Introduction
Riverine runoff has a profound effect on the production and
consumption of organic carbon in coastal ecosystems (e.g., Dagg et al.,
2004; Hedges et al., 1997 and the references therein). Accompanying freshwater
discharge, a substantial amount of dissolved inorganic nutrients (DINs) is
routinely dispensed into coastal regions, thus enhancing primary productivity
(PP; e.g., Dagg et al., 2004; Nixon et al., 1996). In addition, a large
quantity of particulate and dissolved organic matter is discharged via
riverine input (e.g., Wang et al., 2012), and high rates of microbial
metabolism associated with this discharge have been observed in marine
environments (e.g., Hedges et al., 1994; Malone and Ducklow, 1990). River
plumes can extend for hundreds of kilometers along the continental shelf, as
in the case of the Amazon River (e.g., Müller-Karger et al.,
1988).
Overall, the effects of river plumes on coastal ecosystems are strongly
related to the volume of the freshwater discharged (e.g., Chen et al.,
2009; Dagg et al., 2004; Tian et al., 1993). Thus, understanding how
freshwater discharge influences coastal ecological processes is an important
factor in modeling global carbon cycling in the ocean. Under projected
climate change scenarios, such heavy freshwater discharge events are
predicted to become even more pronounced in the near future because of the
dramatic frequency and magnitude increases in extreme rainfall events and
floods predicted to occur throughout the world in the coming decades
(Christensen and Christensen, 2003; Knox, 1993; Milly et al., 2002;
Palmer and Ralsanen, 2002).
Contour plots of salinity (SSS) and concentrations of nitrate
(NO3-), phosphate (PO43-), and chlorophyll a (Chl a) in
the surface water (2–3 m) in the ECS during non-flooding (2009; left
panels) and flooding (2010; right panels) periods. Bottom depth
contours are shown as dashed lines both here and in Fig. 2. The sampling
stations in both periods are marked by an (x) both here and in Fig. 2.
The contour intervals of SSS and concentrations of nitrate, phosphate, and
Chl a are 0.5, 1.0 µM, 0.1 µM, and 0.5 mg Chl m-3,
respectively, and the values of the respective contour lines (bold)
are 31, 3.0 µM, 1.0 µM, and 1.0 mg Chl m-3, respectively. The
range for each parameter is shown at the top of each panel.
The East China Sea (ECS) has an approximate area of 0.5 × 106 km2
and is the largest marginal sea in the Western Pacific. A large amount of
freshwater (956 km3 yr-1) is discharged annually into the ECS,
notably by the Changjiang (also called the Yangtze) River, which is the fifth largest
river in the world in terms of volume discharge (Liu et al., 2010).
On average, the maximum amount of discharge occurs in July, and the mean monthly
discharge has ranged from 33 955 to 40 943 m3 s-1 in years of
normal weather during the past decade (Gong et al., 2011; Xu and
Milliman, 2009). After being discharged into the ECS, freshwater mixes
with seawater to form the Changjiang diluted water (CDW) zone. The sea
surface salinity (SSS) of the CDW is ≤ 31 (e.g., Beardsley et al.,
1985; Gong et al., 1996). In the CDW, especially in summer, the regional
carbon balance is regulated by high rates of plankton community respiration
(CR) and PP (Chen et al., 2006; Gong et al., 2003). The CR rates are
positively associated with riverine flow rates (Chen et al., 2009).
In July 2010, a large flood occurred in the Changjiang River (Gong et
al., 2011). This event provided an opportunity to understand how flooding
affects the ECS shelf ecosystem. Comparative analyses were conducted in
which a number of physical, chemical, and biological parameters (notably CR)
were measured not only during this flood, but also during a period (July
2009) when the riverine flow was relatively low. The main objective of this
study was to reveal the effects of riverine input, particularly the
associated DINs, on plankton activities (e.g., phytoplankton,
heterotrophic bacteria, and zooplankton; > 330 µm) and how
they impact the CR in the ECS between periods of non-flooding and flooding.
In addition, the relationship between CR and the fugacity of CO2
(fCO2) was examined to determine the contribution of the plankton
communities to variations in fCO2 in periods of non-flooding and
flooding.
Materials and methods
Study area and sampling protocol
This study is part of the Long-term
Observation and Research of the East China Sea (LORECS) program. Samples
were collected from the ECS in the summers of 2009 (29 June to 13 July) and
2010 (6 to 18 July) during two cruises on the R/V Ocean Researcher I. The sample stations were
located throughout the ECS shelf region (Fig. 1). In July 2010, the
discharge from the Changjiang River reached 60 527 m3 s-1, which
was significantly higher than in the non-flooding year of 2009 (Gong et
al., 2011; Yu et al., 2009). Water samples were collected using
Teflon-coated Go-Flo bottles (20 L; General Oceanics Inc., Miami, FL, USA) mounted on a
General Oceanics Rosette® assembly (model 1015; General
Oceanics Inc.). At each station, six to nine samples were taken at depths of
3 to 50 m, depending on the depth of the water column. Subsamples were
taken for immediate analysis of DINs, chlorophyll a (Chl a), and bacterial
abundance. Plankton CR was also measured onboard from seawater subsamples.
