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The effect of seasonality in phytoplankton community composition onCO2 uptake on the Scotian Shelf
Susanne E. Craig a,⁎, Helmuth Thomas a, Chris T. Jones a, William K.W. Li b, Blair J.W. Greenan b,Elizabeth H. Shadwick c, William J. Burt a
a Department of Oceanography, Dalhousie University, 1355 Oxford Street, P.O. Box 15000, Halifax, Nova Scotia B3H 4R2, Canadab Bedford Institute of Oceanography, Department of Fisheries and Oceans, Dartmouth, Nova Scotia B2Y 4A2, Canadac Antarctic Climate & Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia
We characterise seasonal patterns in phytoplankton community composition on the Scotian Shelf, northwestAtlantic Ocean, through a study of the numerical abundance of different cell sizes — pico-, nano- andmicrophytoplankton. Cell abundances of each size class were converted to cellular carbon and their seasonalpatterns compared with the partial pressure of carbon dioxide (pCO2) also measured at the study site. We ob-served a persistent drawdown of CO2 throughout the summer months, despite nutrient depleted conditionsand apparent low biomass suggested by the chlorophyll record. This drawdown was associated with a summer-time phytoplankton assemblage numerically dominated by small phytoplankton that reach their peak abun-dance during this period. It was found that phytoplankton carbon during this period accounted forapproximately 10% of spring bloom phytoplankton carbon and pointed to the importance role that small cellsplay in annual CO2 uptake.
Representing only 7% of the surface area of the global ocean (Borges,2005), coastal andmarginal seas are estimated to account for 14–30% oftotal ocean primary production (Gattuso et al., 1998; Muller-Kargeret al., 2005). Primary production in these waters is stimulated by bothhigh nutrient inputs and their efficient use, and draws down amountsof atmospheric CO2 that are estimated to be ~30% of the total openocean uptake of CO2 (Chen and Borges, 2009) via processes knowncollectively as the ‘shelf sea pump’ (Thomas et al., 2004; Tsunogaiet al., 1999).
On the Scotian Shelf in the western North Atlantic (Fig. 1), phyto-plankton production and its associated biological drawdown of at-mospheric CO2 are punctuated by the spring bloom that typicallyoccurs during late March to early April near the surface water tem-perature minimum (Shadwick et al., 2011). Diatoms in themicrophytoplankton (20–200 μm) size range dominate the bloom(Johnson et al., 2012; Li et al., 2006), and over a period of just a fewweeks, approximately one third of the total carbon fixed during theannual cycle in this region is drawn down (Fournier et al., 1977;Mills and Fournier, 1979). The spring bloom collapses precipitously
when nitrate and silicate are largely depleted (Greenan et al., 2008;Mousseau et al., 1996). In the warming, and relatively nutrientpoor conditions that follow, the diatom-dominated bloom issucceeded by a new assemblage of cells comprised of dinoflagellates(microphytoplankton) and pico- (0.2–2 μm, e.g. Synechococcus) andnanophytoplankton (2–20 μm, e.g. nanoflagellates and small dia-toms) that are numerically more abundant than both diatoms anddinoflagellates by several orders of magnitude (Li et al., 2006). Theabundance of both of these size classes increases steadily throughoutthe summer months and their maxima coincide with the water tem-perature maximum and the minimum diatom abundance (Li et al.,2006).
Themost commonly used proxy of phytoplankton biomass is chlo-rophyll a concentration (Chl a; mgm−3) measured either in situ or es-timated from satellite radiance (e.g. Boyce et al, 2010; Siegel et al.,2013; Uitz et al., 2006). However, it should be appreciated that thisbulk community property is strongly influenced by phytoplanktoncommunity composition. As total Chl a increases, the fractional contri-bution of small cells to the standing crop of phytoplankton decreases(Chisholm, 1992). Raimbault et al. (1988) demonstrated this behav-iour by isolating size classes of phytoplankton using filters of differentpore sizes. They showed that the total amount of chlorophyll in eachsize fraction reached an upper limit, and that beyond this limit, chlo-rophyll could only be added to the system by the addition of largercells such as diatoms (Chisholm, 1992; Raimbault et al., 1988). There-fore, in regions such as the Scotian Shelf where a diatom dominated
Fig. 1.Map of study area showing station HL2 on the Scotian Shelf, off the coast of Nova Scotia, eastern Canada.
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spring bloom is succeeded by an assemblage of smaller cells, seasonalpatterns in chlorophyll essentially mirror diatom numerical abun-dance (Li et al., 2006). The numerical abundance of the smaller sizeclasses, however, may not be well represented by chlorophyll,evidenced by the fact that small cells reach their peak abundance inthis region during late summer–early autumn during the period ofminimum Chl a values (Li, 2002; Li et al., 2006). Additionally,photoacclimation brought about by decreases in summer mixedlayer depth and nutrient depleted conditionsmay decrease intracellu-lar chlorophyll content relative to carbon (Falkowski and Owens,1980; MacIntyre et al., 2002; Moore et al., 2006), further complicatingthe relationship between phytoplankton biomass and Chl a (Cullen,1982). A combination of some or all of these factors means that Chl amay be a poor proxy for phytoplankton biomass during summertimeconditions on the Scotian Shelf.
Chl a has been used as a variable in predictive models of the partialpressure of CO2 (pCO2; μatm) (e.g. Lohrenz et al. (2010); Lohrenz andCai (2006); Shadwick et al. (2011); Shadwick et al. (2010)). However,in the Scotian Shelf region, there is evidence to suggest that the biolog-ical uptake of CO2 may be underestimated during the post-bloomsummer period when based on models that use Chl a as the biomassproxy (Shadwick and Thomas, 2014; Shadwick et al., 2010, 2011).
