"Science, Faculty of"@en . "Resources, Environment and Sustainability (IRES), Institute for"@en . "DSpace"@en . "UBCV"@en . "Cheung, Wai Lung"@en . "2011-02-14T18:55:18Z"@en . "2007"@en . "Doctor of Philosophy - PhD"@en . "University of British Columbia"@en . "Fishing has become a major conservation threat to marine fishes. Effective conservation of threatened species requires timely conservation risk assessment and formulation of socio-economically viable policies. A fuzzy logic expert system is developed to predict the intrinsic vulnerability and depletion risk of marine fishes to fishing. Firstly, the expert system predicts intrinsic vulnerability (i.e., fishes' inherent ability to withstand fishing mortality) from simple parameters of life history and ecology. Secondly, the system predicts the relative depletion risk of marine fishes from their intrinsic vulnerability and exploitation status inferred from catch time-series. These methods reveal the increasing dominance in global catches by fishes with low intrinsic vulnerability, particularly those in coral reefs. The opposite trend is observed in seamounts where species are highly vulnerable to fishing and are increasingly being exploited and serially depleted in recent years. Moreover, risk of population depletion increased greatly from the 1970s to 2000s. Among all extant marine fishes, 10 to 20% are predicted to have high depletion risk. In the northern South China Sea (NSCS), relative abundance of 15 out of the 17 studied taxa declines by over 70% in 15 years. The rate of decline is con-elated with the intrinsic vulnerability of the taxa. Using the Ecopath with Ecosim modelling approach, the structures of the NSCS ecosystem in the 1970s and 2000s are reconstructed and compared. The models show that the NSCS ecosystem has chanced from being demersal-dominated to pelagic-dominated, with a large decline in overall biomass and decrease in ecosystem maturity. Primary production is largely utilized by the fisheries compared to some 30 years ago when primary production was mainly utilized by marine fauna. The model is able to emulate the changes of observed relative abundance of commercial taxa. Using Ecosim, trade-off between conservation status (indicated by a depletion index) and economic benefits is identified as convex-shaped. The 2000s ecosystem appears sub-optimal ecologically and economically, thus improvement in conservation and economic benefits can be achieved simultaneously. However, the resulting social problems due to loss of fishing-related jobs need to be addressed first. Thus, developing viable alternative livelihoods for fishers is a priority to meet conservation and economic objectives."@en . "https://circle.library.ubc.ca/rest/handle/2429/31272?expand=metadata"@en . "VULNERABILITY OF MARINE FISHES TO FISHING: FROM GLOBAL OVERVIEW TO THE NORTHERN SOUTH CHINA SEA by W A I L U N G C H E U N G B . S c , The University of Hong Kong , 1998 M . P h i l . , The University of Hong Kong, 2001 A THESIS S U B M I T T E D I N P A R T I A L F U L F I L L M E N T O F T H E R E Q U I R E M E N T S F O R T H E D E G R E E O F D O C T O R O F P H I L O S O P H Y in T H E F A C U L T Y O F G R A D U A T E S T U D I E S (Resource Management and Environmental Studies) T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A May 2007 \u00C2\u00A9 Wai Lung Cheung, 2007 A B S T R A C T Fishing has become a major conservation threat to marine fishes. Effective conservation of threatened species requires timely conservation risk assessment and formulation of socio-economically viable policies. A fuzzy logic expert system is developed to predict the intrinsic vulnerability and depletion risk of marine fishes to fishing. Firstly, the expert system predicts intrinsic vulnerability (i.e., fishes' inherent ability to withstand fishing mortality) from simple parameters of life history and ecology. Secondly, the system predicts the relative depletion risk of marine fishes from their intrinsic vulnerability and exploitation status inferred from catch time-series. These methods reveal the increasing dominance in global catches by fishes with low intrinsic vulnerability, particularly those in coral reefs. The opposite trend is observed in seamounts where species are highly vulnerable to fishing and are increasingly being exploited and serially depleted in recent years. Moreover, risk of population depletion increased greatly from the 1970s to 2000s. Among all extant marine fishes, 10 to 20% are predicted to have high depletion risk. In the northern South China Sea (NSCS) , relative abundance of 15 out of the 17 studied taxa declines by over 70% in 15 years. The rate of decline is correlated with the intrinsic vulnerability of the taxa. Using the Ecopath with Ecosim modelling approach, the structures of the N S C S ecosystem in the 1970s and 2000s are reconstructed and compared. The models show that the N S C S ecosystem has changed from being demersal-dominated to pelagic-dominated, with a large decline in overall biomass and decrease in ecosystem maturity. Primary production is largely utilized by the fisheries compared to some 30 years ago when primary production was mainly utilized by marine fauna. The model is able to emulate the changes of observed relative abundance of commercial taxa. Using Ecosim, trade-off between conservation status (indicated by a depletion index) and economic benefits is identified as convex-shaped. The 2000s ecosystem appears sub-optimal ecologically and economically, thus improvement in conservation and economic benefits can be achieved simultaneously. However, the resulting social problems due to loss of fishing-related jobs need to be addressed first. Thus, developing viable alternative livelihoods for fishers is a priority to meet conservation and economic objectives. ii T A B L E OF C O N T E N T S Abstract ii Table of Contents iii List of Tables viii List of Figures x Acknowledgements xiii Dedications xiv Co-authorship statement xv 1. General Introduction 1 1.1. Fishing as a major conservation threat to marine fishes 2 1.2 Marine fishes may be vulnerable to extinction 4 1.3. Life history and vulnerability 7 1.4. Approaches to assessing conservation status of fishes 10 1.5. Integrating biodiversity conservation into fishery management 13 1.6. Trade-offs between conservation and socio-economic objectives 16 1.7. Modell ing interactions between ecosystem and fisheries 17 1.8. Ecopath with Ecosim 19 1.9. Fisheries in the Northern South China Sea (NSCS) 20 1.10. Research objectives.. 23 1.11. References................^ \u00E2\u0080\u00A2..\u00E2\u0080\u009E'.....' 28 2. A Fuzzy Logic Expert System to Estimate Intrinsic Vulnerabilities of Marine Fishes to Fishing 44 2.1. Introduction 44 2.1.1. Life history and ecological characteristics as proxies for intrinsic vulnerability 45 2.1.2. Fuzzy logic expert system 46 2.2. Methods 47 2.2.1. Structure and functioning of the fuzzy expert system 47 2.2.2. System evaluations 54 2.2.3. Validity tests on vulnerability estimates 55 2.3. Results 57 2.4. Discussion 64 2.5. References 68 i i i 3. Intrinsic Vulnerability in the Global Fish Catch 75 3.1 Introduction 75 3.2. Methods 78 3.2.1. Predicting intrinsic vulnerability 78 3.2.2. Intrinsic vulnerability index of marine fishes 79 3.2.3. Mean intrinsic vulnerability index of catch 81 3.2.4. Comparison with distributions of globally threatened fishes 82 3.3. Results 83 3.3.1. Intrinsic vulnerability of fish with different associated habitats 83 3.3.2. Average intrinsic vulnerability index of catch 86 3.3.3. Comparing average vulnerability of catch and number of red listed fishes 89 3.4. Discussion 91 3.4.1. Intrinsic vulnerability of fish with different associated habitats 91 3.4.2. Intrinsic vulnerability and geographic range 91 3.4.3. Average intrinsic vulnerability of catch 92 3.5. References 96 4. An Index that Expresses Risk of Severe Population Depletion of Marine Fish from Fishing 103 4.1. Introduction 103 4.2. Methodology 109 4.2.1. Analysis of temporal patterns of catch time-series 109 4.2.2. Combining 'rules-of-thumb' 113 4.2.3. Comparing the predicted depletion risk with the I U C N categories....... 116 4.2.4. Depletion risk of all exploited marine fishes 119 4.3. Results..... 120 4.4. Discussion 125 4.5. Reference 128 5. Evaluating the Status of Exploited Fishes in the Northern South China Sea Using Intrinsic Vulnerability and Spatially Explicit Catch-per-unit-effort Data 136 5.1. Introduction 136 5.1.1. Data availability 138 5.1.2. Intrinsic vulnerability 139 5.2. Methods 141 5.2.1. C P U E data 141 5.2.2. Interpolation of C P U E 143 5.2.3. Standardization of C P U E 145 iv 5.2.4. Intrinsic vulnerability and rate of decline 147 5.3. Results 150 5.3.1. Fishing effort distribution 150 5.3.2. Changes in C P U E 150 5.3.3. Intrinsic vulnerability against C P U E changes 157 5.3.4. Intrinsic vulnerability of demersal and benthopelagic fish in N S C S 159 5.4. Discussion 159 5.4.1. Increased fishing mortality 159 5.4.2. Environmental changes 161 5.4.3. Observation error and the non-proportionality of C P U E to abundance 162 5.4.4. Status of fishery resources in the N S C S 163 5.5. References 166 6. Ecosystem Modelling of the Northern South China Sea for the 1970s and 2000s 170 6.1 Introduction 170 6.2 Methods 173 6.2.1. Model structure and parameterization 173 6.2.2. Uncertainty and sensitivity analysis 179 6.3 Results 185 6.3.1. Biomass changes 185 6.3.2. Mortalities 189 6.3.3. System index 190 6.3.4. Uncertainty and sensitivity analysis 195 6.4. Discussion \u00E2\u0080\u00A2\u00E2\u0080\u00A2\u00E2\u0080\u00A2 206 6.5 References 210 7. A Depletion Index as an Indicator of Biodiversity Status in Ecosystem Simulation Model 216 7.1 Introduction 216 7.2. Methods 219 7.2.1. Depletion index 219 7.2.2. Validation with the N S C S model 224 7.3. Results 228 7.3.1. Time-series fitting 228 7.3.2. Depletion index (DI) 233 7.4. Discussion 239 7.5. References 243 v 8. Trade-offs between Conservation and Socio-economic Objectives in Managing a Tropical Marine Ecosystem 247 8.1. Introduction 247 8.1.1. Buy-back as means to restructure fishing fleets 251 8.2. Methods 252 8.2.1. Ecopath with Ecosim modelling 252 8.2.2. Policy optimization 253 (a) Maximizing economic rent 253 (b) Employment 254 (c) Ecosystem structure 255 (d) Conservation of vulnerable species 255 8.2.3. Cost of fleet restructuring 257 8.3. Results 258 8.3.1. The Pareto-frontiers 258 8.3.2. Ecosystem structures 262 8.3.3. Restructuring the fishing fleets 264 8.3.4. Buy-back cost 265 8.3.5. Sensitivity analysis 266 8.4. Discussion 268 8.4.1. Trade-offs between policy objectives 268 8.4.2. Economics of restructuring the fishing fleets 270 8.4.3. Model assumptions and uncertainties 272 8.5. References 275 9. Conclusions 280 9.1. General summary 280 9.2. Applications and recommendations 283 9.3. Limitations and future improvements 287 9.4. Final conclusions 291 9.5. References 292 Appendices 298 2.1 Development of the fuzzy expert system 298 2.2. Operation of the fuzzy system 300 a. Fuzzification 300 b. Rule firing and fuzzy reasoning 300 c. Defuzzification 300 2.3. Assignment of strength of spatial behaviour of fish 301 3.1. Intrinsic vulnerability index of fish taxa represented in the global catch 303 vi 4.1. Life history and recruitment parameters of 21 marine fishes 321 6.1 Parameterizations of the 1970s and 2000s model 322 6.2. Diet composition matrices of the 1970s and 2000s N S C S models 342 7.1. Screenshots of an interface for calculating the depletion index in Ecosim.. . . 349 Appendix references 352 ) vii LIST OF T A B L E S Table 2.1. Heuristic rules defined in the fuzzy system to assign relative vulnerabilities to fishes 51 Table 2.2. The definitions of classical (Boolean) sets used to classify life history and ecological characteristics into different categories, and the rules that connected them to different level of intrinsic vulnerabilities 56 Table 3.1. Habitat categories used here, and for which global maps are available in the Sea Around Us Project, with some of the terms typically associated with them (in FishBase and other sources) 82 Table 3.2. Comparisons of intrinsic vulnerability between fishes associated with different habitats 84 Table 3.3. Results of the generalized linear model for the relationships between the environmental attributes (depth, latitude and position in water column) and the index of intrinsic vulnerability of fishes 85 Table 4.1. Categorization of exploitation status based on fishery catch time-series .. 112 Table 4.2. Heuristic rules that relate intrinsic vulnerability and exploitation status (premises) with depletion risk (conclusions) 115 Table 4.3. Extrapolation of catch data to all exploited marine fish 116 Table 5.1. The 17 taxa reported in the catch and effort surveys and their composite species(s) 143 Table 5.2. Data on life history and ecology traits of the 16 taxa reported in the catch and effort survey 148 Table 5.3. Analysis of variance ( A N O V A ) with index of vulnerability and estimated C P U E change being the independent and dependent variables respectively 158 Table 5.4. Linear regression model between the index of intrinsic vulnerability and the estimated C P U E change (n = 17) 158 Table 6.1. Functional groups and their basic parameters used in the Northern South China Sea ecosystem models for (a) the 1970s and (b) the 2000s states... 175 Table 6.2. Estimated fishery catch (t-km 2) by functional groups and gear types 178 Table 6.3. Pedigree categories of the basic parameters used in the Northern South China Sea ecosystem models 181 Table 6.4. Pedigree indices of the basic parameters used in the Northern South China Sea ecosystem models 183 Table 6.5. Estimated fishing (F), natural (M) and other mortalities (M\u00E2\u0080\u009E) of the 1970s and 2000s N S C S models 190 Table 6.6. Estimated system indices of the 1970s and 2000s models 192 vni Table 6.7. Sensitivity of the estimated parameters 197 Table 7.1. Heuristic rules for the relationship between intrinsic vulnerability, relative abundance and the depletion index (DI) 221 Table 7.2. Alternative heuristic rules that describe the relationship between intrinsic vulnerability, relative abundance and the depletion index (DI) 227 Table 8.1. Landed value, total cost and profitability of the six fishing fleets in the 2000s N S C S ecosystem model 254 Table 8.2. The estimated relative jobs per value for the six fishing fleets in the N S C S ecosystem model 255 ix LIST OF F IGURES Figure 1.1. A specimen of Bahaba taipingensis ( > 2m) 2 Figure 1.2. Proportion of extant vertebrates that have been assessed under the I U C N Red List of Endangered Species 11 Figure 1.3. Number of marine fishes that have been assessed under the I U C N Red List. 12 Figure 1.4. Schematic presentations of the proposed framework to identify policy options that integrate conservation into fisheries management 16 Figure 1.5. Map of the northern shelf of the South China Sea 22 Figure 1.6. A flow diagram showing the structure of this thesis 24 Figure 2.1. Fuzzy sets defining the input life history and ecological characteristics 49 Figure 2.2. Output fuzzy sets for the intrinsic vulnerability of marine fishes 50 Figure 2.3. Estimated intrinsic vulnerability from the fuzzy logic expert system when threshold value varied from 0 to 0.9 57 Figure 2.4 Sensitivity of the calculated intrinsic vulnerability to individual attributes.58 Figure 2.5. Plot of population trends of 40 species of marine fishes listed in the I U C N list of threatened species and (a) A F S ' s productivity - productivity categories estimated by the A F S scheme, (b) maximum length (log), and (c) fuzzy system intrinsic vulnerability 61 Figure 2.6. Plot of the observed population trends of the 24 species of demersal fish in the North Sea and the proxies of extinction vulnerability: (a) A F S ' s productivity, (b) maximum length, (c) age at first maturity, (d) fuzzy system intrinsic vulnerability 62 Figure 2.7. Plots between the observed population trends of the 13 species of reef fish in Fij i (a) maximum length, (b) intrinsic vulnerability estimated by the fuzzy system based on information from FishBase only, and (c) intrinsic vulnerability 63 Figure 3.1. Mean intrinsic vulnerability index of marine fishes that are categorized as: coral reef-associated, estuaries-associated, seamount, seamount-aggregating, all fish, species listed under the I U C N Red List. 84 Figure 3.2. Average intrinsic vulnerability index weighted each year by the annual catch 87 Figure 3.3. Surface plot of catch of fishes with different intrinsic vulnerability index from 1950 to 2003 89 Figure 3.4. Number of marine fishes listed under the I U C N Red List of Threatened Species in the world ocean represented by a map 90 Figure 4.1. Schematic presentations of the proposed framework to identify depletion risk of marine fishes 105 x Figure 4.2. Schematic diagram showing the classification of exploitation status of a population based on a catch time-series 106 Figure 4.3. Catch time-series and the estimated depletion risk index for fish with different life history and exploitation patterns 110 Figure 4.4. Schematic diagram of the structure of a fuzzy expert system to predict depletion risk of marine fishes to fishing 113 Figure 4.5. Proportion of the 460 species of marine fishes with different classes of calculated depletion risk index 120 Figure 4.6. Average depletion risk index of different fish groups 121 Figure 4.7. Proportion of described mammals, birds, amphibians categorized as critically endangered, endangered and vulnerable 122 Figure 4.8. Comparisons of Type I and II errors between threatened status predicted by the I U C N Red List procedure and the rule-based model 123 Figure 4.9. Probability of under-estimating (type I) and over-estimating (type II) risk using the depletion risk index predicted 124 Figure 5.1 Change in landings, fishing power and C P U E of the three coastal Chinese provinces (Guangdong, Guangxi and Hainan) in the N S C S 137 Figure 5.2 Estimated demersal fishery resources in the northern South China Sea... 137 Figure 5.3. The seven fishing areas delineated for the northern South China Sea continental shelf 142 Figure 5.4. Diagram illustrating the interpolation of C P U E for cell without estimate of C P U E '. 145 Figure 5.5 Percentage distribution of sampled fishing effort of (a) stern trawlers and (b) pair trawlers from the government survey from 1973 to 1987 (original data records for 1977 and 1988 are missing) 152 Figure 5.6. Average decline in C P U E of 16 commercially exploited taxa in N S C S . . . 153 Figure 5.7. Standardized C P U E of demersal fish in the N S C S 156 Figure 5.8. Sensitivity of the estimated C P U E decline from different statistical assumptions in analyzing the spatial data 157 Figure 5.9. Linear regression analysis of change in C P U E (log) and the estimated index of intrinsic vulnerability 158 Figure 6.1. Map of the northern South China Sea 170 Figure 6.2. Change in biomasses between the 1970s and the 2000s N S C S models.... 186 Figure 6.3. Comparisons of biomass and throughput of demersal, benthopelagic and pelagic fish groups in the 1970s and 2000s N S C S models 187 Figure 6.4. Ratio of fishes to invertebrates landings from N S C S from 1950 to 2000. 188 Figure 6.5. Biomasses by trophic level of the 1970s and 2000s N S C S models 189 xi Figure 6.6. Primary production required (PPR) in the 1970s and 2000s models 194 Figure 7.1. Fuzzy membership functions for the inputs 222 Figure 7.2. Time-series of predicted biomasses and the observed relative biomasses of the 14 functional groups in the northern South China Sea model 229 Figure 7.3. Time-series of predicted biomasses and the observed relative biomasses in the northern South China Sea model with alternative 'vulnerability factor' settings 232 Figure 7.4. Comparison between the predicted phytoplankton biomass from fitting the N S C S ecosystem model with time-series catch rate data and the observed winter monsoon strength index 233 Figure 7.5. Simulated changes in biomass of the 37 functional groups in the N S C S model 235 Figure 7.6. Comparisons of the depletion index with published ecological indices.... 236 Figure 7.7. Correlations between Q90 biodiversity index and mean trophic level of catch with the depletion index 237 Figure 7.8. Correlations between Q90 biodiversity index and mean trophic level of catch with the depletion index calculated from simulations by assuming different 'vulnerability factor' 238 Figure 7.9. Predicted average depletion index of the N S C S ecosystem from 2000 to 2030. The pessimistic scenario assumes a 3-fold linear increase in fishing effort from 2000 to 2030 while the optimistic scenario assumes a linear decrease in fishing effort to a quarter of the 2000 level in 2030 239 Figure 8.1. Possible trade-off relationships between fisheries management objectives. 248 Figure 8.2. Schematic diagram comparing revenue and cost that can theoretically be obtained from a multi-species fishery 249 Figure 8.3. Pareto-frontier between the net present value of benefits (profit) of the fisheries and the estimated depletion index 259 Figure 8.4. Trade-off relationship between social and conservation objectives 260 Figure 8.5. The approximated Pareto-frontiers between the net present value of benefits relative ecosystem maturity, and Q-90 biodiversity index 261 Figure 8.6. Estimated changes in biomass of fishes, invertebrates and charismatic megafauna 263 Figure 8.7. Relative fishing effort of the six fishing sectors required to achieve the best conservation status, the highest net economic benefits and the maximization of social benefits 265 Figure 8.8. The estimated buy-back cost to reduce fishing capacity 266 Figure 8.9. The approximated Pareto-frontiers between the net present value of benefits and the calculated depletion index 267 xii A C K N O W L E D G E M E N T S I offer my gratitude to my supervisor, Tony Pitcher, for his advice, assistance and support. I am grateful to my supervising committee: Les Lavkulich, Daniel Pauly, Yvonne Sadovy and Rashid Sumaila for their advice and inspiration. I thank the faculty, researchers, students and administrative staff of the Fisheries Centre whom have made the completion of this thesis possible. I would like to extend my gratitude to Reg Watson, Telmo Morato, Adrian Kitchingman, Fredelito Valdez, John Meech, Cheng Jiahua, Cai Wengui, Jia Xiaping, Qiu Yongsung, Rachel Wong, Valerie Ho and Anna Situ for their help, stimulating discussions and useful information. I am thankful to the Hong Kong Agriculture, Fisheries and Conservation Department for permission to use their data. I owe particular thanks to Jonathan Anticamara, Robyn Forrest, Dale Marsden, Marivic Pajaro and Louise Teh for commenting on and editing parts of this thesis, which improved its quality considerably. This thesis would not have been completed without funding supports from the Sir Robert Black Trust Fund (Hong Kong) scholarship for overseas studies, the University of British Columbia Graduate Fellowship, Canada's National Scientific and Engineering Research Council , and the Pew Charitable Trusts through the Sea Around Us Project. I offer special thanks to my wife, V i c k y Lam, whom has always been by my side supporting me. A thousand thanks are owed to my parents, who have supported me in every way to pursue my goals. I am grateful to Uncle Lawrence for providing me with a lovely shelter. xi i i To my grandma, parents, sister and wife xiv C O - A U T H O R S H I P S T A T E M E N T Versions of Chapters 1, 2, 3, 4, 5 and 8 of this thesis have been or are being published: Chapters 1 and 4: Cheung, W . W . L . , Pitcher, T. J. & Pauly, D . 2007 Using an expert system to evaluate vulnerabilities and conservation risk of marine fishes from fishing. In: Lipshitz A . P. (ed.). Progress in Expert Systems Research. New York: Nova Science Publishers, [in press]. Chapter 2: Cheung, W . W . L . , Pitcher, T. J. & Pauly, D . 2005 A fuzzy logic expert system to estimate intrinsic extinction vulnerability of marine fishes to fishing. Biological Conservation 124, 97-111. Chapter 3: Cheung, W . W . L . , Watson, R., Morato, T., Pitcher, T. J. & Pauly, D . 2007 Intrinsic vulnerability in the global fish catch. Marine Ecology Progress Series 333, 1-12. Chapter 5: Cheung, W . W . L . & Pitcher, T. J. Evaluating the Status of Exploited Taxa in the Northern South China Sea Using Intrinsic Vulnerability and Spatially Explici t Catch-per-unit-effort Data. Fisheries Research, [in review]. Chapter 8: Cheung, W . W . L . & Sumaila, U . R. Trade-offs between Conservation and Socio-economic Objectives in Managing a Tropical Marine Ecosystem. Ecological Economics, [in review]. This thesis was designed, conducted and written by me, with the guidance of Tony Pitcher and Daniel Pauly. Reg Watson helped and advised me with the analysis of the global catch data, while Telmo Morato compiled the data for the seamount fishes in Chapter 3. Rashid Sumaila guided the work on economic analyses in Chapter 8. xv 1. G E N E R A L I N T R O D U C T I O N 1 In the early 1950s, a large bodied croaker (Sciaenidae) called the Chinese bahaba (Bahaba taipingensis), endemic to the region, was common along the coast of the South and East China Sea (Figure 1.1). Its swimbladder was particularly valued as a tonic in traditional Chinese medicine. During spawning in the major estuaries, local fishers targeted bahaba aggregations using artisanal purse seines and gillnets. Catches of over one tonne per haul were common and individuals over 50 kg were often caught. A n indirect estimate of annual catch from Hong Kong is over 50 tonnes per year in the 1940s (Sadovy & Cheung 2003). However, in the 2000s, only a few individuals per year were caught along the entire Chinese coast. The species is now listed as 'Critically Endangered' in the I U C N Red List of Endangered Species (Baillie et al. 2004). Other stories of fishes being threatened by fisheries exploitation have been documented, such as the Common skate {Raja batis) in the North Sea (Brander 1981), the Nassau grouper (Epinephelus striatus) in the Caribbean (Sadovy 1993; Sala et al. 2001; Sadovy 2005), and the Humphead wrasse in the Indo-Pacific region (Sadovy et al. 2003). Exploitation of the ocean has increased rapidly in recent decades and fishing has been a major form of direct utilization (Pauly et al. 2002). Based on fishery statistics compiled by the United Nations Food and Agriculture Organization (FAO) , total reported landings from the sea increased from less than 20 to over 82 million tonnes from 1950 to the 2000s. If discards and illegal, unreported and unregulated catches are included, global catches peaked at almost 150 mill ion tonnes in the late 1980s, after which they declined slowly (Pauly et al. 2002). In 2003, about one-quarter of the stocks monitored by F A O were said to be underexploited or moderately exploited (3 percent and 21 percent, respectively), 52 percent were fully-exploited (production close to their maximum sustainable limits), while approximately one-quarter were overexploited, depleted or recovering from depletion (16 percent, 7 percent and 1 percent, respectively). These 1 A version of this Chapter has been accepted for publication. Cheung, W. W. L., Pitcher, T. J. & Pauly, D. 2007 Using an expert system to evaluate vulnerabilities and conservation risk of marine fishes from fishing. In Lipshitz A. P. (ed.). Progress in Expert Systems Research. New York: Nova Science Publishers.[in press] 1 represented an increase in the proportion of overexploited and depleted stocks from about 10 percent in the mid-1970s to close to 25 percent in early 2003 ( F A O 2004). Figure 1.1. A specimen of Bahaba taipingensis ( > 2m) caught 30 December, 1993, outside Castle Peak Bay, western Hong Kong, as incidental trawler bycatch. Photo originally published in Sadovy and Cheung (2003). 1.1. Fishing as a major conservation threat to marine fishes Collapses of major fishery stocks and endangerment of a number of marine fishes suggest that marine species are vulnerable to extreme depletions, or even extinction, resulting directly or indirectly from fishing (Roberts and Hawkins 1999; Powles et al. 2000; Dulvy et al. 2003; Sadovy and Cheung 2003). While the majority of the world's fisheries resources are fully- to over-exploited (Pauly et al. 2002; Hilborn et al. 2004a), fishing is considered to be a major conservation threat to marine fishes (Reynolds et al. 2001; Dulvy et al. 2003). Parallel to the increasing scale of fishing, the abundance of 2 many marine fishes has declined greatly throughout the world over the past five decades. In the north Atlantic, high-trophic-level fishes have declined by two thirds since the 1950s (Christensen et al. 2003). Over the past 50 years, breeding populations of 98 populations of marine fishes from around the world declined by a median of 65%, with over 28 populations declining by more than 80% (Hutchings & Reynolds 2004; Reynolds et al. 2005a). Commercially-important species can be fished down to a vulnerable level because of their economic value, e.g., Chinese bahaba (Bahaba taipingensis, Sciaenidae) (Sadovy & Cheung 2003), Southern bluefin tuna (Thunnus maccoyii, Scombridae) (Hayes 1997). However, species with little or no commercial value are not safe from the threats of fishing, since non-targeted species may be threatened through bycatch (e.g., Common skate, Raja batis, Rajiidae, Brander 1981; Barndoor skate, Raja laevis, Rajiidae, Casey & Myers, 1998). Moreover, fishing activities can create large disturbances and damage to benthic habitats (Jennings et al. 2001; Kaiser et al. 2002; Kaiser et al. 2003). Declines and extinctions can be associated with loss of essential habitats critical to complete the life cycle of the species (McDowal l 1992; Watling & Norse 1998). Fishing may also cause loss of genetic diversity (Law 2000). Some populations of New Zealand snapper (Pagrus auratus; Hauser et al. 2002) and Atlantic cod (Gadus morhua; (Hutchinson et al. 2003) exhibit significant declines in genetic diversity over their exploitation history. Also , effective population size, which determines the genetic properties of a population, was about one-fifth of the census population size (estimated total abundance) in some exploited populations of fishes (Hauser et al. 2002; Hutchinson et al. 2003; Hoarau et al. 2004). The low effective population size may result in inbreeding in the population and a loss of genetic diversity (Hauser et al. 2002; Hutchinson et al. 2003; Hoarau et al. 2004). Thus, there is a need to monitor and manage genetic diversity of exploited marine populations (Kenchington et al. 2003). The loss of biodiversity may directly or indirectly affect the functioning of the ecosystem (Loreau et al. 2001; Worm & Duffy 2003; Worm et al. 2006). Removal of keystone species, which include species that are critical to the ecological function of a community or habitat in their current states (Zacharias & Roff 2001), can result in a state shift in marine ecosystems. For instance, the removal of sea otters in the Aleutian Archipelago resulted in sea urchin population expansion, which virtually excluded fleshy 3 macroalgae such as kelp and greatly affected their associated communities (Tegner & Dayton 2000; Jackson et al. 2001). On the other hand, depletion of algae grazers such as parrotfish led to overgrowth of algae on coral surfaces which largely affected the coral reef ecosystem (Bellwood et al. 2004). Some studies suggested that biodiversity is positively correlated with ecosystem function (Tilman et al. 1997; Symstad et al. 1998; Worm et al. 2006), and. the stability and resilience of ecosystems (Tilman & Downing 1994; Tilman 1996; Scheffer et al. 2001). This is supported by meta-analysis of data showing significant correlations between marine biodiversity and ecosystem functions (Worm et al. 2006). Thus, it is generally agreed that a higher species richness is needed to maintain stability of ecosystem processes against environmental variability (Loreau et al. 2001). 1.2 Marine fishes may be vulnerable to extinction There was a general belief in the past that marine fishes would never be extirpated because a single female could produce millions of eggs and have large geographic ranges. Although it has been realized that elasmobranchs (sharks and rays) are vulnerable to extinction because of their life history and ecology (Smith et al. 1998; Stevens 1999; Stevens et al. 2000), wide-ranging highly fecund fishes were still considered inexhaustible by some. Such perceptions persist since Jean Baptiste de Lamarck stated in the early 19 t h century that: 'Animals living in the waters, especially the sea waters, are protected from the destruction of their species by man. Their multiplication is so rapid and their means of evading pursuit or traps are so great that there is no likelihood of his being able to destroy the entire species of these animals' (Lamarck 1809, reprinted in 1984). This means that a small number of adult individuals remaining in the ocean could f i l l the sea with fishes quickly, assuming that most eggs could develop into adult fish again a few years later. Also, marine fishes generally have wide geographic ranges and produce pelagic eggs that can draft with ocean currents. Thus populations 'somewhere' could always re-colonize a locally depleted area. 4 The misconception that highly fecund fishes (i.e., the majority of teleosts) are resilient to fishing (the ability of a population to recover) have been seriously scrutinized and largely disproved (Sadovy 2001; Dulvy et al. 2003). High fecundity does not equate with high resilience to fishing (the ability of a population to recover from being depleted by fishing). Resilience depends largely on the survivorships from the egg to the adult stages, instead of the fecundity per se (Sadovy 2001). Fishes that spawn millions of eggs at a time usually have a 'bet-hedging' strategy, in which the production of large number of eggs is evolved to compensate for the low survivorship from egg to spawner (Phillipi & Seger 1989). Life history theory predicts that fishes (and other vertebrates) that are large in size (generally highly fecund in teleosts) and late maturing have low intrinsic rates of population increase (r) (Smith et al. 1998; Musick 1999b; Reynolds et al. 2001). Animals with low r have less ability to recover after reduction of the population, and thus low resilience to fishing. There are examples of highly fecund fishes that are endangered by fishing, e.g., the Chinese bahaba (Sadovy & Cheung 2003), Nassau grouper (Epinephelus striatus) (Sadovy 1993; Sala et al. 2001) and Humphead wrasse (Cheilinus undulates) (Sadovy et al. 2003). The relationship between life history of fishes and their vulnerability to fishing wi l l be explored in detail later. A large geographic range does not offer much protection to fishes from being threatened by fishing (Dulvy & Reynolds 2002; Dulvy et al. 2003; Reynolds et al. 2005a). Genetic studies suggested that dispersal of pelagic eggs and larvae might be limited (Swearer et al. 1999; Cowen et al. 2000). Also, large-scale fishing activities have spread to most parts of the oceans and there are few unexploited refugia left (Pauly et al. 2002; Pauly et al. 2005). Thus, fishes with high fecundity and large geographic range should not be assumed to be resilient or invulnerable. Fishing was considered a conservation threat to marine fish only in recent years (Powles et al. 2000). In fact, whether the depletion of fish populations by fishing should be a genuine conservation concern was a subject of debate by fisheries scientists and conservation biologists (Carlton et al. 1999; Mace & Hudson 1999; Powles et al. 2000; Hutchings 2001). For instance, conventional fisheries stock assessment based on simple surplus production models suggests that maximum equilibrium catches from a population could be achieved by reducing stock abundance to a level close to half of the unexploited 5 stock size - a decline level (under a certain time-frame) that may fall into vulnerable categories under the I U C N Red List criteria. The I U C N Red List, maintained by the World Conservation Union, are widely accepted as the authority for determining extinction risk of animals and plants (Rodrigues et al. 2006), although their validity for marine fishes had been questioned (Punt 2000; Reynolds et al. 2005a). To resolve this problem, the I U C N included a higher decline thresholds for species in which 'the causes of the reduction in population size are clearly reversible, and understood and have ceased' ( I U C N 2001). However, even if the causes of population reduction are reversible, and understood, and management policies are in place, depleted populations may still not be able to recover (Hutchings 2000; Hutchings & Reynolds 2004). Moreover, no contemporary extinctions of marine fishes have been documented, leading to the uncertainty of whether marine fishes can ever be fished to extinction (Dulvy et al. 2003; Reynolds et al. 2005a). However, the lack of contemporary extinctions may be due to the difficulty in detecting marine extinctions (Carlton 1993). A study demonstrated a median of 44 years of delayed reporting of possible extinction from the time the last individual of the marine species was sighted since the 1900s, (Dulvy et al. 2003). Although reporting ability has improved in recent years (Dulvy et al. 2003), given that intensive large scale fishing mainly occurred since the 1950s, extinctions or extirpations caused by fishing may still not be apparent. A change in our perception of risk of extinction and threat to conservation has led to various studies on the biology of extinction vulnerability of marine fishes in the last decade (Reynolds et al. 2005a). Some have suggested treating marine fishes differently from other animals in extinction risk assessments (Musick 1999a). On the other hand, many marine fish stocks have collapsed because of over-exploitation (Hutchings & Reynolds 2004). Although it appears that fish stocks recovered after fishing pressure had been eased (Russ & Alcala 1996; Myers & Worm 2005), the rate of recovery depends on the productivity (intrinsic rate of population increase, r) of the stocks and the level of depletion (Safina et al. 2005). Also, many stocks have shown little or no sign of recovery after up to 15 years, while those that have recovered are mainly clupeoid fishes, which are suggested to be intrinsically more resilient (Hutchings 2000; Hutchings & Reynolds 2004). The northern cod is a classic case of the lack of recovery after severe depletion 6 (Shelton & Healey 1999; Hutchings & Reynolds 2004). The lack of recovery of some fish stocks suggested that fishes could be depleted to a level in which their recovery may be impaired. Thus, unless the non-recovery thresholds for fishes are different from those for other organisms, marine fishes should be treated like other animals in extinction risk assessment (Hutchings 2001). However, because of the lack of documented contemporary extinctions of marine fishes, the definition of their true extinction risk is still unclear (Reynolds et al. 2005a). This question may be resolved by improving the understanding of fish population dynamics at low stock sizes, which may help to determine their minimum viable population size. Meta-analysis of stock-recruitment relationships to evaluate stock productivity at low spawner abundance provided useful information to help answer this question (Myers et al. 1999; Goodwin et al. 2006). 1.3. Life history and vulnerability Life-history traits, which have evolved to ensure persistence in the face of biotic and abiotic variability (Winemiller & Rose 1992; Wootton 1996; Rochet et al. 2000; Winemiller 2005), affect the responses of fish population to exploitation (Adams 1980; Roff 1984; Kirkwood et al. 1994). Trade-offs between growth, reproduction and mortality appear to be invariant across a wide range of vertebrates (Charnov 1993; Jensen 1996; Charnov & Downhower 2002). For instance, the ratio of natural mortality to growth rate is very similar across different animal groups. Also, species that are large-bodied, long-lived and late-maturing generally have slow growth, low natural mortality, high life-time reproductive outputs but a low intrinsic rate of population increase. The latter directly affect the ability of a species to resist exploitation. It is possible to put species into a continuum between extreme life history strategies. For instance, based on comparative life-history analysis of 216 North American freshwater and marine fishes, three general life-history patterns were proposed (Winemiller & Rose 1992; Winemiller 2005): \"periodic\" (large, late-maturing fishes), \"opportunistic\" (small, early-maturing fishes) and \"equilibrium\" (intermediate size show 7 parental care to offspring). Later studies adopted these groupings to relate compensatory responses of fish populations to life history patterns (Rose et al. 2001). Fagan et al. (1999) proposed parallel categories for a wide range of vertebrates: \"persistent\" (low population variability relative to their growth rates), \"refuge-dependent\" (high population variability relative to growth rate, thus population is dependent on the existence of refugia for re-colonization), \"carrying capacity-dependent\" (low population growth rate, low variability, thus would require larger population size). Populations in the same life history groups may respond similarly to disturbances such as fishing and habitat destruction. Thus, qualitative predictions on the responses of populations to disturbances based on these groupings could be made (Rose et al. 2001; Winemiller 2005). Correlations between life history, population regulation and thus vulnerability to fishing are supported by empirical evidence. Meta-analysis using 54 stock-recruitment time-series showed that large-sized, late-maturing fishes had strong density-dependence in low abundance (i.e., have smaller maximum spawner per spawner), but high equilibrium spawner per recruit without exploitation (Goodwin et al. 2006). Analysis including data from other vertebrate groups produced similar conclusions, suggesting that the correlations between life history and population dynamics may be applicable to most vertebrates (Fagan et al. 1999). Also , empirical studies using historical abundance data of exploited fish populations find significant correlations between the rate of population declines (a proxy of vulnerability to fishing) and life history parameters such as maximum body size and age at maturity (Jennings et al. 1998; Jennings et al. 1999a, b). Indeed, all current evidence suggests that body size is an important factor in determining vulnerability to fishing (Jennings et al. 1998; Jennings et al. 1999a, b; Cardillo & Dromham 2001; Reynolds et al. 2001; Gaston & Blackburn 2003; Reynolds et al. 2005a). Correlations of life history traits of fishes with other anthropogenic disturbances may be different from those associated with fishing. Freshwater fishes are mainly threatened by habitat destruction and introduction of exotic species (Ricciardi & Rasmussen 1999). This may have led to a weak correlation between body size and vulnerability for some freshwater fishes (Reynolds et al. 2005b). In fact, small freshwater fish may have higher vulnerability (Reynolds et al. 2005b) because their distribution range is correlated with body size, and species with a restricted range are generally more 8 vulnerable to habitat destruction. The ability of species to adapt to changing temperature has been suggested as a factor determining the vulnerability of marine fishes to climate change (Roessig et al. 2004; Perry et al. 2005). Distribution ranges of 36 species of demersal fishes in the North Sea and their relationship with temperature changes over the past three decades suggested that fishes with shorter life cycles and smaller body sizes were able to shift their distribution more easily than other species (Perry et al. 2005). This implies that fishes with these characteristics may be better able to adapt to climate change. Ecological and behavioural characteristics may also affect fishes' vulnerability to fishing. For example, reduction of population below a depensatory abundance threshold can lead to extinction (Liermann & Hilborn 1997). Depensation, termed the Allee effect in the ecology literature (Stephens & Sutherland 1999; Stephens et al. 1999), occurs when fitness (number of offspring per spawner) or per capita growth rate decreases at low population size (Stephens et al. 1999; Petersen & Levitan 2001). This is contrary to the compensation effect often assumed to be the norm, in which fitness or per capita growth rate increases at low population size (described by a logistic function) (Liermann & Hilborn 1997). Depensation may result from juvenile predation saturation at high population size (Liermann & Hilborn 1997; Petersen & Levitan 2001), trophic cascade effects in which predation on juveniles increases because of increased predator abundance (Walters & Kitchell 2001), or disruption of spawning aggregations because of reduced spawner abundance or distorted sex-ratio (Sala et al. 2003; Sadovy & Domeier 2005). Meta-analysis of stock-recruitment data showed that depensation might be uncommon in marine fishes (Myers et al. 1995; Liermann & Hilborn 1997). However, re-analysis accounting for the high variance in the original data suggests that it might still be likely that depensation would be more common in marine fishes than previously assumed (Liermann & Hilborn 1997). Species forming large aggregations can be easily targeted by fishers. Aggregative or shoaling behaviour often results in hyperstability of catch-per-unit-effort (CPUE) , which masks the depletion of populations (Hilborns & Walters 1992; Pitcher 1995, 1997; Walters 2003). Moreover, hyperstability of C P U E implies that economic incentives to fishing can be sustained under low resource abundance (Hutchings 1996) and as a result, 9 bionomic equilibrium may not be reached until populations are depleted to a dangerously low level (Hilborns & Walters 1992; Mackinson et al. 1997). In particular, species which form spatially and temporally predictable spawning aggregations are especially vulnerable. Depletion of spawning aggregations may permanently prevent reproduction in these populations (Sala et al. 2003; Sadovy & Domeier 2005). At the same time, species with certain reproductive strategies such as hermaproditism or a high level of parental care may also be particularly prone to the effects of fishing (Rowe & Hutchings 2003; Hutchings & Reynolds 2004). 1.4. Approaches to assessing conservation status of fishes Despite the wide range of impacts from fishing on marine ecosystems and the potential vulnerability of marine fishes to fishing, our understanding of the conservation status of marine fishes - the largest group of vertebrates in the sea - lags behind the increasing rate of their utilization. Relative to other vertebrate groups, the proportion of fish species that have been assessed with the I U C N Red List criteria is very low (Figure 1.2). If we consider marine fishes only, less than 7% of the 15,723 extant species have been assessed using the Red List criteria (Baillie et al. 2004). Among this 7%, over 35% of the assessed species were considered 'data deficient', i.e., at the time of the assessment, there were not enough data to determine the status of the species. 10 \u00E2\u0080\u0094 100 CU co V) 0) 10 10 TO 10 o Q) Q. (/> 80 60 40 I 20 x LU 0 Mamma ls Birds Amph ib ians Rept i les Fishes Figure 1.2. Proportion of extant vertebrates that have been assessed under the IUCN Red List of Endangered Species (Baillie et al. 2004). The black and grey bars represent marine and freshwater fishes, respectively. If the current rate of Red List assessment is extrapolated, about 20% of extant marine fish species could be assessed by year 2020 (Figure 1.3). To cover half of the marine fishes under the Red List assessment in this timeframe, the current rate of assessment would have to be tripled. However, the Convention on Biological Diversity has set a \"2010 Biodiversity Target\" which has a mission statement: \"to achieve by 2010 a significant reduction of the current rate of biodiversity loss at the global, regional and national level as a contribution to poverty alleviation and to the benefit of all life on earth.\" (Decision VI/26, the Convention on Biological Diversity). To achieve such a target, species that are threatened or likely to be threatened should be identified. Given the current rate of Red List assessment for marine fishes, this target seems overly ambitious. The I U C N and its Species Specialist Commission realized the pressing need to increase assessment coverage in fishes and were devising strategies to increase their rate of assessments (Sadovy, Y . J., Chair of the I U C N Specialist Group of Groupers and Wrasses, pers. comm.). 11 8000 (8 6000 o Fuzzy Logic Expert System -Fisheries Simulation model Policy . options \ / } Figure 1.4. Schematic presentations of the proposed framework to identify policy options that integrate conservation into fisheries management. 1.6. Trade-offs between conservation and socio-economic objectives An important concern in marine conservation is its trade-offs with other resource management objectives (Walters & Martell 2004). A trade-off can be defined as giving up some of one thing to get more of something else. On one hand, catches from fisheries may have to be reduced to lower the risk of stock collapse in the long term (Hilborn et al. 2004b). On. the other hand, catch should be maximized in the short term to provide economic rent and to maintain the livelihoods of fishing communities. Moreover, in multi-species or multi-stocks fisheries, conservation of species or stocks with lower productivity (or higher vulnerability) may require reduction of catches of the more productive species or stocks. Instances of trade-offs between conservation and fisheries become more apparent as exploitation threatens increasing number of vulnerable species (Hilborn et al. 2004a). For example, in New England in the U S A , valuable scallop dredging fisheries were 16 closed in some areas to protect the essential habitat of the depleted groundfish stocks (Worm & Myers 2003; Borodziak et al. 2004). Moreover, rebuilding New England groundfish stocks required reduction in fishing effort, and it was expected that some fishing companies might have to be closed because of reduced profitability (Borodziak et al. 2004). Also, evidence suggested a top-down trophic relationship between the cod (Gadus morhua) and benthic crustaceans such as shrimps and crabs in the north Atlantic (Worm & Myers 2003). Therefore, recovery of the cod populations might reduce the productivity of the valuable invertebrate fisheries. Conservation policies are more likely to be successful i f they receive stakeholder support. Thus, reconciling the trade-offs between conservation and other fisheries management objectives in an ecosystem context should be important for effective marine conservation. The first step to such reconciliation is to explicitly display the trade-offs between the benefits and costs of different policies in terms of different objectives, to allow stakeholders to discuss and achieve consensus on management approaches (Walters & Mattel 1 2004). One problem is the difficulty in understanding and predicting the impact of fisheries and other physical and biological factors on the ecosystem. Single species approaches to fisheries and conservation assessments have been useful in understanding the dynamics of populations and quantify population status, and provide specific reference points with well-quantified uncertainty to management. However, a more holistic view of the ecosystem is needed to compliment the single species approach. Ecosystem simulation modelling is useful to generate alternative hypotheses about responses of ecosystems to fishing, and to reveal trade-offs between different objectives from the ecosystem perspective. 1.7. Modelling interactions between ecosystem and fisheries A n array of modelling approaches, with a range of different assumptions and complexity, has been developed to evaluate interactions between organisms and fisheries (Fulton et al. 2003). Approaches such as multi-species yield-per-recruit models (Murawski 1984) that incorporate interactions between fishing gears generally assume no biological interactions between species (Hollowed et al. 2000). More complicated models that incorporate age-specific dynamics and trophic interactions include multi-species 17 virtual population analysis ( M S V P A ) (Sparre 1991). M S V P A extends single species virtual population analysis to a number of trophically-linked populations. In M S V P A , trophic links between the modelled populations are represented explicitly by expressing natural mortality as a function of the abundance and diet composition matrix of the predators (Sparre 1991). However, because M S V P A is data-intensive, requiring long time-series of catch-at-age and diet composition, its applications are limited to a few well-studied fisheries (Christensen 1996). Ecosystem modelling approaches based on the principle of mass-balance have been more widely used in the last decade (Christensen & Walters 2004a). Mass-balance refers to the principle of conservation of energy in which the energy or biomass entering a system equals the amount produces from it. One of the most widely known approaches in fisheries science is the Ecopath with Ecosim suit of models (Polovina 1984; Christensen & Pauly 1992; Walters et al. 1997; Walters et al. 1999; Pauly et al. 2000). Ecopath was first developed to study coral reefs in Haiwaii (Polovina 1984) and later applied to a wide range of systems. Ecopath is a steady-state, mass-balance model which describe a snapshot of the whole ecosystem at a particular time period. Ecosim, a time-dynamic ecosystem simulation framework based on the Ecopath mass-balanced model, was later developed to allow exploration of the effects of fishing on ecosystems (Walters et al. 1997). 18 1.8. Ecopath with Ecosim Based on the mass-balance principle, Ecopath can be used to develop hypotheses of ecosystem structures that are thermodynamically possible (Polovina 1984; Christensen & Pauly 1992). In most Ecopath models, species, usually those with similar biology and ecology, are aggregated into functional groups to reduce the number of modeled units. The model is governed by the mass-balance principle, which is based on two basic equations. The first one ensures a balance between production, consumption, predation, fishery catch, migrations and other mortalities among and between groups: (PIB)t \u00E2\u0080\u00A2 Bj \u00E2\u0080\u00A2 (1 - EEt)-Bj(QIB)j \u00E2\u0080\u00A2 DCjt - Y; - Et - BAt =0 eq. 1.1 The second equation ensures a balance between consumption, production and respiration within a group: Q i = P i + R i + G E j Q j e q . 1.2 where (P/B)j is the production to biomass ratio of functional group i; B[ is the total biomass; EEt is the ecotrophic efficiency (1-EE, represents mortality other than predation and fishing); 7, the total catch; Et is net migration; BAt is the biomass accumulation; QIBj are consumption to biomass ratio for predator groups j; DCjj is the proportion of group i in the diet of predator groups j; R is respiration; and GE is the proportion of unassimilated food (Christensen & Walters 2004a). The model maintains mass-balance by solving equations 1.1 and 1.2 for all groups simultaneously. Thus, any of the four basic input parameters (B, P/B, Q/B, EE) in each group has to be estimated to ensure mass-balance. Since EE is difficult to measure empirically, it is usually estimated through the mass-balance process provided that data to estimate other parameters are available. In cases where data for one of B, P/B or Q/B are unavailable, EE is often assumed to be 0.95, or lower in case of top predators (Christensen et al. 2004). Ecosim is a dynamic simulation model which simulates changes of ecosystem that are described under Ecopath. It estimates changes of biomass among functional groups in the ecosystem as functions of abundance among other functional groups, and time-varying harvest rates, taking into account predator-prey interactions and foraging 19 behaviors (Pauly et al. 2000; Walters et al. 2000). Ecosim is governed by the basic equations (Walters et al. 1997): dt 8iT,cJ'-Zc'j + l ' - w > + F'+ei)-B< eq. 1.3 and eq. 1.4 vij +v'ij+aij BJ where equation 1.3 gives the biomass growth rate of group i, g,is growth efficiency, M and F are natural and fishing mortalities, I and e are immigration and emigration rates, C), is the consumption of group j organisms by group i organism, v and v ' represent rates of behavioural exchange between invulnerable and vulnerable states and ay represents rate of effective search by predator j for prey type i. The behaviour of functional groups in dynamic simulations is heavily affected by the 'vulnerability factor' (v), which determines the foraging behaviour (i.e., movement between refugia and foraging area) of the functional groups in predator-prey interactions (Walters et al. 1997; Walters & Martell 2004). 1.9. Fisheries in the Northern South China Sea (NSCS) The various issues regarding the challenges in conservation of marine biodiversity are particularly relevant to developing countries fisheries such as those in the northern South China Sea (NSCS). We defined the N S C S as the continental shelf (less than 200 m depth) ranging from 106\u00C2\u00B053'-119\u00C2\u00B048' E to 17\u00C2\u00B010'-25\u00C2\u00B052' N (Figure 1.5). The continental shelf (less than 200 m depth) falls largely within the Exclusive Economic Zone of the People's Republic of China, but Vietnam also shares part of the Gul f of Tonkin. It is a tropical ecosystem where diverse habitats including coral reefs, estuaries, mangroves, seagrass beds, and others can be found (Morton & Blackmore 2001). Diverse fauna and 20 flora have been recorded in the area, with over 900 species of fishes (Ni & Kwok 1999), at least five species of sea turtles (Marque 1990), eight species of marine mammals (Jefferson et al. 1993) and many invertebrates (Jia et al. 2004). Fishery resources are exploited mainly by trawlers (demersal, pelagic and shrimp), gillnets, hook and line, purse seine and other fishing gears such as traps. Similar to other fisheries in the region (Pauly & Chua 1988), fisheries in the N S C S have undergone dramatic changes over the past five decades. Since the foundation of the People's Republic of China (PRC) in 1949, there was a rapid growth in marine capture fisheries. The growth slowed down towards the 1970s. From the 1950s to 1970s, the fishing fleets were mostly state-owned. However, since the end of 1978, following economic reform, fishing fleets started to be privatized and investment in fisheries increased (Pang & Pauly 2001). This resulted in a large increase in the number of fishing boats and improvement in fishing technology. From 1978 to 2000, the number of mechanized fishing boats from Guangdong, Guangxi and Hainan - the three provinces bordering the coast of the N S C S - increased from 8,109 to 79,249 with their total engine power increasing from 0.55 to 3.6 million K J (Department of Fishery, Ministry of Agriculture, The People's Republic of China 1991, 1996, 2000). The apparent decline in engine power per boat from the 1970s to the 2000s is due to the large influx of unemployed inland workers and farmers to the fishing sectors in recent years. Many of these fishers fished with small boats with limited technology and mechanization (Pang & Pauly 2001; presentation by Qiu, Y . South China Sea Fisheries Institute, October 2005). 21 J \u00E2\u0080\u00A2 I Figure 1.5. Map of the northern shelf of the South China Sea. The dramatic expansion of fishing fleets, accompanied by mechanization and other technological advancements, resulted in over-exploitation of near-shore, and later, offshore fisheries resources (Shindo 1973; Cheung & Sadovy 2004) - a change that is similar to most other fisheries globally. The trends continued and catch rates of Chinese trawlers in the N S C S dropped by more than 70% from 1986 to 1998 (Lu & Y e 2001). Modell ing studies and analysis of landings data suggested a decline in trophic level of catch along the coast of N S C S (Buchary et al. 2003; Cheung & Sadovy 2004). A range of species with high vulnerability to exploitation were extirpated locally or regionally by fishing (Sadovy & Cornish 2000; Sadovy & Cheung 2003; Cheung & Sadovy 2004). For instance, the large yellow croaker (Larimichthys crocea) was one of the most important fisheries in the East and South China Sea. The stocks were greatly depleted starting in the 1970s and supplies of this fish in the market now rely almost solely on aquaculture (Liu & Sadovy unpublished data). Also, two decades ago, fishers supplied the local markets with highly esteemed large reef fishes such as groupers and snappers that were caught from the inshore and, later, offshore reefs in the N S C S (Sadovy 2005). Nowadays, fish traders have to import these fishes from distant locations such as Indonesia, Australia and the South Pacific islands because large-sized fishes, which are more susceptible to overfishing, have been depleted locally (Johannes & Riepen 1995; Sadovy & Vincent 22 2002; Sadovy 2005). In addition, critical habitats for marine species such as coral reefs and seagrass beds have been damaged or degraded as a result of the use of destructive fishing methods and coastal development (Hutchings & W u 1987). Therefore, over-exploitation in the N S C S raises serious fishery management and biodiversity conservation concerns. Revisions of the current management policy and tactics are needed to conserve fishery resources and biodiversity in the N S C S . The Chinese fishery management authorities recognized the current status of fishery resources (Lu & Y e 2001) and has initiated a range of fishery management policies. These include limiting new entry to fisheries and prohibiting the use of some destructive fishing methods (He 2001). However, the degree to which regulations have been enforced has been questioned (Li et al. 1999). Since 1998, the Chinese authorities have implemented a seasonal moratorium (June and July) in the N S C S . So far, published empirical studies that evaluate the effect of the moratorium on the exploited populations or ecosystem dynamics in the N S C S are lacking. Studies using ecosystem simulation models suggested that the effects of the moratorium should be small given the sustained high fishing effort in the region (Pitcher et al. 2002; Cheung & Pitcher 2006). Evaluating alternative policy options and management scenarios should provide useful information on their relative pros and cons for the authorities to make policy decisions. 