Scientia Marina 86 (1)
March 2022, e027
ISSN: 0214-8358, eISSN: 1886-8134
https://doi.org/10.3989/scimar.05232.027

Influence of climate on seawater quality and green mussel production

Influencia del clima en la calidad del agua de mar y la producción de mejillón verde

Chayarat Srisunont

Environmental Science Programme, Faculty of Science and Technology, Bansomdejchaopraya Rajabhat University, 1061 Itsaraphap 15 Road, Thonburi, Bangkok 10600, Thailand.

https://orcid.org/0000-0002-4680-2581

Treeranut Srisunont

Faculty of Science and Technology, Valaya Alongkorn Rajabhat University under Royal Patronage, Khlong Luang, Pathum Thani 13180, Thailand.

https://orcid.org/0000-0003-4444-0158

Alongot Intarachart

Sriracha Fisheries Research Station, Faculty of Fisheries, Kasetsart University, 101/12 Moo.9 Sukhumvit Rd., Sriracha, Chonburi Province 20110, Thailand.

https://orcid.org/0000-0002-0402-7070

Sandhya Babel

School of Biochemical Engineering and Technology, Sirindhorn International Institute of Technology (SIIT), Thammasat University, P.O. Box 22, Pathum Thani 12121, Thailand.

https://orcid.org/0000-0003-2378-4891

Summary

This study aimed to investigate the relationships between atmospheric parameters, seawater quality and green mussel production which were cultured in pond, estuary and coastal areas. Seawater and mussel samples were collected from mussel farms in the inner Gulf of Thailand from January to December 2019. Climate data were obtained from the Thai Meteorological Department. The correlations between selected atmospheric and seawater parameters were developed using linear and non-linear models. The influence of seawater quality on mussel production was evaluated using principal component analysis and stepwise multiple linear regression. The effects of atmospheric variation on green mussel productivity were simulated. The results showed that high air temperature and rainfall caused an increase in seawater temperature and a decrease in salinity, respectively. It was observed that the most influential factors affecting mussel production were nutrients and dissolved oxygen in ponds, temperature and salinity in estuaries, and nutrients and pH in coastal areas. The simulation indicated that mussel production can deteriorate when air temperature reaches 34°C and rainfall is higher than 200 mm per month. Our results suggest that under climate change events, locations with less riverine influence can provide higher mussel productivity. These results can be used as a guideline for farmers during a climate change event.

Keywords: 
air temperature; rainfall; seawater quality; climate change; cultivation area; Perna viridis
Resumen

Esta investigación tuvo como objetivo investigar las relaciones entre los parámetros atmosféricos, la calidad del agua de mar y la producción de mejillones verdes que se cultivaron en estanques, estuarios y áreas costeras. Se recolectaron muestras de agua de mar y mejillones de granjas de mejillones en el interior del Golfo de Tailandia de enero a diciembre de 2019. Los datos climáticos se obtuvieron del Departamento Meteorológico de Tailandia. Las correlaciones entre parámetros atmosféricos y de agua de mar seleccionados se desarrollaron utilizando modelos lineales y no lineales. La influencia de la calidad del agua de mar en la producción de mejillones se evaluó mediante análisis de componentes principales y regresión lineal múltiple paso a paso. Se simularon los efectos de la variación atmosférica sobre la productividad del mejillón verde. Los resultados mostraron que la alta temperatura del aire y las precipitaciones provocaron un aumento de la temperatura del agua de mar y una disminución de la salinidad, respectivamente. Se observó que los factores más influyentes que afectaron la producción de mejillón fueron los nutrientes y el oxígeno disuelto (OD) en los estanques; temperatura y salinidad en estuarios; y nutrientes y pH en zonas costeras. La simulación indicó que la producción de mejillón puede deteriorarse cuando la temperatura del aire alcanza los 34°C y la precipitación supera los 200 mm por mes. Nuestros resultados sugieren que, bajo eventos de cambio climático, los lugares con menos influencia fluvial pueden proporcionar una mayor productividad de mejillón. Estos resultados se pueden utilizar como guía para los agricultores durante un evento de cambio climático.

Palabras clave: 
temperatura del aire; lluvia; calidad del agua de mar; cambio climático; área de cultivo; Perna viridis

Received: September  2,  2021. Accepted: January  13,  2022. Published:   March 31,  2022.

Editor: M. Gaspar.

Citation/Cómo citar este artículo: Srisunont C., Srisunont T., Intarachart A., Babel S. 2022. Influence of climate on seawater quality and green mussel production. Sci. Mar. 86(1): e027. https://doi.org/10.3989/scimar.05232.027

CONTENT

INTRODUCTION

 

A higher frequency and magnitude of extreme weather events were reported during the 20th century (Hansen et al. 2006Hansen J., Sato M., Ruedy R., et al. 2006. Global temperature change. Proc. Natl. Acad. Sci. U.S.A. 103: 14288-14293. https://doi.org/10.1073/pnas.0606291103 , IPCC 2013Intergovernmental Panel on Climate Change (IPCC). 2013. Summary for policymakers. In: Stocker T.F., Qin D., Plattner G.-K. et al. (eds), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, United Kingdom and New York, NY, USA.). These caused atmospheric variations, including temperature, moisture and precipitation. By the end of the 21st century (2081-2100), the IPCC (2013)Intergovernmental Panel on Climate Change (IPCC). 2013. Summary for policymakers. In: Stocker T.F., Qin D., Plattner G.-K. et al. (eds), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, United Kingdom and New York, NY, USA. predicts that the global mean surface temperature will increase by 1.5 to 2.0°C. Extreme precipitation events are becoming more intense and frequent. The areas affected by monsoon systems will increase. These weather disturbances can have devastating impacts on marine ecosystems for seawater quality and living organisms (Hader and Barnes 2019Hader D.-P., Barnes P.W. 2019. Comparing the impacts of climate change on the responses and linkages between terrestrial and aquatic ecosystems. Sci. Total Environ. 682: 239-246. https://doi.org/10.1016/j.scitotenv.2019.05.024 ).

Many researchers have documented that surface water quality is highly sensitive to climate. Its physical, chemical and biological properties respond rapidly to climate-related changes (Coulliette and Noble 2008Coulliette A.D., Noble R.T. 2008. Impacts of rainfall on the water quality of the Newport River Estuary (Eastern North Carolina, USA). J. Water Health 06: 473-482. https://doi.org/10.2166/wh.2008.136 , Park et al. 2011Park J.-H., Inam E., Abdullah M.H., et al. 2011. Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia. J. Hydrol. 400: 323-332. https://doi.org/10.1016/j.jhydrol.2011.01.050 , Alosairi et al. 2019Alosairi Y., Alsulaiman N., Karam Q. 2019. Responses of salinity and chlorophyll-a to extreme rainfall events in the northwest Arabian Gulf: Emphasis on Shatt Al-Arab. Mar. Pollut. Bull. 149: 1-7. https://doi.org/10.1016/j.marpolbul.2019.110564 ). Climate change (including global warming) has led to unstable air temperature and rainfall patterns (Al-Awadhi et al. 2014Al-Awadhi J.M., Al-Dousari A.M., Khalaf F.I. 2014. Influence of land degradation on the local rate of dust fallout in Kuwait. Atmos. Clim. Sci. 4: 437. https://doi.org/10.4236/acs.2014.43042 ). An increase in air temperature causes an increase in surface water temperature owing to the enhanced heat content of water by solar radiation (Morrill et al. 2001Morrill J.C., Bales R.C., Conklin M.H. 2001. The relationship between air temperature and stream temperature. American Geophysical Union, Spring Meeting 2001, abstract id. H42A-09, Harvey et al. 2013Harvey R., Lye L., Khan A., Paterson R. 2013. The influence of air temperature on water temperature and the concentration of dissolved oxygen in Newfoundland rivers. Can. Water Resour. J. 36: 171-192. https://doi.org/10.4296/cwrj3602849 ). Rainfall events cause disturbances of water quality by diluting nutrients and other materials and increasing sediment erosion, leading to more suspended particles in the water (Pilditch et al. 2008Pilditch C.A., Widdows J., Kuhn N.J., et al. 2008. Effects of low tide rainfall on the erodibility of intertidal cohesive sediments. Cont. Shelf Res. 28: 1854-1865. https://doi.org/10.1016/j.csr.2008.05.001 , Park et al. 2011Park J.-H., Inam E., Abdullah M.H., et al. 2011. Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia. J. Hydrol. 400: 323-332. https://doi.org/10.1016/j.jhydrol.2011.01.050 ). Moreover, during and after the rainfall events, salinity in estuarine and coastal waters decreases significantly in response to river discharges (Valiela et al. 2012Valiela I., Camilli L., Stone T., et al. 2012. Increased rainfall remarkably freshens estuarine and coastal waters on the Pacific coast of Panama: Magnitude and likely effects on upwelling and nutrient supply. Glob. Planet. Change 92-93: 130-137. https://doi.org/10.1016/j.gloplacha.2012.05.006 , Alosairi et al. 2019Alosairi Y., Alsulaiman N., Karam Q. 2019. Responses of salinity and chlorophyll-a to extreme rainfall events in the northwest Arabian Gulf: Emphasis on Shatt Al-Arab. Mar. Pollut. Bull. 149: 1-7. https://doi.org/10.1016/j.marpolbul.2019.110564 ). Heavy rainfall occurring during a short period can also result in a rapid decrease in salinity caused by flash flooding. Rainfall affects the pH of seawater. In coastal areas, rainwater can input sodium and calcium ions, which come from sea salt and land dust, into seawater (Wirmvem et al. 2014Wirmvem M.J., Ohba T., Fantong W.Y., et al. 2014. Origin of major ions in monthly rainfall events at the Bamenda Highlands, North West Cameroon. J. Env. Sci. 26: 801-809. https://doi.org/10.1016/S1001-0742(13)60502-1 , Khan et al. 2018Khan M.F., Maulud K.N.A., Latif M.T., et al. 2018. Physicochemical factors and their potential sources inferred from long-term rainfall measurements at an urban and a remote rural site in tropical areas. Sci. Total Environ. 613-614: 1401-1416. https://doi.org/10.1016/j.scitotenv.2017.08.025 ). In contrast, in estuarine areas, heavy rainfall can dilute the alkalinity of seawater through river runoff. These changes in seawater properties could affect the health of many aquatic species, including green mussels (Goh and Lai 2014Goh B.P.L., Lai C.H. 2014. Establishing the thermal threshold of the tropical mussel Perna viridis in the face of global warming. Mar. Pollut. Bull. 85: 325-331. https://doi.org/10.1016/j.marpolbul.2013.10.041 , Hader and Barnes, 2019Hader D.-P., Barnes P.W. 2019. Comparing the impacts of climate change on the responses and linkages between terrestrial and aquatic ecosystems. Sci. Total Environ. 682: 239-246. https://doi.org/10.1016/j.scitotenv.2019.05.024 ).