In this report we utilize observations from a long-term study site onthe Scotian Shelf of seasonal phytoplankton community composition,characterised using flow cytometer and microscopic measurements,and its relationship with CO2 uptake measured using a pCO2 sensor.Over an annual cycle, the water column at this site transitions fromcold (sub-zero), well mixed and nutrient rich in winter, to a columnthat is stratified, with warm (~20 °C), nutrient poor surface waters inthe summer. This results in a shift from a diatom-dominatedphytoplankton assemblage in spring to a small cell-dominated assem-blage in summer, and allows an investigation of biological CO2 uptakerates associated with this change. We use numerical abundances ofdifferent phytoplankton size classes as a metric of biomass, rather thanChl a, and convert these to phytoplankton carbon values to examinethe effect of seasonal phytoplankton succession on patterns in CO2
uptake. In so doing, we aim to characterise the role of post-bloomsummertime primary production in annual CO2 uptake that may notbe fully appreciated if estimated using Chl a.
2. Materials and methods
2.1. In situ data
Phytoplankton, chemical, hydrographic, and pCO2 data were collect-ed from station Halifax Line 2 (HL2; 44.4°N, 63.3°W) on the ScotianShelf, eastern Canada (Fig. 1), a site of regular monitoring since 1998by the Department of Fisheries and Oceans (DFO) Canada as part ofthe Atlantic Zone Monitoring Program (AZMP; http://www.bio.gc.ca/science/monitoring-monitorage/azmp-pmza-eng.php). Samplingmethods, experimental procedures and methods have been describedin detail previously (Li and Dickie, 2001; Li and Harrison, 2001;Shadwick et al., 2011). Briefly, high temporal resolution measurementsof pCO2 in the upper mixed layer were obtained from a CARIOCA buoymoored at station HL2 at approximately 2m. Ship-basedmeasurementswere also collected bi-weekly from station HL2 and included CTD casts,microscopic enumeration of microphytoplankton (20–200 μm), andwater sample analyses for Chl a and nutrients using the methodologiesdetailed by Mitchell et al. (2002). Mixed layer depth (MLD; m) was de-termined by identifying the depth at which the density gradient esti-mated from CTD profiles was ≥0.01 kg m−4. Microphytoplanktoncounts (cells m−3) were performed on a depth integrated sample(Mitchell et al., 2002), i.e. a sample comprised of 50 mL aliquots fromeach of the ten depths sampled (1, 5, 10, 20, 30, 40, 50, 75, 100,140 m) to give a 500 mL combined volume. Historically, this methodwas implemented by the AZMP to account for vertical inhomogene-ity in phytoplankton distribution. Analyses of water column stratifi-cation features, Chl a vertical distributions (Fig. 3(e)) and therelationship between Chl a and diatoms (Fig. 5(b)) indicated thatthe counts were very heavily weighted to surface values. It was de-cided, therefore, to use the counts unaltered, whilst appreciatingthat this step may introduce a degree of uncertainty into subsequentcalculations.
Pico- (0.2–2 μm) and nanophytoplankton (2–20 μm) abundanceswere determined by flow cytometry at both HL2 and a nearby coastalmonitoring site in Bedford Basin (44.69°N, 63.64°W). At HL2, flowcytometer samples were routinely acquired from surface waters inspring and fall and occasionally, during other periods throughout theyear. In Bedford Basin, samples from 5 m were acquired weekly, and
54 S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
provided a dataset with which to compare the less frequent data fromHL2.
2.2. Phytoplankton carbon inventory calculations
Phytoplankton abundance (cellsm−3)was converted into total phy-toplankton carbon concentration (mol C m−3) using carbon cell−1
values. Mean carbon cell−1 values were calculated from literature car-bon cell−1 values for diatom (Mullin et al., 1966) and dinoflagellate(Menden-Deuer and Lessard, 2000; Mullin et al., 1966) species thatare known to occur at HL2 (Kevin Pauley, pers. comm.). These werecalculated as 1.492 × 10−9 and 4.386 × 10−9 gC cell−1 for diatomsand dinoflagellates respectively. Carbon cell−1 values for pico- andnanophytoplankton were estimated using the Li (2002) relationship
pic
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BBBB modelHL2HL2 model
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Fig. 2. (a) Relationship between pico- and nanophytoplankton cell abundances (pico +nano) and surface water temperature for weekly Bedford Basin (BB) data and spring/autumn station Halifax Line 2 (HL2) data. Lines represent the linear regression modelfitted to each data set. Bedford Basin model: log10[pico + nano] = 0.085 + 9.564 T,R2 = 0.564 (N = 648), p b b0.001. HL2 model: log10[pico + nano] = 0.135 + 9.135 T,R2 = 0.812 (N = 48), p b b0.001. (b) HL2 modelled versus measured pico + nano,where modelled values were derived from surface water temperature using the HL2model shown in (a).
between the combined abundance of pico- and nanophytoplanktonand carbon cell−1 (their Fig. 2(b)), and a geometric mean was calculat-ed to provide a value of 0.4 × 10−12 gC cell−1. In order to obtain fullyseasonally resolved estimates of cell abundances for this calculation, amodel of HL2 cell abundance (obtained approximately twice per year)versus water temperature was derived (see Section 3.1 below for fulldetails). These more highly temporally resolved values were binnedinto monthly values (spanning 1999–2011).
The phytoplankton carbon inventory (mol C m−2) was obtained bymultiplyingmol Cm−3 by themixed layer depth inm. It should be care-fully noted that cell abundance estimates and pCO2 measurements per-tain only to the upper mixed layer, which varies between 9 m insummer and 53 m in winter. Deep Chl a maxima do form below thepycnocline in this region (Longhurst, 1995), but our calculations anddiscussions concern only the upper mixed layer.
2.3. Rate of change of phytoplankton carbon
To resolve the important contribution of seasonal biological signalsto total carbon dynamics, the rate of change of phytoplankton carbonfrom climatological month to month (ΔCp; mol C m−2 month−1) wascalculated from:
ΔCp ¼ Cp t2ð Þ−Cp t1ð Þt2−t1
ð1Þ
where Cp(t) is the total phytoplankton carbon at time t (t2 N t1), andwhere positive values represent an increase in phytoplankton carbon.ΔCp may be thought of as analogous to net community production(NCP), but depends only on the balance between gross photosynthesisand loss terms such as mortality and grazing. In the calculation of ΔCpby this method, it must be assumed that the water masses from onemonth to the next are invariant. Shadwick et al. (2011) measuredboth across and along shelf horizontal gradients in mixed layer dis-solved inorganic carbon (DIC), and found rather low values on theorder of 1–2 × 10−3 μmol kg−1 m−1. Furthermore, these authorsshowed that the magnitude of the horizontal and vertical transportterms is small compared with the magnitude of NCP throughout theyear except for the autumn months. Based on these findings, we as-sumed that advective terms were small compared with the biologicalsignal.
