Title: Ultimate control on Southern Ocean phytoplankton biomass by zooplankton grazing
Authors (tbc): Corinne Le Quéré, Erik T. Buitenhuis, Rósín Moriarty, Olivier Aumont, Laurent Bopp,
Clare Enright, Sandy P. Harrison, Christine Klaas, Louis Legendre, Trevor Platt, I. Colin Prentice,
Richard B. Rivkin, Shubha Sathyendranath, Meike Vogt, Dieter Wolf-Gladrow
To be submitted to Science as a Report (up to ~2500 words including references, notes and captions or
~3 printed pages) present important new research results of broad significance. Reports should include
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and Methods should usually be included in supporting online material, which should also include
information needed to support the paper's conclusions.
Affiliations (please fill in your affiliation and full address):
CLQ, ETB, CE: Tyndall Centre for Climate Change Research, School of Environmental Sciences,
University of East Anglia, Norwich, NR4 7TJ, UK.
CLQ and RM: British Antarctic Survey, High Cross, Madingley Road, CB3 0ET, Cambridge, UK.
RM: Now at University of Manchester
MV: ETH
OA:
LP:
CK and DWG:
SPH: Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
SH and CP:
LL:
TP and SS:
RBR:
One Sentence Summary: Low phytoplankton biomass in the Southern Ocean is caused by
zooplankton grazing rather than iron limitation.
Abstract (100-150 words):
A major puzzle in oceanography is why phytoplankton biomass is low in the Southern ocean in spite
of the abundance of macro nutrients. This phenomenon has been attributed to low iron availability
because of the remoteness from continental dust sources. Ocean biogeochemistry models fail to
reproduce this behaviour, raising doubts that the ‘iron-limitation hypothesis’ is an adequate
explanation. Here we use a new model that includes zooplankton dynamics to show that zooplankton
grazing and trophic dynamics, rather than iron limitation, are responsible for maintaining low biomass
concentrations in the Southern Ocean during summer. These results have implications for
understanding the effects of fishing pressure on marine resources and the potential of iron fertilisation
for mitigating climate change.
Main Text (2543 words):
Phytoplankton are at the base of the marine food chain. Their biomass, as inferred from remote sensing
of ocean colour, is low in the Southern Ocean compared to the Northern Hemisphere (Fig. 1), in spite
of adequate levels of macro nutrients there. These ‘High Nutrients Low Chlorophyll’ (HNLC) regions
have been explained by lack of the micro nutrient iron [1] as the Southern Ocean is far away from
sources of iron-rich continental dust. Indeed, this has been regarded as the iconic iron-limited ocean
region for several decades [1, 2]. The ‘iron-hypothesis’ has been extensively examined in over a dozen
iron fertilisation experiments [3, 4]; is proposed as a driver for the drawdown of atmospheric CO2
during glaciations [5, 6], and it is being considered commercially as a means to geoengineering
climate and sell carbon credits [7]. However, ocean biogeochemistry models have failed to reproduce
the co-existence of high chlorophyll (chla) concentrations in the Northern Hemisphere and the HNLC
regions in the Southern Ocean [8]. Studies that have incorporated iron dynamics perform better, but
still produce excessive concentrations of chla in the Southern Ocean during summer [9-11].
The low chla in HNLC regions results from the balance between phytoplankton growth and loss
processes. Phytoplankton growth rate varies with temperature, light, and nutrient availability, while
losses result mainly from grazing by zooplankton. Thus, the covariance between chla and nutrients or
zooplankton biomass should reflect the processes controlling phytoplankton biomass. We analysed the
relationship between observed chla concentrations and nitrate (NO3-), phosphate (PO43-), and iron (Fe)
concentrations, and protozooplankton, mesozooplankton and macrozooplankton abundance (Fig. 2).
We focus this analysis on the 25-75% interquartile range of each variable, to identify relationships
characterising the open ocean.