1.10. Research objectives The main objectives of this thesis are to predict the extinction vulnerabilities of marine fishes to fishing, and evaluate the trade-offs between conservation and the socio-economic objectives of fisheries management in an ecosystem context. This includes an understanding of the intrinsic vulnerability to fishing (inherent capacity to respond to fishing in relation to their susceptibility to depletions and extirpations) which represents the first step to idenfity priority species for conservation and research efforts. Also, extinction or extirpation vulnerabilities of exploited species can be evaluated by combining intrinsic vulnerabilities with estimated levels of fishing exploitation. In the first half of this thesis, new methods to evaluate vulnerabilities to fishing are developed and then used to perform global analyses on the vulnerability of fishes. Using the new 23 methods, the second half of the thesis focuses on a case study of the Northern South China Sea ecosystem. The thesis is structured into nine chapters, with Chapters 1 and 9 being the general introduction and conclusion of the thesis, respectively. The structure of the thesis is summarized in a flow diagram (Figure 1.6). Ecosystem modelling -Life-history -Ecology Chapter 4 W / Risk of X i 1 population V V depletion J '\u00E2\u0080\u00A2| Fuzzy Logic ^ Expert System Global catch data Chapter 3 Intrinsic vulnerability of global fish catch Policy trade-offs Chapters 6, 7 & 8 Figure 1.6. A flow diagram showing the structure of this thesis. As understanding intrinsic vulnerabilities to fishing can be considered the first step to assessing the status of exploited marine fishes, Chapter 2 aims to develop a method that integrates easily-obtainable life history and ecological characteristics of marine fishes to provide quantitative indicator of intrinsic vulnerability. Specifically, a fuzzy logic expert system is developed to calculate intrinsic vulnerabilities to fishing. Fuzzy set theory, originally developed by Zadeh (1965), can classify a subject to different categories with a gradation of membership (instead of classifying membership as either 'true' or 'false' as in the classical logic system) and is suggested to be particularly suitable to fisheries analyses (Mackinson et al. 1999; Mackinson 2000a). In fact, fuzzy algorithms have been widely applied in fisheries science (Saila 1996), including stock-recruitment models (Mackinson et al. 1999; Chen 2001), predicting fish shoaling 24 behaviour (Mackinson 2000b), identifying sub-stocks of fish (Zhang 1994), and assessing species for the I U C N Red List (Akcakaya et al. 2000). Tinch (2000) proposed the use of fuzzy logic to assess extinction risks for different Pacific salmon stocks. A n expert system is an artificial intelligence system which is designed to mimic how expert(s) solve problems. It is usually a computer program that uses heuristic rules to describe the available expert knowledge. In this Chapter, rules (expressed in I F - T H E N clauses) (Kasabov 1996) are extracted from published literature describing known relationships, between biological characteristics and vulnerability. Input and output variables are defined by fuzzy sets. Conclusions from different lines of evidence are combined to provide qualitative and quantitative predictions on the intrinsic vulnerability of marine fishes to fishing. Predictions from the expert system are validated using comparisons with empirical data and with other published estimates of the intrinsic vulnerability of fishes. Chapter 3 identifies marine fishes most vulnerable to exploitation in different environments by comparing life history traits, represented by an index of intrinsic vulnerability predicted from the methodology developed in Chapter 2. I evaluate the difference in intrinsic vulnerabilities of fishes, and the global changes in the mean intrinsic vulnerability of fishes in catches comprising different communities, including those on coral reefs, at seamounts and in estuaries. This reveals the changes in the species composition of catch in. relation to the intrinsic vulnerability of the species. In addition, the relationship between the spatial distributions of fishes listed under the I U C N Red List of Threatened Species and the rates of decline of average vulnerability of the taxa in the catches is evaluated. The findings help understand the mechanism leading to changes in fish community structures from fishing, and identify species assemblages that potentially suffer high conservation risks from fishing. Chapter 4 predicts the risk of population depletion of exploited fishes from fishing. Exploitation status of fishes is inferred from temporal features of their catch time-series. Combining the intrinsic vulnerability from the methods developed in Chapter 2 with inferred exploitation status, this chapter aims to predict the relative depletion risk of 460 exploited marine fishes from fishing. This analytical approach is validated by comparison with the I U C N Red List categories using simulated data from a population model. Extrapolating the depletion risk of the 460 species to marine fishes globally, the 25 proportion of marine fishes facing high depletion risk from fishing is estimated. The results allow comparison of depletion risk from large-scale human activities between marine fishes and other vertebrates. Chapter 5 evaluates the status of 17 species of demersal taxa in the northern South China Sea (NSCS) using spatially explicit C P U E data of demersal trawlers from 1973 to 1988. The data are standardized with a generalized linear model to obtain the time-series changes in relative catch rate during this period. Intrinsic vulnerabilities of the 17 taxa are estimated and their relationship with the changes in C P U E is examined. The relationship between the intrinsic vulnerability predicted from the expert system developed in Chapter 2 and the catch rate decline is evaluated, with a hypothesis that the two are positively related (i.e., species with higher intrinsic vulnerability would have a stronger rate of decline). The estimated vulnerability is then applied to extrapolate the population status of other taxa in the region. Chapter 6 describes the past (the early 1970s) and present (the 2000s) status of the N S C S ecosystem using the Ecopath with Ecosim modelling approach. Mass-balanced ecosystem models of the early 1970s and the 2000s are constructed based on published literature, unpublished reports from government surveys, and global databases. B y comparing the structure and dynamics of the past and present systems, ecosystem changes over the past three decades are evaluated. Parameter uncertainty is addressed by the estimation 'pedigrees', and by sensitivity and perturbation analyses. Chapter 7 aims to improve the N S C S ecosystem model by fitting the 1970s N S C S model with time-series C P U E data obtained from Chapter 5 in Ecosim dynamic simulation modelling. Parameters that control the types of trophic control between predators and preys are estimated by the time-series fitting. The estimated parameters are then transferred to the 2000s N S C S model. Moreover, a 'Depletion index' is developed to infer the risk of depletion of stocks or species that have been aggregated into functional groups. This index becomes an objective function for the conservation of vulnerable fishes in the multi-criteria policy optimization analysis in Chapter 8. Chapter 8 identifies the trade-offs between conservation and socio-economic objectives of fisheries management in the N S C S . The analyses are based on dynamic 26 simulation models using Ecopath with Ecosim. 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A F U Z Z Y L O G I C E X P E R T S Y S T E M TO E S T I M A T E INTRINSIC V U L N E R A B I L I T I E S OF M A R I N E FISHES T O FISHING 2 2.1. Introduction Growing evidence indicates that marine species may be under the threat of local, and ultimately global, extinction, due to the direct or indirect effects of fishing (Pitcher 1998; Roberts & Hawkins 1999; Wolf f 2000; Reynolds et al. 2001; Dulvy et al. 2003). Commercially important species can be fished down to a vulnerable level because of their economic value (Clark 1973; Sumaila 2004), e.g., Chinese bahaba (Bahaba taipingensis, Sciaenidae) (Sadovy & Cheung 2003), and Southern bluefin tuna (Thunnus maccoyii, Scombridae) (Hayes 1997). However, species with little or no commercial value are also not safe from the threats of fishing. Non-targeted species may be threatened through bycatch (e.g., Common skate, Raja batis, Rajiidae, Brander 1981; Barndoor skate, Raja laevis, Rajiidae, Casey & Myers 1998), or by fishing activities that create large disturbance and damages to benthic habitats (Jennings et al. 2001; Kaiser et al. 2002, 2003). Declines and extinctions can be associated with the loss of essential habitat critical to complete the life cycle of the species (McDowall 1992; Watling & Norse 1998). Given the overexploited status of most fishery resources in the world (Jackson et al. 2001; Pitcher 2001a; Pauly et al. 2002; Hilborn et al. 2004), timely identification of species or populations that are vulnerable to local extinction (= 'extirpation') is urgently needed so that appropriate counter-measures can be formulated and implemented (Jennings et al. 1999a; Dulvy et al. 2004). Conventional assessments of extinction vulnerability involve an in-depth understanding of population dynamics (e.g., Matsuda et al. 2000), and so lack of data limits rapid assessment for marine fish species. Currently, the required population parameters are available only for a small number of marine fishes, mainly commercially targeted species in developed countries. Collecting the necessary quantitative data on the 2 A version of this Chapter has been published. Cheung, W. W. L., Pitcher, T. J. & Pauly, D. 2005 A fuzzy logic expert system to estimate intrinsic extinction vulnerability of marine fishes to fishing. Biological Conservation 124, 97-111. 44 population status is costly (Reynolds et al. 2001; Dulvy et al. 2003). The problem is most apparent in tropical, developing country fisheries where species diversity is high but resources for monitoring are limited (Jennings & Lock 1996; Johannes 1998). Moreover, the intrinsic rate of increase r, a key population parameter for conventional assessment, is particularly difficult to estimate reliably (Musick 1999; Reynolds et al. 2001; Dulvy et al. 2003). 2.1.1. Life history and ecological characteristics as proxies for intrinsic vulnerability Life-history and ecological traits, which have evolved to ensure persistence in the face of biotic and abiotic variability, have been suggested as 'rule-of-thumb' proxies to evaluate the intrinsic vulnerability of marine fishes to fishing (see Chapter 1; Jennings et al. 1998 1999 a, b; Reynolds et al. 2001). Here, extinction risk is a combination of intrinsic vulnerability and exposure to some threatening factors. Intrinsic vulnerability to fishing is the inherent capacity to respond to fishing that relates to the fish's maximum rate of population growth and strength of density dependence. Responses of fish populations to exploitation are, at least in part, determined by life history and ecological characteristics (Adams 1980; Roff 1984; Kirkwood et al. 1994; Dulvy et al. 2003; Sadovy & Domeier 2005). Selected life history parameters and ecological characteristics are correlated with intrinsic vulnerabilities (Jennings et al. 1999a, b; Denney, et al. 2002; Rowe & Hutchings 2003; Sadovy & Domeier 2005), some of which are suggested to be used as 'rules-of-thumb' for the triage of vulnerable species (Dulvy et al. 2004). However, while these 'rules-of-thumb' are available, little effort is given to how they may be combined and applied to assess a large number of species. Since life history and ecological traits contribute concurrently to increasing fishing vulnerability, an indicator conflating them should be useful in comparing vulnerability across species. Moreover, information for the majority of species is incomplete. Therefore, it is difficult to establish an index of extinction vulnerability from a wide range of life history and ecological characteristics using conventional techniques. Rule-based systems that classify fishes into ordinal extinction vulnerability levels are available (Dulvy et al. 2004). These systems are based on population parameters and biological characteristics and generally employ classical logic, which classifies fish 45 exclusively to categories of each biological characteristic. A n example is the scheme of Musick (1999), adopted by the American Fisheries Society (AFS) that aims to identify the 'productivity' (assumed the inverse of vulnerability) of fishes (hereafter called ' A F S ' s scheme'). A F S ' s scheme determines fish productivity level (high, medium, low, very low) from pre-defined categories of life history and population characters such as intrinsic rate of increase, longevity, age at first maturity, fecundity and the von Bertalanffy growth parameter, K. The productivity estimates are then used to assess threshold population levels for extinction (Musick 1999; Musick, et al. 2000). 2.1.2. Fuzzy logic expert system Fuzzy set theory can be useful in deriving an index of intrinsic vulnerability. Our knowledge of fish biological and ecological characteristics is associated with vagueness. Vagueness or uncertainty also occurs when we infer vulnerability to fishing from a variety of intrinsic characteristics. For example, we know that large fishes tend to be associated with higher extinction risk. However, it is difficult to provide a clear cut definition of what 'large fish' is, i.e., to separate large and small body size, and thus high and low extinction vulnerability. Moreover, other biological characteristics may confer low risk on a species despite large size. Such vagueness and uncertainty can be addressed by fuzzy set theory (or 'fuzzy logic'). In fuzzy set theory, originally developed by Zadeh (1965), a subject can belong to one or more fuzzy set(s) with a gradation of membership, instead of classifying membership as either 'true' or 'false' as in the classical logic system. The degree of membership is defined by fuzzy membership functions (e.g., Figure. 2.1). For instance, based on the fuzzy membership functions presented in Figure 2.1a, fish with a maximum length of 68 cm can be classified as medium and large size, with degree of membership (from 0 to 1) of 0.7 and 0.3, respectively. Fuzzy logic also allows conclusion(s) to be reached from premise(s) with a gradation of truth. Membership can be viewed as a representation of the 'possibility' of association with the particular set (instead of the 'probability' used in frequentist or Bayesian statistics ) (Zadeh 1995; Cox 1999). Kandel et al. (1995), Laviolette et al. (1995) and Zadeh (1995) provide discussion on the applications of fuzzy logic and probability theory. 46 A n expert system is an artificial intelligence system which is designed to mimic how expert(s) solve problems. It is usually a computer program that uses heuristic rules to describe the available expert knowledge. Rules are expressed in the form: IF A T H E N B where A is the premise while B is the conclusion (Kasabov 1996). The actions defined by the rules are 'fired' (= operated) when the degree of membership of the premises exceeds certain threshold values. The threshold values define the minimum required membership of the premises that an expert would expect for that particular rule to be fired and are generally defined by subjective criteria. Conflicting rules are allowed to fire jointly. In this paper, a fuzzy expert system is used to develop an index of the intrinsic vulnerability of marine fishes based on published relationships between life history and ecological characteristics and intrinsic vulnerability of marine fishes. Individual species are treated as the unit of assessment here, but the methodology can be applied to individual populations or sub-stocks. The new index is validated by correlations with empirical data. The empirical data include the observed rate of population decline of fishes in the North Sea (Jennings et al. 1999a) and Fiji (Jennings et al. 1999b), and species listed in the I U C N list of threatened species (Baillie et al. 2004). We evaluate the robustness of the system and its assumptions using various sensitivity analyses. We compare the pros and cons of the fuzzy expert system with other approaches in terms of its practical application. The technical details of fuzzy set theory and the fuzzy expert system are presented in Appendix 2.1 and 2.2. 2.2. Methods 2.2.1. Structure and functioning of the fuzzy expert system We developed a fuzzy expert system (hereafter called fuzzy system, developed using Microsoft Excel and Visual Basic for Applications) which aimed to evaluate the extinction vulnerability of marine fishes based on easily-obtainable life history and ecological characteristics i.e., features available through FishBase (Froese & Pauly 2004, www.fishbase.org). The input variables include maximum length, age at first maturity, 47 longevity, von Bertalanffy growth parameter K, natural mortality rate, fecundity (minimum number of eggs or pups per female per year), strength of spatial behaviour, and geographic range (Figure 2.1). The outputs are expressed as four verbal categories referring to the levels of intrinsic vulnerability to extinction: (1) very high, (2) high, (3) moderate and (4) low (Figure 2.2). Intrinsic vulnerability is also expressed on an arbitrary scale from 1 to 100, with 100 being the most vulnerable. Membership (maximum of 1) to each of the input and output verbal category is defined by a fuzzy membership function (Figures 2.1, 2.2). The fuzzy system includes sets of heuristic rules that allow the inferences of the intrinsic vulnerability based on the inputs. Essentially, fishes are classified into different verbal categories of life history and ecology with associated degrees of membership based on the input fuzzy sets (Figure 2.1). The inputs trigger the pre-specified rules that relate the different input verbal categories to intrinsic vulnerability. The heuristic rules were developed based on relationships described in the published literature (Table 2.1), excluding publications overwhelmingly disproved by empirical data. Each rule is weighted and we made an initial assumption of equal weighting with 0.5 for all rules. We assumed the minimum membership required to trigger the rules (threshold value) to be 0.2. This means that the system considers the premises to be false unless they have membership of 0.2 or more. Thus the system screens out premises that have very low degree of membership. At the end, the system estimates the degree of membership to the four categories of intrinsic vulnerability for a fish taxon (Figure 2.2), and provides a quantitative index of vulnerability. The system also provides lower and upper bounds of the vulnerability index (see Appendix 2.1 and 2.2 for details on the development of the fuzzy sets and heuristic rules, and functioning of the fuzzy expert system). 48 a) b) 50 100 150 Maximum length (cm) 0 1 2 3 4 5 6 7 Age at first maturity (year) c) d) 0 100 200 300 0 20 40 60 80 100 Fecundity (egg/pup individual\"' year\"') Spatial behaviour strength Figure 2.1. Fuzzy sets defining the input life history and ecological characteristics: (a) maximum body length, (b) age at first maturity (Tm), (c) von Bertalanffy growth parameter K, (d) natural mortality rate (M), (e) maximum age (T, r a x), (f) geographic range (km2), (g) annual fecundity (egg or pup female-1 year-1), (h) strength of aggregation behaviour (see Appendix 2.3). V L w - very low, Lw - low, NLw - not low, M -medium/moderate, Ft - high, V H - very high, L - large, V L -very large, R - restricted, V R - very restricted, N R V - not restricted, S - small. A fish species with maximum body length of 68 cm corresponds to 'medium body size' and 'large body size' with membership of 0.7 and 0.3 respectively (threshold value = 0.2) 49 1 20 40 60 80 100 Intrinsic vulnerability Figure 2.2. Output fuzzy sets for the intrinsic vulnerability of marine fishes. The 'Low' and 'Very high' vulnerabilities are defined by trapezoid membership functions while the 'Moderate' and 'High' vulnerabilities are defined by triangle membership functions. Intrinsic vulnerability was scaled arbitrary from 1 to 100. 50 Table 2.1. Heuristic rules defined in the fuzzy system to assign relative vulnerabilities to fishes. Attribute Rule Conditions Consequences Supporting evidence1 Opposing evidence2 1 1 IF Maximum length3 is very targe THEN Vulnerability is very high 8, 11, 13, 14, 15, 16, 17, 1 2 IF Maximum length3 is large THEN Vulnerability is high 21,24, 27,28,29 1 3 IF Maximum lenguV is medium THEN Vulnerability is moderate 1 4 IF Maximum length3 is small THEN Vulnerability is low 2 5 IF Age at first maturity (fm) is very high THEN Vulnerability is very high 1, 2, 3, 4, 5, 11, 14, 15, 28 2 6 IF Age at first maturity (tm) is high THEN Vulnerability is high 19, 20, 24, 33 2 7 IF Age at first maturity (fm) is medium THEN Vulnerability is moderate 2 8 IF Age at first maturity (tm) is low THEN Vulnerability is low 3 9 IF Maximum age (tmax) is very high THEN Vulnerability is very high 13, 19, 33 14 3 10 IF Maximum age ( t \u00E2\u0080\u009E ) is high THEN Vulnerability is high 3 11 IF Maximum age (tmtLX) is medium THEN Vulnerability is moderate 3 12 IF Maximum age (tmllx) is low THEN Vulnerability is low Table 2.1.Con't Attribute Rule Conditions Consequences Supporting evidencel Opposing evidence2 4 13 IF VBGF (k) is very low OR 5,6, 13, 19,28,33 11 Natural mortality (M) is very low THEN Vulnerability is very high4 4 14 IF VBGF K is low OR Natural mortality (M) is low THEN Vulnerability is high4 4 15 IF VBGF K is medium OR Natural mortality (M) is medium THEN Vulnerability is medium4 4 16 IF VBGF K is high OR Natural mortality (M) is high THEN Vulnerability is low4 5 17 IF Geographic range is restricted^1 THEN Vulnerability is high 8, 19,22 5 18 IF Geographic range is very restricted THEN Vulnerability is very high 6 19 IF Fecundity is low6 THEN Vulnerability is high 1,2,3,4,5, 19,20 11, 14, 18, 23, 26, 6 20 IF Fecundity is very low THEN Vulnerability is very high 28, 31 7 20 IF Spatial behaviour strength is low THEN Vulnerability is low 7,9, 10, 12,25,32 7 21 IF Spatial behaviour strength is moderate THEN Vulnerability is moderate 7. 22 IF Spatial behaviour strength is high THEN Vulnerability is high 7 23 IF Spatial behaviour strength is very high THEN Vulnerability is very high Table 2.1.Con't Attribute Rule Conditions Consequences Supporting evidencel Opposing evidence2 8 24 IF Spatial behaviour is related to feeding aggregation THEN Vulnerability resulted from spatial behaviour decreases 25 8 25 IF Spatial behaviour is related to spawning aggregation THEN Vulnerability resulted from spatial behaviour increases 30, 32 1 Literature supporting the assertions of the specific rules; 2 Literature opposing the assertions of the specific rules; 12References: 1. Holden (1973), 2. Holden (1974), 3. Holden (1977), 4. Brander (1981), 5. Hoening & Gruber (1990), 6. Pratt & Casey (1990), 7. Hilborn & Walters (1992), 8. Brown (1995), 9. Pitcher (1995), 10. Pitcher (1997) 11. Jennings et al. (1998), 12. Mackinson et al. (1997), 13. Russ & Alcala (1998), 14. Smith et al. (1998), 15. Walker & Hislop (1998), 16. Jennings et al. (1999a), 17. Jennings et al. (19996), 18. Myers et al. (1999), 19. Musick (1999), 20. Stevens (1999), 21. Dulvy et al. (2000), 22. Hawkins et al. (2000), 23. Stevens et al. (2000), 24. Frisk et al. (2001), 25. Pitcher (20016), 26. Sadovy (2001), 27. Dulvy & Reynolds (2002), 28. Denney et al. (2002), 29. Cardillo (2003), 30. Rowe & Hutchings (2003) 31. Sadovy & Cheung (2003), 32. Sadovy & Domeier (2005); 3 Asymptotic length was used preferentially. However, if asymptotic length was not available, we used maximum length as surrogate; 4 Growth rate of fish is represented by the von Bertalanffy growth parameter (VBGF) K. Since natural mortality and von Bertalanffy growth parameter K of fish are highly correlated (Pauly 1980). They were combined using an \"OR\" operator; 5 Geographic range is grossly estimated from the known distribution of fish in Exclusive Economic Zones (EEZs) and Food and Agriculture Organization (FAO) statistical areas. For instance, if a fish species is known to occur in China and in FAO statistical area 61. Its geographic range is represented by the area of the EEZ of China that falls within FAO statistical area 61; 6 Strong evidence suggests that high fecundity does not reduce the extinction vulnerability of fishes. However, evidence suggesting that lower fecundity (less than 100) increases vulnerability of fishes is valid. Therefore, the rule relating low fecundity to increased extinction vulnerability is retained. Fecundity is expressed as the minimum number of eggs or pups produced per individual per year; 7 Spatial behaviour was defined as groups of fish aggregate together at varying time and spatial scale. Spatial behaviour may be related to spawning, feeding, migration, or defense (schooling and shoaling). The strength of the spatial behaviour is defined by an arbitrary scale that ranges from 0 to 100. The method that assigns strength of spatial behaviour onto the arbitrary scale was described in Appendix 2.3. 2.2.2. System evaluations We examined the distribution of the fuzzy system output generated from ranges of realistic life history and ecological characteristics input. We extracted from FishBase a list of all marine fishes which, at the time of the query (February 2004), had full records of the life history and ecological characteristic (N = 159). Using the life history and ecological information available for these fishes, we calculated their intrinsic vulnerability based on the fuzzy system. We evaluated the impacts of individual attributes to the output of the system using a jackknife approach (Sokal & Rohlf 1995), where the calculations of the intrinsic vulnerability were repeated, while excluding one or more attribute(s) each time. If the system outputs were greatly sensitive to the removal of individual attributes, the outputs may also be sensitive to the weighting factors on the attributes. Thus, through this test, we aimed to evaluate our assumption on weighting individual attributes equally. The degree of deviation (Dev), represented by the changes in the predicted intrinsic vulnerability, was calculated for each species when attribute j was removed from the system: Dev - RT_j - RT where R is the estimated output from the system with full set of attributes (7) and attributes j being removed. We repeated the analysis by randomly removing increasing number of attributes except maximum length, as maximum length was the most readily available parameter for marine fishes. We repeated the latter 50 times to obtain a distribution of the estimated deviations. We tested the sensitivity of the system output to different threshold values. We systematically varied the threshold value of the fuzzy expert system and recorded the output for the 159 marine fishes from FishBase. We examined the differences in the system output for different threshold values. 54 2.2.3. Validity tests on vulnerability estimates We examined the validity of the intrinsic vulnerability estimated from the fuzzy system using empirical data with three tests that used three independent sets of data in which historical abundance trends of the marine taxa in the datasets were known. We used population decline as an indicator of vulnerability to fishing because it was readily available for a large number of marine fish species. A similar approach had been used in other comparative analysis between life history traits and vulnerability of marine fishes (e.g. Jennings et al. 1999 a, b). Species included in the data sets represent examples from wide geographic and habitat ranges. The three datasets included: (1) extinction risk categories of 40 species of marine fishes in the I U C N Red List of threatened species (Baillie et al. 2004); (2) population trends of 24 species of demersal fishes in the northern North Sea (Jennings et al. 1999a); (3) population trends of 13 species of reef fishes (Scaridae, Serranidae and Lutjanidae) in Fij i [species in Jennings et al. (1999&) with at least 15% of their observed population trends explainable by fishing]. We used the goodness-of-fit of the test statistics as an indicator for the accuracy of the intrinsic vulnerability predicted from the explanatory variables. For dataset 1, since the independent variable ( I U C N extinction risk categories) is ordinal, logistic regression was used (Agresti 1996). For dataset 2 and 3, linear regression was used. Whenever the required biological parameters for the species were unavailable in the original data sets, we obtained the data for the same species from FishBase (Froese & Pauly 2004). We repeated the tests using two other selected proxies of extinction vulnerability: (1) whichever life history parameters (maximum or asymptotic length, age at first maturity, longevity or von Bertalanffy growth parameter K) provided the best fit; (2) productivity categories evaluated using the A F S ' s scheme (see Musick 1999 for details on the methodology). Since A F S ' s productivity (assumed inverse of vulnerability) is expressed in ordinal categories, we used a Chi-square test for dataset 1 (species from the I U C N Red List), and A N O V A for datasets 2 and 3 (species from Jennings et al. 1999 a, 55 b). We compared the intrinsic vulnerability from the fuzzy system with these two proxies using two attributes: (1) predictive ability - represented by the goodness-of-fit with the empirical data, (2) data requirement - the amount and flexibility of data required in the calculation of the proxies. We conducted an additional test to compare the performance of the expert system with classical logic. We constructed an expert system with attributes and rules that were exactly the same as the fuzzy system. However, classical (Boolean) sets were used instead of fuzzy sets (Table 2.2). Thus, fish species were classified exclusively to a single category for each biological attribute. If the input parameters of a species resulted in multiple conclusions, the final conclusion would be the highest resulting vulnerability category (Musick 1999). We evaluated the vulnerability of the species in the three test data sets using this system and compared the goodness-of-fit to the empirical data with other vulnerability proxies. Table 2.2. The definitions of classical (Boolean) sets used to classify life history and ecological characteristics into different categories, and the rules that connected them to different level of intrinsic vulnerabilities. Life history characteristics and the resulting vulnerability Life history characteristics Low Moderate High Very high Maximum length (cm) 50 > Lmax 50 T\u00E2\u0080\u009E, 2K Natural mortality rate (year\"1) 0.5 M Maximum age (year) 3 > T \u00E2\u0080\u00A2J \u00E2\u0080\u0094 * max 3 Range Fecundity (egg/pup individual\"1 year\"1) - - 50 < Fee < 100 50 > Fee Spatial behaviour strength 40 > SB 40 < SB < 60 60 < SB < 80 80<5B 56 2.3. Results Based on the input life history and ecological parameters, the fuzzy system estimated the intrinsic vulnerability with associated possibilities. For instance, using the biological parameters available from FishBase, we estimated that Atlantic cod (Gadus morhua, Gadidae) has an intrinsic vulnerability of 61 (100 being the most vulnerable) with lower and upper bounds (membership = 0.5) of 48 to 72. It was identified as being highly to very highly vulnerable, with possibility of 0.78 to 0.63 respectively. Sensitivity analysis showed that the estimated intrinsic vulnerabilities were insensitive to the pre-defined threshold value (Figure 2.3). The estimated intrinsic vulnerabilities varied slightly as we increased the threshold value from 0 to 0.9. Variations in the estimated outputs increased when the threshold value increased to 0.6 and more. >\u00C2\u00AB 100 \u00C2\u00A31 TO *-u a> = 10 c 2 0 \u00E2\u0080\u0094 (u (0 c 8,= c re .c o -20 --30 -0 50 -j \u00E2\u0080\u00A2o CD o 40 -TJ V c 20 -ange vul 10 -o 0 -3 4 5 Attributes 2 3 4 5 6 No. of attributes removed Figure 2.4 Sensitivity of the calculated intrinsic vulnerability to individual attributes incorporated in the fuzzy system evaluated using the jackknife approach (Sokal & Rohlf 1998). The black dots are the median of the deviations of the 159 marine fishes from FishBase when individual (a) attributes were removed, and (b) increasing number of attributes were randomly included (absolute magnitude of changes). The bars are the 25% and 75% quartiles of the deviations (inner bars in Figure 2.4a). The other bars in Figure 2.4a are the maximum and minimum ranges of the deviations. 58 Deviations of the output from the baseline increased when we randomly removed increasing number of attributes from the system (Figure 2.4b). The median of deviated vulnerability ranged from about 1 (maximum deviation is about 18) when one attribute was randomly removed, to about 12 (maximum deviation is about 42) when only maximum length was used. Predicted vulnerability tended to be under-estimated when only maximum length was considered by the system. The intrinsic vulnerabilities estimated from the fuzzy system were significantly related to the extinction risk categories of marine fishes in the I U C N threatened species list with better goodness-of-fit than the two other vulnerability proxies (Figure 2.5). Both A F S ' s scheme and maximum length could not significantly explain the differences in the I U C N categories of the tested species at 5% significant level (AFS ' s productivity: P = 0.085, L m a x : 0.0731), while the estimated intrinsic vulnerability could significantly explain them (Intrinsic vulnerability index: P = 0.0253). Intrinsic vulnerabilities were also significantly related to the population trends of demersal fishes in the North Sea (Jennings et al. 1999a) with the higher goodness-of-fit (Figure 2.6) than the other proxies. When we considered dragonet (Callionymus lyra) and spurdog (Squalus acanthias) as outliers, A F S ' s scheme and individual life history parameters (maximum length and age at first maturity) explained 34% and 28% of the variance, respectively, whereas our fuzzy system explained over 36% of the variance. The relationships remained significant when we included dragonet and spurdog in the analysis; however, its goodness-of-fit was higher than the other two vulnerability proxies by a smaller margin (Figure 2.6). We did not obtain significant relationships between the three vulnerability proxies and the observed population trends of the Fi j i reef fishes based only on the information available from FishBase (Figure 2.7). Lack of life history data meant that we could estimate A F S ' s productivity for only seven species, preventing us from statistically analyzing the data. There was also no relationship between individual life history parameters (enough data only available for maximum length) and the fuzzy system intrinsic vulnerabilities with the observed population trends ( A N O V A p-vaue = 0.142 and 0.170, respectively). 59 A significant relationship between the fuzzy system intrinsic vulnerabilities and the population trends of Fiji reef fishes was shown to occur when we employed supplemental information on occurrence of spawning aggregation available from the global database of the Society for the Conservation of Reef Fish Spawning Aggregation ( S C R F A Global Database 2004) (Figure 2.7d). The fuzzy system is then able to explain about 34% of the variance in population trends ( A N O V A P = 0.03). When the fuzzy sets were replaced by classical sets (Table 2.2), the estimated intrinsic vulnerabilities did not correlate with the population trends in the three empirical datasets. The test statistics for the three tests were: marine fishes from the I U C N Red List - Likelihood ratio Chi-square P = 0.206; demersal fishes in the North Sea - A N O V A P = 0.313; reef fishes in Fiji - A N O V A P = 0.133. 60 a) vu + o \u00E2\u0080\u00A2g EN \u00E2\u0080\u00A2 z o 2 CR-b) vu o \u00C2\u00BB EN O 2 CR 2 3 4 AFS's productivity \u00E2\u0080\u00A2 \u00E2\u0080\u00A2 \u00E2\u0080\u00A2\u00E2\u0080\u00A2 2 4 6 8 Maximum length (log cm) C) VU o O) 2 EN a o = CR -\ \u00E2\u0080\u00A2 mi 0 20 40 60 80 100 Fuzzy intrinsic vulnerability Figure 2.5. Plot of population trends of 40 species of marine fishes listed in the IUCN list of threatened species (Critically Endangered, Endangered and Vulnerable) and (a) AFS's productivity - productivity categories estimated by the AFS scheme (Musick 1999), (b) maximum length (log), and (c) fuzzy system intrinsic vulnerability. We only included species that were categorized by criteria A : reduction in population size (IUCN Species Survival Commission 2001). CR - critically endangered, E N -endangered, V U - vulnerable. a) b) C) d) 0.02 -r c o -0.02 -j l Z3 a. -0.04 -a. -0.06 -R = 0.276 O 2 4 6 8 10 12 Age at first maturity (year) R =0 .367 30 40 50 60 70 Fuzzy intrinsic vulnerability Figure 2.6. Plot of the observed population trends of the 24 species of demersal fish in the North Sea and the proxies of extinction vulnerability: (a) AFS's productivity ( A N O V A P = 0.024), (b) maximum length ( L m i x ) ( A N O V A P = 0.034), (c) age at first maturity (Tm) ( A N O V A P = 0.014), (d) fuzzy system intrinsic vulnerability ( A N O V A P = 0.004). Population trends refer to the slope of linear relationship between standardized catch rate (number h\"1) and time (years). AFS's productivity was expressed in ordinal scale: 1 = high, 2 = medium, 3 = low, 4 = very low. When we included dragonet (Callionmyrus lyra) and spurdog (Squalus acanthias) (open dots) in the analysis, AFS's productivity was only marginally significant (R 2 = 0.272, A N O V A P = 0.042), The goodness-of-fits of age at first maturity and the fuzzy system intrinsic vulnerability became: T m (R 2 = 0.207, A N O V A P = 0.029) and intrinsic vulnerability (R 2 = 0.246, A N O V A P = 0.016). The dotted lines represent the upper and lower bounds estimated from the fuzzy system, based on an assumed membership of 0.5. 62 Q. \u00C2\u00B0 - -0.006 -J 1 , , , , , 0 20 40 60 80 100 120 Maximum length (cm) .2 -0.004 -I \u00C2\u00B0\" -0.006 -I 1 1 1 1 10 30 50 70 90 Fuzzy intrinsic vulnerability (without suppl. info.) Q. -0.006 -I , , , 1 10 30 50 70 90 Fuzzy intrinsic vulnerability (with suppl. Info) Figure 2.7. Plots between the observed population trends of the 13 species of reef fish in Fiji (a) maximum length ( A N O V A P = 0.142), (b) intrinsic vulnerability estimated by the fuzzy system based on information from FishBase only ( A N O V A P = 0.170), and (c) intrinsic vulnerability estimated by the fuzzy system with supplementary information from SCRFA Global Database (2004) ( A N O V A P = 0.03). Population trends were expressed as the slope of the relationship between biomass and fishing effort (Jennings et al. \999b). The dotted lines represent the upper and lower bounds estimated from the fuzzy system based on an assumed membership of 0.5. The lack of the necessary life history data prevented us from analyzing the AFS's productivity. 63 2.4. Discussion Comparisons with empirical population abundance trends showed that this fuzzy system could be used to predict the intrinsic vulnerability of marine fishes. It is also a better predictor of rate of population decline than other proxies proposed earlier. The population trends included in the analysis were confounded by factors such as differences in fishing intensities between species. Therefore, they could only be viewed as rough indicators of the vulnerability to fishing. Thus, intrinsic vulnerability is expected to be able to explain only a fraction of the variance in population trends among species. However, the fuzzy system predicted intrinsic vulnerability still explained a considerable proportion of such variance; indeed, the proportions of variance explainable by the predicted intrinsic vulnerability were higher than two suggested proxies of vulnerability. Furthermore, the tests suggest that the use of fuzzy logic in the expert system provides a better predictor of intrinsic vulnerability than a system employing classical logic. These support the validity of the fuzzy system. In addition, the fuzzy system could be applied to species from a wide range of geographic locations, habitats and ecosystem types, and for which different levels of knowledge is available. We did not account for the number of required input parameters in the comparisons between different vulnerability proxies. The fuzzy system has more attributes than the other proxies. Also, the jackknife analysis suggested that deviations of the outputs increased when attributes were removed from the system. Therefore, its ability to predict vulnerability, and therefore its performance relative to other methods, may decrease when information becomes scarce. On the other hand, the fuzzy system can provide estimates of intrinsic vulnerability for species with different data availability and can explicitly represent a degree of confidence in the output. In the fuzzy system, the conclusions (level of intrinsic vulnerability) are linked to the inputs concurrently by the heuristic rules. Thus intrinsic vulnerability can still be estimated by the rules fired from the inputs where data are available. The jackknife analysis suggests that the estimated intrinsic vulnerability tends to converge when more attributes are included. Thus the deviation of the estimated output could be reduced by increased data availability and more rules linking the input attributes to intrinsic vulnerability. It is noted that some attributes only relate to either high or low 64 vulnerability (fecundity, geographic range and type of spatial behaviour), their removals may result in unsymmetrical deviations in the predicted intrinsic vulnerability. Although removals of attributes result in relatively small deviations of predicted vulnerabilities for the majority of the tested species, some species may have a suite of biological characteristics that render them sensitive to the weighting of particular attribute(s). For instance, by incorporating information on reef fish aggregation from S C R F A Global Database (2004), the fuzzy system greatly increased the goodness-of-fit between the estimated vulnerabilities and the empirical population trends of Fi j i ' s reef fishes. Therefore, weighting of individual rules according to subjective expert judgment (Cox 1999), or availability of evidence supporting the particular rules or attributes (Mackinson 2000) may improve the performance of the system. However, since we defined the attributes and rules from published literature, expert weighting of individual rules was not possible. Moreover, the amount of literature describing a rule (which has been suggested as a weighting factor) does not necessarily reflect the importance of this rule. Future studies may include systematically collating experts' opinions to decide the relative importance of different attributes and thus their weighting factors. Fecundity may not be a significant attribute to be included in the fuzzy system. Ample evidence suggests that fecundity does not relate to the intrinsic vulnerability of fishes when other life history traits, such as maximum length and age at maturity, are accounted for (see Sadovy 2001 for review). In particular, the notion that highly fecund fish are resilient to fishing has been rejected. Our results suggest that removal of fecundity as an attribute results in small deviations in the predicted intrinsic vulnerability. This appears to support the low importance of the relationship between low fecundity and high vulnerability. On the other hand, we considered all available evidence to develop heuristic rules in the fuzzy system. Also , some literature suggests that low fecundity is a factor causing high intrinsic vulnerability, although majority of them focus on' limited group of species (elasmobranchs). Moreover, inclusion of rules that relate low fecundity to high vulnerability makes the system more conservative i.e., the system tends not to underestimate the vulnerability. Therefore, in this thesis, low fecundity (i.e, minimum total fecundity is less than 100 eggs or pups year' 1) remained as one of the attributes in the fuzzy system for calculating intrinsic vulnerability of fishes. 65 The fuzzy system can adapt to new information from both quantitative studies and qualitative experts' knowledge, and enables an integration of local and scientific knowledge (Mackinson & N0ttestad 1998). Currently, some rules in the system are based on literature that does not represent species with full range of life history and ecological traits, and thus these rules were extrapolated from a smaller range of species. Thus the heuristic rules, fuzzy membership functions, and the values that defined them, can be modified based on expert knowledge or newly available information (Cox 1999). The weighting on the rules can also be adjusted when new evidence or experts' opinions are obtained. Therefore, a fuzzy expert system can be particularly useful in facilitating workshop or focus group discussions on the assessment of extinction vulnerability of marine species (see Hudson & Mace 1996). In this case, the discussions and opinions from the experts can act as the knowledge base. The knowledge engineer who maintains the expert system can use the knowledge base to revise and update the expert system (Mackinson & N0ttestad 1998; Cox 1999). The approach described here can facilitate the identification of vulnerable species so that management and conservation efforts can be focused. Current monitoring and management efforts mainly concentrate on commercially important species, which, however, may not necessarily be the most vulnerable. Bycatch and other indirect fishing impacts may threaten non-commercial species (Dulvy et al. 2003). The near extinctions of the Common and Barndoor skates, both low-value bycatch species in bottom trawl fisheries are clear examples (Brander 1981; Casey & Meyers 1998). A large reduction in the abundance of pelagic sharks in the Gul f of Mexico was unnoticed previously because of their relatively low value compared to the tunas; they had life history characteristics which made them highly vulnerable (Baum & Myers 2004). This is particularly true for tropical fisheries where diverse species are caught and resources for monitoring and management are low (Silvestre & Pauly 1997; Johannes 1998; Johannes et al. 2000). The intrinsic vulnerability estimated from the fuzzy system could provide a p r i o r i indicator on the vulnerability of the species. As such, prioritization of species according to their potential intrinsic vulnerabilities can help to allocate limited research and monitoring resources, and develop more effective fishery management and conservation policies (Dulvy et al. 2004). For instance, Chapter 3 of this thesis applied the fuzzy system 66 presented in this Chapter to evaluate the intrinsic vulnerability a large number of extant marine fishes and found that seamount fishes had significantly higher vulnerability than non-seamount fishes. Therefore, this suggests the need for conservation concerns about the increasing fishery exploitations of seamount assemblages. In conclusion, we suggest that the fuzzy expert system approach described here is a useful tool to predict intrinsic vulnerability of marine fishes. It may also be easily extended and further improved. Intrinsic vulnerability may be combined with the other, external factors in estimating the total vulnerability of the species. External factors such as fishing intensity, degradation of essential habitat and climate change contribute significantly to the extinction risk associated with each species (Dulvy et al. 2003). These external factors, together with intrinsic vulnerability, should be integrated in assessing overall extinction risk. In fact, these external factors can be represented at a higher hierarchical level in the fuzzy system. Rules describing the effects of these external factors, and their synergistic effect with the intrinsic vulnerability, can be incorporated into the fuzzy system through which outputs representing the total vulnerability of the species can be obtained (see Chapter 4). This may provide a decision support tool on local or global extinction risk assessment and categorization such as the I U C N Red List of threatened species of the Wor ld Conservation Union or the species listing under the Canada's Species At Risk Act. 67 2.5. References Adams, P. 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A . 1995 Discussion: Probability theory and fuzzy logic are complementary rather than competitive. Technometrics 37(3), 271-276. 74 3. INTRINSIC V U L N E R A B I L I T Y IN T H E G L O B A L FISH C A T C H 3 3.1 Introduction Fishing is a major agent of disturbances to marine ecosystems (Watling & Norse 1998; Pauly et al. 2002; Kaiser et al. 2003). It has caused a general decline in fish biomass, and placed many marine species under serious conservation concern (e.g. Casey & Myers 1998; Pauly et al. 2002; Baum et al. 2003; Dulvy et al. 2003; Sadovy & Cheung 2003). Among marine fishes that are listed under the I U C N Red List of Threatened Species (Baillie et al. 2004), the majority are endangered directly or indirectly by fishing (Dulvy etal. 2003). The life history of a fish species affects its vulnerability to fishing - a feature here called intrinsic vulnerability (Jennings et al. 1999b; Reynolds et al. 2001; Cheung et al. 2005). Generally, species with larger body size (maximum body length or asymptotic length), higher longevity, higher age at maturity, and lower growth rates have higher vulnerability to fishing (Smith et al. 1998; Jennings et al. 1999a; Jennings et al. 1999b; Denney et al. 2002; Dulvy & Reynolds 2002). Species with these life history traits are generally less able to sustain fishing mortality, and thus, differences in life history result in structural changes in the exploited fish community (Jennings et al. 1999a). In this thesis, a community is.defined as the species that occur together in space and time (Fauth et al. 1996; Begon et al. 2005). Correlations between life history traits and intrinsic vulnerability to fishing are supported by empirical evidence (Chapter 2). Empirical studies using historical abundance data of exploited fish populations found significant correlations between the rate of population declines (a proxy of vulnerability to fishing) and life history parameters such as maximum body size and age at maturity, but not fecundity (Jennings et al. 1998; Jennings et al. 1999b). Also, meta-analysis using 54 stock-recruitment time-series showed that large-sized, late-maturing fishes had strong density-dependence in low abundance but high equilibrium spawner per recruit without exploitation (Goodwin et al. 3 A version of this Chapter has been published. Cheung, W. W. L., Watson, R., Morato, T., Pitcher, T. J. & Pauly, D. 2007 Intrinsic vulnerability in the global fish catch. Marine Ecology Progress Series 333, 1-12. 75 2006). Analysis including data from other vertebrate groups produced similar conclusions, suggesting that the correlations between life history and population dynamics may be applicable to most vertebrates (Fagan et al. 1999). Current evidence suggests that body size is one of the most important factor in determining vulnerability to hunting (Jennings et al. 1998; Jennings et al. 1999b; Cardillo & Dromham 2001; Reynolds et al. 2001; Gaston & Blackburn 2003; Reynolds et al. 2005). The correlations between life history and vulnerability to fishing may explain the serial depletion of fish populations, with fishing activities in heavily exploited areas progressing from large-bodied species that tend to have high vulnerability to species with less vulnerable life histories occurred in heavily exploited areas (Pauly et al. 1998; Pitcher 2001; Pauly et al. 2002; Myers & Worm 2003). More vulnerable species decline faster in abundance given similar fishing rates and thus are more readily over-exploited (Jennings et al. 1998; Jennings et al. 1999b; Reynolds et al. 2001; Cheung et al. 2005; Reynolds et al. 2005). Therefore, the change in relative abundance of vulnerable species can be reflected in the catch composition. In fact, the serial replacement of intrinsically more vulnerable by less vulnerable species may be the reason for the \"fishing down marine food webs\" phenomenon (Pauly et al. 1998), as fishing generally targets large predatory (often intrinsically more vulnerable) species, but progressively moves to lower trophic level species (often less vulnerable) as the predatory species become over-exploited. On the other hand, it has been argued that declines in the relative proportion of predatory species, in some cases, may be the result of a mere expansion of the fisheries to lower trophic level species, without reduction of the catches of predatory species (Essington et al. 2006). Understanding the relationship between intrinsic vulnerability and changes in catch composition may provide insights to this debate. Different life history traits are evolved to adapt to different environments or habitats (Roff 1984; Beverton 1992; Winemiller & Rose 1992; Charnov 1993; Jensen 1996; Vila-Gispert et al. 2002; Winemiller 2005). For instance, many of the coral reef and seamount fishes are thought to be particularly vulnerable to fishing because of life-history traits such as slow growth and late maturation (Koslow et al. 2000; Birkeland 2001; Choat & Robertson 2002; Morato et al. 2006a). Particularly, coral reef fishes depend strongly on coral reef for refuges. This renders them more vulnerable to direct 76 and indirect impacts from habitat damages (Pandolfi et al. 2003; Wilson et al. 2006). Estuarine fishes, on the other hand, have a mixture of life history strategies that are adapted to both fluctuating estuarine environments and more stable marine inshore habitats (Whitfield 1990; Winemiller & Rose 1992; Roessig et al. 2004). Their communities include a mixture of diadromous, marine- and freshwater- migratory and estuary resident fishes (Blaber 2000). The relationships between the life history characteristics and the effects of the threats on the estuarine populations may vary widely between species (Reynolds et al. 2005). Thus, effects of fishing on structure of coral reef and seamount fishes may be more apparent than on estuarine fish communities. Given the different characteristics of fish communities in different habitat and the threats of fishing to them, it would be useful to understand the intrinsic vulnerability of fishes in these communities. Also, tracking and comparing the impacts of fishing on these communities over time could help us understand their current conservation status and formulate conservation and fisheries management strategies. Intrinsic vulnerability may be predicted from life history traits (Cheung et al. 2005; Reynolds et al. 2005) while the effects of fishing on the fish communities may be tracked by evaluating changes in composition of catch over time. This study attempted to test two hypotheses. (1) We tested i f marine fishes that are adapted to different environments have different vulnerabilities to fishing. Based on the life history information available from Fishbase (Froese & Pauly 2004), an index of intrinsic vulnerability to fishing is calculated for all marine fish taxa. Regression analysis is used to evaluate the relationship between the niches and habitats which the fishes inhabit, and their intrinsic vulnerability. (2) We investigated whether changes in species composition of catches are related to the intrinsic vulnerability of the exploited taxa. Finally, we discuss the relative intrinsic vulnerability of fishes and their overall conservation status in the major habitats. 77 3.2. Methods 3.2.1. Predicting species intrinsic vulnerability Using the fuzzy logic expert system developed in Chapter 2 (Cheung et al. 2005), intrinsic vulnerability index of fishes (the index values ranging from 1 to 100, with 100 being the most vulnerable) were estimated based on the fishes' life history and ecological characteristics. The input variables consisted of traits that were considered to be related to the species intrinsic vulnerability and were obtained from literature review (Cheung et al. 2005; Chapter 2). These traits included maximum length, age at first maturity, longevity, von Bertalanffy growth parameter K, natural mortality, fecundity (only low fecundity is considered), spatial behaviour and geographic range. As the operations of the fuzzy logic expert system are detailed in Chapter 2, only a brief description of the system is given here. The expert system classified fishes into different life history categories with different degrees of membership or association (e.g. 'large' maximum length, 'moderate' age at maturity, etc.). The degree of membership or association to each category ranged from 0 to 1 (0 - no association, 1 - full association) and was determined by pre-defined fuzzy logic membership functions (Cheung et al. 2005, Chapter 2). The expert system was flexible in terms of data availability. The minimum required input was the maximum length. Rules expressed in IF (predicate)-T H E N (conclusion) clauses were used to infer the levels of intrinsic vulnerability. A n example of the rule is: IF maximum length is large T H E N intrinsic vulnerability is high The rules were developed from published literature and expert opinions (Cheung et al. 2005; Chapter 2). The degrees of membership of different conclusions (the level of intrinsic vulnerability) were based on the membership to the predicates and were accumulated by the expert system through: Membership e = Membership^ + Membership \u00E2\u0080\u00A2 ( 1 - Membership^) eq.3.1 where Membership,, is the degree of membership of the conclusion after combining the conclusions from e pieces of rules, and Membership, is the degree of membership of the conclusion of rule /. An index of intrinsic vulnerability was estimated from the mean 78 values of the conclusions fuzzy membership functions weighted by the degrees of membership to each conclusion category. 3.2.2. Intrinsic vulnerability index of marine fishes A list of the world marine fishes (n = 15,723) was obtained from FishBase (Froese & Pauly 2004; www.fishbase.org). Their life history and ecological characteristics were obtained from published literature, notably those that were recorded in FishBase. These characteristics include: maximum length, age at first maturity, longevity, von Bertalanffy growth parameter K, natural mortality, fecundity (only low fecundity is considered), spatial behaviour and geographic range. To reduce the uncertainty of the predicted vulnerability, species in which total length was the only available life-history parameter were excluded. As a result, the number of species included in the analysis was 1,353. Marine fishes in the analysis were categorized by according to their association to the four major habitats where fishes may have evolved different sets of life history traits and received different characteristics of threats. The habitat categories are: (1) coral reef-associated, (2) estuarine, (3) seamount and (4) seamount-aggregating fishes. The lists of coral reefs and estuaries-associated species were based on information recorded in FishBase. Seamount fishes are defined as fishes that have been recorded on seamount. Species that aggregate in association with seamounts and similar topographic bathymetric features were categorized as seamount-aggregating fishes (Koslow 1997; Morato et al. 2006a). Thus seamount-aggregating fishes are a subset of seamount fishes. Seamount and seamount-aggregating fish were based on the list published by Morato et al. (2006a). The average predicted intrinsic vulnerability index of fishes associated to the four habitats were compared using Turkey-Kramer H S D test. Fishes were also categorized according to their depth range, latitudinal range and positions in the water column. These attributes were considered to be related to the fishes' life history (Macpherson & Duarte 1994; Brown 1995). In general, marine fishes inhabiting deeper environments or with a higher latitudinal range were considered to have larger maximum body size and wider distribution range (Brown & Maurer 1989; Macpherson & Duarte 1991; 1994). These traits (maximum body size, in particular) may 79 then affect the intrinsic vulnerability to fishing. Depth and latitude were represented by the median of the species depth and latitude range and the range sizes, while position in water column was categorized as: (1) demersal, (2) pelagic, (3) benthopelagic, (4) bathypelagic, and (5) bathydemersal. Median depth ranges were log-transformed to correct for the non-linear relationship between depth and intrinsic vulnerability. Information on these attributes was taken from FishBase. Relationships between the environmental attributes (depth, latitude and ecological niche) and the index of intrinsic vulnerability (V) of fishes were evaluated using a generalized linear model ( G L M ) (Kutner et al. 2005). To test the effects of each of the factors that may correlate with the intrinsic vulnerability, the full G L M model was structured as: v = a + Plat * L a t + PLatRange \" LatRange + PDepth \u00E2\u0080\u00A2 \og{Depth) + 5 4 e 3 2 X PnicKi \u00E2\u0080\u00A2 N i c h e i + X 0habitat, j ' Habitat j+\u00C2\u00A3 where a is the average intrinsic vulnerability of all marine fishes, Lat is median value of latitudinal range, LatRange is the latitudinal range, Depth is median of the depth range, Niche and Habitat are categorical factors representing the positions in water column i and habitat types j, respectively. BtatRa^ BDepth, Bniche and Bhabitat are the coefficients for the factors: lattudinal range, median depth range, position in water column and habitat type, respectively, e is the error term. The significance of the factors was evaluated with t-tests. As geographic range (closely correlated with latitudinal range) is an attribute in the expert system, it might appear that the dependency of the intrinsic vulnerability index (V) on the latitudinal ranges of the fishes would invalidate the G L M . However, geographic range is positively correlated with maximum body length (Brown 1995). On the contrary, the rules in the expert system stipulated that geographic range and maximum length contribute to intrinsic vulnerability in an opposite way, i.e. vulnerability increases with maximum body size but decreases with distribution range. Here, the G L M 80 explored how such covariation affects the intrinsic vulnerability of fishes to fishing in different environments (e.g. depth and latitude). 