Data from: Accuracy of climate-based forecasts of pathogen spread

Species distribution models (SDMs) are a tool for predicting the eventual
geographical range of an emerging pathogen. Most SDMs, however, rely on an
assumption of equilibrium with the environment, which an emerging
pathogen, by definition, has not reached. To determine if some SDM
approaches work better than others for modelling the spread of emerging,
non-equilibrium pathogens, we studied time-sensitive predictive
performance of SDMs for Batrachochytrium dendrobatidis, a devastating
infectious fungus of amphibians, using multiple methods trained on
time-incremented subsets of the available data. We split our data into
timeline-based training and testing sets, and evaluated models on each set
using standard performance criteria, including AUC, kappa, false negative
rate and the Boyce index. Of eight models examined, we found that boosted
regression trees and random forests performed best, closely followed by
MaxEnt. As expected, predictive performance generally improved with the
length of time series used for model training. These results provide
information on how quickly the potential extent of an emerging disease may
be determined, and identify which modelling frameworks are likely to
provide useful information during the early phases of pathogen expansion.