3. Results and discussion
3.1. Estimates of pico- and nanophytoplankton abundances
Flow cytometer cell counts of pico- (Synechococcus) andnanophytoplankton (nanoflagellates and small diatoms) at HL2 existedonly during spring and fall, meaning that no directlymeasured informa-tion on small cell abundance was available outwith these periods. Stud-ies performed by others in the North Atlantic have shown that watertemperature is a ‘holistic simplifier’ of themechanistically complex pro-cesses that control small cell abundance, and can beused to estimate theabundance of pico- and nanophytoplankton (Li et al., 2006;Morán et al.,2010). It should be clearly stated, however, that temperature is not con-sidered the proximate controller of cell size. Rather, it co-varies withother complex physical and biological mechanisms that control theavailability of resources, e.g. decreased nutrient supply due to increasedstratification favours small cells (Li et al., 2013; Maranón et al., 2012).We used this proxy approach and examined the relationship betweencombined pico- and nanophytoplankton abundances (pico + nano;cells m−3) and surface water temperature at HL2 (T; °C) (Fig. 2(a)).Bedford Basin weekly pico + nano data was also plotted to allow anexamination of the relationship at higher temporal frequency andthroughout the entire seasonal cycle (Fig. 2(a)). In agreement with pre-vious studies in the region (Li et al., 2006; Morán et al., 2010), a strong
55S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
relationship was observed between cell abundance and temperature atboth HL2 and Bedford Basin. The regression model for Bedford Basinwas log10[pico + nano] = 0.085 + 9.564 T, R2 = 0.564 (N =648),p bb0.001, and for HL2, log10[pico + nano] = 0.135 + 9.135 T, R2 =0.812 (N = 48), p b b0.001. Despite the reduced temporal resolution,the HL2 relationship showed the same general pattern as the morehighly temporally resolved data in Bedford Basin, with small cell abun-dance steadily increasing with temperature. This coherence betweengeographically distinct sites on the Scotian Shelf was also demonstratedby Li et al. (2006), but analysis of covariance (ANOCOVA) performed onour dataset revealed that the slopes of the models were statisticallydifferent.
A comparison of modelled andmeasured values of pico+ nanowasmade (Fig. 2(b)), and results are shown in log-log space to facilitatevisualisation over the large dynamic range. Due to the semi-logarithmic nature of the model (Fig. 2(a)), errors calculated on non-logarithmically transformed values are quite large: R2 = 0.482 (N =43), NRMSE = 99.740%, bias = −5.300 × 109 cell m−3, where NRMSEis the root mean square error divided by the mean of measured values,and where the negative bias indicates underestimation by the model.On the basis of its reasonable predictive skill and confirmation that
MLD
a b
c
e
month
Fig. 3. Climatological biogeochemical properties of station Halifax Line 2 (HL2). Climatologies a(d) Silicate and (e) Chlorophyll a (Chl a). The solid black line represents mixed layer depth (M
seasonal trends were very similar to the higher temporal resolutionmodel at nearby Bedford Basin, the HL2 model was used to estimatepico + nano abundances for all seasons.
3.2. Seasonal patterns
Diatoms dominate the spring bloom, which occurs late March–earlyApril (Figs. 3 and 4), and they reach their climatologicalmaximumof 2.4× 108 cells m−3 in April. When nitrate and silicate are largely depleted(Fig. 3(c)–(d)), the spring bloom collapses and, beginning approxi-mately in May, a new assemblage of cells dominated by much smallerbut numerically more abundant pico- (0.2–2 μm) andnanophytoplankton (2–20 μm) flourish in the warming, relatively nu-trient poor conditions (Figs. 3–4). Throughout the summer, pico- andnanophytoplankton cell abundances increase steadily, reaching itsmaximum of ~4 × 1011 cells m−3 in August (Fig. 4(b)). The prolifera-tion of the small cells during the nutrient poor conditions of the sum-mer months is likely to arise from a combination of factors. Small cellsare believed to have a competitive advantage over larger cells becauseof their higher nutrient affinity that allows them to maintain high up-take rates under low nutrient conditions (Agawin et al., 2000;
d
re constructed from data spanning 1999–2011 for (a) Temperature (b) Salinity (c) NitrateLD).
0
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Fig. 4. (a) Seasonal cycle of diatoms, nanophytoplankton (nano), picophytoplankton(pico), pico and nano combined (pico + nano) and dinoflagellates (dinos) measured inBedford Basin to illustrate seasonal succession in this region. (b) HL2 seasonal cycle oftemperature, Chl a, diatoms, dinoflagellates and pico + nano. Pico + nano abundancesis estimated from the HL2 model shown in Fig. 2. The grey dotted lines indicate thecollapse of the spring bloom in May at ~6–7 °C, the shoaling of the mixed layer depthand the transition to a phytoplankton assemblage numerically dominated by smaller cells.
Fig. 5. The relationship between the abundances of (a) log10[pico+ nano], (b) log10[diatom],and (c) log10[dino] versus log10[Chl a].
56 S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
Chisholm, 1992; Fogg, 1986). Additionally, it is now appreciated thatsmall cells are mixotrophic, making them less dependent on inorganicnutrients than previously thought (Hartmann et al., 2012; Mitra et al.,2014; Zubkov and Tarran, 2008).
