Conserving the critically endangered Hangul (Cervus hanglu hanglu) - Future distribution and efficiency of protected areas under climate change: Implications for the conservation of Dachigam landscape

Conserving the critically endangered Hangul (Cervus hanglu hanglu) - Future distribution and efficiency of protected areas under climate change: Implications for the conservation of Dachigam landscape | 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 Research Article Conserving the critically endangered Hangul (Cervus hanglu hanglu) - Future distribution and efficiency of protected areas under climate change: Implications for the conservation of Dachigam landscape Shiekh Marifatul Haq, Muhammad Waheed, Łukasz Walas, Shirin Alipour, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3874320/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate change is impacting species distribution, posing a significant threat to biodiversity. Special attention is needed for vulnerable species like the Kashmir Red Deer ( Cervus hanglu hanglu ). Despite being a global conservation symbol, holistic management is hindered by limited research. A comprehensive study mapping the potential habitat changes for the Hangul in the Dachigam landscape is crucial to enhance conservation efforts. We examined the prospective effects of expected global warming on the distribution of Hangul by assessing species range shifts and employing a maximum entropy approach. The Hangul was anticipated to be sensitive to upcoming global warming and would raise its risk of local extinction. The severity of repercussions from climate change grew as the time horizon increased and decreased the species' suitable habitat. By 2080, predictions indicated a gradual reduction in range or, in some scenarios, the complete loss of habitat, regardless of the potential for Hangul to disperse indefinitely. We estimated that the overall very highly suitable habitat in the protected region is currently 2220 ha, while its huge distribution area in the unprotected zone is 30,445 ha, emphasizing the necessity of establishing corridor connectivity between fragment populations and promoting conservation efforts. Among various climate conditions, the core-to-edge ratio is at its highest level in the current conditions. Our study reveals two critical findings: Firstly, endangered species unique to a particular region are highly susceptible to the ramifications of global warming. Secondly, when evaluating the outcomes of global warming, the highly suitable habitat is expected to shift under predicted climatic changes, with an average altitudinal migration of 700m. Consequently, conservation strategies must consider the expected regional shifts and are designed with a clear understanding of the accuracy in projecting climate change effects. Altitudinal migration Corridor Climatic change Hangul Conservation Dachigam landscape Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Global warming is an additional threat to high-altitude ecosystems, and as a result, their entire faunal assemblages, particularly critically endangered species, may become extinct (Schneiderbauer et al. 2021 ; Albrich et al. 2020; Weiskopf et al. 2020 ; Hoffmann et al. 2019 ). The survival and geographic ranges of the species may be impacted by global warming, and this influence may be severe especially for endemic species (Manes et al. 2021 ; Leclerc et al. 2020 ; Silva et al. 2019 ). For conservation, it is vital to forecast how the distribution of these animals differs under various future scenarios of global warming (De Frenne et al. 2021 ). Global warming can prompt species to migrate from their original distribution areas to new habitats, highlighting their potential to consistently track their preferred climate niche, even in the face of evolving geographic conditions over time (Antao et al, 2022; Littlefield et al. 2019 ). Local extinctions may also be a result of this dynamic process, particularly for species with little capacity for dispersal and adaptation (Román-Palacios and Wiens, 2020 ). Phenotypic plasticity, genetic changes, and climate equilibrium all affect a species' capacity to adapt to climatic change; without these factors, the species may go extinct (Buchholz et al. 2019 ). Additionally, variables including a species' reproductive rates, spatial extent, migration capability, and niche specificity will affect how that species reacts to global warming (Viana et al. 2022; Ghisbain et al. 2021 ). It seems unlikely that individual species would react to climate change in a predictable manner. Therefore, in order to forecast how specific species' distributions may vary in response to climate change, we must concentrate on them individually. Temperature and precipitation are just two climate variables that affect how species distributions evolve (Brun et al. 2020 ; Waheed et al. 2023 ; DeMarche et al. 2019 ), but the main difficulty faced by ecologists and conservation biologists is predicting which components of climate will have the biggest impacts on species distributions. We have recently been helped to grasp possible species distribution shifts more thoroughly by better methods of simulating species distributions and climate change. It has been shown that the Maxent Model works very adept at forecasting the geographic ranges of species because of climate change (Ab Lah et al. 2021 ; Gilani et al. 2020 ; Haq et al. 2023 ). While acknowledging the temporal variability, mapping species distributions under climate change offers valuable insights into understanding its comprehensive impact. Utilizing geospatial analysis enables the anticipation of habitat shifts, both in terms of their direction and extent, offering insights into the future distribution of biodiversity (Yang et al. 2023 ; Li et al. 2022). Conservation biologists and pertinent authorities can leverage these projections as a foundation for developing proactive solutions aimed at addressing and mitigating the potential impacts of environmental changes. Hangul, classified as the world's most endangered red deer, faces uncertain impacts from climate change on its future ranges and current precarious status (Mukesh et al. 2015). Many studies have assumed that the Dachigam landscape is critical for Hangul foraging habitats and provides key habitats (Ahmad 2004; Sharma et al. 2010 ; Mukherjee et al. 2021 ). The population of Hangul has drastically dropped due to habitat loss, overgrazing, poaching, and disturbances caused by human activities (Shah et al. 2011 ; Hassan et al. 2013 ; Ahmad 2022 ). Furthermore, Hangul migrates to alpine pastures in summer, which have been disturbed due to heavy livestock grazing and turmoil for the last few decades, resulting in a negative impact on the Hangul's population and distribution (Kaul et al. 2020; Bhat et al. 2020 ). The species primarily inhabits the temperate grasslands, dense riverine forests, and high-altitude meadows of the Dachigam landscape (Ahmad et al. 2016 ), and is typically found in small family groups (outside mating period) or herds consisting of females and their offspring. Males are usually solitary and only join the groups during the mating season. The Hangul mating season starts in early autumn (September) and continues till mid to late autumn (October-November) (Bhat et al. 2009 ; Ahmad et al. 2009 ), and the calves are produced in Spring (March-April) with the snowmelt and new plant growth. We employed ensemble species distribution models to assess the influence of climate change on the future abundance and spatial distribution of over 250 + individuals. This analysis aimed to investigate potential shifts in distribution patterns attributed to forthcoming climate changes (Jain 2022 ). The predictive ability of these studies is increased by novel approaches developed here for using biological information to enhance the selection of pseudo-absence points. The findings suggest a complicated combination of future habitat persistence and reduction. We identified locations in desperate need of conservation, each exhibiting diverse levels of current forest cover and protection, and each characterized by distinct climatic and ecological conditions. We employed Species Distribution Models (SDMs) to forecast suitable habitats under multiple climate change scenarios for the years 2020, 2050, and 2080. This analysis aimed to assess the potential impact of anticipated climate changes on Hangul. We specifically asked (1) Whether the existing Hangul suitable habitat would be altered by expected global warming. (2) If there would be more or less suitable habitat? (3) How much of a threat global warming might be to Hangul in the future? The findings of this research will provide valuable perspectives for the preservation of landscape connectivity, considering the existing distribution of suitable habitats. This information can facilitate and guide future species movements counter to the challenges confronted by climate change. Our study will provide pertinent decision-makers and conservation authorities with information on the possible climate change vulnerabilities and can direct future conservation planning for both the Hangul and other threatened ungulate species. Most importantly, the outcomes of this study suggest that the distributions for the Hangul will change and in the future be located in some cases outside of the protected zones. To ensure the prolonged viability of Hangul, it is imperative to initiate and sustain conservation efforts aimed at establishing and safeguarding ecological connectivity. Such connectivity will enable Hangul and other species to migrate to suitable habitats in response to the shifting patterns of global climate. 2. Materials and Methods 2.1 Study area The central element of the study area is Dachigam National Park (DNP), as it holds the only viable population of Kashmir stag (Hangul) on earth. The 141 km 2 DNP, with an altitudinal range of 1700 to 4250 m is located in the Zanskar Mountain Range of the northwestern Himalayas (Fig. 1 ). Zanskar range runs from Gurez to Kishtwar bordering the region from northwest to southeast and covers the historical distribution range of Hangul almost fully. Other important mammals that habitat the DNP include large mammals in the area include musk deer ( Moschus chrysogaster ), serow ( Capricornis sumatraensis ), Asiatic black bear ( Ursus thibetanus ), brown bear ( Ursus arctos ), and common leopard ( Panthera pardus ), Himalayan grey langur ( Semnopithecus ajax ), red fox ( Vulpes vulpes ), golden jackal ( Canis aureus ), small Indian civet ( Viverricula indica ) and yellow-throated marten ( Martes flavigula ) (Ahmad et al. 2016 ). The area exhibits five primary forest categories: Pinus wallichiana , Oak, Acacia , Broad-leaved, and Scrub forests. Within the Dachigam National Park (DNP), deciduous trees and shrubs, such as Populus alba , Prunus armeniaca , and Pinus wallichiana , predominate, while species like Salix alba and Juglans regia characterize riparian areas. The northern aspects host extensive forests of Pinus wallichiana and Prunus armeniaca , with cultivated intentionally invasive tree species, Quercus robur , and Robinia pseudoacacia , serving as a food and habitat source for wildlife in the park. For comprehensive details about the research area, further information is available (Haq et al. 2022). The climate of the region is classified as sub-Mediterranean, marked by four distinct distant seasons viz. spring, summer, autumn, and winter. The dry periods spanning from April to June and September to November. Autumn in Kashmir is known for its vibrant colors as the foliage turns into hues of gold and red. Winter in Kashmir is cold and often snowy, especially in higher elevations and the temperatures can drop below freezing, fluctuating between − 2°C to 10°C in the plains with even low temperatures at higher elevations. Temperature variations in the Zanskar region typically range from approximately range between − 2°C and 30°C. Snow serves as the primary source of precipitation in the Zanskar range, and its melting continues until June in higher altitudes and has a significant impact on the annual precipitation levels. The Zanskar Range experiences a considerable range in minimum and maximum rainfall, varying from 32 mm to 546 mm annually. This fluctuation in precipitation is vital for the ecological dynamics and sustenance of the Hangul population (Haq et al., 2023 ). 2.2 Data regarding the presence of species In this investigation, information related to the existence of Hangul in the research area was gathered through a combination of methods. To acquire information about the locations where the species was found (presence points), a two-year-long field survey was conducted, and existing data from information from existing literature and the GBIF (Global Biodiversity Information Facility) database, covering the period from 1864 to 2019, was gathered. To prevent any concentration in specific regions, duplicate records were carefully removed. Choosing these occurrence points was a meticulous process aimed at avoiding spatial pseudo-replication. It was based on the resolution of raster data (specifically, 2.5 arc-minutes). We selected and filtered GPS locations, retaining only those with a 15-meter accuracy, and ensured that each 5 km 2 pixel contained just one presence point. This was done to prevent the potential bias that multiple presence points within a small area could introduce to the results. We conducted field surveys between 2019 and 2022 to record the occurrence of Hangul in the Greater Dachigam landscape. We gathered data on species presence through interviews with local wildlife scholars, trackers, livestock herders, and wildlife field staff operating in the landscape. In our study, we employed binoculars and perceiving scopes to observe the Hangul species from various vantage points in its potential habitat. During these observations, we meticulously documented habitat characteristics, including vegetation cover, type, physical attributes, and ruggedness. Additionally, we recorded precise location and elevation using GPS devices while also noting any illicit activities such as habitat destruction, illegal poaching, and natural disturbances (Photo plate 1). To further enhance our understanding of Hangul's presence and assess illegal activities within its habitat, we conducted a questionnaire survey, engaging with local individuals and departmental staff. These efforts resulted in the collection of 47 unique Hangul presence points, which served as the foundation for developing the Distribution Prediction Model. 2.3 The compilation of environmental data and the procedure for variable selection Our study involved the gathering of a comprehensive set of environmental and topographic data to enhance the analytical framework. Nineteen bioclimatic variables, with a resolution of 2.5 arc-minutes, and elevation data, at a resolution of 30 arc-seconds, were sourced from the WorldClim website (ver. 2.1) ( www.worldclim.org , accessed on 15 March 2023) (Supplementary Table 1). To supplement the dataset, topographic information, including altitude, slope gradient, and aspect, was acquired through ArcGIS 10.5 software, enabling the creation of a Digital Elevation Model (DEM) via spatial analysis tools. In addition to natural variables, four anthropogenic factors were included as indicators of human influence. These factors encompassed land cover, road proximity, population density, and the Human Footprint (HFP), measuring human perturbation. The HFP map, obtained from the Socio-Economic Data and Applications Centre ( http://sedac.ciesin.columbia.edu ), amalgamates global data layers associated with various factors affecting ecosystems, including human population distribution, urbanization, navigable rivers, roads, and diverse agricultural land uses. The HFP layer spans a range from 0, signifying areas in proximity to natural or pristine conditions, to 50, indicating regions that have experienced significant degradation. The study incorporated comprehensive data sources and variables for a thorough analysis. The Global Land Cover data, outlining nine categories (including grassland, forest, savannah, shrubland, cropland/natural vegetation, snow/ice, wetland, urban, and barren/sparsely vegetated areas), was sourced from the International Geosphere-Biosphere Program (MODIS Global Land Cover Classification v2). To consider the influence of human population density on species distribution, a separate layer of population density data was extracted from Oak Ridge National Laboratory. For vegetation analysis, the Enhanced Vegetation Index (EVI) derived from MODIS products (MOD13A3) was used, covering both mean annual EVI and EVI seasonality. The choice of EVI over NDVI was based on its ability to minimize background interference and handle issues like smoke and sub-pixel cloud contamination. The EVI data was obtained from Terra MODIS ready-to-use files (MOD11 and MOD13). Future simulations included two distinct sets of Shared Socioeconomic Pathways (SSPs), specifically SSPs 245 and SSPs 585, obtained from the Coupled Model Intercomparing Project, Phase 6 (CMIP6). These simulations were carried out using the Global Climate Model BCC-CSM2-MR, operating at a resolution of 2.5 arc-minutes. This comprehensive approach facilitated a thorough analysis of the potential impact of environmental and climatic variables on species distribution under different scenarios. The distribution of the Hangul is influenced by a multitude of features, including precipitation, temperature, natural impediments, physical land structures, and various biological variables, as previously evidenced in research by Gao et al. ( 2022 ) and Zhang et al. (2015). In our model, we sought to evaluate the specific ecological aspects that impact the species' dispersion. To achieve this, we incorporated a comprehensive set of variables, encompassing 19 bioclimatic factors, four anthropogenic indicators (land cover, human footprint, human population density, and road proximity), and three ecological parameters (slope, aspect, and elevation). These factors, in conjunction with vegetation data, enabled a comprehensive evaluation of the factors influencing the western Hangul's distribution. To ensure data independence and eliminate potential correlations among variables, we employed a two-step approach in this study. First, we utilized the initial model with default configurations to evaluate the influence of each variable, establishing a threshold of > 1% to exclude variables that didn't meet this requirement. Subsequently, to further remove possible spatial associations, we subjected the remaining variables (those surpassing the contribution threshold) to pairwise Pearson's correlation (r) analysis. To streamline the variable count, a threshold value of r ≥ ± 0.8 was implemented. In cases where two variables displayed an r value surpassing this threshold, the one with the lower contribution was excluded (Arshad et al., 2022 ; Bosso et al., 2016 ; Gao et al., 2022 ; Hassan et al., 2021 ; Hijmans et al., 2005 ; Khattak et al., 2022 ). Following this procedure, eight significant bioclimatic and topographic variables were identified: mean diurnal range (Bio02), mean temperature of the wettest quarter (Bio08), mean temperature of the coldest quarter (Bio11), annual precipitation (Bio12), precipitation of the driest month (Bio14), precipitation of the warmest quarter (Bio18), precipitation of the coldest month (Bio19), and elevation (elev) (Graham, 2003 ). 2.4. Modeling procedures In the realm of habitat modeling, the calibration and optimization of MaxEnt species distribution models stand as pivotal steps in achieving accurate and reliable results. This involves a systematic exploration of various combinations of regularization multiplier (RM) values and feature classes (FC) to strike a balance between predictive power and overfitting prevention. Threshold-dependent evaluation metrics, such as the omission rate, are harnessed to identify the optimal MaxEnt model settings, ensuring that the model can be effectively applied to different contexts. To illustrate, researchers in this study investigated multiple permutations of eight Regularization Multiplier values (ranging from 1 to 4 with a 0.5 interval) and six Feature Classes, including Linear, Quadratic, Hinge, and more (Eyring et al., 2019 ; Khattak et al., 2022 ). Once the optimal MaxEnt model configuration was established, the study proceeded to construct a bias file by leveraging occurrence and environmental data with the ENMEval package in R. Subsequently, utilized MaxEnt version 3.4.4 to evaluate the data and predict the most suitable habitats for Hangul in the research area, following established methodologies (Awan et al., 2021 ; Hijmans et al., 2005 ; Phillips et al., 2006 ; Singh et al., 2020 ). MaxEnt, grounded in ecological niche theory and informed by presence data, has emerged as a preeminent tool for predicting the potential distribution of species within defined geographic regions (Graham, 2003 ; Bai et al., 2018 ). Its exceptional predictive accuracy has led to its widespread adoption as the preferred species distribution modeling method over competing alternatives (Bai et al., 2018 ; Fourcade et al., 2014 ; Elith et al., 2006 ; Summers et al., 2012 ). The study employed MaxEnt, a robust machine-learning method, to explore the intricate connection between ecological conditions and the spatial arrangement of wildlife (Díaz et al., 2022; Haq et al., 2021 ). To enhance model accuracy and performance, a range of MaxEnt configurations, including cross-validation, output format selection, and response curve generation, were meticulously employed. Model evaluation was conducted using receiver-operator characteristic (ROC) curves and the calculation of area under the curve (AUC) values, with AUC scores above 0.9 indicative of well-performing models (Fourcade et al., 2014 ; Hijmans et al., 2005 ; Merow et al., 2013 ; Phillips et al., 2006 ; Phillips et al., 2009 ; Summers et al., 2012 ). This comprehensive approach not only aids in optimizing SDMs but also contributes to advancing our knowledge of the intricate interplay between environmental factors and species distribution. The results generated by MaxEnt prediction for the suitability of Hangul habitat varied from 0 to 1, categorized into four levels: low (0.21–0.4), moderate (0.41–0.6), high (0.61–0.80), and very high (0.81-1) (Elith et al., 2006 ; Phillips et al., 2006 ; Waheed et al., 2023 ). 