Where Bears Roam in Ladakh: Landscape Determinants of Himalayan Brown Bear Distribution in India’s Trans-Himalayas

Where Bears Roam in Ladakh: Landscape Determinants of Himalayan Brown Bear Distribution in India’s Trans-Himalayas | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Where Bears Roam in Ladakh: Landscape Determinants of Himalayan Brown Bear Distribution in India’s Trans-Himalayas Niazul H. Khan¹ˑ², Ayan Sadhu¹, Dhruv Jain¹, Raza Ali Abidi², and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6888317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract The Trans-Himalayan region of Ladakh is home to an endangered species, the Himalayan brown bear, which is least studied in high-altitude regions. Brown bears in the Indian Himalayas are threatened by low density, and insular populations. To understand the habitat ecology of the brown bear in Ladakh, we used the polygon search method and trail transect surveys using systematic grid-based sampling in 275 grids of 100 km² covering 27500 km² with 4012 trails ranging from 0.5 to 6.78 km. We recorded 2530 brown bear signs from surveyed grids. We used occupancy analysis to address detection bias and to model factors determining brown bear occupancy. We used SDM using MaxEnt model to delineate suitable brown bear habitats in the Trans-Himalayan region. Based on the AUC value and TSS values, best model out of 35 combinations showed that temperature change, elevation, human footprint, and land use land cover determine suitability for brown bears in Ladakh. Apart from temperature, limiting resources in a specific elevation (3000–4500 m) may influence brown bear distribution in western Ladakh. This study emphasises the influence of temperature, elevation, and rangelands on brown bear distribution and stresses the impact of climate change that is likely to fragment the brown bear habitat as major threats in Ladakh. Biological sciences/Ecology Earth and environmental sciences/Ecology Habitat selection Habitat suitability Kargil Occupancy PRESENCE MaxEnt Figures Figure 1 Figure 2 Figure 3 Introduction The history of rapid development in the Himalayas has brought human habitation near to habitats close to wild animals (Fox et al. 1994 ; Mishra 2001 ), and with humans moving towards the Anthropocene era, their utilisation of the natural resource subsequently surged. As people search for new settlements, they are destroying wildlife habitats and wild animals migrate due to the uncontrolled use of natural habitats (Rawat & Satyakumar 2002 ). It was found that both proximal and ultimate factors determined the habitat preference and utilisation by a species. Using proximal criteria to assess habitat, terrain, vegetation cover, slope, and the presence or absence of competitors in the habitat and the ultimate factors are those that have produced evolutionary links between habitat and species (Garshelis 2000 ). Understanding habitat selection and suitability is crucial for the conservation of the wildlife and for formulation of conservation management policies (Doligez 2008). However, widespread extirpation of large carnivore populations has occurred in association with increasing human population and anthropogenic activities (Woodroffe 2000 ). Brown bears ( Ursus arctos ) are found globally on four main continents: North America, South America, Europe, and Asia; in Asia, they are found in Turkey, Iran, and Afghanistan; along the Himalayan range of Pakistan, India, Nepal, covering northern China, Mongolia, Russia, and Japan but have perhaps become extinct from Bhutan (McLellan et al. 2017 ). Only a few studies have been conducted on the habitat selection and suitability of large carnivores, in the Indian Himalayan Region (Raina et al. 2025) especially bears (Rathore 2008 ; Sharief et al. 2020; Dar et al. 2021 ; Sathyakumar et al. 2013 ). In India, a subspecies of brown bear Ursus arctos isabellinus is found in the Greater and Trans-Himalayan region. It is primarily confined to rolling uplands, alpine, and rarely subalpine regions of the Greater Himalayas and some parts of Trans-Himalayas (Sathyakumar 2006 ). However, there are a few regions in the Greater Himalaya where the brown bear uses the subalpine areas to some extent, thereby overlapping with the distribution of Asiatic black bears (Thakur et al. 2023 ). In addition, it is believed that there are only 130–220 brown bears living in the Himalayas and Trans-Himalayan mountain ranges of India and Pakistan (Bellemain et al. 2006 ; Abbas et al. 2015 ). According to a study conducted by Sathyakumar in 2006, the possible range for brown bears in India is 36,800 km², with 28,000 km² in the northwestern and upper western Himalayan region and 8,800 km² in the Trans-Himalayan region of Ladakh However, only 10% of this area is protected by India's current network of protected areas (Sathyakumar & Qureshi 2003 ). Within the Indian Himalayan Range (IHR), brown bears are primarily found in two union territories, viz. Ladakh and Jammu & Kashmir, and in the states of Himachal Pradesh, Uttarakhand, and some parts of upper Sikkim. In Ladakh, brown bears are prominently found in the western part, covering upper parts of Suru Valley, and Zanskar Valley (Mallon 1991 ). In Jammu & Kashmir, brown bears are found in eight protected areas, viz., Dachigam National Park (NP), Gulmarg Wildlife Sanctuary (WS), Hirapora WS, Overa Aru WS, Limber WS, Lachipora WS, Kishtwar NP. In the state of Himachal Pradesh, brown bears are found in ten protected areas, while in Uttarakhand, brown bears are reported from Gangotri NP, Govind NP, and the Bhagirathi basin (Sathyakumar 2006 ; Pal et al. 2016 ). According to a study (Sathyakumar 2001 ), the upper reaches of Kanchenjunga National Park in Sikkim are also home to brown bears but their status there is unknown. Brown bears have a strong relationship between population density and habitat productivity (Ferguson & Mcloughlin 2000 ; Hilderbrand et al. 1999 ). Asiatic brown bears are habitat specialists of sparse patchy resource habitats of cold regions and are found in the Arctic Tundra, boreal forests of Russia, and the trans Himalayas (Servheen 1999 ). Brown bears' omnivorous nature enables them to adapt to various landscapes. In Alaska and Columbia, bears were found to use a variety of habitats, including old-growth forests, coastal sedge meadows, and south-facing slopes of mountains. During summer, most bears use alpine and subalpine meadows (Nawaz 2008 ; Rathore 2008 ). In India, brown bears use grasslands, alpine meadows, valleys, agricultural fields, mixed forests, wet temperate forests, dry alpine scrub characterised by Juniper species, exposed rocky slopes with pastures, and sub-alpine scrub dominated by Rhododendron species (Rathore 2008 ). When humans collect fuelwood and non-timber forest produce (NTFP) from bear habitats, it can lead to conflict situations (Chauhan 2003 ). The Himalayan brown bear occurs at a very low density in the alpine and subalpine areas in the elevation range of 3000 m to 5000 m in the Greater Himalaya and the Trans-Himalayan region exclusively confined to the northwestern and western Himalayan region of India (Sathyakumar 2006 ). Few studies on the human-brown bear conflict have been done in the Kargil region of Ladakh (Sathyakumar & Qureshi, 2003 ; Chavan et al. 2021 ; Ali 2024 ). Ladakh has recently been declared a Union Territory by the Government of India and is receiving large amounts of resources for the development of the region especially infrastructure. In light of this rapid development, it is crucial and timely to understand the habitat ecology of the brown bear so as to incorporate its’ conservation needs while formulating policy and management strategies (Zedrosser et al. 2011 ; Penteriani et al. 2018). Results We effectively sampled 351 grids covering 35,100 km² of habitat area by walking approximately 6149 km of spatially independent trails across the landscape. We recorded 2530 signs of brown bears from across Ladakh, primarily from the Kargil District (Fig. 1 ). Most of the eco-geographical covariates used for Occupancy and MaxEnt models were poorly correlated except for elevation and terrain ruggedness (see Supplementary Table S3) and we used only one of these in any given model. We ran 36 models (see Supplementary Table S4) and found that important factors such as elevation, slope, ruggedness, drainage density, land surface temperature, distance to water, distance to settlement, land surface temperature, permafrost, and distance to road depicted a significant role in determining where brown bears are distributed in the high-altitude cold desert of Ladakh. Occupancy Modeling The detection history dataset was made up of 680 grids, each 100 km², sampling covariate (trail length) and site covariates. It also had field-collected covariates like wild prey, domestic animals, and wild rose. We used Excel to normalise the site covariates and sampling covariates by dividing the values by the standard deviation of those values. This is an important step before the analysis because it makes the data more consistent and gives it a range. We imported the data into the PRESENCE software and performed single-season occupancy modelling. Based on the hypothesis, we have conducted single-species, single-season occupancy models and identified the ones with the lowest AIC values (see Supplementary Table S5). The naïve occupancy estimate is 0.098529. Model 1: psi(R + RL + DW + LST),p(.) Table 1 Himalayan brown bear occupancy model. First model parameter estimates Himalayan brown bear occupancy (Ψ) and detection (P) in Ladakh. The sign and magnitude of the ß estimate provides the relative influence of the ecological variables on Himalayan brown bear occupancy. Parameter Model ß estimate SE Ψ Intercept -2.43 0.37 Ψ Terrain Ruggedness Index 0.82 0.26 Ψ Rangelands 0.45 0.18 Ψ Distance to Water -2.35 0.49 Ψ Land Surface Temperature 0.77 0.22 P Detection-Intercept -0.06 0.11 We also analysed the model using trail length as a detection covariate (p(trail length)) and found no significant results due to the high standard error. We imported the PRESENCE model output into ArcGIS, combined them with the Ladakh grid file, and then prepared an occupancy map. With a beta estimate of 0.82 and standard error of 0.26, the variables determining brown bear occupancy is favourably influenced by rough terrain, or landscape roughness. Rangelands have a notable effect on the occupancy of brown bears in Ladakh, with a beta estimate of 0.45 and a standard error of 0.18. The distance to water gave a beta estimate of -2.34 with a standard error of 0.49, which indicates that as the distance to water bodies increases, brown bear occupancy decreases. Brown bear occupancy shows a positive trend with the land surface temperature indicated by a beta estimate of 0.77 and a standard error of 0.22. The model provided a spatial representation of habitat selection for the Himalayan brown bear in Ladakh, with different colours and percentages indicating the likelihood of bear occupancy across the landscape. The values (ranging from 0.50 to 0.73) represent the predicted probability of brown bear occupancy in each grid cell. The colour scale goes from yellow (a low occupancy probability) to dark blue (a high occupancy probability). The dark blue grids with a 0.68–0.73% probability represent