Ecological niche modelling as a technique for

Diversity and Distributions, (Diversity Distrib.) (2009) 15, 289–298
Blackwell Publishing Ltd
BIODIVERSITY
RESEARCH
Ecological niche modelling as a technique
for assessing threats and setting
conservation priorities for Asian slow
lorises (Primates: Nycticebus)
J. S. Thorn*, V. Nijman, D. Smith and K. A. I. Nekaris
Nocturnal Primate Research Group, School of
Social Sciences and Law, Oxford Brookes
University, Oxford OX3 0BP, UK
ABSTRACT
Aim Data on geographical ranges are essential when defining the conservation
status of a species, and in evaluating levels of human disturbance. Where locality data
are deficient, presence-only ecological niche modelling (ENM) can provide insights
into a species’ potential distribution, and can aid in conservation planning. Presenceonly ENM is especially important for rare, cryptic and nocturnal species, where
absence is difficult to define. Here we applied ENM to carry out an anthropogenic
risk assessment and set conservation priorities for three threatened species of Asian
slow loris (Primates: Nycticebus).
Location Borneo, Java and Sumatra, Southeast Asia.
Methods Distribution models were built using maximum entropy (MaxEnt)
ENM. We input 20 environmental variables comprising temperature, precipitation
and altitude, along with species locality data. We clipped predicted distributions to
forest cover and altitudinal data to generate remnant distributions. These were then
applied to protected area (PA) and human land-use data, using specific criteria to
define low-, medium- or high-risk areas. These data were analysed to pinpoint priority
study sites, suitable reintroduction zones and protected area extensions.
Results A jackknife validation method indicated highly significant models for all
three species with small sample sizes (n = 10 to 23 occurrences). The distribution
models represented high habitat suitability within each species’ geographical range.
High-risk areas were most prevalent for the Javan slow loris (Nycticebus javanicus)
on Java, with the highest proportion of low-risk areas for the Bornean slow loris (N.
menagensis) on Borneo. Eighteen PA extensions and 23 priority survey sites were
identified across the study region.
*Correspondence: J. S. Thorn, Nocturnal Primate
Research Group, School of Social Sciences and
Law, Oxford Brookes University, Oxford OX3
0BP, UK. E-mail: [email protected]
Main conclusions Discriminating areas of high habitat suitability lays the foundations for planning field studies and conservation initiatives. This study highlights
potential reintroduction zones that will minimize anthropogenic threats to animals
that are released. These data reiterate the conclusion of previous research, showing MaxEnt is a viable technique for modelling species distributions with small sample sizes.
Keywords
Lorisidae, MaxEnt, pet trade, protected areas, risk assessment, Sundaland.
Restricted spatial distribution patterns in correlation with
anthropogenic disturbance can be used to define species rarity
(Rabinowitz, 1981; Kattan, 1992; Gaston, 1997). The geographical
extent of a species will continue to shrink as a result of ongoing
habitat loss and anthropogenic pressures (Cowlishaw & Dunbar,
2000). Data on geographical ranges are therefore of paramount
importance when delineating the conservation status of a species
(Anderson & Martinez-Meyer, 2004; Hernandez et al., 2006) and
in evaluating current levels of threat and protection (Fuller et al.,
2006). Applying ecological niche modelling (ENM) can provide
insights into a species’ potential distribution when sufficient
survey data do not exist, and can aid in conservation planning
© 2008 The Authors
Journal compilation © 2008 Blackwell Publishing Ltd
DOI: 10.1111/j.1472-4642.2008.00535.x
www.blackwellpublishing.com/ddi
INTRODUCTION
289
J. S. Thorn et al.
(Guisan & Zimmermann, 2000; Loiselle et al., 2003) by highlighting unknown populations (Pearson et al., 2007), suitable
sites for reintroduction (Hernandez et al., 2006), key areas for
fieldwork (Papes & Gaubert, 2007) and in improving assessment
of risk status (Solano & Feria, 2007).
