Using natural history to guide supervised machine learning for cryptic species delimitation with genetic data.

ABSTRACT: The diversity of biological and ecological characteristics of organisms, and the underlying genetic patterns and processes of speciation, makes the development of universally applicable genetic species delimitation methods challenging. Many approaches, like those incorporating the multispecies coalescent, sometimes delimit populations and overestimate species numbers. This issue is exacerbated in taxa with inherently high population structure due to low dispersal ability, and in cryptic species resulting from nonecological speciation. These taxa present a conundrum when delimiting species: analyses rely heavily, if not entirely, on genetic data which over split species, while other lines of evidence lump. We showcase this conundrum in the harvester Theromaster brunneus, a low dispersal tax

SUBMITTER: Derkarabetian S 

PROVIDER: S-EPMC8862334 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature