Constraining the distribution of photosynthetic parameters in the global ocean

Global and regional ocean primary production estimates are highly dependent on assumptions concerning the photosynthetic potential of the resident phytoplankton communities. Little is known, however, about global patterns in the distribution of photosynthetic potential and their causes. Here, we review existing literature reporting photosynthetic characteristics of natural populations. From this, we formulate hypotheses regarding abiotic and biotic factors of potential importance in determining photosynthetic performance. These hypotheses are then tested using data we have compiled from nearly all major ocean basins on the maximum rate of photosynthesis, P Bmax , and the slope of the photosynthesis vs. light curve, B α (both parameters normalized to chlorophyll) as well as standard environmental variables, size fractioned chlorophyll, taxonomic data (to group), size, and biovolume data for pico-, nano-, and micro-phytoplankton. In terms of abiotic variables, depth of sampling, temperature, and nutrient availability all can be related to photosynthetic parameters. The most important biotic variable inﬂuencing photosynthetic performance was found to be community size distribution and the small component (i.e., the proportion of the phytoplankton community passing through a 10 µ m ﬁlter) is shown to have both higher P Bmax and B α than the larger phytoplankton component. A simple model was used to derive best ﬁt values for P Bmax (1.53/2.50 µ gC l − 1 h − 1 ) and B α (0.025/0.040) for the large/small groups in the subset of the data where taxonomic data were available (both surface and sub-surface samples) using fractioned chlorophyll data and bulk community photosynthetic parameters. Non-metric multidimensional scaling (NMDS) was used to relate the distribution of photosynthetic parameters and dominant (by biovolume) phytoplankton groups. High P Bmax was recorded in communities dominated by dinoﬂagellates, small ﬂagellates and in warmer waters, picoeukaryotes, and Synecococcus . Diatom dominated communities exhibited lower P Bmax and were associated with high inorganic nutrients and colder temperatures. That photosynthetic parameters appear closely related to community size distributions and taxonomic group provides some hope for improving the parameterization of photosynthetic performance in global ocean primary production estimates as both of these parameters can be made from remotely sensed optical characteristics of surface waters.

