Unsupervised Machine Learning for Species Delimitation, Integrative Taxonomy, and Biodiversity Conservation

Abstract excerpt

Integrative taxonomy combining data from multiple axes of biologically relevant variation is a major recent goal of systematics. Ideally, such taxonomies would be backed by similarly integrative species-delimitation analyses. Yet, most current methods rely solely or primarily on molecular data, with other layers often incorporated only in a post hoc qualitative or comparative manner. A major limitation is the dif...

Topics

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• Biodiversity
• Caudata
• Climate
• Climate Change
• Phenotype
• Phylogeny
• Species Specificity
• Unsupervised Machine Learning

Identifiers and source

Literature Corpus work

621830c9-a2f0-5947-ae2c-8dd45e32fb25

DOI

10.1101/2023.06.12.544639