A bioenergetics model for juvenile flounder Platichthys flesus
Related to:Stevens, M.; Maes, J.; Ollevier, F.P. (2006). A bioenergetics model for juvenile flounder Platichthys flesus, in: Stevens, M. Intertidal and basin-wide habitat use of fishes in the Scheldt estuary = Getij- en bekkengebonden habitatgebruik door vissen in het Schelde-estuarium. pp. 81-92, more
|  |
| Keywords |
Feeding
Population functions > Growth
Platichthys flesus (Linnaeus, 1758) [WoRMS]
ANE, British Isles, Scotland, Grampian, Ythan Estu [Marine Regions]
Marine/Coastal; Fresh water |
| Project | Top | Authors |
- Habitat quality of flounder (Platichthys flesus) in the Scheldt estuary: a field and modelling study, more
|
| Abstract |
Despite the numerous physiological studies on flatfish and their economic and ecologic importance, only a few attempts have been made to construct a bioenergetics model for these species. Here we present the first bioenergetics model for European flounder (Platichthys flesus), using experimentally derived parameter values. We tested model performance using literature derived field-based estimates of food consumption and growth rates of an estuarine flounder population in the Ythan estuary, Scotland. The model was applied to four age-classes of flounder (age 0–3). Sensitivity of model predictions to parameter perturbation was estimated using error analysis. The fit between observed and predicted series was evaluated using three statistical methods: partitioning mean squared error, a reliability index (RI) and an index of modelling efficiency (MEF). Overall, model predictions closely tracked the observed changes of consumption and growth. The results of the different validation techniques show a high goodness-of-fit between observed and simulated values. The model clearly demonstrates the importance of temperature in determining growth of flounder in the estuary. A sex-specific estimation of the energetic costs of spawning in adult flounder and a more accurate description of the thermal history of the fish may further reduce the error in the model predictions. |
|