Chla covaries with NO3- at concentrations below about 3 μmol/L, and with PO43- at concentrations
between about 0.3 and 0.5 μmol/L (Fig. 2). These relationships are consistent with our understanding
of the growth limitation of phytoplankton in the sub-tropics, where NO3- and PO43- concentrations are
low (ref?). There is no covariance between chla and Fe concentration [12] below 0.9 mol/L, contrary
to expectations if Fe limitation exerted a significant control on phytoplankton biomass. This is also
true when examining only Southern Ocean data [13]. Covariance with iron at concentrations above 0.9
mol/L reflects data from coastal regions, which are atypical. The strongest covariance is found
between chla and protozooplankton and mesozooplankton biomass [12] for concentrations below 0.6
μmol/L and 0.3 μmol/L, respectively. There is no covariance between chla and macrozooplankton. The
combined set of observations suggests an important role for proto and mesozooplankton in controlling
surface chla concentration, although this cannot be inferred from Fig.1 alone.
To understand the functional relationships between chla and growth and loss processes, we use two
global ocean biogeochemical models: the PlankTOM6 and PlankTOM10 models, representing
respectively six and ten Plankton Functional Types (PFTs) [8, 13, 14] and incorporating a reasonable
representation of grazing dynamics [15, 16]. Both models are based on growth and grazing rates from
observations [13] and are forced by iron deposition averaged from three studies [17]. PlankTOM10
has been tuned to reproduce the observed covariance between chla and limiting factors (Fig. S1). Both
models reproduce the surface chla concentration in the Northern Hemisphere summer with a similar
level of skill (Fig. 1), but the PlankTOM6 model produces excessive chla concentration over the
Southern Hemisphere summer whereas the PlankTOM10 model produces concentrations close to
observed.
We use the North/South ratio in surface chla over the Pacific, where there is the most pronounced
contrast, as a metric to quantify model performance. PlankTOM10 has a North/South chla ratio of
1.980.10 (1998-2009 mean  2SD of annual values), compared to 2.160.35 in the observations and
1.290.12 in PlankTOM6 (Fig. 3). The different behaviour between the two models is caused by the
cascading effect of grazing from several zooplankton groups on chla. In PlankTOM10,
macrozooplankton concentration is high in winter in the North Pacific where the mixed layer is well
stratified, but not in the South Pacific where it is well mixed (Fig. S2). Thus, when the spring bloom
starts in the North, it is initially controlled by proto and mesozooplankton, but after a few weeks the
biomass of macrozooplankton increases leading to a reduction of smaller zooplankton and allowing
chla to remain high. In the South however, the macrozooplankton biomass never increases enough to
limit grazing by the smaller zooplankton, which supress the bloom. The important role of cascading
effects is also highlighted by the absence of covariance between chla and macrozooplankton, and the
presence of a strong covariance between chla and proto and mesozooplankton both in the observations
(Fig. 2) and the model (Fig S2).
We tested the specific control of macrozooplankton on chla by running four additional model
experiments, where: (Z1) we added macrozooplankton to PlankTOM6, (Z2) we parameterised
mesozooplankton in PlankTOM6 using the same growth and loss rates as macrozooplankton, (Z3) we
removed the macrozooplankton from PlankTOM10, and (Z4) we parameterised macrozooplankton in
PlankTOM10 using the same growth and loss rates as mesozooplankton. Experiments (Z1) and (Z2)
both represent macrozooplankton but at different trophic levels. They maintain a North/South ratio of
1.81 and 1.53, respectively (Fig. 3). In contrast, experiments (Z3) and (Z4) do not represent
macrozooplankton, but their trophic structure is the same as PlankTOM6 and PlankTOM10. They do
not maintain the observed North/South ratio, with ratios of 1.16 and 1.27, respectively. Considered
together, these experiments show that the representation of a slow growing zooplankton such as
macrozooplankton plays a pivotal role in determining the relative average concentrations of chla in the
Northern versus Southern hemisphere. The addition of a food-web compartment further improves the
results.
The model results can be influenced by a number of choices including the model structure and
parameters, the physical transport and meteorological data, and the choice of dust deposition fields.
We assessed the combined effects of model choices by comparing our analysis with outputs from
seven other published models [18]. These models produce a North/South chla ratio ranging from 0.60
to 1.36 (median of 1.2). The low ratio found in most models may be a consequence of the greater
efforts put in quantifying and representing the phytoplankton compared to zooplankton rates and
dynamics.
