Synoptic relationships between surface Chlorophyll-<i>a</i> and diagnostic pigments specific to phytoplankton functional types

Abstract

Abstract. Error-quantified, synoptic-scale relationships between chlorophyll-a (Chl-a) and phytoplankton pigment groups at the sea surface are presented. A total of ten pigment groups were considered to represent three Phytoplankton Size Classes (PSCs, micro-, nano-and picoplankton) and seven Phytoplankton Functional Types (PFTs, i.e. diatoms, dinoflagellates, green algae, prymnesiophytes (haptophytes), pico-eukaryotes, prokaryotes and Prochlorococcus sp.). The observed relationships between Chl-a and PSCs/PFTs were well-defined at the global scale to show that a community shift of phytoplankton at the basin and global scales is reflected by a change in Chl-a of the total community. Thus, Chl-a of the total community can be used as an index of not only phytoplankton biomass but also of their community structure. Within these relationships, we also found nonmonotonic variations with Chl-a for certain pico-sized phytoplankton (pico-eukaryotes, Prokaryotes and Prochlorococcus sp.) and nano-sized phytoplankton (Green algae, prymnesiophytes). The relationships were quantified with a leastsquare fitting approach in order to enable an estimation of the PFTs from Chl-a where PFTs are expressed as a percentageCorrespondence to: T. Hirata (tahi@ees.hokudai.ac.jp) of the total Chl-a. The estimated uncertainty of the relationships depends on both PFT and Chl-a concentration. Maximum uncertainty of 31.8% was found for diatoms at Chla = 0.49 mg m −3 . However, the mean uncertainty of the relationships over all PFTs was 5.9% over the entire Chl-a range observed in situ (0.02 < Chl-a < 4.26 mg m −3 ). The relationships were applied to SeaWiFS satellite Chl-a data from 1998 to 2009 to show the global climatological fields of the surface distribution of PFTs. Results show that microplankton are present in the mid and high latitudes, constituting only ∼10.9% of the entire phytoplankton community in the mean field for 1998-2009, in which diatoms explain ∼7.5%. Nanoplankton are ubiquitous throughout the global surface oceans, except the subtropical gyres, constituting ∼45.5%, of which prymnesiophytes (haptophytes) are the major group explaining ∼31.7% while green algae contribute ∼13.9%. Picoplankton are dominant in the subtropical gyres, but constitute ∼43.6% globally, of which prokaryotes are the major group explaining ∼26.5% (Prochlorococcus sp. explaining 22.8%), while pico-eukaryotes explain ∼17.2% and are relatively abundant in the South Pacific. These results may be of use to evaluate global marine ecosystem models.

Bio-Optical Characterization and Ocean Colour Inversion in the Eastern Lagoon of New Caledonia, South Tropical Pacific

“…The specific absorption coefficient aphy*(443) (normalized by Chla) varied from 0.032 to 0.067 m 2 ·mgChla −1 , with an average of 0.049 ± 0.008 m 2 ·mgChla −1 ( Figure 4A), typical of populations mostly dominated by nanoplankton cells [28], as found in New Caledonia coastal lagoons [10,43,44]. The lowest aphy*(443) values (~0.032 m 2 ·mgChla −1 ) were obtained at the coastal stations (e.g., stations 14, 16, 20 and 24), which had higher TChla (>0.6 mg·m 3 ) and higher proportions of Fucoxanthin, suggesting the dominance of diatoms (>40%) (following the relation proposed by Uitz et al [68] and updated by Hirata et al [69] with the diagnostic pigments determined by HPLC). The highest aphy*(443) values (~0.067 m 2 ·mgChla −1 ) were obtained at the stations across the coral reef barrier, at the shelf slope (>300 m deep) (e.g., stations 22 and 23), with lower TChla (<0.2 mg·m 3 ) and higher proportions of diagnostic pigments of picoplankton groups (Zeaxanthin, Chlb and DVChla), suggesting the dominance of these groups (>60%) [68,69].…”

“…The lowest aphy*(443) values (~0.032 m 2 ·mgChla −1 ) were obtained at the coastal stations (e.g., stations 14, 16, 20 and 24), which had higher TChla (>0.6 mg·m 3 ) and higher proportions of Fucoxanthin, suggesting the dominance of diatoms (>40%) (following the relation proposed by Uitz et al [68] and updated by Hirata et al [69] with the diagnostic pigments determined by HPLC). The highest aphy*(443) values (~0.067 m 2 ·mgChla −1 ) were obtained at the stations across the coral reef barrier, at the shelf slope (>300 m deep) (e.g., stations 22 and 23), with lower TChla (<0.2 mg·m 3 ) and higher proportions of diagnostic pigments of picoplankton groups (Zeaxanthin, Chlb and DVChla), suggesting the dominance of these groups (>60%) [68,69]. The negative relation between aphy*(443) and Chla associated to the dominance of different phytoplankton groups (and size classes) has been well reported in other works [23,34,70].…”

Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific

“…The global satellite data, which have the horizontal resolution of 0.042 • , were linearly interpolated to the grid (size 1/10 and 1/6 • ) in the model domain (Fig. 1a), and the nitrogen-converted concentrations of both PL and PS were estimated based on a satellite PFT algorithm (Hirata et al, 2011). The µ-GA cost function was defined from the 1998 monthly averaged PL and PS concentrations.…”

Temporal changes in total and size-fractioned chlorophyll-a in surface waters of three provinces in the Atlantic Ocean (September to November) between 2003 and 2010

“…Global analysis of in-situ data illustrates coherent relationships between PSC and TCHLa, which have been quantified using various statistical methods and applied to remotely-sensed observations of TCHLa to map PSCs at regional to global scales (Brewin et al, 2010;Brotas et al, 2013;Hirata et al, 2011;IOCCG, 2014;Marañón et al, 2012;Uitz et al, 2006;Vidussi et al, 2001). Even standard empirical algorithms used for estimating TCHLa from satellite ocean-colour data implicitly assume a fixed relationship between TCHLa and PSC (Dierssen, 2010, IOCCG, 2014.…”

“…Size-fractionated chlorophyll is commonly used as a metric for phytoplankton size structure and can be estimated through either HPLC diagnostic pigment analysis (e.g. Brewin et al, 2010;Devred et al, 2011;Hirata et al, 2011;Uitz et al, 2006;Vidussi et al, 2001), or through size-fractionated filtration (e.g. Brewin et al, 2014a, b;Marañón et al, 2012).…”