regions with the highest probability of occupancy and are likely core habitat zones. These areas are critical for the survival and conservation of the species because they offer the environmental conditions most favourable for the brown bear, such as suitable terrain, access to water, and an optimal land surface temperature. The C-hat value of the model psi(LST + DW + R + RL), p(.) is 1.24, which indicates the adequacy of model fit. For second and third model see Supplementary Data S6. Habitat Suitability modeled by MaxEnt The model exhibited strong performance, attaining an average test AUC (Area Under Curve) of 0.950, with a standard deviation of 0.007. The True Scale Statistic (TSS) value is 0.71 with Kappa as 0.02,Kappa max value at 0.39, and the Prevalence Value was 0.09. Table 2 Relationship between Himalayan brown bear and environmental variables. Bioclimatic variables (bio4 (highest contribution), bio1 and bio2), Land Use Land Cover (LULC), Terrain Ruggedness Index (TRI), Human Footprint Index (HFP) and Digital Elevation Model (DEM) with percent contribution and permutation importance. Variables Percent contribution Permutation importance 1. Bioclimatic (bio4) 47.7 32.5 2. Bioclimatic (bio1) 32.3 52 3. Land Use Land Cover (LULC) 11.2 1.7 4. Bioclimatic (bio2) 4.1 5.9 5. Terrain Ruggedness Index (TRI) 1.8 2.5 6. Human Footprint Index (HFP) 1.5 2.7 7. Digital Elevation Model (DEM) 1.5 2.7 The below graph presented illustrates the average sensitivity in relation to specificity for brown bears. Owning a standard error of 0.007, the mean training AUC across the replicate runs is 0.950 indicating good performance of model (Elith et al. 2011 ). The outcomes of the jackknife test for variable significance highlights the environmental variable with the highest gain when used in isolation is bio4 (Temperature Seasonality) followed by bio1 (Annual Mean Temperature) and DEM (Digital Elevation Model). The following graphs and maps show a MaxEnt model results along with the variables map generated using only the corresponding variable. These graphs show how predicted suitability changes based on both the chosen variable and the relationships that happen when that variable is correlated with other variables and interpret the strong correlations between variables. The Jackknife of AUC for Himalayan brown bear resulted from MaxEnt SDM (see Supplementary Graph S7). The graphs infer the relation of the response curves (species response) and the spatial representation of environmental variables in understanding the habitat preferences. It is found that the Himalayan brown bear uses a specific elevation range in the Trans-Himalayan region of Ladakh from 3000 m asl to 4500 m asl, indicating its less suitability in the eastern region as appears in the elevation map of Ladakh. It is predicted that brown bears in Ladakh use a terrain rugged index (TRI) between 50 to 150 m, ranging between flat, nearly level, and slightly rugged terrain, thus avoiding moderately rugged, highly rugged and extremely rugged terrain (Riley et al. 1999 ). The land use and land cover (LULC) used by brown bears in Ladakh along with the visualisation of LULC map of Ladakh. However, the rangeland played a significant role in the habitat suitability of brown bears but it’s crucial to understand the role of other factors in determining the habitat use in the cold desert of Ladakh. In western region of Ladakh, the temperature seasonality (bio4) affects brown bear habitat suitability. Brown bears often choose environments marked by notable seasonal temperature changes, which probably influences food availability, denning habit, thermal regulation, and thermal control. The western seasonal temperature fluctuations provide a range of food sources year-round, provide ideal circumstances for denning, and provide chances for thermal control, thereby defining it as the suitable habitat for brown bears. Successful brown bear population management and protection depend on an awareness of these habitat preferences. With emphasis on western region of Ladakh, temperature seasonality (bio4) influences the habitat appropriateness. Brown bears presumably prefer places with notable seasonal temperature variations because of their influence on food supply, changes in behaviour, adaptations, and temperature control. Kargil's seasonal temperature fluctuations produce a range of food supplies year-round, ideal circumstances for denning, and chances for thermal control, therefore defining it as a suitable habitat for brown bears. In accordance with the Himalayan brown bears mean diurnal range (bio2) from 10°C and 13°C in Ladakh. It is reflecting their predisposition towards temperature fluctuations during the day time, this range seems to represent the optimal temperature variability brown bears like in their habitat in Ladakh. With a scale from 0 to 100, the Human Footprint Index (HFI) measures the degree of human effect on terrestrial ecosystems; 0 denotes little human involvement and 100 denotes maximal influence. The Human Food Print Index (HFP) offers an interesting window into brown bear habitat choices. Brown bears avoid highly crowded areas and usually live in areas with low to moderate habitat fragmentation potential, more precisely between 5 and 25. This suggests that brown bears show habitat selection behaviour swayed by human presence, therefore favouring areas with less human effect. The habitat appropriateness of the Himalayan brown bear inside the Ladakh terrain is shown on the map below. The data suggests a substantial tendency of brown bears for the western portion of Ladakh, with a projected suitability range extending from 0.66 to 0.99. This observation emphasises a strong favourable link between the presence of brown bears in the western Ladakh region and temperature seasonality. Moreover, the study reveals that brown bears especially in valleys like to dwell near bodies of water. This implies that the species uses rangeland and valley environments very effectively (Sharief et al. 2020). Discussion This study provides the first comprehensive investigation into the habitat ecology of the Himalayan brown bear ( Ursus arctos isabellinus ) in the high-altitude cold desert of Ladakh. By identifying key environmental factors influencing habitat suitability, the study contributes valuable insights for conservation planning. The species distribution model (SDM) highlights Drass Valley, Suru Valley, Shargole, and Zanskar Valley as critical habitats for sustaining brown bear populations. The findings emphasize that terrain ruggedness, land surface temperature, rangelands, and proximity to water sources significantly influence brown bear distribution. Rugged terrain provides essential cover and vegetation, while reduced human activity in these areas minimizes disturbances (Ferguson & McLoughlin, 2000 ; Sharief et al. 2020). Land surface temperature plays a crucial role, with warmer conditions in cold deserts being favorable for feeding and denning (Nawaz et al. 2014 ). This suggests that climate change could impact habitat suitability, potentially forcing bears to migrate to higher elevations, increasing competition for scarce resources (Dar et al. 2021 ). When working with statistical models, beta estimates show the relationships between predictor variables and a response variable. Positive Beta (β > 0) estimate suggests a positive relationship between the predictor variable and occupancy. Negative Beta (β < 0) estimate suggests a negative relationship between the predictor variable and bear occupancy. Zero Beta (β = 0) estimate means that there is no linear relationship between the predictor variable and the response variable. The magnitude of the beta estimate (i.e., how far it is from zero) indicates the strength of the relationship. Affinity for terrain ruggedness implies that, brown bears secured shelter and denning habitat, areas with challenging topography might be more suited residence. From high-altitude rangelands to rocky alpine meadows, rugged environment of western region of Ladakh offers a spectrum of habitats. This preference is may be because of less human disturbance and access to several food sources. Brown bears are more likely to be present in areas with higher quality or widespread rangeland habitat. Rangelands can have high productivity, supporting a variety of plant and animal species in the Ladakh region. Brown bears have a higher propensity to reside in proximity to water sources. This preference may stem from the presence of water sources such as streams, rivers, and lakes in the trans-Himalayan region, which indicate valleys and serve as food sources for brown bears, facilitating access to vegetation like berries, grasses, and prey. The positive relationship between brown bear occupancy and land surface temperature suggests that greater land surface temperatures is promotion to increase in probability of brown bear occurrence. Warmer temperatures indicate more suitable brown bear habitats, therefore influencing food availability in the short vegetation period, activity patterns, habitat choices, behaviour, and reproduction. Proximity to water sources and valleys emerged as another major factor influencing bear occupancy. Water bodies support vegetation growth, enhance prey availability, and facilitate movement across challenging terrain (Mishra et al. 2001). These findings align with previous research emphasizing the importance of rangelands and valleys in sustaining brown bear populations (Nawaz et al. 2014 ; Rathore 2008 ). Species distribution modeling indicates that western Ladakh is the most suitable region for brown bears. This underscores the need for targeted conservation efforts in areas such as Drass Valley, Suru Valley, Rangdum, Zanskar Valley, and Shargole Valley. Protecting rangelands, water sources, and critical habitats can help mitigate human-wildlife conflicts and ensure long-term population stability (Woodroffe 2000 ). While extensive surveys were conducted in Aryan Valley, Sham Valley, Leh, Changthang, and Shakar-Chiktan, no significant brown bear presence was detected, likely due to high elevations, insufficient rangelands, and low temperature variations. However, an isolated record of brown bear presence in Nubra Valley suggests the need for further investigation. Despite its contributions, this study faced several limitations, including restricted survey access due to military security concerns, difficult terrain, and extreme weather conditions. Future research should focus on long-term monitoring, habitat connectivity assessments, and the impact of climate change on brown bear distribution. This study highlights the complex habitat requirements of the Himalayan brown bear and emphasizes the need for adaptive conservation strategies to ensure the long-term survival of this threatened subspecies in the Trans-Himalayan region. A deeper understanding of movement ecology and the reduction of human disturbances are key to preserving brown bear populations in high-altitude landscapes. It provides an in-depth assessment of the habitat selection and suitability of the Himalayan brown bear in Ladakh’s high-altitude cold desert. The findings highlight that terrain ruggedness, land surface temperature, rangelands, and proximity to water sources are key factors shaping brown bear distribution. Among these, rugged terrain and land surface temperature are particularly influential, offering essential cover, food resources, and favorable conditions for denning and movement. Additionally, brown bears prefer elevations between 3,000–5,000 m asl, aligning with their adaptations to extreme environments. The results have significant implications for conservation in Ladakh. Identifying priority habitats particularly in western Ladakh will enable more targeted conservation strategies. Protecting rangelands, maintaining water resources, and mitigating human disturbances in these areas are crucial for sustaining bear populations. Integrating habitat requirements into Ladakh’s wildlife management policies can enhance long-term conservation efforts for this threatened subspecies. Furthermore, climate change poses a major challenge to brown bear conservation. Rising temperatures may force bears to migrate to higher elevations, increasing competition for limited resources and potentially altering movement patterns (Su et al . 