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Assessment of the Red Sea ecosystem with emphasis on fisheries Tesfamichael, Dawit 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  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.  ii  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. iii  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 iv  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 v  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  vi  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  vii  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  viii  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 ix  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  x  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.  xi  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 xii  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.  xiii  Dedication  To my loving family  xiv  CHAPTER 1: Introduction  1  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 2  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.  3  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 4  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 overfished (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 5  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 6  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 semiquantitative 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 7  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 8  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  9  (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 10  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 11  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 12  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 13  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  14  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.  15  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  16  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).  17  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  18  (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 nonmechanized 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 19  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.  20  CHAPTER 2: Multidisciplinary assessment of the sustainability of Red Sea fisheries using Rapfish  21  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.  22  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 23  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 24  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 2.3.1  Materials and methods 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  25  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  26  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 27  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 (jackknifing), 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 Yaxis 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-offit 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  28  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.  29  Ecological  15 6  26 25 2  18  23 10  9  Bad  17  1  0  8  Sudan  Yemen  Eritrea  Saudi  Shark  16 4 11  22  3  24 7  Egypt  12  19  20  14  Good  100  13  5 21  Economic  17  Bad  2 6 16  0  19 20  10  8  3  5  Good  9  7  23 24  25 12 14  11 13  100  1 26 18  4  21  15  22  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  Social  15  4 21 13  Bad 0  5  6  22 18  11  9  26  7 14 12 17 19 23  20  Good  10  3  1  25  8  100  2  16  24  2  Technological  10  Bad  Good  9 1  0  25  18 4  24  7  16 12  3  8  17  100  6 19 21  23 26 11  22 13  14  20  15  5  Ethical 10 2  18  17  9  6  Bad 0  23  25  26  24 7 16 8  14  1 13  19-22 15  5 3 11  12  Good 100  4  31  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.  15 6 18  26 25 2  9  Bad 0  23 10  17 19  20 1  8  3  12  22  24 7  16 4 11  Good 100  14 13 5 21  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 Xaxis 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.  32  Table 2.3 Leverage of attributes, given by mean standard error (SE), in their respective evaluation field. Ecological Attribute  Economic  Social  Technological  SE  Attribute  SE  Attribute  SE  Attribute  3.41  Marketable right  5.82  Fishing sector  4.93  Selective gear  Ethical SE  Attribute  SE  Just management  4.56  Change in Trophic level  6.07  Recruitment  Mitigation of habitat  variability  3.39  Ownership/transfer  5.76  Conflict status  4.74  Pre-sale processing  5.43  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  Environmental  Fish  attraction  Mitigation of  Catch < maturity  3.32  Subsidy  4.71  knowledge  4.14  devices (FADS)  4.67  ecosystem depletion  3.28  Species caught  3.13  Market  4.05  Fishing income  3.66  Catching power  4.43  Illegal fishing  2.98  New entrants into the  Adjacency &  Range Collapse  3.04  GDP/person  3.89  fishery  3.10  Landing sites  2.85  reliance  2.30  Discarded bycatch  2.49  Limited entry  3.68  Kin participation  2.44  Gear side effects  2.63  Discards & wastes  1.52  2.20  Trip length  1.84  Socialization of Exploitation status  1.83  Average wage  3.58  fishing  33  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.  34  A  ecological 100  B  ethical  economic  ecological 100  ethical  economic 0  0  technological  social West  East  technological  social Industrial  Artisanal  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, nonselective 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.  35  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 36  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 37  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., airconditioners) 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.  38  CHAPTER 3: Analysing changes in fisheries using interviews to generate long time series of catch per effort  39  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 socioeconomic 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.  40  3.2 3.2.1  Introduction 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. 41  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 42  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 43  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 44  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 45  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 3.3.1  Materials and methods 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 46  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 47  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 48  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 49  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  50  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. 51  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.  52  <30  Fishers' age groups in the samples (%)  40  31-45  46-60  >61  30  20  10  0 Eritrea (n = 284)  Sudan (n = 66)  Yemen (n = 73)  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.  53  Catch (Kg/crew/day)  a  300  Y = 4E+63e-0.071x  300  200  100  100  0  1960 300  Catch (kg/crew/day)  R² = 0.47, n = 57  200  0 1970  1980  1990  1960  2000 Y = 2E+90e-0.103x  c  R² = 0.60, n = 55  150  200  100  100  50  1970  1980  1990  2000  Y = 2E+33e-0.036x  d  R² = 0.58, n = 45  0  0 1950  800  Catch (kg/crew/day)  Y = 4E+58e-0.066x  b  R² = 0.44, n = 96  1960  1970  1980  1990  2000  1960  1970  1980  1990  Y = 5E+39e-0.043x  e  R² = 0.64, n = 37  Y = 5E+77e-0.088x  f  R² = 0.66, n = 36  200  600  2000  400 100  200 0 1940  0  1950  1960  1970 Year  1980  1990  2000  1960  1970  1980  1990  2000  Year  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 54  (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 biphasic 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) Statistic  Yemen (Figure 3.5b)  One segment  Two segments  One segment  Two segments  SSQ  1.83  0.38  2.8  1.64  F calculated  71.79  -  13.16  -  <0.05 (3,57)  -  < 0.05 (3,56)  -  p  55  a  Relative CPUE  8 6 4 2 Y = 4E+23e-0.027x R² = 0.49, n = 61  0 1950  1960  1970  1980  1990  2000  30  b  Relative CPUE  25 20 15 10 5  Y = 6E+37e-0.043x R² = 0.64 n = 61  0 1940  1950  1960  1970  1980  1990  2000  Year f ishing started  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  1.2  Annual rate of CPUE decline  a Y = 0.0725x - 144.47 R² = 0.7944  0.8  0.4 Y = 0.17  0.0 1950  Annual rate of CPUE decline  1.0  1960  1970  1980  b  1990  2000  Y = 0.0709x - 141.25 R² = 0.3737  0.8 0.6 Y = 0.34  0.4 0.2 0.0 1940  1950  1960  1970  1980  1990  2000  Year f ishing started  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  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 58  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% 59  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: 60  “  61  CHAPTER 4: Catch reconstruction of the Red Sea fisheries  62  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.  63  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 64  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, 65  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).  66  Figure 4.1 The fate of a fish since its first encounter with a fishing gear, (Based on Mohammed, 2003).  67  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 mid1980s. 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; 68  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 overreporting 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 69  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.  70  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 71  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.  72  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.  73  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 74  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 75  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-ofpearl) 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 76  80  Egypt  6  Catch (10 3 t)  Sudan 60 4  40 2  20  0 1950  1960  1970  1980  1990  2000  30  0 1950  1960  1970  1980  1990  2000  1990  2000  80  Eritrea  Yemen  Catch (10 3 t)  60  20 40  10 20  0 1950  1960  1970  1980  1990  2000  0 1950  1960  1970  1980  1.2  50  Jordan and Israel  Saudi Arabia  Ca tch (10 3 t)  40  0.8 30 20  0.4  10 0 1950  1960  1970  1980 Yea r  1990  2000  0 1950  1960  1970  1980  1990  2000  Year  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 77  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. 100  Catch (10 3 t)  75  50  25  0 1950  1960  1970  1980  1990  2000  Year  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.  78  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.  79  40  Gillnet  Handlining 30  Catch (10 3 t)  30  Others Barracudas Jacks Snappers  20  10  Groupers  20  Other Jacks Tunas  10  Indian mackerel Kingfish  Emperors 0 1950  1960  Catch (103 t)  30  1970  1980  1990  0 1950  2000  Others  Beach seine  20  8  1960  1970  1980  1990  2000  Sharks  Yemen  6  Sardines Sa udi Ara bia  4  10  Eritrea  Anchovies  0 1950  1960  1970  1980  2  1990  2000  Year  0 1950  1960  1970  1980  1990  2000  Year  Figure 4.4 Catch composition of major artisanal fisheries of the Red Sea.  80  30  Ca tch (10 3 t)  Trawl - retained  40  20  Others Cuttlefish  Snappers Shrimp Threadfin bream  10  Lizardfish 0 1950  1960  1980  1990  30  Others 20  Gapers  10  0 1950  2000  Pony fishes 1960  1970  1980  1990  2000  Year  Purse seine  30  Catch (10 3 t)  1970  Trawl - discard  20  10  Others Gapers  Goldstripe sardinela  Round herring 0 1950  Horse mackerel & scads 1960  1970  Year  1980  1990  2000  Figure 4.5 Catch composition of Red Sea industrial fisheries.  81  CHAPTER 5: Estimating the unreported catch: a case study of Eritrean Red Sea fisheries  82  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%.  83  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 84  (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.  85  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 smallscale 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:  86  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  87  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 88  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 89  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.  90  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  0.03  0.03  0.02  0.04  0.01  0.03  0.01  0.01  0.01  0.01  0.11  Shrimp  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  5a  Small pelagic 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  91  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  Small pelagic  0 - 1.67  0 - 1.67  1.67 - 3.33  3.33 - 5  0 - 1.67  0 - 1.67  Finfish trawl  0  36 - 54  72 - 90  30 - 90  0 - 18  0 - 18  18 - 36  0 – 18  36 - 54  72 - 90  73 - 90  0 - 18  0 - 18  18 - 36  Shrimp  1980-84  1985-89  1990-94  1995-99  2000-04  0 - 1.67  0 - 1.67  0 - 1.67  0 - 18  54 - 72  18 - 36  36 - 54  0 - 18  72 - 90  54 - 72  36 - 54  1.67 - 3.33 1.67 - 3.33  92  Table 5.6 Estimates of unreported catch (103 t). Lower and upper refer to the range of unreported catch estimates. Fishery Small pelagic  Finfish trawl  Shrimp  1950-54  1955-59  1960-64  1965-69  1970-74  1975-79  1980-84  1985-89  1990-94  1995-99  2000-04  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  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  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  93  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.  94  A. Small pleagic  Catch (103 t)  B. Finfish trawl  12  20  8 10 4  0 50-54  60-64  70-74  80-84  90-94  00-04  0 50-54  60-64  70-74  80-84  C. Shrimp trawl  Catch (10 3 t)  00-04  30  0.3  D. Total  0.2  20  0.1  10  0 50-54  90-94  60-64  70-74  80-84 Yea r  90-94  00-04  0 50-54  60-64  70-74  80-84  90-94  00-04  Yea r  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  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. 96  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.  97  CHAPTER 6: Ecosystem based assessment of the Red Sea fisheries  98  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.  99  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 ecosystembased 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; 100  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, 101  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 102  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 massbalanced 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  103  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).  104  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. 105  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):  C DE  !  #  ,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.  106  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  O F CP # Q= HF RES # T F UCD  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). 107  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; AriasGonzález, 1998), Indonesia (Buchary, 1999; Ainsworth et al., 2007), and French Frigate ShoalsHawaii (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 108  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 109  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,  110  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). 111  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 112  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 6.4.1  Results 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. 