Struebig MJ, Kingston T, Zubaid A, Adnan AM - Personal Webspace ...

Conservation value of forest fragments to Palaeotropical bats 

Matthew J. Struebig a , Tigga Kingston b , Akbar Zubaid c , Adura Mohd-Adnan c , 

Stephen J. Rossiter a, * 

a School of Biological and Chemical Sciences, Queen Mary, University of London, London E1 4NS, United Kingdom 

b Department of Biological Sciences, Texas Tech University, Lubbock, TX 79409-3131, United States 

c Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Malaysia 

ARTICLE INFO 

Article history: 

Received 18 March 2008 

Received in revised form 

3 June 2008 

Accepted 12 June 2008 

Available online 26 July 2008 

Keywords: 

Chiroptera 

Habitat fragmentation 

Nestedness 

Species–area relationship 

Isolation 

Malaysia 

Oil palm 

1. Introduction 

ABSTRACT 

Habitat fragmentation is a major contributor to biodiversity 

loss (Whitmore, 1997). Nowhere is this more dramatic than 

in Southeast Asia, where tropical forests are becoming 

increasingly disturbed and fragmented, and are rapidly being 

BIOLOGICAL CONSERVATION 141 (2008) 2112– 2126 

available at www.sciencedirect.com 

journal homepage: www.elsevier.com/locate/biocon 

Forested landscapes in Southeast Asia are becoming increasingly fragmented, making this 

region a conservation and research priority. Despite its importance, few empirical studies 

of effects of fragmentation on biodiversity have been undertaken in the region, limiting 

our ability to inform land-use regimes at a time of increased pressure on forests. We estimated 

the biodiversity value of forest fragments in peninsular Malaysia by studying fragmentation 

impacts on insectivorous bat species that vary in dependence of forest. We 

sampled bats at seven continuous forest sites and 27 forest fragments, and tested the influence 

of fragment isolation and area on the abundance, species richness, diversity, composition 

and nestedness of assemblages, and the abundance of the ten most common 

species. Overall, isolation was a poor predictor of these variables. Conversely, forest area 

was positively related with abundance and species richness of cavity/foliage-roosting bats, 

but not for that of cave-roosting or edge/open space foraging species. The smallest of fragments 

(300 ha contribute substantially to landscape-level bat diversity, and that small fragments 

also have some value. However, large tracts are needed to support rare, forest specialist 

species and should be the conservation priority in landscape-level planning. Species that 

roost in tree cavities or foliage may be more vulnerable to habitat fragmentation than those 

that roost in caves. 

Ó 2008 Elsevier Ltd. All rights reserved. 

lost to agriculture (Sodhi et al., 2007). Despite this, only a few 

detailed studies of fragmentation have been conducted in the 

region (e.g. Lynam and Billick, 1999; Pattanavibool and Dearden, 

2002; Brühl et al., 2003; and Benedick et al., 2006). This 

paucity of information hinders both our understanding 

of the consequences of fragmentation for biodiversity in 

* Corresponding author: Tel.: +44 2078827528. 

E-mail addresses: m.struebig@qmul.ac.uk (M.J. Struebig), tigga.kingston@ttu.edu (T. Kingston), zubaid@ukm.my (A. Zubaid), 

adura@pkrisc.cc.ukm.my (A. Mohd-Adnan), s.j.rossiter@qmul.ac.uk (S.J. Rossiter). 

0006-3207/$ - see front matter Ó 2008 Elsevier Ltd. All rights reserved. 

doi:10.1016/j.biocon.2008.06.009

Southeast Asia, and our ability to advise stakeholders and 

land owners on potential mitigation strategies (Meijaard and 

Sheil, 2007). 

Fragmentation reduces suitable habitat area and isolates 

patches within a matrix of modified habitat. Island biogeography 

theory (MacArthur and Wilson, 1967) predicts that smaller, 

more isolated fragments support smaller populations, and 

fewer species than are supported by larger or less isolated 

fragments. However, a recent synthesis has reported that, 

while fragment area is a significant predictor of species richness 

in most studies, the effects of isolation remain ambiguous 

(Watling and Donnelly, 2006). Species exhibit variable 

responses to fragmentation. These responses are inconsistent 

across taxonomic groups, but are more commonly correlated 

with population size and fluctuation, disturbance sensitivity, 

matrix use, biogeographic position and rarity (Henle et al., 

2004). Area-dependent declines in abundance and species 

richness can result in predictable local patterns of species 

extinction, with depauperate smaller fragments harbouring 

nested subsets of assemblages found in larger fragments 

(Wright et al., 1998). Thus, to evaluate the long-term conservation 

value of forest fragments, it is essential to describe 

species diversity patterns and to elucidate the mechanisms 

that produce them. 

Bats constitute the second most species-rich order of 

mammals (Wilson and Reeder, 2005) and up to half of mammal 

species in tropical forests (Findley, 1993). In recent decades, 

bat populations have experienced global declines, a 

trend linked to extensive, recent habitat loss (Mickleburgh 

et al., 2002). In Southeast Asia, 20% of bat species are predicted 

to become extinct by 2100 (Lane et al., 2006). Nonetheless, 

bats are frequently overlooked in biodiversity 

assessments and fragmentation research, possibly because 

they are widely perceived to be at low risk of extinction due 

to their ability to fly. 

The perception of bats as low priority subjects for conservation 

research may be overly optimistic because these animals 

exhibit combinations of traits that may increase their 

sensitivity to habitat loss and disturbance. Ecomorphological 

factors such as wing shape (Schnitzler and Kalko, 2001), 

behaviours including coloniality and strong site fidelity (Miller-Butterworth 

et al., 2003) and slow rates of reproduction 

(Barclay and Harder, 2003) constrain their ecological flexibility. 

In addition, bats are dependent on the availability of suitable 

roosting sites. Consequently, populations of species that 

roost in trees (in hollows and cavities of standing trees, under 

fallen trees and logs), may be adversely impacted by fragmentation 

via the direct loss of rare roosting sites in fragments 

(Schulze et al., 2000) or changes to roost suitability resulting 

from edge effects (e.g. disturbance levels and microclimate 

changes; Laurance et al., 2002). These edge effects also have 

the potential to influence the persistence of bats that roost 

in foliage (under modified or unmodified leaves), because 

these types of roost are more exposed to abiotic conditions 

and disturbance. Similarly, fragmentation can also lead to 

the separation of cave roosts from foraging habitat (fragments). 

However, given the natural patchy distribution of 

caves, and that they typically support large populations of 

bats, communal cave roosting species are likely to have been 

selected for greater vagility over evolutionary time. As a re- 

BIOLOGICAL CONSERVATION 141 (2008) 2112– 2126 2113 

sult, these species typically commute great distances to and 

from foraging grounds each night (Altringham, 1999), and so 

may be better adapted to persist in a fragmented landscape 

than their tree and foliage-roosting counterparts. 

To date, fragmentation studies of tropical bats have focused 

almost exclusively on assemblages in the Neotropics 

(but see Law et al., 1999 for a study in Australia), where bat 

assemblages in forests are dominated by members of the 

family Phyllostomidae. These studies have suggested that 

bat species richness may be largely unaffected by fragmentation 

(Schulze et al., 2000; Estrada and Coates-Estrada, 2002; 

Pineda et al., 2005; Faria, 2006; Bernard and Fenton, 2007), 

though subtle impacts on assemblage structure have been detected 

(Cosson et al., 1999; Gorresen and Willig, 2004). However, 

meaningful comparisons between studies are 

complicated by differences in sampling effort and fragmentation 

history, as well as variation in potential determinants of 

assemblage structure in fragments, such as the degree of contrast 

between fragments and the matrix (low contrast, Estrada 

and Coates-Estrada, 2002; Pineda et al., 2005; Faria, 2006; 

versus high contrast, Cosson et al., 1999; Bernard and Fenton, 

2007), and the availability and size of forest patches in a landscape 

(Gorresen and Willig, 2004). 

