How competition for resources drive specific niches and community structure of phytoplankton by using a trait-based model

Abstract : Understanding the phenology of phytoplankton species is still a challenge and despite a lot of theoretical work on competition for ressources this process is under-represented in deterministic models. To study the main driver of the species selection, we thus used a trait-based model that keeps phenotypic variability through physiological trait parameterization. We next validate the results by using a key species, the toxic dinoflagellate Alexandrium minutum. Due to their monitoring, we show that harmful algae are ideal models for studying ecological niches and for contributing to this more global challenge. As a first step, a dimensionless model of an estuary (France) was built with water temperature and water exchanges deduced from a hydro-dynamic model. The biological parametrisation takes into account the size (from pico- to microphytoplankton) and the type of assimilation. The results show that temperature, competition for nutrients and dilution are important factors regulating the community structure and \\textit\Alexandrium minutum\ dynamics (more especially the bloom initiation and magnitude). These drivers contribute to determine the ecological niche of A. minutum, to influence the shape of its blooms and to provide potential explanations of its interannual variability. This approach mainly introduce more flexibility of the community structure to study how environmental forcing could drive its evolution.
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Article dans une revue
Frontiers in Marine Science, Frontiers Media, 2017, 4, 〈10.3389/fmars.2017.00052〉
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http://hal.univ-brest.fr/hal-01510343
Contributeur : Luis Tito de Morais <>
Soumis le : mercredi 19 avril 2017 - 13:54:50
Dernière modification le : mercredi 19 septembre 2018 - 01:24:24

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Marc Sourisseau, Martin Plus, Annie Chapelle, Valérie Le Guennec, Guillaume Le Gland. How competition for resources drive specific niches and community structure of phytoplankton by using a trait-based model. Frontiers in Marine Science, Frontiers Media, 2017, 4, 〈10.3389/fmars.2017.00052〉. 〈hal-01510343〉

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