113  Table 6.1 The basic parameters of the balanced Red Sea model. Group No. 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  Group name Cetaceans Dungongs Birds Turtles Trawler fishes Purse seine fishes Beach seine fishes Handlining fishes Gillnet fishes Whale shark Sharks Rays Reef top predators Large reef carnivores Medium reef carnivores Small reef carnivores Reef omnivores Reef herbivores Large pelagic carnivores Small pelagic carnivores Pelagic omnivores Demersal top predators Large demersal carnivores Medium demersal carnivores Small demersal carnivores Demersal omnivores Demersal herbivores Benthopelagic fish Bathypelagic fish Bathydemersal fish Shrimp Cephalopods Echrnoderms Crustaceans Molluscs Meiobenthos Corals Other sessile fauna Zooplankton Phytoplankton Sea grass Algae Detritus  Trophic level 3.84 2.00 4.04 2.69 3.38 3.53 3.09 3.54 4.07 3.28 4.16 2.88 3.76 3.51 3.43 3.21 2.88 2.00 3.82 3.44 2.64 3.58 3.31 3.04 2.96 2.16 2.00 2.78 3.11 2.91 2.09 2.92 2.10 2.19 2.05 2.07 2.28 2.28 2.11 1.00 1.00 1.00 1.00  Biomass (t·km-²) 0.0610 0.0029 0.0068 0.0555 0.0402 0.0210 0.1080 0.0700 0.0265 0.0038 0.0076 0.0040 0.0197 0.1100 0.1380 0.3800 0.2630 0.2880 0.1050 0.2740 0.2660 0.0073 0.0160 0.0620 0.2230 0.2960 0.3600 0.2350 0.0020 0.0040 0.0100 0.3990 0.5960 0.8160 0.3680 0.2950 0.9280 0.8500 14.0000 21.5000 11.0000 38.0000 80.0000  P/B (year-1) 0.044 0.025 0.380 0.150 2.680 3.085 3.250 1.300 2.000 0.035 0.750 0.373 1.052 1.240 1.728 2.800 2.700 3.200 0.722 3.162 2.828 1.300 1.500 1.990 3.189 3.200 3.500 1.800 1.749 1.260 9.000 3.500 2.500 6.667 9.000 26.000 2.800 3.200 52.000 110.000 9.000 14.000 -  Q/B (year-1) 5.914 11.000 20.000 3.500 11.380 14.150 15.000 7.887 8.000 4.000 4.371 3.000 4.000 5.500 7.324 10.000 13.890 16.000 6.508 10.000 10.000 6.000 7.000 8.000 12.000 14.000 16.500 6.000 12.720 6.940 25.000 12.000 8.000 20.000 30.000 100.000 9.000 12.000 178.000 -  EE 0.025 0.000 0.026 0.137 0.972 0.945 0.800 0.688 0.950 0.500 0.950 0.400 0.950 0.344 0.576 0.636 0.950 0.950 0.960 0.950 0.950 0.946 0.439 0.920 0.960 0.940 0.975 0.970 0.126 0.831 0.609 0.549 0.553 0.451 0.556 0.402 0.527 0.368 0.363 0.955 0.015 0.027 0.034  GE 0.007 0.002 0.019 0.043 0.236 0.218 0.217 0.165 0.250 0.009 0.172 0.124 0.263 0.225 0.236 0.280 0.194 0.200 0.111 0.316 0.283 0.217 0.214 0.249 0.266 0.229 0.212 0.300 0.138 0.182 0.360 0.292 0.313 0.333 0.300 0.260 0.311 0.267 0.292 -  114  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.  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 predefined 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.  116  Table 6.2 Comparison of the Red Sea model with other tropical ecosystem models using system summary statistics.  Criteria Total boxes Living groups Pedigree index Sum of all consumption (t/km²/year) Sum of all exports (t/km²/year) Sum of all respiratory flows (t/km²/year) Sum of all flows into detritus (t/km²/year) Total system throughput (t/km²/year) Sum of all production (t/km²/year) Mean trophic level of the catch Gross efficiency (catch/net p.p.) Calculated total net primary production (t/km²/year) Total primary production/total respiration Net system production (t/km²/year) Total primary production/total biomass Total biomass/total throughput Total biomass, excluding detritus (t/km²) Total catches (t/km²/year) Connectance Index System Omnivory Index Total market value (US$) Total value (US$) Total variable cost (US$) Total cost (US$) Profit (US$)  Red Sea 43.00 42.00 0.433 2615.82 1665.10 1330.97 1723.53 7335.00 3756.00 3.40 0.000085 2996.00 2.25 1665.03 32.49 0.01 92.22 0.25 0.31 0.24 234.88 234.88 187.90 187.90 46.97  Great Barrier Reef 32.00 30.00 0.139 4314.13 1119.89 1732.15 4038.89 11205.00 3920.00 2.49 0.002971 2846.24 1.64 1114.09 9.82 0.03 289.87 8.46 0.28 0.23 1.20 1.20 0.61 0.61 0.59  Laguna Bay, Philippines 17.00 16.00 0.499 7793.81 5901.51 3137.23 6544.32 23377.00 10838.00 2.08 0.031380 8950.30 2.85 5813.06 49.99 0.01 179.05 280.86 0.21 0.14 -  San Miguel Bay, Philippines 16.00 15.00 0.286 769.38 516.19 381.56 931.41 2599.00 1080.00 3.00 0.016502 897.75 2.35 516.19 28.65 0.01 31.34 14.82 0.34 0.17 -  West Florida shelf USA 59.00 55.00 0.630 18501.20 903.44 5977.33 17273.88 42656.00 14071.00 3.51 0.000051 6986.95 1.17 1009.62 9.74 0.02 717.61 0.36 0.23 0.26 0.28 0.28 0.00 0.00 0.28  117  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  0.05 MTI index  -0.05  Impacting group color code  -0.15  Trawl  -0.25  Purse seine  -0.35  Gillnet Handlining  -0.45  Beach seine  Impacted group 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 6.1.1.1  Ecosim 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.  119  Catch (t.km-2)  0.09  Gillnet  0.09  0.06  0.06  0.03  0.03  0.00  0.00  Catch (t.km-2)  0.02  Shark  0.04 0.01 0.02  0.09  Catch (t.km-2)  Beach seine  0.06  0.00  0.00 0.06  Purse seine  Trawl  0.04  0.06  0.02  0.03 0.00 1950  0.00  0.009  Catch (t.km-2)  Handlining  1960  1970  Year  1980  1990  2000  Shrimp  0.006  0.003  0.000 1950  1960  1970  1980  1990  2000  Year  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  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). 121  Baseline fishing  Zero fishing  Biomass (t.km-2)  0.03  0.40  0.02  0.025 0.20  0.01  0.000  0.00  0.00  0.10  0.60  Biomass (t.km-2)  Handlining  Gillnet  0.050  Increasing fishing  0.40  0.06  0.05 0.20  0.00  0.00  0.03  0.00  0.18  Biomass (t.km-2)  Biomass (t.km-2)  Beach seine  Shark  0.008 0.12  0.004  0.06  0.006  0.003  0.000  0.00  0.000  0.15  0.15  0.18  0.10  0.10  0.12  0.05  0.05  0.06  0.00  0.00  0.00  122  0.03 0.02 Biomass (t.km-2)  Purse seine  0.18 0.02  0.12 0.01  0.01  0.06  0.00  0.00  0.00  0.08 Biomass (t.km-2)  Trawl  0.04 0.04  0.06 0.04  0.02  0.02 0.02 0.00  0.00  0.00  0.012  0.01 Biomass (t.km-2)  Shrimp  0.04  0.008 0.02  0.01  0.00 2006  0.004  2012  2018 Year  2024  2030  0.00 2006  2012  2018 Year  2024  2030  0.000 2006  2012  2018 Year  2024  2030  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  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 Trawler fishes Purse seine fishes Beach seine fishes Handlining fishes Gillnet fishes Sharks Shrimp  6.1.1.1  5% 0 100 0 100 0 100 0  10% 4 100 0 100 74 100 5  15% 38 100 0 100 100 100 25  20% 78 100 0 100 100 100 47  30% 99 100 0 100 100 100 90  40% 100 100 0 100 100 100 98  50% 100 100 0 100 100 100 100  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.  124  0.02  0.1  0.0  0.5  1.0  1.5  2.0  Biomass (t.km-2)  0.04  0.2  Catch (t.km-2 )  0.00  0.4 0.04  0.2  0.02  0.0 0.00  0.25  0.00 0.75  0.50  0.12  Shark  0.06  Beach seine  0.009  0.003  0.0  0.4  0.8  Purse seine  0.20  Biomass (t.km-2)  1.2  0.00  0.06  0.09  0.04  0.06  0.10 0.02  0.00  0.0  0.6  1.2  1.8  0.018  2.4  0.0  0.5  1.0  1.5  2.0  0.00 0.04  Trawl  0.02 0.03  0.00  0.00  0.02  0.04  0.000  Biomass (t.km-2)  0.0  0.08  0.0  1.0  F (yr-1)  2.0  Catch (t.km-2 )  0.1  Biomass (t.km-2)  0.006  Catch (t.km-2 )  0.04  Catch (t.km-2 )  Biomass (t.km-2 )  0.2  3.0  Catch (t.km-2)  Biomass (t.km-2)  0.3  0.0  Handlining  0.06  Catch (t.km-2 )  0.6  Gillnet  0.00  Shrimp  Biomass (t.km-2)  0.002  0.006  0.000  0.0  0.4  0.8  1.2  Catch (t.km-2 )  0.004 0.012  0.000  F (yr-1)  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).  125  1.6 Gillnet Purse seine 1.2  Trawl  0.8  Shark Handlining  0.4  Shrimp Beach seine  0 0.0  0.2  0.4  0.6  0.8  Models' s optimum fishing mortality  1.0  1.2  (Fmsy, year-1)  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).  Catch (t.km-2 )  0.06  0.04  0.02  0.00  Figure 6.9 Maximum sustainable yields (MSY) comparison of single species (open bars) and multispecies (black bars) equilibrium analysis.  126  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. 1.5  a  Beach seine Handlining Gillnet Shark  1.0  1.5  b Purse seine Trawl  1.0  Beach seine Shrimp  Shrimp 0.5  0.0 2006  Trawl Purse seine 2012  2018  Year  2024  2030  0.5 Handlining 0.0 2006  Gillnet Shark  2012  2018  Year  2024  2030  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).  127  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 128  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 129  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 130  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  131  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 132  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 nontrophic 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 133  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).  134  CHAPTER 7: Conclusion  135  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;  136  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. 137  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. 138  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  139  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 140  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.  b  a CPUE (kg /kilowatt.hours)  Catch (kg/crew/day)  300  y = 2E+90e-0.103x R² = 0.5975  200  100  0 1950  1960  1970  1980  1990  2000  0.4 y = 2E+68e-0.081x R² = 0.8137  y = 2E+93e-0.11x R² = 0.8435  0.2 y = -0.0017x + 3.4743 R² = 0.9444 0.0 1950  Year  1960  1970  1980  1990  2000  Year  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 141  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 142  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.  143  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. 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US Fishery Bulletin 105(2): 266-277. Zeller, D. and Pauly, D. (2005) Good news, bad news: global fisheries discards are declining, so are total catches. Fish and Fisheries 6(2): 156-159.  168  Appendices Appendix A Supplementary material for Chapter 2 Table A. 1 Rapfish attributes in their respective fields and notes on their scoring.  Attributes  Good  Bad  Notes  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  Ecological analysis  (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)  169  Attributes  Good  Bad  Notes  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  Economic analysis  (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  0  3  Growth over past ten years: <10% (0); 10-20% (1); 20 - 30% (2); >30% (3)  0  2  Households containing fishers in the community: few, <10% (0); some, 10-30% (1); many, >30%  fishery Fishing sector  (2) Environmental  2  0  knowledge  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)  170  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)  0  4  Have fishers altered gear and vessel to increase catching power over past 5 years?: No (0); very  Change  in  catching  power Gear side effects  little (1); little (2); somewhat (3); a lot, rapid increase (4) 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)  171  Attributes Equity in entry to fishery  Good  Bad  Notes  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); comgmt/comm. leading (3); genuine co-mgmt with all parties equal (4)  Mitigation  –  habitat  4  0  destruction Mitigation – ecosystem  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)  4  0  depletion  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” .  172  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  173  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  174  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  175  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.  Year  Artisanal Categorized Uncategorized  Retained  Industrial Discard Uncategorized  1950 1951 1952 1953 1954  47662 47651 48307 48405 48598  3595 3399 3841 3710 3519  503 523 543 564 584  1481 1517 1551 1582 1612  0 0 0 0 0  FAO 12913 13913 19499 19806 21234  1955 1956 1957 1958 1959  48436 45448 41912 38827 35408  3465 3549 3084 3068 3401  604 393 1698 1877 1925  1640 966 4794 5084 5101  0 0 0 0 0  24561 24613 25986 25774 29689  1960 1961 1962 1963  34396 38043 34721 34114  3427 3218 3414 3643  3967 7584 13441 14813  3063 6668 11533 16080  0 0 0 0  30383 34595 46102 44988  1964 1965 1966 1967  36377 46018 49710 45250  3790 4059 4217 4025  12295 12040 9892 11066  20090 20378 13096 11862  0 0 0 0  40665 42540 40884 40472  1968 1969 1970 1971 1972 1973  43745 45854 50295 51193 42355 37271  3699 5012 4574 3793 4238 3934  12363 13732 15765 18192 17996 6132  10776 9796 11514 10366 11893 6468  0 0 0 0 0 0  38245 38820 40639 46462 43358 31470  1974 1975 1976  39606 39926 40502  3757 3291 3430  17878 16528 25397  6777 13303 12838  0 0 0  34322 30772 35974  1977 1978 1979 1980  41378 45823 45660 43695  2917 3090 6276 5530  20694 19633 24564 18365  10633 10091 7634 8415  0 0 0 0  33498 36049 44875 45133 176  Year  Artisanal Categorized Uncategorized  Retained  Industrial Discard Uncategorized  1981  48919  5928  17136  9037  0  FAO 47075  1982 1983 1984 1985  51249 45036 45155 54087  6329 5147 6235 5531  21140 28993 32149 24846  9737 9283 8595 8303  104 218 320 434  44035 51101 49436 64186  1986 1987 1988 1989  57110 64211 68847 74329  5213 7692 9060 9962  23151 25797 28962 38956  10090 8419 10004 8046  546 674 483 713  65136 70746 78778 96197  1990  71368  8145  38536  7501  606  99145  1991 1992 1993 1994  84697 88693 102024 100188  9912 13140 17238 15133  34687 35596 43059 32951  10498 8669 10517 17210  672 808 794 822  109716 114251 127653 133493  1995 1996 1997 1998  82058 68123 86853 84980  14008 11099 15486 10628  35931 32699 35060 32642  25220 24336 27021 27340  779 798 980 851  133649 128270 137474 136554  1999 2000 2001 2002 2003 2004  83150 60614 61794 67675 61516 57757  9983 6330 5926 10952 7193 9374  36073 52781 45650 48473 47656 47859  31550 34087 36736 39632 42742 35279  1054 1362 1287 1467 1688 1595  158399 148643 147144 145372 138609 133193  2005 2006  60450 62014  8811 8347  34966 36125  30778 26806  722 1167  116503 124057  177  Table C. 2 Catch (t) composition of reconstructed Red Sea handlining fishery. Year  Emperors Groupers Snappers Jacks  Barracuda  Bream  Parrot fishes  Cobia Grunts  Cutlass fish  Rabbit fish  Goggle eye  Surgeon fish  Wrasses  Scombridae  Tunas  Goat fish  Unicorns  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  178  Year  Emperors Groupers Snappers Jacks  Barracuda  Bream  Parrot fishes  Cobia Grunts  Cutlass fish  Rabbit fish  Goggle eye  Surgeon fish  Wrasses  Scombridae  Tunas  Goat fish  Unicorns  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  179  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  180  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  181  Table C. 4 Catch (t) composition of reconstructed Red Sea beach seine fishery. Years 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978  Anchovy 18133 18142 18154 18166 18180 18159 15849 13539 11229 8458 7426 9716 7150 6309 7691 12644 14169 15339 8895 8254 11595 12136 8961 3664 4083 2433 1047 1072 1081  Sardine 9023 9028 9033 9038 9044 9035 7879 6722 5566 4180 3662 4806 3522 3100 3790 6265 7026 377 384 4065 5735 6005 412 421 432 442 453 463 467  Mullets 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 50 49 48 46 45 44 43  Queenfish 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 98 95 93 90 88 85  Jacks 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 250 244 238 231 225 219 213  Little tuna 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 80 80 0 0 0 80 78 76 74 72 70 68  Others 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 40 40 0 0 0 40 39 38 37 36 35 34  Years 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Anchovy 1102 1145 1258 1262 1266 1269 1271 