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Improving the management of global and regional tuna fisheries Bailey, Megan Lynn 2012-12-31

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Improving the management of global and regional tuna fisheries by Megan Bailey  B.Sc., The University of Western Ontario, 2003 MSc., The University of British Columbia, 2007  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 c Megan Bailey 2012 ⃝  Abstract Tuna can travel thousands of kilometers throughout their lifetime, and are often found in the waters of several nations and the high seas. These “straddling stocks” are difficult to manage due to competition between the large number of interested fishing nations, all of which can be asymmetric in their economies, management capacity and conservation concerns. This is compounded by the possibility of new members and free riders. It is no surprise then, that tuna fisheries management has, by and large, been unsuccessful in promoting sustainable fisheries. Populations of several of the world’s tuna species are fully or over-exploited. This dissertation identifies and addresses areas where improvements in the management of global and regional tuna fisheries may facilitate the continued contribution of these fisheries to livelihoods and food security. I analyze private and social resource rent derived from fishing for different tuna species and by different gear types. From these results I identify key management targets. Management efforts are formalized through Regional Fisheries Management Organizations (RFMOs), groups which are mandated to promote cooperative agreements and fair and equitable allocation approaches. Stable cooperative agreements, however, have been hard to come by for tuna RFMOs, in part because the issue of allocations has not been appropriately targeted. I propose a combined socio-economic and ecological approach formulated from the perspective of fisheries benefits, as opposed to just catch, which could facilitate stable cooperative agreements for sustaining tuna stocks into the distant future. Tuna fisheries in the western and central Pacific provide over half of the world’s tuna, but lack of effective management capacity in Indonesia and the Philippines threatens the sustainability of these fisheries. I argue that countries that fish in this region, most specifically Papua New Guinea, would be wise to help facilitate improved management capacity in these countries. One of the major management challenges in this region is the bycatch of juvenile yellowfin and bigeye tuna in the skipjack purse seine fishery. Through applied game-theoretic modelling, I conclude that reduction in juvenile bycatch brought about by cooperative management of these fisheries would provide long-term ecological and economic benefits.  ii  Preface Apart from thesis Chapters 1 and 7, all of the Chapters in this dissertation have been prepared for publication. Chapters 3 and 5 are published, and Chapter 6 is in press. Chapters 2 and 4 are being prepared for submission. I am the senior author on all of the papers, and I led the design, implementation, analysis and writing of the papers. Chapter 2 is coauthored by Andrew Dyck, Vicky Lam, and Rashid Sumaila. I formulated the concept and methods for the study, analyzed the data, and prepared the manuscript. Andrew Dyck assisted with use of the subsidies and price databases, while Vicky Lam assisted with use of the cost datebase. Rashid Sumaila provided guidance throughout the development of the paper. A version of this Chapter is in preparation for submission. Chapter 3 is coauthored by Rashid Sumaila and Marko Lindroos. I identified the need for a contemporary review piece, conducted the research and wrote the manuscript. Marko Lindroos offered his expertise in coalition games to strengthen that section of the paper, while Rashid Sumaila provided guidance throughout. A version of this Chapter was published 2010 in Fisheries Research, Volume 102, pages 1-8. Chapter 4 is coauthored by Gakushi Ishimura, Richard Paisley and Rashid Sumaila. I formulated the concept for this paper, conducted research, and prepared the manuscript. Gakushi Ishimura contributed to the section on climate change, while Richard Paisley provided expertise on international water agreements. Rashid Sumaila provided guidance throughout. A version of this Chapter is in preparation for submission. Chapter 5 is coauthored by Jimely Flores, Sylvester Pokajam, and Rashid Sumaila. I initiated this study following field work in the Philppines, collated and analyzed information on the countries and wrote the manuscript. Jimley Flores conducted interviews in the Philippines, and commented on the Philippine portion of the analysis. Sylvester Pokajam, who works for the National Fisheries Authority in Papua New Guinea, contributed to the analysis of that country. Rashid Sumaila helped guide what was initially a thorough but chaotic piece into a publishable manuscript. A version of this Chapter was published in 2012 in Ocean and Coastal Management, Volume 63, pages 30-42. Chapter 6 is coauthored by Rashid Sumaila and Steven J.D. Martell. I designed this study, developed the model, conducted the analysis and prepared the manuscript. Rashid iii  Preface Sumaila provided guidance on the economic analysis, whereas Steve Martell provided guidance on the biological modelling methodology. A version of this Chapter is in press at Strategic Behavior and the Environment.  iv  Table of Contents Abstract  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ii  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  iii  Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  v  Preface  List of Tables  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii  List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  x  1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1  2 Informing global tuna fisheries management: Private versus social resource rent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12  2.2  Global tuna fisheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13  2.3  Subsidies, welfare economics and the fishery  2.4  Methods  2.5  Results  2.6  Discussion  . . . . . . . . . . . . . . . . . 15  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24  3 Application of game theory to fisheries over three decades  . . . . . . . 28  3.1  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28  3.2  Early years: The two-player game  3.3  Major movement: Coalitions . . . . . . . . . . . . . . . . . . . . . . . . . . 34  3.4  Looking forward: Catch privileges and resilience . . . . . . . . . . . . . . . 38  3.5  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . 30  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43  4 Present and future allocation approaches for shared tuna fisheries 4.1  Introduction  . . 44  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44  v  Table of Contents 4.2  Allocation by tuna RFMOs . . . . . . . . . . . . . . . . . . . . . . . . . . . 46  4.3  The future of allocation schemes . . . . . . . . . . . . . . . . . . . . . . . . 52  4.4  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61  5 Towards better management of Coral Triangle tuna . . . . . . . . . . . . 63 5.1  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63  5.2  Coral Triangle tuna  5.3  Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68  5.4  Philippines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72  5.5  Papua New Guinea  5.6  Regional options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81  6 Can cooperative management of tuna fisheries in the western Pacific solve the growth overfishing problem?  . . . . . . . . . . . . . . . . . . . . 91  6.1  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91  6.2  Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100  6.3  Results  6.4  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119  7 Conclusion: Moving beyond the status quo  . . . . . . . . . . . . . . . . . 122  Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126  Appendices A Rent Analysis  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147  B Allocation by non-tuna RFMOs . . . . . . . . . . . . . . . . . . . . . . . . . 173 B.1 Pacific Salmon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 B.2 Pacific hake B.3 Pacific halibut  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175  B.4 Northwest Atlantic: NAFO . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 B.5 Northeast Atlantic: NEAFC  . . . . . . . . . . . . . . . . . . . . . . . . . . 177  vi  List of Tables 1.1  Information on tuna species, fishing gears, markets supplied, 2010 catches (FAO, 2012), and conservation status (iucn.org). . . . . . . . . . . . . . . .  2.1  5  Tuna RFMOs, species managed, and performance at meeting best practices criteria. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16  2.2  Mean price per tonne by species (weighted by catch) and number of observations used for calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . 18  2.3  Private and social rent (USD) for bluefin fishing nations (all bluefin species combined). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22  2.4  Species summary: mean unit rent, private and social rent. . . . . . . . . . . 24  4.1  Summary of RFMO allocation information  5.1  Summary of main tuna species fished in the Coral Triangle, along with the  . . . . . . . . . . . . . . . . . . 53  gears used, markets supplied and status of the stocks. . . . . . . . . . . . . 65 5.2  Summary of Indonesia’s tuna fisheries and management. . . . . . . . . . . . 73  5.3  Summary of the Philippine’s tuna fisheries and management. . . . . . . . . 81  5.4  Summary of Papua New Guinea’s tuna fisheries and management. . . . . . 86  5.5  Summary of 2008 catches (SPC, 2009), presence (P) and absence (A) of management measures, EEZ size (Sea Around Us Project (seaaroundus.org)) and 2003 subsidies (Sumaila et al., 2010)) in Indonesia, the Philippines and Papua New Guinea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87  6.1  Summary of fisheries and markets for WCPO tuna species used in the model. 95  6.2  Variable definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102  6.3  Biological and fishing parameter inputs for skipjack tuna. . . . . . . . . . . 109  6.4  Biological and fishing parameter inputs for yellowfin tuna. . . . . . . . . . . 111  6.5  Biological and fishing parameter inputs for bigeye tuna. . . . . . . . . . . . 112  6.6  Scenario results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117  A.1 Summary table of rent analysis results . . . . . . . . . . . . . . . . . . . . . 147  vii  List of Figures 1.1  Global catches of tuna species since 1950. Data from seaaroundus.org. . . .  1.2  2005 catches, in tonnes, of skipjack, albacore, bigeye and yellowfin tuna (seaaroundus.org). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1.3 1.4  2 3  2005 catches, in tonnes, of Atlantic, southern and Pacific bluefin tuna (seaaroundus.org). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4  Map of the Coral Triangle, shown within the WCPFC Convention area. c WCPFC, used with permission. . . . . . . . . . . . Convention area map ⃝  9  2.1  2005 tuna catches (in tonnes) from the world’s oceans (Data from seaaroundus.org). 14  2.2  Social rent by country. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21  2.3  Private rent per tonne (difference in price per tonne and cost per tonne) by tuna species and gear type, aggregated over all fishing nations. bf refers to bluefin.  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23  4.1  c Chatham House, used with Map of tuna RFMOs (Lodge et al., 2007). ⃝  4.2  permission. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 c Journal of NorthGrand Banks fishery model schematic (Lane, 2008). ⃝ west Atlantic Fisheries Science, with permission through Creative Commons Attribution-Non Commercial 2.5 Canada. . . . . . . . . . . . . . . . . . . . 56  5.1  Total bigeye catch by gear, compiled from SPC (2010). . . . . . . . . . . . . 66  5.2  Map of the statistical area of the Western and Central Pacific Fisheries c WCPFC, used with permission), shown by solid lines, and Commission (⃝ regional coverage of SPC (small circle) and FFA (large circle). . . . . . . . . 67  5.3  Papau New Guinea catch trends, compiled from SPC (2009). PS: purse seine; PL: pole and line; LL: longline; HL: handline. . . . . . . . . . . . . . 82  6.1  Status quo vulnerability to gears at age for three tuna species. . . . . . . . 104  viii  List of Figures 6.2  Potential profits to the longline fleet at varying levels of relative purse seine effort (x axis). 1.0 refers to the status quo, 0.5 refers to 50% of the status quo effort, and 1.5 refers to 150% of the status quo effort. Varying levels of longline effort are represented by the coloured lines. . . . . . . . . . . . . . 107  6.3  Adjusted vulnerability at age to purse seine gear for yellowfin and bigeye tuna. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110  6.4  Ratio of vulnerable biomass (to the purse seine gear) to spawning biomass. Levels above 1 imply juveniles are vulnerable to the gear. . . . . . . . . . . 115  6.5  Sensitivity analysis: scenario rents when fuel costs are increased by 10% and 25%, compared to the base runs (assuming responsive prices). nc refers to the noncooperative games, while c1 and c2 refer to cooperative games one and two, which assume less FAD and no FAD use, respectively. . . . . . . . 118  ix  Acknowledgements The first year of my Doctoral work was funded by World Wildlife Fund (U.S.), World Wide Fund for Nature (Philippines), the University of British Columbia (through a UGF) and the province of BC (through the Pacific Century Award). Years two to four were funded by the Social Science and Humanities Research Council of Canada. I also received support from GeoEye through their James Joseph Memorial Scholarship to present my work at the 61st Tuna Conference. The initial impetus for Chapter 4 came from a project funded by Fisheries and Oceans Canada, through a contract with Tamee Karim. Thanks to every individual and organization who thought that some present or future benefits would come from my work. I was fortunate enough to spend time in Indonesia and the Philippines speaking with industry, government and conservation groups. Thanks to WWF employees Kate Newman, Lida Pet-Soede, Jose Ingles and Katherine Short for this opportunity. Thanks also to everyone who took the time to speak with me, including, but not limited to, Benjamin Tobias, Dexter Teng, Glennville Castrence, Noel Barut, Rene Subido, Bayani Fredeluces, Augusto Natividad, and Mark Philipe. Drs. Steve Martell, Jennifer Jacquet, Gakushi Ishimura, Meaghan Darcy Bryan, Carie Hoover, Kerrie O’Donnell, Martin Hall, Dale Squires and Pierre Kleiber have all assisted me in various ways in my academic career and for this I am thankful. Thanks also to my friends and colleagues at the Fisheries Economics Research Unit, especially to Wilf Swartz and Andres Cisneros, who have made my journey through fisheries economics a most enjoyable one. Special thanks to Vicky Lam for her expertise in creating maps for Chapters 1 and 2. A huge thank you to the countless number of other students, professors and staff at the Fisheries Centre who have honoured me with their time, advice, help and friendship. To my non-collegiate loved ones, thank you all for your support throughout the years. Special thanks to the Bailey, Henry and Bourne families. Extra special thanks to Alex Henry. I would like to thank my committee members, Drs. Gordon Munro and Carl Walters, for their support and guidance throughout this PhD journey. Both are giants in their respective fields of economics and ecology, and it has been such a privilege to learn from them over the years. Specific thanks to Dr. Munro for his comments on drafts of Chapters x  Acknowledgements 3 and 4, and to Dr. Walters for his comments on the modelling work in Chapter 6. Thanks also to Dr. Marko Lindroos, who has served as an informal advisor for my Doctoral work. My last, and largest, thank you is directed to my supervisor, Dr. Rashid Sumaila. I was fortunate enough to do my Masters degree under Dr. Sumaila’s supervision as well, and have very much enjoyed the past six years we have spent working together. His dedication to his students and to our field is inspiring, and his patience and compassion never falter. Although they might initially seem at odds, Dr. Sumaila’s two favourite sayings are “Just keep pushing” and “Don’t worry be happy”. It is this effortlessly productive aura that I admire most about him. I know I will be leaving UBC a much more educated and humane person, and I thank Rashid for his large contribution to that.  xi  Chapter 1  Introduction An estimated 80-90 million tonnes of fish are caught from the world’s oceans each year (FAO, 2010). In 2000, this catch was worth an estimated US $80 billion in landed value (Sumaila et al., 2007). The annual catch, which increased steadily throughout the 1950s1990s, recently stagnated, and is now likely declining (Pauly and Watson, 2001; Mora et al., 2009). Many scientists argue that we are facing a crisis in world fisheries (Clark, 2006). Some researchers have predicted a 90% global removal of predatory fish (Myers and Worm, 2003), and warn that shortfalls in the supply of fish could have devastating consequences for human populations (Pauly et al., 2002). Furthermore, overfishing has ecosystem effects (Worm et al., 2006), many of which we don’t yet understand, but which will undoubtedly affect human populations in the future. The degree to which our world is facing this crisis in global fisheries is a hotly contested subject today, a debate which eventually took place publicly in Sea Monster (2011). When purely catch-based data are used to analyze the status of global fisheries, it appears that fish stocks are in trouble and that catches are declining as a result (Worm et al., 2006; Kleisner et al., 2012). Assessments based on catch (or catch per unit effort), however, can bias the results towards being more pessimistic (Branch et al., 2011; Carruthers et al., 2011). When single-species stock assessments are analyzed, improvements in fisheries management, and in the status of stocks, can be seen (Worm et al., 2009; Branch et al., 2011). Although stock assessments offer higher-resolution data (Worm et al., 2009), they are not available for many of the world’s fisheries, for example, those in developing countries (Kleisner et al., 2012). Most fish stocks that have regular assessments done are highly managed, and often quite valuable. Species that are often caught as bycatch in these fisheries receive less attention from stock assessment scientists, as do species targeted only in developing countries, or that are not seen as particularly valuable from a global perspective. Therefore, those stocks that seem to be doing well, and which lend evidence to the argument that global fisheries are performing well, are precisely those fisheries that are in fact managed. It is not my intention here to pick one side of the debate, but no matter where we actually fall on the spectrum of poorly- to well-managed global fisheries, common ground can be found in that we are not yet at a place where improvements are unnecessary. 1  500 1000  2000  Albacore Bigeye Atlantic bf Pacific bf Southern bf Skipjack Yellowfin  0  Catch (1,000 t)  Chapter 1. Introduction  1950  1960  1970  1980  1990  2000  Year  Figure 1.1: Global catches of tuna species since 1950. Data from seaaroundus.org. With that in mind, this thesis explores the concerns and opportunities with regards to the management of one particular group of species: the tunas. In recent years, over 4 million tonnes of tuna have been extracted annually from the world’s oceans, amounting to about 5% of the global catch total. In 2005, US $17 billion worth of tuna was landed at ports throughout the world (seaaroundus.org). Tuna products are ubiquitous, consumed as everything from smoked skipjack geared towards the domestic market, to low- and medium-grade tuna in cans, to high-priced bluefin sashimi exports, served in Japanese restaurants. Since 1950, over 117 million tonnes of tuna have been removed from the ocean (Figure 1.1 (seaaroundus.org)). Further to their role in global food supply, the world’s tuna fisheries also support the livelihoods of fishers in over half of all maritime countries, providing employment and revenue. The importance of tuna fisheries to regional and global economies has been well articulated (Majkowski, 2007; Williams and Terawasi, 2009; Pala, 2011; McKenna, 2008; Collette et al., 2011; Sumaila and Huang, 2012). There are seven large species of tuna fished throughout the world’s oceans. In this thesis, I focus on the management of these seven species, which include the three bluefin species (Atlantic (Thunnus thynnus), southern (T. maccoyi ) and Pacific (T. orientalis), yellowfin (T. albacares), bigeye (T. obesus), albacore (T. alalunga), and skipjack (Ketsuwonis pelamis). Figures 1.2 and 1.3 show the 2005 catches by ocean area of non-bluefin and bluefin species, respectively. Information on how these species are targeted, the markets  2  Chapter 1. Introduction  Figure 1.2: 2005 catches, in tonnes, of skipjack, albacore, bigeye and yellowfin tuna (seaaroundus.org). they supply, 2010 catches and their conservation status is summarized in Table 1.1. Tuna are highly migratory fish: throughout their lifetime they can travel thousands of kilometers. This often means that one population of fish will spend part of its life in the waters of different countries, and in the waters of the high seas. In management jargon, this behaviour makes tuna populations known as “straddling” stocks. In 1982, the United Nations Convention on the Law of the Sea (UNCLOS) (United Nations, 1982) was convened to address some of the problems leading to overexploitation of shared fish stocks. At that time, however, issues surrounding straddling stocks were not seen as a big problem, as it was thought that catches from the high seas were a minor concern (Alexander and Hodgson, 1975; Lodge et al., 2007). With UNCLOS came the 200 nautical mile exclusive economic zone (EEZ), which resulted in the redistribution of fishing effort targeting straddling stocks from EEZs to the high seas. Today, the management of straddling stocks, which is no easy task (Bjorndal et al., 2000), is considered one of the biggest challenges to sustainable global fisheries, as they may represent as much as a third of fisheries catches 3  Chapter 1. Introduction  Figure 1.3: 2005 catches, in tonnes, of Atlantic, southern and Pacific bluefin tuna (seaaroundus.org).  4  Table 1.1: Information on tuna species, fishing gears, markets supplied, 2010 catches (FAO, 2012), and conservation status (iucn.org). Common name Albacore  Atlantic bluefin Pacific bluefin Southern bluefin Skipjack  Number of stocks 6  Gears used  Markets supplied  IUCN status  Canned/ frozen  2010 catch (1,000 t) 255.3  4  Longline and pole and line Longline  Sashimi  358.7  Vulnerable  Thunnus thynnus Thunnus orientalis Thunnus maccoyii Katsuwonus pelamis  2  Purse seine Longline  Canned Sashimi  13.0  Endangered  1  Longline  Sashimi  12.2  Least concern  1  Longline  Sashimi  9.2  Endangered  5  Purse seine  Canned  2,523  Least concern  Domestic  4  Handline and pole and line Longline  1,165  Near threatened  Purse seine  Canned  Yellowfin -  Thunnus albacares  Frozen  Near threatened Chapter 1. Introduction  Bigeye  Scientific name Thunnus alalunga Thunnus obesus  5  Chapter 1. Introduction (Munro et al., 2004). The existence of dynamic externality leads to competition among fishing countries or sectors (Levhari and Mirman, 1980). According to several sources, many of the world’s tuna stocks are either fully exploited or overexploited (Table 1.1) (Collette et al., 2011; FAO, 2010; ISSF, 2012; Miyake et al., 2010). This raises questions about the ability of these populations to continue supporting the livelihoods of millions of fishers, and to continue contributing to global food security. Management systems that take into consideration ecological and economic arguments, capacity concerns, strategic behaviour and fisher decisions are necessary to promote productive global tuna fisheries. In this thesis, I address these necessities through the development of five core papers, which provide information and options that can help improve tuna fisheries management. We know from biological studies focussing on stock abundance and distribution that the populations of most tuna species are reaching the point where increased catches will not be possible in the future. In fact, for some species, such as Atlantic and southern bluefin, we have known for decades that populations were overfished. Biological arguments, however, have not resulted in major shifts towards improved management of overfished tuna stocks, nor have they prevented the subsequent overexploitation of other tuna stocks, for example Pacific bigeye. Can an understanding of the economics of global tuna fisheries contribute to a shift in tuna management? In Chapter 2, I explore if and how information about the profitability of tuna fishing can inform management. By combining several global databases created by theSea Around Us Project and the Fisheries Economics Research Unit, both at the Fisheries Centre, I analyze the rent generated by fishing for different tuna species, fishing gear types and fishing nations. We expect that those fisheries where positive rents are being generated are likely to attract more fishing effort in the future, whereas, those fisheries generating negative rents, might be places that management should target for effort reductions. In this Chapter, I also analyze the difference between the private rent obtained by fishing companies, more conventionally called profit, and the resource rent accruing to society, i.e., the net benefits from the fishery once corrected for distortions. This comparison is possible by incorporating national subsidies into the calculations. Market distortions, for example subsidies that artificially inflate ex-vessel prices or deflate fishing costs, can make fisheries appear profitable to fishers. Yet, once these distortions are identified, these same fisheries may seem less attractive to society as a whole. The aim here is to provide information to managers about where effort is likely to increase or decrease in the future. Furthermore, this Chapter asks whether or not the gap between private rent, as the fishers see it, and the social resource rent perceived by society as a whole is an issue from society’s point of view. 6  Chapter 1. Introduction Even if, based on economic (or ecological) arguments, we know where and how to target management efforts, we need to understand how management of global tuna fisheries is actually institutionalized. In 1995, the United Nations convened a special session to address this very question. The UN Fish Stocks Agreement (FSA) formalized the management of tuna stocks (and other shared fish stocks) through groups called Regional Fisheries Management Organizations (RFMOs) (UN, 1995). The earlier UNCLOS Agreement, directs coastal states sharing a resource to cooperate in its management, but does not require states to actually reach an agreement (United Nations, 1982). This essentially allows for non-cooperation to be the default option (Munro et al., 2004). Unfortunately, with very few exceptions, cooperation between the states targeting tuna stocks is essential for sustainable fisheries management (Lodge et al., 2007; Munro, 2006). The theory of cooperative games may provide a particularly useful lens through which to view the formation and stability of cooperation within RFMOs. In Chapter 3, I provide a literature review of the use of game theory in our field since its first application to fisheries by Munro (1979). What insights has the application of this tool provided to the management of joint fisheries resources? I explore the scope for cooperation in the management of highly migratory stocks (i.e., tunas), and speculate on where game-theoretic considerations should be targeted to improve tuna management in the future. How should we tackle the possibility of catch privileges (or allocation) in shared fisheries? And how will changes in climate affect cooperative solutions? As part of their mandate, RFMOs are required to perform the function of agreeing “on participatory rights such as allocations of allowable catch or levels of fishing effort” in internationally-shared fisheries (UN, 1995). Issues of shared fisheries allocation are some of the most challenging in fisheries management (MRAG, 2006), however, most RFMOs have attempted some type of sharing program in the past, or are in the process of formulating one in the present. Five tuna RFMOs exist, managing tuna in different global oceans. The effectiveness of these RFMOs has been questioned, however, as tuna stocks have continued to decline. A recent report analyzed the performance of all RFMOs in meeting best practices criteria both in theory (as evidenced through RFMO mandates) and in practice (as evidenced by stock status reports) (Cullis-Suzuki and Pauly, 2010). On average, tuna RFMOs met best practices criteria only 59% of the time in theory, and 43% of the time in practice, meaning that their mandates are not strong enough to fully effect conservation of their target tuna stocks (Cullis-Suzuki and Pauly, 2010). We could thus conclude that there is definite room for improvement in how tuna fisheries are managed through RFMOs. In Chapter 4, I discuss the current approaches taken by the world’s tuna RFMOs to allocate benefits to member nations. These allocation approaches are often based on 7  Chapter 1. Introduction historical catches, stock abundance estimates, and distribution information. The current approach has failed to truly address declining stocks, and thus, a new approach is warranted. Some RFMOs, for example, the Western and Central Pacific Fisheries Commission (WCPFC) (MRAG, 2006) and the International Commission for the Conservation of Atlantic Tuna (ICCAT) (Cox, 2009), have discussed incorporating more than just biological criteria into their allocation programs, but neither have documented how they would quantitatively do that. Interestingly, Hardin (1968) called for multiple weighted criteria to address the Tragedy of the Commons almost fifty years ago. RFMOs are in a position now to answer this call. In Chapter 4, I ask if a new approach, where the socio-economics of interested parties are also considered, could improve global tuna management. There are other global resources that are shared between nations, for example, fresh water. In Chapter 4, I also draw on the relevant literature from internationally-shared water agreements, particularly a new approach in this field, called the “Mutual Gains Approach” (Grzybowski et al., 2010). This Approach also draws on the issues of strategic interaction between users and the need for cooperation (i.e., game theory), but does so from the perspective of the interests of a nation, as opposed to its political position (Grzybowski et al., 2010). To what extent can we learn from this in fisheries, and move away from merely thinking about allocation from a catch perspective, to thinking about it in terms of other mutual benefits, such as rent, employment, or domestic consumption? Asymmetry in players, that is, difference in perspectives and interests (e.g., differences in rates of discount and costs of fishing), can affect the outcome arrived at in game theoretic models of fishing (Munro, 1979; Sumaila, 2005), as can incomplete information (Jensen and Vestergaard, 2002). When we consider tuna fisheries management, we are often dealing with ten, twenty or thirty fishing states, all of whom have different preferences, economies, management capacities and objectives. In the Western and Central Pacific Ocean (WCPO), over thirty countries exploit four main tuna stocks: albacore, bigeye, skipjack and yellowfin. Some industrial fishing nations, such as Japan, Taiwan and Spain, have powerful fishing fleets that pay for access to fish in the waters of small Pacific Island Countries (PICs) such as Samoa and Palau. These groups of nations have obvious asymmetries. Cooperation among these fishing nations is formalized through the WCPFC, one of the RFMOs considering socio-economics in the development of their allocation scheme mentioned above. The Coral Triangle (CT) is in the western end of the WCPO, and contains parts or all of the waters of Indonesia, the Philippines, Malaysia, Papua New Guinea, Solomon Islands and Timor Leste (Figure 1.4). Over 150 million people live in the area, and an estimated 2.25 million fishers depend on marine resources for their livelihood (The Nature Conservancy, 2004). Recent figures suggest that as much as a third of all tuna catch from 8  Chapter 1. Introduction the western and central Pacific Ocean can be attributed to the fleets of Indonesia, the Philippines and Papua New Guinea (SPC, 2010), the three major tuna fishing nations in the CT. Despite their regional and global importance, however, few papers have focused on confronting the challenges these countries face with regards to tuna management. Rather, emphasis has primarily been placed on analyzing asymmetries and challenges that PICs face in obtaining adequate rents from their fisheries (Bertignac et al., 2000; Gillett et al., 2001; Parris and Grafton, 2006; Petersen, 2006; Campling et al., 2007; Walmsley et al., 2007).  WCPFC area  Coral Triangle  Figure 1.4: Map of the Coral Triangle, shown within the WCPFC Convention area. Conc WCPFC, used with permission. vention area map ⃝ Indonesia, the Philippines and Papua New Guinea all face socio-economic, institutional and management circumstances that differ from one another, and from the other larger and smaller fishing nations in the region. Tagging studies have demonstrated a high degree of interaction between CT tuna fisheries and those to the east (Vera and Hipolito, 2006; Ingles et al., 2008), while a recent stock assessment for yellowfin reports that the domestic fisheries of the Philippines and Indonesia are in part responsible for stock depletion (Langley et al., 2009b). Tuna fisheries and their management in these countries, therefore, impacts other nations fishing for tuna in the WCPO. How are fisheries managed in these countries? Are there programs in place that have been particularly effective at promoting sustainable fisheries in the region? In this Chapter, I compare and contrast tuna fisheries in the three countries, as well as their management regimes and current 9  Chapter 1. Introduction management challenges. This analysis is aimed at improving CT management capacity, in hopes of facilitating improved regional management of a valuable transboundary resource. One of the major management challenges facing the WCPFC is the bycatch of juvenile yellowfin and bigeye tuna in the purse seine skipjack fishery. The regional purse seine fishery has increased its reliance on fish aggregating devices (FADs), essentially floating objects that attract adult skipjack and yellowfin, along with juvenile yellowfin, bigeye and other non-tuna species such as dolphinfish and marlin. Much of the juvenile tuna bycatch occurs in the waters of the Coral Triangle countries. Adult yellowfin and bigeye are targeted by countries inside and outside the Coral Triangle, and thus there is a very intriguing and important conflict of interest between the two groups that needs to be explored. Chapter 6 addresses this conflict by estimating the potential benefits of cooperative management of tuna fisheries in the WCPO. Fisheries in the region currently operate in a non-cooperative, or competitive way, whereby each fishing group makes decisions based on its own self-interest. I develop a bioeconomic game-theoretic model to determine if, at equilibrium, moving away from non-cooperation through the elimination of juvenile fishing could bring economic benefits to the region. Specifically, I examine non-cooperative and cooperative outcomes for a three player game: purse seine; longline; and handline, and incorporate skipjack, yellowfin and bigeye as target species. Given a long-term perspective, what would be the optimal effort allocation between different fishing gears if we seek to maximize cooperative rent? How does this compare with the current effort allocations we see in the WCPO? I hypothesize that reductions in juvenile bycatch will, in fact, have a positive impact on resource rent from the fisheries, as it will eliminate (or at least reduce) growth overfishing, whereby fish are harvested when they are too small. This will probably require a decrease in effort by purse seine vessels. Chapters 2 through 4 of this dissertation highlight broad issues worth tackling in the quest for more effective management of global tuna fisheries. The objectives here are to improve our understanding of how rent, cooperation and allocation approaches can facilitate the move towards sustainability. Chapters 5 and 6 tackle issues associated the world’s most important tuna-producing region, the Western and Central Pacific Ocean. The objectives of these Chapters are to analyze the management systems of Indonesia, the Philippines and Papua New Guinea, to provide some recommendations for improved regional management, and to estimate the possible economic gains to the region from cooperative management. Over half of the world’s tuna supply comes from this region, so improving tuna management here could help move the majority of the world’s tuna supply to a more sustainable model. Such improvements could also help to inform and improve tuna management in other regions. 10  Chapter 1. Introduction Fisheries management is complex, and requires ecological, economic, social and institutional perspectives to facilitate adequate and effective management. Current management of global tuna fisheries is falling short of promoting a sustainable resource base, long-term employment, a steady revenue stream, and a reliable supply of food. The aim of this thesis is to use economic tools, arguments and methods to increase our understanding of the current issues in, and barriers to, sustainability in the tuna fishing sector, and to provide inputs that can help us move toward improved management of global and regional tuna fisheries.  11  Chapter 2  Informing global tuna fisheries management: Private versus social resource rent 2.1  Introduction  Fisheries are a global economic sector, providing both income and food for virtually every country on earth. In 2000, the landed value of the world’s marine capture fisheries was estimated at about US $80 billion (Sumaila et al., 2007). One particular group of fish, the tunas, is of immense global economic importance, with various species being fished by 82 countries, or 56% of all maritime states, and having a landed value of US $17 billion in 2005 (seaaroundus.org). Tuna products are consumed all over the world, including everything from smoked skipjack consumed domestically, to low- and medium-grade tuna in cans to high-priced bluefin sashimi served in Japanese restaurants. Since 1950, over 117 million tonnes of tuna have been removed from the ocean (Figure 1.1), averaging about 2.06 million tonnes per year (seaaroundus.org). The importance of tuna fisheries to regional and global economies has been stated several times in diverse places, everywhere from management reports (Majkowski, 2007; Williams and Terawasi, 2009), media and outreach pieces (Pala, 2011; McKenna, 2008; Bailey, 2012), to scientific literature (Collette et al., 2011; Sumaila and Huang, 2012). Often times, however, economic value is viewed solely from the perspective of the “landed value”, that is, the gross revenue attained for landing the fish at port. With few exceptions (Sumaila and Huang, 2012), the costs associated with fishing these species are generally not reported on, and as such, net revenue, or resource rent, is not discussed. In light of this, I provide the first estimate of the net economic rent of global tuna fisheries in this Chapter. Rent is calculated in two ways. Firstly, private rent is calculated from the perspective of fishers or fishing companies. This is the difference between revenues obtained and costs incurred through harvesting, and is in fact producer surplus. Secondly, the social resource rent is calculated from the perspective of fishing countries (i.e., society). This estimate  12  2.2. Global tuna fisheries includes national subsidies, and thus better represents what society is gaining (or losing) through the global tuna sector. In this paper, I also demonstrate the method of utilizing large global databases to infer economic realities about fisheries.  2.2  Global tuna fisheries  There are seven large tuna species targeted globally, split into 23 stocks (ISSF, 2012). The seven large species, all members of the Scombridae family, include albacore (Thunnus alalunga), yellowfin (T. albacares), bigeye (T. obesus), southern bluefin (T. maccoyii), Atlantic bluefin (T. thynnus), Pacific bluefin (T. orientalis) and skipjack (Katsuwonus pelamis). Tuna are considered a straddling stock in that they are found in the exclusive economic zones (EEZs) of more than one country, and also in the high seas. But they are a special type of straddling stock, namely, “highly migratory species”, a term which became prominent in the literature after the 1995 United Nations Straddling Fish Stocks Agreement (UN, 1995). The Agreement was primarily an attempt to facilitate cooperation between fishing nations exploiting a common pool resource, as cooperative management is generally preferred to non-cooperation if sustainable use is the goal (Singh and Ballabh, 1996; Ostrom et al., 1999; Sumaila, 1999; Bailey et al., 2010). Generally speaking, the state of global tuna stocks is worrisome. Of the seven species reported on in this paper, the International Union for Conservation of Nature (IUCN) lists Atlantic and southern bluefin as endangered, bigeye as vulnerable, albacore and yellowfin as near threatened, and (only) Pacific bluefin and skipjack as of least conservation concern (Table 1.1) (IUCN, 2011). All three bluefin species exhibit life history traits that make them particularly vulnerable to over exploitation, including slow growth and late maturity (De Roos and Persson, 2002), compared to their smaller con-specifics. Furthermore, they are temperate water species, which are generally less productive than tropical species (Majkowski, 2007). For species such as bigeye and yellowfin, their association with skipjack around floating objects, specifically in the Pacific, makes them susceptible to growth overfishing1 due to juvenile bycatch (Miyake et al., 2010; Bailey et al., In press; Langley et al., 2009a,b). Skipjack stocks in the Pacific are probably underexploited, and so planned future increases in fishing effort for this target species are likely to have a negative impact on yellowfin and bigeye stocks in the region if today’s fishing practices continue. Albacore stocks are considered near threatened. Table 1.1 gives the number of separately managed stocks for each tuna species. Several gear types are used to fish for tuna, depending on the species being targeted 1  Growth overfishing occurs when fish are harvested before the point at which individuals reach the maximum yield per recruit.  13  2.2. Global tuna fisheries  Figure 2.1: 2005 tuna catches (in tonnes) from the world’s oceans (Data from seaaroundus.org). and the markets being supplied (Table 1.1). Purse seines target mostly skipjack tuna and adult yellowfin, often taking advantage of tuna’s propensity to aggregate around floating objects. Most purse-seine caught skipjack and yellowfin are sent to canneries, providing ‘light’ tuna. Pole and line, troll, and longline are used to target albacore, which supplies both the canned tuna market (sold as ‘white’ tuna) and the frozen tuna steak market. Longlines are usually the gear of choice to catch bigeye and bluefin species, which supply the sashimi market. Artisanal gears are also utilized to catch tuna, such as ringnet, gillnet and handline. Figure 2.1 shows the catches of aggregated tuna species by ocean area in 2005. Effective management of shared fish stocks often requires cooperation by several fishing nations (Chapter 3). This essential cooperation is facilitated by Regional Fisheries Management Organizations (RFMOs) (UN, 1995). Five tuna RFMOs exist (see Figure 4.1), managing tuna in different global oceans. The effectiveness of these RFMOs has been questioned, however, as tuna stocks have continued to decline. A recent report analyzed the performance of all RFMOs in meeting best practices criteria (set out by Chatham House in Lodge et al. (2007)) both in theory, as evidenced through RFMO mandates, and in practice, as evidenced by stock status reports (Cullis-Suzuki and Pauly, 2010). On average, tuna RFMOs met best practices criteria only 59% of the time in theory, and 43%  14  2.3. Subsidies, welfare economics and the fishery of the time in practice, meaning that their mandates are not strong enough to facilitate conservation of their target tuna stocks (Cullis-Suzuki and Pauly, 2010). The main tuna RFMOs are reviewed in Table 2.1, along with their performance in meeting these best practices criteria.  2.3  Subsidies, welfare economics and the fishery  Fisheries economists have generally focused on tackling the issues of inefficiency in global fisheries, for example overcapacity, and have, by and large, ignored issues of distribution and equity (Bromley, 1977; Charles, 1988; Weninger and McConnell, 2003; Tietze et al., 2005; Beddington et al., 2007). That being said, any fisheries management decision or policy tool will have some impact on the distribution of resources, be they in the form of labor and employment, or in the form of food security. While the focus in fisheries economics has largely been placed on judging a policy tool based on its economic efficiency, welfare economics allows us to judge a policy tool based on how it changes the utility (or value) of the resource to members of society. Instead of maximizing profit to one small subset of society (fishers), incorporating the concepts of welfare economics would have us maximizing benefits to society as a whole (Arrow, 1963). Put simply, welfare economics allows us to evaluate the economic well-being within a society resulting from the allocation of resources. Subsidies are any direct, or indirect, transfer from a public entity (such as the government), to a fishing sector, which essentially gives the fishing sector an economic advantage, encouraging fishers to fish more than they otherwise would (Sumaila et al., 2010). In this way, government subsidies to the fishing sector are a choice on the allocation of public resources to a small fraction of society. Plainly stated, fisheries subsidies exacerbate the problems of overcapacity and overfishing (Arnason, 1998; Clark et al., 2005; Clark, 2006). Two studies in the 1990s estimated that between US $14-54 billion were being transferred to the global fishing sector annually (FAO, 1992; Milazzo, 1998). The World Bank, Organization for Economic Cooperation and Development (OECD), FAO, and conservation groups such as Pew and World Wide Fund for Nature (WWF) have all focused in on fisheries subsidies as an issue to be tackled. A more recent estimate of global fisheries subsidies was calculated by the Sea Around Us Project and the Fisheries Economics Research Unit through the development of a subsidies database containing information on 148 maritime countries for the year 2003 (Sumaila et al., 2010). This updated subsidies database estimated global fisheries subsidies to be between US $25-29 billion, with fuel subsidies making up about 15-30% (Sumaila et al., 2010). In this paper, I consider the benefits from fishing that accrue to the fishing 15  RFMO  Full title  CCSBT  Commission for the Conservation of Southern Bluefin Tuna Inter-American Tropical Tuna Commission International Commission for the Conservation of Atlantic Tunas Indian Ocean Tuna Commission  1994  Southern bluefin  1949  Western and Central Pacific Fisheries Commission *Cullis-Suzuki and Pauly (2010)  2004  Albacore, skipjack, yellowfin, bigeye and Pacific bluefin Albacore, skipjack, yellowfin, bigeye and Atlantic bluefin Albacore, skipjack, yellowfin, bigeye, Southern bluefin Albacore, skipjack, yellowfin and bigeye  IATTC ICCAT IOTC WCPFC  Year of entry  1969 1996  Tuna species covered  Performance (%) (theory, practice)* 44, 0 60, 33 57, 38 58, 78 74, 67  2.3. Subsidies, welfare economics and the fishery  Table 2.1: Tuna RFMOs, species managed, and performance at meeting best practices criteria.  16  2.4. Methods sector itself as private resource rent, also known as profit. Unfortunately, this private rent contains market distortions resulting from subsidies, and consequently yields an incomplete understanding of the benefits of global tuna fisheries to society as a whole. Subsidies can be divided into those that positively affect stock sustainability (“good”), negatively affect stock sustainability (“bad”), and those whose impact is not always clear (“ugly”) (Sumaila et al., 2010). Bad subsidies include things that increase capacity, such as fuel subsidies, or processing and storage infrastructure support. Although we often think of subsidies as lowering the cost of fishing, it is also important to remember that they can act through increasing revenue instead, for example through elevated prices due to favourable trade conditions. In this paper, I subsequently incorporate subsidies into the resource rent calculation, thus accounting for these market distortions. Viewing the economic benefits derived from the fishery from the perspective of social resource rent, as opposed to private rent, is better-aligned with the concepts of welfare economics and speaks to the broader benefits (or lack thereof) of fisheries as common pool resources.  2.4  Methods  For over a decade, the Sea Around Us Project and the Fisheries Economics Research Unit at the University of British Columbia have been collecting and aggregating fisheries data for most commercially targeted fish species and maritime countries. Here, I combine catch, price, cost and subsidies databases to construct a picture of the current economic condition of global tuna fisheries. Particular emphasis is given to the difference between private and social rent originating from global tuna fisheries.  Catches The global catch database is based on data provided by the Food and Agriculture Organization of the United Nations (FAO), which are then supplemented by unreported and unregulated catch reconstruction data (Zeller et al., 2006). Catches are assigned to geophysical marine areas either through the existence of direct data of where a catch occurred, or through a rules-based allocation algorithm taking into account which countries have access to what species, and where and how species are distributed throughout the oceans (Watson, 2004; Watson et al., 2005). The catch database begins reporting catches in 1950, and, at the time of writing, contains estimates of catches by country, fish species, and fishing gear up to the year 2006. Catches (h) of species (s) by gear type (g) and maritime country(m) for the 2005 year are used in this study.  17  2.4. Methods Table 2.2: Mean price per tonne by species (weighted by catch) and number of observations used for calculations. Species Skipjack Albacore Yellowfin Southern bluefin Bigeye Atlantic bluefin Pacific bluefin  Mean price (USD/t) 3,818 4,003 4,341 15,684 4,533 3,929 6,307  Number of observations 265 220 355 28 224 111 23  Prices Although the FAO publishes information on the price of processed fish products, data on ex-vessel prices (i.e., first-hand prices that fishers receive when they land their fish) are not always easy to come by. To fill this information gap, an ex-vessel price database was constructed in 2007 as a way of turning ecological information, catches, into economic information, landed values (Sumaila et al., 2007). This combination of prices and catches allows users to attach landed values to species in time and in space. In developing the database, prices were entered either directly from sources such as governmental agencies, national websites, expert knowledge, published literature, or, if records on prices could not be found, they were calculated from a rules-based algorithm (Sumaila et al., 2007). The algorithm allowed weighted means to be applied within years, countries and/or taxa, with the quality of the data being tracked along the way (Sumaila et al., 2007). The mean ex-vessel prices (weighted by catch tonnage) used in this analysis are shown for each tuna species in Table 2.2. Price (p), and the catch volume (h), determine the landed value of the catch, or the gross revenue (TR) a fisher (or country) attains from a given fishing trip. The 2005 landed value is computed for each of our seven tuna species of interest (s) and for each maritime country (m). Thus, the total revenue country m receives for fishing species s with gear g is calculated as: T Rm,s,g = pm,s hm,s,g  ∀m, s, g  (2.1)  The total revenue to country m is then simply the sum of the total revenues for each tuna species harvested and for each gear type used. T Rm = Σs,g T Rm,s,g ,  ∀m  (2.2)  18  2.4. Methods Similarly, total or mean revenue by species or gear can be calculated by summing across all maritime countries for each gear and species.  