Photoacclimation of natural phytoplankton communities · Mar Ecol Prog Ser 542: 51–62, 2016 chl:C...
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MARINE ECOLOGY PROGRESS SERIESMar Ecol Prog Ser
Vol. 542: 5162, 2016doi: 10.3354/meps11539
Published January 19
Photoacclimation is the phenotypic response ofcells to changes in their light environment (Fal -kowski & LaRoche 1991, MacIntyre et al. 2002). Thisresponse includes changes in enzymatic processes,morphology, and biochemical composition. One ofthe most extensively documented forms of photoac-climation is the change in cellular pigment concen-
tration as a function of growth irradiance, a responsethat can be quantified by changes in chlorophyll a:phytoplankton carbon (chl:Cphyto) ratios. Chl:Cphytovariability, however, is not simply a function ofgrowth irradiance but also registers changes in nutrient-driven division rate and other environmen-tal conditions.
Phytoplankton cultures grown under nutrient re -plete conditions in the laboratory exhibit maximum
Inter-Research 2016 www.int-res.com*Corresponding author: [email protected]
ABSTRACT: Phytoplankton regulate internal pigment concentrations in response to light andnutrient availability. Chlorophyll a to phytoplankton carbon ratios (chl:Cphyto) are commonlyreported as a function of growth irradiance (Eg) for evaluating the photoacclimation response ofphytoplankton. In contrast to most culture experiments, natural phytoplankton communities expe-rience fluctuating environmental conditions, making it difficult to compare field and lab observa-tions. Observing and understanding photoacclimation in nature is important for decipheringchanges in chl:Cphyto resulting from environmental forcings and for accurately estimating net pri-mary production (NPP) in models which rely on a parameterized description of photoacclimation.Here we employ direct analytical measurements of Cphyto and parallel high-resolution biomassestimates from particulate backscattering (bbp) and flow cytometry to investigate chl:Cphyto in nat-ural phytoplankton communities. Chl:Cphyto observed over a wide range of Eg in the field was con-sistent with photoacclimation responses inferred from satellite observations. Field-based photoac-climation observations for a mixed natural community contrast with laboratory results for singlespecies grown in continuous light and nutrient-replete conditions. Applying a carbon-based NPPmodel to our field data for a northsouth transect in the Atlantic Ocean results in estimates thatclosely match 14C depth-integrated NPP for the same cruise and with historical records for the dis-tinct biogeographic regions of the Atlantic Ocean. Our results are consistent with previous satel-lite and model observations of cells growing in natural or fluctuating light and showcase howdirect measurements of Cphyto can be applied to explore phytoplankton photophysiology, growthrates, and production at high spatial resolution in situ.
KEY WORDS: Phytoplankton carbon Chlorophyll Growth irradiance Photoacclimation Growth rate Primary production Nutrients
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Photoacclimation of natural phytoplankton communities
Jason R. Graff1,*, Toby K. Westberry1, Allen J. Milligan1, Matthew B. Brown1,Giorgio DallOlmo2, Kristen M. Reifel1, Michael J. Behrenfeld1
1Oregon State University, Department of Botany and Plant Pathology, 2082 Cordley Hall, Corvallis, OR 97330, USA2Plymouth Marine Laboratory, United Kingdom and National Centre for Earth Observation, Prospect Place, The Hoe,
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chl:Cphyto values and minimum growth rate () at lowgrowth irradiances (Eg) and minimum chl:Cphyto andmaximum at high Eg (e.g. Fig. 1, dashed line) (Clo-ern et al. 1995, Geider et al. 1997, Halsey & Jones2015). In contrast, nutrient-limited phytoplanktongrown under constant Eg exhibit maximum chl:Cphytoat high and minimum chl:Cphyto at low (Laws &Bannister 1980, Halsey et al. 2013). Such laboratoryresults emphasize that chl:Cphyto is dependent onlight and nutrient status, both of which must alwaysbe considered when evaluating phytoplankton pho-tophysiology in natural phytoplankton populations.
In situ light fields fluctuate on timescales rangingfrom seconds in near surface waters (Schubert et al.2001) to vertical mixing dynamics that range fromhours to days (Denman & Gargett 1983, Backhaus etal. 1999, DAsaro 2008) to seasonal patterns in inci-dent light. Phytoplankton respond to fluctuating lightby altering their cellular chl and carbon content ontime scales less than 1 h (Lewis et al. 1984, Cullen &Lewis 1988, Havelkov-Douov et al. 2004) andemploy photoprotective mechanisms such as non-photochemical quenching (Havelkov-Douov et al.2004, Miloslavina et al. 2009, van de Poll et al. 2010,Milligan et al. 2012, Alderkamp et al. 2013) that areactive on timescales of microseconds to minutes.
