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UBC Theses and Dissertations

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

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Improving the management of global and regional tuna sheries 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 dicult to manage due to competition between the large number of interested shing 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 sheries management has, by and large, been unsuccessful in pro- moting sustainable sheries. Populations of several of the world's tuna species are fully or over-exploited. This dissertation identies and addresses areas where improvements in the management of global and regional tuna sheries may facilitate the continued contribution of these sheries to livelihoods and food security. I analyze private and social resource rent derived from shing for dierent tuna species and by dierent gear types. From these results I identify key management targets. Man- agement eorts 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 appropri- ately targeted. I propose a combined socio-economic and ecological approach formulated from the perspective of sheries benets, as opposed to just catch, which could facilitate stable cooperative agreements for sustaining tuna stocks into the distant future. Tuna sheries in the western and central Pacic provide over half of the world's tuna, but lack of eective management capacity in Indonesia and the Philippines threatens the sustainability of these sheries. I argue that countries that sh in this region, most specically 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 yellown and bigeye tuna in the skipjack purse seine shery. Through applied game-theoretic modelling, I conclude that reduction in juvenile bycatch brought about by cooperative management of these sheries would provide long-term ecological and economic benets. 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 for- mulated 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 identied the need for a contemporary review piece, conducted the research and wrote the manuscript. Marko Lindroos oered 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 eld work in the Philppines, collated and analyzed informa- tion 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 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 2 Informing global tuna sheries management: Private versus social re- source rent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Global tuna sheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3 Subsidies, welfare economics and the shery . . . . . . . . . . . . . . . . . 15 2.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3 Application of game theory to sheries over three decades . . . . . . . 28 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2 Early years: The two-player game . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Major movement: Coalitions . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.4 Looking forward: Catch privileges and resilience . . . . . . . . . . . . . . . 38 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4 Present and future allocation approaches for shared tuna sheries . . 44 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.3 Indonesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 5.4 Philippines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 5.5 Papua New Guinea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.6 Regional options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 6 Can cooperative management of tuna sheries in the western Pacic solve the growth overshing problem? . . . . . . . . . . . . . . . . . . . . 91 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 Pacic Salmon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 B.2 Pacic hake . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 B.3 Pacic halibut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 B.4 Northwest Atlantic: NAFO . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 B.5 Northeast Atlantic: NEAFC . . . . . . . . . . . . . . . . . . . . . . . . . . 177 vi List of Tables 1.1 Information on tuna species, shing gears, markets supplied, 2010 catches (FAO, 2012), and conservation status (iucn.org). . . . . . . . . . . . . . . . 5 2.1 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 obser- vations used for calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Private and social rent (USD) for bluen shing nations (all bluen species combined). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.4 Species summary: mean unit rent, private and social rent. . . . . . . . . . . 24 4.1 Summary of RFMO allocation information . . . . . . . . . . . . . . . . . . 53 5.1 Summary of main tuna species shed in the Coral Triangle, along with the gears used, markets supplied and status of the stocks. . . . . . . . . . . . . 65 5.2 Summary of Indonesia's tuna sheries and management. . . . . . . . . . . . 73 5.3 Summary of the Philippine's tuna sheries and management. . . . . . . . . 81 5.4 Summary of Papua New Guinea's tuna sheries 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 sheries and markets for WCPO tuna species used in the model. 95 6.2 Variable denitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.3 Biological and shing parameter inputs for skipjack tuna. . . . . . . . . . . 109 6.4 Biological and shing parameter inputs for yellown tuna. . . . . . . . . . . 111 6.5 Biological and shing 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. . . . 2 1.2 2005 catches, in tonnes, of skipjack, albacore, bigeye and yellown tuna (seaaroundus.org). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 2005 catches, in tonnes, of Atlantic, southern and Pacic bluen tuna (seaaroundus.org). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.4 Map of the Coral Triangle, shown within the WCPFC Convention area. Convention area map c
 WCPFC, used with permission. . . . . . . . . . . . 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 (dierence in price per tonne and cost per tonne) by tuna species and gear type, aggregated over all shing nations. bf refers to bluen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1 Map of tuna RFMOs (Lodge et al., 2007). c
 Chatham House, used with permission. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2 Grand Banks shery model schematic (Lane, 2008). c
 Journal of North- 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 Pacic Fisheries Commission ( c
WCPFC, used with permission), shown by solid lines, and 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 prots to the longline 
eet at varying levels of relative purse seine eort (x axis). 1.0 refers to the status quo, 0.5 refers to 50% of the status quo eort, and 1.5 refers to 150% of the status quo eort. Varying levels of longline eort are represented by the coloured lines. . . . . . . . . . . . . . 107 6.3 Adjusted vulnerability at age to purse seine gear for yellown 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 rst 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 Pacic 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 benets 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 New- man, 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 sheries 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 sta 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 elds of economics and ecology, and it has been such a privilege to learn from them over the years. Specic 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 eld 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 eortlessly 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 sh 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 1950s- 1990s, 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 sheries (Clark, 2006). Some researchers have predicted a 90% global removal of predatory sh (Myers and Worm, 2003), and warn that shortfalls in the supply of sh could have devastating consequences for human populations (Pauly et al., 2002). Furthermore, overshing has ecosystem eects (Worm et al., 2006), many of which we don't yet understand, but which will undoubtedly aect human populations in the future. The degree to which our world is facing this crisis in global sheries is a hotly con- tested 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 sheries, it appears that sh 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 eort), how- ever, 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 sh- eries management, and in the status of stocks, can be seen (Worm et al., 2009; Branch et al., 2011). Although stock assessments oer higher-resolution data (Worm et al., 2009), they are not available for many of the world's sheries, for example, those in developing countries (Kleisner et al., 2012). Most sh stocks that have regular assessments done are highly managed, and often quite valuable. Species that are often caught as bycatch in these sheries 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 sheries are performing well, are precisely those sheries 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 sheries, common ground can be found in that we are not yet at a place where improvements are unnecessary. 