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.
The PICES CCCC (North Pacific Marine Science Organization, ClimateChange and Carrying Capacity program) MODEL Task Team achieved a consensus on the structure of a prototype lower trophic level ecosystem model for the North Pacific Ocean, and named it the North Pacific Ecosystem Model for Understanding Regional Oceanography, "NEMURO". Through an extensive dialog between modelers, plankton biologists and oceanographers, an extensive review was conducted to define NEMURO's process equations and their parameter values for distinct geographic regions. We present in this paper the formulation, structure and governing equations of NEMURO as well as examples to illustrate its behavior. NEMURO has eleven state variables: nitrate, ammonium, small and large phytoplankton biomass, small, large and predatory zooplankton biomass, particulate and dissolved organic nitrogen, particulate silica, and silicic acid concentration. Several applications reported in this issue of Ecological Modelling have successfully used NEMURO, and an extension that includes fish as an additional state variable. Applications include studies of the biogeochemistry of the North Pacific, and variations of its ecosystem's lower trophic levels and two target fish species at regional and basin-scale levels, and on time scales from seasonal to interdecadal.5
Error-quantified, synoptic-scale relationships between chlorophyll-a (Chla) and phytoplankton pigment groups at the sea surface are presented. A total of nine pigment groups were considered to represent nine phytoplankton functional types (PFTs) including microplankton, nanoplankton, picoplankton, diatoms, dinoflagellates, green algae, picoeukaryotes, prokaryotes and Prochlorococcus sp. The observed relationships between Chla and pigment groups were well-defined at the global scale to show that Chla can be used as an index of not only phytoplankton abundance but also community structure; large (micro) phytoplankton monotonically increase as Chla increases, whereas the small (pico) phytoplankton community generally decreases. Within these relationships, we also found non-monotonic variations with Chla for certain pico-plankton (pico-eukaryotes, Prokaryotes and Prochlorococcus sp.) and for Green Algae and nano-sized phytoplankton. The relationships were quantified with a least-square fitting approach in order to estimate the PFTs from Chla alone. The estimated uncertainty of the relationships quantified depends on both phytoplankton types and Chla concentration. Maximum uncertainty over all groups (34.7% Chla) was found from diatom at approximately Chla = 1.07 mg m−3. However, the mean uncertainty of the relationships over all groups was 5.8 [% Chla] over the entire Chla range observed (0.02 < Chla < 6.84 mg m−3). The relationships were applied to SeaWiFS satellite Chla 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 ~9.0 [% Chla] of the phytoplankton community at the global surface, in which diatoms explain ~6.0 [% Chla]. Nanoplankton are ubiquious throught much of the global surface oceans except subtropical gyres, acting as a background population, constituting ~44.2 [% Chla]. Picoplankton are mostly limited in subtropical gyres, constituting ~46.8 [% Chla] globally, in which prokaryotes are the major species explaining 32.3 [% Chla] (prochlorococcus sp. explaining 21.5 [% Chla]), while pico-eukaryotes are notably abundant in the Southern Pacific explaining ~14.5 [% Chla]. These results may be used to constrain or validate global marine ecosystem models
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