Predicting the distributions of predator (snow leopard) and prey (blue sheep) under climate change in the Himalaya

Abstract

Future climate change is likely to affect distributions of species, disrupt biotic interactions, and cause spatial incongruity of predator–prey habitats. Understanding the impacts of future climate change on species distribution will help in the formulation of conservation policies to reduce the risks of future biodiversity losses. Using a species distribution modeling approach by MaxEnt, we modeled current and future distributions of snow leopard (Panthera uncia) and its common prey, blue sheep (Pseudois nayaur), and observed the changes in niche overlap in the Nepal Himalaya. Annual mean temperature is the major climatic factor responsible for the snow leopard and blue sheep distributions in the energy‐deficient environments of high altitudes. Currently, about 15.32% and 15.93% area of the Nepal Himalaya are suitable for snow leopard and blue sheep habitats, respectively. The bioclimatic models show that the current suitable habitats of both snow leopard and blue sheep will be reduced under future climate change. The predicted suitable habitat of the snow leopard is decreased when blue sheep habitats is incorporated in the model. Our climate‐only model shows that only 11.64% (17,190 km2) area of Nepal is suitable for the snow leopard under current climate and the suitable habitat reduces to 5,435 km2 (reduced by 24.02%) after incorporating the predicted distribution of blue sheep. The predicted distribution of snow leopard reduces by 14.57% in 2030 and by 21.57% in 2050 when the predicted distribution of blue sheep is included as compared to 1.98% reduction in 2030 and 3.80% reduction in 2050 based on the climate‐only model. It is predicted that future climate may alter the predator–prey spatial interaction inducing a lower degree of overlap and a higher degree of mismatch between snow leopard and blue sheep niches. This suggests increased energetic costs of finding preferred prey for snow leopards – a species already facing energetic constraints due to the limited dietary resources in its alpine habitat. Our findings provide valuable information for extension of protected areas in future.

Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling

“…MaxEnt modeling approach has been implemented successfully to build current and future habitats under climate change scenarios for the sympatric macrohabitat dwellers red and giant panda (Li, Xu, Wong, Qiu, Li, et al., ; Li, Xu, Wong, Qiu, Sheng, et al., ; Liu et al., ; Songer, Delion, Biggs, & Huang, ; Sun, ). However, MaxEnt modeling has certain limitations, and recent studies suggest species‐specific tuning of the default manipulation improves model performance (Anderson & Gonzalez, ; Aryal et al., ; Radosavljevic & Anderson, ). Here, our model was corrected for sampling biases, calibrated with MaxEnt default settings, and model evaluation was based on robust evaluation statistics to overcome limitations.…”

“…We used mostly default settings in MaxEnt, except for the following settings: random test percentage equals 25% with 10-fold cross-validation and varying the values of the regularization multiplier. We manipulated the regularization multiplier values setting to 0.5, 1, 2, 3, 4, and 5 following recommendations (Anderson & Gonzalez, 2011;Aryal et al, 2016;Muscarella et al, 2014;Radosavljevic & Anderson, 2014;Su et al, 2015). We averaged the results of multiple runs from different models using three scenarios and six regularization multipliers.…”

“…MaxEnt modeling approach has been implemented successfully to build current and future habitats under climate change scenarios for the sympatric macrohabitat dwellers red and giant panda Li, Xu, Wong, Qiu, Sheng, et al, 2015;Liu et al, 2016;Songer, Delion, Biggs, & Huang, 2012;Sun, 2011). However, MaxEnt modeling has certain limitations, and recent studies suggest species-specific tuning of the default manipulation improves model performance (Anderson & Gonzalez, 2011;Aryal et al, 2016;Radosavljevic & Anderson, 2014).…”

Expected impacts of climate change threaten the anuran diversity in the Brazilian hotspots

“…The assessment of these climate change impacts on biodiversity mostly depends on extensive historical information of species distributions, which is unavailable or imprecise for most species in the biodiverse tropics. Therefore, the use of Ecological Niche Models (ENM; or Species Distribution Models: SDM; Araújo & Peterson, ) has become a widely used tool to anticipate the climate change effects on species distribution of a wide range of taxa and also to generate conservation strategies based on a dynamic changing climate in different regions of the world (Araújo, Alagador, Cabeza, Nogués‐Bravo, & Thuiller, ; Aryal et al., ; Cuevas‐Yáñez, Rivas, Muñoz, & Córdoba‐Aguilar, ; Fois, Cuena‐Lombraña, Fenu, Cogoni, & Bacchetta, ; Lemes & Loyola, ).…”

Identifying climate refugia and its potential impact on Tibetan brown bear (Ursus arctos pruinosus) in Sanjiangyuan National Park, China

“…Understanding such impacts is a matter of urgency (Aryal, Brunton, & Raubenheimer, ; Garcia, Cabeza, Rahbek, & Araujo, ; Vegas‐Vilarrúbia, Nogué, & Rull, ). Strong evidence indicates that climate change has significant impacts on species' phenology (Cohen, Lajeunesse, & Rohr, ; Tomotani, Gienapp, Beersma, & Visser, ), behavior (Papaj, Mallory, & Heinz, ; Rockwell & Gormezano, ), distribution and richness (Aryal et al, ; Ihlow et al, ), population size and interspecies relationships (Cohen et al, ), and ecosystem structure and function (Cramer et al, ; Li, Li, Zhao, Zheng, & Bai, ), all of which exacerbate the rate of species extinction (Lewis, ; Mammola, Goodacre, & Isaia, ). The Intergovernmental Panel on Climate Change (IPCC) estimates that 20%–30% of species are facing extinction in this century if the global average temperature rises 2–3°C above preindustrial levels (IPCC, ).…”