1296 1312 1298 1316 1334 1409 1443 1445 1458 1567 1581 1571 1582 1583 1582 1599 1555 1560 1563 1563 1560  Sardine 476 495 544 546 547 548 549 560 567 561 569 576 609 624 624 630 677 683 679 684 684 684 691 672 674 676 676 674  Mullets 41 38 34 30 26 23 19 15 11 8 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Queenfish 83 75 68 60 53 45 38 30 23 15 8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Jacks 207 188 169 150 131 113 94 75 56 38 19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Little tuna 66 60 54 48 42 36 30 24 18 12 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Others 33 30 27 24 21 18 15 12 9 6 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  182  Table C. 5 Catch (t) composition of reconstructed Red Sea shark fishery by countries. Years 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978  Eritrea 413 413 413 413 413 413 413 413 413 413 413 413 413 413 394 937 1146 3174 5508 1900 1500 2300 1100 400 500 30 100 14 14  Sudan 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 17 18 19 21 22 23 34 32 34  Yemen 483 490 499 509 519 503 516 529 542 555 569 584 599 614 628 642 655 667 678 690 702 702 714 766 866 1022 1095 1211 1260  Egypt 3 3 3 4 4 5 5 6 6 7 7 8 8 8 9 9 10 10 11 11 12 12 13 13 14 14 14 15 15  Saudi Arabia 343 343 343 343 351 360 369 377 386 394 403 411 420 429 413 477 509 549 600 676 676 676 694 711 729 747 765 765 968  Years 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Eritrea 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 14 16 14 19 42 143 120 159 135 91 49 255  Sudan 68 62 118 105 0 42 40 38 36 34 33 94 106 109 98 96 105 117 86 95 110 99 102 97 104 117 127 146  Yemen 1204 1173 1349 1474 1497 493 1548 1133 997 747 776 690 3282 7233 7798 7756 5352 4265 5645 5220 6187 2075 1327 414 762 869 1309 1434  Egypt 16 13 10 30 12 14 16 14 12 12 15 19 10 5 10 9 8 10 12 7 3 3 5 4 4 4 5 3  Saudi Arabia 850 776 976 976 1030 690 419 436 556 600 600 556 556 556 600 600 310 308 348 347 489 471 516 480 457 320 471 473  183  Table C. 6 Catch (t) composition of reconstructed Red Sea trawl (retained) fishery. Years 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978  Lizardfish 182 193 204 215 225 236 123 822 908 934 626 1507 2587 3622 4506 4664 3129 2897 2711 2551 2280 2058 2677 1522 1666 3555 3461 2839 2593  Threadfin bream 31 33 35 36 38 40 21 139 167 171 195 511 757 966 1053 1078 829 779 766 774 693 632 721 462 408 713 694 507 444  Shrimp 69 71 74 76 78 80 56 205 220 225 134 267 477 686 896 930 599 553 507 461 1380 1286 1166 608 562 881 887 822 912  Snappers 47 50 53 56 58 61 32 213 230 237 126 287 541 796 1050 1091 690 634 578 522 467 419 585 312 381 876 852 725 668  Cuttlefish 11 11 12 12 13 14 7 48 51 53 28 64 121 178 235 244 154 142 129 117 105 94 131 70 85 196 191 162 150  Emperors 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Mullets 14 15 16 17 18 19 10 65 86 88 147 401 556 676 676 687 577 547 553 580 519 476 507 346 271 403 391 253 209  Horse Mackerel & Scad 16 17 18 19 20 21 11 73 79 82 44 99 187 275 363 377 238 219 200 180 161 145 202 108 131 303 294 250 231  Grunts 0 0 0 0 0 0 0 0 0 0 1 2 3 3 3 3 3 3 3 3 3 3 3 2 1 1 1 0 0  184  Years 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Lizardfish 2224 2298 2879 2443 2141 1085 1302 2782 1917 1033 839 640 927 1261 1409 2821 4287 3567 4345 4845 7636 9574 9962 12571 10014 10885 8314 7905  Threadfin bream 397 501 430 389 418 397 416 408 325 253 289 235 252 316 628 1308 1891 1718 2287 2661 4752 5109 4959 5408 4824 4256 3604 3221  Shrimp 497 604 646 824 953 1319 1255 1117 1097 1443 1141 1281 1848 1228 1381 2082 2847 2526 2761 2390 2279 2510 2825 2142 3488 2112 2209 2036  Snappers 566 699 621 630 530 564 568 538 508 679 1117 966 708 862 952 805 676 687 674 712 915 1064 1065 1141 1226 1058 904 609  Cuttlefish 127 126 184 180 239 380 377 415 535 349 481 457 476 575 681 813 752 787 943 970 1152 1285 1386 1098 1924 3474 3028 2720  Emperors 0 0 0 63 131 193 261 329 406 291 429 365 405 486 554 718 798 1461 972 949 1199 1383 1367 1326 1225 927 992 949  Mullets 196 178 168 288 134 101 117 180 206 285 269 259 323 516 423 357 397 316 329 264 287 296 336 428 487 458 469 392  Horse Mackerel & Scad 196 215 284 164 203 323 232 243 261 235 170 160 207 102 161 291 143 148 290 273 160 189 278 241 260 82 148 355  Grunts 0 0 0 17 36 53 73 91 113 81 119 101 112 135 133 282 337 500 175 188 708 1121 552 480 261 383 246 560  185  Table C.6 continued. Years Jacks Catfish 1950 0 0 1951 0 0 1952 0 0 1953 0 0 1954 0 0 1955 0 0 1956 0 0 1957 0 0 1958 0 0 1959 0 0 1960 0 0 1961 0 0 1962 0 0 1963 0 0 1964 0 0 1965 0 0 1966 0 0 1967 0 0 1968 0 0 1969 0 0 1970 0 0 1971 0 0 1972 0 0 1973 0 0 1974 0 0 1975 0 0 1976 0 0 1977 0 0 1978 0 0 1979 0 0  Barracuda 0 0 0 0 0 0 0 0 1 1 6 17 22 24 20 20 20 20 21 23 21 19 18 14 9 8 7 2 0 1  Crab 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Indian mackerel 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Leopard flounder 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Goat fish 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1  Sole 0 0 0 0 0 0 0 0 0 0 1 3 4 4 4 4 4 4 4 4 4 4 3 3 2 1 1 0 0 0  Others 0 0 0 0 0 0 0 0 1 1 9 26 32 35 29 29 30 29 31 34 31 29 27 21 13 11 11 3 0 2  186  Years 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Jacks 0 0 0 0 0 0 0 0 0 0 0 0 0 0 213 306 804 1 114 344 1876 901 603 270 172 97 266  Catfish 0 0 21 44 64 87 110 136 97 144 122 135 163 160 258 290 169 197 316 335 954 547 319 284 322 154 403  Barracuda 1 0 0 0 0 0 0 0 0 0 0 0 0 0 181 260 174 3 27 118 604 432 328 1869 360 191 570  Crab 0 0 30 62 91 123 155 191 137 202 172 190 229 233 249 244 257 317 288 269 290 300 289 320 190 310 244  Indian mackerel 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 38 58 0 0 20 196 99 13 5 6 4 269  Leopard flounder 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 32 0 0 1 23 119 126 69 53 73 20 85  Goat fish 0 1 4 4 4 7 10 13 16 19 22 6 6 6 6 6 6 6 12 18 19 19 20 23 63 60 60  Sole 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Others 2 0 0 0 0 0 0 0 0 0 0 0 0 0 267 383 244 3 118 375 1219 1374 611 682 435 334 1438  187  Table C. 7 Catch (t) composition of reconstructed Red Sea trawl (discard) fishery. Years 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979  Pony Fish 675 693 710 725 739 753 424 2294 2529 2538 1579 3422 5829 8067 9996 10136 6584 5975 5454 4992 5298 4751 5624 3118 3283 6508 6287 5117 4793 3774  Gaper 296 305 313 321 328 335 170 1105 1165 1169 609 1351 2488 3571 4602 4674 2886 2593 2313 2044 1786 1567 2144 1117 1336 3010 2867 2389 2158 1791  Flounder 118 122 125 128 131 134 68 442 466 468 244 541 995 1428 1841 1870 1154 1037 925 817 714 627 857 447 534 1204 1147 956 863 716  Crab 82 84 86 88 90 92 48 297 317 318 174 384 691 982 1253 1272 796 717 643 574 532 470 615 326 376 818 782 648 591 484  Tigerfish 9 9 9 9 9 9 9 9 22 22 41 81 96 105 89 89 92 89 94 102 195 183 151 100 69 55 63 38 54 22  Sand dollars 79 81 83 86 87 89 45 295 311 312 162 360 663 952 1227 1247 770 692 617 545 476 418 572 298 356 803 764 637 575 478  Cutlassfish 5 5 5 5 5 5 5 5 13 13 23 46 55 60 51 51 52 51 53 58 111 104 86 57 39 31 36 22 31 12  Mojarra 5 5 5 5 5 5 5 5 13 13 23 46 55 60 51 51 52 51 53 58 111 104 86 57 39 31 36 22 31 12  Sponge 47 49 50 51 52 54 27 177 186 187 97 216 398 571 736 748 462 415 370 327 286 251 343 179 214 482 459 382 345 287  Jacks 46 46 46 46 46 46 46 46 6 6 12 23 28 30 26 25 26 25 27 29 519 490 359 185 127 82 90 111 174 6  Flatheads 3 3 3 3 3 3 3 3 6 6 12 23 28 30 26 25 26 25 27 29 56 52 43 29 20 16 18 11 15 6  Puffers 3 3 3 3 3 3 3 3 6 6 12 23 28 30 26 25 26 25 27 29 56 52 43 29 20 16 18 11 15 6  188  Years 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Pony Fish 4125 4445 5154 4923 4648 4502 5382 4484 5605 4361 4145 6026 4704 5981 10336 15352 14825 16482 16799 19449 21111 22727 24466 26387 21720 18859 16437  Gaper 2033 2158 1706 1610 1339 1274 1699 1429 1227 1237 1018 1044 1324 1140 937 1023 969 1039 844 866 770 873 1030 1109 1017 1039 885  Flounder 813 863 682 644 536 510 679 572 491 495 407 417 529 456 375 409 388 416 338 347 308 349 412 444 407 416 354  Crab 544 580 511 484 421 403 517 433 429 391 340 405 419 423 523 702 674 742 713 803 837 910 997 1075 906 818 709  Tigerfish 7 15 197 192 222 221 224 184 356 213 238 445 232 418 955 1502 1455 1626 1706 2000 2210 2369 2530 2729 2223 1895 1656  Sand dollars 542 575 455 429 357 340 453 381 327 330 272 278 353 304 250 273 258 277 225 231 205 233 275 296 271 277 236  Cutlassfish 4 8 112 110 127 126 128 105 203 122 136 254 133 239 546 858 831 929 975 1143 1263 1354 1446 1559 1270 1083 946  Mojarra 4 8 112 110 127 126 128 105 203 122 136 254 133 239 546 858 831 929 975 1143 1263 1354 1446 1559 1270 1083 946  Sponge 325 345 273 258 214 204 272 229 196 198 163 167 212 182 150 164 155 166 135 139 123 140 165 177 163 166 142  Jacks 2 4 56 55 64 63 64 52 102 61 68 127 66 119 273 429 416 465 487 572 631 677 723 780 635 541 473  Flatheads 2 4 56 55 64 63 64 52 102 61 68 127 66 119 273 429 416 465 487 572 631 677 723 780 635 541 473  Puffers 2 4 56 55 64 63 64 52 102 61 68 127 66 119 273 429 416 465 487 572 631 677 723 780 635 541 473  189  Table C.7 continued. Years Soles Goatfish 1950 3 1 1951 3 1 1952 3 1 1953 3 1 1954 3 1 1955 3 1 1956 3 1 1957 3 1 1958 6 3 1959 6 3 1960 12 6 1961 23 12 1962 28 14 1963 30 15 1964 26 13 1965 25 13 1966 26 13 1967 25 13 1968 27 13 1969 29 15 1970 56 28 1971 52 26 1972 43 22 1973 29 14 1974 20 10 1975 16 8 1976 18 9 1977 11 5 1978 15 8 1979 6 3  Mantis shrimp 1 1 1 1 1 1 1 1 3 3 6 12 14 15 13 13 13 13 13 15 28 26 22 14 10 8 9 5 8 3  Lizard fish 38 38 38 38 38 38 38 38 0 0 0 0 0 0 0 0 0 0 0 0 405 383 276 137 94 58 63 88 139 0  Threadfin Bream 32 32 32 32 32 32 32 32 0 0 0 0 0 0 0 0 0 0 0 0 347 328 237 117 81 49 54 75 119 0  Grunt 12 12 12 12 12 12 12 12 0 0 0 0 0 0 0 0 0 0 0 0 130 123 89 44 30 19 20 28 45 0  Catfish 8 8 8 8 8 8 8 8 0 0 0 0 0 0 0 0 0 0 0 0 87 82 59 29 20 12 13 19 30 0  Barracudas 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 0 0 0 29 27 20 10 7 4 4 6 10 0  Cuttlefish 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 14 14 10 5 3 2 2 3 5 0  Others 12 12 12 12 12 12 12 12 29 29 52 104 124 134 115 114 118 114 120 131 251 235 194 128 89 71 80 49 69 28  190  Years 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Soles 2 4 56 55 64 63 64 52 102 61 68 127 66 119 273 429 416 465 487 572 631 677 723 780 635 541 473  Goatfish 1 2 28 27 32 32 32 26 51 30 34 64 33 60 136 215 208 232 244 286 316 338 361 390 318 271 237  Mantis shrimp 1 2 28 27 32 32 32 26 51 30 34 64 33 60 136 215 208 232 244 286 316 338 361 390 318 271 237  Lizard fish 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Threadfin Bream 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Grunt 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Catfish 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Barracudas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Cuttlefish 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Others 8 19 253 247 286 284 288 236 457 274 306 572 299 537 1228 1931 1871 2091 2193 2572 2841 3046 3252 3508 2858 2436 2129  191  Table C. 8 Catch (t) composition of reconstructed Red Sea purse seine fishery. Years 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978  Horse mackerel & scads 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2400 3142 3884 4627 5968 5476 1221 6572 4388 8522 6930 6606  Round herring 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1163 1523 1883 2243 2893 2654 592 3186 2127 4131 3359 3202  Goldstripe sardinella 122 122 122 122 122 122 122 122 122 122 2438 4046 7502 6943 2322 1955 2429 524 686 848 1010 1303 1196 267 1435 958 1861 1513 1442  Indian mackerel 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 341 446 551 657 847 777 173 933 623 1209 983 938  Slimy mackerel 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Spotted sardinella 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 2 629 823 1018 1212 1564 1435 320 1722 1150 2233 1816 1731  Barracudas 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Kingfish 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Queenfish 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  Others 11 11 11 11 11 11 11 11 11 11 212 352 652 604 1136 957 1189 183 240 297 354 456 418 93 502 335 651 530 505  192  Years 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Horse mackerel & scads 9324 6294 5461 7328 11856 13531 9721 8046 9626 11700 16385 16450 14102 14338 17635 10622 10641 9157 10293 8411 6551 11205 8296 9216 8602 8976 4999 5160  Round herring 4519 3051 2647 3489 6133 6952 4860 3900 4652 5875 8205 8309 7023 7058 8813 5081 5120 4322 4803 4036 3180 5629 4088 4579 4255 4585 2444 2464  Goldstripe sardinella 2036 1374 1192 1572 796 903 631 506 604 763 1065 1079 912 917 1144 660 665 561 624 524 413 731 531 821 935 1252 695 698  Indian mackerel 1323 893 775 1193 948 1194 1181 1273 1556 1359 1960 1796 1780 2007 2153 1841 1773 1728 2074 2197 2644 3001 2857 2836 2761 3414 2989 2987  Slimy mackerel 0 0 0 0 2331 2642 1847 1482 1768 2233 3118 3158 2669 2683 3349 1931 1946 1643 1826 1534 1209 2139 1554 1740 1617 1743 929 937  Spotted sardinella 2443 1649 1431 1886 956 1083 757 608 725 916 1279 1295 1094 1100 1373 792 798 674 749 629 496 877 637 939 1045 1372 758 762  Barracudas 0 0 0 33 69 101 137 173 213 153 226 192 213 256 251 260 247 253 310 269 222 234 234 243 249 219 341 321  Kingfish 0 0 0 21 43 64 86 109 134 96 142 121 134 161 158 164 155 159 195 170 173 182 183 203 217 254 255 161  Queenfish 0 0 0 16 34 50 68 85 105 75 111 94 105 126 124 128 121 124 153 132 111 117 117 114 112 90 104 181  Others 713 481 417 550 932 1056 738 592 707 893 1246 1262 1067 1072 1339 772 778 657 730 613 483 855 621 696 646 697 371 374  193  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  50-54  55 - 59  60 - 64  65 - 69  Small  Finfish  pelagic  trawl  Shrimp  Rational  Duration  Ref.*  Growing operation of small pelagic fishery  Increasing effort  50 - 54  1  Shrimp fishery trial  New operation  50 - 54  1  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  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  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  194  Influence Small  Finfish  pelagic  trawl  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  Little recovery of the fishing industry  More effort  83 - 90  7  Resource survey  Resource knowledge  84  8  Establishment of marine and fisheries institute  Resource knowledge  86 - 90  4  Period  80 - 84  85 - 89  Event summary  Shrimp  195  Influence Finfish  pelagic  trawl  Rational  Duration  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  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  Period  Event summary  90 - 94  95 - 99  Small  Shrimp  Ref.*  196  Influence Small  Finfish  pelagic  trawl  Rational  Duration  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  Period  Event summary  00 - 04  Shrimp  *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)  Ref.