3.2.3. Mean intrinsic vulnerability index of catch Catches of the world from 1950 to 2003 were obtained from the Sea Around Us Project (SAUP) global catch database (www.seaaroundus.org). The S A U P catch database was constructed from catch or landing statistics from around the world. The fisheries statistics of the United Nation Food and Agriculture Organization were the major data source. Based on predicted geographic distributions of the exploited organisms and a rule-based model, the original catch data were disaggregated spatially (by 30 min latitude x 30 min longitude cells covering the world's oceans) and taxonomically (Watson et al. 2004). We classified the exploited fishes reported in the catch database (858 taxa) by their associated habitat. In the S A U P catch database, each exploited group (reported as a taxonomically aggregated group by family, genus or species) was given a \"habitat affinity\" (Appendix 3.1). Exploited group's affinities to the major habitats (coral reef, estuary, seamount, Table 3.1) were expressed as an index that ranged from 0 to 1. The affinity values represented the frequency of occurrence or the relative density of the taxon in the particular habitat. These values had been used to allocate reported annual catches from fisheries statistics (e.g. F A O fisheries statistics) to different area of the world oceans. The affinities were determined from qualitative descriptions from the published literature, databases such as FishBase and/or through personal communications from experts (Table 3.1). For instance, striped bass (Morone saxatilis) prefers estuaries and it also occurs in 'other habitats'; thus, it received a score of 0.75 for estuaries and 0.5 for 'other habitats'. This was repeated for all exploited taxa in the Sea Around Us Project global catch database. If specific habitat association information for a group aggregated at a higher taxonomic level (e.g., Genus, Family) was not available, their weighting factors were approximated from the average habitat association values of their composite taxa at a lower taxonomic level (e.g., species). A group may be associated with multiple habitats. The assigned habitat affinity values are available online (www.seaaroundus.org). 81 Table 3.1. Habitat categories used here, and for which global maps are available in the Sea Around Us Project, with some of the terms typically associated with them (in FishBase and other sources). Categories Terms often used to describe these categories Estuary Estuaries, mangroves, river mouth Coral Coral reef, coral, atoll, reef slope Seamounts Seamounts Other habitats Muddy/sandy/rocky bottom The average intrinsic vulnerability index of the taxa in the catch (hereafter called 'average vulnerability of catch') over the past five decades was calculated from the arithmetic mean of the intrinsic vulnerability index of fishes weighted by their annual catch. Firstly, annual catches by exploited fish groups from 1950 to 2003 that had been disaggregated into a world ocean map (represented by 30 min latitude x 30 min longitude cells) were obtained from the Sea Around Us Project database. Secondly, time-series of average vulnerability of catch for all the 858 fish groups with'non-zero affinity to coral reef, estuary and seamount-associations were calculated separately. We overlaid global maps of coral reefs ( U N E P World Conservation Monitoring Centre, Cambridge, U K ) , estuaries (Alder 2003) and seamounts (Kitchingman & Lai 2004) onto the spatial catch data. For each habitat and exploited taxon, we calculated the annual catch from areas where the particular habitat occurred. Catches were considered to be originated from a particular habitat if: (a) the catches came from groups that are associated with that habitat, and (b) the catches came from areas where that particular habitat existed. Thirdly, the intrinsic vulnerability index for each of the 858 groups were predicted using the fuzzy expert system (Appendix 3.1). Using these data, the average vulnerability of catch by habitat over the past five decades was calculated. To understand the changes in community structure that led to any observed trends in average vulnerability of catch, surface-plots of intrinsic vulnerability and total catch of fishes against time were created. 3.2.4. Comparison with distributions of globally threatened fishes Marine fishes that were listed under the I U C N Red List of Threatened Species (Baillie et al. 2004) were mapped onto the world ocean map represented by 30 min 82 latitude x 30 min longitude cells. These included 161 species of fishes that are listed under the vulnerable, endangered and critically endangered categories. Distributions of species were obtained from published literature, maps, and reports. For species without published distribution maps, their distributions were predicted using a rule-based model based on information such as depth and latitudinal range, occurring ocean basins, etc (see Watson et al. 2004 and www.seaaroundus.org for details). The number of red listed species and the time-series of average vulnerability of catch of demersal fishes in each cell was estimated. For each cell in the world ocean map, the slope of the changes in average vulnerability of catch between 1950 and 2003 were estimated using linear regression. Slopes with negative values indicate declines in average vulnerability of catch and otherwise for those with positive values. Correlations between the Red List species number and the slope of the time-series in each spatial cell were then tested using the Kendall correlation test (Kutner et al. 2005). Although the choice of fishes that had been assessed by the Red List was biased (e.g., species that were known to be more vulnerable were more likely to be assessed), the I U C N Red List represented one of the most authoritative global list of threatened marine fishes. It should be useful in revealing large-scale general qualitative patterns. The validity of using the I U C N Red List in this analysis is further elaborated in the discussion section. 3.3. Results 3.3.1. Intrinsic vulnerability of fish with different associated habitats The estimated indices of intrinsic vulnerability of marine fishes varied between habitats (Figure 3.1, Table 3.2). The average intrinsic vulnerability of coral reef-associated and estuarine fishes (43.3 \u00C2\u00B1 1 . 2 s.e. and 45.3 \u00C2\u00B1 1 . 0 s.e., respectively) had similar level of intrinsic vulnerability (Turkey-Kramer H S D test, P > 0.05). However, coral reef fishes were slightly less vulnerable than all analyzed marine fishes (Turkey-Kramer H S D test, P < 0.05). The predicted intrinsic vulnerabilities of seamount and seamount fishes were significantly higher than coral reef and estuarine fishes (Table 3.2) (Turkey-Kramer H S D test, P < 0.05). Particularly, seamourit-aggregating fish, a sub-set of seamount fishes, had the highest average intrinsic vulnerability index among all groups 83 (63.9 \u00C2\u00B1 3.1 s.e.). The average vulnerability index of seamount-aggregating fishes was 63.9 \u00C2\u00B1 3 . 1 s.e., and was similar to fishes that were listed under the I U C N Red List of Threatened Species (Figure 3.1). Table 3.2. Comparisons of intrinsic vulnerability between fishes associated with different habitats. The values represent the difference between the mean intrinsic vulnerability index of the assemblages associated to the habitats listed on the first column and the first row. The asterisk indicated that the pair comparison is significant at 0.05 confidence level in the Turkey-Kramer HSD test (q = 2.574). Seamount-agg: seamount-aggregating fishes Marine fish communities All Coral reef Estuarine Seamount Seamount-agg All 4.10* 2.04 -5.44* -16.56* Coral reef 4 In' ' * i ' '' '\u00E2\u0080\u00A2 -2.06 -9.54* -20.67* Estuarine -2.04 2.06 -7.41* -18.61* Seamount 5.44* 9.54* 7.47* -11.13 Seamount-agg 16.56* 20.67* 18.61* \"\"-11.13 80 70 A g 60 c > .52 50 40 30 All fish IUCN Red List Coral reef Estuaries Seamount Seamount (aggregating) Figure 3.1. Mean intrinsic vulnerability index of marine fishes that are categorized as: coral reef-associated (N = 243), estuaries-associated (N = 381), seamount (172), seamount-aggregating (N = 15), all fish (N = 1,353), species listed under the IUCN Red List (N = 161). The error bars represent 95% confidence limits. 84 The intrinsic vulnerability index values were significantly related to factors relating to the position in water column, depth and latitudinal ranges (Table 3.3). Fishes occupying the bathypelagic region (i.e., the oceanic zone between 1,000 m to 4,000 m deep) showed significantly lower vulnerability. When position in the water column was the only independent factor considered, bathydemersal fishes had the highest vulnerability index, followed by benthopelagic, then demersal and pelagic fishes. The significance of the position in the water column (except for bathypelagic and bathydemersal fishes) was lost when latitudinal and bathymetric variables were added. The occurrence depth (represented by the log-transformed median of their depth range) and latitudinal range was positively and significantly related to the species' vulnerability index. When positions in water column, depth and latitudinal factors were accounted for, habitat type was only marginally significant in explaining the variations in the vulnerability index. Coral reef-associated, seamount and estuarine fishes did not have significantly different indices, while seamount-aggregating fishes were significantly (5% confidence level) more vulnerable. Table 3.3. Results of the generalized linear model for the relationships between the environmental attributes (depth, latitude and position in water column) and the index of intrinsic vulnerability of fishes of 1,514 species of marine fishes. This shows fishes in the bathypelagic zone to have significantly lower vulnerability than all fishes in general. Fishes inhabit deeper water, have bigger latitudinal range and are seamount-aggregating have higher vulnerability index. Factors Coefficients Standard error t-values Probability>|t| Intercept 20.47 2.89 7.074 <0.001** Pelagic 0.31 2.10 0.149 0.882 Demersal 0.69 1.06 0.653 0.514 Benthopelagic 4.62 2.41 1.912 0.056 Bathypelagic -28.46 3.11 -9.168 <0.001** Bathydemersal 6.64 2.39 2.775 0.006** log(median depth) 4.94 0.46 10.765 <0.001** Latitudinal range 0.07 0.02 4.134 <0.001** Coral reef-associated -0.84 1.80 -0.467 0.641 Estuarine 2.36 1.79 1.315 0.189 Seamount -0.23 2.25 -0.102 0.919 Seamount- aggregating 10.85 3.75 2.893 0.004** ** Significant at the 0.01 confidence level 85 3.3.2. Average intrinsic vulnerability index of catch The average vulnerability of catches of marine fishes declined from 1950 to 2003 (Figure 3.2). The trends were similar whether all exploited fishes or only coastal fishes were considered (Figure 3.2a). The large fall and rise of average vulnerability of catch mainly resulted from the large catches of Peruvian anchovy (with low vulnerability) catch and its collapse in the 1970s and 1980s. When small pelagic fishes were excluded from the analysis, the average vulnerability of catch declined more smoothly (Figure 3.2b). The average vulnerability of catches from coral reefs and estuaries declined, while the trend was less clear for seamount fishes (Figure 3.2c-f). The decline was stronger for coral reef-associated fishes (Fig. 2c), from a mean average vulnerability of catch of 50 (out of 100) in 1950 to 40 in the 2000s. Estuarine fishes also showed a consistent decline (Figure 3.2d). When all exploited seamount fishes were considered, average vulnerability of catches fluctuated widely over the past five decades (Fig. 2e). The fluctuations, however, were mainly attributed to the high catch of small pelagic fish. When small pelagic fishes were excluded from the analysis, the average vulnerability of catches for seamount fishes increased consistently from the 1970s to the late 1990s, then levelling from 2000 on (Figure 3.2f). 86 a) c) e) a e b) J3 g 1950 M u O \u00C2\u00AB o S 3 d) f) JS \u00C2\u00BB \u00C2\u00AB w cm \u00C2\u00A9 \u00C2\u00AB 80) recently, particularly those from offshore waters, their contributions to the global catches were relatively small. Current evidence suggests that the increasing exploitations of the offshore deepwater stocks that generally have vulnerable life histories are not sustainable (Morato et al. 2006b). The apparent increase in catch was sustained by serial depletions of previously unexploited and inaccessible stocks. The likelihood of alternative explanations for the observed changes in average vulnerability of catches that were independent of exploited stock status was small. These alternative explanations include changes in market demand and accessibility to fishing grounds. However, a large scale shift in market demand for smaller or less vulnerable fishes independent of the exploited stock status was not apparent in the past five decades. Moreover, changes in accessibility to fishing grounds would have likely affected the catches of fishes similarly across the spectrum of intrinsic vulnerability. On the other hand, the consistent patterns observed in the different habitats and niches and the supporting evidence from empirical studies suggested that the changes in average vulnerability of catch can be contributed mainly to serial depletion of fishes. Coral reef fishes showed the strongest decline in average vulnerability of catch over the last five decades, thus the more vulnerable reef fishes might have been depleted rapidly. Catches of intrinsically vulnerable reef fishes such as groupers (Serranidae) declined, while those from less vulnerable fishes such as rabbitfishes (Siganidae), goatfishes (Mullidae) and bigeyes (Priacanthidae) increased greatly. As the coral reef fish community is relatively more stable compared to other communities such as estuarine, changes in composition of coral reef fishes resulting from serial depletion of fishes with different vulnerabilities can be detected more easily. On the contrary, the high volatility 93 of estuarine communities may partly explain their weaker decline in the average vulnerability of catch. This study also showed that high concentrations of threatened fishes occurred in the Indo-Pacific and the Caribbean, where coral reefs were extensive (Bellwood & Hughes 2001; Spalding et al. 2004). Together with other direct and indirect threats such as destructive fishing (Jennings & Lock 1996), geographic expansions of the live reef fish trade (Sadovy & Vincent 2002; Sadovy 2005), coastal development, climate change (Pandolfi et al. 2003; Birkeland 2004) and data limitations (Sadovy 2005), coral reef habitat should be warranted high conservation attention. The increasing exploitation of deepwater (Morato et al. 2006b) and seamount fishery resources is of concern (Koslow 1997; Koslow et al. 2000; Watson & Morato 2004). Seamount assemblages are generally more vulnerability to fishing. Also, fisheries on seamounts are often 'boom-and-bust', i.e., rapidly over-exploiting a seamount soon after their discovery, followed by a move to the next to be discovered, resulting in serial depletions of seamount populations. This might explain the increase in catches of highly vulnerable offshore fishes and the consistent increase in average vulnerability of catch in seamounts. Moreover, the high vulnerability of the seamount communities means that the populations may be over-exploited rapidly once fishing has developed before management plans and regulations are in place (Boyer et al. 2001). The sustainability of such fisheries is in doubt (Clark 2001). The positive spatial correlation between the number of fishes listed under the I U C N Red List and the decline in average vulnerability of catch provided further support for the over-exploitation of more vulnerable stocks. Although fishes that were selected for assessment by the I U C N Red List might have been biased towards the more vulnerable species, this also means that the distribution of the red-listed species reflects the area where intrinsically more vulnerable and currently endangered species were concentrated. Thus the broad-scale patterns of distributions of the Red-listed fishes should be useful in revealing general patterns of threatened species distributions. The often poor quality of the original catch data should not affect the general conclusion of the analysis. The taxonomic and spatial resolutions of the original data (mainly from F A O ) are poor in some regions of the world. This may have limited the 94 sensitivity of our method to detect changes in average vulnerability of catch (Pauly & Palomares 2005). For example, a mixture of species might have been reported in a single group. These species may have different intrinsic vulnerabilities. As they are aggregated within a single group, their serial depletion would not be detected by the analysis in this study. Also, it is difficult to reveal spatial serial depletion of different populations (e.g. fishing shifts further offshore as inshore stocks are depleted) from landings data reported at country or regional level. Thus we believe that the uncertainty of the data quality can result in underestimation of the decline in the average intrinsic vulnerable of catch over time (Pauly & Palomares 2005). This study demonstrated the large-scale effects of fishing on structures of fish communities that are related to their intrinsic vulnerability to fishing. Although seamount assemblages showed distinctively higher vulnerability, the nature of threats from fishing shared many similarities with coral reef fishes. This study suggests that the coral reef assemblages in the Indo-Pacific and the Caribbean, deepwater demersal fishes and the seamount-aggregating fishes worldwide are particularly threatened by fishing. If present trends persist, it is likely that the more vulnerable species can be further depleted or, at worst, at risk of extinction. In Chapter 4, I attempted to predict the relative conservation risk of marine fishes from fishing in the world. 95 3.5. 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O. 2005 Life history strategies, population relation, and implications for fisheries management. Canadian Journal of Fisheries and Aquatic Science 62, 872-885. Winemiller, K . O. & Rose, K . A . 1992 Patterns of life-history diversification in North American fishes: implications for population regulation. Canadian Journal of Fisheries and Acjuatic Science 49, 2176-2218. 102 4. AN INDEX THAT EXPRESSES RISK OF SEVERE POPULATION DEPLETION OF MARINE FISH FROM FISHING4 4.1. Introduction Unsustainable fishing is occurring over most of the ocean (Botsford et al. 1997; Pauly et al. 2002), which poses serious conservation threats to marine taxa (Dulvy et al. 2003). Such threats are increasingly being recognized as more cases of severe population depletion or extirpation induced by fishing mortality are documented (Casey & Myers 1998; Dulvy et al. 2003; Sadovy & Cheung 2003; Hutchings & Reynolds 2004; Myers & Worm 2005). Although documented marine extinctions are rarer than terrestrial taxa, extirpations (local extinctions) of marine populations are numerous, especially in coastal systems (Carlton 1993; Casey & Myers 1998; Sadovy 2001; Dulvy et al. 2003), with extinction following the last extirpation (Pitcher 2001). On the other hand, lack of long-term population data limits the quantification of conservation threats to marine organisms at a global scale (Dulvy et al. 2004). Currently, under the I U C N Red List, less than 1% of marine fish species have been evaluated (Baillie. et al. 2004). Thus developing approaches that allow assessment of extinction risk with limited data is an important step to conservation of marine populations (Dulvy et al. 2003). Conventional methods to assess extinction risk of animals rely strongly on demographic data (Burgman et al. 1993); thus their applications to extinction risk assessment of most marine fishes are limited because of a lack of data. Conventional methods can range from diffusion methods to individual-based population models (Boyce 1992; Brook et al. 2000; Dulvy et al. 2004). These methods require at least time-series data of abundance or an index of abundance. However, such data are generally lacking for the majority of marine fish species except for some exploited populations in well-studied areas (e.g., North America). The data-limitation problem prevents identification of extinction risk for a broad range of populations affected by anthropogenic impacts 4 A version of this Chapter has been accepted for publication. Cheung, W. W. L., Pitcher, T. J. & Pauly, D. in press Using an expert system to evaluate vulnerabilities and conservation risk of marine fishes from fishing. In: Lipshitz A. P. (ed.). Progress in Expert Systems Research. New York: Nova Science Publishers. [in press]. 103 such as fishing. The problem of data-limitation is particularly serious in tropical, developing country fisheries where species diversity is high, but resources for monitoring are low (Pauly 1980; Jennings & Polunin 1996; Johannes 1998). Another obstacle to estimating the extinction risk of marine fishes is the difficulties in determining their minimum viable population size (Reynolds et al. 2005). Three major reasons that have led to this problem. Firstly, experience of contemporary marine extinction is limited (Dulvy et al. 2003). Secondly, population dynamics of marine fishes in small population size are poorly understood. For instance, the extent to which depensation or Allee effect occurs in marine fish populations is not clear. Depensation, termed the Allee effect in the ecology literature (Stephens & Sutherland 1999; Stephens et al. 1999), occurs when fitness (number of offspring per spawner) or per capita growth rate decreases at low population size (Stephens et al. 1999; Petersen & Levitan 2001). Meta-analysis of stock-recruitment data showed that depensation might be uncommon in marine fish (Myers et al. 1995; Liermann & Hilborn 1997). However, re-analysis accounting for the high variance in the original data suggests that it might still be likely that depensation would be more common in marine fishes than previously assumed (Liermann & Hilborn 1997). Thirdly, dynamics of meta-population - spatially separated populations of the same species which interact with each other (Levins 1969) - are poorly studied for most marine fishes. It is difficult to predict extinction risk accurately without understanding the mechanisms of local extinctions and migrations from meta-populations. Thus, here, conservation status of marine fishes was expressed in terms of the risk of severe depletion i.e., the risk that the abundance of the concerned species is reduced to very low level. Although the uncertainty of the true risk of extinction in greatly reduced marine fish population was admitted, it is reasonable to assume that extinction risk increases largely by severe population depletion. To rapidly assess the relative depletion risk and short-list priority species for detailed assessment, 'rule-of-thumb' approaches were proposed (Fagan et al. 2001; Reynolds et al. 2001; Dulvy et al. 2003; Dulvy et al. 2004). Such approaches use easily-obtainable information to approximately identify vulnerable or \"priority\" species that are in need of immediate conservation attention. For instance, in Chapter 2, the 'rule-of-thumb' approaches were incorporated into a fuzzy logic expert system framework to 104 provide quantitative predictions of intrinsic vulnerabilities to fishing. The 'rule-of-thumb' approaches are especially useful if their applications are combined with large databases, for instance, FishBase (Froese & Pauly 2004; www.fishbase.org) and the Sea Around Us Project database (www.seaaroundus.org), which presents a wide range of fisheries data ranging from spatially disaggregated catch data to prices of fishery catches (Watson et al. 2004; Sumaila et al. 2007). Results obtained from 'rule-of thumb' approaches can also help focus longer term research on the priority species so that data could be made available for more accurate extinction risk assessments (Figure 4.1). IMMEDIATE TERM Rules of thumb 4 ^ LONGER TERM Detailed data gathering and assessments Figure 4.1. Schematic presentations of the proposed framework to identify depletion risk of marine fishes. Among fisheries data, catch time-series are relatively more widely available than data such as absolute abundance or index of relative abundance. Catch time-series can be useful in understanding the overall status of a population (Grainger & Garcia 1996; Easily obtainable data 105 Fiorentini et al. 1997; Caddy 2004). B y definition, a population is under-exploited when a fishery relying on it is developing; in such cases, catch increases as fishing effort increases (Hilborn & Walters 1992). As fishing effort approaches or exceeds maximum productivity, the population becomes over-exploited, and the catch declines, and eventually collapses. A recovery phase may follow if fishing is reduced to a low level (Figure 4.2). Catch time-series had been used as an indicator to reflect the approximate population status at large spatial scale (Grainger & Garcia 1996; Pauly et al. 1998; Caddy 2004; Worm et al. 2006). o \u00E2\u0080\u00A24\u00E2\u0080\u0094< ns O Stage 5 Time Figure 4.2. Schematic diagram showing the classification of exploitation status of a population based on a catch time-series. Stage 1: under-exploited; Stage 2: fully exploited; Stage 3: over-exploited; Stage 4: depleted; and Stage 5: recovering. The relationship between catch and population status becomes less tight when there are confounding ecological, environmental, economic and management effects. Firstly, catches can be maintained by spatial changes in fishing effort and targeted sub-population. In such cases, catch may increase as fishing expands spatially (serial depletion), or catchability increases when a population reduces its range. Also, catches can be reduced by the implementation of more stringent fisheries management policies, while population size may remain roughly constant. Moreover, change in market demand, catch value and costs of fishing may affect the operation of fleets without strong change in population abundance. On the other hand, given that these ecological, environmental and economic data are not available for many targeted fishes and fisheries, we have to 106 rely on catch time-series to identify potentially over-exploited or depleted populations for more detailed analysis. Apart from the extrinsic factors such as fishing mortality rate, life history and ecology are also important intrinsic factors affecting the depletion risk of marine species (Jennings et al. 1998; Reynolds et al. 2001; Rowe & Hutchings 2003; Cheung et al. 2005; Reynolds et al. 2005). Species with certain features (e.g., large size, late maturation) are less able to withstand high fishing mortalities and thus have a higher risk of extinction than less vulnerable species under similar fishing pressure (Musick 1999). Also, theoretical and empirical studies suggest that fishes with 'periodic' life history characteristics (large size, high longevity, late at maturation and high fecundity) have a high compensation ability (i.e., increase in population growth rate as population size decreases), but a low productivity when their population is greatly reduced. On the other hand, 'opportunistic' fishes (small to medium size, short-lives, early maturation and moderate fecundity) have a low compensation ability, but high productivity at small population size (Winemiller & Rose 1992; Fagan et al. 1999; Rose et al. 2001; Winemiller 2005; Goodwin et al. 2006). Thus fishes with 'periodic' life history characteristics have a low resilience (the ability to recover from disturbance) when populations are over-exploited or depleted, while the 'opportunistic' life history characteristics confer high resilience (Winemiller 2005; Goodwin et al. 2006). Based on the relationship between the life history and ecology of a species and its vulnerability to fishing, Cheung et al. (2005) developed an expert system that predicts the intrinsic vulnerability of marine fishes using simple life history and ecology parameters. These parameters are readily available from easily assessable databases, such as FishBase (www.fishbase.org) (see Chapter 2). This expert system estimates an index of intrinsic vulnerability for each species or populations from one or more of the following parameters: maximum length, age at maturity, longevity, von Bertalanffy growth parameter K, natural mortality rate, fecundity, geographic range and scale of spatial behaviour (e.g., schooling, aggregating, etc.). However, the index of intrinsic vulnerability developed in Chapter 2 can only indicate species' inherent capacity to withstand fishing pressure. To understand the overall risk of depletion, extirpation or 107 extinction from fishing, we need to understand both the intrinsic vulnerability and the level of fishing exploitation on the exploited species. Given the above theoretical and empirical bases, we can develop some qualitative relationship between catch time-series, exploitation status, life history and depletion risk of exploited marine fishes. For instance, a consistent decline in catch over a large geographic range after reaching a peak may indicate over-exploitation. While life history traits of the exploited species indicate a high intrinsic vulnerability (or low resilience), the species may likely have high depletion risk when the population is over-exploited. On the other hand, depletion risk may be moderate if the life history of the species confers low intrinsic vulnerability. Developing and collating these qualitative relationships systematically could be useful to evaluate the conservation status of marine fishes to fishing (Jennings et al. 1998; Jennings et al. 1999; Reynolds et al. 2001). A fuzzy logic expert system could be useful in combining the above qualitative relationships to provide an indicator of depletion risk (see Chapter 2). Fuzzy logic, or fuzzy set theory (Zadeh 1965), allows a subject to be \"associated\" with one or more set(s) with a gradation of membership defined by fuzzy membership functions, while an expert system is an artificial intelligence system that helps solve problems based on pre-specified knowledge-base. The knowledge can be expressed in the form of rules such as: IF A T H E N B where A is the premise while B is the conclusion (Kasabov, 1996). The actions defined by the rules are 'fired' (= operated) when the degree of membership of the premises exceed certain threshold values. Conflicting rules are allowed to fire jointly. Thus conclusions from a fuzzy logic expert system can be reached from premise(s) with a gradation of truth. Membership can be viewed as a representation of the 'possibility' of association with the particular set (Zadeh 1995; Cox 1999). Fuzzy set theory is particularly useful because vagueness is a crucial aspect of our knowledge of fishes' biological characteristics, and their relationships with the depletion risk from fishing. Fuzzy expert systems have been proposed and applied to study fisheries and conservation biology (Saila 1996). The applications range from assessing stock-recruitment relationships (Mackinson et al. 1999; Chen 2001), predicting fish shoaling 108 behaviour (Mackinson 2000) and identifying stock structure of fishes (Zhang 1994). They have also been applied to develop an analytical tool to assess conservation threats (Todd & Burgman 1998; Regan & Colyvan 2000) and to assist the I U C N Red List 's species assessment (Akcakaya et al. 2000). Fuzzy logic was also proposed to be used to assess extinction risks of different Pacific salmon stocks (Tinch 2000). Here, based on a rule-based fuzzy model, we used readily available catch and life history data to evaluate the relative depletion risk of exploited marine fish. Depletion risk from fishing was defined here as the possibility of severe population depletion (near extirpation) because of fishing and the intrinsic properties of the species. 4.2. Methodology 4.2.1. Analysis of temporal patterns of catch time-series We analyzed 460 species of marine fish that had at least 10 years of catch time-series data, and catches of at least 100 tonnes in the United Nations Food and Agriculture Organization (FAO) fishery statistics. Catch time-series were aggregated by 19 F A O statistical areas (a total of 1,313 aggregates). Since catch fluctuations caused by environmental variability (e.g., primary productivity or temperature fluctuations) might mask any trends due to fishing, catch time-series were smoothed with a running average: c t=y- eq. 4.1 . v ( y - y ) ' where the averaged catch C at year y is equal to the average of annual catch from year y ' . As smaller species tend to respond more strongly to environmental variability than larger species (Spencer & Coll ie 1997), the running averages were scaled inversely by species' maximum length (maximum length<30 cm: y - y' = 9 years average, maximum length = 30-90 cm: y - y' = 5 years average, maximum length>90: y - y' - 3 years average). Figure 4 illustrates this for Rainbow sardine and Nassau grouper. 109 a) b) 100 T3 C CD U) 3 o O 4 -, 3 ? 2 O T 100 x T3 c o. O 1950 1960 1970 1980 Year 1990 2000 0 1965 100 80 60 40 20 0) \u00E2\u0080\u00A2o c c o Q. l (Mackinson et al. 1997): b is the biomass relative to the unexploited biomass and e is the uniformly distributed error with values between 0 and 1. Time-series of catch and C P U E were recorded for 100 years of the tested species. The extinction risk of each population in each simulation was determined using the I U C N Red List criterion E (based on probability of extinction). Probability of quasi-extinction of the population was determined in each simulation. Quasi-extinction was defined here as a population declines to reach the point of non-viability (Ginzburg et al. 1982; Burgman et al. 1993). For each species, the population dynamics described by the above model was run 100 times. Probability of quasi-extinction was measured as the frequency of a population reaching 1/1000 of the unfished equilibrium biomass in the 100 simulations (Punt 2000). We assumed that populations that have been reduced by 99.9% are not viable, thus the estimated quasi-extinction probability is an approximate estimation on the true extinction probability. Therefore, the population was classified as critically endangered, endangered or vulnerability i f the probability of quasi-extinction is at least 50% in 10 years or three generations, at least 20% in 20 years or 5 generations, and at least 10% in less than 100 years, respectively ( I U C N 2001). Simultaneously, using the generated catch-per-unit-effort time-series, we estimated the threatened status as defined by the I U C N Red List criterion A - trends of index of abundance ( I U C N 2001). The population was categorized as critically endangered, endangered and vulnerable i f the C P U E declined by 80%, 50% and 30% in three generations or 10 years, whichever is longer. 118 In each simulation, depletion risk of each population was also estimated using the expert system developed in this study. The intrinsic vulnerability of each test species was estimated based on the available life history parameters (Table 4.4). At each time-step, the exploitation status was inferred from the catch time-series recorded from the simulation model. Depletion risk was then estimated from the predicted intrinsic vulnerability and exploitation status. Populations were classified as having moderate, high and very high depletion risk i f the calculated depletion risk index was above 40, 55 and 70, respectively. The depletion risk calculated from the expert system was compared with the extinction risk identified by using the I U C N criteria E and A . We considered that the extinction risk identified based on the probability of quasi-extinction ( I U C N criteria E) was accurate, while the I U C N criteria A is most widely used to assess extinction risk of marine fishes (Punt 2000). We compared the depletion risk categories and the I U C N categories determined based on criteria A with the threatened categories determined by criteria E . Considering that the depletion risk categories of moderate, high and very high correspond to the I U C N categories of vulnerable, endangered and critically endangered, respectively, we calculated the probability of under- and over- estimating threatened status (Type I and II errors) from predictions of rule-based model presented here using the simulated data from the above population model. 4.2.4. Depletion risk of all exploited marine fishes To obtain an approximate estimate on the depletion risk of exploited marine fishes globally, we extrapolated the results from our analyses on the 460 selected species to all exploited marine fishes (3,503 species). To correct for biases in our sample towards more vulnerable and targeted species, we grouped species by types (pelagic bony fish, demersal bony fish and elasmobranchs) and fishery importance (highly commercial, commercial, minor commercial) using information available from FishBase (Froese & Pauly 2004). We excluded classes with low sample size (sample to global species number ratio < 5%) before we extrapolated the predicted threatened status in each class (Table 4.3). 119 4.3. Results Our results showed that depletion risk of the 460 exploited fishes increased rapidly over the past three decades (Figure 4.5). In 2001, about 24% of the evaluated species were associated with a very high depletion risk level (depletion risk index > 70), compared to none in the mid 1950s and 4% in 1970. The average depletion risk index of all species in 2001 is about 44. Fishes that are in the very high depletion risk category include gadids, polynemids, haemulids, epinephelids, triglids, dasyatids, while those in moderate depletion risk (depletion risk index around 44) include platycephalids, sciaenids, priacanthids and sparids, etc. Fishes with lower depletion risk include mainly small-bodied clupeids, engraulids, carangids and tetraodontids. 1.0 i V o 30 cm) and elasmobranchs had depletion risk index significantly higher than average of all marine fishes, including small demersal and pelagic fishes, at the 95% confidence level (logistic regression, P = 0.003 and 0.022 respectively) (Figure 4.6). Among the 14-28%, 20-56% and 8-24% of the 460 species that were in the \"very high\", \"high\" and \"moderate\" categories in 2001, elasmobranchs 120 (sharks and rays) had the highest proportion in the \"moderate\" or higher categories (73%), followed by large demersal (61%), large pelagics (48%) and then small pelagic bony fish (36%). 60 -i -g 50 i o 8\" 30 -20 Small pelagic fishes Medium-large Small demersal Large demersal Elasmobranchs pelagic fishes fishes fishes Figure 4.6. Average depletion risk index of different fish groups. Standard errors are indicated by the error bars. B y extrapolating to all exploited marine fish, we found that the proportion of marine fishes that have moderate to very high depletion risk (a depletion risk index over 55) was considerable. Of the 3,503 species of marine fish that FishBase (Froese & Pauly 2004) classifies as being commercially exploited, 500-957 (3-6% of all marine fish), 218-730 (2-3%) and 641-1,763 (5-11%) were categorized as having very high, high and moderate depletion risk, respectively. The statistically non-significant difference between the accuracy of the depletion risk index and the I U C N Red List categories suggests that the very high, high and moderate depletion risk may be used as proxies to indicate a species being in the critically endangered, endangered and vulnerable categories, respectively. Thus our results suggest that 3-6%, 2-3% and 5-11% of all marine fishes may be critically endangered, endangered, and vulnerable. This is in the same order of magnitude as for other vertebrates (mammals, birds and amphibians), for which, however, a much higher number of species has been evaluated under the I U C N Red List procedure (Baillie et al. 2004) (Figure 4.7). 121 40 30 Mammals Birds Amphibia Marine fish Figure 4.7. Proportion of described mammals, birds, amphibians categorized as critically endangered (white), endangered (grey) and vulnerable (black). Status of mammals, birds and amphibians are obtained from the IUCN Red List (Baillie et al. 2004). while the status of marine fish was inferred from our rule-based model. The error bars are the upper and lower 95% confidence limits. Analyses using the simulated data suggested that the performance of the depletion risk index as a proxy to predict the extinction risk category of fishes is similar to the I U C N Red List criteria A (Figure 4.8). Using threshold depletion risk index values of 70, 55 and 40 to define \"critically endangered\", \"endangered\" and \"vulnerable\" categories, the probabilities of categorizing a species to a category that is lower than the prediction using the I U C N criterion E (based on probability of extinction from the population model) (Type I error) are 0.03, 0.14, 0.36 for the three threatened categories, respectively. The probabilities of assigning a higher threatened status of \"critically endangered\", \"endangered\" and \"vulnerable\" than those predicted from the I U C N criterion E (Type II error) are 0.33, 0.2, and 0.05, respectively. Comparing with the predictions based on the I U C N criterion A , the probability of over-estimating risk (Type II error) from predictions of the depletion risk index was significantly lower than the I U C N criterion A , while probability of under-estimation (Type I error) appeared slightly higher. The relatively higher Type I and Type II errors in predicting the vulnerable and critically endangered 122 categories, respectively, means that we might underestimate the risk of extirpation of less threatened taxa but overestimate the risk of those that seem to be highly threatened. b) C R E N or higher V U or higher IUCN Red List category Figure 4.8. Comparisons of Type I and II errors between threatened status predicted by the IUCN Red List procedure (criterion E, solid bars) and the rule-based model (white bars). CR - critically endangered; E N -endangered; V U - vulnerable. The error bars represent the 95% confidence limits, assuming that errors are binomially distributed. Results from the two extreme sets of criteria and rules that represented conservative and liberal interpretations of depletion risk (Tables 4.1, 4.2) showed that our assumed fuzzy sets and heuristic rules performed best (Figure 4.9). Type I errors did not differ significantly while the 'liberal scenario' performed poorly on Type II error, suggesting that predictions from our 'moderate' scenario were robust to the assumed rules and criteria. 123 a) 0.6 0.5 a> 0.4 a o g-'B CO Si O 0.3 0.2 0.1 0 C R E N or higher i V U or higher b) D Conservative \u00E2\u0080\u00A2 Moderate \u00E2\u0080\u00A2 Liberal C R E N or higher V U or higher IUCN category Figure 4.9. Probability of (a) under-estimating (type I) and (b) over-estimating (type II) risk using the depletion risk index predicted from conservative (open bars), moderate (default, gray bars) and liberal (dark bars). 4.4. Discussion Our study shows that the estimated conservation status of marine fish resulting from fishing was consistent with that of other, mainly terrestrial, vertebrates. This is surprising in view of the frequently-expressed notion that marine fish populations should be inherently more resilient than other vertebrates (Hutchings 2001a). However, our findings are supported by abundant empirical evidence. For instance, population parameters such as the intrinsic rate of population increase, and population variability of marine fish were shown to be similar to value for other vertebrates (Fagan et al. 2001; Hutchings 2001b). Maximum reproductive potential, a population parameter that reflects the ability to withstand fishery-induced losses, is similar among fish groups, and similar to values among mammals of similar sizes (Myers et al. 1999), while the geographic range of many fish need not render them less vulnerable, given their propensity for range collapse when abundance declines (Pitcher 1997; Jennings et al. 1998; Sadovy 2001; Dulvy et al. 2003). Studies on fish stock recovery after depletions showed that, except for clupeids, recovery rate was generally low (Hutchings & Reynolds 2004). Although known contemporary marine extinction was rare, it might partly a result of poor detection ability (Dulvy et al. 2003). The rapid increase in our estimated depletion risk over the last three decades coincides with the dramatic expansion of global fisheries (Pauly et al. 2002). Over the past few decades,- fishes have lost their natural refuges (in the form of inaccessible habitats) owing to improved technology (e.g., G P S , sea-floor mapping, echo-acoustic; (Pauly et al. 2002, 2004). Bio-economic factors (e.g. diminishing return from depleted stocks) might not prevent extirpation (or even extinction) as, in some case, market value increased with resource rarity (Sadovy & Cheung 2003). Factors such as government subsidies to fisheries or the lack of alternative livelihood to fisher can maintain fishing effort even i f profitability of fishing becomes low or negative (Khan et al. 2006; Pauly 2006). Thus depletion of populations across their geographic range should greatly increase their depletion risk. Vulnerable species such as elasmobranchs and other large predatory fishes, particularly demersal fish should be prioritized for monitoring and conservation. The life 125 history of elasmobranchs (large-size, late maturation) renders them highly vulnerable to fishing (Stevens et al. 2000; Dulvy & Reynolds 2002; Baum et al. 2003), while large predatory fish are traditionally targeted by fishing (Pauly et al. 1998; Myers & Worm 2003), which contribute to the higher extinction risk in these groups (Hutchings & Reynolds 2004). Our results are also consistent with predictions from simulation modelling that suggest 20-50% of bony fish and 40-100% of sharks might be driven to extinction under a typical fishery removal rate i.e., 40% of the population size removed per year (Myers & Worm 2005). Our results parallel the estimation that 65% of exploited fish and invertebrate taxa have been collpased (>90% decline in reported catches) since 1950 (Worm et al. 2006). Our global estimate for threatened marine species is uncertain. Firstly, because of the poor understanding of the dynamics of fish in small population sizes, the depletion risk index may not represent the 'true' risk of extinction (Dulvy et al. 2004). For instance, the extend of depsensation or allele effects at low population size in marine fishes is uncertain (Myers et al. 1995; Liermann & Hilborn 1997). Future studies on the dynamics of small population size are needed (Pitcher 1998; Dulvy et al. 2003). Secondly, the estimation was dependent on the assumed heuristic relationships and categorization criteria. Also, the structure of catch time-series might be confounded by non-fishery factors (i.e., error in the statistical recording system) and not accurately depict actual fishing yields. Statistical data uncertainties are particularly serious in tropical developing regions and reef fisheries where fishery monitoring was less effective but threats to their high biodiversity were acute (Johannes 1998). Many of the exploited species are not reported explicitly in the catch statistics and thus are excluded from our analysis. Thus our predictions may underestimate the threatened status in these regions. Moreover, indirect effects of fishing, e.g., habitat destruction (Kaiser et al. 2003), ecosystem effects (Jackson et al. 2001), or genetic effects (Law 2000), are not considered in our estimates. Other threats to biodiversity such as climate change (Roessig et al. 2004) were also not accounted for. These factors would likely increase our predicted extinction risk. On the other hand, our sensitivity analysis suggested that estimations from the assumed rules ('moderate' scenario) were robust and preformed best among the various scenarios. 126 Given the limited available data, accuracy of predictions from our model was similar to the widely applied I U C N Red List criteria. This study suggested that exploited marine fishes are vulnerable to severe depletion by fishing and their risk of extinction should not be dismissed. Given the current problems of data limitations and the urgent need to increase coverage of extinction risk assessment of marine fishes (Reynolds et al. 2005), this study provides a way to roughly evaluate the scope of the threats using currently available data. Hopefully, this would attract attention and further researches on the depletion risk of exploited marine fishes. Unsustainable fishing has caused extirpations and large historical depletions of abundance of marine fish (Myers & Worm 2003; 2005). 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The reported fishing power of the three major Chinese fishing provinces of Guangzhou, Guangxi and Hainan increased by almost 20 times from 1950 to 2000 (Figure 5.1). At the same time, the total catch landings from N S C S have increased 16 times, from 0.2 mill ion tonnes in the 50s to 3.5 million tonnes in 2002 (Department of Fishery, Ministry of Agriculture, The People's Republic of China 1991, 1996, 2000). During this period, the total catch-per-unit-effort (CPUE) decreased by half. Moreover, the total demersal fishery resources in the N S C S have declined in abundance by about 75% from the unexploited level, a trend which is consistent in both the Gul f of Tonkin and the entire continental shelf of N S C S (Jia et al. 2004) (Figure 5.2). In fact, analyses that are based on China's national landing statistics could largely underestimate the true decline given the inaccuracy and known over-reporting in Chinese fisheries statistics (Pang and Pauly 2001; Watson & Pauly 2001). 3 5 A version of this chapter has been submitted for publication. Cheung, W. W. L. & Pitcher, T. J. Evaluating the Status of Exploited Taxa in the Northern South China Sea Using Intrinsic Vulnerability and Spatially Explicit Catch-per-unit-effort Data. Fisheries Research, [in review]. 136 1950 1960 1970 1980 Year 1990 2000 Figure 5.1 Change in landings (left axis, solid line, filled circle), fishing power (left axis, solid line, open circle) and CPUE (right axis, broken line, filled circle) of the three coastal Chinese provinces (Guangdong, Guangxi and Hainan) in the NSCS (Department of Fishery, Ministry of Agriculture, The People's Republic of China 1991, 1996, 2000; Qiu, Y . South China Sea Fisheries Institute, pers. comm.). CM i E C/> 2 (0 i 1 CD I I B o 1935 1956 1962 1967 1993 1998 Year Figure 5.2 Estimated demersal fishery resources in the northern South China Sea. Total demersal biomass declined from the unexploited level (Bo) in Gulf of Tonkin (black bar) and the continental shelf in general (open bar) from the 1930s to 1990s. 137 Other sources of information such as local fishers' knowledge corroborate this decline of populations of exploited fisheries in the N S C S . For instance, collations of anecdotal fishers' knowledge indicate that a number of populations, particularly those with life history and ecology that make them vulnerable to exploitation, are in a severely depleted state (Sadovy & Cornish 2000; Sadovy & Cheung 2003; Cheung & Sadovy 2004). Given the continued increase in fishery exploitation in the N S C S , more fish populations can be expected to undergo decline and even extirpation in the future (Cheung & Pitcher 2006). Therefore, accurate evaluation of the current status of fisheries in the N S C S is needed in order to properly guide the adjustment of fishing exploitation in this region to non-destructive and more sustainable levels. 5.1.1. Data availability Assessment of the current status of most exploited species in N S C S is difficult because of poor availability of data (Cheung & Pitcher 2006). Quantitative information such as time-series of abundance indices is virtually non-existent for the majority of exploited fish species. Chinese researchers conducted a number of independent fisheries surveys and assessments (Jia et al. 2004); however, most of these data are classified as 'national security documents' and therefore are unavailable for public use (Xiaping Jia, Director of the South China Sea Fisheries Institute, pers. comm.). More consistent historical time-series data were collected from the N S C S by the Hong Kong government. The Agriculture and Fisheries Department (renamed as Agriculture, Fisheries and Conservation Department in 1998) in Hong Kong carried out catch and effort surveys with fishing vessels operating in N S C S during the 1970s and 1980s. The Hong Kong government's surveys focused on demersal trawlers that were registered in Hong Kong but were allowed to fish in most parts of N S C S , including the inshore Hong Kong waters and the continental shelf (Cheung & Sadovy 2004). These catch and effort data can help reveal the status of exploited fisheries in the N S C S . Using C P U E data as an index of stock abundance has a number of limitations for evaluating the population status of exploited fisheries. In fisheries stock assessment, _ C CPUE B = \u00E2\u0080\u0094 = eq. 5.1 fq q 138 where B is stock abundance, C is catch, / ' is fishing effort and q is catchability coefficient (Hilborn & Walters 1992). It is often assumed that catchability is a constant, thus stock abundance is proportional to C P U E . However, C P U E may not represent the actual proportion of the fish populations because catchability of different fish species may vary according to stock size, time and space (Hilborn & Walters 1992; Walters 2003). For instance, in some shoaling fish species, population decline may lead to range collapse (Pitcher 1995; Mackinson et al. 1997). These fish may continue to maintain dense schools even the population abundance is reduced by fishing (Pitcher 1997). Thus catches can be maintained even when population size (and effort) decline, through an increase in catchability. Also, catch from fishers targeting fish aggregations or schools may be limited by the time to deplete an aggregations rather than the actual fish population size. This means that catch rate of fishers may not decrease despite the overall drop in the abundance of targeted fisheries (a term known as 'hyperstability') (Walters 2003). Moreover, non-random search for targeted species using accurate and advance fish tracking technology could maintain high catch rate when stock abundance declines and thus severely bias C P U E data (Walters & Mattel 1 2004). Fish population estimates from C P U E data are also sensitive to the underlying statistical assumptions of the analysis, particularly when the estimate is based on commercial fishing, wherein samplings were spatially non-random (Walters 2003). For instance, fishers may select fishing grounds that maximize their C P U E (Gill is 2003). Thus, if we assume that C P U E in unfished areas is equivalent to the average C P U E of fished areas, we may overestimate the true C P U E of the entire exploited population. On the other hand, assuming very low or zero C P U E for unfished areas could underestimate the true C P U E (i.e., 'hyperdepletion') (Walters 2003). Therefore, in order for C P U E data to accurately reflect actual fish population trends, it is important to find an objective way to determine a realistic potential C P U E in unfished areas. 5.1.2. Intrinsic vulnerability Proper management of degraded systems such as the N S C S (see Chapter 1) requires understanding of the population status of a wide array of species. However, the 139 currently available C P U E data from N S C S are biased towards commercially important taxa. Therefore a method for estimating the status of fish populations that can extend the assessment to non-commercial species is needed. Calculation of intrinsic vulnerability is one way to determine the status of fish populations that are not represented well in survey data (Cheung et al. 2005). Intrinsic vulnerability, defined as the inherent capacity of the species to respond to fishing, is correlated with life history traits such as body length, age at sexual maturation, longevity, etc. (Reynolds et al. 2001; Dulvy et al. 2003; Cheung et al. 2005). Intrinsic vulnerability can be expressed by an index that is predicted using a fuzzy logic expert system based on one or more of the following input parameters: maximum body length, age at sexual maturation, longevity, von Bertalanffy growth parameter K, natural mortality rate, fecundity (only low fecundity is considered), geographic range and a ranking on the type and strength of aggregation behaviour (Cheung et al. 2005; Chapter 2). These input data are relatively easy to obtain for most exploited species from the literature or an online database (e.g., FishBase; www.fishbase.org). The index of fish vulnerability can be applied to fishes from different areas and taxonomic groups and is positively correlated with rate of decline of marine populations. (Cheung et al 2005, Chapter 2). Here this relationship is applied to demersal fish populations in N S C S to evaluate the status of fish populations. The major steps in this study are (1) evaluating the changes in relative abundance of 17 commercially important demersal fishes in the N S C S , and (2) testing the correlation between the intrinsic vulnerability index and fish population decline. If the correlation between vulnerability index and population decline is significant, then the intrinsic vulnerability index can be applied to evaluate the status of the rest of the exploited demersal fish populations in the region that do not have C P U E data. The possible contributions of fishing, environmental changes and non-fishing anthropogenic impacts (e.g., pollution, coastal development) to the observed changes in N S C S demersal fish populations are discussed. 140 5.2. Methods Firstly, spatially explicit C P U E data of 17 commercially exploited demersal taxa in N S C S from 1973 to 1988 were compiled. The data were standardized to evaluate the time-series changes in relative catch rate during this period. Then, intrinsic vulnerabilities of these 17 taxa were estimated and the relationship with changes in C P U E was examined. The estimated vulnerability was then applied to extrapolate the population status of other taxa in the region. 5.2.1. C P U E data The Hong Kong government conducted surveys between 1973 and 1988 to estimate the catch per unit effort ( C P U E ) of bottom trawlers (stern and pair trawlers) registered in Hong Kong that fished in the N S C S (Hong Kong Agriculture, Fisheries and Hong Kong Department or A F C D , unpublished data). Fishing effort data were obtained once per week by interviewing skippers to estimate the number of days fishing. These data were spatially agregated into spatial cells of 30 minutes latitude by 30 minutes longitude. Each cell fell within one of the seven major fishing zones in the N S C S (Figure 5.3). Data for the catch composition by weight were obtained from sales vouchers reported in the government wholesale market (Fish Marketing Organization or F M O ) . For a single trip in which more than one spatial cell were fished, the landed catch was split by the proportion of the total effort spent in each grid ( A F C D , unpublished data). The original data existed in hard copy, so they were firstly transcribed into an electronic database for quantitative analysis. 141 Figure 5.3. The seven fishing areas delineated for the northern South China Sea continental shelf. Raw data from 17 commercially important demersal taxa, selected because they were well-represented in the landings of commercial trawlers, were compiled for further analyses (Table 5.1). They represented a wide range of different life-history characteristics (and thus intrinsic vulnerability), which facilitated the test of correlations between intrinsic vulnerability and rate of decline. Estimated distribution of fishing effort from the survey between 1973 and 1988 were plotted using ArcGIS 9.0. 