2018). This shift could also escalate human-wildlife conflicts, emphasizing the need for proactive strategies, such as habitat corridors and climate-adaptive conservation plans. Future research should focus on monitoring these ecological shifts and developing mitigation measures to safeguard Himalayan brown bear populations in the high-altitude landscape. Materials and methods Study Area The Trans-Himalayan region of the Union Territory of Ladakh covers an area of186,200 km²and is categorised as 1A High Altitude Cold Desert Biogeographic Region (Rodger & Panwar 1988). The Indian Trans-Himalaya is an extension of the Tibetan plateau covering Ladakh and Lahaul Spiti in the state of Himachal Pradesh. Elevation Ladakh varies from 2533 m in valleys to 7742 m as Saltaro Kangri. The regions characterised by low precipitation, a short growing season, low primary productivity, but having a high livestock density (Mishra 2000 ). The climate is harsh with cold and arid temperatures dipping below − 30°C between November and March, with a short season for crop cultivation (Bagchi et al. 2012). The region represents an ecosystem where the common livelihood source is traditional agro-pastoralism (Ladon et al. 2023 ). Individual families own livestock, whereas the grazing land is common to the village with equal access (Mishra et al. 2001). Ladakh landscapes are unique considering that the wild animals are not restricted to protected areas but found across the landscape (Mishra et al. 2010 ). The landscape of the area is mountainous, rugged, and interspersed with valleys drained by the river Indus and its tributaries. Ladakh has been divided administratively into two districts. Kargil and Leh. The region is characterised by a few natural valleys viz., Suru Valley, Drass Valley, Zanskar Valley, Aryan Valley, Shakar-Chiktan, and Shargole. This study is carried out intensively in Kargil district and extensively across Ladakh because, based on the literature and local knowledge, the Himalayan brown bear is predominantly reported from the western region (Kargil) of Ladakh. Field Data Collection Field sampling was carried out using ‘MSTrIPES’ digital application equipped with collecting the spatial and temporal characteristics of occupancy trails of Himalayan brown bear along with the geotagged photos of sign detections. Data captured using the app was imported into desktop programs designed for data organisation and archiving. This guarantees long-term archiving in the digital infrastructure, transparency, and precise and simple data entry. The sample, which amounted to over 30,000 working-days (‘man-days’) over two years, was carried out in cooperation with local field assistants and researchers and employees of the Wildlife Protection Department. We ensured that each survey team was formed up of experts in these different filed data collection methods. Furthermore, each sign was recorded using mobile devices, which experts could later verify. Occupancy survey Based on the accessibility of the terrain and logistics, the landscape was divided into a grid spanning 10 x 10 km. In order to account for the natural variation in habitats, the grid was further segmented into 5 x 5 km sub-grids so that our sampling could be evenly distributed across each cell. We assessed the occupancy of brown bears in each of the 25 km \(\:²\) sub-grid cells. During the training sessions, the sampling team was given a pictorial guide to help them identify the signs of key species. We conducted at least one sign-search survey of about 5 km each in every sub-grid cell to record signs of the species. We targeted human trails, ridgelines, and valleys to maximise the chances of encountering signs. We recorded signs such as tracks, scats, and sightings that could be assigned to the presence of brown bears to spatially spread the search pathways. We surveyed Ladakh landscape collectively to validate the species' distribution and suitability of their habitats in the region. All field data was collected using a phone-based polygon search application developed for occupancy surveys (MSTrIPES, Qureshi et al. 2023 ). We plotted all brown bear sign locations (2530) to gain a preliminary understanding of the distribution of Himalayan brown bears in the Ladakh region. Environmental Variable Collection We used ecogeographic variables that were procured using ArcGIS and available in public domain at a resolution of 1 km 2 (see Supplementary Table S1 ). These variables included those that were likely to influence the occurrence of brown bears as per our ecological knowledge of the species and our a priori hypotheses (see Supplementary Table S2). Analytical Framework a. Habitat selection modelling We use occupancy modelling that corrects for detection bias and model’s species occurrence using relevant eco-geographical covariates (MacKenzie et al. , 2006). We conducted single species single season occupancy analysis using PRESENCE software version 2.13.47 (Hines 2006 ). We modeled detection probability with survey effort and occupancy with site characteristics. We selected the most parsimonious model with the lowest Akaike Information Criterion (AIC) and used model average Akaike weights when two or models differed by less than 5 AIC. This approach is particularly suitable for large-scale population dynamics monitoring and habitat selection (Haroldson et al. 2021). To avoid collinearity in the model we first estimated the correlation between all ecogeographical variables and then used only one of a pair of correlated variables in any given model. Following the extraction of the PRESENCE results from each model, we exported the Psi-conditional [Pr(occ | detection history)] values into Excel. We then transformed them into inverse logit, the logistic function defined by exp(x)/(1 + exp(x)). The inverse logit transformation takes values on the real line and converts them to be between zero and one. We used clog-log to determine the prediction of values for spatial representation of Himalayan brown bear distribution. We processed the analysis at 100 bootstrap iterations to estimate C-hate values. A diagnostic metric called C-hat evaluates the data-fit of the model. It basically checks for overdispersion, the condition whereby the observed data variability surpasses what the model projects. A good model fit is indicated by a c-hat value of 1; values higher than 1 imply overdispersion. For occupancy modelling we hypothesised that the probability of a site being occupied by a brown bear is influenced by ‘ruggedness, ‘rangelands,’ ‘distance to water,’ and ‘land surface temperature’ with detection probability as constant, p(.). b. Habitat Suitability Modelling We investigated habitat suitability to learn more about how species are spread in the western part of Ladakh by looking at occurrence data, landscape covariates, and bioclimatic variables. We used the Maximum Entropy (MaxEnt) modelling methodology (Phillips et al. 2006 ) in MaxEnt, SDM V 3.4.4, software known for robust handling of presence-only data. MaxEnt creates a probability surface showing areas with acceptable conditions for the species by integrating known occurrence locations with environmental predictors, therefore evaluating species habitat suitability. This method is quite useful in remote and demanding surroundings like Ladakh, where absence data might be irregular or difficult to get. The habitat suitability analysis was carried out exercising occurrence data and landscape covariates through the MaxEnt species distribution modelling method, SDM V 3.4.4 (Phillips et al. 2006 ). Employing ArcGIS v. 10.8 software, we identified 14 potential landscape covariates and transformed the variables into ASCII (American Standard Code for Information Interchange) file format. The model was trained using 80% of the locations, while the accuracy was evaluated on the remaining 20%. Additionally, the model, which integrates 34 distinct models, underwent processing and was utilised in a jackknife test to assess the significance of predictors. To ensure precise predictions, the models were executed 1, 10, and 100 times utilising bootstrap methods. The True Scale Statistics (TSS) value (MaxEnt: Background Prediction, Sample Prediction Test, and Threshold Value 10 Clog-log) was utilised to select the optimal model. This approach enabled us to assess the probability of species presence at multiple locations by using ecological and climatic variables. Combining species occurrence records with key ecological traits like elevation, land cover, temperature, and precipitation patterns, we generated a predicted habitat suitability map for the region. We selected the final model based on True Skill Statistics (TSS) values (Hanssen et al. 1965). Conservation planning depends on an understanding of habitat suitability and species distribution in Ladakh as it helps to identify significant places, evaluate probable dangers, and protection managements. Declarations Acknowledgement(s): We thank the Chief Wildlife Wardens (Brij Mohan Sharma, Sajjad Hussain Mufti, Jigmet Takpa, Preet Pal Singh and Mohd. Sajid Sultan), officers, wildlife department staff, researchers and field assistants for supporting this study and participating in data collection. We thank Jigmet Takpa for his motivation, continuous support, and guidance throughout the project. We thank Wildlife Warden, Kargil, Raza Ali Abidi, for his valuable and constant support to conduct fieldwork. We are grateful to the Ministry of Environment, Forests, and Climate Change, Govt. of India; National Mission on Himalayan Studies; GB Pant National Institute of Himalayan Environment, Almora; and the Wildlife Institute of India for their support. I thank Zainab, Aamir, Sameeha, Kumudini and Bhim for always supporting in the persuade of my research work. We acknowledge the expertise and effort of our wildlife guards and field assistants without which this research would not have been possible. Data availability: The datasets generated and analysed during the current study are available from the corresponding author upon reasonable request. Author contributions: PR, BP, YVJ designed the research and obtained funding. NHK and PR conducted the study, NHK AS and DJ exercised the data analysis, interpretation and wrote the manuscript of the paper with significant contributions by YVJ, BP and PR. RAA helped in field data collection strategies and logistics in western Ladakh. All authors provided comments and suggestions that significantly improved the manuscript and approved the last version. Additional Information: Funding source : The work is supported by Department of Wildlife Protection, Leh and implemented under the National Mission for Himalayan Studies (No. NMHS/2016-17/MG13/06) in Union Territory of Ladakh. Supplementary information is attached herewith. Competing Interests: The authors declare no competing financial interests. References Abbas, F. I., Bhatti, Z. I., Haider, J., & Mian, A. Bears in Pakistan: distribution, population biology and human conflicts. Journal of Bioresource Management, 2 (2), 1 (2015). Ali, I. Examining Human Wild-carnivore conflicts in Kargil Trans-Himalayas, India , Doctoral dissertation (2024). Bagchi, S. Conserving large carnivores amidst human-wildlife conflict: the scope of ecological theory to guide conservation practice. Food Webs, 18 , e00108 (2019). Bellemain, E., Zedrosser, A., Manel, S., Waits, L. P., Taberlet, P., & Swenson, J. E. 