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Data from: A genomic evaluation of taxonomic trends through time in coast horned lizards (genus Phrynosoma)

Citation

Leache, Adam D.; McElroy, Matt T.; Trinh, Anna; McElroy, Matthew T. (2018), Data from: A genomic evaluation of taxonomic trends through time in coast horned lizards (genus Phrynosoma), Dryad, Dataset, https://doi.org/10.5061/dryad.k7k4m

Abstract

Determining the boundaries between species and deciding when to describe new species are challenging practices that are particularly difficult in groups with high levels of geographic variation. The coast horned lizards (Phrynosoma blainvillii, P. cerroense, and P. coronatum) have an extensive geographic distribution spanning many distinctive ecological regions ranging from northern California to the Cape Region of Baja California, Mexico, and populations differ substantially with respect to external morphology across much of this range. The number of taxa recognized in the group has been re-evaluated by herpetologists over 20 times during the last 180 years, and typically without the aid of explicit species delimitation methods, resulting in a turbulent taxonomy containing anywhere from one to seven taxa. In this study, we evaluate taxonomic trends through time by ranking 15 of these species delimitation models (SDMs) using coalescent analyses of nuclear loci and SNPs in a Bayesian model comparison framework. SDMs containing more species were generally favored by Bayesian model selection; however, several three-species models outperformed some four and five species SDMs, and the top-ranked model, which contained five species, outperformed all SDMs containing six species. Model performance peaked in the 1950s based on marginal likelihoods estimated from nuclear loci and SNPs. Not surprisingly, SDMs based on genetic data outperformed morphological taxonomies when using genetic data alone to evaluate models. The de novo estimation of population structure favors a three-population model that matches the currently recognized integrative taxonomy containing three species. We discuss why Bayesian model selection might favor models containing more species, and why recognizing more than three species might be warranted.

Usage Notes

Funding

National Science Foundation, Award: NSF-DBI-1144630

Location

Baja California
Mexico
California