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The short-term effects of estuarine acidification on the algae filtration rate of the Sydney rock oyster (Saccostrea glomerata)

Amelia Phelan 2019


The Sydney rock oyster Saccostrea glomerata (Gould, 1980) plays a major role in water filtration and nutrient cycling in the estuaries and bays of Eastern Australia. The farming of S. glomerata also generates millions of dollars a year for Australia’s aquaculture industry. However, many S. glomerata populations regularly experience dramatic acidification events due to acid sulfate soil (ASS) runoff flowing into estuaries from intertidal areas. The short-term effects of low pH on S. glomerata algae filtration rate are poorly understood. Therefore, the aim of this study was to investigate the short-term effects of estuarine acidification on S. glomerata algae filtration rate. Two laboratory experiments were conducted. The first examined algae filtration rates in sulphuric acid contaminated seawater at four different pH levels, and the second examined post-acidification oyster mortality. Our results indicated that exposure to acidified water (pH< 6.9) reduced algae filtration rate but did not cause mortality in S. glomerata oysters. Further studies should examine the interacting effects of ASS trace metals and pH on algae filtration rate as this would be a more realistic assessment of the issue.


The Sydney rock oyster, Saccostrea glomerata (Gould, 1980), is endemic to Australia and inhabits the intertidal zones of estuaries and bays along the coastline of New South Wales (NSW) and Southeast Queensland (Schrobback et al. 2014). S. glomerata have extensive economic and ecological value. Regulated farming of this species in NSW dates back to 1872 and currently contributes to more than half of the total aquaculture production in NSW, valuing at $48.7 million per annum in 2018 (DPI 2018). S. glomerata oyster reefs also provide complex structural habitats to a diversity of fish and invertebrate communities, and are a food source to crabs, fish, and whelks (Wilkie et al. 2013). Most notable, however, are their water filtration and nutrient cycling services. S. glomerata are indiscriminate filter feeders that can process up to 3L of seawater·oyster-1·hour-1 (Bayne et al. 1999) feeding on suspended organic matter, bacteria and planktonic algae in the water column (Dove and Sammut 2007b). Therefore, S. glomerata populations minimise eutrophication and over-sedimentation in estuary, coral reef, and seagrass ecosystems (Jonathan et al. 2012).

S.glomerata are well attuned to minor fluctuations in pH (between 6.5-8.5) that are characteristic of estuarine environments (Dove 2003). However, dramatic drops in pH have been found to cause damage to the gills and mantle soft tissues of the animal (Dove 2003; Dove and Sammut 2007a).The primary sources of acidification in Eastern Australian estuaries are acid sulfate soils (ASS) (Sammut et al. 1996). ASS are naturally occurring sediments containing iron pyrite that are found in low-energy, vegetated, intertidal environments (Sammut et al. 1996), such as mangrove forests and salt marshes. When heavy rainfall or floodplain drainage dislodges ASS from these low-oxygen environments, the iron pyrite oxidates to form sulfuric acid, resulting in water acidification and the mobilisation of heavy metal ions into the water column (Sammut et al. 1996). Acidification events as low as pH 3 have been recorded in NSW, some lasting up to 3 weeks (Dove and Sammut 2007a).

The short-term effects of estuarine acidification on S. glomerata, at present, are not well known. There is an abundance of research on the in situ growth and mortality rates for S. glomerata exposed to frequent acidification (Amaral et al. 2011; Amaral et al. 2012; Dove 2003; Dove and Sammut 2007b; Paterson et al. 2003; Sammut et al. 1995). Whereby, the general consensus of these studies is that ASS-affected sites tend to have reduced population densities and higher mortality rates compared to reference sites. However, there is minimal research that explores the immediate physiological responses of S. glomerata to acidification. Two studies have shown that S. glomerata silt filtration rate decreases in response to short-term acidification (<6 hours) (Dove 2003; Dove and Sammut 2007a),yet no work has been done on algae filtration rate specifically. Knowing the effects of estuarine acidification on S.glomerata algae filtration rate has two major implications: (1) S. glomerata algae filtration plays an important role in minimising eutrophication in estuarine, coral reef and seagrass ecosystems in eastern Australia (Jonathan et al. 2012), and (2) knowing how estuarine acidification affects oyster feeding can be informative for farmers in helping them decide where to place their spat.