142 Table 5.1. The 17 taxa reported in the catch and effort surveys conducted by the Hong Kong Agriculture and Fisheries Department* and their composite species(s). Only commercial species that are likely to be vulnerable to demersal trawl gears were included. Reported taxon Scientific name(s) of component species Yellow croaker Larimichthys crocea Slate cod croaker Protonibea diacanthus Other croakers Sciaenids (except the above) Two-spotted red snapper Lutjanus bohar Red snapper Lutjanus argentimaculatus Golden threadfin bream Nemipterus virgatus Flathead Platycephalids Groupers Epinephelus bruneus Scads Alepes djedaba, Decapterus russelli Bigeyes Priacanthus tayenus, P. marcracanthus Lizardfish Synodontids Hairtail Trichiurus lepterus, T. nanhaiensis White pomfret Pampus argenteus Red goatfish Upeneus molluccensis Melon seed Psenopsis anomcda Sharks Hemiscylliids, carcharhinids Skates and rays Daysatis akajei, D. kuhlii, Himantura gerrardi * Then the Agriculture, Fisheries and Conservation Department. 5.2.2. Interpolation of C P U E Estimated C P U E data (measured as kg-day\"1 hp\"1-metre haul length\"') of each of the 17 taxa from the Hong Kong survey were spatially and temporally interpolated. The fishing vessels that were sampled by the original survey had not fished in the entire study area. Thus, expected catch rate in the unfished areas remained unknown. However, fishing areas selected by the fishers were non-random as fishers might have targeted areas with high stock abundance. In this case, C P U E in unfished areas might be assumed to have very low abundance (e.g., C P U E = 0). On the other hand, it might be possible that area of high stock abundance were not fished because of the costs of fishing, market prices or accessibility of the fishing grounds. Therefore, C P U E in unfished areas might be assumed to have the same level of C P U E as the other fished areas (Walters 2003). 143 However, the assumption of C P U E = 0 in unfished areas may result in underestimation of C P U E in the region while the latter assumption may lead to over-estimation. In fact, C P U E in an area may generally be similar to its immediate surrounding areas. C P U E in one year can also reflect the level of C P U E in preceding years in the same area. Therefore, spatial and temporal interpolation of C P U E from available data in the surrounding areas and successive years should provide better estimates of C P U E than the simple assumptions mentioned above. It was assumed that C P U E in cells without data (i.e., not fished by the sampled fishing vessels) was dependent on (1) the historical occurrence of the taxa, (2) C P U E in the neighbouring cells and (3) C P U E in the same cell in different years. To decide on the appropriate C P U E in cells (Figure 5.4), the distribution range for each taxa was established from all the cells with positive C P U E in the time-series (1973-1988). If a cell fell within the historical range but was not fished in a particular year, its C P U E was assumed to be equal to the mean of its immediate surrounding cells in the same year (if at least two cells with positive C P U E existed). If the number of surrounding cells with positive C P U E was less than two, then the C P U E from the nearest succeeding year was included in the calculation. However, if the cell was not fished in any succeeding years and no fishing occurred in its surrounding cells either, its C P U E was assumed to be the average for this species in the entire fishing area. These procedures allow the use of all the available information in the dataset to extrapolate C P U E from fisheries-dependent catch and effort surveys. I repeated the analysis by assuming that C P U E in all cells without data was equal to the average of cells with data at the same year only. The latter was to evaluate how alternative assumption in data treatment would affect the conclusions of this study. 144 Year=1 Year=1+t Figure 5.4. Diagram illustrating the interpolation of CPUE for cells without estimate of CPUE. For instance, assuming that the cell marked with '?' falls within the historical range of the taxa, but was not fished at year 1. If CPUE estimates are available for two or more of its immediate surrounding grids (e.g. A l , B l , CI) , its CPUE is assumed to be the average of these surrounding cells. However, if CPUE estimate is available for only one of its immediate surrounding grids, CPUE estimate of the same grid and in the nearest succeeding year (D 1 + 1) is included in calculating the average CPUE. Otherwise, CPUE of the cell with '?' is assumed to be the average of the fishing area. 5.2.3. Standardization of C P U E The original C P U E data were aggregated to reduce the effects of data errors in evaluating the changes of C P U E over time. The original survey method relied solely on the fishing locations reported by fishers in landing sites to estimate catch and effort on a spatial grid map. Thus, the spatial precision of the estimates was limited. Also , the C P U E of a taxon obtained from areas (cells) in close proximity and from the same year might belong to the same sub-population. If so, these data points might be spatially auto-correlated. Thus, the spatial C P U E data were aggregated into the seven fishing zones delineated by the original survey (Figure 5.3). This helps minimize the effects of spatial 145 auto-correlation and low spatial resolution of the data (Agnew et al. 2000). The analysis was repeated using the non-aggregated data to evaluate the sensitivity of the results to this procedure. Also, inter-annual variability in catch rates resulting from random error might mask any trends in C P U E . Therefore, analysis of the C P U E data was repeated by aggregating the data into three time periods: (1) 1973-1978, (2) 1979-1983, (3) 1984-1988. C P U E data were standardized across gear types, fishing areas and time using a generalized linear model ( G L M ) (Allen & Punsly 1984; Hilborn & Walters 1992; Venables & Ripley 1999; Agnew et al. 2000; Maunder 2001; Baum & Myers 2004). C P U E from two gear types were included: stern (otter) trawl and pair trawl. Fishing areas were included in the model to correct for the effects of localized stock depletion. The model included observation error only (Hilborn & Mangel 1997). Previous studies suggest that C P U E data are generally log-normally distributed (Hilborn & Walters 1992; Agnew et al. 2000; Maunder 2001). Thus, a log-normal G L M was employed. This assumption was evaluated by comparing the performance of the log-normal G L M with alternative models. The mathematical form of the G L M becomes: l o g ( \u00C2\u00A3 / , M ) = log(c/, \u00E2\u0080\u009E ) + log(or,.) + logoff,) + l o g ( n ) + e eq.5.2 where f/y^is the observed C P U E at year or period i, by gear j, and at fishing area k. a, B and y are effects due to change in stock abundance, difference in fishing gear, and difference in fishing areas, e is the normally distributed observation error (because of the logarithmic transformation of the variables). The change in the abundance effect (a) between periods provides an estimate of the relative change in standardized C P U E (index of stock abundance) (Hilborn & Walters 1992). Confidence limits of the change in C P U E were obtained from the estimated standard errors of the G L M : 95% confidence intervals = log(\u00C2\u00AB \u00C2\u00B1se-\ .96) eq.5.3 For each dataset, the assumption that C P U E is log-normally distributed was tested by repeating the G L M analysis with Gaussian distribution. The use of a log-transformation 146 2 would be justified i f the goodness-of-fit (R ) from the log-normal G L M was higher than the alternative model. 5.2.4. Intrinsic vulnerability and rate of decline Indices of intrinsic vulnerability of the 17 taxa were calculated based on their life history and ecology using a fuzzy expert system (Cheung et al. 2005; Chapter 2). The life history and ecology data included maximum length, age at first maturity, longevity, von Bertalanffy growth parameter K, natural mortality rate, fecundity (only low fecundity, i.e. annual total fecundity < 100 eggs or pups-year\"1, was considered), geographic range, and an arbitrary score on the strength of their aggregation behaviour (see Chapter 2 for details). These data were obtained from published literature and FishBase (www.fishbase.org) (Table 5.2). The estimated index was in a scale of 1 to 100 (100 being maximum vulnerability). When the tax on was composed of more than one species, its intrinsic vulnerability was assumed to be the median of the vulnerabilities of species belonging to the taxon. Correlation between C P U E changes and intrinsic vulnerability was tested. The decline in standardized C P U E of the 17 taxa between the mid 1970s and late 1980s was regressed against their estimated indices of intrinsic vulnerability. If the two factors were correlated, the intrinsic vulnerabilities could be used as a rough predictor of relative C P U E decline between fishes that were exploited at similar level. When the correlation between C P U E changes and intrinsic vulnerability was significant, the relative status of demersal species in N S C S was extrapolated by their estimated index of vulnerability. As demersal trawling was the major fishing activity on the continental shelf of N S C S , it was assumed that demersal and benthopelagic species were subjected to similar level of fishing pressure. Thus, their overall changes in C P U E between the 1970s and 1980s were likely to follow similar relationship between the index of vulnerability and the C P U E changes of the 17 studied taxa. The list of demersal and benthopelagic fishes in N S C S and their life history data were obtained from FishBase (www.fishbase.org). Indices of intrinsic vulnerability were then estimated. Based on the relationships between the intrinsic vulnerability and C P U E change, the status of these fishes could then be predicted. 147 Table 5.2. Data on life history and ecology traits of the 16 taxa reported in the catch and effort survey. Details on each parameter were described in Chapter 2. Taxon Lmax (cm) T (year) T 4 max (year) K (year\"1) M (year\"1) Fecundity* (eggs year\"1) Spatial behaviour Geograpahic range (km2) Yellow croaker Larimichthys crocea 80 2.3 11 0.27 - - 80 5,217 Slate cod croaker Protonibea diacanthus 150 2 9 0.33 0.83 24,613 Other croakers Atrobucca nibe 45 4.4 19.1 0.15 - - - 21,548 Nibea semifasciata 24 - - - 5,303 Nibeasoldado 60 - - 16,730 Otolithes ruber 90 1.3 5.6 0.51 - - - 30,931 Pennahiaanea 30 0.6 2.2 1.27 - - - 13,889 Pennahia macrocephalus 23 - - - 13,116 Two-spotted red snapper Lutjanus bohar 90 2 9 0.33 - - 40 49,090 Golden threadfin bream Nemipterus virgatus 35 1.6 6 0.45 - - - 18,106 Flathead Platycephalus indicus 100 1.5 7 0.41 - - - 30,974 Groupers' Epinephelus bruneus 128 6.6 32 0.09 - - - 5,347 Scads Alepes djedaba 40 1.2 4.7 0.61 - - 80 37,652 Decapterus russelli 45 1.1 4.4 0.65 - 50,905 90 35,363 Bigeyes Priacanthus tayenus 35 1.1 4.2 0.68 - - 24 22,529 P. macracanthus 30 0.6 2.2 1.31 - - 32 24,955 Table 5.2 Con't Taxon (cm) T * m (year) T A max (year) K (year1) M (year\"1) Fecundity Spatial behaviour Geograpahic range (km2) Lizardfish Saurida tumbil Saurida undosquamis Synodus variegatus Trachinocephalus myops 60 50 40 40 1.1 1.1 1.7 0.4 4.4 4.4 6.6 1.8 0.65 0.65 0.43 1.6 - - - 26,531 28,037 47,761 54014 Red snapper Lutjanus argentimaculatus 150 3.2 15.2 0.19 - - 6 49,979 Hairtail Trichiurus lepturus Trichiurus nanhaiensis 58.9 60.2 1.7 7 0.167 0.207 - - 30 85,613 White pomfret Pampus argenteus 60 2.8 12 0.24 - - 95 17,844 Melon seed Psenopsis anomala 30 1.4 5.2 0.54 - - - 3,861 Sharks Chiloscyllium griseum Cliiloscyllium plagiosum Chiloscyllium punctatum 74 83 104 - -- - --21,001 19,133 22,629 Skates and rays Dasyatis akajei Dasyatis kuhlii Himantura gerrardi 200 70 200 3.3 1.4 3.3 17.1 6.2 17 0.17 0.46 0.17 - 10 2 - 7,544 32,146 18,378 Red goatfish Upeneus molluccensis 20 0.8 2.9 0.97 111,600 95 29,702 * Fecundity estimates are either not available or over 100 eggs year'1 for most species without fecundity input. The fuzzy expert system does not consider fecundity of over 100 eggs year'1 to have any effect on the intrinsic vulnerability (see Chapter 2). 5.3. Results 5.3.1. Fishing effort distribution Distributions of fishing efforts of stern and pair trawlers changed from the 1970s to the 1980s (Figure 5.5). During the early 1970s, the effort of stern trawlers distributed mainly along the coast of Guangxi and southern Guangdong provinces. The effort distribution of pair trawlers was similar, but they spread further offshore and to the south around Hainan Island. Since then, effort distributions expanded along the coast. The effort of stern trawlers shifted to northeast in the late 1970s and early 1980s, while the effort of pair trawlers spread slightly eastward to the Taiwan Strait and southward to southern Hainan. Towards the later half of the 1980s, fishing effort of sterns trawlers moved backed to the south, which fished mostly around the Pearl River Estuary and the southwest of Hainan. Distribution of pair trawler efforts remained relatively stable with a slight shift to the south in the late 1980s. 5.3.2. Changes in C P U E The G L M with time, fishing gears and fish areas as explanatory variables significantly explained the variations of the log-transformed C P U E s for all taxa. The median R~ from the log-normal G L M of all the taxa was 0.47 (lower and upper quartiles = 0.45 and 0.52, respectively). The log-normal G L M performed considerably better (i.e., higher R~) than the alternative model with Gaussian error. The median R for the alternative model was only 0.33 (lower and upper quartiles = 0.25 and 0.44, respectively). This supported the use of log-normal transformation in the analysis. Thus, results from the log-normal G L M were used throughout this thesis. 150 1986 1987 Figure 5.5a 151 1986 1987 Figure 5.5 Percentage distribution of sampled fishing effort of (a) stern trawlers and (b) pair trawlers from the government survey from 1973 to 1987 (original data records for 1977 and 1988 are missing). The original data were obtained from the Hong Kong government fisheries survey (AFCD, unpublished data). 152 Among the 17 studied taxa, C P U E of 15 taxa declined by more than 70% on average from the mid 1970s to late 1980s, while the average decline was more than 80% (Figure 5.6a). Skates and rays (Rajidae and Dasyatidae) suffered the greatest decline (99%). Other taxa that showed declines over 85% included yellow croaker, red goatfish, red snapper, slate-cod croaker and sharks, while those with the least decline (<70%) included other croakers, bigeyes, golden threadfin bream and melon seed. Confidence intervals for the estimated declines were generally wide, with average 95% confidence intervals of +26% and -8% from the mean. The most uncertain estimates were other croakers, melon seed, golden threadfin and bigeyes, with the ranges between the upper and lower limits of over 60% (Figure 5.6). The decline of C P U E for all taxa could also be demonstrated from the time-series of standardized C P U E (Figure 5.7). When the standardized C P U E time-series of each of the 17 taxa were linearly regressed, significantly negative slopes were obtained for all cases. C P U E declined by 4% to 16% per year (8% on average) from 1973 to 1988. Percentage decline in CPUE 0 20 40 60 80 100 Other croakers Melon seed Golden threadfin Bigeyes Hairtail Groupers Lizardfish Flathead White pomfret Sharks Slate cod croaker Scads Two-spotted red snapper Red snapper Yellow croaker Red goatfish Skate and rays -+- \u00E2\u0080\u00A2+-Figure 5.6. Average decline in CPUE of 17 commercially exploited taxa in NSCS from the mid-1970s to late 80s (black dots) estimated from spatially interpolated data. The solid lines mark the 95% confidence intervals. 153 1.5 3 a. U 1.0 TJ TJ 0.5 c re 55 0.0 Bigeyes 72 74 76 78 80 82 84 86 88 1.5 re 0.5 in 0.0 Hairtail \u00E2\u0080\u00A2 72 74 76 78 80 82 84 86 1.2 i UJ 1.0 -0. o 0.8 -TJ fi) N 0.6 -TJ re TJ 0.4 -c re 55 0.2 -0.0 -Flathead 1.4 ui 12 & 1 0 0 0.8 N 1 0.6 re 1 0.4 -I 0.2 0.0 in Lizardfish 72 74 76 78 80 82 84 86 88 72 74 76 78 80 82 84 86 2.0 1.5 1.0 A \u00E2\u0080\u00A2 re 0.5 H 55 o.o Golden threadfin \u00E2\u0080\u00A2 bream 72 74 76 78 80 . 82 84 86 3.5 ui 3.0 & 2.5 ^ \u00C2\u00BB 2.0 H N 1 1.5 re \u00E2\u0080\u00A2o E 1.0 0.5 0.0 in Red snapper 72 74 76 78 80 82 84 86 88 3.5 w 3.0 & 2.5 ? 2.0 N 1 1.5 n 1 1.0 re 55 0.5 0.0 Groupers 72 74 76 78 80 82 84 86 Year 3.0 S 2.5 a. O 2.0 TJ a \u00C2\u00A3 1-5 5 1.0 -l c re 55 0.5 0.0 Two-spot red snapper 72 74 76 78 80 82 84 86 Year Figure 5.7. 154 cfq' C -t CD u\ o o D Standardized CPUE o o o o o - * - * - * Standardized CPUE o o Q. O EC ct> 6 ' 00 7T Standardized CPUE o \u00E2\u0080\u0094* ro c*i b b b b T3 O Standardized CPUE o o . ro b cn b bi b 2L o on Standardized CPUE Standardized CPUE 2.0 -, Slate-cod croaker UJ 0.0 72 74 76 78 80 82 84 86 88 Year Figure 5.7. Standardized CPUE of demersal fish in the NSCS. Dotted line represents the result from linear regression of the time-series. Estimated declines in C P U E of some taxa were sensitive to the assumptions of the spatial interpolation (Figure 5.4). When alternative statistical models and assumptions in spatial interpolations were used, the mean declines in C P U E varied by less than 30% for most groups, with the largest C P U E declines occurring in sharks, skates and rays, and slate-cod croaker. However, taxa with smaller declines in C P U E showed high sensitivity to model assumptions, particularly bigeyes, groupers, golden threadfin bream and melon seed (Figure 5.8). 156 200 -i Figure 5.8. Sensitivity of the estimated CPUE decline between the early 1970s and late 1980s from different statistical assumptions in analyzing the spatial data. The sensitivity is represented by the difference between the maximum and minimum estimated change in CPUE. 5.3.3. Intrinsic vulnerability against C P U E changes The index of intrinsic vulnerability of the 17 taxa ranged from 19 to 65 (most vulnerable = 90). Taxa with the highest vulnerability index were skates and rays, followed by red snapper, yellow croaker and slate-cod croaker. Taxa with the least vulnerability index included bigeyes, other croakers and melon seed. The index of vulnerability was significantly negatively correlated with C P U E change (log-transformed) between the early 1970s and late 1980s (Figure 5.9). Linear regression analysis showed that the indices of vulnerability explained about 50% (adjusted R2 = 0.501) of the variations in C P U E between the 17 taxa (Table 5.3). The slope of the regression line was significantly negative at 95% confidence level (P = 0.001, Table 5.4). The skates and rays group showed the greatest decline among all taxa, but removing it from the analysis did not alter the significance of the relationship. Moreover, correlation between vulnerability and rate of decline continued to be significant when the original spatially disaggregated C P U E data (30' x 30' grids) without interpolation were used (P = 0.005). 157 0 i Intrinsic vulnerabil i ty index Figure 5.9. Linear regression analysis of change in CPUE (log) and the estimated index of intrinsic vulnerability (R2 = 0.5, P = 0.001). Removal of the data point for skates and rays (open circle) does not affect the significance of the relationship (P = 0.002). Table 5.3. Analysis of variance (ANOVA) with index of vulnerability and estimated CPUE change being the independent and dependent variables respectively. Df -degree of freedom. Model Sum of squares Df Mean square _ F Sig Regression 10.987 1 10.987 17.087 0.001** Residual 9.645 15 0.634 Total 20.632 16 - -Table 5.4. Linear regression model between the index of intrinsic vulnerability and the estimated CPUE change (n = 17). Factor Coefficient Standard error t-value P value Intercept 0.407 0.682 0.596 0.560 Intrinsic vulnerability -0.0629 0.015 -4.134 0.001** 158 5.3.4. Intrinsic vulnerability of demersal and benthopelagic fish in NSCS A n index of intrinsic vulnerability of 176 species (belonging to 62 families) of the demersal and benthopelagic non reef-associated fishes were evaluated. Species with the highest index of vulnerability (= 90) included Bahaba taipingensis (Sciaenidae), Lamiopsis temminckii (Carcharhinidae), Carcharhinus hemiodon (Carcharhinidae), Eusphyra blochii (Sphyrnidae), Hemipristis elongate (Hemigaleidae) and Muraenesox cinereus (Muraenesocidae). O f the 22 species with a vulnerability index over 60, over 50% (n = 12) were elasmobranchs (sharks and rays), while the remaining belonged to the families Sciaenidae, Muraenesocidae, Serranidae, Cynoglossidae and Sparidae. 5.4. Discussion This study showed that C P U E of the majority of the 17 studied taxa declined greatly in 15 years in the N S C S . Three possible reasons that could explain the declines include the following: (1) increased mortality from fishing, (2) environmental changes, (3) observation error and the non-proportionality of C P U E to abundance. The first two possibilities assume that C P U E is proportional to abundance and the results would then indicate a genuine decline in populations. The third explanation is dependent on the validity of the C P U E assumption as discussed above. 5.4.1. Increased fishing mortality During the period of 1973 to 1988, nominal fishing effort and fishing power in the region increased 13- and 7-fold, respectively (Department of Fishery, Ministry of Agriculture, The People's Republic of China 1991, 1996, 2000). With improvements in fishing power and technology, fishing extended to most of the area of the N S C S shelf before the 80s. Fishing mortalities of commercially valuable species in the 80s and 90s were estimated to be very high, with exploitation rates (F/Z) ranging from 0.5 to 0.7 for major commercial taxa (Jia et al. 2004). At the same time, total demersal biomass in N S C S declined by over 50% from the 60s to the 90s, which agrees with the estimated declines for major demersal groups in this study. 159 The estimated declines in C P U E also agree well with other estimates on local and global declines in fish abundance. The South China Sea Fisheries Institute (SCSFI) conducted trawl surveys in the Gulf of Tonkin - the south-western part of the N S C S - in 1962 and the 1990s. Estimated density of major commercial species, including the 17 taxa in this study, generally declined by more than 80% during this period 6. Most species with relatively more vulnerable life history such as sharks and rays, slate-cod, yellow croaker and red snapper, had the greatest declines and species composition became dominated by less vulnerable species (see Chapter 6 for comparisons between possible ecosystem structure in N S C S between the 1970s and 2000s). These species generally have larger body size, later maturation or higher longevity. Particularly, the collapse of the yellow croaker (Larimichthys crocea) has been well documented. It was one of the most important targeted species in China (including Hong Kong, Macau and Taiwan) between the 1950s and early 1980s. Using data independent from the present study, yellow croaker catches from China dropped by 99% from the 1970s to 1990s (Liu & Sadovy, unpublished data), a value that matches the decline in C P U E from this study. Yel low croaker that is currently sold in the market is mostly cultured while the wild-caught yellow croaker has become an expensive delicacy (Liu & Sadovy, unpublished data). The C P U E decline of red goatfish (Upeneus moluccensis) was larger than expected (96%) considering its relatively low intrinsic vulnerability (= 39). This species mainly inhabits coastal muddy substrate where trawling effort is concentrated. Its benthic nature might also make it more vulnerable to demersal trawl gear. These factors were not considered in calculating the index of intrinsic vulnerability and might be a reason for the discrepancy between the observed decline rate and those predicted by the intrinsic vulnerability index. Declines of vulnerable taxa in N S C S agree with the estimates for average global decline of large predatory fish (Christensen et al. 2003; Myers & Worm 2003). Global populations of large predators such as large sharks, skates and rays and large tuna were suggested to have declined at an average rate of 80% in 15 years as a result of over-6 Data from the surveys were classified by the Chinese authorities and thus the exact figures could not be cited in this study (Prof. Jia Xiaping, Director of the South China Sea Fisheries Institute, pers. com.). 160 exploitation (Myers & Worm 2003). Although validity of the estimated declines to the studied taxa has been contested and alternative estimates yield more conservative declines (Sibert et al. 2006), the estimates for the adult populations of these large predators are still dramatic. Also, another study estimated that biomass of high-trophic level fishes in the North Atlantic had declined by two-third from 1950 to 1999. Declines of major demersal fishes that are largely predatory species in N S C S (80-99% in 15 years) fall in the upper extreme of the range of values from these studies. Further support on the effects of fishing was obtained from ecosystem simulation modelling (Chapter 7). In Chapter 7, changes of N S C S from the early 70s to late 80s were simulated using observed fishing effort changes. B y comparing the changes in abundance of ecosystem groups with observed C P U E time-series, the results suggested that fishing alone could cause the observed decline of abundance of exploited fish. The evidence presented above suggests that fishing is likely to be the major factor leading to the decline in C P U E . 5.4.2. Environmental changes Natural and human-induced environmental changes may also contribute to the decline in resource abundance. These included changes in primary productivity induced naturally by climate variations, or anthropogenically through eutrophication from pollutions, habitat damage and destruction from coastal development and uses of destructive fishing gear (e.g., bottom trawling, blast fishing). For instance, Qiu et al. (unpublished manuscript) showed that catches of several commercial taxa in N S C S in the past five decades correlated with climate and environmental variables such as monsoon strength, sea surface salinity, and precipitation, besides fishing. However, taxa that had strong correlations between their catches and environmental factors were mainly small pelagic fish and invertebrates which were not included in this study (Qiu et al. unpublished manuscript). Given the general decadal decline in fishery resources in N S C S and commercial taxa in the Gulf of Tonkin, natural environmental fluctuations are likely to be secondary compared to the direct and indirect effects of fishing. The extent to which human-induced environmental changes contributed to the declines has not been quantified. In future studies, proxies for the degree of coastal development or habitat 161 damage may be included as factors in the analysis to evaluate their relative contributions to the change in C P U E . 5.4.3. Observation error and the non-proportionality of CPUE to abundance The original survey that collected the catch and effort data was interview-based and fishery-dependent, thus considerable errors were inherent in the C P U E data. Firstly, the survey only covered a sub-set of fishing boats in the region. Fishing performance might differ between fishing boats and skippers. Thus sampling bias may have skewed the estimated change in C P U E . For instance, i f fishing boats that performed relatively better (e.g., more experienced skipper, boats with better equipment) were included in interviews during the earlier period, and poorer performing boats in the latter period, the decline in C P U E might be over-estimated. A possible way to diagnose this error is to look at the distribution of reported landings among boats fishing within the same cell and compare the variance between years. Systematic changes in variance structure between years may suggest biases in the standardized C P U E . Since the original C P U E data only included aggregated records from the surveyed skippers or boats, such validation exercise was not possible in this study. Nevertheless, the overall strong trend of C P U E declines shown in this study should not be greatly affected by such uncertainty. Also, the estimated C P U E for different taxa were subjected to similar biases, thus the validity of comparisons between taxa should not be greatly compromised, although the exact values of decline rates for individual taxon should be taken with a grain of salt. Errors may also have resulted from the spatial interpolation of the C P U E data. Spatial C P U E data depended on where the sampled commercial fishing boats fished, which were non-randomly distributed and had changed during the survey periods. Thus estimating time-series C P U E depended strongly on the assumptions of C P U E in unfished cells. Here, unfished spatial cell was assumed to have similar C P U E as its surrounding cell, and the same area in the next consecutive years. Commonly-used quantitative fishing effort dynamic models such as the ideal free distribution model (Gill is 2003) or the gravity model (Caddy 1975) predict that cells with similar C P U E would be fished at similar rate given similar fishing cost. Thus, it was implicitly assumed in this study that incomplete coverage of all fishing boats in the sample resulted in the absence of data in 162 those cells. As such, cells without C P U E data were given an average C P U E from the surrounding cells or proceeding years. On the other hand, it was possible that the cell with no data had low potential C P U E (perceived by the fishers) in that area and thus were uneconomical for the fishers to fish). This was especially likely when fishing led to range collapse following depletion (Pitcher 1995; Pitcher 1997). However, this assumption may increase the chance of over-estimating the decline in C P U E (i.e., hyper-depletion). Repeating the analysis using alternative assumptions in spatial interpolation suggested that estimated declines of vulnerable species were consistent, while the less vulnerable species were more sensitive to assumptions on spatial interpolation. Therefore, the large declines of vulnerable taxa estimated in this study are based on robust estimates. Changes in abundance may not be proportional to C P U E . Catchability is likely to increase with improved fishing technology, better boats and experience (Hilborn & Walters 1992). Thus, C P U E may appear to be stable when stock abundance declines. However, since this analysis did not correct for such changes in catchability, the estimated decline in C P U E may actually underestimate the declines in abundance.Overall, although the aforementioned errors may bias the C P U E estimates, they were unlikely to solely explain the strong declines in C P U E obtained from this study. Over-exploitation should be a major reason that explains the declines in C P U E of the 17 taxa. 5.4.4. Status of fishery resources in the NSCS This study showed that the abundance of traditional food fishes in the N S C S has been severely over-exploited with the abundance index of major food fishes declining by about 80% in slightly over a decade. As fishing effort continued to expand from the 80s to now, abundance is likely to decrease further from the 1980s' level. Large predators such as sharks and rays suffered the most in abundance decline. These agreed with the qualitative descriptions from interviewing fishers in Hong Kong (Cheung & Sadovy 2004), and quantitative results from comparing ecosystem structure between the 70s and 2000s (Chapter 6). This also confirms the analysis of Pang & Pauly (2001). The rate of decline of the exploited fishes in the N S C S is also consistent with findings elsewhere in the world. Meta-analysis of global declines in predatory fishes (e.g., sharks and tunas) revealed a rate of decline of about 80% within 15 years of exploitation 163 (Myers & Worm 2003) - an estimate close to the findings here. Moreover, elasmobranchs had previously been shown to be highly vulnerable to exploitation as a result of their life history and ecology (Dulvy et al. 2000; Stevens et al. 2000). In fact, this analysis suggested that population of skates and rays might have collapsed during the study period and it is highly likely that the populations had little recovery over the past two decades. During the latest survey by the South China Sea Fisheries Institute, elasmobranchs were rarely caught in inshore waters (less than 40 m deep), while the overall catch rate in the 1990s had declined by over 80% comparing to survey conducted in the 1960s (Jia et al. 2004). Using the index of intrinsic vulnerability as a predictor of C P U E decline suggests that other large predatory species may have been depleted at a similar rate. The most vulnerable fish families, as shown from the index of vulnerability, include groupers, large croakers and elasmobranchs among others. These taxa were well represented in fishery catch. Among these groups, some highly vulnerable species such as the Chinese bahaba had already been extirpated or severely depleted (Sadovy & Cheung 2003). Given similarly high fishing pressure on all these species, their status may be similar to the Chinese bahaba. If population status of these vulnerable taxa does parallel the status of the bahaba, further conservation and research efforts from local authorities and the international communities should be dedicated to the N S C S region. The large decline in demersal fish assemblages should have resulted in considerable changes in ecosystem. In fact, such changes have already been observed along the coast of the N S C S . For instance, the Hong Kong marine ecosystem changed from being dominated with long-lived, K selected species to one with mostly small pelagics and benthic invertebrates that have fast turn-over rate (Pitcher et al. 2002a; Pitcher et al. 2002b; Buchary et al. 2003; Cheung & Sadovy 2004). Such changes do not only dissipate direct economic benefits to the society (Chapter 8) but may also adversely affect ecosystem functions (Worm et al. 2006). Changes of the N S C S ecosystem between the 1970s and 2000s are evaluated in Chapter 6 of this thesis. Despite the high vulnerability and the potential conservation concerns of the exploited species, fishery and biological information on these species were insufficient. Catch of almost all of these species were not reported separately as the data are 164 aggregated into groups. The problem was amplified by the inaccuracy of landing statistics in recent decades (Watson & Pauly 2001). Moreover, fishery and biological studies focused mainly on commercially important species while knowledge on more vulnerable, although less economically important, species was scare. Thus, more resources should be given to conserve and better understand and monitor them. The intrinsic vulnerable index can be used as one of the tool to identify key taxa for conservation and research. However, improved monitoring and more accurate fisheries statistics are still essential for effective management of fisheries resources 165 5.5. References Agnew, D . J., Nolan, C. P., Beddington, J. R. & Baranowski, R. 2000 Approaches to the assessment and management of multispecies skate and ray fisheries using the Falkland Islands fishery as an example. Canadian Journal of Fisheries and Aquatic Science 57, 429-440. 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Watson, R. & Pauly, D . 2001 Systematic distortions in world fisheries catch trends. Nature 414, 534-536. Worm, B . , Barbier, E . B . , Beaumont, N . , Duffy, J. E . , Folke, C , Halpern, B . S., Jackson, J. B . C , Lotze, H . K . , Miche l i , F. , Palumbt, S. R., Sala, E . , Selkoe, K . A . , Stachowicz, J. J. & Watson, R. 2006 Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787-790. 169 6. ECOSYSTEM MODELLING OF THE NORTHERN SOUTH CHINA SEA FOR THE 1970S AND 2000S 6.1 Introduction The northern shelf of the South China Sea (NSCS) is a tropical ecosystem with a very diverse fauna and flora (Morton & Blackmore 2001). Over 900 species of fishes (Ni & K w o k 1999), at least five species of marine turtles (Marque, 1990), 8 species of marine mammals (Jefferson et al. 1993) and hundreds of invertebrates (Jia et al. 2004) have been recorded from the area. The N S C S also features diverse habitats including coral reefs, estuaries, mangroves and seagrass beds (Morton & Blackmore 2001). The N S C S ecosystem provides important fishery resources which are exploited mainly by trawls (demersal, pelagic and shrimp), gillnets, hook and line, purse seine and other fishing gears such as traps. The continental shelf (i.e., areas less than 200 m depth), ranging from 106\u00C2\u00B053'-119\u00C2\u00B048' E to 17\u00C2\u00B010'-25\u00C2\u00B052' N , falls largely within the Exclusive Economic Zone of the People's Republic of China (PRC), but Vietnam also shares part of the Gulf of Tonkin. This study focuses mainly on the shelf within the Chinese E E Z (Figure 6.1). Figure 6.1. Map of the northern South China Sea, emphasizing the study area (from the coast to the broken line). As fishers from the PRC and Vietnam traditionally fish in the Gulf of Tonkin, the PRC and Vietnam governments agreed to establish common fishing zone in the Gulf of Tonkin, at least for a limited period (Vietnam-China Tonkin Gulf Fishing Co-operation Agreement). 170 Over the past five decades, a dramatic expansion of the fishing fleets, accompanied by mechanization and other technological advancements, has resulted in over-exploitation of fisheries resources (Shindo 1973; Pang & Pauly 2001; Cheung & Sadovy 2004). Following an over 8-fold increase in fishing effort from 1970 to 2000 (Department of Fishery Ministry of Agriculture, The People's Republic of China 1996, 2000), total landings from Chinese fishing fleets in the regions increased from 570,000 to 3,400,000 tonnes. On the other hand, catch-per-unit-effort ( C P U E - a rough index of resource abundance) dropped by more than 70% from 1986 to 1998 (Lu & Y e 2001). If over-reporting of landings by China is considered (Watson & Pauly 2001), the decline in catch rates becomes even stronger. Most traditional large-sized food fish have been depleted and catches are now dominated by small-sized, high turnover rate species (Cheung & Sadovy 2004; Jia et al. 2004). In Chapter 5 of this thesis, I found that the C P U E of 17 commercial demersal taxa in the N S C S declined by over 70% from the early 1970s to the late 1980s. In fact, numerous species with high intrinsic vulnerability to exploitation have probably been extirpated locally or regionally by fishing. For instance, the Chinese bahaba (Bahaba taipingensis, Sciaenidae), endemic to the coast of China, is nearly extinct (locally and globally) as a result of over-exploitation (Sadovy & Cheung 2003). The previously abundant Red grouper (Epinephelus akaara, Serranidae) and some other large reef-associated fishes in Hong Kong have disappeared in commercial catches (Sadovy & Cornish 2000). Non-target species such as skates and rays have also been largely depleted, especially in the heavily fished coastal areas (Sadovy & Cornish 2000; Jia et al. 2004). Over-exploitation and extirpation of these species have altered the ecosystem structure in the N S C S . Such ecosystem changes raise serious fishery management and biodiversity conservation concerns. To properly manage the fisheries resources, restore the ecosystem and conserve the threatened species in the N S C S , it is important to understand the ecosystem effects of fishing (e.g., trophic interactions, bycatch and habitat destruction) (Botsford et al. 1997; Pitcher & Pauly 1998; Pitcher 2001; Kaiser et al. 2003). Ecosystem modelling is a useful tool for such purposes (Cochrane 2002). It can generate alternative hypotheses about ecosystem structure, and on the interactions among biological groups and with the 171 fisheries (Trites et al. 1999; Butterworth 2000; Christensen & Walters 2004a). Particularly, comparing models of the past and present ecosystem can reveal changes in ecosystem structure and dynamics, and help diagnose management problems (Buchary et al. 2003; Pitcher 2004; Bundy 2005; Pitcher et al. 2005). In addition, dynamic ecosystem simulations allow explorations of the effects of fishing, environmental changes, and fishery management policies (Walters et al. 1997; Cochrane 2002; Christensen & Walters 2004a). Such models can identify important ecological indicators and critical information gaps for efficient use of limited resources for ecological monitoring and field studies (Walters 2000; Walters et al. 2000; Cochrane 2002b; Walters & Martell 2004; Cheung & Pitcher 2006). Ecopath with Ecosim was used as the modeling approach in this study (Polovina 1984; Pauly et al. 2000; Christensen & Walters 2004). Ecopath is a steady-state, mass-balance model which can be used to describe a snap-shot of the whole ecosystem at a particular time period. Species, usually those with similar biology and ecology, are aggregated into functional groups to reduce the number of modelled units. The model is governed by the mass-balance principle which is based on two basic equations. The first one ensures balance between production, consumption, predation, fishery, migrations and other mortalities among functional groups: (P/B), \u00E2\u0080\u00A2 5, \u00E2\u0080\u00A2 (1 - EE, )-Br(QI B)J \u00E2\u0080\u00A2 DC- Yl- E, - BAi = 0 eq. 6.1 The second equation ensures balance between consumption, production and respiration within a group: Qi=Pi+Ri+GErQi eq. 6.2 where (P/B)j is the production to biomass ratio; B, the total biomass; EE, the ecotrophic efficiency (l-EEt represents mortality other than predation and fishing); y, the total catch; Ej the net migration; BA, the biomass accumulation of functional group i; (Q/B)j are consumption to biomass ratio for predator groups j; DCjj is the proportion of group i in the diet of predator groups j ; R is respiration while GE is the proportion of unassimilated food (Christensen & Walters 2004). 172 The model maintains mass-balance by solving equations 6.1 and 6.2 for all groups simultaneously. Thus any of the four basic input parameters (B, P/B, Q/B, EE) in each group has to be estimated to ensure mass-balance. Since it is difficult to measure EE empirically, it is usually estimated through the mass-balance process when other input parameters are available. In the case where data for B, P/B or Q/B are unavailable, EE is often assumed to be 0.95 in a heavy exploited ecosystem (Christensen et al. 2004). Ecosystem models of a number of sub-systems of the N S C S have been built. These include models of Hong Kong waters (Buchary et al. 2003; Cheung & Sadovy 2004), and coastal waters of the N S C S (10-50 m) between Cambodia and China (Pauly & Christensen 1995b). A l l of these studies used Ecopath with Ecosim as the modeling platform. Attempt to model the entire N S C S ecosystem have not previously been undertaken. In this chapter, I attempt to describe the past and present status of the N S C S ecosystem using a mass-balance modelling approach (Ecopath with Ecosim) (Christensen & Walters 2004). Models of the early 1970s and 2000s were constructed based on published literature, unpublished reports from government surveys and global databases. B y comparing the structure and dynamics of the past and present systems, I evaluated the ecosystem changes over the past three decades. Parameter uncertainty was addressed through the 'pedigree' of the model, and analyses of its sensitivity. 6.2 Methods 6.2.1. Model structure and parameterization Using Ecopath, two ecosystem models of the N S C S representing the status in the early 1970s and 2000s (hereafter called the 1970s and 2000s models) were constructed. In the 1970s and 2000s N S C S models, species were aggregated based on their commercial importance, body size, ecology, and the available data. Each model had 38 functional groups composed of 2 primary producers, 10 invertebrates, 21 fishes, 2 marine mammals, 1 marine turtle and 1 seabird groups (Table 6.1). The 8 commercially-important functional groups of fishes included threadfin breams (Nemipteridae), hairtails 173 (Trichiuridae), pomfrets (Stromateidae), Iizardfishes (Synodontidae), groupers (Serranidae), snappers (Lutjanidae), croakers (Sciaenidae), and melon seed (Centrolophidae). Other species were aggregated by their maximum body size (i.e., small < 30 cm T L and large > 30 cm T L ) and ecology (demersal, pelagic or benthopelagic). To represent the difference in ecology between juvenile and adult stages, some groups with longer life-span were split into juvenile and adult stanzas using the multi-stanza routine (Christensen et al. 2004). These groups included: hairtail, large croakers (> 30 cm T L ) , large demersal fish (> 30 cm T L ) and large pelagic fishes (> 30 cm T L ) . 174 Table 6.1. Functional groups and their basic parameters used in the Northern South China Sea ecosystem models for (a) the 1970s and (b) the 2000s states. Values in parentheses were estimated by the model. (a) Basic parameters of the 1970s NSCS model Group no. Functional group B P/B Q/B EE 1 Phytoplankton 323 399 - (0.035) 2 Benthic producer 153 11.89 - (0.020) 3 Zooplankton 33.8 32 192 (0.052) 4 Jellyfish (0.146) 5.00 20.0 0.950 5 Polychaetes (3.421) 6.75 22.5 0.950 6 Echinoderms 3.065 1.20 3.58 (0.398) 7 Benthic crustaceans 2.649 5.65 26.9 (0.624) 8 Non-ceph molluscs 13.747 3.00 7.00 (0.383) 9 Sessile/other invertebrates 3.114 1.00 9.00 (0.845) 10 Shrimps (0.422) 5.40 28.9 0.950 11 Crabs (0.731) 3.00 12.0 0.950 12 Cephalopods (0.465) 3.10 8.00 0.500 13 Threadfin bream (nemipterids) 1.04 0.74 8.10 (0.469) 14 Bigeyes (priacanthids) 0.318 1.21 11.3 (0.328) 15 Lizard fish (synodontids) 0.30 2.30 5.41 (0.241) 16 Juvenile Hairtail (trichiurids) 0.034 2.30 13.41 (0.329) 17 Adult hairtail (trichiurids) 0.0426 1.50 6.21 (0.327) 18 Pomfret (stromateids) (0.065) 1.30 6.38 0.950 19 Snappers (0.014) 1.34 8.98 0.950 20 Adult groupers 0.040 0.85 6.10 (0.375) 21 Croakers (< 30 cm) 0.289 2.36 11.28 (0.784) 22 Juvenile large croakers 0.0425 2.36 15.65 (0.913) 23 Croakers (> 30 cm) 0.095 1.43 6.23 (0.541) 24 Demesral fish (< 30 cm) (1.541) 2.70 13.03 0.950 25 Juvenile demersal fish (> 30 cm) 0.112 2.60 15.46 (0.599) 26 Adult demersal fish (>30 cm) 0.195 1.44 6.21 (0.355) 27 Benthopelagic fish (0.470) 3.00 15.0 0.950 28 Melon seed 0.114 2.24 24.7 (0.964) 29 Pelagic fish (< 30 cm) (1.050) 2.87 12.22 0.950 30 Juvenile large pelagic fish 0.118 2.87 . 14.37 (0.62) 31 Pelagic fish (> 30 cm) 0.158 0.90 6.28 (0.283) 32 Demersal sharks and rays 0.04 1.26 6.30 (0.364) 33 Pelagic sharks and rays (0.028) 0.39 1.95 0.500 34 Seabirds 0.0022 0.06 67.76 (0.005) 35 Pinnipeds 0.0046 0.045 14.77 (0.679) 36 Other mammals 0.0158 0.112 10.52 (0.068) 37 Marine turtles 0.0002 0.100 3.50 (0.503) 38 Detritus 100 - - (0.017) 175 (b) Basic parameters of the 2000s NSCS model Group no. Functional group B P/B Q/B EE 1 Phytoplankton 323 398 (0.010) 2 Benthic producer 153 11.89 (0.010) 3 Zooplankton 9.0 32.0 192 (0.306) 4 Jellyfish 1.53 5.00 20.0 (0.520) 5 Polychaetes 2.24 6.75 22.5 (0.673) 6 Echinoderms 1.98 1.20 3.58 (0.444) 7 Benthic crustaceans 1.43 5.65 26.9 (0.617) 8 Non-ceph molluscs 2.68 3.50 11.7 (0.951) 9 Sessile/other invertebrates 2.61 1.00 9.00 (0.575) 10 Shrimps (0.194) 7.60 28.94 (0.950) 11 Crabs (0.368) 3.00 12.0 (0.950) 12 Cephalopods 0.68 3.10 8.00 (0.393) 13 Threadfin bream (nemipterids) 0.26 3.08 15.4 (0.847) 14 Bigeyes (priacanthids) 0.13 3.33 11.3 (0.550) 15 Lizard fish (synodontids) 0.032 1.60 5.407 (0.658) 16 Juvenile Hairtail (trichiurids) 0.015 3.08 14.894 (0.749) 17 Adult hairtail (trichiurids) 0.012 1.47 6.207 (0.545) 18 Pomfret (stromateids) 0.108 3.03 (15.15) 0.950 19 Snappers (0.0013) 1.75 8.984 0.950 20 Adult groupers (0.0064) 1.75 6.10 0.950 21 Croakers (< 30 cm) 0.07 3.30 11.276 (0.958) 22 Juvenile large croakers 0.04 3.30 16.366 (0.564) 23 Croakers (> 30 cm) 0.0094 1.43 6.232 (0.587) 24 Demesral fish (< 30 cm) (0.316) 4.70 23.5 0.950 25 Juvenile demersal fish (> 30 cm) 0.143 3.50 16.144 (0.722) 26 Adult demersal fish (>30 cm) 0.021 2.10 6.207 (0.747) 27 Benthopelagic fish 0.922 3.08 15.42 (0.479) 28 Melon seed 0.070 2.41 24.0 (0.994) 29 Pelagic fish (< 30 cm) 1.772 4.26 17.04 (0.740) 30 Juvenile large pelagic fish 0.289 4.26 16.12 (0.622) 31 Pelagic fish (> 30 cm) 0.079 1.40 6.27 (0.759) 32 Demersal sharks and rays 0.001 1.20 6 (0.867) 33 Pelagic sharks and rays (0.0011) 0.68 3.4 0.950 34 Seabirds 0.0022 0.06 67.759 (0.046) 35 Pinnipeds 0.0046 0.045 14.768 (0.290) 36 Other mammals 0.0158 0.112 10.523 (0.034) 37 Marine turtles 0.0002 0.10 3.50 (0.300) 38 Detritus 100 - - (0.005) 176 Basic model input parameters were estimated from government surveys, published literature, empirical equations and global databases (see Table 6.1 and Appendix 6.1 for detailed descriptions of parameter estimations). Biomasses of most commercially important groups in the 2000s model were estimated based on trawl and acoustic surveys conducted by the South China Sea Fisheries Research Institute (Jia et al. 2004). In the 1970s model, their biomasses were back-calculated from the observed changes in relative abundance between the 1970s and 2000s as reported in published literature and government reports (see Appendix 6.1 for details). P/B ratios were based on mortality estimates from length-based studies and empirical equations (Pauly 1980). Q/B ratios were estimated from empirical equations (Palomares & Pauly 1998). Diet compositions were based on local surveys (Xu et al. 1994) and the information available from FishBase (Froese & Pauly 2004) (Appendix 6.2). The 2000s ecosystem was exploited by six fleets categorized by their fishing gears: pair and stern trawls, shrimp trawl, purse seine, hook and line, gillnet and 'others'; all fishing gears were aggregated into one fishing fleet in the 1970s model. The specifications of fishing sectors in the 2000s model facilitated the identification of optimal fleet configurations through dynamic ecosystem simulations (Chapter 8). However, the limited resolution of catch data by fishing fleets in the 1970s did not allow segregation of catches by fishing fleet types for that period. On the other hand, this did not affect the comparison of ecosystem structures between the 1970s and 2000s as the models only accounted for the total catches by all fishing fleets. Catches in the 1970s and 2000s model were based on landings statistics reported by the P R C government. However, the P R C landings data have been suggested to be largely over-estimated (Watson & Pauly 2001). The Sea Around Us Project (SAUP) provided (www.seaaroundus.org) catch estimates for China that had been adjusted downward based on a meta-analysis of global fisheries catches (Watson & Pauly 2001). Thus the S A U P data were also used to estimate the catches in the two models (Appendix 6.1). Catches by fishing sectors and functional groups were estimated from national and regional fisheries statistics. Catches from the six fishing sectors in the 2000s model 177 were obtained from the P R C fisheries statistics. The national statistics did not report catch composition by fishing fleets. However, such data were available for Hong Kong fisheries and as fishing vessels and gears used in Hong Kong are typical of those in the N S C S region, species composition by fleets was prorated according to the relative catch compositions of Hong Kong fishing fleets (Pitcher et al. 1998) (Table 6.2). The initial input parameters did not result in a model that met the mass-balance criteria. Thus the input parameters were adjusted iteratively until the model achieved mass-balance i.e., the values of E E of all functional groups were below 1 (Appendix 6.1). The diet composition matrix was the primary parameter adjusted because it was relatively more uncertain than the other input parameters. However, changing the diet composition matrix alone was not enough for the models to achieve mass-balance. Thus the P /B , Q/B and biomass inputs were adjusted based on the relative accuracy of the data sources and the available information. The procedures for defining the relative accuracy of the input parameters are described in a later section. Table 6.2. Estimated fishery catch (t-km'2) by functional groups and gear types in the 1970s and 2000s models. PSt - pair and stern trawl; ShT - shrimp trawl; PS - purse seine; H&L - hook and line; GN - gillnet; Others - other fishing gears; inverts -invertebrates; Ju. - juvenile; Ad. - adult; Dem. - demersal. Catches by functional groups (t-km\"2) Functional groups 1970s 2000s model model Total PSt ShT PS H&L GN Others Total Phytoplankton 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Benthic producer 0.0056 0.0000 0.0000 0.0000 0.0000 0.0000 0.0056 0.006 Zooplankton 0.0100 0.0000 0.0000 0.0065 0.0000 0.0000 0.0883 0.095 Jellyfish 0.0012 0.0126 0.0000 0.0314 0.0000 0.0000 0.0000 0.044 Polychaetes 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Echinoderms 0.0039 0.0001 0.0019 0.0000 0.0000 0.0000 0.0001 0.002 Benthic crustaceans 0.0019 0.0003 0.0388 0.0000 0.0000 0.0000 0.0000 0.039 Non-ceph molluscs 0.0056 0.0763 0.1800 0.0000 0.0000 0.0000 0.5060 0.762 Sessile/other inverts 0.0011 0.0001 0.0029 0.0000 0.0000 0.0000 0.0000 0.003 Shrimps 0.0350 0.0801 0.5760 0.0001 0.0000 0.0000 0.0226 0.679 Crabs 0.0100 0.0400 0.1000 0.0000 0.0000 0.0421 0.0176 0.200 Cephalopods 0.0244 0.1530 0.0243 0.0627 0.0009 0.0000 0.0324 0.273 Threadfm bream 0.0440 0.4590 0.0089 0.0000 0.0651 0.0994 0.0246 0.657 Bigeyes 0.0350 0.1420 0.0029 0.0000 0.0210 0.0321 0.0080 0.206 Lizard fish 0.0840 0.0012 0.0015 0.0000 0.0000 0.0166 0.0041 0.023 178 Table 6.2 Con't Catches by functional groups (tkm 2) Functional groups 1970s model 2000s model Total PSt ShT PS H&L GN Others Total Juv. Hairtail 0.0038 0.0206 0.0005 0.0001 0.0000 0.0054 0.0014 0.028 Ad. hairtail 0.0152 0.0047 0.0000 0.0001 0.0009 0.0012 0.0003 0.007 Pomfret 0.0053 0.0809 0.0076 0.0154 0.0299 0.0843 0.0209 0.239 Snappers 0.0053 0.0003 0.0001 0.0001 0.0002 0.0003 0.0001 0.001 Ad. groupers 0.0029 0.0013 0.0003 0.0000 0.0012 0.0049 0.0012 0.009 Croakers (< 30 cm) 0.0160 0.0159 0.0117 0.0000 0.0003 0.0027 0.0045 0.035 Juv. large croakers 0.0110 0.0371 0.0033 0.0000 0.0060 0.0168 0.0078 0.071 Croakers (> 30 cm) 0.0440 0.0046 0.0000 0.0000 0.0001 0.0032 0.0001 0.008 Dem. fish (< 30 cm) 0.0902 0.0085 0.0760 0.0100 0.0055 0.0262 0.0526 0.179 Juv. Dem. fish (> 30 cm) 0.0288 0.0230 0.2070 0.0100 0.0000 0.0613 0.0152 0.317 Ad. Dem. fish (>30 cm) 0.1150 0.0285 0.0000 0.0000 0.0023 0.0035 0.0009 0.035 Benthopelagic fish 0.0303 0.3420 0.0278 0.0607 0.0010 0.0580 0.1130 0.603 Melon seed 0.0057 0.0284 0.0023 0.0050 0.0000 0.0048 0.0094 0.050 Pelagic fish (< 30 cm) 0.1460 0.5120 0.0000 1.0860 0.0000 0.1030 0.6440 2.345 Juv. large pelagic fish 0.0096 0.2080 0.0000 0.0829 0.0000 0.3750 0.0725 0.738 Pelagic fish (> 30 cm) 0.0380 0.0185 0.0000 0.0074 0.0165 0.0333 0.0064 0.082 Dem. sharks and rays 0.0158 0.0006 0.0002 0.0000 0.0000 0.0002 0.0000 0.001 Pelagic sharks and rays 0.0051 0.0002 0.0000 0.0000 0.0002 0.0003 0.0000 0.001 Seabirds 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Pinnipeds 0.0002 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Other mammals 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Marine turtles 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Detritus 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.000 Total 0.8500 2.2990 1.2740 1.3780 0.1510 0.9750 1.6590 7.736 6.2.2. Uncertainty and sensitivity analysis Uncertainties of the input parameters were specified under a 'pedigree' in the Ecopath with Ecosim package (Christensen et al. 2004). The 'pedigree' is a matrix that allowed systematic categorisation of the reliability of the input parameters - specifically the biomass (B); production to biomass ratio (P/B); and the consumption to biomass ratio (Q/B); the diet composition matrix and the catch of each functional group. In the pedigree routine, a coded statement categorizing the origin (data type and associated uncertainty) of a given input was given to each of these parameters. Inputs were rated based on how they had been derived: local data, other locations, 'best guesses', empirical relationships, other Ecopath models, or estimated by the current model. Associated with each of these categories was an index of data quality which ranged from 0 to 1, with 0 being the lowest quality (estimated by the model while solving the mass4jalance equations) while 1 being the highest quality (e.g. data from a quantitative study conducted in the study area). By summing across these pedigrees, an index (P) of the overall quality of the input information in Ecopath can be calculated: 179 i=ij=l where 7,y is the pedigree index for model group i and parameter/, n is the total number of modelled groups. This index summarizes how well the models are rooted in local data (Christensen et al. 2004). The pedigrees were entered for the 1970s and 2000s model based on the source of the data and the author's knowledge on the accuracy of the sources (Table 6.3). The level of confidence corresponded to each pedigree category was based on the default values in Ecopath (Table 6.4). The indices of uncertainty of the 1970s and 2000s models were compared. 180 Table 6.3. Pedigree categories of the basic parameters used in the Northern South China Sea ecosystem models for (a) the 1970s and (b) the 2000s states. (a) Pedigree for the basic parameters of the 1970s NSCS model Pedigree categories* Functional group B P/B Q/B Diet Catch Phytoplankton 4 4 N/A N/A N/A Benthic producer 4 5 N/A N/A 3 Zooplankton 3 4 4 2 3 Jellyfish 3 4 4 2 3 Polychaetes 4 2 2 1 N/A Echinoderms 4 2 2 1 2 Benthic crustaceans 4 4 2 1 3 Non-ceph molluscs 4 2 2 1 2 Sessile/other invertebrates 4 2 2 1 2 Shrimps 0 4 3 2 3 Crabs 0 2 2 2 2 Cephalopods 0 2 2 2 3 Threadfin bream (nemipterids) 3 3 3 4 3 Bigeyes (priacanthids) 3 3 3 4 3 Lizard fish (synodontids) 3 3 3 4 3 Juvenile Hairtail (trichiurids) 0 1 3 4 3 Adult hairtail (trichiurids) 3 3 3 4 3 Pomfret (stromateids) 0 3 3 4 3 Snappers 0 3 3 4 3 Adult groupers 3 3 3 4 3 Croakers (< 30 cm) 3 1 3 4 3 Juvenile large croakers 1 3 3 2 3 Croakers (> 30 cm) 3 3 3 4 3 Demesral fish (< 30 cm) 0 3 3 4 3 Juvenile demersal fish (> 30 cm) 0 1 3 2 3 Adult demersal fish (>30 cm) 3 3 3 4 3 Benthopelagic fish 0 3 3 4 3 Melon seed 3 3 3 4 3 Pelagic fish (< 30 cm) 0 3 3 4 3 Juvenile large pelagic fish 0 1 3 2 3 Pelagic fish (> 30 cm) 3 3 3 4 3 Demersal sharks and rays 3 3 3 4 3 Pelagic sharks and rays 0 3 3 4 3 Seabirds 2 4 2 2 N/A Pinnipeds 2 4 2 2 0 Other mammals 2 4 2 2 0 Marine turtles 2 2 2 2 N/A 181 (b) Pedigree for the basic parameters of the 2000s NSCS model Pedigree categories* Functional group B P/B Q/B Diet Catch Phytoplankton 5 7 N/A N/A N/A Benthic producer 4 5 N/A N/A 3 Zooplankton 4 5 5 2 3 Jellyfish 3 2 2 1 3 Polychaetes 5 2 2 1 N/A Echinoderms 5 2 2 1 2 Benthic crustaceans 3 4 . 2 1 2 Non-ceph molluscs 3 2 2 1 2 Sessile/other invertebrates 4 2 2 1 2 Shrimps 0 4 3 1 3 Crabs 0 2 2 1 3 \u00E2\u0080\u00A2 Cephalopods 4 2 2 3 3 Threadfin bream (nemipterids) 5 7 3 4 3 Bigeyes (priacanthids) 4 7 3 4 3 Lizard fish (synodontids) 5 7 3 4 3 Juvenile Hairtail (trichiurids) 3 5 3 3 3 Adult hairtail (trichiurids) 4 5 3 3 3 Pomfret (stromateids) 4 5 3 3 3 Snappers 3 5 3 4 2 Adult groupers 3 5 3 4 2 Croakers (< 30 cm) 4 5 3 4 2 Juvenile large croakers 3 5 0 1 0 Croakers (> 30 cm) 3 5 3 4 3 Demesral fish (< 30 cm) 3 5 3 4 2 Juvenile demersal fish (> 30 cm) 3 5 0 2 0 Adult demersal fish (>30 cm) 3 5 3 4 2 Benthopelagic fish 4 5 3 4 2 Melon seed 4 5 3 4 3 Pelagic fish (< 30 cm) 4 5 3 4 2 Juvenile large pelagic fish 2 5 0 0 0 Pelagic fish (> 30 cm) 4 7 3 4 2 Demersal sharks and rays 3 5 0 1 2 Pelagic sharks and rays 3 5 3 1 2 Seabirds 2 1 2 1 0 Pinnipeds 2 1 2 2 0 Other mammals 2 1 2 2 0 Marine turtles 2 2 2 2 N/A * For biomass: 1-estimated by model; 2-frbm other model; 3-guesstimate; 4-approximate or indirect method; 5-sampling-based, low precision; 6-sampling-based, high precision; for P/B and Q/B ratios: 4-empirical equation; 5-similar group/species system; similar system; 6-similar group/species, same system; 7-same group/species, similar system; 8-same group/species, same system; for diet composition: 1-general knowledge of related groups/species; 2-from other model; 3-general knowledge for same group/species; 4-qualitative diet composition study; 5-quantitative but limited diet composition study; 6-quantitative, detailed diet composition study; for catch: 1-guessitimate; 2-from other model; 3-FAO statistics; 4-national statistics; 5-local study; low precision/incomplete; 6-local study; high precision/complete; N/A-Not applicable. 182 Table 6.4. Pedigree indices of the basic parameters used in the Northern South China Sea ecosystem models for (a) the 1970s and (b) the 2000s states. (a) Pedigree indices for the basic parameters of the 1970s NSCS model Pedigree index Functional group B P/B Q/B Diet Catch Phytoplankton 0.00 0.60 \u00E2\u0080\u0094 . . . \u00E2\u0080\u0094 Benthic producer 0.00 0.70 . . . . . . 