Journal of Threatened Taxa |
www.threatenedtaxa.org | 26 July 2022 | 14(7): 21331–21346
ISSN 0974-7907 (Online) | ISSN 0974-7893
(Print)
https://doi.org/10.11609/jott.7483.14.7.21331-21346
#7483 | Received 01 June 2021 | Final
received 18 February 2022 | Finally accepted 05 July 2022
The Javan Leopard Panthera
pardus melas (Cuvier, 1809) (Mammalia: Carnivora: Felidae) in West Java,
Indonesia: estimating population density and occupancy
Anton Ario 1, Senjaya
Mercusiana 2, Ayi Rustiadi 3, Robi Gumilang 4 ,
I Gede Gelgel Darma Putra Wirawan 5
& Toni Ahmad Slamet
6
1 Konservasi Indonesia, Jl. Pejaten
Barat No. 16A, Kemang, Jakarta Selatan 12550, Indonesia.
2 Gunung Halimun Salak National
Park, Jl. Raya Cipanas, Kabandungan, Sukabumi, Jawa Barat 43368, Indonesia.
3 Gunung Gede Pangrango National
Park, Jl. Raya Cibodas, Cipanas, Cianjur, Jawa Barat 43253, Indonesia.
4 Gunung Ciremai National Park, Jl.
Raya Kuningan-Cirebon Km 9 No.1 Manislor, Jalaksana, Kuningan, Jawa Barat
45554, Indonesia.
5,6 West Java Natural Resources
Conservation Agency, Jl. Gedebage Selatan No.117, Rancabolang, Gedebage, Kota
Bandung, Jawa Barat 40294, Indonesia.
1–4 Javan Leopard Conservation Forum
(Formata), Jl. Taman Safari Indonesia, Cisarua, Bogor, Jawa Barat 16750,
Indonesia.
1 aario@konservasi-id.org
(corresponding author), 2 mercusianahalimun@gmail.com, 3 ayi.rustiadi@gmail.com,
4 robi_479@live.com, 5 gddharma@yahoo.co.id, 6 toniahmadhutan@gmail.com
Abstract: The Javan Leopard is endemic to
the Indonesian island of Java and has been classified as Endangered. Reliable
information about its population status, distribution, and density is lacking
but are essential to guide conservation efforts and provide a benchmark for
management decisions. Our study represents the first empirical density and
occupancy estimates for the Leopard in West Java and provides baseline data for
this region. We used camera trap data collected from February 2009 to October
2018 in six study areas comprising a sampling effort of 10,955 camera trap days
in a total area of 793.5 km2. We identified 55 individual Leopards
in these areas and estimated Leopard density using spatially explicit
capture-recapture. Population density estimates range from 4.9 individuals/100
km2 in Gunung Guntur-Papandayan Nature Reserve to 16.04
individuals/100 km2 in Gunung Gede Pangrango National Park. Latter
is among the globally highest Leopard densities. Based on detection data, we
modelled single-season Leopard occupancy using three sampling covariates and
eight site covariates. Modelling revealed that the two covariates forest cover
and presence of Wild Boar are the strongest predictors for Leopard occupancy in
our study areas. We recommend assessing and monitoring Leopard distribution,
density and occupancy in other areas of Java and emphasize that a landscape
approach for conservation of the Javan Leopard is imperative.
Keywords: Camera trap, conservation
management, habitat use, spatially explicit capture-recapture.
Bahasa:
Macan Tutul Jawa adalah satwa endemik pulau Jawa di Indonesia dan
diklasifikasikan sebagai Endangered species. Informasi terpercaya
tentang status populasi, distribusi, dan kepadatannya masih kurang, namun
sangat penting sebagai pedoman dalam upaya konservasi dan memberikan tolok ukur
untuk intervensi pengelolaan. Studi kami mewakili perkiraan kepadatan dan
hunian empiris pertama untuk Macan Tutul di Jawa Barat dan menyediakan data
dasar untuk wilayah ini. Kami menggunakan data camera trap yang
dikumpulkan dari Februari 2009 hingga Oktober 2018 di enam wilayah studi yang
meliputi upaya pengambilan sampel selama 10.955 hari rekam di total area seluas
793,5 km². Kami mengidentifikasi 55 individu Macan Tutul di seluruh wilayah
studi dan memperkirakan kepadatan Macan Tutul menggunakan spatially explicit
capture-recapture. Perkiraan kepadatan berkisar dari 4,9 individu/100 km²
di Cagar Alam Gunung Guntur-Papandayan hingga 16,04 individu/100 km² di Taman
Nasional Gunung Gede Pangrango, yang merupakan salah satu kepadatan macan tutul
tertinggi secara global. Berdasarkan data deteksi, kami memodelkan hunian Macan
Tutul satu musim menggunakan tiga kovariat pengambilan sampel dan delapan
kovariat lokasi. Pemodelan mengungkapkan bahwa dua kovariat yaitu tutupan hutan
dan keberadaan Babi Hutan adalah prediktor terkuat untuk hunian Macan Tutul di
wilayah studi kami. Kami merekomendasikan untuk menilai dan memantau
distribusi, kepadatan dan hunian Macan Tutul di wilayah lain di Jawa dan menekankan
bahwa pendekatan lansekap sangat penting untuk konservasi Macan Tutul.
Editor: Angie Appel,
Wild Cat Network, Germany. Date
of publication: 26 July 2022 (online & print)
Citation: Ario, A., S. Mercusiana, A.
Rustiadi, R. Gumilang, I.G.G.D.P. Wirawan & T.A. Slamet (2022). The Javan Leopard Panthera
pardus melas (Cuvier, 1809) (Mammalia: Carnivora: Felidae) in West Java,
Indonesia: estimating population density and occupancy. Journal of Threatened Taxa 14(7): 21331–21346. https://doi.org/10.11609/jott.7483.14.7.21331-21346
Copyright: © Ario et al. 2022. 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: Sea World and Busch Garden, Daikin
Corporation and Star Energy.
Competing interests: The authors declare no competing
interests.
Author details: Anton Ario’s educational background is in
biology, environment and forestry with an emphasis on conservation and ecology.
He holds a Doctor from the IPB University and has over 25 years of conservation
experience, including biodiversity of Javan Leopard and Javan Gibbon. He joined
Conservation International Indonesia in 1999, and is now the Sundaland
Biodiversity Conservation Strategy Senior Manager in Konservasi Indonesia. Senjaya Mercusiana has been working as
forest ecosystem technician for Gunung Halimun Salak National Park, Ministry of
Environment and Forestry since 2004. He is experienced in camera trapping for
wildlife conservation, including the Javan Leopard. Ayi Rustiadi holds a BSc from University of Pakuan. He has been working in Ministry of Environment
and Forestry since 2002 and is assigned to Gunung Gede Pangrango National Park
as a forest ecosystem technician. He is experienced in species conservation
including the Javan Leopard. Robi
Gumilang holds a BSc from IPB University and has been working in
Ministry of Environment and Forestry since 2008. He is assigned to Gunung
Ciremai National Park as a forest ecosystem technician and is experienced in
ecology and conservation of mammals. I
Gede Gelgel Darma Putra Wirawan has a Master of Arts from the
International Institute of Social Studies at Eramus University Rotterdam. He
has been working in Ministry of Environment and Forestry since 1999 and is
assigned to West Java Natural Resources Conservation Agency. He is experienced
in regional development and biodiversity conservation. Toni Ahmad Slamet has been working as a forest ranger for
West Java Natural Resources Conservation Agency, Ministry of Environment and
Forestry since 2005. He is experienced in forest protection and wildlife
conservation.
