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Daniel S. Caetano

Publications and source records attributed to Daniel S. Caetano.

2 recordsLinked to original sources

Have coral snake mimics diversified more than non-mimics?

Dipsadidae is the most diversified family of snakes, composed of species showing an impressive variety of color patterns. Some species are cryptic whereas others have contrasting patterns comprised by bright colors alternated with darker shades, including particular combinations of vivid colors characteristic of coral snakes (Elapidae). Species with such patterns are thought to be mimics of coral snakes based on their color pattern similarity, predator avoidance of such patterns in field experiments, and the geographical concordance between models and mimics. Here we test whether color patterns associated with coral snake mimicry and contrasting color patterns in general influenced the diversification dynamics of the group. We compile the largest database of color patterns among reptiles to date, with color descriptions for the majority (594 species) of dipsadids. We used trait-dependent diversification models along with extensive simulations to deal with the recently described statistical bias associated with such methods. Despite the apparent survival advantage associated with coral snake mimicry, we show that there is no detectable influence of color types in the dynamics of diversification in Dipsadidae. We discuss insights into the function of color patterns and argue that non-mimic contrasting patterns might serve as pre-adaptations to mimicry of coral snakes.\n\nData archival locationBEAST XML file and BiSSE MCMC results: http://dx.doi.org/10.6084/m9.figshare.831493\n\nR code for analyses and simulations: https://github.com/Caetanods/Dipsadidae_color_evolution

Evolutionary Biology

Comparative analysis of principal components can be misleading

Quantitative geneticists long ago recognized the value of studying evolution in a multivariate framework (Pearson, 1903). Due tolinkage, pleiotropy, coordinated selection and mutational covariance, the evolutionary response in any phenotypic trait can only be properly understood in the context ofother traits (Lande, 1979; Lynch and Walsh, 1998). This is of course also well-appreciated bycomparative biologists. However, unlike in quantitative genetics, most of the statistical and conceptual tools for analyzing phylogenetic comparative data (recently reviewed in Pennell and Harmon, 2013) are designed for analyzing a single trait (but see, for example Revell and Harmon, 2008; Revell and Harrison, 2008; Hohenlohe and Arnold, 2008; Revell and Collar, 2009; Schmitz and Motani, 2011; Adams, 2014b). Indeed, even classical approaches for testing for correlated evolution between two traits (e.g., Felsenstein, 1985; Grafen, 1989; Harvey and Pagel, 1991) are not actually multivariate as each trait is assumed to have evolved under a process that is independent of the state of the other (Hansen and Orzack, 2005; Hansen and Bartoszek, 2012). As a result of these limitations, researchers with multivariate datasets are often faced with a choice: analyze each trait as if they were independent or else decompose the dataset into statistically independent set of traits, such that each set can be analyzed with the univariate methods.

Evolutionary Biology