0.50 Zooplankton 0.00 0.60 0.60 0.20 0.50 Jellyfish 0.00 0.60 0.60 0.20 0.50 Polychaetes 0.00 0.20 0.20 0.00 \u00E2\u0080\u0094 Echinoderms 0.00 0.20 0.20 0.00 0.20 Benthic crustaceans 0.00 0.60 0.20 0.00 0.50 Non-ceph molluscs 0.00 0.20 0.20 0.00 0.20 Sessile/other invertebrates 0.00 0.20 0.20 0.00 0.20 Shrimps 0.00 0.60 0.50 0.20 0.50 Crabs 0.00 0.20 0.20 0.20 0.20 Cephalopods 0.00 0.20 0.20 0.20 0.50 Threadfin bream (nemipterids) 0.00 0.50 0.50 0.70 0.50 Bigeyes (priacanthids) 0.00 0.50 0.50 0.70 0.50 Lizard fish (synodontids) 0.00 0.50 0.50 0.70 0.50 Juvenile Hairtail (trichiurids) 0.00 0.10 0.50 0.70 0.50 Adult hairtail (trichiurids) 0.00 0.50 0.50 0.70 0.50 Pomfret (stromateids) 0.00 0.50 0.50 0.70 0.50 Snappers 0.00 0.50 0.50 0.70 0.50 Adult groupers 0.00 0.50 0.50 0.70 0.50 Croakers (< 30 cm) 0.00 0.10 0.50 0.70 0.50 Juvenile large croakers 0.00 0.50 0.50 0.20 0.50 Croakers (> 30 cm) 0.00 0.50 0.50 0.70 0.50 Demesral fish (< 30 cm) 0.00 0.50 0.50 0.70 0.50 Juvenile demersal fish (> 30 cm) 0.00 0.10 0.50 0.20 0.50 Adult demersal fish (>30 cm) 0.00 0.50 0.50 0.70 0.50 Benthopelagic fish 0.00 0.50 0.50 0.70 0.50 Melon seed 0.00 0.50 0.50 0.70 0.50 Pelagic fish (< 30 cm) 0.00 0.50 0.50 0.70 0.50 Juvenile large pelagic fish 0.00 0.10 0.50 0.20 0.50 Pelagic fish (> 30 cm) 0.00 0.50 0.50 0.70 0.50 Demersal sharks and rays 0.00 0.50 0.50 0.70 0.50 Pelagic sharks and rays 0.00 0.50 0.50 0.70 0.50 Seabirds 0.00 0.60 0.20 0.20 . . . Pinnipeds 0.00 0.60 0.20 0.20 0.10 Other mammals 0.00 0.60 0.20 0.20 0.10 Marine turtles 0.00 0.20 0.20 0.20 . . . 183 (b) Pedigree for the basic parameters of the 2000s NSCS model Pedigree index Functional group B P/B Q/B Diet Catch Phytoplankton 1.00 1.00 N/A N/A N/A Benthic producer 0.70 0.70 N/A N/A 0.50 Zooplankton 0.70 0.70 0.70 0.20 0.50 Jellyfish 0.40 0.20 0.20 0.00 0.50 Polychaetes 1.00 0.20 0.20 0.00 N/A Echinoderms 1.00 0.20 0.20 0.00 0.20 Benthic crustaceans 0.40 0.60 0.20 0.00 0.20 Non-ceph molluscs 0.40 0.20 0.20 0.00 0.20 Sessile/other invertebrates 0.70 0.20 0.20 0.00 0.20 Shrimps 0.00 0.60 0.50 0.00 0.50 Crabs 0.00 0.20 0.20 0.00 0.50 Cephalopods 0.70 0.20 0.20 0.50 0.50 Threadfin bream (nemipterids) 1.00 1.00 0.50 0.70 0.50 Bigeyes (priacanthids) 0.70 1.00 0.50 0.70 0.50 Lizard fish (synodontids) 1.00 1.00 0.50 0.70 0.50 Juvenile Hairtail (trichiurids) 0.40 0.70 0.50 0.50 0.50 Adult hairtail (trichiurids) 0.70 0.70 0.50 0.50 0.50 Pomfret (stromateids) 0.70 0.70 0.50 0.50 0.50 Snappers 0.40 0.70 0.50 0.70 0.20 Adult groupers 0.40 0.70 0.50 0.70 0.20 Croakers (< 30 cm) 0.70 0.70 0.50 0.70 0.20 Juvenile large croakers 0.40 0.70 0.00 0.00 0.10 Croakers (> 30 cm) 0.40 0.70 0.50 0.70 0.50 Demesral fish (< 30 cm) 0.40 0.70 0.50 0.70 0.20 Juvenile demersal fish (> 30 cm) 0.40 0.70 0.00 0.20 0.10 Adult demersal fish (>30 cm) 0.40 0.70 0.50 0.70 0.20 Benthopelagic fish 0.70 0.70 0.50 0.70 0.20 Melon seed 0.70 0.70 0.50 0.70 0.50 Pelagic fish (< 30 cm) 0.70 0.70 0.50 0.70 0.20 Juvenile large pelagic fish 0.00 0.70 0.00 0.00 0.10 Pelagic fish (> 30 cm) 0.70 1.00 0.50 0.70 0.20 Demersal sharks and rays 0.40 0.70 0.00 0.00 0.20 Pelagic sharks and rays 0.40 0.70 0.50 0.00 0.20 Seabirds 0.00 0.10 0.20 0.00 0.10 Pinnipeds 0.00 0.10 0.20 0.20 0.10 Other mammals 0.00 0.10 0.20 0.20 0.10 Marine turtles 0.00 0.20 0.20 0.20 N/A To test the sensitivity of the Ecopath estimated parameters, two approaches were used. Firstly, input parameters, including biomass, P/B and Q/B ratios and E E , were varied (increased or decreased) by up to 50%. Changes of the output parameters relative to the initial values were noted. This approach tested sensitivity of the model estimates to the major input parameters except diet composition and catches. Secondly, a perturbation analysis (Bundy et al. 2005) was conducted using the 'Autobalance routine' (Kavanagh et al. 2004) of Ecopath. In this routine, values of the input parameters of the N S C S models were randomly selected from statistical distributions predefined in the 'pedigree' (Christensen et al. 2004). This was repeated until the combination of parameter values resulted in a mass-balanced model (i.e., ecotrophic efficiency of all functional group is positive and less than 1). The sampling and balancing of the models was repeated 30 times and parameter values that resulted in mass-balanced models were recorded. Based on the recorded values from each mass-balanced model, confidence limits for all input and output parameters of the 1970s and 2000s N S C S models could be estimated. 6.3 Results 6.3.1. Biomass changes The models indicated a large change in ecosystem structure (Figure 6.2). Total biomass of consumer groups (i.e., excluding primary producers and detritus) in the 1970s (67.6 tkm\" 2) was 2.5 times higher than in the 2000s (27 t-knf 2). Biomasses of functional groups lower in the trophic level increased from the 1970s. These groups included mainly jellyfish, pelagic, small-bodied and juvenile fishes. Biomass of most other groups, particularly the demersal fishes, decreased by an average of 60% from the 1970s level. Groups with the largest declines included sharks and rays, large demersal fish, snappers, groupers, large croakers, lizardfish and hairtail. 185 V) o o> o SZ 1000 900 A 2 200 i a) O) c re j= u a) O) re c CD U i\u00E2\u0080\u0094 o> a. 100 E 0 \u00E2\u0080\u00A2100 P o ~ -E E E ^ X E \u00C2\u00A3 Figure 6.2. Percentage change in biomasses between the 1970s and the 2000s NSCS models. The models also showed a shift from a demersal-dominant to pelagic-dominant system between the 1970s and the 2000s (Figure 6.3). When pelagic, benthopelagic and demersal feeders were grouped together, biomasses of pelagic and benthopelagic groups increased significantly by about 1.6 times from the 1970s level. On the other hand, biomass of demersal groups declined by more than 60% (Figure 6.3a). Thus, the ratio of total demersal to total pelagic biomass (including fish and invertebrates) declined from 15:1 in the 1970s to 3.7:1 in the 2000s. Moreover, total throughput, a measure of biomass going into and out of a group, increased by 1.8 times for the pelagic fish groups. On the other hand, total throughput of demersal fish groups declined by 2.5 times from the 1970s to 2000s (Figure 6.3b). Energy flows in the 2000s system were mainly through the pelagic groups, which were 3 times the total energy flows of demersal groups. However, 30 years ago, flows of demersal groups were higher than the pelagic groups. 186 a) b) \u00C2\u00A3 3 H in in 20 o 10 Demersal fishes Benthopelagic fishes Demersal fishes Benthopelagic fishes Pelagic fishes Pelagic fishes Figure 6.3. Comparisons of (a) biomass and (b) throughput of demersal, benthopelagic and pelagic fish groups in the 1970s (open bars) and 2000s (gray bars) NSCS models. The error bars represent the standard errors generated from the perturbation analysis (N = 30). The proportion of invertebrates in the total landings of the N S C S increased substantially from the 1970s to the 2000s. The ratio of landings of fishes to invertebrates in the N S C S declined largely from 24:1 to 4:1 since the 1970s, after a period of rapid increase in fish landings between the 1950s and 1970s (Department of Fishery, P R C 1996, 2000) (Figure 6.4). The landings included the Chinese official reported landings from the three provinces that fished in the N S C S (Guangzhou, Guangxi and Hainan). 187 .2 25 ll 0 J , r , , , 1950 1960 1970 1980 1990 2000 Year Figure 6.4. Ratio of fishes to invertebrates landings from NSCS from 1950 to 2000. For the demersal groups, benthic invertebrates became dominant in N S C S in the 2000s. Although biomass of invertebrates (shrimps, crabs, benthic crustaceans, etc.) decreased by almost 30%, the ratio of demersal invertebrates to demersal fish biomass increased 2.8 fold from the 1970s to 2000s. This showed an increase in the proportion of invertebrates in the demersal system despite a decline in the overall demersal biomass. The proportion of low trophic level demersal groups increased substantially in the 2000s. Overall, total biomass from all trophic levels declined from the 1970s to the 2000s (Figure 6.5). Biomass of trophic level 2 to 3 dropped greatly because of the substantial decrease in biomass of benthic invertebrates such as crustacean and echinoderms. While the majority of the demersal groups in trophic level 3 to 4 decreased strongly (>70%), the biomass of benthopelagic and pelagic groups with this trophic level increased. As such, only a moderate decline in biomass of groups in trophic level 3 to 4 was observed. When only demersal groups were considered, the decline in total biomass of trophic level 3 to 4 was greater than in the lower trophic levels. Decline in abundance of pelagic sharks and rays was responsible for the drop in biomass of functional groups with trophic levels higher than 4. 188 1970s 2000s Trophic level 0.03 0.01 4 to 5 5.07 3 54 3 to 4 62.55 ^^^^^^^^ 23.48 2 to 3 -70 -50 I I -30 -10 0 1 10 1 1 30 50 70 B i o m a s s ( t k m 2 ) Figure 6.5. Biomasses by trophic level of the 1970s and 2000s NSCS models. 6.3.2. Mortalities The mortality of the functional groups changed substantially between the two periods. Generally, fishing mortalities of functional groups in the 2000s model were much higher than the 1970s model. Total mortalities of fish groups increased by 15 times on average from the 1970s to 2000s (Table 6.5). This was a logical consequence of the dramatic increase in fishing effort over the past three decades. Changes in predation mortalities between the two time periods varied between groups. Predation mortalities of juvenile and small fish groups decreased because of the decline in abundance of their predators. However, predation mortalities of large fish groups increased slightly, which might be attributed to the declines in average body size and age of the groups. Smaller-size and younger fishes should generally have higher predation mortalities. 189 Table 6.5. Estimated Fishing (F), natural (M) and other mortalities (M0) of the 1970s and 2000s NSCS models. 1970s model 2000s model Functional group F M Mo F M Mo Phytoplankton 0.00 14.10 385.00 0.00 3.79 394.00 Benthic producer 0.00 0.24 11.65 0.00 0.12 11.80 Zooplankton 0.00 1.67 30.33 0.01 9.77 22.20 Jellyfish 0.01 4.74 0.25 0.03 2.57 2.40 Polychaetes 0.00 6.41 0.34 0.00 4.54 2.21 Echinoderms 0.00 0.48 0.72 0.00 0.53 0.67 Benthic crustaceans 0.00 3.52 2.13 0.03 3.46 2.16 Non-ceph molluscs 0.00 1.15 1.85 0.29 3.05 0.17 Sessile/other invertebrates 0.00 0.84 0.16 0.00 0.57 0.43 Shrimps 0.08 5.05 0.27 3.49 3.73 0.38 Crabs 0.01 2.84 0.15 0.54 2.31 0.15 Cephalopods 0.05 1.50 1.55 0.40 0.82 1.88 Threadfin bream (nemipterids) 0.04 0.31 0.39 2.53 0.18 0.47 Bigeyes (priacanthids) 0.11 0.29 0.81 1.62 0.21 1.50 Lizard fish (synodontids) 0.28 0.27 1.75 0.73 0.52 0.55 Juvenile Hairtail (trichiurids) 0.11 0.64 1.54 1.86 0.45 0.77 Adult hairtail (trichiurids) 0.36 0.13 1.01 0.58 0.23 0.67 Pomfret (stromateids) 0.08 1.15 0.07 2.21 0.67 0.15 Snappers 0.38 0.90 0.07 0.78 0.88 0.09 Adult groupers 0.07 0.25 0.53 1.39 0.28 0.09 Croakers (< 30 cm) 0.06 1.80 0.51 0.50 2.66 0.14 Juvenile large croakers 0.26 2.00 0.21 1.77 0.19 1.44 Croakers (> 30 cm) 0.46 0.41 0.66 0.84 0.10 0.59 Demesral fish (< 30 cm) 0.06 2.51 0.14 0.57 3.90 0.24 Juvenile demersal fish (> 30 cm) 0.26 1.40 1.04 2.21 0.51 0.97 Adult demersal fish (>30 cm) 0.59 0.02 0.93 1.70 0.07 0.53 Benthopelagic fish 0.06 2.79 0.15 0.65 0.92 1.61 Melon seed 0.05 2.11 0.08 0.71 1.68 0.01 Pelagic fish (< 30 cm) 0.14 2.58 0.14 1.32 1.51 1.43 Juvenile large pelagic fish 0.08 1.63 1.16 2.55 0.09 1.62 Pelagic fish (> 30 cm) 0.24 0.01 0.65 1.04 0.02 0.34 Demersal sharks and rays 0.40 0.06 0.80 0.98 0.06 0.16 Pelagic sharks and rays 0.18 0.02 0.20 0.62 0.03 0.03 Seabirds 0.00 0.00 0.06 0.00 0.00 0.06 Pinnipeds 0.03 0.00 0.01 0.01 0.00 0.03 Other mammals 0.01 0.00 0.10 0.00 0.00 0.11 Marine turtles 0.05 0.00 0.05 0.03 0.00 0.07 6.3.3. System index System indices obtained from the models suggested that the ecosystem changed considerably from the 1970s to the 2000s (Table 6.6). Total catch from the N S C S ecosystem increased from 0.85 to 7.35 t-km\"2 during this period. Mean trophic level of catch in the 1970s, calculated from the average trophic level of the functional groups weighted by their total annual catches, declined from 3.19 to 2.85. In the 1970s, 190 functional groups with trophic level lower than 3 contributed only 31% to the total catch. However, in the 2000s, 71% of the catch came from these groups. Thus, although total catches increased, lower trophic level groups contributed a higher fraction of the catch. The system indices derived from Odum's attributes of ecosystem maturity (Odum 1969; Christensen 1995) indicated that the 1970s system had higher ecosystem maturity than the 2000s system. The ratio of total primary production to total respiration of the system increased 3-fold between the 1970s and 2000s. Also system overhead, an index that is positively related to the system's reserved strength, and hence to its resilience to unexpected perturbations (Ulanowicz 1986), decreased substantially from the 1970s to the 2000s. Total consumption and respiratory flows, and the resulting total system throughput in the 1970s model were higher than the 2000s model. Total consumption and respiratory flows are measures of the total biomass flows through consumption and respiration of all the functional groups (except detritus) in the system. Average trophic transfer efficiency of the system, calculated from the geometric mean of the transfer efficiency of all trophic levels, was lower in the 1970s model (from 6.6% in the 70s to 10.2% in the 2000s). Transfer efficiency of each trophic level was estimated from the proportion of input that was transferred to the next trophic level. In general, trophic transfer efficiency declined with higher trophic level (Christensen & Pauly 1995). Thus the lower transfer efficiency in the 1970s relative to the 2000s level might indicate that the flows in higher trophic level constituted a large fraction of the total throughput in the 1970s than the 2000s. This paralleled the 8-fold increase in gross efficiency of the system (ratio of catch to net primary production) between the 1970s and 2000s (Table 6.6). 191 Table 6.6. Estimated system indices of the 1970s and 2000s models. Standard errors (s.e.) estimated from the perturbation analysis (N = 30) were noted in the parentheses. System index 1970s 2000s Sum of all consumption (t-km\"') 6,869 1,994 (s.e. = 187) (s.e. = 32) Sum of all exports (t-km\"2) 127,037 129,132 (s.e. = 1,561) (s.e. = 916) Sum of all respiratory flows (t-km\"2) 3,659 1,242 (s.e. = 100) (s.e. = 20) Sum of all flows into detritus (t-km\"2) 129,187 129,751 (s.e. = 1,532) (s.e. = 906) Total system throughput (t-km\"2) 266,752 262,118 (s.e. = 2,976) (s.e. = 1,800) Sum of all production (t-km\"2) 131,881 130,725 (s.e. = 1,505) (s.e. = 906) Mean trophic level of catch 3.19 2.85 (s.e. = 0.003) (s.e. = 0.003) Gross efficiency (catch/net primary production) 7xl0\"6 5.6xl0\"5 (s.e. = 9.5xl0\"8) (s.e. =4.3xl0\"7) Total primary production/total respiration 35.72 104.99 (s.e. = 1.67) (s.e. =2.17) Total primary production/total biomass 240.4 259.2 (s.e. = 1.67) (s.e. = 1.61) Connectance index 0.317 0.303 (s.e. = 8.5xlO\"5) (s.e. = 7.7xl0\"5) System omnivory index 0.185 0.181 (s.e. = 0.001) (s.e. = 9.1xl0\"4) Pedigree index 0.393 0.417 The primary production required (PPR) for consumption by the functional groups and for the fisheries changed between the 1970s and 2000s (Figure 6.6). PPR was calculated from all the flows from primary production (trophic level = 1) required to support the higher trophic levels or the fisheries (Pauly & Christensen 1995a). The PPR for consumption by animals in the N S C S ecosystem decreased by 75% from the 1970s (19,926 +/- s.e. 3,098 t-km\"2) to the 2000s (5,090 +/- s.e. 130 t-km\"2) (Figure 6.6a). However, PPR for fisheries increased by about 78% (from 431 +/- s.e. 95 t-km\"2 to 769 192 2 +/- s.e. 37 t-km\" ) (Figure 6.6b). Thus, the ratio between PPR for fisheries and PPR for consumption increased greatly from 1:46 to 1:6.6 during this period (Figure 6.6c). In other words, in the 2000s, a higher fraction of the total biomass flows from primary production was required to support the fisheries, relative to those consumed by the organisms in the higher trophic levels (except human). However, the P P R per unit of catch decreased by 80% from 507 to 99 (+/- s.e. 4.7) over the three decades. This indicated that a greater proportion of catch in the 2000s were made up of the lower trophic level species. These species required less energy from primary production per unit of biomass. Based on the estimated PPR generated from alternative input parameters (resulting from the perturbation analysis), the values of the above indicators were significantly different between the 1970s and the 2000s model (P<0.05). 193 a) b) c) 25 c o '\u00E2\u0080\u00A2\u00C2\u00A7. 20 u E 0 *- l o \u00C2\u00B0 XI 1 3 10 ra o rr a. o. E \u00E2\u0080\u00A2a c IS U) 1.2 1.0 0.8 0.6 OC o. a. 0.4 0.2 0.0 0.20 i 0.15 DC a. a. a> (A 0.10 f 0.05 c OL a. 0.00 1970s 1970s llBtltlliilltllli 2000s 2000s 1970s 2000s Figure 6.6. Primary production required (PPR) in the 1970s and 2000s models to support (a) consumption by predators (excluding fishing), (b) by fisheries, (c) ratio of fisheries to consumptions. The error bars represent the standard errors generated from the perturbation analysis (N = 30). 194 6.3.4. Uncertainty and sensitivity analysis The input parameters for the 1970s model were estimated from less certain sources than the 2000s model. Based on the assigned pedigree matrices, the estimated index of uncertainty from the 2000s model was slightly higher than the 1970s model. Pedigree index of the 2000s and 1970s model was 4.1 and 3.9, respectively. This indicated that parameter values of the 2000s model were based on slightly more reliable sources than the 1970s model. Data sources for the 2000s model were mainly survey-based, while many of the input parameters for the 1970s model were indirectly estimated from global databases and empirical formulae. In our pedigree analysis, the latter are regarded as having lower reliability than survey-based data. The estimated parameters were sensitive to the input parameters within a functional group, while the outputs were generally robust to parameters from other functional groups (Table 6.7). For instance, when one input parameter of a functional group was reduced by 50%, the output parameters of that group may vary by more than 90%. This was expected as the input parameters of a functional group (e.g., P /B , Q /B , E E ) are tightly linked with each other. Excluding this within-group effect, the estimated parameters were reasonably robust to changes in input parameter values of other functional groups in the 2000s model. A 50% change in input parameter values led to, on average, 10% change in the output values (25% and 75% quartile = 4.3% - 13%). The most sensitive sets of parameters were the effect of the assumed ecotrophic efficiency (EE) of small demersal fish groups on the estimated EEs of benthic crustaceans and melon seed, and the biomass of snappers. The 1970s model was more sensitive to the input parameter values, even when within-group effects were excluded. The effects of P/B and E E of pelagic sharks on the estimated E E of seabirds were most sensitive among all parameter sets, with a maximum change of over 200% in the seabird E E when the input parameters of pelagic sharks and rays changed by 50%. Other more sensitive pairs of input/output parameters included small pelagic fish/juvenile pelagic fish, small demersal fish/melon seed, and cephalopods/pomfret. These indicated the higher uncertainties associated with the estimated parameters of the 1970s model. 195 Results from the perturbation analysis showed that the system estimates of the 1970s and 2000s models were robust to the uncertainty of the input parameters. Based on the alternative model parameters (N = 30 for each model) generated from the perturbation analysis, the confidence intervals of the system indices presented in this study (e.g., system throughput, primary production required by the fisheries) were narrow for both models. The coefficient of variation of these system indices were generally within 30%. 196 Table 6.7. Sensitivity of the estimated parameters when input parameters value were varied (a) for the 1970s model Sensitivity by group (average change in estimations) No Perturbing group Perturbations 1 2 3 4 5 6 7 8 9 10 1 Phytoplankton Reduce by 50% Increase by 50% 1.00 0.33 2 Benthic producer Reduce by 50% Increase by 50% 1.00 0.33 3 Zooplankton Reduce by 50% Increase by 50% 0.50 0.50 1.00 0.33 4 Jellyfish Reduce by 50% Increase by 50% 0.22 0.04 4.31 0.50 5 Polychaetes Reduce by 50% Increase by 50% 1.00 0.33 6 Echinoderms Reduce by 50% Increase by 50% 0.08 0.08 0.59 0.25 0.21 0.21 7 Benthic crustaceans Reduce by 50% Increase by 50% 0.15 0.15 0.09 0.09 1.00 0.33 0.18 0.18 8 Non-cephalopods mollusks Reduce by 50% Increase by 50% 0.20 0.20 0.04 0.04 0.22 0.22 0.97 0.32 0.18 0.18 9 Sessile/other invertebrates Reduce by 50% Increase by 50% 0.15 0.15 1.00 0.33 10 Shrimps Reduce by 50% Increase by 50% 0.06 0.03 1.00 0.33 11 Crabs Reduce by 50% Increase by 50% 0.06 0.02 0.09 0.04 0.06 0.02 0.07 0.03 0.17 0.08 12 Cephalopods Reduce by 50% 0.12 0.34 0.26 0.09 0.08 0.11 Increase by 50% 0.04 0.12 0.09 0.03 0.02 0.04 13 Threadfin bream (nemipterids) Reduce by 50% 0.03 0.05 0.05 0.10 0.13 0.13 0.05 Increase by 50% 0.03 0.05 0.05 0.10 0.13 0.13 0.05 14 Bigeyes (priacanthids) Reduce by 50% Increase by 50% 0.05 0.05 0.04 0.04 0.07 0.07 0.07 0.07 Table 6.7a Con't Sensitivity by group (average change in estimations) No Functional group Perturbations 1 2 3 4 5 6 7 8 9 10 15 Lizard fish (synodontids) Reduce by 50% Increase by 50% 0.03 0.03 0.06 0.06 0.06 0.06 0.09 0.09 18 Pomfret (stromateids) Reduce by 50% Increase by 50% 0.44 0.21 21 Croakers (< 30 cm) Reduce by 50% Increase by 50% 0.04 0.04 0.05 0.05 0.06 0.06 0.13 0.13 22 Juvenile large croakers Reduce by 50% Increase by 50% 23 Croakers (> 30 cm) Reduce by 50% Increase by 50% 0.05 0.05 24 Demesral fish (< 30 cm) Reduce by 50% 0.07 0.18 0.49 0.15 0.08 0.53 0.20 0.09 0.35 Increase by 50% 0.02 0.08 0.20 0.06 0.03 0.22 0.08 0.04 0.15 26 Adult demersal fish (> 30 cm) Reduce by 50% Increase by 50% 0.05 0.05 27 Benthopelagic fish Reduce by 50% Increase by 50% 0.09 0.04 0.09 0.04 28 Melon seed Reduce by 50% Increase by 50% 0.04 0.04 29 Pelagic fish (< 30 cm) Reduce by 50% Increase by 50% 0.14 0.06 0.07 0.03 0.18 0.08 33 Pelagic sharks and rays Reduce by 50% Increase by 50% 0.17 0.06 36 Other mammals Reduce by 50% Increase by 50% 0.03 0.03 Table 6.7a Con't Sensitivity by group (average change in estimations) No. Functional group Perturbations 21 22 23 24 25 26 27 28 29 30 12 Cephalopods Reduce by 50% Increase by 50% 0.10 0.03 0.08 0.03 0.36 0.13 0.29 0.10 13 Threadfin bream (nemipterids) Reduce by 50% Increase by 50% 0.12 0.12 0.04 0.04 0.07 0.07 0.09 0.09 -14 Bigeyes (priacanthids) Reduce by 50% Increase by 50% 0.23 0.23 0.39 0.39 0.11 0.11 0.25 0.25 0.06 0.06 15 Lizard fish (synodontids) Reduce by 50% 0.08 0.10 0.04 0.07 0.09 0.07 Increase by 50% 0.08 0.10 0.04 0.07 0.09 0.07 16 Juvenile Hairtail (trichiurids) Reduce by 50% Increase by 50% 0.04 0.04 0.03 0.03 20 Adult groupers Reduce by 50% Increase by 50% 0.04 0.04 21 Croakers (< 30 cm) Reduce by 50% 1.00 0.05 0.14 0.05 0.07 0.05 Increase by 50% 0.33 0.05 0.14 0.05 0.07 0.05 22 Juvenile large croakers Reduce by 50% Increase by 50% 0.06 0.06 1.00 0.33 23 Croakers (> 30 cm) Reduce by 50% Increase by 50% 1.00 0.33 24 Demesral fish (< 30 cm) Reduce by 50% 0.92 0.37 0.20 0.62 0.08 0.07 Increase by 50% 0.27 0.16 0.08 0.26 0.03 0.03 25 Juvenile demersal fish (> 30 cm) Reduce by 50% Increase by 50% 1.00 0.33 26 Adult demersal fish (> 30 cm) Reduce by 50% Increase by 50% 0.24 0.24 1.00 0.33 27 Benthopelagic fish Reduce by 50% Increase by 50% 0.80 0.25 0.07 0.03 0.24 0.11 0.19 0.08 28 Melon seed Reduce by 50% Increase by 50% 0.99 0.33 29 Pelagic fish (< 30 cm) Reduce by 50% Increase by 50% 0.10 0.04 0.16 0.07 0.84 0.26 0.79 0.34 Table 6.7a Con't No. Functional group Perturbations 21 Sensitivity by group (average change in estimations) 22 23 24 25 26 27 28 29 30 30 Juvenile large pelagic fish Reduce by 50% 0.04 0.98 Increase by 50% 0.04 0.33 31 Pelagic fish (> 30 cm) Reduce by 50% 0.08 0.08 Increase by 50% 0.08 0.08 33 Pelagic sharks and rays Reduce by 50% 0.10 0.08 0.11 0.13 Increase by 50% 0.02 0.01 0.04 0.04 34 Seabirds Reduce by 50% 0.04 Increase by 50% 0.04 Table 6.7a Con't Sensitivity by group (average change in estimations) No. Functional group Perturbations 31 32 33 34 35 36 37 31 Pelagic fish (> 30 cm) Reduce by 50% 1.00 Increase by 50% 0.33 32 Demersal sharks and rays Reduce by 50% 0.64 Increase by 50% 0.23 33 Pelagic sharks and rays Reduce by 50% 1.55 1.69 Increase by 50% 0.35 0.56 34 Seabirds Reduce by 50% 1.00 Increase by 50% 0.33 35 Pinnipeds Reduce by 50% 1.00 Increase by 50% 0.33 36 Other mammals Reduce by 50% 1.00 Increase by 50% 0.33 37 Marine turtles Reduce by 50% 1.00 Increase by 50% 0.33 o o (b) The 2000s model Sensitivity by group (average change in estimations) No. Functional group Perturbations 1 2 ' 3 4 5 6 7 8 9 10 1 Phytoplankton Reduce by 50% Increase by 50% 1.00 -0.33 2 Benthic producer Reduce by 50% Increase by 50% 1.00 -0.33 3 Zooplankton Reduce by 50% Increase by 50% -0.49 0.49 1.00 -0.33 4 Jellyfish Reduce by 50% Increase by 50% -0.16 0.16 0.28 -0.01 5 Polychaetes Reduce by 50% Increase by 50% 1.00 -0.33 6 Echinoderms Reduce by 50% Increase by 50% -0.10 0.10 0.47 -0.11 -0.24 0.24 7 8 Benthic crustaceans Non-cephalopods mollusks Reduce by 50% Increase by 50% Reduce by 50% Increase by 50% -0.16 0.16 -0.13 0.13 -0.10 0.10 -0.15 0.15 1.00 -0.33 -0.17 0.17 0.97 -0.32 -0.10 0.10 9 Sessile/other invertebrates Reduce by 50% Increase by 50% -0.08 0.08 1.00 -0.33 10 Shrimps Reduce by 50% Increase by 50% 0.03 0.00 1.00 -0.33 11 Crabs Reduce by 50% Increase by 50% 0.06 -0.01 0.03 0.00 0.08 -0.01 12 Cephalopods Reduce by 50% Increase by 50% -0.17 0.17 -0.07 0.07 13 Threadfin bream (nemipterids) Reduce by 50% Increase by 50% -0.05 0.05 -0.06 0.06 -0.12 0.12 -0.11 0.11 -0.04 0.04 to o Table 6.7b Con't. Sensitivity by group (average change in estimations) No. Functional group Perturbations 1 2 3 4 5 6 7 8 9 10 14 Bigeyes (priacanthids) Reduce by 50% Increase by 50% -0.06 0.06 -0.04 0.04 18 Pomfret (stromateids) Reduce by 50% Increase by 50% -0.10 0.10 21 Croakers (< 30 cm) Reduce by 50% Increase by 50% -0.04 0.04 24 Demesral fish (< 30 cm) Reduce by 50% 0.13 0.45 0.15 0.08 0.23 Increase by 50% -0.04 -0.12 -0.04 -0.02 -0.06 25 Juvenile demersal fish (> 30 cm) Reduce by 50% Increase by 50% -0.06 0.06 27 Benthopelagic fish Reduce by 50% Increase by 50% -0:06 -0.04 0.06 0.04 -0.07 0.07 29 Pelagic fish (< 30 cm) Reduce by 50% Increase by 50% -0.13 0.13 30 Juvenile large pelagic fish Reduce by 50% Increase by 50% -0.04 0.04 to o Table 6.7b Con't. Sensitivity by group (average change in estimations) No. Functional group Perturbations 11 12 13 14 15 16 17 19 20 11 Crabs Reduce by 50% Increase by 50% 1.00 -0.33 12 Cephalopods Reduce by 50% Increase by 50% -0.13 0.13 0.53 -0.14 13 Threadfin bream (nemipterids) Reduce by 50% Increase by 50% 1.00 -0.33 -0.06 0.06 14 Bigeyes (priacanthids) Reduce by 50% Increase by 50% -0.12 0.12 0.98 -0.33 -0.05 0.05 15 Lizard fish (synodontids) Reduce by 50% Increase by 50% 0.62 -0.20 16 Juvenile Hairtail (trichiurids) Reduce by 50% Increase by 50% -0.11 0.11 0.97 -0.32 17 Adult hairtail (trichiurids) Reduce by 50% Increase by 50% -0.04 0.04 0.63 -0.20 18 Pomfret (stromateids) Reduce by 50% Increase by 50% 19 Snappers Reduce by 50% Increase by 50% 1.00 -0.33 20 Adult groupers Reduce by 50% Increase by 50% 1.01 -0.33 24 Demesral fish (< 30 cm) Reduce by 50% Increase by 50% 0.24 -0.07 0.47 -0.13 27 Benthopelagic fish Reduce by 50% Increase by 50% -0.07 0.07 -0.05 0.05 31 Pelagic fish (> 30 cm) Reduce by 50% Increase by 50% -0.05 0.05 36 Other mammals Reduce by 50% Increase by 50% -0.05 0.05 -0.08 0.08 to o Table 6.7b Con't. Sensitivity by group (average change in estimations) No. Functional group Perturbations 21 22 23 24 25 26 27 28 29 30 12 Cephalopods Reduce by 50% Increase by 50% -0.09 0.09 13 Threadfin bream (nemipterids) Reduce by 50% Increase by 50% -0.16 0.16 -0.06 0.06 14 Bigeyes (priacanthids) Reduce by 50% -0.20 -0.14 -0.05 -0.04 Increase by 50% 0.20 0.14 0.05 0.04 16 Juvenile Hairtail (trichiurids) Reduce by 50% Increase by 50% -0.05 0.05 21 Croakers (< 30 cm) Reduce by 50% Increase by 50% 1.00 -0.33 22 Juvenile large croakers Reduce by 50% Increase by 50% -0.07 0.07 1.00 -0.33 23 Croakers (> 30 cm) Reduce by 50% Increase by 50% 1.00 -0.33 24 Demesral fish (< 30 cm) Reduce by 50% 1.18 0.08 0.08 0.42 Increase by 50% -0.35 -0.02 -0.02 -0.11 25 Juvenile demersal fish (> 30 cm) Reduce by 50% Increase by 50% 0.98 -0.33 26 Adult demersal fish (> 30 cm) Reduce by 50% Increase by 50% 1.00 -0.33 27 Benthopelagic fish Reduce by 50% Increase by 50% 0.91 -0.30 -0.04 0.04 -0.12 0.12 28 Melon seed Reduce by 50% Increase by 50% 1.00 -0.33 to O Table 6.7b Con't. Sensitivity by group (average change in estimations) No. Functional group Perturbations 21 22 23 24 25 26 27 28 29 30 29 Pelagic fish (< 30 cm) Reduce by 50% 1.00 Increase by 50% -0.33 30 Juvenile large pelagic fish Reduce by 50% -0.10 -0.08 1.00 Increase by 50% 0.10 0.08 -0.33 34 Seabirds Reduce by 50% -0.04 Increase by 50% 0.04 35 Pinnipeds Reduce by 50% -0.04 Increase by 50% 0.04 36 Other mammals Reduce by 50% :0.04 Increase by 50% 0.04 Table 6.7b Con't. Sensitivity by group (average change in estimations) No. Functional group Perturbations 31 32 33 34 35 36 37 31 Pelagic fish (> 30 cm) Reduce by 50% 1.00 Increase by 50% -0.33 32 Demersal sharks and rays Reduce by 50% 0.94 Increase by 50% -0.31 33 Pelagic sharks and rays Reduce by 50% 1.05 Increase by 50% -0.34 34 Seabirds Reduce by 50% 1.00 Increase by 50% -0.33 35 Pinnipeds Reduce by 50% 1.00 Increase by 50% -0.33 36 Other mammals Reduce by 50% 1.00 Increase by 50% -0.33 37 Marine turtles Reduce by 50% 1.00 Increase by 50% -0.33 O 6.4. Discussion This study showed that the N S C S ecosystem underwent great change between the 1970s and the 2000s. Particularly, the ecosystem had changed from being dominated by demersal species to a heavily-exploited system dominated by pelagic species with a high turn-over rate. Such changes have been observed in other exploited ecosystems such as the Gulf of Carpentaria in Australia (Harris & Poiner 1991), northeastern coast of the United States (Link & Brodziak 2002), and the Mediterranean and Black Seas (de Leiva Morena et al. 2000). Two possible hypotheses that may explain such changes are: (1) increased fishing effort especially by bottom trawlers increased fishing mortality of benthic and demersal groups; (2) eutrophication enhanced the productivity of the benthopelagic and pelagic groups. Both of these hypotheses may have contributed to the observed ecosystem changes but there is more evidence to support the hypothesis related to fishing. In the N S C S , a large fraction of the region's increase in nominal fishing effort (over 8-folds in the past five decades) was from the demersal trawl sectors (Lu & Y e 2001). The detrimental effects of bottom trawling on benthic and demersal communities are well known (Sainsbury et al. 1997; Watling & Norse 1998). Moreover, fishing power and technology also improved dramatically, e.g. the widespread use of global positioning system, acoustic fish finder (Cheung & Sadovy 2004). The technological improvement enabled trawling in most part of the N S C S continental shelf. Thus, the increased fishing effort of bottom trawlers might have largely increased the fishing mortalities of the demersal groups. These factors might have contributed to the heavier depletion of demersal resources relative to the benthopelagic and pelagic groups (Chapter 5). The latter were generally less catchable by the demersal gears. Growing populations, increased agriculture and industrial development in China and neighboring countries increased the run-offs of nutrients and organic pollutants into the coastal area and continental shelf of the NSCS, (Morton & Blackmore 2001). These were suggested to be the cause of the increased incidence of harmful algal blooms in some coastal regions (Lam & Ho 1989; Huang & Qi 1997). However, due to the lack of consistent time-series data, the effects of changes in primary productivity on the N S C S continental shelf 206 ecosystem could not be shown (Chapter 7). When better time-series data of primary productivity are available in the future, it wi l l be possible to evaluate the relative contributions of these two effects. Fisheries became increasingly dependent on lower trophic level species. This agreed with the decreasing ratio of fish to invertebrate landings in the N S C S during the same period. Such changes may be explained either by the shift in target species following the increase in shrimp trawl effort that targeted primarily benthic invertebrates, or genuine changes in ecosystem structure. Results from this study suggested that the latter was the likely explanation of the estimated changes. This agrees with previous studies that showed depletion of large predatory demersal fishes in the N S C S (Chapter 5) and elsewhere in the South China Sea (Christensen 1998; Pitcher & Pauly 1998). When time-series of catch or landing data by fishing sectors in the region become available, the relative contribution of the gear-change effect on the declining trophic level of catch can be better understood. Ecosystem maturity generally declined between the 1970s and 2000s with the depletion of the older, long-lived species that had accumulated large amounts of biomass when the system was relatively underexploited. This is reflected by comparing various system indices against Odum's attributes of ecosystem maturity (Odum 1969; Christensen 1995). For instance, the 1970s system had a more balanced production to respiration ratio, smaller system production to biomass ratio, more diverse trophic network and higher system overheads. The more mature 1970s ecosystem should be more stable and resilience to perturbations (Vasconcellos et al. 1997). Such perturbations may include environmental variability and anthropogenic changes e.g., climate change. The reduced stability of the 2000s ecosystem, the increased dominance of the pelagic system and the increased dependence on lower trophic level species by the fisheries might increase the volatility of the ecosystem and fisheries (Pauly et al. 1998). Population dynamics of fishes that are small-bodied, fast growing and with high fecundity are often strongly affected by the environment (Winemiller 2005) and have large inter-annual variability (Spencer & Coll ie 1997). Particularly, as intensive fishing had removed a large proportion of the adult biomass, the populations were dominated by 207 juveniles. Such truncation of age-class in fish populations may further intensify the variability of populations (Hsieh et al. 2006). Increased variability of catches due to the stock variabilities might have considerable socio-economic impacts to the fishing communities. Particularly, fishing fleets that build up fishing capacity during the 'good' fishing years may suffer from economic hardship when environmental factors reduce fishery productivity. Restoring the 2000s system back to a state with abundant predatory and demersal species may be beneficial ecologically and economically (Pitcher & Pauly 1998; Pitcher 2004). Ecologically, restoration prevents depletion, extirpation or even extinction of some vulnerable species that have been heavily fished in the N S C S . A restored system would have higher ecosystem maturity, stability and resilience, which would helps dampen out the impacts of environmental variability (Peterson et al. 1998; Hsieh et al. 2006). This is especially important as global climate change may further increase environmental fluctuations in the future (Roessig et al. 2004). Economically, as many of the commercially valuable species were strongly over-exploited, the potential economic productivity from these resources were dissipated (Gordon 1954). Restoration can increase stock abundance and improve the profitability of the fisheries. On the other hand, as over-exploitation by fishing appears to be the major driver of ecosystem changes in the N S C S , any restoration effort would likely require reduction of the current level of fishing effort. Thus fishers may suffer from short-term social (j\u00C2\u00B0bs) and economic difficulties. Moreover, restoration, conservation and fisheries management measures are sometimes costly to implement. Assessments of trade-offs between ecological and socio-economic objectives in the N S C S are useful to determine viable policy options (Chapter 8). Parameters values were estimated with higher certainty in the 2000s model than the 1970s model. Input parameter values of this model such as biomass and mortalities were mainly from the latest survey carried out by the Chinese research institute (Jia et al. 2004). Data obtained from these studies were relatively more accurate. On the other hand, parameter values of the 1970s were mostly estimated from indirect methods. For instance, the biomass of the fish groups in the 1970s were back calculated from the changes in catch rates between different periods, while gross assumptions were made for estimating the P/B ratios. Moreover, the 1970s model was more sensitive to the uncertain parameters 208 as the leverages of individual input parameter values to the outputs were higher in the 1970s model. Biomasses of the high trophic level groups were generally large in the 1970s model. Also, predation mortalities constituted a relatively higher proportion to total mortalities in the 1970s system. Thus relatively small change in parameter values such as biomass and Q/B ratio strongly increased total consumption or production, which resulted in a large influence on the mortalities of their prey and competitors. This situation could be improved by replacing or validating the indirectly estimated input parameters with more precise survey-based estimates. Although large-scale surveys in the N S C S were carried out by the Chinese authorities during the 1960s, 1970s and 1980s, the data are classified by the authorities and not available for use during the course of this study. Declassification of these data could greatly improve the understanding on the changes of the ecosystem, which could help provide more sensible resource assessments and management policy options. On the other hand, the comparisons of the N S C S ecosystem in the two time periods were robust to alternative ecosystem states generated based on the uncertainties of the parameters. This was shown by the generally small coefficient of variation of the system indices estimated based on the perturbation analysis. In addition, in Chapter 7 of this thesis, the validity of the two models was assessed through fitting time-series relative abundance data using the dynamic Ecosim simulation model. This analysis demonstrates that estimated changes in biomasses from simulations based on the initial parameters generated by the 1970s model agreed reasonably with observed data. Therefore, the conclusions from this study should be valid, although the absolute values of the estimates may be uncertain. 209 6.5 References Botsford, L . W . , Castilla, J. C. & Peterson, C. H . 1997 The management of fisheries and marine ecosystem. Science 277, 509-515. Buchary, E . , Cheung, W . W . L . , Sumaila, U . R. & Pitcher, T. J. 2003 Back to the future: A paradigm shift for restoring Hong Kong's marine ecosystem. In (ed. B . Phillips, B . A . Megrey & Y . Zhou), p. 727-746. 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A D E P L E T I O N INDEX AS A N INDICATOR OF BIODIVERSITY STATUS IN E C O S Y S T E M S I M U L A T I O N M O D E L 7.1 Introduction The impacts of fishing on both targeted species and their ecosystem can be accounted for using ecosystem-based approach to fisheries assessment ( E B M ) . During the past decade, E B M has become widely advocated (Pitcher & Pauly 1998; Hall 1999; Pope & Symes 2000; Pitcher 2001; Pauly et al. 2002; Hal l & Mainprize 2004; Pikitch et al. 2004; Jennings 2005). Governments throughout the world have declared their support through the Reykjavik Declaration on Responsible Fisheries in the Marine Ecosystem, and various national instruments. Numerous authors have proposed and discussed various tools and approaches (ecological, economic, managerial) to put the E B M concept in operation (Sainsbury et al. 2000; Sainsbury & Sumaila 2003; Hilborn 2004; Browman & Stergious 2005; Jennings 2005; Zeller & Pauly 2005). Examples of these tools and approaches include the use of indicators in fisheries management framework to evaluate the state of ecosystem and decide appropriate management actions (Link et al. 2002; Jennings 2005; Livingston et al. 2005) and developing ecosystem models to explore the effects of fishing and management policies (Pauly et al. 2000; Pitcher et al. 2005). Ecosystem simulation modelling is a useful tool to evaluate the effects of fishing and fisheries management policies on ecosystems. Various modelling approaches have tried to simulate interactions between organisms and fisheries, over a range of different assumptions and complexity (Fulton et al. 2003). One of the more widely used approaches is Ecopath with Ecosim, or E w E (Walters et al. 1997; Pauly et al. 2000). Ecopath is a mass-balance model which can be used to describe a snap-shot of the whole ecosystem at a particular time period. Species, usually those with similar biology, are aggregated into functional groups to reduce the number of state variables (see Chapter 6 for details of Ecopath modelling). Ecosim is a dynamic simulation model which simulates changes in the ecosystem that is described in Ecopath. It estimates changes in biomass of functional groups in the ecosystem as functions of abundance of other functional groups and time-varying harvest rates, taking into account predator-prey 216 interactions and foraging behaviors (Walters et al. 1997; Pauly et al. 2000). Ecosim is governed by the basic equations: where dB/dt gives the growth rate of group i in terms of its biomass; g, is growth efficiency; M and F are natural and fishing mortalities; / and e are immigration and emigration rates; C,-,- is the consumption of organism j by organism i, v and v' represent rates of behavioural exchange between invulnerable and vulnerable states; and ay-represents rate of effective search by predator j for prey type i. The behaviours of functional groups in dynamic simulations are heavily affected by the 'vulnerability factor' - a scaling factor of v which determines the foraging behavior of the functional groups in predator-prey interactions (Walters et al. 1997; Walters & Martell 2004). Applications of ecosystem modelling tools to evaluate impacts of fishing on biodiversity are often limited. This is because population dynamics at the species level are difficult to represent explicitly in the models. Hence, in models, the large degree of structural complexity in marine ecosystems is usually simplified or approximated in models. Species with similar functional roles or trophic level are aggregated into a functional group (Fulton et al. 2003). This enables better model performance by reducing the number of required parameters and increasing computational efficiency (Fulton et al. 2003). For instance, the northern South China Sea (NSCS) models aggregated thousands of species into 38 functional groups (Chapter 6). On the other hand, aggregations may compromise the resolution of the model and reduce the accuracy of the results (Fulton et al. 2003; Pinnegar et al. 2005). Aggregations in ecosystem models also mask the differential responses of species within a functional group that may have different intrinsic vulnerability to fishing and consequently a different rate of decline when they are fished (Jennings et al. 1999a,b; eq. 7.1 and eq. 7.2 Vij+v'ij+ayBj 217 Chapters 2 and 5). However, such variations need to be considered in models with aggregated functional groups. This is critical particularly with vulnerable species in a functional group where the threat of over-exploitation may not be evident when they are grouped with numerous other species. Knowledge about intrinsic vulnerabilities of marine fishes within functional groups can be used to predict species' responses to fishing. Intrinsic vulnerability of fishes is shown to be correlated with their rate of population decline under fisheries exploitation (Jennings et al. 1999a, b; Chapters 2 and 5). Therefore, when an ecosystem simulation model predicts that the abundance of a functional group (representing multiple species) is driven down by fishing, species with higher intrinsic vulnerability should decline at a faster rate than those with lower vulnerability. B y accounting for the relative intrinsic vulnerability of species within a functional group, we could predict species' relative rates of decline based on the dynamics at the functional group level. Cheung et al. (2005) (Chapter 2) developed an index that quantified the relative intrinsic vulnerability of fishes using the species' life history and ecology. Other indices have been suggested as biodiversity indicators for ecosystem modelling. The two indices that have been incorporated in the E w E software are the diversity index (Q-90) and the mean trophic level of catch (or the marine trophic index, MTI) . The Q-90 index is a variant of the Q-index developed by Kempton and Taylor (1976) which indicates the diversity of functional groups in an ecosystem. It is calculated from the slope of the cumulative functional group abundance curve between the 10th and 90th percentiles (see Ainsworth and Pitcher 2005 for details). The index represents both functional group richness and evenness. The M T I is calculated from the average of the trophic level of species weighted by their annual catch (Pauly et al. 1998). M T I declines when catches of higher tropic level species (usually more intrinsically vulnerable) decline because of their over-exploitations while species lower in the food web (usually less vulnerable) dominate in the catch. Such decline in M T I indicates deteriorating conservation status. In fact, the M T I is considered a major indicator of marine biodiversity by the I U C N - W o r l d Conservation Union (Butchart et al. 2004; Pauly & Watson 2005). 218 This study describes a model that combines simulation modelling with the intrinsic vulnerability of the species to predict the effects of fishing and different fisheries management strategies at the species level. Ecosim simulations using the N S C S Ecopath model (described in Chapter 6) were used as a case study to test this depletion index. The 2000s N S C S ecosystem has been largely depleted compared to the ecosystem in the 1970s (Chapters 5 & 6). Also, the ecosystem has changed from a demersal-dominated to a pelagic-dominated system. To validate the N S C S model for testing the index, the 1970s N S C S model was fitted with historical time-series catch-per-unit effort data (from Chapter 5). The estimated parameters were then -transferred to the 2000s model. The validity of the depletion index was studied by evaluating the correlations between the depletion index and other previously used biodiversity indices. 7.2. Methods 7.2.1. Depletion index A depletion index (DI) was developed to represent the possible responses of a species as a member of a single functional group in an ecosystem model. The DI was based on the assumption that intrinsically more vulnerable species aggregated within a functional group should decline faster than the less vulnerable species. Thus, the DI was calculated from prior knowledge o f the intrinsic vulnerability, population status of the functional group at starting year and the estimated changes in functional group biomasses. Intrinsic vulnerability was represented by the index developed in Cheung et al. (2005) (Chapter 2) while population status was expressed as the ratio of current to unexploited biomass. The relationships between intrinsic vulnerability, population status, biomass change and depletion levels were governed by sets of rules (Table 7.1). The rules represented qualitative descriptions of how depletion risks were inferred from indices, i.e., the higher the intrinsic vulnerability and the larger the decline in biomass of the functional group, the higher the DI . In principle, the rule matrix is similar to other decision matrices, e.g., the 'traffic light' approach (Caddy 2002) or a 'Consideration Matrix ' suggested for the management of cod and haddock ( F R C C 2002). These 219 approaches are systems of status indicators, reference-limits, and corresponding actions which implicitly infer risk of over-exploitation or depletion (and thus leads to management actions) based on pre-specified rules. There are attempts to implement the traffic light approach in a fuzzy logic system (Silvert 2001). The levels of intrinsic vulnerability, biomass decline and DI were represented by fuzzy membership functions (Figure 7.1). Fuzzy membership functions are mathematical functions that determine the degree of membership to different heuristic categories based on the input parameters. As prior knowledge about the choice of fuzzy membership functions for the input attributes was lacking, the simplest forms were employed: trapezoid membership functions at the upper and lower limits and triangular membership functions at intermediate positions on the axis. Other options include membership functions having a sigmoid, gamma, or irregular shapes (Cox 1999), which may be explored to test whether their uses are justified. The memberships to different levels of depletion were calculated from the levels of biomass decline and the intrinsic vulnerability of the species. The memberships to different levels of biomass decline were estimated based on the predicted change in functional group biomass from the simulation model. The predicted biomass changes became the independent variable of the respective fuzzy membership functions (Figure 7.1a). These membership functions then provided the memberships to different levels of depletion. For instance, Chinese bahaba (Bahaba taigingensis) belongs to the group 'large croakers' in the N S C S models (Chapter 6). Based on its life history and ecology, its intrinsic vulnerability was estimated to be very high, with a degree of membership of 0.5. If the Ecosim model predicted that biomass of 'large croakers' declined by 84% relative to the starting biomass, based on the fuzzy membership functions specified in Figure 7.1a, the memberships to 'high' and 'very high' biomass decline would be 0.55 and 0.45, respectively. Based on the pre-defined rules (Table 7.1): IF intrinsic vulnerability is very high (0.5) and decline in biomass is large (0.55) T H E N depletion is high (0.5) IF intrinsic vulnerability is very high (0.5) and decline in biomass is very large (0.45) T H E N depletion is very high (0.45) 220 Table 7.1. Heuristic rules for the relationship between intrinsic vulnerability, relative abundance and the depletion index (DI). Decline in abundance of species within group (relative to B0)* Very low Low Moderate High Very high Extremely high Intrinsic vulnerability Low Minimum DI Minimum DI Very low DI Low DI Low DI Moderate Minimum DI Very low DI Low DI Low DI Moderate DI High Minimum DI Low DI Low DI Moderate DI High DI Very high Minimum DI Low DI Moderate DI High DI Very high DI High/Very high DI Very high DI Very high DI Very high DI Default decline in population is calculated as biomass at ti/biomass at to, where to is the starting time of the simulation. However, if knowledge on unfished biomass (6\u00E2\u0080\u009E) is known, the starting biomass can be replaced by B\u00E2\u0080\u009E. Predicates Conclusions c) Depletion index Figure 7.1. Fuzzy membership functions for the inputs: (a) predicted decline in functional group biomass; (b) estimated intrinsic vulnerability of the species; and (c) output: depletion index. The upper two figures (a and b) are fuzzy membership functions for the predicate while the lower figure (b) is fuzzy membership functions for the conclusions. Each attribute are categorized into different levels based on the fuzzy membership functions: V L - very low; L - low; M - medium; H - high; V H - very high; E H - extremely high; Mod - moderate; Min - minimum. The broken line in Figure c indicates the centroid of the 'moderate' fuzzy membership function. 222 Chinese bahaba would have memberships to 'high depletion risk' and 'very high depletion risk' of 0.5 and 0.45, respectively (values in parentheses are the degree of memberships). The memberships of the conclusion (levels of depletion) were calculated from the minimum of the memberships to their predicates (i.e., biomass decline and intrinsic vulnerability) (Zadeh 1965). We obtained the degree of membership of the final conclusions by combining the conclusions from each heuristic rule. Membership of the conclusion from each rule was combined using the knowledge accumulation method from Buchanan and Shortliffe (1984): Membershipe \u00E2\u0080\u0094 Membershipe_i + Membership,\u00E2\u0080\u00A2 \u00E2\u0080\u00A2 (1 - Membershipe_r) eq. 7.3 where Membership,, is the degree of membership of the conclusion after combining the conclusions from e pieces of rules, and Membership, is the degree of membership of the conclusion of rule /. DI was then calculated from the average of the centroids of each output fuzzy membership function weighted by the membership associated with each conclusion (/) (Figure 7.1): 5 ^ Centroidj \u00E2\u0080\u00A2 membership j DI = ^ eq. 7.4 membership j (=1 In a triangular membership function, the centroid was assumed to be the input variables corresponding to the peak of the triangle. For a trapezoid membership function, the centroid was assumed to be the mid-point between the two ends of the plateau. Following the example of the Chinese bahaba, the estimated DI was 80 (scale from 1 to 100, maximum depletion index = 100). I programmed the calculation of the DI using Visual Basic 6 and incorporated this sub-routine into the E w E modelling software. B y specifying the lists of species within the functional group of a particular Ecopath model, this new routine automatically obtained the required life history parameters for each species from FishBase and calculated the 223 intrinsic vulnerability indices based on the available inputs. Specifically, the species composition and their life history information (maximum body length with, i f available, the von Bertalanffy growth parameter K, natural mortality rate, age-at-maturity, longevity, fecundity, spatial aggregation strength and geographic range) (see Chapter II for details) was based on the information available from FishBase (Froese and Pauly 2004, www.fishbase.org). Secondly, this list (442 species of fishes) was uploaded into the newly developed routine in Ecosim (Appendix 7.1). When the Ecosim simulation was run, the routine calculated the DI of the functional group for each time-step (see Appendix 7.1 for screenshots of the interface). Thus the DI can be used as an indicator of the conservation status of marine fishes in Ecosim. The use of this index in policy optimization routine of Ecosim is discussed in Chapter 8. 7.2.2. Validation with the NSCS model a. Time-series fitting The 2000s N S C S model described in Chapter 6 was used to test the DI. This model has 38 functional groups and six fishing fleet. To run the model in Ecosim, one of the most important input parameters was the vulnerability parameter that determined the form of the predator-prey relationships. However, the vulnerability parameter in Ecosim cannot be measured empirically. Therefore, it was estimated from fitting the simulation model to time series of the observed index of abundance (CPUE) . To estimate the vulnerability parameters, standardized C P U E data from 1973 to 1988 for 17 commercially exploited taxa (Chapter 5) were fitted to Ecosim dynamic simulations using the 1970s N S C S model. Firstly, changes in the ecosystem groups in the 1970s model were simulated using nominal fishing effort from 1973 to 1988 (total engine power of boats in the N S C S (Department of Fishery Ministry of Agriculture, People's Republic of China, 1996). Then vulnerability factors by prey groups were varied iteratively using a numerical search routine to minimize the sum-of-square error between the observed and predicted abundance trends (Christensen et al. 2004). Basic Ecopath input parameters were also adjusted based on their estimated ranges to improve the fit. The estimated vulnerability factors were then transferred to the 2000s model. 224 Phytoplankton biomasses were varied iteratively to evaluate the possibility of the ecosystem being driven by variations in primary production. After estimating the vulnerability factors by time-series fitting, phytoplankton biomasses in each simulation time-step were varied until the sum-square-error between the observed catch rates data and the simulation results was minimized. Primary productivity and fisheries production in the N S C S were suggested to be partially dependent on environmental factors driven by the winter monsoon (Qiu et al. South China Sea Fisheries Institute, unpublished data; Le and Ning 2006). The duration (in hours) of the strong winter monsoon signals in winter (from November to March) issued by the Hong Kong Observatory (http://www.hko.gov.hk) was used as an index of the strength of the monsoon. The same index was compared with the estimated primary production anomalies from the model to identify any correlation between the two. b. Relationship between system indicators Changes to the N S C S ecosystem were simulated in Ecosim and the DI was calculated for each simulation. To calculate the depletion index, firstly, a list of the major species that were likely to be represented in the catches was prepared. Then, using the N S C S model that had been fitted with time-series C P U E data, changes of the N S C S ecosystem in 30 years were simulated with over 180 different combinations of fishing efforts of the six fisheries sectors. For each simulation, the average DI was calculated from: 4-< ' eq. 7.5 DI =-! \u00E2\u0080\u0094 N where DI'j is the depletion index of species /. AT is the total number of species in the functional group specified in the simulation model. For each of the 180 simulations, the Q-90 and M T I were also calculated from the simulation results. Correlations of the DI with the Q-90 and M T I were calculated. 225 c. Sensitivity analysis The sensitivity of the calculated D I to vulnerability factors in Ecosim and the rule matrix in calculating the DI was tested. The vulnerability factors are scaling parameters in the Ecosim dynamic simulation model which determine the degree of predator-prey interactions by determining the rate of transfer from a pool of invulnerable prey to a pool of prey vulnerable to predation (Walters et al. 1997; Walters & Martell 2004). Ecosim simulations are generally sensitive to the vulnerability factors. Thus, in this study, simulations were repeated with three different assumptions of vulnerability factors. The assumptions represented top-down (vulnerability factors = 10), bottom-up (vulnerability factors = 1) and 'mixed' (vulnerability factors proportional to the trophic level of prey groups) controlled ecosystem. The relatively extreme bottom-up and top-down assumptions of predator-prey interactions were used to test the responses of the DI under these extreme assumptions. Moreover, a major assumption in calculating the DI was the rule matrix that determined the relationship between the intrinsic vulnerability, functional group abundance and the depletion levels. Therefore, the analysis was repeated with alternative rule matrices representing 'conservative' and 'liberal' rule sets. The effects of varying these parameters on the calculated D I and its relationship with other indicators were evaluated. 226 Table 7.2. Alternative heuristic rules that describe the relationship between intrinsic vulnerability, relative abundance and the depletion index (DI) representing the more optimistic (upper table) and pessimistic (lower table) scenarios. Decline in abundance of species within group (relative to B0)* Very low Low Moderate Large Very large Extremely large Intrinsic vulnerability Low Moderate High Very high Minimum DI Minimum DI Minimum DI Minimum DI Minimum DI Minimum DI Very Low DI Very Low DI Minimum DI Very low DI Very Low DI Low DI Very low DI Very Low DI Low DI Moderate DI Very Low DI Low DI Moderate DI High DI Low/Moderate DI High DI High DI High DI Decline in abundance of species within group (relative to B0)* Very low Low Moderate Large Very large Extremely large Intrinsic vulnerability Low Moderate High Very high Minimum DI Minimum DI Minimum DI Minimum DI Minimum DI Very low DI Low DI Low DI Very low DI LowDl Low DI Moderate DI Low DI Low DI Moderate DI High DI Low DI Moderate DI High DI Very high DI High/Very high DI Very high DI Very high DI Very high DI 'Default decline in population is calculated as biomass at t 0 + i /biomass at to, where to is the starting time of the simulation. However, if knowledge on unfished biomass (/5\u00E2\u0080\u009E) is known, the starting biomass can be replaced by B\u00E2\u0080\u009E. to 7.3. Results 7.3.1. Time-series fitting Simulated biomasses from the fitted model generally matched with the observed relative biomass trends (Figure 7.2). The total sum-of-square error was minimized to around 77.9 from over 1,000 after varying the vulnerability factors iteratively. However, the model could not emulate some of the large fluctuations in the observed relative biomass time-series of some groups (e.g., large croakers, threadfin breams, pomfrets). The high variability of the C P U E data was likely a result of the high uncertainty of the C P U E estimations due to the original data collection methodology (see Chapter 5 for details). The sum-of-square error increased substantially when vulnerability factors were assumed to be bottom-up (vulnerability factors = 1), top-down (vulnerability factors = 10) or proportional to the trophic level of prey groups throughout the system, which were estimated to be 122, 2266 and 1980, respectively (Figure 13). Particularly, under these alternative rule assumptions, the model failed to reproduce the rapid decline in C P U E in the 1980s for the major commercial groups such as the threadfin breams, bigeye, cephalopods and small croakers. Varying the phytoplankton biomasses improved the goodness-of-fit. The sum-of-square error was reduced by about 35%. However, the predicted phytoplankton biomass anomalies did not correlate with the index of monsoon strength (Figure 7.4). In fact, the short time-series (16 years) available limited the utility of simulation results for evaluating the possible ecosystem effects of primary production variations. 