The methods used to collect the hydrographic data and analyze the
aforementioned response variables followed Chen et al. (2006, 2013,
2009). Descriptions of the methods used are presented briefly in the
following sections. It should also be noted that portions of these results
were published by Chung et al. (2014) and Gong et al. (2011).
Physical and chemical hydrographics
Seawater temperature, salinity, and
transparency were recorded throughout the water column using a Sea-Bird CTD
(Sea-Bird Scientific, Bellevue, WA, USA). Photosynthetically active radiation (PAR) was measured throughout the
water column using an irradiance sensor (4π; QSP-200L; Biospherical Instruments Inc., San Diego, CA, USA). The depth of
the euphotic zone (ZE) was taken as the penetration depth of 1 % of
the surface light. The mixed layer depth (MD) was based on the
potential density criterion of 0.125 units (Levitus, 1982).
A custom-made flow injection analyzer was used for dissolved inorganic
nutrient (e.g., nitrate, phosphate, and silicate) analysis (Gong et al.,
2003). Integrated values for the nitrates and other variables assessed in
the water column above the ZE were estimated using the trapezoidal
method, in which depth-weighted means are computed from vertical profiles
and then multiplied by the ZE (e.g., Smith and
Kemp, 1995). The average nitrate concentration over the ZE was calculated
from the vertically integrated value divided by the ZE. This calculation
was adopted to determine the values of the other measured variables.
The fugacity of CO2 (fCO2) in the surface waters was calculated
from dissolved inorganic carbon (DIC) and total alkalinity (TA) data using a
program designed by Lewis and Wallace (1998). For details of
the TA and DIC measurements, please see Chou et al. (2007).
Biological variables
The water samples taken for Chl a analysis were
immediately filtered through GF/F filter paper (Whatman, Maidstone, UK; 47 mm) and stored
in liquid nitrogen. The Chl a retained on the GF/F filters was quantified
fluorometrically (Turner Designs, San Jose, CA, UAA; 10AU-005; Parsons et al., 1984).
When applicable, Chl a was converted to carbon units using a C : Chl ratio of
52.9, which was previously estimated from the shelf waters of the ECS (Chang
et al., 2003). Surfer 11 (Golden Software, LLC, Golden, CO, USA) was used to estimate the total
Chl a content integrated over the ZE of both the ECS and the CDW (please
see below for details). This estimation was also adopted to determine the
total quantities for heterotrophic bacteria and zooplankton across the ZE.
To compare, total plankton biomass was the summed biomass of phytoplankton,
bacterioplankton, and zooplankton over the ZE.
Heterotrophic bacteria samples were fixed in paraformaldehyde at a final
concentration of 0.2 % (w/v) in the dark for 15 min. They were then
immediately frozen in liquid nitrogen and kept at -80 ∘C prior to
analysis. The heterotrophic bacteria were stained with the nucleic
acid dye SYBR® Green I (emission = 530 ± 30 nm)
at a 104-fold diluted commercial solution (Molecular Probes, Eugene,
OR, USA; Liu et al., 2002). They were then identified and enumerated
using a flow cytometer (FACSAria; BD Biosciences, Franklin Lakes, NJ, USA). Known
numbers of fluorescent beads (TruCOUNT tubes; BD Biosciences) were
simultaneously used to calculate the original cell abundance in each sample.
The bacterial abundance was converted to carbon units using a conversion factor
of 20 × 10-15 g C cell-1 (Hobbie et al., 1977; Lee and Fuhrman,
1987).
Zooplankton samples were collected across the whole water column (ranging
from 20 to 198 m, depending on the station) at selected stations using a
330 µm mesh net with a 160 cm diameter opening. Upon retrieval of the
net, the contents of the cod-end were immediately preserved in 10 %
buffered formalin. The zooplankton samples were digitized to extract size
information (i.e., body width and length) using the ZooScan integrated
system (Hydroptic, L'Isle-jourdain, France), and the size information was used to calculate the ellipsoidal
bio-volume of zooplankton (Garcia-Comas, 2010). The biomass (carbon
units) of zooplankton was then calculated using the estimated bio-volume
following the equations of Alcaraz et al. (2003). To estimate the
biomass over the ZE, the total biomass of zooplankton over the whole water
column was multiplied by the fraction of the ZE relative to the depth of the
water column at all stations.
The mean ± SD values for the different variables measured in the
surface water of the ECS during non-flooding (2009) and flooding (2010)
periods with the range of values in parentheses. The mean ± SD values for
stations in the area of the Changjiang diluted water (CDW) region are in
brackets. Variables include transparency (CTDTM; %), salinity (SSS),
temperature (SST; ∘C), fugacity of CO2 (fCO2; µatm), nitrate concentration (NO3-; µM), phosphate
concentration (PO43-; µM), silicate concentration
(SiO4-; µM), chlorophyll a concentration (Chl a; mg Chl m-3), bacterial biomass (BB; mg C m-3), and plankton community
respiration (CR; mg C m-3 d-1). The euphotic depth (ZE; m)
and mixed layer depth (MD; m) are also shown for each year.
