Physical forcing and phytoplankton distributions · response of phytoplankton to available light is...

INTRODUCTION

In the natural environment the photosyntheticresponse of phytoplankton to available light is notconstant, but varies according to the physiology ofthe cells, which is governed by their size, taxonom-ic composition and the environmental conditions(temperature, nutrients, light) in which they live.For various applications, such as the computation ofprimary production at large geographic scales (Plattand Sathyendranath, 1999), we often need to quan-tify this response for places and times at which we

have no observational data. Quantifying theresponse is equivalent to assigning magnitudes tothe parameters of the relationship between photo-synthesis and available light. This means that wemust extrapolate the (usually) limited observationaldata to cover all the times and places of interest.One approach to the problem of extrapolating thephotosynthesis parameters of marine phytoplanktonover large spatial scales (Platt and Sathyendranath,1988) is to partition the ocean into provinces, eachhaving seasonally-predictable algal growth dynam-ics. The criteria for the assignment of provinces are

PHYTOPLANKTON AND ENVIRONMENT 55

SCI. MAR., 69 (Suppl. 1): 55-73 SCIENTIA MARINA 2005

PROMOTING MARINE SCIENCE: CONTRIBUTIONS TO CELEBRATE THE 50TH ANNIVERSARY OF SCIENTIA MARINA. C. MARRASÉ and P. ABELLÓ (eds.)

Physical forcing and phytoplankton distributions

TREVOR PLATT1, HEATHER BOUMAN2, EMMANUEL DEVRED2,CÉSAR FUENTES-YACO2,1 and SHUBHA SATHYENDRANATH2

1 Bedford Institute of Oceanography, Box 1006, Dartmouth, Nova Scotia, Canada B2Y 4A2. E-mail: [email protected] Oceanography Department, Dalhousie University, Halifax, Nova Scotia, Canada B3H 4J1.

SUMMARY: At the global and regional scales, the distribution and abundance of marine phytoplankton are under the con-trol of physical forcing. Moreover, the community structure and the size structure of phytoplankton assemblages also appearto be under physical control. Areas of the ocean with common physical forcing (ecological provinces) may be expected tohave phytoplankton communities that respond in a similar fashion to changes in local forcing, and with ecophysiological rateparameters that are predictable from local environmental conditions. In modelling the marine ecosystem, relevant parame-ters may be assigned according to a partition into ecological provinces. To the extent that physical forcing of the ocean isnot constant within or between years, the boundaries of the provinces should be considered as dynamic. The dynamics andthe associated changes in taxa can be revealed by remote sensing.

Keywords: phytoplankton, pigments, remote sensing, ocean colour, diatoms, hurricane, biogeochemical provinces.

RESUMEN: INFLUENCIA DE FACTORES FÍSICOS Y DISTRIBUCIÓN DE FITOPLANCTON. – A escalas global y regional, la distribu-ción y abundancia del fitoplancton marino están controladas por factores físicos. Además, la composición y la estructura detamaños de las comunidades fitoplanctónicas también parecen estar gobernadas por factores físicos. En áreas del océanosujetas al mismo tipo de influencias físicas (provincias ecológicas) cabría esperar que las comunidades fitoplanctónicas res-pondieran de forma similar a cambios locales y presentaran tasas de parámetros ecofisiológicos predecibles a partir de lascondiciones ambientales locales. Por ello, parámetros relevantes para la modelización de ecosistemas marinos pueden asig-narse en base a una partición del océano en provincias ecológicas. Teniendo en cuenta que los factores físicos que influyenen los océanos no son constantes ni intra ni interanualmente, los límites de estas provincias deberían ser considerados comovariables. La dinámica de esta variabilidad y los cambios de composición taxonómica asociados a ella pueden ser observa-dos mediante sensores remotos.

Palabras clave: fitoplancton, pigmentos, sensores remotos, color del océano, diatomeas, huracan, provincias biogeoquímicas.

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based on our knowledge of biological responses inthe phytoplankton to oceanographic forcing.

In a practical implementation, the boundaries ofthe provinces may be drawn as if fixed in time andspace. However, it is understood that in reality, theboundaries are free to vary (Platt andSathyendranath, 1999) in response to variations inthe physical forcing on which they are based. If theassignment of provincial boundaries is to bedynamic, it should be based on remotely-sensedobservables, such as sea-surface chlorophyll andtemperature fields, so that the boundaries can betreated with high spatial resolution and updatedaccording to the available repeat coverage.Assigning each pixel in the remotely-sensed imageto a particular province based on that image and onother remotely-sensed data would be the first stepin the computation of primary production at largescales in the operational mode.

It has been shown that phytoplankton size struc-ture and taxonomic composition vary with changesin physical forcing and nutrient availability. In gen-eral, large cells tend to dominate where nitrate con-centrations are high, whereas smaller cells are char-acteristic of highly-stratified waters in which regen-erated forms of nitrogen are found (Margalef, 1978;Yentsch and Phinney, 1989; Chisholm, 1992; Cullenet al., 2002). Although the evidence for a linkbetween physical forcing and phytoplankton sizestructure is largely qualitative, a few quantitativestudies also support this view. Using an empiricalmodel, Rodríguez et al. (2001) demonstrated thatthe relative abundance of large cells increases withthe magnitude of mesoscale vertical motion. Li(2002) reported a strong relationship with size struc-ture of phytoplankton and the degree of water-col-umn stability. Of course, it is difficult to separate therelative influences that turbulence and nutrientavailability have on phytoplankton communitystructure, since hydrodynamic forcing is also theprincipal mechanism responsible for the transport ofnutrients from the deep ocean to the photic zone.

