[1]¿p#1 newcommands Title: Range redistribution under climate change reshapes connectivity without genetic fragmentation in a Himalayan carnivore

[1]¿p#1 newcommands Title: Range redistribution under climate change reshapes connectivity without genetic fragmentation in a Himalayan carnivore | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2026 V1 Latest version Share on [1]¿p#1 newcommands Title: Range redistribution under climate change reshapes connectivity without genetic fragmentation in a Himalayan carnivore Authors : Shahid Ahmad Dar , Vinaya Singh , Vinnet Kumar , Amar Singh , Amira Sharief , Hemant Singh , Ritam Dutta , Bheeem Joshi , Gopinathan Maheswaran , Mukesh Thakur , and Lalit Kumar Sharma 0000-0003-1214-7416 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177022458.82813366/v1 197 views 124 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract [1]¿p#1 newcommands Climate change and accelerating land-use transformation are expected to profoundly alter habitat suitability, connectivity, and genetic structure of wide-ranging mammals, particularly in high-elevation ecosystems. We evaluated climate-driven changes in habitat suitability and population genetic structure of the Asiatic black bear (Ursus thibetanus) in the western Himalaya. We integrated species occurrence records and individual level nuclear DNA data with environmental predictors to examine climate-driven changes in distribution, genetic diversity, and population structure. Environmental variables were first screened using MaxEnt to identify a parsimonious set of predictors, which were then incorporated into an ensemble species distribution modelling framework comprising eleven modelling algorithms to predict current habitat suitability. Future distributions were projected under low- (RCP 2.6) and high-emission (RCP 8.5) climate scenarios for the mid-century (2050s) and late-century (2070s). In parallel, genetic and environmental data were integrated within a hierarchical Bayesian framework (POPS) to infer spatial patterns of genetic structure and assess their stability under future climate change. Current habitat suitability was primarily governed by climatic variables, with annual mean temperature emerging as the most influential predictor. Future projections indicated significant redistribution of suitable habitat, characterized by high stability under low-emission scenarios but pronounced spatial turnover and net habitat loss under high-emission scenarios by the 2070s. Genetic analyses revealed weak population structuring and widespread admixture across the landscape, indicating high levels of connectivity among Asiatic black bears. Nevertheless, projected shifts in habitat suitability, particularly toward higher elevations, may alter historical and existing connectivity patterns and potentially disrupt existing gene flow pathways. Our findings highlight the importance of integrating habitat suitability projections with population genetic analyses to identify climate-driven risks to connectivity and genetic diversity. Incorporating future habitat dynamics into conservation planning will be essential for maintaining long-term population viability of Asiatic black bears in the Himalaya under ongoing climate change. Introduction Climate change, coupled with rapidly accelerating human-driven land-use transformation, poses a major threat to global biodiversity conservation by altering the spatial distribution and configuration of suitable habitats (Garcia et al. 2014; Parmesan, 2006; Shirk et al. 2018). As suitable habitats contract, expand, or shift geographically, species will likely experience increased fragmentation of populations, reduced population size and heightened exposure of anthropogenic pressure, thereby possibly undermining the effectiveness of existing conservation and management strategies (Hannah et al. 2007; Carroll et al. 2010). Such alterations are expected to be more pronounced for wide-ranging species inhabiting in diverse habitats, where environmental change can disrupts the ecological stability across large spatial scales (Ripple et al. 2014). Under such settings, effective conservation planning requires an understanding of the ecological processes that governs the species distributions, habitat requirements, and long-term population persistence (Reside et al. 2018). Therefore to anticipate the conservation challenges and develop the effective conservation strategies, it is crucial to integrate the projections of future environmental change with ecological and population-level responses. High elevation environments are particularly more prone to climate change due to their steep environmental gradients and the limited opportunities they provide to species for range shifts (Beniston, 2003, 2005; IPCC 2013). Climate estimates indicate rising temperatures, changes in monsoon dynamics, and an increasing frequency of extreme climatic events including heat extremes and droughts across the Himalayan region. Together, these changes are likely to make a climate change a powerful driver of future change in spatial configuration of potential habitats and species distributions in this region (Kanagaraj et al. 2019; Chevuturi et al. 2018; IPCC 2021). Himachal Pradesh, located in western Himalaya, supports ecologically important large carnivores such as Asiatic black bear, Himalayan brown bear, and leopard. In this region the distribution of species is largely shaped by climate, topography and vegetation structure, which determine the availability of suitable conditions across the landscape. Past studies have indicated that temperature and precipitation regimes play a key role in defining the habitat suitability for large mammals in the Himalayan region, emphasizing their sensitivity to climate variability and change (Aryal, et al. 2014; Su et al. 2018; Dai et al. 2021; Dar et al. 2021; Dar et al. 2022; Mukherjee et al. 2021). Therefore, predicted climate changes are likely to alter the spatial extent and configuration of potential habitats for these species across their range in Himachal Pradesh. Furthermore, apart from distributional alterations, climate-driven modifications in potential habitats can have significant consequences for genetic diversity and population genetic structure (Crooks et al., 2011, 2017; Wasserman et al., 2013). Within-species genetic variation is vital for preserving the evolutionary potential and enabling species to cope and respond to environmental change. However, fragmentation and reductions in habitat availability and population size potentially can lead to losses of genetic diversity and increased population differentiation (Frankham et al. 2010; Yannic et al. 2014). In Himalayan mountain ecosystems, these effects are likely to be more prominent because many species occupy narrow climatic niches and are constrained by complex topography, steep elevation gradients, and fragmented habitat patches. The genetic variation within a species reflects the combined effects of gene flow, genetic drift and natural selection influenced by geographic distance and environmental factors. Numerous studies have shown how past climatic events have shaped the population genetic structure, emphasizing the importance of understanding how ongoing and future climate change may impact the genetic patterns and ultimately, species persistence (Carnaval & Bates, 2007; Jay et al., 2012; Lima et al., 2017). While numerous studies have observed how historical and current climatic fluctuations have shaped population genetic structure across a wide range of taxa (Carnaval and Bates 2007; Row et al. 2014; Inoue et al. 2015; Ye et al. 2015), comparatively few have explored how predicted future climate change may influence population genetic structure (Jay et al. 2012; Lima et al. 2017; Wróblewska and Mirski 2018). In this study, we used environmental variables and species presence data to predict the habitat suitability of Asiatic black bear for current and future climatic scenarios. We further combined environmental data and genetic data to assess the patterns of population genetic structure and their potential changes under future climate change. We hypothesised that areas predicted to be highly suitable would exhibit higher levels of admixture and weaker genetic structuring, reflecting greater connectivity and gene flow. We also hypothesised that future climatic change would negatively impact habitat suitability of black bears, and likely leading to reduced admixture and increased genetic differentiation across the study region. The findings of this study provide a forward-looking strategy to identify areas susceptible to genetic erosion and to guide effective conservation and management strategies for large carnivores in the Himalayas in the face of ongoing climate change. Materials and methods Study area The study includes the Western Himalaya (30°22’ N and 33°12’ N and 75°47’ E and 79°04’ E), located in western Himalaya (Fig. 1). The region is known for its unique high elevation ecosystems, with diverse climatic and topographic conditions. This region is ecologically rich, with heterogeneous landscapes that harbours many important mammalian species such as Asiatic black bear ( Ursus thibetanus ), Himalayan brown bear ( Ursus arctos issabellinus ), leopard ( Panthera pardus ), snow leopard (Panthera uncia), Himalayan wolf ( Canis lupus ), and musk deer ( Moschus leucogaster ) (Sathyakumar & Bashir 2010). Elevations range from low elevation valleys to alpine zones, resulting in strong ecological heterogeneity over comparatively small geographic scales. The ecological complexity of this region is further enhanced by seasonal variations in precipitation and temperature, which are mostly driven by monsoonal weather systems. Study design, Sample collection and DNA extraction In this study we used a grid-based sampling strategy to collect the non-invasive (scats) genetic samples of the study species (Asiatic black bear) across the study landscape. To do this we first divided the study area into 5 × 5 km sampling units (grid cells) based on the home range size of the study species. Then we selected the sampling grids based on the forest cover of the study landscape, likely the potential habitats of the study species. A total of 1514 grids were selected both inside and outside protected areas. In each selected sampling grid (I.e., 1514), non-invasive genetic samples were collected opportunistically as well as systematically along animal and manmade trails (n = 3, with a maximum length of 3 km) in the study area. The intend of this sampling strategy was to cover all major eco-regions and habitat types across the study region, thereby ensuring representative samples within the known distribution range of the study species in this landscape. All the field activities were conducted in collaboration with the forest department. Altogether 1200 samples of Asiatic black bear were collected across the study region, and were used as occurrence points and source for genomic DNA. Genomic DNA was extracted from the suitable samples using the Qiagen Kit method. Following DNA extraction, the samples were screened for species identification using species-specific mitochondrial control region (Taberlet and Bouvet 1994), and species identity was confirmed using the BLAST function on NCBI. Out of 1200 samples, we identified 567 to be of Asiatic black bear origin in species screening process. The remaining samples of both species either belonged to non-target species or didn’t amplify due to degraded DNA, common in non-invasive genetic samples. Microsatellite selection, screening and genotyping To assess the population genetic patterns of our study species, we initially screened 20 polymorphic microsatellite markers following the necessary criteria suggested by several non-invasive genetic studies (Waits et al. 2001; Bellemain and Taberlet, 2004). These markers were widely used in Asiatic black bear genetic studies (Table S1). However, based on PCR amplification success rate, 12 loci were selected to genotype all the field-collected samples of Asiatic black bear. Multiplex panels were designed to include the full set of loci. The description of all the microsatellite markers are given in supplementary Tables S1. The PCR reaction of each multiplex was setup with a 10 μl reaction volume containing 5 μl of 2X Multiplex MasterMix with HotStart Taq Polymerase (Qiagen), 2 μm BSA, 1 μl of fluorescence labelled primer (10μM) cocktail, and 2 μl of extracted DNA. The PCR conditions were as follows: initial denaturation (95 °C for 15 min), 40 cycles of denaturation (94 °C for 35 s), annealing (varied between 50°C – 62°C for 1 min) and extension (72 °C for 90 s) and a final extension (70 °C for 30 min). All the PCR products were genotyped using capillary electrophoresis on an ABI 3500XL sequencer and GeneScan–500 LIZ® as the Size Standard (Applied Biosystems, Carlsbad, California). The alleles were scored using the program GeneMapper 3.7 (Applied Biosystems), and validated through visual inspection. After data validation, we identified 350 unique genotypes of Asiatic black bears, of which 19 individuals were determined as recaptures, and 24 as first-degree relatives, which were subsequently removed to minimize the bias in downward population genetic analysis. In total 307 unique individuals of Asiatic black bear were identified and used for subsequent population genetic analysis. Habitat variable data We selected habitat variables known to effect the habitat selection and distribution of Asiatic black bear (Ursus thibetanus), based on the previous ecological studies (Zahoor et al. 2021; Kichloo and Sharma, 2021; Rehan et al. 2024; Cheng et al. 2025). These variables characterize environmental heterogeneity, vegetation structure, vegetation composition, topography, climate and anthropogenic disturbance across the study region climate (Supplementary Table S2). Land-use and land-cover variables, along with time-series vegetation indices, were obtained from the MODIS database (https://lpdaac.usgs.gov). Multiple temporal metrics (minimum, maximum, mean, and standard deviation) were calculated form time-series vegetation indices to capture the long-term trends relevant to black bear habitat selection. Habitat heterogeneity variables were obtained from EarthEnv database (http://www.earthenv.org/texture; Tuanmu and Jetz 2015). Topographic variables were derived from the Shuttle Radar Topography Mission (SRTM) elevation data obtained from the CGIAR-CSI database database (Jarvis et al. 2008). Then elevation data was used to calculate the other topographic variables such as aspect, slope, roughness, compound topographic index using different tools of ArcGIS, reflecting the complexity of mountainous landscape inhabited by black bears. The climatic data including precipitation, temperature and 19 bioclimatic variables were obtained from the WorldClim database (http://www.worldclim.org; Hijmans et al. 2005). An additional set of 16 bioclimatic variables was sourced from the ENVIREM database (http://envir-em.github.io; Title & Bemmels 2018), to better capture the ecophysiological constraints in modelling the habitat suitability of black bears. Forest canopy height data were downloaded from the Jet Propulsion Laboratory, California Institute of Technology (https://landscape.jpl.nasa.gov). Global human footprint dataset obtained from the Wildlife Conservation Society (http://sedac.ciesin.columbia.edu/wildareas) was used as human disturbance