Abstract
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.
Highlights
Environmental fluctuations across different time scales pose a challenge for the evolution of organisms subjected to them, as well as for the researchers studying them (Botero et al 2015; Tufto 2015)
S2, S7-8), extreme specialists are always favored, except when between-generation fluctuations σΘ are larger than the squared width of the fitness function (Bull 1987). This well-known result that is usually interpreted as the threshold above which among-individual phenotypic variation (i.e. diversifying bet-hedging (DBH)) becomes adaptive
An increase in σw is adaptive in the rightmost panels because it lowers genotype variance in fitness across generations
Summary
Environmental fluctuations across different time scales pose a challenge for the evolution of organisms subjected to them, as well as for the researchers studying them (Botero et al 2015; Tufto 2015). What appears suboptimal when considered from an individual’s point of view may be optimal in the long term because it lowers genotype variance in fitness and increases geometric mean fitness across generations (Seger & Brockmann 1987; Simons 2002; Starrfelt & Kokko 2012). Such a CBH strategy often represents a generalist or ‘compromise’ phenotype that avoids doing badly in any one environment by doing moderately well in a range of environments, but gains lower arithmetic mean fitness across environments (Seger & Brockmann 1987; Starrfelt & Kokko 2012; Crowley et al 2016)
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