Phytoplankton Growth and Microzooplankton Grazing in
the Subtropical Northeast Atlantic
Carlos Cáceres*, Fernando González Taboada, Juan Höfer, Ricardo Anadón
Departamento de Biologı́a de Organismos y Sistemas, Universidad de Oviedo, Oviedo, Asturias, Spain
Abstract
Dilution experiments were performed to estimate phytoplankton growth and microzooplankton grazing rates during two
Lagrangian surveys in inner and eastern locations of the Eastern North Atlantic Subtropical Gyre province (NAST-E). Our
design included two phytoplankton size fractions (0.2–5 mm and .5 mm) and five depths, allowing us to characterize
differences in growth and grazing rates between size fractions and depths, as well as to estimate vertically integrated
measurements. Phytoplankton growth rates were high (0.11–1.60 d21), especially in the case of the large fraction. Grazing
rates were also high (0.15–1.29 d21), suggesting high turnover rates within the phytoplankton community. The integrated
balances between phytoplankton growth and grazing losses were close to zero, although deviations were detected at
several depths. Also, O2 supersaturation was observed up to 110 m depth during both Lagrangian surveys. These results
add up to increased evidence indicating an autotrophic metabolic balance in oceanic subtropical gyres.
Citation: Cáceres C, Taboada FG, Höfer J, Anadón R (2013) Phytoplankton Growth and Microzooplankton Grazing in the Subtropical Northeast Atlantic. PLoS
ONE 8(7): e69159. doi:10.1371/journal.pone.0069159
Editor: David L. Kirchman, University of Delaware, United States of America
Received November 14, 2012; Accepted June 11, 2013; Published July 23, 2013
Copyright: ß 2013 Cáceres et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This research has been supported by CARPOS (MEC, REN2003-09532-C03-03) and DOS MARES (CTM2010-21810-C03-03) projects. CC was supported by
a FPU fellowship by MEC (AP2008-03658) and FGT by a FICYT ’’Severo Ochoa’’ fellowship (PCTI2006-09, Gobierno del Principado de Asturias). JH was supported by
research contracts from CARPOS (MEC) and RADIAL (IEO- Universidad de Oviedo) projects. The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Carlos.l.caceres@gmail.com
of the phytoplankton community would prevail. Herbivores,
mainly microzooplankton and nanozooplankton, would play an
important role in maintaining high growth rates by controlling
producers biomass [13], avoiding severe competition for nutrients,
and by taking an active part in nutrient regeneration [14]. On the
contrary, without herbivory, low phytoplankton growth rates
would result due to severe nutrient limitation. In this case a
bottom-up regulation of the phytoplankton community would
prevail. Also, because the contribution of the biological pump [15]
to net carbon sequestration depends on the balance between
primary production and respiration, the role of subtropical gyres
in atmospheric CO2 regulation depends upon how the ecosystem
functions, which is directly impacted by microzooplankton grazing
activities.
The objective of this study was to assess phytoplankton growth
rates and microzooplankton grazing rates in order to clarify the
functioning of the microbial food web in the Northeast Atlantic
subtropical gyre. To this purpose, we conducted a series of dilution
experiments [16] during two Lagrangian surveys in the Eastern
North Atlantic Subtropical Gyre (NAST-E province) [17]. In
contrast to previous studies in the North East Atlantic [18,19,20],
we measured growth and mortality rates of phytoplankton at
different depths in the water column down to the Deep Chlorophyll
Maximum (DCM), allowing us to characterize vertical variation and
to estimate vertically integrated measurements. Lagrangian surveys
were conducted near the center and at the eastern boundary of the
North East Atlantic subtropical gyre, providing two ecologically
contrasting scenarios encompassing the range of conditions found in
this part of the Atlantic Ocean. Finally, we considered two different
size fractions of phytoplankton, which allowed us to study potential
Introduction
Oligotrophic subtropical oceans cover around 40% of the
Earths surface and are currently expanding [1]. Nutrient
concentrations are very low during most of the year mainly as a
consequence of phytoplankton activity and vertical stratification
[2]. For this reason, phytoplankton biomass is typically lower than
in other marine environments, and there is a higher contribution
of picophytoplankton to total phytoplankton biomass [3]. However, these properties do not necessarily mean low phytoplankton
growth rates, or low primary production: subtropical gyres
resemble desserts in their low biomass, but regarding their growth
rates they could be more similar to tropical forests [4].
There are a wide range of phytoplankton growth rate estimates
(from ,0.1 d21, e.g.[5]; to more than 1 d21, e.g.[4]), which surely
arises from spatiotemporal heterogeneity [5], but maybe also from
the lack of agreement between different measurement methods
[6,7]. Hence, phytoplankton growth rates derived from primary
production estimates based on the 14C method [8] have frequently
resulted in values near the lower range. In oligotrophic subtropical
environments, high grazing rates [9], together with the release of
dissolved organic carbon compounds [10], resulting in isotope
cycling, might explain apparent low rates obtained with the 14C
method [11]. This is not a trivial matter since the magnitude of
phytoplankton growth rates is a key feature to understand the
functioning of these ecosystems and their role in biogeochemical
cycles.
Oligotrophic subtropical gyres could sustain high phytoplankton
growth rates if nutrient utilization by phytoplankton was coupled
to nutrient regeneration [12]. In this case, a top-down regulation
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Phytoplankton Growth and Grazing in the Atlantic
seven times per day. Water samples were obtained with a rosette
equipped with 24 Niskin bottles of 12 L. Winklers method was
employed to calibrate the SBE-43 oxygen sensor (R2 = 0.96). The
depth of the photic layer (depth at which photosynthetic active
radiation was 1% of the surface irradiance) was determined in situ
from radiometer data. Nutrient analyses (NO3–, NO2– and PO4–)
were carried out from water collected between one and six times
per day in polystyrene tubes, which were immediately frozen and
preserved at 280uC for analysis using a Technicon AAII
autoanalyser [22]. Only the data obtained on the days when
dilution experiments were performed were retained for analysis.
differences in growth and grazing rates within the phytoplankton
community.
Materials and Methods
Study Area and Survey
The study was conducted as part of the CARPOS project
(Plankton and CARbon fluxes in Subtropical Oligotrophic environments: a
Lagrangian approach) aboard the RV ‘Hespérides’. Dilution experiments were carried out in the context of two Lagrangian surveys
located around 25u N, 36u W (WL) and 25u N, 26u W (EL) (Fig. 1),
within the NAST-E province. Experiments during WL were
performed between October 25th and 30th, 2006, while experiments during EL were conducted between November 15th and
20th, 2006. Experiments in each Lagrangian survey were
conducted during five consecutive days. Only one experiment
was performed at each depth each day. The Lagrangian survey
presents some advantages including the possibility of working in
the same water body for several days, which allowed us to perform
dilution experiments at several depths over consecutive days. The
water body was tracked with a buoy joined to a drogue installed at
25 m depth. We obtained relative current velocities by using an
Acoustic Doppler Current Profiler (ADCP) installed at the buoys
line, allowing us to estimate the deviation of the buoy with respect
to the tracked water body (see Aranguren et al. [21] for further
details).
Plankton Abundance
Size-fractionated Chl a concentrations (mg Chl a m–3) were
determined from initial samples of the dilution experiments, which
were performed at 10, 30, 50 and 80 m depth, and at the DCM.
Only one depth was sampled each day, corresponding to the depth
of the dilution experiment for that day. We processed two 1000 ml
samples from each depth. Samples were sequentially filtered
through 5 mm and 0.2 mm pore diameter polycarbonate filters,
which were arranged in line filter funnels. The filters were frozen
and stored 24 h in dark. They were subsequently submerged in
90% acetone for 8–12 h. Chl a concentration was determined
using the non-acidification technique [23] with a Turner Designs
(TD-700) fluorometer calibrated with pure Chl a. From the two
samples measured at each depth, we estimated the mean and the
standard deviation (S.D.) of the Chl a concentration. We used
those mean Chl a estimates to calculate the integrated Chl a up to
125 m depth by trapezoidal integration. Total Chl a concentration
at each depth was determined by adding the two size-fractionated
measurements.
