A new dawn? Population baselines of snow leopards and other mammals of the Kishtwar High Altitude National Park, India
新的曙光？印度基什特瓦尔高海拔国家公园雪豹和其他哺乳动物的种群基线
Oyndrila Sanyal with equal contribution as the first author.
Editor-in-Chief: Ahimsa Campos-Arceiz.
Handling Editor: Lingyun Xiao.
Abstract
enAccurately 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.
摘要
zh准确评估濒危物种的现状需要可靠的种群数据。尽管如此，全球范围内对易危物种雪豹（Panthera uncia）进行系统采样监测的区域却很少。大喜马拉雅山脉印度段，包括基什特瓦尔高海拔国家公园（KHANP），拥有潜在的雪豹栖息地。然而，针对 KHANP 特有物种的生态和保护研究十分有限，对大喜马拉雅山脉更广泛的生物多样性的研究也相对较少。本研究使用空间显式捕获-重捕（SECR）模型，首次提供了来自卡罕坝国家公园可靠的雪豹种群密度和多度估计。本研究还提供了非飞行哺乳动物（不包括小型啮齿动物）的相对多度指数 (RAI)。在 2023 年5月至6月的45天时间里，使用44台红外相机对基什特瓦尔高海拔国家公园达昌地区 (Dachhan region) 的三个流域——吉伯尔、南特和基亚尔——进行监测。在九个相机位点的 15 次探测中识别出 4 只不同的雪豹个体。SECR分析结果显示每100平方公里范围内有0.50只雪豹（95%置信区间:0.13-1.86），相当于研究区内有4只（4-9只）成年雪豹。通过红外相机监测共发现16种哺乳动物，其中包括濒危物种克什米尔麝（Moschus cupreus）。此外，长尾旱獭（Marmota caudata）是本研究中监测到相对多度最高的物种（RAI:21.3 ，SD:± 0.2)。尽管雪豹在我们研究区域的估计密度和丰度的95%置信区间相对较宽，但我们结合雪豹密度和猎物（如北山羊和旱獭）的相对多度指数结果表明，KHANP是雪豹重要的潜在栖息地。鉴于印度查谟和克什米尔地区（KHANP所在地区）的地缘政治历史，野生动物研究仍然是一个优先级较低的事项。本研究是评估 KHANP在印度政府“雪豹项目”下作为景观单元参与保护潜力探索的第一步，我们希望通过本研究促进当地政府对以后更多研究的支持。【审阅:耿颖】
Plain language summary
enAlthough the snow leopard is a threatened species, there is still much to learn about its population across large sections of its global range. Establishing a population baseline is crucial for understanding its current status and ultimately working towards its conservation. We used camera traps to estimate the population size of snow leopards in Kishtwar High Altitude National Park (KHANP) in Jammu and Kashmir, India. We surveyed three valleys within KHANP, namely Kibber, Nanth and Kiyar, using 44 camera traps. Our study identified four unique snow leopards, with the possibility of up to nine individuals being present in the study area. Additionally, we recorded 16 mammal species, including the Endangered Kashmir Musk Deer (Moschus cupreus). The long-tailed Marmot (Marmota caudata) was the most abundant mammal in our survey. Given the geopolitical history of Jammu and Kashmir, the region within which KHANP is situated, limited wildlife research has been conducted. In recent years, with active engagement from the Department of Wildlife Protection and the Government of Jammu and Kashmir, there has been a renewed focus on enhancing research in KHANP, using snow leopards as the flagship species.
