River basin management using a stochastic model of the salmon life cycle

International audience Effective management of natural populations of Atlantic salmon (Salmo Salar L.) requires the ability to evaluate the impact on salmon survival of human activities such as agricultural practices (nitrogen pollution, suspended solids emission, deforestation, .), river management...

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Bibliographic Details
Main Authors: Faivre, Robert, Dumas, J., Charron, M.H., Badia, Jacques, Prouzet, P.
Other Authors: Unité de Biométrie et Intelligence Artificielle (UBIA), Institut National de la Recherche Agronomique (INRA), Station d'hydrobiologie
Format: Conference Object
Language:English
Published: HAL CCSD 1997
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Online Access:https://hal.inrae.fr/hal-02770786
Description
Summary:International audience Effective management of natural populations of Atlantic salmon (Salmo Salar L.) requires the ability to evaluate the impact on salmon survival of human activities such as agricultural practices (nitrogen pollution, suspended solids emission, deforestation, .), river management (e.g. fish-ways, dams), industries practices and catches by fisheries and angling. The salmon life is also influenced by natural environmental constraints: characteristics of the river basin, local home-river quality, climate, flows. A stochastic model of the life cycle was built in order to have a better understanding of effect of these constraints on the abundance. The model considers the life of a salmon as a succession of different stages: embryo-larval, juvenile, adult and reproduction. The total population is split up into different sub-populations depending on life stage and age: salmon can live one or several years in river then in sea. From one stage to the following one, a survival rate is applied to the sub-population. This rate is age-dependent and depends on cons traints: some are juvenile growth areas, competitors density or catch levels. Because of uncontrolled conditions (flows, erosion, .), some rates are considered random and are specified in the model by a distribution. A software, written in Splus, is able to compute simulations after specifying interactively or not the parameters characteristics, the horizon and the number of simulations. Graphical analyses of river management strategies are available. We are able to compare influence of catch levels, juvenile growth areas increase (due to fish-ways), agricultural practices (which control the level of suspended solids) on the distribution of the abundance. Applied on River Adour and its Gaves characteristics, analysis of river managements highlights the main under-gravel survival sensitivity compared to catch levels or juvenile growth areas.