Bioenergetics and the use of Near Infra-
Red Spectroscopy for measuring health and stress response in common
Australian bivalve species
A thesis to meet the requirements of a Higher Degree by Research (Doctor of
Philosophy)
Jill Kathleen Bartlett
University of Canberra
August 2018
Abstract
Coastal estuaries are among the most productive areas of the ocean, both ecologically and economically. Increases in anthropogenic stress factors such as climate change, habitat alteration, contamination and high levels of disturbance in many estuaries have combined with natural stressors inherent in these systems to alter stress regimes on organisms. In
NSW, Australia, coastal and estuarine environments support urban and industrial development activities, with many estuarine areas supporting valuable aquaculture farming.
Bivalves are filter-feeding sessile organisms that inhabit a wide range of habitats. Bivalve species inhabiting estuarine and coastal environments have been used in environmental biomarker and monitoring studies due to their longevity, filter-feeding, sessile nature, ability to accumulate toxins and global distribution.
The acquisition, transfer and use of energy is key to the growth, repair and reproduction of organisms, including bivalves. Changing environmental conditions influence an organism’s capacity to undertake these fitness-related activities and can be limited through their tolerance to stress. Measuring energy stores in the form of protein, lipid and glycogen and potential energy use in the form of electron transport activity (ETS) provides an approach to understanding how organisms are responding to these changing environmental conditions and to assess their capacity for fitness activities. Energy stores versus energy usage can be incorporated into the energetic measure of Cellular Energy Allocation (CEA) that can provide quantitative data on an organism’s energetic response to stress.
Measurement of energetic components using classical chemical and enzymatic measurements is time consuming, difficult and can be quite costly. An alternative approach is to develop
iii quantitative modelling using near infra-red spectroscopy (NIRS). NIRS is one of the classical methods for structure determination of small molecules. Using NIRS and statistical analysis techniques, it is possible to develop a quantitative predictive model for bioenergetic components in bivalves, using spectral image capture and entering the sample’s related analytical chemistry data. Application of quantitative NIRS models reduces both the time and cost associated with energy condition assessment of bivalves.
NIRS quantitative models for total protein, lipid and glycogen stores and ETS activity in five
Australian estuarine bivalve species (Saccostrea glomerata, Ostrea angasi, Crassostrea gigas, Mytilus galloprovincialis and Anadara trapezia) were developed, along with a comparison of single species versus multi-species models for protein, lipid and glycogen, with the benefits of multi-species models demonstrating greater model range and applicability. Model quality was assessed using the ratio of performance to the interquartile distance (RPIQ) and root mean square error of prediction (RMSEP). All five-species models had an RPIQ greater than 3 and RMSEP less than 12%, indicating that combining like species into models provides robust, reliable results.
Comparisons between individual species models and aggregated models for three oyster species and five bivalve species for each storage component indicates that aggregating data from like species produces high quality models with robust and reliable quantitative application.
The use of bivalve energy assessment is demonstrated by applying bioenergetic assessments to two bivalve stress scenarios. The first is the assessment of energetic changes in an
Australian aquaculture oyster species, S. glomerata, over four seasons to demonstrate the seasonal fluctuations in bioenergetic measurements associated with naturally changing
iv environmental conditions. The study demonstrated clear seasonal trends, with glycogen and lipid content decreasing from summer maximums to autumn minimums then slowly increasing through winter and into spring. Protein and ETS activity demonstrated an opposite trend, with increases in autumn and stable through the other seasons. CEA showed a seasonal change, with higher values over summer, gradually decreasing over autumn and winter then starting to increase again in spring. These results indicate that seasonal changes have a significant influence on S. glomerata storage and allocation of energy.
The second assessment demonstrates the energetic stress response of two oyster species, S. glomerata and O. angasi, exposed to a known metal contamination gradient in Lake
Macquarie, NSW Australia. All energetic measures showed a downward trend in both species in response to sediment metal contamination, demonstrating the value of measuring energetics in bivalves as a measure of stress.
Future studies into the NIRS modelling process should include determining methods for a more direct measure of energy consumption potential. Further useful NIRS models may be built for specific stress proteins such as heat shock proteins and metallothioneins.
