a gap analysis of southeast asian mammals based on habitat suitability models

B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 2 7 3 0 – 2 7 4 4

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A gap analysis of Southeast Asian mammals basedon habitat suitability models

Gianluca Catulloa, Monica Masia, Alessandra Falcuccib, Luigi Maioranob,*,Carlo Rondininib, Luigi Boitanib

aIstituto di Ecologia Applicata, via Bartolomeo Eustachio 10, 00161 Rome, ItalybDepartment of Animal and Human Biology, Sapienza Universita di Roma, Viale dell’Universita 32, 00185 Rome, Italy

A R T I C L E I N F O

Article history:

Received 9 November 2007

Received in revised form

7 August 2008

Accepted 11 August 2008

Available online 23 September 2008

Keywords:

Southeast Asian mammal databank

Gap analysis

Protected areas

Distribution models

Mammal distribution ranges

Threatened species

0006-3207/$ - see front matter � 2008 Elsevidoi:10.1016/j.biocon.2008.08.019

* Corresponding author: Tel.: +39 0649694262E-mail addresses: [email protected] (

[email protected] (L. Maiorano), ca

A B S T R A C T

Southeast Asia is one of the richest reservoirs of biodiversity on earth and home to one of

the highest concentrations of endemic species. Many protected areas (PA) have been estab-

lished across the region, but to date no systematic evaluation of their efficacy has been

published because no comprehensive dataset was available which could be fed into an

analysis of conservation gaps. We collected the geographic range for 1086 mammal species

of Southeast Asia and we built species-specific habitat suitability models for 901 of them.

We performed two gap analyses (one based on a combination of distribution models and

distribution ranges and one based on distribution ranges only) for each mammalian spe-

cies, to evaluate the effectiveness of the existing network of PA and to identify priority

regions and priority species for expanding and consolidating the network. Our results indi-

cate that 7.5–8.2% of species are not covered by any PA, and 51.6–59.1% are covered only

partially. These species are distributed throughout the entire study area and their conser-

vation requires the creation of new PA that can help fill this existing conservation gap. This

would be particularly important for species which are endemic of small islands, where

species survival is often threatened by the presence of introduced species and habitat con-

version. Yet PAs cannot be considered as the ending point of a conservation strategy,

because overall, 34% of the species we analyzed (many of which already covered by exist-

ing PAs) were at risk of extinction when considering the IUCN red-list criteria. PAs should

therefore be considered in a broader framework of all local ecological and socio-economic

trends, including the growing human population, growing economy and infrastructure

development.

� 2008 Elsevier Ltd. All rights reserved.

1 Introduction

Southeast Asia is one of the richest reservoirs of biodiversity

on earth and home to one of the highest concentrations of en-

demic species (Sodhi et al., 2006a). The region encompasses

four hotspots (Myers et al., 2000), several of the most valuable

eco-regions (Olson and Dinerstein, 1998), and a megadiversity

er Ltd. All rights reserved

; fax: +39 06491135.G. Catullo), [email protected]@uniroma1.

country system composed by Indonesia, Malaysia and the

Philippines (McNeely et al., 1990). This extraordinary species

richness encompasses all taxa, and mammals are no excep-

tion (Brooks et al., 1999; Sodhi and Brook, 2006). In fact, roughly

one quarter of the mammal species of the world occurs in the

area, with many new families and species which have been

discovered recently (Jenkins et al., 2005; Musser et al., 2005).

.

taly.org (M. Masi), [email protected] (A. Falcucci),it (C. Rondinini), [email protected] (L. Boitani).

mailto:[email protected]






B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 2 7 3 0 – 2 7 4 4 2731

The rapid and extensive destruction of habitats occurring

worldwide across the tropical belt has not spared Southeast

Asia, which indeed has one of the highest relative rates of

deforestation among the major tropical regions (Laurance,

1999). Its native biota is seriously threatened by forest conver-

sion, forest fires, unsustainable subsistence hunting and

wildlife trade (Sodhi et al., 2004a). In this context, protected

areas (PAs) constitute a valuable tool for assuring the conser-

vation of viable populations within natural ecosystems (Bru-

ner et al., 2001; Redford and Richter, 1999; Groves, 2003;

Rosenzweig, 2003; Cardillo et al., 2006; Lee et al., 2007). Over

the years, Southeast Asian countries have established

(though at different paces) national PA systems. However,

PAs are often not completely representative of the biodiver-

sity of a region (Pressey et al., 1993; Scott et al., 1993; Rodri-

gues et al., 1999; Margules and Pressey, 2000; Rondinini

et al., 2005) as their location and design is frequently based

on socio-economic, aesthetic and political values rather than

biological criteria. Consequently, unrepresentative sites of

lesser conservation value may be set aside for conservation

while sites of the higher value remain unprotected.

Gap analysis (assessing to what extent animal and plant

species are being protected) is a powerful approach to explore

the effectiveness of a PA system in representing local biodi-

versity (Scott et al., 1993). Gap analysis has been applied to

various taxa globally (Rodrigues et al., 2004a), at continental

level, and in many countries worldwide (Scott et al., 1993,

2001; Fearnside and Ferraz, 1995; Ramesh et al., 1997; Rodri-

gues et al., 1999; Powell et al., 2000; De Klerk et al., 2004; Fjeld-

sa et al., 2004; Oldfield et al., 2004; Yip et al., 2004; Dietz and

Czech, 2005; Maiorano et al., 2006, 2007; Rondinini et al.,

2006a), but so far it has never been applied to PAs of Southeast

Asia because no comprehensive dataset has been available

which could be fed into the GAP process.

Rodrigues et al. (2004b) and Brooks et al. (2004) suggested

the necessity of fine scale mapping works that, starting from

the results of the global gap analysis, provide the local context

that is necessary for conservation. The Southeast Asian Mam-

mal Databank project (SAMD – a joint effort between Istituto

di Ecologia Applicata, European Commission and IUCN) for

the first time provides a comprehensive, fine-grained, and

large scale biodiversity dataset, consisting of extent of occur-

rence maps, species–habitat relationships and habitat suit-

ability models for all mammals of the region. The dataset

was compiled in 2002–2006 in close collaboration with the

IUCN Global Mammal Assessment process and it is freely

available at SAMD’s website (http://www.ieaitaly.org/samd/).

