Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 July 2023 | 15(7): 23463–23471
ISSN 0974-7907
(Online) | ISSN 0974-7893 (Print)
https://doi.org/10.11609/jott.8291.15.7.23463-23471
#8291 | Received 01
December 2022 | Final received 17 June 2023 | Finally accepted 30 June 2023
Predicting suitable habitat for
the endangered Javan Gibbon in a submontane forest in
Indonesia
Rahayu Oktaviani
1, Amaël Borzée
2, Andi Nugraha Cahyana
3, Susan Lappan 4, Ani Mardiastuti 5 & Misbah
Satria Giri 6
1,3 Yayasan Konservasi
Ekosistem Alam Nusantara
(KIARA), Komplek Laladon
Indah, Jl. Kamojang C4/14, Ciomas,
Bogor, Jawa Barat, 16110, Indonesia.
2 Laboratory of Animal Behavior and
Conservation, College of Biology and Environment, Nanjing Forestry University, No.
159 Longpan Road, Nanjing, 210037, Jiangsu, China.
4 Department of Anthropology,
Appalachian State University, Boone, North Carolina, 28608, United States of
America.
5 Department of Forest Resources
Conservation and Ecotourism, Faculty of Forestry and Environment, IPB
University, Dramaga, Bogor, Jawa Barat, 16680, Indonesia.
6 Gunung Halimun
Salak National Park, Kabandungan,
Sukabumi, Jawa Barat,
43368, Indonesia.
1 rahayu_oktaviani@yahoo.com
(corresponding author), 2 amaelborzee@gmail.com, 3 cahyana.an@gmail.com,
4 lappansm@appstate.edu, 5 aniipb@indo.net.id,
6 satriagr1@gmail.com
Abstract: Species distribution modeling is
an essential tool for understanding the ecology of species and has many
applications in conservation. Using maximum entropy (MaxEnt)
modeling, we identify the key factors shaping the potential distribution of the
endangered Javan Gibbons Hylobates moloch in one of the main remnant habitats, Gunung Halimun Salak National Park (GHSNP), Indonesia, using presence-only
data collected between October and November 2015, and in April and May 2016.
Maxent results showed that forest canopy density and annual temperature were
the principal variables predicting the distribution of Javan Gibbons, with
contribution scores of 53.9% and 35.6%, respectively. The predictive
distribution map indicated that suitable habitat for Javan Gibbons is not
uniformly distributed within GHSNP, i.e., suitable habitat is not located
evenly throughout the region, with some areas more suitable than others. Highly
suitable habitat comprises the largest proportion of habitat, with 42.1% of
GHSNP classified as highly suitable habitat, whereas 24.7% was moderately
suitable, and 33.2% of habitat was of low suitability for Javan Gibbons.
Priority should be given to increasing habitat quality in degraded areas and
law enforcement patrols to reduce degradation in peripheral regions of the park
as part of the conservation management strategy.
Keywords: Conservation, forest canopy, Hylobates moloch,
maximum entropy, West Java.
Abbreviations: AUC—Area under the curve |
DEM—Digital elevation model | GHSNP—Gunung Halimun Salak National Park |
IUCN—International Union for Conservation of Nature and Natural resources. | MaxEnt—Maximum entropy | MoEF—Ministry
of Environment and Forestry | SDM—Species distribution models | PCA—Principal
components analyses | ROC—Receiver operating characteristic | SRTM—Shuttle
radar topography mission.
Indonesian abstract: Pemodelan distribusi
spesies menjadi sebuah alat penting
untuk memahami ekologi suatu spesies
dan telah banyak diaplikasikan dalam bidang konservasi.
Melalui pemodelan Maximum
Entropy (Maxent), kami mengidentifikasi faktor-faktor kunci untuk menentukan sebaran potensial bagi Owa Jawa
Hylobates moloch
yang terancam punah di
salah satu habitat tersisa
di Taman Nasional Gunung Halimun
Salak (TNGHS), Indonesia, antara
bulan Oktober dan November 2015, serta bulan April dan Mei 2016. Hasil analisis Maxent menunjukkan bahwa kerapatan tajuk pohon dan
suhu menjadi faktor utama dalam
memprediksi sebaran Owa Jawa. Kedua
faktor utama tersebut memiliki skor kontribusi masing-masing sebesar 53.9% dan 35.6%. Sementara, peta prediksi sebaran Owa Jawa menunjukkan
bahwa habitat yang sesuai tersebar secara tidak merata di dalam kawasan TNGHS. Habitat yang
memiliki kesesuaian tinggi memiliki proporsi terbesar, dimana 42.1% kawasan TNGHS diklasifikasikan sebagai habitat dengan tingkat kesesuaian tinggi, sedangkan 24.7% dari total luas kawasan memiliki
tingkat kesesuaian sedang, dan 33.2% merupakan habitat dengan kesesuaian rendah. Prioritas pengelolaan kawasan harus difokuskan
untuk meningkatkan kualitas habitat di kawasan terdegradasi, serta perlu dilakukan patroli rutin dan
penegakan hukum untuk mengurangi kerusakan habitat di Kawasan TNGHS.
