Influence of light and temperature on Prochlorococcus ecotype distributions in the Atlantic Ocean

Transcription

1 Limnol. Oceanogr., 52(5), 2007, E 2007, by the American Society of Limnology and Oceanography, Inc. Influence of light and temperature on Prochlorococcus ecotype distributions in the Atlantic Ocean Erik R. Zinser Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, , Cambridge, Massachusetts 02139; Department of Microbiology, University of Tennessee, M409 WLS, Knoxville, Tennessee Zackary I. Johnson Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, , Cambridge, Massachusetts 02139; Department of Oceanography, University of Hawaii, Marine Sciences Building #614, 1000 Pope Road, Honolulu, Hawaii Allison Coe Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, , Cambridge, Massachusetts Erdem Karaca Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 1-348, Cambridge, Massachusetts 02139; U.S. Geological Survey, P.O. Box 25046, MS-966, Denver, Colorado Daniele Veneziano Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 1-348, Cambridge, Massachusetts Sallie W. Chisholm 1 Department of Civil and Environmental Engineering and Department of Biology, Massachusetts Institute of Technology, , Cambridge, Massachusetts Abstract In a focused analysis of Prochlorococcus population structure in the western North Atlantic, we found that the relative abundances of ecotypes varied significantly with depth and, at seasonally stratified locations, with degree of vertical mixing. More limited regional variation was observed (e.g., Sargasso Sea, Gulf Stream, continental slope, and equatorial current), and local patchiness was minimal. Modeling of a combined North and South Atlantic data set revealed significant, independent effects of light and temperature on ecotype abundances, suggesting that they are key ecological determinants that establish the different habitat ranges of the physiologically and genetically distinct ecotypes. This was in sharp contrast with the genus Synechococcus, whose total abundance was related to light but did not vary in a predictable way with temperature. Comparisons of field abundances with growth characteristics of cultured isolates of Prochlorococcus suggested the presence of ecotypespecific thermal and light adaptations that could be responsible for the distinct distribution patterns of the four dominant ecotypes. Significantly, we discovered that one low light-adapted ecotype, enatl2a, can thrive in deeply mixed surface layers, whereas another, emit9313, cannot, even though they have the same growth optimum for (low) light. 1 Corresponding author (chisholm@mit.edu). Acknowledgments We thank the captains and crews of the R/V Endeavor and the R/V Seward Johnson, B. Binder (chief scientist, Endeavor cruises), and E. Carpenter (PIRHANA/MANTRA). We thank M. Polz, D. Lindell, R. Sarma-Rupavtarm, G. Rocap, and N. Ahlgren for valuable discussions and the anonymous reviewers for their helpful comments. This work was supported by grants from the National Science Foundation (NSF), U.S. Department of Energy, the Seaver Foundation, and the Gordon and Betty Moore Foundation to S.W.C.; from the NSF and the University of Tennessee to E.R.Z.; from the NSF, U.S. National Oceanic and Atmospheric Administration, and the University of Hawaii to Z.I.J.; and from the NSF to D.V When first revealed by flow cytometry, the marine unicellular cyanobacterium Prochlorococcus was distinguishable from the cyanobacterium Synechococcus by a smaller size and lack of phycoerythrin-generated orange fluorescence (Chisholm et al. 1988). Consequently, flow cytometry has been applied worldwide to assess Prochlorococcus contribution to the global phytoplankton community (Partensky et al. 1999). Results from these field studies indicate that it is the most abundant marine photosynthetic organism, being found ubiquitously throughout the euphotic zone in tropical and subtropical oligotrophic waters between 40uN and 40uS (Partensky et al. 1999), and, in recent studies, has been found as high as 48uN (Johnson et al. 2006). Prochlorococcus has been shown to contribute

2 2206 Zinser et al. a significant fraction of the net primary production in the tropical and subtropical oceans (Vaulot et al. 1995; Campbell et al. 1997; DuRand et al. 2001), making it an important participant in the global carbon cycle. Understanding how this genus is able to establish numerical dominance over such broad vertical and meridional scales is crucial to our understanding of its role in the global ecosystem. One contributor to the numerical abundance and broad habitat of Prochlorococcus appears to be the genetic and physiological diversity within the lineage. Recently developed molecular approaches probe hybridization and quantitative (real-time) polymerase chain reaction (QPCR) have revealed that different lineages (i.e., ecotypes) dominate at different depths and latitudes, and these different distributions are reflected in their genetic and physiological profiles (West and Scanlan 1999; Ahlgren et al. 2006; Zinser et al. 2006). Ecotypes emed4 and emit9312, which have a high light optimum for growth (Moore et al. 1995, 1998; Moore and Chisholm 1999), dominate the upper euphotic zone in stratified waters of the Atlantic Ocean, Mediterranean Sea, Red Sea, and Arabian Sea (West et al. 2001; Fuller et al. 2005; Johnson et al. 2006) (the e prefix distinguishes the ecotype from the type strain from which it was named [Rocap et al. 2002]). In contrast, ecotypes with relatively lower optimal light intensities for growth, and low compensation light intensities (Moore et al. 1995, 1998; Moore and Chisholm 1999), are found deeper in the stratified euphotic zone (West et al. 2001; Fuller et al. 2005; Johnson et al. 2006). The abundance maxima of the low light-adapted ecotypes in deeper water is also consistent with the ability of some of these ecotypes to use nitrite as a nitrogen source, which is absent in the upper euphotic zone but is typically maximal at the base of the euphotic zone (Moore et al. 2002). In a recent study, our group characterized the Prochlorococcus ecotype composition along a meridional transect from the United Kingdom to the Falkland Islands in the Atlantic Ocean (Johnson et al. 2006). In this study, we used QPCR to quantify the six known ecotypes throughout the euphotic zone and discovered that ecotypes emed4 and emit9312, which dominate the total Prochlorococcus population, further partition the habitat range of Prochlorococcus meridionally (Johnson et al. 2006). Within the 30uN to 30uS band of the Atlantic, the emit9312 ecotype was most abundant by several orders of magnitude, whereas the higher latitudes that abut the observed geographical limit of Prochlorococcus (.40uN or S) are dominated by