Carbon dioxide fluxes from two typical mariculture polyculture systems in coastal China

Aquaculture 521 (2020) 735041

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Carbon dioxide fluxes from two typical mariculture polyculture systems in coastal China

T

Dongxu Zhanga, Xiangli Tiana, , Shuanglin Donga, Yan Chenb, Jie Fenga, Rui-Peng Hea, Kai Zhangc ⁎

a

Key Laboratory of Mariculture, Ministry of Education of China, Ocean University of China, Qingdao 266003, PR China Beijing Aquatic Product Technology Promotion Department, Beijing 100029, PR China c Pearl River Fisheries Research Institute, Chinese Academy of Fishery Science, Guangzhou 510380, PR China b

ARTICLE INFO

ABSTRACT

Keywords: Carbon dioxide flux pH Clam farming Polyculture system Marine ponds

During the farming season of 2013 and 2014, carbon dioxide (CO2) fluxes at the water-air interface were determined from two typical seawater polyculture systems. The mean CO2 fluxes of 2013 and 2014 were − 0.316 ± 0.0674 μmol m−2 s−1 and -0.173 ± 0.242 μmol m−2 s−1 in the bi-species polyculture system of swimming crab (Portunus trituberculatus) with kuruma shrimp (Marsupenaeus japonicus) (PM) and 0.249 ± 0.251 μmol m−2 s−1 and 0.426 ± 0.151 μmol m−2 s−1 in the tri-species polyculture system of swimming crab with shrimp and short-necked clam (Ruditapes philippinarum) (PMR) (Negative flux values refer to CO2 uptake and positive values refer to CO2 emission). During the farming season, the CO2 budgets in PM and PMR were − 113.1 g CO2 m−2 and 154.0 g CO2 m−2, respectively. Water pH seemed like a stable indicator for CO2 flux and Chlorophyll a concentration was a key factor regulating the CO2 flux. Subsequent decreasing in water pH and Chl a concentration was supposed to be the main contributor changing the carbon source/sink function from a CO2 sink of PM to a CO2 source of PMR. Under the condition of current study, pH of 8.26 could be considered to be the critical value between influxes and effluxes in seawater crab-shrimp and crab-shrimpclam polyculture systems.

1. Introduction Carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) are major greenhouse gases (GHGs) contributing to the global warming, the atmospheric concentrations of which have been dramatically increasing since the beginning of the industrial era. CO2 contributes over 64% of radiative forcing by GHGs and its concentration has increased from 280.0 ppm in 1800 to 400.0 ppm in 2015 (WMO, 2016). The increasing atmospheric concentrations of GHGs have promoted a number of studies to determine the GHGs emissions especially CO2 in both terrestrial and aquatic ecosystems (e.g., Keenan et al., 2013; Raymond et al., 2013). Aquatic ecosystems play important roles in providing resources for human, as well as affecting the atmospheric CO2 (Duarte and Prairie, 2005). In the past several decades, CO2 fluxes from different types of aquatic ecosystems have been studied, which concern most on the natural aquatic ecosystems, such as coastal waters (e.g., Thomas et al., 2005; Friederich et al., 2008; Jiang et al., 2008), rivers (e.g., Kling et al., 1992; Wang et al., 2007; Sun et al., 2013), freshwater lakes (e.g.,

⁎

Huttunen et al., 2003; Xing et al., 2005; Hirota et al., 2007; Zhu et al., 2010) and reservoirs(e.g., Casper et al., 2000; Huttunen et al., 2002; Soumis et al., 2004; Abril et al., 2005). It is widely believed that oceans play a major role in reducing atmospheric CO2 concentration. And inland waters, collectively, are generally considered as carbon sources (Cole et al., 2007; Raymond et al., 2013), despite that some individual systems may be carbon sinks (e.g., Soumis et al., 2004; Zhu et al., 2010). Due to the uncertainties in three factors (i.e., the amount of CO2 in water; the global surface area of streams, rivers, lakes and reservoirs; and the gas transfer velocity) (Raymond et al., 2013), inland waters CO2 emission has much likely been underestimated (often around 1 Pg C yr−1) for a long time (Cole et al., 2007; Battin et al., 2009; Aufdenkampe et al., 2011). According to the latest data, the global inland waters CO2 evasion was estimated at 3.9 Pg C yr−1 (Drake et al., 2018). Recently, researchers (e.g., Chen et al., 2016) started to concern about the CO2 flux of mariculture ponds, an artificial aquatic system. According to statistical data maintained by the Agriculture Ministry of China, the combined area of mariculture ponds in 2015 was 4.6 × 103 km2, nearly double the area of that in 2008, and it is very

Corresponding author. E-mail address: [email protected] (X. Tian).

https://doi.org/10.1016/j.aquaculture.2020.735041 Received 29 January 2018; Received in revised form 2 January 2020; Accepted 29 January 2020 Available online 30 January 2020 0044-8486/ © 2020 Elsevier B.V. All rights reserved.

