Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna

 

Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna

Abstract

The biota of cave habitats faces heightened conservation risks, due to geographic isolation and high levels of endemism. Molecular datasets, in tandem with ecological surveys, have the potential to precisely delimit the nature of cave endemism and identify conservation priorities for microendemic species. Here, we sequenced ultraconserved elements of Tegenaria within, and at the entrances of, 25 cave sites to test phylogenetic relationships, combined with an unsupervised machine learning approach for detecting species. Our analyses identified clear and well-supported genetic breaks in the dataset that accorded closely with morphologically diagnosable units. Through these analyses, we also detected some previously unidentified, potential cryptic morphospecies. We then performed conservation assessments for seven troglobitic Israeli species of this genus and determined five of these to be critically endangered.

Steiner, H.G., Aharon, S., Ballesteros, J. et al. Machine learning approaches to assess microendemicity and conservation risk in cave-dwelling arachnofauna. Conserv Genet (2024). https://doi.org/10.1007/s10592-024-01627-5