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Coastal estuaries are among the most productive areas of the ocean, both ecologically and economically (Lannig et al. 2006). Fisheries and aquaculture industries relying on coastal and estuarine environments in Australia have a combined annual worth of almost AUD $1.1 billion to the Australian economy. Oyster farming is one of the top five mariculture industries in terms of production value (Australian Fisheries Department 2014). Along the NSW coast, there are around 300 oyster farming businesses that produce around 44% of Australia's oysters with an industry value over AUD--$35 million (DPI 2014, 2017).

The Sydney rock oyster (Saccostrea glomerata) is a native Australian bivalve species, which is an important species in intertidal and shallow subtidal habitats of the southeast coast of Australia and can tolerate periods of air exposure in the intertidal zone (Parker et al. 2011). They are active suspension feeders, filtering suspended phytoplankton and particles from the water column. These oysters are found attached to hard substrate such as rocks and mangrove roots (Parker et al. 2010). They are a broadcast spawner, with many more offspring produced in the warmer spring/summer months; however, offspring experience a high mortality rate during the larval phase with environmental conditions such as pH, temperature, and salinity influencing survival and development (Parker et al. 2012).

Organism function and sustainability in populations, communities, and ecosystems is primarily driven by the input, use, and transfer of energy (Sokolova et al. 2012). Organisms store and use energy for basal metabolism, growth, reproduction, and repair (Sokolova 2013). The storage and production of energy is influenced by external factors, both natural and anthropogenic, such as changes in temperature, salinity, and pH as well as internal factors such as energy-limited tolerance to stressors (Sokolova et al. 2012, Sokolova 2013). Biomarkers assessing energetic responses to physiochemical conditions can demonstrate subcellular- to population-level effects (Amiard-Triquet & Amiard 2012). Bioenergetics provides information on key metabolic processes in an organism's energy acquisition and expenditure and can link cellular, individual, and reproductive effects using energy distribution as a "currency" (Smolders et al. 2003). A positive energy balance needs to be maintained for growth, reproduction, and fitness (Moolman et al. 2007). Limitations in the available energy, the rate of energy acquisition, and metabolic conversions result in trade-offs between basal maintenance and fitness-related functions (growth, reproduction, and development) (Sokolova et al. 2012). Assessing energy allocation to biological activities is also emerging as an effective method for assessing organism response to environmental changes (Mouneyrac et al. 2012). Energetic changes in bivalves has been used previously for Crassostrea gigas and Mytilus galloprovincialis to develop an understanding of how local environmental conditions affect the species storage and metabolism in relation to gametogenic cycles (Dridi et al. 2007, Erk et al. 2011). Developing an understanding of Saccostrea glomerata energetic response to Australian environmental conditions will facilitate a more detailed understanding of growth potential in response to physiochemical conditions.

Total glycogen, protein, and lipid provide a measure of energy stores available for metabolic and fitness-related activities, whereas electron transport system (ETS) activity gives a measure of potential maximum energy consumption (Sokolova et al. 2012, Wang et al. 2012). Cellular energy allocation (CEA) incorporates these components by transforming them into energetic equivalents and allows evaluation of energetic costs of stress to organisms (De Coen & Janssen 1997, Smolders et al. 2004, Wang et al. 2012). Cellular energy allocation has demonstrated ecological relevance in its capacity to demonstrate suborganismal response with population-level parameters such as survival and fertility (De Coen & Janssen 1997). The application of CEA analyses to bivalve populations has demonstrated its value in assessing energetic stress responses to seasonal and physiochemical changes (Dridi et al. 2007, Gagne et al. 2007, Ivanina et al. 2013, Nahrgang et al. 2013) as well as toxicological exposures to metals and other contaminants (Smolders et al. 2004, Turja et al. 2014, Gomes et al. 2016).

Glycogen is recognized as the primary energy reserve in bivalves and has been shown to vary significantly between seasons (Nahrgang et al. 2013). Glycogen variations in bivalve species have been attributed to gametogenesis, temperature changes, and food availability (Berthelin et al. 2000, Dridi et al. 2007, Li et al. 2009), all of which are closely linked to each other.