228 Croaker(>30cm) Threadfin breams Lizardfishes Pomfrets Large demersal fishes E jt: 0) v c c o (/> (0 (0 E o ffi i 1 r 1975 1980 1985 Bigeye 1975 1980 1985 Cephalopod 1 9 7 5 1 9 R 0 1 g a s T r 1975 1980 1985 Groupers 1975 1980 1985 Hairtails 1 9 7 5 1 9 R n 1 9 8 5 i 1 r 1975 1980 1985 Snappers i 1 r 1975 1980 1985 Demersal sharks and rays 1 9 7 5 1 9 R 0 1 9 R 5 1 9 7 5 1 9 8 0 1 9 8 5 Melon seeds n 1 r 1975 1980 1985 Croakers (<30cm) 1 9 7 5 1 9 R 0 1 9 R 5 8-r 1975 1980 1985 Pelagic fishes 1 9 7 5 1 9 8 0 1 9 8 5 Year Figure 7.2. Time-series of the observed relative biomasses (solid lines) and predicted biomasses (broken lines) of the 14 functional groups in the northern South China Sea model. The observed relative biomasses are CPUE estimated from survey (Chapter 5) and has been scaled by the model to obtain the best fit with the predicted biomasses. The total sum-of-square error between the predicted and observed data is 78. a) Croaker(>30cm) Threadfin breams Lizardfishes Pomfrets Large demersal fishes 1975 1980 1985 1975 1980 1985 1975 1980 1985 1975 1980 1985 Year Figure 7.3. o Lizardfishes Pomfrets Large demersal fishes i 1 1 1 1 1 1 r~ 1975 1980 1985 1975 1980 1985 Year c) Croaker(>30cm) Threadfin breams Lizardfishes Pomfrets Large demersal fishes (A 0 C c o w ns E o 15 -i 1 r 1975 1980 19 Cephalopod -i 1 r 1975 1980 1985 Hairtails 1975 1980 19 Demersal sharks and rays I 1 r 1975 1980 1985 Croakers (<30cm) 1 r 1975 1980 1985 Year Figure 7.3. Time-series of the observed relative biomasses (solid lines) and predicted biomasses (broken lines) of the 14 functional groups in the northern South China Sea model, assuming that the ecosystem is: (a) bottom-up (assuming 'vulnerability factor' = 1), (b) top-down (assuming 'vulnerability factor' =10) and (c) 'mixed' (assuming 'vulnerability factor' is proportional to the group's trophic level). The sum-of-square error between the predicted and observed data of the bottom-up, top-down and \"mixed\" assumptions are 122, 2266 and 1980, respectively. to to Figure 7.4. Comparison between the predicted phytoplankton biomass (solid dots) from fitting the NSCS ecosystem model with time-series catch rate data and the observed winter monsoon strength index (open circles). 7.3.2. Depletion index (DI) The estimated DI was correlated with the biomasses of predatory or highly vulnerable groups (e.g., sharks and rays), particularly those that had been depleted (Figure 7.5). In the N S C S model, simulations with low DI (= 7.8) had large increases in biomasses of intrinsically vulnerable or previously depleted groups, such as the sharks and rays, groupers, threadfin breams and croakers. On the contrary, these groups were greatly depleted in the ecosystem with relatively high DI (= 51.5). The system with moderate DI (= 25.3) fell within these two extremes. Therefore, the DI calculated from the simulation results could largely represent the general conservation status of the ecosystem. Under different scenarios of fishing effort, the resulting DI was significantly and negatively correlated with the Q-90 index (Kendall's correlation test, correlation coefficient = 0.87, p<0.01) and the marine trophic index (MTI, Kendall 's correlation test, 233 correlation coefficient = -0.72, p<0.01) (Figure 7.6). Correlations between the depletion index, Q-90 and the M T I remained strong and significant under different the assumptions in the heuristic rules matrix (Kendall's tests, p<0.01) (Figure 7.7), although correlations between the indicators were slightly lowered. Moreover, the absolute values of the depletion index changed with different sets of rule. A more conservative set of rules lowered the predicted DI while the more liberal set of rules increased the predicted DI. The changes in absolute values would not affect the validity of using the depletion index provided that a consistent set of rules was used throughout the analysis. Varying the assumptions about vulnerability in Ecosim had stronger effects on the relationship between DI and the Q-90 index (Figure 7.8). When the N S C S ecosystem was assumed to be bottom-up or 'mixed' controlled (i.e., vulnerability factors of all predator-prey interactions were set to be 1 and proportional to the trophic level of prey groups, respectively), the relationship of DI with the M T I and Q-90 index remained significant and strong (Kendall's correlation test, p<0.01; correlation coefficients = 0.80 and 0.52, respectively for the bottom-up assumption and 0.95 and 0.95, respectively for the 'mixed' assumption). However, when the system was assumed to be top-down controlled, i.e., vulnerability factors were set to be 10, the plot between the M T I and DI became more scattered, although the correlation was significant (Kendall's correlation test, p<0.01, correlation coefficient = 0.63). The top-down assumption had little effect on the correlation between the DI and Q-90 index (Kendall's correlation test, p<0.01, correlation coefficient = 0.82). The estimated DI was able to track the changes in the conservation status of the N S C S ecosystem (Figure 7.9). When changes in the N S C S ecosystem due to linear increase in the fishing effort of all sectors from the status quo were projected for 30 years, the DI increased over time. The increase in DI indicated a deterioration of the conservation status of the ecosystem. On the contrary, with linear decline in fishing effort from the status quo, the DI decreased consistently, indicating an improvement in conservation status. 234 E o-.2 \u00C2\u00AB Lfi, Figure 7.5. Simulated changes in biomass of the 37 functional groups of living organisms in the NSCS model relative to the status quo (Ecopath base) (log-transformed) when the estimated depletion index is (a) low, DI = 7.8, (b) medium, DI = 25.3 and (c) high, DI = 51.5. 235 Figure 7.6. Comparisons of the depletion index with published ecological indices (a) Q-90 index (Ainsworth and Pitcher 2006) and (b) mean trophic level of catch (Pauly et al. 1998). The dotted lines represent the results of linear regression between the two indices. 236 a) b) 0 10 20 30 40 50 0 '0 20 30 40 50 Depletion index Depletion index c) d) 50 Depletion index Depletion index Figure 7.7. Correlations between (a, b) Q90 biodiversity index and (c, d) mean trophic level of catch with the depletion index using (a, c) conservative rule matrix and (b, d) liberal rule matrix. 237 e) f) 0 20 40 60 80 0 20 40 60 Depletion index Depletion index Figure 7.8. Correlations between (a, c, e) Q90 biodiversity index and (b, d, f) mean trophic level of catch with the depletion index calculated from simulations by assuming: (a, b) bottom-up ecosystem control, i.e. vulnerability factors = 1, (c, d) top-down ecosystem control, i.e. vulnerability factors = 10, and (e, f) 'mixed' ecosystem control, i.e. vulnerability factors are proportional to the trophic level of prey groups. 238 60 -, Pessimistic TJ C o 40 A Q. TJ 20 4 a> <0 Optimistic > < 0 2000 2010 2020 2030 Year Figure 7.9. Predicted average depletion index of the NSCS ecosystem from 2000 to 2030. The pessimistic scenario assumes a 3-fold linear increase in fishing effort from 2000 to 2030 while the optimistic scenario assumes a linear decrease in fishing effort to a quarter of the 2000 level in 2030. 7.4. Discussion The depletion index presented in this study provided a means to evaluate conservation status at the species level through ecosystem simulation modelling. The DI accounts for the life history and ecology of the composite species within a functional group. Also, the rule matrix was developed from well-established empirical trends. These provided the biological rationale to the calculation of the index. The DI was able to indicate the conservation status of the species in all test cases. Firstly, predictions from the DI were consistent with other biodiversity indicators (the Q-90 and MTI) . Secondly, the DI calculated from the simulation results matched with the overall conservation status as indicated by the increase in biomass of depleted and vulnerable groups predicted by the ecosystem simulation models when DI was low and vice versa. For instance, a high DI was predicted for a highly depleted ecosystem with fishing pressure. A low DI was predicted for an ecosystem with low fishing pressure that 239 was lightly depleted. These results demonstrate that the DI can be a valid indicator of the conservation status of an ecosystem. The DI was robust to major assumptions in the calculation of the index. A major assumption in the calculation of DI was the rule matrix. This matrix determined how the input parameters (intrinsic vulnerability and decline of functional group abundance) were related to the outputs (depletion risk of the species within a functional group). Sensitivity analysis showed that the estimated DI was robust to this assumption. In fact, the critical factor in determining the rule matrix is to capture the positive relationship between the intrinsic vulnerability of a species, the rate of decline of the functional group biomass and the resulting depletion risk of the species. The exact form of the rules had a small effect on the relative level of the DI. The DI was also robust to uncertainties in the ecosystem model. The performance of the DI remained good when different trophic controls (vulnerability factors in Ecosim) were assumed. Calculations of DI were applied to fish groups only, but results of this study demonstrate that the DI is a valid and stable indicator of the conservation status of the ecosystem. The DI was based on easily obtainable information available from open-access online databases and may potentially be applied to most marine ecosystems. In fact, the required biological information for calculating the index is readily available from FishBase (www.fishbase.org). The list of species within the functional groups can be compiled from the species database in FishBase. Also, the new sub-routine developed in this study can automatically extract the required biological parameters from a FishBase database. Moreover, the intrinsic vulnerability index, a key parameter in calculating the DI, wi l l soon be made available as a standard index in FishBase (Froese, R. fFM-Geo-Mar, K i e l , Germany, pers. comm.). The validity of the predictions from the N S C S model was supported by fitting the model with time-series of catch rates. Although some variations in the data could not be explained by the model, such variations mainly resulted from data uncertainty and environmental changes. The original time-series data were collected using low-accuracy methodology (based on fisher interviews at fishing ports). Therefore, the time-series data should have high inherent uncertainty and variations. Secondly, the N S C S is strongly 240 influenced by the monsoon (Morton & Blackmore 2001). The seasonal variation of ocean currents driven by the monsoon may explain the fluctuations in the catch rates of some taxa. However, the time-series (16 years) was too short for any meaningful time-series analysis to evaluate the correlations between the data and potential environmental drivers. Such analysis could be done when more consistent data are collected in the future. Given these uncertainties, the model successfully reconstructed the average trends of the C P U E time-series included in the analysis. Thus the model was believed to provide reasonable predictions of ecosystem changes and could be used as a reference model to test the DI. The DI was applied to fish groups only, but its application can be extended to other marine fauna. The DI bases strongly on the index of intrinsic vulnerability to fishing which is developed from life history theory (Chapter 2). As life history theory is generally invariant to a wide range of fauna (from mammals to invertebrates) (Charnov 1993; Charnov & Downhower 2002), correlates between life history and vulnerability to fishing in non-fish marine animals should be similar to those in fishes. Also, the inputs for calculating the intrinsic vulnerability index and DI (e.g., maximum body size, age at maturity and longevity) are available for non-fish groups. The DI should be applicable to a wide range of marine fauna. Future studies can compare the predictions from the indices with empirical data to test the applicability of the indices to non-fish groups. In summary, the DI is proved to be a valid indicator for evaluation of the conservation status of fish species in models with highly aggregated groups. The DI helps to overcome a fundamental difficulty in addressing the conservation concerns at the species level while evaluating the changes at the ecosystem level. This is particularly useful in understanding the trade-offs between objectives that focus on the species level (e.g., conservation of vulnerable species) and those that focus on the ecosystem level (e.g., economic benefits from the ecosystem). Applications of the DI in evaluating various policy trade-offs in the N S C S are documented in Chapter 8. Here, an E w E model of the N S C S was used as operating model to test the indicator. 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T R A D E - O F F S B E T W E E N C O N S E R V A T I O N A N D SOCIO-E C O N O M I C O B J E C T I V E S IN M A N A G I N G A T R O P I C A L M A R I N E E C O S Y S T E M 7 8.1. Introduction Conservation of marine species that are threatened by fishery exploitation is a growing concern. Over the past few decades, over-exploitation of fishery resources has caused depletion, or extirpation in some extreme cases, of marine populations (Pauly et al. 2002; Dulvy et al. 2004; Hilborn et al. 2004a). The need to conserve these populations is being formally recognized by international and national treaties and legislations. Developing fishery management policy that conserves marine biodiversity is an important step towards addressing the above problems. A challenge to managing exploited marine ecosystems is the trade-offs between ecological, social and economic objectives. A trade-off can be thought of as giving up some of one thing to get more of something else. Given a set of alternative allocations or system configurations, a system is considered to be in Pareto-optimum i f improvement in any one individual's benefits results in a reduction in benefits to others. The Pareto-frontier is the set of system configurations that are all in Pareto-optimum (Baumol et al. 1998). Examples of the Pareto-frontier of a trade-off between two objectives are shown in Figure 8.1. Points located within the Pareto-frontier are inefficient or sub-optimal, because one objective can be improved without causing a reduction in the other. Thus, evaluation of the trade-off relationships can help reveal the 'efficiency' of the fisheries in achieving specified management objectives, and the costs and benefits of policy decisions that change the position on a trade-off state-space diagram. 7 A version of this chapter has been submitted for publication. Cheung, W. W. L. & Sumaila, U. R. Trade-offs between Conservation and Socio-economic Objectives in Managing a Tropical Marine Ecosystem. Ecological Economics{in review}. 247 Objective B Figure 8.1. Possible trade-off relationships between two fisheries management objectives: (A) linear, (B), convex, and (C) concave (Walters & Martell 2004). A wide range of trade-offs can be found in fishery management. Among these, trade-offs between ecological and socio-economic objectives are particularly important (Walters & Martell 2004). On one hand, catch may have to be reduced to minimize the risk of stock collapse in the long term (Hilborn et al. 2004b). On the other hand, catch should be maximized in the short term to provide economic rent and maintain the livelihoods of the fishing communities. The trade-offs between conservation and socio-economic objectives are especially complex in a multi-species or ecosystem context. Most conventional resource management approaches aim to maximize the long term sustainable yield of the resources being targeted (Rosenberg et al. 1993; Pitcher 1998). However, most fisheries (particularly tropical multi-species fisheries) catch a mixture of species and stocks with different productivity. Thus, the fishing effort required to achieve the maximum sustainable yield may over-exploit, deplete or even extirpate some of the least productive species or stocks, while the most productive stocks may be under-exploited. Particularly, 248 if the fisheries are open access or i f illegal fishing is prevalent, the ecosystem may be fished to a bionomic equilibrium (BE) - the point at which the total revenue from fishing is just enough to cover the total cost (Figure 8.2). A t BE, the less productive species/stocks may be over-exploited or extirpated (Walters et al. 2005) (Figure 8.2). To restore and conserve over-exploited and vulnerable species, fisheries management may need to consider the trade-off between exploitation of species that are not trophically linked, but which have different productivity and are targeted by the same fisheries. to W O O TJ C (0 0) 3 c > DC R(MSY) Revenue Species C F(MSY) F(BE) Fishing effort Figure 8.2. Schematic diagram comparing revenue and cost that can theoretically be obtained from a multi-species fishery (without tropic interaction between the species) (Sparre & Venema 1998). The broken lines represent the hypothetical fishery revenue from catching the three species: Species A , B, C have high, medium and low productivity, respectively. Total revenue is represented by the solid line. The shape of this curve is due to the disappearance of less productive species as fishing effort increases. The fishing effort, F(MSY), that achieves the revenue at Maximum Sustainable Yield R(MSY) for all three species leads to under-exploitation of Species A, over-exploitation of Species B and extirpation of Species C. Unregulated fisheries (fishing effort at BE) leads to the over-exploitation of all species, with the less productive Species B and C extirpated. Fisheries subsidies generally lower the cost of fishing, thus reduces the slope of the cost line and increases the open access equilibrium effort, F(BE). The trade-off becomes more complicated when trophic linkages between the species or stocks are considered explicitly. For instance, the recovery of predatory, charismatic or less productive species may reduce the productivity of their commercially valuable prey. The release from predation pressure from cod on commercially valuable 249 benthic crustaceans in the Northwest Atlantic following the northwest Atlantic cod collapse is a good example (Worm & Myers 2003). Moreover, following the depletion of a predator by fishing, the increased productivity of prey that feed on the juveniles of the preys' predators may prevent recovery of the depleted predators (Walters & Kitchell 2001). A more holistic approach would help understand the trade-offs between different management objectives at the ecosystem level. Trade-offs between social values (e.g., as a source of livelihoods) and conservation or economic values of the fisheries may be acute. Particularly, when the ecosystem has been over-exploited, most management policies aimed at restoration of depleted populations or improvement of the profitability of fisheries would require reduction of fishing capacity. The associated social problems may be more serious in developing countries where alternative livelihoods for fishers are lacking and the social benefit system is not well-developed. Therefore, to make well-informed policy decisions, policy makers and the public need to understand the costs and benefits associated with such trade-offs. Understanding the trade-offs between conservation and socio-economic benefits are of considerable interest in the northern South China Sea (NSCS), which is defined as the continental shelf (less than 200 m depth) ranging from 106\u00C2\u00B053'-119\u00C2\u00B048' E to 17\u00C2\u00B010'-25\u00C2\u00B052' N . It falls largely within the Exclusive Economic Zone of the People's Republic of China, but Vietnam also shares part of the Gu l f of Tonkin (Chapter 1). It is a tropical ecosystem where coral reefs, estuaries, mangroves, seagrass beds and others, provide habitats for a rich array of species (Morton & Blackmore 2001). Rapid expansion of fisheries in the region has resulted in depletion of most fishery resources and loss of biodiversity. From 1978 to 2000, the number of mechanized fishing boats from Guangdong, Guangxi and Hainan - the three provinces bordering the coast of the N S C S - increased from 8,109 to 79,249 (Department of Fisheries, Ministry of Agriculture, People's Republic of China 1991, 1996, 2000). Although N S C S fisheries appear to have a reasonably comprehensive set of legislation and regulations, the high level of illegal fishing (e.g., fishing without a license) may have driven the system towards bionomic equilibrium (BE). Some vulnerable species, such as the Chinese 250 bahaba (Bahaba taipingensis) and Red grouper (Epinephelus akaara), were extirpated locally while numerous other species were depleted or over-exploited (Sadovy & Cornish 2000; Cheung & Sadovy 2004). Previous modelling analysis suggested that approximately 20% of fishing grounds should be closed to fishing and fishing effort should be reduced at an annual rate of 5% for 30 years in order for the N S C S ecosystem to recover (Cheung & Pitcher 2006). However, the cited study did not evaluate the socio-economic implications of the suggested policy initiatives and its cost-effectiveness. 8.1.1. Buy-back as means to restructure fishing fleets Fishing vessel or license buy-back has been proposed as a means to reduce fishing capacity and restructure fishing fleets to achieve specific management objectives (Cunningham & Greboval 2001). This is a financial mechanism designed to buy fishing vessels or licences from fishers or fishing firms in order to reduce fishing effort and capacity. Various factors, however, may render a buy-back scheme ineffective in removing fishing capacity (Cunningham & Greboval 2001). For instance, the bought-out fishing capacity may seep back into the fishery through improved technology, increased fishing knowledge and experience. Also, i f fishers expect a buy-back scheme to be implemented in the near future, they may build up excessive capacity in anticipation of the buy-back program (Clark et al. 2005). Internalizing the cost of a buy-back through a 'fisher-paid' scheme may improve its effectiveness (Clark et al. 2005). Conventional economic theory predicts that, for an over-exploited system, the net benefits from the system should increase i f fishing capacity is reduced to a level that produces the highest rent (largest positive difference between the total revenue and cost) or Max imum Economic Rent ( M E R ) (Gordon 1954). It has been suggested that benefits gained by fishers who remain in the fisheries can be used, through taxation, for instance, in vessel buy-back schemes (Pauly et al. 2002; Clark et al. 2005). This paper attempts to identify the trade-offs between conservation and socio-economic objectives in managing a tropical marine ecosystem exploited by multi-species fisheries. Moreover, the possibility of using vessel buy-backs to achieve conservation goals is assessed. 251 8.2. Methods The analyses in this study were based on dynamic simulation models using Ecopath with Ecosim, or E w E (Christensen & Walters 2004a). A n ecosystem model of the N S C S was used as a case study to evaluate the trade-offs between different objectives in managing tropical multi-species fisheries. Multi-objective decision analysis was applied in order to identify and display policy trade-offs under conflicting objectives (Enriquez-Andrade & Vaca-Rodriguez 2004). Particularly, E w E incorporates formal numerical optimization routines to search for optimal fishing fleet configurations that maximize the benefits under stated management objectives (see below for details). Fishing efforts that would maximize the benefits to specified conservation, economic and social objectives were estimated. The possible trade-offs between the objectives were then mapped out quantitatively. Finally, strategies to facilitate fishing fleet restructuring to achieve the management goals were discussed. 8.2.1. Ecopath with Ecosim modelling The N S C S ecosystem model represents a hypothesis of the ecosystem structure in the early 2000s (see Chapter 6). The Ecopath model consists of 38 functional groups with six fishing fleets characterized by their gears (stern and pair trawl, shrimp trawl, purse seine, gillnet, hook and line, and others). The model had been fitted with time-series survey data (see Chapter 7). This supports the validity of using the N S C S model in Ecosim dynamic simulations. Ecosim is a dynamic model which simulates changes in ecosystems that have been described with Ecopath (Walters et al. 1997). It estimates changes of biomass among functional groups in the ecosystem as functions of abundance of other functional groups and time-varying catch rates, taking into account predator-prey interactions and foraging behaviors (Walters et al. 1997; Pauly et al. 2000). Ecosim is governed by the basic equations: eq. 8.1 dt and 252 eq. 8.2 where equation 8.1 gives the rate of change of biomass of functional group i, g,is growth efficiency, M and F are natural and fishing mortalities, I and e are immigration and emigration rates, Q, is the consumption of group j organisms by group i organism, v and v ' parameters represent rates of behavioral exchange between invulnerable and vulnerable states and ay represents rate of effective search by predator j for prey type /. The behaviours of functional groups in dynamic simulations are heavily affected by the 'vulnerability factor' - a scaling factor of v which determines the foraging behavior of the functional groups in predator-prey interactions (Walters et al. 1997; Walters & Martell 2004). The 'vulnerability factors' in the N S C S model were estimated from empirically observed catch-per-unit-effort data (see Chapter 7). Alternative 'vulnerability factors' representing a complete 'bottom-up' (vulnerability factor = 1), 'top-down' (vulnerability factor = 10) controlled ecosystem (Christensen et al. 2004) and 'mixed' (i.e. vulnerability factors proportional to the trophic level of the prey groups) (Cheung et al. 2002) were used to test the sensitivity of the analysis to these settings. 8.2.2. Policy optimization The policy optimization routine in Ecosim (Christensen & Walters 2004b) was used to identify the optimal fishing efforts that maximize the benefits from the N S C S given the specific policy objectives. This analysis included four policy objectives: (a) Maximizing economic rent Economic rent is represented by the net present value (NPV) of the flow of profits from the different fisheries over time. N P V of the profits was calculated by taking the difference between landed values and cost, and discounting this over time with a specified discount rate, i.e., m n q eq. 8.3 (=0 y=l k=l 253 where Y and P are the annual catch and price of species k, C is the total cost of fishing of fishery sector j, i is number of years from the present to the end of the time horizon of the analysis and 5 is the discount rate. The discount rate applicable in China was assumed to be 3%, calculated from the interest rate of 9-year Chinese government bonds from 2000-2004 (http://www.chinabond.com.cn). Alternative discount rates of 1% and 5% were used to test the sensitivity of results to this assumption. Since accurate economic data for the fisheries were not available from China, data from Hong Kong were assumed to be reasonable for the N S C S region. Fishing boats registered in Hong Kong were generally similar to those non-Hong Kong boats in mainland China in recent years. In fact, most new fishing boats in Hong Kong were built in China (Sumaila et al. in press). Also , Hong Kong fishers reported that wholesale prices were similar between landing ports (Several members, Aberdeen Fishermen Association, Hong Kong, pers. com.). Skippers of fishing boats in Hong Kong, particularly the trawlers, mostly employ workers and purchase supplies from the Mainland (Sumaila et al. in press). Thus, the cost of fishing in the N S C S continental shelf in the 2000s between the fleets from Hong Kong and China should be similar (Table 8.1). A l l values in H K $ were converted to U S $ by assuming the fixed exchange rate of U S $ 1 = H K $ 7.785. Table 8.1. Landed value, total cost and profitability of the six fishing fleets in the 2000s NSCS ecosystem model. Fishing fleets Total landed value (US$ thousand k m 2 ) Total cost (US$ thousand km' 2) Profitability (%) Stern & Pair trawl 2.86 2.64 7.69 Shrimp trawl 15.91 10.02 28.07 Purse seine 1.07 0.77 37.02 Hook and line 0.31 0.29 6.45 Gillnet 2.12 2.03 4.23 Others 2.03 1.19 41.60 (b) Employment Employment was measured by the number of jobs that could be generated from the fisheries. Data on the average total amount spent on wages by each fishing sector, and the total value of the catches of Hong Kong fishing fleets were obtained from the Hong 254 Kong government (Agriculture, Fisheries and Conservation Department, Hong Kong, unpublished data). The ratios of wage to landed values in a year between fishing fleets in Hong Kong and the Mainland were assumed to be similar. The amount spent on wages per unit of catch value was calculated and used as a proxy of the amount of employment provided by each fishery sector (Table 8.2). Table 8.2. The estimated relative jobs per value for the six f ishing fleets in the N S C S ecosystem model . Fishing fleet Number of jobs Stern and pair trawls 2 Shrimp trawl 2 Purse seine 3 Hook and line 6 Gillnet 3 Others 2 1. Estimated number of jobs per catch value was relative between fishing fleet. The relative number of jobs were used in the optimization analysis, thus the absolute values were not important in the trade-off analysis. (c) Ecosystem structure Two aspects of ecosystem structure were evaluated: (1) ecosystem maturity (Odum 1969) - measured by the longevity-weighted biomass of all functional groups in the model (Christensen & Walters 2004b), (2) biomass diversity - measured by a relative index calculated from a modified Kempton's Q (Q90) index (Kempton & Taylor 1976; Ainsworth & Pitcher 2006). Average longevity was approximated by the biomass to production ratio of the groups. Thus, ecosystem maturity can be maximized by increasing the biomass of long-lived groups. The Q-90 index, a variant of the Q-index developed by Kempton and Taylor (1976), indicates the diversity of the functional groups in an ecosystem. The Q-90 index is calculated from the slope of the cumulative functional group abundance curve between the 10 and 90 percentile (Ainsworth & Pitcher 2006). (d) Conservation of vulnerable species A n index called the Depletion Index (DI) that had been developed to represent the relative degree of species depletion by fishing in ecosystem simulation modelling was used as a performance indicator of conservation status. Detailed methodology for 255 calculating the DI is reported separately in Chapter 7. In brief, to calculate the DI , firstly, list of species within the aggregate groups were uploaded to a sub-routine specifically developed to calculate the DI in Ecosim (see Chapter 7). Then, life history parameters for each species were obtained from FishBase (www.fishbase.org). These parameters included: maximum body length, maximum length, age at first maturity, longevity, von Bertalanffy growth parameter K, natural mortality, fecundity (only low fecundity is considered), spatial behaviour and geographic range. The life history data were combined with simulated changes in the abundance of the aggregate groups in the ecosystem model using a rule-based expert system (Chapter 7). The expert system then estimated the DI for each aggregate group. The DI ranges from 1 to 100, with 100 indicating the most severe population depletion. The DI was validated and shown to be robust to its major assumptions in comparing the relative conservation status between alternative ecosystem states (Chapter 7). The average DI of all the specified species in the ecosystem model was used as a proxy for the conservation status of the ecosystem. The proxy (FuncDi) is calculated from: N 4\" ' eq. 8.4 Funcn. =100\u00E2\u0080\u0094'\u00E2\u0080\u00A2\u00E2\u0080\u0094 ;\u00E2\u0080\u0094 D/ N where DIj is the depletion index of species i. AT is the total number of specified species. The policy search routine employs a multi-criterion non-linear numerical search method (Fletcher 1964; Fletcher & Reeves 1964). It iteratively varies fishing efforts (by fleets) and simulates the changes in fisheries and the ecosystem accordingly until the objective function is maximized. The objective function is calculated from the weighted-sum of the indices that represent the above policy objectives: 256 FlmcTou\u00E2\u0080\u009E = WE,\u00E2\u0080\u009E\u00E2\u0080\u009E * NPV + wemploymau \u00E2\u0080\u00A2 Employment + w e c o s v f l O T \u00E2\u0080\u00A2 Ecol(i) where eq. 8.5 Ecol{\) = ecosystem maturity = (2_i \u00E2\u0080\u0094 * ^ i - * functional group diversity = Q90 I Ecol(i = Conservation status) = FuncD1 where w is the weighting factor for objectives specified in the subscript, NPV is the net economic value (calculated from eq. 8.3), B and P are the biomass and biomass production of functional group i, respectively. Trade-offs between policy objectives were evaluated by running the policy search routine with different weighting factors for each objective. Particularly, the trade-offs between conservation of vulnerable fish species and socio-economic objectives were plotted. To ensure the numerical optimization routine had not become trapped by local maxima, each optimization was initiated with random fishing fleet efforts and repeated a number of times (Christensen & Walters 2004b). Benefits in conservation status (expressed as average DI of the ecosystem) from the optimal policies were plotted against other policy objectives. The optimal solutions identified by the optimization routine were approximated as the Pareto-frontier of the trade-offs. 8.2.3. Cost of fleet restructuring The cost of restructuring the fishing fleets to achieve the Pareto-optimum outcome was estimated by evaluating the cost of a hypothetical buy-back scheme. The cost of buying-back one unit of fishing effort was calculated by treating the fisheries as a small business (Walters et al. in press). Since the theoretical buy-back scheme was assumed to happen once at time-step = 0 in the simulation, the buy-back price was calculated by multiplying the revenue per unit effort (/?,\u00E2\u0080\u00A2) for each fishing sector i by 5 times (Walters et al. in press). The total buy-back cost (cB) required to achieve the management goal was calculated by multiplying the required reduction in fishing effort for each sector i (AAT,) by the per unit effort buy-back price: 257 6 cB = ^ b K r R r 5 e q . 6 /=1 where AKi > 0 The above approach is usually used to value small businesses (Desmond & Marcello 1987), which is appropriate here because most of the fishing enterprises in the Northern South China Sea can be classified as small businesses (Desmond & Marcelo, 1987; Walters et al. in press). Also, it is assumed that fleet capital is perfectly non-malleable while human capital is malleable (Clark et al. 1979; Clark et al. 2005). Therefore, fishers were able to move to other job sectors, and in fact, the P R C authorities have been offering retraining programmes to facilitate switching to other sectors, e.g. aquaculture (Qiu, Y , South China Sea Fisheries Institute, pers. comm.). Thus, the buy-out of human capital is not included in the calculation. 8.3. Results 8.3.1. The Pareto-frontiers A convex-shaped Pareto-frontier was obtained from the trade-off between net present value of benefits from the fisheries and the average depletion risk of the system (Figure 8.3). The shape of the Pareto-frontier was approximated by a quadratic function. The maximum N P V of benefits (of about U S $ 180,000 km\"2) from the fisheries over 30 years was achieved at a system state with a depletion index of 27. Further improving the conservation status (reducing the depletion index from 27) reduced the maximum benefits from the fisheries. At a depletion index of 20, the marginal decrease in the N P V of benefits was US$ 5,600,000 km\" 2 per depletion index, while the rate of decrease was more than doubled at a depletion index of 10. The multi-criteria policy optimization routine could not find any 'optimal' solution when DI was over 27. In other words, any combinations of fishing fleet structure that result in a depletion index of over 27 are sub-optimal in economic and conservation 258 objectives. Thus, increasing the DI from an index of 27 would reduce the maximum N P V of benefits from the fisheries. The state of the N S C S fisheries in the 2000s was sub-optimal in terms of both conservation and economic objectives (Figure 8.3). Therefore, by definition, the conservation status (indicated by the DI) could be improved without reducing the economic benefits and vice versa. Under the status quo of the 2000s and a discount rate of 3% over a 30-year time horizon, the estimated average depletion index of the ecosystem was around 34 and the net present value of the profits was U S $ 117,000 km\" 2. Based on the predicted trade-off frontier, reducing the average depletion index of the system from the status quo level (34.3) to 15 should not compromise the maximum net economic benefits from the fisheries. Moreover, restructuring the fishing fleets could increase the maximum economic benefits from the status quo by over 50% (Figure 8.3). 200 w E 150 I s \u00C2\u00A3 2> 100 o o > \u00C2\u00A3 z \u00C2\u00AB 50 R =0.89 10 20 Depletion index 30 40 Figure 8.3. Pareto-frontier between the net present value of benefits (profit) of the fisheries and the estimated depletion index assuming a 3% discount rate and an exchange rate between US$ and HK$ of 7.785. The equation of the fitted function is: Y = -0.3 l x 2 + 17.66x - 69.48 where Y is the net present value (NPV) of benefits from the fisheries and x is the depletion index. The position of the status quo on the trade-off space is marked by an asterisk. The area enclosed by arrows shows the potential improvement from the status quo in which neither conservation nor economic benefits would have to be reduced to achieve an increase in the other. 259 The Pareto-frontier between the depletion index and the social benefits (number of jobs) from the fisheries was approximated by a linear relationship (Figure 8.4). Improving the conservation status (i.e., reducing the DI) from the status quo led to an approximately linear decline in the number of jobs provided by the fisheries. For instance, reducing the depletion index from 30 to 10 halved the number of jobs provided by the fisheries. Hence, to increase the relative number of jobs, conservation performance has to be reduced. S * 4 O 3 V _ w .2 o o ~ o o> > CD > 2! \u00E2\u0080\u00A2- 2\ F T = 0 . 7 9 o o o o o o\u00C2\u00B0e 20 40 60 Deplet ion index \u00E2\u0080\u00A2 o 80 Figure 8.4. Trade-off relationship between social (expressed in number of jobs created relative to the status quo) and conservation (depletion index) objectives. The relationship is approximated by a linear function: Y = 0.05x +0.32 (R\" = 0.79). The relationship between ecosystem maturity and economic benefits was sigmoid-shaped (Figure 8.5a). Economic profits of the fisheries remain relatively constant at around US$ 180,000 km\" 2 as relative ecosystem maturity index increased from 11 to 12.2 (region of economic stability). However, increasing the index from 12.2 to 12.5 led to a decline in the net economic profits of almost U S $ 200,000 km\" 2. Further improvement in. ecosystem maturity led to a rapid drop in economic benefits which turned a net gain into a large net loss of over US$ 1,200,000 km\" 2. This represents a region of economic collapse. Therefore, the highest ecosystem maturity level that could be achieved by keeping the net economic benefits at the same level as the status quo was about 12. 260 The shape of the trade-off frontier between the net economic benefits and the Q-90 biodiversity index is similar to the one with ecosystem maturity (Figure 8.5b). A s the Q-90 index increased from 4, the maximum N P V of the net benefits from the fisheries decreased gradually. However, when the Q-90 index increased to over 4.7, the maximum N P V of benefits dropped rapidly from over US$ 130,000 km\" 2 to below US$ 50,000 km\" 2. The maximum net benefits remain relatively constant when the Q-90 index increased from 4.8. a) b) <2 E \u00C2\u00A3 -* 0) TJ I s \u00C2\u00A3i m i - 3 O O > \u00C2\u00A3 z en \u00E2\u0080\u00A2D -1000 -1200 -1400 400 200 0 -200 -400 -600 -800 A Region of economic stability \u00E2\u0080\u00A2 JZ O. Z (A 50 2, 4.0 4.2 4.4 4.6 4.8 Q-90 index 5.0 5.2 14 Region of economic Region of economic stability collapse j ^ \u00C2\u00BB ; o Hso ; CP \ : \ \u00E2\u0080\u00A2 \ : VD i 1 ; i : \ = O \ \u00C2\u00B0 = r * . 5.4 Figure 8.5. The approximated Pareto-frontiers between the net present value of benefits and (a) relative ecosystem maturity, and (b) Q-90 biodiversity index. The horizontal broken line in (a) represents zero net benefit (i.e., points below the dotted line represent fisheries operating at a loss). The vertical dotted lines delineate the regions regions of economic stability and collapse. 261 8.3.2. Ecosystem structures The biomass of most vertebrate groups increased when policy objectives focused primarily on conservation (Figure 8.6). The biomass of pelagic sharks and rays show the largest increases of over 400% in 30 years. Such dramatic biomass appears possible given the severely depleted state of the pelagic sharks and rays group (Chapter 5). This is followed by other targeted demersal fish groups such as the groupers, demersal sharks and rays, lizardfish and threadfin breams with more than 10-fold increases in biomasses from the 2000s level (Figure 8.6a). Moreover, abundance of charismatic fauna such as seabirds, marine turtles and marine mammals also increased. Conversely, biomasses of invertebrates (e.g., jellyfish, shrimps and crabs) and small fishes declined. The declines were mainly caused by the increased predation mortalities because of increased predator abundance (Figure 8.6a). 262 0.001 J l\u00E2\u0080\u0094l Pelagic Demersal Demersal fish Groupers Lizardfish Threadfin sharks and sharks and (>30cm) {synodontids) bream rays rays (nemipterids) 0.1 I Shrimps and crabs Benthic Cephalopods Jellyfish crustaceans 0.01 J Seaturt les S e a b i r d s P inn ipeds Figure 8.6. Estimated changes in biomass of: (a) fishes, (b) invertebrates and (c) charismatic megafauna from the status quo (2000s NSCS model) after 30-year simulation with maximized conservation (black bars), economic (grey bars) and social (open bars) objectives. Policy focusing heavily on maximizing economic benefits led to further depletion of many functional groups although the biomass of some target groups increased slightly (Figure 8.6b). Many targeted demersal fish groups (e.g., lizard fish, groupers, adult demersal fish, hairtail, small and large croakers, etc) and charismatic fauna (pinnipeds, sea turtles and seabirds) decreased by 20% to 70% from the 2000s level. The biomass of benthic invertebrates, jellyfishes and pelagic fishes increased slightly. Also , biomass of some targeted groups such as threadfin bream and pomfret and the heavily depleted sharks and rays increased. The model predicted very high level of depletion when policy objectives focused only on maximizing the number of jobs (social objective) (Figure 8.6c). Most of the demersal groups were depleted to less than 30% of the status quo biomass. Some groups were fished to near extinction (Adult demersal fish). The less-affected groups included benthic invertebrates, jellyfish and pelagic fishes. 8.3.3. Restructuring the fishing fleets Moving from the status quo to the Pareto-optimum solutions requires restructuring the fishing fleets (Figure 8.7). To improve the conservation status from the status quo level (depletion index = 34), fishing effort of the pair and stern trawl sector and the gillnet fleet was reduced in most scenarios. The effort of most fishing sectors decreased in scenarios with a heavy focus on conservation status. A n exception was the purse seine fleet which achieved an increase in effort (Figure 8.7). To maximize the net economic benefits, fishing effort of the 'others' fleet increased as much as 4.5 times the status quo level. This is due to the high profitability of this fleet. Effort by the shrimp trawlers remained the same, while efforts from the other fleet decreased (Figure 8.7). When heavy weight was put on the social objective (i.e., maximizing the number of jobs provided by the fisheries), fishing effort by most fleets, except the stern and pair trawls and the gillnet fleets, was increased (Figure 8.6). In particular, the shrimp trawl sector experienced an almost 14 times increase in fishing effort from the status quo level. 264 Stern & Shrimp Purse Hook and Gillnet Others Pair trawl trawl seine line F i sh ing sec to r Figure 8.7. Relative Fishing effort of the six fishing sectors required to achieve the best conservation status (depletion index <10) (black bars), the highest net economic benefits (30 year time-horizon, 3% discount rate) (grey bars) and the maximization of social benefits (number of jobs) (white bars). The dotted line represents the fishing effort level at status quo. 8.3.4. Buy-back cost To move the fishery from its current Pareto inefficient state under the status quo to the Pareto-frontier, the estimated cost of a buy-back scheme increased exponentially as the depletion index of the N S C S ecosystem decreased (i.e., improvement in conservation status) (Figure 8a). When fishers who remain active are paid for the cost of buy-back (cost internalization), profits remained higher than the status quo level for a slight improvement in conservation status (Figure 8b). Internalization of the buy-back cost was calculated by subtracting the N P V of net profits by the buy-back cost required to restructure the fishing fleets to achieve the optimal points (Munro & Sumaila 2002). With the internalized buy-back cost, the overall shape of the frontier remained convex. The fisheries could potentially achieve higher profits relative to the status quo i f the DI was above 17. However, further reduction of the DI reduced the profits from the status quo level. The fisheries would be running at a loss if the DI were to be reduced to below 10. 265 b) E T J C \u00C2\u00A3 5 -O m O O > \u00C2\u00A3 3 Pareto-frontier (buy-back cost internalized) 150 i ^ 100 50 0 -50 -100 A A A ' A A ' Status quo Break-even line 15 25 Depletion index 35 Figure 8.8. The (a) estimated buy-back cost to reduce fishing capacity in order to achieve the Pareto-optimal outcome and (b) the estimated Pareto-frontier after internalizing the cost of buy-back to achieve the Pareto-optimum. The upper and lower horizontal lines represent the net present value of benefits (using 3% discount rate over 30-year time horizon), and the break-even point (zero discounted profit), respectively. 8.3.5. Sensitivity analysis While absolute values varied, the shapes of the Pareto-frontiers are similar under different discount rate scenarios (Figure 8.9). As expected, a higher discount rate reduced the net present value of profits from the fisheries. N P V of profits started to decline rapidly when the DI of the ecosystem was reduced to below 25. Moreover, the steepness 266 of the decline in profits increased with higher discount rate. In general, the trade-off of relative economic and conservation benefits was qualitatively robust to the discount rates used in this study (1% to 5%). a) c) e) 250 5 Depletion index B a s e est imat ion oo- \u00C2\u00B0o 8 25 35 Depletion index b) d) 1200 1000 800 600 400 200 B a s e est imat ion Depletion index 0 ' O cP o 20 30 Depletion index Figure 8.9. The estimated Pareto-frontiers between the net present value of benefits and the calculated depletion index under (a) annual discount rates =1%, (b) annual discount rates = 5%, (c) bottom-up controlled (vulnerability parameters =1), (d) top-down controlled (vulnerability parameters = 10) and (e) vulnerability parameters proportional to the trophic level of prey groups. The 'base estimation' (discount rate = 3% and vulnerability parameters estimated from fitting with time-series data, Chapter 7) was included in the figures for comparisons. The data points in (a), (b), (c) and (e) were fitted with quadratic functions. The results from (d) were too scattered to fit an assumed relationship. 267 The estimated 'optimal' solutions varied considerably when the entire system was assumed to be either bottom-up (vulnerability factor - 1), top-down (vulnerability factor = 10) or 'mixed' (vulnerability factor proportional to trophic level of the prey groups) controlled (Figure 8.9). When the ecosystem was assumed to be bottom-up controlled, the Pareto-frontier between economic profits and conservation status became closer to a linear relationship. Also , when the DI was less than 30, the estimated N P V of profits was lower than that from scenarios with vulnerability parameters obtained from time-series fitting ('base estimation'). Otherwise, higher profits were predicted. Under the top-down controlled assumption, plot of estimated maximum achievable conservation and economic benefits was scattered. The estimated N P V of profits from both top-down and 'mixed' controlled ecosystem were generally higher than that from the 'base case'. 8.4. Discussion 8.4.1. Trade-offs between policy objectives This study has revealed the possible trade-offs between conservation, economic and social objectives in fisheries management in the N S C S . The trade-off analyses show that N S C S fisheries in the 2000s are in a sub-optimal state in terms of achieving conservation and economic objectives. The fisheries had depleted most commercial stocks (Chapters 6 and 7) and largely dissipated the potential economic benefits that could be obtained from an optimally-exploited ecosystem. This agrees with conventional economic theory in which over-capitalization leads to over-exploitation and dissipation of potential economic benefits (Gordon 1954). Thus, the status quo of the N S C S fisheries should have room to improve its conservation status without compromising the overall economic benefits from the fisheries. Such an improvement could be achieved by reducing the effort of fleets that have large impacts on intrinsically vulnerable stocks, while contributing low economic benefits (e.g., stern and pair trawlers, which have low profit margins relative to other sectors because of their larger fuel consumption and labour cost). Therefore, well-designed conservation policies aiming for a moderate improvement in conservation status should also improve the net economic benefits from 268 the fisheries. However, the major problem is the transaction costs (economic and social) that are required to reduce excess fishing capacity. The trade-offs between conservation and socio-economic objectives wi l l become severe when policy targets a high conservation status. Largely improving conservation status (reduce risk of depletion, increase biodiversity and ecosystem maturity) would require restoring and protecting over-exploited and intrinsically vulnerable species. This would also reduce the catch of the less vulnerable or more productive species (Hilborn et al. 2004c; Walters & Martell 2004). For instance, fishing effort in the shrimp trawl sector was largely reduced when the management goal focused on conservation. Because shrimp trawlers targeted highly-valuable benthic invertebrates (e.g., mantis shrimp) and were among the most profitable fleets in the N S C S (Cheung and Sadovy 2004), reducing their effort substantially lowered the net economic benefits from the fisheries. Moreover, recovery of predatory species, particularly, the large demersal fishes, increased the predation mortality of their valuable invertebrate prey and reduced their productivity. This may result in severe trade-offs between conservation and socio-economic objectives. Trade-offs between restoring predatory species and loss of fisheries productivity have occurred in other ecosystems. For instance, following the collapse of the Northeast Altantic cod (Gadus morhua) stocks, lucrative invertebrate fisheries, such as lobster, crabs and shrimp, bloomed. There was a concern that restoring the cod stocks would negatively affect the economic values of the fisheries because of the reduced productivity of the invertebrates stocks that this may entail (Worm & Myers 2003). Also, salmon fisheries along the coast of British Columbia were reduced to protect a number of less productive salmon stocks that were considered endangered, foregoing potential catches from other more productive salmon stocks (Walters & Martell 2004). Similar trade-offs occurred in the central Pacific Ocean where longline fisheries captured both yellowfin tuna {Thunnus albacares) and relatively less productive blue sharks (Prionace glauca) (Schindler et al. 2002). In the past few decades, N S C S fisheries appear to have developed with a strong focus on social benefits (i.e., maximizing the number of jobs). In the late 1980s, fishing firms previously owned by the state were privatized in the People's Republic of China 269 (PRC). The majority of the fisheries were operated by small units in the 2000s, with many of the fishers previously unemployed workers or farmers (Jia, S.P., Director of the P R C South China Sea Fisheries Institute, pers. comm.). Although the nominal fishing effort was theoretically restricted through a licensing system, the fisheries were grossly over-capitalized. Also, illegal fishing (fishing without proper licenses) was common in the region (Yang 2001). Thus, it was inevitable that the fisheries resources were over-exploited, with accompanying large depletion and extirpation of targeted fish stocks in the region (see Chapter 5). Fishers maintained their income from fishing by targeting species further down the food web and, in some cases, using destructive fishing methods. The coastal trawl sectors catch mostly juveniles, small fishes and invertebrates that are more resilient to fishing. Benthic invertebrates such as shrimps and crabs have high market prices. Also, the booming mariculture of predatory fishes (e.g., groupers) in China has created a large demand for small and juvenile fishes used as feed (Chau 2004; Cheung & Sadovy 2004; Funge-Smith et al. 2005). Moreover, destructive fishing methods such as fishing with dynamite or trawl nets with high-voltage electric current have been used (Sumaila et al. in press). Fishers in the region generally have low education levels and limited employment opportunities other than fishing. Therefore, they have few alternatives to fishing harder on the remaining resources. These are clear symptoms of Malthusian overfishing - a situation in which overfishing is driven by poverty, population growth and lack of alternative livelihoods (Pauly 1993, 2006; Teh & Sumaila, in press). This can be the major reason for the largely sub-optimal state of N S C S fisheries in the Pareto-frontier between net economic benefits and conservation. This study suggests that i f fishing continues to serve as a source of new job opportunities, the ecosystem is likely to be further depleted. On the other hand, reducing effort without a proper programme to help fishers find alternative livelihoods may create considerable social problems. 8.4.2. Economics of restructuring the fishing fleets A buy-back scheme may potentially reduce fishing capacity and reduce the direct economic impact on the fishing communities resulting from the management policies. 270 However, this study shows that the cost of a buy-back scheme may increase exponentially with the targeted conservation status. Who should pay the cost? Buy-back schemes that are paid for by the remaining fishers might be implemented through taxation or license fees (Munro & Sumaila 2002; Clark et al. 2005). This approach helps to internalize the environmental externalities of fishing. A n underlying assumption is that the net economic benefits from the fisheries would increase in the future. Therefore, fishers who would benefit from the conservation policy should contribute to the cost of fleet re-organization. This study showed that the increase in net benefits would only be sufficient to pay for the buy-back cost to achieve a moderate conservation level in the N S C S . Given the highly depleted N S C S ecosystem, it appears that a moderate conservation level might not allow sufficient recovery of some vulnerable and highly depleted groups. Public funds through government or non-governmental organizations might be needed i f a high conservation level were to be targeted. Besides commercial fishers, society in general could also obtain benefits from a restored ecosystem, directly (e.g., through ecotourism, recreational fishing, etc) and indirectly (e.g., through increased non-market value, restored ecosystem function, etc.) (Costanza et al. 1998; Balmford et al. 2002; Berman & Sumaila 2006; Worm et al. 2006). A cost benefit analysis of the private and societal sectors is a possible way to evaluate the potential sharing of costs between these two groups. A study focusing on market values was conducted for the Hong Kong marine ecosystem. It showed that conservation policies could provide several times more economic benefits to society than the fishing sectors (Sumaila et al. in press). Therefore, it may be reasonable for society to share part of the cost of conservation. So far, buy-back schemes have been assumed to be perfectly effective, i.e., buying-out each fishing unit resulted in equivalent reduction in fishing effort - an assumption that is hardly true in reality. Fisheries often have latent effort (Brodziak et al. 2004). For instance, fishers may hold valid fishing licenses, but not practice fishing for reasons such as fishing being unprofitable, or the opportunity cost of fishing being high. These fishers may become active when profitability of the fisheries increases. Also, i f the buy-back scheme is expected by the fishers before the scheme is implemented, 271 investments in fishing capacity would possibly increase substantially, as any excessive fishing capacity would likely be bought out when the buy-back scheme is in effect (Clark et al. 2005). Moreover, fishing effort may seep back into the fisheries through improved fishing technology and power, increased fishing time, etc. Therefore, buying out fishing vessels may not reduce fishing effort proportionally. Moreover, effective regulation and monitoring of fishing effort is essential. However, these are weak in the N S C S . In fact, a review of previous experiences of fishing vessel buy-back programmes, such as the British Columbia and Washington State salmon fisheries, Australian northern prawn fishery, the Canadian Atlantic groundfish fishery, concludes that such programmes are generally not an effective way to reduce fishing capacity (Holland et al. 1999). Thus, although this study shows that a buy-back scheme to reduce fishing capacity is economically sound, the P R C government should carefully consider its practical feasibility and effectiveness. It appears that a priority for the P R C government is to improve monitoring, surveillance and control of its fisheries, and develop alternative livelihoods for fishers. Other means of reducing fishing capacity may be through removal of subsidies given to the fisheries. The P R C government is subsidizing fisheries (non-fuel subsidies amount to US$ 1.3 billion annually) (Khan et al. 2006; Sumaila et al. 2006). Therefore, removal of subsidies may lower the profitability of fishing and thus discourage continued investment in maintaining or increasing fishing capacity. However, social problems may still be created because of the lack of alternative livelihoods for the fishers. Clearly, viable alternative livelihood programmes for the fishing communities are essential for the success of fisheries management and conservation measures. 