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likely to increase with food production pressure (Fisheries Department of Agriculture Ministry of China, 2016). The CO2 source or sink function of the mariculture ponds, therefore, should be studied for its considerable area and production, which can not only help understand the impact of aquaculture activities from mariculture ponds on the atmospheric CO2 concentration as well as refine GHGs emission inventories. The swimming crab Portunus trituberculatus has been widely cultured on the coast of China with the production reaching 117,772 tons in 2015 (Fisheries Department of Agriculture Ministry of China, 2016). The multi-species polyculture in marine ponds is very popular for this species. Among them, polyculture of swimming crab with shrimp and clam are the two representative systems (Dong et al., 2013). In this study, two typical polyculture systems, i.e., polyculture of swimming crab with kuruma shrimp (Marsupenaeus japonicus) and that of swimming crab with kuruma shrimp and short-necked clam (Ruditapes philippinarum) in mariculture pond, were chosen and the CO2 fluxes at the water-air interface were determined during the farming season in 2 consecutive years. The aims of this study are: 1) to quantify the CO2 fluxes at the water-air interface from the two typical polyculture systems in mariculture ponds and their differences; 2) to figure out the main environmental factors influencing the CO2 fluxes in mariculture polyculture systems.

avoid high temperature inside the chamber resulting from direct sunlight. A small vertical vent stopped by silicon septum on the top was used for sampling, and a 4.5 V fan driven by dry battery was applied inside the chamber to mix the air better but not to disturb the water-air interface. The chambers were placed on a floatation device installed at the measurement sites when sampling, keeping the lower parts of the chambers about 5 cm below the water surface. All the samples were taken between 9:00 and 12:00 am, and four gas samples of 100 mL were transferred from the chamber into the vacuum sampling bags via polypropylene syringes at 0, 10, 20, 30 min after deployment. All the gas samples were placed in a cool box first and then taken to the laboratory. Gas samples were analyzed as soon as possible with the GC-2010 plus Gas Chromatograph (Shimadzu) which is connected with an MGS4 gas sampler and a MTN-1 methanizer. After driven into the MGS-4 gas sampler, CO2 in the gas samples were separated from column SS2 m × 2 mm at 40 °C packed with PQ (60–80 mesh) and then converted into CH4 in the MTN-1 methanizer by Nickel catalyst at 375 °C. CH4 was detected by a flame ionization detector (FID) at 220 °C. The flow rate of carrier gas (N2) was set at 22 mL min−1, and flame gases (H2 and compressed air) were at 20 and 30 mL min−1, respectively. In order to evaluate the precision of measurements as well as determine the sample concentrations, standard gases were measured every four gas samples.CO2 fluxes were calculated from the linear regression of change in the CO2 concentrations over time. Positive CO2 fluxes correspond with CO2 emission from the atmosphere and negative values to CO2 uptake. The CO2 budgets during the farming season were calculated as, CO2 budgets = mean CO2 fluxes × farming time. The mean CO2 budgets for PM and PMR are the average of the CO2 budgets in the farming season 2013 and 2014.

2. Materials and methods 2.1. Experimental ponds The study was carried out in the Modern Agriculture Industrial Park in Ganyu County, Jiangsu province, China (34.97 N, 119.20 E) both in 2013 and 2014, which represented typical temperate monsoonal climate. Two typical polyculture systems in marine ponds were selected, of which the PM was stocked with swimming crab and kuruma shrimp and the PMR was stocked with swimming crab, shrimp and shortnecked clam. 3 ponds were sampled for each system and all the ponds were oriented north-south (170.0 m length × 60.0 m width × 2.3 m depth). The information of stocking biomass and yield are shown in Table 1. During the farming season, the crab and shrimp were fed mainly with Aloidis laevis daily supplementing with frozen fish waste and the total feed input in PM was the same as that in PMR both in 2013 and 2014. The input amount of Aloidis laevis and frozen trash fish was 28.5 ton and 3.8 ton, respectively, during the farming season. The seawater in the ponds was routinely exchanged though water in-outlet during spring tides.

2.3. Environmental factors determination Meteorological factors were in situ measured when collecting gas. Wind velocity (3 m above the water surface) and air temperature were measured by a portable anemometer (AVM-03, TES Corp, Taiwan, China) in each pond and data were then averaged. At the same time, 9 water samples (samples of surface, middle and bottom water layers in 3 different sites) were taken from each pond with a horizontal sampler and stored in the 1-L polyethylene bottles separately. Water temperature and dissolved oxygen (DO) were measured with a DO Meter (Model 5000-230 V, YSI Incorporated, Yellow Springs, OH, USA) and water pH was determined with an acidometer (PHS-3C, Shanghai REX Instruments, Shanghai, China) in situ when collecting the water samples. All water samples were taken to the laboratory and analyzed immediately. TA (Total alkalinity) of water samples was determined using a potentiometric titrator (848 Titrino plus, Metrohm, Switzerland). TN (Total nitrogen) and TP (Total phosphorus) of water samples were analyzed by the method of simultaneous digestion introduced by Valderrama (1981). After water samples were filtrated through GF/F glass microfiber filters, Chl a (Chlorophyll a) was extracted with acetone (90%) in darkness for 24 h and then determined according to the method of National standardization management council (2007).