the emed4 ecotype. One of the environmental variables that differs most significantly with latitude is water temperature, and temperature was found to correlate well with emit9312 abundance in the euphotic zone (Johnson et al. 2006). Laboratory studies of cultured representatives of the emit9312 and emed4 ecotypes reflected their relative abundances: emit9312 strains outgrow emed4 strains at high temperatures, whereas the reverse is true for colder temperatures (Johnson et al. 2006). Other environmental variables also appeared to play a role in determining ecotype abundances. Light (emed4), ammonium (emit9313), and nitrate (emit9313) correlated with abundance, whereas phosphate (emed4 and enatl2a) and nitrate (emed4) anticorrelated with abundance (Johnson et al. 2006). The anticorrelation with nutrients suggested that at higher nutrient concentrations, ecotypes emed4 and enatl2a are competitively excluded by other phytoplankton. Hence, ecotypes vary in their relative contributions to the total Prochlorococcus abundances at different depths and oceanic regions and have different relationships with environmental variables. In a more recent study, Bouman et al. (2006) circumnavigated the Southern Hemisphere and characterized the ecotypic composition of Prochlorococcus using a molecular probing technique, focusing their work on the surface mixed layer. Abundances of some of the ecotypes correlated with variables associated with degree of vertical mixing. Abundances of the high light II (HLII) ecotype (which our group designated emit9312) positively correlated with temperature, mean daily surface irradiance, and relative abundance of photoprotective pigments and mixed layer depth and, hence, a more stratified condition, whereas abundance of the LL (low light) cluster (consisting at least of the emit9313 and enatl2a ecotypes, but not ess120) was inversely correlated with these features and consistently were more abundant in colder and deeper mixed surface layers. However, some ecotypes, the high light I (HLI, emed4) and SS120 (ess120), showed no relationship with physical forcing, demonstrating that such interactions occur in an ecotype-specific manner. From these prior studies, it is clear that a complete understanding of factors that regulate total abundance of Prochlorococcus requires consideration of the individual responses of the diverse lineages that make up the whole. In this work, we further examine the abundance patterns of the Prochlorococcus ecotypes and investigate the underlying factors that establish their different distributions. We focus on the populations in the Gulf Stream, North American continental margin, and western Sargasso Sea, to determine how their structures vary regionally and seasonally. In the Sargasso Sea, we also monitored changes that occur over short timescales (days) to understand the extent to which single-profile snapshots of population structures account for the variability that can occur in these fluid and dynamic environments. We combine this data set with that of the meridional transect of the Atlantic (Johnson et al. 2006) and apply problem-tailored regression analysis to assess the roles that light and temperature play in defining the vertical and meridional distribution patterns of the individual ecotypes. Finally, we compare the growth properties of the different ecotypes at varied light and temperature levels to investigate the direct influence that light, temperature, or both might play in establishing ecotype abundances in seawater. Methods Samples in the western Sargasso Sea were collected from 31 March to 09 April 2001 and from 25 August to 04 September 2002 aboard the R/V Endeavor (cruises EN351 and EN375, respectively; Fig. 1). Time series studies for both cruises were performed by sampling the same location

3 Prochlorococcus ecotype distributions 2207 Fig. 1. Stations sampled on three cruises. Symbols indicate when the stations were sampled, and shading indicates location: continental margin (black), Gulf Stream (gray), or Sargasso Sea (open). Positions of the central Sargasso Sea (CSS) and equatorial current (EQ) stations are shown in the insert. Numbered symbols indicate spring 2001 (1) and summer 2002 (2) stations examined as time series in Figs. 2 and 3. Sta. 1 is at the BATS site. over the course of a week. Samples in the central Sargasso Sea and North Equatorial Current were collected on 14 and 21 January 2001 aboard the R/V Seward Johnson (cruise MP01) as part of the MANTRA/PIRANHA study ( biology.usc.edu/bc/). CTD measurements (conductivity, temperature, barometric pressure, chlorophyll a [Chl a], fluorescence, and photosynthetically available radiation) were made simultaneously by sample collection with a Niskin rosette. Acoustic Doppler current profiler (ADCP) measurements of current velocity were measured with an RDI 75 khz Ocean Surveyor system, which included heading input from an Ashtech ADU2 attitude GPS. Sample collection for QPCR (100 ml filtration per replicate filter) and flow cytometry (FCM) measurements was performed as described (Zinser et al. 2006). Percent surface irradiance was calculated with a case I radiative transfer model following Morel (1988). This model estimates photosynthetically active radiation (PAR) attenuation on the basis of depth profiles of Chl a concentrations. Chl a concentrations were determined with a Turner 10-AU fluorometer as described (Johnson et al. 1999). Quantitative PCR QPCR for the six Prochlorococcus ecotypes was performed as described in Zinser et al. (2006) with the same sets of culture-based standards and PCR conditions. Measurements of template concentrations were deemed valid if they met two criteria: (1) their threshold cycle (C T ) values were lower than that of the negative controls, which lacked template DNA and (2) the melt curve analysis of the products showed an absence of nonspecific amplifications. Sample template concentrations lower than the most dilute standard were also reported in the data, as long as they had lower C T values than the negative controls and with the caveat that they are extrapolated values beyond the standard set. The theoretical limit of detection in this assay is 0.65 cells ml 21, or one cell per PCR reaction. For the purposes of graphical presentation, values found below this limit were changed to equal this limit, as were values from samples showing nonspecific products amplified (these were only from a few field samples and gave artifactual values of,10 cells ml 21 ). For the log-linear modeling of the data, values,0.065 cells ml 21 (one-tenth the limit of detection) were set to cells ml 21, whereas values cells ml 21 were left unchanged. For some analyses, the abundance estimates of the ecotypes were summed to yield total Prochlorococcus