Author contributions: All authors contributed equally
to conceiving and designing the study and assisted in data collection. AA
performed the analyses and wrote the manuscript.
Acknowledgements: This project is part of a
collaboration between the Indonesian Ministry of Environment and Forestry
(MoEF) and Conservation International Indonesia started in 2009–2021. We thank
the MoEF for the permission to use the collected data. We are grateful to Sea
World and Busch Garden, Daikin Industries Ltd., and Star Energy for funding
support. We thank Semak Foundation, Javan Gibbon Foundation, Rawayan, Raksa
Giri Sawala, Eagle and Tepala community groups, the Javan Leopard Conservation
Forum (Formata) and Konservasi Indonesia for supporting this research. We thank
Angie Appel and anonymous reviewers for their valuable inputs.
INTRODUCTION
With a range extending from Africa to eastern and southeastern Asia, the
Leopard Panthera pardus has the widest distribution of the wild Felidae
(Stein & Hayssen 2013). It inhabits arid and rugged montane regions,
savanna grasslands, shrubland, temperate forests and rainforests (Nowell &
Jackson 1996). Despite its adaptability to a wide range of habitats, it is
primarily threatened by habitat fragmentation and depletion of its natural prey
base (Stein et al. 2020). Endemic to the Indonesian island of Java, the Javan
Leopard P. p. melas is classified as Endangered in the IUCN Red List of
Threatened Species (Wibisono et al. 2021). It is listed on CITES Appendix I and
nationally protected by Indonesian law (Ministry of Environment and Forestry
2018). Yet, reliable information on the Javan Leopard’s population status,
habitat use and density is lacking (Wibisono et al. 2018).
Java is home to 141 million people, and with 1,115 people/km² (Badan
Pusat Statistik 2020) has one of the highest human population densities in the
world (Dsikowitzky et al. 2019). West Java is the most densely populated
province in Indonesia with 1,394 people/km² (Badan Pusat Statistik 2020). Human
pressure on the Leopard’s remaining natural habitat continues to increase in
Java and has restricted its distribution to an extent that remaining suitable
landscapes has been estimated at 11,599 km2, which corresponds to
8.9% of the island (Wibisono et al. 2018). Both the Leopard and its prey are
threatened by retaliatory killing and poaching, habitat loss and fragmentation,
large-scale degradation by plantation companies and human encroachment into
protected areas (Ministry of Environment and Forestry 2016; Gunawan et al.
2017). Leopards increasingly approach settlements in search for prey, which
results in conflict with people over livestock (Ministry of Environment and
Forestry 2016). An annual average of 4.6 Leopards have been removed from the
wild between 2007 and 2019 (Adhiasto et al. 2020). In this period, 29 Leopards
were captured due to conflict, of which four individuals were released into the
wild, five died in captivity, and 20 are still kept in zoos and rescue centres
(Adhiasto et al. 2020). These incidents also fuel illegal trade in body parts
with 51 Leopards confiscated in 41 seizures between 2011 and 2019 (Gomez &
Shepherd 2021).
In 1990, Leopards were known to be present in 12 protected areas with a
guesstimated population of 350–700 individuals (Santiapillai & Ramono
1992). By 2013, the Leopard population was estimated at 491–546 individuals
occurring in 48 habitat patches across Java’s remaining natural forests, based
on data collated during a workshop in 2013 (Ministry of Environment and
Forestry 2016). Occurrence records obtained from 2013 to 2018 in 22 sites
across Java indicate a Leopard population of 188–571 individuals at most
(Wibisono et al. 2021). As the potential population loss is uncertain, reliable
data and robust analyses are essential for a better understanding of the
present Leopard status and viability, and for guiding management decisions
(Traylor-Holzer et al. 2020).
Assessing Leopard density is necessary to provide a baseline for future
reference and is a useful way to increase the precision of island-wide Leopard
status assessments (Ministry of Environment and Forestry 2016). Furthermore,
information on population density and distribution are crucial for assessing
the effectiveness of conservation interventions and provides considerations to
help management authorities for making decisions on conservation planning. In
view of suitable habitat patches being small and isolated, it is equally
important to understand the distribution and habitat use of the Javan Leopard
(Traylor-Holzer et al. 2020; Wibisono et al. 2021). With our study, we aimed at
estimating Leopard population density and occupancy in six forest areas in West
Java province using camera traps in a closed population spatially explicit
capture-recapture (SECR) design and single-season occupancy modelling. These
two methods complement each other by providing a more nuanced assessment of the
population status than a density estimate alone. We anticipate that our results
will form a basis for a comprehensive conservation management plan for the
Javan Leopard.
STUDY AREAS
Our study areas were located in six protected areas in the province of
West Java (Figure 1), comprising three national parks, one strict nature
reserve, one wildlife reserve and one protected forest (Table 1). They are all
situated in Java’s Southern Mountains, which are part of the Sunda Volcanic Arc
that derived from stratovolcano complexes with thermal springs and fumaroles
emitting hot fumes, gases, and vapors (Carranza et al. 2008). These six
protected areas constitute 14% of Java’s Leopard priority landscape (Wibisono
et al. 2018). They are located in eight districts with a total population of
about 20.54 million people (Badan Pusat Statistik Provinsi Jawa Barat 2021).
The tropical climate in the entire region is influenced by the
southeastern Asian and Indo-Australian monsoon winds; the former brings
rainfall from December to January, and the latter causes a dry season from June
to August (Rahayu et al. 2018).
Potential prey species of the Leopard in the study areas include Wild
Boar Sus scrofa, Red Muntjac Muntiacus muntjac, Javan Chevrotain Tragulus
javanicus, Javan Gibbon Hylobates moloch, Javan Lutung Trachypitecus
auratus, Javan Surili Presbytis comata, Long-tailed Macaque Macaca
fascicularis, and Javan Slow Loris Nycticebus javanicus (Ministry of
Environment and Forestry 2016; Ario et al. 2018b).
MATERIAL AND METHODS
Data collection
During the survey period from 2009 to 2018, we had three camera trap
models at our disposal comprising Cuddeback® Digital Scouting Camera model
1125, Cuddeback® X-change white flash model 1279 (NonTypical Inc., Park Falls,
WI, USA) and Bushnell Trophy Cam HD model 119547c. They were set to be active
for 24 hours per day with one minute interval between consecutive photographs.
We set them to take one to three photographs to select the best photograph for
identification.
In each study area, we deployed camera traps in grids of 2 x 2 km2
cells to maximize the chances that all individuals would be photographed, based
on the smallest known Leopard home ranges in Asia (Grassman 1999; Ario et al.
2018b). Similar camera trapping designs were implemented by Borah et al.
(2014), Noor et al. (2020) and Kittle et al. (2021). However, we excluded cells
in the close perimeters of volcanoes that were difficult or potentially
dangerous to access.
Most camera traps were positioned along animal trails where we found
signs such as pugmarks, scrapes or faeces, and oriented in a north-south
direction to avoid direct sunlight. They were mounted perpendicular to trails
at a distance of 3–7 m from the trails’ centre and at a height of 40 cm above
ground to obtain photographs of the Leopard’s flank, body and genitals. This
height corresponds roughly with the shoulder height of an adult Leopard
(Henschel & Ray 2003).
