On phenology and bet-hedging

Environmental fluctuations may cause natural selection to favor phenologies that systematically deviate from the resource maximizing strategy. In temporally variable environments, because fitness is taken from the geometric (not arithmetic) mean growth rate (Gillespie, 1974), this permits the evolution of bet-hedging strategies (Simons, 2011), which are strategies that maximises total fitness by reducing temporal variation at the cost of arithmetic mean fitness (Ripa et al. 2009). Bet-hedging may either involve a single conservative bet-hedging strategy (Philippi and Seger, 1989), or where polyphenism (variable phenotypic expression of the same genotype) is possible (Moran, 1992), lead to diversified bet-hedging strategies in partially predictable environments (Bull, 1987), or adaptive coin-flipping strategy in environments where predictive cues are not available (Cooper and Kaplan, 1982).

A common phenologically-related bet-hedging strategy reported for many taxa, including plants (Childs et al., 2010), rotifers (Garcıa-Roger et al., 2014), and insects (Hopper, 1999), is the variable dormancy period. For example, annual killifish can enter a reversible metabolic and developmental arrest at three different stages in their life history, a bet-hedging strategy against the unpredictable dessication and refilling of the emphemeral pools they inhabit (Podrabsky et al., 2010; Polacik et al., 2014). Consequently, individuals pursuing phenological bet-hedging strategies may be asynchronous to current resource conditions and thus appear maladapted in any given year, when in fact they are maximising fitness over the long-run.

References

Bull, J. 1987. Evolution of phenotypic variance. – Evolution 41: 303–315.

Childs, D. Z. et al. 2010. Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants. – Proc. R. Soc. B. 277: 3055–3064.

Cooper, W. S. and Kaplan, R. H. 1982. Adaptive “coin-flipping” a decision-theoretic examination of natural selection for random individual variation. – J. Theor. Biol. 94(1): 135–151.

Garcıa-Roger, E. M. et al. 2014. Bet-hedging in diapausing egg hatching of temporary rotifer populations – A review of models and new insights. – Int. Rev. Biol. 99(1-2): 96–106.

Gillespie, J. H. 1974. Natural selection for within-generation variance in offspring number. – Genetics 76(3): 601–606.

Hopper, K. R. 1999. Risk-spreading and bet-hedging in insect population biology. – Ann. Rev. Entomol. 44(1): 535–560.

Moran, N. A. 1992. The evolutionary maintenance of alternative phenotypes. – Am. Nat. pp. 971–989.

Philippi, T. and Seger, J. 1989. Hedging one’s evolutionary bets, revisited. – TREE 4(2): 41–44.

Podrabsky, J. E. et al. 2010. Metabolic dormancy and responses to environmental desiccation in fish embryos. – In: Dormancy and Resistance in Harsh Environments. Springer, pp. 203–226.

Polacik, M. et al. 2014. Alternative intrapopulation life-history strategies and their trade-offs in an African annual fish. – J. Evol. Biol. 27(5): 854–865.

Ripa, J. et al. 2009. What is bet-hedging, really? – Proc. R. Soc. B. rspb20092023

Simons, A. M. 2011. Modes of response to environmental change and the elusive empirical evidence for bet hedging. – Proc. R. Soc. B. 278: 1601–1609.

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