It should be noted that the pico- and nanophytoplanktonabundances presented in Fig. 4(a) are from Bedford Basin flowcytometer counts and are shown simply to illustrate the seasonal suc-cession of different phytoplankton groups. The HL2 model used to esti-mate pico + nano does not allow resolution of the separate pico- andnanophytoplankton contributions, but the seasonal patterns are verysimilar (see Fig. 2(a) and Li et al. (2006)). This seasonal pattern is mir-rored in the dinoflagellate population, although their abundance is ap-proximately four orders of magnitude less than the pico- andnanophytoplankton fractions. Dinoflagellates are generally categorizedin the microphytoplankton size class, and, in the context of size scaling
with temperature (Peters, 1983) – i.e. warmer temperatures are associ-ated with smaller organisms – their co-occurrence with the smaller cellsizes seems incongruous. However, dinoflagellates possess a collectionof unique features (e.g. mixotrophy and the ability to vertically migrate(Smayda, 1997)), which collectively may reduce their dependence onnutrients delivered from aphotic depths increasing the likelihood ofpeak abundance during the summer months.
57S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
Following the spring bloom, the diatom contribution to total cellabundance remains low at b0.5 × 108 cells m−3, although occasionally,a modest autumnal bloom occurs caused by wind-driven mixing(Greenan et al., 2004), and this is reflected in the small elevation inthe diatom climatology in November (Fig. 4(a), (b)). It should benoted that, despite being numerically more abundant than diatoms byseveral orders of magnitude in the summer months, pico- andnanophytoplankton biomass is not correlated with chlorophyll a stand-ing stock (Fig. 5(a)), an important observation also made by Li et al.(2006) in the northwest Atlantic, by Claustre (1994) in several oceanicprovinces, and inferred by Shadwick et al. (2011; 2010) from pCO2 stud-ies in the same region. Chl a is persistently less than 1mgm−3 followingthe collapse of the spring bloom, yet the cell count estimates reveal sub-stantial biomass in the pico- and nanophytoplankton fractions, empha-sizing the fact that Chl a primarily mirrors patterns in the diatomfraction of the assemblage (Fig. 5(b)), but not the dinoflagellate fraction(Fig. 5(c)). This lack of relationship between Chl a and the abundance ofdinoflagellates and pico + nano may, in part, be explained byphotoacclimation during the summer when mixed layer depths de-crease to ~9 m (Fig. 3, Fig. 7(a)). Cells respond to potentially damagingirradiance, caused by being trapped in a shallow surfacemixed layer, bydecreasing intracellular photosynthetic pigment (MacIntyre et al.,2002). For this reason, Chl a should not be considered a robust proxyfor biomass in all seasons (e.g. Cullen (1982)).
In contrast to our results, Agawin et al. (2000) found a rather strongrelationship between the percent contribution of picophytoplankton tototal phytoplankton and Chl a (their Fig. 6(b)). The reasons for the dif-ference between their results and ours are not entirely clear, but maybe related to the conditions in the mesocosm used to generate their
a
b
Fig. 6. (a) Relationship between total phytoplankton carbon inventory and surface watertemperature. Note that the y-axis is log10 transformed to facilitate visualisation of the dataover a large range. The grey dotted line corresponds to the month of May, the collapse ofthe spring bloom and the transition to an assemblage numerically dominated by smallercells. Seasons and corresponding months are indicated in the legend. (b) Compositeseasonal cycle of pCO2, norm constructed from data spanning 2007–2009 redrawn afterShadwick et al. (2011).
data, which featured a duration of 21 days, an almost constant temper-ature, a height of 14 m and regular addition of nutrients. All of theseconditions are very different compared to the conditions at our studysite and may have resulted in differences in photoacclimative cellularchlorophyll content, phytoplankton community composition andmixotrophic behaviour (Hartmann et al., 2012; Mitra et al., 2014;Zubkov and Tarran, 2008) that may also alter cellular chlorophyllcontent (Jones et al., 1995). Additionally, our plot includes contributionsfrom pico- and nanophytoplankton, whilst Agawin et al. (2000)consider only picophytoplankton.
3.3. Phytoplankton community composition and pCO2
In Fig. 6(a), we examine the relationship between water tempera-ture and phytoplankton carbon inventory (mol C m−2). We presenttemperature on the abscissa rather than date to account for the factthat the same temperature may occur in multiple seasons. Visualisationof the data in this way allows a direct comparison with pCO2 data plot-ted in the same manner (Fig. 6(b)) and so, illustrates how patterns inmixed layer phytoplankton carbon inventory correspond to features inmixed layer total carbon dynamics.
Minimum phytoplankton carbon values of ~0.01 mol C m−2 oc-curred at approximately 6 °C (Fig. 6(a)), corresponding to both the col-lapse of spring bloom (~May, pink circles) and early winter (December/January, purple circles). The phytoplankton carbonmaximum(~4.5molC m−2) was observed close to the temperature minimum that occurredduring the diatom dominated spring bloom. Over the post spring bloomwarming period (~6–20 °C ≈ May–August, Fig. 4(b)), phytoplanktoncarbon concentration, in concert with pico- and nanophytoplanktonabundances, steadily increased by an approximate order of magnitudeto reach values of ~0.3 mol C m−2, comparable to those observed inthe spring. Throughout this period, Chl a is persistently b1 mg m−3
(Fig. 4(b)), reinforcing the fact that this important fraction of the phyto-plankton assemblage is almost completely decoupled from the chloro-phyll standing stock (Fig. 5(a); c.f. Claustre (1994) and Li et al.(2006)) — significant given the fact that Chl a is used ubiquitously asthe biomass term in many different types of global and regional oceanmodels (e.g. Behrenfeld et al. (2006); Fennel et al. (2008); Boyce et al.(2010)). The inability to accurately estimate carbon uptake based onChl a as a proxy for biomass during this summer period was also identi-fied by Shadwick and colleagues (Shadwick et al., 2010, 2011). This re-inforces the concept that Chl a closely mirrors the patterns ofassemblages with high intracellular Chl a, e.g. large diatoms (Li andHarrison, 2008; Li et al., 2006) (Fig. 4(b), 5(b)), but does not accuratelyrepresent the more numerically abundant smaller size fractions withlower intracellular Chl a that dominate in the summer months(Fig. 5(a)).
