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.

Although island endemic bats are a source of considerable conservation concerns, their biology remains poorly known. Here, we studied the phenology and roosting behavior of a tropical island endemic species: the Reunion free‐tailed bat ( Mormopterus francoismoutoui ). This widespread and abundant species occupies various natural and anthropogenic environments such as caves and buildings. We set up fine‐scale monitoring of 19 roosts over 27 months in Reunion Island and analyzed roost size and composition, sexual and age‐associated segregation of individuals, as well as the reproductive phenology and body condition of individuals. Based on extensive data collected from 6721 individuals, we revealed a highly dynamic roosting behavior, with marked seasonal sex‐ratio variation, linked to distinct patterns of sexual aggregation among roosts. Despite the widespread presence of pregnant females all over the island, parturition was localized in a few roosts, and flying juveniles dispersed rapidly toward all studied roosts. Our data also suggested a 7‐month delay between mating and pregnancy, highlighting a likely long interruption of the reproductive cycle in this tropical bat. Altogether, our results suggest a complex social organization in the Reunion free‐tailed bat, with important sex‐specific seasonal and spatial movements, including the possibility of altitudinal migration. Bat tracking and genetic studies would provide additional insights into the behavioral strategies that shape the biology of this enigmatic bat species. The fine‐scale spatiotemporal data revealed by our study will serve to the delineation of effective conservation plans, especially in the context of growing urbanization and agriculture expansion in Reunion Island.

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The effect of future climate change is poorly documented 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. Nevertheless, the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predicted 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 considered potential differences related to baseline climate data and/or did 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 baseline climate (CHELSA versus Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap the uncertainty related to baseline climate became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We eventually 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 baseline climate data. We recommend the use of multiple approaches, including spatial overlap, when assessing uncertainty in species distribution models.

Invasion risks may be influenced either negatively or positively by climate change, depending on the species. These can be predicted with species distribution models, but projections can be strongly affected by the source of the environmental data (climate data source, Global Circulation Models GCM and Shared Socio-economic Pathways SSP). We modelled the distribution of Phelsuma grandis and P. laticauda, two Malagasy reptiles that are spreading globally. We accounted for drivers of spread and establishment using socio-economic factors (e.g., distance from ports) and two climate data sources, i.e., Climatologies at High Resolution for the Earth’s and Land Surface Areas (CHELSA) and Worldclim. We further quantified the degree of agreement in invasion risk models that utilised CHELSA and Worldclim data for current and future conditions. Most areas identified as highly exposed to invasion risks were consistently identified (e.g. in Caribbean and Pacific Islands). However, projected risks differed locally. We also found notable differences in quantitative invasion risk (3% difference in suitability scores for P. laticauda and up to 14% for P. grandis) under current conditions. Despite both species native distributions overlapping substantially, climate change will drive opposite responses on invasion risks by 2070 (decrease for P. grandis, increase for P. laticauda). Overall, projections of future invasion risks were the most affected by climate data source, followed by SSP. Our results highlight that assessments of current and future invasion risks are sensitive to the climate data source, especially in islands. We stress the need to account for multiple climatologies when assessing invasion risks.