Protein is a major energetic component that can contribute to energy storage and gametogenesis. Although some studies report that protein has a minimal contribution to gametogenetic effort (Berthelin et al. 2000) or little seasonal variation (Nahrgang et al. 2013), other studies have observed seasonal changes in protein content associated with the intense energy demand associated with gonad development (Dridi et al. 2007, Li et al. 2009, Baek et al. 2014) with changes in some bivalve protein content varying with tissue type (Berthelin et al. 2000, Cherkasov et al. 2006, Li et al. 2009).

In bivalves, lipid reserves are a main energetic component and, like protein, have demonstrated seasonal changes, which have been attributed to temperature, food availability, and gametogenesis. In Crassostrea gigas, lipid built up over spring decreased after spawning, and then remained constant for the remainder of the sampling period (Berthelin et al. 2000). Other studies have attributed lipid content changes to temperature (Nahrgang et al. 2013) and food availability (Dridi et al. 2007, Nahrgang et al. 2013).

Energy consumption, as measured by ETS activity, has also been shown to change seasonally in bivalves. The zebra mussel (Dreissena polymorpha) has shown clear trends associated with seasonal temperature changes (Fanslow et al. 2001). Likewise, Nahrgang et al. (2013) observed seasonal patterns in bivalve ETS activity, with increased activity found during autumn and winter that were attributed to gametogenesis and high maintenance costs in periods of low food abundance and quality (Nahrgang et al. 2013).

The aim of this study is to demonstrate the seasonal bioenergetic changes in Saccostrea glomerata in two aquaculturerelevant age groups. This will provide greater understanding of how natural physiochemical changes affect S. glomerata energetic condition, enabling future understanding of growth potential and whether energetic changes are a result of stress or natural fluctuations due to seasonal changes in temperature, salinity, and food availability. The demonstrated energy assessment method used may have future applications in aquaculture farming methods such as optimizing stocking densities, cultivation methods, and farm location viability. Energetic composition may also serve as a potential quality control measure for production and marketing of oysters. Ecological study application includes assessing bivalve population energetics such as in reef restoration projects using bivalves as re efforming organisms.


Site Selection and Sample Collection

The Clyde River at Batemans Bay, NSW, is an important oyster-growing area with increasing threats from human encroachment both around the river and within the catchment (DPI 2014). Clyde River has a catchment area of 1,791 [km.sup.2] and a water area of 19.898 [km.sup.2], making it one of the larger river catchments in NSW (Roy et al. 2001). The 10-y decade average of oyster production in Clyde River is 381.5 t (DPI 2017).

Oysters were collected in summer (January 9, 2015), autumn (April20, 2015), winter (August 6, 2015), and spring (October 6, 2015) from Signature Oysters Pty Ltd. in the Clyde River with samples categorized by age (24 and 36 mo) and sex (Fig. 1). Only female oysters were assessed for seasonal variability because of being the only sex identifiable across all four seasons. Oysters were shucked, sex-identified via the presence of eggs or sperm, weighed, and frozen at -80[degrees]C using dry ice within 4 h of collecting and maintained at -80[degrees]C until processing. Sample wet weights were obtained and the samples were freeze-dried for 48 h. Dry weights were obtained and the samples were ground to a fine powder (less than 20 gm) using an analytical mill (230 v All Basics S5 IKA mill).

Temperature and Salinity

Temperature and salinity data were collected by the NSW Food Safety Authority one to four times per month within the estuary.

Cellular Energy Allocation

Energetic components were determined using wet chemistry methods of De Coen and Janssen (1997) with some modifications described in the following paragraphs, and then transformed into energetic equivalents.