Palaeotropical bat assemblages are dominated by members 

of the families Rhinolophidae and Hipposideridae, and 

the Vespertilionidae subfamilies Kerivoulinae and Murininae. 

These species are not present in the Neotropics, and 

many of them are typically highly adapted for foraging in 

the clutter of the forest interior (‘narrow-space’ ensemble, 

sensu Schnitzler and Kalko, 2001). Consequently, these species 

may be more sensitive to forest loss and exhibit greater 

avoidance of disturbed and open habitats than Neotropical 

bats (reviewed in Kingston et al., 2003). Because of this 

dependence on forest, we expect these species to be adversely 

affected by deforestation and other forest disturbance 

events (Lane et al., 2006). 

We determined the conservation value of forest fragments 

in the Palaeotropics by using bats as a focal animal group, and 

quantifying abundance, species richness, diversity, assemblage 

composition and nestedness in a fragmented landscape 

in central peninsular Malaysia. We focused on the narrowspace 

ensemble of insectivorous species due to their predicted 

vulnerability and because they can be readily captured 

in forests using a single, standardised sampling technique. 

Landscapes in Malaysia have undergone major changes over 

the last century as forests have been rapidly cleared for timber, 

urbanisation and plantation agriculture (Kathirithamby- 

Wells, 2005). Increasing demand for plantation products such 

as oil palm (Elaeis guineensis) places pressures on land owners 

to increase yields; one way to do this is to increase production 

area by clearing forest remnants. Hence, we sought to inform 

these management decisions regarding the value of such 

remnants at a time of increased pressure on remaining forest 

habitats. 

We hypothesised that (1) species richness, abundance and 

diversity of bats is lower in smaller or more isolated forest 

fragments than in larger or less isolated fragments; (2) fragmentation 

effects are stronger in species that roost in tree 

cavities and/or foliage than in more vagile species that roost 

in caves and (3) assemblages in smaller or more isolated frag-

2114 BIOLOGICAL CONSERVATION 141 (2008) 2112– 2126 

ments represent nested subsets of those in larger or less isolated 

fragments. 

2. Methods 

2.1. Study landscape 

The Krau landscape in central Pahang state (3°40 0 N, 102°10 0 E; 

Fig. 1) represents 562,060 ha of land and is bounded by continuous 

forest to the north and west. Historically this landscape 

has experienced little deforestation, but in recent years (1966– 

2002), 39 % of the forest has been felled (DAPM, 2005). Today, 

large blocks of undisturbed continuous forest remain protected 

as the Krau Wildlife Reserve and neighbouring Forest 

Reserves, while 43% of land is covered by rubber (Hevea brasiliensis) 

and oil palm plantations, surrounding smaller forest 

fragments. 

The majority of natural vegetation in the Krau landscape is 

lowland or hill dipterocarp forest, with associated dominant 

tree species including Dipterocarpus cornutus, D. baudii, Hopea 

sangal, Shorea acuminata and S. ovalis, orAnisoptera laevis, D. 

grandiflorus, S. leprosula, S. cutisii and Vatica cuspidata respectively 

(Yusof and Sorenson, 2000). The annual 24-h mean temperature 

is 26 °C, and monthly precipitation typically exhibits 

two periods of maximum rainfall between September and 

December, and March and May, separated by two periods of 

minimum rainfall (Yusof and Sorenson, 2000). 

2.2. Forests sampled and patch metrics 

We sampled bats between May 2002 and June 2007 at 35 lowland 

forest sites. These comprised five undisturbed sites and 

two disturbed sites within Krau Wildlife Reserve (S01-S07, 

mean distance between sites 17 km); and 27 forest fragments 

varying in size from 3 ha to 11,339 ha (F01-F27, Fig. 1). Fragments 

were identified from land-use maps (DAPM, 2005) verified 

by visual interpretation of 2002 Landsat ETM satellite 

images; they represent the range of forest remnant sizes 

and land-use histories in the landscape. Fragments were subject 

to ongoing disturbance: all exhibited evidence of logging 

as well as hunting of wild pigs (Sus scrofa) and mouse deer 

(Tragulus spp.). 

We used ArcView version 3.2 to calculate forest area and 

two measures of isolation widely used in fragmentation studies 

(Watling and Donnelly, 2006): the shortest Euclidean distance 

to nearest continuous forest, and the distance to the 

nearest forest patch. All metrics were independent from each 

other (Pearson’s r < 0.3; p > 0.3), and were log transformed to 

approximate to normal distributions. 

2.3. Bat sampling 

We minimised methodological heterogeneity and capture 

biases (Kingston et al., 2003) by restricting sampling to insectivorous 

species that are readily captured in the forest under- 

Fig. 1 – Locations of sampling sites in the Krau landscape, peninsular Malaysia, including those in continuous forest (S prefix) 

and forest fragments (F prefix). Dark grey areas represent forest cover in 2002 and light grey areas represent additional forest 

cover in 1966, both according to Malaysian Ministry of Agriculture maps. White areas consist of a plantation mosaic, 

primarily oil palm and rubber, but also with substantial areas of durian (Durio spp.) and Acacia spp. Forest cover for all 

peninsular Malaysia is shown in the inset, and black lines indicate state boundaries.

storey, conducting fieldwork only in dry seasons, and avoiding 

periods of heavy rain. Bats were captured using up to fifteen 

four-bank harp traps positioned across flight paths (trails, logging 

skids, streams, or swamp beds) each night and then 

moved to a new position the following day – hence one trap 

set for one complete night constituted one harp trap night 

(HTN), following Kingston et al. (2003). 

Bats were collected and identified following the procedures 

of Kingston et al. (2006) and were marked either with 

uniquely numbered forearm bands or wing biopsies, so that 

recaptures could be recognised and excluded from analyses. 

Individuals were released within 12 h at the capture point. 

We classified species into three classes based on dispersal 

capabilities inferred from wing morphology and roosting 

ecology. Wing morphology was first used to define species 

at the level of ensemble by distinguishing bats that forage 

in narrow spaces (‘narrow-space’ bats, sensu Schnitzler and 

Kalko, 2001; ‘Strategy I’ bats, sensu Kingston et al., 2003) from 

those that primarily forage in edges or open spaces (‘Strategy 

I’ and ‘Strategy III’ bats, sensu Kingston et al., 2003). Species in 

the narrow-space ensemble were then further partitioned 

based on their roosting ecology into two classes: (1) tree cavity/foliage-roosting 

species and (2) cave-roosting species 

(including rock crevices). 