1 Chapter 1. Introduction 1950 1960 1970 1980 1990 2000 0 50 0 10 00 20 00 Year Ca tc h (1, 00 0 t ) Albacore Bigeye Atlantic bf Pacific bf Southern bf Skipjack Yellowfin 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 bluen 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 sheries also support the livelihoods of shers in over half of all maritime countries, providing employment and revenue. The importance of tuna sheries 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 shed throughout the world's oceans. In this thesis, I focus on the management of these seven species, which include the three bluen species (Atlantic (Thunnus thynnus), southern (T. maccoyi) and Pacic (T. orientalis), yellown (T. albacares), bigeye (T. obesus), albacore (T. alalunga), and skipjack (Ketsu- wonis pelamis). Figures 1.2 and 1.3 show the 2005 catches by ocean area of non-bluen and bluen 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 yellown tuna (seaaroundus.org). they supply, 2010 catches and their conservation status is summarized in Table 1.1. Tuna are highly migratory sh: throughout their lifetime they can travel thousands of kilometers. This often means that one population of sh will spend part of its life in the waters of dierent 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 con- vened to address some of the problems leading to overexploitation of shared sh 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 shing eort targeting strad- dling 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 sheries, as they may represent as much as a third of sheries catches 3 Chapter 1. Introduction Figure 1.3: 2005 catches, in tonnes, of Atlantic, southern and Pacic bluen tuna (seaaroundus.org). 4 C h a p ter 1. In tro d u ction Table 1.1: Information on tuna species, shing gears, markets supplied, 2010 catches (FAO, 2012), and conservation status (iucn.org). Common name Scientic name Number of stocks Gears used Markets supplied 2010 catch (1,000 t) IUCN status Albacore Thunnus alalunga 6 Longline and pole and line Canned/ frozen 255.3 Near threatened Bigeye Thunnus obesus 4 Longline Sashimi 358.7 Vulnerable - Purse seine Canned Atlantic bluen Thunnus thynnus 2 Longline Sashimi 13.0 Endangered Pacic bluen Thunnus orientalis 1 Longline Sashimi 12.2 Least concern Southern bluen Thunnus maccoyii 1 Longline Sashimi 9.2 Endangered Skipjack Katsuwonus pelamis 5 Purse seine Canned 2,523 Least concern - Handline and pole and line Domestic Yellown Thunnus albacares 4 Longline Frozen 1,165 Near threatened - Purse seine Canned 5 Chapter 1. Introduction (Munro et al., 2004). The existence of dynamic externality leads to competition among shing 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 shers, and to continue contributing to global food security. Management systems that take into consideration ecological and economic arguments, capacity concerns, strategic behaviour and sher decisions are necessary to promote productive global tuna sheries. In this thesis, I address these necessities through the development of ve core papers, which provide information and options that can help improve tuna sheries 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 bluen, we have known for decades that populations were overshed. Biological arguments, however, have not resulted in major shifts towards improved management of overshed tuna stocks, nor have they prevented the subsequent overexploitation of other tuna stocks, for example Pacic bigeye. Can an understanding of the economics of global tuna sheries contribute to a shift in tuna management? In Chapter 2, I explore if and how information about the protability of tuna shing 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 shing for dierent tuna species, shing gear types and shing nations. We expect that those sheries where positive rents are being generated are likely to attract more shing eort in the future, whereas, those sheries generating negative rents, might be places that management should target for eort reductions. In this Chapter, I also analyze the dierence between the private rent obtained by shing companies, more conventionally called prot, and the resource rent accruing to so- ciety, i.e., the net benets from the shery once corrected for distortions. This comparison is possible by incorporating national subsidies into the calculations. Market distortions, for example subsidies that articially in
ate ex-vessel prices or de
ate shing costs, can make sheries appear protable to shers. Yet, once these distortions are identied, these same sheries may seem less attractive to society as a whole. The aim here is to provide information to managers about where eort is likely to increase or decrease in the future. Furthermore, this Chapter asks whether or not the gap between private rent, as the shers 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 tar- get management eorts, we need to understand how management of global tuna sheries 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 man- agement of tuna stocks (and other shared sh stocks) through groups called Regional Fisheries Management Organizations (RFMOs) (UN, 1995). The earlier UNCLOS Agree- ment, 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 essen- tial for sustainable sheries 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 eld since its rst application to sheries by Munro (1979). What insights has the application of this tool provided to the management of joint sheries 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 sheries? And how will changes in climate aect 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 shing eort" in internationally-shared sheries (UN, 1995). Issues of shared sheries allocation are some of the most challenging in sheries 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 dierent global oceans. The eectiveness 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 eect conservation of their target tuna stocks (Cullis-Suzuki and Pauly, 2010). We could thus conclude that there is denite room for improvement in how tuna sheries are managed through RFMOs. In Chapter 4, I discuss the current approaches taken by the world's tuna RFMOs to allocate benets 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 war- ranted. Some RFMOs, for example, the Western and Central Pacic Fisheries Commission (WCPFC) (MRAG, 2006) and the International Commission for the Conservation of At- lantic 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 fty 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 eld, 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 sheries, and move away from merely thinking about allocation from a catch perspective, to thinking about it in terms of other mutual benets, such as rent, employment, or domestic consumption? Asymmetry in players, that is, dierence in perspectives and interests (e.g., dier- ences in rates of discount and costs of shing), can aect the outcome arrived at in game theoretic models of shing (Munro, 1979; Sumaila, 2005), as can incomplete information (Jensen and Vestergaard, 2002). When we consider tuna sheries management, we are often dealing with ten, twenty or thirty shing states, all of whom have dierent pref- erences, economies, management capacities and objectives. In the Western and Central Pacic Ocean (WCPO), over thirty countries exploit four main tuna stocks: albacore, bigeye, skipjack and yellown. Some industrial shing nations, such as Japan, Taiwan and Spain, have powerful shing 
eets that pay for access to sh in the waters of small Pacic Island Countries (PICs) such as Samoa and Palau. These groups of nations have obvious asymmetries. Cooperation among these shing 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 shers depend on marine resources for their livelihood (The Nature Conservancy, 2004). Recent gures suggest that as much as a third of all tuna catch from 8 Chapter 1. Introduction the western and central Pacic Ocean can be attributed to the 
eets of Indonesia, the Philippines and Papua New Guinea (SPC, 2010), the three major tuna shing 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 sheries (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. Con- vention area map c