Total Glycogen and Protein Concentrations and Energy Equivalents

Total glycogen and protein concentrations were measured using 0.02 g of freeze-dried tissue rehydrated in 5 mL of deionized water. Trichloroacetic acid (15%, w/v) was added to the tissue in a ratio of 3:1 (v/v/v; tissue to trichloroacetic acid), and then incubated for 20 min at -20[degrees]C (Rajalingam et al. 2009). Extracts were then centrifuged at x 5000 g for 10 min at 4[degrees]C. The supernatant was removed for glycogen analysis and the pellet retained for protein determination. Glycogen was determined by incubating 100 gL of supernatant with 200 gL of 5% w/v phenol and 800 gL of 18M sulfuric acid ([H.sub.2]S[O.sub.4]) at 25[degrees]C for 30 min (DuBois et al. 1956); 150 gL was measured colorimetrically at 492 nm in triplicate and using glucose as standard. Protein was determined by digesting the pellet in 500 gL 1M sodium hydroxide for 30 min at 60[degrees]C. The sample was allowed to cool, and then 300 gL 1.67M of hydrochloric acid was added to neutralize the samples. Concentration of protein was determined using Bradford reagent with bovine albumin serum as standard and measured colorimetrically at 590 nm (Bradford 1976).

Glycogen and protein results were then converted to energy of combustion equivalents (Gnaiger 1983): 17,500 mJ [mg.sup.-1] for glycogen and 24,000 mJ [mg.sup.-1] for protein.

Total Lipid Concentrations and Energy Equivalents

Total lipid concentration was measured using the 2:1 w/v chloroform/methanol extraction method (Folch et al. 1956). This method was selected as previous studies have indicated that bivalve tissues can have lipid concentrations greater than 2% w/w lipids (Brown 2011) and Folch's method has been shown to be more effective at recovering lipids in samples with lipid concentrations greater than 2% w/w (Iverson et al. 2001). In brief, chloroform, methanol, and 0.88% (m/v) sodium chloride was added to 0.3 g dried tissue samples to obtain a tissue to solution ratio of 1:20 (m/v) and a ratio of 8:4:3 (v/v/v) of chloroform/methanol/water. The samples were agitated for 10 min (Intelli Mixer RM2M, 10 min, 35 rpm), and then centrifuged at x 5000 g for 5 min. Supernatants were removed and pellets re-extracted to ensure complete lipid recovery (Dickinson et al. 2012). The supernatants were combined, and then centrifuged at x 1000 g for 10 min and a subsample of the lower chloroform phase removed. The chloroform subsample was mixed with sulfuric acid and charred at 200[degrees]C for 15 min, then diluted with 2 mL of water, and measured colorimetrically at 370 nm using tripalmitin as standard (De Coen & Janssen 1997). Lipid results were then converted to energy of combustion equivalents of 39,500 mJ [mg.sup.-1] (Gnaiger 1983).

Electron Transport System Activity

A slightly modified version of previously published ETS methods was used to determine maximum energy usage potential (Owens & King 1975, De Coen & Janssen 1997). Briefly, freezedried tissue was homogenized in 750.tL cold homogenizing buffer (0.1M sodium phosphate buffer, 75 [micro]M MgSO4, 15% (w/v) polyvinylpyrrolidone and 0.2% (v/v) Triton-x 100) for 2 min, and then centrifuged at 4[degrees]C for 10 min at X8500 g (Owens & King 1975). One part supernatant was then added to a microplate and mixed with three parts cold substrate solution (0.1M sodium phosphate buffer, 1.7mM NADH, 0.25nM NADPH, and 0.2% (v/v) Triton-x 100) (Owens & King 1975) and one part room temperature reagent solution [8mM 2-p-iodo-phenyl 3-pnitrophenyl 5-phenyl tetrazolium chlorine (INT)] (De Coen & Janssen 1997). The microplate was mixed on low speed for 5 min, and then read kinetically for 5 min at 490 nm.

The quantity of oxygen consumed by each oyster sample was transformed into energetic equivalents using the specific oxyenthalpic equivalents of an average lipid, protein and carbohydrate mix of 484 mJ mol [O.sup.-1.sub.2] (Gnaiger 1983).

Cellular Energy Allocation

Cellular energy allocation was calculated as [E.sub.a]/[E.sub.c], where [E.sub.a] is the combined energetic equivalent of total protein, lipid, and glycogen concentrations, whereas [E.sub.c] is the converted ETS activity (De Coen & Janssen 1997). By transforming the measured concentrations of total glycogen, lipid, and proteins into their relative energetic equivalents, it was possible to compare their relative contributions to the energy storage budget (Erk et al. 2011).