Green mussel (Perna viridis) is a commercial bivalve species in Southeast Asia. It has been the most productive mollusc in Thailand for decades (Department of Fisheries 2020Department of Fisheries. 2020. Fisheries Statistics of Thailand 2018. Department of Fisheries, Ministry of Agriculture and Cooperatives, Thailand. 88 pp.). Loss of green mussels has tremendous impacts on local income and global food security. Changes in atmospheric parameters influence seawater quality and subsequently affect green mussel growth and survival. Recent studies have documented climate change─related stressors in green mussels (Firth et al. 2011Firth L.B., Knights A.M., Bell S.S. 2011. Air temperature and winter mortality: Implications for the persistence of the invasive mussel, Perna viridis in the intertidal zone of the south-eastern United States. J. Exp. Mar. Biol. Ecol. 400: 250-256. https://doi.org/10.1016/j.jembe.2011.02.007 , Wendling et al. 2013Wendling C.C., Huhn M., Ayu N., et al. 2013. Habitat degradation correlates with tolerance to climate-change related stressors in the green mussel Perna viridis from West Java, Indonesia. Mar. Pollut. Bull. 71: 222-229. https://doi.org/10.1016/j.marpolbul.2013.03.004 , Goh and Lai 2014Goh B.P.L., Lai C.H. 2014. Establishing the thermal threshold of the tropical mussel Perna viridis in the face of global warming. Mar. Pollut. Bull. 85: 325-331. https://doi.org/10.1016/j.marpolbul.2013.10.041 ). The cold air temperature during the winter season can cause green mussel mortality (Firth et al. 2011Firth L.B., Knights A.M., Bell S.S. 2011. Air temperature and winter mortality: Implications for the persistence of the invasive mussel, Perna viridis in the intertidal zone of the south-eastern United States. J. Exp. Mar. Biol. Ecol. 400: 250-256. https://doi.org/10.1016/j.jembe.2011.02.007 ). Based on the IPCC prediction that surface seawater temperature will rise as a result of global warming, experimental results to study thermal stress showed that when the seawater temperature reaches 34ºC, only 25% to 75% of mussels survive (Wendling et al. 2013). The mussels die at 8°C and 38ºC (Goh and Lai 2014Goh B.P.L., Lai C.H. 2014. Establishing the thermal threshold of the tropical mussel Perna viridis in the face of global warming. Mar. Pollut. Bull. 85: 325-331. https://doi.org/10.1016/j.marpolbul.2013.10.041 ). Heavy rainfall can lower the salinity in seawater. This stress results in the degradation of mussel haemocytes and lysosomal content (Wang et al. 2012Wang Y., Hu M., Cheung S.G., et al. 2012. Immune parameter changes of hemocytes in green-lipped mussel Perna viridis exposure to hypoxia and hyposalinity. Aquaculture 356-357: 22-29. https://doi.org/10.1016/j.aquaculture.2012.06.001 ). Variations in pH in seawater caused by changing rainfall patterns can affect physiology and immune responses in green mussel (Sivalingam 1977Sivalingam P.M. 1977. Aquaculture of the green mussel, Mytilus viridis Linnaeus, in Malaysia. Aquaculture 11: 297-312. https://doi.org/10.1016/0044-8486(77)90079-5 ). The above studies have demonstrated the influence of seawater temperature, pH and salinity on green mussels, but none of them has evaluated green mussel production based on environmental fluctuations caused by climate variation.

We hypothesize that disturbances in atmospheric parameters such as air temperature and rainfall cause changes in seawater quality and subsequently a decrease in green mussel productivity. The novelty of the research is that it combines and links atmosphere variation, seawater quality and mussel production. A literature review reveals that no reported scientific research combines all three parameters and investigates impacts on mussel production. Moreover, differences in cultivation methods (for pond, estuary and coastal areas) can also affect green mussel production in different ways.

Therefore, this study emphasizes how climate change affects green mussels cultivated by different methods. The objectives of the study are 1) to investigate correlations between atmospheric parameters and seawater quality; 2) to evaluate the influence of seawater quality on green mussel production; and 3) to simulate the effect of atmospheric variations (air temperature and rainfall) on green mussel productivity with mussels cultured in pond, estuary and coastal areas. The results of our study can provide information to farmers on how the cultivation method used is sensitive to climate change events. They can also assist farmers and provide adaptation strategies for green mussel production under extreme weather events occurring due to global warming and climate change.

MATERIALS AND METHODS

 

Study area

 

In this study, mussel farms in the inner Gulf of Thailand, which is an important green mussel production area in Southeast Asia, were selected. Three cultivation areas and methods which are practised in Thailand (pond, estuarine and coastal areas) were chosen to investigate how each method will be affected by climate change and in which of them extreme events will have the most impact. To compare the climate change effect between these methods, Bangkhuntein (BKT), Maeklong river mouth (MK), and Sriracha (SRI) were chosen as a case study for green mussel cultivation in pond, estuary and coastal areas, respectively (Fig. 1). The farm areas, numbers of cultivation ropes and depths of each station are shown in Table 1.

medium/medium-SCIMAR-86-01-e027-gf1.png
Fig. 1.  Study area: A, inner Gulf of Thailand and location map showing sampling stations for each green mussel farm; B, Bang Khun Tien (BKT); C, Maeklong river mouth (MK); and D, Sriracha (SRI). Panels B, C and D represent the pond, estuary and coastal areas, respectively.
Table 1.  Characteristics of green mussel farms.
Station Cultivation method Farm area (m2) No. of cultivation ropes Depth (m)
BKT 1 floating raft 26935 336 2.0
BKT 2 floating raft 26077 3280 2.5
BKT 3 floating raft 179 48 1.8
MK 1 horizontal rack 8064 33660 6.6
MK 2 horizontal rack 360 1680 3.0
MK 3 long-line 2000 4200 3.0
SRI 1 floating raft 1344 891 3.9
SRI 2 floating raft 1224 1460 4.5
SRI 3 floating raft 1716 1152 4.5

The first sampling location was in Bang Khun Thian district, Bangkok (UTM unit zone 47P 655299 m E, 1497465 m N). At this location, green mussels are cultured in ponds. Juvenile mussels (shell length 2 cm) were brought from Chonburi province, Thailand. They were attached to cultivation ropes and hung on floating rafts. Seawater was circulated by opening the gate at high tide and closing the gate at low tide each day. The mussel farm is located inland at a distance of 4.69 km from the seashore. As shown in Figure 1, there are numerous ponds in this area, but most of them culture shrimp, and the few ponds that culture green mussel were selected in this study. The sampling stations at this location were BKT 1 and BKT 2, which represent a low and high density of mussel cultivation, respectively, and BKT 3, where mussels are cultivated inside a water canal where seawater can flow through all the time.

Another sampling location, where mussels are cultivated in an estuarine area, was the Maeklong river mouth, Samut Songkhram province (UTM unit zone 47P 612675 m E, 1475015 m N). This mariculture site has riverine influences. The juvenile mussels were obtained from this site. Three survey locations were selected across a wide area of the Maeklong river mouth (Fig. 1). Station MK 1 is the farthest green mussel farm from the estuary at a distance of 8.61 km east from the river mouth and 6.24 km from the shore. At this station, the mussels are cultured in horizontal racks made from rope. The mussel spats are attached to a rope and grow naturally. Station MK 2 is close to the river mouth, but the cultivation method is the same as at MK 1. Station MK 3 is at the southern boundary of the river mouth. Small mussels were brought from the mussel farms around MK 2. Then, they were attached to cultivation ropes or fishing nets and hung in long lines.

Green mussel farms in Sriracha, Chonburi province (UTM unit zone 47P 708208 m E, 1458524 m N) were selected for mussel cultivation in a coastal area. This cultivation site is outside of estuarine influences and is about 26 to 35 km south of Bang Pakong estuary. As shown in Figure 1, there are three sampling stations along the Sriracha coastal area, and each station is about 1 km from the seashore. Green mussels at all stations were cultured from floating rafts, and juvenile mussels were obtained from this site and attached to cultivation ropes.

Sampling and analysis

 

Green mussels and seawater samples were collected monthly during the low tide from January to December 2019. All mussels that were attached to one cultivation rope were collected from each sampling station. Epifauna and attachments were removed from the mussels. The fresh weight of the flesh of each mussel was measured using a 4-digit analytical balance. Then, the mussel production (kg m-2) for each mussel farm was determined by multiplying the fresh mussels’ weight by the number of cultivations ropes and dividing by the total farm area (Table 1).

At each station, a 1 L sample of seawater was collected in a glass bottle. Then, the seawater temperature, pH, salinity, conductivity and dissolved oxygen (DO) were measured in situ using a handheld multi-parameter probe (YSI 600QS, USA). Salinity was measured in practical salinity units (psu). Transparency was analysed using a Secchi disc. Seawater samples were brought to the environmental laboratory at Bansomdejchaopraya Rajabhat University, Bangkok, Thailand for chlorophyll a (Chl a), total suspended solids and nutrient concentration analysis. Chl a concentration was analysed as described in Parsons et al. (1984). Total suspended solids (TSS) were determined by filtering the seawater through filter paper (GF/C) and drying it in a hot-air oven at 105°C for three days. Then, the seawater sample was filtered through GF/F filter paper (pore size 0.7 µm) to remove all suspended particles and bacteria. The filtered sample was kept frozen at −20°C for nutrient analysis. Finally, concentrations of ammonium-nitrogen (NH4+-N), nitrite and nitrate-nitrogen (NO2-+NO3--N), phosphate-phosphorus (PO43--P) and silicate-silicon (Si(OH)4-Si) were determined using a TrAAcs 2000 segmented flow analyser (Seal Analytical, USA) (sensitivity ± 0.01 µg L-1).

Data analysis

 

In this study, seawater quality includes only the water variables that may affect green mussel production. To investigate the correlations between atmospheric parameters and seawater quality in the study area, atmospheric data were used. The average of the highest air temperature (HAT) and the monthly total rainfall were obtained from the Thai Meteorological Department (TMD). Then, in each station, the atmospheric data were correlated with seawater parameters (temperature, pH, salinity, DO, NH4+-N, NO2-+NO3--N, PO43--P and Si(OH)4-Si) from monthly field observations for one year (n=12) by model fitness (linear and non-linear) using Microsoft Excel 365 software.