*  17 Shaebia.org (2005)  197  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. FishBase Code  Group Whale shark  Family  Scientific name  Rhincodontidae  Rhincodon typus  Rays  Myliobatidae Myliobatidae Myliobatidae Dasyatidae Dasyatidae Dasyatidae Dasyatidae Dasyatidae Myliobatidae Myliobatidae Dasyatidae Dasyatidae Dasyatidae Torpedinidae Torpedinidae Torpedinidae Dasyatidae  Aetobatus flagellum Aetobatus narinari Aetobatus ocellatus Dasyatis bennetti Dasyatis kuhlii Himantura gerrardi Himantura imbricata Himantura uarnak Manta ehrenbergii Mobula thurstoni Pastinachus sephen Taeniura lymma Taeniura meyeni Torpedo panthera Torpedo sinuspersici Torpedo suessii Urogymnus asperrimus  8973 1250 12600 15387 4508 15483 13150 5507 54614 2588 8203 5399 6482 27060 7970 61378 5400  Porcupine ray  Belonidae Serranidae Carangidae Antennariidae Antennariidae Antennariidae Antennariidae Antennariidae Antennariidae Lutjanidae Lutjanidae Lutjanidae Carangidae Bothidae  Ablennes hians Aethaloperca rogaa Alectis indicus Antennarius coccineus Antennarius commerson Antennarius hispidus Antennarius nummifer Antennarius pictus Antennarius striatus Aphareus furca Aphareus rutilans Aprion virescens Atule mate Bothus mancus Brachysomophis cirrocheilos Carangoides bajad Carangoides chrysophrys Carangoides coeruleopinnatus Carangoides dinema Carangoides fulvoguttatus Carangoides gymnostethus Carangoides malabaricus  972 6441 10 5402 7293 8074 5403 10276 5474 81 83 84 1893 7641  Flat needlefish Redmouth grouper Indian threadfish Scarlet frogfish Commerson' s frogfish Shaggy angler Spotfin frogfish Painted frogfish Striated frogfish Small toothed jobfish Rusty jobfish Green jobfish Yellowtail scad Flowery flounder  12886 1923 4441  Stargazer snake eel Orangespotted trevally Longnose trevally  1924 1925 1926  Coastal trevally Shadow trevally Yellowspotted trevally  1905 4443  Bludger Malabar trevally  Reef top predators  Ophichthidae Carangidae Carangidae Carangidae Carangidae Carangidae Carangidae Carangidae  2081  FishBase common name Whale shark Longheaded eagle ray Spotted eagle ray Bennett' s stingray Bluespotted stingray Sharpnose stingray Scaly whipray Honeycomb stingray Smooth-tail mobula Cowtail stingray Bluespotted ribbontail ray Blotched fantail ray Panther electric ray Marbled electric ray  198  Group  Family Carangidae Carangidae Carangidae Carangidae Carangidae Odontaspididae Serranidae Serranidae Serranidae Serranidae Serranidae Serranidae Labridae Labridae Apogonidae Chirocentridae Congridae Serranidae Muraenidae Serranidae Serranidae Serranidae Serranidae Serranidae Serranidae Serranidae Fistulariidae Fistulariidae Scombridae Scombridae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Muraenidae Antennariidae Labridae Lethrinidae Lethrinidae Lutjanidae  Scientific name Carangoides orthogrammus Carangoides plagiotaenia Caranx ignobilis Caranx melampygus Caranx sexfasciatus Carcharias taurus Cephalopholis argus Cephalopholis boenak Cephalopholis hemistiktos Cephalopholis miniata Cephalopholis oligosticta Cephalopholis sexmaculata Cheilinus undulatus Cheilio inermis Cheilodipterus macrodon Chirocentrus dorab Conger cinereus Diploprion drachi Echidna nebulosa Epinephelus coeruleopunctatus Epinephelus fuscoguttatus Epinephelus hexagonatus Epinephelus lanceolatus Epinephelus malabaricus Epinephelus polyphekadion Epinephelus tukula Fistularia commersonii Fistularia petimba Grammatorcynus bilineatus Gymnosarda unicolor Gymnothorax elegans Gymnothorax favagineus Gymnothorax flavimarginatus Gymnothorax griseus Gymnothorax meleagris Gymnothorax moluccensis Gymnothorax nudivomer Gymnothorax pictus Gymnothorax punctatofasciatus Gymnothorax punctatus Gymnothorax rueppellii Gymnothorax undulatus Histrio histrio Hologymnosus annulatus Lethrinus lentjan Lethrinus olivaceus Lutjanus ehrenbergii  FishBase Code 1909 1910 1895 1906 1917 747 6396 6444 6447 6450 6451 6453 5604 5623 5781 6358 6654 24437 5388  FishBase common name Island trevally Barcheek trevally Giant trevally Bluefin trevally Bigeye trevally Sand tiger shark Peacock hind Chocolate hind Yellowfin hind Coral hind Vermilion hind Sixblotch hind Humphead wrasse Cigar wrasse Large toothed cardinalfish Dorab wolf-herring Longfin African conger Yellowfin soapfish Snowflake moray  6440 4460 6660 6468 6439  Whitespotted grouper Brown-marbled grouper Starspotted grouper Giant grouper Malabar grouper  6473 5525 5444 3276  Camouflage grouper Potato grouper Bluespotted cornetfish Red cornetfish  104 106 23130 5391  Double-lined mackerel Dogtooth tuna Elegant moray Laced moray  5392 8058 5394 27334 7465 6395  Yellow-edged moray Geometric moray Turkey moray Moluccan moray Starry moray Peppered moray  27341 27325 5396 4905 3089 5637 1863 1864 793  Red Sea whitespotted moray Banded moray Undulated moray Sargassumfish Ring wrasse Pink ear emperor Longface emperor Blackspot snapper  199  Group  Family Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Carangidae Cirrhitidae Mullidae Mullidae Ephippidae Haemulidae Haemulidae Haemulidae Haemulidae Haemulidae Serranidae Haemulidae Haemulidae Priacanthidae Priacanthidae Scorpaenidae Holocentridae Holocentridae Synodontidae Carangidae Carangidae Scorpaenidae Scorpaenidae Scorpaenidae Carangidae Carangidae Carangidae Sphyraenidae Sphyraenidae Sphyraenidae Sphyraenidae Sphyraenidae Sphyraenidae Sphyraenidae Muraenidae Synanceiidae Synodontidae Synodontidae Ephippidae  Scientific name Lutjanus erythropterus Lutjanus fulvus Lutjanus johnii Lutjanus lemniscatus Lutjanus malabaricus Lutjanus monostigma Lutjanus quinquelineatus Lutjanus rivulatus Lutjanus russellii Lutjanus sanguineus Lutjanus sebae Macolor niger Megalaspis cordyla Paracirrhites forsteri Parupeneus cyclostomus Parupeneus heptacanthus Platax teira Plectorhinchus flavomaculatus Plectorhinchus gaterinus Plectorhinchus gibbosus Plectorhinchus harrawayi Plectorhinchus sordidus Plectropomus areolatus Pomadasys maculatus Pomadasys stridens Priacanthus blochii Pristigenys niphonia Pterois volitans Sargocentron macrosquamis Sargocentron melanospilos Saurida gracilis Scomberoides lysan Scomberoides tol Scorpaenopsis barbata Scorpaenopsis diabolus Scorpaenopsis gibbosa Selar crumenophthalmus Seriola dumerili Seriolina nigrofasciata Sphyraena barracuda Sphyraena flavicauda Sphyraena forsteri Sphyraena jello Sphyraena obtusata Sphyraena putnamae Sphyraena qenie Strophidon sathete Synanceia verrucosa Synodus variegatus Trachinocephalus myops Tripterodon orbis  FishBase Code 1406 262 264 157 162 166 172 173 176 177 178 187 384 5952 5990 5991 5739  FishBase common name Crimson snapper Blacktail snapper John' s snapper Yellowstreaked snapper Malabar blood snapper Onespot snapper Five-lined snapper Blubberlip snapper Russell' s snapper Humphead snapper Emperor red snapper Black and white snapper Torpedo scad Blackside hawkfish Goldsaddle goatfish Cinnabar goatfish Tiera batfish  7625 7703 6366 52851 7626 6082 4447 7708 9903 7905 5195  Lemon sweetlip Blackspotted rubberlip Harry hotlips Sordid rubberlip Squaretail coralgrouper Saddle grunt Striped piggy Paeony bulleye Japanese bigeye Red lionfish  23251  Bigscale squirrelfish  5345 4534 1951 1953 12767 4921 7918 387 1005 1962 1235 7937 5734 4827 4493 7938 7939 8595 5825 5398 2724 7694  Blackblotch squirrelfish Gracile lizardfish Doublespotted queenfish Needlescaled queenfish Bearded scorpionfish False stonefish Humpback scorpionfish Bigeye scad Greater amberjack Blackbanded trevally Great barracuda Yellowtail barracuda Bigeye barracuda Pickhandle barracuda Obtuse barracuda Sawtooth barracuda Blackfin barracuda Slender giant moray Stonefish Variegated lizardfish Snakefish African spadefish  200  Group  Family Belonidae Belonidae  Large reef carnivores  Balistidae Balistidae Albulidae Albulidae Carangidae Monacanthidae Serranidae Tetraodontidae Tetraodontidae Balistidae Ophidiidae Ophichthidae Ophichthidae Balistidae Carangidae Labridae Labridae Sparidae Platycephalidae Labridae Labridae Haemulidae Diodontidae Diodontidae Diodontidae Drepaneidae Echeneidae Muraenidae Carangidae Labridae Serranidae Serranidae Serranidae Carangidae Lethrinidae Muraenidae Muraenidae Muraenidae Muraenidae Labridae Labridae Priacanthidae Kuhliidae Kyphosidae Lethrinidae Lethrinidae Lethrinidae  Scientific name Tylosurus acus melanotus Tylosurus crocodilus crocodilus Abalistes stellaris Abalistes stellatus Albula glossodonta Albula vulpes Alectis ciliaris Aluterus monoceros Anyperodon leucogrammicus Arothron hispidus Arothron stellatus Balistoides viridescens Brotula multibarbata Callechelys catostoma Callechelys marmorata Canthidermis maculata Carangoides ferdau Cheilinus fasciatus Cheilinus lunulatus Cheimerius nufar Cociella crocodila Coris aygula Coris formosa Diagramma pictum Diodon holocanthus Diodon hystrix Diodon liturosus Drepane longimana Echeneis naucrates Echidna polyzona Elagatis bipinnulata Epibulus insidiator Epinephelus coioides Epinephelus fasciatus Epinephelus morrhua Gnathanodon speciosus Gymnocranius grandoculis Gymnomuraena zebra Gymnothorax hepaticus Gymnothorax javanicus Gymnothorax monochrous Hemigymnus fasciatus Hemigymnus melapterus Heteropriacanthus cruentatus Kuhlia mugil Kyphosus cinerascens Lethrinus erythracanthus Lethrinus microdon Lethrinus xanthochilus  FishBase Code 1317  FishBase common name Keel-jawed needle fish  977  Hound needlefish  9 58334 11512 228 988 4274  Starry triggerfish Roundjaw bonefish Bonefish African pompano Unicorn leatherjacket  4922 5425 6526 6026 7297 12888 12889 4278 1921 5600 12780 444 7895 5624 7736 4465 4659 1022 6552 7692 2467 5389 412 5606 6465 5348 5353 4464  Slender grouper White-spotted puffer Starry toadfish Titan triggerfish Goatsbeard brotula Black-striped snake eel Marbled snake eel Spotted oceanic triggerfish Blue trevally Redbreast wrasse Broomtail wrasse Santer seabream Crocodile flathead Clown coris Queen coris Painted sweetlips Long-spine porcupinefish Spot-fin porcupinefish Black-blotched porcupinefish Concertina fish Live sharksucker Barred moray Rainbow runner Slingjaw wrasse Orange-spotted grouper Blacktip grouper Comet grouper Golden trevally  1834 7880 6498 6380 7285 5635 5636  Blue-lined large-eye bream Zebra moray Liver-colored moray eel Giant moray Drab moray Barred thicklip Blackeye thicklip  1150 5790 5805 1862 1845 1852  Glasseye Barred flagtail Blue seachub Orange-spotted emperor Smalltooth emperor Yellowlip emperor  201  Group  Family Lutjanidae Lutjanidae Malacanthidae Megalopidae Lethrinidae Ophichthidae Ophichthidae Acanthuridae Balistidae Ophichthidae Labridae Platycephalidae Lutjanidae Ophichthidae Lutjanidae Ophichthidae Ephippidae Haemulidae Haemulidae Haemulidae Haemulidae Haemulidae Haemulidae Sparidae Haemulidae Haemulidae Haemulidae Balistidae Balistidae Rachycentridae Echeneidae Sparidae Holocentridae Carangidae Carangidae Carangidae Muraenidae Muraenidae Blenniidae  Medium reef carnivores  Pomacentridae Pomacentridae Sparidae Centriscidae Soleidae Carangidae Ambassidae Labridae Labridae  Scientific name Lutjanus argentimaculatus Lutjanus bohar Malacanthus latovittatus Megalops cyprinoides Monotaxis grandoculis Myrichthys colubrinus Myrichthys maculosus Naso hexacanthus Odonus niger Ophichthus erabo Oxycheilinus digramma Papilloculiceps longiceps Paracaesio xanthura Phaenomonas cooperae Pinjalo pinjalo Pisodonophis cancrivorus Platax orbicularis Plectorhinchus albovittatus Plectorhinchus nigrus Plectorhinchus obscurus Plectorhinchus playfairi Plectorhinchus schotaf Plectorhinchus umbrinus Polysteganus coeruleopunctatus Pomadasys commersonnii Pomadasys furcatus Pomadasys kaakan Pseudobalistes flavimarginatus Pseudobalistes fuscus Rachycentron canadum Remora remora Rhabdosargus sarba Sargocentron spiniferum Trachinotus baillonii Trachinotus blochii Ulua mentalis Uropterygius concolor Uropterygius polyspilus Xiphasia setifer Abudefduf bengalensis Abudefduf septemfasciatus Acanthopagrus bifasciatus Aeoliscus punctulatus Aesopia cornuta Alepes djedaba Ambassis commersonii Anampses caeruleopunctatus Anampses meleagrides  FishBase Code  FishBase common name  1407 1417 5796 227 1869 8053 2650 1263 1311 15682 5599 7896 194 15691 196 8054 5737  Mangrove red snapper Two-spot red snapper Blue blanquillo Indo-Pacific tarpon Humpnose big-eye bream Harlequin snake eel Tiger snake eel Sleek unicornfish Redtoothed triggerfish Fowler' s snake eel Cheeklined wrasse Tentacled flathead Yellowtail blue snapper Short-maned sand-eel Pinjalo Longfin snake-eel Orbicular batfish  6362 23485 6368 7705 7706 60760  Two-striped sweetlips  7935 5126 7707 6006  Giant sweetlips Whitebarred rubberlip Minstrel sweetlip Blueskin seabream Smallspotted grunter Banded grunter Javelin grunter  6027 4466 3542 1751 5368 6507 1978 1963 1930 7283 27347 7563  Yellowmargin triggerfish Yellow-spotted triggerfish Cobia Common remora Goldlined seabream Sabre squirrelfish Smallspotted dart Snubnose pompano Longrakered trevally Unicolor snake moray Large-spotted snake moray Hairtail blenny  6517 5687 4543 7986 7850 1889 13415  Bengal sergeant Banded sergeant Twobar seabream Speckled shrimpfish Unicorn sole Shrimp scad Commerson' s glassy perchlet  4888 4889  Bluespotted wrasse Spotted wrasse  202  Group  Family Labridae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Sparidae Congridae Congridae Tetraodontidae Tetraodontidae Tetraodontidae Bothidae Atherinidae Serranidae Balistidae Labridae Labridae Labridae Labridae Bothidae Caesionidae Caesionidae Caesionidae Caesionidae Caesionidae Caesionidae Plesiopidae Monacanthidae Monacanthidae Tetraodontidae Carangidae Carapidae Centriscidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Apogonidae Apogonidae Labridae Labridae Cirrhitidae Labridae Labridae  Scientific name Anampses twistii Apogon aureus Apogon kallopterus Apogon multitaeniatus Apogon taeniatus Apogon truncatus Argyrops filamentosus Ariosoma balearicum Ariosoma scheelei Arothron diadematus Arothron immaculatus Arothron nigropunctatus Asterorhombus intermedius Atherinomorus lacunosus Aulacocephalus temminckii Balistapus undulatus Bodianus anthioides Bodianus axillaris Bodianus diana Bodianus opercularis Bothus pantherinus Caesio caerulaurea Caesio lunaris Caesio striata Caesio suevica Caesio varilineata Caesio xanthonota Calloplesiops altivelis Cantherhines dumerilii Cantherhines pardalis Canthigaster margaritata Carangoides armatus Carapus homei Centriscus scutatus Chaetodon auriga Chaetodon austriacus Chaetodon collare Chaetodon falcula Chaetodon fasciatus Chaetodon kleinii Chaetodon lineolatus Chaetodon melannotus Chaetodon semilarvatus Chaetodon trifasciatus Chaetodon vagabundus Cheilodipterus arabicus Cheilodipterus lachneri Choerodon robustus Cirrhilabrus blatteus Cirrhitus pinnulatus Coris caudimacula Coris cuvieri  FishBase Code 4893 4837 5758 8009 127 58304 4541 1744 7672 25413 7188 6400 8123 1303 7701 6025 5497 5498 5500 25754 1321 918 920 921 922 924 927 12655 5836 6635 12778 1916 4832 6510 5557 6514 7803 8014 12274 5446 5564 5566 12300 5579 5582 6669 12630 6926 25759 5831 8026 52844  FishBase common name Yellowbreasted wrasse Ring-tailed cardinalfish Iridescent cardinalfish Smallscale cardinal Twobelt cardinal Flagfin cardinalfish Soldierbream Bandtooth conger Tropical conger Masked puffer Immaculate puffer Blackspotted puffer Intermediate flounder Hardyhead silverside Goldribbon soapfish Orange-lined triggerfish Lyretail hogfish Axilspot hogfish Diana' s hogfish Blackspot hogfish Leopard flounder Blue and gold fusilier Lunar fusilier Striated fusilier Suez fusilier Variable-lined fusilier Yellowback fusilier Comet Whitespotted filefish Honeycomb filefish Longfin trevally Silver pearlfish Grooved razor-fish Threadfin butterflyfish Blacktail butterflyfish Redtail butterflyfish Blackwedged butterflyfish Diagonal butterflyfish Sunburst butterflyfish Lined butterflyfish Blackback butterflyfish Bluecheek butterflyfish Melon butterflyfish Vagabond butterflyfish Tiger cardinal Robust tuskfish Purple-boned wrasse Stocky hawkfish Spottail coris African coris  