i UNIVERSITY OF GHANA COLLEGE OF BASIC AND APPLIED SCIENCES SCHOOL OF BIOLOGICAL SCIENCES MODELLING THE RELATIONSHIP BETWEEN THE WEST AFRICAN MANGROVE OYSTER (Crassostrea tulipa, L.1819) AND THE AQUATIC AND CLIMATIC ENVIRONMENT FOR USE AS A BIO-INDICATOR IN THE DENSU ESTUARY BY SANDRA AKUGPOKA ATINDANA (10638040) THIS THESIS IS SUBMITTED TO UNIVERSITY OF GHANA, LEGON, IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF PHD IN FISHERIES SCIENCE DEGREE DEPARTMENT OF MARINE AND FISHERIES SCIENCES SEPTEMBER, 2021 University of Ghana http://ugspace.ug.edu.gh ii DECLARATION I hereby declare this thesis as the result of original research conducted by Sandra Akugpoka Atindana, of the Department of Marine and Fisheries Sciences, University of Ghana, under the supervision of Professors Francis K. E. Nunoo, Patrick K. Ofori- Danson and Dr. Samuel Addo and that no part of it has been presented for another degree in this University or elsewhere. ………………………………….. Date: 15th September, 2021 Sandra Akugpoka Atindana (PhD student) …………………………….. Prof. Francis K. E. Nunoo (Principal Supervisor) Date: 15th September, 2021 ………………………………….. Prof. Patrick K. Ofori-Danson (Supervisor) Date: 15th September, 2021 ………………………………….. Date: 15th September, 2021 Dr. Samuel Addo (Supervisor) University of Ghana http://ugspace.ug.edu.gh iii ABSTRACT Crassostrea tulipa (Lamarck, 1819) in the Densu estuary was investigated from March 2019 to August 2020 for aspects of its ecology; it’s potential as a bio-indicator of environmental variability; and long-term effects of climate variability on shellfish production in Ghana’s artisanal fisheries and its implication on sustainable management of oyster fisheries. Oyster samples were collected monthly and physicochemical parameters namely Temperature (OC), Dissolved Oxygen (mg/L), pH, Total Dissolved Substances (mg/L), Conductivity (µS/cm) and Salinity (0/00) measured in situ in triplicates. Silicates, Total Alkalinity, Chlorophyll a, microbes (Total Viable Counts, faecal coliform and Escherichia coli) and heavy metals (Lead, Cadmium and Mercury) were measured ex situ following standards of APHA (2015). Relative abundance was measured as Catch Per Unit Effort (CPUE) and growth pattern determined using the TropFishR package in R programming software. The numerical and frequency of occurrence methods were used to determine its food habits. Species-environmental driver relationship was analyzed following Canonical Correspondence Analysis (CCA) approach using the Vegan package (version 2.5-4.) in R studio software (version1.3.1056). CPUE from experimental fishing was significantly (p < 0.05) higher (6-200; 233.33 ± 6.00 kg/hr/fisher/day) than commercial fishing (3-100;78.12 ± 7.11 kg/hr/fisher/day). CPUE was significantly higher (p = 0.0161) at low tide (115-500;50.10 ± 5.3 kg/hr/fisher/day) than high tide (6-200; 62.58 ± 3.12 kg/hr/fisher/day). CPUE was higher (p = 0.023) in the dry season (150.87 ± 1.12 kg/hr/fisher/day) than the rainy season (57.45 ± 0.55 kg/hr/fisher/day). Crassostrea tulipa has a fast growth rate (K= 0.81; L∞ = 13.24 cm). Higher condition index (60 %) was recorded in the rainy season than the dry season (39 %). University of Ghana http://ugspace.ug.edu.gh iv The diet of the oyster was predominated by golden algae (IRI=595), red algae (IRI=209), green algae (IRI=131.37) and diatoms (IRI =172). Densu estuary is a dynamic shallow system with high concentration of total alkalinity and aragonite. Water depth, silicates, e coli and revelle factor were significantly higher (p < 0.05) at high tide than low tide. Also, mean water depth, cadmium, total alkalinity, pH, carbon dioxide, lead, total carbon dioxide, carbon dioxide fugacity and chlorophyll a were significantly higher (p<0.05) in the rainy season than the dry season. The simple linear regression models for forecasting shell height, shell width, condition factor and relative abundance are respectively described as follows: Shell Height (n=1800) = -0.006.673 faecal coliform (CFU/ml) - 0.002933 Total carbon dioxide -0.0002556 carbon dioxide fugacity The coefficient of determination, R2, of 0.5226 explained about 52.26 % of the variability in shell height. Shell Width (n = 1800) = -0.02262 faecal coliform (CFU/ml) + 0.00089 Total carbon dioxide -0.0000722 carbon dioxide fugacity Approximately 41.02 % (R2) of the variability in shell width is attributable to faecal coliform, total carbon dioxide and carbon dioxide fugacity. Also, an R2, of 0.5743 shows that 57.43 % of the changes in condition factor was explained by aragonite content and the model describing it is, Condition Factor (n = 1800) = 65.646 Aragonite About 26.05 % (R2) of the oyster abundance is due to temperature. Oyster Catch Per Unit Effort (n = 1080) = - 35.51973 Surface Water Temperature (OC) Also 91 % (R2) of the variations in shellfish catch is due to temperature following the model. University of Ghana http://ugspace.ug.edu.gh v Shellfish catch per unit effort = −7788.067 + (265.312 SST) Except for mercury, small- sized oyster (2.5-3.5g) tissues significantly (p < 0.05) bio accumulated Pb and Cd than big- sized (4.5-5.4g) tissues. Lead, mercury, TVC, faecal coliform and Escherichia coli also bio accumulated in C. tulipa tissues more (BAF > 1) than in the water medium which suggests that it has the ability to provide a measurable response to changes in the estuarine environment. Therefore, C. tulipa in the Densu estuary has the ability to accumulate pollutants from the environment and its morphometric features could give clues on the state of environmental variables. C. tulipa is a good bioindicator for assessing; lead, mercury, Total viable counts, E coli and faecal coliform in the Densu estuary. Densu estuary is high in aragonite and total alkalinity. The predictor variables for; Condition factor is aragonite, shellfish catch is temperature and Shell height & Width are faecal coliform, total carbon dioxide and carbon dioxide fugacity. There is the occurrence of contamination and therefore the need for regular monitoring, enactment of control measures and depuration prior to consumption. Also, the use of refuse dam and sewage outlet should be prohibited. It is recommended that laboratory and field-controlled experiments be conducted on oyster responses to extremes of temperature, aragonite and total alkalinity. There is an urgent need for the collation of data on estuarine/lagoonal shellfisheries in Ghana by Fisheries Commission and other stakeholders on catch trends, gears, effort and income of artisanal oyster collectors. University of Ghana http://ugspace.ug.edu.gh vi DEDICATION This research work is dedicated to YOD HAY WAH HAY (YaHWeH) for His divine presence and provision in my life (Daniel 11:32b). University of Ghana http://ugspace.ug.edu.gh vii ACKNOWLEDGEMENTS Abba Father, see how far You have brought me! I am eternally grateful. To my supervisors, Professor Francis K. E. Nunoo, Professor Patrick K. Ofori-Danson and Dr. Samuel Addo of the Department of Marine and Fisheries Sciences, you have been so phenomenal in your individual capacities in guiding, mentoring and supervising this piece without which, would not have seen the light of day. Thank you very much and God richly bless you. I am grateful to the University for Development Studies and the Department of Fisheries and Aquatic Resources Management for granting me study leave with pay and supporting the study, the Ga East Municipal Assembly for offering me the GETFUND scholarship during the research phase of the study, and all staff of the Department of Marine and Fisheries Sciences, University of Ghana for the academic training and rich source of experience. I am thankful to Dr. Emmanuel Lamptey and staff of ENVARSERVE for encouraging me to pursue a PhD in Legon and assisting in the laboratory analyses respectively. My profound gratitude goes to my husband Mr. Roland A. Akayagre, my sons Caleb A. Asapaka, Glory Y. Asapaka, daughter Excellencia Y. Asapaka and mother, Stella A. Apasnaba for the unflinching support, encouragement and prayers throughout the programme. While you held the torch, God kept the flame burning. God bless you. I appreciate Dr. Tetteh Akiti of SNAS-UG, Dr. Evans K. Arizi of UCC, Dr. Mrs.Eunice Asamoah, Dr. Berchie Asiedu, Mr. Charles Teye and Mr. James Akomeah of blessed memory for their support. A word of gratitude to all individuals and parties for their cooperation, particularly the oyster collectors at Densu, Promise Hunya, the staff of Development Action Association (DAA) and all local fishers who facilitated in the data collection. University of Ghana http://ugspace.ug.edu.gh viii TABLE OF CONTENTS DECLARATION…………………………………………………………………... ii ABSTRACT………………………………………………………………………...iii DEDICATION……………………………………………………………………...vi ACKNOWLEDGEMENTS………………………………………………………...vii TABLE OF CONTENTS…………………………………………………………...viii LIST OF TABLES………………………………………………………………….xv LIST OF FIGURES………………………………………………………………... xix LIST OF ABBREVIATIONS AND ACRONYMS……………………………….. xxi CHAPTER ONE………………………………………………………………….. 1 1.0 GENERAL INTRODUCTION……………………………………………..1 1.1 Background…………………………………………………………………1 1.2 Problem Statement and Relevance of Study………………………………..4 1.3 Objectives………………………………………………………………….. 6 1.3.1 Specific Objectives………………………………………………………… 6 1.4 Research Questions………………………………………………………....6 1.5 Delimitation of the Study…………………………………………………...8 1.5 Limitations of the Study…………………………………………………….8 1.6 Organisation of the Study………………………………………………….. 9 CHAPTER TWO………………………………………………………………….11 2.0 LITERATURE REVIEW………………………………………………….. 11 2.1 Taxonomy, Classification and Distribution of Oysters……………………..11 2.1.1 Feeding……………………………………………………………………...19 2.2. Hydro Dynamics of Estuaries in Relation to Oysters……………………… 20 2.2.1 Physicochemical Parameters Influencing Oyster Abundance………………20 University of Ghana http://ugspace.ug.edu.gh ix 2.2.2 Character of Bottom………………………………………………………...21 2.2.3 Sediment Particle Size……………………………………………............... 21 2.2.4 Total Dissolved Solids……………………………………………………... 22 2.2.5 Exchange of Water………………………………………………………….22 2.2.6 Tides………………………………………………………………………...23 2.2.6 Temperature……………………………………………………………....... 23 2.2.7 pH…………………………………………………………………………...26 2.2.8 Conductivity…………………………………………………………….......26 2.2.9 Salinity……………………………………………………………………... 27 2.2.10 Turbidity and Transparency……………………………………………… 31 2.2.11 Nutrients…………………………………………………………………….32 2.2.12 Silicates…………………………………………………………………......33 2.2.13 Primary Productivity in Estuaries………………………………………….. 34 2.2.14 Food Habits of the West African Mangrove Oyster………………………...35 2.2.15 Negative Drivers of Oyster Abundance…………………………………….37 2.2.16 Trace Metal Accumulation in Estuarine Environments…………………….37 2.2.17 Mercury in Estuarine Environments and its Effects on Oyster Growth ……38 2.2.18 Lead in Estuarine Environments and its Effects on Oyster Growth ……… 40 2.2.19 Cadmium in Estuarine Environments and its Effects on Oyster Growth … 40 2.2.20 Microbial Contamination in Estuarine Environments and its Effects on Oyster Growth…….…………………………………………………………………41 2.3. Climate Factors Influencing Oyster Growth………………………………...43 2.4. Climate Variability and its Impact on Aquatic Life in Ghana……………....45 2.5. Local Effects of Climate Change on Oysters……………………………….46 2.6 Effects of Climate Change on Shellfish…………………………………….47 University of Ghana http://ugspace.ug.edu.gh x 2.7 Estuarine Acidification……………………………………………………..48 2.8 Review of Conventional Water Quality Monitoring Techniques………….. 49 2.9 Review of Conventional Bio Indicators…………………………………….54 2.10 Challenges in Using Aquatic Bio Indicators………………………………..55 2.11 Overview of the Physico-Chemical Conditions of the Densu estuary….…..58 2.12 Review of Studies on the Ecology and Biology of Brackish Populations in Ghana………………………………………………………. 60 CHAPTER THREE………………………………………………………………..62 3.0 ASPECTS OF THE ECOLOGY OF THE West African Mangrove Oyster (Crassostrea tulipa) IN THE DENSU ESTUARY RELEVANT FOR CONSERVATION……………………….. 