Genome-scale evidence for species boundaries in an insular lizard radiation: phylogenomics of Gallotia galloti
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2026
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Oxford University Press
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Cottam, Dayna E., et al. «Genome-Scale Evidence for Species Boundaries in an Insular Lizard Radiation: Phylogenomics of Gallotia Galloti». Zoological Journal of the Linnean Society, vol. 207, n.o 3, julio de 2026, p. zlag117. DOI.org (Crossref), https://doi.org/10.1093/zoolinnean/zlag117.
Abstract
IIsland systems provide powerful natural laboratories for studying the processes that maintain species boundaries. The lacertid lizard Gallotia galloti, endemic to the islands of Tenerife and La Palma, comprises four traditionally recognized subspecies distinguished by differences in morphology, coloration, ecology, and geography. We used ultraconserved element (UCE) phylogenomics, coalescent-based species’ delimitation, and network analyses to test species’ limits and to evaluate introgression. Phylogenomic analyses consistently recovered G. g. palmae as an early-diverging lineage, with G. g. galloti sister to a clade comprising G. g. eisentrauti and G. g. Insulanagae. Species’ delimitation strongly supported the distinctiveness of G. g. palmae under all prior settings. In addition, support for separating G. g. eisentrauti and G. g. insulanagae was consistent in most models. Principal coordinates analyses revealed strong genetic clustering concordant with subspecies boundaries, and no evidence of reticulation, despite geographic contact zones between G. g. galloti and G. g. eisentrauti. This indicates long-term isolation consistent with divergence since the Mid-Pleistocene. Based on phenotypic diagnosability and genomic distinctiveness, we elevate G. palmae stat. nov. to species’ status and recommend further integrative study of the remaining lineages. Our findings highlight the roles of isolation, ecological specialization, and selection in maintaining species boundaries in insular vertebrates.
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The Biology department at Edge Hill University provided funding for the project. M.R.P. was funded by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil (grant 303491/2024).