Phytoplankton growth conditions in the field con-trast starkly with static light and nutrient-repleteconditions commonly used in laboratory cultureexperiments. Because field and laboratory growthconditions can be very dissimilar, it is reasonable toexpect differences in chl:Cphyto photoacclimation pat-terns between laboratory and field populations. Thisexpectation is illustrated by the photoacclimationresponse derived from natural phytoplankton com-munities using satellite observations and that fromthe laboratory. Specifically, the satellite-based rela-tionship (solid black line in Fig. 1) developed usingthe 99th percentile of estimated chl:Cphyto and monthlymedian Eg for the surface mixed layer exhibits only amodest decrease of ~2-fold in chl:Cphyto from mini-mum to maximum values of Eg (Behrenfeld et al.2005, Westberry et al. 2008). Compared to this result,static light nutrient-replete culture experimentsshow a wider range in chl:Cphyto ratio across the samegrowth irradiances. An extreme example of this isDunaliella tertiolecta which, when grown in constantlight, exhibits a nearly 8-fold change in chl:Cphytofrom minimum to maximum Eg (Behrenfeld et al.2005, grey dashed line in Fig. 1). Behrenfeld et al.(2002) showed a similarly large range (~6-fold) inchl:Cphyto for 21 different phytoplankton species from23 previously published laboratory studies (see Fig. 1
inset in Behrenfeld et al. 2002). Still, other laboratorystudies have reported less dramatic changes, albeitlarger than observed in satellite data, in chl:Cphyto(~3-fold) over a wide range of growth irradiances(e.g. Laws & Bannister 1980, Geider et al. 1997). Anoptimality model of resource allocation to light har-vesting for cells grown under dynamic and staticlight conditions was recently described by Talmy etal. (2013). Model results yielded a photoacclimationresponse curve with a more limited range in lightharvesting for a mixed light environment (2.5-fold)than for a static light environment (8-fold) (seeFig. 10 in Talmy et al. 2013). These model resultsreflect a higher investment of resources into carbonfixation and photoprotection compared to pigmentsynthesis under mixed light conditions (Talmy et al.2013), suggesting alternative strategies to pigmentsynthesis when cells are in a low mean buthighly variable light environment. Importantly, thechl:Cphyto response from the dynamic light model isvery similar to the photoacclimation response de -rived from satellite data (Fig. 1, solid black and solidgrey lines).
Many ecosystem models and satellite algorithmsspecify relationships between chl:Cphyto and Eg to
Fig. 1. Photoacclimation relationships (chlorophyll a: phyto-plankton carbon [chl:Cphyto] ratio vs. growth irradiance [Eg])for satellite retrievals (solid black line), light-harvesting allo-cation data from an optimality model for cells experiencingfluctuating light (solid grey line from Talmy et al. 2013), anda steady-state nutrient-replete culture of Dunaliella terti-olecta (dashed grey line, after Behrenfeld et al. 2005). Data
normalized to the minimum chl:Cphyto at high light
Graff et al.: Photoacclimation of natural phytoplankton
determine phytoplankton and net primary produc-tion (NPP) (e.g. Blackford et al. 2004, Westberry et al.2008). Because relationships describing photoaccli-mation can differ greatly between field and culturedata, parameters from cultures may not be applicableto ecosystem models that do not resolve phytoplank-ton species, cell size, functional types, etc. More so,the choice of parameters used may differ betweenstudies. For example, the chl:Cphyto maxima at lowlight from Blackford et al. (2004) and the carbon-based production model (CbPM) (Westberry et al.2008) differ by almost 2-fold (0.075 vs. 0.042, respec-tively). Such differences may lead to incompatibleresults between the different models. Furthermore,interpretations of long-term changes in the historicalchlorophyll records require an accurate characteriza-tion of photoacclimation to discriminate changes dueto biomass, light, or nutrients (Behrenfeld et al. 2009,Siegel et al. 2013). And, while different light regimesmay result in different photoacclimation responses,as described above, defining a photoacclimation re -sponse relationship for natural phytoplankton assem-blages in a dynamic light environment is essential foradvancing our understanding of ocean ecosystemsand the response of microalgal communities to cli-mate forcings.
One of the critical obstacles to evaluating chl:Cphytovariability in the field has been the determination ofphytoplankton biomass (Cphyto). While measurementsof chl a are routine (Wright et al. 2005), Cphyto has tra-ditionally been estimated by proxy or conversion ofphytoplankton-related properties (e.g. chlorophyll,cell number or cell volume, total particulate organiccarbon). Proxy measurements are subject to largeerrors and contamination (Banse 1977, Graff et al.2012). Fortunately, direct measurements of Cphyto arenow possible by combining sorting flow-cytometryand elemental analysis (Graff et al. 2012, 2015, Caseyet al. 2013). These methods can be used to explorephytoplankton biomass distributions in the field(Casey et al. 2013, Graff et al. 2015), constrain globalestimates of Cphyto from space (Graff et al. 2015),investigate photophysiology, and revise descriptionsof phytoplankton physiology and biomass in ecosys-tem models.