62 3.1 Introduction…………………………………………………………………62 3.2. Materials and Methods……………………………………………………...64 3.2.1 Study Area…………………………………………………………………. 64 3.2.2. Site Description……………………………………………………………. 65 3.2.3. Sampling Design……………………………………………………………67 3.2.4. Relative Abundance of Oyster……………………………………... ……... 68 3.2.5 Commercial Catches……………………………………………………….. 68 3.2.6. Experimental Catches……………………………………………………… 69 3.3. Data Analyses……………………………………………………………… 70 3.3.1 Sampling for Determination of Growth Pattern…………………………….70 3.3.2 Data Analyses……………………………………………………………… 70 3.3.3 Determination of Condition Factor…………………………………………73 3.4. Data Analyses……………………………………………………………… 74 3.4.1 Data collection for Analysis of Food Habits………………………………..74 University of Ghana http://ugspace.ug.edu.gh xi 3.5. Results……………………………………………………………………... 76 3.5.1 Oyster Catch………………………………………………………………...76 3.5.2 Experimental Catches……………………………………………………… 76 3.5.3 Growth Pattern……………………………………………………………...79 3.5.4 Condition Index……………………………………………………………. 81 3.5.5 Food Habits…………………………………………………………………83 3.5.6 Overall Species Richness, Diversity, Evenness and Similarity……………. 89 3.5.7 Similarity of Food Items in Guts and Water………………………………..90 3.6 Discussion…………………………………………………………………..90 3.6.1. Oyster Catch………………………………………………………………….90 3.6.2 Growth Pattern……………………………………………………………...93 3.6.3 Condition Index……………………………………………………………. 94 3.6.4 Food Habits………………………………………………………………... 96 3.7 Conclusions…………………………………………………………………97 CHAPTER FOUR…………………………………………………………………99 4.0 ASSESSMENT OF THE POTENTIAL USE OF THE WEST AFRICAN OYSTER AS A BIO-INDICATOR OF ENVIRONMENTAL VARIABILITY……………………………………………………………..99 4.1 Introduction.………………………………………………………………100 4.2 Materials and Methods……………………………………………………101 4.2.1 Study Design………………………………………………………………101 4.2.2 Measurement of Physicochemical Parameters……………………………101 4.2.3 Sampling and Determination of Silicates…………………………………102 4.2.4 Sampling and Measurement of Primary Productivity (Chlorophyll a)……103 4.2.5 Field Data Collection and Measurement of Microbial Loads…………….103 University of Ghana http://ugspace.ug.edu.gh xii 4.2.6 Sampling for Heavy Metals Determination………………………………... 104 4.2.6.1 Preparation of Standard Reference Materials……………………………...105 4.2.6.2 Washing of Teflon Bombs and Negative Controls………………………...105 4.2.6.3 Positive Control……………………………………………………………106 4.2.6.4 Determination of Arsenic Concentration in Water and Oyster……………106 4.2.6.5 Data Analyses……………………………………………………………...106 4.2.6.6 Intake Rate Limits………………………………………………………….106 4.2.6.7 Daily Intake Limit………………………………………………………….106 4.2.7 Sampling for Measurement of Estuarine Acidification Factors…………...108 4.2.7.1 Data Analyses for Estuarine Acidification Factors………………………..109 4.2.7.2 Data Analyses of Physicochemical Parameters……………………………110 4.2.7.3 Analysis of Species-Environmental Driver Relationship………………….110 4.3 Results……………………………………………………………………..112 4.3.1 Monthly Variations in Physicochemical Parameters………………………112 4.3.2 Seasonal Changes in Physicochemical Parameters………………………. 117 4.3.3 Tidal Influences on Physicochemical Parameters…………………………119 4.4.4 Relationship between Physicochemical Factors, Relative Abundance and Morphological Characteristics of the West African Mangrove Oyster…………………………………………………….123 4.4.5 Results on Multiple Linear Regression Models on Factors Influencing C. tulipa Size and Abundance……………………………………128 4.4.5.1 Predictive Shell Height Model……………………………………………129 4.4.5.2 Predictive Shell Width Model…………………………………………….131 4.4.5.3 Predictive Condition Factor (CF) Model………………………………….133 4.4.5.4 Predictive Relative Abundance (Catch Per Unit Effort) Model………….136 University of Ghana http://ugspace.ug.edu.gh xiii 4.4.5.5 Predictive Shell Weight Model……………………………………………..137 4.4.6 Relationship between size of West African Mangrove Oyster and Concentration of Contaminants………………………………...139 4.4.6.1 Trace Metals………………………………………………………………...139 4.4.6.1 Bio-accumulation of Trace Metals and Health Risk Assessment…………...141 4.4. Discussions ………………..………………………………………………..143 4.4.1 Monthly Variations in Physicochemical Parameters………………………..143 4.3.2 Seasonal Changes in Physicochemical Parameters………………………....146 4.3.3 Tidal Influences on Physicochemical Parameters…………………………..150 4.4.4 Relationship between Physicochemical Factors, Relative Abundance and Morphological Characteristics of the West African Mangrove Oyster………………………………………………….. 153 4.4.5 Relationship between Size of West African Mangrove Oyster and Concentration of Contaminants………………………………………...155 4.4.5.1 Trace Metals and Microbial Load Contamination…………………………. 155 4.4.5.1 Trace Metals and Microbe Bio-accumulation and Health Risk Assessment…………………………………………………….156 4.5 Conclusions…………………………………………………………………157 4.4.1 Multiple Linear Regression Models for Predicting Shell Size, Condition Index and Relative Abundance…………………………………. 158 4.6 Conclusions and Recommendations……………………………………….. 160 University of Ghana http://ugspace.ug.edu.gh xiv CHAPTER FIVE…………………………………………………………………. 161 5.0 MODEL PROJECTIONS OF LONG-TERM EFFECTS OF CLIMATE VARIABILITY ON SHELLFISH PRODUCTION IN GHANA’S ARTISANAL FISHERIES AND ITS IMPLICATIONS ON SUSTAINABLE MANAGEMENT OF OYSTER FISHERIES………………………………………161 5.1 Introduction……………………………………………………………….161 5.2 Materials and Methodology………………………………………………165 5.3 Description of Study Areas…………………………………………….…165 5.4 Sampling Design………………………………………………………….167 5.4.1 Collection of Historic Data on Shellfish Production………………………167 5.4.2 Meteorological Data……………………………………………………….168 5.5 Statistical Analyses…………………………………………………………168 5.6 Results ……………………………………………………………………..170 5.6.1 Historic Shellfish Production and Climate Indices…………………………170 5.6.2 Predictive Shellfish Catch Model………………………………………….. 173 5.7. Discussions………………………………………………………………….176 5.8 Conclusions and Recommendations………………………………………...180 CHAPTER SIX…………………………………………………………………… ..182 SUMMARY CONCLUSIONS AND RECOMMENDATIONS……………………182 6.1 Conclusions…………………………………………………………………..182 6.2 Recommendations……………………………………………………………..184 REFERENCES…………………………………………………………………….. 186 APPENDICES……………………………………………………………………... 222 University of Ghana http://ugspace.ug.edu.gh xv LIST OF TABLES Table 1: A Summary of Temperature Requirements for Crassostrea………………25 Table 2: Salinity Ranges for the Genera Crassostrea in Selected Countries………………………………………………………………… 29 Table 3: List of Heterotrophic Bacterial Genera Frequently Found in Estuaries………………………………………………………………….41 Table 3: Criteria for Selection of Aquatic Bio Indicator…………………………... 55 Table 3.1: Parameter Estimates of SSR and AIC Generated from Fitting Three Growth Models to Age-Height Data on C. tulipa in Densu estuary of Ghana (2019 And 2020)……………………………………………………………80 Table 3.2: Species Occurrence in the Guts of C. tulipa and Water of Densu estuary (2019 and 2020)…………………………………………..84 Table 3.2 contd: Species Occurrence in the Guts of C. tulipa and Water of Densu estuary (2019 and 2020) ………………………………………….85 Table 3.3: Frequency of Occurrence and Numerical Percentages of Gut Contents of C. tulipa of Gut Contents of C. tulipa of the Densu estuary…………….…86 Table 3.3 contd: Frequency of Occurrence and Numerical Percentages of Gut Contents of C. tulipa of the Densu estuary……………………87 Table 3.4: Diversity Indices Gut Content of C. tulipa and Water in Densu (2019 And 2020).………………………………….…….90 Table 4.1: Standard Reference Material (1566b Oyster Tissue) Used for Validation……………………………………………………….105 Table 4.2: Range and Mean (± Standard Error) Values of Physicochemical Parameters in The Densu estuary from 2019 to 2020………………………………………………………..115 University of Ghana http://ugspace.ug.edu.gh xvi Table 4.3: Seasonal Variations in Physicochemical Parameters in the Densu estuary, 2019-2020………………………………………………117 Table 4.4: Tidal Variations in Physicochemical Parameters of Densu estuary for the Study Period 2019-2020…………………………121 Table 4.4 cont’d: Tidal Variations in Physicochemical Parameters of Densu estuary for the Study Period 2019-2020…………………………122 Table 4.5: Constrained and Unconstrained Variance Output……………………124 Table 4.6: Eigenvalues for Constrained and Unconstrained Axes………………125 Table 4.7: ANOVA Summary of the CCA Model………………………………125 Table 4.8: ANOVA summary of the Canonical Correspondence Analysis (CCA) Terms………………………………………………….125 Table 4.9: ANOVA Summary of the Canonical Correspondence Analysis Axes…………………………………………………………...125 Table 4.10: Eigen Values of Variables and Axis Generated from CCA………...127 Table 4.11 Descriptive Statistics of Biological Parameters of the Mangrove Oyster in Densu……………………………………………..129 Table 4.12 Model Selection from the Akaike Information Criterion (AIC) for Predicting Shell Height……………………………129 Table 4.13 Model Summary Relating Shell Height and Physicochemical Parameters of the Densu estuary…………………….130 Table 4.14 Regression between Predictor Variables and Shell Height in Densu (2019-2020)……………………………………130 Table 4.15 Model Selection from the Akaike Information Criterion (AIC) for Predicting Shell Width……………………………132 Table 4.16 Model Summary of Shell Width and Physicochemical Parameters…132 University of Ghana http://ugspace.ug.edu.gh xvii Table 4.17 Regression Analyses…………………………………………………133 Table 4.18 Model Selection from the Akaike Information………………………134 Table 4.19 Model Summary of the Relationship between Condition Factor and Physicochemical Parameters………………………134 Table 4.20 Regression between Predictor Variables and Condition Index of C.tulipa in Densu (2019-2020)…………………………………. 135 Table 4.21 Model Selection Using the Akaike Information Criterion Physicochemical Parameters of the Densu………………………………..136 Table 4.22 Summary of The Model Relating CPUE and Physicochemical Parameters of the Densu estuary…………………………………………………………………137 Table 4.23 Regression between Predictor Variables and Relative Abundance in Densu……………………………………………137 Table 4.24 The Akaike Information Criterion Selection of Best Model…………138 Table 4.25 Model Summary……………………………………………………...138 Table 4.26 Regression between Predictor Variables and Shell Weight in Densu (2019-2020) .………………………………………138 Table 4.27: Mean Concentration of Trace Metals in Water and Tissues of C. tulipa in Densu estuary……………………………………141 Table 4.28: Bio-indicator and Health Risk Assessment Indices of C. tulipa in Densu estuary in Ghana (2019-2020)………………………142 Table 5.1 Model Summary Relating Shellfish Production and Mean Sea Surface Temperature Obtained from MOFAD and Gmet (Tema), March 2016 to March 2017………………175 Table 5.2 Analyses of Variance and Coefficients of Regression between Catch and Temperature Obtained from University of Ghana http://ugspace.ug.edu.gh xviii MOFAD and GMET (Tema), March 2016–March 2017………………175 Table 5.3 Pearson Correlation Matrices Showing Relationship between Climate Indices and Shellfish Catch, March 2016–March 2017………176 University of Ghana http://ugspace.ug.edu.gh xix LIST OF FIGURES Figure 3.1: Map of Ghana Showing the Densu estuary Estuary and Areas of Oyster Presence and Spat Availability (Chuku, 2019)…………………………… 67 Figure 3.2: Map of Densu estuary showing grids where Sampling for Estimation of Catch Per Unit Effort (kg/hr/fisher/day) were done …….........................................68 Figure3.3: Tidal variations in CPUE of experimental fishing of the Densu Oyster fishery………………………………………………..77 Figure 3.4: Monthly Variations in CPUE of Commercial and Experimental Oyster Fishing in Densu estuary in 2019 and 2020……………………….78 Figure 3.5: Seasonal Trends in Catch for Experimental and Commercial Oyster Fishing……………………………………………….79 Figure 3.6. Resultant Growth Curves Fitted to the Monthly Length-Frequency Distribution of West African Oyster in the Densu estuary in 2019 and 2020. …………………………………80 Figure 3.7: Monthly Length-Frequency Distribution of Crassostrea tulipa Visualized in Terms of Catches (Top) and Restructured Distribution (Bottom) Fitted with Growth Curves with a Moving Average (MA) Set to 5 (n = 1926) (2019, 2020)………………………….81 Figure 3.8: Monthly Variations in Condition Index of C. tulipa in Densu estuary, 2019 and 2020 (n = Numerical Counts of Individual Specimens Examined) ……………………………………….82 Figure 3.9: Seasonal Variation in Condition Index……………………………..82 Figure 3.10: Percentage Abundance of Food Items in Water of Densu estuary……………………………………………………….