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Basic and Applied Ecology 10 (2009) 237–245 www.elsevier.de/baae
Butterfly species and traits associated with selectively
logged forest in Borneo
Daniel F.R. Clearya,b,�, Martin J. Gennera,c, Lian P. Kohd, Timothy J.B. Boylee,Titiek Setyawatif, Rienk de Jongb, Steph B.J. Menkena
aInstitute for Biodiversity and Ecosystem Dynamics, University of Amsterdam,
P.O. Box 94766, 1090 GT Amsterdam, The NetherlandsbNational Museum of Natural History, ‘Naturalis’, P.O. Box 9517, 2300 RA Leiden, The NetherlandscSchool of Biological Sciences, University of Bristol, Woodland Road, Bristol, BS8 1UG, UKdDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544-1003, USAeUnited Nations Development Program, GEF Unit, 304 E. 45th, 10th Floor, New York, NY 10017, USAfInstitute of Land and Food Resources, University of Melbourne, Victoria 3010, Australia
Received 15 February 2006; accepted 20 March 2008
Abstract
Logging can significantly change the structure of rainforest communities. To better understand how logging drivesthis change, butterflies and environmental variables were assessed within both unlogged and logged forest inIndonesian Borneo. In the whole dataset, we found local environmental variables and geographic distance combinedcaptured 53.1% of the variation in butterfly community composition; 29.6% was associated with measured localenvironmental variables, 13.6% with geographic distance between sites, and 9.9% with covariation betweengeographic distance and environmental variables. The primary axis of variation in butterfly community compositionrepresented a disturbance gradient from unlogged to logged forest. Subsequent axes represented gradients influencedby variables such as canopy cover and total tree density. There were significant associations between environmentalvariables and geographic range and larval host plant use of species. Specifically, butterflies using trees as larval hostplants and those with distributions limited to Borneo were more likely to be present in unlogged forest. By contrast,species that tended to be more abundant in logged forest were those with widespread distributions and those usinglianas and grasses as larval host plants. The results of this study highlight the importance of environmental variablesand disturbance, e.g., selective logging, in structuring rainforest community diversity. Moreover, they confirm howspecies traits, such as larval food use and geographic distributions can determine patterns of species abundancefollowing environmental change.r 2008 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie.
Zusammenfassung
Der Holzeinschlag kann die Struktur von Lebensgemeinschaften in Regenwaldern signifikant verandern. Um besserzu verstehen, wie der Holzeinschlag diese Veranderungen vorantreibt, wurden die Schmetterlinge und die
e front matter r 2008 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie.
ae.2008.03.004
ing author at: National Museum of Natural History, ‘Naturalis’, P.O. Box 9517, 2300 RA Leiden, The Netherlands.
56623; fax: +31 20 5255402.
esses: [email protected], [email protected] (D.F.R. Cleary).
ARTICLE IN PRESSD.F.R. Cleary et al. / Basic and Applied Ecology 10 (2009) 237–245238
Umweltvariablen in Waldern mit und ohne Holzeinschlag im indonesischen Borneo erfasst. Im gesamten Datensetfanden wir, dass Umweltvariablen in Kombination mit geographischer Distanz 53.1% der Variation in derZusammensetzung der Schmetterlingsgemeinschaften abdeckte: 29.6% war mit den gemessenen lokalen Umweltvar-iablien assoziiert, 13.6% mit der geographischen Distanz zwischen den Standorten und 9.9% mit der Kovariationzwischen geographischer Distanz und Umweltvariablen. Die primare Achse der Variation in der Zusammensetzungder Schmetterlingsgemeinschaften reprasentierte einen Storungsgradienten von ungenutzten zu abgeholzten Waldern.Die folgenden Achsen reprasentierten Gradienten, die von Variablen wie Kronendeckung und Gesamtbaumdichtebeeinflusst waren. Es gab signifikante Assoziationen zwischen den Umweltvariablen, den geographischen Verbreitun-gen und der Wirtspflanzennutzung durch die Raupen der Arten. Insbesondere waren Schmetterlinge, die Baume alsWirtspflanzen nutzen, und diejenigen, deren Verbreitung auf Borneo beschrankt war, mit großerer Wahrscheinlichkeitin ungenutzten Waldbereichen zu finden. Im Unterschied dazu waren Arten, die in eingeschlagenen Waldern haufigerwaren, solche, die Lianen und Graser als Wirtspflanzen fur die Raupen nutzen. Die Ergebnisse dieser Untersuchungbetonen die Wichtigkeit der Umweltvariablen und der Storung, z. B. durch selektivem Holzeinschlag, fur dieStrukturierung der Diversitat von Regenwaldgemeinschaften. Zusatzlich bestatigen sie, wie Arteigenschaften, wie dieNahrungsnutzung von Raupen und die geographische Verbreitung, die Muster bestimmen mit denen Artenabundan-zen auf Umweltveranderungen reagieren.r 2008 Published by Elsevier GmbH on behalf of Gesellschaft fur Okologie.
