Assessing the Indicator Properties of Assemblages for Natural Areas Monitoring Author(s): Claire Kremen Source: Ecological Applications, Vol. 2, No. 2 (May, 1992), pp. 203-217 Published by: Ecological Society of America Stable URL: http://www.jstor.org/stable/1941776 . Accessed: 07/02/2014 15:50

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This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions Ecological Applications,2(2), 1992, pp. 203-217 ? 1992 by the Ecological Society of America

ASSESSING THE INDICATOR PROPERTIES OF SPECIES ASSEMBLAGES FOR NATURAL AREAS MONITORING'

CLAIRE KREMEN Centerfor Conservation Biology, Stanford University, Stanford, California 94305 USA

Abstract. The diversityof organismsand complexityof ecosystemsprevent thorough inventoryand monitoringof protectedareas, yet sound databases are needed to manage ecosystems for long-termpersistence. One strategyis thereforeto focus monitoringon indicatororganisms, but guidelinesare lackingfor selecting appropriate species or groups. This paper presentsa simple protocolbased on ordinationtechniques for establishing the indicatorproperties of a group of organismsand forselecting an indicatorspecies subset formore intensivemonitoring. Use of ordinationallows inclusionof many more taxa than have been traditionallyused fornatural areas monitoring,and need not relyon detailed knowledgeof species biology.As an example,I studiedthe indicator properties of a taxocene in a rain forestin . Butterflieshave been suggestedas particularly good environmentalindicators due to their sensitivityto micro-climateand light level changes,and theirinteractions as larvae and adults with differentsets of host plants. The indicatorproperties of butterflyassemblages were evaluated in this studywith respectto a known patternof environmentalheterogeneity along topographic/moistureand distur- bance gradients.Butterfly assemblages were found to be excellentindicators of heterogeneity due to the topographic/moisturegradient, limited indicatorsof heterogeneitydue to an- thropogenicdisturbance, and poor indicatorsof plant diversity.The protocol definedin this studyis widely applicable to othergroups of organisms,spatial scales, and environ- mentalgradients. By examiningthe environmentalcorrelates of the distributionof species assemblages,this protocol can assess the indicatorproperties of targetspecies groups. Keywords: diversity;dominance; ecological monitoring; indicator species assemblage; Madagas- car; naturalareas conservation;ordination; rarity; ; tropical .

INTRODUCrION lation trendsor habitat quality (Landres et al. 1988). Most ecosystemstoday are subject to one or more Given the difficultiesinherent in using one or a few formsof anthropogenicdisturbance, especially pollu- species as indirectassays of complex ecosystemstruc- tion and acidification,habitat modificationand frag- tureand function(Ward 1978, Kimball and Levin 1985, mentation,and invasions by introducedspecies (Soule Cairns 1986, Soule 1987, Landres et al. 1988, Noss needed to establishand testcri- and Wilcox 1980, Burgessand Sharpe 1981, Petersand 1990), furtherwork is Darling 1985,Soule 1986, Schreiberand Newman 1988, teria forselecting indicators. Carleton 1989, Fajer 1989, Fajer et al. 1989, Klein Much previous workinvolving indicators has relied 1989). Given the pervasive spatial and rapid temporal on utilizingone or a few species (e.g., Management scale of currentanthropogenic environmental changes, Indicator Species of the Forest Service, Landres et al. methods are needed forchoosing appropriatespecies 1988). The failingof thisapproach is its narrowfocus, or species assemblages for establishingconservation which can resultin protectionof one organismat the prioritiesand monitoringbiotic responsesto local and expense of others(Kushlan 1979, Landres et al. 1988). global environmentalchange (Kimball and Levin 1985, By contrast,use of a greatervariety of indicatorspecies Soule 1990). could provide more fine-grainedinformation (Noss Outside of a substantialliterature on singleor multi- 1990): thedegree of detail gainedwould in turndepend species indicatorsof specificenvironmental contami- on variation in microhabitatuse, niche breadth,eco- nants (Cairns 1985, 1986), few practical guidelines logical function,and responseto environmentalchange currentlyexist forselecting indicators for monitoring among membersof the indicatorassemblage. By using naturalareas. A recentreview criticallyevaluated se- ordinationtechniques, one can easily examine the dis- lectionof vertebrate indicators, concluding that no sin- tributionsof many species simultaneouslyand their gle traditionalcriterion (e.g., highsensitivity to habitat relationship to environmental parameters (Gauch modification,large size, habitat specialization, low 1982a, Ter Braak 1987, Peet et al. 1988). These pow- population or species turnoverrates, or large area re- erfulmultivariate tools allow assessment of the indi- quirements)can be safelyused for monitoringpopu- cator propertiesof a much wider array of organisms, and could be used to broaden monitoringconcepts to I Manuscriptreceived 7 January1991; revised and accepted include detectionof environmentalpatterns based on 15 August1991. the response of a group of species.

