Prediction of potential habitat suitability of snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) and niche overlap in the parts of western Himalayan region

Abstract

The snow leopard (Panthera uncia) and blue sheep (Pseudois nayaur) are the inhabitants of remote areas at higher altitudes with extreme geographic and climatic conditions. The habitats of these least-studied species are crucial for sustaining the Himalayan ecosystem. We employed the Maximum Entropy (MaxEnt) species distribution model to predict the potential habitat suitability of snow leopards and blue sheep and extracted common overlapped niches. For this, we utilised presence location, bio-climatic and environmental variables, and correlation analysis was applied to reduce the negative impact of multicollinearity. A total of 134 presence locations of snow leopards and 64 for blue sheep were selected from the Global Biodiversity Information Facility (GBIF). The annual mean temperature (Bio1) was found to be the most useful and highly influential factor to predict the potential habitat suitability of snow leopards. Annual mean temperature, annual precipitation and isothermality were the major influencing factors for blue sheep habitat suitability. Highly influential bio-climatic, topographic and environmental variables were integrated to construct the model for predicting habitat suitability. The area under the curve (AUC) values for snow leopard (0.87) and blue sheep (0.82) showed that the models are under good representation. Of the total area investigated, 47% was suitable for the blue sheep and 38% for the snow leopards. Spatial habitat assessment revealed that nearly 11% area from the predicted suitable habitat class of both species was spatially matched (overlapped), 48.6% area was unsuitable under niche overlap and 40.5% area was spatially mismatched niche. The presence of snow leopards and blue sheep in some highly suitable areas was not observed, yet such areas have the potential to sustain these elusive species. The other geographical regions interested in exploring habitat suitability may find the methodological framework adopted in this study useful for formulating an effective conservation policy and management strategy.

INTRODUCTION

Climate change has emerged as one of the potential threats to biodiversity across the world (Garcia et al., 2014). Global warming has influenced the species distribution, and habitat structure (Parmesan & Yohe, 2003) has caused variation in the population of prey–predator species (Gilg et al., 2009), and produced spatial inconsistency between predator–prey habitat ranges (Schweiger et al., 2012). Montane and polar species have already been facing extreme contractions in their natural habitat ranges (Walther et al., 2002). The high altitudes have been identified as highly vulnerable regions to climate change (Shrestha et al., 2012). Climate change has significantly affected the habitat ranges of snow leopards and blue sheep in high-altitude mountains.

Snow leopard inhabiting the extreme climatic condition and rugged mountain terrain of the Himalayas is one of the apex predator in this high-altitude energy-deficient ecosystem (Jackson & Ahlborn, 1984). Hence, its presence is crucial for sustaining such an ecosystem (Ripple et al., 2014). The current and potential habitat ranges of the snow leopards and their primary prey blue sheep assume greater significance for sustaining and conserving the Himalayan ecosystems (Lyngdoh et al., 2014). Assessment of habitat suitability for snow leopards and blue sheep is particularly helpful for formulating conservation-related policy, devising adaptation strategies, predicting the degree of vulnerability, and minimising the potential biodiversity loss (Kujala et al., 2013; Nazeri et al., 2012, 2014; Shrestha & Bawa, 2014). To sustain a healthy Himalayan ecosystem at high altitudes, blue sheep biomass must be maintained (Aryal et al., 2014; Oli & Rogers, 1996). Snow leopards have a smaller population size. It is estimated that their population ranged between 4678 and 8745 globally (Nyhus et al., 2016). The International Union for Conservation of Nature (IUCN) declared the snow leopard under vulnerable species mainly because of the threat to their habitat, declining prey population, and poaching (McCarthy et al., 2017). It was estimated that the population of snow leopards has declined by 20% over the last two decades (Jackson et al., 2008). Most of the habitats of the snow leopard and blue sheep overlap with the regions having large populations of domestic ungulates (Aryal et al., 2014; Bagchi & Mishra, 2004; Oli, 1994). Snow leopard habitat modelling and its primary prey blue sheep would provide a realistic prediction of their potential distribution.

