Comprehensive spatial risk assessment using community-based species distribution models stratified by ant damage types

Global occurrence coordinates for 15 ant species, including Solenopsis invicta, Linepithema humile, Anoplolepis gracilipes, Wasmannia auropunctata, Pheidole megacephala, Acromyrmex octospinosus, Tapinoma melanocephalum, Monomorium destructor, Myrmica rubra, Solenopsis richteri, Brachyponera chinensis, Paratrechina longicornis, Monomorium pharaonis, Solenopsis geminata, and Technomyremex albipes, were obtained from the Global Biodiversity Information Facility (GBIF) to predict their potential distributions under climate change. The ant species were categorized into community groups based on the types of damage they could cause, including impacts on biodiversity, direct or indirect effects on agriculture, and threats to human health such as physical attacks and disease transmission. To reduce sampling bias and improve model performance, spatial filtering was conducted using ArcGIS. Meteorological data were obtained from WorldClim (https://www.worldclim.org) at a 10-minute spatial resolution for the period 1992–2021, including average maximum and minimum temperatures and precipitation. These data were then converted into 19 bioclimatic variables using the biovars function in the dismo package of R software. To assess the impact of climate change on future ant habitats, we considered two shared socioeconomic pathways (SSPs): SSP245 and SSP585. For both scenarios, we used the 19 bioclimatic variables at a 10-minute spatial resolution. Climate projections were derived from four global circulation models (GCMs)—ACCESS-CM2, MIROC6, MPI-ESM1-2-HR, and UKESM1-0-LL—sourced from WorldClim. Global soil temperature data were obtained from a previous study (Lembrechts et al., 2022). The soil bioclimatic variables (SBio) represent global soil temperatures at a 30-arcsecond resolution and are available for two depth intervals: 0–5 cm and 5–15 cm. To match the resolution of the bioclimatic variables, soil temperature layers were interpolated using kriging to achieve a 10-minute resolution. We used ENMeval in the R package to determine the model features and regularization multiplier (RM). We also additionally used R for random forest. Files and variables File: occurrence data.zip: Raw data from GBIF and occurrence data for MaxEnt or random forest models. File: Code_R.zip: Example code for selecting optimal model structure of MaxEnt and operating Random forest. File: bio_damage type.zip: Selected bioclimatic variables for the models of ants by damage types File: model results.zip: The species distribution model results of ants by damage types Access information Other publicly accessible locations of the data: GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.jdtx9b GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.h2y2wc GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.9q4s7b GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.546yyn GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.52ku8u GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.zvjbb9 GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.6c9bmw GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.dmb42b GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.px9r7d GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.h5rs65 GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.x39m7n GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.uxsp5q GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.hdtc7d GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.tfv4qa GBIF.org (26 December 2024) GBIF Occurrence Download https://doi.org/10.15468/dl.jb32n8