Keywords: Community composition; Endemism; Indonesia; Kalimantan; Lepidoptera; Logging; Rainforest; RDA; RLQ
Introduction
The influence of commercial logging on biodiversity isone of the most important issues facing forest ecologists(Summerville & Crist, 2002), and the effective manage-ment of logged forests will probably be one of the majordeterminants of global rainforest biodiversity in thecoming decades (Costa & Magnusson, 2002). Manystudies have been undertaken of the effects of selectivelogging on forest biodiversity, and in general rainforestcommunities tend to lose few species as a result of thisactivity. However, there is often a marked impact oncommunity composition (Cleary, 2004; Cleary, Boyle,Setyawati, Angraeti, & Menken, 2007; Hamer et al.,2003; Lewis, 2001; Summerville & Crist, 2002; Willott,Lim, Compton, & Sutton, 2000). Species abundancechanges are likely to be linked to both the physicaleffects that logging has on the environment, and theecological and life history traits of species in thecommunity. The primary change that selective loggingbrings is an increase in frequency and the size of gaps inthe canopy leading to rapid germination of pioneer treespecies and lianas. This is likely to favour some groupsof species, but at the cost of those that thrive in pristinehabitat.
In this study, spatial variation in butterfly composi-tion was assessed within a rainforest in CentralKalimantan province, Indonesian Borneo. The studysite is located within the Sundaland biodiversity hotspot(Myers, Mittermeier, Mittermeier, da Fonseca, & Kent,2000), and, like many rainforests of the region, loggingcontinues to supply international demand for timber(Sodhi, Koh, Brook, & Ng, 2004). In SoutheastAsia, butterflies have been identified as importantindicators for assessing biodiversity and monitoring
ecosystem responses to environmental perturbations(Cleary, 2004). In particular, butterflies of the regionshow substantial responses to logging (Ghazoul, 2002),but little is known of the life history characters andspecies traits that are likely to influence speciesresponses to the environmental changes brought aboutby logging. However, two species traits have beenshown to influence species responses to disturbance,namely, larval feeding guild and geographic range(Charette et al., 2006; Koh, Sodhi, & Brook, 2004).Larval feeding habits are important in determiningspecies responses to disturbance because many speciesare host-specific, and adults locate these plants forcourtship and egg laying. As such, with a change in thelocal host plant availability we may expect concomitantchanges in butterfly community composition. Geo-graphic range has been linked to vulnerability todisturbance, with more narrowly distributed speciesbeing subject to higher extinction risk (Charette et al.,2006). A likely reason is that narrowly distributedspecies also tend to occupy narrow ecological niches andare less adaptable to temporal environmental changes(Koh et al., 2004).
Here, we investigated the responses of a diversebutterfly community to commercial selective logging.Firstly, we investigated differences between butterflycommunities of pristine unlogged forests and selectivelylogged forests. Secondly, we tested to which extentbutterfly communities are dependent upon a series oflocal environmental variables. Third, we investigated ifthe distance between sampling sites influences commu-nity composition, possibly due to dispersal limitation.Finally, we tested if spatial patterns of butterfly speciesabundance are linked to two species traits, namely,larval host plant use and geographic range.