A literature survey of recent studies revealed that very small areas under the snow leopard habitat range had been assessed using an effective scientific methodology (Rashid et al., 2021). Most of the past studies conducted on snow leopards and primary prey blue sheep in the western Himalayan region of India have not widely utilised the variables such as temperature and precipitation for suggesting conservation-related policy (Watts et al., 2019). Annual temperature and precipitation cycles govern many elements affecting snow leopard survival, such as water availability, ecosystem health, snow leopard behaviour and human activities (Peters et al., 2017). The species habitat suitability model is a realistic approach for assessing the potential habitat range for vulnerable and threatened species of ecosystems (Guisan & Thuiller, 2005; Guisan & Zimmermann, 2000; Rooper et al., 2016). Evaluation of the species' habitats and their distribution is also critical for the long-term survival of endangered species (Ebrahimi et al., 2017). This is particularly relevant when analysing the conservation of keystone species such as snow leopards (Farashi & Shariati, 2018). The loss of apex predators in an ecosystem can trigger a trophic cascade (Berger et al., 2001; Diamond, 1990). In turn, the absence of prey species adversely affects the restoration of predator populations (Berger, 2007; Ripple & Beschta, 2004).

A large number of models varying from common rule-based explanations to complex mathematical and statistical machine learning have been developed for assessing distribution and habitat (Franklin, 2010). The accuracy of these models is generally determined by input data quality and quantity, sampling of occurrence, and presence-absence records (e.g., Baasch et al., 2010; Bateman et al., 2012; Güthlin et al., 2011). Maximum entropy (MaxEnt) has been an extremely popular model to predict the potential distribution of species and their habitat ranges (Clements et al., 2012; Phillips et al., 2006; Wilting et al., 2010). Ecological niche has been utilised for predicting future distribution under climate change (e.g., Hu & Jiang, 2011; Kalkvik et al., 2012). MaxEnt uses the presence-only data for climatic variables for estimating the niche and future geographical distribution of the species (Phillips et al., 2006). It does not rely on the absence of records of particular species (Li et al., 2011). All of these characteristics have brought the MaxEnt model to be considered one of the best models for habitat suitability assessment (Elith et al., 2006). Earlier studies on species distribution models predominantly highlighted the snow leopard habitat and climate change induced habitat shifts (Bai et al., 2018; Li et al., 2020). However, prey availability owing to the food abundance in an ecosystem influences predator distribution. Therefore, the predator snow leopard habitat suitability model should be compared to the prey blue sheep habitat suitability model (Aryal et al., 2016). The study analyses the existing habitats of snow leopards and blue sheep to predict their habitat suitability and overlap niche. The species occurrence data were examined by incorporating bio-climatic and topographical factors.

MATERIALS AND METHODS

The study area covers three states of India: Himachal Pradesh, Uttarakhand, and parts of Jammu and Kashmir (Figure 1). Based on habitat preference by snow leopards and occurrence record availability, earlier studies were conducted on Himachal Pradesh (Bhatnagar, 1997; Bhatnagar et al., 2008; Maheshwari et al., 2013; Mishra, 1997; Mishra et al., 2004; Suryawanshi et al., 2013; Vinod & Sathyakumar, 1999), Uttarakhand (Bhattacharya et al., 2012; Kandpal & Sathyakumar, 2010; Sathyakumar, 1994), Kashmir and Kargil region (Chundawat & Qureshi, 1999; Fox et al., 1991; Mallon, 1991). The geographical extent of the region is 28°43′28.97″ N, 81°2′19.73″ E. It spreads over an area of approximately 205,026.86 km² with elevation ranging between 165 and 7740 m above mean sea level.

FIGURE 1. Location of the study area with presence record and protected areas.

The study area has a good representation of protected areas and wildlife sanctuaries (WLS), including the Hemis national park, Karakoram wildlife sanctuary (WLS), Sechu tuan nala WLS, Changthand WLS, Cold desert biosphere reserve, Gangotri national park, Govind pashu vihar WLS, Nanda devi biosphere reserve, Kugti WLS, Great Himalaya national park and Kedarnath WLS. Most of the study area is under glaciers, permafrost and rock faces, and sometimes the temperature falls as low as −20°C (Maheshwari et al., 2013). The area is marked by extremely harsh climatic conditions and topography. The region is dotted with alpine meadows and subalpine forest cover (Chetri et al., 2017).

