Evolutionarily Stable Strategies for Fecundity and Swimming Speed of Fish (2016)
Type of ContentJournal Articles
PublisherUniversity of Canterbury. Mathematics and Statistics
AuthorsPlank, M.J., Pitchford, J.W., James, A.show all
Many pelagic fish species have a life history that involves producing a large number of small eggs. This is the result of a trade-off between fecundity and larval survival probability. There are also trade-offs involving other traits, such as larval swimming speed. Swimming faster increases the average food encounter rate but also increases the metabolic cost. Here we introduce an evolutionary model comprising fecundity and swimming speed as heritable traits. We show that there can be two evolutionary stable strategies. In environments where there is little noise in the food encounter rate, the stable strategy is a low-fecundity strategy with a swimming speed that minimises the mean time taken to reach reproductive maturity. However, in noisy environments, for example where the prey distribution is patchy or the water is turbulent, strategies that optimise mean outcomes are often outperformed by strategies that increase inter-individual variance. We show that, when larval growth rates are unpredictable, a high-fecundity strategy is evolutionarily stable. In a population following this strategy, the swimming speed is higher than would be anticipated by maximising the mean growth rate.
CitationPlank, M.J., Pitchford, J.W., James, A. (2016) Evolutionarily Stable Strategies for Fecundity and Swimming Speed of Fish. Bulletin of Mathematical Biology, 78(2), pp. 280-292.
This citation is automatically generated and may be unreliable. Use as a guide only.
Keywordsfirst passage time; fish egg size; fish growth rate; genetic algorithm; patchiness; stochastic growth
ANZSRC Fields of Research01 - Mathematical Sciences::0102 - Applied Mathematics::010202 - Biological Mathematics
01 - Mathematical Sciences::0104 - Statistics::010406 - Stochastic Analysis and Modelling
07 - Agricultural and Veterinary Sciences::0704 - Fisheries Sciences