Density of leopard in a moist-temperate forest of western Himalaya, India

Noor, Athar; Mir, Zaffar Rais; Veeraswami, Gopi Govindan; Habib, Bilal

doi:10.1007/s42965-020-00090-w

Cited by 10 publications

(10 citation statements)

References 40 publications

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“…Athreya et al (2013) found a similar pattern of density estimates of leopard (4.8 ± 1.2) in human-dominated regions where leopards relied on domestic prey intake. The density estimates of leopard in this study are higher than the lowest estimates of leopard density (0.74 ± 0.18) recorded in India using the secr framework (Noor et al, 2020). Overall, the density estimates of tigers and leopards were lower compared to other studies in India.…”

Section: Modelcontrasting

confidence: 89%

“…The observed density of tigers and leopards in the buffer zone may be regarded as moderate and not too low or high for a disturbed habitat. A literature survey indicated a huge disparity in the density estimates of leopards in India, varying from as low as 0.74 ± 0.18 (Noor et al, 2020) to the 28.9 ± 7.2 per 100 km 2 reported in Mudumalai (Kalle et al, 2011). The densities of tigers in India have been found to correlate with the prey densities (Karanth & Nichols, 1998;Karanth et al, 2004a).…”

Section: Modelmentioning

confidence: 99%

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Density estimation of tiger and leopard using spatially explicit capture–recapture framework

Rather

Kumar

Khan

2021

PeerJ

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The conservation of large carnivores often requires precise and accurate estimates of their populations. Being cryptic and occurring at low population densities, obtaining an unbiased population estimate is difficult in large carnivores. To overcome the uncertainties in the conventional capture–recapture (CR) methods used to estimate large carnivore densities, more robust methods such as spatially explicit capture-recapture (SECR) framework are now widely used. We modeled the CR data of tiger (Panthera tigris tigris) and leopard (Panthera pardus fusca) in the SECR framework with biotic and abiotic covariates likely believed to influence their densities. An effort of 2,211 trap nights resulted in the capture of 33 and 38 individual tigers and leopards. A total of 95 and 74 detections of tigers and leopards were achieved using 35 pairs of camera traps. Tiger and leopard density were estimated at 4.71 ± 1.20 (3.05–5.11) and 3.03 ± 0.78 (1.85–4.99) per 100 km2. Our results show that leopard density increased with high road density, high terrain ruggedness and habitats with high percentage of cropland and natural vegetation. The tiger density was positively influenced by the mosaic of cropland and natural vegetation. This study provides the first robust density estimates of tiger and leopard within the study area. Our results support the notion that large carnivores can attain moderate densities within human-dominated regions around protected areas relying on domestic livestock. Broader management strategies aimed at maintaining wild prey in the human-dominated areas around protected areas are necessary for large and endangered carnivores’ sustenance in the buffer zones around protected areas.

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Section: Modelcontrasting

confidence: 89%

Section: Modelmentioning

confidence: 99%

Density estimation of tiger and leopard using spatially explicit capture–recapture framework

Rather

Kumar

Khan

2021

PeerJ

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“…Here, the terminal node with 20 surveys from nine studies (Hearn et al 2016, Ramesh et al 2017, Wang et al 2017, Balme et al 2019, Lamichhane et al 2019, Tempa et al 2019, Kittle et al 2021, Palmero et al 2021, Searle et al 2021) with the number of individuals >20 had the highest precision (mean CV = 0.16; SD = 0.06). The lowest precision results (mean CV = 0.49; SD = 0.17) were obtained in the first group and a terminal node with 19 surveys from 10 studies (Ramesh et al 2012, Sunarto et al 2013, Singh et al 2014, Thornton & Pekins 2015, Ramesh et al 2017, Hearn et al 2019, Mohamed et al 2021, Naing et al 2019, Noor et al 2020, Duľa et al 2021) with low numbers of individuals (≤5) and recaptures (≤22). When the number of individuals was between 11 and 20, more than 22 recaptures, that is more than one recapture per each individual, helped obtain moderate precision (mean CV = 0.25).…”

Section: Resultsmentioning

confidence: 94%

Sampling variables and their thresholds for the precise estimation of wild felid population density with camera traps and spatial capture–recapture methods

et al. 2023

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Robust monitoring, providing information on population status, is fundamental for successful conservation planning. However, this can be hard to achieve for species that are elusive and occur at low densities, such as felids. These are often keystones of functioning ecosystems and are threatened by habitat loss and human persecution. When elusive species can be individually identified by visible characteristics, for example via camera‐trapping, observations of individuals can be used in combination with capture–recapture methods to calculate demographic parameters such as population density. In this context, spatial capture–recapture (SCR) outperforms conventional non‐spatial methods, but the precision of results is inherently related to the sampling design, which should therefore be optimised. We focussed on territorial felids in different habitats and investigated how the sampling designs implemented in the field affected the precision of population density estimates. We examined 137 studies that combined camera‐trapping and SCR methods for density estimation. From these, we collected spatiotemporal parameters of their sampling designs, monitoring results, such as the number of individuals captured and the number of recaptures, as well as SCR detection parameters. We applied generalised linear mixed‐effects models and tree‐based regression methods to investigate the influence of variables on the precision of population density estimates and provide numerical thresholds. Our analysis shows that the number of individuals, recapture frequency, and capture probability play the most crucial roles. Surveys yielding over 20 captured individuals that were recaptured on average at least once obtain the most precise population density estimates. Based on our findings, we provide practical guidelines for future SCR studies that apply to all territorial felids. Furthermore, we present a standardised reporting protocol for study transparency and comparability. Our results will improve reporting and reproducibility of SCR studies and aid in setting up optimised sampling designs.

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“…In each study area, we deployed camera traps in grids of 2 x 2 km 2 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.…”

Section: Data Collectionmentioning

confidence: 99%

The Javan Leopard Panthera pardus melas (Cuvier, 1809) (Mammalia: Carnivora: Felidae) in West Java, Indonesia: estimating population density and occupancy

Ario¹,

Mercusiana²,

Rustiadi³

et al. 2022

J. Threat. Taxa

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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.

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Density of leopard in a moist-temperate forest of western Himalaya, India

Cited by 10 publications

References 40 publications

Density estimation of tiger and leopard using spatially explicit capture–recapture framework

Density estimation of tiger and leopard using spatially explicit capture–recapture framework

Sampling variables and their thresholds for the precise estimation of wild felid population density with camera traps and spatial capture–recapture methods

The Javan Leopard Panthera pardus melas (Cuvier, 1809) (Mammalia: Carnivora: Felidae) in West Java, Indonesia: estimating population density and occupancy

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