Here be dragons: important spatial uncertainty driven by climate data in forecasted distribution of an endangered insular reptile

Abstract

AbstractThe effect of future climate change is poorly studied in the tropics, especially in mountainous areas, yet species living in these environments are predicted to be strongly affected. Newly available high‐resolution environmental data and statistical methods enable the development of forecasting models, but the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predictive studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies consider potential differences related to the source of climate data and/or do not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and data source (CHELSA vs. Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap, the uncertainty related to data source became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on the spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and data sources. We recommend the use of multiple approaches, including spatial overlap when assessing uncertainty in species distribution models.