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Assessment of the Red Sea ecosystem with emphasis on fisheries 2012

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ASSESSMENT OF THE RED SEA ECOSYSTEM WITH EMPHASIS ON FISHERIES  by Dawit Tesfamichael  B.Sc., University of Asmara, 1996 M.Sc., Wageningen University, 2001  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY  in  THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  July 2012  © Dawit Tesfamichael, 2012  ii Abstract  A comprehensive assessment of the Red Sea large marine ecosystem (LME), with emphasis on fisheries, was carried out using several approaches. The assessment started with a multidisciplinary rapid appraisal of the sustainability of the fisheries using standardized attributes in ecological, economic, social, technical and ethical fields. Then a time-series assessment of the fishery was carried out using data from interviews and the reconstruction of catch from 1950 - 2006. A case study to estimate the unreported catch by quantifying qualitative information on incentives to misreport was carried out for Eritrean fisheries. Finally, a comprehensive and detailed assessment was done in an ecosystem-based framework using the modelling tool Ecopath with Ecosim (EwE), which quantifies the trophic interactions of the organisms and fisheries. It was used to predict the impact of different scenarios of fisheries on the ecosystem and explore the conflict between artisanal and industrial fisheries. Uncertainty analysis was carried out for the different assessment methods employed.  The results of the assessments have varying levels of detail: relative ranking of the sustainability of fisheries in the rapid appraisal assessment, relative quantitative changes over time in the interview analysis, actual historic quantitative assessment of the catches in the catch reconstruction, and finally a quantitative assessment with potential to predict future scenarios using ecosystem modelling. The results give a holistic understanding of the Red Sea ecosystem and its fisheries. The data and resources needed increased as the details of the outputs increased. The assessments complemented each other and there are similarities in the results. They all showed declines in all fisheries, except for beach seining. Sharks, the top predator of the system, showed the worst decline in all the assessments; and the interview and catch reconstruction methods gave strikingly similar results for sharks. The ecosystem modelling did not show direct impact between artisanal and industrial fishery sectors due to the lack of trophic interactions. In addition, the thesis demonstrates that fishery researchers and practitioners can utilize different assessment tools, given the resources at their disposal, to assist the management of resources to conserve ecosystems and livelihoods.   iii Preface  Some of the chapters of this thesis and the results therein have been published or submitted, or are ready to be submitted. All chapters except 6 are published either fully or partly in peer reviewed journals. Chapter 2 is published: Tesfamichael, D. and Pitcher, T.J. (2006) Multidisciplinary evaluation of the sustainability of Red Sea fisheries using Rapfish. Fisheries Research 78: 227-235. It was part of a bigger research project supervised by Dr. Tony J. Pitcher. I was involved in the development of the assessment technique, scored the Red Sea fisheries and provided my scores as test data for the routine developed to do the computation; I also wrote most of the manuscript, which was reviewed and edited by Dr. Tony J. Pitcher. A paper containing parts of Chapter 3 and 4 is published: Tesfamichael, D. and Pauly, D. (2011) Learning from the Past for Future Policy: Approaches to Time-series Catch Data Reconstruction. Western Indian Ocean J. Mar. Sci. 10: 99-106. I did the data collection, analysis and writing under the supervision of Dr. Daniel Pauly who also reviewed and edited the manuscript. Chapter 5 is published: Tesfamichael, D. and Pitcher, T.J. (2007) Estimating the Unreported Catch of Eritrean Red Sea Fisheries. African Journal of Marine Science 29: 55-63. I did the data collection, analysis and writing under the supervision of Dr. Tony J. Pitcher, who reviewed and edited the manuscript.  Manuscripts submitted include: a paper including part of the results from Chapter 3 and 4. I am a junior author and my contributions include parts of the results and discussion. A second manuscript submitted is one which takes a case study from Chapter 3. Chapter 3 as a whole is ready to be submitted and Chapters 4 and 6 are in preparation to be submitted.  My supervisors: Drs. Daniel Pauly and Tony J. Pitcher, besides the contributions mentioned above in the publications of the papers, were part of the development of the proposals of the chapters of the thesis, supervision during the process, and provided edits and suggestions for improvement of the drafts of each chapter.  This research received ethics approval B06-0818 from UBC’s Behavioural Research Ethics Boards.  iv Table of Contents  Abstract ........................................................................................................................................ ii Preface ......................................................................................................................................... iii Table of Contents ........................................................................................................................ iv List of Tables .............................................................................................................................. vii List of Figures ............................................................................................................................. ix Acknowledgements ..................................................................................................................... xi Dedication .................................................................................................................................. xiv Chapter  1: Introduction .............................................................................................................. 1 1.1 Motivation and development ......................................................................................... 2 1.2 The rational for fisheries assessment ............................................................................. 4 1.3 Thesis outline ................................................................................................................. 6 1.4 The Red Sea ................................................................................................................... 9 1.5 The Red Sea countries ................................................................................................. 17 Chapter  2: Multidisciplinary assessment of the sustainability of Red Sea fisheries using Rapfish ......................................................................................................................................... 21 2.1 Synopsis ....................................................................................................................... 22 2.2 Introduction.................................................................................................................. 23 2.3 Materials and methods ................................................................................................. 25 2.3.1 Sources of information ............................................................................................ 25 2.3.2 Rapfish analysis ....................................................................................................... 27 2.4 Results ......................................................................................................................... 28 2.1 Discussion .................................................................................................................... 35 Chapter  3: Analysing changes in fisheries using interviews to generate long time series of catch per effort ............................................................................................................................ 39 3.1 Synopsis ....................................................................................................................... 40 3.2 Introduction.................................................................................................................. 41 3.2.1 Data needs in fisheries science and management .................................................... 41 3.2.2 Tapping into fishers’ memory or knowledge........................................................... 42 3.2.3 Methodological, standardization and accuracy issues ............................................. 43  v 3.3 Materials and methods ................................................................................................. 46 3.3.1 Questionnaire ........................................................................................................... 46 3.3.2 Sampled areas .......................................................................................................... 46 3.3.3 Sampling .................................................................................................................. 47 3.3.4 Standardizing data ................................................................................................... 49 3.3.5 Validation of interview data .................................................................................... 50 3.3.6 Data fitting ............................................................................................................... 51 3.4 Results ......................................................................................................................... 52 3.1 Discussion .................................................................................................................... 58 Chapter  4: Catch reconstruction of the Red Sea fisheries ..................................................... 62 4.1 Synopsis ....................................................................................................................... 63 4.2 Introduction.................................................................................................................. 64 4.3 Materials and methods ................................................................................................. 71 4.3.1 Sources ..................................................................................................................... 71 4.3.2 Interviews ................................................................................................................ 73 4.3.3 Missing data ............................................................................................................. 74 4.3.4 Compilation ............................................................................................................. 74 4.4 Results and discussion ................................................................................................. 75 Chapter  5: Estimating the unreported catch: a case study of Eritrean Red Sea fisheries . 82 5.1 Synopsis ....................................................................................................................... 83 5.2 Introduction.................................................................................................................. 84 5.3 Materials and methods ................................................................................................. 88 5.1 Results and discussion ................................................................................................. 94 Chapter  6: Ecosystem based assessment of the Red Sea fisheries ........................................ 98 6.1 Synopsis ....................................................................................................................... 99 6.2 Introduction................................................................................................................ 100 6.2.1 The Ecopath model ................................................................................................ 101 6.2.2 Ecosim ................................................................................................................... 103 6.3 Materials and methods ............................................................................................... 105 6.3.1 Ecopath .................................................................................................................. 105 6.3.1.1 Fish species .................................................................................................... 105  vi 6.3.1.2 Non-fish groups ............................................................................................. 107 6.3.1.3 Diet matrix ..................................................................................................... 108 6.3.1.4 Fishery ........................................................................................................... 108 6.3.1.5 Parameterizing / balancing the model............................................................ 109 6.3.2 Ecosim ................................................................................................................... 109 6.3.2.1 Fitting to time series data ............................................................................... 109 6.3.2.2 Model stability and uncertainty analysis ....................................................... 111 6.3.2.3 Equilibrium analysis ...................................................................................... 112 6.3.2.4 Fishing policy exploration ............................................................................. 112 6.4 Results ....................................................................................................................... 113 6.4.1 Ecopath .................................................................................................................. 113 6.1.1 Ecosim ................................................................................................................... 119 6.1.1.1 Fitting to time series ...................................................................................... 119 6.1.1.2 Stability and uncertainty ................................................................................ 121 6.1.1.1 Equilibrium analysis ...................................................................................... 124 6.1.1.2 Fishery policy exploration ............................................................................. 127 6.2 Discussion .................................................................................................................. 128 Chapter  7: Conclusion ............................................................................................................. 135 7.1 Summary .................................................................................................................... 137 7.2 Data, knowledge, management and conservation...................................................... 141 7.3 Contextualizing science ............................................................................................. 142 References .................................................................................................................................. 144 Appendices ................................................................................................................................ 169 Appendix A Supplementary material for Chapter 2 .............................................................. 169 Appendix B Supplementary material for Chapter 3 .............................................................. 173 Appendix C Supplementary material for Chapter 4 .............................................................. 176 Appendix D Supplementary material for Chapter 5 .............................................................. 194 Appendix E Supplementary material for Chapter 6 .............................................................. 198 E.1 Ecopath input data ................................................................................................. 198 E.1 Ecosim input supplementary data .......................................................................... 231   vii List of Tables   Table 2.1 Red Sea fisheries analysed using Rapfish, their numbers in the MDS ordination graphs and categories. ............................................................................................................................. 26 Table 2.2 Kruskal’s stress and RSQ for the different evaluation fields. ..................................... 29 Table 2.3 Leverage of attributes, given by mean standard error (SE), in their respective evaluation field. ........................................................................................................................... 33 Table 3.1 Results of the statistical test comparing the fitting of CPUE change rate data when they were treated as one segment or divided into segments. ....................................................... 55 Table 5.1 Reported catch (mean of 5 years) of three Eritrean fisheries (103 t). .......................... 91 Table 5.2 Qualitative categories of incentives to misreport catch based on the influences from Table (D.1) in the Appendix. ....................................................................................................... 91 Table 5.3 Anchor points as a percentage of total extracted catch (reported plus unreported), bold entries are anchors chosen as references. .................................................................................... 91 Table 5.4 The interpolated values (in %) of unreported catch for the different qualitative categories. .................................................................................................................................... 92 Table 5.5 The interpolated ranges of estimates of unreported catch as a percentage of the total extracted catch. ............................................................................................................................ 92 Table 5.6 Estimates of unreported catch (103 t). Lower and upper refer to the range of unreported catch estimates. .......................................................................................................... 93 Table 6.1 The basic parameters of the balanced Red Sea model............................................... 114 Table 6.2 Comparison of the Red Sea model with other tropical ecosystem models using system summary statistics. ..................................................................................................................... 117 Table 6.3 Biomass depletion risk probabilities for the major fishery groups in the Red Sea below different levels of biomasses, as a ratio of the baseline (2006), at the end of 24 years simulation (2030). ........................................................................................................................................ 124  Table A. 1 Rapfish attributes in their respective fields and notes on their scoring. .................. 169   viii Table C. 1 Red Sea reconstructed catch (t) by sector, compared with the Red Sea total catch data submitted to FAO by member countries. ................................................................................... 176 Table C. 2 Catch (t) composition of reconstructed Red Sea handlining fishery. ...................... 178 Table C. 3 Catch (t) composition of reconstructed Red Sea gillnet fishery. ............................. 180 Table C. 4 Catch (t) composition of reconstructed Red Sea beach seine fishery. ..................... 182 Table C. 5 Catch (t) composition of reconstructed Red Sea shark fishery by countries. .......... 183 Table C. 6 Catch (t) composition of reconstructed Red Sea trawl (retained) fishery. ............... 184 Table C. 7 Catch (t) composition of reconstructed Red Sea trawl (discard) fishery. ................ 188 Table C. 8 Catch (t) composition of reconstructed Red Sea purse seine fishery. ...................... 192  Table D. 1 Summary of the major influences on the incentives to misreport, arrows indicate whether the influence increases or decreases the incentive. ...................................................... 194  Table E. 1 Fish species included in the Red Sea model grouped by functional groups. ........... 198 Table E. 2 Key data on fish groups of the Red Sea ecosystem model. ..................................... 222 Table E. 3 Input parameters of some invertebrates groups. ...................................................... 225 Table E. 4 Diet composition matrix of Red Sea model. ............................................................ 226 Table E. 5 Sources used for the reconstruction of effort of the Red Sea fisheries. ................... 232 Table E. 6 Parameters of exponential and logistic fitting of effort reconstruction.................... 233 Table E. 7 Reconstructed effort of Red Sea fisheries by gear type from 1950-2006. ............... 236 Table E. 8 Flow parameter (vulnerabilities) for the Red Sea model. ........................................ 238 Table E. 9 Feeding rate parameters for the Red Sea model....................................................... 241   ix List of Figures  Figure 1.1 The Red Sea and the bordering countries................................................................... 10 Figure 2.1 Two dimensional Rapfish plots of the MDS ordination of the Red Sea fisheries. The numbers represent the fisheries as given in Table 2.1 (figures continue next pages). ................ 30 Figure 2.2 The inter-quartile (IQ) ranges, or 50% of the scatter, of the Red Sea fisheries in the ecological field............................................................................................................................. 32 Figure 2.3 Kite representation of the evaluation of Red Sea fisheries grouped by countries...... 34 Figure 2.4 Comparison of the different aspects of Red Sea fisheries. A) West and east coast fisheries. B) Industrial and artisanal fisheries.............................................................................. 35 Figure 3.1 Map of the Red Sea indicating the areas (in Sudan, Eritrea and Yemen) where interviews were conducted. ......................................................................................................... 47 Figure 3.2 Age frequency distribution of interviewees by country. ............................................ 53 Figure 3.3 Change in best CPUE fishers recalled for: a = Eritrean gillnet; b = Eritrean hook and line; c = Eritrean shark; d = Sudanese hook and line; e = Yemeni gillnet; f = Yemeni hook and line. Note that axes have different scales..................................................................................... 54 Figure 3.4 Ratio at which the average CPUE changed for interviewees from the year they started fishing, relative to the 2007 CPUE: a = Eritrea, b = Yemen. ...................................................... 56 Figure 3.5 Annual decline of CPUE over the years of fishing experience of fishers in two Red Sea countries. (a) Eritrea, where the rate of decline increased in 1995 after the independence in 1991; (b) Yemen, with an increase in the rate of decline in 1997, which is after  the unification of the country in 1990 and the start of its oil economy. .............................................................. 57 Figure 4.1 The fate of a fish since its first encounter with a fishing gear, (Based on Mohammed, 2003). ........................................................................................................................................... 67 Figure 4.2 Total reconstructed catch (solid line) compared to the data submitted by the Red Sea countries to FAO (broken line). As the Sudanese FAO data do not include shellfish, a version of the reconstructed catch not including shellfish is also included (thicker line). Note: Y-axes have different scales. ............................................................................................................................ 77 Figure 4.3 Total reconstructed landed catch of artisanal (solid line) and industrial (broken line) fisheries for the Red Sea. ............................................................................................................. 78 Figure 4.4 Catch composition of major artisanal fisheries of the Red Sea.................................. 80  x Figure 4.5 Catch composition of Red Sea industrial fisheries..................................................... 81 Figure 5.1 Estimated total extractions by three fisheries in the Eritrean Red Sea. The broken line is the reported catch and the full line is the total including the unreported catch. Error bars are the 95% confidence intervals. Note that the scales of the Y-axes are different. ......................... 95 Figure 6.1 Flow diagram of the food web of the Red Sea ecosystem. Rectangles represent the biomass of the functional groups. The names of the major fishing groups are colored red. The numbers on the left are trophic levels. ....................................................................................... 115 Figure 6.2 Biomass (left, in t·km-2) and flow pyramids (right, in t·km-2·year-1) for the Red Sea model. ........................................................................................................................................ 116 Figure 6.3 Mixed trophic impact (MTI) of the functional groups in the Red Sea model. The upward dark bars and downward lighter bars show the positive and negative impact, respectively, that a small increase of the biomass of an impacting group (Y-axis) would have on all other groups (X-axis). ........................................................................................................... 118 Figure 6.4 Mixed trophic impact of the fisheries of the Red Sea. The gears in the x axis are the impacted groups, while the colours in each cluster are the impacting group. ........................... 119 Figure 6.5 Times series of observed catch data from the Red Sea (dots) and catch predicted by the fitted Red Sea EwE model (line) from 1950 – 2006 for the functional groups important in fisheries. The model was driven by independently estimated fishing effort data. .................... 120 Figure 6.6 Ecosim simulation test at three scenarios (zero, baseline and effort increasing at 5% per year). The lines are the biomasses of the major fishery groups predicted by the model for 24 year simulations from 2006 – 2030, error bars show 1 SD around the mean. .......................... 123 Figure 7.1 Change in catch rate of shark fishery from interview (a) and catch reconstruction (b).  ................................................................................................................................................... 141  Figure E. 1 Ratios of beach seine (full line), handlining (broken line) and gillnet (line with circles) fisheries in the Eritrean artisanal fishery effort allocation from 1950 – 1991. ............. 235   xi Acknowledgements  My sincere and heartfelt thanks go to my supervisors Drs. Daniel Pauly and Tony J. Pitcher for taking me under their wings before the starting of my PhD study until the end. They first welcomed me to the Fisheries Centre as a visiting student while I was still doing my MSc in the Netherlands and gave me the opportunity to study for PhD, for which I am most grateful as it was always my dream to study with such world renowned scientists. They  inspire me in many ways, and have been instrumental and wonderful in every aspect of my study: giving me the freedom to do my study in the Red Sea - where my passion for marine science started – and challenging me and helping me to broaden my horizons. Their support and guidance both at the Fisheries Centre and during my field work have been plentiful that I could count on. Their care and support were above the call of duty, especially when I was struggling with my health after returning from my field work.  I am thankful for the encouragement and guidance I received from my supervisory committee members Dr. Les Lavkulich, and Dr. Rashid Sumaila. Dr. Jackie Alder contributed significantly in the formation of the structure of the thesis at the beginning of my study, for which I am very thankful. I enjoyed working with my committee, who helped refine the focus of my thesis and held me at the moments I was faltering. My personal thanks go to Dr. Les Lavkulich for helping me navigate the bureaucracy at UBC, and helpful advice over the years  and to Dr. Rashid Sumaila, for making himself available for various discussions, not only on fisheries, which helped me to “keep pushing”.  I am grateful to the comments and constructive criticisms I received from the research groups of the Sea Around Us Project (SAUP) and the Policy and Ecosystem Restoration in Fisheries (PERF); belonging to two research groups comes with some perks. I am also thankful to the faculty, students and staff members of the Fisheries Centre. Thank you Janice Doyle and your family for being the first person to welcome me to Vancouver and your home; and also for the many times I needed help at the Fisheries Centre.   xii My research would not have been possible without the generous financial support from the following sources: the research assistantship I received from the Sea Around Us Project, a scientific collaboration between the University of British Columbia and the Pew Environment Group, was the main support of my study and field work. Thanks also for the contribution from Dr. Tony J. Pitcher’s NSERC fund. I am grateful for the extra funding from Eritrea’s Coastal, Marine & Island Biodiversity Conservation Project (ECMIB) in the Ministry of Fisheries, The State of Eritrea, during my field trip in Eritrea.  I am tremendously indebted to the hundreds of fishers in the Red Sea who let me interview them during my field trip. They were generous with their time, knowledge and hospitality. They gave the human touch to my work, something I will always treasure. My thanks extend to the fisheries administrations and their personnel in Egypt, Sudan, Eritrea and Yemen, which facilitated my field work. They also allowed me to access their fishery databases and reports in which they have invested a lot of resources. The College of Marine Sciences and Technology (COMSAT) of Eritrea hosted me during my field trip. My field research assistants: Aron, Ahmed, Yonathan and Bokretsion (driver) in Eritrea, Khalid and Mohammed in Sudan, and Hesham and Fahad in Yemen deserve special thanks. They adopted the interview procedure quickly and made the experience memorable.  There are a few people I would like to mention and thank for the insightful discussions I had with them and for their help: Pat Kavanagh and Dr. Jackie Alder for their help in Rapfish analysis; the constructive discussions I had with Kerrie O’Donnell on the use of fishers’ knowledge; Sally Taylor who helped me dig into old materials for my catch reconstruction including the ‘library morgue’; Dr. Dirk Zeller for his insights on catch reconstructions; Cameron Ainsworth for his help in the analysis of unreported catch; Chiara Piroddi, Divya Varkey, Rajeev Kumar, Lingbo Li and Dr. Villy Christensen for their help with my ecosystem modelling.  I am also grateful to my family for their unwavering support and standing behind me throughout the study especially my parents Tesfamichael and Tabetu. My sister Luchia and her lovely kids have been my continuous family companions over the phone and my visits to them, thanks for  xiii making me feel warm when I needed it the most. Thanks are also due to my sister Mihret and her family for hosting me warmly during the two trips I made to Sudan. My brother Marikos, who came to Vancouver in July 2011, has been a new breath of family; thanks for your support. I appreciate the support of my other siblings: Biniam (and his family), Bereket, Simon and Wintana. My landlords Dr. and Mrs. North have been my second family in Canada. I have been lucky to have them for nice chats after a long day in front of the computer and for the occasional kick in the back through their subtle humor. I also would like to thank Eny Buchary, my former house mate, for lively discussions over the years.  I have a lot of friends who helped me in many ways: through discussions both related to my research and other bigger issues, social events, playing and watching football (i.e., soccer), hiking the beautiful mountains of British Columbia and a lot of other activities. The list is too long  to mention them all, but I cannot pass without mentioning the following: Ahmed, Ben, Binega, Dawit, Cristina, Edu, Ementu, Eskiel, Eunyoung (Julie), Georgeo, Haben, Jean, Miho, Mike, Misa, Pablo and Chiara, Sami, Simret, Steve, Suzanne, Yemane, Zaid, and a lot more. Thanks for your friendship.  xiv Dedication         To my loving family       1         CHAPTER  1: Introduction    2 1.1 Motivation and development  When I introduce this thesis, I would like to take you for a short tour of my journey in marine science. My first academic encounter with marine sciences started when I joined the Department of Marine Sciences at the University of Asmara, Eritrea where I did my undergraduate degree. Before that, what I remember is the fascination I had with people wearing unusual gear (astronauts and divers) I used to watch on TV and the fish I played with in seasonal lakes near our house. When I began to study marine sciences, the most fascinating part was the field trips I did to the Red Sea coast collecting samples, preparing herbaria, preserving animals, measuring physical and biological parameters, and just being in the sea. A blow to my fascination happened when I learned that I could not dive because I cannot balance pressure due to some problem on my left ear. I was frustrated and depressed because I was not able to fulfil my desire to dive.  When I finished my undergraduate studies, I convinced myself to pay more attention to those areas of marine science which do not require diving for further study and research. The first choice was fishery science. My decision to focus on fishery was not only a reaction to the deflating of my diving fantasy, but also because I enjoyed mathematics, which is a big part of fisheries science and it was my favourite subject in school. So, for my master’s degree (MSc) I studied fisheries at Wageningen University, in the Netherlands.  While doing my MSc in the Netherlands, I visited the Fisheries Centre, University of British Columbia and was offered the opportunity to study for PhD. This was a dream come true, because I always wanted to study in some of the best schools in fisheries, of which the Fisheries Centre is one, if not the best. I started taking courses and attending seminars, and soon I started to feel overwhelmed. The kind of research discussed and the amount of data needed seemed something I could not find for the Red Sea. An example of a shocking experience I had is when I volunteered for one weekend in one of the salmon research projects in British Columbia. I joined because I missed going to the ocean on field trips and also wanted to see and learn the local research activities. Although I enjoyed the trip, it shocked me, because microchips that cost more than 200 USD a piece were surgically inserted to salmon fingerlings to estimate their  3 mortality when they migrate down the river to the ocean. I quickly thought how impossible it would be for me to do similar research in the Red Sea simply because it costs a lot of money. I started to hear more about salmon, which I came across in many of the textbooks I used in previous studies, but I never had direct experience of salmon. People talked passionately and romantically about the fascinating life-history of salmon, their migration and jumping over obstacles in streams. However, none of that made a lot of sense to me growing up in a dry area – similar to the many Yemenites in the novel “Salmon fishing in the Yemen” (Torday, 2008). I was familiar with fisheries exploiting groupers, snappers, sharks, emperors and other coral reef fishes of the tropics.  In terms of using fisheries science in sustainable management of fisheries, an even bigger shock came when I started to learn details about the collapse of many fish stocks in Canada, especially the Newfoundland cod. I was puzzled how this could happen in a country with some of the best fishery scientists in the world and how their knowledge was not translated to stop the collapse from happening. This challenged my ambition of ‘saving’ the fisheries of the Red Sea and helping the poor fishing communities by learning the best science available, and later applying it.  So while sitting in class or thinking about my research, there were moments I felt lost: not necessarily a bad position to be when starting one’s research project. When I tried to imagine implementing in the Red Sea what I was learning in classes, I would not go too far. I had many discussions with my supervisors, who were very helpful throughout my study, and finally I decided to do the best I could in learning the different research tools at the Fisheries Centre and apply them to the Red Sea. Thus, I set out to do my research in ecosystem modelling of the Red Sea ecosystem, the new cutting edge tool in fisheries science, which was originally developed for a coral reef ecosystem (Polovina, 1984), the same ecosystem I planned to study. I also wanted to apply other assessment tools to the Red Sea fisheries.   4 1.2 The rational for fisheries assessment  Because of the vastness of oceans and seas, they were thought, for a long time, to harbor inexhaustible fish and other resources (Costanza, 1999), and that any waste material could  be disposed into them without any problem (Sankovitch, 1994). Time and research have proven that both of these ideas were wrong. We have witnessed the collapse or decline of fishery resources globally and pollution threatens many ecosystems. The collapse of Peruvian anchoveta (Engraulis ringens) (Boerema and Gulland, 1973); Northern cod (Gadus morhua) off the coast of Newfoundland, Canada (Myers et al., 1996); the proportion of large predators declining in the catch (Pauly et al., 1998; Myers and Worm, 2003); the dramatic decline of catches from Southeast Asia (Silvestre and Pauly, 1997; Christensen, 1998) and Western Africa (Kaczynski and Fluharty, 2002) are few examples of the common stories of fisheries almost everywhere. The global catch from marine ecosystems has reached or is beyond maximum biological sustainable limit and cannot be increased further by increasing effort (Watson and Pauly, 2001; FAO, 2005). However, the fishing pressure continues to increase well  beyond sustainable levels, notably  because of the economic incentives given to fishers in the form of subsidies, without which their activities would not be economically feasible (Sumaila et al., 2010). Thus, a proper assessment of the status of the resources and the level of fishing pressure is a critical starting point to manage the marine resources.  Aquaculture is erroneously perceived to be able to solve some of the problems posed by declining fishery catch, by meeting the increasing demand for seafood. However, except for the planktivore or omnivore fish used by small-scale farms, aquaculture aggravates the problem of fisheries decline as the feed for the most lucrative (and carnivorous) farmed fishes comes from marine ecosystems (Pauly et al., 2002). Discarding of unwanted by-catch is another serious issue in fisheries. Based on data from the late 1980s, global discards were estimated to be 17.9 to 39.5 million tonnes per year, while the (retained) global marine catch given by FAO was around 85 million tonnes in the mid-1990s (Alverson et al., 1994; Zeller and Pauly, 2005). The geographic distribution of illegal, unreported and unregulated (IUU) fisheries are global and because in many cases the benefits of IUU activities exceed the cost of being apprehended, penalties have not been effective deterrent tools (Sumaila et al., 2006). The estimated discarded  5 catch decreased in later years, however, as did the total catch (Zeller and Pauly, 2005). In addition to discards, due attention should also be given to illegal and unregulated fishing (Pitcher et al., 2002).  For centuries, tropical waters were fished by small-scale artisanal fisheries that were more or less in harmony with their environment simply because they did not have the capacity to deplete the resources. They used small, non-motorized crafts, usually sporting sails. Since the colonization of many of the tropical countries, motorization and introduction of bigger fishing vessels became common everywhere, without any adequate monitoring and management programs. This resulted in the destruction of ecosystems and was a threat to the livelihood of small-scale fishers. For example, the productive Gulf of Thailand was fished predominantly by small scale artisanal fisheries until the early 1960s, when trawlers were introduced (Silvestre and Pauly, 1997). Soon after, the catch per unit of effort of the trawlers decreased by an order of magnitude, and the catch composition was greatly altered, toward smaller fishes and invertebrates, notably cephalopods (Christensen, 1998). Nets with very small mesh sizes that are destructive to the ecosystem were used. By 1973, the Gulf of Thailand was considered over- fished (Boonyubol and Pramokechutima, 1984) and in 1980, it was severely depleted (Christensen, 1998). Similar developments occurred in Taiwan and the Saharan Banks off West Africa (Balguerías et al., 2000; Lu, 2002). Fisheries in developing countries are very important globally. They contribute a large proportion to the world catch (Chuenpagdee et al., 2006). In addition, the fact that they employ so many people gives them more social weight (Pauly, 2006).  The decline of fisheries is often attributed to a combination of factors including variation in environmental conditions, the stochastic nature of fish stocks and other factors over which we do not have much control. Human exploitation of the oceans, however, nowadays is the most significant factor in the decline and we can do something about it. Human effects have directly (e.g., fishing) and indirectly (e.g., greenhouse gas emission leading to ocean warming and acidification) affected fishery resources (Cheung et al., 2011). As fisheries or ecosystem services of oceans are not infinite, care should be taken on how to use the resources. This calls for proper management. The general objective of fisheries management is sustainable use of the resources so that future generations will have as fair a chance of using them as the present  6 generation. Fisheries managers need information to know the status of the resource and to monitor the effectiveness of the management strategies. Of course, in the implementation of the management policies, enforcement is key.  Fisheries management and research have focused for many years on the species that are economically important; and estimated their potential and status to decide on the total catch allowed to be fished. The concept of Maximum Sustainable Yield (MSY) has been guiding the management of many failed  fisheries, and hence its demise has often been proclaimed (Larkin, 1977), though it keeps inspiring legislation, especially at the international level. A valid point of criticism, however, is that MSY is difficult to apply in an ecosystem context. In addition to the targeted organisms, fishing affects all the organisms which are directly or indirectly connected to the targeted species, i.e., the effects of exploiting one species are felt throughout the ecosystem, and this should be taken into consideration (Hall, 1999). Fishery, as natural resource exploitation, is not only a biological issue, but also socio-economic and political; thus raising issues of public policy (Pauly and Maclean, 2003; Pauly and Zeller, 2003). However, in this complicated system, starting with the assessment of the status of the resources and the fisheries will always be a step in the right direction.  1.3 Thesis outline  The overarching objective of the thesis is to assess the Red Sea ecosystem and the status of its fishery resources, which will be explored in the 5 major chapters (not including the introductory and concluding sections) introduced below. I will introduce them in the way they were conceived and developed, rather than in their order in the Table of Contents. The first study I started doing was the ecosystem model of the Red Sea, which is the last Chapter (6) of the thesis in its current format. The objectives of this chapter were first to develop a quantitative description of the ecosystem and the trophic interactions of the organisms, i.e., the flux of energy from one group to another, and second to quantify and evaluate the effect of fisheries on the ecosystem. The model was also to be used to explore different fishing scenarios and if the development of industrial fishery in the Red Sea affects the catch of artisanal fisheries. This has been a cause for some serious conflicts between the two sectors in the Red Sea. Thus, Ecopath  7 with Ecosim (EwE) ecosystem modelling (Christensen et al., 2008) was used to assess the Red Sea in an ecosystem-based framework.  Ecosystem models are very data-hungry, thus I started collecting data. Most of the biological data (e.g., growth and mortality) were acquired from published papers and FishBase (Froese and Pauly, 2012). The first serious practical obstacle was faced when I started looking for fishery catch data from the countries bordering the Red Sea; getting long time series of catches proved to be very difficult. For the Red Sea countries, it was not only a question of whether the fisheries authorities of the country would cooperate or not, but whether such data existed. Using the fishery data the countries reported to the Food and Agricultural Organization (FAO) of the United Nations was considered; however, the reliability of the database was questionable (Pauly and Zeller, 2003). At the same time, the Sea Around Us Project, based at the Fisheries Centre, University of British Columbia, was embarking on a project to improve the global fishery catch by ‘reconstructing’ the catches of each of the world’s maritime countries. So reconstructing the Red Sea fishery catch was a logical step to do.  I started familiarizing myself by reviewing the fisheries of the Red Sea countries and evaluating them using a rapid appraisal method called ‘Rapfish’, Chapter 2 of the thesis details this approach. Rapfish, which stands for ‘Rapid Appraisal of Fisheries’, is a multidisciplinary technique which evaluates the sustainability status of fisheries based on transparent and semi- quantitative scoring of sets of ecological, economic, social, technological and ethical attributes. It uses a non-parametric statistical ordination technique (multidimensional scaling, MDS) to rank the relative sustainability of fisheries in each field. Thus, the main objective of this chapter is to conduct a comprehensive review of the Red Sea fisheries and evaluate their sustainability.  Once the fisheries were reviewed, searching and collecting materials for data to reconstruct the Red Sea fisheries started. The search started first at the library and borrowing materials through interlibrary loan system of the University of British Columbia from libraries and data repositories in the world. When those sources were exhausted and there were still many gaps, a field trip was planned to the Red Sea to search data sources from local organizations. One source of information to explore during the field trip was the knowledge accumulated in the  8 fishers and local communities who have been depending on the Red Sea resources for their livelihoods for centuries. This became a new chapter for the thesis.  Chapter 3 deals with the use of interviews to capture fishers’ knowledge and their perception about the resources and the changes over time. The main objective of this chapter is to quantify the patterns in the fisheries over a long period by interviewing different age groups of fishers, community elders and fishery administrators. The data from the interviews were used to analyze relative changes in catch rates over a long period, and to fill in data gaps, such as unreported catch. This can be done in two ways: first by asking fishers direct quantitative questions about some parts of the catch that never get reported, e.g., the amount of fish consumed by the crew, and given to family and friends, or  by asking fishers to give qualitative information about periods where catch data was not readily available. Interviews were also used to double-check conflicting data in reports.  With all the possible data sources acquired, the catch reconstruction of the Red Sea fisheries is carried out in Chapter 4. The main objective of this chapter is to reconstruct a  set of comprehensive and standardized catch data for the Red Sea fisheries from 1950 – 2006, the most recent data available during the research. This will help to understand the development of the fisheries over time, identify major shifts in effort and target species, and will form the basis for any subsequent quantitative analysis to be carried out on the fisheries sector. The catch reconstruction was done by taxonomic composition of the catch for each type of fishing gear.  Chapter 5 looks at estimating the unreported catch using qualitative information about events or situations that can potentially influence fishers to misreport. Although their presence is not debated, unreported catches, as the name indicates, are not available in official fishery statistics. However, information, mainly qualitative, is usually available either in reports or from the experts in the field about events that happened in the history of the fisheries that could affect the incentives to misreport catch. The main objective of this chapter is to quantify the unreported catch based on those qualitative clues. This case study was done for Eritrea, my home country, where I had better access to documents and people involved in fisheries. This chapter also demonstrates uncertainty analysis in estimating the unreported catch. All the information from  9 Chapters 2 – 5 are used in the ecosystem based assessment of the Red Sea (Chapter 6). These chapters are written as papers able to be published independent of the other chapters and some of them are already published. Hence, some facts about the Red Sea may be repeated.  1.4 The Red Sea  In the next few pages, the Red Sea ecosystem and the countries bordering the Red Sea are briefly introduced. The Red Sea is an elongated narrow sea between Northeastern Africa and the Arabian Peninsula, ranging from 300N to 12030’N with a total length of 2000 km, and from 320E to 430E with an average width of 208 km (Figure 1.1). The maximum width is 354 km in the southern part (Morcos, 1970). The total area is 4.51 x 105 km2. It is connected with the Indian Ocean in the south through a small strait of Bab al Mandab, meaning door of fortune, which is only 29 km wide. Bal al Mandab has a sill, 137 m below sea level, which limits the circulation of water between the Red Sea and the Gulf of Aden. The Red Sea is also connected to the Mediterranean Sea through the Suez Canal since its opening in 1869. The average depth is 491 m and the maximum recorded is 2850 m. In the north, the Red Sea is divided into the Gulf of Suez and Aqaba. The Gulf of Suez is generally wide, shallow and muddy, while the Gulf of Aqaba is narrow and deep.  Geological evolution  The Red Sea is formed by the divergence of the African and the Arabian plates. It is part of a larger rift system that includes the Dead Sea and the East African rift systems. Geologically it is categorized as a young ocean and is still growing or spreading (Braithwaite, 1987). The zone was already structurally weak during the Pan-African orogeny 600 Ma. The split of the Arabian and African plates is believed to have started in the Tertiary period between the Eocene and Oligocene periods and it accelerated during the late Oligocene with intense magmatic activity and the development of a continental rift (Makris and Rihm, 1991). It was formed as an embayment due to the expansion of the Mediterranean Sea. The Red Sea depression is believed to have been flooded by the Mediterranean as a result of extensive sinking in the early Miocene  10 (Girdler and Southren, 1987). Since the starting of its formation, the Red Sea went through a series of connection and disconnection with the Mediterranean in the North and Indian Ocean in  Figure 1.1 The Red Sea and the bordering countries.   the south. At the end of Miocene, upheaval of land occurred and the Red Sea was disconnected from the Mediterranean to become a separated salty lake. At the beginning of the Pliocene, the Red Sea was reconnected with the Mediterranean and for the first time it was connected with the Indian Ocean. At the end of Pliocene, only the northern connection with the Mediterranean was  11 closed off due to crustal plate movement. Later the connection with the Indian Ocean was closed off during the Pleistocene, the glacial period, when the Red Sea became an isolated sea again. At the end of the glacial period, its connection with the Indian Ocean was re-established, whereas the connection with the Mediterranean remained closed until it was artificially opened via the Suez Canal in 1869 (Goren, 1986; Getahun, 1998). The Red Sea being young and still expanding is used as a case study to understand and explain plate tectonics, mid ocean ridges and formation of oceans.  Origin of biota  The connections of the Red Sea with its neighbouring waters explain the kind of species it was colonized by at different times. Though the Red Sea was first populated by Mediterranean species, its current biota resembles more that of the Indian Ocean. When the Red Sea was disconnected with Mediterranean and for the first time connected with the Indian Ocean in the beginning of the Pliocene period (about 5 – 6 million years ago), it was populated by Indian Ocean fauna. Later during the glacial period of the Pleistocene, the level of the world’s oceans was low. The Red Sea was isolated with high level of salinity (about 50 psu at the surface) and low temperature (about 20C lower than the present) (Thunell et al., 1988). This resulted in the massive extinction of many species. Then later, when it was reconnected with the Indian Ocean at the end of the glacial period, 10 – 12 thousand years ago, it created an opportunity for the Indian Ocean species to re-populate the Red Sea (Goren, 1986).  Physical oceanography  The Red Sea area is generally arid, rainfall is very sparse with annual average ranging from 1 mm – 180 mm (Edwards, 1987). Evaporation, with annual average of 2 m (Morcos, 1970), exceeds precipitation. The deficiency is made up by the flow of water from the Indian Ocean through Bab al Mandab. The water flows over a sill which is 137 m below the sea level. In winter, warmer and less saline water flows into the Red Sea in the surface layer; while cooler and saltier water flows into the Gulf of Aden in the lower layer. In summer, there are three layers of water flow in the strait. In addition to the two flows of winter, warm water flows on the surface from the Red Sea to the Gulf of Aden (Smeed, 2004). Sea and air temperatures are high  12 in the Red Sea with mean annual sea surface temperature of 28oC. Another remarkable characteristics of the Red Sea is its high salinity, about 35 psu on average at the surface; readings as high as 40.5 psu are also reported. The high salinity is due to a combination of its geological history and its location in dry and hot environment. Though originally the Red Sea depression was flooded with Mediterranean water, it soon started to become more saline due to high evaporation. Later during the glacial period, the Red Sea was an isolated salty lake with salinity higher than the present by a value of 10. The highly saline water was diluted by water from Indian Ocean when the Red Sea was reconnected with the Indian Ocean (Thunell et al., 1988). However, it is still more saline than the Indian Ocean water due to high evaporation (Morcos, 1970).  Biological oceanography  The Red Sea is not very productive, mainly due to lack of nutrient-rich terrestrial run off; also, there is no circulation of the nutrient rich deep water to the surface where photosynthesis takes place. The vertical mixing of water is prevented by a permanent thermocline as the temperature of the sub-surface water is always lower than the warm surface temperature. The depth of the thermocline is deeper in winter than summer (Edwards, 1987). Generally, the southern part of the Red Sea is more productive than the northern part due to the flow of nutrient rich water from the Indian Ocean, the main nutrient input, and the re-suspension of nutrients from the bottom sediments by turbulent mixing from its broad and shallow shelf area (Sheppard et al., 1992). The shallow water of the Gulf of Suez is also productive and supports many exploited fish populations.  The high and relatively stable temperature of the Red Sea is favourable for the formation of coral reefs. They are more developed in the northern part starting from the tip of Sinai Peninsula going south parallel to the coast. The longest continuous fringing reef in the Red Sea extends from Gubal (at the mouth of the Gulf of Suez) to Halaib, at the Egyptian border with Sudan (Pilcher and Alsuhaibany, 2000). In the south, more patchy reefs are observed as the turbid water of the shallow shelf does not allow the formation of extensive reefs. Sanganeb Atoll, located in Sudan near the border with Egypt, is the only atoll in the Red Sea. It is unique reef  13 rising from 800 m depth to form an atoll that has been recognized as regionally important conservation area. It was proposed to UNESCO for World Heritage Status in the 1980s (Pilcher and Alsuhaibany, 2000). Coral reefs have a self-sustained nutrient cycle and have high productivity, much like an oasis in a desert. They attract fisheries, mainly small-scale artisanal, and tourists.  The Red Sea has very high diversity, more than 1200 species of fishes are reported (Froese and Pauly, 2011). It is also characterized by high degree of endemism. Some research put the percentage of Red Sea endemic fishes between 10 - 17% (Ormond and Edwards, 1987); its semi-closed nature and unique ecological conditions contribute to this high number. Because the Red Sea has very low nutrient input, species that can survive its environment have very good chance to dominate as there are fewer competitors. One good example is the phytoplankton Trichodesmium erythraeum. It is a blue green alga (cynobacterium) that can overcome nitrate depletion by fixing atmospheric nitrogen dissolved in the water. In calm waters the filaments of the blue green algae float to the sea surface and form a rather reddish scum, probably the origin of the name Red Sea.  On the shores of coastal lagoons and sheltered bays mangroves are common. The most common species is Avicennia marina. Bruguiera gymnorhiza and Ceriops tagal also occur, though they are less common. The shallow waters of the lagoons and bays are home to sea grass beds. About 500 species of algae are reported from the Red Sea. Most algae in the north and central part are macroscopic, non-calcareous, brown, green and red algae. In the south, large brown algae such as Sargassum dominate (Walker, 1987).  Five sea turtle species are reported from the Red Sea: Hawksbill, Green, Oliver Ridley, Loggerhead and Leatherback. Hawksbill and Green turtles are the most common and are reported to nest in the Red Sea (Frazier et al., 1987). There is no active hunting for sea turtles in the Red Sea. However, they are accidentally caught in fishing nets. The rich seagrass beds support dugongs. They are reported from Gulf of Suez in the north and the coast of Sudan and the Dahlak Archipelago in Eritrea (Preen, 1989). The reports of Cetaceans from the Red Sea are sparse. Seven species of dolphins are commonly reported. Occasional spotting of Killer whale  14 and False killer whale are also reported. Frazier et al., (1987) suggested that the narrow strait of Bab al Mandab and the low productivity in the Red Sea as reasons for the low population of cetaceans. As far as seabirds are concerned, the enclosed nature of the Red Sea acts as a barrier for pelagic fishes on which many birds feed. As a result pelagic seabirds, such as shearwaters and petrels, are poorly represented. Because of its elongated shape, the Red Sea has high coast to sea area ratio and its seabird fauna is dominated by coastal species (Evans, 1987). It is also a migratory route for many birds.  Human aspects  According to archeological evidence, human settlement on the Red Sea coast started centuries ago (Horton, 1987) and the Red Sea has the oldest archeological records of human use of marine resources based on the middens of giant clams and others shells (Walter et al., 2000). It was used as an important trade route between the Indian Ocean and the Mediterranean. To date, in contrast with the rest of the world, where most of the population lives in a narrow strip of land along the coast (Edgren, 1993), the population density on the Red Sea coast is still very low, except for very few major ports and cities. This is mainly due to the arid and hot climate and as a result most of the settlements have been farther inland in milder climate, where there are enough fresh water supplies. This has greatly limited the degree of coastal shoreline alteration, pollution and resource abstraction. The local traditional societies depend on harvesting marine resources for subsistence using traditional methods of shell collection and fishing. However, in the last few decades, the wider availability of technology coupled with cheaper oil, at least for the oil producing countries, is changing the demography of the Red Sea coast. The major port cities are metropolitan, with diverse economic activities where trades other than fishing are common. Egypt has a strong recreational and tourism industry, and its coast is quite populated, creating pressure on the coastal ecosystems. Air conditioners and desalination plants are making life easier. A typical example is the Saudi Arabia coast where exciting cities, such as Jeddah, have grown fast and new cities (e.g., Yanbu) are developing. In such cities, reclamation and dredging are becoming common for residential, commercial and industrial purposes. Pollution is prevalent around urban areas and ports. Lack of sewage treatment is a serious problem  15 throughout the Red Sea damaging ecosystems. The major industries along the Red Sea coast are refineries. Overall the impact of human activities is growing (Frihy et al., 1996).  