constraint, and human population density data were sourced from the WorldPop database (https://www.worldpop.org). Hydrographic data for illustrating river networks was gathered from HydroSHEDS database (http://hydrosheds.cr.usgs.gov), from which the variable Euclidean distance to nearest river network was calculated. All predictor layers were projected to the 44N UTM projection and resampled to a spatial resolution of 1 km in ArcGIS. Nearest neighbourhood resampled method was used for categorical variables, whereas bilinear interpolation method was used for continuous variables. The complete list of environment predictors, their details, description and data source are provided in Supporting Information Table S2. [1]¿p#1 newcommands Candidate Model Development and Variable Selection We employed a two-step modelling framework following Kanagaraj et al. (2019) to predict the present and future potential habitat suitability of Asiatic black bear across the study region. First, we reduced the set of environmental variables by eliminating the highly correlated variables and identified the most parsimonious set of predictors through MaxEnt models (Phillips et al. 2006) based on different hypothesis related to habitat selection of the study species. Second, we develop an ensemble species distribution models using the set of variables from the most parsimonious model from step one for spatial and temporal projections of habitat suitability of Asiatic black bear in the study landscape. In first step, we compiled a comprehensive set of environmental variables based on the ecology of the study species representing the ecological conditions of the study region. Then these variables were grouped into three broad categories reflecting hypothesized drivers of black bear habitat selection: natural habitat (N), human disturbance (D), and climatic settings (C) (Supplementary Table S3). Natural habitat represent the vegetation, topography and landscape heterogeneity, disturbance variables represent the human influence, and climatic variables describe the temperature and precipitation patterns of the study region. Then we constructed the candidate models by combining the above variable groups to test both simple and complex habitat-selection hypothesis. These included models comprised variables from a single category (e.g., N, D, or C), as well as more comprehensive models combining variables from two or all three categories (e.g., N + D, N + C, D + C, and N + D + C). In addition, we defined three reduced minimal models within the natural habitat group that separately represented vegetation (NV), topography (NT), and habitat heterogeneity (NH). Altogether, by employing this approach we constructed 23 candidate models of varying complexity, along with a global model comprising all predictors (Supplementary Table S3). Prior the MaxEnt model fitting, we applied a pairwise Pearson correlation analysis to all predictors within each candidate model. When two variables showed high correlation (r > 0.7), we retained the variable with greater ecological importance. This way we ensured that each candidate model comprise a reduced and interpretable set of variables. Thereafter, we fitted all the candidate models using the MaxEnt a widely used presence only modelling approach estimates species-habitat relationships by contrasting the environmental settings at observed localities with those at background localities (Phillips et al. 2006). Model calibration was performed using the 5000 randomly generated background points distributed across the study landscape. We implemented a spatially structured k-fold cross-validation using a block partition method for spatial autocorrelation and model evaluation. Presence and background data were divided into four bins based on latitudinal and longitudinal splits, ensuring that training and testing data sets were spatially independent (Madon et al. 2013; Muscarella et al. 2014; Kanagaraj et al.2023). Three bins were used to train the models and evaluated on the remaining bin, with an additional model fitting using the full dataset. In addition, we explored a range of MaxEnt settings by varying feature classes and regularization multipliers for each model. Specifically, we fitted the models across six feature classes and eight regularization multipliers, resulting in 300 individual models per candidate model (6 feature classes × 10 regularization values × 5 data partitions). This resulted a total of 7200 individual model fits across all candidate models and the global model. [1]¿p#1 newcommands Model Selection and Ensemble Projection We identified the best-performing individual model based on the lowest Akaike Information Criterion (AIC) within each candidate model. Then we compared these best-performing models across all models and selected the most parsimonious model with lowest AICc and strong discriminatory power, as indicated by area under the receiver curve (AUC) Muscarella et al. 2014). Thereafter, the predictors identified in the best model was subsequently used to construct the ensemble SDMs to predict the potential habitat of black bear across the study region. For our ensemble approach, we implemented eleven models with varying techniques, including artificial neural network (ANN), classification tree analysis (CTA), flexible discriminant analysis (FDA), generalized linear model (GLM), generalized additive model (GAM), generalized boosting model (GBM), multivariate adaptive regression splines (MARS), maximum entropy (MAXENT), random forests (RF), random forests (RFd) and surface range envelope (SRE). All these models were executed within an ensemble modelling framework using the BIOMOD2 package in R (Thuiller et al., 2023; Guéguen et al. 2025). To account the predictive performance, the data were randomly partitioned such that 80% of data were used for training and the remaining 20% for evaluating the fitted models. True skill statistic (TSS), the area under the receiver operating characteristic curve (AUC), and Boyce index was used as an accuracy metrics to assess the performance of the models. Each modelling algorithm were iterated five times using different training and testing data partitions, and only models exceeding TSS 0.7 were retained for the consensus ensemble prediction. The final ensemble prediction model was produced by calculating the weighted mean of relative occurrence probabilities for each pixel, with greater weight assigned to models demonstrating higher predictive accuracy as measured by TSS. Continuous ensemble outputs were subsequently converted to binary suitability classes (suitable vs. unsuitable) using the TSS-derived optimal threshold. To quantify the contribution of individual predictors to the ensemble model, we estimated variable importance using a machine-learning procedure implemented in the BIOMOD2 package of R (Thuiller et al. 2023; Guéguen et al. 2025).This method involves permuting each predictor variable in turn, generating a new prediction, and computing the correlation between the permuted and original ensemble outputs. Variable importance was calculated as one minus this correlation coefficient, with higher values indicating a stronger influence of the predictor on the ensemble prediction. Future climate change scenarios Using the final consensus model identified above, we projected the future habitat suitability of black bears for the mid-century (2050s; 2041-2060) and late-century (2070s; 2061-2080) periods under alternative climate change scenarios. Future predictors were derived from the same source as our present climatic variables (i.e. WorldClim, Fick & Hijmans, 2017). In addition, variables such as embergerQ were estimated using the ‘envirem’ R package (Title & Bemmels, 2018). We subsequently