203  Group  Family Labridae Labridae Syngnathidae Diodontidae Dactylopteridae Carangidae Scorpaenidae Scorpaenidae Syngnathidae Syngnathidae Drepaneidae Clupeidae Carapidae Bothidae Serranidae Serranidae Chaetodontidae Chaetodontidae Pomacanthidae Gerreidae Gerreidae Gerreidae Gerreidae Gerreidae Labridae Serranidae Caesionidae Lethrinidae Muraenidae Muraenidae Syngnathidae Syngnathidae Syngnathidae Labridae Labridae Labridae Labridae Labridae Labridae Pseudochromidae Chaetodontidae Chaetodontidae Congridae Syngnathidae Syngnathidae Pentacerotidae Hemiramphidae Labridae  Scientific name Coris gaimard Coris variegata Corythoichthys schultzi Cyclichthys orbicularis Dactyloptena orientalis Decapterus macrosoma Dendrochirus brachypterus Dendrochirus zebra Doryrhamphus dactyliophorus Doryrhamphus multiannulatus Drepane punctata Dussumieria elopsoides Encheliophis gracilis Engyprosopon grandisquama Epinephelus merra Epinephelus stoliczkae Forcipiger flavissimus Forcipiger longirostris Genicanthus caudovittatus Gerres argyreus Gerres filamentosus Gerres longirostris Gerres oblongus Gerres oyena Gomphosus caeruleus Grammistes sexlineatus Gymnocaesio gymnoptera Gymnocranius griseus Gymnothorax buroensis Gymnothorax pindae Halicampus dunckeri Halicampus grayi Halicampus macrorhynchus Halichoeres bimaculatus Halichoeres hortulanus Halichoeres margaritaceus Halichoeres marginatus Halichoeres scapularis Halichoeres zeylonicus Haliophis guttatus Heniochus intermedius Heniochus monoceros Heteroconger hassi Hippocampus histrix Hippocampus kuda Histiopterus typus Hyporhamphus affinis Iniistius pavo  FishBase Code 5625 5485 5965 5196 4485 1938  FishBase common name Yellowtail coris Dapple coris Schultz' s pipefish Birdbeak burrfish Oriental flying gurnard Shortfin scad  4912 5828  Shortfin turkeyfish Zebra turkeyfish  5972  Ringed pipefish  14286 454 1454 9204  Many-banded pipefish Spotted sicklefish Slender rainbow sardine Graceful pearlfish  1324 4923 7364 5584 5585 11132 5799 4463 7699 5801 5996 7744 4925 929 1833 6493 7447 5974 7727  Largescale flounder Honeycomb grouper Epaulet grouper Longnose butterflyfish Longnose butterflyfish Zebra angelfish Common mojarra Whipfin silverbiddy Longtail silverbiddy Slender silverbiddy Common silver-biddy Green birdmouth wrasse Sixline soapfish Slender fusilier Grey large-eye bream Vagrant moray Pinda moray Duncker' s pipefish Gray' s pipefish  10225 50017 12663  Ornate pipefish Checkerboard wrasse  5630 5631 5633 13050 4428 12309 5590 12619 5954 5955 7892 7710 5613  Pink-belly wrasse Dusky wrasse Zigzag wrasse Goldstripe wrasse African eel blenny Red Sea bannerfish Masked bannerfish Spotted garden-eel Thorny seahorse Spotted seahorse Sailfin armourhead Tropical halfbeak Peacock wrasse  204  Group  Family Synanceiidae Ostraciidae Ophichthidae Leiognathidae Lethrinidae Lethrinidae Lethrinidae Lethrinidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Lutjanidae Malacanthidae Menidae Monocentridae Monodactylidae Mullidae Mullidae Ophichthidae Holocentridae Holocentridae Holocentridae Holocentridae Carangidae Holocentridae Labridae Labridae Opistognathidae Ostraciidae Ostraciidae Labridae Labridae Labridae Gobiidae Pinguipedidae Carangidae Soleidae Mullidae Mullidae Mullidae Mullidae Terapontidae Pempheridae Pempheridae Pempheridae Gobiidae Platycephalidae Plesiopidae  Scientific name Inimicus filamentosus Lactoria cornuta Lamnostoma orientalis Leiognathus equulus Lethrinus borbonicus Lethrinus harak Lethrinus obsoletus Lethrinus variegatus Lutjanus bengalensis Lutjanus coeruleolineatus Lutjanus fulviflamma Lutjanus gibbus Lutjanus kasmira Malacanthus brevirostris Mene maculata Monocentris japonica Monodactylus falciformis Mulloidichthys flavolineatus Mulloidichthys vanicolensis Muraenichthys schultzei Myripristis berndti Myripristis hexagona Myripristis murdjan Myripristis xanthacra Naucrates ductor Neoniphon sammara Novaculichthys macrolepidotus Novaculichthys taeniourus Opistognathus muscatensis Ostracion cubicus Ostracion cyanurus Oxycheilinus arenatus Oxycheilinus bimaculatus Oxycheilinus mentalis Oxyurichthys papuensis Parapercis hexophtalma Parastromateus niger Pardachirus marmoratus Parupeneus forsskali Parupeneus indicus Parupeneus macronema Parupeneus rubescens Pelates quadrilineatus Pempheris oualensis Pempheris schwenkii Pempheris vanicolensis Periophthalmus argentilineatus Platycephalus indicus Plesiops nigricans  FishBase Code 6403 6399 11728 4451 1844 1851 1847 1850 1409 1425 261 265 156 5795 390 8183 7858  FishBase common name Two-stick stingfish Longhorn cowfish Oriental worm-eel Common ponyfish Snubnose emperor Thumbprint emperor Orange-striped emperor Slender emperor Bengal snapper Blueline snapper Dory snapper Humpback red snapper Common bluestripe snapper Quakerfish Moonfish Pineconefish Full moony  5983  Yellowstripe goatfish  5984 7290 4910 7305 5408 7822 998 4911  Yellowfin goatfish Maimed snake eel Blotcheye soldierfish Doubletooth soldierfish Pinecone soldierfish Yellowtip soldierfish Pilotfish Sammara squirrelfish  5609 5610  Seagrass wrasse Rockmover wrasse  8000 6555 12743 5595 5596 12779 8030 7866 1947 8917 10994 5992 7878 6373 7945 5802 12908 10350  Robust jawfish Yellow boxfish Bluetail trunkfish Speckled maori wrasse Two-spot wrasse Mental wrasse Frogface goby Speckled sandperch Black pomfret Finless sole Red Sea goatfish Indian goatfish Longbarbel goatfish Rosy goatfish Fourlined terapon Silver sweeper Black-stripe sweeper Vanikoro sweeper  7480 950 24438  Barred mudskipper Bartail flathead Whitespotted longfin  205  Group  Family Plotosidae Priacanthidae Serranidae Labridae Caesionidae Caesionidae Scorpaenidae Scorpaenidae Scorpaenidae Sparidae Balistidae Balistidae Balistidae Balistidae Holocentridae Holocentridae Holocentridae Holocentridae Holocentridae Ophichthidae Ophichthidae Nemipteridae Nemipteridae Nemipteridae Nemipteridae Scorpaenidae Scorpaenidae Sillaginidae Soleidae Solenostomidae Labridae Labridae Engraulidae Balistidae Balistidae Syngnathidae Synodontidae Terapontidae Terapontidae Ostraciidae Labridae Labridae Labridae Labridae Labridae Monacanthidae Platycephalidae Syngnathidae  Scientific name Plotosus lineatus Priacanthus hamrur Pseudanthias squamipinnis Pteragogus flagellifer Pterocaesio chrysozona Pterocaesio pisang Pterois miles Pterois radiata Pterois russelii Rhabdosargus haffara Rhinecanthus aculeatus Rhinecanthus assasi Rhinecanthus rectangulus Rhinecanthus verrucosus Sargocentron caudimaculatum Sargocentron diadema Sargocentron ittodai Sargocentron punctatissimum Sargocentron rubrum Scolecenchelys gymnota Scolecenchelys laticaudata Scolopsis bimaculatus Scolopsis ghanam Scolopsis taeniatus Scolopsis vosmeri Scorpaenopsis oxycephala Scorpaenopsis venosa Sillago sihama Soleichthys heterorhinos Solenostomus cyanopterus Stethojulis strigiventer Stethojulis trilineata Stolephorus indicus Sufflamen albicaudatum Sufflamen fraenatum Syngnathoides biaculeatus Synodus indicus Terapon jarbua Terapon theraps Tetrosomus gibbosus Thalassoma hebraicum Thalassoma lunare Thalassoma purpureum Thalassoma rueppellii Thalassoma trilobatum Thamnaconus modestoides Thysanophrys chiltonae Trachyrhamphus  FishBase Code 4706 5791  FishBase common name Striped eel catfish Moontail bullseye  6568 8022 932 936 7797 4913 6404 8166 5839 25420 5840 6028  Sea goldie Cocktail wrasse Goldband fusilier Banana fusilier Devil firefish Radial firefish Plaintail turkeyfish Haffara seabream Blackbar triggerfish Picasso triggerfish Wedge-tail triggerfish Blackbelly triggerfish  4907 4699 6573  Silverspot squirrelfish Crown squirrelfish Samurai squirrelfish  4906 6625 7288  Speckled squirrelfish Redcoat Slender worm eel  15672 5886 5888  Redfin worm-eel Thumbprint monocle bream Arabian monocle bream Black-streaked monocle bream Whitecheek monocle bream Tassled scorpionfish Raggy scorpionfish Silver sillago  5889 5883 5822 7919 4544 22544 7987 5641 6622 569 25419 1312 5980 7942 4458 4829 8129 8019 5645 5647 25787 5649  Ghost pipefish Three-ribbon wrasse Three-lined rainbowfish Indian anchovy Bluethroat triggerfish Masked triggerfish Alligator pipefish Indian lizardfish Jarbua terapon Largescaled therapon Humpback turretfish Goldbar wrasse Moon wrasse Surge wrasse Klunzinger' s wrasse Christmas wrasse  7855 12902 5981  Modest filefish Longsnout flathead Double-ended pipefish  206  Group  Family Mullidae Mullidae Mullidae Uranoscopidae Carangidae Carangidae Gobiidae Gobiidae Blenniidae Labridae Labridae Gobiidae  Small reef carnivores  Syngnathidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Pomacentridae Gobiidae Labridae Antennariidae Antennariidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae  Scientific name bicoarctatus Upeneus moluccensis Upeneus tragula Upeneus vittatus Uranoscopus sulphureus Uraspis helvola Uraspis uraspis Valenciennea helsdingenii Valenciennea puellaris Xiphasia matsubarai Xyrichtys melanopus Xyrichtys pentadactylus Yongeichthys nebulosus Acentronura tentaculata Amblyeleotris diagonalis Amblyeleotris periophthalma Amblyeleotris steinitzi Amblyeleotris sungami Amblyeleotris wheeleri Amblyglyphidodon leucogaster Amblygobius esakiae Anampses lineatus Antennarius rosaceus Antennatus tuberosus Apogon angustatus Apogon annularis Apogon bandanensis Apogon coccineus Apogon cookii Apogon cyanosoma Apogon exostigma Apogon fasciatus Apogon fraenatus Apogon guamensis Apogon heptastygma Apogon isus Apogon kiensis Apogon lateralis Apogon latus Apogon leptacanthus Apogon nigripinnis Apogon nigrofasciatus Apogon pselion Apogon pseudotaeniatus Apogon savayensis Apogon semiornatus Apogon taeniophorus Apogon timorensis Apogon zebrinus Apogonichthys perdix Archamia bilineata  FishBase Code 4444 5443 4821 13512 1983 1984 7224 7246 6078 23517 7747 7228  FishBase common name Goldband goatfish Freckled goatfish Yellowstriped goatfish Whitemargin stargazer Whitemouth jack Whitetongue jack Twostripe goby Maiden goby Japanese snake blenny Yellowpatch razorfish Fivefinger wrasse Shadow goby  16862 13152 7231 7195 12699 7196  Periophthalma prawn-goby Steinitz'prawn-goby Magnus'prawn-goby Gorgeous prawn-goby  5691 27553 7800 7296 11150 5766 56240 5763 5752 9240 4600 5756 6605 5757 5765 50885 50886 8230 5761 60370 5773 8012 4836 4839 26632 5764 8008 5767 12658 58157 5741 58158  Yellowbelly damselfish Snoutspot goby Lined wrasse Spiny-tufted frogfish Tuberculated frogfish Broadstriped cardinalfish Ringtail cardinalfish Bigeye cardinalfish Ruby cardinalfish Cook' s cardinalfish Yellowstriped cardinalfish Narrowstripe cardinalfish Broad-banded cardinalfish Bridled cardinalfish Guam cardinalfish Rifle cardinal Humpback cardinal Threadfin cardinalfish Bullseye Blackstripe cardinalfish Doublebar cardinalfish Samoan cardinalfish Oblique-banded cardinalfish Reef-flat cardinalfish Timor cardinalfish Perdix cardinalfish  207  Group  Family Apogonidae Apogonidae Apogonidae Blenniidae Gobiidae Gobiidae Gobiidae Bythitidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Callionymidae Callionymidae Gobiidae Gobiidae Tetraodontidae Tetraodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Chaetodontidae Apogonidae Pseudochromidae Syngnathidae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Labridae Cirrhitidae Cirrhitidae Syngnathidae Syngnathidae Syngnathidae Syngnathidae Gobiidae Gobiidae Gobiidae Gobiidae  Scientific name Archamia fucata Archamia irida Archamia lineolata Aspidontus taeniatus taeniatus Asterropteryx ensifera Bathygobius cyclopterus Bathygobius fuscus Brosmophyciops pautzkei Bryaninops erythrops Bryaninops loki Bryaninops natans Bryaninops ridens Bryaninops yongei Callionymus delicatulus Callionymus flavus Callogobius bifasciatus Callogobius maculipinnis Canthigaster coronata Canthigaster pygmaea Chaetodon citrinellus Chaetodon guttatissimus Chaetodon larvatus Chaetodon melapterus Chaetodon mesoleucos Chaetodon paucifasciatus Chaetodon trifascialis Cheilodipterus quinquelineatus Chlidichthys johnvoelckeri Choeroichthys brachysoma Chromis flavaxilla Chromis nigrura Chromis ternatensis Chromis weberi Cirrhilabrus rubriventralis Cirrhitichthys calliurus Cirrhitichthys oxycephalus Corythoichthys flavofasciatus Corythoichthys nigripectus Cosmocampus banneri Cosmocampus maxweberi Cryptocentrus caeruleopunctatus Cryptocentrus cryptocentrus Cryptocentrus fasciatus Cryptocentrus lutheri  FishBase Code 5776 58159 7854 6066 7247 11801 7201 7299 7204 52430 7205 7250 7251 17467 56497 46389 7206 7845 25414 5561 7791 12287 12533 25428 12296 5578 5482  FishBase common name Orangelined cardinalfish Shimmering cardinal False cleanerfish Miller' s damsel Spotted frillgoby Dusky frillgoby Slimy cuskeel Erythrops goby Loki whip-goby Redeye goby Ridens goby Whip coral goby Delicate dragonet Doublebar goby Ostrich goby Crowned puffer Pygmy toby Speckled butterflyfish Peppered butterflyfish Hooded butterflyfish Arabian butterflyfish White-face butterflyfish Eritrean butterflyfish Chevron butterflyfish Five-lined cardinalfish  23591  Cerise dottyback  5958 26638 12424 5677 5680  Short-bodied pipefish Arabian chromis Blacktail chromis Ternate chromis Weber' s chromis  12781 46372  Social wrasse Spottedtail hawkfish  5830  Coral hawkfish  5959  Network pipefish  5962 5966 5968  Black-breasted pipefish Roughridge pipefish Maxweber' s pipefish  12748  Harlequin prawn-goby  25797 12679 25800  Ninebar prawn-goby Y-bar shrimp goby Luther' s prawn-goby  208  Group  Family Gobiidae Gobiidae Gobiidae Gobiidae Syngnathidae Engraulidae Tripterygiidae Pegasidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Apogonidae Apogonidae Apogonidae Apogonidae Apogonidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Microdesmidae Apogonidae Syngnathidae Labridae Labridae Clupeidae Atherinidae Atherinidae Gobiidae Gobiidae Labridae Labridae Gobiesocidae Serranidae Serranidae Gobiidae Labridae Labridae Blenniidae Syngnathidae  Scientific name Ctenogobiops crocineus Ctenogobiops feroculus Ctenogobiops maculosus Discordipinna griessingeri Doryrhamphus excisus abbreviatus Encrasicholina punctifer Enneapterygius abeli Eurypegasus draconis Eviota distigma Eviota guttata Eviota pardalota Eviota prasina Eviota sebreei Eviota zebrina Flabelligobius latruncularia Fowleria aurita Fowleria marmorata Fowleria punctulata Fowleria vaiulae Fowleria variegata Fusigobius longispinus Gladiogobius ensifer Gnatholepis anjerensis Gobiodon citrinus Gobiodon reticulatus Gunnellichthys monostigma Gymnapogon melanogaster Halicampus mataafae Halichoeres iridis Halichoeres nebulosus Herklotsichthys quadrimaculatus Hypoatherina barnesi Hypoatherina temminckii Istigobius decoratus Istigobius ornatus Labroides dimidiatus Larabicus quadrilineatus Lepadichthys lineatus Liopropoma mitratum Liopropoma susumi Luposicya lupus Macropharyngodon bipartitus bipartitus Macropharyngodon bipartitus marisrubri Meiacanthus nigrolineatus Micrognathus andersonii  FishBase Code 13153 7238 27561 7212 7718 558 16974 4606 7261 25452 46398 7270 7275 25462  FishBase common name Silverspot shrimpgoby Sandy prawn-goby Spikefin goby Buccaneer anchovy Yellow triplefin Short dragonfish Twospot pygmy goby Spotted pygmy goby Leopard dwarfgoby Green bubble goby Sebree' s pygmy goby  25463 8010 5744 5743 8592 5745 12834 11174 23595 7789 46399  Poison goby Reticulate goby  12678  Onespot wormfish  60031 5975 12790 6663 1494 1305 1307 4328 4322 5459 25788 23229 8432 7318 23719 7801  Fan shrimp-goby Crosseyed cardinalfish Marbled cardinalfish Spotcheek cardinalfish Mottled cardinalfish