Research expeditions  One of the earliest scientific expeditions to the Red Sea is the Danish Arabia Felix of 1761 – 1767, which spent October 1762 – August 1763 in the Red Sea area. It included the Swedish naturalist Peter Forsskål, a student of Linnaeus, who made an extensive collection of plants and animals, and particularly fish. The report was later published posthumously by Carsten Niebuhr, the sole survivor, in 1775 (Forsskål, 1775). There were many fragmented records of expeditions, most of them unsuccessful, to the Red Sea in the 18th and 19th centuries. One important and outstanding work in describing the Red Sea ecosystem and its organisms is that of Carl Benjamin Klunzinger, a German medical doctor who worked as a quarantine inspector in the Egyptian Red Sea port of Qusier from 1863 – 1869 and 1872 – 1875. His descriptions include coral fauna, fish, Crustacea, hemichordates and meteorological observations (Klunzinger, 1870, 1872), and the culture of the society (Klunzinger, 1878). An Austrian research vessel Pola conducted an expedition in 1895 – 1896 to the northern Red Sea (Luksch, 1898) and 1897 – 1898 to the south (Luksch, 1900). It conducted the first oceanographic studies and sampling the deep sea life up to 2000 m (Head, 1987b). The specimens from the expedition are kept in the Natural History Museum in Vienna (Stagl et al., 1996). The more recent expeditions include the John Murray expedition carried out using the Egyptian research vessel Mabahiss 1933 – 1934 (Tesfamichael, 2005). It collected oceanographic and biological samples throughout the Red Sea and the Arabian Sea. The report is written by Norman (1939) and samples are stored at British Natural History Museum (see Tesfamichael, 2005). From 1959 – 1964 the International Indian Ocean Expedition brought some vessels to sample the Red Sea. The oceanographic data was reviewed and report compiled by Morcos (1970). An Israeli expedition to the southern Red Sea in 1962 and 1965 (Ben-Tuvia, 1968), and the Israeli Marine Biological Station at Eilat which was opened in 1968, also contributed to the knowledge of the Red Sea.    16 Resource use  The Red Sea has multiple uses, the main one being as a route from the Indian Ocean to Europe. As far as resource extraction is concerned, fishery is the main one. Recently, interest in the tourism industry has been increasing. Egypt has a well developed marine tourism industry along its northern coast. Historically, fishing has been an important economic activity for the coastal population. The traditional artisanal fisheries, which account for 70% of the total landing (52,700t/year) (Sheppard, 2000), have been generally in harmony with the ecosystem because of low population; non-destructive traditional fishing technology; and poor communication and infrastructure. However, recently, more advanced and destructive methods are being used. At the present, fishing operations in the Red Sea range from foot fishermen, who fish mainly for their own consumption, to very large trawlers with freezing facilities. The fisheries in the Red Sea are typical tropical fisheries, multi-gear and multi-species. Most of the fishery is done with wooden boats of size range between 5 – 18 meters, locally called ‘Sambuk’ and ‘Houris’. Sambuks are bigger in size and have inboard engines. Houris are smaller and use outboard engines. Both Sambuks and Houris use similar fishing gears. The most commonly used gears are handlining and gillnet. The main difference in the operation of Sambuk and Houri are length of the fishing trip, crew size and capacity.  The total annual potential landing from the Red Sea was estimated to be 360,000 t (Gulland, 1971). Though the Red Sea accounts for 0.12% of the total world ocean area, its contribution to the world catch is only 0.07% (Head, 1987c). Nevertheless, it is significant to the countries in the region. Fishery produces a cheap source of protein and provides livelihood for the communities on the coast. Since the countries on the Red Sea coast are generally less industrialized, fisheries can be a good source of employment.  Of the seven countries that border the Red Sea, Jordan and Israel have too small coastlines to support any major fishery. Of the other countries, Egypt and Yemen have well established fisheries and have been utilizing their resource for a long time. Egyptian and Yemen fishermen also fish in other countries’ waters. Sudan and Eritrea are the countries which utilize their  17 fisheries resources the least. Saudi Arabia has recently established an industrial fishery, in addition to the artisanal fishery that has been active for many years.  1.5 The Red Sea countries  Seven countries border the Red Sea. These are (counter clockwise): Egypt, Sudan, Eritrea, Yemen, Saudi Arabia, Jordan and Israel (Figure 1.1). The access Jordan and Israel have to the Red Sea is through a small strip of coast in the Gulf of Aqaba. Yemen, Egypt, Saudi Arabia and Israel have also coastlines outside of the Red Sea, which posed some problems with their fisheries catch data, particularly in the case of Yemen and Saudi Arabia (see below).  Egypt  Egypt has access to both the Mediterranean Sea and the Red Sea. The catch from the Mediterranean Sea is slightly higher than from the Red Sea. Most of the Egyptian fishery in the Red Sea is in the shallow waters of the Gulf of Suez, which is favorable for purse seining and trawling. The continental shelf area of the Gulf of Suez (8,400 km2) is equivalent to the continental shelf of Egypt in the rest of the Red Sea. Foul Bay, in the south close to the border with Sudan, is also an important fishing ground. Purse seining, which accounts for more than 50% of the total catch, is carried out at night using lighted dinghies to attract fish (Sanders and Morgan, 1989); the main landings from this gear are horse mackerel and scads (Carangidae). The second most important fishery is trawling. It operates from September to May and its catch is dominated by lizardfish (Synodontidae), snappers (Lutjanidae) and threadfin breams (Nemipteridae). The prime target of trawlers is shrimp, which accounts for around 10% of the total catch. Reef associated artisanal fisheries contribute only a little to the total catch. They use handlines, longlines and to lesser extent gillnets and trammel nets. Egyptian fishery is the most industrialized in the Red Sea and the Egyptian coast is the most exploited. The Gulf of Suez is believed to be over-fished (Hariri et al., 2000). The number of motorized boats decreased since the mid of 1990s, but the total power more than doubled in order to fish in more distant areas (PERSGA, 2004).    18 Sudan  The main fishery along the Sudanese coast is handlining, representing 80% of the total catch (Hariri et al., 2000). The most productive areas are the inner edges of the offshore coral reefs which are 5 – 10 km from the shore. The species dominant in the catch are groupers (Serranidae), emperors (Lethrinidae) and snappers (Lutjanidae). Pelagic species including Spanish mackerel, barracuda, trevallies and jacks are caught by trolling to and from the fishing grounds (Kedidi, 1984). Small boats are used closer to the shore and the larger motorized boats are used further offshore. Gillnet is used in areas very close to the landing sites. The catch tripled from 1975 – 1984, but started to decrease steadily because projects helping the artisanal fishery phased out, production cost increased and credits given from the Agricultural Bank of Sudan were too expensive (PERSGA, 2004). Industrial fishery is under-developed in Sudan (Hariri et al., 2000). A few trawlers operate in Sudanese water off the Tokar delta, in the south, for shrimp. There is also purse seine fishery in the north, mainly in Foul Bay. An important fishery for trochus shell (Trochus dentatus) and black mother-of-pearl shell (Pinctata margaritifera) exists in Sudan. The main fishing ground is Danganab Bay. Shells are collected by free diving.  Eritrea  The Eritrean fishery was at its peak in the 1950s and 1960s and was dominated by beach seine targeting small pelagic species, mainly sardine (Herklotsichthys quadrimaculatus) and anchovies (Encrasicholina heteroloba and Thryssa baelama) (Grofit, 1971). They were converted to fish meal to be sold in Europe and sun dried for human consumption markets in Asia. Off-shore trawlers fishing for lizard fish and threadfin bream and inshore trawlers for shrimp were also active. The industry was rendered close to non-existent by war in the 1970s and 1980s, leaving only the reef-based fishery. After the war had stopped in 1991, the fishery was restructured and the catches started to increase steadily. For the first few years, it was almost exclusively dominated by artisanal fisheries which operate around coral reefs using handlining, and gillnets. Later, larger commercial trawlers, chartered from other countries  19 (mainly Egypt), were introduced to target shrimp and fish. A local industrial fishery using longline, which targets coral reef fishes, and pelagic species near coral reefs, is also present.  Yemen  Fishery catches in the Gulf of Aden are higher than catches from the Red Sea for Yemen. However, of the countries in the Red Sea, Yemen has the largest catch, which can be attributed to the productive waters of the southern Red Sea and the large size of the fishing industry (Hariri et al., 2000). While the catch from Gulf of Aden is decreasing, that of the Red Sea is increasing. The Red Sea fishery is dominated by artisanal fisheries ranging from small non- mechanized to relatively larger (10 – 15 m) boats with inboard engines. More than 90% of the total landing is by artisanal fisheries (PERSGA, 2004). Some of the boats are used to trawl for shrimp, mainly Penaeus semisulcatus. The gear most used for fish are drift net and handline (Hariri et al., 2000). Indian mackerel, king fish, jacks, emperor, barracuda and shark are dominant in the catch. Extensive subsidies made fishery very profitable and allowed dramatic expansion even to the waters of neighboring countries (Sheppard, 2000). The industrial trawlers in Yemen target demersal fishes, mainly shrimp; there are foreign joint venture companies involved (Hariri et al., 2000).  Saudi Arabia  Saudi Arabia has access to both the Red Sea and the Persian Gulf. More than 50% of its marine catch comes from the Red Sea. There is more potential in the Red Sea than the Persian Gulf, which is fished intensively. Saudi Arabia has a high population density on the coast compared to other Red Sea countries; as a result there are many fishing villages. Fish landings per unit of area increases from north to south (PERSGA, 2004). Artisanal fishery was the only fishery operating in the Red Sea coast of Saudi Arabia until 1981, when trawlers were introduced to fish shrimp. Since then, the catch of trawlers has increased significantly. At the same time, the artisanal gillnet fishery expanded dramatically, with the introduction of fiber glass vessels accounting for the major share of the landing (Sanders and Morgan, 1989). The artisanal catch is almost equally divided between pelagic and benthic species associated with coral reefs; in the  20 northern part it is dominated by mackerels and jacks whereas in the south it is mullets (Mullidae), groupers (Serranidae) and snappers (Lutjanidae). The inner passages around Frasan Bank and Gizan are the main trawling grounds (Hariri et al., 2000).  Jordan and Israel  Jordan and Israel have very small coast in the Gulf of Aqaba, and neither country has a major fishery along its Red Sea coast. Israel used to fish in the southern Red Sea off the coast of Eritrea, mainly in the 1950s and 1960s (Ben-Yami, 1964; Grofit, 1971). Israel has access to the Mediterranean Sea, while Jordan’s only marine access is to the Red Sea.   21         CHAPTER  2: Multidisciplinary assessment of the sustainability of Red Sea fisheries using Rapfish    22 2.1 Synopsis  A multidisciplinary comparative evaluation of the “health” or sustainability status of 26 major Red Sea fisheries from 5 countries was performed using 44 scored attributes in ecological, economic, social, technological and ethical fields. A multidimensional scaling (MDS) technique (“Rapfish”) was employed to visualize the status of the fisheries for each evaluation field. Comparisons were made among the countries bordering the Red Sea coast, between artisanal and industrial fisheries, and between west and east coast fisheries. Monte Carlo sampling simulation was used to analyze uncertainty. Leverage analysis examined the sensitivity of status results to each attribute in the five evaluation fields. Lack of reliable fisheries stock assessment data is not unusual in many tropical countries; however, this chapter demonstrates that the approximate relative status of fisheries can be obtained using attributes which are relatively easy to score in a transparent fashion with defined uncertainty.    23 2.2 Introduction  There is little published information on the status of fisheries in the Red Sea, a sea almost enclosed at both ends with little water exchange with neighbouring water bodies. This chapter employs a transparent semi-quantitative multi-disciplinary evaluation method (Pitcher and Preikshot, 2001), in order to provide a preliminary assessment of the sustainability status of the major fisheries in the Red Sea.  Seven countries border the Red Sea, namely Egypt, Sudan, Eritrea, Yemen, Saudi Arabia, Jordan and Israel (Figure 1.1). The Red Sea coasts of Jordan and Israel are too small to support any major fisheries, and they are not considered here. Of the other five countries, Egypt and Yemen have long-established domestic Red Sea fisheries, and they both fish in other countries’ waters. Saudi Arabia has recently established an industrial fishery and an artisanal fishery has been operating for many years. Sudan and Eritrea are the countries which utilize their Red Sea fisheries resources the least.  The Red Sea has low productivity on account of its situation in an arid region with no major river inflows. In addition, the presence of a permanent thermocline inhibits benthic nutrients from circulating to the surface where most primary production occurs (Edwards, 1987). The main nutrient input is from the Indian ocean through the southern part of the Red Sea (Sheppard et al., 1992). However, the coral reefs in the Red Sea support an array of organisms. Though the Red Sea accounts for 0.123% of the total world ocean area, its contribution to the world fish catch is only 0.07% (Head, 1987c). Nevertheless, it has fish resources that are significant to the countries in the region, providing a good source of protein and livelihood for the communities on the coast. Since Red Sea countries are generally less industrialized, fisheries can also provide useful employment.  Sanders and Morgan (1989) reviewed Red Sea fisheries and described the resources and stock assessment results for some of the commercially important fish. Head (1987c) gives a brief description of the Red Sea fisheries. Fishing operations in the Red Sea range from foot fishers, without a boat, who fish in the shallow coastal waters mainly for their own consumption, to very  24 large trawlers with freezing facilities. Recently Saudi Arabia has the most advanced fleets and covers a wider fishing area in the Red Sea. The fishery in the Red Sea is a typical tropical fishery, multi gear and multi species, which is done using wooden boats between 5 – 18 meters in size. The most commonly-used gears are handlines and gillnets, operated from large “Sambuks”, which have inboard engines, and small “Houris”, which use outboard engines. The main differences in the operation of Sambuk and Houri are the length of the fishing trip, crew size and capacity. Fishers tend to concentrate their effort in the coral reef areas, especially artisanal fishers as they do not have powerful vessels to go far from the shore. There are some reports of conflict between the artisanal and industrial fisheries and most of the countries have rules which prohibit industrial vessels from fishing close to the shore. Nevertheless, because of lack of enforcement, they are frequently reported operating in the shallow inshore waters.  Red Sea fisheries are data poor, so conventional stock assessment may only be performed for a minority of species. Moreover, biological assessment alone is not adequate for proactive fishery management and the multidisciplinary nature of fisheries demands a multidisciplinary approach in management and policy making (Salz and De Wilde, 1996). “Rapfish”, which stands for Rapid Appraisal of Fisheries, is a novel multidisciplinary technique which evaluates the sustainability status of fisheries based on the transparent and semi-quantitative scoring of sets of ecological, economic, social, technological and ethical attributes. The general definition of sustainability is based on the Oxford English Dictionary “Capable of being maintained at a certain rate or level ... for a long time or indefinitely”. Scores in each evaluation field of “Rapfish” are therefore related to the sustainability of the exploited fish populations and their ecosystem. Each fishery is scored for the standardized attributes in each of the five fields. All the fields have 9 attributes each, except ethical that has 8 (Table A.1). Then the scores are converted into relative ranks of the fisheries in two dimensional graphs using the statistical method multidimensional scaling (MDS). MDS uses a non-parametric ordination technique to calculate, from multiple scores of attributes, to provide values that indicate the relative sustainability of fisheries in relation to some fixed extremes in one axis. The technique of Rapfish is thoroughly described by Pitcher (1999) and Pitcher and Preikshot (2001), and its statistical basis by Alder et al., (2000) and Kavanagh and Pitcher (2004). Rapfish does not require quantities of biomass or effort data, which is usually expensive and difficult to obtain in  25 countries with limited resources for fisheries research, but instead relies on easily-obtained indicators or expert opinion with defined uncertainties in scores. Rapfish provides a rapid assessment as to the “heath” or sustainability status of fisheries separately for each of the five evaluation fields. Results can also suggest where to emphasise future research and the wise use of limited resources. The works of Preikshot et al., (1998), Preikshot and Pauly (1998), Pauly and Chuenpagdee (2003) and Baeta et al., (2005) for tropical and small-scale fisheries are good examples. However, Rapfish is not intended to replace conventional stock assessment procedures used to formulate management tools like quotas (Pitcher, 1999). In this chapter the status of 26 major fisheries from 5 countries in the Red Sea are evaluated using Rapfish.  2.3  Materials and methods  2.3.1 Sources of information  Based on the information available during this research, 26 major fisheries in the Red Sea were identified for Rapfish analysis. Since there was limited information these 26 fisheries cannot be taken, by any means, to be exhaustive. Table (2.1) lists the Red Sea fisheries included in this research, the code given to them for graphical purposes and whether they belong to artisanal or industrial sector. Because of their migratory behaviour and since information available during this research for shark fishery was for the whole Red Sea, the shark fishery is for all the Red Sea countries together. I used five evaluation fields, namely ecological, economic, technological, social and ethical, comprising a total of 44 attributes to evaluate the sustainability of the fisheries. The list of attributes, their definitions, scoring ranges in their respective evaluation fields are given in the Appendix (Table A.1). In order to be able to compare results from different Rapfish analysis of different fisheries, it is recommended to use the same attributes.  An exhaustive search of published papers, reports and fishery statistics literature supported the scoring of the attributes. The Regional Organization for the Conservation of the Environment of the Red Sea and Gulf of Aden (PERSGA) provided information for all the countries in the region (Hariri et al., 2000). Saudi Arabian fisheries were scored using official annual fisheries  26 reports (MFD, 1997; MAW, 2008) while Habteselassie and Habte (2000) was useful for Eritrean fisheries.  Table 2.1 Red Sea fisheries analysed using Rapfish, their numbers in the MDS ordination graphs and categories. Fishery     Number for graphing  Category  Egyptian purse seine     1   Industrial Egyptian trawling    2   Industrial Egyptian reef associated fishery   3   Artisanal Sudan artisanal, fin fish    4   Artisanal Sudan artisanal, shell fish   5   Artisanal Sudan industrial    6   Industrial Yemen Houri     7   Artisanal Yemen Sambuk     8   Artisanal Yemen trawlers     9   Industrial Yemen shrimp     10   Industrial Eritrea-Houri hook and line   11   Artisanal Eritrea-Houri gillnet    12   Artisanal Eritrea-Sambuk Hook and line   13   Artisanal Eritrea-Sambuk Gillnet    14   Artisanal Eritrea-diving     15   Industrial Eritrea-longline     16   Industrial Eritrea-trawlers     17   Industrial Eritrea-shrimp     18   Industrial Saudi Arabia-handline    19   Artisanal Saudi Arabia-gillnet    20   Artisanal Saudi Arabia-trolling    21   Artisanal Saudi Arabia-trap    22   Artisanal Saudi Arabia-trawlers    23   Industrial Saudi Arabia-purse seine   24   Industrial Saudi Arabia-shrimp    25   Industrial Shark fishery (whole Red Sea)   26   Artisanal   27 Some information for ecological attributes were collected from FishBase (Froese and Pauly, 2012), and some socio-economic information was obtained from the CIA world fact book (CIA, 2004). Personal contacts with fishery experts from the region and my own observation and experience in the Red Sea provided additional sources. Since information was not available for all the fisheries about the attribute “equity in entry to fishery” of the ethical analysis, the mid score was given to all the fisheries so that it would not have an influence on the distance matrix and the final relative status results.  2.3.2 Rapfish analysis  In order to have fixed reference points with which the fisheries can be compared, Rapfish includes a “good” or “perfect” fishery (defined as 100% sustainability score), consisting of the best possible scores for all the attributes in the respective evaluation fields, and a “bad” or “worst” fishery (defined as 0% sustainability score), which has the worst scores. In addition, two “half-way” scores, which are mirror images of each other to scale the vertical dimension, and a set of pre-defined anchor points in order to avoid vertical “flipping” of the MDS ordinates are included. A more detailed account of the reference and anchor points is given in Kavanagh and Pitcher (2001).  Scores were normalised to Z-values so that all have equal weight in the distance matrix, Euclidean distance squared was used as a measure of distance. The scores, including the reference fisheries, in each evaluation fields were analysed with MDS using the well-known ALSCAL method (Kavanagh and Pitcher, 2004). By convention, the MDS output was rotated so that the “good” to “bad” reference vector is horizontal and scaled between zero and 100%. Since the ALSCAL iteration is an optimisation procedure, ordination errors are indicated by a “stress” value greater than zero: stress values more than 0.25 are considered unreliable (Clarke and Warwick, 1997), but none of the analyses used here exceeded that value. Scoring uncertainty was expressed for each evaluation field using Monte Carlo sampling from a triangular distribution with maximum and minimum values for each score. A 100 simulation runs were made and the median and the 50% inter-quartile range of the scatter were obtained (Alder et al., 2000). The paper (Tesfamichael and Pitcher, 2006) published based on this chapter is the first to  28 apply the uncertainty analysis that has been recommended in previous Rapfish applications (Pitcher and Preikshot, 2001).  In order to determine which attributes have proportionally larger influence on the results, each attribute was sequentially dropped from the MDS analysis for each evaluation field (jack- knifing), providing a value for the percentage influence of each attribute on the overall ordination “leverage” (see Pitcher and Preikshot, 2001). Further analysis was carried out by combining the fishery scores to enable overall comparisons among countries using a kite diagram (Pitcher and Preikshot, 2001). In addition, results were pooled to enable comparison between fisheries on the west and east coasts, and between artisanal and industrial fisheries of the Red Sea.  2.4 Results  The two-dimensional plots show the sustainability status of the fisheries from the MDS ordinations (Figure 2.1). The fisheries are distributed on the X-axis according to their sustainability in the specified evaluation field. The vertical distribution of the fisheries on the Y- axis shows that different combinations of scores can result in similar sustainability values in the ordination. It expresses differences not related to sustainability (Pitcher and Preikshot, 2001). Kruskal’s stress formula 1 and squared correlation (RSQ) provide diagnostic and goodness-of- fit statistics for the MDS (Table 2.2).   The ecological and technological ordinates for the fisheries have a wider distribution on the X axis than the other fields (Figure 2.1), while economic results are clumped and the social and ethical ordinations have a main clump with a few outliers. In all the ordinations, except ecological and technological, there is a general trend for most fisheries to lie in the left half of the sustainability axis, lower than 50%. Most ecological ordinates on the other hand are shifted to the right, the average always higher than any other field. This effect has been observed in most previous Rapfish ecological analyses, (e.g., Pitcher and Preikshot, 2001; Baeta et al., 2005) and, before the sources of this upward shift are investigated in more detail, I have adopted the  29 convention of adjusting the overall mean of ecological field results to 50%. The averages for all other fields are generally close to 50%.  In the ecological evaluation field, the top quartile fisheries for sustainability are Sudanese artisanal fin fish (fishery #4) and Eritrean artisanal fisheries (11, 12 and 13); whereas in the social ordination the top quartile is made up of only industrial fisheries (10, 1, 9, 2, 6, 24 and 16). The best fisheries in the economic field are the Eritrean diving fishery (15) and shark fishery (26). Technologically the most sustainable fisheries are the Eritrean diving (15), Saudi Arabian trap (22) and Saudi Arabian gillnet (20) fisheries. In the ethical ordination, most of the fisheries are clumped about mid way and there are many overlaps; industrial fisheries lie to the left, i.e., are evaluated as less sustainable (e.g. 6, 17 and 2).  Table 2.2 Kruskal’s stress and RSQ for the different evaluation fields.  Evaluation fields        Kruskal’s stress*   RSQ  Ecological    0.20    0.91 Economic    0.17    0.92 Social     0.19    0.88 Technological    0.18    0.87 Ethical     0.20    0.91 * Kruskal’s stress value less than 0.25 indicates a good fit.     30   Figure 2.1 Two dimensional Rapfish plots of the MDS ordination of the Red Sea fisheries. The numbers represent the fisheries as given in Table 2.1 (figures continue next pages).  Ecological 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 25 24 23 22 21 20 19 26 GoodBad 0 100 Egypt Sudan Yemen Eritrea Saudi Shark Economic 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Bad Good 0 100  31     Social 3 2 1 6 54 109 8 7 18 17 16 15 14 13 12 11 25 24 23 2221 20 19 26 GoodBad 0 100 Technological 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1516 17 18 19 20 21 22 23 24 25 26 Bad Good 0 100 Ethical 3 2 1 6 5 4 10 9 8 7 18 17 16 15 14 13 12 11 25 2423 19-22 26 GoodBad 0 100  32 The 50% inter-quartile range (IQ) is chosen to display uncertainty rather than the 95% confidence interval on the median, because the error bars for the latter were very small. The IQ error bars on the median positions for each fishery are quite narrow in all fields. An example is given in Figure (2.2) for the ecological field; IQ error bars for the other fields are not presented as they are similarly narrow. A 100 random runs were found sufficient to stabilize the error variance.  Figure 2.2 The inter-quartile (IQ) ranges, or 50% of the scatter, of the Red Sea fisheries in the ecological field.  The leverage results indicate how much each attribute influences the estimated ordination status of the fisheries (Table 2.3). The values given are the mean standard error of the shift on the X- axis when that specific attribute is dropped. All of the attributes have leverage less than 10% and are relatively similar; this is interpreted as meaning that no single attribute dominates the analysis, all are of roughly equal importance, and there are no candidates to be dropped on statistical grounds. Relatively, the technological field has the widest range of leverage values; “selective gear” and “trip length” are the highest and the lowest, respectively. 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 109 8 7 6 5 4 3 2 1 GoodBad 0 100  33 Table 2.3 Leverage of attributes, given by mean standard error (SE), in their respective evaluation field. Ecological Economic Social Technological Ethical Attribute SE Attribute SE Attribute SE Attribute SE Attribute SE Change in  Trophic level 3.41 Marketable right 5.82 Fishing sector 4.93 Selective gear 6.07 Just management  4.56 Recruitment variability 3.39 Ownership/transfer 5.76 Conflict status 4.74 Pre-sale processing 5.43 Mitigation of habitat destruction 3.60 Migratory range 3.38 Other income 5.70 Education level 4.54 On-board handling 4.79 Equity in entry 3.51 Size of fish caught 3.36 Sector employment 4.86 Fisher influence 4.34 Vessel size 4.74 Alternatives 3.35 Catch < maturity 3.32 Subsidy 4.71 Environmental knowledge 4.14 Fish attraction devices (FADS) 4.67 Mitigation of  ecosystem depletion 3.28 Species caught 3.13 Market 4.05 Fishing income 3.66 Catching power 4.43 Illegal fishing 2.98 Range Collapse 3.04 GDP/person 3.89 New entrants into the fishery 3.10 Landing sites 2.85 Adjacency & reliance 2.30 Discarded bycatch 2.49 Limited entry 3.68 Kin participation 2.44 Gear side effects 2.63 Discards & wastes 1.52 Exploitation status 1.83 Average wage 3.58 Socialization of  fishing 2.20 Trip length 1.84    34  All countries have similar values ethically and economically (Figure 2.3), and in the technological field all countries have similar values except Egypt, which has lower sustainability. The main differences are in the ecological and social fields. Eritrea and Sudan have the best scores ecologically. In the social field, Egypt has the best score, then Yemen followed by Sudan, Eritrea and Saudi Arabia. The west and east coast of the Red Sea scored the same in all fields, except ecological where west coast fisheries scored better than the east (Figure 2.4a). The economic sustainability evaluation of industrial and artisanal fisheries is similar, but the artisanal sector does better in ecological, technological, and ethical fields. Surprisingly, the industrial sector rated higher in the social evaluation (Figure 2.4b).   Figure 2.3 Kite representation of the evaluation of Red Sea fisheries grouped by countries.    	   	 
 	  	       	   	   35   Figure 2.4 Comparison of the different aspects of Red Sea fisheries. A) West and east coast fisheries. B) Industrial and artisanal fisheries.  2.1 Discussion  The Rapfish evaluations suggest that the sustainability of fishing activities is quite similar in all the countries bordering the Red Sea. Generally, the largest difference is observed between artisanal and industrial fisheries (Figure 2.4b). The artisanal fisheries are ranked better than industrial in ecological, ethical and technological evaluation fields, while it is the reverse in the social field and they have similar economic status. The pressure on the ecosystem from artisanal fisheries is not very high because they are mainly for subsistence and their number is not very big as the coast is less populated. Technologically, these Red Sea artisanal fisheries use relatively more selective and passive gears, while the industrial fisheries use active, non- selective gears, which are more destructive to the ecosystem. Industrial fisheries also use more sophisticated technology and have bigger boats with better handling capacity, which adds up to higher pressure on the ecosystem. In the Red Sea, most small-scale fisheries do not have well developed technology at their disposal, especially for handling the catch. Sometimes a considerable amount of the catch perishes due to lack of ice and freezing facilities (Sanders and Morgan, 1989). This hinders them from taking large amounts of fish from the ecosystem. In countries like Sudan, ice is available only in the major fish landing harbours, and most fishers far from these locations are mainly involved in shell collection.  A 0 100 ecological economic socialtechnological ethical West East B 0 100 ecological economic socialtechnological ethical Industrial Artisanal 36  Economically, there is the general perception that industrial fisheries are more efficient and profitable than small-scale artisanal. However, according to the Rapfish analysis, which looks into factors that affect the long term economic sustainability of the fisheries, the artisanal fisheries have very close economic sustainability status to industrial ones (Figure 2.4b). Thus, the argument that artisanal fisheries should be replaced by industrial for economic reasons, which is common in many developing countries such as the Red Sea countries, should be challenged in relation to long term sustainability.  Industrial fisheries ranked better than artisanal in social status, and so did the Egyptian fisheries, which are mainly industrial. This high social evaluation could be a combined influence of attributes such as “fishing sector”, “educational level” and “fisher influence” (see Table A.1 in the Appendix for the definitions of the attributes) in the social field where the industrial fisheries scored higher than the artisanal. In addition, as can be seen in Table (2.3), these attributes have a higher leverage (influence) on the ordination than attributes where artisanal fisheries scored better such as “kin participation” and “socialization of fishing”. The outcome of this research, however, does not negate the general thinking that artisanal fisheries provide more employment opportunities than industrial. This is reflected in Rapfish in the attribute “sector employment” in the economic field where artisanal fisheries scored better. It is important to note that the interpretation of the results of Rapfish should be performed in relation to the definition of the attributes in the different evaluation fields (Table A.1 in the Appendix).  Another major difference between artisanal and industrial fisheries, which directly affects their overall ethics of resource exploitation, is their geographic proximity and historic connection with the ecosystem. Artisanal fisheries in the Red Sea are local to the coast and the ecosystem has been major part of their livelihood and has traditional and cultural values. However, the industrial fisheries are not usually based in the coastal area, most of them are from foreign countries, and their main interest is in making quick money and moving to another place when it is no longer economically feasible to fish. The artisanal fisheries have longer term attitude than the industrial fisheries and this attitude is a critical element of sustainability, so due attention should be given in decision making and not only to the short term profit making. Status values for the ethical ordination are clumped together except for the industrial fisheries, which are to 37  the left of the artisanal (Figure 2.1), i.e., industrial scored worse than artisanal. The artisanal fisheries of the region are more or less similar in terms of their “fishing habits” and hence in their ethical status. The fact that the same score was given to all the fisheries for the attribute “equity in entry to fishery” in the ethical field may have contributed to the clumping of the results.  The difference between the artisanal and industrial is interesting because there are some conflicts between the two sectors. Based on the analysis, the following management recommendations can be made. It seems better to encourage artisanal fisheries so that the long term sustainability of the resource is better ensured. Because the only field in which the artisanal fisheries ranked less than the industrial is in the social field, helping the artisanal fishers in those attributes, especially education and direct involvement in the management, can be a helpful incentive to the overall sustainability of the ecosystem and the fisheries.  The difference between countries is not as obvious as it is between artisanal and industrial. In fact, the fishing operations of the artisanal fisheries, in terms of boats, facilities, gear, fish storage, are similar in all Red Sea countries. There are differences in other aspects, though. For example per capita fish consumption is the highest in Yemen, followed by Egypt, while the lowest consumption is in Eritrea and Sudan (Sanders and Morgan, 1989). Head (1987c) predicted Sudan and Eritrea would be the countries to benefit the most from expanding the fishing industry in the Red Sea. The fact that Sudan and Eritrea are the countries which utilize their resources least is clearly seen in Figure (2.3); these two countries have the highest ecological sustainability status. This is reflected also in the comparison between the west (Egypt, Sudan and Eritrea) and east (Saudi Arabia and Yemen) Red Sea fisheries (Figure 2.4a). The Red Sea fisheries have been sustainable, with some exceptions such as the Egyptian shrimp fishery in the Gulf of Suez, due to lack of efficiency and proper market structure (Sheppard, 2000).  Malthusian overfishing may be a problem in many small-scale fisheries in developing tropical countries (Pauly, 1994). It is characterised by pressure on fishery resources from rapid population growth, poverty, shortage of food and lack of alternative economic activities 38  combined with open access to the fisheries. However, until now the problem has not been evident in the Red Sea, as the coast is less populated because of its harsh weather. Nevertheless, recent developments may change this situation. Increased technological development (e.g., air- conditioners) associated with relatively cheap energy from oil (at least for the oil-producing countries), are making the coast an easier place to live; a typical example is the development of many towns on the Red Sea coast of Saudi Arabia. Another booming development is tourism. The Red Sea, especially its diverse coral reef ecosystems, attracts many tourists: in many cases this is not well controlled and can have a deleterious effect on the coral reefs.  The error bars for the estimated median in the Monte Carlo simulation are narrow. This indicates that uncertainty in the analysis is low. It is obvious from Table (2.3), that all the attributes do not have equal influence in their respective evaluation fields. The attributes which scored high in leverage should be given due attention in the future planning of sustainable fishery in the Red Sea. For example, the attribute “selective gear” in technological field has high influence on the ordination; this is a cue for management to take steps to improve the selectivity of fishing gears employed. This is true for the other attributes as well.  As the scoring was done by experts’ judgments based on reports from the region and their personal experiences, it did not necessarily need new quantitative data for the attributes, although, of course, the analysis would definitely benefit from that. Indeed, for robust management advice, new quantitative data from the field are very helpful. So, further empirical research to improve the accuracy of the attribute scores is required. Nevertheless, the results of this preliminary research can be used to improve Red Sea fisheries management, and to identify characteristics of the fisheries to measure in the field. This information provides crucial guidelines where financial, human and institutional resources for fisheries are very limited, as in the Red Sea countries.     39          CHAPTER  3: Analysing changes in fisheries using interviews to generate long time series of catch per effort  40  3.1 Synopsis  The data requirements for most quantitative fishery assessment models are extensive and most of the fisheries in the world lack time series of the required detailed biological and socio- economic data. Many innovative approaches have been developed to improve statistical data collection for fisheries. Here, I explore the use of data from fishers’ interviews to generate time series of catch rates. A total of 472 standardized interviews were conducted with 423 fishers along the southern Red Sea coast in 2007 recording the best catch they recalled having made, and the change in average catch rates compared to when they started fishing. The results showed decline in the catch rates in all fisheries, ranging from 3.6% - 10.3% per year for more than 50 years. The rate of decline of the typical catch was higher for fishers who started fishing in recent years, suggesting that the resource base is declining, which agrees with other indicators. It is suggested that this can be generalized, and that artisanal fisheries research can be designed around data acquired from fishers through interviews, and their subsequent analysis. This method can be used as a quick and less costly approach to generate time series data, which can be used to supplement other data recording systems, or used independently to document the changes that occurred in fisheries over up to a lifetime.    41  3.2 Introduction  3.2.1 Data needs in fisheries science and management  The data requirements for the empirical assessment of fishery systems, with humans as part and parcel of the system, are extensive, and generally due attention is given to local, regional and international organizations that usually generate such data. Also, sophisticated statistical methods for data sampling and analysis have been developed to bridge the gap between data requirements and availability. Generally, fishery data are divided into fishery independent and fishery dependent, and usually, a combination of both is used for actual assessment. Fishery independent data are usually gathered by research organizations, and obtained from platforms others than fishing crafts, typically research vessels, while the other data type is obtained from the fishery itself, as the name indicates. The most basic and informative data in fisheries science are time series of catch and effort (Caddy and Gulland, 1983; Pauly and Zeller, 2003), from which catch per unit effort (CPUE) can be calculated and, with caution, it can be used to infer abundance (Harley et al., 2001). The caveat is due to the fact that CPUE is, in some cases, not proportional to abundance; rather, it may remain stable (‘hyperstability’) while abundance is declining, for example when schooling fishes or spawning aggregations of non-schooling fish are exploited (Hilborn and Walters, 1992; Pitcher, 1995; Sadovy and Domeier, 2005). On the other hand, CPUE may decline more than the actual decline of abundance, a phenomenon called ‘hyperdepletion’ (Hilborn and Walters, 1992). This can happen, for example, when only a portion of the population is vulnerable to the fishery (Walters and Bonfil, 1999; Kleiber and Maunder, 2008). In most cases, however, CPUE can be a good indicator of resource abundance, again when used with caution.  The most common practices in fishery dependent data collection have been log books filled by the fishers themselves, data collection by technicians at fish landing sites, data collected by onboard observers, and recently, technology-intensive vessel monitoring system (VMS). One common denominator for both the fishery-dependent and fishery-independent data gathering methods is that they both provide a metric of the fishery or the resources at the moment the sampling is done, i.e., they can only generate contemporary data. 42   Most data collection by fishery management organizations emphasize industrial fisheries (McCluskey and Lewison, 2008). Small scale fisheries, which account for more than 95% of the world’s fishers and are critical to the socio-economic life of the communities in which they are embedded (Berkes et al., 2001; Pauly, 2006; Andrew et al., 2007) do not, however, get due attention. It is also estimated that about a third of the global catch (Chuenpagdee and Pauly, 2008) and half of the sea food directly consumed by humans originate from small scale fisheries (Pauly, 2006). In the Red Sea, the small scale fisheries contributed up to 70% of the total retained catch since 1950 (see Figure 4.3 in Chapter 4). Thus, a form of fisheries research which takes the small-scale fisheries into full consideration appears imperative (Berkes et al., 2001).  3.2.2 Tapping into fishers’ memory or knowledge  Even if information about the small-scale (or ‘artisanal’) fisheries is not available in official records, it does not mean there is no information at all. Considering only official records, as has been the common practice in traditional stock assessments, and not using the information that the fishers themselves hold is limiting ourselves (Johannes et al., 2000). Indeed, re-acquiring information from the memory of resource users is gaining more attention in fisheries research (Johannes et al., 2000; Sáenz–Arroyo et al., 2005; Haggan et al., 2007). In the process, several methods have been developed to incorporate fishers experience, knowledge and information into fisheries assessment. These approaches depend on the recollection of people who have been involved in fishing, i.e., fishers who have lived in close proximity to the fishery resources, such that they could witness the changes that occurred, and use interviews to capture historic trends evolution from individuals’ memories. These approaches can be an important source of information and sometimes the only one, for example in societies with strong oral traditions.  Most of interview-based research with natural resource users so far has been of an anthropological nature, or with an emphasis on the socio-economic dynamics of the communities, with little or no attention devoted to the status of the resources exploited in the community in question (Pauly, 2006; Anadón et al., 2009). In addition, anthropological research is usually qualitative, and aims to understand the perceptions, values, opinions and institutions 43  of resource users (Salmi, 1998). These are important and an integral part of resource management, because following even the best stock assessment, policies must be implemented which affect people, i.e., which have implications for resource users and their livelihoods. However, qualitative anthropological research (i.e., much of the tropical fisheries research performed by maritime anthropologists) remains incomplete because it fails to use a metric for the main activity of the people it studies, who we might recall, spend most of their waking time fishing, or improving their tools and methods to catch fish (Pauly, 2006).  Interview-based methods to acquire quantitative information have been used to comprehend past systems, e.g., to describe historic change in the abundance of a target species or change in species composition of the catch of depleted fishing grounds (Sáenz-Arroyo et al., 2005; Sáenz– Arroyo et al., 2005; Bunce et al., 2008; Lozano-Montes et al., 2008). Fuzzy logic has been applied, in some cases, to standardize and quantify qualitative data collected through interviews (Mackinson, 2001; Ainsworth et al., 2008). However, interview-based approaches have been used not only to acquire past data, but also for contemporary analyses as well, for example preliminary assessment of the ecological and socio-economic sustainability of fisheries (Teh et al., 2005), or to obtain information on the by-catch that is omitted in landing recording systems (Moore et al., 2010). Also, since different fisher age groups can be interviewed, interview-based methods have been very useful in quantifying cases of the shifting baseline syndrome (Pauly, 1995; Sáenz-Arroyo et al., 2005).  3.2.3 Methodological, standardization and accuracy issues  Interview-based methods depend on the cognitive faculty of interviewees and have been used for collecting data in wide space and time relatively at low cost (Neis et al., 1999; Anadón et al., 2009; Moore et al., 2010). However, interview protocols that are not standardized hinder comparison as estimates derived from interviews can be sensitive to the methodology used (Fowler Jr, 2009; Moore et al., 2010). The main liability of interview-based data collection has been its questionable reliability. There are not many studies that investigated this issue directly, because most fisheries researchers have used interviews mainly to fill in data gaps (Baelde, 2003). There are studies, however, which used interview simultaneously with other methods to 44  assess fisheries. Some of the researches, when studying the same fisheries, found similar trends and reached similar or complementary conclusions (e.g., Neis et al., 1999; Otero et al., 2005; Begossi, 2008; Lozano-Montes et al., 2008), while some generated mixed results, i.e., there were similarities in some indices and not in others (Daw, 2008; Silvano and Valbo-jørgensen, 2008).  There are two main kinds of biases, which may affect the accuracy of responses (Daw 2010): retrospective bias and a tendency to distort facts because of their perceived potential to affect management or policy (Bradburn et al., 1987; Henry et al., 1994; O'Donnell et al., 2010). The research on the accuracy of people’s memory has been mainly in psychology, where the use of retrospective methods to reconstruct past events has been widely debated (Henry et al., 1994); however, empirical research on the related bias is rare even in psychology (Koriat and Goldsmith, 2000). In a paper evaluating retrospective methods for comparing past data collected through interview (retrospective) and data measured independently in the past, such as archival material (prospective), Henry et al., (1994) reported that cognitive and motivational factors may lead to inefficient and inaccurate processing of past information. They also found that for variables measured along dimensional scale (quantitatively measurable variables), there was a strong correlation between the retrospective and prospective data, while the correlation was poor for psychosocial variables (subjective psychological states). And even for strongly correlated quantitative variables, accuracy was poor, mainly because of a systematic tendency by the interviewees to shift their estimates toward desirable states. However, this bias was not a memory recall error as contemporaneous reports also showed similar bias. Similarly, interviews used in fisheries research can be used to track relative changes (e.g., patterns), while the utmost caution is required when absolute values are in play. Thus, the values gathered through interviews should be checked against independently measured parameters, which can be used as an anchor to translate the interview data to an absolute scale, as is done, for example, when estimating unreported fisheries catches from anecdotes (Pitcher et al., 2002; Tesfamichael and Pitcher, 2007).  There are few studies in fisheries that assessed quantitatively the accuracy of fishers’ interview data. O'Donnell et al., (2010) examined the possible effects of interview accuracy in 45  conservation assessment by running two scenarios: one where the interview data was assumed to be accurate and the other where the fishers were assumed to overestimate or exaggerate their responses. They found out that accuracy can be a serious problem in the assessment of the resource and suggested that the accuracy assumption built into the interview data must be explicitly stated. Otero et al., (2005) compared catch rate and total catch from interviews with official reports. They found that the two sources correlate positively, but that the total catch from interview was higher than the official one, which, they suggested, was due to unreported catch not being included in the official statistics. In this case, interview gave more accurate results than official statistics. Daw et al., (2011) compared CPUE data from interviews, official report and underwater visual census and found disagreement among them. They concluded that each data source had its own limitations and bias, and that none can be taken as the ‘true’ value. Even the most independent abundance measurement, underwater visual census, had sampling problems (e.g., depth limitation) and there was also a mismatch between the area sampled by the visual census area and the fishing grounds (Daw, 2008; Daw et al., 2011). O’Donnell et al., (in press) compared CPUE data from interview, logbook and official catch landing records. While they found that all sources showed similar trends, absolute CPUE values from interviews were higher and more variable, and there was no correlation between interview reports and official landing records. Again the higher CPUE could be due to the inclusion of unreported catch in the interview reports similar to Otero et al., (2005), or exaggeration of their catch by the interviewed fishers.  In this chapter, I describe a protocol to collect time series catch and effort data through interviews and discuss the results and lessons learned. The design of the questionnaire in relation to the objectives of the research, the interview procedure within the context of the research and the culture of the society being interviewed, and an analysis of the data acquired and the results are presented. For comparison purposes, the same protocol was used in three countries and 6 fisheries which were identified by the type of gear. The analysis was used to: (1) quantify the change in catch rate by interviewing fishers recruited to fishing at different times and using the best catch they recalled having made; (2) quantify changes in the typical (average) catch rates of fishers between the time they started fishing and 2007, when the interviews were held. Additionally, present theoretical considerations, from different fields, and empirical 46  examples of the use of interview in fishery research data collection is presented; and the lessons learned for further refinement of interview based procedures to collect quantitative time series catch and effort data is documented.  3.3 Materials and methods  3.3.1 Questionnaire  A semi-structured questionnaire, with some questions open-ended and some not, was used in the interviews, which were carried out in 2007. A semi-structured interviewing method was chosen because it both provides a general framework and also flexibility for the interviewer to probe new ideas as the interview progresses. It also gives a more natural flow of discussion between the interviewer and interviewee (Wengraf, 2001). The questionnaire was subjected to ethical review by the Behavioural Research Ethics Board of the University of British Columbia and restructured according to the reviews. Field testing of the first version of the questionnaire showed that it was too long. The shortened version, as used for the research, is given in the Appendix (B). It had three parts: general bio-data which were asked of all interviewees; specific questions, based on the kind of fishing activity that was involved (usually defined by the kind of fishing gear operated); and finally catch data. As some fishers operated different gears, when they were willing and time allowed, they were asked about these different gears, thus increasing the coverage of gear types.  3.3.2 Sampled areas  The research was carried out in fishing communities in three countries in the southern Red Sea: Eritrea, Sudan, and Yemen (Figure 3.1). Interviews were not done in Egypt and Saudi Arabia, because the Egyptian authorities did not allow field research, and an entry visa could not be secured for Saudi Arabia. In Eritrea, a wide range of fishing villages, from the main port city of Massawa in the north to the Djibouti border in the south, and the villages in Dhalak Kebir Island, were covered. This wide range was possible because of extra support for assistance and transportation funding was available. In Sudan, the main port city, Port Sudan, Mohamed Qol 47  and Dungunab in the north, and Suakin in the south were sampled. In Yemen, only the Red Sea coast was sampled; most of the interviews were conducted in the main fishing port of Hodeidah with a few in Al Koka, in the south (Figure 3.1).  Figure 3.1 Map of the Red Sea indicating the areas (in Sudan, Eritrea and Yemen) where interviews were conducted.   3.3.3 Sampling  In the three countries sampled, an official permit was secured from the authority responsible for the management of the marine sources. A combination of random, snowball and targeted sampling methods were then applied. Assistants who spoke the local languages were trained in the interviewing procedure. Potential interviewees were approached usually at fish landing sites                   !	  "  48  or in their villages. A brief account of the research and what was expected of them was explained to them, and their consent to be interviewed was obtained before any interview was carried out. Effort was made not to interfere with their operations. For example, no interviews were requested when they were operating fishing gear or landing their catch, the latter is critical given that fish quickly starts to spoil in the hot sun of the Red Sea coast. The best time was when they were done with most of their activities and were relaxing, mending their nets or during their days off in their villages. When visits were made to the fishing villages, the elders were first approached and once they gave their blessings for the work to continue, fishers were then interviewed. The elders were very helpful in securing the collaboration of fishers for the interviews. Each interview took on average 30 – 45 minutes, except in the first few pilot interviews, which took longer. The guidelines and recommendations of the Behavioural Research Ethics Board of the University of British Columbia were followed during the interview. In order to protect the privacy of the interviewees, each was given a unique code and no names were written on the questionnaire. In addition, methodological recommendations from Bunce et al., (2002) and Huntington (2000) were considered.  A sample unit in this research is not the individual fisher, but a combination of interviewee and gear type. For example, there were few fishers who were interviewed for two gears; those were taken as two separate samples. Though fishers were interviewed randomly, emphasis was given to the fisheries that have a high contribution to the total catch, rather than spreading the sampling effort thinly over a wide range of fisheries. The gears selected were gillnet, hook and line, and shark for Eritrea; hook and line for Sudan; and gillnet and hook and line for Yemen.  Effort was made to have a wide age range of fishers in the sampling. The samples were reviewed throughout the process to check age distribution. It was not easy to find older interviewees, so targeted requests were made for them. Women could not be interviewed, due to cultural sensitivities, even though they were involved in fishing, usually on foot in shallow waters, and supplied much of the fish consumed in their families (as in the South Pacific; Chapman, 1987). In addition to fishers, community elders and managers were also interviewed for general understanding and historic development of fisheries in their respective areas. The 49  data from the interviews were entered in to a Microsoft Access database with interviewee-gear type combination as unique record identifier.  3.3.4 Standardizing data  Fishers often did not report their catches and efforts in units that could be analyzed and compared directly. For example, catches were given in number of boxes, kilograms, number of fishes etc…. The following standardizations were carried out on the raw data:  • In Eritrea sometimes fish landings were reported in number of sacks, especially in the past; one sack contains 45 kg of fish; • In Yemen, boxes, locally called ‘banker’, are used especially for Indian mackerel, and are equivalent to 40 kg. Bundles of fishes tied in a rope, called ‘mihkal’ are also common in Yemen. It was estimated a bundle holds 5 – 10 kg of fish, and the mid- value of 7.5 kg was used for conversions; • Sometimes, fishers described their catches by the number of fish caught. In such cases, they were asked to identify the species and their average length. Then the data were converted to weight using length - weight relationships in FishBase (Froese and Pauly, 2012).  Almost none of the shark catch data were provided in total wet (or ‘live’) weight (TWW), but as dried fin weight, dried meat or wet dressed carcass (gutted, headed, and all fins removed). Also, irrespective of the nature of the product, either fin or meat, most shark data were given in ‘farasila’, a common measurement unit for trade in the Indian Ocean for many centuries, and which is equivalent to 16 kg (Campell, 1993). First, all products were converted from farasila to kilogram. Dried fin weight (DFW), in kg, was converted to wet fin weight (WFW) using a regression equation fitted to data from Fong (1999).       


