Using a state-space population model to detect age-dependent species interactions.

Models that incorporate species interactions and their effects on the dynamics of commercially important fish stocks are needed to better understand the importance of ecological interactions and to facilitate sustainable fisheries. We developed a dynamic age-structured population model for the North-East Arctic stock of Atlantic haddock (Melanogrammus aeglefinus) based on scientific survey and commercial landings data. Our goal was to investigate climate effects and ecological interactions within the haddock food-web. A Bayesian state-space framework was used to separate information from ecological noise and observation error. Our results indicate significant impacts of species interactions on haddock dynamics. Haddock survival was associated with indices of cod (negative effect), and capelin biomass (positive effect). The latter may reflect lower predation by predators such as marine mammals at high capelin biomass. We further detect weak density dependence in the survival of young haddock, and a convex relationship between haddock abundance and the scientific survey indices. Our findings highlight the importance of considering natural resources as part of an ecosystem with its diverse interactions both within and between species. This study shows that it is possible to detect ecological interactions with a population model based on noisy data.

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