2.2. CO2 gas collection and determination Our study was carried out during farming seasons from July to October/November in 2013 and 2014 with a sampling interval of about 15 days. Three different sites in each pond were selected for the measurement of CO2 flux at the water-air interface using a static chamber technique (Xing et al., 2005; Xing et al., 2006). The sampling chambers, made of transparent acrylic resin, were open-bottomed cylindrical (inner diameter 30 cm, height 50 cm) and covered by aluminum foil to Table 1 Information of stocking density and yield in two mariculture systems. Culture system PM PMR

4

−2

Stocking density (10 ind hm ) Yield (kg hm−2) Stocking density (104 ind hm−2) Yield (kg hm−2)

Swimming crab

Kuruma shrimp

Short-necked clam

7.20 768 ± 64.3 7.20 842 ± 188

48.0 288 ± 23.2 48.0 268 ± 55.6

– 50.0 1840 ± 215

Note: The stocking density are the same in 2013 and 2014; the yield represents the mean value of 2013 and 2014 (mean ± S.D.). Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam. 2

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3

21.7 ± 5.04 21.9 ± 4.17 2.00 ± 1.01 2014

Note: Data with different superscript in the same column of the same farming season means significantly different (P < .05). Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam. WV, wind velocity; AT, air temperature; WT, water temperature.

± ± ± ± 60.8 17.6 44.5 15.8 0.211 ± 0.0591b 0.0974 ± 0.0351a 0.219 ± 0.0647b 0.142 ± 0.0318a 0.683b 0.411a 0.517b 0.396a ± ± ± ± 3.13 1.95 3.66 3.13 0.0334 0.0380 0.0542 0.0272 ± ± ± ± 0.0634 0.0622 0.0626 0.0517 24.7 ± 5.43

24.2 ± 5.25

0.313 0.480 0.209 0.468

± ± ± ±

0.331 0.298 0.159a 0.246b

0.0229 0.0284 0.0570 0.0893

± ± ± ±

0.0171 0.0172 0.0386 0.0301

NH4+-N (mg L−1) NO2−-N (mg L−1) NO3−-N (mg L−1) WT (°C)

PM PMR PM PMR

The variations of water pH in PM and PMR are shown in Fig. 1. The pH of PM and PMR ranged from 8.61 to 8.97 and 7.96 to 8.51, respectively in 2013 and from 7.96 to 8.80 and 7.78 to 7.98, respectively in 2014. Both in farming season 2013 and 2014, water pH of PM was significantly higher than PMR (n = 27, P < .05). Compared with 2014, water pH in 2013 was significantly higher both in PM and PMR (n = 27, P < .01). DO concentration of PM in 2013 ranged from 5.20 to 10.41 mg L−1, significantly higher than PMR (4.98–6.80 mg L−1) (n = 27, P < .05), while no significant difference was found between PM (6.13–10.72 mg L−1) and PMR (5.95–8.63 mg L−1) in 2014 (n = 27, P > .05) (Fig. 1). Between the 2 farming seasons, no significant difference was found in PM, while in PMR DO concentration in 2013 was lower than that in 2014 significantly (n = 27, P < .05). TA increased with farming time both in PM and PMR and no difference was

2013

3.2. Characteristics of water environment in PM and PMR during farming season

AT (°C)

The variations of wind velocity and temperature are shown in Table 2. Air and water temperature varied similar to each other, with the maximum occurring in mid-August and the minimum occurring at the end of farming season. The air and water temperature during farming season in 2013 were higher than 2014 but with no difference (P > .05).

WV (m s−1)

3.1. Variations of environmental factors during farming season

Culture system

3. Results

Farming season

Table 2 Variations of meteorological variables and water nutrient in two culture systems during farming season in 2013 and 2014 (n = 27).

PO43−-P (mg L−1)

The data were analyzed with the statistical software SPSS 17.0. The differences in environmental variables between different culture systems were analyzed using Student's t-test. Correlations between CO2 flux and the measured variables were tested by using Pearson correlation analysis. The stepwise multiple regression analysis was used to identify the relationships between CO2 flux and environmental variables. In the models, CO2 flux was as the dependent variable and environmental factors measured were as the independent variables. The best-fit multiple regression equations for CO2 flux in different culture systems as well as the P value and adjusted R2 value of the models were reported. The contribution of each newly introduced variable was computed by comparing the difference of R2 value between the two models that contain the variable or do not. The linear mixed-effect models (LMMs) in SPSS 17.0 were fitted to estimate the influence of farming season, sampling time and environmental variables on CO2 flux. In the model, farming season, sampling time and environmental variables were considered both into the fixed and random effects. AR (1) was selected as the most parsimonious random effects structure based on the information criteria.

