Hydrographic properties and turbulent mixing were derived from a microstructure turbulent profiler MSS equipped with a high-precision conductivity–temperature–depth (CTD) probe, two microstructure shear sensors (type PNS06), and also a sensor to measure the horizontal acceleration of the profiler. Measurements of dissipation rates of turbulent kinetic energy (ε) were conducted to the bottom, or to 137–323 m over deep waters (Table ). The number of microstructure turbulence profiles used for computing nitrate fluxes at each station were always deployed successively. Sets include 2–11 in the tropical and subtropical Atlantic (37±18 min), 6–7 in the Mediterranean (76±22 min), and 3–402 in the Galician coastal upwelling (65±246 min) (Fig. b in Appendix). Episodic bursts of turbulence can induce episodic inputs of nutrient supply, which can be easily missed in sets of a low number of profiles. In coastal regions where short-term variability in mixing processes is expected to be higher, our dataset includes two high-frequency samplings carried out in the outer part of Ría de Vigo (Galician upwelling ecosystem) in August 2013 (CHAOS cruises). During these cruises two 25 h series of turbulent microstructure and current observations were carried out during spring and neap tides. Turbulent kinetic energy dissipation at the interface between upwelled and surface waters was enhanced by 2 orders of magnitude during the ebbs, as the result of the interplay of the bidirectional upwelling circulation and the tidal current shear . Diffusive nitrate fluxes due to the enhanced dissipation observed during spring tide could be responsible for about half of the phytoplankton primary production estimated in this system during periods of upwelling relaxation–stratification .

The profiler was balanced to have negative buoyancy and a sinking velocity of 0.4 to 0.7 m s-1. The frequency of data sampling was 1024 Hz. The sensitivity of the shear sensors was checked after each use. Due to significant turbulence generation close to the ship, only the data below 5 (HERCULES1, HERCULES2, HERCULES3, DISTRAL, ASIMUTH, CHAOS, and NICANOR) and 10 m (CARPOS, TRYNITROP, FAMOSO1, FAMOSO2, FAMOSO3) were considered reliable. Data processing and calculation of dissipation rates of (ε) were carried out with the commercial software MSSpro. The squared Brunt–Väisälä frequency (N2) was computed from the CTD profiles according to the equation N2=-gρw∂ρ∂zs-2, where g is the acceleration due to gravity (9.8 m s-2), ρw is seawater density (1025 kg m-3), and ∂ρ/∂z is the vertical potential density gradient. Vertical diffusivity (Kz) was estimated as Kz=ΓεN2m2s-1, where Γ is the mixing efficiency, here considered to be 0.2 .

Samples for the determination of nitrate (NO3) + nitrite (NO2) were collected from 5±2 (Galician coastal upwelling), 7±1 (Mediterranean), and 11±2 (tropical and subtropical Atlantic Ocean) different depths in rinsed polyethylene tubes and stored frozen at -20 ∘C until analysis on land, according to standard methods using the automated colorimetric technique . The frequency histogram of sampling depths collected for nitrate concentration in each region is indicated in Fig. c, whereas the maximum sampling depth at which the microstructure turbulence profiler was deployed is indicated in Table . At one station carried out during the NICANOR cruises, where nitrate concentrations were not available, concentration values were obtained by using a nitrate–density relationship built by using all samples (n=52) collected during the NICANOR sampling period. The relationship showed a linear behavior (NO3 =9.7788×σt-256.38; Adj-r2=0.87; p<0.001) for density ranging between 26.1 and 27.1 kg m-3 (Fig. d).

Vertical diffusive fluxes of nitrate into the euphotic zone were calculated following Fick's law as FluxNO3=Kz‾ΔNO3, where ΔNO3 is the nitrate vertical gradient obtained by linear fitting of nitrate concentrations in the nitracline, determined as a region of approximately maximum and constant gradient, and (K‾z) is the averaged turbulent mixing over the same depth interval. In the Galician coastal upwelling, nitrate diffusive fluxes were estimated over a fixed depth interval using the same procedure (10–40 m) except at the shallowest stations at which we compute the surface–bottom flux.