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D., ... & van Manen, F. T. Brown bear ( Ursus arctos ; North America). Bears of the World . Cambridge University Press, Cambridge, United Kingdom, 162-195 (2021). Hanssen, A. W., & Kuipers, W. J. On the relationship between the frequncy of rain and various mateorological parameters: with reference to the problem of objective forecasting . na. (1965). Hilderbrand, G. V., Schwartz, C. C., Robbins, C. T., Jacoby, M. E., Hanley, T. A., Arthur, S. M., & Servheen, C. The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Canadian Journal of Zoology, 77 (1), 132-138 (1999). Hilderbrand, G. V., Gustine, D. D., Mangipane, B. A., Joly, K., Leacock, W., Mangipane, L. S., ... & Cambier, T. Body size and lean mass of brown bears across and within four diverse ecosystems. Journal of Zoology, 305 (1), 53-62 (2018). Hines, J. E. 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Multiple use of Trans‐Himalayan rangelands: reconciling human livelihoods with wildlife conservation. Wild rangelands: conserving wildlife while maintaining livestock in semi‐arid ecosystems, 291-311 (2010). Mohanta, R. K., & Chauhan, N. P. S. Ecology of brown bear (Ursus arctos) with special reference to assessment of man-brown bear conflicts in Kugti Wildlife Sanctuary, Himachal Pradesh (p. 3). India. Technical Report. Wildlife Institute of India, Dehradun (2011). Nawaz, M. A. Ecology, genetics and conservation of Himalayan brown bears (pp. x+-44). Ås, Norway: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences (2008). Nawaz, M. A., Martin, J., & Swenson, J. E. Identifying key habitats to conserve the threatened brown bear in the Himalaya. Biological Conservation, 170 , 198-206 (2014). Pal, R., Arya, S., Thakur, S., Mondal, K., Bhattacharya, T., & Sathyakumar, S. Bibliography on the Mammals of the Indian Himalayan Region. ENVIS Bulletin: Wildlife and Protected Areas, 17 , 10-52 (2016). Penteriani, V., Huber, D., Jerina, K., Krofel, M., López-Bao, J. V., Ordiz, A., ... & Dalerum, F. Trans-boundary and trans-regional management of a large carnivore: Managing brown bears across national and regional borders in Europe. In Large Carnivore Conservation and Management (pp. 291-313), (2018). Phillips, S. J., Anderson, R. P., & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecological modelling, 190 (3-4), 231-259 (2006). Qureshi, Q., Jhala, Y. V., Yadav, S. P., & Mallick, A. Status of tigers, co-predators and prey in India, 2022. National Tiger Conservation Authority, Government of India, New Delhi, and Wildlife Institute of India, Dehradun (2023). Rawat, G. S., & Satyakumar, S. Conservation issues in the Himalayan region of India. Envis Bulletin, Wildlife and Protected Areas, 1 (1), 50-56 (2002). Rathore, B. C. Ecology of brown bear (Ursus arctos) with special reference to assessment of human-brown bear conflicts in Kugti Wildlife Sanctuary, Himachal Pradesh and mitigation strategies (Doctoral dissertation, Saurashtra University), (2008). Riley, S. J., DeGloria, S. D., & Elliot, R. Index that quantifies topographic heterogeneity. intermountain Journal of sciences , 5 (1-4), 23-27 (1999). Rodgers, W. A., & Panwar, H. S. Planning a wildlife protected area network in India. Sathyakumar, S. (2001). Status and management of Asiatic black bear and Himalayan brown bear in India. Ursus, 21-29 (1988). Sathyakumar, S., & Qureshi, Q. Brown bear-Human Conflicts in Zanskar and Suru Valleys, Ladakh-A Report. Wildlife Institute of India, Dehradun, 21pp. (2003). Sathyakumar, S. Status and distribution of Himalayan Brown Bear ( Ursus arctos isabellinus ) in India: an assessment of changes over ten years (2006). Sathyakumar, S., Sharma, L. K., & Charoo, S. A. Ecology of Asiatic Black Bear in Dachigam National Park, Kashmir, India. Final project report, Wildlife Institute of India, Dehradun (2013). Servheen, C. Bears: status survey and conservation action plan (Vol. 44). IUCN (1999). Sharief, A., Joshi, B. D., Kumar, V., Kumar, M., Dutta, R., Sharma, C. M., ... & Chandra, K. Identifying Himalayan brown bear ( Ursus arctos isabellinus ) conservation areas in Lahaul Valley, Himachal Pradesh. Global Ecology and Conservation, 21 , e00900 (2020). Su, J., Aryal, A., Hegab, I. M., Shrestha, U. B., Coogan, S. C., Sathyakumar, S., ... & Ji, W. Decreasing brown bear ( Ursus arctos ) habitat due to climate change in Central Asia and the Asian Highlands. Ecology and Evolution, 8 (23), 11887-11899 (2018). Thakur, S., Pal, R., Kahera, N. S., & Sathyakumar, S. Forced sympatry? Spatiotemporal interactions of ursids, the Himalayan brown bear and the Asiatic black bear, along a gradient of anthropic disturbances in Western Himalaya. Journal of Zoology, 321 (1), 59-74 (2023). Woodroffe, R. Predators and people: using human densities to interpret declines of large carnivores. Animal Conservation, 3 (2), 165-173 (2000). Zedrosser, A., Steyaert, S. M., Gossow, H., & Swenson, J. E. Brown bear conservation and the ghost of persecution past. Biological Conservation, 144 (9), 2163-2170 (2011). Graphs Graphs 1-3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementarysheet.docx Graph1.png Graph 1: Relationships between Himalayan brown bear and environmental variables. Occupancy probability of brown bears in Ladakh in response to a) Terrain Ruggedness Index, b) proportion of Rangeland in a grid, c) average grid distance to perineal water source, and d) Land surface temperature between April - October season. Graph2.png Graph 2: The graph showing average sensitivity vs specificity for Himalayan brown bear. Graph3.png Graph 3: Response curves of Himalayan brown bear and the spatial representation of environmental in Ladakh. Graphs: Himalayan brown bear response to environmental variables (Digital Elevation Model (DEM), Terrain Ruggedness Index (TRI), Land Use Land Cover (LULC), Temperature Seasonality (bio4), Annual Mean Temperature (bio1), Mean Diurnal Range (bio2), and Human Footprint Index (HFP-2020)). Maps: Mapping of respective environmental variables in the Ladakh. Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviewers agreed at journal 22 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers invited by journal 20 Jun, 2025 Editor assigned by journal 19 Jun, 2025 Editor invited by journal 19 Jun, 2025 Submission checks completed at journal 16 Jun, 2025 First submitted to journal 16 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6888317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475479177,"identity":"381a4d94-cc80-4840-8139-a90c4410c1c1","order_by":0,"name":"Niazul H. Khan¹ˑ²","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACZhBx4AADg/zj4z8+ANls7ERrYUhLkJwB0sJMlFVgLTkG0jxwQ/AAc3bmxx8YztyRN2c4Y2Bs82ubPB8zA+OHjzm4tVg2sxkYMNx4Zrizsa0gObfvtmEbMwOz5MxtuLUYHGYwSGD4cJhxw2HmDYdze24zArWwMfPi1cL+4QBQi/2GYwyGzZY9t+2J0MJj2MBw43DihjMsxswMP24nEqOlmCHhzOHkDTfY0hh7G24ntzEzNuP3y/njmz98OHbYdsMN5mMMP/7ctp3f3nzww0c8WsAgAcZgbAOTDQTUo4A/pCgeBaNgFIyCkQIAwGVXXszA94MAAAAASUVORK5CYII=","orcid":"","institution":"¹Wildlife Institute of India","correspondingAuthor":true,"prefix":"","firstName":"Niazul","middleName":"H.","lastName":"Khan¹ˑ²","suffix":""},{"id":475479178,"identity":"ec386907-7472-4ebd-8912-6fb5732b2009","order_by":1,"name":"Ayan Sadhu¹","email":"","orcid":"","institution":"¹Wildlife Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Ayan","middleName":"","lastName":"Sadhu¹","suffix":""},{"id":475479179,"identity":"18c74e3e-b152-45dc-ab7f-cd023731bbde","order_by":2,"name":"Dhruv Jain¹","email":"","orcid":"","institution":"¹Wildlife Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Dhruv","middleName":"","lastName":"Jain¹","suffix":""},{"id":475479180,"identity":"dda2576c-aaeb-4d69-b5f0-628cb3dfe89f","order_by":3,"name":"Raza Ali Abidi²","email":"","orcid":"","institution":"Union Territory of Ladakh","correspondingAuthor":false,"prefix":"","firstName":"Raza","middleName":"Ali","lastName":"Abidi²","suffix":""},{"id":475479181,"identity":"84003656-ae20-41b3-88b3-b79a551fe5ad","order_by":4,"name":"Bivash Pandav¹","email":"","orcid":"","institution":"¹Wildlife Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Bivash","middleName":"","lastName":"Pandav¹","suffix":""},{"id":475479182,"identity":"6bd162fe-84c7-43fe-8ff9-e3871be3484f","order_by":5,"name":"Yadvendradev Jhala","email":"","orcid":"","institution":"³Indian National Science Academy","correspondingAuthor":false,"prefix":"","firstName":"Yadvendradev","middleName":"","lastName":"Jhala","suffix":""},{"id":475479183,"identity":"e985156f-dc8a-44ef-922d-7ae43c14a7c0","order_by":6,"name":"Pankaj Raina¹ˑ²","email":"","orcid":"","institution":"¹Wildlife Institute of India","correspondingAuthor":false,"prefix":"","firstName":"Pankaj","middleName":"","lastName":"Raina¹ˑ²","suffix":""}],"badges":[],"createdAt":"2025-06-13 12:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6888317/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6888317/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-26632-7","type":"published","date":"2025-11-27T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85363587,"identity":"2ff6d772-103b-4764-bcb6-903686831300","added_by":"auto","created_at":"2025-06-25 06:24:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":479483,"visible":true,"origin":"","legend":"\u003cp\u003eMap showing the study area with sampling grids of 10x10 km, survey trails and brown bear presence locations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/33c512569f70f119b44eed5f.png"},{"id":85363594,"identity":"43bb5331-b957-48b0-ae1f-cb75cb8c7228","added_by":"auto","created_at":"2025-06-25 06:24:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":361700,"visible":true,"origin":"","legend":"\u003cp\u003eOccupancy map of the first model with the lowest AIC value (Natural Breaks, Jenks). The table displays the break values as a percentage.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/9d511c901b4e19098b91ea0b.png"},{"id":85364421,"identity":"31331eef-2415-4550-bf0c-df2c97438776","added_by":"auto","created_at":"2025-06-25 06:32:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":274918,"visible":true,"origin":"","legend":"\u003cp\u003eHabitat suitability map for the Himalayan brown bear in the Ladakh landscape shows spatial distribution for potential habitats classified by different degrees of suitability resulting from environmental, topographic, and anthropogenic elements.