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Vulnerability of marine fishes to fishing : from global overview to the northern South China Sea Cheung, Wai Lung 2007

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VULNERABILITY OF MARINE FISHES TO FISHING: FROM GLOBAL OVERVIEW TO THE NORTHERN SOUTH CHINA SEA by  WAI LUNG CHEUNG  B . S c , The University of Hong K o n g , 1998 M . P h i l . , The University of Hong K o n g , 2001  A THESIS S U B M I T T E D IN P A R T I A L F U L F I L L M E N T OF THE REQUIREMENTS FOR T H E D E G R E E OF  DOCTOR OF PHILOSOPHY  in  THE F A C U L T Y OF G R A D U A T E STUDIES  (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH C O L U M B I A M a y 2007  © W a i Lung Cheung, 2007  ABSTRACT  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 ( N S C S ) , 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 convexshaped. 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 CONTENTS 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. Modelling interactions between ecosystem and fisheries  17  1.8. Ecopath with Ecosim  19  1.9. Fisheries in the Northern South China Sea ( N S C S )  20  1.10. Research objectives..  23  1.11. References................^  •..„'.....'  28  2. A Fuzzy Logic Expert System to Estimate Intrinsic Vulnerabilities of Marine Fishes to Fishing 44 2.1. Introduction 2.1.1. Life history and ecological characteristics as proxies for intrinsic vulnerability 2.1.2. Fuzzy logic expert system 2.2. Methods 2.2.1. Structure and functioning of the fuzzy expert system 2.2.2. System evaluations 2.2.3. Validity tests on vulnerability estimates  44 45 46 47 47 54 55  2.3. Results  57  2.4. Discussion  64  2.5. References  68  iii  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 3.2.3. Mean intrinsic vulnerability index of catch 3.2.4. Comparison with distributions of globally threatened fishes  79 81 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 3.4.1. Intrinsic vulnerability of fish with different associated habitats 3.4.2. Intrinsic vulnerability and geographic range 3.4.3. Average intrinsic vulnerability of catch 3.5. References  91 91 91 92 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. 4.2.2. 4.2.3. 4.2.4.  Analysis of temporal patterns of catch time-series 109 Combining 'rules-of-thumb' 113 Comparing the predicted depletion risk with the I U C N categories....... 116 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 5.2.1. C P U E data 5.2.2. Interpolation of C P U E 5.2.3. Standardization of C P U E  141 141 143 145  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 5.3.3. Intrinsic vulnerability against C P U E changes 5.3.4. Intrinsic vulnerability of demersal and benthopelagic fish in N S C S  150 157 159  5.4. Discussion 5.4.1. 5.4.2. 5.4.3. 5.4.4.  159  Increased fishing mortality Environmental changes Observation error and the non-proportionality of C P U E to abundance Status of fishery resources in the N S C S  5.5. References  159 161 162 163 166  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 6.3.1. Biomass changes 6.3.2. Mortalities 6.3.3. System index 6.3.4. Uncertainty and sensitivity analysis 6.4. Discussion 6.5 References  7. A Depletion Index as an Indicator of Biodiversity Status in Ecosystem Simulation Model  185 185 189 190 195 ••• 206 210  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 8.1.1. Buy-back as means to restructure fishing fleets 8.2. Methods 8.2.1. Ecopath with Ecosim modelling 8.2.2. Policy optimization (a) M a x i m i z i n g economic rent (b) Employment (c) Ecosystem structure (d) Conservation of vulnerable species 8.2.3. Cost of fleet restructuring 8.3. Results 8.3.1. 8.3.2. 8.3.3. 8.3.4. 8.3.5.  The Pareto-frontiers Ecosystem structures Restructuring the fishing fleets Buy-back cost Sensitivity analysis  8.4. Discussion 8.4.1. Trade-offs between policy objectives 8.4.2. Economics of restructuring the fishing fleets 8.4.3. Model assumptions and uncertainties 8.5. References  9. Conclusions  247 251 252 252 253 253 254 255 255 257 258 258 262 264 265 266 268 268 270 272 275  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 b. Rule firing and fuzzy reasoning  300 300  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 ) 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  2  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„) 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  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  Table 8.2.  197  254  The estimated relative jobs per value for the six fishing fleets in the N S C S ecosystem model 255  ix  LIST OF FIGURES 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.  M a p 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 Fiji (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  Figure 4.1.  90  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  Schematic diagram of the structure of a fuzzy expert system to predict depletion risk of marine fishes to fishing  113  Figure 4.4. 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 CPUE '. 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.  M a p of the northern South China Sea  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  170  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  Trade-off relationship between social and conservation objectives  260  Figure 8.4.  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. Figure 8.7.  Estimated changes in biomass of fishes, invertebrates and charismatic megafauna  263  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  ACKNOWLEDGEMENTS 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, Q i u Yongsung, Rachel W o n g , Valerie H o and Anna Situ for their help, stimulating discussions and useful information. I am thankful to the Hong K o n g 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 L a m , 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.  xiii  To mygrandma, parents, sister and wife  xiv  CO-AUTHORSHIP STATEMENT 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. N e w Y o r k : 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 Explicit Catch-perunit-effort Data. Fisheries Research, [in review]. Chapter 8: Cheung, W . W . L . & Sumaila, U . R. Trade-offs between Conservation and Socioeconomic 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 H o n g K o n g 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 R e d 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 ( F A O ) , 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 million 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  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  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 ( M c D o w a l 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. A l s o , 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; W o r m & Duffy 2003; W o r m 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; W o r m 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 Marinefishesmay 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 century that: th  '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 fill 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; D u l v y 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; M u s i c k 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 w i 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; D u l v y 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). F o r 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 W o r l d 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). T o 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 & W o r m 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; K i r k w o o d 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 largebodied, 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 recolonization), "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). A l s o , 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, reanalysis 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). A t 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). A m o n g 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  — 100 80  CU  co  V) 0) 10 10 TO 10  o  60  Q) Q. (/>  40  Ix  20  LU  0 Mammals  Birds  Amphibians  Reptiles  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). T o cover half of the marine fishes under the R e d 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). T o 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 (c) Triple  (8 6000 o  <u aV)  (b) Double  o 4000 0) .0  . - (a) Same  E = 2000 4  1995  2000  2005  2010  2015  2020  Year  Figure 1.3. The number of marine fishes that have been assessed under the IUCN Red List since 1998 (solid line) and the projected number of assessed marine fishes to 2020 (dotted line) assuming the rate of assessment (number of species per year) (a) remain the same as the average between 2002 and 2005, (b) doubling the average between 2002 and 2005, and (c) tripling the average between 2002 and 2005.  