The
coastal Gulf of Mexico (GOM) and coastal sea off the Korean Peninsula (CSK)
both suffer from human-induced eutrophication. We used a nitrogen (N) mass
balance model in two different regions with different nitrogen input sources
to estimate organic carbon fluxes and predict future carbon fluxes under
different model scenarios. The coastal GOM receives nitrogen predominantly
from the Mississippi and Atchafalaya rivers and atmospheric nitrogen
deposition is only a minor component in this region. In the CSK, groundwater
and atmospheric nitrogen deposition are more important controlling factors.
Our model includes the fluxes of nitrogen to the ocean from the atmosphere,
groundwater and rivers, based on observational and literature data, and
identifies three zones (brown, green and blue waters) in the coastal GOM and
CSK with different productivity and carbon fluxes. Based on our model
results, the potential primary production rate in the inner (brown water)
zone are over 2 gC m
Industrial expansion and anthropogenic emissions are major factors leading to increased coastal productivity and potential eutrophication (Sigman and Hain, 2012). Coastal primary production is controlled largely by nitrogen (N) and phosphorus (P), and the relative supply of each determines which element limits production (Paerl, 2009); freshwater inputs and the distance from sources such as river mouths are also important (Dodds and Smith, 2016). Changes in nutrient loading from airborne, river-borne and groundwater sources can also affect which element limits coastal productivity (Sigman and Hain, 2012). Most coastal regions are N-limited; however, at certain times conditions can change from N-limited to P-limited (Dodds and Smith, 2016; Howarth and Marino, 2006). Sylvan et al. (2006), for example, suggested that the coastal Gulf of Mexico (GOM), especially near the Mississippi River delta mouth, is P-limited at certain times.
Several studies have shown that increasing atmospheric nitrogen deposition
(AN-D) is contributing to ocean production globally, including to
eutrophication, and is potentially of future importance in the GOM (Cornell
et al., 1995; Doney et al., 2007; Duce et al., 2008; He et al., 2010;
Kanakidou et al., 2016; Kim, 2018; T. W. Kim et al., 2011; Lawrence et al.,
2000; Paerl et al., 2002). Recently, T. W. Kim et al. (2011), using a model
simulation, showed that AN-D controls approximately 52 % of the coastal
productivity in the Yellow Sea. Global
Most previous model studies in the GOM have been used to predict the size of the hypoxic zone (e.g., Fennel et al., 2006, 2011, 2013; Green et al., 2008; Hetland and DiMarco, 2008; Justic et al., 2002; Scavia et al., 2004; Turner et al., 2006, 2008), although Bierman et al. (1994) used a mass balance model to estimate carbon flux and oxygen exchange. The mass balance model is a useful tool to calculate nutrient or carbon fluxes and to estimate production in the coastal ocean (J. S. Kim et al, 2010; G. Kim et al., 2011), and such models have been successfully used in many regions and individual coastal systems to estimate ecosystem metabolism, e.g., in the Patuxent River estuary of the Chesapeake Bay (Hagy et al., 2000; Testa et al., 2008) and in the LOICZ (Land Ocean Interactions in the Coastal Zone) project (e.g., Ramesh et al., 2015). However, there are few such model studies in the GOM and CSK. All previous models for the GOM and the CSK have considered only riverine N as the predominant input source, and AN-D as an input in either region has not been considered.
In this study, we aimed to (1) build a mass balance model considering not only riverine N input but also airborne and groundwater-borne N; (2) use it to calculate potential primary production in the three regions defined by Rowe and Chapman (2002, henceforth RC02, see next section) and their associated coastal productivity; and (3) use the mass balance model to test the RC02 hypothesis. Because RC02 did not quantify their model with nutrient data, and because this model has not been applied to another region, we tested the RC02 hypothesis using data from both the GOM and the CSK that include low salinity samples. We used historical data from the mid-western part of the CSK and evaluated the theoretical model of RC02 in both areas where freshwater with high terrestrial nutrient input mixes into the coastal ocean.