Here, we describe patterns in chl:Cphyto observed inthe open ocean where both chl a and Cphyto concen-trations were measured analytically. Field sampleswere collected from a wide range of environmentalconditions including the Equatorial Pacific Ocean(EPO) and a northsouth transect of the AtlanticOcean from temperate to oligotrophic waters. Ob -served chl:Cphyto variability is evaluated in the con-
text of mixed-layer light conditions, as well as nutri-ent concentrations and supply patterns. The primaryfocus of this report is to compare the new field datawith previously described relationships from labora-tory- and satellite-based studies, where photoaccli-mation has been described as a function of averageor median light levels (Eg). For the current analysis,we assume that the photoacclimation state of phyto-plankton can be described as a function of themedian mixed-layer light level (i.e. cells acclimate tothe integrated daily mean light level in the mixedlayer), but note that this assumption has yet to be rig-orously evaluated from the perspective of cellularmechanisms regulating chlorophyll synthesis. Wefind that the relationship between field-basedchl:Cphyto data and Eg is more consistent with thesatellite-based photoacclimation response than labo-ratory results which exhibit more extreme responses,e.g. D. tertiolecta. To explore the utility of these newresults for estimating NPP, we then apply the CbPM(Westberry et al. 2008), which was originally devel-oped for application to satellite data, to the northsouth Atlantic Ocean transect data. We compare theresultant production estimates from the CbPM with14C uptake rates for the Atlantic Meridional Transect(AMT) and assess the estimated growth rates relativeto surface nutrient data for which the model employsa relationship between Chl:Cphyto ratios and nutrientlimited growth rates.
MATERIALS AND METHODS
Field samples from the surface mixed layer andoptical measurements were collected during 2cruises (Fig. 2). The first cruise took place in the EPOfrom 7 May to 28 June 2012 in conjunction with theNational Oceanic and Atmospheric Administrations(NOAA) Tropical Atmospheric Ocean project aboardthe NOAA Vessel Kaimimoana (Fig. 2). The secondfield effort was part of the 22nd Atlantic MeridionalTransect (AMT-22) aboard the RSS James Cookfrom 10 October to 24 November 2012 (Fig. 2). During both cruises, surface optical properties werecontinuously measured as described in DallOlmo etal. (2009). Discrete samples were collected from theships flowing seawater supply for Cphyto, high- performance liquid chromatography (HPLC) pig-ments and total particulate organic carbon (POC)analyses. During the EPO cruise, samples from thesurface mixed layer and from depths below themixed layer were also collected using a CTD rosettewith Niskin bottles.
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Phytoplankton biomass and pigments
Three methods were used to estimate Cphyto in thefield: (1) direct analytical measurement (Graff et al.2012, 2015), (2) biomass particulate backscattering(bbp) at 470 nm (Graff et al. 2015), and (3) cell countsof Prochlorococcus converted to carbon. First, directmeasurement of Cphyto employs sorting flow-cytome-try to collect cells for elemental analysis. Details ofthis procedure can be found in Graff et al. (2015) butare reviewed here briefly. Whole seawater sampleswere processed on a Becton Dickson Influx CellSorter (BD ICS) flow-cytometer and the collectedmaterial was analyzed on a Shimadzu TOC-N usingmanual injection. A sample blank, consisting of arti-ficial seawater (de-ionized water plus sodium chlo-ride) used as sheath fluid in the BD ICS, was ana-lyzed to correct for non-algal carbon contributed bythe sheath water to the sorted sample. Second, opti-cally derived Cphyto was calculated using measure-ments of bbp similar to Behrenfeld et al. (2005) andWestberry et al. (2008). However, a newly refinedand validated relationship between Cphyto and bbp inthe field reported in Graff et al. (2015) is used here(Cphyto = 12128 bbp470 + 0.59). Last, Prochlorococcus-specific carbon was calculated using flow-cytometrydetermined cell concentrations multiplied by 100 fgC cell1. Published values of carbon cell1 forProchlorococcus span an order of magnitude from35 to 350 fg carbon cell1 (Caron et al. 1995 and refer-enes therein, Veldhuis & Kraay 2004). Prochlorococ-cus-specific carbon was determined because divinylchl a (DV chl) is unique to these cells and can be usedto independently evaluate chl:Cphyto ratios for thisthis group. These ratios, however, should be cau-tiously interpreted given the potential range in car-bon for this group.