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions 204 CLAIRE KREMEN Ecological Applications Vol. 2, No. 2 This paper presentsan analyticalmethod based on time for adult courtshipand oviposition, Watt et al. ordinationtechniques for assessing the indicatorprop- 1968, Kingsolver 1983a, b; severe climate and popu- ertiesof a given species assemblage (guild,taxon, tax- lation extinction,Ehrlich et al. 1972). Since butterfly ocene, or community).The method firsttests whether populations respondto habitatmodifications affecting the distributionaldata fromthe chosen species assem- local climates and light levels, it has been suggested blage indicateenvironmental patterns at thegeograph- that changes in butterflypopulations could serve to ic scale of interest.It then determineswhich environ- herald local or global climate change (Murphy et al. mental parametersthe species assemblage serves to 1990). indicate. Finally,it providesa frameworkfor selecting While studiesof butterflieshave contributedto con- the most importantindicator species fromwithin the servationbiology in both a populationand community originalspecies assemblage. context(e.g., Brown 1982, Ehrlichand Murphy 1987, Murphyet al. 1990, Singletonand Courtney1991), no Butterfliesas indicators studiesto date have evaluated the indicatorproperties The taxon Rhopalocera (butterfliesand skippers)was of butterflyspecies or assemblages. This studyexam- the group chosen forthis studyof a rain foresthabitat ines the abilityof butterflycommunity data to reveal in Madagascar. Littleecological informationexists on patternsof habitat heterogeneitydue to topography these species or theirhabitat, and some of the taxa are and anthropogenicdisturbance. Variation related to poorlydefined at the species level. This studytherefore thesetwo parametersstrongly influences micro-climate requireda method forchoosing indicatorspecies that and plant community composition (Swanson et al. did not depend on detailed knowledgeof theirbiology. 1988), and thusprovides one appropriateassay ofsome This propertyof the method developed below makes of the proposed indicatorproperties of butterflies. it particularlyimportant for use in testingand selecting METHODS indicatorsin regionswhere the ecological information- base is poor. Fieldsite In addition, the choice of the Rhopalocera allowed The fieldsite was located in the southeasternmon- a test of the claim, advanced by several authors,that tane rain forestof Madagascar, 7 km southwestof Ra- butterflieshave a particularvalue as ecological indi- nomafanaon route45 in the provinceof Fianarantsoa. cators (Gilbert 1980, 1984, Pyle 1980, Brown 1982, The area is topographicallydiverse, with steeply wood- Murphy et al. 1990). On a practical basis, butterflies ed hills, dissected by numerous streamsdraining into (in comparisonto otherinsect taxa) have a manageable the Namorona River. The site had a treespecies rich- level of diversity,are betterknown taxonomically, and ness on the order of 95 species/0.2 ha (D. Overdorff, can in many areas be reliably identifiedin the field unpublisheddata) includingFicus spp. (Moraceae), Eu- (Pollard 1977, Thomas 1983, Thomas and Mallorie genia spp. (Myrtaceae), Weinmannia spp. (Cunoni- 1985, Murphy and Wilcox 1986). On biological aceae), Symphonia spp. (Guttiferae),Ravensara and grounds,it has been suggestedthat butterflydiversity Ocotea spp. (Lauraceae), Pittosporumspp. (Pittospora- could be used as an index ofplant diversity (Pyle 1980), ceae), and many others. Common understoryplants since coevolution between butterfliesand theirlarval include many Psychotreaand other Rubiaceae, Pan- host-plantshas in some cases led to high butterfly- danus (Pandanaceae), palms,grasses and bamboos,tree- plant specificity(Ehrlich and Raven 1964, Gilbertand ferns,and ferns.The forestis rich in lianas and epi- Smiley 1978). However, positive correlationsbetween phytes,and receives a total rainfallof ;2300-2600 butterflyand plant diversitymay in factbe the excep- mm per year. The soils are acid, and of low natural tion (e.g., Thomas and Mallorie 1985) ratherthan the fertility(P. Sanchez, personal communication). rule (e.g., this study,Sharp et al. 1974, Vane-Wright Much of the forestat the study site experienceda 1978; M. Singer,personal communication, P. Brussard, patchworkcycle of slash and burn farmingwithin the personal communication).Of perhaps more interestis past 50-100 yr. Slashed field sizes were small (50 x that butterfliesinteract with plants both as larval her- 50 m) and regenerationof a diverse rain forestcom- bivoresand as adult pollinators,potentially influencing munityin the absence of furtherdisturbance has been plant population dynamics in both interactions(Gil- rapid (C. Kremen, unpublishedobservations). For ex- bert 1980, Rausher and Feeny 1980, Jennersten1988). ample, the site supports 12 species of lemur, one of Thus, with sufficientsystem-specific knowledge, one the highestlevels of primatediversity anywhere in the mightbe able to predictchanges in plant populations world (P. Wright,personal communication).The un- fromknowledge of butterflypopulation changes. Fi- derstoryvegetation is also relatively sparse, except nally,populations of butterfliesare stronglyinfluenced whererecent disturbance has led to the invasion of the by local weather,microclimates and lightlevels acting introducedguyava, Psidium cattleianum. in a varietyof ways at all stages of the life cycle (e.g., In this study,two foresttypes were studiedwithin a oviposition site selection and larval development, distance of 4.2 km: forestat Vatoharanana that had Rausher 1979; larval developmentand host plant phe- been selectivelylogged 25 yr previously (referredto nology,Weiss et al. 1987, 1988; availability of flight fromnow on as "old forest"),and forestat Talatakely

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions May 1992 ASSESSING INDICATOR PROPERTIES 205

that had been selectivelylogged withinthe previous 2 of the Malagasy Satyrinae (Nymphali- yr ("new forest").The old forestwas fartherfrom the dae) is poorlyestablished; hence morpho-specieswere main road, and probablynever experiencedthe same designatedaccording to characteristicsof themale gen- intensityof loggingas the new forest.In both forest italia, a standardcharacter set fordefining taxa in this types, 100-m transectswere situation along trails on subfamily(Miller 1968; R. Robbins, personal com- ridges(new forest,N = 3; old forest,N = 2) and stream munication).Once morpho-specieswere resolved ac- banks (new forest,N = 3; old forest,N = 2). Three cording to genitalic characters,the remainingspeci- additional transectswere located withinthe new forest mens were identified by associated wing pattern in highlydisturbed areas slashed to provide logging characteristics.Names given to morpho-species are trails.In addition to disturbanceregime, the old forest precededby question marks,and representan attempt was found at a slightlyhigher elevation than the new to synonymizethese species with establishednames. forest(50 to 100 m) and may receive higherrainfall (D. Overdorif,personal communication).Both forest Habitat characterization typeshad closed canopy, but the old forestcontained At each transect,three randomly located plots of 25 a much higherproportion of trees >20 m in height m2each wereestablished for plant sampling.All plants (11.5%) than the new forest(2.5%) (F. White and D. within these plots > 1 cm diameter at breast height Overdorif,unpublished data). (dbh) were identifiedby a local guide using common Butterflysampling Malagasy names. Vouchercollections made in thisarea since 1986 have allowed the identificationof many of The study was begun at the end of the cool, low- these species (D. Overdorifand P. Wright,personal rainfall"winter." Increasingmean daily temperatures communication).Measurements of dbh and estimates and monthlyrainfall at thistime appeared to stimulate of heightto top of crown were taken on all plants > 1 eclosion in a wide varietyof butterflyspecies. During m in height.These measurementsand the plant iden- each of five 10-d samplingperiods betweenthe end of tificationswere used to determineaverage transectval- August and the beginningof November, each of the ues for plant species diversity,plant basal area, and 13 transectswas sampled forbutterflies twice, 1 h dur- heightof the canopy. Altitudesat each transectwere inga morningtime period (1000- 1200) and 1 h during measured with an altimeter. an afternoontime period (1200-1400). Morning and Flowing phenologywas examined at each transect afternooncollecting dates were randomized between four times during the study. Flower abundance and sites in any one sampling period, and transectswere richnesswere assessed along the lengthof the transect onlysampled undersunny conditions. An equal sample in ten 10 m wide by 5 m deep plots,spaced alternately effortwas therebydevoted to each transect,for a total every 10 m on eitherside of the transect.All flowers of 130 person-hoursof samplingtime. (whetherlianas, epiphytes,herbs, shrubs, or trees)were Sampling was conducted by continuouslywalking noted by threeobservers equipped with 7 x 35 power back and forthalong the transectfor 1 h, collectingall binoculars.Species wereidentified by Malagasy names, butterfliesthat were observed withina 5-m band on and detailed notes on floralcharacteristics were taken eitherside ofthe transect (modification of Pollard 1977, to ensureconsistent identifications between plots. The Thomas 1983). The samplingmethod assessed the rel- numberof floweringindividuals was counted foreach ative abundance of understorybutterfly species. While flowertype in each plot to derive an estimateof floral it ignoredthe canopy (i.e., Charaxes species) and un- abundance (numberof individuals in flowerat a given derestimatedthe fast-flyingmid-level to canopy spe- time) and floralrichness (number of typesof plants in cies (most papilionids, some nymphalids),it was con- flowerat a given time). In general, few flowerswere sistentbetween transects. Observations and understory observed in any one of the ten 50-nM2plots; therefore and canopy trap-datacollected duringthe same sam- the data forthe 10 plots were pooled at each site. Av- plingperiod permittedan assessmentof which species erage floralabundance and richnessat a site referto were not quantitativelyrepresented in these samples. these measurementsaveraged over time. Butterflyidentification Butterfliesin most families were identifiedby the Analysis author using D'Abrera (1980), Paulian (1956), and Ordination.-Species abundance matrices for but- Paulian and Viette(1968). Dr. RobertK. Robbins and terflyor plant data were analyzed by ordination,using Dr. Donald Harvey (National Museum of NaturalHis- DetrendedCorrespondence Analysis (DCA; detrended tory,Washington, D.C.) assisted with identifications by polynomials,CANOCO program,Ter Braak 1988). of lycaenid and riodinid species, respectively.Dr. DCA displays the patternsof covariation in species Jacques Pierre(Musee Nationale d'Histoire Naturelle, distributions,by reducingthe dimensionalityof N spe- Paris) assistedwith identifications in thefamily Acreai- cies among M samples to just a few ordinationaxes dae. Skipperswere identified by theauthor using Viette (Knox and Peet 1989). The eigenvalueassociated with (1956) and Evans (1937). each axis can be thoughtof as the proportionof vari-