The Louisiana–Texas (LATEX) shelf in the northern Gulf of Mexico is affected
by coastal nutrient loading, leading to hypoxia, coming from two major
terrestrial sources (the Mississippi and Atchafalaya rivers that together
form the Mississippi–Atchafalaya river system, MARS). These two major rivers
have different nutrient concentrations. The Gulf of Mexico (GOM) is a
semi-enclosed oligotrophic sea and the MARS is the major source of nutrients
and freshwater to the northern GOM (Alexander et al., 2008; Rabalais et al.,
2002; Robertson and Saad, 2014). The MARS drains 41 % of the contiguous
United States (Milliman and Meade, 1983) and discharges approximately
20 000 m
Study sites and sampling areas in the Gulf of Mexico and the Korean
Peninsula. Panel
At the Old River Control Structure on the lower Mississippi River approximately 25 % of the Mississippi River's water is diverted into the Atchafalaya River, where it mixes with the water in the Red River. The flow in the Atchafalaya River totals 30 % of the total MARS flow (Fig. 1a). Several projects have investigated the relationship between nutrients and the marine ecosystem, and how this leads to hypoxia in the GOM (e.g., Bianchi et al., 2010; Diaz and Rosenberg, 1995, 2008; Forrest et al., 2011; Hetland and DiMarco, 2008; Laurent et al., 2012; Quigg et al., 2011; Rabalais and Smith, 1995; Rabalais et al., 2007; Rabalais and Turner, 2001; Rowe and Chapman, 2002). Strong stratification due to the high freshwater discharge from the MARS, local topography (DiMarco et al., 2010), wind direction and high nitrate concentration all affect hypoxia formation, with upwelling-favorable wind facilitating its development (Feng et al., 2012, 2014).
The Rowe and Chapman three-zone hypothesis, which describes the physical and biochemical processes that initiate and sustain hypoxia on the Louisiana–Texas shelf (Rowe and Chapman, 2002). RMEPs are reduced metabolic end products. Reprinted with permission of Gulf of Mexico Science.
In the northern GOM, the major factor controlling coastal productivity is
riverine N input. Rowe and Chapman (2002) defined three theoretical zones
over the LATEX shelf close to the Mississippi and Atchafalaya river mouths to
predict the effects of nutrient loading on hypoxia along the river plumes and
over the shelf. They named these the brown, green and blue zones (Fig. 2).
Nearest the river mouths is a “brown” zone, where the nutrient
concentrations are high, but the discharge of sediment from the river reduces
light penetration and limits primary productivity within the plume. Further
away from the river plume is a stratified “green” zone with available light
and nutrients that result in high productivity. In this region, the rapid
depletion of nutrients is due to biological uptake processes that depend on
the season and river flow (Bode and Dortch, 1996; Dortch and Whitledge, 1992;
Lohrenz et al., 1999; Turner and Rabalais, 1994). Still further offshore, and
also along the river plume to the west, there is the so-called “blue” zone,
defined arbitrarily by nitrate concentrations of 1
The western CSK forms the eastern side of another
semi-enclosed basin (the Yellow Sea) and is affected by freshwater discharge
from river plumes in the same way as the coastal GOM, although the
freshwater flow is considerably less. The Yellow Sea covers about 380 000 km
There is a strong tidal front in the coastal area near the Taean Peninsula
due to sea floor topography and the coastal configuration (Park, 2017; Park
et al., 2017). The region also contains several bays (Garolim Bay, Gomso Bay
and Cheonsu Bay), and is affected by discharges from a large artificial lake
(Saemangeum Lake) as well as the freshwater discharge from the Keum River
plume that contains high concentrations of nutrients (Lim et al., 2008).
Conditions in the mid-western CSK near the Taean Peninsula are similar to the
coastal GOM, because of mixing of two different water masses from
Gyunggi Bay (Han River) and the
Keum River (Choi et al., 1998, 1999). The annual mean flow
rates within the Keum River were about 70 m
Unlike the coastal GOM, the CSK has increased nitrogen inputs from atmospheric nitrogen deposition (AN-D, which is approximately 5 times higher than in the GOM, Table 2; J. Y. Kim et al., 2010; Luo et al., 2014; Shou et al., 2018; Zhao et al., 2015) and nutrient inputs from the groundwater discharge (J. S. Kim et al., 2010; G. Kim et al., 2011). AN-D has increased in the CSK owing to industrial development in China during the last few decades, which has led to increased atmospheric N emission.
Hydrographic data from the MCH (Mechanisms Controlling Hypoxia – MCH Atlas)
projects in the Gulf of Mexico were collected from the National
Oceanographic Data Center (
Sampling dates for data from Gulf of Mexico projects and the coastal sea of the Korean Peninsula. Winter data are listed for the Gulf of Mexico cruises.