The marine fish species that need to be assessed are numerous, while population data for the majority are lacking. Data limitations restrict the application of conventional assessment approaches to the full spectrum of species, which require understanding of population dynamics (Dulvy et al. 2004). Currently, the required population parameters can be estimated only for a small number of marine fishes, mainly commercially-targeted species in developed countries. A t the same time, quantitative data on fisheries and population status of exploited species are costly to collect (Silvestre and Pauly 1997; Dulvy et al. 2003). Even in cases where time-series of population data are available, such as the North Sea, the power of large-scale monitoring survey to detect population decline in <10 years was low (Maxwell & Jennings 2005). The problem is most apparent in tropical, developing country fisheries where species diversity is high, but resources for monitoring are low (Jennings & Polunin 1996; Johannes 1998). Moreover, the intrinsic rate of increase (r), a population parameter that is a key to conventional assessment, is particularly difficult to estimate reliably (Musick 1999a; Reynolds et al. 2001). To rapidly assess the relative conservation status and short-list priority species for detailed assessment, 'rule-of-thumb' approaches were proposed (Fagan et al. 2001;  12  Reynolds et al. 2001; Dulvy et al. 2003; D u l v y et al. 2004). These appraoches use easilyobtainable information to approximately identify vulnerable or "priority" species that are in need of immediate conservation attention. Such approaches are especially useful if their applications are combined with large databases, for instance, FishBase (Froese & Pauly 2004) and the Sea A r o u n d Us Project database (containing a wide range of fisheries data ranging from spatially disaggregated catch data to prices of fishery catches) (Watson et al. 2004; Sumaila et al. in press). Results can also help focus longer term research on the priority species so that data could be made available for more accurate extinction risk assessments. A s life history and ecology determine, at least in part, how fish populations respond to exploitation, these attributes could be used to develop 'rule-of-thumb' proxies to evaluate the intrinsic vulnerability of marine fishes to fishing (Reynolds et al. 2001; Dulvy et al. 2003). In this thesis, vulnerability of fishes is defined as a combination of intrinsic vulnerability and exposure to some external 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. The intrinsic factors act synergistically with external threatening factors, such as fisheries exploitations, climate change or coastal development, to the susceptibility of species or populations to depletion, extirpation or extinction. For instance, when species with high intrinsic vulnerability to exploitation are being intensively fished, they are likely to have high risk of population depletion. Proxies of intrinsic vulnerability and the depletion risk resulted from their interactions with the external threatening factors could be determined from easily obtainable information through these 'rules-of-thumb'.  1.5. Integrating biodiversity conservation into fishery management Developing fishery management policy that conserves marine biodiversity is an important step towards addressing the above problems. Fishing is the largest remaining w i l d hunting activity in the world (Cury & Cayre 2001) and has been suggested to be a major conservation threat to marine species (Dulvy et al. 2003). Although the objectives of most conventional fishery management policies are to maximize the sustainable catch  13  of target species, they share many similarities with the goals of biodiversity conservation. For instance, the Convention on Biological Diversity ( C B D ) states that "the sustainable  use of its components and the fair and equitable sharing of the benefits". Thus, the Convention includes both sustainable use of resouces and the conservation of biological diversity. Traditional approaches to fishery management may not be adequate to ensure effective conservation of marine species. Although approaches to management of fishery resources have been well developed, they mostly aim to maximize the long term sustainable yield of the resources being targeted (Rosenberg et al. 1993; Pitcher 1998). Using a simulation model, Punt (2000) demonstrated that risk of extinction could still be high even when the stock was managed for maximum sustainable yield. On the other hand, it has been suggested that the I U C N R e d List criteria are consistent with reference points that provide warning of potential stock collapse in fisheries stock assessment (Dulvy et al. 2005). However, conventional approaches to fisheries assessments often focus on a few commercially important species only, while the trophic linkages among ecosystem groups and the effects on non-target species are overlooked. These non-target species may sometimes be more vulnerable and warrant higher conservation concerns than the target species. They can be threatened directly by being caught as bycatch, or indirectly from trophic interactions or habitat modifications (Pauly et al. 1998; Dulvy et al. 2000; Stobutzki et al. 2001). The insufficiency of traditional approaches has generally been recognized and the ecosystem approach to fishery management is being advocated widely (Pitcher & Pauly 1998; H a l l 1999; Pitcher 2001; Pauly et al. 2002; Hall & Mainprize 2004; Pikitch et al. 2004). This is evidenced by the Reykjavik Declaration on Responsible Fisheries in the Marine Ecosystem, proclaimed in 2001. The Declaration was built on the principles of fisheries management suggested in the United Nations Convention on the L a w of the Sea ( U N C L O S ) , the Code of Conduct for Responsible Fisheries and the Convention on Biological  Diversity. The  Declaration emphasized  that  "...  including ecosystem  considerations in fisheries management provides a framework within which States and fisheries performance,  management ...incorporation  organizations  would  enhance  management  of ecosystem considerations implies more effective 14  conservation  of the e c o s y s t e m  a n d sustainable  i n t e r a c t i o n s , s u c h as p r e d a t o r - p r e y living marine  use a n d an i n c r e a s e d  r e l a t i o n s h i p s , a m o n g different  resources; furthermore  that  attention  s t o c k s a n d s p e c i e s of  it e n t a i l s an u n d e r s t a n d i n g of the i m p a c t of  h u m a n a c t i v i t i e s o n the e c o s y s t e m , i n c l u d i n g the p o s s i b l e s t r u c t u r e d d i s t o r t i o n s they  cause  in the ecosystem...".  to  Incorporation  of this concept  can  practically into fishery  management is still in the initial phase (Hall & Mainprize 2004). Numerous authors have proposed and discussed various tools and approaches (ecological, economic, management, etc.)  to put the E B M concept  in operation  (Sainsbury et al. 2000; Sainsbury & Sumaila 2003; H i l b o m 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 the 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). Approaches to integrate conservation with fisheries management should generally include a series of steps from assessing vulnerability to exploring policy options (Figure 1.4). Firstly, species that are more vulnerable to extirpation (or extinction), and regions that are associated with higher conservation concerns, should be identified based on approaches that can be applied under data-limited conditions. This is followed by a more detailed assessment on the status of the ecosystem and the associated species in the region. Then, using various analytical tools such as computer simulation models, fisheries management policy options could be explored and the ecological, social and economic consequences resulting from different scenarios evaluated. These should provide useful insights to identify policies to meet conservation and other fisheries management objectives.  15  -Life-history -Ecology -Fisheries  Simulation model  >  Fuzzy Logic Expert System  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 A n 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 N e w 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 timedynamic 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  • Bj • (1 - EE )-Bj(QIB)j t  • DC  - Y; - E  jt  t  - BA  t  =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  is the production to biomass ratio of functional group i; B[ is the total  where (P/B)j  biomass; EE is the ecotrophic efficiency (1-EE, represents mortality other than predation t  and fishing); 7, the total catch; E is net migration; BA is the biomass accumulation; QIBj t  t  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 timevarying 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):  8 T, J'-Z 'j c  dt  c  +  i  l  '-w>  +  eq. 1.3  ' i)- <  F  +e  B  and eq. 1.4 ij 'ij ij  v  +v  +a  J  B  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°53'-119°48' E to 17°10'-25°52' 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 G u l 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 & K w o k 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 ( P R C ) 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 •  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). Modelling 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 ( L i u & 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, overexploitation  in  the  NSCS  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 ( L i 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 socioeconomic 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 / Risk of X i 1 population V V depletion J '• |  Fuzzy Logic ^ Expert System  W Global catch data  Policy trade-offs  Chapter 3 Intrinsic vulnerability of global fish catch  Chapters 6, 7 & 8  Figure 1.6. A flow diagram showing the structure of this thesis.  A s 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 stockrecruitment 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 ( N S C S ) 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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Marine Ecology Progress Series 274, Zhang, C . 1994 A fuzzy distinguishability on similar degree of composition of spawning stock of yellow croaker in the south Fufian fishing ground. Journal of Fisheries of China 18(4), 335-339.  