We surveyed each study area once using one camera trap per location due
to the limited number of camera traps at our disposal. We did not use bait and
covered all of GHSNP, GGPNP, GCNP, GSWR and GMPF in one survey block each, but
two survey blocks in GGPNR. We determined coordinates and elevation of each
location using a GPS device Garmin 64s that was set to WGS 84 datum. The
distance between locations was 966–1,830 m. We kept camera traps at locations
for 92 to 102 days to satisfy the assumption of population closure within each
survey (Karanth 1995; Rostro-García et al. 2018). We tested population closure
using the statistical program CAPTURE (Otis et al. 1978).
We consider photographs of single individuals and social units of
several individuals as one detection of the species. Our definition of the term
‘independent detection’ refers to a) successive photographs of different
individuals or social units of the same species, b) non-consecutive photographs
of the same species, and c) one or several consecutive photographs of the same
individual taken at the same location within an interval of 30 minutes.
Spatially Explicit Capture–Recapture
We identified individual Leopards by their distinct rosette patterns on
both flanks, gauged their age class and sexed them by the size of their heads
and bodies, and the presence of testes and dewlaps in males as described by
Balme et al. (2012) (Image 1). Six observers independently verified
identification of individuals. Blurred photographs were excluded for analysis.
Due to using a single camera trap per location, we separated photographs
showing left and right flanks and used the flank with the highest number of
identified individuals for analysis following O’Brien et al. (2003). We
cross-checked identified individuals across neighbouring study areas, where
surveys were conducted over the same period.
We estimated Leopard density in each of the six study areas using the
spatially explicit capture-recapture (SECR) package in R version 3.1.5 (Efford
2018; R Core Team 2018). The SECR method combines information about the capture
locations of individuals with their capture probability at point locations to
estimate density (Efford et al. 2009; Royle et al. 2009). This method is less
biased than conventional closed capture-recapture methods by study design,
sample sizes and variation in detection probabilities for effective
conservation and management (Sollmann et al. 2012; Ramesh & Downs 2013).
To avoid bias in determining the population size estimates for each
study area, we used the effective sampled area and calculated SECR as the basis
for the size of forest area. We analysed the spatial capture histories of
camera traps in a likelihood-based density estimation framework, a method that
does not require the addition of a buffer to the trapping polygon for estimating
effective trapping area resulting in less biased estimates (Efford et al. 2009).
As recommended by Tobler et al. (2013), we used sex covariates to
improve density estimates and to show biologically important differences in
movement patterns and detection probabilities between the two sexes. We used
locations and detections of identified individuals on one or more sampling
occasions, i.e. their detection histories, as input data for the SECR. We then
separated the results of SECR analyses in group according to sex for each location.
The impact of sex on the parameter probability of capture at the
activity centre of an individual (g0) and the spatial scale parameter describe
the decline in probability of capture with distance from the activity centre (σ)
(Efford et al. 2009). We tested g0 and σ through the comparison of four
alternative models using the Akaike Information Criterion (AIC) adjusted for
small sample size (AICc; Burnham & Anderson 2002): “secr.0” (null model),
“secr.sex.g0” (g0 varies between males and females), “secr.sex.σ” (σ varies
between males and females), and “secr.sex” (both g0 and σ vary between males
and females) (Efford 2015; Boron et al. 2016). This model assumes that the
detection of all individuals is governed by the same detection versus distance
curve at all detectors on all occasions (Efford 2018).
Occupancy probability
We used single-season occupancy modelling to estimate occupancy
probability (ψ) of Leopards at each site, with maximum likelihood estimation
based on detection-nondetection data. The single-season model has three
assumptions: 1) the method used to detect the species must generate
non-equivocal presence data, 2) all the sampled sites must be ‘closed’ to
change in occupancy for the duration of the survey period, and 3) detection of
the species at a site should be independent from the detections at any other
site. In order to allow for the estimator (ψ) to be interpreted as the
proportion of area occupied, the following assumptions of an occupancy model
were made: 1) sites are closed to changes in occupancy, i.e. they are either
occupied or not by the species for the survey duration; 2) species are correctly
identified; 3) detections are independent; and 4) heterogeneity in occupancy or
detection probability are modelled using covariates (MacKenzie et al. 2006).
We reconstructed the Leopard camera trap history in each study area and
divided the data into sampling occasions. We constructed a
detection-nondetection matrix for all camera traps and occasions, with an entry
of 1 if a Leopard had been detected at a particular location and occasion, and
an entry of 0 otherwise. We categorized photographs into binary detection
histories (1 = detected, 0 = not detected) by aggregating 15 survey days as a
single survey occasion. The goodness-of-fit of the most complex model that
included all contributing covariates (see below) was tested in four different
collapsing scenarios (7-, 10-, 12-, and 15-day periods; MacKenzie & Bailey
2004). The 15-day period represented the optimum period length to maximize
model fit (Tan et al. 2017). We entered the data into PRESENCE 2 version 12.41
(Hines 2006).
We used a constant model comprising the two components (ψ) and detection
probability (p), and included three sampling covariates that potentially affect
detection probability: camera traps were placed on animal trails (trail);
trigger speed of camera trap model (camera) (Strampelli et al. 2018), and
number of days the cameras traps were active in each location (effort) (Tan et
al. 2017). We also included five site covariates, namely elevation, forest
cover, distance to river, distance to village, and distance to road (Table 2), that
potentially affect Leopard habitat use and detection probability (Ngoprasert et
al. 2007; Erfanian et al. 2013; Mondal et al. 2013; Havmøller et al. 2019). We
determined elevation using a GPS device Garmin 64s. We extracted values for
forest cover and distances from the database of Badan Informasi Geospasial
(2013) in ArcGIS version 10.4.1. We used the top ranked model on sampling
covariates and site covariates with the lowest AIC score as a constant for
building models that influenced habitat use and detection probability (Athreya
et al. 2015).
Additionally, we included relative abundance index (RAI) values of three
potential ungulate prey species from every camera trap location in each
protected area as site covariates. RAI values are calculated as independent
detections of these species per 100 days of camera trapping.
We ranked models based on the AICc values and identified those with the
lowest AICc values as the best output models (MacKenzie et al. 2006). The best
approximating models were selected based on the AICc and Akaike weights (wi).
We then designated models with ΔAIC ≤ 2 as the top candidate (Burnham &
Anderson 2002). From those models, we considered covariates to be important if
they had relatively high-summed Akaike weights and outcompeted the null model
[ψ(.), p(.)] with constant occupancy and detection to provide the most useful
information regarding covariates that relate to Leopard occupancy.
RESULTS
Camera trapping
Between 1 February 2009 and 10 October 2018, we covered a total of 152
locations in an effective sampled area of 793.5km2 with a total
sampling effort of 10,955 camera trap days. We lost 12 camera traps due to
theft, and seven were moved by people and covered with large leaves and
branches. The surveys yielded 368 independent detections of 55 individual
Leopards, comprising 161 of right flanks and 207 of left flanks; they were
recorded at 85 locations at an elevation range of 818–2,635 m (Table 3). We
discarded 69 blurred photographs for analysis. All identified individuals were
adult (Images 2 to 5) and included five melanistic ones (Image 6).
Leopard density
Statistical tests support the population closure assumption for Javan
Leopard in GGPNP (z = -0.31; p = 0.37), GCNP (z = 0.45; p = 0.67), GMPF (z =
-0.01, p = 0.16), GSWR (z = -0.34, p = 0.37), GGPNR (z = -0.61, p = 0.27) and
GHSNP (z = 0.28; p = 0.61).
