Decomposing phenotypic skew and its effects on the predicted response to strong selection

Abstract

the lab 12;23-28 and ad-hoc methods have been used to test for skew at the genetic level 29;30 . Nevertheless, to our knowledge, no study has 1) relaxed the normality assumptions when making statistical inferences to examine the origin and extent of skew at different levels, and 2) explored how observed patterns of natural selection interact with skew to determine how well these two equations predict selection response in the wild.Juvenile body size is under strong, persistent, directional selection across taxa 31 , yet is known to show little response to this selection 32 . We show that juvenile body size is highly negatively skewed (long tail of small individuals) across bird species, but the origin of this skew is unknown. To determine this, we developed statistical methods to decompose the phenotypic distribution into a set of skew-t distributions, and predict the shape of PO-regression based on the estimated skew. We applied these methods to data from a long-term cross-fostering experiment of a wild bird population. By estimating survival selection acting on juvenile body size, we tested the robustness of the predicted response to selection from the breeder's and gradient equations. Results Prevalence of Phenotypic SkewAcross 27 species of birds, tarsus length (a common measure of structural size) was substantially negatively skewed (long tail of small individuals) in juveniles (coefficient of skew: -1.054 [-1.394, -0.686], pMCMC<0.001), but not adults (-0.302 [-0.641, 0.052], pMCMC=0.086), with tarsus length being significantly more skewed in juveniles than adults (difference = -0.752

Dynamical BLUP modeling of reaction norm evolution, accommodating changing environments, overlapping generations, and multivariate data

“…The fundamental reason for such errors is that the multivariate breeder's equation involves only mean and variance values of the additive genetic and nonadditive effects, while influences from third and higher order statistical moments are ignored. See for example discussions in Bonamour et al ( 2017 ) and Pick et al ( 2022 ).…”

A BLUP derivation of the multivariate breeder's equation, with an elucidation of errors in BLUP variance estimates, and a prediction method for inbred populations

“…The fundamental reason for such errors is that the multivariate breeder's equation involves only mean and variance values of the additive genetic and environmental effects, while influences from third and higher order statistical moments are ignored. See for example discussions in Bonamour et al (2017) and Pick et al (2022).…”

A quantitative genetics framework for understanding the selection response of microbial communities

“…If one does drop the infidelity and nonlinearity terms, the Price equation reduces to the breeder’s equation (Figure 1D; see also [17]). Conversely, some authors argue that such a simple linear relationship between heritability and selection efficacy is often an inaccurate description of biological reality [18, 19, 20]. Regardless, even if infidelity and nonlinearity are present, as long as they are uncorrelated with h 2 , we may still safely say that greater heritability is associated with more efficient selection.…”