Current IssueSpecial IssueEditorial BoardOnline First ArticlesArchive
Current IssueSpecial IssueEditorial BoardOnline First ArticlesArchive

Full Research Article

Corridor Planning for Asiatic Black Bear in Sikkim Himalaya: A Synergistic Approach of Technology and Indigenous Insights

N
Nischal Gautam1
K
Kapila Sharma2,*
https://orcid.org/0000-0003-3904-4177
B
Bijoy Chettri3
S
Samarjeet Borah2
R
Ratika Pradhan4
1Department of Forest and Environment, Government of Sikkim, Deorali-737 101, Sikkim, India.
2Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Pakyong-737 132, Sikkim, India.
3Department of Zoology, Shri Ramasamy Memorial Sikkim University, Gangtok-737 102, Sikkim, India.
4Department of Computer Applications, Sikkim University, Gangtok-737 102, Sikkim, India.

ABSTRACT

Background: Habitat fragmentation and human encroachment of the Asiatic black bear (Ursus thibetanus) in the Sikkim Himalaya has resulted in isolation of populations along with human-wildlife conflict. Frequent crop and livestock depredation incidents have resulted in many farmers giving up their farming practices. Though bear presence in these regions is frequent, studies on migratory route and habitat corridors are limited. This study provides spatial mapping of habitat and corridor connectivity for Asiatic black bear with focus on Fambonglho Wildlife Sanctuary (FWLS).

Method: Habitat suitability raster generated by MaxEnt software from 64 Asiatic black bear occurrence records and 48 environmental variables was used as a conductance layer in Circuitscape environment for modeling potential movement routes. Indigenously obtained data from structured survey of 117 farmers around FWLS was analyzed in IBM-SPSS framework for corridor connectivity and habitat impacts.

Result: The model had an overall accuracy of 96.37% with a Kappa Coefficient of 0.9621. Corridors that connect FWLS, Kyongnosla Alpine Sanctuary and Pangolakha Wildlife Sanctuaries have been found to be highly developed, whereas bottlenecks have been observed on the highways and rivers like Teesta River and Rangeet River. The surveys revealed a high level of awareness amongst the local community regarding existence of wildlife corridors; only 8.5% of the survey respondents were unaware. Areas like Barapathing Reserve Forest, Bulbulay Reserve Forest and Assam Reserve Forest, critical for connectivity, remain outside protected boundaries, necessitating immediate conservation action. A p-value <0.005 confirmed statistically significant corridor usage patterns.

INTRODUCTION

Wildlife populations increasingly face habitat fragmentation, leading to connectivity loss and isolation due to both habitat disjunction and human-induced pressures, often escalating human-wildlife conflicts (Kopatz et al., 2012). In particular, conflicts involving bears near protected areas pose significant challenges for local communities, as their seasonal movement patterns bring them into close contact with human settlements (Pettigrew et al., 2012; Li et al., 2013). The indigenous knowledge on wildlife and their behaviour in conservation is fading away with the modernization of the society (Bussa, 2023).
       
While global studies on Asiatic black bear (Ursus thibetanus) migration exist, research specific to Sikkim remains limited. Although Bhattacharya et al., (2025) conducted similar work using MaxEnt and Circuitscape in this area, their study did not include accuracy assessment of the generated maps, particularly in relation to Kappa statistics. However remains a valuable reference for further studies in this area. Corridors play a vital role in facilitating species movement between large habitat patches (Freemark et al., 2002), functioning alongside habitat patches and matrices as key landscape components (Forman and Godron 1986). Connectivity can be categorized into structural (physical landscape features) and functional (how well landscapes support species movement) forms (Meiklejohn et al., 2010). Jongman and Pungeti (2004) classify corridors into three types: migration corridors (seasonal movements), dispersal corridors (gene flow) and commuting corridors (foraging and breeding within home ranges). Engineered interventions such as wildlife overpasses and underpasses further strengthen connectivity.
       
While the terms “linkages” are often considered synonymous, it describes larger connectivity zones which are vital for maintaining the process of ecology (Meiklejohn et al., 2010). Fragmentation of habitats may cause isolation of animal population and increase the risk of inbreeding and local extinction (Forman et al., 2002; Foresman, 2004). While linear corridors may increase the danger of poaching and diseases, corridors can be designed in a way to preserve ecological integrity (Noss, 1987). The fragmentation occurs because of both natural causes (landslides, flooding) and human impact (roads, agriculture) (McNeely et al., 1995), while highways act as a barrier for animals’ migration (Hanski 1989; Meniam and Lanoue 1990). Modern strategies of wildlife conservation focus on connecting fragmented habitats to the natural ecosystems (Soule, 1991; Gautam and Pradhan 2020). Engagement of local communities is critical in reaching conservation goals (Songorwa et al., 2000).
       
Different methods are used for assessment of connectivity, such as Least Cost Modelling (Adriaensen et al., 2003), Centrality Analysis (Estrada and Bodin, 2008) and Circuit Theory (McRae et al., 2008). Least Cost Modeling, popular in GIS-based modelling of corridors, allows to find optimal migration routes (Beier et al., 2008). MaxEnt is a species distribution model which produces a map of habitat suitability (Phillips and Dudik, 2008; Phillips et al., 2006). Circuitscape, grounded in electrical circuit theory, models genetic connectivity by integrating spatial data into graph-based networks (McRae et al., 2008; Flory et al., 2012). MaxEnt-derived suitability raster serves as conductance surfaces in Circuitscape to produce cumulative current maps (Saura et al., 2011; Roscioni et al., 2014; Cushman et al., 2013). GIS-based modelling ensures objective, reproducible and spatially explicit planning (Doko et al., 2011).
       
Circuitscape is a Python-based tool that uses circuit theory to model movement patterns, gene flow and genetic differentiation across heterogeneous landscapes (McRae 2006; Hodgson et al., 2012; Lawler et al., 2013; Dutta et al., 2016). Each raster cell is treated as a node connected to four or eight neighboring cells via resistors, excluding cells of infinite resistance (McRae et al., 2008). Habitat patches with zero resistance act as single nodes. Current flow represents the probability of random movement (Fig 3). McRae et al., (2008) also demonstrate models with ground resistors, where added resistors simulate mortality and nodes reflect dispersal survival. This modelling approach evaluates multiple dispersal routes and settlement probabilities. Consequently, this study aims to:
i) Identify suitable habitats for Asiatic black bear conservation in the Sikkim Himalaya and
ii) Identify historic and existing corridors and analyze connectivity between protected areas by integrating technology and indigenous knowledge, focusing on Fambonglho Wildlife Sanctuary.

MATERIALS AND METHODS

Study area
 
Sikkim
 
Sikkim (Fig 1), India’s second smallest state, spans 7,096 sq. km (Sharma et al., 2012; FSI, 2017) in the northeastern Himalayas (27°5' S-28°9' N, 87°9’ W-88°56’ E) and receives 2700-3200 mm of annual rainfall (Sharma and Thapa, 2021; FSI, 2019). Its diverse elevation results in a climate ranging from sub-zero in winter to 28°C in summer. Sikkim harbours over 4000 flowering plants, rare orchids, medicinal herbs and rhododendrons. It supports 574 bird species, 45% of India’s avifauna (Sundriyal et al., 2004; Acharya and Vijayan, 2011), including the Blood Pheasant and Himalayan Monal, along with mammals like the Asiatic black bear, snow leopard and musk deer. The state has seven wildlife sanctuaries and one national park. The Khangchendzonga National Park, a UNESCO World Heritage Site, helps protect the high-altitude ecosystems. In Gangtok, the FWLS helps conserve the Himalayan black bear and red panda, while the Kyongnosla Alpine Sanctuary helps conserve the snow leopard. Other significant reserves include the Pangolakha Wildlife Sanctuary, Kitam Bird Sanctuary and the Meanam Wildlife Sanctuaries. This study explores the human-bear conflict, habitat suitability and connectivity corridors of Asiatic black bears in FWLS.

Fig 1: Map showing Sikkim Himalaya and the FWLS (Study area).


 
Fambonglho wildlife sanctuary
 
Fambonglho Wildlife Sanctuary (51.76 km2), located to the west of Gangtok in East Sikkim (27°10'-27°23' N, 88°29'-88°35' E), was declared as a wildlife sanctuary in 1984 to address the problems of ecological degradation due to urbanization (Fig 1). This sanctuary consists of schedule I species and serves as a water reservoir and an ecological buffer for the city of Gangtok. Sedimentary and metamorphic rock formations characterize the sanctuary with elevation ranging from 1,524-2,749 m, having temperatures of 2°C-5°C in January and 10°C-20°C in May. Average annual rainfall in this area is 300 cm with July receiving the maximum amount of rain. Snowfall is short lived in the winter season on the ridges at higher altitudes. Frost is experienced between December and February. In the biogeographic zones 2C (Central Himalayas) (Rodgers and Panwar, 1988), the sanctuary harbours East Himalayan sub-tropical wet hill forests (8B/C1) and East Himalayan wet temperate forests (11B/C1) consisting of Schima wallichii, Castanopsis spp., Oaks, Magnolias and Rhododendrons. Commonly seen fauna in the sanctuary includes common leopard (Panthera pardus), Asiatic black bear, red panda (Ailurus fulgens), flying squirrel, wild boar (Sus scrofa) and barking deer (Muntiacus muntjak). The sanctuary is surrounded by 31 revenue villages and governed by 10 Eco-Development Committees (EDCs) and agricultural practices along with cardamom plantations being the main occupations (Forest and Environment Department, Government of Sikkim, 2008).
 
