Journal of Marine Systems 147 (2015) 52–60
Contents lists available at ScienceDirect
Journal of Marine Systems
journal homepage: www.elsevier.com/locate/jmarsys
The effect of seasonality in phytoplankton community composition on
CO2 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
b
c
Department of Oceanography, Dalhousie University, 1355 Oxford Street, P.O. Box 15000, Halifax, Nova Scotia B3H 4R2, Canada
Bedford Institute of Oceanography, Department of Fisheries and Oceans, Dartmouth, Nova Scotia B2Y 4A2, Canada
Antarctic Climate & Ecosystems Cooperative Research Centre, University of Tasmania, Hobart, Tasmania, Australia
a r t i c l e
i n f o
Article history:
Received 19 March 2014
Received in revised form 11 June 2014
Accepted 8 July 2014
Available online 15 July 2014
Keywords:
CO2 drawdown
Small phytoplankton
Cell abundance
a b s t r a c t
We characterise seasonal patterns in phytoplankton community composition on the Scotian Shelf, northwest
Atlantic Ocean, through a study of the numerical abundance of different cell sizes — pico-, nano- and
microphytoplankton. Cell abundances of each size class were converted to cellular carbon and their seasonal
patterns compared with the partial pressure of carbon dioxide (pCO2) also measured at the study site. We observed a persistent drawdown of CO2 throughout the summer months, despite nutrient depleted conditions
and apparent low biomass suggested by the chlorophyll record. This drawdown was associated with a summertime phytoplankton assemblage numerically dominated by small phytoplankton that reach their peak abundance during this period. It was found that phytoplankton carbon during this period accounted for
approximately 10% of spring bloom phytoplankton carbon and pointed to the importance role that small cells
play in annual CO2 uptake.
© 2014 Elsevier B.V. All rights reserved.
1. Introduction
Representing only 7% of the surface area of the global ocean (Borges,
2005), coastal and marginal seas are estimated to account for 14–30% of
total ocean primary production (Gattuso et al., 1998; Muller-Karger
et al., 2005). Primary production in these waters is stimulated by both
high nutrient inputs and their efficient use, and draws down amounts
of atmospheric CO2 that are estimated to be ~ 30% of the total open
ocean uptake of CO2 (Chen and Borges, 2009) via processes known
collectively as the ‘shelf sea pump’ (Thomas et al., 2004; Tsunogai
et al., 1999).
On the Scotian Shelf in the western North Atlantic (Fig. 1), phytoplankton production and its associated biological drawdown of atmospheric CO 2 are punctuated by the spring bloom that typically
occurs during late March to early April near the surface water temperature minimum (Shadwick et al., 2011). Diatoms in the
microphytoplankton (20–200 μm) size range dominate the bloom
(Johnson et al., 2012; Li et al., 2006), and over a period of just a few
weeks, approximately one third of the total carbon fixed during the
annual cycle in this region is drawn down (Fournier et al., 1977;
Mills and Fournier, 1979). The spring bloom collapses precipitously
⁎ Corresponding author.
E-mail address: [email protected] (S.E. Craig).
http://dx.doi.org/10.1016/j.jmarsys.2014.07.006
0924-7963/© 2014 Elsevier B.V. All rights reserved.
when nitrate and silicate are largely depleted (Greenan et al., 2008;
Mousseau et al., 1996). In the warming, and relatively nutrient
poor conditions that follow, the diatom-dominated bloom is
succeeded by a new assemblage of cells comprised of dinoflagellates
(microphytoplankton) and pico- (0.2–2 μm, e.g. Synechococcus) and
nanophytoplankton (2–20 μm, e.g. nanoflagellates and small diatoms) that are numerically more abundant than both diatoms and
dinoflagellates by several orders of magnitude (Li et al., 2006). The
abundance of both of these size classes increases steadily throughout
the summer months and their maxima coincide with the water temperature maximum and the minimum diatom abundance (Li et al.,
2006).
The most commonly used proxy of phytoplankton biomass is chlorophyll a concentration (Chl a; mg m−3) measured either in situ or estimated from satellite radiance (e.g. Boyce et al, 2010; Siegel et al.,
2013; Uitz et al., 2006). However, it should be appreciated that this
bulk community property is strongly influenced by phytoplankton
community composition. As total Chl a increases, the fractional contribution of small cells to the standing crop of phytoplankton decreases
(Chisholm, 1992). Raimbault et al. (1988) demonstrated this behaviour by isolating size classes of phytoplankton using filters of different
pore sizes. They showed that the total amount of chlorophyll in each
size fraction reached an upper limit, and that beyond this limit, chlorophyll could only be added to the system by the addition of larger
cells such as diatoms (Chisholm, 1992; Raimbault et al., 1988). Therefore, in regions such as the Scotian Shelf where a diatom dominated
S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
53
47 oN
46 oN
a
o
45 N
a
ov
ti
co
S
80
N
72
64
HL2
o
44 N
56
o
N
o
N
o
N
o
N
48 o
N
Canada
o
126 o
W
43 oN
66 oW
64oW
54
108 oW
o
90 W
62 oW
W
o
72 W
60 oW
Fig. 1. Map of study area showing station HL2 on the Scotian Shelf, off the coast of Nova Scotia, eastern Canada.