Editor: Mewa Singh,
University of Mysore, Mysuru, India. Date
of publication: 26 July 2023 (online & print)
Citation: Oktaviani, R., A. Borzée,
A.N. Cahyana, S. Lappan, A.
Mardiastuti & M.S. Giri
(2023).
Predicting suitable habitat for the endangered Javan Gibbon in a submontane forest in Indonesia. Journal of Threatened Taxa 15(7): 23463–23471. https://doi.org/10.11609/jott.8291.15.7.23463-23471
Copyright: © Oktaviani et al. 2023. Creative Commons Attribution 4.0
International License. JoTT allows unrestricted use,
reproduction, and distribution of this article in any medium by providing
adequate credit to the author(s) and the source of publication.
Funding: This work was funded by The Rufford Foundation, with grant number: 16654-1, and Idea Wild provided equipment for the field surveys.
Competing interests: The authors declare no competing interests.
Author details: RAHAYU OKTAVIANI (RO) is a
primate conservationist focused on gibbon conservation, currently leading an
Indonesian-based non-profit organization, Yayasan Konservasi Ekosistem Alam Nusantara (KIARA) and the co-vice chair of IUCN
Primate Specialist Group - Section on Small Apes. AMAËL BORZÉE (AB) is a
professor at Nanjing Forestry University and the co-chair of the IUCN SSC
Amphibian Specialist Group, interested in conserving multi-species populations
over large landscapes, including multiple type of approaches and analytical
tools. ANDI NUGRAHA CAHYANA (ANC) is a wildlife conservationist interested in
quantitative ecology analysis, spatial modeling, and wildlife habitat mapping.
Susan Lappan (SL) is a professor at Appalachian State
University and a behavioral ecologist interested in the relationships among
habitat characteristics, social organization, and male and female reproductive
strategies. Ani Mardiastuti (AM) is a senior
Indonesian researcher and a professor at IPB University with extensive
experience in ornithology and wildlife ecology. Misbah
Satria Giri (MSG) is a GIS
Specialist at Gunung Halimun
Salak National Park, Ministry of Environment and
Forestry, Indonesia.
Author
contributions: RO and ANC
developed the study concept and collected data; RO and ANC analyzed the data
and RO wrote the manuscript; AB, SL, ANC, AM, and MSG provided feedback and
contributed to completing the final manuscript. All authors contributed to the
article and approved the submitted version.
Acknowledgements: We thank the authorities of Gunung
Halimun Salak National Park
for granting us research permission (SIMAKSI No. 05/P/TNGHS/2015 and SIMAKSI
No. 09/P/TNGHS/2016) and for their assistance during the field surveys. We
thank the following individuals for their contributions to the fieldwork: A.M.
Putra, Arya F., Djamaludin, E.M. Aaf
Afnan, Fahri Budiman, Hery Sudarno, Kuntoro
Bayu Aji, M. Faesal R.K., M. Choerudin, M. Sukri, Putra Caeruleus, and Yanuar I.D. We also thank Hariyo
T. Wibisono, the editor, and the anonymous reviewers
for their valuable comments and suggestions that improved the manuscript.
INTRODUCTION
Understanding the distribution of animals in space and
ecological predictors of abundance are crucially important for designing
effective conservation plans (Sarma et al. 2015).
However, for most species, resources are not adequate to permit detailed
surveys across every area of their potential distribution range. To address
this problem, various modeling techniques have been
developed to predict species distributions and identify suitable habitats by
combining occurrence records with digital layers of environmental variables
(Peterson 2001; Guisan & Thuiller
2005; Ortega-Huerta & Peterson 2008). Species distribution models (SDM)
have been applied to various conservation problems. For instance, SDM have been
used to prioritize areas for conservation (Araújo & Williams 2000), to
predict geographical patterns of species occurrence (Peterson 2003), to discover
unknown populations (Pearson et al. 2006), to improve the assessment of risk
status (Solano & Feria 2006), and to predict
species displacement patterns resulting from climate change (Borzée et al. 2019).