calculated the changes in the potential distribution of black bears between current and future climatic scenarios using the binary habitat maps of current and future climate scenarios from above. Then, we classified areas as either ‘gain’ (areas identified as habitat in the future scenario but not in the current scenario), ‘loss’ (areas identified as habitat in the current scenario but not in the future scenario), or ‘stable’ (areas identified as habitat in both the current and future scenarios). Assessing population genetic structure and its stability under climate change We used the software POPS (Jay et al. 2005; Jay et al. 2012, http://membres-timc.imag. fr/Olivier.Francois/pops.html), to characterize the population genetic structure, which estimates individual admixture coefficients by modelling individual level genetic data, environmental variables and spatial coordinates within a hierarchical Bayesian regression framework. This method works on the principle that individuals sharing similar environmental settings and geographical proximity are more likely to exhibit similar genetic ancestry. We first inferred the current population genetic structure and then evaluated its stability under alternative future climate change scenarios in 205s and 2070s. Environmental variables associated with the most parsimonious model were used to represent the environment component while spatial coordinated of individuals represent the geographic component of the analysis. In our analysis, we held the genetic data constant across the projections, ensuring that changes in population genetic structure to be attributed solely to shifts in environmental conditions. The analysis was conducted for minimum of 2 to a maximum of 10 genetic clusters, with 20 iterations for each K value. The POPS runs were performed using admixture model, and each independent iteration consisting of 10,000 MCMC following a burn-in of 5,000 sweeps. The optimal number of genetic clusters was determined using the deviance information criterion (DIC), with the optimal K where improvements in model fit began to plateau. Then we projected the changes in population genetic structure for 2050s and 2070s under alternative climate change scenarios (RCP 2.6 and RCP 8.5), by substituting the current climate variables with future climatic variables of the respective climate change scenario. Variable selection for species distribution models Among the MaxEnt models, the model illustrating the combined effects of climate and vegetation (C + Nv) was identified as the most parsimonious model (Supplementary Table S4). This model showed high discriminatory power (AUC > 0.92) and the lowest degree of overfitting relative to all other models (Supplementary Table S4). All variables of this model were subsequently used to develop ensemble species distribution models. [1]¿p#1 newcommands Ensemble species distribution models Only models that met the predetermined accuracy threshold (TSS ≥ 0.75) were retained for ensemble model construction. These models included classification tree analysis (CTA), generalized linear models (GLM), multivariate adaptive regression splines (MARS), maximum entropy (MAXENT), random forest (RF), and random forest with down-sampling (RFd). Among the retained variables, the climatic variables contributed the most to the consensus prediction model. Annual mean temperature (Bio1) was the most important variable, accounting for 46.9% of total relative contribution (Table 1). Other climatic variables with moderate contribution included precipitation of the wettest month (Bio13) (5.9%), mean diurnal temperature range (Bio2) (5.6%), and Emberger’s pluviothermic quotient (5.2%) (Table 1). However, vegetation variables contributed comparatively less to the consensus prediction model. Among these barrenland (11.8) showed the highest contribution, followed by canopy height (6.3%), savannas (4.6%), and grasslands (1.6%) (Table 1). Response curves from the consensus prediction model revealed distinct and largely nonlinear relationships between black bear occurrence and key environmental variables (Fig. 2). Black bear occurrence showed a unimodal response to annual mean temperature, with highest occurrence at intermediate temperatures, suggesting an optimal thermal window preference in this region. Mean diurnal range and precipitation of wettest month showed a negative relationship with black bear occurrence, suggesting avoidance of areas experiencing high daily temperature fluctuations and excessive precipitation. In addition, black bear showed a weak negative relationship and relatively flat response to topographic wetness index and emberger’s pluviothermic quotient, respectively, suggesting a broad tolerance to moisture-temperature balance within the study landscape. Furthermore, vegetation composition and structure had a moderate but prominent effect on probability of black bear in this region. The black bear occurrence was predicted to be highest in areas with high amounts of canopy cover. In contrast, black bear occurrence decreased with increasing barrenland proportion, and exhibited low probability across the gradients of grasslands and savannas, indicating avoidance of sparsely vegetation and open habitats. The probability of occurrence patterns of black bear based on the ensemble modeling approach is shown in Figure 3. A total of 10,229 km 2 was found to be highly suitable for black bears in this region, based on the optimal threshold value identified by TSS. This suitable habitat represents around 18% of the total study region. Future projections of habitat suitability The total area of suitable habitat for Asiatic black bear was estimated at 10,229 km 2 , which becomes the basis for comparison across the alternative future climate scenarios (Fig. 3, Table 2). Our model predicted that 84.5% of the current habitat to remain stable under low emission scenario in 2050s, however, habitat gains increased to 1725 km 2 (16.9%) and 1585 km 2 (15.5%) of habitat was lost, resulting in small net gain of 140 km 2 relative to current scenario (Table 2, Fig. 4, Supplementary Figs. S1, S2). This indicated limited spatial reorganization of suitable habitat under a low-emission scenario in 2050s. In contrast, the high-emission scenario in 2050s showed greater spatial turnover. Only 78.5% of the current suitable habitat was predicted to be stable, while habitat gains increased to 2578 km 2 (25.2%) and losses to 2203 km 2 (21.5%) (Table 2, Fig. 4, Supplementary Figs. S1, S2). Despite higher rates of both gain and loss, the net change remain positive, indicating a redistribution rather than contraction by mid-century under high-emission scenario. In 2070s, our models indicated that the proportion of stable habitat under RCP 2.6 is similar to the mid-century low-emission scenario (83.4%; 8,536 km²). Habitat losses were balanced by the habitat gains, resulting a net gain of 103 km 2 (1.01%) (Table 2, Fig. 4, Supplementary Figs. S1, S2). Whereas, under high emission scenario in 2070s, our results revealed a marked decline in suitable habitat. Though 86.7% of habitat remains stable, no new habitat gains were predicted, while 1361 km 2 (-13.31%) of habitat was lost. This resulted in a net loss of 1361 km 2 ((-13.31%), indicating a significant contraction of suitable habitat under high emission scenario in 2070s (Table 2, Fig. 4, Supplementary Figures. S1, S2). Genetic diversity and population genetic structure patterns for current and future climate change scenarios The cumulative probability of identity (PID biased) value of the microsatellite marker panel was 9.2 × 10–19, and the probability of identity (PID sibs) was 2.1 × 10–6 (Table 1). All the 12 microsatellite markers used were polymorphic, and the number