DATABASE AND METHODOLOGY

Due to the very harsh climatic condition, inaccessible rugged mountain tract, and huge areal extent, it is very difficult to conduct a systematic survey for collecting the presence data of snow leopards and blue sheep in the study area (Araújo & Williams, 2000). The presence data record of snow leopards and blue sheep in their habitat ranges are limited in the study area (Elith et al., 2011). We collected snow leopard presence data from the Global Biodiversity Information Facility (GBIF) and World-Wide Fund for Nature (WWF India). Blue sheep presence data were collected from GBIF and published literature (Watts et al., 2019). A total of 134 presence locations of snow leopards and 64 presence location data for blue sheep were used to predict the potential habitat suitability.

We analysed the interaction of the presence locations of snow leopards and blue sheep with potential influencing factors (Bai et al., 2018; Peterson et al., 2011; Wolf & Ale, 2009). Two major categories of influencing factors (bio-climatic and abiotic) related to the habitat suitability of both species were recognised (Aryal et al., 2016; Riordan & Shi, 2016). Bio-climatic variables were collected from WorldClim (http://www.worldclim.org/). A land use/land cover map was prepared from Landsat 8 satellite data. Thirty-metre resolution ASTER digital elevation model data were acquired from USGS (https://earthexplorer.usgs.gov/) to prepare the elevation, slope, aspect and ruggedness of the study area.

All the raster variables were resampled into 1 km resolution using a resample tool in ArcGIS to match the original resolution of WorldClim data. The attribute values of all influencing factors were obtained using the spatial analyst tool in ArcGIS. SPSS was used to analyse the Pearson correlation coefficient of the attribute values of all variables for avoiding random error and uncertainty in the model (Lham et al., 2021). Variables having coefficient values equal to and greater than 0.75 were excluded from the habitat suitability analysis. Finally, we considered slope, aspect, elevation, ruggedness, land use/land cover, annual mean temperature (bio1), mean diurnal range (bio2), isothermality (bio3), temperature seasonality (bio4), and annual precipitation (bio12) for habitat suitability modelling (Table 1). The details of the methodological steps are presented in Figure 2. The raster layers of elevation, slope, aspect, land use/land cover, and bias grid are shown in Figure 3.

TABLE 1 Selected variables for habitat suitability.

S. No	Variables	Category/sub class	References
1.	Bio 1: annual mean temperature	Continuous	Watts et al. (2019)
2.	Bio 2: mean diurnal range	Continuous	Aryal et al. (2016)
3.	Bio 3: isothermality	Continuous
4.	Bio 4: temperature seasonality	Continuous
5.	Bio12: annual precipitation	Continuous	Li et al.  (2016)
6.	Ruggedness	Continuous	Jackson and Hunter (1996)
7.	Elevation (m)	165–120, 1250–2600, 2600–4000, 4000–5000, 5000–7740	La Sorte and Jetz (2010)
8.	Slope (degree)	0–10, 10–20, 20–30, 30–40, 40–50, 50–70	Schaller (1977); Fox et al. (1991)
9.	Aspect	North: 0–22.5, Northeast: 22.5–67.5, East: 67.5–112.5, Southeast: 112.5–157.5, South: 157.5–202.2, Southwest: 202.5–247.5, West: 247.5–292.5, Northwest: 292.5–337	Marque and Mora (1992)
10.	LULC	Cropland, open forest, close forest, sandy area, mosaic herbaceous, shrub land, grassland, sparse vegetation, urban area, water bodies, snow cover and ice	Jackson and Ahlborn (1989)

FIGURE 2. Methodological framework for habitat suitability assessment.

FIGURE 3. Upper left to right: elevation, slope, aspect; lower left to right: land use/land cover, roughness, bias file.

Sampling biases

Generally, the presence data collected by systemic field survey are considered best for habitat suitability assessment, but often those data are not accessible for this region for these endangered and vulnerable species. One of the basic drawbacks to this dataset is the sampling bias in data collection, where certain parts of the study region are analysed more intensively than others (Elith et al., 2011). Important occurrence data for these species are extracted from natural history museums and open-access databases (Elith et al., 2006). In MaxEnt, presence data are usually picked from a wide rectangular area that may include some suitable habitats where no snow leopard and blue sheep presence has been reported (Brown, 2014) and these background points may yield spurious results of habitat suitability (Brown, 2014; Holt et al., 2018). To resolve this sampling bias, we reduced the area using the minimum convex polygon (MCP) based on the occurrence record. For this, the species distribution model was added to ArcGIS and a bias file was generated separately for both species using the presence location. We created a 2 km buffer to select background data from the buffer zone. Snow leopard and blue sheep movement were tracked along a straight distance of 2 km in the Himalayan region. No seasonal variation in the habitat range was observed (Bhattacharya et al., 2020; Jackson & Hunter, 1996).