43  2. A F U Z Z Y L O G I C E X P E R T S Y S T E M T O E S T I M A T E INTRINSIC V U L N E R A B I L I T I E S O F M A R I N E FISHES T O F I S H I N G 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; W o l f f 2000; Reynolds et al. 2001; D u l v y 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 ( M c D o w a l l 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; D u l v y 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  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.  2  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 & L o c k 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; K i r k w o o d 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 ( A F S ) 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). W e evaluate the robustness of the system and its assumptions using various sensitivity analyses. W e 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. W e 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  0  1  Maximum length (cm)  2  3  4  5  6  7  Age at first maturity (year)  c)  d)  0  100  200  300  0  F e c u n d i t y ( e g g / p u p individual"' year"')  20  40  60  80  100  S p a t i a l b e h a v i o u r strength  Figure 2.1. Fuzzy sets defining the input life history and ecological characteristics: (a) maximum body length, (b) age at first maturity (T ), (c) von Bertalanffy growth parameter K, (d) natural m  mortality rate (M), (e) maximum age (T, ), (f) geographic range (km ), (g) annual fecundity (egg 2  rax  or pup female-1 year-1), (h) strength of aggregation behaviour (see Appendix 2.3). V L w - very low, L w - 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  Consequences  Conditions  Supporting evidence  1  1  1  IF  Maximum length is very targe  THEN  Vulnerability is very high  8, 11, 13, 14, 15, 16, 17,  1  2  IF  Maximum length 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 length is small  THEN  Vulnerability is low  2  5  IF  Age at first maturity (f ) is very high  THEN  Vulnerability is very high  1, 2, 3, 4, 5, 11, 14, 15,  2  6  IF  Age at first maturity (t ) is high  THEN  Vulnerability is high  19, 20, 24, 33  2  7  IF  Age at first maturity (f ) is medium  THEN  Vulnerability is moderate  2  8  IF  Age at first maturity (t ) is low  THEN  Vulnerability is low  3  9  IF  Maximum age (t ) is very high  THEN  Vulnerability is very high  3  10  IF  Maximum age ( t „ ) is high  THEN  Vulnerability is high  3  11  IF  Maximum age (t  THEN  Vulnerability is moderate  3  12  IF  Maximum age (t ) is low  THEN  Vulnerability is low  3  3  3  m  m  m  m  max  ) is medium  mtLX  mllx  13, 19, 33  Opposing evidence  2  28  14  Table 2.1.Con't Attribute  Rule  4  13  Conditions IF  VBGF (k) is very low  OR  Natural mortality (M) is very low 4  14  IF  VBGF K is low  15  IF  THEN  VBGF K is medium  16  IF  11  Vulnerability is very high  4  Vulnerability is high  4  OR  Natural mortality (M) is medium 4  THEN  5,6, 13, 19,28,33  Opposing evidence2  OR  Natural mortality (M) is low 4  Supporting evidencel  Consequences  VBGF K is high  THEN  Vulnerability is medium  4  OR  Natural mortality (M) is high  THEN  Vulnerability is low  4  5  17  IF  Geographic range is restricted^  THEN  Vulnerability is high  5  18  IF  Geographic range is very restricted  THEN  Vulnerability is very high  6  19  IF  Fecundity is low  THEN  Vulnerability is high  6  20  IF  Fecundity is very low  THEN  Vulnerability is very high  7  20  IF  Spatial behaviour strength is low  THEN  Vulnerability is low  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  1  6  8, 19,22  1,2,3,4,5, 19,20  11, 14, 18, 23, 26, 28, 31  7,9, 10, 12,25,32  Table 2.1.Con't Attribute  Rule  8  24  Conditions IF  Spatial behaviour is related to feeding  Consequences THEN  25  IF  Spatial behaviour is related to spawning aggregation  Opposing evidence2  25  spatial behaviour decreases  aggregation 8  Vulnerability resulted from  Supporting evidencel  THEN  Vulnerability resulted from  30, 32  spatial behaviour increases  Literature supporting the assertions of the specific rules; Literature opposing the assertions of the specific rules; References: 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); Asymptotic length was used preferentially. However, if asymptotic length was not available, we used maximum length as surrogate; 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; 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; 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; 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. 1  2  12  3  4  5  6  7  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. W e 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. W e 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 - R _j - R T  T  where R is the estimated output from the system with full set of attributes (7) and attributes j being removed. W e 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. W e repeated the latter 50 times to obtain a distribution of the estimated deviations. W e tested the sensitivity of the system output to different threshold values. W e systematically varied the threshold value of the fuzzy expert system and recorded the output for the 159 marine fishes from FishBase. W e 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. W e 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 Fiji [species in Jennings et al. (1999&) with at least 15% of their observed population trends explainable by fishing]. W e 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). W e 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 M u s i c k 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 R e d List), and A N O V A for datasets 2 and 3 (species from Jennings et al. 1999 a,  55  b). W e 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. W e conducted an additional test to compare the performance of the expert system with classical logic. W e 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). W e 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 Low Moderate High Very high  Life history characteristics Maximum length (cm) Age at first maturity (year) VBGF parameter K (year" ) Natural mortality rate (year" ) Maximum age (year) Geographic range (10 km ) Fecundity (egg/pup individual" year" ) Spatial behaviour strength 1  1  J  2  1  1  50 > L 2 > T„, 0.8 < K 0.5 <M 3>T  max  •  J  — * max  -  40 > SB  50<L„ ,< 100 2<r„,<4 0.5 < K<0.8 0.35 <M< 0.5 3 <?;„„< 10 M  -  40 < SB < 60  100<L„,„ < 150 150 <£,„„, 6 < T„, 4<T <6 0.2 < K < 0.5 0.2>K 0.2 <M<0.35 0.2 >M 10<r„,„,<30 30 < T 3.2 < Range< 5.7 3.2 > Range 50 < Fee < 100 50 > Fee 60 < SB < 80 80<5B V  m  max  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.  >« 100  unn\  £1  TO 80 <D C  60  o '55  40  .E  20  £  0  0.0  0.2  0.4  0.6  0.8  1.0  Threshold Figure 2.3. Estimated intrinsic vulnerability from the fuzzy logic expert system for the 159 species of marine fishes when threshold value varied from 0 to 0.9. The dots represent the median, while the bars represent the 25% upper and lower quartiles.  Jack-knifing showed that the deviations in the estimated intrinsic vulnerabilities were relatively small for majority of species when individual attributes were removed from the fuzzy system (Figure 2.4a). In most cases, upper and lower quartiles (75% and  57  25%, respectively) of the deviations in the predicted intrinsic vulnerability were small, within 5 (maximum of 100) relative to the baseline estimates (i.e., all attributes included). However, maximum deviations were up to 20 to 30 for some species. In some cases, deviations were particularly strong when attributes number three and seven  were  removed (maximum age and spatial behaviour strength, respectively). Removal of attributes three (maximum age), five (geographic range) and six (fecundity) tended to result in unsymmetrical negative' bias on the predicted vulnerability, while removal of attribute eight (nature of spatial aggregation) tended to result in positive bias, a)  30 0> *-  20  a> =  10  u  c 2(u 0 — (0 c 8,= cre -20 .c -30 o 0  3  4  5  Attributes b)  50 -j  •o CD o  40 -  TJ <D  i_ —  Q. XI C i_ V u>  ange vul  c  o  30 20 10 0 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 ( A F S ' 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). W e did not obtain significant relationships between the three vulnerability proxies and the observed population trends of the Fiji 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 •g z o 2  EN •  CR-  2  3  4  AFS's productivity  b) vu o  • • ••  » EN  O 2  CR  2  4  6  8  Maximum length (log cm)  C) VU  •  mi  o  O)  2 a  EN  o  = CR -\  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) A F S ' s productivity - productivity categories estimated by the A F S 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). C R - critically endangered, E N endangered, V U - vulnerable.  a)  b)  C)  d) R  0.02 -r <A "D C  = 0.276  R  =0.367  0.00 -  a> c  -0.02 -  o  j l  Z3  a. a.  -0.04 -  O  -0.06 2  4  6  8  10  12  30  40  50  60  70  F u z z y intrinsic vulnerability  Age at first maturity (year)  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  mix  ) ( A N O V A P = 0.034), (c) age at first maturity (T ) ( A N O V A P = 0.014), m  (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" ) and time (years). AFS's 1  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 = 0.272, A N O V A P = 0.042), 2  The goodness-of-fits of age at first maturity and the fuzzy system intrinsic vulnerability became: T  (R = 0.207, A N O V A P = 0.029) and intrinsic vulnerability (R = 0.246, A N O V A P = 0.016). 2  m  2  The dotted lines represent the upper and lower bounds estimated from the fuzzy system, based on an assumed membership of 0.5.  62  Q.  °-  -0.006 -J  1  ,  ,  ,  ,  ,  0  20  40  60  80  100  120  Maximum length (cm)  .2 -0.004 -  I °"  -0.006 -I 1 1 1 1 10 30 50 70 90 Fuzzy intrinsic vulnerability (without suppl. info.)  