   R2 = 0.99      …1)  WFW accounts for about 5% of the dressed carcass weight (NMFS, 1993). This commonly used ratio has been challenged as not being sufficiently species-specific (Ariz, 2006; Cortes and 50  Neer, 2006; Biery and Pauly, 2012). The research aims to examine the Red Sea shark fishery in general and there was not enough data to analyze species separately, so the mean ratio is used. Once the fin and dressed weight are accounted for, what remains is the head and viscera, which account for 18% of TWW (Meliane, 2003). Substituting the ratios, TWW from dried fin, all in kg, is given as:       
                              …2)  The other common product of shark fishery reported by fishers is dried shark meat. Based on controlled drying processes, moisture content was found to be 40% of total wet weight when shark meat was dried to a ‘safe moisture content’ (Sankat and Mujaffar, 2004). The dried shark meat, which is dressed (DDW), was converted to TWW using:               …3)  3.3.5 Validation of interview data  The validity of the data obtained from the interviewees was verified at different phases of the research. It started during the interview where the answers of the interviewees were queried for extreme and unrealistic answers, e.g., a catch too large to be accommodated by a boat. Interviewers were also able to verify the time references the interviewees used. In most of the interviewed communities, people do not know their ages with any precision, as birth certificates do not exist and the culture is predominantly oral. Thus, all references to calendar time made by the interviewees, e.g., the year they had their best catch, were double checked with major events in the history of the communities, which are anchored in most people’s memories (Means and Loftus, 1991). Once the closest historic moment was established, then they were asked how many years before or after that event.  For the amount of catch, they were first asked to express it in kilograms, which they were able to do for the recent times because it is the unit used at landing sites. For earlier events, other 51  common measurements such as number of sacks or boxes were used. When their catch amount in kg seemed doubtful, they were asked to express it in the other measurements.  A question with a clear empirical answer was built into the questionnaire to check the validity of responses. The question was ‘size of largest fish ever caught’, and then the answer was compared with the maximum size reported in FishBase (Froese and Pauly, 2012). The interviewers were able to evaluate the overall reliability of the information they had provided at the end of each interview.  Final validation was done after the data were standardized and entered in to the database, using box plot to identify outliers. Any data point which is less than the first quartile minus 1.5 times the interquartile range or greater than the third quartile plus 1.5 times the interquartile range was considered an outlier hence dropped from analysis, i.e.,                 …4)  3.3.6 Data fitting  An exponential function was fitted to the best CPUE fishers recalled ever having experienced. An exponential function was selected because the resulting slopes (instantaneous rates of change) can be compared among different fisheries irrespective of the actual (scale) value of the catch. In addition, exponential function, unlike linear, does not cross the x-axis, which is realistic. There cannot be negative CPUE. This follows the principles outlined by Silvert (1981) for selecting a mathematical model, which should be useful, but also in agreement with conceptual framework, behaving reasonably over the entire range of data, and also be compatible with a scientific explanation, i.e., not be selected only because it provides a good fit to raw data. The equation which fulfills these criteria is:     	 	                         …5) where: x is year, c is a constant and r is the instantaneous rate at which CPUE changed over time. 52  Besides the best catch they experienced, fishers were also asked to compare their average, or typical, catch rates between the year they started fishing and 2007, when the interviews were held. This was used to examine changes in the ratio of catch rates since the fishers were recruited into fishing. Regression analysis was carried out between the ratios and the year the fishers started fishing. Since not all fishers started in the same year, the ratio of their average catch rate between the year they started and 2007 is affected by the number of years they have been fishing, which prevents direct comparisons. Hence, the comparisons were enabled by annualizing the ratios, i.e., re-expressing the ratios after normalizing for years fishing. Two types of regression analyses were carried out on the data, one where the whole time series data were considered as a set and another where the data were divided into two sets (segments).  3.4 Results  In total, 472 samples (interview units) were collected from 423 different fishers, ranging from 12 – 83 years in age and with fishing experience of 1 – 65 years. Except for a few cases, most fishers approached agreed to be interviewed, albeit only after the objectives of the interviews were explained to them, and their questions were answered. Four interviews had to be canceled because the interviewees left in the middle of the interview to attend to some urgent business. Effort was made to obtain a relatively good representation of all age groups; however, the oldest age group (>61) was difficult to sample, especially in Eritrea and Yemen. In Yemen, the youngest age group (<30) was better represented in the sample than the other age groups (Figure 3.2). The intermediate age groups (31 – 45 and 46 – 60) were well represented in all three countries.  53   Figure 3.2 Age frequency distribution of interviewees by country.  The analysis of the best CPUE fishers recalled was carried out by gear type because gear characterizes the fisheries very well. There is more similarity in terms of the operation within a fishery of the same gear type (Tesfamichael, 2001). They all showed decline in CPUE (Figure 3.3) in the range of 3.6% - 10.3% per year, the lowest rate of change applying to the Sudanese fishery, and the highest to the Eritrean shark fishery. The other fisheries in terms of CPUE decline were Yemeni gillnet (4.3%), Eritrean hook and line (6.6%), Eritrean gillnet (7.1%) and Yemeni hook and line (8.8%). In addition, comparisons were made among countries, but did not show any clear pattern. Between Eritrea and Yemen, the change in CPUE appeared related to the type of fishery, rather than by geography.   0 10 20 30 40 Eritrea (n = 284) Sudan (n = 66) Yemen (n = 73) Fi sh er s'  ag e gr o u ps  in  th e sa m pl es  (% ) <30 31-45 46-60 >61 54       Figure 3.3 Change in best CPUE fishers recalled for: a = Eritrean gillnet; b = Eritrean hook and line; c = Eritrean shark; d = Sudanese hook and line; e = Yemeni gillnet; f = Yemeni hook and line. Note that axes have different scales.  The ratio of typical (average) CPUE from the time the fisher entered to that of the year 2007, when the interviews were held, exhibited wide ranges, i.e., 1.17 – 8 for Eritrea and 1.6 – 25 for Yemen (Figure 3.4). The x-axis is the year the fishers started fishing, which is the independent variable affecting the CPUE change ratio. The declining functions in Figure (3.4) indicate that fishers who started fishing earlier have seen the average catch rate decline more than the fishers who joined recently. The decline in CPUE over time is inescapable in any exploited fishery Y = 4E+63e-0.071x R² = 0.44, n = 96 0 100 200 300 1960 1970 1980 1990 2000 Ca tc h (K g/ cr ew /d ay ) a Y = 4E+58e-0.066xR² = 0.47, n = 57 0 100 200 300 1960 1970 1980 1990 2000 b Y = 2E+90e-0.103x R² = 0.60, n = 55 0 100 200 300 1950 1960 1970 1980 1990 2000 Ca tc h (kg /c re w /d ay ) c Y = 2E+33e-0.036x R² = 0.58, n = 45 0 50 100 150 1960 1970 1980 1990 2000 d Y = 5E+39e-0.043x R² = 0.64, n = 37 0 200 400 600 800 1940 1950 1960 1970 1980 1990 2000 C a tc h (kg /cr e w /d a y) Year e Y = 5E+77e-0.088x R² = 0.66, n = 36 0 100 200 1960 1970 1980 1990 2000 Year f 55  (Beverton and Holt, 1957; Hilborn and Walters, 1992). What is interesting is that it was possible to use interviews to quantify the rate at which the decline is occurring. The data in Figure (3.4) do not incorporate the number of years the fishers have been fishing, so when the ratios are divided by the number of years the fishers have been fishing; it yields the annual rate at which the typical CPUE is changing.  When the rates at which the CPUE’s change were plotted on a scatter plot, they formed a bi- phasic patterns (Figure 3.5). Two types of regression were used to fit a trend to the points. In the first, one trend line was fitted, assuming there was only one general trend. For the second regression type, the data points were divided into two sets (segments), assumed to represent two distinct patterns. To compare the two types of fitting and test if there is any statistical significant difference between them, an F test was carried out on the sum of squares of the residuals (SSR). The result showed that there is significant difference (Table 3.1); hence, the segmented fittings were used (Figure 3.5), which shows that the decline in CPUE is accelerating in recent years. The breakpoints were determined by the least sum of square of residuals, which were at 1995 and 1997 for Eritrea and Yemen, respectively. To check if the breakpoints were statistically significant than if they were in the neighbouring years, an F test was carried out. In both cases, Eritrea and Yemen (Figure 3.5), the tests showed that they were not significant. Nevertheless, the years with the least SSR were chosen. For Eritrea the least SSR was 0.38 for 1995 followed by 0.44 for 1997; while for Yemen it was 1.64 for 1997, followed by 1.75 for 1994.  In both figures, the early portion of the data sets resulted in slopes which are not significantly different from zero, so horizontal lines, which are the averages are used. But in the second segments, there are clear increases in the trends.  Table 3.1 Results of the statistical test comparing the fitting of CPUE change rate data when they were treated as one segment or divided into segments.  Eritrea (Figure 3.5a) Yemen (Figure 3.5b) Statistic One segment Two segments One segment Two segments SSQ 1.83 0.38 2.8 1.64 F calculated 71.79 - 13.16 - p  <0.05 (3,57) - <  0.05 (3,56) -  56      Figure 3.4 Ratio at which the average CPUE changed for interviewees from the year they started fishing, relative to the 2007 CPUE: a = Eritrea, b = Yemen.   Y = 4E+23e-0.027x R² = 0.49, n = 61 0 2 4 6 8 1950 1960 1970 1980 1990 2000 Re la tiv e CP UE  a Y = 6E+37e-0.043x R² = 0.64 n = 61 0 5 10 15 20 25 30 1940 1950 1960 1970 1980 1990 2000 Re la tiv e CP UE Year f ishing started b 57      Figure 3.5 Annual decline of CPUE over the years of fishing experience of fishers in two Red Sea countries. (a) Eritrea, where the rate of decline increased in 1995 after the independence in 1991; (b) Yemen, with an increase in the rate of decline in 1997, which is after  the unification of the country in 1990 and the start of its oil economy.   Y = 0.17 Y = 0.0725x - 144.47 R² = 0.7944 0.0 0.4 0.8 1.2 1950 1960 1970 1980 1990 2000 An n ua l r at e o f C PU E de cl in e a Y = 0.34 Y = 0.0709x - 141.25 R² = 0.3737 0.0 0.2 0.4 0.6 0.8 1.0 1940 1950 1960 1970 1980 1990 2000 An nu al  ra te  o f C PU E de cl in e Year f ishing started b 58  3.1 Discussion  In this study I have demonstrated how interview methods can be used to access knowledge lodged in fishers’ memory and how various analyses of this information can lead to the recovery of quantitative data. Although the use of fishers’ knowledge is getting more attention in fisheries research, how it can be used is still debated. One area where a lot of researchers agree is that a systematic approach during the interview is crucial. What I found in this research is, asking about exceptional experiences of fishers (e.g., the best catch they ever made) and comparing different experiences (e.g., typical catch at different times) allowed fishers to answer the questions more easily than by posing more general or vague questions (e.g., how much is your catch rate changing?). This confirms similar fisheries studies (e.g., Sáenz-Arroyo et al., 2005; Daw, 2008), which found that it is easier to recall events that are unusual or rare.  Besides empirical fisheries studies, there is more evidence of this phenomenon from cognitive psychology as well, which confirms that while it is difficult to recall if memory has many events, unique events can straightforwardly be recalled (Bradburn et al., 1987). These vivid memories of interviewees are referred as ‘flashbulb’ memories and are characterized by having high personal importance (Rubin and Kozin, 1984). Fishers describe their best catch ever with pride and vividly, similar to the best trophy kill of hunters.  Eliciting memories of best catch requires work. During pilot interviews, fishers were asked a direct question “what is your best catch ever” and almost all the time their answer was “the catch varies as the sea gives”. Later, a different approach was used where the question was not directly put forward, rather it was woven into a story “when you go to the sea to fish you do not always catch the same amount, when you are lucky you catch a lot and other days you may even come back empty and lose money. But if you look back, there must be one day where you caught a lot of fish and came back happy”. When the question is put in this way, I observed, almost all the time, a light going on in the interviewees face. They smile and start telling their stories with details and do not want to be interrupted. They tell how they went at certain time of the day from a specific dock, the state of the sea, the hotness or coldness of the air, the phase of the moon, the names of all the crew members, how long it took them to pull the net or that they 59  required help from other boats, how tired they were pulling their lines etc… At the end of their stories, they were able to tell the amount of the catch. Thus, I confirm that giving appropriate hints helps as a cue to recall memories, with cue about location and social occasions (e.g., you came back quickly, and all the crew were happy and singing) increasing recall accuracy (Bradburn et al., 1987).  The resulting time series trends and the quantitative comparison between different fisheries they enabled are informative and useful in fishery assessment and management. For example, knowing the rates at which the different fisheries are declining can be used in prioritizing the attention of the fisheries management system, or they can be used as bench marks to evaluate the effectiveness of management schemes. One major challenge, however, is the use of absolute values rather than relative changes. I do not claim the results to be precise estimates of the actual fisheries change over time. However, these values are as informative as other fishery sampling schemes. In some cases they may be even more accurate because they incorporate the unreported catch which is missed by some data recording systems (Otero et al., 2005; Anadón et al., 2009). Besides, many quantitative (non-interview) methods in fisheries are used only to infer relative changes (except for those methods used to set quotas, which this research is not aiming at). In terms of patterns, they are similar to those observed for the Red Sea fisheries using ecosystem modelling (Chapter 6) and rapid appraisal method (Tesfamichael and Pitcher, 2006).  Showing a declining function to fit the best catch rates fishers remembered is not a striking finding, as a declining trend is expected for any strongly exploited fishery resource (Beverton and Holt, 1957; Hilborn and Walters, 1992). However, it was gratifying that it could be quantified so straightforwardly from interviews. This helps to objectively evaluate the states of the fishery over a long period of time (more than 50 years in this case). Also, the rates can be compared to each other. Out of the 6 fisheries analyzed here, the Eritrean shark fishery exhibited the highest decline rate, 10.3% per year. There has been a long history of shark fishing in the Red Sea (Ben-Yami, 1964). The high global demand for shark fin and the life history of sharks combined is having a toll on the shark population. The least decline of Sudanese fishery (3.6% 60  per year) is not surprising, as the pressure on marine fishes in Sudan is relatively low, because more than 90% of the fish in the country is supplied by fresh water fishery (FA, 2007).  The rapid decline of the annual CPUE for Eritrea in Figure (3.5a) after 1995 fits with the political changes in the region. Eritrea has been in a war for independence until 1991 and the fishery was stagnating for a long time, being conducted only for the daily subsistence of the local coastal population. However, after Eritrea became independent in 1991, programs were introduced to revive the fishery, with investment in infrastructure and financial facilities. After the preparatory phase, the fishery took off and the CPUE decline rate increased starting in 1995 (the breakpoint in Figure 3.5a). This was similar for Yemen (Figure 3.5b); although the change is not as clear as in Eritrea, the decline rate increased after 1997. This matches with the relative stability of Yemen after the civil war, which ended in 1970s, with the unification of the North Yemen and South Yemen in 1990. At the same time, oil revenues started to increase general investments in the country. These two cases are good examples of the significant impacts human actions can have on the ecosystem when the situation allows it, stability in this case. The samples for this analysis were only from Sudan, Eritrea and Yemen. However, some of the results were used for the general Red Sea, i.e., the results were extrapolated to Egypt and Saudi Arabia as well, where sampling was not possible. The artisanal fisheries of the region have very similar culture and their fishing traditions are similar too. For example, in all the countries artisanal fishers give part of their catch to family and friends. The amount was estimated for Sudan, Eritrea and Yemen using interviews, while for Egypt and Saudi Arabia, it was deduced based on the data from the other countries.  The approach described here can be useful to complement data gaps for traditional fishery assessment; alternatively, it can be used independently for a quick, low-cost assessment of a fishery without historic data. For effective use of the methodology, a clear definition of objective and proper preparation (e.g., adequate design of questionnaire) is important. In addition, an understanding of the culture and communication style of the society being interviewed is crucial. The scientific community and the system in general can benefit by giving due attention and respect to the knowledge available in fishers and their communities. I would like to conclude with a quote from the late Robert Johannes’s book Words of the Lagoon: 61  “    	