Approximated carbon biomass was derived from size-fractionated Chl a data applying the C: Chl a ratios presented by Marañón
Water Column Properties
Vertical distributions of temperature, salinity, fluorescence,
dissolved oxygen concentration (mg O2 L–1) and the percentage of
oxygen saturation (O2 sat, %) were obtained using a SBE-19 CTD,
equipped with a SeaPoint fluorometer and a SeaBird SBE-43
oxygen meter. These variables were recorded between three and
Figure 1. Map showing the study area and the location of the surveys. Two Lagrangian surveys were conducted during the CARPOS cruise
near the center of the subtropical gyre, i.e. the West Lagrangian (WL), and near the eastern boundary, i.e. the East Lagrangian (EL).
doi:10.1371/journal.pone.0069159.g001
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Phytoplankton Growth and Grazing in the Atlantic
et al. [24] for the North Atlantic Subtropical gyre: they were 103
at the upper mixed layer (UML) and 21 at the deep chlorophyll
maximum (DCM) for the ,2 mm phytoplankton fraction. Values
for .2 mm phytoplankton were 247 at the UML and 60 at the
DCM. C: Chl a ratios for phytoplankton ,2 mm were used for
phytoplankton ,5 mm, while values for algae .2 mm were used
for the algae .5 mm. Note that this is a conservative approach
since C: Chl a ratios usually increase with the size of phytoplankton. Ratios for the UML were used from the surface up to the
beginning of the DCM layer, defined as the depth where Chl a
concentration was half of the DCM. The DCM was determined
after examining SeaPoint fluorometer profiles. Finally, the relative
abundance of diatoms, dinoflagellates, ciliates, radiolarians and
copepod nauplii was estimated during EL at the same depths in
which dilution experiments were performed. Samples (2.0 L) were
processed using a FlowCam [25] configured in the autoImaging
mode, with a 100 mm flow cell and a 10x objective.
The picophytoplankton community in EL was also analyzed by
flow cytometry (FCM). Samples were fixed with a 1% paraformaldehyde plus 0.05% glutaraldehyde solution and stored at
280uC until analysis. A FacScan flow cytometer (Becton,
Dickinson and Company) was used. Phytoplankton were grouped
and enumerated according to side-scattered light (SSC), orange
fluorescence (FL2, 585621 nm) and red fluorescence signal (FL3,
.650 nm). Samples were run at a flow rate between 38.6 and
43.2 mL min21. Three groups were identified: Prochlorococcus,
Synechococcus and picoeukaryotes. Cell abundances (mean 6 S.D.)
were obtained from the four initial undiluted samples analyzed at
each depth (two from carboys with nutrient addition and another
two from carboys without nutrient addition). We estimated the
amount of biomass (carbon) in each cell detected by the flow
cytometer using the conversion factors reported by Zubkov et al.
[26] for Prochlorococcus (32 fg C cell21) and Synechococcus (103 fg C
cell21), and Zubkov et al. [27] for picoeukaryotes (1.5 pg C cell21).
We integrated these average biomass values from each experimental depth over all depths sampled down to 125 m in each
Lagrangian survey to estimate the integrated C biomass of each
picoplankton group.
Pt
Pt ~P0 ert ? r~ 1t ln
P0
where P0 and Pt are observed population abundance (Prochlorococcus cells ml21) or biomass (mg C m23, calculated from size
fractionated Chl a measurements) at initial and final times,
respectively.
Different nutrient availabilities along dilution treatments could
make m change with the dilution, and cause non-linearities in the
dilution relationship to occur. This problem can be avoided by
providing an appropriate mixture of inorganic nutrients [16]. We
followed this recommendation in the first experiments, conducted
at 80 m depth and DCM in WL. Phytoplankton apparent growth
rates were compared between treatments with added nutrients (rn)
and no added nutrients (r0). Assuming that mortality rates were
unaffected by the nutrient additions, these treatments allowed the
calculation of growth rates (m0) in natural seawater [28]:
m0 ~r100%zm
where r100% is the net growth rate observed in non-enriched
undiluted containers. However, in those two first experiments, we
did not find any effect of nutrient addition on phytoplankton net
growth rates in the undiluted containers, which contain the higher
biomass and possibilities of nutrient effect. Thus, in the rest of
experiments (except at 50 m depth) nutrients were only added to
two undiluted containers to check possible effects.
Particulate net primary production (pNPP, mg C m23 d21) and
grazing losses (G, mg C m23 d21) were estimated using C: Chl a
ratios (see the subsection Plankton abundance) and the equations
proposed by Landry et al. [29] based on Frost [30]:
pNPP~m0 Pm G~m Pm
1
Pm~
t{t0
The Dilution Method
The dilution method provides estimates of phytoplankton
growth rate (m, d21) and phytoplankton mortality rate (m, d21).
The basis of the method is to uncouple phytoplankton growth rate
from microzooplankton grazing by the addition of filtered
seawater [16].The addition of filtered seawater dilutes the sample,
reducing the encounter rates between phytoplankton and grazers
and consequently phytoplankton grazing mortality (m) in an
amount assumed to be linearly related to the dilution factor (f).
Then, linear regression analysis of phytoplankton apparent growth
rate (r) against f yields a slope and an intercept which corresponds
to the rates of microzooplankton grazing and phytoplankton
growth (m), respectively.
t0
t0 ~0
P0 eðm{mÞx dx ? Pm~
P0(eðm{mÞt 1)
(m0 )t
where Pm is the mean concentration of phytoplankton (mg C m23)
during each experiment.
We estimated net changes of Chl a in the sea at the same depths
and times of the dilution experiments (less than 2h of difference) to
check their relationship with m-m balances (considering both size
fractions together) obtained from dilution experiments. To do that,
we used CTD fluorescence data near the initial and final times of
each dilution experiment and assumed an exponential phytoplankton growth model. Data below the UML were not included
in the analysis to avoid the influence of processes like vertical
displacement of the DCM that might affect net changes in Chl a
concentration and hamper the detection of any relationship with
m-m balance.
Despite conducting the dilution experiments at each depth on
consecutive days within a Lagrangian transect, we integrated the
rates vertically to obtain a synoptic view of the ecosystem. We
ignored in this way potential changes between days which we
considered secondary with respect to changes through the water
column and between the locations of each Lagrangian survey.
This view was reinforced by the low temporal variation in
physical-chemical conditions and the absence of abrupt temporal
r~m{mf :
The balance between phytoplankton production and consumption is estimated as the difference between m and m (i.e., m-m
balance, d21). It can be expressed in relative values, as the
percentage of production grazed (% pNPP), dividing m by m.
Apparent phytoplankton growth rate is estimated assuming an
exponential growth model during the incubation:
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ðt
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Phytoplankton Growth and Grazing in the Atlantic
Figure 2. Properties of the water column during dilution experiments. Vertical profiles of temperature (A, H), salinity (B, I), fluorescence (C, J),
oxygen (D, K), O2 saturation (E, L), nitrate plus nitrite (F, M) and phosphate (G, N) in the upper 200 m during dilution experiments in WL (top) and EL
(bottom). Each grey line represents a different profile. Grey points represent nutrient concentrations. An additive transparency factor was applied to
appreciate the agreement between values, being color intensity proportional to the number of overlapped lines or points, respectively. Black solid
lines and points stand for average values.
doi:10.1371/journal.pone.0069159.g002
changes in Chl a concentrations (Fig. 2, see also [21]). Hence, h-S
plots from different vertical profiles conducted during the days in
which dilution experiments were performed overlapped quite well,
except at the base of the thermocline, suggesting a good tracking of
the water body (Fig. 3). Integrals (0–125 m) of pNPP, G, and
pNPP-G were determined using the trapezoidal method. Then,
these integrals were divided by phytoplankton carbon biomass,
resulting in integrated values for m, m, and m-m, which has the
effect of accounting for differences in biomass between size
fractions and depths.