Global and regional ocean primary production estimates are highly dependent on assumptions concerning the photosynthetic potential of the resident phytoplankton communities. Little is known, however, about global patterns in the distribution of photosynthetic potential and their causes. Here, we review existing literature reporting photosynthetic characteristics of natural populations. From this, we formulate hypotheses regarding abiotic and biotic factors of potential importance in determining photosynthetic performance. These hypotheses are then tested using data we have compiled from nearly all major ocean basins on the maximum rate of photosynthesis, P B max , and the slope of the photosynthesis vs. light curve, B α (both parameters normalized to chlorophyll) as well as standard environmental variables, size fractioned chlorophyll, taxonomic data (to group), size, and biovolume data for pico-, nano-, and micro-phytoplankton. In terms of abiotic variables, depth of sampling, temperature, and nutrient availability all can be related to photosynthetic parameters. The most important biotic variable influencing photosynthetic performance was found to be community size distribution and the small component (i.e., the proportion of the phytoplankton community passing through a 10 µm filter) is shown to have both higher P B max and B α than the larger phytoplankton component. A simple model was used to derive best fit values for P B max (1.53/2.50 µgC l −1 h −1 ) and B α (0.025/0.040) for the large/small groups in the subset of the data where taxonomic data were available (both surface and sub-surface samples) using fractioned chlorophyll data and bulk community photosynthetic parameters. Non-metric multidimensional scaling (NMDS) was used to relate the distribution of photosynthetic parameters and dominant (by biovolume) phytoplankton groups. High P B max was recorded in communities dominated by dinoflagellates, small flagellates and in warmer waters, picoeukaryotes, and Synecococcus. Diatom dominated communities exhibited lower P B max and were associated with high inorganic nutrients and colder temperatures. That photosynthetic parameters appear closely related to community size distributions and taxonomic group provides some hope for improving the parameterization of photosynthetic performance in global ocean primary production estimates as both of these parameters can be made from remotely sensed optical characteristics of surface waters.
Keywords: photosynthesis, photosynthetic parameters, global ocean, pico-phytoplankton, P max , alpha INTRODUCTION Focus has in recent years increasingly been moving toward developing a more complete understanding of the functioning of the Earth System (ES) and how it may be changing in response to human activities. Although there are numerous processes within the ES where anthropogenic influence can be detected at the global level (Steffen et al., 2015), climate change is an obvious driver in the search for a better understanding of ES function. As changes induced by human activities on the global carbon cycle lie at the root of human-caused climate change, much scientific focus is levied toward describing this cycle.
Processes occurring in the ocean are critical in the global carbon cycle. Consequently, a better quantification of the fluxes and transformations of carbon in the ocean currently lies at the frontier of marine science. Both physically and biologically mediated processes contribute to ocean carbon cycling. Although the magnitudes of the contributions from physical processes are better quantified than the biological, it is nevertheless believed that changes in rates of biological processes can have profound effects on ocean-atmosphere carbon flux (e.g., Sigman and Boyle, 2000;Segschneider and Bendtsen, 2013) and, thereby, global climate conditions. Photosynthesis, i.e., the biological transformation of dissolved inorganic carbon (DIC) to particulate (POC) and dissolved (DOC) organic carbon is arguably the most important biological process contributing to the ocean carbon cycle. In order to constrain the global ocean carbon cycle, it therefore becomes necessary to obtain precise estimates of photosynthetic rates in the global ocean. Estimates of global ocean photosynthesis, i.e., primary production (PP), are often generated from remotely sensed optical characteristics of the surface ocean determined with the help of satellite-mounted sensors. A number of different algorithms have been developed to convert this surface ocean data to estimates of PP. (Note, however, that all of these algorithms address only the production of POC thus ignoring the not insignificant production of DOC by phytoplankton).
Comparisons (Campbell et al., 2002;Carr et al., 2006) examining how PP estimates generated by these different algorithms compare to particulate PP estimates generated in situ using the 14 CO 2 method originally described by Steemann Nielsen (1951), as well as how the algorithms compare to one another, demonstrate considerable variability between model estimates, and that agreement between estimates from the "best performing" (Campbell et al., 2002) algorithms and in situ estimates is normally only within ±100%. Clearly, this level of accuracy is not sufficient to detect subtle changes in ocean PP that may occur (or be occurring) in response to changing ocean conditions. There is, thus, a need to better constrain estimates of global PP.
All of the algorithms used to estimate global ocean PP today from data collected by remote sensing include a component describing the photosynthetic potential of the community (Behrenfeld and Falkowski, 1997a and several studies e.g., Behrenfeld and Falkowski, 1997b;Carr et al., 2006) have identified this model component as being particularly important in driving model results. Thus, they have argued that improvement in the estimation of ocean PP will require a better understanding and parameterization of the factors influencing photosynthetic potential.
The purpose of this study, therefore, was to examine empirical physiological data (photosynthetic parameters) collected from natural phytoplankton communities in relation to abiotic and biotic variables in an effort to identify patterns in the distribution of photosynthetic potential in the global ocean. The analyses presented are carried out on a single dataset (see Materials and Methods) comprised of data from all major ocean basins and where sampling was carried out by a single research group consisting of a small group of operators using the same equipment and protocols. This is important as relatively large variations between results obtained using different applications of the 14 CO 2 method of estimating PP on the same water sample have earlier been documented (Richardson, 1991).
In what is probably the most commonly applied algorithm for estimating ocean PP from remotely sensed surface optical characteristics, the Vertically Generalized Production Model (Behrenfeld and Falkowski, 1997b), phytoplankton photosynthetic potential, i.e., the maximum rate of photosynthesis normalized to chlorophyll, P B max 1 , is described as a function of temperature, where the relationship to temperature is derived from empirical data collected in two different geographic regions. Two observations regarding the P B max -temperature relationship employed in the VGPM are worth noting: Firstly, there is considerable variability in this relationship, i.e., temperature alone does not well describe P B max and secondly, the fact that P B max varies as a function of temperature does not necessarily imply a direct temperature effect on P B max as temperature co-varies with a number of other ocean parameters, including nutrient availability and phytoplankton community size distribution .
We, therefore, wanted to examine in more detail the relationship between photosynthetic characteristics of naturally occurring phytoplankton populations and environmental conditions. Candidate abiotic and biotic variables for controlling photosynthetic parameters were identified on the basis of a literature survey of studies in which P B max values have been reported for naturally occurring phytoplankton communities ( Table 1). As this survey identified community size distribution and taxonomic characteristics as being potentially important biotic factors in controlling photosynthetic characteristics of a given community, a particular focus of this study is on the elaboration of the potential influence of these two parameters on community photosynthetic characteristics.