Whole seawater samples (13 l) were filtered ontopre-combusted (450C, 4 h) glass fiber filters (GF/F)for HPLC pigment analysis. Each filter was immedi-ately placed into a cryovial and stored in liquid nitro-gen (LN). Samples were analyzed at the NASA God-dard Space Flight Center Ocean Ecology Laboratory(Van Heukelem & Thomas 2001). Total chl a and DVchl are considered here.
Optics and light
Optical parameters were continuously measuredon the flow-through seawater systems of both re -search vessels using a WETLabs ECO-BB3 backscat-tering sensor (470, 532, and 670 nm) mounted in acustom-made backscattering flow chamber (Dal-lOlmo et al. 2009). Particulate backscattering at470 nm (bbp) was calculated following DallOlmo etal. (2009). Values reported here represent up to60 min averages centered on the time of sample col-lection for discrete measurements of pigments and/orCphyto. A smaller temporal averaging period was usedin temperate waters where increased spatial hetero-geneity in particle fields exist.
For the EPO cruise, Eg (mol quanta m2 h1) wascalculated for stations sampled with the CTD rosetteusing ship-mounted incident photosyntheticallyactive radiation (PAR) measurements and extinctioncoefficients (Kd = 0.121 chl0.428) (Morel & Maritorena2001). Eg values within the mixed layer were calcu-lated as the median light level following Behrenfeldet al. (2005). For the AMT-22 transect, Eg was calcu-lated from the daily noon CTD casts and interpolatedto each sample station using the Matlab shape- preserving interpolant, which fits a piecewise poly-nomial function to the dataset, i.e. fitting a smoothfunction for Eg between latitudinal data. Mixed-layerdepths were determined using the criteria of achange in potential density of 0.125 kg m3 relative tothat observed at a depth of 10 m (Levitus 1982).
High spatial-resolution estimates of , NPP, andnutrient stress were obtained by applying the CbPM(Westberry et al. 2008) to the AMT-22 transect fielddata. Growth rates and NPP were calculated usingthe photoacclimation and growth rate parametersdeveloped in Westberry et al. (2008). In the CbPM,the maximum phytoplankton growth rate () is set at2 divisions (div) d1 as observed in the field (Banse
Fig. 2. Ship tracks (blue lines) for 2 cruises used to collectdiscrete samples and continuous optical measurements:(1) in the Equatorial Pacific Ocean (EPO) (May/June 2012)and (2) the 22nd Atlantic Meridional Transect (AMT-22)
(Nov/ Dec 2012)
Graff et al.: Photoacclimation of natural phytoplankton
1991). This maximum is then scaled to account forboth light and nutrient limitation of growth rate(Westberry et al. 2008, their Eq. 5). In this approach,the satellite-derived field photoacclimation rela -tionship (Fig. 1, black line) defines the maximumchl:Cphyto as a function of Eg where the maximumchl:Cphyto = 0.022 + (0.045 0.022)e3PAR(z) (from West-berry et al. 2008, their Eq. 4, z is depth). Where lightis saturating for growth, the maximum division rate is2 and when it is limiting is scaled to a lower valueaccording to the relationship 1 e(5 Eg). A decreasein under low light conditions results from a de -crease in light absorption as chl synthesis is not suffi-cient to match the reduction of the light field (Geider1987, 1996). The exponent 5 describes the strengthof light limitation on observed in laboratory photo -acclimation experiments (Westberry et al. 2008 usingM. J. Behrenfeld unpubl. data 2007). This model alsoincorporates nutrient effects on growth rates. De -creases in chl:Cphyto resulting from nutrient limitationare directly proportional to changes in (Laws &Bannister 1980, Halsey & Jones 2015). Therefore, anobservation of chl:Cphyto which is less than the pre-dicted maximum at its determined Eg is attributed tonutrient stress. Fig. 3 is a diagram of this approach fora single observation. In this example, we have anobservation (open circle) of chl:Cphyto at a particularEg which falls below the maximum value describedby the photoacclimation response curve. The ratioA/B quantifies the reduction in chl:Cphyto between
the maximum and minimum ( = 0) growth rates atthat Eg due to nutrient limitation. Because it isdirectly proportional, the ratio of A/B is used to scale for nutrient effects. Nutrient stress is calculatedfrom the CbPM as 1 A/B where a value of zero rep-resents nutrient replete conditions and a valueapproaching 1 indicates of a high degree of nutrientstress. Thus, we can calculate the division rate of aphytoplankton community for an observed chl:Cphytoand Eg and discern which, if any, of these environ-mental parameters are responsible for limitinggrowth. Mixed-layer NPP values were calculated as Cphyto and integrated to the base of the mixedlayer. These values are further divided into broadbiogeographic regions (after Zubkov et al. 2000) andcompared to depth-integrated 14C measurementsreported for corresponding provinces for the AMT-22cruise (Tilstone & Lange 2013) and the historicalAMT dataset (Tilstone et al. 2009).