spring bloom is succeeded by an assemblage of smaller cells, seasonal
patterns in chlorophyll essentially mirror diatom numerical abundance (Li et al., 2006). The numerical abundance of the smaller size
classes, however, may not be well represented by chlorophyll,
evidenced by the fact that small cells reach their peak abundance in
this region during late summer–early autumn during the period of
minimum Chl a values (Li, 2002; Li et al., 2006). Additionally,
photoacclimation brought about by decreases in summer mixed
layer depth and nutrient depleted conditions may decrease intracellular chlorophyll content relative to carbon (Falkowski and Owens,
1980; MacIntyre et al., 2002; Moore et al., 2006), further complicating
the relationship between phytoplankton biomass and Chl a (Cullen,
1982). A combination of some or all of these factors means that Chl a
may be a poor proxy for phytoplankton biomass during summertime
conditions on the Scotian Shelf.
Chl a has been used as a variable in predictive models of the partial
pressure of CO2 (pCO2; μatm) (e.g. Lohrenz et al. (2010); Lohrenz and
Cai (2006); Shadwick et al. (2011); Shadwick et al. (2010)). However,
in the Scotian Shelf region, there is evidence to suggest that the biological uptake of CO2 may be underestimated during the post-bloom
summer period when based on models that use Chl a as the biomass
proxy (Shadwick and Thomas, 2014; Shadwick et al., 2010, 2011).
In this report we utilize observations from a long-term study site on
the 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 from
cold (sub-zero), well mixed and nutrient rich in winter, to a column
that is stratified, with warm (~20 °C), nutrient poor surface waters in
the summer. This results in a shift from a diatom-dominated
phytoplankton assemblage in spring to a small cell-dominated assemblage in summer, and allows an investigation of biological CO2 uptake
rates associated with this change. We use numerical abundances of
different phytoplankton size classes as a metric of biomass, rather than
Chl a, and convert these to phytoplankton carbon values to examine
the effect of seasonal phytoplankton succession on patterns in CO2
uptake. In so doing, we aim to characterise the role of post-bloom
summertime primary production in annual CO2 uptake that may not
be fully appreciated if estimated using Chl a.
2. Materials and methods
2.1. In situ data
Phytoplankton, chemical, hydrographic, and pCO2 data were collected from station Halifax Line 2 (HL2; 44.4°N, 63.3°W) on the Scotian
Shelf, eastern Canada (Fig. 1), a site of regular monitoring since 1998
by the Department of Fisheries and Oceans (DFO) Canada as part of
the Atlantic Zone Monitoring Program (AZMP; http://www.bio.gc.ca/
science/monitoring-monitorage/azmp-pmza-eng.php).
Sampling
methods, experimental procedures and methods have been described
in detail previously (Li and Dickie, 2001; Li and Harrison, 2001;
Shadwick et al., 2011). Briefly, high temporal resolution measurements
of pCO2 in the upper mixed layer were obtained from a CARIOCA buoy
moored at station HL2 at approximately 2 m. Ship-based measurements
were also collected bi-weekly from station HL2 and included CTD casts,
microscopic enumeration of microphytoplankton (20–200 μm), and
water sample analyses for Chl a and nutrients using the methodologies
detailed by Mitchell et al. (2002). Mixed layer depth (MLD; m) was determined by identifying the depth at which the density gradient estimated from CTD profiles was ≥ 0.01 kg m− 4. Microphytoplankton
counts (cells m− 3) were performed on a depth integrated sample
(Mitchell et al., 2002), i.e. a sample comprised of 50 mL aliquots from
each 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 method
was implemented by the AZMP to account for vertical inhomogeneity in phytoplankton distribution. Analyses of water column stratification features, Chl a vertical distributions (Fig. 3(e)) and the
relationship between Chl a and diatoms (Fig. 5(b)) indicated that
the counts were very heavily weighted to surface values. It was decided, therefore, to use the counts unaltered, whilst appreciating
that this step may introduce a degree of uncertainty into subsequent
calculations.
Pico- (0.2–2 μm) and nanophytoplankton (2–20 μm) abundances
were determined by flow cytometry at both HL2 and a nearby coastal
monitoring site in Bedford Basin (44.69°N, 63.64°W). At HL2, flow
cytometer samples were routinely acquired from surface waters in
spring and fall and occasionally, during other periods throughout the
year. 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 from
HL2.
