Cranial Anatomical Integration and Disparity Among Bones Discriminate Between Primates and Non-primate Mammals

The primate skull hosts a unique combination of anatomical features among mammals, such as a short face, wide orbits, and big braincase. Together with a trend to fuse bones in late development, these features define the anatomical organization of the skull of primates—which bones articulate to each other and the pattern this creates. Here, I quantified the anatomical organization of the skull of 17 primates and 15 non-primate mammals using anatomical network analysis to assess how the skulls of primates have diverged from those of other mammals, and whether their anatomical differences coevolved with brain size. Results show that primates have a greater anatomical integration of their skulls and a greater disparity among bones than other non-primate mammals. Brain size seems to contribute in part to this difference, but its true effect could not be conclusively proven. This supports the hypothesis that primates have a distinct anatomical organization of the skull, but whether this is related to their larger brains remains an open question.

On the Trail of Spatial Patterns of Genetic Variation

The accurate determination of the spatial trends on the variability of a species’ gene pool is essential to elucidate the underlying demographic-evolutionary events, thus helping to unravel the microevolutionary history of the population under study. Herein we present a new software called GenoCline, mainly addressed to detect genetic clines from allele, haplotype, and genome-wide data. This program package allows identifying the geographic orientation of clinal genetic variation through a system of iterative rotation of a virtual coordinate axis. Besides, GenoCline can perform complementary analyses to explore the potential origin of the genetic clines observed, including spatial autocorrelation, isolation by distance, centroid method, multidimensional scaling and Sammon projection. Among the advantages of this software is the ease in data entry and potential interconnection with other programs. Genetic and geographic data can be entered in spreadsheet table formatting (.xls), whereas genome-wide data can be imported in Eigensoft format. Genetic frequencies can also be exported in a format compatible with other programs dealing with population genetic and evolutionary biology analyses. All illustrations of results are saved in.svg format so that there will be high quality and easily editable vectorial graphs available for the researcher. Being implemented in Java, GenoCline is highly portable, thus working in different operating systems.

A New Method for Landmark-Based Studies of the Dynamic Stability of Growth, with Implications for Evolutionary Analyses

A matrix manipulation new to the quantitative study of develomental stability reveals unexpected morphometric patterns in a classic data set of landmark-based calvarial growth. There are implications for evolutionary studies. Among organismal biology’s fundamental postulates is the assumption that most aspects of any higher animal’s growth trajectories are dynamically stable, resilient against the types of small but functionally pertinent transient perturbations that may have originated in genotype, morphogenesis, or ecophenotypy. We need an operationalization of this axiom for landmark data sets arising from longitudinal data designs. The present paper introduces a multivariate approach toward that goal: a method for identification and interpretation of patterns of dynamical stability in longitudinally collected landmark data. The new method is based in an application of eigenanalysis unfamiliar to most organismal biologists: analysis of a covariance matrix of Boas coordinates (Procrustes coordinates without the size standardization) against their changes over time. These eigenanalyses may yield complex eigenvalues and eigenvectors (terms involving $$i=\sqrt{-1}$$ i = - 1 ); the paper carefully explains how these are to be scattered, gridded, and interpreted by their real and imaginary canonical vectors. For the Vilmann neurocranial octagons, the classic morphometric data set used as the running example here, there result new empirical findings that offer a pattern analysis of the ways perturbations of growth are attenuated or otherwise modified over the course of developmental time. The main finding, dominance of a generalized version of dynamical stability (negative autoregressions, as announced by the negative real parts of their eigenvalues, often combined with shearing and rotation in a helpful canonical plane), is surprising in its strength and consistency. A closing discussion explores some implications of this novel pattern analysis of growth regulation. It differs in many respects from the usual way covariance matrices are wielded in geometric morphometrics, differences relevant to a variety of study designs for comparisons of development across species.

New Avenues for Old Travellers: Phenotypic Evolutionary Trends Meet Morphodynamics, and Both Enter the Global Change Biology Era

Evolutionary trends (ETs) are traditionally defined as substantial changes in the state of traits through time produced by a persistent condition of directional evolution. ETs might also include directional responses to ecological, climatic or biological gradients and represent the primary evolutionary pattern at high taxonomic levels and over long-time scales. The absence of a well-supported operative definition of ETs blurred the definition of conceptual differences between ETs and other key concepts in evolution such as convergence, parallel evolution, and divergence. Also, it prevented the formulation of modern guidelines for studying ETs and evolutionary dynamics related to them. In phenotypic evolution, the theory of morphodynamics states that the interplay between evolutionary factors such as phylogeny, evo-devo constraints, environment, and biological function determines morphological evolution. After introducing a new operative definition, here we provide a morphodynamics-based framework for studying phenotypic ETs, discussing how understanding the impact of these factors on ETs improves the explanation of links between biological patterns and processes underpinning directional evolution. We envisage that adopting a quantitative, pattern-based, and multifactorial approach will pave the way to new potential applications for this field of evolutionary biology. In this framework, by exploiting the catalysing effect of climate change on evolution, research on ETs induced by global change might represent an ideal arena for validating hypotheses about the predictability of evolution.