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Quantitative uncertainty estimation in biophysical models of fish larval connectivity in the Florida Keys

Abstract : The impacts of seven uncertain biological parameters on simulated larval connectivity in the Florida Keys are investigated using Polynomial chaos surrogates. These parameters describe biological traits and behaviours-such as mortality, swimming abilities, and orientation-and modulate larval settlement as well as dispersal forecasts. However, these parameters are poorly constrained by observations and vary naturally between individual larvae. The present investigation characterizes these input uncertainties with probability density functions informed by previous studies of Abudefduf saxatilis. The parametric domain is sampled via ensemble calculations, then a polynomial-based surrogate is built to explicitly approximate the dependence of the model outputs on the uncertain model inputs, which enables a robust statistical analysis of uncertainties. This approach allows the computation of probabilistic dispersal kernels that are further analyzed to understand the impact of the parameter uncertainties. We find that the biological input parameters influence the connectivity differently depending on dispersal distance and release location. The global sensitivity analysis shows that the interactions between detection distance threshold, orientation ontogeny, and orientation accuracy, are the dominant contributors to the uncertainty in settlement abundance in the Florida Keys. Uncertainties in swimming speed and mortality, on the other hand, seem to contribute little to dispersal uncertainty.
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Contributeur : Frédérique COUFFIGNAL Connectez-vous pour contacter le contributeur
Soumis le : jeudi 5 mai 2022 - 14:25:34
Dernière modification le : mercredi 3 août 2022 - 04:06:10

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Romain Chaput, Pierre Sochala, Philippe Miron, Vassiliki Kourafalou, Mohamed Iskandarani. Quantitative uncertainty estimation in biophysical models of fish larval connectivity in the Florida Keys. ICES Journal of Marine Science, Oxford University Press (OUP), 2022, 79 (3), pp.609 - 632. ⟨10.1093/icesjms/fsac021⟩. ⟨hal-03659934⟩



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