Identifying the Risk Regions of Wild Boar (Sus scrofa) Incidents in China.

Simple Summary: Identifying the risk regions of human–wildlife conflict is a pertinent topic in current human–wildlife conflict research. Most of the existing studies obtained their wildlife incident information through field surveys to identify risk regions. However, field surveys require a lot of personnel, time, and money. Therefore, they are generally used for meso- and microscale studies as they are difficult to apply to macroscale explorations. In this paper, we attempted to expand the data sources of wildlife incident information to enable macroscale wildlife incident risk region identification more efficiently and at a lower cost. We chose wild boar incidents in China as an example and used web crawling technology to obtain reports of wild boar incidents from internet databases. We then extracted the spatial location information of wild boar incidents from the reports. Subsequently, a system of environmental variables was constructed. Finally, the Maxent model, which provides predictions with higher accuracy and requires less location information, was applied to identify the risk regions of wild boar incidents in China. We observed that approximately 12.18% of China was at a high-risk level, mainly on the eastern side of the Huhuanyong Line. The risk of wild boar incidents was related to the climate, landscape, and topography as well as human disturbance. Variables such as the annual precipitation, GDP index, mean annual temperature, distance from forestland, distance from cultivated land, and elevation strongly influenced the risk of wild boar incidents. The objectives of this study were to identify the risk regions of wild boar incidents in China and to draw a risk map. Risk maps can be used to plan the prioritization of preventive measures, increasing management effectiveness from both a short- and a long-term perspective. We used a web crawler (web information access technology) to obtain reports of wild boar incidents from China's largest search engine (Baidu) and obtained 196 valid geographic locations of wild boar incidents from the reports. Subsequently, a system of environmental variables—with climate, topography, landscape, and human disturbance as the main variable types—was constructed, based on human–land-system thinking. Finally, the Maxent model was applied to predict the risk space of wild boar incidents in China by integrating the geographic location information for wild boar incidents with the environmental variables. We observed that the types of environmental variables that contributed to wild boar incidents were in the descending order of climate (40.5%) > human disturbance (25.2%) > landscape (24.4%) > topography (9.8%). Among the 14 environmental variables, annual precipitation, the GDP index, and the mean annual temperature were the main environmental variables. The distance from woodland, distance from cultivated land, and elevation were the secondary environmental variables. The response curves of the environmental variables demonstrated that the highest probability of wild boar incidents occurred when the annual average temperature was 16 °C, the annual precipitation was 800 mm, and the altitudes were 150 m and 1800 m. The probability of wild boar incidents decreased with an increase in the distance from cultivated and forested land, and increased sharply and then levelled off with an increase in the GDP index. Approximately 12.18% of China was identified as being at a high risk of wild boar incidents, mainly on the eastern side of the Huhuanyong Line. [ABSTRACT FROM AUTHOR]