Most stations carried out in the Galician coastal upwelling were conducted inside three different rias (Ría de Vigo, Ría de Pontevedra, and Ría de A Coruña). The rias are coastal embayments affected by seasonal wind-driven coastal upwelling of cold, nutrient-rich North Atlantic Central Water . The Galician rias, despite being, in general, longer and narrower than many open bays in upwelling areas, they resemble them in that its primary hydrographic and circulation features are determined by the extension of wind-driven flow on the external continental shelf throughout the bay . Fertilization in the rias occurs essentially by coastal upwelling, with fresh and rain water inputs being residual (2 %) . The total nitrate supply in the Galician rias was computed as the sum of nitrate vertical diffusion plus nitrate vertical advection due to coastal upwelling. A simplified estimate of nitrate supply through vertical advection due to upwelling was computed considering the Galician rias as single boxes divided into two layers , the deeper one influenced by upwelled inflowing waters and the surface layer dominated by the outgoing flow. Assuming that the bottom layer volume is conservative and stationary, the vertical advective flux (QZ, m3 s-1) would be equivalent to the incoming bottom flux (QB, m3 s-1), computed as the product of the upwelling index (IW, m3 s-1 km-1) and the lengths of the mouth of the rias (ca. 10–11.5 km). IW was averaged over the 3-day period before each cruise from wind data recorded by meteorological buoys located in Cabo Vilán (HERCULES, NICANOR) and Cabo Silleiro (DISTRAL, ASIMUTH, CHAOS, ASIMUTH), or modeled by the Fleet Numerical Meteorology and Oceanography Center (FNMOC) model when buoy data were not available (http://www.indicedeafloramiento.ieo.es; last access: 18 October 2018). Finally, the transport of nitrate into the euphotic zone through vertical advection was computed as NO3Advectiveflux=QzAbasin[NO3]D, where Abasin is the surface area of the Galician rias, QZ is the vertical advective flux, and [NO3]D is the averaged nitrate concentration at the base of the euphotic layer. Abasin is 141 km2 for Ría de Pontevedra (ASIMUTH), 174 km2 for Ría de Vigo (CHAOS, ASIMUTH, DISTRAL), and 145 km2 for Ría de A Coruña (HERCULES, NICANOR) (see ; ; ; , for details).

Picoplankton samples (1.8 mL) for the determination of picoplankton abundance and cell properties were taken from three to nine depths and measured immediately onboard (TRYNITROP), or preserved with paraformaldehyde plus glutaraldehyde (P+G) and frozen at -80 ∘C until analysis in the laboratory (the other cruises). Unfortunately, due to problems with sample preservation, only heterotrophic or autotrophic picoplankton subgroup data were available for the DISTRAL and ASIMUTH cruises, respectively. Two aliquots from the same sample were used for the study of picophytoplankton (0.6 mL) and heterotrophic prokaryotes (0.4 mL), analyzed at high (mean 60 µL min-1) and low (mean 18 µL min-1) flow rates for 4 and 2 min, respectively. Before the analysis, the DNA of heterotrophic prokaryotes was stained with nucleic-acid-specific fluorescent dye (SYTO-13 or SYBR1). A FACSCalibur flow cytometer (Becton, Dickinson and Company) equipped with a laser emitting at 488 nm was used to measure and count picoplankton. Autotrophic cells were separated into two groups of cyanobacteria (Synechococcus and Prochlorococcus) and one group of small picoeukaryotes, based on their fluorescence and light scatter signals (size scatter, SSC), as explained in . Two groups of heterotrophic prokaryotes (LNA and HNA) were distinguished based on their relative green fluorescence, which was used as a proxy for nucleic acid content .

