Environmental predictors of vascular plant richness at large spatial scales based on protected area data of China

Vascular plant richness contributes significantly to ecosystem function and services. Understanding the environmental predictors of vascular plant richness can therefore support conservation efforts of biodiversity and ecosystem services at large spatial scales. This study used protected area data to explore the environmental predictors of vascular plant richness at different taxonomic levels (i.e., family, genus, and species), to improve the integrity and function of protected areas. Climate variables (i.e., annual mean temperature, minimum temperature of the coldest month, and annual precipitation) and soil parameters (i.e., soil texture, soil pH, bulk density, organic carbon stock, and coarse-fragment volume) were found to largely predict vascular plant richness across different taxonomic levels. Moreover, these were the most effective at the family level. The following measures are therefore proposed to improve conservation management of protected areas. 1) The effects of climate change on vascular plant richness should be accounted for during the spatial planning of protected areas. 2) The interaction between vascular plant richness and soil variables should be considered for the management of protected areas. 3) Human activities should be controlled to establish a balance between strict protection and the sustainable use of natural resources in the protected area network. However, the effect of human activities alone could not explain the variance of vascular plant richness at large spatial scales. This study provides effective guidelines for the sustainable management of plant diversity in protected areas at large spatial scales. •This study developed environmental predictors of vascular plant richness.•Protected area data of China was used to develop these predictors.•Climate and soil predicted vascular plant richness at different taxonomic levels.•Conservation of plant diversity can be managed with these environmental predictors.