Niskin bottle were filtered through a Gelman SuporCap 100
capsule filter (0.2 mm) to obtain the water added to diluted
treatments. Water filtered through the capsules showed undetectable Chl a concentrations and negligible numbers of fluorescent
particles when they were examined by FCM. Unfiltered,
prescreened seawater was gently mixed with filtered seawater in
2.3 L Nalgene polycarbonate containers to obtain two replicated
dilutions with f = 0.25, 0.50, and 0.75. Replicated dilutions with
f = 1 (with and without nutrient enrichment) were obtained by
filling containers with unfiltered prescreened seawater. Initial Chl
a concentration for each treatment were calculated as the product
of the measured initial Chl a concentration at f = 1 and the
different dilution factors.
The nutrient mixture added to the enriched treatments resulted
in a final concentration of 1 mM ammonium (NH4Cl), 0.5 mM
phosphate (H3PO4), 5 nM iron (FeSO4) and 0.1 nM manganese
(MnSO4). Powder-free plastic gloves were used during all
operations. Containers were kept in dark during the whole process
until placed in on-deck incubators. Incubations started always
1.5 h after water collection and lasted approximately 21 h. Blue
sheets of light filters were combined to simulate in-situ light
intensity and spectra. When necessary, the combination of light
filters was corrected after light measurements in the morning, thus
correcting possible small shifts in the amount of irradiance
reaching the different depths. Temperature was kept within
60.5oC of in situ temperature by connecting a cooler and a heater
to two thermostats. Water inside the incubator was homogenized
Experimental Setup, Sampling and Analysis Procedure
Water for the dilution experiments was collected at five depths:
10, 30, 50, 80 m and at the DCM (115 m in WL and 110 m in
EL). Each day, water from one depth was collected at 4:30–5:00 h
GMT, always before dawn, using two 30 L Niskin bottles. Lights
onboard were turned off during sampling, except for minimal
safety requirements. Carboys, containers, capsule filters and
auxiliary pipes were stored in 10% HCl-Milli-Q water between
experiments, and rinsed sequentially with Milli-Q water and
0.2 mm filtered seawater immediately before use. Capsules were
changed every four experiments. We checked that water filtration
did not increase the inorganic nitrogen and phosphorous
concentrations. From one 30 L Niskin bottle, 25 L were gently
transferred to a polycarbonate carboy fully wrapped in black
plastic, using silicone tubing fitted with 200 mm mesh to eliminate
mesozooplankton. At the same time, another 25 L from the other
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8 and R [31] softwares. Graphs were plotted using Grapher
software and the R package ggplot2 [32].
Results
Oceanographic Conditions
Temperature and salinity in WL were warmer and saltier than
in EL, and the thermocline and halocline were deeper in EL
(Fig. 2A, B, H, I). As a result, the upper mixed layer was also
deeper in EL. The depth of the photic layer was around 105 m
during both Lagrangian surveys. Nutrient concentrations were
very low in the photic layer in both Lagrangians (NO32 plus
NO22 ,0.3 mM and PO42 ,20 nM). There was a sharp
nutricline at 140 m depth in WL and at 130 m depth in EL
(Fig. 2F, G, M, N). The DCM was located at 115 m and 110 m
depth in WL and EL respectively (Fig. 2C, J). Average oxygen
saturation was above 100% down to 108 m in WL and down to
113 m depth in EL (Fig. 2E and 2L). These levels of O2 saturation
imply a net autotrophic balance since the last mixing event, when
atmosphere and ocean O2 concentrations were equilibrated.
Indeed, maximum values were found at 73 and 74 m depth in
WL and EL, respectively. Oxygen concentration profiles followed
similar patterns (Fig. 2D and 2K).
Temporal variation of physical-chemical variables was in
general low within each Lagrangian survey, suggesting that we
did indeed sample the same parcel of water over the 5 day survey
(Fig 2, 3; see Aranguren et al. [21] for further details). A greater
scatter of h-S pairs was found between isotherms 22.5uC and
20.75uC in WL (located around 77 m and 137 m depth,
respectively) and between isotherms 22.65uC and 19.85uC in EL
(located around 77 m and 138 m depth, respectively) (Fig. 3). This
was probably caused by turbulent mixing promoted by KelvinHelmholtz instability associated to internal waves, and by salt
fingers (see Thorpe [33]).
Plankton Abundance
Figure 3. h-S diagrams during dilution experiments. h-S plots
obtained from all the potential temperature (h) and S measurements
conducted in the upper 200 m during dilution experiments in WL (top)
and EL (bottom). The color intensity of the dots indicates the depth
associated to each h-S pair. Black lines are isopycnals. Numbers above
them point out sigma-theta values (sh = potential density 21000 Kg
m23). Dotted lines are isotherms between which h-S plots dispersion
was higher.
doi:10.1371/journal.pone.0069159.g003
Total integrated phytoplankton biomass was higher in EL than
in WL, although if biomasses are expressed in C units values are
similar (Table 1). Small phytoplankton integrated biomass was
higher than the biomass of the large size fraction in both
Lagrangian surveys (Table 1), although these differences diminish
if biomasses are expressed in C units due to the higher C:Chl a
ratios of the large size fraction. The contribution of small
phytoplankton to total Chl a biomass was higher near the DCM
(Fig. 4). The picophytoplankton community in EL was numerically
dominated by Prochlorococcus, with abundances two orders of
magnitude higher than Synechococcus and picoeukaryotes (Fig. 5).
Both groups of cyanobacteria followed a similar depth distribution
pattern, with lower abundances at the DCM. In contrast,
picoeukaryotes were slightly more abundant at the DCM.
Prochlorococcus was also dominant in terms of integrated C biomass
(633 mg C m22), followed by picoeukaryotes (139 mg C m22) and
Synechococcus (16 mg C m22). Maximum abundances of diatoms,
ciliates, radiolarians and copepod nauplii were found around 80 m
depth in EL, where we also found a relative maximum abundance
of dinoflagellates.
by a submersible pump, which also shook gently the containers
[20].
Samples taken at times t0 and tf were also examined under in vivo
FlowCam and a stereo microscope (Leica Z12.5) to check for
potential damages to microzooplankton during sample gathering
and handling, and during the incubations. FlowCam images
showed undamaged microzooplankters, suggesting a reduced
damage to microzooplankton during sample handling and during
incubations. Samples processed by flow cytometry during EL were
taken from each container at times t0 and tf. We only show the
results of dilution experiments for Prochlorococcus because of the
generally low R2 values and high standard errors of regressions
obtained for the other groups. A solution of 1 mm fluorescent latex
beads was added to each sample and used as a standard to
estimate relative FL3 signals of Prochlorococcus. We used these data
as proxies for chlorophyll fluorescence changes during the
incubations, as these changes would affect Chl a-based growth
rate estimates. Statistical analyses were conducted using Statistica
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Dilution Experiments
Phytoplankton apparent growth rates increased linearly with the
dilution factor in most of the experiments. However, in six
experiments out of 20, the relationship significantly improved by
fitting a quadratic function (p,0.05, see fig. 6), although explained
variances by simple linear regression were quite high in all those
cases (R2.0.53). Integrated phytoplankton growth and
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Phytoplankton Growth and Grazing in the Atlantic
Figure 5. Picophytoplankton abundance during dilution experiments in EL. Vertical profiles of Prochlorococcus (rectangles),
Synechococcus (circles) and picoeukaryotes (triangles) mean abundances up to the DCM. Symbols also point out sampling depths. Horizontal
bars represent 6 S.D. The 105 cells mL21 scale is for Prochlorococcus,
whereas Synechococcus and picoekaryotes scale is 103 cells mL21.
doi:10.1371/journal.pone.0069159.g005
were similar or lower than the rates of the large fraction at all the
depths analyzed (Table 3). Differences in growth rates between
both phytoplankton size fractions were high below the UML in the
two Lagrangian surveys, coinciding with the maximum phytoplankton growth rates detected for the large phytoplankton size
fraction (Table 3).