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions 206 CLAIRE KREMEN Ecological Applications Vol. 2. No. 2 ation in sample or species dispersionexplained by that TABLE 1. Environmentalvariables used in CanonicalCor- axis (Gauch 1982a). Detrended correspondenceanal- respondenceAnalysis (CCA) ofbutterfly data. ysis was chosen over other available techniques be- Nom- cause: (1) thisalgorithm removes several artifactssuch Environ- inal as the arch effectand compressionof gradientsat ends, mental van- (2) it ordinatessites and species simultaneouslywithin variable able Description the same ordinationspace by reciprocalaveraging, and Topography* 0 ridges (3) it requiresno a prioriassumptions about directions 1 slopes of environmentalgradients or weightingof species or 2 streams Disturbance* 0 selectivelylogged 25 yrago sites (Gauch 1982a, Peet et al. 1988, Knox and Peet 1 selectivelylogged 2 yrago 1989). DCA, an indirectgradient analysis technique, 2 slashedfor logging trails ordinates samples based only on the distributionof Altitude metres(measured) species abundance values. In the absence of otherin- Plantspecies Margalefsindex formation,it is thereforea good preliminarytechnique richness forassessing the ability of floralor faunaldistributional Canopyheight metres(estimated) data to indicate a particularenvironmental pattern. Floral numberof individuals in flower abundance (averagedover time) For analysis of the butterflydata set by DCA, the Floral numberof speciesin flower abundance of each species was partitionedinto male richness (averagedover time) and female abundances, since males and femalesof a Butterfly Berger-Parkerindex species may flyin differenthabitats (e.g., oviposition dominance vs. feedinghabitat). Relative abundance was estimated * Variablesalso usedin CCA ofplant community data. as the sum of individuals of each sex of each species five periods at each site. capturedduring the sampling RESULTS Relative abundances for the plant data set were esti- mated as the totalnumber of individualsof a morpho- Sampleeffort species > 1 cm dbh observed at a site (the sums of Cumulative species diversitybegan levelingoff un- abundance measuresfrom the three separate quadrants der the sampling scheme used in this study afterthe totaling75 m2). fifthsample period (a total of 10 person-hoursof sam- Axis interpretation.-Canonical Correspondence plingtime per transect).Fig. 1, which shows a sample Analysis (CCA) was used to examine the relationship effortcurve for the five sample periods of the study, between species distributionsand environmentalpa- includes additional data froma sixthand seventhpe- rameters.In CCA, a directgradient analysis technique, riod sampled at 9 ofthe 13 sites.When differenthabitat axis interpretationis performedwithin the ordination types were considered separately,cumulative species algorithmusing a set of supplied environmentalvari- diversityalso began to level offat the fifthsample ables (Ter Braak 1987). A major differencebetween periods (Fig. 1). This suggeststhat samplingeffective- CCA and DCA is that ordinationof species and sam- ness was similar in each of the habitat types despite ples are constrainedto lie along axes determinedby topographicand vegetativedifferences that might have the environmentalvariables. The significanceof a par- renderedcertain habitat types easier to sample than ticularenvironmental variable can be assessed through others. Monte Carlo testing(bootstrapping) of the axis asso- The possibilitythat sample curves would rise again ciated with that variable, using the axis eigenvalue as with continued samplingcannot be excluded, since a the teststatistic (Ter Braak and Prentice 1988). Thus, new phenological guild of species could begin flying in CCA, one can test the significanceof an environ- later in the season. However, inventorytaken during mentalvariable in establishingthe ordinationpattern. a January-Marchfield season (C. Kremen,unpublished Environmentalvariables included in the CCA anal- data from1990) added only a fewmore species to the ysis of butterflydata were altitude, topography,and list compiled fromJuly to November (1988 data), de- level of disturbance(see Table 1 for scoringsystems spitedistinct increases in temperatureand rainfalldur- fordisturbance and topography).Vegetative and plant ing this period. communitycharacteristics were canopy height,stem The data fromthe five sample periods used in the basal area per square metre,plant species richness, ordinationanalysis contained a total of 57 species of DCA site ordinationscores based on plant community Rhopalocera (Appendix) among 659 specimens.Using data, and average richnessand abundance of plants in themaximum likelihood method of Cohen (1959, 1961, flower.A characteristicof butterfly species assemblages in Krebs 1989), thedistribution of species' abundances was dominance(Berger-Parker index, d = Nma,,/N,where was not significantlydifferent from a lognormal,with Nmaxis thenumber of individualsof the most abundant a mode of 13 species in the 4th octave of abundance species,Magurran 1988). For axis interpretationof the (data analyzed included all seven sample periods with plant DCA analysis, environmentalvariables consid- 59 species distributedamong 8 octaves of abundance, ered were topographyand level of disturbance. N= 882, x2 = 5.852, df = 5, P > .5).

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions May 1992 ASSESSING INDICATOR PROPERTIES 207 Environmentalpatterns indicated by 70 butterflyspecies abundances Allhabitats . Disturbedhabitats A Detrended CorrespondenceAnalysis (DCA) of the 60 butterflyspecies abundance by samples matrixresulted a) Streamhabitats. in strongclustering of the samples along the firstaxis 5 50 Ridgehabitats a into two clustersat eitherends of the axis, representing ridgeand streamgroups, and a third,middle, cluster of highlydisturbed logging trails (D sites) (Fig. 2A). (.D 40- Within ridge and stream clusters,samples also clus- C.) tered according to whetherthey were located in the 0 - forestselectively logged 25 yrago (old forest)vs. forest selectivelylogged within the previous2 yr(new forest). 520- Eigenvalues forthe firsttwo principalaxes were high E (0.676, 0.436). The ordinationbased on butterflydata effectivelyclustered sites accordingto the patternsof 10 known environmentalheterogeneity due to the topo- graphic/moistureand disturbancegradients. In the above ordination,sexes of each species were 0 consideredseparately. When DCA was run on data in Person-Hoursper Transect which male and female abundance values were com- FIG. 1. Sample effortcurves for all habitats,disturbed bined for each species, the firsteigenvalue declined habitats(logging trails), stream habitats, and ridgehabitats. from0.676 to 0.618, althoughthe overall orderingof Curvesbegan to leveloff in all habitattypes after 20 person- sites along this axis was qualitativelysimilar (data not hoursof sampling time per transect (five sample periods con- sistingof one and one shown).Thus, theseparation of species abundance data morning afternoonhour). by sex resultedin a slightlybetter ordination of the sites, suggestingthat the distributionof males vs. fe- ysis ordination technique. Out of eight supplied en- males among sites conveyed additional information. vironmental variables (including one descriptor of All subsequentordination analyses therefore separated butterflyspecies assemblages, see Table 1), only the species abundance values by sex. nominal variables definingthe topographic/moisture gradientsignificantly explained the variation The size effect along the firstCCA axis (P = .01, exact probabilityfrom 99 Previous studies have questioned whetherordina- Monte Carlo runs). This axis had an eigenvalue of tionand clusteringtechniques reveal actual covariation 0.616, and the relativeordering of sites closely resem- of species distributionsor resultfrom a samplingar- bled thatproduced by DCA (Fig. 2B, note orientation tifactin which species clusteraccording to their fre- of the axes appears reversedin CCA relativeto DCA, quency of occurrenceamong samples (the "size effect" but axis orientationis arbitrary[Knox and Peet 1989]). of Jacksonet al. 1989). In DCA, ordinationof samples Given thesimilarity between the ordering of sites along and species occurs simultaneouslyby reciprocal av- the firstaxis in both ordinations,one can assume that eraging;thus the "size effect,"if present,could influ- topographyalso significantlyexplains variationamong ence theclustering of sites and affectthe environmental sample and species scores in the firstaxis of the DCA interpretation.Prior to axis interpretation,it was im- ordination. portantto determinewhether such an artifacthad con- Axis interpretation: tributedto establishingthe ordinationpattern. disturbance For each of the DCA axes, I ran Spearman's rank In Fig. 2B, the significantfirst axis is plottedfor ease correlation analyses (Statgraphics 4.0; Statistical of viewing against a nonsignificantaxis. The vectors GraphicsCorporation 1989) betweenspecies' frequen- indicatethe strengthand directionof the topographic/ cies of occurrenceamong sitesand species' DCA scores. moisture and disturbancegradients. Lack of signifi- No significantcorrelations were observed, although the cance of the axis definedprincipally by disturbancein second axis showed a weak trend toward a negative the CCA analysis may be due to the coding of distur- correlation(r = -0.1802, P = .0877). Similarly,no bance, whichwas based only on knownanthropogenic significantcorrelations were observedbetween species' effects(time since selectivelogging or clearing),and did overall masses and species' DCA scores on eitheraxis. not consider the contributionof natural disturbance These analyses thereforerule out the possibilityof the (e.g., size and shape of streambeds and treefallgaps). DCA ordinationbeing trivially based on a "size effect." Ridge areas are likelyto experiencedifferent natural disturbanceregimes from stream areas (Swanson et al. Axis interpretation:topography 1988), and the intensityof anthropogenicdisturbance To interpretthe axes produced by DCA, I ran ca- fromselective loggingprobably also differedbetween nonical correspondenceanalysis, a directgradient anal- these two habitats.In addition,the largeseparation of

