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Title
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Stronger evidence for genetic ancestry than environmental conditions in shaping the evolution of a complex signalling trait during biological invasion
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Author
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Abstract
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| Introductions of invasive species to new environments often result in rapid rates of trait evolution. While in some cases these evolutionary transitions are adaptive and driven by natural selection, they can also result from patterns of genetic and phenotypic variation associated with the invasion history. Here, we examined the brown anole ( Anolis sagrei ), a widespread invasive lizard for which genetic data have helped trace the sources of non‐native populations. We focused on the dewlap, a complex signalling trait known to be subject to multiple selective pressures. We measured dewlap reflectance, pattern and size in 30 non‐native populations across the southeastern United States. As well, we quantified environmental variables known to influence dewlap signal effectiveness, such as canopy openness. Further, we used genome‐wide data to estimate genetic ancestry, perform association mapping and test for signatures of selection. We found that among‐population variation in dewlap characteristics was best explained by genetic ancestry. This result was supported by genome‐wide association mapping, which identified several ancestry‐specific loci associated with dewlap traits. Despite the strong imprint of this aspect of the invasion history on dewlap variation, we also detected significant relationships between dewlap traits and local environmental conditions. However, we found limited evidence that dewlap‐associated genetic variants have been subject to selection. Our study emphasizes the importance of genetic ancestry and admixture in shaping phenotypes during biological invasion, while leaving the role of selection unresolved, likely due to the polygenic genetic architecture of dewlaps and selection acting on many genes of small effect. |
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Language
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English
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Source (journal)
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Molecular ecology. - Oxford
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Publication
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Oxford
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2023
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ISSN
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0962-1083
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DOI
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10.1111/MEC.17123
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Volume/pages
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32
:20
(2023)
, p. 5558-5574
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ISI
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001065809800001
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Pubmed ID
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37698063
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Full text (Publisher's DOI)
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Full text (publisher's version - intranet only)
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