For estimating Leopard density, the model based on no variation between
sexes ranks top in three study areas, whereas variation between sexes ranked
top in two study areas (Table 4).
Leopard density ranged from 4.92 ± 2.29 individuals/100 km² in GGPNR to
16.04 ± 6.29 individual/100 km² in GGPNP. The movement parameter (σ) was lowest
in GGPNP with 1,070 m ± 1.81 for males and 676 m ± 1.24 for females, and
highest in GGPNR with 4,227 m ± 1.21 and 2,564 m ± 6.69 for males and females,
respectively. The probability of detection at home range centre (g0) was lowest
in GSWR with 0.01 ± 0.012 for males and 0.031 ± 0.056 for females, and highest
in GGPNR with 0.053 ± SE 0.051 for males and 0.064 ± SE 0.051 for females
(Table 5).
Based on calculations, the analysis revealed an estimated population of
about 20 Leopards in 125.8 km² of GGPNP (95% CI: 9.68–42.38), seven Leopards in
85 km² of GMPF (95% CI: 2.62–18.87), five Leopards in 86.9 km² of GSWR (95% CI:
1.62–17.20), nine Leopards in 186.8 km² of GGPNR (95% CI: 3.98– 22.04), and 15
Leopards in 159 km² of GHSNP (95% CI: 7.47–30.04).
Detection probability
The estimated Leopard detection probability (p) ranges from 0.13 in GCNP
to 0.22 in GMPF and GGPNP. The top ranked model showed that the detection
probability of Leopard was affected by the distance of camera traps from animal
trails in GGPNP, GCNP, GMPF, and GHSNP, but by the number of camera trap days
in GSWR and GGPNR (Table 6).
Leopard occupancy
The estimated Leopard occupancy (Ψ) ranges from 0.51 (±SE 0.21) in GCNP
to 0.94 (±SE 0.13) in GMPF, with a naïve estimate from 0.35 in GGPNR to 0.92 in
GMPF (Table 7).
DISCUSSION
With an effective sampled area of 793.5 km2 in six study
areas, our camera trapping surveys covered about 6.8% of the total landscape
identified by Wibisono et al. (2018) as suitable for the Javan Leopard. We
identified 55 adult individuals during 578 sampling occasions in the period
from February 2009 to October 2018. Although our surveys encompassed all seasons,
none of the 31 identified female Leopards was recorded with a cub, which is a
matter of concern. In contrast, female Leopards with cubs were recorded between
June and November in protected areas in Nepal and Iran (Odden & Wegge 2005;
Farhadinia et al. 2009; Ghoddousi et al. 2010), and between February and May in
southern Sri Lanka (Kittle et al. 2017).
We recorded only one Leopard in Gunung Ciremai National Park (GCNP)
despite an effective sampled area of 150 km2 in 1,070 camera trap
days. Doubts about the small population led the GCNP management to continue the
camera trap survey during 2014 to 2018, but not even a single photograph of a
Leopard was obtained (R. Gumilang, pers. comm. 20 November 2018). For the
recovery of a Leopard population in this area, a male Leopard was translocated
to GCNP in July 2019 (Wibisono et al. 2021), and a female Leopard was released
in March 2022 (R. Gumilang, pers. comm. 10 March 2022).
Leopard density
Our study provides the first estimate for Leopard density and distribution
in montane protected areas of West Java using the spatially explicit
capture-recapture method. Our study area in Gunung Halimun Salak National Park
yielded the highest number of 70 independent detections (IDs). This is the only
study area, in which the sex of 15 identified individuals affects both
detection and spatial parameters as best model for estimating density. The
slightly lower number of 55 IDs in Gunung Gede Pangrango National Park affects
only the spatial parameter as top model, despite 18 identified individuals. The
influence of the variation between sexes on density estimates is considerably
lower in the remaining study areas, where we identified between five and nine
individuals in 21 to 56 IDs. We therefore assume that a minimum of 56 IDs with
at least 15–18 identified individuals represent the threshold necessary for
modelling sex-specific Leopard density. A higher sample size facilitates
modelling sex-specific differences in detectability and spatial patterns
(Goldberg et al. 2015; Kittle et al. 2021; Vinks et al. 2021), whereas a
smaller sample size is insufficient for this model (Strampelli et al. 2020).
Our study area in Gunung Gede Pangrango National Park covered about 52%
of the park’s total size and exhibited the highest Leopard density estimate of
our study areas, followed by Gunung Halimun Salak National Park. Our study area
in latter national park covered about 18.2% of its total size of 876.99 km².
Giri & Munawir (2021) estimated that suitable Leopard habitat in Gunung
Halimun Salak National Park is limited to about 476 km². Follow-up surveys are
necessary to see whether our density estimates hold for all of the extents of
these two national parks, and also to assess whether they can indeed support 50
and 100 Leopards, respectively, as assumed by Wibisono et al. (2018).
The Leopard density of 8.30 ± SE 4.46 in a non-conservation area like
Gunung Malabar Protected Forest corroborates its suitability as Leopard
habitat. The rather low Leopard density of 6.12 ± SE 4.01 and low detection
probability at home range centre in Gunung Sawal Wildlife Reserve coincides
with the highest frequency of conflict between local people and Leopards
documented in Java; 48 cases were reported between 2001 and 2015 (Gunawan et
al. 2017). Leopard density was lowest in Gunung Guntur-Papandayan Nature
Reserve with 4.92 ± SE 2.29 individuals per 100 km2 despite a high
survey effort of 3,614 camera trap days at 60 locations.
Our density estimates for all study areas are bounded by wide confidence
intervals, probably because of the low number of recaptures indicating that
Leopards were not always detected when present. Several sampling covariates may
have impacted differences in detection probabilities. The surveys were
conducted during different seasons, and the sampling effort and duration
differed between study areas. Habitat features around locations ranged from
open to close vegetation. Avoiding disturbed sites is a common behaviour of the
Leopard that has been documented across range countries and study areas
(Ngoprasert et al. 2007; Khorozyan et al. 2008; Rosenblatt et al. 2016;
Havmøller et al. 2019; Kittle et al. 2021; Islam et al. 2021).
With 16.04 ± SE 6.29 individuals/100 km², our study area in Gunung Gede
Pangrango National Park holds a higher density than reported for Ujung Kulon
National Park in southwestern Java by Rahman et al. (2018). At present, it
ranks high in comparison with other study areas in Leopard range countries
(Table 8).
Detection probability
The detection probability was positively correlated with proximity of
camera traps to animal trails in Gunung Gede Pangrango National Park, Gunung
Ciremai National Park, Gunung Malabar Protected Forest and Gunung Halimun Salak
National Park. This reasserts the notion that animal trails facilitate Leopard
movement (Borah et al. 2014; Ngoprasert et al. 2017), and that the placement of
camera traps close to trails enhances the chances of detecting a Leopard
(Strampelli et al. 2018). In contrast, the sampling covariate ‘effort’, i.e.
number of camera trap days, was the principal predictor for detection
probability in Gunung Sawal Wildlife Reserve and Gunung Guntur-Papandayan
Nature Reserve. In these two study areas, the ratio of 21–33 independent
detections per 5–7 identified individuals was lower than in afore-mentioned
study areas. This lower detection rate may be the reason for the site covariate
‘trail’ being less significant than the sampling covariate ‘effort’.
