DOI: 10.5061/dryad.gtht76hp6
title: Publication release: How well do species distribution models predict occurrences in exotic ranges?

description: Species distribution models (SDMs) are widely used predictive tools to
 forecast potential biological invasions. However, the reliability of SDMs
 extrapolated to exotic ranges remains understudied, with most analyses
 restricted to few species and equivocal results. We examined the spatial
 transferability of SDMs for 647 non-indigenous species extrapolated across
 1,867 invaded ranges, and identify what factors may help differentiate
 predictive success from failure. We performed a large-scale assessment of
 the transferability of SDMs using two modelling approaches: generalized
 additive models (GAMs) and MaxEnt. We fitted SDMs on the native ranges of
 species and extrapolated them to exotic ranges. We examined the influence
 of general factors and factors related to biological invasions on spatial
 transferability. Here, we provide the code and data for publication in
 Global Ecology and Biogeography as part of Nguyen and Leung 2022 "How
 well do species distribution models predict occurrences in exotic
 ranges?". Provided are the files and scripts necessary to fit and
 validate the SDMs using distirbutional data from their native and exotic
 ranges, respectively, formulated as generalized additive models (GAMs) or
 MaxEnt models. Additionally, provided is a script to validate the SDMs on
 their native fitting range using 10-fold cross-validation, and to fit the
 transferability model, as a linear mixed model (LMM), with a provided
 cleaned data.frame. The dataset provided includes a full species list with
 GBIF occurrence records, target-group background (TGB) records to use with
 model fitting and validation, as well as environmental data associated
 with the sightings.
description: Bioclimatic and elevation data was gathered from WorldClim
 version 2 at 2.5-arcmin resolution (~ 5-km grid size). Terrain ruggedness
 index (TRI) was derived from the elevation data using the
 'terrain' function in the 'raster' package. NDVI as an
 index of vegetation cover ('greenness') was restrieved from
 National Aeronautics and Space Administration (NASA) Land Processes
 Distributed Active Archive Center and calculated as the maximum values
 across months within a year for a given site, then averaged across all
 available years (2000 to 2020). Prior to model fitting, we accounted for
 collinearity by removing highly correlated variables using pairwise
 correlation cofficient value of |r| &gt; .7, resulting the final set
 of predictors used in all SDMs: annual mean temperature (bio1), mean
 diurnal range (mean of monthly maximum and minimum temperatures; bio2),
 temperature annual range (bio7), mean annual precipitation (bio12),
 precipitation seasonality (coefficient of variation; bio15), elevation,
 TRI and maximum annual NDVI. All variables were standardized to a mean of
 zero and standard deviation of one. Sightings records
 for the 647 species were obtained from the Global Biodiversity Information
 Facility (GBIF), and gridded to environmental data at 2.5-arcmin
 resolution. Records were cleaned using the 'CoordinateCleaner'
 package, and sightings with missing environmental data were excluded. Grid
 cells with multiple occurrences were treated as a single point. Records
 were additionally classified as native or exotic using Centre for
 Agriculture and Bioscience International (CABI) Invasive Species
 Compendium and the International Union for Conservation of Nature (IUCN)
 Global Invasive Species Database.
description: All data should be placed in the "./data/" directory,
 relative to the R working directory, and files generated by running the
 code are placed in the "./output/" directory, by
 default.