Dataset
The major part of the dataset used for this study is comprised of samples taken on the globally circumventing Galathea 3 1 In fact, Behrenfeld and Falkowski (1997b) use at photosynthesis parameter, P B opt, which will be the equivalent of P B max when sufficient light is available to support maximal rates of photosynthesis.

Primary Production
Samples from the surface layer (5 m at latitude >50 • ; 10 m at latitude <50 • ) and the depth of the deep chlorophyll maximum (DCM) were incubated (2 h in artificial light incubators mimicking the spectral distribution of daylight and adjusted to ambient temperature for the two depths) following the addition of 14 CO 2 at 12 different light intensities (∼5-750 µmol photons m −2 s −1 ). Two samples were incubated in darkness. When no DCM was present, a second depth was arbitrarily chosen for incubation (usually 20 m). 14 C incorporation in POC was determined following filtration on a GFF filter and converted to total DIC incorporation on the basis of alkalinity and pH determinations or direct determination of pCO 2 made at each station. Curves were fitted to the photosynthesis vs. light (P vs. E) relationships resulting from the incubations in order for find P max (maxiumum hourly rate of photosynthesis) and the slope of the P vs. E relationship when P < P max , i.e., alpha (α) (µg C (µg Chl a h) −1 µmol −1 photons m 2 s) Both P max and α were normalized to the chlorophyll a content of the sample. Thus, P B max and α B in the text refer to the chlorophyll normalized values of P max (µg C fixed l −1 h −1 ) and α, respectively. The complete primary production dataset (in all 209 samples taken at 121 stations) comprises results obtained in the period ± ∼100 days from the summer solstice with determinations being more or less evenly distributed in this period. No seasonal signal was found in the data. Sampling was also carried out throughout the day, although most sampling took place in daylight. All of the highest values of P B max and α B were recorded between 0800 and 1600 (Figure 2). However, a full range (low to high) of values was recorded in this time interval and the lowest values for these photosynthetic parameters were also recorded within this time frame. Even in the surface data, where the signal is most pronounced, only ∼20 data points for P B max , and many fewer for α B , are found to be higher than those found in the remainder of the dataset. Thus, while the apparent diurnal signal in photosynthetic parameters should be acknowledged, we argue that it does not drive the relationships we discern between these parameters and abiotic/biotic variables in the following analyses.

Chlorophyll Determination
Samples from selected depths and filtered onto GFF to determine total sample chlorophyll (which could be used both in the analysis of fractionated chlorophyll and to calibrate the profiling fluorometer mounted on the rosette) and 10 µm filters, respectively. These were extracted in 96% ethanol and chlorophyll a fluorescence determined using a Turner fluorometer following the US EPA method 445.0 as suggested by Turner Designs.

Abundance and Biovolume-Micro and Nano-Phytoplankon
At 59 stations on the Galathea 3 cruise, samples (90 in all) for phytoplankton analysis were taken from Niskin bottles closed at the surface (5 or 10 m) and/or in the DCM. These were preserved in acidified Lugol's solution (approximately 2% final concentration) and stored in darkness at ∼5 • C until later analysis. Identification of organisms (> ∼3 µm) was made using quantitative light microscopy following the protocol used in the Danish National Water and Nature Monitoring Program (Henriksen and Kaas, 2004) according to Utermöhl (1958). Axial dimensions were measured from a subset of each taxon and used to calculate cell biovolumes using appropriate geometric volume formulas. Analyses were carried out by Orbicon A/S (Århus Denmark).