2.4. Sampling design 

The 27 fragments varied substantially in isolation history, as 

well as in distances to other fragments and to areas of karst 

limestone, which hosted large populations of cave-roosting 

bats that dominated assemblages. Because these factors were 

likely to obscure the effects of fragmentation on bat assemblages, 

we analysed a subset of 15 fragments, each of which 

was a minimum of 500 m from other fragments and 2 km 

from karst sites, and had a sampling effort of at least 15 

HTN. Isolation distances for these sites ranged from 2.1 to 

11.0 km (mean 6.4 km) from continuous forest, and 0.6 to 

2.3 km (mean 1.3 km) from other fragments. For comparisons 

with undisturbed continuous forest, we classified fragments 

by size, which ranged across three orders of magnitude: small 

(mean 70 ha, range 31–102); medium (mean 353 ha, range 251– 

443); and large (mean 5410 ha, range 2025–11 339). Data from 

sites within each size class were then pooled to provide sufficient 

sample sizes for statistical analyses. 

2.5. Statistical analyses 

A suite of analyses was designed to evaluate the effects of forest 

fragmentation on bat abundance, species richness, diversity, 

assemblage composition and nestedness. We used the 

Simpson index as our measure of diversity because this measure 

is weighted toward common species, and allows for 

examination of patterns of species dominance, or in its reciprocal 

form, species evenness. Because of the exploratory 

nature of our study and extensive debate regarding the use 

of adjustments for multiple tests in the ecological literature 

(e.g. Roback and Askins, 2005), we report the exact p-values 

for all analyses. We tested the influence of fragment area 

and isolation on assemblage variables (total bat abundance, 


observed and predicted species richness, and diversity), using 

generalised linear models (GLMs) undertaken in the R-statistical 

package version 2.5.1 (http://www.r-project.org). This approach 

focussed on site-level species richness or total bat 

abundance (i.e. standardised richness or abundance for all 

species pooled in all traps at each site), and was also used 

separately to test the influence of fragment metrics on the 

abundance and species richness for each of the three classes 

of bats (i.e. edge/open space foraging species, cave-roosting 

narrow-space species, and tree cavity-foliage roosting narrow-space 

species). Observed species richness (Sobs) and reciprocal 

Simpson diversity (1/D, evenness) were derived from 

sample-based rarefaction curves (Colwell, 2004), and species 

richness was predicted at a standard number of individuals 

(200) using the Shen multinomial model (Shen et al., 2003; 

Chao and Shen, 2003-2005). Square root transformations were 

used on abundances and predicted species richness to 

approximate normal distributions without special treatment 

of zeros (McCune and Grace, 2002). 

Sample sizes were sufficient to warrant testing the responses 

in the abundance of the ten most common species 

to fragment area and isolation. However, the low abundances 

of these species at some sites limited our ability to detect 

these responses using the GLM approach. Therefore, we 

quantified the responses in species abundance to area and 

both isolation metrics with a procedure that focused on 

trap-level abundance of these species using a generalised linear 

mixed-effects model (GLMM) with Poisson error terms. 

Modelling sites as random effects in GLMMs also controlled 

for pseudoreplication within a site and accounted for variance 

attributable to particular sites. All GLMMs were undertaken 

in R with the lmer function from the lme4 package 

(Bates, 2008). The three fragment metrics were modelled as 

fixed effects, sites were modelled as random effects, and 

the response variable was a species’ abundance in a trap (15 

traps per site). p-values were generated from 10 000 Markov 

chain Monte Carlo (MCMC) simulations using the languageR 

package (Baayan, 2008). 

Bat abundance, species richness and reciprocal Simpson 

diversity (i.e. evenness) among different size fragments and 

continuous forest were compared using non-parametric 

Kruskal–Wallis tests with post-hoc pairwise Mann–Whitney 

U tests. This procedure was also undertaken for the abundances 

of the ten species for which we had sufficient sample 

sizes. Estimates of observed species richness and Simpson 

diversity were partitioned into additive components within 

sites (a); between sites of similar sizes (b1); and between sites 

of different sizes (b 2), using an individual-based randomisation 

procedure (Veech and Crist, 2007). Because additive partitioning 

resulted in a and b components being measured in the 

same units (Crist et al., 2003), we could assess the relative 

contributions of size classes of fragments to overall (c) insectivorous 

bat species richness and Simpson diversity (i.e. dominance) 

over the Krau landscape. 

Species abundance distributions within fragment size 

classes and continuous forest were determined using standardised 

rank abundance (Whittaker) plots, on which the differences 

of species rank between the pooled assemblages 

could be visually inspected. Differences in species abundances 

distributions between fragment size classes and con-


tinuous forest were assessed using a v 2 randomisation test in 

Ecosim version 7 (Gotelli and Entsminger, 2007). Because this 

test uses the same expected abundance values for both observed 

and simulated data (i.e. 1000 simulations) the results 

were not sensitive to small expected values arising from rare 

species. 

To determine variation in the compositional structure of 

bat assemblages among sites, we used non-metric multidimensional 

scaling (NMDS) with the Bray-Curtis dissimilarity 

index. Bray-Curtis coefficients were based on species abundances, 

which were square-root transformed to compress 

values of abundant species relative to those of rare species 

without the need to adjust zeros (i.e. species absences) 

(McCune and Grace, 2002). Ordinations were implemented 

using the software PC-ORD version 5 (McCune and Mefford, 

2006) with 500 iterations and 250 runs of both real and randomised 

data. Because assemblage data are often composed 

largely of rare or absent species, removing some of these 

species may enhance the detection of relationships between 

composition and causal factors, such as fragment metrics 

(McCune and Grace, 2002). Therefore, we performed several 

ordinations starting with the inclusion of all species, and 

then removed subsets of species based on ensemble or rarity. 

The final ordination was chosen based on reducing stress 

from additional axes but also retaining enough species for 

the ordination to remain biologically meaningful. A GLM 

was used to evaluate whether forest metrics determined 

the positions of forest sites in ordination space, and hence 

the compositional differences of bat assemblages between 

forest sites. 

Finally we determined the extent to which bat assemblages 

were nested by calculating nestedness derived from 

presence–absence matrices of species in fragments. We performed 

separate analyses using matrices of all bat species 

and sub-matrices for each of the three classes of bat. The 

resulting temperature metric T describes the level of ‘heat 

disorder’, a measure of the distribution of unexpected presences 

and absences in a matrix. Maximum order, or perfect 

nestedness, is indicated by a temperature of zero, and significance 

can be assessed by comparing the observed temperature 

to a null distribution based on MCMC simulations. We 

used the binary matrix nestedness calculator (BINMATNEST, 

Rodríguez-Gironés and Santamaría, 2006), an algorithm that 

overcomes limitations of other calculators concerning the 

reordering and packing of matrices, the definition of the isocline 

of perfect order, and the appropriateness of null models 

used to assess significance. BINMATNEST provides three 

alternative null models on which to assess significance, with 

model 3 being the most conservative according to the 

authors. We therefore used this model to evaluate significance, 

and based our p-values on 5000 simulated matrices. 

To determine if the maximally nested matrix produced an 

ecologically meaningful nested arrangement, in terms of forest 

fragmentation, the order of forest fragments in the maximally 

nested matrix was correlated (Spearman rank 

coefficient) with forest fragments ordered by area or isolation, 

as surrogates of fragmentation intensity. Hence, a significant 

correlation coefficient would suggest a nested arrangement 

that resulted from the fragmentation process (Rodríguez-Gironés 

and Santamaría, 2006). 

3. Results 

We captured a total of 10 343 insectivorous bats of 46 species 

from 1830 HTN over seven sites in continuous forest and 491 

HTN over 27 fragments (Appendix 1). Of the 7488 individuals 

captured in continuous forest, tree cavity/foliage-roosting 

and cave-roosting narrow-space species represented a similar 

proportion of all bats captured: 47% of individuals (21 species) 

were tree cavity/foliage-roosting; 52%, (12 species) were caveroosting; 

and 1% (7 species) were edge/open space foragers. 