 WCPFC, used with permission. Indonesia, the Philippines and Papua New Guinea all face socio-economic, institu- tional and management circumstances that dier from one another, and from the other larger and smaller shing nations in the region. Tagging studies have demonstrated a high degree of interaction between CT tuna sheries and those to the east (Vera and Hipolito, 2006; Ingles et al., 2008), while a recent stock assessment for yellown reports that the domestic sheries of the Philippines and Indonesia are in part responsible for stock de- pletion (Langley et al., 2009b). Tuna sheries and their management in these countries, therefore, impacts other nations shing for tuna in the WCPO. How are sheries managed in these countries? Are there programs in place that have been particularly eective at promoting sustainable sheries in the region? In this Chapter, I compare and contrast tuna sheries 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 yellown and bigeye tuna in the purse seine skipjack shery. The regional purse seine shery has increased its reliance on sh aggregating devices (FADs), essentially 
oating objects that attract adult skipjack and yellown, along with juvenile yellown, bigeye and other non-tuna species such as dolphinsh and marlin. Much of the juvenile tuna bycatch occurs in the waters of the Coral Triangle countries. Adult yellown and bigeye are targeted by countries inside and outside the Coral Triangle, and thus there is a very intriguing and important con
ict of interest between the two groups that needs to be explored. Chapter 6 addresses this con
ict by estimating the potential benets of cooperative management of tuna sheries in the WCPO. Fisheries in the region currently operate in a non-cooperative, or competitive way, whereby each shing 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 shing could bring economic benets to the region. Specically, I examine non-cooperative and cooperative outcomes for a three player game: purse seine; longline; and handline, and incorporate skipjack, yellown and bigeye as target species. Given a long-term perspective, what would be the optimal eort allocation between dierent shing gears if we seek to maximize cooperative rent? How does this compare with the current eort 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 sheries, as it will eliminate (or at least reduce) growth overshing, whereby sh are harvested when they are too small. This will probably require a decrease in eort by purse seine vessels. Chapters 2 through 4 of this dissertation highlight broad issues worth tackling in the quest for more eective management of global tuna sheries. 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 Pacic 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 institu- tional perspectives to facilitate adequate and eective management. Current management of global tuna sheries 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 the- sis is to use economic tools, arguments and methods to increase our understanding of the current issues in, and barriers to, sustainability in the tuna shing sector, and to provide inputs that can help us move toward improved management of global and regional tuna sheries. 11 Chapter 2 Informing global tuna sheries 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 sheries was estimated at about US $80 billion (Sumaila et al., 2007). One particular group of sh, the tunas, is of immense global economic importance, with various species being shed 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 bluen 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 sheries 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 scientic 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 sh at port. With few exceptions (Sumaila and Huang, 2012), the costs associated with shing 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 rst estimate of the net economic rent of global tuna sheries in this Chapter. Rent is calculated in two ways. Firstly, private rent is calculated from the perspective of shers or shing companies. This is the dierence 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 shing countries (i.e., society). This estimate 12 2.2. Global tuna sheries 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 sheries. 2.2 Global tuna sheries 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), yellown (T. albacares), bigeye (T. obesus), southern bluen (T. maccoyii), Atlantic bluen (T. thynnus), Pacic bluen (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 shing 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 bluen as endangered, bigeye as vulnerable, albacore and yellown as near threatened, and (only) Pacic bluen and skipjack as of least conservation concern (Table 1.1) (IUCN, 2011). All three bluen species exhibit life history traits that make them particularly vulnerable to over exploitation, including slow growth and late matu- rity (De Roos and Persson, 2002), compared to their smaller con-specics. Furthermore, they are temperate water species, which are generally less productive than tropical species (Majkowski, 2007). For species such as bigeye and yellown, their association with skip- jack around 
oating objects, specically in the Pacic, makes them susceptible to growth overshing1 due to juvenile bycatch (Miyake et al., 2010; Bailey et al., In press; Langley et al., 2009a,b). Skipjack stocks in the Pacic are probably underexploited, and so planned future increases in shing eort for this target species are likely to have a negative impact on yellown and bigeye stocks in the region if today's shing 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 sh for tuna, depending on the species being targeted 1Growth overshing occurs when sh are harvested before the point at which individuals reach the maximum yield per recruit. 13 2.2. Global tuna sheries 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 yellown, often taking advantage of tuna's propensity to aggregate around 
oating objects. Most purse-seine caught skipjack and yellown 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 bluen 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. Eective management of shared sh stocks often requires cooperation by several sh- ing 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 dierent global oceans. The eectiveness 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 shery 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 shery Fisheries economists have generally focused on tackling the issues of ineciency in global sheries, 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 sheries 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 sheries economics has largely been placed on judging a policy tool based on its economic eciency, 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 prot to one small subset of society (shers), incorporating the concepts of welfare economics would have us maximizing benets 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 govern- ment), to a shing sector, which essentially gives the shing sector an economic advantage, encouraging shers to sh more than they otherwise would (Sumaila et al., 2010). In this way, government subsidies to the shing sector are a choice on the allocation of public resources to a small fraction of society. Plainly stated, sheries subsidies exacerbate the problems of overcapacity and overshing (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 shing sector annually (FAO, 1992; Milazzo, 1998). The World Bank, Or- ganization 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 sheries subsidies as an issue to be tackled. A more recent estimate of global sheries 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 sheries 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 benets from shing that accrue to the shing 15 2.3. S u b sid ies, w elfare econ om ics an d th e  sh ery Table 2.1: Tuna RFMOs, species managed, and performance at meeting best practices criteria. RFMO Full title Year of entry Tuna species covered Performance (%) (theory, practice)* CCSBT Commission for the Conservation of Southern Bluen Tuna 1994 Southern bluen 44, 0 IATTC Inter-American Tropical Tuna Com- mission 1949 Albacore, skipjack, yellown, bigeye and Pacic bluen 60, 33 ICCAT International Commission for the Conservation of Atlantic Tunas 1969 Albacore, skipjack, yellown, bigeye and Atlantic bluen 57, 38 IOTC Indian Ocean Tuna Commission 1996 Albacore, skipjack, yellown, bigeye, Southern bluen 58, 78 WCPFC Western and Central Pacic Fish- eries Commission 2004 Albacore, skipjack, yellown and bigeye 74, 67 *Cullis-Suzuki and Pauly (2010) 1 6 2.4. Methods sector itself as private resource rent, also known as prot. Unfortunately, this private rent contains market distortions resulting from subsidies, and consequently yields an incomplete understanding of the benets of global tuna sheries to society as a whole. Subsidies can be divided into those that positively aect stock sustainability (\good"), negatively aect 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 shing, 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 benets derived from the shery 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 benets (or lack thereof) of sheries 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 sheries data for most commercially targeted sh 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 sheries. Particular emphasis is given to the dierence between private and social rent originating from global tuna sheries. Catches The global catch database is based on data provided by the Food and Agriculture Or- ganization 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 oc- curred, 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, sh species, and shing 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 Mean price (USD/t) Number of observations Skipjack 3,818 265 Albacore 4,003 220 Yellown 4,341 355 Southern bluen 15,684 28 Bigeye 4,533 224 Atlantic bluen 3,929 111 Pacic bluen 6,307 23 Prices Although the FAO publishes information on the price of processed sh products, data on ex-vessel prices (i.e., rst-hand prices that shers receive when they land their sh) are not always easy to come by. To ll 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 sher (or country) attains from a given shing 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 shing species s with gear g is calculated as: TRm;s;g = pm;shm;s;g 8m; 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. TRm = s;gTRm;s;g; 8m (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 shers and thus shing 
