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Morphological data quantifying sexual dimorphism of Anolis carolinensis in presence and absence of congener


Veen, Thor; Stuart, Yoel; Kamath, Ambika; Sherwin, William (2021), Morphological data quantifying sexual dimorphism of Anolis carolinensis in presence and absence of congener, Dryad, Dataset,


Natural selection favors sexual dimorphism that reduces resource competition between the sexes of the same species. However, niche partitioning among interspecific competitors should counter such divergence, as partitioning the niche results in smaller total niche widths for each individual species, leaving less room for the sexes to diverge. A straightforward (and long-standing) hypothesis emerges: species in competitor-rich ecological communities should show less sexual dimorphism than species in competitor-poor ecological communities. Here, we test this prediction using a well-documented natural experiment generated by the recent arrival of Anolis sagrei to a set of small islands in Mosquito Lagoon, Florida, containing Anolis carolinensis. Despite known interspecific habitat partitioning and rapid evolution in habitat-use traits by A. carolinensis in this system, sexual dimorphism between male and female A. carolinensis was not reduced as predicted on two-species islands relative to islands with only A. carolinensis. This is consistent with a small but growing body of empirical tests of the dimorphism-richness hypothesis that have been ambiguous in their support at best. A rethinking of the validity of this intuitive hypothesis is needed.


See details in the Methods section of main paper. See Stuart et al., 2014, Science 346: 463-466 and its supporting information for coordinates of island location.

Outlier detection was performed with outlier defined as values > 3 standard deviations from the mean for a given island (see For individuals with many outliers in the size corrected, the size (SVL) data were checked against the original data sheet and corrected where errors were detected in data entry. A small number of individuals were detected with one or two outliers which is to be expected given the large data set used. One individual with nine outliers was excluded from the analysis.

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National Science Foundation, Award: DEB-1110521

National Science Foundation, Award: DEB-1456462