of alleles at each locus ranged from 9 to 19, with a total of 158 alleles (Table 4). The global mean observed heterozygosity (Hobs) and expected heterozygosity (Hexp) were 0.35 ± 0.03 and 0.84 ± 0.03 across 12 polymorphic loci, respectively (Table 3). POPS analysis supports the presence of six genetic clusters in asiatic black bear, based on the DIC curve. The DIC curve showed a marked decrease up to K = 6, and marginally stabilized thereafter, suggesting diminishing gains in model fit with additional clusters (Figure S3). Therefore, K = 6 was selected as the optimal number of clusters, demonstrating the most parsimonious description of population genetic structure under the POPS framework (Fig. 5, Supplementary Figure S4). Under ongoing climatic conditions, the clustering results showed weak genetic differentiation, with varying degrees of genetic admixture among all identified clusters. This pattern suggests a substantial level of genetic exchange and high levels of gene flow among Asiatic black bears across the different regions within the study area. In future scenarios, under both low and high emission scenarios for 2050 and 2070, similar diffuse clustering patterns persisted, with no emergence of strongly differentiated genetic clusters (Fig. 5, Supplementary Figure S4). However, minor to moderate increases in heterogeneity of individual membership coefficients were evident across predicted future climates, suggesting localized shifts in ancestry composition among individuals rather than clear population subdivision. Overall, expected climate change scenarios suggest genetic redistribution rather than increased population subdivision, with population connectivity largely maintained across the study region despite altered habitat suitability patterns. [1]¿p#1 newcommands Discussion Our ensemble model identified climate as the main determinant of Asiatic black bear occurrence in the Western Himalaya. This is in consistent with previous studies which showed climate as the primary factor of Asiatic black bear habitat suitability (Zahoor et al. 2021; Rehan et al. 2024). Our results revealed that annual mean temperature was the most important predictor, accounting for nearly half of the total contribution, indicating strong thermal constraints on the species distribution. The unimodal response to annual mean temperature suggests that black bears prefer areas with moderate temperatures, consistent with the physiological sensitivity and elevational limits reported for this species in high elevation environments (Sathyakumar 2001; Rehan et al. 2024). This highlights temperature as a key factor of habitat availability under ongoing climate change. Other climatic variables, including precipitation of wettest month, mean diurnal range, and embergers pluviothermic quotient, contributed moderately and mostly showed negative or weak associations with black bear occurrence. Avoidance of regions with excessive precipitation and large temperature fluctuations likely reflects energy constraints, reduced foraging efficiency, and increased environmental unpredictability in rugged, and highland Himalayan landscapes (Qi et al. 2011; Sathyakumar et al. 2011; Bashir et al. 2018). Land cover and vegetation structure had a secondary but ecologically meaningful effect on habitat suitability. Our results indicated a positive association of canopy height with the black bear occurrence, underscoring the importance of structurally complex forests that provide diverse range of food resources, shelter, and thermal refugia (Qi et al. 2011; Sathyakumar et al. 2011; Bashir et al. 2018; Kichloo and Sharma, 2021). Moreover, dense vegetation cover provides black bears with concealment sites or settings for survival. On the contrary, barrenland excreted a strong negative effect, and the gradients of grasslands and savannas showed low probability, indicating avoidance of open and sparsely vegetated regions. This pattern aligns with previous studies demonstrating strong forest dependence and sensitivity to human disturbance and habitat degradation (Sathyakumar et al. 2011; Bashir et al. 2018; Kichloo and Sharma, 2021). Our consensus model predicted a total of 10,229 km 2 (18% of the study area) as the highly suitable current habitat for Asiatic black bears across the study landscape. This comparatively limited portion of suitable habitat highlights the inherently fragmented nature of bear habitat in the Western Himalaya, where steep topography, climatic gradients, and increasing anthropogenic disturbances limit the spatial extent of optimal settings. Similar estimates of limited suitable habitat have been reported by other studies on this species and other large carnivores in the Himalayan mountain ecosystems, highlighting the conservation importance of remaining intact forest landscapes in this region (Dar et al. 2021; Zahoor et al. 2021). Our future projections revealed contrasting response of black bear occurrence under low and high emission climate scenarios, with important implication for long-term conservation and management. Under the low-emission scenarios, both 2050s and 2070s predictions shown high stability (>83%), accompanied by moderate habitat gains that largely balances the predicted losses. This suggests that under low-emission climate mitigation pathways, the potential habitats of Asiatic black bear may undergo limited spatial reorganization rather than significant contraction. Climate change is expected to induce an upward shift of the treeline in the Himalaya, leading to the establishment of newly forested areas at higher altitudes that may become climatically suitable for Asiatic black bears (Schickhoff et al. 2015; Mainali et al. 2020; Zahoor et al. 2021). Such habitats could possibly facilitate range shifts and redistribution of populations into previously unsuitable alpine regions. However, these new potential habitats are likely to be spatially fragmented, structurally immature, and temporally unstable, possibly limiting their long-term capacity to support viable populations. Such changes may reduce the availability of natural resources and increase the dependency of Asiatic black bears on anthropogenic food, thereby elevating the risk of human-bear conflicts and consequent retaliatory bear mortalities (Hetem et al., 2014; Krosby et al., 2016; Penteriani et al., 2019; Zahoor et al. 2021). Moreover, climate-driven habitat gains at higher elevations may significantly alter the existing patterns of connectivity among Asiatic black bear populations by facilitating dispersal into newly suitable areas (Segelbacher et al., 2010; Hermes et al., 2018). While such changes improve gene flow in parts of the landscape, they may also disrupt the historical connectivity corridors, thereby influencing population genetic structure. Importantly, these newly emerging suitable habitats are unlikely to align with the existing protected areas network, which were largely established based on current species distributions. This mismatch may perhaps limit the ability of current protected areas to maintain the long-term connectivity and population viability, thereby emphasizing the need to integrate future habitat projections into conservation genetics and reserve planning frameworks (Hannah et al. 2007; Carroll et al. 2010; Jay et al. 2012; Lima et al. 2017). Therefore, there is a need for practical conservation actions, including the identification and management of climate refugia through habitat corridors, expansion and upgrading of existing protected areas, and strengthening the adaptive management capacity, to mitigate the adverse impacts of future climate change on Asiatic black bear populations across the Himalayan region. In contrast, high-emission scenarios revealed significantly