Q.  -0.006 -I 10  , 30  , 50  , 70  1 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 S C R F A 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. W e 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 Fiji'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. A l s o , 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' ) remained as one of the attributes in 1  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 & M a c e 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; C o x 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 G u l f of M e x i c o 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. A s 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 W o r l d Conservation Union or the species listing under the Canada's Species A t Risk Act.  67  2.5. References Adams, P. B . 1980 Life history patterns in marine fishes and their consequences for fisheries management. Fishery Bulletin 78(1), 1-12. Agresti, A . 1996 An Introduction to Categorical Data Analysis N e w York: Wiley. Baillie, J. E . M . , Hilton-Taylor, C . & Stuart, S. N . 2004 I U C N R e d List of Threatened Species - A Global Species Assessment. I U C N , Gland, Switzerland Baum, J. K . & Myers, R . A . 2004 Shifting baselines and the decline of pelagic sharks in the G u l f of M e x i c o . Ecology Letters 7, 135-145. Brander, K . 1981 Disappearance of common skate Raja batis from the Irish Sea. Nature 290, 48-49. Brown, J. H . 1995 Macroecology London: University of Chicago Press. Cardillo, M . 2003 Biological determinants of extinction risk: why are smaller species less vulnerable? Animal Conservation 6, 63-69. Casey, J. M . & Myers, R . A . 1998 Near extinction of a large, widely distributed fish. Science 281, 690-692. Clark, C . W . 1973 Profit maximization and the extinction of animal species. The Journal of Political Economy 81(4), 950-961. C o x , E . 1999 The fuzzy systems handbook: a practitioner's guide to building, using, and maintaing fuzzy systems San Diego, C a l i f : A P Professional. Denney, N . H . , Jennings, S. & Reynolds, J. D . 2002 Life-history correlates of maximum population growth rates in marine fishes. Proceedings of the Royal Society of London: Biological Science 269, 2229-2237. Dulvy, N . K . , Ellis, J. R., Goodwin, N . B . , Grant, A . , Reynolds, J. D . & Jennings, S. 2004 Methods of assessing extinction risk in marine fishes. Fish and Fisheries 5, 255276. Dulvy, N . K . , Metcalfe, J. D . , Glanville, J., Pawson, M . G . & Reynolds, J. D . 2000 Fishery stability, local extinctions, and shifts in community structure in skates. Conservation Biology 14(1), 283-293. Dulvy, N . K . & Reynolds, J. D . 2002 Predicting extinction vulnerability in skates. 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J. 2001a Fish schooling: Implications for pattern in teh oceans and impacts on human fisheries. In Encyclopaedia of Ocean Sciences (ed. J . H . Steele, K . K . Turekian & S. A . Thorpe), pp. 975-987. U K : Academic Press. Pitcher, T. J. 2001b Fisheries managed to rebuild ecosystems? Reconstructing the past to salvage the future. Ecological Applications 11, 601-617. Pratt, H . L . & Casey, J. G . 1990 Shark reproductive strategies as a limiting factor in directed fisheries, with a review of Holden's method of estimating growth parameters. In Elasmobranchs as Living Resources: Advances in the Biology, Ecology, Systematics, and the Status of the Fisheries. Proceedings of the Second United States-Japan Workshop East-West Center, Honolulu, Hawaii, 9-14 December 1987 (ed. H . L . Pratt, S. H . Gruber & T. Taniuchi), pp. 97-109. N O A A Technical Report N M F S 90.  72  Reynolds, J. D . , Jennings, S. & D u l v y , N . 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Conservation biology 12, 1180-1197. Wolff, W . J. 2000 Causes of extirpations in the Wadden Sea, an estuarine area in the Netherlands. Conservation Biology 14(3), 876-885. Zadeh, L . 1965 Fuzzy sets. Information and Control 8, 338-353. Zadeh, L . 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 F I S H 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; D u l v y et al. 2003; Sadovy & Cheung 2003). A m o n g 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; D u l v y & 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 timeseries 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.  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. 3  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 & W o r m 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 overexploited. 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 lifehistory 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) W e 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) W e 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. A s 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 = Membership^ e  + Membership • ( 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 /. A n 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. T o reduce  the  uncertainty of the predicted vulnerability, species in which total length was the only available life-history parameter were excluded. A s 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 reefassociated, (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). T o 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 + P "  5  X PnicKi •  4  N i c h e  i +  X  0habitat, j  • \og{Depth) +  Depth  ' Habitat  e  3 2  j+£  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 Bh b a  itat  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. A s 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 ( S A U P ) 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). W e 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 Coral Seamounts Other habitats  Estuaries, mangroves, river mouth Coral reef, coral, atoll, reef slope Seamounts 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. W e overlaid global maps of coral reefs ( U N E P W o r l d Conservation Monitoring Centre, Cambridge, U K ) , estuaries (Alder 2003) and seamounts (Kitchingman & L a i 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. T o 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 R e d 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 largescale 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 ± 1 . 2 s.e. and 45.3 ± 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 (TurkeyKramer 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 ± 3.1 s.e.). The average vulnerability index of seamount-aggregating fishes was 63.9 ± 3 . 1 s.e., and was similar to fishes that were listed under the I U C N R e d 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  All 4 In'  Coral reef  Coral reef 4.10* '* i  ' '' '•  Estuarine 2.04 -2.06  Estuarine  -2.04  2.06  Seamount  5.44*  9.54*  7.47*  Seamount-agg  16.56*  20.67*  18.61*  Seamount -5.44*  Seamount-agg -16.56*  -9.54*  -20.67*  -7.41*  -18.61* -11.13  ""-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 I U C N 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 offishesof 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 seamountaggregating 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  b)  a)  JS »  J3  g c)  « w  1950  M O u  d)  «  cm ©  «  o  S 3  <u a  a  a 'u a  e e)  f)  Figure 3.2. Average intrinsic vulnerability index weighted each year by the annual catch of: (a) all exploited fishes (•) and all coastal exploited fishes (o); (b) all exploited fishes except small pelagic fishes; (c) coral reef-associated fish communities; (d) estuarine fish communities; (e) seamount fish communities; (f) seamount fish communities (except small pelagics). The average intrinsic vulnerability of the catch can range from 1 to 100. Higher value represents greater vulnerability.  87  The declines in average vulnerability of catches generally resulted from the slight decrease in catches of more vulnerable species and the increases in catches of low vulnerability species (Figure 3.3). Catches of fishes with intrinsic vulnerability indices of around 60 increased from the 1950s, peaked in the 1990s and appeared to be declining since then (Figure 3a). A t the same time, catches of fishes with low vulnerability (vulnerability indices below 60) continued to increase rapidly. When we only included catches of demersal fishes from coastal areas (defined here as less than 50 m deep or within 100 km from the nearest coast), such trends became clearer (Figure 3b, c). In the offshore areas (the complement of coastal areas), highly vulnerable fish catches peaked in the 1980s. On the other hand, the pattern of increasing catches of low vulnerability fishes is less clear. Catches of very high vulnerability fishes (vulnerability index = 70 to 90) showed a stronger increase since the late 1980s (Figure 3 b, d).  88  million t  million t  1950  1960  1970  1980  1990  2000  1950  1960  1970  1980  1990  2000  Year  million t  million t 90  0.008  80 70 Miry  0.006  <o ai  0.004  50  a Ei 0.002  50  40 30 20  0.000 1950  1960  1970  1980 Year  1990  2000  1950  1960  1970  1980  1990  2000  Yeai  Figure 3.3. Surface plot of catch of fishes with different intrinsic vulnerability index from 1950 to 2003 of (a) all exploited fishes, (b) coastal exploited fishes, (c) coastal demersal fishes, (d) offshore demersal fishes.  