Statistical Analyses

Statistical analyses were completed using R and PAST. Data were tested for normality (Shapiro-Wilk test) and homoscedasticity (Bartlett's test). If assumptions were met, a factorial ANOVA was used to investigate the fixed factors, age (24 and 36 mo) and season, with Tukey-Kramer post hoc analysis for multiple comparisons if there were no interactions between variables. If there were interactions between variables, post hoc differences were interpreted based on a marginal means plot. If data did not meet the assumptions, a log transformation was performed and the assumptions were retested. If the data still failed to meet the assumptions, nonparametric Kruskal-Wallis test was used with a Dunn's test for multiple comparisons. Significance testing was performed using the Mann-Whitney pairwise test (nonparametric test, does not assume normal distribution) and one-way ANOVA.

Graphs were created using SigmaPlot and Microsoft Excel (column graphs).


A total of 78 female Saccostrea glomerata were collected from the Clyde River over the four sampling events (Table 1).

Temperature and Salinity

The large temperature variations in the Clyde River followed a relatively predictable seasonal pattern with the occasional dip resulting from rainfall events. Maximum water temperature in summer was 24[degrees]C and the minimum temperature was 10.8[degrees]C in winter (July).

Salinity changes within the Clyde River demonstrated small fluctuations between 20 and 27 psu, averaging 23.5 psu over the year. Sharp fluctuations were of small duration and occurred in conjunction with rainfall events increasing the freshwater flows. The Clyde River has significant influence from both tidal and riverine inputs, resulting in short-term spikes that generally return to the average conditions within a few days. Sustained rainfall events, however, can reduce salinity for periods of up 3 wk (preventing oysters harvesting). No sustained rainfall events occurred over the sampling period.

Energy Stores

Energy stores of glycogen, protein, lipid, and total energy available ([E.sub.a]) are shown in Table 1.


Glycogen energy content significantly changed with season (F = 40.01, d.f. = 3.70; P < 0.001) and age cohorts measured (F = 9.87, d.f. = 1.70; P < 0.01), but there was no statistically significant interaction between season and age (P > 0.05). Glycogen did not demonstrate significant correlations with protein or lipid in either age group.

Glycogen energy content in Clyde River Saccostrea glomerata followed the same pattern for both age groups Fig. 2A). Glycogen energy content was highest in summer, and then decreased to its lowest level in autumn, gradually increasing into winter and spring. The only significant difference in glycogen energy content between the age groups was in spring, where 24-mo-old S. glomerata had significantly higher glycogen energy content than 36-mo-old oysters (P = 0.006) (Table 1).

In summer and autumn, glycogen was the highest contributor to the total energy stores budget (Fig. 3).


Protein energy content significantly changed with season (Kruskal--Wallis chi-square = 55.8, df. = 3, P < 0.001) and age cohorts measured Kruskal--Wallis chi-square = 3.8, d.f. = 1, P = 0.05).

Protein energy content appeared to follow an inverse pattern to glycogen energy content, with highest protein energy content occurring in autumn, followed by decreases in winter and spring (Fig. 2B); however, the correlation between protein and glycogen was not significant. Likewise, the correlation between protein and lipid was not significant.

Significant differences in protein energy content were seen between age groups only in autumn (P = 0.0001) with 24-moold oysters having significantly higher protein energy content, which subsequently dropped into winter to be almost equal with 36-mo-old oysters.

In winter and spring, protein energy content contributed the most energy to the total stored energy budget (Fig. 3).


Lipid energy content significantly changed with season (Kruskal--Wallis chi-square = 32.4, df. = 3, P < 0.001), but not age cohort (Kruskal--Wallis chi-square = 0.3, d.f. = 1, P = 0.6).

Lipid energy content exhibited a similar seasonal pattern as glycogen energy content, with high summer stores decreasing into autumn (P = 0.001), followed by a winter increase (P < 0.05). Unlike glycogen, there was little change in lipid energy content into spring (Fig. 2C).