Temperature is one of the most frequently-meas-ured environmental variables in the ocean and it canbe observed routinely over large scales by satellite.Although temperature itself is an important factorgoverning the distribution of phytoplankton in theocean, it also provides a quantitative index of thephysico-chemical state of the marine environment.For example, we know that the seasonal warming ofsurface waters in the temperate open ocean is theprimary mechanism responsible for strengthening

the stability of the water column, which causes areduction in the flux of nutrients from below themixed layer during the summer months. Thus,increasing sea-surface temperatures are associatedwith a strengthening in the density gradient, and acorresponding decrease in the diffusion of nutrientsacross the pycnocline. This link between sea-surfacetemperature and nitrate concentration has beenexploited in studies that attempt to estimate the con-centration of nitrate over regional (Sathyendranathet al., 1991) to global scales (Carder et al., 1999;Switzer et al., 2003), using temperature fieldsobtained by satellites.

The importance of temperature as a predictor ofmany of the patterns and processes in phytoplanktonecology has been shown repeatedly in the literature.Using measurements of pigment composition, cellsize and phytoplankton absorption, Bouman et al.(2003) showed that the size and gross taxonomicstructure of phytoplankton follow changes in tem-perature for a variety of oceanic regions. Li (inpress) has shown that the abundance of the ubiqui-tous picoplankter Synechococcus is strongly corre-lated with temperature for samples collected in theNorth-West Atlantic.

The rate of light-saturated photosynthesis (theassimilation number) has also been shown to betemperature dependent in temperate marine ecosys-tems. Although this relationship has usually beenattributed in the past to the enzymatic nature of thedark reactions of photosynthesis (Geider andOsborne, 1992), some workers have also suggestedthat the relationship may be a result of the concomi-tant change in phytoplankton size and taxonomicstructure with temperature (Platt and Jassby, 1976;Côté and Platt, 1983; Bouman et al., in press). Usinga pelagic food web model, Laws et al. (2000) foundthat temperature explained a significant proportionof the variance in measurements of the ratio ofexport to total production taken from the literature.Thus, the potential use of temperature to provideinformation on the structure and function of thepelagic ecosystem is well established.

Chlorophyll is the pigment involved in the con-version of inorganic carbon to organic compoundsusable as a substrate to sustain the aquatic ecosys-tem. Because it is unique to photosynthetic organ-isms, it provides a useful marker of the biomass ofthe autotrophic community. Its function to absorblight for photosynthesis gives it an optical signaturethat can be detected from space. In addition to itsprimary role as an indicator of phytoplankton bio-

56 T. PLATT et al.

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mass, chlorophyll concentration has also been pro-posed as a proxy index of phytoplankton size struc-ture. A conventional view is that there exists a con-stant background of picoplanktonic cells and that, asnutrient availability increases, there is a correspon-ding increase in the abundance of larger cells(Yentsch and Phinney, 1989; Chisholm,1992). Datacollected in the North-West Atlantic, however showthat the pico-phytoplankton actually decrease aspigment concentration increases and that the nano-phytoplankton show limited variability with chloro-phyll concentration (Li, 2002). Nevertheless, thehypothesis that the relative proportion of micro-phy-toplankton increases as chlorophyll concentrationincreases, still holds (Rodríguez et al., 1998).

Although the main product of ocean-colour datais chlorophyll concentration, recent studies haveshown that information on the size structure and tax-onomic composition of marine phytoplankton maybe derived from the remotely-sensed signal (Ciotti etal., 1999, 2002), using relationships betweenchlorophyll and cell size of the type outlined above.Sathyendranath et al. (2004) established an algo-rithm to detect the presence of diatoms using satel-lite data of ocean colour. Thus, if robust relation-ships could be established between phytoplanktonfunctional groups and photosynthetic performance,then information on their relative contribution to theautotrophic community might be used as an aid tothe assignment of photosynthesis-irradiance param-eters over basin scales.

Here, we attempt to establish an ecological geog-raphy of the North-West Atlantic Ocean using twoocean observables: sea-surface temperature andchlorophyll concentration. We begin by examiningthe correlations of both temperature and chloro-phyll-a concentration with two indices of phyto-plankton community structure (pigment composi-tion and phytoplankton absorption characteristics)and with photosynthetic parameters. We then pro-pose a method for assigning dynamic boundaries tothe biogeochemical provinces of the North-WestAtlantic using remotely-sensed data on sea-surfacetemperature and chlorophyll. These boundaries arecompared with the distribution of diatom popula-tions as determined by remote-sensing, to assesswhether the boundaries are consistent with theknown ecological diversity of the study region.Next, using hurricanes off the Grand Banks in a casestudy, we demonstrate how this dynamic approach isable to embrace ecologically-important episodicevents in physical forcing.

REMOTELY-SENSED OBSERVABLES ANDPHYTOPLANKTON STRUCTURE ANDFUNCTION

To examine the predictablity of phytoplanktoncomposition and photosynthetic performance fromremotely-detectable variables, we used data collect-ed on 15 cruises to the Scotian Shelf and LabradorSea during the period of May 1996 to December2003 (Fig. 1). Measurements of temperature wereobtained from a SeaBird CTD profiler. Pigmentcomposition was obtained using High PerformanceLiquid Chromatography (HPLC) according to themethod of Head and Horne (1993).

Two indices of phytoplankton community struc-ture were used in this study. The first is the pigmentindex Fµ, which represents the contribution ofmicro-phytoplankton cells to the total pigment bio-mass (Claustre, 1994) based on the relative concen-tration of accessory pigments obtained by HPLC.The index is simply the sum of diagnostic pigmentsassociated with large cells (diatoms and dinoflagel-lates) divided by the sum of diagnostic pigmentsassociated with all phytoplankton size classes:

(1)

where Df and Dp represent the concentrations of thecarotenoids fucoxanthin and peridinin, respectively.

