Dissecting fluctuating selection: A unified population and quantitative genetics

One of the longstanding debates in evolutionary biology is the effect of fluctuating selection on genetic changes in populations. However, the extent to which these periodic forces influence organisms at both genomic and phenotypic levels remains unclear. Despite the compelling evidence of fluctuating selection from recent studies, there is a disconnect between empirical and theoretical findings concerning the underlying mechanisms due to the limited evidence regarding the scale and processes that generate stable genome-wide oscillations. This study aims to elucidate the genetic and ecological factors driving fluctuating selection and to identify the parameters that produce consistent oscillatory patterns. We developed a modeling framework integrating quantitative and population genetics to simulate a population under various selection regimes. We applied spectral analysis to detect periodicity, indicating cyclical selective environments. Our simulations highlight the conditions sustaining oscillations in allele frequencies over time. Spectral analysis successfully identifies the periodic patterns from allele frequency, even under highly complex selection regimes. Not only does our study clarify the conditions that yield long-term oscillatory behaviors, but these parameters are also relatively easy to predict from natural populations, providing a possibility of empirically testing these models. Significance statementAs genomic data is becoming increasingly available for different species across time, one observation are patterns where alleles oscillate in a seasonal pattern, which has been interpreted as a signature of fluctuating selection. However, the field lacks theoretical models that predict these persistent oscillations in allele frequencies caused by fluctuating selection. We develop such a theoretical model, defining the conditions under which persistent, strong oscillations in allele frequencies are predicted to occur because of fluctuating selection. In addition, we develop a novel method to detect patterns of fluctuating selection from genomic data using spectral analysis. Our model considers key parameters that are measurable in real populations, which is an added advantage to test them empirically. Our research paves the way for field biologists to test our predictions and brings us closer to reliably forecasting how populations will evolve in environments that are frequently changing.