Where data are deficient on a species’ geographical distribution, conservationists often rely on its presence within and
around protected area (PA) networks in order to assess its
conservation status. Species persistence in these areas may,
however, be heavily affected by extrinsic factors such as habitat
loss and over exploitation (Lande, 1998). Many studies highlight
the necessity of incorporating ecological risk assessments
into quantifying vulnerability to human disturbance and
conservation planning (Williams, 1998; Araujo & Williams, 2000;
Harwood, 2000; Villa & Mcleod, 2002; Wilson et al., 2005;
Solano & Feria, 2007). Measuring the level of threat posed to the
ecological viability of an area by factors such as agricultural
encroachment, proximity to human settlements, road building
or hunting pressure is necessary for developing conservation
strategies for species inhabiting human dominated landscapes
(Oates, 1986; Eudey, 1987).
We utilized MaxEnt ENM software (Phillips et al., 2006) to
carry out such a study on a group of cryptic, little-known
primates, the Asian slow lorises (Nycticebus). Although other
modelling algorithms have been more widely tested with
vertebrate species, recent research has demonstrated greater
modelling accuracy with MaxEnt and limited datasets (i.e. < 25
localities) (Hamel et al., 2006; Hernandez et al., 2006; Phillips
et al., 2006; Pearson et al., 2007). MaxEnt is a general purpose
method for generating predictions or inferences from
incomplete information (Phillips et al., 2006). Probabilities of
occurrence are generated across the entire study region based on
the environmental conditions in areas where the species have
been observed. MaxEnt requires presence-only data, using a
random selection of background pixels from the study area as
pseudo-absences (Phillips et al., 2006). When modelling species
distribution, inferring absences with any amount of certainty can
be problematic (Sutherland, 2000; Hirzel et al., 2002; Brotons
et al., 2004; Elith et al., 2006; Phillips et al., 2006; Gibson et al.,
2007), particularly when the taxa are rare or cryptic, or biotic or
human-induced factors prevent colonization of all suitable
habitat areas (Hirzel et al., 2001; Engler et al., 2004). Absence can
more easily be determined if the species is conspicuous and easily
identified (Sutherland, 2000).
Slow lorises are cryptic and nocturnal primates that, to date,
have been little-studied in the wild (Nekaris & Bearder, 2007),
including few data on their precise geographical ranges. Low
encounter rates of Nycticebus (Nekaris & Nijman, 2007a; Nekaris
et al., 2008) confirm the inherent difficulties in determining
absence for this genus. Presence-only modelling is therefore
highly suitable to generate urgently needed data on their
distribution. Already listed as Vulnerable (VU a2cd) or
Endangered (EN a2cd) (IUCN, 2006), recent studies show
greater taxonomic diversity and variable ecology (Groves, 2001;
Roos, 2003; Brandon-Jones et al., 2004; Chen et al., 2006;
Nekaris et al., 2006; Nekaris & Bearder, 2007; Nekaris & Jaffe,
290
2007; Groves & Maryanto, 2008), urging a more quantitative
analysis of their distribution and threats. In addition to habitat loss
plaguing all wildlife in southeast Asia, Nycticebus spp. in particular
are threatened by unsustainable harvesting for the pet trade and
traditional medicines (Nekaris & Bearder, 2007), even resulting
in their elevation to Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora
(CITES, 2007; Nekaris & Nijman, 2007b). It is essential that
organizations involved in the management of Nycticebus are able
to identify suitable study areas, extensions of the PA network and
sites for reintroduction of individuals confiscated from the illegal
wildlife trade (Schulze & Groves, 2004). ENM can play a pivotal
role in making these recommendations.
Our study focused on modelling the distribution of three
threatened species of Nycticebus: on Borneo the Bornean slow
loris (N. menagensis), on Java the Javan slow loris (N. javanicus),
and on Sumatra the greater slow loris (N. coucang) (~8.00° N–
9.00° S, 94.00° E–120.00° E). We conducted an anthropogenic risk
assessment in order to pinpoint distributional regions that are
undergoing varying levels of human-induced threat. Finally, we
used data from the MaxEnt predictions and anthropogenic risk
assessment to set conservation priorities for Nycticebus.