Variegated cardinalfish Orange-spotted sand-goby Gladiator goby  Samoan pipefish Nebulous wrasse Bluestripe herring Barnes'silverside Samoan silverside Decorated goby Ornate goby Bluestreak cleaner wrasse Fourline wrasse Doubleline clingfish Pinstriped basslet Meteor perch Vermiculate wrasse  13137 12641 5977  Blackline fangblenny Shortnose pipefish  209  Group  Family Labridae Apogonidae Pomacentridae Pomacentridae Pomacentridae Tripterygiidae Gobiidae Cirrhitidae Monacanthidae Gobiidae Labridae Gobiidae Gobiidae Pempheridae Scorpaenidae Scorpaenidae Pseudochromidae Anomalopidae Syngnathidae Serranidae Serranidae Plesiopidae Gobiidae Pomacentridae Gobiidae Gobiidae Pomacentridae Apogonidae Serranidae Serranidae Serranidae Serranidae Labridae Labridae Pseudochromidae Pseudochromidae Pseudochromidae Pseudochromidae Pseudochromidae Pseudochromidae Pseudochromidae Pseudochromidae Serranidae Labridae Microdesmidae  Scientific name Minilabrus striatus Neamia octospina Neopomacentrus cyanomos Neopomacentrus miryae Neopomacentrus xanthurus Norfolkia brachylepis Oplopomus oplopomus Oxycirrhites typus Oxymonacanthus halli Palutrus meteori Paracheilinus octotaenia Paragobiodon echinocephalus Paragobiodon xanthosomus Parapriacanthus ransonneti Parascorpaena aurita Parascorpaena mossambica Pectinochromis lubbocki Photoblepharon steinitzi Phoxocampus belcheri Plectranthias nanus Plectranthias winniensis Plesiops coeruleolineatus Pleurosicya mossambica Pomacentrus pavo Priolepis cincta Priolepis randalli Pristotis obtusirostris Pseudamia gelatinosa Pseudanthias cichlops Pseudanthias heemstrai Pseudanthias lunulatus Pseudanthias taeniatus Pseudocheilinus evanidus Pseudocheilinus hexataenia Pseudochromis dixurus Pseudochromis flavivertex Pseudochromis fridmani Pseudochromis olivaceus Pseudochromis pesi Pseudochromis sankeyi Pseudochromis springeri Pseudochromis xanthochir Pseudogramma megamycterum Pteragogus cryptus Ptereleotris evides  FishBase Code 25781 8593  FishBase common name Minute wrasse Eightspine cardinalfish  8209 12461  Regal demoiselle Miry' s demoiselle  12464 14209 7218 5833 25418 25042 4840  Red Sea demoiselle Tropical scaly-headed triplefin Spinecheek goby Longnose hawkfish Red Sea longnose filefish Meteor goby Red Sea eightline flasher  7219  Redhead goby  7220  Emerald coral goby  5803 27438  Pigmy sweeper  5810 12742 17085 7742 15118 12799 8005 23079 5726 7221 46409 8127 4362 6945 24434 23329 12776 5616  Mozambique scorpionfish  5617 24442 12738 12741 24440 12653 24443 24441  Flashlight fish Rock pipefish Bownband perchlet Redblotch basslet Crimsontip longfin Toothy goby Sapphire damsel Girdled goby Randall' s goby Gulf damselfish Gelatinous cardinalfish Orangehead anthias Lunate goldie Striated wrasse Pyjama Forktail dottyback Sunrise dottyback Orchid dottyback Olive dottyback Pale dottyback Striped dottyback Blue-striped dottyback  23434 49434 5620 4375  Cryptic wrasse Blackfin dartfish  210  Group  Family Microdesmidae Microdesmidae Microdesmidae Apogonidae Apogonidae Holocentridae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Scorpaenidae Syngnathidae Solenostomidae Clupeidae Labridae Labridae Pomacentridae Tetraodontidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Labridae Xenisthmidae  Reef omnivores  Pomacentridae Pomacentridae Acanthuridae Acanthuridae Acanthuridae Monacanthidae Monacanthidae Pomacentridae Gobiidae Gobiidae Gobiidae Pomacentridae Pomacanthidae  Scientific name Ptereleotris heteroptera Ptereleotris microlepis Ptereleotris zebra Rhabdamia cypselura Rhabdamia nigrimentum Sargocentron inaequalis Scorpaenodes corallinus Scorpaenodes guamensis Scorpaenodes hirsutus Scorpaenodes parvipinnis Scorpaenodes scaber Scorpaenodes varipinnis Scorpaenopsis vittapinna Sebastapistes bynoensis Sebastapistes cyanostigma Sebastapistes strongia Siokunichthys bentuviai Solenostomus paradoxus Spratelloides delicatulus Stethojulis albovittata Stethojulis interrupta Teixeirichthys jordani Torquigener flavimaculosus Trimma avidori Trimma barralli Trimma fishelsoni Trimma flavicaudatus Trimma mendelssohni Trimma sheppardi Trimma taylori Trimma tevegae Valenciennea sexguttata Valenciennea wardii Vanderhorstia delagoae Vanderhorstia mertensi Wetmorella nigropinnata Xenisthmus polyzonatus Abudefduf sexfasciatus Abudefduf sordidus Acanthurus gahhm Acanthurus mata Acanthurus xanthopterus Aluterus scriptus Amanses scopas Amblyglyphidodon flavilatus Amblygobius albimaculatus Amblygobius hectori Amblygobius nocturnus Amphiprion bicinctus Apolemichthys xanthotis  FishBase Code 4378 4381 4384 5746 46488 23249 27363 5819 5815 4915 7314 5818 59507 59579 5811 5814 7194 7312 1457 8025 6633 10742  FishBase common name Blacktail goby Blue gudgeon Chinese zebra goby Swallowtail cardinalfish Lattice squirrelfish Guam scorpionfish Hairy scorpionfish Lowfin scorpionfish Pygmy scorpionfish Blotchfin scorpionfish Yellowspotted scorpionfish Barchin scorpionfish Harlequin ghost pipefish Delicate round herring Bluelined wrasse Cutribbon wrasse Jordan' s damsel  26639 28069 28063 28070 28071 28072 28073 12752 12754 7227 12615 8033 23647 4870 13766  Yellow cave goby Blue-striped cave goby Sixspot goby Ward' s sleeper Candystick goby Mertens'prawn-goby Sharpnose wrasse Bullseye wriggler  5688 5689 17471 1255 1261 4275 6672  Scissortail sergeant Blackspot sergeant Black surgeonfish Elongate surgeonfish Yellowfin surgeonfish Scrawled filefish Broom filefish  11834  Yellowfin damsel  6675 7242 7243 11837 10940  Butterfly goby Hector' s goby Nocturn goby Twoband anemonefish Yellow-ear angelfish  211  Group  Family Blenniidae Gobiidae Blenniidae Blenniidae Scaridae Pomacanthidae Pomacanthidae Chaetodontidae Chanidae Scaridae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Mugilidae Pomacentridae Pomacentridae Pomacentridae Sparidae Blenniidae Tripterygiidae Tripterygiidae Blenniidae Gobiidae Gobiidae Gobiidae Tripterygiidae Hemiramphidae Hemiramphidae Hemiramphidae Scaridae Mugilidae Balistidae Blenniidae Acanthuridae Acanthuridae Acanthuridae Pomacentridae Mugilidae Blenniidae Lutjanidae Monacanthidae Monacanthidae Blenniidae Blenniidae  Scientific name Aspidontus taeniatus tractus Asterropteryx semipunctata Blenniella cyanostigma Blenniella periophthalmus Bolbometopon muricatum Centropyge bicolor Centropyge multispinis Chaetodon leucopleura Chanos chanos Chlorurus gibbus Chromis dimidiata Chromis pelloura Chromis pembae Chromis trialpha Chromis viridis Chrysiptera annulata Chrysiptera unimaculata Crenimugil crenilabis Dascyllus aruanus Dascyllus marginatus Dascyllus trimaculatus Diplodus noct Ecsenius midas Enneapterygius altipinnis Enneapterygius tutuilae Exallias brevis Exyrias belissimus Fusigobius neophytus Gnatholepis cauerensis cauerensis Helcogramma steinitzi Hemiramphus far Hyporhamphus balinensis Hyporhamphus gamberur Leptoscarus vaigiensis Liza vaigiensis Melichthys indicus Mimoblennius cirrosus Naso annulatus Naso brevirostris Naso elegans Neoglyphidodon melas Oedalechilus labiosus Omobranchus punctatus Paracaesio sordida Paramonacanthus japonicus Pervagor randalli Plagiotremus rhinorhynchos Plagiotremus tapeinosoma  FishBase Code  FishBase common name  8040 7200 16946 6051 5537 5454 6549 8083 80 4979 11861 12428 12429 12432 5679 12438 5702 5653 5110 11985 5112 8112 7561 13574 47045 6032 370 7215  Starry goby Striped rockskipper Blue-dashed rockskipper Green humphead parrotfish Bicolor angelfish Dusky angelfish Somali butterflyfish Milkfish Heavybeak parrotfish Chocolatedip chromis Duskytail chromis Pemba chromis Trispot chromis Blue green damselfish Footballer demoiselle Onespot demoiselle Fringelip mullet Whitetail dascyllus Marginate dascyllus Threespot dascyllus Red Sea seabream Persian blenny Highfin triplefin High hat triplefin Leopard blenny Mud reef-goby Common fusegoby  9950 26343 5404 16813 53427 4360 5656 7634 46416 6019 6021 60074 5707 5657 7566 192  Eyebar goby Red triplefin Blackbarred halfbeak Balinese garfish Red Sea halfbeak Marbled parrotfish Squaretail mullet Indian triggerfish Fringed blenny Whitemargin unicornfish Spotted unicornfish Elegant unicornfish Bowtie damselfish Hornlip mullet Muzzled blenny Dirty ordure snapper  7977 4372  Hairfinned leatherjacket  6071  Bluestriped fangblenny  6072  Piano fangblenny  212  Group  Family  Pomacanthidae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Pomacentridae Haemulidae Gobiidae Pomacentridae Labridae Pomacanthidae Blenniidae Clupeidae Scaridae Scaridae Scaridae Siganidae Siganidae Pomacentridae Pomacentridae Mugilidae  Scientific name Plectroglyphidodon lacrymatus Pomacanthus asfur Pomacanthus imperator Pomacanthus maculosus Pomacanthus semicirculatus Pomacentrus albicaudatus Pomacentrus aquilus Pomacentrus leptus Pomacentrus sulfureus Pomacentrus trichourus Pomacentrus trilineatus Pomadasys olivaceus Priolepis semidoliata Pristotis cyanostigma Pseudodax moluccanus Pygoplites diacanthus Salarias fasciatus Sardinella albella Scarus caudofasciatus Scarus collana Scarus fuscopurpureus Siganus javus Siganus stellatus Stegastes lividus Stegastes nigricans Valamugil seheli  Acanthuridae Acanthuridae Acanthuridae Acanthuridae Blenniidae Blenniidae Scaridae Scaridae Scaridae Scaridae Pomacentridae Blenniidae Blenniidae Acanthuridae Blenniidae Blenniidae Blenniidae Blenniidae Blenniidae Scaridae Blenniidae Blenniidae Kyphosidae Kyphosidae Acanthuridae  Acanthurus nigricans Acanthurus nigrofuscus Acanthurus sohal Acanthurus tennentii Aspidontus dussumieri Atrosalarias fuscus fuscus Calotomus viridescens Cetoscarus bicolor Chlorurus genazonatus Chlorurus sordidus Chrysiptera biocellata Cirripectes castaneus Cirripectes filamentosus Ctenochaetus striatus Ecsenius aroni Ecsenius frontalis Ecsenius gravieri Ecsenius nalolo Enchelyurus kraussii Hipposcarus harid Istiblennius edentulus Istiblennius rivulatus Kyphosus bigibbus Kyphosus vaigiensis Naso unicornis  Pomacentridae Pomacanthidae Pomacanthidae Pomacanthidae  Reef herbivores  FishBase Code  FishBase common name  5712 11194 6504 7903  Whitespotted devil Arabian angelfish Emperor angelfish Yellowbar angelfish  5663 12478 12480 12494 12503 12504 12505 5518 12885 12507 5594 6572 6058 1502 7908 14379 14381 4618 4622 4351 4352 5659  Semicircle angelfish Whitefin damsel Dark damsel Slender damsel Sulphur damsel Paletail damsel Threeline damsel Olive grunt Half-barred goby Bluedotted damsel Chiseltooth wrasse Royal angelfish Jewelled blenny White sardinella Redbarred parrotfish Red Sea parrotfish Purple-brown parrotfish Streaked spinefoot Brownspotted spinefoot Blunt snout gregory Dusky farmerfish Bluespot mullet  6011 4739 4740 1259 6065 17462 4358 5538 14382 5556 5693 4387 4389 1262 25794 12634 12635 25451 6062 7906 6049 23697 5804 5806 1265  Whitecheek surgeonfish Brown surgeonfish Sohal surgeonfish Doubleband surgeonfish Lance blenny Viridescent parrotfish Bicolour parrotfish Sinai parrotfish Daisy parrotfish Twinspot damselfish Chestnut eyelash-blenny Filamentous blenny Striated surgeonfish Aron' s blenny Smooth-fin blenny Red Sea mimic blenny Nalolo Krauss'blenny Candelamoa parrotfish Rippled rockskipper Grey sea chub Brassy chub Bluespine unicornfish  213  Group  Large pelagic carnivores  Small pelagic carnivores  Family Blenniidae Blenniidae Pomacentridae Scaridae Scaridae Scaridae Scaridae Scaridae Scaridae Siganidae Siganidae Siganidae Acanthuridae Acanthuridae  Scientific name Petroscirtes mitratus Plagiotremus townsendi Plectroglyphidodon leucozonus Scarus ferrugineus Scarus frenatus Scarus niger Scarus psittacus Scarus russelii Scarus scaber Siganus argenteus Siganus luridus Siganus rivulatus Zebrasoma veliferum Zebrasoma xanthurum  Coryphaenidae Elopidae Istiophoridae Istiophoridae Molidae Molidae Scombridae Carangidae Istiophoridae Scombridae Belonidae Xiphiidae Carangidae Clupeidae Scombridae Scombridae Bregmacerotidae Bregmacerotidae Carangidae Exocoetidae Exocoetidae Chirocentridae Coryphaenidae Exocoetidae Carangidae Clupeidae Engraulidae Hemiramphidae Exocoetidae Hemiramphidae Clupeidae Clupeidae Exocoetidae Exocoetidae Malacanthidae Hemiramphidae  FishBase Code 6074 12788  FishBase common name Floral blenny Townsend' s fangblenny  5713 14380 5546 5550 5553 7912 7913 4614 4613 4545 1266 12023  Singlebar devil Rusty parrotfish Bridled parrotfish Dusky parrotfish Common parrotfish Eclipse parrotfish Fivesaddle parrotfish Streamlined spinefoot Dusky spinefoot Marbled spinefoot Sailfin tang Yellowtail tang  Coryphaena hippurus Elops machnata Istiophorus platypterus Makaira indica Mola mola Ranzania laevis Sarda orientalis Scomber sansun Tetrapturus audax Thunnus albacares Tylosurus choram Xiphias gladius  6 5512 77 217 1732 1750 114 53238 223 143 26633 226  Common dolphinfish Tenpounder Indo-Pacific sailfish Black marlin Ocean sunfish Slender sunfish Striped bonito  Alepes vari Amblygaster leiogaster Auxis rochei rochei Auxis thazard thazard Bregmaceros mcclellandii Bregmaceros nectabanus Carangoides ciliarius Cheilopogon cyanopterus Cheilopogon pinnatibarbatus altipennis Chirocentrus nudus Coryphaena equiselis Cypselurus oligolepis Decapterus macarellus Dussumieria acuta Engraulis encrasicolus Euleptorhamphus viridis Exocoetus volitans Hemiramphus marginatus Herklotsichthys lossei Hilsa kelee Hirundichthys rondeletii Hirundichthys socotranus Hoplolatilus geo Hyporhamphus xanthopterus  1891 1500 93 94 8421 8422 53230 7695  Striped marlin Yellowfin tuna Red Sea houndfish Swordfish Herring scad Smooth-belly sardinella Bullet tuna Frigate tuna Spotted codlet Smallscale codlet Margined flyingfish  23233 1452 7 15365 993 1453 66 3156 1032 9963 1492 1595 1035 60693 54468  Smallhead flyingfish Whitefin wolf-herring Pompano dolphinfish Largescale flyingfish Mackerel scad Rainbow sardine European anchovy Ribbon halfbeak Tropical two-wing flyingfish Yellowtip halfbeak Gulf herring Kelee shad Black wing flyingfish  25044  Red-tipped halfbeak  214  Group  Family Scombridae Lactariidae  Belonidae Echeneidae Echeneidae Scombridae Sphyraenidae Clupeidae Engraulidae  Scientific name Katsuwonus pelamis Lactarius lactarius Parexocoetus brachypterus Parexocoetus mento Platybelone argalus platura Remora brachyptera Remorina albescens Scomber japonicus Sphyraena chrysotaenia Spratelloides gracilis Thryssa setirostris  Pelagic omnivores  Bregmacerotidae Leiognathidae Mugilidae Monodactylidae Clupeidae  Demersal top predator  Exocoetidae Exocoetidae  Large demersal carnivores  FishBase Code 107 363  FishBase common name Skipjack tuna False trevally  1037 4904  Sailfin flyingfish African sailfin flyingfish  58272 3546 3548 117 16905 1458 599  Spearfish remora White suckerfish Chub mackerel Yellowstripe barracuda Silver-stripe round herring Longjaw thryssa  Bregmaceros arabicus Leiognathus oblongus Liza carinata Monodactylus argenteus Sardinella longiceps  23168 58321 13673 5807 1511  Oblong ponyfish Keeled mullet Silver moony Indian oil sardine  Muraenesocidae Serranidae Serranidae Leiognathidae Gobiidae Muraenidae Lophiidae Muraenesocidae Psettodidae Paralichthyidae Synodontidae Synodontidae Uranoscopidae Uranoscopidae Uranoscopidae  Congresox talabonoides Epinephelus epistictus Epinephelus radiatus Gazza minuta Glossogobius giuris Gymnothorax johnsoni Lophiomus setigerus Muraenesox cinereus Psettodes erumei Pseudorhombus arsius Synodus hoshinonis Synodus macrops Uranoscopus bauchotae Uranoscopus dahlakensis Uranoscopus oligolepis  11713 7341 7360 4462 4833 7882 7517 298 513 1325 7941 8299 56492 56493 8303  Sparidae Ariidae  Argyrops megalommatus