The geographical distribution and production of the Barents Sea capelin (Mallotus villosus, Osmeridae) is modelled by the use of a state-variable optimization technique (dynamic programming), where the main objective of individuals always is re, maximize fitness, or total expected reproduction (R(o)), by selecting the most profitable habitats through time. Fitness is gained by successful reproduction (a function of size) during the spawning season on the breeding grounds off northern Norway. The environment (predators, temperature and zooplankton prey) is determined by a meteorologically forced circulation model for the year 1980, creating a spatial and seasonal fluctuation in the environment. Predation from cod is the main source of mortality, and the distribution of the cod (Gadus morhua) stock is assumed to vary with temperature. Growth is predicted from a bioenergetic model, incorporating the cost of swimming between feeding areas and spawning grounds. Field data of the capelin stock recorded during autumn cruises from 1979 is implemented at the start of the model, and then this stock is modelled through 1980 and the first months of 1981. Model predictions are compared with the observed distribution of capelin in autumn 1980. Habitat selection has consequences for the dynamics of the population and growth of individuals, demonstrating the importance of combining external (environmental) and internal (evolutionary) forcing to understand and predict the dynamics of fish populations. This study is the first application of dynamic programming to model the dynamics and ecology of horizontal fish migration, and we suggest that the method may be developed into a useful tool for the management of short-lived species.
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