2.2. Phytoplankton carbon inventory calculations
Phytoplankton abundance (cells m−3) was converted into total phytoplankton carbon concentration (mol C m−3) using carbon cell− 1
values. Mean carbon cell−1 values were calculated from literature carbon cell− 1 values for diatom (Mullin et al., 1966) and dinoflagellate
(Menden-Deuer and Lessard, 2000; Mullin et al., 1966) species that
are known to occur at HL2 (Kevin Pauley, pers. comm.). These were
calculated as 1.492 × 10−9 and 4.386 × 10− 9 gC cell−1 for diatoms
and dinoflagellates respectively. Carbon cell− 1 values for pico- and
nanophytoplankton were estimated using the Li (2002) relationship
between the combined abundance of pico- and nanophytoplankton
and carbon cell−1 (their Fig. 2(b)), and a geometric mean was calculated to provide a value of 0.4 × 10−12 gC cell−1. In order to obtain fully
seasonally resolved estimates of cell abundances for this calculation, a
model of HL2 cell abundance (obtained approximately twice per year)
versus water temperature was derived (see Section 3.1 below for full
details). These more highly temporally resolved values were binned
into monthly values (spanning 1999–2011).
The phytoplankton carbon inventory (mol C m−2) was obtained by
multiplying mol C m−3 by the mixed layer depth in m. It should be carefully noted that cell abundance estimates and pCO2 measurements pertain only to the upper mixed layer, which varies between 9 m in
summer and 53 m in winter. Deep Chl a maxima do form below the
pycnocline in this region (Longhurst, 1995), but our calculations and
discussions concern only the upper mixed layer.
2.3. Rate of change of phytoplankton carbon
12
To resolve the important contribution of seasonal biological signals
to total carbon dynamics, the rate of change of phytoplankton carbon
from climatological month to month (ΔCp; mol C m−2 month−1) was
calculated from:
10
a
11
pico + nano (cells m 3)
10
ΔC p ¼
10
10
9
10
BB
BB model
HL2
HL2 model
8
10
0
5
10
15
20
Modelled pico + nano (cells m 3)
b
11
10
C p ðt 2 Þ−C p ðt 1 Þ
t 2 −t 1
ð1Þ
where Cp(t) is the total phytoplankton carbon at time t (t2 N t1), and
where 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 photosynthesis
and loss terms such as mortality and grazing. In the calculation of ΔCp
by this method, it must be assumed that the water masses from one
month to the next are invariant. Shadwick et al. (2011) measured
both across and along shelf horizontal gradients in mixed layer dissolved inorganic carbon (DIC), and found rather low values on the
order of 1–2 × 10−3 μmol kg− 1 m− 1. Furthermore, these authors
showed that the magnitude of the horizontal and vertical transport
terms is small compared with the magnitude of NCP throughout the
year except for the autumn months. Based on these findings, we assumed that advective terms were small compared with the biological
signal.
3. Results and discussion
3.1. Estimates of pico- and nanophytoplankton abundances
10
10
9
10
10
9
10
10
10
11
Measured pico + nano (cells m 3)
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 model
fitted 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 HL2
model shown in (a).
Flow cytometer cell counts of pico- (Synechococcus) and
nanophytoplankton (nanoflagellates and small diatoms) at HL2 existed
only during spring and fall, meaning that no directly measured information on small cell abundance was available outwith these periods. Studies performed by others in the North Atlantic have shown that water
temperature is a ‘holistic simplifier’ of the mechanistically complex processes that control small cell abundance, and can be used to estimate the
abundance of pico- and nanophytoplankton (Li et al., 2006; Morán et al.,
2010). It should be clearly stated, however, that temperature is not considered the proximate controller of cell size. Rather, it co-varies with
other complex physical and biological mechanisms that control the
availability of resources, e.g. decreased nutrient supply due to increased
stratification favours small cells (Li et al., 2013; Maranón et al., 2012).
We used this proxy approach and examined the relationship between
combined 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 an
examination of the relationship at higher temporal frequency and
throughout the entire seasonal cycle (Fig. 2(a)). In agreement with previous studies in the region (Li et al., 2006; Morán et al., 2010), a strong
S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
seasonal trends were very similar to the higher temporal resolution
model at nearby Bedford Basin, the HL2 model was used to estimate
pico + nano abundances for all seasons.
relationship was observed between cell abundance and temperature at
both HL2 and Bedford Basin. The regression model for Bedford Basin
was 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 more
highly temporally resolved data in Bedford Basin, with small cell abundance steadily increasing with temperature. This coherence between
geographically distinct sites on the Scotian Shelf was also demonstrated
by Li et al. (2006), but analysis of covariance (ANOCOVA) performed on
our dataset revealed that the slopes of the models were statistically
different.