Leopards exhibited marked variation in movement parameters. The high
detection probability and high movement parameters of both female and male
Leopards in Gunung Guntur-Papandayan Nature Reserve may indicate that they used
a high proportion of the surveyed area but avoid the central volcanic part. The
lower movement parameters in Gunung Gede Pangrango National Park may indicate a
high prey abundance in the surveyed area.
Leopard occupancy
Leopard occupancy in all our study areas was high in forests, a site
covariate that has also been shown to be the preferred habitat type of the
Leopard across Sri Lanka (Kittle et al. 2017). This stresses the importance of
forest cover for Leopard distribution and persistence, especially in rather
small isolated areas that do not afford the protection level of national parks
like Gunung Sawal Wildlife Reserve and Gunung Malabar Protected Forest. As
pointed out by Wibisono et al. (2018), the Javan Leopard has been under high
pressure because of habitat isolation as a result of severe forest
fragmentation since at least the turn of this century.
In Gunung Gede Pangrango National Park, the Leopard occupancy model
based on the relative abundance index (RAI) of Wild Boar ranked even higher
than the one based on forest cover. It also ranked high in four study areas,
followed by RAI of Red Muntjac in three study areas. This result underscores
the significance of integrating RAIs of potential prey species into modelling
Leopard occupancy. Lamichhane et al. (2021) showed that the presence
of the Wild Boar is a strong predictor of Leopard occupancy in a forested
mountain range in Nepal. In several study areas in Asia, the Wild Boar
constitutes a major proportion of the Leopard’s diet (Sharbafi et al. 2016; Kandel et al.
2020), especially when other prey species are depleted (Ghoddousi
et al. 2017). It also exhibits a higher temporal and spatial overlap with the
Leopard than other ungulates (Ghoddousi et al. 2020; Kittle et al. 2021; Sehgal
et al. 2022).
The remaining site covariates elevation, distance to road, village and
river were less important predictors for Leopard occupancy in all our study
areas.
Management implications and recommendations
Density estimates are not equally robust, and under- or over-estimating
densities can have substantial implications for conservation management and
policy (Foster & Harmsen 2012; Hayward et al. 2015). We recommend to
maximise capture and recapture probabilities in future surveys by implementing
a closer-knit camera trapping design with a maximum spacing of 1,500 m between
locations and placing two opposite camera traps per location. Regular
monitoring surveys in all our study areas and beyond are essential for
assessing changes in Leopard densities as a baseline for readjusting management
interventions.
Efforts to recover the Javan Leopard need focus on maintaining landscape
integrity and reducing poaching (Wibisono et al. 2018). Integrated management
of suitable Leopard habitat in West Java is utmost important, because Leopards
inhabit forest types under three different management regimes, namely
conservation forests, protected forests and production forests, which are
currently managed by three different authorities. Priority management
interventions inside and outside protected areas must be aimed at preventing
further habitat fragmentation and decline of prey species. Degraded habitats
need to be restored to improve habitat quality, ideally with the support of
multiple stakeholders. Since a large part of landscapes suitable for Leopard
survival includes production and secondary forests (Wibisono et al. 2018), we
strongly recommend identifying and mapping potential wildlife corridors with
low conflict risk that are suitable to increase connectivity between forest
patches and protected areas. We emphasize that both a landscape approach and
conflict mitigation is imperative to ensure the long-term viability of both
Leopard and prey populations.
Table
1. List of protected areas in West Java, Indonesia, and their key
characteristics.
|
Name |
Size (km²) |
Elevation (m) |
IUCN Protected Area category |
Description |
|
Gunung Gede Pangrango National Park (GGPNP) |
242.8 |
500–3,019 |
II |
GGPNP was designated a biosphere reserve in 1977 and established as
national park in 1980. It encompasses two stratovolcanoes with seven craters
located at elevations of 2,600–2,927 m. Its topography is hilly and
mountainous with forest cover classified as submontane, montane and subalpine
forests; annual rainfall is 4,000–6,000 mm, and temperature ranges from
18–23°C (Harris 1996). |
|
Gunung Ciremai National Park (GCNP) |
155 |
500–3,078 |
II |
GCNP was established in 2004. It surrounds a stratovolcano with a more
than 20km2 summit crater in its centre. The topography is hilly
and mountainous with submontane, montane and subalpine forests; annual
rainfall is 2,500–4,500 mm with temperatures ranging from 18–22°C (Kebun Raya
Bogor 2001). |
|
Gunung Malabar Protected Forest (GMPF) |
88.9 |
1,000–2,300 |
VI |
GMPF is a production forest managed by the Forestry State Enterprise
Perhutani (Ario et al. 2018a). It encompasses a stratovolcano with fumaroles,
hot springs, mud pools and altered ground on its southern slope (Bogie et al.
2008). The topography is hilly and mountainous with submontane and montane
forests; annual rainfall is 2,000–2,500 mm with temperatures of 18–23°C (Ario
et al. 2018a). It is disturbed due to encroachment for farming and hunting of
wildlife (Ario et al. 2018a) |
|
Gunung Sawal Wildlife Reserve (GSWR) |
110 |
500–1,766 |
IV |
GSWR was established in 1979. It encompasses an extinct volcano. The
topography is hilly and mountainous with lowland and submontane forests;
annual rainfall is around 2,000–2,500 mm with temperatures of 18–22°C (BBKSDA
Jawa Barat 2018). |
|
Gunung Guntur-Papandayan Nature Reserve (GGPNR) |
153 |
773–2,678 |
IA |
GGPNR was established in 2013. It encompasses two stratovolcanoes with
active fumarole fields. Its topography is hilly and mountainous with
submontane and montane forests; annual rainfall is 2,000–2,500 mm with
temperatures from 19–27°C (BBKSDA Jawa Barat 2018). |
|
Gunung Halimun Salak National Park (GHSNP) |
877 |
500–2,211 |
II |
GHSNP was established in 1992. It encompasses two stratovolcanoes with
several cone craters at the summit. Its topography is hilly and mountainous
with forest cover classified as lowland, submontane and montane forests;
annual rainfall amounts to 4,000–6,000 mm, and temperature ranges from
19–23°C (Simbolon et al. 1998) |
Table
2. Covariates included in potential candidate detection and occupancy models.
|
Covariate name |
Description |
|
Sampling covariates |
|
|
Trail |
Camera trap placed on animal trail (1) or not (0) |
|
Camera |
Trigger speed of camera trap models (range of 0.2–0.6 seconds) |
|
Effort |
The number of days a camera trap was active during each sampling
occasion (range of 19–97 days) |
|
Site covariates |
|
|
Elevation |
Elevation of the camera trap location (range of 818–2,635 m) obtained
from GPS device Garmin 64s and cross-checked with database of Badan Informasi
Geospasial (2013) |
|
Forest |
Percentage of forest cover around camera trap locations (range of
65–98%) using values from Badan Informasi Geospasial (2013) database |
|
River |
Distance of the camera trap to the nearest river (range of 15–1,151 m)
using values from Badan Informasi Geospasial (2013) database |
|
Road |
Distance of the camera trap to the nearest road (range of 215–4,943 m)
using values from Badan Informasi Geospasial (2013) database |
|
Village |
Distance of the camera trap to the nearest human settlement (range of
481–6,152 m) using values from Badan Informasi Geospasial (2013) database |
|
Boar |
RAI of Wild Boar (range of 4.02–12.99 independent detections/100
camera trapping days) |
|
Muntjac |
RAI of Red Muntjac (range of 2.32–10.56 independent detections/100
camera trapping days) |
|
Chevrotain |
RAI of Javan Chevrotain (range of 1.58–6.07 independent detections/100
camera trapping days) |
Table
3. Sampling effort in six study areas with number of right (R) & left
flanks (L), and adult male (M) & female (F) Javan Leopards detected. The
bolded independent detections represent the flank used for identification of
individuals.