Conversely, of the 2857 individuals captured in fragments, 

the proportion of tree cavity/foliage-roosting species was lower 

(26% 17 species), while that of cave-roosting species (68% 12 

species) and edge/open space foraging species (6% 9 species) 

was higher. 

Only six individuals were recaptured between sites, all of 

which were cave-roosting species (Table 1). No species were 

recorded in every fragment, but four species (Rhinolophus affinis, 

R. lepidus, R. trifoliatus and Murina suilla) were widespread 

(present in >70% of fragments and all continuous forest sites). 

Sixteen species were uniformly rare (< 1% of captures in both 

continuous and fragment forest sites, Appendix 2) and six 

(Coelops robinsoni, Hipposideros armiger, Harpiocephalus mordax, 

Kerivoula krauensis, Murina rozendaali and Myotis siligorensis) 

were only recorded in continuous forest. Three species captured 

in fragments (Hesperoptenus blanfordi, Hipposideros lylei 

and Scotophilus kuhlii) were absent from our surveys in continuous 

forest, but have been recorded in that habitat by other 

studies reviewed in Kingston et al. (2006). 

3.1. Patterns of abundance and assemblage composition 

More bats were captured in larger fragments than smaller 

fragments (Fig. 2; Table 2), but there was no response in total 

or ensemble abundance based on either measure of fragment 

isolation. Fragment area explained the majority of variation 

in total bat abundance (72.7% Fig. 2a), with measures of isolation 

consistently removed from GLMs. When partitioning this 

relationship by ensemble and roosting class, area explained 

variation in abundance of tree cavity/foliage-roosting bats 

(54.5% Fig. 2b), but not for cave-roosting bats (Fig. 2c) or 

edge/open space foraging bats (Fig. 2d). There was no response 

to fragment isolation exhibited by any ensemble or 

roosting class. 

The total bat abundance of the smallest fragments was 

significantly lower than continuous forest (Kruskal–Wallis 

v 2 = 11.78, p = 0.008; all pairwise Mann–Whitney comparisons, 

U < 0.001, p = 0.009), but abundance in medium and large fragments 

was similar to continuous forest (U = 7.0–11.0, p > 0.05). 

When considered by roosting class, fewer tree cavity/foliageroosting 

bats were captured in fragments of all size classes 

compared to continuous forest sites (v 2 = 10.73, p = 0.013; all 

pairwise analyses, U > 1, p < 0.05). However, for cave-roosting 

and edge/open space foraging bats no differences between 

size classes were detected (v 2 = 5.38, p = 0.15; and v 2 = 5.01, 

p = 0.17, respectively). 

Species abundance distributions were unequal across fragment 

size classes (observed v 2 = 913.17, simulated 

v 2 = 111.18 ± 13.08, p < 0.0001; Fig. 3). Kerivoula intermedia was 

among the most dominant tree cavity/foliage-roosting spe-

Table 1 – Individuals recaptured in forest fragments in the Krau landscape during this study, with the Euclidean distance 

between sites of capture and recapture 

Species Sex and 

reproductive condition a 

Fig. 2 – Relationships between insectivorous bat abundance (a–d) or species richness (e–h) and forest area, for ensembles of 

bats with different roosting/foraging ecology. Black circles indicate forest fragments and grey circles indicate continuous 

forest sites. Regression models performed using fragment sites only. Fragment F15 was identified as an outlier with a hyperabundance 

of cave-roosting species and so was excluded from regressions. 

cies in continuous forest (rank = 2) and large fragments 

(rank = 1), but was much rarer in medium (rank = 19) and 

small fragments (rank = 20). In contrast, Rhinolophus affinis 

was a rare cave-roosting species in continuous forest 

(rank = 18), but was much more abundant in fragments (ranks 

from large fragments to small = 3, 1, 3). 

For species-level analyses of abundance significantly fewer 

bats were captured in fragments compared to continuous forest 

for two tree cavity/foliage-roosting species – K. intermedia 

(v 2 = 9.11, p = 0.028) and K. papillosa (v 2 = 11.99, p = 0.007). 

These results were also supported by GLMM models, which 

showed that more individuals of these species were captured 

in traps set in larger fragments than in smaller fragments (K. 

intermedia, p = 0.010; K. papillosa, p < 0.0001), and that both isolation 

metrics were not significant predictors of abundance. 

Similarly, species abundance of two cave-roosting species, 


Identity band 

number 

Site captured Site recaptured Distance between 

sites (km) 

Hipposideros cervinus # A MBCRU A6251 S05 F10 12.5 

$ A PL MBCRU A8593 S05 F12 10.7 

Rhinolophus affinis # A MBCRU A5326 S05 F09 10.4 

Rhinolophus stheno $ A P MBCRU B2183 S01 F08 4.9 

$ A PL THK 34227 F17 F18 1.9 

# A MBCRU A6546 S05 F10 10.9 

a A, mature adult; PL, post-lactating; P, pregnant. 

Hipposideros cervinus and H. larvatus, was greater in larger fragments 

than smaller fragments (p = 0.005 and p = 0.014 respectively), 

but also increased with greater distance from 

continuous forest (p = 0.030 and p = 0.032 respectively). The 

six other common species (tree cavity/foliage roosting: Murina 

suilla, Rhinolophus trifoliatus; and cave-roosting: H. bicolor 131, 

H. bicolor 142, R. affinis, R. lepidus) exhibited no response in 

abundance to any fragment metric (p > 0.1). 

3.2. Species richness and diversity 

Smaller fragments typically supported fewer bat species than 

larger fragments or continuous forest. Significant positive 

relationships existed between species richness and log-area 

using observed and predicted species richness (r 2 = 0.309, 

p = 0.01, Fig. 2e; and r 2 = 0.324, p = 0.02 respectively). When ob-


Table 2 – Landscape metric and insectivorous bat assemblage characteristics for fragment and continuous forest sites used 

for analyses 

Forest class and site Landscape metrics Assemblage characteristics 

Area (ha) Isolation a (km) Nearest forest b (km) N c 

Small fragments 

F03 RTP Lembah Klau 100 7.4 2.3 39 10 11.8 4.7 

F09 Paya Parit 31 5.0 0.6 41 11 27.3 6.3 

F11 Ulu Rugan 122 8.8 1.7 23 5 8.9 3.2 

F22 Desa Bakti 107 5.6 2.1 42 12 17.0 6.1 

F25 Jambu Rias 32 4.6 1.2 20 7 7.4 6.3 

Medium fragments 

F06 Klau Kecil 443 3.7 1.4 63 17 19.1 10.7 

F08 Paya Luas 353 2.1 1.0 43 11 12.7 6.8 

F10 Hutan Kerdau 319 8.1 0.6 71 14 19.8 5.4 

F14 Kampung Lebu 400 7.4 2.3 77 17 18.9 8.2 

F15 Rumpun Makmur 251 4.6 1.8 157 19 20.4 7.0 

Large fragments 

F01 Kemasul 1 2883 7.7 0.6 65 15 19.8 4.2 

F02 Kemasul 2 11 339 7.5 1.2 113 17 18.3 8.8 

F21 Belungu 5225 11.0 1.8 104 14 18.9 4.2 

F23 Klau Besar 5581 5.5 0.7 91 17 19.5 9.1 

F24 Jengka 2025 7.6 0.6 112 13 14.5 4.9 

Continuous forest 

S01 Kuala Lompat 137 000 – – 67 16 20.0 10.0 

S02 Lubuk Baung 137 000 – – 66 13 16.6 7.9 

S03 Kuala Serloh 137 000 – – 49 11 11.9 7.4 

S04 Kuala Gandah 137 000 – – 61 16 19.4 8.0 

S05 Jenderak Selatan 137 000 – – 162 12 12.5 3.5 

a The shortest straight-line distance to continuous forest. 