This study investigates the short-term effects of estuarine acidification on S.glomerata algae filtration rate. To address this, two laboratory experiments were conducted. The first experiment examined the algae filtration rates of oysters in sulphuric acid contaminated seawater at four different pH levels, and the second experiment recorded post-acidification oyster mortality. I hypothesised that (1) algae filtration rates would be lower in the acidic treatments compared to the control and (2) post-acidification mortalities would be higher in the acidic treatments compared to the control.

Figure 1

Materials and Methods

Acclimation period

30 adult S. glomerata oysters of similar length (10 ± 1cm) were sourced from Hastings Pontoon Oysters in Port Macquarie, NSW and transported on ice via plane to Frank’s Seafood wholesalers in West End, QLD. The oysters were acclimated for 7 days to stabilise their metabolism (Thompson et al. 2012). They were held in the Goddard marine genomics research aquarium system at the University of Queensland, in 3 x 60L tanks within a 3800L recirculating system (25.1°C, pH 8.29, 35ppt salinity, 0ppm NH3, 0ppm NO, 3.4ppm PO3-4, 1560ppm Mg2+,470ppm Ca2+, 0ppm Fe2+, 110mV redox, 12:12 light darkcycle) (Challen, C., personal communication). The tanks were supplied a constant inflow of algae feed mixture (1mL·94L-1 seawater).The species of algae in this mixture were based on recommendations from the NSW Department of Primary Industries’ S.glomerata Hatchery Manual (O'Connor et al. 2008) (containing equal amounts Thalassiosira weissflogii sp, T-ischochrysis, Nannochloropsis sp, and Pavlova sp from Reed Mariculture). Once acclimated, the algae inflow to the tanks was shut off for 24 hours prior to experimentation to starve the oysters.

Algae filtration rate experiment

Experimental set-up

I measured the algae filtration rates of the oysters at four different pH levels. 4 x 10L buckets of pH 8.1 seawater were sourced from the Goddard aquarium and filtered using a 100 micron filter. Using a pH probe and varying amounts of 0.05M sulphuric acid, three of the buckets were set to the pH levels of 7.4, 6.9 and 6.0, respectively. These pH levels resembled the actual range of conditions found in Hastings river, NSW (Dove 2003), a popular S. glomerata farming location that drains into the Port Macquarie coral reef network. 15x15x15cm square plastic containers were set-up in a 4x4 matrix and each container was assigned a treatment (8.1 control, 7.4, 6.9 or 6.0) (Figure 2) using a random number generator app. Each container was filled with 2L of their treatment’s seawater and aerated with compressed air using a network of plastic tubing. One oyster was placed in each container and allowed to settle in their new environment for 30 minutes.

Calibrating the mass spectrometer

In a new container, I made up a mixture containing 2L filtered seawater and 1mL of algae feed mixture (the same feed used during the acclimation period). From this mixture, I transferred a 1mL aliquot into a cuvette and measured its optical density at wavelengths 560, 580, 600, 620, 640, 660, 680, 700, 720 and 740nm to produce an optical density (OD) calibration curve (as done by Adeeyinwo et al. (2013)) (Figure 3). OD is a measure of how much light is refracted by a medium and therefore can be used to quantify relative amounts of suspended particles in a medium (Thierie 2014). The calibration curve showed maximal refraction at 680nm, which indicated that 680nm would be the optimal wavelength for quantifying the algae particles present in our algae feed mixture (Thierie 2014).

