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

Rather, Tahir Ali; Kumar, Sharad; Khan, Jamal A.

doi:10.7717/peerj.10634

Cited by 10 publications

(9 citation statements)

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“…This study provides the first estimates of leopard abundance and density in the Erode Forest Division (EFD), which were made using spatially explicit capturerecapture methods with extensive camera-trap sampling of the area in 2018 and 2020. The density of leopards in both years is comparable to estimates from the buffer zone of the Bandhavgarh Tiger Reserve 3.03 ± 0.78 per 100 km 2 (Rather et al 2021). Leopard density is much lower than in the Mudumalai Tiger Reserve 13.17 Tiger Reserve is a rich tropical deciduous forest with the highest level of protection, whereas Bandhavgarh is a largely drier mixed deciduous forest and more similar to our study area.…”

Section: Discussionsupporting

confidence: 61%

“…This is significantly lower than the estimates reported from the adjoining Sathyamangalam Tiger Reserve (STR) with the chital density of 39.66 (± 5.39) per km 2 and that of sambar of 8.97 (± 1.19) per km 2 (Jhala et al 2020). Prey density estimates exclusively from the buffer zone of Bandhavargh are not available, but (Rather et al 2021) used disturbance levels of forest as a proxy for prey abundance and found that leopard density was positively associated with disturbed habitats, i.e., lower prey density. During the sampling period we photo-captured ~25 other species, including the tiger (Panthera tigris), elephant (Elephas maximus indicus), gaur (Bos gaurus), sambar (Rusa unicolor), chevrotain (Moschiola indica), pangolin (Manis crassicaudata), and the rusty-spotted cat (Prionailurus rubiginosus).…”

Section: Discussionmentioning

confidence: 99%

“…We used spatially explicit capture recapture (SECR) models, implemented in the package secr in R (version 3.4.0) to estimate leopard density. SECR methods have been widely used in large carnivore population estimations (Thapa et al 2014;Rather et al 2021). We used a 15 km habitat mask around the trapping block that comprised an evenly spaced mesh of potential activity within the habitat areas.…”

Section: Methodsmentioning

confidence: 99%

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Population status and density estimate of Leopard Panthera pardus fusca in dry thorn forests of southern India

Nagarajan¹,

Ramesh²,

Boominathan³

et al. 2023

Zoology and Ecology

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The study aims to assess the population status and density of the Leopard (Panthera pardus fusca) in the Erode Forest Division, Tamil Nadu. The Erode Forest division has numerous villages within its boundaries and is exposed to associated biotic pressures. In order to improve the management practices of the wildlife in the area and to enhance its conservation, it is relevant to assess its status. In 2018 and 2020, we estimated the population density of the leopard in the entire forest division using 2 km2 sample grids and the camera trapping method. In each grid, we deployed a pair of camera traps, the cumulative number of trap-nights being 22983 (10732 trap nights in 2018 and 12251 in 2020). We analysed the obtained data using spatially explicit capture-recapture models (SECR). The surveys yielded 198 images of the leopard in 2018 and 272 images in 2020, of which 48 and 44 leopard individuals were identified in the respective years based on the rosette pattern. The density estimates were 5.16 (SE = ±0.89) and 4.00 (SE ± 0.72) individuals/100 km2 in 2018 and 2020, respectively. We found that in some regions overlapping with the human-use area, leopard densities were high, indicating that the species successfully exploits areas near human habitation and highlighting its high potential for interaction with humans. This baseline estimate and insights will help prioritize management actions, strengthen large mammal conservation beyond the boundaries of protected areas (PA), and plan human-wildlife conflict mitigation measures to enable the persistence of large carnivores in multi-use forests.

