Grain-dependent responses of mammalian diversity to land-use and the implications for

conservation set-aside

Oliver R. Wearn1,2*, Chris Carbone2, J. Marcus Rowcliffe2, Henry Bernard3, Robert M. Ewers1

1Department of Life Sciences, Imperial College London, Silwood Park, Ascot SL5 7PY, UK

2Institute of Zoology, Zoological Society of London, Regent’s Park, London NW1 4RY, UK

3Institute for Tropical Biology and Conservation, Universiti Malaysia Sabah, Jalan UMS, 88400

Kota Kinabalu, Sabah, Malaysia

*Corresponding author: Wearn, O. R. (oliver.wearn@gmail.com)

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Abstract

Diversity responses to land-use change are poorly understood at local scales, hindering our

ability to make forecasts and management recommendations at scales which are practical. A key

barrier in this has been the under-appreciation of grain-dependent diversity responses and the

role that β-diversity – the variation in community composition across space – plays in this.

Decisions about the most effective spatial arrangement of conservation set-aside, for example

High Conservation Value areas, have also neglected β-diversity, despite its role in determining

the complementarity of sites. We examinedlocal-scale mammalian species richness and β-

diversity across old-growth forest, logged forest and oil palm plantations in Borneo, using

intensive camera- and live-trapping. For the first time, we were able to investigate diversity

responses, as well as β-diversity, at multiple spatial grains, and across the whole terrestrial

mammal community (both large and small mammals). β-diversity was quantified by comparing

observed β-diversity with that obtained under a null model, in order to control for sampling

design effects, and we refer to this measure as the β-diversity signal. Community responses to

land-use were grain-dependent, with large mammals showing reduced richness in logged forest

compared to old-growth forest at the grain of individual sampling points, but no change at the

overall land-use level. Responses varied with species group, however, with small mammals

increasing in richness at all grains in logged forest compared to old-growth forest. Both species

groups were significantly depauperate in oil palm. Large mammal communities in old-growth

forest became more heterogeneous at coarser spatial grains and small mammal communities

became more homogeneous, whilst this pattern was reversed in logged forest. Both groups,

however, showed a significant β-diversity signal at the finest grain in logged forest, likely due to

logging-induced environmental heterogeneity. The β-diversity signal in oil palm was weak, but

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heterogeneity at the coarsest spatial grain was still evident, likely due to variation in landscape

forest cover. Our findings suggest that the most effective spatial arrangement of set-aside will

involve trade-offs between conserving large and small mammals. Greater consideration in the

conservation and management of tropical landscapes needs to be given to β-diversity at a range

of spatial grains.

Key words: land-use change, species richness, β-diversity, spatial grain, environmental

heterogeneity, mammals, selective logging, oil palm agriculture, Borneo, camera-trapping

Introduction

It is widely acknowledged that global biodiversity is in decline, primarily due to unprecedented

rates of habitat loss and degradation (Hansen et al. 2013). Many attempts have been made to

quantify this biodiversity loss due to land-use change at coarse scales and forecast losses into the

future (Sodhi et al. 2004; Koh & Ghazoul 2010; Wearn et al. 2012), with the aim of informing

policy-making at the highest administrative levels. In reality, biodiversity loss at coarse scales is

a summation of the changes occurring at the local scale of landscapes, such as forestry

concessions or private landholdings. Local stakeholders often make management decisions that

have substantial impact on the outcomes for biodiversity in these landscapes. At this local scale,

however, there is little consensus about the community responses to land-use, which hinders our

ability to make management recommendations and biodiversity forecasts at scales relevant to

local stakeholders.

Much confusion surrounding local-scale biodiversity responses has arisen due to an under-

appreciation of spatial grain (Sax & Gaines 2003). At the smallest scales (e.g. those of a quadrat

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or plot), species richness has been shown to be stable (Dornelas et al. 2014) or even increasing in

post-disturbance areas (Vellend et al. 2013). On the other hand, a number of other meta-analyses

focussing on overtly disturbed areas, and which did not account for spatial grain, have shown the

contrasting result of declines in species richness (Dunn 2004; Gibson et al. 2011; Burivalova et

al. 2014). It is difficult to completely reconcile these two apparently conflicting findings, and

make firm conclusions with respect to local-scale biodiversity responses, due to the lumping

together of studies using vastly different spatial grains. For example, in a review of past studies,

Hill & Hamer (2004) found that the effects of disturbance on Lepidoptera and birds were

strongly grain-dependent. Specifically, in response to disturbance, Lepidoptera richness often

increased at small scales (< 1 ha) and decreased at intermediate scales (1 – 25 ha), while bird

richness also decreased at intermediate scales but then increased at still larger scales (> 25 ha).

Although consideration of spatial grain has largely been neglected in global meta-analyses, it

offers the potential of uniting seemingly contradictory results and allowing better forecasting of

biodiversity changes at the local-scale. An essential component in this framework will be a better

understanding of community variance, or β-diversity, which is an emergent property of a set of

communities and is itself generated by processes such as dispersal limitation and habitat filtering.

Importantly, the β-diversity present among communities largely determines the relationship

between spatial grain and richness (Scheiner 2004). Indeed, changes in β-diversity can

potentially explain how, in response to land-use change, species richness might remain constant

or even increase at the level of a sampling point, yet decline at the level of a study site.

β-diversity patterns are important in systematic conservation planning, as they determine the

complementarity of communities across sites (Ferrier 2002). This also applies, at smaller scales,

to decisions about how to allocate conservation set-aside. Major certification schemes, including

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those of the Forest Stewardship Council (FSC), Round-table on Responsible Soy (RTRS) and

Round-table on Sustainable Palm Oil (RSPO), require concession holders to identify and set-

aside forest patches with High Conservation Value (HCV), but do so without explicit

consideration of local-scale β-diversity. β-diversity is a crucial determinant of the conservation

values, such as the number of species, ultimately conserved within a concession’s set-aside

patches, and should play an important role in management decisions about the spatial distribution

of patches and how large each patch should be (Nekola & White 2002). This is relevant in the

context of the expansion of both cropland and tree plantations into forested landscapes, which is

ongoing at a rapid rate (Wilcove et al. 2013), and of the increasing uptake of sustainability

principles by logging companies, as required under certification schemes such as the FSC, but

also more broadly under the banner of retention forestry (Lindenmayer et al. 2012).

Selective logging is the main driver of tropical forest degradation worldwide (Asner et al. 2009)

and, by modifying the structure (Cannon et al. 1994), resources (Johns 1988) and micro-climate

(Hardwick et al. 2015) of forests through space, may act as a strong environmental filter on the

occurrence patterns of species post-logging. Only a handful of studies have investigated β-

diversity in logged forests, mostly focussing on arthropods, but these support the notion that

environmental heterogeneity in logged forests increases β-diversity (Hill & Hamer 2004; Berry

et al. 2008; Woodcock et al. 2011, but see Kitching et al. 2013). Plantation habitats, by contrast,

may be more homogeneous in space than natural forest, not only in terms of floral species

composition, but also in terms of structure, resources and micro-climate (Scales & Marsden

2008). This may be true of oil palm (Elaeis guineensis) plantations (Luskin & Potts 2011), which

are expanding across the tropics at a rapid rate, particularly in Southeast Asia (Wilcove et al.