numbers, as determined by QPCR, to compare with total numbers assessed by flow cytometry (see below). The latter is considered the true number of cells (Zinser et al. 2006). Any differential between these two numbers is attributed to deficiencies in primer design for the QPCR, resulting from the limitations of our culture collection which served as the data base for the design of the primers. Flow cytometry Samples (laboratory and natural seawater samples) were prepared for FCM by fixing them in 0.125% (v/v) glutaraldehyde during a 10-min dark incubation and freezing in liquid nitrogen until analysis. All field samples were quantified with an EPICS flow cytometer (Beckman Coulter), and all laboratory samples used to prepare QPCR standards were quantified with a FACScan (Becton Dickinson) with modified optics (Dusenberry and Frankel 1994; Cavender-Bares et al. 1998). Prochlorococcus and Synechococcus were differentiated and enumerated on the basis of their distinct red and orange fluorescence and light scatter signals (Dusenberry and Frankel 1994; Cavender-Bares et al. 1998). For the log-linear modeling of the data, values,6.5 cells ml 21 were set to 6.5 cells ml 21. Culture experiments Prochlorococcus strains NATL2A and MIT9313 were grown in 25 ml of Pro99 natural seawater based medium (Andersen et al. 2005) in glassware soaked overnight in 1 mol L 21 HCl, followed by a rinse in 18 MW Milli-Q water. Experiments with growth at different temperatures was performed in a temperature bar (Moore and Chisholm 1999) under 14 : 10 h light : dark (LD) photoperiod under cool white fluorescent lamps, at an irradiance of mmol quanta m 22 s 21, as performed by Johnson et al. (2006) for strains MED4, MIT 9515, MIT 9312, and MIT Experiments with growth at different light levels were performed under a 14 : 10 LD photoperiod under cool white fluorescent lamps, at 24uC. Varying light levels were provided through the use of neutral density (gray) filters (Roscolux, Stamford, CT). Triplicate (at least) cultures were grown for several generations at the test temperature or light level before measuring growth. For experiments for growth at different temperatures, growth at temperatures exceeding the limits for individual strains was attempted several times.

4 2208 Zinser et al. Data modelling Atlantic Ocean data from this study and from the meridional transect reported earlier (Johnson et al. 2006) were combined to model the effects of temperature (T) and light (L) on the abundance distributions of Prochlorococcus ecotypes. To separate the effects of T and L and assess their possible interactions, we have used a problem-tailored regression analysis. This improves on the multiple regression analysis used in our previous work (Johnson et al. 2006), in which we assumed a linear relation among the log cell concentration (C) and log L or T. By contrast, here, we considered two types of constraints (or cases): (1) no constraint, except for some degree of smoothness. In this case we let the data dictate the best fitting surface. (2) We also consider the independent effects constraint, in which the concentration is assumed to be the product of an unspecified function of log light times an unspecified function of temperature, and again let the data tell us what those two functions are. For case 1, we estimate log C as a function of T and log L exclusively on the basis of the data. A standard procedure for doing this is to average or weighted-average the empirical log C values in a local neighborhood of each (T, log L) point of interest. However, this procedure tends to bias the results if the empirical data is not uniformly distributed around the point of interest, as for example happens near the edge of the (T, log L) region covered by the data. To reduce this bias, we use locally weighted least squares (LWLS) linear regression (Cleveland 1979). This nonparametric technique estimates the local value of log C at a point (T, log L) by performing a weighted linear regression of the log concentration values in a suitable neighborhood of that point. The size of the neighborhood depends on the local density of the measurements; hence, smoothing is higher in regions with fewer data points. The weights reflect both the distance from the (T, log L) point under consideration and the accuracy of the log concentration measurements, which decreases as the concentration decreases. As is often done in nonparametric regression, we use various weighting schemes and judgmentally select the optimal one (as the weighting that produces minimal smoothing while not being sensitive to individual measurements). To detect and remove outliers, LWLS regression is implemented in an iterative way. Following each iteration, data points that are more than three (local) standard deviations from the value predicted by the local regression are considered outliers and removed for the next iteration. When applied to our data set, this criterion results in very few points being tagged as outliers. The end result is a best fitting nonparametric surface, say log C 5 g LWLS (T, log L) over a grid of points on the (log L, T) plane, and the estimated variance at each point. Notice that the data have a nonregular arrangement and variable density on the (T, log L) plane. In particular, high temperatures do not occur simultaneously with low light intensity, and very few measurements are available at temperatures below 12uC. Moreover, low log concentrations are more difficult to estimate empirically than high log concentrations. Hence, the mean log C values are determined with different levels of accuracy depending on light and temperature conditions. For case 2, we fit a simpler model under the assumption that T and L have independent (IND) multiplicative effects on the concentration C. Therefore this second function has the log additive form log C 5 g IND (T, log L) 5 g T (T) + g L (log L), where the functions g T (T) and g L (log L) are smooth but otherwise unrestricted. This step uses as data the values of the function g LWLS (T, log L) from case 1, read on a regular grid. The accuracy with which g LWLS itself is estimated at different (T, log L) points (because of the variable density and accuracy of the original data) is taken into consideration when fitting the independent model (i.e., case 2). The method used to fit the independent model is somewhat different from that of case 1, but it too builds on the idea of weighted least squares, in which the weights depend on the accuracy with which g LWLS (T, log L) is estimated for different T and log L. Although one could adapt existing weighted least squares regression software to perform the analysis, various peculiarities of our problem made it more expedient to write our own computer programs. Results