Novel models of trait evolution via an expansion of Lande's fitness function: The Ornstein-Uhlenbeck process meets the Little Prince's boa

Abstract

Adaptive topographies form the foundation for much of our understanding of evolutionary change. Lande's 1976 influential paper on the adaptive topography of phenotypes demonstrated how the concept is inherent in both phenotypic and genetic models of evolution, and how the concept can be used to test evolutionary hypotheses given data. Here, we revisit and generalize Lande's original derivation of an equation analogous to Wright's genotypic adaptive topography to the case of two fitness components. A move to two fitness components yields novel predictions about the shape and mechanistic underpinnings of the adaptive topography. The optimum of this updated fitness function is a weighted average of the optima of the two fitness components, with weights given by the relative strengths of stabilizing selection on each component. Temporal or spatial heterogeneity in the strengths of selection for each fitness component create novel shapes (asymmetry, bi-modality, or lack thereof) of the overall fitness function, a possibility demonstrated with a case-study from the published literature. Finally, when combined with Lande's approach to generate an Ornstein-Uhlenbeck (OU) model for the evolution of the average phenotype, our fitness formulation leads to a previously unrecognized family of stochastic differential equation models of trait evolution. These results provide mechanistic justification for non-Gaussian fitness functions (often observed in natural systems), provide a path for testing alternative models generating non-Gaussian fitness functions, and pave the way for future study of the interplay of ecological and evolutionary dynamics, such as in the study of evolutionary rescue.