3.3.3. Comparing average vulnerability of catch and number of red listed fishes The map of the global distribution of the number of marine fishes in the I U C N Red  List showed that high concentration of red-listed fishes mainly occur along the  continental shelf (Figure 3.4). In the world ocean map, where the number of red-listed fishes i n each 30 min latitude x 30 min longitude cell was calculated, cells with the highest quartile of the number of red-listed species were all found along the continental shelf. In terms of ocean basins, high concentration of red-listed fishes was observed in the  89  Indo-Pacific, Northwest Pacific and Northwest and East-central Atlantic (particularly the Caribbean). The distribution of red-listed marine fishes agrees with the spatial patterns of changes in the average vulnerability of catch. In the world ocean map, most 30 min latitude x 30 min longitude cells in inshore and continental shelf showed declines in average vulnerability of catch from 1950 to 2003. These were also the areas where the bulk of fishes were being caught. The slopes were mostly positive or very small (+ 0.01) in the cells representing the high seas (i.e. area outside the Exclusive Economic Zones or any national jurisdiction), indicating a slight increase or no change in  average  vulnerability of catch over the past five decades. Cells with negative slope concentrated more in the Indo-Pacific, Northwest Pacific, North Atlantic and the Caribbean. Kendall correlation test showed that the slopes of average vulnerability of catch and number of fishes listed under the I U C N Red List were significantly and negatively correlated (P < 0.01). This means that more threatened fishes occur in areas where average vulnerability of catch (of demersal fishes) declined from the 1950s to the 2000s.  Figure 3.4. Number of marine fishes listed under the IUCN Red List of Threatened Species (Baillie et al. 2004) in the world ocean represented by a map with 30 min x 30 min cells.  90  3.4. Discussion 3.4.1. Intrinsic vulnerability of fish with different associated habitats This study supports the proposed hypothesis that fish communities differ in intrinsic vulnerabilities as a result of different life histories and ecology. Particularly, the findings  agreed  with  previous conclusions that  extremely vulnerable (Koslow  seamount-aggregating  fishes  are  1996; 1997; Morato et al. 2006a). Seamount  fish  communities consist of demersal and benthopelagic species inhabiting deeper waters. Deepwater fishes, represented here as bathydemersal, are highly vulnerable because of their larger sizes, slower growth and late maturity (Koslow 1996; 1997). Such life history patterns allow them to adapt to the high stability of deepwater environment (Steams 1977). On the contrary, deepwater pelagic (mesopelagic and bathypelagic) fishes are generally small-sized and fast-growing (Childress et al. 1980), and thus have lower intrinsic vulnerability (Rex & Etter 1998). Examples of deepwater demersal and benthopelagic species associated with seamounts include orange roughy (Hoplostethus atlanticus), deepwater oreos (Family: Oreosomatidae) and rockfish  (Sebastes spp).  Besides their vulnerable life history patterns (Koslow 1996; 1997), these fishes have a high tendency to form aggregations around seamount, which renders them even more vulnerable to exploitation. Although the coral reef assemblages appeared to have low average vulnerability index to fishing, this can be attributed to the large diversity of small-bodied species, which evolved to utilize the many niches provided by the complex coral reef structure (Sale 1977). On the other hand, high species diversity in coral reef communities also means that the absolute number of fish with vulnerable life histories may be considerable. Estuarine fish assemblages consisted of a mixture of freshwaterand marine- migrants and residents (Blaber 2000). Thus the assemblage structures are relatively more volatile. Therefore, the average intrinsic vulnerability of estuarine fishes is statistically similar to all marine fishes.  3.4.2. Intrinsic vulnerability and geographic range The  significant  positive  relationship  between  latitudinal range  and  the  vulnerability index suggest that fishes with a large geographic range may be more  91  vulnerable  to  fishing.  Macroecological theory  predicts  that  geographic  range  (approximated by latitudinal range here) is positively related to maximum body size, as large-bodied animals tend to be generalists, have higher mobility and require more resources (Gaston 1988; Brown 1995; Brown et al. 1996). A s body size is positively correlated with intrinsic vulnerability (Dulvy et al. 2003; Reynolds et al. 2005), vulnerability and latitudinal range are thus correlated. This relationship implies that wide ranging fishes  may be more vulnerable to fishing -  which contradicts  previous  conclusions that fishes with large geographic ranges should be less vulnerable. In the fuzzy expert system employed in this study, geographic range was an attribute used to calculate the vulnerability index. However, the 'rule' in the model specified that species with small geographic range should have high vulnerability (Cheung et al. 2005). Thus the positive geographic range and vulnerability relationship obtained from the results should not be an artifact of the model.  3.4.3. Average intrinsic vulnerability of catch The results from this study support the hypothesis that global fisheries catches were  increasingly dominated  by  less  intrinsically  vulnerable  fishes  while  more  intrinsically vulnerable fishes became over-exploited or depleted. The consistent declines of average vulnerability of catch were generally caused by the reduced catches of more vulnerable species, while catches of less vulnerable fishes increased. This trend was particularly prominent in coastal regions. The findings agree well with empirical evidence of serial depletion from more vulnerable to less vulnerable species worldwide. Firstly, large declines in abundance of animals in coastal  and estuarine  ecosystems  had been  estimated  from historical  ecosystem reconstructions (Lotze et al. 2006). Secondly, empirical evidence at regional scale showed significant relationships between intrinsic vulnerability and changes in community structure because of fishing (Jennings et al. 1998; Cheung et al. 2005; Cheung, W . W . L .  unpublished data). In general, abundances  of intrinsically  more  vulnerable fishes declined faster than those of less vulnerable fishes. In fact, the majority of the currently over-exploited, depleted or collapsed fishery stocks are large demersal  92  fishes (Grainger & Garcia  1996). Here, these species were shown to have high  vulnerability. Moreover, the large-scale depletion of predatory fishes (Baum et al. 2003; Christensen et al. 2003; Hutchings 2000; Myers & W o r m 2003, 2005) and numerous accounts of local extinction of highly vulnerable species (Casey & Myers 1998; D u l v y et al. 2003; Sadovy & Cheung 2003; Donaldson & D u l v y 2004) support the hypothesis that the decline in average vulnerability of catch was largely a result of over-exploitation of the more vulnerable fishes. Although catches of extremely vulnerable species also increased (intrinsic vulnerability index > 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. O n 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. A s 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 & L o c k 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 R e d 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. A s 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. 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J. 2006a Vulnerability of seamount fish to fishing: fuzzy analysis of life-history attributes. Journal of Fish Biology 68, 209221. Morato, T., Watson, R., Pitcher, T. J. & Pauly, D . 2006b Fishing down the deep. Fish and Fisheries 7(1), 24-34.  99  Myers, R. A . & W o r m , B . 2003 Rapid  worldwide depletion  of predatory  fish  communities. Nature 423, 280-283. Myers, R. A . & W o r m , B . 2005 Extinction, survival, or recovery of large predatory fishes. Philosophical Transactions of the Royal Society of London: B Pandolfi, J. M . , Bradbury, R. H . , Sala, E . , Hughes, T. P., Bjomdal, K . A . , Cooke, R. G . , M c A r d l e , D . , McClenachan, L . , Newman, M . J. H . , Paredes, G . , Warner, R. R. & Jackson, J. B . C . 2003 Global trajectories of the long-term decline of coral reef ecosystems. Science 301 (5635), 955-958. Pauly, D . , Christensen, V . , Dalsgaard, J., Froese, R. & Torres Jr., F. 1998 Fishing down marine food webs. Science 279, 860-863. Pauly, D . , Christensen, V . , Guenette, S., Pitcher, T. J., Sumaila, U . R., Walters, C . J., Watson, R. & Zeller, D . 2002 Towards sustainability in world fisheries. Nature 418, 689-695. Pauly, D . & Palomares, M . L . 2005 Fishing down marine food web: it is far more pervasive than we thought. Bulletin of Marine Science 76, 197-211. Pitcher, T. J. 2001 Fisheries managed to rebuild ecosystems? Reconstructing the past to salvage the future. Ecological Applications 11, 601-617. Rex, M . A . & Etter, R. J. 1998 Bathymetric patterns of body size: implications for deepsea biodiversity. Deep-Sea Research II45, 103-127. Reynolds, J. D . , Dulvy, N . K . , Goodwin, N . B . & Hutchings, J. A . 2005 Biology of extinction risk in marine fishes. Proceedings of the Royal Society of London: Biological Science 272, 2337-2344. Reynolds, J. D . , Jennings, S. & Dulvy, N . K . 2001 Life histories of fishes and population responses to exploitation. In Conservation of exploited species (ed. J. D . Reynolds, G . M . Mace, K . H . Redford & J. G . Robinson), pp. 147-168. Cambridge: Combridge University Press. Roessig, J. M . , Woodley, C . M . , Cech, J. J. & Hansen, L . J. 2004 Effects of global climate change on marine and estuarine fishes and fisheries. Reviews in Fish Biology and Fisheries 14, 251-275. Roff, D . A . 1984 The evolution of life history parameters in teleosts. Canadian Journal of Fisheries and Aquatic Science 41, 989-1000.  100  Sadovy, Y . 