and discussion Day-to-day variability of Prochlorococcus ecotype distributions Seasonally stratified water column: Analysis of spatial structuring of Prochlorococcus ecotype populations have been thus far limited to basin-level lateral variation (on the scale of thousands of kilometers) from transect studies and to vertical variation within the euphotic zone (on the scale of meters) from single depth profiles (Bouman et al. 2006; Johnson et al. 2006; Zinser et al. 2006); the influence of mesoscale ( km) to microscale (on the order of 1 mm) processes in promoting spatiotemporal variation in Prochlorococcus ecotype abundance is largely unknown. Lateral population heterogeneity of marine microbes (i.e., patchiness), as determined by abundance or diversity, has been shown to be fairly low, within the range of m, but to increase significantly in the mesoscale range (Zubkov et al. 2002; Ghiglione et al. 2005; Hewson et al. 2006). A high-resolution study of Synechococcus and heterotrophic bacteria in the Celtic Sea, however, revealed as much as 50-fold differences in cell concentrations at stations only 12 km apart (Martin et al. 2005). Hence, sampling oceanic regions at varying scales is necessary to fully interpret basin-scale variations observed at low resolution. To investigate how well a single depth profile describes the Prochlorococcus population in small spatial and temporal scales, we performed a pair of week-long Eulerian time series analyses of ecotype distributions in the seasonally stratified western Sargasso Sea near Bermuda (Fig. 1): one in late August 2002 (Fig. 2B E) and one in early April 2001 (Fig. 2F I). Time series were performed during summer and spring to investigate variation in highly stratified and deeply mixed water columns, respectively. Water temperature and density, Chl a fluorescence, and total Prochlorococcus and Synechococcus cell densities, determined by flow cytometry, were collected over time (e.g., Fig. 2), as well as the abundances of the six known

5 Prochlorococcus ecotype distributions 2209 ecotypes of Prochlorococcus by quantitative PCR (e.g., Fig. 2). Abundances of all six ecotypes were summed and compared with flow cytometry counts to assess the ability of the existing suite of PCR primers to account for the total population (Fig. 2). The short duration of the time series restricted our analysis to the variation resulting from smallscale physical (e.g., lateral patchiness and vertical mixing) and biological processes. The role of mesoscale events (i.e., eddy-driven nutrient flux), acting on scales of 10 2 km and approximately 1 month (McGillicuddy et al. 1998; Dickey et al. 2001), were thus not assessed. Depth profiles for days 0 and 6 are reported in Fig. 2B E as representatives of the summer 2002 time series (Fig. 1, Sta. 2). Day 3 of the series was reported elsewhere (Zinser et al. 2006), and the data from day 4 are not shown here for simplicity. Throughout this time series, the water column remained well-stratified with a warm ( uC) shallow ( m) surface mixed layer (Fig. 2B,D). The chlorophyll maximum was well below the mixed layer, at around 100 m. Total Prochlorococcus and Synechococcus distributions remained relatively stable over the time series, and their distributional features were consistent with patterns observed for summer stratified columns at the nearby Bermuda Atlantic time series study (BATS) for the years (DuRand et al. 2001). Prochlorococcus was abundant throughout the upper 200 m, with maximal abundance at around 100 m, which is below the mixed layer (Fig. 2B). In the surface mixed layer, Prochlorococcus ranged from 2.5 (Fig. 2B) to 7.5 (Fig. 2D) times greater abundance than Synechococcus, and this difference increased dramatically below the seasonal mixed layer. As expected (Zinser et al. 2006), the depth distributions of the Prochlorococcus ecotypes were strikingly different from one another, and this difference was maintained over the 7-d period of the time series (Fig. 2C,E). Ecotype emit9312 was present throughout the euphotic zone (0 200 m), although it was most abundant in the upper 100 m, with peak abundance (.100,000 cells ml 21 ) at around 80 m, which is below the surface mixed layer. It was by far the most abundant ecotype in the surface mixed layer (outnumbering the next most abundant, emed4, by greater than four orders of magnitude), and its integrated abundance in the euphotic zone exceeded that of the other ecotypes by two orders of magnitude (Fig. 2C,E). For all time points, ecotypes ess120 and emit9211 were,10 cells ml 21 and found only in the deeper waters. Ecotypes emed4, enatl2a, and emit9313 were moderately abundant throughout the time series, with maximal abundance on the order of cells ml 21. As we have noted before (Zinser et al. 2006), the relative depth distributions of these three ecotypes reveal a very consistent and interesting feature over the time series: Their distributions overlapped and were offset with respect to depth, with emed4 the most shallow and emit9313 the most deep (Fig. 2C,E). The significance of these relationships is discussed below. Despite overall consistency of population distribution patterns over the times series, some day-to-day variation was observed. Total Prochlorococcus and Synechococcus,as well as the six ecotypes, showed variation in abundance over one order of magnitude at some depths over this short time interval (e.g., emit9312 at 125 m, Fig. 2B E). Deeper waters showed more variation than shallow, which was best characterized by a shift in population depth, rather than a change in population size or shape of the depth distribution curve. Consistent with the greater variation in ecotype abundance, the deeper waters also showed a greater daily variation in both water mass density and direction of flow, as determined by ADCP profiling (data not shown), indicating that different water masses, distinct from the surface mixed layer, were passing through the lower euphotic zone at this site during the time course. Because Prochlorococcus have been documented to grow at rates of about 0.1 d 21 (Mann et al. 2002) below the mixed layer in the Sargasso Sea, where variation in ecotype abundance is greatest, it is very unlikely that any changes in growth or mortality rates could account for the order of magnitude changes in abundance observed. Thus, it appears that physical, rather than biological, forces dominated the variation in ecotype abundance in the deep euphotic zone. This interpretation is supported by results from a Lagrangian study at this site, in which we followed water masses with a holey sock drogue set at 20 or 60 m and observed (at most) a twofold difference in the abundances of the ecotypes present (emit9312, emed4, enatl2a) between the 0- and 28-h time