We examined environmental and anthropogenic factors drive range loss in large mammals, using presence data of Amur tigers opportunistically collected between 2000 and 2012, and anthropogenic and environmental variables to model the distribution of the Amur tiger in northeastern China. Our results suggested that population distribution models of different subregions showed different habitat factors determining tiger population distribution patterns. Where farmland cover was over 50 km2 per pixel (196 km2), distance was within 15 km to the railway in Changbaishan and road density (length per pixel) increased in Wandashan, the relative probability of Amur tiger occurrence exhibited monotonic avoidance responses; however, where distance was within 150 km of the Sino‐Russia border, the occurrence probability of Amur tiger was relatively high. We analyzed the avoidance or preference responses of Amur tiger distribution to elevation, snow depth and Viewshed. Furthermore, different subregional models detected a variety of spatial autocorrelation distances due to different population clustering patterns. We found that spatial models significantly improved model fits for non‐spatial models and made more robust habitat suitability predications than that of non‐spatial models. Consequently, these findings provide useful guidance for habitat conservation and management.

A residual population of Amur tigers Panthera tigris altaica probably survives in the eastern Wanda Mountains (EWM) in China, where the main prey species are red deer Cervus elaphus, eastern roe deer Capreolus pygargus and wild boar Sus scrofa ussuricus. We used 53 snow sample plots each containing about 29 km of transects to detect ungulate presence and determined their total density in EWM in 2002 to be 87.9 6 8.9 kg km -2 . We then applied these data to three published models that predict the relationship between tiger density and prey biomass density to obtain three estimates of tiger carrying capacity in EWM. Existing estimates of tiger density suggest that tigers were below carrying capacity estimates. Relationships between prey density and tiger density from 15 studies indicate a threshold prey biomass of 195 kg km -2 (CI: 33-433), below which a tiger population cannot be sustained. We therefore concluded that the EWM population of tigers is in peril. We compared densities between the years 2002 and 2008 using comparable data and found that the EWM populations of the three ungulate prey species all experienced decreases of 40-45%, apparently due to intense poaching. This rapid decline in prey density and pervasive threats to tigers and their prey in the EWM demands immediate and effective protection of ungulate and tiger populations from poaching if tigers are to persist and recover.

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With the installation and use of large-scale photovoltaic systems around the world, the detection of photovoltaic system operation and maintenance has become increasingly important. This research uses a convolutional neural network training model to detect and classify the infrared near-field images of photovoltaic modules from small-scale photovoltaic plants in the laboratory. This model classifies the images into two categories: with and without hot spots, with a classification accuracy of 96.58%. The experimental results show that the convolutional neural network training model has a good classification result

When working with widespread large carnivores, most conservation organizations can only perform direct conservation actions for a specific population, but the extinction risk of a species is evaluated at a global scale. Here, we aim to bridge this impact gap by assessing the work and opinions from many organizations. We combines knowledge from scientific literature with the observations of 24 front-line staff working at 18 Chinese snow leopard research and conservation organizations. Through attending two group-meetings and by filling in threat-scoring spreadsheets, we identified 21 threats and ranked them at both the national and provincial levels. The five main snow leopard distribution provinces are Qinghai, Tibet, Xinjiang, Sichuan and Gansu. Also, we analyzed 17 conservation actions conducted by these conservation organizations, as well as the threats these actions attempted to address. The top three threats in China are the insufficient capacity of local conservation departments (9.5 points), climate change (8.0 points), and the lack of conservation incentive among local communities (6.8 points), although large differences exist between provinces. There is currently no action being taken in response to climate change. Although some conservation actions have addressed the insufficient capacity of local conservation departments and the lack of conservation awareness in local communities, such as by building up the capacity of conservation areas and monitoring by communities, respectively, the spatial coverage of these actions is still far from sufficient.