A comparison of modelled and measured values of pico + nano was
made (Fig. 2(b)), and results are shown in log-log space to facilitate
visualisation over the large dynamic range. Due to the semilogarithmic nature of the model (Fig. 2(a)), errors calculated on nonlogarithmically transformed values are quite large: R2 = 0.482 (N =
43), NRMSE = 99.740%, bias = −5.300 × 109 cell m−3, where NRMSE
is 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
a
55
3.2. Seasonal patterns
Diatoms dominate the spring bloom, which occurs late March–early
April (Figs. 3 and 4), and they reach their climatological maximum of 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 approximately in May, a new assemblage of cells dominated by much smaller
but numerically more abundant pico- (0.2–2 μm) and
nanophytoplankton (2–20 μm) flourish in the warming, relatively nutrient poor conditions (Figs. 3–4). Throughout the summer, pico- and
nanophytoplankton cell abundances increase steadily, reaching its
maximum of ~4 × 1011 cells m−3 in August (Fig. 4(b)). The proliferation of the small cells during the nutrient poor conditions of the summer months is likely to arise from a combination of factors. Small cells
are believed to have a competitive advantage over larger cells because
of their higher nutrient affinity that allows them to maintain high uptake rates under low nutrient conditions (Agawin et al., 2000;
b
MLD
c
d
e
month
Fig. 3. Climatological biogeochemical properties of station Halifax Line 2 (HL2). Climatologies are constructed from data spanning 1999–2011 for (a) Temperature (b) Salinity (c) Nitrate
(d) Silicate and (e) Chlorophyll a (Chl a). The solid black line represents mixed layer depth (MLD).
56
S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
3
a
108
diatoms
2
1
1010
0
1.5 nano
1
with temperature (Peters, 1983) – i.e. warmer temperatures are associated with smaller organisms – their co-occurrence with the smaller cell
sizes seems incongruous. However, dinoflagellates possess a collection
of unique features (e.g. mixotrophy and the ability to vertically migrate
(Smayda, 1997)), which collectively may reduce their dependence on
nutrients delivered from aphotic depths increasing the likelihood of
peak abundance during the summer months.
0.5
12
0.6
0.3
1011
0
pico + nano
0.9
0.6
0.3
0
1.2 dinos
107
0.8
0.4
0
2
4
6
8
10
pico + nano (log 10[cells m-3])
10
pico
1011
cells m-3
0
0.9
12
R2= =
0.069
= 12)
R2
0.0686
(N(N
= 12)
pp
= 0.41103
>> 0.05
11
10
10
10
a
9
10
month
0
10
Chl a (log 10[mg m-3])
b
diatoms (log10[cells m-3])
R22 =
= 12)
R
= 0.738
0.738(N(N
= 12)
p =<<
0.00034231
p
0.01
10
8
10
7
b
10
Fig. 4. (a) Seasonal cycle of diatoms, nanophytoplankton (nano), picophytoplankton
(pico), pico and nano combined (pico + nano) and dinoflagellates (dinos) measured in
Bedford Basin to illustrate seasonal succession in this region. (b) HL2 seasonal cycle of
temperature, Chl a, diatoms, dinoflagellates and pico + nano. Pico + nano abundances
is estimated from the HL2 model shown in Fig. 2. The grey dotted lines indicate the
collapse of the spring bloom in May at ~6–7 °C, the shoaling of the mixed layer depth
and the transition to a phytoplankton assemblage numerically dominated by smaller cells.
Chl a (log 10[mg m-3])
10
8
R2
R2==0.00554
0.006 (N
(N==12)
12)
pp= >>
0.8182
0.05
dinos (log10[cells m-3])
Chisholm, 1992; Fogg, 1986). Additionally, it is now appreciated that
small cells are mixotrophic, making them less dependent on inorganic
nutrients than previously thought (Hartmann et al., 2012; Mitra et al.,
2014; Zubkov and Tarran, 2008).
It should be noted that the pico- and nanophytoplankton
abundances presented in Fig. 4(a) are from Bedford Basin flow
cytometer counts and are shown simply to illustrate the seasonal succession of different phytoplankton groups. The HL2 model used to estimate pico + nano does not allow resolution of the separate pico- and
nanophytoplankton contributions, but the seasonal patterns are very
similar (see Fig. 2(a) and Li et al. (2006)). This seasonal pattern is mirrored in the dinoflagellate population, although their abundance is approximately four orders of magnitude less than the pico- and
nanophytoplankton fractions. Dinoflagellates are generally categorized
in the microphytoplankton size class, and, in the context of size scaling
0
10
10
7
c
6
10
0
Chl a (log 10[mg m-3])
Fig. 5. The relationship between the abundances of (a) log10[pico + nano], (b) log10[diatom],
and (c) log10[dino] versus log10[Chl a].
S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
Following the spring bloom, the diatom contribution to total cell
abundance remains low at b 0.5 × 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 in
the diatom climatology in November (Fig. 4(a), (b)). It should be
noted that, despite being numerically more abundant than diatoms by
several orders of magnitude in the summer months, pico- and
nanophytoplankton biomass is not correlated with chlorophyll a standing stock (Fig. 5(a)), an important observation also made by Li et al.
