An individual‐based modelling approach was developed to investigate the spatial and temporal patterns in the recruitment processes of North Sea haddock, Melanogrammus aeglefinus. The approach was based on the realization that the survivors to recruitment of an annual cohort are most probably not drawn at random from the initial population of eggs, but represent the fastest‐growing individuals. Individual growth rates reflect the unique exposure of each larva to the environment along its drift trajectory. In this context, the environment refers to a wide range of factors affecting growth such as food, turbulence and temperature. A combination of a model of egg production by the adult stock, a particle‐tracking scheme, and a model of larval growth and mortality rate was used to simulate the dispersal trajectories, and the survival of haddock larvae spawned at different times and locations on the continental shelf. The particle tracking was driven by flowfields from a climatological implementation of the Hamburg Shelf–Ocean Model (HAMSOM) for the North Sea and NE Atlantic. The system was able to resolve spatial and temporal patterns in the recruitment process and indicated that the surviving population of larvae was drawn from a restricted part of the spawning distribution. The results have the potential to guide the development of future conservation measures in fisheries management.
The large‐scale distribution of haddock (Melanogrammus aeglefinus) larvae in the northern North Sea was mapped in a grid survey carried out in late April 1996. A drifting buoy was deployed in the centre of one of the areas of concentration of larvae located off the east coast of the Shetland Isles, where intensive sampling was carried out for ≈ 10 days. Daily larval haddock growth variability, estimated from otolith microstructure analysis, was independent of the measured variability of the physical and biological environment of the larvae. The survey coincided with the onset of the spring plankton production bloom, and a likely explanation for the absence of environmental effects on larval growth was high food availability and larval feeding rates. Nevertheless, differences in growth were observed between cohorts, with larvae hatched later in the spring displaying higher growth at age than those hatched earlier. Particle‐tracking modelling suggested that differences in temperature history between cohorts, on their own or compounded by a potential interaction between temperature and the development of plankton production, may explain the higher growth rate of the larvae hatched later in the season.
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