Abundance and Biovolume-Pico-Phytoplankton
At the same stations where micro and nano-phytoplankton were determined on the Galathea cruise, pico-phytoplankton were also quantified. Water from the CTD was stored in darkness and cool until sampling could be performed (within 30 min after collection). Samples of 4 ml were preserved in filter-sterilized glutaraldehyde to a final concentration of 2% and stored darkness at 4 • C. Pico-phytoplankton abundance was determined by flow cytometry (FACS Calibur, Becton Dickinson) within 2 days of sampling. The flow rate of samples through the FACS Calibur was determined using BD Biosciences TruecountTM tubes. The gatings for Prochlorococcus and Synechococcus, and pico-phytoplankton were defined using cultures of Prochlorococcus, Synechococcus and picophytoplankton (Phaeocystis spp., Rhodomonas spp., Emiliana huxleyi, Pycnococcus spp., Pelagococcus subviridis, Pelagococcus spp.), and were verified onboard by comparison with microscopy counts. XY plots were used with data from forward scatter, side scatter and fluorescence signals. Several plots in different combination were used for Prochlorococcus, Synechococcus, and pico-phytoplankton, to ensure that there was no overlapping gating for any of the gating groups. The sizes of the pico-phytoplankton (Prochlorococcus, Synechococcus and eukaryotic pico-phytoplankton) were estimated in all samples from forward scatter and calibrated against 8 cultures of Prochlorococcus, Synechococcus and eukaryotic picophytoplankton (Micromonas pusilla strains K-0024 and K-0023, Nannochloropsis oculata, Thalassiosira pseudonana) representing a cell diameter range from 0.6 to 3.1 µm.

Chlorophyll a Content of Dominant Phytoplankton Groups
The combined datasets on biovolume of nano + micro-and pico-phytoplankton were assumed to represent the majority of the phytoplankton community in each sample. Different taxa were then grouped together based on their taxonomy and size and their greatest axial linear dimension (GALD). Thus, in the nano + micro-phytoplankton data set, taxa were grouped as ciliates, diatoms < 50 µm, diatoms > 50 µm, dinoflagellates < 50 µm, dinoflagellates > 50 µm and green nanoflagellates. In the picophytoplankton data, set all taxa were small and we only differentiated between taxa; i.e., the cyanobacteria, Prochlorococcus and Synecococcus, and the picoeukaryotes. Assuming that cell volume is linearly related to chlorophyll a content (see Marañón et al., 2007), we calculated the chlorophyll content of each group in each sample by multiplying the chlorophyll a concentration by the relative contribution that each group contributed to the total biovolume.

Size Dependence of Photosynthetic Parameters
A simple model was developed on the basis of 142 samples taken at the 86 stations where fractionated chlorophyll had been determined to test for size-dependence of the photosynthetic parameters, α B and P B max . The model considered two discrete size classes representing small (i.e., chlorophyll containing organisms passing through a 10 µm filter) and large cells, respectively. Their respective fractions, i.e., f (small) and f (large) , were defined from the observed concentration of chlorophyll a associated with the two size classes: [0.7-10 µm] and [> 10 µm]. Thus, the two fractions were related as: Each size class was assumed to be characterized by a photosynthetic parameter given by ϕ (small) or ϕ (large) where ϕ represented the photosynthetic parameter, i.e., α B or P B max . The bulk photosynthetic parameter of the sample was then determined from: Best fit values of the two free parameters ϕ (small) and ϕ (large) were then found by minimizing the residual defined by: where ϕ (obs) represented the photosynthetic parameters obtained from the incubations described above and the summation included all observations. Finally, a normalized residual (R norm ) was calculated by scaling R with the residual from the best fit solution (R min ):