Several algorithms for modeling
distributions use evidence of the presence or absence of a species in different
locations. However, reliably determining that a species is absent is not often
possible, limiting these algorithms’ applicability. Alternatively, maximum
entropy models (MaxEnt) aim to characterize species
probability distributions using presence-only data, and can be applied even in
situations with incomplete information from limited datasets (Pearson et al.
2006; Phillips et al. 2006; Guisan et al. 2007). MaxEnt can accurately predict habitat suitability based on
relatively few variables (Liu et al. 2001; Dayton & Fitzgerald 2006) and
these models can conform to the realized niche of species (Stone et al. 2013).
This approach has been used to develop SDM in a wide range of primate species,
including Asian Slow Lorises Nycticebus spp.
(Thorn et al. 2009), Spider Monkey Ateles
geoffroyi (Vidal-García & Serio-Silva 2011),
Ecuadorian Capuchin Cebus albifrons (Campos & Jack 2013), Peruvian Night
Monkey Aotus miconax
(Shanee et al. 2015), Eastern Hoolock Gibbon Hoolock
leuconedys (Sarma et
al. 2015), Western Hoolock Gibbon Hoolock hoolock
(Naher et al. 2021), Southern Yellow-Cheeked Gibbon Nomascus gabriellae
(Nhung et al. 2021), and Bornean Agile Gibbon Hylobates
albibarbis (Singh et al. 2018).
Javan Gibbons H. moloch
are endemic to Java, Indonesia, and are generally restricted to the western and
central parts of the island (Nijman 2004). Globally, Javan Gibbons are listed
as ‘Endangered’ on the IUCN Red List (Nijman 2020). This species is sensitive
to habitat alteration because of their dependence on closed-canopy forests for
food (Kim et al. 2012), locomotion (Bertram 2004), and sleeping trees (Ario et al. 2018). Deforestation and forest degradation are
primary threats as they disrupt the forest canopy and result in habitat
fragmentation (Geissmann 2003; Smith et al. 2017).
It is estimated that up to 96% of the original Javan
Gibbons habitat has been lost (Supriatna 2006; Nijman
2013; Malone et al. 2014), and most of the remaining habitat is located in protected
areas such as Gunung Halimun
Salak National Park (GHSNP). GHSNP is the largest
remaining forest block in the region and represents the last stronghold for the
species, likely harboring 25% and 50% of the global
Javan Gibbon population (Nijman 2004). However, estimates of the total
population within GHSNP vary dramatically, and populations within GHSNP may be
effectively isolated from each other by enclaves of human activity within the
park. The probability of persistence for these populations in the long term is
likely to be affected by the total carrying capacity and the degree of
isolation among subpopulations within GHSNP (Smith et al. 2017). Therefore, a
better understanding of the total carrying capacity of GHSNP and the factors
affecting habitat suitability is critical for effective conservation planning.
Two habitat suitability analyses for Javan Gibbons in
GHSNP have been conducted using principal components analyses (PCA). Helianthi et al. (2007) estimated that 71.43% of the total
area of GHSNP is highly suitable for Javan Gibbons, while Ikbal
et al. (2008), in an analysis restricted to the Mount Salak
region within GHSNP, estimated that only 13.20% of the habitat was highly
suitable. Given changes in forest management and ongoing habitat alteration,
habitat quality for Javan Gibbons in GHSNP may have changed in recent years;
thus, a new approach and update are needed. We used MaxEnt
modeling to identify environmental factors that
contribute to the Javan Gibbon presence and to identify areas in GHSNP where
habitat characteristics best align with the ecological niche of the species.
The results of this study may help identify priority areas for conservation
efforts and may lead to improved management practices within the park to ensure
the continued survival of Javan Gibbons as one of the key species in GHSNP.