Analysis of variance (ANOVA) and Turkey-HSD were used to test for the spatial and temporal variations of green mussel production and seawater quality. The sample size (n) was 108 and the degree of freedom (df) was 8 for spatial analysis and 11 for temporal analysis. Principal component analysis (PCA) was used to determine the main parameters that cause changes in the environment at each sampling site (BKT, MK and SRI). Only principal components (PCs) with an eigenvalue exceeding 1 were analysed with Varimax rotation. Then, multiple linear regressions were performed to evaluate the influence of seawater quality on green mussel production. Based on the results from PCA (PC 1) and stepwise analysis, only significant variables were used in the regression models. Moreover, cultivation duration and operation methods were different at all stations, resulting in a different age of the mussels between stations. Thus, in addition to seawater quality, cultivation time is another important parameter affecting green mussel density. Hence, to obtain reliable results, cultivation time was included in the regressions analysis. All statistical parameters were computed using IBM SPSS Statistic Version 23 for Windows.

Scenarios on effects of climate variation on green mussel production

 

To simulate the effects of atmospheric variation on green mussel productivity, seawater parameters were correlated with the variation in monthly rainfall or the HAT using a multiple linear regression model to evaluate the mussel production. In this study, the models were simulated under two different scenarios: 1) drought conditions; and 2) storms or rapid heavy rainfall. In the first scenario, the variation in HAT was considered with fixed rainfall of 0.1 mm (lowest data used in the models). In the second scenario, the variation in monthly rainfall was considered with fixed HAT. For both scenarios, simulations were done for 12 months of cultivation time, and the effects were assessed on the healthiest mussels that were ready for harvest.

RESULTS

 

Effects of air temperature and rainfall on seawater parameters

 

The results indicated that variations in air temperature and rainfall caused changes in seawater quality (Figs 2-5Figs 2, 3, 4, 5). This is explained by the model equations, as shown in the supplementary data (Table S1). The results demonstrated that in all green mussel cultivation areas a high air temperature caused an increase in seawater temperature. An increase in monthly rainfall resulted in a decrease in salinity and an increase in seawater pH, except at station MK 3. At this station, excessive rainfall may have lowered the water pH due to the amount of runoff. The correlation between rainfall and DO at each cultivation area was found to be different owing to site-specific characteristics and influence of wind, wave and pollution through river runoff. The results also showed that an increase in monthly rainfall caused a decrease in nutrient concentration. The correlations fitted well with the logarithm model, with the exception of NO2-+NO3--N and Si(OH)4-Si at some stations (Figs 4-5).

medium/medium-SCIMAR-86-01-e027-gf2.png
Fig. 2.  Correlation between the average of the highest air temperature (HAT) and seawater temperature (A, B and C), and correlation between rainfall and pH of seawater (D, E, and F) at each sampling station in the pond area of Bangkhuntein (BKT), the estuary area of the Maeklong river mouth (MK) and the coastal area of Sriracha (SRI) based on monthly measurements from January to December 2019.
medium/medium-SCIMAR-86-01-e027-gf3.png
Fig. 3.  Correlation between rainfall and salinity (A, B and C), and correlation between rainfall and dissolved oxygen (DO) (D, E, and F) at each sampling station in the pond area of Bang Khun Tien (BKT), the estuary area of the Maeklong river mouth (MK), and the coastal area of Sriracha (SRI) based on monthly measurements from January to December 2019.
medium/medium-SCIMAR-86-01-e027-gf4.png
Fig. 4.  Correlation between rainfall and ammonium-nitrogen (NH4+-N) (A, B and C), and correlation between rainfall and nitrite and nitratenitrogen (NO2-+NO3--N) (D, E, and F) at each sampling station in the pond area of Bang Khun Tien (BKT), the estuary area of the Maeklong river mouth (MK) and the coastal area of Sriracha (SRI) based on monthly measurements from January to December 2019.
medium/medium-SCIMAR-86-01-e027-gf5.png
Fig. 5.  Correlation between rainfall and phosphate-phosphorus (PO43--P) (A, B and C), and correlation between rainfall and silicate-silicon (Si(OH)4-Si) (D, E, and F) at each sampling station in the pond area of Bang Khun Tien (BKT), the estuary area of the Maeklong river mouth (MK) and the coastal area of Sriracha (SRI) based on monthly measurements from January to December 2019.

Variation of green mussel production and seawater quality

 

Results from statistical analysis showed that green mussel production and seawater parameters in the three cultivation areas (BKT, MK and SRI) were significantly different, except for seawater temperature and Chl a concentration (Table 2). The pH level of seawater and DO at MK and SRI were found to be similar, and both were higher than at BKT. The highest values of salinity and conductivity were found at SRI, followed by MK and BKT, respectively. The nutrient concentrations at BKT were significantly higher than at MK and SRI, showing that BKT received more nutrient loading than the other areas. Spatial and temporal variations of green mussel production and seawater parameters in each cultivation area are shown in the supplementary data (Table S2-S4Table S2, S3, S4).

Table 2.  Green mussel production and seawater parameters (mean±sd, n=36) in the green mussel cultivation area at Bang Khun Tien (BKT), Maeklong River Mouth (MK), and Sriracha (SRI).
One-way ANOVA Turkey-HSD
Parameter BKT MK SRI Cultivation Areas BKT MK SRI
GM (kg m-2) 0.11 ± 0.13 12.64 ± 8.29 1.03 ± 0.67 *** a b a
Temp (oC) 29.27 ± 2.20 29.13 ± 2.19 29.76 ± 1.80 ns - - -
pH 7.68 ± 0.55 8.16 ± 0.26 8.21 ± 0.33 *** a b b
Sal 21.56 ± 3.52 23.91 ± 5.60 28.14 ± 3.10 *** a a b
Conduct (ms) 25.43 ± 2.18 25.85 ± 4.13 30.12 ± 3.00 *** a a b
DO (mg L-1) 4.87 ± 1.74 5.74 ± 0.95 5.95 ± 0.90 ** a b b
Trans (cm) 131.17 ± 57.32 119.09 ± 77.90 230.45 ± 98.17 *** a a b
TSS (mg L-1) 32.75 ± 32.36 26.54 ± 15.08 19.48 ± 5.91 * b a,b a
NH4+-N (µg L-1) 200.01 ± 143.17 90.78 ± 73.91 67.23 ± 66.97 *** b a a
NO2-+NO3--N (µg L-1) 50.12 ± 31.44 23.63 ± 19.63 7.59 ± 8.18 *** a c b
PO43--P (µg L-1) 77.23 ± 59.76 13.39 ± 12.41 5.75 ± 4.83 *** b a a
Si(OH)4-Si (µg L-1) 1151.98 ± 716.70 492.08 ± 261.29 657.71 ± 300.80 *** b a a
Chl a (µg L-1) 10.31 ± 16.21 8.24 ± 7.05 5.62 ± 7.20 ns - - -

Significance level of one-way ANOVA: ***, P <0.001; **, P <0.01; *, P <0.05; ns = not significant Groups of data from Turkey-HSD are shown as a, b and c

Results from PCA revealed that the three cultivation areas were differently affected by seawater variables. The PCs with eigenvalues higher than 1 and the variables whose loading exceeded 0.5 were mentioned. As shown in Figure 6, only PC 1 and PC 2 were emphasized because they explain most of the important variables. The first five PCs were responsible for more than 82.17% of the data scatter in the mussel cultivation in a pond (BKT). PC 1 explained 28.70% of the variance. The variables were NH4+-N, NO2-+NO3--N, and PO43--P with positive correlations and DO with a negative correlation. PC 2 explained 19.05% of the variance, comprising the negative correlation of temperature and the positive correlations of salinity and conductivity, while PC 3 explained 16.19% of the variance with TSS and transparency. PC 4 was pH and Si(OH)4-Si and PC 5 was Chl a concentration, which accounted for 9.47% and 8.77% of the variance, respectively.

medium/medium-SCIMAR-86-01-e027-gf6.png
Fig. 6.  Bar chart and scatter plots showing the results of principal component analysis (significant factor of PC 1 [circle] and PC 2 [square]) for seawater variables [temperature (Temp), pH, salinity (Sal), conductivity (Conduct), dissolved oxygen (DO), transparency (Trans), total suspended solids (TSS), ammonium-nitrogen (NH4+-N), nitrite and nitrate-nitrogen (NO2-+NO3--N), phosphate-phosphorus (PO43--P), silicate-silicon (Si(OH)4-Si), and chlorophyll a (Chl a)] at the three sampling sites [Bang Khun Tien (BKT), Maeklong river mouth (MK) and Sriracha (SRI)].

For mussel cultivation in an estuarine area (MK), salinity, conductivity, and temperature were identified as the most important component (PC 1), with 25.35% of the variance, followed by TSS, transparency and Chl a concentration (PC 2) with 18.0% of the variance. PC 1 was negatively correlated with temperature and positively correlated with salinity and conductivity (Fig. 6). Nutrient concentrations accounted for PC 3 (Si(OH)4-Si and NO2-+NO3--N) and PC 4 (NH4+-N and PO43--P), which explained 16.53% and 12.13% of the variance, respectively. Finally, DO and pH (PC 5) accounted for 9.94% of the variance.

For the coastal area (SRI), nutrient concentrations (NH4+-N, PO43--P and Si(OH)4-Si) were positively correlated and pH was negatively correlated. The most important components explained 32.32% (PC 1) and 20.56% (PC 2) of the variance (Fig. 6). PC 2 was positively correlated with NO2-+NO3--N and negatively correlated with salinity and conductivity. PC 3 comprised the temperature, DO and Chl a concentration, whereas PC 4 comprised TSS and transparency, and represented 15.21% and 9.64 % of the variance, respectively.

Influence of seawater quality on green mussel production

 

PCA and stepwise analysis were employed to determine the most important parameters affecting green mussel production in each cultivation area. The selected parameters were used in the multiple linear regression model. As shown in Table 3, each sampling station had an individual model equation with a high R-squared. This is due to differences in the seawater quality and cultivation method at each location. The results showed that DO, NH4+-N, NO2-+NO3--N, and PO43--P significantly affected green mussel productivity cultured in a pond (BKT). The mussel productivity at the estuary station (MK) was dependent on the distance from the river mouth owing to the different seawater parameters. For mussel cultivation in a coastal area (SRI), the most important factors were pH, NH4+-N, PO43--P and Si(OH)4-Si.

