ORIGINAL ARTICLE

Linking phytoplankton community compositionto seasonal changes in f-ratio

Bess B Ward1,2, Andrew P Rees1, Paul J Somerfield1 and Ian Joint1

1Marine Life Support Systems, Plymouth Marine Laboratory, Plymouth, Devon, UK and 2Departmentof Geosciences, Princeton University, Princeton, NJ, USA

Seasonal changes in nitrogen assimilation have been studied in the western English Channel bysampling at approximately weekly intervals for 12 months. Nitrate concentrations showed strongseasonal variations. Available nitrogen in the winter was dominated by nitrate but this was close tolimit of detection from May to September, after the spring phytoplankton bloom. The 15N uptakeexperiments showed that nitrate was the nitrogen source for the spring phytoplankton bloom butregenerated nitrogen supported phytoplankton productivity throughout the summer. The averageannual f-ratio was 0.35, which demonstrated the importance of ammonia regeneration in thisdynamic temperate region. Nitrogen uptake rate measurements were related to the phytoplanktonresponsible by assessing the relative abundance of nitrate reductase (NR) genes and the expressionof NR among eukaryotic phytoplankton. Strong signals were detected from NR sequences that arenot associated with known phylotypes or cultures. NR sequences from the diatom Phaeodactylumtricornutum were highly represented in gene abundance and expression, and were significantlycorrelated with f-ratio. The results demonstrate that analysis of functional genes provides additionalinformation, and may be able to give better indications of which phytoplankton species areresponsible for the observed seasonal changes in f-ratio than microscopic phytoplanktonidentification.The ISME Journal advance online publication, 5 May 2011; doi:10.1038/ismej.2011.50Subject Category: geomicrobiology and microbial contributions to geochemical cyclesKeywords: f-ratio; seasonal variation; nitrate reductase gene expression; microarrays

Introduction

The western English Channel is one of the best-studied marine regions, with a long time series ofmeasurements. Research was initiated in the latenineteenth century as an investigation to explainvariability in the fishery (Southward et al., 2005).Among the earliest factors to be studied weretemperature and a variety of chemical measurements.For example, the first determination of phosphateconcentration was made in 1916 (Matthews, 1917)and frequent, regular measurements began in 1923.The studies from the English Channel in this periodlaid the basis for much subsequent understanding ofchemical oceanography, including observations onnitrate to phosphate ratio in the English Channel(Cooper, 1937)—observations that were to be collatedfrom a range of geographical provinces to form theRedfield ratio hypothesis (Redfield et al., 1963).

Nutrient measurements have been a particularlyimportant part of studies to understand productivity

in the English Channel. Russell (1936) made an earlylink between phosphate concentration and pelagicbiology when he noted a correspondence betweenthe abundance of young fish and phosphate con-centration. Initially concerned with interannualvariation, Russell showed that for the period 1923–1935, there was good correspondence between meanwinter phosphate maxima and young fish abun-dance (Russell, 1936). Later research focused onthese winter concentrations of phosphate (that is,the yearly maxima), which were thought to be animportant driver of long-term fluctuations in theChannel ecosystem (Russell et al., 1971; Southwardet al., 2005), fluctuations that were referred to as theRussell Cycle (Cushing and Dickson, 1976). Laterworkers found that temperature was a major deter-minant of the timing of maximum fish abundance(Genner et al., 2010). A number of variations inspecies abundance were observed between the1920s and the 1980s, including a decline inabundance of zooplankton, a change of the planktoncommunity from that characterized by the chaetognathSagitta elegans to one characterized by the moreneritic species Sagitta setosa and the failure of theherring fishery off Plymouth in 1936 (Southwardet al., 2005).

Throughout this long period, phosphate concen-trations were measured routinely and frequently in

Received 4 November 2010; revised 11 March 2011; accepted24 March 2011

Correspondence: BB Ward, Marine Life Support Systems,Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth,Devon PL1 3DH, UK.E-mail: bbw@princeton.edu

The ISME Journal (2011), 1–12& 2011 International Society for Microbial Ecology All rights reserved 1751-7362/11

www.nature.com/ismej

every season and the methodologies were adaptedand improved (Joint et al., 1997). The time series ofdissolved inorganic nitrogen concentrations in thewestern English Channel is less complete, partlybecause a reliable method of nitrate analysis wasdeveloped much later than that for phosphate.Although interannual variations in nutrient concen-trations have been described for the region (Jordanand Joint, 1998), the processes that influence thenitrogen cycle, and result in season changes in con-centration, have been less thoroughly investigated.

In this paper, we describe a seasonal study ofphytoplankton uptake of nitrate and ammonium inthe context of changes in nutrient concentration.These uptake measurements are linked to phyto-plankton community structure through the use offunctional gene microarrays targeted to eukaryoticphytoplankton nitrate reductase (NR) genes. Allmeasurements are placed within the context of alarge body of data on physics, chemistry and biologyof the English Channel (Harris, 2010; http://www.westernchannelobservatory.org.uk/). The aim of thestudy is to provide better understanding of howchanges in community composition and gene ex-pression of the phytoplankton assemblage mightcorrelate with activity and biogeochemical cycles. Itis hoped that this will improve parameterization formodels involved in assimilating data derived fromsatellite remote sensing (Smyth et al., 2010).

Materials and methods

Study site and sample collectionAt approximately weekly intervals between March2004 and February 2005, water samples werecollected from Station L4 (Harris, 2010) in theEnglish Channel (501150N, 41130W). The WesternChannel has been studied for more than 100 years,with a large contextual data set of physical,chemical and biological data (Southward et al.,2005; Smyth et al., 2010) within which to place newmeasurements. Station L4 is typical of UK temperatecoastal waters, and is largely composed of centralEnglish Channel water with, occasionally, influ-ences of river run-off (Siddorn et al., 2003, Reeset al., 2009). The station is now part of a long-termocean observatory, the Western English Channel(http://www.westernchannelobservatory.org.uk/).

Water samples were taken by pump from justbelow the sea surface (B1 m depth) as part of thestandard sampling protocol and transported to thelaboratory in 20 l carboys. Immediately upon returnto the laboratory (B2–4 h after sampling), the waterwas processed for different measurements. Ammo-nium and nitrate uptake incubations were initiatedimmediately; nutrient concentrations were mea-sured within 2 h of the water arriving in thelaboratory; samples for molecular and microarrayanalyses were filtered within 2 h onto Sterivex(Millipore, Billerica, MA, USA) filter capsules

(4–6 litre per capsule) and frozen at –80 1C. Approxi-mately 20 min was required to filter 4–6 litre of waterand two capsules were handled simultaneously.