Costs Fishing costs play a major role in determining the behaviour of fishers and thus fishing fleets. Up until 2011, however, reliable estimates on the cost of fishing were not consistently published or adequately summarized. There are several reasons for the lack of data, including the extensive amount of effort required to collect cost information and the lack of reporting requirements for this type of information by government agencies (Lam et al., 2011). Therefore, a fishing cost database was developed in 2011, aimed at quantifying costs for various types of fishing gears in all maritime countries for the 2005 year (Lam et al., 2011). Data were gathered from secondary sources such as grey literature, and government, FAO and consultant reports, along with requests for information from global partners (Lam et al., 2011). The authors were able to source information on, or interpolate data for, countries that made up 98% of the global fisheries catch (Lam et al., 2011). Fishers face two main types of costs, fixed and variable. The former are costs not dependent on fishing operations directly, often called sunk costs, for example, the cost of the vessel itself. Variable costs are those that vary with the level of fishing activity, for example, fuel, gear maintenance and labour costs. Costs reported in the Lam et al. (2011) database, and used in this analysis, include a normal profit estimate, and are thus economic costs of fishing, as opposed to accounting costs. For the purposes of this paper, cost estimates for purse seine, pole and line, longline, gillnet and hook and line are of particular interest, as they combined for over 96% of all tuna catches in 2005. Unit costs (c) are expressed on a per tonne basis for each gear type g. The lowest costs of fishing, US $259/t as published in (Lam et al., 2011), were for purse seining in some South American and Caribbean countries. The highest unit cost of fishing, US $7,092/t, were for longlining by South Pacific Island countries (Lam et al., 2011). Where cost data were missing for a particular geo-political entity for which I had catch and price data, mean unit costs, weighted by catch tonnage, were used. This occurred for territories of certain countries. For example, a cost estimate for tuna fishing in American Samoa did not exist because it is a United States entity. To avoid making a judgement between whether U.S. costs or costs similar to other Pacific Island nations were more representative of American Samoa, the weighted means were used for the gears utilized. Countries for which weighted means were applied are indicated with an asterisk in Appendix A. The total cost (TC) for country m fishing with gear g in 2005 is thus given as:  19  2.4. Methods  T Cm,g = cm,g hm,g ,  ∀m, g  (2.3)  The total cost of fishing to country m is then calculated by summing over all gears and species.  Subsidies In addition to specific subsidies estimates, the subsidies database (Sumaila et al., 2010) contains the computed subsidy intensity (λ), or the proportion of a country’s total landed value that is subsidized (all subsidy categories combined). Because it is not currently known what amount (absolute or relative) of a nation’s subsidies go directly to supporting the tuna fishing sector, I use the intensity as a proxy and apply it to the landed value of fishing for tuna species. For example, if a country had a reported subsidy intensity of 0.25 in Sumaila et al. (2010), and its landed value of all tuna species combined in 2005 was US $1 million (based on the price and catch databases), then we would conclude that subsidies amounting to US $250,000 were transferred by that country’s government to the tuna fishing sector. The subsidy intensity ranged from 0 to 2.92, with a mean value of 0.405 (Sumaila et al., 2010). This intensity is applied to the estimated landed value (or TR, as defined above) for the 2005 year for each country and as follows: T S m = λm T Rm ,  ∀m  (2.4)  Rent estimates Resource rent as applied to fisheries is formally defined as the difference between the total revenue and the total cost of fishing (Clark, 2006). It is important to note that for this to be true, the total cost estimate must incorporate the opportunity cost of a country (or gear type) using its resources in some other sector, thus allowing for normal profit (Clark, 2006). This is true for the cost estimates developed in Lam et al. (2011), and used in this analysis. In this paper, I calculate rent in two different ways. Firstly, private rent is computed from the simple definition of subtracting total costs from total revenues. This is done for the 2005 year for each country and species caught with each gear as such: πm,g,s = T Rm,g,s − T Cm,g,s ,  ∀m, g, s  (2.5)  Secondly, the subsidies-adjusted resource rent (π λ ) for each country in 2005 is computed. This is what I consider the social resource rent:  20  2.5. Results  Figure 2.2: Social rent by country.  λ πm = πm − T Sm ,  2.5  ∀m  (2.6)  Results  The private rent generated from global tuna fisheries, or that which is perceived by the fishing industry, was an estimated US $4.70 billion in 2005. This ranged from a maximum private rent of US $1.62 billion (Japan) to a low of US -$816 million (South Korea). This private rent is the difference between the total revenues generated by fishing for the seven key tuna species of interest (all gears combined) and the total costs incurred through these fishing operations (again, all gears combined). When subsidies are accounted for, the social rent is an estimated US -$951 million. Japan and South Korea generate the most and least social resource rent, respectively. The difference between the private and the social rent can be thought of as a social opportunity cost, essentially the amount of money that society could choose to put elsewhere, into its ‘next best option’. The sum of the opportunity cost over all countries amounted to US $5.63 billion in 2005. Table 2.3 shows the private (before subsidies) and social (after subsidies) resource rent each country derives from the fishing of specifically bluefin tuna species, while Figure 2.2 shows countries generating positive, zero and negative social rent from fishing all tuna species combined. Only Japan, Italy, New Zealand and Croatia derive substantial positive social rents from fishing for bluefin tuna, with Ireland and the U.S. also having positive social rents, although to a lesser extent. 21  2.5. Results  Table 2.3: Private and social rent (USD) for bluefin fishing nations (all bluefin species combined). Country Spain France Morocco Tunisia Malta Mexico Algeria Indonesia Taiwan Portugal Cyprus Greece Denmark Uruguay USA Ireland Croatia New Zealand Italy Japan  Private rent (USD) -2,885,881 -532,373 -2,456,061 -1,453,750 -92,536 -666,347 -424,065 -296,140 12,035 -62,395 24,649 359 -265 -47 20 430 323,792 852,044 7,666,626 94,125,476  Social rent (USD) -6,830,267 -3,709,356 -2,664,997 -1,617,400 -798,714 -729,192 -445,313 -384,296 -360,456 -79,143 -22,509 -1,247 -284 -49 12 170 242,810 800,795 4,142,360 56,645,033  22  2.5. Results skipjack  southern bf  yellowfin 20000 15000 10000  GN  gillnet  HL  handline  LL PL  longline pole and line  PS  purse seine  Unit rent (USD/t)  5000 0 −5000  albacore  Atlantic bf  bigeye  Pacific bf  20000 15000 10000 5000 0 −5000 GN  HL  LL  PL  PS  GN  HL  LL  PL  PS  GN  HL  LL  PL  PS  GN  HL  LL  PL  PS  Gear type  Figure 2.3: Private rent per tonne (difference in price per tonne and cost per tonne) by tuna species and gear type, aggregated over all fishing nations. bf refers to bluefin. Complete disaggregated results can be found in the Appendix (A.1). Private rent estimates by species and by gear type are summarized below.  Species and gears I calculated the mean rent per tonne, or difference in price and cost per tonne, for each major gear type employed in global commercial tuna fisheries. There is wide variability from fishing the various tuna species with different gears (Figure 2.3). Gillnets have the highest rent per tonne at US $3,859 per tonne, followed by purse seine (US $3,093/t), hook and line (US $3007/t), pole and line (US $2,329/t), and with longline (US $464/t) having the lowest mean rent per tonne. Note that these means are aggregated across all species caught. While fishing for Atlantic bluefin offers the highest possible individual rent per tonne (Figure 2.3), the mean is actually the lowest of all of the species at US $981/t (Table 2.4). The highest mean unit rent is for southern bluefin tuna (Table 2.4). The total private and social rents, as discussed above, were disaggregated by species, shown in Table 2.4. From  23  2.6. Discussion a species perspective, only fishing for yellowfin appears to be bad business, as this species contributes negatively to overall private rent (Table 2.4). Once subsidies are included, however, fishing for albacore, skipjack, yellowfin, and Atlantic bluefin yields negative social rents. This suggests that, with the exception of southern and Pacific bluefin and skipjack, subsidies are making unprofitable fisheries seem otherwise profitable to fishers. Table 2.4: Species summary: mean unit rent, private and social rent. Species  Mean rent (USD/t)  Albacore Bigeye Skipjack Yellowfin Atlantic bf Southern bf Pacific bf  2,116 2,510 2,829 2,153 981 5,865 3,533 Total  2.6  Total private rent (million USD) 125 633 4,057 -371 46 170 20 4,681  Total social rent (million USD) -183 -77 792 -1,562 -6.2 76 9.5 -951  Discussion  No doubt tuna fisheries provide substantial revenues in the form of landed value for fishing nations. Once costs and subsidies are accounted for, however, the net social rent from global tuna fisheries is negative. There are vast differences in the distribution of rent by country, species, and by fishing gear. Furthermore, there is a substantial difference in the private and social resource rent. Currently, subsidies amounting to over US $5 billion are being transferred to the tuna fishing sector from national governments. This is money that countries are choosing to put into various fishing sectors that may not be providing positive economic returns for the country, and may be fueling overexploitation of tuna stocks.  Bluefin Fishing for bluefin tuna still remains a potentially profitable endeavor, with the private mean rent per tonne (difference in per tonne revenues and costs) for southern and Pacific bluefin species being higher than for non-bluefin species. Atlantic bluefin, however, offers the lowest unit rent of all tuna species analyzed in this study. Both Atlantic and southern bluefin tuna are overfished (MacKenzie et al., 2009; Collette et al., 2011; ISSF, 2012),  24  2.6. Discussion yet remain, to varying degrees, profitable. This is especially true with regards to private rent, or the subsidized amount perceived by fishers. Once subsidies have been accounted for and social rent estimated, however, fishing for Atlantic bluefin is no longer profitable. This should offer even more impetus to follow rebuilding plans as suggested in MacKenzie et al. (2009), to reduce subsidies that are probably encouraging overexploitation, and to remove capacity that is not generating positive rent. The results here suggesting that profitability is not for high for Atlantic bluefin fisheries agrees with work conducted by Bjorndal and Brasao (2009), which concluded that profits could be much higher for those involved in fishing for Atlantic bluefin in the Eastern Atlantic and Mediterranean if an increase in stock size resulted from a recovery program. The authors make the case that allowing overexploitation to continue has large economic costs in terms of forgone future income, and make a solid case for rebuilding (Bjorndal and Brasao , 2009). Some fishing nations (notably Italy, Japan and New Zealand) still stand to have positive social rents from fishing bluefin tuna, while other countries, such as Spain and France, collect only negative rents. Many other fishing nations are fishing right around the zero social resource rent point. Policy recommendations based on decreasing effort (and catch), like those argued for in Bjorndal and Brasao (2009), could be targeted at those countries whose subsidies are negating any positive rents. This may prove to be more effective than targeting those countries that are seeing positive economic benefits. If, as the Bjorndal and Brasao (2009) paper argues, substantial increases in rent are possible by reducing effort and allowing stock rebuilding to take place, then there may in fact be a strong case for exploring the notion of side payments to facilitate this process (see Chapter 3 for more on side payments).  Albacore, skipjack, yellowfin and bigeye Fishing for albacore and yellowfin, species that are considered near threatened by the IUCN and reported as fully exploited by scientists (ISSF, 2012; Langley et al., 2009b; IUCN, 2011; Collette et al., 2011), still offers positive private rents, albeit lower than most of the other species. The sum of the private rents from yellowfin fishing, however, is negative, despite the positive unit mean. Overall, therefore, fishing for yellowfin tuna is a losing endeavor, even before subsidies have been considered. Fishing for skipjack tuna, an underexploited species, and bigeye, which is of conservation concern (Harley et al., 2010; ISSF, 2012), also have positive mean private rents per tonne. Once subsidies are considered, however, fishing for bigeye contributes negative social rent. Skipjack tuna make up over half of all global catches (ISSF, 2012). That fishing for this species offers positive social rent does suggest that increasing effort in these fisheries is  25  2.6. Discussion likely. Some of the cost savings from skipjack fishing comes from the use of fish aggregating devices (FADs) used by purse seiners, which reduces fuel consumption (Miyake et al., 2010). Conservation measures put in place by the Western and Central Pacific Fisheries Commission to limit the use of FADs (due to the issue of juvenile tuna bycatch) (WCPFC, 2009), could result in increased costs to purse seiners in this region, and a decrease in the private and social rents generated by this fishery in the future. This in turn would most likely result in less effort or capacity moving into this particular fishery than would otherwise be predicted.  Conclusion This analysis finds that the mean unit rents through fishing for all tuna species offers potential positive returns, from the fishers’ point of view. This could suggest that effort will continue moving into these fisheries, even though several of the stocks are in danger of overexploitation. It is important to note, however, that the data used here, specifically the cost estimates, are based on fuel costs prior to the large increases occurring since 2008. It is likely that the costs of fuel have been increasing more quickly than the ex-vessel price of fish, and thus the unit rent in 2005, as estimated here, might be higher than what we would calculate based on current costs. Cost data are equally as important as revenue data in determining resource rent, yet to date, the cost database used here is the only publicly available global reference. Improvements in cost estimates of all components of fishing operations will likely lead to improved estimates of fisheries rents, from tuna stocks and others, in the future. Subsidies can alter the perceived rent possibilities, encouraging overcapitalization (Arnason, 1998; Clark, 2006; Sumaila et al., 2010). For fish populations that are fully or overexploited, increased effort resulting from overcapitalization can lead to decreased stock size, as well as reduced resource rent for all fishing nations. Furthermore, excess capacity in global tuna fisheries is thought to contribute to management challenges and hinder effectiveness of RFMOs (Miyake et al., 2010). In this analysis, national subsidies to global tuna fisheries amounted to US $5.63 billion in 2005. Due to the fact that, besides skipjack and Pacific bluefin, the world’s tuna species are fully or overexploited, these subsidies are essentially society’s contribution to depletion of its tuna stocks. To what extent is society benefiting from this disinvestment in fisheries capital? This is a question that should be tackled through a better incorporation of welfare economics into decisions about tuna management. It seems for many countries that positive social rents are not being generated by fishing for tuna. Society’s support for this disinvestment is therefore leading to economic losses, in addition to ecological losses. More national accountability, coupled  26  2.6. Discussion with improved management by RFMOs is going to be necessary to reduce the gap between private and social resource rents generated from global tuna stocks.  27  Chapter 3  Application of game theory to fisheries over three decades 3.1  Introduction  Background Game theory is a tool for explaining and analyzing problems of strategic interaction (Eatwell et al., 1989). Essentially, it uses mathematics to describe player strategies in sources of conflict and common interest, and predicts what rational players should do (Luce and Raiffa, 1957). A game consists of a set of players, a set of strategies available to those players, and a set of possible payoffs for each combination of strategies. A strategy refers to any option that a player can take and it must specify what action will happen in each contingent state of the game (i.e., if player A chooses strategy x, player B will choose strategy y or z). Modern approaches to game theory are usually attributed to von Neumann and Morgenstern (1947), although Luce and Raiffa (1957) point out that there are earlier contributions. Following the von Neumann and Morgenstern work, game theory was expanded on by John Nash, who is probably best known for his work on non-cooperative (Nash, 1951) and cooperative (Nash, 1953) solutions (for which he was awarded the Nobel Prize in economics in 1994). Subsequently, game theory has been used in a number of worldwide applications, including political science, evolutionary biology, military strategies, economics, including natural resource and environmental economics, and computer science (Eatwell et al., 1989). Game theory deals with the strategies decision makers choose, as individuals or in some forms of collusion, to maximize their outcome in a given situation (Luce and Raiffa, 1957). We can see that the issues of fisheries management fit well within this game-theoretic framework as fishers and/or managers seek to maximize the benefits from a given fishery. Games are structured around players, the constraints they face, the information sets they possess, and the possible outcomes players expect. The players in game-theoretic analyses are assumed to be rational, essentially each player seeks to maximize their potential outcome through an understanding that all other players are seeking the same goal (Luce and 28  3.1. Introduction Raiffa, 1957). The rationality assumption helps us to identify preferred outcomes among a set of possible outcomes (Davis, 1997). The value of an outcome is usually expressed as ‘utility’ in game theory (Luce and Raiffa, 1957). In a game, utility represents the motivations of a player. A utility function is a value assigned to each player for each possible outcome of the game. As the utility function increases, the respective outcome is viewed as more desirable. For example, a player will prefer outcome L1 to outcome L2 if and only if the expected utility of L1 is greater than that of L2 .  Game theory and fisheries From society’s point of view, overfishing is wasteful, both biologically and economically, yet it happens often (Clark, 2006). The theory of games offers some insights into why fishers may be driven to adopt strategies that seem to be irrational; why overfishing may in fact be an economically rational action (Kaitala and Lindroos, 2007). Game theory is particularly applicable to the study of resource management, such as fisheries, as many of the world’s natural resources are common pool in nature (Sumaila, 1999). We can divide shared fisheries resources into four main categories: 1. Domestic shared stocks: those stocks fished by more than one entity within a coastal state’s exclusive economic zones (EEZ); 2. Transboundary resources: those occurring in the EEZs of 2 (or more) coastal states; 3. Straddling stocks: those occurring in the EEZs of at least one coastal state and the high seas (including highly migratory species, i.e., tuna); 4. Discrete high seas stocks: those occurring only in the high seas. Generally speaking, the list above is in increasing order of the level of management difficulty. The first relevant paper analyzing fisheries in a game-theoretic context was authored by Munro (1979). The author was motivated to write his seminal paper by the increasing acceptance of extended fisheries jurisdiction which he believed would, and in fact did, lead to increased management of fisheries by individual coastal states (Munro, 1979)2 . He argued that the issue of managing transboundary fish stocks, those that moved between 2 Munro also credits the inspiration for this paper to Hnyilicza and Pindyck (1976), a report analyzing cooperative behaviour in pricing policies by the Organization of Petroleum Exporting Countries (OPEC). The majority of game theoretic work in economics had focused on non-cooperative or competitive games, and this report was one of the first to start viewing world situations in a cooperative way (Munro, personal communication).  29  3.2. Early years: The two-player game two or more EEZs, would require a joint approach, and as such, he applied the theory of bargaining, or cooperative games, to the problem (Munro, 1979). Interestingly, the United Nations Convention on the Law of the Seas, a result of which was the 200 nautical mile EEZ, suggested that although coastal states sharing a resource must seek to cooperate, they are not required to reach an agreement (United Nations, 1982). This essentially allows for non-cooperation to be the default option (Munro et al., 2004). This outcome is often referred to as the Prisoner’s Dilemma, where players are driven to adopt sub-optimal strategies, from the perspective of the group. Note, however, that non-cooperation does not automatically imply a negative situation. Cooperation is a more flexible outcome because players could, of course, choose the non-cooperative payoff as their solution, so a point could exist where cooperation and non-cooperation result in the same outcome. Munro et al. (2004) point to the North Atlantic scallop fishery off the east coast of Canada and the U.S. as an example where non-cooperation puts players in no worse state than cooperation would. In this example, there is limited interaction between the fleets of the two countries, primarily because adult scallop are fairly sedentary. It has been thirty years since Munro’s work was published. We can now reflect on three decades worth of academic and practical applications of game theory to fisheries and ask how influential this paper has been in terms of shaping fisheries management today. In the following section, I summarize the earlier game-theoretic analyses, which involved mostly two-player approaches. The last decade has produced major gains in the theory of games as applied to fisheries, specifically with the incorporation of coalition theory into the analyses, which allows for the development of game-theoretic models with greater than two players. These gains are discussed in Section 3.3. By drawing on current issues in international fisheries, and international environmental issues as a whole, Section 3.4 highlights where fisheries economists are directing their focus today, with respect to game-theoretic applications, and where that focus is likely headed in the next decade.  3.2  Early years: The two-player game  Munro (1979) investigated how asymmetry in players, for example, players facing different rates of discount and costs of fishing, can impact the cooperative solution when considering a fishery resource that is shared between two coastal states. One of the most relevant conclusions in Munro’s analysis is that, given that players often have different preferences and perspectives, joint management of a resource is greatly simplified with the possibility of side payments, or what is also called transferable utility (Munro, 1979). Transferable utility is a term used in cooperative game theory and in economics. Utility is transferable if one player can ‘costlessly’ transfer part of its utility to another player. Such transfers 30  3.2. Early years: The two-player game are possible if the players have a common currency that is valued equally by all. Interestingly, the term ‘side payment’ has been met with scepticism by the international fisheries management community. Fisheries economists may be well advised to rename this policy tool if it is, in fact, going to be a valuable aide in reaching cooperative agreements3 .  Dynamic externality Levhari and Mirman (1980) published their influential paper on ‘fish wars’ a year after Munro (1979). In their two-player analysis, the authors highlight two important gametheoretic features of fisheries management: that the underlying stock is affected by both players’ decisions; and that each player must take into account the other players’ actions (Levhari and Mirman, 1980). These two features create what is known as ‘dynamic externality’ (Levhari and Mirman, 1980) and it is this fundamental situation that allows game theory, the study of strategic interaction, to be applied to fisheries (Sumaila, 1999). That same year, Clark (1980) published a game-theoretic paper exploring restricted access to common property resources. Clark was motivated to apply game theory to the fishery problem due to the increase in limited entry programs being initiated by fishing countries. This insightful analytical work demonstrated that, for a limited entry system with at least two players, the competitive (or non-cooperative) game results in overfishing, which is in fact what we readily observe in reality. Following the Munro, Levhari and Mirman, and Clark papers, many other contributions were published, mostly in the 1990s, applying game theory to highlight several of the most pressing issues in fisheries management, specifically how to manage shared stocks. Generally, these games took the form of cooperative and non-cooperative games, with authors usually illustrating the gains to the system through cooperative management (Sumaila, 1999). Nash defined cooperation as occurring when players in the game are able to discuss and agree upon a joint plan (they can communicate), and that the agreement is ‘assumed to be enforceable’, or binding (Nash, 1953). It thus follows that non-cooperative games are those in which agreements are non-existent and/or non-binding, and where parties cannot communicate. For a two-player cooperative outcome to be stable, it must meet two conditions, namely, Pareto Optimality (no player can increase their payoff without decreasing the payoff to another player) and the Individual Rationality Constraint (the cooperative payoff to any player must be equal to or greater than the payoff under noncooperation, essentially the player’s threat point). Miller and Munro (2004), in the context of climate change, add a third condition to these two, that of flexibility and resilience of the cooperative solution. This third condition is discussed in Section 3.4. 3  Recently the term ’negotiation facilitators’ has been proposed (Munro, personal communication).  31  3.2. Early years: The two-player game These early contributions, thoroughly reviewed in an article published ten years ago by Sumaila (1999), usually modeled fisheries shared between only two players. Although one could envision several fisheries situations where there are greater than two players, authors can reduce complexity in their models by aggregating players into two groups. This can be done by gear type, as in the case of the Arcto-Norwegian cod fishery where Armstrong and Flaaten (1991) and Sumaila (1995, 1997a) modeled the interaction between offshore trawlers and coastal vessels. Fisheries game-theoretic methods were also applied to study how cannibalism by adult cod on juveniles can affect the optimal catch shares between two entities that fish different age classes (Armstrong and Sumaila, 2000). Similarly, players can be grouped by country, which was the way Munro had originally envisioned the application of game theory when he wrote about the management of transboundary resources (Munro, 1979). Kennedy (1987) developed a two-player game of the fishery between Australia and Japan, targeting Southern bluefin tuna. The author concluded that the optimal outcome is, in fact, joint management, or cooperation, resulting in the total exclusion of Australia from the fishery (compensated through side payments) (Kennedy, 1987). In the case of the industrial pelagic fishery shared by Chile and Peru, Aguero and Gonzalez (1996) also applied a 2-country analysis. The authors also conclude that appropriate joint management can lead to benefits, specifically through eliminating the tendency for overcapitalization and overfishing to occur in open access fisheries (Aguero and Gonzalez, 1996).  The two-player application expanded In the decade following the Sumaila (1999) review, the two-player framework was expanded upon to analyze more than just catch shares between two entities. Game theory was used to study the efficiency of marine protected areas (MPAs). MPAs are areas of the ocean or inter-tidal regions, which have been reserved by law or other means in an effort to protect the ecosystems within those areas. Sumaila (2002) developed a two-agent bioeconomic game-theoretic model to assess the difference in expected MPA effectiveness under cooperative and non-cooperative management. Not surprisingly, the paper concludes that both cod stock biomass and rent from the fishery are higher under an MPA program that is managed cooperatively by the two players (Sumaila, 2002). A subsequent paper to this addressed the distributional effects of MPAs to different players through a game-theoretic analysis (Sumaila and Armstrong, 2006). The authors conclude that the management plan in place before and after the implementation of an MPA can influence which players may win or lose (Sumaila and Armstrong, 2006). Studies like this can help illustrate to policy makers that simply creating an MPA is not necessarily a sufficient plan  32  3.2. Early years: The two-player game to enable sustainable fisheries. Measures may need to be put in place to ensure that the management plan is equitable and honored by all players. Not only are we interested in the gains to the system through cooperation, but we would also like to understand what factors are likely to aide cooperation. Trisak (2005) attempts to answer this question by analyzing the biological characteristics of a fished stock that affect fishers in a co-management group. The author concludes that the size and the internal growth rate of the stock do in fact influence fishers’ decisions to cooperate, but fishers’ attitudes toward risk are also highly influential (Trisak, 2005). These conclusions are related to if and when a player chooses to cooperate: essentially the timing of cooperation. This issue of timing of the cooperative agreement has gained attention recently, and is likely to be even more important in the coming years. Kaitala and Lindroos (2004) helped to initiate this conversation within the fisheries game theory realm. Applying game theory in this type of analysis can help policy makers better understand how the biological characteristics of a fishery can help or hinder cooperation.  Stage and sequential games Most fisheries game-theoretic studies have used a single stage structure. Players make one decision at the beginning of the game, usually based on known states of the future system. There have been a few attempts at multiple-stage games, where players make a decision about inputs in stage one, and in the second stage, the players use those inputs to engage in competitive behaviour. Sumaila (1995) developed a two-stage game, where players decide on the fishing effort to maximize rent in stage one, and in stage two, take their optimal catch shares. In a similar style, Ruseski (1998) formulates a two-player game where players choose the number of allowable firms in the fishery, or a fishery subsidy amount, and then optimize their catch shares in a competitive second stage. Kronbak and Lindroos (2006) take the stage-game further, by combining the idea of coalition formation by fishers with the level of government regulation and enforcement. The authors use a four stage game. In stage one, authorities choose their level of regulation (centralized, decentralized, etc.,). In stage two, authorities choose a level of effort control. Fishers choose their coalition structure in stage three, and in stage four, fishers choose their optimal effort strategy. In sequential games, one player makes their decision first, followed by the other player(s). This type of structure probably resembles how international agreements are decided in the real world, where often a player may wait to sign onto an agreement until a certain other player has done so. Hannesson (1995) develops a sequential game and considers the possibility of cooperative harvesting being a self-enforcing equilibrium. A two-player game was developed by Laukkanen (2003), where the author allows the catch  33  3.3. Major movement: Coalitions of agent one to occur first because they target fish in the feeding grounds, followed by agent two determining their catch from the stock in the spawning grounds, as the second decision. McKelvey (1997) also develops a sequential game, but instead of looking at a domestically-shared resource, the author applies the sequential model to a transboundary stock. These types of stage and sequential games may, in fact, be more realistic, as fishers, nations or management authorities do not necessarily all make one single decision simultaneously. More work of this kind may help to yield insights into the resiliency of cooperation, as discussed later in the paper. I have explained how the application of game theory to the management of transboundary resources has illuminated some of the issues present in non-cooperative management, and illustrated possible gains from cooperation. After about 20 years of game theoretic work involving mostly two-player games, fisheries economists began to work on the issues present in situations involving greater than two players, particularly as it relates to the management of straddling stocks, specifically tuna.  3.3  Major movement: Coalitions  Coalitions: Characteristic function approach As stated earlier, both analytical and computational methods are often easier when only two players are considered, and the two-player approach seemed a logical simplification for the first game theoretic applications. At the time of extended fisheries jurisdiction, about 90% of the world’s capture fisheries were believed to be located in the EEZs of countries (Alexander and Hodgson, 1975). The creation of EEZs gave management jurisdiction over coastal marine resources to the states themselves, and it was thought that this would make sustainable management more of a reality. The management of internationally shared fish stocks, where interested fishing parties include coastal states, Distant Water Fishing Nations (DWFNs) and high seas fishing fleets, has required models involving greater than two players. And in fact, the issue of the management of straddling stocks, that is, those that migrate between the EEZs of several countries and the high seas, may now be one of the biggest challenges to global sustainable fisheries, as these fisheries represent as much as one third of marine capture fisheries catches (Munro et al., 2004). The application of game theory to fisheries has recently expanded to allow for this possibility of coalitions in games involving greater than two players (Kaitala and Lindroos, 1998; Arnason et al., 2000; Brasao et al., 2000; Duarte et al., 2000). A coalition framework allows for cooperation among a group that is smaller than the total number of players  34  3.3. Major movement: Coalitions in the game (Kronbak and Lindroos, 2007). Coalitions are common in the real world. Examples include several countries joining together to form an oil cartel such as OPEC (Organization of the Petroleum Exporting Countries) and the creation of a political unit such as the European Union. The formation of coalitions is a vital part of economic activity (Yi, 2003). The management of fisheries occurring in both the EEZ of countries and in the high seas can call for a coalition approach due to the potentially large number of interested countries (Lindroos et al., 2007). Following the 1995 United Nations Migratory Fish Stocks Agreement (UNFSA) (UN, 1995), Kaitala and Munro (1997) realized that the two-player analysis would not be sufficient to tackle one of the most pressing of fisheries management issues, namely, management of straddling stocks. The UNFSA effectively mandated the management of straddling stocks to be carried out through regional fisheries management organizations (RFMOs) (UN, 1995). Kaitala and Munro (1997) observed that, while the bargaining process among two players proceeds in a straightforward manner, the standard game-theoretic models that had been developed thus far were not capable of dealing with a larger number of players. The limitations of the 2-player game were also raised by Hannesson (1997), again in relation to the UNFSA. Hannesson (1997) develops a repeated game model of infinite duration (known as a supergame), one of the results of which is that the payoffs to playing non-cooperatively increase as the number of players in the game increases. Thus, there is a large incentive to deviate from cooperation given a sufficiently large group of players. This may be particularly relevant for management of tuna fisheries, as the potential number of interested players can be quite large. Some of the earliest fisheries studies involving greater than two players found in the literature, no doubt inspired by the Kaitala and Munro (1997) and Hannesson (1997) suggestions, used characteristic-function games, or C-games, to assign a value to a given coalition (Kaitala and Lindroos, 1998; Duarte et al., 2000; Lindroos, 2004). To apply a C-game approach, we first compute and compare the relative payoff of each coalition, with respect to the grand coalition, where the grand coalition is the outcome where all players in the game play cooperatively. The next step, which is the primary function of C-games, is to calculate the sharing imputation - that is, what fraction of the benefits should each player in a coalition receive? There are different methods for assigning sharing rules, and in fisheries, these methods generally include the Shapely value (Shapley, 1953), the nucleolus (Schmeidler, 1969), and the Nash bargaining solution (Nash, 1950). The Shapley value essentially weights players based on their marginal contributions (Shapley, 1953), while the nucleolus is a unique solution that maximizes the benefits of the least-satisfied coalition (Schmeidler, 1969). The Nash bargaining solution is an egalitarian approach, essentially assuming that all players in the coalition are equally important because full 35  3.3. Major movement: Coalitions cooperation would not succeed without all of them, and thus the payoff should be shared equally (Nash, 1950). Note that there is no guarantee that all or any of these approaches will lead to a stable coalition structure, that is, one that is rational to all players. A review of a coalitional fisheries games was undertaken in Lindroos et al. (2007). The issue of stability of the cooperative solution soon emerged, with models comparing core and free-rider stability (Kronbak, 2004; Kronbak and Lindroos, 2007). A given coalition is stand-alone stable if and only if no player is better off by leaving the coalition to become a singleton, or free-rider (internal stability), and no player wishes to join the coalition (external stability) (Pintassilgo, 2003). In an early coalitional game of the Baltic Sea fishery, Kronbak (2004) determines that the sum of the players’ threat points if operating as singletons is greater than the sum of the grand coalition’s payoff. In light of this, Kronbak and Lindroos (2007) apply a novel sharing rule that combines a cooperative and non-cooperative game and considers free-rider threat points, those payoffs that each player would get if deviating from the grand coalition. Their model indicates that there can be a large enough increase in benefits through the formation of the grand coalition to satisfy all players, (Kronbak and Lindroos, 2007), where all players are ‘satisfied’ if their payoff through cooperation is at least equal to their payoff from free-riding (the individual rationality constraint). This approach, which incorporates the issues of externalities in coalition formation, developed in parallel to a complimentary approach, called the partition-function approach, as discussed in the next section.  Externalities: Partition function approach One major drawback to the conventional C-game approach, is that a given coalition value is calculated based only on the makeup of that coalition, not on the entire coalition structure of the game. This results in C-games ignoring the influence of group externalities. As Yi (1997) explains, many coalition formations exert positive or negative externalities on other players/coalitions in the game. For example, an oil cartel’s decision to limit supply has a positive effect on other oil-producing non-members, as the price they command for their oil will be higher based on the actions of the cartel. Negative externalities can be seen with the example of established trading blocs, whereby non-members may suffer by not joining the bloc coalition. We can determine if externalities are present by observing whether a merger of coalitions changes the payoff to a player not involved in the merger (Kronbak and Lindroos, 2007). These externalities are considered positive if, upon the merger of coalitions, the payoff to a player not involved in the merger increases (Yi, 2003). The term ‘free-rider’ has been given to describe a player benefiting from coalition formation but not involved  36  3.3. Major movement: Coalitions in the merger. The issue of these group externalities in fisheries has been tackled by Pintassilgo (2003) and, as described above, by Kronbak and Lindroos (2007). Pintassilgo (2003) applies a partition-function game to the management of Northern Atlantic bluefin tuna, stating that fair sharing rules on their own can’t guarantee stability of cooperation, but rather suggests that legal frameworks need to be in place. This is in fact quite an important conclusion that policy makers may benefit from understanding. Taken together, the Pintassilgo (2003) and Kronbak and Lindroos (2007) papers illustrate that full cooperation is not always an economically rational decision at the level of an individual player, and may help us to understand why in fact so much non-cooperative behaviour exists in internationally shared fish stocks management. Highly migratory stocks The application of game theory has proved useful in understanding some of the management issues concerning a specific group of straddling stocks, namely, highly migratory stocks (Duarte et al., 2000; Pintassilgo, 2003). The term ‘highly migratory stock’ pertains “to all intents and purposes, to tuna” (Kaitala and Munro, 1997). As highly migratory stocks, tuna tend to occur in the exclusive economic zones of multiple countries, and in the high seas, resulting in substantial management challenges (Bjorndal et al., 2000). The ability to model multi-player games is essential for joint management. This is particularly the case, as Kaitala and Munro (1997) revealed, given the UN mandate encouraging countries exploiting these highly migratory species to cooperate in their management by the initiation of RFMOs (UN, 1995)4 . RFMOs are formed by groups of countries with relevant interest in fishing shared stocks, be they coastal states or DWFNs. Resolution of negotiations between different groups can be studied through the use of coalition theory (Lindroos et al., 2007). However, the major problem that remains is that even if an international cooperative agreement is reached, it is not binding or enforceable (Bjorndal et al., 2000), which contradicts one of the main requirements for the existence of cooperative solutions (Nash, 1953). However, Munro (2006) specifically states that, with very few exceptions, cooperation between the states targeting highly migratory fish stocks is essential for sustainable fisheries management. Game theory may provide a particularly useful lens through which to view the formation and stability of RFMOs. Recent work by Pintassilgo et al. (2008) illustrates that, although higher cooperative gains can be expected from RFMOs with a large num4 Note that RFMOs exist to manage numerous fish stocks, and were not created solely for tuna management.  37  3.4. Looking forward: Catch privileges and resilience ber of members, the likelihood of cooperative stability decreases as number of members increases. Two of the main issues in the management of highly migratory stocks are unregulated fishing, or free riders, and what has been termed the ‘New Member Problem’ (Kaitala and Munro, 1993). Fishing nations that are not party to the RFMO agreement (and therefore probably not abiding by RFMO guidelines), but fishing on the high seas, can be said to be engaging in unregulated fishing. Currently, there is very little RFMO member countries can do to address this issue. Unfortunately, it seems cooperation is not likely if unregulated fishing, and thus free-riding, is allowed to persist (Pintassilgo and Lindroos, 2008). The second issue arises from the fact that the RFMO is not justified in excluding any interested party from joining the organization (UN, 1995). As such, possible entrants may participate in unregulated fishing (or no fishing) until the state of the stock is rebuilt to such a level that they choose to join the RFMO. This new entrant is free-riding, essentially benefiting from the stock rebuilding program without bearing any of the management costs (Munro, 2006). In order for RFMOs to be effective in managing stocks sustainably, as they are mandated to do, these two issues will need to be addressed. The next section discusses the current ideas being formulated to tackle these issues, the resolution of which may come through the application of game theory to fisheries.  3.4  Looking forward: Catch privileges and resilience  Catch privileges and the principal-agent problem Although game-theoretic models of shared stocks have been somewhat successful in elucidating the benefits of joint management, actually obtaining this cooperation is another question. There are two levels of cooperation, as identified by Gulland (1980). The primary level is scientific cooperation, where players in the game communicate and share research information (Gulland, 1980). Even this first level can be hard to achieve because some players may suspect that their ‘rivals’ may use that information against them (Munro et al., 2004). In fact, McKelvey et al. (2003) demonstrate that if non-cooperation, which is often the default option in shared stocks management, prevails, more information can actually be harmful to the sustainability of the resource. The authors suggest side payments as a way to encourage cooperation in asymmetrical information situations (McKelvey et al., 2003). Gulland (1980) describes the secondary level as cooperation in active management, which is, in effect, the formation of joint management arrangements, such as RFMOs. One of the possible underlying challenges in creating effective cooperative regimes, even at the primary level, is the lack of ‘property rights’ bestowed on fishing  38  3.4. Looking forward: Catch privileges and resilience nations. Without property rights, if one country agrees to actively cooperate in management, what guarantee do they have that they will, in fact, be the ones to benefit from that cooperation? With so many vested interests in a straddling stock fishery, unregulated fishing and cheating are bound to occur. Unregulated fishing can lead to an underestimate of catch and effort in the fishery (Pitcher et al., 2002), and can severely undermine management programs (FAO, 2002). It has been suggested that de facto property rights granted to member countries (including for catch on the high seas) would effectively change unregulated to illegal fishing (Kaitala and Munro, 1997; Munro, 2008), thus allowing RFMO member countries to take action against such illegal fishers. Perhaps game-theoretic modeling could be used to illustrate the differences in optimal outcomes between ‘open access’ and ‘privatized’ fisheries. Of course, the granting of access rights, or catch privileges, comes with a suite of its own challenges, including distribution and equity arguments (Clark, 2006). In this case, allocation of catch privileges could be seen as just one of several tools that would bestow greater ownership to, and hence possibly greater likelihood of cooperation by, RFMO member countries. Munro (2007) points out, however, that development of state property rights in straddling stock fisheries is far less straightforward than in transboundary fisheries, but stresses that private fishery access rights should enhance cooperative management. In Chapter 4, I examine the challenges of current allocation schemes in shared fisheries, and propose a way forward for RFMOs. A branch of game theory, called principal-agent analysis5 , could possibly be applied to address these issues. The majority of game theoretic applications in fisheries rely on the assumption of perfect information (Jensen and Vestergaard, 2002). However, this assumption is not met in many circumstances, as Nash (1953) himself admitted. Principalagent analysis, part of a class of games called incomplete or asymmetric games, is applied in systems of imperfect information and uneven power (Clarke and Munro, 1987). This type of analysis focuses on the problem of devising compensation rules (incentives) that induce an agent to act in the best interest of a principal (Sappington, 1991). To my knowledge, there are only a handful of principal-agent analyses applied to fisheries in the literature. The first two were analytical pieces by Clarke and Munro (1987, 1991) that analyze the optimal catch and effort tax scheme to be employed by coastal states on DWFNs. Jensen and Vestergaard (2002) analyze a tax on the effort of EU member states (agents) to be enforced by the EU (principal) in an attempt to correct for imperfect information in the system. An empirical piece analyzing illegal fishing in Indonesia has been conducted by Bailey and Sumaila (2008b), where the authors use principal agent analysis to devise a 5  This is also sometimes referred to as a Stackelberg or leader-follower game (Mesterton-Gibbons, 1993).  39  3.4. Looking forward: Catch privileges and resilience penalty scheme to discourage illegal fishing. Given that these are the only analyses to date, there appears to be more scope for incorporation of principal-agent analyses into fisheries modeling. It has been suggested that in the context of principal-agent analysis, granting of catch privileges may in fact strengthen the information and control that a principal has over the agents in the system (Munro et al., 2009). This may mean that implementing a catch privileges/allocation scheme within the context of RFMO fisheries may lead to RFMO member states having more control over the management of the resource (Chapter 4). Although catch privileges have been suggested as a way of helping reduce or eliminate the occurrence of unregulated fishing, to my knowledge, this has not been modelled in a game-theoretic framework. Collective catch privileges have also been suggested as a way of increasing stability of a cooperative agreement in light of the new member problem. This was addressed by Pintassilgo and Duarte (2001). The authors explore three possible solutions to deal with new members, including transferable membership, a waiting period, and a fair sharing rule. They point out that in a quota or allocation scheme, transferable memberships in the cooperative group can take on the attributes of individual transferable quotas (Pintassilgo and Duarte, 2001). However, the authors are quick to point out that, at the time of writing their paper, international quota markets, were not common in fisheries. This condition does not appear to have changed much over the past few years. The new member problem falls under the bigger issue of resilience of the cooperative solution.  