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The red panda is an endangered species. Although they are protected by national laws in their range countries, its population continues to decline due to habitat loss and fragmentation. Estimating and mapping suitable habitat plays a critical role in red panda conservation planning and policy. In this study, the red panda habitat in Nepal was predi...
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... is solitary small sized (weight is 3 to 6.2 kg and length from head to body is 50 to 64 cm, and the tail is 28 to 59 cm) herbivorous carnivore (Roberts and Gittleman, 1984). This species is native to five countries of Asia: Bhutan, China, India, Myanmar and Nepal ( Glatston et al., 2015) (Figure 1). The presence of red panda is confirmed from 13 districts of Bhutan ( Dorji et al., 2012), three provinces of China (Sichuan, Yunnan and Tibet), three states of India (Sikkim, West Bengal and Arunachal Pradesh), and Northern Kachin province of Myanmar ( Glatston et al., 2015). ...
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... curves show how the response changes for a particular variable (Phillips, 2017). The response curves of Figure 10 were derived from the model using all four categories of variables (i.e., bio-climatic, topographic, vegetation-related, and anthropogenic variables) in the model. ...
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... of the important variables for prediction of suitable habitat for red pandas are presented in Figure 10. Red panda habitat is limiting to a certain range of the annual mean temperature (Bio1) and mean monthly diurnal temperature range (Bio 2). ...
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... which is located at the higher and lower temperatures from this range is not suitable for red pandas. The higher canopy height and the denser forests are an indication of suitable habitat for red pandas ( Figure 10). The probability of the presence of suitable habitat for red pandas is higher in medium (around 0) but lower in very low and very high minimum NDVI. ...
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... are lots of paths near to the red panda habitat. The probability of distribution the red panda habitat is higher near to the paths and trails (Figure 10). ...
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... model was run to find the current and future suitable habitat (2070) for red pandas by using bio-climatic (version 1.4) and topographical variables. A total of 16,920 km 2 of habitat is identified as current suitable habitat for red pandas in Nepal (Figure 11), but that will be 18,019 km 2 in 2070 due to climate change ( Figure 12). Suitable habitat for red pandas will be increased by 6.5% in 2070 due to climate change. ...
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... model was run to find the current and future suitable habitat (2070) for red pandas by using bio-climatic (version 1.4) and topographical variables. A total of 16,920 km 2 of habitat is identified as current suitable habitat for red pandas in Nepal (Figure 11), but that will be 18,019 km 2 in 2070 due to climate change ( Figure 12). Suitable habitat for red pandas will be increased by 6.5% in 2070 due to climate change. ...
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... model was run by using land use and land cover, bio-climatic and topographical variables. A total of 16,725 km 2 of habitat is identified as current suitable habitat for red pandas in Nepal ( Figure 13), but that will be 16,649 km 2 in 2070 due to climate and land use and land cover change ( . Suitable habitat for red pandas will be reduced by 0.5% in 2070 due combined effect of climate, and land use and land cover change ( Figure 15). ...
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... total of 16,725 km 2 of habitat is identified as current suitable habitat for red pandas in Nepal ( Figure 13), but that will be 16,649 km 2 in 2070 due to climate and land use and land cover change ( . Suitable habitat for red pandas will be reduced by 0.5% in 2070 due combined effect of climate, and land use and land cover change ( Figure 15). The threshold 0.220 was used to convert the continuous map (habitat suitability) to binary map. ...
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... to the previous findings, the variables related to the temperature such as mean annual temperature and mean monthly diurnal temperature range were identified as most influencing variables to the red panda habitat suitability model (Figure 9). The red panda prefers the 5-10֯ C annual mean temperature and around 10֯ C monthly diurnal temperature range ( Figure 10). ...
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... height, forest cover and minimum NDVI were crucial to model the suitable habitat for red pandas (Figure 9). The red panda prefers the denser forest cover with higher canopy height ( Figure 10). Winter NDVI was significantly correlated with understory bamboo ( Wang et al., 2009) so this study used minimum NDVI as a surrogate of understory bamboo which is the major food and habitat indicator of the red panda. ...
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... NDVI was significantly correlated with understory bamboo ( Wang et al., 2009) so this study used minimum NDVI as a surrogate of understory bamboo which is the major food and habitat indicator of the red panda. The red panda prefers the medium to high minimum NDVI (Figure 10). ...
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... from the human paths, livestock density, and population density were most important variables to model the suitable habitat for red pandas ( Figure 9). Red pandas are living in forests, so their suitable habitat was almost zero with a high density of livestock and population ( Figure 10). Distance from the motor road is less useful but distance from paths is more useful to explain the suitable habitat for red pandas. ...
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... from the motor road is less useful but distance from paths is more useful to explain the suitable habitat for red pandas. Therefore, the impact of paths is higher than the impact of the motor road to the suitable habitat for red pandas ( Figure 10). Peoples are living in very high mountains of Nepal, and they are managing the facilities for tourists. ...