Phytoplankton growth was not nutrient-limited in the experiments. Median phytoplankton growth rates obtained in nutrient
addition treatments were indistinguishable from median phytoplankton growth rates without added nutrients in the case of small
(Wilcoxon matched pairs test, p = 0.12, n = 8) and large (Wilcoxon
matched pairs test, p = 0.67, n = 8) fractions. The ratios between
phytoplankton growth rates without and with added nutrients (m0:
mn), were in general close to one (Table 3).
We obtained a very good relationship between m-m balances
estimated from dilution experiments and net sea Chl a changes
(data not shown; R2 = 0.71, n = 6), providing further support to our
experimental balances and rates. The total m-m integrated balance
was close to zero in WL (Table 2). The integrated balance for
Figure 4. Vertical profiles of Chl a concentration up to the DCM
during dilution experiments in WL and EL. Solid lines stand for
total Chl a average values. Dashed lines indicate average Chl a ,5 mm.
Horizontal bars represent 6 S.D. Numbers point out the percentage of
Chl a ,5 mm.
doi:10.1371/journal.pone.0069159.g004
microzooplankton grazing rates obtained in both Lagrangian
surveys were high and similar (Table 2), suggesting a similar global
functioning of the ecosystem in the two areas of study.
Nevertheless, rates in WL and EL were different at some depths
(Table 3). Regarding the comparison between phytoplankton size
fractions, integrated phytoplankton growth and microzooplankton
grazing rates were lower for the small phytoplankton fraction
(Table 2). Hence, rates of the small phytoplankton size fraction
Table 1. Phytoplankton integrated biomass in the water column during our experiments.
Biomass (mg Chl a m22)
% Chl a ,5
Biomass (mg C m22)
% C ,5
,5
15.19
78
801
52
.5
4.23
total
19.42
,5
20.83
.5
3.26
531
total
24.09
1501
Site
Size fraction (mm)
WL
EL
724
1525
87
970
65
Carbon based measurements were estimated using the C:Chl a ratios reported by Marañón et al. 2007 (see Methods). WL and EL refer to the West and East Lagrangian,
respectively (see Figure 1). % Chl a ,5: Contribution of phytoplankton Chl a biomass ,5 mm to the total Chl a biomass. % C ,5: Contribution of phytoplankton C
biomass ,5 mm to the total C biomass.
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Figure 6. Plots of dilution experiments based on Chl a measurements. Simple linear regressions between dilution factor and
phytoplankton apparent growth rate for both phytoplankton size fractions and Lagrangians. Quadratic fits are showed if they
significantly (p,0.05) improve the relationship. White dots indicate phytoplankton apparent growth rate in treatments with no nutrients added. Black
dots point out phytoplankton apparent growth rates in treatments with nutrient added. Dashed lines indicate apparent growth rate = 0.
doi:10.1371/journal.pone.0069159.g006
small phytoplankton was slightly positive, mainly due to the
positive balance in the UML (Fig. 7). On the contrary, the
integrated balance of large phytoplankton was slightly negative
(Table 2), with positive values only around 80 m depth (Table 3,
Fig. 7). The % pNPP consumed ranged between 61% and 136%
for small phytoplankton and between 93% and 137% for large
phytoplankton. Total m-m integrated balance was also balanced in
EL, being the m-m integrated balances of both phytoplankton size
fractions close to zero too (Table 2). The % pNPP grazed ranged
between 53% and 187% for small phytoplantkon and between
45% and 129% in the case of the large fraction (Table 3). The m-m
balance was positive at 80 m depth, near the maximum % O2
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saturation, in both Lagrangians and for both phytoplankton size
fractions.
Prochlorococcus growth rates at EL were nearly constant in the
UML and relatively low (0.2 d21), reaching maximum and
lowest values at 80 m depth and DCM, respectively (Fig. 8;
Table 4). Grazing rates were also maximum at 80 m depth;
nevertheless they changed along the UML (Table 4). In all the
experiments analyzed, relationship between dilution factor and
Prochlorococcus apparent growth rate did not significantly improve
by fitting a quadratic function (p.0.13). No differences between
net growth rates of Prochlorococcus in treatments with and without
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Phytoplankton Growth and Grazing in the Atlantic
Table 2. Integrated m, m and m-m balance (d21) for both phytoplankton size fractions and Lagrangians.
Site
Size fraction (mm)
Int. m (d21)
Int. m (d21)
Int. m-m (d21)
% Int. pNPP
WL
,5
0.57 (483)
0.44 (373)
0.13 (110)
77
.5
1.04 (709)
1.17 (799)
20.13 (290)
113
total
0.78 (1192)
0.77 (1172)
0.01 (20)
98
EL
,5
0.56 (529)
0.58 (557)
20.02 (228)
104
.5
1.12 (614)
1.04 (568)
0.08 (46)
93
total
0.76 (1143)
0.75 (1124)
0.01 (19)
98
In brackets besides each rate integral are their associated integrated pNPP, G and pNPP-G balances (mg C m22 d21). The final column (% Int. pNPP) indicates the
percentage of phytoplankton grazed in the upper 125 m of the water column.
doi:10.1371/journal.pone.0069159.t002
nutrients were detected (Wilcoxon matched pairs test, p = 0.86,
n = 9).
Initial and final relative FL3 signals of Prochlorococcus were
different (Sign test, p,0.001, n = 39). Relative FL3 increased
during incubations at all depths except at the DCM. Differences
between initial and final relative SSC, an indicative of cell size,
were also observed (Wilcoxon matched pairs test, p = 0.003,
n = 39) and followed a similar pattern as relative FL3 signal,
although the magnitude of the changes was lower.
Discussion
Dilution experiments were performed to assess ecosystem
functioning and phytoplankton productivity in the eastern border
and near the center of the North Atlantic Subtropical gyre.
Despite the low nutrient concentrations, chlorophyll-based phytoplankton growth and grazing rates were high, suggesting a very
dynamic ecosystem similar to the proposed by Goldman et al.
[12]. In the following, we discuss the operation of the microbial
community taking into account the high phytoplankton growth
Table 3. Estimated growth and grazing rates for small (,5 mm) and large (.5 mm) phytoplankton size fractions.
Site
Depth
(m)
Size fraction
(mm)
mn ± SE (d21)
m0± SE (d21)
m0:mn
m ± SE (d21)
r
m-m balance
(d21)
% pNPP
WL
210
,5
0.8
1.1060.17
1.38
0.7560.25
0.78*
0.35
68
210
.5
0.99
1.0360.10
1.04
1.0860.14
0.96**
20.05
105
230
,5
0.87
0.9960.05
1.14
0.6060.07
0.99**
0.39
61
230
.5
1.12
1.0160.07
0.9
1.2260.10
0.98**
20.21
121
250
,5
0.1560.26
0.23
20.04
136
EL
250
.5
280
,5
0.1160.20
0.9460.27
0.5660.08
1.2960.35
0.79**
20.35
137
0.66
1.18
0.5660.12
0.91**
0.1
85
1.21
1.1260.23
0.89**
0.09
93
0.2660.37
0.33
20.04
118
280
.5
1.0060.16
1.21
2115
,5
0.0060.28
0.22
2115
.5
0.8460.16
0.95
1.13
1.0760.23
0.89**
20.12
113
210
,5
0.83
0.7560.18
0.9
0.5060.28
0.62
0.25
67
129
210
.5
0.8
0.7760.20
0.96
0.9960.29
0.81**
20.22
230
,5
0.21
0.3160.15
1.48
0.5860.22
0.73*
20.27
187
230
.5
1.45
1.3960.09
0.96
1.1160.13
0.97**
0.28
80
90
250
,5
0.8460.20
0.7660.30
0.72*
0.08
250
.5
1.1760.07
1.2160.10
0.98**
20.04
103
280
,5
1.45
0.2460.14
0.61
0.21
53
45
0.31
0.4560.09
280
.5
1.68
1.6060.14
0.95
0.7260.20
0.82**
0.88
2110
,5
0.64
0.4560.05
0.7
0.8060.09
0.97**
20.35
178
2110
.5
0.93
0.8660.16
0.92
0.8860.24
0.84**
20.02
102
mn: phytoplankton growth rate estimated from treatments with added nutrients. m0: phytoplankton growth rate estimated from treatments with no nutrients added. m:
microzooplankton grazing rate. m-m balance: balance between m and m. % pNPP: % Particulated net primary production grazed. SE: Standard error of regression
parameters. **r significant at p,0.01. *r significant at p,0.05.
