beginning plant operations . Thus, one has eachstation acting as its own control through time as well as having acontrol station upstream from the possible source of pollution.

The data upon which this study was based were obtained at sta-tions classified as healthy or semi-healthy according to the systemdescribed by PATRICK (1949) . One of us (CAIRNS, 1967) has proposeda definition of optimal conditions which is similar in intent toPATRICK'S definition of healthy. "For biologically oriented readers myown operational definition of optimal is the ability to support anaquatic community in a pattern which does not vary more than 20percent from the empirically estimated maximum steady-statediversity possible in each particular locale ."

Samples upon which species determinations were based werecollected from a sampling area or station which usually includedapproximately 100-300 yards of stream or river from bank to bankand which commonly included a riffle, slack water, and a pool area .Generally about 20 half-pint samples were collected, and approxi-mately a dozen different examinations were made from each sample .Since it was quite unlikely that the species densities or numbers of

4 19

individuals per species would be identical in each sample, some of thespecies reported from a single station were probably widely separatedspecially. Since water for chemical and physical determinationscould not be collected from each microhabitat, it is also unlikely thatthe chemical determinations represent the precise environmentalconditions in the microhabitats from which the protozoans werecollected . In fact, one would expect that the chemical and physicalconditions in the microhabitats within a station would probablydiffer in several environmental characteristics from those reportedfor the station as a whole . CAIRNS & YONGUE (1968) have shown thateven on a relatively homogeneous substrate the numbers and kindsof species vary either due to sampling error or due to the fact thatspecies below a certain threshold density are not easily observed andcollected . On the other hand, by restricting the sampling to thoseorganisms associated with the substrate, local environmental condi-tions are probably reflected more accurately than would be possiblefrom an analysis of the "planktonic" species that are merely passingthrough the area .

The succession or replacement of protozoan species in a fresh-water habitat is a well known phenomenon . It should be emphasizedthat the samples at each station were taken only once on a survey,though in some cases additional surveys of the same stations weremade the following year. However, successive samples of a singlestation taken a few days apart were not characteristic of these sur-veys. Therefore, any sequential relationships through time ratherthan in space would not be detected . In addition, it is quite likelythat the presence of at least some of the species is related to thepresence of certain bacterial species, none of which were identifiedin these studies . It is also quite likely that trace compounds such ascarbohydrates, glycolic acid, Vitamin B 72, and other substances ofthis sort which are not included in the chemical analyses might beextremely important in determining the presence or absence of aspecies .

Other sources of error are possible, although these are of minorimportance in analyses of this type. First of all, in certain cases agroup of species may be lumped under a single specific name. Thisis undoubtedly the case with the organisms collectively groupedunder Vahlkampfia limax (BOVEE, personal communication) . An-other possibility is that a number of physiological species may be"hiding behind" a similar morphological facade . Ecologists havebeen well aware of this possibility and have confirmed its existencesufficiently to make further discussion of this point unnecessary . An-other possible source of error was the use of a number of protozoolo-gists (six) for these determinations . Since surveys were made over a

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considerable period of time (over 10 years), it is also possible that asingle investigator's view of taxonomic relationships and specificcharacters might have changed during this period . Since protozoancommunities are difficult to preserve intact and since many of thespecies are present in very low densities and these are difficult toobserve when associated with various debris and other inert particles,the difference in reported number of species from the number actu-ally inhabiting an area due to these causes is virtually impossible toassess. Collections made and examined independently followed by acomparison of results suggests that the errors resulting from differ-ences of opinion or identification are considerably less than onemight expect and probably are not nearly as important as thesampling errors. Identification problems are, of course, reduced bythe cosmopolitan distribution of protozoans and the use of a relativelyfew basic taxonomic keys .

