Tuning Evolution Towards Generalists Through Resonant Environmental Cycling

Natural environments can present diverse fitness pressures, but some genotypes remain fit across a wide range of challenges. Such 'generalist' genotypes can be hard to evolve because there may be entropic or absolute fitness costs relative to specialist genotypes. Here, we study the conditions under which time-dependent evolutionary protocols stabilize generalists even when static protocols fail. We find that cycling environments on timescales tuned to match fixation times can reliably evolve generalists when the landscape is too rugged, or deleterious selection too adverse, for static protocols to succeed. We discuss 'chirp' protocols that circumvent the need for protocol fine-tuning. Our work reveals regimes in which time-dependent 'seascapes’ can find and stabilize populations around genotypes that are fundamentally unstable in any static protocol.