Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining species distribution and their influences on ecological responses. This study explores multi‐scale habitat relationships of the snow leopard ( Panthera uncia ) in two study areas on the Qinghai–Tibetan Plateau of western China. Our primary objectives were to evaluate the degree to which snow leopard habitat relationships, expressed by predictors, scales of response, and magnitude of effects, were consistent across study areas or locally landcape‐specific. We coupled univariate scale optimization and the maximum entropy algorithm to produce multivariate SDMs, inferring the relative suitability for the species by ensembling top performing models. We optimized the SDMs based on average omission rate across the top models and ensembles’ overlap with a simulated reference model. Comparison of SDMs in the two study areas highlighted landscape‐specific responses to limiting factors. These were dependent on the effects of the hydrological network, anthropogenic features, topographic complexity, and the heterogeneity of the landcover patch mosaic. Overall, even accounting for specific local differences, we found general landscape attributes associated with snow leopard ecological requirements, consisting of a positive association with uplands and ridges, aggregated low‐contrast landscapes, and large extents of grassy and herbaceous vegetation. As a means to evaluate the performance of two bias correction methods, we explored their effects on three datasets showing a range of bias intensities. The performance of corrections depends on the bias intensity; however, density kernels offered a reliable correction strategy under all circumstances. This study reveals the multi‐scale response of snow leopards to environmental attributes and confirms the role of meta‐replicated study designs for the identification of spatially varying limiting factors. Furthermore, this study makes important contributions to the ongoing discussion about the best approaches for sampling bias correction.

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. AbstractAim: We explore the phylogeography of Himalayan wolves using multiple genetic markers applied on a landscape-scale dataset and relate our findings to the biogeographic history of the region. Location: Himalayas of Nepal, the Tibetan Plateau of China and mountain ranges of Central Asia. Taxon: Himalayan wolf (also called the Tibetan wolf), Canis lupus chanco. Methods: We present a large-scale, non-invasive study of Himalayan wolves from across their estimated range. We analysed 280 wolf scat samples from western China, Kyrgyzstan and Tajikistan at two mtDNA loci, 17 microsatellite loci, four nonsynonymous SNPs in three nuclear genes related to the hypoxia pathway, and ZF genes on both sex chromosomes. | 1273 WERHAHN Et Al.

Identification of the effect of anthropogenic threats on ecosystem is crucial. We used molecular tools and remote sensing to evaluate the population status of an isolated Asian elephant population in southwestern China in response to changes in habitat suitability between 1989 and 2019. A total of 22 unique genotypes were identified from 117 dung samples collected between March and June 2018 using microsatellite DNA analysis, including 13 males and 9 females. Based on the size of fecal boli, 1 animal was a juvenile, 9 were subadults, and 12 were adults, indicating that recruitment was limited. The effective population size was small (15.3) but there was no signature of a recent population bottleneck. We observed a low genetic diversity (H e = 0.46 ± 0.05) and a high level of inbreeding (F is of 0.43 ± 0.11), suggesting low population viability and high risk of extinction. In total, these elephants lost nearly two thirds (62%) of their habitat in 3 decades. The expansion of agriculture and rubber plantations followed by an increase in human settlements after 1989 increased the isolation of this population. We recommend that resettlement of 800 inhabitants of 2 villages and the abandonment of associated farmland and rubber plantations would make an additional 20 km 2 of suitable habitat available. This could allow a population increase of 14 elephants, possibly by translocating individuals from elsewhere in China. Our findings can be applied to the management and conservation of other fragmented populations in China or in other range countries of Asian elephants.

Habitat evaluation constitutes an important and fundamental step in the management of wildlife populations and conservation policy planning. Geographic information system (GIS) and species presence data provide the means by which such evaluation can be done. Maximum Entropy (MaxEnt) is widely used in habitat suitability modeling due to its power of accuracy and additional descriptive properties. To survey snow leopard populations in Qomolangma (Mt. Everest) National Nature Reserve (QNNR), Xizang (Tibet), China, we pooled 127 pugmarks, 415 scrape marks, and 127 non-invasive identifications of the animal along line transects and recorded 87 occurrences through camera traps from 2014–2017. We adopted the MaxEnt model to generate a map highlighting the extent of suitable snow leopard habitat in QNNR. Results showed that the accuracy of the MaxEnt model was excellent (mean AUC=0.921). Precipitation in the driest quarter, ruggedness, elevation, maximum temperature of the warmest month, and annual mean temperature were the main environmental factors influencing habitat suitability for snow leopards, with contribution rates of 20.0%, 14.4%, 13.3%, 8.7%, and 8.2% respectively. The suitable habitat area extended for 7 001.93 km2, representing 22.72% of the whole reserve. The regions bordering Nepal were the main suitable snow leopard habitats and consisted of three separate habitat patches. Our findings revealed that precipitation, temperature conditions, ruggedness, and elevations of around 4 000 m a.s.l. influenced snow leopard preferences at the landscape level in QNNR. We advocate further research and cooperation with Nepal to evaluate habitat connectivity and to explore possible proxies of population isolation among these patches. Furthermore, evaluation of subdivisions within the protection zones of QNNR is necessary to improve conservation strategies and enhance protection.

Conservation interventions for threatened species must be based on accurate assessments of the effects of anthropogenic pressures on habitat suitability. We used multiscale multivariable species‐distribution modeling to evaluate habitat suitability for an Asian elephant (Elephas maximus) population in Shangyong Reserve, Yunnan Province, southwestern China. We investigated the scales at which measurements of environmental variables best reflected elephant habitat selection, and examined whether these responses changed over 2 decades (2000–2010 and 2011–2020) in response to 20 environmental variables, including 14 variables reflecting landscape fragmentation, the extent of buildings, and transport infrastructure. Elephant presence was sensitive to the scale of each variable, and the effects differed among variables within and between decades. More than half of the variables influenced elephant presence at coarse scales of 8 or 16 km, including 12 variables reflecting anthropogenic pressures in 2000–2010 and 10 in 2011–2020. Overall, multivariate models with variables at their optimal scales had higher discrimination than models at uniformly fine scales of 1 km or 2 km. The extent of suitable habitat for elephants declined by 24% over 2 decades. Less than half of elephant habitat was located within Shangyong Reserve (49% in 2000–2010, 40% in 2011–2020), indicating the importance of managing suitable habitat beyond reserve boundaries. Roads and buildings reduced the probability of elephant presence, with effects that extended beyond their immediate footprint. We advocate that infrastructure be planned with buffers, ≥8 km wide, between roads or buildings and core elephant habitat. Multiscale multivariable species‐distribution modeling should be employed to ensure that all suitable habitat for the remaining fragmented elephant populations in Yunnan is identified, mapped, and protected.