8.4.3. Model assumptions and uncertainties A key parameter uncertainty was the predator-prey vulnerability factors. Here, observed time-series catch rate data were used to estimate the vulnerability factors, which provided some empirical support to the model (Chapter 7). However, the results were very sensitive to the vulnerability factor settings, particularly if a top-down controlled system was assumed. A top-down controlled system predicted large fluctuations in biomass of prey and predator groups. Such unrealistic predictions led to highly scattered 272 estimates from the multi-criteria policy optimization routine. Thus, extreme top-down control assumptions in the simulations may be invalid. When time-series abundance data in the N S C S are available in the future, the model can be further validated by comparing the observed trends with the model predictions. The effects of other (non-fishing) anthropogenic changes, economic and environmental fluctuations were not included in the model. Oceanographic and other physical changes (e.g., coastal nutrient inputs) may have considerable effects on the N S C S ecosystem. Also, following the rapid economic development of China, anthropogenic disturbance such as reclamation and pollution may significantly affect the ecosystem (Zhang et al. 2002; A n & H u 2006). These factors are likely to have negative effects on the benefits from conservation policies. However, limited historical time-series data prevented the analysis of the effects of these factors. There is a need to better understand the effects of environmental and non-fishing anthropogenic factors on the ecosystem dynamics in the N S C S . In addition, the costs of fishing and prices of catch are assumed to be constant, which may vary as the ecosystem and market conditions change in reality. Accurate economic data of the N S C S fisheries, such as cost profiles of different fishing fleets, were also limited. These may render the absolute values of benefits and costs calculated from the ecosystem model uncertain. One of the major assumptions in the multi-criteria optimization analysis is that all fishing fleets cooperate to maximize the overall benefits of the fisheries. Thus, in some scenarios, benefits of some fishing sectors were maximized at the expense of other sectors. Although this assumption may affect the optimal fleet structures, the overall trade-off relationships revealed from the present analyses should remain valid. On the other hand, the evaluations of trade-offs and allocations of resources between different fishing sectors is an important topic and should be areas for future studies. The aim of this study was to quantify the potential trade-offs between conservation and other socio-economic objectives. The N S C S ecosystem has been greatly depleted by fishing and is considered to be currently in a sub-optimal state both in terms of ecological and economic objectives. We believe that the findings in this paper should be generally applicable to other tropical marine ecosystems, as the symptoms of 273 Malthusian and ecosystem overfishing are commonly observed, particularly in developing countries (Pauly 1993, 2006; Teh & Sumaila in press). To prevent more degradation and extirpations, and improve economic benefits from exploiting these ecosystems, restructuring the fishing fleets is urgently needed. However, fishing communities might oppose a restoration policy because of the inevitable need to reduce fishing capacity in the short term. Moreover, the restructuring of fishing fleets and the unbalanced share of costs and benefits may create tension among fishing sectors (Cheung and Sadovy 2004). Understanding the trade-offs between management objectives may allow the stakeholders and the public to hold informed discussions on future management policies. 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General summary In the first chapter of this thesis, the case of the disappearance of the Chinese bahaba (Bahaba taipensis) along the coast of China was discussed (Sadovy & Cheung 2003). In fact, given the sustained high fishing pressure in the South China Sea (and globally in most of the oceans), the Chinese bahaba is not likely to be an isolated case. Then, how can we identify fishes that are in a similar situation and reduce the threat from fishing in a timely fashion? The major aim of this thesis was to provide answers to this question. The issue was addressed by following the analytical framework described in Chapter 1: (i) using approaches that can be applied under data-limited conditions to identify species that are more vulnerable to extirpation (or extinction), and regions that are associated with higher risk of population depletion or extirpation (Chapters 2, 3 and 4); (iii) assess the status of the ecosystem and its associated species (Chapters 5 and 6); and (iv) explore the effects of fisheries management policies and identify socio-economically viable conservation and fisheries management options (Chapters 7 and 8). The fuzzy logic expert system developed in Chapter 2 provided an intrinsic vulnerability index that correlated significantly with observed population declines in different areas and ecosystems. Such correlation was higher than those from other vulnerability indicators that had previously been proposed e.g., the approach adopted by the American Fisheries Society (Musick 1999) and individual life history traits (e.g., Jennings et al. 1998). The fuzzy expert system had the additional advantages of flexible requirements for input data and ease of incorporating new knowledge. The system could quantitatively and consistently assess the relative vulnerability of wide ranges of marine fishes and identify priority species for protection and further studies using readily available life history parameters, such as those in FishBase (www.fishbase.org). This index was applied to the analyses in most chapters of this thesis. Using the intrinsic vulnerability index developed in Chapter 2, the differences among marine fishes in different habitats, latitudinal and depth zones were evaluated. The 280 study found that deepwater demersal fishes, particularly those aggregating around seamounts, had higher intrinsic vulnerability to fishing. Moreover, when the average intrinsic vulnerability indices of species weighted by their annual catch over the past 50 years were calculated for coral reef, estuary and seamount fish communities, a consistent decline became apparent in coral reefs and estuaries, while the opposite was observed in seamounts. When coral reef and estuarine communities were compared, decline in average vulnerability of fishes in the catch was much stronger in coral reefs. Catches from coral reefs have been increasingly dominated by low intrinsic vulnerability species, while the highly vulnerable species have been rapidly depleted. Moreover, an increasing average vulnerability of fishes in the catch was observed in seamounts, where the highly vulnerable species have been serially depleted in recent years (Koslow 1997; Clark 2001; Roberts 2002). Estuarine fish communities consist of a mixture of freshwater- and marine- migrants and residents (Blaber 2000). Thus community structure is more volatile, which may explain the weaker decline in average vulnerability of catch. The link between average vulnerability of catches and status of fish communities agrees with the correlation between the number of fishes listed under the I U C N Red List of Threatened Species and the rates of decline of mean vulnerability of catches. These findings quantitatively revealed patterns of changes in fish community structure in coral reef, estuary and seamount since 1950. Also , the results showed that changes in the structure of these fish communities were closely related to the species' intrinsic vulnerability. A n analytical approach to predict the depletion risk of exploited fishes from fishing using life history and catch time-series data was developed and applied to analyze the conservation status of marine fishes globally (Chapter 4). The study found that a considerable proportion of the exploited fishes had moderate, high or very high risk of population depletion. The proportion of species that had at least moderate depletion risk increased greatly over the past 50 years, particularly large-bodied demersal fishes and elasmobranchs. These results agreed with the conclusions from Chapters 2 and 3. The proportion of species facing moderate to very high depletion risk from fishing was comparable to other vertebrate groups. Thus, this chapter showed that the scale of the conservation problems faced by marine fishes might be similar to those of other 281 terrestrial vertebrates. This chapter, together with Chapters 2 and 3, highlighted the need to incorporate conservation plans into fisheries management. The Northern South China Sea (NSCS) ecosystem was used as a case study to evaluate the conservation concerns resulting from fishing (Chapter 5). The N S C S was heavily exploited by fishing, but baseline data were limited. Using standardized time-series C P U E data of demersal trawlers from 1973 to 1988, it was shown that the relative abundance of most of these species declined by over 70% over the 15-year period. Relative abundance of some intrinsically vulnerable species (e.g., sharks, rays and yellow croaker) declined by an average of over 90%. The rate of decline was significantly correlated with the species' predicted intrinsic vulnerability. Using the predicted intrinsic vulnerability, this study suggested that many exploited fish species in the N S C S might be threatened by fishing. This is in accord with the global predictions of depletion risk of marine fishes in Chapter 4. To understand the impacts of fishing in the N S C S at the ecosystem level, past (the early 1970s) and present (the 2000s) status of the N S C S ecosystem was evaluated using the Ecopath with Ecosim modelling approach (Chapter 6). The models indicated a large decline in system biomass. Also , biomass and energy flows of the demersal groups reduced greatly from the 1970s to the 2000s, resulting in a shift from being a demersal-dominated to a pelagic-dominated system. Moreover, most of the primary production that had originally been consumed by marine organisms through the foodweb went to capture fisheries. This showed that intensive and ill-managed fishing does not only directly threaten vulnerable species (Chapter 5), but also exerted large ecosystem impacts that indirectly threatened other non-target species. Based on the Ecopath with Ecosim dynamic simulation models developed in Chapter 6 and validated in Chapter 7, trade-offs between conservation and socio-economic objectives of fisheries management in the N S C S were evaluated. The depletion index developed and validated in Chapter 7 was used as an indicator of conservation status. Using a numerical optimization routine, this study identified convex pareto-frontiers in the trade-offs between conservation and socio-economic objectives. The 2000s N S C S ecosystem was sub-optimal for achieving either conservation or economic 282 objectives. Thus improvement in both the conservation status and the net present value of benefits from the fisheries could be made by restructuring the fishing fleets. However, this would require reduction of fishing capacity and the number of jobs provided by the fisheries. Under the current socio-economic situation in China, such changes might lead to considerable social problems. A buy-back scheme that is funded by the fishing industry might be possible, as the buy-back cost could be offset by the higher profit expected from improved management. However, public funds would be required i f a higher conservation target were to be achieved. This might be justified by the direct or indirect benefits to society that could be provided by a well-conserved ecosystem. On the other hand, social problems associated with the lack of alternative livelihood for fishers might hinder any management and restoration plans. The livelihood problems appeared to be a priority for improving fisheries management and conservation policies in the N S C S . 9.2. Applications and recommendations It is important to ensure proper management and conservation of marine ecosystem and species that are particularly vulnerable to fishing. These vulnerable species may include those that are targeted by fisheries and bycatch. The need for such efforts can be supported by a number of reasons. Firstly, loss of marine biodiversity may have significant socio-economic implications (Chapin III et al. 2000). The depletion or extirpation of valuable exploited species may result in direct economic losses. These can result in large repercussion to the fishing industries and the community depending on the resources. For example, the depletion of Atlantic cod (Gadus morhua, Gadiidae) and the subsequent moratorium on the Canadian cod fisheries resulted in extensive socio-economic problems to the fishing communities. Secondly, loss of biodiversity may directly or indirectly affect the functioning of the ecosystem (Loreau et al. 2001; Worm and Duffy 2003; Worm et al. 2006) and can alter the productivity of the ecosystem and the recovery of depleted species. Removal of keystone species, which include species that are critical to the ecological function of a community or habitat in their current states (Zacharias and Roff 2001), can result in state shift in marine ecosystem. For instance, the removal of sea otters in the Aleutian Archipelago resulted in sea urchin population 283 expansion, which virtually excluded fleshy macroalgae such as kelp and greatly affected their associated communities (Tegner and Dayton 2000). Moreover, biodiversity can be positively correlated with stability and resilience of an ecosystem (Tilman and Downing 1994; Tilman 1996; Tilman et al. 1997; Scheffer et al. 2001; Worm et al. 2006). For instance, in this thesis, I demonstrated that the depletion of the intrinsically vulnerable, predatory species (both targeted and non-targeted species) such as sharks, rays, large-bodied sciaenids and groupers in the N S C S had lead to the dominance of less vulnerable species with generally high population turn-over rate (e.g., small pelagic and juvenile fishes, and invertebrates). Populations of the latter are generally more variable (Spencer & Coll ie 1997) and their dominance reduces the stability of the ecosystem. The N S C S fisheries are currently supported by fishing for these high turn-over species. The resulted increase in variability of catches due to the stock variabilities might have considerable socio-economic impacts to the fishing communities. Particularly, fishing fleets that build up fishing capacity during the 'good' fishing years may suffer from economic hardship when environmental factors reduce fishery productivity. Given that losses of species diversity may be irreversible and can have large socio-economic consequences, based on the precautionary principle, instead of protecting only the valuable fishery targeted species, we should aim to conserve the full spectrum of species in the ecosystem (Worm & Duffy 2003), particularly those that are more vulnerable to fishing. The analytical approaches developed in this thesis may allow conservation assessment to cover a wider range of marine fishes. Currently, only a small proportion of marine fishes have been assessed by the I U C N Red List criteria (Baillie et al. 2004). A major obstacle that hindered the assessments was data limitation (Dulvy et al. 2003). The analytical approaches developed in this study (e.g., intrinsic vulnerability index) and elsewhere (Dulvy et al. 2003; Reynolds et al. 2005) can facilitate rapid assessment of the relative conservation status of fishes with limited data. Thus, fisheries managers or conservation practitioners can make use of these approaches to understand the status of the ecosystem of concern and identify priority areas and species for conservation planning and undertaking more detailed assessment (e.g., the I U C N threatened species assessment). In fact, the intrinsic vulnerability index wi l l become a standard parameter for most species in FishBase in 2007 (Rainer Froese, IFM-Geo-Mar , K i e l , Germany, pers. 284 comm.). This should facilitate the utilization of this index for research, conservation planning and fisheries management. Particularly, the use of fuzzy expert system enabled integration of local and scientific knowledge and adaptation to new knowledge (Mackinson & N0ttestad, 1998). The fuzzy expert systems presented in this thesis allowed better use of available information to predict intrinsic vulnerability and depletion risk of marine fishes and ecosystems. Moreover, the heuristic rules, fuzzy membership functions, and the defining values, can be modified based on expert knowledge or newly available information (Cox, 1999). The weighting on the rules can also be adjusted when new evidence or experts' opinions are obtained. The fuzzy expert system, and the indices developed from it, are particularly useful in data limited situations, e.g., tropical fisheries where diverse species are caught and resources for monitoring and management are low (Silvestre & Pauly 1997; Johannes 1998; Johannes et al. 2000). This may provide a decision support tool on local or global extinction risk assessment and categorization such as the I U C N Red List of threatened species of the World Conservation Union or the species listing under Canada's Species At Risk Act. Deepwater demersal and seamount, as well as coral reef fish assemblages should warrant high conservation concerns. The many threats to coral reef ecosystems (e.g., overfishing, habitat destruction, climate change) have been well understood (Pandolfi et al. 2003; Bellwood et al. 2004; Birkeland 2004). However, fishing in the deep seas (including the continental slope) and seamount ecosystems have only been recognized as a major concern recently (Pankhurst 1999; Koslow et al. 2000; Roberts 2002; Morato et al. 2006b). This thesis has quantitative showed the high intrinsic vulnerability of demersal deepwater fishes and seamount-associated fishes to fishing. It has also shown that these species have been increasingly exploited (Chapter 3). As many of the deepsea and seamount species occur in the high seas, international conservation efforts are necessary. On the other hand, although protection and conservation of these species and their vulnerable habitats (e.g., deepwater coral) are advocated by conservation groups and scientists, international conservation actions are lacking. Results from this thesis demonstrated an urgent need to increase international conservation efforts. 285 In addition to the conventional approaches to assessment of target species, assessments should expand to non-target species and the ecosystem. Even i f conventional management goals such as the Maximum Sustainable Yie ld ( M S Y ) are achieved, non-target species that are biologically more vulnerable to fishing can be susceptible to over-exploitation (Walters et al. 2005)., In this thesis, through analyses of intrinsic vulnerability and depletion risk from life history and fisheries data, and an empirical case study of the N S C S , the elasmobranchs (sharks and rays) were clearly shown to be intrinsically vulnerability and had been largely depleted by fishing. Sharks and rays are bycatch of many fisheries, e.g., the bycatch of blue shark (Prionace glauca) from the tuna and swordfish longline fisheries in the Central Pacific Ocean, North Atlantic Ocean and Mediterranean Sea. (e.g., Buencuerpo & Moron 1998; Schlinder et al. 2002). This problem is also illustrated in tropical multi-species fisheries such as the high bycatch rate of shrimp trawling (Andrew & Pepperell 1992). The index can improve understanding on intrinsic vulnerabilities of bycatch species, and therefore, should be a useful tool to reveal potential impacts of the bycatch. Moreover, fishing may affect ecosystem dynamics and functions (Pitcher & Pauly 1998; Worm et al. 2006). The analytical tools presented in this study (e.g., ecosystem modelling) should be useful to facilitate the assessments on the effects of fishing on the non-target species and ecosystem. These tools can be combined with alternative management goals such as the 'Optimal Restorable Biomass' - an optimized form of historical ecosystem - proposed by Ainsworth (2006) to achieve ecosystem-based management objectives. As fisheries management involves a multitude of objectives (e.g. ecological, economic, social), the trade-offs of benefits and costs between the different objectives should be evaluated and presented to the decision makers and stakeholders. Ideally, consensus on the desirable set of management objectives could be made from informed discussions among stakeholders. Fisheries managers could then develop management tactics that could effectively achieve the management goals (Cochrane 2002) such as the 'Optimal Restorable Biomass' mentioned previously (Pitcher 2005; Ainsworth 2006). Particularly, in the N S C S , effective management and conservation actions are urgently needed. This thesis clearly showed that the fisheries resources in the N S C S are 286 heavily over-exploited and that the fisheries contributed largely to the adverse ecosystem changes. A major root cause of this problem is Malthusian overfishing (Pauly 1993, 2006): unemployed workers and farmers moved to the coast and became fishers (Huang & Guo 2001), many of them fishing illegally (e.g., without fishing license) (Pang and Pauly 2001). In the N S C S , with the depletion of fisheries resources (Chapters 5 and 6) and the lack of alternative livelihood, destructive fishing methods (e.g., fishing with dynamite, electric net) were sometimes employed, leading to further depletion. Any measures that improve the ecological and economic benefits of fisheries would involve effort reduction and loss of fisheries-related jobs (Chapter 8). Thus, it is critical for the P R C government to help develop sustainable alternative livelihoods for potentially displaced fishers. The government provided funding to help fishers develop alternative o livelihoods in aquaculture, distant water fishing, tourism and other land-based industries . However, the P R C government should also carefully consider whether some of the current alternative livelihood programmes for fishers are ecologically and economically sustainable. The P R C government should also improve monitoring, surveillance and control (MSC) of fishing effort. This thesis showed that a buy-back scheme funded by the fishing industry could be an economically viable option to facilitate reduction in fishing capacity. However, the effectiveness of any buy-back scheme relies heavily on the ability to monitor and control fishing effort. M S C is weak in China and strengthening this area is a pre-requisite for improvements from the status quo. The ecosystem models developed in this thesis can be used as an operating model to test the performance of different monitoring and management system through 'closed loop' analysis (a.k.a. Management Strategy Evaluation framework) (Watlers 1998; Smith et al. 1999; Plaganyi & Butterworth 2004). 9.3. Limitations and future improvements The extent and accuracy of the analyses in this thesis was partly limited by our current understanding on the biology of extinction of marine fishes. Conservation risks of 8 News article (Chinese). China Report 5 December 2005. http://big5.chinabroadcast.en/gate/big5/gb.cri.cn/41 /2003/12/05/108 @ 13466.htm 287 marine fishes (particularly the highly fecund commercial species) have only become seriously recognized in recent decades (Sadovy 2001; Dulvy et al. 2003). Although knowledge about the extinction biology of fishes is growing rapidly, a number of key issues and questions are still being researched (Reynolds et al. 2005). For instance, the dynamics of fish populations in small population size are still poorly understood and it is difficult to determine the minimum viable population size for most marine fishes. Future studies on the dynamics of fish at small population size are needed (Pitcher 1998; Dulvy et al. 2003). Also, the responses of marine fishes to other environmental and anthropogenic changes such as climate change (Roessig et al. 2004; Perry et al. 2005) and genetic effects from fishing (Law 2000) are being studied. Further understanding on these factors would improve the predictions of extinction risk for marine fishes. Thus the expert system developed here should be updated regularly to incorporate new knowledge in these areas. Although the analytical approaches developed here allowed rapid assessment of intrinsic vulnerability and depletion risk under data-limited situation, good data are necessary to improve our understanding on the threats of fishing, human and environmental changes to marine fishes. Data limitations are particularly serious in tropical developing regions and reef fisheries where fishery monitoring is less effective but threats to their high biodiversity are acute (Johannes 1998). For instance, the life history parameters of many fishes in the N S C S are unknown. Also , catch or landings data are available only for a few commercial species, and many species are not reported explicitly in the catch. Better monitoring systems should be developed to improve the collection of basic biology and fisheries data. The problem of data limitation is serious in the N S C S . Fisheries statistics from the P R C government are suggested to be inaccurate, especially those in recent decades (Pang & Pauly 2001, Watson & Pauly 2001). Although government and research institutes in China have conducted sporadic surveys in the N S C S , access to the data was generally restricted by the government (Jia, S.P., Director of the South China Sea Fisheries Institute, pers. comm.). Therefore, this thesis relied on the available data from past surveys conducted by the Hong Kong government. However, the temporal coverage of the data was short (from the mid-1970s to the late 1980s only), and the accuracy was limited 288 because of the survey methodology. These affected the accuracy of the study results. It would be helpful to all i f the Chinese authorities allowed legitimate researchers to access their past survey data so that the status of the N S C S ecosystem and the dynamics of its changes can be better understood. Moreover, if monitoring could be improved in the future, the collected data could be used to validate the predictions (e.g., through Ecosim modelling) from this thesis. On the other hand, meta-analysis and global databases could be used for studies in cases with limited data. Uncertainties associated with the, Ecopath with Ecosim approach have been reviewed in detail (Plaganyi & Butterworth 2004). Some of the stated uncertainties have been addressed in this study, while the remaining may be addressed through future development of the Ecopath with Ecosim programme: \u00E2\u0080\u00A2 Functional group aggregations Specification of the structure of the N S C S ecosystem model may affect the model predictions (Fulton et al. 2003; Pinnegar et al. 2005). The current model structure (i.e., specifications of the functional groups) was determined based on the characteristics of the N S C S ecosystem, the objectives of the study, and the available data (Chapter 4). Moreover, the model provided reasonable agreement with historical time-series data (Chapter 7). When more data on the biology, ecology and fisheries of the N S C S become available, it would be useful to test the sensitivity of the model predictions to different functional group structures. \u00E2\u0080\u00A2 Steady-state assumption The N S C S ecosystem in two time periods (the 1970s and 2000s) was constructed to reveal changes in biological and ecological parameters over time (Bundy 2001). However, the long-term changes of some biological and fisheries parameters (e.g. consumption rate, fishing cost, landing prices, etc) could not be reflected in the dynamic simulations. Modification of the modelling approach to incorporate systematic changes of some of these parameters in dynamic simulations may allow analysis on the sensitivity of the model predictions to such changes. 289 Prey selection by predators Prey selection in the current N S C S model was specified by a diet composition matrix that reflected the proportion of biomasses of different prey items in the predators' diet. Accurate diet composition data are difficult and costly to collect (Hyslop 1980; Cortes 1997). Particularly, prey items that form a tiny proportion of the predators' diet (e.g., juvenile fishes) are easily missed in determining the diet composition matrix. However, such predator-prey linkages may control important ecosystem dynamics (Walters & Martell 2004). Alternative specification of diet composition can be expressed as a function of predators' grip-sizes, prey body-sizes and availabilities (Fulton et al. 2003). Currently, such a 'size-based' approach to specification of diet composition in ecosystem model is being developed for Ecopath with Ecosim (Cameron Ainsworth, Fisheries Centre, University of British Columbia, pers. comm.). It would be useful to cross-validate the results from the current diet composition matrix in Ecopath with this newly developed approach. The vulnerability settings (foraging arena theory) One of the most important components in Ecosim modelling is the vulnerability parameters that determine the foraging behaviour and predator-prey interactions in the ecosystem model (Walters & Juanes 1993; Walters et al. 1997). Chapters 7 and 8 and other previous studies (Hollowed et al. 2000) indicated that predictions from Ecosim were sensitive to the vulnerability parameters. Some have suggested that modelling predator-prey relationships through explicit specification of functional responses may perform better than the foraging arena model in Ecosim (Koen-Alonso & Yodzis 2005). However, these studies mainly criticized the assumption of either top-down or bottom-up trophic control (high or low vulnerability parameters, respectively) throughout the ecosystem. Here, each prey group's vulnerability parameter was estimated by fitting the model with time-series C P U E data. This represents a more systematic approach to estimating the vulnerability parameters. Moreover, alternative approaches in representing 290 trophic control may be developed in the future to generate alternative hypothesis on ecosystem changes. In addition, a new version (version 6) of the Ecopath with Ecosim programme is being developed. One of the characteristics of this version is to allow users to incorporate alternative sub-routines in the model (e.g., incorporating alternative trophic control routine) (Vi l ly Christensen, Fisheries Centre, University of British Columbia, pers. comm.). The potential flexibility to incorporate user-defined sub-routines greatly widens the possibility of testing the effects of alternative model assumptions and structures (e.g., on recruitment, compensation and considerations of long-term life history changes). 9.4. Final conclusions The various analytical approaches developed in this thesis can improve our understanding of the intrinsic vulnerability and depletion risk of marine fishes to fishing. Currently, the I U C N Red List assessment - the authority on extinction risk assessment -covers only a small proportion of marine fishes compared to other vertebrate groups. A major problem that prevents assessing a wider range of marine fish species is insufficient data. Thus, the true scale of conservation problems associated with fishing remains inaccessible. The analytical approaches developed here can provide rapid conservation risk assessments with limited data and inform conservation practitioners, policy makers and the public for conservation actions. Using analytical approaches and ecosystem modelling developed in this thesis, conservation and socio-economic problems associated with over-exploitation in the N S C S have been identified. The results could allow policy makers, fishers, fisheries managers and the public to realize the seriousness of the problems and inform them about the cost and benefits of managing fisheries under alternative objectives. These should facilitate discussions between management authorities and stakeholders to decide on fisheries management and conservation policies in the future. These approaches should be applicable to fisheries assessments in other regions, especially in areas where species diversity is high but available data and resources for researches are limited. 291 9.5. References Ainsworth, C . H . 2006 Strategic Marine Ecosystem Restoration in Northern British Columbia. Ph.D. thesis, Resource Management and Environmental Studies, The University of British Columbia, Vancouver. Andrew, N . L . & Pepperell, J. G . 1992. The by-catch of shrimp trawl fisheries. Oceanography and Marine Biology 30, 527-565. Baillte, J. E . M . , Hilton-Taylor, C . & Stuart, S. 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Aquatic Conservation: Marine and Freshwater Ecosystems 11, 59-76. 297 APPENDICES 2.1 Development of the fuzzy expert system We collated known relationships between life-history and ecological characteristics to intrinsic vulnerability from the published literature (Table A2.1), excluding those overwhelmingly disproved by empirical data. For instance, high fecundity had been suggested to be associated with low vulnerability. However, both theoretical and empirical studies lately do not support such relationship (see Table A2.1 for the list of evidence). Thus the rules relating high fecundity and low vulnerability are excluded from the system. The published relationships were transformed into IF-THEN rules relating life history and ecological characteristics to the four vulnerability categories (Table A2.1). Firstly, we transformed the input biological attributes into verbal categories, defined by fuzzy sets (Figure A2.1), and based on an existing vulnerability categorization scheme: AFS's scheme (Musick 1999a) and scheme proposed by Rainer Froese and is presented in FishBase (Rainer Froese, IFM-Geo-Mar, Kiel , Germany, pers. comm.). However, studies reported in Table A2.1 may not represent the full range of each trait for marine fishes. Thus we have to extrapolate the reported qualitative relationships between biology/ecology and vulnerability to fishes with wider range of traits. As prior knowledge about the choice of fuzzy membership functions for the input attributes was lacking, we employed the simplest forms: trapezoid membership functions at the upper and lower limits and triangular membership functions at intermediate positions on the axis. Other options include membership functions in sigmoid, gamma, and irregular shapes (Cox 1999) which may be explored if their uses are justified by experts. Trapezoid and triangular membership functions can be defined by values of independent variables that give minimum (0) and maximum (1) memberships. These values are modified from AFS's scheme; maximum length, geographic range and spatial behavior strength were not included and so, for consistency with other attributes, we classified each of them with four verbal categories (only two categories of geographic range are associated with rules). We defined the membership functions for maximum length and geographic range from the lower quartile, median, and upper quartile of each attribute from all marine fishes recorded in FishBase (over 15,000 species) (Froese & Pauly 2004). Membership functions of spatial behavior strength were defined by arbitrary values (Appendix 2.3). For all the fuzzy membership functions, I assumed high degree of overlap between fuzzy sets. This assumption reflects our uncertainty on the exact 298 relationship between the premises (the biological and ecological characteristics) and the conclusion (intrinsic vulnerability) (Kosko 1993). We defined four verbal categories referring to the levels of intrinsic vulnerability: (1) very high vulnerability, (2) high vulnerability, (3) moderate vulnerability and (4) low vulnerability. These verbal categories were defined by fuzzy sets on an arbitrary 'intrinsic vulnerability' scale from 1 to 100. Without prior knowledge, we assumed the simplest forms of fuzzy membership function: trapezoid and triangular membership functions. A trapezoid membership function was used for the 'very high vulnerability' and the 'low vulnerability' categories, while symmetric triangular membership functions were used for the other two categories (Figure A2.2). We assumed the minimum membership in the premises (conditions) required to fire the rules (threshold value) to be 0.2. This means that we considered the premises to be totally false unless they had membership of trueness of 0.2 or more. Thus the system screens out premises that have very low degree of membership. We evaluated the sensitivity of the system outputs to different threshold values. We made an initial assumption of equal weighting with 0.5 for all rules. The weighting factor represents the level of belief associated with the rule. Thus a weighting factor of 0.5 means we have 50% of belief to the validity of the rule. That is: Membership com.Uiswn = Membershippremise \u00E2\u0080\u00A2 CF eq. A2.1 where CF represents the weighting factor. Thus the conclusion of a particular rule can only have a maximum degree of membership of 0.5 to its fuzzy set. We tested the validity of the equal weighting assumption using a jackknife approach. We obtained the degree of membership of the final conclusions (four levels of intrinsic vulnerability) by combining the conclusions from each heuristic rule. Membership of the conclusion from each rule was combined using the knowledge accumulation method in (Buchanan & Shortliffe 1984): Membership^ \u00E2\u0080\u0094 Membership^ + Membershipt \u00E2\u0080\u00A2 (1 - Membershipe_,) eq. A2.2 where Membership,, is the degree of membership of the conclusion after combining the conclusions from e pieces of rules, and Membershipt is the degree of membership of the conclusion of rule i. 299 2.2. Operation of the fuzzy system a. Fuzzification Fuzzification is a process that determines the degree of membership to the fuzzy set based on the fuzzy membership function. We input the life history and ecological parameters into the fuzzy system. The input parameters were categorized into the different verbal categories (e.g. large maximum size, low value of von Bertalanffy growth parameter K) with the corresponding membership based on the pre-defined fuzzy membership functions (Figure 2.2). Categories with membership exceeding the threshold values would fire the corresponding rules. For example, for a fish species with maximum body length of 68 cm, the input parameters would correspond to \"medium body size\" and \"large body size\" with membership of 0.7 and 0.3 respectively (threshold value = 0.2) (Figure 2.1). b. Rule firing and fuzzy reasoning A l l premises with membership exceeding the threshold values (Membership,,^ triggered the fuzzy system to fire their corresponding rules. Following the example used in the fuzzification sessions, the rules: IF fish maximum body size is medium, T H E N intrinsic vulnerability is moderate IF fish maximum body size is large, T H E N intrinsic vulnerability is high would be fired. When several rules with the same conclusion were fired, the conclusions were combined and accumulated using the method of Buchanan and Shortliffe (1984). c. Defuzzification Defuzzification refers to the reduction of a range of conclusions with different membership to a single point output. The conclusions reached from the rules were defuzzified based on the output fuzzy membership functions. Defuzzification was based on the centroid weighted-average method (Cox, 1999), i.e., the output intrinsic vulnerability factor was calculated from the average of the supremums of each output fuzzy membership function weighted by the membership associated with each conclusion. In a triangular membership function, the supremum is equivalent to the intrinsic vulnerability factor with the highest membership (peak of the triangle). For trapezoid membership function, the supremum was assumed to be the mid-point between the two ends of the plateau. The upper and lower bounds of the output were estimated by using the smallest and largest intrinsic vulnerability factors that fall within the particularly fuzzy 300 sets at the specified membership level, instead of using the supremums. They represent the range of intrinsic vulnerability that falls within the pre-specified membership of the conclusion fuzzy membership functions. Therefore, Intrinsic vulnerability - -1 ^ Membership, f 4 I ^ Membership, \u00E2\u0080\u00A2 Supj eq. A2.3 Bounds: y Membership, ^Membership, \u00E2\u0080\u00A2 f^MLu^) eq. A2.4 where Supi is the supremums of conclusion fuzzy membership functions /, and f(ML) is the estimated upper or lower bounds (U and L respectively) of the conclusion fuzzy membership functions at the specified membership level (ML). 2.3 Assignment of strength of spatial behaviour of fish We obtained qualitative descriptions on the spatial behaviour of the fish from FishBase. We looked for keywords that verbally describe the spatial behaviour of fish (Table A 2.1). We assumed a baseline spatial behaviour strength of 1 for species forming groups or colonies, 40 for aggregations and shoals and 80 for schools (Pitcher 2002). The baseline spatial behaviour strength (B) was then adjusted by a multiplication factor (A) based on their verbal descriptions (Table A 2.1). That is: S = B-(l+ A] + A2 + . . .AJ eq. A2.5 where 5 is the total spatial behaviour strength (0 to 100) of the species. If S is above 100, it is rounded to 100. n is the number of verbal terms included. Moreover, i f spatial behaviour only occurs in either juvenile or adult stage, the total spatial strength was divided by two. For example, Callionymus limiceps (Round-headed dragonet) is described as \"usually in small aggregations\" in FishBase. The baseline spatial behaviour strength for 'aggregation' is 40, the multiplication factors for 'usually' and 'small ' are 40% and -40% respectively. Therefore, the spatial behaviour strength is calculated as: 301 S = (1 + 40% - 40%) \u00E2\u0080\u00A2 40 = 40 Table A 2.1. Keywords that verbally describe the strength of spatial behaviour and their corresponding multiplication factors. Verbal descriptions Multiplication % (A) Usually Solitary/Pair -40 Occasionally/Sometimes/Alternately/May/Probably/Loose/small -40 Sometimes Solitary/Pair -20 Presumably/Apparently -20 Frequently/Often 20 Commonly/Usually/Large/Dense 40 302 3.1. Intrinsic vulnerability index of fish taxa represented in the global catch, based on the Sea Around Us database (www.seaaroundus.org). Taxonomic Intrinsic level Taxon Common name vulnerability Family Pristidae Sawfishes 88 Squatinidae Angel sharks 80 Anarhichadidae Wolffishes 78 Carcharhinidae Requiem sharks 77 Hammerhead, bonnethead, Sphyrnidae scoophead shark 77 Macrouridae Grenadiers or rattails 75 Rajidae Skates 72 Alepocephalidae ^ Slickheads 71 Lophiidae Goosefishes 70 Torpedinidae Electric rays 68 Belonidae Needlefishes 67 Emmelichthyidae Rovers 66 Nototheniidae Cod icefishes 65 Ophidiidae Cusk-eels 65 Trachichthyidae Slimeheads 64 Channichthyidae Crocodile icefishes 63 Myliobatidae Eagle and manta rays 63 Squalidae Dogfish sharks 62 Congridae Conger and garden eels 60 Serranidae Sea basses, groupers, etc. 60 Exocoetidae Flyingfishes 59 Malacanthidae Tilefishes 58 Scorpaenidae Scorpionfishes or rockfishes 58 Polynemidae Threadfins 56 Triakidae Houndsharks 56 Istiophoridae Billfishes 55 Petromyzontidae Lampreys 55 Rhinobatidae Guitarfishes 54 Bramidae Pomfrets 53 Lethrinidae Emperors or scavengers 53 Muraenidae Moray eels 53 Scombridae Mackerels, tunas, bonitos 52 Scyliorhinidae Cat sharks 52 Herrings, shads, sardines, Clupeidae menhadens 51 Lutjanidae Snappers 51 Labridae Wrasses 50 Latridae Trumpeters 50 Pomacanthidae Angelfishes 50 Stromateidae Butterfishes 50 Moridae Morid cods 49 Mugilidae Mullets 49 Oreosomatidae Oreos 49 Scophthalmidae Scophthalmidae 49 303 Taxonomic Intrinsic level Taxon Common name vulnerability Family Pleuronectidae Righteye flounders 48 Scaridae Parrotfishes 48 Batrachoididae Toadfishes 47 Sciaenidae Drums or croakers 47 Zeidae Dories 47 Carangidae Jacks and pompanos 46 Cottidae Sculpins .46 Platycephalidae Flatheads 46 Soleidae Soles 46 Ammodytidae Sand lances 45 Centrolophidae Medusafishes 45 Echeneidae Remoras 45 Scomberesocidae Sauries 45 Sparidae Porgies 45 Engraulidae Anchovies 44 Haemulidae Grunts 43 Mullidae Goatfishes 43 Chlorophthalmidae Greeneyes Spadefishes, batfishes and 42 Ephippidae scats 42 Trichiuridae Cutlassfishes 42 Ariidae Sea catfishes 40 Cynoglossidae Tonguefishes 40 Dasyatidae Stingrays 40 Sillaginidae Smelt-whitings 40 Trachinidae Weeverfishes 40 Atherinidae Silversides Threadfin breams, Whiptail 39 Nemipteridae breams 39 Triglidae Searobins 38 Balistidae Triggerfishes 37 Salmonidae Salmonids 36 Tetraodontidae Puffers Boxfishes (cowfish and 36 Ostraciidae trunkfish) Surgeonfishes, tangs, 33 Acanthuridae unicornfishes 31 Myctophidae Lanternfishes Slimys, slipmouths, or 31 Leiognathidae ponyfishes 30 Gobiidae Gobies 29 Bothidae Lefteye flounders 28 Caproidae Boarfishes 28 Centriscidae Snipefishes and shrimpfishes 28 Holocentridae Squirrelfishes, soldierfishes 28 Gerreidae Moj arras 22 Synodontidae Lizardfishes 22 304 Taxonomic Intrinsic level Taxon Common name vulnerability Family Ambassidae Asiatic glassfishes 21 Apogonidae Cardinalfishes 10 Genus Muraenesox Conger eels 90 Mycteroperca Grouper 89 Alopias Thresher 79 Dissostichus Toothfish 79 Anarltichas Wolffish 78 Macrourus Grenadier 78 lsurus Mako 76 Molva Ling 76 Callorhincluts Elephantfish 75 Genypterus ' Cusk-eel 75 Gymnura Gymnura 75 Lopliius Angler/Monk fishes 75 Dasyatis Stingrays 73 Macruronus Grenaiders 73 Raja Rays 72 Bathyraja Skates 70 Salvelinus Charr 70 Torpedo Torpedo 68 Trachinotus Pompanos 68 Tylosurus Needlefishes 67 Etmopterus Lanternsharks 66 Thunnus Tuna 64 Austroglossus Southern soles 63 Epinephelus Groupers 63 Sphyraena Barracudas 62 Squalus Squalus 62 Trachurus Cutlass fishes 62 Argentina Argentines 61 Pseudotolithus Croakers 61 Scorpaena Scorpionfish 61 Beryx Alfonsinos 60 Chirocentrus Wolf herring 60 Hydrolagus Chimaeras 60 Lepidorhombus Megrims 60 Merluccius Hakes 59 Micropogonias Western croakers 59 Chrysoblephus Seabreams 58 Sebastes Redfishes 58 Caranx Jacks 57 Rhinobatos Guitarfish 57 Lithognathus Lithognathus 56 Lutjanns Snappers 56 Mustelus SmooUVhounds 56 Nemadactylus Porae 55 305 Taxonomic level Taxon Common name Intrinsic vulnerability Genus Seriola Amberjacks 55 Trig la Gurnards 55 Centropomus Snooks 53 Pagellus Pandoras 53 Scyliorhinus Lesser catsharks 53 Dentex Seabreams {Dentex spp) 52 Pampus Silver pomfrets 52 Paralichthys American flounders 52 Epigonus Cardinalfishes 51 Hemiramphus Halfbeaks 51 Scomberoides Queenfishes 51 Solea Soles 51 Alosa Shads 50 Caesio Fusiliers 50 Cynoscion Weakfishes 50 Diplodus Seabreams {Diplodus spp) 50 Salmo Salmons 50 Scomberomorus Seerfishes 50 Seriolella Barrelfishes 50 Trachipterus Deaf fishes 50 Coregonus Whitefishes 49 Phycis Gunnels, forkbeards 49 Batrachoides Toadfish 47 Calamus Porgies 47 Kyphosus Sea chubs 47 Rhinochimaera Rhinochimaera 47 Ammodytes Spookfishes 45 Prionotus Sandlances 45 Rastrelliger Indo-Pacific mackerels 45 Sardinella Sardine 45 Dicentrarchus European/spotted seabass 44 Mullus Goatfishes 44 Oncorhynchus Pacific salmon 43 Gymnocranius Large-eye breams 42 Platax Batfishes 42 Terapon Trumpeters 42 Harengula Herring 41 Pagrus Seabreams {Pagrus spp) 41 Stolephorus Anchovies 41 Lycodes Eelpouts 40 Peprilus Harvestfishes 40 Menticirrhus Kingcroakers 39 Nemipterus Threadfin breams 39 Rhodichthys Rhodichthys 39 Siganus Rabbitfishes 39 Microchirus Microchirus 38 Plotosus Eel catfishes 36 306 Taxonomic Intrinsic level Taxon Common name vulnerability Genus Cantherhines Filefishes 35 Rhombosolea Flounders 35 Trematomus Antarctic rockcods 35 Spicara Picarels 34 Serranus Groupers 32 Uranoscopus Stargazers 32 Leiognathus Pony fishes 30 Notoscopelus Notoscopelus 30 Gobius Gobies 29 Muraenolepis Moray cods 27 Myoxocephalus Sculpins 27 Priacanthus Bigeyes 27 Upeneus Goatfishes (Upeneus spp) 27 Auxis Frigate tuna 26 Decapterus Scads 26 Gaidropsams Rocklings 24 Sphoeroides Puffers 23 Gerres Morrajas \u00E2\u0080\u00A2 22 Hypomesus Hypomesus 20 Scatophagus Scats 10 Scolopsis Monocle breams 10 Centrophorus granulosus Gulper shark 90 Centrophorus squamosus Leafscale gulper shark 90 Lampris guttatus Opah 90 Lepidocybium flavobrunneum Escolar 90 Lichia amia Leerfish 90 Muraenesox cinereus Daggertooth pike conger 90 Oxynotus centrina Angular roughshark 90 Regalecus glesne King of herrings 90 Ruvettus pretiosus Oilfish 90 Somniosus microcephalus Greenland shark 90 Somniosus pacificus Pacific sleeper shark 90 Dipturus laevis Barndoor skate 87 Dipturus batis Blue skate 86 Dipturus oxyrinchus Longnosed skate 86 Megalops atlanticus Tarpon 84 Megalops cyprinoides Indo-Pacific tarpon 84 Ca rcha rh inusbra chyu rus Copper shark 83 Carcharhinus obscurus Dusky shark 83 Petrus rupestris Red steenbras 82 Squatina argentina Argentine angelshark 80 Squatina squatina Angelshark 80 Alopias superciliosus Bigeye thresher 79 Alopias vulpinus Thintail thresher 79 Dissostichus eleginoides Patagonian toothfish 79 Dissostichus mawsoni Antarctic toothfish 79 307 Taxonomic Intrinsic level Taxon Common name vulnerability Species Gingly mo stoma cirratum Nurse shark 79 Anarliiclias lupus Wolf-fish 78 Anarhichas minor Spotted wolffish 78 Carcharhinus falciformis Silky shark 77 Carcharhinus limbatus Blacktip shark 77 Centroscyllium fabricii Black dogfish 77 Deania calcea Birdbeak dogfish 77 Lepidopus caudatus Silver scabbardfish 77 Notorynchus cepedianus Broadnose sevengill shark 77 Prionace glauca Blue shark 77 Sphyrna lewini Scalloped hammerhead 77 Sphyrna zygaena Smooth hammerhead 77 lsurus oxyrinchus Shortfin mako 76 lsurus paucus Longfin mako 76 Aphanopus carbo Black scabbardfish 75 Argyrosomus hololepidotus Southern meagre 75 Argyrosomus regius Meagre 75 Coryphaenoides rupestris Roundnose grenadier 75 Lota lota Burbot 75 Molva dypterygia Blue ling 75 Molva molva Ling 75 Reinhardtius stomias Arrowtooth flounder 75 Conger orbignyanus Argentine conger 73 Rexea solandri Silver gemfish 73 Callorhinchus capensis Cape elephantfish 72 Callorhinchus milii Ghost shark 72 Centroscymnus coelolepis Portuguese dogfish 72 Centroscymnus cryptacanthus Shortnose velvet dogfish 72 Conger oceanicus American conger 72 Lophius americanus American angler 72 Lophius vomerinus Cape monk 72 Raja microocellata Small-eyed ray 72 Totoaba macdonaldi Totoaba 72 Alepocephalus bairdii Bairds smooth-head 71 Argyrozona argyrozona Carpenter seabream 71 Macruronus magellanicus Patagonian grenadier 71 Macruronus novaezelandiae Blue grenadier 71 Mola mola Ocean sunfish 71 Pseudosciaena crocea Croceine croaker 71 Bathyraja eatonii Eatons skate 70 Genypterus blacodes Pink cusk-eel 70 Gobionotothen gibberifrons Humped rockcod 70 Lepidonototlien squamifrons Grey rockcod 70 Macrourus berglax Onion-eye grenadier 70 Macrourus whitsoni Whitsons grenadier 70 Salvelinus alpinus Charr 70 Dalatias licha Kitefin shark 69 308 Taxonomic Intrinsic level Taxon Common name vulnerability Species Eleutheronema tetradactylum Fourfinger threadfin 69 Genypterus cliilensis Red cusk-eel 69 Leucoraja circularis Sandy ray 69 Leucoraja fullonica Shagreen ray 69 Polyprion americanus Wreckfish 69 Polyprion oxygeneios Hapuka 69 Raja clavata Thornback ray 69 Raja undulata Undulate ray 69 Gnathanodon speciosus Golden trevally 68 Hippoglossus hippoglossus Atlantic halibut 68 Hippoglossus stenolepis Pacific halibut 68 Lobotes surinamensis Atlantic tripletail 68 Channichthys rhinoceratus Unicorn icefish 67 Lepidopsetta bilineata Rock sole 67 Chaenocephalus aceratus Blackfin icefish 66 Emmelichthys nitidus nitidus Redbait 66 Lamna nasus Porbeagle 66 Ophiodon elongatus Lingcod 66 Orthopristis chrysoptera Pigfish 66 Scomberesox saurus saurus Atlantic saury 66 Lophius piscatorius Angler 65 Stereolepis gigas Giant sea-bass 65 Carcharodon carcharias Great white shark 64 Champsocephalus gunnari Mackerel icefish 64 Hoplostethus artanticus Orange roughy 64 Makaira mazara Indo-Pacific blue marlin 64 Notothenia coriiceps Yellowbelly rockcod 64 Notothenia rossii Marbled rockcod 64 Orcynopsis unicolor Plain bonito 64 Pogonias cromis Black drum 64 Polydactylus quadrifdis Giant African threadfin 64 Pterogymnus laniarius Panga seabream 64 Thunnus orientalis Pacific bluefin tuna 64 Xiphias gladius Swordfish 64 Atractoscion aequidens Geelbeck croaker 63 Atractoscion nobilis White weakfish 63 Centroberyx ajfinis Redfish 63 Cheimerius nufar Santer seabream 63 Elagatis bipinnulata Rainbow runner 63 Epinephelus aeneus White grouper 63 Epinephelus flavolimbatus Yellowedge grouper 63 Epinephelus goreensis Dungat grouper 63 Epinephelus nigritus Warsaw grouper 63 Epinephelus niveatus Snowy grouper 63 Epinephelus tauvina Greasy grouper 63 Spondyliosoma cantharus Black seabream 63 Thunnus maccoyii Southern bluefin tuna 63 309 Taxonomic level Intrinsic Taxon Common name vulnera Thunnus tonggol Longtail tuna 63 Eopsetta jordani Petrale sole 62 Epineplielus marginatus Dusky grouper 62 Lateolabrax japonicus Japanese seaperch 62 Leucoraja naevus Cuckoo ray 62 Macquaria ambigua Golden perch 62 Makaira indica Black marlin 62 Makaira nigricans Atlantic blue marlin 62 Squalus acanthias Piked dogfish 62 Thunnus alalunga Albacore 62 Anoplopoma fimbria Sablefish 61 Argyrops spinifer King soldierbream 61 Galeorhinus galeus Tope shark 61 Plectorhinchus mediterraneus Rubberlip grunt 61 Pseudotolithus senegallus Law croaker 61 Scorpaena scrofa Largescaled scorpionfish 61 Tliyrsites atun Snoek 61 Alectis alexandrinus African threadfish 60 Beiyx decadactylus Alfonsino 60 Chirocentrus dorab Dorab wolf-herring 60 Conger conger European conger 60 Conger myriaster Whitespotted conger 60 Euthynnus affinis Kawakawa 60 Euthynnus alletteratus Little tunny 60 Euthynnus lineatus Black skipjack 60 Hydrolagus novaezealandiae Dark ghost shark 60 Megalaspis cordyla Torpedo scad 60 Mycteroperca venenosa Yellowfin grouper 60 Mycteroperca xenarcha Broomtail grouper 60 Paralonchurus peruanus Peruvian banded croaker 60 Pahstiopterus labiosus Giant boarfish 60 Pseudopercis semifasciata Pigletfish 60 Reinhardtius evermanni Kamchatka flounder 60 Reinhardtius hippoglossoides Greenland halibut 60 Rhabdosargus globiceps White stumpnose 60 Semicossyphus pulcher California sheephead 60 Stenotomus chrysops Scup 60 Thunnus albacares Yellowfin tuna 60 Thunnus atlanticus Blackfin tuna 60 Thunnus obesus Bigeye tuna 60 Thunnus thynnus Northern bluefin tuna 60 Cheilopogon agoo Japanese flyingfish 59 Helicolenus dactylopterus dactylopterus Blackbelly rosefish 59 Larimichthys polyactis Yellow croaker 59 Merluccius australis Southern hake 59 Merluccius capensis Shallow-water Cape hake 59 Pleuronectes plat ess us European plaice 59 Species 310 Taxonomic level Taxon Common name Intrinsic vulnerability Pleuronectes quadrituberculatus Alaska plaice 59 Rhomboplites aurorubens Vermilion snapper 59 Gadus macrocephalus Pacific cod 58 Lares calcarifer Barramundi 58 Lopliolatilus chamaeleonticeps Great northern tilefish 58 Merluccius senegalensis Senegalese hake 58 Mugil liza Liza 58 Pollachius pollachius Pollack 58 Pollachius virens Saithe 58 Sebastes entomelas Widow rockfish 58 Sebastes flavidus Yellowtail rockfish 58 Sebastes melanops Black rockfish 58 Sebastes mentella Deepwater redfish 58 Sebastes viviparus Norway redfish 58 Amblyraja georgiana Antarctic starry skate 57 Amblyraja radiata Thorny skate 57 Brotula barbata Bearded brotula 57 Caranx ruber Bar jack 57 Liza saliens Leaping mullet 57 Merluccius gayi peruanus Peruvian hake 57 Merluccius merluccius European hake 57 Rachycentron canadum Cobia 57 Mustelus mustelus Smooth-hound 56 Theragra chalcogramma Alaska pollack 56 Austroglossus microlepis West coast sole 55 Austroglossus pectoralis Mud sole 55 Epinephelus analogus Spotted grouper 55 Epinephelus morio Red grouper \u00E2\u0080\u00A2 55 Ethmidium maculatum Pacific menhaden 55 Katsuwonus pelamis Skipjack tuna 55 Nemadactylus macropterus Tarakihi 55 Petromyzon marinus Sea lamprey 55 Pomatomus saltator Bluefish 55 Trigla lyra Piper gurnard 55 Brosme brosme Tusk 54 Caranx rhonchus False scad 54 Cetorliinus maximus Basking shark 54 Elops lacerta West African ladyfish 54 Elops saurus Ladyfish 54 Morone americana White perch 54 Morone saxatilis Striped sea-bass 54 Mycteroperca phenax Scamp 54 Nibea mitsukurii Nibe croaker 54 Rhinobatos percellens Chola guitarfish 54 Rhinobatos planiceps Pacific guitarfish 54 Sarda orientalis Striped bonito 54 Sardinops sagax South American pilchard 54 311 Taxonomic Intrinsic level Taxon Common name vulnerability Species Sebastes alutus Pacific ocean perch 54 Sebastes capensis False jacopever 54 Sebastes paucispinis Bocaccio 54 Tautoga onitis Tautog 54 Trachinotus blochii Snubnose pompano 54 Trachinotus carolinus Florida pompano 54 Albula vulpes Bonefish 53 Brama brama Atlantic pomfret 53 Caranx crysos Blue runner 53 Caranx hippos Crevallejack 53 Centropomus undecimalis Common snook 53 Clupea harengus Atlantic herring 53 Clupea harengus membras Baltic herring 53 Clupea pallasii Pacific herring 53 Girella nigricans Opaleye 53 Girella tricuspidata Luderick 53 Holtbyrnia anomala Bighead searsid 53 Lethrinus atlanticus Atlantic emperor 53 Lithognathus lithognathus White steenbras 53 Lithognathus mormyrus Striped seabream 53 Merluccius hubbsi Argentine hake 53 Mora mow Common mora 53 Mustelus henlei Brown smooth-hound 53 Mustelus lenticulatus Spotted estuary smooth-hound 53 Mustelus schmitti Narrownose smooth-hound 53 Mycteroperca bonaci Black grouper 53 Mycteroperca microlepis Gag 53 Myxine glutinosa Hagfish 53 Paralabrax humeralis Peruvian rock seabass 53 Sarda chiliensis lineolata Pacific bonito 53 Sciaenops ocellatus Red drum 53 Scomber australasicus Blue mackerel 53 Sebastolobus alascanus Shortspine thorny head 53 Dentex dentex Common dentex 52 Eleginops maclovinus Patagonian blennie 52 Gadus morhua Atlantic cod 52 Gadus ogac Greenland cod 52 Kathetostoma giganteum Giant stargazer 52 Pagellus bogaraveo Blackspot seabream 52 Pampus argenteus Silver pomfret 52 Paralichthys californicus California flounder 52 Pleuragramma antarcticum Antarctic silverfish 52 Scyliorliinus canicula Smallspotted catshark 52 Scyliorhinus stellaris Nursehound 52 Seriola quinqueradiata Japanese amberjack 52 Dentex angolensis Angola dentex 51 Epigonus telescopus Bulls-eye 51 312 Taxonomic Intrinsic level Taxon Common name vulnerability Species Galeus melastomus Blackmouth catshark 51 Heiniramphus brasiliensis Ballyhoo 51 Isacia conceptionis Cabinza grunt 51 Lepidotrigla dieuzeidei Spiny gurnard 51 Melamphaes leprus Melamphaes leprus 51 Merluccius gayi gayi South Pacific hake 51 Pagellus erythrinus Common pandora 51 Scomber japonicus Chub mackerel 51 Scomber scombrus Atlantic mackerel 51 Scophthalmus maximus Turbot 51 Strangomera bentincki Araucanian herring 51 Trachurus murphyi Inca scad 51 Trachurus picturatus Blue jack mackerel 51 Acanthocybium solandri Wahoo 50 Antimora rostrata Blue antimora 50 Chimaera monstrosa Rabbit fish 50 Clupeonella cultriventris Black Sea sprat 50 Coryphaena hippurus Common dolphinfish 50 Diplodus cervinus cervinus Zebra seabream 50 Galeoides decadactylus Lesser African threadfin 50 Lophius budegassa Black-bellied angler 50 Lophius vaillanti Shortspine African angler 50 Pseudopleuronectes americanus Winter flounder 50 Pseudopleuronectes herzensteini Littlemouth flounder 50 Salilota australis Tadpole codling 50 Sarpa salpa Salema 50 Seriola dumerili Greater amberjack 50 Seriola lalandi Yellowtail amberjack 50 Trachurus declivis Greenback horse mackerel 50 Trachurus trecae Cunene horse mackerel 50 Zenopsis conchifer Silvery John dory 50 Zenopsis nebulosus Mirror dory 50 Acanthistius brasilianus Sea bass 49 Alosa aestivalis Blueback shad 49 Alosa alosa Allis shad 49 Alosa fallax Twaite shad 49 Alosa pontica Pontic shad 49 Alosa sapidissima American shad 49 Atrobucca tribe Longfin kob 49 Coregonus albula Vendace 49 Dentex macrophthalmus Large-eye dentex 49 Epinephelus guttatus Red hind 49 Epinephelus striatus Nassau grouper 49 Genypterus capensis Kingklip 49 Genypterus maculatus Black cusk-eel 49 Mallotus villosus Capelin 49 Mugil cephalus Flathead mullet 49 313 Taxonomic Intrinsic level Taxon Common name vulnerability Species Mugil soiuy So-iuy mullet 49 Parana signata Parona leatherjacket 49 Phycis blennoides Greater forkbeard 49 Phycis phycis Forkbeard 49 Pseudochaenichthys georgianus South Georgia icefish 49 Raja asterias Starry ray 49 Raja rnontagui Spotted ray 49 Sardina pilchardus European pilchard 49 Sebastes marinus Ocean perch 49 Seriolina nigrofasciata Blackbanded trevally 49 Trachurus symmetricus Pacific jack mackerel 49 Brevoortia aurea Brazilian menhaden 48 Brevoortia pectinata Argentine menhaden 48 Cynoscion analis Peruvian weakfish 48 Cynoscion regalis Gray weakfish 48 Lutjanus purpureus Southern red snapper 48 Merluccius bilinearis Silver hake 48 Merluccius polli Benguela hake 48 Merluccius productus North Pacific hake 48 Parophrys vetula English sole 48 Pseudophycis bachus Red codling 48 Pteroscion peli Boe drum 48 Salmo salar Atlantic salmon 48 Salmo trutta trutta Sea trout 48 Sarda chiliensis chiliensis Eastern Pacific bonito 48 Sarda sarda Atlantic bonito 48 Sciaena gilberti Sciaena gilberti 48 Sciaena umbra Brown meagre 48 Scomberomorus guttatus Indo-Pacific king mackerel 48 Selaroides leptolepis Yellowstripe scad 48 Seriolella brama Common warehou 48 Sparisoma cretense Parrotfish 48 Tautogolabrus adspersus Cunner 48 Tenualosa ilisha Hilsa shad 48 Tenualosa toli Toli shad 48 Acanthopagrus latus Yellowfin seabream 47 Acanthopagrus schlegeli Black porgy 47 Brevoortia patronus Gulf menhaden 47 Brevoortia tyrannus Atlantic menhaden 47 Kyphosus cinerascens Blue seachub 47 Melanogrammus aeglefinus Haddock 47 Paralichthys dentatus Summer flounder 47 Paralichthys olivaceus Bastard halibut 47 Percophis brasiliensis Brazilian flathead 47 Pomadasys jubelini Sompat grunt 47 Pseudocaranx dentex White trevally 47 Pseudotolithus elongatus Bobo croaker 47 314 Taxonomic level Taxon Common name Intrinsic vulnerability Pseudotolithus senegalensis Cassava croaker 47 Rhinochimaera atlantica Spearnose chimaera 47 Scomberomorus maculatus Spanish mackerel 47 Scomberomorus sierra Pacific sierra 47 West African Spanish Scomberomorus tritor mackerel 47 Selene dorsalis African moonfish 47 Selene setapinnis Atlantic moonfish 47 Seriolella caerulea White warehou 47 Seriolella porosa Choicy ruff 47 Seriolella punctata Silver warehou 47 Zeus faber John dory 47 Etrumeus teres Round herring 46 Etrumeus wliiteheadi Whiteheads round herring 46 Glyptocephalus cynoglossus Witch 46 Glyptocephalus zachirus Rex sole 46 Istiopliorus albicans Atlantic sailfish 46 Istiophorus platypterus Indo-Pacific sailfish 46 Parastromateus niger Black pomfret 46 Platichthys flesus Flounder 46 Platycephalus indicus Bartail flathead 46 Psettichthys melanostictus West American sand sole 46 Rhizoprionodon terraenovae Atlantic sharpnose shark 46 Scomberomorus brasiliensis Serra Spanish mackerel 46 Scomberomorus cavalla