Statistics
All statistical analyses were performed in the open source statistical software, R (R Core Team, 2016). In addition to the core software, we used the R -package "vegan" (Oksanen et al., 2016).
Patterns in the distribution of the dominant phytoplankton groups in relation to P B max , α B and the environmental variables (temperature, depth, nitrate, phosphate and silicate) were investigated by performing a non-metric multidimensional scaling analysis (Legendre and Legendre, 2012) on the distribution of the group specific chlorophyll a content. The underlying dissimilarity matrix was calculated using the Bray-Curtis dissimilarity index and the analysis was run multiple times to avoid getting trapped in a local optimum. When the global optimum had been estimated, we projected the distribution of P B max and α B into the ordination space using thin plate regression splines (Wood, 2003). In addition, the environmental variables were fitted as linear vectors indicating the direction of the association between the community composition and changes in the environment.
As our data suggested an effect of both temperature and depth of sampling, we split the data set into four groups: Group 1: depth > 10 m and temperatures ≥ 15 • C; Group 2: depth < 10 m and temperatures ≥ 15 • C; Group 3: depth > 10 m and temperatures ≤ 15 • C and; Group 3: depth ≤ 10 m and temperatures ≤ 15 • C. We then calculated the median values of all the variables measured and assessed if the values in each group could be considered to come from the same distribution using a Kruskal-Wallis rank sum test.

Relationship between Photosynthetic Parameters and Environmental Variables
Both P B max and α B are seen in the dataset to vary in relation to temperature, the depth of sample collection (possibly a proxy for light availability), ambient inorganic nutrient (nitrate and phosphate) concentrations, and the size distribution of the phytoplankton community (fractionated chlorophyll) (Figures 3, 4A). Not all of the relationships appear to be linear and all of these variables correlate to some degree with one another. Thus, it is not possible to ascertain cause and effect in these relationships or to rank these variables in order of their potential importance in terms of controlling photosynthetic performance. Nevertheless, some generalizations can be made about the global distribution of P B max when these relationships are considered together. From Figure 4B, the generalization can be made that the highest values of P B max will be found in warm (>∼15 • C) surface waters. Although communities dominated by both large and small organisms can exhibit high P B max , communities dominated by small organisms appear particularly likely in this data set to be associated with high P B max . Relatively high P B max can be recorded down to ∼100 m. Furthermore, P B max exhibits a negative relationship with the inorganic nutrient concentrations (Figure 3C), where all of the highest values of P B max were recorded in waters where the ambient concentration of nitrate was <5 µmol kg −1 .
Patterns associated with the distribution of α B (Figures 5, 6) are less clear. Also this photosynthetic parameter can be related to temperature (bell-shaped curve), the depth of sample collection and nutrient availability. Although the highest values of α B were recorded in surface waters, the relationship between depth of sampling and value of α B is not as strong as is the case for P B max . Alpha was most clearly related to the size structure of the community with the highest values recorded being associated with communities dominated by small phytoplankton. Thus, the overall patterns in the distribution of α B are less clear ( Figure 6B) than those found for P B max.
That the association with α B and the variables examined is weaker than for P B max is confirmed when the median values for the two photosynthetic parameters and the variables shown to be associated with them are compared ( Table 2). The median values of each of the parameters and variables at the depths of the DCM and in surface samples in warm (≥15 • C) and cold (<15 • C) waters are examined individually. In all cases, there is a significant difference between the values when the four environment types, i.e., warm and cold waters in surface and DCM, respectively, are compared. However, the level of significance when α B is compared in the four different environments is less (p = 0.016) than is the case for P B max (p = 0.006).

Photosynthetic Parameters-Large and Small Cells
When the entire dataset was considered, best fit solutions were found for [α B (small) = 0.040, α B (large) = 0.025] and [P B max (small) = 2.56, P B max (large) = 1.53]. The resulting best fit solutions of the bulk photosynthetic parameters were then calculated to α B = 0.038 µg C (µg Chl a h) −1 (µmol photons) −1 m 2 s and P B max = 2.38 µg C (µg Chl a h) −1 , respectively (Figure 7). When data collected from surface and DCM samples were considered separately, the differences between photosynthetic performance of the large and small cells were even more pronounced ( Table 3). In the depth separated analysis, P B max (small) was calculated to be nearly twice as high in surface (≤10 m) waters than in deeper waters while P B max (large) was lower in surface than in deeper waters. For both size groups, α B increased with depth, as would be expected in response to adaptation of the photosynthetic apparatus to low light (Richardson et al., 1983).