Nutrient data from the AMT database for samplescollected between 2 and 5 m during the 22nd AMTCruise are included in this study for qualitativeanalyses of chl:Cphyto and CbPM results for andnutrient stress terms. Nutrient data for AMT-22includes inorganic nitrogen (nitrate plus nitrite),phosphate, and silicate.
For the 41 total direct measurements of Cphyto pre-sented here, 20 were from discrete samples collectedfrom the EPO and 21 were from the AMT-22 cruise.Biomass directly measured for these samples rangedfrom 4 to 58 g l1 (Graff et al. 2015). Applying the bbpvs. Cphyto relationship described by Graff et al. (2015)yielded 200 additional indirect estimates of Cphyto,and conversions of the Prochlorococcus cell counts tobiomass added another 159 values specific for thatgroup. Biomass estimates from measurements of bbpalong the AMT-22 transect ranged from 6 to 47 g l1
(Fig. 4B). Chlorophyll was lowest in the southernsubtropical region at 0.026 g l1 and highest in thesouthern temperate region at 1.13 g l1 (Fig. 4A). Egranged from 0.0022 to 1.9 mol quanta m2 h1 wheredirect measurements of Cphyto were available. Inter-polating Eg between CTD stations and to whereproxy estimates of biomass were available extendedthe upper range of growth irradiance for this analysisfrom 1.9 to 2.8 mol quanta m2 h1 (Fig. 4D).
Chl:Cphyto from direct measurements ranged from0.0023 to 0.032 (Fig. 5A), exhibiting almost 15-foldvariability that reflects both nutrient- and light-
Fig. 3. Diagram of growth rate () calculation used in the car-bon-based productivity model (CbPM) (Westberry et al.2008). Growth rate is scaled from a theoretical maximum of2 (Banse 1991) by nutrient limitation when observations fallbelow the solid black line and by light limitation at low light.A: observed chl:Cphyto; B: maximum chl:Cphyto possible for a
given equation; O: observed chl:Cphyto and Eg
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driven changes in cellular pigmentation (Geider1987, Geider et al. 1997, Behrenfeld et al. 2005).Inclusion of indirect Cphyto estimates from bbp orProchlorococcus cell counts significantly increasedthe number of chl:Cphyto observations with collocatedestimates of median mixed-layer light level. When
biomass was derived using bbp or Prochlorococcuscell counts the range of chl:Cphyto was 0.0030.027(Fig. 5B) and 0.0010.05 (Fig. 5C), respectively.
Field-derived photoacclimation parameters (chl:Cphytoand Eg) were used to model phytoplankton andNNP. We calculated phytoplankton division rates fol-
Fig. 4. 22nd Atlantic Meridional Transect (AMT-22) data for (A) high-performance liquid chromatography chlorophyll a, (B)biomass particulate backscattering (bbp)-estimated phytoplankton carbon (Cphyto), (C) chl:Cphyto, (D) growth irradiance (Eg), (E)inorganic nitrogen (nitrate plus nitrite), (F) phosphate, and (G) silicate concentrations for samples collected in the upper 10 m
during the AMT-22 Cruise
Graff et al.: Photoacclimation of natural phytoplankton
lowing the approach used in the CbPM (Westberry etal. 2008) and detailed again in this paper. Here, weused Cphyto estimates from bbp for the AMT-22 transectto provide high-resolution estimates of and NPP.Along the northsouth transect, estimates of rangedfrom 0.03 to 1.9 div d1 (Fig. 6B). Division rates werehighest at the southern end of AMT-22, and anotherpeak was observed in the vicinity of the IntertropicalConvergence Zone (ITCZ) at approximately 12 N.Lower values were observed in the oligotrophic gyres(~0.3 div d1). NPP in the CbPM is calculated by defini-tion as Cphyto. Mixed-layer NPP from the CbPM ap-proach ranged from 45 to 1447 mg C m2 d1 (Table 1),exhibiting a local peak near the Equator (731 mg C m2
d1) and the maximum in southern temperate waters(Fig. 6C). Both subtropical gyres had a lower range inNPP, as did the northern temperate region, spanningfrom 45 to 558 for these 3 regions combined (Table 1).
