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Accurately assessing the status of threatened species requires reliable population estimates. Despite this necessity, only a small proportion of the global distribution range of the vulnerable snow leopard (Panthera uncia) has been systematically sampled. The Indian section of the Greater Himalayas, which includes Kishtwar High Altitude National Park (KHANP), harbours potential snow leopard habitat. Nevertheless, there has been limited ecological and conservation research focusing on species that are specific to KHANP, as well as limited research on the broader biodiversity of the Greater Himalayas. We used Spatially Explicit Capture‐Recapture (SECR) models to provide—to our knowledge—the first robust snow leopard population density and abundance estimates from KHANP. We also provide a Relative Abundance Index (RAI) for non‐volant mammals (excluding small rodents). Our study sampled three catchments within the Dachhan region of KHANP—Kibber, Nanth and Kiyar—using 44 cameras over a 45‐day period between May and June 2023. We identified four unique snow leopard individuals across 15 detections in nine camera locations. SECR analysis estimated a density of 0.50 snow leopards per 100 km2 (95% confidence interval: 0.13–1.86), corresponding to an abundance of four individual (4–9) adults. Camera trapping revealed a total of 16 mammal species, including the endangered Kashmir musk deer (Moschus cupreus). Marmots (Marmota caudata) had the highest RAI of 21.3 (±0.2). Although the estimated density and abundance of snow leopards in our study area had relatively wide 95% confidence intervals, our combined results of snow leopard densities and RAIs of prey species such as ibex and marmots indicate that KHANP is a potentially important area for snow leopards. Given the geopolitical history of Jammu and Kashmir in India, the region where KHANP is located, wildlife research remains a low priority. We hope our study encourages authorities to support further research. This study is an initial step towards evaluating the potential of KHANP as a conservation landscape under the Government of India's Project Snow Leopard.
Accurately assessing the status of threatened species requires reliable population estimates. Despite this necessity, only a small proportion of the global distribution range of the vulnerable snow leopard (Panthera uncia) has been systematically sampled. The Indian section of the Greater Himalayas, which includes Kishtwar High Altitude National Park (KHANP), harbours potential snow leopard habitat. Nevertheless, there has been limited ecological and conservation research focusing on species that are specific to KHANP, as well as limited research on the broader biodiversity of the Greater Himalayas. We used Spatially Explicit Capture‐Recapture (SECR) models to provide—to our knowledge—the first robust snow leopard population density and abundance estimates from KHANP. We also provide a Relative Abundance Index (RAI) for non‐volant mammals (excluding small rodents). Our study sampled three catchments within the Dachhan region of KHANP—Kibber, Nanth and Kiyar—using 44 cameras over a 45‐day period between May and June 2023. We identified four unique snow leopard individuals across 15 detections in nine camera locations. SECR analysis estimated a density of 0.50 snow leopards per 100 km2 (95% confidence interval: 0.13–1.86), corresponding to an abundance of four individual (4–9) adults. Camera trapping revealed a total of 16 mammal species, including the endangered Kashmir musk deer (Moschus cupreus). Marmots (Marmota caudata) had the highest RAI of 21.3 (±0.2). Although the estimated density and abundance of snow leopards in our study area had relatively wide 95% confidence intervals, our combined results of snow leopard densities and RAIs of prey species such as ibex and marmots indicate that KHANP is a potentially important area for snow leopards. Given the geopolitical history of Jammu and Kashmir in India, the region where KHANP is located, wildlife research remains a low priority. We hope our study encourages authorities to support further research. This study is an initial step towards evaluating the potential of KHANP as a conservation landscape under the Government of India's Project Snow Leopard.
In an era where global biodiversity hotspots are under unprecedented threat, understanding the intricate balance between land use land cover (LULC) changes and their implications on ecosystem services value (ESV) becomes paramount. The region of Jammu and Kashmir, with its distinctive ecological importance, is well known for these challenges and opportunities. This region embodies various conservation reserves and national parks, and one of the most ecologically rich is called Kishtwar High Altitude National Park. It is often considered an example of biodiversity richness in the Indian subcontinent, as it protects a myriad of species and provides essential ecosystem services. However, despite its significance, it faces pressures from both peripheral human activities, such as seasonal grazing by nomadic communities and broader climatic changes. This study aims to investigate the complex relationship between these LULC shifts and their consequent effects on the park’s ESV. We used the cellular automata (CA)–Markov model to simulate the LULC for the future. Using the LULC from 1992 to 2020 and projecting for 2030, 2040, and 2050, we employed the global value coefficient method to understand the ESV contributions of different LULC types. Our results revealed a 7.43% increase in ESV from 1992 to 2020, largely due to the increase of forests and waterbodies. In contrast, our projections for 2020 to 2050 intimate a 7.55% decline in ESV, even amidst anticipated grassland expansion. These results highlight the role of forests in securing resilient ecosystem services. These findings shall help offer informed conservation strategies, that are relevant both regionally and globally.
The aim of this paper is to prepare, describe and discuss the models of the current and future distribution of Phthiracarus longulus (Koch, 1841) (Acari: Oribatida: Euptyctima), the oribatid mite species widely distributed within the Palearctic. We used the maximum entropy (MAXENT) method to predict its current and future (until the year 2100) distribution based on macroclimatic bio-variables. To our best knowledge, this is the first-ever prediction of distribution in mite species using environmental niche modelling. The main thermal variables that shape the current distribution of P. longulus are the temperature annual range, mean temperature of the coldest quarter and the annual mean temperature, while for precipitation variables the most important is precipitation of the driest quarter. Regardless of the climatic change scenario (SSP1-2.6, SSP2-4.5, SSP5-8.5) our models show generally the northward shift of species range, and in Southern Europe the loss of most habitats with parallel upslope shift. According to our current model, the most of suitable habitats for P. longulus are located in the European part of Palearctic. In general, the species range is mostly affected in Europe. The most stable areas of P. longulus distribution were the Jutland with surrounding southern coasts of Scandinavia, islands of the Danish Straits and the region of Trondheim Fjord.
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