Application of energetic assessments to aquaculture-relevant scenarios such as basket and lease density stocking, potential new oyster growing areas and effectiveness of aquaculture techniques may allow for increased productivity. Application of the energetics method to new ecological scenarios and species may also be useful to allow health assessments of other aquatic organisms.
v
Acknowledgements
For my primary supervisor and mentor, Bill Maher. You gave me opportunities and support to achieve more than I realised I could. You gave me the time and flexibility to complete this mammoth task without doubt. Your support through my years of study has been invaluable.
To Tariq Ezaz, my secondary supervisor, your support, time and ideas helped me get this study to what it is.
To my fellow authors of my published and drafted papers, thank you for contributions to this work that couldn’t have happened without your input and hard work.
To the Institute for Applied Ecology, Ecochemistry laboratory and the University of Canberra for facilitating and supporting my studies.
For my husband and collaborator, Matthew Purss. Without your support and input, I could never have achieved such an outcome. From the massive input in developing cutting edge software to allow the computer modelling to take place, to the time and emotional support to get our whole family over the finish line.
To my children, Oliver, Morgan and Teagan. You give me a life I never dreamed possible and distractions from this task when I needed them (and when I didn’t!).
To my parents, Gwen and Michael – for being my Igor’s in the lab and the field and supporting my study dreams from the beginning.
To my fellow PhD students and undergrad volunteers, your help and support has been invaluable.
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My PhD journey was marked by great highs and debilitating lows. From attaining a scholarship that allowed me to take this journey, achieving results and outcomes that gave me a wondrous thrill, to the mind-bending frustrations of working out what it all meant. My personal difficulties and health issues along the way spurred me on to fulfil my dreams and appreciate every day as it comes. I plan to take these lessons in life and continue to apply them to all I continue to do.
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Table of Contents
Abstract ...... iii
Certificate of Authorship ...... vii
Acknowledgements ...... ix
Table of Contents ...... xi
Table of Tables ...... xxiii
Table of Figures...... xxv
Glossary of acronyms ...... xxix
Chapter 1. Introduction ...... 1
Background and rationale ...... 1
Aims and Objectives ...... 3
Chapter 2. The Australian context and relevance of using bioenergetics ...... 5
Setting the scene – Changing environments and the Australian context ...... 5
Australian context ...... 6
Coastal environments ...... 6
Estuarine environments ...... 6
Physical structure and function ...... 7
Ecology ...... 8
Economic value ...... 9
2.2.2.3.1. Aquaculture ...... 9
xi
2.2.2.3.2. Tourism and recreation...... 10
Threats to estuarine health ...... 10
2.2.2.4.1. Industrialisation, urbanisation and contamination ...... 11
2.2.2.4.2. Climate change and ocean acidification in estuaries ...... 12
2.2.2.4.3. Temperature fluctuations...... 14
2.2.2.4.4. Salinity Changes ...... 15
2.2.2.4.5. Hypoxia ...... 16
Bivalve species in Australia ...... 17
Ecology ...... 19
Threats from diseases ...... 19
Sydney Rock oyster – Saccostrea glomerata ...... 21
Flat oyster – Ostrea angasi ...... 24
Pacific oyster – Crassostrea gigas ...... 27
Australian Blue mussel – Mytilus galloprovincialis ...... 29
Sydney cockle – Anadara trapezia ...... 31
Development of whole energy measurement and using Near Infra-Red Spectral modelling ...... 33
Quantifying organism response to environmental change ...... 33
Biological change and biomarkers as measurable biological response to
physiochemical change ...... 33
Energy as a biomarker of stress ...... 34
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Cellular Energy Allocation ...... 37
Total Lipids...... 44
Total Proteins ...... 45
Total Glycogen ...... 46
Electron Transport System (ETS) activity ...... 47