Table 3.  Multiple linear regression model equation of each sampling station.
Model equation Multiple R R-squared Adjusted R-squared Standard error
G M B K T   1 = 0.0028 D   -   ( 7.4261   ×   10 - 6 ) A   +   0.0003 P + ( 3.9282   ×   10 - 6 ) N   +   0.0027 C t   -   0.0708  
0.9885 0.9772 0.9202 0.0021
G T B K T   2 = - 0.0016 T   -   0.1167 p H +   0.0031 S   +   0.0079 D + 0.0056 C t   +   0.8119  
0.9187 0.8439 0.7139 0.0183
G M B K T   3 =   - 0.0683 D   -   0.0012 A   +   ( 1.7320   ×   10 - 5 ) N + 0.0019 P   -   0.0380 C t   +   1.0635  
0.9334 0.8711 0.7101 0.0741
G M M K   1 = - 0.0009 T   -   0.1387 p H   +   0.0385 S   +   0.0865 D - 0.0017 P   +   0.0013 A   -   0.0001 N   +   2.9414 C t + 1.8603  
0.9128 0.8332 0.7359 0.2487
G M M K   2 = - 1.1487 T + 18.3636 p H - 0.2184 S - 4.2752 D + 0.0871 C t - 75.1478  
0.7742 0.5994 0.2656 4.6909
G M M K   3 = - 0.1655 D + 0.0088 P - 0.0031 S i + 0.6674 C t - 3.4299  
0.8425 0.7098 0.4776 1.8867
G M S R I   1 = 0.0039 T - 0.5995 p H + 0.1369 S - 0.3015 D + 0.3022 C t + 2.4448  
0.9676 0.9363 0.8725 0.2975
G M S R I   2 = - 0.4718 p H - 0.0020 A + 0.2629 P - 0.0038 S i - 0.0344 C t + 6.8927  
0.8562 0.7331 0.5106 0.3273
G M S R I   3 = 0.1698 p H - 0.0043 A + 0.0161 P - 0.0009 S i - 0.0085 C t - 0.5888  
0.9185 0.8437 0.7135 0.0797

Note: GM is green mussel production (kg m-2); T is seawater temperature (°C); pH is pH level of seawater; S is salinity; D is dissolved oxygen(DO) (mg L-1); A is ammonium-nitrogen (NH4+-N) (µg L–1); N is nitrite and nitrate-nitrogen (NO2+NO3-N) (µg L-1); P is phosphate-phosphorus (PO43–-P) (µg L-1); Si is silicate-silicon (Si(OH)4-Si) (µg L-1); and Ct is cultivation time (months)

Scenarios of effects of atmospheric variation on green mussel production

 

Results from model simulations on atmospheric parameters affecting seawater quality (Table S1) and the influence on green mussel production in each cultivation area (Table 3) are shown in Figure 7. For mussel cultivation in a pond (BKT 1 and BKT 3), the results showed that under drought conditions (high air temperature with negligible rainfall) the air temperature does not affect mussel production. However, mussel production tends to decrease when the air temperature increases at station BKT 2, with the largest mussel population (Fig. 7). The results also demonstrated that heavy rainfall causes a decrease in green mussel productivity, except at BKT 3, where mussels are cultured inside a water canal. The simulations revealed that a high air temperature with low rainfall sharply decreases production of green mussels cultured close to the river mouth (MK). As shown in Figure 7, in a coastal area (SRI), high air temperature did not influence mussel production. Heavy storms (rapid high rainfall) caused a decrease in mussel survival at station SRI 1, which is closer to Bang Pakong estuary than the other stations.

medium/medium-SCIMAR-86-01-e027-gf7.png
Fig. 7.  Simulations of green mussel production (GM) based on variations of the average of the highest air temperature (HAT) and rainfall in the pond area of Bang Khun Tien (BKT), the estuary area of the Maeklong river mouth (MK) and the coastal area of Sriracha (SRI).

DISCUSSION

 

Seawater quality under variations of atmospheric parameters

 

It was found that increased air temperature resulted in a higher seawater temperature, and the correlations fitted well with simple linear regression (Fig. 2). An increase in the atmospheric temperatures is due to solar radiation and results in heat exchange processes that occur at the water surface (Morrill et al 2001Morrill J.C., Bales R.C., Conklin M.H. 2001. The relationship between air temperature and stream temperature. American Geophysical Union, Spring Meeting 2001, abstract id. H42A-09). Our results are in agreement with other studies in New Zealand streams (Johnson 1971Johnson F. 1971. Stream temperatures in an alpine area. J. Hydrol. 14: 322-336. https://doi.org/10.1016/0022-1694(71)90042-4 ), Minnesota streams (Song et al. 1973Song C., Pabst A., Bowers C. 1973. Stochastic analysis of air and water temperatures. J. Environ. Eng. 99: 785-800. https://doi.org/10.1061/JEEGAV.0000117 ) and the rivers of Newfoundland and Labrador, Canada (Harvey et al. 2013Harvey R., Lye L., Khan A., Paterson R. 2013. The influence of air temperature on water temperature and the concentration of dissolved oxygen in Newfoundland rivers. Can. Water Resour. J. 36: 171-192. https://doi.org/10.4296/cwrj3602849 ), where the general relation between the monthly means of the surface water and air temperature was described by linear regression. However, different sampling locations resulted in a different model because environmental conditions such as water depth, flow velocity, stream discharge and location were not the same.

Rainfall can increase runoff, increasing the cations in seawater and subsequently increasing the pH level. In the pond area (BKT), intense rainfall corresponded to high pH in seawater, and the plateau was found with rainfall of over 50 mm per month (Fig. 2). This can be explained by the enhancement of dissolved cations from soil erosion. At BKT, there is an embankment surrounding the cultivation pond, and this area connects to a canal. Intense rainfall can increase sediment erosion from the exposed marsh surfaces and creek banks, and thus elevate the suspended sediment concentration in water channels (Pilditch et al. 2008Pilditch C.A., Widdows J., Kuhn N.J., et al. 2008. Effects of low tide rainfall on the erodibility of intertidal cohesive sediments. Cont. Shelf Res. 28: 1854-1865. https://doi.org/10.1016/j.csr.2008.05.001 ). As a result of increased suspended solids, enhanced solubility of Na+, K+, Ca2+, and Mg2+ in a water body is observed (Budhavant et al. 2009Budhavant K.B., Rao P.S.P., Safai P.D., Ali K. 2009. Chemistry of monsoon and post-monsoon rains at a high altitude location, Sinhagad, India. Aerosol Air Qual. Res. 9: 65-79. https://doi.org/10.4209/aaqr.2008.07.0033 ). Therefore, more suspended-solid loading can cause high dissolved cations and saturation. In the estuary (MK) and coastal (SRI) areas, high pH levels were observed with increased rainfall. Probable sources of cations were soil erosion and atmospheric deposition. Rainfall washes off aerosols and deposits them into seawater. Budhavant et al. (2009)Budhavant K.B., Rao P.S.P., Safai P.D., Ali K. 2009. Chemistry of monsoon and post-monsoon rains at a high altitude location, Sinhagad, India. Aerosol Air Qual. Res. 9: 65-79. https://doi.org/10.4209/aaqr.2008.07.0033 found that rainwater was alkaline throughout the year. Other studies (Wirmvem et al. 2014Wirmvem M.J., Ohba T., Fantong W.Y., et al. 2014. Origin of major ions in monthly rainfall events at the Bamenda Highlands, North West Cameroon. J. Env. Sci. 26: 801-809. https://doi.org/10.1016/S1001-0742(13)60502-1 , Khan et al. 2018Khan M.F., Maulud K.N.A., Latif M.T., et al. 2018. Physicochemical factors and their potential sources inferred from long-term rainfall measurements at an urban and a remote rural site in tropical areas. Sci. Total Environ. 613-614: 1401-1416. https://doi.org/10.1016/j.scitotenv.2017.08.025 ) also indicated that rainwater contains many cations from sea salt (Na+) and land dust (NH4+, Ca2+, and Mg2+). These cation fluxes in seawater cause an increase in pH. However, at station MK 3, heavy rainfall caused a decrease in the pH of seawater. This area is south of the Maeklong river mouth and is influenced by strong winds from the land. Rainwater in this area may be more acidic than in other areas owing to aerosol pollutants (NO3- and SO42-) from human activity (Khan et al. 2018), indicating that this area is sensitive to anthropogenic stress.

This study found that high rainfall caused a decrease in salinity that can be described by simple linear regression (Fig. 3, Table S1). In estuarine and coastal areas, the salinity of seawater is diluted by river water. In addition, intense rainfall events cause water discharges, decreasing the salinity. This phenomenon has been widely observed in coastal ecosystems, as shown by recent research carried out on the pacific coast of Panama (Valiela et al. 2012Valiela I., Camilli L., Stone T., et al. 2012. Increased rainfall remarkably freshens estuarine and coastal waters on the Pacific coast of Panama: Magnitude and likely effects on upwelling and nutrient supply. Glob. Planet. Change 92-93: 130-137. https://doi.org/10.1016/j.gloplacha.2012.05.006 ), in the Depeng lagoon, Taiwan (Meng et al. 2017Meng P.-J., Tew K.S., Hsieh H.-Y., Chen C.-C. 2017. Relationship between magnitude of phytoplankton blooms and rainfall in a hyper-eutrophic lagoon: A continuous monitoring approach. Mar. Pollut. Bull. 124: 897-902. https://doi.org/10.1016/j.marpolbul.2016.12.040 ), and in the northwest Arabian Gulf (Alosairi et al. 2019Gu H., Shanga Y., Clements J., et al. 2019. Hypoxia aggravates the effects of ocean acidification on the physiological energetics of the blue mussel Mytilus edulis. Mar. Pollut. Bull. 149: 1-7. https://doi.org/10.1016/j.marpolbul.2019.110538 ). These studies confirmed that rainfall decreases salinity at the sea surface. The present study provides site-specific model equations for evaluating seawater salinity caused by variations in rainfall (Table S1).