In order to estimate biovolume (BV), we used an empirical calibration between SSC and cell diameter , assuming spherical shape for all groups. The following volume-to-carbon conversion factors were used for picoautotrophic groups: 230 fg C BV for Synechococcus, 240 fg C BV for Prochlorococcus, and 237 fg C BV for picoeukaryotes . For bacteria, BV was converted into carbon biomass by using the allometric relationship: 108.8 fg C BV0.898 . More details about the processing and analysis of flow cytometry samples are provided in (, TRYNITROP), (, FAMOSO), (, CHAOS), and (, NICANOR). Abundance data obtained at different depths for each station were combined to compute depth-integrated biomass for the photic layer.

A generalized additive model (GAM) approach was used to predict depth-integrated biomass of each picoplankton subgroup, the contribution of LNA prokaryotes to heterotrophic picoplankton, the cyanobacteria-to-picoeukaryote ratio, and the autotrophic-to-heterotrophic ratio based on observations and estimates of three environmental factors: sea surface temperature (SST), daily surface PAR, and the transport of nitrate into the euphotic zone (NO3 flux), including both diffusive and advective processes. GAMs assume that the effect of each predictor on the response variable can be described by smoothed functions whose effects are additive. Due to the large number of zero observations, data overdispersion, and the need for a single parsimonious model to make predictions for a large number of groups, we assumed that the depth-integrated biomass of each picoplankton subgroup, relative contribution values, and biomass ratios all followed negative binomial distributions. Those niche descriptors that did not follow normal distributions were log transformed. The complete model structure for the biomass of each picoplankton subgroup was yj=I+s(SST)+s(PAR)+s(log⁡(NO3Flux))+Error, where yj represents the depth-integrated biomass for each picoplankton subgroup j, and s a cubic regression spline used for fitting the observations to the model .

Generalized models include a function linking the mean value of yj and the predictors. For those response variables that followed a negative binomial distribution, the used link function was the natural logarithm. The LNA contribution to total heterotrophic prokaryotes was adjusted using a Gaussian distribution and an identify link . The inclusion of the different predictors to explain the response variable (the biomass of each picoplankton subgroup, its relative contribution, and biomass ratios) was assessed via stepwise model selection using the minimum Akaike information criterion .

Smooth terms were tested using a Bayesian test to prevent overfitting. GAMs were evaluated based on explanatory power (explained variance) and goodness of fit (GOF). GOF was assessed via quantile–quantile (QQ) plots of Pearson residuals (provided in SM Fig. ). All calculations were carried out using the mgcv package in R .

The estimation of niche overlap among different picoplankton subgroups based on nonparametric kernel density functions (NOK) was calculated following : NOKi,j,t=1-12∫fit(x)-fjt(x)dx, where NOKi,j,t is the niche overlap between picoplankton subgroups i and j for the environmental factor t, and fit and fjt are the kernel population density functions of factor t for species i and j, respectively. In order to correct the correlation among niche predictors, we used the estimator in a dependent sample (EDS) proposed by .

To assess the statistical niche differences between subgroups, null model permutation tests were performed to verify whether the niche overlaps were significantly lower than 100 % . When the contribution of depth-integrated biomass for each picoplankton subgroup exceeded that expected by chance (one-third for autotrophic and one-half for heterotrophic picoplankton), niche predictors for each station were selected. Statistical null distributions (the distribution of the statistic test under the null hypothesis of no niche differentiation) were generated by calculating pseudo-values through randomly permuting group labels in the corresponding dataset over 10 000 runs. The distributions of the average niche overlaps for the null model were then computed. Niche overlap calculations and associated null model tests were performed using the density function and the source code provided as supporting information in . All calculations were performed using R .