eets. Up until 2011, however, reliable estimates on the cost of shing were not consistently published or adequately summarized. There are several reasons for the lack of data, including the extensive amount of eort 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 shing cost database was developed in 2011, aimed at quantifying costs for various types of shing 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 sheries catch (Lam et al., 2011). Fishers face two main types of costs, xed and variable. The former are costs not dependent on shing operations directly, often called sunk costs, for example, the cost of the vessel itself. Variable costs are those that vary with the level of shing 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 prot estimate, and are thus economic costs of shing, 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 shing, 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 shing, US $7,092/t, were for longlining by South Pacic 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 shing 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 Pacic 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 shing with gear g in 2005 is thus given as: 19 2.4. Methods TCm;g = cm;ghm;g; 8m; g (2.3) The total cost of shing to country m is then calculated by summing over all gears and species. Subsidies In addition to specic subsidies estimates, the subsidies database (Sumaila et al., 2010) contains the computed subsidy intensity (), 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 shing sector, I use the intensity as a proxy and apply it to the landed value of shing 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 shing 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 dened above) for the 2005 year for each country and as follows: TSm = mTRm; 8m (2.4) Rent estimates Resource rent as applied to sheries is formally dened as the dierence between the total revenue and the total cost of shing (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 prot (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 dierent ways. Firstly, private rent is computed from the simple denition 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: m;g;s = TRm;g;s  TCm;g;s; 8m; g; s (2.5) Secondly, the subsidies-adjusted resource rent () for each country in 2005 is com- puted. This is what I consider the social resource rent: 20 2.5. Results Figure 2.2: Social rent by country. m = m  TSm; 8m (2.6) 2.5 Results The private rent generated from global tuna sheries, or that which is perceived by the shing 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 dierence between the total revenues generated by shing for the seven key tuna species of interest (all gears combined) and the total costs incurred through these shing 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 dierence 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 shing of specically bluen tuna species, while Figure 2.2 shows countries generating positive, zero and negative social rent from shing all tuna species combined. Only Japan, Italy, New Zealand and Croatia derive substantial positive social rents from shing for bluen 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 bluen shing nations (all bluen species combined). Country Private rent (USD) Social rent (USD) Spain -2,885,881 -6,830,267 France -532,373 -3,709,356 Morocco -2,456,061 -2,664,997 Tunisia -1,453,750 -1,617,400 Malta -92,536 -798,714 Mexico -666,347 -729,192 Algeria -424,065 -445,313 Indonesia -296,140 -384,296 Taiwan 12,035 -360,456 Portugal -62,395 -79,143 Cyprus 24,649 -22,509 Greece 359 -1,247 Denmark -265 -284 Uruguay -47 -49 USA 20 12 Ireland 430 170 Croatia 323,792 242,810 New Zealand 852,044 800,795 Italy 7,666,626 4,142,360 Japan 94,125,476 56,645,033 22 2.5. Results Gear type Un it r en t (U SD /t) −5000 0 5000 10000 15000 20000 albacore Atlantic bf bigeye Paci"c bf skipjack southern bf −5000 0 5000 10000 15000 20000 yellow"n GN HL PS PL LL gillnet handline longline pole and line purse seine GN HL PSPLLL GN HL PSPLLLGN HL PSPLLLHL PSPLLLGN Figure 2.3: Private rent per tonne (dierence in price per tonne and cost per tonne) by tuna species and gear type, aggregated over all shing nations. bf refers to bluen. 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 dierence in price and cost per tonne, for each major gear type employed in global commercial tuna sheries. There is wide variability from shing the various tuna species with dierent 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 shing for Atlantic bluen oers 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 bluen 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 shing for yellown appears to be bad business, as this species contributes negatively to overall private rent (Table 2.4). Once subsidies are included, however, shing for albacore, skipjack, yellown, and Atlantic bluen yields negative social rents. This suggests that, with the exception of southern and Pacic bluen and skipjack, subsidies are making unprotable sheries seem otherwise protable to shers. Table 2.4: Species summary: mean unit rent, private and social rent. Species Mean rent (USD/t) Total private rent (million USD) Total social rent (million USD) Albacore 2,116 125 -183 Bigeye 2,510 633 -77 Skipjack 2,829 4,057 792 Yellown 2,153 -371 -1,562 Atlantic bf 981 46 -6.2 Southern bf 5,865 170 76 Pacic bf 3,533 20 9.5 Total 4,681 -951 2.6 Discussion No doubt tuna sheries provide substantial revenues in the form of landed value for shing nations. Once costs and subsidies are accounted for, however, the net social rent from global tuna sheries is negative. There are vast dierences in the distribution of rent by country, species, and by shing gear. Furthermore, there is a substantial dierence in the private and social resource rent. Currently, subsidies amounting to over US $5 billion are being transferred to the tuna shing sector from national governments. This is money that countries are choosing to put into various shing sectors that may not be providing positive economic returns for the country, and may be fueling overexploitation of tuna stocks. Bluen Fishing for bluen tuna still remains a potentially protable endeavor, with the private mean rent per tonne (dierence in per tonne revenues and costs) for southern and Pacic bluen species being higher than for non-bluen species. Atlantic bluen, however, oers the lowest unit rent of all tuna species analyzed in this study. Both Atlantic and southern bluen tuna are overshed (MacKenzie et al., 2009; Collette et al., 2011; ISSF, 2012), 24 2.6. Discussion yet remain, to varying degrees, protable. This is especially true with regards to private rent, or the subsidized amount perceived by shers. Once subsidies have been accounted for and social rent estimated, however, shing for Atlantic bluen is no longer protable. This should oer 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 protability is not for high for Atlantic bluen sheries agrees with work conducted by Bjorndal and Brasao (2009), which concluded that prots could be much higher for those involved in shing for Atlantic bluen 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 shing nations (notably Italy, Japan and New Zealand) still stand to have posi- tive social rents from shing bluen tuna, while other countries, such as Spain and France, collect only negative rents. Many other shing nations are shing right around the zero social resource rent point. Policy recommendations based on decreasing eort (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 eective than targeting those countries that are seeing positive economic benets. If, as the Bjorndal and Brasao (2009) paper argues, substantial increases in rent are possible by reducing eort 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, yellown and bigeye Fishing for albacore and yellown, 