RT Journal Article
SR Electronic
T1 Testing the predictive performance of comparative extinction risk models to support the global amphibian assessment
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.02.08.526823
DO 10.1101/2023.02.08.526823
A1 P.M. Lucas
A1 M. Di Marco
A1 V. Cazalis
A1 J. Luedtke
A1 K. Neam
A1 M.H. Brown
A1 P. Langhammer
A1 G. Mancini
A1 L. Santini
YR 2023
UL http://biorxiv.org/content/early/2023/02/08/2023.02.08.526823.abstract
AB Assessing the extinction risk of species through the IUCN Red List is key to guiding conservation policies and reducing biodiversity loss. This process is resource-demanding, however, and requires a continuous update which becomes increasingly difficult as new species are added to the IUCN Red List. The use of automatic methods, such as comparative analyses to predict species extinction risk, can be an efficient alternative to maintaining up to date assessments. Using amphibians as a study group, we predict which species were more likely to change status, in order to suggest species that should be prioritized for reassessment. We used species traits, environmental variables, and proxies of climate and land-use change as predictors of the IUCN Red List category of species. We produced an ensemble prediction of IUCN Red List categories by combining four different model algorithms: Cumulative Link Models (CLM), phylogenetic Generalized Least Squares (PGLS), Random Forests (RF), Neural Networks (NN). By comparing IUCN Red List categories with the ensemble prediction, and accounting for uncertainty among model algorithms, we identified species that should be prioritized for future reassessments due to high prediction versus observation mismatch. We found that CLM and RF performed better than PGLS and NN, but there was not a clear best algorithm. The most important predicting variables across models were species range size, climate change, and landuse change. We propose ensemble modelling of extinction risk as a promising tool for prioritizing species for reassessment while accounting for inherent models’ uncertainty.Competing Interest StatementThe authors have declared no competing interest.