points (data not shown). A striking feature of the time series results is that the relative vertical positions of the emed4, enatl2a, and emit9313 ecotypes are maintained, despite the variation in the actual depths they occupy (Fig. 2C,E; Zinser et al. 2006). This pattern has been observed in all well-stratified water columns in the Atlantic to date (Ahlgren et al. 2006; Johnson et al. 2006; Zinser et al. 2006; see below) and is likely shaped by gradients in light, temperature, or both, a relationship that is analyzed in detail in the sections that follow. Deeply mixed water column: At the BATS station near Bermuda (Fig. 1), decreased surface temperatures in the winter and spring leads to deep mixing that drives nutrients into the euphotic zone (Steinberg et al. 2001). During this period, the integrated abundance of Prochlorococcus is significantly and consistently lower than during the stratified, nutrient poor summer that follows (DuRand et al. 2001). To assess short-term variability in this type of water column, we sampled the water column at BATS twice within 1 week. On 31 March 2001, the water column was cold and weakly stratified, with a surface mixed layer extending to 35 m (Fig. 2F). Prochlorococcus and Synechococcus were in equal abundance in the surface mixed layer, and both genera were present in high numbers throughout the entire euphotic zone, with maximal abundance at or just above a well-defined deep chlorophyll maximum at 62 m. This is consistent with prior studies showing an increased abundance of Synechococcus relative to Prochlorococcus during deep mixing at this location (DuRand et al. 2001). The features of the Prochlorococcus ecotype depth distributions (Fig. 2G) differed dramatically from those of the strongly stratified summer profile (Fig. 2C,E), with all six ecotypes showing abundance maxima at the same depth:

6 2210 Zinser et al. Fig. 2. Changes in community structure of Prochlorococcus and Synechococcus in the Sargasso Sea near Bermuda on weekly timescales. (A) Left panel defines the symbols (consistent through all figures) for total Prochlorococcus determined by flow cytometry (black circles), for all ecotypes by summed QPCR (open triangles), and for total Synechococcus by flow cytometry (orange squares). Right panel is a schematic of the phylogenetic tree (after Rocap et al. 2002) illustrating the relationships between the six ecotype clades of Prochlorococcus and the colors

7 Prochlorococcus ecotype distributions 2211 at or just above the deep chlorophyll maximum. Another significant difference was the relatively high abundance of emed4 and enatl2a in the surface mixed layer. enatl2a is a member of the low light-adapted clade but, as discussed below, clearly behaves differently from other low light-adapted ecotypes such as emit9313. Similar to the summer profile, the emit9211 and ess120 ecotypes were found at very low (or zero) abundance. Finally, although only about 10% of the deeper populations are accounted for by the current suite of QPCR primers in the summer (Zinser et al. 2006; Fig. 2B,D), essentially all of the cells in the spring populations are accounted for by QPCR (Fig. 2F,H). This result implies that the unknown ecotypes that dominate the deep euphotic zone in stratified waters lose dominance under periods of deeper mixing. A week later at this station (Fig. 2H), the physical and biological characteristics were significantly different. The mixed layer was deeper extending to 130 m as was the chlorophyll maximum, located just below the mixed layer (Fig. 2F). It was evident from the CTD profiles that a deep mixing event or events occurred between the first and second profile of the time series; mixed layer depth increased progressively from 35 to 130 m, while temperature decreased from 20.3uC to 19.6uC (data not shown). The outcome of this deeper mixing was a disruption via homogenization of the well-defined maxima for chlorophyll; total Prochlorococcus and Synechococcus; and the ecotypes emit9312, emed4, and enatl2a (Fig. 2H,I). The ecotypes most strongly affected by mixing were emit9313 and ess120, which shared the same relatively shallow depth of maximal abundance as total Prochlorococcus and the other ecotypes in the weakly stratified profile (Fig. 2F,G) but were virtually excluded from the deeply penetrating mixed layer of the second profile (Fig. 2I). Although our data consist of only two time points, it is nevertheless intriguing to view the dynamics of ecotype population structuring as controlled by two opposing factors: one that causes all the ecotypes to become maximally abundant at the same, relatively shallow, position and the other being the overwhelming homogenization of the deep mixing events. Mechanisms responsible for the depth positioning of the ecotypes in the absence of mixing are unclear, but in the absence of vertical forcing, they might be biological. In any case, our results suggest that the frequency of deep mixing events could be the dominant factor regulating ecotype positioning within the water column in seasons of weak or no stratification. Clearly, greater water column sampling at higher frequency during this highly dynamic period of deep mixing is required to test this hypothesis. Variation of Prochlorococcus ecotypes within the western Sargasso and neighboring waters Several stations were sampled in the northwestern Sargasso Sea during spring and summer transects (Fig. 1), and their ecotype distributions were very similar to the ones observed near Bermuda (data not shown). Hence, in this localized area of the Sargasso Sea, population structure is relatively conserved and fairly well-represented by the profiles taken near Bermuda. Although the Gulf Stream originates from lower latitudes and its waters have different temperature and density profiles than the neighboring waters, late summer and spring ecotype abundance profiles from these waters shared similar features with those in the Sargasso: domination of the upper euphotic zone by the emit9312 ecotype, moderate abundance of emed4 and enatl2a, and essentially no ess120 or emit9211 (Fig. 3A D). One significant difference between the spring Sargasso and Gulf Stream profiles was the relative abundance of enatl2a and emed4 ecotypes in the mixed layer (Figs. 2G,I, 3D). These two ecotypes were more than an order of magnitude less abundant in the Gulf Stream compared with the Sargasso. The Gulf Stream mixed layer is several degrees warmer than the Sargasso, and this could favor the emit9312 ecotype over the other two, similar to what is observed in summer profiles. The summer Continental Slope station (Fig. 3E) had a very shallow mixed layer relative to the Gulf Stream (Fig. 3A) and Sargasso Sea (Fig. 2B,D), with the deep chlorophyll maximum immediately below