AN-D data from around the USA are sparse (Table 2). Most US data have been
collected along the east coast of the USA, and the only data in the GOM
region were collected near Corpus Christi (
Atmospheric nitrogen deposition (AN-D) in the USA and in the Yellow Sea.
Our model consists of three sub-regions based on sampling locations during
MCH cruises (Fig. 3), each of which contains a series of 0.25
The N mass balance box model is modified from previous models to calculate
the net removal of dissolved inorganic nitrogen (DIN) inside each box, which
represents potential primary production (PPP; De Boer et al., 2010; G. Kim et
al., 2011; Eq. 1). In this model, DIN concentration includes ammonium
(
Definitions and values used in N mass balance model to calculate DIN removal by biological production.
In order to calculate the net removal of DIN in a box above the pycnocline
layer, we used our N mass balance model in Eq. (2).
Below the pycnocline layer we used the revised Eq. (3).
In the GOM, benthic sediments provide excess ammonium to overlying water by
regeneration processes such as remineralization (Lehrter et al., 2012;
Nunnally et al., 2014; Rowe et al., 2002). Generally, there is an uptake of
nitrate and nitrite mainly by sedimentary denitrification (McCarthy et al.,
2015) or dissimilatory nitrate reduction to ammonium (DNRA) and assimilation
by benthic microalgae (Christensen et al., 2000; Dalsgaard, 2003; Thornton et
al., 2007). Due to this, net DIN flux was used as the value of
The output terms are (1)
Mean ocean current velocities
Water transport in the region is generally from the east, i.e., from near the
Mississippi River in sub-region A to the west, near the Atchafalaya River in
sub-region C during non-summer periods. During summer, the winds change
direction from easterly to westerly, blocking the water flow to the west (Cho
et al., 1998). We calculated advection from current meter data collected
during the LATEX program (Nowlin et al., 1998a, b) from April 1992 to
December 1994, from which we determined
To run the box model, we assumed three factors: (1) the study area is in a
steady state condition, with equal input sources and outputs, (2) AN-D is
evenly distributed across each area and (3) DIN is fully utilized by
phytoplankton growth in the layer above the pycnocline, so we can neglect
other removal factors. However, in the layer below the pycnocline, as we
mentioned above, denitrification, which leads to a main loss of DIN as
nitrogen gas, is considered as another output term in Eq. (3). Because we
assumed that all DIN removed is fully consumed by primary production above
the pycnocline, we can calculate potential carbon fluxes and oxygen
consumption using the Redfield ratio
(C : N :
The existence of the three zones suggested by RC02 has been verified from
winter data using nutrient/salinity relationships (Kim, 2018). Figure 5 shows
the contour graph based on the mean concentration of DIN at each station
during the MCH M4 (March 2005) cruise. For operational and modeling purposes,
stations were grouped into three sub-regions – near the Mississippi (A),
near the Atchafalaya (C) and an intermediate region (B) between
Extent of the three zones defined by RC02 based on the mean concentration of nutrient (DIN) at each station during the MCH M4 cruise in March 2005, showing their correspondence with the three sub-regions used in the box model. Red, grey and blue stations correspond to sub-regions A (near the Mississippi River), B (between the Mississippi and Atchafalaya) and C (near the Atchafalaya), respectively.
A range of N input values from various sources were used in the N mass
balance model to estimate PPP and carbon fluxes in the coastal GOM. The PPP
rates were highest near the river mouth and we set the boundaries of
production for each zone based on our N mass balance model results and mean
DIN data. We defined the brown zone as having the PPP rate of over
2 gC m
Areal distributions of the three zones using data from above the
pycnocline
The edges of the three zones above and below the pycnocline layer, based on
our N mass balance model results, are shown in Fig. 6a and b. The patterns of
the boundaries above and below the pycnocline differ from the edges of the
zones. The brown zone was found above the pycnocline on all cruises close to
the Mississippi River mouth because of the high nutrient concentrations, but
only appeared off the Atchafalaya River in March 2005 (MCH M4). However,
below the pycnocline it was found only in April 2004 (MCH M1) in sub-region
A. This suggests that vertical transport across the pycnocline rapidly
removes the high levels of suspended material that cause light limitation
above the pycnocline. In the green zones, the nutrient source is mostly
supported directly by the river, with minor additional sources of N from
vertical sinking, AN-D and benthic fluxes. We utilized the vertical sinking
flux from the sediment trap data from Qureshi (1995) below the pycnocline
layer to estimate PPP. This varied between 0.1 and
1.0 gN m
The model calibration was done with historic literature data. The literature
data suggest that observed PP rates in the green and brown zones of the
coastal GOM vary between 0.4 gC m
The actual PP ranges were similar to our model-based PPP (Fig. 6). However,
this was different from RC02's brown zone. This might be due to the
differences between methods such as
Note that our model assumed all the biological uptake could be converted
directly to production rates, which we considered as PPP. The PPP from
cruises MCH M1–M8 for samples from above the pycnocline calculated using our
model is reasonable based on comparison with previous PP values (Fig. 6a).
