Forecasting the Fate of Forest Dwellers: Comparative Modeling of Barking Deer Habitats Against Climatic and Anthropogenic Shifts in the Western, Central, and Eastern Himalayas

doi:10.21203/rs.3.rs-4819699/v1

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Forecasting the Fate of Forest Dwellers: Comparative Modeling of Barking Deer Habitats Against Climatic and Anthropogenic Shifts in the Western, Central, and Eastern Himalayas

https://doi.org/10.21203/rs.3.rs-4819699/v1

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Barking deer (Muntiacus vaginalisBoddaert, 1785), or Northern Red Muntjac, is a solitary forest-dwelling cervid distributed from eastern Pakistan to Indonesia, including Southeast Asia. Despite its wide range, habitat assessments for the species are limited. This study models the distribution of Barking deer in the Indian Himalayan Region (IHR) using primary data (camera traps, sign surveys) and secondary data (literature, GBIF). Bioclimatic, land use, topographic, and anthropogenic variables were used to predict current and future habitat suitability for 2050 and 2090 under SSP2.6 and SSP8.5 emission scenarios.

We analysed three biogeographic provinces of the IHR: Western Himalaya, Central Himalaya, and Eastern Himalaya. The estimated current suitable habitat is 7,363 km² in the Eastern Himalaya, 1,932 km² in the Central Himalaya and 30,573 km² in the Western Himalaya. Future projections indicate a decrease in suitable habitat in the Eastern and Central Himalayas under combined climate and land use change scenarios. Conversely, the Western Himalaya shows an increase in suitable habitat. Key variables influencing distribution include temperature and precipitation patterns, forest cover, and human impact indices.

The landscape metrics and fragmentation analysis revealed significant differences across the provinces. The number of suitable habitat patches in the Western Himalaya is currently estimated at 347, with an increase in patch size projected under future scenarios. In contrast, the Central and Eastern Himalayas have 33 and 54 patches, respectively, with future projections indicating a decline in both number and size of patches. This fragmentation suggests that Barking deer in these regions may face greater habitat loss and reduced connectivity. Effective conservation planning and habitat management are crucial for sustaining Barking deer populations and the larger ecosystem, including predators that rely on them. The study emphasizes the need for enhanced conservation efforts, particularly in the Eastern and Central Himalayas.

Species distribution modelling

Indian Himalayan Region

suitable habitat

protected areas

climate change impact

Over the decades, the Indian Himalayan Region (IHR) has faced major challenges due to increasing human interference and global climate change, resulting in habitat decline for many wildlife species (Mills 2009; Morrison et al. 2012). Rapid human-induced environmental changes have left many wildlife species unable to adapt quickly enough to maintain their populations, leading to declines (Myers and Knoll 2001; Kaszta et al. 2020). Consequently, evaluating species-habitat relationships is crucial for suggesting effective conservation strategies, as many global wildlife species are on the brink of extinction due to habitat degradation, fragmentation, and loss (Marzluff and Sallabanks 1998). However, conservation efforts are often delayed until the species and their habitats are severely affected (Myers et al. 2000; Brooks et al. 2002), with limited focus on species of least concern.

Barking deer are prevalent throughout the IHR and are an important natural prey for large carnivores (Karanth et al. 2004). A decline in the abundance of such ungulates may negatively affect carnivore populations (Karanth et al. 2004). Therefore, it is equally important to understand and conserve species like the Barking deer, their habitats, and management regimes at both local and landscape levels (Carbone and Gittleman 2002). The geographical distribution and habitat use of ungulate species are mainly influenced by the landscape composition, and assessing their distribution in the Himalayan landscape is slightly challenging (Radeloff et al. 1999; Marmolejo et al. 2015).

The northern red muntjac (Muntiacus vaginalis), or Barking deer, belongs to the family Cervidae and is one of the most primitive deer groups in the world. Barking deer are solitary browsers and forest-dwelling species, distributed throughout South and Southeast Asia in a wide variety of habitats up to 3500 m elevation in the Himalayan regions (Barrette 2004; Negi 1992; Timmins et al. 1998; Duckworth et al. 1999; Teng et al. 2004; Wegge et al. 2006; Ashokkumar and Nagarajan 2023). In India, Barking deer are distributed throughout the country, including the Himalayan regions, and form an important biomass as an ungulate and prey species for carnivores in the Himalayas, in the absence of other medium-sized ungulates (Schaller 1967; Johnsingh 1983; Seidensticker 1990; Johnsingh et al. 1993; Venkatraman et al. 1995; Grassman 1999; Karanth and Sunquist 2000; Selvan et al. 2013). Thus, understanding the distribution and identifying potential habitats of Barking deer is essential for sustaining populations of large predators in the IHR landscape. Despite Barking deer's wide distribution, the habitat and population from some regions are declining at an alarming rate due to human-centric development and land use modification (bin Kassim 1987; Steinmetz et al. 2010; Ngoprasert et al. 2007; Lau et al. 2010; Kral et al. 2017; Habiba et al. 2020). Additionally, hunting throughout its distribution range is another reason for its population decline (bin Kassim 1987; Madhusudan and Karanth 2002; Hansel 2004; Kaul et al. 2004; Steinmetz et al. 2008). Despite being a crucial component of the mammalian fauna in India and throughout the Indian Himalayan Region (Schaller 1977; Ilyas et al. 2003; Bagchi and Ritchie 2010), Barking deer have received less attention for conservation and are listed as LC species in the IUCN’s Red List and Schedule I species in India’s Wildlife Protection Act 1972, amended in 2022. Furthermore, some studies have evaluated regional levels of habitat ecology and distribution (bin Kassim 1987; Hameed et al. 2009; Bhattacharya et al. 2010; Singh and Kushwaha 2011; Paudel et al. 2015; Trisurat et al. 2015; Habiba et al. 2020; Letro et al. 2021; Neupane et al. 2022), but exclusively lacking in the IHR.