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions 208 CLAIRE KREMEN EcologicalApplications Vol. 2, No. 2 A. DA B.0OA 400-400 - *40~CCA ~~~~~~~~~~DCA ~ .40

ridge stream disturbance(P .34) 200 200 _ CM1 disturbed MAdi

-0 -20 -0 0 10 30-20020 30 40 -200 00 20 30 C3

DCA axis 1 CCA axis 1 FIG. 2. Ordinationof samplingsites by butterfly data. O = siteslocated in theold forest(selectively logged 25 yrago); OR = old ridge,OS = old stream.* = sitesin thenew forest (selectively logged 2 yrago); R = ridge,S = stream.* = sites alongslashed logging trails; D = disturbed.(A) DetrendedCorrespondence Analysis ordination plot (DCA), showingridge, stream,and disturbed area clusters. Eigenvalues are 0.676 and 0.436 for the first and second axis. (B) CanonicalCorrespondence Analysis(CCA) ordinationplot, constrained according to topography and disturbancevariables (see Table 1); arrowsindicate thestrength and directionof gradients. The orderingof siteson thefirst axis was significant(A = 0.616,P = .01); fromthe diagramthis axis can be interpretedas topography.As no subsequentaxes werefound to be significant,the disturbance gradientin thisordination is notsignificant.

ridge vs. stream sites in the DCA ordinationin Fig. and female scores are side by side). The ordination 2A indicates thatthe butterflyassemblages occupying space is identicalto thatshown forthe sitesordination ridgevs. streamsites are distinct.Given these consid- (Fig. 2A), and the location of habitat clusters (new erations,a separate CCA analysis was conducted for stream,old stream,etc.) is indicated in the diagram. each of two site clustersdefined empirically by DCA A complex of species, includingHenotesia ?iboinaand axis 1 (Fig. 2A): siteswith values <- 100 (ridgecluster) Strabena andriana (:Satyrinae) and Sari- and sites with values > + 100 (streamcluster). bia ?perroti() had ordinationoptima within The ridge clustercontained all of the new and old the ridgecluster. Another species complex, including ridgesites, plus one of the loggingtrail sites (Dl). The Henotesia ?angulifascia,H. ?undulosa(male), H. ?sub- firstaxis of CCA of the data fromthese sitesalone was similis (male), H. ?anceps and H. ?pauper,had ordi- significantlyrelated to the disturbancevariables (X = nation optima withinthe streamcluster. 0.469, P = .01), but not to the six otherenvironmental The relationshipbetween the DCA ordinationscores variables. In contrast,the stream cluster,containing and species distributionaldata can be seen by com- two of the new stream sites and all of the old stream paringTable 2 withFig. 3. Henotesia species ?anceps, sites,showed no significantrelationship to disturbance ?subsimilis,and ?pauperwere each consistentlyfound in CCA analysis (X = 0.541, P = .32), or to any of the at the new streamsites, but were almost never found otherenvironmental variables. at the old stream sites. Their ordination optima fall withinthe domain of the new stream sites. Male H. Identifyingbutterfly indicator speciesassemblages TABLE 2. Distributionof Henotesiaspecies among stream In correspondenceanalysis, species ordinationscores siteslocated in newand old forest.* representthe optimal localization of the species within the ordinationspace, and the abundance or probability Abundance(individuals) of occurrenceof a species decreaseswith distance from Speciest Si S2 S3 OSI OS2 the location of its score. I used the DCA species or- Henotesia?anceps 18 4 0 0 0 dination diagram as an aid to findassemblages of spe- Henotesia?subsimils 16 5 1 1 0 cies characteristicof points along the topographic/ Henotesia?pauper 20 1 2 0 1 Henotesia?undulosa 19 3 0 6 6 moistureand disturbancegradients. Henotesia?angulifascia 5 5 3 29 18 Fig. 3 shows the DCA species scores forthe 12 most * S1-S3: newstream sites; OS 1-OS2: old streamsites. abundant species (the scores are separatedby sex as in t See Appendixfootnote t forinformation on Malagasy the analysis,but only the male score is shown if male butterflytaxa.