Mann–Whitney rank-sum tests were used to identify temporal differences. For
reference, it should be noted that the difference between the CDW zone and
the other regions in the ECS in each year was significant for most of the
variables (p < 0.05), except nitrate and phosphate in 2009.
Variable
2009 (non-flooding period)
2010 (flood)
ZE
38.9 ± 36.4 (1.3–190.6)
33.4 ± 17.3 (10.1–82.2)
[16.8 ± 7.4]
[24.8 ± 10.7]
MD
13.7 ± 7.3 (5–37)
11.3 ± 6.6 (4–35)
[7.3 ± 3.6]
[7.9 ± 2.6]
CTDTM
76.7 ± 12.2 (37.2–86.3)
80.5 ± 5.4 (67.7–88.5)
[70.0 ± 4.9]
[78.4 ± 4.3]**
SSS
32.62 ± 2.07 (23.80–34.11)
30.32 ± 3.60 (19.33–34.27)*
[29.24 ± 2.52]
[27.95 ± 3.03]
SST
26.8 ± 1.7 (23.3–29.6)
26.1 ± 2.2 (21.0–30.0)
[25.0 ± 0.9]
[25.1 ± 1.7]
fCO2
362.9 ± 101.2 (118.7–599.8)
297.6 ± 79.0 (178.7–454.2)*
[230.4 ± 105.3]
[248.6 ± 54.5]
NO3-
2.0 ± 5.3 (0.0–24.3)
6.2 ± 9.8 (0.0–37.6)*
[4.0 ± 9.1]
[10.3 ± 11.3]*
PO43-
0.13 ± 0.17 (0.00–0.83)
0.17 ± 0.30 (0.00–1.71)
[0.13 ± 0.07]
[0.23 ± 0.37]
SiO4-
5.8 ± 5.9 (1.5–24.5)
6.4 ± 7.8 (0.6–36.4)
[9.8 ± 7.2]
[9.1 ± 9.2]
Chl a
0.98 ± 1.52 (0.12–4.41)
1.26 ± 1.27 (0.03–5.32)
[2.23 ± 1.46]
[1.83 ± 1.35]
BB
39.8 ± 33.7 (10.6–184.8)
20.4 ± 16.5 (3.6–90.2)**
[54.9 ± 39.6]
[24.4 ± 18.6]**
CR
73.2 ± 76.9 (2.7–311.9)
105.6 ± 66.7 (10.9–325.3)*
[172.0 ± 109.2]
[142.0 ± 61.2]
* p < 0.01. ** p < 0.001.
The plankton CR, which was calculated as the decrease in dissolved oxygen
(O2) during dark incubation (Gaarder and Grann, 1927), was
measured in samples collected from most stations, with two initial and two
dark treatment samples taken from four to six depths (depth intervals of 3, 5, 10,
15, 20, and/or 25 m depending on the depth of the water column) within the
ZE at each station. The treatment samples were siphoned into 350 mL
biological oxygen demand (BOD) bottles and incubated for 24 h in a dark
chamber filled with running surface water. The maximum temperature changes were
1.33 ± 0.81 and 2.70 ± 1.43 ∘C (mean ± SD)
during each incubation in 2009 and 2010, respectively. The concentration of
O2 was measured by a direct spectrophotometry method (Pai et
al., 1993). The precision of this method was calculated as the root mean
square of the difference between the duplicate samples and was found to be
0.02 and 0.03 mg L-1 in 2009 and 2010, respectively. The precision
for the initial samples in both periods was < 0.01 mg L-1. The
difference in the O2 concentration between the initial and the dark
treatment was used to compute the CR. A respiration quotient of 1 was
assumed in order to convert the respiration from oxygen units to carbon
units (Hopkinson, 1985; Parsons et al., 1984).
Results and discussion
Comparison of hydrographic patterns between flooding and non-flooding
periods
In 2010, the Changjiang River began to flood in late May or early June. The
mean monthly water discharge was 60 527 m3 s-1, and the suggested
discharge rate for flooding was 4–6 × 104 m3 s-1, making it
the largest recorded flooding of the Changjiang River over the last decade
(http://yu-zhu.vicp.net/). This rate was almost 2 times larger than that
recorded in the non-flooding period in July 2009 (33 955 m3 s-1;
Gong et al., 2011; Yu et al., 2009). During the flood, a tremendous
quantity of freshwater was delivered into the ECS, and the low salinity of
the sea surface (SSS ≤ 31) covered almost two-thirds of the continental
shelf (Fig. 1b). The SSS in the ECS during the 2010 flood was significantly
lower than during the 2009 non-flooding survey period; the mean (±SD
for this and all parameters discussed henceforth) values were 30.32 (± 3.60) and 32.62 (± 2.07), respectively (Table 1). During periods of
high discharge from the river, particularly during the summer, the CDW zone
is generally distributed within the 60 m isobath region between the
latitudes of 27 and 32∘ N along the coast (e.g.,
Beardsley et al., 1985; Gong et al., 1996). During the 2010 flood, the CDW
dispersed towards the south and east and reached as far as the 100 m isobath
(Fig. 1b). The substantial quantity of freshwater discharged into the ECS is
also reflected in the coverage area of the CDW (e.g., Gong et al., 2011);
in the 2010 flood, the CDW area (111.7 × 103 km2) was
approximately 6 times larger than in the 2009 non-flooding period (19.0 × 103 km2).