 	
  	  	  
 

  		 	 
    

 	       	
     		 
  	    
 
  	   
 	
			
	 		 		 
		
	
	 	 62          CHAPTER  4: Catch reconstruction of the Red Sea fisheries  63  4.1 Synopsis  Reliable time-series catch data are fundamental for fisheries assessment and management; however, such data are usually not readily available. The catches of Red Sea fisheries are reconstructed from 1950 – 2006. Historical documents, published and unpublished reports, grey literature, databases, surveys, anecdotal information, interviews, and information on processed seafood products were used as sources. When reliable data were available for a number of years, they were used as anchor points to interpolate for missing data, based on assumptions given the best knowledge of the fisheries available. The catches of each country bordering the Red Sea are reconstructed by gear type and the catches of each gear divided according to its taxonomic composition. The reconstructed catches were compared to the catch data submitted by each country to the Food and Agricultural Organization (FAO) of the United Nations. The resulting catch trends provide interesting historical records and important guidance for the development of future fisheries management policies on resource conservation and sustaining the livelihoods of the coastal communities.  64  4.2 Introduction  The Red Sea has a long history (and prehistory) of resource exploitation by humans. Archaeological studies of middle stone age middens from the Eritrean Red Sea coast indicate that humans were eating giant clams and other molluscs about 125,000 years ago, possibly the most ancient such practice on record in the world (Walter et al., 2000). A key part of documenting such exploitation is reporting on its catch. Given the catch level of a given fishery, inferences can be drawn on the intensity of the exploitation, and the approximate number of people involved in, and/or dependant on that fishery. Also, from additional information on the catch composition, inferences can be drawn on the technology that is deployed, the trade linkages that a fishing community has with its neighbours, its income from fishing, etc. In fact, reliable catch data are the most straightforward source of information for a variety of disciples, ranging from history and maritime anthropology to fisheries economics (Pauly, 2006).  For fisheries scientists, the value of catch data is even greater; indeed, catch data are crucial to their main task, which is to perform fish stock assessments in support of fisheries management. Herein, the key feature of stock assessments is to evaluate the status or level of fishing activity in relation to the productivity of the ecosystem, so that fish from a given stock can be caught in such a way that the various components of the system and its regeneration potential are not compromised. If such conditions are met the system will sustain fishing for a long time. To accomplish this task, there are two different subtasks to be considered: first establishing the potential of the system and second knowing where the fishery is relative to that potential. Many assessment tools have been developed to estimate the biological potential of a fishery system and use them as benchmarks for the level of exploitation. Maximum sustainable yield (MSY), and the ratio between the estimated original (un-fished) biomass and the current biomass are two of the many metrics used globally to establish levels beyond which the catch is not advised to go (Beverton and Holt, 1957; Hilborn and Walters, 1992). Of course, there are criticisms of those approaches, the assumptions they use and their applicability to different systems, and they even share part of the blame for the decline of many fisheries (Larkin, 1977; Punt and Smith, 2001). Until some better alternatives are made available to replace the traditional stock assessment tools, they will be used despite their limitations. However, while new approaches are being 65  developed, many fisheries in the world do not have estimates of those metrics and/or are not managed at all.  Overall, reliable catch data, jointly with the methods to estimate the biomass of fish and their productivity, are crucial components of effective assessment and management of fisheries. Time series of total catch, preferably by species, is thus the most basic and important information that can be gathered about a fishery (Caddy and Gulland, 1983; Pauly and Zeller, 2003). It is even more useful when coupled with fishing effort data. Notably, catch and effort data can help with preliminary assessment of the status of population upon which fisheries depend. However, this should be done with caution (Harley et al., 2001), because catch per unit of effort (CPUE), although an indicator of fish biomass, is not always proportional to abundance. CPUE can stay stable while abundance is declining, a phenomenon called ‘hyperstability’ , observed on schooling pelagic fish and spawning aggregations (Hilborn and Walters, 1992; Pitcher, 1995; Sadovy and Domeier, 2005). On the other hand, CPUE can decline more than the actual decline of abundance called ‘hyperdepletion’  (Hilborn and Walters, 1992). This can happen, for example, when only a portion of the population is vulnerable to the fishery (Walters and Bonfil, 1999; Kleiber and Maunder, 2008). However, for many fisheries, CPUE is the best type of information available for assessment, and not using it is short-sighted.  There are many ways catch data can be collected. The most common are log books filled in by the fishers, observers onboard the fishing vessels and data collection at the landing sites and from markets (e.g., auction and exports). For the Red Sea countries, many of these methods are very difficult to implement. Most of the local (artisanal) fishers cannot write. The communities are predominantly based on oral traditions, so log books are out of question. The majority of the boats are small, thus on-board observers are impractical to deploy. Data recording at landing sites, although still arduous, is the most practical way for routine catch and effort data collection. The challenge with that is that the number of landing sites along the coast is quite big, and some of them are not even known to the fisheries administrations. Setting up proper data collection systems is not straightforward, given the complexity of fisheries and fish marketing. There are many fates of a fish following its encounter with fishing gear (Figure 4.1). For some Red Sea countries, more than half of the fish catch does not go through fish market, 66  where official recording occurs (Chakraborty, 1983). Thus, proper planning and systematic collection procedures are needed (Gulland, 1975; Sparre, 2000). This requires resources, so developed countries usually have better catch and related statistics than developing countries (Alder et al., 2010), while the latter also have to contend with a generally higher biodiversity, which makes the catch highly diverse, and hence comprehensive catch statistics difficult to produce (Pauly and Watson, 2008). Note as an aside the irony that even in developed countries with better statistics, overfishing is rampant, e.g., in the North Atlantic (see e.g., Christensen et al., 2003).  The Food and Agricultural Organization (FAO) of the United Nations compiles and distributes global data on fisheries since the late 1940s, issued annually since 1950 (Garibaldi, 2012; Pauly and Froese, 2012). Garibaldi (2012) gives a comprehensive description of the FAO database and its evolution. Data submission to FAO is based on voluntary reports by member countries, which are required to send annually updated accounts of their fisheries catches to the FAO Statistic Division, which standardizes them to a set format, and incorporates them in their publicly available global database of fisheries statistics (see http://www.fao.org/fishery/statistics/en; (Pauly and Zeller, 2003). Because it consists of continuous, long time series and is easy to access, the FAO database is used extensively to guide local, regional or international decisions in countries where local data recording systems are lacking, such as the Red Sea countries. Especially for regional and international analyses, it has been heavily used (e.g., 600 refereed journals cited the FAO database in the last 15 years) because its standardized data makes comparisons straightforward (Garibaldi, 2012). 67     Figure 4.1 The fate of a fish since its first encounter with a fishing gear, (Based on Mohammed, 2003). 68  FAO’ s mandate is very broad, and when it comes to fishery data, it can only compile what is submitted to it. This is the main bottleneck to the quality of the data. Countries do not necessarily have the incentive to submit reliable data, except as moral obligation to contribute to a global system. Thus, it is not uncommon for countries to send incorrect fishery data records (Pauly and Froese, 2012), and FAO does not have a legal or procedural mandate to refuse such data. Even more problematic, the technical reports produced by FAO itself are not reflected in the database. Thus, the global estimates of discards documented in successive Technical Papers and other FAO documents were never included in the FAO statistics (Zeller and Pauly, 2005), even though you can only discard fish that have been previously caught. Another example, applying specifically to the Red Sea, is that most of the early fishery data for the Red Sea comes from national or regional projects executed by FAO, especially the project ‘Development of fisheries in areas of the Red Sea and Gulf of Aden’ , which ran from the late 1970s to the mid- 1980s. Through the agreements in those projects, FAO would send staff or consultants to assess the national fisheries and recommend their future developments. Among other things, the projects surveyed the fisheries and estimated the effort and catch (Chakraborty, 1984), but these results were not incorporated into the FAO catch database.  Moreover, while the countries around the Red Sea are all members of FAO, and hence they send their fishery data to FAO, many suffer from political and institutional instability, which affects their fishery agencies, and thus there are gaps and inconsistencies in the data supplied to FAO. FAO’ s mandate, while broad, does not include detailed analysis and review of the data supplied by member countries, which thus remain limited in their reliability and usefulness. Data submitted to FAO by over half of the developing and a quarter of developed countries is not of good quality (Garibaldi, 2012). The following are the major constraints with the fishery statistics in the FAO database. These issues are not specific to the Red Sea countries, but affect the database in general. 1. The FAO database reports global marine catches spatially only to the extent that they are allocated to 19 giant ‘statistical areas’ . In the cases of Red Sea catches, this is area 51, the ‘Western Indian Ocean’ , extending from the tip of the Gulf of Suez in the North to the Antarctic Convergence in the South, and from Sri Lanka in the East to South Africa in the West; 69  2. The level of taxonomic aggregation of the catch is usually very high, and a large part of the catch is reported as ‘miscellaneous’  or unidentified species, which masks qualitative changes occurring in the ecosystem; 3. The member countries often send catch data to FAO (usually emanating from a Department of Fisheries or similar institution) through their Ministry of Trade, or some central statistics office or other government agency not directly connected with fisheries, where they are often over-aggregated and/or otherwise modified before being sent off; 4. Some countries may have political reasons to misreport their catch, including over- reporting of catches for political reasons as China did to FAO for at least two decades (Watson and Pauly, 2001) and, gravest of all; 5. When data for certain fisheries are not available (because the fisheries in question were not monitored), no estimate for the missing catch data are submitted. Subsequently, absent catch data for a given year become an annual catch of precisely ‘0’  tonne. Thus, the FAO database does not account for illegal, unreported and unregulated (IUU) catch (Alverson et al., 1994; Kelleher, 2004).  The use of FAO fishery data by many organizations will not stop anytime soon, and neither should it, but one can hope that such use becomes more critical (Pauly and Froese, 2012). Also, there is at least one research project initiative, the Sea Around Us project (www.seaaroundus.org), which aims to improve the quality of the global marine fishery data. As a university-based research project, it is not limited by legal procedures, as the FAO is to its members. Hence, country catch reports are criticized, scrutinized, alternative sources are used, and when data are missing, they are estimated with transparent assumptions given the best knowledge of the fishery available at the time. In effect, the major issues with the FAO database can be overcome through reconstructing historical catch time series (Pauly, 1998; Pauly and Zeller, 2003; Pauly and Froese, 2012). Reconstructed time series of catch (and effort) data from the past are not merely useful for historical purposes. Rather, they provide a basis for overcoming the shifting baseline syndrome (Pauly, 1995), i.e., for accurate assessment of the impact of fishing on marine ecosystems, and for ecological restoration (Scott Baker and Clapham, 2004; Pitcher, 2005). The lessons learned from catch reconstruction in different 70  circumstances of the fisheries can be informative, similar to ‘scenarios’  in adaptive management of resources (Walters, 1986).  Catch reconstructions, which can be performed at any scale, allow for the effect of items (1) to (5) in the above to be mitigated. Thus, for example issue (1) was addressed here by reconstructing the catch of Yemen within the Red Sea separately from that in the Gulf of Aden, which are in the same FAO area. There is a similar issue with the west and east coast of Saudi Arabia. Item (2) is addressed by identifying and researching the fisheries (including the gears) which generated all catches, which usually allows a reduction of the unidentified components of the catch.  Catch reconstruction involves quantifying the catch of each fishery known to have existed, based (when ‘hard’  catch data are not available) on the ‘shadow’  that this fishery throws on the society in which it is embedded. This shadow may consist of household fish consumption figures, number and income of fishers, export figures, etc. (Pauly, 1998). In either case, when item (3) above leads to cases of item (5), catch can be estimated; these estimates, while approximate, will generally be closer to reality that the precise estimate of zero in the official databases (Pitcher et al., 2002; Zeller et al., 2007).  The main objective of this chapter is to reconstruct catches of the Red Sea fisheries from 1950, the year FAO started to publish annual statistical reports on the fisheries of the world, up to the most recent fishery statistics data available. Included here are all the Red Sea countries: Egypt, Sudan, Eritrea, Yemen, Saudi Arabia, Jordan and Israel and all the fishing sectors of these countries. Jordan and Israel have very short coastlines in the Red Sea in the inner Gulf of Aqaba, i.e., they do not have major fisheries in the Red Sea. Thus, this analysis will be driven by data from the other countries, though data from Jordan and Israel are also included. The output will be a time series of standardized fishery catch for the Red Sea, divided by sector, gear and catch composition.   71  4.3 Materials and methods  The main methodology in catch reconstruction is digging into different sources reporting the catches of the countries, critically analyzing them, and organizing them to a common standard, which can be used for comparison and carrying out analysis for the assessment of the resources. The sources include peer-reviewed published papers, grey literature (mainly government, consultant, and FAO reports), and national databases complemented by field trips to Egypt, Sudan, Eritrea, and Yemen from December 2006 to September 2007. The information collected was enriched by the insights of local experts and colleagues who provided data through personal communications. The catch reconstruction for the whole Red Sea was first compiled in the form of individual country reports, co-authored by country experts: Egypt (Tesfamichael and Mehanna, 2012), Sudan (Tesfamichael and Elawad, 2012), Eritrea (Tesfamichael and Mohamud, 2012), Yemen (Tesfamichael and Rossing, 2012a), Saudi Arabia (Tesfamichael and Rossing, 2012b), and Jordan and Israel (Govender and Pauly, 2012). In them the specific details of the reconstruction for each country are given. Here the summary of the general methodology and the procedure to establish one coherent data set for the whole Red Sea are described.  4.3.1 Sources  A continuous database of fishery catch, starting from 1950, does not exist for any of the Red Sea countries and had to be assembled from different sources. The earliest data sources for the Red Sea countries were technical reports of the assessments of the fishery resources for planning the development of the fishing industry, starting in the decades following WWII. The 1950s was also a period where several of these countries became independent and started to run their national economies, and food security became a critical issue. These assessments/surveys were made by foreign experts (except for Egypt), who were usually recruited through FAO. The earliest sources available were for Saudi Arabia (El-Saby and Farina, 1954), Sudan (Kristjonsson, 1956), Eritrea (Ben-Yami, 1964), Egypt (Al-Khol and El-Hawary, 1970) and Yemen (Lisac, 1971; Losse, 1973). Some of the early assessment work was done through bilateral arrangements or consultants hired directly by the countries (e.g. see Ben-Yami, 1964; Atkins, 1965; Grofit, 1971 for Eritrea). In the 1970s and 1980s, in part because of the Cold War 72  and ensuing East-West competition, development aid was pouring into the Red Sea countries and a fraction of that was assigned to fisheries development. A regional project for the Red Sea area, ‘Development of fisheries in areas of the Red Sea and Gulf of Aden’ , was carried out from the end of the 1970s until the mid-1980s and led to an improvement of the quality (comprehensiveness and taxonomic resolution) of fishery catch data. Additional sources were also used, notably tax offices and export records. For example, the catch of the Eritrean beach seine small pelagic fishery was reconstructed from export figures for fish meal, which was the output of the fishery (Ben-Yami, 1964).  Organized databases and/or annual fishery statistical reports are a relatively new development for the Red Sea countries. The oldest database is that of Egypt, which starts in 1979, while Saudi Arabia started publishing its annual fishery statistics in the 1980s. Eritrea has had annual reports since its independence in 1991, but an organized database started only in 1996. Sporadic annual reports are available for Yemen and a database system is being established. Sudan does not have any fishery data reporting system yet; however, daily catch data are collected at the main fishing market of Port Sudan, which are stored, but not issued as annual reports. All these sources were accessed for the catch reconstruction of the respective countries.  Once the sources were accessed, they were analyzed for their spatial, temporal and sectoral coverage. Some reports were written only for a certain section of the countries or only a specific sector of the fisheries. Then the sources were critically examined with regards to the method(s) and assumptions used in collecting their data. Only after the data were scrutinized were they used for catch reconstruction. For some years, data were available from different sources, some simply regurgitating previous reports. In such cases an effort was made to locate the original reports. When there were multiple independent sources, the ones which have detailed explanations of the methodology and comprehensive coverage were selected. In a few cases, the information from one source was used to correct data from another report.  73  4.3.2 Interviews  Interviews were conducted with fishers ranging from 15 – 82 years in age, and with fishing village elders and the employees of fisheries administrations. The main goal of the interviews was to assess long-term change in fisheries productivity using fishers’  memories. A separate analysis of the interview data is given in Chapter 3, but with respect to catch reconstruction, interviews had two major aspects. First, they were very useful in filling data gaps. For some periods there were no records at all, so interviewees were asked to explain what happened in those periods and whether the catches were higher, lower or about equal to the adjacent periods with records. The other type of information supplied by the interviews was the amount of unreported catch, i.e., the catch missed by official records. For many artisanal fisheries in the Red Sea, this included the amount of catch given freely to some members of the community and the catch landed at remote landing places, where there are no data collectors. Regarding the former, there is a strong tradition, shared by the maritime cultures of Red Sea countries, that part of the catch is expected to be given freely to family, friends and people who need assistance (e.g., the elderly, disabled, and widows… ). The amount given freely is called ‘kusar’  and is a form of food security social network. Not to give ‘kusar’  leads to loss of prestige, which may have serious consequences, e.g., with regards to market transactions and eventual marriages. The amount was about half of the total catch in the 1950s and 1960s; however, as the catches started to decrease and the fish accrued market value, the proportion of the catch devoted to kusar started to decrease.  The second useful input from the interviews was explanations of discrepancies among reports. The insights from older fishers and people who have been involved in the management of fisheries for a long time were able to explain ambiguities in reports and other records. Although they did not give specific quantitative values, their ability to give comparative qualitative information helped to base the assumptions used in quantifying the catch.  74  4.3.3 Missing data  For the years data were missing, interpolations or extrapolations were made to fill in the data gaps. These were made on the basis of explicitly stated assumptions, given the best knowledge of the fisheries available at the time. Population size and per capita consumption were also frequently used as a proxy, to infer catches.  4.3.4 Compilation  Once the catches were reconstructed for each country, they were added together to represent the catches of the Red Sea as a whole. This addition was made in the way that appeared most informative, i.e., by fishing sector (industrial or artisanal) and by gear types. Then, the catch composition was calculated for each gear category. Dividing the catch by sector and gear is based on practical uses of the information. Almost all countries divide their fishery into artisanal (a long traditional fishing practice), and industrial, which is usually operated by foreign fleets, except for Egypt (for a long time) and Saudi Arabia (only recently). These two sectors are different in their economic and cultural settings, and conflicts between the two are common (Pauly, 2006). Gears reflect the technical aspect of human interaction with the resources, and thus can serve as management units (Tesfamichael, 2001), as also used in the ecosystem model of the Red Sea (see Chapter 6). The main gears, based on their contribution to total catch are: handlining, gillnet and beach seine fisheries in the artisanal, and trawl and purse seine gear in the industrial sectors.  The catches of each country were divided by the fishery administrations of the countries into artisanal and industrial, but not by gears for all countries. When catches were not divided by gear, the taxonomic groups were allocated to a specific gear based on the life history and habitat of the species, following the classification of global fisheries performed by Watson et al., (2006). For example, in the artisanal fishery, small pelagic species are categorized under beach seine, carnivorous coral reef fishes under handlining and large pelagic under gillnet. The Eritrean catch was already divided by gear, while the Sudanese catch was presented in the categories ‘artisanal’ , ‘trawling’  and ‘purse seining’ . Since the artisanal fishery in Sudan is 75  predominantly handlining, all of it was categorized under handlining. The Egyptian catch was divided by gears for the industrial sector, but not for the artisanal catch; this was here divided into gillnet and handlining based on the species composition of the catch and qualitative description of the fishery. The Yemeni industrial fishery is all trawling, but the artisanal catch needed to be divided into handlining and gillnet. Here, account was taken of taxonomic groups that were caught by both gears, namely barracudas and breams; their catch was divided equally between the two gears. The Saudi artisanal catch was originally not divided into gears, but most of the catch was from handlining (Sakurai, 1998; MAW, 2000, 2008), so all taxa could be allocated to handlining, except species which are predominantly pelagic and known to be caught mainly by gillnet (Spanish mackerel, tunas, Indian mackerel, queenfish and mullets). The Saudi industrial fishery catch was not divided by gears either. This was done based on the composition of the catch. For all countries, the catch of trawl was divided into retained and discarded catch. The latter can be very significant proportion, usually ignored in the data recording systems. The division was necessary because the taxonomic compositions of the retained and discarded catch are different. Gears with very small contribution to the total catch and unidentified groups, which cannot be assigned to any gear due to lack of taxonomic resolution were placed under ‘uncategorized catch’  (Appendix A1).  4.4 Results and discussion  The total reconstructed catch was different from the data submitted by the countries to FAO, and in most cases, the reconstructed catch was higher (Figure 4.2). Overall, from 1950 – 2006 the total catch taken from the Red Sea is 1,312,259 t or 34% higher than suggested by the FAO database. In the following, a brief per-country account is given, starting with Egypt and moving counter-clockwise along the Red Sea coast.  For Egypt, the reconstructed catch is higher than the fisheries catch statistics that Egypt submits to FAO from the beginning of 1960s until the beginning of 1990s, but the reverse after the mid 1990s. This discrepancy may be due to the fact that Egypt fishes outside its own waters (e.g., in Eritrean waters starting early 1990s (Tesfamichael and Mohamud, 2012) and these catches are not included in the reconstruction (Tesfamichael and Mehanna, 2012), as the objective of the 76  reconstruction is to quantify the amount fished in the waters of various countries, and not where they landed. The catch of Egyptian vessels from Eritrean waters is reported in the reconstruction of Eritrea.  The Sudanese data submitted to FAO does not include catch of the shell (trochus and mother-of- pearl) fishery, which was very important before 1980s. Hence, in Figure (4.2), the reconstructed catch without shells is presented (along with the total) to enable comparisons. Generally there is no large difference between the reconstructed data and data submitted to FAO for Sudan. The sudden spike of Sudanese catch reported to FAO in 1983, on the other hand, is likely due to a reporting error, as there was no major change in the fisheries likely to cause such a sudden jump for only one year. The higher catches reported to FAO after the 1990s are suspicious, as the locally available data do not indicate such a high level of total catch (Tesfamichael and Elawad, 2012).  For Eritrea, Yemen and Saudi Arabia, the reconstructed catches are higher than those reported to FAO (Figure 4.2), due to the latter not including various fisheries and omitting discards. The major discrepancies between the reconstructed data and data submitted to FAO for Eritrea are in the early decades (1950s and 60s) and later after 2000. In between those periods the fishery was not active, hence catches were low (Tesfamichael and Mohamud, 2012). For Yemen in the Red Sea, the reconstructed catch is continuously higher than reported catch, the difference being more consistent for Yemen than for any other country. It shows a continuous omission of part of the catch in the reporting system (Tesfamichael and Rossing, 2012a). There is clear difference between the reconstructed and reported catch for Saudi Arabia in the Red Sea until the mid 1980s. After the mid 1980s the Saudi fishery became more industrialized with trawlers, and the gap between the two data sets is mainly the discard (Tesfamichael and Rossing, 2012b). The reconstructed catches of Israel and Jordan are negligible compared to those of the other countries (Govender and Pauly, 2012), which is understandable given their minuscule footholds in the inner Gulf of Aqaba. They also exhibited less fluctuation than the FAO data. Overall, Egypt and Yemen are the heavyweights of the Red Sea fisheries, followed by Saudi Arabia. Sudan has the lowest catch once Israel and Jordan are discounted  77     Figure 4.2 Total reconstructed catch (solid line) compared to the data submitted by the Red Sea countries to FAO (broken line). As the Sudanese FAO data do not include shellfish, a version of the reconstructed catch not including shellfish is also included (thicker line). Note: Y-axes have different scales.  Based on the reconstructed catch, the contribution of the artisanal fishery to the total catch in the Red Sea is higher than the industrial sector (Figure 4.3) Thus, from 1950 – 2006 the artisanal was more than 2.5 times the industrial catch. This has major economic and social implications. Artisanal fisheries employ a higher number of fishers per tonne of catch (Pauly, 2006), which translates to higher employment and livelihood in the communities. Note that the industrial catch in Figure (4.3) does not include discards, which are not landed and do not have any 0 20 40 60 80 1950 1960 1970 1980 1990 2000 C a tc h (10 3 t) Egypt 0 2 4 6 1950 1960 1970 1980 1990 2000 Sudan 0 10 20 30 1950 1960 1970 1980 1990 2000 C a tc h (1 03 t) Eritrea 0 20 40 60 80 1950 1960 1970 1980 1990 2000 Yemen 0 10 20 30 40 50 1950 1960 1970 1980 1990 2000 Ca tc h (10 3 t) Year Saudi Arabia 0 0.4 0.8 1.2 1950 1960 1970 1980 1990 2000 Year Jordan and Israel 78  economic value; however, they are important ecologically, hence are reported in Figure (4.5). The major increase in the total catch of artisanal fisheries happened in the mid-1980s, the time when motorization of local boats started gaining momentum.  Figure 4.3 Total reconstructed landed catch of artisanal (solid line) and industrial (broken line) fisheries for the Red Sea.  In the artisanal sector, the major fisheries are handlining, gillnet and beach seine. The contribution of handlining is the highest followed by gillnet. The catch composition of the gears is usually very diverse. However, a few taxonomic groups dominate (Figure 4.4). For better graphic presentation, all the minor groups are lumped together under ‘others’ , while the detailed catch compositions by gear are given in Appendix (C.2 – C.8). Sharks are caught by deepwater gillnet and handlining, but the shark fishery is treated separately because of its unique importance (Bonfil, 1994; Bonfil and Abdallah, 2004), in particular because of the singular life history of sharks (Frisk et al., 2001), and high demand for sharks i.e., for shark fins (Fong, 1999; Biery and Pauly, 2012). Indeed, this study shows that shark suffered the worst decline in the Red Sea (see Chapters 2, 3 and 6). The sharks’  catch by countries is given in Figure (4.4), which shows that the catches of sharks from Egypt and Sudan are negligible compared with those of other countries.  0 25 50 75 100 1950 1960 1970 1980 1990 2000 Ca tc h (10 3 t) Year 79  For non-shark handlining and gillnets, the catches started to increase in the mid-1970s, reaching a peak in the early 1990s and then declining. On the other hand the catch of the beach seine fishery was higher in the earlier years and declined later, mainly due to the collapse of the fish meal industry in Eritrea because of political instability (Tesfamichael and Mohamud, 2012).  As in the artisanal sector, the catch composition of the industrial sector is also dominated by a few taxonomic groups (Figure 4.5). The catch of trawlers was low until the 1990s, when the industrial fishery of Saudi Arabia became well established (Tesfamichael and Rossing, 2012b) and Egyptian trawlers were operating widely in other countries’  waters (Tesfamichael and Elawad, 2012; Tesfamichael and Mohamud, 2012). The purse seine fishery is almost exclusively Egyptian and has been active for a long time (Rafail, 1970, 1972), operating mainly in Egyptian waters and the northern part of Sudan (Tesfamichael and Elawad, 2012). The fishery started with few purse seiners (Rafail, 1972) and their numbers increased gradually resulting in increased catch (Barrania and El Shennawi, 1979; Sanders et al., 1984a). The decline of the purse seine catch after its peak in 1992 appears to be due to a decline in the number of trips per year carried out by the vessels (GAFRD, 2010).  Despite the above, no attempt is made in this chapter to draw inferences on the state of the fisheries resources. Such an attempt is made in Chapter 6, using an ecosystem model of the Red Sea which incorporates the catch data presented here, and time series of fishing effort aggregated by gear type.   80   Figure 4.4 Catch composition of major artisanal fisheries of the Red Sea.   0 10 20 30 40 1950 1960 1970 1980 1990 2000 C a t c h  ( 1 0 3 t ) Handlining Emperors Others Barracudas Jacks Snappers Groupers 0 10 20 30 1950 1960 1970 1980 1990 2000 Gillnet Kingfish Other Jacks Tunas Indian mackerel 0 10 20 30 1950 1960 1970 1980 1990 2000 C a t c h  ( 1 0 3 t ) Year Beach seine Anchovies Sardines Others 0 2 4 6 8 1950 1960 1970 1980 1990 2000 Year Sharks Eritrea Saudi Arabia Yemen  81   Figure 4.5 Catch composition of Red Sea industrial fisheries. Snappers 0 10 20 30 1950 1960 1970 1980 1990 2000 C a t c h  ( 1 0 3 t ) Trawl - retained Lizardfish Cuttlefish Shrimp Threadfin bream Others 0 10 20 30 40 1950 1960 1970 1980 1990 2000 Year Trawl - discard Pony fishes Gapers Others 0 10 20 30 1950 1960 1970 1980 1990 2000 C a t c h  ( 1 0 3 t ) Year Purse seine Horse mackerel & scads Gapers Others Round herring Goldstripe sardinela  82           CHAPTER  5: Estimating the unreported catch: a case study of Eritrean Red Sea fisheries   83 5.1 Synopsis  Unreported catch from three major fisheries in the Eritrean Red Sea is investigated in order to estimate the impact of the total extraction of fish from the ecosystem, which will help the assessment of the resource and its management. The fisheries target small pelagics, demersal finfish and shrimps, and were chosen for their major contribution to the total Eritrean catch, economic importance and/or significant contribution to unreported catch. The analysis is carried out from 1950 – 2004, subdivided into blocks of 5 years. Factors that provide incentives to fishers to misreport are obtained by examining the historical development of the fisheries. The analysis is based on interpolations, guided by the incentives, between independent quantitative estimates of unreported catch (“ anchor points” ). Errors are estimated using a Monte Carlo sampling technique. The fishery industry in Eritrea operated smoothly from the mid 1950s to the end of 1960s, when it was disrupted by political instability. Fishing operations were normalized again at the beginning of the 1990s. Of the three fisheries, the small pelagic fishery has the least unreported catch; a maximum of 5% of the total extracted. The total catch from the three fisheries has been under-reported on average by 21%.   84 5.2 Introduction  Much fisheries research used in decision-making depends on data that are acquired from the fishery industry itself. For example, quota setting using virtual population analysis (VPA) depends on catch data from the fishing fleets (Shepherd and Pope, 2002). However, catch officially reported to fishery organizations is generally not the amount extracted from the ecosystem (see Figure 4.1 in Chapter 4). Some of the fish caught are discarded because they have low or no economic value, some are not reported, or are reported as something else, because the fishing operation is illegal, while others are not recorded simply because they are not regulated (Pitcher et al., 2002). If these components of fishing activity are not included in the catch analysis, actual extractions will be underestimated, encouraging the notion that more is still available to be fished. This may result in severe depletion or even extirpation or extinction of species. The effect of unreported catches can be worse when parameters estimated from the catch are used in other analyses, where errors will have a compounded effect. The magnitude of unreported catch can be very big; for example in shrimp fisheries discards are usually more than the retained catch, in some cases by an order of magnitude. So, the closer we can get to the actual amount extracted, the better will be the inferences we can make about the status of a fishery.  With the exception of discards in those countries with an observer system, e.g., USA (Harrington et al., 2005), estimates of unreported catch are not available in the official reports of many countries. The challenge is, therefore, to estimate what is not reported but is known to be taking place. Estimating unreported catch in the form of discards has been receiving more attention. Based on data from the late 1980s, Alverson et al., (1994) estimated the global unreported discards to be 17.9 to 39.5 million tonnes per year, while the maximum global catch given by the Food and Agricultural Organization (FAO) of the UN was around 85 million tonnes in the mid 1990s. Starting in the early 1990s discards decreased because of technological innovation, better management and increased utilization of catch. Using data from 1992 to 2001, Kelleher (2004) estimated global discards to be 7.3 million tonnes. Though the decline of discards is a good sign of effective use of extracted marine resources, the overall decline of total catch (landing and discarding) at a steeper rate than previously thought is a serious concern  85 (Zeller and Pauly, 2005). Besides discards there are also illegal and unregulated fishing activities, which are not reported. Pitcher et al., (2002) estimated unreported catch from the different sources in Morocco and Iceland based on knowledge of the development of the fishery and some clues about the unreported catch. Similar methodology with some minor refinement for British Columbian fisheries is used in Ainsworth and Pitcher (2005). Patterson (1998) explored the effect of misreporting on parameter estimates by comparing stock assessment models that use catch reports and estimates made from survey data only.  Estimating unreported catch is tricky as it deals with what is known to happen but no data are given, hence the term ‘unreported catch’ . In the absence of data records, it is not uncommon for researchers to depend on information gained from people knowledgeable with the system and the issue being investigated. For example, oral traditions have been a valuable source of information about historical events in fisheries (e.g., Neis et al. 1999; Sáenz-Arroyo et al. 2005). See also Chapter 3 where interviews are used to analyze long-term trends in catch rates. Pauly (1995) argues when there is no data record, anecdotes can be “ as factual as temperature records” . Sometimes the only information available is expert or traditional knowledge, and not using it may mean putting the fisheries at risk (Johannes et al., 2000). In many fishery analyses, unless unreported catch is accounted for explicitly, it is implicitly assumed to be zero, which is misleading and unacceptable (Pitcher et al., 2002). Patterson (1998) found that estimates of fishing mortality were imprecise when catches were under-reported off the coast of west Scotland. Bias from subjectivity provides a caution about using “ expert”  knowledge or judgment in fishery analysis, but it is not a good reason not to use it at all. Error due to subjectivity, which is present in almost any observation, can be systematically minimized and can be acknowledged by reporting error ranges explicitly.  In this chapter I estimate the unreported catch based on expert judgments, guided by influences to misreport in the history of the fishery and by independent quantitative estimates of unreported catch as “ anchor points” . From these anchors, estimates are interpolated for the years when quantitative data are not available. I used Monte Carlo simulation to determine unreported catch and the error range for three fisheries from the Eritrean coast of the Red Sea.   86  The Eritrean fishery in the Red Sea is a typical tropical fishery, multi-species and multi-gears. It can be categorized into small-scale artisanal and large-scale commercial fisheries. The artisanal fisheries are characterized by selective gears operating in shallow coastal water on coral reefs. The commercial fisheries use more powerful vessels and operate in deeper waters. The small- scale fisheries, which are mainly handlining and gillnet, are not included in this research because they use selective gear, hence discards are very small, and their catches are well recorded as there are a very few fish landing sites where almost all the catches are landed, and they are well monitored.  Based on operation and management, the Eritrean fisheries can be divided into two clear periods: before and after the independence of Eritrea from Ethiopia in 1993. The industry was larger before independence, starting from the mid 1950s until the end of the 1960s, and was dominated by a small pelagic fishery for fish meal exported to Europe and Asia (Sanders and Morgan, 1989). There was no strong management or fish landing data collection as it was only a small branch of a bigger government body stationed far away from the coast. The most important data available were the amount of fish meal exported, kept for tax purposes. After independence the fishery started to gain momentum, following a complete destruction of its infrastructure during the independence war. Nowadays, the commercial sector is mainly dominated by trawl fisheries. There is a stronger management and data collection system. Fish landings are monitored by the Ministry of Fisheries. The fish landing sites are very few, which makes the monitoring easier.  Three fisheries are included in this chapter. They were selected based on their contribution to the total catch (they account for more than 80% of the total catch), economic importance and/or for being known to have a relatively high contribution to the unreported catch. The three fisheries also have relatively better data records and there are some independent estimates of unreported catch either from surveys or onboard observations. They are:    87 Small pelagic fishery  This beach seine fishery was the most important fishery in the 1950s and 1960s especially for its volume, accounting for up to 90% of the total reported catch (Grofit, 1971). Its main target species were sardines (Herklotsichthys quadrimaculatus) and anchovies (Encrasicholina heteroloba and Thryssa baelama) used mainly in the production of fish meal, which was exported to Europe and Asia. A small proportion of the catch was sun-dried for human consumption for markets in Asia (Sanders and Morgan, 1989). Since the catch was used for fish meal production, nothing was discarded; however, there was some misreporting. Relatively, it was a well-documented fishery but its infrastructure was dismantled before Eritrean independence, and this fishery no longer exists despite the continued presence of its target species.  Finfish trawl fishery  Bottom trawls for finfish, operating on both hard and soft bottoms, are important fisheries both before and after 1993. They are operated almost exclusively by 25 – 40 m long foreign vessels, mainly from Egypt and Saudi Arabia under joint venture, with enough power to trawl in deeper waters (450 – 1500 HP). Since 1993 this fishery has provided the largest contribution to the total catch. The dominant species in the catch are lizard fish (Saurida undosquamis and S. tumbil) and threadfin bream (Nemipterus japonicus). The unit price of these fishes is not very high, but large catches make it economically worthwhile. This fishery has intensive grading and huge discarding.  Shrimp trawl fishery  This trawl fishery does not make a big contribution to the total catch; however, it is very lucrative because of high prices in the market. It operates only in soft bottom and has a large amount of discarding. Its operation, mainly by Egyptian and Saudi trawlers, has been sporadic. Its total catch has never been as high as the estimated maximum sustainable annual yield of 500  88 tonnes (Giudicelli, 1984). The species commonly caught are: Peneus semisulcatus, P. japonicus and P. latisulcatus.  In this chapter only the total estimated unreported catch is given. It comes mainly from one component: “ misreporting”  for the small pelagic fishery and “ discarding”  for the other two fisheries. As part of the agreement with the trawl fishery, observers are sent with the trawlers, especially after 1993. The source of unreported catch in these fisheries is, therefore, mainly from discarding and not from misreporting or illegal operation. Some rare incidences of illegal fishing are known to happen, however the amounts are likely insignificant.  5.3 Materials and methods  In the absence of quantitative data on the unreported catch, I used qualitative ranks or categories of “ incentives to misreport”  based on expert judgments and qualitative descriptions of the fisheries in published and unpublished reports. The categories are high, medium/high, medium, low/medium and low. These categories are used in order to have the same standards as all previous similar researches. The categories are converted to quantitative values using anchor points.  The procedure starts with a time series of the reported catch, which was obtained from the Ministry of Fisheries, Eritrea and other records. Though FAO has a global database of fishery catches, data on reported catch was sought first from Eritrea, as the accuracy of the FAO catch data is questionable (Watson and Pauly, 2001; Pauly and Zeller, 2003). An extensive search of published papers, reports and expert consultation allowed to construct the catch from 1950 – 2004. This case study was carried out in 2005, before the catch reconstruction (Chapter 4) to try out the unreported catch estimation method. It covers the period from 1950 – 2004, while the catch reconstruction, which includes unreported catch, goes from 1950 – 2006. This chapter shows how detailed analysis of unreported catch and uncertainty analysis can be done.  Table (5.1) shows the reported catch of the three fisheries included in this chapter. Since the analysis is made in blocks of 5 years, the catch is the average over the 5 years. The catch after  89 1993 was obtained from a database maintained by the Ministry of Fisheries Eritrea (MOF, 2007), which is well-organized and even has estimates of unreported catch for trawling. The catch of small pelagic species in the past was estimated from export of fish meal (Ben-Yami, 1964; Grofit, 1971).  The next step is to get the qualitative categories of incentives to misreport catch. Though the categories for each 5 year block can be acquired directly from expert opinions and/or inferring from qualitative descriptions in reports, I guided the ranking by tabulating the major developments in the fisheries that could influence the incentive to misreport. These guidelines minimize the subjectivity in the ranking. The development of the fisheries through time was investigated to pinpoint changes that would influence the fishers to misreport their catch. The changes can be technical (e.g. change in catching power), economic such as markets and prices, changes in the management scheme, and political or any other change. An extensive literature search and expert opinions were used to document changes in the fisheries, and a table showing the influences on the incentives to misreport was prepared (Table D.1 in the Appendix). It is important to note that these influences are by no means complete; however, they capture the major changes in the fisheries which could affect reporting. The table also shows if the influences have a positive or negative effect on the incentives to misreport. Established facts in fisheries sciences were applied, when appropriate, to evaluate the effects of influences on the incentives to misreport. For example, using smaller mesh size at the cod-end of a trawl net increases discard amounts.  Once the table of influences is prepared, the qualitative categories of incentives to misreport are established (Table 5.2) based on those influences. I acknowledge that the expert judgments used in this part can be the most subjective part of the procedure. However, expert judgments are valuable and sometimes the only information available for estimating what is not reported (Pauly, 1995; Johannes et al., 2000).  For some years there were some quantitative estimates of the unreported catch either from surveys or onboard observers (Table 5.3). Those estimates were used as “ anchors”  to convert the qualitative categories of incentives to quantitative percentages of the total catch. At least one  90 anchor point is needed for each fishery; however, if more anchors are available, they can be used to double check the interpolated results. I chose anchor points that are more reliable than the others (bold face entries in Table 5.3). Using the anchors, interpolation values were set for the different categories of incentives in such a way that ‘medium high’  is 80% of the upper bound, ‘medium’  is 60%, ‘low medium’  is 40%, and ‘low’  is 20% (Ainsworth and Pitcher, 2005), see Table (5.4). It is basically a matter of distributing the five categories into five equally spaced ratios, the scaling factor in Table (5.4) i.e., the range for “ high”  will be 1 – 0.8, medium/high 0.6 – 0.8,… low 0 – 0.2. The bold face entries in Table (5.4) are anchors used for interpolation and the italic entries are interpolated values.  Based on Table (5.4), all the qualitative categories of the unreported catch in Table (5.2) were converted to quantitative values as shown in Table (5.5). The percentage values were converted to absolute values using the reported catch given in Table (5.1). The estimated ranges of unreported catches are given in Table (5.6).  To examine the uncertainty in the estimates of the unreported catch, a Monte Carlo simulation was done. Five thousand samples were taken from asymmetrical triangular distributions with end points being the upper and lower estimates for each value as given in Table (5.6). An asymmetrical triangular distribution was chosen because the likely limits were neither symmetrical nor normally distributed. The extreme values far away from the median were regarded as less likely (Kalikoski et al., in press). The mean and the 95% confidence intervals were calculated.  91 Table 5.1 Reported catch (mean of 5 years) of three Eritrean fisheries (103 t). Fishery 1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Small pelagic 8.00 18.84 6.66 13.94 9.70 0.54 0.16 0.08 0.09 0.04 0.01 Finfish trawl 0 0.01 1.04 1.28 0.82 0.28 0.07 0.20 1.10 2.18 1.59 Shrimp 0.03 0.03 0.02 0.04 0.01 0.03 0.01 0.01 0.01 0.01 0.11  Table 5.2 Qualitative categories of incentives to misreport catch based on the influences from Table (D.1) in the Appendix. Fishery 1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Small pelagic L L LM M L L LM LM L L L Finfish trawl  - M H H L L LM L MH LM M Shrimp L M H H L L LM L H MH M  Table 5.3 Anchor points as a percentage of total extracted catch (reported plus unreported), bold entries are anchors chosen as references. Fishery 1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Small pelagic    5a Finfish trawl   90b 30-50a   26-40c   18 – 40d 25 – 32d Shrimp     90b 90a     32c     60 – 95d 20 – 66d a  Grofit (1971): estimate from onboard observation    c Blindheim (1984): from survey data b  Ben-Yami (1964): estimate from onboard observation   d MOF (1996): estimate from onboard observation   92 Table 5.4 The interpolated values (in %) of unreported catch for the different qualitative categories. Bold entries are anchors used as references and italic are interpolated values. Categories Scaling factor Small pelagic Finfish trawl Shrimp H 1 8.33 90 90 MH 0.8 6.67 72 72 M 0.6 5.00 54 54 LM 0.4 3.33 36 36 L 0.2 1.67 18 18  Table 5.5 The interpolated ranges of estimates of unreported catch as a percentage of the total extracted catch. Fishery 1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Small pelagic 0 - 1.67 0 - 1.67 1.67 - 3.33 3.33 - 5 0 - 1.67 0 - 1.67 1.67 - 3.33 1.67 - 3.33 0 - 1.67 0 - 1.67 0 - 1.67 Finfish trawl 0 36 - 54 72 - 90 30 - 90 0 - 18 0 - 18 18 - 36 0 - 18 54 - 72 18 - 36 36 - 54 Shrimp 0 – 18 36 - 54 72 - 90 73 - 90 0 - 18 0 - 18 18 - 36 0 - 18 72 - 90 54 - 72 36 - 54    93 Table 5.6 Estimates of unreported catch (103 t). Lower and upper refer to the range of unreported catch estimates. Fishery  1950-54 1955-59 1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 Small pelagic lower 0 0 0.11 0.48 0 0 0 0 0 0 0   upper 0.14 0.32 0.23 0.73 0.16 0.01 0.01 0 0 0 0 Finfish trawl lower 0 0.01 2.67 0.55 0 0 0.02 0 1.29 0.48 1.25   upper 0 0.01 9.34 11.52 0.18 0.06 0.04 0.04 2.84 1.23 2.61 Shrimp lower 0 0.02 0.05 0.11 0 0 0.01 0 0.01 0.01 0.06   upper 0.01 0.04 0.18 0.36 0.01 0.01 0.02 0.01 0.02 0.03 0.13    94 5.1 Results and discussion  The estimated overall extractions by the three fisheries from the Eritrean Red Sea are higher than the official report (Figure 5.1). The total extraction (full line in Figure 5.1) is the reported plus the unreported catches. The latter is the mean of 5000 Monte Carlo samples and its 95 % confidence intervals are given by the error bars. The results are averages over the 5 year periods in which the analysis was carried out. The small pelagic fishery has the smallest unreported catch, a maximum of 5% of the total extracted catch. Finfish trawl and shrimp fisheries have a high proportion of unreported catch. When the fishery industry was operating smoothly in the 1950s and 1960s and after 1993, the finfish trawl fishery was underreported by 26 – 84% and the shrimp fishery by 18 – 89%. Adding the three fisheries together (Figure 5.1d), the catch is underreported by 21%. The interpolated quantitative ranges match quite well with those periods where anchor points exist, except in one period, finfish trawl 1965 – 69. For this period, the extreme upper and lower values from the anchor and the interpolated values were taken. Though only one anchor point can be enough to carry out the analysis, having more anchor points helps to double check the results.  All the fisheries show a clear decline in catch in the 1970s and 1980s, mainly due to instability in the region. While finfish trawl and shrimp fisheries revived after the independence of Eritrea in 1993, the small pelagic fishery did not as the fish meal factories and their infrastructure were destroyed. Comparing the three fisheries, the small pelagic is the “ cleanest”  fishery because it has the smallest unreported catch. As its main end product is fish meal, all the catch is used and nothing is discarded. Also, the beach seine gear, dragged manually in shallow waters, is not so destructive to the ecosystem. It had the least problem of misreporting as well. All the fish meal was exported and there is a good record of the export.    95   Figure 5.1 Estimated total extractions by three fisheries in the Eritrean Red Sea. The broken line is the reported catch and the full line is the total including the unreported catch. Error bars are the 95% confidence intervals. Note that the scales of the Y-axes are different.  0 10 20 50-54 60-64 70-74 80-84 90-94 00-04 C a t c h  ( 1 0 3 t ) A. Small pleagic 0 4 8 12 50-54 60-64 70-74 80-84 90-94 00-04 B. Finfish trawl 0 0.1 0.2 0.3 50-54 60-64 70-74 80-84 90-94 00-04 C a t c h  ( 1 0 3 t ) Year C. Shrimp trawl 0 10 20 30 50-54 60-64 70-74 80-84 90-94 00-04 Year D. Total  96 Both finfish and shrimp trawl fisheries have a high level of unreported catch, almost completely from discarding. They both use unselective trawl gear. Once the net is hauled onboard, a large proportion of the catch is thrown back to the sea. These are species which do not have any value in the market or are the small sizes of valuable fishes. The unreported catches of finfish trawl and shrimp fisheries are higher in the 1950s and 1960s than after 1993. This can be attributed three major factors. First, in the 1950s and 1960s, these fisheries were in an experimental stage (Ben-Yami, 1964; Grofit, 1971). Second, the technology used was not as advanced as that used at the present. Third, fishery regulations hardly existed at the time. There was no catch monitoring program, and fisheries were managed by a small division within the port administration of the then Ethiopian government (Ben-Yami, 1964), based in Addis Ababa, far from the coast. On the other hand, after 1993, the fisheries started based on the knowledge accumulated earlier. The technology used is more advanced. The regulation is also better, being managed by a full-fledged ministry, Ministry of Fisheries of the Eritrean government, stationed on the coast. It has regulation mechanisms such as the fishery proclamation of 1998 (MOF, 1998), which aims to regulate fishing activities. It also has monitoring and surveillance programs. For example, trawlers are not allowed to fish in coastal waters shallower than 30 m (Hartmann, 1997). Moreover, an observer is placed in every trawler to monitor the operation and report the retained and discarded catch.  For the shrimp fishery, the catch increased rapidly from the mid 1990s to 2000, however the increase in the unreported catch is less. It could be that the new shrimp grounds found by the industry in the late 1990s have good concentrations of shrimp and low by-catch (Gebremichael et al., 2001). The shrimp fishery has a potential for future expansion. If so, methods of by-catch reduction (Kennelly and Broadhurst, 2002) should be encouraged.  Providing quantitative estimates of unreported catch demands some daring assumptions and they can rightly be criticized. I believe this research will trigger some discussions among researchers and will bring forth feedbacks. There are many subjective opinions in the analysis that need to be reviewed by experts so that the results can be used with more confidence in decision making. However, the procedure is easy to understand and is fully transparent so that values can be adjusted to take account of such comments.  97 This chapter focuses the method of estimating unreported catch using qualitative data and anchors. It does not examine the composition of the unreported catch. This would be useful especially for discarding, which is done in Chapter 4 (see Figure 4.5 and Table C.7 in the Appendix for composition of trawl discards). The composition of the discards can provide information for management. The life history and behaviour of the discarded species can be used to at least minimize their incidental catch, e.g. mesh size can be regulated based on the maturity size of the discarded species. Eritrea has a policy of increasing effort as the current catch level, based on reported catch, is lower than the estimated potential; however the unreported catch should be considered in calculating the total extraction from the ecosystem and effort increase in the future. In addition, it is highly recommended that the increase in effort to focus on the small pelagic fishery. First, it is a resource with big potential, which has not been used since the fishery revived in 1993. Second, it is a cleaner fishery in terms of discard than the others.     98           CHAPTER  6: Ecosystem based assessment of the Red Sea fisheries   99 6.1 Synopsis  An ecosystem-based framework was used to examine the Red Sea ecosystem with emphasis on the fisheries. Ecopath with Ecosim (EwE) modelling tool was used to examine the organisms in the Red Sea, their interactions, including human impacts. Time dynamic simulations were run to quantify the impact of fishery, which is the main direct anthropogenic impact on the ecosystem. The model was fitted to a time series of observed catch and effort to validate its ability to emulate the processes in the ecosystem. Then the model was used to predict the consequences of different fishing scenarios: maintaining the status quo, banning all fishing, and increasing the fishing rate at the average it has been increasing by in the last 10 years. Monte Carlo simulation was used to examine the sensitivity of the predictions to changes in the model input parameters and the risk of the biomasses of the groups falling beyond certain percentages of the starting biomass value of the model were calculated. Equilibrium surplus yield analysis was carried out on the major groups affected by the fishery. Last but not least, the model was used to examine the conflict between artisanal and industrial fisheries in the Red Sea by running scenarios where the fishing effort of each sector was doubled one at a time and the impact on the biomasses of the groups fished by the other sector were calculated.    100 6.2  Introduction  Quantitative assessment of fisheries has evolved in the last 6 decades from the single species assessment (Beverton and Holt, 1957) to multispecies evaluation and recently into ecosystem- based management, although the latter is still embryonic (Browman, 2000; Pikitch et al., 2004). Each step in this progression addressed certain questions pertinent at the time of their development. This progression is continuing as new knowledge is acquired about ecosystems, including human interactions, and drawbacks of the already existing approaches are identified. The more recent approach, ecosystem-based management (EBM) attempts to put fisheries management into a ‘holistic’  framework, trying to avoid the pitfalls of reductionism. A lot has been written about EBM, some attempting to define and/or frame it (Link, 2002; Pikitch et al., 2004) to others developing conceptual or software tools for its implementation (Brodziak and Link, 2002; Smith et al., 2007). EBM’ s acceptance has grown over time and it is under serious consideration by both researchers and practitioners, although poorly implemented as yet (Pitcher et al., 2009). Ecosystem modelling is an important component of EBM, as it enables us to translate the ideas of EBM into workable quantitative assessment tools (Plagányi, 2007). Ecopath with Ecosim (EwE) is one of these tools (Pauly et al., 2000), and it has been used widely, in different ecosystem types. Here, I document the construction and application of an EwE model of the Red Sea, to assess the fisheries in an ecosystem-based framework.  The Red Sea is one of the Large Marine Ecosystems (LME), the large regions of the world oceans, based on its physical parameters, ecology, and exploitation history (Sherman and Alexander, 1986). Although the management of the fisheries is performed by the different countries in their own respective waters, it is helpful to obtain a general ecological understanding of the whole system. Thus, the model incorporates all Red Sea organisms from primary producers to top predators, and human impact through the fisheries.  The habitat and trophic parameters of the organisms are very important for modelling. The following habitat definitions based on FishBase (Froese and Pauly, 2012) are used explicitly in the building the model and to categorize organism by their habitats: Reef associated: living and/or feeding on or near coral reefs, between 0 – 200 m;  101 Pelagic: occurring mainly in the water column between 0 and 200 m, not feeding on benthic organisms; Demersal: living and/or feeding on or near the bottom, between 0 – 200 m; Benthopelagic: living and/or feeding on or near the bottom, as well as in midwater, between 0 – 200m; Bathypelagic: Region of the oceanic zone between 1,000 m to 4,000 m; between the mesopelagic layer above and the abyssopelagic layer below. Living or feeding in open waters at depths between 1,000 and 4,000 m. In FishBase this term is used to include the depth range from 200 m to the bottom and thus the zones mesopelagic, bathypelagic and abyssopelagic; Bathydemersal: living and/or feeding on or near the bottom, below 200m.  These are habitat descriptions in relation to the location of mainly fishes in the ecosystem given in FishBase. However, these are not exhaustive list of habitats. For example, in the model sea grass and sea weed habitats are explicitly included.  6.2.1 The Ecopath model  Ecopath is an ecosystem modelling tool used to account for the energy transfers in an ecosystem (Polovina, 1984; Christensen and Pauly, 1992). Its basic feature is that energy can be transferred from one ecosystem group to another, but the overall transfers are in equilibrium for a period of arbitrary duration. This is in line with the first law of thermodynamics (law of conservation), which states energy can be changed from one form to another, but it cannot be created or destroyed.  The first Ecopath model (Polovina, 1984) was developed to study the ecosystem of the French Frigate shoals, an atoll near the centre of the Northwestern Hawaiian islands. Different scientists were researching and estimating different aspects of the ecosystem and Ecopath was used to put together the estimates in order to get a quantitative picture of the atoll’ s ecosystem. Ecopath was then applied to a wide range of ecosystems (Christensen and Pauly, 1993). In the early development of Ecopath, its steady-state or equilibrium assumption was understood to mean that the mean annual biomass for each species group does not change from year to year (Polovina,  102 1984). In the later development of EwE (Christensen and Pauly, 1992), this assumption was replaced by an emphasis on ‘mass- balance’ , implying that there could be change in biomass over time (i.e., biomass accumulation), but the net change over the whole system remains zero.  Ecopath has two master equations. The first one states biological production within a group equals the sum of mortalities by predation and fisheries, net migration, biomass accumulation and other unexplained mortality as expressed in the equation: !" # $%!&"  '" (!)  # * )+, !)  # -") ." !/"  !" % !"  #   .."  Where Bi and Bj are biomasses of prey (i) and predator (j), respectively; P/Bi is the production/biomass ratio; Yi is the total fishery catch rate of group (i); Q/Bj is the consumption/biomass ratio; DCij is the fraction of prey (i) in the average diet of predator (j); Ei is the net migration rate (emigration – immigration); and BAi is the biomass accumulation rate for group (i). EEi is the ecotrophic efficiency; the fraction of group mortality explained in the model.  The second equation called the energy equation, states consumption within a group equals the sum of production, respiration and unassimilated food as expressed in the equation:  ! # $!&  ! # $ % !&   01 #     2 # %  ! $  !& # 01  Where GS is the fraction of the food that is not assimilated; and TM is the trophic mode expressing the degree of heterotrophy; 0 and 1 represent autotrophs and heterotrophs, respectively. Intermediate values represent facultative consumers.  Predation mortality is the parameter that connects the different groups in the system. What is predation mortality for the prey is consumption to the predator and Ecopath uses a set of algorithms to simultaneously solve the above linear equations for all the functional groups under the assumption of mass balance. The basic inputs of Ecopath are biomass, production per unit  103 biomass (P/B), consumption per unit biomass (Q/B). Because of the mass-balance assumption, Ecopath can estimate one free parameter of the basic input for each group. Diet composition is also basic input for Ecopath and has to be entered, not estimated by the model.  6.2.2 Ecosim  Ecopath gives a snapshot of the ecosystem at one time. Ecosim, on the other hand, is time dynamic simulation (Walters et al., 1997) and can be used in policy exploration. A mass- balanced Ecopath model is used for Ecosim runs driven by fishing mortality. Change in biomass rates over time and the flux of biomass among the groups is expressed by varying biomasses and harvest rates. Simulation is used to fit the predicted biomass to independent time series data. The model can also be driven by climate or nutrient. It is in Ecosim that the effect of fishing on the ecosystem is addressed. In the policy exploration facility, four policy objectives are included: maximize fisheries rent, social benefits, mandated rebuilding of species and ecosystem structure or ‘health’  (Christensen et al., 2000). The basic differential equation used in Ecosim is:  3!" 34  5"(6!) !"7 (!) !" * )+, 8" 2" "  "  # !" * )+,   where dBi/dt represents biomass change rate of group (i) during the interval dt; gi represents the net growth efficiency (production/consumption ratio); Ii is the immigration rate; Mi and Fi are natural and fishing mortality rates of group (i), respectively; ei is emigration rate; and ƒ(Bj,Bi) is a function used to predict consumption rates of predator (j) on prey (i) according to the assumptions of foraging arena theory (Walters and Martell, 2004; Walters and Christensen, 2007). It is modified by the predator-prey vulnerability parameter assigned to the interaction.  Besides a snapshot of the ecosystem (Ecopath) and time dynamics (Ecosim), the EwE package also has a dynamic spatial simulation called Ecospace (Walters et al., 1999). It remedies the assumption of homogenous spatial behavior of organisms which is implicit in Ecopath and Ecosim. The use of Ecospace so far has been mainly in placement and evaluation of marine  104 protected areas (MPA) (Walters, 2000; Varkey et al., 2012). Ecotracer is another component of EwE which deals with the movement and accumulation of contaminants and tracers in the food web (Christensen and Walters, 2004). For further accounts of EwE, notably for the theoretical and mathematical backgrounds see (Walters et al., 1997; Christensen et al., 2008). Plagányi and Butterworth  (2004) and Plagányi (2007) present critical reviews of the EwE approach.  The main objective of this chapter is to assess the Red Sea fisheries in ecosystem based framework. This was accomplished by building an ecosystem model of the Red Sea which: • Presents a quantitative description of the structure of the ecosystem in terms of the ‘players’ (groups), which include the organisms living in that sea and the fisheries, and their interactions, i.e., the flux of energy from one group to another, and including basic ecosystem parameters for each group in the model; • Quantifies and evaluates the effect of fisheries on the system; • Explores the interaction between the different fisheries, and their policy implications. The specific question addressed is whether the industrial and artisanal fisheries have negative impact on each other (the assumption that they do has been a frequent cause of conflict).  105 6.3 Materials and methods  6.3.1 Ecopath  Defining the boundaries of an ecosystem to be modeled can be difficult, especially in marine systems where the boundary can be elusive, and varies through time. However, this is not a problem here, as the whole Red Sea is taken into consideration. The fact that the Red Sea is an enclosed sea with little exchange with neighboring ecosystems makes it ideal to be modeled as a unit.  The data needed to build an Ecopath model is extensive. The Red Sea organisms included in the model are divided into two categories, fish and non-fish, for the convenience of data source and calculating parameters.  6.3.1.1 Fish species  The Red Sea, a subtropical system, has high diversity. There are more than 1290 fish species reported for the Red Sea (Froese and Pauly, 2012), the list of fish species is given in the Appendix (Table E.1). It is neither practical nor necessary for each species to be represented as a group by itself in the model. Grouping of similar species is possible and necessary. Here, grouping was done using parameters that define the trophic interaction of the organisms: habitat, trophic level and size. Using these parameters the fish species were grouped into 20 ecologically meaningful functional groups (Table E.2 in the Appendix). The fish species that are major contributors to the catch of the different major gears in the Red Sea (see Figures 4.4 and 4.5 of Chapter 4) were kept in separate groups, so that detailed analysis on these groups could be carried out.  The two important Ecopath input parameters, consumption rate and production rate for the fish, were calculated using population parameters from FishBase. First priority was given to data from the Red Sea, but when data from the Red Sea could not be found, data were taken from similar ecosystems, i.e., coral reef ecosystems with similar mean annual temperature.  106 Consumption  The food consumption per unit biomass (Q/B) values for the fish species were taken from FishBase, preferably from the Red Sea. When the Q/B value was not given, the empirical equation developed by Palomares and Pauly (1998) was used:   !  9 # 