2.50 ± 1.09

2.4. Data analysis

0.0162 ± 0.0101 0.00929 ± 0.00516 0.0143 ± 0.00501 0.0220 ± 0.0146

TN (mg L−1)

TP (mg L−1)

Chl a (mg m−3)

The filtrate was used to analyze NO3−-N, NO2−-N, NH4+-N and PO43−P concentrations. NO3−-N was determined with the cadmium‑copper column reduction method according to Grasshoff et al. (1999). NO2−-N was measured by the method as Bendschneider and Robinson (1952). NH4+-N was determined with the indophenol blue method according to Sagi (1966). And PO43−-P was analyzed by the method introduced by Murphy and Riley (1962). POC (Particulate organic carbon) concentration of the water was measured by a Vario EL III elemental analyzer (Elementar, Germany). 25 mL water samples were firstly filtered through pre-weighed Whatman GF/F filters (450 °C pre-combusted), and then acidizing the filters for 4 h with 1 N HCl to remove carbonate. DOC (Dissolved organic carbon) and DIC (dissolved inorganic carbon) contents in water samples were analyzed with the filter liquor above by using the multi2100 s TOC analyzer (Analytikjena, German).

19.8b 7.07a 28.7b 11.2a

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Fig. 1. Variations of water pH (a), dissolved oxygen (DO) (b), total alkalinity (c) and Chl a (d) in the PM and PMR culture systems during farming seasons in 2013 and 2014. The data were means ± S.D. (n = 3). Solid and short dash lines represent the year 2013 and 2014, respectively. Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam.

found between them (n = 27, P > .05) (Fig. 1). Mean TA of PM and PMR were 2.74 mmol L−1 and 2.48 mmol L−1, respectively in 2013 and 2.71 mmol L−1 and 2.53 mmol L−1, respectively in 2014. Between the 2 seasons, the differences of TA were not significant both in PM and PMR (n = 27, P > .05). As shown in Fig. 1, Chl a concentration of PM and PMR in 2013 ranged from 37.23 to 93.21 mg m−3 (mean 60.82 mg m−3) and 9.02 to 29.79 mg m−3 (mean 17.64 mg m−3), respectively. In 2014, Chl a concentration ranged from 21.19 to 103.01 mg m−3 in the PM (mean 44.50 mg m−3) and 7.78 to 30.34 mg m−3 in the PMR (mean 15.80 mg m−3). During the 2 farming seasons, Chl a concentration of PM were significantly higher than PMR (n = 27, P < .05). For PMR, the difference of Chl a concentration between the 2 seasons were not significant (n = 27, P > .05), while in PM Chl a concentration in season 2013 was considerably higher than that in 2014 (n = 27, P < .05). The water quality of PM and PMR are shown in Table 2. The concentrations of NO3−-N, NO2−-N, NH4+-N, PO43−-P in PM had no difference with that in PMR both in 2013 and 2014 (n = 27, P > .05), except that the NO3−-N concentration in the PM was significantly lower than that in PMR in 2014 (n = 27, P < .05). TN and TP concentrations of PM were higher than PMR significantly both in the 2 seasons (n = 27, P < .05). Compared with 2013, TN and NO2−-N concentrations in 2014 were higher significantly both in PM and PMR (n = 27, P < .01), while no differences of NO3−-N, NH4+-N, PO43−-P and TP concentrations between the 2 seasons were observed (n = 27, P > .05). As shown in Fig. 2, DOC and POC concentrations in PM were significantly higher than PMR (P < .05) both in 2013 and 2014. The mean DOC concentrations for PM and PMR were 3.23 mg L−1 and 0.63 mg L−1, respectively in 2013 and 3.38 mg L−1 and 1.77 mg L−1, respectively in 2014. And the mean POC concentrations were 2.37 mg L−1 for PM and 0.59 mg L−1 for PMR in 2013 and 2.41 mg L−1 for PM and 0.88 mg L−1 for PMR in 2014. Between season 2013 and

2014, no differences of DOC and POC concentrations in PM were found (n = 27, P > .05), while in PMR DOC and POC concentrations in 2014 were considerably greater than that in 2013 (n = 27, P < .05). DIC concentrations of PM and PMR showed a similar seasonal trend (Fig. 2) both in 2013 and 2014, and no difference was found between them (P > .05). DIC concentrations of PM and PMR ranged from 10.93 to 21.25 mg L−1 and 11.34 to 23.41 mg L−1, respectively in 2013 and from 20.60 to 29.28 mg L−1 and 13.71 to 27.11 mg L−1, respectively in 2014. Compared with 2013, DIC concentrations in 2014 were greater significantly (n = 27, P < .01) both in PM and PMR. 3.3. CO2 fluxes of PM and PMR during farming season The variations of CO2 fluxes of PM and PMR are shown in Fig. 3. During the farming season of 2013, the mean CO2 flux of PM was −0.316 μmol m−2 s−1, and ranged from −0.468 to −0.260 μmol m−2 s−1, indicating that the PM was a CO2 sink to the atmosphere. In PMR, CO2 flux (mean, 0.249 μmol m−2 s−1) ranged from −0.171 to 0.662 μmol m−2 s−1, and positive values were observed in the early and middle farming season while negative values were measured at the end of the season. The PMR, on the whole, acted as a source of atmospheric CO2. In 2014, CO2 flux of PM (mean, −0.173 μmol m−2 s−1) varied from −0.445 to 0.260 μmol m−2 s−1. Negative values were observed on most of the sampling time except August 3 and September 18. Consistent with the results in 2013, the PM performed as a sink of atmospheric CO2. CO2 flux of PMR (mean, 0.426 μmol m−2 s−1) ranged from 0.155 to 0.616 μmol m−2 s−1, showing that the PMR acted as a source of CO2 emission agreeing with the results in 2013. Compared with 2013, CO2 fluxes in 2014 were significantly higher both in PM and PMR (n = 27, P < .01). During the 2013 and 2014 farming seasons, the mean CO2 budgets were estimated to be −113.1 g CO2 m−2 in PM and 154.0 g CO2 m−2 in PMR, respectively. 4

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Fig. 3. Variations of CO2 fluxes in two culture systems during farming seasons in 2013 and 2014. The data were means ± S.D. (n = 3). Solid and short dash lines represent the year 2013 and 2014, respectively. Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam.