The only significant difference between the age groups was during summer, with 24-mo-old oysters having significantly higher lipid energy content than 36-mo-old oysters (P < 0.001). In all groups, lipid energy content contributed the smallest portion of energy to the overall energy stores (Fig. 3).

Total Energy Stores

Total energy stores (Ea) significantly changed with season (F = 14.03, d.f. = 3, P < 0.001) and age cohort (F = 23, d f = 1; P < 0.001), but there was no interaction between season and age (P = 0.2).

Total energy stores were highest in summer and autumn, which were not significantly different, with a significant decrease into winter (P < 0.001), and then a slight increase into spring that was not significant (Fig. 2D). The 24-mo-old oysters had higher average [E.sub.a] than 36-mo-old oysters in all seasons (Table 1).

Total Energy Consumption

Total energy consumption (Er), as determined by maximum electron transport activity (ETS), varied between the age cohorts, with the highest activity generally occurring in either autumn or winter. The seasonal pattern of Et. showed that energy consumption increased significantly (P < 0.001) from summer into autumn. From autumn to winter, energy consumption continued to increase (but not significantly) or remained stable. Winter to spring witnessed a nonsignificant decrease in energy consumption for both groups (Fig. 2E).

Energy consumption did not demonstrate significant correlations with glycogen, protein, lipid, or total energy available.

Cellular Energy Allocation

Cellular energy allocation significantly changed with season (F= 44.14, d.f. = 3, P< 0.001) and age cohort (F= 5.29, d.f. =1, P < 0.01) as well; there was a significant interaction between season and age (P < 0.01).

Cellular energy allocation was highest in summer for both age groups, with CEA declining over autumn and winter, and then slightly increasing into spring (Fig. 2F). Age cohort --related significant differences in CEA occurred only in autumn, and in both instances, 24-mo-old oysters had a higher CEA (Table 1).



Seasonal changes in glycogen energetic content for both age and location comparisons reflected changes in temperature in summer, autumn, and spring (Fig. 2A). Previous studies demonstrating the relationship between seasonal temperature change and glycogen energy content are partly in agreement with these results (Dridi et al. 2007). The timing and peaks of glycogen energetic content varied in the literature, both with the results of this study and with each other; however, the published literature attributes glycogen changes to food availability and/or gametogenesis with temperature having some influence (Erk et al. 2011, Acarli et al. 2015).

The differences between age groups indicate that the younger oysters have equal or higher glycogen energy stores (Fig. 2A). The significantly higher energy content in younger oysters occurred in winter and spring, indicating that the younger oysters are either more effective at feeding or are storing additional energy to support more rapid growth and future reproduction.


The inverse relationship of energy stored in protein relative to glycogen may be a result of the increased need for proteins to build and repair tissues during and following gametogenesis and energy use (ETS) in summer and autumn. The pattern of protein energy content for Clyde River oysters is largely similar to that of energy consumption (Fig. 2B and E). Seasonal changes in protein energy have been reported in the literature (Berthelin et al. 2000, Baek et al. 2014), but the timing of these changes differed in this study. In other studies, the peak of protein energy contents occurred in spring or summer, with protein energy content decreasing in autumn. These changes were attributed to gametogenesis (Dridi et al. 2007, Baek et al. 2014). In this study, the protein energy maximum occurred in autumn, post spawning, and as temperatures were declining, indicating that protein content changes were not in response to reproductive development.

There were minimal differences in protein energy content between the different age groups in the Clyde River oysters examined (Fig. 2B), indicating that protein energy storage is not likely to be influenced by age in this study.


The lipid energy seasonal pattern indicates that seasonal changes influence the storage of lipid in Saccostrea glomerata tissues (Fig. 2C). This is consistent with other bivalve studies measuring seasonal change in lipid energy content (Berthelin et al. 2000, Dridi et al. 2007, Nahrgang et al. 2013), but because of lack of data, it cannot be determined if these changes were a result of temperature, food availability, reproduction, or a combination of factors.

The decrease in lipid energy moving into spring was seen in all but one group, which may indicate the use of lipid for vitellogenesis early in the spawning cycle.