2013).

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Across taxa, β-diversity may vary depending on dispersal capacity, as well as the typical home-

range sizes of individuals: all else being equal, poor dispersers with small home-ranges will both

be more dispersal-limited and less able to buffer spatial variation in habitat quality, leading to

higher β-diversity. Soininen et al. (2007) found evidence across past studies that larger-bodied

organisms, which have higher dispersal capacity and larger home-ranges, generally exhibited

lower levels of β-diversity. Despite the expected differences among taxa, few studies have

explored this at the local scale using data collected simultaneously on multiple species groups at

the same spatial locations (but see: Kessler et al. 2009; Gossner et al. 2013).

The primary aim of our study was to quantify the species richness and β-diversity of mammal

communities across a land-use gradient and investigate whether diversity responses to land-use

were dependent on spatial grain. In doing so, we used robust estimators and comparisons with

null models to control for the specific properties of our sampling design. As a secondary aim, we

also investigated differences in richness and β-diversity among large and small mammals across

a range of spatial grains. We chose mammals as our focus due to the fact that they are a high-

profile group that are often the targets of policy and land-use decisions, and are often given

strong weighting in conservation planning, especially the HCV assessment process.

We made three specific hypotheses with regard to β-diversity. We hypothesised that logged

forest areas would be more environmentally heterogeneous than old-growth forest, therefore

giving rise to higher levels of β-diversity (HI), whilst oil palm would be environmentally

homogeneous, giving rise to lower levels of β-diversity (HII).. We also hypothesised that small

mammals (< 1 kg) would be more dispersal-limited than large mammals, owing to their smaller

body size, and less able to buffer fine-grained variation in habitat quality (HIII). We therefore

expected small mammals to exhibit greater levels of β-diversity than large mammals. To address

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these hypotheses, we gathered one of the most comprehensive datasets on local-scale mammal

occurrence from the tropics that we are aware of, using multiple sampling methods to

incorporate nearly the entire non-volant community, from the smallest murid rodents (~0.03 kg)

up to the Asian elephant Elephas maximus (~2700 kg). Our findings with respect to the

importance of spatial grain and β-diversity have important implications for the conservation and

management of biodiversity in these systems and, in particular, with regard to optimal designs

for conservation set-aside.

Methods

Study Sites and Sampling Design

We sampled mammals in three different land-uses, taking advantage of the experimental design

of the Stability of Altered Forest Ecosystems (SAFE) Project in Sabah, Malaysian Borneo

(Ewers et al. 2011). This consists of old-growth forest within the Maliau Basin Conservation

Area, repeatedly-logged forest within the Kalabakan Forest Reserve and two adjacent oil palm

plantations straddling the Kalabakan Forest Reserve boundary (Supporting Information).

We used a hierarchical nested sampling design in order to explore β-diversity at three different

spatial grains (Fig. 1). We based this on the fractal sampling design of the SAFE Project (Ewers

et al. 2011), which is an especially efficient design for quantifying β-diversity (Marsh & Ewers

2012). At the lowest level in the hierarchy were individual sampling points. These were clustered

into rectangular sampling grids, which we call here plots, of (4 x 12 =) 48 points separated by

23m (covering an area of 1.75 ha). In turn, 3 to 6 plots were clustered together into blocks

(covering an average minimum convex polygon of 25 ha; range: 24.1 – 25.4), and there were 3 to

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4 blocks per land-use (Fig. 1). These were arranged differently in the logged forest compared to

the other two land-uses (Fig. 1), in order to overlay the locations of future experimental

fragments (Ewers et al. 2011), but separation distances between plots (170 to 290 m) and

between blocks (0.6 to 3 km) were similar across the land-uses. The spatial arrangement of

sampling points at the SAFE Project has been deliberately designed to minimise confounding

factors across the land-use gradient, including latitude, slope and elevation (Ewers et al. 2011),

and this applied equally to our sampling design for mammals.

Mammal Sampling

Small mammal trapping was conducted at the level of the plot, with a session consisting of seven

consecutive days. Two locally-made steel-mesh traps (18 cm wide, 10-13 cm tall and 28 cm in

length), baited with oil palm fruit, were placed at or near ground level (0 - 1.5 m) within 10 m of

each of the 48 grid points. Traps were checked each morning and captured individuals were

anaesthetised using diethyl ether, measured, permanently marked using a subcutaneous passive

inductive transponder tag (Francis Scientific Instruments, Cambridge, UK), identified to species

using Payne et al. (2007) and released at the capture location. Trapping was carried out between

May 2011 and March 2014, during which there were no major mast-fruiting events. Some plots

(8 of 31) were sampled more than once over this period (mean effort per plot = 925 trap nights).

We deployed camera traps (Reconyx HC500, Holmen, Wisconsin, USA) at a random subset of

grid points within plots (mean points sampled per plot = 13), setting the cameras as close to the

points as possible and strictly within 5 m. The deployment of cameras randomly with respect to

space has rarely been used before, though is essential for revealing species-specific patterns of

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space-use (Wearn et al. 2013), which is a contributor towards β-diversity. Cameras were fixed to

trees or wooden poles, or placed within locally-made steel security cases in areas of high human

traffic, with the camera sensors positioned at a height to maximise detection for a range of

species (most often 30 cm, though this was flexible depending on the terrain encountered at each

location). No bait or lure was used and disturbance to vegetation was kept to a minimum.

Camera traps were active between May 2011 and April 2014, during which most plots (39 of 42)

were sampled for multiple sessions (mean effort per plot = 625 trap nights). We were able to

sample more plots using camera traps (n = 42) than we could using small mammal traps (n = 31),

owing to the lower labour demands in the former case.

In total, 543 points were camera-trapped and 1,488 points were live-trapped, and we used these

datasets for estimating large and small mammal species richness, respectively. Both trapping

protocols were used at 430 points and we used only this subset of the data for the β-diversity

analyses. This subset included data from 31 plots nested in 8 blocks (9 plots in 3 blocks for old-

growth forest; 16 plots in 3 blocks for logged forest, and 6 plots in 2 blocks for oil palm)..

Data Analysis

All analyses were ultimately derived from the separate community matrices from live-trapping

and camera-trapping, with trap nights forming rows of the matrices, species forming the columns

and each cell containing the number of independent capture events. Unlike live traps, camera

traps are continuous-time detectors, so we considered photographic capture events to be

independent if they a) contained unambiguously different individuals or b) were separated by >

12 hours, which matches the approximate minimum separation between live trap events.

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Our hierarchical sampling design allowed us to partition species richness and β-diversity into

multiple spatial grains across the three land-uses, by aggregating the community matrices to the

appropriate grain. However, unequal levels of effort, replication and sample completeness across

spatial grains and across land-uses makes comparisons of richness and β-diversity problematic,

an issue that has often been neglected in past studies (Beck et al. 2013).