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 oers positive private rents, albeit lower than most of the other species. The sum of the private rents from yellown shing, however, is negative, despite the positive unit mean. Overall, therefore, shing for yellown 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, shing for bigeye contributes negative social rent. Skipjack tuna make up over half of all global catches (ISSF, 2012). That shing for this species oers positive social rent does suggest that increasing eort in these sheries is 25 2.6. Discussion likely. Some of the cost savings from skipjack shing comes from the use of sh 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 Pacic 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 shery in the future. This in turn would most likely result in less eort or capacity moving into this particular shery than would otherwise be predicted. Conclusion This analysis nds that the mean unit rents through shing for all tuna species oers potential positive returns, from the shers' point of view. This could suggest that eort will continue moving into these sheries, even though several of the stocks are in danger of overexploitation. It is important to note, however, that the data used here, specically 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 sh, 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 shing operations will likely lead to improved estimates of sheries rents, from tuna stocks and others, in the future. Subsidies can alter the perceived rent possibilities, encouraging overcapitalization (Ar- nason, 1998; Clark, 2006; Sumaila et al., 2010). For sh populations that are fully or over- exploited, increased eort resulting from overcapitalization can lead to decreased stock size, as well as reduced resource rent for all shing nations. Furthermore, excess capacity in global tuna sheries is thought to contribute to management challenges and hinder eectiveness of RFMOs (Miyake et al., 2010). In this analysis, national subsidies to global tuna sheries amounted to US $5.63 billion in 2005. Due to the fact that, besides skipjack and Pacic bluen, 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 soci- ety beneting from this disinvestment in sheries 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 gen- erated by shing 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 sheries 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 con
ict and common interest, and predicts what rational players should do (Luce and Raia, 1957). A game consists of a set of players, a set of strategies avail- able to those players, and a set of possible payos 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 Raia (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 Raia, 1957). We can see that the issues of sheries management t well within this game-theoretic framework as shers and/or managers seek to maximize the benets from a given shery. 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 out- come through an understanding that all other players are seeking the same goal (Luce and 28 3.1. Introduction Raia, 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 Raia, 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 sheries From society's point of view, overshing is wasteful, both biologically and economically, yet it happens often (Clark, 2006). The theory of games oers some insights into why shers may be driven to adopt strategies that seem to be irrational; why overshing 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 sheries, as many of the world's natural resources are common pool in nature (Sumaila, 1999). We can divide shared sheries resources into four main categories: 1. Domestic shared stocks: those stocks shed 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 di- culty. The rst relevant paper analyzing sheries 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 sheries jurisdiction which he believed would, and in fact did, lead to increased management of sheries by individual coastal states (Munro, 1979)2. He argued that the issue of managing transboundary sh stocks, those that moved between 2Munro 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 rst 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 
exible outcome because players could, of course, choose the non-cooperative payo 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 shery o 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 
eets 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 re
ect on three decades worth of academic and practical applications of game theory to sheries and ask how in
uential this paper has been in terms of shaping sheries 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 sheries, specically 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 sheries, and international environmental issues as a whole, Section 3.4 highlights where sheries 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 dierent rates of discount and costs of shing, can impact the cooperative solution when consider- ing a shery 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 dierent preferences and perspectives, joint management of a resource is greatly simplied 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. Interest- ingly, the term `side payment' has been met with scepticism by the international sheries 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 in
uential paper on `sh wars' a year after Munro (1979). In their two-player analysis, the authors highlight two important game- theoretic features of sheries management: that the underlying stock is aected 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 exter- nality' (Levhari and Mirman, 1980) and it is this fundamental situation that allows game theory, the study of strategic interaction, to be applied to sheries (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 shery problem due to the increase in limited entry programs being initiated by shing 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 overshing, which is in fact what we readily observe in reality. Following the Munro, Levhari and Mirman, and Clark papers, many other contri- butions were published, mostly in the 1990s, applying game theory to highlight several of the most pressing issues in sheries management, specically 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 dened 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 par- ties cannot communicate. For a two-player cooperative outcome to be stable, it must meet two conditions, namely, Pareto Optimality (no player can increase their payo without decreasing the payo to another player) and the Individual Rationality Constraint (the cooperative payo to any player must be equal to or greater than the payo under non- cooperation, 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 
exibility and resilience of the cooperative solution. This third condition is discussed in Section 3.4. 3Recently 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 sheries shared between only two players. Although one could envision several sheries 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 shery where Armstrong and Flaaten (1991) and Sumaila (1995, 1997a) modeled the interaction between oshore trawlers and coastal vessels. Fisheries game-theoretic methods were also applied to study how cannibalism by adult cod on juveniles can aect the optimal catch shares between two entities that sh dierent 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 shery between Australia and Japan, targeting Southern bluen tuna. The author concluded that the optimal outcome is, in fact, joint management, or cooperation, resulting in the total exclusion of Australia from the shery (compensated through side payments) (Kennedy, 1987). In the case of the industrial pelagic shery 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 benets, specically through eliminating the tendency for overcapitalization and overshing to occur in open access sheries (Aguero and Gonzalez, 1996). The two-player application expanded In the decade following the Sumaila (1999) review, the two-player framework was ex- panded upon to analyze more than just catch shares between two entities. Game theory was used to study the eciency 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 eort to protect the ecosystems within those areas. Sumaila (2002) developed a two-agent bioeconomic game-theoretic model to assess the dierence in expected MPA eectiveness under cooperative and non-cooperative management. Not surprisingly, the paper con- cludes that both cod stock biomass and rent from the shery are higher under an MPA program that is managed cooperatively by the two players (Sumaila, 2002). A subsequent paper to this addressed the distributional eects of MPAs to dierent 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 in