greater spatial turnover, particularly by the 2070s. Even though a large amount of current habitat was predicted to remain stable, the absence of habitat gains and substantial losses resulted in a net decline of more than 13%. This, however, suggests that under increased warming, suitable climatic conditions may shift beyond the limits of available forested regions, leading to habitat contraction rather than redistribution. Similar results under high-emission scenario has been observed in other studies on this species and other highland mammals, where upward range shifts are constrained by topographic and land-use barriers (Su et al. 2018; Zahoor et al. 2021; Dar et al. 2021). The POPS-based clustering analysis indicate that Asiatic black bears across the study landscape are characterized by weak population genetic structure with widespread admixture, both under ongoing climatic conditions and across expected future climate scenarios in 2050s and 2070s. The constantly low individual membership coefficients (Q values ranging between ~0.6 and 0.18 across clusters) fall well below the commonly used thresholds for strong clustering (e.g., Q>0.7), demonstrating the absence of clearly distinct genetic populations. Such patterns reflects the ecology of large-bodied and wide-ranging mammals, which typically have high dispersal ability and broad habitat tolerance, particularly in continuous and semi-continuous high elevation landscapes (McRae et al. 2005; Frantz et al., 2009; Cushman et al. 2010; Cushman & Landguth 2012a,b; Kendall et al. 2016). The lack of clear plateau in the DIC curve further supports these findings, indicating that genetic variation is better described by gradual spatial transitions rather than distinct population clusters. Similar patterns of diffuse genetic structure have been reported for brown bears and Asiatic black bears across heterogeneous mountain regions, where topographic complexity supports isolation-by-environment and isolation-by-distance rather than strong population subdivision (Waits et al. 2000). Comparisons across alternative climate scenarios reveal that future change in temperature and precipitation is unlikely to results in sudden genetic fragmentation at the regional scale. Instead, the increased heterogeneity in individual membership coefficients under both future low and high emission scenarios suggests redistribution of ancestry composition, potentially driven by climate-induced shifts in habitat suitability and current movement corridors. Similar results have been reported in spatially explicit simulations where climate change alters dispersal corridors without causing strong changes in population genetic clustering (Shirk et al. 2010; Landguth et al. 2017; Shirk et al. 2017). The lack of distinct genetic structuring even under high emission scenarios indicates that projected habitat changes may facilitate continued genetic exchange among the black bears in this landscape, particularly through newly shifting suitable habitats at higher elevations. However, the increased admixture of ancestry composition also suggests that past connectivity patterns may be altered, resulting in possible changes genetic diversity rather than population structuring. This is consistent with empirical findings, that particularly in species with large effective population sizes, climate-driven range shifts often modify the spatial configuration of gene flow without causing immediate significant changes in population structuring (Excoffier et al. 2009; Yannic et al. 2014). Moreover, this relative stability in genetic structure reflects the limited net loss of suitable habitat across future climate scenarios as revealed by our results, consistent with findings that low to moderate climate change impacts may result in redistribution rather than population fragmentation populations (Hannah et al. 2007; Carroll et al. 2010). However, small changes in the ancestry composition may indicate early responses to habitat alteration due to climate change, even in the absence of strong genetic restructuring. As suitable conditions shift, dispersal corridors and local mating patterns may be subtly changed, resulting in gradual changes in ancestry composition over time (Jay et al. 2012; Wróblewska & Mirski 2018). These processes may lead to detectable changes in classical genetic diversity matrices and thus signify an early warning indication of future genetic change. Importantly, the persistence of extensive admixture across future climate scenarios should not be interpreted as resilience to climate change. High-elevation regions are expected to experience pronounced warming and upward shifts in treeline, potentially creating novel suitable habitats that are temporally unstable and spatially fragmented (IPCC 2021; Pecl et al. 2017). Though these emerging shifting habitats may facilitate short-term dispersal and genetic exchange, they are unlikely to sustain long-term viable populations, particularly due to increased exposure to human-dominated landscapes and associated anthropogenic threats (Ripple et al. 2014; Pritchard et al. 2019). Overall, these results indicate that Asiatic black bears in this region are largely connected, with future climate change expected to influence genetic variation more than the genetic structuring. This highlights the importance of maintaining landscape-scale connectivity and incorporating future habitat projections into conservation planning to preserve long-term evolutionary processes. In conclusion, our findings reveal that climate in the main driver of Asiatic black bear distribution in the study landscape, with temperature influencing strongly on habitat availability under both ongoing and future climatic conditions. Ensemble model projections suggest that low emission scenarios may promote shifting in habitat rather than reduction, whereas high emission scenarios are likely lead to significant habitat loss by the late 21 st century. Despite these changes in suitable habitat, POPS-based genetic analysis indicate weak population differentiation and genetic structure with widespread admixture among the Asiatic black bears, reflecting high habitat connectivity, dispersal capacity and high levels of gene flow in this species. Therefore, future climate change is expected to alter the patterns of genetic exchange and ancestry coefficients rather than sudden genetic fragmentation. However, newly emerging suitable areas at higher elevations are likely to be isolated, and poorly align with the existing protected areas, potentially elevating human-bear interactions and undermining long-term viability of populations. Together, these findings highlight the need for active, climate-informed conservation strategies that prioritize landscape connectivity, protection and management of climate refuge areas, and integration of future habitat predictions into genetic and spatial planning frameworks to safeguard the evolutionary potential of Asiatic black bears in the Himalaya. math_shortcuts References Aryal, A., Brunton, D. & Raubenheimer, D. (2014) Impact of climate change on human wildlife ecosystem interactions in the Trans-Himalaya region of Nepal. Theor. Appl. Climatol. 