For species richness, there are non-parametric estimators which can be used to make richness

values more robust to sampling design variation. We used the Abundance-based Coverage

Estimator (ACE) to estimate overall richness in each land-use, because we were confident that

sufficient sampling had been done to estimate the minimum asymptotic richness (Gotelli & Chao

2013), whilst we standardised point richness to 90% sample coverage (Colwell et al. 2012). We

hereafter refer to overall richness in each land-use and point richness as γ-diversity and α-

diversity, respectively. For both γ- and α-diversity, we used the full camera trap and live trap

datasets to make estimates for large and small mammals, respectively. We modelled the α-

diversity of either large or small mammals as a function of land-use using a Poisson generalised

linear mixed-effects model, with the hierarchical sampling design specified in the random effects

(points nested within plots, in turn nested within blocks), as well as a point-level random effect

to account for overdispersion. We also made estimates of γ- and α-diversity across large and

small mammals for the subset of points which had been sampled using both live traps and

camera traps. In this case, we were able to model α-diversity as a function of both land-use and

species group (large or small mammal), as well as their interaction.

Commonly used metrics of β-diversity are also sensitive to the specific sampling design

employed (Supporting Information). Instead of using β-diversity values directly, we compared

observations with an appropriate null model, an approach which has been underexploited to date

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(Lessard et al. 2012). Differences from the null model, calculated using simple subtraction

(βobserved – βnull), can be interpreted as a measure of β-diversity due to non-random community

assembly processes (including those of intraspecific aggregation, environmental filtering and

dispersal limitation), over and above that due to the vagaries of the sampling process itself. We

refer to this difference between observed and null β-diversity as the β-diversity signal (as

opposed to the random β-diversity noise). Observed β-diversities were calculated using Lande’s

(1996) additive formulation, in which β-diversity at a given level, i, in a hierarchy is the average

richness at the given level substracted from that in the level above: βi = αi+1 – αi. This was done

for each combination of land-use (old-growth forest, logged forest and oil palm) and species

group (small mammals, large mammals or both combined), for each of three spatial grains:

points (camera detection zone = 0.02 ha), plots (1.75 ha) and blocks (25 ha). It follows from

Lande’s (1996) additive diversity partitioning that overall observed γ-diversity of each land-use

is: αpoint + βpoint + βplot + βblock. We used additive partitioning because β-diversity is in units of

species richness in this framework, which means differences from null models are also in units of

species richness, allowing more straightforward comparisons between land-uses, between

species groups and between hierarchical levels.

To estimate null β-diversities, we used null models based on the sample-based randomisations of

Crist et al. (2003). For each spatial grain i in the hierarchy, we randomly shuffled (without

replacement) the community samples at the level below (i – 1), whilst constraining the random

placements to maintain the integrity of any higher-level (i + 1) spatial nesting. For example, null

β-diversity for the plot level was derived by randomly shuffling point-level communities

amongst plots, but only amongst plots within the same block. By constraining the null model in

this way, we were able to test for differences from null at the specific spatial grain of interest.

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We extended this to the case of multiple sampling methods, by keeping the matrices derived

from live-trapping and camera-trapping separate and conducting the randomisations in parallel,

mimicking how the data were generated. This also allowed us to specifically control for the

different sampling efforts achieved during live-trapping and camera-trapping.

By repeating the randomisation process, we obtained distributions of differences from the null.

We calculated the 95% quantiles of these distributions and deemed differences to be significant

if the quantiles did not overlap zero. Sample size necessarily declines at the higher spatial levels

of a fractal sampling design, causing a loss in the precision of β-diversity estimates (Marsh &

Ewers 2012). This was also true of our null model approach, because we had fewer community

samples to shuffle at higher levels. We used 1000 randomisations in all cases, except for our oil

palm sampling design, in which there were few possible combinations of placing plots within

blocks. In this case we restricted the number of randomisations to the number of combinations (n

= 40).

We modelled the differences from null using linear mixed-effects models in order to explore

differences across land-use, across spatial grains and across the two species groups. Since β-

diversity at a given hierarchical level is, in the additive framework, the mean of the number of

“missing” species in each sample (species which are absent from a sample but present at the

level above), we took advantage of this by extracting the un-averaged number of missing species

for each sample. We calculated the difference from null for each of these values and accounted

for the lack of independence between values by specifying the hierarchical sampling design in

the random effects structure. Point-level values were nested within plots and blocks, whilst plot-

level values were nested within blocks. For the block-level model, no random effects were

specified because this was the highest level in the hierarchy.

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Finally, using the approach outlined by Baselga (2010), we differentiated between the two broad

proximate causes of β-diversity – species turnover and nestedness (see Supporting Information

for more information) – to investigate which was primarily responsible for β-diversity at each

spatial grain in the three land-uses. Species turnover (the replacement of some species by others)

can be calculated using a multiple-site generalization of the Simpson index, whilst the β-diversity

generated by nestedness (variation in species richness without turnover) can be calculated by

subtracting the Simpson measure from the total β-diversity, as measured using a multiple-site

Sørensen index (i.e. βnestedness = βSørensen - βSimpson). Given the dependence on sample size of these

measures, we calculated them over 100 random sub-samples of our data (Baselga 2010a), taking

the minimum sample sizes at each hierarchical level across the whole dataset each time (8 points

per plot, 3 plots per block and 2 blocks per land-use). This would still not enable fair

comparisons across spatial grains, so we calculated values as a proportion of the total β-diversity,

as given by the Sørensen index (Baselga 2010a). We modelled the proportion of β-diversity in

the nestedness component using beta regression models with a log link and constant dispersion

parameter, constructing separate models with land-use, species group or spatial grain as the

explanatory variable. We used the combined live trap and camera trap dataset for this analysis,

removing 12 points which had been camera-trapped for less than 7 days.

All analyses were done in R version 3.1.0 (R Core Team 2014), using the additional packages

vegan 2.0-10 (Oksanen et al. 2013), iNEXT 1.0 (Hsieh 2013), lme4 1.1-6 (Bates et al. 2014) and

betareg 3.0-5 (Cribari-Neto & Zeileis 2010).

Results

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Live-trapping resulted in a total of 4,046 captures of 25 species (over 28,681 trap nights), whilst

camera-trapping resulted in a total of 12,788 independent captures of 58 species (over 26,251

trap nights). This gave a total of 65 mammal species (Supporting Information), of which 19

species were captured using both protocols. Over the points sampled using both live traps and

camera traps (n = 430), we obtained 11,579 captures of 61 species (over a combined effort of

27,176 trap nights).