In this paper we present the SAMD dataset and provide an

assessment of the effectiveness of the existing PAs for the

conservation of terrestrial mammals in Southeast Asia. While

geographic ranges grossly overestimate species distribution

(conditional on species range size as suggested by Jetz et al.,

2008), thus hiding a number of gap species that overlap PAs

only in unsuitable portions of their range (Rondinini et al.,

2005), distribution models may potentially underestimate

species distribution, providing an overly restrictive picture

of the current conservation status. As suggested by Rodrigues

et al. (2004b) and by Brooks et al. (2004), we focused on the

overlap of PAs with estimated (and validated) suitable areas

for species, because these are more likely to host the species

under analysis (Rondinini et al., 2006b). However, we per-

formed our analyses also considering geographic ranges to

obtain a range of reasonable estimates for gap species. Our

analysis is unique in providing indications of conservation

status for a large study area and at a scale that is useful for

conservation planning purposes.

2. Materials and methods

Our study area encompasses the entire Southeast Asia,

including all countries south of China and east of India: Bru-

nei Darussalam, Cambodia, Indonesia, Lao PDR, Malaysia,

Myanmar, Papua New Guinea, The Philippines, Singapore,

Thailand, and Viet Nam.

We characterized the landscape of the study area consider-

ing datasets from different sources and covering: (a) land-cov-

er (Global Land Cover 2000 [GLC2000]; European Commission,

Joint Research Center, 2003; http://www-gvm.jrc.it/glc2000);

(b) elevation (Hastings et al., 1999; http://www.ngdc.noaa.-

gov/mgg/topo/globe.html); (c) hydrology (Digital Chart of the

World; ESRI, 1993); (d) human settlements (Digital Chart of

the World; ESRI, 1993); (e) administrative boundaries (Digital

Chart of the World; ESRI, 1993); (f) PAs (World Database on

Protected Areas [WDPA] Consortium, 2006; http://maps.geo-

g.umd.edu/WDPA/WDPA_info/English/index.html). All layers

were re-sampled for the analyses using a common origin

and a 1-km cell size. From the WDPA, we selected 1635 PAs

presently mapped for SE Asia, irrespective of their IUCN clas-

sification (IUCN, 1994). From analyses, we excluded 90 PAs for

which no information was available on their boundaries and/

or on their area. Of the 1635 PAs, 1088 had information on

their geographical boundaries, while 547 had the coordinates

of their geographical center and the information on the size

of the PA. In order to merge all PAs into a single layer, we built

circular buffers of the same size as the PA around these cen-

tral points.

We compiled a geographical database (freely available

through SAMD’s website) covering the entire study area with

information on 1086 mammal species, belonging to 17 orders

(Table 1).

For each species, we collected from the scientific literature

all available information on the species–habitat relationships,

the elevation range, and the Extent of Occurrence (EOO; the

full list of papers is available from the authors upon request).

Species–habitat relationships (sensu Corsi et al., 2000) fol-

lowed the GLC2000 land-cover classes, and each class was

classified as suitable (main or preferred habitats) or unsuit-

able (secondary and unsuitable habitats). Elevation was clas-

sified according to two classes: inside known elevation

range, outside known elevation range.

All the information collected was reviewed, discussed, and

integrated during five dedicated workshops which involved a

selection of the most eminent local and international experts

on various taxa. Each workshop involved from 21 to 48 ex-

perts for 3–5 days of intensive analysis and updating of the

available data (Mammals of Southeast Asia – Thailand, May

2004; Mammals of the Philippines – Philippines, April 2006;

Bats and Large Mammals of Southeast Asia – Indonesia,

May 2006; Small Carnivores of Southeast Asia – Vietnam, July

2006; Primates of Southeast Asia – Cambodia, September

http://www.ieaitaly.org/samd/

http://www-gvm.jrc.it/glc2000

http://www.ngdc.noaa.gov/mgg/topo/globe.html


http://maps.geog.umd.edu/WDPA/WDPA_info/English/index.html

http://maps.geog.umd.edu/WDPA/WDPA_info/English/index.html

Table 1 – Orders and number of species included in theSoutheast Asian Databank

Order All species Threatened species

Monotremata 4 3

Dasyuromorphia 16 3

Peramelemorphia 12 2

Diprotodontia 58 30

Proboscidea 1 1

Scandentia 17 11

Dermoptera 2 1

Primates 76 62

Rodentia 388 114

Lagomorpha 6 1

Erinaceomorpha 7 1

Soricomorpha 56 5

Chiroptera 328 73

Pholidota 3 3

Carnivora 54 24

Perissodactyla 3 3

Artiodactyla 52 34

Total 1086 371

Threatened species include critically endangered, endangered,

vulnerable, and near threatened.

Table 2 – Overlay of environmental layers

Land-covera Elevationb Suitability score

1 1 Suitable

1 2 Not-suitable

2 1 Not-suitable

2 2 Not-suitable

a Suitability scores for land-cover as defined in the method sec-

tion: 1 = suitable; 2 = unsuitable.

b Suitability scores for elevation as defined in the method section:

1 = inside known elevation range; 2 = outside known elevation

range.

2732 B I O L O G I C A L C O N S E R V A T I O N 1 4 1 ( 2 0 0 8 ) 2 7 3 0 – 2 7 4 4

2006). Our dataset was also integrated with the information

gathered during two additional workshops run by IUCN in

the framework of the Global Mammal Assessment project

(Mammals of Australia and Papua New Guinea – Australia,

August 2005; Rodents of Southeast Asia - United States, May

2006). During these workshops, species conservation status

was reviewed in accordance to the IUCN red-list criteria ver-

sion 3.1 (Table 1; IUCN, 2001).

2.1. Distribution models

We considered 1077 species (99.2% of the total 1086 species in

the database) in all the subsequent analyses. We excluded

four introduced species and five species with no reliable infor-

mation on their distribution. For 901 species, we built deduc-

tive distribution models (DM; sensu Corsi et al., 2000) using the

available species–habitat relationships and environmental

layers. First of all, land-cover and elevation were reclassified

following the available suitability scores (see paragraph above

for more details on the definition of the scores). Then, for

each species we combined the suitability scores of the two

layers into a synthetic suitability index (Table 2).