The seasonal cycle of pCO2 shown in Fig. 6(b) is a composite of datafrom deployments during 2007–2009 and was constructed to accountfor breaks in data acquisition due to buoy maintenance. To account forthe effect ofwater temperature,we present pCO2 corrected to a constantannual mean temperature (pCO2, norm; μatm) (c.f. Takahashi et al.(2002); Shadwick et al. (2011)). Comparing Fig. 6(a) and (b), it isclear that the seasonal evolution and succession of the different phyto-plankton communities correspondwith various features in the bulk CO2
system parameters. For example, springtime maximum phytoplanktoncarbon concentration (Fig. 6(a), pink circles), which is associated withlarge diatoms that bloom around the temperature minimum(Fig. 4(b)), is reflected in the rapid drop in pCO2, norm (Fig. 6(b)). The col-lapse of the bloom and the onset of surface warming in May results inrising pCO2, norm concentrations, and at a water temperature of ~5–6 °C inMay/June, thediatom community is succeeded bydinoflagellatesand smaller pico- and nanophytoplankton (Fig. 4(b)). The increase inbiomass and concomitant uptake of carbon by these communities(Fig. 6(a)) consistently lowers pCO2, norm (Fig. 6(b)) throughout thesummer months until the temperature reaches its maximum. At the
ph
yto
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C (
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Fig. 7. (a) Total phytoplankton carbon concentration andmixed layer depth (MLD) over aclimatological year. (b) Seasonal pattern of the rate of change of phytoplankton carbon,ΔCp. (e) ΔCp and net community production (NCP) with error estimates shown for thisstudy, Shadwick et al. 2011 (S2011) and Thomas et al. 2012 (T2012).
58 S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
end of the summer period, respiration resulting from the decay ofphytoplankton biomass and wind-induced or convective entrainmentof CO2 from deeper waters (Greenan et al., 2004; Shadwick et al.,2011) raises pCO2, norm back to pre-bloom winter conditions. Theaccentuated impact of the summertime phytoplankton community onthe surface layer CO2 system (i.e. pCO2) also becomes visible whenrelating Figs. 6 and 7(a). Whilst the summertime inventory ofphytoplankton carbon (Fig. 6(a)) is certainly lower than that of thespring bloom, the phytoplankton carbon concentration (Fig. 7(a); mgC m−3) in the respective surface layer is comparable during both sea-sons – spring bloom: ~380 mg C m−3 compared with August/September: ~200 mg C m−3 (Fig. 7(a)). Since it is the phytoplanktoncarbon concentration, rather than its inventory, that is responsible forregulating the biologically driven variability of the surface water pCO2,the magnitude of the post-bloom CO2 drawdown ~150 μatm;Fig (6(b)) is also comparable to that of the spring bloomCO2 drawdown(~230 μatm, Fig. 6(b)).
3.4. Changes in phytoplankton carbon, ΔCp
The rate of change of phytoplankton carbon (ΔCp; mol C m−2 -month−1) was calculated by integrating phytoplankton carbon overthemixed layer (Section 2.2 and Fig. 7(a)). Itwas found that a springtimeΔCp maximum of 0.616 mol C m−2 month−1 occurs in April (Fig. 7(b))and is influenced primarily by the rapid increase in diatom cell numbersduring the spring bloom. The precipitous decrease in diatom abundancein May drives ΔCp to a negative value of −0.439 mol C m−2 month−1,approximately 70% of the absolute magnitude of the spring value. ΔCpis positive throughout June–August reflecting the steady increase inpicophytoplankton, nanophytoplankton and dinoflagellate abundances,which, along with temperature, reach their maxima in August(Fig. 4(b)). In September, ΔCp becomes negative and remains sothroughout thewinter, corresponding to the decline in all phytoplanktonsize classes throughout the autumn and winter. ΔCp is positive duringthe spring (months 3–4) and the summer (months 6–8), and summingtheΔCp values during these two periods revealed that summertime phy-toplankton uptake of carbon (0.101 mol C m−2) represents approxi-mately 10% of the springtime uptake (0.963 mol C m−2), reinforcingthe fact that summertime productivity is significant. This summertimevalue is less than the 40% of spring bloom production estimated byShadwick et al. (2011). However, it seems reasonable to postulate thatthe uncertainties in both this and the Shadwick et al. (2011) estimatemay account for the difference.
A comparison ofΔCp values with NCP values calculated by Shadwicket al. (2011) from pCO2 measurements is shown in Fig. 7(c) along withtheir respective error estimates. NCP values from Thomas et al. (2012)are also shown for comparison. Error bars for this study represent thecombination of uncertainties calculated using standard propagation oferror techniques. There are a number of uncertainties involved in thiscalculation and the most significant by far are those for pico + nanocell abundance estimates (~100% for temperature-abundance model,Fig. 2) and for carbon cell−1 uncertainties (~17% derived from themean of coefficient of variation values calculated by Menden-Deuerand Lessard (2000)). Except for months 4 and 10, these independentlyderived estimates agree within their upper and lower bounds of uncer-tainty. Factors contributing to the differences between the two esti-mates include uncertainty in pico + nano abundance estimation(±100%, Fig. 2), the difference in methods used to construct seasonalcycles in each study (13-year climatology for this study and a 2-yearcomposite cycle by Shadwick et al. (2011)), selection of carbon cell−1
values, and the fact that, except for diatoms, all the other phytoplanktongroups enumerated are mixotrophic to greater (dinoflagellates) or less-er (prasinophytes in the nanophytoplankton size class) extents (Flynnet al., 2013). This latter pointmeans that mixotrophswould be includedin the ΔCp calculation based on cell abundance estimates, despite thefact that part of their metabolic activity is heterotrophic and does notconstitute net CO2 drawdown. However, mixotrophy would drive thediscrepancy in the opposite direction to that observed, i.e. make thecell abundance-based ΔCp values larger than the pCO2-based NCPvalues. It is not currently possible to fully explain the differencesobserved, and further work is required to reconcile them.