ARTICLE IN PRESSD.F.R. Cleary et al. / Basic and Applied Ecology 10 (2009) 237–245 239
Materials and methods
Sampling sites and species
Sampling was conducted within the 300,000 ha KayuMas logging concession in Central Kalimantan. Theconcession contained a mosaic of forest types, includingunlogged forest or forest that had been logged once.Logging in the area has been selective, with a cuttingcycle of 35 years, extracting mainly dipterocarp treespecies. In total, 37 sites were sampled representing threehabitat types; unlogged ‘primary’ forest (hereafterlabelled ‘P’: 14 sites), forest logged in 1993/94 (‘L93’:13 sites), and forest logged in 1989/90 (‘L89’: 10 sites).Survey sites were 3 ha (300� 100m2) and were desig-nated a-priori on a digitalised elevation map using threephysiographical classes: lower, middle and upper slopehabitats. For a more detailed description of sites seeCleary, Genner, et al. (2005). Butterflies were sampledfrom July to October 1998 using methods described inCleary (2003). Briefly, butterflies were sampled whenencountered within ca. 15m on either side of a 300mtransect in each site. Transects were traversed on foot ata steady pace until 200 butterflies were sampled. Thenumber of days spent sampling a site was on average 5.2days for L89, 6.4 days for L93 and 7.75 days forP. Individuals were caught with nets and identified in thefield. Most of these individuals were marked andreleased to avoid multiple observations of the sameindividual. Sampling took place between 9:00 and16:00 h, except during rain. Capture date and locationwere noted for each individual. Voucher specimens ofselected species were preserved and deposited at theZoological Museum of the University of Amsterdam.Individuals were identified to species following Mar-uyama and Otsuka (1991), Otsuka (1988), and Seki,Takanami, and Maruyama (1991). In a few cases, it wasnot possible to identify beyond a species-pair or species-group because morphological diagnostic characteristicscould not be determined in the field. In total, datacomprised 7400 individuals belonging to 332 butterflyspecies (mean 58712 S.D. species per sampling site). Allthe species sampled and their abundance per sample siteis presented in Appendix A.
Habitat structure variables
Habitat structure variables were recorded in six200m2 (10� 20m) subplots in each site using asystematic sampling design that constituted 4.0% ofthe total site area. The following variables were recordedwithin each 200m2 subplot: the volume of (1) fresh deadwood, (2) dead wood with sound wood, but flakingbark, (3) dead wood with sound wood, but no bark, (4)dead wood with rotting wood, but firm, (5) dead wood
with wood rotten and soft, (6) fallen dead wood,(7) standing dead wood, (8) total volume of dead wood;the abundance of (9) non-woody lianas, (10) small-woody lianas (stem diameter o5 cm), (11) large-woodylianas (stem diameter 45 cm), (12) epiphytes, (13) bryo-phytes; the ground cover of (14) seedlings, (15) herbs,(16) ferns, (17) grasses, (18) small woody debris (deadwood less than 10 cm diameter), (19) mesophyll leaflitter, (20) notophyll leaf litter, (21) microphyll leaf litter,(22) mean litter depth (23) dbh (diameter at breastheight) (24) tree height, (25) bifurcation index,(26) crown depth, (27) crown radius; the density of(28) short (o5m) saplings (o5 cm dbh), (29) tall(45m) saplings (o5 cm dbh), (30) short (o10m) poles(5–10 cm dbh), (31) tall (410m) poles (5–10 cm dbh),(32) trees (410 cm dbh), (33) mean canopy coverand (34) standard deviation in canopy cover.The importance of geographic distance between samp-ling sites in explaining variation in butterfly commu-nity composition was assessed by supplementing thespatial UTM coordinates (easting ‘x’ and northing‘y’) with all the terms of a bi-cubic trend surface,(i.e., x, y, x2, xy, y2, x3, x2y, xy2 and y3; see Borcard,Legendre, & Drapeau, 1992). See Cleary, Genner, et al.(2005) for a detailed description of habitat structurevariables.