Community Taxonomic Structure and Photosynthetic Parameters
Results of the NMDS ordinations relating the dominant (by biovolume) phytoplankton group in the community to the measured photosynthetic parameters of the total community are shown in Figure 8 (P B max ) and 9 (α B ). The highest P B max determinations were associated with communities dominated by dinoflagellates, small (<10 µm) unidentified flagellates, Synecococcus and pico-eucaryotes. Of the large phytoplankton, only dinoflagellate dominated communities in warm water are associated with high P B max . The large dinoflagellate dominated communities also are associated with the highest values of α B recorded (Figure 9).
Large and small diatom dominated communities occupy similar positions (intermediate both with respect to P B max and α B ) in the NMDS ordinations of both photosynthetic parameters. Diatom dominated communities are associated with colder waters and high inorganic nutrient concentrations. The "ciliates" recorded in the NMDS ordinations are primarily comprised by Mesodinium rubrum. These are associated in this dataset with relatively warm waters and again, exhibit intermediate values for both of the photosynthetic parameters examined. Prochlorococcus is associated with deep waters and with low values for both photosynthetic parameters. This genus dominated in samples taken in the Sargasso Sea where the DCM is found at depths >100 m and this likely explains the placement of this genus in the ordinations.

DISCUSSION
As background for this study, we surveyed the literature for reports of P B max determined on natural phytoplankton communities ( Table 1). All of the studies found in the survey described conditions in a limited geographic region. A number of the studies found had identified specific abiotic and/or biotic variables as being correlated with photosynthetic performance and we examine here all of the variables identified in these studies as potentially being related to photosynthetic characteristics (i.e., light, temperature, ambient nutrient concentration, size structure of the phytoplankton community and taxonomic group) in relation to characteristics of the P vs. E curves constructed from incubations of samples from the surface and the deep chlorophyll maximum in samples collected from nearly all major ocean basins. Although the northern Pacific is not represented in our dataset, the literature survey includes reports of many studies carried out in the Pacific. On the basis of those reports, we see no reason to suspect that the general patterns in the distribution of photosynthetic characteristics demonstrated here would not also be applicable to phytoplankton in the Pacific. While it would have been possible, for some of the environmental variables examined, to combine our FIGURE 6 | (A) α B vs. the fraction of small phytoplankton (i.e., proportion of total chlorophyll passing through a 10 µm filter). (B) α B vs. depth of the sample and temperature. The size of the rings represents the value of α B and the color represents the size distribution. Red color shows samples where more than 90% of the chlorophyll originated from phytoplankton smaller than 10 µm. Data originate from Galathea 3 and the Dana cruise in 2008. Units of α B as in Figure 1. observations with data for P B max reported in the literature in an attempt to better constrain the relationship between P B max and these variables, we refrained from doing so because comparisons (Richardson, 1991) of results obtained by different workers/protocols on similar samples have demonstrated considerable variability. By limiting our analysis to photosynthetic data we had collected ourselves using the same equipment and protocols, we could minimize the error introduced into the analyses from operator differences.
The range (up to ∼8) we found in our P B max determinations agrees well with P B max reported for natural populations in the literature (Table 1). However, some studies report (usually a small number of) P B max values that are considerably higher (up to >60). Further study is needed to ascertain whether these outliers actually represent regions of extreme efficiency in photosynthetic performance or data artifacts.
As noted in the introduction, one of the most commonly employed algorithms used to estimate water column primary production from remotely sensed surface ocean data is the VGPM model (Behrenfeld and Falkowski, 1997b). This model parameterizes photosynthetic performance as a function of sea surface temperature. We note that, while the VGMP estimates of the maximum rate of photosynthesis lie within the range of P B max estimates reported in the literature, they lie at the upper end of the range of the P B max values that we find ( Figure 3A) and of the averages reported by other workers (Table 1). We have no basis upon which to argue which estimates of P B max are the most "correct" but note simply the fact that the estimates used in VGPM appear to be on the high end of the range most often reported. Thus, there is the possibility that VGPM may be overestimating global ocean POC production.