A significant amount of scatter is seen in allchl:Cphyto observations (Fig. 5 AC) and nearly allvalues fall below the photoacclimation responsecurves for the Dunaliella tertiolecta culture andsatellite ocean-color retrievals (Fig. 5D). Theseresponse curves (lines in Fig. 5D) representchl:Cphyto under nutrient-replete conditions in cul-ture or optimal growth conditions observed in thefield for a particular Eg. Observations that lie belowthe maximal photoacclimation response curves areattributed in the CbPM to a reduction in chl:Cphytoresulting from nutrient-limited growth rate (Laws &Bannister 1980, Halsey & Jones 2015) but may alsoreflect Eg being an incomplete descriptor of themixed-layer light level to which phytoplanktonphoto acclimate. While the field chl:Cphyto reported
Fig. 5. Chlorophyll a: phytoplankton carbon ratio (chl:Cphyto) for field samples vs. growth irradiance (Eg) using (A) direct meas-urements of Cphyto, (B) a proxy relationship of biomass particulate backscattering (bbp) (Graff et al. 2015), and (C) divinylchl:Cphyto using carbon from conversions of Prochlorococcus (Pro) cell concentrations. (D) All data combined. Dashed/solidlines are same as shown in Fig. 1 but are not normalized here as in Fig. 1. Note y-axis scale change in (D) to accommodate the
continuous light culture data
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here fall below the more extreme laboratory (repre-sented here by D. tertiolec ta) and satellite-derivedphotoacclimation responses, the observed maximaclosely match those from satellite retrievals (Fig. 5D,solid line). This does not strictly rule out the possi-bility that the satellite-based photoacclimation rela-tionship or the field observations reported heredeviate from the laboratory results due to nutrientlimitation. However, the limited range in chl:Cphytofrom satellite retrievals in cludes observations inregions where nutrients are seasonally replete.Field data also represent a mixed phytoplankton
community that may dampen the photoacclimationresponse curve relative to culture studies whichobserve larger changes. For example, the range ofchl:Cphyto of Prochlorococcus (Fig. 5C), not account-ing for nutrient impacts on this ratio, appears to bemore enhanced relative to the total community(Fig. 5D). Disentangling nutrient, light, and species-specific impacts on the field photoacclimationresponse curve cannot be fully accomplished here.However, the variability and broad patterns inchl:Cphyto along the AMT transect can be exploredrelative to light and nutrient concentrations.
Fig. 6. Application of the carbon-based production model (CbPM) to the 22nd Atlantic Meridional Transect (AMT-22) data (seeFig. 2) provides estimates of (A) nutrient stress, (B) growth rate (), and (C) CbPM net primary production (NPP). Regional
ranges for AMT-22 14CNPP estimates shown by the boxes around CbPMNPP estimates
Region of Atlantic Ocean Net primary production (mg C m2 d1)CbPM AMT-22 14C AMT-22 14C (19982005)
All Regions 451447a 1001600 511245Northern temperate (50 N, 40 N) 91379 ~400 51678Northern subtropical (40 N, 12 N) 122417 100300 137590Western tropical Atlantic (12 N, 3 S) 161779a 500650 51459Southern subtropical (3 S, 37 S) 45558 150250 89542Southern temperate (37 S, 50 S) 571447 9001600 1101245Total observations N = 180 N = 37 N = 91
aExcludes 2 observations of 1579 and 2004 mg C m2 d1 at the Equator
Table 1. Comparison of surface mixed-layer net primary production (NPP) using the carbon-based production model (CbPM)(Westberry et al. 2008) with depth-integrated 14C measurements for the same 22nd Atlantic Meridional Transect (AMT-22)
cruise and with the historical AMT record (19982005)
Graff et al.: Photoacclimation of natural phytoplankton
Chl:Cphyto variability along the AMT
In this subsection, we focus on the AMT-22 transectand evaluate the observed patterns in chl:Cphytoagainst in situ Eg and nutrient conditions. Enhancedchl:Cphyto ratios in the northern temperate region ofthe transect correspond to low Eg (Fig. 4C,D) andmoderately elevated nutrients (Table 2, Fig. 4C &EG). A more dramatic increase in chl:Cphyto is seenin the southern temperate region (Fig. 4) sampledduring austral spring. Nitrogen (nitrate + nitrite) andphosphate were maximal in this region (Fig. 4D,E),and Eg was often as low as that encountered in thetemperate northern waters (Fig. 4). Thus, low lightand increased nutrients underlie the highestchl:Cphyto observed during AMT-22. Conversely, thelowest chl:Cphyto were found in the North AtlanticSubtropical Gyre (~2035 N), where nutrients wereat a minimum and light was moderately high in com-parison to other regions (Table 2, Fig. 4).