简明语言摘要
zh尽管雪豹是一种受威胁的物种，但其全球范围内的大部分区域的种群现状仍有待深入研究。建立种群基线对于了解雪豹生存现状以及促进最终的保护至关重要。通过布设44台红外相机，调查印度查谟和克什米尔的基什特瓦尔高海拔国家公园（KHANP）三个流域，即 Kibber、Nanth 和Kiyar雪豹种群的大小。通过红外相机识别出四只不同的雪豹个体，而整个研究区内可能最多存在九只雪豹个体。此外，我们还记录了16种哺乳动物，包括濒危物种种克什米尔麝（Moschus cupreus）。长尾旱獭（Marmota caudata）是本研究中相对多度最高的哺乳动物。鉴于查谟和克什米尔地区（KHANP 所处地区）的地缘政治历史，该地区开展的野生动物研究十分有限。近年来，在野生动物保护部门以及查谟和克什米尔政府的积极参与下，以雪豹为旗舰物种在KHANP的研究工作再次受到重视。
Practitioner points
en
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Our study provides initial evidence for evaluating the potential of Kishtwar High Altitude National Park (KHANP) as a unit for landscape-based participatory conservation under the Government of India's Project Snow Leopard.
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Further studies are needed to understand the threats faced by snow leopards in KHANP.
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The Kiyar Valley within KHANP is especially important for snow leopards; future research and conservation projects should focus on facilitating snow leopard movement by ensuring connectivity between this valley and adjacent valleys.
实践者要点
zh
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我们的研究为评估KHANP在印度政府“雪豹项目”下作为景观单元参与保护的潜力提供了初步证据。
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需要开展进一步研究了解雪豹在基什特瓦尔高海拔国家公园（KHANP）面临的威胁。
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基什特瓦尔高海拔国家公园（KHANP）内的基亚尔流域对雪豹尤为重要；未来的研究和保护应侧重于通过确保该流域与相邻流域的连通性来促进雪豹的移动。
1 INTRODUCTION
The mountain regions of High Asia harbour several biodiversity hotspots (Hanson et al., 2009), including the highest mountain range in the world—the Himalayas. There is ample evidence of habitat loss and negative human–wildlife interactions impacting biodiversity across the Himalayas (Mishra et al., 2022). The snow leopard has emerged as a flagship species for the conservation of the high mountains of Central and South Asia, including the Himalayas (Mishra et al., 2022). Negative human–wildlife interactions and habitat loss significantly affect snow leopard populations (Nowell et al., 2016). Political conflicts and military presence have been shown to negatively impact these populations (Maheshwari, 2020). The geopolitical position of the Indian Himalaya, with its proximity to various international borders, has limited research on population baselines, conservation conflicts and efforts to mitigate them (Bhatnagar et al., 2009).
Additionally, across India, much of the research on snow leopards has focused on the trans-Himalayan region. While the Greater Himalayas harbour potential snow leopard habitats, these areas have seen limited surveys and research efforts on the species (Pal et al., 2022). Snow leopard habitats in the Greater Himalayas tend to be in remote, inaccessible regions that lack sufficient research infrastructure. In the border region of Jammu and Kashmir (hereafter J&K), Kishtwar High Altitude National Park (KHANP) falls within the global snow leopard distribution range (Sanyal et al., 2024). Due to its unique biogeographic location, acting as a bridge between the Greater and Trans-Himalayan regions and encompassing various habitats, KHANP is home to a diverse and unique assemblage of mammals (Naqash, 2013). In recent years, with active engagement from the Government of J&K's Department of Wildlife Protection, there has been a renewed focus on enhancing research in KHANP, using snow leopards as a flagship species.
In the present study, we aim to establish the first robust baseline of snow leopard populations in KHANP while also understanding the relative abundance of other high-altitude mammalian species, including key prey species. Our focus is on the Dachhan region of KHANP. Since robust population estimates over time help determine population trends (Mihoub et al., 2017), an initial population reference can aid in framing conservation objectives by assessing feasibility, concentrating effort and defining time periods within which progress can be evaluated (Bull et al., 2014). A linked aim of this study is to use these robust outputs to encourage the research community to prioritise high-altitude research in the region.
