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Size-fractionated filtration (SFF) is a direct method for estimating pigment concentration in various size classes. It is also common practice to infer the size structure of phytoplankton communities from diagnostic pigments estimated by high-performance liquid chromatography (HPLC). In this paper, the three-component model of Brewin et al. (2010) was fitted to coincident data from HPLC and from SFF collected along Atlantic Meridional Transect cruises. The model accounted for the variability in each data set, but the fitted model parameters differed for the two data sets. Both HPLC and SFF data supported the conceptual framework of the three-component model, which assumes that the chlorophyll concentration in small cells increases to an asymptotic maximum, beyond which further increase in chlorophyll is achieved by the addition of larger celled phytoplankton. The three-component model was extended to a multicomponent model of size structure using observed relationships between model parameters and assuming that the asymptotic concentration that can be reached by cells increased linearly with increase in the upper bound on the cell size. The multicomponent model was verified using independent SFF data for a variety of size fractions and found to perform well (0.628 r 0.989) lending support for the underlying assumptions. An advantage of the multicomponent model over the three-component model is that, for the same number of parameters, it can be applied to any size range in a continuous fashion. The multicomponent model provides a useful tool for studying the distribution of phytoplankton size structure at large scales.

We describe an approach to partition a vertical profile of chlorophyll‐a concentration into contributions from two communities of phytoplankton: one (community 1) that resides principally in the turbulent mixed‐layer of the upper ocean and is observable through satellite visible radiometry; the other (community 2) residing below the mixed‐layer, in a stably stratified environment, hidden from the eyes of the satellite. The approach is tuned to a time‐series of profiles from a Biogeochemical‐Argo float in the northern Red Sea, selected as its location transitions from a deep mixed layer in winter (characteristic of vertically well‐mixed systems) to a shallow mixed layer in the summer with a deep chlorophyll‐a maximum (characteristic of vertically stratified systems). The approach is extended to reproduce profiles of particle backscattering, by deriving the chlorophyll‐specific backscattering coefficients of the two communities and a background coefficient assumed to be dominated by non‐algal particles in the region. Analysis of the float data reveals contrasting phenology of the two communities, with community 1 blooming in winter and 2 in summer, community 1 negatively correlated with epipelagic stratification, and 2 positively correlated. We observe a dynamic chlorophyll‐specific backscattering coefficient for community 1 (stable for community 2), positively correlated with light in the mixed‐layer, suggesting seasonal changes in photoacclimation and/or taxonomic composition within community 1. The approach has the potential for monitoring vertical changes in epipelagic biogeography and for combining satellite and ocean robotic data to yield a three‐dimensional view of phytoplankton distribution.

Abstract:The unicellular cyanobacterium Prochlorococcus is the most dominant resident of the subtropical gyres, which are considered to be the largest biomes on earth. In this study, the spatial and temporal variability in the global distribution of Prochlorococcus was estimated in the Atlantic Ocean using an empirical model based on data from 13 Atlantic Meridional Transect cruises. Our model uses satellite-derived sea surface temperature (SST), remote-sensing reflectance at 443 and 488 nm, and the water temperature at a depth of 200 m from Argo data. The model divides the population of Prochlorococcus into two groups: ProI, which dominates under high-light conditions associated with the surface, and ProII, which favors low light found near the deep chlorophyll maximum. ProI and ProII are then summed to provide vertical profiles of the concentration of Prochlorococcus cells. This model predicts that Prochlorococcus cells contribute 32 Mt of carbon biomass (7.4 × 10 26 cells) to the Atlantic Ocean, concentrated mainly within the subtropical gyres (35%) and areas near the Equatorial Convergence Zone (30%). When projected globally, 3.4 × 10 27 Prochlorococcus cells represent 171 Mt of carbon biomass, with 43% of this global biomass allocated to the upper ocean (0-45 m depth). Annual cell standing stocks were relatively stable between the years 2003 and 2014, and the contribution of the gyres varies seasonally as gyres expand and contract, tracking changes in light and temperature, with lowest cell abundances during the boreal and austral winter (1.4 × 10 13 cells m −2 ), when surface cell concentrations were highest (9.8 × 10 4 cells mL −1 ), whereas the opposite scenario was observed in spring-summer (2 × 10 13 cells m −2 ). This model provides a three-dimensional view of the abundance of Prochlorococcus cells, revealing that Prochlorococcus contributes significantly to total phytoplankton biomass in the Atlantic Ocean, and can be applied using either in situ measurements at the sea surface (r 2 = 0.83) or remote-sensing observables (r 2 = 0.58).

Phytoplankton (> 15 µm) was investigated in three shallow coastal areas at Admiralty Bay (AB) between the summers of 2002-03 and 2008-09. Phytoplankton abundance was low (10 3 cells l -1 ) and, over time, the prevailing cell size decreased due to a shift in phytoplankton dominant species from diatoms to dinoflagellates. In situ and remote sensing data showed that oscillations in sea surface temperature, precipitation, ice formation/melting, irradiance (cloud cover) and bottom circulation (indexed by the Antarctic Oscillation Index; AAO) were shown to govern the structure of the phytoplankton. Under negative AAO, diatoms prevailed, with the dominance of large (> 80 µm) benthic diatoms (e.g. Corethron pennatum and Navicula directa) in periods of low production (10 2 cells l -1 in 2002-03), and medium-sized (31-80 µm) centrics (e.g. Thalassiosira spp. and Stellarima microtrias) when the abundance was higher (10 4 cells l -1 in 2003-04). Conversely, positive AAO led to the co-dominance of dinoflagellates and planktonic diatoms (e.g. Pseudo-nitzschia spp.) in the summers of 2007-08 and 2008-09. These results suggest that the AAO can be a good predictor of phytoplankton in coastal areas around the western Antarctic Peninsula, and may help our understanding of changes in other trophic levels of the food web.

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