Comparison between observed (in red) and modeled (in blue) abundance of <i>Acartia tonsa</i> (log₁₀(x+1)) at three sampling stations in the Gironde estuary. A

<p>. Station F. <b>B</b>. Station E. <b>C</b>. Station K. Modeled data originated from the realized niche assessed from monthly water temperature and salinity and using a mixed Gausian-linear model (see Fig. 2C).</p

Observed and modeled ecological niche of <i>Acartia tonsa</i> in the Gironde estuary. A

<p>. Observed niche of <i>A. tonsa</i> (log₁₀ (x+1)) as a function of monthly water temperature and salinity (considering all available data). <b>B</b>. Realized niche of <i>A. tonsa</i> (log₁₀ (x+1)) as a function of monthly water temperature and salinity. <b>C</b>. Realized niche of <i>A. tonsa</i> (log₁₀ (x+1)) as a function of monthly water temperature and salinity modeled by a mixed Gausian-linear model. <b>D</b>. Fundamental niche of <i>A. tonsa</i> (occurrence probability) modeled by the NPPEN model.</p

Monthly water temperature and salinity of each record of <i>Acartia tonsa</i> at Station E. A

<p>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1978–1983 (all months). <b>B</b>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1978–1983 (August). <b>C</b>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1984–2011 (all months). <b>D</b>. Occurrences of <i>Acartia tonsa</i> (blue dots) for period 1984–2011 (August). The estimated realized niche was superimposed (see Fig. 2A). Both temperature and salinity maxima from period 1978–1983 were also superimposed as black dotted lines.</p

Comparison between observed (in red) and modelled (in blue) abundance of <i>Acartia tonsa</i> (log₁₀(x+1)) at three sampling stations in the Gironde estuary. A

<p>. Station F. <b>B</b>. Station E. <b>C</b>. Station K. Modeled data originated from the NPPEN model (see Fig. 2D).</p

Map of the Gironde estuary with the three sampling sites (F, E, and K).

<p>Map of the Gironde estuary with the three sampling sites (F, E, and K).</p

Comparison between observed (in red) and estimated (in blue) abundance of <i>Acartia tonsa</i> (log₁₀(x+1)) at three sampling stations in the Gironde estuary. A

<p>. Station F. <b>B</b>. Station E. <b>C</b>. Station K. Estimated data originated from the realized niche assessed by discretization from monthly water temperature and salinity (see Fig. 2B).</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