2 MATERIALS AND METHODS
2.1 Study area
Nestled in the Greater Himalayan region of the Western Himalaya, KHANP lies roughly between 33°43′ and 33°99′N and 75°65′ and 76°28′E. The elevation in this region ranges from 2300 to over 6000 m, and KHANP covers a total area of 2191.50 km2 (Figure 1). KHANP comprises four catchments: Renai, Kiyar, Nanth and Kibber, located within the Marwah and Dachhan regions of Jammu and Kashmir. Large areas of KHANP consist of permanent glaciers, which are rapidly melting due to climate change (Kichloo et al., 2023).
The vegetation primarily consists of moist temperate broadleaved and coniferous forests, which give way to subalpine scrub, alpine meadows and rocky outcrops further north. The climate of KHANP is largely cold and arid. Most of the lowest elevations, around 1700 m, are villages situated outside the national park (Figure 1, Image 1). These areas are typically followed by conifer forests (Figure 1, Image 2). Further into the valleys, around the 3000 m region, there is a transition from conifer forest to birch and alpine meadows (Figure 1, Image 3). This marks the beginning of the potential snow leopard habitat. The treeline then transitions into alpine meadows and cliffs with mostly shrub and herbaceous vegetation (Figure 1, Image 4), which, during our camera installation, were covered with snow from the winter months (November–March). This snow rapidly melts with the approach of summer (June–August). Beyond this, around and above 5000 m, the valleys have inaccessible glaciers throughout much of the year (Figure 1, Image 5).
Although designated as a national park, KHANP is vital for the traditional livelihoods of various human communities. Several villages are settled around the southwest boundary of the national park, primarily dependent on agriculture for subsistence and income. Some of these villages have seasonal homes within the national park, which are usually occupied during the summer months for grazing local livestock (mostly large-bodied livestock such as cattle and cattle–yak hybrids) and agricultural activities. From late May to early September, the nomadic Bakarwal communities visit ancestral pastures in the higher reaches of the national park to graze their livestock (mainly sheep and goats).
2.2 Data collection
Sanyal et al. (2024) recently published the first photographic records of snow leopards from the Kishtwar National Park, emphasising the need for a comprehensive study to understand snow leopard abundance and densities. Building on this work, our study aimed to estimate the abundance and densities of snow leopards using the spatially explicit capture-recapture (SECR) analytical framework. A total of 44 cameras were used for this study, with 34 cameras placed specifically for this research and data from 10 cameras from Sanyal et al. (2024) that fit the study duration and design incorporated.
Camera trapping surveys were conducted in accordance with SECR requirements. Key aspects to consider during such surveys include ensuring the closure assumption is met (Otis et al., 1978), sampling a sufficient area to avoid bias (Suryawanshi et al., 2019) and placing cameras to obtain multiple captures of individual snow leopards (Efford & Fewster, 2013). The camera trapping surveys were conducted from 4 May 2023 to 17 June 2023 over a 45-day period to ensure a closed population (Otis et al., 1978). This period was chosen because it is late spring to early summer when the snow has melted enough to enable access into the valley, but before the nomadic herders arrive, which often leads to wildlife disturbance, especially snow leopards (Ghoshal et al., 2019). A total of 44 camera traps (Reconyx HyperFire 2) were placed in the study area, including 17 in Kiyar, 17 in Nanth and 10 in Kibber. Due to logistical constraints, cameras could not be placed in the renai catchment (see Figure 1). Camera placement was designed to fit SECR requirements, ensuring the capture of different individuals across multiple locations (Efford & Fewster, 2013). We aimed for an effective sampling area of c. 400 km2, considering the sampling bias of studying snow leopards in smaller regions (Suryawanshi et al., 2019). Camera trap locations were selected after several reconnaissance surveys, which involved hiking through all accessible areas of the three valleys to look for signs of snow leopards. If a location with signs indicating snow leopard presence was found, a GPS point was recorded for camera placement. Camera traps were installed based on the prevalence of snow leopard signs, which included hair, scat, scrape or urine spray marks and any other suitable microhabitats (e.g., overhanging boulders in cliffs) to maximise detection at each location. We prioritised placing cameras at urine spray sites, angled to ideally capture both the flanks of the snow leopard.