Thus, the present study attempts to predict the distribution of Barking deer across the IHR, which extends over four biogeographic provinces (Rodgers and Panwar 1988). These four biogeographic provinces are located in Jammu and Kashmir as North West Himalaya (2A), Himachal Pradesh and Uttarakhand as Western Himalaya (2B), Sikkim and Northern West Bengal as Central Himalaya (2C), and Arunachal Pradesh as Eastern Himalaya (2D). The entire range of the IHR, with its heterogeneous environment, provides a unique habitat to the widely distributed species like Barking deer in each of the provinces of the Indian Himalaya. The current study focused on understanding Barking deer’s spatial distribution pattern and their relationship with environmental variables. This study is useful for suggesting species conservation and habitat management strategies.

Study area

The Indian Himalayan Region (IHR) is among the most diversified and versatile mountain ecosystems globally. Its topography extends almost continuously from the Siwalik foothills in the south, through the lesser Himalayas in Himachal Pradesh, up to the Tibetan Plateau in the north (Trans-Himalaya). The region's altitudinal range spans from 300 to 8,611 meters, creating substantial variability in topographic and climatic conditions (Xu et al., 2009). This exceptional variation supports a variety of habitat conditions, enabling the coexistence of numerous unique and threatened species of flora and fauna (Xu et al., 2009). Covering about 16% of India's total geographic area, the IHR hosts over 36% of the country's faunal species, supported by one of the world’s largest altitudinal gradients (Rodgers and Panwar, 1988).

Biogeographically, the IHR is divided into four provinces: North West Himalaya (2A), including Jammu and Kashmir and Himachal Pradesh; West Himalaya (2B), encompassing Himachal Pradesh and Uttarakhand; Central Himalaya (2C), covering Sikkim and the northern part of West Bengal; and Eastern Himalaya (2D), consisting of Arunachal Pradesh (Rodgers and Panwar, 1988) (Figure S1).

The Western Himalaya, ranging from the Sutlej to the Gandak rivers in Nepal, features a generally drier climate and harsher winters. Despite its lower diversity, the region exhibits interesting vegetation zoning along its altitudinal gradient, categorized into Temperate (2,500 to 3,000m), Subalpine (3,000 to 3,350m), and Alpine (3,300 to 4,500m) zones. The Temperate zone consists primarily of conifer and broad-leaved forests, including oak species (Singh and Singh, 1987), while the Subalpine zone contains high-altitude oak, birch, and Rhododendron forests below the tree line, adjacent to alpine meadows (Mani, 1974). The Alpine zone is characterized by dwarf Rhododendron and juniper scrubs that merge into alpine meadows, rocks, and perpetual snow (Rawat, 1998).

Central Himalaya, marking the end of the Western elements, stretches from Gandaki in Nepal through Darjeeling in West Bengal and Sikkim to Central Bhutan. This region is predominantly moist and is covered largely by the states of Sikkim and northern Bengal. It is known for its Temperate-Broadleaf forests, influenced by elevation (Mani, 1974; Tambe, 2007). The overlapping ecological factors and altitudinal zones in Darjeeling and Sikkim support diverse fauna, including the Himalayan black bear, common leopard, clouded leopard, serow, takin, red panda, leopard cat, Barking deer, and yellow-throated marten.

The Eastern Himalaya, encompassing Central Bhutan and all of Arunachal Pradesh, is noted for its moist conditions and higher tree line, along with an exceptional diversity of Rhododendron species (Myers, 1988; Rodgers and Panwar, 1988). This region has been highlighted as a Global Biodiversity Hotspot due to its significant conservation value.

Each province harbors distinctive floral and faunal diversity, reflecting their unique characteristics. Numerous studies have shown that the species and ecosystems in the Himalayan region are particularly susceptible to global warming, making them more vulnerable to climate change (Xu et al., 2009; Chettri et al., 2020). The visible impacts include changing weather patterns, receding glaciers (Parry et al., 2020), and species habitat fragmentation and loss (Bagaria et al., 2020; Harrison et al., 2021). The primary study was conducted across five districts of the IHR, representing all three provincial divisions: Uttarkashi district of Uttarakhand for the Western Himalaya, East Sikkim and Darjeeling district of West Bengal for Central Himalaya, and West Kameng district of Arunachal Pradesh for Eastern Himalaya.