The problem of low density species has already been mentioned .However, a few additional comments may benefit those readers notfamiliar with fresh-water protozoan populations . Certain rather largespecies such as Spirostomuum ambiguum (about 1000 microns) arevirtually impossible to miss and are, therefore, highly likely to berecorded when populations are at or above the minimal densitiesdecided upon for inclusion in the species list . However, species ofBodo (about 9-18 microns) and other relatively small protozoansmay be difficult to detect particularly when there are large numbersof one or two species in a genus and relatively small numbers of a third .Since it is quite difficult to examine each specimen carefully, theprobability of omitting a low density small species under these con-ditions is considerably greater than for the medium to large species .Because it is difficult to preserve an entire fresh-water protozoancommunity and retain species in their original density relationshipsas well as with sufficient taxonomic characters for good identifica-tion, all samples must be examined with dispatch . Thus, from thetime the sample is collected species may be encysting, excysting,reproducing, or dying. Usually an immediate scanning of all samplesafter collection followed by as rapid determination as possible willinsure that no great distortions occur in the estimation of the numberand kinds of species present . However, it is inevitable that some errorsoccur that are related to the perishable nature of the sample and thatthese errors may not be uniform from one river system to another orat all seasons of the year in a single area .

It is quite widely recognized that protozoans and other micro-organisms produce a variety of substances of use to species otherthan the species producing the substance . Since all of the data onwhich this paper is based came from sampling areas with flowing

421

water, it is possible that the critical relationships are linear andrelated to the direction of flow . In this event, the protozoans andother micro-organisms above the sampling area which might not beidentical to those in the sampling area would have more influenceupon the species present in the sampling area proper than wouldother species in the established community within the sampling area .Thus, when we attempt to define the niche of an aquatic inhabitantof a river or stream we might find the critical relationships are thosewith upstream organisms rather than with organisms occupying thesame territory .

PROGRAM METHODS AND PROCEDURES

The method of cluster analysis used in this study involves foursteps. The first step is computation of a matrix of coefficients ofassociation. A great many coefficients of association have been pro-posed in the literature ; many of these have been reviewed and evalu-ated by COLE (1949, 1957), DAGNELIE (1960), SIMPSON (1960), LONG(1963), SOKAL & SNEATH (1963), and KAESLER (1966) . For our analysis,we have chosen one of the simplest, most straight forward coefficients,the Jaccard coefficient (JACCARD, 1908) . The equation for thiscoefficient is :

aSj = a+b+c ,

where a, b and c are standard notation for a 2 x 2 contingency table(e.g., SIMPSON, Row & LEWONTIN, 1960, p. 187) ; a is the number oftimes species included in the study occur at both of two stationsbeing compared ; b is the number of times they occur at one stationand not the other ; c is the number of times they occur at the otherstation and not the one.

An important feature of the Jaccard coefficient is that it omitsnegative matches from consideration . Thus, if a protozoan specieswas found at one or more of the stations occupied but not at stationsi and j, its mutual absence from i and j would not contribute to simi-larity between i and j . Many other commonly used coefficients donot have this feature. The simple matching coefficient, for example,includes the number of negative matches in both the numerator andthe denominator . KAESLER (1966) made use of this characteristic ofthe simple matching coefficient in clustering stations from a marinebay that showed gradual change from shallow to deep water ofnearly all physical properties . In such an environment, negative

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matches might be nearly as important as positive matches, givenadequate sampling. MADDOCKS (1966), studying a much more diversearea, found that use of simple matching coefficients was essentially astudy of negative association, because most species occurred at onlya few of the many stations sampled . She used the Jaccard coefficientto eliminate this effect .

Data for our study are much like those Of MADDOCKS (1966) .Species rarely occur in more than a few samples . For this reason weconsidered the Jaccard coefficient to be appropriate . Furthermore, acoefficient of similarity that includes negative matches measures twokinds of similarity by mutual absences . For example, temperature atstations i and j may be too high for a species to live . The stations,then, will be properly indicated as similar for their mutual intolera-bility to that species . However, station k may be too cold for thesame species; if so, station k will be indicated as similar to i and j forits absence of the species . But the similarity coefficient will containno information relating to the cause of the mutual absence of thespecies . Use of the Jaccard coefficient avoids this difficulty .

The second step in cluster analysis is the clustering itself . We haveused the unweighted pair-group method (SOKAL & SNEATH, 1963),which has been found generally to introduce less distortion into theclusters than other methods of clustering .

The third step is graphic display of clusters . Dendrograms forthis study are shown in Fig . 2 through 8. The scale of similarityis shown across the top of the dendrogram, and similarity level ofany cluster may be determined by drawing a vertical line fromthe level of branching to the scale . SOKAL & SNEATH (1963) havediscussed interpretation of dendrograms, and the appendix of theirbook contains an example of the computations necessary to obtainone.

