phytoclass: A pigment‐based chemotaxonomic method to determine the biomass of phytoplankton classes

Abstract

Pigment-based chemotaxonomy is a widely utilized tool to determine the biomass of phytoplankton classes from pigment biomarkers. The CHEMTAX approach is sensitive to the initial estimates of pigment-tochlorophyll a (Chl a) ratios for the phytoplankton classes required, even though these are modified by the CHEMTAX process. We present an alternative chemotaxonomic method that utilizes simulated annealing with a steepest descent algorithm to derive class abundances and pigment-to-Chl a ratios. The simulated annealing algorithm is tested on two synthetic datasets of Southern Ocean phytoplankton communities. Each dataset is composed of 1000 inversion samples (set of phytoplankton class abundances, pigment ratios, and pigment profiles) with sizes ranging between 5 and 60 individual samples. We show that the new simulated annealing approach displays higher accuracy than two common configurations of the CHEMTAX method, with lower differences between true and estimated class abundances. Symmetric mean absolute percentage error were 4.8-11%, compared to 18-70% with CHEMTAX approaches. Proportions of variance explained (R 2 ) between true and estimated class abundances using the simulated annealing approach were 0.98-0.99 compared to 0.71-0.89 for CHEMTAX. Overall, this new methodology is capable of determining phytoplankton class abundances at higher accuracy than CHEMTAX without sensitivity to initial estimates of pigment-to-Chl a ratios.

New tools and recommendations for a better management of harmful algal blooms under the European Marine Strategy Framework Directive

“…These groups can be structured either taxonomically (i.e., diatoms/dinoflagellates), by size (microplankton, nanoplankton, picoplankton, etc. ), based on their pigment signatures (Bustillos-Guzmán et al, 2004;Havskum et al, 2004;Not et al, 2007;Hayward et al, 2023), or according to their functional traits such as autotrophs-to-heterotrophs ratios or ecological assemblages (Nogueira and Figueiras, 2005;Weithoff and Beisner, 2019;Lehtinen et al, 2021;Litchman, 2023). This structuring of indicators offers a multifaceted approach to understanding phytoplankton communities and their ecological roles.…”

Twenty-six years of phytoplankton pigments reveal a circumpolar Class Divide around the Southern Ocean

“…This method allows for dynamic pruning of dendrogram branches based on their significance and has been proven effective at clustering potential outliers. A minimum cluster size was set to 13 to account for sensitivity to sample sizes, as suggested by Hayward et al, 2023. The samples split into 720 clusters, which were inverted to phytoplankton groups using the phytoclass program (Hayward et al, 2023).…”

“…A minimum cluster size was set to 13 to account for sensitivity to sample sizes, as suggested by Hayward et al, 2023. The samples split into 720 clusters, which were inverted to phytoplankton groups using the phytoclass program (Hayward et al, 2023).…”

“…In this manuscript, we reveal consistent zonal differences in phytoplankton community structure over the circumpolar Southern Ocean, based on a large dataset of phytoplankton pigments samples (n = 14,824) amassed over 26 years from 58 oceanographic voyages between 32°S and the Antarctic coast (see Methods). We present analysis of this pigment dataset from the novel inversion method "phytoclass" (Hayward et al, 2023), which determined the differences in the chlorophyll a (Chl a) biomass and relative abundance of seven phytoplankton groups at Class level (diatoms, haptophytes, cryptophytes, dinoflagellates, green algae, pelagophytes, and Synechococcus) across the Southern Ocean. Further, we identify the distributions of four distinct community types, revealed by cluster analysis, and show their distribution relative to the APF.…”

Toward a synthesis of phytoplankton community composition methods for global‐scale application

“…Many methods exist to characterize PCC from field samples, with varying taxonomic resolution, quality control and standardization criteria, and scales of observation (Sosik et al 2014;Johnson and Martiny 2015;Lombard et al 2019). Common methods include microscopy (Karlson et al 2010), high-performance liquid chromatography (HPLC) pigments (e.g., Mackey et al 1996;Uitz et al 2015;Kramer and Siegel 2019;Hayward et al 2023), flow cytometry (FCM; e.g., Zubkov et al 1998;Sosik et al 2010), quantitative cell imaging (e.g., with the Imaging FlowCytobot [IFCB]; Olson and Sosik 2007), and amplicon sequencing of "barcode" genes (e.g., Needham and Fuhrman 2016;Catlett et al 2020). This list is not exhaustive and does not include optical proxy methods developed for use with in situ and remote sensing approaches (Thibodeau et al 2014;Uitz et al 2015).…”