Species accumulation curves in each land-use closely approached asymptotes (Supporting

Information), all with an estimated sample coverage > 98%. Logged forest had the highest

observed and estimated mammal γ-diversity, though the 95% confidence intervals overlapped

with those for old-growth forest (Fig. 2). Of the 44 species found in old-growth forest, 38 species

(86%) were also detected in the logged habitats. Oil palm plantations were a significantly

depauperate habitat (Fig. 2), harbouring just 22 of the 63 species (35%) found in the forest

habitats, in addition to the invasive domestic dog (Canis familiaris) and plantain squirrel

(Callosciurus notatus). Three of these species were recorded only within a 200 m wide margin of

forest-scrub habitat connected to a 45 km2 block of logged forest, meaning that just 19 forest

species (31%) were found in the oil palm crop itself.

The overall γ-diversity differences between land-uses were in large part due to the small

mammals. Observed and estimated large mammal γ-diversities were very similar for old-growth

and logged habitats (Fig. 2) and, for the full camera trap dataset, the 95% confidence intervals

for oil palm overlap, albeit slightly, with those of old-growth forest (Supporting Information). In

contrast, small mammal estimated γ-diversity was significantly different among all three land-

use contrasts, except for a slight overlap in 95% confidence intervals between old-growth forest

and oil palm in the combined live trap and camera trap dataset (Fig. 2; Supporting Information).

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Mixed-effects models of α-diversity (standardised to 90% sample coverage) indicated a

significant effect of land-use, for both small mammals from the live trap data (χ2(2) = 119, p <

0.0001) and large mammals from the camera trap data (χ2(2) = 21.7, p < 0.0001). As with γ-

diversity, large and small mammals showed markedly different responses for α-diversity (Fig. 2),

which resulted in a significant interaction term between land-use and species group using the

combined live trap and camera trap dataset (χ2(2) = 251, p < 0.0001). In this model, logged forest

had a significantly higher α-diversity than old-growth forest for small mammals (3.7 times

higher, z = 14.2, p < 0.0001) and a significantly lower α-diversity for large mammals (24%

lower, z = -2.51, p = 0.01). This difference between the two forest habitats was also significant

for small mammal α-diversity with the full live trap dataset (5 times higher in logged forest, z =

6.76, p < 0.0001), but was not significant for large mammals when the full camera trap dataset

was used (19% lower in logged forest, z = -1.54, p = 0.12). Oil palm was, again, highly

depauperate compared to the forest habitats (either with or without the points in the forest-scrub

boundary; Table S2), and this difference was significant for both small mammals from the live

trap data (compared to old-growth forest: z = 4.61, p < 0.0001) and large mammals from the

camera trap data (compared to logged forest: z = -3.47, p < 0.01).

Diversity partitioning suggested that the majority of the γ-diversity was contained in the β-

diversity components (Fig. 2): 83% in old-growth forest and 84% in both logged forest and oil

palm. The percentages for each of the spatial grains also appear broadly similar for overall

mammal diversity (Fig. 2): 38%, 38% and 30% as βpoint-diversity; 20%, 25% and 27% as βplot-

diversity, and 25%, 20% and 28% as βblock-diversity for old-growth forest, logged forest and oil

palm, respectively. However, the proportion of diversity contained within the β components

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across land-use, especially βplot and βblock, is markedly different for large and small mammals (Fig.

2).

Null model comparisons demonstrated that most community samples had a significant signal of

non-random assembly processes (as evidenced by 95% confidence intervals which did not

overlap zero; Supporting Information). In old-growth forest, the β-diversity signal at large spatial

grains was increasingly strong for large mammals and increasingly weak for small mammals,

whilst this pattern was reversed in logged forest (Fig. 3). The β-diversity signal in oil palm was

found to be much lower overall, due in part to the depauperate nature of the mammal community

that exists there, especially for small mammals. However the β-diversity signal for large

mammals in oil palm was still comparable at the point level to that found in old-growth forest,

and did not decline at the block level as it did in logged forest (Fig. 3).

Mixed-effects models of βpoint differences from null showed significant differences among the

land-uses (χ2(2) = 7.70, p = 0.02) and among the species groups (χ2

(1) = 13.94, p < 0.001). These

significant differences were due to: larger differences from null in old-growth compared to

logged habitats (showing support for HI); smaller differences from null in oil palm (showing

support for HII), and the consistently higher differences from null, irrespective of land-use, for

large mammals (showing no support for HIII). The interaction between land-use and species

group was not significant at this spatial grain (χ2(2) = 3.31, p = 0.19). There were no consistent

differences in βplot or βblock departures from null, either between land-uses (plot-level: χ2(2) = 0.87,

p = 0.65; block-level: F(2, 10) = 0.30, p = 0.75) or species groups (plot-level: χ2(1) = 0.92, p = 0.34;

block-level: F(1, 10) = 1.17, p = 0.30), showing no support at these spatial grains for any of HI to

HIII. The interaction terms in both models were also not significant (plot-level: χ2(2) = 0.28, p =

0.87; block-level: F(2, 10) = 0.63, p = 0.55).

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β-diversity was predominantly generated by species turnover rather than nestedness, with

turnover forming the larger component in all cases except for small mammals at the plot level in

oil palm and block level in logged forest (Fig. 4). Nestedness formed a larger component of β-

diversity for small mammals compared to large mammals (z = 2.09, p = 0.04). There was a trend

for nestedness to be more important in oil palm (compared to logged forest: z = 1.68, p = 0.093),

but no obvious patterns across spatial grains (χ2(2) = 2.28, p = 0.32).

Discussion

Our finding that the vast majority of old-growth species are retained in logged forest is in

agreement with the emerging consensus, from studies of a large variety of taxa, that logged

forest has substantial conservation value (Putz et al. 2012; Edwards et al. 2014). Logging

responses are strongly taxon- and continent-specific (Burivalova et al. 2014), and our study also

adds to a relatively small body of literature on Southeast Asian mammals, supporting the general

notion that large areas of logged forest in the region retain much of the terrestrial mammal

diversity of old-growth forest (Wells et al. 2007; Bernard et al. 2009; Brodie et al. 2015), despite

log extraction rates that may be an order of magnitude higher than on other continents (Putz et al.

2012).

Whilst supporting this general notion, our study also offers a more comprehensive assessment of

mammal community responses to logging than has been possible before. For the first time, we

were able to examine mammal diversity responses at multiple spatial grains, and across the

whole terrestrial mammal community, including both large and small mammals. This revealed a

more nuanced view of community responses to logging: logged habitats had either a higher or

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lower richness of large mammals depending on spatial grain, whilst small mammals were richer

in logged forest across all spatial grains. Moreover, large mammal communities became more

heterogeneous at increasing spatial grains in old-growth forest but more homogeneous in logged

forest, whilst the reverse pattern was seen in small mammal communities.

Large mammal richness at small spatial grains was reduced by 19-24% in logged forest, even

though species richness at larger spatial grains was maintained. Similarly, Brodie et al. (2015)

found a reduction in large mammal richness of 11% at the sampling point level in recently-

logged (< 10 years) areas, similar to our logged areas (last logged 3 to 6 years before data

collection). Therefore, whilst logged forests in the region do appear to retain much of the

mammal γ-diversity of old-growth forest, logging may in fact be having subtle but pervasive

impacts on the diversity of mammals utilising resources within any given forest patch, with

unknown consequences for ecosystem functioning. We also note that many of the large mammal

species in our study are long-lived, and therefore there is the potential that a long-term extinction

debt remains to be paid off, with communities gradually “relaxing” to a lower equilibrium

richness in logged forest compared to old-growth forest.