NutrientsMicromolar (mmol l�1) nitrate, nitrite, silicate andphosphate concentrations were measured on a5-channel Technicon segmented flow colorimetricautoanalyzer (BranþLuebbe, AAII, Seal Analytical,Hampshire, UK) with detection limits of 0.1 mmol l�1

for all channels. Further details may be foundelsewhere (Woodward and Rees, 2001). Ammoniumconcentration was measured by the orthophthaldial-dehyde method (Holmes et al., 1999). As with allfluorometric determinations, there are challenges inboth establishing a baseline and obtaining ammonia-free water. In addition, the fluorescence of ortho-phthaldialdehyde method is different in distilledwater and sea water. In this study, low-ammonia seawater for blank determination was obtained byexploiting the diffusion of ammonia through apolytetrafluoroethylene membrane (Gibb et al.,1995). Polytetrafluoroethylene tubing was filledwith concentrated H2SO4, sealed and immersed in1 litre filtered sea water, taking care to ensure that noacid was introduced into the sea water. After 24 h,the ammonia concentration in the sea water wassignificantly reduced as ammonia, after diffusingthrough the polytetrafluoroethylene, was scavengedby the acid. For each determination of ammonia insea water, standard curves were constructed fromammonia additions to this low-ammonia sea water;standard additions of known ammonia concentra-tions were also added to each environmental sampleto check the fluorometric response of the ortho-phthaldialdehyde method.

Phytoplankton counts and pigmentsPaired water-bottle samples were collected from adepth of 1 m and preserved with 2% Lugol’s iodinesolution (Throndsen, 1978) and 4% bufferedformaldehyde. Between 10 and 100 ml of sample,depending on cell density, were settled for 448 h andall phytoplankton cells were identified where possi-ble to species level and enumerated (Widdicombeet al., 2010). Cell volumes were calculated accordingto the equations of Kovala and Larrance (1966) andconverted to carbon (Menden-Deuer and Lessard,2000). Pigments were extracted from filtered phyto-plankton and analyzed by high-performance liquidchromatography as previously described (Llewellynet al., 2005).

15N-nitrate and -ammonium uptake ratesAliquots from the 20 l carboys were measured intosix 250 ml bottles; three received 0.5 mmol l�1

15NO3 and three received 0.05 mmol l�1 15NH4 finalconcentrations. Experiments were done in a large

Phytoplankton assemblage and f-ratioBB Ward et al

2

The ISME Journal

temperature-controlled tank of sea water in aconstant temperature laboratory that was main-tained at ambient seawater temperature, asdescribed by Joint and Jordan (2008). Photon fluxsimilar to sunlight was achieved using three quartzhalogen lamps (Osram Power Star, 150 W, Munich,Germany); heating was not a problem because thesamples were incubated in a large volume of waterin the water tank (1000 l). Throughout the annualstudy, samples were incubated 0.5 m from the lightsource and all experiments received the samephoton flux—550 mmol quanta m�2 s�1.

For each tracer addition, two bottles were incu-bated in the light and one in the dark. As the aim ofthe study was to investigate nitrogen uptake byphytoplankton associated with photosynthetic car-bon fixation, rather than by the whole microbialassemblage, uptake rates measured in the darkincubations have been subtracted from those in thelight, to give an estimate of the phytoplankton-specific nitrogen assimilation. After 3 h, the entirecontents of each bottle were filtered onto 25 mm GF/Ffilters and dried. The particulate carbon, particulatenitrogen (PN) and 15/14N ratio of the PN weredetermined using an isotope ratio mass spectro-meter (Europa 20/20, SerCon, Cheshire, UK). Uptakerates were calculated using the equations of Dugdale andGoering (Dugdale and Goering, 1967) assuming con-stant substrate atom-% over the short incubation.

Probe and array designThe phytoarray is a functional gene microarraycontaining probes representing functional genesfrom major eukaryotic phytoplankton groups.Oligonucleotide probes representing NR genes wereselected from sequences contained in the publicdatabases (Genbank). The phytoarray is an arche-type array, in which each group of similar sequencesis represented by a single archetype probe, whichdiffers from all other archetypes by at least 15%sequence identity, chosen to represent the relevantdatabase (for example, all marine algae) whileminimizing overlap between probes. The probeselection method (Bulow et al., 2008), complete listof probes, probe sequences and database sequencesused to design the archetypes have been describedpreviously (Ward, 2008). The phytoarray contained16 NR probes, which include 8 diatom probes,4 chlorophyte probes and 3 NR probes representingsequences of unknown affiliation. Arrays containedtwo sets of replicate blocks such that each specificprobe was represented in two blocks, providingeight replicate features in each array. Two to eightfeatures in each block were spotted with equimolarmixtures of all gene-specific probes present on thearray. These MIXALL probes were used for signalnormalization (see below).

The probes were manufactured as 90-bp oligonu-cleotides, each containing the 70-mer probesequence described above plus a 20-mer universal

reference sequence (50-GATCCCCGGGAATTGCCATG-30). The presence of the same reference sequencein each feature was intended for use in quantifica-tion and normalization (Dudley et al., 2002). The90-mer oligo probes (Integrated DNA Technologies,Coralville, IA, USA) were adjusted to a concentra-tion of 0.05 mg ml�1 in 50% dimethylsulfoxide andwere spotted on CMT-GAPS amino silane-coatedglass slides (Corning Inc., Corning, NY, USA) usinga robotic style arrayer (DeRisi et al., 1996), in thePrinceton University Molecular Biology ArrayFacility. After printing, the slides were baked at80 1C for 3 h and stored in the dark at roomtemperature under slight vacuum.

DNA and RNA purificationParticulate material from sea water collected onSterivex filters was lysed and the DNA extractedusing a slight modification of the Gentra PureGenetissue protocol (Qiagen, Valencia, CA, USA).Volumes of the various buffers were doubled inorder to improve recovery from the filter capsulesand lysis was performed inside the filter capsule.The resulting DNA was ready for PCR withoutfurther purification and was stored at �20 1C.

RNA was recovered from the sterivex filtercapsules using the RNAqueous kit from Ambion(Applied Biosystems, Foster City, CA, USA) bydoubling the buffer volumes and carrying out thelysis step inside the filter capsules. Immediatelyfollowing elution of the RNA from the spin column,it was transcribed using Superscript III from Invitro-gen (Carlsbad, CA, USA), using either oligo-d(T) orrandom hexamer primers. The remaining RNA andcomplementary DNA (cDNA) was stored at �80 1Cuntil further use.

PCR amplificationTo produce PCR targets for hybridization with thearray, DNA was amplified with gene-specific pri-mers using the nested PCR approach (Allen et al.,2005). The NR primers amplify NR genes from alldiatoms tested so far, several other algal groups inculture and field samples (Bhadury and Ward,2009), but do not amplify NR genes from chloro-phytes or prokaryotes (Allen et al., 2005). PrimersNRPt907F and NRPt2325R were used for the first(outer) round of NR amplification, and NRPt1000Fand NRPt1389 for inner amplification. The secondround of amplification used 1–5 ml of the outerreaction as a template with the same PCR protocol.