Butterfly species traits
Data on two species traits were compiled for eachspecies from published data. These included (1) larvalfeeding guild and (2) geographic distribution. Wedistinguished seven larval feeding guilds: (1) herb feeders(feed on dicotyledonous or monocotyledonous herbs),(2) grass feeders, (3) liana feeders (feed on monocoty-ledonous and/or dicotyledonous vines), (4) palm feeders,(5) tree feeders (feed on woody dicotyledonous shrubsand/or trees), (6) carnivores (feed on insects suchas aphids, coccids, membracids and psyllids) and(7) generalists (members of at least two of the previouslymentioned guilds). Data on host plant use was obtainedfrom Eliot, Corbet, Pendlebury, and D’Abrera (1992),Igarashi and Fukada (1997, 2000), and Robinson,Ackery, Kitching, Beccaloni, and Hernandez (2001)For the geographic distribution, each species was rankedon a scale of 1–4, as follows: (1) endemic to the island ofBorneo; (2) endemic to the Sundaland Region (Malay-sia, Southern Thailand, Sumatra, Java, Borneo, andsatellite islands); (3) found in Sundaland and surround-ing biogeographic regions; (4) Sundaland and extendinginto more distant biogeographic regions (e.g., Australia,Africa, Europe). Geographic distribution data wereobtained from Igarashi and Fukada (1997, 2000),Otsuka (1988), Maruyama and Otsuka (1991), and Sekiet al. (1991).
ARTICLE IN PRESSD.F.R. Cleary et al. / Basic and Applied Ecology 10 (2009) 237–245240
Data analysis
Community composition was assessed with uncon-strained ordination, principal components analysis(PCA) and with constrained ordination, redundancyanalysis (RDA), in CANOCO 6.1 (ter Braak &Smilauer, 1998). Input for these analyses consisted oflog10(x+1) species abundance data transformed usingthe programme Transformation (http://www.fas.umon-treal.ca/biol/casgrain/en/labo/transformations.html).Through this transformation, species abundance datawere adjusted so that subsequent analyses preserved thechosen distance among sample sites. Here the Hellingerdistance was used, as recommended by Legendre andGallagher (2001).
The environmental dataset used in the RDA consistedof the following variables: elevation, slope position(lower: 1, middle: 2 and upper: 3), logging (P: 1, L89: 2and L93: 3) and the log10(x+1) mean values ofpreviously mentioned habitat structure variables. With-in CANOCO, a forward selection procedure using aMonte Carlo permutation test (999 permutations) andthe full model option (ter Braak & Smilauer, 1998) wasused to test environmental and spatial variables forsignificance (ter Braak & Verdonschot, 1995). Onlyvariables with Po0.1 were included in the final model.The significance of associations between species andenvironmental datasets was also assessed using MonteCarlo simulations (999 permutations) of constrainedordination scores against environmental variables.Variance partitioning (Borcard et al., 1992; Legendre,Borcard, & Peres-Neto, 2005; Økland, 2003), usingpartial RDA’s within CANOCO, was subsequently usedto partition the spatial variation in composition intovariation only explained by environmental (vegetation
-2PCA
-2.5
0
2.5
PC
A a
xis
2
PL89L93
Fig. 1. Ordination of butterfly community structure based on prin
between primary unlogged forest (P), forest logged in 1989/90 (L89
structure) variables, only by spatial variables, or by acombination of both (see Økland, 2003).
Species traits were directly linked to environmentalvariables with a three-table ordination method knownas RLQ analysis (Doledec, Chessel, ter Braak, &Champely, 1996; Ribera, Doledec, Downie, & Foster,2001) using the ADE4 software package (http://pbil.univ-lyon1.fr/ADE-4/) within R (http://www.r-project.org/). See Rachello-Dolmen and Cleary (2007) for adetailed description of the method.