Rainfall can cause an increase or decrease in DO. Our results revealed that at sampling stations MK 1 and SRI 3 intense rainfall resulted in high DO in seawater. This is because there were more wind and waves than at other stations. This finding is in line with a study on gas exchange across an air-water interface by Liss (1973)Liss P.S. 1973. Processes of gas exchange across an air-water interface. Deep-Sea Res. Oceanogr. Abstr. 20: 221-238. https://doi.org/10.1016/0011-7471(73)90013-2 , who reported that the exchange of oxygen between the air and surface water increased as a result of higher wind velocity and breaking of the water surface. In contrast, at some sampling stations (MK 2, MK 3, SRI 1, and SRI 2), rainfall caused oxygen depletion in seawater (Fig. 3). These areas were influenced by water discharges from the Maeklong and Bang Pakong river mouths, which may be polluted with suspended sediments and dissolved organic matter. In agreement with previous studies, heavy rainfall events increase wastewater discharges (Park et al. 2011Park J.-H., Inam E., Abdullah M.H., et al. 2011. Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia. J. Hydrol. 400: 323-332. https://doi.org/10.1016/j.jhydrol.2011.01.050 ) and faecal coliform bacteria (Coulliette and Noble 2008Coulliette A.D., Noble R.T. 2008. Impacts of rainfall on the water quality of the Newport River Estuary (Eastern North Carolina, USA). J. Water Health 06: 473-482. https://doi.org/10.2166/wh.2008.136 ). Consequently, this phenomenon enhances decomposition processes that require oxygen, resulting in oxygen depletion. In the pond area (BKT), it was found that an increase in rainfall increases DO; however, with rainfall over 50 mm per month, DO became steady. With high rainfall, dispersion of suspended sediments and organic matter occurred, so DO did not increase continuously.

The results of this study also revealed that rainfall causes a decrease or increase in nutrient concentrations. During high rainfall periods, low nutrient contents were observed in the pond area (BKT), with the exception of silicate-silicon. Sipaúba-Tavares et al. (2007)Sipaúba-Tavares L.H., Guariglia C.S.T., Braga F.M.S. 2007. Effects of rainfall on water quality in six sequentially disposed fishponds with continuous water flow. Braz. J. Biol. 67: 643-649. https://doi.org/10.1590/S1519-69842007000400008 also reported that rainfall positively affected a pond’s water quality. In an unpolluted pond, high rainfall tends to lower the nutrient concentration owing to an increase in the water flow. However, it was found that silicate-silicon increased with high rainfall. This is because heavy rainfall increases sediment erosion from the creek banks (Pilditch et al. 2008Pilditch C.A., Widdows J., Kuhn N.J., et al. 2008. Effects of low tide rainfall on the erodibility of intertidal cohesive sediments. Cont. Shelf Res. 28: 1854-1865. https://doi.org/10.1016/j.csr.2008.05.001 ), dissolving silicon content into the water body. In estuarine (MK) and coastal (SRI) areas, nutrient concentrations tend to decrease with high rainfall, and the correlation between these two parameters fitted the logarithm model (Figs 4-5, Table S1). Although rainfall caused excessive nutrient loading through river runoff, it has a short-term impact. These nutrients can be effectively absorbed by phytoplankton (Meng et al. 2017Meng P.-J., Tew K.S., Hsieh H.-Y., Chen C.-C. 2017. Relationship between magnitude of phytoplankton blooms and rainfall in a hyper-eutrophic lagoon: A continuous monitoring approach. Mar. Pollut. Bull. 124: 897-902. https://doi.org/10.1016/j.marpolbul.2016.12.040 ). The concentrations returned to their normal levels within five days after rainfall (Xu et al. 2019Xu H., Zhang Y., Zhu X., Zheng M. 2019. Effects of rainfall-runoff pollution on eutrophication in coastal zone: A case study in Shenzhen Bay, southern China. Hydrol. Res. 50: 1062-1074. https://doi.org/10.2166/nh.2019.012 ). Moreover, the concentrations of dissolved ions tend to be higher during dry periods (Park et al. 2011Park J.-H., Inam E., Abdullah M.H., et al. 2011. Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia. J. Hydrol. 400: 323-332. https://doi.org/10.1016/j.jhydrol.2011.01.050 ) as a result of high water evaporation. In this study, high nitrite nitrate-nitrogen and silicate-silicon with intense rainfall were found in the estuarine area (MK). This is because DO during weather disturbances can induce nitrification processes that transform ammonium to nitrite and nitrate. In addition, as observed for the pond area (BKT), rainfall causes an increase in suspended solids, which are the main source of silicate-silicon in seawater. Thus, the amount of rainfall affects the nutrient variability. The model developed in this study (Table S1) can predict nutrient concentrations with variations in rainfall. This can assist the estimation of seawater quality during atmospheric disturbances such as typhoons, depressions and tropical storms.

Influential seawater parameters for green mussel production

 

It was found that seawater quality in pond, estuary and coastal areas was significantly different and affected green mussel production in different ways. Research on green-lipped mussel Perna canalicus by Ren et al. (2019)Ren J.S., Fox S.P., Howard-Williams C., et al. 2019. Effects of stock origin and environment on growth and reproduction of the green-lipped mussel Perna canaliculus. Aquaculture 505: 502-509. https://doi.org/10.1016/j.aquaculture.2019.03.011 found that there were site-specific influences on the mussel filtration, ingestion, respiration and growth rates owing to differences in environmental conditions. Results from PCA revealed that the most important parameters affecting the environmental variability in a pond (BKT) were NH4+-N, NO2-+NO3--N, PO43--P and DO. These parameters were considered by multiple linear regression to estimate the mussel production at stations BKT 1 and BKT 3, where the mussels are cultured in low density and in a water channel, respectively. In these areas, nutrients are important factors because they are sensitive areas that receive pollutant loading from anthropogenic activities such as residences, restaurants, local industry and shrimp farms. Excessive nutrients affect green mussel production by inducing large phytoplankton blooms (Riani et al. 2018Riani E., Cordova M.R., Arifin Z. 2018. Heavy metal pollution and its relation to the malformation of green mussels cultured in Muara Kamal waters, Jakarta Bay, Indonesia. Mar. Pollut. Bull. 133: 664-670. https://doi.org/10.1016/j.marpolbul.2018.06.029 ), which are a dominant dietary source for mussels (Irisarri et al. 2014Irisarri J., Fernández-Reiriz M.-J., De Troch M., Labarta U. 2014. Fatty acids as tracers of trophic interactions between seston, mussels and biodeposits in a coastal embayment of mussel rafts in the proximity of fish cages. Comp. Biochem. Physiol. B 172-173: 105-115. https://doi.org/10.1016/j.cbpb.2014.04.006 ). However, extreme ammonia concentrations (2.0 mg L-1) are toxic to green mussels because they reduce lysosomal integrity (Fang et al. 2008Fang J.K.H., Wu R.S.S., Chan A.K.Y., et al. 2008. Influences of ammonia-nitrogen and dissolved oxygen on lysosomal integrity in green-lipped mussel Perna viridis: laboratory evaluation and field validation in Victoria Harbour, Hong Kong. Mar. Pollut. Bull. 56: 2052-2058. https://doi.org/10.1016/j.marpolbul.2008.08.003 ), and the concentration in BKT was closer to this level than at other locations (Table 2). Furthermore, in the pond, water circulation significantly influenced the available DO in the water column. High water flows result in an increase in the oxygen concentration; this results in high growth and survival rates of green mussels (Sanjayasari and Jeffs 2019Sanjayasari D., Jeffs A. 2019. Optimising environmental conditions for nursery culture of juvenile Greenshell™ mussels (Perna canaliculus). Aquaculture 512: 1-10. https://doi.org/10.1016/j.aquaculture.2019.734338 ). Therefore, good mussel productivity could be found with high nitrite and nitrate-nitrogen, phosphate-phosphorus, oxygen and low ammonium-nitrogen concentrations. Results from multiple linear regression demonstrated that the model parameters at station BKT 2 differed from those at the other stations (Table 3). At BKT 2, there was less water circulation, and seawater quality depended on the air temperature. High air temperature caused an increase in water temperature (Fig. 2), leading to higher water vaporization and denser cations in the water column (Park et al. 2011Park J.-H., Inam E., Abdullah M.H., et al. 2011. Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia. J. Hydrol. 400: 323-332. https://doi.org/10.1016/j.jhydrol.2011.01.050 ). This influences salinity and the pH level of seawater. The lethal level of these parameters can be reached faster than that of nutrient concentrations. Therefore, at this station, temperature, salinity and pH were included in the model instead of nutrients, which had less influence.

In an estuarine area (MK), our results from PCA demonstrated that variation of temperature and salinity were the most important parameters. Salinity and temperature are affected by freshwater discharges through river runoff. In line with our findings, Sasikumar and Krishnakumar (2011)Sasikumar G., Krishnakumar P.K. 2011. Aquaculture planning for suspended bivalve farming systems: The integration of physiological response of green mussel with environmental variability in site selection. Ecol. Indic. 11: 734-740. https://doi.org/10.1016/j.ecolind.2010.06.008 also found that in a riverine influenced area, water discharges can transport significant amounts of particulate matter into coastal waters, leading to variations in seawater temperature and salinity. These parameters significantly affect green mussel growth and survival (Wendling et al. 2013Wendling C.C., Huhn M., Ayu N., et al. 2013. Habitat degradation correlates with tolerance to climate-change related stressors in the green mussel Perna viridis from West Java, Indonesia. Mar. Pollut. Bull. 71: 222-229. https://doi.org/10.1016/j.marpolbul.2013.03.004 ). Hence, in the present study, they contributed to the mussel productivity model equations. Our results indicated that in an offshore area (MK 1), the riverine areas had less influence and the abundance of nutrients became a more important factor. Therefore, NH4+-N, NO2-+NO3--N, and PO43--P were the main factors in the mussel production model. These nutrients are essential for phytoplankton growth, which corresponds to mussel integrity (Irisarri et al. 2014Irisarri J., Fernández-Reiriz M.-J., De Troch M., Labarta U. 2014. Fatty acids as tracers of trophic interactions between seston, mussels and biodeposits in a coastal embayment of mussel rafts in the proximity of fish cages. Comp. Biochem. Physiol. B 172-173: 105-115. https://doi.org/10.1016/j.cbpb.2014.04.006 ). Unlike at other stations, Si(OH)4-Si was found to be a significant variable affecting green mussel production at MK 3. Intensive suspended solids led to dissolved silicate in the water body. This influences the mussel production by providing essential nutrients for phytoplankton, especially diatoms and dinoflagellates, which are major food sources for mussels (Irisarri et al. 2014Irisarri J., Fernández-Reiriz M.-J., De Troch M., Labarta U. 2014. Fatty acids as tracers of trophic interactions between seston, mussels and biodeposits in a coastal embayment of mussel rafts in the proximity of fish cages. Comp. Biochem. Physiol. B 172-173: 105-115. https://doi.org/10.1016/j.cbpb.2014.04.006 ).