Resilience of the cooperative solution As Kaitala and Lindroos (2004) point out, the timing of international agreements can either facilitate or destabilize cooperation. The costs players face, and how players in the game perceive the size of the stock biomass, among other variables, can affect whether or not and when they choose to cooperate (Kaitala and Lindroos, 2004). Similarly, one can imagine that changes in the future state of the system, such as new members or shifting climate regimes, can hinder a cooperative agreement created today. With regard to the new member problem, as discussed above, this might involve a potential fishing nation waiting until the stock has been rebuilt to join the cooperative agreement. The immediate response by the RFMO may be then to keep the stock at such a level to discourage new entrants, as suggested by McKelvey et al. (2002). However, the authors are quick to explain that this is perhaps a desperate action, which may entail large economic and ecological losses to RFMO members (McKelvey et al., 2002). They conclude that instead of trying to deal aggressively with non-RFMO fishers by discouraging them to join the RFMO or to engage in unregulated fishing, (what they call ‘interlopers’), working out a cooperative  40  3.4. Looking forward: Catch privileges and resilience solution would probably be the optimal action (McKelvey et al., 2002). As such, there seems to be even more impetus on reaching a cooperative solution in the present day that is resilient to changes in the future. One way may be to develop a better understanding of how to negotiate the reallocation of property rights to new RFMO entrants in the future, as called for by Bjorndal et al. (2000), but I are unaware of any studies to date that have analyzed this issue. The issue of ‘resilience’ to shocks in the system in the cooperative solution was raised by Kaitala and Pohjola (1988) twenty years ago, and reiterated by Munro (1990). However, it has not yet been tackled properly either in theory or in practice (Munro, 2008). Deterministic models, such as Kaitala and Pohjola (1988), illustrate how changes in the system can lead to an unstable equilibrium. Game-theoretic stochastic models, such as those developed in Sumaila (2002), Laukkanen (2003), and McKelvey et al. (2003), although rare, are insightful and can help policy makers anticipate how shocks in the system may affect the cooperative solution. However, practical evidence suggests that predicting these shocks is difficult, both in magnitude and direction (Munro, 2008). If, however, cooperation is to succeed, for example in RFMOs, then stochasticity in models should be the norm (where it is currently the exception), and our time frame must be increased in an attempt to incorporate future conditions. The issue of future states of the ocean, biomass, and economy, brings up the issues of discounting, where we prefer benefits to be received today, over benefits to be received in the future. In conventional discounting, often the benefit of a fishery in 50 years is negligible to the decision-making of today. This means that we are essentially unable to predict how future changes could affect cooperation. New methods for discounting, including those by Sumaila and Walters (2005) and Weitzman (2001), are worthwhile attempts to address the discounting issue. Shifting climate Recent work has illustrated how shifts in climate may affect fish, and thus fishing, distribution globally (Cheung et al., 2009). One of the major suggestions is that many fish populations will move away from the equator and toward the poles (Cheung et al., 2009), which would almost certainly result in losses of benefits to tropical countries. Furthermore, species naturally occurring in northern regions are quite sensitive to temperature changes, rendering them susceptible to shocks from climate shifts (Cheung et al., 2009), which could result in economic losses to northern fisheries. A recent publication by Brandt and Kronbak (2010) analyzes how changes in climate could impact Baltic Sea fisheries. The authors determine that if changes in climate result in decreases in future payoffs to the fishery, stability of the cooperative solution is not guaranteed. Hopefully, similar studies  41  3.4. Looking forward: Catch privileges and resilience can be undertaken to address implications for both domestic and internationally-shared fish stocks as a result of possible climate shifts. What is also necessary is a move away from just modelling of these scenarios to a real solutions-based discussion of how to get to where we want to be. The impact of climate shifts on the stability of the cooperative agreement between Canada and the United States, formed to manage the Pacific salmon transboundary resource, was summarized by Miller and Munro (2004). The authors describe how warming of coastal waters on the west coast of North America in 1977 led to an increase in the abundance of salmon in Alaskan waters, and a sharp decrease in abundance in salmon found in California, Oregon, Washington and southern Canada (Miller and Munro, 2004). The benefits expected by the southern players at the outset of the cooperative agreement did not materialize, and non-cooperative behaviour ensued (Miller and Munro, 2004). One major criticism to the Canada-US Pacific Salmon treaty was that it did not explicitly include the scope for side payments (Munro, 1990). This retrospective analysis helps to illustrate why resiliency in a cooperative agreement is important for stability, however, testing the resiliency of straddling stock cooperative agreements, such as those through RFMOs, to changing circumstances has yet to be adequately addressed in the fisheries game theory literature (Munro, 2008). One further development that should begin to surface is the use of game theory in a broader, ecosystem-based context. The majority of game-theoretic analyses in fisheries have been applied to single stocks. There are a few exceptions, for example, the predatoryprey piece analyzed by Sumaila (1997b), where the author looks at the optimal exploitation for cod and capelin in the Barents Sea. Chapter 6 in this dissertation develops a multispecies model that addresses bycatch and growth overfishing in an effort to address this gap in modelling. Game theory is also being applied in many other environmental contexts, notably the possibility for cooperation in international environmental agreements geared towards mitigating the impacts of climate change. Interestingly, the progress that has occurred recently in fisheries coalitions has paralleled the developments in coalitional models to address the issue of climate change negotiations. Finus et al. (2008) discuss new developments in coalition theory as applied to this issue. The authors model heterogeneity in players (i.e., asymmetric players) and explore the issues of open and restricted membership (where fisheries coalition models are generally developed as open membership games) (Finus et al., 2008) and transferability (broadly paralleled to side payments in fisheries). In addition to their predictable result that gains through the cooperative solution are large, one of the key outcomes in their study is that it may be more beneficial to have the most important players (those whose marginal contributions to cooperation are largest) 42  3.5. Conclusion within the cooperative agreement than to insist on full cooperation by all members (Finus et al., 2008). Work on this front may offer interesting new angles that should be addressed by fisheries economists in the next few years. It seems that these approaches are being merged, as evidenced by the recent joint work of Finus, Pintassilgo, Lindroos, and Munro (Pintassilgo et al., 2008).  3.5  Conclusion  It seems fair to conclude, given the extensive literature available on the application of game theory to fisheries, that indeed, Munro’s 1979 paper was influential in directing attention to how fisheries can be modeled as strategic dynamic interaction between fishing entities. The impetus for publishing the paper was the issue of extended jurisdiction and transboundary resources. These issues were tackled for fisheries in Norway and Russia (Sumaila, 1997a), Canada and the US (Miller and Munro, 2004), Australia and Japan (Kennedy, 1987), among others. However, it is equally, or perhaps even more useful, to view the management of straddling stocks, such as tuna, through the lens of game theory. It is in this realm that much of the work over the past decade has focused, beginning with applying game theory to the management of North Atlantic bluefin tuna (Duarte et al., 2000; Pintassilgo, 2003). The recent work on coalition theory through the partition function approach has illuminated many challenges in achieving cooperation (both primary and secondary) in straddling stocks management (Pintassilgo and Lindroos, 2008). Recent work in fostering cooperation in international climate change agreements may help inform future game-theoretic models, and may help facilitate cooperation by fishing states. One further detail that may need better incorporation in game theoretic models to facilitate cooperative management is improved cost functions. The costs of achieving cooperation, be they institutional, technical, or other, are generally not properly factored into the fisheries game-theoretic analyses that have been developed to date. The application of game theory to fisheries has provided insightful predictions about stability of cooperation in internationally shared fish stocks management. This has been shown both in theory and in practice (Munro, 1990). As Munro (2008) points out, the continued broadening of game theory from the theoretical to the applied may go a long way in aiding cooperation in the management of the world’s shared fish stocks.  43  Chapter 4  Present and future allocation approaches for shared tuna fisheries 4.1  Introduction  Shared fisheries resources are susceptible to the “tragedy of the commons” (Hardin, 1968). Although Hardin (1968) formally explored the impact of individual shepherds increasing their heads of cattle on a shared pasture, his thesis is just as relevant to shared marine pastures, or the global ocean commons. Fish stocks are common pool resources that face the problem of overuse (i.e., overfishing) due to dynamic (Munro, 1979; Levhari and Mirman, 1980), market (Dockner et al., 1989; Sumaila, 1999; Datta and Mirman, 1999) and stock (Koenig, 1984; Fischer and Mirman, 1992; Sumaila, 1997b) externalities. This challenge to economically and ecologically viable common pool fisheries was identified as early as the 1950s (Gordon , 1954), even though the idea was better-popularized by Hardin. Economists took up the challenge by analyzing the difference between noncooperative and cooperative management of these shared fish stocks (see Chapter 3), concluding that cooperation could alleviate some of the problems of the overuse of common pool resources as it seeks to find the optimum solution (Munro, 1979; Clark, 1980; Levhari and Mirman, 1980). In the case of fisheries shared by several fishing nations, a race to the fish fueled by national interests has historically ensued, leading to both biological and economic losses. Some countries recognized the sub-optimal nature of such interactions and formed joint management arrangements to facilitate cooperation and improved fishing strategies. Canada and the United States, for example, formed a joint committee as early as 1923 to improve management of Pacific halibut. The United Nations Convention on the Law of the Sea (United Nations, 1982) admonished fishing states to seek regional or sub-regional organizational groups to improve management of transboundary and straddling stocks. In 1995, the United Nations Fish Stocks Agreement (UNFSA) furthered this sentiment, and  44  4.1. Introduction formalized these joint arrangements into what are called Regional Fisheries Management Organizations (RFMOs) (UN, 1995). Among other responsibilities, RFMOs are required to perform the function of agreeing “on participatory rights such as allocations of allowable catch or levels of fishing effort” in internationally-shared fisheries (UN, 1995). And, although the degree to which an allocation program is seen as equitable and effective can have a large impact on all other effectiveness measures of an RFMO, it is often one of the least structured elements of RFMO activities (Lodge et al., 2007). In order for cooperative management to succeed, parties must be confident that they are better off through cooperation than through noncooperation: known as the individual rationality constraint as described in Chapter 3. The allocation of catches (or other benefits) can largely influence whether or not cooperation is rational. Issues surrounding the allocation of shared fisheries resources are some of the most challenging in fisheries management (MRAG, 2006; Metzner et al., 2010). While RFMOs have often relied only on biological information, economists have been using the theory of games to derive the conditions under which fishing states sharing a resource would be encouraged to cooperate in management, including how effort or catches should be allocated. Most applied game-theoretic analyses, which usually focus on maximizing economic rent from the shared fishery, have concluded that cooperative agreements between fishing nations bring benefits above and beyond non-cooperative management (Chapter 3). Two of the formidable barriers that impede international cooperative agreements are the new member problem, by which a new country seeks access to the shared resources (Kaitala and Munro, 1997; Munro et al., 2004), and issues related to free-riding, whereby a country not engaging in the cooperative agreement benefits from the conservation measures of compliant countries. Such issues are usually present in fisheries that involve a substantial catch from the high seas, in addition to EEZ catches, such as fisheries for tuna species. Cooperation in such systems is inherently difficult to reach (Pintassilgo, 2003; Pintassilgo et al., 2008). In this Chapter, I summarize how the current allocation programs for the tuna RFMOs came to be. These results are summarized in Table 4.1. In Section 4.3, I speculate on future considerations for allocation programs, both for new schemes and those schemes that may need to be renegotiated in the near future. The issues present in the management of shared fish stocks are also present in the management of internationally-shared water resources. I therefore draw on various parallels with, and conclusions from, international water agreements. By highlighting current allocation practices, criteria to be considered in the future, and allocation programs present in sharing other natural resources, I propose a way forward for tuna RFMOs with regard to their responsibilities for allocation schemes. 45  4.2. Allocation by tuna RFMOs  4.2  Allocation by tuna RFMOs  Due to their migratory nature, managing tuna stocks in a cooperative manner is remarkably difficult. Several RFMOs exist to do just that, although according to Cullis-Suzuki and Pauly (2010), they have had variable degrees of success in meeting management objectives, be they catch limits or otherwise. This could be partly due to the lack of quantifiable guiding principles on which RFMOs can draw for their allocation decisions (Lodge et al., 2007). Figure 4.1 shows the RFMOs that are charged with the management of tuna (and tuna-like) species (Lodge et al., 2007). Most tuna RFMOs currently have some type of catch allocation or apportionment scheme in place. Although RFMO members are under a legal obligation to cooperate as per the UNFSA (UN, 1995), groups have often failed to reach agreement on the allocation of catches, and overages have been common (Lodge et al., 2007). Current allocation schemes fall short in their ability to address the problem of new member allocations, of adequately considering the needs of developing states, and of limiting non-compliance with catch allocations (MRAG, 2006; Lodge et al., 2007).  ICCAT: Atlantic bluefin tuna The RFMO in charge of Atlantic bluefin is the International Commission for the Conservation of Atlantic Tuna (ICCAT). In the early 1970s, tuna fishing nations in the Atlantic began to worry about overexploitation of Atlantic (northern) bluefin tuna. In 1974, minimum size limits were implemented, but by 1981, it was evident that more drastic conservation measures would be required (Palma, 2010). The United States proposed allowable catches be allocated based on 1970-1974 catch histories, but this was not agreed upon. Further delegations resulted in the TAC being divided among Canada, Japan, and the U.S., with Brazil and Cuba having no catch restrictions. Reportedly, allocations were determined by a combination of historical catches, economic factors, and monitoring needs (Palma, 2010). These initial bluefin delegations paved the way for further TAC allocation schemes to be developed for other North Atlantic species, such as swordfish and albacore tuna. For these latter schemes, instead of catches being explicitly allocated, management instead suggested to set the allowable fishing mortality (Palma, 2010). This resulted in an implicit sharing arrangement. However, problems with uncertainty in mortality estimates and the inability to enforce this measure, meant that catch allocations were eventually favoured. Similar to earlier allocation schemes, sharing was based on historical catches. Pathological underreporting of catches, however, has occurred (Lodge et al., 2007). Today, ICCAT has developed an extensive set of criteria to inform allocation schemes of individual stocks. The inclusive nature, however, makes consensus difficult, and leaves 46  4.2. Allocation by tuna RFMOs  c Chatham House, used with Figure 4.1: Map of tuna RFMOs (Lodge et al., 2007). ⃝ permission. room for various concessions and opportunities for ineffective management (Cox, 2009). One of their more questionable allocation criteria is based on aspirations. For example, in 2002, ICCAT allocated 25 tonnes of bluefin tuna to Mexico and various amounts of swordfish to Morocco, Mexico, Barbados, Venezuela and China, among others, because of the aspirations of these countries (MRAG, 2006; Cox, 2009). Unfortunately, such practice resulted in the 2002 allocated TAC for bluefin being significantly higher than the scientifically-recommended TAC (MRAG, 2006). ICCAT outlines the conditions for applying their allocation criteria as follows (Cox, 2009): 1. Applied in a fair and equitable manner; 2. Applied by relevant panels on a stock by stock basis; 3. Applied to all stocks in gradual manner; 4. Takes into account contributions to conservation;  47  4.2. Allocation by tuna RFMOs 5. Applied consistent with international instruments in a manner to prevent overfishing; 6. Applied so as to not legitimize illegal, unreported and unregulated catches (IUU); 7. Applied in a manner that encourages cooperating non-members to become contracting parties; 8. Applied in a manner that encourages cooperation between developing states; 9. No qualifying participant shall trade or sell allocated quota. Some of these criteria appear to be at odds with one another. For example, to apply an allocation program to stocks in a gradual manner (3), may in fact not be consistent with preventing overfishing (5). Interestingly, ICCAT does not assign area-specific TAC allocations, rather, allocation of a TAC to a party allows that party to fish throughout the whole convention area (access to foreign EEZs has to be applied for) (MRAG, 2006). This is due to the migratory nature or tuna (and tuna-like species) and is something for other tuna RFMOs to consider. Agreed-upon ICCAT allocations are valid for three years (IOTC, 2011).  WCPFC: Western Pacific tuna The Western and Central Pacific Fisheries Commission (WCPFC) is the RFMO responsible for tuna management in the western Pacific. The Commission was established under the Convention on the Conservation and Management of the Highly Migratory Fish Stocks of the Western and Central Pacific Ocean in 2000, in an effort to more effectively manage fish stocks in the area. It came into being in 2004, after both UNCLOS and FSA, and thus their guidelines are more considerate of the the issues around straddling stocks management, including issues of allocation. The WCPFC has a strong sub-coalition within its membership through the Nauru Group, made up of Pacific Island Countries (PICs) with plentiful tuna resources within their EEZs. They have had success in bargaining together as a group (Lodge et al., 2007), and influence the development and direction of the WCPFC (Munro et al., 2004). The WCPFC does not presently allocate specific tuna catches to member states, however, they recognize the future need for such a program, and have therefore developed a list of criteria to consider upon development of an allocation program (MRAG, 2006):  48  4.2. Allocation by tuna RFMOs 1. Stock status; 2. Past and present fishing patterns and practices of participants, extent to which catch is used for domestic consumption; 3. Historical catch in an area; 4. Needs of small island states with highly fisheries-dependent economies; 5. Contributions by participants to conservation and management; 6. Record of compliance; 7. Needs of coastal communities; 8. EEZ size, with special consideration for states with limited EEZs due to proximity of neighbours; 9. Geographical situations of island states; 10. Fishing interests and aspirations of coastal states. Although these practical criteria exist, there does not appear to be any indication of how they would be weighted in an effort to calculate and distribute allocations. The sub-coalition mentioned above, the Parties to the Nauru Agreement (PNA), use the vessel day scheme (VDS), which is an effort allocation program. VDS was adopted by the PNA under the Palau Arrangement for the Management of the Western Pacific Purse Seine Fishery (the Palau Arrangement), to regulate purse seine fishing days in the waters of PNA countries. VDS came into effect in December 2007, and was implemented as a way to provide for effective management in the face of declining fish stocks, and in an attempt to improve economic returns by creating a limit on the number of fishing days. Fishing days are allocated to all bilateral fishing partners, and these days are monitored using Vessel Monitoring System (VMS) technology. Effort allocation is based on equal weighting of historical effort levels and the level of estimated biomass in different EEZs (MRAG, 2006). Work within the WCPFC is ongoing in an effort to develop an allocation approach that will be accepted by its members. A recent analysis outlined four possible allocation schemes for WCPFC tuna (Parris and Lee, 2009): 1. Effort model: calculate allocated shares based on historical effort; 2. Harvest model: calculate relative allocations based on historical harvest data; 49  4.2. Allocation by tuna RFMOs 3. Biomass model: calculate allocations based on biomass distribution data; 4. Spatial model: calculate relative allocations based on size of EEZs. Unfortunately, no combination model was analyzed and socio-economic factors were not suitably incorporated. One important element for WCPFC to note, and other RFMOs who are currently contemplating initiation of allocation programs, is that it is easier to meet the needs of members through allocation when the stock status is considered healthy, i.e., prior to overexploitation (Lodge et al., 2007) (or perhaps after rebuilding). In this regard, setting up catch quotas for skipjack, yellowfin and albacore should proceed quickly, as reaching agreement in the future may be harder if conservation measures are not put in force today. CCSBT: Southern bluefin tuna Southern bluefin tuna is managed under the Commission for the Conservation of Southern Bluefin Tuna (CCSBT), which came into force in 1994. Prior to the Commission, southern bluefin was managed through a voluntary cooperative agreement between Australia, Japan and New Zealand, but this agreement failed to adequately conserve the resource.6 Kennedy (1987) developed an applied two-player game of the fishery between Australia and Japan, targeting Southern bluefin. Due to the heterogenous markets for sashimi (Japan) and canned (Australia) products, the optimal outcome in the early 1980s was joint management whereby Australia was totally excluded from the fishery (compensated through side payments) (Kennedy, 1987). In reality, of course, no country was excluded and membership increased instead of decreased. CCSBT was faced with the new member problem when South Korea and Chinese Taipei wanted access to the resource. CCSBT simply increased the total allowable catch for southern bluefin, despite concerns about the health of the stocks (Lodge et al., 2007). CCSBT originally inherited the allocation scheme that the three founding fishing nations had developed in 1986, but there is no record of how that allocation program was decided upon (MRAG, 2006). In 2005, CCSBT initiated a changing TAC procedure, but this did not change national TAC shares that were initially negotiated in 1986 (MRAG, 2006). However, in 2009, members agreed on a proportional allocation program based on catches and distribution (CCSBT, 2011). Like ICCAT, fishing nations can fish their allocated TAC throughout the convention area (Harwood, 1997). CCSBT is in the process of redefining their national allocation approach, which currently allocates based on proportions of the TAC (CCSBT, 2011). Upon any increase in the calculated TAC, those 6  http : //www.ccsbt.org/site/originso ft hec onvention.php  50  4.2. Allocation by tuna RFMOs countries who took voluntary decreases in allocation (New Zealand and Australia) will have the difference in their TAC returned to them, providing a system with some type of incentive for voluntary conservation (CCSBT, 2011). Any decrease in TAC will result in a decrease in national allocation consistent with allocation proportions (CCSBT, 2011). CCSBT allows for nations to carry forward any unused TAC in the subsequent year, however it does not allow for transfers between nations. IATTC: Eastern Pacific tuna Tuna and tuna-like species in the eastern Pacific have been managed through the InterAmerican Tropical Tuna Commission (IATTC) since 1969. Original allocations were based on historical catches, with disregard for the migratory nature of tuna and stock distribution information (MRAG, 2006). This original program collapsed in the mid 1970s. IATTC has since promoted management measures supplementary to allocations, such as area closures. IATTC manages its purse seine and longline fisheries differently. The purse seine fishery is managed through capacity (effort) allocations using four main criteria (MRAG, 2006; IATTC, 2007): 1. Catch history of national fleets (1985-1998); 2. Amount of catch taken from zones where nations have jurisdiction; 3. Landings of tuna in each nation; 4. Contribution of each nation to the IATTC conservation program. The longline fishery is managed through a catch limit program. The benefit to allocating catches instead of capacity is that IATTC found some fleets were manipulating their vessel capacity and this resulted in capacity allocation being ineffective (MRAG, 2006). National catch allocations are based on stock abundance and distribution, as well as historical catches during the 2000-2002 period (MRAG, 2006). IOTC: Indian Ocean tuna In 1996, the Indian Ocean Tuna Commission was formed and today, consists of 30 Member states. Its stated objective is to promote cooperation among its Members, and to use appropriate management to encourage the conservation and sustainable use of tuna stocks. A total of sixteen tuna and tuna-like species are managed by the IOTC, including southern bluefin, yellowfin, skipjack and bigeye tuna, among others. Similar to IATTC, IOTC has tried to use restrictions on vessel capacity (through measurement of gross registered tonnage) as their allocation program, however the restrictions are reportedly not binding 51  4.3. The future of allocation schemes (MRAG, 2006). A resolution was passed in 2006 encouraging members to limit their capacity, but allows for much flexibility in meeting capacity targets (MRAG, 2006). IOTC has, however, produced a report documenting allocation approaches by other RFMOs in an attempt to begin their allocation process (Indian Ocean Tuna Commission, 2007). The report documents their struggles with using capacity limits to impact conservation, and discusses the possibility for allocations based on historical catch (Indian Ocean Tuna Commission, 2007). In 2012, some IOTC Members submitted reports with their suggested allocation approaches in response to IOTC Resoultion 10/01, requiring the adoption of a quota allocation program (or other suitable approach) (Indian Ocean Tuna Commission: Japan, 2012; Indian Ocean Tuna Commission: EU, 2012; Indian Ocean Tuna Commission: Seychelles, 2012). The proposal put forth by the Republic of Seychelles suggests historical catches and catches per area be used as the basis for allocation, but they make note that for some developing coastal states, catch records have not been consistently collected and this could negatively impact their catch allocations (Indian Ocean Tuna Commission: Seychelles, 2012). Thus, the proposal suggests that, where catch records are not of good quality, socio-economic factors be incorporated (Indian Ocean Tuna Commission: Seychelles, 2012). The EU proposal is also firmly attached to the idea that historical catches should form the basis of the allocation program, but it suggests that a percentage of the TAC be put aside to be redistributed to developing coastal states and new members (Indian Ocean Tuna Commission: EU, 2012). Similarly, the third proposal, put forth by Japan, states that allocation should initially be based on historical catches, specifically over the past 10 years (Indian Ocean Tuna Commission: Japan, 2012). These base allocations are subsequently altered using different mathematical relationships, based on criteria such as if the Member has contributed financially to the IOTC, or has had any occurrences of non-compliance (Indian Ocean Tuna Commission: Japan, 2012). These proposals all use catch histories as their basis, but also recognize, in different ways, that this singular criteria is not the most effective and equitable strategy.  4.3  The future of allocation schemes  Table 4.1 summarizes the major tuna RFMOs and their various approaches to allocation programs. The table also includes references to several non-tuna RFMOs. More detailed information about the allocation approaches of these specific RFMOs is included in Appendix B. A recent report analyzed the performance of all RFMOs in meeting best practices criteria in theory (based on written mandates) and in practice (based on stock status reports) (Cullis-Suzuki and Pauly, 2010). These rankings are included in 52  Table 4.1: Summary of RFMO allocation information Species  Data for allocation  What is allocated  NAFO (ICNAF) NEAFC  Groundfish  Stock assessment and historical catch Zonal attachment principle and historical catch  Stock assessment, historical catch, bycatch Stock assessment and historical catch Gross registered tonnage (plus historical catch in future) Vessel carrying capacity  Catch effort Catch  Stock assessments and historical catches, distribution, economic dependence  ICCAT CCSBT IOTC IATTC  WCPFC  PSC  Herring, mackerel, blue whiting Tuna species Southern bluefin Tuna species Tuna and tuna-like species Tuna and tuna-like species  53  Pacific salmon IPHC Pacific halibut Sources: MRAG (2006);  Transferability  Catch  Penalties for noncompliance Yes  Allowed  Ranking (theory, practice) 52,53  Catch  Yes  Allowed  52,72  Yes  57,38  Yes  No sale, exchange ok None  Yes  None  58,78  and  Yes  None  60,33  No current regional allocation, but sub-regional effort program (VDS) Percentage of TAC Catch  Yes  Currently being discussed  74,67  Unknown  None  43,NA  Unknown  None  52, 33  and  Effort Catch effort  Historical catch, bilateral negotiations Stock abundance and distribution Cox (2009); Cullis-Suzuki and Pauly (2010)  44,0  4.3. The future of allocation schemes  RFMO  4.3. The future of allocation schemes Table 4.1 to relate the allocation schemes in place with one measure effective or ineffective management. The first question to be addressed in developing an allocation approach is what, in fact, is to be allocated. There is an obvious precedent in internationally shared fish stocks management for historical catches (by proportion) to provide the basis for allocation. The assumption here is that a fair way to distribute shares is based on historical participation, with the added benefit of catches being a relatively easily measured and quantified reference (Cox, 2009). The PNA countries (a WCPFC sub-coalition) employ an effort allocation scheme, instead of allocating catches, called the vessel day scheme. But apart from this, allocation schemes for existing RFMOs are based on catch tonnage. Using catch histories is not always the most ecologically-sound method (Caddy, 1996), and gives an incentive for members to block allocation agreements until they have built up their capacity and catches (Lodge et al., 2007). Furthermore, the allocation schemes that have been put in place so far, based on catch histories or abundances, have been unsuccessful in facilitating sustainable fisheries. It may be time to start reconsidering what is being allocated. Perhaps potential rent can be allocated, or some other benefit. One way to do this would be to try to put different types of benefits into equivalent units. This has been suggested several times with regards to the Pacific Salmon Commission, the RFMO put in place to manage Pacific salmon between Canada and the U.S.. Sockeye are the most valuable of the five Pacific salmon species harvested. It was argued that “sockeye” equivalents could be used so that catches, overages and interceptions are measured in a similar fashion, and could perhaps facilitate trading. This type of relativity would allow the two countries to compare apples to oranges, that is, to put all salmon species in the same currency. Unfortunately, this scheme has never been realized because groups within both countries were unable to agree on a way forward.7 As discussed later in the paper, some international water allocation agreements have explicitly allowed each interested party to develop their own apples- or oranges-based utility function (Sanderson, 2009). Currently, no program for internationally-shared tuna stocks is based on revenue or rent allocations. The addition of socio-economic factors into allocation decision-making was argued for as early as 1996 (Caddy, 1996). Several tuna RFMOs have begun using qualitative criteria in assisting with the allocation process, for example economic dependence and domestic consumption. How to explicitly incorporate these into some type of allocation algorithm is a challenging next step. One possible way to incorporate other criteria would be to develop objective functions of resource use for each country and then 7  Sandy Argue, Argus Bioresources Ltd., personal communication.  54  4.3. The future of allocation schemes test possible allocation schemes in their ability to most closely meet both (all) countries’ needs. For example, if employment is an important target, then incorporating a layer of fishery dynamics into allocation modelling could suggest employment outcomes for various schemes. Optimization approaches could be used to calculate the weighting system that best meets nations’ objectives. Some possible factors to consider including are: historical catches; species distribution within EEZs; spawning and nursery areas; contribution to habitat and environmental health; contribution to research and monitoring; amount of catch for domestic consumption; and interactions between catch and employment in the fisheries and processing sectors. Currently most RFMOs produce some type of annual report that summarizes stock dynamics, catches, and sometimes effort, for the fishery. Producing an annual report that includes social, environmental and economic assessments of RFMO-managed fisheries, in addition to these biological reports, could help highlight the broader benefits of reaching an optimal sharing agreement (Bjorndal, 2009). One of the first papers in the literature to start theorizing about the future of allocation schemes suggested an objective framework where national allocations depend on multiple factors which are given different weights by individual parties (Caddy, 1996). One important point to note in developing an allocation criteria based on multiple factors is the fact that for every new factor introduced into the negotiations, the importance of all other factors goes down. For example, if biomass distribution is the sole factor, then only it has importance. However, when economic considerations are entered, the importance of biomass must be less than 1. As per the Caddy (Caddy, 1996) approach, allocation negotiations essentially break down into three parts: 1. What factors are relevant (catch histories, domestic consumption, biomass distribution, employment, etc.)? 2. How do we calculate/measure values for each factor for each interested party? 3. How do we weight the different factors? One of the drawbacks associated with solely using catch as a way of measuring fleet performance and stock sustainability is that it explicitly ignores human drivers of fishing behaviour and does nothing to illustrate tradeoffs in policy decisions (allocations) with community well-being. This is of course an argument that can be made across many forms of fisheries management and is not at all exclusive to the challenges of internationallyshared stocks, but it is worth mentioning here. Importantly, the incorporation of shortterm social, economic and political criteria can also pave the way for opportunities to overexploit and ignore conservation goals (Lane, 2008). Many allocation schemes do utilize penalties for lack of compliance to discourage TAC overages (Cox, 2009). For example, 55  4.3. The future of allocation schemes NAFO and CCSBT reduce the quotas in the subsequent year of members who overfish their allocation. If countries cooperate in defining their objectives in participating in the joint fishery (above and beyond catch), that could help in developing some sort of tradeoff matrix. What mix of targets is optimal? What costs and amount of risk are communities and governments willing take to promote economically viable fisheries? Although no tuna RFMOs have taken seriously the task of developing a multi-criteria allocation algorithm, academic studies have been discussing this issue. One such study involving NAFO fisheries, developed a model linking catches to processing and community livelihoods in Canadian maritime regions, taking into account fleet dynamics of Spanish and Portuguese fisheries (Lane, 2008). The schematic developed, shown in Figure 4.2, displays how the annual catch scenario (or allocation rule) feeds into the socio-economics of the communities (Lane, 2008). In this way, allocations are directly linked with their outcomes to the community at large, and are thus representative of benefits above and beyond catches.  c Journal of Northwest Figure 4.2: Grand Banks fishery model schematic (Lane, 2008). ⃝ Atlantic Fisheries Science, with permission through Creative Commons Attribution-Non Commercial 2.5 Canada.  Rationality, flexibility and reviews In order for members to agree on a cooperative management solution, they must be better off in doing so than by continuing in a non-cooperative manner, the so-called rationality 56  4.3. The future of allocation schemes assumption. Ensuring equitable distribution is an essential component of an agreement, as agreements perceived as inequitable often lead to non-compliance (Lodge et al., 2007; Cox, 2009). Having flexibility built into the cooperative agreement, often called resilience (Miller and Munro, 2004; Munro, 2008), is of paramount importance to ensure the rationality constraint continues to be met through time. One of the major impediments to long-term stability of allocation agreements is the new member problem. A stipulation in the UNFSA (Articles 10 and 11) states that any party with genuine interests in a fishery can seek to join the RFMO (and thus have access to the resource) at a later date. How to deal with these new members is something that RFMOs to date have not adequately addressed. Most RFMOs have chosen to accommodate new members by increasing the total allowable catch instead of reallocating from within the catch limits (Lodge et al., 2007). This has been done with disregard to the conservation status of the resource (for example, the case with CCSBT), and thus is at obvious odds with RFMO mandates for conservation. The scope for bargaining and renegotiation of allocations needs to be widened, and access rights should certainly stop trumping conservation concerns. Both conservation and access are part of RMFO mandates so novel ways of trading them off against each other resulting in the best outcomes are necessary. One possible option would be to put aside part of the total catch allowance, say 5%, for new members. Each year, if no new members have been added to the RFMO, that 5% gets redistributed to existing members, but it should be seen as a bonus, not as a right. An additional, or supplemental, mechanism would be to relax the ban on trading of quota that most RFMOs have in place and allow existing members to lease out or sell part of the allocation to new members (MRAG, 2006; Lodge et al., 2007). If these methods were combined, new members would be afforded initial allocation (from the 5% surplus) with the chance to increase their share through trading. As discussed in Chapter 3, this was addressed by Pintassilgo and Duarte (2001). The authors explore three possible solutions to deal with new members, including transferable membership, a waiting period, and a fair sharing rule. They point out that in a quota or allocation scheme, transferable memberships in the cooperative group can take on the attributes of individual transferable quotas (Pintassilgo and Duarte, 2001). One way may be to develop a better understanding of how to negotiate the reallocation of property rights to new RFMO entrants in the future, as called for by Bjorndal et al. (2000). Renegotiation of the allocation scheme should take place, and an appeals process should be developed (Caddy, 1996), if one is not already in place. It has been suggested that renegotiation should be considered on a medium to long term basis, for example, every 10 years (MRAG, 2006). 57  4.3. The future of allocation schemes Currently, no RFMO has any type of independent review panel in place to assess suitability of catch allocations (Cox, 2009), even though this can be a useful measure (Caddy, 1996) and has even been outlined in the UNFSA (UN, 1995). NAFO does, however, have an appeals process in place, whereby a contracting party is able to file an objection to any conservation or management measure, along with an explanation for the objective and an alternative policy. This objection can then go to an independent ad-hoc panel, who will make a subsequent recommendation to NAFO. Ad-hoc panels made up of external experts should be a more frequently-used tool. Anticipated and unanticipated climate shifts can change local fish distributions. If the allocation scheme is fixed and based on fish distributions, such changes can affect the viability of national fisheries and can give participating countries an incentive to deviate from cooperative agreements. For example, climate shifts impacted the stability of the cooperative agreement formed between Canada and the U.S. to manage Pacific salmon (Miller and Munro, 2004). Warming of coastal waters on the west coast of North America in 1977 led to an increase in the abundance of salmon in Alaskan waters, and a sharp decrease in abundance in salmon found in California, Oregon, Washington and southern Canada (Miller and Munro, 2004). The benefits expected by the southern players at the outset of the cooperative agreement did not materialize, and non-cooperative behaviour ensued (Miller and Munro, 2004). One major criticism to the Canada-US Pacific Salmon Treaty was that it did not explicitly include the scope for side payments (Munro, 1990), which would have been a way to compensate the losing party subsequent to any unforeseen shifts in abundance. This retrospective analysis helps to illustrate why resiliency and flexibility in a cooperative agreement is important for stability. This is becoming of increasing importance as climate forecasts coupled with models of fish stock distributions suggests there could be major shifts in terms of future access to shared resources (Cheung et al., 2009).  Efficiency and transferability Economic efficiency does not seem to play into allocation decisions for any tuna RFMO (Cox, 2009). This is probably because most efficiency gains from allocation programs are seen to derive from some loss in equity (Pinkerton and Edwards, 2009).8 Ex-vessel prices, fishing costs, and fleet capacity are rarely mentioned in stock assessment reports describing allocation. One argument that has been put forth in the literature is the possibility for 8 A tradeoff between efficiency and equity does not have to occur. A lack of dialogue between economists and non-economists about efficiency and equity has bred continued confusion about this apparent tradeoff. Economists have continually suggested that side payments be utilized to facilitate cooperation. This is one way that equity could be strengthened, while at the same time improving efficiency.  58  4.3. The future of allocation schemes auctioning quota or allocation shares (Copes and Charles, 2004) to increase efficiency. This has not been taken seriously to date. Given that cooperation must bring benefits above and beyond non-cooperation, the added economic burden of paying for allocation shares could result in non-cooperation being the more economically-sound decision for some states (Cox, 2009). Most RFMOs do not allow trading or selling of quota among participating members. This is inefficient from an economic perspective, however, as transferability allows for the most efficient vessels or nations to harvest fish (Gibbs, 2009). Efficiency gains have been seen through allowing a secondary market for transferring quota (Morgan, 1995), and some RFMOs have recognized the future need for transferability of allocated quota (IATTC, 2007). The issues around limiting greenhouse gas emissions parallel those around sharing fisheries resources. Allocated quota and trading programs for greenhouse gas emissions were initiated based on setting national targets. A market for international trading has emerged as the primary policy tool to promote efficiency and benefit those who choose to lower their contribution to the problem, although improvements in the system are still being sought. The allocation schemes in place to deal with greenhouse gas emissions have incorporated economic efficiency as a major objective in their design. There will likely be lessons learned about the international quota markets for carbon trading that could help guide the way towards an international trading mechanism for catches or revenues from shared fisheries.  Allocation and shared water agreements Like the United Nations Convention on the Law of the Sea, the United Nations Convention on the Law of the Non-Navigational Uses of International Watercourses exists to provide a framework for allocating water resources that are shared internationally (United Nations, 1997). The Convention states three main rules that govern the conduct of states who share a watercourse (United Nations, 1997): 1. The watercourse is to be used in an equitable and reasonable manner; 2. States are to take appropriate measures to prevent significant harm to another state; 3. States are to consult with, and provide timely notification to, other states about any possible adverse effects resulting from new policies or a change in policy. A novel approach to negotiations between states sharing watercourse, called the “Mutual Gains Approach”, has been proposed by Grzybowski et al. (2010). The authors outline two possible negotiation scenarios, one in which the position of the states is the 59  4.3. The future of allocation schemes primary driver of negotiations, and one in which states negotiate based on their interests. The conclusions reached suggest that when institutional egos can be left off the bargaining table, mutual gains to all cooperating parties are attainable based on the interests they represent (Grzybowski et al., 2010). The authors draw on historical examples of successful cooperative agreements, writing in length about the Columbia River Basin, a watercourse shared by Canada and the U.S.. One of the more interesting, and important, parts of the Columbia River Treaty, is that the responsibility for calculating the benefits and costs of non-cooperative and cooperative management lies with each individual country (Sanderson, 2009). In this way, each country calculates and communicates what it is likely to gain through cooperation, but these perceived benefits, or utility functions, need not be comparable between states (Sanderson, 2009). Rather, each country lays out what it hopes to get from cooperation, and as long as those hopes are met, cooperation can ensue. The Columbia Treaty suggests a 50/50 sharing of the benefits of cooperation, but in the event that one party would end up being worse off than through non-cooperation, a renegotiation of the sharing rules takes place (Sanderson, 2009). In a more applied assessment not related to the Columbia, van der Zaag et al. (2002) suggested three alternative allocation algorithms: equal sharing; shared in proportion to each country’s area in the water basin; and equal sharing per capita. The authors report that once equitable allocation has been reached, parties should be free to trade or transfer their allocated water amongst themselves (van der Zaag et al., 2002). In terms of allocation of shared water within a nation, historical usage patterns have been a common starting for allocation programs, although this is as much for political reasons as for any other (Cox, 2009). Market-based approaches have been employed in Australia, South Africa, the western states of the U.S. and Chile (Cox, 2009), but it’s hard to imagine that these can be at all equitable. A two-tiered approach has, however, reportedly been successful in the U.S. and Australia, whereby some amount of reliability or security of the entitlement is combined with the actual allocated amount (Peterson et al., 2004). In this way, allocations that are highly secure (or can be met 96-99 times out of 100) have priority before general secure allocations are met (those that are to be met 75 times out of 100) (Peterson et al., 2004). Efficiency is achieved through market-based trading allowances. The implications for fisheries would be as follows: one proportion of the TAC is allocated to nations as fixed, with the remaining quota classified as flexible, distributed on an annual basis to members either through auction or some other mechanism (Cox, 2009).  60  4.4. Conclusion  4.4  Conclusion  This study has provided a review of tuna allocation approaches used by groups managing internationally-shared fisheries resources. Many RFMOs have found it a tedious and tiring process to formulate allocation programs that are agreed-upon by all members, or have avoided making explicit allocation decisions all together (Metzner et al., 2010). In most cases, allocation has generally been decided based on historical catches, and more recently, combining historical catches with current biomass distribution trends (MRAG, 2006). Most current programs are based solely on biomass and catch information, without consideration of economic or social factors in allocation decisions. Socio-economic factors can include such items as economic dependency on the fisheries stock, and national economic wealth (Palma, 2010). Incorporating these may offer alternative allocation possibilities that could increase the scope for cooperation in internationally-shared fish stocks management. And although the United Nations Fish Stocks Agreement states that there should be development of transparent allocation criteria (UN, 1995), transparency has not been a priority to date (Lodge et al., 2007). The “Mutual Gains Approach” (Grzybowski et al., 2010) for shared international watercourses, offers some insights into the future of fisheries management. The authors suggest that the interests of nations sharing a resource should be the central tenant that drives negotiations (Grzybowski et al., 2010). This is akin to states moving away from “how much” of the resource they should be allowed to extract, to “what” they hope to gain from participating in a sharing system. Allocation in shared fisheries has invariably been based on a political process (Lodge et al., 2007), something that has not served sustainability well. In the Grzybowski et al. (2010) paper, the authors draw on historical examples of side payments (or negotiation facilitators) in shared watercourses, whereby the party who stands to gain the most through cooperation compensates those parties who may not be better off under cooperation. One of the earliest such schemes was contained within the Treaty of Versailles in 1919 (Carnegie Endowment for International Peace, 1924), one of the post-World War I treaties. Article 358 of the Treaty gives France “the exclusive right to the power derived from works of regulation on the river, subject to the payment to Germany of the value of half the power actually produced” (Carnegie Endowment for International Peace, 1924). A more relatable example is the 1911 agreement between the the U.S., Russia, Canada and Japan, all of whom targeted fur seals. In the early 1900s, the fur seal population had declined to the point that the economic benefits from the fishery were brought into question. While the U.S. and Russia harvested seals from land, Canada and Japan targeted individuals at sea. To maximize economic returns, all harvesting was to take place from 61  4.4. Conclusion land, essentially removing Canada and Japan from the harvest (Barrett, 2003). All of the catch was taken by the U.S. and Russia, with Canada and Japan compensated, through side payments, with a fixed percentage of the annual sealskins (Barrett, 2003). The need for side payments to factor more heavily in cooperative fisheries schemes is evident today, and has been raised before (Munro, 1979; Lodge et al., 2007; Bailey et al., 2010). Although Hardin’s most memorable contribution to our understanding of the problems associated with shared resources is the idea that self-interest almost always trumps collective interest,9 he also explored briefly the fact that incommensurable goods could in fact be compared, simply through subjective judgement and a weighting system (Hardin, 1968). In this regard, he was encouraging us to combine different objectives with different measurements in a joint utility function to improve the management of common pool resources. His challenge to the future was to “work out an acceptable theory of weighting” (Hardin, 1968). That challenge needs to be taken up and applied to the ocean commons. Allocation models with multiple weighted criteria would be a good starting point. Further to this, economic efficiency has not routinely been a component of international allocation schemes. Socio-economics have been largely ignored in allocation formulations in part because, although RFMO members are required to report some biological and catch statistics, there is no requirement to report statistics related to fishing costs, employment, or subsidies. In the very least, developing a bioeconomic allocation approach with which to compare the strictly ecological program currently in place would provide an interesting starting point for dialogue among RFMOs. Clearly, the allocation programs developed thus far have not provided the right incentive structure to promote sustainable fisheries. Most RFMOs, especially those tasked with managing highly migratory fish like tunas, face problems of illegal, unregulated and unreported fishing (IUU), TAC overages, competing sector interests, and challenges associated with multi-species and multi-gear fisheries, such as juvenile bycatch. Perhaps a de-politicized incentive structure whereby allocations are afforded based on more than just catch histories and abundance estimates is required to address these problems and improve RFMO management of shared fisheries resources.  