2005 Trouble on the reef: the imperative for managing vulnerable and valuable fisheries. Fish and Fisheries 6, 167-185. Sadovy, Y . & Cheung, W . L . 2003 Near extinction of a highly fecund fish: the one that nearly got away. Fish and Fisheries 4, 86-99. Sadovy, Y . J. & Vincent, A . C . J. 2002 Ecological issues and the trades in live reef fishes. In Coral Reef Fishes: Dynamics and Diversity in a Complex Ecosystem (ed. P. F. Sale), pp. 391-420. San Diego: Academic Press. Sale, P. F . 1977 Maintenance of high diversity in coral reef fish communities. The American Naturalist 111(978), 337-359. Smith, S. E . , A u , D . W . & Show, C . 1998 Intrinsic rebound potentials of 26 species of Pacific sharks. Marine Fisheries Research 49, 663-678. Spalding, M . D . , Ravilious, C . & Green, E . P. 2004 World Atlas of Coral Reefs Berkeley, C A : University of California Press. Steams, S. C . 1977 The evolution of life history traits: a critique of theory and a review of the data. Annual Review of Ecology and Systematics 8, 145-171. Vila-Gispert, A . , Moreno-Amich, R. & Garcia-Berthou, E . 2002 Gradients of life-history variation: an intercontinental comparison of fishes. Reviews in Fish Biology and Fisheries 12, 411-421. Watling, L . & Norse, E . A . 1998 Disturbance of the seabed by mobile fishing gear: a comparison to forest clearcutting. Conservation biology 12, 1180-1197. Watson, R., Kitchingman, A . , Gelchu, A . & Pauly, D . 2004 Mapping global fisheries: sharpening our focus. Fish and Fisheries 5(2),  168-177.  Watson, R . & Morato, T. 2004 Exploitation patterns in seamount fisheries. In Seamounts: Biodiversity and Fisheries, pp. 61-66. Vancouver: Fisheries Centre Research Report 12(5). Whitfield,  A . K . 1990 Life-history styles of fishes  in South African  estuaries.  Environmental Biology of Fishes 28, 295-308. Wilson, S. K . , Graham, N . A . J., Pratchett, M . S., Jones, G . P. & Polunin, N . V . C . 2006 Multiple disturbances and the global degradation of coral reefs: are reef fishes at risk or resilient. Global Change Biology 12, 2220-2234.  101  Winemiller, K . 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 FISHING 4  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 & W o r m 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; D u l v y et al. 2003), with  extinction following the last extirpation (Pitcher 2001). On the other hand, lack of longterm 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; D u l v y 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 wellstudied areas (e.g., North America). The data-limitation problem prevents identification of extinction risk for a broad range of populations affected by anthropogenic impacts  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]. 4  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, reanalysis 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; D u l v y et al. 2003; Dulvy et al. 2004). Such approaches use easilyobtainable information to approximately identify vulnerable or "priority" species that are in need of immediate conservation attention. For instance, in Chapter 2, the 'rule-ofthumb' 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 i f 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).  Easily obtainable data  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. A m o n g 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;  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). A s 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; W o r m et al. 2006).  o  4 •—<  ns  Stage 5  O  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 subpopulation. 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  theoretical  under similar fishing pressure (Musick  and empirical studies  suggest that fishes  with  'periodic'  1999). Also, 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. T o 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 prespecified 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; C o x 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 stockrecruitment 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 W e analyzed 460 species of marine fish that had at least 10 years of catch timeseries data, and catches of at least 100 tonnes in the United Nations Food and Agriculture Organization ( F A O ) 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:  .  eq. 4.1  t=y-  c v  (y-y)  '  where the averaged catch C at year y is equal to the average of annual catch from year y ' . A s smaller species tend to respond more strongly to environmental variability than larger species (Spencer & Collie 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)  100  T  100  T3  x T3  C CD U)  3 o  c  O  o. O  1950  1960  1970  1980  1990  2000  Year  b)  4 -,  100 80 60  3 ? 2  40  c o  20  <u Q  O  0 1965  1975  1985  0)  •o c  Q.  1995  Year  Figure 4.3. Catch time-series (solid black line: original; dotted: smoothed) and the estimated depletion risk index (grey line) for fish with different life history and exploitation patterns, (a) Rainbow sardine (Dussumieria acuta) is an example of small pelagic fish with low intrinsic vulnerability and (b) Nassau grouper (Epinephelus striatus) is an example of large demersal fish with high intrinsic vulnerability.  Based on the smoothed catch time-series, each population in each year were categorized into different exploitation status. Firstly, the smoothed catch time-series were re-expressed as the ratio of each year's annual catch to the maximum catch in the smoothed time-series. Each data point in the time-series was also classified by its position relative to the maximum catch in the data-series (i.e., before or after the maximum catch  110  is reached). Based on the relative position in the time-series and the ratio to the maximum catch (Table 4.1), each data point was then categorized into exploitation status categories adopted by the F A O : (1) under-exploited, (2) fully exploited, (3) over-exploited, (4) depleted, (5) recovering (Grainger & Garcia 1996). Each data point can belong to multiple categories, each with an associated degree of membership estimated from predefined membership functions for the categories (Table 4.1). The simplest form of membership functions, trapezoidal and triangular, were used:  Membership = 0  if x<a.  Membership =  if a<x<b  eq. 4.2a ^  b- a Membership = 1  if b<x<c  d-x Membership —  eq. 4.2c eq. 4.2d  if c<x<d d-c  where x is the independent variable, and in this case represents the ratio of annual catch to the maximum catch of the time-series. A l l x values between a and d are in the particular fuzzy set; b and c are the independent variables with maximum membership. For the triangular membership function, b and c are equal (Table 4.1). For instance, the catch of Rainbow sardine (Dussumieria acuta) smoothed over nine years in year 1970 is about 11,000 tonnes. This is less than 17% of the maximum smoothed catch (63 thousand tonnes in 1992). Based on the fuzzy member functions in Table 4.1, Rainbow sardine in 1970 was classified as under-exploited with full membership. However, smoothed catch of Nassau grouper in 1970 was 1.2 thousand tonnes, 40% of the maximum catch in 1967. Thus, Nassau grouper was classified as over-exploited and depleted, with memberships of 0.6 and 0.4, respectively (full membership = 1).  Ill  Table 4.1. Categorization of exploitation status based on fishery catch time-series under three scenarios: conservative (minimize overestimation), liberal (minimize under-estimation), and moderate (intermediate). Exploitation status  Domain of fuzzy sets in different scenarios" Catch relative to maximum in time-series* Conservative  Moderate  Position in time-series'  7  Fuzzy membership function''  Liberal  0-1 (0-0.75)  0-0.75 (0-0.5)  0-0.5 (0-0.25)  Before maximum  Trapezoidal  Fully exploited  0.75-1 (1)  0.5-1 (0.75)  0.25-1 (0.5-0.75)  Before maximum  Trapezoidal  Fully exploited  0.5-1 (1)  0.75-1 (1)  After maximum  Triangular  0.5-1 (0.75-1)  Before maximum  Trapezoidal  Under-exploited  Over-exploited Over-exploited Depleted Recovering  -  -  -  0.1-0.75 (0.5)  0.25-1 (0.75)  0.75-1 (1)  After maximum  Triangular  0-0.25 (0.1)  0-0.5 (0.25)  0-0.75 (0.5)  After maximum  Trapezoidal  Catch remained  Catch remained  Catch remained  After maximum and after  Trapezoidal  stable/increasing for  stable/increasing for  stable/increasing for  conditions for 'over-exploited' or  at least 10 years  'collapsed' occur  at least 3 years  at least 5 years  Domain of a set represents its all possible values of an independent variable of a function. Values in parentheses represent the value (or range) of an independent variable with full membership to the set; ''Estimated from the ratio of catch at year t to the maximum catch (using catch time-series smoothed by running average); 'Position of data-point in the catch time-series (after running average) relative to the maximum attained catch in the data-series;. '' Types of membership functions assumed in the model. Each year of catch-time series belongs to set(s) of exploitation status with degree of membership to the set(s) determined by the specified fuzzy membership function (trapezoidal and triangular membership functions).  to  4.2.2. Combining 'rules-of-thumb' A fuzzy expert system was constructed to predict the depletion risk of marine fishes to fishing (Figure 4.4). The expert system is composed of two stages: the first stage infers intrinsic vulnerability to fishing while the second stage predicts depletion risk from the intrinsic vulnerability (from first stage) and time-series catch data. The structure of the first stage (prediction of intrinsic vulnerability) and its validation are detailed elsewhere (Cheung et al. 2005; Chapter 2).  