Measuring filtration rate

The temperature of the water was recorded as 24.8°C. To commence feeding, 1mL of algae feed mixture was added to each of the containers. This is approximate to the daily feed quota recommended for adult S. glomerata by the NSW Department of Primary Industries’ S. glomerata Hatchery Manual (O'Connor et al. 2008). Immediately after, 2 x 1mL aliquots were taken from each container and transferred into a series of 32 x 1mL cuvettes. The OD680 of each cuvette was recorded using a mass spectrometer and the average OD680 of each tank was calculated. Prior to each reading, I applied parafilm to the cuvettes and rotated them in case any particles had settled at the bottom. This process was repeated again at 90 minutes. Aerators were then turned off and after 5 minutes the presence/absence of faeces was recorded.

A hemocytometer was used to estimate the number of algae cells per 1 mL of seawater (N) in the containers. The hemocytometer was loaded with 10µL of the algae:seawater mixture (1:2000mL) and placed on a compound microscope, where I counted the number of algae cells in 10 individual 0.04mm2 squares. From this, an average cell count of 383x106cells·mL-1 was calculated. I then measured the OD680 of known dilutions of the algae:seawater mixture and used this to derive the linear equation (N =0.0316*OD680 + 0.0157, R2 = 0.98), which was used to convert the OD680 values to cells·mL-1. Lastly, filtration rate was calculated with the equation ∆N/1.5*1000 = 106·cells·mL-1·hour-1.

Statistical analyses

The mean algae filtration rate and standard deviation of each pH treatment was calculated in Excel. All other analyses were done in RStudio. Firstly, a preliminary Shapiro-Wilks test was done to test the normality of filtration rate. Upon finding that filtration rate was normally distributed, the effect of pH treatment on algae filtration rate was tested using a one-way ANOVA test. TukeyHSD tests at 95% confidence were applied post hoc to assess differences between treatment means.

Figure 2
Figure 3

Post-acidification mortality experiment

Upon completion of the algae filtration experiment, the oysters were placed back into the Goddard marine genomics research aquarium system for 7 days in normal pH conditions. Due to limited tanks in the aquarium, the oysters were kept in 4 x 20L buckets, respective of their treatment. The buckets were filled with water from the recirculation system, aerated with air stones, and fed 1mL of algae feed mixture per bucket every 3 days. The number of mortalities within each treatment during the 7-day period was recorded.


Algae filtration rate experiment

The mean algae filtration rates of the oysters across the pH treatments have been summarised in Figures 3 and 4. All 16 oysters consumed some amount of algae during the 90 minute period and no faeces were present in the containers at the end of the experiment. The one-way ANOVA test indicated that filtration rate varied significantly between pH levels (F3,12= 7.663, p = 0.004). Post hoc TukeyHSD comparisons revealed that the mean filtration rate for the pH 6.0 treatment varied significantly to pH 7.4 and pH 8.1, but not to pH 6.9. Additionally, the mean filtration rate for pH 6.9 varied significantly to pH 8.1. All other comparisons were not significant. Within-group variation was highest for pH 6.0 (sd = 158.1 106·cells·h-1) and lowest for pH 6.9 (sd = 53.8 106·cells·h-1).

Post-acidification mortality experiment

Zero oysters died during the 7-day post-acidification period.

Figure 4
Figure 5


Algae filtration rate

As hypothesised, exposure to acidified water (pH < 6.9) caused a decline in algae filtration rate compared to the control (pH 8.1). All oysters opened their valves during the experiment, exposing their soft tissue to the sulphuric-acid contaminated water. Therefore, we can infer that acidity adversely affected the gill or mantle soft tissue (or both) of the oysters within the 90 minute feeding period. Dove and Sammut (2007a) conducted a similar study with silt filtration rate and found a reduction of filtration rate within short-term (<6 hour) exposure to pH 5.5 seawater. Using histopathology, they found significant inflammation and cell necrosis in the gills of the acidified oysters, which they attribute to the corrosive nature of the acid ions in the water (Dove and Sammut 2007a). I suspect that similar degenerative effects were the primary cause of reduced filtration rate in this study. However, additional histological research is required to validify this claim.