Data collection

Monitoring wildlife through sign surveys has been extensively utilized to understand habitat availability and use, as well as to determine population abundance and density (Neu, 1974; Joshi et al., 2020; Proctor et al., 2022). Recently, the introduction of camera trapping has expanded the scope of non-invasive sampling techniques in this field (Nichols et al., 2011). Drawing on existing literature and on-site reconnaissance surveys, we adopted a combined approach of sign surveys and camera trapping. To navigate the challenges of fieldwork in rugged and steep terrain, we adhered to field sampling methodologies outlined by Sathyakumar (1994) and Vinod and Sathyakumar (1999) for data collection.

The primary sampling was conducted in the selected districts of all three zones (Western, Central and Eastern) of Himalayas. These disticts inclues Uttarkashi, Darjeeling, East Sikkim, East Siang and West Kameng of the IHR. Initially, the study districts was segmented into 5 sq km blocks, ranging from 300m to 3,500m in elevation and representing all habitat types of the species (Fig. 1). To enhance sampling efficiency, these blocks were further subdivided into 2 sq km grids, tailored to accommodate the home range of the species (Dinerstein, 1979; Sukmasuang, 2001; Odden and Wegge, 2007). Grids falling under settlements, agricultural land, inaccessible terrain, and areas occupied by the humans were excluded from sampling. Intensive sign surveys were conducted across all accessible grids, following old forest trails and animal paths, complemented by camera traps. Each accessible grid (ranging from 1.5 to 5 km) was meticulously searched for direct and indirect evidence of Barking deer, such as sightings, fecal samples, hoof marks, and feeding or nesting grounds. Due to the Barking deer's elusive behavior, nocturnal activity, and preference for dense forests, which reduce visibility, they are particularly challenging to monitor (Rahman et al., 2017; Phumanee et al., 2020). Additionally, a total of 402 camera traps were strategically placed along active natural trails, near water sources, and across different elevational ranges to capture the distribution of Barking deer.

In addition to primary data from camera traps and sign surveys, secondary data on barking deer occurrences were gathered from open-source platforms such as the Global Biodiversity Information Facility (GBIF; doi: https://doi.org/10.15468/dl.q952bk) and from publications accounting to 143 records as of November 2022 (Table S3). The total dataset comprised 1,113 occurrence locations of Barking deer—885 from primary sources (210 from camera traps and 675 from sign surveys) and 228 from secondary sources (85 from GBIF and 143 from literature reviews). To avoid spatial autocorrelation, we rarefied the occurrence locations to a 1 km² resolution, aligning with the minimum home range of Barking deer, using the ‘spThin’ package in R (Barrette, 1977; Chaplin, 1977; Dinerstein, 1979; Sukmasuang, 2001; Karanth and Kumar, 2005; Odden and Wegge, 2007).

Environmental predictors

Based on previous studies, we selected variables known to influence Barking deer habitats for our analysis (Frid and Dill, 2002; Ilyas and Khan, 2003; Kushwaha et al., 2004; Pokharel and Chalise, 2010; Rahman, 2017; Habiba et al., 2021). The predictor variables were categorized into four groups for analysis: climatic, topographic, anthropogenic, and land use land cover (LULC) (Table S1). Climatic Variables: We utilized 19 bioclimatic variables from WorldClim version 2.1, covering historical data from 1970–2000 and projections for 2050 (2041–2060) and 2090 (2081–2100) under the Shared Socioeconomic Pathways (SSPs). SSP2.6 corresponds to low emission scenarios, while SSP8.5 represents the highest greenhouse gas emissions scenarios (Riahi et al., 2017) (WorldClim). Topographic Variables: Elevation and proximity to water streams, downloaded from Diva-GIS (Diva-GIS), were included. Additional landscape variables such as aspect, slope, and ruggedness were derived from elevation data using the Spatial Analyst extension in ArcGIS 10.4. Distances to water/stream lines from species occurrence points were calculated using the Euclidean distance algorithm in ArcGIS. Anthropogenic Variables: The Human Influence Index (HII), representing the impact of human activity on the landscape, was sourced from the Socioeconomic Data and Applications Center (SEDAC), NASA (SEDAC, 2005). Road networks, also obtained from Diva-GIS, were analyzed to determine the proximity of roads to species occurrence points using the Euclidean distance algorithm in ArcGIS. Land Use Land Cover (LULC) Variables: Current and future LULC data, which includes 10 general classes, was downloaded from the Finer Resolution Observation and Monitoring Global Land Cover database (1km resolution) (http://data.ess.tsinghua.edu.cn/) (Li et al., 2016). We aligned the LULC data with bioclimatic data for 2020, 2050, and 2090 under RCP 2.6 and RCP 8.5 scenarios (Yu et al., 2019). Multicollinearity Assessment: To address potential multicollinearity among the predictor variables, we conducted a correlation analysis using the usdm package in R (Babak Niami 2023). Variables exhibiting a Pearson’s correlation coefficient less than 0.7 were further analyzed, and selected variables based on the computation of their variance inflation factor (VIFs). Variables with a VIF greater than ten were excluded from the analysis (Naimi et al., 2014). After evaluating multicollinearity, we selected a tailored set of variables for each biogeographic province and for the overall IHR. Specifically, we used 10 variables for each of the Eastern and Central Himalaya, 9 for Western Himalaya, and 13 for the overall IHR. These variables were then utilized to perform the ensemble species distribution modeling (Table S2).