|
Study area |
Sampling period |
Elevation |
Effective sampled area in km² |
Locations |
Camera trap days |
Sampling occasion (days) |
Independent detections |
Adult individuals |
|
GGPNP |
01.ii.–03.v.2009 |
855–2,828 |
125.8 |
23 |
2,082 |
92 |
R = 55, L = 31 |
M = 8, F = 10 |
|
GCNP |
14.i.–15.iv.2013 |
1,168–2,012 |
150 |
12 |
1,070 |
92 |
R = 10, L = 8 |
M = 1, F = 0 |
|
GMPF |
01.xi.2013 –04.ii.2014 |
1,500–2,226 |
85 |
12 |
1,102 |
96 |
L = 56, R = 30 |
M = 3, F = 4 |
|
GSWR |
27.x.2016–01.ii.2017 |
818–1,766 |
86.9 |
14 |
1,317 |
98 |
R = 21, L = 9 |
M = 2, F = 3 |
|
GGPNR |
01.vii.–10.x.2018 |
1,489–2,678 |
186.8 |
60 |
3,614 |
102 |
L = 33, R = 23 |
M = 3, F = 6 |
|
GHSNP |
05.vii.–10.x.2018 |
964–1,962 |
159 |
31 |
1,770 |
98 |
L = 70, R = 22 |
M = 7, F = 8 |
|
|
|
Total |
793.5 |
152 |
10,955 |
578 |
R = 161, L = 207 |
M = 24, F = 31 |
Table
4. Model selection parameters for spatially explicit capture-recapture models.
|
Study area |
Model |
Description |
AICc |
ΔAICc |
AICc wi |
K |
|
GGPNP |
g0~1, σ~h2 (secr.sex.σ) |
Variation between sexes
affecting σ |
179.34 |
0.00 |
0.90 |
5 |
|
|
g0~1, σ~1 (secr.0) |
No variation between sexes |
184.40 |
5.07 |
0.07 |
4 |
|
|
g0~h2, σ~1 (secr.sex.g0) |
Variation between sexes
affecting g0 |
186.83 |
7.50 |
0.02 |
5 |
|
|
g0~h2, σ~h2 (secr.sex) |
Variation between sexes
affecting g0 and σ |
188.71 |
9.38 |
0.01 |
5 |
|
GCNP |
|
Not Applicable (NA) |
NA |
NA |
NA |
NA |
|
GMPF |
g0~1, σ~1 (secr.0) |
No variation between sexes |
242.47 |
0.00 |
1 |
4 |
|
|
g0~h2, σ~h2 (secr.sex) |
Variation between sexes
affecting g0 and σ |
280.23 |
37.76 |
0 |
5 |
|
|
g0~1, σ~h2 (secr.sex.σ) |
Variation between sexes
affecting σ |
284.06 |
41.59 |
0 |
5 |
|
|
g0~h2, σ~1 (secr.sex.g0) |
Variation between sexes
affecting g0 |
284.44 |
41.97 |
0 |
5 |
|
GSWR |
g0~1, σ~1 (secr.0) |
No variation between sexes |
270.78 |
0.00 |
0.60 |
4 |
|
|
g0~1, σ~h2 (secr.sex.σ) |
Variation between sexes
affecting σ |
265.01 |
5.64 |
0.24 |
5 |
|
|
g0~h2, σ~1 (secr.sex.g0) |
Variation between sexes
affecting g0 |
264.37 |
8.19 |
0.12 |
5 |
|
|
g0~h2, σ~h2 (secr.sex) |
Variation between sexes
affecting g0 and σ |
260.10 |
10.20 |
0.04 |
5 |
|
GGPNR |
g0~1, σ~1 (secr.0) |
No variation between sexes |
222.66 |
0.00 |
0.79 |
4 |
|
|
g0~h2, σ~h2 (secr.sex) |
Variation between sexes
affecting g0 and σ |
225.61 |
2.95 |
0.18 |
5 |
|
|
g0~1, σ~h2 (secr.sex.σ) |
Variation between sexes
affecting σ |
229.76 |
7.10 |
0.02 |
5 |
|
|
g0~h2, σ~1 (secr.sex.g0) |
Variation between sexes
affecting g0 |
231.40 |
8.74 |
0.01 |
5 |
|
GHSNP |
g0~h2, σ~h2 (secr.sex) |
Variation between sexes
affecting g0 and σ |
317.14 |
0.00 |
0.48 |
5 |
|
|
g0~1, σ~1 (secr.0) |
No variation between sexes |
317.38 |
0.24 |
0.43 |
4 |
|
|
g0~1, σ~h2 (secr.sex.σ) |
Variation between sexes
affecting σ |
321.93 |
4.78 |
0.04 |
5 |
|
|
g0~h2, σ~1 (secr.sex.g0) |
Variation between sexes
affecting g0 |
322.01 |
4.87 |
0.04 |
5 |
Notes: the values probability of
capture at the home range centre (g0), spatial parameter related to home range
size (σ), Akaike information criterion adjusted for small sample size (AICc),
difference from best ranking model (ΔAICc), model weighting (AICc wi), and
number of model parameters (K).
Table
5. Results from SECR analyses for Leopard density in six study areas.
|
Study area |
Gender |
D (± SE) adult individuals/100
km² |
LCL (CI 95%) |
UCL (CI 95%) |
σ (±SE) m |
g0 (±SE) |
|
GGPNP |
M |
4.94 (1.86) |
2.46 |
10.12 |
1,070 (1.81) |
0.026 (0.012) |
|
|
F |
11.1 (4.43) |
5.24 |
23.58 |
676 (1.24) |
0.036 (0.012) |
|
|
|
16.04 (6.29) |
|
|
|
|
|
GCNP |
M |
Not Applicable (NA) |
NA |
NA |
NA |
NA |
|
GMPF |
M |
3.42 (1.99) |
1.19 |
9.85 |
2,091 (4.51) |
0.023 (0.011) |
|
|
F |
4.88 (2.47) |
1.91 |
12.45 |
1,719 (3.59) |
0.024 (0.006) |
|
|
|
8.30 (4.46) |
|
|
|
|
|
GSWR |
M |
1.96 (1.39) |
0.56 |
6.8 |
2,120 (3.94) |
0.010 (0.012) |
|
|
F |
4.16 (2.62) |
1.33 |
12.95 |
1,447 (5.66) |
0.031 (0.056) |
|
|
|
6.12 (4.01) |
|
|
|
|
|
GGPNR |
M |
1.50 (0.85) |
0.51 |
4.22 |
4,227 (1.21) |
0.053 (0.051) |
|
|
F |
3.42 (1.44) |
1.57 |
7.58 |
2,564 (6.69) |
0.064 (0.051) |
|
|
|
4.92 (2.29) |
|
|
|
|
|
GHSNP |
M |
4.36 (1.65) |
1.25 |
7.51 |
1,996 (5.61) |
0.025 (0.006) |
|
|
F |
5.08 (1.80) |
3.45 |
11.39 |
1,827 (3.57) |
0.031 (0.008) |
|
|
|
9.44 (3.45) |
|
|
|
|
Notes: values for density (D),
standard error (SE), confidence interval (CI), lower confidence limit (LCL),
upper confidence limit (UCL), movement parameter (σ), the probability of
detection at home range centre (g0).