METHODS
Modelling data
We compiled locality data for the three study species using zoological collections, published literature and communication with
researchers. Zoological collections were accessed through the
online databases of the Mammal Networked Information System
(MaNIS) and the Global Biodiversity Information Facility (GBIF).
We also visited collections at the Natural History Museum, London,
the Natural History Museum, Oxford, Naturalis, Leiden, and
the Zoological Museum, Amsterdam. A questionnaire and species
photo sheet were emailed to researchers to gather data on the
occurrence of and perceived threats to Nycticebus. A total of
147 independent localities for Nycticebus were collected from the
above sources, with 48 of these localities selected for use in the
final modelling process. Selection was based on exclusion of
localities that were now outside of suitable habitat regions, i.e.
areas completely denuded of forest. We also aimed to generate
spatial independence between localities and to lessen the effects
of spatial autocorrelation present at low sample sizes (Hernandez
et al., 2006; Phillips et al., 2006; Pearson et al., 2007). Where
localities fell within 10 km of each other, only one locality from
the cluster was selected to represent species presence in that area.
Twenty environmental layers comprising temperature, precipitation and altitude were utilized (see Appendix S1 in Supporting
Information). We extracted eleven temperature and eight
precipitation variables from the WorldClim database (Hijmans
et al., 2005). The set of bioclimatic variables available are derived
from monthly temperature and rainfall values to produce more
ecologically relevant layers on a grid resolution of ~1 km2.
Elevation data were derived from the digital elevation model
(DEM) GTOPO30 (US Geological Survey, 1996). All environmental
© 2008 The Authors
Diversity and Distributions, 15, 289–298, Journal compilation © 2008 Blackwell Publishing Ltd
ENM and slow loris conservation
variables were resampled to the WGS84 geographical coordinate
system at a resolution of ~1 km2 (0.008333 decimal degrees).
Ecological niche modelling
Potential distributions of N. menagensis, N. javanicus and
N. coucang were predicted by generating statistically significant
models of occurrence based on their ecological niche
requirements. MaxEnt software version 3.1.0 (Computer
Sciences Department – Princeton University, 2004) was utilized
for this purpose. Species occurrence data and environmental
variables were entered into MaxEnt and predicted distributions
projected using ArcGIS 9.2. The distribution of each study
species was modelled separately, with the study region divided
into three subregions. Fifteen spatially independent localities on
Sumatra (N. coucang), 10 on Java (N. javanicus) and 23 on
Borneo (N. menagensis) were utilized.
As MaxEnt generates a cumulative probability distribution
output between 0 and 100%, we selected a decision threshold to
distinguish presence from absence, enabling validation and
visual interpretation of model predictions (Liu et al., 2005;
Pearson et al., 2007). Binary maps of presence or absence were
then created based on this threshold value. A lowest presence
threshold (LPT) (Pearson et al., 2007) was used, thus maintaining
zero omission error in the model localities, and also representing
areas of habitat that are at least as suitable as those where the
species has been observed (Hernandez et al., 2006; Pearson et al.,
2007). For a detailed explanation of ENM methodology using
MaxEnt see Phillips et al. (2006).
Model validation
We explicitly followed the jackknife validation methodology
developed by Pearson et al. (2007), which is shown to be effective
for sample sizes of 25 or less. Using this approach, one locality
point was removed from the dataset, and the model built using
the remaining n–1 localities. Thus, for a species with n localities,
n individual models were built for testing. Model accuracy and
significance were evaluated based on the ability of each model to
predict the one excluded test locality as present (Pearson et al.,
2007). Predictions were also made for each species using all n
localities to gain best-fit models across the entire study region for
visualization and spatial analysis in ArcGIS 9.2.