MaxEnt model simulation

Habitat suitability models for both species were constructed in MaxEnt Version 3.4.1 (Phillips et al., 2004). We randomly used 75% of the presence record of both species for training the model and the remaining 25% for testing the model (Aryal et al., 2016; Bai et al., 2018; Holt et al., 2018; Zhang et al., 2019). Outcomes of the MaxEnt model were validated through the AUC values. The model permeation was good for both the species with AUC values of 0.87 for snow leopards and 0.82 for blue sheep. The habitat suitability map was classified into four suitable classes (high, moderate, low, and very low) and an unsuitable class. The area of the predicted habitat suitability map of both species was calculated using zonal geometry in ArcGIS.

Niche overlap among snow leopard and blue sheep

The habitat suitability map was further reclassified into the binary map by using the reclassify tool in ArcGIS. The habitat suitability map was reclassified into two classes. In the reclassify tool we selected all five classes and then assigned new values (0 and 1). Moderate, low and very low suitable, and unsuitable were assigned a ‘0’ value; the high suitability class was assigned 1. Likewise, the same procedure was replicated for all classes of both species. For niche overlap analysis, we superimposed all the classified layers of both species in ArcGIS. The higher area is extracted through the lower area to identify the same niche of both species by using the extract-by-mask tool in ArcGIS. The area with spatial habitat similarity (same niche for both species) and spatial habitat dissimilarity was calculated by a raster calculator in ArcGIS.

RESULTS Potential influencing factors for habitat suitability

The analysis (Figure 4) of the contribution of influencing variables in habitat suitability shows that annual mean temperature (Bio1) was identified as the major influencing factor with a 52.4% contribution rate in the prediction of the potential habitat of snow leopard, followed by Bio3: isothermality (15.3%), Bio 4: temperature seasonality (9.9%), Bio12: annual precipitation (8.7%) and Bio2: mean diurnal range (5.1%).

FIGURE 4. Contribution of variables in habitat suitability prediction.

Blue sheep habitat suitability was also mostly influenced by Bio 1: annual mean temperature (63.4%), followed by Bio3: isothermality (19.7%), Bio12: annual precipitation (7.7%), land use land cover (4.6%), and Bio2: mean diurnal range (1%). Slope, aspect, elevation and ruggedness did not contribute to habitat suitability prediction for both species (Figure 4).

Potential habitat suitability of snow leopard and blue sheep

Out of the total area, nearly 38% area was suitable for snow leopards and 47% area was suitable for blue sheep (Table 2 and Figure 5). Snow leopards prefer very steep slopes over 40° and avoid level terrain (Jackson & Ahlborn, 1989). The findings revealed that a suitable habitat for snow leopards is located between 3000 and 5400 m and these places are generally moderately to very steep (>40–50° slopes). Blue sheep often avoid steep slopes (over 15°) and lower elevations (below 4000 m). Their presence is seen at higher altitudes of around 5100 m (Aryal et al., 2014; Filla et al., 2021; Lyngdoh et al., 2014; Shrestha et al., 2018). Snow leopards primarily depend on blue sheep and constantly hunt these ungulates preferentially across their range. Blue sheep often preferred the shrub or grass-covered open slope along the cliff, which helped them escape from snow leopards (Jackson & Ahlborn, 1984). The findings demonstrated that unsuitable habitats incorporated comparably lower elevations with flattened slopes, less ruggedness, high maximum temperature and dense forests, as well as human disturbances such as transportation and agricultural operations. Snow leopard habitat was often constrained by climatic parameters, such as diurnal temperature range, driest quarter precipitation, roughness and biological aspects, such as prey availability and livestock activity (Yang et al., 2021).

TABLE 2 Area under various classes of habitat suitability and overlap niche for snow leopard and blue sheep.

Suitability classes	Habitat suitability area in (km2)		Overlap niche area in (km2)	Niche overlap (spatially matched area)
	Blue sheep Snow leopard
High	15,111.2	9686.2	3011.2	Highly suitable for both species
Moderate	19,412.2	13,021.9	3180.5	Moderately suitable for both species
Low	24,358.01	22,363.6	6128.3	Low suitable for both species
Very low	36,714.1	33,800.7	9996.1	Very low suitable for both species
Unsuitable	109,431.4	126,154.5	99,803.5	Unsuitable for both species

FIGURE 5. Reclassified map of snow leopard habitat suitability (left) and reclassified map of blue sheep habitat suitability (right).