MATERIALS AND METHODS
Study Area
This study was conducted at GHSNP,
Indonesia (6.739° S, 106.530° E), located within three administrative
districts: Bogor and Sukabumi in West Java Province
and Lebak in Banten Province. The Halimun
area was established as a national park in 1992. To reduce forest loss, the
Indonesian government increased the size of the protected area in 2003 by
merging Halimun National Park and Salak
Reservation Area, including the production forest. Currently, GHSNP covers an
area of approximately 87,699 ha. Besides protecting water catchment areas for
several big cities near the national park, it also protects essential habitat
for endangered species such as Javan Gibbons, Javan Leopards Panthera pardus melas, and Javan Hawk-Eagles Nisaetus
bartelsii. The park includes forests ranging from
500–2,200 m, a tropical climate with annual temperatures between 19° C
and 31° C, and average precipitation of 4,000–6,000 mm. This national
park experiences various pressures, including illegal gold mining, poaching,
and forest encroachment for agricultural land & settlements, which cause
fragmentation and degradation. Forest encroachment for agriculture is the
biggest threat to GHSNP, driving fragmentation that may threaten the
persistence of protected species in the area (Iwanda
et al. 2019). Moreover, social conflicts related to land ownership, intensive
land use, and ongoing timber exploitation by the rural community are
significant problems in managing this national park (Rosleine
et al. 2014).
Method
Field Survey
We conducted field surveys to
determine the occurrence of Javan Gibbons at 10 locations across the GHSNP
(Figure 1). We selected survey areas by combining historical information from Ikbal et al. (2008) and information obtained during a
meeting in October 2015 with two GHSNP officers: Mr. Wardi
Septiana from Conservation Area Affairs and Mr. Momo Suparmo from Biodiversity
Conservation Affairs. In total, we obtained 73 occurrence records of Javan
Gibbons across 10 survey sites representing ten resorts (the smallest
administrative unit of the national park); 80.8% of occurrence records were
based on direct observation, and 19.2% were based on indirect observation.
Field surveys were conducted in
both rainy and dry seasons. The survey for the rainy season was undertaken
between October and November 2015, while the dry season survey took place
between April and May 2016 along the transect lines. To minimize negative
impacts on the survey area, the survey team (2–3 people for each site,
including at least one of the authors) walked along existing trails in the
forest for 1–2 km depending on the difficulty of the terrain. Surveys were
conducted for four hours in the morning (0700–1100 h) and three hours in
the afternoon (1400–1700 h) each day of a four-day survey. This schedule
was followed during both seasons except on heavy rainy days when we stopped the
observation and repeated it the next day. The survey times were chosen based on
the activity patterns of the species. During the walks, we recorded the time
and location for all direct (visual) and indirect (auditory) encounters using a
GPS Garmin 64s (Kansas, United States), by estimating the distance from the
observers the individuals sighted by using Bushnell Digital Laser Rangefinder
850 (Utah, United States), and sighting angle between the transect line and the
observers to species line.
Data Analysis
We included seven environmental
variables in our models that were also used in previous modeling
for the same species (Helianthi et al. 2007; Suheri et al. 2014; Widyastuti et
al. 2020), and as they were found to be likely to influence habitat use by
Javan Gibbons (Table 1).
We used MaxEnt
v3.3.3 (Phillips et al. 2006) to produce a map of suitable habitats for Javan
Gibbons in GHSNP. Of the 73 occurrence data points, 75% of points were used as
a training sample and 25% of points as references for model validation.
Environmental variables that predicted >10% of the variance in gibbon
presence in the models were identified as important, following Norris et al.
(2011).
We classified habitat with values
< 0.25 as having low suitability, values between 0.25–0.75 as having
moderate suitability, and values >0.75 as having high habitat suitability
for Javan Gibbons. In most cases, values greater than 0.5 indicate suitable
habitat (Yang et al. 2013). The default value of 1 has been identified as the
most suitable to prevent overfitting (Merow et al.
2013).
Model accuracy should be tested in
a modeling approach to evaluate model performance. We
used a receiver operating characteristic (ROC) value closer to 1 to assess the
model. This method does not require arbitrary threshold selection and has been
widely used. The ROC generates a single measure of model performance called
area under the curve (AUC) with AUC values >0.9 indicating high accuracy of
the model (Elith et al. 2006; Phillips et al. 2006).
RESULTS
The final ecological niche model for Javan Gibbons
provided a ROC with an AUC of 0.936 for the training data, indicating good
performance and suggesting that the model can be used to predict species
occurrence. Among the seven environmental variables investigated, forest canopy
density and mean annual temperature contributed the most to the model and to
predicting Javan gibbon distribution, accounting for 53.9% and 35.6% of the
variation in habitat suitability, respectively (Table 2). No other variables in
the model were identified as important predictors of habitat suitability for
Javan Gibbons.