Remnant distributions
We used data on remaining habitat and altitudinal limits of
Nycticebus to clip the MaxEnt predictions to generate remnant
distributions. An altitudinal layer was applied to the model;
however, with the tendency for MaxEnt to over-predict, it was
necessary to further process the results using known altitudinal
limits. We did not incorporate land-cover data in modelling
because it is necessary for species localities and environmental
layers to correspond temporally (Phillips et al., 2006). Hence,
land-cover data are not suitable for use with occurrence data
from a number of decades, particularly in regions of rapid
deforestation and development. The habitat layer was created
using the most up-to-date uniform land-cover map available for
Southeast Asia (GLC, 2000). We considered four vegetation
types to be suitable habitat for Nycticebus: evergreen montane
forest, evergreen lowland forest, mangrove forest and swamp
forest. Vegetation patches of less than 10 km2 were considered as
unsuitable for maintaining viable populations of Nycticebus
and were therefore removed. Based on our locality data we set the
altitudinal limit at 1000 m on Borneo and 1500 m on Java and
Sumatra.
Anthropogenic risk assessment
Suitable habitat regions occupied by Nycticebus according to the
MaxEnt predictions, and situated outside of PA boundaries were
classified using the criteria in Table 1. In our list of PAs, we also
included some areas of global conservation importance but with
less stringent status, such as the Leuser Ecosystem and the ‘Heart
of Borneo’ that are made up of a series of PAs linked by lessprotected forested land. We classified habitat patches as low risk
(LR), medium risk (MR) or high risk (HR). Layers of agriculture
from the GLC 2000, and populated places, roads and PAs from
the World Database on Protected Areas (WDPA) were overlaid
with remnant distributions of Nycticebus.
We selected a distance of more than 10 km from human
access points as a conservative estimate of walking threshold,
and therefore anything beyond this distance was considered
LR (Peres & Lake, 2002). We then calculated percentages of
each species’ suitable habitat area qualifying as LR, MR or
HR in order to apply a chi-squared (χ2) test to assess varying
risk levels between species.
Table 1 Criteria for qualifying habitat patches as low, medium or high risk. The ranking indicates the suitability of habitat patches for
supporting viable populations of Nycticebus.
Measure
Low risk
Medium risk
High risk
Size of forest patch
Proximity to protected areas
Proximity to populated areas
Proximity to roads
Proximity to agriculture
> 40 km2
Within 20 km*
> 10 km
> 10 km
> 5 km
> 20 km2
Within 20–30 km
> 5 km
> 5 km
> 2.5 km
> 10 km2
Within 30–40 km
Adjacent
Adjacent
Adjacent
*For N. menagensis ‘Proximity to protected area’, the low risk criterion was within 20 km of protected area network or inside the Heart of Borneo.
© 2008 The Authors
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291
J. S. Thorn et al.
Conservation priority areas
RESULTS
We made recommendations for key PAs to be prioritized as
potential survey sites, as well as for extensions of the existing PA
Network. Priority survey sites were based on PAs where there is
not yet active research on Nycticebus and where MaxEnt
predictions identified suitable ecological conditions. Recommendations for PA extensions were based on LR areas for
Borneo, and LR/MR areas for Java and Sumatra due to higher
densities of human land-use features. In order to minimize the
potential level of human–wildlife conflict, we selected PA
extensions only in areas where habitat was classified as LR or MR.
The jackknife method for model testing (Pearson et al., 2007)
demonstrated high predictive success rates for all three species,
with highly significant models for N. coucang and N. javanicus
(P < 0.01) and significant models for N. menagensis (P < 0.05)
(Table 2). The predicted distributions in Fig. 1 reflect the output
generated by MaxEnt before further application of altitudinal
limits and ‘current’ forest cover to create the remnant distributions.