b The shortest straight-line distance to the nearest forest fragment. 

c Number of individuals captured in 15 harp traps set on trails in a site. 

d Number of observed species. 

e Predicted number of species at the 200 individual abundance level using the model proposed by Shen et al. (2003). 

f Reciprocal Simpson index – higher values indicate a more diverse assemblage with even species abundances. 

served species richness was considered separately for each 

ensemble and roosting class, only tree cavity/foliage-roosting 

bats exhibited a positive response to log-area (Fig. 2f–h). No 

significant relationships existed between reciprocal Simpson 

diversity (i.e. evenness) and any of the fragment metrics. Fewer 

species were recorded in small fragments than continuous 

forest (v 2 = 10.73, p = 0.013; U = 2.00, p = 0.027), but fragment 

size classes were similar in terms of predicted species richness 

(v 2 = 7.20, p = 0.066), or reciprocal Simpson diversity 

(v 2 = 4.45, p = 0.217). 

Additive partitioning revealed that species richness of bats 

within sites, between sites of similar size, and between sites 

of different size, contributed almost equal proportions to 

the overall insectivorous bat species richness (a = 36.8%; 

b1 = 35.7%; b2 = 27.7%). However, the majority of species dominance, 

as measured by Simpson diversity, was attributed to 

within sites (a = 89.7% b1 = 6.9%; b2 = 3.3%) suggesting that 

sites were highly dominated by common species. 

3.3. Bat assemblage composition 

The final NMDS ordination of species dissimilarity among 

sites consisted of two axes and was based on a matrix that included 

all bats except for the edge/open space foraging 

ensemble (27 species, stress = 16.7, (Fig. 4)). Other ordinations 

S obs d 

based on assemblages with all species, or with rare species 

excluded, had higher stress (20.0) and so were less reliable, 

but showed a similar pattern. The final ordination represented 

82% of variation in dissimilarity, and showed that 

small fragments (

Fig. 4 – Nonmetric multimensional scaling (NMDS) 

ordination for Bray-Curtis species dissimilarity of 

insectivorous bat assemblages in 15 forest fragments and 

five continuous forest sites in the Krau landscape. A 2dimensional 

ordination that excluded edge/open space 

species was the best solution (stress = 16.7), and 

represented the majority of species (27) and variance in 

dissimilarity (82%). Points are scaled to log transformed 

forest area, the sole significant predictor of assemblage 

composition. Grey points represent sites in continuous 

forest and black points represent forest fragments. 


Fig. 3 – Rank abundance (Whittaker) plots for insectivorous bats in three size classes of forest and continuous forest in the 

Krau landscape. Species are ranked according to the abundance of each species (n) and the total abundance of all species for 

each forest class (N). Species codes are in Appendix 2. 

also significantly nested when assessed separately (observed 

T = 20.00, expected T = 36.81 ± 5.23, p < 0.001), and the rank order 

of fragments exhibited a similar negative relationship 

with fragment area (r s = 0.693, p = 0.001). Cave-roosting bats 

exhibited nested subsets, but were not as strongly nested as 

other groups of bats (observed T = 17.22, expected 

T = 34.06 ± 6.54, p < 0.01), and no significant correlation between 

fragment rank and area was evident (rs = 0.261, 

p = 0.174). No nested pattern was evident for edge/open space 

foraging bats (observed T = 14.69, expected T = 21.20 ± 6.71, 

p = 0.216), and no correlation was observed between the 

nested rank order of fragments and either measure of isolation 

for any of the matrices (p > 0.1). Hence, nested analyses 

suggested that bat assemblages in smaller fragments were 

subsets of those in larger fragments, that this was more evident 

for tree cavity/foliage-roosting and cave-roosting narrow-space 

bats, and that fragment area rather than 

isolation played a causal role in this nested structure. 

4. Discussion 

We recorded diverse insectivorous bat assemblages and 

found evidence that fragmentation has negative effects on 

bat abundance, species richness and assemblage composition. 

Overall bats responded to changes in fragment area, 

but not isolation, and assemblages in small fragments were 

nested subsets of those in large fragments. Moreover, tree


cavity/foliage-roosting species appeared more susceptible to 

fragmentation than cave-roosting species or edge/open foraging 

species. 

We found that fragment area, but not fragment isolation, 

influenced assemblage-level abundance, species richness, 

composition and nestedness rankings of bat species, in agreement 

with the majority of fragmentation studies (Watling and 

Donnelly, 2006). At the level of individual species, we also 

found that the abundance of four species (Hipposideros cervinus, 

H. larvatus, K. intermedia and K. papillosa) responded positively 

to increases in fragment area; however, contrary to 

expectations, the abundance of two of these (H. cervinus and 

H. larvatus) also increased with greater isolation distance from 

continuous forest. Given that both of these species roost in 

caves, we suggest that this response was an artefact of the 

distribution of caves; several large cave systems are known 

in the east and west of the Krau landscape, but only a few 

small caves are known near the continuous forest sites, and 

none of these support large bat populations. Thus it is likely 

that, at least for some cave-roosting species, their roosts are 

simply of sufficient distance from continuous forest for bats 

not to be recorded in great numbers at the sites we studied. 

This notion is also supported by the greater abundance rankings 

of the cave-roosting species H. larvatus, Rhinolophus affinis 

and R. lepidus in fragments compared to continuous forest 

(Fig. 3). 

Our inability to detect a clear effect of fragment isolation 

on bat assemblage structure might reflect the comparatively 

limited distribution in values of isolation distance compared 

to area in the study (see Watling and Donnelly, 2006). However, 

we also suspect that the lack of an impact of isolation 

is real, and is attributable to several aspects of the study area 

and focal species. First, the Krau landscape is characterised 

by a low level of matrix contrast. The structural contrast between 

fragments and matrix determines the extent to which 

animals can move across fragment boundaries, and is highly 

dependent on a species’ vagility and its perception of habitat 

(see Ewers and Didham, 2006). In an example of extreme matrix 

contrast, Meyer and Kalko (in press) studied land-bridge 

islands in Panama and found that island (fragment) isolation, 

rather than area, was linked to patterns of nestedness in bat 

assemblages. In Krau, the more hospitable matrix consisting 

of plantations and village gardens is likely to be more easily 

traversed by bats. Indeed, the tolerance of a species to different 

habitats defines its effective isolation, which might differ 

from that described by Euclidean distances, and so further 

complicate our ability to detect isolation effects (Ricketts, 

2001). 

The extent to which isolation impacts assemblage structure 

in fragments will also be influenced by the history of 

the landscape. Fragmentation is usually an ongoing process, 

and although this process began in the Krau landscape ca. 

50 years ago, it has been much more recently that most 

changes have occurred. In addition, the rate of fragmentation 

has been much faster in some areas than others. Thus, there 

might be a delay in the realisation of isolation effects, with 

current patterns dominated by the effects of area, which will 

reflect both the prey base available to insectivorous bats, as 

well as viable roosting opportunities for tree cavity/foliage 

roosting species. 