TN, Chl a, DOC and POC were found to have significant contributions to CO2 flux, explaining 62.1%, 9.50%, 4.50%, 2.00% and 2.30% of the variation in CO2 emission, respectively, with a total of 80.4%. The bestfit regression equation for PMR could be expressed as, F = 1560–180 pH - 18.1 TN - 1.03 Chl a + 10.2 DOC - 14.4 POC, with R2 = 0.804 and P = .000. Statistical details of the two models see in Table 3. Obviously, water pH was the most powerful factor related to CO2 flux both in PM and PMR. The fixed effects of the linear mixed-effect models (Table 4) showed that farming season was found an insignificant factor affecting CO2 fluxes both in PM and PMR (P > .05), though CO2 fluxes generally varied notably over farming time (P < .01). Water pH had remarkably negative effects on CO2 fluxes both in PM and PMR (P < .01), which was consistent with the results of Pearson correlation and multiple regression analysis above. Chl a concentration was significant in the model fitted to PM (P < .05), and in PMR Chl a concentration also had a significant negative effect on CO2 flux (P < .05) while POC concentration positively affected CO2 emission (P < .05). According to the AR1 structures (Table 5), the random errors associated with all involved variables were observed insignificant both in PM and PMR (P > .05), indicating that the overall random effects of variables added into the models were not evident.

Fig. 2. Variations of DOC (a), POC (b) and DIC (c) concentrations in the PM and PMR culture systems during farming seasons in 2013 and 2014. The data were means ± S.D. (n = 3). Solid and short dash lines represent the year 2013 and 2014, respectively. Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam. DOC, dissolved organic carbon; POC, particulate organic carbon; DIC, dissolved inorganic carbon.

4. Discussion

3.4. Correlation between CO2 flux and environment factors measured

4.1. Affecting factors of CO2 flux in PM and PMR

The correlation analysis showed that in PM the CO2 flux was negatively correlated with pH (r = −0.761, P < .01, n = 54) and POC (r = −0.515, P < .01, n = 54) and a positive correlation was found between CO2 flux and NH4+-N (r = 0.624, P < .01, n = 54), TN (r = 0.410, P < .05, n = 54), NO2−-N (r = 0.345, P < .05, n = 54) and DIC (r = 0.341, P < .05, n = 54). In PMR, CO2 emission correlated significantly negatively with pH (r = −0.788, P < .01, n = 54) and Chl a (r = −0.499, P < .01, n = 54), while a positive correlation was observed between NO2−-N and CO2 flux (r = 0.310, P < .05, n = 54). According to the results of multiple stepwise regression, water pH, POC and Chl a concentration had significant contributions to CO2 flux in PM. Water pH, POC and Chl a concentrations explained 57.9%, 6.80% and 8.70% of the CO2 flux variation at the water-air interface, respectively, with a total of 73.4%. The best-fit regression equation for PM could be expressed as, F = 636–78.7 pH - 10.3 POC + 0.440 Chl a, with R2 = 0.734 and P = .000. In PMR, water pH, concentrations of

Generally, the main purpose of introducing filter-feeding clams into the polyculture system is to improve water quality and reduce the pollutant emissions from the aquaculture systems (Nizzoli et al., 2006; Zhang et al., 2016). In the present study, the concentrations of Chl a, Table 3 Statistical details of the CO2 multiple regression models with explanatory environment factors in two culture systems. Culture system

R2 model

P model

Contribution of each variable

PM

0.734

0.000

PMR

0.804

0.000

pH 57.9% pH 62.1%

POC 6.80% TN 9.50%

Chl a 8.70% Chl a 4.50%

DOC 2.00%

POC 2.30%

Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and shortnecked clam. 5

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Table 4 Estimates of fixed effects in the models fitted to PM and PMR. Parameters

Estimate

S.E.

t

Sig.