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/06f9755c1bf38c6cedcfad76.png"},{"id":97178347,"identity":"1ccac5fc-7fd9-457d-b9bc-23ed1224f666","added_by":"auto","created_at":"2025-12-01 16:08:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1713415,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/02f765e8-2d42-4015-ba1c-43a7041c0cbc.pdf"},{"id":85363588,"identity":"740df8a5-2def-4914-aa18-b6fea6fc15d0","added_by":"auto","created_at":"2025-06-25 06:24:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1610779,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarysheet.docx","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/0d2f6ab2f71afd1490a081c0.docx"},{"id":85363591,"identity":"8e7218ed-794a-49f4-9d34-e369d1377e2c","added_by":"auto","created_at":"2025-06-25 06:24:28","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":283266,"visible":true,"origin":"","legend":"\u003cp\u003eGraph 1: Relationships between Himalayan brown bear and environmental variables. Occupancy probability of brown bears in Ladakh in response to a) Terrain Ruggedness Index, b) proportion of Rangeland in a grid, c) average grid distance to perineal water source, and d) Land surface temperature between April - October season.\u003c/p\u003e","description":"","filename":"Graph1.png","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/4c976bc72ce808c762d351e4.png"},{"id":85363599,"identity":"304cde93-16d7-4e2c-aa17-7fcb713f4c82","added_by":"auto","created_at":"2025-06-25 06:24:29","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":52807,"visible":true,"origin":"","legend":"\u003cp\u003eGraph 2: The graph showing average sensitivity vs specificity for Himalayan brown bear.\u003c/p\u003e","description":"","filename":"Graph2.png","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/6e9ae98ac2a5bcf81b2d4dad.png"},{"id":85364419,"identity":"965bc258-d1b7-4d3d-95ef-e794d4f55a05","added_by":"auto","created_at":"2025-06-25 06:32:28","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":823064,"visible":true,"origin":"","legend":"\u003cp\u003eGraph 3: Response curves of Himalayan brown bear and the spatial representation of environmental in Ladakh. Graphs: Himalayan brown bear response to environmental variables (Digital Elevation Model (DEM), Terrain Ruggedness Index (TRI), Land Use Land Cover (LULC), Temperature Seasonality (bio4), Annual Mean Temperature (bio1), Mean Diurnal Range (bio2), and Human Footprint Index (HFP-2020)). Maps: Mapping of respective environmental variables in the Ladakh.\u003c/p\u003e","description":"","filename":"Graph3.png","url":"https://assets-eu.researchsquare.com/files/rs-6888317/v1/f3ce9b40174c8eb28d909ca8.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Where Bears Roam in Ladakh: Landscape Determinants of Himalayan Brown Bear Distribution in India’s Trans-Himalayas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe history of rapid development in the Himalayas has brought human habitation near to habitats close to wild animals (Fox et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Mishra \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and with humans moving towards the Anthropocene era, their utilisation of the natural resource subsequently surged. As people search for new settlements, they are destroying wildlife habitats and wild animals migrate due to the uncontrolled use of natural habitats (Rawat \u0026amp; Satyakumar \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). It was found that both proximal and ultimate factors determined the habitat preference and utilisation by a species. Using proximal criteria to assess habitat, terrain, vegetation cover, slope, and the presence or absence of competitors in the habitat and the ultimate factors are those that have produced evolutionary links between habitat and species (Garshelis \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Understanding habitat selection and suitability is crucial for the conservation of the wildlife and for formulation of conservation management policies (Doligez 2008). However, widespread extirpation of large carnivore populations has occurred in association with increasing human population and anthropogenic activities (Woodroffe \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Brown bears (\u003cem\u003eUrsus arctos\u003c/em\u003e) are found globally on four main continents: North America, South America, Europe, and Asia; in Asia, they are found in Turkey, Iran, and Afghanistan; along the Himalayan range of Pakistan, India, Nepal, covering northern China, Mongolia, Russia, and Japan but have perhaps become extinct from Bhutan (McLellan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Only a few studies have been conducted on the habitat selection and suitability of large carnivores, in the Indian Himalayan Region (Raina et al. 2025) especially bears (Rathore \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sharief \u003cem\u003eet al.\u003c/em\u003e 2020; Dar et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sathyakumar et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, a subspecies of brown bear \u003cem\u003eUrsus arctos isabellinus\u003c/em\u003e is found in the Greater and Trans-Himalayan region. It is primarily confined to rolling uplands, alpine, and rarely subalpine regions of the Greater Himalayas and some parts of Trans-Himalayas (Sathyakumar \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, there are a few regions in the Greater Himalaya where the brown bear uses the subalpine areas to some extent, thereby overlapping with the distribution of Asiatic black bears (Thakur et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, it is believed that there are only 130\u0026ndash;220 brown bears living in the Himalayas and Trans-Himalayan mountain ranges of India and Pakistan (Bellemain et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Abbas et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). According to a study conducted by Sathyakumar in 2006, the possible range for brown bears in India is 36,800 km\u0026sup2;, with 28,000 km\u0026sup2; in the northwestern and upper western Himalayan region and 8,800 km\u0026sup2; in the Trans-Himalayan region of Ladakh However, only 10% of this area is protected by India's current network of protected areas (Sathyakumar \u0026amp; Qureshi \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the Indian Himalayan Range (IHR), brown bears are primarily found in two union territories, viz. Ladakh and Jammu \u0026amp; Kashmir, and in the states of Himachal Pradesh, Uttarakhand, and some parts of upper Sikkim. In Ladakh, brown bears are prominently found in the western part, covering upper parts of Suru Valley, and Zanskar Valley (Mallon \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). In Jammu \u0026amp; Kashmir, brown bears are found in eight protected areas, viz., Dachigam National Park (NP), Gulmarg Wildlife Sanctuary (WS), Hirapora WS, Overa Aru WS, Limber WS, Lachipora WS, Kishtwar NP. In the state of Himachal Pradesh, brown bears are found in ten protected areas, while in Uttarakhand, brown bears are reported from Gangotri NP, Govind NP, and the Bhagirathi basin (Sathyakumar \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pal et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to a study (Sathyakumar \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the upper reaches of Kanchenjunga National Park in Sikkim are also home to brown bears but their status there is unknown.\u003c/p\u003e \u003cp\u003eBrown bears have a strong relationship between population density and habitat productivity (Ferguson \u0026amp; Mcloughlin \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hilderbrand et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Asiatic brown bears are habitat specialists of sparse patchy resource habitats of cold regions and are found in the Arctic Tundra, boreal forests of Russia, and the trans Himalayas (Servheen \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Brown bears' omnivorous nature enables them to adapt to various landscapes. In Alaska and Columbia, bears were found to use a variety of habitats, including old-growth forests, coastal sedge meadows, and south-facing slopes of mountains. During summer, most bears use alpine and subalpine meadows (Nawaz \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rathore \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, brown bears use grasslands, alpine meadows, valleys, agricultural fields, mixed forests, wet temperate forests, dry alpine scrub characterised by Juniper species, exposed rocky slopes with pastures, and sub-alpine scrub dominated by Rhododendron species (Rathore \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). When humans collect fuelwood and non-timber forest produce (NTFP) from bear habitats, it can lead to conflict situations (Chauhan \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The Himalayan brown bear occurs at a very low density in the alpine and subalpine areas in the elevation range of 3000 m to 5000 m in the Greater Himalaya and the Trans-Himalayan region exclusively confined to the northwestern and western Himalayan region of India (Sathyakumar \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFew studies on the human-brown bear conflict have been done in the Kargil region of Ladakh (Sathyakumar \u0026amp; Qureshi, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Chavan et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ali \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Ladakh has recently been declared a Union Territory by the Government of India and is receiving large amounts of resources for the development of the region especially infrastructure. In light of this rapid development, it is crucial and timely to understand the habitat ecology of the brown bear so as to incorporate its\u0026rsquo; conservation needs while formulating policy and management strategies (Zedrosser et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Penteriani \u003cem\u003eet al.\u003c/em\u003e 2018).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe effectively sampled 351 grids covering 35,100 km\u0026sup2; of habitat area by walking approximately 6149 km of spatially independent trails across the landscape. We recorded 2530 signs of brown bears from across Ladakh, primarily from the Kargil District (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most of the eco-geographical covariates used for Occupancy and MaxEnt models were poorly correlated except for elevation and terrain ruggedness (see Supplementary Table S3) and we used only one of these in any given model. We ran 36 models (see Supplementary Table S4) and found that important factors such as elevation, slope, ruggedness, drainage density, land surface temperature, distance to water, distance to settlement, land surface temperature, permafrost, and distance to road depicted a significant role in determining where brown bears are distributed in the high-altitude cold desert of Ladakh.