Nutrient impacts on chl:Cphyto become more appar-ent when comparing regions of the AMT transect asa function of irradiance (Fig. 7). Chl:Cphyto for thesouthern portion of the transect (Fig. 7, dark blue) fallwell above those collected in the northern temperatewaters (Fig. 7, dark red) despite having similar Egvalues in both regions. A local peak in chl:Cphyto wasalso present just north of the equator at ~15 N in theITCZ. Nutrient inputs into the ITCZ include up -welling (Robinson et al. 2006) and nitrogen and iron-rich dust blowing off the African continent, whichpeak in concentration in this region (Baker et al.2013). Samples from the ITCZ create a cluster ofpoints (Fig. 7, light green, yellow, and light orange)that fall above samples with similar Eg values. Ele-vated concentrations of silicate are visible in thenutrient data in this region (Table 2, Fig. 4G), andenhanced inputs of iron in the ITCZ would elevatechl:Cphyto in this region (Sunda & Huntsman 1997).However, inorganic nitrogen and phosphate do notshow increases in the ITCZ. It is likely that one or
both of these nutrients are limiting in this region(Moore et al. 2013). Any supply of these nutrientswould be quickly taken up and incorporated into bio-mass and, thus, would not likely be measured asfreely available resources.
and NPP along the AMT
Patterns in chl:Cphyto observed for AMT-22 are con-sistent with our understanding of the environmentalparameters which impact these values. Thus, we canevaluate the patterns in and NPP derived from theCbPM as it was applied to our field data. In the oligo-trophic gyres where is low, nutrient stress appearsto be relatively high (Fig. 6A). Reduced growth in
Region of Atlantic Ocean Nitrate + Nitrite Phosphate Silicate Eg Chl:Cphyto(mol l1) (mol l1) (mol l1) (mol quanta m2 h1) (g l1:g l1)
Northern temperate (50 N, 40 N) 0.041.24 0.061.1 0.556.5 0.090.36 0.0120.017Northern subtropical (40 N, 12 N) 0.020.07 0.020.72 0.571.96 0.151.72 0.0060.018Western tropical Atlantic (12 N, 3 S) 0.010.06 0.030.12 1.067.85 0.241.34 0.0090.02Southern subtropical (3 S, 37 S) 0.010.04 0.070.19 0.971.38 0.242.85 0.0090.027Southern temperate (37 S, 50 S) 0.047.4 0.160.64 0.241.22 0.021.13 0.0090.028
Table 2. Range in nutrients, median mixed-layer light (Eg), and the chlorophyll a: phytoplankton carbon (chl:Cphyto) ratio by region along the 22nd Atlantic Meridional Transect (AMT-22) cruise transect. Eg: growth irradiance
Fig. 7. Chlorophyll a: phytoplankton carbon ratio (chl:Cphyto)as a function of growth irradiance (Eg) for the 22nd AtlanticMeridional Transect (AMT-22) using the biomass particulatebackscattering (bbp) Cphyto relationship (bbp vs. Cphyto). Samplesare color-coded by latitude. Distinct regional groupings canbe seen, e.g. elevated chl:Cphyto for samples collected in theIntertropical Convergence Zone (ITCZ) (0 to ~17 N) where
nitrogen and iron inputs are elevated along this transect
Mar Ecol Prog Ser 542: 5162, 2016
these regions is the result of nutrient stress, as Eg isgenerally high and not limiting, especially in theSouth Atlantic subtropical gyre. Moore et al. (2013)reported that phytoplankton growth and productionin as much as 30% of low latitude surface oceanwaters is limited by nitrogen supply with the remain-der of the ocean limited by iron or other micronutri-ents. However, light can also limit , and this isaccounted for in the CbPM. A reduction in due tolight limitation was observed in the northern temper-ate waters above 35 N. Eg (Fig. 4D) and (Fig. 6B)decline sharply moving northward in this regionwhile nutrient stress (Fig. 6A) remains relatively con-stant.
A broader understanding of how nutrients differ-entially influence phytoplankton group-specificgrowth rates in the field can be ascertained from thisdata. For example, bulk phytoplankton in the ITCZregion ranged from 0.8 to 1.6 d1. Flow cytometricanalysis of samples from this region during AMT-22showed that the phytoplankton community includedProchlorococcus, Synechococcus, and eukaryotes(data not shown). Division rates for Prochlorococcusfrom laboratory and field studies range from ~ 0.3 to1 d1 (Liu et al. 1998, Shalapyonok et al. 1998, Parten-sky et al. 1999). Prochlorococcus division ratesappear to be insensitive to increased nutrient sup-plies relative to other phytoplankton groups (Parten-sky et al. 1999) and take up nutrients at high rateseven in oligotrophic waters (Zubkov et al. 2003).Modeled growth rates of the total phytoplanktoncommunity in these regions are above and beyondthe published range for Prochlorococcus and suggestthat the remainder of the community, composed ofSynechococcus and eukaryotic phytoplankton, havehigher rates of division as a result of nutrient suppliesin this region.