(2006) in the northwest Atlantic, by Claustre (1994) in several oceanic
provinces, and inferred by Shadwick et al. (2011; 2010) from pCO2 studies in the same region. Chl a is persistently less than 1 mg m−3 following
the collapse of the spring bloom, yet the cell count estimates reveal substantial biomass in the pico- and nanophytoplankton fractions, emphasizing the fact that Chl a primarily mirrors patterns in the diatom
fraction of the assemblage (Fig. 5(b)), but not the dinoflagellate fraction
(Fig. 5(c)). This lack of relationship between Chl a and the abundance of
dinoflagellates and pico + nano may, in part, be explained by
photoacclimation during the summer when mixed layer depths decrease to ~9 m (Fig. 3, Fig. 7(a)). Cells respond to potentially damaging
irradiance, caused by being trapped in a shallow surface mixed layer, by
decreasing intracellular photosynthetic pigment (MacIntyre et al.,
2002). For this reason, Chl a should not be considered a robust proxy
for biomass in all seasons (e.g. Cullen (1982)).
In contrast to our results, Agawin et al. (2000) found a rather strong
relationship between the percent contribution of picophytoplankton to
total phytoplankton and Chl a (their Fig. 6(b)). The reasons for the difference between their results and ours are not entirely clear, but may
be related to the conditions in the mesocosm used to generate their
a
b
Fig. 6. (a) Relationship between total phytoplankton carbon inventory and surface water
temperature. Note that the y-axis is log10 transformed to facilitate visualisation of the data
over a large range. The grey dotted line corresponds to the month of May, the collapse of
the spring bloom and the transition to an assemblage numerically dominated by smaller
cells. Seasons and corresponding months are indicated in the legend. (b) Composite
seasonal cycle of pCO2, norm constructed from data spanning 2007–2009 redrawn after
Shadwick et al. (2011).
57
data, which featured a duration of 21 days, an almost constant temperature, a height of 14 m and regular addition of nutrients. All of these
conditions are very different compared to the conditions at our study
site and may have resulted in differences in photoacclimative cellular
chlorophyll content, phytoplankton community composition and
mixotrophic behaviour (Hartmann et al., 2012; Mitra et al., 2014;
Zubkov and Tarran, 2008) that may also alter cellular chlorophyll
content (Jones et al., 1995). Additionally, our plot includes contributions
from 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 temperature and phytoplankton carbon inventory (mol C m− 2). We present
temperature on the abscissa rather than date to account for the fact
that the same temperature may occur in multiple seasons. Visualisation
of the data in this way allows a direct comparison with pCO2 data plotted in the same manner (Fig. 6(b)) and so, illustrates how patterns in
mixed layer phytoplankton carbon inventory correspond to features in
mixed layer total carbon dynamics.
Minimum phytoplankton carbon values of ~ 0.01 mol C m− 2 occurred at approximately 6 °C (Fig. 6(a)), corresponding to both the collapse of spring bloom (~May, pink circles) and early winter (December/
January, purple circles). The phytoplankton carbon maximum (~4.5 mol
C m−2) was observed close to the temperature minimum that occurred
during the diatom dominated spring bloom. Over the post spring bloom
warming period (~6–20 °C ≈ May–August, Fig. 4(b)), phytoplankton
carbon concentration, in concert with pico- and nanophytoplankton
abundances, steadily increased by an approximate order of magnitude
to reach values of ~ 0.3 mol C m− 2, comparable to those observed in
the spring. Throughout this period, Chl a is persistently b 1 mg m−3
(Fig. 4(b)), reinforcing the fact that this important fraction of the phytoplankton assemblage is almost completely decoupled from the chlorophyll standing stock (Fig. 5(a); c.f. Claustre (1994) and Li et al.
(2006)) — significant given the fact that Chl a is used ubiquitously as
the biomass term in many different types of global and regional ocean
models (e.g. Behrenfeld et al. (2006); Fennel et al. (2008); Boyce et al.
(2010)). The inability to accurately estimate carbon uptake based on
Chl a as a proxy for biomass during this summer period was also identified by Shadwick and colleagues (Shadwick et al., 2010, 2011). This reinforces the concept that Chl a closely mirrors the patterns of
assemblages with high intracellular Chl a, e.g. large diatoms (Li and
Harrison, 2008; Li et al., 2006) (Fig. 4(b), 5(b)), but does not accurately
represent the more numerically abundant smaller size fractions with
lower 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 data
from deployments during 2007–2009 and was constructed to account
for breaks in data acquisition due to buoy maintenance. To account for
the effect of water temperature, we present pCO2 corrected to a constant
annual mean temperature (pCO2, norm; μatm) (c.f. Takahashi et al.