…88 Figure 3.11: Index of Relative Abundance of Food Items in University of Ghana http://ugspace.ug.edu.gh xx Water of Densu estuary…………………………………………………89 Figure 4.1 Map of Ghana showing the location of the Densu estuary Estuary in Ghana and Study Grids ………………………………………………………………..101 Figure 4.2: Canonical Correspondence Analysis Ordination Plot Relating Oyster Abundance and Morphological Features with Physicochemical Factors of the Densu estuary Estuary, Ghana (2019-2020)……………128 Figure 4.3: Mean Concentration of A) pb B) Hg and C) Cd in C. tulipa Tissues in Densu estuary (2019-2020) (Error bars = standard error of means)…………………………………………………………140 Figure 4.4: Bioaccumulation Factors of Microbial Contaminants in C. tulipa, Densu estuary (2019-2020)…………………………………142 Figure 5.1: Map of the Coastline of Ghana Showing Ghana’s Coastal Regions……………………………………………………….165 Figure 5.2: Historic Shellfish Catch from 1970 to 2015 in Marine Artisanal Fisheries, Ghana……………………………………………170 Figure 5.3: Climate Indices (Tema) from 1970 to 2015 in Marine Artisanal Fisheries, Ghana……………………………………………172 University of Ghana http://ugspace.ug.edu.gh xxi LIST OF ABBREVIATIONS AND ACRONYMS ABBREVIATION ACRONYM APHA - American Psychological Health Association AIC - Akaike Information Criterion BAF - Bio-accumulation Factor Balk - Buffer alkalinity CCA - Canonical Correspondence Analysis CPUE - Catch Per Unit Effort C02 - Carbon dioxide C03 - Carbonates HC03 - Bicarbonates CI - Condition Index CF - Condition Factor Chla - Chlorophyll a Cd - Cadmium DAA - Development Action Association DO - Dissolved Oxygen Dsi - Dissolved silicates DIC - Dissolved Inorganic Carbon ELEFAN - Electronic Length Frequency Analysis EPA - Environmental Protection Agency (United States) FC02 - Carbon dioxide Fugacity FAO - Food and Agriculture Organisation FAO/FiSAT FAO ICLARM - Fish Stock Assessment Tool GSS - Ghana Statistical Service University of Ghana http://ugspace.ug.edu.gh xxii Hg - Mercury IRI - Index of Relative Importance IUBS -International Union of Biological Sciences DMAFS -Department of Marine and Fisheries Sciences MoFAD - Ministry of Fisheries and Aquaculture Development NERR - National Estuarine Research Reserve NOAA - National Oceanic and Atmospheric Administration NRC - National Research Council NTU - Nephelometric Turbidity Unit OH - Hydroxyl ions Pb - Lead pC02 - Partial pressure of carbon dioxide SDG - Sustainable Development Goals SE - Standard Error SLR -Sea Level Rise Si - Silicates Si Alk - Silica Alkalinity SH - Shell Height SL - Shell Length SW - Shell Width SFMP -Sustainable Fisheries Management Project TDS - Total Dissolved Substances TVC -Total Viable mesophilic Counts TCO2 -- Total carbon dioxide USD - United States Dollar University of Ghana http://ugspace.ug.edu.gh xxiii USEPA -United States Environmental Protection Agency VIF -Variance Inflation Factor VBGF -von Bertalanffy Growth Function WAMO - West African Mangrove Oyster WHO - World Health Organization University of Ghana http://ugspace.ug.edu.gh University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1.0 GENERAL INTRODUCTION 1.1 Background Oysters are a source of nutrients in the form of protein to many people in West Africa including Ghana (Sutton et al., 2012). The shells provide calcium and are used in the preparation of poultry and livestock feeds. In addition, the shells serve as an ingredient in paint preparations, a rough base for footpaths, cement for building and raw material for pharmaceutical industries (Obodai, 1999; Ansa & Bashir, 2007). Ecologically, sessile organisms like oysters are important in the aquatic food chain. Oysters are filter feeders and while they feed on plankton, they help improve on water quality. The settling behavior of oyster spats with time, form reefs which provide structured habitat in estuaries and lagoons for many fish species and crabs. In Ghana, the West African Mangrove Oyster (WAMO) is widely distributed occurring in mangroves, sediments and compact substrates of coastal water bodies (Obodai, 1999; Ampofo -Yeboah, 2014). In the country, oyster populations in estuaries and lagoons are declining. As at 1996, about nine (9) out of the 41 wetland ecosystems lost their oyster populations (Yankson & Obodai, 1999). The statistics is likely to increase as the study dates back to two (2) decades ago with this possibly to be exacerbated by recent state of increase in pollution of Ghana’s water resources. Meanwhile, there is a huge potential for most of these wetlands to be used for culture of oysters on commercial basis for economic gains to the country as practiced in some countries like the Gambia, Egypt and Australia (Obodai, 1999). Furthermore, according to Yankson & Obodai, (1999), 48 % of the estuaries and lagoons in Ghana were found to be suitable for commercial cultivation of University of Ghana http://ugspace.ug.edu.gh 2 oysters suggesting a high potential for their use for aquaculture to augment catches from the wild which the Densu estuary is no exception. The Densu estuary in Ghana is a habitat for Crassostrea tulipa, Lamarck 1819 (West African Mangrove Oyster). The population is currently harvested for commercial use and support the livelihood of riparian coastal communities. There exist several studies on oyster fisheries in Ghana and elsewhere. Most of these studies are centred on oyster socio-economics, biology and culture potential of oysters (e.g Ansa & Bashir, 2007; Mekawy & Madkour, 2013; Asare, Obodai & Acheampong, 2019). Meanwhile, there is no information on the interactions between the size and abundance of the wild mangrove oyster (Crassostrea tulipa) with the environment for use as an indicator of natural aquatic variability in the Densu estuary. Water quality factors, invertebrates, algae, foraminefera, birds, macrophytes and fish have been the conventional proxies used in Ghana for assessing environmental health and managing aquatic systems (Ndanu, 1998; Essuman & Nortsu, 2008; Mahu, 2010; Amoah et al., 2011; Debrah et al., 2011; Apau et al., 2012; Klake et al., 2012; Osei et al., 2013; Anim-Gyampoh et al., 2013; Ansah et al., 2018; Asare et al., 2018; Botwe, 2018; Okyere & Nortey, 2019). The high mobility of birds, fin fish, short life span of algae, frequent changes in water quality requiring a longer period of monitoring, presents a challenge to their sustainable usage. However, oysters are sedentary organisms that are cosmopolitan in nature, has the ability to filter pollutants and store biogeochemical data thereby reflecting the health of estuarine environments better than other known aquatic bio indicators (Amoah et al., 2011). University of Ghana http://ugspace.ug.edu.gh 3 In many regions of the world, extensive studies have been carried out on oysters and mussels as ecological indicators of environmental variability (Rudolf et al., 1995; Kirby et al.,1998; Harper et al.,2000; Brander 2007; Barbour et al., 2010; Mekawy & Madkour, 2013; Crampton et al., 2016). However, in Ghana scanty scientific studies have been done on the use of oysters (Katikiro & Macusi, 2012; Parker et al., 2013; Zougmore et al., 2016). Also, it has been documented (Mekawy & Madkour, 2013. Crampton et al., 2016) that as filter feeders, oysters filter a lot of pollutants and are considered as good indicators of environmental changes in water. Conversely, due to the possibly different environmental conditions of the Densu estuary, and the rising need for identification of proxies in the current environmental uncertainty, there is a need to undertake this current research to guide stakeholders to make informed decisions on the possible adoption or otherwise of the West African Mangrove Oyster as an early warning signal of changes in estuarine environments for the development, management and sustainable exploitation of oyster fisheries in Ghana. University of Ghana http://ugspace.ug.edu.gh 4 1.2 Problem Statement and Relevance of Study Out of ten estuaries investigated by Yankson & Obodai (1999) in the coast of Ghana for their suitability for culture of oysters, two, namely the Densu and Lower Volta are located in the Greater Accra Region. The Densu estuary is the most important in terms of Oyster fishery in the region (Entsua-Mensah, 1998). It currently supports a thriving commercial oyster fishery under the Development Action Association (DAA) project funded by USAID and remains an important coastal water body in Greater Accra. Information on aspects of the ecology of C. tulipa bordering on food habits of the West African Mangrove Oyster and its interaction with the natural environment in the Densu estuary is lacking. Also, despite scientific proofs on the detrimental impacts of estuarine acidification on oysters, there is dearth of information on the influences of acidification factors on the Densu oyster population in Ghana. There are indications that many coastal wetlands along the Gulf of Guinea Large Marine Ecosystem (GLME) of Ghana which the Densu estuary estuary is no exception is under increasing threat of contamination from incessant human activities such as oil drilling, farming activities, improper disposal of waste from household and industrial sources (McGlade et al., 2002). Several works reiterate the deteriorating state of the Densu estuary and its tributary with impacts arising from nutrient and trace metal loads (Entsua- Mensah,, 1998; Karikari & Ansa Asare 2006; Fianko et al. 2010; Mahu, 2010). Feedback from oyster collectors in Densu indicates oyster size and harvest have currently reduced. Currently most of the brackish systems in Ghana which had thriving oyster populations are recording decreases (Obodai et al., 1999; personal communication, Yankson, 2016). The declining state of oyster stocks in the country and the concurrent contamination of the delta give concern for detailed studies to heighten the need for conservation and urgent management attention. University of Ghana http://ugspace.ug.edu.gh 5 No known study on the Densu estuary and any other coastal wetland in Ghana has attempted to investigate the interactions between C. tulipa and the aquatic environment for use as a proxy of environmental changes. Meanwhile, in this period of climate change and variability, estuaries are classified as highly vulnerable due to their close tie to the sea (World Bank, 2017). The likely impacts will stem from rise in the sea level, changing temperature and estuarine acidification. These impacts are likely to have profound effects on shell bearing organisms like the West African oyster. According to reports of the regional climate vulnerability studies by the World Bank (2017), Ghana will be significantly impacted by variability in climate. The impacts will arise from high annual temperatures from heat stress and precipitation with warming which will impact greatly on fisheries resources in sub-Saharan Africa. Despite these envisaged challenges, the uniqueness of this sub-artisanal fishery lies in the fact that, although fishing is generally recognized as a male-dominated field in Ghana, the harvesting, processing and marketing of oysters is dominated by women (Ebinimi & Bashir, 2007; Bagne et al., 2011; Carney et al., 2017; Chuku, 2019). These roles of women in the oyster industry have contributed significantly to the reduction of poverty and enhanced food security within deprived communities (Goodwin et al., 2012; Darboe, 2015). Sustainable management of the fishery will ensure a continual nutritional and economic support to this vulnerable group and the society as a whole. Therefore, for a viable management and development of the oyster industry as well as its culture, there is a need to acquire scientific knowledge on the environment and aspects of the biology of the species. This work will not only be useful for the management of the fishery but will inform frontiers of knowledge on the growth and use of the oyster as an early warning signal of environmental perturbations. University of Ghana http://ugspace.ug.edu.gh 6 The findings of this research will be useful in policy formulation by being leveraged into the Ghana national fisheries policy in its bid to enhancing and deepening marine stock recovery as planned in the 2020 Ghana budget and economic policy. 