it at 50 m (Fig. 3E). The slope summer mixed layer was colder than the summer Sargasso and Gulf Stream, and though dominated by emit9312, it also contained significant numbers of emed4 and enatl2a (Fig. 3F). In contrast to the Sargasso, the subsurface maxima of the ecotypes were relatively shallow (Fig. 3F), perhaps as a result of the sharp thermocline (Fig. 3E). In striking contrast to the Gulf Stream and the Sargasso, the deep euphotic zone ( m) of this station was dominated by the emit9312 ecotype (Fig. 3E), whose abundances match well with total Prochlorococcus abundance determined by flow cytometry, indicating that, indeed, this is the dominant ecotype in these cold dimly lit waters. The most dramatic seasonal differences observed in this study occurred at the continental slope. Contrasting spring to summer, we see that total Prochlorococcus is several r used to identify them throughout all figures. Depth profiles taken (B, C) at Sta. 2 (from Fig. 1) on 26 Aug 02 and (D, E) 01 Sep 02, which were the first and last days in the summer time series. Depth profiles taken (F, G) at Sta. 1 (from Fig. 1) on 31 Mar 01 and (H, I) 05 Apr 01. In panels B, D, F, and H, total Prochlorococcus is determined both by QPCR and flow cytometry and Synechococcus, temperature (red line), and in situ bulk fluorescence in arbitrary units (green line). Asterisks indicate counts that were below detection. In panels C, E, G, and I, depth distributions of the six known ecotypes of Prochlorococcus were determined by QPCR. Values on the black vertical dotted line at 0.65 cells ml 21 were below detection.

8 2212 Zinser et al. Fig. 3. Community structures in (A D) the Gulf Stream and (E H) the continental slope for summer 2002 (A, B, E, F) and spring 2001 (C, D, G, H) cruises, and (I, J) in the central Sargasso Sea (SS) for winter See Fig. 2 for legend.

9 Prochlorococcus ecotype distributions 2213 orders of magnitude higher in summer than spring (Fig. 3E,G) and that emed4 and emit9313 are undetectable in the deep mixed layer (Fig. 3F,H), suggesting that the latter undergo local extinction in the winter/spring and repopulate in the summer. Potential sources of the summer restocking include local patches of survivors or reintroduction from warm-core eddies from the Gulf Stream. The source for recovery of these populations could have important consequences in terms of gene flow, which in turn can affect the ecology. Alternatively, the apparent extinction of these two ecotypes might not be real because very low concentrations of cells, below the limit of detection by QPCR (0.65 cells ml 21 ), could persist during these periods. A third cruise in January 2001 allowed us to sample stations in the central Sargasso Sea, roughly 1,800 km due east of Bermuda, and in the North Equatorial Current (at 10uN), which forms the southern boundary of the Sargasso (Fig. 1). These two stations had very similar features; thus, we present only the central Sargasso Sea station here (Fig. 3I,J). These stations were characterized by a relatively deep, though well-stratified, surface mixed layer that contained a greater abundance of the emed4 and enatl2a ecotypes than the waters near Bermuda (Fig. 2), although these were still only minor contributors to the total Prochlorococcus cell concentrations. emit9312 was by far the dominant ecotype in the mixed layer. These patterns closely resemble those seen at 1uN the eastern North Atlantic in September 2003 (Johnson et al. 2006). As in the western Sargasso, the most abundant of the known ecotypes in the deep euphotic zone was emit9313, although comparisons of flow cytometry and QPCR counts of the total population (Fig. 3I) showed that unknown ecotypes could dominate at these depths. From these seasonal and spatial comparisons of ecotype abundances focused on the western North Atlantic, we conclude that seasonal variability was greater than regional variability and that the relative abundances of Prochlorococcus ecotypes are greatly influenced by local hydrography. In this region of the North Atlantic, the emit9312 ecotype dominated total abundance in the euphotic zone, regardless of season. However, the extent of this dominance varied and generally decreased during the spring, likely because of the decrease in temperature and increase in mixed layer depth. Mixed layer depth also appeared to affect the abundance and depth distributions of the other ecotypes in the euphotic zone and appears to be a strong ecological determinant in this region, consistent with the findings of Bouman et al. (2006) for the mixed layers in the Southern Hemisphere. Ecotype abundances as a function of temperature andlight In the two large-scale transect studies of Prochlorococcus ecotype populations, temperature and light (as well as chemical and biological parameters) were cited as key determinants of abundance and depth distribution (Johnson et al. 2006). The effects of light and temperature on abundance are complicated by the fact that both tend to decrease with depth, making relative contributions and potential interactions difficult to ascertain. In this study, we have therefore used a more sophisticated approach, applying models of these interactions to a combined Atlantic Ocean data set that includes the data reported here and the one reported in Johnson et al. (2006) for the North and South Atlantic meridional transects. Our goal was to assess the influence of temperature and light on ecotype abundances and to understand whether they have interacting effects. The method works by comparing two types of light or temperature versus abundance curves: one that is simply a best fit of the actual data (using nonparametric LWLS linear regression) (Figs. 4, 5), and one that is generated by a model that assumes light and temperature have independent multiplicative effects on abundance (see Methods) (Figs. 4, 5). Where the two curves are in agreement, the hypothesis that light and temperature act on abundance independently and multiplicatively is supported. Where they diverge, interactions are occurring: Light levels modulate the effect of temperature on abundance, or vice versa. To look for temperature versus light interactions on a finer scale, different colors were used to plot the data in different temperature or light bins: every 4uC or every log unit of light expressed as a percentage of surface irradiance (Morel 1988). We first examined the total Prochlorococcus and Synechococcus abundance data (determined by flow cytometry) as a function of the temperature and light levels at the depth from which they were sampled. The overall relationship between temperature and abundance and between light and abundance was strongly positive for Prochlorococcus (Fig. 4A,C). However, at the highest light and temperature