uence 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 sucient plan 32 3.2. Early years: The two-player game to enable sustainable sheries. 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 shed stock that aect shers in a co-management group. The author concludes that the size and the internal growth rate of the stock do in fact in
uence shers' decisions to coop- erate, but shers' attitudes toward risk are also highly in
uential (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 Lin- droos (2004) helped to initiate this conversation within the sheries game theory realm. Applying game theory in this type of analysis can help policy makers better understand how the biological characteristics of a shery can help or hinder cooperation. Stage and sequential games Most sheries 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 shing eort 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 rms in the shery, or a shery 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 shers 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 eort control. Fishers choose their coalition structure in stage three, and in stage four, shers choose their optimal eort strategy. In sequential games, one player makes their decision rst, 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 rst because they target sh 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 sh- ers, 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 transbound- ary 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, sheries 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, specically 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 simplication for the rst game theoretic applications. At the time of extended sheries jurisdiction, about 90% of the world's capture sheries 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 sh stocks, where interested shing parties include coastal states, Distant Water Fishing Nations (DWFNs) and high seas shing 
eets, 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 sheries, as these sheries represent as much as one third of marine capture sheries catches (Munro et al., 2004). The application of game theory to sheries 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 sheries 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 sucient to tackle one of the most pressing of sheries management issues, namely, management of straddling stocks. The UNFSA eectively mandated the management of straddling stocks to be carried out through regional sheries 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 innite duration (known as a supergame), one of the results of which is that the payos 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 suciently large group of players. This may be particularly relevant for management of tuna sheries, as the potential number of interested players can be quite large. Some of the earliest sheries 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 rst compute and compare the relative payo 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 benets should each player in a coalition receive? There are dierent methods for assigning sharing rules, and in sheries, 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 benets of the least-satised 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 payo 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 sheries games was undertaken in Lindroos et al. (2007). The issue of stability of the cooperative solution soon emerged, with models compar- ing 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 o 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 shery, Kronbak (2004) determines that the sum of the players' threat points if op- erating as singletons is greater than the sum of the grand coalition's payo. 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 payos that each player would get if deviating from the grand coalition. Their model indicates that there can be a large enough increase in benets through the formation of the grand coalition to satisfy all players, (Kronbak and Lindroos, 2007), where all players are `satised' if their payo through cooperation is at least equal to their payo from free-riding (the individual rationality constraint). This approach, which incorporates the issues of exter- nalities 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 in
uence 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 eect 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 suer by not joining the bloc coalition. We can determine if externalities are present by observing whether a merger of coali- tions changes the payo to a player not involved in the merger (Kronbak and Lindroos, 2007). These externalities are considered positive if, upon the merger of coalitions, the payo to a player not involved in the merger increases (Yi, 2003). The term `free-rider' has been given to describe a player beneting from coalition formation but not involved 36 3.3. Major movement: Coalitions in the merger. The issue of these group externalities in sheries 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 bluen 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 benet 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 sh stocks management. Highly migratory stocks The application of game theory has proved useful in understanding some of the manage- ment issues concerning a specic 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 particu- larly 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 shing shared stocks, be they coastal states or DWFNs. Resolution of negotiations between dierent 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) specically states that, with very few exceptions, cooperation between the states targeting highly migratory sh stocks is essential for sustainable sheries manage- ment. 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 num- 4Note that RFMOs exist to manage numerous sh stocks, and were not created solely for tuna man- agement. 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 shing, 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 shing on the high seas, can be said to be engaging in unregulated shing. Currently, there is very little RFMO member countries can do to address this issue. Unfortunately, it seems cooperation is not likely if unregulated shing, and thus free-riding, is allowed to persist (Pintassilgo and Lindroos, 2008). The second issue arises from the fact that the RFMO is not justied in excluding any interested party from joining the organization (UN, 1995). As such, possible entrants may participate in unregulated shing (or no shing) 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 beneting from the stock rebuilding program without bearing any of the management costs (Munro, 2006). In order for RFMOs to be eective 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 sheries. 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 elu- cidating the benets of joint management, actually obtaining this cooperation is another question. There are two levels of cooperation, as identied by Gulland (1980). The pri- mary level is scientic cooperation, where players in the game communicate and share research information (Gulland, 1980). Even this rst level can be hard to achieve be- cause 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 informa- tion 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 eect, the formation of joint management arrangements, such as RFMOs. One of the possible underlying challenges in creating eective coopera- tive regimes, even at the primary level, is the lack of `property rights' bestowed on shing 38 3.4. Looking forward: Catch privileges and resilience nations. Without property rights, if one country agrees to actively cooperate in manage- ment, what guarantee do they have that they will, in fact, be the ones to benet from that cooperation? With so many vested interests in a straddling stock shery, unregulated shing and cheating are bound to occur. Unregulated shing can lead to an underestimate of catch and eort in the shery (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 eectively change unreg- ulated to illegal shing (Kaitala and Munro, 1997; Munro, 2008), thus allowing RFMO member countries to take action against such illegal shers. Perhaps game-theoretic mod- eling could be used to illustrate the dierences in optimal outcomes between `open access' and `privatized' sheries. 