For four strictly water dependent species (Aonyx cinerea, Lu-

tra lutra, Lutra sumatrana, Lutrogale perspicillata) the habitat

suitability scores were computed inside a 3-km buffer around

water bodies and courses. For 63 species no information on

the elevation range was available; in this case the suitability

was calculated considering only land-cover.

We defined as area of potential species presence the area

inside the EOO that was classified as highly suitable by the

DM. We did not build a DM for 144 species with EOO < 1000 km2

(too small an extent compared with the extent of our study

area) and for other 32 species that had unreliable species–

habitat relationships (i.e. species whose ecology was not

known). For these 176 species, we used their entire EOO as the

area of potential species presence. The complete species list is

available at SAMD’s website (http://www.ieaitaly.org/samd).

2.2. Validation

To test the predictive power of the DMs, we measured the per-

formance of the models in predicting species potential pres-

ence. In particular, we measured the agreement between

each DM and a set of points of presence which were indepen-

dently collected in the field. We also measured the agreement

between each DM and a set of random points and we com-

pared the results using a permutation test.

We obtained point data on species presence gathering the

available published and unpublished datasets, consisting

mainly in observations and captures. In particular, we ob-

tained data for Chiroptera, Rodentia, Artiodactyla, Carnivora,

Primates and Scandentia covering Laos, Thailand, Malaysia,

Brunei, Vietnam, Cambodia and Indonesia (Kalimantan).

The datasets covered a total of 351 species and were kindly

provided by C. Francis (unpublished data), R. Steinmetz

(unpublished data), R. Boonratana (unpublished data), G.

Csorba (unpublished data), J. MacKinnon (unpublished data),

D. Lunde (Lunde et al., 2003), E. Meijaard (unpublished data),

and J. Walston (unpublished data).

We integrated these existing datasets with point data on

species presence directly collected in the field in Indonesia,

The Philippines and Vietnam. We selected these three coun-

tries for several reasons including available logistic support,

political stability, security for the field crew, cost of expedition

and permission to conduct research activities. The field crews

were composed by two researchers from the Institute of Ap-

plied Ecology (IEA) and by two zoologist from the Indonesian

Institute of Sciences for Indonesia, by one researcher from

IEA and by two zoologist from the Institute of Biology of the

University of the Philippines–Diliman for the Philippines, by

two researchers from IEA and by two zoologists from the

Institute of Ecology and Biological Resources of the Vietnam-

ese Academy of Science and Technology in Vietnam. In each

country, we followed a systematic random sampling design

to select 100 points (for a total of 300 points). The field crews

reached the village that was closest to each random point and

conducted a direct and standardized interview (Boitani et al.,

1999) with local hunters and/or villagers (1–5 villagers/hunt-

ers actively participating in the interviews) aimed at collect-

ing information on the current presence of 148 species of

medium and large sized mammals (we considered species

easier to recognize and of greater economic value for the local

http://www.ieaitaly.org/samd


populations) in the immediate neighborhood of the point.

Interviews were conducted in the local language with support

of pictures of the species; the ability of the interviewees in

recognizing species was tested using dummy pictures of com-

parable European and/or African species. Only (subjectively

judged) reliable information was retained. Not all 300 points

were reached by the field crews because of logistic reasons:

70 out of the original 100 points were sampled in Indonesia

and in the Philippines, 80 out of the original 100 points were

sampled in Vietnam, for a total of 220 sampled points freely

accessible through SAMD’s website.

Merging all datasets, we obtained points of presence for

399 species. To decrease spatial dependence, we removed all

locations closer than 5 km one from each other from each

species dataset. Moreover, we excluded all species with less

than five presence points from the validation analyses,

obtaining a final list of 190 species (21% of all the DMs) for

which model validation was possible.

To account for possible location errors and the spatial

uncertainty naturally associated with each point of presence,

we built a circular buffer (1.5 km radius) around each point of

presence (thus a total of nine grid cells was considered for

each point). A point of presence and the corresponding DMs

were considered to agree if at least one of the nine cells was

classified as highly suitable. In this way, for each species we

calculated the percentage of presence points that agreed with

the DM.

To test the significance of the agreement between points of

presence and the DMs, we used a permutation test following

Maiorano et al. (2007). We compared the percentage of agree-

ment calculated for the points of presence with that obtained

with 1000 sets of random points sharing the same character-

istics as the set of points of presence (same number of points,

distance among points equal or greater than 5 km). If the per-

centage of agreement calculated for the points of presence

was in the top 5% of the agreements obtained from the 1000

random samples, the model was considered validated.

To test whether the 190 species for which the points of

presence were available were representative of the ecology

of all 901 species with a DM, we compared the distribution

of the suitability scores among the two groups. In particular,

for each land-cover class, we calculated the percentage of

the 190 species for which the class represented a suitable

habitat; we calculated the same percentage for the 711 spe-

cies with a DM but without validation points, and we com-

pared the two distributions using a Kolmogorov–Smirnov test.

2.3. Potential species richness

We used the 901 DMs and the 176 EOOs to build a map of po-

tential species richness, calculated as the sum of all the spe-

cies’ areas of potential presence (i.e. for species with a DM the

areas inside the EOO classified as suitable, or the entire EOO

for species without a DM as defined in the Distribution models

section above). Moreover, to highlight the areas with a high

concentration of endemic or restricted-range species, we also

built a map of potential species richness in which each spe-

cies was weighted according to the inverse of its area of po-

tential presence (hereafter called potential weighted

richness). To highlight the areas with a high concentration

of threatened species, we built a map of potential species

richness for threatened species considering only those spe-

cies classified as critically endangered, endangered, vulnera-

ble, or near threatened following the IUCN red-list criteria

version 3.1. The same maps of potential species richness were

calculated considering the EOOs only for all 1077 species.

2.4. Gap analysis

We performed two gap analyses: one considering a combina-

tion of the 901 DMs and 176 EOOs, and one considering EOOs

only. Gap analysis requires the identification of a representa-

tion target (Scott et al., 1993). We used a species-specific repre-

sentation target depending on the area of potential presence

for each species. We performed our analyses following the

representation target defined in the ‘‘global gap’’ project

(Rodrigues et al., 2004a): species with a narrow distribution

(area of potential presence smaller than 1000 km2) should be

protected in 100% of the area of potential presence; wide-

spread species (area of potential presence greater than

250,000 km2) should be protected in 10% of the area of poten-

tial presence; species with area of potential presence greater

than 1000 km2 and smaller than 250,000 km2 have a represen-

tation target that is obtained interpolating between the two

extremes using a linear regression on the log-transformed

area of potential presence.