FD D

D D D D D D Df p

f p h b a c z

µ =+

+ + + + + +


FIG. 1. – Map showing stations sampled during 15 cruises of the Scotian Shelf and Labrador Sea.

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The concentrations of 19’ hexanoyloxyfucoxanthin,19’ butanoyloxyfucoxanthin and alloxanthin are rep-resented by Dh, Db and Da respectively. These arethree carotenoids associated with the nano-phyto-plankton size class. Chlorophyll-b (Dc) and zeaxan-thin (Dz) are pigments associated with the pico-phy-toplankton size class.

The second index of phytoplankton communitystructure is the chlorophyll-a specific absorptioncoefficient at 676 nm, a*(676) (Bouman et al.,2003). Phytoplankton absorption coefficients wereobtained using the filter pad method. Briefly, algalcells were filtered onto a glass fibre filter, and thefilter was scanned from 350 to 750 nm using aShimadzu dual-beam spectrophotometer with anintegrating sphere assembly. Pigments wereextracted from the GF/F filter as described inBouman et al. (2003) and the filter was re-scannedto obtain the optical density of detrital material.Optical density was converted to absorption coeffi-cient (Hoepffner and Sathyendranath, 1992, 1993),and the difference between total particulate absorp-tion and the absorption by detrital material yieldedthe absorption by phytoplankton. The absorption

coefficient of phytoplankton at each wavelengthwas divided by the concentration of chlorophyll-ain the sample, to obtain the specific absorptioncoefficient (m2 mg Chl-a-1). The specific absorp-tion coefficient at the red absorption peak ofchlorophyll-a (676 nm) is little affected by changesin pigment composition, but is affected by the sizeof the cells, through the flattening effect (Duysens,1956). Hence, the specific absorption coefficient at676 nm is used here as an index of phytoplanktoncell size, as in Bouman et al. (2003).

Photosynthesis-irradiance experiments were con-ducted at sea. Water samples were inoculated with14C radioisotope and incubated in an artificial lightgradient for three hours. Photosynthesis-irradiancecurves derived from the experiments were fitted tothe equation of Platt et al. (1980) to obtain the pho-tosynthesis-irradiance parameters, Pm

B, the photo-synthetic rate at saturation (also known as assimila-tion number) and αB, the initial slope of the curve.

The results from samples collected only in thetop 20 m of the water column are shown in thisstudy, for two reasons. First, the remotely-sensedsignals of both temperature and ocean colour are

58 T. PLATT et al.

FIG. 2. – Plot of two indices of community structure against temperature for low-latitude and high-latitude regions. A) The pigment index Fµ(Equation 1) and B) the chlorophyll-a specific absorption coefficient at 676 nm, a*(676).

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heavily weighted towards the sea surface. Second,by focussing on data collected near the sea surface,variability in the optical and photosynthetic charac-teristics of phytoplankton caused by photoacclima-tion of cells to low light levels at depth is minimised.

For samples collected south of 55ºN a cleartemperature-dependence for both indices of phyto-plankton community structure is observed (Fig. 2).A possible explanation for this relationship lies inthe link between vertical density structure and tem-perature. At higher latitudes, however, neither phy-toplankton pigment composition nor specificabsorption appear to be related to temperature.This lack of correlation between temperature andphytoplankton community structure in higher lati-tudes may be, in part, a result of a significant con-tribution of salinity, rather than temperature, to thevertical density structure.

Chlorophyll and our indices of community struc-ture show similar relationships for both latitudinalranges (Fig. 3). At chlorophyll-a concentrations <5mg m-3, values of both Fµ and a*(676) are highlyvariable, covering the entire range possible (0 to 1for Fµ, and 0.003 to 0.06 m2 (mg Chl-a)-1 fora*(676)). Thus at low-to-intermediate concentrationsof chlorophyll, cells covering a wide range of sizes

and taxonomic groups may be present. At chloro-phyll-a concentrations > 5 mg Chl-a m-3, however,values of Fµ and a*(676) imply the dominance oflarge cells (diatoms) exclusively, with high relativeconcentrations of the indicator pigment fucoxanthinand low specific absorption coefficients. Thus itwould appear that in bloom conditions in thisregion, where chlorophyll concentrations reach val-ues greater than 5 mg Chl-a m-3, the phytoplanktonare often dominated by diatoms, though Phaeocystisblooms are also known to occur.

Since community structure appears to vary in aregular way with temperature, and since both phyto-plankton taxonomic composition and temperatureare known to be sources of variability in the photo-synthetic parameters in the natural environment, wewould anticipate that temperature would be a goodindicator of the photosynthetic properties of marinephytoplankton in this region. When the light-satu-rated photosynthetic rate, represented by the param-eter, Pm

B, is plotted against temperature, a strongtemperature-dependence is revealed (Fig. 4). This isin agreement with numerous studies conducted intemperate waters that also show a positive correla-tion between temperature and light-saturated photo-synthesis. Many studies have attributed the correla-


FIG. 3≠. – Plot of two indices of community structure against chlorophyll-a concentration for low-latitude and high-latitude regions.