Step four is comparison of similarities shown by the dendrogramwith those of the original matrix of coefficients of association . Thisstep is necessary because the clustering method involves averagingof similarities in order to express the multidimensional Jaccard co-efficient matrix as a two-dimensional, hierarchic relationship . SOKAL& ROHLF (1962) have developed a method of making this comparisonin which similarity values from the dendrogram are expressed as amatrix of cophenetic values . A correlation coefficient is then com-puted between this matrix and the original matrix of coefficients ofassociation. The correlation coefficient is a measure of the amount ofdistortion introduced by the clustering method . The unweightedpair-group method commonly yields a higher cophenetic correlationthan other clustering methods .

Cluster analysis has several important disadvantages, some of them

423

general and some of them specific to ecological applications . One ofthe most important of these is the distortion introduced by averaging.The extent of distortion can be measured, but it cannot be correctedfor. A more fundamental objection is that cluster analysis produceshierarchic clusters regardless of the structure of the original matrix .That is, stations distributed uniformly in an ecological hyperspacewould be clustered into a hierarchy although no such structure existsin nature. Furthermore, the clusters produced by most clusteringmethods, including the one used here, are hyperspheres . This couldbecome a serious disadvantage in an ecological study of a river, whereunder some circumstances as mentioned previously one might expectsequential similarity of stations along the stream with a few aberrantstations .

In spite of these shortcomings, cluster analysis has much to re-commend it. It can be applied to cases in which assumptions ofrigorous statistical methods are not met and thus in which tests ofsignificance may be meaningless, and it provides convenient, albeitsomewhat distorted, two-dimensional graphic display of clusters . Ifcophenetic correlations are high (i .e ., greater than about 0.8), onecan assume that averaging similarities, forcing stations into a hierarchy,and clustering hyperspheroidally have not introduced too great adistortion of the information content of the original matrix ofcoefficients of association .

An important characteristic of the method of cluster analysis usedis that it gives equal weight to each species included in the study .Equal weighting presents few conceptual difficulties if all organismsincluded in the study are of the same kind, e.g., Protozoa. In laterphases of the analysis of the Potomac surveys data, we plan to includeall aquatic organisms from algae to fish . It may seem irrational toweight minute, single-celled organisms equally with vertebrates .However, it is important in this regard to remember that the occur-rence of the species and not the size or activity of the specimensthemselves is recorded as data . And although a large predator mayexert a strong influence on the population of its prey, the predatoris in turn dependent upon and therefore influenced by the occurrenceof species upon which it feeds. It is possible to program weightingprocedures, but no operationally definable basis for a priori weightinghas been proposed. In the absence of such a criterion for unequalweighting, equal weighting is to be preferred .

A posteriori weighting of species, on the other hand, could be madeconsistent with the dendrograms' ordination of stations and couldlead to the identification of species that are specific indicators ofcertain environmental conditions . If ease of collecting and identifyingindicator species were also considered, cost of limnological surveys

424

might be greatly reduced while information obtained remained asuseful as that from more extensive surveys .

RESULTS

Dendrograms in Figures 2 through 8 show results of cluster analysis .The first digit of the aggregation designator is the station number .An R or L after station 2 indicates right or left substation; a 2 withno letter indicates pooling of all data from station 2 . An H after thespace indicates a high-water survey ; an L indicates a low-watersurvey; and the last two digits refer to the year .

Figures 2 and 3 show, respectively, clusters of all high-water andall low-water aggregations . It is interesting to note that, with theexception of aggregations 3 H62 and 1 L62, all stations within asurvey form a relatively tight cluster with fairly high within-groupsimilarities . This indicates a similar protozoan population at allstations during any year at high or low water and strongly suggeststhat pollution has not had a significant effect on the protozoan po-pulation in the area .

JACCARD COEFFICIENTS

0 .001

1 .001 H563 H562R H562 H562L H56

H602 H603 H60

2R H622L H622 H62I H612R H612L H612 H613 H61

Fig. 2. Dendrogram prepared by the unweighted pair-group method witharithmetic averages (UPGMA) showing similarities among all high-wateraggregations. An R or L after station 2 indicates right or left substation ; a 2with no letter indicates pooling of all data from station 2 . An H after the spaceindicates a high water survey ; an L indicates a low-water survey ; and the lasttwo digits refer to the year .