values, these trends were less pronounced. The tight mapping of the solid lines onto the dashed lines in Fig. 4A and C supports that the effects of light and temperature on total Prochlorococcus abundance are independent and multiplicative. Similar to Prochlorococcus, Synechococcus abundance increases with light intensity (Fig. 4D), but unlike Prochlorococcus, it does not covary with temperature (Fig. 4B). The striking difference in the relationship between temperature and abundance for these two closely related genera might help to explain the different seasonal patterns of abundance observed in seasonally stratified systems. At the BATS station near Bermuda, Synechococcus abundances reach a maximum during the spring bloom, when deep convective mixing brings inorganic nitrogen and phosphorus into the upper euphotic zone (DuRand et al. 2001). After a decline in nutrients and the onset of stratification, integrated Synechococcus cell abundances drop. In contrast, Prochlorococcus does not bloom during spring mixing, but rather during the temperature increase that leads to stratification (DuRand et al. 2001). Prochlorococcus ecotypes examined to date cannot utilize nitrate (Moore et al. 2002), and several have been shown to lack the gene for nitrate reductase (Dufresne et al. 2003; Rocap et al. 2003). In fact, Prochlorococcus abundance has been shown to be anticorrelated with nitrate in the North Atlantic, whereas the inverse is true for Synechococcus (Johnson et al. 2006). It appears then that in such seasonally stratified waters, Synechococcus abundances could be regulated primarily by nutrient concentration, whereas Prochlorococcus abun-

10 2214 Zinser et al. Fig. 4. Dependence of Prochlorococcus or Synechococcus cell abundances (A, B) on temperature over different ranges of light and (C, D) on light over different ranges of temperature and log(par), where PAR is photosynthetically active radiation expressed as a percentage of surface irradiance, and T is temperature (uc). The data points are cell abundances determined by flow cytometry. The dashed lines are fits produced by nonparametric locally weighted regression, and the solid lines are nonparametric regression fits, assuming that temperature and light have independent multiplicative effects on the concentrations (see text for details). dances are regulated by temperature, either directly or indirectly via its role in regulating mixed layer nutrient concentrations. However, an important caveat to these interpretations is that flow cytometry measures Prochlorococcus and Synechococcus populations at the genus level, so these analyses reflect only the dominant ecotypes. Ecotype-specific relationships with light and temperature could therefore be masked, and indeed, differences at the ecotype level are observed for Prochlorococcus (see below) and might also exist for Synechococcus. We next applied this same regression approach to the abundances of the four most abundant individual ecotypes. emed4, enatl2a, and emit9313 have similarly shaped relationships of abundance as a function of temperature, peaking around 19uC and declining dramatically above 25uC (Fig. 5A,C,D). In contrast, ecotype emit9312 abundances increase steadily with temperature, reaching near-constant maximum values between about 25uC and 28uC (Fig. 5B), reflecting its numerical dominance over the other ecotypes at high temperature found here and in Johnson et al. (2006). Because emit9312 is the numerically dominant ecotype by orders of magnitude in this dataset, it is clear that the patterns seen for total Prochlorococcus (Fig. 4A) reflect those of emit9312. The abundances of emed4 and (to a lesser extent) emit9312 are positively correlated with light at all light values, irrespective of temperature (Fig. 5E,F). In contrast, emit9313 shows a strong photoinhibition at high light, irrespective of temperature (Fig. 5H). These results are consistent with the respective light physiologies of these ecotypes, with the emit9312 and emed4 strains having a higher growth optimum than the emit9313 strains (Moore and Chisholm 1999). Interestingly, the abundance versus light curves for the enatl2a ecotype (Fig. 5G) was intermediate between that of emed4 and emit9313 (Fig. 5E,H), showing some photoinhibition at high light levels (.1% surface PAR). The relative sensitivities of emed4, enatl2a, and emit9313 to high light, regardless of water temperature, have a striking relationship to the relative depth distributions of these ecotypes in stratified waters of the Sargasso (Fig. 2B,C). In contrast, no clear relationship was found between relative abundance and temperature (Fig. 5A,C,D). Together, these data suggest that the light gradient (but not the temperature gradient) in the euphotic zone is likely playing a significant role in establishing the stacked distribution curves observed. The relative influence of light and temperature on abundance was found in this study to be ecotype de-

11 Prochlorococcus ecotype distributions 2215 Fig. 5. Dependence of Prochlorococcus ecotype concentrations on temperature under different ranges of light (A D) and on light under different ranges of temperature (E H). See Fig. 4 for legend, except that open circles are QPCR-determined ecotype abundances.

12 2216 Zinser et al. Fig. 6. Ecotype abundance in field samples (open circles) as a function of temperature and light, and the growth rate of cultured strains (closed circles and triangles, and open triangles) belonging to these ecotype clades as a function of growth temperature and light intensity. (A D) Growth rates for ecotype strains grown under a 14 : 10 LD cycle with 66 mmol quanta m 22 s 21 light (dashed and solid lines), and cell abundances for all Atlantic field samples (Johnson et al. 2006; this study) collected from depths corresponding to light intensities of 1 10% surface value (open circles). (E H) Growth rates for strains belonging to different ecotypes grown at 24uC (dashed and solid lines), and cell abundances for all field data within a 22 26uC range (open circles). Strains tested are MED4 (panels A and E, open triangles), MIT9515 (panels A and E, closed circles), MIT9312 (panels B and F, open triangles), MIT9215 (panels B and F, closed circles), NATL2A (panels C and G), and MIT9313 (panels D and H). Data points on the horizontal dotted lines are below the limits of detection for QPCR. Growth data for panels A and B are from Johnson et al. (2006), and panels E, F, and H are from Moore and Chisholm (1999). The relationship between the growth rate scale and the cell abundance scale is arbitrary; the maximum growth rate achieved by all of the strains (0.8 d 21 ) was aligned with the maximum cell density in the field (emit9312), and the rest of the values were set relative to this.