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tion between PmB and temperature to a direct effect

of temperature impacting the enzyme-controlleddark reactions of photosynthesis (Geider andOsborne, 1992). It should be pointed out, however,that the temperature-dependence of light-saturatedphotosynthesis has been shown to be weak or absentin other oceanic regimes (Behrenfeld et al., 2002,Bouman et al., in press). This lack of dependencehas been attributed to several factors, including vari-

ability in the concentration of the enzymes involvedin the Calvin cycle and in the intracellular chloro-phyll concentration with changes in ambient tem-perature (Behrenfeld et al., 2002). In addition,changes in nutrient stress (Eppley et al., 1972) andphytoplankton community structure (Bouman et al.,in press) have also been implicated.

When the initial slope of the photosynthesis-irradi-ance curve, αB, is plotted against temperature, a clear

60 T. PLATT et al.

Fig. 4. – Plot of the photosynthetic parameters against A) temperature, B) chlorophyll-a concentration C) the chlorophyll-specific absorption coefficient at 676 nm, a*(676) and D) the pigment index Fµ (Equation 1).

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positive relationship is also found (Fig. 4A). It is diffi-cult to invoke a direct causative factor to explain thisrelationship: unlike the case for Pm

B, no physiologicalbasis has been invoked to explain this apparentdependence of αB on temperature. Thus we must con-clude that there is another co-variate or a group ofcovariates governing the relationship. To examine theinfluence of phytoplankton community structure onthe photosynthetic parameters, Pm

B and αB are plottedagainst a*(676) and Fµ (Fig. 4C and D). Both the pho-tosynthetic parameters Pm

B and αB appear to be strong-ly correlated with a*(676). The relationship betweenthe initial slope of the photosynthesis-irradiance curve(αB) and a*(676) is not surprising, since αB is relatedto the absorptive efficiency of phytoplankton accord-ing to the following relationship:

αB = φma–* (2)

where a–* is the spectrally-averaged specific absorp-tion coefficient of phytoplankton and φm is therealised maximum quantum yield of photosynthesis(Platt and Jassby, 1976; Geider and Osborne, 1992).The relationship between Pm

B and a*(676) is moredifficult to interpret. Yet, if we assume that a*(676)is an index of phytoplankton cell size, then perhapsthis relationship can be rationalised. Geider et al.(1986) demonstrated that both a–* and the light-satu-rated maximum growth rate (µ m) decrease with anincrease in cell size. Since under light-saturated con-ditions, µ m should be proportional to Pm

B, and sincea–* is proportional to αB, both photosynthetic param-eters should be size dependent. The positive correla-tion of a*(676) with the two photosynthesis param-eters implies that smaller cells have higher rates oflight-limited and light-saturated photosynthesis perunit biomass than larger cells. The relationshipbetween the pigment index Fµ and the photosyn-thetic parameters also supports the view than largercells (diatoms) have lower biomass-specific produc-tion rates than smaller cells. These results are con-sistent with other field (Yentsch and Ryther, 1957;Côté and Platt, 1983, 1984; Bouman et al., in press)and laboratory (Taguchi, 1976; Geider et al., 1986;Finkel, 2001) studies which show that larger cellstend to have lower biomass-specific photosyntheticrates than small cells.

Results obtained in this study point to both tem-perature and to indices of the size and taxonomicstructure of phytoplankton cells as potential indica-tors of the values of photosynthetic parameters forthe North-West Atlantic region. Since temperature

and indices of phytoplankton community structurecan be obtained by remote sensing (at least indirect-ly), they could be used as the basis of a protocol forassignment of photosynthesis parameters to estimateprimary production from ocean colour in an opera-tional mode.

Although these relationships appear to be robustfor the North-West Atlantic, they may not hold forother oceanic regions. For example, in the ArabianSea, no relationship between temperature and com-munity structure was apparent in the data collectedover a series of cruises during the monsoon andinter-monsoon seasons (Bouman et al., 2003;Bouman et al., in press). However, the relationshipsof total chlorophyll concentration and pigmentindices of taxonomic composition with the photo-synthetic parameters were similar to those in theNorth-West Atlantic. Such regional differences lendadditional support to a province-based approach tolarge-scale computations of primary production.

A DYNAMIC ASSIGNMENT OF ECOLOGICALPROVINCES USING SATELLITEOBSERVATIONS

As shown in the previous section, sea-surfacetemperature and chlorophyll biomass can be used inthe North-West Atlantic to provide informationabout phytoplankton functional groups (related tocell size) and their physiological state, and thesevariables are now measured on a routine basis byseveral satellite sensors. This opens the avenue todevelop tools to retrieve comprehensive informationon phytoplankton in the ocean at regional scalesusing remotely-sensed data. In this section, theboundaries of the ecological provinces of the North-West Atlantic (Fig. 5) as defined by Sathyendranathet al. (1995) and Longhurst (1998) are refined usingsatellite-derived chlorophyll-a concentrations andsea-surface temperatures. An algorithm to discrimi-nate diatom-dominated phytoplankton populationsfrom other populations is also applied and the resultsare interpreted in the context of the dynamic bound-aries of the ecological provinces. Spring andAutumn 2003 are the two seasons selected to illus-trate the relationship between the boundaries of theecological provinces and the distribution of phyto-plankton functional groups.

Daily images of sea-surface temperature andchlorophyll-a concentration were acquired betweenApril 24th and May 15th, 2003 (Spring) and between


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October 13th and October 28th, 2003 (Autumn) usingAVHRR and SeaWiFS data, respectively. These datescorrespond to the dates of scientific cruises carriedout as part of the Canadian SOLAS (Surface-OceanLower Atmosphere) programme in which in situ datawere collected to validate the products (not presentedin this paper). Discrimination of diatoms from otherpopulations (hereafter referred to as “mixed popula-tion”) was also performed on the ocean-colour data.The algorithm used to achieve this (Sathyendranath etal., 2004) is based on the absorptive properties ofdiatoms, which differ from those of other phyto-plankton populations (typically smaller cells), and atheoretical reflectance model (Sathyendranath andPlatt, 1997), which relates water-leaving reflectancesto chlorophyll concentration.