Validation of the model

The validation of habitat suitability maps produced through MaxEnt with the AUC revealed that the model performed well with an AUC value of 0.87 for snow leopards and 0.82 for blue sheep (Figure 6).

FIGURE 6. Area under the curve for snow leopard (left) and blue sheep (right).

Niche overlap (spatially matched and spatially mismatched)

Spatially matched (overlaps) and mismatched habitat distribution of snow leopards and blue sheep in the western Himalayan region indicated that about 1.4% of the area from the highly suitable class is spatially matched (overlap). This area is characterised by high altitude, the least snow cover, alpine and subalpine grassland, sufficient food availability, and cold and dry climatic conditions. Nearly 4.9% area was found under very low suitable overlapped followed by low suitable overlapped (2.9%) and moderately suitable overlapped (1.6%). About 48.6% of the area was found under unsuitable overlapped while 40.5% of the potential niche was spatially mismatched between snow leopards and blue sheep (Table 3 and Figure 7).

TABLE 3 Area under overlap niche (spatially matched) and spatially mismatched between snow leopard and blue sheep.

Blue sheep	Snow leopard habitat suitability (area in km2)
High	High	Moderate	Low	Very low	Unsuitable
	3011.21	2639.47	4102.72	3909.12	1448.67
Moderate	2727.42	3180.53	5657.77	5790.45	2056.02
Low	2185.86	3631.30	6128.28	7887.06	4525.47
Very low	1440.37	2604.46	4352.50	9996.11	18,320.69
Unsuitable	321.33	966.12	2122.31	6217.95	99,803.56
Total spatially mismatched niche is 89,907.06 km2

Note: Bold entries show spatially matched areas (overlap niche); all other entries show spatially mismatched areas (niche mismatched).

FIGURE 7. Overlap niche between snow leopard and blue sheep.

These areas experience a dry and warmer climate, and high human disturbance along the dense patched forest region. Changes in temperature and precipitation patterns cause habitat shifting and niche mismatching between snow leopards and blue sheep (Shen, 2020, August). Wild prey like blue sheep in the Himalayan region moves to higher elevations due to food shortages. Spatially mismatched and unsuitable overlapped habitats were found in areas with high human disturbance. Excessive herding and grazing also caused the snow leopard's natural prey to shift to higher elevations. At lower altitudes, the blue sheep population declined due to the overgrazing of wild grasses and shrubs by livestock herds. This led to a food shortage for the snow leopard. Due to the scarcity of primary wild prey, snow leopards were compelled to prey on livestock, creating conflict between herders (Bagchi & Mishra, 2006).

DISCUSSION

Climate change is acting as a significant challenge for species inhabited in high-altitude mountain ranges. Mountain ecosystems are experiencing habitat degradation due to unprecedented changes in annual mean temperature and precipitation cycle. Bio-climatic and topographical simulations were used to predict the potential habitat suitability and distribution. About 30% of snow leopard natural habitats are vulnerable to climate change in the Himalayan region (Jessica et al., 2012). The species distribution model can effectively be utilised for conservation planning (Segurado & Araujo, 2004). However, the non-availability of species distribution data is a great challenge (Kadmon et al., 2003; Wisz, et al., 2008). To overcome this limitation, a bias file at a 2 km buffer was created for both species using the presence record in ArcGIS. Validation of the prediction model with receiver operating characteristics (ROC) curves showed AUC values greater than 0.8 for both species. Thus, the model proved to be effective for habitat suitability analysis. Our findings revealed that the contribution of annual mean temperature was highest in potential habitat suitability prediction of both snow leopards and blue sheep in the western Himalayan region of India. The finding is in tune with Aryal et al. (2016). Aspect, altitude and ruggedness have the least contribution in determining the potential habitat suitability for snow leopards and blue sheep. Precipitation and temperature cycles control habitat ranges, habitat degradation, water availability, and behaviour of the snow leopards and blue sheep. Aspect might have an indirect relationship due to variations in vegetation patterns (Dearborn & Danby, 2017).