Results
Principal component and redundancy analyses
Principal component axis 1 captured 11.8% of thevariance in community composition while axis 2captured 7.1% of the variance. There was a cleardifferentiation between sites (Fig. 1), strongly indicatingthat commercial selective logging had considerableinfluence on butterfly community composition. Alongaxis 1, sites from L89 appeared intermediate on averageto sites from P and L93 suggesting that the greater thetime since logging in L89 has allowed it to more closelyresemble unlogged forest, at least in as far as butterflycomposition is concerned. The cluster of L93 sitesclosest to P sites along axis 1 are all sites that wereadjacent to unlogged forest in P, thereby indicating thatthe proximity to unlogged forest has a strong influenceon the composition of butterflies in logged forest. Alongaxis 2, the sites in L89 seem distinct from sites in P andL93, which may be due to the greater distance betweenL89 and both other areas or a function of some global
0 2 axis 1
cipal components analysis (PCA). There are clear differences
) and forest logged in 1993/94 (L93).
ARTICLE IN PRESSD.F.R. Cleary et al. / Basic and Applied Ecology 10 (2009) 237–245 241
environmental difference between L89 and both otherareas.
In the RDA, the sum of all constrained (canonical)eigenvalues was 0.531 (Monte Carlo test of trace;Po0.001). The environmental variables and geographicdistance between sites thereby captured 53.1% of thevariation in the dataset of which 13.6% (P ¼ 0.031) wasdue to geographic distance only, 9.9% due to covarianceof geographic distance and local environmental vari-ables, and 29.6% (Po0.001) due to local environmentalvariables only. The eigenvalues of the first four axes ofthe ordination were 0.112, 0.064, 0.050 and 0.042 for thefirst, second, third and fourth axes respectively. Thespecies-environment correlations of the first four axeswere high (range: 0.929–0.978) indicating a strongassociation between the species matrix and the environ-mental matrix.
RDA ordinations linking butterfly species responses tosignificant environmental variables are shown in Fig. 2.Axis 1 represents a disturbance gradient characterised byvariables including logging, non-woody liana abun-dance, tree density and elevation. Species such as Eurema
-0.8
0.0
0.8
RD
A A
xis
2
-0.6
0.0
0.6
RD
A A
xis
4
-0.6
RDA Axis 3
0.0 0.6
-0.6
RDA Axis 1
0.0 0.6
hecabe, and the grass feeding Ypthima baldus andYpthima fasciata were associated with selectively loggedhabitats, while the tree feeding Euthalia iapis andArhopala borneensis, and the generalist Drupadia theda
were associated with unlogged habitats. Axis 2 wasprimarily associated with canopy cover, axis 3 with treeand liana abundance and axis 4 with grass cover.
RLQ analysis
RLQ analysis revealed a highly significant (Permuta-tion test; Po0.001) relationship between environmentalvariables and species traits. We only consider the firsttwo RLQ axes, which together captured 80.37% ofvariance in the analysis. The RLQ analysis (Fig. 3A andB) showed that differences between logged forest with ahigh abundance of non-woody and small woody lianas,mesophyll leaf litter and small woody debris, andunlogged forest, with a high tall sapling, pole and treedensity, microphyll leaf litter, and mean tree heightaccounted for most of the variation in species traits in asfar as this could be related to the available set ofenvironmental predictors (variance explained: 67.4%).Species with widespread distributions and those withlarvae that feed on lianas were linked to environmental
Fig. 2. Ordination based on redundancy analysis (RDA)
showing species and environmental variables on (A) the first
and second axes and (B) the third and fourth axes. Arrows
represent significant environmental variables, and their direc-
tion and length indicates their contribution to variation along
those axes. The broad scatter of species on the plots is
indicative of a broad range of species responses to these
environmental variables. Log: logging, Slo: slope position, Ele:
elevation, Sap: small sapling abundance, Tre: tree density,
Gra: grass cover, Lia: non-woody liana abundance, Lit: mean
litter depth, Aca: mean canopy cover and Sca: standard
deviation in canopy cover. Selected species are indicated by
four-letter codes: Al-su: Allotinus substrigosus, Al-un: Allotinus
unicolor, Ap-ly: Appias lyncida, Ar-at: A. atosia, Ar-bo: A.