A species not represented at all in any PA was considered a

total gap, a species whose representation target is only par-

tially met was considered a partial gap, and a species whose

representation target is met was considered covered.

3. Results

PAs do not cover the countries in the study area with similar

proportions (Table 3). For example, more than 44% of Brunei

Darussalam and less than 6% of Singapore is protected. PAs

cover a disproportionate percentage of mountainous area,

the median elevation inside PAs being 438 m (interquartile

range = 753 m) while the median elevation inside the study

area is 190 m (interquartile range = 516 m).

3.1. Validation

The 190 species that we considered in the validation proce-

dure were representative of the distribution patterns of all

species. We found no significant difference (p = 0.95) between

the distribution of the high suitability scores for the 190 spe-

cies and those for the 711 species that were not processed for

validation (Fig. 1). The main difference (although not statisti-

cally significant) was for the land-cover class ‘‘Tree Cover,

Regularly Flooded, Fresh Water’’, for which the 190 species

with validation show a higher percentage of high suitability

scores compared to the entire species dataset.

On average, 32 presence points (median = 20) were avail-

able for the 190 species with a maximum of 233 points for Par-

adoxurus hermaphroditus and a minimum of five points for

nine species (Babyrousa babyrussa, Sus cebifrons, Hipposideros

rotalis, Myotis annectans, Phoniscus atrox, Presbytis chrysomelas,

Pygathrix nigripes, Hylobates agilis, Hylopetes nigripes). For 140

out of 190 species (73.7%), the DMs gave positive validation re-

Table 3 – Country area, percentage being protected, and number of protected areas in each Southeast Asian country

Country Country area (km2) % In protected areas Number of protected areas

Brunei Darussalam 5898 44.25 44

Cambodia 182,062 24.02 30

Indonesia 1,911,253 14.10 431

Lao PDR 230,662 16.22 25

Malaysia 331,165 16.88 516

Myanmar 669,660 6.31 48

Papua New Guinea 397,160 11.43 35

Philippines 296,940 10.81 198

Singapore 592 5.57 6

Thailand 516,906 20.04 203

Viet Nam 328,809 5.96 99

Fig. 1 – Distribution of the high suitability scores for the species with a distribution model. Land-cover class: 1 – Tree Cover,

Broadleaved, Evergreen; 2 – Tree Cover, Broadleaved, Deciduous, Closed; 3 – Tree Cover, Needle-Leaved, Evergreen; 4 – Tree

Cover, Regularly Flooded, Fresh Water; 5 – Tree Cover, Regularly Flooded, Saline Water; 6 – Mosaic: Tree Cover/Other Natural

Vegetation; 7 – Shrub Cover, Closed-Open, Evergreen; 8 – Shrub Cover, Closed-Open, Deciduous; 9 – Herbaceous Cover,

Closed-Open; 10 – Sparse Herbaceous Or Sparse Shrub Cover; 11 – Cultivated and Managed Areas; 12 – Mosaic: Cropland/Tree

Cover/Other Natural Vegetation; 13 – Mosaic: Cropland/Shrub and/Or Grass Cover; 14 – Bare Areas; 15 – Water Bodies; 16 –

Artificial Surfaces.


sults, which were statistically significant for 46 species

(24.2%) at the a = 0.05 level, and for 77 species (40.6%) at the

a = 0.1 level. For 50 species (26.3%), the percentage of agree-

ment among DMs and points of presence was lower than

the agreement obtained with 1000 sets of random points,

but only for eight species (4.2%) the difference was signifi-

cantly lower (p < 0.05). We found no taxonomic bias in the

validation results, with the exception of Chiroptera and Pri-

mates. Considering the proportion of Chiroptera species over

the total sample of species with validation points, we found a

percentage of positively validated species greater than ex-

pected. On the contrary the validation results for Primates

showed a percentage of species with a negative validation

that was greater than expected.

3.2. Potential species richness

In general, the richness maps estimated using only EOOs were

similar to those estimated using both EOOs and DMs, although

the former maps depicted a much coarser spatial pattern. The

paragraphs below focus on describing potential species rich-

ness maps estimated using both EOOs and DMs, and point

out noteworthy differences with the maps calculated using

only EOOs. All potential species richness maps calculated

using the EOOs only are available in Appendix 1 online.

Considering potential species richness, the richest areas

(Fig. 2a) are found in Borneo (mainly Sarawak and Sabah),

Western Sumatra, Annamites mountains, and Malay Penin-

sula (Malaysia). Other important areas of high potential spe-

cies richness were also found in the Cardamom mountains

in Cambodia and Myanmar, along the mountain ranges of Pa-

pua New Guinea, and in Sulawesi. The relative importance of

Papua New Guinea and of Sulawesi was lower if considering

potential species richness calculated with EOOs only (Fig. 1a

in Appendix 1).

The map of potential weighted richness (Figs. 1b and 2b in

Appendix 1) showed a different pattern, with higher values

being concentrated mainly along mountain ranges (rough-

ly > 500 m a.s.l.) and in small islands. In particular, the richest

areas were found in the Annamite mountains (Vietnam and

Laos) and in the far north of Myanmar, which was the most

important area for weighted species richness in Indochina.

Fig. 2 – (a) Potential species richness calculated as the sum of each species’ area of potential presence (EOO or DM); (b)

potential weighted species richness calculated as the sum of each species’ area of potential presence (EOO or DM): each

species is weighted according to the inverse of its area of potential presence; (c) potential threatened species richness

calculated as the sum of each species’ area of potential presence (EOO or DM) considering only critically endangered,

endangered, vulnerable or near threatened species. All maps are represented using a histogram equalize stretch in ArcGis;

the legends of (a) and (c) report the actual ranges of potential richness values, the legend of (b) reports percentages.


Fig. 2 (continued)


The Malay peninsula (Malaysia) was also a very important

area, as well as the Philippines (especially Palawan, Mindoro,

and Luzon), Sulawesi, New Guinea and a number of small is-

lands (Moluccas, Singapore, Natuna Besar, Mentawai, Engg-

ano, Simeleu, Sangihe, Talaud, Peleng, Waigeo, and Geelvink

Bay islands).

