A comprehensive county-level distribution database of alien and invasive plants in China

Abstract

Over the past half century, international trade and exchange have continued to increase in China, resulting in the widespread introduction of alien plant species. The accumulation of these alien species has accelerated invasion events, posing serious threats to local ecological security and economic development. Comprehensive and accurate species distribution records are extremely important for early detection, understanding dispersal dynamics, and supporting various management strategies and research initiatives. However, biodiversity databases, both global and local, often lack comprehensive and high-resolution distribution data for alien invasive plant species (AIPs). This limitation is particularly evident in China, where local databases typically provide coarse spatial data, often restricted to the provincial level, leading to a substantial underestimation of the actual distribution of AIPs. Here, we fill this gap by creating the most comprehensive distribution database for AIPs in China at a much finer spatial resolution. By integrating 73,469 distribution records from China's online herbarium, biodiversity databases, flora, published literature, and 173,396 georeferenced records from GBIF, we built the county-level distribution database for 400 AIPs and report for the first time their presence in 2684 administrative counties in China (92.5% of the total counties). Notably, our database provides 2.58 times more distribution records than global biodiversity data repositories such as GBIF and also includes the earliest introduction dates for each AIP. The temporal range of the records spans from 1607 to 2023, capturing over 400 years of AIP presence in China. These rigorously quality-controlled georeferenced data can be used to examine the dynamics and influencing factors of plant invasions in China. They can also serve as the most updated data reference for policy makers in designing effective AIP management policies in China. We encourage users to cite this data paper when utilizing the data, and there are no restrictions on its use for non-commercial purposes.