Spatial patterns of tree species richness and structural diversity in China’s forests

Two fundamental aspects that characterize the diversity of natural forests are species richness and structural diversity. Our understanding of the fine-grained patterns and drivers of tree species richness and structural diversity in many regions has been limited by lack of spatially representative vegetation-plot data. Based on the distributions of China’s forests, 3396 natural forest plots (20 × 50 m) were established in China between 2011 and 2015. The natural forests considered in our study included two types: preexisting primary forests and secondary forest. Specifically, secondary forests resulted from natural regeneration after deforested or natural disturbance (e.g., fire). For each 20 × 50m plot, all trees with a diameter at breast height (DBH) ≥ 5 cm were tagged, and their taxonomic information, DBH, and height were recorded. Tree height was measured using a telescopic stick, while DBH was measured with a diameter tape. A total of 322,391 trees were surveyed in all plots. Species richness was quantified as the number of tree species with a DBH greater than 5 cm within the plot. The Gini coefficient is used to characterize the structural diversity. Based on these datasets and environmental factors, we upscaled the spatial patterns of species richness and structural diversity at a 1 km resolution using machine learning models. These atlases will provide critical support for biodiversity conservation in China.