The map of potential richness for threatened species (Figs.

1c and 2c in Appendix 1) clearly shows the importance for con-

servation of areas like the Annamite mountains, Borneo (Sara-

wak in particular), Western Sumatra and peninsular Malaysia.

3.3. Gap analysis

Considering a combination of DMs (901 species) and EOOs

(176 species), only 88 species (8.2%) of the Southeast Asian

mammals that we considered were total gap. This number re-

mained almost unchanged considering EOOs only, with 81

species (7.5%) being considered total gap (as it was expected

given that 72% of the 88 total gap species above do not have

a distribution model). Considering both EOOs and DMs, the

orders with the highest numbers of total gap species were

the Rodentia (37 species) and the Chiroptera (28 species). Per-

amelemorphia, Dasyuromorphia, and Artiodactyla showed

relatively low numbers of total gap species (respectively

two, two, and six species), but extremely high percentages,

with Peramelemorphia being the order with the highest per-

centage of total gap species (Table 4). Considering EOOs only,

the results were almost unchanged, with the exception of

Dasyuromorphia (for which no species was classified as total

gap according to the EOO only analyses) and Artiodactyla

(that passed from five total gap species considering EOOs

and DMs to three species with EOOs only).

Considering a combination of EOOs and DMs, more than

59% of all the species (636 species) were partial gap species

and 33% (352) were fully covered (Table 4). The taxa with the

highest percentages of partial gap species were Soricomor-

pha, Rodentia, Diprotodontia, Lagomorpha and Primates,

but many other taxa showed high numbers of partial gap spe-

cies (Table 4). Considering EOOs only, 51.6% of all the species

(556 species) were partial gap species and 41% (440) were fully

covered (Table 4). Once more, Soricomorpha, Rodentia, Diprot-

odontia, Lagomorpha and Primates, although with different

percentages, were the taxa with the highest percentage of

partial gap species. The complete list of total and partial

gap species is available in Appendix 2 available online.

Considering threatened species (i.e. those species classi-

fied as critically endangered, endangered, vulnerable, or near

threatened according to the IUCN criteria), from 73.9% (EOOs

plus DMs) to 68.2% (EOOs only) are classified as total or partial

gap (Table 4), with Dasyuromorphia (only when considering

EOOs plus DMs), Diprotodontia and Rodentia being the taxa

with the highest percentages of threatened species not cov-

ered by any PA, and Erinaceomorpha, Peramelemorphia, and

Soricomorpha being the taxa with the highest percentages

of threatened species only partially covered by PAs. Notable

are also the cases of the Dermoptera, Lagomorpha and Probo-

scidea that, considering threatened species, are completely

covered by existing PAs (Table 4).

The areas with the highest numbers of total and partial

gap species (Fig. 3a) are Sulawesi, Papua New Guinea, and

Mentawai islands, together with many mountainous areas

(Annamite mountains at the boundaries between Laos and

Vietnam, peninsular Malaysia, North Myanmar, Luzon in

the Philippines, and northern Borneo). The same distribution

Table 4 – Percentage of total gap species, partial gap species, and covered species for each order calculated using acombination of DMs and EOOs

Order Total gap species (%) Partial gap species (%) Covered species (%)

All species Threatened species All species Threatened species All species Threatened species

Artiodactyla 9.6 (5.8) 3.8 (0.0) 51.9 (51.9) 63.8 (61.8) 38.5 (42.3) 32.4 (38.2)

Carnivora 1.9 (1.9) 0.0 (0.0) 29.6 (18.5) 20.8 (16.7) 68.5 (79.6) 79.2 (83.3)

Chiroptera 8.5 (7.9) 4.1 (4.1) 57.9 (48.8) 79.5 (72.6) 33.5 (43.3) 16.4 (23.3)

Dasyuromorphia 12.5 (0.0) 33.3 (0.0) 50.0 (50.0) 33.3 (66.7) 37.5 (50.0) 33.3 (33.3)

Dermoptera 0.0 (0.0) 0.0 (0.0) 50.0 (50.0) 0.0 (0.0) 50.0 (50.0) 100.0 (100.0)

Diprotodontia 6.9 (6.9) 13.3 (13.3) 67.2 (55.2) 76.7 (70.0) 25.9 (37.9) 10.0 (16.7)

Erinaceomorpha 0.0 (0.0) 0.0 (0.0) 57.1 (57.1) 100.0 (100.0) 42.9 (42.9) 0.0 (0.0)

Lagomorpha 0.0 (0.0) 0.0 (0.0) 66.7 (66.7) 0.0 (0.0) 33.3 (33.3) 100.0 (100.0)

Monotremata 0.0 (0.0) 0.0 (0.0) 50.0 (50.0) 33.3 (33.3) 50.0 (50.0) 66.7 (66.7)

Peramelemorphia 16.7 (16.7) 0.0 (0.0) 50.0 (41.7) 100.0 (100.0) 33.3 (41.7) 0.0 (0.0)

Perissodactyla 0.0 (0.0) 0.0 (0.0) 33.3 (33.3) 33.3 (33.3) 66.7 (66.7) 66.7 (66.7)

Pholidota 0.0 (0.0) 0.0 (0.0) 33.3 (33.3) 33.3 (33.3) 66.7 (66.7) 66.7 (66.7)

Primates 6.6 (6.6) 8.1 (8.1) 63.2 (52.6) 66.1 (54.8) 30.3 (40.8) 25.8 (37.1)

Proboscidea 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) 100.0 (100.0) 100.0 (100.0)

Rodentia 9.7 (9.4) 13.2 (12.3) 63.1 (57.9) 69.3 (68.4) 27.2 (32.7) 17.5 (19.3)

Scandentia 5.9 (5.9) 9.1 (9.1) 35.3 (23.5) 36.4 (18.2) 58.8 (70.6) 54.5 (72.7)

Soricomorpha 5.4 (5.4) 0.0 (0.0) 76.8 (64.3) 100.0 (100.0) 17.9 (30.4) 0.0 (0.0)

All orders 8.2 (7.5) 8.1 (7.3) 59.1 (51.6) 65.8 (60.9) 32.7 (40.9) 26.1 (31.8)

Results obtained considering EOOs only are given in parenthesis. Threatened species include critically endangered, endangered, vulnerable,

and near threatened. The percentages are calculated considering the number of species in Table 1.


pattern was found considering total and partial gap species

potential richness calculated using EOOs only (Fig. 2a in the

Appendix 1), with the exceptions of North Vietnam and Min-

danao (Philippines), whose potential species richness was

higher.

