Does Hormonal Pleiotropy Shape the Evolution of Performance and Life History Traits?

Hormonal pleiotropy occurs when a part of the endocrine system (e.g., hormone concentrations) influences the expression of two or more phenotypes. Although hormonal pleiotropy may have similar evolutionary consequences as genetic pleiotropy, most conceptual and empirical work on its putative evolutionary consequences to date has focused on identifying whether the different components of an endocrine axis (titer, receptor expression, etc.) that affect trait expression are themselves able to evolve independently from one another. This is important because if these different components evolve together, the expression of two traits affected by the same hormone may be yoked and evolve non-independently. Here, we first describe methodological approaches used to identify how hormonal pleiotropy could cause the co-evolution of performance and life history traits. We then focus on a similar but less studied concept about how hormonal pleiotropy can affect phenotypic responses to selection. If the expression of two traits is affected by the same hormone, the magnitude of the phenotypic response to selection may be exacerbated or retarded compared to the absence of this hormonal pleiotropy. We use classical concepts from quantitative genetics to discuss an approach for identifying whether hormonal pleiotropy has such evolutionary consequences using data collected from longitudinal studies of wild animals. We develop a simple quantitative genetics model to derive predictions about the conditions under which hormonal pleiotropy would affect the response to selection. We focus on performance and life history traits and how the effects of hormonal pleiotropy on the evolution of these traits depend upon the genetic correlations between the hormone and traits as well as the direction and strength of selection on the two traits. Finally, we review the literature for examples that have estimated these model parameters to characterize the studies that have or have not found support for these model predictions.