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions May 1992 ASSESSING INDICATOR PROPERTIES 209 ?undulosawere most frequently observed at new stream 400- sites, but also regularlyoccurred at old stream sites, and its ordinationoptimum is intermediatein position betweenthe new and old streamdomains. The female also frequenteddisturbed sites, and its optimum re- *Hen angM flectsthis. While H. ?angulifasciawas observed fre- _) :Hen iboM Os quentlyat new streamsites, its position withinthe old SarperM R streamdomain reflectsits extremedominance at these *Hen angF *Hen undM sites (comprising44-66% of the total butterflyabun- O ovsacM 5OR HensubF dance). O ?r0 andM D HenundF S Interpretingthe species ordination diagram - oMyiphiM Hen subM *AcrstrM Hen pauM * Typically,many of the species found at the center Hen ancM -200- a Jan eurM of the DCA ordinationare ubiquitous species, bimod- ally distributedspecies, or species whose distribution otherwisedeparts from a unimodal responsecurve (Ter -300 -200 -100 0 100 200 300 400 Braak and Prentice 1988). In Fig. 3, Mylothrisphileris DCA axis 1 ) is a good example of this phenomenon. De- FIG. 3. DetrendedCorrespondence Analysis (DCA) or- spiteits placementin thedomain of thedisturbed sites, dinationshowing the 12 mostabundant species. The ordi- this species occurredabundantly in all of the habitat nationspace is identicalto theDCA sitesordination in Fig. 2A. The lettersindicate the typessampled. approximateaverage locations of newridge (R), old ridge(OR), disturbedlogging trails (D), In contrast,species found at the edges of the ordi- newstream (S), and old stream(OS). Speciesscores represent nationdiagram are frequently"rare" species,and occur theoptima of Gaussian response surfaces. Here they are sep- therebecause theyare associated with "extreme" en- aratedby sex, as in theanalysis; however, only the male score vironmentalconditions (Ter Braak and Prentice1988). is shownfor species whose male (M) and female(F) scores are locatedside by side. Species names are placedas closeto In Fig. 4, the species scores of the "rare" species (i.e., themarker as possible,usually to theright. Acr str = Acraea species individuallycontributing < 1% ofthetotal sam- strattipocles,Hen anc = Henotesia ?anceps,Hen ang = Hen- ple abundance) are plotted. Fig. 4A shows scores for otesia ?angulifascia,Hen ibo = Henotesia ?iboina,Hen sub = those species whose "rarity"was known by indepen- Henotesia ?subsimilis,Hen pau = Henotesia ?pauper,Hov sac = Hovala = dent observations to be due to sampling error (i.e., saclavus, Jun eur Junonia eurodoce,Myl phi = Mylothrisphileris, Sar per = Saribia ?perroti,and Str = Stra- species that were frequentlyobserved in one or more bena andriana. of the habitat typesat the studysite but were not fre- quently captured during the study sample hours; C. Kremen,personal observations).As expected,ordina- species would make good indicators of undisturbed tion scoresfor these species werespread over theentire conditions. ordinationdiagram due to random samplingerror. Ordination scores for the remaining"rare" species (those that appeared to be low in abundance during Dominance and diversitycharacteristics relative both 1988 and 1990 fieldseasons) are plottedin Fig. to DCA ordination 4B. A subset of these species had scores at the edges Characteristiclevels of dominance were associated of the ordinationdiagram. In particular,several Hen- with differentsite types.In and of itself,ranking sites otesia species werenoted thatwere found only at either by dominance(Berger-Parker index) organized the sites: old or new ridgesites (see H. ?parva,H. ?obscura,and low dominancecharacterized newly disturbed sites plus H. ?turbans).The distributionsof these species between new stream sites; and high dominance characterized sites were consistentalthough the number of obser- new ridge, old ridge,and old stream sites (Fig. 5A). vations was small, and independentrecords of these Species richnesswas negativelycorrelated with dom- species support their habitual occurrenceat these or inance (Fig. 5B, r = -0.56, P = .047). No density similar sites. H. ?parva and H. ?obscuraoccurred at compensationwas observed; sitesthat were low in spe- both the old ridge sites, and nowhere else, while H. cies richnessdid not have a compensatoryincrease in ?turbansoccurred at two out of threeof the new ridge abundance of one or more species (Fig. 5C). sites,and nowhereelse. These species mightqualify as The dominance index distinguished significantly eithergenuinely rare species characteristic ofthese hab- (ANOVA, Berger-Parker,F = 15.504, P = .0292) be- itats, or as species that were sampled at the edges of tween butterflyspecies assemblages found at old vs. their range. In the lattercase, the two species found new stream sites, although species richness did not only at the old ridgesites (H. ?parvaand H. ?obscura) distinguishbetween the two typesof sites (F = 1.681, may have optima underless disturbedconditions (e.g., P = .2856). In contrast,dominance did not distinguish primaryforest). Further work could distinguishbe- betweenold and new ridgesites (F= 2.516, P = .2109), tweenthese two hypothesesto determinewhether these althoughspecies richnessdid (F = 30.943, P = .01 15).

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions 210 CLAIRE KREMEN EcologicalApplications Vol. 2, No. 2 A. r* B. l 400 400_

- - oHenturM U 200- 200-

R O R Os Hen obsM o o P OR 0 HenparM DO * S I eRU D * OS a

-200 * -200

-300 -200 -100 0 100 200 300 400 -300 -200 -100 0 100 200 300 400 DCAaxis 1 DCAaxis 1 FIG.4. DetrendedCorrespondence Analysis (DCA) ordinationshowing the rare species (< 1% oftotal sample abundance). As in Fig. 3, theletters indicate the approximate average locations of newridge (R), old ridge(OR), disturbedlogging trails (D), new stream(S), and old stream(OS). Speciesscores, shown by markers,represent the optima of Gaussianresponse surfaces.Here theyare separatedby sex,as in theanalysis; however, only the male scoreis shownexcept when male (M) and female(F) scoresdid notoccur side by side.* = 1 species,LI = 2 species,0 = 4 species,A = 5 species.(A) Species knownfrom additional observations to be raredue to samplingerror (i.e., speciescommonly seen butseldom captured); scoresare distributedacross the ordinationspace. Speciesare: Acraeafornax (M and F markersshown), sabina, Catopsiliathauruma, Celaenorrhinus humbloti (M and F), Eagrissabadius (M and F), Eicochrysopssanguigutta (M and F), Graphiumcyrnus, Henotesia vola (M and F), Heteropsisdrepana (M and F), Houlbertiasp., Junonia goudoti, Neptis saclava, Papiliooribazus, Pseudacrea glaucina, Pseudacrea lucretia (M and F). (B) Speciesthat appeared to be trulylow in abundance duringboth 1988 and 1990field seasons. The rarespecies near the edges of the ordination diagram may indicate the presence oflonger gradients. The specieslocated at theedge of the topography gradient are labeled by name. Henotesia ?obscura (Hen obs) and Henotesia?parva (Hen par) wereonly found at old ridgesites, and maybe morecharacteristic of a lessdisturbed pointon thedisturbance gradient (e.g., primary forest). Henotesia ?turbans (Hen tur)was foundonly at newridge sites. The othermarkers identify rare species not at theedge of thetopography gradient. Species are: Acraeahova, Aterica rabena, Henotesiaankova, Iolaus argentarius,Mylothris smithii, Strabena ?aurivilliusi, Strabena smithii.

Environmentalpatterns indicated by To see whetherlocal butterflydiversity was a good plantspecies abundance data indicatorof plant species richnessor diversity,I used linear regression.Butterfly species richness In comparison with the butterflyDCA ordination, was not a good predictorof either the plant DCA ordination was less successfulin re- plant species richness(Mar- galef'sindex, F = 0.608, r2= = vealing the known environmentalgradients (Fig. 6). -0.05, P .45) or plant species diversity = = To test whetherplant communitydata were signifi- (Shannon's index, F 0.257, r2 0.02, P = .62). cantlyrelated to the two knowngradients, a CCA was However, butterflyrichness showed a weak relationshipwith average floralabundance (F= performedusing the topographicand disturbancevari- 4.349, r2 = 0.28, P = .061) and was ables (Table 1). The firstCCA axis was not significantly significantlyrelated to average floral = = relatedto eitherof thesevariables (X = 0.399, P = .47), richness(F 18.65, r2 0.63, P = .0012) (Fig. 7). Floral richness and therewas thereforeno need to look at subsequent and abundance were axes. both highestin sunnyregions by the largerstreams (S2 and S3) and the loggingtrails (D1-D3), whichalso had Relationshipbetween butterfly species the greatestbutterfly species diversityand abundance assemblagesand plant communities (Fig. 5C).