Nycticebus javanicus is significantly more vulnerable to
anthropogenic activity (χ2 = 2689, d.f. = 4, P < 0.0001). Intensive
human land use can be found across much of the study region,
Figure 1 Species predicted and remnant distributions for (a) Nycticebus coucang on Sumatra, (b) N. menagensis on Borneo and (c) N. javanicus
on Java. Predicted distributions (grey) represent the MaxEnt output before clipping to the altitude and vegetation layers. Remnant distributions
(black) represent the predicted distribution clipped to the altitude and vegetation layers.
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ENM and slow loris conservation
Table 2 Results of the jackknife validation method of model testing for Nycticebus coucang, N. javanicus and N. menagensis, showing the sample
sizes included for modelling, and the data used to calculate the P-values.
Species
Locality sample size
Number of successes
Mean fractional predicted area
Lowest presence threshold (LPT)
P-value
N. coucang
N. javanicus
N. menagensis
15
10
23
13
9
21
0.54
0.49
0.72
18.664
12.523
4.886
0.006
0.003
0.027
Figure 2 Percentage of suitable habitat classified as low risk,
medium risk or high risk for Nycticebus menagensis, N. javanicus and
N. coucang according to the risk assessment criteria.
and is most prevalent on Java. The lowest vulnerability levels are
for N. menagensis, which has both the highest percentage of
LR and the lowest percentage of HR areas across all species (Fig. 2).
Percentage of MR areas are most evenly spread across species.
Conservation priority areas
The risk assessment on Sumatra identified potential for six PA
extensions (Fig. 3a). Six areas were identified for PA extensions
on Borneo, providing connectivity among ten PAs (Fig. 3b). Due
to the fragmented nature of the habitat on Java, only one PA
extension has potential to provide connectivity between two PAs,
on the west side between Gunung Tilu and Gunung Simpang.
A total of five further PA extensions were identified (Fig. 3c). Six
PAs have been recommended as priority survey sites on Sumatra,
seven on Borneo, and ten on Java (see Table 3).
DISCUSSION
MaxEnt as a conservation technique
Application of the jackknife validation method confirms that
MaxEnt is effective at predicting distributions that are representative of species’ actual distribution, using limited occurrence
data. The models were significantly validated for all taxa based
on the calculated P-values. It is important to treat model
predictions using low sample sizes as representing areas of high
habitat suitability as opposed to presence of the species (Pearson
et al., 2007). Based on the jackknife tests, significant predictive
ability can be achieved with as few as ten localities. Both Pearson
et al. (2007) and Hernandez et al. (2006) even suggest that useful
predictions using MaxEnt can be attained with sample sizes
below ten, and this presents positive implications for the scope of
applying MaxEnt when setting conservation priorities. Reducing
the minimum number of occurrences required to produce useful
predictions of species distribution will greatly increase the
number of species that can be studied (Pearson et al., 2007). This
is particularly important for rare and cryptic species, where
inclusion of known distributions is vital for effective conservation
planning.
As with other studies of this kind (e.g. Raxworthy et al., 2003;
Papes & Gaubert, 2007; Pearson et al., 2007; Sergio et al., 2007;
Ward, 2007), the resulting MaxEnt predictions demonstrate how
ENM uses environmental variables to generate outputs not
possible purely from observed localities. The model indicates
these areas as having similar environmental conditions to areas
where the species is known to occur. The distribution maps
clearly show how the models project into areas of unknown
occurrence when the training localities are overlaid with their
respective predicted distributions. This highlights the value of
MaxEnt in discriminating unrecorded populations and areas of
high habitat suitability without relying on confirmed species
presence. As more field studies are undertaken for Nycticebus,
further records of occurrence will influence the shape of
predictions, particularly if the new locality data are sourced from
an area with unique environmental conditions not yet represented
by the current data set (Pearson et al., 2007).
Model limitations based on taxonomy
Taxonomic uncertainty can contribute to decreased modelling
accuracy, with models performing most poorly for species that
have high taxonomic confusion (Hernandez et al., 2006).