Small mammals, on the other hand, appeared to respond positively to logging, which is

consistent with the broader literature from across the tropics (Isabirye-Basuta & Kasenene 1987;

Lambert et al. 2006). Small mammals may be resilient to logging due to their apparently high

dietary flexibility (Langham 1983; Munshi-South et al. 2007) and to the greater availability of

their preferred microhabitats post-logging (Cusack et al. 2015). Small mammal communities in

old-growth habitats are also likely constrained by supra-annual cycles of mast-fruiting in

dipterocarp forests (Curran & Leighton 2000), in contrast to more consistent food resources in

logged forests (Munshi-South et al. 2007).

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Oil palm mammal communities were highly depauperate for both large and small mammals at all

spatial grains, even when including non-native species and species occurring in plantation

margins. This finding agrees with studies of a range of other taxa (Foster et al. 2011), as well as a

small number of studies on mammals (Maddox et al. 2007; Bernard et al. 2009; Yue et al. 2015),

and underlines the grave threat to wildlife populations that oil palm expansion represents

(Wilcove et al. 2013). This is especially the case given that our results likely represent something

of a best-case scenario for oil palm biodiversity: plantations were in close proximity to a large

block of well-protected forest, riparian forest margins existed in the broader landscape and

hunting levels were relatively low (only three incidences of hunting activity were photographed

in 3,104 camera trap nights).

We hypothesised that that logged forest would be more environmentally heterogeneous than old-

growth forest, giving rise to higher β-diversity (HI). We found that the β-diversity signal was

more strongly evident in logged forests compared to the other land-uses consistently only at the

smallest grains, though small mammal communities showed a stronger β-diversity signal in

logged forest compared to the other land-uses at more coarse spatial grains as well. This appears

to match with the spatial grain of heterogeneity imparted by the logging process: felling of

individual dipterocarp trees usually creates initial canopy gaps of less than 600 m2 (Sist et al.

2003) and these gaps are mostly less than 10 m in length (i.e. 100 m2) after a decade or more of

regeneration (Bebber et al. 2002). In contrast, gaps are rare in old-growth forest, typically

occupying less than 1% of forest area (Sist et al. 2003). Other forms of disturbance – e.g. the

creation of skid-trails, roads and log landings – also impart heterogeneity at a more coarse grain

than the felling process, as does variation among logging compartments in the intensity of

extraction (Cannon et al. 1994). This variation may be by as much as an order of magnitude

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(Berry et al. 2008). For small mammals, which show strong preferences for specific

microhabitats (Cusack et al. 2015), this latter source of environmental heterogeneity may have

driven the strong signal of β-diversity we observed at larger spatial grains. Note, however, that

small mammal β-diversity at the block level was primarily driven by nestedness rather than

turnover in logged forest, which may suggest that the processes of local extinction and dispersal

limitation are also important at this scale. For large mammals, communities may not respond as

strongly to forest structure per se, and the greater homogeneity at coarse grains may reflect the

greater homogeneity of tree communities in logged forest at coarse grains, overwhelmingly

dominated by a single pioneer species, Macaranga pearsonii, in this forest.

We also hypothesised that oil palm would be environmentally homogeneous, giving rise to

lower β-diversity (HII). Oil palm communities, overall, were more homogeneous than forest

communities, but this was not consistently the case: large mammal communities at the block

level showed a stronger β-diversity signal in oil palm compared to logged forest. This was likely

due to the substantial differences in management practices between blocks – for example in the

year of planting and the extent of undergrowth clearance – and, perhaps more crucially, due to

differences in the proximity to forest across blocks. β-diversity in oil palm was also generated

comparatively more by nestedness than in the other land-uses.

Our final hypothesis was that small mammal communities would be more dispersal-limited than

large mammals, and would therefore show higher levels of β-diversity (HIII). Support for this

hypothesis was only found at the block level in logged forest and large mammals otherwise

showed a stronger signal of β-diversity. Given the greater dispersal abilities expected of larger

bodied mammals (Sutherland et al. 2000), this does not suggest a primary role for local-scale

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dispersal limitation in the assembly of communities in these systems, and niche-based assembly

may prevail.

Our findings have implications for the management and conservation of mammal biodiversity at

local scales. In the context of logging, our results point to the importance of spatial

heterogeneity, particularly at fine grains, in maintaining the diversity of mammal communities at

similar levels to old-growth forest. Small mammal diversity may also be increased by

heterogeneity in forest structure at larger spatial grains, but the high levels of nestedness at this

scale also suggests that populations could benefit from interventions to increase connectivity

amongst populations. For large mammals, heterogeneity in forest structure at larger spatial grains

was apparently less important, and the maintenance instead of floristically diverse areas of old-

growth forest may have greater benefits for large mammal diversity. In the context of plantation

landscapes, our findings point to the key role that the maintenance of heterogeneity could play in

improving biodiversity values, for example by deliberately varying the year of planting across

coupes within a concession and, more importantly, by retaining forested areas in the broader

landscape.

An understanding of β-diversity patterns is essential for the effective identification of HCV set-

aside in forest landscapes. In Southeast Asia, these forest landscapes are overwhelmingly

composed of logged and degraded forest (Margono et al. 2012; Bryan et al. 2013), and HCV

assessments are made in the context of re-entry logging under sustainable certification or

conversion to tree plantation. Typically ~10% or more of a concession may be considered for

set-aside (WWF-Malaysia, 2009), in patches of approximately 30 ha (Tawatao et al. 2014) or

more. Given this, our results suggest that the specific placement of set-aside for the conservation

of large mammal communities, which we have shown are homogeneous in logged forest at

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spatial grains < 30 ha, will be less critical and we would tentatively suggest an approach of

maximising the size of set-aside patches. Such patches, even when isolated from surrounding

natural forest, may have considerable value for mammals (McShea et al. 2009; Bernard et al.

2014). For small mammals on the other hand, logged forest communities showed substantial

heterogeneity at the scale of conservation set-aside (tens of hectares), which may favour a

distributed network of patches. Although the long-term viability of these meta-populations is

largely unknown, patches would ideally be connected, for example by riparian margins, and

positioned according to robust HCV baseline surveys. Trade-offs in the most effective spatial

arrangement of conservation areas often exist between different species groups (Schwenk &

Donovan 2011), and our findings for large and small mammals suggest that a diversified strategy

including a small number of large patches and a network of smaller stepping-stone patches

would be necessary for the conservation of both groups. These recommendations for large and

small mammals are supported by simulation studies, albeit of sessile taxa, of randomly-occurring

and aggregated species communities undergoing logging, in which a single large set-aside patch

was optimal for maximising yield and biodiversity in the case of homogeneous communities, but

multiple smaller reserves were favoured for aggregated communities (Potts & Vincent 2008).