3.5. Summertime phytoplankton production
The increase in phytoplankton biomass, and thus ΔCp, during thesummer months is in spite of apparent depletion of surface mixedlayer nitrate (Fig. 3(c)), a finding also reported at this site by Shadwickand colleagues (Shadwick and Thomas, 2011; Shadwick et al., 2011).They observed supersaturation of O2 with respect to atmospheric levelsand a decrease in DIC in surface waters at HL2 throughout the postbloom summer months, which they attributed to phytoplanktonprimary production. In the North Atlantic, the phenomenon of elevatedcarbon consumption relative to nitrogen that exceeds the classicalRedfield ratio (Redfield et al., 1963) of 6.6 – so called “carbon
59S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
overconsumption” – has been reported extensively in the literature(Jiang et al., 2013; Koeve, 2004; Körtzinger et al., 2001; Osterroht andThomas, 2000; Sambrotto et al., 1993; Shadwick and Thomas, 2014;Shadwick et al., 2011; Taucher et al., 2012; Thomas et al., 1999;Toggweiler, 1993), and the authors of these studies observed that over-consumption appears to be associated with summertime nutrient poorconditions. Based on our observations of both the inorganic carbon andnutrient systems, we speculate that carbon overconsumption by a sum-mertime assemblage of small cells may play a role in the carbon cycle atour study site. However, it is impossible to verify this withoutmeasure-ments of both the particulate and dissolved organic carbon and nitrogenpools — data that were, unfortunately, not available. Future studiesshould include such measurements to allow a more thorough charac-terisation of the various processes at play at the study site during thenutrient depleted summer months.
Other factors that may contribute to on-going summertimeproduction despite nutrient depletion include the elevation of nutrientrecycling and phytoplankton metabolism at higher water temperatures(Taucher and Oschlies, 2011; Taucher et al., 2012) and, as discussedpreviously, the ability of some species to harvest nutrients throughmixotrophy (Hartmann et al., 2012; Mitra et al., 2014; Zubkov andTarran, 2008)
4. Conclusions
We have provided a mechanistic explanation of CO2 drawdown inthe context of seasonal phytoplankton succession, despite uncertaintiesin derived parameters. It has been shown that numerically abundantpico- and nanophytoplankton are correlated with the persistent uptakeof carbon throughout the nutrient poor, post bloom summer months.During the summer, this fraction of the phytoplankton assemblage isuncoupled from the Chl a standing stock, yet accounts for approximate-ly 10% of spring bloom carbon uptake. The lack of relationship betweenChl a and the small cells and dinoflagellates that dominate the assem-blage during the summer is likely caused, in part, by photoacclimativereduction in intracellular photosynthetic chlorophyll as a result ofhigh irradiances in a shallow mixed layer. An additional factor thatmay explain this behaviour might also be found in the fact that smallcells may only attain certain threshold values of Chl a, and that onlythe addition of larger cells will allow Chl a to increase beyond thesethresholds.
We speculated that carbon overconsumption may be taking placeduring the summer, but a lack of information on the dissolved nutrientpool prevented any robust conclusions from being drawn. It is alsolikely that mixotrophy played a role during the nutrient poor summermonths, and future work should investigate both of these phenomenain order to more thoroughly characterise the biological influence oncarbon dynamics at this site. Finally, our findings may be relevant inthe context of the shift towards smaller phytoplankton assemblagespredicted to occur under warming ocean conditions (Doney et al.,2012; Falkowski and Oliver, 2007).
Acknowledgments
This work was supported by the Canadian Foundation for Climateand Atmospheric Sciences (CFCAS, grant no. GR-C-01). Data from sta-tion HL2 and Bedford Basin were provided by the Department of Fish-eries and Oceans (DFO) Canada Atlantic Zone Monitoring Project(AZMP) and Bedford Basin Plankton Monitoring Program respective-ly. We thank Tim Perry, Carla Caverhill and Heidi Maas of Bedford In-stitute of Oceanography (BIO) for their assistance in data assembly,and Kevin Pauley (BIO) for guidance on phytoplankton taxonomy.The paper benefitted from the constructive comments of two anony-mous referees.
References
Agawin, N.S., Duarte, C.M., Agusti, S., 2000. Nutrient and temperature control of thecontribution of picoplankton to phytoplankton biomass and production. Limnol.Oceanogr. 45, 591–600.
Borges, A., 2005. Do we have enough pieces of the jigsaw to integrate CO2 fluxes in thecoastal ocean? Estuaries 28, 3–27.
Boyce, D.G., Lewis, M.R., Worm, B., 2010. Global phytoplankton decline over the pastcentury. Nature 466, 591–596.
Chen, C.-T.A., Borges, A.V., 2009. Reconciling opposing views on carbon cycling in thecoastal ocean: continental shelves as sinks and near-shore ecosystems as sources ofatmospheric CO2. Deep-Sea Res. II Top. Stud. Oceanogr. 56, 578–590.
Chisholm, S.W., 1992. Phytoplankton size. In: Falkowski, P.G., Woodhead, A.D. (Eds.),Primary Productivity and Biogeochemical Cycles in the Sea. Plenum Press, New York.
Claustre, H., 1994. The trophic status of various oceanic provinces as revealed byphytoplankton pigment signatures. Limnol. Oceanogr. 39, 1206–1210.
Cullen, J.J., 1982. The deep chlorophyll maximum: comparing vertical profiles ofchlorophyll a. Can. J. Fish. Aquat. Sci. 39, 791–803.
Falkowski, P.G., Oliver, M.J., 2007. Mix and match: how climate selects phytoplankton.Nat. Rev. Microbiol. 5, 813–819.
Falkowski, P.G., Owens, T.G., 1980. Light-shade adaptation. Plant Physiol. 66, 592–595.Fennel, K., Wilkin, J., Previdi, M., Najjar, R., 2008. Denitrification effects on air–sea CO2 flux
in the coastal ocean: simulations for the northwest North Atlantic. Geophys. Res. Lett.35, L24608.
Flynn, K.J., Stoecker, D.K., Mitra, A., Raven, J.A., Glibert, P.M., Hansen, P.J., Granéli, E.,Burkholder, J.M., 2013. Misuse of the phytoplankton–zooplankton dichotomy: theneed to assign organisms as mixotrophs within plankton functional types. J. PlanktonRes. 35, 3–11.