Due to the rugged landscape, we deployed camera traps in a clustered manner rather than a uniform design, following the recommendations of Sharma et al. (2019). Clustering can improve data quality (i.e., increased recaptures of snow leopards), especially in landscapes where features like glaciers and rocky outcrops (as in our study area) can impede uniform sampling (Sollmann et al., 2013). To facilitate clustering while ensuring spatial coverage, we tried to maintain two or more cameras per 4 × 4 km grid (Suryawanshi et al., 2021). Each camera was treated as an independent sampling point (Sharma et al., 2019). Since these grids are smaller than a snow leopard's home range, this study design allows for individuals to be captured across multiple traps, which is key to estimating movement scale in a spatially explicit analysis. Overall, our study area was extensive enough to encompass multiple snow leopard home ranges (Johansson et al., 2016).
We intentionally maintained camera deploys above 3,000 m because areas below this elevation appeared to be outside the limits of snow leopard habitats (Suryawanshi et al., 2021). This is illustrated in Images 1 and 2 (Figure 1).
2.3 Data analysis
Upon retrieval of the cameras, photos were tagged at the species level using ‘digiKam’ software (https://www.digikam.org/), and the tags were read using the ‘camtrapR’ package (Niedballa et al., 2016). To mitigate the major source of bias in population assessments of snow leopards, specifically errors in individual identification (Johansson et al., 2020), we employed a two-step process as recommended by Suryawanshi et al. (2021). Step 1 is the identification phase and step 2 is the review phase. In step 1, two researchers independently catalogued all photos of snow leopards from each site. Different individuals were identified using unique rosette patterns on their various body parts. Following this, in step 2, a third researcher compared and reviewed all the individual identifications, resolving any disagreements between the initial two researchers. All reviewers were trained using the application ‘camtraining’ (camtraining.globalsnowleopard.org).
We used a maximum-likelihood-based SECR model to estimate the population densities of snow leopards (Efford & Fewster, 2013). SECR models use the spatial information of detection location at camera traps, accounting for capture heterogeneity due to the location of the animal's activity centre. The heterogeneity may arise because the probability of capture at a specific camera trap depends on its proximity to the animal's activity centre, with the detection probability decreasing as the distance from the detector to an individual's activity centre increases (Efford et al., 2009). A habitat mask was created using a buffer width of 18 km around the traps and a spacing of 1 km between mask points to create a state space of 1794 km2 (roughly the size of the entire KHANP). Non-habitat areas were removed by clipping the habitat mask to elevations between 3000 and 6000 m, excluding non-snow leopard habitats (below 3000 m) and high glaciers (above 6000 m) (Suryawanshi et al., 2019). Density was estimated using a single-session SECR model (D ~ 1, g0 ~ 1, sigma ~ 1) and a half-normal detection function was used to model detection probability. Here, sigma can be interpreted as an index of home range size, while g0 is an indication of detectability. Abundance was estimated for a region of 464.57 km2, determined using a 4 km buffer around the convex hull of all camera traps, which best represented the sampled region. The SECR analysis was carried out using the ‘secr’ package (Efford, 2023) in R (version 3.2.3) (R Core Team, 2023).
Here A is the total number of independent encounters of a species by all cameras, and N is the total number of camera trap days, calculated as the total number of cameras multiplied by 45 (the survey period duration) (Jenks et al., 2011). We considered all consecutive pictures that show different species as independent events. Pictures taken more than 30 min apart were also taken as independent events (O'Brien et al., 2003). While there is debate about how accurately RAIs reflect species abundance, especially as they ignore species detectability (Sollmann et al., 2013), they are still widely used to compare species capture frequency at different sites (Rovero et al., 2014). RAI was calculated for data from 34 cameras placed by researchers from the Nature Conservation Foundation (NCF) (15 in Kiyar, 12 in Nanth and 7 in Kibber), as the remaining 10 cameras (placed by National Development Foundation researchers) only provided snow leopard data for this study.