We should underline that our results are relevant for set-aside at the local-scale, for example of a

single concession, and a different approach may be necessary at the regional scale of large forest

management units or other administrative regions.

We have shown that diversity responses are strongly grain-dependent and that patterns of β-

diversity at each spatial grain play a fundamental role in this. Better forecasting of local-scale

responses to land-use will require consideration of this grain-dependency. Our data also suggest

that management decisions taken at the local scale, including optimising the spatial arrangement

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of conservation set-aside, may be made more effective by considering patterns of β-diversity.

Given the increased uptake of sustainable forestry principles, in particular FSC, in the

management of logged forests in the region (Dennis et al. 2008), as well as rising membership of

the RSPO and other crop certification schemes (Edwards et al. 2012), it is now critical that the

scientific underpinnings of HCV are improved, and this should include consideration of β-

diversity at a range of spatial grains.

Acknowledgements

We are grateful to Yayasan Sabah, Benta Wawasan, Sabah Softwoods, the Sabah Forestry

Department and the Maliau Basin Management Committee for allowing access to field sites. We

thank the Royal Society South East Asia Rainforest Research Programme for supporting this

research, and in particular Glen Reynolds. Fieldwork was greatly aided by the logistical support

received from Edgar Turner, MinSheng Khoo, Johnny Larenus, Sarah Watson and Ryan Gray.

Data collection would not have been possible without the efforts of Leah Findlay, Jeremy

Cusack, Matiew bin Tarongak, James Loh, Matthew Holmes, Faye Thompson, Jack Thorley,

Jessica Haysom, Mohd Sabri bin Bationg, Aleks Warat Koban bin Lukas and all of the SAFE

Project field staff. We also thank two anonymous reviewers for their constructive criticism of a

draft manuscript. This research was conducted with the permission of the Economic Planning

Unit of Malaysia and Sabah Biodiversity Council. Full funding was provided by the Sime Darby

Foundation.

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Figure Legends

Figure 1. Sampling design across (a) old-growth forest, (b) oil palm and (c) logged forest used in

this study, illustrating the three spatial grains within each land-use: individual sampling points,

1.75 ha rectangular plots (consisting of clusters of points) and blocks (consisting of clusters of

plots). Blocks were arranged identically in old-growth forest and oil palm, and were arranged to

coincide with future experimental forest fragments in logged forest. Separation between points,

plots and blocks was nonetheless similar across land-uses. Shaded areas lie outside the

Kalabakan Forest Reserve, consisting of a 2,200 ha Virgin Jungle Reserve (Brantian-Tatulit) to

the south and an extensive (>1 million ha) area of logged forest to the north (Mount Louisa

Forest Reserve and connecting reserves). Insets show the location within insular Southeast Asia

and the spatial proximity of the three land-uses within southeast Sabah, Malaysian Borneo.

Figure 2. Diversity partitions for all mammals, large mammals and small mammals across a

gradient of land-uses, including observed values (± SD) at four spatial grains and estimated α-

and γ-diversities (± 95% CI). Estimates of α-diversity (standardised to 90% sample coverage) are

predictions from a mixed-effects model which accounted for the hierarchical nested sampling

design. Estimates of γ-diversity were calculated using the Abundance-based Coverage Estimator

(ACE). Observed β-diversity at a given spatial grain is the average richness at the given grain

subtracted from that in the grain above (for example, βplot = αblock – αplot). Only data from sampling

points which were both camera-trapped and live-trapped were used in this figure (see Supporting

Information for full results).

Figure 3. β-diversity differences from null models (± SE) with increasing spatial grain, for all

mammals, large mammals and small mammals. Panels show results across a gradient of land-

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uses. The horizontal line at y=0 represents the case of no difference between observed β-

diversity and expected β-diversity from null models. Dashed vertical lines show the three spatial

grains of β-diversity sampling within each land-use (points, plots and blocks). Smoothed lines

between data points are to aid interpretation. Overlapping data points have been spaced apart

slightly. See Supporting Information for 95% CIs.

Figure 4. Percentage of overall β-diversity generated by nestedness (variation in species richness

without species composition changes) and species turnover (changes in species composition)

across species groups and land-use types.

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Figures

Figure 1

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Figure 2

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Appendices

Appendix A: Detailed study site descriptions.

The Maliau Basin Conservation Area (1,054 km2 including the buffer zone), one of the last

remaining examples of lowland undisturbed habitat in the region (Reynolds et al. 2011),

represented our old-growth control site. The sampled area consisted mostly of pristine hill forest,

dominated by dipeterocarps including Shorea johorensis, Dryobalanops lanceolata and

Parashorea melaanonan. One-third of the sampled sites (lying within the buffer zone) were in a

water catchment that had been subjected to low levels of ironwood (Eusideroxylon zwageri)

extraction in the 1970s and 1990s; some old skid-trails were present in the area, though the

structure and community composition of the canopy and understorey were comparable to the

surrounding unlogged forest (Ewers et al. 2011).

Part of the Kalabakan Forest Reserve (2,240 km2), the SAFE Project experimental area (94 km2

including a Virgin Jungle Reserve) represented our logged forest site. This was connected to a

large (> 1 million ha) area of logged forest to the north and was otherwise surrounded by oil

palm plantations. Similar to our old-growth forest site, the SAFE Project experimental area was

composed of hill dipterocarp forest, but had been affected by multiple, intense rounds of

extraction, beginning in 1978 (Chong et al. 2005). Logging ended as recently as 2008, by which

time timber restrictions had been lifted in anticipation of future clearance, and a total of 179 m3

ha-1 had been removed from the area (Yayasan Sabah, unpublished data). This land-use history,

in combination with topographical constraints on access, means there was substantial spatial

variability in the intensity and timing of logging, as well as the methods used (tractor-based and

cable yarding), creating a highly heterogeneous forest landscape. There were few old-growth

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trees remaining, and pioneer species such as Macaranga pearsonii, M. hypoleuca and

Neolamarckia cadamba were dominant. Indeed, M. pearsonii alone formed ~10% of tree basal

area (SAFE Project, unpublished data). In addition, logging created a network of regenerating

skid trails, roads of varying width and heavily degraded log-landing areas, which means there

were also some areas of grassland and low scrub, often containing non-native shrubs including

Clidemia hirta and Chromolaena odorata.