(2002); Shadwick et al. (2011)). Comparing Fig. 6(a) and (b), it is
clear that the seasonal evolution and succession of the different phytoplankton communities correspond with various features in the bulk CO2
system parameters. For example, springtime maximum phytoplankton
carbon concentration (Fig. 6(a), pink circles), which is associated with
large diatoms that bloom around the temperature minimum
(Fig. 4(b)), is reflected in the rapid drop in pCO2, norm (Fig. 6(b)). The collapse of the bloom and the onset of surface warming in May results in
rising pCO2, norm concentrations, and at a water temperature of ~ 5–
6 °C in May/June, the diatom community is succeeded by dinoflagellates
and smaller pico- and nanophytoplankton (Fig. 4(b)). The increase in
biomass and concomitant uptake of carbon by these communities
(Fig. 6(a)) consistently lowers pCO2, norm (Fig. 6(b)) throughout the
summer months until the temperature reaches its maximum. At the
S.E. Craig et al. / Journal of Marine Systems 147 (2015) 52–60
400
a
350
0
3.4. Changes in phytoplankton carbon, ΔCp
10
The rate of change of phytoplankton carbon (ΔCp; mol C m− 2 month− 1) was calculated by integrating phytoplankton carbon over
the mixed layer (Section 2.2 and Fig. 7(a)). It was found that a springtime
ΔCp 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 numbers
during the spring bloom. The precipitous decrease in diatom abundance
in 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. ΔCp
is positive throughout June–August reflecting the steady increase in
picophytoplankton, nanophytoplankton and dinoflagellate abundances,
which, along with temperature, reach their maxima in August
(Fig. 4(b)). In September, ΔCp becomes negative and remains so
throughout the winter, corresponding to the decline in all phytoplankton
size classes throughout the autumn and winter. ΔCp is positive during
the spring (months 3–4) and the summer (months 6–8), and summing
the ΔCp values during these two periods revealed that summertime phytoplankton uptake of carbon (0.101 mol C m−2) represents approximately 10% of the springtime uptake (0.963 mol C m−2), reinforcing
the fact that summertime productivity is significant. This summertime
value is less than the 40% of spring bloom production estimated by
Shadwick et al. (2011). However, it seems reasonable to postulate that
the uncertainties in both this and the Shadwick et al. (2011) estimate
may account for the difference.
A comparison of ΔCp values with NCP values calculated by Shadwick
et al. (2011) from pCO2 measurements is shown in Fig. 7(c) along with
their respective error estimates. NCP values from Thomas et al. (2012)
are also shown for comparison. Error bars for this study represent the
combination of uncertainties calculated using standard propagation of
error techniques. There are a number of uncertainties involved in this
calculation and the most significant by far are those for pico + nano
cell abundance estimates (~ 100% for temperature-abundance model,
Fig. 2) and for carbon cell− 1 uncertainties (~ 17% derived from the
mean of coefficient of variation values calculated by Menden-Deuer
and Lessard (2000)). Except for months 4 and 10, these independently
derived estimates agree within their upper and lower bounds of uncertainty. Factors contributing to the differences between the two estimates include uncertainty in pico + nano abundance estimation
(±100%, Fig. 2), the difference in methods used to construct seasonal
cycles in each study (13-year climatology for this study and a 2-year
composite cycle by Shadwick et al. (2011)), selection of carbon cell−1
values, and the fact that, except for diatoms, all the other phytoplankton
groups enumerated are mixotrophic to greater (dinoflagellates) or lesser (prasinophytes in the nanophytoplankton size class) extents (Flynn
et al., 2013). This latter point means that mixotrophs would be included
in the ΔCp calculation based on cell abundance estimates, despite the
fact that part of their metabolic activity is heterotrophic and does not
constitute net CO2 drawdown. However, mixotrophy would drive the
discrepancy in the opposite direction to that observed, i.e. make the
cell abundance-based ΔCp values larger than the pCO2-based NCP
values. It is not currently possible to fully explain the differences
observed, and further work is required to reconcile them.
phytopankton C
MLD
300
20
250
200
30
150
40
100
50
0
MLD (m)
phytoplankton C (mg C m -3 )
58
50
2
4
6
8
10
12
month
b
c
Fig. 7. (a) Total phytoplankton carbon concentration and mixed layer depth (MLD) over a
climatological 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 this
study, Shadwick et al. 2011 (S2011) and Thomas et al. 2012 (T2012).
end of the summer period, respiration resulting from the decay of
phytoplankton biomass and wind-induced or convective entrainment
of CO2 from deeper waters (Greenan et al., 2004; Shadwick et al.,
2011) raises pCO2, norm back to pre-bloom winter conditions. The
accentuated impact of the summertime phytoplankton community on
the surface layer CO2 system (i.e. pCO2) also becomes visible when
relating Figs. 6 and 7(a). Whilst the summertime inventory of
phytoplankton carbon (Fig. 6(a)) is certainly lower than that of the
spring bloom, the phytoplankton carbon concentration (Fig. 7(a); mg
C m−3) in the respective surface layer is comparable during both seasons – spring bloom: ~ 380 mg C m− 3 compared with August/
September: ~ 200 mg C m−3 (Fig. 7(a)). Since it is the phytoplankton
carbon concentration, rather than its inventory, that is responsible for
regulating 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 bloom CO2 drawdown
(~230 μatm, Fig. 6(b)).
3.5. Summertime phytoplankton production
The increase in phytoplankton biomass, and thus ΔCp, during the
summer months is in spite of apparent depletion of surface mixed
layer nitrate (Fig. 3(c)), a finding also reported at this site by Shadwick
and colleagues (Shadwick and Thomas, 2011; Shadwick et al., 2011).
