Global potential invasion maps of traded birds under climate and land-cover change
Naimi, Babak et al. (2022), Global potential invasion maps of traded birds under climate and land-cover change, Dryad, Dataset, https://doi.org/10.5061/dryad.qz612jmj8
Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585).
The data are the outcomes of the species distribution models (SDMs), trained by using 609 species data (known to be traded from appendix II of CITES [Convention on International Trade in Endangered Species] database; available online at https://trade.cites.org) and the Worldclim climate dataset (version 2.1). The sdm R package (https://onlinelibrary.wiley.com/doi/full/10.1111/ecog.01881) was used to fit the models and generate the ensemble of predictions (for the current time) and projections (for the future times). The details are provided in the main manuscript published in Global Change Biology.
The zip files contain the species distribution maps for each individual species (for the current and future times), and the individual GeoTiff files (not those that are within the zip files) are the maps based on combining all the species to generate potential invasion risk (also presented in the manuscript).
Any software that can handle GIS data (raster formats) can be used to open and explore the data (e.g., ArcGIS, QGIS, R).
Fundação para a Ciência e a Tecnologia, Award: PTDC/BIA-ECO/30931/2017-POCI-01-0145-FEDER-030931