Our database covered a wide environmental gradient from oligotrophic to eutrophic conditions. Stations sampled in the tropical and subtropical Atlantic Ocean (T) were, on average, characterized by warm surface waters (26±2 ∘C, mean ± SD) where the supply of nitrate through vertical diffusion from deeper waters (0.7±1.6 mmol N m-2 d-1) and surface chlorophyll a were low (0.1±0.1 mg m-3) (Table  and Fig. ). The Mediterranean Sea, sampled from March to September, was characterized by cooler surface waters (16±4 ∘C) and intermediate values of both nitrate vertical diffusive supply (41±113 mmol N m-2 d-1) and also surface chlorophyll a (0.9±0.9 mg m-3). Finally, the stations sampled in the Galician coastal upwelling system, which included year-round samples, were characterized by relatively cold surface waters (16±2 ∘C), enhanced rates of nitrate supply (30±46 mmol N m-2 d-1), and high values of surface chlorophyll a (2.2±2.5 mg m-3). No statistically significant differences were observed in averaged surface PAR among the three regions.

Differences in picoplankton biomass and composition were also observed among the three domains. Averaged photic layer depth-integrated total picoplankton biomass (including both LNA and HNA prokaryotes, Prochlorococcus, Synechococcus, and picoeukaryotes) was higher in the tropical and subtropical Atlantic (1052±215 mg C m-2) and the Mediterranean (1038±485 mg C m-2), compared to the Galician coastal upwelling (216±36 mg C m-2) (Table ). In the tropical and subtropical Atlantic, the contribution of Prochlorococcus to total picoplankton biomass was 41 %, followed by LNA (27 %) and HNA (22 %) prokaryotes, with smaller contributions of Synechococcus and picoeukaryotes (<5 % each). In the Mediterranean, picoplankton biomass was on average dominated by Synechococcus (50 %), followed by LNA and HNA prokaryotes (∼17 % for each group), picoeukaryotes (10 %), and Prochlorococcus (5 %). Finally, HNA (55 %) and LNA (21 %) prokaryotes dominated in the Galician coastal upwelling system, followed by picoeukaryotes (11 %), Synechococcus (6 %), and Prochlorococcus (1 %).

Vertical distributions of temperature, nitrate concentration, and the biomass of autotrophic and heterotrophic picoplankton groups for each domain are shown in Fig. .

In order to explore the role of temperature, light, and nitrate supply in the composition of the picoplankton community, we first used generalized linear models to investigate simple linear relationships among each of these factors and the depth-integrated biomass of each picoplankton subgroup, the contribution of LNA prokaryotes to heterotrophic picoplankton biomass, the ratio of cyanobacteria (Prochlorococcus and Synechococcus) to picoeukaryote, and the autotrophic-to-heterotrophic picoplankton ratio (Fig.  and Table ). All picoplankton groups except picoeukaryotes exhibited statistically significant relationships with SST. This relationship was negative for Synechococcus and positive for all the other subgroups (Fig. 3). Only LNA and HNA prokaryotes and Synechococcus exhibited significant, positive relationships with surface radiation. All groups except the picoeukaryotes were negatively correlated with nitrate fluxes. The contribution of LNA prokaryotes to heterotrophic picoplankton biomass only exhibited a significant negative relationship with nitrate fluxes, whereas the cyanobacteria-to-picoeukaryote ratio was positively correlated with surface temperature and negatively correlated with nitrate fluxes. Finally, the ratio of autotrophic to heterotrophic biomass was not linearly correlated with any of the studied environmental factors. In order to exclude cross correlation among the three environmental factors and consider the possibility of nonlinear relationships, we subsequently fitted the data to GAMs (Fig.  and Table ). Temperature was the only factor included in the models built for predicting the depth-integrated biomass of all picoplankton subgroups. HNA prokaryotes exhibited a positive relationship with temperature above 19 ∘C, whereas Prochlorococcus and LNA prokaryotes showed a nearly sigmoid curve relationship with a transition between ca. 16 and 25 ∘C (Fig. 4). The relationship between the biomass of both Synechococcus and picoeukaryotes and temperature showed a negative trend until ∼20 ∘C and remained relatively constant above this temperature. PAR was included in the models of all picoplankton groups except picoeukaryotes. Whereas the biomass of Prochlorococcus exhibited a saturation-type relationship with PAR, heterotrophic prokaryotes and Synechococcus showed a linear positive relationship. Finally, only LNA prokaryotes, Synechococcus, and picoeukaryotes exhibited statistically significant relationships with nitrate supply. This relationship was negative for LNA prokaryotes and Synechococcus, whereas picoeukaryotes showed a unimodal function, peaking at ∼1 mmol NO3 m-2 d-1. Nitrate flux was the only factor selected in the models to predict the contribution of LNA prokaryotes to heterotrophic picoplankton biomass and both the cyanobacteria-to-picoeukaryote biomass ratio and the autotrophic-to-heterotrophic biomass ratio. This relationship was negative in the three models. Temperature was also negatively correlated with the contribution of LNA prokaryotes to heterotrophic biomass and the ratio of autotrophic to heterotrophic biomass. Finally, PAR showed a positive correlation with the contribution of LNA prokaryotes to heterotrophic biomass and the cyanobacteria-to-picoeukaryote biomass ratio.