1.2 Objectives The main aim of this study is to assess the possible use of the West African Mangrove Oyster (Crassostrea tulipa) as a bio-indicator of aquatic environmental variability to enhance sustainable management of the fishery in the perspective of global environmental change in Ghana. 1.2.1 Specific Objectives The specific objectives of the study are to: I. Study aspects of the ecology of the West African Mangrove Oyster in the Densu estuary necessary for its conservation II. Assess the potential use of C. tulipa as a bio-indicator of environmental variability of the Densu estuary III. Model projections of long-term effects of climate variability on artisanal shellfisheries production and its implications for sustainable management of oyster fisheries. 1.3 Research Questions a) Does Crassostrea tulipa catch vary with season and tide in Densu estuary? b) Is there seasonal variation in condition factor of Crassostrea tulipa? c) Is there (dis)similarity in gut contents and food items in water? University of Ghana http://ugspace.ug.edu.gh 7 d) How is C. tulipa abundance influenced by physicochemical parameters of the environment? e) Does seasonality and tidal changes in physicochemical parameters have influences on CPUE? f) Is the concentration of heavy metals and microbes significantly different among different sizes of oyster? g) Is there a significant correlation between heavy metals and microbial content in tissues and water? h) Which aspects of the biodata on the shell fish best serve as bio indicators? i) What are the major determinant climate factors influencing artisanal shell fish production in Ghana? The following hypotheses were therefore tested based on the set objectives: 1. H0: Tide and season does not influence the abundance of Crassostrea tulipa 2. H0: There is no seasonal variation in condition factor of C. tulipa 3. H0: There is no similarity in food items found in guts and estuarine water 4. H0: Physicochemical parameters have no effect on size of C. tulipa 5. H0: Seasonal and tidal changes in physicochemical parameters have no influence on oyster catch. 6. H0: Tide and season does not influence the abundance of C. tulipa 7. H0:The concentration of heavy metals and microbes are not different among different sizes of oyster University of Ghana http://ugspace.ug.edu.gh 8 8. H0: The concentration of heavy metals and microbes are not different among different sizes of oyster 9. 9. H0: There is no correlation between heavy metals and microbial content in meat and water. 9. 10. H0: There are no major determinant climate factors influencing marine artisanal shell fish production in Ghana. 1.4 Delimitation of the Study Densu estuary is not the only estuary where oysters thrive in coastal Ghana. It was however, selected because of the presence of an active commercial oyster fishery and the fishery being one of the main sources of livelihood support for nearby coastal communities. Although in literature there are many other water quality parameters which could have been monitored, it was empirically unattainable to monitor all so the study was restricted to studying the correlation between oyster abundance and size with some species of microbes, heavy metals, chlorophyll a, estuarine acidification and Physicochemical Parameters. These environmental parameters were chosen based on existing land use activities around the site, the ecology of the West African Mangrove Oyster and human health implications. 1.5 Limitations of the Study A motorized canoe was used for catch assessment in the experimental fishing because it was the available fishing craft of the oyster collector. Although this limitation was uncontrollable, their influences precluded any scientific interferences. The incidences influenced mainly the scale of the research and only emphasizes the complexity of sampling techniques in open aquatic systems which are prone to natural harsh University of Ghana http://ugspace.ug.edu.gh 9 conditions and disasters. 1.6 Organisation of the Study There are a total of six chapters in this thesis. Chapter 1 is an introduction to the whole concept of the study, highlighting the background of the study and stating the problem and relevance of the study, objectives, research questions, hypotheses to be tested, delimitations and limitations. Chapter 2 is a review of literature relevant to the study. In-depth review of the literature on classification of oysters, population parameters, hydrodynamics of estuaries affecting oyster abundance, impacts of estuarine acidification on oysters, conventional bio indicators in use and a review of studies on ecology and biology of brackish populations in Ghana was presented. Chapter 3, presents introduction on the relevance of assessing aspects of ecology of the West African Oyster. The materials and methods employed in assessing commercial and experimental catch, growth pattern, feeding regime and condition index are elaborated. The results are presented on charts and tables followed by a detailed discussion. Chapter 4, investigates the relationship between physicochemical parameters and the morphometric factors of the West African Oyster and the establishment of the use of C. tulipa as a bio-indicator using physicochemical parameters. The materials and methodology used in gathering data, data analytical procedures and tools, results and discussions are elaborated into detail. Chapter 5 presents an introduction, methodology, results and discussions on modelling projections of long-term effects of climate variability on shellfish production in University of Ghana http://ugspace.ug.edu.gh 10 Ghana’s artisanal fisheries sub sector and the implications on management of oyster fisheries. Chapter 6 presents general conclusions and recommendations of the study. University of Ghana http://ugspace.ug.edu.gh 10 1 CHAPTERTWO 2 2.0 LITERATURE REVIEW 3 This chapter reviews relevant literature for the study. It discusses into detail the taxonomy, 4 classification and distribution of oysters, population parameters, hydrodynamics of 5 estuaries in relation to oysters, climatic factors influencing oyster growth, effects of ocean 6 acidification on estuaries and bivalves, bio indicators of estuarine environments and review 7 of conventional and aquatic bio indicators. An in-depth review of studies on ecology and 8 biology of brackish populations in Ghana is presented. 9 2.1 Taxonomy, Classification and Distribution of Oysters 10 The classification of oysters was first done by Linnaeus in 1958. This was an attempt to 11 describe some species of oysters including Ostrea. Among bivalve molluscs, oysters are 12 known to belong to the class Bivalvia, subclass Pteriomorphia, order Ostreida, and 13 superfamily Ostreoidea (Linnaeus, 1958). This resulted from a broad definition of the genus 14 Ostrea and many bivalves across different families. 15 A detailed study of the fossil and living oysters by Stenzel (1971) showed that oysters can 16 be classified according to shell morphology and anatomic characters into the families 17 Gryphaeidae and Ostreidae (Liu et al., 2011; Salvi et al., 2014; Raith et al., 2016). The oyster 18 fossils revealed that both families have no recognizable intermediate type (Triassic) 19 suggesting the two oyster families might be diphyletic (Stenzel 1971; Liu et al., 2011). 20 Further to this, molecular data obtained from phylogenetic analyses reiterated the separation 21 of the two families but not the diphyletic origin of Ostreoidea. Conclusively in all analyses, 22 the two distinct branches of a single monophyletic clade of oysters are 23 Gryphaeidae and Ostreidae (Liu et al., 2011; Salvi et al., 2014; Raith et al., 2016; Salvi & 24 University of Ghana http://ugspace.ug.edu.gh 12 Mariottini, 2017). In 1971, the family Gryphaeidae was divided into three subspecies: 25 Gryphaeinae, Exogyrinae, and Pycnodonteinae by Stenzel. Except for the family 26 Pycnodontidae where all living species are placed, the remaining first two subfamilies are 27 extinct. And with the family Ostreidae, the author classified it into two subfamilies, 28 Lophinae and Ostreinae, both containing extinct species. On the other hand, Quayle in 1989, 29 grouped all oysters of the world into one family called the Ostreidae under three main 30 groups or genera called Ostrea, Crassostrea and Pycnodonta. Later, Torigoe (1981) 31 considered Gryphaeidae as a fossil-only family and elevated subfamily Pycnodonteinae to 32 family Pycnodonteidae. 33 Crassostrea and Saccostrea were later moved out of Ostreinae and a new subfamily, 34 Crassostreinae established (Torigoe,1981). In 1985, Crassostreinae was accepted as a sub 35 family under Ostreidae while the sub family Pycnodonteinae was retained under 36 Gryphaeidae and several new genera proposed under Ostreinae and Lophinae (Harry, 37 1985). Later, Angell (1986) confirmed the existence of many genera of living oysters in 38 literature but concluded that the genera Crassostrea, Saccostrea and Ostrea contain the most 39 commercially important 40 species. 41 Li (2013) in a quest to support his findings with molecular data on these groups, revealed 42 new phylogenetic structures needful for taxonomic consideration. The study of Li (2013) 43 showed high levels of divergence between Crassostrea and Saccostrea and proposed that 44 Saccostrea be placed into a separate subfamily Saccostreinae. 45 The works of Salvi et al. (2014) further confirmed the significant differences between the 46 two families Crassostrea and Saccostrea. The author concluded by proposing a new 47 subfamily Saccostreinae. 48 University of Ghana http://ugspace.ug.edu.gh 13 The genera Striostrea were originally placed under the subfamily Ostreinae. Contrary to 49 this, Raith et al. (2016) found that Striostrea prismatica was highly varied from other 50 Ostreinae species in mitochondrial DNA sequences. The author later suggested the new 51 subfamily Striostreinae with the single genus Striostrea. This ideology was supported by 52 Salvi and Mariottini (2017). On the basis of morphological characters, the subfamilies 53 Lophinae and Lstreinae are well documented and studied (Stenzel, 1971; Torigoe, 1981; 54 Harry, 1985 as cited in Guo et al., 2018). On the basis of phylogenetic analyses, brooding 55 oysters from these families (land ostreinae) are often intertwined, and so suggested to be 56 combined (Salvi et al., 2014; Raith et al., 2016). Anatomically, the main difference between 57 the Ostreinae species and Lophinae species is the intestine looping around the stomach 58 (Torigoe, 1981; Li & Qi, 1994). In terms of taxonomy, there is little scientific 59 documentation on Ostreinae and Lophinae. Therefore, it is confusing and unscientific to 60 merge the two subfamilies. Currently, many biologists suggest the two subfamilies be kept 61 independent of each other and accept the Ostreidae: Crassostreinae, Lophinae, Ostreinae, 62 Saccostreinae, and Striostreinae as sub families (Bayne et al., 2017; Bayne et al., 2018; Guo 63 et al., 2018). 