Arius thalassinus Branchiostegus sawakinensis Cheilinus abudjubbe Cynoglossus arel Cynoglossus bilineatus Epinephelus latifasciatus Gorgasia cotroneii Gorgasia sillneri Gymnothorax angusticauda Gymnothorax tile Gymnura poecilura Lagocephalus lunaris Lagocephalus spadiceus Platycephalus micracanthus Plectorhinchus faetela  61176 10220  Malacanthidae Labridae Cynoglossidae Cynoglossidae Serranidae Congridae Congridae Muraenidae Muraenidae Gymnuridae Tetraodontidae Tetraodontidae Platycephalidae Haemulidae  7649 60813 7523 5455 7350 58702 55167 27319 17266 8260 8263 8180  Indian pike conger Dotted grouper Oblique-banded grouper Toothpony Tank goby Whitespotted moray Blackmouth angler Daggertooth pike conger Indian spiny turbot Largetooth flounder Blackear lizardfish Triplecross lizardfish  Giant seacatfish Freckled tilefish Largescale tonguesole Fourlined tonguesole Striped grouper  Longtail butterfly ray Green rough-backed puffer Half-smooth golden pufferfish  52981 60766  215  Group  Family Haemulidae Haemulidae Haemulidae Lutjanidae Platycephalidae Nettastomatidae Congridae Ophichthidae  Medium demersal carnivores  Sparidae Sparidae Ambassidae Apistidae Apogonidae Ariommatidae Soleidae Bothidae Bothidae Soleidae Callionymidae Callionymidae Synanceiidae Gobiesocidae Cynoglossidae Cynoglossidae Cynoglossidae Cynoglossidae Cynoglossidae Cynoglossidae Cynoglossidae Dactylopteridae Syngnathidae Bothidae Platycephalidae Muraenidae Tripterygiidae Congridae Narcinidae Syngnathidae Syngnathidae Syngnathidae Syngnathidae Syngnathidae Malacanthidae Leiognathidae Triglidae Liparidae Syngnathidae  Scientific name Pomadasys argenteus Pomadasys hasta Pomadasys multimaculatum Pristipomoides multidens Rogadius pristiger Saurenchelys lateromaculatus Uroconger lepturus Yirrkala tenuis Acanthopagrus berda Acanthopagrus latus Ambassis gymnocephalus Apistus carinatus Apogon fleurieu Ariomma dollfusi Aseraggodes sinusarabici Bothus myriaster Bothus tricirrhitus Brachirus orientalis Callionymus filamentosus Callionymus gardineri Choridactylus multibarbus Chorisochismus dentex Cynoglossus dollfusi Cynoglossus gilchristi Cynoglossus kopsii Cynoglossus lachneri Cynoglossus lingua Cynoglossus pottii Cynoglossus sealarki Dactyloptena peterseni Dunckerocampus boylei Engyprosopon maldivensis Grammoplites suppositus Gymnothorax herrei Helcogramma obtusirostre Heteroconger balteatus Heteronarce bentuviai Hippichthys cyanospilus Hippichthys spicifer Hippocampus fuscus Hippocampus jayakari Hippocampus lichtensteinii Hoplolatilus oreni Leiognathus fasciatus Lepidotrigla bispinosa Liparis fishelsoni Lissocampus bannwarthi  FishBase Code 399 55178  FishBase common name Silver grunt  5517 208 15225  Cock grunter Goldbanded jobfish Thorny flathead  58723 7590 15697  Slender conger Thin sand-eel  5526 6356 4806 6383 4838 60525 58956 1322 58972 8312 225 1318 6387 23222 9250 7681 7647 7682 8238 56480 17158 7691 54745  Picnic seabream Yellowfin seabream Bald glassy Ocellated waspfish Cardinalfish Indo-Pacific oval flounder Oriental sole Blotchfin dragonet Longtail dragonet Orangebanded stingfish Rocksucker Ripplefin tonguesole Shortheaded tonguesole Lachner' s tonguesole Long tongue sole Starry flying gurnard Broad-banded Pipefish  13970 28128 7491  Olive wide-eyed flounder Spotfin flathead  8046 55140 53919 7728 7495 25955 53814  Hotlips triplefin  53909 15379 4452 28127 58827 46165  Elat electric ray Blue-spotted pipefish Bellybarred pipefish Sea pony Jayakar' s seahorse Lichtenstein' s Seahorse Striped ponyfish Bullhorn gurnard  216  Group  Small demersal carnivores  FishBase Code 6388 22602 5851 4554 5852 5855 7867 56473 10297 5856 5858  Family Synanceiidae Ophichthidae Nemipteridae Nemipteridae Nemipteridae Nemipteridae Pinguipedidae Pinguipedidae Pinguipedidae Nemipteridae Nemipteridae  Scientific name Minous monodactylus Myrophis microchir Nemipterus bipunctatus Nemipterus peronii Nemipterus randalli Nemipterus zysron Parapercis robinsoni Parapercis simulata Parapercis somaliensis Parascolopsis aspinosa Parascolopsis eriomma  Nemipteridae Nemipteridae Pempheridae Polynemidae Polynemidae Haemulidae Priacanthidae Paralichthyidae Labridae Platycephalidae Platycephalidae Samaridae Holocentridae Serranidae Ophidiidae Ophichthidae Soleidae Soleidae Syngnathidae Batrachoididae Syngnathidae Trichonotidae Mullidae Mullidae Uranoscopidae Uranoscopidae Muraenidae Muraenidae Labridae Labridae Labridae Soleidae  Parascolopsis inermis Parascolopsis townsendi Pempheris mangula Polydactylus plebeius Polydactylus sextarius Pomadasys punctulatus Priacanthus sagittarius Pseudorhombus elevatus Pteragogus pelycus Rogadius asper Rogadius prionotus Samaris cristatus Sargocentron marisrubri Serranus cabrilla Sirembo jerdoni Skythrenchelys lentiginosa Solea elongata Synaptura commersonnii Syngnathus safina Thalassothia cirrhosa Trachyrhamphus longirostris Trichonotus nikii Upeneus pori Upeneus sulphureus Uranoscopus dollfusi Uranoscopus guttatus Uropterygius genie Uropterygius golanii Xyrichtys bimaculatus Xyrichtys javanicus Xyrichtys niger Zebrias quagga  23124 27323 46375 4445 46424 56494 47872 50765 14342 56499 8444 8194  Ambassidae Gobiidae Gobiidae Gobiidae Caproidae Apogonidae Apogonidae Apogonidae  Ambassis urotaenia Amblyeleotris triguttata Amblygobius magnusi Amoya signata Antigonia indica Apogon gularis Apogon hungi Apogon micromaculatus  9235 26636 56463 17033 59052 56481 56482 56483  5860 5859 25449 7901 4470 46379 9913 1333 8023 8305 7897 8290 5347 1353 10527 59468 14394 14395 61282 6390  FishBase common name Grey stingfish Delagoa threadfin bream Notchedfin threadfin bream Randall' s threadfin bream Slender threadfin bream Smallscale grubfish Somali sandperch Smooth dwarf monocle bream Rosy dwarf monocle bream Unarmed dwarf monocle bream Scaly dwarf monocle bream Black-edged sweeper Striped threadfin Blackspot threadfin Lined grunt Arrow bulleye Deep flounder Sideburn wrasse Olive-tailed flathead Blackblotch flathead Cockatoo righteye flounder Comber Brown-banded cusk-eel Elongate sole Commerson' s sole Toadfish  Por' s goatfish Sulphur goatfish Dollfus'stargazer  Two-spot razorfish Fringefin zebra sole Banded-tail glassy perchlet Triplespot shrimpgoby Tusk goby  217  Group  Family Apogonidae Apogonidae Apogonidae Scorpaenidae Callionymidae Callionymidae Callionymidae Callionymidae Callionymidae Gobiidae Gobiidae Gobiidae Apogonidae Apogonidae Pseudochromidae Pseudochromidae Pomacentridae Aploactinidae Gobiidae Gobiidae Callionymidae Callionymidae Bothidae Bothidae Bothidae Tripterygiidae Tripterygiidae Tripterygiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Gobiidae Kraemeriidae Leiognathidae Leiognathidae Leiognathidae Leiognathidae Gobiesocidae Triglidae Creediidae Synanceiidae Synanceiidae Pomacentridae Ophidiidae Gobiidae Blenniidae  Scientific name Apogon quadrifasciatus Apogon smithi Apogon spongicolus Brachypterois serrulata Callionymus bentuviai Callionymus erythraeus Callionymus marleyi Callionymus muscatensis Callionymus oxycephalus Callogobius amikami Callogobius dori Callogobius flavobrunneus Cheilodipterus novemstriatus Cheilodipterus pygmaios Chlidichthys auratus Chlidichthys rubiceps Chromis axillaris Cocotropus steinitzi Coryogalops anomolus Cryptocentroides arabicus Diplogrammus infulatus Diplogrammus randalli Engyprosopon hureaui Engyprosopon latifrons Engyprosopon macrolepis Enneapterygius clarkae Enneapterygius obscurus Enneapterygius pusillus Favonigobius reichei Fusigobius humeralis Fusigobius maximus Gobius koseirensis Gobius leucomelas Hetereleotris diademata Hetereleotris vulgaris Isthmogobius baliurus Kraemeria nudum Leiognathus berbis Leiognathus klunzingeri Leiognathus leuciscus Leiognathus lineolatus Lepadichthys erythraeus Lepidotrigla spiloptera Limnichthys nitidus Minous coccineus Minous inermis Neopomacentrus taeniurus Ophidion smithi Opua elati Parablennius cyclops  FishBase Code 53017 59514 56484 9203 56496 46382 7650 46387 56498 26993 56134 17050 12629 12881 56486 56487 11854 56490 46394 46397 17029 49452 15567 15569 5344 16975 25377 16979 9945 59445 59446 61336 61337 56465 46402 52799 60799 7748 27024 4453 4563 55729 10366 16931 10726 46368 5705 16788 56467 56471  FishBase common name Twostripe cardinal Smith' s cardinalfish  Smallhead dragonet Sand dragonet Muscat dragonet  Slimy goby Indian Ocean twospot cardinalfish  Grey chromis Anomolous goby Arabian goby Sawspine dragonet Hureau' s flounder Barred triplefin Highcrest triplefin Indo-Pacific tropical sand goby  Common goby Berber ponyfish Whipfin ponyfish Ornate ponyfish Spotwing gurnard Sand submarine Onestick stingfish Alcock' s scorpionfish Freshwater demoiselle  218  Group  Family Microdesmidae Plesiopidae Gobiidae Gobiidae Gobiidae Aploactinidae Microdesmidae Scorpaenidae Leiognathidae Leiognathidae Gobiidae Gobiidae Gobiidae Syngnathidae Apogonidae Opistognathidae Labridae Labridae Synanceiidae Callionymidae Syngnathidae Gobiidae Tetrarogidae  Demersal omnivores  Blenniidae  FishBase Code 56470 27000 56468 9191 59404 52867 4374 56488 4455 4811 56197 56469 9996 7190 56485 56472 4409 4413 12085 25699  FishBase common name Moustache longfin Marbled goby  Pugnose ponyfish Deep pugnose ponyfish Phantom goby  Spottail wrasse Russell' s wrasse Red Sea stonefish  46212 28064 56489  8059  Wedgetail filefish  Monacanthidae Monacanthidae Blenniidae Monacanthidae  Alloblennius pictus Antennablennius adenensis Antennablennius australis Antennablennius hypenetes Brachaluteres baueri Chelon macrolepis Crenidens crenidens Enneapterygius destai Leiognathus bindus Leiognathus elongatus Leiognathus splendens Liza subviridis Omobranchus fasciolatus Omobranchus steinitzi Paraluteres arqat Paramonacanthus frenatus Paramonacanthus oblongus Paramonacanthus pusillus Petroscirtes ancylodon Stephanolepis diaspros  53239 54624 46423 14343  Hair-finned filefish  Blenniidae Blenniidae Cyprinodontidae  Alticus kirkii Alticus saliens Aphanius dispar dispar  46411 6031 4813  Blenniidae Blenniidae Blenniidae Monacanthidae Mugilidae Sparidae Tripterygiidae Leiognathidae Leiognathidae Leiognathidae Mugilidae Blenniidae Blenniidae Monacanthidae Monacanthidae  Demersal herbivores  Scientific name Paragunnellichthys springeri Plesiops mystaxus Pleurosicya prognatha Pomatoschistus marmoratus Psilogobius randalli Ptarmus gallus Ptereleotris arabica Scorpaenodes steinitzi Secutor insidiator Secutor ruconius Silhouettea aegyptia Silhouettea chaimi Silhouettea insinuans Siokunichthys herrei Siphamia permutata Stalix davidsheni Suezichthys caudavittatus Suezichthys russelli Synanceia nana Synchiropus sechellensis Syngnathus macrophthalmus Trimma filamentosus Vespicula bottae  52391 46412 8042  Aden blenny Moustached rockskipper  46413 54554 4816 7931 56507 4449 4450 4454 4819 8038 59659 54621  Arabian blenny Largescale mullet Karenteen seabream Orangefin ponyfish Slender ponyfish Splendid ponyfish Greenback mullet Arab blenny  Arabian fangblenny Reticulated leatherjacket Kirk' s blenny Leaping blenny  219  Group  Family Blenniidae Blenniidae Blenniidae Blenniidae Blenniidae Blenniidae Mugilidae Mugilidae  Benthopelagic fish  Apogonidae Sciaenidae Ariommatidae Ateleopodidae Syngnathidae Balistidae Carangidae Gerreidae  22835 16944 27245 27015 25453 4820 4700  Bramidae Trichiuridae Terapontidae Gempylidae Trichiuridae Stomiidae Sciaenidae Myctophidae Champsodontidae Stomiidae Paralepididae Sternoptychidae Nemichthyidae Stomiidae  Astronesthes martensii Atrobucca geniae Benthosema pterotum Champsodon capensis Chauliodus sloani Lestrolepis luetkeni Maurolicus mucronatus Nemichthys scolopaceus Stomias affinis  10213 15959 10238 10296 1786 27423 51615 2660 10167  Acropomatidae Congridae Bothidae Percophidae  Acropoma japonicum Ariosoma mauritianum Arnoglossus marisrubri Bembrops caudimacula  1267 7671 60532 23546  Lutjanidae Lutjanidae Carangidae Opistognathidae Stromateidae Synodontidae  Bathydemersal fish  FishBase Code 27295  Apogon queketti Argyrosomus regius Ariomma brevimanus Ateleopus natalensis Bryx analicarens Canthidermis macrolepis Decapterus russelli Gerres methueni Hoplostethus mediterraneus mediterraneus Lithognathus mormyrus Lobotes surinamensis Mugil cephalus Oncorhynchus mykiss Physiculus sudanensis Pomadasys striatus Pristipomoides filamentosus Pristipomoides sieboldii Seriola lalandi Stalix histrio Stromateus fiatola Synodus randalli Taractichthys steindachneri Tentoriceps cristatus Terapon puta Thyrsitoides marleyi Trichiurus lepturus  Trachichthyidae Sparidae Lobotidae Mugilidae Salmonidae Moridae Haemulidae  Bathypelagic fish  Scientific name Ecsenius dentex Entomacrodus epalzeocheilos Hirculops cornifer Istiblennius flaviumbrinus Istiblennius pox Istiblennius unicolor Liza tade Valamugil cunnesius  FishBase common name Fringelip rockskipper Highbrow rockskipper Scarface rockskipper Pallid rockskipper Tade mullet Longarm mullet  8011 418 10513 10662 46105 46433 374 7700  Spotfin cardinal Meagre  4964 706 1077 785 239 60891 7301  Mediterranean slimehead Striped seabream Atlantic tripletail Flathead mullet Rainbow trout  Pink pipefish Large-scale triggerfish Indian scad Striped silver biddy  Striped grunter  201 209 382 23505 1198 58509  Crimson jobfish Lavender jobfish Yellowtail amberjack  3561 7947 7946 7698 1288  Sickle pomfret Crested hairtail Small-scaled terapon Black snoek Largehead hairtail  Blue butterfish  Skinnycheek lanternfish Gaper Sloane' s viperfish Naked barracuda Slender snipe eel Günther' s boafish Glowbelly Blunt-tooth conger  220  Group  Family Champsodontidae Cynoglossidae Synaphobranchidae Nettastomatidae Bythitidae Synodontidae Syngnathidae Ophidiidae Tetrarogidae Scorpaenidae Gobiidae Nemipteridae Moridae Gobiidae Congridae Nettastomatidae Setarchidae Acropomatidae Trichiuridae Mullidae Uranoscopidae Congridae  Scientific name Champsodon omanensis Cynoglossus acutirostris Dysomma fuscoventralis Facciolella karreri Grammonus robustus Harpadon erythraeus Hippocampus kelloggi Neobythites stefanovi Neocentropogon mesedai Neomerinthe bathyperimensis Obliquogobius turkayi Parascolopsis baranesi Physiculus marisrubri Priolepis goldshmidtae Rhynchoconger trewavasae Saurenchelys meteori Setarches guentheri Synagrops philippinensis Trichiurus auriga Upeneus davidaromi Uranoscopus marisrubri Uroconger erythraeus  FishBase Code 15604 10204 15591 58715 15659 15605 53815 15598 61244  FishBase common name Sharpnose tonguesole  Great seahorse  61433 56466 15368 15597 59388 57764 58724 5029 10338 8666 60913 56495 15590  Deepwater scorpionfish Pearly hairtail  221  Table E. 2 Key data on fish groups of the Red Sea ecosystem model.  Group No.  Group name  10  Whale shark  12  Rays  13  Reef top predators  14  Large reef carnivores  15  No. of spp.  Trophic level Min  L (cm)  Max  Min  Max  1  3.55  3.55  1683.0  1683.0  17  3.1  4.5  68.4  347.4  122  3.98  4.5  9.5  421.1  86  3  3.98  51.4  315.8  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; 222  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 223  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 224  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) Other sessile fauna 0.85 Cephalopods 0.399 Molluscs 0.368 Echinoderms 0.596 Crustaceans 0.816 Meiobenthos 0.295 Zooplankton* 14 * modified after (van Couwelaar, 1997)  P/B (year-1) 3.2 3.5 9 1.6 3 26 52  Q/B (year-1) 12 12 30 8 10 100 178  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).  225  Table E. 4 Diet composition matrix of Red Sea model. 