Total gap species were distributed throughout the study

area (Fig 3b), from the extreme north of Myanmar to the SE

coast of Papua New Guinea. Many small islands (among

which Tawitawi, Dinagat, and Camiguin in the Philippines,

Natuna Besar, Simeleu, Sangir, and Peleng in Indonesia, Obi,

Kai, and Aru among the Moluccas) hosted total gap species.

A particularly important case is in the Mentawai archipelagos

(particularly Sipura and North Pagai), west of Sumatra, where

the highest number of total gap species (five species) was

found. Considering total gap species richness calculated

using EOOs only (Fig. 2b in Appendix 1), the general pattern

is almost the same, with the main exceptions of Central Prov-

ince in Papua New Guinea and of the mountain range be-

tween Irian Jaya and Papua New Guinea where no total gap

species was present.

The areas with the highest number of partial and total gap

species classified as threatened (Fig. 3c) are mainly located in

Sulawesi, in Malaysian Borneo, and in the Mentawai islands.

Considering mainland Southeast Asia, the Annamite Moun-

tains and the Malay Peninsula (Malaysia) have a particularly

important role. Also in this case, the potential richness pat-

tern obtained considering EOOs only was almost the same

(Fig. 2c in Appendix 1), with higher richness values in West

Java and in Mindanao, Negros and Mindoro (Philippines).

Most of the species whose conservation target was com-

pletely met have a relatively large distribution range (Fig. 4a).

On the contrary, total gap species (Fig. 4b) have a small to

medium distribution range, a distribution which is similar to

that of the species completely enclosed in PAs (Fig. 4d).

4. Discussion

Our study represents an important contribution to mammal

conservation in Southeast Asia. It provides a new and com-

plete dataset on all mammal species of the region and pre-

sents a synthetic view of the conservation status in relation

to the network of existing PAs. Several multi-species gap anal-

yses have been carried out globally (e.g. Rodrigues et al.,

2004a) or on a sub-continental scale (e.g. Fjeldsa et al., 2004;

De Klerk et al., 2004) but no comprehensive gap analysis has

ever been performed for the Southeast Asian region. More-

over, extensive modeling of species distribution has been ap-

plied at national level (e.g. Maiorano et al., 2006) or for other

continents (e.g. Rondinini et al., 2005), but no systematic ef-

fort has ever been applied to the Southeast Asian region.

Many international conservation efforts have considered our

study area (McNeely et al., 1990; Olson and Dinerstein, 1998;

Myers et al., 2000; Wikramanayake et al., 2002) and many dif-

ferent PAs have been established (currently more than 1700

according to the WPDA, 2006). Although the effectiveness of

networks of PAs in protecting biodiversity is often debated

and different studies have produced different results (e.g.

Bruner et al., 2001 vs. Sodhi et al., 2004a), in Southeast Asia

the contribution of PAs to conservation of an important taxo-

nomic group such as mammals has never been tested. Our

analysis clearly indicates that the existing PAs are inadequate

in assuring the conservation of mammals across the region.

We based our results both on deductive distribution mod-

els and on extents of occurrence, with a few discrepancies

among the two analyses as already discussed in Rondinini

et al. (2005) and Loiselle et al. (2003). Deductive distribution

models have been successfully used elsewhere (Rondinini

et al., 2005; Maiorano et al., 2006, 2007) reducing the level of

commission errors that is naturally present in species distri-

Fig. 3 – (a) Potential gap species richness calculated as the sum of each species’ area of potential presence (EOO or DM)

considering only total and partial gap species; (b) potential presence of total gap species (EOO or DM); (c) potential threatened

gap species richness calculated as the sum of each species’ area of potential presence (EOO or DM) considering only total and

partial gap species that were classified as critically endangered, endangered, vulnerable or near threatened. Maps a and c are

represented using a histogram equalize stretch in ArcGIS and report the actual ranges of potential richness values. The list of

total and partial gap species can be found in the Appendix 2 available online.


Fig. 3 (continued)


bution maps (Guisan and Zimmermann, 2000; Scott et al.,

2002; Loiselle et al., 2003; see Jetz et al. 2008 for an analysis

of the ecological correlates). However, there is no inherent

assurance that a distribution model portrays reality (Guisan

and Zimmermann, 2000; Johnson and Gillingham, 2004) and

a model that poorly represents the presence of a species

may result in harmful conservation and management actions

(Loiselle et al., 2003; Wilson et al., 2005). Therefore, model val-

idation is an essential step in any application of models to

conservation, and it is an important component of our study.

We were able to perform a validation analysis for only 21% of

all the distribution models that we developed (190 out of 901

distribution models), but we were able to cover a wide taxo-

nomic range (12 out of 17 orders), a wide geographic range

(from Thailand to Philippines/Sulawesi), and we represented

the distribution patterns of all mammal species. Moreover,

the results of the validation process can be considered to be

positive. The distribution models predicted potential pres-

ence significantly better than random at the a = 0.05 for

22.2% of the species, and for only 4.2% of the species were

the models significantly worse than random at the same level

of significance. For all other distribution models, no statisti-

cally significant result was found, either for the large percent-

age of suitable areas in the models or the low number of

available points of presence.

However, raising the significance level to a = 0.1, the per-

centage of distribution models which portray species distri-

bution worse than random was almost stable (6.3%), while

almost 41% of the models can be considered to be signifi-

cantly better than random. We can conclude that our models

represent a reasonable baseline for conservation planning at

the scale of our study area.

We found no particular bias in the validation results with

reference to the different taxonomic groups. The only excep-

tion were the Chiroptera, for which we obtained better valida-

tions than expected, and the Primates for which the

proportion of validated species was lower than expected.

Clearly, the positive results obtained for Chiroptera are linked

to the higher quality of the validation points; for almost all

the species of Chiroptera considered for validation, we ob-

tained presence locations from data collected in the field

(mainly captures and scientific observations). Considering

Primates, most of the validation points were of lower quality

(if compared with those available for Chiroptera), being col-

lected mainly through interviews and often located close to

small patches of suitable habitat (observed directly in the

field) that are almost invisible at the scale of our distribution

model.