Model species
 
Asiatic black bear (Ursus thibetanus) lives in different parts of southern and eastern Asia, in the Himalayas till 3,750 meters (Schaller, 1977; Sathyakumar, 2001). Though the geographical distribution of the species has been recorded in the northeastern Indian hills (Sathyakumar and Chaudhary, 2007; Charoo et al., 2011), little information on the population status of the species is available from the Sikkim Himalayas (Lepcha et al., 2017). The animal falls under the Schedule I of the Wildlife Protection Act of 1980, its survival is threatened by habitat fragmentation and increasing man-animal conflicts.
 
Input dataset
 
The habitat suitability data was used in the form of a raster (Fig 2), as produced by Gautam et al., (2025) using 64 occurrence points of black bears along with 48 environmental variables collected from various data sources such as WorldClim, EarthExplorer DEM, MODIS NDVI and DIVA-GIS land use, representing vegetation condition (NDVI), precipitation, temperature and land-use/land-cover and were selected based on their ecological relevance to Asiatic black bear habitat suitability and spatial availability for the study area. For this study, the MaxEnt model (v 3.3.3k.d) was applied for the analysis of bioclimatic variables. The model showed highly accurate results (AUC: 0.7-0.9) with good validation statistics (Training AUC: 0.948±0.01 SD; Test AUC: 0.86±0.04 SD). The variable contribution in the study indicated that August NDVI (17.6%), driest-month precipitation (15.7%) were the major contributors, while permutation importance was highest for monthly mean diurnal temperature range (19.4%) and September NDVI (15.3%). The complete contribution and permutation-importance values are presented in Table 1 (Gautam et al., 2025).

Fig 2: Map for the predicted distribution model, showing different threshold categories for bears.



Table 1: Environmental and associated variables and their percentage representation in the prediction for the probable bear availability.


 
Corridor modeling using circuit theory
 
The circuit theory models the movements of wildlife using the analogy of electric current movements (Ament et al., 2014). In such modelling, the landscape is viewed as electric circuits where the nodes are viewed as habitat patches and the resistors as the landscape matrix between them. The resistance is the permeability of the habitat, whereas the voltage is the probability of movement (McRae et al., 2008). Circuitscape is used to compute the cumulative current flow that is used as the indicator of connectivity.
       
Fig 3 shows that each cell is modelled as a node linked with resistors to four or eight adjacent cells when raster grids of an electrical network are transformed to the electrical network. Short-circuited areas are combined into a node, but cells having infinite resistance are not included in the electrical network (McRae et al., 2008).

Fig 3: Representation of simple electric circuit.


 
Corridor calibration with circuitscape
 
Corridors were created using Circuitscape 5.0 software with the “One to All” model to create connectivity between source nodes and all possible destination nodes. Resistance surface generated using the MaxEnt model’s friction layer was used for estimation of current density and probability of movements.
 
Corridor performance assessment
 
Model validation was conducted using 64 known black bear occurrence points and 500 pseudo-absence points. A 3 km buffer was applied (Fig 4) to account for estimated home ranges (Gopalaswami et al., 2012; Hazumi and Maruyama, 1986; Sunar et al., 2012). Performance metrics included a confusion matrix, overall accuracy and the Kappa coefficient. The established benchmarks of 70% accuracy and Kappa range of 0.40-0.85 (Congalton, 1991) indicates the descent model performance. The overall Accuracy was calculated utilizing the following equation:
               
       
         
The degree of agreement between truth value and the classification is measured by the Kappa Coefficient. Perfect agreement is expressed by a kappa value of 1 and no agreement when the kappa value is 0. The Kappa Coefficient was calculated by following equation:
 
  
       
Where,
r = Number of rows in the error matrix.
xii = Number of observations in row i and column i (on the major diagonal).
xi+ = Total of observations in row i (shown as marginal total to right of the matrix).
x+i = Total observations in column i (shown as marginal total to bottom of the matrix).
N = Total number of observations included in matrix.

Fig 4: Presence and pseudo-absence point of the target species with 3 km buffer range in the study area (Gautam, 2025).


 
Indigenous insight
 
To assess historical bear movement corridors around Fambonglho Wildlife Sanctuary, a structured survey was conducted with 117 farmers from 2022-2023 (Gautam et al., 2024). The research involved a mixed method research approach, incorporating both qualitative and quantitative approaches like participatory observation, focus group discussion and structured interview, which included both close- and open-ended questions.
       
The questionnaire adopted was based on already existing methodology frameworks (Farhadinia et al., 2017; Goursi et al., 2021) covering demographic features, human-bear conflicts, attitudes towards conservation, disturbances, history of use of the corridor and damage to crops/animals in the last three years. Data for observation was analyzed using IBM SPSS Statistics (2023). This methodology improves the reliability of information through triangulation and understanding of socio-ecological dynamics influencing bear mobility.
       
The fieldwork and spatial modelling for the study were conducted under the Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, from January 2021 to December 2025.

RESULTS AND DISCUSSION

The corridor model identified potential pathways connecting key protected areas and suitable habitats for Asiatic black bears across the Sikkim Himalayas (Fig 5). Most suitable habitats are already protected within Sikkim’s protected-area network. However, important suitable areas also occur outside protected areas, particularly in Barapathing, Bulbulay, Tendong, Kabi, Tingda and Assam Reserve Forests, where protection is comparatively limited. Bhattacharya et al., (2025) similarly identified Pangthang and Kabi as important pinch points in Sikkim. This agrees with our findings and local reports of bear movement through the Pangthang-Kabi-Mangshila landscape, highlighting the importance of forest linkages beyond protected areas. At the broader Eastern Himalaya scale, modelling across Bhutan and neighbouring Indian states found that many Asiatic black bear corridors lie outside protected areas, highlighting the need to conserve forest linkages beyond the protected-area network (Penjor et al., 2024), consistent with the current study in the Sikkim Himalaya.

Fig 5: Potential connectivity corridor map with PAs for black bear in the Sikkim Himalayas. Low values represent weak connectivity.


       
Heterogeneity of connectivity is seen in the central areas that have optimal movement corridors. Pinch points can be seen in areas that are fragmented habitats and areas where human and wildlife conflicts occur, such as near rivers like the Teesta and Rangeet and highways like NH 10 (Gautam, 2025). Recent modelling in China also found that Asiatic black bears prefer mountainous, densely vegetated habitats and avoid highly disturbed areas (Cheng et al., 2025).
       
In case of Sikkim, out of the eight PAs, connectivity among black bears was found in seven; while Kitam Bird Sanctuary stood out as the exception based on primary and secondary data. The Circuitscape model gave an accuracy of 96.37%, having Kappa value of 0.9621 and thus was able to successfully define corridors that were surpassing the benchmark established by Congalton, 1991. In addition to this, field survey conducted among 117 farmers around Fambonglho Wildlife Sanctuary corroborated these results; with 107 respondents identifying bear corridors while 10 reporting no awareness. Local perceptions and knowledge can play an important role in wildlife conservation and human-wildlife coexistence (Kumar et al., 2017; Bussa, 2023). The following connectivity in Fambonglho Wildlife Sanctuary were highlighted during the field survey:
 
• Fambonglho Wildlife Sanctuary-Ransang-Lingee RF-Sokpay RF-Dzongu (34 responses)-Most frequently reported, highlighting its role in northward connectivity.
 
•​ Pangthang-Bulbulay Reserve Forest-Kyongnosla Alpine Sanctuary-Pangolakha Wildlife Sanctuary (46 responses)-Strong eastward connectivity requiring conservation efforts.
 
•​ Pangthang-Kabi Reserve Forest-Mangshila Reserve Forest-Dzongu (27 responses)– Alternative route linking key habitats across fragmented landscapes.
       
Since the p-value (0.0000366) is significantly below 0.05, the variation in bear corridor usage is not random, indicating that certain corridors are more heavily used. This has significant implications from a conservation perspective, highlighting the necessity of action in movement pathways. Respondents from agricultural background spoke about bear migration to the Kyongnosla Alpine Sanctuary (KAS) and Pangolakha Wildlife Sanctuaries (PWLS), moving eastward through the reserve forest called Bulbulay, with some mentioning also the route Ransang-Lingee-Sokpay-Dzongu. However, there is low awareness among residents in urban areas, which can be due to loss of traditional knowledge. On the other hand, only 10 people admitted not being aware, which means that the majority knows about bear corridor use. The high local awareness observed in this study highlights the value of community knowledge in identifying wildlife movement routes. Similar findings from Sikkim and the Uttarakhand Himalaya show that local knowledge can complement scientific studies by providing useful information on wildlife activity and corridor use (O’Neill  et al., 2017; Yadav et al., 2022; Balodi and Anwar, 2018).
 
CONSERVATION IMPLICATIONS
Comparison of the corridors with the protected areas reveals that while a good percentage of the bears’ habitat lies within protected areas, there are still some vital locations like Barapathing Reserve Forest (RF), Bulbulay RF and Assam RF which do not lie in any wildlife sanctuary. Key observations include:
• Strong connectivity exists between FWLS, KAS and PWLS via Assam RF and Barapathing RF.
• Weak connectivity between FWLS and Maenam Wildlife Sanctuary because of dense settlements, the Teesta River and the highways that act as barriers.
• Fragmentation hotspots include Pam-Bhusuk, Tumin-Namrang, Dikchu and urban areas like Burtuk, Upper Chanmari and Tashi Viewpoint.
• Strategic locations such as Dzuluk, Kupup and Gnathang face connectivity challenges due to NH-310, rivers, defence installations and human settlements.
• Hydropower projects and tourism in North Sikkim (Lachen and Lachung) and agricultural expansion in Barasamdong and Phunchebong threaten corridor continuity.
• Southern Sikkim (Damthang and Kewzing) faces habitat loss due to road expansion and settlements.
       