Data processing and model building

In this study, we employed an ensemble modeling approach to predict the species distribution of Barking deer, leveraging its advantages over single statistical methods (Elith et al., 2006; Brave et al., 2011). The models were implemented using the ‘sdm’ package in R version 4.2.1 (Naimi and Araujo, 2016). We calibrated the models using 70% of the occurrence data as training data, while the remaining 30% served as test data for model evaluation. Accurate information on species absence at specific locations is crucial for species distribution modeling (SDM) (Lobo et al., 2010; Miller, 2010). However, true absence data are often difficult to obtain. To address this, pseudo-absence points were randomly generated across the entire study area (Figure S2) (Jimenez et al., 2008; Lobo et al., 2010), with twice the number of absence points generated using a random selection method. The models built in the study underwent 20 replicate runs using a subsampling method for model calibration and validation. The final ensemble model was built by integrating the five modelling techniques: Generalized Linear Models (GLM), Boosted Regression Trees (BRT), Multivariate Adaptive Regression Splines (MARS), Support Vector Machines (SVM), and Random Forests (RF). Each predictor variable’s importance was assessed using the Area Under the Curve (AUC) and correlation metrics across all algorithms. We ran models under three distinct scenarios viz., Combined climate and land use/land cover (M1); Future climate change only (M2); Future land use and land cover only (M3). Topographic and anthropogenic variables were held constant across all scenarios.

The distribution probability layers for each biogeographic province and the overall IHR were classified into two categories: ‘suitable’ and ‘unsuitable.’ This classification was based on a threshold value derived from the True Skill Statistic (TSS) at the point where specificity and sensitivity intersected (Cantor et al., 1999). Areas above the threshold were deemed suitable habitats, while those below were considered unsuitable (Menel et al., 2001; Stockwell and Peterson, 2002). This method was applied consistently across all probability layers and scenarios for 2050 and 2090.

To assess the change in suitable habitat, the current suitable habitat maps for each biogeographic province (Western, Central, and Eastern Himalaya) were analyzed to determine changes over time in terms of ‘gain’ (areas not currently occupied but predicted to become suitable), ‘loss’ (currently suitable areas predicted to become unsuitable), and ‘stability’ (areas that remain suitable across time frames). Cross-tabulation was used to calculate these changes in habitat over time and space (Pontius et al., 2004).

Landscape Metrics and Fragmentation Analysis

The impact of landscape structure, size, and connectivity on species distribution was also considered (MacArthur and MacArthur, 1961; Ortner and Wallentin, 2020). Fragmentation of habitat patches can significantly affect species dispersal, potentially leading to declines in population and diversity (Kruess and Tscharntke, 1994). We assessed patch dynamics using the SDM layer by estimating the number of patches (NP), the largest patch index (LPI), and the radius of gyration (RGY), which measures the extent to which a species can move within a patch before reaching its edge. These metrics were calculated using Fragstats v4.2 software (McGarigal and Marks, 1995; Oliver et al., 2015).

Habitat Suitability Model Performance

The performance of the species distribution models varied across the Indian Himalayan Region (IHR), reflecting the diversity of environmental conditions in different provinces. The Area Under the Curve (AUC) values for the models ranged from 0.93 to 1.0 for training and 0.93 to 0.95 for model evaluation in Western Himalaya (WH), 0.81 to 1.00 for training and 0.79 to 0.89 for model evaluation in Central Himalaya (CH), and 0.94 to 1.0 for training and 0.90 to 0.92 for model evaluation in Eastern Himalaya (EH) as detailed in Table 1. These robust AUC values, alongside other model evaluation metrics such as the Receiver Operating Characteristic (ROC), True Skill Statistic (TSS), and Kappa, demonstrated substantial agreement between predicted and observed data. This underscores the models' effectiveness in capturing the Barking deer's distribution dynamics across the Himalaya’s varied biogeographic provinces of the IHR. The distribution probability maps were converted into binary suitability maps using specifically defined threshold values: 0.43 in WH, 0.45 in CH, and 0.49 in EH, with an overall threshold of 0.49 applied across the IHR. This method facilitated a clear demarcation of suitable versus unsuitable habitats for Barking deer, based on the validated model outputs.