2.5. Range and corridor estimation Guidos Toolbox (Vogt and Riitters 2017 ) software was used to estimate the division of potential range into the core and edge using the “Entropy Map” method. The same software was used to prepare the morphological-spatial analysis (MSPA), which allows to categorization of the potential distribution of the species into functional classes (Soille and Vogt 2009 ). For Guidos Toolbox analyses, the MaxEnt output with suitability above 0.2 was used as an input raster. The area of each MSPA class and Edge/Core/Remote Core class in Entropy Map was calculated in QGIS 3.32.1 “Lima” (QGIS Development Team 2023). CIRCUITSCAPE was employed to estimate the location of potential corridors for species migration between the existing core of the range and several potential cores. As potential future cores, six locations were chosen: four of them are current protected areas and two, in the southern part of the analyzed area, were estimated by MaxEnt as regions with high suitability. The final connectivity raster was calculated using the output of three CIRCUITSCAPE analyses: one with suitability as a conductance, second with altitude as a resistance (Danielson and Gesch 2011 ), and third with a land cover (Zanaga et al. 2021), where built-up areas had high resistance, crop fields medium resistance and natural vegetation low resistance. As a potential corridor, we have chosen an area that connects the current range core to the potential range core via a continuous path with high conductance. Zonation 5.0 (Moilanen et al. 2022 ) was used to identify areas that should be prioritized for protection to save the species. Analysis was performed with the following raters: potential current range as input with weight 1.0, future models as inputs with weight 0.5 each, land cover (Zanaga et al. 2021) as a condition raster, population density as a cost raster. The protected areas were used as a retention raster to exclude them from analysis. Results were visualized using QGIS 3.32.1 “Lima” (QGIS Development Team 2023). 3. Results 3.1. Model evaluation MaxEnt was utilized to compute a forecasted ecological suitability score for the Hangul species within the Jammu and Kashmir region. The suitability score exhibited a range from 0 to 0.96, representing the anticipated appropriate conditions of various habitats for the species. In this investigation, the top-performing model attained an AUC score of 0.963, signifying a high level of accuracy and a close alignment between the model's predictions and the actual distribution of the Hangul species (Supplementary data: Figure S1 ). Among the climatic variables considered, the precipitation of the driest month (Bio14) and precipitation of the coldest month (Bio19), elevation (elev), and the mean temperature of the wettest quarter (Bio08) contributed 85% in the distribution Hangul. Bio14 and 19 had the highest contribution, accounting for 41.4% and 15.3%, respectively. So, the Jackknife test revealed that those variables were influential in shaping the species' distribution (Supplementary data: Figure S2, S3). Subsequently, both elevation (elev) and the mean temperature of the wettest quarter (Bio08) made contributions of 15.1% each. The least contributed variable, the mean temperature of the coldest quarter (Bio11) accounted for 13.1% of the variance, while the mean diurnal range (Bio02) contributed 4.5%. Additionally, both the precipitation of the warmest quarter (Bio18) and the annual precipitation (Bio12) demonstrated equal contributions of 0.7% each to the model's overall accuracy (Supplementary data: Figure S2). As depicted in Figure S4, when precipitation levels exceeded 40 mm, the probability of encountering Hangul was notably higher than 0.6, signifying a region of high suitability. The highest probability of occurrence was observed around 500 mm of precipitation, where the likelihood reached approximately 0.7. The elevation range that proved most conducive to Hangul habitats spanned from 1600 to 2200 meters. Notably, habitats considered suitable with a high likelihood exhibited a bio19 range falling between 400 and 600 mm. The optimal range of bio11 for Hangul is -5°C to 5°C. Likewise, the mean diurnal range is considered most suitable when it falls within 5°C to 12°C, as illustrated in Figure S4. Furthermore, the ideal range for annual precipitation lies between 800 mm and 1000 mm, while the bio18 variable is found to be most conducive within the range of 100 mm to 300 mm. 3.2. Current distribution Employing a species distribution model, we successfully identified the current potential locations for finding the Hangul, as depicted in Fig. 2 . Traditionally, Hangul is known to inhabit the coniferous, oak, broadleaved, and grassland areas within Dachigam National Park, the adjacent Zabarwan mountains, Lidder, and Sindh valleys. However, the allocation of favorable habitats across the upper Himalayan region exhibits significant variation. The results of our study indicate that regions with very high and high suitability for the species are dispersed in a patchwork pattern throughout central Kashmir, spanning from Kulgam, Anantnag, and Pulwama, and extending to Ganderbal. Furthermore, there are constant strips of moderately suitable habitats along the elevated areas of Bandipura and Budgam districts under the prevailing climatic conditions (from the 1970s to the 2000s). 3.3. The assessment of habitat suitability under prospective scenarios of climate change Subsequently, a thorough investigation of the potential consequences of climate change on Hangul spreading, our findings suggest the present habitat that is currently the most suitable for the species is possible to contract and shift northward across varying elevation gradients. In the context of the 2050s and 2070s, encompassing all four Shared Socioeconomic Pathways (SSPs) scenarios, the migratory trajectory of Hangul's suitable habitat is poised to shift northward, encompassing regions like Kishtwar, Ganderbal, and Bandipora. This anticipates the emergence of an extended expanse of highly suitable habitats in the northern sector of Jammu and Kashmir and its neighboring areas, as visually depicted in Fig. 3 . As we approach 2050, the availability of suitable habitats for Hangul is expected to dwindle, particularly under the SSPs 245 and SSPs 585 scenarios, where the predominant suitable areas will be primarily concentrated in the northern domains of Kashmir. Under the SSPs 346 scenario, there is an envisaged reduction in habitat availability between Pulwama and Haramukh by 2050, leaving only slender segments of viable habitat in locales such as Bandi Bagh and Takya Sangi Reshi. Consequently, Hangul might confront a scarcity of suitable habitats in the Kulgam, Ramban, Budgam, and Shopian regions by 2050, particularly within the ambit of the SSPs 245 and SSPs 585 scenarios (Fig. 3 ). Across all four of the climate change scenarios under investigation, it is anticipated that the once highly suitable habitats near Anantnag, Mattan, and Kishtwar will no longer be viable by the 2070s. Furthermore, regions like Budgam and Shopian face a critical risk of losing all of their present suitable and highly suitable habitats as early as the 2050s and continuing into the 2070s, particularly under the influence of the SSPs 245 and SSPs 585 scenarios. Additionally, during the transitional period between 2050 and 2070, there may only be limited availability of marginally suitable locations in areas like Kargil, Bandipora, and Ganderbal, predominantly at higher altitudes (Fig. 3 ). In our analysis, we have estimated the likelihood of Hangul occurrence within different habitat suitability categories. Our findings suggest that the overall potential habitat suitability for Hangul, with a probability greater than 0.2 (p > 0.2), is expected to decline in the future. In a more concise and comparative format, the projected reduction in potential habitat suitability is expected to be -5.06% in comparison to the current distribution range. This translates to 11,851 km² potential habitat suitability under SSPs 245 for the 2050s, and a more significant decline of -10.31%, resulting in 9,687 km² potential habitat suitability under SSPs 585 for the 2050s. Similarly, for the 2070s, under SSPs 245, potential habitat suitability is forecasted to decrease to 10,163 km², representing a -9.15% change, and to 10,310 km² (-9.33% change) under SSPs 585 for the 2070s. Taking a closer look, considering SSPs 245 in the 2050s, there is a forecasted slight reduction in very high suitability habitat (VHSR) compared to its current climatic conditions, specifically decreasing from 2,147 km² to 7,838 km² at a rate of -1.54%. Likewise, in the highly suitable habitat (VHSR), the potentially suitable land area is expected to diminish as follows: a decrease to 1,837 km² (-2.29%) under SSPs 585 for the 2050s, a reduction to 2,095 km² (-1.66%) under SSPs 245 for the 2070s, and a decline to 2,232 km² (-1.45%) under SSPs 585 for the 2070s (Table 1 ). Table 1 The expected likelihood of Hangul's habitat suitability under different climate change scenarios offers valuable perspectives on the potential consequences of evolving environmental conditions for this particular species. Climate change scenario The projected probability of Hangul occurrence within the assessed habitat suitability categories NSR LSR MSR HSR VHSR Total suitable land area (km 2 ) (≤ 0.2) (0.21–0.4) (0.41–0.6) (0.61–0.8) (≥ 0.81) Current climate 27267 5345 3421 2387 2783 13936 SSPs_245_2050 29352 4303 3245 2156 2147 11851 Rate of change (%) 5.06 -2.52 -0.42 -0.56 -1.54 -5.06 SSPs_585_2050 31516 3423 2512 1915 1837 9687 Rate of change (%) 10.31 -4.66 -2.20 -1.14 -2.29 -10.31 SSPs_245_2070 31040 3795 1582 2691 2095 10163 Rate of change (%) 9.15 -3.76 -4.46 0.73 -1.66 -9.15 SSPs_585_2070 31793 3513 1834 2731 2232 10310 Rate of change (%) 9.33 -4.62 -3.94 0.69 -1.45 -9.33 3.4. The Altitudinal shift of habitat The projected areas suitable for Hangul fluctuated across various periods and were determined using different climate scenarios. However, the overarching pattern remained consistent, indicating a shrink of very highly and highly suitable regions, a rise in areas moderately suitable and less suitable, and a notable enhancement in habitat fragmentation. Considering future climate conditions, the centroids of the highly suitable and moderate regions gradually shifted northwestward over time, whereas the centroids of the least suitable areas moved northeastward. In general, the centroids migrated toward higher altitudes under future climate change. In 2050 under SSP245 and SSP585 the highly suitable habitat will shift from 3000m to 4000m elevation with an average 700m altitudinal migration habitats. Similarly, in 2070 the habitat shifts from 3000 to 5000m with an average 1300m altitudinal shift (Supplementary data: Figure S5). 3.5. Potential corridors for Hangul movement The corridors associated with Hangul’s potential movements are illustrated in Fig. 4 , while the values denote potential dispersal distances from the core to new habitats. The total very highly suitable area in the protected region was (7%) 2220 ha and in the unprotected region (93%) 30445 ha. These corridors can bridge isolated remnant habitat patches, thereby enhancing connectivity and offering opportunities for the establishment of new habitats. A comprehensive ecological network spanning 1200 km² (depicted in violet) has been delineated. Among this, approximately 200 km² is designated as continuous corridors. The shortest corridor, spanning approximately 30 km, traverses the Haramukh mountain region adjacent to Wular Lake, which holds protected status. On the other hand, the most extensive corridor, with a length of around 70 km, extends along the PirPanjal Range in proximity to the Dachigam protected area. A point of significance is that the corridors denoted as C2, C3, and C4 prominently exhibit a persistent linkage between the core hotspot of Hangul distribution (depicted in red) and the designated protected area (highlighted in yellow). Notably, the corridor labeled as C1 terminates at potential new habitats (as shown in Fig. 4 ). It's worth noting that while additional potential corridors exist, they lack the feasibility of maintaining a continuous connection to the central core. 3.6. Functional landscape changes The prevalent morphological type within the species' current distribution and projected future distribution (under SSP585-2070) is the "core" type, which collectively constitutes 54.17% of the total study area. The percentage varies across scenarios, with proportions of 49.90% under the SSPs245-2050 scenario, 37.24% under the SSPs245-2070 scenario, and 35.59% under the SSPs585-2050 scenario (Table S1 ). The findings indicate that the suitable habitat for the Hangul might undergo a northward shift, reaching the Zanskar range of the Himalayas. Anticipated changes include the expansion of core areas to this new location in future scenarios, signifying a potential change in the species distribution (Fig. 5 ). Despite the alterations observed in Loop and Islet configurations, both classes exhibit similarities between their patterns under present and future conditions (Table S1 ; Fig. 5 ). Although the percentage of changes in perforation is relatively small, there is a noticeable internal fragmentation process within the Core habitat, particularly under the SSPs245-2050 scenario. These alterations are primarily concentrated in the Kashmir region, encompassing districts like Kulgam, Anantnag, Pulwama, and Ganderbal. Importantly, these changes result in a loss due to perforation, constituting 1.39% of the total range (Table S1 ; Fig. 5 B). Under the SSPs585-2050 and SSPs245-2070 scenarios, there was a notable reduction in the intensity of Core habitat loss, marked by contraction. Moreover, there were hardly any significant large-area cores observed in the PirPanjal Range when moving towards the northern direction. This observation was accompanied by an increasing percentage of bridges and branches within the habitat (Table S1 ; Fig. 5 C and D). Interestingly, the pessimistic scenario for 2070 predicts an expansion of the core area to higher altitudes (Fig. 5 E). In Fig. 6 A, among various climate conditions, the core-to-edge ratio is at its highest level in the current conditions. The spatial patterns of habitat edges in both optimistic and pessimistic scenarios for 2050, when compared to the present condition, exhibit a clear rise in the percentage of edges and remote edges (including perforation, loop and bridge connectors, and branches). Notably, the results indicate an increase in edge length due to habitat gain in the SSPs585-2070 scenario, while the processes of fragmentation become more pronounced under the SSPs245-585-2050 and SSPs245-2070 scenarios (Fig. 6 B, C, D, and E). Consequently, there is an observed disparity in the abundance of edge habitat versus core habitat in the southern and northern regions within the study area. This discrepancy suggests greater habitat fragmentation in these areas. Conversely, the central region generally displays a higher ratio of core-to-edge habitat, which suggests a less fragmented habitat configuration (Fig. 6 ). Table S1 Area of each class of species potential range according to the MSPA analysis. Climate condition Core Loop Bridge Islet Edge Branch Perforation Total Area (km 2 ) Current 7549.45 1114.85 1064.55 663.65 1970.69 1498.48 74.32 13936 SSPs_245_2050 5913.99 781.56 1322.47 400.72 1871.24 1395.71 165.31 11851 SSPs_585_2050 3447.95 577.24 1973.52 562.49 1377.12 1748.67 0 9687 SSPs_245_2070 3784.29 765.21 2308.57 229.97 1690.4 1341.03 43.52 10163 SSPs_585_2070 5585.16 824.78 787.57 490.98 1457.94 1108.59 54.99 10310 Percentage Current 0.5417 0.08 0.0764 0.0476 0.1414 0.1075 0.0053 1 SSPs_245_2050 0.499 0.0659 0.1116 0.0338 0.1579 0.1178 0.0139 1 SSPs_585_2050 0.3559 0.0596 0.2037 0.0581 0.1422 0.1805 0 1 SSPs_245_2070 0.3724 0.0753 0.2272 0.0226 0.1663 0.132 0.0043 1 SSPs_585_2070 0.5417 0.08 0.0764 0.0476 0.1414 0.1075 0.0053 1 3.7. Priority area for conservation We developed a prioritization framework utilizing our methodology to select priority areas. This strategy identified core regions that hold significant conservation promise due to their available space with suitable conditions, their capacity to enhance connectivity, and a preference for locations with minimal human impact (Fig. 7 ). The core areas that are currently most conducive to the species' well-being and have been classified as priority areas fall into two categories: "very high priority," covering an area of 456.87 km², and "high priority," encompassing 930.51 km². These areas include a narrow strip along the Himalayan range and northwest broadleaf forests, concentrated in both higher and lower elevations of the range. Based on the results, it's evident that an opportunity exists to extend certain existing protected areas, such as the Overa-Aru Wildlife Sanctuary, towards the direction of the Dachigam National Park (Fig. 7 ). 4. Discussion Forecasting the outcomes of anthropogenic climate change on the geographical distribution of species is crucial, given that these shifts may result in mass extinctions (Wallingford et al., 2020 ). The vast majority of existing data points to climate change as the primary driver of widespread species extinctions, and its effects on species distributions or hastening the rate of extinction should be anticipated (Román-Palacios and Wiens, 2020 ; Kemp et al., 2022 ). Predicting how a species like Hangul, with only 250 + individuals left, will react to climate change is difficult because unsuccessful reproduction and colonization are dependent on diverse factors, including geographic range, ecologically isolated populations, low reproductive rates, and a high degree of specific habitat requirements (Maviya Majid et al., 2019 ; Mukherjee et al., 2019; Malhi et al., 2020 ; Inouye, 2022 ). Hangul is already facing the risk of low reproductive rates and isolated populations (Ahmad et al., 2023 ) and thus climate change may aggravate it further. However, SDMs offer a valuable method for integrating future conditions into approaches and choices in conservation and management, when the consequences of incorrect conservation strategies or inaction are balanced against the uncertainties of model forecasts (Hu and Jiang, 2011 ). In light of this, we investigated any potential changes in the Greater Dachigam landscape’s distribution of endangered species caused by climate change. Based on the current suitable habitat in the Greater Dachigam landscape, our findings highlight the complexity of changes in species distributions under climate change (Table 1 ). Hangul's distribution is therefore anticipated to decrease in the Dachigam landscape (Table 1 ). According to earlier findings, individual species' distributions are uniformly influenced by climate change because they react to it in the same patterns (Semenzato et al., 2021 ; Haq et al., 2023 ). The ability of individuals from the same species to adapt to local climate change is probably similar (Thurman et al., 2020 ), the likelihood of those individuals surviving in their current habitats in the face of large climate change decreased as the time horizon grew longer and the severity of the consequences from climate change increased (Pearce-Higgins et al., 2022 ). Similar findings were also reported by Hu and Jiang ( 2011 ) and Mason et al. ( 2014 ) when predicting the likely responses of endangered ungulates to future climatic conditions. When Thorstad et al. ( 2021 ) studied the effect of climate change on the survival of Atlantic salmon, they came to similar findings. Our conclusions are supported by Scapini et al. ( 2019 ). Our model's goal was to elucidate if environmental factors may independently predict the habitat that is suitable for Hangul within the Himalayas surrounding the Dachigam landscape. Bio14 was the most important variable, showing that the precipitation in the driest month may have a greater impact on species distributions. According to Bradie and Leung ( 2017 ), precipitation and temperature had the greatest overall contributions to species distribution for terrestrial species. Similarly, Jiang ( 2018 ), and Chen et al. ( 2023 ) observed that the driest months' precipitation exerted a notable influence on the distribution of endemic, rare, and threatened species, similar to our findings. This is corroborated by numerous other investigations, e.g., for Pygathrix cinerea (Tran et al., 2018 ); Himalayan Goral (Haq et al., 2023 ), Red panda (Thapa et al., 2018 ) and Tragopan melanocephalus (Jameel et al., 2023). The second most important variable was bio19 (coldest month precipitation), the third elevation, and the fourth was bio08 (wettest quarter mean temperature). Previous studies revealed that average precipitation alone may not accurately predict bioclimatic variance and that the mean temperature of the bio19 (coldest month precipitation), (Jiang et al., 2019 ); wettest quarter (bio08) (Zhong et al., 2022 ) and elevation (Campos et al., 2020 ; Jameel et al., 2023) may also play a key role in species distributions potentially causing species to extend or decrease their ranges. Our findings underline that rather than focusing solely on rising global temperatures, it is necessary to incorporate precipitation and other climate variables (including extreme circumstances) into future expected species distributions. When