Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km2 and 343 km2 respectively, while the suitable area determined by GARP was 1074 km2 and 714 km2 respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

Studying habitat overlap between sympatric species is one of the best ways to identify interspecies relationships and to direct conservation efforts so that multiple species can benefit. However, studies exploring interspecies relationships are very limited in Nepal, making it difficult for the government of Nepal and conservation partners to manage wildlife in their habitats, especially in Himalayan protected areas. In this study, we identified habitat overlap between Asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) as well as important habitat types for both species in the Makalu Barun National Park, Nepal using Maximum Entropy (MaxEnt) modeling. GPS points of species occurrence were collected from the field, and environmental variables were extracted from freely available sources. We found that the study area contained 647 km2 of Asiatic black bear habitat and 443 km2 of the red panda habitat. 368 km2 supported both species, which constituted 57% of the Asiatic black bear habitat and 83% of the red panda habitat. We found that conifer forest was the most important habitat type for both species. Because the largest portions of both species’ habitat were located inside the buffer zone, a peripheral zone of national park, conservation efforts for these sympatric species should be focused inside the buffer zone to be most effective.

14 ABSTRACT15 Recent centuries have experienced drastic changes in land cover around the world where 16 Himalayan countries like Nepal have undergone changes in the past several decades because of 17 increasing anthropogenic pressure, natural risks and climatic factors. Accordingly, forest 2 18 fragmentation has also been increasing alarmingly, which is a matter of concern for natural 19 resource management agencies and biodiversity conservation communities. In this study, we 20 assessed land cover change and forest fragmentation trends in Dhorpatan Hunting Reserve of 21 Nepal by implementing landscape fragmentation and recovery process models, and calculating 22 landscape indices based on five-date land cover maps derived from Landsat satellite images from 23 1993 to 2018. Six land cover types including forest, grass land, barren land, agriculture & built-24 up, water bodies and snow & glaciers were determined after an intensive field survey. Diverse 25 derived image features were fed to the Support Vector Machines classifier to create land cover 26 maps, followed by a validation procedure using field samples and reference data. Land cover maps 27 showed an increase in forest area from 37.32% (1993) to 39.26% (2018) and snow & glaciers from 28 1.72% (1993) to 2.15% (2018) while a decrease in grassland area from 38.78% (1993) to 36.41% 29 (2018) and agriculture & built-up area from 2.39% (1993) to 1.80% (2018). Barren land and water 30 body showed negligible changes. The spatial explicit process of forest fragmentation indicated that 31 shrinkage was the most responsible factor of forest loss while expansion was dominant to 32 increment for forest restoration. High dependency of people persists on the reserve for subsistence 33 resources being a cause of forest fragmentation and posing threats to biodiversity. Focus should 34 be made on strategies to decrease the anthropogenic pressure on the reserve. This requires 35 approaches that provide sustainable alternative resources to the local people and innovations that 36 will help them become less reliant on natural resources. 37 39 3 40 Introduction 41 Land use land cover (LULC) change is considered as one of the most important variables of global 42 ecological change since its impacts are profound and range from global carbon and hydrologic 43 cycle to aerosols and local biodiversity [1]. Land use refers to human activity on a piece of land 44 for different purposes such as industrial zones, residential zones, and land cover refers to its 45 surface features such as forest, grassland, etc [2, 3]. Land use is one of the main factors through 46 which humans influence the environment. It involves both the manner in which the biophysical 47 attributes of the land are manipulated and the intent underlying that manipulation [4]. There's a 48 direct link between land cover and the actions of people in their environment, i.e., land use may 49 lead to land cover change. 50 There have been drastic changes in LULC over recent centuries where Nepal has undergone 51 constant c...

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