Target preparationHybridization targets were produced from PCRproducts. The product obtained from the standardPCR above was labeled by random priming usingKlenow fragment and random octomers supplied inthe BioPrime labeling kit (Invitrogen). The standard

Phytoplankton assemblage and f-ratioBB Ward et al

3

The ISME Journal

dNTP mixture was replaced with a mixture of1.2 mM dNTP containing A/C/G/T/U in the ratio of6:6:6:2:4, in which the U was the amino-allyl-modified base dUTPaa. Parallel reactions werepooled and again cleaned with Qiaquick columns(Qiagen), and the eluted dUTPaa-labeled DNA wasdried under vacuum and stored as a dry pelletat �20 1C.

The dUTPaa-labeled fragments (200–2000 ng)were labeled with Cy3 (Cy3 mono-functional NHSester; GE Healthcare Bio-Sciences Corp., Piscataway,NJ, USA) as previously described (Ward, 2008).Immediately before hybridization, the target wasdissolved in water using a volume calculated toprovide a convenient addition to the hybridizationchamber. The concentration of target was computedby measuring the DNA concentration and Cy3concentration of the Qiaquick eluate before the lastdrying step. DNA concentration was quantifiedusing the PicoGreen kit (Invitrogen) and a JASCOfluorometer (Essex, UK).

Hybridization, scanning and quantification of array dataCy3-labeled target (200 ng for arrays using targetsproduced by PCR) plus 100 pmol of the reverse Cy5-labeled 20-mer reference oligonucleotide in a totalvolume of 200 ml was pipetted into the gasket area ofthe coverslip (Agilent Technologies, Santa Clara,CA, USA). The slides were placed inside hybridiza-tion chambers (Agilent), and rotated at 8–12 cyclesper min at 55 1C for B16 h. After hybridization, thecoverslip was removed and the slides washed inthree successive 10–20 min washes at roomtemperature (wash 1¼ 1� SSC (salt, sodium citratesolution), 0.05% SDS (sodium dodecyl sulfate);wash 2¼ 0.1� SSC, 0.05% SDS; wash 3¼ 0.1�SSC). After the last wash, the slide was dried bycentrifugation. The dried slides were stored at roomtemperature in the dark and scanned with an Axon4200 laser scanner (Molecular Devices, Sunnyvale,CA, USA).

The scanner determined Cy3 (green) and Cy5 (red)fluorescence for each spot in the array using GenePix Pro 6.0 (Axon Instruments). The fluorescencedata were transferred to Excel spreadsheets formanipulation and quantification and filtered forquality control as follows. The average backgroundvalue for all spots was computed and spots withbackgrounds exceeding the average value by 2 s.d.were removed. This usually involved removing 1 or 2features (obvious printing problems or slideblemishes) from each array whose backgroundsexceeded the average by B1000�. The averageand s.d. of the background for all dots wererecomputed. Spots whose red or green fluorescencedid not exceed the background value by 1 s.d. werenot considered to contain significant signal andwere removed from further calculations. For spotsthat passed the background filter cutoff, the Cy3/Cy5ratio was computed and an average and s.d. of the

ratio were computed for each set of replicatefeatures. The average Cy3/Cy5 ratio for replicatefeatures was considered significant if the T-statistic(Wurmbach et al., 2003) exceeded 3 (it usuallyexceeded 5).

The main source of signal variability was betweenreplicate blocks, whereas within-block replicationwas excellent (T often 420 for n¼ 4 to 5). Botharrays included features that contained constructedmixtures (MIXALL probes) of the specific probes ineach block, and these provided excellent correctionfor between- and within-block variability in hybri-dization. The average Cy3/Cy5 ratio for the MIX-ALLs in each block was computed and used tocorrect for differences in hybridization intensitybetween duplicate blocks on the same array. Theaverage coefficient of variation (s.d. as a percent ofthe mean) for replicate Cy3/Cy5 ratios within arrayswas 16%, the highest variance being associated withsmallest ratios. For each target, duplicate arrayswere hybridized and the average relative fluores-cence ratio (RFR) for each archetype—a measure ofthe hybridization signal—was used in subsequentanalyses. Average RFR values for replicate arraysvaried by 8–15%, as reported previously in anextensive investigation of the reproducibility of thisarray format (Ward, 2008).

All of the original array files are available at GEO(Gene Expression Omnibus; http://www.ncbi.nlm.nih.gov/projects/geo/) at NCBI (National Center forBiotechnology Information) under GEO accessionnumber GSE27998.

Statistical analysesBiogeochemical, hydrographic and microarrayhybridization data were subjected to multivariatestatistical analyses using PRIMER (Clarke andWarwick, 2001; Clarke and Gorley, 2006) and R(http://cran.r-project.org/). Two-dimensional non-metric multidimensional scaling was used to clusterthe archetypes according to their temporal patterns,whereas principal components analysis was used toinvestigate relationships among archetypes andchemical and physical variables. Physical, chemicaland rate data were log transformed before usingPRIMER, and f-ratio and array RFR values were arcsintransformed before performing regression and corre-lation analysis with R. When results are stated assignificant, it means that the relationship wassignificant at the level of Pp0.05.

Results

Nutrients concentrations over the annual cycleDuring the period of this study, temperature variedfrom a minimum of 9.0 1C in January to a maximumof 18.8 1C in late August. Salinity was relativelyinvariant at 35.11±0.24 over the year; the lowestsalinities were associated with late autumn storms

Phytoplankton assemblage and f-ratioBB Ward et al

4

The ISME Journal

when there was a significant increase in run-off.Nutrient concentrations in the surface water atStation L4 underwent a typical seasonal cycle(Figure 1). All three major nutrients (N, P, and Si)were present at high concentrations in winter(December–March), decreased in spring (lateMarch–April) as a consequence of the increase inphytoplankton during the spring bloom, remained atconsistently low levels throughout the summer(May–July/August) before increasing again in latefall (August–November). There were a few sporadicincreases in phosphate or silicate—but not nitrate—during the summer, in which either nutrient waspresent at relatively high concentrations at a singletime point. The molar ratio of nitrate (N) to silicate(Si) varied from 0.02 to 2 (mean¼ 0.83±0.72).Nitrate concentrations were higher than silicate inthe winter, but the ratio was o1 in summer,suggesting potential nitrate limitation of diatomgrowth. The nitrate/phosphate ratio was very closeto 16 in January of 2004, and then varied widelyfrom 0.1 to 490 and leveled out at B6 by the end ofthe year. Nitrate, nitrite and silicate were allpositively correlated with each other and withsalinity, but were negatively correlated with tem-

perature, suggesting Atlantic Ocean rather thanfreshwater river sources for the nutrients. Phosphateconcentration was weakly correlated with silicateconcentration but was not related to any othernutrients or physical/chemical variables (Supple-mentary Table 1).