3 RESULTS
Our camera trapping effort comprised 1980 trap nights, resulting in 175 photos capturing 15 independent snow leopard detections (defined as sets of images taken at 30-min intervals) across 9 camera locations, from which we identified 4 unique individuals (see Table 1 for further details). Sets of images where both flanks of individual snow leopards were captured were prioritised for analysis, leading to the exclusion of one snow leopard encounter. The SECR analysis estimated a density of 0.50 snow leopards per 100 km2 (95% confidence intervals [CI]: 0.13–1.86 snow leopards per 100 km2). This corresponds to an estimated abundance of four individuals (95% CI: 4–9) in the study area. The detection probability at the activity centres (g0) was estimated to be 0.018 (95% CI: 0.007–0.048), and the sigma value from our study was estimated to be 4330 m (95% CI: 2031–9229 m) (see Table 2 for more details).
| Area sampled (km2) | 465 |
| Number of camera traps placed | 44 |
| Number of occasions (days) | 45 |
| Trap nights | 1980 |
| Number of identifiable independent snow leopard detections | 14 |
| Number of snow leopard individuals identified | 4 |
| Number of individuals captured only once | 1 |
| Parameter | Estimate | Standard error | Lower confidence interval | Upper confidence interval |
|---|---|---|---|---|
| Density (individuals per 100 km2) | 0.50 | 0.37 | 0.13 | 1.86 |
| g0 | 0.018 | 0.0090 | 0.0070 | 0.048 |
| Sigma (m) | 4330 | 1736 | 2031 | 9229 |
| Realised abundance | 4 | 0.8 | 4 | 9 |
Additionally, we recorded a total of 16 species of mammals (Figure 2). Figure 2 displays the RAI for these mammals across the data set, while Table 3 presents key camera trapping statistics. Long-tailed Marmots (Marmota caudata) had the highest RAI (21.3 ± 0.2), followed by Pikas (Ochotona sp.) (18.1 ± 0.3). Both species had the highest number of detections and were captured by the most cameras (Table 3). Among carnivores, the Red Fox (Vulpes vulpes) had the highest RAI (7.4 ± 0.1), followed by the Stone Marten (Martes foina) (4.3 ± 0.1). The Asiatic Ibex (Capra sibirica), the main prey of the snow leopard, had an RAI of 1.1 ± 0.02, nearly twice that of the snow leopard (0.6 ± 0.01). Both bear species were present, with the Brown Bear (Ursus arctos isabellinus) having a higher RAI (2.5 ± 0.04). Table 3 also displays the RAI for species across each of the catchments within our study area (RAI values are different from Figure 2 as they are scaled to effort in each catchment). It is evident from Table 3 that Kibber had the lowest overall RAI for species, whereas Kiyar had the highest. Interestingly, most snow leopard encounters were concentrated in Kiyar, with some captures in Nanth and none in Kibber (Table 3). Cameras captured the most mammal species in Kiyar (15 species), followed by Nanth (11 species) and Kibber (8 species) (Table 3; Figure 3).