The study shows that combining GIS-based connectivity modelling with local ecological knowledge can make conservation planning more practical and locally relevant. By identifying potential corridors and bottlenecks, the study provides useful inputs for habitat restoration and protection of forest linkages, supporting India’s ongoing efforts to improve SDG 15 (Life on Land) through biodiversity conservation, ecosystem restoration and sustainable management of terrestrial ecosystems, thereby contributing to global SDG goals.
 
RECOMMENDATION
Habitat protection and restoration
 
It is important to demarcate the critical wildlife corridors as conservation sites along with the establishment of monitoring schemes to protect them from future degradation. In addition to this, afforestation with bear amenable species can play an important role in improving landscape connectivity.
 
Infrastructure solutions
 
To avoid habitat fragmentation, it is imperative to construct wildlife under passes and overpasses along major highways, along-with the use of environment-friendly road and settlement planning that minimizes ecological disruptions.
 
Community-based conservation
 
Make conservation efforts more effective by incorporating traditional knowledge and geospatial corridor mapping, conducting awareness campaigns to increase community involvement in the conservation effort and by including them in habitat restoration projects.
 
Adaptive management and policy implementation
 
Strengthen the legal protection of high-priority wildlife corridors, apply incentive-based approaches to reduce human-wildlife conflicts and strengthen multi-sectoral collaboration between the conservationists, policy makers to the local communities.

CONCLUSION

The study identified important potential connectivity corridors for Asiatic black bears in and around Fambonglho Wildlife Sanctuary. While several corridors provide strong links between protected areas, bottlenecks occur around fragmented habitats, villages and highways intersections like Pangthang-Bulbulay junction, posing serious threats to the bear movement and genetic exchange. Protecting these forest linkages, restoring fragmented habitats and reducing barriers to wildlife movement will be important for maintaining bear populations and strengthening connectivity across Sikkim and the wider Eastern Himalayas, thus avoiding any intrusions in search of food or passage. High-potential patches should be declared as conservation reserves for ecological integrity and species survival.
 

ACKNOWLEDGEMENT

The authors gratefully acknowledge the Forest and Environment Department, Government of Sikkim, for granting permission to conduct field surveys and for providing the necessary data for this study. The authors also thank the Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, for providing the academic and research support.
 
Disclaimers
 
The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect loss resulting from the use of this content.
 
Informed consent
 
No animals were captured, handled, or subjected to experimental procedures during this study. All fieldwork was non-invasive and conducted with the necessary permissions.

CONFLICT OF INTEREST

The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

REFERENCES


  1. Acharya, B.K. and Vijayan, L. (2011). The birds of Sikkim: An analysis of elevational distribution, endemism and threats. In Biodiversity of Sikkim.

  2. Adriaensen, F., Chardon, J.P., De Blust, G., Swinnen, E., Villalba, S., Gulinck, H. andMatthysen, E. (2003). The application of “least-cost” modeling as a functional landscape model. Landscape and Urban Planning. 64(4): 233-247. https:// doi.org/10.1016/S0169-2046(02)00242-6.

  3. Ament, R., Callahan, R., McClure, M., Reuling, M. and Tabor, G. (2014). Wildlife connectivity: Fundamentals for conservation action. Center for Large Landscape Conservation, Bozeman, Montana, USA, 41.

  4. Balodi, K. and Anwar, M. (2018). Community attitude and religious bonding in human-wildlife conflict mitigation: A study of Kilpura-Khatima-Surai Corridor, Terai Arc Landscape, India. Asian Journal of Environment and Ecology. 7(1): 1-13. https://doi.org/10.9734/AJEE/2018/41957. 

  5. Beier, P., Majka, D. and Spencer, W.D. (2008). Forks in the road: Choices in procedures for designing wildlife linkages. Conservation Biology. 22(4): 836-851. https://doi.org/ 10.1111/j.1523-1739.2008.00942.x.

  6. Bhattacharya, M., Sarkar, D., Pandey, S., Mondal, I., Sathyakumar, S., Kumar, R.S. and Talukdar, G. (2025). Identifying corridors for Asiatic black bear (Ursus thibetanus) in a part of Eastern Himalayas, India. Frontiers in Conservation Science. 6: 1470223. https://doi.org/10.3389/fcosc. 2025.1470223.

  7. Bussa, B. (2023). Community perceptions and challenges to wildlife conservation: The case of Borana National Park, Southern Ethiopia. Agricultural Science Digest. 43(4): 540-545. doi: 10.18805/ag.DF-455.

  8. Charoo, S.A., Sharma, L.K. and Sathyakumar, S. (2011). Asiatic black bear-human interactions around dachigam national park, Kashmir, India. Ursus. 22(2): 106-113.: https://doi. org/10.2192/URSUS-D-10-00021.1.

  9. Cheng, J., Zheng, W., Guo, X., Wang, Y., Zhou, Y., Zhao, S., Song, X. and Xu, A. (2025). Large carnivores persisting in a human-dominated landscape: Suitable habitat and connectivity for Asiatic black bears in China. Ecology and Evolution. 15(10): e72181. https://doi.org/10.1002/ ece3.72181. 

  10. Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. 37(1): 35-46. https://doi.org/10.1016/ 0034-4257(91)90048-B.

  11. Cushman, S.A., Landguth, E.L. and Flather, C.H. (2013). Evaluating population connectivity for species of conservation concern in the American Great Plains. Biodiversity and Conservation. 22(11): 2583-2605. https://doi.org/10.1007/s10531-013- 0541-1. 

  12. Doko, T., Fukui, H., Ichinose, T. and Chen, W. (2011). Ecological network planning for the Asiatic black bears in Fuji-Tanzawa region, Japan. In 34th International Symposium on RemoteSensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring.

  13. Dutta, T., Sharma, S., McRae, B.H., Roy, P.S. and DeFries, R. (2016). Connecting the dots: Mapping habitat connectivity for tigers in central India. Regional Environmental Change. 16(Suppl.1): 53-67. https://doi.org/10.1007/s10113-015-0877-z. 

  14. Estrada, E. and Bodin, Ö. (2008). Using network centrality measures to manage landscape connectivity. Ecological Applications. 18(7): 1810-1825. https://doi.org/10.1890/07-1419.1.

  15. Farhadinia, M.S., Johnson, P.J., Hunter, L.T. and Macdonald, D.W. (2017). Wolves can suppress goodwill for leopards: Patterns of human-predator coexistence in northeastern Iran. Biological Conservation. 213(Part A): 210-217. https://doi.org/10.1016/j.biocon.2017.07.011.

  16. Flory, A.R., Kumar, S., Stohlgren, T.J. and Cryan, P.M. (2012). Environmental conditions associated with bat white nose syndrome mortality in the northeastern United States. Journal of Applied Ecology. 49(4): 680-689. https:// doi.org/10.1111/j.1365-2664.2012.02188.x.

  17. Foresman, K.R. (2004). The effects of highways on fragmentation of small mammal populations and modifications of crossing structures to mitigate such impacts (No. FHWA/MT- 04-005/8161). Montana. Dept. of Transportation. Research Section. https://doi.org/10.21949/1518360.

  18. Forest and Environment Department, Government of Sikkim. (2008). Management Plan: Fambonglho Wildlife Sanctuary (2008-2018). Government of Sikkim.

  19. Forest Survey of India. (2017). The State of Forest Report. Government of India-Ministry of Environment and Forest. https://fsi. nic.in/sikkim-isfr-2017.pdf.

  20. Forest Survey of India. (2019). The State of Forest Report. Government of India-Ministry of Environment and Forest. https:// fsi.nic.in/forest-report-2019. 

  21. Forman, R.T.T. and Godron, M. (1986). Landscape Ecology. New York, NY: John Wiley and Sons.

  22. Forman, R.T., Sperling, D., Bissonette, J.A., Clevenger, A.P., Cutshall, C.D., Dale, V.H., Fahrig, L., Goldman, C.R. et al. (2002). Road Ecology: Science and Solutions.

  23. Freemark, K., Bert, D. and Villard, M.A. (2002). Patch-, landscape- and regional-scale effects on biota. In Applying landscape ecology in biological conservation (pp. 58-83). Springer. 

  24. Gautam, N. (2025). A GIS-based approach to ecological network planning for Asiatic black bear in Fambonglho Wildlife Sanctuary in Sikkim, India (Master Thesis). Sikkim Manipal University, Department of Computer Applications. http://hdl.handle.net/10603/678005.

  25. Gautam, N. and Pradhan, R. (2020). Optimal wildlife corridor: A review. Indian Forester. 146(10): 911-916. https://doi.org/ 10.36808/if/2020/v146i10/155428.

  26. Gautam, N., Borah, S., Pradhan, R. and Sharma, K. (2024). Harmony and discord: Unravelling spatial distribution and seasonal dynamics of human-bear (Ursus thibetanus) conflict in fambonglho wildlife sanctuary, Sikkim Himalaya. Indian Journal of Animal Research. 58(8): 1402-1409. doi: 10. 18805/IJAR.B-5329. 

  27. Gautam, N., Borah, S., Pradhan, R., Chhetri, B. and Sharma, K. (2025). Spatially explicit conservation planning: Habitat modelling and connectivity exploration for Asiatic black bears in Sikkim Himalaya. In H. Zen, N. M. Dasari, Y. M. Latha and S. S. Rao (Eds.), Soft computing and signal processing (Vol. 1200): 393-404. Springer. https://doi.org/10.1007/ 978-981-97-9926-8_30.