borneensis, Ar-el: A. elopura, Ar-ep: A. epimuta, Ar-ev: A.
evansi, Ar-ps: A. pseudomuta, Ar-si: A. silhetensis, Ca-el:
Caleta elna, Ce-hy: Cethosia hypsea, Ch-pe: Chersonesia
peraka, Cu-er: Cupha erymanthis, Dr-th: Drupadia theda, Er-
ar: Erites argentina, Er-el: E. elegans, Eu-an: Eurema
andersoni, Eu-he: E. hecabe, Eu-ia: Euthalia iapis, Ga-ha:
Gandaca harina, Id-st: Idea stolli, Ja-pu: Jamides pura, Le-ni:
Leptosia nina, Lo-ma: Logania marmorata, Lo-ml: L. malayica,
My-an: Mycalesis anapita, My-ho: M. horsfieldi, My-th: M.
thyateira, Na-be: Nacaduba beroe, Ne-hy: Neptis hylas, Ne-le:
N. leucoporos, Pa-ar: Pachliopta aristolochiae, Pa-di: Panto-
poria dingdinga, Pa-te: Paralaxita telesia, Ra-ma: Ragadia
makuta, Ra-va: Rapala varuna, Ta-ar: Taractrocera ardonia,
Ta-ha: Taxila haquinus, Th-no: Thaumantis nouredin, Tr-am:
Troides amphrysus, Vi-de: Vindula dejone.Xa-bu: Xanthotaenia
busiris, Yp-ba: Ypthima baldus, Yp-fa: Y. fasciata, Yp-pa: Y.
pandocus.
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RLQ
Axi
s 2
0.4-0.4
-0.2
4-3
4
-3
RLQ Axis 1
0.4
Fig. 3. The results of the RLQ analysis indicating associations
along the first two axes between (A) environmental variables
and (B) species traits. Positions of points relative to the origin
indicate relative contributions to RLQ axes, and similar
positions relative to the origin in both plots indicate close
associations between environmental variables and species
traits. Environmental variable: Bry: bryophytes, Can: mean
canopy cover, CrD: crown depth, CrR: crown radius, DbH:
diameter at breast height, DwF: dead wood fallen, DwS:
DWT: dead wood total, dead wood standing, DW1: dead
wood state 1, DW2: dead wood state 2, DW3: dead wood state
3, DW4: dead wood state 4, DW5: dead wood state 5, Ele:
elevation, Epi: epiphytes, Fer: ferns, Fur: bifurcation index,
Gra: grasses, Hei: height, Her: herbs, LiL: large woody lianas,
Lit: litter depth, LiS: small woody lianas, Log: logging, LiN:
non-woody lianas, Mes: mesophyll sized leaf litter, Mic:
microphyll sized leaf litter, Not: notophyll sized leaf litter,
Sed: seedlings, S10s: short poles, S5s: short saplings, Slo: slope,
Swc: small woody chips, StC: standard deviation in canopy
cover, S10t: tall poles, S5t: tall saplings, Tre: trees. Species
traits: H.Ins: carnivores, H.Her: herb specialists, H.Gen: host
plant generalists, H.Gra: grass specialists, H.Lia: liana
specialists, H.Pal: palm specialists, and H.Tre: tree specialists,
R.E_Bor: Bornean endemics, R.Mod: species with a moderate
distribution, R.E_Sun: Sundaic endemics, and R.Wide: species
with a wide distribution.
D.F.R. Cleary et al. / Basic and Applied Ecology 10 (2009) 237–245242
conditions found in logged forest. Species that feed ongrasses were linked to areas, mainly found in loggedforest, with abundant non-woody lianas. By contrastspecies with distributions limited to Borneo and thosewith tree feeding larvae were linked to unlogged primaryforest. The second RLQ axis accounted for anadditional 12.4% of variation. Species with traitsincluding larval feeding of herbs and palms were linkedto areas with abundant herbs and grasses whereasspecies that feed on insects were linked to areas withabundant dead wood state 4, notophyll sized leaf litterand trees.