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  Prey \ Predator Cetaceans Dungongs Birds Turtles Trawler fishes Purse seine fishes Beach seine fishes Handlining fishes Gillnet fishes Whale shark Sharks Rays Reef top predators Large reef carnivores Medium reef carnivores Small reef carnivores Reef omnivores Reef herbivores Large pelagic carnivores Small pelagic carnivores Pelagic omnivores Demersal top predators Large demersal carnivores Medium demersal carnivores Small demersal carnivores Demersal omnivores Demersal herbivores Benthopelagic fish Bathypelagic fish Bathydemersal fish Shrimp Cephalopods Echinoderms Crustaceans Molluscs Meiobenthos Corals Other sessile fauna Zooplankton Phytoplankton Sea grass Algae Detritus  1  2  3  0.010 0.013  0.020 0.059  0.004  0.001  0.013 0.066 0.131 0.010 0.053 0.065 0.008  4  0.011 0.010 0.020 0.112 0.112 0.112  0.131 0.026 0.026 0.131  0.010 0.169  0.047 0.131 1.000 0.012  0.033  7  0.004 0.151 0.002 0.003  0.106 0.271 0.217 0.026 0.020 0.180  0.020  5  0.100 0.148 0.015  0.002 0.002 0.005 0.001  0.001 0.001 0.001 0.015 0.015 0.020 0.002 0.111 0.015  0.005 0.052 0.262 0.152 0.202  0.000  0.001  0.011 0.011 0.017 0.020 0.001  0.006 0.006 0.006 0.006  0.006 0.006 0.006 0.006  0.050  0.020  0.110  0.020  0.197 0.100  0.522 0.180  0.001  0.044  0.043  0.010 0.112 0.057 0.226 0.057  0.233 0.070 0.230 0.137 0.056  0.003 0.001 0.060 0.060 0.060 0.006 0.127 0.040  8  0.101  0.011 0.009 0.197 0.065  226  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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  Prey \ Predator Cetaceans Dungongs Birds Turtles Trawler fishes Purse seine fishes Beach seine fishes Handlining fishes Gillnet fishes Whale shark Sharks Rays Reef top predators Large reef carnivores Medium reef carnivores Small reef carnivores Reef omnivores Reef herbivores Large pelagic carnivores Small pelagic carnivores Pelagic omnivores Demersal top predators Large demersal carnivores Medium demersal carnivores Small demersal carnivores Demersal omnivores Demersal herbivores Benthopelagic fish Bathypelagic fish Bathydemersal fish Shrimp Cephalopods Echinoderms Crustaceans Molluscs Meiobenthos Corals Other sessile fauna Zooplankton Phytoplankton Sea grass Algae Detritus  1  2  3  0.010 0.013  0.020 0.059  0.004  0.001  0.013 0.066 0.131 0.010 0.053 0.065 0.008  4  0.011 0.010 0.020 0.112 0.112 0.112  0.106 0.271 0.217 0.026 0.020 0.180  0.000 0.011 0.011 0.017 0.020 0.001  0.010 0.169 0.020  0.047 0.131 1.000 0.033  6  0.004 0.151 0.002 0.003  0.131 0.026 0.026 0.131  0.012  5  0.100 0.148 0.015  0.010 0.112 0.057 0.226 0.057  0.233 0.070 0.230 0.137 0.056  0.101  7  8  0.002 0.002 0.005 0.001  0.003 0.001 0.060 0.060 0.060 0.006 0.127 0.040  0.001 0.001 0.001 0.015 0.015 0.020 0.002 0.111 0.015  0.001 0.006 0.006 0.006 0.006  0.006 0.006 0.006 0.006  0.050  0.020  0.110  0.020  0.197 0.100  0.522 0.180  0.001  0.044  0.043  0.005 0.052 0.262 0.152 0.202  0.011 0.009 0.197 0.065  227  9 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  0.030 0.114 0.003 0.002  0.002 0.011 0.015 0.015 0.015 0.015 0.020 0.459 0.088 0.002 0.001 0.006 0.006 0.006 0.006 0.003  0.015 0.045 0.031 0.076 0.003  10  0.008 0.005  0.022 0.020 0.163 0.009 0.041 0.001  0.170  11 0.002 0.002 0.034 0.004 0.004 0.003 0.011 0.009 0.002 0.005 0.010 0.004 0.090 0.124 0.131 0.168 0.113 0.113 0.008 0.024 0.004 0.006 0.005 0.004 0.002 0.024 0.004 0.002 0.004 0.024 0.002 0.002  0.004 0.366 0.184  0.010  0.012  12  0.051  13  14  15  0.003 0.002 0.002 0.007 0.003  0.002 0.004 0.002 0.008 0.003  0.002 0.004 0.002 0.007 0.002  0.055 0.163 0.159 0.202 0.003 0.002 0.000 0.003 0.000 0.003 0.003 0.003 0.003  0.001 0.003 0.004 0.186 0.128 0.151 0.002 0.001 0.000 0.002 0.000 0.002 0.002 0.002 0.002  0.069 0.072 0.278 0.154 0.154  0.021 0.021  0.000 0.003 0.003 0.003 0.003  16  17  18  0.000 0.001 0.000 0.001  19  0.005 0.015 0.001  0.003 0.001 0.003 0.012 0.028  0.011 0.002 0.006 0.018 0.018 0.011 0.021 0.236 0.085  0.001 0.003 0.009 0.009 0.040  0.076 0.090 0.080 0.021  0.014 0.004 0.150 0.088 0.229 0.229 0.023 0.023  0.198  0.007 0.007 0.007 0.215 0.007  0.010 0.014 0.004 0.242 0.068 0.007  0.014  0.022  0.005 0.012 0.026 0.051 0.091 0.064 0.104 0.042 0.062  0.038  0.002 0.008 0.033 0.100 0.047 0.151 0.176 0.022 0.151  0.015  0.001 0.009 0.028 0.050 0.041 0.041 0.070 0.043 0.405 0.100 0.073 0.074  0.276 0.003 0.224 0.011  0.015 0.049 0.098 0.804 0.049  0.020  228  20 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  21  0.001 0.037  0.005  0.001  0.000  22  23  24  25  0.010  0.010  0.010  0.010  26  27  28  29  0.015  0.010  0.030  0.037 0.019 0.007  30  0.006 0.001 0.001 0.001 0.001 0.001 0.081 0.154  0.006 0.003 0.010 0.011 0.114 0.128 0.138 0.142 0.150 0.061  0.110  0.002 0.100 0.105 0.105 0.105 0.041  0.013 0.136 0.090 0.092  0.000 0.002 0.020 0.114 0.170  0.002 0.023 0.023 0.023 0.023 0.010  0.040 0.090 0.082 0.082 0.010  0.060  0.012 0.100 0.020 0.004 0.030 0.012  0.005 0.081 0.020  0.013 0.049 0.049 0.061 0.049 0.003 0.003  0.545  0.052 0.106 0.052 0.105 0.010 0.052 0.035 0.017  0.005 0.005 0.051 0.136 0.068 0.082 0.051 0.056  0.417 0.402 0.025 0.025  0.013  0.102  0.204  0.001  0.001  0.020 0.091 0.019 0.090 0.060 0.200 0.010  0.020 0.015 0.012 0.039 0.005 0.002  0.192  0.071 0.014 0.400 0.360  0.140 0.460 0.400  0.100 0.082  0.142 0.040 0.200 0.142  0.015 0.012 0.114 0.008 0.020 0.300  0.290  0.057 0.010 0.154 0.107 0.309 0.005  0.030 0.225  229  31 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  32  33  34  35  36  0.002 0.009 0.001 0.011 0.041 0.002 0.001 0.101 0.078  0.009 0.001 0.010 0.013 0.003 0.002 0.007  0.015 0.001 0.000 0.047  0.114 0.638  0.069 0.886  0.047 0.890  37  38  39  0.250 0.600  0.250 0.600  0.100 0.900  0.150  0.150  0.012  0.012 0.017  0.001 0.001  0.012 0.022  0.005 0.005 0.005  0.005 0.004 0.016 0.001 0.020 0.010 0.008 0.012 0.118 0.178 0.628  0.040 0.005 0.068 0.100 0.091  0.009 0.003 0.037 0.030 0.008 0.004  0.356 0.047  0.202  0.374 0.535  230  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.  231  Table E. 5 Sources used for the reconstruction of effort of the Red Sea fisheries.  Country Year Sudan 1955 1976 1979 1981 1982 2001 2006 Eritrea 1964 1968 1969 1970 1981 1984 Yeman 1972 1975 1976 1978 1992 1997 1998 2000 2001 2002 2003 2004 2005 2006 1954 Saudi Arabia 1971 1980 1984 1991 1992 1993 1994 1995 1996 1997 1998 1999  Effort data Data* Source 200 Kristjonsson (1956) 418 ODA (1983) 437 Barrania (1979) 664 ODA (1983) 605 Chakraborty (1983) 743 FA (2007) 967 FA (2007) 3543 Grofit (1971) 4167 Grofit (1971) 3022 Grofit (1971 3000 Giudicelli (1984) 875 Giudicelli (1984) 250 Giudicelli (1984) 1000 Agger (1976) 1066 Walczak (1977) 1071 Campleman (1977 ) 1597 Campleman (1977) 1771 Herrera and Lepere (2005) 2686 Brodie et al., (1999) 3390 FAO (2002) 1781 MoFW (2010) 2254 MoFW (2010) 2562 MoFW (2010) 2737 MoFW (2010) 4510 MoFW (2010) 5000 MoFW (2010) 5727 MoFW (2010) 2500 Neve and Al-Aiidy (1973) 3250 Neve and Al-Aiidy (1973) 3678 Barrania et al., (1980) 2408 Kedidi et al., (1984) 2993 MAW (2008) 3443 MAW (2008) 3907 MAW (2008) 4063 MAW (2008) 4316 MAW (2008) 4212 MAW (2008) 4145 MAW (2008) 4209 MAW (2008) 4764 MAW (2008)  Year 1956 1979 1982 2006  Motorization data % Source 1.93 Kristjonsson (1956) 22.57 Barrania (1979) 61.98 Chakraborty (1983) 95.00 FA (2007)  1960 1963 1964 1969 1974  1.00 2.20 3.72 42.10 75.00  Grofit (1971) Grofit (1971) Grofit (1971 Grofit (1971 Giudicelli (1984)  1972 1975 1978 2006  10.00 26.45 60.66 96.00  Agger (1976) Walczak (1977) Campleman (1977) MoFW (2010)  1955 1965 1969 1991  0.20 30.77 41.43 97.00  Ferrer (1958) Neve and Al-Aiidy (1973) Neve and Al-Aiidy (1973) Sakurai (1998)  232  Country Year 2000 2001 2002 2003 2004 2005 2006  Effort data Data* Source 5037 MAW (2008) 6116 MAW (2008) 6389 MAW (2008) 6927 MAW (2008) 7266 MAW (2008) 6880 MAW (2008) 7533 MAW (2008)  Year  Motorization data % Source  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  WX YHB  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.  Sudan Eritrea Yemen Saudi Arabia  Exponential fitting a b 1.00E-22 0.0287 5.00E+106 -0.121 4.00E-32 0.04 9.00E-16 0.022  2  R 0.89 0.92 0.78 0.64  logistic fitting ln a b 275.63 0.1389 861.09 0.4369 277.36 0.1399 487.04 0.2467  R2 0.96 0.98 0.89 0.88  Using the logistic curve fitting results, the total effort was divided into motorized and nonmotorized. 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 233  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 234  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)  1  Effort ratio  0.75 0.5 0.25 0  1950  1960  1970  1980  1990  Year 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  Table E. 7 Reconstructed effort of Red Sea fisheries by gear type from 1950-2006.  Year 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988  Beach seine 3260163 3261646 3263347 3265170 3267117 3265557 3267845 2906275 2586142 2302782 2052021 1830176 1633982 1547353 1541115 1639968 1874024 1698521 1253924 2070943 3633916 4557226 4391027 3515606 2330559 969981 226857 227999 229451 232530 233061 243346 239757 236236 232767 229389 229581 229020 223816  Effort (kilowatt.hours) Gillnet Handlining 2409164 5506687 2478914 5631296 2550989 5758717 2625647 5889065 2703097 6022413 2792947 6175856 2878727 6327502 2968792 6415349 3063659 6515860 3163932 6628825 3270385 6754326 3383946 6892765 3505746 7044914 3637240 7227335 3780044 7436921 3936025 7680376 4107317 7965751 4296088 8875907 4504499 10294204 4734683 10679160 4988745 10483532 5325151 11111007 6017858 13512905 6879881 16761498 7927032 20442238 9168813 24444877 10605296 28017197 14923601 29883329 18027390 34001708 21624492 38502704 25751504 43659249 30438437 49390693 35737595 55144940 41674966 61550744 48285591 68425327 55602881 75773910 63655847 83602553 72475883 91921930 82090575 100744526  Purse seine 122247 153416 152412 153022 189598 185090 184876 200680 207646 237638 131544 136918 133406 145992 146457 375787 429322 418719 479157 291247 283388 253944 299842 310256 476401 414977 487028 790741 333317 781688 533880 992719 304570 213003 296883 295036 312653 340611 563564  Trawl 1685304 2010420 2396194 2486810 2633673 2753224 2842540 2625311 2771840 2676854 1542295 1617308 1634785 1874036 1858988 2276185 2408709 2402342 2977451 2244378 2469150 2525112 2509215 2510686 3259823 3195146 3338693 3902215 2921845 4367463 3498433 5165161 2874781 3461937 3722514 3740346 3340348 3320155 3634536 236  Year 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006  Beach seine 223996 224544 243948 257200 264943 275326 305033 317172 325100 337872 349180 360541 374877 387660 400572 413612 426779 444458  Effort (kilowatt.hours) Gillnet 92514901 103772729 115874916 129457088 143725688 158905420 175009249 191560732 212058648 230889840 252378357 274450818 292928692 316286259 340375293 366520099 395004639 423651161  Handlining 110082153 119952344 130359461 142265035 154935802 168173310 181984519 196924712 214737143 230875197 250997094 270357983 283670425 305417442 309461472 338014037 352666794 374812480  Purse seine 728561 727433 722734 909693 838570 695858 724406 820456 1109682 1426178 2103248 1992639 2494183 2815688 2525939 2963705 3032281 3726970  Trawl 3304907 3516169 3711930 3930491 4382493 6669252 6344746 8639737 8730950 10096325 13445928 12963567 14137305 14396368 14079268 15941449 16652515 26874663  237  Table E. 8 Flow parameter (vulnerabilities) for the Red Sea model. 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  Prey \ Predator Cetaceans Dungongs Birds Turtles Trawler fishes Purse seine fishes Beach seine fishes Handlining fishes Gillnet fishes Whale shark Sharks Rays Reef top predators Large reef carnivores Medium reef carnivores Small reef carnivores Reef omnivores Reef herbivores Large pelagic carnivores Small pelagic carnivores Pelagic omnivores Demersal top predators Large demersal carnivores Medium demersal carnivores Small demersal carnivores Demersal omnivores Demersal herbivores Benthopelagic fish Bathypelagic fish Bathydemersal fish Shrimp Cephalopods Echinoderms Crustaceans Molluscs Meiobenthos Corals Other sessile fauna Zooplankton Phytoplankton Sea grass Algae Detritus  1  2  3  1.01 2.26  1.01 2  2.26  2  2.26 2.26 2.26 2.26 2.26 2.26 2.26  4  2.26 2.26 2.26 2.26 2.26 2.26  2 2 2 2 2 2  2.26 2.26 2.26 2.26 2.26 2.26  2.26 2.26 2  2 2.26 2 2  6  1 2 1 2  2.26 2.26 2.26 2.26  2  5  2.26 2.26 2.26  2.26 2.26 2.26 2.26 2.26  2.26 2.26 2.26 2.26 2.26  2.26  7  8  3.25 1.01 3.25 12  20 20 20 20 20 20 20 20  3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25  20 20 20 20 20  3.25 3.25 3.25 3.25  12 12 12 12 12  20  3.25  12 12 12 12  20  3.25  20 20  3.25 3.25  12  3.25  12  9  10  1.01 1.01 7.65 2  1.01 2  7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65 7.65  1.5 7.65 7.65 7.65 7.65  2 2 2 2 2 2  2  2 2  2  2  238  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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  11 3  12  3 3 3 1.01 3 3 2.5 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3  3  3  13  14  15  2 1.01 2 2 2  2.26 1.01 2.26 2.26 2.26  3.25 1.01 3.25 2.5 3.25  2.26 2.26 2.26 2.26 2.26 2.26 2.26 2.26 2.26 2.26 2.26 2.26 2.26  3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25  16  17  18  3.25 1.01 3.25 2.5  19  20  21  1.01 2  1.01 2.26  3.25  2  2.26  3.25  22  23  24  2  2  2  2 2 2 2 2 2  2 2 2 2  2  2 2 2 2 2 2 2 2  2 2 2 2 2 2 2 2  2  2  2  2 2 2 2 2 2  2 2  2 2 2 2 2 2 2 2  2  2 2 2 2 2  2 2 2 2 2  2.26 2.26 2.26 2.26 2.26 2.26  2.26  2.26  3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25  3.25  2 2 2 2 2  2 2 2 2 2 2 2 2 2  3.25 3.25 3.25 3.25 3.25  2.26 2.26 2.26 2.26 2.26 2.26 2.26  3.25 3.25 3.25 2 2 2 2 2 2 2  2 2 2  2 2 2 2 2 2 2 2 2  2  3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25 3.25  2  2.26  2 2 2 2  2.26 2.26 2.26 2.26  2 2 2 2 2  2  2.26  3.25 3.25 3.25  2 2 2 2 2 2  3.25 3.25 3.25 3.25  239  25 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 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43  26  27  2  28  29  2  2  30  31  32  33  34  35  36  2 2 2 2 2 2 2 2 2  2 2 2 2 2 2 2  2 2 2 2  2 2  2 2  2 2  37  38  39  2 2  2 2  2 2  2  2  2  2 2  2 2 2 2 2 2  2 2 2  2  2 2 2 2 2 2 2 2 2 2  2  2 2 2 2 2 2 2 2 2 2 2  2 2  2 2 2  2 2 2 2 2 2  2 2 2 2 2 2 2 2  2 2 2 2 2  2 2 2 2  2 2 2 2 2 2  2 2 2  15 15 15 15 15 15 15 15  2  2 2  2 2  15 15 15  2 2 2 2 2  2 2 2 2 2 2  2 2  2  2 2  240  Table E. 9 Feeding rate parameters for the Red Sea model.  Group Cetaceans Dungongs Birds Turtles Trawler fishes Purse seine fishes Beach seine fishes Handlining fishes Gillnet fishes Whale shark Sharks Rays Reef top predators Large reef carnivores Medium reef carnivores Small reef carnivores Reef omnivores Reef herbivores Large pelagic carnivores Small pelagic carnivores  Max rel. feeding time 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2  Feeding time adjust rate [0,1] 0.5 0.5 0.5 0.5 0 0 0 0 0 0.5 0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5  Group Pelagic omnivores Demersal top predators Large demersal carnivores Medium demersal carnivores Small demersal carnivores Demersal omnivores Demersal herbivores Benthopelagic fish Bathypelagic fish Bathydemersal fish Shrimp Cephalopods Echinoderms Crustaceans Molluscs Meiobenthos Corals Other sessile fauna Zooplankton  Max rel. feeding time 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2  Feeding time adjust rate [0,1] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5  241  

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