# **Data and Method**

The data for this study have been provided by the Global Biodiversity Information Facility (GBIF), a global network, funded by various national governments, that offers open access to comprehensive data on global biodiversity. The GBIF provides a repository of georeferenced, time-stamped species occurrence records that are constantly updated in collaboration with the Catalogue of Life partnership (https://www.catalogueoflife.org), Biodiversity Information Standards (https://www.tdwg.org/), Consortium for the Barcode of Life (CBOL) <u>(http://www.barcodeoflife.org/), Encyclopedia of Life (EOL) (http://eol.org), Global Earth</u> Observation System of Systems (GEOSS) (https://earthobservations.org/geoss.php) and other organizations. The GBIF records span multiple taxa, including animals, plants, fungi, and microbes. Users can directly access these records through GBIF's Occurrence and Maps APIs <u>(https://www.gbif.org/developer/occurrence, https://www.gbif.org/developer/maps),</u> and access to the database is also provided by cloud platforms like Google BigQuery and Amazon Web Services (AWS).

For the analysis presented here, we processed large-scale datasets using Google BigQuery, which efficiently handles vast amounts of biodiversity data. The GBIF occurrence records were filtered to include only those that met specific criteria: we selected records from 1970 onwards and focused on species that had at least three unique reporting locations and five occurrence reports. Additionally, to manage computational efficiency, we limited species with excessive data, such as the American Robin ( _Turdus migratorius_ ), to a maximum of 20,000 randomly sampled records. This filtering approach ensured that computational resources were effectively used, without sacrificing the integrity of the data.

We adhered to GBIF’s occurrence reporting protocols, which ensure consistency in the way species records are documented and shared across the global community. This adherence to protocol is essential for maintaining the reliability and comparability of biodiversity data.

## **Species Occurrence Mapping**

To generate species occurrence maps, we employed machine-based techniques for pattern recognition and cluster analysis. For most species (93.6% of the dataset), we applied a spatial boundary construction method known as the alphahull algorithm (Pateiro-López and RodríguezCasal, 2010). This method, well-established in the literature (Guo et al., 2022; Kass et al., 2022), allowed for the precise delineation of species' occurrence regions across terrestrial, freshwater, coastal, and marine environments. For the remaining species, we utilized a k-means clustering algorithm combined with convex hulls to estimate their geographic distributions.

To validate the accuracy of our generated maps, we compared them with expert-verified maps from well-regarded datasets of mammals (Marsh et al., 2022), ants (Kass et al., 2022), and vascular plants (Borgelt et al., 2022). These comparisons demonstrated strong correlations between our maps and those generated by subject-matter experts. Figure 2 provides two examples of species occurrence regions: one for an endemic species, _Scherya bahiensis_ , which is 7