By using nonparametric kernel density functions, we investigated the overlapping in the ecological niches of the autotrophic and heterotrophic picoplankton subgroups defined by using the three variables previously considered together with surface nitrate concentration (Fig. ). Photic layer depth-integrated biomass of each picoplankton group was used for this analysis. These results revealed three ecological niches in the distribution of picoplankton subgroups. Prochlorococcus and LNA prokaryotes were more abundant in warm waters, where nitrate supply was low. HNA prokaryotes and Synechococcus dominated in cooler regions with medium to high nitrate supply, and picoeukaryotes were more abundant in cold waters with high nitrate supply. A large degree of overlapping of the ecological niches for all picoplankton subgroups was observed when only surface light was considered. For each picoplankton subgroup Table shows the partial weighted overlap of the ecological niches defined by the four factors: SST, PAR, nitrate flux, and surface nitrate concentration. According to these data only nitrate supply enabled a statistically significant separation of the niches of both heterotrophic (HNA and LNA prokaryotes) and autotrophic (Prochlorococcus, Synechococcus, and picoeukaryotes) picoplankton subgroups. Although the minimum overlap between Prochlorococcus and the other autotrophic picoplankton subgroups was also well defined by temperature, only nitrate supply could statistically distinguish the niche partitioning between the two groups of heterotrophic prokaryotes (p<0.05) and between Synechococcus and picoeukaryotes (p<0.1).

Picoplankton community composition and concurrent estimates of nitrate supply into the euphotic zone from highly contrasting marine environments allowed us to conclude that SST and nitrate supply are the main factors controlling the variability in the biomass of different subgroups, whereas surface light generally played a minor role. As far as we know, only one study had previously investigated the role of these environmental factors in the distribution of, in this case, the two major groups of cyanobacteria. By using a large flow cytometry dataset from all major ocean regions, concluded that temperature and light were the most important predictors of the abundances of Prochlorococcus and Synechococcus, with nitrate availability exerting a negligible effect. Although this conclusion seems to be contradictory with the results presented here, some important differences between these studies should be noted. Firstly, used bulk estimates of seawater nitrate concentration as a proxy for nitrate availability in the euphotic zone. However, in near-steady-state systems such as the subtropical gyres, where diffusive nutrient supply into the euphotic zone is slow, nitrate concentrations are kept close to the detection limit due to phytoplankton uptake. For this reason, nitrate concentrations and actual nitrate supply into the euphotic zone in the vast oligotrophic regions are often largely disconnected . Moreover, whereas used Prochlorococcus and Synechococcus abundances determined at several depths in the upper 200 m, we used depth-integrated biomass of both autotrophic and heterotrophic picoplankton subgroups in the photic layer.