The PPP ranges (0.01–5.05 gC m
Based on our model calculation, which assumes all the nutrients are available for production, the PPP showed maxima at all times in sub-region A (near the Mississippi River) and minima in sub-region B (between the Mississippi and Atchafalaya rivers), except for MCH M2 in June 2004, when sub-region C had the lowest PPP (Fig. 6a). The high values in sub-region A are due largely to under-utilization of nutrients in regions of high turbidity. As the water flows west under the influence of the Coriolis effect, PPP is expected to decrease as a result of declining nutrient concentrations because of dilution and nutrient uptake during biological production while the water flows to sub-region B. In sub-region C, MCH M4 (March 2005) had the highest PPP among the all MCH cruises. This probably depended on high nutrient concentrations being present during the winter period, when the region was affected by Atchafalaya River nutrient input.
We tested the sensitivity of the model to changes in input/output parameters
such as increasing AN-D and decreasing riverine N input. Assuming the model
is robust, we investigated three model scenarios based on the nutrient
distributions seen during the MCH1 cruise (note that using data from other
cruises gives very similar results). In the first scenario, we cut riverine N
input 60 % and increased the AN-D input by a factor of 2 based on
increasing N emission predictions (Duce et al., 2008; He et al., 2010;
Kanakidou et al., 2016; T. Kim et al., 2011; Lawrence et al., 2000; Paerl et
al., 2002). In the second scenario, we doubled the amount of AN-D as in
scenario 1 and decreased riverine N input by 30 % based on the hypoxia
management plan goal (Gulf Hypoxia Action Plan Report, 2001, 2008; Rabalais
et al., 2009). In the third scenario, we increased riverine N input by
20 %, assuming the failure of the hypoxia management plan, while we set
the AN-D amount equal with the first and second scenarios. Based on our
N mass balance model calculation and model scenarios, we can initially
estimate carbon fluxes from our PPP rate, and, using the Redfield
carbon-to-oxygen stoichiometry ratio (
Simulation results for selected model scenarios based on MCH M1
(5–7 April 2004), as described in the text. Biological production is
calculated using our N mass balance model, while oxygen demand is calculated
by the Redfield stoichiometry ratio (C:
As can be seen in the scenario results for MCH M1 data (Table 4), the riverine N input source is still the major controlling factor in the coastal GOM region even when its contribution is greatly reduced and the AN-D source is doubled. For instance, if we fail to reduce riverine N input in the future (scenario 3), the potential carbon fluxes will increase by 17 % relative to current conditions. In contrast, the AN-D input source only increased to a maximum of 5 % of the total input term; this indicates that AN-D input is still a minor factor in the GOM. If the production is increased, overall oxygen demand will also be increased. The MCH M1 scenario result indicated that the overall oxygen demand would increase approximately 21 % if we fail to reduce riverine N input, likely increasing considerably the area of the hypoxia.
As we have done in the GOM, we used our N mass balance model to estimate the
PPP in the CSK and define the three different zones (Fig. 7). Similar to the
GOM region, the PPP rates were highest near the river mouth, and we set the
boundaries of each zone based on our N mass balance model results. Based on
nutrient data, as was done for the GOM, we defined the brown zone as having a
PPP rate above 1.5 gC m
The distribution of the three zones of the mid-western CSK above the
pycnocline
The seasonal results shown in Fig. 7a and b show that the boundaries of the three zones above and below the pycnocline layer were roughly consistent with the main change coming in summer (August), which is the wet season and sees the highest river discharge. The large size of the green zone in all seasons suggests that AN-D is consistently adding extra nitrogen to the surface ocean along with the riverine N input. This is supported by the fact that the PPP in the blue zone is an order of magnitude higher than for the GOM. Around 90 % of the grid cells in the CSK are in the same zones above and below the pycnocline (Fig. 7a and b) during all four cruises; however, in the GOM (Fig. 6a and b) this was found for fewer than half of the grid cells. This is probably due to the difference in freshwater discharge rate in the two regions, which leads to the much larger stratified area in the GOM than in the CSK.