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Mammals have experienced a massive decline in their populations and geographic ranges worldwide. The sloth bear, Melursus ursinus (Shaw, 1791), is one of many species facing conservation threats. Despite being endangered in Nepal, decades of inattention to the situation have hindered their conservation and management. We assessed the distribution a...
Citations
... Species distribution and zonation are essential to identifying ecologically valuable areas for species conservation (Karimi, Brown & Hockings, 2015). Maximum Entropy (MaxEnt) model is a widely used tool for predicting the distribution of the species in Nepal (Aryal et al., 2016;Bista, Panthi & Weiskopf, 2018;Panthi, 2018;Panthi, Aryal & Coogan, 2019;Sharma et al., 2020;Karki & Panthi, 2021;Panthi, Pariyar & Low, 2021). This model demands only presence points (Phillips, Anderson & Schapire, 2006) so this is popular and useful to model the habitat of threatened species, whose occurrence points are not available in large number. ...
... MaxEnt is a standard and widely used species distribution software. This software is already used to model the suitable habitat of several faunas in Nepal (Aryal et al., 2016;Bista, Panthi & Weiskopf, 2018;Panthi, 2018;KC et al., 2019;Sharma et al., 2020). Variance Inflation Factors (VIFs) less than 10 indicate that multicollinearity is not a severe problem (Forthofer, Lee & Hernandez, 2007). ...
Background
Biodiversity conservation is becoming challenging day by day. For this, it is essential to understand the distribution, habitat, and impact of anthropogenic activities on animals at risk. We assessed the suitable habitats and anthropogenic impacts on Asiatic black bears, common leopards, musk deer, and snow leopards in and outside the protected areas of Gandaki Province, Nepal.
Methods
We collected the presence locations of Asiatic black bears, common leopards, musk deer, and snow leopards based on scats and other signs. We employed the Maximum Entropy (MaxEnt) tool to identify suitable habitats of our studied species and their anthropogenic impacts on them.
Results
The total suitable habitat of the common leopard was found to be 6,052 km ² , followed by the Asiatic black bear (5,819 km ² ), snow leopard (4,447 km ² ), and musk deer (1,690 km ² ) in Gandaki Province. Most of the areas of suitable habitat for common leopards and Asiatic black bears were outside the protected areas, and for musk deer and snow leopards were inside the protected areas. Elevation was the most important variable determining habitat suitability of Asiatic black bear, common leopard, and musk deer, whereas the distance to water was the most important variable determining habitat suitability of snow leopard. Asiatic black bears, common leopards, and musk deer face significant anthropogenic impacts, but snow leopards face some anthropogenic impacts.
Conclusion
Managing these animals’ habitats inside and outside protected areas is essential. Hence, biodiversity conservation and livelihood opportunities should be balanced in the Himalayas on a win-win basis.
... There are settelement of the indigenous people, mostly in the higher hill range and Eastern part of Nepal in the Red panda region, while the majority in the western part are Chetri / Others, but again indigenous people at very high altitudes (Panthi, 2018). The economic condition overall is low compared to the people from the urban and Terai regions (DNPWC and DFSC, 2018). ...
The Red panda (Ailurus fulgens) habitat has been providing several ecosystem services (ES) to the people; however, the differences in local stakeholders' perceptions and preferences of these ecosystem services based on differences in their location, caste, gender, age, and engagement in CFUG are still understudied. This study was conducted using a telephone interview with 120 households from 28 Red panda habitat districts in the Hima-layan range of Nepal. Respondents were asked to: (1) prioritize and rank the environmental (regulatory ES), economic (provisional ES), social, cultural, and spiritual importance of the Red panda habitat areas; (2) identify and prioritize the provisional ES; and (3) share their perceptions about the current state of the forest and biodiversity in comparison to the past decade to assess the change in Red panda habitat condition. Key findings include: (1) gender, caste, location, and involvement in community forest had a significant influence on people's perception and preference for ecosystem services (p < 0.05); (2) in overall, the environmental value of forests was significantly prioritized over the social, cultural, and economic values (p < 0.05); (3) provisional services such as fuelwood and fodder were significantly prioritized by Dalit and indigenous people and CFUG members, whereas timber was given the highest priority by the Brahmin and Chhetri caste groups (p < 0.05); and (4) forest cover, biodiversity, and forest condition have significantly improved in East Nepal over the past 10 years, while the reverse was true in West Nepal (p < 0.05). Information on the preferences of local communities could assist in planning, policymaking, and effective management of natural ecosystems and ecosystem services. More importantly, the findings provide a better understanding of the nature-human interactions in the Red panda region and indicate that people from marginalized groups (ethnic communities, Dalit, and women) still rely on forests (community forests in many cases), and any consideration in future policies should take this into account.
... Geographic factors are responsible for the spatial distribution of the snow leopard (Wolf & Ale, 2009). These geographic variables were used to model the habitat of this species and other Himalayan carnivores in Nepal (Aryal et al., 2016;Bista, Panthi & Weiskopf, 2018;Panthi, 2018). A Digital Elevation Model (DEM) with 30 m resolution was downloaded from the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov/). ...