In this study, the dominant parameters affecting environmental variability in a coastal area (SRI) were pH, NH4+-N, PO43--P and Si(OH)4-Si. These variables were found to have a significant effect on green mussel production. This is because the study area is located farther from the river mouth. There was less input of freshwater, and the riverine influences were reduced along the coast. Therefore, temperature and salinity become less impactful factors. Seawater quality in this area is associated with precipitation and water circulation inside the inner Gulf of Thailand. The amount of rainfall affects the variation of pH levels in seawater (Fig. 2). Extremely low or high pH levels cause unhealthy green mussels (Sasikumar and Krishnakumar 2011Sasikumar G., Krishnakumar P.K. 2011. Aquaculture planning for suspended bivalve farming systems: The integration of physiological response of green mussel with environmental variability in site selection. Ecol. Indic. 11: 734-740. https://doi.org/10.1016/j.ecolind.2010.06.008 ), and tremendous die-offs can occur (Sivalingam 1977Sivalingam P.M. 1977. Aquaculture of the green mussel, Mytilus viridis Linnaeus, in Malaysia. Aquaculture 11: 297-312. https://doi.org/10.1016/0044-8486(77)90079-5 ). Low pH levels (<7.3) induce physiological and biochemical stress in bivalves, such as immune deterioration (Sui et al. 2016Sui Y., Kong H., Shang Y., et al. 2016. Effects of short-term hypoxia and seawater acidification on hemocyte responses of the mussel Mytilus coruscus. Mar. Pollut. Bull. 108: 46-52. https://doi.org/10.1016/j.marpolbul.2016.05.001 , Wu et al. 2016Wu F., Lu W., Shang Y., et al. 2016. Combined effects of seawater acidification and high temperature on hemocyte parameters in the thick shell mussel Mytilus coruscus. Fish Shellfish Immunol. 56: 554-562. https://doi.org/10.1016/j.fsi.2016.08.012 ), lowered clearance rates, reduction of absorption efficiency, decline of scope for growth and increased excretion rates (Gu et al. 2019Gu H., Shanga Y., Clements J., et al. 2019. Hypoxia aggravates the effects of ocean acidification on the physiological energetics of the blue mussel Mytilus edulis. Mar. Pollut. Bull. 149: 1-7. https://doi.org/10.1016/j.marpolbul.2019.110538 ). Moreover, a decrease in pH has negative effects on the calcification ability of other mussel species (Duarte et al. 2014Duarte C., Navarro J.M., Acuña K., et al. 2014. Combined effects of temperature and ocean acidification on the juvenile individuals of the mussel Mytilus chilensis. J. Sea Res. 85: 308-314. https://doi.org/10.1016/j.seares.2013.06.002 , Fitzer et al. 2014Fitzer S.C., Cusack M., Phoenix V.R., Kamenos N.A. 2014. Ocean acidification reduces the crystallographic control in juvenile mussel shells. J. Struct. Biol. 188: 39-45. https://doi.org/10.1016/j.jsb.2014.08.007 ). The results demonstrated that unlike at other stations, the significant variables affecting green mussel production at SRI 1 were seawater temperature, pH, salinity and DO, which can be influenced by river runoff (Sasikumar and Krishnakumar 2011Sasikumar G., Krishnakumar P.K. 2011. Aquaculture planning for suspended bivalve farming systems: The integration of physiological response of green mussel with environmental variability in site selection. Ecol. Indic. 11: 734-740. https://doi.org/10.1016/j.ecolind.2010.06.008 ). This is because SRI 1 has more riverine influences because it is located closer to the Bang Pakong river mouth than the other stations.

Impact of atmospheric variation on green mussel production

 

The results of the simulations indicated that at all sampling stations variations in air temperature and rainfall influence green mussel production in different ways. For mussels cultured in a pond, drought strongly affected green mussel production (Fig. 7). A high air temperature caused an increase in seawater temperature (Fig. 2). A very low or absence of rainfall caused a decrease in pH and DO and an increase in ammonia concentration (Figs 2-4Figs 2, 3, 4). Consequently, the immune-related functions and metabolism of green mussels were reduced (Fang et al. 2008Fang J.K.H., Wu R.S.S., Chan A.K.Y., et al. 2008. Influences of ammonia-nitrogen and dissolved oxygen on lysosomal integrity in green-lipped mussel Perna viridis: laboratory evaluation and field validation in Victoria Harbour, Hong Kong. Mar. Pollut. Bull. 56: 2052-2058. https://doi.org/10.1016/j.marpolbul.2008.08.003 , Donaghy and Volety 2011Donaghy L., Volety A.K. 2011. Functional and metabolic characterization of hemocytes of the green mussel, Perna viridis: in vitro impacts of temperature. Fish Shellfish Immunol. 31: 808-814. https://doi.org/10.1016/j.fsi.2011.07.018 , Wang et al. 2011Wang Y., Menghong H., Shin P.S.K., Cheung S.G. 2011. Immune responses to combined effects of hypoxia and high temperature in the green-lipped mussel Perna viridis . Mar. Pollut. Bull. 63: 201-208. https://doi.org/10.1016/j.marpolbul.2011.05.035 ). However, when mussels were cultured in a water canal (BKT 3), the seawater temperature was not a significant variable for mussel production. This is because thermal stress can be reduced by enhanced aeration in seawater (Wang et al. 2011Wang Y., Menghong H., Shin P.S.K., Cheung S.G. 2011. Immune responses to combined effects of hypoxia and high temperature in the green-lipped mussel Perna viridis . Mar. Pollut. Bull. 63: 201-208. https://doi.org/10.1016/j.marpolbul.2011.05.035 ). Although our results showed that there was not much difference in DO between pond stations (BKT) (Fig. 3), water can effectively flow through the mussel body at BKT 3, and there is minimal oxygen competition between living organisms. Increasing the water flow can provide positive effects on green mussel growth and survival (Sanjayasari and Jeffs 2019Sanjayasari D., Jeffs A. 2019. Optimising environmental conditions for nursery culture of juvenile Greenshell™ mussels (Perna canaliculus). Aquaculture 512: 1-10. https://doi.org/10.1016/j.aquaculture.2019.734338 ).

Based on simulation results, drought did not influence green mussel production in estuary (MK) and coastal (SRI) areas (Fig. 7). This is because, although seawater temperature was found to be linearly dependent on the air temperature (Fig. 2 and Table S1), it was not a significant variable for mussel production (Table 4). However, there was a negative effect at the mussel farm that was closest to the river mouth (MK 2). This is because the seawater temperature was an influential parameter in this area. The results are in line with those of White and Toumi (2014)White R.H., Toumi R. 2014. River flow and ocean temperatures: The Congo River. J. Geophys. Res. Oceans 119: 2501-2517. https://doi.org/10.1002/2014JC009836 , who found that seawater temperature fluctuates when there is a tremendous flux of freshwater from rivers. This fluctuation can affect mussel immunity and integrity (Donaghy and Volety 2011Donaghy L., Volety A.K. 2011. Functional and metabolic characterization of hemocytes of the green mussel, Perna viridis: in vitro impacts of temperature. Fish Shellfish Immunol. 31: 808-814. https://doi.org/10.1016/j.fsi.2011.07.018 , Goh and Lai 2014Goh B.P.L., Lai C.H. 2014. Establishing the thermal threshold of the tropical mussel Perna viridis in the face of global warming. Mar. Pollut. Bull. 85: 325-331. https://doi.org/10.1016/j.marpolbul.2013.10.041 ), and subsequently mussel production

Scenarios of green mussel production under variations of rainfall showed that intense rainfall resulted in a decrease in production of green mussel cultured in a pond (Fig. 7). As shown in Figures 4-5, high rainfall corresponded to nutrient reduction. The lack of essential nutrients was not suitable for phytoplankton growth. Consequently, there was not sufficient available food for green mussels (Irisarri et al. 2014Irisarri J., Fernández-Reiriz M.-J., De Troch M., Labarta U. 2014. Fatty acids as tracers of trophic interactions between seston, mussels and biodeposits in a coastal embayment of mussel rafts in the proximity of fish cages. Comp. Biochem. Physiol. B 172-173: 105-115. https://doi.org/10.1016/j.cbpb.2014.04.006 ). The mussels that were cultured in a water canal (BKT 3) were found to be more productive than those in a pond (BKT 1 and BKT 2). The mussel production increased with an increase in rainfall and become steady when rainfall was over 50 mm per month (Fig. 7). Although there were fewer available nutrients during intense rainfall, the multiple regression analysis indicated that mussel production depends more on DO than on the other parameters (Table 3). A high water flow rate enhances the aeration of seawater and promotes mussel growth (Sanjayasari and Jeffs 2019Sanjayasari D., Jeffs A. 2019. Optimising environmental conditions for nursery culture of juvenile Greenshell™ mussels (Perna canaliculus). Aquaculture 512: 1-10. https://doi.org/10.1016/j.aquaculture.2019.734338 ).

Surprisingly, the simulations revealed that heavy rainfall did not affect green mussels that were cultured in the estuary area (Fig. 7). The multiple linear regression analysis showed that for mussel cultivation far from shore the seawater quality did not change (much) during intense rainfall. This is because there is less impact from river runoff in this area. However, the mussel production next to the river mouth (MK 2) was found to be higher when rainfall increased (Fig. 7). This can be explained by the enhancement of phytoplankton during heavy rainfall events. Heavy storms cause excessive nutrients in an estuary through river runoff. These nutrients were effectively absorbed by phytoplankton (Meng et al. 2017Meng P.-J., Tew K.S., Hsieh H.-Y., Chen C.-C. 2017. Relationship between magnitude of phytoplankton blooms and rainfall in a hyper-eutrophic lagoon: A continuous monitoring approach. Mar. Pollut. Bull. 124: 897-902. https://doi.org/10.1016/j.marpolbul.2016.12.040 , Xu et al. 2019Xu H., Zhang Y., Zhu X., Zheng M. 2019. Effects of rainfall-runoff pollution on eutrophication in coastal zone: A case study in Shenzhen Bay, southern China. Hydrol. Res. 50: 1062-1074. https://doi.org/10.2166/nh.2019.012 ). Then, this phytoplankton was filtered by green mussels as their dominant dietary source (Irisarri et al. 2014Irisarri J., Fernández-Reiriz M.-J., De Troch M., Labarta U. 2014. Fatty acids as tracers of trophic interactions between seston, mussels and biodeposits in a coastal embayment of mussel rafts in the proximity of fish cages. Comp. Biochem. Physiol. B 172-173: 105-115. https://doi.org/10.1016/j.cbpb.2014.04.006 ). Finally, enhanced green mussel production was observed.

