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Nutrient quotas and carbon content variability of Prorocentrum minimum (Pavillard) Schiller, 1933
Affiliation:1. Université Lille Nord de France, Université Lille 1 Sciences et Technologies, Laboratoire d''Océanologie et de Géosciences, CNRS, UMR 8187, Station Marine de Wimereux, 28 Avenue Foch, 62930 Wimereux, France;2. Université Lille Nord de France, Université du Littoral Côte d''Opale, Laboratoire d''Océanologie et de Géosciences, CNRS, UMR 8187, Maison de la Recherche en Environnement Naturel, 32 Avenue Foch, 62930 Wimereux, France
Abstract:Frequency, severity, and geographic range of harmful blooms caused by a dinoflagellate Prorocentrum minimum have been increasing significantly over the past few decades. The ability to adapt nutrient quotas and carbon content to a wide range of environmental conditions is one of the key factors for the proliferation of P. minimum. Understanding the limits of stoichiometric variability in terms of nutrient quotas and carbon content would help explain the observed trends and assist in P. minimum growth model creation. This manuscript aggregates information from 15 studies to investigate variability in nutrient quotas and carbon content for a broad range of P. minimum isolates and clonal lines. Nitrogen quota, phosphorus quota, and carbon content in the studies varied between 11–107.5 pgN cell−1, 1.45–17.58 pgP cell−1, and 70–656.36 pgC cell−1, respectively. Regression analysis was used to estimate average nitrogen and phosphorus quotas as functions of carbon, and to show that carbon content variability explains 55% of nitrogen and 23% of phosphorus quota variability. Confidence intervals for data (CID) found during the analysis were used to define maximal and minimal nutrient quotas as functions of carbon content. The ratios of the upper and lower CID ranges can, therefore, be used to estimate nutrient storage capacity as a function of carbon content. The new results and comparison with other species show that, at least for P. minimum, carbon-based quotas are more suitable for modelling than cell-based quotas. Finally, results indicate that environmental nutrient availability affects quotas more than light does: while quota variability due to light remains within 80% CID, nutrient variability covers the 95% CID.
Keywords:Nitrogen quota  Phosphorus quota  Carbon content  Phytoplankton model  Stoichiometric constraints
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