One question that has not been investigated is the temperature dependence of
primary productivity in the two areas. While the GOM is temperate throughout
the year, winter temperatures in the CSK fall to
The AN-D input source comes mainly from the Chinese side of the East China Sea (ECS) and this affects the boundaries of the green and blue zones above the pycnocline as it is deposited uniformly across the region. There is also nutrient input from offshore, as the Yellow Sea Bottom Cold Water Mass can up-well during the mixing process and is assumed to supply additional nutrients to the outer shelf (Lim et al., 2008).
AN-D is currently considerably more important (by approximately an order of magnitude) in the CSK than in the GOM, and it is anticipated that AN-D will likely be a major controlling factor here in the future (Duce et al., 2008; He et al., 2010; T. Kim et al., 2011; Lawrence et al., 2000; Paerl et al., 2002). Because of the lack of research on potential hypoxia scenarios in the Korean Peninsula, we used the same three scenarios in the CSK as were used for the GOM. Similar to GOM results, riverine N input remains the major controlling factor; however, in this area, the AN-D source is more critical than in the GOM region (Table 5). The AN-D input source increased from 20 % to 47 % of the total input under scenario 1, while based on our scenario 3 results, increases in the AN-D input source and riverine N input together will affect biological production by increasing carbon fluxes up to 25 % and oxygen demand up to 32 % if we fail to reduce N input in future (Table 5).
Simulation results for selected model scenarios based on CSK
(February 2008) data. Biological production is calculated by our N mass
balance model. Oxygen demand is calculated by the Redfield stoichiometry
ratio (C:
Most previous model studies in the GOM were focused on predicting the hypoxia
area (Bierman et al., 1994; Fennel et al., 2011, 2013; Justic et al., 1996,
2002, 2003; Scavia et al., 2004). For example, Justic et al. (1996, 2003)
used a two-layer model incorporating vertical oxygen data, from one station
(LUMCON station C6; 28.867
Distribution of the three zones during cruises MCH M1–M3 based on salinity data (Lahiry, 2007). Areas identified as brown, green or blue zones are shaded accordingly.
In contrast to our model, traditional predictive models have also ignored different nitrogen input sources such as AN-D and SGD. While this is probably reasonable on the Louisiana–Texas shelf, where riverine inputs dominate, it may not apply in other coastal regions. As a result, model studies in this region have concluded that reducing riverine N input is the only solution to decrease the size of the hypoxia area in the GOM (Gulf Hypoxia Action Plan Report, 2001, 2008; Rabalais et al., 2009; Scavia et al., 2013). According to our model results, AN-D is still a minor controlling factor in the GOM; however, in the CSK, the AN-D contributed more to the total nitrogen budget and may be a major controlling factor in the future. This indicates that AN-D should be considered as another input term for nutrient managements, especially in Asia or in other regions where high concentrations are expected. Similarly, nitrogen input from either sediment fluxes or groundwater also need to be considered.
Our zonal boundaries can be compared with the results of Lahiry (2007), who used salinity to define the edges of each zone for the three cruises MCH M1, M2 and M3 (Fig. 8) and defined the edges of the RC02 zones in the coastal GOM based solely on salinity. Lahiry's limited simulation results indicated similar patterns to our model based on DIN concentration near the Mississippi River mouth (e.g., during MCH M1, M2 and M3). Mixing was more conservative in this region than further west because the low salinity water with high nutrient concentrations was less diluted with offshore water.
Away from the MR in sub-regions B and C, however, Lahiry's results gave very different boundaries for the three zones compared with our results (Fig. 8). In particular, the results near the Atchafalaya River were very different (compare Figs. 6 and 8). For example, our data showed only green and blue zones off Atchafalaya Bay during MCH M1, with no brown zone. Similarly, the extent of the blue zones in sub-region C during MCH M2 and M3 is also very different. We believe that our N-model-based classification can cover more complex biological processes than the Lahiry (2007) method, which considers only advection and mixing, and that our N-model is a more sensible way to look at biological processes in the GOM.