The reduced filtration rate in the acidic treatments could also be explained by oxidative stress. Other bivalve studies, including one on the hard clam, Mercenaria mercenaria (Haider et al. 2016), and one on the thick shell mussel Mytilus coruscus (Hu et al. 2015), have shown that low pH leads to increased production rates of reactive oxygen species (ROS) in the gills of these animals. Elevated ROS levels are known to cause degeneration in cell structures (oxidative stress). Therefore, it is possible that oxidative stress in the gills of the oysters led to a decrease in their metabolism and subsequent decline in filtration rate. Further research in ROS production in acidified oysters would be needed to validate this claim.

From this experiment alone, we cannot infer that pH was the sole cause for declines in filtration rate. For example, Dove and Sammut (2007a) conducted a similar study with silt filtration rate and found that at pH 5.5, there were higher concentrations of total particulate matter (TPM) and particulate inorganic matter (PIM) in the water, consisting largely of harmful oxidation products such as iron floccules (Dove and Sammut 2007a). There was no iron in the water used in this study, however, it is possible that some oxidation products might have formed as a result of contaminants in the water (e.g. Fe, Al) reacting with the sulphate ions from the sulphuric acid, which could have affected the filtration abilities of the oysters. This is difficult to assess in our experiment without TPM and PIM readings.

An unexpected result in this experiment, was the high variability of the pH 6.0 treatment (Figure 3 and 4). I suspect this was a result of variability in pH tolerance among individuals in the treatment. Amaral et al. (2011) show that S. glomerata are capable of long-term acclimation and genetic selection, which allows certain populations to survive in ASS-affected waters. Therefore, because we do not know exactly where in Hastings River these oysters were harvested, it is possible that variability in pH tolerance led to this result.

Post-acidification mortality

I hypothesised that post-acidification mortalities would be higher in the acidic treatments compared to the control. Unexpectantly, there was 100% survival of the post-acidification oysters. Amaral et al. (2012) found a similar result of nearly 100% survival of S. glomerata after 20-weeks of ASS runoff exposure. Amaral et al. (2012) suggest that these impressive survival rates are likely an outcome of S. glomerata’s in-built adaptations to the daily pH fluctuations that occur in the intertidal zone.


The oyster bioenergetics models of Cerco (2011)and Powell et al. (1992) show that filtration rate is the major determinant of growth in oysters. Therefore, here I assume that algae filtration rate is an indicator of growth. In doing so, there are two major implications that arise from this study. Firstly, S.glomerata are known to play an important role in minimising eutrophication in estuarine, coral reef and seagrass ecosystems (Jonathan et al. 2012). Therefore, from this study,we can infer that S. glomerata in ASS-affected areas are likely to have reduced growth and be less efficient at mitigating eutrophication in such ecosystems. Secondly, this study is informative for S. glomerata farmers. Here we show that low acidity causes decreased growth in S.glomerata. Therefore, to optimise the value of their product we recommend that farmers place their spat in sheltered areas that are less vulnerable to ASS outflow (e.g. near mangroves or in bays).

Limitations and recommendations

There were notable limitations in this study. Experimentally, there is a need for more treatment replicates to overcome variability in pH-tolerance across individuals. Furthermore, histopathology, TPM and PIM measurements would have been useful additions to the method to help explain the trends seen in the data. It would also be worthwhile testing if ASS trace metals (e.g. Al, Fe, Mn, Zn (Dove and Sammut 2007a)) have an interacting effect with pH on filtration rate because this would be a more realistic assessment of the issue. I must also address the underlying assumption of this study, which is that algae filtration rate is an indicator of oyster growth. Whilst there is literature to support this assumption (Cerco 2011; Powell et al. 1992), it may be worthwhile in future to test this relationship in the context of S. glomerata and the experiment.

Overall, I recommend that further work is done on the effects of estuarine acidification on S. glomerata. Currently, literature is limited on this topic, yet, there is great economic and ecological benefit to be gained from furthering research in this area.


I would like to thank Bernard and Sandie Degnan for their assistance with my experimental design and analysis. I would also like to thank Chris Challen for his help in the aquarium, and Mattias for his helpful tips along the way.


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