Timing of ontogenetic changes of two cranial regions in Sotalia guianensis (Delphinidae)

Despite the fact that heterochronic processes seem to be an important process determining morphological evolution of the delphinid skull, previous workers have not found allometric scaling as relevant factor in the differentiation within the genus Sotalia. Here we analyzed the skull ontogeny of the estuarine dolphin S. guianensis and investigate differential growth and shape changes of two cranial regions the neurocranium and the face in order to evaluate the relevance of cranial compartmentalization on the ontogeny of this structure. Our results show that, even though both cranial regions stop growing at adulthood, the face has higher initial growth rates than the neurocranium. The rate of shape changes is also different for both regions, with the face showing a initially higher, but rapidly decreasing rate of change, while the neurocranium shows a slow decreasing rate, leading to persistent and localized shape changes throughout adult life, a pattern that could be related to epigenetic regional factors. The pattern of ontogenetic shape change described here is similar to those described for other groups of Delphinidae and also match intra and interspecific variation found within the family, suggesting that mosaic heterochrony could be an important factor in the morphological evolution of this group. (C) 2012 Deutsche Gesellschaft fur Saugetierkunde. Published by Elsevier GmbH. All rights reserved.

MODULARITY, NOISE, AND NATURAL SELECTION

Most biological systems are formed by component parts that are to some degree interrelated. Groups of parts that are more associated among themselves and are relatively autonomous from others are called modules. One of the consequences of modularity is that biological systems usually present an unequal distribution of the genetic variation among traits. Estimating the covariance matrix that describes these systems is a difficult problem due to a number of factors such as poor sample sizes and measurement errors. We show that this problem will be exacerbated whenever matrix inversion is required, as in directional selection reconstruction analysis. We explore the consequences of varying degrees of modularity and signal-to-noise ratio on selection reconstruction. We then present and test the efficiency of available methods for controlling noise in matrix estimates. In our simulations, controlling matrices for noise vastly improves the reconstruction of selection gradients. We also perform an analysis of selection gradients reconstruction over a New World Monkeys skull database to illustrate the impact of noise on such analyses. Noise-controlled estimates render far more plausible interpretations that are in full agreement with previous results.