Research on the spatial patterns of human-wildlife conflict is fundamental to understanding the mechanisms underlying it and to identifying opportunities for mitigation. In the state of Xishuangbanna, containing China’s largest tropical forest, an imbalance between nature conservation and economic development has led to increasing conflicts between humans and Asian elephants (Elephas maximus), as both elephant numbers and conversion of habitable land to rubber plantations have increased over the last several decades. We analyzed government data on the compensation costs of elephant-caused damage in Xishuangbanna between 2008 and 2012 to understand the spatial and temporal patterns of conflict, in terms of their occurrence, frequency and distribution. More than 18,261 incidents were reported, including episodes involving damage to rubber trees (n = 10,999), damage to crops such as paddy, upland rice, corn, bananas and sugarcane (n = 11,020), property loss (n = 689) and attacks on humans (n = 19). The conflict data reconfirmed the presence of elephants in areas which have lacked records since the late 1990s. Zero Altered Negative Binomial models revealed that the risk of damage to crops and plantations increased with proximity to protected areas, increasing distance from roads, and lower settlement density. The patterns were constant across seasons and types of crop damaged. Damage to rubber trees was essentially incidental as elephants searched for crops to eat. A predictive map of risks revealed hotspots of conflict within and around protected areas, the last refuges for elephants in the region, and along habitat corridors connecting them. Additionally, we analyzed how mitigation efforts can best diminish the risk of conflict while minimizing financial costs and adverse biological impacts. Our analytical approach can be adopted, adjusted and expanded to other areas with historical records of human-wildlife conflict.

When densities of large carnivores fall below certain thresholds, dramatic ecological effects can follow, leading to oversimplified ecosystems. Understanding the population status of such species remains a major challenge as they occur in low densities and their ranges are wide. This paper describes the use of non-invasive data collection techniques combined with recent spatial capture-recapture methods to estimate the density of snow leopards Panthera uncia. It also investigates the influence of environmental and human activity indicators on their spatial distribution. A total of 60 camera traps were systematically set up during a three-month period over a 480 km² study area in Qilianshan National Nature Reserve, Gansu Province, China. We recorded 76 separate snow leopard captures over 2,906 trap-days, representing an average capture success of 2.62 captures/100 trap-days. We identified a total number of 20 unique individuals from photographs and estimated snow leopard density at 3.31 (SE = 1.01) individuals per 100 km². Results of our simulation exercise indicate that our estimates from the Spatial Capture Recapture models were not optimal to respect to bias and precision (RMSEs for density parameters less or equal to 0.87). Our results underline the critical challenge in achieving sufficient sample sizes of snow leopard captures and recaptures. Possible performance improvements are discussed, principally by optimising effective camera capture and photographic data quality.

The leopardPanthera pardus, categorized globally as Near Threatened on the IUCN Red List, has the widest distribution of any wild felid species, although in Asia it has declined dramatically and five subspecies are Endangered or Critically Endangered. In China at least three subspecies have been reported to occur throughout much of the country, and in 1998 the population was estimated to be 1,000. However, recent studies have indicated that leopards have disappeared from large areas, probably as a result of habitat loss, a low prey base and poaching, indicating this species may not be as common in China as previously believed. To examine this we reviewed recent literature and interviewed specialists to determine the current status and distribution of the leopard in China. Our findings indicate that the species has declined dramatically, with confirmation of presence at only 44 sites in 11 provinces, despite extensive surveys. Current populations are small and fragmented, and occur mainly in isolated nature reserves. We estimate a total population of only 174–348P. pardus japonensis(the north Chinese leopard), which is endemic to China, and < 30 individuals for each of the other subspecies whose distributions extend beyond China. We recommend that a separate IUCN assessment be made forP. pardus japonensis, and that this subspecies be categorized as Critically Endangered. Our findings are the first reliable estimates of the current distribution and status of the leopard in China, and provide valuable information that will help guide conservation efforts.

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, QNNR) National Nature Reserve, 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 7001.93 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 4000 m 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.

Human population growth and concomitant increases in demand for natural resources pose threats to many wildlife populations. The landscapes used by the endangered snow leopard (Panthera uncia) and their prey is increasingly subject to major changes in land use. We aimed to assess the influence of 1) key human activities, as indicated by the presence of mining and livestock herding, and 2) the presence of a key prey species, the blue sheep (Pseudois nayaur), on probability of snow leopard site use across the landscape. In Gansu Province, China, we conducted sign surveys in 49 grid cells, each of 16 km² in size, within a larger area of 3392 km². We analysed the data using likelihood-based habitat occupancy models that explicitly account for imperfect detection and spatial auto-correlation between survey transect segments. The model-averaged estimate of snow leopard occupancy was high [0.75 (SE 0.10)], but only marginally higher than the naïve estimate (0.67). Snow leopard segment-level probability of detection, given occupancy on a 500 m spatial replicate, was also high [0.68 (SE 0.08)]. Prey presence was the main determinant of snow leopard site use, while human disturbances, in the form of mining and herding, had low predictive power. These findings suggest that snow leopards continue to use areas very close to such disturbances, as long as there is sufficient prey. Improved knowledge about the effect of human activity on large carnivores, which require large areas and intact prey populations, is urgently needed for conservation planning at the local and global levels. We highlight a number of methodological considerations that should guide the design of such research.

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.