assessing important environmental variables, our model of the appropriateness of the current Hangul habitat functioned well (Table 1 ). Our best model suggested that the overall suitable Hangul habitat spanned 13936 km 2 based on a 10-percentile criterion, accounting for 8% of moderately suitable habitat and 7% of high-suitability habitat. Ali et al. ( 2021 ) compare well to our findings, indicating that under current climatic conditions, 9% of the overall study region was suitable for blue sheep from the Hindu Kush ranges. When the repercussions of global warming are considered, the highly suitable habitats for Hangul distributions in the Dachigam Landscape will decrease in the future (from 5.06 to 9.33 km 2 ). Numerous other studies also emphasized how threatened species will face a considerable loss of their habitats due to the altering climate, particularly in the Himalayan regions (Ali et al., 2021 ; Liang et al., 2021 ; Jameel et al., 2023; Haq et al., 2023 ). Examples include Himalayan musk deer (Khadka and James 2017 ) blue sheep (Islam et al., 2023 ), and NilgiriTahr (Sony et al. 2018 ). The current distribution of the Hangul is anticipated to shift along both elevational and latitudinal gradients because of climate change. Instead of an expansion of their range to higher altitudes, the projected changes along the elevational gradient were mostly attributable to a significant reduction in their geographic distribution. This result was consistent with the idea put forth by Luo et al. (2015), and it is possible that the geography of the Dachigam terrain, covering a relatively narrow range, may have had a major role in these results. Our data indicate that Hangul is mostly found at the elevation of 2000 to 4000m, which highlights the species' preference for high-elevation habitats with colder climates and less anthropogenic disturbance. It aligns with the findings of Ahmad et al. ( 2016 ) in Dachigam National Park, reporting Hangul occurrence at elevations ranging from 1700–2300 m and an altitudinal range of 1700–3500 m (Qureshi et al. 2009). Our findings confirmed what was previously known about the altitudinal habitat range of Hangul and this helps to understand the ecological needs and niche of Hangul for formulating an effective conservation strategy. This altitudinal preference can be seen as a result of different bioclimatic and anthropogenic factors, including vegetation types occurring at that elevation and less snow cover. Similar preferences for vegetation types were determined by Sharma et al. ( 2010 ) and Qureshi et al. (2016). However, our results indicate that climate change is likely to cause the Hangul population to move upwards to northern regions as its habitat range shifts to higher elevations. Similarly, Chen et al. ( 2011 ) noted that species were rapidly moving towards higher elevations to combat heat due to climate change. This is further corroborated by the study results of Lenoir et al. ( 2008 ) who determined an upward shift of plant species especially in mountainous regions due to climate change. Agnihotri et al. ( 2017 ), He et al. ( 2019 ), and Yadav et al. ( 2021 ) also documented the climate change-driven altitudinal shift of Himalayan vegetation. As a result, animal species will follow, e.g. Himalayan Goral (Haq et al. 2023 ). We suggest that climate change is causing altitudinal shift in Hangul and there is a need to conserve the present and future habitat range for Hangul so that climate change and elevation shifts could not pose a threat to the survival of this species. Currently, Hangul populations are also found outside the protected areas, particularly during summer. Of the 34975 km 2 of predicted habitat, 30445 km 2 (93%) lie outside the protected areas, which is a serious concern for this highly threatened species. The effectiveness of this unprotected network and potential changes in protected habitat suitability for the Hangul have not yet been assessed under climate change. Different parts of the unprotected habitat are under varying levels of restrictions on human activity. As a result, the effectiveness of some of these sites under climate change may be significantly influenced by the management techniques used in the future. This holds particularly true for unprotected regions but has the potential to have high conservation value in the future. Considering the crucial role that unprotected regions play in the survival of the Hangul, projecting the species' future distribution to guide the selection of new sites or expanding the current array of protected zones could be an efficient conservation approach to confronting the challenges posed by climate change. More crucially, in response to climate change, the effectiveness of the network of protected zones might be significantly increased by sustaining crucial corridors that permit Hangul mobility. Given the expansion of anthropogenic activities, several protected sites already become unsuitable for Hangul. The Zonation analysis (Fig. 7 ) has given a very good and practical result for Hangul conservation. The securement and protection of very high and high-priority zones occurring between the existing protected areas can boost the conservation of hangul for the long term. For example, the areas between Tral and Achabal PAs, between Dachigam and Wanghat, Wanghat and Ajas, and Ajas and Kishan Ganga (Fig. 7 ). Securing such an area will provide a contiguous landscape for long-term conservation for a mobile species like Hangul. We identified critical sites with the potential for future Hangul conservation. We also identified corridors for Hangul that would last well into the future, enabling affected populations to relocate in response to climate change. Enhancing habitat availability and accessibility is crucial for species adjusting to the challenges of climate change. Improving landscape connectivity, and the facilitation of species movement emerge as a key conservation strategy. Identifying and maintaining well-suited corridors through methods like Species Distribution Models (SDMs) enhances animal resilience by providing avenues to escape unsuitable conditions. Our forecast identified south Kashmir, notably the high-altitude conifer forest areas of Anantnag, Kulgam, and Pulwama districts, as highly suitable habitats for Hangul conservation in the Kashmir Himalayas. Kaul et al. ( 2018 ) also found Cervus hangul hanglu in Central/north Kashmir conifer woodlands such as Wanghat-Naranag, Rajparian-Daksum, Overa-Aru, and Thajwas-Baltal, confirming our predictions with field-based survey data. 4.1. Implications for conservation of Dachigam landscape Habitat conservation through protected areas has been a primary issue in maintaining the survival and sustainability of the critically endangered Hangul population. However, isolated protected areas cannot help to conserve the hangul population for the long term due to the issues of inbreeding, and thus connectivity among these isolated protected areas becomes crucial. Existing PAs cover only 7.7% (2220 km 2 ) of the expected future Hangul habitat. This indicates that most of the Hangul habitat (93%) is currently unprotected, highlighting a huge risk of extinction as anthropogenic disturbance and climate change increase. As a result, we propose that nearby core habitat regions must be protected to connect the Hangul subpopulations. Our results revealed that 9.33% of high-suitability areas for the Hangul movement might become unsuitable by 2070 (Fig. 9), adding to the issues of Hangul conservation. On the other hand, overexploitation in the form of illicit wildlife poaching, habitat degradation, and fragmentation pose serious threats to its main abode, the Greater Dachigam landscape. These issues have led to ecological isolation, population decline, and inbreeding and brought the species to the brink (Ahmad, 2022 ; Ahmad et al., 2023 ). Although the establishment of protected areas is a helpful conservation strategy, it has been found that poor management and smaller size may negate the advantages of all conservation initiatives aimed at enhancing biodiversity. There restricted distribution of Hangul and its core areas in the context of existing and prospective climate change scenarios implies that the Dachigam Landscape is seriously lacking in space. As a result, Dachigam Landscape urgently needs to be expanded or connected to the potential hangul sites including the neighboring reserves and the hotspots/core areas. Ecological corridors around greater Dachigam landscape should be prioritized for conservation and management to secure it and the Hangul. According to our findings, hotspots may mostly move outside of the Dachigam landscape due to climate change. Therefore, our suggestions can assist in developing dynamic protected areas within the Dachigam landscape over an extended period. 5. Conclusions It is crucial to foresee how anthropogenic global warming could have an impact on species distributions because these changes are expected to result in widespread extinctions of species. According to the projected distribution of Hangul under various climate change scenarios, the available habitat for this species would become significantly scarcer. In the future, it is anticipated that a sizable section of the current habitat with a high likelihood of occurrence will become unsuitable due to climate change. Our model detected a robust Hangul response to bioclimatic factors associated with precipitation in the Himalayas, with a notable emphasis on rainfall during the driest month and precipitation during the coldest quarter. Despite these factors, Dachigam Landscape’s some of the current meta-populations and potential future sites of Hangul are connected through a contiguous forest landscape which can act as corridors and can play a crucial role for the long-term conservation of Hangul. We recommend that such corridors and sites be surveyed and secured on priority to improve habitat connectivity and ensure Hangul conservation in the realm of global warming and the critical status of Hangul. Declarations Author Contribution Conceptualization S.M.H; Study Design S.M.H; Data collection- R.A., S.M.H; Data analysis- L.W; S.A; M.W; S.M.H; Original draft writing S.M.H; Review and editing-R. A; R.W.B; L.W; S.A; M.W; S.M.H. All authors read and approved the final manuscript.We have no conflict of interest to disclose. Moreover, the sequence of authorships mentioned in the article is based upon the contribution of the work by all authors. References Ab Lah NZ, Yusop Z, Hashim M, Mohd Salim J, Numata S (2021) Predicting the habitat suitability of Melaleuca cajuputi based on the MaxEnt species distribution model. Forests. 12(11), 1449. Agnihotri P, Husain T, Shirke PA, Sidhu OP, Singh H, Dixit V, Khuroo AA, Amla DV, Nautiyal CS (2017) Climate change-driven shifts in elevation and ecophysiological traits of Himalayan plants during the past century. Curr. sci. 595-601. Ahmad K (2022) Ecology and Conservation of Mountain Ungulate in the Western and Trans Himalayas, India.https://doi.org/ 10.5772/intechopen.108809 Ahmad K, Mirelli M, Charoo S, Nigam P, Qureshi Q, Naqash RY, Focardi S (2023) Is the hangul Cervus hangluhanglu in Kashmir drifting towards extinction? Evidence from 19 years of monitoring. Oryx. 57(5), 585-591. Ahmad K, Qureshi Q, Agoramoorthy G, Nigam P (2016) Habitat use patterns and food habits of the Kashmir red deer or Hangul ( Cervus elaphushanglu ) in Dachigam National Park, Kashmir, India. Ethol. Ecol. Evol. 28(1), 85-101. Ahmad, K., Sathyakumar, S., Qureshi, Q., 2009. Conservation status of the last surviving wild population of hangul or Kashmir deer Cervus elaphushanglu in Kashmir, India. J. Bombay Nat. Hist. 106(3), 245. Ali H, Din JU, Bosso L, Hameed S, Kabir M, Younas M, Nawaz MA (2021) Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS One.16(12), e0260031. Almasieh K, Mohammadi A, Alvandi R (2022) ‘Identifying core habitats and corridors of a near threatened carnivore, striped hyaena Hyaena hyaena ) in southwestern Iran’, Sci. Rep. 12(1), 3425. Antão LH, Weigel B, Strona G, Hällfors M, Kaarlejärvi E, Dallas T, Opedal ØH, Heliölä J, Henttonen H, Huitu O, Korpimäki E (2022) Climate change reshuffles northern species within their niches. Nat. Clim. Change. 12(6), 587-592. Arshad F, Waheed M, Fatima K, Harun N, Iqbal M, Fatima K, Umbreen S (2022) Predicting the suitable current and future potential distribution of the native endangered tree Tecomellaundulata (Sm.) Seem. in Pakistan. Sustainability. 14(12), 7215. Aryal A, Brunton D, Raubenheimer D (2014) Impact of climate change on human-wildlife-ecosystem interactions in the Trans-Himalaya region of Nepal. Theor. Appl. Climatol. 115, 517-529. Awan MN, Saqib Z, Buner F, Lee DC, Pervez A (2021) Using ensemble modeling to predict the breeding habitat of the red-listed Western Tragopan (Tragopanmelanocephalus) in the Western Himalayas of Pakistan. Glob. Ecol. Conserv. 31, e01864. Azfar Hussain, SA, Begum S, Ali IHH (2019) Climate change perspective in mountain area: Impact and adaptations in Naltar valley, western Himalaya, Pakistan. Fresenius Environ. Bull. 28, 6683-6691. Bai DF, Chen PJ, Atzeni L, Cering L, Li Q, Shi K, 2018. Assessment of habitat suitability of the snow leopard (Panthera uncia) in Qomolangma National Nature Reserve based on MaxEnt modeling. Zool. Res. 39(6), 373. Bellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol. Lett. 15(4), 365-377. Bhat BA, Ahmad R, Fazili MF, Haq IU, Bhat GA (2020) Threatened fauna of Jammu and Kashmir state. Biodiversity of the Himalaya: Jammu and Kashmir State, 997-1009. Bhat BA, Shah GM, Jan U, Ahangar FA, Fazili MF (2009) Observations on rutting behaviour of Hangul deer Cervus elaphus hanglu (cetartiodactyla: Cervidae) in Dachigam National Park, Kashmir, India. J. Threatened Taxa. 355-357. Bhat KA, Bhat BA, Ganai BA, Majeed A, Khurshid N, Manzoor M (2023) Food habits of the Red Fox Vulpes vulpes (Mammalia: Carnivora: Canidae) in Dachigam National Park of the Kashmir Himalaya, India. J. Threat. Taxa. 15(1), 22364-22370. Bhattacharyya S, Mungi NA, Kawamichi T, Rawat GS, Adhikari BS, Wilkening JL (2019) Insights from the present distribution of an alpine mammal Royle’spika ( Ochotona roylei ) to predict future climate change impacts in the Himalayas. Reg. Environ. Change. 19, 2423-2435. Bosso L, Di Febbraro M, Cristinzio G, Zoina A, Russo D (2016) Shedding light on the effects of climate change on the potential distribution of Xylellafastidiosa in the Mediterranean basin. Biol. Invas. 18, 1759-1768. Bradie J, Leung B (2017) A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44(6), 1344-1361. Brun P, Thuiller W, Chauvier Y, Pellissier L, Wüest RO, Wang Z, Zimmermann NE (2020) Model complexity affects species distribution projections under climate change. J. Biogeogr. 47(1), 130-142. https://doi.org/10.1111/jbi.13734 Buchholz R, Banusiewicz JD, Burgess S, Crocker-Buta S, Eveland L, Fuller L (2019) Behavioral research priorities for the study of animal response to climate change. Anim. Behav. 150, 127-137. Campos PV, Villa, PM, Schaefer CEGR, Nunes JA, Porembski S, Neri AV (2020) Community composition, beta diversity, and structure of high altitude grasslands along an altitudinal gradient in southeastern Brazil. Rev. biol. trop. 68(3), 977-986. Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science. 333 (6045), 1024-1026. Chen W, Miao K, Guo K, Qian W, Sun W, Wang H, Chang Q, Hu C (2023) Potential Geographic Range of the Endangered Reed Parrotbill Paradoxornisheudei under Climate Change. Biology. 12 (4), 560. Chhetri B, Badola HK, Barat S (2021) Modeling climate change impacts on the distribution of Himalayan pheasants. Ecol. Ind. 123, 107368. Dahal N, Lamichhaney S, Kumar S (2021) Climate change impacts on Himalayan biodiversity: evidence-based perception and current approaches to evaluating threats under climate change. J. Indian Inst. Sci. 101(2), 195-210. Danielson JJ, Gesch DB (2011) Global multi-resolution terrain elevation data 2010 (GMTED2010). De Frenne P, Lenoir J, Luoto M, Scheffers BR, Zellweger F, Aalto J, Ashcroft MB, Christiansen DM, Decocq G, De Pauw K, Govaert S (2021) Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Change Biol. 27(11), 2279-2297. DeMarche ML, Doak DF, Morris WF (2019) Incorporating local adaptation into forecasts of species distribution and abundance under climate change. Glob. Change Bio. 25(3), 775-793. Díaz S, Malhi Y, (2022) Biodiversity: Concepts, patterns, trends, and perspectives. Annu Rev Environ Resour. 47, 31-63. Elith JH, Graham CP, Anderson R, Dudík M, Ferrier S, Guisan AE, Zimmermann N (2006) Novel methods improve the prediction of species distributions from occurrence data. Ecography, 29(2), 129-151. Eyring V, Cox PM, Flato GM, Gleckler PJ, Abramowitz G, Caldwell P, Williamson MS (2019) Taking climate model evaluation to the next level. Nat. Clim. Change 9(2), 102-110. Fourcade Y, Engler JO, Rodder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PloS one. 9(5), e97122. Gao X, Liu J, Huang Z (2022) The impact of climate change on the distribution of rare and endangered tree Firmianakwangsiensis using the Maxent modeling. Ecol. Evol. 12(8), e9165. Ghisbain G, Gérard M, Wood TJ, Hines HM, Michez D (2021) Expanding insect pollinators in the Anthropocene. Biol. Rev. 96(6), 2755-2770. Gilani H, Goheer MA, Ahmad H, Hussain K (2020) Under predicted climate change: Distribution and ecological niche modeling of six native tree species in Gilgit-Baltistan, Pakistan. Ecol. Ind. 111, 106049. Graham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology. 84(11), 2809-2815. Haq SM, Yaqoob U, Calixto ES, Kumar M, Rahman IU, Hashem A, Ijaz F (2021) The long-term impact of transhumance pastoralism and associated disturbances in high-altitude forests of Indian Western Himalaya. Sustainability. 13(22), 12497. Haq SM, Waheed M, Ahmad R, Bussmann RW, Arshad F, Khan AM, Casini R, Alataway A, Dewidar AZ, Elansary HO (2023) Climate Change and Human Activities, the Significant Dynamic Drivers of Himalayan Goral Distribution ( Naemorhedus goral ). Biology, 12 (4), 610.https://doi.org/10.3390/biology12040610 Hassan T, Hamid M, Wani SA, Malik AH, Waza SA, Khuroo AA (2021) Substantial shifts in flowering phenology of Sternbergiavernalis in the Himalayas: Supplementing decadal field records with historical and experimental evidence. Sci. Total Environ. 795, 148811. Hassan U, Shah GM, Jan U (2013) A Study of Interaction between Hangul Deer and Livestock at Dachigam National Park Kashmir (Doctoral dissertation). He X, Burgess KS, Yang XF, Ahrends A, Gao LM, Li DZ (2019) Upward elevation and northwest range shifts for alpine Meconopsis species in the Himalaya–Hengduan Mountains region. Ecol. Evol. 9(7), 4055-4064. Hijmans RJ, Cameron SE, Parra JL, Jones P G, Jarvis A (2005) Very high-resolution interpolated climate surfaces for global land areas. Int J Climatol. 25(15), 1965-1978. Hoffmann AA, Rymer PD, Byrne M, Ruthrof KX, Whinam J, McGeoch M, Bergstrom DM, Guerin GR, Sparrow B, Joseph L, Hill SJ (2019) Impacts of recent climate change on terrestrial flora and fauna: Some emerging Australian examples. Aust. Ecol. 44(1), 3-27. Hu J, Jiang Z (2011) Climate change hastens the conservation urgency of an endangered ungulate. PloS one. 6(8), e22873. Inouye DW (2022) Climate change and phenology. Wiley Interdiscip. Rev.: Clim. Change. 13 (3), e764. Islam M, Sahana M, Areendran G, Jamir C, Raj K, Sajjad H (2023) Prediction of potential habitat suitability of snow leopard ( Panthera uncia ) and blue sheep ( Pseudoisnayaur ) and niche overlap in the parts of the western Himalayan region. Geo: Geogr. Environ. 10 (1), e00121. Jain P (2022) Endangered Hangul population increasing in Kashmir. November 11, 2022Animals, Environment, Kashmir Report, States. Jiang F (2018) Bioclimatic and altitudinal variables influence the potential distribution of canine parvovirus type 2 worldwide. Ecol. Evol.8(9), 4534-4543. Jiang F, Li G, Qin W, Zhang J, Lin G, Cai Z, Gao H, Zhang T (2019) Setting priority conservation areas of wild Tibetan gazelle ( Procaprapicticaudata ) in China's first national park. Glob. Ecol. Conserv. 20, e00725. https://doi.org/10.1016/j.gecco.2019.e00725 Kaboodvandpour S, Almasieh K, Zamani N (2021) ‘Habitat suitability and connectivity implications for the conservation of the Persian leopard along Iran – ­ Iraq border’, Ecol. Evol. 11(19), 13464–13474. Kaul R, Chatterjee M, Bhattacharya T, Bodhankar S, Ahmad R, Sofi MN, Charoo SA (2018) Conservation prospects of the Kashmir Red Deer ( Cervus hangluhanglu ) beyond Dachigam National Park, in Jammu and Kashmir, India. Curr. Sci. 2123-2130. Kemp L, Xu C, Depledge J, Ebi KL, Gibbins G, Kohler TA, Rockström J, Scheffer M, Schellnhuber HJ, Steffen W, Lenton TM (2022) Climate Endgame: Exploring catastrophic climate change scenarios. Proc. Natl. Acad. Sci. 119(34), e2108146119.https://doi.org/10.1073/pnas.2108146119 Khadka KK, James DA (2017) Modeling and mapping the current and future climatic niches of endangered Himalayan musk deer. Ecol. Infor. 40, 1-7. Khanal L, Chalise MK, Jiang X (2018) Ecological niche modeling of Himalayan langur ( Semnopithecus entellus ) in the southern flank of the Himalayas. J. Sci. Technol. 23(1), 1-9. Khattak RH, Teng L, Ahmad S, Bari F, Rehman EU, Shah AA, Liu Z (2022) In Pursuit of New Spaces for Threatened Mammals: Assessing Habitat Suitability for Kashmir Markhor ( Capra falconericashmeriensis ) in the Hindukush Range. Sustainability. 14(3), 1544. Khuroo AA, Mehraj G, Muzafar I, Rashid I, Dar GH (2020) Biodiversity conservation in Jammu and Kashmir state: current status and future challenges. Biodiversity of the Himalaya: Jammu and Kashmir State , 1049-1076. Leclerc C, Courchamp F, Bellard C (2020) Future climate change vulnerability of endemic island mammals. Nat comm. 11(1), 4943.https://doi.org/10.1038/s41467-020-18740-x Lenoir J, Gégout JC, Marquet PA, de Ruffray P, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century. science 320 (5884), 1768-1771. Li G, Huang J (2022) Multi-Directional Rather Than Unidirectional Northward-Dominant Range Shifts Predicted under Climate Change for 99 Chinese Tree Species. Forests. 