Scope for Growth...... 48
Near Infra-red spectroscopy (NIRS) ...... 51
Data Pre-treatment ...... 52
Band width selection ...... 53
Knowledge-based band width selection ...... 53
Algorithm-based band width selection ...... 54
Regression modelling...... 55
Single-term linear regression ...... 55
Multi-term regression ...... 55
Applying quantitative analysis to NIRS data ...... 56
Measures of model “quality” ...... 59
Chapter 3. Near Infra-red spectroscopy quantitative modelling of bivalve protein, lipid and glycogen composition using single-species versus multi-species calibration and validation sets ...... 63
Abstract ...... 67
Introduction ...... 69
xiii
Methods...... 74
Sample Collection ...... 74
Near Infra-red spectra collection ...... 78
Wet chemical analysis...... 78
Total glycogen and protein concentrations ...... 78
Total Lipid concentration ...... 79
NIRS measurements...... 79
NIRS- Quantitative data modelling ...... 79
Model performance assessment ...... 81
Results ...... 82
Protein ...... 83
Single species models ...... 83
Oyster model...... 84
Bivalve model ...... 84
Lipid ...... 93
Single species models ...... 93
Oyster model...... 94
Bivalve model ...... 94
Glycogen ...... 103
Single species models ...... 103
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Oyster species model ...... 104
Bivalve model ...... 104
Discussion ...... 113
Protein ...... 115
Lipids ...... 116
Glycogen ...... 117
Conclusions ...... 119
Chapter 4. Cellular energy allocation analysis of multiple marine bivalves using near infrared spectroscopy ...... 121
Abstract ...... 125
Introduction ...... 127
Materials and procedures ...... 130
Sample collection ...... 130
Near Infra-red spectra (NIRS) collection ...... 130
Chemical and enzymatic analysis ...... 131
Total glycogen and protein concentrations ...... 131
Total lipid concentration ...... 131
Electron Transport System activity (ETS) ...... 132
NIRS Quantitative data modelling ...... 132
Model performance assessment ...... 133
Results and Discussion ...... 135
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Protein ...... 140
Lipids ...... 141
Glycogen ...... 143
ETS activity ...... 143
Cellular Energy Allocation ...... 145
General considerations ...... 150
Demonstrated application of results to natural environmental conditions and a
metal contamination gradient ...... 151
Seasonal changes ...... 151
Metal contamination gradient ...... 152
Potential application of models for quantitative bioenergetic analysis ...... 154
Chapter 5. Review of bivalve responses to stress ...... 157
Physiochemical factors and effects on biotic condition ...... 157
Temperature ...... 158
Salinity ...... 164
Carbon dioxide and pH ...... 168
Oxygen ...... 177
Contamination and metal exposure ...... 178
Effects of multiple stressors on organisms ...... 179
Salinity and temperature ...... 179
Salinity and pH ...... 180
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Salinity and oxygen ...... 181
Temperature and pH ...... 181
Temperature and oxygen ...... 182
pH and oxygen ...... 183
Physiochemical changes and metal toxicity ...... 183
Rates of change influencing stress response ...... 189
Pulse versus press ...... 189
Short-term ...... 190
Medium term ...... 191
Seasonal changes ...... 192
Summary ...... 193
Chapter 6. Seasonal bioenergetic changes in Saccostrea glomerata from two age cohorts farmed in Clyde River, NSW Australia ...... 195
Abstract ...... 196
Introduction ...... 197
Methods...... 201
Site Selection and Sample Collection ...... 201
Temperature and Salinity ...... 203
Bioenergetic measurements ...... 203
Total glycogen and protein concentrations ...... 203
Total Lipid concentrations ...... 204
xvii
Electron Transport System activity (ETS) ...... 204
Cellular Energy Allocation ...... 205
Statistical Analysis ...... 205
Results ...... 207
Temperature and Salinity ...... 207
Energy stores ...... 210
Glycogen ...... 210
Protein ...... 210
Lipids ...... 211
Total Energy Stores ...... 212
Total Energy Consumption ...... 212
Cellular Energy Allocation ...... 212
Discussion ...... 217
Glycogen ...... 217
Protein ...... 217
Lipid ...... 218
Total Energy Stores...... 219
ETS activity ...... 220