Although the mean SSS differed significantly between the flooding and
non-flooding periods, there was no difference in the temperature of the sea
surface (SST; Table 1). The mean values of the SST in 2009 (26.8 ± 1.7)
and 2010 (and 26.1 ± 2.2 ∘C) were within the range of
the mean SST in the ECS in summer (Chen et al., 2009). The mixed layer
depth (MD) did not significantly vary between survey periods: 13.7
(± 7.3) m in 2009 and 11.3 (± 6.6) m in 2010 (Table 1). However,
the average MD was shallower than documented previously in the summer
in the ECS (with a range from 16.8 to 28.2 m; Chen et al., 2009). The euphotic
depth (ZE) was not significantly deeper in 2009 (38.9 ± 36.4 m)
than in 2010 (33.4 ± 17.3 m; Table 1). Regarding the ZE, the
average ZE in the ECS was also shallower than in a previous study
conducted during the summer (Chen et al., 2009). The shallower ZE
could have been indirectly influenced by the transmittance of the seawater.
The average transparency in summer in the ECS over the 2003–2008 period was
81.9 % (C. C. Chen, unpublished data). The average transparency values of
the ECS in 2009 and 2010 were 76.7 and 80.5 %, respectively (Table 1).
The average transparency for the CDW zone was lower in 2009 (70.0 %) and
higher high in 2010 (78.4 %) compared to the previous 6-year average
(72.7 %; C. C. Chen, unpublished data). This might also explain why the ZE
in the CDW in 2009 was only 16.8 m (Table 1).
These findings suggest that the growth of phytoplankton might be limited by
the availability of light, especially in the CDW zone in 2009. Generally,
the transparency of the coastal ocean might be low during flooding periods
due to the riverine discharge of terrestrial matter. A low transparency value
was documented in June 2003 in the ECS, during which the CDW area was 43.1 × 103 km2 (∼ 40 % of the CDW area of the 2010
flood; Chen et al., 2009), and the average transparency values for the ECS
and the CDW were 70.9 and 66.0 %, respectively (C.C. Chen, unpublished
data). The average transparency in the CDW in 2010 (78.4 %) was higher
than the previous 6-year average (72.7 %). This could be partially
explained by the fact that most large particulates from terrestrial sources
might have been confined to and precipitated in the coastal region, not in
the expanded CDW region (e.g., Chung et al., 2012). Furthermore, it
should also be noted that the 2010 sampling period was 1 month after the
beginning of this flood. In estuarine and coastal regions, phytoplankton
blooms normally occur within 2–3 weeks after a heavy rainfall event
(e.g., Hsieh et al., 2012; Meng et al., 2016, 2015;
Mulholland et al., 2009). Therefore, it is reasonable to speculate that
plankton communities were in the late phase of succession in this flood
event. The transparency during the 2010 sampling period might have then
increased due to organic matter (particulate and dissolved) having been
taken up and transferred to higher trophic levels.
In general, a large quantity of dissolved inorganic nutrients is delivered
from the Chinese coast to the ECS during the wet season (May to
September; Chen et al., 2013, 2009; Gong et al., 1996). A high
concentration of nitrates in the fluvial discharge of the Changjiang River
was documented in the ECS during the 2010 flood. Furthermore, there was (1) a
negative linear relationship between SSS and nitrate concentration
(r2= 0.37, p < 0.001, n= 37), (2) a negative linear
relationship between SSS and silicate concentration (r2= 0.60, p < 0.001, n= 37), and (3) no correlation between SSS and phosphate
concentration. The nitrate concentration (Table 1) was significantly higher in
the surface waters of the ECS in the 2010 (6.2 ± 9.8 µM) flood
than in the 2009 non-flooding period (2.0 ± 5.3 µM), and similar
nitrate concentration differences were perpetuated between sampling times
over the ZE (data not shown). During the 2010 flood, the mean nitrate
concentration, either in the surface water or averaged over the ZE, was
higher than or comparable to that documented during periods of high riverine
discharge in the ECS (Chen et al., 2009; Gong et al., 1996). Nitrate
levels reached 37.6 µM in the surface water during the 2010 flood, and
the highest nitrate concentrations were observed within the CDW (Fig. 1d).
The phosphate concentration in the surface water (Table 1) did not differ
between the 2009 non-flooding period (0.13 ± 0.17 µM) and the
2010 flood (0.17 ± 0.30 µM), nor did it differ in the CDW zone
between study years (0.23 and 0.13 µM, respectively). However, it
should be noted that there was one station with an extremely high phosphate
concentration (1.71 µM) in the surface water in the CDW zone during the
2010 flood (Fig. 1f), during which the mean molar ratio of nitrate to
phosphate (N / P) over the entire ECS was 22.3 ± 20.9. The high N / P
molar ratio was even more pronounced in the CDW; it was higher than the
Redfield ratio for N : P (i.e., 16) at 14 of the 20 stations and averaged at 40.4 (± 22.6). This value was comparable to that of the CDW during high
riverine flow periods in the ECS in summer (Chen et al., 2006). During
the non-flooding period, the N / P molar ratio was lower than 16 with a mean
value of 11.5 (± 20.8).
