Understanding patterns and potential drivers of forest diversity in northeastern China using machine‐learning algorithms

Abstract

AbstractQuestionForest ecosystems are the most important global repositories of terrestrial biodiversity. The mixed temperate forests in northeastern China constitute one of the most biodiverse temperate regions globally and provide nearly one‐third of China's wood supply. We ask what are the spatial patterns and potential drivers of tree species diversity in mixed temperate forests.LocationTemperate, mixed forests of northeastern China.MethodsUsing a large set of ground‐source forest inventory data (FIN) and geospatial covariates derived from published raster layers, we compared different machine‐learning and statistical models to study spatial patterns of tree species diversity and their underpinning drivers.ResultsThe spatial distribution of tree species diversity (species richness and evenness) varied greatly across northeastern China. Tree species diversity varied most with climatic (annual precipitation and annual mean temperature), topographic (elevation and slope), and anthropogenic factors. Anthropogenic factors affected tree species evenness (importance value = 13%) more than tree species richness (importance value = 9%). Based on these relationships, we mapped spatial patterns of tree diversity throughout the region at a 1 km × 1 km resolution.ConclusionsOur findings shed light on the processes behind community assembly and biodiversity patterns in mixed temperate forests in northeastern China, and provide a benchmark for future assessment of biodiversity. Our high‐resolution tree species diversity maps can be useful to landowners and land management agencies in their decision‐making processes about sustainable forest management, biodiversity conservation, and forest restoration — a priority task outlined by the recently implemented 2050 China National Forest Management Plan.