Phytoplankton community compositionThe seasonal patterns of phytoplankton speciescomposition and photosynthetic pigments havebeen described previously for this site (Rodriguezet al., 2000; Llewellyn et al., 2005). Here weemphasize the diatom component because thisgroup was best represented in the microarrayanalysis. Although highly variable on a weeklybasis, the annual cycle usually includes two majorblooms, to which both diatoms and flagellatescontribute major portions of the biomass. In 2004,chlorophyll concentration indicated two bloomperiods (Figure 2), the first from mid-April to mid-May, and the second, of slightly lower maximumchlorophyll concentration but longer duration, fromearly July to late September. Fucoxanthin concen-tration—a marker for diatoms—followed a verysimilar temporal pattern and was highly correlatedwith chlorophyll concentration (R2¼ 0.761,P50.001), as reported previously for a 3-year dataset at this station (Llewellyn et al., 2005). Neitherchlorophyll a nor fucoxanthin concentrations werecorrelated with total phytoplankton carbon, diatomcarbon or nitrate concentration. As reported byLlewellyn et al. (2005), the relationship betweenchlorophyll a and total phytoplankton carbon wasa significant positive correlation (R2¼ 0.74,P¼ 0.0003) for samples in which the chlorophylla/total phytoplankton carbon ratio was 40.04, butwas more variable for warm season samples whenthe ratio was lower (o0.04) and more variable. Both

Figure 1 Concentrations (mmol l�1) of nitrate, nitrite, ammo-nium, silicate and phosphate (right axis) measured in the surfacewaters at L4 during the study. (Bottom panel) Expanded scale forsummer months.

Figure 2 Chlorophyll a, peridinin, fucoxanthin and hexfucox-anthin concentrations (ng l�1) determined by high-performanceliquid chromatography (HPLC) analysis. Right axis is the organicPN concentration determined by mass spectroscopy in the15N uptake experiments.

Phytoplankton assemblage and f-ratioBB Ward et al

5

The ISME Journal

chlorophyll a (R2¼ 0.59, P50.001) and fucoxanthin(R2¼ 0.53, P50.001) concentration were positivelycorrelated with PN concentration, as measured bymass spectrometry in the 15N uptake experiments.Total carbon estimated from phytoplankton countsand PN measured by mass spectrometry were notwell correlated and suggested an average carbon/nitrogen ratio of 2.4, well below that usuallyreported for biomass. The total carbon/nitrogen ratioaveraged over 2004 from the WCO (http://www.westernchannelobservatory.org.uk/) was 8.1, muchcloser to the usual average for biomass, andsuggesting slight N limitation.

Correlations between fucoxanthin and chloro-phyll a concentrations imply that diatoms weremajor contributors to the overall biomass repre-sented by chlorophyll throughout the year. Thecorrelations between both chlorophyll a and fucox-anthin and PN again suggest that diatoms weremajor contributors to the live biomass, and that mostof the PN was associated with live phytoplanktonrather than non-living material. Lack of significantcorrelations between pigments other than chloro-phyll a and fucoxanthin with PN and particulatecarbon estimated from the microscopy countssuggests that these estimates did not reflect livebiomass accurately. Additionally, fucoxanthin andPN were positively correlated with the rate of nitrateassimilation (see below; Supplementary Table 1),consistent with a major role for diatoms in assim-ilating nitrate and driving new production in thisregion.

Several diatom species achieved their maximumabundance (as determined by microscope counts)for the year in the week of, or the week following,the large silicate injection in late August. Presum-ably the silicate injection and maximum nitrateassimilation rates measured on 25 August led to thebiomass accumulation within the next few days,which we captured in the weekly sampling on 31August. Several of the same species contributed tothe smaller maxima in diatom carbon abundancein May, June and July. These species, includingChaetoceros socialis, Guinardia delicatula, Lepto-cylindrus danicus, Nitzschia closterium, Pseudo-nitzschia spp., Rhizosolenia stolterfothii andother Rhizosolenia spp., and several Thalassiosiraspecies, were the major components of the diatomassemblage in terms of cell carbon over the annualperiod.

Nitrate and ammonium uptake ratesNitrate and ammonium uptake rates both varied ontime scales of several weeks (Figure 3). The firstspring maximum in nitrate uptake preceded the firstmaximum in ammonium uptake rate, and wasassociated with the spring maximum in chlorophyllconcentration. A second brief maximum in uptakerates of both N sources occurred during a minimumin the chlorophyll concentrations, but the highest

nitrate uptake rates occurred at the time of the lateAugust chlorophyll maximum. The peak nitrateuptake rate on 25 August coincided with a largesilicate injection that was not accompanied by anincrease in nitrate concentration, suggesting thatphytoplankton assimilation of nitrate maintainedlow concentrations. The second highest nitrateassimilation measured over the year was recordedin late September, when both nitrate and silicateconcentrations were at or near their fall maxima.

Specific uptake rates (V) for nitrate and ammo-nium varied in parallel to their total uptake rates (r).These rates suggest maximum growth rates (assum-ing a 12 h light period) in terms of nitrogen of o0.5per day (0.26 per day for NH4

þ on 21 June and0.21 per day for NO3

� on 25 August). The averagegrowth rates, however, were much lower, 0.037 perday±0.042 for NO3

� and 0.071 per day±0.062 forNH4

þ . These rates are well below the maximumpredicted for nutrient replete growth at the observedtemperatures (Eppley, 1972), but many of the speciesenumerated from these samples have optimalgrowth temperatures higher than those observed atL4. Thus, both temperature and nutrient availabilitycould be factors controlling the growth rate andspecific uptake rates of the overall assemblage. Thef-ratio, computed on the basis of light minus darkuptake rates, ranged from 0.05 (late June) to 40.8(mid-April, late August). When calculated as a time-averaged value over the period of the study, theannual f-ratio was 0.35, indicating the importanceof regenerated production in this temperatecoastal zone.

Correlation-based principal components analysisof DIN concentrations and uptake rates across allmonths identified important temporal patterns(Figure 4). The first principal component essentiallycontrasts dates with high uptake rates with dateswith high levels of nitrate and nitrite, and explains33.6% of the variance. The second principalcomponent, explaining a further 26.6% of thevariance, separates dates with high f-ratio and NO3

�

Figure 3 Uptake rates (r) of nitrate and ammonium (light minusdark) determined in 15N tracer experiments and f-ratio.

