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Traits associated with naturalization in Anolis lizards: comparison of morphological, distributional, anthropogenic, and phylogenetic models

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Abstract

The worldwide spread of invasive species affects native biodiversity and causes economic loss, but also allows better understanding of historical biogeographic patterns. Prediction of likely invaders facilitates economic and conservation decisions and gives insight into characteristics that have allowed natural colonization over evolutionary time. However, it is not clear what types of characters best predict naturalization or even whether naturalization is predictable at all. Squamate reptiles have been understudied subjects for invasion biology. Lizards of the genus Anolis have been highly successful colonizers both recently and over evolutionary time. Nineteen of the approximately 350 described species of Anolis have established naturalized populations. We constructed models of naturalization using morphological, distributional, anthropogenic, and phylogenetic characters and compared these single character class models to each other and to a composite model incorporating all four classes. We show that (1) each class of variables significantly predicts invasion, (2) a composite model significantly outperforms each of the submodels, and (3) the final composite model displays extraordinary ability to objectively identify naturalized species of Anolis.

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Acknowledgments

We thank Cam Webb for help with Phylocom. Jim Brown and Mason Ryan reviewed earlier versions of this manuscript and provided useful comments. This work was supported by National Science Foundation Grant DEB-0844624 to Steven Poe and a University of New Mexico Student Research Allocation Committee Grant to Ian Latella.

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Correspondence to Ian M. Latella.

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Latella, I.M., Poe, S. & Tomasz Giermakowski, J. Traits associated with naturalization in Anolis lizards: comparison of morphological, distributional, anthropogenic, and phylogenetic models. Biol Invasions 13, 845–856 (2011). https://doi.org/10.1007/s10530-010-9873-x

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  • DOI: https://doi.org/10.1007/s10530-010-9873-x

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