Perhaps the most obvious explanation for a lack of isolation 

effect is that the bats studied are sufficiently vagile to 

cover distances between the fragments. However, we advocate 

caution in drawing this conclusion for several reasons, 

not least because while empirical studies demonstrate that 

some of the bat species recorded are highly mobile, the vagility 

of several groups appears to be more limited. Indeed, edge/ 

open space foraging bats, together with several species in our 

cave-roosting class, may be able to commute between forest 

patches, as well as utilise matrix habitats. The relatively 

small, long and narrow wings characteristic of these species 

(Kingston et al., 2003) result in high wing loading and high aspect 

ratios, which have been linked to fast energy-efficient 

flight (Norberg and Rayner, 1987). Radio-tracking studies have 

shown that cave-roosting rhinolophid and hipposiderid species 

can commute several kilometres in a single night (e.g. 

H. speoris, Pavey et al., 2001; R. hipposideros, Bontadina et al., 

2002), and recapture data from our study (Table 1) confirm 

that dispersal distances can exceed the distances between 

some fragments. In contrast, bat species in our tree cavity/foliage 

roosting class appear to be more restricted to areas 

around available roosts in forest fragments. These species 

are characterised by low wing loading and low aspect ratios 

(Kingston et al., 2003), associated with slow, manoeuvrable 

but energetically expensive flight that is suited to clutter but 

poorly adapted to long distances (Norberg and Rayner, 1987). 

This prediction is well supported by banding records from 

the Krau Wildlife Reserve; of 3900 bat recaptures, none of 

the recapture distances for tree cavity/foliage roosting species 

exceeded 1 km (Sujarno-Kudus, 2006). Moreover, radiotracking 

studies of four of these species (Kerivoula papillosa, Hipposideros 

ridleyi, Rhinolophus sedulus and R. trifoliatus) have 

revealed that home ranges are limited to < 100 ha and do 

not extend beyond forest boundaries (Allen, 2005; Fletcher, 

2006). In light of such empirical evidence, we speculate that 

the combined effects of low matrix contrast, heterogeneous 

fragmentation rates and variation in bat species vagility are 

likely to have ameliorated the impacts of isolation for at least 

some of our study species. 

Area-dependent relationships with species richness, nestedness 

and assemblage structure suggest that in our study 

area large tracts of forest are needed to conserve intact bat 

assemblages. Nonetheless, the greatest differences in assemblage 

structure were among the smallest fragments, and 

many of the medium- and large-sized fragments (>300 ha) 

retained substantial bat diversity, in come cases equalling 

or even exceeding that of continuous forest sites. In fact, 

additive partitioning revealed that almost a third of bat species 

richness at the landscape level was generated by diversity 

between sites of different size classes (i.e. b2), which 

was a similar contribution to that of species richness from 

within sites (i.e. a). Although this suggests that smaller fragments 

have substantial value for bat diversity when considered 

together at the landscape level, again, there are several 

reasons to be cautious. First, most sites were highly dominated 

by common species that were relatively mobile; the 

greatest component of Simpson diversity (i.e. species dominance) 

was attributed to individual sites, and species abundance 

distributions in fragments indicated that dominant 

bats were frequently cave-roosting species. Second, species

predicted to be vagile contributed most to the differences in 

assemblage composition between small fragments. In particular, 

there was no nested pattern for edge/open space foraging 

bats, the nestedness of cave-roosting bats could not be 

predicted by fragment area, and some small fragments were 

seen to host edge/open space foraging species that were not 

recorded elsewhere during the study (Hesperotenus blanfordi 

and Scotophilus kuhlii). Third, our analyses have not fully accounted 

for the rare specialist bat species known to occur 

in peninsular Malaysia, which are likely to be at a greater 

risk of extinction than common generalists (Davies et al., 

2004). Although 11 of the 46 species we captured are IUCN 

red-listed (Appendix 2), these were typically found in larger 

fragments and continuous forest. Despite a large cumulative 

sampling effort over the landscape, six species were only 

found in continuous forest; three of these (Coelops robinsoni, 

Harpiocephalus mordax and Murina rozendaali) are red-listed, 

and one (Kerivoula krauensis) has only recently been described, 

is currently considered endemic to Krau Wildlife Reserve, 

and has not yet been assessed by the IUCN. Finally, 

because of the recent history of fragmentation in some parts 

of the Krau landscape, crowding effects are likely to be substantial 

over the short term (see Ewers and Didham, 2006). 

Hence, many small and isolated fragments may still owe 

an extinction debt (Tilman et al., 1994), at least for the few 

cavity/foliage roosting species that currently persist in them, 

and the long-term responses of bat assemblages to fragmentation 

in Malaysia may yet be realised. 

This study represents one of the first to date of bats and 

fragmentation in the Palaeotropics, with the vast majority of 

previous tropical fragmentation research having been undertaken 

in the Neotropics. Comparisons between our results 

and those of other studies of bats and fragmentation are 

not only complicated by the fundamental differences of bat 

assemblage composition between the Neo- and Palaeotropics, 

but also by fragmentation history. Studies in naturally fragmented 

landscapes in the Neotropics suggest that fragmentation 

has had limited impact on bat assemblages, with species 

richness and composition remaining similar between fragments 

and continuous forest (Montiel et al., 2006; Bernard 

and Fenton, 2007). However, in historical examples of fragmentation, 

processes are likely to have selected for species 

traits that confer resistance to habitat change (Balmford, 

1996), and hence patterns in these landscapes do not necessarily 

predict those that arise from more rapid human-induced 

fragmentation. In some cases, subtle impacts on 

assemblage structure have often been detected in these situations, 

and researchers have suggested characteristics that 

may influence the resilience of bat species to fragmentation. 

In Guatemala, for example, the most abundant bats in fragments 

were found to be typically large frugivores (Schulze 

et al., 2000), while in French Guiana they were large frugivores 

that were also canopy specialists (Cosson et al., 1999). In a 

study of insectivorous bats in Australia, resilient species appeared 

to be fast flying, poorly manoeuvrable species (Law 

et al., 1999)ßand in a detailed study of land-bridge islands in 

Panama, edge-sensitivity was suggested to be the key influence 

on vulnerability (Meyer et al., 2008). However, single 

traits are often poor predictors of species sensitivity to frag- 


mentation, and profiles describing groups of traits that may 

act synergistically may be more accurate (Davies et al., 2004; 

Henle et al., 2004). In this regard, our study suggests that multiple 

traits correlated with roosting ecology (e.g. vagility, population 

size, foraging behaviour, see Altringham, 1999) might 

have important roles in determining the differential responses 

of bat species to fragmentation. 

4.1. Conservation implications 

The ability of bats to fly calls into question whether these animals 

are a poor model group to infer the impacts of land-use 

change, or on which to base landscape management policies. 