It has been suggested that phytoplankton growth might be regulated by the
availability of nutrients or the N / P ratio of the available nutrient pool
in the ECS (Gong et al., 1996; Harrison et al., 1990). The results of
this study indicate that in the 2009 non-flooding period, the phytoplankton
biomass might have been regulated by the availability of dissolved inorganic
nitrogen to a greater extent than it was during the 2010 flood.
The phytoplankton biomass might have also been limited by nitrate and silicate
levels in 2010. Based on nutrient levels and the N / P molar ratio, however,
phytoplankton growth was more likely limited by phosphate, especially in the
CDW zone during the 2010 flood (please refer to Sect. 3.2 for details.).
Phytoplankton growth limited by different inorganic nutrients has been
observed in estuaries and coastal regions, such as Chesapeake Bay in the
United States (Fisher et al., 1992; Harding, 1994). In the ECS,
phosphates have been frequently found as a factor limiting phytoplankton
growth, especially in the CDW (Chen et al., 2004; Gong et al., 1996;
Harrison et al., 1990).
Plankton activity associated with the Changjiang River flood
Following the discharge of fluvial nutrients into the ECS, phytoplankton are
generally abundant in the CDW region. The Chl a concentration in the CDW has even
reached bloom criteria (> 20 mg Chl m-3) in past years in
the ECS (Chen et al., 2009, 2003). Surprisingly, the
phytoplankton biomass was not as high as expected in this study, even though
a high nitrate concentration was observed during the 2010 flood. The mean
values of Chl a in the surface water of the ECS in 2009 and 2010 were 0.98 (± 1.52) and 1.26 (± 1.27) mg Chl m-3,
respectively (Table 1). However, these mean values were still at the high end of the Chl a
concentration range normally documented in the ECS in the mid-summer through
July–August (Chen et al., 2009). In both periods, the
phytoplankton biomass in the surface water was generally higher in the CDW
than in other regions of the ECS (Fig. 1g and h). For example, in the 2010
flood, the maximum Chl a value reached 5.32 mg Chl m-3 in the CDW (Table 1; Fig. 1h). In the 2010 flood, the Chl a values were positively correlated
with nitrate and silicate concentrations (all p < 0.001), but not
phosphate concentrations (p= 0.09), in the surface water. The linear
relationship between Chl a and phosphate values in the surface water,
however, became significant (p < 0.001) if one outlier with a
markedly high phosphate concentration (1.71 µM) was excluded from the
analysis (Fig. 1f). In the 2009 non-flooding period, the Chl a concentration
was significantly, positively, and linearly correlated with concentrations
of all measured nutrients: nitrate, silicate, and phosphate (p < 0.01
in all cases).
The spatial distribution pattern of Chl a documented in this study was
similar to that found in previous studies of the ECS (Gao and Song, 2005;
Gong et al., 2011), and phytoplankton biomass in the surface water (Table 1) or averaged over the ZE (data not shown) did not differ significantly
between 2009 and 2010. In the 2010 flood, primary production (PP) in the
surface water was 62.1 (± 33.8) mg C m-3 d-1, which is comparable to
values documented in the ECS in summer by Chen et al. (2009). In
contrast, the PP : Chl a value was higher in the 2010 flood
(27.1 ± 17.2 mg C mg Chl-1 d-1) compared to the value documented (19.7 ± 5.5 mg C mg Chl-1 d-1)
by Chen et al. (2009). Gong et al. (2011) estimated that over the past decade, the average rate of carbon
fixation during flooding periods was about 3 times higher than during
non-flooding periods, and the carbon fixation rate reached
176.0 × 103 t C d-1 in the CDW during the 2010 flood (Gong et al., 2011).
Total area (×103 km2) of the East China Sea (ECS) and
Changjiang diluted water (CDW) region (in brackets), as well as bacterial
(BB; × 106 kg C) and zooplankton (Zoo; × 106 kg C) biomass over
the euphotic depth integrated for the entire ECS and CDW region (in
brackets) during non-flooding (2009) and flooding (2010) periods.
Variables
2009
2010
(non-flooding period)
(flood)
Area
186.0 [19.0]
182.7 [111.7]
BB
222.5 [21.0]
87.3 [47.7]
Zoo
410.3 [6.2]
920.6 [560.8]
In summer, heterotrophic bacterioplankton are generally more abundant in the
CDW of the ECS than in other regions (Chen et al., 2006,
2009). Chen et al. (2006) suggested that the growth of bacteria along the
coast might be stimulated by the substantial amount of organic matter
derived from both autochthonous marine production and fluvial runoff. This
spatial distribution pattern was also observed in 2009 and 2010. In the 2009
non-flooding period, the mean bacterial biomass in the surface water of the
CDW was 77.5 (± 55.7) mg C m-3, over 2-fold higher than in all
other areas (31.0 ± 18.6 mg C m-3). Their mean values in the 2010
flood were 24.4 (± 18.6) and 15.0 (± 11.5) mg C m-3 in the
CDW and other regions, respectively. Further analyses revealed that the
bacterial biomass in the surface water was positively and linearly
associated with Chl a concentrations in both 2009 (p < 0.01) and 2010
(p < 0.05). This finding applies to the values averaged over the ZE
in both periods (both p < 0.01). However, the mean Chl a
concentrations in the surface water were slightly higher in 2010 than in
2009 (Table 1).