Phytoplankton assemblage and f-ratioBB Ward et al

6

The ISME Journal

uptake in the light from dates with high NO3�

concentration. For example, the two dates in lateApril (late spring bloom: low NO3

� concentration)clustered opposite all three March dates and theearly April date (early spring: high NO3

� concentra-tion, low NO3

� assimilation rates). The five dates inJune were not clustered coherently, which is con-sistent with the great variability observed in Nuptake and N concentrations over that month(Figures 1 and 3). Most significantly, the late Augustdate (very high f-ratio and high N uptake rate) waswell separated from all other dates in Figure 4. Thisis consistent with the high NO3

� uptake measure-

ments and high f-ratio measured in late August.The large temporal signal in most environmentalvariables (Figure 1) probably explains the lack ofsimple correlations between temporally contiguoussamples and single physical/chemical variables.The f-ratio was positively correlated with bothchlorophyll a (R2¼ 0.47, P50.001) and fucoxanthin(R2¼ 0.25, P50.006) concentrations (Figure 5). Thehighest f-ratios coincided with the spring and latesummer maxima in nitrate uptake rates and pigmentconcentrations.

NR archetype relative abundance and gene expressionArray analysis was performed on samples from sixdates, all before the August silicate injection. Thestrongest hybridization signals (RFR) determinedfrom NR PCR targets prepared from DNA indicatedthe presence of archetypes representing Phaeodac-tylum tricornutum, Thalassiosira oceanica and twoarchetypes representing unknown NR sequences(Figure 6). The two unknown archetypes,LEO212154 and MBAP19CONSENS, representmultiple similar sequences originally derived fromNew Jersey coastal waters (both archetypes) and

Figure 5 Chlorophyll a concentration (Chl) and fucoxanthin(Fuco) concentration plotted against f-ratio. Chl (ng l�1)¼3215.8(f-ratio)þ 164.13, R2¼ 0.6023, Po0.0001; Fuco (ng l�1)¼784(f-ratio)þ 41.92, R2¼0.5056, Po0.0001.

Figure 6 RFR for DNA hybridization signals from each NRarchetype at six dates. Errors for individual points not shown forclarity; see Materials and methods.

8 MonthMarchAprilMayJuneJulyAugustSeptemberOctober

6

NovemberDecemberJanuaryFebruary

4

2

PC

2

0

-2

-4

PC1-4 -2 0 2 4 6

Figure 4 Principal components analysis based on a correlationmatrix of chemical (nutrients), physical (temperature, salinity)and biological (uptake rates, f-ratio, pigments) factors at eachsample date.

Figure 7 RFR for cDNA hybridization signals from each NRarchetype at six dates. Errors for individual points not shown forclarity; see Materials and methods.

Phytoplankton assemblage and f-ratioBB Ward et al

7

The ISME Journal

Monterey Bay (MBAP19CONSENS) that were avail-able at the time this probe set was designed. Thesefour high signal probes all showed different temporalpatterns.

Three of the archetypes that yielded high DNARFR were also large RFR signals in the PCR targetsprepared from cDNA. Of the four high DNA RFRarchetypes, only MBAP19CONSENS cDNA was notsignificantly different from the rest of the arche-types, which showed generally lower and lessvariable signals (Figure 7).

Low hybridization signals were observed for thearchetype probes representing chlorophyte algal NRgenes. The NR PCR primers used here weredesigned to target diatoms and they amplify knowngreen algal sequences not at all or with very lowefficiency. The RFR for the chlorophyte archetypeswas similar to that observed for several of the low-signal diatom archetypes, essentially backgroundwith little temporal variability. They will not be thefocus of further discussion.

Multivariate analysis of normalized DNA RFRdata was used to capture the temporal patterns inthe RFR data, such that archetypes that covary intime are clustered together. The analysis identifiedtwo groups of archetypes and two individualarchetypes (LEO212154 and PTRICORNUTUM)whose temporal patterns (variation, that is, increaseor decrease with time, rather than absolute signalstrength) were significantly different from eachother (Figure 8a), as determined by a similarityprofiles test. The two archetypes with distinctbehavior were among those with the highest RFR,and archetypes within a second group, whichincluded TOCEANICA, were characterized bywidely varying magnitude of RFR but similartemporal patterns. The largest group of archetypes,which included MBAP19CONSENS, had generallylow signals with little temporal variability.

Groupings of temporal patterns among archetypesdiffered markedly when cDNA RFR data weresubjected to a similar analysis (Figure 8b). Anonparametric Mantel test based on the underlyingresemblance matrices derived from DNA and cDNARFR values indicates that the grouping in temporalpatterns among archetypes are significantly related(Spearman’s r¼ 0.516, P¼ 0.003). However, thecorrelation is not close to 1.0, and hence differencesbetween patterns in abundance and expression ofNR genes are important. In the analysis of cDNA, thearchetype LEO212154 remained distinct from allothers, but MBAP19CONSENS now grouped withTOCEANICA and TSTPSKENAS. PTRICORNUTUMgrouped with MBAP22 and LEO212152. LEO21213no longer grouped with TOCEANICA but with thelarger grouping of generally low RFR archetypes,and CHLAMY was distinct from all other arche-types. In comparing Figures 6–8, it may be seen thatthe major archetypes, with highest RFR, separate outin the multidimensional scaling analysis becausetheir peaks never coincided in time, as expected, but

indicating variation in individual archetyperesponse to environmental conditions.

Using multiple correlation analyses betweennormalized RFR and biogeochemical, hydrographicand uptake rate data, we found that TOCEANICAwas positively correlated with NH4

þ concentration(R2¼ 0.761, P¼ 0.098) and with the rate of ammo-nium uptake (R2¼ 0.88, P¼ 0.026) but not with anyother variables (Supplementary Table 1). MBAP19-CONSENS was positively correlated with the uptakerates of both NO3

� and NH4þ (R2¼ 0.9, P¼ 0.013).

None of the other probes identified as distinct in themultidimensional scaling analysis, nor any of theclusters of probes, were significantly correlated withenvironmental or rate data.

Discussion

The seasonal patterns in temperature, leading towater column stratification, have been welldescribed for this temperate region in the westernEnglish Channel (Southward et al., 2005). Stratifica-tion usually occurs in April, and the springphytoplankton bloom follows causing nutrient de-pletion in the surface waters that extends through

Standardise Samples by TotalResemblance: S17 Bray Curtis similarity

simpr

d

b

aLEO212154

AMORPHA

CHAETCOSC

2D Stress: 0.05

c

CHLAMY

DUNALIELLA

VOLVOX

LEO 21213

a

b

TOCEANICA

TSTPSKENAS

CHLORELLALEO21212

LEO212152

MBAP22

MBAP19CONSENS

MBAP244

PTRICORNUTUM

Standardise Samples by TotalResemblance: S17 Bray Curtis similarity

simprdebaLEO212154

PTRICORNUTUM

2D Stress: 0.05

c

LEO212152

MBAP22

CHLAMYCHLORELLA

DUNALIELLA

LEO 21213 MBAP19CONSENSMBAP244

VOLVOXLEO21212

AMORPHACHAETCOSCTOCEANICA

TSTPSKENAS

Figure 8 Two-dimensional nonmetric multidimensional scalingplots of RFR data for (a) DNA and (b) cDNA. Archetypes thatshowed similar temporal patterns (a hypothesis of no multivariatestructure rejected at P¼0.05) are shown in the same color. A fullcolor version of this figure is available at The ISME Journalonline.