Although our results point to both temperature and nitrate supply as important factors controlling the distribution of the picoplankton subgroups (Fig.  and Table ), nitrate supply was the only factor that allowed the distinction among the ecological niches of autotrophic and heterotrophic picoplankton subgroups (Fig.  and Table ). Our attempt to sort out the ecological niches of picoplankton subgroups gave rise to three distinct categories. Prochlorococcus and LNA prokaryotes were more abundant in warmer waters (above 20 ∘C) where the availability of nitrate was low. Synechococcus and HNA prokaryotes prevailed mainly in cooler (below 20 ∘C) marine environments characterized by intermediate and high levels of nitrate supply, and finally, the niche for picoeukaryotes was characterized by low temperatures and high nitrate supply. These results underline the physiological and ecological features of the distinct picoplankton functional subgroups. Our results confirm the previously reported ecological differences between the two major groups of unicellular cyanobacteria .

Moreover, the ecological niche alignment of the two cyanobacteria genera with the two heterotrophic prokaryotes subgroups is consistent with taxa that prevail in oligotrophic regions (e.g., SAR11) being included in the LNA prokaryotes, whereas copiotrophic and more diverse taxa (Gammaproteobacteria, Bacteroidetes/Flavobacteria, etc.) are generally grouped under HNA . Although the relationship among stratification, mixing, and nutrient supply is not obvious , our results are in general consistent with the patterns described by . These authors, by using vertical density stratification as a proxy for the three main environmental factors influencing phytoplankton growth (temperature, light, and nutrients) in subtropical regions of the Pacific, Atlantic, and Indian oceans, described the dominance of photosynthetic picoeukaryotes in well-mixed waters and the prevalence of cyanobacteria in strongly stratified conditions.

Although previous studies have revealed that Prochlorococcus may have acquired the ability to use nitrate by horizontal gene transfer, their photosynthetic activity primarily relies on regenerated forms of nitrogen . Our results support this view and substantiate that, after controlling for the concurrent effects of light and seawater temperature, Prochlorococcus biomass was uncorrelated with nitrate fluxes. However, it is important to note that we could not discriminate between high-light (HL) and low-light (LL) ecotypes and that the presence of nitrate reductase seems to be more relevant in LL . Evolutionary adaptation to light-limiting conditions makes Prochlorococcus the most efficient light harvesters among Earth's photosynthetic organisms . Their competitive ability under light-limiting conditions could explain the negative effect of light as a predictor for Prochlorococcus biomass. Ultimately, the photo-physiological strategy of Prochlorococcus leads to (i) thermal sensitivity of photosystem II , which limits its fundamental niche to temperatures greater than 15 ∘C , and (ii) high sensitivity to ultraviolet (UV) radiation in surface waters . This could explain that, after removing the effect of light, our data analysis revealed that the effect of temperature on Prochlorococcus biomass showed a sigmoid relationship as temperature increased.

Synechococcus is able to use both new and regenerated forms of nitrogen , which largely explains its wider geographical distribution range . The fact that it is more abundant at intermediate levels of nitrate supply is consistent with the lower intracellular nitrogen quota of Synechococcus relative to Prochlorococcus and hence their higher growth rate under saturating nutrient conditions . Conversely, the large affinity of Prochlorococcus to acquire nutrients and absorb light under severe nutrient- and light-limiting conditions precludes the supremacy of Synechococcus in warm and stratified oligotrophic systems . Although Prochlorococcus and Synechococcus are not very different in cell size and they usually coexist in oligotrophic regions, differences in adaptation to light conditions and UV stress lead to segregate their maximal distributions across space (vertical segregation) and through time .