They observed supersaturation of O2 with respect to atmospheric levels
and a decrease in DIC in surface waters at HL2 throughout the post
bloom summer months, which they attributed to phytoplankton
primary production. In the North Atlantic, the phenomenon of elevated
carbon consumption relative to nitrogen that exceeds the classical
Redfield ratio (Redfield et al., 1963) of 6.6 – so called “carbon
S.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 and
Thomas, 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 overconsumption appears to be associated with summertime nutrient poor
conditions. Based on our observations of both the inorganic carbon and
nutrient systems, we speculate that carbon overconsumption by a summertime assemblage of small cells may play a role in the carbon cycle at
our study site. However, it is impossible to verify this without measurements of both the particulate and dissolved organic carbon and nitrogen
pools — data that were, unfortunately, not available. Future studies
should include such measurements to allow a more thorough characterisation of the various processes at play at the study site during the
nutrient depleted summer months.
Other factors that may contribute to on-going summertime
production despite nutrient depletion include the elevation of nutrient
recycling and phytoplankton metabolism at higher water temperatures
(Taucher and Oschlies, 2011; Taucher et al., 2012) and, as discussed
previously, the ability of some species to harvest nutrients through
mixotrophy (Hartmann et al., 2012; Mitra et al., 2014; Zubkov and
Tarran, 2008)
4. Conclusions
We have provided a mechanistic explanation of CO2 drawdown in
the context of seasonal phytoplankton succession, despite uncertainties
in derived parameters. It has been shown that numerically abundant
pico- and nanophytoplankton are correlated with the persistent uptake
of carbon throughout the nutrient poor, post bloom summer months.
During the summer, this fraction of the phytoplankton assemblage is
uncoupled from the Chl a standing stock, yet accounts for approximately 10% of spring bloom carbon uptake. The lack of relationship between
Chl a and the small cells and dinoflagellates that dominate the assemblage during the summer is likely caused, in part, by photoacclimative
reduction in intracellular photosynthetic chlorophyll as a result of
high irradiances in a shallow mixed layer. An additional factor that
may explain this behaviour might also be found in the fact that small
cells may only attain certain threshold values of Chl a, and that only
the addition of larger cells will allow Chl a to increase beyond these
thresholds.
We speculated that carbon overconsumption may be taking place
during the summer, but a lack of information on the dissolved nutrient
pool prevented any robust conclusions from being drawn. It is also
likely that mixotrophy played a role during the nutrient poor summer
months, and future work should investigate both of these phenomena
in order to more thoroughly characterise the biological influence on
carbon dynamics at this site. Finally, our findings may be relevant in
the context of the shift towards smaller phytoplankton assemblages
predicted to occur under warming ocean conditions (Doney et al.,
2012; Falkowski and Oliver, 2007).
Acknowledgments
This work was supported by the Canadian Foundation for Climate
and Atmospheric Sciences (CFCAS, grant no. GR-C-01). Data from station HL2 and Bedford Basin were provided by the Department of Fisheries and Oceans (DFO) Canada Atlantic Zone Monitoring Project
(AZMP) and Bedford Basin Plankton Monitoring Program respectively. We thank Tim Perry, Carla Caverhill and Heidi Maas of Bedford Institute 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 anonymous referees.
59
References
Agawin, N.S., Duarte, C.M., Agusti, S., 2000. Nutrient and temperature control of the
contribution of picoplankton to phytoplankton biomass and production. Limnol.
Oceanogr. 45, 591–600.
Behrenfeld, M.J., O'Malley, R.T., Siegel, D.A., McClain, C.R., Sarmiento, J.L., G.C., F., Milligan,
A.J., Falkowski, P.G., Letelier, R.M., Boss, E.S., 2006. Climate-driven trends in
contemporary ocean productivity. Nature 444, 752–755.
Borges, A., 2005. Do we have enough pieces of the jigsaw to integrate CO2 fluxes in the
coastal ocean? Estuaries 28, 3–27.
Boyce, D.G., Lewis, M.R., Worm, B., 2010. Global phytoplankton decline over the past
century. Nature 466, 591–596.
Chen, C.-T.A., Borges, A.V., 2009. Reconciling opposing views on carbon cycling in the
coastal ocean: continental shelves as sinks and near-shore ecosystems as sources of
atmospheric 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 by
phytoplankton pigment signatures. Limnol. Oceanogr. 39, 1206–1210.
Cullen, J.J., 1982. The deep chlorophyll maximum: comparing vertical profiles of
chlorophyll a. Can. J. Fish. Aquat. Sci. 39, 791–803.
Doney, S.C., Ruckelshaus, M., Emmett Duffy, J., Barry, J.P., Chan, F., English, C.A., Galindo, H.
M., Grebmeier, J.M., Hollowed, A.B., Knowlton, N., Polovina, J., Rabalais, N.N.,
Sydeman, W.J., Talley, L.D., 2012. Climate change impacts on marine ecosystems.
Annu. Rev. Mar. Sci. 4, 11–37.