Cellular Energy Allocation ...... 220
Implications for oyster and environmental monitoring ...... 222
xviii
Chapter 7. Saccostrea glomerata and Ostrea angasi exposed to a metal contamination gradient in Lake Macquarie, NSW Australia: biochemical and whole organism responses 225
Abstract ...... 227
Introduction ...... 229
Methods...... 234
Site selection ...... 234
Species selection ...... 235
Sediment collection ...... 237
Oyster collection and exposure ...... 237
Metal Analysis ...... 237
Sediments...... 237
Bivalves ...... 238
Subcellular biomarkers ...... 238
Total Antioxidant Capacity & Lipid Peroxidation ...... 238
Lysosomal stability ...... 239
Cellular Energy Allocation ...... 240
Total glycogen and protein concentrations ...... 240
Total lipid concentration ...... 241
Electron Transport System activity (ETS) ...... 241
Bioenergetic cost of exposure...... 242
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Embryo Development ...... 242
Statistical Analysis ...... 243
Results ...... 244
Sediment metal concentrations ...... 244
Bivalves...... 246
Tissue Metal concentrations – Saccostrea glomerata ...... 246
Tissue Metal concentrations – Ostrea angasi...... 246
Species-specific differences in metal concentrations ...... 246
Oxidative stress response - S. glomerata ...... 247
7.4.2.4.1. Total Antioxidant Capacity ...... 247
7.4.2.4.2. Lipid Peroxidation (LP) ...... 247
7.4.2.4.3. Lysosomal Membrane Stability ...... 247
7.4.2.4.4. Oxidative biomarker interactions ...... 247
Oxidative stress response – O. angasi ...... 248
7.4.2.5.1. TAOC ...... 248
7.4.2.5.2. Lipid peroxidation ...... 248
7.4.2.5.3. Lysosomal Membrane Stability ...... 248
7.4.2.5.4. Oxidative biomarker interactions ...... 248
Energy stores – S. glomerata ...... 251
7.4.2.6.1. Protein ...... 251
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7.4.2.6.2. Lipids ...... 251
7.4.2.6.3. Glycogen ...... 251
7.4.2.6.4. Total available energy ...... 252
7.4.2.6.5. Energy biomarker interactions ...... 252
Energy stores – O. angasi ...... 252
7.4.2.7.1. Protein ...... 252
7.4.2.7.2. Lipids ...... 253
7.4.2.7.3. Glycogen ...... 253
7.4.2.7.4. Total available energy ...... 253
7.4.2.7.5. Energy biomarker interactions ...... 254
Energy consumption – S. glomerata and O. angasi ...... 254
Cellular energy allocation– S. glomerata and O. angasi ...... 254
Bioenergetic “cost” of exposure ...... 255
Embryo Development ...... 261
Mortality ...... 262
Discussion ...... 263
Sediment metal concentrations ...... 263
Metal bioaccumulation...... 264
Oxidative response ...... 268
Bioenergetic responses to metal exposure ...... 269
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Embryo Development ...... 274
Conclusions ...... 276
Chapter 8. Synopsis and Conclusions ...... 277
NIRS modelling ...... 277
Using bioenergetics to assess bivalve condition and responses...... 278
Improvements and future studies ...... 283
NIRS quantitative modelling and chemical data sets ...... 283
Application of CEA approach ...... 284
Conclusions ...... 285
Chapter 9. References ...... 287
Appendix 1 – Traditional chemistry testing methods assessment ...... 323
Appendix 2 – Data in Brief article ...... 343
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Table of Tables
Table 2.1 - Classification of species examined ...... 18
Table 2.2 - Glycogen changes in bivalve response to physiochemical seasonal changes . 41
Table 2.3 - Protein changes in bivalves in response to seasonal changes ...... 42
Table 2.4 - Lipid energy composition changes in bivalves in response to seasonal changes ...... 43
Table 2.5 - NIRS model performance assessment parameters ...... 61
Table 3.1 - Sample collection details for NIRS modelling ...... 76
Table 3.2 - Protein Calibration model for individual species models versus multi-species model ...... 92
Table 3.3 - Lipid Calibration model results for individual species models versus multi- species model ...... 102
Table 3.4 - Glycogen Calibration model results for individual species models versus multi-species model ...... 112
Table 4.1 - Chemical composition of bivalve tissues used in the calibration and validation data sets, showing the number of samples (n), median and range. All results are for tissue dry mass...... 136