We have provided no measure of commission or omission

error associated to our distribution models. Both types of er-

rors are important in any conservation planning exercise,

with omission errors that affect the comprehensiveness of a

network of PAs, and commission errors that affect represen-

tativeness and adequacy of a reserve network (Rondinini

et al. 2006a). No direct measure of the commission error rate

was possible with out dataset, and yet it has been argued that

conservation decision makers should prefer models that min-

imize commission errors because such errors lead to the

selection of reserves that do not actually contain the target

species (Loiselle et al. 2003; Rondinini et al. 2005; Jetz et al.

(2008)) have demonstrated that EOOs can overestimate the

real range occupancy (conditional on EOO size) with propor-

tions going from 0% to 91%, with an average of 39%. However,

Jetz et al. (2008) performed their analyses on birds in North

(a) area of potential presence x protection

log area of potential presence (km2)

% p

rote

cted

5

5

5

10

10

10

20

201 2 3 4 5 6 7

020

4060

8010

0

5

5

5

10

10

10

20

2030 30

60 90

120

partial gapcovered

(b) 0% protected


num

ber o

f spe

cies

1 2 3 4 5 6 7

015

(c) 20% protected


num

ber o

f spe

cies

1 2 3 4 5 6 7

075

150

(d) 100% protected


num

ber o

f spe

cies

1 2 3 4 5 6 7

010

20

Fig. 4 – (a) Number of species for each class of range size and

for each class of proportion of area of potential presence

inside protected areas (EOOs plus DMs). The grey area

represents the protection target we set for species. Isolines

enclose areas with equal numbers of species (dotted lines

620 species, continuous lines >20 species). (b) Number of

total gap species for each range size class. (c) Number of

species for each range size class with 20% of their area of

potential presence protected. This graph corresponds to the

peak number of species in (a). (d) Number of species for each

range size class with 100% of their area of potential

presence protected. Same results (not shown) were obtained

for EOOs only.


America, South Africa and Australia. We performed our anal-

yses on mammals in South East Asia, with a much smaller le-

vel of knowledge on species ecology and distribution,

particularly for Rodentia, Soricomorpha, and Chiroptera that

make up 71% of our sample. Thus we can assume that the

commission error associated with the distribution models is

much smaller if compared to the commission error associated

to the available EOOs, probably with a proportion that is

greater than that measured by Jetz et al. (2008).

The omission error rate can be easily obtained from the

percentages of agreement that we calculated during the val-

idation procedure. In particular, an average 32% (standard

deviation = 21%) of the occurrence points were not covered

by suitable areas in the DMs. We recognize that the omission

error rate cannot be considered negligible and that it may

have relevant consequences in gap analyses (Rondinini et

al., 2006). However, we used two general approaches to esti-

mate the frequency of gap, partial gap, and covered species,

one using EOOs only and the other using a combination of

DMs and EOOs. The results of these two approaches should

provide a range of reasonable estimates that likely brackets

the true value of the parameter of interest. In fact, while

estimates based on EOOs only should minimize omission er-

rors at the cost of potentially high commission errors, esti-

mates based on a combination of DMs and EOOs should

reduce commission errors at the expenses of higher omis-

sion errors.

A further point regarding our distribution models and ex-

tents of occurrence calls for caution in the interpretation of

our results. We have produced (validated) estimates of habitat

suitability and reliable maps of distribution ranges but we do

not have any insurance that the species are effectively pres-

ent in their entire EOO or even in the suitable part of their

EOO only. This is the well-known problem of the ‘‘empty for-

est syndrome’’, with large animals (mainly primates, carni-

vores and ungulates) that are extinct in vast areas of their

former EOO because of commercial hunting, even if the vege-

tation is still intact (Redford, 1992; Milner-Gulland et al., 2003;

Corlett, 2007). This can have profound influences on our re-

sults, as it can be demonstrated considering the case of the

banteng (Bos javanicus) and of the Hose’s lead monkey (Presby-

tis hosei). According to our results (see the Appendix 2 and

consider the distribution model) the representation target

for the banteng is more than met (123% of the representation

target is covered by PAs). However, the species is gone from

most of the highly suitable areas (even though Java still repre-

sent one of the species’ stronghold), mainly because of illegal

hunting for the trade in horns (Hedges, 2000; Steinmetz, 2004).

In the case of the Hoses’s leaf monkey the representation tar-

get is almost completely met (87% according to our results)

but a recent report (Nijman, 2005) outlines that hunting for

medicinal bezoar stones have produced local extinction of

the species even in highly suitable areas as the huge and re-

mote Kayan Mentarang National Park in Indonesia. However,

our results are still extremely important, because we provide

at least an estimate of how much suitable habitat remains in

relation to the existing PAs, an estimate that suffers with low-

er commission errors if compared to the original EOO (the

representation target met as measured using the EOOs is

180% for the banteng and 130% for the Hoses’s leaf monkey)

and that is important for planning from the standpoint of po-

tential recovery for populations.

As in any gap analysis, our results heavily depend on the

dataset and on the pre-determined representation targets.

In particular, even though we applied a representation target

that has been used in the previous studies, we are not explic-

itly accounting for species viability (Svancara et al., 2005).

Even considering all the inherent limitations and uncer-

tainties that characterize analyses on a large scale and our

dataset, our results give a clear indication of important pat-

terns in the current distribution of mammals in Southeast

Asia. The analysis of potential species richness demonstrates

that many large areas are still potentially occupied by a high

number of species, even though many of the flat areas, where


most of the human pressure is concentrated, are relatively

poor in mammal species. Particular caution is necessary in

considering the results on potential weighted species rich-

ness. In fact, the areas of high species richness located at

the boundaries of our study area are probably influenced by

the presence of species that have a much wider distribution

outside of our study area but that seem to have a small range

when considering only the zone within our study area. For

example, this is the case of the tufted deer (Elaphodus cephalo-

phus), a total gap species, and of the red panda (Ailurus ful-

gens): both species occupy a tiny range in northern

Myanmar, but outside of our study area the first is distributed

throughout central and southern China and the latter occurs

in Nepal, India, Bhutan, and South China. However, the areas

with high potential weighted richness in the smaller islands

and in the central parts of our study area are clearly an indi-

cation of important centers of restricted species ranges. For

example, at least 15 endemic species are present in the Men-

tawai archipelago, with one Chiroptera species (classified as

data deficient) that is total gap, five threatened and gap Pri-

mates species (among which the total gap and critically

endangered Macaca pagensis and the partial gap and critically

endangered Simia concolor), eight threatened and gap Rodentia

species (among which the total gap and endangered Hylopetes

sipora), and the endangered and total gap Tupaia chrysogaster.