Our oil palm sites were spread across two neighbouring plantation estates: Selangan Batu estate

(operated by Benta Wawasan Sendirian Berhad) and Mawang estate (operated by Sabah

Softwoods Sendirian Berhad). Oil palms within the Selangan Batu estate were mostly planted in

2006 and were 1-4 m in height, forming a discontinuous canopy. Oil palms within the Mawang

estate were mostly planted in 2000, with some trees up to 10 m in height and forming a

continuous canopy layer. Palms in both estates were planted with approximately 10 m

separation. Understorey communities within the plantations consisted of various grasses, ferns,

other herbs such as Ageratum conyzoides, and vines, including the highly invasive Mikania

cordata (T. Döbert, personal communication). Herbicide use in the plantations meant that the

area directly around each palm was bare in most cases, but there was otherwise substantial

variation in the extent of understorey growth. In the older plantations, where the canopy was

unbroken, much of the ground was bare except for the oil palm fronds which are cut during

harvesting and stacked between the rows of palms. Some small areas within the younger

plantations had been planted with seedlings of subsistence crops, or had been burned in

anticipation of doing so. Small riparian buffers of degraded logged forest existed in the broader

landscape, as well as a 45 km2 block of logged forest (managed by the Sabah Forestry

Department) immediately to the west of the sampling points. Interviews with the estate managers

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indicated that there were no active rodent control programmes operating in the plantations (W.

Lojinin, personal communication; R. Hussein, personal communication), with no recent use of

rodenticide or biocontrol by barn owls (Tyto alba javanica).

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Appendix B: Additional information on β-diversity quantification.

β-diversity patterns remain poorly characterised at least in part because of uncertainty

surrounding how to define β-diversity (Tuomisto 2010) and how best to measure it (Jost 2007;

Baselga 2010a; Chao et al. 2012; Legendre & De Cáceres 2013). β-diversity may be separated

into those components which vary due to sampling effects, including the effects of sampling

extent (Soininen et al. 2007b), grain (Mac Nally & Fleishman 2004; Steinbauer et al. 2012;

Olivier & Aarde 2014), replication (Crist & Veech 2006; Chao et al. 2012) and sample

completeness (Cardoso et al. 2009; Beck et al. 2013), and those components which vary

depending on the assembly of communities, including patterns of species abundance, occupancy,

co-occurrence and intraspecific aggregation (Veech et al. 2003; Veech 2005). It is these latter

components that are typically of interest to researchers.

In the context of diversity partitioning, there is the additional problem that β-diversity is

calculated using values for α- and γ-diversity, and as a result there has been a recurring debate

about whether β-diversity calculated in this way is truly independent (Jost 2007, 2010; Baselga

2010b; Ricotta 2010; Veech & Crist 2010a, 2010b). Chao et al. (2012) recently synthesised this

debate, showing conclusively that neither additive β-diversity (= γ-α) nor multiplicative β-

diversity (= γ/α) are free of this dependence, and recommended a normalisation to overcome this.

However, it remains unclear whether sampling effects on α- and γ-diversity are completely

controlled for using this normalisation.

The effects of the sampling process, as well as the size of regional species pools (Lessard et al.

2012), can be accounted for by comparing observations with a null model which specifically

includes these details (Crist et al. 2003; Kraft et al. 2011). Any differences between observations

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and the null model which remain are taken to be indicative of non-random processes that were

excluded from the null model, for example community assembly processes. This is the approach

we chose to use here (see Methods for more details).

β-diversity sensu lato includes community variance due both to the turnover of species and due

to variation in species richness independent of turnover, i.e. the nestedness of communities.

Baselga (2007, 2010a) and others (for a review see Legendre, 2014) have argued for a separation

of turnover and nestedness and for β-diversity sensu stricto to be measured independently of the

effects of nestedness. The predominance of turnover or nestedness in communities is related to

the assembly processes at work. For example, niche assembly and random community drift will

often be responsible for patterns of turnover at local scales, whilst differential dispersal

capacities and selective extinction are more likely to create nested communities across space.

The distinction between turnover and nestedness is particularly important in the context of

conservation set-aside; if β-diversity is driven by species turnover, a distributed network of set-

aside patches would be required to ensure representation of all species, whilst if β-diversity is

completely driven by nestedness patterns, the optimal solution would simply be to prioritise the

conservation of the most diverse forest patches. Following the approach of (Baselga 2010a), we

therefore separated observed β-diversity into its turnover and nestedness components (more

details on this approach are given in Methods).

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Appendix C: Species accumulation curves.

Figure C1. Sample-based species accumulation curves across land-use types, based on a

Bernoulli product model (Colwell et al. 2012). Only data from sampling points which were both

camera-trapped and live-trapped were used. Solid lines show interpolated values, whilst dashed

lines show extrapolated values. Filled circles show the observed γ-diversities.

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Appendix D: Additional results tables.

Table D1. Observed α-diversity and model estimates of standardised α-diversity across land-use

types.

Species group Land-use Dataset αobserveda αstandardised

b 95% CI

Mammals Old-growth forest

Locations both camera- and live-trapped 7.21 7.6 6.53 - 8.83

Logged forest Locations both camera- and live-trapped 8.19 9.29 7.68 - 11.24

Oil palm plantation

Locations both camera- and live-trapped 3.29 3.19 2.42 - 4.21

Oil palm cropc Locations both camera- and live-trapped within oil palm crop 3.22 3.16 2.39 - 4.17

Large mammals

Old-growth forest All camera trap locations 5.99 6.07 4.94 - 7.45

Logged forest All camera trap locations 4.94 4.91 3.75 - 6.42Oil palm plantation All camera trap locations 3 2.91 2.12 - 3.98

Oil palm cropc All camera trap locations within oil palm crop 2.97 2.91 2.12 - 3.99

Old-growth forest

Locations both camera- and live-trapped 5.99 5.95 5.04 - 7.02

Logged forest Locations both camera- and live-trapped 4.77 4.53 3.66 - 5.6

Oil palm plantation

Locations both camera- and live-trapped 3.13 2.96 2.19 - 3.99

Oil palm cropc Locations both camera- and live-trapped within oil palm crop 3.09 2.96 2.20 - 4.00

Small mammals

Old-growth forest All live trap locations 0.53 0.45 0.30 - 0.69

Logged forest All live trap locations 2.66 2.72 1.62 - 4.57Oil palm plantation All live trap locations 0.1 0.08 0.04 - 0.16

Oil palm cropc All live trap locations within oil palm crop 0.1 0.07 0.04 - 0.14

Old-growth forest

Locations both camera- and live-trapped 1.21 1.14 0.95 - 1.36

Logged forest Locations both camera- and live-trapped 3.42 5.34 4.32 - 6.61

Oil palm plantation

Locations both camera- and live-trapped 0.16 0.29 0.15 - 0.59

  Oil palm cropc Locations both camera- and live-trapped within oil palm crop 0.13 0.23 0.11 - 0.51

aMean observed α-diversity across sampling points

bStandardised to 90% sample coverage (Chao & Jost 2012). Estimates are from mixed-effects models and include shrinkage.cExcluding sampling locations within scrub habitat at the oil palm plantation margins

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Table D2. Observed and estimated γ-diversity across land-use types.