:;<=    

>/  
?  
>3  where W is asymptotic weight of the species, T is mean annual temperature of the Red Sea, 27.71oC, expressed as 1000/(ToC+273.1), A is the aspect ratio obtained from FishBase, h and d refer to the types of food consumed (i.e., for herbivores h=1, d=0; for carnivores h=0, d=0; for detritivores d=1, h=0).  When W was not directly given it was calculated from length-weight relationship:  =  @ # A= B   where L is the asymptotic length, and a and b are constants from FishBase.  When the aspect ratio was not available, a different empirical equation developed by Pauly (1986) was used to calculate the consumption per unit biomass (Q/B):   !  
CDE # 

  ,FFF G  # = HF,CI # >JK # >LM  where T is the Red Sea mean annual temperature in degree Celsius (27.71oC), Pf is feeding mode parameter set to 1 for predators and zooplankton feeders, and Zero for other fish species, Hd is diet composition parameter set to 1 for herbivores and zero for omnivores and carnivores.    107 Production  The production per unit biomass (P/B) is equal to the total mortality, which is the sum of natural mortality and fishing mortality (Z = M + F). For species not exploited P/B equals M. For all the species M value was searched in FishBase and when it was not available the empirical formal of Pauly (1980) was used. N OFCP # Q= HFRES # TFUCD  Where K is the von Bertalanffy growth constant and L is the asymptotic length both obtained from FishBase and T is Red Sea mean annual temperature (27.71oC).  Biomass  Detailed biomass data was not available for all the fish species included in the model. However, extensive search resulted in some data, which were used as a starting point to parameterize the model. For pelagic fishes an acoustic survey in the southern Red Sea (Massé and Araia, 1997), for demersal fish a trawl survey (Blindheim, 1984), for coral reef fish visual censuses (Roberts and Ormond, 1987; Bouchon-Navaro and Bouchon, 1989; Zekaria, 2003) were used. Abundance values of a wider range of organisms were also available (Antoine et al., 1997; Price et al., 1998; Tsehaye, 2007).  6.3.1.2 Non-fish groups  The non fish groups are diverse with different taxonomic composition. They include marine mammals, turtles, birds, invertebrates and primary producers. Shrimp is the most important of the non-fish groups for fisheries. Hence, it is given its own functional group, as the main focus of the model is ecosystem-based assessment of fisheries in the Red Sea. Data of non-fish groups were searched for the Red Sea; in additional, data from similar ecosystems were also used. For invertebrates, SeaLifeBase (Palomares and Pauly, 2012) and benthic invertebrate population dynamics database (Brey, 2001) were used as sources. The list of the non-fish groups together with their parameters and sources is given in the Appendix (E.1.1).  108  6.3.1.3 Diet matrix  Diet data for the fish species, unless specified otherwise, was obtained from FishBase. Priority was given for data from the Red Sea, but when not available, data from similar ecosystems were used. For the non-fish group, diet compositions were compiled based on similar coral reef ecosystem models of the Eritrean Red Sea (Tsehaye, 2007), Caribbean (Opitz, 1996; Arias- González, 1998), Indonesia (Buchary, 1999; Ainsworth et al., 2007), and French Frigate Shoals- Hawaii (Polovina, 1984). The diet matrix table is given in the Appendix (Table E.4)  6.3.1.4 Fishery  The fishery data for the model were taken from the data compiled for the catch reconstruction of Red Sea fisheries, as presented in Chapter (4). These fisheries can be divided into two main categories: artisanal and industrial. The major fishing gears from each group are represented in the model. For the artisanal sector the major gears are handlines, gillnets and beach seines; while the major industrial fishing methods are bottom trawling and purse seining. As the main objective of the model is to explore the Red Sea fisheries at the ecosystem level, the species which contribute the highest proportion to the catch of the various fishing gears were assigned to distinct functional groups in the model (for each gear). Their names in the model are the gear name followed by ‘fishes’  e.g., fishes targeted by handlining are called ‘handlining fishes’ . The major taxonomic groups for each gear that are given a separate functional group in the model accounted for more than 80% of the catch by respective gears (see Figure 4.4 for artisanal and Figure 4.5 for industrial gears in Chapter 4, and Appendices C.2 - C8). The minor portions were divided among other functional groups by matching the catch compositions to the functional groups. The shark catch was similarly divided between handlining and gillnet, as both gears are used to catch sharks in the Red Sea. Discard from the trawl fishery was included in the model, and was made to flow to detritus. The total catch values were expressed per unit area (t·km-2·year-1). The five fisheries are named by their respective gears: handline, gillnet, beach seine, trawl and purse seine; while the functional groups in the model which are their target are: handline fishes, gillnet fishes, beach seine fishes, shark, trawl fishes, purse seine fishes and  109 shrimp. Sharks are targeted by both handline and gillnet, while shrimps are targeted by trawl; however, because of their importance for the fisheries they are given separate groups. So in the following part when ‘shrimp’  is mentioned, it is the trawl fishery, but the shrimp catch is analyzed separately from the fishes caught by trawling.  6.3.1.5 Parameterizing / balancing the model  Parameterizing a model is making sure the mass balance equations for each group are fulfilled simultaneously. The model was parameterized following the procedure outlined in the Ecopath with Ecosim manual (Christensen et al., 2008), i.e., the input that were less reliable or whose value had been assumed were changed progressively, and the model was run to check the progress of balancing. The diet matrix, being the most uncertain, was the input that was adjusted the most during balancing the model, while P/B and Q/B were changed less, if at all. Model balancing was terminated when it fulfilled the requirements of balanced models: all EE less than 1, the gross food conversion efficiency (GE, i.e., production/consumption) with the range of 0.1 – 0.3 for fish, and all respiration/biomass ratios within a physiologically reasonable range.  6.3.2 Ecosim  Unlike Ecopath, which is static, Ecosim is a time dynamic simulation. The latter was the fitted to time series data. This enabled verifying the parameterization of the Ecopath model, and after some adjustments, to performing an equilibrium analysis with Ecopath, and an exploration of fishery policy scenarios with Ecosim.  6.3.2.1 Fitting to time series data  A time series simulation was made to fit the model predictions to independently calculated catch time series. This fitting exercise helps to validate the ability of the model to mimic the actual process in the ecosystem, including its fishery. A time series of fishing effort was needed for this exercise.  The procedures and the results of the fishing effort reconstruction for the artisanal fisheries are given in the Appendix (E.2.1), while effort data for industrial.were kindly extracted  110 by Dr. Reg Watson from the database compiled in support of the publication by Anticamara et al., (2011).  In order to test the ability of the model (which pertains to 2006) to mimic the functioning of the Red Sea ecosystem, e.g. to predict the catch data from 1950 – 2006, it was made to run from 1930 – 2006, i.e., first to let the model mimic the situation before 1950 (with restored biomass of the predators that have been depleted by the fishery), so that it will be ready for the procedure of fitting to the independent data (Cox et al., 2002; Villy Christensen pers. comm.). The procedure consists of first scaling the time series of effort between 0 and 1, and taking the effort of 2006 to be 1. Then, the relative effort of 1950 is carried backward for few years (20-30 years, i.e, starting 1920 or 1930), and the simulations was run in Ecosim, until they stabilize in 1950. Because the simulation stabilized when it was run from 1930, the simulation from 1920 was discarded and all simulations were done from 1930 – 2006. However, the time series fitting was done only from 1950 – 2006. The fishing effort levels of 1950 were very small compared to 2006, except for the beach seine fishery (Table E.7 in the Appendix), which was a strong vibrant fishery in the 1950s, especially in Eritrea. Later, this fishery was largely abandoned. Thus, for the effort ratio of beach seine, instead of the high value of 1950, an arbitrary low ratio of 0.02 was used for the period from 1930 to 1950. The small effort values for all the fisheries from 1930 – 1950 allowed the model to assume an equilibrium characterized by high biomasses of top predators by the time the actual simulation started in 1950. This is a reasonable assumption that the biomasses of top predators were higher in 1950 before they were fished out in the following decades. More importantly, those values were to be used only as a starting point for the time series fitting, which works by minimizing the sum of squares of the differences between the observed catch and CPUE data and the ones predicted by the model.  During the time series fitting, some of the basic Ecopath input parameters (biomass, P/B. Q/B and diet composition) were modified and the fit rechecked. This procedure was repeated iteratively, and the model fine tuned (particularly the diet compositions, and secondarily the P/B ratios) until the best fit was achieved. Catch per unit effort (CPUE) was used as proxy for biomass to guide the time series fitting. Note that the emphasis of the fitting was not on CPUE,  111 but on the catch time series data, which appear more reliable, given the catch reconstruction documented in Chapter 4.  Trophic flow parameter  A key parameter to be adjusted during time series fitting is vulnerability, a parameter that regulates the flow between different trophic level groups or foraging arena parameter (Walters and Martell, 2004; Walters and Christensen, 2007). Vulnerability depicts the effect of the biomasses of prey and predator on the predation mortality. The minimum value used is 1 when an increase in the biomass of predator does not cause noticeable change in predation mortality, a situation known as prey or bottom-up control. The other extreme occurs when an increase in biomass of predator produces noticeable change in predation mortality known as predator or top-down control. Here, the parameterization of the vulnerability values for the Red Sea was done using both the automated vulnerability search routine in EwE and manually. The vulnerability search routine is an iterative procedure to identify predator-prey interactions that are critical for the model (and presumably the ecosystem) to function. It uses a least-square method to optimize those critical vulnerabilities in order to recreate the observed time series of catch and CPUE. The search begins with all the interactions in the diet matrix, but then later it is focused on the few that are highly influential. Another parameter which affects the feeding behavior of the animals and was adjusted during the fitting process was ‘feeding time factor’ . It is a measure of how fast organisms adjust their feeding behavior (i.e., their feeding times) so as to stabilize consumption rate per biomass. It ranges between 0, causing feeding time and hence time exposed to predation risk to remain constant, to 1, causing fast time response, which reduces vulnerability to predation (Christensen et al., 2008).  6.3.2.2 Model stability and uncertainty analysis  I tested the stability of the model by subjecting it to three scenarios and running the corresponding three simulations: (i) maintaining the baseline fishing rates, (ii) assuming zero fishing rates for all the gears, and (iii) increasing the fishing rates of each gear by 5% each year, which is the overall increase of the fishing rates in the last 10 years (Table E.7 in the Appendix).  112 The stability test showed the model’ s behavior under varying functional group parameters and fishing pressure. If the model behaves in a realistic fashion, then it can be used for fishing policy exploration; otherwise, if unstable results are produced by the model, its use for policy development will not be warranted.  Under the three scenarios, the sensitivity of the model to changes in the basic input parameters was explored using Monte Carlo simulations. The biomasses of all the functional groups were allowed to vary +/- 20% of their original Ecopath values, while P/Q, Q/B and EE were varied +/- 10%, then 100 Monte Carlo draws were made from a uniform distribution. The mean and the standard deviation (SD) were calculated for each simulation to establish a range of error for predictions. In addition, the depletion risk of the fishery groups in a population was explored through a viability analysis, i.e., an estimation of the probability that the biomass can drop below a certain ratio of the original biomass.  6.3.2.3 Equilibrium analysis  Once the model’ s stability was established and uncertainty analyses were performed, equilibrium analysis was carried out, which provides both important diagnostics and analytical results. The pertinent routine calculates the biomass and catch of the functional groups at different fishing mortality rates. EwE allows this analysis either by taking one group at a time and keeping all the other groups constant (which is thus similar to traditional single species stock assessment; or allowing interaction between groups (which is similar to multi-species stock assessment). For the Red Sea model, the latter was used.  6.3.2.4 Fishing policy exploration  Besides the three scenarios mentioned above, two additional scenarios were run using Ecosim simulations to explore the interaction between the artisanal and industrial fisheries in the Red Sea. This is very important for the region as conflicts between the two fisheries types are common, which has serious impact on the decision-making process. The two scenarios involved are one where the fishing effort of the industrial sector was doubled without changing the  113 artisanal effort, and a second scenario where this was reversed. The simulations were run to predict the biomasses of all the groups until 2030.  6.4 Results  6.4.1 Ecopath  EwE is an ecosystem modelling tool with a wide suite of routines which allows numerous analyses once it is balanced and validated. Here, the general structure of the Red Sea ecosystem model is presented, as are a number of analyses relevant to the main objective of building the model, which is an ecosystem-based assessment of the Red Sea fisheries. The key result of the Ecopath modelling part is a snapshot of the ecosystem with all the basic parameters satisfying all features as outlined above, i.e., all the ecotrophic efficiencies (EE) are less than 1, respiration values are positive (Christensen et al., 2008). This balanced model of the Red Sea (Table 6.1) was used to explore the Red Sea ecosystem using the diagnostic tools provided in  EwE.  The food web with all the flows of energy among different groups placed on the order of the trophic level of the groups is given in Figure (6.1). Since the main objective of the model is to explore the fishing activities, the names if the groups which are the prime targets for fishery are colored red. The size of the squares is proportional to the biomass of the groups. Of all the living groups, the primary producers (phytoplankton, sea grass and algae) have biomasses that are notably larger than all other groups. This is summarized in the food web pyramids both for the biomasses and flows (Figure 6.2). The flow pyramid shows flow by trophic level, the bottom plane is the first order consumers and the volume of the compartments is proportional to the sum of all flows at the level or throughput. When drawn to the same scale, pyramids are useful to compare different systems, especially since the top angle of the flow pyramid is inversely proportional to the mean trophic transfer efficiency at trophic level II-IV (Christensen et al., 2008). The Red Sea model is compared with some tropical ecosystem models built using EwE and whose files were available in the Ecopath website (www.ecopath.org; Table 6.2). In terms of fisheries, it is worth noting that the Red Sea has a very low total catch in relation to total biomass (excluding detritus), indicating a lower exploitation.  114 Table 6.1 The basic parameters of the balanced Red Sea model. Group No. Group name Trophic level Biomass (t·km-²) P/B (year-1) Q/B (year-1) EE GE 1 Cetaceans 3.84 0.0610 0.044 5.914 0.025 0.007 2 Dungongs 2.00 0.0029 0.025 11.000 0.000 0.002 3 Birds 4.04 0.0068 0.380 20.000 0.026 0.019 4 Turtles 2.69 0.0555 0.150 3.500 0.137 0.043 5 Trawler fishes 3.38 0.0402 2.680 11.380 0.972 0.236 6 Purse seine fishes 3.53 0.0210 3.085 14.150 0.945 0.218 7 Beach seine fishes 3.09 0.1080 3.250 15.000 0.800 0.217 8 Handlining fishes 3.54 0.0700 1.300 7.887 0.688 0.165 9 Gillnet fishes 4.07 0.0265 2.000 8.000 0.950 0.250 10 Whale shark 3.28 0.0038 0.035 4.000 0.500 0.009 11 Sharks 4.16 0.0076 0.750 4.371 0.950 0.172 12 Rays 2.88 0.0040 0.373 3.000 0.400 0.124 13 Reef top predators 3.76 0.0197 1.052 4.000 0.950 0.263 14 Large reef carnivores 3.51 0.1100 1.240 5.500 0.344 0.225 15 Medium reef carnivores 3.43 0.1380 1.728 7.324 0.576 0.236 16 Small reef carnivores 3.21 0.3800 2.800 10.000 0.636 0.280 17 Reef omnivores 2.88 0.2630 2.700 13.890 0.950 0.194 18 Reef herbivores 2.00 0.2880 3.200 16.000 0.950 0.200 19 Large pelagic carnivores 3.82 0.1050 0.722 6.508 0.960 0.111 20 Small pelagic carnivores 3.44 0.2740 3.162 10.000 0.950 0.316 21 Pelagic omnivores 2.64 0.2660 2.828 10.000 0.950 0.283 22 Demersal top predators 3.58 0.0073 1.300 6.000 0.946 0.217 23 Large demersal carnivores 3.31 0.0160 1.500 7.000 0.439 0.214 24 Medium demersal carnivores 3.04 0.0620 1.990 8.000 0.920 0.249 25 Small demersal carnivores 2.96 0.2230 3.189 12.000 0.960 0.266 26 Demersal omnivores 2.16 0.2960 3.200 14.000 0.940 0.229 27 Demersal herbivores 2.00 0.3600 3.500 16.500 0.975 0.212 28 Benthopelagic fish 2.78 0.2350 1.800 6.000 0.970 0.300 29 Bathypelagic fish 3.11 0.0020 1.749 12.720 0.126 0.138 30 Bathydemersal fish 2.91 0.0040 1.260 6.940 0.831 0.182 31 Shrimp 2.09 0.0100 9.000 25.000 0.609 0.360 32 Cephalopods 2.92 0.3990 3.500 12.000 0.549 0.292 33 Echrnoderms 2.10 0.5960 2.500 8.000 0.553 0.313 34 Crustaceans 2.19 0.8160 6.667 20.000 0.451 0.333 35 Molluscs 2.05 0.3680 9.000 30.000 0.556 0.300 36 Meiobenthos 2.07 0.2950 26.000 100.000 0.402 0.260 37 Corals 2.28 0.9280 2.800 9.000 0.527 0.311 38 Other sessile fauna 2.28 0.8500 3.200 12.000 0.368 0.267 39 Zooplankton 2.11 14.0000 52.000 178.000 0.363 0.292 40 Phytoplankton 1.00 21.5000 110.000 - 0.955 - 41 Sea grass 1.00 11.0000 9.000 - 0.015 - 42 Algae 1.00 38.0000 14.000 - 0.027 - 43 Detritus 1.00 80.0000 - - 0.034 -      115   Figure 6.1 Flow diagram of the food web of the Red Sea ecosystem. Rectangles represent the biomass of the functional groups. The names of the major fishing groups are colored red. The numbers on the left are trophic levels.  116   ·  Figure 6.2 Biomass (left, in t·km-2) and flow pyramids (right, in t·km-2·year-1) for the Red Sea model.  The data requirement for an EwE model is huge, and models can be categorized by the quality of the data used for constructing them. This is done using pedigree analysis. It is a routine in EwE which allocates the likely uncertainty associated with input parameters based on pre- defined categories according to the sources of the inputs. Parameters from quantitative research in the model area receive a higher pedigree index, which also means low uncertainty value. On the other hand, parameters estimated by Ecopath receive a lower pedigree index and a higher uncertainty value. Once the indices are assigned for all input parameters, then the routine calculates an overall average ranging between 0 and 1 (inclusive); 1 being model built from local data with high precision (Christensen et al., 2005). There does not exist a single Ecopath model with a pedigree value of 1 (Morissette, 2007). The Red Sea model scored 0.433, while analysis of 50 other models with average of 27 groups resulted in a mean pedigree of 0.44 (Morissette, 2007). Table (6.2) gives the pedigrees of four other tropical ecosystem models compared with that of the Red Sea model. The mixed trophic impact, MTI, (Figure 6.3) shows the combined direct and indirect trophic impacts that a small change in the biomass of one group could have on other groups. If we zoom in only on the fishery groups, the main impacts of the fisheries are, as one would expect, on the group they target (Figure 6.4) but not on the other fishery groups.  117 Table 6.2 Comparison of the Red Sea model with other tropical ecosystem models using system summary statistics. Criteria Red Sea Great Barrier Reef Laguna Bay, Philippines San Miguel Bay, Philippines West Florida shelf USA Total boxes 43.00 32.00 17.00 16.00 59.00 Living groups 42.00 30.00 16.00 15.00 55.00 Pedigree index 0.433 0.139 0.499 0.286 0.630 Sum of all consumption (t/km²/year) 2615.82 4314.13 7793.81 769.38 18501.20 Sum of all exports (t/km²/year) 1665.10 1119.89 5901.51 516.19 903.44 Sum of all respiratory flows (t/km²/year) 1330.97 1732.15 3137.23 381.56 5977.33 Sum of all flows into detritus (t/km²/year) 1723.53 4038.89 6544.32 931.41 17273.88 Total system throughput (t/km²/year) 7335.00 11205.00 23377.00 2599.00 42656.00 Sum of all production (t/km²/year) 3756.00 3920.00 10838.00 1080.00 14071.00 Mean trophic level of the catch 3.40 2.49 2.08 3.00 3.51 Gross efficiency (catch/net p.p.) 0.000085 0.002971 0.031380 0.016502 0.000051 Calculated total net primary production (t/km²/year) 2996.00 2846.24 8950.30 897.75 6986.95 Total primary production/total respiration 2.25 1.64 2.85 2.35 1.17 Net system production (t/km²/year) 1665.03 1114.09 5813.06 516.19 1009.62 Total primary production/total biomass 32.49 9.82 49.99 28.65 9.74 Total biomass/total throughput 0.01 0.03 0.01 0.01 0.02 Total biomass, excluding detritus (t/km²) 92.22 289.87 179.05 31.34 717.61 Total catches (t/km²/year) 0.25 8.46 280.86 14.82 0.36 Connectance Index 0.31 0.28 0.21 0.34 0.23 System Omnivory Index 0.24 0.23 0.14 0.17 0.26 Total market value (US$) 234.88 1.20 - - 0.28 Total value (US$) 234.88 1.20 - - 0.28 Total variable cost (US$) 187.90 0.61 - - 0.00 Total cost (US$) 187.90 0.61 - - 0.00 Profit (US$) 46.97 0.59 - - 0.28  118  Figure 6.3 Mixed trophic impact (MTI) of the functional groups in the Red Sea model. The upward dark bars and downward lighter bars show the positive and negative impact, respectively, that a small increase of the biomass of an impacting group (Y-axis) would have on all other groups (X-axis).  119   Figure 6.4 Mixed trophic impact of the fisheries of the Red Sea. The gears in the x axis are the impacted groups, while the colours in each cluster are the impacting group.  6.1.1 Ecosim  6.1.1.1 Fitting to time series  After fine tuning the basic Ecopath input parameters, searching for vulnerability values and fitting the time factor, the best fit between the observed and predicted catch was obtained (Figure 6.5). The pattern for the two sets of data was similar for almost all the fisheries. However, a clear distinction is observed between the artisanal and industrial fisheries. The fit is generally better for the industrial fisheries (purse seine, trawl and shrimp). The best fits are for trawl (fishes) and shrimp. For the groups in the artisanal fishery (gillnet, handlining, shark and beach seine fishes), the fits were poor at the beginning of the fitting run. The model was responding to changes in CPUE, which was used as measure of biomass. The CPUE calculated for the artisanal can be divided into two periods, before and after motorization, which started in the 1960s but got its momentum in the 1970s. The expansion of the fishing effort was higher after motorization, and that CPUE calculated after motorization show better representation for the whole Red Sea, the area considered in the model. So, more emphasis was given to the fitting after 1970 and the model predicted the pattern.  -0.45 -0.35 -0.25 -0.15 -0.05 0.05 M TI  in de x Impacted group Impacting group color code Trawl Purse seine Gillnet Handlining Beach seine  120       Figure 6.5 Times series of observed catch data from the Red Sea (dots) and catch predicted by the fitted Red Sea EwE model (line) from 1950 – 2006 for the functional groups important in fisheries. The model was driven by independently estimated fishing effort data.  0.00 0.03 0.06 0.09 Ca tc h ( t.k m - 2 ) Gillnet 0.00 0.03 0.06 0.09 Handlining 0.00 0.01 0.02 C at ch  (t. km - 2 ) Shark 0.00 0.02 0.04 0.06 Beach seine 0.00 0.03 0.06 0.09 C at ch  (t. km - 2 ) Purse seine 0.00 0.02 0.04 0.06 1950 1960 1970 1980 1990 2000 Year Trawl 0.000 0.003 0.006 0.009 1950 1960 1970 1980 1990 2000 Ca tc h ( t.k m - 2 ) Year Shrimp  121 For the vulnerability search routine, the most important functional groups were sharks, gillnet fishes, i.e., the major species targeted by gillnet fishery, and handlining fishes. Changes in these three groups, which are on top part of the food web (Figure 6.1), had a high impact on the foraging arena dynamics of the model. Once the vulnerability values for the three groups were adjusted, the minor groups were easily accommodated, along with the feeding time factor. For all the groups, important in fisheries the latter value was adjusted to zero, which means that the feeding time and hence the time they were exposed to predation risk remained constant. The final vulnerability and the feeding time factor values are given in the Appendix (Tables E.8 and E.9).  6.1.1.2 Stability and uncertainty  Three scenarios run to test the stability of the model generated largely predicable results. When the fishing mortality was kept at the baseline, the biomasses of all the fishery important groups remained more or less constant. When the fishing mortality was set to zero, the biomasses of all the groups increased, except for the fish exploited by beach seines, which decreased slightly at first, then stabilized at a slightly higher level, and the biomass of fish exploited by trawlers, which increased drastically at first, then stabilized at a lower level (but still higher than the initial level). In the third scenario, when the fishing mortality was increased by 5% per year, the biomass of all groups decreased except those exploited by beach seines, which consist of low trophic level fishes. Thus, once the biomasses of predators are decreased, the biomasses these fishes increased, due to reduced predation. The Monte Carlo uncertainty analysis showed all the estimated values were within +/- 1 standard deviation (Figure 6.6).  The depletion risk of the fishery groups in a population viability analysis, i.e., the probability the biomass falling below a certain fraction of the original biomass. For the zero and baseline fishing scenarios did not cause any depletion beyond 50% of the baseline biomass. On the other hand, in the scenario where fishing was increased 5% per year, the probability of the biomass in 2030 dropping below 5% of the baseline was 100% for purse seine, handlining and sharks. Beach seine fishes would not go below 50% of the baseline, while trawler, gillnet fishes and shrimps exhibited varying degrees of depletion (Table 6.3).  122 Baseline fishing   Zero fishing    Increasing fishing     0.000 0.025 0.050 B i o m a s s  ( t . k m - 2 ) 0.00 0.20 0.40 0.00 0.01 0.02 0.03 0.00 0.05 0.10 B i o m a s s  ( t . k m - 2 ) 0.00 0.20 0.40 0.60 0.00 0.03 0.06 0.000 0.004 0.008 B i o m a s s  ( t . k m - 2 ) 0.00 0.06 0.12 0.18 0.000 0.003 0.006 0.00 0.05 0.10 0.15 B i o m a s s  ( t . k m - 2 ) 0.00 0.05 0.10 0.15 0.00 0.06 0.12 0.18 G i l l n e t  H a n d l i n i n g  S h a r k  B e a c h  s e i n e   123     Figure 6.6 Ecosim simulation test at three scenarios (zero, baseline and effort increasing at 5% per year). The lines are the biomasses of the major fishery groups predicted by the model for 24 year simulations from 2006 – 2030, error bars show 1 SD around the mean. 0.00 0.01 0.02 0.03 B i o m a s s  ( t . k m - 2 ) 0.00 0.06 0.12 0.18 0.00 0.01 0.02 0.00 0.02 0.04 B i o m a s s  ( t . k m - 2 ) 0.00 0.02 0.04 0.06 0.08 0.00 0.02 0.04 0.00 0.01 0.01 2006 2012 2018 2024 2030 B i o m a s s  ( t . k m - 2 ) Year 0.00 0.02 0.04 2006 2012 2018 2024 2030 Year 0.000 0.004 0.008 0.012 2006 2012 2018 2024 2030 Year P u r s e  s e i n e  T r a w l  S h r i m p   124 Table 6.3 Biomass depletion risk probabilities for the major fishery groups in the Red Sea below different levels of biomasses, as a ratio of the baseline (2006), at the end of 24 years simulation (2030).  End state (2030) biomass as a percentage of baseline (2006) Groups 5% 10% 15% 20% 30% 40% 50% Trawler fishes 0 4 38 78 99 100 100 Purse seine fishes 100 100 100 100 100 100 100 Beach seine fishes 0 0 0 0 0 0 0 Handlining fishes 100 100 100 100 100 100 100 Gillnet fishes 0 74 100 100 100 100 100 Sharks 100 100 100 100 100 100 100 Shrimp 0 5 25 47 90 98 100  6.1.1.1 Equilibrium analysis  The equilibrium analysis provided, for all the groups important for fisheries, estimates of equilibrium biomass and catch values at different fishing mortality rates and the value of the current fishing mortality rate in relation to that generating maximum sustainable yield (Fmsy; Figure 6.7). Gillnet, handlining, shark and purse seine fisheries are operating at fishing mortality rate beyond Fmsy, while trawl and shrimp are near Fmsy level. The beach seine fishery was the only fishery operating at a level much lower than Fmsy (Figure 6.8). The baseline fishing mortality rate of the shark fishery is 3.6 times the optimum calculated by the model, the furthest from Fmsy of all the fisheries, i.e., the shark fishery is the most depleted resource.    125       Figure 6.7 Result of the multispecies equilibrium analysis for major Red Sea fishery groups. Curved full line shows surplus yield, broken line shows equilibrium biomass and vertical line is the baseline fishing mortality rate (see text).  0.00 0.02 0.04 0.06 0.0 0.1 0.2 0.3 0.0 0.5 1.0 1.5 2.0 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) Gillnet 0.00 0.02 0.04 0.06 0.0 0.2 0.4 0.6 0.00 0.25 0.50 0.75 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) Handlining 0.000 0.003 0.006 0.009 0.0 0.1 0.2 0.0 0.4 0.8 1.2 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) Shark 0.00 0.02 0.04 0.00 0.04 0.08 0.12 0.0 0.5 1.0 1.5 2.0 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) Beach seine 0.00 0.02 0.04 0.06 0.00 0.10 0.20 0.0 0.6 1.2 1.8 2.4 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) Purse seine 0.00 0.02 0.04 0.00 0.03 0.06 0.09 0.0 1.0 2.0 3.0 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) F (yr-1) Trawl 0.000 0.002 0.004 0.000 0.006 0.012 0.018 0.0 0.4 0.8 1.2 C at ch  (t. km - 2 ) B io m as s (t. km - 2 ) F (yr-1) Shrimp  126  Figure 6.8 Baseline fishing mortality rate (Fbase) in relation to the optimum fishing mortality calculated by the model (Fmsy). The 45o line indicates where Fbase is equal to Fmsy.  The equilibrium analysis considers multispecies interactions, which is more realistic and closer to the actual ecosystem functioning than single species assessment. For this reason, the yields from multispecies are lower than from single species assessments for all the fisheries except for shrimp (Figure 6.9).   Figure 6.9 Maximum sustainable yields (MSY) comparison of single species (open bars) and multispecies (black bars) equilibrium analysis. Gillnet Handlining Shark Beach seine Purse seine Trawl Shrimp 0 0.4 0.8 1.2 1.6 0.0 0.2 0.4 0.6 0.8 1.0 1.2                               Models's optimum fishing mortality (Fmsy, year-1) 0.00 0.02 0.04 0.06 Ca tc h (t. km - 2 )  127  6.1.1.2 Fishery policy exploration  The conflict between artisanal and industrial fisheries was explored by doubling the effort of one sector at a time. This caused, as expected the biomasses of the groups targeted by the respective sector in question to decrease drastically (Figure 6.10). What was interesting and contrary to expectations were the effects of one sector on the other. An increase in the effort of one sector did not decrease the biomass of the groups targeted by the other sector; rather, it increased slightly. When industrial fishing effort was doubled, the increase in the biomass of groups targeted by the artisanal fisheries was higher (Figure 6.10a) than the converse (Figure 6.10b). Doubling the industrial sector did not have an impact on the shark biomass (Figure 6.10a), while beach seine fish biomass benefited from it. The small pelagic beach seine fishes are the main prey for the purse seine fishes, and when the industrial sector effort is doubled, the biomass of the purse seine fishes decreases strongly. This implies that the beach seine fishes experience a predatory release, resulting in an increased biomass.  Figure 6.10 Change in the biomass ratios of the major fishing groups as a result of doubling only the industrial fishery effort (a) or the artisanal (b).  Trawl Purse seine Beach seine Handlining Gillnet Shrimp Shark 0.0 0.5 1.0 1.5 2006 2012 2018 2024 2030          Year a Trawl Purse seine Beach seine Handlining Gillnet Shrimp Shark0.0 0.5 1.0 1.5 2006 2012 2018 2024 2030 Year b  128 6.2 Discussion  The Red Sea, as many coral reef ecosystems, is a complex system with a multitude of interactions among the organisms, and with humans within the ecosystem. The EwE model represents the ecosystem quantitatively and can act as the map to understand the ecosystem in some detail. But the model did not capture all interactions, by far. However, the model gives a reasonable picture of the dominant interactions, and more specifically, about those that affect the fishery, as intended. The Red Sea ecosystem has a large biomass at its base (the primary producers), which tapers off as one ascends the trophic pyramid. All the groups important in the fishery are in the upper part of the food web (upper left corner of Figure 6.1) and have trophic level > 3, except for shrimp. This is reflected in the mean trophic level of fisheries catch, which was 3.4 in 2006 (Table 6.2). This shows that the fishery still can catch top predators, which is uncommon for most of the exploited reef ecosystems of the world (Jackson et al., 2001; Worm et al., 2005).  In terms of the impact of change in biomass of one group on another, increase in shark biomass has the most negative impact on certain groups: cetaceans, birds, turtles, whale shark and rays (Figure 6.3). Shark is the main (for some the only) predator for these groups; hence it has a direct impact on their biomasses through predation. Sea grass has the direct positive impact on the biomass of dugongs, which feed extensively on the sea grass. Most of the other impacts are positive, although at a moderate level. The pedigree value of the Red Sea model is about average for the largest pedigree analysis done yet (Morissette, 2007), despite the fact that some key parameters (especially biomass) were not available. This is surprising, because the Red Sea is reputed to be very data-sparse. It may be mentioned, in this context, that the most comprehensive source of information on the Red Sea was FishBase (www.fishbase.org), especially for the three other main inputs of the model, i.e., P/B, Q/B and diet composition.  Although a high pedigree value, implying abundant sources of input, can lead to better quality model, a more useful validation of a model is its ability to predict independent observations, i.e., fit to a time-series data. Indeed, the fitting of the model to time series catch data was the most important part in validating the EwE model of the Red Sea. During the time series fitting, all  129 parameters and possible interactions (diet matrix and trophic flow parameter vulnerabilities) are scrutinized. At the end of the fitting, some important changes were made to the model. An interesting observation during the fitting was, how difficult it was to fit both the early years of the time series (1950s and 60s), and the final decade. When the whole time series was considered the model at first did not track the independent time series catch at all. It rather produced a horizontal line that went through the observed data. A close examination of the latter data revealed that there was a major shift in the Red Sea fisheries starting in the 1970s. Before the 1970s, most of the fishery, especially the artisanal, was non-motorized and exploited in shallow inshore waters.  With motorization, fishers started to venture out to new fishing grounds further offshore. However, the catch and effort data do not differentiate between inshore and offshore fishing grounds. Hence, the CPUE data do not necessarily reflect trends occurring in the whole ecosystem. Also, Ecosim uses biomass to guide the fitting process. Because a time series of independent observation of biomasses of the different groups does not exist for the Red Sea, the temptation was great to use CPUE data as a proxy for biomass. Using CPUE as a proxy for biomass is problematic. A declining CPUE, while locally accurate, may document only a local depletion, leaving the bulk of the biomass of the group in question unaffected (Hilborn and Walters, 1992) as probably occurred in this case (see below). Thus here, after a few (unsuccessful) attempts to fit the CPUE data, emphasis was given to fitting the catches, as the more reliable data now available from the Red Sea. Also, during the fitting process, emphasis was given to the years after 1970, under the assumption that, after motorization, wider areas of the Red Sea were covered, whereas only the inshore waters were fished before 1970.  This brings us back to the issue of localized depletion in the Red Sea, mainly in fishing grounds near major settlements. Even though the Red Sea still has a relatively high predator biomass, some areas which fishers frequent have shown signs of localized depletion (Tesfamichael, 2001; Tsehaye, 2007). The effect of the spatial distribution of the fishing effort on the fitting procedure can be easily seen by comparing the industrial and artisanal fishery in the Red Sea. Unlike the artisanal fishery, the industrial fishery used motorized vessels from the beginning, giving it a wider coverage. The fits for the industrial fisheries were reasonably good throughout  130 the time series (1950 – 2006), with no change over time, contrary to the artisanal fisheries, where the fits improve markedly (Figure 6.5).  The model stability tests, based on three scenarios (zero, baseline and increasing effort fishing) not only showed that the model was behaving well, but also that the result were moderately precise (+/- 1 SD) when the input parameters were allowed to change within certain range in a random fashion. Decreasing fishing effort, for example, is predicted to have a positive impact on the biomasses of the groups that are fished. On the other hand, if the effort is allowed to increase at the rate it has been increasing the last 10 years (about 5% increase per year), the model predicts that all the groups important to the fisheries (except beach seine fishes) will collapse within the next two decades (Figure 6.6). The probability that the biomasses of the groups falls below 5% of the baseline value is very high (100% for purse seine, handlining and sharks) for all the groups except beach seine fishes. Thus, increasing the effort level by the rate it has been increasing for the last 10 years would have dire consequences in the long term.  These predictions were confirmed by analysing the fishing level of each fishery important group using the equilibrium analysis, which showed that most of the fisheries are operating at an effort level higher than that required to generate MSY (Fmsy; Figures 6.7 and 6.8), except for beach seines fishes, which is at a very low level, and shrimp and trawl fishes, which operate around Fmsy. These results are compatible with the general understanding of the situation of the fisheries, and their trends. It seems conflicting that there are still big sized top predators in catches of the Red Sea artisanal fisheries, but the equilibrium analysis shows that the fisheries are operating beyond the MSY level. This is explained by the fact that the big predators are not common in the catches throughout the Red Sea. They are rather common in Sudan and Eritrea, the countries with the least intensity of fishing, which is demonstrated both in Chapters 2 and 3. Even in those countries, the big predators appear in the catches when fishers venture out to newer fishing grounds, otherwise there are evidences of localized depletions (Tesfamichael, 2001; Tsehaye, 2007). The pockets of fishing grounds with still relatively unexploited biomasses are easily overshadowed in the ecosystem modelling analysis which deals with the whole Red Sea. Second, this occurrence of top predators in the catches of the Red Sea fisheries is sometimes taken as an indicator that the Red Sea fisheries are doing better only in comparison  131 to similar ecosystems that are worse than the Red Sea, for example southeast Asia (Christensen, 1998; Pet-Soede et al., 2000) and west Indian Ocean (McClanahan, 1995). However, this comparison can be detrimental because the reference is to a worse scenario rather than to the potential of the Red Sea ecosystem as shows in the EwE model.  Using the ecosystem model results in isolation, as if they were the results of stock assessments, may not be advisable. We cannot expect models to generate precise predictions, but rather give coherent representations of the system in question, and its dynamics (Christensen et al., 2008). For the Red Sea model caution is needed, particularly in conjunction with the equilibrium analyses of the artisanal fisheries, as they may be still reflecting only the limited area where that the fisheries operate, which may not translate easily to the whole ecosystem. This may hold true even after the motorization of artisanal boats and expansion of their fishing grounds. It will be worth examining this hypothesis with an explicit spatial dynamics of the fishing effort, which is not available at the moment.  One example that stands out clearly is the estimated MSY for the beach seine fishery (Figure 6.9) is lower than for the gillnet, handlining and purse seine fisheries (depending on whether one takes the single or multispecies analysis). However, previous stock assessment results indicate that the MSY value of beach seine fishes would be higher than almost all the other fisheries (e.g., Walczak and Gudmundsson, 1975; Giudicelli, 1984). Indeed, it appears that the representation of the beach seine fishery in the Red Sea EwE model suffered from its limited size, and the absence of good data. EwE applications benefit immensely, with regards to the trustworthiness of their prediction, from time series historic fishery data of exploited stocks (Villy Christense, pers. comm.; see also Guénette et al., 2008).  Except for shrimp, all the MSY estimates of the fisheries were lower in the multispecies equilibrium analysis than single species analysis (Figure 6.9). The former is more realistic representation of the system, and that is why an ecosystem based fishery assessment and management can produce a more holistic and reasonable results. One possible explanation for a higher MSY for shrimp in multispecies equilibrium analysis is that shrimp is at the lower  132 trophic level and in multispecies analysis the biomasses of the predators are reduced, which means less mortality by predation, which in turn translates to a higher level of MSY.  Perhaps the most important question about the fisheries situation in the Red Sea is whether artisanal and industrial fisheries interact, and if they do, to what extent. The complaints of the artisanal fishers about the industrial fisheries (which are foreign companies in most of the Red Sea countries) are common and sensitive issue. Although their conflict may have many facets, one of the main aspects of the competition between these two fisheries is the effect of the industrial sector on the catch of the artisanal fisheries. In 472 interviews conducted in the Red Sea countries of Sudan, Eritrea and Yemen with the artisanal fishers, 75% of them blamed increase in effort, which includes both artisanal and industrial, as the reason for decline in their catch (D. Tesfamichael, unpublished data). Most of that blame, however, is laid on the industrial sector. This is the reason why the trophic interactions between groups and fisheries were studied using EwE. The model simulation supported that increase in effort in general is the cause of the decline (Figures 6.6 and 6.10), but did not support the contention that that one sector is causing the decline of the other (Figure 6.10). Actually, to a small extent, the sectors appear to be synergistic, i.e., their interactions are not zero-sum game. This is contrary to the general perception (e.g. in Pauly, 2006); it is also not commonly seen in ecosystem models (Daniel Pauly, pers. comm.). Looking at the mixed trophic impact of the fisheries on each other shows that, the main negative impact of the groups is on themselves (Figures 6.4 and 6.10), but there is no negative impact on others, except for the slight effect that handlining has on the purse seine and gillnet fisheries.  Another crucial insight comes from the nature of the two sectors. They do not target the same groups, thus avoiding direct competition. They operate on groups which inhabit different habitats, and even when they target similar habitat (e.g., pelagic by purse seine, gillnet and beach seine) their gears and operations differ. Trawl and handlining fisheries target mainly muddy and reef habitats, respectively, which are not targeted by any of the other fisheries. Possible conflicts would be among the fisheries that target pelagic species. Purse seiners target small and medium pelagic species, but not close to the shore, while gillnet fishery targets large pelagic species using bigger mesh size gillnets than the mesh size used by purse seiners. The  133 main potential conflict would be between beach seine, which also targets small and medium pelagic fishes, and purse seine, which is shown in the mixed trophic impact analysis (Figure 6.4). This is reflected by the increase in beach seine fish biomass in the simulation when the biomass of purse seine is decreased due to increased industrial fishery effort levels (Figure 6.10a). However, the beach seine fish biomass increase is not very big, because beach seines operate mainly on shallow beaches as opposed to purse seiners which operate in relatively deeper water; thus, there is not overlap of habitats to see a big impact of purse seine on beach seine. Second, at the present, the beach seine fishery is almost non-existent, i.e., the group’ s biomass is almost at its highest carrying capacity (Figure 6.7) with no room for large increase. For the pelagic species, even if beach seine and purse seine fisheries operate in different habitats and use different gears (mesh sizes), one could argue that the very mobile (or migratory) behavior of the target species would cause mixing and possible conflict. Simulation runs where the fishing pressures of the industrial fisheries (trawl and purse seine) were increased ten folds were run to examine how far the effort can increase before it starts to affect the artisanal fisheries. There was no impact on the biomasses of the groups targeted by the artisanal, except sharks, when the trawl effort was increased ten times.  The lack of major impact among the fisheries is helped by almost non-existent mixed trophic impact among the groups (Figure 6.3). This scenario may not be common in many other ecosystems, but the Red Sea still has a wide range of low and high trophic level fishes appearing in the catch (e.g., the mean trophic level is 3.4, see Table 6.2). A possible hypothesis to explain this singular behaviour of the Red Sea model (and, hopefully, of the Red Sea itself) is that because of the wide range of fish available for the fisheries, they can still target different sections of the ecosystem with no direct competition. One can conjecture that the fewer top predators, the main target of the artisanal fisheries, are available, the more they will start to target lower trophic levels, as they do in many fisheries (Pauly et al., 1998), making them rely on the resources which the industrial sector also exploits. However, it is important to note that these results apply only to the trophic interactions between the industrial and artisanal fisheries. In real life, these two fisheries are not totally separate from each other and there are many non- trophic interactions that are not dealt in with EwE. For example, there are complaints by artisanal fisheries that the industrial fisheries operate close inshore (forbidden in almost all Red  134 Sea countries) and destroy coastal habitats, and sometimes even the fishing gears of the artisanal fishery. Although, the trophic model of the Red Sea does not deal with such issues, it does deal with an important aspect of the conflict, and thus can be useful, in conjunction with other approaches, for exploring policies for the Red Sea fisheries.  The total catch for 2006, the base year of the model, was 122,370 t (only 95,564 retained), which was calculated to be 0.2 t·km-2, the unit used in the model. This may not sound high in a global context. Nevertheless, it is significant to the countries in the region. Fish is the main staple food for the coastal communities. It is a cheap source of protein and provides livelihood for the communities. The Red Sea area is very dry and population density is low, which may explain why there are still large sized predators in the catch. Since the countries on the Red Sea coast are generally less industrialized, fisheries can be a good source of employment. The fishery may be expanded further to supply more protein and employment for the local people, but that expansion should target the small pelagic beach seine fishery, all the other fisheries are already at or beyond their sustainable level (Figures 6.7 and 6.8).    