Picoeukaryotes, like Synechococcus, also exhibited a negative relationship with seawater temperature, again reflecting the superior competitive ability of Prochlorococcus under severe nutrient-limiting conditions . The relative dominance of cyanobacteria in oligotrophic systems results from the fact that cyanobacteria are less negatively affected by nutrient diffusion limitation than picoeukaryotes . It is widely accepted that small cells are at an advantage over large cells in stratified open-ocean environments, where nutrient recycling dominates biogeochemical fluxes . First, the surface-to-volume ratio increases with decreasing cell size, which narrows the nutrient diffusion boundary layer around the cell and facilitates the acquisition of nutrients in nutrient impoverished environments. Second, small-sized cells have lower sinking rates than their larger counterparts, which allow them to extend their chances of survival in the euphotic layer .

Our analysis indicates that among the picophytoplankton, picoeukaryotes were the most responsive to nutrient fluxes. This is consistent with experimental observations under laboratory-controlled conditions revealing that, within the picoplankton size range, the maximum attainable growth rate increases with increasing cell size . This positive relationship between maximum growth rate and cell size in the picophytoplankton to small nanophytoplankton size range has been explained as a trade-off between intracellular nitrogen quotas (N requirements) and mass-specific nitrate uptake rates (N uptake) . Whereas nitrogen uptake rate exhibits an isometric relationship with cell size, smaller picoplankton cells have substantially larger intracellular nitrogen quotas, which reduce their capability to maximize carbon-specific growth rates. Conversely, high maximum growth rates represent an advantage for picoeukaryotes, compared to any other organism, as they attenuate the effect of loss processes such as predation or the washout of plankton communities in highly dynamic, turbulent systems . For instance, microzooplankton is thought to maintain the biomass of their prey under tight control, and thus slight variations in picophytoplankton growth rate may substantially alter the resulting biomass of the different picophytoplankton subgroups .

The unimodal relationship observed between the biomass of picoeukaryotes and nitrate supply (Fig. ) could seem at first contradictory with the rising hypothesis proposed by , which describes that improved growth conditions benefit all phytoplankton size classes, including picoplankton. In this regard, by using data collected along the Atlantic Meridional Transect cruises showed that <2 µm size-fractionated chlorophyll was positively correlated with total chlorophyll only until a value of 1 mg m-3, and then it did not show any positive relationship with total chlorophyll. It is also important to note that surface abundance of picoplankton subgroups reported in our study, which are consistent with previous observations , did show higher surface abundance of picoeukaryotes in the Galician coastal upwelling and the Mediterranean compared to the tropical and subtropical Atlantic (Table  and Fig. ). However, this pattern was diluted when depth-integrated biomasses were computed since the lower limit for the integration (the base of the photic zone) was much shallower in the coastal upwelling domain (ca. 37 m) compared to the Mediterranean (ca. 62 m) and the tropical and subtropical regions (ca. 109 m).

Heterotrophic prokaryotes also use dissolved inorganic nutrients, including nitrate, for growth . Consistent with this, showed a positive relationship between prokaryotic abundance and a proxy for nutrient supply in a latitudinal gradient across the Atlantic. They did not partition this effect on the two subgroups that can universally be differentiated among bacteria and archaea. Our results suggest that LNA prokaryotes respond less markedly to nutrient fluxes than HNA prokaryotes. The effect of nitrate supply on the biomass of LNA prokaryotes showed a linear negative relationship as nitrate supply increases, perhaps associated with their ability to survive under nutrient starving conditions . Under such conditions, proteorhodopsin-containing LNA prokaryotes (e.g., example SAR11) can use energy from light , improving their competitiveness against non-proteorhodopsin-containing prokaryotes. Consistent with this idea, our results showed a positive relationship between the biomass of LNA prokaryotes and PAR. already proposed the ubiquity of this bottom-up control of prokaryotic abundance in oligotrophic environments (<1 mg Chl m3). Therefore, we believe that that the underlying cause for the clear niche difference between LNA and HNA prokaryotes is more the difference in the suite of genes than in cell size .