Phytoplankton assemblage and f-ratioBB Ward et al

8

The ISME Journal

until late September. This seasonal pattern was alsoseen during this study. However, as has beenreported by Jordan and Joint (1998), there were alsosporadic introductions of nutrients into the surfacemixed layer, possibly as riverine input followingrain events (see, for example, Rees et al., 2009),which were not in the Redfield ratio and which werequickly depleted. Jordan and Joint (1998) examined41000 nitrate and phosphate measurements,obtained over a long time period (1930–1987), andfound that the mean molar nitrate/phosphate ratiowas only 11.6, much lower than the Redfield ratio of16:1. Significantly, over that long time period, therewere many transient increases in P that did not haveequivalent increases in N. Interestingly, similarnear-surface enrichments have also been reportedfor the North Pacific Ocean (Karl and Tien, 1992;Haury et al., 1994). However, the cause of thesetransients in nutrient concentration has not beenadequately explained.

In the present study, there were also short-livedincreases in some nutrient concentrations. Althoughwe observed only very minor P transients, comparedwith those reported by Jordan and Joint (1998), therewere occasions when changes in nutrient uptakekinetics implied that the phytoplankton populationswere responding to sporadic nutrient inputs. Inparticular, a large increase in nitrate uptake wasmeasured on 25 August, with a maximum in f-ratio,but there was only a small change detected in nitrateconcentration; this was some weeks before nitrateconcentrations returned to their winter maximum.However, the peak f-ratio was accompanied by alarge change in silicate concentration, suggestingthat silicate was not required at the same concentra-tion as nitrate. Fucoxanthin concentrations werehigh in this sample, suggesting that diatoms werepresent in this late bloom. The dinoflagellateKarenia sp. (which contains some fucoxanthin) isoften an important component of the late summerbloom in the western English Channel but was indecline on this date, having reached a maximum inthe previous 2 weeks. Other dinoflagellates, mainlyProrocentrum sp. that typically do not containfucoxanthin (Berden-Zrimec et al., 2008), wereabundant in August. They may have contributed tothe observed peak in nitrate assimilation, respond-ing to increased nutrient supply by utilizing nitrate(and presumably phosphate) without a large require-ment for silicate. In this case, because nutrientuptake rates were measured, we have been able todetect changes in phytoplankton activity that wouldnot have been predicted from the observed varia-tions in nutrient concentration, or from measures ofphytoplankton abundance. It is clear that there canbe very rapid variations in the supply of nutrients tothe surface mixed layer in this region—and thephytoplankton populations respond very rapidly.

Although we have detailed information on phy-toplankton species (from microscope and pigmentanalyses), it is difficult to draw convincing cause

and effect conclusions from these standard biologi-cal oceanographic measurements. It is impossible tolink specific phytoplankton species, or changes inapparent phytoplankton assemblage, to the observedvariations in nutrient concentration or to nitrogenuptake rate measurements convincingly. For thisreason, we investigated if species-specific NRabundance and expression might provide additionalinformation that would lead to better understandingof phytoplankton–nutrient dynamics. Grazing onphytoplankton also plays a role in the seasonalpatterns of population growth and assemblagecomposition, but less so in the specific phytoplank-ton–nutrient interactions that might be reflected ingene expression.

Significant correlations were detected betweenphysical/chemical variables and a few of the arche-types (Supplementary Table 1). RFR for TOCEANICAand TSTPSKENAS cDNAs were negatively correlatedwith NO3

� and NO2� concentrations, which may

represent a phase phenomenon (higher relativebiomass after the nutrients have been assimilated),whereas PTRICORNUTUM and TSTPSKENAS werepositively correlated with temperature.

A few of the archetypes with largest RFR corre-lated with ammonium assimilation, either in thelight or the dark. Although these archetypes allrepresent diatoms, which are often associated withnitrate uptake, their relative abundance in terms ofNR archetypes was significantly related to ammo-nium uptake and not to nitrate uptake. The positivecorrelations between total chlorophyll and f-ratio,and between fucoxanthin and f-ratio (Figure 5), areconsistent with greater importance of nitrate as an Nsource at higher biomass/productivity levels, butthis simple correlation was not detected for mostindividual archetypes.

PTRICORNUTUM was the only archetype whoseRFR was correlated with the f-ratio, and it waspositively correlated at both DNA and RNA levels.Although the f-ratio was computed from Lightminus Dark rates, PTRICORNUTUM was not corre-lated with absolute rate of nitrate uptake (Lightminus Dark), although it was negatively correlatedwith the rate of ammonium uptake in the light.PTRICORNUTUM thus appears to exhibit a relation-ship with nitrogen uptake that is typically associatedwith diatoms, but which was not exhibited by otherdiatoms represented on the array.

Pearson correlation analysis using arcsin trans-formed RFR values for DNA and cDNA hybridiza-tion signals detected significant correlations amongarchetypes at both the DNA and cDNA levels thatwere consistent with the multidimensional scalingcluster analysis (Figures 8a and b). The compositionof the clusters differed between DNA and cDNA andeach of the archetypes with the largest RFR were indifferent clusters. These clusters represent arche-types that covary in time among the six dates andmay identify ecologically important associations interms of relative abundance and gene expression.

Phytoplankton assemblage and f-ratioBB Ward et al

9

The ISME Journal

It was striking that none of the archetypes showed asignificant correlation between cDNA and DNAwhen analyzed in a pairwise manner. Nonetheless,when grouped across all dates and all archetypes,cDNA and DNA RFR were significantly correlated(R2¼ 0.610, P50.001).

The lack of correlation between cDNA and DNAsignals for individual archetypes could be becauseof differential regulation of NR in response to NO3

�

concentration and time of day. Gene regulationcould result in gene expression varying much morerapidly than abundance, which could not beresolved with our sampling. The cDNA/DNA corre-lation for all archetypes suggests that despitevariability among types, the most abundant typesare the most active in gene expression, which isconsistent with selection for growth of those typesunder favorable conditions.

The finding that two archetypes, MBAP19CON-SENS and LEO212154, which were not associatedwith known diatom species, were among thestrongest signals suggests that either these arche-types represent known species whose genes havenot been sequenced or that abundant phylotypes arenot represented in the culture collection. A screen ofthe GenBank database in April 2010 found thatdespite the presence of hundreds of additional NRsequences since the design of the original phyto-array, the original four NR sequences from New Jerseycoastal waters upon which the LEO212154 probewas designed are still the only sequences with highidentity to the probe. For MBAP19CONSENS, B40NR sequences were found that have high enoughidentities to hybridize with the probe. None of theserepresent cultivated species, and all of them werereported from two studies, one of seagrass epiphytesin Tampa Bay Florida (Adhitya et al., 2007), and onea survey of NR and rbcL diversity in Monterey Bayand L4 phytoplankton assemblages (Bhadury andWard, 2009).