64 Among all these families, the number of extinct oyster species is difficult due to taxonomic 65 confusions and inadequacies from shell morphological features. In terms of speciation, 66 there are twelve species of the family Pycnodonteinae. Saccostrea species which are small- 67 to medium-sized rock oysters are extremely different and difficult to classify due to their 68 small- sized and close attachment to rocks and shells of Saccostrea. The family 69 Crassostreinae contains about 26 species, including others that have not been genetically 70 confirmed: Crassostrea cuttackensis, Crassostrea aequatorialis, Crassostrea iredalei and 71 Crassostrea tulipa. 72 In literature, two species names have often been used to refer to the West African 73 University of Ghana http://ugspace.ug.edu.gh 14 Mangrove Oyster (WAMO), Crassostrea tulipa (Lamarck, 1819) and Crassostrea gasar 74 (Dautzenberg, 1891). A study by Lapegue et al. (2002) on the genetic analysis of the 75 mitochondrial DNA, revealed that the two scientific names referred to the same organism. 76 However, in literature, the use of the name C. tulipa is usually preferred to C. gasar due to 77 the precedence of the former to the latter. On the basis of nomenclature of the species, 78 Yankson (1991) addressed the earlier confusion by giving a comprehensive discussion on 79 the subject, which ended with the recommendation of referring to the West African 80 Mangrove Oyster as C. tulipa. As a result, the species name C. tulipa was used in this 81 document. 82 Crassostrea tulipa is found along the West coast of Africa (Sutton et al., 2012) (Plate 1). It 83 is distributed between Senegal and Angola. Apart from the bloody cockle, Anadara senilis, 84 C. tulipa is the next economically important bivalve mollusc in the West African sub region 85 (Nickles, 1950). In Ghana, C. tulipa is not rare to be found. It occurs in not less than 90 % 86 of the existing coastal wetland systems (Sutton et al., 2012). The West African mangrove 87 oyster, C. tulipa (Lamarck, 1819) occurs in the tropics and has high tolerance to extremes 88 of salinity changes. Therefore, it is referred to as a euryhaline organism. The species can 89 grow well in shallow (2 to 5 m deep) brackish areas such as mangrove swamps and sheltered 90 places. The maturity period for WAMO is 91 approximately 7 – 9 months (Ansa & Bashir, 2007). It attaches itself to stilt roots of red 92 mangrove ecosystems. Along these suitable habitats, oysters survive and thrive well by 93 using their foot to attach themselves to roots of mangroves, surfaces of firm substrate such 94 as sand, rocks/stones and shells of organisms. 95 University of Ghana http://ugspace.ug.edu.gh 15 96 Plate 1: Crassostrea tulipa specimen from Densu estuary, 2019 97 Morphology of oysters in terms of shape and size also change in response to variations in 98 the estuarine environment (Juliana et al., 2008). Under crowded conditions the shells may 99 be thin and long, broken and pitted under fouling conditions and small and white if the 100 oyster is growing in low salinity areas. The significant contribution of bivalve fisheries and 101 its potential globally calls for efficient methods of assessment to safeguard rational 102 production over time (Gosling, 2015). In a given environment, the most frequently used 103 measure of an organism’s vitality is its growth. According to Gosling (2004), growth is said 104 to be an increase in the longest measurement of the shell. Among bivalves, age is used as a 105 direct estimate of size. Therefore, growth is representative of changes in body size and as 106 such its proportional to shell volume, shell length or shell height (Dame, 2002). 107 There is a seemingly general confusion with the shell orientation and dimension of bivalve 108 terminologies in literature. On shell orientation of bivalves, terminologies like anterior and 109 posterior have been used inconsistently even with a given species. 110 Hoggarth (1987) reported that the identification of anterior and posterior ends of the 111 bivalve shell could be speculative in dealing with juveniles and less studied (fossil) groups. 112 University of Ghana http://ugspace.ug.edu.gh 16 According to Bailey (2009), the dominance of posterior elongation among bivalves has 113 fostered a bias, where it is spontaneously assumed that the long end of the shell is the 114 posterior and confusing right and left shells. 115 The confusion in shell dimension, where a given dimension is given different names 116 abounds in literature. Particularly in oysters, the height is determined as the distance 117 between the hinge and the opposite shell margin (Gosling, 2015). Meanwhile, the same 118 dimension has been termed length (Gordon et al., 2017). However, per the conventional 119 shell dimensions according to Galtsoff (1964) and Gosling (2015), the height is the distance 120 from the opposite shell margin to the hinge line; while the widest part across the shell at 90 121 degrees to the height is the length and the width is measured at the thickest part of the two 122 shell valves. These conventional definitions of oyster shell dimensions by these authors 123 were adopted in this study. 124 Thus, the longest dimension of the shell is the height in oysters (Plate 3). Determining 125 growth of bivalves using shell size requires precision. Depending on measurements of 126 single shells to generalize growth of a population can be problematic and may result into 127 complications. This is so because differences in environmental and reproductive conditions 128 may affect growth of these bivalves. Also, variations in growth of shells and soft body tissue 129 will not allow for an equal comparison of these morphometric parameters 130 (Dame, 1972 as cited by Dame, 2002). Thus, generally among bivalves, determination of 131 size relationships using weight of soft body tissue and shell dimensions and usually results 132 in allometric growth patterns (Dame, 2002). There is therefore the need to combine linear 133 shell measurements with weights in the study of the growth of bivalves. 134 University of Ghana http://ugspace.ug.edu.gh 17 135 Plate 2: Anatomical features of C. tulipa (Wikipedia.com) 136 137 Dame (2002) identified four methods useful in assessing growth in bivalves. These are the 138 tracking size frequency distributions, size changes in marked individuals, using biomass 139 and observing radioactive tracer uptake in estimating shell growth rings. Quayle & Newkirk 140 (1989) and Bayne (2017) on the other hand presented five methods for assessing shell 141 growth, namely: measurements of shell dimension of randomly sampled specimens, 142 sequential measurements of tagged individuals, measurements of growth rings (upon 143 validation), acetate peels of cut shells and changes in stable isotope ratios within the shell. 144 The use of size frequency distribution in determining growth rates is only applicable to 145 bivalve species with short reproductive periods however those with extended recruitment 146 period like Crassostrea tulipa have their growth rates being variable (Dame, 2012). In 147 relation to shell rings or growth lines, bivalves form stable environments where 148 environmental conditions are uniform (tropics), generally have inconsistent line formation 149 and visible rings and line formation is variable. Hence, the uncertainty in the use of this 150 method lies in the need for a careful check for reliability of rings in each locality. 151 University of Ghana http://ugspace.ug.edu.gh 18 Given the procedures above, the measurement of individual sizes (particularly the 152 untagged procedure) has been widely used in assessing tropical bivalve fish stocks (Laudien 153 et al., 2003; Mendo & Jurado, 1993). This is because the approach is simple, less time 154 consuming, measures the growth of populations under natural conditions and does not 155 require the sacrifice of specimens. Irrespective of which method is used, growth models are 156 required to relate the age of fish in a population to their length or weight data termed as the 157 final products of growth analyses (Pauly, 1984). With these models on growth, an equation 158 is usually developed to represent the output of the model. The output of population models, 159 relates the estimates of the parameters and growth essential for future comparisons among 160 and within. The most common growth models are von Bertalanffy, Gompertz and Logistic 161 models. To assess growth pattern using these models, length-based procedures which are 162 commonly used in the tropics in fish stock assessment are employed (Pauly, 1984; 163 OforiDanson & Kwarfo-Apegyah, 2008; Osei, 2015). One such example of length-based 164 procedures is the development of the electronic length-frequency analysis (ELEFAN) by 165 FAO. ELEFAN was a separate programme meant for the collection of fishery assessment 166 tools that uses length-frequency data. 167 It was later implemented in the FiSAT II program of FAO-ICLARM Fish Stock 168 Assessment Tools (Gayanilo et al., 2005) and COMPLEAT ELEFAN (Gayanilo, Soriano 169 & Pauly, 1989). FiSAT II has been extensively used in the analysis of several fisheries 170 around the globe since its publication by Pauly & David (1980). In part, due to the cost 171 effectiveness of length data and the insufficiency of catch data. Lately, Pauly & Greenberg 172 (2013) incorporated ELEFAN I into the R software. This innovation led to the development 173 of R-based packages for fish stock assessment. The relevant ones to the current study are 174 TropFishR (Mildenberger et al., 2017); fish methods (provide functions for the application 175 of fisheries stock assessment methods, Nelson, 2018); devtools 176 University of Ghana http://ugspace.ug.edu.gh 19 (provide functions that simplify and facilitate commands, Wickham, Hester & Chang, 177 2018); (kernel smoothing for confidence contours, Duong, 2019) and fishboot (a tool for 178 the study of fish stocks and aquatic resources, Schwamborn et al., 2018). The new 179 optimisation algorithms which are packages built for the R software (R Core Team, 2019), 180 according to Taylor & Mildenberger (2017), have the capability of optimising the search 181 for a combination of four parameters (asymptotic length (L∞), growth coefficient (K), 182 summer point (ts) and strength oscillation (C)) at a reduced computation time, where FiSAT 183 II software fall short. These modern tools were used in this study. 184 2.1.1 Feeding 185 The presence of phytoplankton in water constitutes an important component of the diet of 186 suspension feeders (Dupuy et al., 2000). There is high suspension activity of bivalve 187 populations during incidences of high phytoplankton concentration in water. The activities 188 of these suspension feeders, may have profound influences on phytoplankton abundance 189 (Barille et al., 2003). 190 Studies from gut content analyses and stable isotope carbon analyses have shown that 191 `phytoplankton` particularly benthic diatoms, can be a main food source of oysters (Hsieh 192 et al., 2000; Yokoyama & Ishii, 2003; Kasai et al., 2004). Phytoplankton abundance 193 therefore is indicative of the presence of many benthic species of diatoms (Facca et al., 194 2002; Perissinotto et al., 2002). The existing gap in gut analyses studies lies on difficulty in 195 identifying the preferred algae during feeding. However, direct observation of gut 196 contents is needed to clarify the feeding preference in various natural food sources of 197 bivalves (Dupuy et al., 2000). Feeding has profound influences on the condition of oysters 198 University of Ghana http://ugspace.ug.edu.gh 20 thus the volume of the shell cavity that contains the soft body tissue which is also affected 199 by the hydrodynamics of estuaries. 