115, 517–529. Babar Zahoor, Xuehua Liu, Lalit Kumar, Yunchuan Dai, Bismay Ranjan Tripathy, Melissa Songer, (2021) Projected shifts in the distribution range of Asiatic black bear (Ursus thibetanus) in the Hindu Kush Himalaya due to climate change. Ecological Informatics, 63, 101312, ISSN 1574-9541, https://doi.org/10.1016/j.ecoinf.2021.101312. Bashir, T., Bhattacharya, T., Poudyal, K., Qureshi,Q., Sathyakumar, S. (2018) Understanding pat -terns of distribution and space-use by Ursus thi-betanus in Khangchendzonga, India: Initiativetowards conservation. Mammalian Biology, 9211–20. https://doi:101016/jmambio201804004 Bellemain, E., & Taberlet, P. (2004) Improved noninvasive genotyping method: Application to brown bear (Ursus arctos) faeces. Molecular Ecology Notes , 4(3), 519–522. Beniston, M. (2003) Climatic change in mountain regions: a review of possible impacts. Clim. Change 59, 5–31. Beniston, M. (2005) The risks associated with climatic change in mountain regions. In Global change and mountain regions: an overview of current knowledge: 511–520. Huber, U., Bugmann, H. & Reasoner, M. (Eds.). Dordrecht: Springer. Carnaval AC, Bates JM (2007) Amphibian DNA shows marked genetic structure and tracks pleistocene climate change in northeastern Brazil. Evolution 61:2942–2957. https://doi.org/10.1111/j. 1558-5646.2007.00241.x Carroll C, Dunk JR, Moilanen A (2010) Optimizing resiliency of reserve networks to climate change: Multispecies conservation planning in the Pacific Northwest, USA. Glob Change Biol 16:891–904. https://doi.org/10.1111/j.1365-2486.2009.01965.x Cheng J, Zheng W, Guo X, Wang Y, Zhou Y, Zhao S, Song X, Xu A. (2025) Large Carnivores Persisting in a Human-Dominated Landscape: Suitable Habitat and Connectivity for Asiatic Black Bears in China. Ecol Evol. 30;15(10):e72181. doi: 10.1002/ece3.72181. PMID: 41041397; PMCID: PMC12483940. Chevuturi A, Klingaman NP, Turner AG, Hannah S (2018) Projected changes in the asian-australian monsoon region in 1.5°C and 2.0°C global-warming scenarios. Earths Future 6:339–358. https://doi.org/10.1002/2017EF000734 Crooks, K.R., Burdett, C.L., Theobald, D.M., King, S.R.B., Di Marco, M., Rondinini, C. & Boitani, L. (2017) Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc. Natl. Acad. Sci. USA 114, 7635–7640. Crooks, K.R., Burdett, C.L., Theobald, D.M., Rondinini, C. & Boitani, L. (2011) Global patterns of fragmentation and connectivity of mammalian carnivore habitat. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2642–2651. Cushman, S.A. & Landguth, E.L. (2012a) Ecological associations, dispersal ability, and landscape connectivity in the Northern Rocky Mountains. Res. Pap. RMRS-RP-90. 90 US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO. pp. 21. Cushman, S.A. & Landguth, E.L. (2012b) Multi-taxa population connectivity in the Northern Rocky Mountains. Ecol. Model. 231, 101–112. Cushman, S.A., Chase, M.J. & Griffin, C. (2010) Mapping landscape resistance to identify corridors and barriers for elephant movement in southern Africa. In Spatial complexity, informatics and wildlife conservation: 349–368. Cushman, S.A. & Huettman, F. (Eds). Tokyo: Springer. Dai, Y., Peng, G., Wen, C., Zahoor, B., Ma, X.-D., Hacker, C. & Xue, Y. (2021) Climate and land use changes shift the distribution and dispersal of two umbrella species in the Hindu Kush Himalayan region. Sci. Total Environ. 777, 146207. https://doi.org/10.1016/j.scitotenv.2021.146207. Dar, S.A., Singh, S.K., Wan, H.Y., Kumar, V., Cushman, S.A. & Sathyakumar, S. (2021) Projected climate change threatens Himalayan brown bear habitat more than human land use. Anim. Conserv. 24, 659–676. https://doi.org/10.1111/acv.12671. Dar, S.A., Singh, S.K., Wan, H.Y., Cushman, S.A., Bashir, T. and Sathyakumar, S. (2023). Future land use and climate change escalate connectivity loss for Himalayan brown bears. Anim Conserv, 26: 199-215. https://doi.org/10.1111/acv.12813 Fick, S.E. & Hijmans, R.J. (2017) WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int.J. Climatol. 37, 4302–4315. Frankham R, Ballou JD, Briscoe DA (2010) Introduction to Conservation Genetics. University Press, Cambridge, UK Garcia RA, Cabeza M, Rahbek C, Araujo MB (2014) Multiple dimensions of climate change and their implications for biodiversity. Science 344:1247579. https://doi.org/10.1126/ science.1247579 Guéguen M, Blancheteau H, Lemaire-Patin R, Thuiller W (2025) biomod2: Ensemble Platform for Species Distribution Modeling . R package version 4.3-4 3. https://biomodhub.github.io/biomod2/. Hannah L, Midgley G, Andelman S et al (2007) Protected area needs in a changing climate. Front Ecol Environ 5:131–138. https://doi.org/10.1890/1540-9295 Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25 (15), 1965–1978. Inoue K, Lang BK, Berg DJ (2015) Past climate change drives current genetic structure of an endangered freshwater mussel species. Mol Ecol 24:1910–1926. https://doi.org/10.1111/mec.13156 IPCC (2013) Climate Change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, U. K., and New York IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp. doi:10.1017/9781009157896. Jay F, Francois O, Durand EY, Blum MGB (2015) POPS: a software for prediction of population genetic structure using latent regression models. J Stat Softw. https://doi.org/10.18637/jss.v068.i09 Jay F, Manel S, Alvarez N et al (2012) Forecasting changes in population genetic structure of alpine plants in response to global warming. Mol Ecol 21:2354–2368. https://doi.org/10.1111/j.1365-294X.2012.05541.x Jetz W, Wilcove DS, Dobson AP (2007) Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol 5:e157. https://doi.org/10.1371/journal.pbio.0050157 Kanagaraj R, Araujo MB, Barman R et al (2019) Predicting range shifts of asian elephants under global change. Divers Distrib 25:822–838. https://doi.org/10.1111/ddi.12898 Kichloo M.A. and N. Sharma, 2021. MaxEnt modeling of distribution and habitat preferences of Asiatic black bear in Kishtwar high altitude National Park, Jammu and Kashmir. Asian J. Anim. Sci., 15: 19-26. Lima JS, Ballesteros-Mejia L, Lima-Ribeiro MS, Collevatti RG (2017) Climatic changes can drive the loss of genetic diversity in a neotropical savanna tree species. Glob Change Biol 23:4639–4650. https://doi.org/10.1111/gcb.13685 Madon B, Warton DI, Araujo MB (2013) Community-level vs species-specific approaches to model selection. Ecography 36:1291–1298. https://doi.org/10.1111/j.1600-0587. 2013.00127.x Mainali K, Shrestha BB, Sharma RK, et al. (2020) Contrasting responses to climate change at Himalayan treelines revealed by population demographics of two dominant species. Ecol Evol . 10: 1209–1222. https://doi.org/10.1002/ece3.5968 Mukherjee, T., Sharma, L.K., Kumar, V., Sharief, A., Dutta, R., Kumar, M., Joshi, B.D., Thakur, M., Venkatraman, C. & Chandra, K. (2021) Adaptive spatial planning of protected area network for conserving the Himalayan brown bear. Sci. Total Environ. 754, 142416. https://doi.org/10.1016/j.scitotenv.2020.142416. Muscarella R, Galante PJ, Soley-Guardia M et al (2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for MAXENT ecological niche models. Methods Ecol Evol 5:1198–1205. https:// doi. org/ 10. 1111/ 2041-210X. 12261 Parmesan, C. (2006) Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259. https://doi.org/10.1016/j. ecolmodel.2005.03.026 Qi, Z. X., W. H. Xu, X. Y. Xiong, Z. Y. Ouyang, H. Zheng, and D. X. Gan. (2011) “Assessment of Potential Habitat for Ursus thibetanus in the Qinling Moun- Tains.” Biodiversity Science 19, no. 3: 343–352. Rehan, M., Hassan, A., Zeb, S. et al. (2024) Application of species distribution models to estimate and manage the Asiatic black bear ( Ursus thibetanus ) habitat in the Hindu Kush Mountains, Pakistan. Eur J Wildl Res 70 , 62. https://doi.org/10.1007/s10344-024-01806-2 Reside AE, Butt N, Adams VM (2018) Adapting systematic conservation planning for climate change. Biodivers Conserv 27:1–29. https://doi.org/10.1007/s10531-017-1442-5 Ripple, W.J., Estes, J.A., Beschta, R.L., Wilmers, C.C., Ritchie, E.G., Hebblewhite, M., Berger, J., Elmhagen, B., Letnic, M., Nelson, M.P., Schmitz, O.J., Smith, D.W., Wallach, A.D. & Wirsing, A.J. (2014) Status and ecological effects of the world’s largest carnivores. Science 343, 1241484. Row JR, Wilson PJ, Gomez C et al (2014) The subtle role of climate change on population genetic structure in Canada lynx. Glob Change Biol 20:2076–2086. https://doi.org/10.1111/gcb.12526 Sathyakumar, S. & Bashir, T. (2010) Wildlife of the Himalaya: conservation issues and the way forward. In Mountains, Ecosystem and Man. 1st edn.: 322–343. New Delhi: Soil Conservation Society of India. Schickhoff, U., Bobrowski, M., Böhner, J., Bürzle, B., Chaudhary, R. P., Gerlitz, L., Heyken, H., Lange, J., Müller, M., Scholten, T., Schwab, N., and Wedegärtner, R.: Do Himalayan treelines respond to recent climate change? An evaluation of sensitivity indicators, Earth Syst. Dynam., 6, 245–265, https://doi.org/10.5194/esd-6-245-2015, 2015. Shirk AJ, Landguth EL, Cushman SA. (2017) A comparison of individual-based genetic distance metrics for landscape genetics. Mol Ecol Resour . 