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Section: Discussionsupporting

confidence: 61%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Population status and density estimate of Leopard Panthera pardus fusca in dry thorn forests of southern India

Nagarajan¹,

Ramesh²,

Boominathan³

et al. 2023

Zoology and Ecology

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“…The main objective of the camera trap sampling was to estimate the density of the tiger and leopard within the study area (Rather et al 2021). A total effort of 2211 trap nights resulted in 36 photo captures of the sloth bear.…”

Section: Sloth Bear Occurrence Records and Pseudo-absencesmentioning

confidence: 99%

Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales

2021

Self Cite

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Background Habitat resources occur across the range of spatial scales in the environment. The environmental resources are characterized by upper and lower limits, which define organisms’ distribution in their communities. Animals respond to these resources at the optimal spatial scale. Therefore, multi-scale assessments are critical to identifying the correct spatial scale at which habitat resources are most influential in determining the species-habitat relationships. This study used a machine learning algorithm random forest (RF), to evaluate the scale-dependent habitat selection of sloth bears (Melursus ursinus) in and around Bandhavgarh Tiger Reserve, Madhya Pradesh, India. Results We used 155 spatially rarified occurrences out of 248 occurrence records of sloth bears obtained from camera trap captures (n = 36) and scats located (n = 212) in the field. We calculated focal statistics for 13 habitat variables across ten spatial scales surrounding each presence-absence record of sloth bears. Large (> 5000 m) and small (1000–2000 m) spatial scales were the most dominant scales at which sloth bears perceived the habitat features. Among the habitat covariates, farmlands and degraded forests were the essential patches associated with sloth bear occurrences, followed by sal and dry deciduous forests. The final habitat suitability model was highly accurate and had a very low out-of-bag (OOB) error rate. The high accuracy rate was also obtained using alternate validation matrices. Conclusions Human-dominated landscapes are characterized by expanding human populations, changing land-use patterns, and increasing habitat fragmentation. Farmland and degraded habitats constitute ~ 40% of the landform in the buffer zone of the reserve. One of the management implications may be identifying the highly suitable bear habitats in human-modified landscapes and integrating them with the existing conservation landscapes.

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“…Population density estimates from SECR frameworks have since been shown to produce lower estimates, but with higher precision than estimates conducted previously in the same regions (Lewis et al, 2015;Satter et al, 2019). These analyses allow researchers to examine the influence of environmental variables on density or encounter parameters, which can aid in informing management and recovery actions (Qi et al, 2015;Gogoi et al, 2020;Rather et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Spatial capture-recapture and LiDAR-derived vegetation metrics reveal high densities of ocelots on Texas ranchlands

Lombardi¹,

Sergeyev²,

Tewes³

et al. 2022

Front. Conserv. Sci.

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Reliable estimates of population density and size are crucial to wildlife conservation, particularly in the context of the Endangered Species Act. In the United States, ocelots (Leopardus pardalis pardalis) were listed as endangered in 1982, and to date, only one population density estimate has been reported in Texas. In this study, we integrated vegetation metrics derived from LiDAR and spatial capture-recapture models to discern factors of ocelot encounter rates and estimated localized population estimates on private ranchlands in coastal southern Texas. From September 2020 to May 2021, we conducted a camera trap study across 42 camera stations on the East Foundation’s El Sauz Ranch, which was positioned within a larger region of highly suitable woody and herbaceous cover for ocelots. We observed a high density of ocelots (17.6 ocelots/100 km2) and a population size of 36.3 ocelots (95% CI: 26.1–58.6) with the 206.25 km2 state space area of habitat. The encounter probability of ocelots increased with greater canopy cover at 1-2 m height and decreasing proximity to woody cover. These results suggest that the incorporation of LiDAR-derived vegetative canopy metrics allowed us to understand where ocelots are likely to be detected, which may aid in current and future population monitoring efforts. These population estimates reflect the first spatially explicit and most recent estimates in a portion of the northernmost population of ocelots in southern Texas. This study further demonstrates the importance of private working lands for the recovery of ocelots in Texas.

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

Cited by 10 publications

References 50 publications

Population status and density estimate of Leopard Panthera pardus fusca in dry thorn forests of southern India

Population status and density estimate of Leopard Panthera pardus fusca in dry thorn forests of southern India

Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales

Spatial capture-recapture and LiDAR-derived vegetation metrics reveal high densities of ocelots on Texas ranchlands

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