The existing protected areas in the Western Himalayas cover a large range of the predicted snow leopard and blue sheep habitats. The area under the high suitability class mostly covered the protected area, national parks, wildlife sanctuaries and biosphere reserves. Snow leopard habitat distribution is primarily dependent on the density of wild prey species (Sharma et al., 2015). Blue sheep are the primary prey of snow leopards and determine the snow leopard distribution (Aryal et al., 2010; Oli et al., 1993). Blue sheep inhabit the Western Himalayas at an altitude of between 2500 and 5500 m above sea level. Generally, blue sheep prefer living near cliff slopes covered with grass (Schaller, 1977). Snow leopards are also found at the same altitude. The abundance of blue sheep is mainly observed in the protected areas and biosphere reserves. Gangotri national park in the Uttarkashi district of Uttarakhand, the upper region of Kashmir, was found under a high to moderate class of blue sheep habitat suitability. However, 40% of such protected areas are smaller than the average home range of a single adult species of snow leopard (Johansson et al., 2016). The interaction between pastoral communities and the species often leads to conflict (Bhatnagar et al., 2016). Extension of protected areas to encourage the population of snow leopards and blue sheep, if required, would be prudent. However, expanding the area cannot ensure successful habitat conservation since several other variables influence effective conservation, including the abundance of prey density species, anthropogenic pressures, human–wildlife conflict, and habitat quality (Aryal et al., 2016). In addition to the extension of protected areas, mitigation strategies could be a successful conservation strategy (Hayward, 2011).

There is a substantial overlap in the distribution of snow leopards and blue sheep throughout the Himalayan region. Hence, blue sheep presence indicated the possibility of the presence of snow leopards (Bhattacharya et al., 2020). Bai et al. (2018) explored that snow leopards preferred bare land with relatively high slopes, while the aspect has very little impact on snow leopard habitat suitability. McCarthy et al. (2005) investigated that the prey species distribution and rugged mountain terrain primarily influenced snow leopard habitats. Sharma et al. (2015) also observed that blue sheep distribution intensively influenced the snow leopard presence and distribution in the western Himalayan region. A trade-off between elevation and food availability has resulted in the least abundance of blue sheep in snow leopard habitats (Wolf & Ale, 2009). Snow leopards preferentially stalk prey on the rocks and hillsides of highly rugged landscapes, whereas blue sheep were mostly observed feeding on open slopes (Jackson & Ahlborn, 1984). However, the distribution of snow leopards cannot be examined in isolation from blue sheep (Carbone & Gittleman, 2002; Karanth et al., 2004; Sharma et al., 2015). Hence, the results indicate that areas with high habitat suitability of blue sheep might be classified as highly suitable for snow leopard habitats.

Snow leopards strongly prefer irregular slopes greater than 40° and well defined landform edges, such as mountain ridges, cliffs and ravines to wander about their home range. During the winter, they may migrate to lower altitudes to avoid deep heavy snow and to monitor the movements of blue sheep. Blue sheep also rely on steep, broken, rocky terrain for shelter when resting, escaping predators and maintaining homeothermy (Schaller, 1977; Wegge, 1979; Wilson, 1981). During periods of continued snowfall, steep, broken rocky terrain with southern exposures provides shelter and restricted forage; also less snow accumulates on steep (>35°) slopes. The higher structural variety of broken rocky landscapes also tends to attenuate the impacts of extremes in ambient air temperature, thermal and solar radiation, and wind speed. The influencing factors of snow leopard and blue sheep habitat suitability can vary in different regions. Feng et al. (2012) identified ruggedness, cliff baselines and stream beds as the major influencing factors of snow leopard habitat suitability in the Tomur National Nature Reserve. Li et al. (2013) reported that annual mean temperature and ruggedness were the two critical factors affecting snow leopard habitation.

The annual mean temperate, isothermality and annual precipitation were primary influencing factors for habitat suitability. Changes in these parameters will lead to a lower degree of niche overlap and a higher degree of spatial mismatch of snow leopard and blue sheep habitats. Climate change results in less overlap and more regional mismatch between snow leopard needs and blue sheep availability. It can have a substantial impact on predators by influencing food requirements and food availability (Broitman et al., 2008; Durant et al., 2007). The interaction between factors influencing species distribution and predator–prey relationships is quite complex (Moritz et al., 2008). Variations in the shifting habitat ranges among and within species result in a mismatch of predator–prey requirements (Durant et al., 2007; Peers et al., 2014). The spatial mismatch also indicated that the snow leopards will have to expand their dietary variety to meet their requirements with the common leopard in various protected areas and national parks. It would be detrimental for snow leopard conservation to have a superior competitor for diet and habitat (Lovari & Minder, 2013). Climate change may restrict the snow leopard to a narrow range between unsuitable habitat and marginal habitat (Forrest et al., 2012). It will be fascinating to study how the snow leopard's other prey species will respond to climate change. Changes in dietary patterns among multiple prey species have occurred due to the occurrence of narrow niches (Peers et al., 2014).