The snow leopard ( Panthera uncia ) found in central Asia is classified as vulnerable species by the International Union for Conservation of Nature (IUCN). Every year, large number of livestock are killed by snow leopards in Nepal, leading to economic loss to local communities and making human-snow leopard conflict a major threat to snow leopard conservation. We conducted formal and informal stakeholder’s interviews to gather information related to livestock depredation with the aim to map the attack sites by the snow leopard. These sites were further validated by district forest office staffs to assess sources of bias. Attack sites older than 3 years were removed from the survey. We found 109 attack sites and visited all the sites for geo location purpose (GPS points of all unique sites were taken). We maintained at least a 100 m distance between attack locations to ensure that each attack location was unique, which resulted in 86 unique locations. A total of 235 km ² was used to define livestock depredation risk zone during this study. Using Maximum Entropy (MaxEnt) modeling, we found that distance to livestock sheds, distance to paths, aspect, and distance to roads were major contributing factors to the snow leopard’s attacks. We identified 13.64 km ² as risk zone for livestock depredation from snow leopards in the study area. Furthermore, snow leopards preferred to attack livestock near livestock shelters, far from human paths and at moderate distance from motor roads. These identified attack zones should be managed both for snow leopard conservation and livestock protection in order to balance human livelihoods while protecting snow leopards and their habitats.
... along with our occurrence data to determine habitat suitability for wild Asian elephants within our study area. The MaxEnt program is widely used to map wildlife habitat and identify the influence of environmental variables on species occurrence in similar study areas Bista, Panthi, & Weiskopf, 2018;KC et al., 2019;Panthi, 2018;Panthi, Multicollinearity between environmental variables described in Table 1 is acceptable (|r| < .70) (Dormann et al., 2013), so we used all variables in the model. ...
... along with our occurrence data to determine habitat suitability for wild Asian elephants within our study area. The MaxEnt program is widely used to map wildlife habitat and identify the influence of environmental variables on species occurrence in similar study areas Bista, Panthi, & Weiskopf, 2018;KC et al., 2019;Panthi, 2018;Panthi, Multicollinearity between environmental variables described in Table 1 is acceptable (|r| < .70) (Dormann et al., 2013), so we used all variables in the model. ...
... The TSS = Sensitivity + Specificity − 1 and ranges from −1 to 1, where values less than 0 indicate a performance no better than random and 1 indicates a perfect fit (Allouche, Tsoar, & Kadmon, 2006). We calculated TSS for all 10 model outputs in R software (R Core Team, 2018), and the final TSS was averaged from all ten replications (Bista et al., 2018;Jiang et al., 2014;Panthi, 2018). For species distribution models, presence-only data threshold to maximize the TSS is recommended (Liu, White, & Newell, 2013); so, we used this threshold to convert the continuous habitat suitability map to a suitable/unsuitable binary map. ...
Background: There is currently very little available research on the habitat suitability,
the influence of infrastructure on distribution, and the extent and connectivity of
habitat available to the wild Asian elephant (Elephas maximus). Information related to
the habitat is crucial for conservation of this species.
Methods: In this study, we identified suitable habitat for wild Asian elephants in the
Western Terai region of Nepal using Maximum Entropy (MaxEnt) software.
Results: Of 9,207 km2, we identified 3194.82 km2 as suitable habitat for wild Asian
elephants in the study area. Approximately 40% of identified habitat occurs in existing
protected areas. Most of these habitat patches are smaller than previous estimations
of the species home range, and this may reduce the probability of the species
continued survival in the study area. Proximity to roads was identified as the most
important factor defining habitat suitability, with elephants preferring habitats far
from roads.
Conclusions: We conclude that further habitat fragmentation in the study area can
be reduced by avoiding the construction of new roads and connectivity between
areas of existing suitable habitat can be increased through the identification and
management of wildlife corridors between habitat patches.
... We selected variables suspected to influence species presence based on the existing literature and expert opinion from the field. We used ArcGIS and ENVI software to process the variables [55][56][57][58]. We created a raster file with 30 m resolution of all environmental variables to fit the MaxEnt, a species distribution model. ...
... We selected elevation, aspect, and slope as variables for our model, as these are the most important topographical factors impacting habitat selection by terrestrial animals [20,58]. We downloaded a 30 m resolution Digital Elevation Model (DEM) from the U.S. Geological Survey (USGS) website (https://earthexplorer.usgs.gov/), ...
... TSS = Sensitivity + Specificity − 1, and ranges from −1 to 1, where 1 indicates a perfect fit, and values less than 0 indicate a performance no better than random [71]. We calculated TSS for all model outputs, and final TSS was averaged from all ten replications [58]. The threshold to maximize the TSS is recommended for species distribution models which have presence-only data [72] so we used this threshold to convert the continuous probability map to a suitable/unsuitable binary map. ...
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.