PM

PMR

PM

PMR

PM

PMR

PM

PMR

Intercept Y2013

1130 −72.2

1060 58.1

621 166

213 78.5

1.81 −0.435

4.98 0.741

0.0830 0.668

0.000 0.460

Y2014 (reference) T1 (Y2013) T2 T3 T4 T5 T6 T7 T8

396 234 509 139 670 115 452 169

−17.6 27.4 −10.9 −25.1 −39.8 −24.3 −2.27 −33.1

813 676 972 355 1040 254 587 323

95.5 102 106 92.7 71.8 57.4 46.0 32.7

0.487 0.346 0.524 0.392 0.642 0.451 0.770 0.522

−0.184 0.269 −0.103 −0.271 −0.554 −0.423 −0.0490 −1.01

0.631 0.732 0.606 0.699 0.528 0.656 0.450 0.607

0.855 0.791 0.919 0.789 0.585 0.676 0.961 0.323

T9 (reference) T1 (Y2014) T2 T3 T4 T5 T6 T7 T8

206 283 222 375 −72.6 78.3 99.5 −21.2

−27.4 −63.4 −28.4 −20.2 −10.6 15.1 9.59 1.33

553 725 582 925 342 214 485 141

79.4 92.0 91.6 94.4 71.5 45.7 38.6 24.8

0.373 0.390 0.382 0.405 −0.213 0.366 0.205 −0.150

−0.345 −0.689 −0.310 −0.214 −0.149 0.330 0.249 0.0530

0.713 0.700 0.706 0.689 0.834 0.718 0.839 0.882

0.734 0.498 0.759 0.833 0.883 0.745 0.806 0.958

T9 (reference) WV WT pH TA DO Chl a NO3−-N NO2−-N NH4+-N TN TP DOC DIC POC

165 −7.40 −169 −7.50 −1.42 −1.04 40.0 1250 −171 −14.5 −1.24 0.611 0.534 1.37

−2.46 2.70 −135 14.4 −3.23 −1.94 −9.23 −288 −215 15.1 7.92 −2.70 0.310 18.1

281 40.8 59.4 24.0 4.83 0.239 24.8 1190 308 12.0 115 3.47 0.810 3.26

5.41 6.37 26.1 23.6 3.45 0.796 33.3 193 199 13.4 121 6.44 2.00 8.51

0.588 −0.181 −2.85 −0.312 −0.293 −4.37 1.61 1.05 −0.556 −1.20 −0.0110 0.176 0.659 0.420

−0.454 0.424 −5.19 0.609 −0.937 −2.43 −0.277 −1.49 −1.08 1.13 0.0650 −0.419 0.155 2.13

0.563 0.858 0.00900 0.758 0.772 0.0480 0.122 0.304 0.584 0.242 0.992 0.862 0.517 0.679

0.654 0.676 0.000 0.549 0.359 0.0160 0.784 0.151 0.292 0.273 0.949 0.680 0.878 0.0450

Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam. Y2013, farming season of 2013; Y2014, farming season of 2014. T1-T9, 9 sampling time in 2013 and 2014.

DOC, POC, TN and TP in the PMR were found to be significantly lower than those in the PM (P < .05). CO2 emission patterns in aquatic ecosystems were strongly driven by their internal biological and physicochemical characteristics (Thornton et al., 1990). Therefore, any factor that has impacts on the internal biogeochemical process (e.g. management methods, biological characteristics of stocking species, weather changes etc.) might probably influence the CO2 emission patterns in aquaculture systems, among which the cultured species could be the most significant factor. In fact, the clam, considering its biological characteristics, could increase the CO2 partial pressure in the water and eventually promote the CO2 emission in the following direct and indirect ways. Firstly, compared with PM, the additional respiration from clam in PMR increased CO2 concentration in the water. At the

same time, the calcification of clam which can be showed as follows, Ca2+ + 2HCO3− ⇌ CaCO3 + H2O + CO2, could also release CO2 by one mole for each mole of generated CaCO3 (Frankignoulle, 1994; Frankignoulle et al., 1994). According to the estimation of Mistri and Munari (2012), due to the respiration and calcification clam emitted 998.8 and 244.6 g CO2 m−2 yr−1 to seawater, respectively. Secondly, the clam, being filter feeders, feed on the particulate matter in the water and thus significantly reduced the phytoplankton biomass in the water (Nakamura, 2001). As a matter of fact, phytoplankton biomass was regarded as a primary factor affecting CO2 flux because of its consumption of CO2 by photosynthesis (Xing et al., 2005, 2006; Trolle et al., 2012; Li et al., 2015). In the present study, the Chl a concentration in PMR was observably lower than that in PM (P < .05)

Table 5 Estimates of covariance parameters in the models fitted to PM and PMR. Parameters

Residual Intercept + variablesa

Estimate

AR1 diagonal AR1 rho

S.E.

Wald Z

Sig.

PM

PMR

PM

PMR

PM

PMR

PM

PMR

41.7 0.0180 0.565

98.7 0.604 0.976

12.9 0.131 6.95

30.6 1.70 1.27

3.24 0.133 0.0810

3.23 0.284 0.757

0.00100 0.894 0.935

0.00100 0.777 0.449

Variables a: variables included farming season, sampling time of each faming season, wind velocity, water temperature, water pH, total alkalinity (TA), dissolved oxygen (DO), chlorophyll a (Chl a), NO3−-N, NO2−-N, NH4−-N, TN, TP, DOC, DIC, POC. Abbreviation: PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam. 6

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Fig. 5. Relation between CO2 flux and water pH (n = 108) in the mariculture system. The intersection of the two dashed line indicates that pH 8.26 is the critical value between influxes and effluxes.

