Published April 3, 2026
| Version v1
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Figure 1 from: Chan KO, Grismer LL (2026) Extending GroupStruct2: a Bayesian and machine-learning framework for testing taxonomic hypotheses using morphometric data. ZooKeys 1276: 125-138. https://doi.org/10.3897/zookeys.1276.182331
Authors/Creators
- 1. Department of Integrative Biology, MSU Museum, Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, United States of America
- 2. Department of Biology, La Sierra University, Riverside, United States of America|Department of Herpetology, San Diego Natural History Museum, San Diego, United States of America|El Serpentario y C.E.M.A de Baja California Sur, La Paz, Mexico
Description
Figure 1 An example workflow for morphometric delimitation using GroupStruct2. Step 0 (grey box) is not part of GroupStruct2 and should be performed independently before using the application. Steps 1–5 (blue boxes) represent submodules within the main Morphometric module. Each submodule contains a suite of stand-alone analyses that can be performed to answer different questions (tan boxes). Analyses in the Inferential Statistics (Step 4.1) and Morphometric Delimitation (Step 4.2) submodules are based on fundamentally different conceptual frameworks, and the results of each should be compared to obtain more holistic taxonomic conclusions. Bayesian GMM and machine-learning analyses introduced by Tiburtini et al. (2025) and this study are implemented in the Morphometric Delimitation (Step 4.2) submodule.
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- Journal article: 10.3897/zookeys.1276.182331 (DOI)
- Journal article: https://zenodo.org/record/19426479 (URL)