  28. Gopalaswamy, A.M., Karanth, K.U., Kumar, N.S. and Macdonald, D.W. (2012). Estimating tropical forest ungulate densities from sign surveys using abundance models of occupancy. Animal Conservation. 15(6): 669-679. https://doi.org/ 10.1111/j.1469-1795.2012.00565.x.

  29. Goursi, U.H., Anwar, M., Bosso, L., Nawaz, M.A. and Kabir, M. (2021). Spatial distribution of the threatened Asiatic black bear in northern Pakistan. Ursus. 32(e13): 1-5. https:// doi.org/10.2192/URSUS-D-19-00031.3.

  30. Hanski, I. (1989). Metapopulation dynamics: Does it help to have more of the same? Trends in Ecology and Evolution. 4(4): 113-114. https://doi.org/10.1016/0169-5347(89) 90106-6.

  31. Hazumi, T. and Maruyama, N. (1986). Movements and home ranges of Japanese black bears in Nikko. In Bears: Their Biology and Management. 7. pp. 275-279).

  32. Hodgson, J.A., Thomas, C.D., Dytham, C., Travis, J.M.J. and Cornell, S.J. (2012). The speed of range shifts in fragmented landscapes. PLOS ONE. 7(10): e47141. https://doi.org/ 10.1371/journal.pone.0047141.

  33. Jongman, R.H. and Pungetti, G. (Eds.). (2004). Ecological Networks and Greenways: Concept, Design, Implementation. Cambridge University Press.

  34. Kopatz, A., Eiken, H.G., Hagen, S.B., Ruokonen, M., Esparza-Salas, R., Schregel, J., Kojola, I. et al. (2012). Connectivity and population subdivision at the fringe of a large brown bear (Ursus arctos) population in North Western Europe. Conservation Genetics. 13: 681-692.

  35. Kumar, M., Meena, H.R., Paul, P. and Meena, B.S. (2017). An analytical approach to assess the level of tolerance among farmers towards wildlife conflict. Agricultural Science Digest. 37(1): 22-26. doi: 10.18805/asd.v0iOF.7335.

  36. Lawler, J.J., Ruesch, A.S., Olden, J.D. and McRae, B.H. (2013). Projected climate-driven faunal movement routes. Ecology Letters. 16(8): 1014-1022. https://doi.org/10.1111/ele. 12132.

  37. Lepcha, T.W., Chandy, T., Rao, C.S., Tamang, N.W., Subba, N., Sathyakumar, S., Ramesh, K. et al. (2017). Wildlife Population Estimation for all Protected Areas of Sikkim 2016-2017. Department of Forests, Environment and Wildlife Management, Government of Sikkim, Gangtok. pp. 72.

  38. Li, X., Buzzard, P., Chen, Y. and Jiang, X. (2013). Patterns of livestock predation by carnivores: Human-wildlife conflict in Northwest Yunnan, China. Environmental Management.  52(6): 1334-1340. https://doi.org/10.1007/s00267-013- 0192-8.

  39. McNeely, J.A., Gadgil, M., Lévêque, C., Padoch, C. and Redford, K. (1995). Human influences on biodiversity. In V. H. Heywood and R.T. Watson (Eds.), Global biodiversity assessment (pp. 711-821). Cambridge University Press.

  40. McRae, B.H. (2006). Isolation by resistance. Evolution. 60(8): 1551- 1561. https://doi.org/10.1111/j.0014-3820.2006.tb00500.x. 

  41. McRae, B.H., Dickson, B.G., Keitt, T.H. and Shah, V.B. (2008). Using circuit theory to model connectivity in ecology, evolution and conservation. Ecology. 89(10): 2712-2724. https:// doi.org/10.1890/07-1861.1.

  42. Meiklejohn, K., Ament, R. and Tabor, G. (2010). Habitat corridors and landscape connectivity: Clarifying the terminology. Center for Large Landscape Conservation.

  43. Meniam, G. and Lanoue, A. (1990). Corridor use by small mammals: Field measurement for three experimental types of Peromyscus. Landscape Ecology. 4(2): 123-131. https://doi.org/10. 1007/BF00133777.

  44. Noss, R.F. (1987). Corridors in real landscapes: A reply to Simberloff and Cox. Conservation Biology. 1(2): 159-164. https:// doi.org/10.1111/j.1523-1739.1987.tb00039.x

  45. O’Neill, A.R., Badola, H.K., Dhyani, P.P. and Rana, S.K. (2017). Integrating ethnobiological knowledge into biodiversity conservation in the Eastern Himalayas. Journal of Ethnobiology and Ethnomedicine. 13: 21. https://doi.org/ 10.1186/s13002-017-0148-9.

  46. Penjor, U., Kaszta, Z.M., Macdonald, D.W. and Cushman, S.A. (2024). Identifying umbrella and indicator species to support multispecies population connectivity in a Himalayan biodiversity hotspot. Frontiers in Conservation Science. 5: 1306051. https://doi.org/10.3389/fcosc.2024.1306051.

  47. Pettigrew, M., Xie, Y., Kang, A., Rao, M., Goodrich, J., Liu, T. and Berger, J. (2012). Human–carnivore conflict in China: A review of current approaches with recommendations for improved management. Integrative Zoology. 7(2): 210- 226. https://doi.org/10.1111/j.1749-4877.2012.00303. xDigitalObject Identifier (DOI)

  48. Phillips, S.J. and Dudík, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography. 31(2): 161-175. https://doi.org/ 10.1111/j.0906-7590.2008.5203.x.

  49. Phillips, S.J. anderson, R.P. and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190(3-4): 231-259. https://doi.org/ 10.1016/j.ecolmodel.2005.03.026.

  50. Rodgers, W.A. and Panwar, H.S. (1988). Planning a Wildlife Protected Area Network in India. 2 Vols. Wildlife Institute of India, Dehra Dun. https://api.semanticscholar.org/ CorpusID: 126742690.

  51. Roscioni, F., Rebelo, H., Russo, D., Carranza, M.L., Di Febbraro, M. and Loy, A. (2014). A modelling approach to infer the effects of wind farms on landscape connectivity for bats. Landscape Ecology. 29(5): 891-903. https://doi.org/ 10.1007/s10980-014-0030-2.

  52. Sathyakumar, S. (2001). Status and management of Asiatic black bear and Himalayan brown bear in India. Ursus. 12(1): 21-30. https://www.jstor.org/stable/3873225.

  53. Sathyakumar, S. and Choudhury, A. (2007). Distribution and status of the Asiatic black bear (Ursus thibetanus) in India. Journal of the Bombay Natural History Society. 104(2): 316-323.

  54. Saura, S., Estreguil, C., Mouton, C. and Rodríguez-Freire, M. (2011). Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000). Ecological Indicators. https://doi.org/10.1016/j.ecolind.2010.06.011.

  55. Schaller, G.B. (1977). Mountain monarchs: Wild sheep and goats of the Himalaya. University of Chicago Press.

  56. Sharma, K. and Thapa, G. (2021). Analysis and interpretation of forest fire data of Sikkim. Forest and Society. 5(2): 261- 276. doi.10.24259/fs.v5i2.10931.

  57. Sharma, R.K., Sharma, N., Shrestha, D.G., Luitel, K.K., Arrawatia, M.L. and Pradhan, S. (2012). Study of forest fires in Sikkim Himalayas, India using remote sensing and GIS techniques. In Climate change in Sikkim-Patterns, impacts and initiatives (pp. 233-244).

  58. Songorwa, A.N., Bührs, T. and Hughey, K F. (2000). Community- based wildlife management in Africa: A critical assessment of the literature. Natural Resources Journal. 40(3): 603- 643. https://www.jstor.org/stable/24888539.

  59. Soule, M.E. (1991). Land use planning and wildlife maintenance: Guidelines for conserving wildlife in an urban landscape. Journal of the American Planning Association. 57(3): 313-323. https://doi.org/10.1080/01944369108975502.

  60. Sunar, D., Chakraborty, R., Sharma, B.K., Ghose, P.S., Bhutia, P.T. and Pradhan, S. (2012). Status and distribution of Asiatic black bear and the status of human-bear conflict in Senchal Wildlife Sanctuary, Darjeeling, West Bengal. WWF- India and West Bengal Forest Department, Wildlife Division I. 10(2.1): 1622-3526. https://doi.org/10.2.1/ 1622-3526.

  61. Sundriyal, M., Sundriyal, R.C. and Sharma, E. (2004). Dietary use of wild plant resources in the Sikkim Himalaya, India. Economic Botany. 58(4): 626-638. https://doi.org/10. 1663/0013-0001(2004)058[0626:DUOWPR]2.0.CO;2.

  62. Yadav, V.K., Chauhan, D. S. and Lakhera, P.C. (2022). Asiatic black bear (Ursus thibetanus) activity pattern and human- black bear conflict in the Nanda Devi Biosphere Reserve, Western Himalaya, India. Indian Journal of Ecology. 48(4): 1075-1082.
Published In
Indian Journal of Animal Research
Article Metrics
Metrics Icon
31
Views
0
Citations
Reviewed By
Share Article
Download Article
In this Article
    Contact us
    Follow us

    Full Research Article

    Corridor Planning for Asiatic Black Bear in Sikkim Himalaya: A Synergistic Approach of Technology and Indigenous Insights

    N
    Nischal Gautam1
    K
    Kapila Sharma2,*
    https://orcid.org/0000-0003-3904-4177
    B
    Bijoy Chettri3
    S
    Samarjeet Borah2
    R
    Ratika Pradhan4
    1Department of Forest and Environment, Government of Sikkim, Deorali-737 101, Sikkim, India.
    2Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Pakyong-737 132, Sikkim, India.
    3Department of Zoology, Shri Ramasamy Memorial Sikkim University, Gangtok-737 102, Sikkim, India.
    4Department of Computer Applications, Sikkim University, Gangtok-737 102, Sikkim, India.

