Differential equations used in the numerical simulations.

<p>Meanings of the symbols are the same as indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.t001" target="_blank">Table 1</a>.</p><p>^aEquations modified to include growth based on nutrient uptake following a Michaelis-Menten-Monod function.</p><p>^bEquations modified to include grazing by microciliates or rotifers.</p><p>^cMaximum growth rate was determined at 19°C and was temperature-corrected to 20°C assuming a <i>Q</i>₁₀ of 2.</p><p>^dHourly rates were converted to daily rates, assuming a constant growth over 24h.</p><p>^e<i>T</i> is the temperature (20°C).</p><p>Differential equations used in the numerical simulations.</p

Influence of nanophytoplankton abundance on rotifer grazing of dinospores.

<p>(A) Results from simulations assessing the effect of increasing nanophytoplankton abundances (cells L^–1) on the number of dinospores consumed by rotifers (dinospores rotifer^–1 d^–1) and rotifer growth rates (d^–1). The dashed line indicates the point where rotifer growth = 0. (B–E) Effect of two nanophytoplankton concentrations, 10⁵ and 10⁷ cells L^–1 (B–C and D–E, respectively), on the consumption of dinospores by rotifers during 30-day simulations.</p

Results of Sobol’s sensitivity analysis.

<p>Ranking of first index sensitivities for the most relevant 5 parameters affecting dinoflagellate abundance (<i>H</i>; red bars), infected dinoflagellate cells (<i>I</i>; grey bars) and dinospore abundance (<i>P</i>; black bars) under oligotrophic and eutrophic conditions (1 and 36 μM nitrate, respectively). Parameters are numbered from 1 (most influencing) to 5. Meanings of parameter symbols are the same as indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.t001" target="_blank">Table 1</a>.</p

Effect of the different components of the modeled plankton community on the parasite-dinoflagellate dynamics.

<p>(A) Results of simulations assessing the effect of the initial abundance (cells L^–1) of other phytoplankton (i.e. nanophytoplankton and diatoms) on maximum dinoflagellate abundance (cells L^–1), in the presence (colored mesh plot) and absence (red mesh plot) of parasites, under different nitrate concentrations (μM). The composition of the plankton community was the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.g001" target="_blank">Fig 1C</a>. (B–C) Individual relevance of the different components of the simulated plankton community that negatively affected dinoflagellates and their parasites ineutrophic conditions (36 μM nitrate) under low (B) and high (C) abundance of other phytoplankton (10⁴ and 10⁸ cells L^–1, respectively). The proportion between nanophytoplankton and diatoms was 99:1 in all simulations. Components of the plankton community are identified by the same letters as indicated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.g001" target="_blank">Fig 1C</a>. Arrow thickness indicates the intensity (%) of negative effects (only values higher than 1% are shown). Negative effects were divided into two types: those that acted during the exponential growth phase (i.e. they affected the maximum number of individuals in the population) (black arrows); and those that contributed to the elimination of dinoflagellates and parasites (yellow arrows). Dashed arrows indicate indirect negative interactions.</p

Values of parameters and state variables considered in the numerical simulations.

<p>^aOnly <i>Prorocentrum triestinum</i> was considered because this species contributed to 99% of the total dinoflagellate abundance in Thau Lagoon (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.s001" target="_blank">S1 Appendix</a>, section S1-2).</p><p>^bMaximal growth rate of <i>P</i>. <i>triestinum</i>.</p><p>^cAverage value of half saturation constants for nitrate uptake of dinoflagellate species presented in Table 3.7 of this author.</p><p>^dNitrogen cell quota of <i>Prorocentrum micans</i>.</p><p>^eParameters of <i>Amoebophrya</i> sp. infecting <i>Karlodinium micrum</i>.</p><p>^fAverage value of the mean doubling rates (d^–1) of <i>Leptocylindrus minimus</i>, <i>Leptocylindrus danicus</i>, <i>Cylindrotheca closterium</i> and <i>Thalassionema nitzschioides</i> (the dominant diatoms species in Thau Lagoon; <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.s001" target="_blank">S1 Appendix</a>, section S1-2).</p><p>^gHalf saturation constant for nitrate uptake of <i>Pseudo-nitzschia</i> sp. shown in Table 3.7 of this author.</p><p>^hAverage values of nitrogen cell quota of diatom species presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.t001" target="_blank">Table 1</a> of these authors.</p><p>ⁱAverage value of nanophytoplankton growth rates (d^–1) presented by these authors in their <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.g004" target="_blank">Fig 4</a> (only control experiments, with no nutrient addition, were considered).</p><p>^jValue for nanophytoplankton natural assemblages in Thau lagoon presented by these authors.</p><p>^kAverage values of nitrogen cell quota of nanoplankton species presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.t001" target="_blank">Table 1</a> of these authors.</p><p>^lAverage values of <i>Tiarina fusum</i> feeding on <i>Lingulodinium polyedrum</i> and <i>Scrippsiella trochoidea</i> (values in ng C^–1 were transformed to cells L^–1 considering carbon content per cell) given by the authors.</p><p>^mBased on average values estimated from growth rates of <i>Brachionus plicatilis</i>, <i>Brachionus rotundiformis</i> and <i>Brachionus</i> sp. feeding on <i>Tetraselmis suecica</i> and <i>Nannochloris atomus</i> (prey concentrations presented in ng C^–1 were converted to cells L^–1 by considering the cellular carbon content of a cell with equivalent spherical diameter of 10 μm and the equation for carbon conversion given by [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127623#pone.0127623.ref029" target="_blank">29</a>]).</p><p>Values of parameters and state variables considered in the numerical simulations.</p

Table_1_Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT.xls

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.</p

DataSheet_1_Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT.docx

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.</p