From Traditional Morphology to Genomes and Machine Learning: Navigating Taxonomic Uncertainties in Southeast Asian Agamids

Classifying species, or the process of identifying, naming, and grouping organisms based on shared characteristics and evolutionary relationships, is critical in many aspects of biology. Without species classification, it would be difficult, if not impossible, for us to understand Earth’s biodiversity and the processes that generate it. However, classifying organisms into discrete units can be challenging, particularly when a historical species is named without designating an associated name-bearing type specimen(s) or when available names have been used irregularly in older, historical literature. These issues are exacerbated when a group is difficult to diagnose because phenotypic distinctiveness and genetic divergence do not align, as in the case of cryptic species, or when a group lacks considerable genetic divergence but exhibits variable morphology, as in polymorphic species. My dissertation provides an empirical example of delimiting species boundaries, with nomenclatural issues compounded by unclear signals of genetic and morphological variation and demonstrates how previously ambiguous taxonomies, muddled by historical confusion, can be navigated by careful integration of all available data and use of modern tools.

Name thesis:degree_name

Ph.D.

Discipline thesis:degree_discipline

Ecology & Evolutionary Biology

Grantor dc:publisher

University of Kansas

Year dc:date.issued

2026

Author dc:creator

• Supsup, Christian E.

Advisor dc:contributor.advisor

• Brown, Rafe M.

• genome
• neural network
• phylogenetics
• population genetics
• species delimitation
• taxonomy

Language dc:language.iso

en

Dc Identifier Other

https://www.proquest.com/LegacyDocView/DISSNUM/32697970

OAI identifier oai:identifier

oai:kuscholarworks.ku.edu:1808/39485