13 (10), 1619. Liang J, Ding Z, Jiang Z, Yang X, Xiao R, Singh PB, Hu Y, Guo K, Zhang Z, Hu H (2021) Climate change, habitat connectivity, and conservation gaps: a case study of four ungulate species endemic to the Tibetan Plateau. Landsc. Ecol. 36, 1071-1087. Linshan L, Zhilong Z, Yili Z, Xue W (2017) Using the maxent model to predict suitable habitat changes for key protected species in Koshi Basin, Central Himalayas. J. Resour. Ecol. 8(1), 77-87. Littlefield CE, Krosby M, Michalak JL, Lawler JJ (2019) Connectivity for species on the move: supporting climate‐driven range shifts. Front. Ecol. Environ. 17(5), 270-278. Malhi Y, Franklin J, Seddon N, Solan M, Turner MG, Field CB, Knowlton N (2020) Climate change and ecosystems: Threats, opportunities, and solutions. Philos Trans R Soc Lond B Biol Sci. 37 5 (1794), p.20190104.https://doi.org/10.1098/rstb.2019.0104 Malik JA, Bansal SK (2016) ‘A review of population ecology of Hangul deer (Cervus elaphus Hanglu, Wagner / Cervus Canadensis Hanglu) in Dachigam National Park’, (May). Manes S, Costello MJ, Beckett H, Debnath A, Devenish-Nelson E, Grey KA, Jenkins R, Khan TM, Kiessling W, Krause C, Maharaj SS (2021) Endemism increases species' climate change risk in areas of global biodiversity importance. Biol. Conser. 257, 109070. Mason TH, Stephens PA, Apollonio M, Willis SG (2014) Predicting potential responses to future climate in an alpine ungulate: interspecific interactions exceed climate effects. Glob. Change Biol. 20(12), 3872-3882. Maviya Majid MK, Ahmad W, Gul S (2019) Hungul ( Cervus elaphushanglu ; Red Deer) As Flagship Species of Dachigam National Park: A Brief Review. J. Wildl. Res. 7(03), pp.50-58. McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. PNAS 104:19885–19890.https://doi.org/10.1073/pnas.0706568104 Merow C, Smith MJ, Silander Jr JA (2013) A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography. 36(10), 1058-1069. Moilanen A, Lehtinen P, Kohonen I, Virtanen E, Jalkanen J, Kujala H (2022) Novel methods for spatial prioritization with applications in conservation, land use planning, and ecological impact avoidance. Methods Ecol. Evol. 13:1062–1072. Morueta-Holme N, Fløjgaard C, Svenning JC (2010) Climate change risks and conservation implications for a threatened small-range mammal species. PloS one. 5(4), e10360. Mukesh Kumar VP, Sharma, L.K., Shukla, M., Sathyakumar, S., 2015. Pragmatic perspective on conservation genetics and demographic history of the last surviving population of Kashmir red deer ( Cervus elaphushanglu ) in India. PLoS One. 10(2), e0117069. Mukherjee T, Sharma V, Sharma LK, Thakur M, Joshi BD, Sharief A, Thapa A, Dutta R, Dolker S, Tripathy B, Chandra K (2021) Landscape-level habitat management plan through geometric reserve design for critically endangered Hangul ( Cervus hangluhanglu ). Sci. Total Environ. 777, 146031. Pearce-Higgins JW, Antão LH, Bates RE, Bowgen KM, Bradshaw CD, Duffield SJ, Ffoulkes C, Franco AMA, Geschke J, Gregory RD, Harley MJ (2022) A framework for climate change adaptation indicators for the natural environment. Ecol. indic. 136 , 108690. Phillips SJ, Dudík M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecol. Appl. 19(1), 181-197. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol. Mod. 190(3-4), 231-259. QGIS Development Team 2023. QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.org/en/site/index.html Rao A (2002) Pastoral nomads, the state and a national park: The case of Dachigam, Kashmir. Nomadic Peoples. 72-98. Román-Palacios C, Wiens JJ (2020) Recent responses to climate change reveal the drivers of species extinction and survival. PNAS. 117(8), 4211-4217. Scapini F, Degli EI, Defeo O (2019) Behavioral adaptations of sandy beach macrofauna in face of climate change impacts: A conceptual framework. Estuar. Coast. Shelf Sci. 225, 106236. Schneiderbauer S, Pisa PF, Delves JL, Pedoth L, Rufat S, Erschbamer M, Thaler T, Carnelli F, Granados-Chahin S (2021) Risk perception of climate change and natural hazards in global mountain regions: A critical review. Sci. Total Environ. 784, 146957. Semenzato P, Cagnacci F, Ossi F, Eccel E, Morellet N, Hewison AM, Sturaro E, Ramanzin M (2021) Behavioral heat‐stress compensation in a cold‐adapted ungulate: Forage‐mediated responses to warming Alpine summers. Eco. Lett. 24(8), 1556-1568. Shah GM, Jan U, Bhat BA, Ahanger FA (2011) Causes of decline of critically endangered hangul deer in Dachigam National Park, Kashmir (India): A review. Int. J. Biodivers. Conserv, 3(14), 735-738. Sharma LK, Charoo SA, Sathyakumar S (2010) Habitat use and food habits of Kashmir red deer or hangul ( Cervus elaphushanglu ) at Dachigam National Park, Kashmir, India. Galemys. 22 , 309-329. Sharma LK, Joshi BD, Thakur M, Chandra K (2022) Status, Issues, and Challenges of Biodiversity: Wild Animals. In Biodiversity in India: Status, Issues and Challenges (149-173). Singapore: Springer Nature Singapore. Silva J, Cruz-Neto O, Peres CA, Tabarelli M, Lopes AV (2019) Climate change will reduce suitable Caatinga dry forest habitat for endemic plants with disproportionate impacts on specialized reproductive strategies. PloS one.14(5), e0217028. Singh H, Kumar N, Kumar M, Singh R (2020) Modeling habitat suitability of western tragopan ( Tragopanmelanocephalus ) a range-restricted vulnerable bird species of the Himalayan region, in response to climate change. Clim. Risk Manag. 29 , 100241. Soille P, Vogt P (2009) Morphological segmentation of binary patterns. Pattern Recognit. Lett. 30 (4), 456-459. Sony RK, Sen S, Kumar S, Sen M, Jayahari KM (2018) Niche models inform the effects of climate change on the endangered NilgiriTahr ( Nilgiritragushylocrius ) populations in the southern Western Ghats, India. Ecol. Eng. 120, 355-363. Srivastava T, Vasudevan K (2021) Conservation of hangul, Cervus hangul –paving the way ahead. Curr. Sci. 121(4), 485. Suhail I, Iqbal S, Ahmad K, Lone I, Mansoor M, Zargar R, Hussain S, Baba M (2009) Status and distribution of Hangul Cervus Elaph us Hanglu Wagner in Kashmir, India. J. Bombay Nat. Hist. Soc. 106 (1), 63-71. Summers DM, Bryan BA, Crossman ND, Meyer WS (2012) Species vulnerability to climate change: impacts on spatial conservation priorities and species representation. Glob. Change Biol. 18(7), 2335-2348. Tao Z (2023) Predicting the changes in suitable habitats for six common woody species in Central Asia. Int J Biometeorol. 67(1), 107-119. Thapa A, Wu R, Hu Y, Nie Y, Singh PB, Khatiwada JR, Yan LI, Gu X, Wei F (2018) Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8(21), 10542-10554. Thorstad EB, Bliss D, Breau C, Damon‐Randall K, Sundt‐Hansen LE, Hatfield EM, Horsburgh G, Hansen H, Maoiléidigh NÓ, Sheehan T, Sutton SG (2021) Atlantic salmon in a rapidly changing environment—Facing the challenges of reduced marine survival and climate change. Aquat. Conserv.: Mar. Freshw. 31(9), 2654-2665. Thurman LL, Stein BA, Beever EA, Foden W, Geange SR, Green N, Gross JE, Lawrence DJ, LeDee O, Olden JD, Thompson LM (2020) Persist in place or shift in space? Evaluating the adaptive capacity of species to climate change. Front. Ecol. Evol. (9), 520-528. Tobgay S, Mahavik N (2020) Potential habitat distribution of Himalayan red panda and their connectivity in Sakteng Wildlife Sanctuary, Bhutan. Ecol. Evol. 10(23), 12929-12939. Tran VD, Vu TT, Ta T, Tran Q, Nguyen T, Ha TM, Nguyen HV (2018) Predicting suitable distribution for an endemic, rare, and threatened species (grey-shanked douc langur, Pygathrixcinerea Nadler, 1997) using the MaxEnt model. Appl. Ecol. Environ. Res. 16(2). Varol T, Canturk U, Cetin M, Ozel HB, Sevik H (2021) Impacts of climate change scenarios on European ash tree ( Fraxinus excelsior L.) in Turkey. For. Ecol. Manag. 491, 119199. Viana DS, Chase JM (2022) Increasing climatic decoupling of bird abundances and distributions. Nat. Ecol. Evol. 6(9), 1299-1306. Vogt P, Riitters K (2017) Guidos Toolbox: universal digital image object analysis. Eur. J. Remote. Sens. 50:352–361. Waheed M, Arshad F, Majeed M, Haq SM, Aziz R, Bussmann RW, Ali K, Subhan F, Jones DA, Zaitouny A (2023) Potential distribution of a noxious weed ( Solanum viarum Dunal), current status, and future invasion risk based on MaxEnt modeling. Geol. ecol. landsc.1-16. Waheed M, Haq SM, Arshad F, Jameel MA, Siddiqui MH, Bussmann RW, Manshoor N, Alamri S (2023a) Where Will Threatened Aegle marmelos L., a Tree of the Semi-Arid Region, Go under Climate Change? Implications for the Reintroduction of the Species. Land. 12(7), 1433. Wallingford PD, Morelli TL, Allen JM, Beaury EM Blumenthal DM, Bradley BA, Dukes JS, Early R, Fusco EJ, Goldberg DE, Ibáñez I (2020) Adjusting the lens of invasion biology to focus on the impacts of climate-driven range shifts. Nat. Clim. Change. 10(5), 398-405. Weiskopf SR, Rubenstein MA, Crozier LG, Gaichas S, Griffis R, Halofsky JE, Hyde KJ, Morelli TL, Morisette JT, Muñoz RC, Pershing AJ (2020) Climate change affects biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. 733 , p.137782.https://doi.org/10.1016/j.scitotenv.2020.137782 Yadav RR, Negi PS, Singh J (2021) Climate change and plant biodiversity in Himalaya, India. Proc.Indian Natl. Sci. Acad. 87, 234–259. https://doi.org/10.1007/s43538-021-00034-5 Yang L, Huang L, Zhang H, Lee P, Zhang N, Cai R, Li E, Pan T, Wu X (2023) Habitat Suitability and Distribution Pattern Response to Global Climate Change in a Widespread Species, the Asiatic Toad ( Bufogargarizans ). Asiat. Herpetol. Res. 14(2), 138-146. Zanaga D, Van De Kerchove R, Daems D, De Keersmaecker W, Brockmann C, Kirches G, Wevers J, Cartus O, Santoro M, Fritz S, Lesivn M, Herold M, Tsendbazar NE, Xu P, Ramoino F, Arino O (2022) ESA WorldCover 10 m 2021 v200. Zhong Y, Xue Z, Davis CC, Moreno‐Mateos D, Jiang M, Liu B, Wang G (2022) Shrinking habitats and native species loss under climate change: a multifactorial risk assessment of China's inland wetlands. Earth's Future. 10(6), e2021EF002630. Photo plate 1 Photo plate 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Photoplate1.png Photo plate 1. Photograph representation of (a-c) natural habitat Hangul in Dachigam landscape (d-e) Hangul sighted during the survey in the Dachigam landscape and Hangul antler reported from trader during the survey period. Supplementarydata.docx Cite Share Download PDF Status: Posted Version 1 posted 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-3874320","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267789822,"identity":"34fade13-5cb8-4e2b-9e0e-c9bc5f9fd534","order_by":0,"name":"Shiekh Marifatul Haq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2PrWrEQBRGbyikJrA2NXmDwkBhVEgepGbCwKxpobIiIjWNWfomK9ZU3yWwKu3YgZjErKqIKhTK0pssKyqSYV2hc2B+xHfuNwPgcPxR8HE8BEB2iOniPaFVqYEdldZXg1JYFTgpXutXxyFz6evyrUPMv2FxecdABDpZlxW15PHtlMLrJUPcMbhafZASNvK1zkjZqftiSkEFVe8zYGZoYY3kSIpXVNOK3gPigUE6KuJdct1ZFKMAt8/UEg4KYsKNrcVQy/blJgjr/QM9SQpuqEXM/UWrix4/o2hRyk33VSQp18uu7fN4UjkRDBvSysaksMR/kZ4Tdjgcjv/BD+VXaX1xRV2aAAAAAElFTkSuQmCC","orcid":"","institution":"Ilia State University","correspondingAuthor":true,"prefix":"","firstName":"Shiekh","middleName":"Marifatul","lastName":"Haq","suffix":""},{"id":267789823,"identity":"aa14f9c7-3092-4df4-a26f-24e2b2c468d1","order_by":1,"name":"Muhammad Waheed","email":"","orcid":"","institution":"Ilia State University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Waheed","suffix":""},{"id":267789824,"identity":"d186ba6f-e927-41ed-b960-3ce16a21c6e3","order_by":2,"name":"Łukasz Walas","email":"","orcid":"","institution":"Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Łukasz","middleName":"","lastName":"Walas","suffix":""},{"id":267789825,"identity":"578418b2-e03b-4cc2-886c-ba020cf5cd51","order_by":3,"name":"Shirin Alipour","email":"","orcid":"","institution":"Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shirin","middleName":"","lastName":"Alipour","suffix":""},{"id":267789826,"identity":"e7de8871-a808-4e31-a70f-705a61973b89","order_by":4,"name":"Riyaz Ahmad","email":"","orcid":"","institution":"National Centre for Wildlife","correspondingAuthor":false,"prefix":"","firstName":"Riyaz","middleName":"","lastName":"Ahmad","suffix":""},{"id":267789827,"identity":"bfc5220c-3b5a-4fe2-b42e-d20fa87626dc","order_by":5,"name":"Rainer W. Bussmann","email":"","orcid":"","institution":"Ilia State University","correspondingAuthor":false,"prefix":"","firstName":"Rainer","middleName":"W.","lastName":"Bussmann","suffix":""}],"badges":[],"createdAt":"2024-01-18 01:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3874320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3874320/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49864507,"identity":"bb9676d6-fc3c-41be-883f-d6403048bcf8","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2910979,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the research region and points showing the presence records of Hangul in the Kashmir Valley.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/9c98c9bdda8bc1d157b798f5.png"},{"id":49864906,"identity":"223cca92-e823-4abc-9417-f4000f59b760","added_by":"auto","created_at":"2024-01-19 10:01:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1022707,"visible":true,"origin":"","legend":"\u003cp\u003eThe MaxEnt prediction map presents a comprehensive classification of the potential habitat suitability for Hangul, which is the focus of this study. It provides a detailed visualization of the anticipated suitability of habitats for the Hangul species within the research area.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/2a8d8affe079ba1d775567ec.png"},{"id":49864502,"identity":"2d84e914-2dce-4037-8d84-196a9d6c7d78","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":997343,"visible":true,"origin":"","legend":"\u003cp\u003eThe MaxEnt-generated maps provide a visual representation of forecasted habitat suitability classifications across a range of future climate change scenarios. As illustrated in the figures, we observe: Figure A presents the habitat suitability under the SSPs-245 scenario in the 2050s, while Figure B displays the habitat suitability under the SSPs-585 scenario in the 2070s. Furthermore, Figure C showcases the habitat suitability under the SSPs-245 scenario in the 2070s, and Figure D represents the habitat suitability under the SSPs-585 scenario in the 2070s.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/b180cd6c32ff3415dae6568a.png"},{"id":49864503,"identity":"97850b9d-662a-46b0-91b8-e45e476f67c8","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1710459,"visible":true,"origin":"","legend":"\u003cp\u003eThe potential identified connecting very highly suitable habitat that provides potential movement corridors for Hangul in the Dachigam landscape.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/fe666980098eb883e3df2a7f.png"},{"id":49865399,"identity":"87da1157-2316-477b-b3be-c3442baf676e","added_by":"auto","created_at":"2024-01-19 10:09:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":755268,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in structural elements based on MSPA under (A) current and future scenarios (B) SSPs_245_2050, (C) SSPs_585_2050, (D) SSPs_245_2070, and (E) SSPs_585_2070\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/bf9ab6d4efeb72caafa33f39.png"},{"id":49864508,"identity":"7cb45d57-7115-4bec-8cb9-f20ef9c8b04a","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":655802,"visible":true,"origin":"","legend":"\u003cp\u003eHangul habitat fragmentation process and Core/Edge ratio under (A) current and future scenarios (B) SSPs_245_2050, (C) SSPs_585_2050, (D) SSPs_245_2070, and (E) SSPs_585_2070\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/1d8fdc3413fabbe19b4a1914.png"},{"id":49864505,"identity":"c1b4b0f7-914b-4d0e-b2f7-db9e710ce490","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1999708,"visible":true,"origin":"","legend":"\u003cp\u003eResult of Zonation analyses, showing the areas with the highest priority for conservation.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/d3c2dd93fcb1b2e44d396dbf.png"},{"id":51922703,"identity":"36f71fef-2db2-4097-84a2-4f97888ee29e","added_by":"auto","created_at":"2024-03-03 23:25:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8932140,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/cd07efa7-612d-440f-b243-b2c9617546d5.pdf"},{"id":49864501,"identity":"2ad7528b-455d-4c84-8d24-928ec95767c0","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2209773,"visible":true,"origin":"","legend":"\u003cp\u003ePhoto plate 1. Photograph representation of (a-c) natural habitat Hangul in Dachigam landscape (d-e) Hangul sighted during the survey in the Dachigam landscape and Hangul antler reported from trader during the survey period.\u003c/p\u003e","description":"","filename":"Photoplate1.png","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/47aca5ced0ecee9dc4070529.png"},{"id":49864509,"identity":"55baded9-97a3-4100-9435-f855d08d3b34","added_by":"auto","created_at":"2024-01-19 09:53:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":796687,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-3874320/v1/ac58edd400128aa1b1813ede.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Conserving the critically endangered Hangul (Cervus hanglu hanglu) - Future distribution and efficiency of protected areas under climate change: Implications for the conservation of Dachigam landscape","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal warming is an additional threat to high-altitude ecosystems, and as a result, their entire faunal assemblages, particularly critically endangered species, may become extinct (Schneiderbauer et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Albrich et al. 2020; Weiskopf et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hoffmann et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The survival and geographic ranges of the species may be impacted by global warming, and this influence may be severe especially for endemic species (Manes et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Leclerc et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Silva et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For conservation, it is vital to forecast how the distribution of these animals differs under various future scenarios of global warming (De Frenne et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Global warming can prompt species to migrate from their original distribution areas to new habitats, highlighting their potential to consistently track their preferred climate niche, even in the face of evolving geographic conditions over time (Antao et al, 2022; Littlefield et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Local extinctions may also be a result of this dynamic process, particularly for species with little capacity for dispersal and adaptation (Rom\u0026aacute;n-Palacios and Wiens, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Phenotypic plasticity, genetic changes, and climate equilibrium all affect a species' capacity to adapt to climatic change; without these factors, the species may go extinct (Buchholz et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, variables including a species' reproductive rates, spatial extent, migration capability, and niche specificity will affect how that species reacts to global warming (Viana et al. 2022; Ghisbain et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It seems unlikely that individual species would react to climate change in a predictable manner. Therefore, in order to forecast how specific species' distributions may vary in response to climate change, we must concentrate on them individually.\u003c/p\u003e \u003cp\u003eTemperature and precipitation are just two climate variables that affect how species distributions evolve (Brun et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Waheed et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; DeMarche et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), but the main difficulty faced by ecologists and conservation biologists is predicting which components of climate will have the biggest impacts on species distributions. We have recently been helped to grasp possible species distribution shifts more thoroughly by better methods of simulating species distributions and climate change. It has been shown that the Maxent Model works very adept at forecasting the geographic ranges of species because of climate change (Ab Lah et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gilani et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Haq et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While acknowledging the temporal variability, mapping species distributions under climate change offers valuable insights into understanding its comprehensive impact. Utilizing geospatial analysis enables the anticipation of habitat shifts, both in terms of their direction and extent, offering insights into the future distribution of biodiversity (Yang et al. \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Li et al. 2022). Conservation biologists and pertinent authorities can leverage these projections as a foundation for developing proactive solutions aimed at addressing and mitigating the potential impacts of environmental changes.\u003c/p\u003e \u003cp\u003eHangul, classified as the world's most endangered red deer, faces uncertain impacts from climate change on its future ranges and current precarious status (Mukesh et al. 2015). Many studies have assumed that the Dachigam landscape is critical for Hangul foraging habitats and provides key habitats (Ahmad 2004; Sharma et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mukherjee et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The population of Hangul has drastically dropped due to habitat loss, overgrazing, poaching, and disturbances caused by human activities (Shah et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hassan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Ahmad \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, Hangul migrates to alpine pastures in summer, which have been disturbed due to heavy livestock grazing and turmoil for the last few decades, resulting in a negative impact on the Hangul's population and distribution (Kaul et al. 2020; Bhat et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The species primarily inhabits the temperate grasslands, dense riverine forests, and high-altitude meadows of the Dachigam landscape (Ahmad et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and is typically found in small family groups (outside mating period) or herds consisting of females and their offspring. Males are usually solitary and only join the groups during the mating season. The Hangul mating season starts in early autumn (September) and continues till mid to late autumn (October-November) (Bhat et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ahmad et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and the calves are produced in Spring (March-April) with the snowmelt and new plant growth. We employed ensemble species distribution models to assess the influence of climate change on the future abundance and spatial distribution of over 250\u0026thinsp;+\u0026thinsp;individuals. This analysis aimed to investigate potential shifts in distribution patterns attributed to forthcoming climate changes (Jain \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The predictive ability of these studies is increased by novel approaches developed here for using biological information to enhance the selection of pseudo-absence points. The findings suggest a complicated combination of future habitat persistence and reduction. We identified locations in desperate need of conservation, each exhibiting diverse levels of current forest cover and protection, and each characterized by distinct climatic and ecological conditions. We employed Species Distribution Models (SDMs) to forecast suitable habitats under multiple climate change scenarios for the years 2020, 2050, and 2080. This analysis aimed to assess the potential impact of anticipated climate changes on Hangul. We specifically asked (1) Whether the existing Hangul suitable habitat would be altered by expected global warming. (2) If there would be more or less suitable habitat? (3) How much of a threat global warming might be to Hangul in the future?\u003c/p\u003e \u003cp\u003eThe findings of this research will provide valuable perspectives for the preservation of landscape connectivity, considering the existing distribution of suitable habitats. This information can facilitate and guide future species movements counter to the challenges confronted by climate change. Our study will provide pertinent decision-makers and conservation authorities with information on the possible climate change vulnerabilities and can direct future conservation planning for both the Hangul and other threatened ungulate species. Most importantly, the outcomes of this study suggest that the distributions for the Hangul will change and in the future be located in some cases outside of the protected zones. To ensure the prolonged viability of Hangul, it is imperative to initiate and sustain conservation efforts aimed at establishing and safeguarding ecological connectivity. Such connectivity will enable Hangul and other species to migrate to suitable habitats in response to the shifting patterns of global climate.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe central element of the study area is Dachigam National Park (DNP), as it holds the only viable population of Kashmir stag (Hangul) on earth. The 141 km\u003csup\u003e2\u003c/sup\u003e DNP, with an altitudinal range of 1700 to 4250 m is located in the Zanskar Mountain Range of the northwestern Himalayas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Zanskar range runs from Gurez to Kishtwar bordering the region from northwest to southeast and covers the historical distribution range of Hangul almost fully. Other important mammals that habitat the DNP include large mammals in the area include musk deer (\u003cem\u003eMoschus chrysogaster\u003c/em\u003e), serow (\u003cem\u003eCapricornis sumatraensis\u003c/em\u003e), Asiatic black bear (\u003cem\u003eUrsus thibetanus\u003c/em\u003e), brown bear (\u003cem\u003eUrsus arctos\u003c/em\u003e), and common leopard (\u003cem\u003ePanthera pardus\u003c/em\u003e), Himalayan grey langur (\u003cem\u003eSemnopithecus ajax\u003c/em\u003e), red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e), golden jackal (\u003cem\u003eCanis aureus\u003c/em\u003e), small Indian civet (\u003cem\u003eViverricula indica\u003c/em\u003e) and yellow-throated marten (\u003cem\u003eMartes flavigula\u003c/em\u003e) (Ahmad et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe area exhibits five primary forest categories: \u003cem\u003ePinus wallichiana\u003c/em\u003e, Oak, \u003cem\u003eAcacia\u003c/em\u003e, Broad-leaved, and Scrub forests. Within the Dachigam National Park (DNP), deciduous trees and shrubs, such as \u003cem\u003ePopulus alba\u003c/em\u003e, \u003cem\u003ePrunus armeniaca\u003c/em\u003e, and \u003cem\u003ePinus wallichiana\u003c/em\u003e, predominate, while species like Salix alba and Juglans regia characterize riparian areas. The northern aspects host extensive forests of \u003cem\u003ePinus wallichiana\u003c/em\u003e and \u003cem\u003ePrunus armeniaca\u003c/em\u003e, with cultivated intentionally invasive tree species, \u003cem\u003eQuercus robur\u003c/em\u003e, and \u003cem\u003eRobinia pseudoacacia\u003c/em\u003e, serving as a food and habitat source for wildlife in the park. For comprehensive details about the research area, further information is available (Haq et al. 2022).\u003c/p\u003e\u003cp\u003eThe climate of the region is classified as sub-Mediterranean, marked by four distinct distant seasons viz. spring, summer, autumn, and winter. The dry periods spanning from April to June and September to November. Autumn in Kashmir is known for its vibrant colors as the foliage turns into hues of gold and red. Winter in Kashmir is cold and often snowy, especially in higher elevations and the temperatures can drop below freezing, fluctuating between \u0026minus;\u0026thinsp;2\u0026deg;C to 10\u0026deg;C in the plains with even low temperatures at higher elevations. Temperature variations in the Zanskar region typically range from approximately range between \u0026minus;\u0026thinsp;2\u0026deg;C and 30\u0026deg;C. Snow serves as the primary source of precipitation in the Zanskar range, and its melting continues until June in higher altitudes and has a significant impact on the annual precipitation levels. The Zanskar Range experiences a considerable range in minimum and maximum rainfall, varying from 32 mm to 546 mm annually. This fluctuation in precipitation is vital for the ecological dynamics and sustenance of the Hangul population (Haq et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data regarding the presence of species\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this investigation, information related to the existence of Hangul in the research area was gathered through a combination of methods. To acquire information about the locations where the species was found (presence points), a two-year-long field survey was conducted, and existing data from information from existing literature and the GBIF (Global Biodiversity Information Facility) database, covering the period from 1864 to 2019, was gathered. To prevent any concentration in specific regions, duplicate records were carefully removed. Choosing these occurrence points was a meticulous process aimed at avoiding spatial pseudo-replication. It was based on the resolution of raster data (specifically, 2.5 arc-minutes). We selected and filtered GPS locations, retaining only those with a 15-meter accuracy, and ensured that each 5 km\u003csup\u003e2\u003c/sup\u003e pixel contained just one presence point. This was done to prevent the potential bias that multiple presence points within a small area could introduce to the results.\u003c/p\u003e \u003cp\u003eWe conducted field surveys between 2019 and 2022 to record the occurrence of Hangul in the Greater Dachigam landscape. We gathered data on species presence through interviews with local wildlife scholars, trackers, livestock herders, and wildlife field staff operating in the landscape. In our study, we employed binoculars and perceiving scopes to observe the Hangul species from various vantage points in its potential habitat. During these observations, we meticulously documented habitat characteristics, including vegetation cover, type, physical attributes, and ruggedness. Additionally, we recorded precise location and elevation using GPS devices while also noting any illicit activities such as habitat destruction, illegal poaching, and natural disturbances (Photo plate 1). To further enhance our understanding of Hangul's presence and assess illegal activities within its habitat, we conducted a questionnaire survey, engaging with local individuals and departmental staff. These efforts resulted in the collection of 47 unique Hangul presence points, which served as the foundation for developing the Distribution Prediction Model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The compilation of environmental data and the procedure for variable selection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOur study involved the gathering of a comprehensive set of environmental and topographic data to enhance the analytical framework. Nineteen bioclimatic variables, with a resolution of 2.5 arc-minutes, and elevation data, at a resolution of 30 arc-seconds, were sourced from the WorldClim website (ver. 2.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.worldclim.org\u003c/span\u003e\u003cspan address=\"http://www.worldclim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 15 March 2023) (Supplementary Table\u0026nbsp;1). To supplement the dataset, topographic information, including altitude, slope gradient, and aspect, was acquired through ArcGIS 10.5 software, enabling the creation of a Digital Elevation Model (DEM) via spatial analysis tools. In addition to natural variables, four anthropogenic factors were included as indicators of human influence. These factors encompassed land cover, road proximity, population density, and the Human Footprint (HFP), measuring human perturbation. The HFP map, obtained from the Socio-Economic Data and Applications Centre (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sedac.ciesin.columbia.edu\u003c/span\u003e\u003cspan address=\"http://sedac.ciesin.columbia.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), amalgamates global data layers associated with various factors affecting ecosystems, including human population distribution, urbanization, navigable rivers, roads, and diverse agricultural land uses. The HFP layer spans a range from 0, signifying areas in proximity to natural or pristine conditions, to 50, indicating regions that have experienced significant degradation.\u003c/p\u003e \u003cp\u003eThe study incorporated comprehensive data sources and variables for a thorough analysis. The Global Land Cover data, outlining nine categories (including grassland, forest, savannah, shrubland, cropland/natural vegetation, snow/ice, wetland, urban, and barren/sparsely vegetated areas), was sourced from the International Geosphere-Biosphere Program (MODIS Global Land Cover Classification v2). To consider the influence of human population density on species distribution, a separate layer of population density data was extracted from Oak Ridge National Laboratory. For vegetation analysis, the Enhanced Vegetation Index (EVI) derived from MODIS products (MOD13A3) was used, covering both mean annual EVI and EVI seasonality. The choice of EVI over NDVI was based on its ability to minimize background interference and handle issues like smoke and sub-pixel cloud contamination. The EVI data was obtained from Terra MODIS ready-to-use files (MOD11 and MOD13). Future simulations included two distinct sets of Shared Socioeconomic Pathways (SSPs), specifically SSPs 245 and SSPs 585, obtained from the Coupled Model Intercomparing Project, Phase 6 (CMIP6). These simulations were carried out using the Global Climate Model BCC-CSM2-MR, operating at a resolution of 2.5 arc-minutes. This comprehensive approach facilitated a thorough analysis of the potential impact of environmental and climatic variables on species distribution under different scenarios.\u003c/p\u003e \u003cp\u003eThe distribution of the Hangul is influenced by a multitude of features, including precipitation, temperature, natural impediments, physical land structures, and various biological variables, as previously evidenced in research by Gao et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Zhang et al. (2015). In our model, we sought to evaluate the specific ecological aspects that impact the species' dispersion. To achieve this, we incorporated a comprehensive set of variables, encompassing 19 bioclimatic factors, four anthropogenic indicators (land cover, human footprint, human population density, and road proximity), and three ecological parameters (slope, aspect, and elevation). These factors, in conjunction with vegetation data, enabled a comprehensive evaluation of the factors influencing the western Hangul's distribution.\u003c/p\u003e \u003cp\u003eTo ensure data independence and eliminate potential correlations among variables, we employed a two-step approach in this study. First, we utilized the initial model with default configurations to evaluate the influence of each variable, establishing a threshold of \u0026gt;\u0026thinsp;1% to exclude variables that didn't meet this requirement. Subsequently, to further remove possible spatial associations, we subjected the remaining variables (those surpassing the contribution threshold) to pairwise Pearson's correlation (r) analysis. To streamline the variable count, a threshold value of r\u0026thinsp;\u0026ge;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 was implemented. In cases where two variables displayed an r value surpassing this threshold, the one with the lower contribution was excluded (Arshad et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bosso et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hassan et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hijmans et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Khattak et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Following this procedure, eight significant bioclimatic and topographic variables were identified: mean diurnal range (Bio02), mean temperature of the wettest quarter (Bio08), mean temperature of the coldest quarter (Bio11), annual precipitation (Bio12), precipitation of the driest month (Bio14), precipitation of the warmest quarter (Bio18), precipitation of the coldest month (Bio19), and elevation (elev) (Graham, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Modeling procedures\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the realm of habitat modeling, the calibration and optimization of MaxEnt species distribution models stand as pivotal steps in achieving accurate and reliable results. This involves a systematic exploration of various combinations of regularization multiplier (RM) values and feature classes (FC) to strike a balance between predictive power and overfitting prevention. Threshold-dependent evaluation metrics, such as the omission rate, are harnessed to identify the optimal MaxEnt model settings, ensuring that the model can be effectively applied to different contexts. To illustrate, researchers in this study investigated multiple permutations of eight Regularization Multiplier values (ranging from 1 to 4 with a 0.5 interval) and six Feature Classes, including Linear, Quadratic, Hinge, and more (Eyring et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Khattak et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce the optimal MaxEnt model configuration was established, the study proceeded to construct a bias file by leveraging occurrence and environmental data with the ENMEval package in R. Subsequently, utilized MaxEnt version 3.4.4 to evaluate the data and predict the most suitable habitats for Hangul in the research area, following established methodologies (Awan et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hijmans et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). MaxEnt, grounded in ecological niche theory and informed by presence data, has emerged as a preeminent tool for predicting the potential distribution of species within defined geographic regions (Graham, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Its exceptional predictive accuracy has led to its widespread adoption as the preferred species distribution modeling method over competing alternatives (Bai et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Fourcade et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Elith et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Summers et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study employed MaxEnt, a robust machine-learning method, to explore the intricate connection between ecological conditions and the spatial arrangement of wildlife (D\u0026iacute;az et al., 2022; Haq et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To enhance model accuracy and performance, a range of MaxEnt configurations, including cross-validation, output format selection, and response curve generation, were meticulously employed. Model evaluation was conducted using receiver-operator characteristic (ROC) curves and the calculation of area under the curve (AUC) values, with AUC scores above 0.9 indicative of well-performing models (Fourcade et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hijmans et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Merow et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Summers et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This comprehensive approach not only aids in optimizing SDMs but also contributes to advancing our knowledge of the intricate interplay between environmental factors and species distribution. The results generated by MaxEnt prediction for the suitability of Hangul habitat varied from 0 to 1, categorized into four levels: low (0.21\u0026ndash;0.4), moderate (0.41\u0026ndash;0.6), high (0.61\u0026ndash;0.80), and very high (0.81-1) (Elith et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Phillips et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Waheed et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Range and corridor estimation\u003c/h2\u003e \u003cp\u003eGuidos Toolbox (Vogt and Riitters \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) software was used to estimate the division of potential range into the core and edge using the \u0026ldquo;Entropy Map\u0026rdquo; method. The same software was used to prepare the morphological-spatial analysis (MSPA), which allows to categorization of the potential distribution of the species into functional classes (Soille and Vogt \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). For Guidos Toolbox analyses, the MaxEnt output with suitability above 0.2 was used as an input raster. The area of each MSPA class and Edge/Core/Remote Core class in Entropy Map was calculated in QGIS 3.32.1 \u0026ldquo;Lima\u0026rdquo; (QGIS Development Team 2023).\u003c/p\u003e \u003cp\u003eCIRCUITSCAPE was employed to estimate the location of potential corridors for species migration between the existing core of the range and several potential cores. As potential future cores, six locations were chosen: four of them are current protected areas and two, in the southern part of the analyzed area, were estimated by MaxEnt as regions with high suitability. The final connectivity raster was calculated using the output of three CIRCUITSCAPE analyses: one with suitability as a conductance, second with altitude as a resistance (Danielson and Gesch \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and third with a land cover (Zanaga et al. 2021), where built-up areas had high resistance, crop fields medium resistance and natural vegetation low resistance. As a potential corridor, we have chosen an area that connects the current range core to the potential range core via a continuous path with high conductance.\u003c/p\u003e \u003cp\u003eZonation 5.0 (Moilanen et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was used to identify areas that should be prioritized for protection to save the species. Analysis was performed with the following raters: potential current range as input with weight 1.0, future models as inputs with weight 0.5 each, land cover (Zanaga et al. 2021) as a condition raster, population density as a cost raster. The protected areas were used as a retention raster to exclude them from analysis. Results were visualized using QGIS 3.32.1 \u0026ldquo;Lima\u0026rdquo; (QGIS Development Team 2023).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Model evaluation\u003c/h2\u003e \u003cp\u003eMaxEnt was utilized to compute a forecasted ecological suitability score for the Hangul species within the Jammu and Kashmir region. The suitability score exhibited a range from 0 to 0.96, representing the anticipated appropriate conditions of various habitats for the species. In this investigation, the top-performing model attained an AUC score of 0.963, signifying a high level of accuracy and a close alignment between the model's predictions and the actual distribution of the Hangul species (Supplementary data: Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the climatic variables considered, the precipitation of the driest month (Bio14) and precipitation of the coldest month (Bio19), elevation (elev), and the mean temperature of the wettest quarter (Bio08) contributed 85% in the distribution Hangul. Bio14 and 19 had the highest contribution, accounting for 41.4% and 15.3%, respectively. So, the Jackknife test revealed that those variables were influential in shaping the species' distribution (Supplementary data: Figure S2, S3). Subsequently, both elevation (elev) and the mean temperature of the wettest quarter (Bio08) made contributions of 15.1% each. The least contributed variable, the mean temperature of the coldest quarter (Bio11) accounted for 13.1% of the variance, while the mean diurnal range (Bio02) contributed 4.5%. Additionally, both the precipitation of the warmest quarter (Bio18) and the annual precipitation (Bio12) demonstrated equal contributions of 0.7% each to the model's overall accuracy (Supplementary data: Figure S2).