Journal article
Unsupervised machine learning for species discovery in Eurytoma and Phylloxeroxenus (Hymenoptera: Eurytomidae) parasitoids of oak gall wasps
Insect Systematics and Diversity, Vol.10(3), ixag021
05/2026
DOI: 10.1093/isd/ixag021
Appears in UI Libraries Support Open Access
Abstract
Species discovery (inferring species limits de novo, without a priori hypotheses) from genetic data has become more common as molecular tools have expanded and has been a helpful initial step in tackling the taxonomic impediment for small insects. Often species discovery involves a single locus (eg mitochondrial cytochrome oxidase I [mtCOI]), but the accessibility of techniques for large sub-genomic sequencing projects (1000 s of loci) makes it possible to approach molecular species discovery with more robust datasets. Here, we test unsupervised machine learning (UML) methods for species discovery on a set of ultra-conserved element loci for a large collection of parasitic wasps reared from North American oak galls, all initially thought to be in genus Eurytoma Illiger. UML methods produced species hypotheses that largely aligned with those that emerge from a commonly used mtCOI-based species partitioning method, and that also tended to match existing species descriptions. Results revealed a new genus-level association with oak galls (Phylloxeroxenus Ashmead) hidden among the Eurytoma, 2 distinct lineages of Eurytoma, including a new lineage of Eurytoma more closely related to the South American genus Kavayva Zhang, Gates, and Silvestre, evidence for one or more cryptic Eurytoma species, and a mix of generalist and specialist host ranges. We make recommendations for how best to employ UML methods to similar datasets.
Details
- Title: Subtitle
- Unsupervised machine learning for species discovery in Eurytoma and Phylloxeroxenus (Hymenoptera: Eurytomidae) parasitoids of oak gall wasps
- Creators
- Christian L. Weinrich - University of IowaKristyna Bubenikova - University of Iowa, BiologySofia I Sheikh - University of IowaMaKella J. Steffensen - University of IowaAnna K G Ward - University of IowaYuanmeng Miles Zhang - University of EdinburghAndrew A Forbes - University of Iowa, Biology
- Resource Type
- Journal article
- Publication Details
- Insect Systematics and Diversity, Vol.10(3), ixag021
- DOI
- 10.1093/isd/ixag021
- ISSN
- 2399-3421
- eISSN
- 2399-3421
- Publisher
- Oxford University Press
- Grant note
- US Department of State and the Fulbright Commission American Genetic Association National Science Foundation to AAF: 2418250 European Union's Horizon 2020 Research and Innovation Programme under Marie Sklstrok;odowska-Curie: 101024056 Center for Global and Regional Environmental Research Fulbright US Scholar Program
Collections, sequencing, and analyses were supported by grants from the National Science Foundation to AAF (2418250), from the American Genetic Association to AKGW, and to SIS from the Center for Global and Regional Environmental Research. KB acknowledges financial support for her stay in the Forbes lab by the Fulbright US Scholar Program, which is sponsored by the US Department of State and the Fulbright Commission in the Czech Republic. YMZ was supported by the European Union's Horizon 2020 Research and Innovation Programme under Marie Sk & lstrok;odowska-Curie grant agreement 101024056.
- Language
- English
- Date published
- 05/2026
- Academic Unit
- Biology; University College Courses
- Record Identifier
- 9985166183402771
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