Although lipid energy content contributes the highest energetic equivalent in conversion (39 500 mJ [mg.sup.-1]), lipid content contributed the least to the overall energetic budget (Fig. 3). This may be due to less need to store lipids or a physiological characteristic of this species. Other studies of Saccostrea glomerata have recorded similar lipid energy stores in whole tissue studies (Brown et al. 2012).

The influence of age appears to be minimal on lipid energy content in Clyde River oysters, with 24-mo-old oysters only having higher energetic concentrations during summer (Fig. 2C). This may have been due to a greater energetic input of the older age group putting more energetic resources into egg production (vitellogenesis) rather than growth. Relationships between glycogen and lipid have been reported in the literature to show inverse patterns, with maximum glycogen concentrations occurring when minimum lipid concentrations were found and vice versa (Dridi et al. 2007). This was not demonstrated in the Clyde River Saccostrea glomerata examined in this study, with glycogen and lipid seasonal patterns being similar in their seasonal responses.

Total Energy Stores

The contribution of each energy type to the total energy storage (Ea) showed the same patterns when comparing age differences (Fig. 4). Total energy stores changed with seasons, with protein contributing the most to the energy budget in summer and autumn, whereas glycogen was the greatest contributor during winter and spring. Protein has varied functions and are generally not used as an energy source unless other energy sources are depleted. The high summer and autumn protein energy stores may be attributed to the greater work load of proteins in building and repairing the oyster tissues needed to undertake activities during and after spawning and the metabolism requirements in warmer waters. This is supported by the growth data recorded by Hall-Aspland et al. 2015.

The major contribution of glycogen energy content in winter and spring may be attributed to the reduced requirement for protein (so stores broken down and used) and building up of readily accessible energy stores to support upcoming spawning activity.

When comparing between age groups in the Clyde River, the younger cohort of oysters had consistently higher energy stores than the older group (Fig. 2D). This indicates that younger oysters are storing more energy, likely to support increased growth needs as well as future reproductive output requirements.

The strong similarity of patterns, regardless of age, indicates that total energy stores in Saccostrea glomerata is a complex process likely strongly influenced by internal physiological factors as well as external environmental factors.

Electron Transport System Activity

The pattern of ETS activity in this study does not agree with that reported by Nahrgang et al. (2013), having an opposite pattern, with autumn and winter recording the highest ETS activity. Nahrgang et al. (2013) found higher ETS activity in female blue mussels (Mytilus edulis) in spring and summer and suggested this may reflect the higher energetic investment of female's activities such as vitellogenesis (yolk deposition), which was supported in their study by decreased liver lipid content.

In the Clyde River, in all seasons except winter, the younger oysters had higher rates of energy consumption (Fig. 2E). This indicates more energy is being used, likely for fitness related activities or growth.

Cellular Energy Allocation

As a metric for stress, CEA takes into account both the total energy stores available and the potential energy usage through maximum ETS activity. Ongoing stress is likely to reduce CEA, but the duration and source of stress are likely to determine the effect on CEA. As a measure, the lower the CEA is, the more stressed the organism is considered to be. Under toxic exposure, CEA in bivalves has been seen to go into the negatives (Smolders et al. 2004). The oysters assessed from the Clyde River were least stressed in summer, when the energy available was highest, temperatures were warm, and food was abundant. As temperatures decreased, energy consumption rose and energy stores decreased, resulting in a decrease in CEA. Even with changing bioenergetic conditions, as a result of seasonal changes, all Saccostrea glomerata could not be described as stressed when compared with toxic stress conditions. Energy balance remained highly positive in all groups over all seasons.

The energetic changes in Saccostrea glomerata energy storage components, total energy stores, energy consumption, and CEA demonstrated in this study were supported by findings from the Sustainable Oyster Assessment Program (SOAP) who undertook monitoring of oyster growth and mortality of S. glomerata in multiple oyster-growing areas, including the Clyde River over 2014 to 2015. The SOAP study found dramatic increases in growth and condition index over warmer months, in response to increased algal growth, i.e., food increased in supply (Hall-Aspland et al. 2015). These SOAP findings indicate increased energetic availability and allocation of resources to growth during the time of peak reproductive effort and support the seasonal pattern of S. glomerata energetic changes found in this study.