Photosynthetic Performance in Relation to Abiotic Variables
Sea surface temperature (SST) is readily estimated from remotely sensed surface ocean characteristics. Therefore, photosynthetic performance is often parameterized in relation to SST. The analysis presented here indeed demonstrates a relationship between temperature and both P B max and α B (Figures 3, 5). In the case of both parameters, vs. temperature, the relationships appear to be non-linear.
As noted earlier, the fact that photosynthetic parameters appear to relate to temperature does not necessarily imply a direct temperature effect on these parameters. P B max and α B are also shown here to vary as a function of ambient nutrient concentration and depth of sampling. Both of these environmental variables can be predicted to vary as a function of temperature. Thus, all of these environmental variables are correlated, making cause, and effect in the relationships identified difficult to identify. It is interesting, however, to note that both photosynthetic parameters are, in our dataset negatively correlated with ambient nutrient concentration (Figures 3C,  5C), i.e., all of the highest values were recorded when nitrate concentrations were <5 µmol kg −1 . This result contrasts with the findings of Palmer et al. (2013) who found the highest values of P B max in environments with nitrate concentrations over 10 µmol kg −1 in a study of Arctic waters, thus raising the interesting question of a possible influence of an interaction between temperature and nutrient availability on photosynthetic parameters.

Photosynthetic Performance in Relation to Size Structure of the Phytoplankton Community
Size structure of the phytoplankton community also correlates with both temperature and ambient nutrient concentrations (Hilligsøe et al., 2011;Mousing et al., 2014) and both P B max and α B are here shown to be clearly correlated with phytoplankton size structure, i.e., the relative proportion of total chlorophyll retained on a 10 µm filter (Figures 4, 6). Model fits describing median values of best fit values for photosynthetic parameters (Figure 7, Table 3) indicate that smaller organisms exhibit higher values of both P B max and α B . Recent field studies (Strom et al., 2010;Morán and Scharek, 2015) have also reported higher P B max for small than for large cells.
When the surface and DCM data are considered separately, the best fit model suggests a lower P B max for large cells in the µm; Dino < 50, Dinoflagellates < 50 µm; Dino > 50, Dinoflagellates > 50 µm; Flag < 10, Green nanoflagellates < 10 µm; Peuk, Picoeukariots; Pro, Prochlorococcus; Syn, Synecococcus. Units of P B max as in Figure 1. The NMDS is based on a subset of 90 samples from the Galathea 3 Expedition, i.e., where both taxonomic data and photosynthetic parameters were collected.
surface layer than at the DCM. While this result was not expected, earlier studies (From et al., 2014) suggest that photoinhibition may be more common in the surface layer than normally assumed. For both the large and small components of the phytoplankton community, α B increases with depth ( Table 3). This result would be expected as a universal adaptation to low light is believed to be an increase in alpha (Richardson et al., 1983).
The model results show that the residuals are narrower in the direction of smaller cells than in the direction of large cells (Figures 7A,B) and this implies that both P B max and α B are better constrained for small cell sizes. We can suggest two reasons for why this might be the case. Firstly, the size range of the "small" phytoplankton in this study is well defined as being those organisms not retained on a 10 µm filter. This, presumably, would mean organisms with an axial length of <10 µm. Within this size range, the morphological shape of organisms is usually relatively simple (round or oblong). The "large" organisms in this study would be all those retained on a 10 µm filter, i.e., no maximum size. The variety of morphological forms found in these larger organisms is, everything being equal, greater than for the smaller organisms. One possibility is that that this greater variety of cell forms in the larger phytoplankton might result in a greater variability in light absorption capability and thereby, photosynthetic parameters in the larger phytoplankton than in the smaller. (purple contour lines) as well as temperature, depth and macronutrients (blue arrows). Phytoplantkon group abbreviations: Cil, Mixotrophic ciliates; Dia < 50, Diatoms < 50 µm; Dia > 50, Diatoms > 50 µm; Dino < 50, Dinoflagellates < 50 µm; Dino > 50, Dinoflagellates > 50 µm; Flag < 10, Green nanoflagellates < 10 µm; Peuk, Picoeukariots; Pro, Prochlorococcus; Syn, Synecococcus. Units of α B as in Figure 1. The NMDS is based on a subset of 90 samples from the Galathea 3 Expedition where both taxonomic data and photosynthetic parameters were collected.