200 2.2. Hydro Dynamics of Estuaries in Relation to Oysters 201 2.2.1 Physicochemical Parameters Influencing Oyster Abundance 202 Quayle (1989) suggested a suite of environmental factors which affect tropical oyster 203 populations both positively and negatively. The positive factors facilitate growth and 204 survival. Conversely, negative factors hinder reproductive capabilities and affect 205 population dynamics. Also, excessive bridging of thresholds of conditions may lead to 206 increases in the incidence of disease reduce fattening ability of oysters and impair growth 207 thereby reducing the productiveness of reefs of oysters and body coverings and increases 208 the incidences of predation. The positive factors are character of the bottom, water 209 movements, salinity, temperature and food. 210 2.2.2 Character of Bottom 211 Oysters attach their foot to diverse surfaces. Some of these surfaces ranges from shells of 212 other organisms, compact surfaces and intertidal shores. Ability to attach well provide 213 support to their weight. Silt sand and soft mud are extremely unsuitable bottoms for oyster 214 communities but may be improved by adding dead oyster or clay (Strayer, 2008). 215 2.2.3 Sediment Particle Size 216 According to Tait (1981), differences between benthic communities in water can often be 217 correlated with differences in sediment grain size. The rate of circulation of bottom current 218 and size of particles being transported with the wave influences the type of sediment 219 deposited at shore. 220 University of Ghana http://ugspace.ug.edu.gh 21 Also, the faster the water moves, the coarser is the texture of the substrate, because finely 221 divided materials are easily held in suspension than larger particles of the same density. 222 Sedimentary materials are transported into the estuary from rivers, sea, or are washed in 223 from the land surrounding the estuary (McLusky, 1989). As tidal current enters an estuary, 224 it slackens in speed and deposits first gravel, then sand, and finally silt, which accumulates 225 as mud. Castro & Huber (2005) reported sand and other coarse materials settle out in the 226 upper reaches of the estuary when the river current flows. The fine, muddy particles are 227 carried further down the estuary where many of them settle out when the current slows even 228 more; though the finest particles may be carried far out to sea. The bottoms of most 229 estuaries, therefore, have sand or soft mud substrates (McLusky, 1989). 230 In a study by Mahu (2015), sediments in the Densu estuary showed mean grain size 231 variation between 49.3 μm and 88.3 μm with modal values ranging from 127.6-185.4 μm. 232 The estuary was dominated by silt (70%), Clay (10% at the top and 30% at the bottom) and 233 sand (27%) at the top section of the cores. 234 2.2.4 Total Dissolved Solids 235 The growth of tropical oysters is partly influenced by the amounts of dissolved suspended 236 solids in estuaries. These suspended solids have the ability to clog the gills of oysters as 237 they filter feed. A measure of Total Dissolved Solids (TDS) involves both dissolved and 238 suspended solids. These solids range from silts, clay, soil runoff, plankton, industrial waste 239 to sewage. According to Thommai et al. (2014), total dissolved solids (TDS) in water 240 comprises both inorganic salts and dissolved materials. Salts in natural waters, are made up 241 of chemical compounds of anions and cations such as carbonates, chlorides (Cl-), sulphates 242 (SO4 -2), nitrates (NO4 -), potassium (K+), magnesium (Mg2+), calcium (Ca2+) and sodium 243 (Na+). 244 University of Ghana http://ugspace.ug.edu.gh 22 The design value and design range of TDS for a raw brackish water is 3,394 and 2899-3450 245 mg/L (WHO, 2007). As filter feeding organisms, oysters tend to have their gills blocked by 246 high amounts of dissolved solids are ingested during feeding. Growth is impaired and 247 aerobic respiration affected during such processes. Prasanna & Ranjan (2010) reported the 248 maximum range of TDS in the months of April and May and the minimum range during 249 January and February at Dhamra Estuary. Likewise, Thommai et al. (2014), observed TDS 250 ranges from 144 to 64600 (mg/L) with explanations to the high values as due to heavy 251 rainfall. In summer, TDS level is usually low probably due to the low inflow of fresh water. 252 The second largest estuary in Ghana, Pra estuary is silted from activities of illegal alluvial 253 gold miners upstream. 254 2.2.5 Exchange of Water 255 Among oysters, free exchange of water is crucial for their growth, fattening and 256 reproduction (Angell, 1986). Therefore, an ideal condition for bivalves is that of a steady, 257 non-turbulent flow of water over an oyster bed, capable of providing enough oxygen and 258 food while carrying away the liquid and gaseous metabolites and excreta. 259 Also, for a suitable expansion of oyster beds, water currents should be able to transport 260 larvae at the required time during spat settlement for adequate contact with clean, hard 261 surfaces (Strayer, 2008). This phenomenon is particularly distinct in estuaries and places 262 these ecosystems at an advantage for the expansion of oyster communities and for the 263 annual rehabilitation of reduced oyster populations . This is so because during the 264 movement, some larvae which are carried back and forth by the oscillating movements of 265 tidal waters, eventually settle beyond the place of their origin. 266 267 University of Ghana http://ugspace.ug.edu.gh 23 2.2.6 Tides 268 Most tropical oyster species like Crassostrea tulipa, prefer shallow intertidal waters to 269 avoid desication and less predation. They grow to dense populations along narrow bands or 270 concentrate more at a tidal height of intertidal regions (Angell, 1986). Among the genera 271 Crassostrea, Crassostrea paraibanensis is one of the few species which is predominantly 272 or wholly subtidal (Singaraja, 1980). The susceptibility of tropical oysters to heavy fouling 273 and tolerance to desiccation when continuously immersed directly influence culture 274 technology. The interactions between oysters and their physical and chemical environment 275 according to Angell (1986), Pieterse (2013), Lodeiros et al. (2017) and Chumkiew et al. 276 (2018), affect the fauna’s distribution and abundance and greatly influence the health and 277 sustainability of aquatic ecosystems. 278 2.2.6 Temperature 279 Temperature is among one of the most principal physical factors influencing growth, 280 survival, reproduction and abundance of aquatic life (Landford, 1990). In tropical estuaries, 281 variability in water temperature is mostly attributable to shallow water depth, low water 282 volume and freshwater inflow from land drainage (Fatema et al., 2014). Generally, among 283 shallow estuaries, temperature changes is controlled by atmospheric temperature. 284 The life of oysters such as rate of water transport, feeding, respiration, gonad formation, 285 and spawning are influenced largely by changes in temperature regime. 286 Among estuaries, temperature varies between 18.33 °C and -29.44°C (WHO, 2007). 287 Though in other parts of the world, Crassostrea is known to thrive between temperatures 288 of 23 °C - 31 °C, there is little documented data on the effects of prolonged temperatures 289 above 32 °C to 34 °C on oyster populations. However, deductions may be drawn from a 290 few physiological observations that long, sustained exposure to high temperature is 291 University of Ghana http://ugspace.ug.edu.gh 24 unfavorable and impedes the normal rate of water transport by the gills (Quayle, 1989). 292 Crassostrea tulipa is known to thrive well in temperatures varying between 25-30°C in 293 estuaries in Ghana (Sutton et al., 2012). In an experimental trial by Yankson (1990) to 294 determine the effects of changing temperature on larval growth, temperature was identified 295 to have intense influence on early stages of C. tulipa. However, in an earlier study by Angell 296 (1986) of tropical and subtropical oysters, changes in temperature were posited to have less 297 influence on growth and mortality. The author on the other hand, mentioned that gonad 298 activity coincides with changes in temperature and salinity. Thus, increasing temperature 299 supports gonadal growth whereas maximum shell growth occurs during the months of 300 highest salinity (20-28 ppt) and lowest temperature of 15- 16°C. Similarly, among the 301 population of Crassostrea virginica in Louisiana, high temperatures correlate with high 302 growth rate (Lowe et al., 2017). The Butuah estuary according to Okyere et al. (2011), has 303 an extremely higher temperature, averaging 32.9oC. 304 In Canada and USA, the works of Cansas et al. (2019) reiterated temperature as a main 305 driver of clearance rate, valve opening duration and oxygen consumption rate. A summary 306 of the temperature requirements by different species of Crassostrea are shown in Table 2.1. 307 308 309 310 311 312 Table 2. 1: A Summary of Temperature Requirements for Crassostrea spp. 313 Species/country Temperature range (o C) Reference University of Ghana http://ugspace.ug.edu.gh 25 Crassostrea belcheri Malaysia 27 – 31 Chin & Lim, 1975 Crassostrea gasar Nigeria 25 – 30 Ajana, 1980 Crassostrea gigas Hong kong 11 – 31 Mok, 1973 Israel 12 – 34 Hughes-Grames, 1977 Fiji 24 – 31 Ritchie, 1977 Hawaii 22 – 28 Brick, 1970 Crassostrea gryphoides India 19 – 33 Durve, 1965 Crassostrea madarensis India 26 – 31 Virabhadra & Nayar, 1956 Crassostrea parabainensis Brazil 24 – 30 Singaraja, 1980 Crassostrea rhizophorae Cuba 18 – 34 Farfarsie, 1954 Venezuela 27 – 30 Angell, 1973 Puerto Rico 24 – 27 Watters & Prindow, 1975 Colombia 27 – 33 Wedler, 1970 St Croix 25 – 32 Forbes, 1973 Crassostrea tulipa 314 Ghana (Benya) Ghana (Pra) 27 – 31.5 27 – 32 Obodai et al., 1997 Crassostrea virginica Mexico 20 – 30 De Buen, 1957 Hawaii 21 – 27 Sakuda, 1966 315 Heat is crucial for biochemical reactions. It accelerates the dissolution of chemical 316 substances affecting the pH and conductivity levels of water thus influencing aquatic 317 species diversity and distribution (Gillooly et al., 2002). 318 319 320 2.2.7 pH 321 University of Ghana http://ugspace.ug.edu.gh 26 According to NERR (1997), most aquatic fauna are known to adapt to pH levels ranging 322 between 5.0 and 9.0. Therefore, knowledge of pH in estuaries is important for sustained 323 growth and wellbeing of brackish life. 324 The pH in estuaries remain fairly constant. The dissolution of carbonate ions available in 325 the saline water of the sea act to minimize or buffer pH changes by reacting with the ions 326 that alter pH. Biological activities, however, may significantly change the pH 327 concentrations of lagoonal systems. For estuaries, a pH range of 6.5 to 9.4 is required 328 (Wood, 1967). Variations in pH of shallow biologically active tropical marine waters is 329 more pronounced during the daytime especially when pH rises up to 9.5 due to 330 photosynthetic activities where communities in these systems are adapted to such variations 331 (NERR, 1997). In addition, the solubility, toxicity and biological availability of several 332 substances such as trace metals are dependent on pH. For instance, among these metals, 333 lower pH levels enhance their solubility and toxicity or otherwise. In UK, the Tweed estuary 334 has a distinct seasonal variation in alkalinity and pH values within the upstream of the 335 estuary, and these can be largely related to changes in freshwater river flows. Also, during 336 high flows, the pH and alkalinity of the system’s water were low whereas at low flows, the 337 pH and alkalinity were high. This is so because of the weathering of rich river bedrock ions 338 thereby affecting the pH and alkalinity of the water (Howland et al., 2000). 339 2.2.8 Conductivity 340 Conductivity and salinity are interrelated. Measurement of conductivity of estuarine water 341 for oyster growth is important as it gives a rapid and practical estimate of the dissolved 342 mineral contents of water, usually mainly due to saline water and in part, leaching 343 (Thommai et al., 2014). 