    ABSTRACT

    Background: Habitat fragmentation and human encroachment of the Asiatic black bear (Ursus thibetanus) in the Sikkim Himalaya has resulted in isolation of populations along with human-wildlife conflict. Frequent crop and livestock depredation incidents have resulted in many farmers giving up their farming practices. Though bear presence in these regions is frequent, studies on migratory route and habitat corridors are limited. This study provides spatial mapping of habitat and corridor connectivity for Asiatic black bear with focus on Fambonglho Wildlife Sanctuary (FWLS).

    Method: Habitat suitability raster generated by MaxEnt software from 64 Asiatic black bear occurrence records and 48 environmental variables was used as a conductance layer in Circuitscape environment for modeling potential movement routes. Indigenously obtained data from structured survey of 117 farmers around FWLS was analyzed in IBM-SPSS framework for corridor connectivity and habitat impacts.

    Result: The model had an overall accuracy of 96.37% with a Kappa Coefficient of 0.9621. Corridors that connect FWLS, Kyongnosla Alpine Sanctuary and Pangolakha Wildlife Sanctuaries have been found to be highly developed, whereas bottlenecks have been observed on the highways and rivers like Teesta River and Rangeet River. The surveys revealed a high level of awareness amongst the local community regarding existence of wildlife corridors; only 8.5% of the survey respondents were unaware. Areas like Barapathing Reserve Forest, Bulbulay Reserve Forest and Assam Reserve Forest, critical for connectivity, remain outside protected boundaries, necessitating immediate conservation action. A p-value <0.005 confirmed statistically significant corridor usage patterns.

    INTRODUCTION

    Wildlife populations increasingly face habitat fragmentation, leading to connectivity loss and isolation due to both habitat disjunction and human-induced pressures, often escalating human-wildlife conflicts (Kopatz et al., 2012). In particular, conflicts involving bears near protected areas pose significant challenges for local communities, as their seasonal movement patterns bring them into close contact with human settlements (Pettigrew et al., 2012; Li et al., 2013). The indigenous knowledge on wildlife and their behaviour in conservation is fading away with the modernization of the society (Bussa, 2023).
           
    While global studies on Asiatic black bear (Ursus thibetanus) migration exist, research specific to Sikkim remains limited. Although Bhattacharya et al., (2025) conducted similar work using MaxEnt and Circuitscape in this area, their study did not include accuracy assessment of the generated maps, particularly in relation to Kappa statistics. However remains a valuable reference for further studies in this area. Corridors play a vital role in facilitating species movement between large habitat patches (Freemark et al., 2002), functioning alongside habitat patches and matrices as key landscape components (Forman and Godron 1986). Connectivity can be categorized into structural (physical landscape features) and functional (how well landscapes support species movement) forms (Meiklejohn et al., 2010). Jongman and Pungeti (2004) classify corridors into three types: migration corridors (seasonal movements), dispersal corridors (gene flow) and commuting corridors (foraging and breeding within home ranges). Engineered interventions such as wildlife overpasses and underpasses further strengthen connectivity.
           
    While the terms “linkages” are often considered synonymous, it describes larger connectivity zones which are vital for maintaining the process of ecology (Meiklejohn et al., 2010). Fragmentation of habitats may cause isolation of animal population and increase the risk of inbreeding and local extinction (Forman et al., 2002; Foresman, 2004). While linear corridors may increase the danger of poaching and diseases, corridors can be designed in a way to preserve ecological integrity (Noss, 1987). The fragmentation occurs because of both natural causes (landslides, flooding) and human impact (roads, agriculture) (McNeely et al., 1995), while highways act as a barrier for animals’ migration (Hanski 1989; Meniam and Lanoue 1990). Modern strategies of wildlife conservation focus on connecting fragmented habitats to the natural ecosystems (Soule, 1991; Gautam and Pradhan 2020). Engagement of local communities is critical in reaching conservation goals (Songorwa et al., 2000).
           
    Different methods are used for assessment of connectivity, such as Least Cost Modelling (Adriaensen et al., 2003), Centrality Analysis (Estrada and Bodin, 2008) and Circuit Theory (McRae et al., 2008). Least Cost Modeling, popular in GIS-based modelling of corridors, allows to find optimal migration routes (Beier et al., 2008). MaxEnt is a species distribution model which produces a map of habitat suitability (Phillips and Dudik, 2008; Phillips et al., 2006). Circuitscape, grounded in electrical circuit theory, models genetic connectivity by integrating spatial data into graph-based networks (McRae et al., 2008; Flory et al., 2012). MaxEnt-derived suitability raster serves as conductance surfaces in Circuitscape to produce cumulative current maps (Saura et al., 2011; Roscioni et al., 2014; Cushman et al., 2013). GIS-based modelling ensures objective, reproducible and spatially explicit planning (Doko et al., 2011).
           
    Circuitscape is a Python-based tool that uses circuit theory to model movement patterns, gene flow and genetic differentiation across heterogeneous landscapes (McRae 2006; Hodgson et al., 2012; Lawler et al., 2013; Dutta et al., 2016). Each raster cell is treated as a node connected to four or eight neighboring cells via resistors, excluding cells of infinite resistance (McRae et al., 2008). Habitat patches with zero resistance act as single nodes. Current flow represents the probability of random movement (Fig 3). McRae et al., (2008) also demonstrate models with ground resistors, where added resistors simulate mortality and nodes reflect dispersal survival. This modelling approach evaluates multiple dispersal routes and settlement probabilities. Consequently, this study aims to:
    i) Identify suitable habitats for Asiatic black bear conservation in the Sikkim Himalaya and
    ii) Identify historic and existing corridors and analyze connectivity between protected areas by integrating technology and indigenous knowledge, focusing on Fambonglho Wildlife Sanctuary.

    MATERIALS AND METHODS

    Study area
     
    Sikkim
     
    Sikkim (Fig 1), India’s second smallest state, spans 7,096 sq. km (Sharma et al., 2012; FSI, 2017) in the northeastern Himalayas (27°5' S-28°9' N, 87°9’ W-88°56’ E) and receives 2700-3200 mm of annual rainfall (Sharma and Thapa, 2021; FSI, 2019). Its diverse elevation results in a climate ranging from sub-zero in winter to 28°C in summer. Sikkim harbours over 4000 flowering plants, rare orchids, medicinal herbs and rhododendrons. It supports 574 bird species, 45% of India’s avifauna (Sundriyal et al., 2004; Acharya and Vijayan, 2011), including the Blood Pheasant and Himalayan Monal, along with mammals like the Asiatic black bear, snow leopard and musk deer. The state has seven wildlife sanctuaries and one national park. The Khangchendzonga National Park, a UNESCO World Heritage Site, helps protect the high-altitude ecosystems. In Gangtok, the FWLS helps conserve the Himalayan black bear and red panda, while the Kyongnosla Alpine Sanctuary helps conserve the snow leopard. Other significant reserves include the Pangolakha Wildlife Sanctuary, Kitam Bird Sanctuary and the Meanam Wildlife Sanctuaries. This study explores the human-bear conflict, habitat suitability and connectivity corridors of Asiatic black bears in FWLS.

    Fig 1: Map showing Sikkim Himalaya and the FWLS (Study area).


     
    Fambonglho wildlife sanctuary
     
    Fambonglho Wildlife Sanctuary (51.76 km2), located to the west of Gangtok in East Sikkim (27°10'-27°23' N, 88°29'-88°35' E), was declared as a wildlife sanctuary in 1984 to address the problems of ecological degradation due to urbanization (Fig 1). This sanctuary consists of schedule I species and serves as a water reservoir and an ecological buffer for the city of Gangtok. Sedimentary and metamorphic rock formations characterize the sanctuary with elevation ranging from 1,524-2,749 m, having temperatures of 2°C-5°C in January and 10°C-20°C in May. Average annual rainfall in this area is 300 cm with July receiving the maximum amount of rain. Snowfall is short lived in the winter season on the ridges at higher altitudes. Frost is experienced between December and February. In the biogeographic zones 2C (Central Himalayas) (Rodgers and Panwar, 1988), the sanctuary harbours East Himalayan sub-tropical wet hill forests (8B/C1) and East Himalayan wet temperate forests (11B/C1) consisting of Schima wallichii, Castanopsis spp., Oaks, Magnolias and Rhododendrons. Commonly seen fauna in the sanctuary includes common leopard (Panthera pardus), Asiatic black bear, red panda (Ailurus fulgens), flying squirrel, wild boar (Sus scrofa) and barking deer (Muntiacus muntjak). The sanctuary is surrounded by 31 revenue villages and governed by 10 Eco-Development Committees (EDCs) and agricultural practices along with cardamom plantations being the main occupations (Forest and Environment Department, Government of Sikkim, 2008).
     
    Model species
     
    Asiatic black bear (Ursus thibetanus) lives in different parts of southern and eastern Asia, in the Himalayas till 3,750 meters (Schaller, 1977; Sathyakumar, 2001). Though the geographical distribution of the species has been recorded in the northeastern Indian hills (Sathyakumar and Chaudhary, 2007; Charoo et al., 2011), little information on the population status of the species is available from the Sikkim Himalayas (Lepcha et al., 2017). The animal falls under the Schedule I of the Wildlife Protection Act of 1980, its survival is threatened by habitat fragmentation and increasing man-animal conflicts.
     