Table 1

Evaluation score of the model training and testing data for each model algorithm used in the ensemble model to predict habitat suitability map of barking deer in three biogeographic provinces of Indian Himalayan Region *viz*., **Eastern Himalaya (EH), Central Himalaya (CH) and Western Himalaya (WH) and overall Indian Himalayan Region (IHR).**
Model Training
	ROC				TSS				KAPPA
Algorithm	CH	EH	WH	Overall IHR	CH	EH	WH	Overall IHR	CH	EH	WH	Overall IHR
GLM	0.81	0.94	0.93	0.91	0.65	0.78	0.70	0.63	0.61	0.76	0.68	0.60
BRT	0.90	0.96	0.97	0.96	0.69	0.83	0.80	0.76	0.71	0.81	0.78	0.74
MARS	0.88	0.97	0.96	0.96	0.72	0.86	0.80	0.78	0.64	0.84	0.78	0.76
RF	1.00	1.00	1.00	1.00	0.97	1.00	0.99	0.99	0.97	1.00	0.99	0.99
SVM	0.88	0.97	0.95	0.96	0.59	0.85	0.78	0.79	0.65	0.84	0.76	0.76
Model Mean	0.89	0.97	0.96	0.96	0.72	0.86	0.81	0.79	0.72	0.85	0.80	0.77
Model Evaluation
	ROC				TSS				KAPPA
Algorithm	CH	EH	WH	IHR	CH	EH	WH	IHR	CH	EH	WH	IHR
GLM	0.79	0.92	0.93	0.90	0.66	0.71	0.71	0.60	0.69	0.69	0.69	0.57
BRT	0.83	0.91	0.95	0.94	0.73	0.72	0.76	0.73	0.70	0.70	0.74	0.70
MARS	0.82	0.90	0.94	0.95	0.65	0.69	0.74	0.74	0.68	0.67	0.72	0.71
RF	0.89	0.92	0.95	0.96	0.83	0.76	0.77	0.78	0.80	0.74	0.75	0.76
SVM	0.81	0.92	0.94	0.94	0.65	0.76	0.76	0.75	0.65	0.74	0.74	0.73
Model Mean	0.83	0.91	0.94	0.94	0.70	0.73	0.75	0.72	0.70	0.71	0.73	0.70
Note: ROC-Receivers Operating Characteristics; TSS-; KAPPA- Cohen’s kappa statistic; TSS- True Skill Statistics. Out of total occurrence data, 70% were used as training for model calibration and the remaining 30% were used as test data for model evaluation.

The most influential variables for predicting Barking deer distribution varied by biogeographic provinces. In the WH, the temperature of the wettest quarter (bio8), forest cover, and precipitation of the driest month (bio14) were pivotal (Figure S3). In case of CH, forest cover, mean diurnal range (mean of monthly maximum temperature minus minimum temperature) (bio2), and slope were crucial for modelling distributions (Figure S3). Whereas, of the EH, precipitation seasonality (bio15), isothermality (bio3), and mean diurnal range were key determinants (Figure S3).

Following the assessment of the comparative species distribution model for Barking deer in different provinces of IHR, an ensemble model was employed to predict and identify the key variables that govern the distribution of Barking deer in overall IHR. Thus, based on the variable importance score, bio8 followed by bio2 and hii were found to be the most important variables for predicting the Barking deer distribution in overall IHR (Figure S4). The mean of model evaluation score for ROC, TSS and KAPPA were 0.96, 0.79 and 0.77 for training data and 0.94, 0.72 and 0.70 respectively for test data (Table 1). The distribution probability map generated was then classified into binary suitability map by using threshold value of 0.49 for IHR.

Suitable habitat dynamics and spatiotemporal change in IHR

Current Habitat and Protected Areas

The observed current suitable habitat of the Barking deer in Western Himalaya was found to be 30,573 km², constituting about 9% of the total area of WH, of which 2,976 km² (9.7%) is under protected area networks (Fig. 1). In Central Himalaya, the suitable habitat was estimated at 1,932 km², representing 19.6% of CH's total area, with 535 km² (27.6%) under protection (Fig. 1). The suitable area in Eastern Himalaya was calculated at 7,363 km², approximately 9.3% of EH's total area, with 444 km² (6%) protected (Fig. 1).