Photosynthetic Performance in Relation to Dominate Taxonomic Group
Phytoplankton size is also, at least to some degree, related to taxonomy. For the stations where we had taxonomic data, we, therefore, attempted to relate the dominant (by biovolume) taxonomic group to photosynthetic characteristics. Here, it should be noted that we have a relatively limited number of samples (90) with taxonomic data. Furthermore, the preservation method we used for phytoplankton samples (acidified Lugol fixation) would not preserve all phytoplankton groups (coccolithophorids are, for example, missing). Therefore, we cannot use the relationships we find here as being universal and diagnostic for the global ocean. Nevertheless, the NMDS ordinations (Figures 8, 9) relating dominant phytoplankton group and the two photosynthetic parameters reveal some interesting patterns that can be used to develop a more nuanced understanding of the general pattern of higher P B max and α B being associated with the smaller phytoplankton.
With respect to P B max (Figure 8), the ordination identifies high values to be associated with dinoflagellates of all sizes, flagellates, and in warmer waters, pico-eukaryotes and Synecococcus. We were surprised to find the dinoflagellates here, i.e., associated with a higher P B max than diatoms, as earlier studies (e.g., Chan, 1980) have suggested that the two groups may perform similarly when their photosynthesis is compared on a per chlorophyll basis. A possible explanation for this finding is that dinoflagellates in our dataset often occur in communities with a relatively large proportion of picoplankton. In such cases, the biovolume of a small number of large dinoflagellates could provide the dominate biovolume in the community, while the photosynthetic profile of the community is determined by the smaller organisms. Another possibility, of course, is that dinoflagellates under natural conditions may be more efficient photosynthesizers than usually assumed.
Prochlorococcus dominated communities show a relatively low P B max in our dataset and are associated here with deep, warm waters. This fits well with the fact that we found them primarily in the DCM samples in the Sargasso Sea, where the DCM is located at >100 m. Diatom dominated communities were associated with cold waters and high nutrient concentrations and exhibited an intermediate P B max . The patterns emerging from the NMDS ordination for α B , are less clear than for P B max (Figure 9). This, however, is not surprising given that the relationships between α B and environmental variables are shown here to be less clear than for P B max . Also here, the highest values for α B were found to be associated with communities where large dinoflagellates dominated the biovolume. In the case of α B , diatom dominated communities appear to be characterized by relatively high values. Thus, a picture emerges of diatom dominated communities being well adapted to utilize low light levels but not among the most efficient in terms of P B max . Such a photosynthetic strategy would make diatoms well adapted to the kinds of conditions where they are known to dominate (i.e., spring bloom in temperate waters).
As noted above, we do not mean to imply that the taxonomic patterns we find in relation to the data on photosynthetic parameters that we present here are diagnostic for the global ocean under all conditions. However, we do believe that the strong relationship we find in this study between phytoplankton community size structure and photosynthetic performance of the community, as well as the fact that nuances in this relationship can be discerned from study of the taxonomic composition of the community, to be of great potential interest in terms of improving the parameterization of photosynthetic potential when collection of physiological data is not possible, i.e., when estimating ocean primary production from remotely sensed surface data.
Estimation of both the size distribution of phytoplankton and to some extent, the taxonomic groupings represented in surface waters is possible using remote sensing techniques to quantify surface optical characteristics (e.g., Le Quere et al., 2005;Chust et al., 2013;Boyce et al., 2015;Cetinić et al., 2015). Thus, a prospective avenue to follow in the pursuit of a better parameterization of the photosynthetic potential of natural phytoplankton population could be to further explore the relationship between community size (and taxonomic) structure and photosynthetic performance.

AUTHOR CONTRIBUTIONS
KR was responsible for all data collection except for picoplankton. KR, EM, and JB conceived the study. EM and JB carried out the analyses. KR wrote the manuscript with input from JB and EM. TK provided data on the picoplankton abundance and sizes.