Discussion
Local environmental variables, geographic distance
between sampling sites and butterfly community
structure
We were able to explain more than 50% of thevariation in composition of a diverse tropical butterflycommunity. A highly significant proportion of thisvariation was associated with local environmentalvariables, thus the results confirm that local environ-mental conditions can significantly influence spatialstructure of rainforest butterfly assemblages. This resultis consistent with the hypothesis that niche differentia-tion among butterflies contributes to large-scale patternsof spatial abundance. Importantly, species compositiondiffered considerably between forest that had beensubject to commercial selective logging and unloggedforest. Moreover, logging was identified as the primaryvariable characterising the main axis in spatial variationin the butterfly community. It would seem that theprincipal drivers of community change in our sampledareas are logging activity and associated environmentalchanges. Several other studies have demonstratedlogging to have significant impacts on insect communitystructure (e.g. Basset, Charles, Hammond, & Brown,2001; Cleary, 2003; Cleary, Boyle, Setyawati, & Men-ken, 2005; Ghazoul, 2002). It is likely that these changesare linked directly to alterations in vegetation structure,as well as physical variables such as light availability andhumidity.
The results of this study also revealed a significanteffect of geographic distance between sampling sites onbutterfly community composition. While some of thiswas linked to co-variance between environmentalvariables and geographic distance, a high proportionwas associated with geographic distance alone. Onefactor driving this may be the relationship betweendispersal ability and population demography, forexample, spatial variation in population growth rateshave been linked to dispersal rates (Baguette &Schtickzelle, 2006). Tropical butterflies can be stronglydispersal limited (Fauvelot, Cleary, & Menken, 2006),and there is also evidence that in general lepidoptera candiffer in rates of dispersal depending on their morphol-ogy and patterns of resource use (Beck & Kitching,2007).
Species traits and responses to commercial logging
Our results indicate that differences in the abundanceof species within logged and unlogged habitats wassignificantly linked to their traits. Species from the larvaltree feeding guild were more abundant in primaryunlogged forest, while species belonging to grass and
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liana feeding guilds were more abundant in recentlylogged forest. Species that feed on herbs and palmswere mainly found in areas of unlogged and recentlylogged forest with a relatively high mean canopy coverwhereas insect feeders were mainly found in areasof older logged forest with a deep litter layer andabundant dead wood. Although butterfly larvae do notfeed on dead wood the presence of dead wood and itsstate of decay are indicators of local environmentalconditions. For example, large volumes of rotten deadwood (state 5) were found in unlogged humid closedcanopy forest.
Within logged forest there was also variation inspecies abundance patterns. Liana feeders and widerange species were associated with logged areas withhigh small woody liana abundance, whereas grassfeeders were associated with areas dominated by non-woody lianas, recently fallen dead wood (DW2) andsmall woody debris. Although there is no directassociation between, for example, dead wood and grassfeeding butterflies, the presence of dead wood influencesthe environmental conditions within the sample site. Assuch dead wood may either influence environmentalconditions critical to the presence of butterflies or becorrelated with other variables that have a more directeffect on butterflies.
Although the specialist guilds were on average moreabundant within logged forest, they tended to occupysomewhat different disturbance types. Interestingly,there was little association between the cover of grassesand grass feeders. There was also no significantdifference in grass cover among the forest types (resultsnot shown); severely disturbed logged areas were mainlydominated by lianas as opposed to grasses. The presenceof grass-feeding butterflies in large open gaps dominatedby non-woody lianas may be due to differences in theactual species composition of grasses in unlogged versuslogged forest. For example, many of the grass feedersrecorded in logged forest feed on invasive grass speciessuch as Imperata cylindrica. In addition to this, thesespecies probably enter the logged forest from thenetwork of logging roads and skid roads where grassesare more abundant. In a previous study we in factfound that the abundance of grass feeders such asYphthima baldus and Mycalesis horsfieldi was muchhigher in open habitat along logging roads than eitherwithin unlogged or logged forest and concomitantlyhigher in logged forest than unlogged forest due to thenetwork of logging roads in the logged forest (Cleary,Boyle, et al., 2005).