Future Projections: Scenario-Based Changes

Under the M1 scenario, significant habitat gains for Barking deer in WH are anticipated, with increases of about 8% and 23% by 2050, and 26% and 18% by 2090, under both low and high emission and development scenarios, respectively. Conversely, in CH, the M1 scenario predicts substantial habitat loss, ranging from 6% under low impact to 12% under high impact scenarios in 2050, and 13–11% by 2090. In contrast, under M2 and M3 scenarios, the impact on potential suitable habitat is less pronounced across all regions (Figs. 2, 3, and 4; Table 2). In the WH, the habitat is expected to expand southward into areas currently deemed unsuitable (Fig. 5). In the case of CH, habitat losses might drive the Barking deer to migrate towards available dense forests, indicating potential shifts in their current distribution patterns (Fig. 6). Further, in the case of EH projections show a significant reduction in suitable habitat, especially at lower elevations, with Barking deer potentially moving to mid-altitude regions by 2090 (Fig. 7). Overall current suitable habitat for Barking deer across IHR totals 47,876 km², with 5,763 km² (12%) protected. Future projections under the M1 scenario suggest a decrease in suitable habitat by 12% and 10% in 2050, with a further decrease of 5% projected for 2090 under low-emission scenarios. Conversely, a 10% increase is expected under high emission scenarios by 2090. Under the M2 scenario, the suitable habitat is expected to decrease by 10% and 17% in 2050 and 1% and 27% in 2090 under low and high-emission scenarios, respectively. Similarly, under the M3 scenario, the suitable habitat is expected to reduce by around 10% in 2050 and around 6% and 1% in 2090 under low development scenarios, respectively. Whereas, in the year 2050, around 3% of suitable habitat is expected to increase under high development scenario (Fig. 8, Table 3). This study predicted that under future climatic scenarios examined, Barking deer are expected to extensively utilize the middle elevation range of the Indian Himalayan Region (Fig. 9).

Table 2

Barking deer’s current suitable habitat and change in area (stable, gain and loss) under future projection according to both SSP assumption considered in this study in three biogeographic provinces viz Eastern Himalaya ( EH), Central Himalaya (CH), and Western Himalaya (WH) of Indian Himalayan Region.
		Total Suitable Habitat (sqkm)			Gain (Sqkm)			Loss (sqkm)			Change of habitat from current (sqkm)
		CH	EH	WH	CH	EH	WH	CH	EH	WH	CH	EH	WH
Current Scenario		1932	7363	30573	-	-	-	-	-	-	-	-	-
Climate and land use change (M1)	2050 SSP1-2.6	1820	6324	33086	298	765	6171	410	1804	3658	-112	-1039	2513
	2050 SSP5-8.5	1693	6211	37721	254	569	9642	493	1721	2494	-239	-1152	7148
	2090 SSP1-2.6	1674	5600	38664	254	425	11126	512	2188	3035	-258	-1763	8091
	2090 SSP5-8.5	1715	6688	36120	273	912	7757	490	1587	2210	-217	-675	5547
Climate change-only (M2)	2050 SSP1-2.6	1766	6312	37460	261	749	8978	427	1800	2091	-166	-1051	6887
	2050 SSP5-8.5	1804	6917	37615	238	1193	8349	366	1639	1307	-128	-446	7042
	2090 SSP1-2.6	1910	7197	39952	344	1225	11035	366	1391	1656	-22	-166	9379
	2090 SSP5-8.5	1823	6945	44076	300	1221	14652	409	1639	1149	-109	-418	13503
Land use change-only (M3)	2050 SSP1-2.6	1786	5942	49829	231	254	19958	377	1675	702	-146	-1421	19256
	2050 SSP5-8.5	1916	6754	38187	274	636	8660	290	1245	1046	-16	-609	7614
	2090 SSP1-2.6	1753	6470	34807	205	362	6143	384	1255	1909	-179	-893	4234
	2090 SSP5-8.5	1932	6709	44076	260	407	3677	384	1061	4456	-124	-654	-779

Table 3

Barking deer’s current suitable habitat and change in area (stable, gain and loss) under future projection according to both SSP assumption considered in this study in overall Indian Himalayan Region
		Total Suitable Habitat (sqkm)	Gain (Sqkm)	Loss (sqkm)	Change of habitat from current (sqkm)
Scenario		47876 (Current Scenario)	-	-	-
Climate and land use change (M1)	2050 SSP1-2.6	42083	5614	11407	-5793
	2050 SSP5-8.5	43239	5687	10324	-4637
	2090 SSP1-2.6	45516	7443	9803	-2360
	2090 SSP5-8.5	52519	11474	6831	4643
Climate change-only (M2)	2050 SSP1-2.6	43240	8075	12711	-4636
	2050 SSP5-8.5	39862	6687	14701	-8014
	2090 SSP1-2.6	47504	10440	10812	-372
	2090 SSP5-8.5	35034	4579	17421	-12842
Land use change-only (M3)	2050 SSP1-2.6	42919	5369	10326	-4957
	2050 SSP5-8.5	49312	9739	8303	1436
	2090 SSP1-2.6	45228	6591	9239	-2648
	2090 SSP5-8.5	47489	8107	8494	-387

Habitat Patch Dynamics The number of suitable habitat patches is currently estimated at 347 in WH and is expected to decrease under future climate scenarios (Figure S5). In Central Himalaya, around 33 patches are currently observed, with a forecasted reduction under future conditions (Figure S6). In Eastern Himalaya, the count stands at 54 patches, also expected to decline (Figure S7). Overall, the IHR shows a current tally of 839 patches, with a predicted decrease across various climatic conditions. However, in WH, an increase in patch size is projected under the M1 scenario, suggesting different responses to climatic and land-use changes within the region (Figure S8).

