Dataset
Data and code from: Unsupervised machine learning for species discovery in Eurytoma and Phylloxeroxenus (Hymenoptera: Eurytomidae) parasitoids of oak gall wasps
Dryad
10/14/2025
DOI: 10.5061/dryad.vq83bk477
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 (e.g., mtCOI) but the accessibility of techniques for large sub genomic sequencing projects (1000s 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 UCE 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, two distinct lineages of Eurytoma including a new lineage of Eurytoma more closely related to the South American genus Kavayva Zhang, Gates, & 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
- Data and code from: Unsupervised machine learning for species discovery in Eurytoma and Phylloxeroxenus (Hymenoptera: Eurytomidae) parasitoids of oak gall wasps
- Creators
- Christian Weinrich - University of IowaKristýna Bubeníková - University of IowaSofia Sheikh - University of IowaMakella Steffensen - University of IowaAnna Ward - University of IowaYuanmeng Zhang - University of EdinburghAndrew Forbes - University of Iowa
- Resource Type
- Dataset
- DOI
- 10.5061/dryad.vq83bk477
- Publisher
- Dryad
- Grant note
- Collaborative Research: How do gall-forming insects escape diverse and evolving clades of parasitic wasps - and how do parasites catch up? - 2418250 / Division of Environmental Biology (https://ror.org/03g87he71)
- Language
- English
- Date published
- 10/14/2025
- Academic Unit
- Biology; University College Courses
- Record Identifier
- 9985164578302771
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