| Relative Abundance Index | |||||||
|---|---|---|---|---|---|---|---|
| Common name | Scientific name | No. of detections | No. of locations detected at | Overall | Kiyar | Nanth | Kibber |
| Herbivore | |||||||
| Long-tailed Marmot | Marmota caudata | 429 | 18 | 21.3 (±0.2) | 42.6 (±1.1) | 6.4 (±0.5) | - |
| Pika | Ochotona sp. | 372 | 17 | 18.1 (±0.3) | 7.8 (±0.3) | 36.5 (±2.0) | 8.8 (±1.0) |
| Asiatic Ibex | Capra sibirica | 22 | 4 | 1.1 (±0.02) | 0.7 (±0.05) | 1.8 (±0.1) | 0.7 (±0.1) |
| Musk Deer | Moschus sp. | 1 | 1 | 0.04 (−) | 0.1 (−) | - | - |
| Omnivore | |||||||
| Himalayan Brown Bear | Ursus arctos isabellinus | 51 | 14 | 2.5 (±0.04) | 4.1 (±0.2) | 1.0 (±0.05) | 1.7 (±0.2) |
| Himalayan Black Bear | Ursus thibetanus laniger | 9 | 4 | 0.4 (±0.01) | 0.3 (±0.02) | 0.8 (±0.05) | - |
| Himalayan Langur | Semnopithecus sp. | 3 | 2 | 0.1 (-) | 0.3 (±0.01) | - | - |
| Rhesus Macaque | Macaca mulatta | 2 | 1 | 0.1 (−) | - | - | 0.5 (−) |
| Carnivore | |||||||
| Red Fox | Vulpes vulpes | 150 | 26 | 7.4 (±0.1) | 10 (±0.4) | 7.0 (±0.2) | 2.6 (±0.2) |
| Stone Marten | Martes foina | 87 | 21 | 4.3 (±0.1) | 5.2 (±0.1) | 4.3 (±0.2) | 2.1 (±0.1) |
| Himalayan Stoat | Mustela erminea | 31 | 10 | 1.5 (±0.02) | 1.4 (±0.07) | 2.4 (±0.2) | 0.2 (±0.1) |
| Snow Leopard | Panthera unica | 15 | 9 | 0.6 (±0.01) | 1.2 (±0.05) | 0.1 (-) | - |
| Mountain Weasel | Mustela altaica | 9 | 3 | 0.3 (0.01) | 0.7 (±0.05) | - | - |
| Leopard Cat | Prionailurus bengalensis | 6 | 3 | 0.3 (±0.01) | - | 0.7 (±0.06) | 0.2 (±0.04) |
| Yellow-throated Marten | Martes flavigula | 3 | 2 | 0.2 (−) | 0.3 (±0.01) | - | - |
| Jackal | Canis aureus | 1 | 1 | 0.04 (−) | - | 0.1 (±0.01) | - |
- Note: This is only for 34 cameras as 10 cameras only had snow leopard-related data used for the SECR section. Number of detections relates to independent detection (i.e., detections 30 min apart), number of location is number of camera traps with the species out of 34, whereas Relative Abundance Index is calculated (1). A is the total number of independent encounters of a species by all cameras and N is the total number of camera trap days by all the cameras throughout the study area. Species arranged to their overall RAI within each group. Brackets indicate the standard error rounded to the first decimal place.
4 DISCUSSION
Our study represents one of the initial systematic sampling efforts to offer a robust assessment of snow leopard populations in KHANP in Jammu and Kashmir, India. Despite being one of the key premises for the designation of the national park in 1981, the area has not yet been validated with robust estimates of snow leopard numbers. The density and abundance estimates of snow leopards from our study area exhibit relatively wide 95% confidence intervals, primarily owing to the low number of individual detections and recaptures. The precision of SECR density estimates is directly related to sample size (Efford & Boulanger, 2019), and precision can be improved by expanding the study area, which would subsequently increase the number of photo-captured individuals (Green et al., 2020; Palmero et al., 2023). More robust snow leopard population estimates can be achieved by conducting a broader landscape-wide study across the Kishtwar region, including adjacent potential snow leopard habitats such as Warwan and Paddar (Sanyal et al., 2024). Such a comprehensive study area is key to implementing a long-term monitoring programme to track changes in snow leopard populations. Additionally, it is important to note that placing detectors along linear features risks biasing the density estimates, especially if these lines are aligned with noncircular home ranges (Efford, 2019). The placement of cameras in our study area was relatively linear due to areas being snowed-in or inaccessible because of the precipitous terrain. However, by comparing captures of snow leopards from this study with those of Sanyal et al. (2024), it is evident that snow leopards are capable of moving between valleys. Given the landscape breaks in each valley, it is plausible that the home ranges of snow leopards do not transverse the entire array, which is an important design consideration for SECR (Efford, 2019; Palmero et al., 2023).