Fogg, G.E., 1986. Review lecture: picoplankton. Proceedings of the royal society of London.Series B. Biol. Sci. 228, 1–30.
Fournier, R.O., Marra, J., Bohrer, R., Van Det, M., 1977. Plankton dynamics and nutrient en-richment of the Scotian Shelf. J. Fish. Res. Board Can. 34, 1004–1018.
Gattuso, J.-P., Frankignoulle, M., Wollast, R., 1998. Carbon and carbonate metabolism incoastal aquatic ecosystems. Annu. Rev. Ecol. Syst. 29, 405–434.
Greenan, B.J.W., Petrie, B.D., Harrison, W.G., Oakey, N.S., 2004. Are the spring and fallblooms on the Scotian Shelf related to short-term physical events? Cont. Shelf Res.24, 603–625.
Greenan, B.J.W., Petrie, B.D., Harrison, W.G., Strain, P.M., 2008. The onset and evolution ofa spring bloom on the Scotian Shelf. Limnol. Oceanogr. 53, 1759–1775.
Hartmann, M., Grob, C., Tarran, G.A., Martin, A.P., Burkill, P.H., Scanlan, D.J., Zubkov, M.V.,2012. Mixotrophic basis of Atlantic oligotrophic ecosystems. Proc. Natl. Acad. Sci. 109,5756–5760.
Jiang, Z.P., Hydes, D.J., Tyrrell, T., Hartman, S.E., Hartman, M.C., Dumousseaud, C., Padin, X.A., Skjelvan, I., Gonzalez-Pola, C., 2013. Key controls on the seasonal and interannualvariations of the carbonate system and air–sea CO2 flux in the Northeast Atlantic (Bayof Biscay). J. Geophys. Res. Ocean. 118, 785–800.
Johnson, C., Harrison, G., Head, E., Spry, J., Pauley, K., Maass, H., Kennedy, M., Porter, C.,Yashayaeva, I., Casault, B., 2012. Optical, chemical and biological conditions in themaritimes region in 2009 and 2010. Canadian Science Advisory Secretariat(CSAS)Bedford Institute of Oceanography.
Jones, H.L.J., Durjun, P., Leadbeater, B.S.C., Green, J.C., 1995. The relationship betweenphotoacclimation and phagotrophy with respect to chlorophyll a, carbon and nitro-gen content, and cell size of Chrysochromulina brevifilum (Prymnesiophyceae).Phycologia 34, 128–134.
Koeve, W., 2004. Spring bloom carbon to nitrogen ratio of net community production inthe temperate N. Atlantic. Deep-Sea Res. Ocean.Res. Pap. 51, 1579–1600.
Körtzinger, A., Koeve,W., Kahler, P., Mintrop, L., 2001. C:N ratios in themixed layer duringthe productive season in the northeast Atlantic Ocean. Oceanogr. Res. Pap. 48,661–688.
Li, W.K.W., 2002. Macroecological patterns of phytoplankton in the northwestern NorthAtlantic Ocean. Nature 419, 154–157.
Li, W.K.W., Carmack, E.C., McLaughlin, F.A., Nelson, R.J., Williams, W.J., 2013. Space-for-time substitution in predicting the state of picoplankton and nanoplankton in achanging Arctic Ocean. J. Geophys. Res.Ocean. 118, 5750–5759.
Li, W.K.W., Dickie, P.M., 2001. Monitoring phytoplankton, bacterioplankton, andvirioplankton in a coastal inlet (Bedford Basin) by flow cytometry. Cytometry 44,236–246.
Li, W.K.W., Glen Harrison, W., Head, E.J.H., 2006. Coherent assembly of phytoplanktoncommunities in diverse temperate ocean ecosystems. Proc. R. Soc. B Biol. Sci. 273,1953–1960.
Li, W.K.W., Harrison, W.G., 2001. Chlorophyll, bacteria and picophytoplankton inecological provinces of the North Atlantic. Deep-Sea Res. II Top. Stud. Oceanogr. 48,2271–2293.
Li, W.K.W., Harrison, W.G., 2008. Propagation of an atmospheric signal to phytoplanktonin a small basin. Limnol. Oceanogr. 53, 1734–1745.
Lohrenz, S.E., Cai, W.-J., Chen, F., Chen, X., Tuel, M., 2010. Seasonal variability in air–seafluxes of CO2 in a river-influenced coastal margin. J. Geophys. Res. 115, C10034.
Lohrenz, S.E., Cai, W.J., 2006. Satellite ocean color assessment of air–sea fluxes of CO2 ina river-dominated coastal margin. Geophys. Res. Lett. 33.
60 S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
Longhurst, A., 1995. Seasonal cycles of pelagic production and consumption. Prog.Oceanogr. 36, 77–167.
MacIntyre, H.L., Kana, T.M., Anning, T., Geider, R.J., 2002. Photoacclimation ofphotosynthesis irradiance response curves and photosynthetic pigments inmicroalgae and cyanobacteria. J. Phycol. 38, 17–38.
Maranón, E., Cermeno, P., Latasa, M., Tadonléké, R.D., 2012. Temperature, resources, andphytoplankton size structure in the ocean. Limnol. Oceanogr. 57, 1266.
Menden-Deuer, S., Lessard, E.J., 2000. Carbon to volume relationships for dinoflaggelates,diatoms and other protist plankton. Limnol. Oceanogr. 45, 569–579.
Mills, E.L., Fournier, R.O., 1979. Fish production and the marine ecosystems of the ScotianShelf, eastern Canada. Mar. Biol. 54, 101–108.
Mitchell, M.R., Harrison, W.G., Pauley, K., Gagné, A., Maillet, G.L., Strain, P., 2002. AtlanticZone Monitoring Program Protocol., Canadian Technical Reports of Hydrographyand Ocean Sciences. Department of Fisheries and Oceans, Maritimes Region.
Mitra, A., Flynn, K.J., Burkholder, J.M., Berge, T., Calbet, A., Raven, J.A., Granéli, E., Glibert,P.M., Hansen, P.J., Stoecker, D.K., Thingstad, F., Tillmann, U., Våge, S., Wilken, S.,Zubkov, M.V., 2014. The role of mixotrophic protists in the biological carbon pump.Biogeosciences 11, 995–1005.