To examine the relationshipbetween the butterfly DISCUSSION and the plant ordinations,DCA site scores fromthe Ordinationtechniques are exploratorytools thathave plant ordinationwere also included as one of the en- frequentlybeen used in communityecology to reveal vironmentalvariables forCCA analysisof the butterfly environmentalpatterns and to generateecological hy- data; however,no significantrelationship was found. potheses based on the covariation of species distri- As noted earlier,none ofthe otherenvironmental vari- butions (Gauch 1982a, b, Digby and Kempton 1987). ables related to vegetativediversity or structurewere As techniques that examine the relationshipbetween foundto explain significantlythe variationalong CCA species distributionsand environmentalgradients, they axes of the butterflydata. are inherentlyuseful for testing the indicatorproperties

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions May 1992 ASSESSING INDICATOR PROPERTIES 211 A.100 the indicator propertiesof an assemblage of species and selectingappropriate sets of indicators from within 80 the largergroup.

C 60 Whyand what to monitor? Beforeelaborating on a protocol fortesting and se- 40 0 lectingindicators for natural areas monitoring(see be- low), it is importantto establish the goals of a moni- 20 toringprogram and the desirablequalities of indicators. o The general functionof a monitoringprogram is to S2 Si Ri S3 D2 Dl R2 D3 OR1 OR2 OS2 R3 OSi provide data that can be used forthe scientificman- Sites B. 35 agementof reservesfor restoration or maintenanceof u) - ADJ thecomposition, structure, and functionof natural eco- 0 30 0 C systems(Franklin et al. 1981, Noss 1990). An ideal eC 25 2D monitoringprogram should thereforebe made up of '602 D3 20-2 two components:(1) a descriptivecomponent that es- 2 5 15 l- OS 3 tablishes the baseline in a large, buffered,"pristine" -S2 CR2 naturalarea, includingstudy of "normal" fluctuations Z 10 SR1 over time, and (2) an experimentalcomponent that 0. R2 10051 X RI3 evaluates the effectsof specificmanagement activities over time using standard ecological experimentalde- 0 0 20 40 60 80 100 sign.The detailsof a monitoringprogram will be highly Dominance specificto a given reserve,depending, for example, on its "naturalness" (how differentit is from baseline), size, shape, bioticand physiographicheterogeneity, and co30- on the impacts of anthropogenicand natural distur- 0kCO 25 - ls I -. C A0 bance regimes,global changes,and managementprac- tices. 020 -CR S2 R Propertiesof species or speciesassemblages can serve 35. Chrceitc srctr as indicators at genetic, population-species or com- CI.forest (S) d fbtefydvriyand R si munity-ecosystemlevels formonitoring composition, 0. 5 C) R3 structure,and functionin naturalareas; however,many 0 otherindicators, both biotic and abiotic, are available 0 0.05 0.1 0.15 0.2 formonitoring natural ecosystems at all biological lev- rig ie;OS=odsra Relative(B)S sies Abundance Speierihsss els (geneticto landscape) (Noss 1990). It is therefore importantto determinethe unique value ofmonitoring FiG.5. Characteristicsofbutterfly diversity and structure of speciesassemblages at each site.(A) Dominance(Berger- properties of species or species assemblages. First, Parkerindex) is lowestin thesunniest sites: streams in new monitoringspecies or species assemblages allows the forest(S) and disturbedsites (D). R = ridgesites; OR = old most direct assessment of fundamentalmanagement OS = old streamsites. richness ridgesites; (B) Species is goals such as the maintenance of viable populations negativelycorrelated with dominance (r = -0.56, P = .041); sunnynew streams and disturbed sites have the highest species and native (Noss 1990). Second, moni- richness.(C) Speciesrichness is positivelycorrelated with the toringat this level can provide direct or indirectas- totalrelative abundance of butterflies at each site (r = 0.786, sessments of ecological function(Gilbert 1980), al- = P .001). At thespatial scale of this study, no densitycom- thoughsuch informationis likelyto be narrowlyfocused pensationfor butterfly abundance was observed. (e.g., studies of specificecological interactions,as in a predator-preycycle) as comparedwith ecosystem stud- of a group of organisms,especially in poorly known ies at the landscape level (e.g., studies of functional systems.However, limitationsof these techniques,in- processes,as in nutrientcycling). cludingambiguity of interpretation (Gauch 1982a) and When monitoringof populations or communitiesis lack of statisticaltests, have perhapsprevented a great- conductedwithin the contextof knownenvironmental er utilization (Ter Braak and Prentice 1988). Recent change (e.g., due to human or natural disturbances, advances thatallow bootstrappingof DCA (Knox and global changes,or managementpractices), it can then Peet 1989 and R. G. Knox, personal communication) provide a basis for improved managementdecision- and CCA (Ter Braak and Prentice 1988) ordination making.There is littlepoint in usingindicators to mea- axes have opened the door forsignificance testing, and sure an environmentalchange that can be measured can now be used creativelyfor testing as well as gen- directlyusing a pH scale, thermometer,or LANDSAT eratinghypotheses. In particular,this facilitygreatly image, forexample; instead,the purpose and value of enhancesthe utility of ordination techniques for testing using indicatororganisms is to observe the biotic re-

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions 212 CLAIRE KREMEN Ecological Applications Vol. 2. No. 2 sponse to environmentalstress, and especiallyto pro- A. 35 vide early warningsof natural responses to environ- ND1 mentalimpacts (Noss 1990). Ifstudies are welldesigned, a) 30 C such informationshould also be useful in developing .2) 25 or refiningrestoration or managementplans, and in 20 - *D2 8D3 /S3 monitoringthe success of these efforts. aD) 20 .0 0~~~S2 An indicatorshould "be aD capable of providinga con- 0. 15 OR- tinuous assessmentover a wide rangeof stress"(Noss C/ 0S R2 c101%I0 --Si R7OS1 1990: 357). Inherently,species assemblages should R2. provide finergauges of biotic responses than single 5 R3 species, just as precision is increased by placing finer gradationsupon a scale. Certain propertiesof assem- 0 0 20 40 60 80 100 120 140 blages can be definedthat might enhance the likelihood of wide-scale sensitivityto a specificenvironmental AverageFloral Abundance stress.For example, assemblages that include species (no. plants in flower) coveringa wide range of spatial heterogeneity(low to B. 35 iDl highvagility, narrow to broad distribution)and micro- *D1 habitat specificity(specialist to generalist),and that g 30 collectivelyoccupy a broad range of micro-habitats, .C 25 mightbe expected to display a greaterrange of sensi- C.) *D2 3

tivitiesto habitat fragmentationand/or modification co 20 S over time (Terborgh 1974). a) 15 VR1V For completeness,a monitoringprogram might use 0. Si VS separateindicator species assemblages representing dif- >, 10 _ Rvs ferenttaxonomic and/orfunctional groups to monitor aE nR3 differentenvironmental impacts, as will be dealt with g 5 more fullyin a separatepaper. This paper is concerned 00 2 4 6 8 10 instead with testingthe indicatorproperties of a uni- formtaxonomic/functional group, and selectinga sub- AverageFloral Richness set of appropriateindicators from within the groupfor (no. types of plants in flower) addressinga specificmanagement problem. As thepro- FIG. 7. Butterflyrichness in relationto floralabundance tocol summarizedbelow is widelyapplicable to differ- and richness.Floral abundance and richnessvalues are av- ent species groups, as well as differentspatial scales eragesof fourphenological samples taken during the study period.D = disturbedareas (loggingtrails), R = new ridge = 200 sites,OR old ridgesites, S = newstream sites, OS = old streamsites. (A) Butterflyrichness was weaklydependent on *S1 PLANTS averagefloral abundance (r2 = 0.28, P = .061). Sunnyareas 150 (D1-D3 and S1-S2) had thegreatest floral abundance. (B) Butterflyrichness was significantlyrelated to averagefloral 100 AD2 AD3 richness(r2 = 0.63, P = .0012).Sunny areas had thegreatest floralrichness. C 50 _" oOSl.S2 and environmentalgradients, its objective is to en- mS3 OR1 0R2 couragethe testing, selection, and monitoringof multi- 0 OS species indicatorgroups fromamong a broad rangeof DQ -50-~~~~o ERl m.R3 taxonomic groups. This protocol can be used in the AD1 absence of detailed -100 autoecologicalor communitydata, and it is probablymost appropriate to situationswhere -150 -R2 such data are lacking.

