How to Explore Morphological Integration in Human Evolution and Development?

Abstract

Most studies in evolutionary developmental biology focus on large-scale evolutionary processes using experimental or molecular approaches, whereas evolutionary quantitative genetics provides mathematical models of the influence of heritable phenotypic variation on the shortterm response to natural selection. Studies of morphological integration typically are situated in-between these two styles of explanation. They are based on the consilience of observed phenotypic covariances with qualitative developmental, functional, or evolutionary models. Here we review different forms of integration along with multiple other sources of phenotypic covariances, such as geometric and spatial dependencies among measurements. We discuss one multivariate method [partial least squares analysis (PLS)] to model phenotypic covariances and demonstrate how it can be applied to study developmental integration using two empirical examples. In the first example we use PLS to study integration between the cranial base and the face in human postnatal development. Because the data are longitudinal, we can model both cross-sectional integration and integration of growth itself, i.e., how cross-sectional variance and covariance is actually generated in the course of ontogeny. We find one factor of developmental integration (connecting facial size and the length of the anterior cranial base) that is highly canalized during postnatal development, leading to decreasing cross-sectional variance and covariance.A second factor (overall cranial length to height ratio) is less canalized and leads to increasing (co)variance. In a second example, we examine the evolutionary significance of these patterns by comparing cranial integration in humans to that in chimpanzees.

Brain structure–function associations identified in large-scale neuroimaging data

“…A fundamental question for the neuroimaging field is what the functional metrics really tell us or what is their generative mechanism from the brain structure (Betzel et al 2016). Many researchers have attempted to associate functional metrics to various phenotypes (Medaglia et al 2015), but have not achieved a fully clear understanding of the biological meaning of these functional metrics. Our study offers a novel perspective that characterizes functional metrics based on their shared information with the structural metrics across individuals.…”

Imaging structural co-variance between human brain regions

“…High phenotypic co-variance is generally interpreted as evidence of developmental coordination or integration, which occurs within modules with components that are subject to similar developmental processes; by contrast, the developmental processes that influence distinct modules are relatively autonomous, resulting in low correlations at the population level 50–55 .…”

“…Sharing a developmental precursor may result in co-variance between distinct parts of a mature organism 56 . Similarly, inductive signalling from one developing tissue to another or simultaneous exposure to signals from a third party may result in synchronized maturation and ultimately co-variation 54,55,57 . Shared genetic influences that are due to could underlie such coordinated development, as could correlational selection that is due to inter-related influences on adaptive fitness 58–60 .…”

Infant growth patterns of the mandible in modern humans: a closer exploration of the developmental interactions between the symphyseal bone, the teeth, and the suprahyoid and tongue muscle insertion sites

“…same age range, same dental stage) are more likely to reflect integration (Mitteroecker et al 2012). We thus use two-block PLS analysis (also called singular warp analysis when applied to Procrustes coordinates; Rohlf & Corty, 2000;Bookstein et al 2003;Mitteroecker & Bookstein, 2007Mitteroecker et al 2012) to study integration within the same age stages. For our data, the amount of allometric shape variation within the DS1 subsample is similar to that of the combined DS2-DS3 subsample (Fig.…”

“…Therefore, growth must be removed because it affects all the developmental units under study, otherwise the observed correlation is unreliable (Mitteroecker & Bookstein, 2007). Thus, we regressed out size (lnCS) from the shape coordinates in each subsample to remove allometric shape variation and variation in developmental timing (Mitteroecker & Bookstein, 2007;Mitteroecker et al 2012). We used the di 2 s in the DS1 subsample and the permanent second incisors (I 2 s) in the DS2-DS3 subsample in order to assess the association between the teeth, the symphyseal bone and the muscle insertions.…”