The TOCEANICA probe has 100% identity onlywith itself, but has identity in the range of 87% witha number of environmental sequences obtained fromthe seagrass study (Adhitya et al., 2007) as well asfrom Monterey Bay (Allen et al., 2005). Sequencesfrom Skeletonema costatum, T. weissflogii andAsterionellopsis glacialis are in the range of85–87% identical, equivalent to the cutoff belowwhich hybridization is negligible (Taroncher-Oldenburg et al., 2003). T. oceanica was notindividually enumerated in the L4 microscopyrecord, but it likely falls into the general categories‘Thalassiosira 5, 10 and 20 mm’, which were abun-dant during the summer bloom period (Widdicombeet al., 2010).

P. tricornutum was not recorded in the microscopydata from L4 and we were initially skeptical that thisspecies, known primarily as a laboratory modelorganism and usually isolated from nutrified orcontaminated sites, could be an important componentof the diatom assemblage of the western English

Channel. The probe representing P. tricornutum isvery specific, however, and there are no publishedsequences within 85% identity of the P. tricornutumNR sequence (all o80% identity). We are confidentthat these results do not result from contaminationduring DNA/RNA analysis. Negative controls in everyPCR reaction ensured that no obvious contaminantswere present in the targets prepared by PCR. It seemsunlikely that seawater samples filtered in one lab andextracted in another could be contaminated bycultures growing elsewhere in the institution.Sterivex samples were extracted in different labora-tories for DNA and RNA (Plymouth Marine Labora-tory and Princeton University, respectively), andhence contamination would have had to be presentin both locations. Thus, technical issues seemunlikely to account for the observations.

The explanation may be that P. tricornutum waspresent in the form of small oval-shaped cells,which would not have been recognized by micro-scopic inspection. P. tricornutum occurs in threeshapes, the tricornered hat, the spindle and a small(3–8 mm diameter) oval (Allen et al., 2008). The ovaldominates most isolates when subjected to stress(De Martino et al., 2007). Using a PCR assaydesigned to be specific for the intergenic transcribedspacer region of the P. tricornutum 18S rRNA genes,De Martino et al. (2007) detected P. tricornutum inthe DNA extracts from April and July from whichthe array targets were prepared. In fact, one of thefirst isolations of a culture of P. tricornutum, thencalled Nitzschia closterium var. minimutis, wasfrom the English Channel (Allen and Nelson,1910). Thus, we conclude that the array detectionof P. tricornutum was probably correct. Even‘known’ species can be cryptic, illustrating thepower of DNA/RNA-based detection methods.

The phytoarray demonstrates the potential toinvestigate the phytoplankton community assem-blage at much higher resolution than can beaccomplished with assays of total chlorophyll,ancillary pigments or cell counts. Even with thesmall number of archetype probes present on thearray used here, differential temporal patterns weredetected for multiple diatom types. The low-frequency array analysis reported here could bevastly improved upon with higher-frequency analy-sis, taking advantage of streamlined protocolsdeveloped recently. One of the interesting findingshere is the relative importance (high RFR) ofarchetypes representing unknown organisms. Itremains to be discovered whether these archetypesrepresent uncultivated types. Using probes derivedfrom unknown types based on gene sequencesretrieved from the environment, however, allowsthe detection of distinct temporal patterns andarchetype associations that were not obvious frombulk measurements or cell counts.

The chemical and physical parameters over thetime course showed the expected seasonal patterns,as did the overall general pattern of phytoplankton

Phytoplankton assemblage and f-ratioBB Ward et al

10

The ISME Journal

biomass and N uptake rates. Simple correlationsamong overall abundances, archetype abundances,nutrient concentrations and chemical and physicalparameters, however, were generally not significantand clearly did not describe the environmentalresponses of the phytoplankton. Observations ofparticular snapshots in time are not capable ofidentifying the processes responsible for patternsthat result from previous conditions. Higher resolu-tion time series of relative abundance and geneexpression might make it possible to link pastconditions with present patterns. Among all arche-types combined, RFR for cDNA and DNA patternscorrelated, suggesting a general relationship be-tween biomass and gene expression. However, onan individual basis, the correlations were weak andthis mismatch may imply transitions in response toenvironmental conditions such as, for example,entering stationary phase or beginning of a growthphase. Higher temporal resolution in the arrayanalysis and environmental sampling could linkthese processes to individual environmental stimuli.

Acknowledgements

We thank the staff of the Western English ChannelObservatory and the crews of the research ships. Monitor-ing data are taken from the WEC Observatory database:nutrients measured by Malcolm Woodward, hydrographicdata by Tim Smyth, phytoplankton counts by DerekHarbour (through 2004) and subsequently by ClaireWiddicombe (beginning in 2005), and pigments by CaroleLlewellyn. The microarray was printed by Donna Storton(Princeton University) and we gratefully acknowledge thePrinceton University Microarray Facility. We thank theNatural Environment Research Council (UK) and theNational Science Foundation (USA) for funding.

References

Adhitya A, Thomas FI, Ward BB. (2007). Diversity ofassimilatory nitrate reductase genes from planktonand epiphytes associated with a seagrass bed. MicrobEcol 54: 587–597.

Allen AE, LaRoche J, Maheswari U, Lommer M, SchauerN, Lopez PJ et al. (2008). Whole-cell response of thepennate diatom Phaeodactylum tricornutum to ironstarvation. Proc Natl Acad Sci USA 105: 10438–10443.

Allen AE, Ward BB, Song BK. (2005). Characterization ofdiatom (Bacillariophyceae) nitrate reductase genesand their detection in marine phytoplankton commu-nities. J Phycol 41: 95–104.

Allen EJ, Nelson EW. (1910). On the artificial culture ofmarine plankton organisms. J Mar Biol Assoc UK 8:421–474.

Berden-Zrimec M, Flander-Putrle V, Drinovec L, ZrimecA, Monti M. (2008). Growth, delayed fluorescence andpigment composition of four Prorocentrum minimumstrains growing at two salinities. Biol Res 41: 11–23.

Bhadury P, Ward BB. (2009). Molecular diversity of marinephytoplankton communities based on key functionalgenes. J Phycol 45: 1335–1347.

Bulow SE, Francis CA, Jackson GA, Ward BB. (2008).Sediment denitrifier community composition andnirS gene expression investigated with functionalgene microarrays. Enviro Microbiol 10: 3057–3069.

Clarke KR, Gorley RN. (2006). PRIMER v6: User Manual/Tutorial. Primer-E Ltd.: Plymouth, UK.

Clarke KR, Warwick RM. (2001). Change in Marine Com-munities: An Approach to Statistical Analysis andInterpretation, 2nd edn. Primer-E Ltd: Plymouth, UK.

