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Predicting mussel population density and age structure: The relationship between model complexity and predictive power

, : Predicting mussel population density and age structure: The relationship between model complexity and predictive power. Marine Ecology Progress Series 208: 131-145, December 8

We use a model to predict the age structure and density of an intertidal mussel Mytilus edulis L. population on the 10 primary mussel beds of the Exe estuary, England. We investigate the relationship between the number of parameters in the model and its accuracy in describing the observed density-age structure. The full detail version (382 parameters) assumes that recruitment rates are bed-specific and mortality rates are age-class, bed- and season-specific, and describes the observed age structure and density very accurately; the predicted estuary-wide density in March is 581 m-2 compared with an observed density of 578 m-2 during 1977 to 1983. The simplest version (24 parameters) describes the age structure and density almost as well (predicted estuary-wide density of 568 m-2) and assumes that: (1) density-dependence is absent in the 3rd-winter and older mussels, and density-independent mortality in these age classes is the same on all beds, but increases with age; (2) in younger mussels, density-dependent mortality operates above a threshold mussel density which is determined by bed exposure and has a strength which is the same on all beds, but which varies with age class; (3) recruitment rate is dependent on the density of adults on a bed and the bed's substrate softness. The model is tested by comparing its predictions, based on data collected during 1976 to 1983, with observed mussel densities during 1992 to 1997; a stable September mussel population of 524 adults m-2 is predicted and, in accord with this, the observed density of adults in September changed relatively little between 1976 and 1983 (541 m-2) and between 1992 and 1997 (450 m-2). We discuss why the number of parameters in the model can be reduced so greatly with very little reduction in the accuracy of predictions, and whether a similar approach could be used to model other shellfish populations.


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