Table 4.2 - NIRS modelling of bivalve composition outcomes for the quality assessment of each component modelled...... 139
Table 4.3 - Measure of model quality using the modelled sample output and measured values. Value is calculated as (measured value/predicted value*100) then averaged and presented with ± Standard Error. They are then converted to a percentage of the
xxiii measured range of values. To assess the model robustness, the closer to 100%, the more accurate the predictive outcomes...... 139
Table 5.1 - Effect of varied temperature exposures on multiple bivalve species ...... 160
Table 5.2 - Effect of salinity changes on multiple bivalve species ...... 166
Table 5.3 - Effect of carbon dioxide dissolution and pH changes on multiple bivalve species ...... 171
Table 5.4 - Effect of multiple stressors on bivalve species...... 185
Table 6.1 - Total energy available, cellular energy allocations, energy available of each storage type and energy consumed as mJ/mg in female S. glomerata from Clyde River,
NSW Australia. Values are means and standard deviations ...... 209
Table 7.1 - Metal concentrations in sediment and two transplanted bivalves from Lake
Macquarie, NSW, Australia. Individual metals are in µg/g dry mass. Values are presented as mean ± standard error...... 245
Table 7.2 - Comparison of bioenergetic equivalents in exposed S. glomerata to those in pre-exposed animals, converted to percentages and designating pre-exposure as 100%
...... 256
Table 7.3 - Comparison of bioenergetic equivalents in exposed O. angasi to those in pre- exposed animals, converted to percentages and designating pre-exposure as 100% .... 256
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Table of Figures
Figure 2.1 - Distribution of S. glomerata from Atlas of Living Australia (Australia 2017)
...... 23
Figure 2.2 - Distribution of Ostrea angasi from Atlas of Living Australia (Australia
2017) ...... 26
Figure 2.3 - Distribution of C. gigas from Atlas of Living Australia 2017 (Australia
2017) ...... 28
Figure 2.4 - Distribution of M. galloprovincialis from Atlas of Living Australia, 2017
(Australia 2017) ...... 30
Figure 2.5 - Distribution of A. trapezia from Atlas of Living Australia 2017 (Australia
2017) ...... 32
Figure 2.6 - Typical structure of a generic GA demonstrating the looped process that is repeated until the optimum outcome of the quantitative modelling process is achieved.
...... 58
Figure 3.1 - Samples sites for bivalve collections in NSW, Victoria and South Australia
(Google, 2017) ...... 75
Figure 3.2 - mean (line) and standard deviation (shading) raw absorbance spectra for the five bivalve species modelled separately and then combined for the oyster and bivalve models ...... 82
Figure 3.3 – Bandwidth selection outcomes for protein modelling in S. glomerata (a), O. angasi (b), M. galloprovincialis (c), A. trapezia (d), 3 oysters (e) and 5 bivalves (f)...... 88
Figure 3.4 - Protein modelling results for S. glomerata (a), O. angasi (b), M. galloprovincialis (c), A. trapezia (d), 3 oysters (e) and 5 bivalves (f)...... 91
xxv
Figure 3.5 - Bandwidth selection outcomes for lipid modelling in S. glomerata (a), O. angasi (b), M. galloprovincialis (c), A. trapezia (d), 3 oysters (e) and 5 bivalves (f)...... 98
Figure 3.6 - Lipid model outcome plots for S. glomerata (a), O. angasi (b), M. galloprovincialis (c), A. trapezia (d), 3 oysters (e) and 5 bivalves (f) ...... 101
Figure 3.7 - Bandwidth selection outcomes for glycogen modelling in S. glomerata (a), O. angasi (b), M. galloprovincialis (c), A. trapezia (d), 3 oysters (e) and 5 bivalves (f)...... 108
Figure 3.8 - Glycogen model outcome plots for S. glomerata (a), O. angasi (b), M. galloprovincialis (c), A. trapezia (d), 3 oysters (e) and 5 bivalves (f)...... 111
Figure 4.1 - Bandwidth selection using GA for protein (a), lipid (b), glycogen (c) and
ETS activity (d). Darker bands indicate higher correlations from spectral peaks ...... 147
Figure 4.2 - NIRS modelling outputs for measuring protein (a), lipid (b), glycogen (c) concentrations and ETS activity (d) ...... 149