Despite the variability in our data, the diverse prey base for snow leopards, such as ibex, musk deer and marmots, and the connection of KHANP with the trans-Himalayan and Greater Himalayan regions affirms its potential as a significant snow leopard habitat. Our estimated abundance of four individuals (95% CI: 4–9) aligns with findings from comparable regions in the neighbouring state of Himachal Pradesh (e.g., Bhaga, Bharmour, Baspa, Kullu and Miyar) (Suryawanshi et al., 2021), and matches the count of four individuals reported by Sanyal et al. (2024). Notably, the sigma parameter for snow leopards in our data was 4330 m (95% CI: 2031–9229), slightly lower than that for nearby regions (Sharma et al., 2021; Suryawanshi et al., 2021), possibly indicating a more restricted population (Efford et al., 2016). A restricted population could be categorised by limited movement across valleys due to reasons such as poor habitat connectivity and/or prey availability. While the sigma value is relatively low, it is consistent with findings from similar landscapes in Nepal (Khanal et al., 2020) and aligns with the global average for snow leopards (Sharma et al., 2019). Further research is needed to understand snow leopard movements between KHANP and adjacent areas. Beyond snow leopards, our camera trapping revealed a rich mammal assemblage across KHANP, similar to Sanyal et al. (2024). However, it is important to exercise caution when comparing RAIs of nontarget species due to potential variations in species detectability (Sollmann et al., 2013). Marmots had the highest RAI in our survey, mirroring findings from Kachel et al. (2022), who showed that snow leopards in the Tajik Pamirs relied on seasonally abundant marmots as prey. This could also be the case in our study area, though further studies are needed to validate this. Wild ungulates, key determinants of snow leopard populations (Suryawanshi et al., 2021), were also present. The main ungulate prey, the ibex, had a slightly higher RAI than the snow leopard in our data set. We recommend future studies undertake robust methods for ungulate monitoring, such as double-observer surveys, as camera trap surveys can underestimate ungulate densities (Khanyari et al., in review). Moreover, since camera placement for snow leopards can influence capture rates of other mammals, future research should consider methods like Camera Trap-based Distance Sampling and Random Encounter Models, which are effective for both marked and unmarked species (Palencia et al., 2021).
Furthermore, the Himalayan brown bear was present across all three catchments in our study area, ranking fifth in terms of RAI. Patel et al. (2023) suggest potential spatial-avoidance behaviour between Himalayan brown bears and snow leopards in Himachal Pradesh. Investigating how brown bears, the largest mammal in the region, interact with snow leopards is crucial, especially considering their partially overlapping diets (Nawaz et al., 2019). Interestingly, Sanyal et al. (2024) reported a higher photo-capture rate of Asiatic black bears compared to Himalayan brown bears. This discrepancy could be due to Sanyal et al.'s sampling at lower elevations, which are more forested and suitable habitats for black bears, particularly between 2300 and 3000 m, which were not included in our sampling. Notably, neither our study nor that of Sanyal et al. (2024) captured images of common leopards (Panthera pardus). In similar regions, such as the Great Himalayan National Park in Himachal Pradesh and the Annapurna region of Nepal, common leopards and snow leopards have shown overlapping habitats (Chetri et al., 2024; Suryawashi et al., 2021). Kichloo et al. (2023) suggest high common leopard local extinction rates in KHANP's higher elevation regions, primarily attributed to the need for natural cover (Kshettry et al., 2017). The potential impact of years of political conflict on the local extinction of common leopards through direct hunting or indirectly through habitat loss warrants further research. Furthermore, there are anecdotal reports of hunting in the region for species like musk deer for their pods, with more research being needed to understand the impact of this hunting on local species populations.