doi:10.1371/journal.pone.0069159.t003
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Phytoplankton Growth and Grazing in the Atlantic
Figure 7. Vertical profiles summarizing the results of dilution
experiments. Phytoplankton growth rates (black squares and lines),
grazing rates (grey squares and lines) and m-m balances (white squares
and black dashed lines) of both phytoplankton size fractions and
Lagrangians. Squares also point out the depths at which dilution
experiments were performed. Grey dashed lines indicate the zero value.
doi:10.1371/journal.pone.0069159.g007
Figure 8. Plots of dilution experiments based on Prochlorococcus abundances. Simple linear regressions between dilution
factor and Prochlorococcus apparent growth rate in EL. White
dots: Prochlorococcus apparent growth rate in treatments without
added nutrients. Black dots: Prochlorococcus apparent growth rates in
nutrient added treatments. Dashed lines indicate apparent growth
rate = 0.
doi:10.1371/journal.pone.0069159.g008
and grazing rates obtained. Also, we discuss the metabolic balance
from m-m balances and the O2 supersaturation found. In the first
part of discussion following, we underline some of the caveats and
uncertainties related to the methods employed.
Potential Caveats of the Dilution Technique
processes. The experiments lasted approximately 21 h, and they
started at the end of the dark period, when a low percentage of
cells are at the G2 phase (% G2), resulting in their FL3 and SSC
relative signals being close to their daily minimum [37]. Then, it
would be possible to detect an increase in those signals, especially if
growth rates were high. Hence, we found a positive logarithmic
correlation between m0 and the increase in the relative FL3
(R2 = 0.99, n = 5) and SSC (R2 = 0.67, n = 5) signals, despite
Prochlorococcus growth rates would have to be low when photoacclimation occurs [38]. Therefore, the rise of those signals could
mean the existence of a Prochlorococcus growth which was not
detected. Thus, Prochlorococcus growth rates might be underestimated.
The dilution technique is based on some assumptions [16] that
must be validated to obtain correct estimates of phytoplankton
growth and grazing rates. One of these assumptions is that dilution
does not affect the estimates of both rates. For instance, several
studies discuss the possibility that nutrient availabilities change
across dilution treatments [16,19]. Another possibility we are not
able to reject is related to the potential effects of dilution on
mixotrophy, especially considering their importance in oligotrophic environments [34]. Indeed, mixotrophs could increase their
content of Chl a with dilution to obtain organic carbon by
photosynthesis, compensating for the reduction of C obtained by
predation (see Arenovski et al. [35] and references therein). This
possible response would overestimate the rates obtained. The
quantification of this effect remains a challenge for future dilution
experiment studies in oligotrophic waters.
The observed increase in relative FL3 and SSC in Prochlorococcus
cells seems to be the result of a light-dark cycle [36], although we
can not entirely discard the occurrence of photoacclimation
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Phytoplankton Growth and Microzooplankton Grazing
Rates
The low nutrient concentrations found in both locations
contrasted with the high phytoplankton growth rates obtained.
They would be promoted by the high micro and nanozooplankton
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Phytoplankton Growth and Grazing in the Atlantic
Table 4. Estimated growth and grazing rates for Prochlorococcus in EL.
Depth
(m)
mn
(d21)
m0± SE
(d21)
m0:mn
m ± SE
(d21)
r
pNPP
(103 cells ml21 d21)
PG
(103 cells ml21 d21)
m-m balance
(d21)
% PP 55
210
0.17
0.2260.02
1.29
0.1260.03
0.90*
39
21.3
0.1
230
0.07
0.2360.06
3.29
0.1360.08
0.6
41.9
23.7
0.1
250
0.3
0.2260.09
0.73
0.2760.13
0.68
35.5
43.6
20.05
123
280
0.83
0.6760.08
0.81
0.3460.12
0.78*
115.1
58.4
0.33
51
2110
0.07
0.1260.06
1.71
0.160.09
0.53
9.2
7.7
0.02
83
57
mn: Prochlorococcus growth rate estimated from treatments with added nutrients. m0: Prochlorococcus growth rate estimated from treatments with no nutrients added.
m: microzooplankton grazing rate. pNPP: Particulate net Prochlorococcus production. PG: Prochlorococcus grazing losses. m-m balance: balance between m and m. % PP:
% Prochlorococcus production grazed. SE: Standard error of regression parameters. *r significant at p,0.05.
doi:10.1371/journal.pone.0069159.t004
motilities [58]. In addition, the storage capacity and vertical
migration of some groups of large phytoplankton like diatoms [59],
dinoflagellates or cyanobacteria of the genus Trichodesmium [60],
could provide an advantage to exploit the nutrient heterogeneity at
the vertical scale too. In this way, we propose that the spatiotemporal variability of nutrient concentrations might explain the
higher growth rates obtained for the large phytoplankton. As Polz
et al. [61] proposed for bacteria, there could be two strategies
followed by phytoplankton in oligotrophic environments: the
passive oligotroph (‘‘SS strategist’’ according to Reynoldss scheme
[58], ‘‘affinity adapted’’ in Sommers [62] scheme), mainly adopted
by small phytoplankton (e. g. Prochlorococcus), exploiting the low
background nutrient concentrations; and the ‘‘opportuni-troph’’,
which might be the strategy adopted by most of the large
phytoplankton fraction (e.g. some phytoflagellates or diatoms),
exploiting the nutrient enriched environments at different
spatiotemporal scales.
In general, the differences in growth between both fractions
were higher below the UML, where large phytoplankton growth
rates were the highest recorded. This observation was especially
evident at 80 m depth, coinciding with the maximum O2
supersaturation. At 80 m depth in EL we found the maximum
abundance of diatoms and a relative maximum of dinoflagellates
too. The higher availability of nutrients resulting from turbulent
mixing processes, together with vertical excursions to take up
nutrients in enriched deeper waters [59], could result in higher
growth rates for large phytoplankton, especially considering their
higher photosynthetic efficiency under non-limiting conditions
[63]. In addition, microzooplankton (ciliates, nauplii and metanauplii) abundances were also maxima at this layer in EL. These
activities might enhance the formation of microscale nutrient
patches and might consequently promote the advantage of
‘‘opportuni-trophs’’, providing further enhancement to the very
high growth rates detected for the large phytoplankton.
grazing rates detected [12], previously reported by Quevedo et al.
[20] in surface waters and in the DCM. On one hand, grazers
diminish phytoplankton biomass relaxing competition for nutrients. At the same time, grazers also take an active part in nutrient
regeneration, increasing the amount of nutrients available to
phytoplankton cells [14]. High phytoplankton growth rates would
also be promoted by a variety of mechanisms known to diminish
nutrient consumption or to improve nutrient uptake, like
mixotrophy [34], phytoplankton associations [39], N2 fixation
[40] and other biochemical and physiological mechanisms [41,42].