Despite this, few empirical studies have compared the responses 

of bats to land-use changes with different taxa within 

the same landscape. The exceptions, based exclusively in 

the Neotropics, have demonstrated that different animal 

groups vary in their response to land-use change, and that 

the response of bat assemblages is not shared by other taxa, 

which typically are more heavily affected (Pineda et al., 

2005; Barlow et al., 2007; Gardner et al., 2008). In fact, Amazonian 

bat assemblages were similar amongst secondary forests 

and plantations, a response that exhibited the poorest congruence 

with other groups of vertebrates, invertebrates and 

plants (Gardner et al., 2008), and which was related to their 

high vagility (Barlow et al., 2007). However, this finding might 

have arisen because analyses were conducted on all bat species 

at the assemblage level. Our study suggests that, in the 

Palaeotropics at least, not all bat species are as mobile as 

might be perceived, and that partitioning assemblage analyses 

based on foraging or roosting strategies may improve 

our ability to detect responses to land-use changes. Studies 

of bats and other animal groups in the same disturbed Palaeotropical 

landscapes are needed to elucidate how the responses 

of tree cavity/foliage roosting bats compare to those 

of other taxa. 

Our study supports the view that larger fragments contain 

more species, with assemblages resembling those in ‘intact’ 

natural habitats. Therefore, conservation strategies in Palaeotropical 

landscapes should favour large areas of forest, at 

least for conserving bat populations. Large fragments in the 

Krau landscape are currently managed for timber production 

as part of the Permanent Forest Estate, echoing trends elsewhere 

in Malaysia; they are therefore likely to remain in the 

landscape given their size and economic value. Small and 

medium sized fragments, however, are typically afforded 

low conservation and economic status and their long-term 

fate rests in the hands of plantation managers, local landowners 

and the state government. Our study suggests that 

fragments > 300 ha can support considerable bat diversity. In 

addition, although small fragments do not appear to support 

the rare, specialist species of most conservation concern, they 

contribute substantially to landscape-level bat diversity, and 

may also facilitate the movements of some species across 

managed landscapes. By comparison, a preliminary study of 

bat diversity in rubber and oil palm plantations suggests that 

these plantation types host a much more depauperate bat 

fauna compared to forest (Danielsen and Heegaard, 1995). 

With demonstrated predation impacts on arthropod popula-


tions in agricultural areas (Williams-Guillén et al., 2008), 

insectivorous bats have an ecosystem value that could benefit 

plantation managers. Hence, protecting large tracts of forest, 

while retaining some forest fragments in plantations should 

form an integral part of landscape planning, and has the potential 

to both benefit plantation management and bat 

conservation. 

Acknowledgements 

We are grateful to Christoph Meyer and an anonymous reviewer 

for critical suggestions that greatly improved the 

manuscript. Thanks to the Economic Planning Unit of the 

Malaysian Government for granting us permission to conduct 

bat research in Malaysia, and the Malaysian Department 

of Wildlife and National Parks (DWNP), the Pahang 

Appendix 1 

State Forestry Department, the Federal Land Development 

Authority (FELDA), and numerous private landowners for 

allowing us access to research sites. Thanks also to Paul 

Banks, Monika Bo _zek, Christine Fletcher, Joanne Kelly, Lee- 

Sim Lim, Juliana Senawi, Rakhmad Sujarno Kudus, Anthony 

Turner and Zamiza Zainal for assistance with fieldwork, and 

to Richard Nichols and Philippa Lincoln for statistical advice. 

Research in fragments was funded by a PhD studentship 

awarded to MJS from the Natural Environment Research 

Council UK, and a grant from Bat Conservation International/US 

Forest Service. Research in Krau Wildlife Reserve 

was supported by grants to TK from Lubee Bat Conservancy, 

National Science Foundation (NSF # 0108384, DEB & East Asia 

and Pacific Program), Earthwatch Institute, and National 

Geographic (Committee for Research & Exploration; Conservation 

Trust). 

Fragment and continuous forest sites visited in the Krau landscape, peninsular Malaysia, between May 2002 and June 2007, with 

a summary of bat survey results 

Site name Surrounding 

land-use a 

F, Fragment; S, Continuous forest (KWR) 

Area (ha) Isolation 

(km) b 

Nearest 

forest (km) c 

Trap nights N d 

F01 Kemasul 1: 3°230 N, 102°110 E A, O 2883 7.7 0.6 28 137 16 

F02 Kemasul 2: 3°260 N, 102°080 E A, O 11 339 7.5 1.2 40 220 19 

F03 RTP Lembah Klau: 3°420 N, 101°580 E O, R 100 7.4 2.3 27 90 12 

F04 h 

FELDA Jenderak: 3°370 N, 102°190 E O 1838 2.5 1.9 27 97 14 

F05 Bukit Besar: 3°220 N,102°150 E A 551 18.1 1.2 14 18 5 

F06 Klau Kecil: 3°470 N, 101°530 E R, O, G 443 3.7 1.4 38 120 20 

F07 g 

Gunung Senyum: 3°410 N, 102°270 E O 1356 12.3 0.6 15 483 15 

F08 Paya Luas: 3°420 N, 102°190 E O, R, G 353 2.1 1.0 16 44 11 

F09 Paya Parit: 3°410 N, 102°230 E R, O, G 31 5.0 0.6 15 43 11 

F10 Hutan Kerdau: 3°390 N, 102°250 E R, O, G 319 8.1 0.6 29 93 16 

F11 Ulu Rugan: 3°360 N, 102°200 E O, R 122 8.8 1.7 15 23 5 

F12 h 

Dato’ Shariff: 3°400 N, 102°230 E C, O 161 6.9 0.4 14 30 10 

F13 f 

Kampung Gun: 3°330 N, 101°580 E O 44 3.0 1.3 9 11 5 

F14 h 

Kampung Lebu: 3°380 N, 101°560 E O, G 400 7.4 2.3 21 85 17 

F15 Rumpun Makmur: 3°430 N, 102°230 E R, G 160 4.6 1.8 14 219 20 

F16 Tebing Tinggi: 3°510 N, 102°230 E C, G, R 93 5.5 0.6 16 33 10 

F17 Bukit Dinding: 3°490 N, 102°240 E R, G, O 32 6.3 0.5 22 75 19 

F18 Bukit Ketupat: 3°480 N, 102°240 E R, G 115 6.6 0.3 17 42 13 

F19 f,h 

Paya Perak: 3°360 N, 102°260 E R, G 100 13.4 0.7 14 35 9 

F20 g 

Batu Sawar: 3°390 N, 102°280 E O 300 14.7 0.7 13 202 8 

F21 Belungu: 3°440 N, 102°330 E O, R 5225 11.0 1.8 22 107 16 

F22 Desa Bakti: 3°480 N, 102°280 E A, P 107 5.6 2.1 16 43 12 

F23 h 

Klau Besar: 3°750 N, 101°890 E O, R 5581 5.5 0.7 17 358 19 

F24 Jengka: 3°590 N, 102°470 E O, R 2025 7.6 0.6 19 105 13 

F25 Jambu Rias: 3°450 N, 102°100 E O, R 32 4.6 1.2 16 20 7 

F26 Karak: 3°410 N, 102°050 E O, R 35 3.6 1.1 8 122 12 

F27 Tasek Chatin: 3°470 N, 102°350 E R 3 10.8 0.6 5 2 2 

S01 Kuala Lompat: 3°430 N, 102°170 E F 137 000 – – 330 998 29 

S02 Lubuk Baung: 3°430 N, 102°130 E F 137 000 – – 356 1491 28 

S03 Kuala Serloh: 3°400 N, 102°100 E F 137 000 – – 355 614 30 

S04 Kuala Gandah: 3°360 N, 102°090 E F 137 000 – – 356 1194 25 

Sobs e

Appendix 1 (continued) 

Site name Surrounding 

land-use a 

F, Fragment; S, Continuous forest (KWR) 