9  It has been argued that Hardin had it wrong (Feeny et al., 1996), and that groups could in fact be counted upon to manage shared resources well (Ostrom, 1990). Although it is probably true that Hardin’s argument does not always hold its ground, the fact that so many shared resources are mismanaged and overexploited certainly gives credence to his insights.  62  Chapter 5  Towards better management of Coral Triangle tuna 5.1  Introduction  The western and central Pacific Ocean (WCPO) encompasses over 94 million km2 (Molony, 2008), and is home to an incredible amount of marine biomass. In 2010, tuna catches from the area provided 59% of the global tuna supply (SPC, 2010), with 2008 catches having an estimated gross value of almost US $5 billion (Williams and Terawasi, 2009). The four main species targeted in the WCPO are albacore (Thunnus alalunga), skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (Thunnus obesus). These four species are highly migratory, resulting in their biomass being present in the exclusive economic zones (EEZs) of many different countries, as well as in the high seas. There are numerous challenges associated with managing these types of resources in a cooperative manner, including asymmetry in national objectives and economic conditions, new members, and the tendency to default to the prisoner’s dilemma, among others (Aguero and Gonzalez, 1996; Munro, 1990, 2007; Munro et al., 2004; Bailey et al., 2010). Despite all of the challenges, the need for cooperation among states in managing shared resources is paramount (Chapter 3). The Coral Triangle (CT) is located in the western part of the WCPO (Figure 1.4); its name resulting from the region’s coral reef biodiversity. This area, approximately 5.7 million km2 in size, spans all or part of the waters of Indonesia, the Philippines, Malaysia, Papua New Guinea, Solomon Islands and Timor Leste. It is considered the world’s most biodiverse marine environment (The Nature Conservancy, 2004), and also one of the most threatened, due to population and poverty pressures faced by the communities that depend on its resources (Allen and Werner, 2002). Over 150 million people live in the area, and an estimated 2.25 million fishers depend on marine resources for their livelihood (The Nature Conservancy, 2004). Although named for its species-rich reefs, it is the Coral Triangle’s tuna stocks that are of immense importance to food security and economic production in the region. Tuna  63  5.2. Coral Triangle tuna fisheries in the CT range from small-scale subsistence and artisanal fishing to large-scale commercial operations. In 2010, about a third of the reported tuna catch in the WCPO was taken by the combined fleets of the Philippines, Indonesia and Papua New Guinea, equating to over 97% of tuna removals by CT countries (SPC, 2010). Tagging studies have demonstrated a high degree of interaction between CT tuna fisheries and those to the east (Vera and Hipolito, 2006; Ingles et al., 2008). The most recent stock assessment for yellowfin reports that the domestic fisheries of the Philippines and Indonesia are in part responsible for stock depletion (Langley et al., 2009b). Despite their regional and global importance, however, few papers have focused on confronting the challenges these countries face with regards to tuna management. Rather, emphasis has primarily been placed on analyzing the challenges that the small Pacific Island Countries (PICs) face in obtaining adequate rents from their fisheries, for example Bertignac et al. (2000), Gillett et al. (2001), Parris and Grafton (2006), Petersen (2006), Campling et al. (2007) and Walmsley et al. (2007). Reporting on the status and management challenges of CT fisheries will fill this information gap, improve tuna management in the CT, and hopefully facilitate better management in the WCPO as a whole.  5.2  Coral Triangle tuna  Tuna species Skipjack, yellowfin and bigeye are the three main tuna species targeted in the Coral Triangle, with skipjack making up almost 75% of the catch by weight (SPC, 2010). Skipjack are often caught by attracting the schools using either drifting or anchored fish aggregating devices (FADs), and then collected with a purse seine or by handline. The skipjack stock in the WCPO is thought to be underexploited (Majkowski, 2007), with the fisheries considered sustainable (Langley and Hampton, 2008). Skipjack catch is primarily sent to canneries, either exported to Thailand, or processed directly in the Philippines, Indonesia or Papua New Guinea. Some skipjack is smoked, or processed into ‘ham’, for domestic consumption. Table 5.1 summarizes the main CT tuna species fished. The biological diversity of the CT, along with the shelter of the archipelagic region, make this area prime nursery habitat for juvenile yellowfin and bigeye. These small juveniles are often captured as bycatch in the skipjack fishery, due to their association with skipjack stocks around FADs, and subsequently sent to canneries. Juvenile fish make up a high percentage of the standing stock biomass for all three species in CT waters, especially in the Philippines (Vera and Hipolito, 2006). As adults, yellowfin and bigeye are targeted by U.S., European (Spain, Portugal, etc.) and Asian (Taiwan, Japan, Korea etc.) longlin-  64  5.2. Coral Triangle tuna Table 5.1: Summary of main tuna species fished in the Coral Triangle, along with the gears used, markets supplied and status of the stocks. Species  Age  Gears  Markets  Skipjack  Adult  Canned, domestic  Yellowfin  Juvenile  Purse seine, pole and line Purse seine (bycatch) Purse seine, handline, longline, pole and line Purse seine (bycatch) Handline, longline, pole and line  Adult  Bigeye  Juvenile Adult  Canned, domestic  Stock status Underexploited Fully exploited  Sashimi, steaks, loins  Canned, domestic  Overfishing occurring  Sashimi  ers, as well as by domestic fisheries in Pacific Island Countries. Juvenile bycatch reduces the possible catch to these other fishing groups due to growth overfishing (see Chapter 6). This results in a conflict of interest between purse seine fisheries in the CT, who would prefer to exploit juveniles now, with longline fisheries outside the CT, who would benefit from reduced juvenile bycatch (Bailey et al., In press; Sumaila and Bailey, 2011; Hanich, 2012). Stock assessments report that yellowfin are fully exploited (Langley et al., 2009b), and that there has been significant depletion of yellowfin in the WCPO due to fishing “by the domestic fisheries of the Philippines and Indonesia and the combined purse seine fishery” (Hampton, 2002c). Yellowfin mature at about one and a half to two years of age, however, juvenile yellowfin are encountered in commercial fisheries in the Philippines and eastern Indonesia when they are only a few months old (Langley et al., 2007). Bigeye purse seine catch is almost exclusively juveniles, and because bigeye is often misidentified as yellowfin in its juvenile years, catch estimates are significantly underestimated (Lawson, 2008a; Reid et al., 2003; Lawson, 2007). As illustrated in Figure 5.1, there has been a rapid increase in purse seine catches of bigeye since the early 1980s, mostly due to the increased use of FADs (Hampton, 2002a; Langley et al., 2009a). Currently, stock assessments indicate that overfishing is occurring on the bigeye population (Harley et al., 2010) (Table 5.1).  65  60000  domestic longline pole/line purse seine other  0  20000  Catch (t)  100000  5.2. Coral Triangle tuna  1950  1958  1966  1974  1982  1990  1998  2006  Year  Figure 5.1: Total bigeye catch by gear, compiled from SPC (2010).  Tuna management Tuna stocks in the region are managed by the Western and Central Pacific Fisheries Commission (WCPFC), a regional fisheries management organization (RFMO). Figure 5.2 shows the statistical area of the WCPFC (solid straight lines), which, at the time of writing, has 25 participating members. Both the Philippines and Papua New Guinea are members, while Indonesia is considered a cooperating non-member. The Commission is a multi-lateral regime that includes PICs, large coastal states, and distant water fishing nations (DWFNs), and has been viewed as an impressive achievement (Parris and Grafton, 2006). The WCPFC received the highest ranking in a recent analysis scoring 18 different RFMOs against best-practices criteria (Cullis-Suzuki and Pauly, 2010). As sustainability issues with regional bigeye and yellowfin fisheries are abundant, however, there is still much room for improvement (Langley et al., 2009c; Cullis-Suzuki and Pauly, 2010; Hanich, 2012). The Secretariat of the Pacific Community (SPC) is another international organization in the area that represents about 8 million people in 22 PICs (Figure 5.2). The SPC has been in existence, in one form or another, for about 60 years, and works to provide technical and policy advice, along with training and research services to PICs. The SPC deals with a variety of issues relevant to its members, including health, human development, agriculture, forestry and fisheries, and contributes substantially to the scientific program of the WCPFC. Of the three countries highlighted in this Chapter, only Papua New Guinea  66  5.2. Coral Triangle tuna  WCPFC  Philippines  SPC Indonesia  PNG  FFA  Figure 5.2: Map of the statistical area of the Western and Central Pacific Fisheries Comc WCPFC, used with permission), shown by solid lines, and regional coverage mission (⃝ of SPC (small circle) and FFA (large circle).  67  5.3. Indonesia is a member of the SPC (Figure 5.2). Finally, the Forum Fisheries Agency (FFA) is a third player in the region. The FFA has 17 members, mostly PICs, but members also include Australia and New Zealand (Figure 5.2). It is essentially a coalition of countries with interest in Pacific tuna stocks. The FFA works to help facilitate effective management of tuna by its member countries through the sharing of information and expertise. Indonesia and the Philippines are not members. Given the existence of these three organizations, it would seem fair to conclude that tuna management in the WCPO is well-institutionalized. In reality, however, availability of information and data in the region, particularly in the Coral Triangle, is limited, and subsequently, the validity of scientific assessments is compromised. This then results in the WCPFC having difficulty setting informed management recommendations, let alone having those recommendations followed. That being said, it is argued here that affiliation with these regional organizations can lead to better management.  5.3  Indonesia  Indonesia is the world’s largest archipelagic nation, comprised of about 17,000 islands. It also has one of the most biodiverse and productive marine areas (Tomascik et al., 1997), making fisheries an important sector economically and culturally, and also in terms of food security. Indonesia catches more tuna in its waters than any other country in the world (Ingles et al., 2008). In 2006, Indonesian fishery exports totalled US $2.1 billion, 12% of which were tuna and tuna products, mostly fresh or frozen (Ministry of Marine Affairs and Fisheries, 2007). About 44% of all Indonesian tuna exports go to Japan, and about 27% go to the USA (Ministry of Marine Affairs and Fisheries, 2007). The current availability of information regarding tuna fishing and fisheries in Indonesia falls short of the information available for neighbouring countries. Catch and effort statistics have not been consistently reported, leading to regional uncertainty in stock assessment reports. The most recent year of catch data reported by the WCPFC for Indonesia’s distant water purse seine fleet is 1989. At the time of writing, Indonesia is not a full member of the WCPFC.  Tuna fisheries Indonesian fishers employ a variety of gears to harvest tuna. A pole and line skipjack fishery has existed in Indonesia since at least the 1940s (Ishida et al., 1994). A major expansion began in 1977, with catches of yellowfin and bigeye (collectively reported as “tunas”) increasing at an average of 10.6% per year, from 1977, to 1989 (Ishida et al.,  68  5.3. Indonesia 1994). The majority of tuna fishing gears used in Indonesia also target other pelagic species and these include Danish and purse seines, four varieties of gillnets, troll and simple handlines. Tuna longlines and tuna handlines are the only two gear types that specifically target large tunas (yellowfin and bigeye) in Indonesia (Ingles et al., 2008). All gears apparently fish only within Indonesian waters, as catch statistics available from the WCPFC suggest that there were no distant water fisheries after 1990 (Lawson, 2008b). However, some Indonesian handliners catch and offload their tuna in the Philippines (Ingles et al., 2008), and it is unclear how these catches are reported. Purse seine In Indonesia, the use of purse seines to catch tuna and other pelagic fish began in the 1960s. After trawling was banned in much of the country, many trawl vessels were converted to seine operations, which resulted in three times the amount of purse seines operating between 1976 and 1983 (Ingles et al., 2008). This exemplifies what is likely to happen when well-intended policies are not broadly considered. Small- and medium-sized purse seine fleets catch tuna seasonally, often targeting other small pelagic species throughout the year. There is also a fleet of large purse seine vessels (> 100 gross registered tonnes, GRT) that works in tandem with several catcher, carrier, skiff and light boats to operate. This fleet uses about 20-30 FADs per catcher vessel, and is not authorized to operate in archipelagic waters, but vessels often violate this law, leading to higher juvenile catches (Ingles et al., 2008). Longline The Indonesian longline sector originated in the 1980s, when the ban on trawling, combined with a government loan scheme (subsidy), created an ideal situation for the development and expansion of a tuna longline fleet (Ishida et al., 1994). Recently, the longline fishery in Indonesia has decreased in terms of its importance in the fisheries sector, which can be attributed to a decline in the availability of bait fish, as well as increasing fuel costs (Ingles et al., 2008). Effort has shifted to smaller-scale fishing gears, such as troll and tuna handline, which can provide high quality fish to the ever-growing sashimi market at a lower cost (Ingles et al., 2008). Processing The hygienic conditions of the landing facilities in Indonesia are far below international standards (Ingles et al., 2008). This, along with poor post-harvest handling practices, generally results in a lower-quality product going to market, and means that Indonesia is 69  5.3. Indonesia unable to supply to those markets willing to pay for high-quality fish. The government has, however, initiated plans to increase and improve the processing sector in an effort to facilitate all tuna caught in their EEZ to be landed and processed directly (Anon., 2007). The new regulations, scheduled to take place in December of 2011, will require all foreign fleets fishing in Indonesian waters to comply (PNA and U.S. News Agency / Asian, 2011). If this plan is to be successful, Indonesia is going to have to improve its processing facilities to remain competitive in the global market. Requiring landed fish to be processed domestically will not only increase activity of the processing sector, but should lead to better catch accounting, as currently tuna caught in the Indonesian EEZ but transhipped elsewhere are not always reported. Possibly due to this underreporting of catches, managers seem to believe that some of their tuna fisheries are underexploited, and are thus increasing their joint-venture relationships with foreign fleet owners (Anon., 2007).  Management measures and challenges In 2004, the Indonesian government enacted Fisheries Act No. 31, resulting in the management of tuna fisheries being segmented into 9 Fisheries Management Areas (FMA), overseen by the Ministry of Marine Affairs and Fisheries (MMAF). In 2009, the number of FMAs was increased to 11 by Ministerial Decree No. 1/2009 (Anon., 2009). FMAs refer to a particular body of water or fishing area, and are thus based on ecological boundaries, not political ones. Although this is relevant from a fisheries point of view, it can make management difficult. Often times several provincial and regency governments must cooperate in one FMA, or one province or district may have to participate in the management of various FMAs. These recent changes make analyzing trends over time difficult because catch statistics, now collected according to FMA, cannot easily be compared to statistics reported prior to institutional re-organization. The 2009 Ministerial Decree committed Indonesia to implementing a vessel monitoring system (VMS) (Anon., 2009), even though the government issued a similar decree in 2003 which did not lead to any changes (Directorate General of Catch Fishery, 2003). The year 2009 also saw Indonesia ratify the UN Fish Stocks Agreement (Anon., 2009). Indonesia’s prior refusal to ratify the Agreement was seen as a major barrier to international conservation efforts. As previously stated, tagging studies have shown a high degree of mixing between tuna found in Indonesia, and those found in the Indian Ocean, and further east in the WCPO (Ingles et al., 2008). Tuna management in Indonesia, therefore, greatly affects tuna fisheries in other countries. Indonesia does not have effective regulations to limit the size of tuna removed from  70  5.3. Indonesia its waters (Ingles et al., 2008). They do, however, issue licenses that can technically be revoked if fishers are caught fishing in areas for which they are not licensed, and for misreporting their catches (Anon., 2008b). New laws and regulations introduced in the mid 2000s to combat illegal, unreported and unregulated (IUU) fishing allow Indonesia to meet its international obligations for fisheries management on paper (Agoes, 2005). However, they fall drastically short in actually promoting conservation, in part because enforcement is so weak. Subsidies Since the late 1960s, the Indonesian government has been encouraging development of its tuna fleet for export-oriented markets (Ishida et al., 1994). The country currently uses subsidies to promote several different parts of their fishing sector. For example, trollers in the Ambon region (FMA-V Banda Sea) have received free boats and motors to enter the fishery (Ingles et al., 2008). The government also provides fishers with materials free of charge to build FADs, thus exacerbating the issues of juvenile bycatch (see below) (Ingles et al., 2008). Furthermore, investments in the processing sector, funded in part by joint-ventures, is also a type of subsidy, which may encourage more fishing than is currently profitable. The MMAF has stated that the country will strive to be the world’s biggest producer of fish, with the goal of increasing its fisheries sector by 300% by 2012 The Jakarta Post (2009). Government-driven fisheries expansion almost always involves subsidies. In 2003, the Indonesian government was estimated to have provided harmful subsidies amounting to almost US $800 million (Sumaila et al., 2010). Data One of the major challenges of fisheries management in Indonesia arises from the grouping of landed fish into categories useful for trade or for sale, not according to biology. For example, the category for landed ‘tuna’ includes both bigeye and yellowfin tuna, and could also include southern bluefin, albacore and long tail tuna (Ingles et al., 2008). Similarly, the ‘skipjack’ category probably includes juvenile yellowfin and bigeye tuna because they are sold together (Ingles et al., 2008). This problem was recognized as early as 1994 (Ishida et al., 1994), but species identification seems to vary within FMA, often due to local language differences. Discrepancies in the tuna species found in abundance at the market, with those recorded as the catch, have been noted (Ingles et al., 2008). In 2004, the national fisheries statistics system began recording catches by species, but this change was not uniformly made in all FMAs. Catch statistics prior to 2004 may not be particularly accurate, and thus the country does not have accurate catch statistics from which to draw 71  5.4. Philippines management recommendations. The WCPFC reports that a total of 182,476 tonnes of skipjack were caught by Indonesia in 2004 (Lawson, 2008b), however, based on MMAF data, fisher interviews and independent port sampling, it was reported that as much as 288,353 tonnes were caught (Ingles et al., 2008). Similarly, the WCPFC reports that officially 52,042 and 31,160 tonnes of yellowfin and bigeye were caught, respectively (Lawson, 2008b), while Ingles et al. (2008) report that the combined landings for these two species was 237,753 tonnes in 2004. A study initiated in eastern Indonesia (Papua province) also found substantial under-reporting of tuna catches, with the authors stating reduced taxes as the major economic incentive driving under-reporting (Varkey et al., 2010). Reported catch figures for 2009 were 210,590 t of skipjack, 94,141 t of yellowfin and 11,568 t of bigeye (SPC, 2009). The WCPFC is apparently working with grossly underestimated catches, leading to management difficulty on a regional scale (ACIAR, 2003). These removals should thus be reformulated to incorporate better catch estimates. Development of data collection and reporting ‘standard operating procedures’ would go a long way in improving the fisheries statistics system in Indonesia. Indonesia and the Philippines have developed a joint data collection program that is a good start to improving Indonesia’s data system. FADs and juvenile bycatch Of the nine FMAs visited by Ingles et al. (2008), the authors found evidence of FAD fishing in all of them, with some (FMAs 6 and 7) having extensive FAD use for multiple gears. The government’s choice to actively subsidize the construction of FADs is worrisome. The increased use of FADs in Indonesia, in part due to these subsidies and the rising cost of fuel, has resulted in increased catches of juvenile yellowfin and bigeye by the purse seine fleet, with these species now making up between 18% and 90% of the total catch weight Ingles et al. (2008). If there are spatial and seasonal differences in these percentages, then it might be worthwhile to limit FAD use during those times, or in those areas, where juvenile bycatch is the highest. Unfortunately, this will most likely result in short term losses for fishers, and require substantial monitoring and enforcement resources.  5.4  Philippines  As an island nation with an EEZ of about 2.2 million km2 , the Philippines is a country highly-dependent on fisheries resources (Barut and Garvilles, 2005). Fisheries contribute about 4% to the country’s Gross Domestic Product (GDP), with tuna fisheries comprising about 20% of marine fisheries production (Barut and Garvilles, 2005). Commercial tuna  72  5.4. Philippines Table 5.2: Summary of Indonesia’s tuna fisheries and management. Fisheries Processing Challenges  Management measures  Purse seine, longline, handline, gillnet, pole and line, small seines, troll Below industry standards, but economically important Unregulated FADs, juvenile bycatch (making up 18-90% of purse seine catch by weight), underreporting, directed subsidies for FADs, inconsistent data collection No size limits, no FADs plan, no unified data collection program, some closed areas  fisheries initially developed in the Philippines during Japanese occupation in the early 1940s (Vera and Hipolito, 2006), where catches were supplied to the local market (Barut and Garvilles, 2005), or delivered to smoking plants for the Japanese market (called ‘katsuobushi’). As catches started to decrease in the Philippine EEZ, and as American and Japanese demand for tuna increased, effort moved into the waters of Indonesia, Papua New Guinea and the high seas (Barut and Garvilles, 2005). Philippine fisheries now supply to both domestic and foreign markets. Capture fisheries are divided into two main sectors: municipal and commercial. Tuna vessels are usually classified as commercial because fishing occurs outside of municipal waters, using vessels larger than 3 GRT (Vera and Hipolito, 2006). Census data from 2002 estimated that the fisheries sector employed almost 1.8 million municipal fishers and about 8,000 commercial fishers10 (Vera and Hipolito, 2006).  Tuna fisheries The Philippines domestic fleets caught about 266,600 t in 2009 (SPC, 2009). Gillnets were used in Philippine tuna fisheries until 1997, and today, purse seines, ringnet, longline and handlines are all used. Lower-value fish, like skipjack or smaller yellowfin, are generally consumed domestically, or sent to the canneries, whereas higher-value fish, such as adult yellowfin and bigeye, are destined for the frozen loin or sashimi market. The main gears used include purse seine and longline, both considered commercial gears, and handline, considered a municipal gear. Because of this designation, handline vessels are not required to report their catches outside of Philippine waters, even though they also fish in Indonesia, Palau, Papua New Guinea and the high seas (Vera and Hipolito, 2006). The only vessels allowed to fish in Philippine waters are those flagged to the country. However, in 1995 10  To avoid double counting, any fisher engaging in both municipal and commercial fishing was counted as only a municipal fisher, and thus commercial fisher numbers are most likely underestimated.  73  5.4. Philippines as much as 10,000 t of tuna, 40% of which was yellowfin, were caught by longline vessels illegally fishing in Philippine waters (Barut and Garvilles, 2005) Purse seine The domestic and distant water purse seine fleets target mostly skipjack and some adult yellowfin, but also catch juvenile yellowfin and bigeye. Skipjack caught in purse seines average 27-35 cm in length, with juvenile tunas being around 15-50 cm, and, although the proportions vary by season, the domestic purse seine tuna catch is generally composed of about 60-70% skipjack, 20-30% yellowfin, and 10% bigeye11 . In 1995, as much as 90% of purse seine catch from commercial fishers in the area of Mindanao (in the southeastern region of the country, where much of the tuna catch is landed) was found to be less than 12 months of age (Aprieto, 1995). The use of FADs has only increased since then, so it is probably safe to assume that juvenile catch composition is not any better today. Purse seiners fish throughout Philippine waters, and the waters of Indonesia, Papua New Guinea and the high seas. An area of water between the Philippines and Indonesia is disputed territory that both countries claim as their own, but it is recognized internationally as Indonesian waters. This catch is treated as ‘domestic’ by the Philippines. There was evidence that large catches by Philippine fleets in these waters has adversely affected smaller-scale tuna operations in northern Indonesia (Naamin et al., 1995). About 60% of purse seine-caught tuna goes directly to the cannery for processing (Vera and Hipolito, 2006). We spoke with TSP Industries, a company owning a sizeable fleet of small, medium and large purse seine vessels, about their operations. The following lists some generalities: • For small- and medium-sized vessels, labour is paid via profit sharing. The boat owner finances the boat, while the master fisher hires the crew. Fishers continue fishing until they have reached the point where their catch volume is enough to cover costs. The owner takes 50% of the gross revenue, and the fishers split the remaining 50%, which could be considered the cost of labour; • TSP has 20-30 large purse seine vessel groups that spend their time catching fish in waters of the high seas and Papua New Guinea; one ‘group’ consists of one catcher boat, 2 carriers with ice, and 3-4 light boats, and employs 70-80 crew members; • About 70% of the vessels are active at any given time, but require dry-docking every 2 years; 11  Glennville Castrence, NSAP, personal communication.  74  5.4. Philippines • Six to seven years ago, larger vessels faced operating costs of US $400/t and they were selling fish for US $550-600/t. In late 2008, costs were about US $1,200/t, while the ex-vessel price was around US $1,625/t. Profitability has therefore increased about twofold; • TSP uses about 30 FADs per catcher vessel, 90% of which are anchored. Each FAD costs about US $3-4,000, and lasts 6-12 months; • The initial cost to using FADs is more than compensated for by the saving on fuel costs (especially following the elimination of fuel subsidies); • Costs are made up of 50% fuel, 14% labour, 18% maintenance, 8% FADs, 4% each to insurance and corruption (such as pilferage at sea), and 2% overhead; • TSP expects on average 4,000 t of tuna to be caught per catcher vessel per year. Handline Handline fishers are the primary Philippine producers of high-grade sashimi fish. They target adult skipjack, yellowfin and bigeye, as well as other species. There are two classifications of handlines: the palaran vessel, which is confined to municipal waters, and the pamariles, which can venture into deep Philippine and international waters (Vera and Hipolito, 2006), fishing as far away as Palau. Although there is uncertainty around the numbers, an estimated three to four thousand handline vessels, probably employing about ten times as many fishers, are active in the Philippines (Vera and Hipolito, 2006). Municipal handline fishers are opportunistic, in that they catch a large variety of species, depending on what is abundant at the time of fishing. On average, a palaran fisher catches about four tuna per week (Vera and Hipolito, 2006). The quality of the fish is of primary importance, and as such, industry and government began discussing a possible subsidy that would help handline fishers on very small vessels, with limited space for ice, maintain a fresh product by providing refrigeration vessels in municipal waters (Vera and Hipolito, 2006). Further to this, World Wide Fund for Nature Philippines has helped facilitate a public-private partnership aimed at promoting handline-caught yellowfin tuna as a more sustainable food choice for consumers12 . Pamariles fishers target only tuna. A mother-boat will carry auxiliary vessels and head out to fish on anchored FADs, known as payaos. Handline-caught tuna, although often fished with FADs, is usually adult-sized therefore the problems of juvenile bycatch 12  http://wwf.panda.org/?199811/Small-scale-fishers-in-the-Coral-Triangle-get-big-break-in-globalmarket  75  5.4. Philippines associated with fishing on FADs are less relevant in the pamariles fishery. Most FADs in Philippine waters are owned by purse seiners, but handliners are allowed to fish on these FADs given that the purse seine fleet has fishing priority. Furthermore, allowing handliners to fish on FADs can give purse seine owners a good idea of the possible catch composition of the school aggregating around the payao. A new handlining mother-boat costs between about US $10-30,000, while used ones are sold for about half of that (Vera and Hipolito, 2006). Operational considerations such as labour can cost up to US $1,900 per fishing trip (Vera and Hipolito, 2006). Profit sharing is employed, with fishers getting a percentage of the value of their catch, which amounts to about US $95 - $150 on average per month for a pamariles fisher (Vera and Hipolito, 2006). Longline The Philippine distant water longline fleet targets adult yellowfin and bigeye in the waters of Papua New Guinea and the high seas. The catch is exclusively landed in the city of Davao, in the province of Mindanao. Landed catch includes Philippine-caught tuna, and catch taken by other countries (mostly Japanese, Taiwanese and Korean vessels) in and around Philippine waters. There is a high degree of vertical integration in this sector with industries owning both fleets and processing plants. Far East Seafood, Inc., shared information about the structure of their longline operations. The following are their generalities: • Trips last about 20 days, with vessels fishing about 200 miles from the shore; • Average vessel catches about 12-15 tonnes of tuna per trip, the majority of which is yellowfin; • Nine workers are employed on one vessel, eight of whom take home about US $250 per trip, with the captain receiving about US $2,000 (unless he is Japanese, then he will earn up to US $5,000); • Fuel accounts for about 50% of the operating costs, with a longline vessel using about 2,000 litres per trip; • Vessels are, almost without exception, second-hand, costing about US $500,000. Vessels are dry-docked for one year (every couple of years), at a cost of about US $10,000; • The longline catch is composed of about 30% Grade B (commanding about US$3.25/kg), 45% Grade A (commanding about US$6/kg), and 25% Highest Quality fish (commanding about US$7.50/kg). 76  5.4. Philippines Processing The catch value, itself substantial, is only part of the economic benefit that tuna fisheries provide to the Philippines. There is a large value-added sector for tuna products, with about 80% of all tuna caught in the Philippines going to the cannery to be processed domestically13 . General Santos City, in the southern part of the province of South Cotabato, is a city founded on the cannery business. In fact, the City hosts an annual ‘Tuna Festival’ to promote its industry. Philippine purse seine vessels and Indonesian handline vessels land their catch here. For the Indonesian fishers, this port is closer for them, based on where they fish, and therefore is a more economical landing site. Compared to Indonesia, the Philippine cannery sector is also more economically efficient. In Indonesia, 3,000 workers, on average, are needed to can every 150 tonnes of tuna, whereas 1,500 are required in the Philippines. This is, in part, due to more holidays and shorter work days in Indonesia to facilitate daily prayers and religious holidays. The average daily wage in the Philippines is US $6.32, compared to US $2.20 in Indonesia (Anon., 2010). The port in General Santos City is managed by the Fisheries Development Authority (FDA, see below). There are about 30,000 direct cannery jobs, and an estimated 100,000 indirect jobs provided by the canning sector. Consequently, there is concern here about the implications that management may have on catch levels, and thus supply and processing14 . Both locally-caught and imported tuna is processed here. The tuna is generally bought at a lower price by the canneries, then sold at a higher price once canned. As such, although the Philippines is a net importer of fish, the total trade earning is positive, an estimated US $445 million in 2003 (Vera and Hipolito, 2006). The Philippines is currently working on internal reforms so that the processing sector better-meets EU health and safety standards. In addition to the large canning industry, some of the domestic skipjack and yellowfin catch is smoked, dried, salted, or processed into sausages and ham (Barut and Garvilles, 2005). Larger yellowfin are often sold as fresh or frozen loins, or exported as lower-grade sashimi.  Management measures and challenges Two national laws provide the fisheries policy framework in the Philippines: the Fisheries Code of 1998, and the Agriculture and Fisheries Modernization Act of 1997 (Vera and Hipolito, 2006). The Fisheries Code outlines policies regarding the development and utilization of fisheries resources, which include measures to control commercial fishing in 13 14  Benjamin Tobias, BFAR, personal communication. Miguel Lamberte, FDA, personal communication.  77  5.4. Philippines municipal waters, managing fisheries with regard to maximum sustainable yield (MSY), implementation of user fees, gear regulations, such as limiting the use of active fishing gears in municipal waters, and policies toward decentralization of fisheries management (Vera and Hipolito, 2006). Interestingly, the 1997 Act is focused on modernizing the fisheries sector, and thus sometimes promotes development-based measures that are in direct conflict with the more conservation-based measures promoted by the Fisheries Code of 1998 (Vera and Hipolito, 2006). There are several different organizations overseeing tuna management in the Philippines. The Bureau of Fisheries and Aquatic Resources (BFAR; www.bfar.gov.ph) is the highest federal entity in charge of fisheries management. BFAR tuna management functions include: monitoring and review of international fishing agreements; authorization of Philippine vessels fishing in international waters; regulation of transhipped products; and enforcement of fisheries laws and rules, except in municipal waters. Licenses are required to fish and are good for three years. The annual revenue from all fisheries licenses is quite low, about 1-3 million Pesos (US $6,000 - $18,000) in 2006 and 2007  15 .  Licensing is given locally for fishing in municipal waters, or federally for fishing  access in national waters. The municipal licenses are inexpensive, and often granted to commercial vessels through bribery. In total, about 1.3 billion Pesos (US $7.8 million) are spent on fisheries management annually in the Philippines, with about 500 million (US $3 million) of those being directed to tuna management16 . In addition to BFAR at the federal level, there is also the National Stock Assessment Program (NSAP). NSAP provides observers at port to take length and age samples of landed fish and its scientists are responsible for conducting stock assessments for domestic fisheries. NSAP has currently entered into a joint agreement with Indonesia called the Indonesia-Philippine Data Collection Project (IPDCP), which is aimed at improving reported catch statistics from the two countries (NFRDI, 2008). Although the Philippines has its own system for management of domestic tuna fisheries, it also participates in management through its membership in the WCPFC. In 2008, the Philippines paid about US $83,000 to the WCPFC as part of its membership obligations, and in return for this, received US $150,000 for management (primarily for data collection and tagging programs)17 . Overseeing of the fishing ports is done by the Fisheries Development Authority (FDA). Throughout the Philippines there are 12 FDA government ports. Some of these have been built with subsidies from Japan. The FDA is currently working on improving product 15  Augusto Natividad, BFAR, personal communication. Benjamin Tobias, BFAR, personal communication. 17 Benjamin Tobias, BFAR, personal communication. 16  78  5.4. Philippines quality and implementing measures to improve traceability, both in order to facilitate better market access. The Bureau of Statistics does its own port sampling, and interviews fishers and dockside observers. The FDA port in General Santos City, home to the country’s canning industry, has invited the private sector to invest in an on-site testing laboratory to check histamine levels in the fish. Histamine is a byproduct of bacterial action and can build up in the muscle tissue of fish if it is not kept at near-frozen temperatures. When consumed by humans, it can cause histamine poisoning, the symptoms of which mimic allergic reactions or other types of food poisoning. Industry is very much involved in tuna management in the Philippines. The National Tuna Industry Council (NTIC) is a coalition of actors, including academic, industry (purse seine and handline producers), non-government and government members. NTIC deals with trade and access issues, and reviews recommended management. The industry representatives serve as liaisons in an effort to ensure that the interests of industry are accounted for in management decision-making, and to help the industry as a whole cope with those decisions. Mesh size The Philippines has put into law a 3.5 inch minimum mesh size requirement for net fisheries (Table 5.3), however, many vessels still use 1 inch meshes for three reasons. Firstly, many fishers in the Philippines use second-hand nets because they are cheaper. They buy these from Japan and Taiwan, where stronger enforcement of measures in place for minimum mesh size requirements mean fishers there can no longer use their 1 inch meshes. And secondly, for Philippine companies who can afford to purchase new nets, they often have to be custom-ordered, sometimes taking more than 2 years to arrive. The third, more perverse, reason is due to demand. Many people rely on fish as a main source of protein, but most residents can only afford cheaper fish, which often means small juveniles. Consequently, there is high domestic demand for juvenile tuna sold at the markets. To this end, the government has issued fish rulers to people frequenting fish markets to discourage them from buying juvenile fish. The Philippines has instituted a management measure reportedly setting 10% as the maximum proportion of the catch that can be made up of small tunas (under 500 g) (Anon., 2008a). For yellowfin and bigeye, however, fish of this size are still juvenile. A proposed “net amnesty” program would allow fishers to trade in their smaller meshed nets in exchange for regulations size mesh.  79  5.4. Philippines Subsidies The Philippines used to subsidize fuel for fishers, but currently domestic fishers pay the full cost of about $1/litre. Commercial distant water fleets (fishing outside the Philippine EEZ), however, can avoid paying federal fuel tax by requesting direct importation of fuel. The removal of fuel subsidies and the increase in fuel prices in early 2008 had two major ramifications. Firstly, fishing effort and landings decreased in the Philippines, and elsewhere in the world. Skipjack catch was down an estimated 60%, and Philippine canneries were seeing an overall decrease in supply by about 50-300 t/day18 . The global supply of tuna decreased and thus the price skyrocketed, with skipjack prices reaching almost $2,000/t (Williams and Terawasi, 2009). Secondly, fishers who were able to fish, used their gear closer to shore where more juvenile fish are found. The removal of fuel subsidies therefore contributed to an increase in the by-catch of juvenile fish. Any policy reform is likely to alter fisher behaviour in ways other than originally intended by the reform. Subsequent enforcement, for example in not allowing purse seines to operate in juvenile tuna habitat, should have been in place to help mitigate undesirable consequences. In 2003, the Philippine government was estimated to have provided harmful subsidies amounting to US $610 million (Sumaila et al., 2010). Their joint-venture relationship with Japan for landing and processing fish, for example, is a form of subsidy. Juvenile catch The catching of juvenile yellowfin and bigeye tuna is recognized by both government and industry as a sustainability issue. Juvenile by-catch in the Philippines tends to involve very young and small fish, for example, bigeye and yellowfin of about 15 cm in length. In Indonesia, juvenile’s are also caught, but they tend to be a bit larger, 20-30 cm in length. In Papua New Guinea, as the tuna have started migrating out of the Coral Triangle area, those caught in purse seines are larger, about 50+ cm in length, but still juvenile. This makes it difficult to enact sweeping management recommendations regarding juvenile by-catch by the WCPFC, because the catch varies so much between countries, and management measures would adversely affect some countries more than others. In the Philippines, juvenile by-catch is highest in coastal waters, with oceanic waters having a smaller catch proportion of juveniles. A recent summary of NSAP data concluded that 100% of the yellowfin and bigeye captured by purse seines in Philippine archipelagic waters were juveniles (Ingles and PetSoede, 2010). In 2009, this resulted in a total of over 61,000 t of juvenile fish, of all three species combined, being removed from the ecosystem (Ingles et al., 2008). The use of 18  Bayani Fredeluces, NTIC, personal communication.  80  5.5. Papua New Guinea FADs in Philippine waters should be monitored, if not controlled. FADs tend to decrease the costs (particularly fuel) associated with fishing, and thus can lead to both overfishing and an overcapitalized fishery. Up to 150 FADs are currently being used per purse seine vessel in the Philippines19 . Many individuals in government and industry thought that a limit of about 25-30 FADs per catcher vessel might be reasonable. Effective enforcement of such a limit is obviously a substantial subsequent issue, however, making fishers register and be accountable for their FADs, may help regulators. One way to do this would be to require documentation on FADs, as suggested by the WCPFC (2009). Table 5.3: Summary of the Philippine’s tuna fisheries and management. Fisheries Processing  Challenges Management measures  5.5  Purse seine, longline, handline, ringnet Very important economically, undergoing improvements to secure EU accessibility, more efficient than Indonesia Unregulated FADs, juvenile bycatch (averaging 15-50 cm in length), subsidies, ineffective controls Mesh size limits (3.5 inch, but ineffective), no FADs plan, juvenile catch limits (10% by weight)  Papua New Guinea  Papua New Guinea (PNG), home to about 6 million people, shares its land mass with the province of Papua, Indonesia. The PNG EEZ is about 2.4 million km2 , and borders the EEZs of Australia, Solomon Islands, Indonesia and Federated States of Micronesia (FSM). The major fisheries in PNG include tuna, prawns, sea cucumber (or bˆeche-de-mer), lobster, trochus shells and shark. PNG is one of the Parties to the Nauru Agreement (PNA), along with Palau, FSM, Marshall Islands, Nauru, Kiribati, Tuvalu and Solomon Islands. The PNA formed a coalition specifically to facilitate multi-lateral cooperation in regional purse seining. In February of 2010, they undertook measures to have skipjack tuna eco-certified as sustainable by the Marine Stewardship Council (MSC). Their request specifies that only tuna caught by purse seines setting on free schools (that is, without the use of FADs or any floating object) in PNA country EEZs should be considered for certification (Marine Stewardship Council, 2010). After going through the MSC appeals process, the fishery was officially declared MSC-certified in December, 2011. . 19  Benjamin Tobias, BFAR, personal communication.  81  5.5. Papua New Guinea  Tuna fisheries The tuna fisheries of PNG are the fishing sector’s biggest and most valuable. The tuna sector includes domestic longline, handline, pole and line (although the WCPFC (Lawson, 2008b) only reports pole and line catches up 1985) and purse seine fleets, as well as a locally-based foreign purse seine fleet, and a foreign access purse seine fleet. Of 194 licensed vessels in 2008, 9 were PNG-flagged, 30 were locally-based foreign vessels, and the other 155 were foreign access distant water fishing vessels. Over 80% of the landed catch is skipjack, with about 20% being yellowfin and less than 1% bigeye. Figure 5.3 shows the catch trends for Papua New Guinea’s fisheries over the past 40 years. Since the late 1990s, the country has seen a major increase in catches of all species, due mostly to the increased use of purse seines. Bigeye catch 5000 1980  1990  2500  2000  1974  1986  1998  Year  Year  Yellowfin catch  Total catches (all gears)  Total SkJ BE YF  0  0  20000  PS PL LL  50 100  Catch (1000 t)  40000  200  1970  Catch (t)  PS LL  0  60  Catch (t)  120  PS PL  0  Catch (1000 t)  180  Skipjack catch  1974  1986 Year  1998  1970  1980  1990  2000  Year  Figure 5.3: Papau New Guinea catch trends, compiled from SPC (2009). PS: purse seine; PL: pole and line; LL: longline; HL: handline.  Processing The importance of the processing sector is also factored into national policy decisions. PNG has many processing plants in place now, and plans further development. When the European Union, PNG’s major tuna export destination, required that imported tuna 82  5.5. Papua New Guinea meet certain food safety standards, PNG undertook measures to be designated a Seafood Competent Authority. Competency for PNG was awarded as a result of the availability of legal instruments empowering the development and implementation of the PNG Standards for Fish and Fishery Products. Furthermore, the system allows for continuous updates on compliance of EU food laws by the National Fisheries Authority, in terms of sanitary control processes, and procedures based on risk application and monitoring mechanisms, such as official controls and laboratory services. The agreement with the EU allows for duty free status of all tuna processed in PNG, and exported to the EU (essentially, a subsidy).  Management measures and challenges The fisheries sector is governed and regulated by two federal initiatives: the Fisheries Management Act of 1998 and the Fisheries Management Regulation of 2000. These initiatives specifically mandate that PNG fisheries resources be managed in a sustainable and equitable way for current and future generations. Under the 1998 Act, the National Fisheries Authority (NFA) is responsible for the management and development of the fisheries sector, under the overall policy direction from the Minister for Fisheries. Tuna fisheries are managed under the National Tuna Management Plan (NTMP), which guides PNG policy. The Plan, adopted in 1999, is based on the precautionary approach and recognizes the responsibilities of PNG given the regional management environment (i.e., WCPFC, FFA, and SPC). Even though customary tenure of land is common in PNG, the government has employed a predominantly top-down approach toward fisheries management. The national program is founded on the basic principle that as the national fishing industry grows, the number of purse seine vessels under foreign access will be reduced, a process called domestication. PNG has taken several regulatory measures to improve management of its tuna stocks. The longline fleet was fully domesticated in 1995, giving the government better management control over that sector. The NTMP has included control measures such as number of licenses; setting of the total allowable catch (TAC); control of fishing effort (i.e., number of boats/day, fishing days); season closures; species length/weight limits; gear type limits; and delineated fishing areas/zones. PNG has also instituted what they call “in-zone measures”, essentially spatial controls within their EEZ. These include: closure of the Morgado Square in the Bismark Sea; archipelagic waters closed to non-domestic fleets; territorial waters closed to purse seining (12 miles); all waters south of 5 degrees latitude closed to FADs; inshore waters closed to longlines (6 miles); and currently in process of closing 50 nautical mile corridor along northern border of PNG and Indonesia to all forms  83  5.5. Papua New Guinea of fishing. In addition to national policies, PNG has also linked their management to several regional arrangements. They are members of the WCPFC, and as such, have taken initiatives encouraged by the Commission to monitor and control FADs. PNG has also adopted the FFA coordinated observer programs, the Niue Treaty, coordinated aerial surveillance, and the Palau Arrangement, which initiated the use of the vessel day scheme. Participation in, and compliance with, regional agreements has greatly facilitated effective tuna management in PNG. Over the past decade, Papua New Guinea has seen improvements in its catch and effort data collection, in part due to the use of the vessel monitoring scheme (VMS), the vessel day scheme (VDS, see below) and pockets of the high seas closed to fishing. PNG’s management measures are summarized in Table 5.4 Pacific Marine Industrial Zone PNG is considering the development of a Pacific Marine Industrial Zone (PMIZ). The Zone would be located on the Vidar Plantation, Madang. It would comprise of 860 hectars across the North Coast Road, and is in close proximity to fishing grounds, thus making it easier for fishing companies to offload their catch at a competitive cost. PNG is also hoping the Zone will increase the level of fishing participation by PNA countries, thus decreasing their reliance on foreign access fees. Furthermore, given the duty-free status of all tuna processed in PNG and exported to the EU, PNA countries would thus have another incentive to process their fish in the Industrial Zone. Currently, the PNG government has allocated about US $7 million to facilitate the project start-up. That the PMIZ is a good thing for Papua New Guinea is not necessarily agreed upon, however. One newspaper article alleged that some residents of Madang do not support the project (Schenk and Simon, 2009). The article goes on to report that the US $300 million plan to build 10 new processing factories will negatively impact local fishers due to closures in the adjacent waters (Schenk and Simon, 2009). Vessel Day Scheme The vessel day scheme (VDS) was adopted by the PNA under the Palau Arrangement for the Management of the Western Pacific Purse Seine Fishery (the Palau Arrangement), to regulate purse seine fishing days in the waters of PNA countries. VDS came into effect in December 2007, and was implemented as a way to provide for effective management in the face of declining fish stocks, and in an attempt to improve economic returns by creating a limit on the number of fishing days. PNG allocates fishing days to all bilateral fishing partners, and monitors these controls using Vessel Monitoring System (VMS) technology. 