STAGE 1 /  y Biological data: y *Life history / "Ecology  Heuristic rules  Intrinsic vulnerability to fishing  STAGE 2 Fisheries data: / • Catch time/ series /  /  Heuristic rules  Exploitation status  \ _  Heuristic rules  Figure 4.4. Schematic diagram of the structure of a fuzzy expert system to predict depletion risk of marine fishes from fishing.  113  a. Estimating intrinsic vulnerability Using the expert system described in (Cheung et al. 2005; see Chapter 2) and life history parameters that were available from Fishbase (www.fishbase.org), we estimated the natural resistance to depletion from fishing (i.e., intrinsic vulnerability) for 460 species of exploited marine fishes. The predicted intrinsic vulnerabilities, originally on a scale from 1 to 100, were expressed as ordinal categories (low, moderate, high, very high) with degrees of membership associated with each category according to the life history and ecology of the species. For instance, Rainbow sardine was predicted to have ' l o w ' intrinsic vulnerability with full membership, while Nassau grouper was predicted to have 'high' and 'very high' vulnerability with membership of 0.4 and 0.6, respectively.  b. Inferring depletion risk Based on sets of heuristic rules, the predicted intrinsic vulnerabilities and exploitation status were combined to infer depletion risk. The heuristic rules were developed from the assumption that the depletion risk of exploited marine  fishes  increases as populations become fully exploited, over-exploited and depleted (Table 4.2). Depletion risk was categorized into four levels: low, moderate, high, and very high each representing a set of relative depletion risk that ranged on a scale from 1 to 100 with increasing risk. Heuristic rules that determined the levels of depletion risk were expressed in I F - T H E N clauses in which exploitation status and intrinsic vulnerability were the premises, while levels of depletion risk were the conclusions (Table 4.2). For instance, Rainbow sardine has low intrinsic vulnerability (membership = 1) and it was 'underexploited' in 1970 (membership = 1), following the rule in Table 4.2:  IF intrinsic vulnerability is low and population is under-exploited  THEN  depletion risk is low.  The membership to the conclusion was calculated from the minimum of the memberships to the premises. Thus Rainbow sardine had low depletion risk with a membership of 1 (full membership) in 1970. Alternative sets of rules were used to the test sensitivity to and validity of the rules (Table 4.2).  114  Table 4.2. Heuristic rules that relate intrinsic vulnerability and exploitation status (premises) with depletion risk (conclusions). The rules were developed based on the rationale that depletin risk index increased as a population becomes fully exploited, over-exploited and depleted, and related positively with intrinsic vulnerability. Alternative sets of rules representing 'conservative' and 'liberal' estimation of risk were used to test sensitivity to and validity of the assumed rules. Premises Vulnerability Low  Moderate  High  Very high  When  Status  Conclusions (depletion risk) of scenarios: Conservative Moderate Liberal  Under-exploited  Low  Low  Low & mod.  Fully exploited Over-exploited Depleted Recovering Under-exploited Fully exploited Over-exploited Depleted Recovering Under-exploited Fully exploited Over-exploited Depleted Recovering Under-exploited Fully exploited Over-exploited Depleted Recovering  Low Low Mod Low Low Low Mod. High Low Low Low & mod. Mod. & high High & v. high Low & mod. Low Mod. & high High High & v. high Mod. & high  Low Low & mod. Mod & high Low Low Low & mod. Mod. & high V. high Low & mod. Low Mod. High V. high Mod. Low High High & v. high V. high High  Mod. Mod & high High & v. high Mod. Low & mod. Mod. High V. high Mod. Low & mod. Mod. & high High & v. high V. high Mod. & high Low & mod. High & v. high V. high V. high High & v. high  different  rules result in the same conclusion, memberships  to the  conclusion were accumulated using the method (Buchanan & Shortliffe 1984):  Accumulated membership = Membership+ Membership  ( + 1  • (1 - Membership;)  where Membership! is the degree of membership to the conclusion resulted from rule /. A n index of depletion risk was estimated from the index value of each depletion risk category weighted by their degrees of membership.  115  Table 4.3. Extrapolation from fish species with catch data to all exploited marine fish, by fishery importance and fish types. Groups  Number of species With catch World Percent Extrapolation ^ data total" represented Pelagics 64 64 100 Included Demersals 73 138 54 Included Elasmobranchs 3 7 43 Included Commercial Pelagics 53 155 34 Included Demersals 152 1272 12 Included Elasmobranchs 9 117 8 Included Minor Pelagics 25 14 185 Included commercial Demersals 61 1038 6 Included Elasmobranchs 13 165 8 Included Others Pelagics 1 1 146 Excluded Demersals 4 267 1 Excluded Elasmobranchs 1 44 2 Excluded a - This classification is based on the level of catch in FAO statistics; see FishBase (www.fishbase.org); b - Pelagics and demersals include bony fish (teleosts) only; c - Groups that have 5% of less of the species with catch time-series data are excluded in extrapolating the number of world's threatened marine fish. Fishery Importance" Highly commercial  Types"  1  Depletion risk was estimated for each exploited stock. Catches of each species from each F A O statistical area were considered as being obtained from an independent stock. Thus the 460 species included in this analysis consist of 1,313 'stocks'. T o ensure that the predictions from this analysis at the species level are conservative, the depletion risk for each species (group) was estimated from the smallest depletion risk among its stocks.  4.2.3. Comparing the predicted depletion risk with the IUCN categories W e compared the predicted depletion risk with the I U C N Red List threatened categories: (vulnerable, endangered, and critically endangered) using simulated data from a dynamic population model. W e selected 21 species of marine fishes with a wide range of intrinsic vulnerability of which estimates of parameters of life history and stockrecruitment functions are available (Myers et al. 1999) (Appendix 4.1). For each species, we developed an age-structured population model (Hilborn & Walters 1992), with assumed variability in recruitment, fishing intensity, density dependent  change in  catchability to fishing (Mackinson et al. 1997). The model was employed to simulate population dynamics for each of the species:  116  N  < l +  where N  ,  =N ^e-^  v + i  eq.4.3.  a  is number of age a individual at year y, F and M are fishing and natural  ay  mortality rates. Recruitment at time t (R ) was specified by a Beverton and Holt function: t  e -  R = — ^  £(0  eq.4.4.  a)  where R, is expressed as a function of the egg production or weight of spawners, a is the maximum annual recruitment per spawner and fi determines the degree of density dependence, and S, is the spawning stock size. Variations of annual recruitment were assumed to be log-normally distributed (mean = 0 and standard deviation = 0.5). Population was in equilibrium without fishing mortality initially (year 0) from when fishing mortality rate (F) increased at a constant rate each year. The rate of increase in F was randomly chosen for each simulation. Selectivity was assumed to be agedependent and follow a logistic function:  a" v  «  =  ,  t  P  p ,  M  e  q  -  4  -  5  '  where v is the probability of capture at age a, t is the age at 50% capture and P is a a  c  constant determining the slope of the selectivity curve (P = 5 in this analysis). Time-series of catch (C) and catch-per-unit-effort ( C P U E ) were generated from each simulation (Hilborn & Walters 1992). In each simulation run, catch and C P U E were calculated from:  CPUE =  F  eq. 4.7.  117  where w is the weight-at-age, q is the actual catchability coefficient while q was the a  assumed catchability coefficient used by the observation model (q  = 0.3). Density-  dependence change in catchability was modelled by:  q = q' " b  \fq<i  <7 = 1  eq. 4.8.  \fq>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 R e d List criterion E (based on probability of extinction). Probability of quasiextinction 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). W e 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 R e d 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). A t 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 . W e 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). W e 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). T o 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). W e 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 m i d 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 smallbodied clupeids, engraulids, carangids and tetraodontids.  1.0 i V  o 0.8 <D. Q W  1970  o 0.6 c o 0.4 r o Q. O  /  0.2 -  a 0.0 4 r~i,n 5  2001  i  15  25  35  45  55  65  75  85  Depletion risk index Figure 4.5. Proportion of the 460 species of marine fishes with different classes of calculated depletion risk index in 1970 (open bars) and 2001 (gray bars). The values in the x-axis are the mid-point of the classes.  