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOccupancy Modeling\u003c/h2\u003e \u003cp\u003eThe detection history dataset was made up of 680 grids, each 100 km\u0026sup2;, sampling covariate (trail length) and site covariates. It also had field-collected covariates like wild prey, domestic animals, and wild rose. We used Excel to normalise the site covariates and sampling covariates by dividing the values by the standard deviation of those values. This is an important step before the analysis because it makes the data more consistent and gives it a range. We imported the data into the PRESENCE software and performed single-season occupancy modelling.\u003c/p\u003e \u003cp\u003eBased on the hypothesis, we have conducted single-species, single-season occupancy models and identified the ones with the lowest AIC values (see Supplementary Table S5). The na\u0026iuml;ve occupancy estimate is 0.098529.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eModel 1: psi(R + RL + DW + LST),p(.)\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHimalayan brown bear occupancy model. First model parameter estimates Himalayan brown bear occupancy (Ψ) and detection (P) in Ladakh. The sign and magnitude of the \u0026szlig; estimate provides the relative influence of the ecological variables on Himalayan brown bear occupancy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter Model \u0026szlig; estimate SE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΨ Intercept -2.43 0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΨ Terrain Ruggedness Index 0.82 0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΨ Rangelands 0.45 0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΨ Distance to Water -2.35 0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΨ Land Surface Temperature 0.77 0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP Detection-Intercept -0.06 0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe also analysed the model using trail length as a detection covariate (p(trail length)) and found no significant results due to the high standard error. We imported the PRESENCE model output into ArcGIS, combined them with the Ladakh grid file, and then prepared an occupancy map. With a beta estimate of 0.82 and standard error of 0.26, the variables determining brown bear occupancy is favourably influenced by rough terrain, or landscape roughness. Rangelands have a notable effect on the occupancy of brown bears in Ladakh, with a beta estimate of 0.45 and a standard error of 0.18. The distance to water gave a beta estimate of -2.34 with a standard error of 0.49, which indicates that as the distance to water bodies increases, brown bear occupancy decreases. Brown bear occupancy shows a positive trend with the land surface temperature indicated by a beta estimate of 0.77 and a standard error of 0.22.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model provided a spatial representation of habitat selection for the Himalayan brown bear in Ladakh, with different colours and percentages indicating the likelihood of bear occupancy across the landscape. The values (ranging from 0.50 to 0.73) represent the predicted probability of brown bear occupancy in each grid cell. The colour scale goes from yellow (a low occupancy probability) to dark blue (a high occupancy probability). The dark blue grids with a 0.68\u0026ndash;0.73% probability represent regions with the highest probability of occupancy and are likely core habitat zones. These areas are critical for the survival and conservation of the species because they offer the environmental conditions most favourable for the brown bear, such as suitable terrain, access to water, and an optimal land surface temperature.\u003c/p\u003e \u003cp\u003eThe C-hat value of the model psi(LST\u0026thinsp;+\u0026thinsp;DW\u0026thinsp;+\u0026thinsp;R\u0026thinsp;+\u0026thinsp;RL), p(.) is 1.24, which indicates the adequacy of model fit. For second and third model see Supplementary Data S6.\u003c/p\u003e\n\u003ch3\u003eHabitat Suitability modeled by MaxEnt\u003c/h3\u003e\n\u003cp\u003eThe model exhibited strong performance, attaining an average test AUC (Area Under Curve) of 0.950, with a standard deviation of 0.007. The True Scale Statistic (TSS) value is 0.71 with Kappa as 0.02,Kappa max value at 0.39, and the Prevalence Value was 0.09.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelationship between Himalayan brown bear and environmental variables. Bioclimatic variables (bio4 (highest contribution), bio1 and bio2), Land Use Land Cover (LULC), Terrain Ruggedness Index (TRI), Human Footprint Index (HFP) and Digital Elevation Model (DEM) with percent contribution and permutation importance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePercent contribution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePermutation importance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Bioclimatic (bio4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Bioclimatic (bio1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Land Use Land Cover (LULC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Bioclimatic (bio2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. Terrain Ruggedness Index (TRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6. Human Footprint Index (HFP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7. Digital Elevation Model (DEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe below graph presented illustrates the average sensitivity in relation to specificity for brown bears. Owning a standard error of 0.007, the mean training AUC across the replicate runs is 0.950 indicating good performance of model (Elith et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe outcomes of the jackknife test for variable significance highlights the environmental variable with the highest gain when used in isolation is bio4 (Temperature Seasonality) followed by bio1 (Annual Mean Temperature) and DEM (Digital Elevation Model).\u003c/p\u003e \u003cp\u003eThe following graphs and maps show a MaxEnt model results along with the variables map generated using only the corresponding variable. These graphs show how predicted suitability changes based on both the chosen variable and the relationships that happen when that variable is correlated with other variables and interpret the strong correlations between variables. The Jackknife of AUC for Himalayan brown bear resulted from MaxEnt SDM (see Supplementary Graph S7). The graphs infer the relation of the response curves (species response) and the spatial representation of environmental variables in understanding the habitat preferences.\u003c/p\u003e\u003cp\u003eIt is found that the Himalayan brown bear uses a specific elevation range in the Trans-Himalayan region of Ladakh from 3000 m asl to 4500 m asl, indicating its less suitability in the eastern region as appears in the elevation map of Ladakh. It is predicted that brown bears in Ladakh use a terrain rugged index (TRI) between 50 to 150 m, ranging between flat, nearly level, and slightly rugged terrain, thus avoiding moderately rugged, highly rugged and extremely rugged terrain (Riley et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The land use and land cover (LULC) used by brown bears in Ladakh along with the visualisation of LULC map of Ladakh. However, the rangeland played a significant role in the habitat suitability of brown bears but it\u0026rsquo;s crucial to understand the role of other factors in determining the habitat use in the cold desert of Ladakh.\u003c/p\u003e \u003cp\u003eIn western region of Ladakh, the temperature seasonality (bio4) affects brown bear habitat suitability. Brown bears often choose environments marked by notable seasonal temperature changes, which probably influences food availability, denning habit, thermal regulation, and thermal control. The western seasonal temperature fluctuations provide a range of food sources year-round, provide ideal circumstances for denning, and provide chances for thermal control, thereby defining it as the suitable habitat for brown bears. Successful brown bear population management and protection depend on an awareness of these habitat preferences.\u003c/p\u003e \u003cp\u003eWith emphasis on western region of Ladakh, temperature seasonality (bio4) influences the habitat appropriateness. Brown bears presumably prefer places with notable seasonal temperature variations because of their influence on food supply, changes in behaviour, adaptations, and temperature control. Kargil's seasonal temperature fluctuations produce a range of food supplies year-round, ideal circumstances for denning, and chances for thermal control, therefore defining it as a suitable habitat for brown bears. In accordance with the Himalayan brown bears mean diurnal range (bio2) from 10\u0026deg;C and 13\u0026deg;C in Ladakh. It is reflecting their predisposition towards temperature fluctuations during the day time, this range seems to represent the optimal temperature variability brown bears like in their habitat in Ladakh. With a scale from 0 to 100, the Human Footprint Index (HFI) measures the degree of human effect on terrestrial ecosystems; 0 denotes little human involvement and 100 denotes maximal influence. The Human Food Print Index (HFP) offers an interesting window into brown bear habitat choices. Brown bears avoid highly crowded areas and usually live in areas with low to moderate habitat fragmentation potential, more precisely between 5 and 25. This suggests that brown bears show habitat selection behaviour swayed by human presence, therefore favouring areas with less human effect.\u003c/p\u003e \u003cp\u003eThe habitat appropriateness of the Himalayan brown bear inside the Ladakh terrain is shown on the map below. The data suggests a substantial tendency of brown bears for the western portion of Ladakh, with a projected suitability range extending from 0.66 to 0.99. This observation emphasises a strong favourable link between the presence of brown bears in the western Ladakh region and temperature seasonality. Moreover, the study reveals that brown bears especially in valleys like to dwell near bodies of water. This implies that the species uses rangeland and valley environments very effectively (Sharief \u003cem\u003eet al.\u003c/em\u003e 2020).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first comprehensive investigation into the habitat ecology of the Himalayan brown bear (\u003cem\u003eUrsus arctos isabellinus\u003c/em\u003e) in the high-altitude cold desert of Ladakh. By identifying key environmental factors influencing habitat suitability, the study contributes valuable insights for conservation planning. The species distribution model (SDM) highlights Drass Valley, Suru Valley, Shargole, and Zanskar Valley as critical habitats for sustaining brown bear populations.\u003c/p\u003e \u003cp\u003eThe findings emphasize that terrain ruggedness, land surface temperature, rangelands, and proximity to water sources significantly influence brown bear distribution. Rugged terrain provides essential cover and vegetation, while reduced human activity in these areas minimizes disturbances (Ferguson \u0026amp; McLoughlin, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sharief \u003cem\u003eet al.