344 Conductivity affects the survival of oyster life and reproduction is effectively favored 345 under higher conductivity levels. Low conductivity and heavy siltation following 346 University of Ghana http://ugspace.ug.edu.gh 27 monsoonal rains may cause mass mortalities of oysters. Generally, specific conductivity is 347 standardized at 25°C because it is highly correlated with temperature. Standardization is 348 necessary for a fair comparability of data of different aquatic ecosystems with different 349 temperatures (Thommai et al., 2014). At high temperatures, specific conductivity increases 350 due to easy movement of ions from water which becomes less viscous during such 351 conditions. As a result, most reports of 352 conductivity reference specific conductivity. 353 The source of regulation of conductivity in estuarine ecosystems could be the rocks’ mineral 354 composition, size of the watershed, wastewaters from industries, sewage treatment works, 355 septic tanks, settlements, agriculture and other sources of ions (Okyere, 2019). Determining 356 conductivity is important as it results in high total dissolved solids concentrations and can 357 have adverse effects on aquatic life (Lodeiros et al., 2017). Geology, precipitation, surface 358 runoff, and evaporation are the driving factors of conductivity and salinity. 359 In the study of the Pra estuary, conductivity and salinity of the estuarine water fluctuated 360 throughout the study indicating similar pattern of changes in these parameters confirming 361 the assertion that conductivity and salinity of water are directly related and has influences 362 on each other (Okyere, 2019). 363 2.2.9 Salinity 364 Daily and storm driven tides, one’s location in the estuary and volume of freshwater flowing 365 into the estuary are some factors which influences the salinity in an estuary (Chumkiew et 366 al., 2018). 367 Furthermore, sea and freshwater inflow from tidal action and land drainage as well as a 368 combined effect of the location of the terminal ends of river systems determines the changes 369 in salinity of estuaries. Low salinity levels especially during the rainy season causes mass 370 University of Ghana http://ugspace.ug.edu.gh 28 mortalities of oysters (Obodai et al., 1997). Among populations in Louisiana, low salinities 371 have been identified as the main cause of reduced mortality. The growth and mortality of 372 C. virginica and as well as reproduction is known to be influenced by salinity changes 373 (Shumway, 1996). 374 Several documented studies along the Gulf of Mexico show evidence of limited or no 375 recruitment at all during low salinity levels of below 10 ppt (Cake, 1983; Chatry et al., 376 1983; Pollack et al., 2011), This has the tendency to affect oyster size and availability of 377 hard substrate. Also, more so than temperature, higher salinities can be associated with 378 greater instances of disease and predation in C. virginica (Ewart & Ford, 1993; Shumway, 379 1996). 380 Considering farmed oysters in Brazil, oysters growing close to the ocean experience best 381 growth performance than those farthest from the ocean. Areas close to the ocean have high 382 salinities and less variations Sites farthest from the ocean are influenced by fluvial discharge 383 rendering oysters to be of relatively smaller sizes (Oliveira et al., 2018). The Pra estuary 384 has an average salinity of the sea varying between 30.0‰ and 35.0‰ (Okyere, 2019), 385 except at the peak of the wet season in June 2012 when a low value of 13.5± 1.3‰ was 386 recorded. The average salinity levels of the Densu system according to Biney (1990) ranges 387 between 2 - 28‰ and the mean values are 1.1-39.3‰. 388 The salinity requirements by different species of the genera, Crassostrea are shown in Table 389 2.2. 390 391 University of Ghana http://ugspace.ug.edu.gh 29 Table 2.2: Salinity Ranges for the Genera Crassostrea in Selected Countries 392 Species/Country Salinity range (ppt) Reference Crassostrea belcheri Malaysia 22 – 28 Chin and Lim, 1975 Crassostrea gasar Nigeria 20 – 30 Ajana, 1980 Crassostrea gigas Hong Kong 2 – 32 Mok, 1973 Israel 41 Hughes-Grames, 1977 Fiji 26 – 36 Ritchie, 1977 Hawaii 31 – 36 Brick, 1970 Crassostrea gryphoides India 3 – 40 Durve 1965 Crassostrea madarensis India 0 – 41 Virabhadra and Nayar, 1956 Crassostrea parabainensis Brazil 3 – 23 Singaraja, 1980 Crassostrea rhizophorae Cuba 22 – 40 Farfarsie, 1954 Venezuela 37 – 39 Angell, 1973 Puerto Rico 11 – 35 Watters & Prindow, 1975 Colombia 15 – 30 Wedler, 1970 St Croix 34 – 37 Forbes, 1973 Crassostrea tulipa 393 Ghana (Benya) Ghana (Pra) 30 – 40 0 – 29 Obodai et al., 1996 Crassostrea virginica Mexico 3 – 12 De Buen, 1957 Hawaii 22 – 32 Sakuda, 1966 Dissolved oxygen Increasing salinity results in a decline in the solubility of oxygen. Unlike in freshwater, the 394 dissolution of oxygen in seawater is far less (20 %) than what occurs in freshwaters under 395 the same condition of temperature (NERR, 1997). 396 University of Ghana http://ugspace.ug.edu.gh 30 During respiration, aerobic aquatic fauna depend on dissolved oxygen for their sustenance 397 and so a critical determinant factor of abundance (NERR, 1997). Oxygen content in 398 estuarine water influences the distribution of organisms. Diffusion of atmospheric oxygen 399 into water and primary productivity by phytoplankton and aquatic macrophytes are the two 400 natural processes influencing the supply of oxygen to estuarine waters. 401 Furthermore, according to McLusky (1989) the rate of absorption of atmospheric oxygen 402 into water is driven by the mixing of surface waters by wind and waves. Similarly, fresh 403 and saline waters flowing into estuaries transport large quantity of oxygen which is 404 consumed rapidly by the many organisms living within the estuaries, especially in the 405 bottom deposits. The levels of dissolved oxygen in water are affected by salinity changes 406 which thereby influences chemical conditions within the estuary. The amount of oxygen 407 that can dissolve in water, decreases as salinity increases. The dissolution of oxygen in 408 water also depends on water temperature, and air pressure. Increase in temperature and a 409 decrease in pressure results in a decline in dissolved oxygen. Recycling of nutrients and the 410 removal of organic substances such as dead vegetation from waterways using oxygen is an 411 important natural process. This dissolved oxygen is not only needed by aquatic fauna but 412 also very useful in maintaining a healthy ecosystem. It is therefore essential to balance the 413 sources and sinks of dissolved oxygen for sustainability of aquatic resources. 414 Biological (e.g., photosynthesis), physical (e.g., wind action, temperature) and chemical 415 (e.g., salinity) interrelated factors all serve as sources of dissolved oxygen which affects its 416 concentration. Spat settlement, growth and survival of oysters are known to be heavily 417 affected during hypoxic conditions in estuaries (Baker & Mann, 2003). 418 419 420 University of Ghana http://ugspace.ug.edu.gh 31 According to Shepard et al. (2019), among bivalves like oysters, a low oxygen event can be 421 classified according to severity: moderate hypoxia (2 mg/ L to 4 mg/ L), severe hypoxia 422 (0.5 mg/L-< 2 mg/ L) and anoxia (< 0.5 mg/L) (Renaud, 1986; Diaz & Rosenberg, 1995; 423 Turner et al., 2005). Among intertidal oysters, low dissolved oxygen is reported to be less 424 likely to be a problem for intertidal oyster reefs. For subtidal oyster reefs, dissolved oxygen 425 levels of > 4mg/L is termed, good (Shepard et al., 2019). Biney (1990) found DO levels in 426 the Densu estuary to be between 3.5-8.4 with mean values ranging between 0 - 8mg/L. 427 In related studies, dissolved oxygen concentration in the Pra estuary was found to be below 428 5 mg/L. Also, Essei lagoon recorded the least average dissolved oxygen concentration of 429 0.7 mg/L (Okyere et al., 2011). The dissolution of gases are dependent also on the amount 430 of particulate suspended particles. 431 2.2.10 Turbidity and Transparency 432 Another condition of importance in an estuary is turbidity and or transparency. Due to 433 constant mixing of freshwater and saline water from land drainage and tidal action, 434 estuarine waters are more subject to sediment transport which influences the turbidity and 435 transparency of water. Turbidity is the degree to which the water loses its clarity due to the 436 presence of suspended particulate matter while transparency is the ability of light to transmit 437 through the water column. Castro & Huber (2005) has reported large amounts of suspended 438 matter, such as algae, sediment particles, detritus or solid waste, greatly reduce water clarity 439 and prevent light from penetrating through the water column; thus, limiting photosynthesis. 440 Suspended particles clog the feeding apparatus of suspension feeders and eventually may 441 result in their death. 442 University of Ghana http://ugspace.ug.edu.gh 32 Fincham (1984) noted that higher turbidity with less clarity occurs in estuaries than in 443 adjacent open sea, because suspended materials in estuaries are derived from the river, the 444 sea and from the resuspension of particles by the activity of currents and tides. 445 Suspended particles in estuarine turbid waters can be removed by flocculation and 446 coagulation induced by the increase in salt concentration seaward in the estuary (Chou & 447 Wollast, 2006). This phenomenon is observed usually at a salinity of 5 ppt associated with 448 a turbidity maximum. In polluted estuaries where there is incessant human activities along 449 the coast, the amount of deposited silt is a controlling factor on the amount of light that may 450 be trapped by surface waters necessary for photosynthetic processes. The amount of light 451 available in estuaries together with other influencing factors like nutrients principally 452 determines primary productivity of the aquatic ecosystems. There is significantly higher 453 turbidity of 180ppm in Butuah estuary than Essei lagoon and Whin estuary (Okyere et al., 454 2011). 455 2.2.11 Nutrients 456 Nutrients content in estuaries influences the growth of filter feeders which are heavily 457 dependent on primary productivity. Phosphates (PO4 -2) and nitrates (NO3 -) are the two basic 458 but limiting nutrients in aquatic systems required for the sustenance of aquatic primary 459 productivity. Marine waters are nitrates limited and phosphorous rich. This is mainly due 460 to differences in their level of nitrogen fixation and the rate of release of phosphates from 461 sediments. In estuaries, the acceptable limits of nitrates and phosphates are 0.1 mg/L and 462 1.0 mg/L respectively (EPA, 2001). As such the availability of these nutrients in the 463 required quantities play essential roles in ecosystem sustenance (EPA, 2001; Wetzel, 2001). 464 At elevated concentrations, nitrates and phosphates can enhance eutrophication thereby 465 posing threats to aquatic life (Zuma, 2010). 466 University of Ghana http://ugspace.ug.edu.gh 33 The availability of these essential inorganic nutrients affect diversity and abundance of 467 shellfish and finfish species (Walker et al., 2007). Nitrates and phosphates in estuarine 468 systems are mostly distributed in the water, fish biomass and the sediments. It is however 469 believed that a large proportion of nutrients end up in the mud as such a crucial role being 470 played by organisms in aquatic ecosystems in nutrient absorption for their sustenance 471 (Walker et al., 2007). 472 Average and mean phosphate levels of the Densu estuary according to Biney (1990) ranges 473 between 0.02- 0.27 mg/L and 0-0.85mg/L respectively. Similarly, the author found mean 474 nitrates concentration to be between 0.02-1.67 mg/L and average values are found between 475 0-8.68 mg/L. In Pra estuary, phosphate concentration was higher in the wet season than the 476 dry season (Okyere, 2019). Phosphate content was 0.01 mg/L in the dry season and 477 0.41±0.04 mg/L in the estuary during the wet season. The highest concentration of 478 phosphate in the estuary was recorded in its riverine reaches. Also, there was seasonal 479 variability in nitrate concentration with the dry season recording the highest (78.2 ±2.3 480 mg/L) in the estuary than in the wet season (1 to 2 mg/L). The riverine reaches of the estuary 481 had the highest nitrate levels in the dry (78.2 mg/L) and wet (24.4 mg/L) seasons. 