    Input dataset
     
    The habitat suitability data was used in the form of a raster (Fig 2), as produced by Gautam et al., (2025) using 64 occurrence points of black bears along with 48 environmental variables collected from various data sources such as WorldClim, EarthExplorer DEM, MODIS NDVI and DIVA-GIS land use, representing vegetation condition (NDVI), precipitation, temperature and land-use/land-cover and were selected based on their ecological relevance to Asiatic black bear habitat suitability and spatial availability for the study area. For this study, the MaxEnt model (v 3.3.3k.d) was applied for the analysis of bioclimatic variables. The model showed highly accurate results (AUC: 0.7-0.9) with good validation statistics (Training AUC: 0.948±0.01 SD; Test AUC: 0.86±0.04 SD). The variable contribution in the study indicated that August NDVI (17.6%), driest-month precipitation (15.7%) were the major contributors, while permutation importance was highest for monthly mean diurnal temperature range (19.4%) and September NDVI (15.3%). The complete contribution and permutation-importance values are presented in Table 1 (Gautam et al., 2025).

    Fig 2: Map for the predicted distribution model, showing different threshold categories for bears.



    Table 1: Environmental and associated variables and their percentage representation in the prediction for the probable bear availability.


     
    Corridor modeling using circuit theory
     
    The circuit theory models the movements of wildlife using the analogy of electric current movements (Ament et al., 2014). In such modelling, the landscape is viewed as electric circuits where the nodes are viewed as habitat patches and the resistors as the landscape matrix between them. The resistance is the permeability of the habitat, whereas the voltage is the probability of movement (McRae et al., 2008). Circuitscape is used to compute the cumulative current flow that is used as the indicator of connectivity.
           
    Fig 3 shows that each cell is modelled as a node linked with resistors to four or eight adjacent cells when raster grids of an electrical network are transformed to the electrical network. Short-circuited areas are combined into a node, but cells having infinite resistance are not included in the electrical network (McRae et al., 2008).

    Fig 3: Representation of simple electric circuit.


     
    Corridor calibration with circuitscape
     
    Corridors were created using Circuitscape 5.0 software with the “One to All” model to create connectivity between source nodes and all possible destination nodes. Resistance surface generated using the MaxEnt model’s friction layer was used for estimation of current density and probability of movements.
     
    Corridor performance assessment
     
    Model validation was conducted using 64 known black bear occurrence points and 500 pseudo-absence points. A 3 km buffer was applied (Fig 4) to account for estimated home ranges (Gopalaswami et al., 2012; Hazumi and Maruyama, 1986; Sunar et al., 2012). Performance metrics included a confusion matrix, overall accuracy and the Kappa coefficient. The established benchmarks of 70% accuracy and Kappa range of 0.40-0.85 (Congalton, 1991) indicates the descent model performance. The overall Accuracy was calculated utilizing the following equation:
                   
           
             
    The degree of agreement between truth value and the classification is measured by the Kappa Coefficient. Perfect agreement is expressed by a kappa value of 1 and no agreement when the kappa value is 0. The Kappa Coefficient was calculated by following equation:
     
      
           
    Where,
    r = Number of rows in the error matrix.
    xii = Number of observations in row i and column i (on the major diagonal).
    xi+ = Total of observations in row i (shown as marginal total to right of the matrix).
    x+i = Total observations in column i (shown as marginal total to bottom of the matrix).
    N = Total number of observations included in matrix.

    Fig 4: Presence and pseudo-absence point of the target species with 3 km buffer range in the study area (Gautam, 2025).


     
    Indigenous insight
     
    To assess historical bear movement corridors around Fambonglho Wildlife Sanctuary, a structured survey was conducted with 117 farmers from 2022-2023 (Gautam et al., 2024). The research involved a mixed method research approach, incorporating both qualitative and quantitative approaches like participatory observation, focus group discussion and structured interview, which included both close- and open-ended questions.
           
    The questionnaire adopted was based on already existing methodology frameworks (Farhadinia et al., 2017; Goursi et al., 2021) covering demographic features, human-bear conflicts, attitudes towards conservation, disturbances, history of use of the corridor and damage to crops/animals in the last three years. Data for observation was analyzed using IBM SPSS Statistics (2023). This methodology improves the reliability of information through triangulation and understanding of socio-ecological dynamics influencing bear mobility.
           
    The fieldwork and spatial modelling for the study were conducted under the Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, from January 2021 to December 2025.

    RESULTS AND DISCUSSION

    The corridor model identified potential pathways connecting key protected areas and suitable habitats for Asiatic black bears across the Sikkim Himalayas (Fig 5). Most suitable habitats are already protected within Sikkim’s protected-area network. However, important suitable areas also occur outside protected areas, particularly in Barapathing, Bulbulay, Tendong, Kabi, Tingda and Assam Reserve Forests, where protection is comparatively limited. Bhattacharya et al., (2025) similarly identified Pangthang and Kabi as important pinch points in Sikkim. This agrees with our findings and local reports of bear movement through the Pangthang-Kabi-Mangshila landscape, highlighting the importance of forest linkages beyond protected areas. At the broader Eastern Himalaya scale, modelling across Bhutan and neighbouring Indian states found that many Asiatic black bear corridors lie outside protected areas, highlighting the need to conserve forest linkages beyond the protected-area network (Penjor et al., 2024), consistent with the current study in the Sikkim Himalaya.

    Fig 5: Potential connectivity corridor map with PAs for black bear in the Sikkim Himalayas. Low values represent weak connectivity.


           
    Heterogeneity of connectivity is seen in the central areas that have optimal movement corridors. Pinch points can be seen in areas that are fragmented habitats and areas where human and wildlife conflicts occur, such as near rivers like the Teesta and Rangeet and highways like NH 10 (Gautam, 2025). Recent modelling in China also found that Asiatic black bears prefer mountainous, densely vegetated habitats and avoid highly disturbed areas (Cheng et al., 2025).
           
    In case of Sikkim, out of the eight PAs, connectivity among black bears was found in seven; while Kitam Bird Sanctuary stood out as the exception based on primary and secondary data. The Circuitscape model gave an accuracy of 96.37%, having Kappa value of 0.9621 and thus was able to successfully define corridors that were surpassing the benchmark established by Congalton, 1991. In addition to this, field survey conducted among 117 farmers around Fambonglho Wildlife Sanctuary corroborated these results; with 107 respondents identifying bear corridors while 10 reporting no awareness. Local perceptions and knowledge can play an important role in wildlife conservation and human-wildlife coexistence (Kumar et al., 2017; Bussa, 2023). The following connectivity in Fambonglho Wildlife Sanctuary were highlighted during the field survey:
     
    • Fambonglho Wildlife Sanctuary-Ransang-Lingee RF-Sokpay RF-Dzongu (34 responses)-Most frequently reported, highlighting its role in northward connectivity.
     
    •​ Pangthang-Bulbulay Reserve Forest-Kyongnosla Alpine Sanctuary-Pangolakha Wildlife Sanctuary (46 responses)-Strong eastward connectivity requiring conservation efforts.
     
    •​ Pangthang-Kabi Reserve Forest-Mangshila Reserve Forest-Dzongu (27 responses)– Alternative route linking key habitats across fragmented landscapes.
           
    Since the p-value (0.0000366) is significantly below 0.05, the variation in bear corridor usage is not random, indicating that certain corridors are more heavily used. This has significant implications from a conservation perspective, highlighting the necessity of action in movement pathways. Respondents from agricultural background spoke about bear migration to the Kyongnosla Alpine Sanctuary (KAS) and Pangolakha Wildlife Sanctuaries (PWLS), moving eastward through the reserve forest called Bulbulay, with some mentioning also the route Ransang-Lingee-Sokpay-Dzongu. However, there is low awareness among residents in urban areas, which can be due to loss of traditional knowledge. On the other hand, only 10 people admitted not being aware, which means that the majority knows about bear corridor use. The high local awareness observed in this study highlights the value of community knowledge in identifying wildlife movement routes. Similar findings from Sikkim and the Uttarakhand Himalaya show that local knowledge can complement scientific studies by providing useful information on wildlife activity and corridor use (O’Neill  et al., 2017; Yadav et al., 2022; Balodi and Anwar, 2018).
     
    CONSERVATION IMPLICATIONS
    Comparison of the corridors with the protected areas reveals that while a good percentage of the bears’ habitat lies within protected areas, there are still some vital locations like Barapathing Reserve Forest (RF), Bulbulay RF and Assam RF which do not lie in any wildlife sanctuary. Key observations include:
    • Strong connectivity exists between FWLS, KAS and PWLS via Assam RF and Barapathing RF.
    • Weak connectivity between FWLS and Maenam Wildlife Sanctuary because of dense settlements, the Teesta River and the highways that act as barriers.
    • Fragmentation hotspots include Pam-Bhusuk, Tumin-Namrang, Dikchu and urban areas like Burtuk, Upper Chanmari and Tashi Viewpoint.
    • Strategic locations such as Dzuluk, Kupup and Gnathang face connectivity challenges due to NH-310, rivers, defence installations and human settlements.
    • Hydropower projects and tourism in North Sikkim (Lachen and Lachung) and agricultural expansion in Barasamdong and Phunchebong threaten corridor continuity.
    • Southern Sikkim (Damthang and Kewzing) faces habitat loss due to road expansion and settlements.
           
