Gene Flow Creates Fuzzy Species Boundaries in Fence Lizards
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ID: 325269
2026
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Abstract
Species delimitation is a fundamental challenge in systematic biology, particularly for geographically variable taxa with hierarchical population structure and gene flow. Gene flow can obscure signals of lineage divergence and blur species boundaries, motivating the use of migration-aware coalescent models to investigate divergence processes and delimit species. In this study, we combine the use of such models with phylogenetic and population structure analyses to delimit species of fence lizards within the Sceloporus undulatus complex, a group with a broad distribution across North America characterized by extensive population subdivision, mitochondrial DNA introgression, and nuclear gene flow. We find that the undulatus complex exhibits uneven variation in genetic, morphological, and bioclimatic traits, resulting in variable distinctiveness among groups. In some cases, species boundaries are recognized by clear genetic discontinuities without gene flow. In others, shallow divergence, paraphyly, and gene flow produce leaky boundaries and fuzzy species limits. Mitochondrial introgression is extensive and concentrated at species boundaries, whereas nuclear gene flow is limited to a few lineage pairs and occurs at much lower levels between species than gene flow within species. Neither within-species populations or species are substantially diverged across morphology or bioclimatic space, highlighting the limited utility of these traits for diagnosing species in this group. By considering estimates of gene flow with phylogenetic and population structure analyses, this study provides a robust and biologically meaningful taxonomic revision for the undulatus complex that identifies independently evolving lineages as species.
| Reference Key |
openalex_W7153268005
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| Authors | Adam D. Leaché, Hayden R. Davis, Edú Baptista Guerra, Aracely Tamayo Herrera, Julio Lemos-Espinal, Matthew K. Fujita, Tanner C. Myers, Sonal Singhal |
| Journal | systematic biology |
| Year | 2026 |
| DOI |
10.1093/sysbio/syag061
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| URL | |
| Keywords | Keywords not found |
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