Analysis of a multitrait population projection matrix reveals the evolutionary and demographic effects of a life history trade-off

It is increasingly recognized that incorporating life history trade-offs into evolutionary demography models requires trade-offs to be decomposed into fixed (a.k.a genetic) and individual (a.k.a dynamic) components. This is fundamental in order to understand how trade-offs are related to fixed and dynamic components of individual heterogeneities and generate variance in individual trajectories. Therefore, embedding such trade-offs into population projection matrices usually requires three categories: a life-history determining trait (e.g., age or stage), a fixed trait incorporating the genetic trade-off, and a dynamic trait modeling the individual component. This has proved a complex exercise until the recent advent of Multitrait Population Projection Matrices (MPPMs).11MPPM: Multitrait (i.e., multi-category) Population Projection Matrix. Recent developments of Trait-Level Analysis (TLA)22TLA: Trait-Level Analysis, a toolbox for MPPMs developed by Coste et al. (2017). tools for MPPMs now allow us to study the demographic and evolutionary consequences of each component of a life history trade-off. Here, we illustrate this by constructing and analyzing an evolutionary demography model that implements both dynamic and fixed components of the costs of reproduction, the trade-off between current/early reproduction and future/later fitness. In particular, we explain and describe the use of the TLA to measure the effects of this trade-off on individual fitness. Here, we focus on the variance of lifetime reproductive success between models implementing the individual costs and asymptotically-equivalent matrices from which they are absent. This allows us to show that dynamic costs decrease that variance and more so for slow organisms. Therefore, accounting for this component of the costs, instead of classically focusing solely on fixed costs of reproduction, is paramount in order to correctly assess the relative importance of the “neutral” and “adaptive” components of individual heterogeneity.