The procedure to identify the phytoplankton fora given pixel in an image includes four steps: i) twochlorophyll-a concentrations were computed withalgorithms for diatom-dominated populations thatused two different ratios of water-leavingreflectances (the two wavelength pairs used were490:670 and 510:555); ii) this was repeated with thealgorithms for mixed populations; iii) the differencein the two chlorophyll-a values obtained for thediatom-dominated case was divided by their meanvalue, to obtain the normalised difference in chloro-

phyll concentrations, and the computation wasrepeated for the non-diatom case; iv) the pixel wasclassified as diatom-dominated or otherwise,depending on which algorithm yielded the smallernormalised difference. The assumption here is thatthe differences in estimated chlorophyll obtainedusing different pairs of wavebands will be less whenthe diatom model is applied to a diatom-dominatedlocation or when a mixed-population model isapplied to a location with mixed populations, ratherthan when there is a mismatch between assumptionand reality. This is a binary approach where pixelsare classified either as dominated by diatoms(assigned a value of 100%) or dominated by a mixedpopulation (assigned a value of 0%). This procedurewas repeated for every pixel and for all imagesavailable during the time of the study. All theprocessed images for Spring and Autumn were usedto generate seasonal composite images which showthe number of times a pixel was classified asdiatoms divided by the total number of images withvalid data in that pixel location (that is to say,images with cloudy conditions or negative chloro-phyll concentrations at that pixel location were notincluded when computing this ratio). These com-posite images may be interpreted as showing on apixel-by-pixel basis the probability that the phyto-plankton was diatom dominated, for the time inter-vals for which the images were created.

Composite images of the North-West Atlanticshowing the probability of the presence of diatomsin Spring and Autumn of 2003, are shown in Figure6. Mapping the distribution of diatoms over largescales reveals features that cannot be observed usingin situ measurements collected from scientific ships.The left-hand-side image shows an intense bloom ofdiatoms during the Spring in the southern region ofthe Scotian Shelf. At high latitudes, diatoms are alsopresent, probably the result of ice melt, and subse-quent increase in nutrient supply. In the Autumnimage, diatoms are dominant on Georges Bank,which is consistent with the field observations ofHoepffner and Sathyendranath (1992, 1993). Thediatom blooms that started in the spring season haveshifted further north. The presence of diatoms offthe mouth of the Saint Lawrence River may be theresult of high nutrients caused by the river discharge(assuming that the algorithm is not in error in thesepotentially Case-2 waters).

Partitioning the ocean into biogeochemicalprovinces is a useful concept to assign biologicalproperties based on physical forcing. To date, such

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Fig. 5. – The ecological provinces in the North-West Atlantic as defined by Sathyendranath et al. (1995).

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partitions have been used mainly for pedagogicalapplications rather than for operational oceanogra-phy. We have developed a simple operationalmethod to divide the North-West Atlantic Oceaninto provinces using sea-surface temperatures andchlorophyll-a concentrations retrieved by satellite asprimary indicators, and location and bathymetry assecondary indicators. According to the convention-al, static partition of Longhurst et al. (1995),Sathyendranath et al. (1995) and Longhurst (1998)the study area was first divided into 7 provinces:

- Three provinces were characterized as warmoligotrophic regions (Eastern and Western part ofthe Subtropical gyre, STGW and STGE respective-ly, and the Gulf Stream, GFST);

- Two provinces were characterized by colderwaters (Polar Boreal current, BPLR, and Arcticwaters, ARCT);

- One province represented the continental shelf(NWCS); and

- The North Atlantic Drift (NADR) representedthe water mass that results from the mixture ofArctic and oligotrophic waters.

For each of the provinces as defined by theboundaries in Figure 5, the frequency distributionsof sea-surface temperature and chlorophyll concen-

tration were plotted (Fig. 7 and 8). Multiple peaks inthe sea-surface temperature histograms indicate thatdifferent water masses are present within the sameputative province. Moreover, the same peaks in sea-surface temperature distribution may be found indifferent provinces: for instance a peak of tempera-ture occurs at 21ºC in the STGW and GFSTprovinces, or at 3.5ºC in the BPLR and ARCTwaters. Similar features are also observed in thechlorophyll histograms. These observations indicatethat the static partition is less than perfect, and thatthe provincial boundaries should be readjusteddynamically.

Therefore, the boundaries of provinces werereorganized using an interative procedure based onvisual analysis of the frequency distributions of thesea-surface temperature and chlorophyll concentra-tion in each province. Thus, the following four cri-teria were applied:

- Only a single sea-surface temperature peakshould appear in each provinces, as far as possible;

- Chlorophyll concentration was used as an indi-cator of water masses when the partitioning couldnot be based on sea-surface temperature alone (forinstance, North Atlantic Drift had similar sea surfacetemperatures as continental shelf);


Fig. 6. – Composite images of diatoms population identification for the Spring and Fall 2003 in the North-West Atlantic.

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- Bathymetry helped to separate provinces alonga front where the gradients of chlorophyll-a concen-tration and sea-surface temperatures on both sides ofthe front did not allow identification of theprovinces (e.g., Boreal Polar waters and Arcticwaters) and;

Location was also used in a broad sense. Thatis to say, we made an a priori decision, based on

knowledge of the regional oceanography, that theprovincial boundaries will be found in the neigh-bourhood of the static boundaries as defined byLonghurst (1998). For example, if similar temper-atures are found in disparate locations, they aretreated as indicators of distinct provinces: forexample, continental shelf waters and the watersoff Greenland may have similar temperatures, but

64 T. PLATT et al.

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do not belong to the same province. However, themethod allows a “peppering” effect in the neigh-bourhood of boundaries, representing incursionsof neighbouring waters across boundaries.