Our results also agree with previous studies that demonstrated that both the
GOM and CSK regions are N-limited for most of the year (Lim et al., 2008;
Turner and Rabalais, 2013). This compares with the results of Sylvan et
al. (2007), who reported that the coastal GOM could be P-limited in the MR
delta mouth area where our brown zone is located, while RC02 suggested light
limitation rather than N- or P-limitation. However, these P-limited
conditions appear to occur when N concentrations are very high. In
particular, the
Dissolved inorganic nitrogen (DIN) against dissolved inorganic
phosphorus (DIP) during sampling periods in GOM and mid-western CSK. Nearly
all samples had an
It should be remembered, however, that the arithmetic
Both the GOM and CSK regions receive nitrogen inputs from AN-D, rivers and benthic fluxes. These different nitrogen input sources control coastal productivity, and this may reflect the different nitrogen cycling in the two regions. In the GOM, the riverine N input source consistently dominates coastal productivity and eutrophication, while, in the CSK, AN-D is also becoming a critical controlling factor. In the CSK, however, there is strong tidal mixing of freshwater from the Keum River and/or Gyunggi Bay with nearby coastal water, which results in a tidal front along the offshore region and off the Taean Peninsula during spring and summer. It is this physical mixing that mostly controls the spatial distribution patterns of nutrients and salinity here, particularly below the pycnocline (Lim et al., 2008). The brown zone in the upper layer in the CSK (August 2008) changed to a green zone region below the pycnocline layer as a result of the strong coastal tidal mixing.
RC02 considered their model to be theoretical. In the brown zone, close to
the river mouth, they assumed turbidity leads to light-limited conditions.
Their results agree well with measured
In the CSK, most previous production studies focused on inshore areas such as estuaries or rivers. Our research focused for the first time on the coastal ocean off the Korean Peninsula. Our results suggest that diverse nitrogen sources need to be recognized as potential issues for future nutrient management concerned with hypoxia, eutrophication and other environmental issues. The agreement between our results and the pattern of production based on satellite-sensing in the CSK (Son et al., 2005) suggests that our model is reasonable.
The results of our changing scenarios represent how the biological processes in these coastal regions may vary as individual nutrient sources change. While our model cannot predict the area of the hypoxic zone, we can investigate the effects of potential flux changes of each factor, such as AN-D, riverine input or benthic fluxes, and calculate the effects of changes in each on PPP and on the overall oxygen balance for the region. We have only considered different input terms of our N mass balance model; output terms such as water mixing rates and the residence time for each box need more detailed study in future work to calculate more realistic production changes in each box.
The model suggests that the three-zone theory of RC02 can be applied not only
in the northern GOM but also in the CSK region and that three zones can be
distinguished based on their nutrient concentration. As a result, we believe
that using our N mass balance model to separate different zones based on RC02
may be appropriate not only for large-scale regions like the GOM and CSK but
also at small scales such as river or estuary systems. The model also
estimates potential primary production and carbon flux based on the inclusion
of AN-D data that have not been considered previously (e.g., Bierman et al.,
1994; T. Kim et al., 2011). Our results agree well with previous
Based on CSK cruise data results, we can initially determine where the three different zones are in the CSK. We evaluated our model and tested its sensitivity based on three different scenarios. Through our scenario results, we assume that the AN-D is a considerable factor in the CSK as well as the riverine N input from the Keum River. Reducing nutrient input from the river is critical for hypoxia management policy (Gulf Hypoxia Action Plan Report, 2001, 2008; Rabalais et al., 2009). In addition, these model scenarios will be helpful for future coastal nutrient management and hypoxia management studies in the CSK, especially as AN-D sources become more important.
Hydrographic and dissolved nutrients data used in this study from the Louisiana–Texas shelf from the years 2004–2009 are available from NOAA NCEI (accession-ID 0088164).
PC and SFD were responsible for data collection. JK wrote the paper; all co-authors discussed results and assisted with writing.
The authors declare that they have no conflict of interest.
The authors would like to thank to the captains and crew of the R/V
This research was made possible by grant SA 12-09/GoMRI-006 to the Gulf Integrated Spill Consortium from the Gulf of Mexico Research Initiative and by grants NA03NOS4780039, NA06NOS4780198 and NA09NOS4780208 from NOAA.
This paper was edited by Mario Hoppema and reviewed by three anonymous referees.