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 sev- eral tools that would bestow greater ownership to, and hence possibly greater likelihood of cooperation by, RFMO member countries. Munro (2007) points out, however, that devel- opment of state property rights in straddling stock sheries is far less straightforward than in transboundary sheries, but stresses that private shery access rights should enhance cooperative management. In Chapter 4, I examine the challenges of current allocation schemes in shared sheries, 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 sheries 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. Principal- agent 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 sheries in the literature. The rst two were analytical pieces by Clarke and Munro (1987, 1991) that analyze the optimal catch and eort tax scheme to be employed by coastal states on DWFNs. Jensen and Vestergaard (2002) analyze a tax on the eort 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 shing in Indonesia has been conducted by Bailey and Sumaila (2008b), where the authors use principal agent analysis to devise a 5This 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 shing. Given that these are the only analyses to date, there appears to be more scope for incorporation of principal-agent analyses into sheries 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 sheries 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 shing, 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 sheries. 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 aect 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 shing 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 shers by discouraging them to join the RFMO or to engage in unregulated shing, (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). Deter- ministic 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 aect the cooperative solution. However, practical evidence suggests that predicting these shocks is dicult, both in magnitude and direction (Munro, 2008). If, however, cooper- ation 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 benets to be received today, over benets to be received in the future. In conventional discounting, often the benet of a shery in 50 years is negligible to the decision-making of today. This means that we are essentially unable to predict how future changes could aect 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 aect sh, and thus shing, dis- tribution globally (Cheung et al., 2009). One of the major suggestions is that many sh populations will move away from the equator and toward the poles (Cheung et al., 2009), which would almost certainly result in losses of benets to tropical countries. Further- more, 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 sheries. A recent publication by Brandt and Kronbak (2010) analyzes how changes in climate could impact Baltic Sea sheries. The authors determine that if changes in climate result in decreases in future payos to the shery, 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 sh 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 Pacic salmon transboundary re- source, 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 benets 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 Pacic Salmon treaty was that it did not explicitly in- clude 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 sheries 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 sheries have been applied to single stocks. There are a few exceptions, for example, the predatory- prey 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 multi- species model that addresses bycatch and growth overshing in an eort 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 sheries coalitions has paralleled the developments in coalitional models to address the issue of climate change negotiations. Finus et al. (2008) discuss new devel- opments 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 member- ship (where sheries coalition models are generally developed as open membership games) (Finus et al., 2008) and transferability (broadly paralleled to side payments in sheries). 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 benecial 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 oer interesting new angles that should be addressed by sheries 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 sheries, that indeed, Munro's 1979 paper was in
uential in directing attention to how sheries can be modeled as strategic dynamic interaction between shing entities. The impetus for publishing the paper was the issue of extended jurisdiction and transboundary resources. These issues were tackled for sheries 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 bluen 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 shing 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 sheries game-theoretic analyses that have been developed to date. The application of game theory to sheries has provided insightful predictions about stability of cooperation in internationally shared sh 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 sh stocks. 43 Chapter 4 Present and future allocation approaches for shared tuna sheries 4.1 Introduction Shared sheries 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., overshing) 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 sheries was identied as early as the 1950s (Gordon , 1954), even though the idea was better-popularized by Hardin. Economists took up the challenge by analyzing the dierence between noncooperative and cooperative management of these shared sh stocks (see Chapter 3), concluding that cooperation could alleviate some of the problems of the overuse of common pool resources as it seeks to nd the optimum solution (Munro, 1979; Clark, 1980; Levhari and Mirman, 1980). In the case of sheries shared by several shing nations, a race to the sh 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 shing strategies. Canada and the United States, for example, formed a joint committee as early as 1923 to improve management of Pacic halibut. The United Nations Convention on the Law of the Sea (United Nations, 1982) admonished shing 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 shing eort" in internationally-shared sheries (UN, 1995). And, although the degree to which an allocation program is seen as equitable and eective can have a large impact on all other eectiveness 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 condent that they are better o through cooperation than through non- cooperation: known as the individual rationality constraint as described in Chapter 3. The allocation of catches (or other benets) can largely in
uence whether or not cooperation is rational. Issues surrounding the allocation of shared sheries resources are some of the most challenging in sheries 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 shing states sharing a resource would be encouraged to cooperate in management, including how eort or catches should be allo- cated. Most applied game-theoretic analyses, which usually focus on maximizing economic rent from the shared shery, have concluded that cooperative agreements between shing nations bring benets 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 coun- try not engaging in the cooperative agreement benets from the conservation measures of compliant countries. Such issues are usually present in sheries that involve a substantial catch from the high seas, in addition to EEZ catches, such as sheries for tuna species. Cooperation in such systems is inherently dicult 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 sh 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 remark- ably dicult. 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 objec- tives, be they catch limits or otherwise. This could be partly due to the lack of quantiable 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 bluen tuna The RFMO in charge of Atlantic bluen is the International Commission for the Con- servation of Atlantic Tuna (ICCAT). In the early 1970s, tuna shing nations in the At- lantic began to worry about overexploitation of Atlantic (northern) bluen 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 al- lowable 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 bluen delegations paved the way for further TAC allocation schemes to be developed for other North Atlantic species, such as swordsh and albacore tuna. For these latter schemes, instead of catches being explicitly allocated, management instead suggested to set the allowable shing 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 dicult, and leaves 46 4.2. Allocation by tuna RFMOs Figure 4.1: Map of tuna RFMOs (Lodge et al., 2007). c
 Chatham House, used with permission. room for various concessions and opportunities for ineective management (Cox, 2009). One of their more questionable allocation criteria is based on aspirations. For example, in 2002, ICCAT allocated 25 tonnes of bluen tuna to Mexico and various amounts of swordsh to Morocco, Mexico, Barbados, Venezuela and China, among others, because of the aspirations of these countries (MRAG, 2006; Cox, 2009). Unfortunately, such prac- tice resulted in the 2002 allocated TAC for bluen being signicantly higher than the scientically-recommended TAC (MRAG, 2006). ICCAT outlines the conditions for ap- plying 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 over- shing; 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 contract- ing 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 overshing (5). Interestingly, ICCAT does not assign area-specic TAC allocations, rather, allocation of a TAC to a party allows that party to sh 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 Pacic tuna The Western and Central Pacic Fisheries Commission (WCPFC) is the RFMO responsi- ble for tuna management in the western Pacic. The Commission was established under the Convention on the Conservation and Management of the Highly Migratory Fish Stocks of the Western and Central Pacic Ocean in 2000, in an eort to more eectively manage sh 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 man- agement, including issues of allocation. The WCPFC has a strong sub-coalition within its membership through the Nauru Group, made up of Pacic 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 in