NPP estimates from the CbPM generally agreewith 14C measurements (Table 1, Fig. 6C). Depth-integrated 14C-NPP measurements ranged from 100to 1600 for AMT-22 and from 51 to 1600 if the histor-ical AMT 14C data is included (Table 1) (Tilstone et al.2009, Tilstone & Lange 2013). A general pattern oflow NPP in the subtropical oligotrophic gyres andhigher values at the Equator and in temperate watersis consistent between the 2 methods (Table 1,Fig. 6C). A benefit of implementing the CbPM is thatit can provide higher resolution data (N = 180) com-pared to the 14C method (AMT-22, N = 37, AMT19982005, N = 91) and can capture variability thatmay otherwise be missed by the spatial resolution ofthe less frequent incubations. Inclusion of the histor-ical AMT dataset further demonstrates that the
CbPM-NPP values fall well within the observedrange of 14C data (Table 1).
A variety of factors may contribute to differences inNPP estimates from the CbPM and 14C approach. Forexample, the CbPM-NPP estimates were calculatedfor the surface mixed layer only, whereas 14C-NPPwas integrated over the euphotic zone. Interpretationof 14C incubation results must also consider incuba-tion time and growth rates of the phytoplankton(Halsey et al. 2013, Pei & Laws 2013). Short-term 14Cincubation experiments provide estimates of grossprimary production (Halsey et al. 2013). Long-termincubations approach net production (Halsey et al.2013) but may over or underestimate NPP as a resultof phytoplankton species composition (Pei & Laws2013) and differences in the environmental parame-ters (e.g. light, temperature) between incubationsand in situ conditions. Additionally, Eg used here wasinterpolated between noon CTD cast stations andmay not accurately represent the water columnwhere samples were collected. Assumptions of themodel, for example maximum division rates of 2 d1
or what defines the growth irradiance, could signifi-cantly bias model results. New methods for assessinggrowth rates in the field may shed light on the maxi-mal growth rates of natural phytoplankton communi-ties (Zubkov 2014) and would directly influencemodeled NPP in ecosystems models such as theCbPM. Additional work is needed to resolve discrep-ancies between modeled and measured rates of NPP,which despite the difference in approach, show goodrelative agreement.
A fundamental question that still needs to beaddressed is how to appropriately evaluate meas-ured chl:Cphyto variability with respect to the oceansmixed-layer light environment. For consistency withearlier studies, we have chosen to express chl:Cphytodata as a function of the median mixed-layer lightlevel. While this assessment of growth irradiance iscertainly mathematically convenient, it is not mecha-nistically based on physiological processes that regu-late chlorophyll synthesis in the field, and it does notconsider how changes in light over the course of thephotoperiod influence these regulatory pathways.Clearly, a major area for future work on photoaccli-mation in natural systems will be the integration offield measurements of chl:Cphyto with mechanisticunderstandings of cellular photoacclimation and reg-ulation in a fluctuating light environment.
Graff et al.: Photoacclimation of natural phytoplankton
Despite using a simplistic description of mixed-layer growth irradiance, a robust conclusion from thecurrent study is that chl:Cphyto variability in the upperopen ocean does not exhibit the extreme low-lightvalues observed in static light laboratory experi-ments. Instead, our analytical field data for naturalphytoplankton communities appear more consistentwith the rather diminished low-light response earlierderived from field data (Behrenfeld et al. 2002), satel-lite data (Behrenfeld et al. 2005, Westberry et al.2008), and the dynamic light model of Talmy et al.(2013). Expanding the field database of measuredchl:Cphyto values, concurrent with water columnmeasurements of the light field and vertical mixing,will be important for refining our understanding ofphotophysiology in nature. This understanding iscritical for advancing ocean ecosystem modeling,improving assessments of global NNP, and interpret-ing the satellite record of chlorophyll and carbonvariability in response to environmental forcings.
Acknowledgements. This work was funded by NASA GrantNNX10AT70G to M. Behrenfeld. This study was also sup-ported by the UK Natural Environment Research CouncilNational Capability Funding to Plymouth Marine Labora-tory and the National Oceanography Centre, Southampton.This is contribution number 261 of the AMT Programme. Wethank the NOAA Tropical Atmospheric Ocean Program andLCDR Brian Lake and the 22nd Atlantic Meridional TransectProgramme and the Principal Scientist Glen Tarran for pro-viding field support to carry out this research.
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Editorial responsibility: Steven Lohrenz, New Bedford, Massachusetts, USA
Submitted: February 5, 2015; Accepted: November 1, 2015Proofs received from author(s): January 8, 2016
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