Indeed we did not find differences in phytoplankton growth rates
between treatments with and without nutrient enrichment, maybe
because of the short incubation times. For all those reasons, the
regulation of the system seems closer to a top-down control than to
a bottom-up one during the time of study, although the low
amount of nutrients available would influence phytoplankton
community composition and its overall biomass.
Our integrated growth rates were within the range of values
reported in other studies carried out in open ocean oligotrophic
regions (e.g. [18,43,44]). Also, similar phytoplankton growth rates
have been reported in the Subtropical Pacific using the 14C
method [45,46], although the rates reported here were greater
than most of the 14C estimates reported in the NAST-E region
[47,48,49]. Consequently, our pNPP estimates were also higher.
However, the 14C method could largely underestimate primary
production in oligotrophic environments [11], and, consequently,
phytoplankton growth rates obtained with this methodology. This
underestimation could be partially caused by the use of small
incubation bottles [50,51]. In addition, the high percentage of
pNPP grazed, typical from tropical regions [9] and also observed
in this study, could prevent the detection of a considerable amount
of the fixed carbon. All these reasons make the existence of
differences in production estimates between 14 C method and
other techniques possible (see Quay et al. [6]).
We obtained higher growth rates for the large phytoplankton
fraction despite the assumed competitive advantage of small
phytoplankton to exploit low nutrient concentrations waters [52].
Slightly higher metabolic rates for the large phytoplankton were
also reached in these latitudes by using the 14C method [50],
although similar [48], or even lower rates [49] were observed using
the 14C method too. McCarthy and Goldman [53] suggested the
existence of microscale nutrient patches resulting from zooplankton excretion and degradation of particulate organic matter. The
existence of such nutrient patches, and the response of algal
flagellates to them, was subsequently proved [54,55,56]. Large
phytoplankton could take advantage of those patches because they
generally have higher maximum nutrient uptake rates [57] and
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Production-grazing Balances and Metabolic Balances
The percentage of pNPP grazed was at some depths far from
100%, especially in EL. This would indicate m-m imbalances that
might be related to predator-prey cycles. In both Lagrangian
surveys, the depth around maximum O2 saturation seemed to be a
net production zone of phytoplankton biomass (positive particulate
m-m balances at 80 m depth from Chl a analysis and Prochlorococcus
counts), while the DCM was a net consumption zone (negative m-m
balances). However, without mixing events or other restoration
processes, the m-m imbalances would not persist for a long time:
the high potential growth and grazing rates of protists [64] and the
low carrying capacity of phytoplankton populations in oligotrophic
10
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Phytoplankton Growth and Grazing in the Atlantic
subtropical gyres, would limit the duration of positive imbalances.
At the same time, the low phytoplankton stocks would prevent
long negative imbalances. According to this idea, the potential
length and amplitude of the imbalances would be maximum in
winter and early spring, when nutrient concentrations and
phytoplankton carrying capacity is maximum, and they would
attenuate in summer, when the strengthened stratification favors
lower nutrient concentrations in the surface.
In spite of the imbalances detected for single depths, the
integrated % pNPP grazed of small and large phytoplankton were
close to 100%, except in the case of the small phytoplankton
fraction in the WL. If both phytoplankton fractions are treated
together, then the integrated % pNPPs were even closer to 100%,
greater than the average 74.5% pNPP grazed reported for the
open ocean [9]. Nevertheless, most dilution experiments analyzed
in Calbet and Landry [9] were performed in the UML, where the
% pNPP grazed is in general lower. Therefore, the coupled
integrated m-m balances for both Lagrangian surveys are the result
of multiple compensated imbalances, increasing the coupling of
the system with the size of the phytoplankton compartment.
The integrated m-m balance reported could be approximated to
a simultaneous metabolic balance (production- respiration) if i)
grazing exerted by mesozooplankton is very low [65,66], and
mesozooplankton respiration is supported by carbon ingested from
zooplankton consumption, ultimately coming from phytoplankton;
and ii) bacterial respiration is fully supported by organic carbon
coming from organisms of the contemporary community [67], i.e.
eventually fixed by phytoplankton. Under these circumstances,
even slightly negative integrated m-m balances might imply
autotrophic metabolic balances, given that part of the consumed
phytoplankton would be finally exported and not respired in the
upper water column. Therefore, according to the obtained m-m
balances, the water column analyzed would be slightly autotrophic
during our experiments. Indeed, our results are within the
confidence intervals reported to the net plankton metabolic
balances during the same Lagragian surveys by using oxygen
in vitro measurements [21]. On the other hand, assuming a 30%
dark phytoplankton respiration of pNPP, and 18% of extracellular
release (PER) of the carbon photoassimilated by phytoplankton
[68], the integrated GPP would be at least 1.68 g C m22 d21 in
the two study areas. This value is higher than the average
community respiration found in the photic layer of the NAST-E
province obtained from oxygen experiments [24,69].
Regarding the metabolic balance during longer time periods,
oxygen supersaturation up to around 110 m depth was observed
during Lagrangian surveys and along the approximately 2600 Km
transect carried out just before the Lagrangian surveys [70]. This
implies an autotrophic metabolic balance too, although the
observed O2 supersaturation could be partially caused by a
temperature increase during spring and summer [71]. Other
studies also reported oxygen saturation levels higher than 100%
[24,72]. Furthermore, net oxygen production and supersaturation
was observed at other subtropical oligotrophic regions [6,73,74],
especially in summer and fall [75].
Despite the m-m balance and the high GPP and the O2
supersaturation found, most studies in the North Atlantic
subtropical gyre report that net heterotrophic metabolic balance
prevails [69,72,76]. These opposing situations could take place if
net heterotrophic events were more common and longer than the
autotrophic ones, which would be infrequent and brief, although
they would allow O2 supersaturation [77]. However, the
overestimation of respiration [78,79], or the probable underestimation of primary production estimates based on O2 incubations
[6,79,80], could produce the same results. If autotrophy is the
common situation [74], subtropical gyres could be an important
carbon sink.
Acknowledgments
We thank the crew and scientific CARPOS team on board R.V.
Herspérides, especially J. Sostres and L. Viesca. A. Calvo-Dı́az kindly
provided FCM calibrations, shared some FCM data and helped, together
with L.A. Suárez and L. Dı́az, with FCM operation. J. Escánez and J. F.
Domı́nguez (IEO Canarias) measured nutrient concentrations. R.
González-Gil assisted with R software use. Comments by N.F. Weidberg,
C. Lobón, E. López and A. Rivera and five anonymous reviewers
improved greatly the quality of this paper and are especially acknowledged.
Author Contributions
Conceived and designed the experiments: RA. Performed the experiments:
FGT RA JH. Analyzed the data: CC FGT RA JH. Contributed reagents/
materials/analysis tools: CC FGT RA JH. Wrote the paper: CC RA FGT
JH.
References
1. Polovina JJ, Howell EA, Abecassis M (2008) Ocean’s least productive waters are
expanding. Geophysical Research Letters 35: L03618. doi:03610.01029/
02007GL031745.
2. Mann KH, Lazier JRN (2006) Vertical Structure of the Open Ocean: Biology of
the Mixed Layer. Dynamics of Marine Ecosystems. Third ed. Oxford: Blackwell
Publishing. 68–117.
3. Teira E, Mouriño B, Marañón E, Pérez V, Pazó MJ, et al. (2005) Variability of
chlorophyll and primary production in the Eastern North Atlantic Subtropical
Gyre: potential factors affecting phytoplankton activity. Deep-Sea Research I 52:
569–588.
4. Sheldon AW (1984) Phytoplankton growth rates in the tropical ocean.
Limnology and Oceanography 29: 1342–1346.
5. Marañon E, Holligan PM, Varela M, Mouriño B, Bale AJ (2000) Basin-scale
variability of phytoplankton biomass, production and growth in the Atlantic
Ocean. Deep-Sea Research I 47: 825–857.