Comparing the predicted depletion risk index in 2001 among different fish groups, large demersal fish (maximum length > 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). A m o n g 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  Medium-large  fishes  pelagic fishes  Small demersal Large demersal Elasmobranchs 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. O f 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), 218730 (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 R e d 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  Figure 4.7. Proportion  Birds  Amphibia  of described  categorized as critically endangered  Marine fish  mammals,  birds,  amphibians  (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)  CR  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 I U C N Red List procedure (criterion E, solid bars) and the rule-based model (white bars). C R - 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  0.3  g-  'B 0.2 CO  Si O  0.1 0 CR  E N or higher  i  V U or higher  b) D Conservative • Moderate • Liberal  CR  E N or higher  V U or higher  IUCN category  Figure 4.9. Probability of (a) under-estimating (type I) and (b) overestimating (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. F o r 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). M a x i m u m 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; D u l v y 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; D u l v y & Reynolds 2002; Baum et al. 2003), while large predatory fish are traditionally targeted by fishing (Pauly et al. 1998; Myers & W o r m 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 & W o r m 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; D u l v y 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). M a n y 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 R e d 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 & W o r m 2003; 2005). 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Robinson), pp. 147-168. Cambridge: Combridge University Press. Roessig, J. M . , Woodley, C . M . , Cech, J. J . & Hansen, L . J. 2004 Effects of global climate change on marine and estuarine fishes and fisheries. Reviews in Fish Biology and Fisheries 14, 251-275. Rose, K . A . , Cowan Jr, J. H . , Winemiller, K . O., Myers, R . A . & Hilborn, R . 2001 Compensatory density dependence in fish populations: importance, controversy, understanding and prognosis. Fish and Fisheries 2, 293-327'. Rowe, S. & Hutchings, J. A . 2003 Mating systems and the conservation of commercially exploited marine fish. Trends in Ecology and Evolution 18(11), 567-572. Sadovy, Y . 2001 The threat of fishing to highly fecund fishes. Journal of Fish Biology 59 (Supplement A ) , 90-108. Sadovy, Y . & Cheung, W . L . 2003 Near extinction of a highly fecund fish: the one that nearly got away. Fish and Fisheries 4, 86-99. Saila, S. B . 1996 Guide to some computerised artificial intelligence methods. In Computers in Fisheries Research (ed. B . A . Megrey & E . Moksness), pp. 8-37. London: Chapman and H a l l . Spencer, P. D . & Collie, J. S. 1997 Patterns of population variability in marine fish stocks. Fisheries Oceanography 6, 188-204.  133  Stephens, P. A . & Sutherland, W . J. 1999 Consequences of the Allee effect for behaviour, ecology and conservation. T r e n d s in E c o l o g y a n d E v o l u t i o n 14(10), 401-405.  Stephens, P . A . , Sutherland, W . J. & Freckleton, R. P. 1999 What is the Allee effect. Oikos  87, 185-190.  Stevens, J. D . , Bonfil, R., Dulvy, N . K . & Walker, P . A . 2000 The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. I C E S J o u r n a l of M a r i n e S c i e n c e 57, 476-494.  Sumaila, U . R . , Marsden, D . , Watson, R . , & Pauly, D . 2007. Global ex-vessel fish price database:  construction,  spatial  and  temporal  applications.  Journal  of  B i o e c o n o m i c s . [in p r e s s ] .  Tinch, R . 2000 Assessing Extinction Risks: A Novel Approach Using Fuzzy Logic. Norwich. University of East Angila. Todd, C . R . & Burgman, M . A . 1998 Assessment of threat and conservation priorities under realistic levels of uncertainty and reliability. C o n s e r v a t i o n B i o l o g y  12(5),  966-974. Watson, R., Kitchingman, A . , Gelchu, A . & Pauly, D . 2004 Mapping global fisheries: sharpening our focus. Fish  a n d F i s h e r i e s 5(2), 168-177.  Winemiller, K . O . 2005 Life history strategies, population relation, and implications for fisheries management. C a n a d i a n J o u r n a l  of F i s h e r i e s a n d A q u a t i c S c i e n c e 62,  872-885. Winemiller, K . O . & Rose, K . A . 1992 Patterns of life-history diversification in North American fishes: implications for population regulation. C a n a d i a n J o u r n a l o f F i s h e r i e s a n d A q u a t i c S c i e n c e 49, 2176-2218.  W o r m , B . , Barbier, E . B . , Beaumont, N . , Duffy, J . E . , Folke, C , Halpern, B . S., Jackson, J. B . C , Lotze, H . K . , M i c h e l i , F . , Palumbi, 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.  Zadeh, L . 1965 Fuzzy sets. I n f o r m a t i o n a n d C o n t r o l 8, 338-353. Zadeh, L . A . 1995 Discussion: Probability theory and fuzzy logic are complementary rather than competitive. Technometrics  37(3), 271-276.  134  Zhang, C . 1994 A fuzzy distinguishability on similar degree of composition of spawning stock of yellow croaker in the south Fufian fishing ground. Journal of Fisheries of China 18(4), 335-339.  135  5. E V A L U A T I N G T H E S T A T U S O F E X P L O I T E D FISHES I N T H E NORTHERN SOUTH CHINA SEA USING INTRINSIC VULNERABILITY AND SPATIALLY EXPLICIT CATCH-PERUNIT-EFFORT DATA 5  5.1. Introduction The fishery resources of the northern South China Sea ( N S C S ) have been heavily exploited in the past and are now in decline (Lu & Y e 2001; Cheung & Pitcher 2006). 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). A t the same time, the total catch landings from N S C S have increased 16 times, from 0.2 million 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 ( C P U E ) 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 G u l 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).  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 Catchper-unit-effort Data. Fisheries Research, [in review]. 3 5  136  1950  1960  1970  1980  1990  2000  Year  Figure 5.1 Change in landings (left axis, solid line, filled circle), fishing power (left axis, solid line, open circle) and C P U E (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 Bo  1935  I  1956  1962  I  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 K o n g government.  The Agriculture and Fisheries Department  (renamed  as  Agriculture, Fisheries and Conservation Department in 1998) in Hong K o n g carried out catch and effort surveys with fishing vessels operating in N S C S during the 1970s and 1980s. The Hong K o n g government's surveys focused on demersal trawlers that were registered in Hong K o n g but were allowed to fish in most parts of N S C S , including the inshore Hong K o n g 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,  _ B =  C  CPUE  fq  q  — =  eq. 5.1  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 (Gillis 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; D u l v y 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 K o n g 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 K o n g that fished in the N S C S (Hong K o n g Agriculture, Fisheries and Hong K o n g 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 A r c G I S 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" hp" -metre haul length"') of each of 1  1  the 17 taxa from the Hong K o n g 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 C P U E estimates are available for two or more of its immediate surrounding grids (e.g. A l , B l , C I ) , its C P U E is assumed to be the average of these surrounding cells. However, if C P U E estimate is available for only one of its immediate surrounding grids, C P U E estimate of the same grid and in the nearest succeeding year (D ) is included in calculating 1+1  the average CPUE. Otherwise, C P U E 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 autocorrelated. 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) 19841988. 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 ( £ / , ) = log(c/, „ ) + log(or,.) + logoff,) + l o g ( n ) + e M  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 = l o g ( « ±se-\  .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" , was considered), geographic range, and 1  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. A s 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. (cm)  T (year)  Yellow croaker Larimichthys crocea  80  Slate cod croaker Protonibea diacanthus Other croakers Atrobucca nibe Nibea semifasciata Nibeasoldado Otolithes ruber Pennahiaanea Pennahia macrocephalus  T  max  K (year" )  M (year" )  Fecundity* (eggs year" )  Spatial behaviour  Geograpahic range (km )  2.3  11  0.27  -  -  80  5,217  150  2  9  0.33  0.83  45 24 60 90 30 23  4.4  19.1  0.15  -  1.3 0.6  5.6 2.2  0.51 1.27  90  2  9  35  1.6  100  Groupers' Scads Epinephelus bruneus Alepes djedaba Decapterus russelli Bigeyes Priacanthus tayenus P. macracanthus  Taxon  Lmax  4  (year)  1  1  1  2  24,613  -  -  -  21,548 5,303 16,730 30,931 13,889 13,116  0.33  -  -  40  49,090  6  0.45  -  -  -  18,106  1.5  7  0.41  -  -  -  30,974  128 40 45  6.6 1.2 1.1  32 4.7 4.4  0.09 0.61 0.65  --  -50,905  80 90  5,347 37,652 35,363  35 30  1.1 0.6  4.2 2.2  0.68 1.31  -  -  24 32  22,529 24,955  Two-spotted red snapper Lutjanus bohar Golden threadfin bream Nemipterus virgatus Flathead Platycephalus indicus  Table 5.2 Con't T  Taxon (cm)  T  *m  A  max  (year)  (year)  K (year )  M (year" )  Fecundity  1  1  Spatial behaviour  Geograpahic range (km ) 2  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  Red goatfish Upeneus molluccensis  20  0.8  2.9  0.97  -  10  -  7,544 32,146 18,378  95  29,702  2  111,600  * Fecundity estimates are either not available or over 100 eggs year' for most species without fecundity input. The fuzzy expert system does not consider fecundity of over 100 eggs year' to have any effect on the intrinsic vulnerability (see Chapter 2). 1  1  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 considerab