King mackerel 46 Scomberomorus lineolatus Streaked seerfish 46 Scorpaenichthys marmoratus Cabezon 46 Sebastes goodei Chilipepper 46 Sebastes pinniger Canary rockfish 46 Sprattus fuegensis Falkland sprat 46 Sprattus sprattus European sprat 46 Sprattus sprattus balticus Baltic sprat 46 Tetrapturus angustirostris Shortbill spearfish 46 Tetrapturus pfluegeri Longbill spearfish 46 Trachurus lathami Rough scad 46 Trachurus mediterraneus Mediterranean horse mackerel 46 Trachurus trachurus Atlantic horse mackerel 46 Ammodytes personatus Pacific sandeel 45 Archosargus probatocephalus Sheepshead seabream 45 Boops boops Bogue 45 Caulolatilus chrysops Atlantic goldeye tilefish 45 Caulolatilus princeps Ocean whitefish 45 Cetengraulis edentulus Atlantic anchoveta 45 Cetengraulis mysticetus Pacific anchoveta 45 Cololabis saira Pacific saury 45 Hyperoglyphe antarctica Antarctic butterfish 45 Hyperoglyphe bythites Black driftfish 45 Joturus pichardi Bobo mullet 45 315 Taxonomic level Taxon Common name Intrinsic vulnerability Lutjanus campechanus Northern red snapper 45 M icromesistius austral is Southern blue whiting 45 Micromesistius poutassou Blue whiting 45 Ocyurus chrysurus Yel lowtai l snapper 45 Pagellus acarne Axi l l a ry seabream 45 Pagellus bellottii bellottii Red pandora 45 Pleurogrammus azonus Okhostk atka mackerel 45 Pleurogrammus monopterygius Atka mackerel 45 Pterothrissus belloci Longfin bonefish 45 Rastrelliger brachysoma Short mackerel 45 Rastrelliger kanagurta Indian mackerel 45 Sardinella brasiliensis Brazi l ian sardinella 45 Solea senegalensis Senegalese sole 45 Solea solea Common sole 45 Spratelloides gracilis Silverstriped round herring 45 Tetrapturus albidus Atlantic white marlin 45 Tetrapturus audax Striped marlin 45 Engraulis anchoita Argentine anchoita 44 Engraulis capensis Cape anchovy 44 Engraulis encrasicolus European anchovy 44 Engraulis japonicus Japanese anchovy 44 Engraulis mordax Californian anchovy 44 Engraulis ringens Anchoveta 44 Mullus barbatus Red mullet 44 Mullus surmuletus Striped red mullet 44 Opisthonema libertate Pacific thread herring 44 Opisthonema oglinum Atlantic thread herring 44 Psettodes belcheri Spottail spiny turbot 44 Psettodes bennettii Spiny turbot 44 Psettodes erumei Indian spiny turbot 44 Sardinella maderensis Madeiran sardinella 44 Trachurus capensis Cape horse mackerel 44 Trachurus japonicus Japanese jack mackerel 44 Alosa mediocris Hickory shad 43 Alosa pseudoharengus Alewife 43 Brachydeuterus auritus Bigeye grunt 43 Cyclopterus lumpus Lumpsucker 43 Sardinella aurita Round sardinella 43 Atherina boyeri Big-scale sand smelt 42 Belone belone belone Garpike 42 Dicentrarclius labrax European seabass 42 Dicentrarchus punctatus Spotted seabass 42 Herklotsichthys quadrimaculatus Bluestripe herring 42 Lepido rhomb us boscii Fourspotted megrim 42 Lepidorhombus whiffiagonis Megr im 42 Lutjanus argentimaculatus Mangrove red snapper 42 Lutjanus argentiventris Y e l l o w snapper 42 316 Taxonomic level Taxon Common name Intrinsic vulnerability Species Lutjanus synagris Lane snapper 42 Sardinella gibbosa Goldstripe sardinella 42 Sardinella lemuru Bali sardinella 42 Sardinella longiceps Indian oil sardine 42 Sardinella zunasi Japanese sardinella 42 Scophthalmus aquosus Windowpane 42 Scophthalmus rhombus Brill 42 Trichiurus lepturus Largehead hairtail 42 Chrysophrys auratus Squirefish 41 Coregonus lavaretus Common whitefish 41 Dicologlossa cuneata Wedge sole 41 Dorosoma cepedianum American gizzard shad 41 Merlangius merlangus Whiting 41 Oncorhynchus tshawytscha Chinook salmon 41 Pagrus auriga Redbanded seabream 41 Pagrus caeruleostictus Bluespotted seabream 41 Pagrus pagrus Common seabream 41 Sparus auratus Gilthead seabream 41 Umbrina canariensis Canary drum 41 Urophycis brasiliensis Brazilian codling 41 Urophycis chuss Red hake 41 Urophycis tenuis White hake 41 Chaenodraco wilsoni Spiny icefish 40 Chionodraco rastrospinosus Ocellated icefish 40 Coregonus huntsmani Atlantic whitefish 40 Coregonus oxyrinchus Houting 40 Dasyatis akajei Red stingray 40 Dasyatis pastinaca Common stingray 40 Dentex canariensis Canary dentex 40 Dentex congoensis Congo dentex 40 Eleginus gracilis Saffron cod 40 Eleginus navaga Navaga 40 Galeichthys feliceps White baggar 40 Hyporhamphus ihi Garfish 40 Hyporhamphus sajori Japanese halfbeak 40 Lepidorhynchus denticulatus Thorntooth grenadier 40 Limanda aspera Yellowfin sole 40 Microstomas kitt Lemon sole 40 Microstomus pacificus Dover sole 40 Peprilus simillimus Pacific pompano 40 Pomadasys argenteus Silver grunt 40 Pomadasys incisus Bastard grunt 40 Pontinus kuhlii Offshore rockfish 40 Pseudopentaceros richardsoni Pelagic armorhead 40 Stromateus fiatola Blue butterfish 40 Trachinus draco Greater weever 40 Nemipterus virgatus Golden threadfin bream 39 317 Taxonomic Intrinsic level Taxon Common name vulnerability Species Ethmalosa Jimbhata Bonga shad 38 Oncorhynchus keta Chum salmon 38 Oncorhynchus mason masou Cherry salmon 38 Oncorhynchus mykiss Rainbow trout 38 Oncorhynchus nerka Sockeye salmon 38 Psenopsis anomala Melon seed 38 Takifugu vermicula lis Takifugu vermicularis 38 Argentina situs Greater argentine 37 Argentina sphyraena Argentine 37 Citharichthys sordidus Pacific sanddab 37 Cynoscion nebulosus Spotted weakfish 37 South American striped Cynoscion striatus weakfish 37 Labrus bergylta Ballan wrasse 37 Narrow-barred Spanish Scomberomorus commerson mackerel 37 Labrus menda Brown wrasse 37 Scomberomorus niphonius Japanese Spanish mackerel 37 Scomberomorus regalis Cero 37 Centropristis striata Black seabass 36 Chelidonichthys cuculus East Atlantic red gurnard 36 Chelidonichthys lucerna Tub gurnard 36 Pegusa lascaris Sand sole 36 Chelidonichthys capensis Cape gurnard 35 Chelidonichthys gurnardus Grey gurnard 35 Chelidonichthys kumu Bluefin gurnard 35 Chelidonichthys lastoviza Streaked gurnard 35 Chloroscombrus chrysurus Atlantic bumper 35 Chloroscombrus orqueta Pacific bumper 35 Clupanodon thrissa Chinese gizzard shad 35 Diplodus sargus sargus White seabream 35 Macrodon ancylodon King weakfish 35 Patagonotothen brevicauda brevicauda Patagonian rockcod 35 Patagonotothen ramsayi Patagonotothen ramsayi 35 Pentanemus quinquarius Royal threadfin 35 Boreogadus saida Polar cod 34 Diplodus puntazzo Sharpsnout seabream 34 Normanichthys crockeri Normans camote 34 Oncorhynchus gorbuscha Pink salmon 34 Oncorhynchus kisutch Coho salmon 34 Spicara maena Blotched picarel 34 Genyonemus lineatus White croaker 33 Lepidoperca pulchella Orange perch 33 Limanda ferruginea Yellowtail flounder 33 Limanda limanda Dab 33 Oblada melanura Saddled seabream 33 Trisopterus esmarkii Norway pout 33 318 Taxonomic Intrinsic level Taxon Common name vulnerability Species Trisopterus hiscus Pouting 33 Trisopterus minutus Poor cod 33 Arctoscopus japonicus Sailfin sandfish 32 Otolithes ruber Tiger-toothed croaker 32 Pennahia argentata White croaker 32 Stephanolepis cirrhifer Thread-sail filefish 32 Thyrsitops lepidopoides White snake mackerel 32 Uranoscopus scaber Atlantic stargazer 32 Cheilodactylus variegatus Peruvian morwong 31 Ilisha africana West African ilisha 31 llisha elongata Elongate ilisha 31 Lampanyctodes hectoris Hectors lanternfish 31 Nemadactylus bergi White morwong 31 Zoarces americanus Ocean pout 31 Zoarces viviparus Viviparous blenny 31 Maurolicus muelleri Pearlsides 30 Osmerus eperlanus European smelt 30 Osmerus mordax mordax Atlantic rainbow smelt 30 Arripis georgianus Australian ruff 29 Arripis trutta Eastern Australian salmon 29 Gobius niger Black goby 29 Hippoglossoides elassodon Flathead sole 29 Hippoglossoides platessoides American plaice 29 Menticirrhus littoralis Gulf kingcroaker 29 Menticirrhus saxatilis Northern kingcroaker 29 Pleuronichthys decurrens Curlfin sole 29 Pseudupeneus prayensis West African goatfish 29 Thaleichthys pacificus Eulachon 29 Batistes carolinensis Grey triggerfish 28 Macroramphosus scolopax Longspine snipefish 28 Muraenolepis microps Smalleye moray cod 27 Parapercis colias Blue cod 27 Priacanthus macracanthus Red bigeye 27 Synagrops japonicus Japanese splitfin 27 Ariomma indica Indian ariomma 26 Hypoptychus dybowskii Korean sandeel 26 Pterygotrigla picta Spotted gurnard 26 Pterygotrigla polyommata Latchet 26 Auxis rochei rochei Bullet tuna 25 Auxis thazard thazard Frigate tuna 25 Conodon nobilis Barred grunt 25 Micropogonias furnieri Whitemouth croaker 25 Micropogonias undulatus Atlantic croaker 25 Anchoa hepsetus Broad-striped anchovy 24 Anchoa mitchilli Bay anchovy 24 Glossanodon semifasciatus Deepsea smelt 24 Konosirus punctatus Konoshiro gizzard shad 24 319 Taxonomic level Taxon Common name Intrinsic vulnerability Microgadus proximus Pacific tomcod 24 Microgadus tomcod Atlantic tomcod 24 Umbrina canosai Argentine croaker 24 Umbrina cirrosa Shi drum 24 Mene maculata Moonfish 23 Sphoeroides maculatus Northern puffer 23 Common two-banded Diplodus vulgaris seabream 22 Harpadon nehereus Bombay-duck 22 Diplodus annularis Annular seabream 21 Diplodus argenteus argenteus South American silver porgy 21 Saurida tumbil Greater lizardfish 21 Saurida undosquamis Brushtooth lizardfish 21 Hypomesus pretiosus Surf smelt 20 Leiostomus xanthurus Spot croaker 20 Diplophos maderensis Diplophos maderensis 19 Lactarius lactarius False trevally 19 Peprilus alepidotus Haryestfish 19 Peprilus triacanthus American butterfish 19 Pellona ditchela Indian pellona 18 Leucoraja erinacea Little skate 17 Leucoraja garmani Freckled skate 17 Meuschenia scaber Velvet leatherjacket 17 Diplectrum formosum Sand seabass 16 Decapterus maruadsi Japanese scad 15 Decapterus russelli Indian scad 15 Selar crumenophthalmus Bigeye scad 15 Ciliata mustela Fivebeard rockling 14 Anodontostoma chacunda Chacunda gizzard shad 10 Apogon pseudomaculatus Twospot cardinalfish 10 Astronesthes micropogon Astronesthes micropogon 10 Bregmaceros mcclellandi Spotted codlet 10 Centrobranchus nigroocellatus Roundnose lanternfish 10 Dussumieria acuta Rainbow sardine 10 Dussumieria elopsoides Slender rainbow sardine 10 Ectreposebastes imus Midwater scorpionfish 10 Gonostoma atlanticum Atlantic fangjaw 10 Hilsa kelee Kelee shad 10 Menidia menidia Atlantic silverside 10 Symbolophorus veranyi Large scale lantern fish 10 Taaningichthys minimus Taaningichthys minimus 10 Uncisudis lohgirostra Uncisudis longirostra 10 320 Appendix 4 . 1 . Life history and recruitment parameters of the 21 species of marine fish that are used to simulate time-series of catch, catch-per-unit-effort and abundance through an age-structured population model. Parameters on L i n f - Asymptotic length, tm - age at first maturity, t^x - longevity, K - von Bertalanffy growth parameter K, t\u00E2\u0080\u009E - theoretical age at zero body length, M - instantaneous natural mortality rate, Fecundity - annual fecundity. Scientific name Linf (cm) tin (year) tmax (year) K (year\"1) to (year) M (year\"1) Fecundity (eggs per female) Vulnerability1 Alosa pseudoharengus 41.7 3.6 14.4 0.20 -0.63 0.41 37932 43.33 Alosa sapidisslma 78.5 4.7 20.6 0.14 -0.78 0.21 75864 62.87 Brevoortia patronus 38.9 4.7 18.9 0.15 -1.06 0.39 27569 47.5 Brevoortia tyrannus 51.0 2.1 8.4 0.34 -0.45 0.55 154849 34.18 Clupea harengus 30.0 3.5 11.0 0.35 0 0.27 37100 40.65 Coilia dussumieri 25.0 0.6 2.3 1.21 -0.14 2.08 2237 10.00 Engraulis encrasicolus 25.0 2.3 8.8 0.32 -0.55 0.59 16125 32.24 Engraulis mordax 31.0 2.3 8.5 0.33 -0.56 0.64 3417 32.72 Gadus morhua 105.0 2.2 25.0 0.19 0 0.23 4287500 53.82 Lutjanus campechanus 94.1 3.7 16.9 0.17 -0.73 0.31 27943 51.98 Melanogrammus aeglefinus 75.5 2.7 12.0 0.24 -0.54 0.27 318206 50.06 Merluccius hubbsi 98.0 3.3 15.1 0.19 -0.64 0.23 28620 52.83 Morone saxatilis 203.4 11.2 57.9 0.05 -2.10 0.09 250999 86.21 Reinhartius hippoglossoides 82.9 10.9 48.2 0.06 -1.81 0.10 45167 74.95 Sardinops sagax 30.0 1.7 6.6 0.43 -0.38 0.80 25496 27.29 Solea vulgaris 42.4 1.8 7.3 0.39 -0.38 0.57 59161 31.32 Spratus spratus 13.0 1.2 4.0 0.70 -0.29 1.08 5478 20.03 Stenotomus chrysops 38.5 2.6 10.5 0.27 -0.58 0.47 6767 37.54 Stizostedion vitreum 107.0 12.1 57.5 0.05 -2.49 0.09 168739 78.77 Thunnus albacares 183.9 2.3 11.6 0.25 -0.41 0.36 647723 56.08 Trachurus trachurus 54.3 5.1 22.0 0.13 -1.12 0.25 103888 59.23 / - Estimated using the expert system developed in Chapter 2. 2 - Based on estimates in Myers et al. (1999). to 6.1 Parameterizations of the 1970s and 2000s model 6.1.1 Phytoplankton Phytoplankton communities in the N S C S were dominated by diatoms (over 97% in numbers during a survey in the late 1990s), followed by dinoflagellates and other groups (Jia et al. 2004). Biomass and P/B ratio of phytoplankton in the 2000s model were estimated from survey data. The survey estimated chlorophyll a concentration by depth in each season. A phytoplankton cell was assumed to consist of an average of 1% (0.5-1.5%) of chlorophyll a by weight (Ahlgren 1970). Thus the average depth-integrated chlorophyll a concentration was converted to wet weight of biomass using a conversion factor of 100. (Table A6.1). The estimated biomass amounted to 323 t-km\"2 (215 - 646 t4mi\"2). The value is within the range of phytoplankton biomasses reported in Pauly and Christensen (1995a) (13-730 t-km\"2). Table A6.1. Estimation of phytoplankton biomass in the NSCS (Jia et al. 2004). Estimated Chlorophyll a density (mg Chl.a-m ) Depth (m) Spring Summer Autumn Winter Average 0 0.10 0.22 0.38 0.50 0.30 20 0.15 0.25 0.35 0.50 0.31 40 0.23 0.32 0.29 0.41 0.31 60 0.22 0.31 0.22 0.31 0.27 80 0.16 0.29 0.16 0.23 0.21 100 0.08 0.20 0.10 0.15 0.13 150 0.01 0.05 0.05 0.05 0.04 200 0.01 0.03 0.03 0.03 0.03 Depth-integrated Chlorophyll a density (mg Chl.a-ml) Estimated phytoplankton biomass (t-km2) 32.26 323. B y summing up the estimated daily phytoplankton production in each season in the late 1990s, I estimated the annual phytoplankton production to be 14,290 t4^m\"2 year\"1. Assuming 1 g C = 9 g (range = 8-10 g) wet weight (Pauly & Christensen 1995a), the estimated annual total production in wet weight was 128,608 t4^m\"2 year\"1. Thus the P/B ratio of phytoplankton was estimated to be 399 (177 - 665) year\"1. A s independent estimates of biomass and P/B ratio in the 1970s were not available, they are assumed to be the same as the 2000s model. 322 6.1.2. Benthic producer Benthic producer consists of benthic algae. Biomass and P/B ratio were based on the values reported in Pauly and Christensen (1995a), i.e., 153 t-km\"2 and 11.9 year\"1. Since I found no evidence of large changes in biomass of this group over the past few decades, I assumed that the biomass and P/B ratio were the same in the 1970s and 2000s models. Catch in the 2000s and the 1970s models were estimated from national landings statistics from Guangdong, Guangxi and Hainan provinces in 2000 and 1973. The group 'algae' amounted to 0.045 t-km\"2 and 0.0056 t-km\"2 in 2000 and 1973, respectively. 6.1.3. Zooplankton Zooplankton biomasses in the three regions in the N S C S : Pearl River Estuary, western Guangdong, and eastern Hainan were estimated to be 10.4, 9.8 and 6.9 t-km\"2, respectively (South China Sea Fisheries Institute, unpublished data). The average was used as the zooplankton biomass in the 2000s model (Average = 9 t-km\"2; range: 6.9 -10.4 t-km\"2). P/B and Q/B ratios were assumed to be the same as the value used in the South China Sea (50-200 m) ecosystem model (Pauly & Christensen 1995b). The biomass of zooplankton in N S C S decreased from 104.6 mg m\" 3 in 1978-1979 to 22.0 mg m\"3 in the late 1990s (South China Sea Fisheries Institute, unpublished data). Thus biomass of zooplankton in the 1970s model was increased by 3.8 times from the 2000s model, which became 33.8 t-km\"2. P /B and Q/B ratios were assumed to be the same between years. Catches in the 2000s and 1970s models were based on national landings statistics from Guangdong, Guangxi and Hainan provinces in 2000 and 1972 (data point only available in the early 1970s), in which the group ' M o shrimp' (Acetes spp.) amounted to 0.095 t-km\"2 and 0.01 t-km\"2, respectively. 323 6.1.4. Jellyfish The jellyfish group consists of medusae of the phylum Cnidaria. Based on trawl survey conducted in Hong Kong in 1982-83, biomass of jellyfish in the early 1980s was estimated to be about 1.529 t-km\" . Since extrapolation of this estimate to the entire N S C S may not be valid, biomass was left to be estimated by assuming ecotrophic efficiency of 0.95 in both the 1970s and 2000s models. Local estimates for P/B and Q/B ratios are not available. Thus estimates from the Gulf of Thailand (P/B = 5 year\"1, Q/B = 20 year\"1) -the closest region where estimates were available - were used in both the 1970s and 2000s models. Catches were based on national landings statistics. There is no reported landing of jellyfish in 2000 and 1973. However, jellyfish should have been caught and landed in both periods (Jiahua Cheng, East China Sea Fisheries Institute, pers. comm.). Thus I used the 1993 and 1977 landings, the closest years when landings data are available, as approximate estimates of jellyfish landings in the 2000s and 1970s (0.044 t-km\"2 and 0.0012 t-km\" , respectively). 6.1.5. Benthos Benthos is sub-divided into five functional groups: polychaetes, echinoderms, benthic crustaceans (excluding shrimps and crabs), non-cephalopod molluscs, and sessile/other invertebrates. Their biomasses in the 2000s model were estimated from survey conducted in 1998 -2000 (Jia et al. 2004) (Table A6.2). Local estimates on P/B and Q/B ratio for these groups were not available, thus I used estimates from other similar systems. Based on estimates for the southern Gul f of Mexico, Q/B ratio of polychaetes in the 2000s model is 22.5 year\"1 (Chavez et al. 1998). P/B ratio was estimated by assuming a P/Q ratio of 0.3. P/B of echinoderms was assumed to be 1.2 year\"1 based on estimates from the Caribbean coral reef (Opitz 1993) while Q/B was 3.58 year\"1 (Pauly et al. 1993). P/B of benthic crustaceans was 5.65 year\"1, the same as the estimated total mortality of Oratosquilla spp. from Hong Kong (Pitcher et al. 1998), while Q/B was 26.9 year\"1 (Opitz 1996). Based on estimates from the Gulf of Thailand (Christensen & Walters 2004b), P /B of non-cephalopod molluscs was 3 year\"1. Q/B was 324 assumed to be 7 year\"1 (Opitz 1993). Based on estimates for sessile invertebrate in the Caribbean coral reef (Opitz 1993), P/B and Q/B of sessile/other invertebrates was assumed to be 1 year\"1 and 9 year\"1, respectively. Biomass estimates of benthos in the 1970s model were based a survey along the Guangdong coast in the late 1970s, while P/B and Q/B ratio were assumed to be similar in the two periods. Table A6.2. Survey estimated biomass for polychaetes, echinoderms, benthic crustaceans, non-cephalopod molluscs, and sessile/other invertebrates in the NSCS in the late 1990s (Jia et al. 2004). Functional groups 2 Biomass (t-km\" ) Polychaetes 2.24 Echinoderms 1.98 Benthic crustaceans 1.43 Non-cephalopod molluscs 2.68 Sessile/other invertebrates 2.61 Echinoderms were caught in the N S C S but were not reported as a separate group in the national catch statistics. Thus catch estimates from the Sea Around Us Project 2 2 catch database was used, which amounted to 0.0019 t-km\" and 0.0039 t-km\" for the 2000s and 1970s models, respectively. Catch of benthic crustaceans was obtained from reported landings of 'crustacean' in the national statistics, subtracted by the landings of shrimps and crabs to avoid double-counting (see below). Thus the catch estimates in the 2000s and 1970s models were 0.039 t-km\"2 and 0.0019 t-km\"2, respectively. Non-cephalopod molluscs were based on the group 'molluscs' and catches were estimated from national fisheries statistics subtracted by the estimated catch of cephalopods (0.763 t- km\" 2 and 0.0056 t-km\"2 in the 2000s and 1970s model, respectively). 6.1.6. Shrimps Based on trawl survey conducted in the late 1990s, biomass of shrimps in the N S C S was estimated to be 0.013 t-km\"2. However, because of the low catchability of shrimps by the survey trawl nets, the biomass of shrimps was likely to be under-estimated. 325 Thus biomasses of shrimps in the 1970s and 2000s models were left to be estimated by the model by assuming EE. P /B ratio in the 2000s was based on the averaged total mortality estimates of Metapenaeopsis palmensis and M. barbata from Hong Kong (7.60 year\"1) (7 .1 -8 .1 year\"1), while estimates for the past model was estimated from assuming that fishing mortality rate was half of their natural mortality rate. Assuming that M = 3.6 year\"1 (Pitcher et al. 1998), P/B ratio for the 1970s model was 5.4 year\"1. Q/B ratios of penaeid shrimps were based on the estimates available from Pauly et al. (1993). Landings of shrimp were estimated based on national statistics. 6.1.7. Crabs The late 1990s trawl survey estimated biomass of crabs in the N S C S to be 0.0045 t-km (Jia et al. 2004). This was subsequently found to be too low to support the fisheries and predation specified in the 2000s model. Also, biomass data of crabs in the 1970s were not available. Thus biomasses in the 1970s and 2000s models were estimated by assuming ecotrophic efficiency equalled 0.95. P /B and Q/B ratios in the 1970s model were assumed to be similar to the Gulf of Thailand during that period (3 year\"1 and 12 year\"1 respectively). I increased the P/B ratio in the 2000s model to 4 year\"1 to reflect the higher exploitation rate. Landings of crab was based on the national fisheries statistics. 6.1.8. Cephalopods In N S C S , Loligo squid was the dominant group of cephalopod, consisting of Loligo edulis and L. chinensis. Estimated Logilo squid biomasses from acoustic and trawl surveys in the late 1990s were 1.24 t4^m\"2 and 0.13 t4cm\"2, respectively (Jia et al. 2004). Report from the surveys suggested that the actual biomass of Loligo squid in N S C S might probably lie between these two values. Thus I used their average (0.68 t4on\" ) as the biomass estimate for the cephalopods in the 2000s model. Biomass in the 1970s model was estimated by assuming EE to be 0.95. P/B and Q/B ratios were assumed to be the same as the cephalopods group in the Gul f of Thailand (Christensen & Walters 2004). Cephalopod landings were not reported in the 2000s period. Thus catch estimates of the N S C S region were obtained from the Sea Around Us Project catch database 326 (www.seaaroundus.org), which amounted to 0.273 t-km\" . Catches of cephalopods in thel970s model was based on the reported landings from the national fisheries statistics in 1973 (0.0244 t-km\"2). 6.1.9. Threadfin bream (nemipterids) This group consists of fish from the family Nemipteridae, including Nemipterus virgatus, N. bathybius and N. japonicus. Based on acoustic survey in the late 1990s, biomass of nemipterids in N S C S was estimated to be 0.26 t-km\"2. This was used in the 2000s model. P /B ratio, approximated by the total mortality rate, was estimated to be 3.08 year\"1 (Wang et al. 2004). Q/B ratio was estimated from empirical equations (Palomares & Pauly 1998). Compared to the. 1990s survey, catch rate of N. virgatus in the N S C S dropped by about 72% from a 1962 survey (Jia et al. 2004) while catch rate of N. bathybius dropped by 68%-88.2% (averaged 78%) from an early 1990s survey (Jia et al 2004). Based on the average between the two estimates, I assumed that biomass of nemipterids (assuming that abundance was roughly proportional to catch rate) declined approximately by 75% from the 1970s to late 1990s. Thus biomass in the 1970s model was estimated to be 1.04 t-km\" . Fishing mortality rate in the 1970s was estimated from the exploitation rate (catch/biomass). With an estimated total catch of 0.062 t-km\"2, total mortality rate was estimated to be 0.74 year\"1 ( M = 0.68 year\"1, Wang et al. 2004). Q/B ratio was assumed to be stationary between years. Catch data in the 2000s and 1970s were obtained from national landings statistics. 6.1.10. Bigeyes (priacanthids) Bigeyes consists of fishes from the family Priacanthidae. Acoustic and trawl surveys estimated that the biomass of Bigeyes in the late 1990s was 0.245 t-km\"2 and 0.009 t-km\" , respectively. The average of the two estimates was used as the biomass in the 2000s model (0.127 t-km\"2). P/B ratio in the 2000s model was estimated from the total mortality rates (3.33 year\"1) (Sun & Qiu 2004), Q/B ratio was estimated from an empirical equation (Palomares and Pauly 1998). 327 Catch rates of priacanthids in the late 1990s was only 40% of the 1962 level (South China Sea Fisheries Institute, unpublished data). Assuming that change in biomass was proportional to catch rate, estimated biomass in the 1970s was 0.318 t-km\" . Fishing mortality rate in the 1970s was approximated from the exploitation rate calculated from the total catch (0.025 t-km\"2) and biomass. The estimated total mortality rate was 1.21 year\"1 ( M = 1.13 year\"1) (Sun and Qiu 2004). Q/B ratio was assumed to be stationary between years. Landings of bigeyes in 2000 were not reported in the national fisheries statistics. However, bigeyes were reported to represent about 2.31% and 1.79% in weight in the catch of the late 1990s and the 1970s fishing surveys in the N S C S , respectively (South China Sea Fisheries Institute, unpublished data). Based on the total landings reported in the national statistics, catch of bigyeyes in the 2000s and 1970s models were estimated to be 0.207 t-km\"2 and 0.035 t-km\"2, respectively. 6.1.11. Lizardfish (synodontids) Fishes of the family Synodontidae are included in this group. The major species in the N S C S include Saurida tumbil and 5. undosquamis. Based on fishing survey in the late 1990s, total biomass of these two species was estimated to be 0.0318 t-km\"2. Total mortality rates of the lizardfish in the N S C S continental shelf and Gulf of Tonkin were estimated to be 1.42 and 1.78 year\"1, respectively (Shu & Qiu 2004). Their average was used as the P/B ratio in the 2000s model. Q/B ratio was estimated from an empirical equation (Palomares and Pauly 1998). Since a historical biomass trend for lizardfish was not available, its biomass in the 1970s model was estimated indirectly. Estimated total demersal fish biomass in the N S C S declined by 30.8% from the early to late 1990s (South China Sea Fisheries Institute, unpublished data). Moreover, standardized catch rate of lizardfish by Hong Kong trawlers declined by 69% from the 1970s to the late 1980s (Chapter 5). Biomass in the 1970s was then back-calculated from the 1980s and 1990s (0.149 t-km\"2). Fishing mortality rate in the 1970s was approximated from the exploitation rate calculated from the total catch (0.024 t-km\"2) and biomass. The total mortality rate was estimated to be 328 0.79 year\"1 ( M = 0.63 year\"1) (Shu & Qiu 2004). This was later found to be too low to support predation and catches on this group. Thus I increased the P/B ratio slightly to 0.85 year\"1. Q/B ratio was assumed to be the same between the 1970s and 2000s. Landings of lizardfish in the late 1990s and early 2000s were not available from the national statistics. Thus catch in the 2000s and 1970s models were estimated based on the S A U P database (0.086 t-km\"2 and 0.084 t-km\"2, respectively). 6.1.12. Hairtails (trichiurids) This group is composed of fishes from the family Trichiuridae. This group has been seriously over-exploited and is now dominated by juvenile (age 1 or less) fishes (South China Sea Fisheries Institute, unpublished data). I segregated this group into two multi-stanza groups - juvenile (age less than 18 months) and adult using the multi-stanza routine in Ecopath (Table A6.3) (Christensen & Walters 2004). In the late 1990s, biomass of Trichiurus lepturus - a dominant species of this group in the N S C S - was estimated to be 0.015 t-km\" by trawl survey (South China Sea Fisheries Institute, unpublished data). Since catch of T. lepturus was mainly composed of juvenile fish (Jia et al. 2004), I allocated the entire estimated biomass to the juvenile stage, and I used the multi-stanza routine to estimate the adult stage biomass. Biomass of the adult stage in the 1970s was back calculated from the estimated biomass in the 1990s. By assuming a 65% decline in biomass from the 1970s to the late 1980s (see Chapter 5), and a 30.8% decline from the early to late 1990s (assuming that biomass of trichiurids declined at the same rate as the overall demersal resources) (Jia et al. 2004), the biomass in the 1970s was estimated to be 0.0426 t-km\"2. 329 Table A6.3. Growth and recruitment parameter values for groups that consist of multi-stanza in the 1970s and 2000s models. Groups No. of von Bertalanffy Recruitment Wmaturity/vV* i n f stanza growth parameter K (year1) power Hairtails (trichiurids) 2 0.41' 1* 0.00071 Croakers (> 30 cm) 2 0.363 l 2 0.154 Demersal fish (> 30 cm) 2 0.315 l 2 0.135 Pelagic fish (> 30 cm) l , \u00E2\u0080\u00A2 , __ . . \u00E2\u0080\u00A2 ,- , , 2 0.596 l 2 0.136 Values reported in Fishbase (www.fishbase.org); 2 Default value; 3 Based on 26 stocks of large croakers in the NSCS; 4 Based on 59 stocks of large croakers in the NSCS; 5 Based on 33 species of large demersal fish in the NSCS; 6 Based on 23 species of large pelagic fish in the NSCS. Hairtails were found to be relatively under-exploited in the 1970s compared to the 2000s (Chapter 5). The P/B ratio was thus approximated as 2 times the natural mortality rate, and P/B of the adult stage hairtails in the 1970s was estimated to be 1.08 year\"1. Landings of hairtail reported in the national statistics in 2000 was 227,202 t. However, an independent survey estimated a maximum annual fishery catch of hairtail from the N S C S of only 13,000 t (South China Sea Fisheries Institute, unpublished data). Because of the higher unreliability of the national statistics and the possibility of over-reporting (Watson & Pauly 2001), the latter estimate (13,000 t) was used in the 2000s model. Since the majority of the hairtails caught in the 2000s were reported to be juveniles (< 1 year), I assumed that 80% of the total hairtail catch in weight was from the juvenile stanza. This resulted in estimated catches of 0.028 t-km\"2 and 0.007 t-km\"2 for the juvenile and adult stanza, respectively. As landings statistics in thel970s were more reliable (Qiu, Y . South China Sea Fisheries Institute, pers. comm.), it was used in the 1970s model (0.019 t-km\"2). Since hairtails in the 1970s were only moderately exploited and the fishery targeted mostly adults, I assumed that 80% of the catch was from the adult stanza. 330 6.1.13. Pomfrets (stromateids) Members of this group are fish from the family Stromateidae. Based on an acoustic survey in the late 1990s, the estimated total biomass of stromateids, ariommids, nomeis and formionids in the late 1990s was 0.43 t-km\"2. As information on the relative composition among these groups was not available, I assumed that the estimated biomass was evenly distribution among the groups. Thus biomass of pomfrets was estimated to be 0.108 t-km\" . Changes in biomass of this group from the 1970s to 2000s were reported to be similar to trends of overall resource changes. Estimated demersal fishery resources in the N S C S in this period declined by approximately 59%. Thus the estimated biomass of this group in the 1970s model was 1.03 t-km\"2. Estimates of total mortality or exploitation rates were mainly available for demersal or pelagic species, but not benthopelagic species. Thus P/B ratios in the 1970s and 2000s models were left to be estimated by the model, assuming P/Q ratio of 0.2. Q/B ratio was calculated from an empirical equation (Palomares & Pauly 1998) and assumed to be similar in both models. Catches in the 2000s and 1970s were based on national statistics (0.230 t-km\"2 and 0.0053 t-km\"2). 6.1.14. Snappers Members of this group are from the family Lutjanidae. Biomass estimates were not available for the 1970s and 2000s. Thus biomasses of snappers in the 1970s and 2000s models were estimated by assuming EE (= 0.95). Natural mortality rate was estimated from Pauly's empirical equation (Pauly 1980). Since independent estimates of fishing mortality were not available, I assumed that the group was under similar exploitation rate (E = F/Z) as the other exploited demersal fish groups. Based on the average E of 0.62 for demersal fish groups in the N S C S (South China Sea Fisheries Institute, unpublished data) and substituting Z = F+M into the equation, total mortality was roughly estimated to be 1.75 year\"1. Populations of snappers was assumed to be under-exploited in the 1970s, thus I assumed F= M and Z = 2M. The total morality rate calculated from this method was 1.24 year\"1. Q/B ratios were calculated from an empirical equation (Palomares & Pauly 1998). 331 Since landings of snappers were not reported in the national statistics in the 2000s, estimates from the S A U P database were used in the model (0.001 t-km\"2). Landings statistics were available in the 1970s, which amounted to 0.0053 t-km\"2. 6.1.15. Groupers Members of this group are from the family Serranidae. Biomass estimates were not available and thus they were estimated in the models by assuming EE to be 0.95. Natural mortality rate was estimated to be 0.67 year\"1 from Pauly's empirical equation. Since independent estimates of fishing mortality were not available, I assumed that the group was under similar exploitation rate (E = F/Z) as other major exploited demersal fish groups (E = 0.62, South China Sea Fisheries Institute, unpublished data) and substituting Z = F+M into the equation, total mortality was roughly estimated to be 1.75 year\"1. Populations of groupers were assumed to be fully-exploited in the 1970s. Thus I assumed F = M and Z = 2M (total morality = 1.24 year\"1). Q/B ratios were calculated from an empirical equation and assumed to be the same in the 1970s and 2000s models. Landings in 2000 and 1973 reported in the national statistics were 0.089 t-km\"2 and 0.0029 t-km\"2, respectively. 6.1.16. Melon seed Melon seed, or Psenopsis anomala, is a benthopelagic fish. Biomass in the late 1990s was estimated from the average between the biomass estimates from a trawl survey and an acoustic survey (0.00091 - 0.11 t-km\"2) (Jia et al. 2004). However, this value was later found to be too small to support the catch and predations. Thus the biomass was adjusted upward slightly to become 0.07 t-km\"2. P/B in the 2000s was estimated from the total mortality rate of P. anomala (2.41 year\"1) (South China Sea Fisheries Institute, unpublished data). Between the 1970s and 2000s, fishery resources in the N S C S declined by approximately 59% (Jia et al. 2004). Based on the estimated biomass in the 2000s model, the estimated biomass of melon seed in the 1970s model was estimated to be 0.114 t-km\". P /B in the 1970s was assumed to be twice the natural mortality rate while 332 Q/B was estimated from an empirical equation. Catches were based on the national fisheries statistics. 6.1.17. Small croakers Members of this group are of the family Sciaenidae, with total length less than or equal to 30 cm. Commercially important species include Agyrosomus spp. and Pennahia spp. Based on an acoustic survey in the late 1990s (Jia et al. 2004), biomass of commercially important small croakers in the N S C S was estimated to be 0.0368 t-km\"2. However, based on a trawl survey, biomass of Pennahia (Agyrosomus) argentatus in the same time period was already 0.392 t-km\" . This group was reported to have been severely over-exploited and age 1 fish dominated the population (South China Sea Fisheries Institute, unpublished data). Given that the annual catch of this group was estimated to be about 0.003 t-km\"2 only, the acoustic survey estimated biomass should be more reasonable. The ratio of commercial to non-commercial demersal fish in the N S C S was about 1:0.9 (Jia et al. 2004). Scaling the biomass of commercially important Agyrosomus spp and Pennahia spp. with this ratio, total biomass of small croakers in the 2000s model was estimated to be 0.07 t-km\"2. Total mortality rate of Pennahia (Agyrosomus) argentatus in 1992-93 was estimated to be 3.3 year\"1 (Yuan, South China Sea Fisheries Institute, unpublished data). Since a similar estimate was not available for recent years, the 1992-93 estimate was used as P/B ratio in the 2000s model. Biomass in the 1970s model was estimated from the biomass estimate for the 2000s model. Catch rates of Pennahia (Agyrosomus) argentatus by trawlers halved from 0.4 kg h\"1 to 0.2 kg h\"1 in the mid 1980s and the late 1990s, respectively (Jia et al. 2004). From the early 1970s to the mid 1980s, its biomass declined by about 52% (Chapter 5, estimated from the average of white croaker and other croakers). Assuming that catch rate was proportional to abundance, biomass in the 1970s was estimated to be 0.29 t-km\"2. A direct estimate of total mortality in the 70s period was not available. Since exploitation of this group should be moderate, fishing mortality rate was assumed to be the same as the natural mortality rate. Thus a total mortality rate of 2.36 year\"1 for the 1970s model was used. Q/B ratios of the 1970s and 2000s models were derived from an empirical 333 equation. Catch of small croakers in the 1970s model was based on the reported landings in 1973 (0.0197 t-km\"2). Catches of small croakers in the 2000s were calculated from the estimated biomasses and fishing mortality rates (0.035 t-km\"2). 6.1.18. Large croakers Large croakers are sciaenids with maximum total length of more than 30 cm. This group was seriously over-exploited, and is currently composed of mostly juveniles. This group was divided into two stanzas which were ontogenically linked through the multi-stanza routine (Christensen et al. 2004). The first stanza represented sexually immature fish (less than 24 months old), while the second stanza represented fishes older than 24 months. The parameter values for the multi-stanza routine were obtained from FishBase (www.fishbase.org) (Table A6.3). Biomass of the adult stanza was estimated from catch composition of bottom trawls and the total demersal biomass in the N S C S . Landings of stern and bottom trawls from Hong Kong were reported to have an average of 1.5% of large croakers (Agriculture, Fisheries and Conservation Department, Hong Kong unpublished data). Because of the low value of juvenile croakers, they were usually landed as \"mixed fish\" instead of \"croakers\". The average proportion of adult croakers in the landings of croakers was assumed to represent its relative abundance in the demersal fishery resources of the N S C S . Total demersal resources in the N S C S in the late 1990s were estimated to be 0.64 t-km\" (Jia et al. 2004). Thus biomass of adult croakers was estimated to be 0.0094 t-km\" . Total demersal biomass in the N S C S declined by 30.8% from the 80s to late 90s while catch rate of commercially important large croakers declined by an average of 85.5% from the 70s to 80s. Assuming that biomass of adult large croakers followed these trends, biomass in the 1970s model was estimated to be 0.095 t-km\"2. P/B ratio in the 2000s model was estimated from natural mortality rate, catch and biomass. Fishing mortality rate of adult large croakers in the 2000s was estimated to be 0.67 year\"1 (Jia et al. 2004). Based on Pauly's empirical equation, natural mortality rate was estimated to be 0.76 year\"1, therefore the total mortality rate was 1.43 year\"1. Since large croakers were already heavily exploited in the 70s, I assumed that P/B ratio in the 334 1970s model was the same as in the 2000s model. P/B ratios of juvenile croakers in both models were assumed to be the same as small croakers. Q/B ratio of adult croakers was estimated from an empirical equation. Based on the above parameter values, the multi-stanza routine estimated the biomass and Q/B ratio of juvenile large croakers to be 0.072 t-km\"2 and 16.47 year\"1 in the 2000s model, and 0.051 t-km\"2 and 15.48 year\"1 in the 1970s model respectively. Landings of yellow croaker {Larimichthys crocea), a commercially important large croaker, was reported to be 0.079 t-km\" in 2000 in the national statistics. As the majority of the catch was dominated by juvenile fish (<1 year), 90% of the landings was assumed to be juveniles. A s catch of this group in 1973 was not reported in the national statistics, estimates were based on the S A U P database (0.055 t-km\"2). In the 1970s model, 80% of the catch was assumed to be adults. 6.1.19. Small demersal fish This group consists of demersal fish with maximum total length less than or equal to 30 cm (except those that have been included in other functional groups). Estimates of total biomasses of small demersal fish in the 1970s and 2000s were not available, so it was estimated by assuming EE to be 0.95. P/B ratio in the 2000s model was estimated from natural mortality rate (1.8 year\"1, averaged from species with available estimates using Pauly's empirical equation) and an averaged exploitation rate (F/Z) of 0.62 in the N S C S (thus Z = 4.7 year\"1). In the 1970s model, as small demersal fish were only moderately exploited, fishing and natural mortality rates were assumed to be half of the fishing mortality, resulting in a total mortality rate of 2.7 year\"1. Q/B ratios in both models were estimated from an empirical equation. Catch of this group in the 2000s and 1970s were based on the S A U P database (0.179 t-km\"2 and 0.0902 t-km\"2 respectively). 335 6.1.20. Large demersal fish This group consists of demersal fish with maximum total\" length greater than 30 cm (except those that have been included in other functional groups). This group was divided into two stanza. The adult stanza represents fish older than 18 months. Biomass was estimated from commercial catch composition and estimated total demersal resources in the N S C S . Large demersal fish contributed about 25% to the total catch of bottom trawls. Based on the estimated total demersal fishery resources, biomass of large demersal fish was estimated to be 0.64 t-km\"2. Since the late 1990s, the majority of large demersal fish stocks had been over-exploited and juveniles (age 1-2) dominated the populations. Thus I assumed that 90% of the biomass was from the juvenile stanza (below age 2) (i.e..143 t-km\"2). In the 2000s model, juvenile large demersal fish were assumed to have similar natural mortality rates as the small demersal fish, while fishing mortality rate was estimated to be 1.68 year\"1. Thus the P/B of juvenile large demersal fish in the 2000s was estimated to be 3.5 year\"1. P /B ratio of adult large demersal fish was obtained from the estimated natural mortality rate (0.8 year\"1) and the averaged exploitation rate (F/Z) of exploited species in the N S C S in the late 90s. This resulted in a P/B ratio of 2.1 year\"1. Q/B ratio was estimated from an empirical equation. Based on the other basic parameter values for the multi-stanza routine, biomass of adult large demersal fish was estimated to be 0.015 t-km\"2. In the 1970s model, average demersal fishery resources in the 1970s was estimated to be 1.51 t-km\"2. Large demersal fish represented about 26% of the catch of bottom trawls in 1973 (Agriculture, Fisheries and Conservation Department, Hong Kong, unpublished data). Assuming that this represents the relative abundance of this group in the demersal fisheries resources, biomass of large demersal fish in the 1970s was estimated to be 0.39 t-km\" . Since the group was relatively under-exploited in the 1970s, I assumed that the adult stanza represented 50% of the estimated biomass. Based on the estimated biomass and catch (0.144 t-km\"2) of adult large demersal fish, fishing mortality was roughly estimated to be 0.74 year\"1. Thus P /B ratio of adult stanza was 1.54 year\"1 ( M = 0.8 year\"2). P/B ratio of juvenile stanza was assumed to be the same as small demersal 336 fish. Q/B ratio was estimated from an empirical equation. Multi-stanza parameters follow those in the 2000s model (Table A6.3). Catches in the 2000s and 1970s models were based on the S A U P database (0.351 2 2 t-km\" and 0.144 t-km\" , respectively). Since catch of this group was dominated by juvenile fish in the late 1990s, I assumed 90% of the catch was from the juvenile stanza. However, the adult stanza should have dominated the catch in the 1970s, thus I assumed 80% of the catch was from adult stanza in the 1970s model. 6.1.21. Small benthopelagic fish This group consists of benthopelagic fish with maximum total length less than or equal to 30 cm. Benthopelagic fish was defined as fish living and feeding near the bottom as well as in mid-water or near the surface (FishBase: www.fishbase.org). Natural mortality rates of fishes in this group were estimated by using Pauly's empirical equation. The average natural mortality rate from the member species was 1.54 year\"'. Exploitation rate (F/Z) in the 2000s was estimated to be about 0.55 (South China Sea Fisheries Institute, unpublished report). Thus total mortality rate was approximately 3.08 year\"1. Q/B ratio was estimated from a P/Q ratio of 0.2. As a biomass estimate was not available, it was estimated by assuming EE to be 0.95. For the 1970s model, biomass was estimated by assuming EE to be 0.95. As the group was only lightly exploited in the 1970s, fishing mortality rate was assumed to be half the natural mortality rate. Thus P/B ratio was estimated to be 2.31 year\"1. Q/B was estimated from an assumed P/Q ratio of 0.2. Catches of this group in the late 1990s and early 1970s were estimated based on the S A U P database (0.643 t-km\"2 and 0.0023 t-km\"2 respectively). 6.1.22. Large benthopelagic fish This group consists of fish with maximum total length of more than 30 cm. Biomass was estimated by assuming EE to be 0.95. Natural mortality rate estimated from Pauly's empirical equation was 0.86 year\"1. Exploitation rate (F/Z) in the 2000s was 337 estimated to be about 0.55 (South China Sea Fisheries Institute, unpublished report). Thus total mortality rate was estimated to be 1.91 year\"1. Q/B ratio was estimated from an empirical equation. For the 1970s model, biomass was of large benthopelagic fish estimated by assuming EE to be 0.95. As the group was only lightly exploited in the 1970s, fishing mortality rate was assumed to be half of the natural mortality rate, thus P /B ratio was estimated to be 1.29 year\"1. Q/B ratio was assumed to be the same between the 1970s and 2000s. Catches in the two periods were based on the S A U P database (0.0079 t-km\"2 and 0.0053 t-km\" , respectively). 6.1.23. Small pelagic fish This group consists of pelagic fish with maximum total length of less than or equal to 30 cm. In the late 1990s, total biomass of commercially valuable small pelagic fish, including sardine, thryssa, anchovy, etc. was estimated to be 1.47 t-km\"2 (Agriculture, Fisheries and Conservation Department, Hong Kong unpublished data). The ratio of commercial to non-commercial pelagic nekton biomass was estimated to be 4.9:1 (Jia et al. 2004). Based on this ratio, small pelagic fish biomass was estimated to be 1.77 t-km\"2. Based on Pauly's empirical equation, natural mortality rate of small pelagic fish was 1.91 year\"1. Assuming that this group had similar exploitation rate (F/Z) as Decapterus maruadsi in the late 1990s (0.55), total mortality rate was estimated to be 4.26 year\"1. Q/B ratio was estimated from empirical equation. A biomass estimate of small pelagic fish in the 1970s was not available, thus it was estimated by the model by assuming EE to be 0.95. As the group was only lightly exploited in the 70s, fishing mortality rate was assumed to be half the natural mortality rate. Thus the P/B ratio was estimated to be 2.87 year\"1. Q/B ratio was assumed to be the same between the 1970s and 2000s. Catch of this group was estimated from the landings of the commercially important small pelagic taxa (anchovy and sardine) reported in the national statistics 338 2 (2.344 bkm ). However, such data were not reported in the early 1970s. Thus catch in the 1970s model was based on the S A U P data (0.146 t-km\"2). 6.1.24. Large pelagic fish This group consists of demersal fish with maximum total length greater than 30 cm (except those that have been included in other functional groups). It was divided into two stanzas. The adult stanza represented fish older than 18 months. Based on acoustic survey, biomass of commercially valuable large pelagic fishes (e.g. scombrids) in the late 1990s was about 0.079 t-km\" . Based on Pauly's empirical equation, natural mortality rate of large pelagic fish was 0.59 year\"1. Assuming that this group had similar exploitation rate (F/Z) as Decapterus maruadsi in the late 1990s (0.55), total mortality rate was estimated to be 1.31 year\"1 (Cheng & Qiu 2003). Q/B ratio was estimated from an empirical equation. For the juvenile stanza, P/B ratio was assumed to be the same as small pelagic fish. Based on the multi-stanza routine, its biomass and Q/B ratio were estimated to be 0.242 t-km\"2 and 16.08 year\"1 respectively. For the 1970s model, biomass of large pelagic fish was suggested to have declined by more than 90% from the 1950s to 1990s (Cheung & Sadovy 2004). Assuming that biomass of this group declined by approximately 50% from the 1970s to 1990s (upper and lower limits are 90% and 10% respectively), biomass of the adult stanza in the 1970s model was estimated to be 0.158 t-km\"2. As the group was only lightly exploited in the 70s, fishing mortality rate was assumed to be half the natural mortality rate. Thus P/B ratio was estimated to be 0.9 year\"1. Q/B was assumed stable in the two periods. Catch of this group was calculated based on landings of commercially important large pelagic taxa. In the 2000s model, since the majority of the catches were juveniles, I assumed that 90% of the catch was from the juvenile stanza. However, such data were not reported in the early 1970s. Thus catch was based on the S A U P data (0.048 t-km\"2). As the group was only moderately exploited in the 1970s, I assumed 80% of the catch was from the adult stanza. 339 6.1.25. Sharks and rays Elasmobranchs were divided into demersal and pelagic groups. Demersal sharks and rays represented about 0.1% of bottom trawlers' catches in the late 1980s. This was assumed to represent the relative abundance of demersal elasmobranchs relative to the total demersal biomass in the late 1990s. Thus biomass was estimated to be about 0.001 t- km\" 2. However, this biomass level was found to be too low to support the fishery. Thus it was left to be estimated by the model by assuming an EE of 0.95. Since pelagic sharks and rays were ill-represented in demersal trawls, its biomass could not be estimated from the catch composition of demersal trawlers. The biomass of this group in the 1970s was estimated by assuming EE to be 0.5. A report in the late 1990s suggested that demersal sharks and rays mostly consisted of small species in the N S C S (Jia et al. 2004). The natural mortality rates of the demersal and pelagic groups estimated from Pauly's empirical equation were about 0.84 year\"1 and 0.26 year\"1. Assuming an averaged exploitation rate (F/Z) of 0.62 in the N S C S , total mortality rates of the demersal and pelagic groups were estimated to be 2.2 year\"1 and 0.68 year\"1, respectively. Q/B ratios were estimated by assuming P/Q ratios to be 0.2. Catch composition of bottom trawls in the 1970s consisted of about 1% of demersal sharks and rays. Based on total estimated demersal resources of 1.51 t-km\"2 in that period, biomass of demersal sharks and rays in the 1970s model was estimated to be 0.015 t-km\" . Biomass of pelagic sharks and rays was estimated by the model with an assumed EE of 0.5. As the group was lightly exploited in the 1970s, fishing mortality rate was assumed to be half the natural mortality rate, thus P/B ratios of the demersal and pelagic groups become 1.26 year\"1 and 0.39 year\"1, respectively. Q/B ratios were estimated by an assumed P/Q ratio of 0.2. Catch of demersal and pelagic sharks and rays in the late 1990s and the 1970s were based on the S A U P database. 340 6.1.26. Seabirds, pinnipeds, other mammals and marine turtles Since parameters for these groups were not available for the whole modeled region, the input parameter values were assumed to be the same as an ecosystem model of Hong Kong waters (Buchary et al. 2003; Cheung and Sadovy 2004; Pitcher et al. 2002). 6.1.27. Detritus Information regarding detritus biomass in the N S C S was unavailable. Since the model was generally insensitivity to detritus biomass, biomasses of detritus in the 1970s and 2000s models were assumed to be 100 t-km\"2. 341 6.2. Diet composition matrices of the 1970s and 2000s N S C S models Preys Predators Zooplanktons Jellyfish Phytoplanktons 0.700 Zooplanktons 0.900 Jellyfish 0.080 Pelagic fish (< 30 cm) 0.019 Juvenile large pelagic fish 0.001 Detritus 0.300 Sum 1.000 1.000 Predators Benthic Non-ceph Sessile/other Preys Polychaetes Echinoderms crustaceans molluscs invertebrates Phytoplanktons 0.003 0.070 0.070 Benthic producers 0.554 0.151 0.151 Zooplanktons 0.003 0.050 0.050 0.600 Polychaetes 0.022 0.054 0.100 Echinoderms 0.059 Benthic crustaceans 0.003 Non-ceph molluscs 0.056 0.080 0.010 Sessile/other invertebrates 0.100 0.001 0.010 Shrimps Detritus 1.000 0.200 0.594 0.609 0.400 Sum 1.000 1.000 1.000 1.000 1.000 Predators Preys Shrimps Crabs Phytoplanktons 0.027 Benthic producers 0.125 0.200 Zooplanktons 0.193 Polychaetes 0.121 0.240 Echinoderms 0.010 Benthic crustaceans 0.020 Non-ceph molluscs 0.140 Sessile/other invertebrates 0.010 Shrimps 0.050 Detritus 0.534 0.330 Sum 1.000 1.000 342 Predators Preys Cephalopods Zooplanktons 0.46600 Echinoderms 0.06200 Benthic crustaceans 0.12500 Non-ceph molluscs 0.06800 Shrimps 0.00500 Crabs 0.05000 Cephalopods 0.04300 Threadfin bream (nemipterids) 0.00010 Lizard fish (synodontids) 0.00010 Juv Hairtail (trichiurids) 0.00010 Pomfret (stromateids) 0.01000 Snappers 0.00001 Adult groupers 0.00001 Demesral fish (< 30 cm) 0.00005 Benthopelagic fish 0.01300 Melon seed 0.00100 Pelagic fish (< 30 cm) 0.15600 Juvenile large pelagic fish 0.00010 Predators Threadfin bream Bigeyes Lizard fish Preys (nemipterids) (priacanthids) (synodontids) Phytoplanktons 0.015 Zooplanktons 0.120 0.322 Polychaetes 0.173 0.086 Echinoderms 0.030 Benthic crustaceans 0.156 0.084 Non-ceph molluscs 0.384 0.015 Sessile/other invertebrates 0.003 Shrimps 0.005 0.007 0.190 Crabs 0.001 0.136 Cephalopods 0.005 0.092 Threadfin bream (nemipterids) 0.014 0.050 Bigeyes (priacanthids) 0.001 0.008 0.038 Lizard fish (synodontids) 0.019 Adult groupers 0.002 Croakers (< 30 cm) 0.060 Juv large croakers 0.001 0.004 Demesral fish (< 30 cm) 0.099 0.239 0.198 Juv demersal fish (> 30 cm) 0.020 Benthopelagic fish 0.015 Melon seed 0.001 Pelagic fish (< 30 cm) 0.407 Predators Juv Hairtail Adult hairtail Pomfret Preys (trichiurids) (trichiurids) (stromateids) Phytoplanktons 0.010 Zooplanktons 0.107 0.040 0.375 Jellyfish 0.478 Polychaetes 0.008 0.020 Echinoderms 0.015 0.002 Benthic crustaceans 0.031 0.010 0.002 Non-ceph molluscs 0.023 Sessile/other invertebrates 0.024 Shrimps 0.092 0.050 Crabs 0.005 0.010 0.024 Cephalopods 0.153 0.100 0.008 Threadfin bream (nemipterids) 0.046 0.034 Bigeyes (priacanthids) Lizard fish (synodontids) 0.034 0.035 Juv Hairtail (trichiurids) 0.010 0.010 Adult hairtail (trichiurids) 0.010 Adult groupers Croakers (< 30 cm) 0.096 0.070 Juv large croakers 0.008 Demesral fish (< 30 cm) 0.236 0.142 Juv demersal fish (> 30 cm) Benthopelagic fish 0.150 0.099 0.013 Melon seed 0.009 0.001 0.001 Pelagic fish (< 30 cm) 0.389 0.020 Predators Preys Snappers Adult groupers Croakers (< 30 cm) Juv large croakers Croakers (> 30 cm) Zooplanktons 0.0500 0.3140 0.3140 0.2230 Jellyfish 0.0090 Polychaetes 0.0100 0.0550 0.1200 Echinoderms 0.0420 0.1000 Benthic crustaceans 0.1000 0.1720 0.2020 0.2460 0.3400 Non-ceph molluscs 0.0500 0.0460 0.0070 0.0070 0.0090 Sessile/other invertebrates 0.0020 0.0110 Shrimps 0.0500 0.0840 0.1300 0.1000 Crabs 0.0500 0.0910 0.0010 0.0010 0.0010 Cephalopods 0.0840 Threadfin bream (Nemipterids) 0.0100 0.0090 Bigeyes (Priacanthids) 0.0020 Lizard fish (Synodontids) 0.0020 Pomfret (Stromateids) 0.0280 Snappers 0.0001 Adult groupers 0.0009 Croakers (< 30 cm) 0.0500 0.0070 Juv large croakers 0.0010 Croakers (> 30 cm) Demesral fish (< 30 cm) 0.6800 0.4570 0.0940 0.0500 0.1250 Juv demersal fish (> 30 cm) 0.0550 0.0003 Adult demersal fish (> 30 cm) 0.0090 0.0010 Benthopelagic fish 0.1000 0.1140 Melon seed 0.0010 0.0130 Pelagic fish (< 30 cm) 0.0500 0.1170 345 Predators Preys Demesral fish (< 30 cm) Juv demersal fish (> 30 cm) Adult demersal fish (> 30 cm) Benthopelagic fish Phytoplanktons 0.0130 0.0130 0.1459 Benthic producers 0.0300 0.0300 0.1459 Zooplanktons 0.3020 0.4244 0.0840 0.4705 Jellyfish 0.0090 0.0090 0.0010 Polychaetes 0.0890 0.0901 0.0470 0.0290 Echinoderms 0.0020 0.0020 0.0150 Benthic crustaceans 0.2200 0.2653 0.4380 0.0490 Non-ceph molluscs 0.1210 0.0511 0.1370 0.0190 Sessile/other invertebrates 0.0100 0.0410 Shrimps 0.0270 0.0020 0.0020 Crabs 0.0250 0.0010 0.0010 0.0100 Cephalopods 0.0040 0.0090 0.0100 0.0060 Threadfin bream (nemipterids) 0.0100 Bigeyes (priacanthids) 0.0100 Lizard fish (synodontids) 0.0100 Juv Hairtail (trichiurids) 0.0100 Snappers 0.0001 Adult groupers 0.0050 Croakers (< 30 cm) 0.0100 Juv large croakers 0.0001 0.0010 0.0004 Croakers (> 30 cm) 0.0004 Demesral fish (< 30 cm) 0.0250 0.0050 0.1940 Juv demersal fish (> 30 cm) 0.0030 0.0050 Benthopelagic fish 0.0110 0.0070 0.0090 Melon seed 0.0070 0.0010 0.0010 Pelagic fish (< 30 cm) 0.0090 0.0070 0.0859 Detritus 0.1010 0.0420 0.0290 346 Predators Preys Melon seed Pelagic fish (< 30 cm) Juvenile large pelagic fish Pelagic fish (> 30 cm) Phytoplanktons 0.0190 0.2200 0.0630 0.0110 Benthic producers 0.0760 Zooplanktons 0.8860 0.7520 0.5890 0.4580 Jellyfish Polychaetes 0.0380 0.0210 0.0160 0.0110 Echinoderms Benthic crustaceans 0.0060 0.0580 Non-ceph molluscs 0.0060 0.0550 0.0090 Sessile/other invertebrates 0.0190 0.0010 0.0230 0.0060 Shrimps 0.0006 Crabs Cephalopods 0.0190 0.0030 0.0180 Juv Hairtail (trichiurids) 0.0005 Adult hairtail (trichiurids) 0.0020 Pomfret (stromateids) 0.0030 Croakers (< 30 cm) 0.0050 Juv large croakers 0.0002 Demesral fish (< 30 cm) 0.0030 Benthopelagic fish 0.0100 0.0600 0.0400 Melon seed 0.0060 0.0040 Pelagic fish (< 30 cm) 0.0030 0.0300 0.3750 Detritus 0.0730 0.0020 347 Predators Preys Demersal sharks and rays Pelagic sharks and rays Seabirds Pinnipeds Other mammals Marine turtles Phytoplanktons 0.0005 Benthic producers 0.1198 0.4000 Zooplanktons 0.0860 0.0030 0.0040 0.2380 Polychaetes 0.2086 Echinoderms 0.0030 0.0350 0.0110 Benthic crustaceans 0.3600 0.0190 0.0110 Non-ceph molluscs 0.1150 0.0006 0.1357 0.0350 0.0110 Shrimps 0.0700 0.0479 0.0380 0.0090 Crabs . 0.0520 0.0010 0.0010 0.0001 0.0110 Cephalopods 0.2420 0.1148 0.2700 0.0800 0.0180 Threadfin bream (nemipterids) 0.0050 0.0200 0.0050 Bigeyes (priacanthids) 0.0050 0.0190 0.0050 Lizard fish (synodontids) 0.0410 0.0050 0.0009 Juv Hairtail (trichiurids) 0.0010 0.0100 0.0500 Adult hairtail (trichiurids) 0.0010 0.0060 Pomfret (stromateids) 0.0660 0.0630 0.0160 Snappers 0.0001 0.0020 0.0006 Adult groupers 0.0050 0.0040 0.0006 Croakers (< 30 cm) 0.0170 0.0130 0.1148 0.2500 0.0140 0.0040 Juv large croakers 0.0030 0.0030 0.0002 0.0001 Croakers (> 30 cm) 0.0020 0.0030 0.0040 Demesral fish (< 30 cm) 0.1860 0.3080 0.0300 0.0070 Juv demersal fish (> 30 cm) 0.0070 0.1130 0.0060 0.0820 Adult demersal fish (> 30 cm) 0.0010 0.0130 0.0010 0.0090 Benthopelagic fish 0.0270 0.1790 0.1068 0.1690 0.0430 Melon seed 0.0070 0.0260 0.0080 0.0330 Pelagic fish (< 30 cm) 0.4320 0.4040 0.0660 Juvenile large pelagic fish 0.0250 0.1148 0.0010 0.0450 - 0.0009 Pelagic fish (> 30 cm) 0.0050 0.0010 0.0110 Demersal sharks and rays 0.0100 0.0001 Pelagic sharks and rays 0.0090 Seabirds 0.0001 Pinnipeds 0.0001 Other mammals 0.0001 Marine turtles 0.0001 Detritus 0.0030 348 7.1. Screenshots of an interface for calculating the depletion index in Ecosim. \u00E2\u0080\u00A2 Iwt (C.WocurncntsandScltfnplw.chcuriKWy^ VllWadclWSCS. trade o(l MMI| NSCS/OOCWif 1_T)AuR0b; - .1 Fit Eo* UrcartMWv MmWma IWwk EcMn Ecotpac* Uctkm Pl* lBl\u00C2\u00AB l ^l\u00C2\u00BBINal\u00C2\u00BBl *1 _LDJIB tM ALsJi8l*l _tJ B I B W O H I L I Qun Ho | &IOgprto| Stjge j M\"lnei**iiet I (jmiaHun j Fwcpfl lunctont | flpplyfF Run I ' j ... | a. A check box 'Depletion Risk Index' was added to the 'Run Ecosim' panel in Ecosim. User needs to check the box to set up the input parameters and calculate the depletion index from the simulation results. An input parameter form will appear after checking the box. * Fuzzy Extinction Risk File Data Analysis |Lg Dem. NRA. Juv j Leiognathus splendens Paraplagusia bilineata Stephanolepis cirrhifer Istigobius hoshmomc Bo thus myriaster Carangoides praeustus Acanthogobius flavtmanus T ridentiger trigonocephalus Istigobius campbelli Upeneus sulphureus Pentaprion longimanus Leiognathus bmdus Leiognathus elongatus Leiognathus leuciscus Seculor insidiator Gazza minuta Polydactyly sextarius Add Delete View rules Parameters Lmax (cml Tm [year] Tmax (year) von Bertalanlly growth K Natural mortality tyear\"-1) Fecundity (egg per year) Spatial behaviour strength Aggregation Feeding Spawning Geogiaphic lange EEZ area (km\"21 Coastline (km) 14 77 23 0 38 2.59 80 r r r 32958 219466 Edit b. This forms allow the user to open the species lists and connect to a database (extracted from FishBase) from which the life history and ecology parameters required to calculate the depletion index wil l be automatically extracted. 349 * EwE Extinction Risk Settings View rules Biomass decline VLw VLw VLw Lw Lw Lw Mod ReLF/lnt. Vul. Lw Ave Hg Lw Ave Hg Lw t Low 0 0 0 0 0 0 0 1 Mod 0 0 0 0 1 1 1 t Hiqh 0 0 0 1 2 2 2 VHiqh 0 0 1 1 2 3 2 "Thesis/Dissertation"@en . "10.14288/1.0074894"@en . "eng"@en . "Resource Management and Environmental Studies"@en . "Vancouver : University of British Columbia Library"@en . "University of British Columbia"@en . "For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use."@en . "Graduate"@en . "Vulnerability of marine fishes to fishing : from global overview to the northern South China Sea"@en . "Text"@en . "http://hdl.handle.net/2429/31272"@en .