84  5.6. Regional options In this way, the government receives real time data relating to vessel position and utilization of allocated fishing days. Furthermore, vessels can provide their catch declaration electronically. FADs PNG also has a very ambitious FAD management plan: of the three countries discussed in this Chapter, they are, in fact, the only one to explicitly include a FAD management plan in their national policy (WCPFC, 2009). The NTMP limits the number of FADs allowed per fisher vessel and includes guidelines on the deployment of FADs. Further to this, they have set an overall limit of 1,000 total allowable FADs in their EEZ (WCPFC, 2009). PNG also requires that the date and position of FAD deployment be recorded, and that an observer must be present at deployment (WCPFC, 2009). Monitoring, control and surveillance PNG operates several monitoring, control and surveillance (MCS) initiatives to enforce their regulatory measures. The first is the vessel monitoring system, VMS, which is operated on both a national scale by PNG, and on a regional scale by the FFA. The system monitors the operations of all licensed vessels operating within PNG waters, and as mentioned earlier, the national system helps to implement VDS. An observer program is also in place, and with 127 observers, is the largest in the region. Recent initiatives in the PNA countries have included closures to all tuna fishing in pockets of the high seas from 20o North and 20o South of the equator and 100% observer coverage on board purse seines has resulted in lower bigeye catches of up to 20%, as well as a reduction in illegal and unreported catches. Vessels are audited randomly to check with compliance, as are processing facilities. Processing facilities also have to meet certification standards regarding food safety. In 2002, PNG began utilizing four Defence Force patrol boats. These naval crafts participate in ten trips per year, undertaking surveillance along the EEZ border. The management measures and MCS of PNG are linked to regional arrangements under the FFA and the Palau Arrangement.  5.6  Regional options  Tuna fisheries in the Coral Triangle provide food and income security to Indonesia, the Philippines and Papau New Guinea. These fisheries also substantially contribute to the world supply of tuna. As described above, both Indonesia and the Philippines face challenges in managing their transboundary tuna stocks. Table 5.5 presents a summary of 85  5.6. Regional options Table 5.4: Summary of Papua New Guinea’s tuna fisheries and management. Fisheries Processing  Challenges Management measures  Purse seine (FADs-free fishery MSC-certified), longline, pole and line Important, plans to expand, opportunities for PICs to use facilities, designated Seafood Competent Authority Some juvenile bycatch, subsidies FADs plan, VDS and VMS used, length/weight limits, seasonal closures  the 2009 reported catches for each CT country analyzed here, the types of management systems that are currently in place, and subsidy estimates for the 2003 year. The major management challenges that Indonesia and the Philippines have to overcome are in their data collection and reporting capacity, and their ability to reduce juvenile bycatch of yellowfin and bigeye tuna through FADs management and size/retention controls. The Philippines has two major tuna landing ports, one for purse seine-caught tuna and one for longline- and handline-caught tuna, allowing for better data handling. Both countries, however, could greatly improve their management regimes and their enforcement programs. Papua New Guinea has a unique opportunity to help facilitate better CT tuna management as they are strategically located between Indonesia and the Philippines, and the Pacific Island community. In paying membership dues to the WCPFC, the Philippines receives more in financial assistance than they put in. Data collection and handling in Indonesia is unacceptable for such a major player in regional tuna fisheries. If financial limitations are deterring the government from improving their collection and analyzing capacity, then Indonesia would do well to join the WCPFC to, at the very least, receive financial help in this context. The joint data collection system between Indonesia and the Philippines is a good start, but the WCPFC needs better access to Indonesian data to improve stock assessments and management recommendations. Given the obvious under-reporting of tuna catches in Indonesia, the government’s goal to increase their fisheries sector production by 300% is quite worrisome.  Juvenile bycatch Both the Philippines and Papua New Guinea have some type of size limit recommendation in their management of tuna. The effectiveness of this in the Philippines has yet to be seen. Weakly enforced mesh limits, if any, and ineffective size controls, result in juvenile yellowfin and bigeye tuna continuingly being captured as bycatch in the Coral Triangle purse seine 86  5.6. Regional options Table 5.5: Summary of 2008 catches (SPC, 2009), presence (P) and absence (A) of management measures, EEZ size (Sea Around Us Project (seaaroundus.org)) and 2003 subsidies (Sumaila et al., 2010)) in Indonesia, the Philippines and Papua New Guinea. Summary statistics Regional memberships Size of EEZ (million km2 ) Skipjack catch (1,000 t) Yellowfin catch (1,000 t) Bigeye catch (1,000 t) Percentage of total WCPFC catch Management measures Catch limits Effort limits FADs plan Closures Mesh size limits Length limits Harmful subsidies (million USD) Harmful subsidies (% of Landed value)  Indonesia None 3.61 211 94.1 11.6 13.6  Philippines WCPFC 2.27 179 81.5 6.3 11.4  Papua New Guinea WCPFC, SPC, FAA 2.40 169 45.6 6.6 9.5  A A A A A A 790 40  A A A A P A 610 32  A P P P P P 427 28  fishery. Further to this, Papua New Guinea is the only country to institute both closures and a FADs management plan. On a regional scale, the WCPFC is initiating a FAD management and monitoring plan, recommending the marking and electronic monitoring of FADs, and limits to the number of FADs deployed and set on (WCPFC, 2009). This should probably encourage the Philippines to hasten their pace at instituting such a policy. As a cooperating non-member, it is hard to say if Indonesia, on the other hand, will be so encouraged. That Indonesia and the Philppines have dragged their feet in implementing a FADs policy is unacceptable both biologically and economically. Juvenile bycatch, highest in archipelagic waters, leads to growth overfishing whereby fish are harvested before they are able to reach a size that results in the maximum yield per recruit. This results in economic waste because the larger fish are more valuable at port. The current recommendations do nothing to counter this, and set up a system that continues to rob tuna-fishing nations of future economic returns from adult harvests, not to mention the ecosystem consequences. The FFA and the SPC include Papua New Guinea, but do not promote observer programs in Indonesia and the Philippines, where juvenile bycatch is high. Being able to monitor and control effort is nearly impossible without some idea of FAD distribution and use. At the very least, VMS should be enabled on board all medium and large tuna vessels. Biological control measures such as gear restrictions, minimum size limits and seasonal/temporal 87  5.6. Regional options closures should be implemented and enforced to discourage growth overfishing of yellowfin and bigeye stocks. The WCPFC has recommended a 30% decrease in fishing mortality on bigeye tuna (from 2001-2004 levels), and limiting the fishing mortality on yellowfin to its 2001-2004 level (Hampton and Harley, 2009). However, decreases in fishing mortality in archipelagic waters are apparently not required, even though this is where the majority of tuna catches from Indonesia, the Philippines, and to a lesser extent, Papua New Guinea, are concentrated (Hampton and Harley, 2009). Recommended decreases in mortality of bigeye will probably not be met because of this, among other limitations (Hampton and Harley, 2009). Interestingly, due to the decrease in fuel required for fishing with FADs, purse seining in general was found to have a lower carbon footprint than other forms of tuna fishing (Tyedmers and Parker, 2012), and thus there may be increasing pressure to continue fishing with these aides in an attempt to reduce the carbon footprint of the industry. An interesting idea proposed in the Philippines was to turn FADs into ‘FEDs’ - fish enhancing devices. These would be safe havens for the fish. Although it is unclear how such a plan may alter the natural migratory patterns of the tuna, if drifting FADs were turned into FEDs, they could almost be thought of as mobile marine protected areas.  Economic measures Papua New Guinea currently subscribes to the vessel day scheme (VDS), as initiated by the PNA. This is a type of effort quota system, that is expected to eliminate some of the competitive nature of shared fisheries. The entire Philippine industry expects that they will soon have to participate in this scheme (Barut and Garvilles, 2005). Philippine distant water fleets operating in the waters of Papua New Guinea are already required to participate. Estimates of fishing effort in both Indonesia and the Philippines are uncertain. Implementing VDS would at least give both countries a better idea of exactly who is operating in their waters, and how many fishing days are being utilized. Limiting effort in order to control catches and capacity would be an obvious next step. Licensing fees are probably an under-utilized economic tool in the Coral Triangle region. No doubt for the large commercial operations in Indonesia and the Philippines, paying for the privilege of harvesting a public resource should be required. The costs of managing a migratory resource like tuna are large, and those costs need to be shared by parties benefiting from the fishery. Given that purse seine fishers are experiencing increased profits margins in recent years, increased licence fees could be used to improve management in both Indonesia and the Philippines. All three countries highly subsidize their fisheries, although it is not known at this  88  5.6. Regional options time, what proportion of those subsidies goes directly to tuna fisheries. Tackling the subsidy problem could be a very good first economic step to promoting more sustainable fisheries (Sumaila et al., 2010). Although the elimination of fuel subsidies is often noted as a conservation initiative (Sumaila et al., 2008), industry in the Philippines acknowledges that the rise in fuel prices increased their dependence on FADs. Removal of fuel subsidies without subsequent economic incentives or enforcement of management regulations may thus be detrimental to stocks. For example, if elimination of fuel subsidies results in higher costs to offshore fishers, then secondary measures need to be in place to ensure that the fleet does not start fishing in inshore waters. The utility of market-based instruments in promoting conservation is on the rise. The desire of the PNA countries to seek MSC-certification speaks to the industry’s growing awareness that retailers and consumers can shift demand. New market-based instruments, such as consumer awareness campaigns and sustainable processor and retailer sourcing, can serve to pull the industry towards more ecologically conscious behaviour. Coupled with a push from top-down improvement in data collection, monitoring, enforcement, and spatial closures, the western Pacific tuna industry could evolve into being a benchmark of sustainability for other tuna RFMOs (Pala, 2011).  Conclusion In order to adequately manage tuna in the western Pacific, a group of highly migratory species, we first need an understanding of life history parameters, distribution and migratory patterns, and the ecological relationship between tuna and other organisms aggregating around FADs. Research is currently being conducted to meet these needs. That being said, there are some simple first steps that the Philippines and especially Indonesia should be encouraged to take to improve regional tuna management regardless of what is not yet fully understood. Better data collection and management and simple gear and size restrictions would be a good start. Because the decisions in these countries have an impact on the potential for tuna fisheries in other countries, the WCPFC community needs to cooperate in facilitating these improvements by the Coral Triangle region. PNG’s involvement in other regional groups, such as the FFA and the SPC may be one reason that they have been more successful in meeting management challenges. Although closed areas, gear restrictions and effort limits (including VDS) may not be completely adequate to correct the biological and economic problems that mis-managed fisheries can create (Joseph et al., 2010), these measures are simple first steps that Indonesia and the Philippines, who have valuable fisheries, should implement. A third of all tuna caught in the WCPO comes from the Coral Triangle, and thus management actions,  89  5.6. Regional options or lack thereof, in this region impact the fisheries potential for other nations in the region. If these fisheries are to continue being of economic and social value to communities in the Coral Triangle and elsewhere, all members of the WCPFC should facilitate some kind of benefits sharing system, a ‘tuna trust fund’ of sorts (Bailey and Sumaila, 2008a), so that all fisheries could share in the possible economic gains from decreasing the bycatch of juvenile fish (see Chapter 6 for a general discussion). This possibility of cooperation has been theorized (Kaitala and Munro, 1993, 1997), quantified (Bertignac et al., 2000; Bailey et al., In press; Campbell et al., 2010), and summarized (Munro, 2008; Bailey et al., 2010) in the literature. Actually implementing such a system on the ground will be vital to encourage Indonesia and the Philippines to contribute to more effective tuna management in region.  90  Chapter 6  Can cooperative management of tuna fisheries in the western Pacific solve the growth overfishing problem? 6.1  Introduction  The western and central Pacific Ocean (WCPO) is home to many species of commercially targeted fish, the most profitable of which are tuna. About 2.4 million tonnes of tuna were caught in the WCPO in 2007 (Williams and Reid, 2007), accounting for about 54% of the world’s tuna supply (Lawson, 2008b). There are four main species found in the WCPO: albacore (Thunnus alalunga), skipjack (Katsuwonus pelamis), yellowfin (Thunnus albacares), and bigeye (Thunnus obesus). The latter three species, which are mainly found between 10 degrees north and south of the equator, are often found in association with one another, especially around floating objects known as fish aggregating devices (FADs). This association leads to the bycatch of juvenile yellowfin and bigeye tuna in the purse seine fishery primarily targeting skipjack and adult yellowfin. The term bycatch has been defined several different ways (Hall, 1996), but for the purposes of this paper, bycatch is considered to be any species caught, whether retained or not, that is not the main target of the fishery. The catching of juvenile fish of a target species can lead to growth overfishing, and can thus lead to a decline in the resource of interest (Gjertsen et al., 2010). In this context, bycatch of juvenile tuna in the WCPO tuna fisheries has been discussed in recent stock assessments and technical reports (Langley et al., 2007, 2009a; Williams and Reid, 2007; Kumoru et al., 2009; Harley et al., 2010), and the possible decrease in economic rent resulting from this has been analyzed (Campbell, 2000). Juvenile bycatch of bigeye and yellowfin tuna is generally higher in the western part of the WCPO, such as in the waters around the Philippines, Indonesia and Papua New  91  6.1. Introduction Guinea, in an area known as the Coral Triangle20 . As juvenile tuna grow, they tend to migrate east, resulting in smaller amounts of juvenile bycatch in the waters of the Pacific Island States, and in the high seas (i.e., tuna fisheries in this area catch larger fish). It has been shown through tagging studies that there is a high degree of interaction between tuna fisheries in the western part of the WCPO with fisheries in the more eastern parts of the WCPO (Vera and Hipolito, 2006; Ingles et al., 2008). A recent study initiated in Papua New Guinea suggests that the mean size of bigeye tuna caught in the purse seine fishery has declined in recent years, with the majority of harvested fish being between about 39 and 64 cm in length (Kumoru et al., 2009), even though bigeye mature at about 100 cm in length (Molony, 2008). It is believed that the introduction of drifting FADs in 1996 has increased the amount of bigeye bycatch in the purse seine fishery (Williams and Reid, 2007). Growth overfishing of bigeye, and probably yellowfin, is occurring, and stock depletion of these species has been linked, in part, to juvenile bycatch. Other types of fishing mortality are also thought to contribute to depletion. If left in the ocean, those yellowfin and bigeye who do not die of natural mortality could mature and spawn, supporting productivity of the stocks. Furthermore, the adults could be targeted by longline and handline fishers, whose catch commands a much higher price than that paid for juvenile fish. There is thus a conflict of interest between purse seine fishers in the Coral Triangle and longline and handline fishers targeting adult yellowfin and bigeye. It is important to ask then, could cooperative management of tuna fisheries in this region reduce the economic losses due to growth overfishing? This question is addressed through the development of a bioeconomic game-theoretic equilibrium model. I examine the potential catches and values of the purse seine, longline and handline fisheries in the WCPO resulting from three alternative management scenarios: (1) the status quo, (2) a regulated FAD plan, and (3) the total elimination of FAD fishing and no juvenile tuna bycatch. All values are calculated at equilibrium, and thus answer the question: what is the best achievable outcome in equilibrium. The status quo assumes that business as usual continues, with purse seine vessels still fishing on FADs with little or no regulation. The regulated FAD plan assumes that national governments institute some sort of management scheme that limits the use of FADs, either seasonally or spatially. Given that sustainability concerns for WCPO tuna stem, in part, from FADs fishing, our third scenario examines the equilibrium solution to the game where there is no fishing on FADs, and thus we assume no juvenile bycatch. We are interested in how the final outcomes could create the necessary incentives to encourage change. 20  The Coral Triangle encompasses part or all of the waters in Philippines, Indonesia, Malaysia, Papua New Guinea, Solomon Islands and Timor Leste.  92  6.1. Introduction  Fishing gears and fisheries Various gears are used to fish tuna in the WCPO. These include several artisanal gears, such as gillnet, hook and line and ring net, as well as commercial gears, including purse seine, longline and handline. There is also a pole and line fishery in the region, but it accounts for only 3%, 6% and 3% of bigeye, skipjack and yellowfin catch, respectively (SPC, 2009). As such, in this paper we are concerned with the three main commercial fisheries. Table 6.1 reviews the stock status and main fisheries for each of the three species of interest in this study. Purse seine The purse seine fishery developed rapidly in the 1970s and 1980s. This was due to improved technology, as well as expanded foreign fleets from Korea, Japan and Taiwan. Furthermore, declining market demand for tuna caught in the eastern Pacific Ocean, where dolphin bycatch can be high, along with changing access due to extended jurisdiction, resulted in fleets moving to the western Pacific. Purse seine vessels from both domestic and distant water fleets target both skipjack and adult yellowfin, and they fish with or without FADs. The term FAD is a catch-all word ranging from simple floating objects, such as a log or a coconut, to high-tech devices capable of transmitting sonar information via satellite. Recent research suggests that the fishery is moving in that direction - increasing their capacity through increased technological innovation (Guillotreau et al., 2011). Tuna and other pelagic fish naturally aggregate around floating objects in the open ocean and the use of FADs greatly increases efficiency of purse seine fishing. Smaller pelagic feed fish gather at the FAD (or are released), which attracts skipjack schools, as well as juvenile yellowfin and bigeye. FADs reduce the fuel costs of fishing, which can be as high as 50% of operating costs21 . In the western parts of the WCPO, most FADs are anchored, that is, they are placed in a fixed area and remain there. It the eastern parts of the WCPO, most FADs are drifting, that is, they are deployed and drift with the ocean’s currents. From a management standpoint, it would seem easier to regulate anchored FADs because their position is known. But in reality, anchored FADs are generally associated with higher levels of juvenile bycatch and are thus more of a management concern. In 2008, there were 1,200 active purse seine vessels in the WCPO tuna fishery (Williams and Terawasi, 2009). About 220 of these were distant water vessels from Japan, Korea, Chinese-Taipei, the US, and from the domestic fisheries of the Pacific Island Countries, while over 1,000 vessels reportedly fished from the Japanese coastal fishery, and from Indonesia and the Philippines (Williams and Terawasi, 2009). In 2008, an overall effort 21  Dexter Teng, TSP Industries, personal communication.  93  6.1. Introduction of about 58,000 fishing days was reported (Williams and Terawasi, 2009), but this is aggregating all days searching and fishing for tuna, regardless of the vessel size or power. The percentage of total logged purse seine sets using FADs has increased in the past few years, from 21% in 2006 and 2007 to 32% in 2008 (WCPFC, 2009). These numbers do not include purse seine sets in Indonesia and the Philippines, which are mostly set on anchored FADs. In the Philippines, it has been suggested that there are over 100 FADs in operation for each catcher vessel,22 while Papua New Guinea has instituted a limit of 30 FADs per catcher vessel for any fleet operating in its waters (WCPFC, 2009). A study of FAD use by the Korean purse seine fleet reported fork lengths for bigeye and yellowfin tuna of 30-52 cm and 28-132 cm, respectively, for FAD purse seine sets (Moon et al., 2008). Almost all purse seine-caught tuna is destined to be canned, where ex-vessel prices are under $2,000/tonne (Williams and Reid, 2007). The two principal canning destinations for purse seine-caught tuna are Bangkok, Thailand and Papua New Guinea. American Samoa, the Philippines and Indonesia also have sizeable canning industries. Longline The longline fleet fishes in deep water, targeting both adult yellowfin and bigeye (Table 6.1). There were reportedly 23 countries longlining for tuna in the WCPO, with a total of 4,869 active vessels engaged in the fishery in 2007 (Lawson, 2008b), however, countries report this differently, so there is uncertainty in this estimate. These vessels represent two categories of the fleet. The first is the large distant water freezer vessels, generally greater than 250 gross registered tonnes (GRT), and taking voyages that can last months. The second category is the smaller, domestically-based vessels, which are most often less than 100 GRT. Longline catch is either destined for the sashimi market, where Japan essentially dominates (Reid et al., 2003), or is destined to become frozen steaks and loins. The longline catch has shifted from a majority yellowfin catch in the 1970s and early 1980s, to a majority bigeye catch in recent years (Williams and Reid, 2007) Longlinecaught yellowfin tuna command ex-vessel prices between about $5,000-$7,000 (Williams and Reid, 2007). Handline Handlining fleets vary in scale from very small vessels, able to fish only in municipal waters, to large operations that include a mother-boat carrying auxiliary vessels that heads out to fish on anchored FADs in deeper waters. Handliners in Indonesia and the Philippines often fish on FADs owned by purse seine companies. Handliners are allowed to fish on 22  Benjamin Tobias, Bureau of Fisheries and Aquatic Resources, Philippines, personal communication.  94  6.1. Introduction these FADs given that they respect the owners of the FAD, and their gear. Furthermore, allowing handliners to fish on FADs can give purse seine owners a good idea of the possible catch composition of the school aggregating around the device. Handline-caught tuna is destined for the same market as longline tuna, but because its quality is sometimes compromised due to rough handling and lack of ice on board some vessels, it commands a lower ex-vessel price, about $4,000 -$6,000/tonne23 . Reporting of the handline fleet is especially poor, with catches often being lumped under “other”. Table 6.1: Summary of fisheries and markets for WCPO tuna species used in the model. Species Skipjack  Stock sta- Target fisheries tus Sustainable Purse seine, artisanal  Fullyexploited Overfishing Bigeye occurring *Source: SPC (2009). Yellowfin  Purse seine, longline, pole and line, artisanal Longline, pole and line, artisanal  Total 2009 catch* 1,783,986 433,275 118,023  Markets Cannery, some domestic Cannery, sashimi, fresh/frozen loin Sashimi  Skipjack The skipjack stock in the western and central Pacific is found between about 40◦ N and 40◦ S of the equator, and exhibits a large and variable degree of migratory movement (Langley and Hampton, 2008). Currently, the stock is estimated at about 5,8 million tonnes, and is thought to be at a sustainable level, that is that current harvests could continue into the future without negative repercussions to the stock (Langley and Hampton, 2008) (Table 6.1). Skipjack are fished with several gear types, including purse seine, pole-and-line, gillnet, hook and line, and ring net (Hampton, 2002b), however, the majority of skipjack catch is by purse seiners. The biomass trends tend to be driven by recruitment, with more recent years (1985-2001) being characterized by high recruitment, thus allowing for high catches (Langley and Hampton, 2008). However, Hampton (2002b) warns that, should a period of low recruitment occur, skipjack catches would have to decrease substantially. The estimated skipjack spawning area in the WCPO is over 17 nautical miles (Fonteneau, 2003). The 2008 assessment indicates that fishing mortality appears to be the highest in the western regions recently (Langley and Hampton, 2008). Skipjack are primarily sent to canneries (exported to Thailand or America Samoa, or processed directly in Philippines 23  J. Ingles, World Wide Fund for Nature Philippines, personal communication  95  6.1. Introduction or Indonesia), where bycatch of other juvenile tuna species is generally purchased at the same price. In addition to the skipjack canned market, there is a domestic market in countries such as Indonesia and the Philippines for whole fish that are often smoked. In 2008, an estimated 1.579 million tonnes of skipjack were caught by purse seines (SPC, 2009), worth about US $2.491 million (Williams and Terawasi, 2009). Yellowfin The WCPO yellowfin tuna stock, estimated at about 2.5 million tonnes, is now believed to be fully exploited (Langley et al., 2009b). This essentially means that they are currently undergoing the maximum amount of exploitation possible, and any increases in exploitation could negatively impact the stock (Langley et al., 2009b) (Table 6.1). Yellowfin in the western and Central Pacific is thought to be a single stock for assessment purposes, but tagging data do suggest a small degree of mixing between the eastern and western stocks (Langley et al., 2009b). Yellowfin is targeted by purse seines and longlines, and in addition to adult fish being caught, there is also a large amount of bycatch of juvenile yellowfin in the skipjack purse seine fishery, where juveniles are found associating with skipjack schools around FADs. Although large yellowfin receive a price premium at the cannery, recent research from Indian Ocean tuna fisheries suggests that this economic incentive does not really influence fisher behaviour to avoid juvenile catch (Guillotreau et al., 2011). Yellowfin biomass declined in the 1990s, primarily due to lower average recruitment in those years, as well as high fishing mortality (Hampton, 2002c). The estimated juvenile fishing mortality used for assessment purposes increased in the 1990s as a result of both an increase in reported catches from Indonesia and the increased use of FADs (Hampton, 2002c). Hampton (2002c) states that there has been a significant depletion in some areas of the WCPO due to fishing “by the domestic fisheries of the Philippines and Indonesia and the combined purse seine fishery”. Yellowfin tend to spawn opportunistically, at water temperatures above 26◦ C, and mature at about one year of age, or 100 cm in length. However, Langley et al. (2007) report that juvenile yellowfin are encountered in commercial fisheries in the Philippines and Eastern Indonesia when they are only a few months old, or as small as 15 cm (Molony, 2008). Generally, purse seiners catch a wide age range of yellowfin tuna, whereas longliners tend to take mostly adult fish (Langley et al., 2007). The longline yellowfin catch in 2009 was estimated at about 69,000 t, while purse seine catch was about 264,000 t (SPC, 2009). The longline-caught yellowfin fishery was worth about US $486 million in 2008 (Williams and Terawasi, 2009).  96  6.1. Introduction Bigeye Bigeye in the WCPO is thought to be one stock for assessment purposes. The current biomass estimate for bigeye is about 525,000 t (Harley et al., 2010). Tagging studies are still underway, but large scale migrations of over 4,000 nautical miles have been noted, leading stock assessments scientists to report that there is potential for gene flow over a wide area (Harley et al., 2010). Overfishing is occurring on the stock, (Langley et al., 2009a), meaning that more fish are being removed from the stock than the stock is capable of regenerating (Table 6.1). By 1970, bigeye had decreased to about half of its initial biomass (estimated in Harley et al. (2010) as about 1.25 million tonnes before fishing began), and has declined an additional 20% in the last decade (Langley et al., 2009a). A reduction in longline fishing mortality may be necessary to help move the stock to a more sustainable level (Langley et al., 2009a). Adult bigeye are targeted by longliners from both distant water fishing states (DWFS) as well as Pacific Island States (PIS). Of all tropical tunas, bigeye commands the highest price in the sashimi market (Langley et al., 2009a). There has been a rapid increase in purse seine catches of juvenile bigeye since the early 1990s (Langley et al., 2009a). Furthermore, it has been suggested that purse seine catches are significantly underestimated (Lawson, 2008a) as bigeye is often mistakenly classified as yellowfin in its juvenile years (Lawson, 2007), especially when under 50 cm in length (Molony, 2008). Recently, reported catches have been adjusted to account for this misidentification (Williams and Reid, 2007). However, data were not available for the domestic fleets of Indonesia and the Philippines (Lawson, 2007), and therefore, whatever adjustments have been incorporated disregard the importance of the catches from these two countries. Bigeye purse seine catch is almost exclusively juveniles, and it is thought that this catch has increased in part because of the increased use of FADs (Hampton, 2002a; Langley et al., 2009a). In the Eastern Pacific Ocean, bycatch of juvenile bigeye tuna is thought to be one of the most non-sustainable bycatch forms (Archer, 2005). The estimated 2009 longline catch of bigeye in the WCPO was about 66,000 t, down from the 2004 high of 91,000 t (SPC, 2009). The 2009 purse seine catch, estimated at 43,000 t, was down from the record high 2008 catch, estimated at 48,000 t (SPC, 2009). In 2007 the landed value of longline-caught bigeye tuna from the statistical area of the Secretariat for the Pacific Community (which does not include catch from Indonesia and Philippines) was approximately US$ 504 million (Williams and Reid, 2007), while the 2008 value was estimated at US $724 million (Williams and Terawasi, 2009).  97  6.1. Introduction  Management The tuna fisheries in the WCPO are managed by the Western and Central Pacific Fisheries Commission (WCPFC), which is the regional fisheries management organization (RFMO) in the area. The WCPFC has 23 participating members, including large domestic countries such as the Philippines, Japan, Korea and the U.S. (most of whom also have distant water fleets fishing in the Pacific), PICs such as Kiribati, Vanuatu and Papua New Guinea, and DWFNs, such as the European Union who, through bilateral or multilateral agreements, have access to fish in the exclusive economic zones (EEZ) of countries in the WCPO. The Commission, established under the Convention on the Conservation and Management of the Highly Migratory Fish Stocks of the Western and Central Pacific Ocean in 2000, is currently faced with the challenge of managing declining tuna stocks in the area, namely, yellowfin and bigeye. Reduction in juvenile and adult fishing mortalities on these stocks would likely result in decreased economic benefits to both purse seine and longline fisheries, at least in the short-term, especially those operating in the Coral Triangle countries, where it appears that the smallest bigeye and yellowfin are caught. It is estimated that over 150 million people live in the Coral Triangle, and that about 2.25 million fishers depend on marine resources for their livelihood (The Nature Conservancy, 2004). It is therefore important to create sustainable fisheries management regimes in an effort to provide the population with continued benefits from regional fisheries, which include the valuable tuna fisheries. The issue of juvenile mortality in the WCPO was explored by Bertignac et al. (2000), who concluded that shifting the fisheries from younger to older fish would improve efficiency. This work, however, notes its limitations in modeling bigeye bycatch in the purse seine fishery due to data deficiencies (Bertignac et al., 2000). Their study estimated that a reduction in effort to about 50% of the 1996 levels would maximize rent generated in the area of the Forum Fisheries Agency (a sub-section of the WCPO). Contrary to this finding, effort has not been reduced over the past decade, but has increased (Williams and Reid, 2007). Of particular interest in the Bertignac et al. (2000) study is the conclusion that a substantial reduction in purse seine effort is required to maximize the combined longline and purse seine profit because of the high level of juvenile bycatch. A more recent bioeconomic modeling paper found similar results: a major reduction in purse seine fishing effort is needed to fully realize economic benefits in the region (Campbell et al., 2010). Here, we tackle the issue specifically from a FADs management perspective through a game-theoretic model, asking whether or not management of FADs fishing, through a decrease in juvenile bycatch, could yield higher joint benefits in the region.  98  6.1. Introduction  Some preliminaries on ‘fisheries game theory’ Game theory is a tool for explaining and analyzing problems of strategic interaction (Eatwell et al., 1989). It is particularly applicable to the study of fisheries management, as many of the world’s fisheries are common pool in nature (Sumaila, 1999), thus having more than one interested user. Fisheries also exhibit dynamic externality (Levhari and Mirman, 1980), that is, the underlying stock is affected by all players’ decisions, and each player must take into account the other players’ actions. Cooperative games occur when players are able to discuss and agree upon a joint plan (they can communicate), and that the agreement is enforceable, or binding (Nash, 1953). It thus follows that non-cooperative games are those in which agreements are non-existent and/or non-binding, and where parties cannot communicate (Nash, 1951). Game theory has been applied to fisheries for over 30 years (Munro, 1979; Bailey et al., 2010). Much attention has been paid to analyzing the management of transboundary and high seas fisheries through the lens of game theory (Munro, 1990; Kaitala and Munro, 1997; Kaitala and Lindroos, 1998; Bjorndal et al., 2000; Bjorndal and Munro, 2002). Tuna fisheries are a special type of transboundary resource because of their highly migratory nature. Any given tuna stock is generally found in the waters of several countries and in the high seas, often at the same time. This, along with the fact that the number of interested parties exploiting the resource is high, and likely to change (Pintassilgo and Duarte, 2001), exacerbates management challenges and makes the study of tuna fisheries management highly amenable to the theory of games. Of particular relevance to this study, are several game theoretic models developed to explore optimal exploitation of southern (Kennedy, 1987) and North Atlantic bluefin tuna (Brasao et al., 2000; Duarte et al., 2000; Pintassilgo and Duarte, 2001; Pintassilgo, 2003). In these studies, researchers analyzed cooperative and noncooperative management (Kennedy, 1987; Brasao et al., 2000), as well as exploring the possibility of coalition formation in management, through the analysis of the characteristic function approach (Duarte et al., 2000), and the partition function approach (Pintassilgo, 2003; Pintassilgo and Lindroos, 2008), and how these decisions affected optimal exploitation. All studies concluded that the fisheries were currently overcapitalized, and that economic benefits could be increased through cooperation. However, some authors also went on to find that cooperation is not a stable outcome, and that players in the tuna fisheries would have incentives to deviate from cooperation (Pintassilgo and Lindroos, 2008). In this paper, I formulate a three-player game, partitioned by gear type: purse seine, longline, and handline. Most purse seine owners (the U.S. excluded) are aligned as a solitary unit through their membership in the World Tuna Purse Seine Organization (WTPO).  99  6.2. Model Here I assume that longline and handline owners are aligned in a similar manner with respective industry organizations. The game is partitioned by gear type because dynamic externality exists at the gear level: in these fisheries all three gear types catch yellowfin and bigeye tuna. Players are assumed to be individually rational, that is, they want to maximize their equilibrium profit, and will choose the strategy that does this. Furthermore, a player will only agree to cooperate if the payoff they receive through cooperation is at least equal to the payoff they would expect from non-cooperation. Players are asymmetric in several ways. The costs of fishing differ, as do the prices the players command for their products. The gears impart different fishing mortalities on the stocks, through differing selectivity. Side payments The term side payments has been used in fisheries economics to describe the transfer of benefits from one player to another. They are a type of cooperation facilitator (see Chapter 3), in that they would allow a player who benefits from cooperation to transfer some of their payoff to a player who may bare a cost from cooperation. Side payments help to meet the individual rationality constraint in game theory: that a player will only cooperate if their payoff through cooperation is at least what they would receive by not cooperating. If the cooperative payoff is lower than the non-cooperative payoff, then a side payment can be used to essentially compensate the player who stands to lose. Side payments are explored in the concluding section of this paper.  6.2  Model  A multi-species, multi-gear bioeconomic game-theoretic model is developed here to address this issue of tradeoffs in fishing effort and economic benefits between purse seine, longline and handline fishers. Given WCPFC recommendations for regional nations to adopt a FAD management plan (WCPFC, 2009), we are interested in knowing the optimal fishing effort each player (gear) will choose in order to maximize individual and joint net benefits from the resource under different management options: status quo, reduced FADs and no FADs. We model the status quo as a non-cooperative outcome, whereby each gear chooses their fishing effort based on their expected rent, not taking into account the implications of their actions on the other players. The two management scenarios, reduced and no FADs, are modeled as cooperative games, where the outcomes are calculated through maximization of the joint payoff, that is the sum of payoffs to all three players.  100  6.2. Model  Population dynamics The population model used here was developed in Botsford and Wickham (1979) and Botsford (1981b,a), and is summarized in Walters and Martell (2004). A yield per recruit model, which considers growth and mortality, is combined with a stock-recruitment model incorporating density dependent population effects. Recruitment of the three fish stocks is assumed to be of the Beverton and Holt (Beverton and Holt, 1957) form, (Langley et al., 2007, 2009a; Langley and Hampton, 2008). Lengths and weights are assumed to follow von Bertalanffy growth, although it has been suggested that growth of yellowfin and bigeye may divert from this pattern for part of their life histories24 (Langley et al., 2009b; Harley et al., 2010). Age-specific survivorship is a function of age-specific natural and fishing mortality, where natural mortality decreases with increases in length (see Lorenzen, 1996, for more details). Selectivity-at-age is assumed to be dome-shaped for the purse seine fishery, and asymptotic for the longline and handline fisheries, and is based on the age at which 50% of the population is fully vulnerable to the gear. A logistic function was used for the asymptotic selectivity curves and a three parameter exponential logistic was used for the dome-shaped selectivity. Selectivity curves for the status quo scenario are shown in Figure 6.1. Catchability is gear-specific. The reader is referred to Table 6.2 for a review of the variable definitions used throughout the text. Growth and mortality We begin by calculating standard age schedule information (Equations 1-4), such as lengths, la , weights, wa , mortality, ma and fecundity, fa , at age, a, for each species, denoted by the i superscript, where i takes values of 1, 2, or 3 for skipjack, yellowfin and bigeye, respectively. Ages go from 0 to the terminal age, A, which is assumed to be 5, 6 and 7 years, for each respective species, i:  lai = Li∞ (1 − e−K  i ai  )  wai = (alai )b ( i ) L∞ mia = M i lai i fai = wai − wm ,  fai ≥ 0  (6.1) (6.2) (6.3) (6.4)  24 This deviation would not have a significant impact on our model as per P. Kleiber, stock assessment scientist at the National Marine Fisheries Service, HI.  101  6.2. Model  Table 6.2: Variable definitions g i la L∞ wa wm va lh sd ma za fa lxa lza R a, b K κ ϕV B ϕB ϕe ϕh heq q y p c TR TC F π  gear type fish species length at age mean asymptotic length weight at age weight at maturity vulnerability at age length at 50% vulnerability standard deviation in vulnerability mortality at age total mortality (natural plus fishing) fecundity at age unfished survivorship at age fished survivorship at age recruits recruitment parameters von Bertalanffy metabolic coefficient Goodyear compensation ratio per recruit vulnerable biomass per recruit biomass per recruit egg production (unfished) per recruit egg production (fished) per recruit yield catchability coefficient total yield ex-vessel price unit cost of effort total revenue total cost fishing effort profit  102  6.2. Model i are the mean asymptotic lengths and weights, respectively, for each where Li∞ and W∞  species, i, and k i is the von Bertalanffy metabolic coefficient. Fecundity is the difference i between the weight at age and the weight at maturity, wmat , and is assumed to be 0 if  wa < w m . Survivorship to age in an unfished population, lxia , is the probability of an individual fish surviving to age a given natural mortality at age: lxia = lxia−1 e−ma−1 , i  given lx0 = 1,  lxiA = lxiA /(1 − e  −mia  0<a≤A ),  (6.5)  a=A  We next calculate the equilibrium eggs per recruit in the unfished population ϕie : ϕie =  A ∑  lxia fai  (6.6)  a=0  Fished population Selectivity curves are generated for each of the three gears targeting each of the three species. The gear types, g, are purse seine (PS), longline (LL) and handline (HL). Purse seines are assumed to exhibit dome-shaped selectivity, with younger yellowfin and bigeye individuals being more vulnerable to the gear than older individuals. Longlines and handlines are assumed to exert asymptotic selectivity, where fish aren’t fully vulnerable to the gear until they are mature. These curves are generated as follows for dome-shaped purse seine selectivity (equation 6.7) and longline and handline logistic selectivity (equation 6.8): [ vai,g  =  ][  1 −1  1 + e−sd1  (lai,g −lhi,g 1 )  [  vai,g  =  ]  1 −1  1 + esd2 1 −1  1 + e−sd1  (lai,g −lhi,f 2 )  (lai,g −ˆ ai,g )  ,  g=1  (6.7)  g = 2, 3  (6.8)  ]  ,  i,g Here, lhi,g 1 and lh2 define the length at which fish are 50% vulnerable to the fishery, and  sd1 is the standard deviation. For the logistic selectivity, the lengths are based on the age at which 50% of the population is fully vulnerable to the gear. Total mortality at age, zai , in the fished population is then calculated as the sum of natural mortality at age, mia and the sum of the gear-specific mortalities imparted by the three fisheries: zai = mia +  ∑  vai,g F g  (6.9)  g  103  6.2. Model  0.4  0.8  purse seine longline handline  0.0  Vulnerability  skipjack  0  1  2  3  4  5  Age  0.8 0.4  purse seine longline handline  0.0  Vulnerability  yellowfin  0  1  2  3  4  5  6  Age  0.8 0.4  purse seine longline handline  0.0  Vulnerability  bigeye  0  1  2  3  4  5  6  7  Age  Figure 6.1: Status quo vulnerability to gears at age for three tuna species. where F i,g is the fishing mortality, which is the product of the gear- and species-specific catchabilities, q i,g , and the fishing effort, f g . Survivorship to age, lzai , in a fished population is calculated in a similar manner to the unfished survivorship, only that it is a function of the total mortality, not just natural mortality: i lzai = lza−1 e−za−1 , i  given lz0 = 1,  i i lzA = lzA /(1 − e  −zai  ),  0<a≤A  (6.10)  given a = A  Equilibrium incidence functions are then calculated for each species in the fished populations, including, eggs per recruit, ϕif , per recruit gear-specific yield for one unit of fishing i i i effort, ϕi,g V B , recruits, Re , spawning biomass, Be , and gear-specific yield, Ye :  104  6.2. Model  ϕif =  A ∑  lzai fai  (6.11)  a A ∑  q i,g vai,g lzai wai (1 − e−za ) = zai a ( ) ϕioe κi − ϕif Rei = Roi , Rei ≥ 0 κi − 1 A ∑ wai Bei = Rei lzaf  ϕi,g VB  i  (6.12)  (6.13) (6.14)  a  where κi is the Goodyear compensation ratio for a given fish stock25 . The unfished recruits parameter, Roi , is used here as a global scalar. Finally, the equilibrium yield of species i for a specific gear g is given by26 : g Yei,g = Rei ϕi,g V BF  (6.15)  Economics Total revenue for a given gear type is calculated as the sum of the product of the equilibrium yield and the ex-vessel price for each species targeted by the gear. Costs are expressed on a per unit effort basis. For the purse seine and handline fleets, one unit of effort is a fishing day. For the longline fleet, one unit of effort is defined as 1 hook. Total cost is therefore the product of the unit cost and the equilibrium effort. Equilibrium resource rent for a given gear type is simply the difference between the total revenue (summed over all three species) and the total cost. We model non-cooperative and cooperative games, where players either seek to maximize their individual or joint rent, respectively. The per season equilibrium total revenue to gear g is: T Rg =  ∑  Yei,g pi,g  (6.16)  i  where pi,g is the ex-vessel price of fish species i caught by gear type g, and Yei,g is the yield. 25 The Goodyear compensation ratio is calculated from reported steepness estimates in the stock assessments, using a conversion equation derived in Appendix B of Martell et al. (2008). 26 Here, our catch equation assumes constant return in catch to changes in fishing mortality.  105  6.2. Model The total cost of a given fishing gear is the product of the unit cost of fishing, cg and the fishing effort,f g : T C g = cg f g  (6.17)  Total rent to the gear is the difference between the total revenue and cost: Πg = T Rg − T C g  (6.18)  We assume that in the non-cooperative game, each player (gear) is trying to maximize this rent without explicitly taking into account implications of their actions on the potential benefits of the other players: ∀g  max Πg ,  (6.19)  From a modeling perspective, we assume that each individual player calculates the optimal effort they should employ to maximize this rent. This is done by calculating the entire space of all possible rent estimates at all possible effort levels. This non-cooperative game is simulated for the status quo scenario, as we assume that little to no cooperation is currently occurring, hence the overfishing of juvenile fish. The competition between 2 players (purse seine and longline) is shown in Figure 6.2. It is clear that major reductions in potential longline profits result at increasing levels of purse seine effort. For the cooperative game, we assume that players seek to maximize the overall, or joint profit: max Π =  ∑  Πg  (6.20)  g  Here, we assume that each player takes into account the actions of the other players, and chooses the effort they should employ to maximize the overall rent, or the sum of the rents of each individual gear. This cooperative game is simulated for both the FAD management and FAD elimination scenarios. We assume here that full cooperation exists between players in the game through these management plans.  Data and simulations Biological parameters were taken from recent stock assessment documents of the relevant species (Harley et al., 2010; Langley et al., 2009a; Langley and Hampton, 2008; Langley et al., 2007), as well as from a summary paper by Molony (2008). These values were used for the empirical simulations. As stated in Reid et al. (2003), there is high variability 106  6.2. Model  Figure 6.2: Potential profits to the longline fleet at varying levels of relative purse seine effort (x axis). 1.0 refers to the status quo, 0.5 refers to 50% of the status quo effort, and 1.5 refers to 150% of the status quo effort. Varying levels of longline effort are represented by the coloured lines.  107  6.2. Model in ex-vessel prices for tuna. Estimates for costs of fishing were taken from Reid et al. (2003), where fishing costs are meant to exclude costs representing a division of profit (for example, access fees) and costs incurred in transhipment. These costs are a static estimate, and we have not included any conditional measures (i.e., changes in costs due to stock size) in our model. In our study, costs are averaged over several different fleets (for example, both domestic and foreign purse seine fleets). Due to these data uncertainties, although the direction of simulation outcomes would most likely not change as a result of price fluctuations and disaggregation of costs, the magnitude may differ. A sensitivity analysis is performed to address uncertainties in costs27 . Parameter values used for each species are shown in Tables 6.3, 6.4 and 6.5.  