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: the
need to assign organisms as mixotrophs within plankton functional types. J. Plankton
Res. 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 enrichment of the Scotian Shelf. J. Fish. Res. Board Can. 34, 1004–1018.
Gattuso, J.-P., Frankignoulle, M., Wollast, R., 1998. Carbon and carbonate metabolism in
coastal 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 fall
blooms 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 of
a 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 interannual
variations of the carbonate system and air–sea CO2 flux in the Northeast Atlantic (Bay
of 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 the
maritimes 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 between
photoacclimation and phagotrophy with respect to chlorophyll a, carbon and nitrogen 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 in
the 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 the mixed layer during
the 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 North
Atlantic Ocean. Nature 419, 154–157.
Li, W.K.W., Carmack, E.C., McLaughlin, F.A., Nelson, R.J., Williams, W.J., 2013. Space-fortime substitution in predicting the state of picoplankton and nanoplankton in a
changing Arctic Ocean. J. Geophys. Res.Ocean. 118, 5750–5759.
Li, W.K.W., Dickie, P.M., 2001. Monitoring phytoplankton, bacterioplankton, and
virioplankton 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 phytoplankton
communities 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 in
ecological 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 phytoplankton
in 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–sea
fluxes 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 in
a 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 of
photosynthesis irradiance response curves and photosynthetic pigments in
microalgae and cyanobacteria. J. Phycol. 38, 17–38.
Maranón, E., Cermeno, P., Latasa, M., Tadonléké, R.D., 2012. Temperature, resources, and
phytoplankton 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 Scotian
Shelf, eastern Canada. Mar. Biol. 54, 101–108.
Mitchell, M.R., Harrison, W.G., Pauley, K., Gagné, A., Maillet, G.L., Strain, P., 2002. Atlantic
Zone Monitoring Program Protocol., Canadian Technical Reports of Hydrography
and 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 response 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 of
small phytoplankton in a warmer ocean. Glob. Chang. Biol. 16, 1137–1144.
Mousseau, L., Legendre, L., Fortier, L., 1996. Dynamics of size-fractionated phytoplankton
and 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. The
importance 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, cell
volume, and area in phytoplankton. Limnol. Oceanogr. 11, 307–311.
Osterroht, C., Thomas, H., 2000. New production enhanced by nutrient supply from nonRedfield 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 the
Ligurian Sea and the Algerian Basin (Mediterranean Sea): size distribution versus
total concentration. Mar. Microb. 3, 1–7.
Redfield, A.C., Ketchum, B.H., Richards, F.A., 1963. The influence of organisms on the
composition 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 relatve
to 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 the
Scotian Shelf region. In: Chircop, A., Coffen-Smout, S., McConnell, M. (Eds.), Ocean
Yearbook 25. Martinus Nijhoff, Leiden/Boston, pp. 171–204.
Shadwick, E.H., Thomas, H., 2014. Seasonal and spatial variability in the CO2 system on the
Scotian 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 the
Scotian 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–sea
CO2 fluxes on the Scotian Shelf: seasonal to multi-annual variability. Biogeosciences
7, 3851–3867.
Siegel, D.A., Behrenfeld, M.J., Maritorena, S., McClain, C.R., Antoine, D., Bailey, S.W.,
Bontempi, P.S., Boss, E.S., Dierssen, H.M., Doney, S.C., Eplee Jr., R.E., Evans, R.H.,
Feldman, G.C., Fields, E., Franz, B.A., Kuring, N.A., Mengelt, C., Nelson, N.B., Patt, F.S.,
Robinson, W.D., Sarmiento, J.L., Swan, C.M., Werdell, P.J., Westberry, T.K., Wilding, J.
G., Yoder, J.A., 2013. Regional to global assessments of phytoplankton dynamics
from the SeaWiFS mission. Remote Sens. Environ. 135, 77–91.
Smayda, T.J., 1997. Harmful algal blooms: their ecophysiology and general relevance to
phytoplankton 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–air
CO2 flux based on climatological surface ocean pCO2, and seasonal biological and
temperature effects. Deep-Sea Res. II Top. Stud. Oceanogr. 49, 1601–1622.
Taucher, J., Oschlies, A., 2011. Can we predict the direction of marine primary production
change under global warming. Geophys. Res. Lett. 38, L02603.
Taucher, J., Schulz, K.G., Dittmar, T., Sommer, U., Oschlies, A., Riebesell, U., 2012. Enhanced
carbon overconsumption in response to increasing temperatures during a mesocosm
experiment. Biogeosciences 9, 3531–3545.
Thomas, H., Bozec, Y., Elkalay, K., de Baar, H.J.W., 2004. Enhanced open ocean storage of
CO2 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 fixation on the Scotian Shelf, northwestern Atlantic Ocean. Biogeosciences 9,
2301–2309.
Thomas, H., Ittekkot, V., Osterroht, C., Schneider, B., 1999. Preferential recycling of nutrients — 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 absorption 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 the
North Atlantic Ocean. Nature 455, 224–226.