We tested the role of atmospheric iron deposition compared to grazing for the North/South distribution
of chla directly by applying different dust deposition fields to the PlankTOM10 and PlankTOM6
models, with: (D1) dust deposition including the effect of dust particle size on iron solubility [19],
(D2-D4) iron deposition using the three individual dust fields [20-22] averaged in [17], and (D0) an
extreme simulation where no dust is deposited from the atmosphere. The differences in the
North/South chla ratio between these experiments (Fig. 3) are smaller than the difference between
PlankTOM10 and PlankTOM6 for any one experiment. The experiment with no iron deposition from
dust still maintains excessive chla in the Southern Ocean summer, using iron sources from below. This
result is consistent with the fact that although Fe is lower in the Southern Ocean than elsewhere, it is
on average ~0.2 mol/L [23], which is near or above the half-saturation for growth of most
phytoplankton [8]. Thus Fe levels may limit phytoplankton growth but not sufficiently to explain the
observed very low chla concentration, even in summer months.
Our results show that zooplankton regulate phytoplankton biomass, and suggest that food-web
structure rather than Fe availability limits productivity in the Southern Ocean. This does not contradict
the results of Fe fertilisation experiments [3], as additional Fe will trigger further growth providing Fe
is not at the optimal concentration. However, our experiments suggest that ecosystem structure
provides an explanation for why some iron fertilisation experiments have led to large phytoplankton
blooms (ref?) while others have not (ref?). The role and response of zooplankton to Fe fertilisation
needs to taken into account if deliberate Fe fertilisation is to be used as a means to geoengineer the
climate and store CO2 in the deep ocean. The importance of grazing in modulating chla also raises the
interesting possibility that observed decadal changes in surface chla may be a consequence of changes
in grazing from fishing pressure rather than changes in climate [24, 25].
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29. CMIP5 reference (suggestion?)
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Acknowledgments. The design of the DGOM was developed through a series of seven
international workshops funded in part by the Max-Planck Institute for Biogeochemistry, and hosted
by the Villefranche Oceanography Laboratory, France. We thank Stéphane Pesant for support with the
data compilations. This work benefited from funding by the European Union EUR-OCEANS,
GREENCYCLES, Carbo-Ocean and Carbo-Change projects and the UK-NERC QUEST programme.
We thank the NEMO team (Nucleus for European Modelling of the Ocean) for support to the physical
model, A. Tagliabue for providing the Fe database and N. Mahowald for providing dust deposition
fields. Model simulations were run on the Escluster of the University of East Anglia.
Figure captions
Fig. 1. Surface chla (mgChl m-3) for (left) June-August and (right) November-January. Data are from
(top) SeaWiFS satellite, (middle) PlankTOM10, and (bottom) PlankTOM6. All datasets are averages
for 1998-2009. The boxes in the North and South Pacific show the regions used in Fig. 3.
Fig. 2. Covariance between chla concentration and (left) limiting nutrients and (right) biomass of
zooplankton groups [24]. Chlorophyll data from SeaWiFS are the same in each panel, and are
averaged over 1998-2009. The NO3- and PO43- fields are from the World Ocean Atlas 2009, updated
from [26]. Fe concentrations are from [23]. The proto- and mesozooplankton biomass data are from
[15, 16], respectively. The macro-zooplankton biomass data are from [27]. The median is shown in
black, with the 25-75% interquartile range shown in grey shading [13].
Fig. 2. Ratio of the surface chla in the North and South Pacific. Observations are from SeaWiFS.
Model results in green represent a slow growing zooplankton, including: PlankTOM10, (Z1)
PlankTOM6 plus macrozooplankton, (Z2) PlankTOM6 with mesozooplankton parameterised like
macrozooplankton, (D0-D4) PlankTOM10 with no dust deposition or with dust fields from [19], [20],
[21], and [22], respectively. Model results in blue do not represent a slow growing zooplankton,
including: PlankTOM6, (Z3) PlankTOM10 minus macrozooplankton, (Z2) PlankTOM10 with
macrozooplankton parameterised like mesozooplankton, and (D0*-D4*) as above with PlankTOM6.
Results from (F1-F4) are model simulations available through the MARine Ecosystem Model
Intercomparison Project [28] and (C1-C4) the Climate Model Intercomparison Project 5 [29].
Supplementary Materials:
Methods
Tables S1 and S2
Figs. S1 and S2
References