    The study shows that combining GIS-based connectivity modelling with local ecological knowledge can make conservation planning more practical and locally relevant. By identifying potential corridors and bottlenecks, the study provides useful inputs for habitat restoration and protection of forest linkages, supporting India’s ongoing efforts to improve SDG 15 (Life on Land) through biodiversity conservation, ecosystem restoration and sustainable management of terrestrial ecosystems, thereby contributing to global SDG goals.
     
    RECOMMENDATION
    Habitat protection and restoration
     
    It is important to demarcate the critical wildlife corridors as conservation sites along with the establishment of monitoring schemes to protect them from future degradation. In addition to this, afforestation with bear amenable species can play an important role in improving landscape connectivity.
     
    Infrastructure solutions
     
    To avoid habitat fragmentation, it is imperative to construct wildlife under passes and overpasses along major highways, along-with the use of environment-friendly road and settlement planning that minimizes ecological disruptions.
     
    Community-based conservation
     
    Make conservation efforts more effective by incorporating traditional knowledge and geospatial corridor mapping, conducting awareness campaigns to increase community involvement in the conservation effort and by including them in habitat restoration projects.
     
    Adaptive management and policy implementation
     
    Strengthen the legal protection of high-priority wildlife corridors, apply incentive-based approaches to reduce human-wildlife conflicts and strengthen multi-sectoral collaboration between the conservationists, policy makers to the local communities.

    CONCLUSION

    The study identified important potential connectivity corridors for Asiatic black bears in and around Fambonglho Wildlife Sanctuary. While several corridors provide strong links between protected areas, bottlenecks occur around fragmented habitats, villages and highways intersections like Pangthang-Bulbulay junction, posing serious threats to the bear movement and genetic exchange. Protecting these forest linkages, restoring fragmented habitats and reducing barriers to wildlife movement will be important for maintaining bear populations and strengthening connectivity across Sikkim and the wider Eastern Himalayas, thus avoiding any intrusions in search of food or passage. High-potential patches should be declared as conservation reserves for ecological integrity and species survival.
     

    ACKNOWLEDGEMENT

    The authors gratefully acknowledge the Forest and Environment Department, Government of Sikkim, for granting permission to conduct field surveys and for providing the necessary data for this study. The authors also thank the Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, for providing the academic and research support.
     
    Disclaimers
     
    The views and conclusions expressed in this article are solely those of the authors and do not necessarily represent the views of their affiliated institutions. The authors are responsible for the accuracy and completeness of the information provided, but do not accept any liability for any direct or indirect loss resulting from the use of this content.
     
    Informed consent
     
    No animals were captured, handled, or subjected to experimental procedures during this study. All fieldwork was non-invasive and conducted with the necessary permissions.

    CONFLICT OF INTEREST

    The authors declare that there are no conflicts of interest regarding the publication of this article. No funding or sponsorship influenced the design of the study, data collection, analysis, decision to publish, or preparation of the manuscript.

    REFERENCES


    1. Acharya, B.K. and Vijayan, L. (2011). The birds of Sikkim: An analysis of elevational distribution, endemism and threats. In Biodiversity of Sikkim.

    2. Adriaensen, F., Chardon, J.P., De Blust, G., Swinnen, E., Villalba, S., Gulinck, H. andMatthysen, E. (2003). The application of “least-cost” modeling as a functional landscape model. Landscape and Urban Planning. 64(4): 233-247. https:// doi.org/10.1016/S0169-2046(02)00242-6.

    3. Ament, R., Callahan, R., McClure, M., Reuling, M. and Tabor, G. (2014). Wildlife connectivity: Fundamentals for conservation action. Center for Large Landscape Conservation, Bozeman, Montana, USA, 41.

    4. Balodi, K. and Anwar, M. (2018). Community attitude and religious bonding in human-wildlife conflict mitigation: A study of Kilpura-Khatima-Surai Corridor, Terai Arc Landscape, India. Asian Journal of Environment and Ecology. 7(1): 1-13. https://doi.org/10.9734/AJEE/2018/41957. 

    5. Beier, P., Majka, D. and Spencer, W.D. (2008). Forks in the road: Choices in procedures for designing wildlife linkages. Conservation Biology. 22(4): 836-851. https://doi.org/ 10.1111/j.1523-1739.2008.00942.x.

    6. Bhattacharya, M., Sarkar, D., Pandey, S., Mondal, I., Sathyakumar, S., Kumar, R.S. and Talukdar, G. (2025). Identifying corridors for Asiatic black bear (Ursus thibetanus) in a part of Eastern Himalayas, India. Frontiers in Conservation Science. 6: 1470223. https://doi.org/10.3389/fcosc. 2025.1470223.

    7. Bussa, B. (2023). Community perceptions and challenges to wildlife conservation: The case of Borana National Park, Southern Ethiopia. Agricultural Science Digest. 43(4): 540-545. doi: 10.18805/ag.DF-455.

    8. Charoo, S.A., Sharma, L.K. and Sathyakumar, S. (2011). Asiatic black bear-human interactions around dachigam national park, Kashmir, India. Ursus. 22(2): 106-113.: https://doi. org/10.2192/URSUS-D-10-00021.1.

    9. Cheng, J., Zheng, W., Guo, X., Wang, Y., Zhou, Y., Zhao, S., Song, X. and Xu, A. (2025). Large carnivores persisting in a human-dominated landscape: Suitable habitat and connectivity for Asiatic black bears in China. Ecology and Evolution. 15(10): e72181. https://doi.org/10.1002/ ece3.72181. 

    10. Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment. 37(1): 35-46. https://doi.org/10.1016/ 0034-4257(91)90048-B.

    11. Cushman, S.A., Landguth, E.L. and Flather, C.H. (2013). Evaluating population connectivity for species of conservation concern in the American Great Plains. Biodiversity and Conservation. 22(11): 2583-2605. https://doi.org/10.1007/s10531-013- 0541-1. 

    12. Doko, T., Fukui, H., Ichinose, T. and Chen, W. (2011). Ecological network planning for the Asiatic black bears in Fuji-Tanzawa region, Japan. In 34th International Symposium on RemoteSensing of Environment - The GEOSS Era: Towards Operational Environmental Monitoring.

    13. Dutta, T., Sharma, S., McRae, B.H., Roy, P.S. and DeFries, R. (2016). Connecting the dots: Mapping habitat connectivity for tigers in central India. Regional Environmental Change. 16(Suppl.1): 53-67. https://doi.org/10.1007/s10113-015-0877-z. 

    14. Estrada, E. and Bodin, Ö. (2008). Using network centrality measures to manage landscape connectivity. Ecological Applications. 18(7): 1810-1825. https://doi.org/10.1890/07-1419.1.

    15. Farhadinia, M.S., Johnson, P.J., Hunter, L.T. and Macdonald, D.W. (2017). Wolves can suppress goodwill for leopards: Patterns of human-predator coexistence in northeastern Iran. Biological Conservation. 213(Part A): 210-217. https://doi.org/10.1016/j.biocon.2017.07.011.

    16. Flory, A.R., Kumar, S., Stohlgren, T.J. and Cryan, P.M. (2012). Environmental conditions associated with bat white nose syndrome mortality in the northeastern United States. Journal of Applied Ecology. 49(4): 680-689. https:// doi.org/10.1111/j.1365-2664.2012.02188.x.

    17. Foresman, K.R. (2004). The effects of highways on fragmentation of small mammal populations and modifications of crossing structures to mitigate such impacts (No. FHWA/MT- 04-005/8161). Montana. Dept. of Transportation. Research Section. https://doi.org/10.21949/1518360.

    18. Forest and Environment Department, Government of Sikkim. (2008). Management Plan: Fambonglho Wildlife Sanctuary (2008-2018). Government of Sikkim.

    19. Forest Survey of India. (2017). The State of Forest Report. Government of India-Ministry of Environment and Forest. https://fsi. nic.in/sikkim-isfr-2017.pdf.

    20. Forest Survey of India. (2019). The State of Forest Report. Government of India-Ministry of Environment and Forest. https:// fsi.nic.in/forest-report-2019. 

    21. Forman, R.T.T. and Godron, M. (1986). Landscape Ecology. New York, NY: John Wiley and Sons.

    22. Forman, R.T., Sperling, D., Bissonette, J.A., Clevenger, A.P., Cutshall, C.D., Dale, V.H., Fahrig, L., Goldman, C.R. et al. (2002). Road Ecology: Science and Solutions.

    23. Freemark, K., Bert, D. and Villard, M.A. (2002). Patch-, landscape- and regional-scale effects on biota. In Applying landscape ecology in biological conservation (pp. 58-83). Springer. 

    24. Gautam, N. (2025). A GIS-based approach to ecological network planning for Asiatic black bear in Fambonglho Wildlife Sanctuary in Sikkim, India (Master Thesis). Sikkim Manipal University, Department of Computer Applications. http://hdl.handle.net/10603/678005.

    25. Gautam, N. and Pradhan, R. (2020). Optimal wildlife corridor: A review. Indian Forester. 146(10): 911-916. https://doi.org/ 10.36808/if/2020/v146i10/155428.

    26. Gautam, N., Borah, S., Pradhan, R. and Sharma, K. (2024). Harmony and discord: Unravelling spatial distribution and seasonal dynamics of human-bear (Ursus thibetanus) conflict in fambonglho wildlife sanctuary, Sikkim Himalaya. Indian Journal of Animal Research. 58(8): 1402-1409. doi: 10. 18805/IJAR.B-5329. 