Table 6. Model selection for
detection probability (p) analyses in six sites in West Java.
|
Study area |
Model |
AICc |
ΔAICc |
AICc wi |
K |
p (±SE) |
−2 log likelihood |
|
GGPNP |
p(trail) |
263.19 |
0.00 |
0.95 |
3 |
0.22 (0.03) |
257.19 |
|
p(effort) |
270.92 |
7.73 |
0.02 |
3 |
0.22 (0.04) |
264.92 |
|
|
p(.) |
271.35 |
8.16 |
0.02 |
2 |
0.22 (0.05) |
267.35 |
|
|
p(camera) |
273.27 |
10.08 |
0.01 |
3 |
0.22 (0.05) |
267.27 |
|
|
GCNP |
p(trail) |
79.97 |
0.00 |
0.76 |
3 |
0.13 (0.04) |
67.97 |
|
p(.) |
77.68 |
3.71 |
0.12 |
2 |
0.12 (0.04) |
73.68 |
|
|
p(effort) |
78.61 |
4.64 |
0.08 |
3 |
0.11 (0.04) |
72.61 |
|
|
p(camera) |
79.68 |
5.71 |
0.04 |
3 |
0.11 (0.05) |
73.68 |
|
|
GMPF |
p(trail) |
180.68 |
0.00 |
0.81 |
3 |
0.22 (0.04) |
174.68 |
|
p(effort) |
184.72 |
4.04 |
0.11 |
3 |
0.22 (0.04) |
178.72 |
|
|
p(.) |
186.00 |
5.32 |
0.06 |
2 |
0.21 (0.04) |
182.00 |
|
|
p(camera) |
187.96 |
7.28 |
0.02 |
3 |
0.21 (0.05) |
181.96 |
|
|
GSWR |
p(effort) |
125.24 |
0.00 |
0.41 |
3 |
0.16 (0.04) |
119.24 |
|
p(camera) |
125.99 |
2.75 |
0.28 |
3 |
0.14 (0.03) |
119.99 |
|
|
p(.) |
126.54 |
4.76 |
0.21 |
2 |
0.14 (0.03) |
122.54 |
|
|
p(trail) |
128.00 |
5.30 |
0.10 |
3 |
0.14 (0.04) |
122.00 |
|
|
GGPNR |
p(effort) |
267.99 |
0.00 |
0.63 |
3 |
0.16 (0.02) |
261.99 |
|
p(.) |
270.14 |
2.15 |
0.21 |
2 |
0.16 (0.04) |
266.14 |
|
|
p(camera) |
272.10 |
4.11 |
0.08 |
3 |
0.14 (0.04) |
266.10 |
|
|
p(trail) |
272.13 |
4.14 |
0.08 |
3 |
0.14 (0.02) |
266.13 |
|
|
GHSNP |
p(trail) |
344.69 |
0.00 |
0.70 |
3 |
0.17 (0.03) |
338.69 |
|
p(effort) |
357.14 |
2.45 |
0.12 |
3 |
0.16 (0.02) |
351.14 |
|
|
p(.) |
358.46 |
3.77 |
0.10 |
2 |
0.16 (0.03) |
354.46 |
|
|
p(camera) |
360.20 |
5.51 |
0.08 |
3 |
0.16 (0.03) |
354.20 |
Table
7. Single-season occupancy models for Javan Leopard distribution in six study
areas in West Java, Indonesia.
|
Study area |
Models |
AICc |
ΔAICc |
AICc wi |
K |
Naïve estimate |
Ψ (±SE) |
p (±SE) |
|
GGPNP |
Ψ(boar),p(trail) |
249.09 |
0.00 |
0.55 |
4 |
0.65 |
0.67 (0.13) |
0.22 (0.03) |
|
|
Ψ(forest),p(trail) |
251.91 |
1.82 |
0.22 |
4 |
0.65 |
0.67 (0.11) |
0.22 (0.03) |
|
|
Ψ(muntjac),p(trail) |
255.56 |
1.98 |
0.20 |
4 |
0.65 |
0.67 (0.12) |
0.22 (0.03) |
|
|
Ψ(village),p(trail) |
256.40 |
7.31 |
0.02 |
4 |
0.65 |
0.66 (0.12) |
0.22 (0.03) |
|
|
Ψ(chevrotain),p(trail) |
259.60 |
10.51 |
0.01 |
4 |
0.65 |
0.66 (0.11) |
0.22 (0.03) |
|
|
Ψ(road),p(trail) |
260.82 |
11.73 |
0.00 |
4 |
0.65 |
0.66 (0.12) |
0.22 (0.03) |
|
|
Ψ(elevation),p(trail) |
263.08 |
13.99 |
0.00 |
4 |
0.65 |
0.66 (0.14) |
0.22 (0.03) |
|
|
Ψ(river),p(trail) |
264.98 |
15.89 |
0.00 |
4 |
0.65 |
0.66 (0.14) |
0.22 (0.03) |
|
|
Ψ(.),p(.) |
271.35 |
22.26 |
0.00 |
2 |
0.65 |
0.67 (0.10) |
0.22 (0.03) |
|
GCNP |
Ψ(forest),p(trail) |
66.90 |
0.00 |
0.66 |
4 |
0.42 |
0.51 (0.21) |
0.13 (0.04) |
|
|
Ψ(village),p(trail) |
70.66 |
3.76 |
0.10 |
4 |
0.42 |
0.51 (0.21) |
0.13 (0.04) |
|
|
Ψ(road),p(trail) |
70.79 |
3.89 |
0.09 |
4 |
0.42 |
0.51 (0.21) |
0.13 (0.04) |
|
|
Ψ(elevation),p(trail) |
70.79 |
3.89 |
0.09 |
4 |
0.42 |
0.51 (0.21) |
0.13 (0.04) |
|
|
Ψ(boar),p(trail) |
74.44 |
7.54 |
0.02 |
4 |
0.42 |
0.50 (0.24) |
0.12 (0.04) |
|
|
Ψ(muntjac),p(trail) |
75.17 |
8.27 |
0.02 |
4 |
0.42 |
0.50 (0.24) |
0.12 (0.04) |
|
|
Ψ(chevrotain),p(trail) |
75.80 |
8.90 |
0.01 |
4 |
0.42 |
0.50 (0.24) |
0.12 (0.04) |
|
|
Ψ(river),p(trail) |
75.94 |
9.04 |
0.01 |
4 |
0.42 |
0.50 (0.24) |
0.11 (0.04) |
|
|
Ψ(.),p(.) |
77.68 |
10.78 |
0.00 |
2 |
0.42 |
0.50 (0.19) |
0.11 (0.04) |
|
GMPF |
Ψ(forest),p(trail) |
178.88 |
0.00 |
0.34 |
4 |
0.92 |
0.94 (0.13) |
0.22 (0.04) |
|
|
Ψ(boar),p(trail) |
178.88 |
1.78 |
0.13 |
4 |
0.92 |
0.94 (0.13) |
0.22 (0.04) |
|
|
Ψ(muntjac),p(trail) |
178.88 |
1.90 |
0.12 |
4 |
0.92 |
0.94 (0.13) |
0.22 (0.04) |
|
|
Ψ(river),p(trail) |
178.88 |
2.15 |
0.10 |
4 |
0.92 |
0.94 (0.13) |
0.22 (0.04) |
|
|
Ψ(chevrotain),p(trail) |
184.56 |
2.36 |
0.10 |
4 |
0.92 |
0.93 (0.14) |
0.22 (0.04) |
|
|
Ψ(elevation),p(trail) |
184.56 |
3.80 |
0.06 |
4 |
0.92 |
0.93 (0.15) |
0.21 (0.04) |
|
|
Ψ(road),p(trail) |
185.68 |
3.80 |
0.06 |
4 |
0.92 |
0.93 (0.15) |
0.21 (0.04) |
|
|
Ψ(village),p(trail) |