\u003c/em\u003e 2020). Land surface temperature plays a crucial role, with warmer conditions in cold deserts being favorable for feeding and denning (Nawaz et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This suggests that climate change could impact habitat suitability, potentially forcing bears to migrate to higher elevations, increasing competition for scarce resources (Dar et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen working with statistical models, beta estimates show the relationships between predictor variables and a response variable. Positive Beta (β\u0026thinsp;\u0026gt;\u0026thinsp;0) estimate suggests a positive relationship between the predictor variable and occupancy. Negative Beta (β\u0026thinsp;\u0026lt;\u0026thinsp;0) estimate suggests a negative relationship between the predictor variable and bear occupancy. Zero Beta (β\u0026thinsp;=\u0026thinsp;0) estimate means that there is no linear relationship between the predictor variable and the response variable. The magnitude of the beta estimate (i.e., how far it is from zero) indicates the strength of the relationship.\u003c/p\u003e \u003cp\u003eAffinity for terrain ruggedness implies that, brown bears secured shelter and denning habitat, areas with challenging topography might be more suited residence. From high-altitude rangelands to rocky alpine meadows, rugged environment of western region of Ladakh offers a spectrum of habitats. This preference is may be because of less human disturbance and access to several food sources. Brown bears are more likely to be present in areas with higher quality or widespread rangeland habitat. Rangelands can have high productivity, supporting a variety of plant and animal species in the Ladakh region. Brown bears have a higher propensity to reside in proximity to water sources. This preference may stem from the presence of water sources such as streams, rivers, and lakes in the trans-Himalayan region, which indicate valleys and serve as food sources for brown bears, facilitating access to vegetation like berries, grasses, and prey.\u003c/p\u003e \u003cp\u003eThe positive relationship between brown bear occupancy and land surface temperature suggests that greater land surface temperatures is promotion to increase in probability of brown bear occurrence. Warmer temperatures indicate more suitable brown bear habitats, therefore influencing food availability in the short vegetation period, activity patterns, habitat choices, behaviour, and reproduction. Proximity to water sources and valleys emerged as another major factor influencing bear occupancy. Water bodies support vegetation growth, enhance prey availability, and facilitate movement across challenging terrain (Mishra \u003cem\u003eet al.\u003c/em\u003e 2001). These findings align with previous research emphasizing the importance of rangelands and valleys in sustaining brown bear populations (Nawaz et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rathore \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpecies distribution modeling indicates that western Ladakh is the most suitable region for brown bears. This underscores the need for targeted conservation efforts in areas such as Drass Valley, Suru Valley, Rangdum, Zanskar Valley, and Shargole Valley. Protecting rangelands, water sources, and critical habitats can help mitigate human-wildlife conflicts and ensure long-term population stability (Woodroffe \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). While extensive surveys were conducted in Aryan Valley, Sham Valley, Leh, Changthang, and Shakar-Chiktan, no significant brown bear presence was detected, likely due to high elevations, insufficient rangelands, and low temperature variations. However, an isolated record of brown bear presence in Nubra Valley suggests the need for further investigation. Despite its contributions, this study faced several limitations, including restricted survey access due to military security concerns, difficult terrain, and extreme weather conditions. Future research should focus on long-term monitoring, habitat connectivity assessments, and the impact of climate change on brown bear distribution.\u003c/p\u003e \u003cp\u003eThis study highlights the complex habitat requirements of the Himalayan brown bear and emphasizes the need for adaptive conservation strategies to ensure the long-term survival of this threatened subspecies in the Trans-Himalayan region. A deeper understanding of movement ecology and the reduction of human disturbances are key to preserving brown bear populations in high-altitude landscapes. It provides an in-depth assessment of the habitat selection and suitability of the Himalayan brown bear in Ladakh\u0026rsquo;s high-altitude cold desert. The findings highlight that terrain ruggedness, land surface temperature, rangelands, and proximity to water sources are key factors shaping brown bear distribution. Among these, rugged terrain and land surface temperature are particularly influential, offering essential cover, food resources, and favorable conditions for denning and movement. Additionally, brown bears prefer elevations between 3,000\u0026ndash;5,000 m asl, aligning with their adaptations to extreme environments.\u003c/p\u003e \u003cp\u003eThe results have significant implications for conservation in Ladakh. Identifying priority habitats particularly in western Ladakh will enable more targeted conservation strategies. Protecting rangelands, maintaining water resources, and mitigating human disturbances in these areas are crucial for sustaining bear populations. Integrating habitat requirements into Ladakh\u0026rsquo;s wildlife management policies can enhance long-term conservation efforts for this threatened subspecies.\u003c/p\u003e \u003cp\u003eFurthermore, climate change poses a major challenge to brown bear conservation. Rising temperatures may force bears to migrate to higher elevations, increasing competition for limited resources and potentially altering movement patterns (Su \u003cem\u003eet al\u003c/em\u003e. 2018). This shift could also escalate human-wildlife conflicts, emphasizing the need for proactive strategies, such as habitat corridors and climate-adaptive conservation plans. Future research should focus on monitoring these ecological shifts and developing mitigation measures to safeguard Himalayan brown bear populations in the high-altitude landscape.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe Trans-Himalayan region of the Union Territory of Ladakh covers an area of186,200 km\u0026sup2;and is categorised as 1A High Altitude Cold Desert Biogeographic Region (Rodger \u0026amp; Panwar 1988). The Indian Trans-Himalaya is an extension of the Tibetan plateau covering Ladakh and Lahaul Spiti in the state of Himachal Pradesh. Elevation Ladakh varies from 2533 m in valleys to 7742 m as Saltaro Kangri. The regions characterised by low precipitation, a short growing season, low primary productivity, but having a high livestock density (Mishra \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The climate is harsh with cold and arid temperatures dipping below \u0026minus;\u0026thinsp;30\u0026deg;C between November and March, with a short season for crop cultivation (Bagchi \u003cem\u003eet al.\u003c/em\u003e 2012). The region represents an ecosystem where the common livelihood source is traditional agro-pastoralism (Ladon et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Individual families own livestock, whereas the grazing land is common to the village with equal access (Mishra \u003cem\u003eet al.\u003c/em\u003e 2001). Ladakh landscapes are unique considering that the wild animals are not restricted to protected areas but found across the landscape (Mishra et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe landscape of the area is mountainous, rugged, and interspersed with valleys drained by the river Indus and its tributaries. Ladakh has been divided administratively into two districts. Kargil and Leh. The region is characterised by a few natural valleys viz., Suru Valley, Drass Valley, Zanskar Valley, Aryan Valley, Shakar-Chiktan, and Shargole.\u003c/p\u003e \u003cp\u003eThis study is carried out intensively in Kargil district and extensively across Ladakh because, based on the literature and local knowledge, the Himalayan brown bear is predominantly reported from the western region (Kargil) of Ladakh.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField Data Collection\u003c/h3\u003e\n\u003cp\u003eField sampling was carried out using \u0026lsquo;MSTrIPES\u0026rsquo; digital application equipped with collecting the spatial and temporal characteristics of occupancy trails of Himalayan brown bear along with the geotagged photos of sign detections. Data captured using the app was imported into desktop programs designed for data organisation and archiving. This guarantees long-term archiving in the digital infrastructure, transparency, and precise and simple data entry. The sample, which amounted to over 30,000 working-days (\u0026lsquo;man-days\u0026rsquo;) over two years, was carried out in cooperation with local field assistants and researchers and employees of the Wildlife Protection Department. We ensured that each survey team was formed up of experts in these different filed data collection methods. Furthermore, each sign was recorded using mobile devices, which experts could later verify.\u003c/p\u003e\n\u003ch3\u003eOccupancy survey\u003c/h3\u003e\n\u003cp\u003eBased on the accessibility of the terrain and logistics, the landscape was divided into a grid spanning 10 x 10 km. In order to account for the natural variation in habitats, the grid was further segmented into 5 x 5 km sub-grids so that our sampling could be evenly distributed across each cell. We assessed the occupancy of brown bears in each of the 25 km\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026sup2;\\)\u003c/span\u003e\u003c/span\u003e sub-grid cells. During the training sessions, the sampling team was given a pictorial guide to help them identify the signs of key species. We conducted at least one sign-search survey of about 5 km each in every sub-grid cell to record signs of the species. We targeted human trails, ridgelines, and valleys to maximise the chances of encountering signs. We recorded signs such as tracks, scats, and sightings that could be assigned to the presence of brown bears to spatially spread the search pathways. We surveyed Ladakh landscape collectively to validate the species' distribution and suitability of their habitats in the region. All field data was collected using a phone-based polygon search application developed for occupancy surveys (MSTrIPES, Qureshi et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We plotted all brown bear sign locations (2530) to gain a preliminary understanding of the distribution of Himalayan brown bears in the Ladakh region.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental Variable Collection\u003c/h2\u003e \u003cp\u003eWe used ecogeographic variables that were procured using ArcGIS and available in public domain at a resolution of 1 km\u003csup\u003e2\u003c/sup\u003e (see Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These variables included those that were likely to influence the occurrence of brown bears as per our ecological knowledge of the species and our a priori hypotheses (see Supplementary Table S2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalytical Framework\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ea. Habitat selection modelling\u003c/h2\u003e \u003cp\u003eWe use occupancy modelling that corrects for detection bias and model\u0026rsquo;s species occurrence using relevant eco-geographical covariates (MacKenzie \u003cem\u003eet al.