To the provinces defined in Sathyendranath etal. (1995) (albeit with their boundaries reassigneddynamically), a new province was added, whichwe named “Slope Waters”, taking in waters lying

between the NWCS province and the warm olig-otrophic waters. These waters have the same rangeof chlorophyll concentration as the oligotrophicwaters, but have the same range of sea-surfacetemperatures as the continental shelf waters.Figure 9 shows the log-normal frequency distribu-tions of sea-surface temperatures and chlorophyll-a concentrations after pixels were classified into


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0

8333

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Fig. 8. – Frequency distribution of chlorophyll concentrations for the spring season extracted from the composite image for the provincesdefined by Sathyendranath et al. (1995).

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ecological provinces using the steps outlinedabove for two of the provinces.

The same method of assigning provincial bound-aries based on satellite observations was applied toimages obtained in Autumn. The sea-surface tem-perature criteria were modified to account for theincrease in sea-surface temperatures as a result ofsummer heating over the study area. Biomass con-centration criteria were also modified to account fordifferences in the intensity and distribution ofblooms. Comparison of provincial boundaries inSpring and Autumn shows a movement of the olig-otrophic warm waters (Gulf Stream and sub-tropicalgyre) towards the north (Figs. 10, 11). It is also note-worthy that the Boreal Polar Region extends southduring the Autumn season into the slope waters, fol-lowing the 1000-to-2000 m isobath, even thoughbathymetry was not used as a criterion to define thisboundary.

The boundaries of biogeochemical provincesand the distribution of diatoms show similar fea-tures (Figs. 10, 11). This is not unexpected, sincechlorophyll biomass was used to define theprovinces and diatom blooms are known to appear

at higher chlorophyll concentrations (see Fig. 3).Similarly, temperature distribution was used toestimate the boundaries of provinces, and tempera-ture is also related to community structure, asshown earlier (Fig. 2). In the southern part of thestudy area, during Spring, the diatom blooms occurin the slope waters and on the North-WestContinental Shelf, and a clear front is observedwith the southern oligotrophic waters where nodiatoms were identified. Diatoms are also identi-fied in the Boreal Polar Region in the Spring andthe Autumn between 50 and 55ºN at the borderwith the Arctic waters. In Autumn, an intensebloom of diatoms occurred in the eastern part ofthe slope waters and spread into the North AtlanticDrift waters. The relationship between water mass-es and groups of phytoplankton is illustrated herefor the first time using remotely-sensed data. Itdemonstrates the complementarity of in situ meas-urements, which showed strong relations betweenphysical properties, phytoplankton groups andtheir physiology, and remotely-sensed ocean-colour data, justifying extrapolation of the in situmodel to wide areas using suitable algorithms.

66 T. PLATT et al.

Fig. 9. – Frequency distribution of sea surface temperatures and chlorophyll concentrations for the spring season extracted from the composite image after application of criteria to determine the provinces dynamic boundaries.

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TEMPORAL DEVELOPMENT OF A DIATOMBLOOM FOLLOWING A STORM

In the ocean, the passage of a hurricane leads toenhanced entrainment of cold, nutrient-rich waterinto the mixed layer. The entrainment process pro-motes increased primary production and thereforehigher phytoplankton biomass (chlorophyll-a). Thearrival of remote sensing has improved our capacityto measure synoptic fields of oceanographicprocesses to assess these powerful transient eventsin different regions of the world (Fuentes-Yaco etal., 1997; Nayak et al., 2001; Subrahmanyam et al.,

2002; Zedler et al., 2002; Lin et al., 2003;McKinnon et al., 2003; Vinayachandran andMathew, 2003; Babin et al., 2004; Vinayachandranet al., 2004). Eastern Canada can experience severalhurricanes each year. In 2003, the hurricane seasonin the Atlantic Ocean was very active, with 14named storms. Typically, the storms progress north-wards along the eastern seaboard of the UnitedStates, and then turn to the east affecting the ScotianShelf and/or the Newfoundland Shelf. Here weexamine the response of the fields of chlorophylland temperature, and the community structure ofphytoplankton, to the passage of Hurricane Kate.


Fig. 10. – Composite image of identification of diatoms during spring. Solid lines correspond to the province boundaries.

Fig. 11. – Composite image of identification of diatoms during autumn. Solid lines correspond to the province boundaries.

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This hurricane crossed the Grand Banks ofNewfoundland, an area with rich fisheries resources.

According to the National Hurricane Center ofthe National Weather Service (NOAA), HurricaneKate was born as a tropical wave that crossed thecoast of Western Africa on September 21st. ByOctober 4th the hurricane reached its strongest windintensity (185 km h-1), when it was east-southeast ofBermuda. On October 5th and 6th, the cyclone turnednorthward then accelerated to north-east over coolerwaters. As reported by the Meteorological Serviceof Canada - Atlantic, Hurricane Kate was in theResponse Zone of the Canadian Hurricane Centrebetween the 7th and 8th of October (Fig. 12), enteringthe region as a Category-1 Hurricane (110 to 120 kmh-1), and was at tropical-storm strength as it movedthrough the South-Eastern and Northern GrandBanks of Newfoundland. The system completed itsextra-tropical transition by October 8th.