In general, an increased amount of organic matter is delivered through
fluvial discharge into the ECS during periods of high riverine flow
(e.g., Wang et al., 2012). Although these results suggest that the
bacterial biomass might be higher in the flooding period than in the
non-flooding period, this difference was not verified through the use of averaged
bacterial biomass values in this study. The bacterial biomass in the surface
water was significantly higher in the 2009 non-flooding period than during
the 2010 flood with mean values of 39.8 (± 33.7) and 20.4 (± 16.5) mg C m-3, respectively (Table 1). The average bacterial biomass
over the ZE was even more pronounced in 2009 than in 2010 (data not shown).
However, the total bacterial biomass in the CDW zone was 2 times higher in
2010 than in 2009 with values of 47.7 and 21.0 × 106 kg C,
respectively (Table 2). A potential cause of the low average bacterial
biomass observed during the 2010 flood might be protozoan grazing. Protozoa
have been recognized as important microbial grazers in the ECS and in many
coastal ecosystems (e.g., Chen et al., 2009, 2003; Sherr and
Sherr, 1984). Although protozoan abundance was not measured in this study, a
high production rate of nanoflagellates was observed in the southern ECS
with mean values of 0.46 µg C L-1 h-1 during periods of high
riverine flow (Tsai et al., 2005).
Contour plots of plankton community respiration (CR; mg C m-3 d-1) over the euphotic zone of the ECS during (a) non-flooding (2009)
and (b) flooding (2010) periods. The contour interval is 10 mg C m-3 d-1. The CR range is shown at the top of each panel.
Zooplankton, especially microzooplankton, are amongst the most important
contributors to plankton CR (Calbet and Landry, 2004;
Hernández-León and Ikeda, 2005; Hopkinson et al., 1989).
Unfortunately, microzooplankton were not measured in this study. Instead,
zooplankton (> 330 µm) were sampled across the whole water
column. However, the average biomass of zooplankton over the ZE can still be
estimated, and the mean values for the 2010 flood and the 2009 non-flooding
period were calculated as 105.7 (± 144.4) and 22.6 (± 25.7) mg C m-3, respectively; this difference was statistically significant
(p < 0.01). The average zooplankton biomass over the ZE for the CDW
zone was 90-fold higher in 2010 than in 2009 (Table 2), suggesting that the
flood may have had a significant effect on zooplankton biomass.
Relationships between plankton community respiration (CR; mg C m-3 d-1), (a) chlorophyll a concentration
(Chl a; mg Chl m-3),
and (b) bacterial biomass (mg C m-3) for all data from non-flooding
(2009; ∘) and flooding (2010; •) periods. Linear regressions of
the
data from 2009 (solid lines) and 2010 (dashed lines), as well as the
respective r2 and p values, have also been included.
Effects of Changjiang River flooding on plankton community
respiration
Plankton CR is typically defined as the integrated rate of organic carbon
consumption by plankton communities (e.g., Hopkinson et al., 1989;
Rowe et al., 1986). In summer, the mean CR rate in the surface waters of the
ECS ranges from 52.2 to 128.4 mg C m-3 d-1 (Chen et al., 2006, 2009), and it is significantly correlated with fluvial
discharge from the Changjiang River (Chen et al., 2009). In this study,
the CR in the surface water ranged from 2.7 to 311.9 mg C m-3 d-1 with a mean value of
73.2 (± 76.9) mg C m-3 d-1 in the 2009 non-flooding period (Table 1). During the 2010 flood,
the mean rate in the surface water of 105.6 (± 66.7) mg C m-3 d-1 was significantly higher than in 2009 (p < 0.01; Table 1),
and CR ranged from 10.9 to 325.3 mg C m-3 d-1 (Table 1). The CR rate
averaged over the ZE was statistically similar in both years (p= 0.08) with mean values of
76.8 (± 53.0) and 66.8 (± 68.4) mg C m-3 d-1, respectively. In terms of spatial distribution, higher CR
rates were mostly observed in the CDW region in both sampling periods,
especially along the coast (Fig. 2). Nevertheless, it should be noted that
the CDW zone was much larger in 2010 than in 2009.
Differences (Δ) between 2009 and 2010 in plankton community
respiration (CR; mg C m-3 d-1) versus (a) chlorophyll a (Chl a; mg Chl m-3) and (b) bacterial
biomass (mg C m-3) over the euphotic
zone at the same station. The r2 and p values have been shown for the
best-fit linear regression line (solid line). For reference, the vertical
and horizontal dashed lines represent inter-year differences of zero (i.e.,
Δ= 0).
