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

Abstract

The 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.

Mining rare Earth elements: Identifying the plant species most threatened by ore extraction in an insular hotspot

“…Our results, obtained on the basis of locally recognised references, historical material and the involvement of local field-experts, thus reveals a hindrance for using this kind of data at a fairly high resolution. Without any kind of cleaning, such work would suffer a substantial geographical bias, adding uncertainty to the identified biases in the bioclimatic datasets (Dubos et al, 2022).…”

Maps of area of habitat for Italian amphibians and reptiles

“…Moreover, a binary classification is not optimal, as the habitat quality may vary across the spatial surface. In the future, habitat maps may be improved by including local predictors and landscape predictors, thus providing additional information on habitat availability (Dubos et al 2022).…”

Choice of climate data influences current and future global invasion risks for twoPhelsumageckos

“…Secondly, we used an approach that takes into account spatial information, i.e., spatial overlap (Muscatello et al 2021;Petford and Alexander 2021;Dubos et al, 2022a). We computed the Schoener's D overlap between projections of current invasion risk between predictions based on the two climate datasets considered.…”

“…Differences may be driven by the selection of different variables (e.g., models calibrated with CHELSA data selected 'Daily temperature range' but not with Worldclim for P. grandis); however, a recent study showed that differences can persist even when the same predictors are selected (Dubos et al 2022a; see also Jiménez-Valverde et al 2021). The mismatch may be better explained by the methods used to compute the climatologies.…”

“…These factors can strongly affect SDMs and induce uncertainty in assessments of climate suitability (Buisson et al 2010). More recently, the choice of the source of climate data (e.g., CHELSA or Worldclim; Fick and Hijmans 2017;Karger et al 2017) used for model calibration has been identified as a major source of uncertainty in SDMs (Baker et al 2016;Dubos et al 2022a). In spite of this, most SDM studies aimed at projecting future distributions use climate data from a single source.…”