Figure 4 shows clustering of aggregations from station 1 for allsurveys taken together and for high-water and low-water surveystaken separately. These clusters are probably the most significantfor evaluation of possible effects of thermal pollution by the PEPCO

425

JACCARD COEFFICIENTS

0 .00

1 .00

r1 L573 L572R L572L L572 L57

L613 L612R L612 161

1AL1 L602 L603 L60I L652R L652 L653 L652L L652R L622L L622 L62

3 L621 1 L62 j1 L632R L632 L632L L633 L63

Fig. 3. Dendrogram (UPGMA) showing similarities among all low-wateraggregations .

Fig. 4. Dendrograms (UPGMA) showing similarities among all station 1aggregations. Top, all ; middle, high water; bottom, low water.

426

JACCARD COEFFICIENTS

t

I

I

I

I0 .00

0 .35

1 H56I L60I L 651 L571

L61I H611 H601 H621 L621 L63

1 H561 H60I H621 H61

1 L57I

1

L611 L601 L651 L621 L63

power station. On the dendrogram relating all high-water aggrega-tions from station 1, note that the 1956 aggregation shows the lowestsimilarity to the other three aggregations . If this low similarity ismeaningful and not a result of sampling error or haphazard fluctua-tions in protozoan populations, it indicates that 1956 was differentfrom other survey years . Any differences from the 1956 aggregationat stations 2 and 3 must be evaluated with this change in mind .Furthermore, in the dendrogram of all low-water aggregations fromstation 1, the early aggregations (1957, 1961, and especially 1960)show close similarity to the late (1965) aggregation, whereas inter-mediate years' aggregations (1962 and 1963) are less similar to eachother and to the aggregations from early and late surveys . A similarpattern is suggested in Figures 2 and 3 . Such a pattern for thecontrol station may indicate increased urbanization upstream fromthe power plant, resultant change in the protozoan population, andsubsequent readjustment to its original balance . This explanation isonly one of many possible ones, and it must be regarded as specula-tion until more data are available . In any event, patterns of this sortin the control station complicate interpretation of relationshipsamong samples from below the power station .

Dendrograms in Figures 5 through 8 show similarity among aggre-gations from substations 2R and 2L, stations 2 (pooled), and station 3for all surveys and for high-water and low-water surveys separately .The same patterns are present as were noted for aggregations fromstation 1 . First, at all stations and substations except station 3, aggre-gates from the 1956 survey show greatest dissimilarity to other high-water aggregations . Second, low-water aggregations for 1963 are

JACCARD COEFFICIENTS

I

I

I

I

I0.00

0.35

2R H562R L622R H612R L612R H622R L652R L572R L63

II2R H562R H612R H62

2R L572R L62R L622R L652R L63

Fig. 5. Dendrograms (UPGMA) showing similarities among all substation 2Raggregations . Top, all ; middle, high water; bottom, low water .

427

most dissimilar to other low-water aggregations, with early- and late-year surveys producing more similar aggregations. Finally, overallsimilarity among high-water aggregations is generally somewhatgreater than among low-water ones .

Similarity relationships among aggregations from substations 2Rand 2L and station 2 (see Figures 2 and 3) are interesting, but moredata are needed to permit fruitful interpretation .

Fig. 6. Dendrograms (UPGMA) showing similarities among all substation 2Laggregations. Top, all ; middle, high water; bottom, low water .

JACCARD COEFFICIENTS

I

I

I

I

I0 .00

0.35

2 H562 L602 L652 H602 H622 H612 L612 L622 L572 L63

2 H562 H602 H622 H61

2 L572 L602 L652 L612 L622 L63

Fig. 7. Dendrograms (UPGMA) showing similarities among all station 2(pooled) aggregations . Top, all ; middle, high water; bottom, low water .

428

JACCARD COEFFICIENTS

I

I I

I I

0.00 0 .35

2L H562L L612L L62

2L L572L L65

41-1 2L H622L H612L L63

2L H62L H612L H62

2L L572L L652L L61I2L L622L L63

Fig. 8. Dendrograms (UPGMA) showing similarities among all station 3aggregations. Top, all ; middle, high water; bottom, low water.