    27. Gautam, N., Borah, S., Pradhan, R., Chhetri, B. and Sharma, K. (2025). Spatially explicit conservation planning: Habitat modelling and connectivity exploration for Asiatic black bears in Sikkim Himalaya. In H. Zen, N. M. Dasari, Y. M. Latha and S. S. Rao (Eds.), Soft computing and signal processing (Vol. 1200): 393-404. Springer. https://doi.org/10.1007/ 978-981-97-9926-8_30.

    28. Gopalaswamy, A.M., Karanth, K.U., Kumar, N.S. and Macdonald, D.W. (2012). Estimating tropical forest ungulate densities from sign surveys using abundance models of occupancy. Animal Conservation. 15(6): 669-679. https://doi.org/ 10.1111/j.1469-1795.2012.00565.x.

    29. Goursi, U.H., Anwar, M., Bosso, L., Nawaz, M.A. and Kabir, M. (2021). Spatial distribution of the threatened Asiatic black bear in northern Pakistan. Ursus. 32(e13): 1-5. https:// doi.org/10.2192/URSUS-D-19-00031.3.

    30. Hanski, I. (1989). Metapopulation dynamics: Does it help to have more of the same? Trends in Ecology and Evolution. 4(4): 113-114. https://doi.org/10.1016/0169-5347(89) 90106-6.

    31. Hazumi, T. and Maruyama, N. (1986). Movements and home ranges of Japanese black bears in Nikko. In Bears: Their Biology and Management. 7. pp. 275-279).

    32. Hodgson, J.A., Thomas, C.D., Dytham, C., Travis, J.M.J. and Cornell, S.J. (2012). The speed of range shifts in fragmented landscapes. PLOS ONE. 7(10): e47141. https://doi.org/ 10.1371/journal.pone.0047141.

    33. Jongman, R.H. and Pungetti, G. (Eds.). (2004). Ecological Networks and Greenways: Concept, Design, Implementation. Cambridge University Press.

    34. Kopatz, A., Eiken, H.G., Hagen, S.B., Ruokonen, M., Esparza-Salas, R., Schregel, J., Kojola, I. et al. (2012). Connectivity and population subdivision at the fringe of a large brown bear (Ursus arctos) population in North Western Europe. Conservation Genetics. 13: 681-692.

    35. Kumar, M., Meena, H.R., Paul, P. and Meena, B.S. (2017). An analytical approach to assess the level of tolerance among farmers towards wildlife conflict. Agricultural Science Digest. 37(1): 22-26. doi: 10.18805/asd.v0iOF.7335.

    36. Lawler, J.J., Ruesch, A.S., Olden, J.D. and McRae, B.H. (2013). Projected climate-driven faunal movement routes. Ecology Letters. 16(8): 1014-1022. https://doi.org/10.1111/ele. 12132.

    37. Lepcha, T.W., Chandy, T., Rao, C.S., Tamang, N.W., Subba, N., Sathyakumar, S., Ramesh, K. et al. (2017). Wildlife Population Estimation for all Protected Areas of Sikkim 2016-2017. Department of Forests, Environment and Wildlife Management, Government of Sikkim, Gangtok. pp. 72.

    38. Li, X., Buzzard, P., Chen, Y. and Jiang, X. (2013). Patterns of livestock predation by carnivores: Human-wildlife conflict in Northwest Yunnan, China. Environmental Management.  52(6): 1334-1340. https://doi.org/10.1007/s00267-013- 0192-8.

    39. McNeely, J.A., Gadgil, M., Lévêque, C., Padoch, C. and Redford, K. (1995). Human influences on biodiversity. In V. H. Heywood and R.T. Watson (Eds.), Global biodiversity assessment (pp. 711-821). Cambridge University Press.

    40. McRae, B.H. (2006). Isolation by resistance. Evolution. 60(8): 1551- 1561. https://doi.org/10.1111/j.0014-3820.2006.tb00500.x. 

    41. McRae, B.H., Dickson, B.G., Keitt, T.H. and Shah, V.B. (2008). Using circuit theory to model connectivity in ecology, evolution and conservation. Ecology. 89(10): 2712-2724. https:// doi.org/10.1890/07-1861.1.

    42. Meiklejohn, K., Ament, R. and Tabor, G. (2010). Habitat corridors and landscape connectivity: Clarifying the terminology. Center for Large Landscape Conservation.

    43. Meniam, G. and Lanoue, A. (1990). Corridor use by small mammals: Field measurement for three experimental types of Peromyscus. Landscape Ecology. 4(2): 123-131. https://doi.org/10. 1007/BF00133777.

    44. Noss, R.F. (1987). Corridors in real landscapes: A reply to Simberloff and Cox. Conservation Biology. 1(2): 159-164. https:// doi.org/10.1111/j.1523-1739.1987.tb00039.x

    45. O’Neill, A.R., Badola, H.K., Dhyani, P.P. and Rana, S.K. (2017). Integrating ethnobiological knowledge into biodiversity conservation in the Eastern Himalayas. Journal of Ethnobiology and Ethnomedicine. 13: 21. https://doi.org/ 10.1186/s13002-017-0148-9.

    46. Penjor, U., Kaszta, Z.M., Macdonald, D.W. and Cushman, S.A. (2024). Identifying umbrella and indicator species to support multispecies population connectivity in a Himalayan biodiversity hotspot. Frontiers in Conservation Science. 5: 1306051. https://doi.org/10.3389/fcosc.2024.1306051.

    47. Pettigrew, M., Xie, Y., Kang, A., Rao, M., Goodrich, J., Liu, T. and Berger, J. (2012). Human–carnivore conflict in China: A review of current approaches with recommendations for improved management. Integrative Zoology. 7(2): 210- 226. https://doi.org/10.1111/j.1749-4877.2012.00303. xDigitalObject Identifier (DOI)

    48. Phillips, S.J. and Dudík, M. (2008). Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography. 31(2): 161-175. https://doi.org/ 10.1111/j.0906-7590.2008.5203.x.

    49. Phillips, S.J. anderson, R.P. and Schapire, R.E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190(3-4): 231-259. https://doi.org/ 10.1016/j.ecolmodel.2005.03.026.

    50. Rodgers, W.A. and Panwar, H.S. (1988). Planning a Wildlife Protected Area Network in India. 2 Vols. Wildlife Institute of India, Dehra Dun. https://api.semanticscholar.org/ CorpusID: 126742690.

    51. Roscioni, F., Rebelo, H., Russo, D., Carranza, M.L., Di Febbraro, M. and Loy, A. (2014). A modelling approach to infer the effects of wind farms on landscape connectivity for bats. Landscape Ecology. 29(5): 891-903. https://doi.org/ 10.1007/s10980-014-0030-2.

    52. Sathyakumar, S. (2001). Status and management of Asiatic black bear and Himalayan brown bear in India. Ursus. 12(1): 21-30. https://www.jstor.org/stable/3873225.

    53. Sathyakumar, S. and Choudhury, A. (2007). Distribution and status of the Asiatic black bear (Ursus thibetanus) in India. Journal of the Bombay Natural History Society. 104(2): 316-323.

    54. Saura, S., Estreguil, C., Mouton, C. and Rodríguez-Freire, M. (2011). Network analysis to assess landscape connectivity trends: Application to European forests (1990-2000). Ecological Indicators. https://doi.org/10.1016/j.ecolind.2010.06.011.

    55. Schaller, G.B. (1977). Mountain monarchs: Wild sheep and goats of the Himalaya. University of Chicago Press.

    56. Sharma, K. and Thapa, G. (2021). Analysis and interpretation of forest fire data of Sikkim. Forest and Society. 5(2): 261- 276. doi.10.24259/fs.v5i2.10931.

    57. Sharma, R.K., Sharma, N., Shrestha, D.G., Luitel, K.K., Arrawatia, M.L. and Pradhan, S. (2012). Study of forest fires in Sikkim Himalayas, India using remote sensing and GIS techniques. In Climate change in Sikkim-Patterns, impacts and initiatives (pp. 233-244).

    58. Songorwa, A.N., Bührs, T. and Hughey, K F. (2000). Community- based wildlife management in Africa: A critical assessment of the literature. Natural Resources Journal. 40(3): 603- 643. https://www.jstor.org/stable/24888539.

    59. Soule, M.E. (1991). Land use planning and wildlife maintenance: Guidelines for conserving wildlife in an urban landscape. Journal of the American Planning Association. 57(3): 313-323. https://doi.org/10.1080/01944369108975502.

    60. Sunar, D., Chakraborty, R., Sharma, B.K., Ghose, P.S., Bhutia, P.T. and Pradhan, S. (2012). Status and distribution of Asiatic black bear and the status of human-bear conflict in Senchal Wildlife Sanctuary, Darjeeling, West Bengal. WWF- India and West Bengal Forest Department, Wildlife Division I. 10(2.1): 1622-3526. https://doi.org/10.2.1/ 1622-3526.

    61. Sundriyal, M., Sundriyal, R.C. and Sharma, E. (2004). Dietary use of wild plant resources in the Sikkim Himalaya, India. Economic Botany. 58(4): 626-638. https://doi.org/10. 1663/0013-0001(2004)058[0626:DUOWPR]2.0.CO;2.

    62. Yadav, V.K., Chauhan, D. S. and Lakhera, P.C. (2022). Asiatic black bear (Ursus thibetanus) activity pattern and human- black bear conflict in the Nanda Devi Biosphere Reserve, Western Himalaya, India. Indian Journal of Ecology. 48(4): 1075-1082.
    In this Article
      Contact us
      Follow us
      Published In
      Indian Journal of Animal Research

      Editorial Board

      View all (0)