Misidentification is particularly significant for cryptic species
such as Nycticebus, both in the wild and in captive environments
(Nekaris & Jaffe, 2007). Many researchers, museum curators and
rescue centres still utilize the 1971 taxonomy of Groves (Molur
et al., 2003; Schulze & Groves, 2004), which will lead to modelling
inaccuracies if using occurrence data from these sources.
Nycticebus is undergoing wide taxonomic revision, and
preliminary evidence has been put forward to suggest the existence
of two forms on Borneo, two on Java and two on Sumatra (Nekaris
& Jaffe, 2007; Nekaris & Nijman, 2007a). A number of major
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293
J. S. Thorn et al.
Figure 3 Recommendations for protected area extensions and priority survey areas based on species remnant distributions and results of the
risk assessment for (a) Nycticebus coucang on Sumatra, (b) N. menagensis on Borneo, and (c) N. javanicus on Java. Protected area extensions are
shown in dark grey and priority survey areas are shown in black.
river systems originated from the highlands of Borneo, Java and
Sumatra, and transected the lowlands during the Pleistocene
period (Voris, 2000). Combined with more recent forest
fragmentation events, these waterways may have represented
effective zoogeographical barriers and agents in population
isolation and speciation (Harrison et al., 2006). Distinct forms
on the outlying islands of Banka (Lyon, 1906) and Natuna
(Chasen, 1935) are also a possibility. It would be interesting to
test whether further models of Nycticebus distributions can be
improved by partitioning occurrence data into the geographical
294
boundaries of the new proposed forms. Such data are still
forthcoming, however, and a study of this kind would be
founded on speculation of geographical extents. Genetic analyses
and field-based morphometrics are urgently needed to solve
these taxonomic discrepancies.
Implications for the conservation status of Nycticebus
Many forest patches highlighted to have suitable ecological
conditions for Nycticebus may actually be devoid of populations
© 2008 The Authors
Diversity and Distributions, 15, 289–298, Journal compilation © 2008 Blackwell Publishing Ltd
ENM and slow loris conservation
Table 3 Table of recommendations for priority survey areas and protected area (PA) extensions for Borneo, Java and Sumatra, based on the
distribution of Nycticebus and results of the risk assessments.
Region
Recommendations for priority survey areas
Recommendations for PA extensions
Sumatra
•
•
•
•
•
Bukit Barisan Selatan
Kerinci Sebelat
Bukit Tigapuluh
Berbak
Way Kambas
Borneo
•
•
•
•
•
•
•
Crocker Range
Kayan-Mentarang
Betung Kerihun
Bukit Batutenobang
Bukit Baka Bukit Raya
Bukit Sepat Haung
Taman Negara Banjaran
Java
•
•
•
•
•
•
•
•
•
•
Ujung Kulon
Halimun
Gunung Gede Pangrango
Gunung Masigit Kareumbi
Gunung Tilu
Gunung Simpang
Gunung Sawal
Bromo Tengger Semeru
Yang Highlands
Meru Betiri
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
•
of Nycticebus, due to unsustainable harvesting for the wildlife
trade, already shown to be true for some areas (Nekaris &
Nijman, 2007a; Nekaris et al., 2008). Nycticebus has been documented as the most prevalent legally protected primate taxon
in live animal markets (Malone et al., 2002; Webber & Nekaris,
2004; Mcgreal, 2007). This is clearly an area of research that needs
to be prioritized with a systematic approach to quantify the effects
of the wildlife trade on wild populations of Nycticebus. The
situation for N. menagensis is optimistic, with a high percentage
of LR areas. For N. javanicus and N. coucang, however, due to
higher human land-use densities, it may be necessary to prioritize
LR and MR areas to create sites that are viable in terms of size as
well as levels of anthropogenic risk. These areas will require
extensive surveys to establish current population densities and
habitat viability (Streicher, 2004b). Ideal sites for reintroduction
will incorporate high arboreal connectivity with low population
density (Nekaris & Nijman, 2007a).