For a coastal area, green mussel production was not affected by heavy rainfall, except at SRI 1 (Fig. 7). The regression analysis indicated that the pH of seawater was the most important parameter affecting mussel production. However, it did not change (much) with variations in rainfall. Therefore, almost consistent mussel production was found. The simulation results demonstrated that the mussel production dramatically decreased with intense rainfall at the farm that was closest to the river mouth (SRI 1), where higher fluctuations in seawater salinity were observed than at the other stations. A magnitude decrease in salinity causes physiological changes in green mussels, such as decreased clearance rates, closed valves (McFarland et al. 2013McFarland K., Donaghy L., Volety A.K. 2013. Effect of acute salinity changes on hemolymph osmolality and clearance rate of the non-native mussel, Perna viridis , and the native oyster, Crassostrea virginica, in Southwest Florida. Aquat. Invasions 8: 299-310. https://doi.org/10.3391/ai.2013.8.3.06 ), and reduced immune responses (Wang et al. 2012Wang Y., Hu M., Cheung S.G., et al. 2012. Immune parameter changes of hemocytes in green-lipped mussel Perna viridis exposure to hypoxia and hyposalinity. Aquaculture 356-357: 22-29. https://doi.org/10.1016/j.aquaculture.2012.06.001 ). Finally, fewer mussels can be found due to high mortality (Firth et al. 2011Firth L.B., Knights A.M., Bell S.S. 2011. Air temperature and winter mortality: Implications for the persistence of the invasive mussel, Perna viridis in the intertidal zone of the south-eastern United States. J. Exp. Mar. Biol. Ecol. 400: 250-256. https://doi.org/10.1016/j.jembe.2011.02.007 ).

The results of field observation demonstrated that the estuary area (MK) had significantly the highest green mussel production (Table 2). However, simulation results showed that during climate disturbance, coastal areas (SRI) are suitable for green mussel culture owing to low fluctuations in temperature and salinity. Variation of these parameters can significantly reduce mussel production and often occurs in the area which is close to the river mouth (Sasikumar and Krishnakumar 2011Sasikumar G., Krishnakumar P.K. 2011. Aquaculture planning for suspended bivalve farming systems: The integration of physiological response of green mussel with environmental variability in site selection. Ecol. Indic. 11: 734-740. https://doi.org/10.1016/j.ecolind.2010.06.008 , White and Toumi 2014White R.H., Toumi R. 2014. River flow and ocean temperatures: The Congo River. J. Geophys. Res. Oceans 119: 2501-2517. https://doi.org/10.1002/2014JC009836 ). Therefore, the location far from shore is less impacted by climate change than locations near the river mouth.

Although some simulations showed that variations in air temperature or rainfall do not affect green mussel production, there are threshold limits. To estimate atmospheric parameters that cause the death of green mussels, the threshold levels of seawater quality (Table 4) were employed in the model equations (Table S1), which were developed from the correlations between atmospheric parameters and seawater quality (Figs 2-5Figs 2, 3, 4, 5). When the air temperature and rainfall reached the threshold levels of seawater quality, high mortalities of green mussels were observed (Fig. 8). When these atmospheric parameters were beyond the maximum and minimum lethal levels, large green mussel mortality was observed. According to Figure 8, the mussels can die-off when the air temperature reaches 34.01°C, 35.64°C and 38.39°C for cultivation in the pond, estuary and coastal areas, respectively. Intense rainfall higher than 204.48, 200.24, and 283.18 mm per month can deteriorate the mussel production in the pond, estuary and coastal areas, respectively. These results can assist green mussel farmers to adapt their operation when weather disturbances occur. The results of the study can guide farmers to select a proper location for mussel cultivation under extreme climate events. During climate disturbance, the coastal zone is the most suitable for mussel cultivation because the lethal levels of air temperature and rainfall are higher than in estuary and pond areas (Fig. 8). In addition, these lethal levels also can be used as warning levels. When air temperature and rainfall reach these levels, green mussels should be harvested to avoid mass mortality. Finally, the farm operators can use our results to plan their cultivation period with less impact of climate variation on green mussel production. For instance, to reach a higher production of mussels under climate change, the cultivation should not start when the air temperature is above the lethal levels (Fig. 8). The findings of our study can provide scientific information to enhance the efficiency of the farm and can be used as a climate change adaptation tool for shellfish farmers. Our suggestion for further study is to increase the number of replications together with controlled cultivation methods, such as mussel density, location, depth, distance from shore, cultivation rope, etc., in order to increase the accuracy of the model. In addition, more yearly data or several cultures could enhance the reliability of the model.

Table 4.  Limits of seawater parameters that cause death and threaten the survival of green mussels (Firth et al. 2011Firth L.B., Knights A.M., Bell S.S. 2011. Air temperature and winter mortality: Implications for the persistence of the invasive mussel, Perna viridis in the intertidal zone of the south-eastern United States. J. Exp. Mar. Biol. Ecol. 400: 250-256. https://doi.org/10.1016/j.jembe.2011.02.007 , Goh and Lai 2014Goh B.P.L., Lai C.H. 2014. Establishing the thermal threshold of the tropical mussel Perna viridis in the face of global warming. Mar. Pollut. Bull. 85: 325-331. https://doi.org/10.1016/j.marpolbul.2013.10.041 , Sivalingam 1977Sivalingam P.M. 1977. Aquaculture of the green mussel, Mytilus viridis Linnaeus, in Malaysia. Aquaculture 11: 297-312. https://doi.org/10.1016/0044-8486(77)90079-5 , Wendling et al. 2013Wendling C.C., Huhn M., Ayu N., et al. 2013. Habitat degradation correlates with tolerance to climate-change related stressors in the green mussel Perna viridis from West Java, Indonesia. Mar. Pollut. Bull. 71: 222-229. https://doi.org/10.1016/j.marpolbul.2013.03.004 ).
Seawater parameter Maximum lethal level* Upper survival level Lower survival level Minimum lethal level*
Temperature (°C) 38.00 36.00 14.00 8.00
pH 9.00 8.65 7.54 3.50
Salinity 80.00 65.00 25.00 24.00
DO (mg L-1) - 8.00 4.00 0.50

Note: *Lethal level is the level of seawater parameters that causes 50% to 100% die-off of green mussels.

medium/medium-SCIMAR-86-01-e027-gf8.png
Fig. 8.  Bar charts representing (from top to bottom), the maximum lethal level, upper survival level, lower survival level and minimum lethal level of atmospheric parameters (average of the highest temperature (HAT) and rainfall) that were estimated from seawater temperature (SW- Temp), pH, salinity, and dissolved oxygen (DO) in the pond area of Bang Khun Tien (BKT), the estuary area of the Maeklong river mouth (MK) and the coastal area of Sriracha (SRI).

CONCLUSIONS

 

It can be concluded that climate change affects green mussel productivity differently according to the cultivation area. Atmospheric parameters affect seawater quality in pond, estuary and coastal areas in different kinetics. A high air temperature significantly increased the seawater temperature. Intense rainfall results in a decrease in salinity and an increase in pH except at MK 3, where the pH of water can be lowered by a high quantity of river runoff. Increased DO was found during heavy rainfall. However, it decreased with excessive suspended sediments and dissolved organic matter caused by water discharges. Rainfall caused variations in nutrient concentrations that were diluted under unpolluted conditions and concentrated by the dispersion of domestic waste and soil erosion. Additionally, results also indicated that the most influential seawater parameters on green mussel production in pond, estuary and coastal areas were different. This can be explained by the results of PCA and the multiple linear regression analysis. Nutrients and DO significantly affected the mussels cultured in a pond. In an estuary, temperature and salinity were influential factors due to freshwater input caused by river runoff. Nutrient and pH levels were found to be more important in coastal waters. Finally, the cultivation areas most sensitive to droughts and heavy storms were ponds, followed by estuary and coastal areas. Drought conditions with high air temperature and negligible rainfall strongly affected the mussel production in the pond. Tropical storms cause intense rainfall that significantly affects mussel production close to the river mouth. This study suggests that cultivating in a coastal area or a large distance from the river mouth can effectively produce green mussels under extreme weather events caused by climate change.

ACKNOWLEDGEMENTS

 

This research was financially supported by the Thailand Research Fund (TRF) and the Office of the Higher Education Commission (OHEC), Grant No. MRG6180223. The authors are also thankful to the Environmental Science and Technology Programme, Faculty of Science and Technology, Bansomdejchaopraya Rajabhat University for providing laboratory space, and the Sriracha Fisheries Station, Faculty of Fisheries, Kasetsart University for providing the green mussels and samples of seawater in this study.

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SUPPLEMENTARY MATERIAL

 

The following supplementary material is available through the online version of this article and at the following link: http://scimar.icm.csic.es/scimar/supplm/sm05232esm.pdf

Table S1. - Simple linear regression and logarithm model equations and coefficient of determination (R2) show correlations between atmospheric parameters and seawater parameters in each sampling station at Bangkhuntein (BKT), the Maeklong river mouth (MK) and Sriracha (SRI).

Table S2. - Spatial and temporal variation of green mussel production and seawater parameters in the mussel cultivation areas at Bangkhuntein (BKT) by using one-way ANOVA and Turkey-HSD.

Table S3. - Spatial and temporal variation of green mussel production and seawater parameters in the mussel cultivation areas at the Meaklong River Mouth (MK) by using one-way ANOVA and Turkey-HSD.

Table S4. - Spatial and temporal variation of green mussel production and seawater parameters in the mussel cultivation areas at Sriracha (SRI) by using one-way ANOVA and Turkey-HSD.