R2 = 0.85, P < .0001, n = 108). According to the regression equation, the pH value 8.26 was expected to be the critical pH value under our experimental conditions, dividing the data set into influxes and effluxes (Fig. 4). Similar pH-CO2 flux relationships were observed in some other aquatic ecosystems and the boundary pH values varied from 8.1 to 9.0, possibly because concentrations of each carbonate system component often differed among different systems. (Soumis et al., 2004; Therrien et al., 2005; Duarte et al., 2008; Finlay et al., 2009). In the present study, the correlation between Chl a concentration and CO2 flux in PMR was significantly negative (r = −0.499, P < .01) while no significant correlation was found in PM (r = −0.148, P = .300). The non-significance in PM could be attributed to the occurrence of positive flux data of August 3 and September 18 in 2014. Heavy rain before sampling days brought about sudden pH changes and consequent CO2 flux alteration. And when the positive flux data were removed, CO2 flux for PM showed a strongly negative correlation with Chl a (r = −0.403, P < .01), which indicated that although Chl a played a critical part in increasing CO2 uptake flux, it seemed to be easily affected by other environmental factors when predicting CO2 flux. Whereas water pH, because of its direct and close relationship with the carbonate system, seemed likely a more stable indicator for CO2 flux. According to Del-Giorgio et al. (1999), there is a baseline respiration in water column fueled by allochthonous carbon. When the allochthonous organic carbon became a major source of CO2, CO2 flux often correlated positively with DOC concentration (Hope et al., 1996; Riera et al., 1999; Huttunen et al., 2003). However, we found no significant correlation between the 2 factors (P > .05) both in PM and PMR. It was speculated that live feed actually made a limited contribution to DOC concentration and additionally, due to the dense phytoplankton in PM (with mean Chl a of 52.7 mg m−3 during 2 farming seasons), baseline respiration might be relatively insignificant. According to the previous studies, temperature usually significantly related to CO2 fluxes in long-term measurements (seasonal or yearround measurements) (Le et al., 2013; Åberg et al., 2015). However, according to the correlation results of the present study, temperature was found to be insignificantly correlated with CO2 fluxes. On the one hand, this might be because the variation range of temperature was relatively narrow (Table 2), and on the other hand, the timescale of observation (approximately 4 months in the present study) was probably too short to obtain a significant correlation between temperature and CO2 fluxes. Water salinity could affect CO2 concentration in the water column through its effect on solubility and thus altered CO2 fluxes across the water-air interface (Woolf et al., 2016). In this study, seawater was routinely exchanged during spring tides and

Fig. 4. Diurnal variations of CO2 fluxes from PM (a) and PMR (b). PM, polyculture system of swimming crab with kuruma shrimp; PMR, polyculture system of swimming crab with kuruma shrimp and short-necked clam.

both in 2013 and 2014. The decreasing phytoplankton biomass in PMR could have resulted in weakened photosynthesis and a negative differential between CO2 production and uptake, turning the PMR into a carbon source. With respect to the diurnal pattern, diurnal variation of photosynthesis by phytoplankton might also exert an effect on CO2 flux. And given this, additional experiments were actually designed to investigate the possible diurnal pattern of CO2 flux at the initial, middle and final stages during the period of present research. The results showed that CO2 flux between 9:00 to 12:00 could be used to represent the daily average value (Fig. 4). And this was the main reason that daylight time of 9:00 to 12:00 was selected as the sampling time in the present study. In fact, a number of previous studies on CO2 fluxes across the water-air interface also took the value determined during the daylight as the daily average CO2 flux to assess the CO2 sink/source function during the observation periods (e.g., Xing et al., 2005; Zhu et al., 2010; Sidik and Lovelock, 2013; Xiao et al., 2013; Chen et al., 2015, 2016; Yang et al., 2015, 2018a, 2018b; Wen et al., 2016). HCO3−, CO32– and CO2 compose the most portion of acid-base substances in water bodies, and therefore, it is generally believed that water pH is mostly controlled by the carbonate system. Given that the close relationship between CO2 concentration and water pH, CO2 flux usually had a good relationship with water pH (Trolle et al., 2012; Li et al., 2015). In our study, the mean water pH of PM and PMR in 2013 and 2014 were 8.55 and 8.05, respectively corresponding to the mean CO2 flux of −0.244 and 0.338 μmol m−2 s−1 and significant negative correlations were observed both in PM (r = −0.761, P < .01) and PMR (r = −0.788, P < .01), and multiple regression analysis and linear mixed-effect model also demonstrated that pH was a key predicator on CO2 flux. Our results not only indicated that water pH was a key factor reflecting CO2 flux at the water-air interface, but also implied that there might be a pH threshold from carbon sink into carbon source for a mariculture system. Regression analysis demonstrated a good and very high significant correlation between CO2 flux and water pH (Fig. 5, 7

