Why the architecture of environmental fluctuation matters for fitness

Abstract

The physical environment provides the very stage upon which the eco-evolutionary play unfolds. How fluctuations in the environment affect demographic fitness is thus central to selection predictions, life history analyses, and viability of populations. Treatment of fluctuating environments typically leverages the mathematics of random variability. However, environmental fluctuations in nature are almost always combinations of random and non-random components. For example, some fluctuations contain feedbacks which generate autocorrelation (e.g. disturbances such as floods, fires, and hurricanes), while others are driven by geophysical forces that create fixed cyclicality (e.g. seasonal, tidal, and diel). Despite theoretical developments, the consideration of non-random characteristics of fluctuations is still rare in empirical work on natural populations, mostly due to convention and partially due to difficulties in measuring and analyzing timeseries of environmental fluctuations. We show why non-randomness matters for fitness. Using a simple demographic model, we systematically compare four major categories of fluctuating environments: stochastic, positively autoregressive, negatively autoregressive, and periodic with error ("Noisy Clock"). The architectures of fluctuations influence the fitness of structured populations even when the modelled environments only differ in the timing of fluctuations, and not in their overall frequency. Importantly, we highlight two quantitative mechanisms through which fitness depends on fluctuation architecture--the consecutiveness of deviations from the environmental mean, and Jensens Inequality acting on nonlinear biological parameters--both relevant features in virtually all populations inhabiting variable environments. Our goal is to argue that non-random structures of environmental variability should be more seriously considered in empirical work. Such an endeavor would tap into the rich diversity of variable environments in nature to expand our understanding of the commensurate diversity of population dynamics.