Implications for Oyster Aquaculture and Environmental Monitoring

The influence of physiochemical changes on oyster condition is clearly recognized in the Australian oyster industry (DPI 2014); however, the effect on oyster condition is generally limited to longer term measures such as oyster growth and mortality. Measuring energetic responses to changing environmental conditions allows identification of changes over shorter time frames and population health implications such as potential growth rates. Quantifying energetic changes to changing environmental conditions provides evidence to support present aquaculture practices, such as cage type, adapt future management practices, such as basket position on lease, and enable ongoing sustainable development of a valuable industry, including determining effective stocking densities. Results from this study demonstrate the value of using energetic conditions to assess potential future growth of Saccostrea glomerata within multiple settings. Future applications using energetic condition as a rapid assessment metric may include identifying whether potential aquaculture sites produce viable, rapid growth in oysters, assessing oyster condition in response to threatening processes such as disease exposure and changing physiochemical conditions as well as potential links to assessing oyster quality in food and taste assessments. As a quality control measure, energetic composition may provide an approach for assessing consistent quality of oysters from different locations with varying physiochemical conditions such as changing salinity.


We thank the NSW Food Safety Authority for the provision of temperature and salinity data; McAsh Oysters for the provision of oysters; Rod Ubrihein for assistance on statistical analyses; and G. Bartlett and M. Bartlett for their volunteer help in the field and laboratory.


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Institute for Applied Ecology, University of Canberra, University Drive, Bruce, ACT 2617, Canberra, Australia

(*) Corresponding author. E-mail:

DOI: 10.2983/035.038.0213
Total glycogen, protein, and lipid energy stores, and total energy
stores ([E.sub.a]), energy use ([E.sub.c]), and CEA, as mJ [mg.sup.-1]
in female Saccostrea glomerata from Clyde River, NSW, Australia.

Age     Sampling    n         Glycogen              Protein

24 mo  Summer 2015  12  4,208 [+ or -] 1,289  4,735 [+ or -] 1,578
       Autumn 2015  14  1,513 [+ or -] 515    9,169 [+ or -] 2,403
       Winter 2015   6  3,469 [+ or -] 1,633  2,508 [+ or -] 1,166
       Spring 2015   6  4,875 [+ or -] 1,252  1,454 [+ or -] 556
36 mo  Summer 2015  13  3,967 [+ or -] 1,217  4,024 [+ or -] 1,768
       Autumn 2015  14  1,137 [+ or -] 567    5,665 [+ or -] 1,601
       Winter 2015   8  2,103 [+ or -] 775    1,964 [+ or -] 348
       Spring 2015   5  2,648 [+ or -] 626    1,253 [+ or -] 801

Age         Lipid             [E.sub.(a)]       [E.sub.(c)]

24 mo  946 [+ or -] 172   9,889 [+ or -] 2,678  3 [+ or -] 1
       502 [+ or -] 177  11,184 [+ or -] 2,388  6 [+ or -] 3
       703 [+ or -] 307   6,680 [+ or -] 2,931  8 [+ or -] 4
       615 [+ or -] 86    6,944 [+ or -] 1,645  4 [+ or -] 1
36 mo  699 [+ or -] 162   8,689 [+ or -] 2,652  3 [+ or -] 1
       550 [+ or -] 100   7,352 [+ or -] 1,858  7 [+ or -] 3
       781 [+ or -] 134   4,848 [+ or -] 990    5 [+ or -] 3
       589 [+ or -] 86    4,491 [+ or -] 1,118  3 [+ or -] 2

Age            CEA

24 mo  3,739 [+ or -] 1,430
       1,992 [+ or -] 679
         953 [+ or -] 293
       1,662 [+ or -] 439
36 mo  3,625 [+ or -] 1,266
       1,179 [+ or -] 467
       1,143 [+ or -] 439
       1,562 [+ or -] 508

Values are means with SD.
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Article Details
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Author:Bartlett, Jill K.; Maher, William A.
Publication:Journal of Shellfish Research
Article Type:Report
Geographic Code:8AUST
Date:Aug 1, 2019

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