CR rates were regressed against the biomass of phytoplankton, heterotrophic
bacteria, and zooplankton (> 330 µm). However, it should be
noted that microzooplankton were not measured in this study and were excluded from
our analysis. In this study, CR was significantly correlated with both Chl a concentration and bacterial biomass for both periods in the surface water and
when averaged over the ZE (all p < 0.01; Fig. 3). The contribution
of phytoplankton and/or bacterioplankton to CR is substantial in the ECS,
even though the relative contribution varies spatially and temporally
(Chen et al., 2006, 2009, 2003) Given the
importance of phytoplankton and bacterioplankton to CR rates in both years,
as well as their high densities measured herein, it seems likely that these
microbial groupings contributed substantially to the CR rate in both 2009
and 2010.
Surprisingly, the mean Chl a concentration was slightly higher in 2010 than
in 2009, though the bacterial biomass was significantly lower in 2010 than in
2009 (Table 1). However, the CR rate was still higher in 2010 than in 2009.
In a further analysis, the differences (i.e., 2010 minus 2009) in the
average CR, Chl a concentration, and bacterial biomass over the ZE at the
same station were calculated. The extent of such differences in CR was
significantly related to differences in Chl a concentration (p < 0.001) and bacterial biomass (p < 0.01; Fig. 4). The linear
relationships were also statistically significant if the values of the
differences in the surface water were used (all p < 0.01; data not
shown). Among the positive CR difference values (i.e., 20 of 33), 15
stations were also characterized by positive differences in Chl aconcentrations;
only 2 stations had positive differences in bacterial biomass.
Interestingly, the stations with positive Chl a values in terms of concentration difference
were mostly located within the CDW region in 2010, with the exception
of the CDW in 2009. These results suggest that the higher CR in the 2010
flood might be attributed to phytoplankton, especially in the CDW. The mean
Chl a concentration was only slightly higher in 2010 than in 2009. Therefore,
it is reasonable to speculate that the differences in the CR rate in both
periods might have been partially caused by variation in the composition of
the phytoplankton communities. Although the CR attributed to different
components of the phytoplankton community was not measured in this study, it
has been documented elsewhere; for instance, dinoflagellates have higher
carbon-specific respiration rates than many other phytoplankton types
(e.g., Lopez-Sandoval et al., 2014).
Relationship between plankton community respiration (CR) and total
plankton biomass (expressed per carbon unit) over the ZE in 2009 (∘; solid
line) and 2010 (•; dashed line). The respective r2 and p values
are shown for each linear regression line. The total plankton biomass is the
summed biomass of phytoplankton, bacterioplankton, and zooplankton. Please
refer to the “Materials and Methods” section for details on the carbon conversion
for plankton communities.
In addition, zooplankton might also be amongst the potential contributors to
the higher CR rate observed in 2010 than in 2009. As stated above, the
biomass of zooplankton was significantly higher in 2010 than in 2009.
However, the linear relationships between CR and zooplankton biomass over the
ZE were not statistically significant in 2009 or 2010. To further
explore how plankton communities contributed to CR, the CR rate was
regressed against the total plankton biomass (i.e., summed biomass of
phytoplankton, bacterioplankton, and zooplankton) for both periods, and the
linear relationships between CR and the total plankton biomass (mg C m-3)
over the ZE were significant in both 2009 (p < 0.001) and 2010 (p < 0.01; Fig. 5).
Similarly significant relationships between CR and total planktonic biomass
have also been observed in the summer in the ECS, and phytoplankton and
bacterioplankton might be the most important components contributing to CR
at such times (Chen et al., 2006). In this study, the autotrophic plankton
biomass (i.e., phytoplankton) accounted for 41.3 and 45.6 % of the total
planktonic biomass in 2009 and 2010, respectively. As for the heterotrophic
plankton biomass, bacterioplankton were attributed to 38.7 and 11.3 % and
zooplankton contributed 20.0 and 43.1 % of the total plankton biomass
in 2009 and 2010, respectively. This suggests that phytoplankton and
bacterioplankton might be the most important components contributing to CR in
the 2009 non-flooding period. In contrast, during the 2010 flood, the CR
rate might have been mostly driven by phytoplankton and zooplankton
metabolic activity.
Relationships between the fugacity of CO2 (fCO2) and
plankton community respiration (CR) in the surface water in 2009 (∘; solid
line) and 2010 (•; dashed line). The respective r2 and p values
are shown for each linear regression line.
All such conjectures are based on stocks, and biomass might not be directly
related to the concurrent CR rate. By using physiological and allometric
relationships of variant plankton communities, the plankton CR rate could be
estimated from stock values, and significant correlations have indeed been
found between measured and estimated rates (Chen et al., 2009).
Furthermore, it should be noted that microzooplankton might be another
important contributor to CR, though they were unfortunately not assessed
herein.