Table S1.  Simple linear regression and logarithm model equations and coefficient of determination (R2) show correlations between atmospheric parameters and seawater parameters in each sampling station at Bangkhuntein (BKT), the Maeklong river mouth (MK) and Sriracha (SRI).
Equation R2 Equation R2
TBKT1 = 2.6753(AHT) - 52.986 R² = 0.7074 ABKT1 = -101.40ln(R) + 637.28 R² = 0.9090
TBKT2 = 2.4945(AHT) - 47.59 R² = 0.7454 ABKT2 = -69.46ln(R) + 478.61 R² = 0.8717
TBKT3 = 2.634(AHT) - 51.855 R² = 0.7494 ABKT3 = -75.00ln(R) + 508.88 R² = 0.8829
TMK1 = 2.5276(AHT) - 52.091 R² = 0.7621 AMK1 = -52.42ln(R) + 272.73 R² = 0.8908
TMK2 = 2.4532(AHT) - 49.874 R² = 0.8169 AMK2 = -43.53ln(R) + 242.78 R² = 0.8336
TMK3 = 2.1928(AHT) - 41.565 R² = 0.7790 AMK3 = -44.47ln(R) + 242.92 R² = 0.8814
TSRI1 = 1.2173(AHT) - 11.377 R² = 0.8820 ASRI1 = -29.76ln(R) + 171.12 R² = 0.7063
TSRI2 = 1.6318(AHT) - 24.948 R² = 0.9107 ASRI2 = -27.61ln(R) + 156.72 R² = 0.7975
TSRI3 = 1.7192(AHT) - 27.999 R² = 0.8137 ASRI3 = -32.21ln(R) + 174.79 R² = 0.7133
pHBKT1 = 0.3787ln(R) + 6.1702 R² = 0.8532 NBKT1 = -8.232ln(R) + 79.446 R² = 0.7588
pHBKT2 = 0.1556ln(R) + 7.3110 R² = 0.7038 NBKT2 = -2.982ln(R) + 53.669 R² = 0.1225
pHBKT3 = 0.3040ln(R) + 6.7289 R² = 0.7649 NBKT3 = -20.84ln(R) + 158.95 R² = 0.8296
pHMK1 = 0.0038(R) + 8.0392 R² = 0.8430 NMK1 = 0.2253(R) + 9.136 R² = 0.6540
pHMK2 = 0.0041(R) + 7.9575 R² = 0.8761 NMK2 = 0.2648(R) + 24.129 R² = 0.7654
pHMK3 = -0.0028(R) + 8.331 R² = 0.8208 NMK3 = 0.1947(R) + 10.276 R² = 0.6859
pHSRI1= 0.0047(R) + 7.9888 R² = 0.6688 NSRI1 = -1.501ln(R) + 9.8385 R² = 0.7181
pHSRI2 = 0.0035(R) + 8.0932 R² = 0.6700 NSRI2 = -0.552ln(R) + 5.6547 R² = 0.7882
pHSRI3 = 0.0046(R) + 7.8071 R² = 0.7854 NSRI3 = -2.325ln(R) + 12.577 R² = 0.6390
SBKT1 = -0.0411(R) + 23.605 R² = 0.6379 PBKT1 = -31.99ln(R) + 200.02 R² = 0.8777
SBKT2 = -0.0439(R) + 24.778 R² = 0.6474 PBKT2 = -38.33ln(R) + 243.27 R² = 0.7533
SBKT3 = -0.0496(R) + 25.142 R² = 0.6118 PBKT3 = -21.57ln(R) + 158.26 R² = 0.9434
SMK1 = -0.0274(R) + 30.415 R² = 0.6852 PMK1 = -3.345ln(R) + 19.965 R² = 0.6171
SMK2 = -0.0665(R) + 28.316 R² = 0.7873 PMK2 = -9.063ln(R) + 49.76 R² = 0.7841
SMK3 = -0.0402(R) + 29.865 R² = 0.7032 PMK3 = -1.312ln(R) + 12.297 R² = 0.6364
SSRI1 = -0.0519(R) + 29.697 R² = 0.8428 PSRI1 = -1.625ln(R) + 11.086 R² = 0.7575
SSRI2 = -0.044(R) + 30.138 R² = 0.7345 PSRI2 = -2.059ln(R) + 11.414 R² = 0.8379
SSRI3 = -0.0481(R) + 30.517 R² = 0.8055 PSRI3 = -1.967ln(R) + 12.541 R² = 0.7388
DBKT1 = 0.2497ln(R) + 3.2924 R² = 0.5765 SiBKT1 = 2.8139(R) + 318.30 R² = 0.7107
DBKT2 = 0.4723ln(R) + 3.4519 R² = 0.6880 SiBKT2 = 5.9321(R) + 463.79 R² = 0.7265
DBKT3 = 0.5528ln(R) + 2.2477 R² = 0.8659 SiBKT3 = 12.407(R) + 626.85 R² = 0.7802
DMK1 = 0.0025(R) + 5.7695 R² = 0.0608 SiMK1 = 2.438(R) + 54.633 R² = 0.7759
DMK2 = -0.0064(R) + 6.0290 R² = 0.7557 SiMK2 = 2.297(R) + 369.44 R² = 0.8922
DMK3 = -0.0153(R) + 7.8423 R² = 0.9633 SiMK3 = 3.4005(R) + 168.67 R² = 0.7645
DSRI1 = -0.0014(R) + 5.7808 R² = 0.0146 SiSRI1 = -125.6ln(R) + 951.33 R² = 0.7260
DSRI2 = -0.0087(R) + 6.2643 R² = 0.6362 SiSRI2 = -145.2ln(R) + 1145.6 R² = 0.7047
DSRI3 = 0.0139(R) + 5.7621 R² = 0.7842 SiSRI3 = -140.1ln(R) + 1155.2 R² = 0.8350

Note: R is rainfall (mm); AHT is average the highest air temperature (°C); T is seawater temperature (°C); pH = pH level of seawater; S is salinity (psu); D is dissolved oxygen (DO) (mg L-1); A is ammonium-nitrogen (NH4+-N) (mg L-1); N is nitrite and nitrate-nitrogen (NO2-+NO3--N) (mg L-1); P is phosphate-phosphorus (PO43--P) (mg L-1); Si is silicate-silicon (Si(OH)4-Si) (mg L-1)

Table S2.  Spatial and temporal variation of green mussel production and seawater parameters in the mussel cultivation areas at Bangkhuntein (BKT) by using one-way ANOVA and Turkey-HSD.
Turkey-HSD
One-way ANOVA Spatial Analysis Temporal Analysis-BKT
Parameter Station Month BKT 1 BKT 2 BKT3 Jan Feb Mar April May Jun Jul Aug Sep Oct Nov Dec
GM (kg m-2) *** ns a a b - - - - - - - - - - - -
Temp (°C) ns *** - - - c e,f d,e g e,f,g b g d f,g d,e c a
pH * * a b a,b a,b a a,b a,b a,b a,b a,b b b a,b a,b a,b
Sal (psu) ns *** - - - c,d c,d b a,b a,b a,b a,b a,b,c a,b a,b b,c d
Conduct (ms) ns *** - - - a,b b,c b,c b,c a c,d a,b a,b,c a,b a a,b c,d
DO (mg L-1) ns ** - - - a a,b a,b a,b,c a,b,c a,b,c a,b,c a,b,c a,b,c a,b a,b,c c
Trans (cm) *** ns a b c - - - - - - - - - - - -
TSS (mg L-1) *** ns a a b - - - - - - - - - - - -
NH4+-N (mg L-1) ns *** - - - c b b b a a a a a a a a
NO2-+NO3--N (mg L-1) * ns a,b a b - - - - - - - - - - - -
PO43--P (mg L-1) ns *** - - - c a,b,c b,c a,b,c a,b a a a a a a a
Si(OH)4-Si (mg L-1) ns * - - - a,b a a,b a,b b a,b a,b a,b a,b a,b a,b a
Chl a (mg L-1) ns ns - - - - - - - - - - - - - - -

Note: Significance level of one-way ANOVA: *** = P < 0.001; ** = P < 0.01; * = P < 0.05; ns = not significant

Table S3.  Spatial and temporal variation of green mussel production and seawater parameters in the mussel cultivation areas at the Meaklong River Mouth (MK) by using one-way ANOVA and Turkey-HSD.
    Turkey-HSD
One-way ANOVA Spatial Analysis Temporal Analysis-MK
Parameter Station Month MK 1 MK 2 MK 3 Jan Feb Mar April May Jun Jul Aug Sep Oct Nov Dec
GM (kg m-2) *** ns b b a - - - - - - - - - - - -
Temp (°C) ns *** - - - f b b,e g e d,e c,d,e b,c,d b,c,d d,e b,c a
pH ns ** - - - a,b a a,b a,b a a,b a,b a,b a,b b a,b a,b
Sal (psu) ns ** - - - b,c b,c a,b,c a,b a,b a,b,c b,c a,b,c a,b a a,b,c c
Conduct (ms) * ns b a a,b - - - - - - - - - - - -
DO (mg L-1) ns ** - - - a,b a,b,c a,b,c b,c a,b,c b,c a,b,c a,b,c a,b,c a,b,c a c
Trans (cm) ** ns b a a - - - - - - - - - - - -
TSS (mg L-1) ns * - - - a a,b a,b a,b a,b a,b b a,b a,b a,b a,b a,b
NH4+-N (mg L-1) ns *** - - - f e,f e d b,c,d a,b,c a,b a,b a,b a a,b,c c,d
NO2-+NO3--N (mg L-1) ns ns - - - - - - - - - - - - - - -
PO43--P (mg L-1) ns ns - - - - - - - - - - - - - - -
Si(OH)4-Si (mg L-1) ns ns - - - - - - - - - - - - - - -
Chl a (mg L-1) ** ns a a,b b a a,b a,b a,b a,b b a,b a,b a,b a,b a,b a,b

Note: Significance level of one-way ANOVA: *** = P < 0.001; ** = P < 0.01; * = P < 0.05; ns = not significant

Table S4.  Spatial and temporal variation of green mussel production and seawater parameters in the mussel cultivation areas at Sriracha (SRI) by using one-way ANOVA and Turkey-HSD.
    Turkey-HSD
One-way ANOVA Spatial Analysis Temporal Analysis
Parameter Station Month SRI 1 SRI 2 SRI 3 Jan Feb Mar April May Jun Jul Aug Sep Oct Nov Dec
GM (kg m-2) ** ns a a b - - - - - - - - - - - -
Temp (°C) ns *** - - - b,c,d,e d c f f e b,c,d,e e b b,c,d b,c a
pH ns * - - - a a,b a a a,b a,b a,b a,b b a,b a,b a,b
Sal (psu) ns *** - - - c,d d c,d c,d b b,c,d b,c b,c,d a d c,d c,d
Conduct (ms) ns *** - - - b,c b,c c b,c b,c b,c b,c a,b a c b,c c,d
DO (mg L-1) ns *** - - - a,b,c a,b,c a,b,c a,b,c a,b b,c,d a a,b,c d a a,b,c c,d
Trans (cm) ns ns - - - - - - - - - - - - - - -
TSS (mg L-1) ns ns - - - - - - - - - - - - - - -
NH4+-N (µg L-1) ns *** - - - b b c b,c a a a a a a a a
NO2-+NO3--N (µg L-1) ns ns - - - - - - - - - - - - - - -
PO43--P (µg L-1) ns *** - - - c,d b,c,d a b,c,d d a a a a,b,c a,b a a
Si(OH)4-Si (µg L-1) ns *** - - - b b b b a a a a a a a a
Chl a (µg L-1) ns *** - - - a a,b a,b a,b a,b a,b a,b b,c c a,b a a

Note: Significance level of one-way ANOVA: *** = P < 0.001; ** = P < 0.01; * = P < 0.05; ns = not significant