While we are just beginning to understand the value of a region such as KHANP for snow leopards, it is important to contextualise this with the increasing threats in the region. KHANP is visited by transhumant Gujjar and Bakarwal nomads during the summer months. The potential negative human–wildlife interactions, both direct and indirect through impacts on prey and habitat due to livestock grazing, need coordinated and concerted studies. Regions across the Indian Greater Himalayas are experiencing some of the fastest rates of infrastructural development, particularly hastily planned hydro-projects, leading to large-scale habitat loss (Asher and Bhandari, 2021). Furthermore, glacial melting in the Greater Himalayas is occurring at nearly twice the rate of the rest of the planet (Maurer et al., 2019), which can drastically impact snow leopard habitats (Kichloo et al., 2023), potentially leading to upward shifts (Li et al., 2016). All of these factors need to be closely monitored to ensure effective snow leopard conservation in the region.
To conclude, our study provides initial evidence supporting the potential of KHANP as a unit for landscape-based participatory conservation under the Government of India's Project Snow Leopard (PSL, 2008). This initiative will need meaningful participation from local communities, including villagers and Bakarwals. Our approach aligns with the Kunming-Montreal Global Biodiversity Framework, which acknowledges the significant role of indigenous people and local communities in global conservation. We hope our study encourages local researchers and authorities to engage in further species research, with results contributing to inclusive conservation models that benefit both wildlife and local populations.
AUTHOR CONTRIBUTIONS
Munib Khanyari: Conceptualisation; Fund acquisition; data collection; data analysis; writing. Oyndrila Sanyal: Data collection; data analysis; writing. Pankaj Chandan: Conceptualization; fund acquisition; supervision; reviewing. Deepti Bajaj: Data analysis; reviewing. Charu Sharma: Data analysis; reviewing. Manoj Rana: Data collection. Tawqir Bashir: Conceptualization; supervision; reviewing. Kulbhushansingh Suryawanshi: Conceptualization; fund acquisition; supervision; reviewing.
ACKNOWLEDGEMENTS
This work would never have been possible without the tremendous support of various local people and researchers. Nishu, Pinku and Sanam from Surnoo villages in particular were helpful. Akram Rahi from Honzar and Rakesh Kumar from Kibber village helped with much of the fieldwork. Several officials of the Jammu and Kashmir Wildlife Protection Department were crucial in ensuring this work happens. Rinchen Togbe, Dorje Angrup and Rigzen Dorjay were key technical leaders in the camera trapping efforts. Without their effort, the camera trapping would not have been successful. Mr. Suresh Gupta, former Chief Wildlife Warden, Mr. Sarvesh Rai, Chief Wildlife Warden, Govt. of J&K, Dr. Kumar MK, Regional Wildlife Warden Jammu, Mr. Majid Bashir DFO-Kishtwar, Mr. Sajad Zaheer, Mr. Mansoor, Mr. Khalid and their entire staff across the National Park (particularly Shadi Lal, Dewan Chand, Lateef and Om Prakash) were instrumental in ensuring this work tookplace. This work is now carrying forward under the leadership of Mr. Chander Shekar, Wildlife Warden Chenab Division, Kishtwar. This work was generously funded by the Panthera Sabin Grant and the National Geographic Meridian Grant to researchers at Nature Conservation Foundation. Also, the Department of Wildlife Protection funded part of the work through a collaborative initiative between National Development Foundation, Jammu, and the CORD, University of Kashmir, Hazratbal.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ETHICS STATEMENT
Camera trapping protocol followed the ethical guidelines suggested by Sharma et al. (2020): Sharma, K., Fiechter, M., George, T., Young, J., Alexander, J. S., Bijoor, A.,… & Mishra, C. (2020). Conservation and people: Towards an ethical code of conduct for the use of camera traps in wildlife research. Ecological Solutions and Evidence, 1(2). Additionally, as this is an observational study this did not require approval from the ethics committee. Any photographs obtained of people were immediately destroyed.
Open Research
DATA AVAILABILITY STATEMENT
All data from our work has been presented in the manuscript. For raw data from camera trap the authors are happy to provide the same upon contacting the corresponding author. We decided not to upload camera trap raw data due to security reasons.