Figure 4.3 - Seasonal changes in CEA (shown in the boxes) of S. glomerata in a natural environment, with temperature shown via the line. Associated data (chapter 6) indicates these changes result from decreased glycogen and lipid stores following spawning and increased ETS activity during cooler seasons. These changes are a result of increased energy demand to meet basal metabolic requirements and decreased food
(plankton)...... 153
Figure 4.4 - CEA Response in S. glomerata exposed to a known metal contamination gradient in Lake Macquarie, NSW, Australia. Plot (a) shows total sediment metal concentrations, while Plot (b) demonstrates the CEA response following a 31 day exposure. CEA decreased from the background metal concentration to the moderate metal concentration in a linear fashion, while at the high metal concentration, CEA did not decrease as much. We suspect this is due to bivalve capacity to reduce feeding and
xxvi lowered energy consumption in response to stress (see chapter 7 for more detailed analysis)...... 153
Figure 6.1 - Sampling location on the NSW coast at Clyde River, Batemans Bay, NSW
Australia...... 202
Figure 6.2 - Temperature and salinity for Clyde River from summer 2014 to mid-spring
2015...... 208
Figure 6.3 - Seasonal changes to energy stores, use and CEA in female S. glomerata from 24 month and 36 month old cohorts in the Clyde River, NSW Australia ...... 214
Figure 6.4 - Total energy stores available as a feature of relative contributions from each energy store type in female S. glomerata in the Clyde River, NSW over 4 seasons.
...... 215
Figure 7.1 - Sites selected along a metal contamination gradient in Lake Macquarie,
NSW Australia ...... 236
Figure 7.2 - Oxidative stress response of S. glomerata exposed to metal contamination gradient in Lake Macquarie, NSW Australia. “a” and “b” indicate significant differences from pre-exposure within oxidative biomarkers, with “a” indicating no significant difference and “b” indicating a significant difference over time of exposure.
...... 249
Figure 7.3 - Oxidative stress response of O. angasi exposed to metal contamination gradient in Lake Macquarie, NSW Australia. “a” and “b” indicate significant differences from pre-exposure within oxidative biomarkers, with “a” indicating no significant difference and “b” indicating a significant difference over time of exposure.
...... 250
xxvii
Figure 7.4 - Total energy stores in Saccostrea glomerata exposed to a metal contamination gradient in Lake Macquarie, NSW Australia, showing the portions of glycogen, protein and lipid...... 257
Figure 7.5 - Total energy stores in Ostrea angasi, shown as glycogen, protein and lipid, exposed to a metal contamination gradient in Lake Macquarie, NSW Australia...... 258
Figure 7.6 – Electron transport system activity (ETS) and cellular energy allocation
(CEA) for S. glomerata exposed to a contamination gradient in Lake Macquarie, NSW
Australia...... 259
Figure 7.7 – Electron transport system activity (ETS) and cellular energy allocation
(CEA) for O. angasi exposed to a contamination gradient in Lake Macquarie, NSW
Australia...... 260
Figure 7.8 - Percentage of normal embryo development of S. glomerata in Lake
Macquarie, NSW ...... 262
xxviii
Glossary of acronyms
Term Definition ATP Adenosine triphosphate BMR Basal metabolic requirements CEA Cellular energy allocation The use of mathematical and statistical methods to improve the Chemometrics understanding of chemical information and to correlate quality parameters or physical properties to analytical instrument data. ETS Electron transport system activity GA Genetic algorithm LV Latent variables NIR Near Infra-red NIRS Near Infra-red spectroscopy PCA Principal component analysis PCR Principle component regression PLS Partial least squares QX Queensland Unknown - an oyster pathogen RMSECV Root mean square error of cross validation RMSEP Root mean square error of prediction RPD Ration of performance to standard deviation RPIQ Ration of performance to the interquartile distance SD Standard deviation SE Standard Error SEC Standard error of calibration SEP Standard error of prediction SFG Scope for growth WM Winter mortality - an oyster pathogen
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