The variation in habitat types following logging maybe linked to different post-logging successional path-ways. In logged forests, lianas tend to dominate gaps,leaving them in prolonged low canopy states (Schnitzer& Bongers, 2002). Alternatively a rich herb layer candevelop under a moderately to relatively undisturbed
canopy largely consisting of species of Zingiberaceae,and Maranthaceae. However, falling woody debris cansmother and kill large proportions of the undergrowthe.g., liana saplings, large herbs and tree seedlings(Wright, 2002), thereby creating different habitats forbutterflies.
It appears species are rapidly and directly respondingto the availability of larval food-plant resources. Thereare also marked differences in how larval guilds respondto ENSO-induced wildfires, extreme events that typi-cally decimate species richness and initiate successionalprocesses (Cleary & Genner, 2004; Cleary & Mooers,2004; Cleary, Fauvelot, Genner, Menken, & Mooers,2006). Following burning, species with narrow larvalniche breadths tend to be much less likely to return toaffected forest than more generalist species, probablybecause the generalists are able to exploit pioneer treesand forbs that rapidly colonise the burned areas(Charrette, Cleary, & Mooers, 2006; Cleary & Grill,2004).
Species responses were also strongly linked togeographical ranges. Unlogged forest was associatedwith range-restricted species, while broadly distributedspecies were more abundant in logged forest. Theseresults highlight the risk of driving local extirpations,and possibly extinction, through selective logging.Moreover, they demonstrate how selective logging mayeventually result in the spatial homogenisation of thebutterfly assemblage. Similar associations between dis-turbed habitat and declining relative abundance ofnarrowly distributed species have been made by severalother studies (Charette et al., 2006; Cleary & Mooers,2006; Ghazoul, 2002; Hamer et al., 2003), and it hasbeen suggested that this may be a consequence ofobserved associations between larval food plant nichebreadth and biogeographic range size (Charrette et al.,2006). Generalist and widespread species may be able toexploit novel resources associated with logged habitats,while in contrast range restricted species tend to bespecialised upon narrowly distributed resources, whichmakes them more vulnerable to local disturbance (Beck& Kitching, 2007; Charrette et al., 2006; Cleary &Mooers, 2006).
Concluding remarks
Here we have provided evidence that selectivecommercial logging can significantly influence butterflycommunity composition within rainforests, and that theeffect on species is influenced by life history traitsincluding their larval host plants and biogeographicdistribution. This study was conducted on a wholeassemblage and over a large spatial scale, thus theassociations between species-responses and the traitsthat we discovered were broad and general. It is highly
ARTICLE IN PRESSD.F.R. Cleary et al. / Basic and Applied Ecology 10 (2009) 237–245244
likely that more fine-scale study of the traits ofindividual species will reveal close associations betweenspecies and environmental variables. It is particularlyimportant to note the importance of environmentalvariables in structuring composition implying that thereis considerable niche differentiation within the butterflyassemblage. This suggests that a key factor in themaintenance of high species richness within theseenvironments is a naturally heterogeneous forest withhigh niche diversity.
Acknowledgments
We thank B. Mackey and colleagues from theDepartment of Geography, Australian National Uni-versity, for deriving and mapping environmentalpredictors from a digital elevation model of the site.G. Hellier is gratefully thanked for field assessmentof habitat structure variables. The staff of P.H.T.Kayu Mas and Wanariset Sangai, together with localDayak field assistants provided valuable support duringfieldwork. Konrad Fiedler, Roger Kitching, KlausHovemeyer and anonymous reviewers provided impor-tant comments that have improved previous versions ofthis manuscript. DFRC was supported by grant895.100.005 of the Netherlands Foundation for theAdvancement of Tropical Research (NWO-WOTRO)within Priority Programme ‘Biodiversity in DisturbedEcosystems’.
Appendix A. Supplementary Material
Supplementary data associated with this article can befound in the online version at doi:10.1016/j.baae.2008.03.004.
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