27  I performed extra scenario runs assuming fuel costs were 10% and 25% higher than the values used in the main section. See Figure 6.5  108  Table 6.3: Biological and fishing parameter inputs for skipjack tuna.  M Fishing q g (PS, LL, HL) lh1 (PS) lh2 (PS) sd1 , sd2 c (PS) p (PS)  Mean asymptotic length (cm) Length at maturity (cm) Weight at maturity (kg) Length-weight relationship Length-weight relationship Growth coefficient Recruitment compentation Adult mortality (per year) Catchabilities Start length of capture (cm) End length of capture (cm) Standard deviation on length of capture Unit cost of effort (per day) (USD) Ex-vessel price (USD/t)  PS = purse seine, LL = longline, HL = handline.  Value 106 43 1.56 8.6388E-06 3.2174 0.3105 36 2 Value 3.35e-06, 0, 0 20 80 5, 1 22,000 1,500  Source Molony (2008) (average) Langley et al. (2005) Langley et al. (2005) Langley and Hampton (2008) Langley and Hampton (2008) Molony (2008) (average) Calculated from Langley and Hampton (2008) Molony (2008) Source Derived from Williams and Reid (2007); Lawson (2008b) Molony (2008) Molony (2008)  Reid et al. (2003) Williams and Reid (2007)  6.2. Model  Biological L∞ Lm Wm a b K κ  109  6.2. Model  0.8 0.4  status quo less FADs no FADs  0.0  Vulnerability  yellowfin  0  1  2  3  4  5  6  Age  0.8 0.4  status quo less FADs no FADs  0.0  Vulnerability  bigeye  0  1  2  3  4  5  6  7  Age  Figure 6.3: Adjusted vulnerability at age to purse seine gear for yellowfin and bigeye tuna. In running simulations, we assume three possible scenarios. The first scenario is intended to represent the status quo where fishing on FADs is permitted and we model the non-cooperative equilibrium. Here, fishing for juvenile fish is current practice, but adult yellowfin are also harvested. This, in effect, means that purse seine fishers must take into account the fact that their removal of juvenile yellowfin fish does in fact affect their ability to harvest adult yellowfin. In the second scenario we consider the cooperative equilibrium with reduced fishing on FADs, perhaps through spatial or temporal closures. The third scenario also assumes a cooperative game where fishing on FADs is not allowed. To implement scenarios two and three, we modify the vulnerability of yellowfin and bigeye juveniles to the purse seine gear (Figure 6.3). For each scenario, we are interested in the equilibrium catch and rent received by each gear type. For all simulations, we calculate outcomes for the entire space of possible fishing effort combinations. The non-cooperative simulations are done in two steps. In the first step, each player chooses the effort, given all possible combinations of effort by the three gears, that will maximize its rent from the resource. This level of effort is then fed into the model for each of the three players, and the individual rents are then calculated at this combination of non-cooperative effort choices. In the cooperative game, efforts are chosen based on the single largest joint rent possibility over the entire space. Data adjustments To simulate a scenario where there is reduced or no FAD fishing, I change the length at which 50% of the population is vulnerable to the purse seine gear (lhi1 ). By changing 110  Table 6.4: Biological and fishing parameter inputs for yellowfin tuna. Biological L∞ Lm Wm a b K κ  a ˆg c (PS, LL, HL) p (PS, LL, HL)  Adult mortality (per year) Catchabilities  Value 175 100 19 2.512E-05 2.9396 0.392 12 1 Value 1.34e-06, 1.09e-9, 2.84e-7  Start length of capture (cm) End length of capture (cm) Standard deviation on length of capture Age at 50% vulernability (LL,HL) (years) Unit cost of effort (USD)  20 100 15, 15  Ex-vessel price (USD/t)  1,500, 5,000, 4,000  PS = purse seine, LL = longline, HL = handline.  Source Molony (2008) Molony (2008) Molony (2008) Langley et al. (2009b) Langley et al. (2009b) Molony (2008) (average) Calculated from Langley et al. (2009b) Molony (2008) (average) Source Derived from Williams and Reid (2007); Lawson (2008b) Molony (2008) Molony (2008)  2, 3  Molony (2008)  0, 1, 50  Reid et al. (2003),J. Ingles, pers. com. Williams and Reid (2007)  6.2. Model  M Fishing q g (PS, LL, HL) lh1 (PS) lh2 (PS) sd1 , sd2  Mean asymptotic length (cm) Length at maturity (cm) Weight at maturity (kg) Length-weight relationship Length-weight relationship Growth coefficient Recruitment compentation  111  Table 6.5: Biological and fishing parameter inputs for bigeye tuna. Biological L∞ Lm Wm a b K κ  a ˆ c (PS, LL, HL) p (PS,LL,HL)  Adult mortality (per year) Catchabilities  Value 180 102 23 1.973E-05 3.0247 0.188 12 0.361 Value 2.26e-06, 1.36e-8, 1.57e-6  Start length of capture (cm) End length of capture (cm) Standard deviation on length of capture Age at 50% vulernability (LL,HL) (years) Unit cost of effort (USD)  25 80 15, 2  Ex-vessel price (USD/t)  1,500, 7,000, 6,000  PS = purse seine, LL = longline, HL = handline.  Source Hampton (2002a) Molony (2008) Molony (2008) Harley et al. (2010) Harley et al. (2010) Harley et al. (2010) (average) Calculated from Harley et al. (2010) (average) Molony (2008) (average) Source Derived from Williams and Reid (2007); Lawson (2008b) Molony (2008) Molony (2008)  2, 3  Molony (2008)  0, 1, 50  Reid et al. (2003), J. Ingles, pers. com. Williams and Reid (2007)  6.2. Model  M Fishing q g (PS, LL, HL) lh1 (PS) lh2 (PS) sd1 , sd2  Mean asymptotic length (cm) Length at maturity (cm) Weight at maturity (kg) Length-weight relationship Length-weight relationship Growth coefficient Recruitment compentation  112  6.2. Model these lengths to larger sizes, I force the model to decrease fishing pressure on juvenile fish, which is what we would probably observe if fishing on FADs was not allowed. In the second scenario, I allow a reduced amount of yellowfin and bigeye bycatch to be taken by the purse seine gear by shifting lhi1 from 20 and 25 cm, to 50 and 60 cm for yellowfin and bigeye, respectively. In the no FADs scenario, I change the parameters so that adult yellowfin can still be caught by purse seiners, but I do not allow the bigeye population to be vulnerable to purse seining at all. This is done by shifting lhi1 to 80 cm for yellowfin, and infinity for bigeye. The end length of capture, lhi2 is also increased to 120 cm for yellowfin, from 100 cm in the status quo simulations. This is done because older, and thus larger, yellowfin are captured when setting on unassociated schools, that is, schools not associated with floating objects. Furthermore, I reduced the catchability of the purse seine gear to all three species by 10% and 30% in the reduced and no-FADs scenarios, respectively. I also assumed that, because the landed yellowfin would now be all adultsized, the average ex-vessel price was increased by 5% and 10%, respectively, for scenarios two and three.28 Responsiveness of tuna prices Tuna is a global commodity. The quantity of tuna caught in the WCPO can, to a certain degree, affect the global price of tuna (Reid et al., 2003). This is especially true for ‘light’ cannery-grade tuna, as the WCPO supplies almost a third of the global market. The WCPO also supplies about 11% of the global yellowfin and bigeye supply (Reid et al., 2003). I incorporate this possibility in a second set of cooperative scenarios, using an equation and derived price elasticities published in Reid et al. (2003). The new price of tuna in these modified simulations, pe, is calculated by the following equation (Reid et al., 2003): ( pe  i,g  =p  i,g  −p  i,g  yei,g − q i,g q i,g  )  1 ϵ  (6.21)  where pi,g is the gear- and species-specific ex-vessel price, as earlier defined, yei,g is the yield, as earlier defined, and ϵ is the price elasticity, which takes the values 1.90 and 9.97 for purse seine and longline caught tuna (Reid et al., 2003). As there were no estimates available for the handline fleet, we used the longline value of 9.97, due to the fact that catches from these two gears supply similar markets. The original quantity of species i supplied by gear g, q i,g , is taken from the catch quantities estimated in the noncooperative status quo scenario. In this way, the non-cooperative outcome is a reference or 28  Reid et al. (2003) explain that there is a size premium paid for larger fish; with fish weighing more than 7.5 kg receiving higher ex-vessel prices.  113  6.3. Results baseline for the cooperative games assuming non-constant prices. Equation (6.21) assumes a downward sloping demand curve, and results in increased (decreased) ex-vessel prices when the catch from that gear type is decreased (increased). When Equation 6.21 is used, we do not include the 5% and 10% increase in the purse seine-caught yellowfin ex-vessel price as stated above.  6.3  Results  Status quo: Non-cooperative game The optimum rent for each gear type is reached at effort levels of about 98,000 purse seine fishing days, 591 million longline hooks, and 1.6 million handline days29 (Table 6.6). At equilibrium, skipjack, yellowfin and bigeye purse seine catches of 2.1 million t, 211,000 t and 44,000 t are possible, respectively. This leads to rent in the purse seine fishery of almost USD $1.4 billion (Table 6.6). Interestingly, in the non-cooperative status quo simulation, longline is not a profitable endeavor, actually yielding negative rents of about US $54 million annually. A constraint on this recalibrates the rent to be 0. The potential negative rent is in spite of yellowfin and bigeye catches of 173,000 t and 38,000 t, respectively. At equilibrium, the total maximum rent attained in the status quo scenario is about US $1.54 billion. For all three species, the ratio of biomass vulnerable to the purse seine gear and spawning biomass is greater than 1, meaning that juvenile fish are being harvested (Figure 6.4).  Reduction in FADs fishing: Cooperative game 1 Our second simulation assumes that the use of FADs is reduced through some sort of management regulation, thereby reducing the vulnerability of juvenile yellowfin and bigeye to the purse seine gear. For this simulation, we assume a cooperative regime, where all players, in this case, cooperatives, unions or organizations based on fishing gear, agree to manage the resource in order to maximize the joint rent, or the sum of all individual rents. As shown in Table 6.6, the maximum rent is achieved with efforts of about 21,000 purse seine days, 830 million longline hooks and over 2 million handling days. This represents quite a large decrease in purse seine effort, resulting in less catch of all three species, and substantially lower overall rent to purse seiners. However, positive rents are possible for each of the gears, namely US $465, $732 and $433 million, respectively, for purse seine, longline and handline. Overall, about US $1.63 billion is attainable at equilibrium, through 29 The estimated number of handline fishing days for small and large Philippine vessels averaged about one million per year over the years 2005-2009 (J. Ingles, pers. comm.)  114  0.5  1.0  1.5  Skipjack Yellowfin Bigeye  0.0  Ratio of vulnerable biomass to spawner biomass  6.3. Results  Status quo  Less FADs  No FADs  Figure 6.4: Ratio of vulnerable biomass (to the purse seine gear) to spawning biomass. Levels above 1 imply juveniles are vulnerable to the gear. the reduction of FADs. This is an increase of about US $100 million annually. Results are summarized in Table 6.6. The spawning biomass of all three species is improved in this scenario by 200%, 120% and 274% for skipjack, yellowfin and bigeye, respectively. Due to this, and the reduction in juvenile vulnerability, the ratio of vulnerable biomass to spawning biomass has decreased to below 1 (Figure 6.4).  No FADs fishing: Cooperative game 2 The third scenario assumes that fishing on FADs no longer occurs, and thus, there is no juvenile bycatch of yellowfin or bigeye tuna. This scenario is also run assuming a cooperative agreement is in place, and thus we are trying to maximize the joint rent from all three fisheries. Again, a major reduction in purse seine effort is needed to maximize joint rent in this scenario. Similar to the reduced FADs situation, efforts of about 20,000 purse seine days, 812 million longline hooks, and 2.0 million handline days maximize rent (Table 6.6). Substantial increases in rent to longliners and handliners are possible here, compared to the status quo. This scenario results in the lowest rent to purse seiners, an estimated US $312 million annually, but the highest rents to longliners and handliners, US $839 and $480 million, respectively. The overall rent in this scenario is quite similar to  115  6.3. Results the reduced FADs scenario, an estimated US $1.63 billion. The gain in rent to longliners and handliners in going from a reduced FADs to no FADs fishing policy is canceled out by the decline in the purse seine rent. The gains in spawning biomass are almost the same as in the reduced FADs scenario, with increases of 203%, 121% and 281% for skipjack, yellowfin and bigeye, respectively. Again, we see that the increase in spawner biomass and reduction in juvenile catch, the ratio of vulnerable biomass to spawning biomass has decreased to below 1 for all three species, reaching almost 0 (Figure 6.4).  Cooperative games when price is not constant In the above cooperative scenarios, we assumed prices remained constant, except in the case of purse seine-caught yellowfin, due to the price premium for large fish. Here, we allow the price to respond to changes in the quantity of fish supplied to the market from the WCPO (i.e., the catch). This results in much higher rent possibilities to the purse seine fleet in both the reduced and no FADs scenarios. The optimal equilibrium effort, estimated at just over 21,000 purse seine fishing days for both scenarios, does not vary greatly from the constant price simulations, yielding catches that are similar to the two cooperative results above. In the reduced FADs cooperative game, 584,665 t of skipjack, 28,438 t of yellowfin and 9,132 t of bigeye are caught, yielding purse seine rents of US $951 million (compared to US $465 in the low FADs non-price responsive model), and an overall equilibrium rent of US $1.885 billion. With the total reduction of FADs, purse seines catch 505,371 t of skipjack and 24,142 t of yellowfin, yielding rents of about US $714 million (compared to US $312 million in the no FADs non-price responsive model). Because of the reduced catchability in the no FADs scenario, the same amount of purse seine effort catches fewer fish, and, even with the increase in price due to the decrease in the quantity supplied, this scenario yields an overall rent of US $1.750 billion. This is less than the reduced FADs scenario incorporating price responsiveness, but it is still higher than both of the cooperative games assuming constant prices. A sensitivity analysis to cost assumptions was performed. For this, I reran the noncooperative and cooperative games assuming fuel costs were 10% and 25% higher for all fleets than the estimates used in the main model. Fuel costs represent about half of purse seine and longline costs30 , and we assumed this was true for the handline fleet as well. Results stated above are robust to these changes: the optimal solution is still the less FAD option, although total rent and effort for all fleets is reduced (Figure 6.5). 30  Dexter Teng, TSP Industries and Mark Filipe, Far East Seafood, Inc., personal communication.  116  Table 6.6: Scenario results Status quo LL 591 0 173,184 37,571 1,129 1,183 0 1,536  HL 1.624 0 28,360 24,392 260 162 98  PS 20,742 575,622 28,025 9,138 921 456 465  Effort* Skipjack catch (t) Yellowfin catch (t) Bigeye (t) Revenue (m. USD) Cost (m. USD) Rent (m. USD) Total rent (m. USD) Increase in skipjack spawning biomass (%) Increase in yellowfin spawning biomass (%) Increase in bigeye spawning biomass (%) *PS=purse seine (effort = num days), LL=longline (effort =  Low FAD LL 830 0 282,972 139,498 2,391 1,659 732 1,630  HL 2.074 0 40,831 79,495 640 207 433  PS 22,062 515,674 14,395 0 797 485 312  200  203  120  121  274  281  No FAD LL 882 0 302,626 155,796 2,604 1,765 839 1,630  num hooks), HL=handline (effort = num days)  HL 2.206 0 43,581 87,840 701 221 480  6.3. Results  PS 97,829 2,138,396 210,543 44,194 3,590 2,152 1,438  117  purse seine longline handline  1000 0  500  Rent, million USD  1500  2000  6.3. Results  nc  nc10  nc25  c1  c1_10  c1_25  c2  c2_10  c2_25  SCENARIO  Figure 6.5: Sensitivity analysis: scenario rents when fuel costs are increased by 10% and 25%, compared to the base runs (assuming responsive prices). nc refers to the noncooperative games, while c1 and c2 refer to cooperative games one and two, which assume less FAD and no FAD use, respectively.  118  6.4. Conclusion  6.4  Conclusion  Tuna fisheries in the WCPO have the potential to be profitable, but evidence suggests that at least two of the targeted species, namely, yellowfin and bigeye, may be fully exploited or overfished (Langley et al., 2007, 2009a). The goal of the WCPFC is to try to manage tuna (and other) stocks in the WCPO in a sustainable way, so the Commission is currently facing tough management decisions regarding the potential for tuna fisheries in the area to continue providing benefits to the region. The conflict between purse seine fishers catching juvenile yellowfin and bigeye tuna, and longline fishers targeting adults of these species, is probably only one important challenge to address, but it has been raised numerous times in WCPFC technical reports (Langley et al., 2009a; Williams and Reid, 2007; Itano, 2009; Kumoru et al., 2009). Furthermore, the WCPFC has itself called for a FADs management plan (CCM 2008-01) mandating member countries to establish FAD regulatory measures within their own waters for their purse seine fleets (WCPFC, 2009). Assuming constant prices, both reduction and total elimination of FADs yield almost equivalent payoffs. Losses to the purse seine sector are evident when making this type of policy change. When we allow for prices to reflect changes in the quantity of tuna supply, these losses are mitigated to a certain extent. Of all four cooperative scenarios run, the regulation of FADs use with responsive prices yields the highest benefits, an improvement of US $458 million per year. Purse seine effort was estimated at about 58,000 vessel days in 2008 (Williams and Terawasi, 2009). The equilibrium effort of about 20,000 vessel days for purse seiners needed to maximize joint rent, in either the constant or non-constant price scenarios, is therefore quite a reduction. The per day rent, however, increases significantly. In the status quo, the rent generated for each purse seine fishing day is about US $14,700. In the reduced and no FADs scenarios, assuming prices are responsive to the quantities supplied, this per day rent is increased to US $45,300 and US $34,000 per day, respectively. If, from a management perspective, the reduction in purse seine days is unacceptable, the second-best solution may be to allow more effort, but the same amount of catch (if there was a way to actually enforce that), and just allow the profitability of the fishery to be reduced. I have incorporated changes in size selectivity by the purse seine gear, which would presumably occur if there was a major shift from fishing on FADs to fishing on unassociated schools. Further to this, I have included both a price premium (of 5% and 10%) and the ability of the price to respond to regional supply. Even given these considerations, however, I acknowledge that improvements in the understanding of the complex relationship between the supply of fish produced by a multispecies fishery and the market price, which, although highly influenced by Bangkok, is also dependent on the local supply and 119  6.4. Conclusion processing abilities of the regional canneries and factories, could help improve our estimates of equilibrium rent. Research is currently being conducted to update and improve the estimates of elasticity, which could also be used to improve future analyses in this context. Side payments are a kind of negotiation facilitator for cooperation, possibly a way to encourage purse seiners to reduce their FAD use (Reid , 2006). Side payments are often envisioned in monetary terms, however, it could be beneficial to think of them in terms of sharing the catches in these fisheries, instead of sharing the rent. For example, purse seine fleets could be given a share of the longline catch, as compensation for not fishing with FADs. If they choose not to enter the longline fishery, their shares could be leased out to other longline fleets, enabling them to derive rent from the fishery. An alternative form of a side payment could be realized through longline fishers leasing catch shares for access to the purse seine fishery, which they would choose to not fish. They would therefore be contributing to offsetting the loss of that fishing ground to purse seiners who are active in the fishery. These types of arrangements could probably be easily achieved for countries that have both purse seine and longline fleets, such as Taiwan. However, as international fisheries quota markets are still in their infancy, trading among countries may prove difficult in the near future (Bailey et al., 2010). The potential of the longline fishery to bring regional benefits may rest on an effective decrease in juvenile fishing by purse seiners. Both the reduction or removal of fishing on FADs yields benefits to the region. In this study, however, we did not address the costs of management. The overall benefit of total elimination of FADs versus just a reduction may be more or less enticing depending on whether it is more or less costly to impart temporal and spatial closures on FADs versus an all-out ban. Gjertsen et al. (2010) discuss several types of economic incentives for reducing bycatch, including market-based, rights-based, and top-down incentives such as taxes and subsidies. With specific reference to the Eastern Pacific Ocean (EPO), the authors suggest that assigning property rights to set on floating objects, perhaps through a spatial management plan, might help to control the use of FADs (Gjertsen et al., 2010). Another alternative would be to lease or rent out FADs during the fishing season, and require that they be returned upon closures, with fines instituted where this does not happen, as alluded to in Jacquet et al. (2011). In any event, spatial analyses in the future could probably help regulators decide on where and when FADs closures should take place, but it’s clear that, in the very least, FADs regulation is necessary. The WCPFC could probably adopt several management measures that the InterAmerican Tropical Tuna Association (IATTC), responsible for management of tuna in the EPO, has considered or implemented. For example, size limits on catch retention might 120  6.4. Conclusion help to decrease the occurrence of juvenile fish, if, for example, a type of quota on bycatch is implemented. Additionally, demand-side measures, such as consumers demanding FAD-free tuna in Britain, may help to force canneries to rethink their purchasing decisions (Pala, 2011). There are several measures currently underway, or in the foreseeable future, that could tackle the sustainability issues associated with growth overfishing and juvenile byctach. Obvious challenges to implementing management measures in WCPO tuna fisheries exist. These challenges, however, are not an excuse to allow the continued growth overfishing of yellowfin and bigeye tuna. The WCPFC should encourage learning by doing, and facilitate the adoption of management measures so this region can continue to provide the world with sustainably-caught tuna well into the future.  121  Chapter 7  Conclusion: Moving beyond the status quo Albert Einstein wisely suggested that problems cannot be solved from the same level of consciousness that created them. Globally, tuna fisheries are important for employment and food security, and the tuna stocks themselves provide ecosystem functions throughout the world’s oceans. Unfortunately, both fisheries and conservation scientists report that the majority of the world’s tuna species are of conservation concern (Miyake et al., 2010; ISSF, 2012; Collette et al., 2011; IUCN, 2011). We are thus faced with a decision: do we continue managing tuna the way we have done in the past, i.e., maintain the status quo, or do we accept that we have not done an adequate management job thus far and alter our methods to head in a new direction? As societies become more affluent, we know that demand for luxury products, of which tuna can be considered a part, will increase (Delgado et al., 2003). If demand increases and our supplies are not managed sustainably, we are likely to see the end of the global tuna era, which has brought economic, ecological and social benefits to fishers, countries, and consumers throughout the past sixty years. Continuing the status quo of tuna fisheries management, however, will lead to a future of increasingly competitive fisheries, overexploited stocks, a culture of ‘haves’ and ‘have-nots’, and biological and economic waste. Furthermore, for the countries that depend on tuna catches for domestic food security, failure to manage tuna stocks sustainably could result in worse circumstances than simply a failing economic sector. In this thesis, I address deficiencies in the way tuna fisheries are currently managed, and provide ways forward to improve global and regional management. Paths to improvement include better incorporation of economic information in policy-making and stronger national accountability with regards to fishing subsidies (Chapter 2), increases in cooperation (Chapter 3), new allocation approaches (Chapter 4), management capacity building (Chapter 5), and incorporation of policies that take a long-term perspective, such as a FADs management plan (Chapter 6). While it is true that tuna fisheries are an important revenue source for many fish-  122  Chapter 7. Conclusion: Moving beyond the status quo ing nations, to what extent these revenues are realized as resource rent has been largely ignored in economic analyses. There are obvious economic asymmetries associated with fishing for different tuna species using different fishing methods (Chapter 2). Management formulated with these asymmetries in mind may have a greater likelihood of being effectively implemented. Fishers employing longline gear are faced with the lowest rent per tonne of all the major tuna fishing gears, whereas gillnet and purse seine fishers are realizing some of the highest per tonne rents. This is likely due to the fact that fuel is a major contributor to operational costs, and both purse seine and gillnets bring tuna to them (through the use of FADs in the cast of purse seines) and thus decrease their fuel use because of this. Fishing for bluefin (Atlantic, Pacific and southern) still brings in positive private rents, even though two of these stocks are overfished. Skipjack fisheries, considered underexploited today, provide the majority of the global tuna supply, and are profitable to fish before and after subsidies are accounted for. As economic theory suggests, effort will continue moving into fisheries that are profitable, and thus fishing with purse seines, and for bluefin species and skipjack, may increase in the short term. Depending on the population status of a given species, this increase in effort could be more or less worrisome. A management regime that is proactive and takes into account where resource rent is generated, and where effort is likely to increase or decrease would be a step forward from where we are today, essentially a management system that is always putting policies in place after the fact. Subsidies have created a gap between social and private rent, creating artificially-higher profits (Chapter 2). In the case of global tuna fisheries, this gap amounts to over US $5.6 billion, money which societies could invest in more sustainable parts of their economy. Civil society needs to have some say in where its economic resources are being allocated, and perhaps the choice of many governments to disinvest in global tuna stocks may be suboptimal for society as a whole. It has been shown both theoretically and empirically that cooperation in fisheries management can bring benefits above and beyond non-cooperative management (Munro, 1979; Sumaila, 1999; Bailey et al., 2010; Hannesson, 2011). Even with the creation of Regional Fisheries Management Organizations (RFMOs), which are mandated to bring about cooperative management, competitive fishing of tuna and non-tuna stocks has continued largely unabated. Tuna RFMOs are often composed of multiple members, which can make cooperation more difficult, and further to this, face the problem of free riders and new members. The evidence that cooperation will facilitate improvements in sustainability has increased over the past thirty years (Chapter 3) and it is time for these theories to transfer into action. According to an analysis by Cullis-Suzuki and Pauly (2010), RFMOs are currently not 123  Chapter 7. Conclusion: Moving beyond the status quo doing enough to enforce their mandates to promote sustainability. One specific way that RFMOs can improve cooperative management is to focus on their allocation programs (Chapter 4), which to this point have by and large failed to prevent the overexploitation of tuna stocks throughout the world (Lodge et al., 2007). Transparent and equitable allocation programs that are accepted by RFMO members could go a long way in promoting sustainability. Most allocation programs have been developed based on catch histories of participating members, and have not taken into account socio-economic factors such as employment, domestic consumption, or management capacity. Global tuna fisheries offer many benefits to nations above and beyond catch quotas. The Western and Central Pacific Fisheries Commission (WCPFC) has developed a fairly inclusive set of criteria to consider in the future when they implement their allocation program. Although this is beyond what most tuna RFMOs have done, it still does not go far enough to provide any kind of guidance on how these inclusive criteria will be valued or weighted. RFMO members need to have open and honest discussions about what they expect to gain from cooperation, and improved development of allocation criteria and weighting that will help facilitate a program that meets these expectations (Chapter 4). A new approach applied today, and in the future, based on multiple allocation criteria and defined by national interests could help us shift the allocation focus from a strictly catch-based perspective to a more benefits-based fisheries management paradigm. Indonesia, the Philippines and Papua New Guinea are found within the WCPFC convention area, and are part of a sub-region known as the Coral Triangle. About a third of all tuna caught in the western and central Pacific Ocean comes from this sub-region where effective management capacity is limited. Indonesia and the Philippines have poor data collection and management programs, non-existent or ineffective fishing restrictions, lack a plan for how to manage fish aggregating devices (FADs) and have limited membership with regional scientific and management groups. Papua New Guinea, on the other hand, is aligned with several regional initiatives and institutions, and they have been proactive in setting up spatial and temporal management plans, including for FADs. The strategic placement of Papua New Guinea between Indonesia and the Philippines, and the rest of the WCPFC, means that they are well-suited to help facilitate improved management of fisheries in the Coral Triangle region (Chapter 5). Cooperation in management of straddling stocks should extend beyond just sitting in on annual meetings. A future in which tuna fishing nations help raise the standards of fishing sectors and management programs in nations with whom they share a resource would be a bright one indeed. The challenge of reducing or eliminating FADs in the WCPFC is important because the use of these devices causes bycatch of juvenile yellowfin and bigeye tuna (Langley et al., 2009a). These stocks are considered fully exploited and overexploited, respectively, 124  Chapter 7. Conclusion: Moving beyond the status quo and thus bycatch of juvenile fish needs to be reduced drastically or eliminated if we are to see long-term sustainability of these stocks. Cooperative management in this region, whereby the joint benefit to the entire region is considered, would increase the spawning biomass of these species, and offer long-term economic benefits to longliners who target adult fish. Further to this, although the amount of purse seine effort would decrease under a cooperative scenario where juvenile bycatch is limited, the potential profitability per purse seine fishing day will increase. The analysis conducted in Chapter 6 is a multispecies 3-player game, that seeks to analyze a highly complex interaction between these different fishing gears. More work is needed here to identify solutions to the conflict of interest. Although my analysis provides evidence that cooperation brings benefits at equilibrium, institutional and governance barriers to cooperation exist and need to be understood. Chapter 6 is an equilibrium approach, meaning that it seeks a long-term solution. If this type of modelling shows us where we would be better off in the future (i.e., through cooperation), then we should focus today on solutions and ways to move us toward this better place. One thing our generation probably needs to accept (and one thing that we have been unable to even consider), is that short-term losses might be necessary now in order to achieve a better tomorrow. This reality is ubiquitous in the news today, evidenced by the austerity plans put forth by several countries, and by the protests and unrest that such measures create. To counter present-day losses in some tuna-fishing nations, side payments have to be employed more effectively. A non-formal agreement has been crafted between Norway and Russia, with Russia agreeing not to target juvenile herring in its waters in exchange for the right to catch adult herring in Norway’s waters (Lodge et al., 2007). Such an agreement could theoretically be struck between a sub-coalition of Indonesia, the Philippines and Papua New Guinea, for example, where purse seiners agree to not fish on FADs, and thus reduce the catch of juvenile yellowfin and bigeye, in exchange for the right to catch adult tuna in the waters of Pacific Island Countries or the high seas. If open discussions surrounding the present day issues in tuna management are encouraged, hopefully tuna RFMOs can begin the process of solidifying their mandates and promoting more sustainable fisheries. A pointed analysis of tuna subsidies, development of transparent and equitable allocation programs, and improved and facilitated cooperation are all going to be necessary for the realization of long-term benefits derived from global and regional tuna fisheries.  125  Bibliography ACIAR, 2003. A review of Indonesia’s Indian Ocean tuna fisheries and extension of catch monitoring at the key off-loading ports. Technical Report. Australian Centre for International Agricultural Research, FIS/2001/079. Agoes, E., 2005. Adequacy of Indonesian laws and regulations to combat IUU fishing: An evaluation of the new law on fisheries, in: National Workshop on IUU Fishing in Indonesia, Agency for Research and Development, Department of Marine Affairs and Fisheries. Aguero, M., Gonzalez, E., 1996. Managing Transboundary Stocks of Small Pelagic Fish: Problems and Options. Technical Report ISBN: 0-8213-3659-2. World Bank. Alexander, L., Hodgson, R., 1975. The impact of the 200-mile economic zone on the Law of the Sea. San Diego Law Review 12, 569–599. Allen, G., Werner, T., 2002. Coral reef fish assessment in the Coral Triangle of southeastern Asia. Environmental Biology of Fishes 65, 209–214. Anderson, E., 1998. The history of fisheries management and scientific advice - the ICNAF/NAFO history from the end of World War II to the present. Journal of Northwest Atlantic Fisheries Science 23, 75–94. Anon.,  2007.  Indonesia. World Fishing News. Accessed January 13,  2011.  http://www.worldfishing.net/features/new-horizons/indonesia2. Anon., 2008a. Regulation on the mesh size of tuna purse seine nets and trading of small tuna. Technical Report. Republic of the Philippines, Department of Agriculture. Anon., 2008b. Report Number PER.05/MEN/2008 Capture Fishery Business. Technical Report. Ministry of Marine Affaris and Fisheries. Indonesia Anon., 2009.  Nomor Per.01/MEN/2009.  Technical Report. Menteri Kelautan dan  Perikanan Republik Indonesia (Minister of Maritime Affairs and Fisheries Republic of Indonesia Report). 126  Anon., 2010. Comparative Wages in Selected Countries. Accessed November 23, 2011. http://nwpc.dole.gov.ph/pages/statistics/Asean%20Wages%202010.pdf. Technical Report, 1 p. Aprieto, V., 1995. Philippine Tuna Fisheries: Yellowfin Tuna and Skipjack. University of the Philippines Press. Quezon City, Philippines. Archer, F., 2005. Report of the ETP purse-seine bycatch reduction workshop. Technical Report. National Marine Fisheries Service Adminstrative Report LJ-05-07. 29 pp. Armstrong, C., Flaaten, O., 1991. Essays on the Economics of Migratory Fish Stocks. Springer, Germany. The Optimal Management of a Transboundary Renewable Resource: The ArctoNorwegian Cod Stock. Armstrong, C., Sumaila, U., 2000. Cannibalism and the optimal sharing of the Northeast Atlantic cod stock: A bioeconomic model. Journal of Bioeconomics 2, 99–115. Arnason, R., 1998. Fisheries subsidies, overcapitalization and economic losses, in: Workshop on Overcapacity, Overcapitalization and Subsidies in European Fisheries. Arnason, R., Magnusson, G., Agnarsson, S., 2000. The Norwegian spring-spawning herring fishery: A stylized game model. Marine Resource Economics 15, 293–319. Arrow, K.J., 1963. Social choice and individual values, 2nd Edition. John Wiley and Sons, Inc. Bailey, M., 2012. A Life Sentence For Yellowfin And Bigeye Tuna. Accessed March 28, 2012. http://pna.atuna.com/ViewArticle.asp?ID=10698. Online. atuna.com. Bailey, M., Sumaila, R., 2008a. Power in diversity: Bringing people together and putting ideas out. Sea Around Us Newsletter 49. Bailey, M., Sumaila, U., 2008b. Destructive fishing in Raja Ampat, Indonesia: An applied principal-agent analysis, in: Ecological and economic analyses of marine ecosystems in the Bird’s Head seascape, Papua, Indonesia: II. Fisheries Centre Research Reports 16(1): 142-169. Bailey, M., Sumaila, U., Lindroos, M., 2010. Application of game theory to fisheries over three decades. Fisheries Research 102, 1–8. Bailey, M., Sumaila, U., Martell, S., In press. Can cooperative management of tuna fisheries in the western Pacific solve the growth overfishing problem? Strategic Behavior and the Environment. 127  Barrett, S., 2003. Environment and Statecraft: The Strategy of Environmental Treaty Making. Oxford University Press. Barut, N., Garvilles, E., 2005. Philippines Fishery Report. Technical Report. National Fisheries Research and Development Institute, Bureau of Fisheries and Aquatic Resources, Philippines. Beddington, J.R., Agnew, D.J., Clark, C.W., 2007. Current problems in the management of marine fisheries. Science 316, 1713–1716. Bertignac, M., Campbell, H., Hampton, J., Hand, A., 2000. Maximizing resource rent from the western and cetnral Pacific tuna fisheries. Marine Resource Economics 15, 151–177. Beverton, R., Holt, S., 1957. On the dynamics of exploited fish populations. Chapman and Hall, London. Bjorndal, T., 2009. Overview, roles, and performance of the North East Atlantic fisheries commission (NEAFC). Marine Policy 33:4, 685–697. Bjorndal, T., Brasao, A. 2006. The East Atlantic bluefin tuna fisheries: Stock collapse or recovery? Marine Resource Economics 21, 193–210. Bjorndal, T., Kaitala, V., Lindroos, M., Munro, G., 2000. The management of high seas fisheries. Annal of Operations Research 94, 183–196. Bjorndal, T., Munro, G., 2002. The management of high seas fisheries resources and the implentation of the UN Fish Stocks Agreement of 1995. Technical Report. Working Paper 06/02, Institute for Research in Economics and Business Administration, University of Bergen. Botsford, L., 1981a. The effects of increased individual growth rates on depressed poulation size. American Naturalist 117, 38–63. Botsford, L., 1981b. Optimal fishery policy for size-specific, density-dependent population models. Journal of Mathematical Biology 12, 265–293. Botsford, L., Wickham, D., 1979. Cyclic phenomena in marine plants and animals. Permagon New York. Population cycles caused by inter-age, density-dependent mortality in youg fish and crustratceans. pp. 73–82.  128  Branch, T., Jensen, O., Ricard, D., Ye, Y., Hilborn, R., 2011. Contrasting global trends in marine fishery status obtained from catches and from stock assessments. Conservation Biology 25, 777–786. Brandt, U., Kronbak, L., 2010. On the stability of fishery agreements under exogenous change: An example of agreements under climate change. Fisheries Research . Brasao, A., Duarte, C., Cunha-e S, M., 2000. Managing the northern Atlantic bluefin tuna fisheries: The stability of the UN Fish Stocks Agreement solution. Marine Resource Economics 15, 341–360. Bromley, D.W., 1977. Distributional implications of the extended economic zone: Some policy and research issues in the fishery. American Journal of Agricultural Economics 59, pp. 887–892. Caddy, J., 1996. An objective approach to the negotiation of allocations from shared living resources. Marine Policy 20, 145 – 155. Campbell, H.F., 2000. Managing tuna fisheries: a new strategy for the Western and Central Pacific Ocean. Marine Policy 24, 159 – 163. Campbell, H., Kennedy, J., Reid, C., 2010. Bioeconomic Modeling and Management of the Western and Central Pacific Ocean Tuna Stocks. Technical Report. Inter- American Tropical Tuna Commission and National Marine Fisheries Service Workshop La Jolla May 13-14, 2010. Campling, L., Havice, E., Ram-Bidesi, V., 2007. Pacific Island countries, the global tuna industry and the international trade regime - A guidebook. Technical Report. Forum Fisheries Agency. Carnegie Endowment for International Peace, 1924. The Treaties of Peace 1919-1923. Technical Report. New York. Carruthers, T.R., Ahrens, R.N., McAllister, M.K., Walters, C.J., 2011. Integrating imputation and standardization of catch rate data in the calculation of relative abundance indices. Fisheries Research 109, 157 – 167. CCSBT, 2011. Resolution on the Allocation of the Global Total Allowable Catch. Technical Report. Commission for the Conservation of Southern Bluefin Tuna. Charles, A.T., 1988. Fishery socioeconomics: A survey. Land Economics 64, pp. 276–295.  129  Cheung, W., Lam, V., Sarmiento, J., Kearner, K., Watson, R., Pauly, D., 2009. Projecting global marine biodiversity impats under climate change scenarios. Fish and Fisheries 10(3), 235–251. Clark, C., 1980. Dynamic Optimization and Mathematical Economics. Plenum Press. Chapter 7. Restricted access to common-property fishery resources: A game-theoretic analysis. pp. 117–132. Clark, C., 2006. The Worldwide Crisis in Fisheries. Economic Models and Human Behavior. Cambridge University Press, Cambridge. Clark, C., Munro, G., Sumaila, U., 2005. Subsidies, buybacks and sustainable fisheries. Journal of Environmental Economics and Management 50, 47–58. Clarke, F., Munro, G., 1987. Coastal states, distant water fishing nations and extended jursdiction: A principal-agent analysis. Natural Resource Modeling 2, 81–107. Clarke, F., Munro, G., 1991. Coastal states and distant water fishing nations: Conflicting views of the future. Natural Resource Modeling 5, 345–369. Cohen Commission, 2010a. International law relevant to the conservation and management of Fraser River sockeye salmon. Technical Report. Cohen Commission Policy and Practice Report. Cohen Commission, 2010b. Overview of Fraser River sockeye salmon harvest management. Technical Report. Cohen Commission Policy and Practic Report. Collette, B.B., Carpenter, K.E., Polidoro, B.A., Juan-Jord, M.J., Boustany, A., Die, D.J., Elfes, C., Fox, W., Graves, J., Harrison, L.R., McManus, R., Minte-Vera, C.V., Nelson, R., Restrepo, V., Schratwieser, J., Sun, C.L., Amorim, A., Brick Peres, M., Canales, C., Cardenas, G., Chang, S.K., Chiang, W.C., de Oliveira Leite, N., Harwell, H., Lessa, R., Fredou, F.L., Oxenford, H.A., Serra, R., Shao, K.T., Sumaila, R., Wang, S.P., Watson, R., Yez, E., 2011. High value and long lifedouble jeopardy for tunas and billfishes. Science 333, 291–292. Copes, P., Charles, A., 2004.  Socioeconomics of individual transferable quotas and  community-based fishery management. Agricultural and Resource Economics Review 33(2), 171–181. Cox, A., 2009. Quota allocation in international fisheries. Technical Report. OECD Food, Agriculture and Fisheries Working Papers, No. 22, OECD Publishing.  130  Cullis-Suzuki, S., Pauly, D., 2010. Failing the high seas: A global evaluation of regional fisheries management organizations. Marine Policy 34(5), 1036–1042. Datta, M., Mirman, L.J., 1999. Externalities, market power, and resource extraction. Journal of Environmental Economics and Management 37, 233 – 255. Davis, M., 1997. Game theory: A nontechnical introduction. Dover Publications. De Roos, A.M., Persson, L., 2002. Size-dependent life-history traits promote catastrophic collapses of top predators. Proceedings of the National Academy of Sciences 99, 12907– 12912. Delgado, C., Wada, N., Rosegrant, M., Meijer, S., Ahmed, M., 2003. Fish to 2020: Supply and demand in changing global markets. Technical Report. WorldFish Center. DFO, 2004. The NAFO objection procedure. Technical Report. Fisheries and Oceans Canada. Directorate General of Catch Fishery, 2003. Number: 7231/DPT.0/PI.340.S4/XI/03 on the Implementation of the Installation of the Vessel Monitoring System Transmitter. Technical Report. Ministry of Marine Affairs and Fisheries. Dockner, E., Feichtinger, G., Mehlmann, A., 1989. Noncooperative solutions for a differential game model of fishery. Journal of Economic Dynamics and Control 13, 1 – 20. Duarte, C., Brasao, A., Pintassilgo, P., 2000. Management of the Northern Atlantic bluefin tuna: An application of C-games. Marine Resource Economics 15, 21–36. Eatwell, J., Milgate, M., Newman, P. (Eds.), 1989. Game Theory. The New Palgrave, W. W. Norton and Company, Ltd, New York. Emery, C., 1997. Pacific salmon: The Canada-United States dispute. Technical Report. Political and Social Affairs Division, Government of Canada, BP-429E. FAO, 1992. Marine fisheries and the law of the sea: A decade of change. Technical Report. FAO Fisheries Circular NO. 853. Rome: FAO. FAO, 2002. Stopping Illegal, Unreported and Unregulated (IUU) Fishing. Technical Report. Food and Agriculture Organization of the United Nations. 11 pp. FAO, 2010. State of World Fisheries and Aquaculture. Technical Report. Food and Agriculture Organization of the United Nations. 197 pp. 131  FAO, 2012. Fishery Information, Data and Statistics Unit, FishStat Plus Software, v.2.32. Rome Feeny, D., Hanna, S., McEvoy, A.F., 1996. Questioning the assumptions of the “Tragedy of the Commons” model of fisheries. Land Economics 72, pp. 187–205. Finus, M., Siz, M., Hendrix, E., 2008. An empirical test of new developments in coalition theory for the design of international environmental agreements. Environmental and Development Economics 14, 117–137. Fischer, R.D., Mirman, L.J., 1992. Strategic dynamic interaction: Fish wars. Journal of Economic Dynamics and Control 16, 267 – 287. Fisheries and Oceans Canada, 2011. 2011 Offshore Pacific hake harvest plan. Technical Report. Fisheries and Oceans Canada, Addendum to the 2011/2013 Integrated Fishery Management Plan for Groundfish. Fonteneau, A., 2003. A comparative overview of skipjack fisheries and stocks worldwide. Technical Report. Working Paper SKJ-6 - 16th Meeting of the Standing Committee on Tuna and Billfish, Mooloolaba, Australia, 9-16, July. Gezelius, S., 2008. Making fisheries management work. Springer Science. Chapter 2 The arrival of modern fisheries management in the North Atlantic: A historical overview. pp. 26–40. Gibbs, M.T., 2009. Individual transferable quotas and ecosystem-based fisheries management: Its all in the T. Fish and Fisheries 10, 470–474. Gillett, R., McCoy, M., Rodwell, L., Tamate, J., 2001. Tuna: A key economic resource in the Pacific. Technical Report. Asian Development Bank, Manila. Gjertsen, H., Hall, M., Squires, D., 2010. Conservation and Management of Transnational Tuna Fisheries. Wiley-Blackwell. Chapter 14 Incentives to address bycatch issues. pp. 225–248. Gordon, H.S., 1954. The economic theory of a common-property resource: The fishery. The Journal of Political Economy, 62(2), 124–142. Grzybowski, A., McCaffrey, C., Paisley, R., 2010. Beyond national water law: Successfully negotiating mutual gains agreements for international watercourses. Global Business and Development Law Journal 22, 139–154.  132  Guillotreau, P., Salladarra, F., Dewalsb, P., Dagorn, L., 2011. Fishing tuna around Fish Aggregating Devices (FADs) vs free swimming schools: Skipper decision and other determining factors. Fisheries Research 109, 234–242. Gulland, J., 1980. Some problems of the management of shared stocks. Technical Report. FAO Fisheries Technical Paper No. 206, Rome. Hall, M., 1996. On byctaches. Reviews in Fish Biology and Fisheries 6, 319–352. Hampton, J., 2002a. Stock assessment of bigeye tuna in the western and central Pacific Ocean. Technical Report. Secretariat of the Pacific Community. Hampton, J., 2002b. Stock assessment of skipjack tuna in the western and central Pacific Ocean. Technical Report. Secretariat of the Pacific Community. Hampton, J., 2002c. Stock assessment of yellowfin tuna in the western and central Pacific Ocean. Technical Report. Secretariat of the Pacific Community. Hampton, J., Harley, S., 2009. Assessment of the potential implications of application of CMM-2008-01 for bigeye and yellowfin tuna. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC5-2009/GN-WP-17. Hanich, Q., 2012. Distributing the bigeye conservation burden in the western and central Pacific fisheries. Marine Policy 36, 327 – 332. Hannesson, R., 1995. Sequential fishing: cooperative and non-cooperative equilibria. Natural Resource Modeling 9, 5159. Hannesson, R., 1997. Fishing as a supergame. Journal of Environmental Economics and Management 32, 309–322. Hannesson, R., 2011. Game theory and fisheries. Annual Review of Resource Economics 3, 181–202. Hardin, G., 1968. The tragedy of the commons. Science 162, 1243–1248. Hare, S., 2010. Assessment of the Pacific halibut stock at the end of 2010. Technical Report. International Pacific Halibut Commission. Harley, S., Hoyle, S., Williams, P., Hampton, J., Kleiber, P., 2010. Stock assessment of bigeye tuna in the western and central Pacific Ocean. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC6-2010/SA-WP-04.  133  Harwood, M., 1997. Biting the allocation bullet - allocation in international fisheries, in: Taking stock: defining and managing shared resources, pp. 125–131. Hnyilicza, E., Pindyck, R., 1976. Pricing policies for a two-part exhaustible resource cartel: The case of opec. European Economic Review 8, 139–154. IATTC, 2007. Staff response to requests from ad-hoc meeting, February 2007. Technical Report. Inter-American Tropical Tuna Commission IATTC-75-05a. ICNAF, 1972. Report of the 22nd Annual meeting of ICNAF. Technical Report. ICNAF, Rome Annual Proceedings Vol 22. Indian Ocean Tuna Commission, 2007. Report of the ninth session of the IOTC working party on tropical tunas. Technical Report. IOTC. Indian Ocean Tuna Commission: teria:  Propsed by Japan.  Japan, 2012.  Proposal on IOTC allocation cri-  Technical Report. Indian Ocean Tuna Commission  IOTC2012TCAC02PropA[E]. Indian Ocean Tuna Commission: EU, 2012. On establishing a quota allocation system for the main targeted species in the IOTC area of competence: Submitted by the European Union. Technical Report. Indian Ocean Tuna Commission IOTC2012TCAC02PropC[E]. Indian Ocean Tuna Commission: Seychelles, 2012. On establishing a quota allocation system for the main targeted species in the IOTC area of competence: Submitted by the Republic of Seychelles.  Technical Report. Indian Ocean Tuna Commission  IOTC2012TCAC02PropB[E]. Ingles, J., Flores, J., Musthofa, I., 2008. Getting off the hook: Reforming tuna fisheries of Indonesia. Technical Report. World Wide Fund for Nature. Ingles, J., Pet-Soede, L., 2010. Solving the Juvenile Tuna Dilemma. Technical Report. World Wide Fund for Nature. IOTC, 2011. Approaches to allocation criteria in other tuna Regional Fishery Management Organizations. Technical Report. Indian Ocean Tuna Commission IOTC-2011-SS403[E]. Ishida, K., Yamamoto, T., Gafa, B., 1994. Development for tuna and tuna like fish in Indonesia with particular reference to the Jakarta-based tuna longline fishery. Technical Report. IPTP/94/WP/26.  