The response of the fields of chlorophyll andtemperature to the passage of Hurricane Kate in theNorth-West Atlantic (39 to 62.5ºN and 42 to 71ºW)was studied using satellite images of ocean colour(SeaWiFS) and sea-surface temperature(NOAA/AVHRR). Daily images of chlorophyll-a,an index of phytoplankton biomass, were createdfollowing a method that accounts for changes in theabsorptive properties of different phytoplanktonpopulations (Sathyendranath et al., 2004). Sea- sur-face temperatures (SST) were calculated as estab-lished by McClain et al. (1985). Composite imageswere produced with all available images from 12days before (September 25th to October 6th, 2003) to14 days after (October 7th to 20th, 2003) the stormpassage. They were made on a spatial scale ofapproximately 1.5 km to preserve the detailed spa-tial structure, and used to extract data in theCanadian region across a band of 700 km followingthe storm track: 350 km on the left side and 350 kmon the right side (Fig. 12).

Changes in sea-surface temperature and chloro-phyll-a concentration

The passage of a storm, with strong associatedwinds, can generate upwelling of cold water fromthe deep layers along its trajectory. This mecha-nism brings nutrients to the surface, supporting thedevelopment of remarkable phytoplankton blooms.Figure 12 (a and b) shows sea-surface temperature,which may be considered as a proxy for nutriententrainment to the surface layers (Sathyendranath

et al., 1991). The left side (a) corresponds to theperiod before the storm passage and the right side(b) to the period after the storm passage. Figure 12(c and d), shows the fields of chlorophyll-a before(c) and after (d) the storm passage. In the compos-ites after the storm passage (right side maps),strong gradients of both sea-surface temperatureand chlorophyll-a are clearly visible in the vicinityof the Grand Banks of Newfoundland. Additionalanalyses based on data extracted from the compos-ite images in a band of 700 km, and averagedacross the storm track (this approach to extractionand averaging was also used for the other figuresshown here from this analysis), confirm that cool-er waters were associated with stronger pigmentconcentrations, and this relationship appeared to bemore robust after, than before, the storm passage(Fig. 12e).

68 T. PLATT et al.

Fig. 12. – Composites of sea surface temperature: a) 12 days beforethe passage of Hurricane Kate, from September 25th to October 6th,and b) 14 days after the passage of the storm, October 7th to 20th,2003. Composites of chlorophyll-a concentration: c) 12 daysbefore, and d) 14 days after the hurricane passage. The storm trackand a band of 700 km (350 km on both sides) are also indicated. e)Scatter plot of sea surface temperature against chlorophyll-a. Thedata are extracted from the composite images under the 700 km

area, and averaged across the storm track.

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A detailed analysis of changes along and acrossthe storm track is shown in the subsequent figures.Figure 13a and 13b show concurrent changes in sea-surface temperature and chlorophyll-a under thearea along the storm track. Figure 13a shows the dif-ference (after - before) in sea-surface temperature,whereas Figure 13b shows relative change in chloro-phyll-a (as ratio of pigment after to pigment before,passage of storm), both computed using the com-posite images of before and after the hurricane pas-sage. The right side of the storm path shows com-plex features in changes of temperature with differ-ences up to 7ºC (Fig. 13a). These structures arecoherent with changes in chlorophyll-a, from three

to six fold (Fig. 13b). It reveals in great detail theimpact of Hurricane Kate on the pelagic ocean.

Data of the concurrent changes in sea-surfacetemperature and phytoplankton pigment (Fig. 13c)show an imbalance in the distribution of the differ-ences in SST and Chl-a. Pixels with small differ-ences appear the left side of the storm, whereasthose with big differences are seen to the right sideof the storm path. These results coincide with previ-ous observations concerning the stronger effect ofthe storms passage on the right side than on the leftside of storm tracks (Iverson, 1997). It is also impor-tant to note that the right side of the hurricanecrossed the South-Eastern slope of the Grand Banksof Newfoundland, the area with the greatest meas-ured variations. The stronger upwelling of coldwater (with high nutrient concentrations) apparentlytriggered a strong biological response.

Impact of physical forcing on ecosystem structure

Margalef (1978) proposed that, in the marineenvironment, species succession is heavily influ-enced by two physical processes: advection and tur-bulence. He also postulated that in addition to lightenergy, the kinetic energy provided by wind andwaves also drives the production in aquatic ecosys-tems by modifying nutrient supply and temperature.Furthermore, Margalef (1978) also proposed a sys-tematization of the functional morphology of phyto-plankton on the basis of two basic environmentalfactors: supply of nutrients and intensity of turbu-lence. In this context, it is known that the taxonom-ic composition of phytoplankton communities isregulated by physical processes, and that tempera-ture is a suitable proxy for nutrient (new nitrogen)supply (Bouman et al., 2003). Estrada and Berdalet(1997) underline that diatoms, non-motile organ-isms with fast potential growth rates, prosper in rel-atively turbulent and nutrient-rich waters. Thesecharacteristics make diatoms ideal organisms toillustrate the response of community structure to thepassage of a major storm, and to test whether turbu-lent and nutrient-rich waters prevailing after a hurri-cane passage can be suitable for their growth.

Here, the use of an algorithm that allows distinc-tion between diatoms from other phytoplanktonpopulations (Sathyendranath et al., 2004) providesadditional information to assess phytoplanktonblooms. Figure 14 shows composite images of theprobability of finding diatoms: the left image (a)


Fig. 13. – Changes in sea surface temperature and chlorophyll-a: a)Difference (after-before) in SST. Green and blue colours showdecreased temperatures. b) Ratio (after : before) of chlorophyll-a.Values above 1.0 indicate areas where the phytoplankton biomassincreased. c) Scatter plot of concurrent changes in SST (difference)and Chl-a (ratio). The data are extracted from the composite imageswithin a 700 km band (see Fig. 12), and averaged across the stormtrack. The low (or high) values of change are related with the left

(or right) side of the storm track.