\u003c/p\u003e \u003cp\u003eAs depicted in Figure S4, when precipitation levels exceeded 40 mm, the probability of encountering Hangul was notably higher than 0.6, signifying a region of high suitability. The highest probability of occurrence was observed around 500 mm of precipitation, where the likelihood reached approximately 0.7. The elevation range that proved most conducive to Hangul habitats spanned from 1600 to 2200 meters. Notably, habitats considered suitable with a high likelihood exhibited a bio19 range falling between 400 and 600 mm. The optimal range of bio11 for Hangul is -5\u0026deg;C to 5\u0026deg;C. Likewise, the mean diurnal range is considered most suitable when it falls within 5\u0026deg;C to 12\u0026deg;C, as illustrated in Figure S4. Furthermore, the ideal range for annual precipitation lies between 800 mm and 1000 mm, while the bio18 variable is found to be most conducive within the range of 100 mm to 300 mm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Current distribution\u003c/h2\u003e \u003cp\u003eEmploying a species distribution model, we successfully identified the current potential locations for finding the Hangul, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Traditionally, Hangul is known to inhabit the coniferous, oak, broadleaved, and grassland areas within Dachigam National Park, the adjacent Zabarwan mountains, Lidder, and Sindh valleys. However, the allocation of favorable habitats across the upper Himalayan region exhibits significant variation. The results of our study indicate that regions with very high and high suitability for the species are dispersed in a patchwork pattern throughout central Kashmir, spanning from Kulgam, Anantnag, and Pulwama, and extending to Ganderbal. Furthermore, there are constant strips of moderately suitable habitats along the elevated areas of Bandipura and Budgam districts under the prevailing climatic conditions (from the 1970s to the 2000s).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The assessment of habitat suitability under prospective scenarios of climate change\u003c/h2\u003e \u003cp\u003eSubsequently, a thorough investigation of the potential consequences of climate change on Hangul spreading, our findings suggest the present habitat that is currently the most suitable for the species is possible to contract and shift northward across varying elevation gradients. In the context of the 2050s and 2070s, encompassing all four Shared Socioeconomic Pathways (SSPs) scenarios, the migratory trajectory of Hangul's suitable habitat is poised to shift northward, encompassing regions like Kishtwar, Ganderbal, and Bandipora. This anticipates the emergence of an extended expanse of highly suitable habitats in the northern sector of Jammu and Kashmir and its neighboring areas, as visually depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As we approach 2050, the availability of suitable habitats for Hangul is expected to dwindle, particularly under the SSPs 245 and SSPs 585 scenarios, where the predominant suitable areas will be primarily concentrated in the northern domains of Kashmir. Under the SSPs 346 scenario, there is an envisaged reduction in habitat availability between Pulwama and Haramukh by 2050, leaving only slender segments of viable habitat in locales such as Bandi Bagh and Takya Sangi Reshi. Consequently, Hangul might confront a scarcity of suitable habitats in the Kulgam, Ramban, Budgam, and Shopian regions by 2050, particularly within the ambit of the SSPs 245 and SSPs 585 scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross all four of the climate change scenarios under investigation, it is anticipated that the once highly suitable habitats near Anantnag, Mattan, and Kishtwar will no longer be viable by the 2070s. Furthermore, regions like Budgam and Shopian face a critical risk of losing all of their present suitable and highly suitable habitats as early as the 2050s and continuing into the 2070s, particularly under the influence of the SSPs 245 and SSPs 585 scenarios. Additionally, during the transitional period between 2050 and 2070, there may only be limited availability of marginally suitable locations in areas like Kargil, Bandipora, and Ganderbal, predominantly at higher altitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn our analysis, we have estimated the likelihood of Hangul occurrence within different habitat suitability categories. Our findings suggest that the overall potential habitat suitability for Hangul, with a probability greater than 0.2 (p\u0026thinsp;\u0026gt;\u0026thinsp;0.2), is expected to decline in the future. In a more concise and comparative format, the projected reduction in potential habitat suitability is expected to be -5.06% in comparison to the current distribution range. This translates to 11,851 km\u0026sup2; potential habitat suitability under SSPs 245 for the 2050s, and a more significant decline of -10.31%, resulting in 9,687 km\u0026sup2; potential habitat suitability under SSPs 585 for the 2050s. Similarly, for the 2070s, under SSPs 245, potential habitat suitability is forecasted to decrease to 10,163 km\u0026sup2;, representing a -9.15% change, and to 10,310 km\u0026sup2; (-9.33% change) under SSPs 585 for the 2070s.\u003c/p\u003e \u003cp\u003eTaking a closer look, considering SSPs 245 in the 2050s, there is a forecasted slight reduction in very high suitability habitat (VHSR) compared to its current climatic conditions, specifically decreasing from 2,147 km\u0026sup2; to 7,838 km\u0026sup2; at a rate of -1.54%. Likewise, in the highly suitable habitat (VHSR), the potentially suitable land area is expected to diminish as follows: a decrease to 1,837 km\u0026sup2; (-2.29%) under SSPs 585 for the 2050s, a reduction to 2,095 km\u0026sup2; (-1.66%) under SSPs 245 for the 2070s, and a decline to 2,232 km\u0026sup2; (-1.45%) under SSPs 585 for the 2070s (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \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\u003eThe expected likelihood of Hangul's habitat suitability under different climate change scenarios offers valuable perspectives on the potential consequences of evolving environmental conditions for this particular species.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eClimate change scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eThe projected probability of Hangul occurrence within the assessed habitat suitability categories\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVHSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal suitable land area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u0026le;\u0026thinsp;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.21\u0026ndash;0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.41\u0026ndash;0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.61\u0026ndash;0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent climate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_245_2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate of change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_585_2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate of change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-10.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_245_2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate of change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_585_2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRate of change (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4. The Altitudinal shift of habitat\u003c/h2\u003e \u003cp\u003eThe projected areas suitable for Hangul fluctuated across various periods and were determined using different climate scenarios. However, the overarching pattern remained consistent, indicating a shrink of very highly and highly suitable regions, a rise in areas moderately suitable and less suitable, and a notable enhancement in habitat fragmentation. Considering future climate conditions, the centroids of the highly suitable and moderate regions gradually shifted northwestward over time, whereas the centroids of the least suitable areas moved northeastward. In general, the centroids migrated toward higher altitudes under future climate change. In 2050 under SSP245 and SSP585 the highly suitable habitat will shift from 3000m to 4000m elevation with an average 700m altitudinal migration habitats. Similarly, in 2070 the habitat shifts from 3000 to 5000m with an average 1300m altitudinal shift (Supplementary data: Figure S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Potential corridors for Hangul movement\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe corridors associated with Hangul\u0026rsquo;s potential movements are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, while the values denote potential dispersal distances from the core to new habitats. The total very highly suitable area in the protected region was (7%) 2220 ha and in the unprotected region (93%) 30445 ha. These corridors can bridge isolated remnant habitat patches, thereby enhancing connectivity and offering opportunities for the establishment of new habitats. A comprehensive ecological network spanning 1200 km\u0026sup2; (depicted in violet) has been delineated. Among this, approximately 200 km\u0026sup2; is designated as continuous corridors. The shortest corridor, spanning approximately 30 km, traverses the Haramukh mountain region adjacent to Wular Lake, which holds protected status. On the other hand, the most extensive corridor, with a length of around 70 km, extends along the PirPanjal Range in proximity to the Dachigam protected area. A point of significance is that the corridors denoted as C2, C3, and C4 prominently exhibit a persistent linkage between the core hotspot of Hangul distribution (depicted in red) and the designated protected area (highlighted in yellow). Notably, the corridor labeled as C1 terminates at potential new habitats (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). It's worth noting that while additional potential corridors exist, they lack the feasibility of maintaining a continuous connection to the central core.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Functional landscape changes\u003c/h2\u003e \u003cp\u003eThe prevalent morphological type within the species' current distribution and projected future distribution (under SSP585-2070) is the \"core\" type, which collectively constitutes 54.17% of the total study area. The percentage varies across scenarios, with proportions of 49.90% under the SSPs245-2050 scenario, 37.24% under the SSPs245-2070 scenario, and 35.59% under the SSPs585-2050 scenario (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The findings indicate that the suitable habitat for the Hangul might undergo a northward shift, reaching the Zanskar range of the Himalayas. Anticipated changes include the expansion of core areas to this new location in future scenarios, signifying a potential change in the species distribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Despite the alterations observed in Loop and Islet configurations, both classes exhibit similarities between their patterns under present and future conditions (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Although the percentage of changes in perforation is relatively small, there is a noticeable internal fragmentation process within the Core habitat, particularly under the SSPs245-2050 scenario. These alterations are primarily concentrated in the Kashmir region, encompassing districts like Kulgam, Anantnag, Pulwama, and Ganderbal. Importantly, these changes result in a loss due to perforation, constituting 1.39% of the total range (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Under the SSPs585-2050 and SSPs245-2070 scenarios, there was a notable reduction in the intensity of Core habitat loss, marked by contraction. Moreover, there were hardly any significant large-area cores observed in the PirPanjal Range when moving towards the northern direction. This observation was accompanied by an increasing percentage of bridges and branches within the habitat (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and D). Interestingly, the pessimistic scenario for 2070 predicts an expansion of the core area to higher altitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, among various climate conditions, the core-to-edge ratio is at its highest level in the current conditions. The spatial patterns of habitat edges in both optimistic and pessimistic scenarios for 2050, when compared to the present condition, exhibit a clear rise in the percentage of edges and remote edges (including perforation, loop and bridge connectors, and branches). Notably, the results indicate an increase in edge length due to habitat gain in the SSPs585-2070 scenario, while the processes of fragmentation become more pronounced under the SSPs245-585-2050 and SSPs245-2070 scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, C, D, and E). Consequently, there is an observed disparity in the abundance of edge habitat versus core habitat in the southern and northern regions within the study area. This discrepancy suggests greater habitat fragmentation in these areas. Conversely, the central region generally displays a higher ratio of core-to-edge habitat, which suggests a less fragmented habitat configuration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\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 S1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea of each class of species potential range according to the MSPA analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClimate condition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoop\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBridge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIslet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEdge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBranch\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePerforation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eArea (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7549.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1114.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1064.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e663.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1970.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1498.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_245_2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5913.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e781.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1322.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e400.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1871.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1395.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e165.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_585_2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3447.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e577.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1973.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e562.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1377.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1748.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_245_2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3784.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e765.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2308.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1690.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1341.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_585_2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5585.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e824.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e787.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e490.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1457.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1108.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_245_2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_585_2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_245_2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.3724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSPs_585_2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\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\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Priority area for conservation\u003c/h2\u003e \u003cp\u003eWe developed a prioritization framework utilizing our methodology to select priority areas. This strategy identified core regions that hold significant conservation promise due to their available space with suitable conditions, their capacity to enhance connectivity, and a preference for locations with minimal human impact (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The core areas that are currently most conducive to the species' well-being and have been classified as priority areas fall into two categories: \"very high priority,\" covering an area of 456.87 km\u0026sup2;, and \"high priority,\" encompassing 930.51 km\u0026sup2;. These areas include a narrow strip along the Himalayan range and northwest broadleaf forests, concentrated in both higher and lower elevations of the range. Based on the results, it's evident that an opportunity exists to extend certain existing protected areas, such as the Overa-Aru Wildlife Sanctuary, towards the direction of the Dachigam National Park (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eForecasting the outcomes of anthropogenic climate change on the geographical distribution of species is crucial, given that these shifts may result in mass extinctions (Wallingford et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The vast majority of existing data points to climate change as the primary driver of widespread species extinctions, and its effects on species distributions or hastening the rate of extinction should be anticipated (Rom\u0026aacute;n-Palacios and Wiens, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kemp et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Predicting how a species like Hangul, with only 250\u0026thinsp;+\u0026thinsp;individuals left, will react to climate change is difficult because unsuccessful reproduction and colonization are dependent on diverse factors, including geographic range, ecologically isolated populations, low reproductive rates, and a high degree of specific habitat requirements (Maviya Majid et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mukherjee et al., 2019; Malhi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Inouye, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Hangul is already facing the risk of low reproductive rates and isolated populations (Ahmad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and thus climate change may aggravate it further. However, SDMs offer a valuable method for integrating future conditions into approaches and choices in conservation and management, when the consequences of incorrect conservation strategies or inaction are balanced against the uncertainties of model forecasts (Hu and Jiang, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In light of this, we investigated any potential changes in the Greater Dachigam landscape\u0026rsquo;s distribution of endangered species caused by climate change. Based on the current suitable habitat in the Greater Dachigam landscape, our findings highlight the complexity of changes in species distributions under climate change (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Hangul's distribution is therefore anticipated to decrease in the Dachigam landscape (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). According to earlier findings, individual species' distributions are uniformly influenced by climate change because they react to it in the same patterns (Semenzato et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Haq et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The ability of individuals from the same species to adapt to local climate change is probably similar (Thurman et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the likelihood of those individuals surviving in their current habitats in the face of large climate change decreased as the time horizon grew longer and the severity of the consequences from climate change increased (Pearce-Higgins et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similar findings were also reported by Hu and Jiang (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Mason et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) when predicting the likely responses of endangered ungulates to future climatic conditions. When Thorstad et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the effect of climate change on the survival of Atlantic salmon, they came to similar findings. Our conclusions are supported by Scapini et al. (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur model's goal was to elucidate if environmental factors may independently predict the habitat that is suitable for Hangul within the Himalayas surrounding the Dachigam landscape. Bio14 was the most important variable, showing that the precipitation in the driest month may have a greater impact on species distributions. According to Bradie and Leung (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), precipitation and temperature had the greatest overall contributions to species distribution for terrestrial species. Similarly, Jiang (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and Chen et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) observed that the driest months' precipitation exerted a notable influence on the distribution of endemic, rare, and threatened species, similar to our findings. This is corroborated by numerous other investigations, e.g., for \u003cem\u003ePygathrix cinerea\u003c/em\u003e (Tran et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Himalayan Goral (Haq et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Red panda (Thapa et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and \u003cem\u003eTragopan melanocephalus\u003c/em\u003e (Jameel et al., 2023). The second most important variable was bio19 (coldest month precipitation), the third elevation, and the fourth was bio08 (wettest quarter mean temperature). Previous studies revealed that average precipitation alone may not accurately predict bioclimatic variance and that the mean temperature of the bio19 (coldest month precipitation), (Jiang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); wettest quarter (bio08) (Zhong et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and elevation (Campos et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jameel et al., 2023) may also play a key role in species distributions potentially causing species to extend or decrease their ranges. Our findings underline that rather than focusing solely on rising global temperatures, it is necessary to incorporate precipitation and other climate variables (including extreme circumstances) into future expected species distributions.