Most of the area within GHSNP was classified as highly
suitable or moderately suitable, with highly suitable habitat comprising the
largest proportion of habitat. A total of 36,921 ha (42.1%) of GHSNP was
classified as highly suitable habitat, whereas 21,662 ha (24.7%) was classified
as moderately suitable, and 29,116 ha (33.2%) was considered to be habitat of
low suitability for Javan Gibbons (Figure 2).
DISCUSSION
The MaxEnt analysis confirmed
that forest canopy density was the most critical predictor of Javan gibbon
distribution in GHSNP and suggested that habitat with dense tree cover is
associated with a greater probability of occurrence for this species. Widyastuti et al. (2020) reported similar results for Javan
Gibbons in the Dieng Highland in Central Java, where
the presence of natural forest with a connected canopy was the most crucial
variable predicting habitat suitability in their MaxEnt
analysis. Gibbons preferentially use high canopy layers for many activities,
including travel, feeding, resting, and singing (Fan et al. 2009; Hamard et al. 2010; Cheyne et al. 2016; Jang et al. 2021).
Because Javan Gibbons, like all small apes, primarily travel through
brachiation (arm-swinging locomotion that can only be performed across a
relatively intact forest canopy), they require high canopy connectivity to
travel efficiently and are particularly susceptible to habitat disturbance.
High forest connectivity may also indicate high tree density or the presence of
large trees, which are associated with the increased availability of plant
foods (Zhang et al. 2022) and protection against predators. Our observations
suggest that avian predators represent a real threat to gibbons, as we observed
the predation attempts from above to the immature individuals by Spilornis cheela (Rahayu Oktaviani pers. obs.
September 27th, 2019 & February 26th, 2020).
Canopy cover and tree height have also been found to
influence the spatial distribution and density of other gibbon species, i.e.,
Agile Gibbons Hylobates agilis
(Pang et al. 2022), Borneon White-Bearded Gibbons
Hylobates albibarbis
(Singh et al. 2018), Hoolock Gibbons Hoolock hoolock
(Alamgir et al. 2015), Yellow Cheeked-Gibbons Nomascus
gabriellae (Gray et al.
2010), and other arboreal primates, i.e., Borneon
Orangutans Pongo pygmaeus (Felton et al.
2003), Pied Tamarins Saguinus bicolor (Vidal & Cintra 2006), Thomas’s Langurs Presbytis thomasi
(Slater 2015), and Red-Crested Tamarins Saguinus
geoffroyi (Kim & Riondato
2016).
Climatic conditions have long been observed to play a
primary role in limiting species distributions (Gaston 2003; Franklin 2009; Kamilar 2009), either directly or indirectly, through their
effects on vegetation (Guisan & Thuiller 2005). Climatic variables may affect the
productivity of food plant species that animals consume and, therefore, affect
animal behavior, abundance, and distribution
(Vidal-García & Serio-Silva 2011). For example, temperature and
precipitation affect the distribution of Hoolock Gibbons, likely because of the
influence of climate variables on the phenology of fruiting trees (Alamgir et
al. 2015; Sarma et al. 2021).
Accordingly, our results showed that mean annual
temperature is the second-most important predictor of Javan Gibbons
distribution in GHSNP. This variable is also correlated with elevation, and the
relationship with Javan Gibbon distribution may result from an indirect influence
of temperature on plant productivity. From an activity budget and behavior perspective, temperature variation may also
influence resting time, an essential determinant of primate distribution (Stone
et al. 2013; Fei et al. 2019). As a result, feeding and traveling time are
generally positively affected by temperature in frugivorous primates (Korstjens et al. 2010; Fan et al. 2012). In future studies,
the inclusion of animals experiencing a broader range of ecological conditions
could shed more light on Javan Gibbons responses to temperature variation.
The model showed that most of the highly suitable habitat
for Javan Gibbons is in the central part of the park, where substantial areas
of sub-montane forest have the optimal physical and biotic resources to support
Javan Gibbons. However, the area of highly suitable habitat is discontinuous,
with some areas fragmented or isolated by areas with lower suitability for
Javan Gibbons, especially in the western and eastern parts of the park.