134  ISSF, 2012. ISSF stock status ratings: Status of the world’s fisheries for tuna. Technical Report. International Seafood Sustainability Foundation 2012-04. Itano, D., 2009. The use of underwater video to characterize the species, size composition and vertical distribution of tunas and non-tuna bycatch around floating objects. Technical Report. Western and Centrfal Pacfiic Fisheries Commission WCPFC-SC52009/FT-IP-2. IUCN, 2011. IUCN Redlist of threatened species. Version 2011.2. Jacquet, J., Boyd, I., Carlton, J., Fox, H., Johnson, A., Mee, L., Roman, J., Spalding, M., Sutherland, W., 2011. Scanning the oceans for solutions. Solutions 2(1), 46–55. Jensen, F., Vestergaard, N., 2002. A principal-agent analysis of fisheries. Journal of Institutional and Theoretical Economics 158, 276–285. Jensen, T., 1986. The United States-Canada Pacific Salmon Treaty: An historical and legal overview. Environmental Law 16, 365–422. Joseph, J., Squires, D., Bayliff, W., Groves, T., 2010. Conservation and Management of Transnational Tuna Fisheries. Wiley-Blackwell. Addressing the problem of excess fishing capacity in tuna fisheries. pp. 11–38. Kaitala, V., Lindroos, M., 1998. Sharing the benefits of cooperation in high seas fisheries: A characteristic-function game approach. Natural Resrouce Modeling 11, 275–299. Kaitala, V., Lindroos, M., 2004. When to ratify an environmental agreement: The case of high seas fisheries. International Game Theory Review 6, 55–68. Kaitala, V., Lindroos, M., 2007. Handbook of Operations Research on Natural Resources. Springer. Chapter 11 Game Theoretic Application to Fisheries. pp. 201–216. Kaitala, V., Munro, G., 1993. The management of high seas fisheries. Marine Resource Economics 8, 313–329. Kaitala, V., Munro, G., 1997. The conservation and management of high seas fishery resources under the New Law of the Sea. Natural Resource Modeling 10(2), 87–108. Kaitala, V., Pohjola, M., 1988. Optimal recovery of a shared resource stock: A differential game with efficient memory equilibrium. Natural Resource Modeling 3, 91–119. Kennedy, J., 1987. A computable game theoretic approach to modelling competitive fishing. Marine Resource Economics 4, 1–14. 135  Kleisner, K., Zeller, D., Froese, R., Pauly, D., In press. Using global catch data for inferences on the worlds marine fisheries. Fish and Fisheries Koenig, E.F., 1984. Controlling stock externalities in a common property fishery subject to uncertainty. Journal of Environmental Economics and Management 11, 124 – 138. Kronbak, L., 2004. A Coalition Game of the Baltic Sea Cod Fishery. Technical Report. University of Southern Denmark, Working Paper. Kronbak, L., Lindroos, M., 2006. An enforcement-coalition model: Fishermen and authorities forming coalitions. Environmental and Resource Economics 35(3), 169–194. Kronbak, L., Lindroos, M., 2007. Sharing rules and stability in coalition games with externalities. Marine Resource Economics 22, 137–154. Kumoru, L., Usu, T., Yaman, L., Baje, L., 2009. Species composition and length frequency of Papua New Guinea purse-seine catch from the 2008 independent tuna port sampling. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC52009/EB-IP-16. Lam, V., Sumaila, U., Dyck, A., Pauly, D., Watson, R., 2011. Construction and first applications of a global cost of fishing database. ICES Journal of Marine Science 68(9), 1–9. Lane, D., 2008. Fishing in the NAFO regulatory area: Integrated modeling of resources, social impacts in Canada and EU fleet viability. Journal of Northwest Atlantic Fisheries Science 39, 119–145. Langley, A., Hampton, J., 2008. Stock assessment of skipjack tuna in the western and central Pacific Ocean. Technical Report. WCPFC-SC4-2008/SA-WP-4. Langley, A., Hampton, J., Kleiber, P., Hoyle, S., 2007. Stock assessment of yellowfin tuna in the western and central Pacific Ocean, including an analysis of management options. Technical Report. Western and Central Pacific Fisheries Commission SC3 SA SWG/WP-01. Honolulu, USA, 13-24 August 2007. Langley, A., Hampton, J., Kleiber, P., Hoyle, S., 2009a. Stock assessment of bigeye tuna in the western and central Pacific Ocean, including an analysis of management options. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC52009/SA-WP-4.  136  Langley, A., Hampton, J., Ogura, M., 2005. Stock Assessment of skipjack tuna in the western and central Pacific Ocean. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC1 SA WP-4. Langley, A., Harley, S., Hoyle, S., Davies, N., Hampton, J., Kleiber, P., 2009b. Stock assessment of yellowfin tuna in the western and central Pacific Ocean. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC5-2005/SA- WP -03. Langley, A., Wright, A., Hurry, G., Hampton, J., Aqorua, T., Rodwell, L., 2009c. Slow steps towards management of the world’s largest tuna fishery. Marine Policy 33, 271 – 279. Laukkanen, M., 2003. Cooperative and non-cooperative harvesting in a stochastic sequential fishery. Journal of Environmental Economics and Management 45, 454–473. Lawson, T. (Ed.), 2006. Tuna Fishery Yearbook 2006. Western and Central Pacific Fisheries Commission. Lawson, T., 2007. Further analysis of the proportion of bigeye in ’yellowfin plus bigeye’ caught by purse seiners in the WCPFC statistical area. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC3-ST SWG/IP-5. Lawson, T., 2008a. Factors affecting the use of species composition data collected by observers and port samplers from purse seiners in the western and central Pacific. Technical Report. Western and Central Pacific Fisheries Commission SC4 ST WP-3. Port Moresby, PNG, 11-22 August. Lawson, T. (Ed.), 2008b. Tuna Fishery Yearbook 2007. Western and Central Pacific Fisheries Commission. Levhari, D., Mirman, L.J., 1980. The great fish war: An example using a dynamic Cournot-Nash solution. The Bell Journal of Economics 11, 322–334. Lindroos, M., 2004. Sharing the benefits of cooperation in the Norwegian spring-spawning herring fishery. International Game Theory Review 6(1), 35–53. Lindroos, M., Kaitala, V., Kronbak, L., 2007. Advance in Fisheries Economics: Festschrift in honour of Professor Gordon Munro. Blackwell. Chapter 11 Coalition Games in Fisheries Economics. pp. 184–195. Lodge, M., Anderson, D., Lobach, T., Munro, G., Sainsbury, K., Willock, A., 2007. Recommended best practices for Regional Fisheries Management Organizations: Report of 137  an independent panel to develop a model for improved governance by Regional Fisheries Management Organizations. Technical Report. Chatham House, London. Lorenzen, K., 1996. The relationship between body weight and natural mortality in juvenile and adult fish: a comparison of natural ecosystems and aquaculture. Journal of Fish Biology 49, 627–647. Luce, R., Raiffa, H., 1957. Games and Decisions. John Wiley and Sons, Inc. MacKenzie, B.R., Mosegaard, H., Rosenberg, A.A., 2009. Impending collapse of bluefin tuna in the Northeast Atlantic and Mediterranean. Conservation Letters 2, 26 – 35. Majkowski, J., 2007. Global fishery resources of tuna and tuna-like species. Technical Report. FAO Technical Paper No. 483. Rome, FAO. Marine Stewardship Council, 2010. PNA western and central Pacific skipjack tuna. Technical Report. Martell, S., Walters, C., Pine III, W., 2008. Parameterizing age-structured models from a fisheries management perspective. Canadian Journal of Fisheries and Aquatic Science 65, 1586–1600. McKelvey, R., 1997. Game-theoretic insights into the international management of fisheries. Natural Resource Modeling 10, 129–171. McKelvey, R., Miller, K., Golubtsov, P., 2003. Risk and Uncertainty in Environmental and Natural Resource Economics. Edward Elgar Publishing. Fish wars revisited: A stochastic incomplete-information harvesting game. pp. 93–112. McKelvey, R., Sandal, L., Steinshamn, S., 2002. Regional fisheries mangement on the high seas: the hit-and-run interloper model. Technical Report. Centre for Fisheries Economics, Institute for Research in Economics and Administrations Working Ppaer No. 57/02, Bergen. McKenna, P., 2008.  Tuna fisheries facing a cod-like collapse. Accessed February  12, 2012. http://www.newscientist.com/article/dn13346-tuna-fisheries-facing-a-codlikecollapse.html. Mesterton-Gibbons, M., 1993. Game-theoretic resource modeling. Natural Resource Modeling 7(2), 93–147.  138  Metzner, R. Isokawa, D. Liu, Y. Wells F. 2010 Sharing the Fish 06: Allocation issues in fisheries management FAO Fisheries and Aquaculture Proceedings. No. 15. Fremantle, Western Australia, 27 February2 March 2006. Rome, FAO. 2010. 253p. Milazzo, M., 1998. Subsidies in world fisheries: A re-examination. Technical Report. World Bank no. 406. Fisheries series. Washington, DC: The World Bank. Miller, K., Munro, G., 2004. Climate and cooperation: A new perspective on the management of shared fish stocks. Marine Resource Economics 19, 367–393. Ministry of Marine Affairs and Fisheries, 2007. . Export of Fishery Products by Major Commodities 2001-2006. Technical Report. Directorate General of Processing and Marketing of Fishery Products, MMAF. Miyake, M., Guillotreau, P., Sun, C.H., Ishimura, G., 2010. Recent developments in the tuna industry. Technical Report. Food and Agriculture Organization of the United Nations 543. Molony, B., 2008. Fisheries biology and ecology of highly migratory species that commonly interact with industrialised longline and purse-seine fisheries in the Western and Central Pacific Ocean. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC4-2008/EB-IP-6. Moon, D.Y., An, D.H., Hwang, S.J., Kim, S.S., 2008. Preliminary information on the catch of small-sized tuna by set type of Korean tuna purse seine fishery in the WCPO. Technical Report WCPFC-SC4-2008/FT-IP-5. Western and Central Pacific Fisheries Commission WCPFC-SC4-2008/FT-IP-5. Port Moresby, PNG. Mora, C., Myers, R.A., Coll, M., Libralato, S., Pitcher, T.J., Sumaila, R.U., Zeller, D., Watson, R., Gaston, K.J., Worm, B., 2009. Management effectiveness of the world’s marine fisheries. PLoS Biology 7, e1000131. Morgan, G., 1995. Optimal fisheries quota allocation under a transferable quota (TQ) management system. Marine Policy 19(5), 379–390. MRAG, 2006. Allocation Issues for WCPFC tuna resources. Technical Report. Marine Resources Assessment Group Ltd, Report for the Western and Central Pacific Fisheries Commission. Munro, G., 1979. The optimal management of transboundary renewable resources. The Canadian Journal of Economics 12, 355–376.  139  Munro, G., 1990. The optimal management of transboundary fisheries: Game theoretic considerations. Natural Resource Modeling 4(4), 403–426. Munro, G., 2006. International allocation issues and the high seas: an economist’s perspective, in: Sharing the Fish Conference Fremantle, Australia, Fremantle Australia. Munro, G., 2007. Internationally shared fish stocks, the high seas, and property rights in fisheries. Marine Resource Economics 22, 425–443. Munro, G., 2008. Game theory and the development of resource management policy: The case of international fisheries. Environment and Development Economics 14, 7–27. Munro, G., Houtte, A.V., Willmann, R., 2004. The conservation and management of shared fish stocks: Legal and economic aspects. Technical Report. FAO Fisheries Technical Paper No. 465, Rome. Munro, G., Turris, B., Clark, C., Sumaila, U., Bailey, M., 2009. Impacts of harvesting rights in Canadian Pacific fisheries. Technical Report. Statistical and Economic Analysis Series, No. 1-3, ISSN 1921-877X, Ottawa, Fisheries and Oceans Canada. Myers, R., Worm, B., 2003. Rapid worldwide depletion of predatory fish communities. Nature 423, 280–283. Naamin, N., Mathews, C., Monintja, D., 1995. Studies of Indonesian tuna fisheries, Part 1: Interactions between coastal and offshore tuna fisheries in Manado and Bitung, North Sulawesi. Technical Report. Food and Agriculture Organization. Nash, J., 1950. The bargaining problem. Econometrica 18, 155–162. Nash, J., 1951. Non-cooperative games. The Annals of Mathematics 54(2), 286–295. Nash, J., 1953. Two-person cooperative games. Econometrica 21, 128–140. NEAFC, 1974. Report of the 12th annual meeting. Technical Report. NEAFC. NFRDI, 2008. IPDCP Progress Report 2008. Technical Report. National Fisheries Research and Development Institute, Philippines. Ostrom, E., 1990. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press. Ostrom, E., Burger, J., Field, C.B., Norgaard, R.B., Policansky, D., 1999. Revisiting the commons: Local lessons, global challenges. Science 284, 278–282. 140  Pala, C., 2011. Experts hail world’s first ’sustainable industrial fishery’ for tuna. Accessed August 1, 2011.  www.theecologist.org/News/news_analysis/978279/experts_  hail_worlds_first_sustainable_industrial_fishery_for_tuna.html.  Palma, M., 2010. Allocation of fishing opportunities in Regional Fisheries Management Organizations: A legal analysis in the light of equity. Master’s thesis. Dalhousie University. Parris, H., Grafton, Q., 2006. Can tuna promote sustainable development in the Pacific? The Journal of Environment and Development 15, 296. Parris, H., Lee, A., 2009. Navigating Pacific Fisheries: Legal and Policy Trends in the Implementation of International Fisheries Instruments in the Western and Central Pacific Region. ANCORS University of Wollongong. Chapter 11 Allocation models in the Western and Central Pacific Fisheries Commission and implications for Pacific Island States. pp. 250–283. Pauly, D., Christensen, V., Guenette, S., Pitcher, T., Sumaila, U., Walters, C., Watson, R., Zeller, D., 2002. Towards sustainability in world fisheries. Nature 418, 689–695. Pauly, D., Watson, R., 2001. Systematic distortions in world fisheries catch trends. Nature 414, 534–536. Petersen, E., 2006. Institutional economics and fisheries management: The case of Pacific tuna. Edward Elgar. Peterson, D., Dwyer, G., Appels, D., JM, F., 2004. Modelling water trade in the Southern Murray-Darling Basin. Technical Report. Productivity Commission Staff Working Paper, Melbourne. Pinkerton, E., Edwards, D.N., 2009. The elephant in the room: The hidden costs of leasing individual transferable fishing quotas. Marine Policy 33, 707 – 713. Pintassilgo, P., 2003. A coalition approach to the management of high seas fisheries in the presence of externalities. Natural Resource Modeling 16(2), 175–196. Pintassilgo, P., Duarte, C., 2001. The new-member problem in the cooperative management of high seas fisheries. Marine Resource Economics 14(4), 361–378. Pintassilgo, P., Finus, M., Lindroos, M., Munro, G., 2008. Stability and success of regional fisheries management organizations. Technical Report. Working Paper No. 20.2008, Fondazione Eni Enrico Mattei, Italy. 141  Pintassilgo, P., Lindroos, M., 2008. Coalition formation in straddling stock fisheries: A partition function approach. International Game Theory Review 10(3), 303–317. Pitcher, T., Watson, R., Forrest, R., Valtysson, H., Gu´enette, S., 2002. Estimating illegal and unreported catches from marine ecosystems: A basis for change. Fish and Fisheries 3, 317–339. PNA and U.S. News Agency / Asian, 2011. GenSan tuna players brace implementation of Indonesia’s new fishing rules. Technical Report. Reid, C., 2006. Economic implications of an implicit allocation of bigeye harvest rights through an across the board reduction in effort levels in the Western and Central Pacific tuna fishery.  Paper prepared for the Sharing the Fish Conference 06, Fremantle,  Australia, 26 February 2 March 2006. Reid, C., Vakurepe, R., Campbell, H., 2003. Tuna prices and fishing costs for bioeconomic modelling of the western and central Pacific tuna fisheries. Technical Report. ACIAR Project No. ASEM/2001/036. Ruseski, G., 1998. International fish wars: the strategic roles for fleet licensing and effort subsidies. Journal of Environmental Economics and Management 36, 70–88. Sanderson, C., 2009. The Columbia River Treaty after 2004. Technical Report. Lawson and Lundell LLP, Paper prepared for Transboundary River Governance in the Face of Uncertainty: The Columbia River Treaty, 2014. Sappington, D., 1991. Incentives in principal-agent relationships. The Journal of Economic Perspectives 5, 45–66. Schenk, K., Simon, D., 2009. Papua New Guinea tuna factories threaten regional ecology, livelihoods. Accessed January 1, 2011. http://indymedia.org.au/2009/10/23/papuanew-guinea-tuna- factories-threaten-regional-ecology-livelihoods. Schmeidler, D., 1969. The nucleolus of a characteristic function game. SIAM Journal of Applied Mathematics 17(6), 1163–1170. Sea Monster, 2011.  Forum on fish, food and people. Accessed March 10, 2012.  http://theseamonster.net/2011/05/forum-on-fish-food-and-people/. Shapley, L., 1953. Contributions to the Theory of Games, Volume II. Princeton University Press. A value for n-person games. pp. 307–317.  142  Shepard, M., Argue, S., 2005. The 1985 Pacific Salmon Treaty: Sharing conservation burdens and benefits. UBC Press Vancouver. Singh, K., Ballabh, V. (Eds.), 1996. Cooperative management of natural resources. Sage Publications India Pvt Ltd. SPC, 2009. Western and Central Pacific Fisheries Commission Tuna Fishery Yearbook. Technical Report. Western and Central Pacific Fisheries Commission. SPC, 2010. Western and Central Pacific Fisheries Commission Tuna Fishery Yearbook. Technical Report. Secretariat of the Pacific Community. Sumaila, U., 1995. Irreversible capital investment in a 2-stage bimatrix fishery game model. Marine Resource Economics 3, 263–283. Sumaila, U., 1997a. Cooperative and non-cooperative exploitation of the Arcto-Norwegian cod stock. Environmental and Resource Economics 10, 147–165. Sumaila, U., 1997b. Strategic dynamic interaction: The case of Barents Sea fisheries. Marine Resource Economics 12, 77–94. Sumaila, U., 1999. A review of game-theoretic models of fishing. Marine Policy 23, 1–10. Sumaila, U., 2002. Marine protected area performance in a model of the fishery. Natural Resource Modeling 15(4), 439–451. Sumaila, U., 2005. Differences in economic perspectives and implementation of ecosystembased management of marine resources. Marine Ecology Progress Series 300, 279–282. Sumaila, U., Armstrong, C., 2006. Distributional and efficiency effects of marine portected areas: a study of the Northeast Atlantic cod fishery. Land Economics 82(3), 321–332. Sumaila, U., Bailey, M., 2011. Sequential fishing of western and central Pacific Ocean tuna stocks. Technical Report. Fisheries Centre Working Paper 2011-02. Sumaila, U., Khan, A., Dyck, A., Watson, R., Munro, G., Tyedmers, P., Pauly, D., 2010. A bottom-up re-estimation of global fisheries subsidies. Journal of Bioeconomics 12, 201–225. Sumaila, U., Teh, L., Watson, R., Tyedmers, P., Pauly, D., 2008. Fuel price increase, subsidies, overcapacity, and resource sustainability. ICES Journal of Marine Science 65(6), 832–840.  143  Sumaila, U., Walters, C., 2005. Intergenerational discounting: A new intuitive approach. Ecological Economics 52, 135–142. Sumaila, U.R., D., M.A., Watson, R., Pauly, D., 2007. A global ex-vessel fish price database: Construction and applications. Journal of Bioeconomics 9(1), 3951. Sumaila, U.R., Huang, L., 2012. Managing bluefin tuna in the Mediterranean Sea. Marine Policy 36, 502 – 511. The Jakarta Post, 2009. DKP targets Indonesia as number one fish producer. Technical Report. http://www.thejakartapost.com/news/2009/11/16/dkp-targets- indonesianumber-one-fish-producer.html. The Nature Conservancy, 2004. Delineating the Coral Triangle, its Ecoregions and Functional Seascapes. Version 1.1. The Nature Conservancy. Tietze, U., Thiele, W., Lasch, W., Thomsen, B., Rihan, D., 2005. Economic performance and fishing efficiency of marine capture fisheries. Technical Report. FAO. Tomascik, T., Mah, A., Nontji, A., Moosa, M., 1997. The Ecology of the Indonesian Seas, Parts One and Two. Technical Report. EMDI and Periplus. Trisak, J., 2005. Applying game theory to analyze the influence of biological characteristics on fishers’ cooperation in fisheries co-management. Fisheries Research 75, 164–174. Tyedmers, P., Parker, R., 2012. Fuel consumption and greenhouse gas emissions from global tuna tisheries: A preliminary assessment. Technical Report. ISSF Technical Report 2012-03. UN, 1995. United Nations Conference on Straddling Fish Stocks and Highly Migratory Fish Stocks. Technical Report. United Nations. United Nations, 1982.  United Nations Convention on the Law of the Sea. UN  Doc.A/Conf.62/122 . United Nations, 1997. United Nations Convention on the Law of the Non-Navigational Uses of International Watercourses. Technical Report. United Nations, May 21. United States Senate, 2004. Agreement with Canada on Pacific Hake/Whiting. Technical Report. 108th Congress 2d Session Treaty Doc. 108-24. van der Zaag, P., Seyam, I., Savenije, H., 2002. Towards measurable criteria for the equitable sharing of international water resources. Water Policy 4, 19–32. 144  Varkey, D., Ainsworth, C., Pitcher, T., Goram, Y., Sumaila, U., 2010. Illegal, unreported and unregulated fisheries catch in raja ampat regency, eastern indonesia. Marine Policy 34(2), 228–236. Vera, C., Hipolito, Z., 2006. The Philippines tuna industry: A profile. Technical Report. International Collective in Support of Fishworkers. SAMUDRA Monograph. von Neumann, J., Morgenstern, O., 1947. Theory of games and economic behavior, Second Edition. Princeton University Press. Walmsley, S., Barnes, C., Payne, I., Howard, C., 2007. Comparitave study of the impact of fisheries partnership agreements - Executive report. Technical Report. MRAG, CRE and NRI. MRAG, CRE, and NRI 35 pages. Walters, C., Martell, S., 2004. Fisheries Ecology and Management. Princeton University Press, Princeton, NJ. Watson, R., 2004. Spatial allocation of global fisheries landings using rule-based procedures, in: Proceedings of Second International Symposium on GIS/Spatial Analysis in Fishery and Aquatic Sciences, 3-6 September, 2002, University of Sussex, Brighton, U.K., pp. 381–390. Watson, R., Alder, J., Kitchingman, A., Pauly, D., 2005. Catching some needed attention. Marine Policy 29, 281–284. WCPFC, 2009. FAD management and monitoring. Technical Report. Western and Central Pacific Fisheries Commission WCPFC-SC5-2009/FT-WP-1. Weitzman, M., 2001. Gamma discounting. American Economic Review 91, 260–271. Weninger, Q., McConnell, K., 2003. Buyback programs in commercial fisheries: efficiency versus transfers. Canadian Journal of Economics 33(2), 394–412. Williams, P., Reid, C., 2007. Overview of the tuna fisheries in the western and central Pacific Ocean, including economic conditions - 2006. Technical Report. Western and Central Pacific Fisheries Commission SC3 GN WP-1. Honolulu, USA 13-24 August. Williams, P., Terawasi, P., 2009. Overview of tuna fisheries in the western and central Pacific Ocean, including economic conditions - 2008. Technical Report. Western and Central Pacific Fisheries Commission.  145  Worm, B., Barbier, E., Beaumont, N., Duffy, J., Folke, C., Halpern, B., Jackson, J., Lotze, H., Micheli, F., Palumbi, S., Sala, E., Selkoe, K., Stachowicz, J., Watson, R., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790. Worm, B., Hilborn, R., Baum, J.K., Branch, T.A., Collie, J.S., Costello, C., Fogarty, M.J., Fulton, E.A., Hutchings, J.A., Jennings, S., Jensen, O.P., Lotze, H.K., Mace, P.M., McClanahan, T.R., Minto, C., Palumbi, S.R., Parma, A.M., Ricard, D., Rosenberg, A.A., Watson, R., Zeller, D., 2009. Rebuilding global fisheries. Science 325, 578–585. Yi, S.S., 1997. Stable coalition structures with externalities. Games and Economic Behavior 20(2), 201–237. Yi, S.S., 2003. The Endogenous Formation of Economic Coalitions. Edward Elgar Publishing. Chapter 3. A survey of the partition function approach. pp. 80–127. Zeller, D., Booth, S., Pauly, D., 2006. Fisheries contribution to the Gross Domestic Product: Understanding small-scale fisheries in the Pacific. Marine Resource Economics 21, 355–374.  146  Appendix A  Rent Analysis Table A.1: Summary table of rent analysis results Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Algeria Algeria Algeria Algeria Algeria Am Samoa* Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Am Samoa Angola Angola Angola Angola Angola Angola Australia Australia Australia Australia Australia  Atlantic bf Atlantic bf Atlantic bf Atlantic bf Atlantic bf yellowfin skipjack bigeye skipjack albacore bigeye skipjack skipjack skipjack albacore yellowfin albacore albacore yellowfin bigeye albacore yellowfin yellowfin bigeye bigeye yellowfin yellowfin yellowfin bigeye bigeye bigeye albacore southern bf yellowfin skipjack bigeye  pole/line -142,531 -147,706 trap -421,624 -436,167 hook/line -3,449 -3,563 purse seine -281,534 -297,607 longline -717,705 -731,923 longline 871,897 386,236 hook/line 288,722 140,292 longline 250,162 110,818 longline 178,939 79,267 longline 8,220,031 3,641,340 hook/line 379,356 184,332 gillnet 165,260 93,035 pole/line 239,850 134,130 purse seine 388,605 224,693 hook/line 3,578,798 1,738,963 hook/line 700,538 340,396 mw trawl 6,301,060 3,776,072 pole/line 5,000,623 2,796,466 pole/line 636,881 356,159 pole/line 149,533 83,623 purse seine 1,971,953 1,140,190 purse seine 1,708,835 988,055 gillnet 535,281 301,344 purse seine 303,417 175,437 gillnet 560 315 pole/line -52,570 -59,831 purse seine -14,816 -18,033 longline -156,576 -168,374 pole/line -40,653 -46,267 purse seine -7,671 -9,336 longline -103,131 -110,902 hook/line 44,839 6,361 longline 33,552,653 -4,668,900 longline 12,255,680 -1,705,396 gillnet 960 372 longline 5,563,754 -774,204 Table continued on next page  Opportunity Cost (USD) 5,175 14,542 115 16,073 14,218 485,661 148,430 139,344 99,672 4,578,691 195,024 72,224 105,720 163,912 1,839,835 360,141 2,524,988 2,204,157 280,722 65,911 831,762 720,780 233,937 127,980 245 7,260 3,217 11,798 5,615 1,666 7,771 38,478 38,221,553 13,961,076 589 6,337,959  Unit Rent (USD/t) -1,453 -1,530 -1,587 -924 -2,663 7,355 7,969 7,355 7,355 7,355 7,969 9,374 9,295 9,713 7,969 7,969 10,224 9,295 9,295 9,295 9,713 9,713 9,374 9,713 9,374 -1,453 -924 -2,663 -1,453 -924 -2,663 8,310 6,260 6,260 11,637 6,260  147  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Australia Barbados Barbados Barbados Barbados Barbados Barbados Barbados Barbados Barbados Barbados Barbados Barbados Bermuda* Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Bermuda Brazil Brazil Brazil Brazil Brazil Brazil Brazil  yellowfin skipjack albacore bigeye southern bf yellowfin albacore yellowfin bigeye albacore skipjack bigeye albacore albacore bigeye bigeye albacore skipjack skipjack yellowfin skipjack yellowfin yellowfin skipjack albacore bigeye albacore Atlantic bf skipjack yellowfin Atlantic bf Atlantic bf bigeye skipjack Atlantic bf albacore bigeye Atlantic bf yellowfin yellowfin albacore bigeye bigeye yellowfin skipjack bigeye skipjack yellowfin  hook/line 41,199 5,845 longline 8,873 -1,235 longline 3,417,754 -475,585 pole/line 46,965 14,429 pole/line 1,816,157 557,978 gillnet 10,339 4,003 pole/line 916,447 281,560 pole/line 416,568 127,982 gillnet 1,135 440 gillnet 18,233 7,060 pole/line 175,942 54,055 purse seine 7,178 4,858 longline 14,577 8,469 purse seine 3,070 2,078 longline 29,158 16,940 pole/line 3,199 2,153 pole/line 1,219 820 pole/line 385 259 purse seine 1,992 1,348 purse seine 212,078 143,552 longline 918 534 longline 214,101 124,388 pole/line 31,468 21,177 pole/line 38 -49 longline -1,292 -1,946 longline -1,245 -1,875 hook/line -1 -1 hook/line -56 -103 purse seine 430 -18 longline -47,753 -71,927 purse seine 69 -3 pole/line 0 0 pole/line 24 -30 longline -529 -797 longline -145 -219 purse seine 102 -4 purse seine 115 -5 trap -10 -19 pole/line 1,215 -1,557 purse seine 21,165 -908 pole/line 19 -24 longline 663,977 153,650 pole/line 41,619 22,201 longline 14,668,088 8,286,138 pole/line 10,721,005 4,274,992 purse seine 26,657 14,485 purse seine 510,939 218,476 purse seine 969,271 592,709 Table continued on next page  Opportunity Cost (USD) 35,354 10,107 3,893,340 32,536 1,258,178 6,335 634,886 288,585 696 11,173 121,888 2,319 6,108 992 12,218 1,046 399 126 643 68,525 385 89,713 10,291 88 654 630 0 47 448 24,175 72 0 54 268 74 106 120 8 2,772 22,073 43 510,327 19,418 6,381,950 6,446,013 12,172 292,463 376,563  Unit Rent (USD/t) 8,310 6,260 6,260 10,294 10,294 11,637 10,294 10,294 11,637 11,637 10,294 1,786 1,377 1,786 1,377 1,765 1,765 1,765 1,786 1,786 1,377 1,377 1,765 352 -1,587 -1,587 -973 -973 771 -1,587 771 352 352 -1,587 -1,587 771 771 -971 352 771 352 598 986 3,409 426 1,007 447 3,818  148  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Brazil Brazil Brazil Brazil Brazil Brazil Belize Belize Belize Belize Belize Belize Belize Belize Belize Belize Belize Belize Solomon Is.* Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Solomon Is. Virgin Is.* Virgin Is. Virgin Is. Canada Canada Canada Cape Verde Cape Verde Cape Verde Cape Verde Cape Verde Cape Verde Cape Verde Cape Verde  albacore yellowfin albacore skipjack albacore albacore bigeye yellowfin skipjack albacore bigeye yellowfin albacore yellowfin bigeye skipjack yellowfin albacore bigeye skipjack yellowfin skipjack bigeye bigeye bigeye Pacific bf yellowfin bigeye skipjack skipjack yellowfin yellowfin skipjack yellowfin yellowfin yellowfin yellowfin albacore albacore albacore yellowfin bigeye bigeye skipjack yellowfin skipjack bigeye yellowfin  pole/line 122,279 70,429 pole/line 10,499,122 6,397,139 hook/line 198 -420 longline 6,995 -39,405 purse seine 1,733 1,008 longline 754,762 339,224 longline 183,491 -31,998 longline 224,825 -39,206 longline 163 -28 longline 811,368 -141,489 gillnet 172 -515 gillnet 450 -1,348 gillnet 35,675 -106,836 pole/line 35,819 2,987 pole/line 2,398 200 pole/line 9,044 754 hook/line 793 -3,033 pole/line 72,065 6,010 longline 11,291 -1,245 longline 7,078,882 -780,850 hook/line 6,983,769 2,128,808 hook/line 16,836,843 5,132,245 pole/line 10,874 4,944 hook/line 25,238 7,693 gillnet 47 25 hook/line 37,306 11,372 longline 5,896,612 -650,437 purse seine 21,998 10,484 purse seine 24,695,154 11,769,668 pole/line 15,288,202 6,951,516 gillnet 6,763,616 3,609,980 purse seine 18,564,381 8,847,752 gillnet 12,214,827 6,519,482 pole/line 6,939,883 3,155,552 pole/line 43 -55 purse seine 627 -27 longline -1,690 -2,546 purse seine -6,581 -8,007 pole/line -212 -331 longline -9,886,093 -11,079,440 purse seine -715,721 -1,361,168 purse seine -258 -492 longline -997 -1,308 pole/line -250,416 -394,043 longline -1,817,750 -2,386,575 purse seine -172,465 -327,997 pole/line -503 -791 pole/line -584,186 -919,249 Table continued on next page  Opportunity Cost (USD) 51,849 4,101,984 618 46,400 726 415,538 215,488 264,031 191 952,857 687 1,798 142,511 32,832 2,198 8,290 3,826 66,055 12,536 7,859,732 4,854,960 11,704,598 5,930 17,545 22 25,934 6,547,049 11,514 12,925,486 8,336,686 3,153,637 9,716,629 5,695,345 3,784,332 98 653 856 1,426 119 1,193,347 645,447 233 312 143,628 568,825 155,532 288 335,064  Unit Rent (USD/t) 1,686 3,797 229 39 1,707 1,298 1,377 1,377 1,377 1,377 405 405 405 1,765 1,765 1,765 335 1,765 3,893 3,893 6,218 6,218 7,927 6,218 9,270 6,218 3,893 8,258 8,258 7,927 9,270 8,258 9,270 7,927 352 771 -1,587 -1,455 -563 -2,613 -924 -924 -2,663 -1,453 -2,663 -924 -1,453 -1,453  149  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Cape Verde Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Sri Lanka Chile Chile Chile Chile Chile Chile Chile Chile Chile China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main China Main Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan  skipjack bigeye yellowfin skipjack skipjack skipjack yellowfin yellowfin bigeye bigeye yellowfin skipjack bigeye skipjack skipjack yellowfin bigeye skipjack yellowfin albacore yellowfin bigeye Atlantic bf albacore yellowfin bigeye albacore albacore Atlantic bf yellowfin bigeye albacore yellowfin yellowfin Atlantic bf skipjack Atlantic bf bigeye albacore yellowfin skipjack albacore Pacific bf bigeye bigeye bigeye yellowfin skipjack  longline -13,444 -17,652 gillnet 2,727 1,965 hook/line 331,750 174,768 pole/line 36,522,592 20,544,546 gillnet 3,146,610 2,267,472 hook/line 2,532 1,334 gillnet 1,595,416 1,149,669 longline 27,842,917 11,930,367 longline 418,075 179,140 pole/line 5,570 3,133 pole/line 4,683,845 2,634,738 longline 26,417,153 11,319,443 purse seine -594 -741 longline -863 -877 purse seine -751 -790 longline -12,216 -12,304 longline -3,100 -3,223 pole/line -33 -35 pole/line -114 -117 longline -388 -418 purse seine -7,066 -7,213 pole/line 1,722,191 502,646 pole/line 8,372 2,444 hook/line 102,733 24,046 hook/line 143,142 33,504 purse seine 1,937,604 482,109 gillnet 24,211 12,245 longline 743,715 -9,000,351 trap 10,492 -8,988 gillnet 60,845 30,772 longline 3,297,799 -39,909,593 purse seine 1,758 438 longline 2,550,863 -30,870,260 purse seine 559,055 139,102 purse seine 12,461 3,100 longline 5,613,180 -67,930,088 longline 163 -1,974 gillnet 110,167 55,716 pole/line 242,716 70,840 pole/line 1,468,147 428,500 purse seine 11,874 9,621 gillnet 2,852,480 1,898,738 longline 460,606 239,645 pole/line 9,103,434 7,056,697 gillnet 251,138 167,168 purse seine 6,109,794 4,950,619 pole/line 17,551,474 13,605,354 longline 157,797,869 82,099,303 Table continued on next page  Opportunity Cost (USD) 4,207 762 156,982 15,978,046 879,139 1,198 445,747 15,912,550 238,935 2,437 2,049,107 15,097,710 147 13 39 88 123 2 2 30 148 1,219,545 5,929 78,687 109,637 1,455,495 11,967 9,744,065 19,481 30,073 43,207,392 1,321 33,421,123 419,953 9,360 73,543,268 2,137 54,451 171,876 1,039,647 2,253 953,742 220,961 2,046,737 83,969 1,159,175 3,946,121 75,698,566  Unit Rent (USD/t) -2,663 3,400 2,008 2,172 3,400 2,008 3,400 1,662 1,662 2,172 2,172 1,662 -78 -582 -173 -623 -487 -194 -236 -388 -215 2,172 2,172 2,008 2,008 2,047 3,111 117 828 3,111 117 2,047 117 2,047 2,047 117 117 3,111 2,172 2,172 2,573 1,460 1,018 2,172 1,460 2,573 2,172 1,018  150  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Colombia Colombia Colombia Colombia Colombia Colombia Colombia Colombia Colombia Comoros Comoros Comoros Comoros Comoros Comoros Comoros Comoros Mayotte* Mayotte Mayotte Mayotte Mayotte Mayotte Mayotte Mayotte Mayotte Mayotte Mayotte Cook Is* Cook Is Cook Is  skipjack yellowfin southern bf albacore yellowfin Pacific bf southern bf albacore yellowfin Pacific bf Pacific bf Pacific bf skipjack yellowfin bigeye albacore albacore bigeye bigeye skipjack skipjack bigeye skipjack yellowfin yellowfin yellowfin bigeye yellowfin skipjack yellowfin yellowfin bigeye yellowfin bigeye bigeye bigeye albacore yellowfin albacore skipjack albacore bigeye yellowfin yellowfin yellowfin bigeye yellowfin yellowfin  pole/line 181,894 140,998 hook/line -22,730 -425,567 longline 934,085 485,987 longline 34,989,508 18,204,392 purse seine 2,800,080 2,268,837 seine 7,082 5,187 gillnet 32,440 21,594 pole/line 5,068,786 3,929,164 longline 113,440,811 59,021,149 mw trawl 261,574 220,996 hook/line -20,406 -382,050 gillnet 128,104 85,272 gillnet 22,177,193 14,762,127 gillnet 794,720 529,001 longline 68,173,408 35,469,359 purse seine 555,954 450,476 hook/line -19,683 -368,511 longline -346,240 -523,384 purse seine 280,095 45,790 purse seine 1,457,143 238,213 longline -695,781 -1,051,757 pole/line 130 4 pole/line 46,267 1,348 longline -2,074,921 -3,136,496 purse seine 2,382,234 389,447 pole/line 29,082 848 pole/line -512 -528 longline -13,753,869 -13,985,738 pole/line -4,649,861 -4,793,539 gillnet -19,943 -20,779 hook/line -124,482 -128,005 gillnet -118 -123 pole/line -928,102 -956,780 longline -91,984 -93,535 pole/line -325 -350 gillnet -96 -104 longline -34,239 -35,317 longline -884,178 -912,025 pole/line -1,739 -1,869 pole/line -663,262 -712,986 gillnet -2,023 -2,184 longline -75,916 -78,307 hook/line -10,966 -11,389 pole/line -45,941 -49,386 gillnet -1,264 -1,365 gillnet 954 900 hook/line 569,532 531,148 purse seine 1,389,270 1,312,449 Table continued on next page  Opportunity Cost (USD) 40,895 402,837 448,098 16,785,116 531,243 1,895 10,847 1,139,622 54,419,662 40,578 361,644 42,832 7,415,065 265,719 32,704,049 105,478 348,829 177,144 234,305 1,218,930 355,976 126 44,918 1,061,575 1,992,787 28,234 16 231,869 143,678 837 3,522 5 28,678 1,551 24 8 1,078 27,847 130 49,724 161 2,391 423 3,444 100 55 38,384 76,821  Unit Rent (USD/t) 2,172 -28 1,018 1,018 2,573 1,825 1,460 2,172 1,018 3,147 -28 1,460 1,460 1,460 1,018 2,573 -28 -254 155 155 -254 134 134 -254 155 134 -1,453 -2,663 -1,453 -1,070 -1,587 -1,070 -1,453 -2,663 -1,405 -1,326 -3,345 -3,345 -1,405 -1,405 -1,326 -3,345 -2,731 -1,405 -1,326 9,374 7,969 9,713  151  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Cook Is Croatia Croatia Croatia Croatia Croatia Cuba Cuba Cuba Cuba Cuba Cuba Cyprus Cyprus Cyprus Cyprus Cyprus Cyprus Cyprus Cyprus Cyprus Benin Benin Benin Dominica Dominica Dominica Dominica Dominica Dominica Dominican Rp Dominican Rp  bigeye yellowfin bigeye albacore skipjack albacore albacore yellowfin bigeye skipjack skipjack skipjack albacore albacore bigeye skipjack yellowfin Atlantic bf Atlantic bf Atlantic bf Atlantic bf Atlantic bf skipjack yellowfin yellowfin skipjack yellowfin skipjack albacore Atlantic bf Atlantic bf albacore albacore Atlantic bf Atlantic bf albacore Atlantic bf bigeye bigeye bigeye skipjack yellowfin yellowfin skipjack skipjack yellowfin yellowfin yellowfin  purse seine 517,456 488,843 longline 708,845 657,084 hook/line 646,963 603,361 hook/line 2,901,959 2,706,381 longline 38,522 35,709 longline 6,665,420 6,178,695 purse seine 1,599,008 1,510,590 gillnet 435,179 410,246 longline 426,633 395,480 hook/line 62,155 57,966 pole/line 51,634 48,651 gillnet 35,577 33,538 mw trawl 5,109,376 4,840,964 pole/line 4,054,883 3,820,577 pole/line 255,018 240,282 purse seine 83,658 79,032 pole/line 517,779 487,860 pole/line -73,701 -99,776 trap -523,153 -596,423 purse seine 323,792 242,810 longline -1,059,655 -1,131,292 hook/line -1,780 -2,358 pole/line 99,193 82,365 purse seine 13,800 11,487 pole/line 2,048 1,700 longline 236,488 185,084 longline 13,931 10,903 purse seine 512,827 426,871 pole/line 190,936 -174,361 longline -57,375 -186,932 purse seine 157,209 10,749 longline -169,567 -552,464 hook/line 206,551 -204,797 hook/line 525 -520 pole/line 24,649 -22,509 purse seine 153,725 10,511 trap 23,138 -109,373 longline -3,986 -4,474 purse seine -1,034 -1,398 pole/line -2,012 -2,462 pole/line 7,319 -14,082 longline 86,520 -237,610 pole/line 12,717 -24,466 purse seine 37,840 -71,472 longline 17,450 -47,922 purse seine 85,703 -161,876 purse seine 79,892 43,950 pole/line 11,854 6,456 Table continued on next page  Opportunity Cost (USD) 28,613 51,762 43,602 195,578 2,813 486,725 88,418 24,933 31,154 4,189 2,984 2,038 268,412 234,307 14,736 4,626 29,919 26,075 73,270 80,983 71,637 578 16,828 2,313 347 51,404 3,028 85,956 365,297 129,557 146,460 382,897 411,348 1,045 47,158 143,214 132,511 487 364 451 21,401 324,131 37,183 109,312 65,371 247,579 35,942 5,398  Unit Rent (USD/t) 9,713 7,355 7,969 7,969 7,355 7,355 9,713 9,374 7,355 7,969 9,295 9,374 10,224 9,295 9,295 9,713 9,295 -699 -1,766 989 -3,659 -762 1,765 1,786 1,765 1,377 1,377 1,786 1,602 -1,357 3,290 -1,357 1,539 1,539 1,602 3,290 535 -2,663 -924 -1,453 1,765 1,377 1,765 1,786 1,377 1,786 1,786 1,765  152  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Dominican Rp Dominican Rp Dominican Rp Dominican Rp Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador El Salvador El Salvador El Salvador Faroe* Is Faroe Is Faroe Is Faroe Is Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji Fiji France France France France France France France  skipjack skipjack yellowfin skipjack bigeye skipjack yellowfin skipjack yellowfin bigeye yellowfin bigeye skipjack yellowfin yellowfin bigeye skipjack Atlantic bf Atlantic bf Atlantic bf Atlantic bf skipjack bigeye yellowfin bigeye bigeye skipjack albacore albacore albacore yellowfin bigeye albacore skipjack skipjack yellowfin albacore skipjack yellowfin yellowfin bigeye Atlantic bf bigeye skipjack bigeye yellowfin albacore yellowfin  pole/line 13,675 7,448 longline 32,603 13,582 longline 80,654 33,598 purse seine 70,700 38,893 pole/line 1,220 367 purse seine 16,617,799 6,605,565 pole/line 58,858 17,701 pole/line 639,511 192,330 longline -3,770,951 -5,160,522 longline -2,086,924 -2,855,942 purse seine 4,386,087 1,743,467 purse seine 2,589,078 1,029,157 longline -7,852,651 -10,746,301 gillnet -26 -28 purse seine 11,919,554 10,418,320 purse seine 1,350,395 1,180,316 purse seine 8,416,745 7,356,680 pole/line -27 -80 purse seine 170 85 longline -234 -254 trap -1,485 -1,661 pole/line 130,681 71,303 longline 434,172 32,492 gillnet 2,072,536 1,267,323 pole/line 418,153 228,155 purse seine 845,909 476,987 longline 60,509 4,528 pole/line 12,981,698 7,083,162 longline 13,244,159 991,139 mw trawl 19,190,300 12,433,186 longline 1,806,865 135,218 gillnet 1,815 1,110 hook/line 10,707,017 5,783,439 hook/line 181,290 97,925 gillnet 104,410 63,845 pole/line 2,126,549 1,160,302 purse seine 5,103,759 2,877,883 purse seine 211,089 119,028 purse seine 5,688,577 3,207,647 hook/line 2,695,704 1,456,095 hook/line 1,222,551 660,366 hook/line -34,380 -44,160 pole/line -111,160 -866,822 longline -207,131 -394,984 longline -22,581,026 -28,515,146 hook/line -1,026,209 -1,220,503 gillnet -10,421 -20,553 purse seine 11,898,120 1,081,833 Table continued on next page  Opportunity Cost (USD) 6,227 19,022 47,056 31,807 853 10,012,234 41,157 447,182 1,389,571 769,018 2,642,620 1,559,921 2,893,651 2 1,501,235 170,078 1,060,066 54 85 19 177 59,378 401,681 805,212 189,997 368,922 55,981 5,898,536 12,253,020 6,757,114 1,671,647 705 4,923,578 83,366 40,565 966,247 2,225,876 92,061 2,480,930 1,239,608 562,185 9,780 755,662 187,853 5,934,120 194,294 10,132 10,816,287  Unit Rent (USD/t) 1,765 1,377 1,377 1,786 134 155 134 134 -254 -254 155 155 -254 -1,226 1,786 1,786 1,786 -83 335 -2,023 -1,406 7,927 3,893 9,270 7,927 8,258 3,893 7,927 3,893 10,229 3,893 9,270 7,832 7,832 9,270 7,927 8,258 8,258 8,258 7,832 7,832 -1,740 -119 -1,526 -3,079 -2,036 -1,032 424  153  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  France France France France France France France France France France France France France France France France France French Polynesia* French Polynesia French Polynesia French Polynesia Gabon Gabon Gabon Ghana Ghana Ghana Ghana Ghana Ghana Ghana Ghana Ghana Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati Kiribati  albacore skipjack yellowfin Atlantic bf Atlantic bf yellowfin skipjack albacore Atlantic bf bigeye Atlantic bf yellowfin skipjack albacore albacore bigeye albacore albacore  purse seine purse seine longline longline purse seine pole/line gillnet pole/line pole/line purse seine trap gillnet pole/line hook/line mw trawl gillnet longline longline  446,443 73,454,314 -165,473,435 -9,957,218 2,261,171 -15,143,715 -3 215,049 -1,091,180 1,101,467 -2,793,544 -445,375 49,273,348 -828,340 2,832,589 -31,761 -1,262,371 18,564,374  232,557 40,901,520 -180,571,778 -11,211,725 706,705 -19,760,526 -832 -315,964 -1,648,898 533,521 -4,416,061 -508,886 1,732,005 -3,105,317 329,698 -48,260 -1,758,473 8,306,821  bigeye yellowfin skipjack yellowfin yellowfin yellowfin skipjack yellowfin skipjack bigeye bigeye skipjack yellowfin yellowfin bigeye skipjack bigeye bigeye bigeye yellowfin skipjack yellowfin skipjack bigeye skipjack yellowfin yellowfin skipjack yellowfin  longline 4,457,215 1,994,427 longline 10,201,580 4,564,802 longline 8,259,823 3,695,943 longline -1,956 -2,048 purse seine -770 -875 pole/line -628 -683 longline -1,649,934 -1,735,222 purse seine -6,734,863 -7,738,149 purse seine -21,165,398 -24,318,387 purse seine -3,597,077 -4,132,930 pole/line -6,999,551 -7,662,722 pole/line -30,731,673 -33,643,340 longline -17,104,838 -17,989,023 pole/line -5,497,130 -6,017,954 longline -13,869,752 -14,586,708 hook/line 8,049,328 3,607,400 hook/line 767,601 344,009 longline 343,392 40,736 gillnet 1,435 904 hook/line 2,250,337 1,008,515 longline 3,384,259 401,469 pole/line 2,236,196 1,268,224 gillnet 5,839,641 3,678,242 purse seine 669,039 391,066 pole/line 7,308,955 4,145,160 gillnet 2,179,399 1,372,748 longline 1,900,030 225,397 purse seine 11,806,211 6,900,954 purse seine 5,981,888 3,496,527 Table continued on next page  Opportunity Cost (USD) 213,886 32,552,794 15,098,343 1,254,507 1,554,466 4,616,811 829 531,013 557,718 567,946 1,622,517 63,511 47,541,342 2,276,977 2,502,891 16,499 496,102 10,257,553  2,094 3,122 -4,224 -3,928 720 -1,264 -4 406 -968 1,569 -852 -2,703 1,434 -365 1,136 -1,557 -2,553 7,355  2,462,788 5,636,777 4,563,880 93 105 55 85,288 1,003,286 3,152,989 535,853 663,171 2,911,667 884,185 520,825 716,956 4,441,929 423,592 302,656 531 1,241,823 2,982,790 967,973 2,161,399 277,973 3,163,795 806,651 1,674,633 4,905,257 2,485,361  7,355 7,355 7,355 -2,663 -924 -1,453 -2,663 -924 -924 -924 -1,453 -1,453 -2,663 -1,453 -2,663 6,218 6,218 3,893 9,270 6,218 3,893 7,927 9,270 8,258 7,927 9,270 3,893 8,258 8,258  Unit Rent (USD/t)  154  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Kiribati Greece Greece Greece Greece Greece Greece Greece Greece Greece Grenada Grenada Grenada Grenada Grenada Grenada Grenada Grenada Grenada Grenada Grenada Grenada Grenada Guam* Guam Guam Guam Guam Guam Guam Guam Guam Guam Guatemala Guatemala Guatemala Guatemala Guatemala Guatemala Guatemala Guatemala Guatemala Guinea Guinea Guinea Guinea Honduras Honduras  bigeye albacore Atlantic bf Atlantic bf albacore Atlantic bf albacore Atlantic bf albacore Atlantic bf bigeye yellowfin albacore yellowfin skipjack albacore albacore bigeye albacore bigeye skipjack yellowfin skipjack skipjack yellowfin yellowfin yellowfin yellowfin skipjack yellowfin skipjack skipjack skipjack yellowfin bigeye yellowfin bigeye skipjack yellowfin bigeye skipjack skipjack yellowfin yellowfin yellowfin yellowfin bigeye yellowfin  pole/line 330,722 187,564 hook/line 242,384 -267,021 trap -102,408 -306,151 purse seine 335,570 110,380 purse seine 275,954 98,600 longline -372,840 -572,042 longline -621,615 -1,095,788 hook/line 359 -1,247 pole/line 232,830 -219,547 pole/line 19,514 -52,994 pole/line 237 -116 pole/line 53,130 -25,991 hook/line 5 -37 longline 361,478 -328,229 longline 10,102 -9,173 longline 32,518 -29,527 pole/line 2,720 -1,330 longline 2,160 -1,961 purse seine 6,849 -3,228 purse seine 532 -251 purse seine 21,907 -10,325 purse seine 358,062 -168,753 pole/line 4,237 -2,073 longline 18,639 8,257 hook/line 17,927 8,711 purse seine 43,730 25,285 pole/line 16,298 9,114 gillnet 13,698 7,712 gillnet 17,215 9,691 longline 22,312 9,884 pole/line 24,984 13,972 hook/line 30,075 14,614 purse seine 40,480 23,406 longline 1,956,191 1,284,433 longline 516,962 339,437 purse seine 5,525,920 4,062,654 pole/line 612,665 448,457 purse seine 7,531,211 5,536,944 pole/line 1,124,457 823,078 purse seine 501,065 368,383 pole/line 5,359,644 3,923,143 longline 268,215 176,109 gillnet -44 -47 hook/line -274 -289 pole/line -2,045 -2,161 longline -30,305 -31,247 purse seine 2,343,858 1,626,272 purse seine 3,069,674 2,129,875 Table continued on next page  Opportunity Cost (USD) 143,158 509,406 203,743 225,190 177,354 199,201 474,173 1,607 452,377 72,508 353 79,120 42 689,708 19,276 62,045 4,050 4,121 10,077 782 32,232 526,816 6,310 10,382 9,216 18,445 7,184 5,987 7,523 12,428 11,013 15,461 17,074 671,758 177,525 1,463,266 164,207 1,994,267 301,379 132,682 1,436,501 92,105 3 14 117 942 717,586 939,799  Unit Rent (USD/t) 7,927 771 -695 2,060 2,522 -2,588 -2,125 309 834 372 1,765 1,765 335 1,377 1,377 1,377 1,765 1,377 1,786 1,786 1,786 1,786 1,765 7,355 7,969 9,713 9,295 9,374 9,374 7,355 9,295 7,969 9,713 1,377 1,377 1,786 1,765 1,786 1,765 1,786 1,765 1,377 -1,070 -1,587 -1,453 -2,663 1,786 1,786  155  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Honduras Honduras Honduras Honduras Honduras Honduras Honduras India India India India India India India India Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Indonesia Iran Iran Iran Iran Iran Ireland Ireland Ireland Ireland Ireland Ireland Ireland Ireland Italy  yellowfin skipjack bigeye skipjack bigeye yellowfin skipjack yellowfin yellowfin skipjack skipjack yellowfin skipjack yellowfin skipjack southern bf albacore southern bf skipjack skipjack yellowfin yellowfin albacore skipjack yellowfin southern bf bigeye yellowfin skipjack bigeye bigeye southern bf albacore bigeye yellowfin yellowfin yellowfin yellowfin skipjack skipjack albacore albacore Atlantic bf Atlantic bf Atlantic bf skipjack skipjack albacore  pole/line 44,535 30,734 purse seine 6,633,604 4,602,687 pole/line 1,367 943 longline 1,732,609 1,044,708 longline 1,500,042 904,477 longline 1,409,408 849,828 pole/line 254,780 175,829 longline -25,768,758 -26,816,385 gillnet -21,370 -34,227 longline -1,412,848 -1,470,287 gillnet -5,559 -8,904 hook/line -140,977 -154,911 hook/line -46 -51 pole/line -1,305,705 -1,437,170 pole/line -2,033,457 -2,238,197 gillnet -296,140 -384,296 longline -8,700,998 -9,206,590 seine -97,422 -136,428 gillnet -34,718,360 -43,483,011 pole/line -34,409,032 -39,653,726 hook/line -24,628,110 -34,155,735 pole/line -2,062,088 -2,424,164 hook/line -13,397,433 -18,580,362 hook/line -101,999,059 -134,908,359 gillnet -4,618,210 -5,992,968 hook/line -597,944 -829,265 gillnet -21,087 -27,365 longline -48,388,633 -51,200,372 longline -95,388,182 -100,311,512 hook/line -17,082,196 -23,690,611 longline -33,879,424 -35,848,070 mw trawl -44,246 -76,160 gillnet -255,052 -330,977 pole/line -114,350 -134,429 longline 4,476,275 -49,278,667 hook/line 1,163,212 346,594 pole/line 10,243,223 3,594,768 gillnet 428,079 234,148 pole/line 175,139,908 61,463,795 pole/line 18 -2,956 hook/line -1,385 -16,061 mw trawl 455,949 293,514 trap -483 -814 longline -171 -207 purse seine 429 270 longline -410 -497 purse seine 8,674 5,454 gillnet 1,854 -1,878 Table continued on next page  Opportunity Cost (USD) 13,800 2,030,917 424 687,901 595,565 559,580 78,951 1,047,626 12,857 57,439 3,345 13,934 5 131,465 204,739 88,156 505,593 39,006 8,764,651 5,244,694 9,527,626 362,076 5,182,929 32,909,300 1,374,758 231,321 6,277 2,811,739 4,923,331 6,608,415 1,968,646 31,914 75,924 20,078 53,754,942 816,618 6,648,455 193,931 113,676,113 2,973 14,675 162,435 332 36 159 87 3,220 3,732  Unit Rent (USD/t) 1,765 1,786 1,765 1,377 1,377 1,377 1,765 -3,445 -233 -3,445 -233 -1,417 -1,417 -1,391 -1,391 -761 -3,897 -566 -806 -1,335 -585 -1,290 -585 -631 -761 -585 -761 -3,897 -3,942 -585 -3,897 -314 -761 -1,290 117 2,008 2,172 3,111 2,172 4 -59 1,763 -914 -2,956 1,692 -2,956 1,692 1,128  156  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Italy Cote d’Ivoire* Cote d’Ivoire Cote d’Ivoire Cote d’Ivoire Cote d’Ivoire Cote d’Ivoire Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan  albacore Atlantic bf Atlantic bf yellowfin bigeye albacore albacore yellowfin albacore yellowfin yellowfin Atlantic bf Atlantic bf skipjack skipjack Atlantic bf bigeye bigeye yellowfin yellowfin skipjack yellowfin skipjack skipjack Atlantic bf skipjack Atlantic bf southern bf bigeye yellowfin Pacific bf southern bf skipjack Atlantic bf yellowfin albacore albacore yellowfin skipjack albacore Atlantic bf bigeye albacore southern bf bigeye albacore Atlantic bf bigeye  pole/line 2,053,337 619,259 purse seine 7,666,626 4,142,360 pole/line 1,624,778 490,012 pole/line 1,745,064 526,288 gillnet 1,089 -1,104 longline 195,425 -1,329,602 purse seine 1,220,483 659,440 gillnet 17,659 -17,892 hook/line 2,262,589 651,133 hook/line 210,189 60,489 longline 1,262,769 -8,591,433 longline 399,497 -2,718,040 hook/line 35,305 10,160 longline 4,946 -33,649 pole/line 13,151,928 3,966,448 trap 1,011,288 -2,177,336 pole/line 10,044 3,029 longline 88,134 -599,629 pole/line -54,993 -59,234 longline -171,117 -178,316 purse seine -596,522 -668,844 purse seine -67,376 -75,544 longline -46,501 -48,458 pole/line -866,136 -932,923 pole/line 9,029,156 5,679,904 hook/line 551 176 hook/line 536,794 324,271 gillnet 9,236,433 5,953,415 pole/line 51,172,425 29,120,078 pole/line 24,834,824 10,906,037 gillnet 10,869,936 6,784,709 longline 62,380,996 35,835,061 longline 9,677,299 -2,378,351 trap 27,518,100 16,635,757 hook/line 674,807 -79,530 gillnet 1,593,085 849,179 pole/line 1,723,684 325,585 longline -6,201,145 -155,349,819 pole/line 1,624,459,449 853,007,414 longline -110,455,479 -185,051,639 longline 10,805,338 6,200,703 longline 434,654,185 116,134,530 purse seine 169,945 14,091 mw trawl 7,123,261 4,595,358 purse seine 56,228,992 31,461,932 hook/line -30,450 -374,814 purse seine 16,379,809 10,262,317 gillnet 525,590 326,546 Table continued on next page  Opportunity Cost (USD) 1,434,078 3,524,266 1,134,766 1,218,776 2,193 1,525,027 561,043 35,551 1,611,456 149,700 9,854,202 3,117,537 25,145 38,594 9,185,480 3,188,624 7,015 687,763 4,240 7,199 72,322 8,169 1,956 66,787 3,349,252 374 212,523 3,283,018 22,052,347 13,928,787 4,085,227 26,545,935 12,055,650 10,882,343 754,337 743,905 1,398,098 149,148,674 771,452,035 74,596,160 4,604,635 318,519,655 155,854 2,527,903 24,767,060 344,364 6,117,493 199,044  Unit Rent (USD/t) 3,251 4,939 3,251 3,251 1,128 291 4,939 1,128 3,188 3,188 291 291 3,188 291 3,251 720 3,251 291 -1,453 -2,663 -924 -924 -2,663 -1,453 21,662 3,169 20,295 22,739 6,812 2,742 8,440 18,993 1,729 20,319 1,376 2,215 1,275 -64 4,535 -1,531 18,856 4,006 1,128 22,775 6,665 -91 21,515 7,752  157  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Kenya Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Korea Rep Latvia Latvia Latvia Liberia Liberia Liberia Liberia Liberia Liberia Libya Libya Libya Lithuania Lithuania Lithuania Malaysia  yellowfin skipjack Pacific bf yellowfin southern bf southern bf southern bf skipjack Pacific bf Pacific bf skipjack Atlantic bf yellowfin yellowfin albacore Atlantic bf Atlantic bf albacore yellowfin yellowfin albacore bigeye southern bf Atlantic bf albacore Atlantic bf albacore bigeye bigeye yellowfin bigeye skipjack yellowfin yellowfin yellowfin yellowfin yellowfin yellowfin bigeye bigeye bigeye yellowfin yellowfin yellowfin skipjack skipjack skipjack bigeye  purse seine 21,680,918 8,830,650 purse seine 413,215,387 210,391,298 mw trawl 1,519,189 950,661 gillnet 580,244 337,863 purse seine 1,164,088 729,550 hook/line 52,006,834 31,435,100 pole/line 864,271 543,831 gillnet 697,933 423,375 longline 3,934,976 1,275,965 hook/line 4,063,559 1,962,205 pole/line -781,798 -911,849 hook/line 2,786 -364 hook/line -13,579 -66,527 longline -192,997,756 -258,228,919 purse seine 936 340 pole/line 184,271 42,130 trap -184,215 -583,622 hook/line 4,828 -26,868 pole/line 322,971 -412,756 purse seine 176,462 50,845 pole/line 65,155 -22,167 pole/line 2,203,040 1,040,601 longline -32,937 -78,965 purse seine 824,761 383,311 gillnet 42,589 8,047 longline -279,444 -669,946 longline -9,135,415 -13,374,382 purse seine 1,972,289 1,072,532 longline 85,137,810 -14,918,574 gillnet 37,827 341 gillnet 70,128 36,257 longline -815,885,692 -905,507,308 pole/line -97,973 -170,788 longline -870,455 -994,070 purse seine 266,977 126,710 pole/line -37,395 -38,798 longline -116,359 -118,740 purse seine -45,815 -48,517 longline -31,891 -32,544 pole/line -16,094 -16,698 purse seine -8,271 -8,759 pole/line -22,940 -23,860 longline -71,380 -72,943 purse seine -28,105 -29,878 longline -14,055 -14,961 purse seine 140,448 106,949 pole/line -91,685 -122,620 pole/line -4,494 -5,833 Table continued on next page  Opportunity Cost (USD) 12,850,268 202,824,089 568,527 242,381 434,539 20,571,734 320,440 274,558 2,659,011 2,101,354 130,051 3,150 52,948 65,231,164 596 142,141 399,407 31,696 735,728 125,617 87,323 1,162,439 46,028 441,450 34,542 390,503 4,238,966 899,757 100,056,384 37,486 33,872 89,621,617 72,815 123,615 140,267 1,403 2,381 2,702 653 604 488 920 1,563 1,773 906 33,498 30,934 1,338  Unit Rent (USD/t) 2,595 4,388 8,476 3,682 21,652 20,433 21,799 5,475 4,694 6,134 -1,453 1,234 -212 -2,444 1,519 1,808 -643 147 363 1,160 722 5,093 -998 2,606 1,193 -998 -2,085 5,891 2,287 833 5,564 -3,551 -699 -3,659 989 -1,453 -2,663 -924 -2,663 -1,453 -924 -1,453 -2,663 -924 -3,659 989 -699 -682  158  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Malaysia Malaysia Malaysia Malaysia Malaysia Malaysia Malaysia Malaysia Malaysia Maldives Maldives Maldives Maldives Maldives Maldives Maldives Maldives Maldives Maldives Maldives Malta Malta Malta Malta Malta Malta Malta Malta Malta Mauritius Mauritius Mauritius Mauritius Mauritius Mauritius Mauritius Mauritius Mauritius Mauritius Mauritius Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico  yellowfin yellowfin bigeye yellowfin yellowfin albacore bigeye albacore yellowfin yellowfin skipjack skipjack skipjack yellowfin bigeye bigeye bigeye yellowfin yellowfin skipjack albacore albacore Atlantic bf albacore Atlantic bf Atlantic bf Atlantic bf albacore Atlantic bf yellowfin yellowfin bigeye skipjack yellowfin albacore albacore bigeye albacore bigeye yellowfin Pacific bf bigeye Pacific bf Atlantic bf Atlantic bf skipjack albacore yellowfin  longline -5,320,537 -5,715,389 gillnet -21,490 -35,689 gillnet -633 -1,052 purse seine -40,764 -51,030 hook/line -32,393 -41,350 gillnet -401 -666 longline -1,768,381 -1,899,618 longline -23,787 -25,553 pole/line -181,160 -235,114 longline 17,230,265 8,896,544 pole/line 284,493,835 223,626,776 longline 880,512 454,637 hook/line 148 114 pole/line 4,820,468 3,789,135 pole/line 22,868 17,976 longline 991,827 512,112 gillnet 8,410 6,881 gillnet 181,407 148,413 hook/line 552,493 426,531 gillnet 136,347 111,548 longline -16,131 -28,732 hook/line -3,610 -17,147 trap -177,288 -464,219 purse seine 1,631 -3,082 longline -359,099 -639,634 pole/line -24,976 -127,089 hook/line -603 -2,866 pole/line -2,941 -14,963 purse seine 109,728 -207,406 gillnet -539 -590 longline -371,587 -385,901 longline -153,482 -159,395 pole/line -22,377 -23,957 hook/line -3,363 -3,581 longline -101,499 -105,409 pole/line -6,694 -7,167 gillnet -197 -216 gillnet -6,083 -6,666 pole/line -854 -914 pole/line -25,074 -26,845 hook/line -954,621 -1,021,493 purse seine -138,531 -151,338 gillnet -2,661 -4,149 purse seine -12,686 -13,859 longline -22,238 -23,432 pole/line -908,404 -1,049,205 longline -13,305 -14,019 longline -15,889,345 -16,742,452 Table continued on next page  Opportunity Cost (USD) 394,852 14,199 418 10,267 8,957 265 131,237 1,765 53,954 8,333,721 60,867,059 425,875 34 1,031,332 4,893 479,714 1,530 32,994 125,962 24,799 12,601 13,538 286,931 4,713 280,534 102,113 2,263 12,022 317,134 52 14,314 5,912 1,580 217 3,910 473 19 583 60 1,770 66,872 12,807 1,488 1,173 1,194 140,802 714 853,107  Unit Rent (USD/t) -2,736 -307 -307 -806 -734 -307 -2,736 -2,736 -682 960 2,170 960 2,037 2,170 2,170 960 2,553 2,553 2,037 2,553 -3,659 -762 -1,766 989 -3,659 -699 -762 -699 989 -1,070 -2,663 -2,663 -1,453 -1,587 -2,663 -1,453 -1,070 -1,070 -1,453 -1,453 -2,115 -1,603 -265 -1,603 -2,760 -828 -2,760 -2,760  159  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Mexico Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Morocco Oman Oman Oman Oman Oman Namibia Namibia Namibia Namibia Namibia Namibia Namibia Namibia Namibia Namibia Namibia  Pacific bf yellowfin albacore skipjack bigeye skipjack yellowfin Pacific bf Atlantic bf Atlantic bf Pacific bf bigeye yellowfin Atlantic bf Atlantic bf bigeye yellowfin yellowfin bigeye bigeye skipjack Atlantic bf Atlantic bf yellowfin Atlantic bf albacore skipjack albacore Atlantic bf skipjack albacore albacore yellowfin yellowfin yellowfin yellowfin skipjack yellowfin albacore southern bf yellowfin bigeye southern bf albacore yellowfin bigeye albacore bigeye  purse seine -651,000 -711,183 purse seine -216,555,343 -236,575,359 pole/line -13,976 -16,889 purse seine -22,240,516 -23,900,339 longline -304,936 -321,308 longline -1,210,073 -1,264,070 gillnet -26 -41 longline -268,880 -283,317 trap -2,440 -2,577 pole/line 0 0 pole/line -22,143 -26,760 pole/line -6,475 -7,825 pole/line -1,910,151 -2,308,393 hook/line -10,799 -11,556 longline -88,615 -92,944 purse seine -134,144 -153,028 purse seine -70,456 -80,374 longline -178,940 -187,680 pole/line -261,031 -284,401 longline -517,239 -542,504 longline -70,084 -73,507 trap -2,442,258 -2,649,958 purse seine -792,445 -903,996 pole/line -57,507 -62,656 pole/line -13,803 -15,039 pole/line -99,759 -108,690 purse seine -897,608 -1,023,963 hook/line -59,223 -64,078 hook/line -334 -361 pole/line -1,302,573 -1,419,192 purse seine -295 -337 longline -190,968 -200,296 gillnet 162,295 86,480 longline 1,697,066 -19,317,758 pole/line 3,883,458 1,284,328 hook/line 441,002 121,756 pole/line 1,557,656 515,144 purse seine -16,896 -18,669 hook/line -61,368 -104,748 pole/line -611 -652 longline -192,363 -199,365 longline -423,193 -438,598 longline -1,543 -1,599 purse seine -13,121 -14,498 pole/line -64,648 -68,962 purse seine -30,968 -34,217 pole/line -1,611,343 -1,718,851 pole/line -166,514 -177,623 Table continued on next page  Opportunity Cost (USD) 60,183 20,020,016 2,914 1,659,823 16,372 53,997 15 14,436 137 0 4,617 1,350 398,242 756 4,329 18,883 9,918 8,741 23,370 25,265 3,423 207,700 111,551 5,149 1,236 8,931 126,355 4,856 27 116,618 42 9,328 75,815 21,014,825 2,599,131 319,247 1,042,512 1,772 43,380 41 7,002 15,405 56 1,376 4,313 3,249 107,508 11,110  Unit Rent (USD/t) -1,603 -1,603 -711 -1,721 -2,760 -2,878 -265 -2,760 -2,644 -711 -711 -711 -711 -2,115 -2,663 -924 -924 -2,663 -1,453 -2,663 -2,663 -1,530 -924 -1,453 -1,453 -1,453 -924 -1,587 -1,587 -1,453 -924 -2,663 3,111 117 2,172 2,008 2,172 -924 -137 -1,453 -2,663 -2,663 -2,663 -924 -1,453 -924 -1,453 -1,453  160  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Namibia Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Nauru Netherlands Netherlands Netherlands New Caledonia* New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia New Caledonia Vanuatu Vanuatu Vanuatu Vanuatu Vanuatu Vanuatu Vanuatu Vanuatu Vanuatu  albacore bigeye skipjack bigeye bigeye yellowfin skipjack bigeye yellowfin bigeye yellowfin skipjack skipjack yellowfin yellowfin skipjack skipjack skipjack skipjack yellowfin yellowfin albacore bigeye bigeye albacore albacore bigeye albacore bigeye yellowfin yellowfin skipjack yellowfin bigeye albacore skipjack skipjack skipjack skipjack yellowfin bigeye albacore albacore bigeye bigeye albacore yellowfin albacore  longline -2,059,890 -2,134,874 gillnet 13 6 hook/line 20,336 3,099 hook/line 6,883 1,049 purse seine 5,999 2,171 purse seine 34,320 12,418 longline 8,550 -3,025 longline 3,079 -1,089 gillnet 12,504 5,395 pole/line 2,966 994 hook/line 12,911 1,967 purse seine 29,828 10,792 pole/line 18,466 6,188 pole/line 12,830 4,300 longline 10,901 -3,857 gillnet 14,754 6,366 purse seine -4,216 -4,356 longline -1,403 -1,407 pole/line -19,878 -20,007 hook/line 617,797 302,744 pole/line 561,659 316,082 hook/line 1,946,063 953,646 longline 147,382 65,948 pole/line 88,097 49,578 pole/line 2,719,217 1,530,283 purse seine 1,072,300 623,643 hook/line 223,496 109,522 longline 4,469,852 2,000,081 purse seine 178,758 103,964 purse seine 1,507,005 876,464 longline 768,917 344,060 longline 311 139 gillnet 472,059 267,410 gillnet 330 187 mw trawl 3,426,363 2,064,371 gillnet 287 163 purse seine 675 392 hook/line 501 246 pole/line 416 234 longline 3,554,934 720,646 purse seine 24,597,987 15,353,048 hook/line 838,251 419,805 purse seine 528,843 330,082 longline 3,319,770 672,975 pole/line 84,385 51,343 pole/line 2,162,156 1,315,526 purse seine 105,895,138 66,095,371 longline 39,156,884 7,937,773 Table continued on next page  Opportunity Cost (USD) 74,984 7 17,237 5,834 3,829 21,902 11,575 4,169 7,109 1,972 10,944 19,035 12,277 8,530 14,758 8,388 140 4 130 315,052 245,576 992,417 81,435 38,519 1,188,934 448,657 113,975 2,469,770 74,793 630,540 424,857 172 204,649 143 1,361,993 124 282 256 182 2,834,287 9,244,940 418,446 198,761 2,646,796 33,043 846,629 39,799,767 31,219,110  Unit Rent (USD/t) -2,663 9,270 6,218 6,218 8,258 8,258 3,893 3,893 9,270 7,927 6,218 8,258 7,927 7,927 3,893 9,270 -411 -5,059 -2,099 7,969 9,295 7,969 7,355 9,295 9,295 9,713 7,969 7,355 9,713 9,713 7,355 7,355 9,374 9,374 10,224 9,374 9,713 7,969 9,295 3,893 8,258 6,218 8,258 3,893 7,927 7,927 8,258 3,893  161  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Vanuatu Vanuatu Vanuatu Vanuatu New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Nicaragua Niue* Niue Niue Niue Niue Niue  skipjack yellowfin skipjack skipjack Pacific bf southern bf skipjack bigeye albacore bigeye bigeye yellowfin southern bf southern bf bigeye skipjack southern bf yellowfin albacore albacore Pacific bf albacore Pacific bf skipjack yellowfin albacore yellowfin skipjack Pacific bf skipjack bigeye yellowfin bigeye skipjack bigeye yellowfin skipjack bigeye bigeye skipjack yellowfin yellowfin albacore yellowfin bigeye skipjack skipjack yellowfin  purse seine 555,333,696 346,616,355 pole/line 435,117 264,740 longline 3,331,983 675,450 pole/line 778,650 473,756 seine 47,349 44,808 hook/line 711,890 650,396 gillnet 24,196,998 22,795,097 longline 543,998 468,946 longline 4,995,017 4,305,883 pole/line 523,927 488,426 purse seine 1,059,885 990,954 pole/line 2,700,685 2,517,692 gillnet 684,203 644,563 mw trawl 348,435 330,140 gillnet 2,274 2,142 pole/line 30,285,212 28,233,148 seine 416,675 394,314 hook/line 2,717,763 2,483,000 mw trawl 7,237,595 6,857,562 hook/line 3,205,691 2,928,780 gillnet 77,750 73,246 pole/line 4,896,030 4,564,285 hook/line 80,897 73,909 longline 14,022,932 12,088,269 purse seine 7,224,408 6,754,556 purse seine 1,924,876 1,799,688 gillnet 2,632,089 2,479,594 purse seine 48,919,942 45,738,351 mw trawl 39,595 37,516 hook/line 33,353,003 30,471,931 hook/line 1,216,027 1,110,985 longline 2,294,692 1,978,107 purse seine 27,246 19,926 pole/line 41,286 30,060 hook/line 1,114 -480 pole/line 6,201 4,515 purse seine 4,356,218 3,185,976 pole/line 1,995 1,452 longline 18,307 11,930 longline 280,761 182,950 longline 236,160 153,887 purse seine 12,746,637 9,322,417 longline 154,617 143,327 purse seine 114,371 108,047 gillnet 43 41 purse seine 10,795 10,198 gillnet 4,591 4,328 gillnet 35,826 33,773 Table continued on next page  Opportunity Cost (USD) 208,717,341 170,378 2,656,533 304,894 2,541 61,494 1,401,902 75,052 689,133 35,500 68,931 182,993 39,641 18,296 132 2,052,064 22,361 234,764 380,033 276,911 4,505 331,745 6,988 1,934,662 469,852 125,188 152,495 3,181,592 2,079 2,881,072 105,042 316,585 7,319 11,226 1,594 1,686 1,170,242 542 6,378 97,811 82,272 3,424,220 11,291 6,324 2 597 263 2,053  Unit Rent (USD/t) 8,258 7,927 3,893 7,927 10,008 6,218 9,270 3,893 3,893 7,927 8,258 7,927 9,270 10,229 9,270 7,927 10,008 6,218 10,229 6,218 9,270 7,927 6,218 3,893 8,258 8,258 9,270 8,258 10,229 6,218 6,218 3,893 1,786 1,765 335 1,765 1,786 1,765 1,377 1,377 1,377 1,786 7,355 9,713 9,374 9,713 9,374 9,374  162  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Niue Niue Niue Niue Niue Niue Niue Niue Niue Niue Niue Niue Niue Niue North Marianus* North Marianas North Marianus North Marianas North Marianas North Marianus North Marianas North Marianus North Marianas North Marianas Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Micronesia Marshall Is Marshall Is Marshall Is  bigeye skipjack albacore albacore bigeye albacore skipjack yellowfin albacore yellowfin bigeye bigeye yellowfin skipjack yellowfin  longline longline hook/line mw trawl purse seine purse seine hook/line pole/line pole/line hook/line pole/line hook/line longline pole/line hook/line  19,392 4,971 67,317 118,522 23,521 37,092 8,020 42,626 94,061 46,886 11,592 29,407 58,355 6,662 26,201  17,976 4,608 62,780 112,296 22,220 35,041 7,480 40,163 88,626 43,726 10,922 27,426 54,094 6,278 12,731  yellowfin skipjack skipjack yellowfin yellowfin yellowfin skipjack skipjack skipjack skipjack skipjack bigeye bigeye albacore albacore yellowfin skipjack albacore skipjack yellowfin albacore skipjack bigeye bigeye albacore yellowfin bigeye yellowfin yellowfin yellowfin yellowfin skipjack  pole/line 23,820 13,321 hook/line 186,466 90,605 purse seine 250,974 145,114 purse seine 63,913 36,955 gillnet 20,020 11,271 longline 32,610 14,446 gillnet 106,730 60,085 longline 115,565 51,193 pole/line 154,903 86,625 purse seine 53,141,729 11,792,879 gillnet 26,285,193 8,065,692 purse seine 927,899 205,914 gillnet 1,991 611 hook/line 239 -8 pole/line 365 69 hook/line 4,476,885 -149,741 pole/line 32,898,816 6,229,622 mw trawl 539 200 longline 15,233,110 -9,910,303 longline 3,779,973 -2,459,162 purse seine 143 32 hook/line 36,231,362 -1,211,851 hook/line 1,064,598 -35,608 pole/line 458,683 86,855 longline 372 -242 purse seine 11,900,537 2,640,892 longline 476,255 -309,840 gillnet 4,335,758 1,330,441 pole/line 4,448,753 842,403 pole/line 8,155,513 3,708,296 hook/line 8,207,083 2,501,702 hook/line 74,407,177 22,680,965 Table continued on next page  Opportunity Cost (USD) 1,416 363 4,537 6,226 1,301 2,051 541 2,463 5,435 3,160 670 1,982 4,261 385 13,470  Unit Rent (USD/t)  10,499 95,861 105,860 26,958 8,750 18,164 46,645 64,371 68,278 41,348,850 18,219,501 721,986 1,380 247 296 4,626,626 26,669,194 339 25,143,414 6,239,135 112 37,443,213 1,100,206 371,828 614 9,259,644 786,096 3,005,317 3,606,351 4,447,217 5,705,381 51,726,211  9,295 7,969 9,713 9,713 9,374 7,355 9,374 7,355 9,295 8,258 9,270 8,258 9,270 6,218 7,927 6,218 7,927 10,229 3,893 3,893 8,258 6,218 6,218 7,927 3,893 8,258 3,893 9,270 7,927 7,927 6,218 6,218  7,355 7,355 7,969 10,224 9,713 9,713 7,969 9,295 9,295 7,969 9,295 7,969 7,355 9,295 7,969  163  Appendix A. Rent Analysis Country  Marshall Marshall Marshall Marshall Marshall Marshall Marshall Marshall Marshall Marshall Marshall Marshall Palau Palau Palau Palau Palau Pakistan Pakistan Pakistan Pakistan Pakistan Panama Panama Panama Panama Panama Panama Panama Panama Panama Panama Panama Panama Panama Papua N Papua N Papua N Papua N Papua N Papua N Papua N Papua N Papua N Papua N Papua N Papua N Papua N  Is Is Is Is Is Is Is Is Is Is Is Is  Guin Guin Guin Guin Guin Guin Guin Guin Guin Guin Guin Guin Guin  Species  Gear  Private Rent (USD)  Social Rent (USD)  bigeye skipjack bigeye yellowfin bigeye skipjack skipjack skipjack yellowfin yellowfin bigeye bigeye yellowfin yellowfin yellowfin yellowfin yellowfin yellowfin yellowfin yellowfin yellowfin skipjack albacore bigeye yellowfin yellowfin yellowfin albacore albacore bigeye skipjack albacore bigeye skipjack skipjack yellowfin albacore skipjack skipjack albacore bigeye albacore yellowfin bigeye bigeye albacore skipjack albacore  longline 996,646 -109,937 longline 31,283,747 -3,450,814 hook/line 2,227,854 679,100 longline 6,929,497 -764,372 pole/line 959,873 436,452 pole/line 67,563,248 30,720,880 gillnet 53,981,057 28,811,586 purse seine 109,135,430 52,013,757 gillnet 7,948,368 4,242,323 purse seine 21,816,226 10,397,576 purse seine 1,941,790 925,454 gillnet 4,166 2,223 purse seine 715 -94 hook/line 269 -135 gillnet 261 -2 pole/line 267 -48 longline 227 -318 pole/line 1,140,878 495,974 hook/line 129,557 50,345 longline 498,562 -4,715,705 gillnet 47,679 28,867 pole/line 7,772,076 3,378,752 hook/line 1,196 -341 longline 7,115,945 4,889,174 longline 13,242,029 9,098,242 purse seine 44,167,459 33,509,789 pole/line 3,156,410 2,385,498 longline 9,432 6,481 purse seine 54 41 purse seine 9,676,058 7,341,211 purse seine 49,378,232 37,463,195 pole/line 11,571 8,745 pole/line 1,488,980 1,125,316 pole/line 11,493,016 8,685,997 longline 6,987,193 4,800,713 hook/line 54,884,611 16,730,052 pole/line 3,045,253 1,384,671 longline 109,403,338 -12,067,946 pole/line 236,277,461 107,434,910 hook/line 1,993,889 607,782 purse seine 7,437,197 3,544,555 purse seine 1,197,243 570,604 purse seine 145,895,246 69,533,422 hook/line 8,532,845 2,601,001 longline 3,817,227 -421,067 longline 3,106,821 -342,704 hook/line 260,211,634 79,318,304 mw trawl 4,501,668 2,599,376 Table continued on next page  Opportunity Cost (USD) 1,106,583 34,734,561 1,548,755 7,693,869 523,421 36,842,368 25,169,471 57,121,673 3,706,045 11,418,651 1,016,336 1,942 809 404 263 315 545 644,905 79,213 5,214,268 18,811 4,393,325 1,536 2,226,770 4,143,787 10,657,670 770,912 2,952 13 2,334,846 11,915,037 2,826 363,664 2,807,019 2,186,481 38,154,558 1,660,583 121,471,283 128,842,551 1,386,107 3,892,642 626,639 76,361,824 5,931,844 4,238,294 3,449,524 180,893,330 1,902,293  Unit Rent (USD/t) 3,893 3,893 6,218 3,893 7,927 7,927 9,270 8,258 9,270 8,258 8,258 9,270 8,258 6,218 9,270 7,927 3,893 2,172 2,008 117 3,111 2,172 335 1,377 1,377 1,786 1,765 1,377 1,786 1,786 1,786 1,765 1,765 1,765 1,377 6,218 7,927 3,893 7,927 6,218 8,258 8,258 8,258 6,218 3,893 3,893 6,218 10,229  164  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Papua N Guin Papua N Guin Papua N Guin Papua N Guin Papua N Guin Papua N Guin Papua N Guin Peru Peru Peru Peru Peru Peru Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Philippines Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal Portugal  yellowfin bigeye bigeye yellowfin yellowfin skipjack skipjack yellowfin yellowfin skipjack skipjack yellowfin skipjack albacore skipjack skipjack albacore yellowfin yellowfin albacore bigeye bigeye skipjack skipjack yellowfin yellowfin skipjack bigeye yellowfin albacore Atlantic bf albacore Atlantic bf bigeye bigeye yellowfin skipjack yellowfin albacore southern bf skipjack Atlantic bf albacore yellowfin yellowfin Atlantic bf bigeye skipjack  longline 46,340,767 -5,111,707 pole/line 3,676,384 1,671,645 gillnet 15,955 8,516 gillnet 53,154,458 28,370,401 pole/line 54,539,722 24,799,107 purse seine 381,660,894 181,898,921 gillnet 188,778,748 100,757,849 pole/line 14,777 2,413 longline -1,132,935 -1,632,470 pole/line 596 97 longline -9,853 -14,197 purse seine 1,164,258 324,918 purse seine 17,642 4,924 longline -20,926 -26,622 hook/line -19,023,580 -42,696,561 longline -58,534,856 -74,431,449 pole/line -379 -1,067 longline -231,579,141 -293,156,435 gillnet 5,942 -964 gillnet 746 -103 gillnet 2,488 -343 pole/line -4,983 -14,039 purse seine -19,438,220 -45,580,483 gillnet 10,101,163 -1,417,873 pole/line -112,668 -303,619 hook/line -19,495 -42,621 pole/line -9,341,951 -26,203,198 longline -56,258,056 -71,571,853 longline -24,988 -141,180 hook/line -16,110 -41,949 pole/line -10,677 -16,467 longline -126,148 -176,926 longline -35,743 -41,082 pole/line 1,062,473 -1,286,764 purse seine 711,197 248,837 hook/line 427 -192 longline -86,556 -93,933 purse seine 278,178 112,917 pole/line 16 -211,484 longline -32,677 -37,558 purse seine 75,869 -152,692 purse seine 6,026 -503 purse seine 685 202 gillnet 19 -128 pole/line 176,965 -1,106 trap -111,915 -143,064 longline -898,890 -1,518,106 pole/line -3,946,223 -4,920,557 Table continued on next page  Opportunity Cost (USD) 51,452,474 2,004,739 7,439 24,784,056 29,740,614 199,761,972 88,020,899 12,364 499,535 499 4,344 839,340 12,719 5,696 23,672,981 15,896,593 689 61,577,294 6,906 849 2,832 9,057 26,142,264 11,519,036 190,951 23,126 16,861,248 15,313,797 116,192 25,839 5,790 50,779 5,338 2,349,238 462,360 619 7,377 165,260 211,500 4,881 228,560 6,529 483 147 178,071 31,148 619,215 974,334  Unit Rent (USD/t) 3,893 7,927 9,270 9,270 7,927 8,258 9,270 134 -254 134 -254 155 155 -2,416 -528 -2,418 -362 -2,436 557 578 578 -362 -488 576 -382 -546 -364 -2,416 -527 -743 -1,125 -2,960 -4,085 703 2,391 1,690 -4,520 4,121 0 -4,085 128 563 1,688 308 2,433 -2,192 -2,257 -1,560  165  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Timor Leste* Timor Leste Timor Leste Timor Leste Puerto Rico Puerto Rico Puerto Rico Puerto Rico Puerto Rico Puerto Rico Reunion* Reunion Reunion Reunion Reunion Reunion Reunion Reunion Reunion Reunion Reunion Russian Fed Russian Fed Russian Fed Russian Fed Russian Fed Russian Fed St Helena* St Helena St Helena St Helena St Helena St Helena St Helena St Helena St Helena St Helena St Helena St Lucia St Lucia St Lucia St Lucia St Lucia St Lucia St Lucia St Lucia St Lucia St Lucia  yellowfin yellowfin yellowfin yellowfin skipjack skipjack yellowfin skipjack yellowfin yellowfin albacore yellowfin bigeye bigeye albacore skipjack yellowfin bigeye yellowfin yellowfin albacore bigeye yellowfin yellowfin bigeye yellowfin bigeye albacore skipjack albacore albacore yellowfin bigeye yellowfin albacore bigeye yellowfin bigeye yellowfin albacore albacore bigeye yellowfin bigeye yellowfin skipjack bigeye skipjack  longline 716 -3,238 pole/line 734 225 hook/line 23 -16 gillnet 166 55 pole/line 884 -1,028 purse seine 9,880 114 purse seine 5,953 68 longline -12,172 -18,012 pole/line 409 -475 longline -16,055 -23,759 pole/line -102,727 -110,429 pole/line -142,236 -152,899 gillnet -2,553 -2,756 longline -2,020,026 -2,083,647 longline -2,022,737 -2,086,443 pole/line -113,822 -122,356 hook/line -33,951 -35,261 pole/line -8,655 -9,304 gillnet -3,914 -4,225 longline -2,737,438 -2,823,654 gillnet -119,536 -129,035 pole/line -242 -461 pole/line -454 -864 purse seine 1,236 446 longline -1,369 -1,606 longline -4,030 -4,726 purse seine 277 100 pole/line -23,268 -25,123 pole/line -451,074 -487,041 hook/line -18,245 -18,994 longline -38,629 -39,923 pole/line -118,091 -127,507 pole/line -9,441 -10,194 purse seine -34,084 -37,953 purse seine -209 -233 purse seine -1,939 -2,159 longline -456,338 -471,625 longline -31,163 -32,206 purse seine 124,922 68,721 pole/line 188 102 longline 2,243 934 purse seine 532 292 pole/line 18,536 10,095 longline 2,160 900 longline 126,114 52,536 purse seine 158,329 87,099 pole/line 237 129 longline 73,013 30,415 Table continued on next page  Opportunity Cost (USD) 3,954 509 39 111 1,912 9,766 5,884 5,841 884 7,704 7,701 10,663 203 63,621 63,706 8,533 1,310 649 311 86,216 9,499 219 410 790 237 696 177 1,855 35,967 749 1,294 9,416 753 3,869 24 220 15,286 1,044 56,201 85 1,308 239 8,441 1,260 73,578 71,230 108 42,598  Unit Rent (USD/t) 278 2,218 893 2,298 352 771 771 -1,587 352 -1,587 -1,405 -1,405 -1,326 -3,345 -3,345 -1,405 -2,731 -1,405 -1,326 -3,345 -1,326 -699 -699 989 -3,659 -3,659 989 -1,405 -1,405 -2,731 -3,345 -1,405 -1,405 -987 -987 -987 -3,345 -3,345 1,786 1,765 1,377 1,786 1,765 1,377 1,377 1,786 1,765 1,377  166  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  St Lucia St Lucia St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent St Vincent Sao Tome Prn Sao Tome Prn Sao Tome Prn Sao Tome Prn Sao Tome Prn Sao Tome Prn Sao Tome Prn Sao Tome Prn Sao Tome Prn Senegal Senegal Senegal Senegal Senegal Senegal Senegal Senegal Senegal Senegal Seychelles Seychelles Seychelles Seychelles Seychelles Seychelles Seychelles Seychelles Seychelles Seychelles Seychelles Singapore Singapore Singapore  skipjack albacore bigeye albacore bigeye albacore skipjack bigeye skipjack yellowfin albacore albacore skipjack yellowfin yellowfin yellowfin skipjack yellowfin bigeye skipjack skipjack yellowfin bigeye bigeye bigeye yellowfin skipjack yellowfin skipjack bigeye skipjack yellowfin yellowfin bigeye albacore bigeye albacore albacore yellowfin bigeye yellowfin skipjack bigeye yellowfin yellowfin skipjack skipjack skipjack  pole/line 30,625 16,679 purse seine 472 260 longline 56,156 23,393 hook/line 12 -16 purse seine 13,824 7,605 pole/line 5,908 3,218 pole/line 55,086 30,002 pole/line 6,162 3,356 longline 131,331 54,709 longline 1,497,973 624,015 purse seine 14,878 8,185 longline 70,643 29,428 purse seine 284,793 156,668 purse seine 1,483,817 816,267 pole/line 220,170 119,913 longline -141,783 -147,747 purse seine -78,652 -88,188 purse seine -55,825 -62,594 pole/line -3,018 -3,250 longline -6,131 -6,389 pole/line -114,201 -123,007 pole/line -45,566 -49,079 longline -5,980 -6,231 purse seine -1,551 -1,739 purse seine -186,354 -213,038 longline -1,274,414 -1,337,735 longline -180,022 -188,967 pole/line -408,996 -446,243 pole/line -3,353,096 -3,658,456 longline -718,552 -754,254 purse seine -2,309,332 -2,640,000 purse seine -500,889 -572,611 gillnet -24 -27 pole/line -362,627 -395,650 gillnet -19,260 -25,718 gillnet -34,445 -45,996 longline -321,360 -364,671 pole/line -21,043 -26,241 hook/line -926,488 -1,136,076 pole/line -149,561 -186,507 longline -102,391,884 -116,191,732 pole/line -66,917,008 -83,447,158 longline -26,877,524 -30,499,938 pole/line -6,909,540 -8,616,366 gillnet -149,183 -199,209 longline 79 -195 gillnet 1,524 1,324 pole/line 1,557 1,265 Table continued on next page  Opportunity Cost (USD) 13,945 212 32,763 28 6,219 2,690 25,084 2,806 76,622 873,958 6,694 41,215 128,125 667,550 100,257 5,965 9,536 6,768 233 258 8,806 3,514 252 188 26,684 63,321 8,945 37,246 305,360 35,702 330,668 71,721 3 33,024 6,458 11,551 43,311 5,198 209,588 36,945 13,799,848 16,530,150 3,622,414 1,706,827 50,026 275 199 291  Unit Rent (USD/t) 1,765 1,786 1,377 335 1,786 1,765 1,765 1,765 1,377 1,377 1,786 1,377 1,786 1,786 1,765 -2,663 -924 -924 -1,453 -2,663 -1,453 -1,453 -2,663 -924 -924 -2,663 -2,663 -1,453 -1,453 -2,663 -924 -924 -1,003 -1,453 -1,070 -1,070 -2,663 -1,453 -1,587 -1,453 -2,663 -1,453 -2,663 -1,453 -1,070 117 3,111 2,172  167  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Singapore Singapore South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa South Africa Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Spain Syria Syria Syria Syria  skipjack skipjack bigeye yellowfin yellowfin yellowfin albacore albacore bigeye bigeye albacore skipjack yellowfin skipjack yellowfin bigeye albacore albacore southern bf skipjack yellowfin albacore albacore skipjack Atlantic bf Atlantic bf albacore yellowfin albacore bigeye Atlantic bf yellowfin skipjack bigeye bigeye skipjack skipjack yellowfin yellowfin Atlantic bf southern bf Atlantic bf albacore bigeye Atlantic bf albacore Atlantic bf albacore  hook/line 2,021 1,611 purse seine 2,275 1,823 gillnet -253 -274 gillnet -220 -239 hook/line -917 -996 longline -1,798,720 -1,860,584 hook/line -647,844 -703,764 longline -2,820,865 -2,917,884 pole/line -120,965 -128,590 purse seine -22,293 -24,502 gillnet -628 -682 longline -253 -262 pole/line -563,805 -599,347 purse seine -149 -163 purse seine -144,681 -159,021 longline -501,753 -519,010 purse seine -17,902 -19,676 pole/line -2,199,102 -2,337,731 longline -10,653 -11,020 pole/line -2,535 -2,694 longline -311,666,952 -351,580,046 longline -1,070,563 -2,258,415 purse seine 411,800 128,599 purse seine 7,790,008 -6,787,961 trap -4,501,736 -6,993,835 longline -2,416,735 -3,050,754 pole/line 13,689,124 -9,178,707 gillnet -467,616 -602,222 hook/line 115,726 -25,101,944 purse seine 4,591,794 -398,271 hook/line -6,291 -9,571 purse seine 2,725,372 -1,508,489 longline -7,315,492 -8,152,446 longline -55,722,119 -65,942,520 pole/line -5,089,532 -8,739,943 gillnet -55 -69 pole/line -155,052,827 -209,757,888 hook/line -2,583,455 -3,150,258 pole/line -14,731,221 -20,802,898 pole/line -530,839 -1,316,179 longline -10,793 -13,624 purse seine 1,615,855 163,567 gillnet 6,482 -14,655 gillnet -53,249 -77,629 purse seine 64,332 16,007 longline 8,555 -103,534 hook/line 450 105 hook/line 157,216 36,799 Table continued on next page  Opportunity Cost (USD) 409 452 22 19 79 61,864 55,920 97,019 7,625 2,210 54 9 35,541 15 14,340 17,257 1,774 138,628 366 160 39,913,095 1,187,853 283,201 14,577,969 2,492,099 634,019 22,867,831 134,605 25,217,670 4,990,064 3,280 4,233,861 836,955 10,220,401 3,650,411 14 54,705,061 566,803 6,071,677 785,340 2,831 1,452,288 21,137 24,380 48,325 112,089 345 120,417  Unit Rent (USD/t) 2,008 2,047 -1,070 -1,070 -1,061 -2,663 -1,061 -2,663 -1,453 -924 -1,070 -2,663 -1,453 -924 -924 -2,663 -924 -1,453 -2,663 -1,453 -4,294 -1,778 2,869 268 -1,705 -3,598 1,181 -1,910 9 671 -1,810 354 -4,380 -3,977 -1,017 -1,996 -1,420 -2,506 -1,334 -638 -3,598 1,050 605 -1,593 2,047 117 2,008 2,008  168  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Syria Syria Syria Syria Syria Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Thailand Togo Togo Togo Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Tonga Trinidad Tob Trinidad Tob Trinidad Tob Trinidad Tob Trinidad Tob Trinidad Tob Trinidad Tob  Atlantic bf albacore Atlantic bf albacore Atlantic bf albacore albacore bigeye skipjack albacore bigeye skipjack yellowfin yellowfin skipjack bigeye yellowfin yellowfin bigeye bigeye bigeye albacore skipjack yellowfin yellowfin albacore yellowfin bigeye yellowfin skipjack albacore albacore bigeye albacore bigeye skipjack skipjack bigeye skipjack yellowfin bigeye albacore yellowfin albacore bigeye bigeye bigeye albacore  pole/line 21,973 6,413 purse seine 55,811 13,887 trap 23,550 -20,173 pole/line 151,011 44,075 longline 3,263 -39,486 pole/line -9,989 -12,692 gillnet 1,357 -2,616 gillnet 436 -840 longline -344 -372 longline -333,174 -359,788 longline -5,006,886 -5,406,833 gillnet 1 -1 longline -5,990,171 -6,468,662 pole/line -218,665 -277,845 pole/line -6,856,457 -8,712,116 pole/line -15,072 -19,151 gillnet 590 -1,137 hook/line -31,974 -39,243 pole/line -8,047 -8,689 longline -15,946 -16,640 purse seine -4,135 -4,654 longline 421,087 -192,927 pole/line 2,841 806 hook/line 235,626 20,505 purse seine 626,344 195,806 pole/line 412,743 117,159 pole/line 234,145 66,463 pole/line 123,568 35,075 gillnet 228,198 88,462 purse seine 4,589 1,435 hook/line 270,244 23,518 purse seine 162,270 50,728 purse seine 249,973 78,146 mw trawl 610,140 271,532 longline 128,302 -58,783 gillnet 2,270 880 hook/line 3,129 272 hook/line 286,799 24,958 longline 1,315 -603 longline 198,946 -91,150 gillnet 536 208 hook/line 110 41 pole/line 77,369 42,138 pole/line 14,452 7,871 longline 149,503 62,279 purse seine 36,803 20,246 pole/line 16,404 8,934 longline 172,809 71,988 Table continued on next page  Opportunity Cost (USD) 15,560 41,924 43,723 106,937 42,748 2,703 3,973 1,275 27 26,614 399,946 2 478,490 59,180 1,855,659 4,079 1,727 7,269 642 694 519 614,014 2,034 215,120 430,538 295,583 167,681 88,492 139,736 3,154 246,727 111,541 171,827 338,607 187,085 1,390 2,856 261,840 1,918 290,096 328 69 35,231 6,581 87,224 16,557 7,470 100,822  Unit Rent (USD/t) 2,172 2,047 828 2,172 117 -860 80 80 -2,914 -2,914 -2,914 80 -2,914 -860 -860 -860 80 -1,024 -1,453 -2,663 -924 3,893 7,927 6,218 8,258 7,927 7,927 7,927 9,270 8,258 6,218 8,258 8,258 10,229 3,893 9,270 6,218 6,218 3,893 3,893 9,270 1,286 1,765 1,765 1,377 1,786 1,765 1,377  169  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Trinidad Tob Trinidad Tob Trinidad Tob Tunisia Tunisia Tunisia Tunisia Tunisia Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Turkey Tuvalu* Tuvalu Tuvalu Tuvalu Tuvalu Tuvalu Tuvalu Tuvalu Tuvalu Tuvalu UK UK Tanzania Tanzania Tanzania Tanzania USA USA USA USA USA USA USA USA USA USA USA USA USA USA USA  yellowfin yellowfin albacore Atlantic bf Atlantic bf Atlantic bf Atlantic bf Atlantic bf albacore Atlantic bf albacore Atlantic bf Atlantic bf albacore Atlantic bf Atlantic bf albacore skipjack yellowfin yellowfin skipjack skipjack skipjack yellowfin skipjack yellowfin yellowfin albacore albacore yellowfin yellowfin yellowfin yellowfin bigeye skipjack Pacific bf bigeye Atlantic bf albacore albacore bigeye yellowfin albacore albacore Pacific bf skipjack albacore Pacific bf  purse seine 521,424 286,842 longline 526,399 219,284 purse seine 36,396 20,022 longline -2,454,550 -2,613,300 trap -1,441,955 -1,604,325 pole/line -487,456 -545,240 purse seine -962,846 -1,142,308 hook/line -11,795 -13,075 longline -18,573 -22,380 hook/line -489 -1,467 purse seine -582 -2,007 longline -591,526 -712,793 purse seine -56,071 -193,159 pole/line -439 -4,071 trap -400,827 -524,859 pole/line -5,334 -49,474 hook/line -2,047 -6,137 hook/line 2,406,014 2,406,014 purse seine 3,363,850 3,363,850 gillnet 1,053,702 1,053,702 purse seine 3,238,376 3,238,376 gillnet 1,377,165 1,377,165 longline 1,491,155 1,491,155 longline 1,716,333 1,716,333 pole/line 1,998,749 1,998,749 hook/line 1,379,011 1,379,011 pole/line 1,253,703 1,253,703 longline -135 -140 mw trawl -110 -276 longline -1,541,446 -1,620,449 hook/line -13,951 -15,151 gillnet -2,235 -2,520 pole/line -104,016 -113,787 gillnet 16,483 9,342 gillnet 3,847 1,396 gillnet 189,154 99,892 longline 7,524,426 2,497,287 pole/line 20 12 pole/line 815,825 -317,781 purse seine -40,519 -147,392 pole/line 1,143,552 614,923 gillnet 859 448 mw trawl 14,275 7,702 longline -11,496,496 -17,868,467 longline 67,606 -7,836 longline -288,773 -629,102 hook/line -970 -1,930 hook/line 200,388 35,062 Table continued on next page  Opportunity Cost (USD) 234,582 307,115 16,374 158,751 162,370 57,784 179,462 1,280 3,808 978 1,424 121,267 137,088 3,633 124,032 44,141 4,090 0 0 0 0 0 0 0 0 0 0 5 166 79,004 1,200 285 9,771 7,141 2,451 89,262 5,027,138 8 1,133,606 106,873 528,629 410 6,573 6,371,971 75,441 340,329 960 165,326  Unit Rent (USD/t) 1,786 1,377 1,786 -2,663 -1,530 -1,453 -924 -1,587 -2,106 -216 -177 -2,106 -177 -52 -1,395 -52 -216 7,969 9,713 9,374 9,713 9,374 7,355 7,355 9,295 7,969 9,295 -4,832 -113 -2,663 -1,587 -1,070 -1,453 7,097 1,620 4,323 4,602 12,516 584 -308 6,651 4,079 1,763 -1,465 1,828 -875 -820 2,473  170  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  USA USA USA USA USA USA USA USA USA USA USA Uruguay Uruguay Uruguay Uruguay Uruguay Uruguay Uruguay Uruguay Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Venezuela Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa Samoa  Atlantic bf yellowfin skipjack skipjack yellowfin yellowfin Atlantic bf bigeye Atlantic bf Atlantic bf Pacific bf Atlantic bf albacore Atlantic bf bigeye Atlantic bf Atlantic bf yellowfin Atlantic bf skipjack skipjack bigeye albacore albacore yellowfin bigeye albacore albacore yellowfin yellowfin yellowfin bigeye skipjack bigeye albacore yellowfin skipjack albacore yellowfin yellowfin albacore skipjack skipjack bigeye albacore bigeye albacore bigeye  purse seine 539,440 295,241 purse seine 32,168,111 9,294,099 purse seine 11,031,485 -29,385,437 pole/line 4,310,042 521,397 longline 2,099,004 -483,945 pole/line 2,120,081 982,737 hook/line 332,610 175,096 purse seine 58,477,642 27,257,642 trap 57,287 28,804 longline 494,516 245,907 purse seine 135,042 42,755 purse seine 12 9 longline -8,118 -9,284 pole/line 3 2 longline -15,729 -17,988 longline -15 -17 hook/line -50 -51 longline -164,645 -188,291 trap -20 -22 purse seine 2,113,427 907,288 pole/line 136,819 46,197 purse seine 17,974 7,716 purse seine 5,381 2,310 longline -54,171 -73,079 pole/line 264,020 89,146 longline -78,247 -105,559 pole/line 1,863 629 hook/line -189 -202 gillnet -7 -8 purse seine 6,015,594 2,582,477 longline -2,046,778 -2,761,197 pole/line 2,886 974 longline -532,366 -718,187 longline 65,690 20,554 pole/line 1,842,028 1,220,417 hook/line 214,107 121,996 pole/line 21,307 14,116 hook/line 1,206,073 687,208 purse seine 569,144 384,794 pole/line 212,762 140,963 purse seine 724,194 489,622 purse seine 34,417 23,269 longline 9,866 3,087 gillnet 275 195 longline 1,879,269 587,999 purse seine 127,986 86,531 mw trawl 2,722,991 2,010,901 pole/line 63,267 41,917 Table continued on next page  Opportunity Cost (USD) 244,199 22,874,013 40,416,922 3,788,645 2,582,949 1,137,344 157,514 31,219,999 28,483 248,610 92,287 3 1,166 1 2,259 2 1 23,645 2 1,206,139 90,622 10,258 3,071 18,908 174,874 27,312 1,234 13 1 3,433,117 714,419 1,911 185,820 45,137 621,610 92,111 7,190 518,865 184,350 71,799 234,571 11,148 6,779 79 1,291,270 41,456 712,090 21,350  Unit Rent (USD/t) 11,624 2,741 282 1,174 1,584 3,633 11,112 5,759 10,583 10,467 2,985 155 -254 134 -254 -254 -1,296 -254 -363 155 134 155 155 -254 134 -254 134 -1,296 -1,226 155 -254 134 -254 3,893 7,927 6,218 7,927 6,218 8,258 7,927 8,258 8,258 3,893 9,270 3,893 8,258 10,229 7,927  171  Appendix A. Rent Analysis Country  Species  Gear  Private Rent (USD)  Social Rent (USD)  Samoa yellowfin longline 180,777 56,563 Samoa skipjack hook/line 23,465 13,370 Samoa bigeye hook/line 146,841 83,669 Samoa skipjack gillnet 17,023 12,111 Samoa yellowfin gillnet 207,358 147,525 Yemen skipjack pole/line 20,249,577 10,942,381 Yemen yellowfin gillnet 345,361 234,567 Yemen yellowfin longline 3,611,318 -27,099,131 Yemen yellowfin pole/line 8,263,908 4,465,616 Yemen yellowfin hook/line 938,443 471,905 Montenegro* Atlantic bf longline -4,017 -5,272 Montenegro Atlantic bf hook/line -23 -33 Montenegro Atlantic bf pole/line -60 -517 Montenegro Atlantic bf trap -2,856 -4,140 Montenegro Atlantic bf purse seine 752 -667 High seas* albacore purse seine -16 -55 High seas yellowfin hook/line -541,563 -662,593 High seas albacore hook/line -5,507 -6,738 High seas skipjack longline -480,493 -561,885 High seas bigeye purse seine -68,744 -233,427 High seas albacore longline -1,830,414 -2,140,474 High seas southern bf seine -14 -50 High seas yellowfin purse seine -528,969 -1,796,176 High seas southern bf hook/line -1,726 -2,112 High seas yellowfin gillnet -44,251 -80,960 High seas skipjack gillnet -4 -7 High seas bigeye gillnet -10,197 -18,656 High seas skipjack pole/line -16,465,509 -28,306,576 High seas southern bf mw trawl 38 -11 High seas southern bf gillnet -81 -147 High seas yellowfin pole/line -2,242,462 -3,855,115 High seas albacore gillnet -55,519 -101,575 High seas skipjack purse seine -924,372 -3,138,811 High seas yellowfin longline -53,090,316 -62,083,454 High seas bigeye pole/line -321,033 -551,902 High seas bigeye longline -16,962,130 -19,835,399 High seas albacore pole/line -33,767 -58,051 * Denotes that weighted means were used in cost calculations for these countries  Opportunity Cost (USD) 124,214 10,095 63,173 4,912 59,833 9,307,196 110,794 30,710,448 3,798,293 466,538 1,255 10 457 1,284 1,419 39 121,030 1,231 81,392 164,683 310,060 36 1,267,207 386 36,709 3 8,459 11,841,067 49 67 1,612,653 46,056 2,214,440 8,993,138 230,869 2,873,269 24,284  Unit Rent (USD/t) 3,893 6,218 6,218 9,270 9,270 2,172 3,111 117 2,172 2,008 -2,023 -1,409 -83 -1,406 335 -179 -1,923 -1,923 -2,537 -179 -2,537 -170 -179 -1,923 -518 -518 -518 -598 331 -518 -598 -518 -179 -2,537 -598 -2,537 -598  172  Appendix B  Allocation by non-tuna RFMOs In Chapter 4, I discussed how the tuna Regional Fisheries Management Organizations (RFMOs) have decided upon their current allocation programs, or how they will develop their programs in the future. In this Appendix, I discuss the allocation programs present in non-tuna RFMOs in order to provide a broader picture of the current allocation landscape. The programs present in these RFMOs are reviewed in Table 4.1.  B.1  Pacific Salmon  Pacific salmon are a transboundary resource, shared by the United States and Canada. In 1985, the Pacific Salmon Treaty (PST) was signed by both parties, after 25 years of negotiations. Prior to the Treaty, both countries engaged in “fish wars”, intentionally over-harvesting in their own waters in order to deny harvesting opportunities to the other country (Jensen, 1986). The Treaty replaced earlier agreements, such as the 1937 Fraser Salmon Convention, which established the International Pacific Salmon Fisheries Commission (IPSFC) charged with sharing Fraser River sockeye 50/50 between Canada and the U.S.. The 1985 Treaty sets out the long-term management goals of both countries. The Pacific Salmon Commission is the regulatory body put in place to implement the Treaty. There are five species of Pacific salmon managed jointly under the treaty: sockeye (Oncorhynchus nerka), chinook (O. tshawytscha), coho (O. kisutch), chum (O. keta), and pink (O. gorbuscha). Pacific salmon return to spawn in the streams they were born in, meaning salmon that originate in Canada will eventually return to Canadian waters. The Treaty acknowledges this, recognizing “that States in whose waters salmon stocks originate have primary interest in and responsibility for such stocks” (Emery, 1997). Annex IV, Chapters 1 to 7 of the Treaty contain agreed management, conservation and allocation measures for each species and interception fishery. These chapters are renegotiated separately every 4 to 12 years. Article III 1(b) requires each country to manage its fisheries and enhancement programs so as to ensure that each country receives “benefits equivalent to the production of salmon originating in its waters”, the so-called equity principle. This provision has never been fully implemented because the Parties cannot agree on what constitutes an “equitable balance” (Shepard and Argue, 2005). 173  B.2. Pacific hake The Commission has long dealt with the issue of “interceptions”: those fish originating in one country but being caught by the other. In 1996, for example, Canada estimated that the accumulated interceptions of both countries favoured the U.S. by about 35 million fish, resulting in a loss of about $500 million (CAD) to Canada (Emery, 1997). Notably, Pacific salmon cannot be fished in the high seas, as per the North Pacific Anadromous Fish Convention (Cohen Commission, 2010a). Bilateral interception limits are negotiated periodically between Canada and the U.S.. However, Canada actually has to negotiate with several states (Oregon, Washington and Alaska), the U.S. government, and the Pacific Northwest Tribes, instead of just one federal group. That negotiations must take place between more than two interested parties increases the challenge of reaching cooperation. In spite of this negotiating complexity, however, in 1999, after 7 years of difficult negotiations, agreement was finally reached amongst the five U.S. jurisdictions and Canada on renewed fishing arrangements for Annex IV. For Fraser River sockeye, an annual international TAC is calculated as follows Cohen Commission (2010b):  T AC = return−sockeye  harvested  (test)−escapement  target−M A−AF E (B.1)  Here, MA is the management adjustment for each Fraser River sockeye stock, and AFE is the Aboriginal Fisheries Exemption. The U.S. TAC is then a fixed percentage of the international TAC, currently 16.5% Cohen Commission (2010b). It is unclear how this fixed percentage was formulated.  B.2  Pacific hake  North Pacific hake (Merluccius productus), also known as Pacific whiting, are found from northern Vancouver Island south to the northern part of the Gulf of California, and are thus shared between Canada and the U.S.. Hake are considered the most populous groundfish species in the California current system. The catch is primarily processed into H&G blocks, fillets or surimi. Prior to 2002, the U.S. was claiming an 80% share of the hake fishery, while Canada was claiming 30%, leading to non-cooperation and overfishing (United States Senate, 2004). This was perhaps due to differences in stock assessments performed by scientists within each country. Thus, in 2003, both countries signed the U.S.-Canada Pacific Hake/Whiting Agreement. While the Agreement was ratified in 2003, it was not formally implemented until 2012 (Fisheries and Oceans Canada, 2011). However, from 174  B.3. Pacific halibut 2003 through 2011, both Canada and the United States operated under the spirit of the Agreement, and complied with the Agreement’s national allocations31 . The document states: “The Agreement establishes, for the first time, agreed percentage shares of the transboundary stock of Pacific hake, also known as Pacific whiting. It also creates a process through which U.S. and Canadian scientists and fisheries managers will recommend the total catch of Pacific hake each year, to be divided by a set percentage formula. (United States Senate, 2004)” A TAC is decided upon jointly, with input from scientific advisory panels from both Canada and the U.S., as well as through consultation with the Hake/Whiting Industry Advisory Panel. Allocations of 26.12% and 73.88% of the coastwide TAC (Total Allowable Catch) go to Canada and the U.S., respectively (United States Senate, 2004). This fixed allotment, determined through bilateral negotiation, is in effect for nine years, and will remain fixed unless both Parties agree to change it.  B.3  Pacific halibut  Pacific halibut (Hippoglossus stenolepis) are found along the continental shelf in the North Pacific as well as the Bering Sea, and have been commercially harvested by Canada and the United States since the late 1880s. Since 1923, the Pacific halibut fishery has been managed by a joint Canada-U.S. convention. This convention resulted in one of the earliest international groups developed to facilitate conservation-based cooperative management between different countries sharing access to a commercially valuable fish stock. It was initially called the International Fisheries Commission, but today is known as the International Pacific Halibut Commission (IPHC). Prior to 2006, halibut was managed under the assumption that there were several separate stocks along the Pacific coast with negligible migrations between regulatory areas. Due to an easterly migration of halibut that was originally not accounted for, a disproportionate share of catches were being taken from the eastern areas, notably the waters of Canada and Washington State (Hare, 2010). Modified stock assessment modelling has led scientists to reformulate this assumption, and now the population is managed based on a single coast-wide stock, although this has not been formally accepted by Canada. Through annual stock assessments, IPHC estimates the coast-wide exploitable biomass. Exploitable biomass by regulatory area (8 areas in total) is then calculated based on survey 31  Bruce Turris, Pacific Fisheries Management Inc., personal communication.  175  B.4. Northwest Atlantic: NAFO data, and a fixed exploitation rate is applied to that biomass to obtain an allowable yield (constant exploitation yield (CEY)) for each regulatory area (Hare, 2010). Presently, an exploitation rate of about 20% of the exploitable biomass is the management target for each area (Hare, 2010). Allocation is currently done by regulatory area, but the result of this process is a proportion of the stock that Canada is allocated to remove, and proportion of the stock that the U.S. is allocated to remove, essentially a bilateral agreement. Given that Canada and the U.S share several commercially-exploited fish stocks, it is conceivable that bargaining for multi-species instead of single-species allocations could facilitate improved cooperative outcomes for both countries. In this case, by giving up some allocated hake, for example, Canada could then ask for more sockeye salmon or halibut in return. The apparent process of several different Canadian and U.S. interests all acting in their own best interest is probably counterproductive to each country obtaining the best outcome.  B.4  Northwest Atlantic: NAFO  The International Commission for the Northwest Atlantic Fisheries (ICNAF), now the Northwest Atlantic Fisheries Organization (NAFO), initiated allocation schemes in the early 1970s (ICNAF, 1972). At that time, the primary stocks of management interest for the Commission were of haddock, cod, pollock, halibut, herring and lobster. Between 1969 and 1972, the ICNAF adopted national TACs for individual stocks based on historical catches (Anderson, 1998; Gezelius, 2008). They used an 80% allocation rule, where national TACs were developed based on long-term (40% in proportion to average catches over a 10 year period32 ) and short term (40% in proportion to average catches over a 3 year period) removal histories ICNAF (1972). Further to this, 10% of the TAC was allocated to Coastal States, with the remaining 10% put aside for special needs (ICNAF, 1972). This was referred to as the 40-40-10-10 formula. This special needs category is too often an overlooked option: why not allocate an amount to the precautionary approach? Upon compliance by all cooperating members, and assuming a healthy stock, the extra share could be further allocated to fishers near the end of the season, or at the beginning of the next season. By 1977, ICNAF had developed nationally-allocated TACs for some 70 different regional stocks (Anderson, 1998). The Commission recognized the need for flexibility in allocation schemes, especially because overfishing was already occurring on some stocks, and TACs needed to be adjusted downward in subsequent years. ICNAF 32  It is unclear why 10 years was thought to be long-term. If this was based on biological considerations of the target stocks, then we have the case where biological reference points are used, with disregard to economic criteria. When dealing with climate science and issues of resilience over time, RFMOs will certainly be forced to expand their considerations of ‘long-term’.  176  B.5. Northeast Atlantic: NEAFC was formally dissolved in 1979, with NAFO being inaugurated that same year (Anderson, 1998). After Canada and the U.S. declared sovereignty over their 200 nautical mile EEZs, many foreign fleets turned their attention to heavy fishing just outside of the EEZ limits, on the so called “nose and tail” of the Grand Banks. Although NAFO continued to recommend annual allocation TACs, these were often exceeded by several European countries (Anderson, 1998) and the area has been plagued by overfishing for decades (Lane, 2008). NAFO was also challenged by non-member fishing fleets, for example those from Panama, Chili and Mexico (Anderson, 1998) who fished the resource without being party to the group, essentially free-riders. Today, the NAFO allocation system is based on fixed shares, as a proportion of the TAC (Cox, 2009). A working group formed to analyze current and possible future allocation programs for NAFO has had difficulty agreeing on a comprehensive set of allocation criteria (MRAG, 2006). NAFO has set out guidelines with how to deal with the the new member problem. They simply state that their stocks are fully allocated, and new members should join NAFO with the understanding that their fishing opportunities will be limited, for example to fisheries that are as of yet unallocated (Lodge et al., 2007). The setting of NAFO allocations, however, has often been met with resistance. In the 1980s and 1990s, for example, and average of 10 objections by member states were launched per year which often resulted in unilateral quota allocations being set by the objecting parties (DFO, 2004).  B.5  Northeast Atlantic: NEAFC  The need for national TACs and allocations was also recognized early by the Northeast Atlantic Fisheries C ommission (NEAFC). NEAFC was established in 1959, and is mainly concerned with herring, mackerel, blue whiting and pelagic redfish (Bjorndal, 2009). Despite recognition in the early 1960s that TACs could serve conservation purposes, the Commission was unable to nudge its members into cooperating in an allocation scheme prior to the collapse of the Norwegian Spring Spawning Herring stocks in the late 1960s. This led some of its members, specifically the former USSR, Iceland and Norway, to initiate their own allocation program. In 1974, NEAFC was able to institute TACs for North Sea herring along with other stocks on an ad-hoc basis (Gezelius, 2008; NEAFC, 1974). Like ICNAF, NEAFC used historical catches as the main criteria for their allocation recommendations, along with special considerations for coastal states and new members (Gezelius, 2008). NEAFC originally ceased overseeing TAC allocation when countries adopted the 200 nautical mile EEZ, leaving individual nations responsible for conservation through smaller 177  B.5. Northeast Atlantic: NEAFC bilateral and multilateral agreements (Gezelius, 2008). Today, they recommend a variety of conservation measures, including the setting of TACs and allocations to member nations (called contracting parties, CPs), which include the European Union, Denmark, Iceland, Norway and the Russian Federation (Bjorndal, 2009). For herring, allocation to CPs is based on the “zonal attachment principle”: the stock size in a given zone multiplied by the duration of the stay determines the allowable biomass removals for that zone (Bjorndal, 2009). Changes in abundance distribution of herring caused a breakdown in cooperation between CPs in 2003, with Norway demanding a higher allocation (Bjorndal, 2009). NEAFC has also encountered trouble facilitating cooperation between CPs targeting blue whiting. In the 1990s, although fishing nations agreed that a cooperative sharing scheme was necessary to prevent overexploitation of blue whiting, CPs could not agree on how to share the TAC, and often set their own quotas, greatly exceeding the recommended TAC (Bjorndal, 2009). In the 2000s, CPs presented alternative ways of allocating the TAC based on the zonal attachment principle described above, on catches from a given zone, or a combination of these two, along with an economic dependency argument in some cases. In 2005, an allocation scheme was agreed upon, which was facilitated by fishermen’s organizations (Bjorndal, 2009). Currently, NEAFC operates their allocation program based on fixed proportions of the TAC (Cox, 2009). A promising sign of improved fisheries management in the North Atlantic is communication between NEAFC and NAFO. The two RFMOs have reportedly initiated the development of a pan-North Atlantic list of vessels engaged in illegal, unregulated and unreported (IUU) fishing (Bjorndal, 2009). IUU vessels flagged on the waters of one RFMO would be reported to the other group.  178  

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