A Dataset of Potential Distributions and Species Richness of Ecosystem-Disrupting Invasive Alien Animal Species in South Korea under Shared Socioeconomic Pathways Climate Scenarios
Year: 2025
Type: article
Abstract: AInvasive alien species are major drivers of biodiversity change, yet spatially explicit datasets that integrate their distributions with future climate scenarios remain scarce. In South Korea, several ecosystem-disrupting animal species have rapidly expanded across multiple ecosystems, highlighting the need for standardized, national-scale spatial data products to support management and research. Here we present a gridded dataset of current and future potential distributions and species richness for seven ecosystem-disrupting animal species designated by the Ministry of Environment in South Korea: Pochazia shantungensis, Lycorma delicatula, Vespa velutina, Pseudemys concinna, Metcalfa pruinosa, Trachemys scripta, and Lithobates catesbeianus. Species occurrence records were compiled from the National Ecosystem Survey (1997-2021), national park monitoring data (2003-2022), and published literature, then filtered and merged to unique 1 km2 grid cells. Environmental predictors include six bioclimatic variables (BIO1, BIO2, BIO3, BIO12, BIO13, BIO14) and three climate-trend indices derived from 1 km2 ensemble climate data provided by the Korea Meteorological Administration under SSP2-4.5 and SSP5-8.5 scenarios for the 2030s, 2050s, 2070s and 2090s. For each species, we fitted Random Forest models using presence and pseudo- absence data, and evaluated performance using receiver operating characteristic (ROC), true skill statistic (TSS), and Cohen’s kappa. Across all species, mean ROC was 0.752 (range, 0.664-0.845), with corresponding TSS and kappa values indicating robust discrimination. Thresholded suitability maps were then summed across species to derive 1 km2 species-richness surfaces (0-7 species) for the baseline and each future time slice. These richness layers were further aggregated to mean values at the sigun- gu administrative level. The resulting dataset consists of species-level suitability maps, grid- and administrative-level species richness, and metadata on model performance and variable importance. It provides reusable spatial information that can be integrated with land-use, socio-economic, or conservation-planning layers to support invasive-species risk assessment, prioritization of management areas, and climate- change impact studies.
Source: GEO DATA
Institutions: Konkuk University, National Institute of Ecology
Language: English
Subfield: Ecological Modeling
Field: Environmental Science
Domain: Physical Sciences
SDGs: Life on land, Climate action
Open Access status: diamond
Awards: G2736946188, G3609728466
OpenAlex ID: W7118694142