Cooper LHN. (1937). On the ratio of nitrogen to phos-phorus in the sea. J Mar Biol Assoc UK 22: 177–182.

Cushing DH, Dickson RR. (1976). The biological responsein the sea to climatic changes. Adv Mar Biol 14: 2–122.

De Martino A, Meichenin A, Shi J, Pan KH, Bowler C.(2007). Genetic and phenotypic characterization ofPhaeodactylum tricornutum (Bacillariophyceae)accessions. J Phycol 43: 992–1009.

DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS,Ray M et al. (1996). Use of a cDNA microarray toanalyse gene expression patterns in human cancer.Nat Genet 14: 457–460.

Dudley AM, Aach J, Steffen MA, Church GM. (2002).Measuring absolute expression with microarrayswith a calibrated reference sample and an extendedsignal intensity range. Proc Natl Acad Sci USA 99:7554–7559.

Dugdale RC, Goering JJ. (1967). Uptake of new andregenerated forms of nitrogen in marine production.Limnol Oceanogr 12: 196–206.

Eppley R. (1972). Temperature and phytoplankton growthin the sea. Fish Bull, NOAA 70: 1063–1085.

Genner MJ, Halliday NC, Simpson SD, Southward AJ,Hawkins SJ, Sims DW. (2010). Temperature-drivenphenological changes within a marine larval fishassemblage. J Plankton Res 32: 699–708.

Gibb S, Mantoura RFC, Liss PS. (1995). Analysis ofammonia and methylamines in natural waters by flowinjection gas diffusion coupled to ion chromato-graphy. Analytica Chimica Acta 316: 291–304.

Harris R. (2010). The L4 time series: the first 20 years.J Plankton Res 32: 577–583.

Haury LR, Fey CL, Shulenberger E. (1994). Surfaceenrichment of inorganic nutrients in the North PacificOcean. Deep-Sea Res Pt 1 41: 1191–1205.

Holmes RM, Aminor A, Kerouel R, Hooker BA, PetersonBJ. (1999). A simple and precise method for measuringammonium in marine and freshwater ecosystems. CanJ Fish Aquat Sci 56: 1801–1808.

Joint I, Jordan MB, Carr MR. (1997). Is phosphate part ofthe Russell Cycle? J Mar Biol Assoc UK 77: 625–634.

Joint IR, Jordan MB. (2008). The effect of short-termexposure to UVA and UVB on potential phytoplanktonproduction in UK coastal waters. J Plankton Res 30:199–210.

Jordan MB, Joint I. (1998). Seasonal variation in nitrate,phosphate ratios in the English Channel 1923–1987.Estuar Coast Shelf Sci 46: 157–164.

Karl DM, Tien G. (1992). MAGIC: a sensitive and precisemethod for measuring dissolved phosphorus in aqua-tic environments. Limnol Oceanogr 37: 105–116.

Kovala PE, Larrance JD. (1966). Computation of phyto-plankton cell numbers, cell volume, cell surface andplasma volume per litre, from microscopical counts.Special Report no. 38. Department of Oceanography,University of Washington.

Llewellyn C, Fishwick J, Blackford J. (2005). Phytoplank-ton community assemblage in the English Channel: a

Phytoplankton assemblage and f-ratioBB Ward et al

11

The ISME Journal

comparison using chlorophyll a derived from HPLC-CHEMTAX and carbon derived from microscopy cellcounts. J Plankton Res 27: 103–119.

Matthews DJ. (1917). On the amount of phosphoric acid inthe sea-water off Plymouth Sound II. J Mar Biol AssocUK 11: 251–257.

Menden-Deuer S, Lessard E. (2000). Carbon to volumerelationships for dinoflagellates, diatoms, and otherprotist plankton. Limnol Oceanogr 45: 569–579.

Redfield AC, Ketchum BH, Richards FA. (1963). Theinfluence of organisms on the composition of seawater. In: Hill MN (ed.). The Sea. Wiley Interscience:New York, pp 26–77.

Rees AP, Hope S, Widdicombe CE, Dixon JL, WoodwardEMS, Fitzsimmons JF. (2009). Alkaline phosphataseactivity in the Western English Channel: elevationsinduced by high summer time rainfall. Estuar CoastShelf Sci 81: 569–574.

Rodriguez F, Fernandez E, Head R, Harbour D, Bratbak G,Heldal M et al. (2000). Temporal variability of viruses,bacteria, phytoplankton and zooplankton in thewestern English Channel off Plymouth. J Mar BiolAssoc UK 80: 575–586.

Russell FR, Southward AJ, Boalch GT, Butler EI. (1971).Changes in biological conditions in the EnglishChannel off Plymouth during the last half century.Nature 234: 468–470.

Russell FS. (1936). The seasonal abundance of the pelagicyoung of teleostean fishes in the Plymouth area. PartIII. The year 1935, with a note on the conditions asshown by the occurrence of plankton indicators. J MarBiol Assoc UK 20: 595–604.

Siddorn JR, Allen JI, Uncles RJ. (2003). Heat, salt and tracertransport in the Plymouth Sound coastal region: a 3-Dmodelling study. J Mar Biol Assoc UK 83: 673–682.

Smyth TJ, Fishwick JR, Al-Moosawi L, Cummings DG,Harris C, Kitidis V et al. (2010). A broadspatio-temporal view of the Western English Channelobservatory. J Plankton Res 32: 585–601.

Southward AJ, Langmead O, Hardman-Mountford NJ,Aiken J, Boalch GT, Dando PR et al. (2005). Long-termoceanographic and ecological research in the WesternEnglish Channel. Adv Mar Biol 47: 1–105.

Taroncher-Oldenburg G, Griner E, Francis CA, Ward BB.(2003). Oligonucleotide microarray for the study offunctional gene diversity of the nitrogen cycle in theenvironment. Appl Environ Microbiol 69: 1159–1171.

Throndsen J. (1978). Productivity and abundance of ultra-plankton and nanoplankton in Oslo Fjorden. Sarsia63: 273–284.

Ward BB. (2008). Phytoplankton community compositionand gene expression of functional genes involvedin carbon and nitrogen assimilation. J Phycol 44:1490–1503.

Widdicombe CE, Eloire D, Harbour D, Harris RP,Somerfield PJ. (2010). Long-term phytoplankton com-munity dynamics in the Western English Channel.J Plankton Res 32: 643–655.

Woodward EMS, Rees AP. (2001). Nutrient distributions inan anticyclonic eddy in the north east Atlantic Ocean,with reference to nanomolar ammonium concentra-tions. Deep-Sea Res II 48: 775–793.

Wurmbach E, Yuen G, Sealfon SC. (2003). Focusedmicroarray analysis. Methods Enzymol 31: 306–316.

Supplementary Information accompanies the paper on The ISME Journal website (http://www.nature.com/ismej)

Phytoplankton assemblage and f-ratioBB Ward et al

12

The ISME Journal