17: 1308–1317. https://doi.org/10.1111/1755-0998.12684 Shirk, A.J., Cushman, S.A., Waring, K.M., Wehenkel, C.A., Leal-S_aenz, A., Toney, C. & Lopez-Sanchez, C.A. (2018) Southwestern white pine (Pinus strobiformis) species distribution models project a large range shift and contraction due to regional climatic changes. For. Ecol. Manage. 411, 176–186. Shirk, A.J., Wallin, D.O., Cushman, S.A., Rice, C.G. & Warheit, K.I. (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol. Ecol. 19, 3603–3619. Su, J., Aryal, A., Hegab, I.M., Shrestha, U.B., Coogan, S.C.P., Sathyakumar, S., Dalannast, M., Dou, Z., Suo, Y., Dabu, X., Fu, H., Wu, L. & Ji, W. (2018) Decreasing brown bear (Ursus arctos) habitat due to climate change in Central Asia and the Asian Highlands. Ecol. Evol. 8, 11887–11899. Taberlet, P. & Bouvet, J. (1994). Mitochondrial DNA polymorphism, phylogeography, and conservation genetics of the brown bear Ursus arctos in Europe. Proc. R. Soc. B. 255, 195–200. Thuiller W, Georges D, Engler R, Breiner F (2016) biomod2: ensemble platform for species distribution modeling. R package version 3(3):r539 Title, P.O. & Bemmels, J.B. (2018). ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41, 291–307. Tuanmu M-N, Jetz W (2015) A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling: Global habitat heterogeneity. Glob Ecol Biogeogr 24:1329–1339. https://doi.org/10.1111/geb.12365 Waits, L. P., et al. (2001). A select panel of polymorphic microsatellite loci for individual identification of American black bears. Molecular Ecology Notes , 1(1), 98–101. Waits, L.P., Luikart, G. and Taberlet, P. (2001) Estimating the probability of identity among genotypes in natural populations: cautions and guidelines. Molecular Ecology, 10: 249-256. https://doi.org/10.1046/j.1365-294X.2001.01185.x Wasserman, T.N., Cushman, S.A. & Littell, J. (2013) Population connectivity and genetic diversity of American marten (Martes americana) in the United States northern Rocky Mountains in a climate change context. Conserv. Genet. 14, 529–541. Wroblewska A, Mirski P (2018) From past to future: impact of climate change on range shifts and genetic diversity patterns of circumboreal plants. Reg Environ Change 18:409–424. https://doi.org/10.1007/s10113-017-1208-3 Yannic G, Pellissier L, Ortego J et al (2014) Genetic diversity in caribou linked to past and future climate change. Nat Clim Change 4:132–137. https://doi.org/10.1038/nclimate20 74 Ye J, Xiao Z, Li C et al (2015) Past climate change and recent anthropogenic activities affect genetic structure and population demography of the greater long-tailed hamster in northern China. Integr Zool 10:482–496. https://doi.org/10. 1111/1749-4877.12150 Table 1. Percent contribution of variables in the final consensus prediction model. Barrenland 11.8 Annual mean temperatureBio1 46.9 Mean diurnal temperature range (Bio2) 5.6 Precipitation of the wettest month (Bio13) 5.9 Canopy height 6.3 embergerQ 5.2 Grasslands 1.6 Savannas 4.6 topographic wetness index 2.6 Table 2. Change in habitat (Stable, Gain and Loss) of Asiatic black bears in Himachal Pradesh, Western Himalaya under future projections. Current 10229 10229 100.0 - - - - - - Climate change-only 2050s RCP 2.6 8644 8644 84.5 1725.0 16.86 1585 15.5 140.0 1.37 2050s RCP 8.5 8026 8026 78.5 2578.0 25.20 2203 21.5 375.0 3.67 2070s RCP 2.6 8536 8536 83.4 1796.0 17.56 1693 16.6 103.0 1.01 2070s RCP 8.5 8868 8868 86.7 0.0 0.00 1361 13.3 -1361.0 -13.31 math_shortcuts Table 3. Genotyping error rates and g enetic characterisation of Asiatic black bear at twelve microsatellite loci in Himachal Pradesh, Western Himalaya. 1 G10H* 284 19 0.261 0.92 0.72 0.1 0.0 0.34 0.011 0.290 2 MSUT3* 274 17 0.321 0.92 0.66 0.0 0.0 0.32 1E-04 0.085 3 MSUT5* 298 18 0.208 0.93 0.77 0.1 0.0 0.37 1E-06 0.025 4 UT3 283 16 0.403 0.93 0.58 0.0 0.0 0.28 1E-08 0.007 5 UT4* 285 13 0.509 0.89 0.43 0.0 0.0 0.21 3.1E-10 0.002 6 UT36* 283 12 0.392 0.85 0.54 0.0 0.0 0.25 1.2E-11 7E-04 7 MSUT2* 313 9 0.393 0.84 0.54 0.0 0.0 0.25 5.1E-13 7E-04 8 MSUT8* 314 16 0.417 0.84 0.50 0.0 0.0 0.23 2.1E-14 8.6E-05 9 UT1* 286 10 0.301 0.84 0.63 0.1 0.0 0.29 9.3E-16 2.9E-05 10 UT29* 278 10 0.403 0.81 0.49 0.0 0.0 0.23 5.6E-17 1.1E-05 11 MSUT7* 321 9 0.374 0.78 0.54 0.0 0.0 0.23 4E-18 4E-06 12 MSUT1* 293 9 0.239 0.56 0.56 0.1 0.1 0.22 9.2E-19 2.1E-06 Mean 292.67 13.16 0.35 0.84 0.58 0.04 0.01 0.27 - - SE (±) 4.46 1.11 0.03 0.03 0.03 - - - Figure 1. Map of the study landscape (Himachal Pradesh) showing occurrences of Asiatic black bears. Figure 2. Response curves for the habitat variables of the ensemble prediction model for Asiatic black bears in study region. Figure 3. The habitat suitability map showing the predicted occurrence of Asiatic black bears based on ensemble modelling in Himachal Pradesh, Western Himalaya. The map displayed areas of low to high suitability represented in a gradient from the lowest probability of brown bear occurrence (blue) to the highest (red). Figure 4. Future changes in potential habitat for Asiatic black bear in Himachal Pradesh, Western Himalaya based on ensemble habitat modelling under climate change scenario in 2050s and 2070s. Figure 5. Current population genetic structure and future projections projected by POPS for Asiatic black bear under future climate change scenarios (RCP2.6 and RCP8.5) in 2050s and 2070s. LES 2050 represents low-emission scenario 2050s, HES 2050 represents high-emission scenario 2050s, LES 2070 represents low-emission scenario 2070s, and HES 2070 represents high-emission scenario 2070s. Membership coefficients were interpolated using kriging and shown within the areas of high habitat suitability for each scenario. Information & Authors Information Version history V1 Version 1 04 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords carnivores climate change ensemble modelling gene flow population genetic structure range shifts Authors Affiliations Shahid Ahmad Dar Zoological Survey of India View all articles by this author Vinaya Singh Zoological Survey of India View all articles by this author Vinnet Kumar Zoological Survey of India View all articles by this author Amar Singh Zoological Survey of India View all articles by this author Amira Sharief Zoological Survey of India View all articles by this author Hemant Singh Zoological Survey of India View all articles by this author Ritam Dutta Zoological Survey of India View all articles by this author Bheeem Joshi Zoological Survey of India View all articles by this author Gopinathan Maheswaran Zoological Survey of India View all articles by this author Mukesh Thakur Zoological Survey of India View all articles by this author Lalit Kumar Sharma 0000-0003-1214-7416 [email protected] Zoological Survey of India View all articles by this author Metrics & Citations Metrics Article Usage 197 views 124 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Shahid Ahmad Dar, Vinaya Singh, Vinnet Kumar, et al. 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