482 Concentrations of phosphates and nitrates in the estuary exceeded the optimum values of 483 0.1 mg/L and 1.0 mg/L respectively in estuaries for prevention of algal bloom (EPA, 2001). 484 2.2.12 Silicates 485 Shell bearing organisms like oysters, require silicates for its shell formation during the early 486 life stages. Dissolved silica (DSi) is more concentrated in rivers than in the ocean. 487 Therefore, estuaries which are formed when freshwater mixes with seawater has dissolved 488 silicates (DSi) decreasing rapidly toward the sea (Chou & Wollast, 2006). 489 University of Ghana http://ugspace.ug.edu.gh 34 Abiotic and biotic processes influence the removal of silicates in estuaries. The biotic 490 processes include, removal of DSi by aquatic siliceous organisms (diatoms, radiolarians 491 and sponges) and removal by phytoplankton (Chou & Wollast, 2006). 492 DSi losses due to abiotic processes namely reactions of silicates with dissolved substances 493 such as clay, formation of colloidal silica due to increase ionic strength during mixing of 494 seawater and freshwater, low biological activity due to environmental stressors and 495 adsorption of silica on ferrihydrites occur in estuarine systems. 496 However, these abiotic factors are of little significance in the water column of estuaries as 497 compared to the sediments (Chou & Wollast, 2006). Optimal conditions required for abiotic 498 uptake of silica to occur are; low salinity (0-5ppt), high concentrations of suspended matter 499 and DSi and concurrent rapid increase in salinity. Biney (1990) recorded mean and average 500 silicate levels of 5.12mg/L and 1.7-17.6mg/L respectively. Silicate concentrations were 501 higher in the rainy season than the dry season. 502 2.2.13 Primary Productivity in Estuaries 503 Primary productivity of aquatic ecosystems can be estimated from chlorophyll a 504 concentration. It is an indicator of phytoplankton abundance and biomass in coastal and 505 estuarine waters. Many models developed for predicting bivalve growth and carrying 506 capacity has relied on chlorophyll as a proxy for determining food availability (Hofmann 507 et al., 2006). Chlorophyll a has been shown to limit growth when concentrations are too 508 high or too low (Hofmann et al., 2006). The presence or lack of food also have influences 509 on shell variations, the colour and condition of oyster meat. In general, among filter feeding 510 brackish organisms, the quantity of food available can possibly be determined by estimating 511 plankton and nannoplankton samples biomass. 512 University of Ghana http://ugspace.ug.edu.gh 35 In terms of productivity, nitrate and phosphate concentrations in the Pra estuary far 513 exceeded the optimum levels (nitrate = 1.0 mg/L; phosphate = 0.1 mg/L) for primary 514 productivity in estuaries which is detrimental to aquatic life by inhibiting light penetration 515 and consequently limiting primary productivity in the estuary (Okyere, 2019). 516 2.2.14 Food Habits of the West African Mangrove Oyster 517 Trophic ecology of fish stocks is necessary in the development of the aquaculture and 518 sustainable management of aquatic life (Adite et al., 2019). Among natural and wild 519 populations of oysters, one of the factors influencing bivalve growth is the availability and 520 quality of food. Phytoplankton is considered one of the main traditional food sources for 521 bivalves (Gosling, 2003). Meanwhile, recent studies indicates that other food sources such 522 as bacteria, detritus and even zooplankton are also dependent upon by oysters (Davenport 523 et al., 2000; Lehane & Davenport, 2006). Grazing in bivalves has the potential to 524 significantly reduce consequences of eutrophication in shallow low flowing estuaries 525 (Lehane & Davenport, 2006). 526 Research by Xu &Yang (2007) also confirms the assertion among the many documented 527 dietary forms of energy, phytoplankton is one of the most important food source for the 528 intertidal oyster, Crassostrea gigas. Kassim & Mukai (2009) works on C. gigas noticed the 529 dominance of benthic diatoms in its diet accounting for 70% in 2003 and 67% in 2004 in 530 the gut contents. 531 In C. madrasensis population of a coastal lake in India, diatoms constituted about 52·8%, 532 detritus 45·7% and animal matter was 1·5% (Thangavelu, 1988). The most predominant 533 algal species fed on were; Navicula, Coscinodiscus, Nitzschia, Pleurosiqma, Rhizosolenia, 534 Amphora and Peridinium. Spatial comparison of the algal groups showed high similarity in 535 species obtained from the beds and the guts of the oyster. 536 University of Ghana http://ugspace.ug.edu.gh 36 Furthermore, the oyster showed preference especially for diatoms like Pleurosigma, 537 Coscinodiscus and Peridinium though the diatoms were in low quantities in the natural bed. 538 With the zooplankton, bivalve veliger was ranked first, followed by ciliate tintinnids. Two 539 peaks of feeding intensities were observed one during December-January and the other 540 during May-June. Oyster fed poorly during monsoon season (October-November) due to 541 prevalence of low saline conditions in the lake. 542 Adite et al. (2019) observed that the Benin population of Crassostrea gasar prefer more of 543 Diatomophycea (33.52%), Chlorophycae (17.19%), Scenedesmacae (13.80%), 544 Dictyosphaeriacae (3.79%), and Pleurococcacae (2.75%). Poly- cystis, Coelosphaerium, 545 Protococcus, Botryoccocus, Crucigenia, Melosira, Cyclotella, and Gyrosigma are the eight 546 (8) genuses of phytoplankton which dominated the diet of C. gasar with a percentage 547 composition of up to 69.06 % of the diet. Percentage occurrence were high for Melosira 548 (n= 263; 41.75%), substrate particles ( n = 211; 33.49%), and Polycystis (n = 151; 23.97%). 549 Elsewhere in Nigeria, the stomach contents of C. gasar consisted mainly of 550 phytoplankton, zooplankton, debris and indeterminate elements. Phytoplankton remained 551 dominant irrespective of the site, the time of year, the size of individuals, sex and sexual 552 maturity. In the findings of Kouakou et al. (2019) in Ivory Coast, the proportion of 553 phytoplankton in Moossou, Bimbresso and Lokodjro sites were 98.91%, 97.23% and 554 98.86%, respectively. Males had 97.64 phytoplankton and those of females had, 98.22% in 555 their stomachs. Taken into account seasons and sites, the percentage of phytoplankton 556 ranged between 91.13% and 99.2%. Many scientists have found and reported good 557 information on the food fed by oysters from the genera Crasssotrea (Kassim & Mukai, 558 2009; Adite et al., 2019; Kouakou et al., 2019). However, there is limited literature on the 559 food habits of the species Crassostrea tulipa in Ghana. 560 University of Ghana http://ugspace.ug.edu.gh 37 2.2.15 Negative Drivers of Oyster Abundance 561 Among the negative factors are competition, sedimentation, climate change, contamination 562 from microbial loads and heavy metals. Competition from boring sponges, clams, mud 563 worms, crabs, fouling organisms and predators like birds, man and fish all affect the growth 564 of oysters. Pollution from domestic, industrial and radioactive waste militate against the 565 sustenance of bivalves. 566 Contaminants have the ability to change the normal environmental conditions of estuaries 567 and render them unsuitable for physiological processes of oysters (Strayer, 2008). To 568 determine the impact of negative factors, scores are assigned to the degree of their 569 harmfulness ranging from 1, for 10 percent effectiveness, to 9, for 90%. excluding 10 570 (100%) because no oyster population can exist under such a condition. Though all coastal 571 water contain some amount of dissolved organic and inorganic solids, excessive settling of 572 suspended material is considered highly destructive to an oyster community. Interstitially 573 sediment pores and other zones of aquatic systems, are in many cases, accumulated trace 574 metals which can be disastrous to aquatic life. 575 2.2.16 Trace Metal Accumulation in Estuarine Environments 576 Among the natural constituents of estuarine ecosystems, are trace metals. In estuaries, traces 577 of all heavy metals are found in their waters, organisms and sediment (Valavanidis & 578 Vlachogianni, 2010). Lead (Pb), Cadmium (Cd), Chromium (Cr), Copper (Cu), Zinc (Zn), 579 Nickel (Ni), Arsenic (As) and Mercury (Hg) are the most common heavy metal pollutants. 580 Within the context of bio accumulation, Hg, Pb, and Cd are of the greatest concern 581 (Stankovic et al., 2014). 582 583 University of Ghana http://ugspace.ug.edu.gh 38 Seemingly, these elements bio accumulate in the bodies of aquatic organisms and are passed 584 on to other fauna and flora species through the food chain and pose toxic health risks to 585 species higher in the food chain (Stankovic & Stankovic, 2013). Metals such as Cd, As, Hg 586 and Pb have become key concerns in recent years due to their potential to negatively affect 587 aquatic organisms at high concentrations (Valavanidis & Vlachogianni, 2010). 588 The background concentrations of some trace metals from anthropogenic inputs may pose 589 physiological threats on fauna. In relation to marine bivalves such as oysters, the organisms 590 play a role by accumulating trace contaminants in their tissues, revealing essentially that 591 fraction in the environment which may be of direct ecotoxicological relevance. The feeding 592 habits and accumulation gradients of estuaries are key drivers of elemental tissue 593 accumulation (Okyere et al., 2011). 594 2.2.17 Mercury in Estuarine Environments and its Effects on Oyster Growth 595 Mercury (Hg) is a relatively non-reactive natural element that exists in elemental volatile 596 form, Hgo. In this form it has a number of toxic mercuric species which comprise Hg2+ , 597 and organic Hg, mainly monomethyl mercury (MeHg), dimethylmercury (Me2Hg), some 598 ethyl (EtHg) and mercury (Ullrich et al., 2001). 599 The natural sources of mercury in estuarine environments include gradual degassing of soil 600 systems and aerial and sub-aerial volcanism. The man-made emissions of Hg are through 601 small scale gold mining, coal-fired power plants, cement production, pyrometallurgy and 602 the use of Hg in industrial processes (Sonke et al., 2013). The factors which influence the 603 mobility and availability of Hg in brackish environments include thermodynamic solubility 604 of Hg and Hg compounds, temperature, pH, redox potential, activity and structure of 605 bacterial community, speciation, age, and the presence of inorganic and organic complexing 606 agents (Ullrich et al., 2001; Randall & Chattopadhyay, 2013). 607 University of Ghana http://ugspace.ug.edu.gh 39 In estuarine sediments, one of the key pathways to Hg speciation is its lower oxidation– 608 reduction potential (ORP). This is because a lower ORP promotes microbial- mediated 609 sulfur reduction that results in the promotion of the methylation of Hg (Randall & 610 Chattopadhyay, 2013). The accumulation of dissolved sulfide (reduced sulfur) results in the 611 precipitation of highly insoluble inorganic Hg (HgS mineral) (Randall & 612 Chattopadhyay, 2013). Continual increases in dissolved sulfide concentrations result in 613 decreases in Hg methylation rates (Gilmour et al., 1992). Mercury is a high priority 614 pollutant, persistent in the environment, high toxicity on organisms and has no biological 615 requirement (Jiang et al., 2006). 616 In laboratory experiments, estuarine organisms have reacted differently to different Hg 617 concentrations. Oysters have shown alterations in larval reproduction, hematological 618 parameters, histopathological changes i