-200 i . l Establishingthe indicator properties of -200 -100 0 100 200 300 a speciesassemblage DCAAxis 1 The firststep in establishingthe indicatorproperties FIG. 6. DetrendedCorrespondence Analysis (DCA) or- of a species assemblage is to ask whetherthe chosen dinationof samplingsites by plantcommunity data. As in group of species is appropriatefor indicating a specific Fig.2, [ = siteslocated in theold forest;OR = old ridge,OS ecological patternat the desired spatial and/or tem- = = = old stream. * sites in the new forest;R ridge, S = poral scale. This can be accomplished using indirect stream.A = sitesalong logging trails; D = disturbed.Eigen- valuesare: axis 1, X = 0.483; axis 2, X = 0.444. Canonical gradientanalysis techniques such as DCA, which are correspondenceconstrained along topographicand distur- based simplyon patternsof species covariation(Knox bancevariables did notproduce a significantfirst axis. and Peet 1989). The patternof species covariation ei-

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions May 1992 ASSESSING INDICATOR PROPERTIES 213 ther will or will not reflectthe ecological patternor species ordination values would then be constrained environmentalimpact of interest.More than one po- along the gradientof interest.However, if knowledge tential indicator propertycan be assessed simulta- of environmentalgradients is minimal, DCA plots neously,dependent on the samplingdesign, as forto- mightbe preferred,because this unconstrainedordi- pographyand disturbancein this study. In addition, nation may show the response of species to environ- the resultsof a DCA ordinationanalysis may suggest mental gradientsnot explicitlyconsidered withinthe otherindicator properties of theassemblage associated studydesign. with environmentalgradients or factorsnot explicitly The selectionof indicatorspecies froman ordination consideredwithin the studydesign. diagramof species scoresrepresents a hypothesisabout Alternativehypotheses of indicator propertiescan the propertiesof these species in relation both to the be testedin the second step, replacingthe need forad entireassemblage and to environmentalgradients. The hoc argumentsto justifyindicator properties. A direct reliabilityof these choices can be assessed throughei- gradientanalysis technique such as CCA can be used ther spatial or temporal replication of the study, or to interpretthe ordinationaxes by looking at the sig- throughautoecological studies. nificanceof correlationsbetween species and environ- Rare species contributelittle to establishingthe or- mental data. The CCA analysis can be used to match dinationaxes (Ter Braak and Prentice1988). However, a species assemblage to environmentalfactor(s) for it may be usefulto identifysuch species, because their which it is a good indicator. "rarity"may resultfrom being sampled farfrom their Once the indicatorproperties of an assemblage are own environmentaloptima. Presence,in thiscase, may established,the ordinationanalyses can be employed indicate the existence of a longer gradientor a rare at two biological levels for monitoringover time. At habitattype (Terborgh 1974, Rabinowitz et al. 1986). the community level, the entire assemblage can be In an ordinationdiagram, such species tendto be found monitored,and CCA ordinationanalysis used to track at the edges of the diagram (e.g., Fig. 4B). In contrast, the communityresponse to the environmentalfactor ordinationscores of species whose rarityresults simply of interest(for an example of time-seriesanalysis using fromrandom samplingerror will be randomlyspread ordination,see Wieglebet al. 1989). For example, bird across the diagram(e.g., Fig. 4A). In thisstudy, several assemblages known to be good indicators of habitat uncommon species associated only withold ridgesites heterogeneityassociated withelevation could be mon- (Henotesia ?parvaand H. ?obscura)were located at the itoredin permanentplots along an elevationalgradient edgesof the ordination diagram (Fig. 4B). These species over a period of years.Each yeafrsdata froma sample may prove to be indicatorsfor more undisturbedcon- plot would then be ordinated as a point in sample ditions(e.g., primaryforest) than were included in this space. Imagine that with time each plot shiftedalong study. the firstCCA axis in the directionof an elevation loss. Therefore,as the trueelevation at a plot is unchanged, Indicatorproperties of a butterfly this indicates that the new species composition at the speciesassemblage plot is more similarto lowerelevation sites from earlier I used the protocol described above to assess the years. Such a resultmight be predicted,for example, indicatorproperties of a butterflyassemblage froma due to the effectsof global warming.As temperatures rain foresthabitat in Madagascar. The studylooked at increase,low-elevation faunas adapted to warmercli- theresponse of butterfly communities to variationalong mates can be expectedto replace high-elevationfaunas topographic/moistureand disturbance gradients in (Murphyand Weiss 1991). The effectof year on changes space. Potentialindicator properties established in this in species composition along the gradientcan also be "snapshot" spatial studywould, by inference,be likely tested forsignificance. to be valid for temporal variation in these same en- At the population/specieslevel, the ordinationdia- vironmentalfactors. grams can be used to select a subset of the species as The DCA ordinationof the butterflydata reflected indicatorsfor more intensivemonitoring (e.g., studies local patternsof habitatheterogeneity associated with of spatial dispersion,population fluctuations,adapta- both the topographic/moistureand disturbancegra- tion, gene flow,Noss 1990). In DCA and CCA ordi- dients, which suggeststhat this assemblage could be nation diagrams,species placementswithin a plot rep- considered as an appropriateindicator of these types resent the optima of unimodal species distributions and this spatial scale of habitat heterogeneity.How- withrespect to environmentalgradients (Ter Braak and ever, Monte Carlo tests of CCA axes were significant Prentice1988). In choosingpotential indicator species, only for the axis principallydefined by topography/ one looks for species whose optima fall at reference moisture. Since the influenceof topographywas so points forthe environmentalgradient of interest.The strong,it may have masked less obvious gradientsre- use of indirect(DCA) vs. direct(CCA) gradientordi- lated to disturbanceat each end of the topographic nations should depend on knowledgeof the system. scale. For example, in the DCA ordinationshown in CCA ordinationplots should be chosen if the impor- Fig. 2A, "gradients" of disturbanceran in opposite tance of a particulargradient is well established,since directionsin the ridge vs. the streamclusters. By an-

