Frequency dependence and the predictability of evolution in a changing environment

Abstract

Frequency-dependent (FD) selection, whereby fitness and selection depend on the genetic or phenotypic composition of the population, arises in numerous ecological contexts (competition, mate choice, crypsis, mimicry, etc.) and can strongly impact evolutionary dynamics. In particular, negative frequency-dependent selection (NFDS) is well known for its ability to potentially maintain stable polymorphisms, but it has also been invoked as a source of persistent, predictable frequency fluctuations. However, the conditions under which such fluctuations persist are not entirely clear. In particular, previous work rarely considered that FD is unlikely to be the sole driver of evolutionary dynamics when it occurs, because most environments are not static but instead change dynamically over time. Here, we investigate how FD interacts with a temporally fluctuating environment to shape the dynamics of population genetic change. We show that a simple metric introduced by Lewontin, the slope of frequency change against frequency near equilibrium, works as a key criterion for distinguishing microevolutionary outcomes, even in a changing environment. When this slope D is between 0 and -2 (consistent with the empirical examples we review), substantial fluctuations would not persist on their own in a large population occupying a constant environment, but they can still be maintained indefinitely as quasi-cycles fueled by environmental noise or genetic drift. However, such moderate NFDS buffers and temporally shifts evolutionary responses to periodic environments (e.g., seasonality). Stronger FD, with slope D < -2, can produce self-sustained cycles that may overwhelm responses to a changing environment, or even chaos that fundamentally limits predictability. This diversity of expected outcomes, together with the empirical evidence for both FD and environment-dependent selection, suggests that the interplay of internal dynamics with external forcing should be investigated more systematically to reach a better understanding and prediction of evolution.

Large haploblocks underlie rapid adaptation in an invasive weed

“…Note that the overdominant selection model is dynamically equivalent to many other balancing selection models provided the differences in fitness among genotypes are small (consistent with our analysis below). Our results based on the overdominance model should, therefore, apply more broadly to other scenarios of balancing selection, including scenarios involving negative frequency-dependence and antagonistic pleiotropy 83,84 .…”

“…Our results based on the overdominance model should, therefore, apply more broadly to other scenarios of balancing selection, including scenarios involving negative frequency-dependence and antagonistic pleiotropy (e.g. 72,73).…”

Laplace's demon in biology: Models of evolutionary prediction

“…Importantly, uncertainty in selection did not lead to erroneous qualitative predictions, as a stable oscillation in stripe frequency was always predicted (this is expected given the step function assumed for NFDS, unlike in Chevin et al, 2022). However, limited knowledge of selection did cause considerable quantitative uncertainty in the evolutionary trajectory (i.e., in the pattern and characteristics of the oscillations), and this was greater than the uncertainty caused by genetic drift (Figure 3F).…”

“…Moreover, our ability to predict evolution can be further compromised when systems exhibit extreme sensitivity to initial conditions, for example leading to chaotic dynamics (e.g., Costantino et al, 1997). Evolutionary theory has raised the possibility of chaos in evolutionary dynamics (Gavrilets & Hastings, 1995;Doebeli & Ispolatov, 2014), including in a changing environment (Rego-Costa et al, 2018;Chevin et al, 2022), but its actual existence and prevalence remains to be investigated empirically. Another situation where initial conditions are critical to the outcome is when evolution occurs on rugged adaptive landscapes caused by strong epistatic interactions (Kauffmanet al , 1993;Nosil et al, 2020), such that randomly occurring mutations or slight differences in standing genetic variation may lead populations into different, irreversible evolutionary paths (e.g., Park et al, 2022).…”

“…Under the data limits hypothesis, the assumption is that with better data and better analysis, evolution by natural selection can be better predicted. Limits to our understanding of evolution by selection can occur because environmental sources of selection, such as climatic conditions or predator abundance, fluctuate in ways that are difficult to predict (Grant & Grant, 2002; Lenormand et al., 2009; Nosil et al., 2018; Chevin et al., 2022). And even if the environment is constant or can be predicted, limited information about how environmental factors affect resource and trait distributions, and thus selection, can make evolution less predictable.…”