\u003c/p\u003e \u003cp\u003eWhen assessing important environmental variables, our model of the appropriateness of the current Hangul habitat functioned well (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Our best model suggested that the overall suitable Hangul habitat spanned 13936 km\u003csup\u003e2\u003c/sup\u003e based on a 10-percentile criterion, accounting for 8% of moderately suitable habitat and 7% of high-suitability habitat. Ali et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) compare well to our findings, indicating that under current climatic conditions, 9% of the overall study region was suitable for blue sheep from the Hindu Kush ranges. When the repercussions of global warming are considered, the highly suitable habitats for Hangul distributions in the Dachigam Landscape will decrease in the future (from 5.06 to 9.33 km\u003csup\u003e2\u003c/sup\u003e). Numerous other studies also emphasized how threatened species will face a considerable loss of their habitats due to the altering climate, particularly in the Himalayan regions (Ali et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liang et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jameel et al., 2023; Haq et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Examples include Himalayan musk deer (Khadka and James \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) blue sheep (Islam et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and NilgiriTahr (Sony et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe current distribution of the Hangul is anticipated to shift along both elevational and latitudinal gradients because of climate change. Instead of an expansion of their range to higher altitudes, the projected changes along the elevational gradient were mostly attributable to a significant reduction in their geographic distribution. This result was consistent with the idea put forth by Luo et al. (2015), and it is possible that the geography of the Dachigam terrain, covering a relatively narrow range, may have had a major role in these results. Our data indicate that Hangul is mostly found at the elevation of 2000 to 4000m, which highlights the species' preference for high-elevation habitats with colder climates and less anthropogenic disturbance. It aligns with the findings of Ahmad et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) in Dachigam National Park, reporting Hangul occurrence at elevations ranging from 1700\u0026ndash;2300 m and an altitudinal range of 1700\u0026ndash;3500 m (Qureshi et al. 2009). Our findings confirmed what was previously known about the altitudinal habitat range of Hangul and this helps to understand the ecological needs and niche of Hangul for formulating an effective conservation strategy. This altitudinal preference can be seen as a result of different bioclimatic and anthropogenic factors, including vegetation types occurring at that elevation and less snow cover. Similar preferences for vegetation types were determined by Sharma et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Qureshi et al. (2016). However, our results indicate that climate change is likely to cause the Hangul population to move upwards to northern regions as its habitat range shifts to higher elevations. Similarly, Chen et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) noted that species were rapidly moving towards higher elevations to combat heat due to climate change. This is further corroborated by the study results of Lenoir et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) who determined an upward shift of plant species especially in mountainous regions due to climate change. Agnihotri et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), He et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Yadav et al. (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also documented the climate change-driven altitudinal shift of Himalayan vegetation. As a result, animal species will follow, e.g. Himalayan Goral (Haq et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We suggest that climate change is causing altitudinal shift in Hangul and there is a need to conserve the present and future habitat range for Hangul so that climate change and elevation shifts could not pose a threat to the survival of this species.\u003c/p\u003e \u003cp\u003eCurrently, Hangul populations are also found outside the protected areas, particularly during summer. Of the 34975 km\u003csup\u003e2\u003c/sup\u003e of predicted habitat, 30445 km\u003csup\u003e2\u003c/sup\u003e (93%) lie outside the protected areas, which is a serious concern for this highly threatened species. The effectiveness of this unprotected network and potential changes in protected habitat suitability for the Hangul have not yet been assessed under climate change. Different parts of the unprotected habitat are under varying levels of restrictions on human activity. As a result, the effectiveness of some of these sites under climate change may be significantly influenced by the management techniques used in the future. This holds particularly true for unprotected regions but has the potential to have high conservation value in the future. Considering the crucial role that unprotected regions play in the survival of the Hangul, projecting the species' future distribution to guide the selection of new sites or expanding the current array of protected zones could be an efficient conservation approach to confronting the challenges posed by climate change. More crucially, in response to climate change, the effectiveness of the network of protected zones might be significantly increased by sustaining crucial corridors that permit Hangul mobility. Given the expansion of anthropogenic activities, several protected sites already become unsuitable for Hangul. The Zonation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) has given a very good and practical result for Hangul conservation. The securement and protection of very high and high-priority zones occurring between the existing protected areas can boost the conservation of hangul for the long term. For example, the areas between Tral and Achabal PAs, between Dachigam and Wanghat, Wanghat and Ajas, and Ajas and Kishan Ganga (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Securing such an area will provide a contiguous landscape for long-term conservation for a mobile species like Hangul.\u003c/p\u003e \u003cp\u003eWe identified critical sites with the potential for future Hangul conservation. We also identified corridors for Hangul that would last well into the future, enabling affected populations to relocate in response to climate change. Enhancing habitat availability and accessibility is crucial for species adjusting to the challenges of climate change. Improving landscape connectivity, and the facilitation of species movement emerge as a key conservation strategy. Identifying and maintaining well-suited corridors through methods like Species Distribution Models (SDMs) enhances animal resilience by providing avenues to escape unsuitable conditions. Our forecast identified south Kashmir, notably the high-altitude conifer forest areas of Anantnag, Kulgam, and Pulwama districts, as highly suitable habitats for Hangul conservation in the Kashmir Himalayas. Kaul et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also found \u003cem\u003eCervus hangul hanglu\u003c/em\u003e in Central/north Kashmir conifer woodlands such as Wanghat-Naranag, Rajparian-Daksum, Overa-Aru, and Thajwas-Baltal, confirming our predictions with field-based survey data.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Implications for conservation of Dachigam landscape\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eHabitat conservation through protected areas has been a primary issue in maintaining the survival and sustainability of the critically endangered Hangul population. However, isolated protected areas cannot help to conserve the hangul population for the long term due to the issues of inbreeding, and thus connectivity among these isolated protected areas becomes crucial. Existing PAs cover only 7.7% (2220 km\u003csup\u003e2\u003c/sup\u003e) of the expected future Hangul habitat. This indicates that most of the Hangul habitat (93%) is currently unprotected, highlighting a huge risk of extinction as anthropogenic disturbance and climate change increase. As a result, we propose that nearby core habitat regions must be protected to connect the Hangul subpopulations. Our results revealed that 9.33% of high-suitability areas for the Hangul movement might become unsuitable by 2070 (Fig.\u0026nbsp;9), adding to the issues of Hangul conservation. On the other hand, overexploitation in the form of illicit wildlife poaching, habitat degradation, and fragmentation pose serious threats to its main abode, the Greater Dachigam landscape. These issues have led to ecological isolation, population decline, and inbreeding and brought the species to the brink (Ahmad, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ahmad et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the establishment of protected areas is a helpful conservation strategy, it has been found that poor management and smaller size may negate the advantages of all conservation initiatives aimed at enhancing biodiversity. There restricted distribution of Hangul and its core areas in the context of existing and prospective climate change scenarios implies that the Dachigam Landscape is seriously lacking in space. As a result, Dachigam Landscape urgently needs to be expanded or connected to the potential hangul sites including the neighboring reserves and the hotspots/core areas. Ecological corridors around greater Dachigam landscape should be prioritized for conservation and management to secure it and the Hangul. According to our findings, hotspots may mostly move outside of the Dachigam landscape due to climate change. Therefore, our suggestions can assist in developing dynamic protected areas within the Dachigam landscape over an extended period.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIt is crucial to foresee how anthropogenic global warming could have an impact on species distributions because these changes are expected to result in widespread extinctions of species. According to the projected distribution of Hangul under various climate change scenarios, the available habitat for this species would become significantly scarcer. In the future, it is anticipated that a sizable section of the current habitat with a high likelihood of occurrence will become unsuitable due to climate change. Our model detected a robust Hangul response to bioclimatic factors associated with precipitation in the Himalayas, with a notable emphasis on rainfall during the driest month and precipitation during the coldest quarter. Despite these factors, Dachigam Landscape\u0026rsquo;s some of the current meta-populations and potential future sites of Hangul are connected through a contiguous forest landscape which can act as corridors and can play a crucial role for the long-term conservation of Hangul. We recommend that such corridors and sites be surveyed and secured on priority to improve habitat connectivity and ensure Hangul conservation in the realm of global warming and the critical status of Hangul.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization S.M.H; Study Design S.M.H; Data collection- R.A., S.M.H; Data analysis- L.W; S.A; M.W; S.M.H; Original draft writing S.M.H; Review and editing-R. A; R.W.B; L.W; S.A; M.W; S.M.H. All authors read and approved the final manuscript.We have no conflict of interest to disclose. Moreover, the sequence of authorships mentioned in the article is based upon the contribution of the work by all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAb Lah NZ, Yusop Z, Hashim M, Mohd Salim J, Numata S (2021) Predicting the habitat suitability of \u003cem\u003eMelaleuca cajuputi\u003c/em\u003e based on the MaxEnt species distribution model. Forests. 12(11), 1449. \u003c/li\u003e\n\u003cli\u003eAgnihotri P, Husain T, Shirke PA, Sidhu OP, Singh H, Dixit V, Khuroo AA, Amla DV, Nautiyal CS (2017) Climate change-driven shifts in elevation and ecophysiological traits of Himalayan plants during the past century. Curr. sci. 595-601.\u003c/li\u003e\n\u003cli\u003eAhmad K (2022) Ecology and Conservation of Mountain Ungulate in the Western and Trans Himalayas, India.https://doi.org/ 10.5772/intechopen.108809\u003c/li\u003e\n\u003cli\u003eAhmad K, Mirelli M, Charoo S, Nigam P, Qureshi Q, Naqash RY, Focardi S (2023) Is the hangul Cervus hangluhanglu in Kashmir drifting towards extinction? Evidence from 19 years of monitoring. Oryx. 57(5), 585-591. \u003c/li\u003e\n\u003cli\u003eAhmad K, Qureshi Q, Agoramoorthy G, Nigam P (2016) Habitat use patterns and food habits of the Kashmir red deer or Hangul (\u003cem\u003eCervus elaphushanglu\u003c/em\u003e) in Dachigam National Park, Kashmir, India. Ethol. Ecol. Evol. 28(1), 85-101. \u003c/li\u003e\n\u003cli\u003eAhmad, K., Sathyakumar, S., Qureshi, Q., 2009. Conservation status of the last surviving wild population of hangul or Kashmir deer Cervus elaphushanglu in Kashmir, India. J. Bombay Nat. Hist. 106(3), 245.\u003c/li\u003e\n\u003cli\u003eAli H, Din JU, Bosso L, Hameed S, Kabir M, Younas M, Nawaz MA (2021) Expanding or shrinking? range shifts in wild ungulates under climate change in Pamir-Karakoram mountains, Pakistan. PLoS One.16(12), e0260031. \u003c/li\u003e\n\u003cli\u003eAlmasieh K, Mohammadi A, Alvandi R (2022) \u0026lsquo;Identifying core habitats and corridors of a near threatened carnivore, striped hyaena \u003cem\u003eHyaena hyaena\u003c/em\u003e) in southwestern Iran\u0026rsquo;, Sci. Rep. 12(1), 3425. \u003c/li\u003e\n\u003cli\u003eAnt\u0026atilde;o LH, Weigel B, Strona G, H\u0026auml;llfors M, Kaarlej\u0026auml;rvi E, Dallas T, Opedal \u0026Oslash;H, Heli\u0026ouml;l\u0026auml; J, Henttonen H, Huitu O, Korpim\u0026auml;ki E (2022) Climate change reshuffles northern species within their niches. Nat. Clim. Change. 12(6), 587-592. \u003c/li\u003e\n\u003cli\u003eArshad F, Waheed M, Fatima K, Harun N, Iqbal M, Fatima K, Umbreen S (2022) Predicting the suitable current and future potential distribution of the native endangered tree \u003cem\u003eTecomellaundulata\u003c/em\u003e (Sm.) Seem. in Pakistan. Sustainability. 14(12), 7215. \u003c/li\u003e\n\u003cli\u003eAryal A, Brunton D, Raubenheimer D (2014) Impact of climate change on human-wildlife-ecosystem interactions in the Trans-Himalaya region of Nepal. Theor. Appl. Climatol. 115, 517-529. \u003c/li\u003e\n\u003cli\u003eAwan MN, Saqib Z, Buner F, Lee DC, Pervez A (2021) Using ensemble modeling to predict the breeding habitat of the red-listed Western Tragopan (Tragopanmelanocephalus) in the Western Himalayas of Pakistan. Glob. Ecol. Conserv. 31, e01864. \u003c/li\u003e\n\u003cli\u003eAzfar Hussain, SA, Begum S, Ali IHH (2019) Climate change perspective in mountain area: Impact and adaptations in Naltar valley, western Himalaya, Pakistan. Fresenius Environ. Bull. 28, 6683-6691.\u003c/li\u003e\n\u003cli\u003eBai DF, Chen PJ, Atzeni L, Cering L, Li Q, Shi K, 2018. Assessment of habitat suitability of the snow leopard (Panthera uncia) in Qomolangma National Nature Reserve based on MaxEnt modeling. Zool. Res. 39(6), 373. \u003c/li\u003e\n\u003cli\u003eBellard C, Bertelsmeier C, Leadley P, Thuiller W, Courchamp F (2012) Impacts of climate change on the future of biodiversity. Ecol. Lett. 15(4), 365-377. \u003c/li\u003e\n\u003cli\u003eBhat BA, Ahmad R, Fazili MF, Haq IU, Bhat GA (2020) Threatened fauna of Jammu and Kashmir state. Biodiversity of the Himalaya: Jammu and Kashmir State, 997-1009.\u003c/li\u003e\n\u003cli\u003eBhat BA, Shah GM, Jan U, Ahangar FA, Fazili MF (2009) Observations on rutting behaviour of Hangul deer Cervus elaphus hanglu (cetartiodactyla: Cervidae) in Dachigam National Park, Kashmir, India. J. Threatened Taxa. 355-357.\u003c/li\u003e\n\u003cli\u003eBhat KA, Bhat BA, Ganai BA, Majeed A, Khurshid N, Manzoor M (2023) Food habits of the Red Fox Vulpes vulpes (Mammalia: Carnivora: Canidae) in Dachigam National Park of the Kashmir Himalaya, India. J. Threat. Taxa. 15(1), 22364-22370.\u003c/li\u003e\n\u003cli\u003eBhattacharyya S, Mungi NA, Kawamichi T, Rawat GS, Adhikari BS, Wilkening JL (2019) Insights from the present distribution of an alpine mammal Royle\u0026rsquo;spika (\u003cem\u003eOchotona roylei\u003c/em\u003e) to predict future climate change impacts in the Himalayas. Reg. Environ. Change. 19, 2423-2435. \u003c/li\u003e\n\u003cli\u003eBosso L, Di Febbraro M, Cristinzio G, Zoina A, Russo D (2016) Shedding light on the effects of climate change on the potential distribution of \u003cem\u003eXylellafastidiosa\u003c/em\u003e in the Mediterranean basin. Biol. Invas. 18, 1759-1768. \u003c/li\u003e\n\u003cli\u003eBradie J, Leung B (2017) A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44(6), 1344-1361. \u003c/li\u003e\n\u003cli\u003eBrun P, Thuiller W, Chauvier Y, Pellissier L, W\u0026uuml;est RO, Wang Z, Zimmermann NE (2020) Model complexity affects species distribution projections under climate change. J. Biogeogr. 47(1), 130-142. https://doi.org/10.1111/jbi.13734\u003c/li\u003e\n\u003cli\u003eBuchholz R, Banusiewicz JD, Burgess S, Crocker-Buta S, Eveland L, Fuller L (2019) Behavioral research priorities for the study of animal response to climate change. Anim. Behav. 150, 127-137.\u003c/li\u003e\n\u003cli\u003eCampos PV, Villa, PM, Schaefer CEGR, Nunes JA, Porembski S, Neri AV (2020) Community composition, beta diversity, and structure of high altitude grasslands along an altitudinal gradient in southeastern Brazil. Rev. biol. trop. 68(3), 977-986. \u003c/li\u003e\n\u003cli\u003eChen IC, Hill JK, Ohlem\u0026uuml;ller R, Roy DB, Thomas CD (2011) Rapid range shifts of species associated with high levels of climate warming. Science. \u003cem\u003e333\u003c/em\u003e(6045), 1024-1026. \u003c/li\u003e\n\u003cli\u003eChen W, Miao K, Guo K, Qian W, Sun W, Wang H, Chang Q, Hu C (2023) Potential Geographic Range of the Endangered Reed Parrotbill \u003cem\u003eParadoxornisheudei\u003c/em\u003e under Climate Change. Biology. \u003cem\u003e12\u003c/em\u003e(4), 560. \u003c/li\u003e\n\u003cli\u003eChhetri B, Badola HK, Barat S (2021) Modeling climate change impacts on the distribution of Himalayan pheasants. Ecol. Ind. 123, 107368.\u003c/li\u003e\n\u003cli\u003eDahal N, Lamichhaney S, Kumar S (2021) Climate change impacts on Himalayan biodiversity: evidence-based perception and current approaches to evaluating threats under climate change. J. Indian Inst. Sci. 101(2), 195-210.\u003c/li\u003e\n\u003cli\u003eDanielson JJ, Gesch DB (2011) Global multi-resolution terrain elevation data 2010 (GMTED2010).\u003c/li\u003e\n\u003cli\u003eDe Frenne P, Lenoir J, Luoto M, Scheffers BR, Zellweger F, Aalto J, Ashcroft MB, Christiansen DM, Decocq G, De Pauw K, Govaert S (2021) Forest microclimates and climate change: Importance, drivers and future research agenda. Glob. Change Biol. 27(11), 2279-2297. \u003c/li\u003e\n\u003cli\u003eDeMarche ML, Doak DF, Morris WF (2019) Incorporating local adaptation into forecasts of species distribution and abundance under climate change. Glob. Change Bio. 25(3), 775-793.\u003c/li\u003e\n\u003cli\u003eD\u0026iacute;az S, Malhi Y, (2022) Biodiversity: Concepts, patterns, trends, and perspectives. Annu Rev Environ Resour. 47, 31-63. \u003c/li\u003e\n\u003cli\u003eElith JH, Graham CP, Anderson R, Dud\u0026iacute;k M, Ferrier S, Guisan AE, Zimmermann N (2006) Novel methods improve the prediction of species distributions from occurrence data. Ecography, 29(2), 129-151. \u003c/li\u003e\n\u003cli\u003eEyring V, Cox PM, Flato GM, Gleckler PJ, Abramowitz G, Caldwell P, Williamson MS (2019) Taking climate model evaluation to the next level. Nat. Clim. Change 9(2), 102-110. \u003c/li\u003e\n\u003cli\u003eFourcade Y, Engler JO, Rodder D, Secondi J (2014) Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PloS one. 9(5), e97122. \u003c/li\u003e\n\u003cli\u003eGao X, Liu J, Huang Z (2022) The impact of climate change on the distribution of rare and endangered tree \u003cem\u003eFirmianakwangsiensis\u003c/em\u003e using the Maxent modeling. Ecol. Evol. 12(8), e9165. \u003c/li\u003e\n\u003cli\u003eGhisbain G, G\u0026eacute;rard M, Wood TJ, Hines HM, Michez D (2021) Expanding insect pollinators in the Anthropocene. Biol. Rev. 96(6), 2755-2770. \u003c/li\u003e\n\u003cli\u003eGilani H, Goheer MA, Ahmad H, Hussain K (2020) Under predicted climate change: Distribution and ecological niche modeling of six native tree species in Gilgit-Baltistan, Pakistan. Ecol. Ind. 111, 106049.