6. Quay PD, Peacock C, Björkman K, Karl DM (2010) Measuring primary
production rates in the ocean: Enigmatic results between incubation and nonincubation methods at Station ALOHA. Global Biogeochemical Cycles 24:
doi:10.1029/2009GB003665.
7. Marra J (2002) Approaches to the measurement of plankton production. In:
Williams PJlB, Thomas DN, Reynolds CS, editors. Phytoplankton productivity.
Oxford: Blackwell Science Ltd. 78–108.
8. Steemann-Nielsen E (1952) The use of radiative carbon (14C) for measuring
organic production in the sea. Journal du Conseil pour lexploration de la Mer
18: 117–140.
9. Calbet A, Landry MR (2004) Phytoplankton growth, microzooplankton grazing,
and carbon cycling in marine systems. Limnology and Oceanography 49: 51–57.
PLOS ONE | www.plosone.org
10. Karl DM, Hebel DV, Björkman K, Letelier RM (1998) The role of dissolved
organic matter release in the productivity of the oligotrophic North Pacific
Ocean. Limnology and Oceanography 43: 1270–1286.
11. Peterson BJ (1980) Aquatic primary productivity and the 14C-CO2 method: A
history of the productivity problem. Annual Review of Ecology and Systematics
11: 359–385.
12. Goldman JC, McCarthy JJ, Peavey DG (1979) Growth rate influence on the
chemical composition of phytoplankton in oceanic waters. Nature 279: 210–215.
13. Strom S (2002) Novel interactions between phytoplankton and microzooplankton: their influence on the coupling between growth and grazing rates in the sea.
Antonie van Leeuwenhoek 480: 41–54.
14. Sterner RW (1986) Herbivores direct and indirect effects on algal populations.
Science 231: 605–607.
15. Volk T, Hoffert MI (1985) Ocean carbon pumps: Analysis of relative strengths
and efficiencies in ocean-driven atmospheric CO2 changes. In: Sundquist ET,
Broecker WS, editors. The carbon cycle and atmospheric CO2: Natural
variations Archean to present. Washington, DC: AGU. 99–110.
16. Landry MR, Hassett RP (1982) Estimating the grazing impact of marine microzooplankton. Marine Biology 67: 283–288.
17. Longhurst A (2007) Ecological geography of the sea. London: Academic Press.
18. Stelfox-Widdicombe CE, Edwards ES, Burkill PH, Sleigh MA (2000)
Microzooplankton grazing activity in the temperate and sub-tropical NE
Atlantic: summer 1996. Marine Ecology Progress Series 208: 1–12.
19. Lessard EJ, Murrell MC (1998) Microzooplankton herbivory and phytoplankton
growth in the northwestern Sargasso Sea. Aquatic Microbial Ecology 16: 173–
188.
11
July 2013 | Volume 8 | Issue 7 | e69159
Phytoplankton Growth and Grazing in the Atlantic
20. Quevedo M, Anadón R (2001) Protist control of phytoplankton growth in the
subtropical north-east Atlantic. Marine Ecology Progress Series 221: 29–38.
21. Aranguren-Gassis M, Serret P, Fernández E, Herrera JL, Domı́nguez JF, et al.
(2012) Balanced plankton net community metabolism in the oligotrophic North
Atlantic subtropical gyre from Lagrangian observations. Deep-Sea Research I
68: 116–122.
22. Tréguer P, Le Corre P (1975) Manuel d’analyse des sels nutritifs dans l’eau de
mer (utilisation de l’AutoAnalyzer Technicon). Brest: Université de Bretagne
Occidentale.
23. Welschmeyer NA (1994) Fluorometric analysis of chlorophyll a in the presence
of chlorophyll b and pheopigments. Limnology and Oceanography 39: 1985–
1992.
24. Marañón E, Pérez V, Fernández E, Anadón R, Bode A, et al. (2007) Planktonic
carbon budget in the eastern subtropical North Atlantic. Aquatic Microbial
Ecology 48: 261–275.
25. Sieracki CK, Sieracki ME, Yentsch CS (1998) An imaging-in-flow system for
automated analysis of marine microplankton. Marine Ecology Progress Series
168: 285–296.
26. Zubkov MV, Sleigh MA, Tarran GA, Burkill PH, Leakey RJG (1998)
Picoplanktonic community structure on an Atlantic transect from 50uN to
50uS. Deep-Sea Research I 45: 1339–1355.
27. Zubkov MV, Sleigh MA, Burkill PH, Leakey RJG (2000) Picoplankton
community structure on the Atlantic Meridional Transect: a comparison
between seasons. Progress in Oceanography 45: 369–386.
28. Andersen T, Schartau AKL, Paasche E (1991) Quantifying external and internal
nitrogen and phosphorus pools, as well as nitrogen and phosphorus supplied
through remineralization, in coastal marine plankton by means of a dilution
technique. Marine Ecology Progress Series 69: 67–80.
29. Landry MR, Constantinou J, Latasa M, Brown SL, Bidigare RR, et al. (2000)
Biological response to iron fertilization in the eastern equatorial Pacific (IronEx
II). III. Dynamics of phytoplankton growth and microzooplankton grazing.
Marine Ecology Progress Series 201: 57–72.
30. Frost BW (1972) Effects of size and concentration of food particles on the feeding
behavior of the marine planktonic copepod Calanus pacificus. Limnology and
Oceanography 17: 805–815.
31. R Core Team (2013). R: A language and environment for statistical computing.
R Foundation for Statistical Computing, Vienna, Austria. Available: http://
www.R-project.org/. Accessed 2013 Jun 6.
32. Wickham H (2009) ggplot2: Elegant graphics for data analysis. New York:
Springer.
33. Thorpe SA (2007) Turbulence in the ocean pycnocline. An introduction to
ocean turbulence. Cambridge: Cambridge University Press. 116–157.
34. Hartmann M, Grob C, Tarran GA, Martin AP, Burkill PH, et al. (2012)
Mixotrophic basis of Atlantic oligotrophic ecosystems. Proceedings of Natural
Academics of Science 109: 5756–5760
35. Arenovski AL, Lim EL, A.Caron D (1995) Mixotrophic nanoplankton in
oligotrophic surface waters of the Sargasso Sea may employ phagotrophy to
obtain major nutrients. Journal of Plankton Research 17: 801–820.
36. Vaulot D, Marie D, Olson RJ, Chisholm SV (1995) Growth of Prochlorococcus, a
photosynthetic prokaryote, in the equatorial Pacific Ocean. Science 268: 1480–
1482.
37. Jacquet S, Partensky F, Marie D, Casotti R, Vaulot D (2001) Cell cycle
regulation by light in Prochlorococcus strains. Applied and Environmental
Microbiology 67: 782–790.
38. Bricaud A, Allali K, Morell A, Marie D, Veldhuis MJW, et al. (1999) Divinyl
chlorophyll a-specific absorption coefficients and absorption efficiency factors for
Prochlorococcus marinus: kinetics of photoacclimation. Marine Ecology Progress
Series 188: 21–32.
39. Villareal TA, Brown CG, Brzezinski MA, Krause JW, Wilson C (2012) Summer
diatom blooms in the North Pacific subtropical gyre:2008–2009. PLoS ONE
7: p. e33109
40. Karl D, Michaels A, Bergman B, Capone D, Carpenter E, et al. (2002)
Dinitrogen fixation in the world’s oceans. Biogechemistry 57/58: 47–98.
41. VanMooy BAS, Fredricks HF, Pedler BE, Dyhrman ST, Karl DM, et al. (2009)
Phytoplankton in the ocean use non-phosphorus lipids in response to phosphorus
scarcity. Nature 458: 69–72.
42. Bonachela JA, Raghib M, Levin SA (2011) Dynamic model of flexible
phytoplankton nutrient uptake. Proceedings of Natural Academics of Science
108: 20633–20638.
