Phytoplankton pigment in situ measurements uncertainty evaluation: an HPLC interlaboratory comparison with a European-scale dataset - Canuti, Elisabetta (Frontiers in Marine Science, 2023)
Phytoplankton pigment data are fundamental in ecological studies. They are used to identify the abundance of different algal groups and to estimate primary production rates. The development of bio-optical algorithms or the validation of satellite data products also require availability of high quality in-situ measurements of Chlorophyll a (TChl a) and other marine pigments. High-performance liquid chromatography (HPLC) technique allows separation and quantification of multiple pigments in a single water sample and is considered the gold standard method to support ocean color studies. The current work aims to investigate the uncertainties associated with phytoplankton pigments quantification by comparing the analyses performed on duplicate samples by two laboratories that implemented the same analytical method: the Joint Research Centre of the European Commission (J) and the DHI Group, Denmark (D) on a dataset of 957 natural samples collected between 2012 and 2017 in various European seas. The samples were representative of different trophic conditions (TChl a concentration varying from 0.083 to 27.35 mg/m3). The observed 10.8% mean Percent Difference between the two independent analyses of TChl a and the 16.9% associated with the primary phytoplankton pigments (TChl a, Chlrorophyll b, Chlorophyll c, Carotens, Fucoxantin, 19’-Butantoloxyfucoxanthin, Diadinoxanthin, Diatoxanthin, 19’-Hexanoloxyfucoxanthin, Peridin and Zeaxanthin) fully satisfy the requirement of 15% and 25% uncertainties associated with marine pigments quantitative measurements applicable for the validation of satellite data products. In addition, the study investigated the extent to which uncertainties in diagnostic pigment values propagate to phytoplankton indexes at the basis of pigment-based algorithms deriving descriptors of phytoplankton populations such functional types.