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions 214 CLAIRE KREMEN EcologicalApplications Vol. 2, No. 2 alyzingeach clusterseparately using CCA, it was pos- floraland butterflydiversity also supporteda higher sible to assess the significanceof disturbancein iso- total abundance of butterflies(compare Figs. 5C and lation fromtopography. Disturbance was then found 7B). Not surprisingly,these sites correspondedto the to be a significantvariable in CCA ordinationsof the sunniest,most disturbed areas (loggingtrails and good- ridgecluster, although not forthe streamcluster. sized streams).Judging by both abundance and species To select indicatorspecies I used the DCA ordina- compositionof butterflies at thesesites, these abundant tion, since the knowledgeof one gradientof interest, and diversenectar resources may have attractedforest disturbancedue to anthropogeniceffects, was limited species to the forestedge (Lovejoy et al. 1986; Brown, to the historicalrecord of lengthof time since selective personalcommunication), so that the pool of species logging.In general,small assemblagesof species rather observed to occur there included both resident dis- than individual species could be said to characterizea turbed area species (Pieridae: Euremafloricola; Ly- particularpoint on a gradient(see the Appendix for caenidae: Cacyreusdarius, Leptotes rabenafer; Nym- species names). Similarly,taxocene composition, in- phalidae: Junoniaeurodoce) and visitingforest species cludinglevel of dominance, identityof dominantspe- (Papilionidae: Papilio delalandei,P. oribazus;Nym- cies, and species richness,were more characteristicof phalidae: Pseudacrealucretia). While forestedges had particularhabitat typesthan were individual species. higherdiversity than eitherdisturbed or foresthabitat Many of the species identifiedas potentialindicators alone, massive, widespreadclearing of foreststo create (Fig. 3, 4B, and Appendix) are members of the edgeswould not enhance landscape-level diversity, since Henotesia (Satyrinae),a species-richgenus containing it reduces habitatarea forforest specialists (e.g., Hen- 41 endemic and one nonendemicspecies in Madagas- otesia?anceps, H. ?parva;see also Wilcove et al. 1986). car (D'Abrera 1980). The membersof this genus and In this study,significance tests of CCA ordination its tribe (Mycalesini) exhibit some primitivecharac- axes allowed evaluation of the hypothesisthat butter- teristicsin Madagascar linkingthem with Indo-Ma- fliesare good ecologicalindicators (Gilbert 1980, 1984, layan ratherthan Africanrelatives, and suggestingan Pyle 1980, Brown 1982, Murphyet al. 1990). Are but- early arrival in Madagascar (late Cretaceous or early terfliesin factgood ecologicalindicators? In some sense, Tertiary),followed by a long period of evolution in the presence,absence or abundance level of any or- isolation (Miller 1968). This genus appears to have ganism must always indicate somethingabout the bi- experienced an extensive evolutionary radiation in otic or abiotic environment,and the question is there- Madagascar and exhibits high beta-diversity.Many fore trivial. The question should be: what does the species appear to be restrictedto particularforest mi- presence/absence/abundanceof species X or the com- cro-habitats(canopy vs. understory,ridge vs. riparian), positionof species assemblage Yindicate?In thisstudy, elevationalzones, and successionalstages, while others butterflytaxocene compositionproved to be an excel- are more widelydistributed, both in rangeand habitat lent indicator of heterogeneitydue to anthropogenic type (this study,R. Van Buskirkand C. Kremen, un- disturbanceby logging(although coding of disturbance publisheddata, C. Kremen, unpublishedmanuscript). levels may have been a factorobscuring the relation- This range of habitat specificityand the diversityof ships between the disturbancegradient and butterfly micro-habitatsoccupied by membersofthis genus may data), and a poor indicator of plant species richness allow them to serve as an indicatorassemblage witha and diversity.Given their strongresponse to topo- wide rangeof sensitivitiesto habitatfragmentation and/ graphic/moistureeffects, one likelyutility of butterflies or modification. Species-rich genera resultingfrom as ecologicalindicators will be formonitoring the biotic evolutionaryradiations and havinghigh beta-diversity responseto climate change,as has also been suggested may frequentlymake good indicatorassemblages, as elsewhere (Weiss and Murphy 1988, Murphy et al. will be elaborated more fullyelsewhere. 1990). Butterfliesas ecological indicators Conclusion Otherauthors have suggestedthat butterfly diversity The modificationof natural environmentsis accel- could provide an index of plant diversity,since larval eratingat an alarming rate, and the responses of or- host-plant relationships are frequentlyso specific ganismsto thesechanges provide informationnot only (Murphyand Wilcox 1986). Interestingly,in thisstudy, on the viabilityof global lifesupport systems, but also butterflycommunity data were not foundto be a good on the efficacyof our protectedarea networksin main- indicator of any environmentalgradients related to tainingbiological diversity.A substantialresearch ef- measured parametersof vegetationstructure, plant di- fortis needed to select appropriateindicators and to versity,or communitycomposition. Nor was butterfly design monitoringprograms. Ordination techniques species richnessa good predictorof plant richnessor provide a simple method forestablishing the indicator diversityat the spatial scale of this study. propertiesof targetassemblages of organisms.Contin- Whilebutterfly diversity was notcorrelated with plant ued use of these techniques in testingproperties of diversityin general,it was stronglycorrelated with the assemblages and selecting multi-species indicator average diversityof plants in flower.Regions of high groupsfor intensive monitoring could increasethe tax-

This content downloaded from 128.32.85.74 on Fri, 7 Feb 2014 15:50:07 PM All use subject to JSTOR Terms and Conditions May 1992 ASSESSING INDICATOR PROPERTIES 215

ic diversity and sensitivity of natural areas monitoring Franklin,J. F., K. Cromack, W. Denison et al. 1981. Eco- programs. logical characteristicsof old-growthDouglas-fir forests. United States Forest Service General Technical Report ACKNOWLEDGMENTS PNW-118. Gauch, H. G. 1982a. Multivariateanalysis in community This manuscriptbenefited greatly from the comments of ecology.Cambridge University Press, Cambridge, England. P. Brussard, R. Colwell, A. Moldenke, D. Murphy,and an . 1982b. Noise reductionby eigenvectorordinations. anonymousreviewer. The workwould not have been possible Ecology 63:1643-1649. withoutthe assistance of the Malagasy Ministriesof Higher Gilbert, L. E. 1980. Food web organization and the con- Education and Water and Forests, who grantedpermission servation of neotropical diversity.Pages 1 1-34 in M. E. to conduct this work at the Ranomafana National Park, P. Soule and B. A. Wilcox, editors. 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APPENDIX Butterflyand skipperspecies list forthe study.

Potential Potential Species indicator* Species indicator* Papilionidae Acraea hova Graphiumcyrnus Acraeastrattipocles Papiliodelalandei Nymphalinae Papilio oribazus Aterica rabena Pieridae Junoniaeurodoce Appiassabina confusa Junoniagoudoti Catopsiliathauruma Neptiskikideli Euremafloricola floricola Neptissaclava saclava Leptosianupta viettei Pseudacreaglaucina Mylothrisphileris Pseudacrealucretia apaturoides Mylothrissmithii Salamisanteva Salamis Nymphalidae dupreii Satyrinaet Riodinidaet Henotesia?anceps S Saribia?perroti R Henotesia?angulifascia OS Saribia?tepahi Henotesia?ankova Henotesia?iboina R Cacyreusdarius Henotesia?obscura OR Iolaus argentarlus Henotesia?parva OR Iolaus mermeros Henotesia?pauper S Eichochrsopssanguigutta Henotesia?subsimilis Eptotesr afer Henotesia?turbans R Leptotesrabenafer Henotesia?undulosa Hesperiidae Henotesia?vola Celaenorrhinushumbloti Heteropsisdrepana Eagrissabadius Houlbertiasp. Fulda coroller Strabenaandriana OR Galergahyposticta Strabena?aurivilliusi Hovala dispar Strabenarakoto Hovalapardalina Strabenasmithii Hovala saclavus OR Strabenatriopthalma Miraja varians Strabenasp. Tagiadesinsularis Acraeinae Acraeacuva villettei Acraeafornax * S = stream;OS = old stream;R = ridge;OR = old ridge. t The taxonomyof some Malagasy butterflytaxa is poorly established. Species names preceded by a question mark refer to morphospeciesand representan attemptto synonymizethese formswith established names.

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