43. Landry MR, Brown SL, Campbell L, Constantinou J, Liu H (1998) Spatial
patterns in phytoplankton growth and microzooplankton grazing in the Arabian
Sea during monsoon forcing. Deep-Sea Research II 45: 2353–2368.
44. Edwards ES, Burkill PH, Stelfox CE (1999) Zooplankton herbivory in the
Arabian Sea during and after the SW monsoon, 1994. Deep-Sea Research II 46:
843–863.
45. Laws EA, DiTuillo GR, Redalje DG (1987) High phytoplankton growth and
production rates in the North Pacific subtropical gyre. Limnology and
Oceanography 32: 905–918.
46. Laws EA, Redalje DG, Haas LW, Bienfang PK, Eppley RW, et al. (1984) High
phytoplankton growth and production rates in oligotrophic Hawaiian coastal
waters. Limnology and Oceanography 29: 1161–1169.
47. Marañón E (2005) Phytoplankton growth rates in the Atlantic subtropical gyres.
Limnology and Oceanography 50: 299–310.
PLOS ONE | www.plosone.org
48. Pérez V, Fernández E, Marañón E, Morán XAG, Zubkov MV (2006) Vertical
distribution of phytoplankton biomass, production and growth in the Atlantic
subtropical gyre. Deep-Sea Research I 53: 1616–1634.
49. Moreno-Ostos E, Fernández A, Huete-Ortega M, Mouriño-Carballido B,
Calvo-Dı́az A, et al. (2011) Size-fractionated phytoplankton biomass and
production in the tropical Atlantic. Scientia Marina 75: 379–389.
50. Huete-Ortega M, Cermeño P, Calvo-Dı́az A, Marañón E (2011) Isometric sizescaling of metabolic rate and the size abundance distribution of phytoplankton.
Proceedings of the Royal Society B 279: 1815–1823
51. Gieskes W, Kraay G, Baars M (1979) Current 14C methods for measuring
primary production: Gross underestimates in oceanic waters. Netherlands
Journal of Sea Research 13: 58–78.
52. Raven JA (1998) The twelfth Tansley Lecture. Small is beautiful: the
picophytoplankton. Functional Ecology 12: 503–513.
53. McCarthy JJ, Goldman JC (1979) Nitrogenous nutrition of marine phytoplankton in nutrient-depleted waters. Science 203: 670–672.
54. Lehman JT, Scavia D (1982) Microscale patchiness of nutrients in plankton
communities. Science 216: 729–730.
55. Seymour JR, Marcos, Stocker R (2009) Resource patch formation and
exploitation throughout the marine microbial food web. The American
Naturalist 173: E15–29.
56. Azam F, Malfatti F (2007) Microbial structuring of marine ecosystems. Nature
Reviews Microbiology 5: 782–791.
57. Litchman E, Klausmeier CA, Schofield OM, Falkowski PG (2007) The role of
functional traits and trade-offs in structuring phytoplankton communities:
scaling from cellular to ecosystem level. Ecology Letters 10: 1170–1181.
58. Reynolds C (2006) The ecology of phytoplankton. New York: Cambridge
University Press.
59. Villareal TA, Altabet MA, Culver-Rymsza K (1993) Nitrogen transport by
migrating diatom mats in the North Pacific Ocean. Nature 363: 709–712.
60. Letelier RM, Karl DM (1998) Trichodesmium spp. physiology and nutrient fluxes
in the North Pacific subtropical gyre. Aquatic Microbial Ecology 15: 265–276.
61. Polz MF, Hunt DE, Preheim SP, Weinreich DM (2006) Patterns and
mechanisms of genetic and phenotypic differentiation in marine microbes.
Philosophical Transactions of the Royal Society 361: 2009–2021.
62. Sommer U (1984) The paradox of the plankton: Fluctuations of phosphorus
availability maintain diversity of phytoplankton in flow-through cultures.
Limnology and Oceanography 29: 633–636.
63. Cermeño P, Estévez-Blanco P, Marañón E, Fernández E (2005) Maximum
photosynthetic efficiency of size-fractionated phytoplankton assessed by 14C
uptake and fast repetition rate fluorometry. Limnology and Oceanography 50:
1438–1446.
64. Sherr EB, Sherr BF (2002) Significance of predation by protists in aquatic
microbial food webs. Antonie van Leeuwenhoek 81: 293–308.
65. Huskin I, Anadón R, Medina G, Head RN, Harris RP (2001) Mesozooplankton
distribution and copepod grazing in the Subtropical Atlantic near Azores:
Influence of mesoscale structures. Journal of Plankton Research 23: 671–691.
66. Isla JA, Llope M, Anadón R (2004) Size-fractionated mesozooplankton biomass,
metabolism and grazing along a 50uN230uS transect of the Atlantic Ocean.
Journal of Plankton Research 26: 1301–1313.
67. Robinson C (2008) Heterotrophic bacterial respiration. In: Kirchman DL,
editor. Microbial Ecology of the oceans. Second ed. New Jersey: Jhon Wiley &
Sons. 299–327.
68. Teira E, Pazó MJ, Quevedo M, Fuentes MV, Niell FX, et al. (2003) Rates of
dissolved organic carbon production and bacterial activity in the eastern North
Atlantic Subtropical Gyre during summer. Marine Ecology Progress Series 249:
53–67.
69. Duarte CM, Agustı́ S, Arı́stegui J, González N, Anadón R (2001) Evidence for a
heterotrophic subtropical northeast Atlantic. Limnology and Oceanography 46:
425–428.
70. Taboada FG, Gil RG, Höfer J, González S, Anadón R (2010) Trichodesmium spp.
population structure in the eastern North Atlantic subtropical gyre. Deep-Sea
Research I 57: 65–77.
71. Emerson S, Stump C, Nicholson D (2008) Net biological oxygen production in
the ocean: Remote in situ measurements of O2 and N2 in surface waters. Global
Biogeochemical Cycles 22: GB3023.
72. Robinson C, Serret P, Tilstone G, Teira E, Zubkov MV, et al. (2002) Plankton
respiration in the eastern Atlantic Ocean. Deep-Sea Research I 49: 787–813.
73. Nicholson D, Emerson S, Eriksen CC (2008) Net community production in the
deep euphotic zone of the subtropical North Pacific gyre from glider surveys.
Limnology and Oceanography 53: 2226–2236.
74. Riser SC, Johnson KS (2008) Net production of oxygen in the subtropical ocean.
Nature 451: 323–325.
75. Karl DM (2007) Microbial oceanography: paradigms, processes and promise.
Nature Reviews Microbiology 5: 759–769.
76. Aranguren-Gassis M, Serret P, Fernández E, Herrera JL, Domı́nguez JF, et al.
(2011) Production and respiration control the marine microbial metabolic
balance in the eastern North Atlantic subtropical gyre. Deep-Sea Research I 58:
768–775.
77. Karl DM, Laws EA, Morris P, Williams PJleB, Emerson S (2003) Metabolic
balance of the open sea. Nature 426: 32.
78. Pomeroy LR, Sheldon JE, Sheldon WM (1994) Changes in bacterial numbers
and leucine assimilation during estimations of microbial respiratory rates in
12
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Phytoplankton Growth and Grazing in the Atlantic
seawater by the precision Winkler Method. Applied and Environmental
Microbiology 60: 328–332.
79. Calvo-Dı́az A, Dı́az-Pérez L, Suárez LÁ, Morán XAG, Teira E, et al. (2011)
Decrease in the autotrophic-to-heterotrophic biomass ratio of picoplankton in
oligotrophic marine waters due to bottle enclosure. Applied and Environmental
Microbiology 77: 5739–5746.
PLOS ONE | www.plosone.org
80. Morán XAG, Perez V, Fernandez E (2007) Mismatch between community
respiration and the contribution of heterotrophic bacteria in the NE Atlantic
open ocean: What causes high respiration in oligotrophic waters? Journal of
Marine Research 65: 545–560.
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