Integrating field data into individual-based models of the migration of European eel larvae

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Abstract

Lagrangian simulations based on coupled physical-biological models can help determine the mechanisms that affect fish recruitment, but only if the key biological and environmental drivers are accurately described. However, it is difficult to obtain experimental measurements for some vital traits, such as mortality and/or movement patterns. The different hypotheses about these traits can be contrasted by comparing simulation outputs with experimental data that can be collected in the field, such as body size distribution at selected transects. We used this approach to study the oceanic migration of European eel larvae. Despite considerable research effort (involving both field surveys and simulation studies), it is still uncertain whether this migration is a purely passive process or the result of the interaction between transport by currents and an active larval movement. Based on present knowledge of eel larvae and predictions of metabolic ecology, we developed a parameterized model that provided a simple, yet biologically reasonable description of the species' key life history traits (body growth, mortality and movement). We contrasted different model settings and identified the most plausible migration scenario by comparing simulation results against experimental data. The best-performing scenario was not purely passive but in - cluded an active larval propulsion proportional to body size. The corresponding migration duration was about 3 yr. Our modelling study succeeded in assimilating experimental data within a conceptual framework that is consistent with that sketched out, almost a century ago, by Danish biologist Johannes Schmidt. © Inter-Research 2013.

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Melià, P., Schiavina, M., Gatto, M., Bonaventura, L., Masina, S., & Casagrandi, R. (2013). Integrating field data into individual-based models of the migration of European eel larvae. Marine Ecology Progress Series, 487, 135–149. https://doi.org/10.3354/meps10368

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