\u003c/em\u003e, 2006). We conducted single species single season occupancy analysis using PRESENCE software version 2.13.47 (Hines \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). We modeled detection probability with survey effort and occupancy with site characteristics. We selected the most parsimonious model with the lowest Akaike Information Criterion (AIC) and used model average Akaike weights when two or models differed by less than 5 AIC. This approach is particularly suitable for large-scale population dynamics monitoring and habitat selection (Haroldson \u003cem\u003eet al.\u003c/em\u003e 2021).\u003c/p\u003e \u003cp\u003eTo avoid collinearity in the model we first estimated the correlation between all ecogeographical variables and then used only one of a pair of correlated variables in any given model. Following the extraction of the PRESENCE results from each model, we exported the Psi-conditional [Pr(occ | detection history)] values into Excel. We then transformed them into inverse logit, the logistic function defined by exp(x)/(1\u0026thinsp;+\u0026thinsp;exp(x)). The inverse logit transformation takes values on the real line and converts them to be between zero and one. We used clog-log to determine the prediction of values for spatial representation of Himalayan brown bear distribution. We processed the analysis at 100 bootstrap iterations to estimate C-hate values. A diagnostic metric called C-hat evaluates the data-fit of the model. It basically checks for overdispersion, the condition whereby the observed data variability surpasses what the model projects. A good model fit is indicated by a c-hat value of 1; values higher than 1 imply overdispersion.\u003c/p\u003e \u003cp\u003eFor occupancy modelling we hypothesised that the probability of a site being occupied by a brown bear is influenced by \u0026lsquo;ruggedness, \u0026lsquo;rangelands,\u0026rsquo; \u0026lsquo;distance to water,\u0026rsquo; and \u0026lsquo;land surface temperature\u0026rsquo; with detection probability as constant, p(.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eb. Habitat Suitability Modelling\u003c/h2\u003e \u003cp\u003eWe investigated habitat suitability to learn more about how species are spread in the western part of Ladakh by looking at occurrence data, landscape covariates, and bioclimatic variables. We used the Maximum Entropy (MaxEnt) modelling methodology (Phillips et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) in MaxEnt, SDM V 3.4.4, software known for robust handling of presence-only data. MaxEnt creates a probability surface showing areas with acceptable conditions for the species by integrating known occurrence locations with environmental predictors, therefore evaluating species habitat suitability. This method is quite useful in remote and demanding surroundings like Ladakh, where absence data might be irregular or difficult to get.\u003c/p\u003e \u003cp\u003eThe habitat suitability analysis was carried out exercising occurrence data and landscape covariates through the MaxEnt species distribution modelling method, SDM V 3.4.4 (Phillips et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Employing ArcGIS v. 10.8 software, we identified 14 potential landscape covariates and transformed the variables into ASCII (American Standard Code for Information Interchange) file format. The model was trained using 80% of the locations, while the accuracy was evaluated on the remaining 20%. Additionally, the model, which integrates 34 distinct models, underwent processing and was utilised in a jackknife test to assess the significance of predictors. To ensure precise predictions, the models were executed 1, 10, and 100 times utilising bootstrap methods. The True Scale Statistics (TSS) value (MaxEnt: Background Prediction, Sample Prediction Test, and Threshold Value 10 Clog-log) was utilised to select the optimal model.\u003c/p\u003e \u003cp\u003eThis approach enabled us to assess the probability of species presence at multiple locations by using ecological and climatic variables. Combining species occurrence records with key ecological traits like elevation, land cover, temperature, and precipitation patterns, we generated a predicted habitat suitability map for the region. We selected the final model based on True Skill Statistics (TSS) values (Hanssen \u003cem\u003eet al.\u003c/em\u003e 1965).\u003c/p\u003e \u003cp\u003eConservation planning depends on an understanding of habitat suitability and species distribution in Ladakh as it helps to identify significant places, evaluate probable dangers, and protection managements.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement(s):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Chief Wildlife Wardens (Brij Mohan Sharma, Sajjad Hussain Mufti, Jigmet Takpa, Preet Pal Singh and Mohd. Sajid Sultan), officers, wildlife department staff, researchers and field assistants for supporting this study and participating in data collection. We thank Jigmet Takpa for his motivation, continuous support, and guidance throughout the project. We thank Wildlife Warden, Kargil, Raza Ali Abidi, for his valuable and constant support to conduct fieldwork. We are grateful to the Ministry of Environment, Forests, and Climate Change, Govt. of India; National Mission on Himalayan Studies; GB Pant National Institute of Himalayan Environment, Almora; and the Wildlife Institute of India for their support. I thank Zainab, Aamir, Sameeha, Kumudini and Bhim for always supporting in the persuade of my research work. We acknowledge the expertise and effort of our wildlife guards and field assistants without which this research would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePR, BP, YVJ designed the research and obtained funding. NHK and PR conducted the study, NHK AS and DJ exercised the data analysis, interpretation and wrote the manuscript of the paper with significant contributions by YVJ, BP and PR. RAA helped in field data collection strategies and logistics in western Ladakh. All authors provided comments and suggestions that significantly improved the manuscript and approved the last version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding source\u003c/strong\u003e: The work is supported by Department of Wildlife Protection, Leh and implemented under the National Mission for Himalayan Studies (No. NMHS/2016-17/MG13/06) in Union Territory of Ladakh.\u003c/p\u003e\n\u003cp\u003eSupplementary information is attached herewith.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare no competing financial interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbas, F. I., Bhatti, Z. I., Haider, J., \u0026amp; Mian, A. 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Journal of Zoology, \u003cstrong\u003e321\u003c/strong\u003e(1), 59-74 (2023).\u003c/li\u003e\n\u003cli\u003eWoodroffe, R. Predators and people: using human densities to interpret declines of large carnivores. \u003cem\u003eAnimal Conservation, \u003cstrong\u003e3\u003c/strong\u003e\u003c/em\u003e(2), 165-173 (2000).\u003c/li\u003e\n\u003cli\u003eZedrosser, A., Steyaert, S. M., Gossow, H., \u0026amp; Swenson, J. E. Brown bear conservation and the ghost of persecution past. Biological Conservation, \u003cstrong\u003e144\u003c/strong\u003e(9), 2163-2170 (2011).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Graphs","content":"\u003cp\u003eGraphs 1-3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Habitat selection, Habitat suitability, Kargil, Occupancy, PRESENCE, MaxEnt","lastPublishedDoi":"10.21203/rs.3.rs-6888317/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6888317/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Trans-Himalayan region of Ladakh is home to an endangered species, the Himalayan brown bear, which is least studied in high-altitude regions. Brown bears in the Indian Himalayas are threatened by low density, and insular populations. To understand the habitat ecology of the brown bear in Ladakh, we used the polygon search method and trail transect surveys using systematic grid-based sampling in 275 grids of 100 km\u0026sup2; covering 27500 km\u0026sup2; with 4012 trails ranging from 0.5 to 6.78 km. We recorded 2530 brown bear signs from surveyed grids. We used occupancy analysis to address detection bias and to model factors determining brown bear occupancy. We used SDM using MaxEnt model to delineate suitable brown bear habitats in the Trans-Himalayan region. Based on the AUC value and TSS values, best model out of 35 combinations showed that temperature change, elevation, human footprint, and land use land cover determine suitability for brown bears in Ladakh. Apart from temperature, limiting resources in a specific elevation (3000\u0026ndash;4500 m) may influence brown bear distribution in western Ladakh. This study emphasises the influence of temperature, elevation, and rangelands on brown bear distribution and stresses the impact of climate change that is likely to fragment the brown bear habitat as major threats in Ladakh.\u003c/p\u003e","manuscriptTitle":"Where Bears Roam in Ladakh: Landscape Determinants of Himalayan Brown Bear Distribution in India’s Trans-Himalayas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 06:24:23","doi":"10.21203/rs.3.rs-6888317/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-02T22:12:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-30T23:07:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295125187575635445800459536523795680890","date":"2025-06-25T17:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118612356166279147456747385542762024151","date":"2025-06-23T04:37:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T22:16:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"145928169645260336709465289744439772337","date":"2025-06-22T22:03:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328910262089271114891837181934969089962","date":"2025-06-22T08:15:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65700399888675719304654461477716036304","date":"2025-06-21T08:24:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-20T08:03:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T12:36:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-19T12:29:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-16T12:45:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-16T12:42:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b92094fa-d8ad-48fa-936b-962933850c77","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50488135,"name":"Biological sciences/Ecology"},{"id":50488136,"name":"Earth and environmental sciences/Ecology"}],"tags":[],"updatedAt":"2025-12-01T16:01:01+00:00","versionOfRecord":{"articleIdentity":"rs-6888317","link":"https://doi.org/10.1038/s41598-025-26632-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-27 15:57:26","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-06-25 06:24:23","video":"","vorDoi":"10.1038/s41598-025-26632-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-26632-7","workflowStages":[]},"version":"v1","identity":"rs-6888317","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6888317","identity":"rs-6888317","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}