CONCLUSION

This paper explored the habitat suitability of snow leopards and blue sheep. We utilised presence location data maintained by GBIF, bio-climatic and abiotic factors to predict potential habitat suitability in the parts of the western Himalayan region. Habitat suitability models for both species were constructed in MaxEnt. The suitability maps prepared through MaxEnt were validated using ROC curves. All the layers were superimposed to obtain niche overlap. The annual mean temperature was identified as the most influential factor for habitat suitability followed by isothermality, temperature seasonality, annual precipitation and mean diurnal range. Of the total area of the region, nearly 38% was found to be a suitable habitat area for snow leopards and 47% for blue sheep. MaxEnt was found to be a suitable model for habitat niche analysis of species with limited presence records and ecological information; however, more climatic variables are needed to understand species distribution and their habitat. The study could not use some non-climatic and anthropogenic factors for habitat suitability; this is a limitation of our study. Factors such as dietary interactions with other species, dispersal strategy, future land-cover changes, and pastoral activities may be included in model construction. Niche overlap analysis revealed that nearly 11% area was overlapped for snow leopard and blue sheep habitats. The methodology will endure to increase the effectiveness of mitigation initiatives in the future and also strengthen the conservation of endangered species. For conservation concerns, it is essential to evaluate the presence record of snow leopards and blue sheep in all areas of the landscape which have been identified as highly suitable in the MaxEnt habitat suitability model. Research also shows that half of the suitable habitat for snow leopards and blue sheep remains outside protected areas. Hence, efforts should be directed either toward the establishment of new protected areas or the expansion of existing protected areas and national parks, enabling the facilitation of more suitable habitats. The study also suggests that only strengthening protected area networks as a habitat management approach may not be effective to address habitat shifting unless continuous poaching and other human disturbances against snow leopards and blue sheep are adequately managed in all existing protected and non-protected areas of the western Himalayan region of India. In the western Himalayan region, the low suitable habitat of snow leopards and blue sheep accounted for about 11% and 12% area, respectively, having lower altitude areas with little shelter and higher anthropogenic pressure. Further investigation of these regions is required to validate the absence of snow leopards and to assess the status of primary prey populations. Low suitable habitats of snow leopards and blue sheep are essential for their existence since they are part of the ecosystem where snow leopards may survive. The highly suitable habitats in the western Himalayan region mainly lie at higher altitudes. Spatial congruity is a major concern as agro-pastoral communities falling in the prey–predator habitat often lead to human–animal conflict.

ACKNOWLEDGMENTS

The author is incredibly grateful to the WWF-India and TERI School of Advanced Studies for their support in conducting this research work under the major project research work of the MSc course of Mohd Islam. The authors are thankful to Mr. Ravi Singh (Secretary general and CEO, WWF-India) and Dr. Sejal Worah (Programme Director, WWF-India) for providing us with valuable support and a workspace for conducting this study. We would like to thank all the WWF-India staff for all of their cooperation during the project work at IGCMC, WWF-India. We are greatly thankful to the anonymous reviewers for their valuable comments and suggestions in improving the quality of the manuscript. We are also thankful to the Managing Editor of the journal GEO: Geography and Environment, Dr. Phil Emmerson from Royal Geographical Society (with IBG) for his comments and recommendations. We are also very thankful to GBIF (the Global Biodiversity Information Facility), journals, articles, and research work for providing us with species occurrence data and other helpful information. The author likes to show deep appreciation to Dr. Rahul Chopra (Director-Centre for Sustainability, Environment, and Climate Change, FLAME University) and Manjula Nair (Senior Research Associate, FLAME University) for their moral support and provided a friendly atmosphere at work for finalizing this research work.

DATA AVAILABILITY STATEMENT

All datafiles produced by this study will be published in the university of Manchester official website datafile portal. Link: https://research.manchester.ac.uk/en/persons/mehebub.sahana/datasets/.