\u003c/li\u003e\n\u003cli\u003eGraham MH (2003) Confronting multicollinearity in ecological multiple regression. Ecology. 84(11), 2809-2815. \u003c/li\u003e\n\u003cli\u003eHaq SM, Yaqoob U, Calixto ES, Kumar M, Rahman IU, Hashem A, Ijaz F (2021) The long-term impact of transhumance pastoralism and associated disturbances in high-altitude forests of Indian Western Himalaya. Sustainability. 13(22), 12497. \u003c/li\u003e\n\u003cli\u003eHaq SM, Waheed M, Ahmad R, Bussmann RW, Arshad F, Khan AM, Casini R, Alataway A, Dewidar AZ, Elansary HO (2023) Climate Change and Human Activities, the Significant Dynamic Drivers of Himalayan Goral Distribution (\u003cem\u003eNaemorhedus goral\u003c/em\u003e). Biology, \u003cem\u003e12\u003c/em\u003e(4), 610.https://doi.org/10.3390/biology12040610\u003c/li\u003e\n\u003cli\u003eHassan T, Hamid M, Wani SA, Malik AH, Waza SA, Khuroo AA (2021) Substantial shifts in flowering phenology of \u003cem\u003eSternbergiavernalis\u003c/em\u003e in the Himalayas: Supplementing decadal field records with historical and experimental evidence. Sci. Total Environ. 795, 148811. \u003c/li\u003e\n\u003cli\u003eHassan U, Shah GM, Jan U (2013) A Study of Interaction between Hangul Deer and Livestock at Dachigam National Park Kashmir (Doctoral dissertation).\u003c/li\u003e\n\u003cli\u003eHe X, Burgess KS, Yang XF, Ahrends A, Gao LM, Li DZ (2019) Upward elevation and northwest range shifts for alpine \u003cem\u003eMeconopsis\u003c/em\u003e species in the Himalaya\u0026ndash;Hengduan Mountains region. Ecol. Evol. 9(7), 4055-4064. \u003c/li\u003e\n\u003cli\u003eHijmans RJ, Cameron SE, Parra JL, Jones P G, Jarvis A (2005) Very high-resolution interpolated climate surfaces for global land areas. Int J Climatol. 25(15), 1965-1978. \u003c/li\u003e\n\u003cli\u003eHoffmann AA, Rymer PD, Byrne M, Ruthrof KX, Whinam J, McGeoch M, Bergstrom DM, Guerin GR, Sparrow B, Joseph L, Hill SJ (2019) Impacts of recent climate change on terrestrial flora and fauna: Some emerging Australian examples. Aust. Ecol. 44(1), 3-27. \u003c/li\u003e\n\u003cli\u003eHu J, Jiang Z (2011) Climate change hastens the conservation urgency of an endangered ungulate. PloS one. 6(8), e22873. \u003c/li\u003e\n\u003cli\u003eInouye DW (2022) Climate change and phenology. Wiley Interdiscip. Rev.: Clim. Change. \u003cem\u003e13\u003c/em\u003e(3), e764. \u003c/li\u003e\n\u003cli\u003eIslam M, Sahana M, Areendran G, Jamir C, Raj K, Sajjad H (2023) Prediction of potential habitat suitability of snow leopard (\u003cem\u003ePanthera uncia\u003c/em\u003e) and blue sheep (\u003cem\u003ePseudoisnayaur\u003c/em\u003e) and niche overlap in the parts of the western Himalayan region. Geo: Geogr. Environ. \u003cem\u003e10\u003c/em\u003e(1), e00121. \u003c/li\u003e\n\u003cli\u003eJain P (2022) Endangered Hangul population increasing in Kashmir. November 11, 2022Animals, Environment, Kashmir Report, States. \u003c/li\u003e\n\u003cli\u003eJiang F (2018) Bioclimatic and altitudinal variables influence the potential distribution of canine parvovirus type 2 worldwide. Ecol. Evol.8(9), 4534-4543. \u003c/li\u003e\n\u003cli\u003eJiang F, Li G, Qin W, Zhang J, Lin G, Cai Z, Gao H, Zhang T (2019) Setting priority conservation areas of wild Tibetan gazelle (\u003cem\u003eProcaprapicticaudata\u003c/em\u003e) in China\u0026apos;s first national park. Glob. Ecol. Conserv. 20, e00725. https://doi.org/10.1016/j.gecco.2019.e00725\u003c/li\u003e\n\u003cli\u003eKaboodvandpour S, Almasieh K, Zamani N (2021) \u0026lsquo;Habitat suitability and connectivity implications for the conservation of the Persian leopard along Iran \u0026ndash; \u0026shy; Iraq border\u0026rsquo;, Ecol. Evol. 11(19), 13464\u0026ndash;13474. \u003c/li\u003e\n\u003cli\u003eKaul R, Chatterjee M, Bhattacharya T, Bodhankar S, Ahmad R, Sofi MN, Charoo SA (2018) Conservation prospects of the Kashmir Red Deer (\u003cem\u003eCervus hangluhanglu\u003c/em\u003e) beyond Dachigam National Park, in Jammu and Kashmir, India. Curr. Sci. 2123-2130. \u003c/li\u003e\n\u003cli\u003eKemp L, Xu C, Depledge J, Ebi KL, Gibbins G, Kohler TA, Rockstr\u0026ouml;m J, Scheffer M, Schellnhuber HJ, Steffen W, Lenton TM (2022) Climate Endgame: Exploring catastrophic climate change scenarios. Proc. Natl. Acad. Sci. 119(34), e2108146119.https://doi.org/10.1073/pnas.2108146119\u003c/li\u003e\n\u003cli\u003eKhadka KK, James DA (2017) Modeling and mapping the current and future climatic niches of endangered Himalayan musk deer. Ecol. Infor. 40, 1-7. \u003c/li\u003e\n\u003cli\u003eKhanal L, Chalise MK, Jiang X (2018) Ecological niche modeling of Himalayan langur (\u003cem\u003eSemnopithecus entellus\u003c/em\u003e) in the southern flank of the Himalayas. J. Sci. Technol. 23(1), 1-9.\u003c/li\u003e\n\u003cli\u003eKhattak RH, Teng L, Ahmad S, Bari F, Rehman EU, Shah AA, Liu Z (2022) In Pursuit of New Spaces for Threatened Mammals: Assessing Habitat Suitability for Kashmir Markhor (\u003cem\u003eCapra falconericashmeriensis\u003c/em\u003e) in the Hindukush Range. Sustainability. 14(3), 1544. \u003c/li\u003e\n\u003cli\u003eKhuroo AA, Mehraj G, Muzafar I, Rashid I, Dar GH (2020) Biodiversity conservation in Jammu and Kashmir state: current status and future challenges. \u003cem\u003eBiodiversity of the Himalaya: Jammu and Kashmir State\u003c/em\u003e, 1049-1076.\u003c/li\u003e\n\u003cli\u003eLeclerc C, Courchamp F, Bellard C (2020) Future climate change vulnerability of endemic island mammals. Nat comm. 11(1), 4943.https://doi.org/10.1038/s41467-020-18740-x\u003c/li\u003e\n\u003cli\u003eLenoir J, G\u0026eacute;gout JC, Marquet PA, de Ruffray P, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century. science\u003cem\u003e320\u003c/em\u003e(5884), 1768-1771. \u003c/li\u003e\n\u003cli\u003eLi G, Huang J (2022) Multi-Directional Rather Than Unidirectional Northward-Dominant Range Shifts Predicted under Climate Change for 99 Chinese Tree Species. Forests. \u003cem\u003e13\u003c/em\u003e(10), 1619. \u003c/li\u003e\n\u003cli\u003eLiang J, Ding Z, Jiang Z, Yang X, Xiao R, Singh PB, Hu Y, Guo K, Zhang Z, Hu H (2021) Climate change, habitat connectivity, and conservation gaps: a case study of four ungulate species endemic to the Tibetan Plateau. Landsc. Ecol. 36, 1071-1087. \u003c/li\u003e\n\u003cli\u003eLinshan L, Zhilong Z, Yili Z, Xue W (2017) Using the maxent model to predict suitable habitat changes for key protected species in Koshi Basin, Central Himalayas. J. Resour. Ecol. 8(1), 77-87. \u003c/li\u003e\n\u003cli\u003eLittlefield CE, Krosby M, Michalak JL, Lawler JJ (2019) Connectivity for species on the move: supporting climate‐driven range shifts. Front. Ecol. Environ. 17(5), 270-278. \u003c/li\u003e\n\u003cli\u003eMalhi Y, Franklin J, Seddon N, Solan M, Turner MG, Field CB, Knowlton N (2020) Climate change and ecosystems: Threats, opportunities, and solutions. Philos Trans R Soc Lond B Biol Sci. 37\u003cem\u003e5\u003c/em\u003e(1794), p.20190104.https://doi.org/10.1098/rstb.2019.0104\u003c/li\u003e\n\u003cli\u003eMalik JA, Bansal SK (2016) \u0026lsquo;A review of population ecology of Hangul deer (Cervus elaphus Hanglu, Wagner / Cervus Canadensis Hanglu) in Dachigam National Park\u0026rsquo;, (May).\u003c/li\u003e\n\u003cli\u003eManes S, Costello MJ, Beckett H, Debnath A, Devenish-Nelson E, Grey KA, Jenkins R, Khan TM, Kiessling W, Krause C, Maharaj SS (2021) Endemism increases species\u0026apos; climate change risk in areas of global biodiversity importance. Biol. Conser. 257, 109070. \u003c/li\u003e\n\u003cli\u003eMason TH, Stephens PA, Apollonio M, Willis SG (2014) Predicting potential responses to future climate in an alpine ungulate: interspecific interactions exceed climate effects. Glob. Change Biol. 20(12), 3872-3882. \u003c/li\u003e\n\u003cli\u003eMaviya Majid MK, Ahmad W, Gul S (2019) Hungul (\u003cem\u003eCervus elaphushanglu\u003c/em\u003e; Red Deer) As Flagship Species of Dachigam National Park: A Brief Review. J. Wildl. Res. 7(03), pp.50-58.\u003c/li\u003e\n\u003cli\u003eMcRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. PNAS 104:19885\u0026ndash;19890.https://doi.org/10.1073/pnas.0706568104\u003c/li\u003e\n\u003cli\u003eMerow C, Smith MJ, Silander Jr JA (2013) A practical guide to MaxEnt for modeling species\u0026apos; distributions: what it does, and why inputs and settings matter. Ecography. 36(10), 1058-1069.\u003c/li\u003e\n\u003cli\u003eMoilanen A, Lehtinen P, Kohonen I, Virtanen E, Jalkanen J, Kujala H (2022) Novel methods for spatial prioritization with applications in conservation, land use planning, and ecological impact avoidance. Methods Ecol. Evol. 13:1062\u0026ndash;1072. \u003c/li\u003e\n\u003cli\u003eMorueta-Holme N, Fl\u0026oslash;jgaard C, Svenning JC (2010) Climate change risks and conservation implications for a threatened small-range mammal species. PloS one. 5(4), e10360. \u003c/li\u003e\n\u003cli\u003eMukesh Kumar VP, Sharma, L.K., Shukla, M., Sathyakumar, S., 2015. Pragmatic perspective on conservation genetics and demographic history of the last surviving population of Kashmir red deer (\u003cem\u003eCervus elaphushanglu\u003c/em\u003e) in India. PLoS One. 10(2), e0117069. \u003c/li\u003e\n\u003cli\u003eMukherjee T, Sharma V, Sharma LK, Thakur M, Joshi BD, Sharief A, Thapa A, Dutta R, Dolker S, Tripathy B, Chandra K (2021) Landscape-level habitat management plan through geometric reserve design for critically endangered Hangul (\u003cem\u003eCervus hangluhanglu\u003c/em\u003e). Sci. Total Environ. 777, 146031. \u003c/li\u003e\n\u003cli\u003ePearce-Higgins JW, Ant\u0026atilde;o LH, Bates RE, Bowgen KM, Bradshaw CD, Duffield SJ, Ffoulkes C, Franco AMA, Geschke J, Gregory RD, Harley MJ (2022) A framework for climate change adaptation indicators for the natural environment. Ecol. indic. \u003cem\u003e136\u003c/em\u003e, 108690.\u003c/li\u003e\n\u003cli\u003ePhillips SJ, Dud\u0026iacute;k M, Elith J, Graham CH, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data. Ecol. Appl. 19(1), 181-197. \u003c/li\u003e\n\u003cli\u003ePhillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol. Mod. 190(3-4), 231-259. \u003c/li\u003e\n\u003cli\u003eQGIS Development Team 2023. QGIS Geographic Information System. Open Source Geospatial Foundation Project. https://qgis.org/en/site/index.html\u003c/li\u003e\n\u003cli\u003eRao A (2002) Pastoral nomads, the state and a national park: The case of Dachigam, Kashmir. Nomadic Peoples. 72-98.\u003c/li\u003e\n\u003cli\u003eRom\u0026aacute;n-Palacios C, Wiens JJ (2020) Recent responses to climate change reveal the drivers of species extinction and survival. PNAS. 117(8), 4211-4217. \u003c/li\u003e\n\u003cli\u003eScapini F, Degli EI, Defeo O (2019) Behavioral adaptations of sandy beach macrofauna in face of climate change impacts: A conceptual framework. Estuar. Coast. Shelf Sci. 225, 106236. \u003c/li\u003e\n\u003cli\u003eSchneiderbauer S, Pisa PF, Delves JL, Pedoth L, Rufat S, Erschbamer M, Thaler T, Carnelli F, Granados-Chahin S (2021) Risk perception of climate change and natural hazards in global mountain regions: A critical review. Sci. Total Environ. 784, 146957. \u003c/li\u003e\n\u003cli\u003eSemenzato P, Cagnacci F, Ossi F, Eccel E, Morellet N, Hewison AM, Sturaro E, Ramanzin M (2021) Behavioral heat‐stress compensation in a cold‐adapted ungulate: Forage‐mediated responses to warming Alpine summers. Eco. Lett. 24(8), 1556-1568. \u003c/li\u003e\n\u003cli\u003eShah GM, Jan U, Bhat BA, Ahanger FA (2011) Causes of decline of critically endangered hangul deer in Dachigam National Park, Kashmir (India): A review. Int. J. Biodivers. Conserv, 3(14), 735-738.\u003c/li\u003e\n\u003cli\u003eSharma LK, Charoo SA, Sathyakumar S (2010) Habitat use and food habits of Kashmir red deer or hangul (\u003cem\u003eCervus elaphushanglu\u003c/em\u003e) at Dachigam National Park, Kashmir, India. Galemys. \u003cem\u003e22\u003c/em\u003e, 309-329.\u003c/li\u003e\n\u003cli\u003eSharma LK, Joshi BD, Thakur M, Chandra K (2022) Status, Issues, and Challenges of Biodiversity: Wild Animals. In \u003cem\u003eBiodiversity in India: Status, Issues and Challenges\u003c/em\u003e (149-173). Singapore: Springer Nature Singapore.\u003c/li\u003e\n\u003cli\u003eSilva J, Cruz-Neto O, Peres CA, Tabarelli M, Lopes AV (2019) Climate change will reduce suitable Caatinga dry forest habitat for endemic plants with disproportionate impacts on specialized reproductive strategies. PloS one.14(5), e0217028. \u003c/li\u003e\n\u003cli\u003eSingh H, Kumar N, Kumar M, Singh R (2020) Modeling habitat suitability of western tragopan (\u003cem\u003eTragopanmelanocephalus\u003c/em\u003e) a range-restricted vulnerable bird species of the Himalayan region, in response to climate change. Clim. Risk Manag.\u003cem\u003e 29\u003c/em\u003e, 100241. \u003c/li\u003e\n\u003cli\u003eSoille P, Vogt P (2009) Morphological segmentation of binary patterns. Pattern Recognit. Lett. \u003cem\u003e30\u003c/em\u003e(4), 456-459. \u003c/li\u003e\n\u003cli\u003eSony RK, Sen S, Kumar S, Sen M, Jayahari KM (2018) Niche models inform the effects of climate change on the endangered NilgiriTahr (\u003cem\u003eNilgiritragushylocrius\u003c/em\u003e) populations in the southern Western Ghats, India. Ecol. Eng. 120, 355-363. \u003c/li\u003e\n\u003cli\u003eSrivastava T, Vasudevan K (2021) Conservation of hangul, \u003cem\u003eCervus hangul\u003c/em\u003e\u0026ndash;paving the way ahead. Curr. Sci. 121(4), 485. \u003c/li\u003e\n\u003cli\u003eSuhail I, Iqbal S, Ahmad K, Lone I, Mansoor M, Zargar R, Hussain S, Baba M (2009) Status and distribution of Hangul Cervus Elaph us Hanglu Wagner in Kashmir, India. J. Bombay Nat. Hist. Soc.\u003cem\u003e106\u003c/em\u003e(1), 63-71.\u003c/li\u003e\n\u003cli\u003eSummers DM, Bryan BA, Crossman ND, Meyer WS (2012) Species vulnerability to climate change: impacts on spatial conservation priorities and species representation. Glob. Change Biol. 18(7), 2335-2348. \u003c/li\u003e\n\u003cli\u003eTao Z (2023) Predicting the changes in suitable habitats for six common woody species in Central Asia. Int J Biometeorol. 67(1), 107-119. \u003c/li\u003e\n\u003cli\u003eThapa A, Wu R, Hu Y, Nie Y, Singh PB, Khatiwada JR, Yan LI, Gu X, Wei F (2018) Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8(21), 10542-10554. \u003c/li\u003e\n\u003cli\u003eThorstad EB, Bliss D, Breau C, Damon‐Randall K, Sundt‐Hansen LE, Hatfield EM, Horsburgh G, Hansen H, Maoil\u0026eacute;idigh N\u0026Oacute;, Sheehan T, Sutton SG (2021) Atlantic salmon in a rapidly changing environment\u0026mdash;Facing the challenges of reduced marine survival and climate change. Aquat. Conserv.: Mar. Freshw. 31(9), 2654-2665. \u003c/li\u003e\n\u003cli\u003eThurman LL, Stein BA, Beever EA, Foden W, Geange SR, Green N, Gross JE, Lawrence DJ, LeDee O, Olden JD, Thompson LM (2020) Persist in place or shift in space? Evaluating the adaptive capacity of species to climate change. Front. Ecol. Evol. (9), 520-528. \u003c/li\u003e\n\u003cli\u003eTobgay S, Mahavik N (2020) Potential habitat distribution of Himalayan red panda and their connectivity in Sakteng Wildlife Sanctuary, Bhutan. Ecol. Evol. 10(23), 12929-12939. \u003c/li\u003e\n\u003cli\u003eTran VD, Vu TT, Ta T, Tran Q, Nguyen T, Ha TM, Nguyen HV (2018) Predicting suitable distribution for an endemic, rare, and threatened species (grey-shanked douc langur, \u003cem\u003ePygathrixcinerea\u003c/em\u003e Nadler, 1997) using the MaxEnt model. Appl. Ecol. Environ. Res. 16(2).\u003c/li\u003e\n\u003cli\u003eVarol T, Canturk U, Cetin M, Ozel HB, Sevik H (2021) Impacts of climate change scenarios on European ash tree (\u003cem\u003eFraxinus excelsior \u003c/em\u003eL.) in Turkey. For. Ecol. Manag. 491, 119199. \u003c/li\u003e\n\u003cli\u003eViana DS, Chase JM (2022) Increasing climatic decoupling of bird abundances and distributions. Nat. Ecol. Evol. 6(9), 1299-1306. \u003c/li\u003e\n\u003cli\u003eVogt P, Riitters K (2017) Guidos Toolbox: universal digital image object analysis. Eur. J. Remote. Sens. 50:352\u0026ndash;361. \u003c/li\u003e\n\u003cli\u003eWaheed M, Arshad F, Majeed M, Haq SM, Aziz R, Bussmann RW, Ali K, Subhan F, Jones DA, Zaitouny A (2023) Potential distribution of a noxious weed (\u003cem\u003eSolanum viarum\u003c/em\u003e Dunal), current status, and future invasion risk based on MaxEnt modeling. Geol. ecol. landsc.1-16. \u003c/li\u003e\n\u003cli\u003eWaheed M, Haq SM, Arshad F, Jameel MA, Siddiqui MH, Bussmann RW, Manshoor N, Alamri S (2023a) Where Will Threatened \u003cem\u003eAegle marmelos\u003c/em\u003e L., a Tree of the Semi-Arid Region, Go under Climate Change? Implications for the Reintroduction of the Species. Land. 12(7), 1433. \u003c/li\u003e\n\u003cli\u003eWallingford PD, Morelli TL, Allen JM, Beaury EM Blumenthal DM, Bradley BA, Dukes JS, Early R, Fusco EJ, Goldberg DE, Ib\u0026aacute;\u0026ntilde;ez I (2020) Adjusting the lens of invasion biology to focus on the impacts of climate-driven range shifts. Nat. Clim. Change. 10(5), 398-405. \u003c/li\u003e\n\u003cli\u003eWeiskopf SR, Rubenstein MA, Crozier LG, Gaichas S, Griffis R, Halofsky JE, Hyde KJ, Morelli TL, Morisette JT, Mu\u0026ntilde;oz RC, Pershing AJ (2020) Climate change affects biodiversity, ecosystems, ecosystem services, and natural resource management in the United States. Sci. Total Environ. \u003cem\u003e733\u003c/em\u003e, p.137782.https://doi.org/10.1016/j.scitotenv.2020.137782\u003c/li\u003e\n\u003cli\u003eYadav RR, Negi PS, Singh J (2021) Climate change and plant biodiversity in Himalaya, India. Proc.Indian Natl. Sci. Acad. 87, 234\u0026ndash;259. https://doi.org/10.1007/s43538-021-00034-5\u003c/li\u003e\n\u003cli\u003eYang L, Huang L, Zhang H, Lee P, Zhang N, Cai R, Li E, Pan T, Wu X (2023) Habitat Suitability and Distribution Pattern Response to Global Climate Change in a Widespread Species, the Asiatic Toad (\u003cem\u003eBufogargarizans\u003c/em\u003e). Asiat. Herpetol. Res. 14(2), 138-146. \u003c/li\u003e\n\u003cli\u003eZanaga D, Van De Kerchove R, Daems D, De Keersmaecker W, Brockmann C, Kirches G, Wevers J, Cartus O, Santoro M, Fritz S, Lesivn M, Herold M, Tsendbazar NE, Xu P, Ramoino F, Arino O (2022) ESA WorldCover 10 m 2021 v200.\u003c/li\u003e\n\u003cli\u003eZhong Y, Xue Z, Davis CC, Moreno‐Mateos D, Jiang M, Liu B, Wang G (2022) Shrinking habitats and native species loss under climate change: a multifactorial risk assessment of China\u0026apos;s inland wetlands. Earth\u0026apos;s Future. 10(6), e2021EF002630. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Photo plate 1","content":"\u003cp\u003ePhoto plate 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Altitudinal migration, Corridor, Climatic change, Hangul, Conservation, Dachigam landscape","lastPublishedDoi":"10.21203/rs.3.rs-3874320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3874320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is impacting species distribution, posing a significant threat to biodiversity. Special attention is needed for vulnerable species like the Kashmir Red Deer (\u003cem\u003eCervus hanglu hanglu\u003c/em\u003e). Despite being a global conservation symbol, holistic management is hindered by limited research. A comprehensive study mapping the potential habitat changes for the Hangul in the Dachigam landscape is crucial to enhance conservation efforts. We examined the prospective effects of expected global warming on the distribution of Hangul by assessing species range shifts and employing a maximum entropy approach. The Hangul was anticipated to be sensitive to upcoming global warming and would raise its risk of local extinction. The severity of repercussions from climate change grew as the time horizon increased and decreased the species' suitable habitat. By 2080, predictions indicated a gradual reduction in range or, in some scenarios, the complete loss of habitat, regardless of the potential for Hangul to disperse indefinitely. We estimated that the overall very highly suitable habitat in the protected region is currently 2220 ha, while its huge distribution area in the unprotected zone is 30,445 ha, emphasizing the necessity of establishing corridor connectivity between fragment populations and promoting conservation efforts. Among various climate conditions, the core-to-edge ratio is at its highest level in the current conditions. Our study reveals two critical findings: Firstly, endangered species unique to a particular region are highly susceptible to the ramifications of global warming. Secondly, when evaluating the outcomes of global warming, the highly suitable habitat is expected to shift under predicted climatic changes, with an average altitudinal migration of 700m. Consequently, conservation strategies must consider the expected regional shifts and are designed with a clear understanding of the accuracy in projecting climate change effects.\u003c/p\u003e","manuscriptTitle":"Conserving the critically endangered Hangul (Cervus hanglu hanglu) - Future distribution and efficiency of protected areas under climate change: Implications for the conservation of Dachigam landscape","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-19 09:53:07","doi":"10.21203/rs.3.rs-3874320/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0ede4bd8-df5b-434d-a565-7a30ec2300a3","owner":[],"postedDate":"January 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-23T07:03:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-19 09:53:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3874320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3874320","identity":"rs-3874320","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}