Identification of Suitable Habitats in different biogeographic provinces of IHR

Identifying suitable habitats that support the long-term survival of species under changing climatic scenarios is crucial for timely management and conservation efforts. This study represents the first effort to predict the distribution of Barking deer under current and future climate and land use patterns, utilizing Shared Socioeconomic Pathways (SSPs) SSP2.6 and SSP8.5 for the years 2050 and 2090 in three provinces of the Indian Himalayan Region (IHR), as well as across the IHR as a whole. The distribution of vegetation in the Himalayas, heavily influenced by variations in topographic and climatic factors (Salick et al. 2014), leads to diverse phytogeographic zones that significantly impact the distribution and ecology of species like the Barking deer (Singh and Kumar 1997).

Acknowledging the distinctiveness of the different biogeographic provinces in the IHR, we conducted region-wise modeling to understand how climatic, topographic, and land use variables influence the distribution of Barking deer in each province—Western, Central, and Eastern Himalayas—and in a combined model of the entire IHR. Forest presence, a key variable, showed a positive association with the probability of Barking deer occurrence in the Western and Central Himalayas, corroborating with previous studies that suggest good canopy cover and leaf litter provide vital food sources for Barking deer (Lekagul and McNeely 1977; Armstrong et al. 1983; Takahashi and Kaji 2001; Ilyas et al. 2003; Teng et al. 2004; Odden and Wegge 2007; Neupane et al. 2022). Among topographic variables, slope was particularly influential, with the probability of occurrence decreasing with increased steepness in the Central Himalayan landscape, where moderate levels of slope positively influence the presence of Barking deer (Singh and Kushwaha 2011; Paudel and Kindlmann 2012; Pokharel et al. 2015; Neupane et al. 2022). In this study, the species was found using a habitat up to the 3650-meter elevation in Eastern and Central Himalayas, whereas the species was found using an elevation up to 3300 meters in Western Himalayas (Fig. 5). This study also predicts that the species in the future will neither move upwards or lower in elevation.

Anthropogenic variables also showed a positive association with Barking deer occurrence up to a Human Influence Index value of 30%, after which a significant decrease was observed, indicating tolerance towards human presence (Oka 1998; Jathanna et al. 2003; Paudel and Kindlmann 2012). This tolerance is likely facilitated by agroforestry practices that provide ample food and other resources, which are easily accessible at forest edges or agricultural fields (Hofmann 1989; Ilyas 2003).

Spatial distribution prediction of Barking deer in three biogeographic province of IHR suggest that the species occupies the habitat outside the protected areas (territorial forest, community forest etc) more than the PAs corroborated with the findings of previous studies (Lekagul and McNeely 1977; Kitamura et al. 2010; Trisurat et al. 2014). The Barking deer distribution range in CH and EH will decline under future land use and climate change scenarios, which could be due to habitat loss, habitat fragmentation and habitat degradation. Whereas the prediction is just the opposite in WH, it was observed that currently, the species occupies the dense canopy forest, but in future, the species may occupy a habitat close to human settlement or cropland or open forest; hence, there will be a shift rather loss of distribution range.

The probability of occurrence of Barking deer across all three biogeographic provinces of the Indian Himalayan Region (IHR) is strongly associated with bioclimatic variables. Notably, in the Western Himalaya (WH) and throughout the IHR, the distribution of Barking deer is significantly influenced by the mean temperature of the wettest quarter (bio8 up to 18°C). The peak growing season for plants coincides with this period of high precipitation and optimal temperatures, which enhances vegetation growth, nutrient availability, forage quality, and water availability—key factors for meeting the deer’s nutritional needs for survival and reproduction (O’donnell et al. 2012; Richardson 2000; Hanley et al. 2012; Felton et al. 2020).

Conversely, the mean monthly temperature (bio2) negatively impacts Barking deer distribution in both Eastern (EH) and Central Himalayas (CH). In EH, Isothermality (bio3) which measures the diurnal temperature oscillations relative to the annual oscillations significantly influences Barking deer occurrences. This variable supports the availability of feeding resources in different seasons and suits the microclimatic preferences of Barking deer, providing a consistent food supply throughout the year (Odden and Wegge 2007; Habiba et al. 2020). Previous studies confirm these observations, noting no seasonal differences in habitat selection, home range size, or elevation shifts among Barking deer (Odden and Wegge 2007; Habiba et al. 2020). However, in the Eastern Himalayas, precipitation seasonality (Coefficient of Variation) (bio15) shows a negative association with the occurrence of Barking deer. Here, an increase in monthly precipitation fluctuation correlates with a sharp decrease in the likelihood of Barking deer presence, suggesting that the species struggles with high variability in precipitation. This finding underlines the critical role of stable hydrological conditions in determining Barking deer distribution at local and landscape levels throughout the IHR.

