Generalists versus specialists in fluctuating environments: a bet‐hedging perspective

Bet‐hedging evolves in fluctuating environments because long‐term genotype success is determined by geometric (rather than arithmetic) mean fitness across generations. Diversifying bet‐hedging produces different specialist offspring, whereas conservative bet‐hedging produces similar generalist offspring. However, many fields, such as behavioral ecology and thermal physiology, typically consider specialist versus generalist strategies only in terms of maximizing arithmetic mean fitness benefits to individuals. Here we model how environmental variability affects optimal amounts of phenotypic variation within and among individuals to maximise genotype fitness, and we disentangle the effects of individual‐level optimization and genotype‐level bet‐hedging by comparing long‐term arithmetic versus geometric mean fitness. For traits with additive fitness effects within lifetimes (e.g. foraging‐related traits), genotypes of similar generalists or diversified specialists perform equally well. However, if fitness effects are multiplicative within lifetimes (e.g. sequential survival probabilities), generalist individuals are always favored. In this case, geometric mean fitness optimization requires even more within‐individual phenotypic variation than does arithmetic mean fitness, causing individuals to be more generalist than required to simply maximize their own expected fitness. In contrast to previous results in the bet‐hedging literature, this generalist conservative bet‐hedging effect is always favored over diversifying bet‐hedging. These results link the evolution of behavioral and ecological specialization with earlier models of bet‐hedging, and we apply our framework to a range of natural phenomena from habitat choice to host specificity in parasites.The code should contain the necessary information to reproduce the results from the paper. If anything is unclear, please do not hesitate to contact Thomas Haaland (corresponding author). Funding provided by: Norges ForskningsrådCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100005416Award Number: 240008Funding provided by: Norges ForskningsrådCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100005416Award Number: 223257This is the R code used for the fitness calculations and producing the figures in the paper and supplementary material