135             CHAPTER  7: Conclusion   136 The rate at which we are exploiting resources cannot continue at the same level without creating major problems for the ecosystems. We humans interact with the environment and depend on the resources for our basic needs and survival. This has affected our spatial distribution and behavior to an ever greater extent (Mannino and Thomas, 2002). With an ever increasing human population, the issue of resource scarcity has caught the attention of policy makers, academics and the general public. The oceans and other water bodies deliver tremendous services to our life on this planet through temperature regulation, water cycle, transportation, food provisioning, etc. It has increasingly become clear that some of the impacts of our activities can have serious, sometimes detrimental, effects on the environment and the organisms that live there. Fishery resources, similar to forest and grazing pasture, have the potential to regenerate, hence can be categorized as renewable resources. However, they cannot regenerate under any circumstances. Their potential to regenerate is limited and conditioned on how much of the resource is taken and how much is left in the water. In theory, fishery resources can be used sustainably, if the exploitation rate does not compromise the inherent regeneration capabilities of the fish populations. This basic idea highlights the need to understand the resources and our interactions with them, i.e., how much of the resource is there, its regeneration capabilities, how much has been taken away and the consequences of the exploitation. So, fishery sciences developed to address these basic questions.  Our perception or assessment of fisheries evolved through different phases over time, from the thoughts that the bounty of the oceans is infinite and cannot be exhausted (Costanza, 1999; Roberts, 2007) to what we know now where most major stocks of the oceans are declining and exhibit serious depletion problems (Myers and Worm, 2003). Accordingly, fishery science, as an applied science responding to the phenomena around us, evolved in parallel to our changing perceptions of the resources. Many different fishery assessment tools have been developed and likely more will be developed in the future as well. In recent history, fishery science has evolved from the classical single-species stock assessments and their many variations (Beverton and Holt, 1957; Hilborn and Walters, 1992), to multispecies stock assessments (May et al., 1979), and ecosystem-based assessments (Pikitch et al., 2004). The inclusion of socio-economic aspects of the resource users explicitly in assessments is becoming very important (Clark, 1973;  137 Jentoft et al., 1998; Berkes et al., 2003). Even with the best possible assessment knowledge of fishery resources, in the end, management must deal with humans, not fish (Hilborn, 2007).  The different assessment approaches were developed to address specific questions, pertinent at the time of their development. But later, when more and new questions were raised and the previous tools were deemed not able to address the new issues, opportunities were created for the birth and growth of different approaches; the process continues. The assessment approaches develop not solely out of the questions asked, but also depend on the resources available to accomplish the task and their applicability to the specific context in which they are to be deployed. It is within such context that the Red Sea ecosystem and its fisheries were assessed in this thesis.  7.1 Summary  Each chapter was written to stand as a separate paper, with its own discussion and concluding remarks, and some of them have already been published. Here I will summarize the main contribution of each chapter to the assessment of the Red Sea and how the findings of each chapter fit in the thesis.  I started, in Chapter 2 of the thesis, with the general review of the fisheries in the Red Sea and evaluated their sustainability using standardized scoring procedure of attributes in the ecological, economic, social, technological, and ethical fields. The standardized attributes enabled comparison of the fisheries. The multidimensional scaling employed in analyzing the scores of the fisheries resulted in two-dimensional plots with the relative positions of the fisheries. This was a good starting point because the data need for this analysis is not extensive and most of the information needed to score the fisheries is available from general description, not necessarily quantitative, of the fisheries, which are contained in annual or technical reports (e.g., those issued by FAO). This exercise allowed me to familiarize myself with the fisheries of all the countries in the Red Sea and also understand their performances in several fields, which was a good starting point. However, it was not a very detailed quantitative assessment; for example it did not show change in patterns or rates over time.  138  In the next Chapter (3) the fishery was assessed based on the information available in the memories of the communities whose livelihoods depend on the Red Sea and its resources. The premise for this chapter was that the absence of written documents on the status of the fisheries can be compensated, at least to a certain extent, by the knowledge stored in the memory of the people involved with the resources. The Red Sea region does not have a strong written culture, but there is a strong oral tradition. The people involved with the extraction of the resources perform observations, although not in the metrics and designs employed by scientific research. The knowledge in the memories of the communities was used to assess the long term changes in status of the resources, using interviews mainly with fishers and also with community elders and fishery administrators. The resources needed for this analysis were more than the assessment done in Chapter 2. One year’ s field work was needed to interview resource users. The results of Chapter 2 were used to guide the interview procedure, e.g., which fisheries to concentrate on. The main output of this analysis was relative quantitative assessment of the resources over a long period of time. The fisheries can be compared in terms of their relative changes, but not in absolute values.  In Chapter 4, the focus shifted from qualitative and relative quantitative assessments toward quantitative and actual values. The actual catch amount of the Red Sea fisheries was reconstructed from 1950 – 2006. This is a more detailed analysis than the previous two approaches; hence, the resources needed for this assessment were also more than the previous two approaches. Hard quantitative data and detailed knowledge of the fisheries were needed. They were obtained by searching any record of quantitative value of catch, scrutinizing it for any missing information and performing corrections with clear assumptions, given the best knowledge available. The main result of this analysis is the first comprehensive and standardized catch statistics of the Red Sea fisheries by gear and species composition. This is the most basic information needed to carry out quantitative analysis of the fisheries. This is a major achievement and probably the portion which will be most used by researchers and managers. Notably, the results can be used as a baseline reference for future policy choices. However, they cannot be used to quantitatively predict what would happen in the future under different scenarios.  139 Chapter 5 analysed in detail the unreported catch and the uncertainty associated with its estimation by taking the case study of Eritrea. Unreported catch affects fishery assessment because it causes an underestimation of the actual amount of catch. The estimation was done using major changes in the history of the fisheries, based on the accounts of the fisheries, that would create an incentive or disincentive to misreport catch. The incentives were then converted to actual amounts based on quantitative estimates of the unreported catch as anchor points. Then, the uncertainties around the estimates were calculated using Monte Carlo simulation runs. This assessment gave quantitative estimates of the magnitude of the unreported catch and also the uncertainty of those estimates, which is a significant addition to the reconstruction of time series catch in Chapter 4.  The last Chapter (6) consists of an assessment of the Red Sea in an ecosystem-based framework, which is the latest approach in fisheries assessment. It used a holistic, quantitative ecosystem modelling approach to assess the ecosystem and the impacts of human interaction, i.e., fisheries. As in the other chapters, the main focus of the assessment was the fisheries. This chapter has the most detailed assessment of the Red Sea. It quantifies not only the fish species that are very important in the fisheries, but also all the other organisms in the ecosystem. In addition, it quantifies the interactions among the organisms and the fisheries. As far as the fisheries are concerned, it presents quantitatively the actual values of the level of exploitation in relation to the potential of the resources. It also reproduces the changes in the fisheries since 1950. And the most important for management is, it can predict what will happen to the fisheries and ecosystem under different scenarios. This is the most significant assessment tool of this chapter, and one that none of the previous chapters could match. It is also the most important section to be considered in any decision-making process. All the previous chapters assess what has happened to the system up to the present, which is important for knowing where we are and how far we have exploited the resources; however, they are not equipped to quantify the future possible scenarios. The ecosystem-based assessment, on the other hand, combines the past up to the present and forecasts the future as well. The data need for this assessment was the most extensive. It incorporates all the information from the previous chapters plus detailed ecological data. It benefited from the long time series of historic data of the Red Sea fisheries assessment  140 and combined that with the ecological data for detailed and comprehensive analysis in actual (not relative) quantitative values covering the past to the present and the future.  The analyses demonstrated an incremental increase in the details of the assessment and the corresponding need for resources (data, manpower and time). It gives a wide range of possibilities from which one can choose to carry out the assessment to answer specific questions with clear understanding of what is needed and available, and the limitations of the assessments. For example, at one end of the spectrum, for a quick and wide but not detailed understanding of fisheries, the rapid appraisal of fisheries (Rapfish) can accomplish the task with minimum effort. At the other end of the spectrum, for detailed quantitative analysis one can utilize the ecosystem-based assessment with its high demand for resources. What is interesting in the analyses is the similarity and complementarity of the assessment results. All the analyses, except the rapid appraisal, which does not have time dynamics, showed decline in the resources. The changes are expressed in different ways, for example in the analysis of interviews, it is relative change, which was highlighted in Figures (3.3 and 3.4), while it was the actual value in relation to the potential of the resources for the ecosystem modelling (Figure 6.7). The only exception, i.e., a fishery that is not declining, is the beach seine fishery, which is a special case, because the fishery used to be active in the early 1950s but was largely abandoned for marketing reasons. Hence, the decline in its catch (Figure 4.4) is not due to depletion of the resource (Figure 6.7).  The most striking result from all the analyses is the assessment of sharks. It ranked as one of the worst in the rapid appraisal of the fisheries in the ecological field (fishery 26 Figure 2.1), has the highest decline of catch rate in the interview analysis (slope of Figure 3.3c), shows high depletion in the ecosystem analysis (Figures 6.7 and 6.8), and could face worst consequences in the future if the fishing effort intensifies (Figure 6.10b). When the catches of sharks from the catch reconstruction (Figure 3.4 and Table C.5) were divided by the total effort of gillnet and handlining fisheries (Table E.7 in the Appendix), which both target sharks, the catch rate (CPUE) of sharks was obtained. The CPUE then was compared to the catch rates from the interview analysis (Figure 7.1). The decline rate according to the interview data is 10.3% per year (Figure 7.1a); while when a continuous regression line is fitted to the CPUE, the broken line in Figure (7.1b), the decline rate is 8.1% per year. A close scrutiny of the CPUE data shows  141 two sets of data: one starting from 1950 – 1964, where the decline is very small, and a second set from 1965 – 2007 where the CPUE peaked, then declined drastically. The main reason for this difference is the introduction of motors for boats in the beginning of the 1960s (Appendix E.2.1). So, if two separate regression lines are fitted (full lines in Figure 7.1b), the decline rate for the latter part, which overlaps with the period of the interview, is 11% per year, similar to the decline rate according to interviews (10.3% per year). This is a good example to show that an assessment with fewer resources (interview in this case) can be as informative as a detailed and resource-intensive approach (the catch reconstruction). Waiting for a detailed assessment and not taking any action until that is fulfilled is a bad excuse for inaction. However, this is not to argue that the less detailed analyses can fully replace the detailed analyses.   Figure 7.1 Change in catch rate of shark fishery from interview (a) and catch reconstruction (b).  7.2 Data, knowledge, management and conservation  Comparison of the different resource assessment approaches raises the question of how much information and knowledge is needed in resource assessment for proper management actions to be taken in order to conserve resources and livelihoods. The quantitative stock assessment tools used in fishery assessment demand a lot of data, which is not available for most of the fisheries of the world (Froese et al., 2012). The situation is worse in many developing tropical countries, of which the Red Sea is a part. The resources needed to collect detailed fishery data and analyze them are not readily available and may not be top priority in many developing countries. The situation is further complicated, as compared with temperate systems, by the multispecies and multiple gears nature of the fisheries. This is a practical challenge faced by both researchers and practitioners of tropical fisheries, and it may not vanish easily in the future either. Creative and y = 2E+90e-0.103x R² = 0.5975 0 100 200 300 1950 1960 1970 1980 1990 2000 C a tc h (kg /c re w /d a y) Year a y = 2E+93e-0.11x R² = 0.8435 y = -0.0017x + 3.4743 R² = 0.9444 y = 2E+68e-0.081x R² = 0.8137 0.0 0.2 0.4 1950 1960 1970 1980 1990 2000 CP U E (kg  /k ilo w a tt . ho u rs ) Year b  142 practical approaches will be needed to overcome this challenge and give information for effective actions.  An interesting aspect to note is the source of information. The societies around the Red Sea do not have a strong written tradition; however, that does not mean there is no information and knowledge useful for assessment. The societies have very strong oral tradition and if accessed systematically, as shown for example in Chapter 3, it can be a source of important insights. The people who interact with the resources experience the events and record them in their memories. Such information can be as good as an independent research observation (Pauly, 1995). When such observations are shared with others it creates a collective memory and knowledge. It will be beneficial to use such knowledge, and not using them because, for example, they do not fit in to the framework of scientific research will be losing an opportunity, which in some cases could be the only one (Johannes et al., 2000; Soto, 2006). Scientists are becoming increasingly interested in accessing such knowledge and some methodologies are being developed, although they are still crude and more refinements are needed. Such knowledge, however, should be used with caution. Some of the understandings, legends or myths may not be realistic. For example, during my interviews with the fishers, the idea that the sea can never be polluted because it is so vast was a common saying in the communities. If the main objective of fisheries assessment is to manage the resources so that they are conserved and sustainably used, using any knowledge will be an asset in the process. Incorporating the resource users in the process helps not only as a source of information, but also to understand their perceptions, which is important for the success of any management scheme.  7.3 Contextualizing science  In additional to data and resources availability, another serious challenge faced by fishery researchers in tropical countries is the applicability of the commonly used assessment tools. Fishery science, as we know it now, developed in temperate systems. However, those approaches proved not directly applicable in tropical systems. The design of the tools and their implementations are characteristically temperate. A typical issue that comes to the forefront is the use of age-based assessments. It is easier to age temperate fishes using growth rings in  143 otoliths, but not for tropical fishes. Of course, there are some clever modifications made to adopt methods to tropical situations, such as using length rather than age (Pauly and David, 1981). Yet, the tropical research tools and knowledge have not developed much further than this. Moreover, even at the moment, most of the research in tropical systems is done by scientists and organizations from the developed nations, in Europe, North America and Australia. Disconnection between the important issues on the ground and the priorities taken by the initiatives is not uncommon (Anderson et al., in press). For example, during my field trip interviews, fishers and community elders repeatedly mentioned that the immediate attention needed in the Red Sea is the conservation of sharks, which is also demonstrated by the different assessment tools described in this thesis. However, none of the few development/research initiatives in the Red Sea is focused on sharks. The result is that local communities feel sidelined from the actual process and their compliance with any management regulations is very low. Taking these important issues in practical assessment and conservation in places like the Red Sea and integrating them in the assessment and management of fisheries will help to move forward in the successful application of the science, which will lead to fisheries really becoming the ‘applied’  science it claims to be.  144 References  Agger, P. (1976) Yemen Arab Republic - Stock Assessment FI: DP YEM/74/003/3. FAO, Rome, 1-42 p. Ainsworth, C. and Pitcher, T. (2005) Estimating illegal, unreported and unregulated catch in British Columbia's marine fisheries. Fisheries Research 75(1-3): 40-55. Ainsworth, C.H., Pitcher, T.J. and Rotinsulu, C. (2008) Evidence of fishery depletions and shifting cognitive baselines in Eastern Indonesia. Biological Conservation 141(3): 848- 859. Ainsworth, C.H., Varkey, D. and Pitcher, T.J. 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Attributes Good Bad Notes  Ecological analysis Exploitation status 0 4 Under- (0); fully- (1); heavily- (2); or over-exploited (3); almost completely collapsed (4)* Recruitment variability 0 3 COV low <20% (0); medium 20-60% (1); high 60-100% (2); very high >200% (3) Change in trophic level 0 2 Is the trophic level of the catch decreasing: no (0), somewhat, slowly (1); rapidly (2) Migratory range 0 2 Number of jurisdictions (international included) encountered during life history: 1-2 (0); 3-4 (1); >4 (2) Range collapse 0 3 Is there evidence of geographic range reduction: no (0); a little (1); a lot, fast (2); very great, rapid (3)* Size of fish caught 0 2 Has average fish size landed changed in past 5 years; no (0); yes, a gradual change (1); yes, a rapid large change (2) Catch before maturity 0 2 Percentage caught before size/age of maturity: none (0); some (>30%) (1); lots (>60%) (2) Discarded by-catch 0 2 Percentage of target catch: low 0-10% (0); medium 10-40% (1); high >40% (2) Species caught 0 2 Number of species caught (retained and discarded): low 1-10 (0); medium 10-100 (1); high >100 (2)    170 Attributes Good Bad Notes Economic analysis Fisheries in GDP 2 0 Importance of fisheries sector in the economy: low (0); medium (1); high(2) Average wage 4 0 Do fishers make more or less than the average person? Much less (0); less (1); the same (2); more (3); much more (4) Limited entry 4 0 Includes informal limitations: Open Access (0); Almost none (1); very little (2); some (3); lots (4) Marketable right 2 0 Marketable right/quota/share? (0); some (1); mix (2); full ITQ, CTQ or other property rights (2) Other income 0 3 In this fishery, fishing is mainly: casual (0), part-time (1); seasonal (2); full-time (3) Sector employment 0 2 Employment in formal sector of this fishery: <10% (0); 10-20% (1); >20% (2) Ownership/Transfer 0 2 Profit from fishery mainly to: locals (0); mixed (1); foreigners (2) Market 0 2 Market is principally: local/national (0); national/regional (1); international (2) Subsidy 0 4 Are subsidies (including hidden) provided to support the fishery?: no (o); somewhat (1); large subsidies (2); heavily reliant (3); almost completely reliant on subsidies (4)* Social analysis Socialization of fishing 2 0 Fishers work as: individuals (0); families (1); community groups (2). New entrants into the fishery 0 3 Growth over past ten years: <10% (0); 10-20% (1); 20 - 30% (2); >30% (3) Fishing sector 0 2 Households containing fishers in the community: few, <10% (0); some, 10-30% (1); many, >30% (2) Environmental knowledge 2 0 Level of knowledge about the fishery resource and its ecosystem and environment: none (0); some (1) ; lots (2) Education level 2 0 Education level compared to population average: below (0); at (1); above (2) Conflict status 0 2 Level of conflict with other sectors: none (0); some (1); lots (2) Fisher influence 2 0 Strength of direct fisher influence on actual fishery regulations: almost none (0); some (1); lots (2)  171 Attributes Good Bad Notes Fishing income 2 0 Fishing income as % of total family income: <50% (0); 50-80% (1); >80% (2) Kin participation 4 0 Do kin sell and/or process fish? None (0); very few relatives (1-2 people) (1); a few relatives (2); some relatives (3); many kin (4)  Technological analysis Trip length 0 4 Average days at sea per fishing trip. 1 or less (0); 2-4 (1); 5-8 (2); 8-10 (3); more than 10 (4) Landing sites 0 3 Are landing sites: dispersed (0); somewhat centralised (1); heavily centralised (2); distant water fleet with little, or no local landings (3) Pre-sale processing 2 0 Processing before sale, e.g. gutting, filleting, salting: none (0); some (1); lots (2) Onboard handling 3 0 None (0); some (e.g. salting, boiling) (1); sophisticated (e.g. flash freezing, champagne ice) (2); live tanks (3) Selective gear 2 0 Device(s) and/or handling of gear to increase selectivity? Few (0); some (1); lots (2) FADS 0 1 Fish attraction devices: not used (0); bait is used (0.5); used (1) Vessel size 0 4 Average length of vessels: <5 m (0); 5-10 m (1); 10-15 (2); 15-20 (3); >20 (4) Change in catching power 0 4 Have fishers altered gear and vessel to increase catching power over past 5 years?: No (0); very little (1); little (2); somewhat (3); a lot, rapid increase (4) Gear side effects 0 3 Does gear have undesirable side effects (e.g. cyanide, dynamite, trawl); no (0); some (1); a lot (2); fishery dominated by destructive fishing practices (3)* Ethical analysis Adjacency and reliance 3 0 Geographical proximity & historical connection: not adjacent/no reliance (0); not adjacent/some reliance (1); adjacent/some reliance (2); adjacent/strong reliance (3) Alternatives 2 0 Alternatives to the fishery within community: none (0); some (1); lots (2)  172 Attributes Good Bad Notes Equity in entry to fishery 2 0 Entry based on traditional/historical access/harvests? not considered (0); considered (1); traditional indigenous fishery (2). Just management 4 0 Inclusion of fishers in management: none (0); consultations (1); co-mgmt/gov’ t leading (2); co- mgmt/comm. leading (3); genuine co-mgmt with all parties equal (4) Mitigation – habitat destruction 4 0 Attempts to mitigate damage to fish habitat: much damage (0); some damage (1); no ongoing damage or mitigation (2); some mitigation (3); much mitigation (4) Mitigation – ecosystem depletion 4 0 Attempts to mitigate fisheries-induced ecosystem change: much damage (0); some damage (1); no damage or mitigation (2); some mitigation (3); much mitigation (4) Illegal fishing 0 2 Illegal catching/poaching/transshipments: none (0); some (1); lots (2) Discards & wastes 0 2 Discard and waste of fish: none (0); some (1); lots (2) * called “ killer”  attribute scores and shift all scores in that evaluation field to the “ bad”  score. For ecological analysis, if the sum of the score of the two “ killer” attributes exceeds 5, then all scores are shifted to “ bad” .  173 Appendix B  Supplementary material for Chapter 3  Questionnaire used to collect data on historic and present utilization of fishery resources in the Red Sea.  GENERAL BIO-DATA  Code_________  Date________  Location____________________________  1. Age/date of birth______________  2. Gender: F    M  3. Place of birth ________________   Current place of residence ______________ When did you move?________________________________________________ 4. Occupation:   Boat owner    Skipper  Crew    Retired/when_________ Other___________________________  Did you change occupation?  Yes   No   Do you do other jobs besides fishing? _________________________________________ Education (formal) level____________ How long have you been in fishing (start - end?)_________________________________ How many generations has your family been in fishery? (Circle one) 1  2  3  4  >4 Number of family members involved in fishing?_________________________________ Any interruption in your fishing career, when and for how long?____________________   Interviewer’ s remarks  174   EFFORT DATA  Code___________ Crew size____________________ Boat: Type: Sambuk  Huri    Other____________ Size__________________ Engine:  Inboard     Outboard  HP______________  Gears: Gillnet Gillnet dimensions_______________ Mesh size_________ Average No. of nets used per setting:_______________________  Hook and line No. of hooks per line?_______ Hook size_________ Do you use circle hooks: Yes  No  How many people are directly involved in handlining?____________________ What bait do you use?______________________________________________ How do you get the bait?___________________________________________  How long, on average, did it take you to go to the fishing ground? Present ____________ Past_____________ How long was a single trip (average or range in days?)___________________________  Anything else you would like to tell?  Interviewer’ s remarks  175 CATCH DATA  Code__________  The best catch ever you recall: Kg __________ Boxes________ Size of box (kg)_______ Sacks________ Size of sack (kg)______ Number: Species 1_________ Length (average or range in cm)_________    Species 2_________ Length (average or range in cm)_________    Species 3_________ Length (average or range in cm)_________    Species 4_________ Length (average or range in cm)_________    Species 5_________ Length (average or range in cm)_________    Estimate of all other minor species (kg)__________________________ Other units_____________________________  Size of largest fish ever caught (cm)___________ Species_________________________  Effort of best catch recalled:  Crew size:_____________________  Trip length (days)_______________  Average/typical catch rate when you started fishing______________________________  Average/typical catch rate at the moment (in the same unit as previous question)_______  Anything else you would like to tell?  Interviewer’ s remarks  176 Appendix C  Supplementary material for Chapter 4 Table C. 1 Red Sea reconstructed catch (t) by sector, compared with the Red Sea total catch data submitted to FAO by member countries.  Artisanal Industrial FAO Year Categorized Uncategorized Retained Discard Uncategorized 1950 47662 3595 503 1481 0 12913 1951 47651 3399 523 1517 0 13913 1952 48307 3841 543 1551 0 19499 1953 48405 3710 564 1582 0 19806 1954 48598 3519 584 1612 0 21234 1955 48436 3465 604 1640 0 24561 1956 45448 3549 393 966 0 24613 1957 41912 3084 1698 4794 0 25986 1958 38827 3068 1877 5084 0 25774 1959 35408 3401 1925 5101 0 29689 1960 34396 3427 3967 3063 0 30383 1961 38043 3218 7584 6668 0 34595 1962 34721 3414 13441 11533 0 46102 1963 34114 3643 14813 16080 0 44988 1964 36377 3790 12295 20090 0 40665 1965 46018 4059 12040 20378 0 42540 1966 49710 4217 9892 13096 0 40884 1967 45250 4025 11066 11862 0 40472 1968 43745 3699 12363 10776 0 38245 1969 45854 5012 13732 9796 0 38820 1970 50295 4574 15765 11514 0 40639 1971 51193 3793 18192 10366 0 46462 1972 42355 4238 17996 11893 0 43358 1973 37271 3934 6132 6468 0 31470 1974 39606 3757 17878 6777 0 34322 1975 39926 3291 16528 13303 0 30772 1976 40502 3430 25397 12838 0 35974 1977 41378 2917 20694 10633 0 33498 1978 45823 3090 19633 10091 0 36049 1979 45660 6276 24564 7634 0 44875 1980 43695 5530 18365 8415 0 45133  177  Artisanal Industrial FAO Year Categorized Uncategorized Retained Discard Uncategorized 1981 48919 5928 17136 9037 0 47075 1982 51249 6329 21140 9737 104 44035 1983 45036 5147 28993 9283 218 51101 1984 45155 6235 32149 8595 320 49436 1985 54087 5531 24846 8303 434 64186 1986 57110 5213 23151 10090 546 65136 1987 64211 7692 25797 8419 674 70746 1988 68847 9060 28962 10004 483 78778 1989 74329 9962 38956 8046 713 96197 1990 71368 8145 38536 7501 606 99145 1991 84697 9912 34687 10498 672 109716 1992 88693 13140 35596 8669 808 114251 1993 102024 17238 43059 10517 794 127653 1994 100188 15133 32951 17210 822 133493 1995 82058 14008 35931 25220 779 133649 1996 68123 11099 32699 24336 798 128270 1997 86853 15486 35060 27021 980 137474 1998 84980 10628 32642 27340 851 136554 1999 83150 9983 36073 31550 1054 158399 2000 60614 6330 52781 34087 1362 148643 2001 61794 5926 45650 36736 1287 147144 2002 67675 10952 48473 39632 1467 145372 2003 61516 7193 47656 42742 1688 138609 2004 57757 9374 47859 35279 1595 133193 2005 60450 8811 34966 30778 722 116503 2006 62014 8347 36125 26806 1167 124057   178 Table C. 2 Catch (t) composition of reconstructed Red Sea handlining fishery. Year Emperors Groupers Snappers Jacks Barra- cuda Bream Parrot fishes Cobia Grunts Cutlass fish Rabbit fish Goggle eye Surgeon fish Wrasses Scom- bridae Tunas Goat fish Uni- corns Others 1950 1008 737 1414 1189 368 264 280 81 0 66 0 0 6 0 6 3 0 5 187 1951 1026 749 1419 1195 370 268 280 82 0 66 0 0 6 0 7 3 0 5 187 1952 1046 760 1424 1200 372 273 280 83 0 66 0 0 6 0 8 4 0 5 187 1953 1066 772 1430 1205 375 277 280 85 0 66 0 0 6 0 8 4 0 5 187 1954 1097 797 1452 1232 383 281 287 87 0 68 0 0 6 0 9 5 0 5 187 1955 1110 822 1468 1259 385 286 294 84 0 69 0 0 6 0 9 5 0 5 187 1956 1143 846 1492 1286 395 290 301 86 0 71 0 0 6 0 10 5 0 5 187 1957 1177 871 1515 1313 404 295 308 88 0 73 0 0 6 0 11 6 0 5 187 1958 1211 896 1539 1340 413 299 315 91 0 74 0 0 6 0 11 6 0 5 187 1959 1245 920 1562 1367 422 303 322 93 0 76 1 0 7 0 12 7 0 5 187 1960 1296 962 1643 1394 432 308 329 95 0 77 1 0 7 0 12 7 0 5 197 1961 1331 987 1668 1421 441 313 336 98 0 79 1 0 7 0 13 8 0 5 197 1962 1351 995 1634 1448 451 317 343 100 0 81 1 0 7 0 14 8 0 5 187 1963 1386 1019 1658 1474 461 321 350 103 0 82 1 0 7 0 14 9 0 5 187 1964 1388 1007 1633 1440 454 326 337 105 0 79 1 0 7 0 15 9 0 5 187 1965 1497 1116 1769 1607 501 331 390 107 0 92 1 0 8 0 15 10 0 5 187 1966 1624 1243 2067 1692 525 335 416 109 0 98 1 0 8 0 16 10 0 5 228 1967 1699 1314 2153 1796 555 339 448 111 0 105 1 0 9 0 16 11 0 5 228 1968 1789 1403 2261 1930 593 343 490 113 0 115 1 0 10 0 17 11 0 5 228 1969 1911 1529 2417 2122 646 346 552 115 0 130 1 0 11 0 18 12 0 5 228 1970 1936 1545 2425 2127 649 349 552 117 0 130 1 0 11 0 18 12 0 5 238 1971 1951 1562 2431 2131 649 351 552 117 0 130 1 0 11 0 19 13 0 6 249 1972 1999 1605 2475 2180 664 354 566 120 0 133 1 0 12 0 19 13 0 6 260 1973 2076 1649 2529 2230 689 356 581 128 0 137 1 0 12 0 20 14 0 6 271 1974 2190 1692 2595 2280 726 360 596 145 0 140 1 0 12 0 20 14 0 7 281 1975 2345 1736 2675 2329 777 363 610 171 0 144 1 0 12 0 21 15 0 7 292 1976 2455 1815 2763 2389 808 366 625 183 0 147 1 0 13 0 22 15 0 11 382 1977 2461 1721 2458 2393 837 371 625 202 0 147 1 0 13 0 22 15 0 10 305 1978 2783 2043 2886 2909 985 379 790 211 0 186 1 0 16 1 23 16 1 11 317 1979 3929 2883 2983 2623 1168 626 694 447 63 163 1 316 14 1 23 16 1 16 342  179 Year Emperors Groupers Snappers Jacks Barra- cuda Bream Parrot fishes Cobia Grunts Cutlass fish Rabbit fish Goggle eye Surgeon fish Wrasses Scom- bridae Tunas Goat fish Uni- corns Others 1980 3846 2841 2915 2452 1105 629 634 435 61 149 1 308 13 0 24 7 0 7 477 1981 4170 3257 2750 2751 1415 674 588 501 70 375 2 354 12 1 21 27 1 21 706 1982 4505 3248 2825 2738 1475 695 588 548 77 375 2 387 12 1 19 17 1 18 638 1983 3613 3172 1759 2608 1173 409 449 49 0 550 4 0 9 2 29 17 2 0 803 1984 4063 2914 2905 2605 1629 413 310 209 0 506 3 513 6 8 22 14 2 12 597 1985 6175 4869 3808 3145 2256 1133 209 1084 185 628 4 416 4 21 25 17 2 12 559 1986 6424 4438 3835 3242 2133 1160 218 649 168 654 4 482 4 22 22 15 2 11 538 1987 6843 5238 4222 3945 2612 1953 278 474 19 833 6 0 6 28 20 13 3 11 503 1988 7354 5606 4450 4201 2730 2011 300 435 73 900 6 0 6 30 19 12 3 10 469 1989 7848 6059 4271 4201 2957 1957 300 454 83 900 6 0 6 30 23 16 3 10 434 1990 7613 6040 3591 3902 2940 1658 278 408 82 833 6 0 6 28 28 20 3 0 495 1991 8654 5741 4567 3885 3089 2432 278 687 379 833 6 0 6 28 17 10 3 0 546 1992 8918 6454 5190 3870 3569 2353 278 1116 676 833 6 0 6 28 11 5 3 0 623 1993 10323 7381 7459 4136 4089 3678 300 1514 900 900 6 0 6 30 17 10 3 0 578 1994 10164 7240 6347 4127 3745 3017 300 1532 1369 900 6 0 6 30 15 9 3 0 568 1995 7587 5012 4090 2366 2107 2501 516 1366 2170 361 688 0 103 138 15 8 34 0 631 1996 7224 5723 3505 2847 1792 2039 399 1110 1574 360 599 0 103 137 17 11 34 0 713 1997 8577 6127 4347 2841 2213 2730 451 1434 2367 406 877 0 116 155 19 13 39 0 579 1998 8005 6331 1988 2796 2401 599 449 1622 3844 405 514 0 116 154 13 7 39 0 619 1999 8056 6359 2220 3401 2634 593 482 1547 2727 226 545 0 198 137 8 3 30 0 738 2000 5100 7404 2202 3285 1712 582 464 236 280 218 525 0 191 132 9 4 29 0 705 2001 6439 6778 2262 3551 2035 602 509 1170 267 239 575 0 209 144 10 5 31 0 699 2002 5952 6479 2431 3334 1910 582 474 132 411 223 536 0 195 134 10 5 29 0 724 2003 5717 4910 1860 3198 2172 566 451 155 280 212 510 0 185 128 10 4 28 0 609 2004 4947 4443 2354 2845 1946 464 278 146 329 0 222 0 186 87 9 4 10 0 791 2005 5692 5699 1473 3274 2375 478 301 270 396 0 237 0 226 77 10 5 19 0 639 2006 6180 6235 2136 2956 2421 504 393 390 514 0 248 0 224 77 8 3 16 0 773   180 Table C. 3 Catch (t) composition of reconstructed Red Sea gillnet fishery. Years Kingfish Indian mackerel Tunas Jacks Mullets Queenfish Barracuda Bream Rays Rabbit fish Guitar fish Other Scombridae Parrotfish Others 1950 2778 3827 804 565 1008 158 130 0 242 97 161 3 9 0 1951 2801 3883 810 573 1016 160 132 0 245 98 163 4 11 0 1952 2829 3951 818 584 1023 163 134 0 250 99 166 4 12 0 1953 2859 4024 826 594 1031 166 137 0 254 101 170 4 14 0 1954 2923 4107 845 606 1050 170 139 0 260 102 173 4 16 0 1955 2904 3987 842 595 1069 164 136 0 252 103 168 5 17 0 1956 2976 4089 863 608 1088 169 139 0 258 105 172 5 19 0 1957 3047 4190 884 622 1107 173 142 0 264 106 176 5 21 0 1958 3120 4293 905 636 1126 177 146 0 271 107 181 6 22 0 1959 3194 4399 926 650 1145 181 149 0 278 109 185 6 24 0 1960 3269 4508 948 664 1165 186 153 0 285 110 190 6 26 0 1961 3346 4622 970 679 1185 191 156 0 292 111 195 6 27 0 1962 3425 4741 993 695 1204 196 160 0 299 113 200 7 29 0 1963 3503 4858 1016 710 1222 200 164 0 307 114 205 7 30 0 1964 3491 4964 1009 725 1209 205 168 0 314 115 209 7 32 0 1965 3769 5093 1096 739 1304 210 172 0 321 117 214 8 34 0 1966 3925 5201 1144 753 1354 214 175 0 327 118 218 8 35 0 1967 4107 5307 1201 765 1414 218 178 0 333 120 222 8 37 0 1968 4332 5415 1271 777 1488 222 181 0 339 121 226 9 39 0 1969 4644 5532 1369 789 1594 225 184 0 345 121 230 9 40 0 1970 4683 5625 1379 801 1598 229 187 0 351 122 234 9 42 0 1971 4683 5625 1380 802 1601 229 187 0 351 123 234 9 44 0 1972 4787 5727 1411 814 1629 233 190 0 357 123 238 10 45 0 1973 5015 6127 1475 859 1657 250 203 0 383 124 255 10 47 0 1974 5397 6902 1580 945 1686 283 228 0 433 125 289 10 49 0 1975 5955 8102 1730 1078 1714 334 267 0 511 126 341 11 50 0 1976 6251 8668 1812 1141 1743 358 286 0 547 127 365 11 52 0 1977 6665 9559 1909 1245 1753 396 318 0 605 128 404 37 54 34 1978 7558 10008 2187 1291 2036 412 331 0 630 131 420 38 55 34 1979 6228 5422 1838 1431 1891 376 592 241 0 133 0 38 57 34  181 Years Kingfish Indian mackerel Tunas Jacks Mullets Queenfish Barracuda Bream Rays Rabbit fish Guitar fish Other Scombridae Parrotfish Others 1980 5901 5268 1727 1406 1808 367 579 235 0 135 0 48 61 48 1981 7047 6085 1849 1591 1676 422 663 270 0 138 0 44 57 44 1982 7374 6615 1036 1713 1690 461 724 295 0 141 0 47 63 49 1983 6888 8298 1550 1528 1410 0 308 0 0 143 0 83 50 89 1984 7665 5044 1287 995 1283 373 667 0 0 144 0 69 50 77 1985 9040 3099 1339 1762 1252 733 783 707 0 146 0 61 59 64 1986 9657 6076 768 1464 1277 724 599 735 0 147 0 60 50 64 1987 10965 7190 1280 1231 1391 447 659 1532 0 146 0 53 44 57 1988 11675 7334 1348 1683 1429 927 622 1592 0 145 0 47 41 49 1989 12320 8195 2586 2263 1423 1117 848 1535 0 144 0 43 54 42 1990 11919 8015 3687 2634 1374 1168 986 1232 0 143 0 40 69 34 1991 12333 9560 3118 4141 1369 2444 1135 2024 0 142 0 35 36 34 1992 12006 2160 4082 4235 1360 1685 1619 1957 0 141 0 58 19 69 1993 12928 1988 3254 3736 1396 1300 1984 3278 0 139 0 63 35 72 1994 13116 2208 4739 3768 1386 2816 1640 2623 0 138 0 63 32 72 1995 6556 3748 4546 3417 1103 3274 1432 1915 0 136 0 72 29 84 1996 5633 1356 3487 3220 1092 2527 1099 1456 0 134 0 54 37 59 1997 7526 1963 5215 4491 1130 3802 1437 2119 0 134 0 102 44 122 1998 8825 5649 4217 3977 1124 2669 1633 0 0 133 0 142 24 178 1999 7620 2376 5490 4678 1077 4178 1515 0 0 132 0 98 11 123 2000 7512 4255 4018 2170 1058 471 633 0 0 131 0 98 13 122 2001 7434 4658 2527 2273 1081 584 851 0 0 129 0 76 44 93 2002 7667 8296 1936 2526 1050 597 811 0 0 128 0 102 95 127 2003 6588 10123 1983 2458 1027 746 1127 0 0 127 0 112 38 141 2004 6037 6385 1398 2324 892 708 1172 0 12 125 0 57 48 69 2005 7244 6076 2047 2493 899 593 1204 0 80 123 0 147 109 186 2006 5841 7095 1924 2270 860 762 1209 0 83 122 0 118 81 149    182 Table C. 4 Catch (t) composition of reconstructed Red Sea beach seine fishery. Years Anchovy Sardine Mullets Queenfish Jacks Little tuna Others Years Anchovy Sardine Mullets Queenfish Jacks Little tuna Others 1950 18133 9023 50 100 250 0 0 1979 1102 476 41 83 207 66 33 1951 18142 9028 50 100 250 0 0 1980 1145 495 38 75 188 60 30 1952 18154 9033 50 100 250 0 0 1981 1258 544 34 68 169 54 27 1953 18166 9038 50 100 250 0 0 1982 1262 546 30 60 150 48 24 1954 18180 9044 50 100 250 0 0 1983 1266 547 26 53 131 42 21 1955 18159 9035 50 100 250 0 0 1984 1269 548 23 45 113 36 18 1956 15849 7879 50 100 250 0 0 1985 1271 549 19 38 94 30 15 1957 13539 6722 50 100 250 0 0 1986 1296 560 15 30 75 24 12 1958 11229 5566 50 100 250 0 0 1987 1312 567 11 23 56 18 9 1959 8458 4180 50 100 250 0 0 1988 1298 561 8 15 38 12 6 1960 7426 3662 50 100 250 0 0 1989 1316 569 4 8 19 6 3 1961 9716 4806 50 100 250 0 0 1990 1334 576 0 0 0 0 0 1962 7150 3522 50 100 250 0 0 1991 1409 609 0 0 0 0 0 1963 6309 3100 50 100 250 0 0 1992 1443 624 0 0 0 0 0 1964 7691 3790 50 100 250 0 0 1993 1445 624 0 0 0 0 0 1965 12644 6265 50 100 250 0 0 1994 1458 630 0 0 0 0 0 1966 14169 7026 50 100 250 0 0 1995 1567 677 0 0 0 0 0 1967 15339 377 50 100 250 80 40 1996 1581 683 0 0 0 0 0 1968 8895 384 50 100 250 80 40 1997 1571 679 0 0 0 0 0 1969 8254 4065 50 100 250 0 0 1998 1582 684 0 0 0 0 0 1970 11595 5735 50 100 250 0 0 1999 1583 684 0 0 0 0 0 1971 12136 6005 50 100 250 0 0 2000 1582 684 0 0 0 0 0 1972 8961 412 50 100 250 80 40 2001 1599 691 0 0 0 0 0 1973 3664 421 49 98 244 78 39 2002 1555 672 0 0 0 0 0 1974 4083 432 48 95 238 76 38 2003 1560 674 0 0 0 0 0 1975 2433 442 46 93 231 74 37 2004 1563 676 0 0 0 0 0 1976 1047 453 45 90 225 72 36 2005 1563 676 0 0 0 0 0 1977 1072 463 44 88 219 70 35 2006 1560 674 0 0 0 0 0 1978 1081 467 43 85 213 68 34  183  Table C. 5 Catch (t) composition of reconstructed Red Sea shark fishery by countries. Years Eritrea Sudan Yemen Egypt Saudi Arabia Years Eritrea Sudan Yemen Egypt Saudi Arabia 1950 413 15 483 3 343 1979 14 68 1204 16 850 1951 413 15 490 3 343 1980 14 62 1173 13 776 1952 413 15 499 3 343 1981 14 118 1349 10 976 1953 413 15 509 4 343 1982 14 105 1474 30 976 1954 413 15 519 4 351 1983 14 0 1497 12 1030 1955 413 15 503 5 360 1984 14 42 493 14 690 1956 413 15 516 5 369 1985 14 40 1548 16 419 1957 413 15 529 6 377 1986 14 38 1133 14 436 1958 413 15 542 6 386 1987 14 36 997 12 556 1959 413 15 555 7 394 1988 14 34 747 12 600 1960 413 15 569 7 403 1989 14 33 776 15 600 1961 413 15 584 8 411 1990 14 94 690 19 556 1962 413 15 599 8 420 1991 14 106 3282 10 556 1963 413 15 614 8 429 1992 14 109 7233 5 556 1964 394 15 628 9 413 1993 14 98 7798 10 600 1965 937 15 642 9 477 1994 14 96 7756 9 600 1966 1146 15 655 10 509 1995 14 105 5352 8 310 1967 3174 15 667 10 549 1996 16 117 4265 10 308 1968 5508 15 678 11 600 1997 14 86 5645 12 348 1969 1900 15 690 11 676 1998 19 95 5220 7 347 1970 1500 17 702 12 676 1999 42 110 6187 3 489 1971 2300 18 702 12 676 2000 143 99 2075 3 471 1972 1100 19 714 13 694 2001 120 102 1327 5 516 1973 400 21 766 13 711 2002 159 97 414 4 480 1974 500 22 866 14 729 2003 135 104 762 4 457 1975 30 23 1022 14 747 2004 91 117 869 4 320 1976 100 34 1095 14 765 2005 49 127 1309 5 471 1977 14 32 1211 15 765 2006 255 146 1434 3 473 1978 14 34 1260 15 968        184  Table C. 6 Catch (t) composition of reconstructed Red Sea trawl (retained) fishery. Years Lizardfish Threadfin bream Shrimp Snappers Cuttlefish Emperors Mullets Horse Mackerel & Scad Grunts 1950 182 31 69 47 11 0 14 16 0 1951 193 33 71 50 11 0 15 17 0 1952 204 35 74 53 12 0 16 18 0 1953 215 36 76 56 12 0 17 19 0 1954 225 38 78 58 13 0 18 20 0 1955 236 40 80 61 14 0 19 21 0 1956 123 21 56 32 7 0 10 11 0 1957 822 139 205 213 48 0 65 73 0 1958 908 167 220 230 51 0 86 79 0 1959 934 171 225 237 53 0 88 82 0 1960 626 195 134 126 28 0 147 44 1 1961 1507 511 267 287 64 0 401 99 2 1962 2587 757 477 541 121 0 556 187 3 1963 3622 966 686 796 178 0 676 275 3 1964 4506 1053 896 1050 235 0 676 363 3 1965 4664 1078 930 1091 244 0 687 377 3 1966 3129 829 599 690 154 0 577 238 3 1967 2897 779 553 634 142 0 547 219 3 1968 2711 766 507 578 129 0 553 200 3 1969 2551 774 461 522 117 0 580 180 3 1970 2280 693 1380 467 105 0 519 161 3 1971 2058 632 1286 419 94 0 476 145 3 1972 2677 721 1166 585 131 0 507 202 3 1973 1522 462 608 312 70 0 346 108 2 1974 1666 408 562 381 85 0 271 131 1 1975 3555 713 881 876 196 0 403 303 1 1976 3461 694 887 852 191 0 391 294 1 1977 2839 507 822 725 162 0 253 250 0 1978 2593 444 912 668 150 0 209 231 0  185 Years Lizardfish Threadfin bream Shrimp Snappers Cuttlefish Emperors Mullets Horse Mackerel & Scad Grunts 1979 2224 397 497 566 127 0 196 196 0 1980 2298 501 604 699 126 0 178 215 0 1981 2879 430 646 621 184 0 168 284 0 1982 2443 389 824 630 180 63 288 164 17 1983 2141 418 953 530 239 131 134 203 36 1984 1085 397 1319 564 380 193 101 323 53 1985 1302 416 1255 568 377 261 117 232 73 1986 2782 408 1117 538 415 329 180 243 91 1987 1917 325 1097 508 535 406 206 261 113 1988 1033 253 1443 679 349 291 285 235 81 1989 839 289 1141 1117 481 429 269 170 119 1990 640 235 1281 966 457 365 259 160 101 1991 927 252 1848 708 476 405 323 207 112 1992 1261 316 1228 862 575 486 516 102 135 1993 1409 628 1381 952 681 554 423 161 133 1994 2821 1308 2082 805 813 718 357 291 282 1995 4287 1891 2847 676 752 798 397 143 337 1996 3567 1718 2526 687 787 1461 316 148 500 1997 4345 2287 2761 674 943 972 329 290 175 1998 4845 2661 2390 712 970 949 264 273 188 1999 7636 4752 2279 915 1152 1199 287 160 708 2000 9574 5109 2510 1064 1285 1383 296 189 1121 2001 9962 4959 2825 1065 1386 1367 336 278 552 2002 12571 5408 2142 1141 1098 1326 428 241 480 2003 10014 4824 3488 1226 1924 1225 487 260 261 2004 10885 4256 2112 1058 3474 927 458 82 383 2005 8314 3604 2209 904 3028 992 469 148 246 2006 7905 3221 2036 609 2720 949 392 355 560    186 Table C.6 continued. Years Jacks Catfish Barracuda Crab Indian mackerel Leopard flounder Goat fish Sole Others 1950 0 0 0 0 0 0 0 0 0 1951 0 0 0 0 0 0 0 0 0 1952 0 0 0 0 0 0 0 0 0 1953 0 0 0 0 0 0 0 0 0 1954 0 0 0 0 0 0 0 0 0 1955 0 0 0 0 0 0 0 0 0 1956 0 0 0 0 0 0 0 0 0 1957 0 0 0 0 0 0 0 0 0 1958 0 0 1 0 0 0 0 0 1 1959 0 0 1 0 0 0 0 0 1 1960 0 0 6 0 0 0 0 1 9 1961 0 0 17 0 0 0 0 3 26 1962 0 0 22 0 0 0 0 4 32 1963 0 0 24 0 0 0 0 4 35 1964 0 0 20 0 0 0 0 4 29 1965 0 0 20 0 0 0 0 4 29 1966 0 0 20 0 0 0 0 4 30 1967 0 0 20 0 0 0 0 4 29 1968 0 0 21 0 0 0 0 4 31 1969 0 0 23 0 0 0 0 4 34 1970 0 0 21 0 0 0 0 4 31 1971 0 0 19 0 0 0 0 4 29 1972 0 0 18 0 0 0 0 3 27 1973 0 0 14 0 0 0 0 3 21 1974 0 0 9 0 0 0 0 2 13 1975 0 0 8 0 0 0 0 1 11 1976 0 0 7 0 0 0 1 1 11 1977 0 0 2 0 0 0 0 0 3 1978 0 0 0 0 0 0 1 0 0 1979 0 0 1 0 0 0 1 0 2  187 Years Jacks Catfish Barracuda Crab Indian mackerel Leopard flounder Goat fish Sole Others 1980 0 0 1 0 0 0 0 0 2 1981 0 0 0 0 0 0 1 0 0 1982 0 21 0 30 0 0 4 0 0 1983 0 44 0 62 0 0 4 0 0 1984 0 64 0 91 0 0 4 0 0 1985 0 87 0 123 0 0 7 0 0 1986 0 110 0 155 0 0 10 0 0 1987 0 136 0 191 0 0 13 0 0 1988 0 97 0 137 0 0 16 0 0 1989 0 144 0 202 0 0 19 0 0 1990 0 122 0 172 0 0 22 0 0 1991 0 135 0 190 0 0 6 0 0 1992 0 163 0 229 0 0 6 0 0 1993 0 160 0 233 0 0 6 0 0 1994 213 258 181 249 26 22 6 0 267 1995 306 290 260 244 38 32 6 0 383 1996 804 169 174 257 58 0 6 0 244 1997 1 197 3 317 0 0 6 0 3 1998 114 316 27 288 0 1 12 0 118 1999 344 335 118 269 20 23 18 0 375 2000 1876 954 604 290 196 119 19 0 1219 2001 901 547 432 300 99 126 19 0 1374 2002 603 319 328 289 13 69 20 0 611 2003 270 284 1869 320 5 53 23 0 682 2004 172 322 360 190 6 73 63 0 435 2005 97 154 191 310 4 20 60 0 334 2006 266 403 570 244 269 85 60 0 1438   188 Table C. 7 Catch (t) composition of reconstructed Red Sea trawl (discard) fishery. Years Pony Fish Gaper Flounder Crab Tigerfish Sand dollars Cutlassfish Mojarra Sponge Jacks Flatheads Puffers 1950 675 296 118 82 9 79 5 5 47 46 3 3 1951 693 305 122 84 9 81 5 5 49 46 3 3 1952 710 313 125 86 9 83 5 5 50 46 3 3 1953 725 321 128 88 9 86 5 5 51 46 3 3 1954 739 328 131 90 9 87 5 5 52 46 3 3 1955 753 335 134 92 9 89 5 5 54 46 3 3 1956 424 170 68 48 9 45 5 5 27 46 3 3 1957 2294 1105 442 297 9 295 5 5 177 46 3 3 1958 2529 1165 466 317 22 311 13 13 186 6 6 6 1959 2538 1169 468 318 22 312 13 13 187 6 6 6 1960 1579 609 244 174 41 162 23 23 97 12 12 12 1961 3422 1351 541 384 81 360 46 46 216 23 23 23 1962 5829 2488 995 691 96 663 55 55 398 28 28 28 1963 8067 3571 1428 982 105 952 60 60 571 30 30 30 1964 9996 4602 1841 1253 89 1227 51 51 736 26 26 26 1965 10136 4674 1870 1272 89 1247 51 51 748 25 25 25 1966 6584 2886 1154 796 92 770 52 52 462 26 26 26 1967 5975 2593 1037 717 89 692 51 51 415 25 25 25 1968 5454 2313 925 643 94 617 53 53 370 27 27 27 1969 4992 2044 817 574 102 545 58 58 327 29 29 29 1970 5298 1786 714 532 195 476 111 111 286 519 56 56 1971 4751 1567 627 470 183 418 104 104 251 490 52 52 1972 5624 2144 857 615 151 572 86 86 343 359 43 43 1973 3118 1117 447 326 100 298 57 57 179 185 29 29 1974 3283 1336 534 376 69 356 39 39 214 127 20 20 1975 6508 3010 1204 818 55 803 31 31 482 82 16 16 1976 6287 2867 1147 782 63 764 36 36 459 90 18 18 1977 5117 2389 956 648 38 637 22 22 382 111 11 11 1978 4793 2158 863 591 54 575 31 31 345 174 15 15 1979 3774 1791 716 484 22 478 12 12 287 6 6 6  189 Years Pony Fish Gaper Flounder Crab Tigerfish Sand dollars Cutlassfish Mojarra Sponge Jacks Flatheads Puffers 1980 4125 2033 813 544 7 542 4 4 325 2 2 2 1981 4445 2158 863 580 15 575 8 8 345 4 4 4 1982 5154 1706 682 511 197 455 112 112 273 56 56 56 1983 4923 1610 644 484 192 429 110 110 258 55 55 55 1984 4648 1339 536 421 222 357 127 127 214 64 64 64 1985 4502 1274 510 403 221 340 126 126 204 63 63 63 1986 5382 1699 679 517 224 453 128 128 272 64 64 64 1987 4484 1429 572 433 184 381 105 105 229 52 52 52 1988 5605 1227 491 429 356 327 203 203 196 102 102 102 1989 4361 1237 495 391 213 330 122 122 198 61 61 61 1990 4145 1018 407 340 238 272 136 136 163 68 68 68 1991 6026 1044 417 405 445 278 254 254 167 127 127 127 1992 4704 1324 529 419 232 353 133 133 212 66 66 66 1993 5981 1140 456 423 418 304 239 239 182 119 119 119 1994 10336 937 375 523 955 250 546 546 150 273 273 273 1995 15352 1023 409 702 1502 273 858 858 164 429 429 429 1996 14825 969 388 674 1455 258 831 831 155 416 416 416 1997 16482 1039 416 742 1626 277 929 929 166 465 465 465 1998 16799 844 338 713 1706 225 975 975 135 487 487 487 1999 19449 866 347 803 2000 231 1143 1143 139 572 572 572 2000 21111 770 308 837 2210 205 1263 1263 123 631 631 631 2001 22727 873 349 910 2369 233 1354 1354 140 677 677 677 2002 24466 1030 412 997 2530 275 1446 1446 165 723 723 723 2003 26387 1109 444 1075 2729 296 1559 1559 177 780 780 780 2004 21720 1017 407 906 2223 271 1270 1270 163 635 635 635 2005 18859 1039 416 818 1895 277 1083 1083 166 541 541 541 2006 16437 885 354 709 1656 236 946 946 142 473 473 473    190 Table C.7 continued. Years Soles Goatfish Mantis shrimp Lizard fish Threadfin Bream Grunt Catfish Barracudas Cuttlefish Others 1950 3 1 1 38 32 12 8 3 1 12 1951 3 1 1 38 32 12 8 3 1 12 1952 3 1 1 38 32 12 8 3 1 12 1953 3 1 1 38 32 12 8 3 1 12 1954 3 1 1 38 32 12 8 3 1 12 1955 3 1 1 38 32 12 8 3 1 12 1956 3 1 1 38 32 12 8 3 1 12 1957 3 1 1 38 32 12 8 3 1 12 1958 6 3 3 0 0 0 0 0 0 29 1959 6 3 3 0 0 0 0 0 0 29 1960 12 6 6 0 0 0 0 0 0 52 1961 23 12 12 0 0 0 0 0 0 104 1962 28 14 14 0 0 0 0 0 0 124 1963 30 15 15 0 0 0 0 0 0 134 1964 26 13 13 0 0 0 0 0 0 115 1965 25 13 13 0 0 0 0 0 0 114 1966 26 13 13 0 0 0 0 0 0 118 1967 25 13 13 0 0 0 0 0 0 114 1968 27 13 13 0 0 0 0 0 0 120 1969 29 15 15 0 0 0 0 0 0 131 1970 56 28 28 405 347 130 87 29 14 251 1971 52 26 26 383 328 123 82 27 14 235 1972 43 22 22 276 237 89 59 20 10 194 1973 29 14 14 137 117 44 29 10 5 128 1974 20 10 10 94 81 30 20 7 3 89 1975 16 8 8 58 49 19 12 4 2 71 1976 18 9 9 63 54 20 13 4 2 80 1977 11 5 5 88 75 28 19 6 3 49 1978 15 8 8 139 119 45 30 10 5 69 1979 6 3 3 0 0 0 0 0 0 28  191 Years Soles Goatfish Mantis shrimp Lizard fish Threadfin Bream Grunt Catfish Barracudas Cuttlefish Others 1980 2 1 1 0 0 0 0 0 0 8 1981 4 2 2 0 0 0 0 0 0 19 1982 56 28 28 0 0 0 0 0 0 253 1983 55 27 27 0 0 0 0 0 0 247 1984 64 32 32 0 0 0 0 0 0 286 1985 63 32 32 0 0 0 0 0 0 284 1986 64 32 32 0 0 0 0 0 0 288 1987 52 26 26 0 0 0 0 0 0 236 1988 102 51 51 0 0 0 0 0 0 457 1989 61 30 30 0 0 0 0 0 0 274 1990 68 34 34 0 0 0 0 0 0 306 1991 127 64 64 0 0 0 0 0 0 572 1992 66 33 33 0 0 0 0 0 0 299 1993 119 60 60 0 0 0 0 0 0 537 1994 273 136 136 0 0 0 0 0 0 1228 1995 429 215 215 0 0 0 0 0 0 1931 1996 416 208 208 0 0 0 0 0 0 1871 1997 465 232 232 0 0 0 0 0 0 2091 1998 487 244 244 0 0 0 0 0 0 2193 1999 572 286 286 0 0 0 0 0 0 2572 2000 631 316 316 0 0 0 0 0 0 2841 2001 677 338 338 0 0 0 0 0 0 3046 2002 723 361 361 0 0 0 0 0 0 3252 2003 780 390 390 0 0 0 0 0 0 3508 2004 635 318 318 0 0 0 0 0 0 2858 2005 541 271 271 0 0 0 0 0 0 2436 2006 473 237 237 0 0 0 0 0 0 2129   192  Table C. 8 Catch (t) composition of reconstructed Red Sea purse seine fishery. Years Horse mackerel & scads Round herring Goldstripe sardinella Indian mackerel Slimy mackerel Spotted sardinella Barracudas Kingfish Queenfish Others 1950 0 0 122 0 0 0 0 0 0 11 1951 0 0 122 0 0 0 0 0 0 11 1952 0 0 122 0 0 0 0 0 0 11 1953 0 0 122 0 0 0 0 0 0 11 1954 0 0 122 0 0 0 0 0 0 11 1955 0 0 122 0 0 0 0 0 0 11 1956 0 0 122 0 0 0 0 0 0 11 1957 0 0 122 0 0 0 0 0 0 11 1958 0 0 122 0 0 0 0 0 0 11 1959 0 0 122 0 0 0 0 0 0 11 1960 0 0 2438 0 0 0 0 0 0 212 1961 0 0 4046 0 0 0 0 0 0 352 1962 0 0 7502 0 0 0 0 0 0 652 1963 0 0 6943 0 0 0 0 0 0 604 1964 0 0 2322 0 0 1 0 0 0 1136 1965 0 0 1955 0 0 1 0 0 0 957 1966 0 0 2429 0 0 2 0 0 0 1189 1967 2400 1163 524 341 0 629 0 0 0 183 1968 3142 1523 686 446 0 823 0 0 0 240 1969 3884 1883 848 551 0 1018 0 0 0 297 1970 4627 2243 1010 657 0 1212 0 0 0 354 1971 5968 2893 1303 847 0 1564 0 0 0 456 1972 5476 2654 1196 777 0 1435 0 0 0 418 1973 1221 592 267 173 0 320 0 0 0 93 1974 6572 3186 1435 933 0 1722 0 0 0 502 1975 4388 2127 958 623 0 1150 0 0 0 335 1976 8522 4131 1861 1209 0 2233 0 0 0 651 1977 6930 3359 1513 983 0 1816 0 0 0 530 1978 6606 3202 1442 938 0 1731 0 0 0 505  193 Years Horse mackerel & scads Round herring Goldstripe sardinella Indian mackerel Slimy mackerel Spotted sardinella Barracudas Kingfish Queenfish Others 1979 9324 4519 2036 1323 0 2443 0 0 0 713 1980 6294 3051 1374 893 0 1649 0 0 0 481 1981 5461 2647 1192 775 0 1431 0 0 0 417 1982 7328 3489 1572 1193 0 1886 33 21 16 550 1983 11856 6133 796 948 2331 956 69 43 34 932 1984 13531 6952 903 1194 2642 1083 101 64 50 1056 1985 9721 4860 631 1181 1847 757 137 86 68 738 1986 8046 3900 506 1273 1482 608 173 109 85 592 1987 9626 4652 604 1556 1768 725 213 134 105 707 1988 11700 5875 763 1359 2233 916 153 96 75 893 1989 16385 8205 1065 1960 3118 1279 226 142 111 1246 1990 16450 8309 1079 1796 3158 1295 192 121 94 1262 1991 14102 7023 912 1780 2669 1094 213 134 105 1067 1992 14338 7058 917 2007 2683 1100 256 161 126 1072 1993 17635 8813 1144 2153 3349 1373 251 158 124 1339 1994 10622 5081 660 1841 1931 792 260 164 128 772 1995 10641 5120 665 1773 1946 798 247 155 121 778 1996 9157 4322 561 1728 1643 674 253 159 124 657 1997 10293 4803 624 2074 1826 749 310 195 153 730 1998 8411 4036 524 2197 1534 629 269 170 132 613 1999 6551 3180 413 2644 1209 496 222 173 111 483 2000 11205 5629 731 3001 2139 877 234 182 117 855 2001 8296 4088 531 2857 1554 637 234 183 117 621 2002 9216 4579 821 2836 1740 939 243 203 114 696 2003 8602 4255 935 2761 1617 1045 249 217 112 646 2004 8976 4585 1252 3414 1743 1372 219 254 90 697 2005 4999 2444 695 2989 929 758 341 255 104 371 2006 5160 2464 698 2987 937 762 321 161 181 374   194 Appendix D  Supplementary material for Chapter 5 Table D. 1 Summary of the major influences on the incentives to misreport, arrows indicate whether the influence increases or decreases the incentive.   