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corresponds to the period before the passage ofHurricane Kate and the right composite (b) corre-sponds to the period after the passage of the storm.Before the storm passage, diatoms seemed to bedominant in the northern area (50 to 55ºN) of thepath; however, after the storm passage, the northernbloom seemed to disappear and a new diatom bloomdeveloped between 45 and 50ºN. The strongestprobabilities to find diatoms were on the right sideof the storm track, corresponding to the slope of theGrand Banks of Newfoundland. Simultaneous dataof sea-surface temperature and the probability offinding diatoms are shown in Figure 14c. It is inter-esting to see how the probabilities to find diatomsincreased after the storm passage, as the temperaturedecreased. This pattern is similar to the relationshipfound between phytoplankton biomass and sea-sur-face temperature (Fig. 12).

The change in probability of finding diatoms canbe estimated by computing the differences in proba-bilities before and after the storm passage, using thecomposite images (Fig. 15a). Figure 15b shows thedifferences of sea-surface temperature against the

differences in the probability of finding diatoms.The distribution shows an asymmetry, with low val-ues on the left and high values on the right side ofthe storm path. It is possible that the extreme mixingcreated by the strong winds related to the hurricanecould restrict the growth of diatoms already presentbefore the storm. After the passage of the hurricane,a new stability gradient was apparently created andtriggered a new phytoplankton succession cycle, ledby diatoms.

Using a temporal series of two-day compositeimages, we also examined the development ofdiatom blooms over time, in response to the pre-sumed entrainment of nitrate into the surface layer

70 T. PLATT et al.

Fig. 14. – Composites of probability to find diatom dominant pop-ulations: a) 12 days before the storm passage, and b) 14 days afterthe hurricane passage. c) Scatter plot of SST against probability tofind diatoms. The data are extracted from the composite images

under the 700 km area, and averaged across the storm track.

Fig. 15. – Differences (composite after - composite before) in theprobability to find diatom-dominated populations (a). Red to yellow(positive values) colour denote increased probabilities. Green to bluecolours (negative values) show decreased probabilities. Scatter plotof differences of SST against differences of probability to finddiatoms dominance (b). The data are extracted from the compositeimages under the 700 km area, and averaged across the storm track.The low (or high) values of change are related with the left (or right)

side of the storm track.

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by wind mixing (Fig. 16). A two-day period allowedreduction in the proportion of cloud cover maskingthe images, but provided sufficient temporal resolu-tion to observe the biological response to the storm.The geographic distributions of the ecologicalprovinces obtained using the method described inthe previous section are also shown for before, andafter, the passage of the storm (Fig. 16). Note thatthe maps of the provinces for both before and afterthe storm are derived from 12-day compositeimages.

The presence of diatoms is apparent before thepassage of the storm in the northern part of theimages corresponding to the Arctic Province. In thesouthern region, which is designated predominantlyas the Slope Province, diatoms are generally absent,except for a few very small patches. The map of theprovinces before the storms shows an incursion ofBoreal Polar waters into the slope waters that tendsto follow the steep slope of the Scotian Shelf. Afterthe passage of the storm (S+1 to S+11), diatoms tendto predominate in the center of the image, between42 and 44ºN and increase in relative abundance thefollowing four days. This event corresponds to anintrusion of Boreal Polar waters into this area, sug-gesting that the mixing of waters by the storm deep-ened the mixed layer, leading to a supply of nutri-ents to the surface ocean. This temporal analysisemphasizes the impact of storms on marine ecosys-tems. The passage of the storm in a nutrient-deplet-ed environment (south part of the image) resulted

not only in an increase in phytoplankton biomass,but also a change in the community structure. Thenorthern part of the area, where diatoms werealready present, were less impacted, as ocean colourdata showed a decrease in chlorophyll concentration(probably due to a redistribution of existing biomassover a deeper mixed layer).

CONCLUDING REMARKS

The broad lines of the majestic partition of theoceans by Longhurst (1998) follow from the prem-ise that the spatial features of phytoplankton distri-butions are under the control of physical forcing,and that the temporal changes in these distributionsreflect changes in the relevant physical forcing. Wehave shown that this premise is justified by theavailable observations on the physiological ecologyof phytoplankton in relation to the physical proper-ties of the environment. Moreover, we have shownfor the first time that the ocean partition ofLonghurst can be implemented in the operationalmode using remotely-sensed data as input. Theresulting partition corresponds broadly with theLonghurst one, but differs in detail and in that theboundaries can be adjusted in real time to reflectchanges in forcing. These boundaries are consistentwith what is known about the community structureof the phytoplankton. Finally, using remotely-sensed data on ocean colour, we have documented


Fig. 16. – Temporal series of two-day composite images of the probability of occurrence of a diatom-dominated population along the stormtrack 12 days before (top) and 12 days after (bottom) the passage of the hurricane. Data were averaged across the storm track within a 700

km band (see Fig. 12). Plots on the right-hand side indicates the biogeochemical provinces before and after the storm.

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the response of the phytoplanktkon biomass, and ofcommunity structure, to the passage of a hurricaneacross the eastern seaboard of Canada. These obser-vations reinforce the views of Margalef (1978) andof Longhurst (1998) concerning the influence ofphysical forcing on phytoplankton distributions(sensu largo) and enhance the value of remotely-sensed data of ocean colour as a means to illustrateand characterise this relationship.

ACKNOWLEDGEMENTS

This paper is part of the Canadian SOLAS pro-gramme. This work is also sponsored by theCanadian Space Agency.

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