Implications of plankton community respiration on coastal ecosystems of
the ECS
A further comparative analysis was conducted to determine whether the CR rate
affected the fugacity of CO2 (fCO2) in the seawater. In 2009, the
fCO2 in the surface water was in the range of 118.7–599.8 µatm
with mean values of 362.9 ± 101.2 µatm (Table 1). This mean value
is close to the mean (369.6 µatm) observed in the ECS in August in prior
years (Chen et al., 2006). In the 2010 flood, the mean value (297.6 µatm) of fCO2 in the surface water was significantly lower than in
2009 and ranged from 178.7 to 454.2 µatm (Table 1). It is well known
that fCO2 is temperature dependent, and it increases as the
temperature increases (e.g., Goyet et al., 1993). The effect
of temperature on the large variation in fCO2 observed between the
2009 non-flooding period and the 2010 flood was trivial; the SST difference
of 0.7 ∘C between 2009 and 2010 would only equal an
fCO2 decrease of approximately 10 µatm (Table 1).
Differences (Δ) between 2009 and 2010 in fCO2 (µatm) and plankton community respiration (CR; mg C m-3 d-1) in the
surface water at the same station. For reference, the vertical and
horizontal dashed lines represent interannual differences of zero
(i.e., Δ= 0).
The effect of freshwater input on fCO2 in the surface water in the ECS
has also been suggested to be relatively minor compared to the interannual
variation in fCO2 (Chen et al., 2013). To evaluate this, conservative
mixing was applied by using TA and DIC data between freshwater and seawater
end-members. Provided that the proportional contributions from freshwater
and seawater end-members are f1 and f2 (f1+f2= 1),
respectively, the conservative mixing TA and DIC values for a given water
sample can be expressed by the following equations:
TAmix=TAfw×f1+TAsw×f2,DICmix=DICfw×f1+DICsw×f2,
where the subscripts “mix”, “fw”, and “sw” represent the values of
conservative mixing, freshwater, and seawater end-members, respectively. The
TA and DIC data reported by Zhai et al. (2007) for the Changjiang River
in summer were used as the freshwater end-members (both TAfw and
DICfw= 1743 µmol kg-1), and the surface data at station K in
July 2009 and 2010 were chosen to represent the seawater end-members
(TAsw= 2241 µmol kg-1 and
DICsw= 1909 µmol kg-1 in 2009; TAsw= 2240 µmol kg-1 and
DICsw= 1904 µmol kg-1 in 2010). Subsequently, the
hypothetical fCO2 from conservative mixing was calculated from the
TAmix and DICmix data using CO2SYS version 2.1
(Pierrot et al., 2006), in which the carbonic acid dissociation
constants were adopted from Mehrbach et al. (1973) and refitted
by Dickson and Millero (1987). The uncertainty in this simulation
mainly derives from errors in the estimations of TAmix and DICmix.
Assuming that the errors in the calculated TAmix and DICmix are ±5 µmol kg-1,
this may result in an uncertainty of ±13 µatm in the simulated fCO2. The simulated results show that the effect
of mixing freshwater and seawater on fCO2 was nearly the same in both
periods. However, a large variation in fCO2 in the surface water was
estimated; it varied from 375.4 to 439.8 µatm as salinity varied from
20.38 to 33.96. This finding implies that surface water fCO2 in the ECS
might increase dramatically, especially during the devastating flood of 2010
where low SSS (≤ 31) characterized almost 70 % of the ECS shelf (Fig. 1b). However, in the 2010 flood, surface water with low fCO2 was
observed in the ECS. Therefore, vigorous photosynthetic processes might be a
potential cause of the reduction in fCO2 in the surface water during
periods of flooding. Compared to PP values observed in summer in the ECS in
previous years (Chen et al., 2009), PP was indeed high during the 2010
flood (Table 1; Chen et al., 2009). Gong et al. (2011) also estimated
that over the past decade, the carbon fixation rate during flooding was
about 3 times higher than during non-flooding periods. However, no
significant correlation was found between fCO2 and PP in the 2010 flood,
though this may simply be due to the small sample size for PP.
Nevertheless, fCO2 was significantly correlated with Chl a concentration
in the pooled 2010 flood dataset (p < 0.001). This significant
relationship indirectly supports the hypothesis that the reduction in
fCO2 in the 2010 flood might be associated with vigorous phytoplankton
metabolic activity. Furthermore, negative linear relationships were observed
between fCO2 and CR in the surface water during both the 2009
non-flooding period (p < 0.01) and the 2010 flood (p < 0.001;
Fig. 6). Significant linear relationships were also found using pooled data
from each period (all p < 0.001). CR has been assumed to be an
integrated response of overall plankton activity. These results imply that
fCO2 in the surface water (or the entire water column) is related to
plankton activities. To explore the variation in fCO2 between the
non-flooding and flooding period, the difference in fCO2 and CR at the
same station was estimated. Surprisingly, a negative linear relationship was
found between the difference in fCO2 and CR for the flooding and
non-flooding periods (p= 0.001; Fig. 7). As previously stated, compared to
the 2009 non-flooding period, the increase in the CR rate in the 2010 flood
might be associated with the increase in phytoplankton biomass (Fig. 4a).
These results indicate that the significant amount of fCO2 absorption in
the 2010 flood was related to the strength of plankton activity,
particularly phytoplankton at stations that were not characterized by low
SSS in the 2009 non-flooding period.