Influence  Period Event summary Small pelagic Finfish trawl  Shrimp  Rational  Duration  Ref.*  50-54 Growing operation of small pelagic fishery    Increasing effort 50 - 54 1  Shrimp fishery trial    New operation 50 - 54 1 55 - 59 Small pelagic fishery at its highest peak    High effort 55 2  First off shore survey to locate trawling grounds by Israeli     New grounds 57 - 58 3  First commercial trawl report available    Start of operation 58 3 60 - 64 Sea Fisheries advisory board of Massawa established     Encouraged investment 60 1  Yemenite fishermen who were expertise stopped from operation    Less effort  60 3  Israeli expert working as advisory in the Eritrean Red Sea    Resource knowledge  60 - 63 3  Experimental inshore shrimp fishery in central and southern part    New grounds 60 - 63 3  Purse seine survey     New grounds 62 4  Carrier ship to Israel stopped operating, trawlers had to do it themselves    Less effective effort 63 - 65 3  Some trawlers stopped operation     Less effort 63 3  First phase of motorization of Dhows for Beach seine    Increased catching power 63 1  Freedom from hunger campaign     Increased demand 60 - 63 3 65 - 69 Yemenite fishermen resume operation again     More effort 65 3  Master plan for the development of fishery    Cleared way for investment 65 5  Israeli expert working as advisory in the Eritrean Red Sea    Resource knowledge  66 - 69 1  195   Influence  Period Event summary Small pelagic Finfish trawl  Shrimp  Rational  Duration  Ref.*  More boat motorization    Increased catching power 66  1  Training of fishermen to use new technology    Technical knowledge 66  1  Closer of Suez Canal due to middle east war    Market 67 1  Synthetic fibers and outboard engines in small Beach seine boats    Increased catching power 67 - 04 1  Tickler chain introduced in Shrimp trawlers    Increased catching power 67 – 04 1  Trial of 57' semi-balloon shrimp trawl    Increased catching power 68 1  Experiment of different size and types shrimp trawls    Increased catching power 68  1  More trawlers added    More effort 68 1  Less demand for Lizard fish in the market    Market and grading 68 1  Tendency to use trawl trash for fish meal    Retained and reported 66 - 69 1  Minimum mesh size of 50 mm at the cod end adopted     Regulation 66 - 69 1  Campaign to increase fish consumption locally    Increased demand 66 - 69 1  Resumption of fishmeal export, because of alternative market    Market  69 -71 6 70 - 74 General political instability in the country    Instability 72 7 75 - 79 Major war in the coastal area    Instability 77  Fishing industry totally collapsed     Less effort 78  7 80 - 84 Little recovery of the fishing industry    More effort 83 - 90 7  Resource survey    Resource knowledge 84 8 85 - 89  Establishment of marine and fisheries institute    Resource knowledge 86 - 90 4  196   Influence  Period Event summary Small pelagic Finfish trawl  Shrimp  Rational  Duration  Ref.* 90 - 94 Major war in the coastal area    Instability 90  Independence of Eritrea    Resumption of operation 91  Formation of Ministry of Marine Resources (later Ministry of Fisheries)    Encouraged investment 91  Log book and onboard observers introduced    Better reporting 92  Foreign trawlers legally operating in Eritrea    More effort 94 - 97  9  Log book system improved and database system working    Better reporting 96 10  Infrastructure development projects    Encouraged investment 92  11 95 - 99 Resource survey    Resource knowledge 97 13  Foreign trawlers stopped operation    Less effort 97 - 98 12  Fisheries proclamation    Regulation 98 14  Political instability    Instability 98  Restarting of trawlers    More effort 99 15  Commencement of large scale shrimp fishery    More effort 99 16  Fish processing plants established    Increased market 99 16  A good shrimp ground found    Cleaner catch 99  16      197   Influence  Period Event summary Small pelagic Finfish trawl  Shrimp  Rational  Duration  Ref.* 00 - 04 War in the southern part of the coast    Instability 00  Existing trawlers increased their effort after the war, mainly in the north    More effort 00 16  New medium sized (11 – 18 m) shrimp trawlers operating    More effort 00 16  New trawlers added    More effort 00 16  New trawlers added    More effort 04 17 *1 Grofit (1971)  2 Jonson (1956)   3 Ben-Yami (1964)  4 Melake (1988)   5 Atkins (1965) 6 Ben-Yami (1975) 7 Giudicelli (1984)  8 Blindheim (1984)  9 Hartmann (1997)  10 MOF (1996) 11 FAO (1993)   12 Tesfamichael and Zeremariam (1998)    13 Antoine et al., (1997)  14 MOF (1998) 15 Habteselassie and Habte (2000)    16 Gebremichael et al., (2001) 17 Shaebia.org (2005)      198 Appendix E  Supplementary material for Chapter 6  E.1 Ecopath input data  Table E. 1 Fish species included in the Red Sea model grouped by functional groups. Group Family Scientific name FishBase Code FishBase common name Whale shark Rhincodontidae Rhincodon typus 2081 Whale shark Rays Myliobatidae Aetobatus flagellum 8973 Longheaded eagle ray Myliobatidae Aetobatus narinari 1250 Spotted eagle ray Myliobatidae Aetobatus ocellatus 12600 Dasyatidae Dasyatis bennetti 15387 Bennett's stingray Dasyatidae Dasyatis kuhlii 4508 Bluespotted stingray Dasyatidae Himantura gerrardi 15483 Sharpnose stingray Dasyatidae Himantura imbricata 13150 Scaly whipray Dasyatidae Himantura uarnak 5507 Honeycomb stingray Myliobatidae Manta ehrenbergii 54614 Myliobatidae Mobula thurstoni 2588 Smooth-tail mobula Dasyatidae Pastinachus sephen 8203 Cowtail stingray Dasyatidae Taeniura lymma 5399 Bluespotted ribbontail ray Dasyatidae Taeniura meyeni 6482 Blotched fantail ray Torpedinidae Torpedo panthera 27060 Panther electric ray Torpedinidae Torpedo sinuspersici 7970 Marbled electric ray Torpedinidae Torpedo suessii 61378 Dasyatidae Urogymnus asperrimus 5400 Porcupine ray Reef top predators Belonidae Ablennes hians 972 Flat needlefish Serranidae Aethaloperca rogaa 6441 Redmouth grouper Carangidae Alectis indicus 10 Indian threadfish Antennariidae Antennarius coccineus 5402 Scarlet frogfish Antennariidae Antennarius commerson 7293 Commerson's frogfish Antennariidae Antennarius hispidus 8074 Shaggy angler Antennariidae Antennarius nummifer 5403 Spotfin frogfish Antennariidae Antennarius pictus 10276 Painted frogfish Antennariidae Antennarius striatus 5474 Striated frogfish Lutjanidae Aphareus furca 81 Small toothed jobfish Lutjanidae Aphareus rutilans 83 Rusty jobfish Lutjanidae Aprion virescens 84 Green jobfish Carangidae Atule mate 1893 Yellowtail scad Bothidae Bothus mancus 7641 Flowery flounder Ophichthidae Brachysomophis cirrocheilos 12886 Stargazer snake eel Carangidae Carangoides bajad 1923 Orangespotted trevally Carangidae Carangoides chrysophrys 4441 Longnose trevally Carangidae Carangoides coeruleopinnatus 1924 Coastal trevally Carangidae Carangoides dinema 1925 Shadow trevally Carangidae Carangoides fulvoguttatus 1926 Yellowspotted trevally Carangidae Carangoides gymnostethus 1905 Bludger Carangidae Carangoides malabaricus 4443 Malabar trevally  199 Group Family Scientific name FishBase Code FishBase common name Carangidae Carangoides orthogrammus 1909 Island trevally Carangidae Carangoides plagiotaenia 1910 Barcheek trevally Carangidae Caranx ignobilis 1895 Giant trevally Carangidae Caranx melampygus 1906 Bluefin trevally Carangidae Caranx sexfasciatus 1917 Bigeye trevally Odontaspididae Carcharias taurus 747 Sand tiger shark Serranidae Cephalopholis argus 6396 Peacock hind Serranidae Cephalopholis boenak 6444 Chocolate hind Serranidae Cephalopholis hemistiktos 6447 Yellowfin hind Serranidae Cephalopholis miniata 6450 Coral hind Serranidae Cephalopholis oligosticta 6451 Vermilion hind Serranidae Cephalopholis sexmaculata 6453 Sixblotch hind Labridae Cheilinus undulatus 5604 Humphead wrasse Labridae Cheilio inermis 5623 Cigar wrasse Apogonidae Cheilodipterus macrodon 5781 Large toothed cardinalfish Chirocentridae Chirocentrus dorab 6358 Dorab wolf-herring Congridae Conger cinereus 6654 Longfin African conger Serranidae Diploprion drachi 24437 Yellowfin soapfish Muraenidae Echidna nebulosa 5388 Snowflake moray Serranidae Epinephelus coeruleopunctatus 6440 Whitespotted grouper Serranidae Epinephelus fuscoguttatus 4460 Brown-marbled grouper Serranidae Epinephelus hexagonatus 6660 Starspotted grouper Serranidae Epinephelus lanceolatus 6468 Giant grouper Serranidae Epinephelus malabaricus 6439 Malabar grouper Serranidae Epinephelus polyphekadion 6473 Camouflage grouper Serranidae Epinephelus tukula 5525 Potato grouper Fistulariidae Fistularia commersonii 5444 Bluespotted cornetfish Fistulariidae Fistularia petimba 3276 Red cornetfish Scombridae Grammatorcynus bilineatus 104 Double-lined mackerel Scombridae Gymnosarda unicolor 106 Dogtooth tuna Muraenidae Gymnothorax elegans 23130 Elegant moray Muraenidae Gymnothorax favagineus 5391 Laced moray Muraenidae Gymnothorax flavimarginatus 5392 Yellow-edged moray Muraenidae Gymnothorax griseus 8058 Geometric moray Muraenidae Gymnothorax meleagris 5394 Turkey moray Muraenidae Gymnothorax moluccensis 27334 Moluccan moray Muraenidae Gymnothorax nudivomer 7465 Starry moray Muraenidae Gymnothorax pictus 6395 Peppered moray Muraenidae Gymnothorax punctatofasciatus 27341 Muraenidae Gymnothorax punctatus 27325 Red Sea whitespotted moray Muraenidae Gymnothorax rueppellii 5396 Banded moray Muraenidae Gymnothorax undulatus 4905 Undulated moray Antennariidae Histrio histrio 3089 Sargassumfish Labridae Hologymnosus annulatus 5637 Ring wrasse Lethrinidae Lethrinus lentjan 1863 Pink ear emperor Lethrinidae Lethrinus olivaceus 1864 Longface emperor Lutjanidae Lutjanus ehrenbergii 793 Blackspot snapper  200 Group Family Scientific name FishBase Code FishBase common name Lutjanidae Lutjanus erythropterus 1406 Crimson snapper Lutjanidae Lutjanus fulvus 262 Blacktail snapper Lutjanidae Lutjanus johnii 264 John's snapper Lutjanidae Lutjanus lemniscatus 157 Yellowstreaked snapper Lutjanidae Lutjanus malabaricus 162 Malabar blood snapper Lutjanidae Lutjanus monostigma 166 Onespot snapper Lutjanidae Lutjanus quinquelineatus 172 Five-lined snapper Lutjanidae Lutjanus rivulatus 173 Blubberlip snapper Lutjanidae Lutjanus russellii 176 Russell's snapper Lutjanidae Lutjanus sanguineus 177 Humphead snapper Lutjanidae Lutjanus sebae 178 Emperor red snapper Lutjanidae Macolor niger 187 Black and white snapper Carangidae Megalaspis cordyla 384 Torpedo scad Cirrhitidae Paracirrhites forsteri 5952 Blackside hawkfish Mullidae Parupeneus cyclostomus 5990 Goldsaddle goatfish Mullidae Parupeneus heptacanthus 5991 Cinnabar goatfish Ephippidae Platax teira 5739 Tiera batfish Haemulidae Plectorhinchus flavomaculatus 7625 Lemon sweetlip Haemulidae Plectorhinchus gaterinus 7703 Blackspotted rubberlip Haemulidae Plectorhinchus gibbosus 6366 Harry hotlips Haemulidae Plectorhinchus harrawayi 52851 Haemulidae Plectorhinchus sordidus 7626 Sordid rubberlip Serranidae Plectropomus areolatus 6082 Squaretail coralgrouper Haemulidae Pomadasys maculatus 4447 Saddle grunt Haemulidae Pomadasys stridens 7708 Striped piggy Priacanthidae Priacanthus blochii 9903 Paeony bulleye Priacanthidae Pristigenys niphonia 7905 Japanese bigeye Scorpaenidae Pterois volitans 5195 Red lionfish Holocentridae Sargocentron macrosquamis 23251 Bigscale squirrelfish Holocentridae Sargocentron melanospilos 5345 Blackblotch squirrelfish Synodontidae Saurida gracilis 4534 Gracile lizardfish Carangidae Scomberoides lysan 1951 Doublespotted queenfish Carangidae Scomberoides tol 1953 Needlescaled queenfish Scorpaenidae Scorpaenopsis barbata 12767 Bearded scorpionfish Scorpaenidae Scorpaenopsis diabolus 4921 False stonefish Scorpaenidae Scorpaenopsis gibbosa 7918 Humpback scorpionfish Carangidae Selar crumenophthalmus 387 Bigeye scad Carangidae Seriola dumerili 1005 Greater amberjack Carangidae Seriolina nigrofasciata 1962 Blackbanded trevally Sphyraenidae Sphyraena barracuda 1235 Great barracuda Sphyraenidae Sphyraena flavicauda 7937 Yellowtail barracuda Sphyraenidae Sphyraena forsteri 5734 Bigeye barracuda Sphyraenidae Sphyraena jello 4827 Pickhandle barracuda Sphyraenidae Sphyraena obtusata 4493 Obtuse barracuda Sphyraenidae Sphyraena putnamae 7938 Sawtooth barracuda Sphyraenidae Sphyraena qenie 7939 Blackfin barracuda Muraenidae Strophidon sathete 8595 Slender giant moray Synanceiidae Synanceia verrucosa 5825 Stonefish Synodontidae Synodus variegatus 5398 Variegated lizardfish Synodontidae Trachinocephalus myops 2724 Snakefish Ephippidae Tripterodon orbis 7694 African spadefish  201 Group Family Scientific name FishBase Code FishBase common name Belonidae Tylosurus acus melanotus 1317 Keel-jawed needle fish Belonidae Tylosurus crocodilus crocodilus 977 Hound needlefish Large reef carnivores  Balistidae Abalistes stellaris 9 Starry triggerfish Balistidae Abalistes stellatus 58334 Albulidae Albula glossodonta 11512 Roundjaw bonefish Albulidae Albula vulpes 228 Bonefish Carangidae Alectis ciliaris 988 African pompano Monacanthidae Aluterus monoceros 4274 Unicorn leatherjacket Serranidae Anyperodon leucogrammicus 4922 Slender grouper Tetraodontidae Arothron hispidus 5425 White-spotted puffer Tetraodontidae Arothron stellatus 6526 Starry toadfish Balistidae Balistoides viridescens 6026 Titan triggerfish Ophidiidae Brotula multibarbata 7297 Goatsbeard brotula Ophichthidae Callechelys catostoma 12888 Black-striped snake eel Ophichthidae Callechelys marmorata 12889 Marbled snake eel Balistidae Canthidermis maculata 4278 Spotted oceanic triggerfish Carangidae Carangoides ferdau 1921 Blue trevally Labridae Cheilinus fasciatus 5600 Redbreast wrasse Labridae Cheilinus lunulatus 12780 Broomtail wrasse Sparidae Cheimerius nufar 444 Santer seabream Platycephalidae Cociella crocodila 7895 Crocodile flathead Labridae Coris aygula 5624 Clown coris Labridae Coris formosa 7736 Queen coris Haemulidae Diagramma pictum 4465 Painted sweetlips Diodontidae Diodon holocanthus 4659 Long-spine porcupinefish Diodontidae Diodon hystrix 1022 Spot-fin porcupinefish Diodontidae Diodon liturosus 6552 Black-blotched porcupinefish Drepaneidae Drepane longimana 7692 Concertina fish Echeneidae Echeneis naucrates 2467 Live sharksucker Muraenidae Echidna polyzona 5389 Barred moray Carangidae Elagatis bipinnulata 412 Rainbow runner Labridae Epibulus insidiator 5606 Slingjaw wrasse Serranidae Epinephelus coioides 6465 Orange-spotted grouper Serranidae Epinephelus fasciatus 5348 Blacktip grouper Serranidae Epinephelus morrhua 5353 Comet grouper Carangidae Gnathanodon speciosus 4464 Golden trevally Lethrinidae Gymnocranius grandoculis 1834 Blue-lined large-eye bream Muraenidae Gymnomuraena zebra 7880 Zebra moray Muraenidae Gymnothorax hepaticus 6498 Liver-colored moray eel Muraenidae Gymnothorax javanicus 6380 Giant moray Muraenidae Gymnothorax monochrous 7285 Drab moray Labridae Hemigymnus fasciatus 5635 Barred thicklip Labridae Hemigymnus melapterus 5636 Blackeye thicklip Priacanthidae Heteropriacanthus cruentatus 1150 Glasseye Kuhliidae Kuhlia mugil 5790 Barred flagtail Kyphosidae Kyphosus cinerascens 5805 Blue seachub Lethrinidae Lethrinus erythracanthus 1862 Orange-spotted emperor Lethrinidae Lethrinus microdon 1845 Smalltooth emperor Lethrinidae Lethrinus xanthochilus 1852 Yellowlip emperor  202 Group Family Scientific name FishBase Code FishBase common name Lutjanidae Lutjanus argentimaculatus 1407 Mangrove red snapper Lutjanidae Lutjanus bohar 1417 Two-spot red snapper Malacanthidae Malacanthus latovittatus 5796 Blue blanquillo Megalopidae Megalops cyprinoides 227 Indo-Pacific tarpon Lethrinidae Monotaxis grandoculis 1869 Humpnose big-eye bream Ophichthidae Myrichthys colubrinus 8053 Harlequin snake eel Ophichthidae Myrichthys maculosus 2650 Tiger snake eel Acanthuridae Naso hexacanthus 1263 Sleek unicornfish Balistidae Odonus niger 1311 Redtoothed triggerfish Ophichthidae Ophichthus erabo 15682 Fowler's snake eel Labridae Oxycheilinus digramma 5599 Cheeklined wrasse Platycephalidae Papilloculiceps longiceps 7896 Tentacled flathead Lutjanidae Paracaesio xanthura 194 Yellowtail blue snapper Ophichthidae Phaenomonas cooperae 15691 Short-maned sand-eel Lutjanidae Pinjalo pinjalo 196 Pinjalo Ophichthidae Pisodonophis cancrivorus 8054 Longfin snake-eel Ephippidae Platax orbicularis 5737 Orbicular batfish Haemulidae Plectorhinchus albovittatus 6362 Two-striped sweetlips Haemulidae Plectorhinchus nigrus 23485 Haemulidae Plectorhinchus obscurus 6368 Giant sweetlips Haemulidae Plectorhinchus playfairi 7705 Whitebarred rubberlip Haemulidae Plectorhinchus schotaf 7706 Minstrel sweetlip Haemulidae Plectorhinchus umbrinus 60760 Sparidae Polysteganus coeruleopunctatus 7935 Blueskin seabream Haemulidae Pomadasys commersonnii 5126 Smallspotted grunter Haemulidae Pomadasys furcatus 7707 Banded grunter Haemulidae Pomadasys kaakan 6006 Javelin grunter Balistidae Pseudobalistes flavimarginatus 6027 Yellowmargin triggerfish Balistidae Pseudobalistes fuscus 4466 Yellow-spotted triggerfish Rachycentridae Rachycentron canadum 3542 Cobia Echeneidae Remora remora 1751 Common remora Sparidae Rhabdosargus sarba 5368 Goldlined seabream Holocentridae Sargocentron spiniferum 6507 Sabre squirrelfish Carangidae Trachinotus baillonii 1978 Smallspotted dart Carangidae Trachinotus blochii 1963 Snubnose pompano Carangidae Ulua mentalis 1930 Longrakered trevally Muraenidae Uropterygius concolor 7283 Unicolor snake moray Muraenidae Uropterygius polyspilus 27347 Large-spotted snake moray Blenniidae Xiphasia setifer 7563 Hairtail blenny Medium reef carnivores Pomacentridae Abudefduf bengalensis 6517 Bengal sergeant Pomacentridae Abudefduf septemfasciatus 5687 Banded sergeant Sparidae Acanthopagrus bifasciatus 4543 Twobar seabream Centriscidae Aeoliscus punctulatus 7986 Speckled shrimpfish Soleidae Aesopia cornuta 7850 Unicorn sole Carangidae Alepes djedaba 1889 Shrimp scad Ambassidae Ambassis commersonii 13415 Commerson's glassy perchlet Labridae Anampses caeruleopunctatus 4888 Bluespotted wrasse Labridae Anampses meleagrides 4889 Spotted wrasse  203 Group Family Scientific name FishBase Code FishBase common name Labridae Anampses twistii 4893 Yellowbreasted wrasse Apogonidae Apogon aureus 4837 Ring-tailed cardinalfish Apogonidae Apogon kallopterus 5758 Iridescent cardinalfish Apogonidae Apogon multitaeniatus 8009 Smallscale cardinal Apogonidae Apogon taeniatus 127 Twobelt cardinal Apogonidae Apogon truncatus 58304 Flagfin cardinalfish Sparidae Argyrops filamentosus 4541 Soldierbream Congridae Ariosoma balearicum 1744 Bandtooth conger Congridae Ariosoma scheelei 7672 Tropical conger Tetraodontidae Arothron diadematus 25413 Masked puffer Tetraodontidae Arothron immaculatus 7188 Immaculate puffer Tetraodontidae Arothron nigropunctatus 6400 Blackspotted puffer Bothidae Asterorhombus intermedius 8123 Intermediate flounder Atherinidae Atherinomorus lacunosus 1303 Hardyhead silverside Serranidae Aulacocephalus temminckii 7701 Goldribbon soapfish Balistidae Balistapus undulatus 6025 Orange-lined triggerfish Labridae Bodianus anthioides 5497 Lyretail hogfish Labridae Bodianus axillaris 5498 Axilspot hogfish Labridae Bodianus diana 5500 Diana's hogfish Labridae Bodianus opercularis 25754 Blackspot hogfish Bothidae Bothus pantherinus 1321 Leopard flounder Caesionidae Caesio caerulaurea 918 Blue and gold fusilier Caesionidae Caesio lunaris 920 Lunar fusilier Caesionidae Caesio striata 921 Striated fusilier Caesionidae Caesio suevica 922 Suez fusilier Caesionidae Caesio varilineata 924 Variable-lined fusilier Caesionidae Caesio xanthonota 927 Yellowback fusilier Plesiopidae Calloplesiops altivelis 12655 Comet Monacanthidae Cantherhines dumerilii 5836 Whitespotted filefish Monacanthidae Cantherhines pardalis 6635 Honeycomb filefish Tetraodontidae Canthigaster margaritata 12778 Carangidae Carangoides armatus 1916 Longfin trevally Carapidae Carapus homei 4832 Silver pearlfish Centriscidae Centriscus scutatus 6510 Grooved razor-fish Chaetodontidae Chaetodon auriga 5557 Threadfin butterflyfish Chaetodontidae Chaetodon austriacus 6514 Blacktail butterflyfish Chaetodontidae Chaetodon collare 7803 Redtail butterflyfish Chaetodontidae Chaetodon falcula 8014 Blackwedged butterflyfish Chaetodontidae Chaetodon fasciatus 12274 Diagonal butterflyfish Chaetodontidae Chaetodon kleinii 5446 Sunburst butterflyfish Chaetodontidae Chaetodon lineolatus 5564 Lined butterflyfish Chaetodontidae Chaetodon melannotus 5566 Blackback butterflyfish Chaetodontidae Chaetodon semilarvatus 12300 Bluecheek butterflyfish Chaetodontidae Chaetodon trifasciatus 5579 Melon butterflyfish Chaetodontidae Chaetodon vagabundus 5582 Vagabond butterflyfish Apogonidae Cheilodipterus arabicus 6669 Tiger cardinal Apogonidae Cheilodipterus lachneri 12630 Labridae Choerodon robustus 6926 Robust tuskfish Labridae Cirrhilabrus blatteus 25759 Purple-boned wrasse Cirrhitidae Cirrhitus pinnulatus 5831 Stocky hawkfish Labridae Coris caudimacula 8026 Spottail coris Labridae Coris cuvieri 52844 African coris  204 Group Family Scientific name FishBase Code FishBase common name Labridae Coris gaimard 5625 Yellowtail coris Labridae Coris variegata 5485 Dapple coris Syngnathidae Corythoichthys schultzi 5965 Schultz's pipefish Diodontidae Cyclichthys orbicularis 5196 Birdbeak burrfish Dactylopteridae Dactyloptena orientalis 4485 Oriental flying gurnard Carangidae Decapterus macrosoma 1938 Shortfin scad Scorpaenidae Dendrochirus brachypterus 4912 Shortfin turkeyfish Scorpaenidae Dendrochirus zebra 5828 Zebra turkeyfish Syngnathidae Doryrhamphus dactyliophorus 5972 Ringed pipefish Syngnathidae Doryrhamphus multiannulatus 14286 Many-banded pipefish Drepaneidae Drepane punctata 454 Spotted sicklefish Clupeidae Dussumieria elopsoides 1454 Slender rainbow sardine Carapidae Encheliophis gracilis 9204 Graceful pearlfish Bothidae Engyprosopon grandisquama 1324 Largescale flounder Serranidae Epinephelus merra 4923 Honeycomb grouper Serranidae Epinephelus stoliczkae 7364 Epaulet grouper Chaetodontidae Forcipiger flavissimus 5584 Longnose butterflyfish Chaetodontidae Forcipiger longirostris 5585 Longnose butterflyfish Pomacanthidae Genicanthus caudovittatus 11132 Zebra angelfish Gerreidae Gerres argyreus 5799 Common mojarra Gerreidae Gerres filamentosus 4463 Whipfin silverbiddy Gerreidae Gerres longirostris 7699 Longtail silverbiddy Gerreidae Gerres oblongus 5801 Slender silverbiddy Gerreidae Gerres oyena 5996 Common silver-biddy Labridae Gomphosus caeruleus 7744 Green birdmouth wrasse Serranidae Grammistes sexlineatus 4925 Sixline soapfish Caesionidae Gymnocaesio gymnoptera 929 Slender fusilier Lethrinidae Gymnocranius griseus 1833 Grey large-eye bream Muraenidae Gymnothorax buroensis 6493 Vagrant moray Muraenidae Gymnothorax pindae 7447 Pinda moray Syngnathidae Halicampus dunckeri 5974 Duncker's pipefish Syngnathidae Halicampus grayi 7727 Gray's pipefish Syngnathidae Halicampus macrorhynchus 10225 Ornate pipefish Labridae Halichoeres bimaculatus 50017 Labridae Halichoeres hortulanus 12663 Checkerboard wrasse Labridae Halichoeres margaritaceus 5630 Pink-belly wrasse Labridae Halichoeres marginatus 5631 Dusky wrasse Labridae Halichoeres scapularis 5633 Zigzag wrasse Labridae Halichoeres zeylonicus 13050 Goldstripe wrasse Pseudochromidae Haliophis guttatus 4428 African eel blenny Chaetodontidae Heniochus intermedius 12309 Red Sea bannerfish Chaetodontidae Heniochus monoceros 5590 Masked bannerfish Congridae Heteroconger hassi 12619 Spotted garden-eel Syngnathidae Hippocampus histrix 5954 Thorny seahorse Syngnathidae Hippocampus kuda 5955 Spotted seahorse Pentacerotidae Histiopterus typus 7892 Sailfin armourhead Hemiramphidae Hyporhamphus affinis 7710 Tropical halfbeak Labridae Iniistius pavo 5613 Peacock wrasse  205 Group Family Scientific name FishBase Code FishBase common name Synanceiidae Inimicus filamentosus 6403 Two-stick stingfish Ostraciidae Lactoria cornuta 6399 Longhorn cowfish Ophichthidae Lamnostoma orientalis 11728 Oriental worm-eel Leiognathidae Leiognathus equulus 4451 Common ponyfish Lethrinidae Lethrinus borbonicus 1844 Snubnose emperor Lethrinidae Lethrinus harak 1851 Thumbprint emperor Lethrinidae Lethrinus obsoletus 1847 Orange-striped emperor Lethrinidae Lethrinus variegatus 1850 Slender emperor Lutjanidae Lutjanus bengalensis 1409 Bengal snapper Lutjanidae Lutjanus coeruleolineatus 1425 Blueline snapper Lutjanidae Lutjanus fulviflamma 261 Dory snapper Lutjanidae Lutjanus gibbus 265 Humpback red snapper Lutjanidae Lutjanus kasmira 156 Common bluestripe snapper Malacanthidae Malacanthus brevirostris 5795 Quakerfish Menidae Mene maculata 390 Moonfish Monocentridae Monocentris japonica 8183 Pineconefish Monodactylidae Monodactylus falciformis 7858 Full moony Mullidae Mulloidichthys flavolineatus 5983 Yellowstripe goatfish Mullidae Mulloidichthys vanicolensis 5984 Yellowfin goatfish Ophichthidae Muraenichthys schultzei 7290 Maimed snake eel Holocentridae Myripristis berndti 4910 Blotcheye soldierfish Holocentridae Myripristis hexagona 7305 Doubletooth soldierfish Holocentridae Myripristis murdjan 5408 Pinecone soldierfish Holocentridae Myripristis xanthacra 7822 Yellowtip soldierfish Carangidae Naucrates ductor 998 Pilotfish Holocentridae Neoniphon sammara 4911 Sammara squirrelfish Labridae Novaculichthys macrolepidotus 5609 Seagrass wrasse Labridae Novaculichthys taeniourus 5610 Rockmover wrasse Opistognathidae Opistognathus muscatensis 8000 Robust jawfish Ostraciidae Ostracion cubicus 6555 Yellow boxfish Ostraciidae Ostracion cyanurus 12743 Bluetail trunkfish Labridae Oxycheilinus arenatus 5595 Speckled maori wrasse Labridae Oxycheilinus bimaculatus 5596 Two-spot wrasse Labridae Oxycheilinus mentalis 12779 Mental wrasse Gobiidae Oxyurichthys papuensis 8030 Frogface goby Pinguipedidae Parapercis hexophtalma 7866 Speckled sandperch Carangidae Parastromateus niger 1947 Black pomfret Soleidae Pardachirus marmoratus 8917 Finless sole Mullidae Parupeneus forsskali 10994 Red Sea goatfish Mullidae Parupeneus indicus 5992 Indian goatfish Mullidae Parupeneus macronema 7878 Longbarbel goatfish Mullidae Parupeneus rubescens 6373 Rosy goatfish Terapontidae Pelates quadrilineatus 7945 Fourlined terapon Pempheridae Pempheris oualensis 5802 Silver sweeper Pempheridae Pempheris schwenkii 12908 Black-stripe sweeper Pempheridae Pempheris vanicolensis 10350 Vanikoro sweeper Gobiidae Periophthalmus argentilineatus 7480 Barred mudskipper Platycephalidae Platycephalus indicus 950 Bartail flathead Plesiopidae Plesiops nigricans 24438 Whitespotted longfin  206 Group Family Scientific name FishBase Code FishBase common name Plotosidae Plotosus lineatus 4706 Striped eel catfish Priacanthidae Priacanthus hamrur 5791 Moontail bullseye Serranidae Pseudanthias squamipinnis 6568 Sea goldie Labridae Pteragogus flagellifer 8022 Cocktail wrasse Caesionidae Pterocaesio chrysozona 932 Goldband fusilier Caesionidae Pterocaesio pisang 936 Banana fusilier Scorpaenidae Pterois miles 7797 Devil firefish Scorpaenidae Pterois radiata 4913 Radial firefish Scorpaenidae Pterois russelii 6404 Plaintail turkeyfish Sparidae Rhabdosargus haffara 8166 Haffara seabream Balistidae Rhinecanthus aculeatus 5839 Blackbar triggerfish Balistidae Rhinecanthus assasi 25420 Picasso triggerfish Balistidae Rhinecanthus rectangulus 5840 Wedge-tail triggerfish Balistidae Rhinecanthus verrucosus 6028 Blackbelly triggerfish Holocentridae Sargocentron caudimaculatum 4907 Silverspot squirrelfish Holocentridae Sargocentron diadema 4699 Crown squirrelfish Holocentridae Sargocentron ittodai 6573 Samurai squirrelfish Holocentridae Sargocentron punctatissimum 4906 Speckled squirrelfish Holocentridae Sargocentron rubrum 6625 Redcoat Ophichthidae Scolecenchelys gymnota 7288 Slender worm eel Ophichthidae Scolecenchelys laticaudata 15672 Redfin worm-eel Nemipteridae Scolopsis bimaculatus 5886 Thumbprint monocle bream Nemipteridae Scolopsis ghanam 5888 Arabian monocle bream Nemipteridae Scolopsis taeniatus 5889 Black-streaked monocle bream Nemipteridae Scolopsis vosmeri 5883 Whitecheek monocle bream Scorpaenidae Scorpaenopsis oxycephala 5822 Tassled scorpionfish Scorpaenidae Scorpaenopsis venosa 7919 Raggy scorpionfish Sillaginidae Sillago sihama 4544 Silver sillago Soleidae Soleichthys heterorhinos 22544 Solenostomidae Solenostomus cyanopterus 7987 Ghost pipefish Labridae Stethojulis strigiventer 5641 Three-ribbon wrasse Labridae Stethojulis trilineata 6622 Three-lined rainbowfish Engraulidae Stolephorus indicus 569 Indian anchovy Balistidae Sufflamen albicaudatum 25419 Bluethroat triggerfish Balistidae Sufflamen fraenatum 1312 Masked triggerfish Syngnathidae Syngnathoides biaculeatus 5980 Alligator pipefish Synodontidae Synodus indicus 7942 Indian lizardfish Terapontidae Terapon jarbua 4458 Jarbua terapon Terapontidae Terapon theraps 4829 Largescaled therapon Ostraciidae Tetrosomus gibbosus 8129 Humpback turretfish Labridae Thalassoma hebraicum 8019 Goldbar wrasse Labridae Thalassoma lunare 5645 Moon wrasse Labridae Thalassoma purpureum 5647 Surge wrasse Labridae Thalassoma rueppellii 25787 Klunzinger's wrasse Labridae Thalassoma trilobatum 5649 Christmas wrasse Monacanthidae Thamnaconus modestoides 7855 Modest filefish Platycephalidae Thysanophrys chiltonae 12902 Longsnout flathead Syngnathidae Trachyrhamphus 5981 Double-ended pipefish  207 Group Family Scientific name FishBase Code FishBase common name bicoarctatus Mullidae Upeneus moluccensis 4444 Goldband goatfish Mullidae Upeneus tragula 5443 Freckled goatfish Mullidae Upeneus vittatus 4821 Yellowstriped goatfish Uranoscopidae Uranoscopus sulphureus 13512 Whitemargin stargazer Carangidae Uraspis helvola 1983 Whitemouth jack Carangidae Uraspis uraspis 1984 Whitetongue jack Gobiidae Valenciennea helsdingenii 7224 Twostripe goby Gobiidae Valenciennea puellaris 7246 Maiden goby Blenniidae Xiphasia matsubarai 6078 Japanese snake blenny Labridae Xyrichtys melanopus 23517 Yellowpatch razorfish Labridae Xyrichtys pentadactylus 7747 Fivefinger wrasse Gobiidae Yongeichthys nebulosus 7228 Shadow goby Small reef carnivores   Syngnathidae Acentronura tentaculata 16862 Gobiidae Amblyeleotris diagonalis 13152 Gobiidae Amblyeleotris periophthalma 7231 Periophthalma prawn-goby Gobiidae Amblyeleotris steinitzi 7195 Steinitz' prawn-goby Gobiidae Amblyeleotris sungami 12699 Magnus' prawn-goby Gobiidae Amblyeleotris wheeleri 7196 Gorgeous prawn-goby Pomacentridae Amblyglyphidodon leucogaster 5691 Yellowbelly damselfish Gobiidae Amblygobius esakiae 27553 Snoutspot goby Labridae Anampses lineatus 7800 Lined wrasse Antennariidae Antennarius rosaceus 7296 Spiny-tufted frogfish Antennariidae Antennatus tuberosus 11150 Tuberculated frogfish Apogonidae Apogon angustatus 5766 Broadstriped cardinalfish Apogonidae Apogon annularis 56240 Ringtail cardinalfish Apogonidae Apogon bandanensis 5763 Bigeye cardinalfish Apogonidae Apogon coccineus 5752 Ruby cardinalfish Apogonidae Apogon cookii 9240 Cook's cardinalfish Apogonidae Apogon cyanosoma 4600 Yellowstriped cardinalfish Apogonidae Apogon exostigma 5756 Narrowstripe cardinalfish Apogonidae Apogon fasciatus 6605 Broad-banded cardinalfish Apogonidae Apogon fraenatus 5757 Bridled cardinalfish Apogonidae Apogon guamensis 5765 Guam cardinalfish Apogonidae Apogon heptastygma 50885 Apogonidae Apogon isus 50886 Apogonidae Apogon kiensis 8230 Rifle cardinal Apogonidae Apogon lateralis 5761 Humpback cardinal Apogonidae Apogon latus 60370 Apogonidae Apogon leptacanthus 5773 Threadfin cardinalfish Apogonidae Apogon nigripinnis 8012 Bullseye Apogonidae Apogon nigrofasciatus 4836 Blackstripe cardinalfish Apogonidae Apogon pselion 4839 Apogonidae Apogon pseudotaeniatus 26632 Doublebar cardinalfish Apogonidae Apogon savayensis 5764 Samoan cardinalfish Apogonidae Apogon semiornatus 8008 Oblique-banded cardinalfish Apogonidae Apogon taeniophorus 5767 Reef-flat cardinalfish Apogonidae Apogon timorensis 12658 Timor cardinalfish Apogonidae Apogon zebrinus 58157 Apogonidae Apogonichthys perdix 5741 Perdix cardinalfish Apogonidae Archamia bilineata 58158  208 Group Family Scientific name FishBase Code FishBase common name Apogonidae Archamia fucata 5776 Orangelined cardinalfish Apogonidae Archamia irida 58159 Apogonidae Archamia lineolata 7854 Shimmering cardinal Blenniidae Aspidontus taeniatus taeniatus 6066 False cleanerfish Gobiidae Asterropteryx ensifera 7247 Miller's damsel Gobiidae Bathygobius cyclopterus 11801 Spotted frillgoby Gobiidae Bathygobius fuscus 7201 Dusky frillgoby Bythitidae Brosmophyciops pautzkei 7299 Slimy cuskeel Gobiidae Bryaninops erythrops 7204 Erythrops goby Gobiidae Bryaninops loki 52430 Loki whip-goby Gobiidae Bryaninops natans 7205 Redeye goby Gobiidae Bryaninops ridens 7250 Ridens goby Gobiidae Bryaninops yongei 7251 Whip coral goby Callionymidae Callionymus delicatulus 17467 Delicate dragonet Callionymidae Callionymus flavus 56497 Gobiidae Callogobius bifasciatus 46389 Doublebar goby Gobiidae Callogobius maculipinnis 7206 Ostrich goby Tetraodontidae Canthigaster coronata 7845 Crowned puffer Tetraodontidae Canthigaster pygmaea 25414 Pygmy toby Chaetodontidae Chaetodon citrinellus 5561 Speckled butterflyfish Chaetodontidae Chaetodon guttatissimus 7791 Peppered butterflyfish Chaetodontidae Chaetodon larvatus 12287 Hooded butterflyfish Chaetodontidae Chaetodon melapterus 12533 Arabian butterflyfish Chaetodontidae Chaetodon mesoleucos 25428 White-face butterflyfish Chaetodontidae Chaetodon paucifasciatus 12296 Eritrean butterflyfish Chaetodontidae Chaetodon trifascialis 5578 Chevron butterflyfish Apogonidae Cheilodipterus quinquelineatus 5482 Five-lined cardinalfish Pseudochromidae Chlidichthys johnvoelckeri 23591 Cerise dottyback Syngnathidae Choeroichthys brachysoma 5958 Short-bodied pipefish Pomacentridae Chromis flavaxilla 26638 Arabian chromis Pomacentridae Chromis nigrura 12424 Blacktail chromis Pomacentridae Chromis ternatensis 5677 Ternate chromis Pomacentridae Chromis weberi 5680 Weber's chromis Labridae Cirrhilabrus rubriventralis 12781 Social wrasse Cirrhitidae Cirrhitichthys calliurus 46372 Spottedtail hawkfish Cirrhitidae Cirrhitichthys oxycephalus 5830 Coral hawkfish Syngnathidae Corythoichthys flavofasciatus 5959 Network pipefish Syngnathidae Corythoichthys nigripectus 5962 Black-breasted pipefish Syngnathidae Cosmocampus banneri 5966 Roughridge pipefish Syngnathidae Cosmocampus maxweberi 5968 Maxweber's pipefish Gobiidae Cryptocentrus caeruleopunctatus 12748 Harlequin prawn-goby Gobiidae Cryptocentrus cryptocentrus 25797 Ninebar prawn-goby Gobiidae Cryptocentrus fasciatus 12679 Y-bar shrimp goby Gobiidae Cryptocentrus lutheri 25800 Luther's prawn-goby  209 Group Family Scientific name FishBase Code FishBase common name Gobiidae Ctenogobiops crocineus 13153 Silverspot shrimpgoby Gobiidae Ctenogobiops feroculus 7238 Sandy prawn-goby Gobiidae Ctenogobiops maculosus 27561 Gobiidae Discordipinna griessingeri 7212 Spikefin goby Syngnathidae Doryrhamphus excisus abbreviatus 7718 Engraulidae Encrasicholina punctifer 558 Buccaneer anchovy Tripterygiidae Enneapterygius abeli 16974 Yellow triplefin Pegasidae Eurypegasus draconis 4606 Short dragonfish Gobiidae Eviota distigma 7261 Twospot pygmy goby Gobiidae Eviota guttata 25452 Spotted pygmy goby Gobiidae Eviota pardalota 46398 Leopard dwarfgoby Gobiidae Eviota prasina 7270 Green bubble goby Gobiidae Eviota sebreei 7275 Sebree's pygmy goby Gobiidae Eviota zebrina 25462 Gobiidae Flabelligobius latruncularia 25463 Fan shrimp-goby Apogonidae Fowleria aurita 8010 Crosseyed cardinalfish Apogonidae Fowleria marmorata 5744 Marbled cardinalfish Apogonidae Fowleria punctulata 5743 Spotcheek cardinalfish Apogonidae Fowleria vaiulae 8592 Mottled cardinalfish Apogonidae Fowleria variegata 5745 Variegated cardinalfish Gobiidae Fusigobius longispinus 12834 Orange-spotted sand-goby Gobiidae Gladiogobius ensifer 11174 Gladiator goby Gobiidae Gnatholepis anjerensis 23595 Gobiidae Gobiodon citrinus 7789 Poison goby Gobiidae Gobiodon reticulatus 46399 Reticulate goby Microdesmidae Gunnellichthys monostigma 12678 Onespot wormfish Apogonidae Gymnapogon melanogaster 60031 Syngnathidae Halicampus mataafae 5975 Samoan pipefish Labridae Halichoeres iridis 12790 Labridae Halichoeres nebulosus 6663 Nebulous wrasse Clupeidae Herklotsichthys quadrimaculatus 1494 Bluestripe herring Atherinidae Hypoatherina barnesi 1305 Barnes' silverside Atherinidae Hypoatherina temminckii 1307 Samoan silverside Gobiidae Istigobius decoratus 4328 Decorated goby Gobiidae Istigobius ornatus 4322 Ornate goby Labridae Labroides dimidiatus 5459 Bluestreak cleaner wrasse Labridae Larabicus quadrilineatus 25788 Fourline wrasse Gobiesocidae Lepadichthys lineatus 23229 Doubleline clingfish Serranidae Liopropoma mitratum 8432 Pinstriped basslet Serranidae Liopropoma susumi 7318 Meteor perch Gobiidae Luposicya lupus 23719 Labridae Macropharyngodon bipartitus bipartitus 7801 Vermiculate wrasse Labridae Macropharyngodon bipartitus marisrubri 13137 Blenniidae Meiacanthus nigrolineatus 12641 Blackline fangblenny Syngnathidae Micrognathus andersonii 5977 Shortnose pipefish  210 Group Family Scientific name FishBase Code FishBase common name Labridae Minilabrus striatus 25781 Minute wrasse Apogonidae Neamia octospina 8593 Eightspine cardinalfish Pomacentridae Neopomacentrus cyanomos 8209 Regal demoiselle Pomacentridae Neopomacentrus miryae 12461 Miry's demoiselle Pomacentridae Neopomacentrus xanthurus 12464 Red Sea demoiselle Tripterygiidae Norfolkia brachylepis 14209 Tropical scaly-headed triplefin Gobiidae Oplopomus oplopomus 7218 Spinecheek goby Cirrhitidae Oxycirrhites typus 5833 Longnose hawkfish Monacanthidae Oxymonacanthus halli 25418 Red Sea longnose filefish Gobiidae Palutrus meteori 25042 Meteor goby Labridae Paracheilinus octotaenia 4840 Red Sea eightline flasher Gobiidae Paragobiodon echinocephalus 7219 Redhead goby Gobiidae Paragobiodon xanthosomus 7220 Emerald coral goby Pempheridae Parapriacanthus ransonneti 5803 Pigmy sweeper Scorpaenidae Parascorpaena aurita 27438 Scorpaenidae Parascorpaena mossambica 5810 Mozambique scorpionfish Pseudochromidae Pectinochromis lubbocki 12742 Anomalopidae Photoblepharon steinitzi 17085 Flashlight fish Syngnathidae Phoxocampus belcheri 7742 Rock pipefish Serranidae Plectranthias nanus 15118 Bownband perchlet Serranidae Plectranthias winniensis 12799 Redblotch basslet Plesiopidae Plesiops coeruleolineatus 8005 Crimsontip longfin Gobiidae Pleurosicya mossambica 23079 Toothy goby Pomacentridae Pomacentrus pavo 5726 Sapphire damsel Gobiidae Priolepis cincta 7221 Girdled goby Gobiidae Priolepis randalli 46409 Randall's goby Pomacentridae Pristotis obtusirostris 8127 Gulf damselfish Apogonidae Pseudamia gelatinosa 4362 Gelatinous cardinalfish Serranidae Pseudanthias cichlops 6945 Serranidae Pseudanthias heemstrai 24434 Orangehead anthias Serranidae Pseudanthias lunulatus 23329 Lunate goldie Serranidae Pseudanthias taeniatus 12776 Labridae Pseudocheilinus evanidus 5616 Striated wrasse Labridae Pseudocheilinus hexataenia 5617 Pyjama Pseudochromidae Pseudochromis dixurus 24442 Forktail dottyback Pseudochromidae Pseudochromis flavivertex 12738 Sunrise dottyback Pseudochromidae Pseudochromis fridmani 12741 Orchid dottyback Pseudochromidae Pseudochromis olivaceus 24440 Olive dottyback Pseudochromidae Pseudochromis pesi 12653 Pale dottyback Pseudochromidae Pseudochromis sankeyi 24443 Striped dottyback Pseudochromidae Pseudochromis springeri 24441 Blue-striped dottyback Pseudochromidae Pseudochromis xanthochir 23434 Serranidae Pseudogramma megamycterum 49434 Labridae Pteragogus cryptus 5620 Cryptic wrasse Microdesmidae Ptereleotris evides 4375 Blackfin dartfish  211 Group Family Scientific name FishBase Code FishBase common name Microdesmidae Ptereleotris heteroptera 4378 Blacktail goby Microdesmidae Ptereleotris microlepis 4381 Blue gudgeon Microdesmidae Ptereleotris zebra 4384 Chinese zebra goby Apogonidae Rhabdamia cypselura 5746 Swallowtail cardinalfish Apogonidae Rhabdamia nigrimentum 46488 Holocentridae Sargocentron inaequalis 23249 Lattice squirrelfish Scorpaenidae Scorpaenodes corallinus 27363 Scorpaenidae Scorpaenodes guamensis 5819 Guam scorpionfish Scorpaenidae Scorpaenodes hirsutus 5815 Hairy scorpionfish Scorpaenidae Scorpaenodes parvipinnis 4915 Lowfin scorpionfish Scorpaenidae Scorpaenodes scaber 7314 Pygmy scorpionfish Scorpaenidae Scorpaenodes varipinnis 5818 Blotchfin scorpionfish Scorpaenidae Scorpaenopsis vittapinna 59507 Scorpaenidae Sebastapistes bynoensis 59579 Scorpaenidae Sebastapistes cyanostigma 5811 Yellowspotted scorpionfish Scorpaenidae Sebastapistes strongia 5814 Barchin scorpionfish Syngnathidae Siokunichthys bentuviai 7194 Solenostomidae Solenostomus paradoxus 7312 Harlequin ghost pipefish Clupeidae Spratelloides delicatulus 1457 Delicate round herring Labridae Stethojulis albovittata 8025 Bluelined wrasse Labridae Stethojulis interrupta 6633 Cutribbon wrasse Pomacentridae Teixeirichthys jordani 10742 Jordan's damsel Tetraodontidae Torquigener flavimaculosus 26639 Gobiidae Trimma avidori 28069 Gobiidae Trimma barralli 28063 Gobiidae Trimma fishelsoni 28070 Gobiidae Trimma flavicaudatus 28071 Gobiidae Trimma mendelssohni 28072 Gobiidae Trimma sheppardi 28073 Gobiidae Trimma taylori 12752 Yellow cave goby Gobiidae Trimma tevegae 12754 Blue-striped cave goby Gobiidae Valenciennea sexguttata 7227 Sixspot goby Gobiidae Valenciennea wardii 12615 Ward's sleeper Gobiidae Vanderhorstia delagoae 8033 Candystick goby Gobiidae Vanderhorstia mertensi 23647 Mertens' prawn-goby Labridae Wetmorella nigropinnata 4870 Sharpnose wrasse Xenisthmidae Xenisthmus polyzonatus 13766 Bullseye wriggler Reef omnivores Pomacentridae Abudefduf sexfasciatus 5688 Scissortail sergeant Pomacentridae Abudefduf sordidus 5689 Blackspot sergeant Acanthuridae Acanthurus gahhm 17471 Black surgeonfish Acanthuridae Acanthurus mata 1255 Elongate surgeonfish Acanthuridae Acanthurus xanthopterus 1261 Yellowfin surgeonfish Monacanthidae Aluterus scriptus 4275 Scrawled filefish Monacanthidae Amanses scopas 6672 Broom filefish Pomacentridae Amblyglyphidodon flavilatus 11834 Yellowfin damsel Gobiidae Amblygobius albimaculatus 6675 Butterfly goby Gobiidae Amblygobius hectori 7242 Hector's goby Gobiidae Amblygobius nocturnus 7243 Nocturn goby Pomacentridae Amphiprion bicinctus 11837 Twoband anemonefish Pomacanthidae Apolemichthys xanthotis 10940 Yellow-ear angelfish  212 Group Family Scientific name FishBase Code FishBase common name Blenniidae Aspidontus taeniatus tractus 8040 Gobiidae Asterropteryx semipunctata 7200 Starry goby Blenniidae Blenniella cyanostigma 16946 Striped rockskipper Blenniidae Blenniella periophthalmus 6051 Blue-dashed rockskipper Scaridae Bolbometopon muricatum 5537 Green humphead parrotfish Pomacanthidae Centropyge bicolor 5454 Bicolor angelfish Pomacanthidae Centropyge multispinis 6549 Dusky angelfish Chaetodontidae Chaetodon leucopleura 8083 Somali butterflyfish Chanidae Chanos chanos 80 Milkfish Scaridae Chlorurus gibbus 4979 Heavybeak parrotfish Pomacentridae Chromis dimidiata 11861 Chocolatedip chromis Pomacentridae Chromis pelloura 12428 Duskytail chromis Pomacentridae Chromis pembae 12429 Pemba chromis Pomacentridae Chromis trialpha 12432 Trispot chromis Pomacentridae Chromis viridis 5679 Blue green damselfish Pomacentridae Chrysiptera annulata 12438 Footballer demoiselle Pomacentridae Chrysiptera unimaculata 5702 Onespot demoiselle Mugilidae Crenimugil crenilabis 5653 Fringelip mullet Pomacentridae Dascyllus aruanus 5110 Whitetail dascyllus Pomacentridae Dascyllus marginatus 11985 Marginate dascyllus Pomacentridae Dascyllus trimaculatus 5112 Threespot dascyllus Sparidae Diplodus noct 8112 Red Sea seabream Blenniidae Ecsenius midas 7561 Persian blenny Tripterygiidae Enneapterygius altipinnis 13574 Highfin triplefin Tripterygiidae Enneapterygius tutuilae 47045 High hat triplefin Blenniidae Exallias brevis 6032 Leopard blenny Gobiidae Exyrias belissimus 370 Mud reef-goby Gobiidae Fusigobius neophytus 7215 Common fusegoby Gobiidae Gnatholepis cauerensis cauerensis 9950 Eyebar goby Tripterygiidae Helcogramma steinitzi 26343 Red triplefin Hemiramphidae Hemiramphus far 5404 Blackbarred halfbeak Hemiramphidae Hyporhamphus balinensis 16813 Balinese garfish Hemiramphidae Hyporhamphus gamberur 53427 Red Sea halfbeak Scaridae Leptoscarus vaigiensis 4360 Marbled parrotfish Mugilidae Liza vaigiensis 5656 Squaretail mullet Balistidae Melichthys indicus 7634 Indian triggerfish Blenniidae Mimoblennius cirrosus 46416 Fringed blenny Acanthuridae Naso annulatus 6019 Whitemargin unicornfish Acanthuridae Naso brevirostris 6021 Spotted unicornfish Acanthuridae Naso elegans 60074 Elegant unicornfish Pomacentridae Neoglyphidodon melas 5707 Bowtie damselfish Mugilidae Oedalechilus labiosus 5657 Hornlip mullet Blenniidae Omobranchus punctatus 7566 Muzzled blenny Lutjanidae Paracaesio sordida 192 Dirty ordure snapper Monacanthidae Paramonacanthus japonicus 7977 Hairfinned leatherjacket Monacanthidae Pervagor randalli 4372 Blenniidae Plagiotremus rhinorhynchos 6071 Bluestriped fangblenny Blenniidae Plagiotremus tapeinosoma 6072 Piano fangblenny  213 Group Family Scientific name FishBase Code FishBase common name Pomacentridae Plectroglyphidodon lacrymatus 5712 Whitespotted devil Pomacanthidae Pomacanthus asfur 11194 Arabian angelfish Pomacanthidae Pomacanthus imperator 6504 Emperor angelfish Pomacanthidae Pomacanthus maculosus 7903 Yellowbar angelfish Pomacanthidae Pomacanthus semicirculatus 5663 Semicircle angelfish Pomacentridae Pomacentrus albicaudatus 12478 Whitefin damsel Pomacentridae Pomacentrus aquilus 12480 Dark damsel Pomacentridae Pomacentrus leptus 12494 Slender damsel Pomacentridae Pomacentrus sulfureus 12503 Sulphur damsel Pomacentridae Pomacentrus trichourus 12504 Paletail damsel Pomacentridae Pomacentrus trilineatus 12505 Threeline damsel Haemulidae Pomadasys olivaceus 5518 Olive grunt Gobiidae Priolepis semidoliata 12885 Half-barred goby Pomacentridae Pristotis cyanostigma 12507 Bluedotted damsel Labridae Pseudodax moluccanus 5594 Chiseltooth wrasse Pomacanthidae Pygoplites diacanthus 6572 Royal angelfish Blenniidae Salarias fasciatus 6058 Jewelled blenny Clupeidae Sardinella albella 1502 White sardinella Scaridae Scarus caudofasciatus 7908 Redbarred parrotfish Scaridae Scarus collana 14379 Red