185.68 |
3.80 |
0.06 |
4 |
0.92 |
0.93 (0.15) |
0.21 (0.04) |
|
|
Ψ(.),p(.) |
186.00 |
7.12 |
0.03 |
2 |
0.92 |
0.93 (0.08) |
0.22 (0.03) |
|
GSWR |
Ψ(forest),p(effort) |
112.85 |
0.00 |
0.39 |
4 |
0.57 |
0.64 (0.15) |
0.16 (0.05) |
|
|
Ψ(boar),p(effort) |
112.85 |
1.98 |
0.20 |
4 |
0.57 |
0.64 (0.15) |
0.16 (0.05) |
|
|
Ψ(river),p(effort) |
126.09 |
3.24 |
0.09 |
4 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
|
Ψ(muntjac),p(effort) |
126.09 |
3.60 |
0.08 |
4 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
|
Ψ(.),p(.) |
126.54 |
3.69 |
0.08 |
2 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
|
Ψ(chevrotain),p(effort) |
127.01 |
5.16 |
0.06 |
4 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
|
Ψ(elevation),p(effort) |
127.01 |
5.16 |
0.01 |
4 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
|
Ψ(village),p(effort) |
127.13 |
5.28 |
0.05 |
4 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
|
Ψ(road),p(effort) |
127.24 |
7.39 |
0.04 |
4 |
0.57 |
0.63 (0.15) |
0.14 (0.03) |
|
GGPNR |
Ψ(forest),p(effort) |
241.55 |
0.00 |
0.34 |
4 |
0.35 |
0.60 (0.08) |
0.16 (0.02) |
|
|
Ψ(boar),p(effort) |
241.55 |
1.87 |
0.18 |
4 |
0.35 |
0.60 (0.08) |
0.16 (0.02) |
|
|
Ψ(elevation),p(effort) |
264.79 |
3.24 |
0.09 |
4 |
0.35 |
0.60 (0.15) |
0.14 (0.02) |
|
|
Ψ(muntjac),p(effort) |
264.79 |
3.40 |
0.08 |
4 |
0.35 |
0.60 (0.15) |
0.14 (0.02) |
|
|
Ψ(chevrotain),p(effort) |
264.79 |
4.87 |
0.07 |
4 |
0.35 |
0.60 (0.15) |
0.14 (0.02) |
|
|
Ψ(village),p(effort) |
266.47 |
4.92 |
0.07 |
4 |
0.35 |
0.59 (0.16) |
0.14 (0.02) |
|
|
Ψ(road),p(effort) |
268.53 |
6.98 |
0.06 |
4 |
0.35 |
0.59 (0.16) |
0.14 (0.02) |
|
|
Ψ(river),p(effort) |
269.91 |
8.36 |
0.06 |
4 |
0.35 |
0.58 (0.16) |
0.14 (0.02) |
|
|
Ψ(.),p(.) |
270.14 |
8.59 |
0.05 |
2 |
0.35 |
0.58 (0.15) |
0.14 (0.02) |
|
GHSNP |
Ψ(forest),p(trail) |
339.57 |
0.00 |
0.51 |
4 |
0.74 |
0.80 (0.11) |
0.17 (0.03) |
|
|
Ψ(muntjac),p(trail) |
339.56 |
1.56 |
0.15 |
4 |
0.74 |
0.80 (0.11) |
0.17 (0.03) |
|
|
Ψ(boar),p(trail) |
349.56 |
1.98 |
0.12 |
4 |
0.74 |
0.80 (0.12) |
0.17 (0.04) |
|
|
Ψ(river),p(trail) |
349.52 |
3.95 |
0.04 |
4 |
0.74 |
0.80 (0.12) |
0.17 (0.04) |
|
|
Ψ(chevrotain),p(trail) |
349.52 |
3.95 |
0.04 |
4 |
0.74 |
0.79 (0.11) |
0.16 (0.03) |
|
|
Ψ(road),p(trail) |
346.57 |
7.00 |
0.04 |
4 |
0.74 |
0.79 (0.11) |
0.16 (0.03) |
|
|
Ψ(elevation),p(trail) |
346.66 |
7.09 |
0.04 |
4 |
0.74 |
0.79 (0.11) |
0.16 (0.03) |
|
|
Ψ(village),p(trail) |
346.69 |
7.12 |
0.04 |
4 |
0.74 |
0.78 (0.11) |
0.16 (0.03) |
|
|
Ψ(.),p(.) |
358.46 |
8.89 |
0.02 |
2 |
0.74 |
0.78 (0.11) |
0.17 (0.03) |
Table
8. Leopard densities in national parks (NP), wildlife sanctuaries (WS), and
protected areas in range countries in Asia.
|
Study area |
Leopard density per 100km2 |
Source |
|
Rajaji Corbett NP, India |
14.99 ± SE 6.9 |
Harihar et al. (2009) |
|
Mudumalai NP, India |
13.17 ± SE 3.15 |
Kalle et al. (2011) |
|
Ujung Kulon NP, Java |
12.8 ± SE 1.99 in dry season 11.24 ± SE 3.16 in wet season |
Rahman et al. (2018) |
|
Kuiburi NP, Thailand |
12.6 ± SE 3.6 |
Steinmetz et al. (2009) |
|
Ruhuna (Yala) National Park, Sri Lanka |
12.1 |
Kittle et al. (2017) |
|
Horton Plains NP, Sri Lanka |
11.7 ± SE 5.5 |
Kittle & Watson (2017) |
|
Kuno WS, India |
11 ± SE 4.6 |
Pawar et al. (2019) |
|
Wilpattu NP, Sri Lanka |
10.4 ± SE 1.9 |
Kittle et al. (2021) |
|
Sarigol NP, Iran |
8.86 ± SE 3.60 |
Farhadinia et al. (2019) |
|
Royal Manas National Park, Bhutan |
6.25–15.93 |
Goldberg et al. (2015) |
|
Mondulkiri Protected Forest, Cambodia |
3.6 ± SE 1.0 |
Gray & Prum (2012) |
|
Manas NP, India |
3.4 ± SE 0.82 |
Borah et al. (2014) |
|
Tembat Forest Reserve, Malaysia |
3 ± SE 1.02 |
Hedges et al. (2015) |
|
Bamu NP, Iran |
1.87 ± SE 0.07 |
Ghoddousi et al. (2010) |
|
Shaanxi Province, China |
2.0 ± SE 0.53; 2.4 ± SE 0.67 |
Yang et al. (2021) |
|
Kamdi Biological Corridor, Nepal |
1.5 ± SE 0.49 |
Kandel et al. (2020) |
|
Jigme Singye Wangchuck NP, Bhutan |
1.04 ± SE 0.01 |
Wang & Macdonald (2009) |
|
Srepok WS, Cambodia |
1 |
Rostro-García et al. (2018) |
For figure &
images - - click here (for full PDF)
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