Appendix 2 

Bat species surveyed in the Krau landscape during this study 

FAMILY/Taxon Species 

code 


Red list 

status a 

Landscape 

distribution b 

Area (ha) Isolation 

(km) b 

Ensemble c 

Nearest 

forest (km) c 

No. continuous 

sites occupied(Nmax =7) d 

Trap nights N d 

S05 Jenderak Selatan: 3°38 0 N, 102°17 0 E F 137 000 – – 370 1766 27 

S06 Lembah Klau: 3°42 0 N, 102°03 0 E F, G 137 000 – – 39 821 15 

S07 Perlok: 3°49 0 N, 102°13 0 E F, G 137 000 – – 24 604 17 

a Land-use surrounding the site in order of increasing area. A, Acacia plantation; O, oil palm plantation; P, pine plantation; R, rubber plantation; 

C, cleared land; F, forest; G, mixed gardens. 

b The nearest straight-line distance to continuous forest. 

c The straight-line distance to the nearest forest fragment. 

d Total number of individuals captured at a site, including recaptures from other sites, but excluding those within a site. 

e Observed species richness for all insectivorous bat species. 

f Sites in which surveys were influenced by heavy rain. 

g The Gunung Senyum fragment (F07) contains an outcrop of karst limestone with very large abundances of cave-roosting bats, which skewed 

analyses for both this fragment and the nearest neighbour Batu Sawar (F20). Hence, these fragments were excluded from subsequent 

analyses on these grounds. 

h Fragments of reduced area since 2002 due to recent or current forest clearance. Area estimates are corrected based on observations on the 

ground and inspection of local forest maps if available. 

No. fragments 

occupied(Nmax= 27) d 

MEGADERMATIDAE 

Megaderma spasma Msp R T 5 1 

NYCTERIDAE 

Nycteris tragata Ntr R T 7 7 

EMBALLONURIDAE 

Emballonura monticola Emo R E 5 2 

RHINOLOPHIDAE 

Rhinolophus affinis Raf W C 7 22 

Rhinolophus lepidus e 

Rle W C 7 22 

Rhinolophus luctus Rlu R T 3 6 

Rhinolophus macrotis Rma R T 1 2 

Rhinolophus robinsoni Rro R C 4 2 

Rhinolophus sedulus Rse NT T 5 13 

Rhinolophus stheno Rst C 7 17 

Rhinolophus trifoliatus Rtr W T 6 23 

HIPPOSIDERIDAE 

Coelops robinsoni Cro NT A T 4 0 

Hipposideros armiger Har A C 1 0 

Hipposideros bicolor 131 f 

Hb31 C 7 18 

Hipposideros bicolor 142 f 

Hb42 C 7 13 

Hipposideros cervinus Hce C 7 19 

Hipposideros cineraceus Hci R C 4 5 

Hipposideros diadema Hdi C 7 17 

Hipposideros doriae e 

Hdo NT R T 4 5 

Hipposideros galeritus Hga R C 1 3 

(continued on next page) 

Sobs e


Appendix 2 (continued) 

FAMILY/Taxon Species 

code 

REFERENCES 

Red list 

status a 

Landscape 

distribution b 

Allen, D. 2005. Foraging ecology of Kerivoula papillosa. MSc thesis, 

University of East Anglia, Norwich. 

Altringham, J.D., 1999. Bats: Biology and Behaviour. Oxford 

University Press, Oxford. 

Baayan, R.H., 2008. The languageR package for R: data sets and 

functions with ‘‘Analyzing linguistic data: A practical 

introduction to statistics’’, version 0.92, . 

Ensemble c 

No. continuous 

sites occupied(Nmax =7) d 

No. fragments 

occupied(Nmax= 27) d 

Hipposideros larvatus Hla C 3 13 

Hipposideros lylei Hly NT F C 0 1 

Hipposideros ridleyi Hri VU T 5 7 

VESPERTILIONIDAE 

Glischropus tylopus Gty E 0 8 

Harpiocephalus mordax Hmo VU A T 2 0 

Hesperoptenus blanfordi Hbl F E 0 1 

Kerivoula hardwickii Kha R T 2 1 

Kerivoula intermedia Kin NT T 7 13 

Kerivoula krauensis e 

Kkr nc A T 4 0 

Kerivoula minuta Kmi NT T 1 1 

Kerivoula papillosa Kpa T 7 19 

Kerivoula pellucida Kpe T 7 15 

Miniopterus medius/schreibersii g Msc NT E 0 6 

Murina aenea Mae VU R T 3 1 

Murina cyclotis Mcy T 7 4 

Murina rozendaali Mro VU A T 4 0 

Murina suilla Msu W T 6 23 

Myotis ater Mat E 1 8 

Myotis horsefieldi Mho E 3 1 

Myotis ridleyi Mri R E 5 4 

Myotis siligorensis Msi A E 1 0 

Phoniscus atrox Pat NT R T 6 8 

Phoniscus jagorii Pja R T 1 2 

Scotophilus kuhlii Sku F E 0 1 

Tylopus pachypus Tpa R E 1 2 

Tylopus robustula Tro R E 1 0 

a IUCN red list status following review by the Southeast Asian Mammal Databank (http://www.ieaitaly.org/samd/. Accessed 31 March 2008): NT, 

Near Threatened; VU, Vulnerable; nc, not yet classified. 

b Landscape distribution of species based on presence and abundance at sites in continuous and fragmented forest. W, widespread, present in 

at least 70% of fragments, and all continuous forest sites; R, rare, comprise < 1% of combined captures from all sites; A, absent from 

fragments; and F, absent from continuous forest. 

c T, cavity/foliage-roosting narrow-space species; C, cave-roosting narrow-space species; E, edge/open space foraging species. 

d The number of sites occupied does not control for the different trapping effort applied to each site. Therefore absence at a site does not 

necessarily reflect true absence. 

e Kingston et al. (2003) included R. lepidus as R. refulgens, H. doriae as H. sabanus, and K. krauensis as K.sp. We follow the updated nomenclature in: 

Simmons (2005) Chiroptera, In: Wilson DE, Reeder DM (eds) Mammal species of the World: a taxonomic and geographic reference. John 

Hopkins University Press, Baltimore, pp 312-529, and Francis, C.M., Kingston, T., Zubaid, A., 2007. A new species of Kerivoula (Chiroptera: 

Vespertilionidae) from peninsular Malaysia. Acta Chiropterologica 9, 1–12. 

f Hipposideros ‘bicolor’ comprises two phonic types with mean echolocation call frequencies of 131 kHz and 142 kHz. They differ genetically 

enough to warrant treatment as separate taxa and can be distinguished in the hand by subtle differences in noseleaf shape, forearm length 

and tibia length (Kingston et al., 2006). 

g Miniopterus individuals captured could not be reliably identified by external measurements to medius or schreibersii. The red list status is Least 

Concern for M. medius, and Near Threatened for M. schreibersii. 

Balmford, A., 1996. Extinction filters and current resilience: the 

significance of past selection pressures for conservation 

biology. Trends in Ecology and Evolution 11, 193–196. 

Barclay, R.M.R., Harder, L.D., 2003. Life histories of bats: life in the 

slow lane. In: Kunz, T.H., Brock-Fenton, M. (Eds.), Bat Ecology. 

University of Chicago Press, Chicago, pp. 209–256. 

Barlow, J., Gardner, T.A., Araujo, I.S., Avila-Pires, T.C., Bonaldo, 

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