Ontogenetic allometry underlies trophic diversity in sea turtles (Chelonioidea)

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Abstract

Despite only comprising seven species, extant sea turtles (Cheloniidae and Dermochelyidae) display great ecological diversity, with most species inhabiting a unique dietary niche as adults. This adult diversity is remarkable given that all species share the same dietary niche as juveniles. These ontogenetic shifts in diet, as well as a dramatic increase in body size, make sea turtles an excellent group to examine how morphological diversity arises by allometric processes and life habit specialisation. Using three-dimensional geometric morphometrics, we characterise ontogenetic allometry in the skulls of all seven species and evaluate variation in the context of phylogenetic history and diet. Among the sample, the olive ridley ( Lepidochelys olivacea) has a seemingly average sea turtle skull shape and generalised diet, whereas the green ( Chelonia mydas) and hawksbill ( Eretmochelys imbricata) show different extremes of snout shape associated with their modes of food gathering (grazing vs. grasping, respectively). Our ontogenetic findings corroborate previous suggestions that the skull of the leatherback ( Dermochelys coriacea) is paedomorphic, having similar skull proportions to hatchlings of other sea turtle species and retaining a hatchling-like diet of relatively soft bodied organisms. The flatback sea turtle ( Natator depressus) shows a similar but less extreme pattern. By contrast, the loggerhead sea turtle ( Caretta caretta) shows a peramorphic signal associated with increased jaw muscle volumes that allow predation on hard shelled prey. The Kemp’s ridley ( Lepidochelys kempii) has a peramorphic skull shape compared to its sister species the olive ridley, and a diet that includes harder prey items such as crabs. We suggest that diet may be a significant factor in driving skull shape differences among species. Although the small number of species limits statistical power, differences among skull shape, size, and diet are consistent with the hypothesis that shifts in allometric trajectory facilitated diversification in skull shape as observed in an increasing number of vertebrate groups.

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ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R

Emmanuel Paradis, Klaus Schliep, Russell Schwartz (2018)

After more than fifteen years of existence, the R package ape has continuously grown its contents, and has been used by a growing community of users. The release of version 5.0 has marked a leap towards a modern software for evolutionary analyses. Efforts have been put to improve efficiency, flexibility, support for 'big data' (R's long vectors), ease of use and quality check before a new release. These changes will hopefully make ape a useful software for the study of biodiversity and evolution in a context of increasing data quantity.

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TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE

Simon Blomberg, Theodore Garland, Anthony Ives (2003)

The primary rationale for the use of phylogenetically based statistical methods is that phylogenetic signal, the tendency for related species to resemble each other, is ubiquitous. Whether this assertion is true for a given trait in a given lineage is an empirical question, but general tools for detecting and quantifying phylogenetic signal are inadequately developed. We present new methods for continuous-valued characters that can be implemented with either phylogenetically independent contrasts or generalized least-squares models. First, a simple randomization procedure allows one to test the null hypothesis of no pattern of similarity among relatives. The test demonstrates correct Type I error rate at a nominal alpha = 0.05 and good power (0.8) for simulated datasets with 20 or more species. Second, we derive a descriptive statistic, K, which allows valid comparisons of the amount of phylogenetic signal across traits and trees. Third, we provide two biologically motivated branch-length transformations, one based on the Ornstein-Uhlenbeck (OU) model of stabilizing selection, the other based on a new model in which character evolution can accelerate or decelerate (ACDC) in rate (e.g., as may occur during or after an adaptive radiation). Maximum likelihood estimation of the OU (d) and ACDC (g) parameters can serve as tests for phylogenetic signal because an estimate of d or g near zero implies that a phylogeny with little hierarchical structure (a star) offers a good fit to the data. Transformations that improve the fit of a tree to comparative data will increase power to detect phylogenetic signal and may also be preferable for further comparative analyses, such as of correlated character evolution. Application of the methods to data from the literature revealed that, for trees with 20 or more species, 92% of traits exhibited significant phylogenetic signal (randomization test), including behavioral and ecological ones that are thought to be relatively evolutionarily malleable (e.g., highly adaptive) and/or subject to relatively strong environmental (nongenetic) effects or high levels of measurement error. Irrespective of sample size, most traits (but not body size, on average) showed less signal than expected given the topology, branch lengths, and a Brownian motion model of evolution (i.e., K was less than one), which may be attributed to adaptation and/or measurement error in the broad sense (including errors in estimates of phenotypes, branch lengths, and topology). Analysis of variance of log K for all 121 traits (from 35 trees) indicated that behavioral traits exhibit lower signal than body size, morphological, life-history, or physiological traits. In addition, physiological traits (corrected for body size) showed less signal than did body size itself. For trees with 20 or more species, the estimated OU (25% of traits) and/or ACDC (40%) transformation parameter differed significantly from both zero and unity, indicating that a hierarchical tree with less (or occasionally more) structure than the original better fit the data and so could be preferred for comparative analyses.

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A generalized K statistic for estimating phylogenetic signal from shape and other high-dimensional multivariate data.

Dean C Adams (2014)

Phylogenetic signal is the tendency for closely related species to display similar trait values due to their common ancestry. Several methods have been developed for quantifying phylogenetic signal in univariate traits and for sets of traits treated simultaneously, and the statistical properties of these approaches have been extensively studied. However, methods for assessing phylogenetic signal in high-dimensional multivariate traits like shape are less well developed, and their statistical performance is not well characterized. In this article, I describe a generalization of the K statistic of Blomberg et al. that is useful for quantifying and evaluating phylogenetic signal in highly dimensional multivariate data. The method (K(mult)) is found from the equivalency between statistical methods based on covariance matrices and those based on distance matrices. Using computer simulations based on Brownian motion, I demonstrate that the expected value of K(mult) remains at 1.0 as trait variation among species is increased or decreased, and as the number of trait dimensions is increased. By contrast, estimates of phylogenetic signal found with a squared-change parsimony procedure for multivariate data change with increasing trait variation among species and with increasing numbers of trait dimensions, confounding biological interpretations. I also evaluate the statistical performance of hypothesis testing procedures based on K(mult) and find that the method displays appropriate Type I error and high statistical power for detecting phylogenetic signal in high-dimensional data. Statistical properties of K(mult) were consistent for simulations using bifurcating and random phylogenies, for simulations using different numbers of species, for simulations that varied the number of trait dimensions, and for different underlying models of trait covariance structure. Overall these findings demonstrate that K(mult) provides a useful means of evaluating phylogenetic signal in high-dimensional multivariate traits. Finally, I illustrate the utility of the new approach by evaluating the strength of phylogenetic signal for head shape in a lineage of Plethodon salamanders.