13 Prochlorococcus ecotype distributions 2217 pendent. For the emed4, enatl2a, and emit9313 ecotypes, abundances covary with temperature and light to similar degrees (Fig. 5). In contrast, for emit9312, the relative contributions of light and temperature to ecotype abundance are clearly different, with temperature having a much stronger influence than light (Fig. 5B,F). For instance, at high temperature, abundance was always found to be high, whereas the same was not true for high light. Temperature has been identified to be a strong ecological determinant for this ecotype (Johnson et al. 2006). Results from this analysis support those previous findings and help to further distinguish temperature effects from light effects, which as stated above, can be complicated by the tendency of light and temperature to covary with depth. In general, we observed little evidence to support interactive effects between light and temperature on the ecotype abundances (Fig. 5). With one exception (see below), the curves representing the two data models matched very well, supporting the hypothesis of independent multiplicative effects of temperature and light on ecotype concentrations. This result was somewhat surprising, given the significant influence of light and temperature on photosynthesis in cultured phytoplankton (Eppley 1972; Lajko et al. 1997; Moore and Chisholm 1999). Clearly, there is much to be learned concerning the roles temperature and light play in Prochlorococcus photophysiology. In the case of emit9312, we did observe one potential synergy between light and temperature. At low temperature, increasing light intensity tended to decrease, rather than increase, emit9312 abundance, in contrast with the model of independent multiplicative effects (Fig. 5F). Consistent with this finding, synergistic inhibitory effects of high light and low temperature have been noted for other cyanobacteria, Synechocystis sp. PCC6803, and Synechococcus sp. PCC7002 (Tasaka et al. 1996; Sakamoto and Bryant 2002). High light at low temperatures is thought to generate energy imbalances that can lead to photoinhibition, although this is not true in all cases (Sakamoto and Bryant 2002) and could be a strainspecific phenomenon. In any case, these conditions of high light and low temperature will deserve special consideration in future laboratory investigations of the emit9312 ecotype. Relationships between field abundance and cell physiology From the analysis above, we see that light and temperature influence abundance of Prochlorococcus ecotypes and that the effects are, for the most part, independent and multiplicative. Interpreting these results is complicated, however, by the fact that both parameters can have direct and indirect effects on abundance by influencing both bottom-up (i.e., nutrients) and top-down (i.e., predation) controls on abundance. Temperature and nutrient import via deep convective mixing covary, and both temperature and light can influence competing phytoplankton. Top-down controls of Prochlorococcus are still not well understood but are generally believed to consist of protozoan grazing and lysis during phage infection (Monger et al. 1999; Calbet et al. 2001; Sullivan et al. 2003). What influence temperature and light might have on these processes remains largely unknown. However, we observed some surprisingly well-defined patterns when comparing growth optima of strains in culture as a function of light and temperature and abundance of the clades they belong to in the field (Fig. 6). That is, there are clear relationships between relative growth rates of some ecotypes in culture as a function of light and temperature and their abundance in the field under similar conditions. For this analysis, we used the combined Atlantic Ocean data set (Johnson et al. 2006; this study), but because temperature and light have multiplicative effects on abundances that vary between the ecotypes (see above), the field abundance data used in this analysis were selected to keep these effects separate. Because the culture experiments for growth as a function of temperature were performed at mmol quanta m 22 s 21 (Johnson et al. 2006; this study), only field abundances from samples experiencing roughly this amount of light (1 10% of surface irradiance, assuming incident light at the surface is 2,000 mmol quanta m 22 s 21 ) were used. Likewise, light versus growth rate experiments were performed at 24uC (Johnson et al. 2006; this study), so comparisons to field abundances were restricted to those samples in the 22 26uC range. The shapes of the temperature versus growth rate curves of the two emit9312 strains, MIT 9215 and MIT 9312 (Johnson et al. 2006), closely followed the shape of the temperature versus ecotype abundance curve (Fig. 6B). The lower temperature limit for growth of cultures (16uC) also corresponded to a precipitous decline in field abundance. Above this threshold, growth rate and field abundance both increased with increasing temperature, and the temperature at which growth rates were maximal (,24 26uC) corresponded to maximal abundances. Temperature has been shown to be a strong determinant of emit9312 abundance (Bouman et al. 2006; Johnson et al. 2006), and this analysis suggests that the direct effect of temperature on growth rate could indeed be the most important factor determining these patterns. For the emed4, enatl2a, and emit9313 ecotypes, there was considerable variation in abundance for most temperatures at which cells were detected (Fig. 6A,C,D), often exceeding three to four orders of magnitude. Johnson et al. (2006) cite additional (unknown) factors contributing to the abundance patterns of these ecotypes, and these results support those findings. Nevertheless, a clear and important trend in the data was observed: for the emed4 and enatl2a ecotypes, it appears that the range of temperatures in which the ecotype is present in the ocean is conferred by intrinsic, physiological constraints: high cell abundances are detectable throughout the range that permits growth of selected strains in culture, but not above or below these limits (Fig. 6A,C; Johnson et al. 2006). Ecotype emit9313 is an interesting exception to these patterns. It was found at high abundance (.1,000 cells ml 21 ) in water a few degrees below the limit for growth of its type strain MIT9313 (Fig. 6D). Two explanations are likely. Either the emit9313 ecotype can consist of genetic variants with different temperature limits that we do not have represented in our culture collection, or we are