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consequently, the water salinity usually remained relatively stable during the farming season (ranging from 26.1 to 26.6 in the present study). This may explain the insignificant correlation between CO2 fluxes and salinity in the present study. Molluscs was known as a useful tool animal for “carbon sink fishery” (fishery activities that can reduce the atmospheric CO2 concentration by direct or indirect ways) for its carbon sequestration by shells. Li et al. (2014) estimated that the amount of carbon sequestration by mariculture molluscs in China was approximately 5.12 × 105 t per year, equivalent to about 1.07 × 104 km2 afforestation for carbon sequestration and could reduce the increasing amount of CO2 in the atmosphere by approximately 0.0109%. However, the above calculation only focused on carbon sequestration by shells and failed to take the CO2 emission by physiological and biological activities of shellfish into account. Recently, a growing number of studies demonstrated that clams, as individuals, were CO2 generators (Chauvaud et al., 2003; Mistri and Munari, 2012; Mistri and Munari, 2013; Munari et al., 2013). From the perspective of farming system, the direction of CO2 flux at the water-air interface changed after culturing clams according to the present results. Whereas biodeposition was also a critical part influencing carbon cycle in clam farming system (Forrest et al., 2009; Sousa et al., 2014). To better understand this impact, more comprehensive, further and detailed studies on how carbon cycles in shellfish farming systems seemed to be necessary in the future.

freshwater grass carp polyculture systems act as CO2 sources, which was mainly regulated by the high nutrient load and pelagic respiration from feeding the fish (Yang et al., 2013; Chen et al., 2015). In contrast, nutrient load seems to have the opposite effect on CO2 flux in mariculture systems. According to Chen et al. (2016), compared with polyculture of shrimp and sea cucumber, the higher nutrient loading levels and net primary productivity (NPP) by phytoplankton in shrimp ponds promoted the absorption of CO2 from the atmosphere, which could also be concluded from our study. The contrast effects of nutrient load on CO2 flux is much probably related to the feed type, ration, efficiency as well as the biological characteristics of stocking species. When the feed couldn't be consumed in time, the easier residual feed be decomposed, the more chance pelagic respiration be enhanced which may eventually exceed the NPP by phytoplankton. Based on the above understanding, more effective feeding strategies should be developed with more attention to the characteristics of feed type and the feeding rhythms of stocking species. Additionally, in order to minimize CO2 efflux from aquaculture systems, reasonable combinations of cultured species together with relevant management methods could also be a possible way which can not only make sure that nutrients and living space be utilized rationally but maintain a proper level of nutrient load and a relatively high water pH during farming season. 5. Conclusion

4.2. Comparisons of CO2 flux with other aquatic systems

Clam farming dramatically altered the carbon source/sink function of the polyculture system, from a CO2 sink in mariculture system of swimming crab with kuruma shrimp (PM) to a CO2 source in system of swimming crab with kuruma shrimp and short-necked clam (PMR). During the farming season, the CO2 budgets in the PM and PMR were estimated to be −113.1 g CO2 m−2 and 154.0 g CO2 m−2, respectively. Water pH seemed like a stable indicator for CO2 flux and Chlorophyll a concentration was a key factor regulating the CO2 flux. And pH of 8.26 was supposed to be the critical value between influxes and effluxes in seawater aquaculture systems.

Table 6 showed the CO2 fluxes in different aquatic ecosystems. In the present study, the mean CO2 flux of PM in 2013 and 2014 were − 0.316 μmol m−2 s−1 and -0.173 μmol m−2 s−1, falling within the range of CO2 flux from Antarctic Lake Mochou (Table 6). The mean CO2 flux of PMR were 0.249 μmol m−2 s−1 in 2013 and 0.426 μmol m−2 s−1 in 2014 which were in the intermediate to high range compared with other aquatic systems such as aquaculture ponds and lakes, but were much lower than that in the Three Gorges Reservoir (Table 6). Inland freshwater systems worldwide were tended to be considered as CO2 sources to the atmosphere (Tranvik et al., 2009; Raymond et al., 2013). However, in terms of aquaculture systems, it seems difficult to draw such a conclusion considering that aquaculture systems are relatively too complicated ecosystems involving feed supply, characteristics of farming animals, management method etc.. Shown as Table 6,

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Table 6 Comparisons of CO2 flux (μmol m−2 s−1) in different aquaculture systems. System Seawater ponds

Sampling time 2013.7–2013.10 2014.7–2014.11 2013.7–2013.10 2014.7–2014.11 2014.3–2014.11

Freshwater ponds

Lakes&reservoirs

2014.3–2015.3 2011.9–2012.1 2013.4–2013.9 2003.4–2004.3 2010.10 2007.12–2008.1 2010.1–2010.12

Mean

Range

Polyculture pond of crab and shrimp −0.316 −0.468~ − 0.260 −0.173 −0.445–0.256 Polyculture pond of crab, shrimp and clam 0.249 −0.171–0.662 0.426 0.155–0.616 Polyculture pond of shrimp −0.0245 / Polyculture pond of shrimp and sea cucumber 0.0299 / Polyculture pond of grass carp and shrimp 0.133 −0.140–0.472 Grass carp polyculture pond 0.617 −0.000820–1.97 Lake Donghu 0.0861 −0.369–1.01 Lake Poyang 0.230 −0.250–0.540 Lake Mochou −0.450 −1.17–0.323 Three Gorges Resevoir 1.51 1.02–2.03

8

Reference Present research Present research Present research Present research Chen et al., 2016 Chen et al., 2016 Yang et al., 2013 Chen et al., 2015 Xing et al., 2005 Liu et al., 2013 Zhu et al., 2010 Zhao et al., 2013

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Acknowledgements

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