We have also shown that an important number of species

are not covered by any PAs. These species are distributed

throughout the entire study area and their conservation calls

for the creation of new PAs that can contribute to filling the

existing conservation gap. This would be particularly impor-

tant for total gap species over small islands, where species

survival is often endangered by the presence of introduced

species as well as habitat vulnerability and other factors (Pur-

vis et al., 2000; Sodhi et al., 2004b).

Considering gap species, many small semi-natural areas

surrounded by cultivated areas, both in the mainland and in

the larger islands, should be considered for the establishment

of new PAs or, better, for the implementation of effective

management plans, but clearly Papua New Guinea (where

many total gap species occur), Sulawesi, and the internal

mountainous areas represent the areas with the highest con-

servation priority (Fig. 3). Particular attention should be

placed towards species like the saola (Pseudoryx nghetinhensis),

a highly distinctive monotypic genus first described 15 years

ago (Dung et al., 1993) that is highly endangered because of

habitat loss and hunting (Timmins et al., 2007): almost half

of its range is covered by PAs but all the necessary efforts

should be made to extend protection to the entire range of

the species along the Annamite mountains (Vietnam–Laos

borders) where the last know populations of the species occur

at extremely low densities.

Our results are comparable in some ways to those ob-

tained by Rodrigues et al. (2004a), who identified some of

the most important areas for the conservation of mammals

in Southeast Asia. Rodrigues et al. (2004a) developed two

possible global scenarios and found that 5.5–11% of all mam-

mal species were not covered by any PA, a percentage that is

similar to our 8.2–7.5% of total gap species. However, for par-

tial gap species, our results are different: using the same

representation target, Rodrigues et al. (2004a) found that

34% of the species were partial gap, while we found that

52–59% of the species were partial gap. Our results were ob-

tained using both distribution models and EOOs, while

Rodrigues et al. (2004a), besides considering a different study

area and working on a different scale, used species’ EOOs

only, introducing a greater proportion of commission errors

in their analyses (Rondinini et al., 2005). This difference

clearly explains the smaller percentage of partial gap species

(Loiselle et al., 2003; Rondinini et al., 2005) and provides an

indication that our results might be more reliable for conser-

vation applications.

The results we obtained for partial gap species are obvi-

ously important for the identification of areas in need of fur-

ther attention from the conservation community, but the

results we obtained for total gap species are particularly

important. In fact, we found a relatively low number of spe-

cies that are total gaps (8.2–7.5% of the species). Yet a con-

siderably higher number of mammal species of the region

are classified as threatened according to the IUCN red-list

criteria. In fact, 34% of all 1086 species considered in the

SAMD database are listed in the IUCN red-list categories as

critically endangered, endangered, vulnerable, or near threa-

tened, while 45% are classified as least concern and 21% as

data deficient or not evaluated, thus being potential candi-

dates for a threatened category (red-list category assess-

ments reviewed during the five workshops; note that not

all the species assessments have already been officially con-

firmed by IUCN).

This is clearly an indication that PAs cannot be considered

as the ending point of our conservation strategies. Defining

PAs is an option that can be pursued relatively easily in many

situations. However, managing effective PAs for conservation

is much more difficult and expensive than just establishing

them and different studies have indicated that PAs in the tro-

pics usually act as ‘‘paper parks’’ (Schwartzman et al., 2000;

Curran et al., 2004; Fuller et al., 2004; Sigel et al., 2006; Sodhi

et al., 2006b; Verburg et al., 2006; Gaveau et al., 2007; but see

Bruner et al., 2001; Nagendra et al., 2004; Nepstad et al., 2006).

Moreover, for a number of species PAs do not ensure prop-

er conservation (Corlett, 2007). Notable examples can be

found among many different taxa, going from the Asian ele-

phant (Elephas maximus), to rhinos (Dicerorhinus sumatrensis

and Rhinoceros sondaicus), orangutans (Pongo pygmaeus and

Pongo abelii), pangolins (Manis spp.) to many other taxa (Cor-

lett, 2007) for which PAs have not been able to stop population

declines.

In this context, it is clear that the role of existing PAs, as

well as the establishment of new PAs, should be considered

in a broader framework of all local ecological and socio-eco-

nomic trends, including the growing human population,

growing economy and infrastructure development. PAs alone

cannot be the solution to all conservation problems. In fact,

we should also focus on off-reserve management and the

preservation of natural processes.

Acknowledgements

A great number of people and organizations supported the

project throughout its implementation. It is impossible to list


them all, but we would like to mention at least the following

individuals and organizations that provided data, collabora-

tion, criticism, review and general support: G. Amori, P. T.

Ahn, K. Aplin, D. Balete, P. Bates, R. Boonratana, L. X. Canh,

D. Cesarini, W. Duckworth, C. Francis, C. Groves, T. Kingston,

L. Heaney, K. Helgen, M. Hobbelink, M. Hoffman, D. Lunde, J.

MacKinnon, I. Marzetti, A. Montemaggiori, E. Meijaard, M.

Pedregosa, H. Q. Quynh, G. Reggiani, S. Roberton, M. Rulli, L.

Ruedas, V. Salvatori, S. Savini, W. Sechrest, M. Sinaga, R. Tim-

mins, N. Van Strien, J. Walston, Sapienza Universita di Roma,

the Asean Regional Centre for Conservation of Biodiversity,

the University of the Philippines–Diliman, the Indonesian

Institute of Sciences, the Vietnamese Institute of Ecology

and Biological Resources. Richard Corlett and three anony-

mous reviewers provided helpful comments that greatly im-

proved our original manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found,

in the online version, at doi:10.1016/j.biocon.2008.08.019.

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a gap analysis of southeast asian mammals based on habitat suitability models

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