Sea parrotfish Scaridae Scarus fuscopurpureus 14381 Purple-brown parrotfish Siganidae Siganus javus 4618 Streaked spinefoot Siganidae Siganus stellatus 4622 Brownspotted spinefoot Pomacentridae Stegastes lividus 4351 Blunt snout gregory Pomacentridae Stegastes nigricans 4352 Dusky farmerfish Mugilidae Valamugil seheli 5659 Bluespot mullet Reef herbivores Acanthuridae Acanthurus nigricans 6011 Whitecheek surgeonfish Acanthuridae Acanthurus nigrofuscus 4739 Brown surgeonfish Acanthuridae Acanthurus sohal 4740 Sohal surgeonfish Acanthuridae Acanthurus tennentii 1259 Doubleband surgeonfish Blenniidae Aspidontus dussumieri 6065 Lance blenny Blenniidae Atrosalarias fuscus fuscus 17462 Scaridae Calotomus viridescens 4358 Viridescent parrotfish Scaridae Cetoscarus bicolor 5538 Bicolour parrotfish Scaridae Chlorurus genazonatus 14382 Sinai parrotfish Scaridae Chlorurus sordidus 5556 Daisy parrotfish Pomacentridae Chrysiptera biocellata 5693 Twinspot damselfish Blenniidae Cirripectes castaneus 4387 Chestnut eyelash-blenny Blenniidae Cirripectes filamentosus 4389 Filamentous blenny Acanthuridae Ctenochaetus striatus 1262 Striated surgeonfish Blenniidae Ecsenius aroni 25794 Aron's blenny Blenniidae Ecsenius frontalis 12634 Smooth-fin blenny Blenniidae Ecsenius gravieri 12635 Red Sea mimic blenny Blenniidae Ecsenius nalolo 25451 Nalolo Blenniidae Enchelyurus kraussii 6062 Krauss' blenny Scaridae Hipposcarus harid 7906 Candelamoa parrotfish Blenniidae Istiblennius edentulus 6049 Rippled rockskipper Blenniidae Istiblennius rivulatus 23697 Kyphosidae Kyphosus bigibbus 5804 Grey sea chub Kyphosidae Kyphosus vaigiensis 5806 Brassy chub Acanthuridae Naso unicornis 1265 Bluespine unicornfish  214 Group Family Scientific name FishBase Code FishBase common name Blenniidae Petroscirtes mitratus 6074 Floral blenny Blenniidae Plagiotremus townsendi 12788 Townsend's fangblenny Pomacentridae Plectroglyphidodon leucozonus 5713 Singlebar devil Scaridae Scarus ferrugineus 14380 Rusty parrotfish Scaridae Scarus frenatus 5546 Bridled parrotfish Scaridae Scarus niger 5550 Dusky parrotfish Scaridae Scarus psittacus 5553 Common parrotfish Scaridae Scarus russelii 7912 Eclipse parrotfish Scaridae Scarus scaber 7913 Fivesaddle parrotfish Siganidae Siganus argenteus 4614 Streamlined spinefoot Siganidae Siganus luridus 4613 Dusky spinefoot Siganidae Siganus rivulatus 4545 Marbled spinefoot Acanthuridae Zebrasoma veliferum 1266 Sailfin tang Acanthuridae Zebrasoma xanthurum 12023 Yellowtail tang Large pelagic carnivores Coryphaenidae Coryphaena hippurus 6 Common dolphinfish Elopidae Elops machnata 5512 Tenpounder Istiophoridae Istiophorus platypterus 77 Indo-Pacific sailfish Istiophoridae Makaira indica 217 Black marlin Molidae Mola mola 1732 Ocean sunfish Molidae Ranzania laevis 1750 Slender sunfish Scombridae Sarda orientalis 114 Striped bonito Carangidae Scomber sansun 53238 Istiophoridae Tetrapturus audax 223 Striped marlin Scombridae Thunnus albacares 143 Yellowfin tuna Belonidae Tylosurus choram 26633 Red Sea houndfish Xiphiidae Xiphias gladius 226 Swordfish Small pelagic carnivores Carangidae Alepes vari 1891 Herring scad Clupeidae Amblygaster leiogaster 1500 Smooth-belly sardinella Scombridae Auxis rochei rochei 93 Bullet tuna Scombridae Auxis thazard thazard 94 Frigate tuna Bregmacerotidae Bregmaceros mcclellandii 8421 Spotted codlet Bregmacerotidae Bregmaceros nectabanus 8422 Smallscale codlet Carangidae Carangoides ciliarius 53230 Exocoetidae Cheilopogon cyanopterus 7695 Margined flyingfish Exocoetidae Cheilopogon pinnatibarbatus altipennis 23233 Smallhead flyingfish Chirocentridae Chirocentrus nudus 1452 Whitefin wolf-herring Coryphaenidae Coryphaena equiselis 7 Pompano dolphinfish Exocoetidae Cypselurus oligolepis 15365 Largescale flyingfish Carangidae Decapterus macarellus 993 Mackerel scad Clupeidae Dussumieria acuta 1453 Rainbow sardine Engraulidae Engraulis encrasicolus 66 European anchovy Hemiramphidae Euleptorhamphus viridis 3156 Ribbon halfbeak Exocoetidae Exocoetus volitans 1032 Tropical two-wing flyingfish Hemiramphidae Hemiramphus marginatus 9963 Yellowtip halfbeak Clupeidae Herklotsichthys lossei 1492 Gulf herring Clupeidae Hilsa kelee 1595 Kelee shad Exocoetidae Hirundichthys rondeletii 1035 Black wing flyingfish Exocoetidae Hirundichthys socotranus 60693 Malacanthidae Hoplolatilus geo 54468 Hemiramphidae Hyporhamphus xanthopterus 25044 Red-tipped halfbeak  215 Group Family Scientific name FishBase Code FishBase common name Scombridae Katsuwonus pelamis 107 Skipjack tuna Lactariidae Lactarius lactarius 363 False trevally Exocoetidae Parexocoetus brachypterus 1037 Sailfin flyingfish Exocoetidae Parexocoetus mento 4904 African sailfin flyingfish Belonidae Platybelone argalus platura 58272 Echeneidae Remora brachyptera 3546 Spearfish remora Echeneidae Remorina albescens 3548 White suckerfish Scombridae Scomber japonicus 117 Chub mackerel Sphyraenidae Sphyraena chrysotaenia 16905 Yellowstripe barracuda Clupeidae Spratelloides gracilis 1458 Silver-stripe round herring Engraulidae Thryssa setirostris 599 Longjaw thryssa Pelagic omnivores Bregmacerotidae Bregmaceros arabicus 23168 Leiognathidae Leiognathus oblongus 58321 Oblong ponyfish Mugilidae Liza carinata 13673 Keeled mullet Monodactylidae Monodactylus argenteus 5807 Silver moony Clupeidae Sardinella longiceps 1511 Indian oil sardine Demersal top predator Muraenesocidae Congresox talabonoides 11713 Indian pike conger Serranidae Epinephelus epistictus 7341 Dotted grouper Serranidae Epinephelus radiatus 7360 Oblique-banded grouper Leiognathidae Gazza minuta 4462 Toothpony Gobiidae Glossogobius giuris 4833 Tank goby Muraenidae Gymnothorax johnsoni 7882 Whitespotted moray Lophiidae Lophiomus setigerus 7517 Blackmouth angler Muraenesocidae Muraenesox cinereus 298 Daggertooth pike conger Psettodidae Psettodes erumei 513 Indian spiny turbot Paralichthyidae Pseudorhombus arsius 1325 Largetooth flounder Synodontidae Synodus hoshinonis 7941 Blackear lizardfish Synodontidae Synodus macrops 8299 Triplecross lizardfish Uranoscopidae Uranoscopus bauchotae 56492 Uranoscopidae Uranoscopus dahlakensis 56493 Uranoscopidae Uranoscopus oligolepis 8303 Large demersal carnivores Sparidae Argyrops megalommatus 61176 Ariidae Arius thalassinus 10220 Giant seacatfish Malacanthidae Branchiostegus sawakinensis 7649 Freckled tilefish Labridae Cheilinus abudjubbe 60813 Cynoglossidae Cynoglossus arel 7523 Largescale tonguesole Cynoglossidae Cynoglossus bilineatus 5455 Fourlined tonguesole Serranidae Epinephelus latifasciatus 7350 Striped grouper Congridae Gorgasia cotroneii 58702 Congridae Gorgasia sillneri 55167 Muraenidae Gymnothorax angusticauda 27319 Muraenidae Gymnothorax tile 17266 Gymnuridae Gymnura poecilura 8260 Longtail butterfly ray Tetraodontidae Lagocephalus lunaris 8263 Green rough-backed puffer Tetraodontidae Lagocephalus spadiceus 8180 Half-smooth golden pufferfish Platycephalidae Platycephalus micracanthus 52981 Haemulidae Plectorhinchus faetela 60766  216 Group Family Scientific name FishBase Code FishBase common name Haemulidae Pomadasys argenteus 399 Silver grunt Haemulidae Pomadasys hasta 55178 Haemulidae Pomadasys multimaculatum 5517 Cock grunter Lutjanidae Pristipomoides multidens 208 Goldbanded jobfish Platycephalidae Rogadius pristiger 15225 Thorny flathead Nettastomatidae Saurenchelys lateromaculatus 58723 Congridae Uroconger lepturus 7590 Slender conger Ophichthidae Yirrkala tenuis 15697 Thin sand-eel Medium demersal carnivores Sparidae Acanthopagrus berda 5526 Picnic seabream Sparidae Acanthopagrus latus 6356 Yellowfin seabream Ambassidae Ambassis gymnocephalus 4806 Bald glassy Apistidae Apistus carinatus 6383 Ocellated waspfish Apogonidae Apogon fleurieu 4838 Cardinalfish Ariommatidae Ariomma dollfusi 60525 Soleidae Aseraggodes sinusarabici 58956 Bothidae Bothus myriaster 1322 Indo-Pacific oval flounder Bothidae Bothus tricirrhitus 58972 Soleidae Brachirus orientalis 8312 Oriental sole Callionymidae Callionymus filamentosus 225 Blotchfin dragonet Callionymidae Callionymus gardineri 1318 Longtail dragonet Synanceiidae Choridactylus multibarbus 6387 Orangebanded stingfish Gobiesocidae Chorisochismus dentex 23222 Rocksucker Cynoglossidae Cynoglossus dollfusi 9250 Cynoglossidae Cynoglossus gilchristi 7681 Ripplefin tonguesole Cynoglossidae Cynoglossus kopsii 7647 Shortheaded tonguesole Cynoglossidae Cynoglossus lachneri 7682 Lachner's tonguesole Cynoglossidae Cynoglossus lingua 8238 Long tongue sole Cynoglossidae Cynoglossus pottii 56480 Cynoglossidae Cynoglossus sealarki 17158 Dactylopteridae Dactyloptena peterseni 7691 Starry flying gurnard Syngnathidae Dunckerocampus boylei 54745 Broad-banded Pipefish Bothidae Engyprosopon maldivensis 13970 Olive wide-eyed flounder Platycephalidae Grammoplites suppositus 28128 Spotfin flathead Muraenidae Gymnothorax herrei 7491 Tripterygiidae Helcogramma obtusirostre 8046 Hotlips triplefin Congridae Heteroconger balteatus 55140 Narcinidae Heteronarce bentuviai 53919 Elat electric ray Syngnathidae Hippichthys cyanospilus 7728 Blue-spotted pipefish Syngnathidae Hippichthys spicifer 7495 Bellybarred pipefish Syngnathidae Hippocampus fuscus 25955 Sea pony Syngnathidae Hippocampus jayakari 53814 Jayakar's seahorse Syngnathidae Hippocampus lichtensteinii 53909 Lichtenstein's Seahorse Malacanthidae Hoplolatilus oreni 15379 Leiognathidae Leiognathus fasciatus 4452 Striped ponyfish Triglidae Lepidotrigla bispinosa 28127 Bullhorn gurnard Liparidae Liparis fishelsoni 58827 Syngnathidae Lissocampus bannwarthi 46165  217 Group Family Scientific name FishBase Code FishBase common name Synanceiidae Minous monodactylus 6388 Grey stingfish Ophichthidae Myrophis microchir 22602 Nemipteridae Nemipterus bipunctatus 5851 Delagoa threadfin bream Nemipteridae Nemipterus peronii 4554 Notchedfin threadfin bream Nemipteridae Nemipterus randalli 5852 Randall's threadfin bream Nemipteridae Nemipterus zysron 5855 Slender threadfin bream Pinguipedidae Parapercis robinsoni 7867 Smallscale grubfish Pinguipedidae Parapercis simulata 56473 Pinguipedidae Parapercis somaliensis 10297 Somali sandperch Nemipteridae Parascolopsis aspinosa 5856 Smooth dwarf monocle bream Nemipteridae Parascolopsis eriomma 5858 Rosy dwarf monocle bream Nemipteridae Parascolopsis inermis 5860 Unarmed dwarf monocle bream Nemipteridae Parascolopsis townsendi 5859 Scaly dwarf monocle bream Pempheridae Pempheris mangula 25449 Black-edged sweeper Polynemidae Polydactylus plebeius 7901 Striped threadfin Polynemidae Polydactylus sextarius 4470 Blackspot threadfin Haemulidae Pomadasys punctulatus 46379 Lined grunt Priacanthidae Priacanthus sagittarius 9913 Arrow bulleye Paralichthyidae Pseudorhombus elevatus 1333 Deep flounder Labridae Pteragogus pelycus 8023 Sideburn wrasse Platycephalidae Rogadius asper 8305 Olive-tailed flathead Platycephalidae Rogadius prionotus 7897 Blackblotch flathead Samaridae Samaris cristatus 8290 Cockatoo righteye flounder Holocentridae Sargocentron marisrubri 5347 Serranidae Serranus cabrilla 1353 Comber Ophidiidae Sirembo jerdoni 10527 Brown-banded cusk-eel Ophichthidae Skythrenchelys lentiginosa 59468 Soleidae Solea elongata 14394 Elongate sole Soleidae Synaptura commersonnii 14395 Commerson's sole Syngnathidae Syngnathus safina 61282 Batrachoididae Thalassothia cirrhosa 6390 Toadfish Syngnathidae Trachyrhamphus longirostris 23124 Trichonotidae Trichonotus nikii 27323 Mullidae Upeneus pori 46375 Por's goatfish Mullidae Upeneus sulphureus 4445 Sulphur goatfish Uranoscopidae Uranoscopus dollfusi 46424 Dollfus' stargazer Uranoscopidae Uranoscopus guttatus 56494 Muraenidae Uropterygius genie 47872 Muraenidae Uropterygius golanii 50765 Labridae Xyrichtys bimaculatus 14342 Two-spot razorfish Labridae Xyrichtys javanicus 56499 Labridae Xyrichtys niger 8444 Soleidae Zebrias quagga 8194 Fringefin zebra sole Small demersal carnivores Ambassidae Ambassis urotaenia 9235 Banded-tail glassy perchlet Gobiidae Amblyeleotris triguttata 26636 Triplespot shrimpgoby Gobiidae Amblygobius magnusi 56463 Gobiidae Amoya signata 17033 Tusk goby Caproidae Antigonia indica 59052 Apogonidae Apogon gularis 56481 Apogonidae Apogon hungi 56482 Apogonidae Apogon micromaculatus 56483  218 Group Family Scientific name FishBase Code FishBase common name Apogonidae Apogon quadrifasciatus 53017 Twostripe cardinal Apogonidae Apogon smithi 59514 Smith's cardinalfish Apogonidae Apogon spongicolus 56484 Scorpaenidae Brachypterois serrulata 9203 Callionymidae Callionymus bentuviai 56496 Callionymidae Callionymus erythraeus 46382 Smallhead dragonet Callionymidae Callionymus marleyi 7650 Sand dragonet Callionymidae Callionymus muscatensis 46387 Muscat dragonet Callionymidae Callionymus oxycephalus 56498 Gobiidae Callogobius amikami 26993 Gobiidae Callogobius dori 56134 Gobiidae Callogobius flavobrunneus 17050 Slimy goby Apogonidae Cheilodipterus novemstriatus 12629 Indian Ocean twospot cardinalfish Apogonidae Cheilodipterus pygmaios 12881 Pseudochromidae Chlidichthys auratus 56486 Pseudochromidae Chlidichthys rubiceps 56487 Pomacentridae Chromis axillaris 11854 Grey chromis Aploactinidae Cocotropus steinitzi 56490 Gobiidae Coryogalops anomolus 46394 Anomolous goby Gobiidae Cryptocentroides arabicus 46397 Arabian goby Callionymidae Diplogrammus infulatus 17029 Sawspine dragonet Callionymidae Diplogrammus randalli 49452 Bothidae Engyprosopon hureaui 15567 Hureau's flounder Bothidae Engyprosopon latifrons 15569 Bothidae Engyprosopon macrolepis 5344 Tripterygiidae Enneapterygius clarkae 16975 Barred triplefin Tripterygiidae Enneapterygius obscurus 25377 Tripterygiidae Enneapterygius pusillus 16979 Highcrest triplefin Gobiidae Favonigobius reichei 9945 Indo-Pacific tropical sand goby Gobiidae Fusigobius humeralis 59445 Gobiidae Fusigobius maximus 59446 Gobiidae Gobius koseirensis 61336 Gobiidae Gobius leucomelas 61337 Gobiidae Hetereleotris diademata 56465 Gobiidae Hetereleotris vulgaris 46402 Common goby Gobiidae Isthmogobius baliurus 52799 Kraemeriidae Kraemeria nudum 60799 Leiognathidae Leiognathus berbis 7748 Berber ponyfish Leiognathidae Leiognathus klunzingeri 27024 Leiognathidae Leiognathus leuciscus 4453 Whipfin ponyfish Leiognathidae Leiognathus lineolatus 4563 Ornate ponyfish Gobiesocidae Lepadichthys erythraeus 55729 Triglidae Lepidotrigla spiloptera 10366 Spotwing gurnard Creediidae Limnichthys nitidus 16931 Sand submarine Synanceiidae Minous coccineus 10726 Onestick stingfish Synanceiidae Minous inermis 46368 Alcock's scorpionfish Pomacentridae Neopomacentrus taeniurus 5705 Freshwater demoiselle Ophidiidae Ophidion smithi 16788 Gobiidae Opua elati 56467 Blenniidae Parablennius cyclops 56471  219 Group Family Scientific name FishBase Code FishBase common name Microdesmidae Paragunnellichthys springeri 56470 Plesiopidae Plesiops mystaxus 27000 Moustache longfin Gobiidae Pleurosicya prognatha 56468 Gobiidae Pomatoschistus marmoratus 9191 Marbled goby Gobiidae Psilogobius randalli 59404 Aploactinidae Ptarmus gallus 52867 Microdesmidae Ptereleotris arabica 4374 Scorpaenidae Scorpaenodes steinitzi 56488 Leiognathidae Secutor insidiator 4455 Pugnose ponyfish Leiognathidae Secutor ruconius 4811 Deep pugnose ponyfish Gobiidae Silhouettea aegyptia 56197 Gobiidae Silhouettea chaimi 56469 Gobiidae Silhouettea insinuans 9996 Phantom goby Syngnathidae Siokunichthys herrei 7190 Apogonidae Siphamia permutata 56485 Opistognathidae Stalix davidsheni 56472 Labridae Suezichthys caudavittatus 4409 Spottail wrasse Labridae Suezichthys russelli 4413 Russell's wrasse Synanceiidae Synanceia nana 12085 Red Sea stonefish Callionymidae Synchiropus sechellensis 25699 Syngnathidae Syngnathus macrophthalmus 46212 Gobiidae Trimma filamentosus 28064 Tetrarogidae Vespicula bottae 56489 Demersal omnivores  Blenniidae Alloblennius pictus 52391 Blenniidae Antennablennius adenensis 46412 Aden blenny Blenniidae Antennablennius australis 8042 Moustached rockskipper Blenniidae Antennablennius hypenetes 46413 Arabian blenny Monacanthidae Brachaluteres baueri 54554 Mugilidae Chelon macrolepis 4816 Largescale mullet Sparidae Crenidens crenidens 7931 Karenteen seabream Tripterygiidae Enneapterygius destai 56507 Leiognathidae Leiognathus bindus 4449 Orangefin ponyfish Leiognathidae Leiognathus elongatus 4450 Slender ponyfish Leiognathidae Leiognathus splendens 4454 Splendid ponyfish Mugilidae Liza subviridis 4819 Greenback mullet Blenniidae Omobranchus fasciolatus 8038 Arab blenny Blenniidae Omobranchus steinitzi 59659 Monacanthidae Paraluteres arqat 54621 Monacanthidae Paramonacanthus frenatus 8059 Wedgetail filefish Monacanthidae Paramonacanthus oblongus 53239 Hair-finned filefish Monacanthidae Paramonacanthus pusillus 54624 Blenniidae Petroscirtes ancylodon 46423 Arabian fangblenny Monacanthidae Stephanolepis diaspros 14343 Reticulated leatherjacket Demersal herbivores Blenniidae Alticus kirkii 46411 Kirk's blenny Blenniidae Alticus saliens 6031 Leaping blenny Cyprinodontidae Aphanius dispar dispar 4813  220 Group Family Scientific name FishBase Code FishBase common name Blenniidae Ecsenius dentex 27295 Blenniidae Entomacrodus epalzeocheilos 22835 Fringelip rockskipper Blenniidae Hirculops cornifer 16944 Highbrow rockskipper Blenniidae Istiblennius flaviumbrinus 27245 Blenniidae Istiblennius pox 27015 Scarface rockskipper Blenniidae Istiblennius unicolor 25453 Pallid rockskipper Mugilidae Liza tade 4820 Tade mullet Mugilidae Valamugil cunnesius 4700 Longarm mullet Bentho- pelagic fish Apogonidae Apogon queketti 8011 Spotfin cardinal Sciaenidae Argyrosomus regius 418 Meagre Ariommatidae Ariomma brevimanus 10513 Ateleopodidae Ateleopus natalensis 10662 Syngnathidae Bryx analicarens 46105 Pink pipefish Balistidae Canthidermis macrolepis 46433 Large-scale triggerfish Carangidae Decapterus russelli 374 Indian scad Gerreidae Gerres methueni 7700 Striped silver biddy Trachichthyidae Hoplostethus mediterraneus mediterraneus 4964 Mediterranean slimehead Sparidae Lithognathus mormyrus 706 Striped seabream Lobotidae Lobotes surinamensis 1077 Atlantic tripletail Mugilidae Mugil cephalus 785 Flathead mullet Salmonidae Oncorhynchus mykiss 239 Rainbow trout Moridae Physiculus sudanensis 60891 Haemulidae Pomadasys striatus 7301 Striped grunter Lutjanidae Pristipomoides filamentosus 201 Crimson jobfish Lutjanidae Pristipomoides sieboldii 209 Lavender jobfish Carangidae Seriola lalandi 382 Yellowtail amberjack Opistognathidae Stalix histrio 23505 Stromateidae Stromateus fiatola 1198 Blue butterfish Synodontidae Synodus randalli 58509 Bramidae Taractichthys steindachneri 3561 Sickle pomfret Trichiuridae Tentoriceps cristatus 7947 Crested hairtail Terapontidae Terapon puta 7946 Small-scaled terapon Gempylidae Thyrsitoides marleyi 7698 Black snoek Trichiuridae Trichiurus lepturus 1288 Largehead hairtail Bathy- pelagic fish Stomiidae Astronesthes martensii 10213 Sciaenidae Atrobucca geniae 15959 Myctophidae Benthosema pterotum 10238 Skinnycheek lanternfish Champsodontidae Champsodon capensis 10296 Gaper Stomiidae Chauliodus sloani 1786 Sloane's viperfish Paralepididae Lestrolepis luetkeni 27423 Naked barracuda Sternoptychidae Maurolicus mucronatus 51615 Nemichthyidae Nemichthys scolopaceus 2660 Slender snipe eel Stomiidae Stomias affinis 10167 Günther's boafish Bathy- demersal fish Acropomatidae Acropoma japonicum 1267 Glowbelly Congridae Ariosoma mauritianum 7671 Blunt-tooth conger Bothidae Arnoglossus marisrubri 60532 Percophidae Bembrops caudimacula 23546  221 Group Family Scientific name FishBase Code FishBase common name Champsodontidae Champsodon omanensis 15604 Cynoglossidae Cynoglossus acutirostris 10204 Sharpnose tonguesole Synaphobranchidae Dysomma fuscoventralis 15591 Nettastomatidae Facciolella karreri 58715 Bythitidae Grammonus robustus 15659 Synodontidae Harpadon erythraeus 15605 Syngnathidae Hippocampus kelloggi 53815 Great seahorse Ophidiidae Neobythites stefanovi 15598 Tetrarogidae Neocentropogon mesedai 61244 Scorpaenidae Neomerinthe bathyperimensis 61433 Gobiidae Obliquogobius turkayi 56466 Nemipteridae Parascolopsis baranesi 15368 Moridae Physiculus marisrubri 15597 Gobiidae Priolepis goldshmidtae 59388 Congridae Rhynchoconger trewavasae 57764 Nettastomatidae Saurenchelys meteori 58724 Setarchidae Setarches guentheri 5029 Deepwater scorpionfish Acropomatidae Synagrops philippinensis 10338 Trichiuridae Trichiurus auriga 8666 Pearly hairtail Mullidae Upeneus davidaromi 60913 Uranoscopidae Uranoscopus marisrubri 56495 Congridae Uroconger erythraeus 15590   222 Table E. 2 Key data on fish groups of the Red Sea ecosystem model. Group No. Group name No. of spp. Trophic level L (cm) Min Max Min Max 10 Whale shark 1 3.55 3.55 1683.0 1683.0 12 Rays 17 3.1 4.5 68.4 347.4 13 Reef top predators 122 3.98 4.5 9.5 421.1 14 Large reef carnivores 86 3 3.98 51.4 315.8 15 Medium reef carnivores 218 3 3.98 15.0 48.9 16 Small reef carnivores 209 3 3.98 2.1 14.8 17 Reef omnivores 87 2.02 2.99 3.1 115.8 18 Reef herbivores 39 2 2 5.8 94.7 19 Large pelagic carnivores 12 3.47 4.58 105.3 350.5 20 Small pelagic carnivores 35 3 4.5 7.3 87.2 21 Pelagic omnivores 5 2.1 2.95 6.4 26.3 22 Demersal top predators 15 4 4.45 20.6 263.2 23 Large demersal carnivores 24 3.02 3.97 50.1 88.2 24 Medium demersal carnivores 82 3 3.95 15.4 48.4 25 Small demersal carnivores 81 3 3.68 1.8 14.7 26 Demersal omnivores 20 2.45 2.99 3.3 72.3 27 Demersal herbivores 11 2 2.04 6.2 51.5 28 Benthopelagic fish 26 2.13 4.45 3.9 210.5 29 Bathypelagic fish 9 3.03 4.5 4.4 100.0 30 Bathydemersal fish 26 3 4.43 8.5 68.6  E.1.1 Non-fish taxa groups included in the model  Cetaceans This group includes the dolphins and whales of the Red Sea, whose list and distributions have been described in the literature (Schmitz and Lavigne, 1984; Frazier et al., 1987; Notarbartolo di Sciara, 2002). All the reported cetaceans are from the suborder Odontocetea (toothed whales) except Balaenoptera edeni (Eden’ s whale) and Megaptera novaeangliae (humpback whale), which are from the suborder Mysticeti. The P/B values for cetaceans were calculated assuming r/2 (Schmitz and Lavigne, 1984), where r is the average intrinsic rate of growth (0.088 year-1) for the Red Sea cetaceans species Stenella attenuate, S. longirostris, S. coeruleoalba and Tursiops truncatus for which data were available. The estimated P/B for the group equals 0.044 year-1. The r/2 method is commonly used to measure P/B of marine mammals (Guénette, 2005;  223 Ainsworth et al., 2007). The Q/B value was estimated based on the body weight of Red Sea cetaceans taken from Schmitz and Lavigne (1984) and Trites and Pauly (1998), from which the ration was determined using the relationship in Trites and Heise (1996). The average Q/B value, 5.91 year-1 was used in the model. Biomass data were not available and were estimated by the model.  Dugongs Dugongs are herbivore marine mammals whose abundance in the Red Sea is estimated to be 4000 animals (Gladstone et al., 2003). With an average weight of 320 kg (Frazier et al., 1987), the biomass is calculated to be 0.00292 t·km2. Similar to the cetaceans, P/B for dugong was calculated using the intrinsic growth rate which is estimated to be 5 % year-1 (Marsh et al., 1997), with P/B = 0.025 year-1. The Q/B ratio is taken to be 11 year-1 as calculated by Ainsworth et al., (2007) based on body weight.  Birds The sea birds covering the whole Red Sea are described in Evans (1987) and recent reviews on the status of the Red Sea birds by country are available (PERSGA/GEF, 2003; Marchi et al., 2009). However, they are very brief with some list of species sighted and habitat distribution with no estimate of abundance. The P/B value of 0.38 year-1 was used based on Russell (1999). Seabird biomass was not available, and was estimated by the model.  Turtles Five species of sea turtles, hawksbill (Eretmochelys imbricata), green (Chelonia mydas), loggerhead (Caretta caretta), olive ridley (Lepidochelys olivacea) and leatherback (Dermochelys coriacea), are reported for the Red Sea (Frazier et al., 1987; Tesfamichael, 1994). The first two are the most abundant, with known records of nesting on the Red Sea beaches (Frazier and Salas, 1984; Frazier et al., 1987; Gladstone et al., 2003). The P/B value for turtles was estimated using the relationship M = - lnS, where M is an estimate of P/B and S is the survival rate, which was 0.948 year-1 for green turtle (Mortimer et al., 2000) and 0.867 year-1 for loggerhead (Chaloupka and Limpus, 2002). This gives an average P/B value of 0.1 year-1. P/B value for all turtles in the Caribbean reef was calculated to be 0.2 year-1 (Opitz, 1996). Since the P/B estimate using  224 survival rate was only for two species, i.e., it does not include all the five species in the Red Sea, an average of the P/B calculated from survival and the Caribbean value, 0.15 year-1, was used for the model. Q/B value of 3.5 year-1 was used based on ecosystem models of the Caribbean reef (Opitz, 1996) and west coast of Peninsular Malaysia (Alias, 2003). Sea turtle biomass was not available and was estimated by the model.  Invertebrates The main invertebrate important for the Red Sea fisheries is shrimp where 64,007 t (14% of total retained trawl fishery) were fished from 1950 – 2006 (see Chapter 4). Hence, shrimps were given a separate functional group. The most common species caught are Penaeus semisulcatus, P. monodon, Marsupenaeus japonicas, Melicertus latisulcatus, Metapenaeus monoceros and Fenneropenaeus indicus. P/B value of 5 year-1 and Q/B of 29 year-1 based on Buchary (1999) were used as a starting parameters to balance the model.  The coral reef structure in the Red Sea is important ecologically and is also the main fishing ground for the artisanal fisheries. Thus, the reef forming corals are categorized as a separate functional group. The high and relatively stable temperature of the Red Sea is favourable for the formation of coral reefs. They are home to more than 200 species of corals (Head, 1987a). The Red Sea coral reef coverage area is estimated to be around 16030 km2 (Spalding et al., 2001). Coral reefs are more developed in the northern part starting from the tip of Sinai Peninsula going south parallel to the coast until the central part (Sheppard et al., 1992). The longest continuous fringing reef in the Red Sea extends from Gubal, at the mouth of the Gulf of Suez, to Halaib, at the Egyptian border with Sudan (Pilcher and Alsuhaibany, 2000). In the south, more patchy reefs are observed as the turbid water of the shallow shelf does not allow the growth of extensive reefs. Sanganeb Atoll, located in Sudan near the border with Egypt, is the only atoll in the Red Sea. It is unique reef rising from 800 m depth to form an atoll that has been recognized as regionally important conservation area. It was proposed to UNESCO for World Heritage Status in the 1980s (Pilcher and Alsuhaibany, 2000). The biomass of corals was calculated based on data from the southern Red Sea (Ateweberhan, 2004; Tsehaye, 2007) adjusted for the total area of the Red Sea and the north-south abundance gradient giving 2.75 t·km-2. The P/B value of corals was calculated based on daily turnover rate of 0.003 day-1 (Crossland et al., 1991), which  225 equals to 1.095 year-1. A Q/B value of 9 year-1 was used based on the Caribbean reef model (Opitz, 1996).  The other invertebrates included in the model are: non-coral sessile fauna such as sponges, sea anemones, and tunicates; cephalopods: squids, octopuses and cuttlefish; other molluscs; echinoderms: starfish, sea urchins and sea cucumber; crustaceans: representing all crustaceans except shrimps (which have a group of their own); and meiobenthos: polychaetes and nematodes. The P/B and Q/B values of these groups were taken from an ecosystem model of the Eritrean coral reef (Tsehaye, 2007) adjusted for the area of the Red Sea fine tuned during balancing and time series fitting (Table E.3).  Table E. 3 Input parameters of some invertebrates groups. Biomass (t·km2) P/B (year-1) Q/B (year-1) Other sessile fauna 0.85 3.2 12 Cephalopods 0.399 3.5 12 Molluscs 0.368 9 30 Echinoderms 0.596 1.6 8 Crustaceans 0.816 3 10 Meiobenthos 0.295 26 100 Zooplankton* 14 52 178 * modified after (van Couwelaar, 1997)  Primary producers There are three functional groups of primary producers in the model: phytoplankton, sea grasses and algae. The phytoplankton biomass of 21.5 t·km-2 and a P/B 110 year-1 were used based on data in (Weikert, 1987; Veldhuis et al., 1997) averaged over all the Red Sea. For sea grass, a biomass of 11 t·km-2 and P/B value of 19 year-1 were used, based on Wahbeh (1988) and Aleem (1979). The biomass estimate of algae was based on Ateweberhan (2004) and Walker (1987), and was averaged for the whole Red Sea, resulting in 38 t·km-2. The P/B value of 14 year-1 was used based on Ateweberhan (2004) and Wolanski (2001), which is similar to the value in other coral reef ecosystems: Caribbean (Opitz, 1996) and Indonesia (Ainsworth et al., 2007).   226 Table E. 4 Diet composition matrix of Red Sea model. Prey \ Predator 1 2 3 4 5 7 8 1 Cetaceans 2 Dungongs 3 Birds 4 Turtles 5 Trawler fishes 0.002 6 Purse seine fishes 0.010 0.020 0.004 0.002 7 Beach seine fishes 0.013 0.059 0.151 0.005 8 Handlining fishes 0.002 0.001 9 Gillnet fishes 0.004 0.001 0.003 10 Whale shark 11 Sharks 12 Rays 13 Reef top predators 0.011 0.001 14 Large reef carnivores 0.010 0.003 0.001 0.005 15 Medium reef carnivores 0.013 0.106 0.020 0.001 0.001 0.052 16 Small reef carnivores 0.066 0.271 0.112 0.060 0.015 0.262 17 Reef omnivores 0.131 0.217 0.112 0.060 0.015 0.152 18 Reef herbivores 0.010 0.026 0.112 0.060 0.020 0.202 19 Large pelagic carnivores 0.053 0.020 0.006 0.002 20 Small pelagic carnivores 0.065 0.180 0.127 0.111 21 Pelagic omnivores 0.008 0.040 0.015 22 Demersal top predators 23 Large demersal carnivores 0.000 0.001 24 Medium demersal carnivores 0.011 0.006 0.006 25 Small demersal carnivores 0.131 0.011 0.006 0.006 26 Demersal omnivores 0.026 0.017 0.006 0.006 27 Demersal herbivores 0.026 0.020 0.006 0.006 28 Benthopelagic fish 0.131 0.001 29 Bathypelagic fish 30 Bathydemersal fish 31 Shrimp 0.010 0.010 32 Cephalopods 0.169 0.112 0.050 0.020 0.011 33 Echinoderms 0.020 0.100 0.057 0.009 34 Crustaceans 0.148 0.226 0.110 0.020 0.197 35 Molluscs 0.015 0.057 0.065 36 Meiobenthos 37 Corals 38 Other sessile fauna 0.047 0.233 39 Zooplankton 0.131 0.070 0.197 0.522 0.001 40 Phytoplankton 0.100 0.180 41 Sea grass 1.000 0.230 42 Algae 0.137 43 Detritus 0.012 0.033 0.056 0.101 0.044 0.043    227  Prey \ Predator 1 2 3 4 5 6 7 8 1 Cetaceans 2 Dungongs 3 Birds 4 Turtles 5 Trawler fishes 0.002 6 Purse seine fishes 0.010 0.020 0.004 0.002 7 Beach seine fishes 0.013 0.059 0.151 0.005 8 Handlining fishes 0.002 0.001 9 Gillnet fishes 0.004 0.001 0.003 10 Whale shark 11 Sharks 12 Rays 13 Reef top predators 0.011 0.001 14 Large reef carnivores 0.010 0.003 0.001 0.005 15 Medium reef carnivores 0.013 0.106 0.020 0.001 0.001 0.052 16 Small reef carnivores 0.066 0.271 0.112 0.060 0.015 0.262 17 Reef omnivores 0.131 0.217 0.112 0.060 0.015 0.152 18 Reef herbivores 0.010 0.026 0.112 0.060 0.020 0.202 19 Large pelagic carnivores 0.053 0.020 0.006 0.002 20 Small pelagic carnivores 0.065 0.180 0.127 0.111 21 Pelagic omnivores 0.008 0.040 0.015 22 Demersal top predators 23 Large demersal carnivores 0.000 0.001 24 Medium demersal carnivores 0.011 0.006 0.006 25 Small demersal carnivores 0.131 0.011 0.006 0.006 26 Demersal omnivores 0.026 0.017 0.006 0.006 27 Demersal herbivores 0.026 0.020 0.006 0.006 28 Benthopelagic fish 0.131 0.001 29 Bathypelagic fish 30 Bathydemersal fish 31 Shrimp 0.010 0.010 32 Cephalopods 0.169 0.112 0.050 0.020 0.011 33 Echinoderms 0.020 0.100 0.057 0.009 34 Crustaceans 0.148 0.226 0.110 0.020 0.197 35 Molluscs 0.015 0.057 0.065 36 Meiobenthos 37 Corals 38 Other sessile fauna 0.047 0.233 39 Zooplankton 0.131 0.070 0.197 0.522 0.001 40 Phytoplankton 0.100 0.180 41 Sea grass 1.000 0.230 42 Algae 0.137 43 Detritus 0.012 0.033 0.056 0.101 0.044 0.043   228  9 10 11 12 13 14 15 16 17 18 19 1 0.002 2 3 0.002 4 0.034 5 0.004 0.003 0.002 0.002 0.000 6 0.030 0.008 0.004 0.002 0.004 0.004 0.001 0.005 7 0.114 0.005 0.003 0.002 0.002 0.002 0.000 0.015 8 0.003 0.011 0.007 0.008 0.007 0.001 9 0.002 0.009 0.003 0.003 0.002 0.001 10 0.002 11 0.005 12 0.010 13 0.002 0.004 0.001 0.011 14 0.011 0.090 0.069 0.003 0.003 0.001 0.002 15 0.015 0.022 0.124 0.072 0.055 0.004 0.001 0.003 0.006 16 0.015 0.020 0.131 0.278 0.163 0.186 0.003 0.009 0.018 17 0.015 0.163 0.168 0.154 0.159 0.128 0.012 0.009 0.018 18 0.015 0.009 0.154 0.202 0.151 0.028 0.040 0.011 19 0.020 0.113 0.003 0.002 0.021 20 0.459 0.041 0.113 0.002 0.001 0.236 21 0.088 0.001 0.008 0.000 0.000 0.085 22 0.002 0.024 0.003 0.002 23 0.001 0.004 0.000 0.000 0.000 24 0.006 0.006 0.003 0.003 0.002 25 0.006 0.005 0.003 0.003 0.002 0.076 26 0.006 0.004 0.021 0.003 0.003 0.002 0.090 27 0.006 0.002 0.021 0.003 0.003 0.002 0.080 28 0.003 0.024 0.021 29 0.004 30 0.002 31 0.015 0.004 0.014 0.007 0.010 0.005 0.002 0.001 32 0.045 0.170 0.024 0.004 0.007 0.014 0.012 0.008 0.009 0.276 33 0.031 0.150 0.007 0.004 0.026 0.033 0.028 0.003 34 0.076 0.002 0.088 0.215 0.242 0.051 0.100 0.050 0.224 35 0.003 0.002 0.229 0.007 0.068 0.091 0.047 0.041 0.011 36 0.229 0.007 0.064 0.151 0.041 37 0.104 0.176 0.070 38 0.004 0.023 0.042 0.022 0.043 39 0.366 0.023 0.014 0.062 0.151 0.405 0.015 40 0.184 0.100 0.049 41 0.098 42 0.073 0.804 43 0.010 0.012 0.051 0.198 0.022 0.038 0.015 0.074 0.049 0.020   229  20 21 22 23 24 25 26 27 28 29 30 1 2 3 4 5 0.010 0.010 0.010 0.010 0.015 0.010 6 0.001 7 0.037 0.005 8 9 0.001 0.000 10 11 12 0.006 13 14 0.001 15 0.001 16 0.001 17 0.001 18 0.001 19 0.006 0.037 20 0.081 0.003 0.019 21 0.154 0.010 0.030 0.007 22 0.011 23 0.114 0.002 0.000 0.002 24 0.128 0.100 0.013 0.002 0.040 0.023 25 0.138 0.105 0.136 0.020 0.090 0.023 26 0.142 0.105 0.090 0.114 0.082 0.023 27 0.150 0.105 0.092 0.170 0.060 0.082 0.023 28 0.110 0.061 0.041 0.010 0.010 29 0.012 30 0.100 0.057 31 0.013 0.052 0.005 0.001 0.001 0.010 32 0.020 0.005 0.049 0.106 0.005 0.142 33 0.004 0.049 0.052 0.051 0.020 0.020 0.040 0.154 34 0.030 0.081 0.061 0.105 0.136 0.091 0.015 0.100 0.200 0.107 35 0.012 0.020 0.049 0.010 0.068 0.019 0.012 0.082 0.142 0.309 36 0.003 0.052 0.082 0.090 0.039 0.005 37 0.035 0.051 0.060 0.005 38 0.003 0.017 0.056 0.200 0.002 0.015 39 0.545 0.417 0.010 0.012 40 0.402 0.071 0.114 41 0.014 0.140 0.008 42 0.025 0.400 0.460 0.020 0.030 43 0.025 0.013 0.102 0.204 0.192 0.360 0.400 0.300 0.290 0.225   230  31 32 33 34 35 36 37 38 39 1 2 3 4 5 6 7 0.012 8 9 10 11 12 13 14 15 16 17 0.012 0.001 18 0.017 0.001 19 20 0.012 21 0.022 22 23 24 25 0.005 26 0.005 27 28 0.005 29 30 31 0.005 32 0.004 0.040 0.002 33 0.016 0.005 0.009 0.009 0.009 34 0.001 0.068 0.003 0.001 0.001 35 0.020 0.100 0.037 0.011 0.010 36 0.010 0.091 0.030 0.041 0.013 0.015 37 0.008 0.002 0.003 0.001 38 0.008 0.004 0.001 0.002 0.000 39 0.012 0.356 0.101 0.007 0.047 0.250 0.250 0.100 40 0.047 0.078 0.600 0.600 0.900 41 0.118 42 0.178 0.374 0.114 0.069 0.047 43 0.628 0.202 0.535 0.638 0.886 0.890 0.150 0.150   231 E.1 Ecosim input supplementary data  E.2.1 Reconstruction the fishing effort data of the Red Sea fisheries  Fishing effort is an important part of fishery assessment; however, it is not usually readily available, worse than even catch data. The Red Sea fisheries are divided into two major sectors industrial and artisanal. The industrial fishery has generally better records than the small scale artisanal fisheries. The effort data for the industrial fishery (trawl and purse seine) of the Red Sea was obtained from the database of the Sea Around Us Project (Anticamara et al., 2011; Watson et al., Submitted)  The artisanal fisheries, on the other hand, do not have an effort recording system and the time series effort for the Red Sea fisheries was derived mainly using on the basis of demographic information (fisher numbers), or boat counts. Table (E.5) lists the references from which the effort data were obtained for each country. For Yemen the available data were total number of boats. Egypt has a database system from which the effort data was reconstructed and for Eritrea, because of data availability, the analysis was divided before and after 1991, when Eritrea became an independent nation.  Except for Egypt and Eritrea after 1991, the effort reconstruction procedure was the same. First, an exponential function was fitted to the available effort data, which was then used to predict effort for years it was missing. The exponential function fitted had the form V  @ 	 B; where a and b are constants, presented in Table (E.6) for each country.   232 Table E. 5 Sources used for the reconstruction of effort of the Red Sea fisheries. Country  Effort data Motorization data Year Data* Source Year  % Source Sudan 1955 200 Kristjonsson (1956) 1956 1.93 Kristjonsson (1956) 1976 418 ODA (1983) 1979 22.57 Barrania (1979) 1979 437 Barrania (1979) 1982 61.98 Chakraborty (1983) 1981 664 ODA (1983) 2006 95.00 FA (2007) 1982 605 Chakraborty (1983) 2001 743 FA (2007) 2006 967 FA (2007) Eritrea 1964 3543 Grofit (1971) 1960 1.00 Grofit (1971) 1968 4167 Grofit (1971) 1963 2.20 Grofit (1971) 1969 3022 Grofit (1971 1964 3.72 Grofit (1971 1970 3000 Giudicelli (1984) 1969 42.10 Grofit (1971 1981 875 Giudicelli (1984) 1974 75.00 Giudicelli (1984) 1984 250 Giudicelli (1984) Yeman 1972 1000 Agger (1976) 1972 10.00 Agger (1976) 1975 1066 Walczak (1977) 1975 26.45 Walczak (1977) 1976 1071 Campleman (1977 ) 1978 60.66 Campleman (1977) 1978 1597 Campleman (1977) 2006 96.00 MoFW (2010) 1992 1771 Herrera and Lepere (2005) 1997 2686 Brodie et al., (1999) 1998 3390 FAO (2002) 2000 1781 MoFW (2010) 2001 2254 MoFW (2010) 2002 2562 MoFW (2010) 2003 2737 MoFW (2010) 2004 4510 MoFW (2010) 2005 5000 MoFW (2010) 2006 5727 MoFW (2010) Saudi Arabia 1954 2500 Neve and Al-Aiidy (1973) 1955 0.20 Ferrer (1958) 1971 3250 Neve and Al-Aiidy (1973) 1965 30.77 Neve and Al-Aiidy (1973) 1980 3678 Barrania et al., (1980) 1969 41.43 Neve and Al-Aiidy (1973) 1984 2408 Kedidi et al., (1984) 1991 97.00 Sakurai (1998) 1991 2993 MAW (2008) 1992 3443 MAW (2008) 1993 3907 MAW (2008) 1994 4063 MAW (2008) 1995 4316 MAW (2008) 1996 4212 MAW (2008) 1997 4145 MAW (2008) 1998 4209 MAW (2008) 1999 4764 MAW (2008)  233 Country  Effort data Motorization data Year Data* Source Year  % Source 2000 5037 MAW (2008) 2001 6116 MAW (2008) 2002 6389 MAW (2008) 2003 6927 MAW (2008) 2004 7266 MAW (2008) 2005 6880 MAW (2008) 2006 7533 MAW (2008) 1* All effort data are number of fishers except for Yemen, which is number of boats.  Motorization of the fishing vessels affects how effort is calculated significantly, so it was considered explicitly. The rate at which motorization took place in the Red Sea countries was fitted by the logistic curve equation: V      WXYHB    where ln a and b are constants, which are presented in Table (E.6) for each country  Table E. 6 Parameters of exponential and logistic fitting of effort reconstruction. Exponential fitting logistic fitting a b R2 ln a b R2 Sudan 1.00E-22 0.0287 0.89 275.63 0.1389 0.96 Eritrea 5.00E+106 -0.121 0.92 861.09 0.4369 0.98 Yemen 4.00E-32 0.04 0.78 277.36 0.1399 0.89 Saudi Arabia 9.00E-16 0.022 0.64 487.04 0.2467 0.88 Using the logistic curve fitting results, the total effort was divided into motorized and non- motorized. For the non-motorized effort, number of fishers were converted to horsepower (hp) using the conversion factor one manpower = 0.18 hp/day (Dalzell et al., 1987). For all the four countries, except Yemen, the total effort was given in number of fishers. The total number of boats in the non-motorized category for Yemen was converted to total number of fishers by the average number of fishers per boat (n = 5). For the motorized part, the horsepower equivalent for each fisher in the motorized boats was first calculated for at least two years in the time series. Two points are needed to account for the  234 change in the hp of the engines installed in the boats over time. Using those points, a time series of hp/fisher was established, which were used to calculate the total hp by multiplying it with the total number of fishers. For Yemen, since the total boats were given instead of total number of fishers, a time series of hp/boat was calculated as a multiplier of the total number of boats. Then the cumulative hp from the non-motorized and that of the motorized effort were added to get the overall total hp for each country. For Egypt, the calculation was done differently. The artisanal fisheries included in the analysis are what are referred by the Egyptian authorities as ‘reef-associated’  and ‘semi-industrial’  (or launch) fishery. Effort data, in total number of trips/landings, was available from 1980 – 2006 (GAFRD, 2010) for the main landing site of Suez for the semi-industrial fishery. First, the number of landing/trips was converted to hp using the average hp/trip calculated from data given in Sanders et al., (1984b). A linear function, was then fitted to the data and used to estimate the effort from 1950 – 1979. The effort from Suez was scaled up to the whole Red Sea using the Suez effort ratio in the whole Red Sea, which was calculated to be 47.7 % (Sanders et al., 1984b). The effort data for Eritrea after 1991 was calculated using effort data available from the Ministry of Fishery (MOF, 2007), which divides it by gear and boat type from 1996 – 2006. For 1992 – 1995, linear interpolation was used to fill the gap. Subsequently, all efforts were re-expressed in kilowatt-hours. Thus, it was assumed that boats operate 2/3 (243 days) of the year, while for the rest of the year, they are docked for maintenance and/or the fishers are selling their catch or performing other land-based activities. Based on interviews with fishers, an average of 10 hours/day was used to calculate hp.hours from hp.days. Horsepower was converted to watts using the conversion ratio of 1hp = 745.7 watt. All the major artisanal fishers are included in this analysis, but there are minor fisheries which are not. So, only 90% of the total effort calculated was used in the analysis. The remaining 10%, which was not included, is the effort spent for the minor fisheries. The final stage of the effort reconstruction is dividing total effort into gears. This was done using effort information from the sources presented in Table (E.5). For Sudan, all the effort is used for handlining, because it is pretty much the only gear used by the artisanal fishers. For Eritrea, the composition changed over time from beach seine being dominant in the early years to handlining being dominant in  235 the later years (Figure E.1). For Yemen, first the effort for the least important of the major fisheries, beach seine, was calculated by allocating 10% of the effort in 1950. The effort for the rest of the time was calculated proportionally to the population size and the 1950 data. This is reasonable because beach seine is carried out by people in their localities pre-dominantly for their own consumption; it is the least commercialized fishery. So, I assumed, as the population grows, more and more people are involved in the fishery. The remaining effort was divided 70% for gillnet and 30% for handlining. Yemen has a dominant gillnet fishery whereas the other countries are dominated by handlining. For Saudi Arabia, the effort was divided 70% handlining and 30% gillnet. The total effort for the whole Red Sea by gear type was calculated by adding total efforts of the same gear from all the Red Sea countries (Table E.7)  Figure E. 1 Ratios of beach seine (full line), handlining (broken line) and gillnet (line with circles) fisheries in the Eritrean artisanal fishery effort allocation from 1950 – 1991.   0 0.25 0.5 0.75 1 1950 1960 1970 1980 1990 Ef fo rt  ra tio Year  236 Table E. 7 Reconstructed effort of Red Sea fisheries by gear type from 1950-2006. Effort (kilowatt.hours) Year Beach seine Gillnet Handlining Purse seine Trawl 1950 3260163 2409164 5506687 122247 1685304 1951 3261646 2478914 5631296 153416 2010420 1952 3263347 2550989 5758717 152412 2396194 1953 3265170 2625647 5889065 153022 2486810 1954 3267117 2703097 6022413 189598 2633673 1955 3265557 2792947 6175856 185090 2753224 1956 3267845 2878727 6327502 184876 2842540 1957 2906275 2968792 6415349 200680 2625311 1958 2586142 3063659 6515860 207646 2771840 1959 2302782 3163932 6628825 237638 2676854 1960 2052021 3270385 6754326 131544 1542295 1961 1830176 3383946 6892765 136918 1617308 1962 1633982 3505746 7044914 133406 1634785 1963 1547353 3637240 7227335 145992 1874036 1964 1541115 3780044 7436921 146457 1858988 1965 1639968 3936025 7680376 375787 2276185 1966 1874024 4107317 7965751 429322 2408709 1967 1698521 4296088 8875907 418719 2402342 1968 1253924 4504499 10294204 479157 2977451 1969 2070943 4734683 10679160 291247 2244378 1970 3633916 4988745 10483532 283388 2469150 1971 4557226 5325151 11111007 253944 2525112 1972 4391027 6017858 13512905 299842 2509215 1973 3515606 6879881 16761498 310256 2510686 1974 2330559 7927032 20442238 476401 3259823 1975 969981 9168813 24444877 414977 3195146 1976 226857 10605296 28017197 487028 3338693 1977 227999 14923601 29883329 790741 3902215 1978 229451 18027390 34001708 333317 2921845 1979 232530 21624492 38502704 781688 4367463 1980 233061 25751504 43659249 533880 3498433 1981 243346 30438437 49390693 992719 5165161 1982 239757 35737595 55144940 304570 2874781 1983 236236 41674966 61550744 213003 3461937 1984 232767 48285591 68425327 296883 3722514 1985 229389 55602881 75773910 295036 3740346 1986 229581 63655847 83602553 312653 3340348 1987 229020 72475883 91921930 340611 3320155 1988 223816 82090575 100744526 563564 3634536  237 Effort (kilowatt.hours) Year Beach seine Gillnet Handlining Purse seine Trawl 1989 223996 92514901 110082153 728561 3304907 1990 224544 103772729 119952344 727433 3516169 1991 243948 115874916 130359461 722734 3711930 1992 257200 129457088 142265035 909693 3930491 1993 264943 143725688 154935802 838570 4382493 1994 275326 158905420 168173310 695858 6669252 1995 305033 175009249 181984519 724406 6344746 1996 317172 191560732 196924712 820456 8639737 1997 325100 212058648 214737143 1109682 8730950 1998 337872 230889840 230875197 1426178 10096325 1999 349180 252378357 250997094 2103248 13445928 2000 360541 274450818 270357983 1992639 12963567 2001 374877 292928692 283670425 2494183 14137305 2002 387660 316286259 305417442 2815688 14396368 2003 400572 340375293 309461472 2525939 14079268 2004 413612 366520099 338014037 2963705 15941449 2005 426779 395004639 352666794 3032281 16652515 2006 444458 423651161 374812480 3726970 26874663       238 Table E. 8 Flow parameter (vulnerabilities) for the Red Sea model. Prey \ Predator 1 2 3 4 5 6 7 8 9 10 1 Cetaceans 2 Dungongs 3 Birds 4 Turtles 5 Trawler fishes 3.25 6 Purse seine fishes 1.01 1.01 1 1.01 1.01 1.01 7 Beach seine fishes 2.26 2 2 3.25 1.01 2 8 Handlining fishes 1 12 7.65 9 Gillnet fishes 2.26 2 2 2 10 Whale shark 11 Sharks 12 Rays 13 Reef top predators 2.26 3.25 7.65 14 Large reef carnivores 2.26 20 3.25 12 7.65 15 Medium reef carnivores 2.26 2 2.26 20 3.25 12 7.65 2 16 Small reef carnivores 2.26 2 2.26 20 3.25 12 7.65 2 17 Reef omnivores 2.26 2 2.26 20 3.25 12 7.65 2 18 Reef herbivores 2.26 2 2.26 20 3.25 12 7.65 2 19 Large pelagic carnivores 2.26 2 20 3.25 7.65 20 Small pelagic carnivores 2.26 2 20 3.25 7.65 2 21 Pelagic omnivores 2.26 20 3.25 7.65 2 22 Demersal top predators 7.65 23 Large demersal carnivores 2.26 20 7.65 24 Medium demersal carnivores 2.26 20 3.25 7.65 25 Small demersal carnivores 2.26 2.26 20 3.25 7.65 26 Demersal omnivores 2.26 2.26 20 3.25 7.65 27 Demersal herbivores 2.26 2.26 20 3.25 7.65 28 Benthopelagic fish 2.26 2.26 7.65 29 Bathypelagic fish 30 Bathydemersal fish 31 Shrimp 2.26 2.26 1.5 32 Cephalopods 2.26 2.26 20 3.25 12 7.65 2 33 Echinoderms 2 2.26 2.26 12 7.65 34 Crustaceans 2.26 2.26 20 3.25 12 7.65 35 Molluscs 2.26 2.26 12 7.65 36 Meiobenthos 37 Corals 38 Other sessile fauna 2 2.26 39 Zooplankton 2.26 2.26 20 3.25 12 2 40 Phytoplankton 20 3.25 2 41 Sea grass 2 2.26 42 Algae 2.26 43 Detritus 2 2 2.26 2.26 3.25 12 2 2    239 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 3 2 3 3 4 3 5 3 2 2.26 3.25 3.25 2 2 2 6 1.01 1.01 1.01 1.01 1.01 1.01 1.01 7 3 2 2.26 3.25 3.25 2 2.26 3.25 8 3 2 2.26 2.5 2.5 9 2.5 2 2.26 3.25 2 2.26 3.25 10 3 11 1 12 3 2 13 3 3.25 2 14 3 2 3.25 2 3.25 2 2.26 15 3 2 2.26 3.25 2 3.25 2 2.26 16 3 2 2.26 3.25 2 3.25 2 2.26 17 3 2 2.26 3.25 2 3.25 2 2.26 18 2 2.26 3.25 2 3.25 2 2.26 19 3 2.26 3.25 2 3.25 20 3 2.26 3.25 2 2.26 3.25 21 3 2.26 3.25 2 2.26 3.25 22 3 2.26 3.25 2 23 3 2 2.26 3.25 2 2 24 3 2 2.26 3.25 2 2 2 25 3 2 2.26 3.25 2 2 2 2 26 3 2 2 2.26 3.25 2 2 2 2 27 3 2 2 2.26 3.25 2 2 2 2 28 3 2 2.26 2 2 29 3 30 3 31 3 2 2 2.26 3.25 2 3.25 2 2 2 32 3 2 2 2.26 3.25 2 3.25 2 2.26 3.25 2 2 2 33 2 2 2.26 3.25 2 3.25 2 2.26 2 2 2 34 3 2 2 2.26 3.25 2 3.25 2 2.26 3.25 2 2 2 35 3 2 2 2.26 3.25 2 3.25 2 2.26 3.25 2 2 2 36 2 2.26 3.25 2 3.25 2 2 2 37 3.25 2 3.25 2 2 38 3 2 3.25 2 3.25 2 2 2 39 2 2.26 3.25 2 3.25 2 2.26 3.25 40 3.25 2 3.25 41 2 42 3.25 2 3.25 43 3 2 2.26 3.25 2 3.25 2 2 3.25 2 2 2    240 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 1 2 3 4 5 2 2 2 6 7 2 8 9 10 11 12 13 14 15 16 17 2 2 18 2 2 19 2 20 2 2 21 2 2 2 22 23 2 2 24 2 2 2 25 2 2 2 2 26 2 2 2 2 27 2 2 2 2 28 2 2 2 29 2 30 2 2 31 2 2 2 15 32 2 15 2 2 33 2 2 2 2 15 2 2 2 2 34 2 2 2 2 2 15 2 2 2 2 35 2 2 2 2 2 15 2 2 2 2 36 2 2 2 15 2 2 2 2 2 37 2 2 2 2 2 2 38 2 2 2 15 2 2 2 2 39 2 2 15 2 2 2 2 2 2 2 40 2 2 2 2 2 2 2 41 2 2 2 15 42 2 2 2 2 15 2 2 2 2 43 2 2 2 2 2 2 15 2 2 2 2 2 2 2    241 Table E. 9 Feeding rate parameters for the Red Sea model. Group Max rel. feeding time Feeding time adjust rate [0,1] Group Max rel. feeding time Feeding time adjust rate [0,1] Cetaceans 2 0.5 Pelagic omnivores 2 0.5 Dungongs 2 0.5 Demersal top predators 2 0.5 Birds 2 0.5 Large demersal carnivores 2 0.5 Turtles 2 0.5 Medium demersal carnivores 2 0.5 Trawler fishes 2 0 Small demersal carnivores 2 0.5 Purse seine fishes 2 0 Demersal omnivores 2 0.5 Beach seine fishes 2 0 Demersal herbivores 2 0.5 Handlining fishes 2 0 Benthopelagic fish 2 0.5 Gillnet fishes 2 0 Bathypelagic fish 2 0.5 Whale shark 2 0.5 Bathydemersal fish 2 0.5 Sharks 2 0 Shrimp 2 0 Rays 2 0.5 Cephalopods 2 0.5 Reef top predators 2 0.5 Echinoderms 2 0.5 Large reef carnivores 2 0.5 Crustaceans 2 0.5 Medium reef carnivores 2 0.5 Molluscs 2 0.5 Small reef carnivores 2 0.5 Meiobenthos 2 0.5 Reef omnivores 2 0.5 Corals 2 0.5 Reef herbivores 2 0.5 Other sessile fauna 2 0.5 Large pelagic carnivores 2 0.5 Zooplankton 2 0.5 Small pelagic carnivores 2 0.5 

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