Understanding what controls the spawning distribution of North Sea whiting (Merlangius merlangus) using a multi-model approach

The processes that control the spatial distribution of North Sea whiting (Merlangius merlangus) spawning adults are investigated using a statistical multi-model approach. Models of external and internal controls on the population, such as environmental conditions, spatial constraints, present or past spatial distribution, and demographic state of the population, are evaluated, compared and ranked to select those that are the best able to predict the observed distribution of spawning adults. Model selection is greatly influenced by the selection method, either based on data fitting or prediction, as well as by the threshold value used to stop the selection. Model selection based on prediction tends to select simpler models than selection based on data fitting. The hypotheses underlying the selected models are inferred to play a significant role in controlling North Sea whiting spatial distribution. The multi-model inference approach developed in this study enables comparison of several theoretical concepts and hypotheses and the results provide important clues on the processes involved in the control of the spatial distribution of whiting. We conclude that whiting has a high spatial fidelity to spawning site which can be linked to either geographical attachment or year-to-year persistence of the spatial distribution of the population. Environmental factors – temperature and salinity – appear to influence the geographical extent of spawning whiting distribution, whereas local abundance levels are primarily controlled by internal factors, i.e., population size and spatial segregation between ages.

Keyword(s)

Akaike information criterion, hypotheses inference, model selection, North Sea, spawning distribution, whiting

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Loots CHRISTOPHE, Vaz Sandrine, Planque Benjamin, Koubbi Philippe (2011). Understanding what controls the spawning distribution of North Sea whiting (Merlangius merlangus) using a multi-model approach. Fisheries Oceanography. 20 (1). 18-31. https://doi.org/10.1111/j.1365-2419.2010.00564.x, https://archimer.ifremer.fr/doc/00022/13313/

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