Are narrow-ranging species doomed to extinction? Projected dramatic decline in future climate suitability of two highly threatened species

Augros²,

Deso³

et al. 2022

Animal Conservation

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.

Section: Introductionmentioning

confidence: 99%

“…To date, very few studies have focused on the potential effects of climate change on the future of Reunion Island's biodiversity (but see Legrand et al, 2016). Among these, Dubos et al (2022) predicted the potential extinction of the endemic Reunion Island reptile, P. inexpectata, driven by climate change.…”

Section: Introductionmentioning

confidence: 99%

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

Augros²,

Deso³

et al. 2022

Animal Conservation

Section: Discussionmentioning

confidence: 99%

Choice of climate data influences current and future global invasion risks for two Phelsuma geckos

Fieldsend

Roesch

et al. 2022

Preprint

Self Cite

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 input environmental data (climate data source, Global Circulation Models 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 overlap 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.

“…The source of climate data was not accounted for in SDM studies until recently (Baker et al 2016; Morales-Barbero and Vega-Álvarez 2019; Datta et al 2020; Ocon 2020; Dubos et al 2022b, a; Stewart et al 2022). To the best of our knowledge, this study is the first to account for multiple sources of climate data in invasion risk assessments.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Fieldsend

Roesch

et al. 2022

Preprint

Self Cite

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 input environmental data (climate data source, Global Circulation Models 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 overlap 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.