Cophenetic correlations (Table I) for all dendrograms are high,some of them extraordinarily so, indicating that little distortion in thesimilarity matrix has been introduced by clustering .

TABLE I

Cophenetic correlations for dendrograms in Figures 2 through 8 .

CONCLUSIONS

1 . With few exceptions, greatest similarity is found among aggre-gates from the same survey.

2. A tendency is indicated for higher within survey similarities inearly and late surveys than in middle-year surveys . If this tendencyis a significant one, cluster analysis has indicated either a cycle in theriver ecology or change and subsequent readjustment of the proto-zoan population.

3. The year 1956 may have been different from other survey years .

429

Figure

Dendrogram2 3 4 5 6 7 8

position

Top 0 .927 0 .929 0 .920 0.829 0.841 0 .895 0 .898

Middle 0 .766 0.914 0.893 0 .991 0 .753

Bottom 0 .971 0.767 0 .879 0 .944 0 .956

JACCARD COEFFICIENTS

I

I0.00

0.35

3 H563 L603 L653 L573 L613 L623 H603 H613 H623 L63

3 H563 H613 H603 H62

3 1573 L60

~- 3 L653 L613 L623 L63

If so, a possible explanation is that in succeeding years increasedurbanization upstream from the power station produced changes inthe protozoan population . High similarities between low-water aggre-gations from 1960 and 1965 may indicate faunal adjustment .

4. No detectable change in protozoan populations can be attributedto thermal loading from the Pepco Dickerson power plant .

5 . The methods of cluster analysis provide a convenient quantita-tive means of studying data from river surveys . Results of theanalysis agree closely with results based on number of species .

ACKNOWLEDGEMENTS

We are indebted to Mr . L. W. CADWALLADER, Vice President of thePotomac Electric Power Company for giving permission to publishthese results and to the staff of the Potomac Electric Power Companyand Sheppard P. POWELL and Associates for help and many courtesiesduring the course of these surveys . The survey program from whichthis paper was taken was designed by Dr . Ruth PATRICK, to whom weare grateful for help and suggestions offered during the preparationof this material. We are also grateful to Dr . F. James ROHLF, Mr . J.R. L. KISHPAUGH, and Mr. Ronald L. BARTCHER, who developed theNT-SYS computer programs used in the analysis of data and whoassisted us in their use . We are indebted to The University of KansasComputation Center for a grant of computer time and to The Uni-versity of Kansas Biomedical Sciences Research Grant 4180-5706-2for funds necessary to enable us to have cards punched for thecomputational work. In addition to the determinations made byCAIRNS, protozoologists who participated in these studies are Dr .Mary GOJDICS (1957 and 1961 surveys), Dr . Stuart S. BAMFORTH(1960 surveys), Dr . J. RusSEL GABEL (1956 survey), and Dr. SamsonMcDOWELL (1965 survey). We are grateful to Professors Conrad A .ISTOCK and Daniel JANZEN for their constructive criticisms of themanuscript .

REFERENCES

BONHAM-CARTER, G . F. - 1967 - Fortran IV program for Q-mode clusteranalysis of nonquantitative data using IBM 7090/7094 computers .Computer Contribution 17, State Geological Survey, Univ. of Kansas,p. 1-28 .

CAIRNS, J., Jr. - 1964 - The chemical environment of common fresh-waterProtozoa . Not. Nat. Acad. Nat. Sci. Philadelphia, No. 365, p . 1-6.

CAIRNS, J . - 1965 - The environmental requirements of Protozoa . BiologicalProblems in Water Pollution, Third Seminar, 1962 PHS Publ . No. 999-WP-25, p. 48-52, Abst . p. 385-386 .

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CAIRNS, J . - 1966 - The Protozoa of the Potomac River from Point of Rocks toWhites Ferry . Not. Nat. Acad. Nat. Sci. Philadelphia, No. 387, p . 1-11 .

CAIRNS, J . - 1967 - The use of water quality control techniques in the manage-ment of aquatic ecosystems . Water Res. Bull ., 3 ; 47-53 .

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Addendum :

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