This study assumes that both temperature and precipitation significantly affect the probability of Barking deer presence, which may extend to other species in the IHR. Such inferences are supported by broader ecological studies documenting similar responses to climatic variables (Dai et al. 2019; Mukherjee et al. 2021). Under the three scenarios (M1, M2, and M3), WH shows a considerable gain in suitable habitat for Barking deer by 2050 and 2090 under both low and high emission and development conditions. Notably, the habitat expansion is most pronounced toward the southern regions, which are currently unsuitable, with the greatest increase projected under the M3 scenario in 2050. This suggests that Barking deer in WH are effectively adapting to changing climatic and land-use conditions, tracking adaptive environmental niches.

In contrast, the suitable habitat for Barking deer in Central (CH) and Eastern Himalaya (EH) is expected to decrease under the combined effects of climate and land-use changes (M1) for both the 2050 and 2090 projections, across all emission and development scenarios. However, isolated impacts of climate change (M2) or land-use change (M3) alone show less detrimental effects on habitat than the combined scenario (M1). Future shifts in Barking deer habitat in CH are anticipated to move from degraded and sparsely forested areas—currently used for agriculture or agroforestry—toward denser forests, driven by the availability of food, water sources, predation pressure, and human activities. Similarly, in EH, the habitat is expected to shift toward mid-altitudes (1000–2000 m), aligning with the species' preference for mid-elevation zones (Bhattacharya et al. 2010; Pokharel and Chalise 2010; Neupane et al. 2022). This habitat transition in CH and EH is directly tied to anticipated changes in climate and land use. The study underscores the Barking deer's difficulty in coping with these projected changes, particularly under both low and high emission and development scenarios. Moreover, overall distribution modeling across the IHR indicates a decrease in suitable habitat under all scenarios (M1, M2, and M3), reinforcing the importance of the middle elevations, which offer adequate resources for the Barking deer.

Landscape metrics reveal that habitat patches in WH, CH, and EH are expected to decrease in number and fragment into smaller sizes in future scenarios. This fragmentation suggests that Barking deer in Central and Eastern Himalayan regions may not effectively track climate change, facing greater habitat loss than their counterparts in Western Himalayas. This study provides significant baseline information on the habitat suitability of Barking deer in the Indian Himalayan Region (IHR). Our findings reveal that the distribution and environmental predictors vary across different provinces of the IHR, necessitating region-specific conservation planning. The models predict that the majority of suitable habitats for Barking deer are clustered at mid-altitudes across all three biogeographic provinces (WH, EH, and CH).

The presence of Barking deer is crucial for ecosystem functioning, particularly in governing the populations of large carnivores in the Himalayas (Anup 2017; Kandel 2019; Baral et al. 2023). This is in contrast to mainland habitats, where diverse sympatric species such as sambar, chital, Indian gazelle, and mouse deer are abundant (Selvan et al. 2013; Rather et al. 2020). In the mid-Himalayas, Barking deer emerge as the dominant ungulate species, thus forming a major component of the diet for large carnivores, including the common leopard (Sathyakumar et al. 2011; Shrestha 2015; Kandel 2019).

Furthermore, our study highlights a concerning trend: most Barking deer habitats are located outside protected areas. These areas are at risk of degradation and fragmentation due to rapid development, emphasizing the urgent need for enhanced conservation efforts to designate these vulnerable areas as Protected Areas (PAs). Specifically, the loss of suitable habitat in the Eastern and Central Himalayas is predominantly at the forest edge, making these regions particularly susceptible to the impacts of rapid development and anthropogenic activities.

Acknowledgements

We express our sincere thanks to the Principal Chief Conservation of Forest (PCCF) of Uttarakhand, Sikkim, West Bengal and Arunachal Pradesh for granting us research permission to undertake field surveys for the research works. We thank all the Divisional Forest Officers and frontline staff of the forest department from the study area for the necessary support and cooperation during the study. We are also thankful to the member of the eco-development committee and Joint management forest committee and local guide for their cooperation during fieldwork. The authors are also grateful to all the team members of the NMHS project who assisted during the fieldwork. Finally, we acknowledge the National Mission for Himalayan Studies, Ministry of Environment, Forest and Climate Change (MoEF&CC), Government of India for the funding support under Grant No. NMHS/2017-18/LG09/ 02.

Statement of Animal Ethics: No ethical approval was required for the work performed

Conflict of Interest Statement: The authors declare no competing interests

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Forecasting the Fate of Forest Dwellers: Comparative Modeling of Barking Deer Habitats Against Climatic and Anthropogenic Shifts in the Western, Central, and Eastern Himalayas

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Version 1

Abstract

Figures

Introduction

Material and Methods

Study area

Data collection

Environmental predictors

Data processing and model building

Landscape Metrics and Fragmentation Analysis

Results

Habitat Suitability Model Performance

Suitable habitat dynamics and spatiotemporal change in IHR

Current Habitat and Protected Areas

Future Projections: Scenario-Based Changes

Discussion

Identification of Suitable Habitats in different biogeographic provinces of IHR

Declarations

References

Supplementary Files

Status:

Version 1