| Summary: | This aims to study the impact of climate warming on the spatial distribution of fish in the North Atlantic, using the new habitat model called the Non-Parametric Probabilistic Ecological Niche Model (NPPEN). The model NPPEN is nonparametric and requires only presence data. It is based on concept of the ecological niche sensu Hutchinson. The model NPPEN tests the Mahalanobis generalised distance by permutations to produce and map the probability of species occurrence. The model is therefore well suited to study expected changes in the biogeography of marine species at macro-scale. Applying this new model on more than fifty marine species in the North Atlantic, has highlighted the impact of global warming on the biogeography of species, structure and trophodynamic of the marine ecosystem. Disruption, already observed in spatial distribution and abundance (probability of occurrence) of fish species such as Atlantic cod and lesser sandeel were found again. The majority of species will move northward to stay in an environment consistent with their ecological niche. The intensity and rapidity of the biogeographic movements expected, as the balance of gains or losses in the spatial range differ among fish; governed by the ability of species movement, their range of environmental tolerance (niche breadth) and the intensity of global warming Cette thèse doctorale, réalisée dans le cadre d’un partenariat avec des professionnels de la pêche, a pour objet l’étude de l’impact du réchauffement climatique sur la distribution spatiale des poissons en Atlantique Nord, à l’aide de l’application d’un nouveau modèle d’habitat appelé le Non-Parametric Probabilistic Ecological Niche Model (NPPEN). Le modèle NPPEN est non-paramétrique et basé sur le concept de niche écologique (sensu Hutchinson). Le modèle ne requiert que des données de présence. Il est donc bien adapté à l’étude à macro-échelle de la biogéographie des espèces marines Le modèle NPPEN teste la distance généralisée de Mahalanobis par un test non-paramétrique de ...
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