Sex, finding date and age of the twenty three sampled harbor seals.

<p>Sex, finding date and age of the twenty three sampled harbor seals.</p

Mean stable isotope compositions of the groups of prey items (error bars show standard deviations) compared to the moving mean of seal vibrissae per season.

<p>The shaded areas represent the isotopic range per season including all standard deviations from each value of the moving mean. Theoretical stable isotope values of prey foraged by seals were computed with TFFs of 3.2 ‰ and 2.8 ‰ for δ¹³C and δ¹⁵N, respectively. Fish species are grouped as planktivorous/piscivorous (PlankPisc), benthivorous/piscivorous (BenthPisc), strictly benthivorous (StricBenth).</p

δ¹³C and δ¹⁵N values (mean ± standard deviation) of the different groups of prey items in the Sylt-Rømø Bight and the North Sea.

<p>n: sample size.</p

Contributions per season of the different trophic groups of prey items to diet of seals.

<p>Contributions were computed by the SIAR mixing model. Higher and lower values of the 95% credibility intervals (CI) are shown for each trophic group and each season. Fish species are grouped as planktivorous/piscivorous (PlankPisc), benthivorous/piscivorous (BenthPisc) or strictly benthivorous (StricBenth).</p

Seasonal Variation of Harbor Seal's Diet from the Wadden Sea in Relation to Prey Availability - Fig 2

<p><b>CPUE_m (A, in g) and CPUE_n (B) of the different groups of fish, shrimp and squid species per seasons.</b> Fish species are grouped as planktivorous/piscivorous (PlankPisc), benthivorous/piscivorous (BenthPisc), or strictly benthivorous (StricBenth).</p

Groups of species used as prey items in the Sylt-Rømø Bight (for biomass and stable isotope analyses) and in the North Sea (for stable isotope analyses).

<p>Groups of species used as prey items in the Sylt-Rømø Bight (for biomass and stable isotope analyses) and in the North Sea (for stable isotope analyses).</p

Location and map of the Sylt-Rømø Bight; Maps created using ArcGIS® 10 Esri software.

<p>Sylt-Rømø Bight map data courtesy of the Schleswig-Holstein’s Government-Owned Company for Coastal Protection, National Parks and Ocean Protection—National Park Authority, Tönning.</p

Summary of Tukey tests following ANOVAs (for the Sylt-Rømø Bight) and Wilcoxon rank sum tests following Kruskal Wallis tests (for the North Sea) between the different groups of prey items.

<p>Fish species are grouped as planktivorous/piscivorous (PlankPisc), benthivorous/piscivorous (BenthPisc), strictly benthivorous (StricBenth).</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