Aide à la décision pour la conservation des populations de saumon atlantique (Salmo salar L.)

• Main Author: Brun, Mélanie
• Other Authors: Ecologie Comportementale et Biologie des Populations de Poissons (ECOBIOP), Institut National de la Recherche Agronomique (INRA)-Université de Pau et des Pays de l'Adour (UPPA), Université de Pau et des Pays de l'Adour, Ecole doctorale des sciences exactes et de leurs applications, Marc Jarry
• Format: Doctoral or Postdoctoral Thesis
• Language: French
• Published: HAL CCSD 2011
• Subjects: these; SALMONIDE; ECOLOGIE; modèle hiérarchique bayésien; [SDV]Life Sciences [q-bio]; Atlantic salmon; Salmo salar
• Online Access: https://hal.inrae.fr/tel-02810463

Diplôme : Dr. d'Université The sustainable management of natural living resources is a major issue in a context of increasing scarcity due to human impact and of pervasive uncertainty. Improving existing tools and developing new ones to advise decision makers on the potential evolution of natural living resources, according to various management and environmental scenarios, is requested. This PhD aims at contributing to the development of a methodology for decision making for natural living resources management, while taking into account major sources of uncertainty. This is achieved through the study case of the Atlantic salmon (Salmo salar L.) population of the Nivelle River (France). This population is subjected to a long term monitoring program and the species has been extensively studied. Atlantic salmon is a threatened species but still targeted by commercial and recreational fisheries. It illustrates the duality between conservation and exploitation which is at the heart of natural living resource management. To manage a population, it is necessary to understand its dynamics and to predict its evolution under various management and environmental scenarios. The Bayesian approach provides a coherent framework to quantify uncertainty in its different forms. Hierarchical models allow the assimilation of multiple sources of data and to make spatio-temporal inferences and predictions. A Bayesian state space model, i.e. a Bayesian dynamic hierarchical model, is constructed to study the dynamics of the population of interest and to predict its evolution. The decision theory under uncertainty provides a framework to help an individual in its choices, but its application still raises difficulties. In theory, a utility function depending on the consequences of alternative actions reflects the preferences of a single individual involved in a decision problem. In practice, its construction is challenging. Firstly, it is difficult to assign a value for each consequence. Secondly, there is usually more than one ...