Moore, C., Suggett, D.J., Hickman, A.E., Kim, Y.-N., Tweddle, J.F., Sharples, J., Geider, R.J.,Holligan, P.M., 2006. Phytoplankton photoacclimation and photoadaptation in re-sponse to environmental gradients in a shelf sea. Limnol. Oceanogr. 51, 936–949.
Morán, X.A.G., López-Urrutia, Á., Calvo-Díaz, A., Li, W.K.W., 2010. Increasing importance ofsmall phytoplankton in a warmer ocean. Glob. Chang. Biol. 16, 1137–1144.
Mousseau, L., Legendre, L., Fortier, L., 1996. Dynamics of size-fractionated phytoplanktonand trophic pathways on the Scotian Shelf and at the shelf break, Northwest Atlantic.Aquat. Microb. Ecol. 10, 149–163.
Muller-Karger, F.E., Varela, R., Thunell, R., Luerssen, R., Hu, C., Walsh, J.J., 2005. Theimportance of continental margins in the global carbon cycle. Geophys. Res. Lett.32, L01602.
Mullin, M.M., Sloan, P.R., Eppley, R.W., 1966. Relationship between carbon content, cellvolume, and area in phytoplankton. Limnol. Oceanogr. 11, 307–311.
Osterroht, C., Thomas, H., 2000. New production enhanced by nutrient supply from non-Redfield remineralisation of freshly produced organic material. J. Mar. Syst. 25, 33–46.
Peters, R.H., 1983. The Ecological Importance of Body Size. Cambridge University Press,Cambridge.
Raimbault, P., Rodier, M., Taupier-Letage, I., 1988. Size fraction of phytoplankton in theLigurian Sea and the Algerian Basin (Mediterranean Sea): size distribution versustotal concentration. Mar. Microb. 3, 1–7.
Redfield, A.C., Ketchum, B.H., Richards, F.A., 1963. The influence of organisms on thecomposition of seawater. In: Hill, M.N. (Ed.), The Sea. Interscience, New York.
Sambrotto, R.N., Savidge, G., Robinson, C., Boyd, P., Takahashi, T., Karl, D.M., Langdon, C.,Chipman, D., Marra, J., Codispoti, L., 1993. Elevated consumption of carbon relatveto nitrogen in the surface ocean. Nature 363, 248–250.
Shadwick, E.H., Thomas, H., 2011. Carbon dioxide in the coastal ocean: a case study in theScotian Shelf region. In: Chircop, A., Coffen-Smout, S., McConnell, M. (Eds.), OceanYearbook 25. Martinus Nijhoff, Leiden/Boston, pp. 171–204.
Shadwick, E.H., Thomas, H., 2014. Seasonal and spatial variability in the CO2 system on theScotian Shelf (Northwest Atlantic). Mar. Chem. 160, 42–55.
Shadwick, E.H., Thomas, H., Azetsu-Scott, K., Greenan, B.J.W., Head, E., Horne, E., 2011.Seasonal variability of dissolved inorganic carbon and surface water pCO2 in theScotian Shelf region of the Northwestern Atlantic. Mar. Chem. 124, 23–37.
Shadwick, E.H., Thomas, H., Comeau, A., Craig, S.E., Hunt, C.W., Salisbury, J.E., 2010. Air–seaCO2 fluxes on the Scotian Shelf: seasonal to multi-annual variability. Biogeosciences7, 3851–3867.
Smayda, T.J., 1997. Harmful algal blooms: their ecophysiology and general relevance tophytoplankton blooms in the sea. Limnol. Oceanogr. 42, 1137–1153.
Takahashi, T., Sutherland, S.C., Sweeney, C., Poisson, A., Metzl, N., Tilbrook, B., Bates, N.,Wanninkhof, R., Feely, R.A., Sabine, C., Olafsson, J., Nojiri, Y., 2002. Global sea–airCO2 flux based on climatological surface ocean pCO2, and seasonal biological andtemperature effects. Deep-Sea Res. II Top. Stud. Oceanogr. 49, 1601–1622.
Taucher, J., Oschlies, A., 2011. Can we predict the direction of marine primary productionchange under global warming. Geophys. Res. Lett. 38, L02603.
Taucher, J., Schulz, K.G., Dittmar, T., Sommer, U., Oschlies, A., Riebesell, U., 2012. Enhancedcarbon overconsumption in response to increasing temperatures during a mesocosmexperiment. Biogeosciences 9, 3531–3545.
Thomas, H., Bozec, Y., Elkalay, K., de Baar, H.J.W., 2004. Enhanced open ocean storage ofCO2 from Shelf Sea pumping. Science 304, 1005–1008.
Thomas, H., Craig, S.E., Greenan, B.J.W., Burt, W., Herndl, G.J., Higginson, S., Salt, L.,Shadwick, E.H., Urrego-Blanco, J., 2012. Direct observations of diel biological CO2 fix-ation on the Scotian Shelf, northwestern Atlantic Ocean. Biogeosciences 9,2301–2309.
Thomas, H., Ittekkot, V., Osterroht, C., Schneider, B., 1999. Preferential recycling of nutri-ents — the ocean's way to increase new production and to pass nutrient limitation?Limnol. Oceanogr. 44, 1999–2004.
Toggweiler, J.R., 1993. Oceanography — carbon overconsumption. Nature 363, 210–211.Tsunogai, S., Watanabe, S., Sato, T., 1999. Is there a “continental shelf pump” for the ab-
sorption of atmospheric CO2? Tellus B. 51, 701–712.Uitz, J., Claustre, H., Morel, A., Hooker, S.B., 2006. Vertical distribution of phytoplankton
communities in open ocean: an assessment based on surface chlorophyll. J. Geophys.Res. 111.
Zubkov, M.V., Tarran, G.A., 2008. High bacterivory by the smallest phytoplankton in theNorth Atlantic Ocean. Nature 455, 224–226.