Isolation in habitat fragments could severely threaten
Javan Gibbons’ long-term survival in these areas. For example, a recent
Population and Habitat Viability Analysis for Javan Gibbons in GHSNP by Smith
et al. (2017) showed that if the population is fragmented under current
pressures, all subpopulations are likely to decline substantially in the next
100 years, and local extinction is very likely for the smallest subpopulations.
Thus, maintaining or reestablishing connectivity of
fragmented habitats and restoring habitat quality in habitat corridors is
critical to facilitating the dispersal of arboreal species like Javan Gibbons
across areas of high-quality habitat in GHSNP. Low suitability habitat mainly
occurs in the peripheral areas of the park, which may limit Javan Gibbons to
more central areas with higher food abundance in GHSNP.
Our species distribution modeling
has limitations because it is based on the current realized niche (i.e., it
considers where Javan Gibbons occur in the present day) rather than the
fundamental niche (the range of places Javan Gibbons could occupy). Other studies
have shown that some areas fall under environmental conditions matching the
species’ ecological environments, although the species does not occur in these
areas (Raxworthy et al. 2003; Pearson et al. 2006;
Thorn et al. 2009; Abolmaali et al. 2018). The model
is also based on surveys at only a limited set of sites within the GHSNP
landscape. A more detailed analysis based on a more extensive data set would
allow the inclusion of more explanatory variables, which might improve our
ability to model the Javan Gibbons ecological niche accurately.
The results of this study add to a growing body of
information about Javan Gibbons distribution and habitat suitability in GHSNP,
one of the most significant remaining habitats for this endangered species
(Nijman 2020). The predictive distribution map indicates that suitable habitats
for Javan Gibbons are not uniformly distributed across GHSNP; some areas in
GHSNP are more suitable than others for the species. Most of the suitable area
is in the central part of the park, which must be protected to optimize the
habitat and ensure the long-term persistence of the species. In addition, some
high-quality habitat is located in peripheral areas of GHSNP. To prevent
further degradation of these areas and to maintain and improve connectivity
between fragments of high-quality habitat, buffer areas surrounding areas of
high-quality habitat should be protected and, where possible, restored.
To ensure the long-term persistence of Javan Gibbons, an
endangered species endemic to Indonesia, we recommend that the Indonesian
Ministry of Environment and Forestry (MoEF) and the
GHSNP authorities prioritize habitat protection to prevent erosion and
degradation of high-quality habitats, including the area of Resort Cikaniki, Gunung Kendeng, and Gunung Bedil. Habitat restoration to increase habitat quality in
degraded habitat in the peripheral areas of the park (i.e., the area of Resort Gunung Bongkok, Cisoka, and Gunung Talaga) is crucial to improve the low-medium suitable
habitat adjacent to higher-quality habitat patches, especially in the corridor
area connected the region of Halimun and Mount Salak as part of their conservation management strategy.
Table 1. Predictor variables of
habitat suitability for Javan Gibbons in GHSNP.
|
|
Environmental variable |
Unit |
Data source |
|
1 |
Annual
precipitation |
Millimeters |
Bioclimatic
map (http://www.worldclim.org/) |
|
2 |
Mean annual
temperature |
°C |
Bioclimatic
map (http://www.worldclim.org/) |
|
3 |
Aspect |
Degrees |
Digital
Elevation Model (DEM) SRTM with a 30-meter spatial resolution
(http://earthexplorer.usgs.gov/) |
|
4 |
Distance
from river |
Meters |
The
Euclidean distance at software QGIS 2.10 |
|
5 |
Elevation |
Meters |
Digital
Elevation Model (DEM) SRTM with a 30-meter spatial resolution
(http://earthexplorer.usgs.gov/) |
|
6 |
Forest
canopy density |
% |
Imagery 8
2013 using the software Forest Canopy Density Mapper V2 |
|
7 |
Slope |
% |
Digital
Elevation Model (DEM) SRTM with a 30-meter spatial resolution
(http://earthexplorer.usgs.gov/) |
Table 2. Environmental variables
and their contribution to habitat suitability in a Maxent model for Javan
Gibbons in GHSNP.
|
|
Environmental variable |
Predictive value and % contribution |
|
1 |
Forest
canopy density |
53.9 |
|
2 |
Annual
temperature |
35.6 |
|
3 |
Annual
precipitation |
6.3 |
|
4 |
Slope |
2.5 |
|
5 |
Distance
from river |
1.7 |
|
6 |
Elevation |
0.1 |
|
7 |
Aspect |
0 |
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