Assessing threats and setting conservation priorities
The human land-use features assessed in this study contribute
strongly to the intensity of harvesting for the pet trade. Virtually
all forest areas in the study region are intersected by roads or
within the vicinity of populated places and much of the suitable
habitat for Nycticebus has already been converted to agriculture.
Ulu Masen
Bukit Barisan
Barumun
Berbak
Siranggas Wildlife Reserve to Leuser Ecosystem
Bukit Hitan Protection Forest to Bukit Balai Rejang Protection Forest
Ulu Temburong
Gunung Mulu
Labi Hills
Ladan Hills
Maliau Basin
Danum Valley
Kayan Mentarang to Betung Kerihun
Bukit Sepat Haung to Betung Kerihun and Bukit Batutenobang
Bukit Batutenobang to Bukit Baka Bukit Raya
Bukit Perai to Bukit Rongga
Halimun
Gunung Gede Pangrango
Gunung Simpang to Gunung Tilu
Effective conservation planning for human-dominated
landscapes needs to address issues of PA connectivity (Fuller
et al., 2006) alongside the requirements of the local human
population (Gillingham & Lee, 1999), possibly through
multicriteria analysis (Moffett & Sarkar, 2006). When time,
money and resources are limited, it is more efficient to build
upon existing protected areas than to create new ones (Noss,
1987; Johns, 1991; Tutin et al., 1997; Marsh, 2003; Fuller et al.,
2006; Strier, 2007). Establishing connectivity between PAs is
especially significant for arboreal primate species such as
Nycticebus which, due to behavioural adaptations, will not cover
large distances terrestrially (Nekaris & Bearder, 2007). Nycticebus
spp. have been encountered dead on power lines, suggesting
failed attempts at dispersal between forest patches, when the
natural habitat has been destroyed (Streicher, 2004a, Jan Beck,
personal communication, July 2007). Animals have also been
observed as victims of road kill, where roads intersect forest
patches (Nekaris & Schulze, 2004). These two factors highlight
the difficulty animals face in trying to disperse in a humandominated landscape, and further emphasize the importance of
establishing arboreal connectivity between PAs.
A challenging issue facing organizations involved in the
conservation of Nycticebus is locating suitable sites for
reintroduction of individuals confiscated from the wildlife trade
(Schulze & Groves, 2004). The MaxEnt predictions and risk
© 2008 The Authors
Diversity and Distributions, 15, 289–298, Journal compilation © 2008 Blackwell Publishing Ltd
295
J. S. Thorn et al.
assessment are a preliminary step towards highlighting areas of
high habitat suitability, which may also be effective reintroduction
zones that minimize anthropogenic threats once the animals are
released. Furthermore, these data reiterate the conclusion of
previous research, showing MaxEnt is a viable technique for
modelling species distributions with small sample sizes.
ACKNOWLEDGEMENTS
We would like to thank all those who responded to email requests
for slow loris data. The following individuals provided localities
for use in the ENM: Imam Basuki Jan Beck, Susan Cheyne,
Wawan Djum, Colin Groves, Kristofer Helgen, Agung Nugroho,
Matt Struebig, Reed Wadley and Serge Wich. Thanks also go to
Richard Pearson for advice on using the Jackknife Validation
Method. We thank the following individuals for access to and
help with their zoological collections: Hein van Grouw (Naturalis Leiden), Daphne Hill and Paula Jenkins (Natural History
Museum London), Adri Rol (Zoological Museum Amsterdam),
and Malgosia Nowak-Kemp (Natural History Museum Oxford).
Finally, we would like to thank Sarah Jaffe for her invaluable
comments and suggestions through various versions of this
manuscript. Funding was provided by the Systematics Research
Fund of the Systematics Association and Linnaean Society of
London, Oxford Brookes University Research Strategy Fund and
SYNTHESYS (NL-TAF-3491). This research formed a part of an
MSc in Primate Conservation by J S Thorn at Oxford Brookes
University.
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