Species group Land-use Dataset γobserved γACEa 95% CI

Mammals Old-growth forest

Locations both camera- and live-trapped 42 46.7 42.0 - 52.7

Logged forest Locations both camera- and live-trapped 51 52.4 51.0 - 59.1

Oil palm plantation

Locations both camera- and live-trapped 21 33.9 28.2 - 39.5

Oil palm cropb Locations both camera- and live-trapped within oil palm crop 18 27.1 22.2 - 32.0

Large mammals

Old-growth forest All camera trap locations 30 31.8 30.0 - 36.8

Logged forest All camera trap locations 32 33.0 32.0 - 38.2Oil palm plantation All camera trap locations 18 27.8 22.6 - 32.9

Oil palm cropb All camera trap locations within oil palm crop 17 28.7 23.6 - 33.9

Old-growth forest

Locations both camera- and live-trapped 30 31.8 30.0 - 36.8

Logged forest Locations both camera- and live-trapped 30 30.3 30.0 - 35.4

Oil palm plantation

Locations both camera- and live-trapped 17 23.7 18.9 - 28.5

Oil palm cropb Locations both camera- and live-trapped within oil palm crop 16 24.5 19.7 - 29.4

Small mammals

Old-growth forest All live trap locations 11 13.2 11.0 - 16.6

Logged forest All live trap locations 21 21.5 21.0 - 25.5Oil palm plantation All live trap locations 4 4.8 4.0 - 6.8

Oil palm cropb All live trap locations within oil palm crop 2c

Old-growth forest

Locations both camera- and live-trapped 12 16.7 13.0 - 20.3

Logged forest Locations both camera- and live-trapped 21 22.2 21.0 - 26.4

Oil palm plantation

Locations both camera- and live-trapped 4 12.8 9.7 - 15.8

  Oil palm cropb Locations both camera- and live-trapped within oil palm crop 2 3.1 2.0 - 4.6

aEstimates of minimum asymptotic richness calculated using the Abundance-based Coverage Estimator (ACE). See Gotelli & Chao (2013).bExcluding sampling locations within scrub habitat at the oil palm plantation marginscInsufficient data to estimate asymptotic minimum richness.

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Table D3. β-diversity differences from null models across land-use types and spatial grains.

Species group Land-useSpatial grain

β-diversity difference from null 95% CIa

MammalsOld-growth forest βpoint 1.54 1.40 - 1.69

βplot 1.64 1.06 - 2.49βblock 1.41 0.49 - 2.82

Logged forest βpoint 2.26 2.12 - 2.41βplot 1.45 0.76 - 2.08βblock 1.75 0.50 - 2.84

Oil palm βpoint 0.82 0.64 - 0.99βplot 0.60 0.20 - 1.04βblock 0.50 -0.01 - 0.99

Large mammalsOld-growth forest βpoint 0.67 0.60 - 0.75

βplot 0.58 0.16 - 1.05βblock -0.17 -0.67 - 0.33

Logged forest βpoint 0.86 0.77 - 0.96βplot 0.63 0.26 - 1.01βblock 1.40 0.48 - 2.14

Oil palm βpoint -0.01 -0.01 - 0.01βplot -0.06 -0.11 - 0.05βblock 0b 0.00 - 0.00

Small mammalsOld-growth forest βpoint 0.87 0.75 - 1.00

βplot 1.05 0.66 - 1.55βblock 1.51 0.68 - 3.01

Logged forest βpoint 1.39 1.28 - 1.50βplot 0.78 0.32 - 1.32βblock 0.34 -0.65 - 1.35

Oil palm βpoint 0.83 0.68 - 0.99βplot 0.65 0.12 - 1.12

βblock 0.52 0.04 - 1.04aConfidence intervals calculated as the quantiles of the distribution of difference from null values across simulations.bAll null simulations returned the same β-diversity as the observed case, likely caused by a sparse dataset in this case.

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Appendix E: Mammal species list.

Table E1. Order, scientific name and common name of mammal species detected by camera-

trapping and live-trapping surveys in the Maliau Basin Conservation Area (including the Maliau

Basin Buffer Zone Forest Reserve), Kalabakan Forest Reserve and nearby oil palm plantations

(Selangan Batu and Mawang estates).

Species Common name Old-growth forest

Logged forest

Oil palmErinaceomorpha

Echinosorex gymnura Moon rat Pholidota

Manis javanica Sunda pangolin Carnivora

Prionailurus bengalensis Leopard cat Pardofelis badia Bay cat Pardofelis marmorata Marbled cat Neofelis diardi Clouded leopard Diplogale hosei Hose's civet Hemigalus derbyanus Banded civet Paguma larvata Masked palm civet Paradoxurus hermaphroditus

Common palm civet Arctictis binturong Binturong Viverra tangalunga Malay civet Prionodon linsang Banded linsang Herpestes semitorquatus Collared mongoose Herpestes brachyurus Short-tailed mongoose Canis familiaris Domestic dog Helarctos malayanus Sun bear *Mydaus javanensis Sunda stink badger Martes flavigula Yellow-throated marten Mustela nudipes Malay weasel Aonyx cinerea Oriental small-clawed otter

CetartiodactylaSus barbatus Bearded pig Tragulus napu Greater mouse-deer Tragulus kanchil Lesser mouse-deer Muntiacus atherodes Bornean yellow muntjac Muntiacus muntjac Red muntjac Rusa unicolor Sambar deer Bos javanicus Banteng

ScandentiaPtilocercus lowii Pen-tailed treeshrew Tupaia minor Lesser treeshrew Tupaia gracilis Slender treeshrew Tupaia longipes Plain treeshrew Tupaia tana Large treeshrew *Tupaia dorsalis Striped treeshrew

PrimatesCephalopachus bancanus Western tarsier Presbytis rubicunda Maroon langur

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Macaca fascicularis Long-tailed macaque Macaca nemestrina Southern pig-tailed

macaque

Hylobates muelleri Bornean gibbon Pongo pygmaeus Orangutan

RodentiaLariscus hosei Four-striped ground

squirrel

Callosciurus prevostii Prevost's squirrel Callosciurus notatus Plantain squirrel *Callosciurus adamsi Ear-spot squirrel Exilisciurus exilis Least pygmy squirrel Sundasciurus lowi Low's squirrel Sundasciurus tenuis Slender squirrel Sundasciurus brookei Brooke's squirrel Sundasciurus hippurus Horse-tailed squirrel Rheithrosciurus macrotis Tufted ground squirrel Aeromys thomasi Thomas's flying squirrel Trichys fasciculata Long-tailed porcupine Hystrix brachyura Malay porcupine Hystrix crassispinis Thick-spined porcupine Leopoldamys sabanus Long-tailed giant rat Sundamys muelleri Müller's rat Niviventer cremoriventer Dark-tailed tree rat Maxomys whiteheadi Whitehead's rat Maxomys surifer Red spiny rat *Maxomys rajah Brown spiny rat Maxomys baedon Small spiny rat Maxomys ochraceiventer Chestnut-bellied spiny rat Rattus exulans Polynesian rat Rattus rattus Black rat

ProboscideaElephas maximus Asian elephant

*Records from scrub habitat at the oil palm plantation margins and not inside the oil palm crop itself

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Literature cited in the Appendices

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