uence the development and direction of the WCPFC (Munro et al., 2004). The WCPFC does not presently allocate specic tuna catches to member states, how- ever, 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 shing 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 sheries-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 eort 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 eort allocation program. VDS was adopted by the PNA under the Palau Arrangement for the Management of the Western Pacic Purse Seine Fishery (the Palau Arrangement), to regulate purse seine shing days in the waters of PNA countries. VDS came into eect in December 2007, and was implemented as a way to provide for eective management in the face of declining sh stocks, and in an attempt to improve economic returns by creating a limit on the number of shing days. Fishing days are allocated to all bilateral shing partners, and these days are monitored using Vessel Monitoring System (VMS) technology. Eort allocation is based on equal weighting of historical eort levels and the level of estimated biomass in dierent EEZs (MRAG, 2006). Work within the WCPFC is ongoing in an eort 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. Eort model: calculate allocated shares based on historical eort; 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, yellown 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 bluen tuna Southern bluen tuna is managed under the Commission for the Conservation of Southern Bluen Tuna (CCSBT), which came into force in 1994. Prior to the Commission, south- ern bluen 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 shery between Australia and Japan, targeting Southern bluen. 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 shery (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 bluen, despite concerns about the health of the stocks (Lodge et al., 2007). CCSBT originally inherited the allocation scheme that the three founding shing na- tions 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, shing nations can sh their allocated TAC throughout the convention area (Harwood, 1997). CCSBT is in the pro- cess of redening their national allocation approach, which currently allocates based on proportions of the TAC (CCSBT, 2011). Upon any increase in the calculated TAC, those 6http : ==www:ccsbt:org=site=originsoftheconvention:php 50 4.2. Allocation by tuna RFMOs countries who took voluntary decreases in allocation (New Zealand and Australia) will have the dierence in their TAC returned to them, providing a system with some type of incentive for voluntary conservation (CCSBT, 2011). Any decrease in TAC will re- sult 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 Pacic tuna Tuna and tuna-like species in the eastern Pacic have been managed through the Inter- American 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 sheries dierently. The purse seine shery is managed through capacity (eort) allocations using four main criteria (MRAG, 2006; IATTC, 2007): 1. Catch history of national 
eets (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 shery is managed through a catch limit program. The benet to al- locating catches instead of capacity is that IATTC found some 
eets were manipulating their vessel capacity and this resulted in capacity allocation being ineective (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 bluen, yellown, 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 
exibility 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 ap- proaches in response to IOTC Resoultion 10/01, requiring the adoption of a quota alloca- tion 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: Sey- chelles, 2012). Thus, the proposal suggests that, where catch records are not of good quality, socio-economic factors be incorporated (Indian Ocean Tuna Commission: Sey- chelles, 2012). The EU proposal is also rmly 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 (In- dian Ocean Tuna Commission: EU, 2012). Similarly, the third proposal, put forth by Japan, states that allocation should initially be based on historical catches, specically over the past 10 years (Indian Ocean Tuna Commission: Japan, 2012). These base alloca- tions are subsequently altered using dierent mathematical relationships, based on criteria such as if the Member has contributed nancially 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 dierent ways, that this singular criteria is not the most eective and equitable strategy. 4.3 The future of allocation schemes Table 4.1 summarizes the major tuna RFMOs and their various approaches to alloca- tion programs. The table also includes references to several non-tuna RFMOs. More detailed information about the allocation approaches of these specic 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 4.3. T h e fu tu re of a llo cation sch em es Table 4.1: Summary of RFMO allocation information RFMO Species Data for allocation What is allo- cated Penalties for non- compliance Transferability Ranking (theory, practice) NAFO (ICNAF) Groundsh Stock assessment and histori- cal catch Catch Yes Allowed 52,53 NEAFC Herring, mackerel, blue whit- ing Zonal attachment principle and historical catch Catch Yes Allowed 52,72 ICCAT Tuna species Stock assessment, historical catch, bycatch Catch and eort Yes No sale, ex- change ok 57,38 CCSBT Southern bluen Stock assessment and histori- cal catch Catch Yes None 44,0 IOTC Tuna species Gross registered tonnage (plus historical catch in future) Eort Yes None 58,78 IATTC Tuna and tuna-like species Vessel carrying capacity Catch and eort Yes None 60,33 WCPFC Tuna and tuna-like species Stock assessments and histor- ical catches, distribution, eco- nomic dependence No current regional allo- cation, but sub-regional eort pro- gram (VDS) Yes Currently be- ing discussed 74,67 PSC Pacic salmon Historical catch, bilateral ne- gotiations Percentage of TAC Unknown None 43,NA IPHC Pacic hal- ibut Stock abundance and distri- bution Catch Unknown None 52, 33 Sources: MRAG (2006); Cox (2009); Cullis-Suzuki and Pauly (2010)53 4.3. The future of allocation schemes Table 4.1 to relate the allocation schemes in place with one measure eective or ineective management. The rst 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 sh 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 participa- tion, with the added benet of catches being a relatively easily measured and quantied reference (Cox, 2009). The PNA countries (a WCPFC sub-coalition) employ an eort 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 sheries. It may be time to start reconsidering what is being allocated. Perhaps potential rent can be allocated, or some other benet. One way to do this would be to try to put dierent types of benets into equivalent units. This has been suggested several times with regards to the Pacic Salmon Commission, the RFMO put in place to manage Pacic salmon between Canada and the U.S.. Sockeye are the most valuable of the ve Pacic 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 depen- dence 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 7Sandy 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 shery 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 sheries and processing sectors. Currently most RFMOs produce some type of annual report that summarizes stock dynamics, catches, and sometimes eort, for the shery. Producing an annual report that includes social, environmental and economic assessments of RFMO-managed sheries, in addition to these biological reports, could help highlight the broader benets of reaching an optimal sharing agreement (Bjorndal, 2009). One of the rst papers in the literature to start theorizing about the future of allo- cation schemes suggested an objective framework where national allocations depend on multiple factors which are given dierent 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 distribu- tion, employment, etc.)? 2. How do we calculate/measure values for each factor for each interested party? 3. How do we weight the dierent factors? One of the drawbacks associated with solely using catch as a way of measuring 
eet performance and stock sustainability is that it explicitly ignores human drivers of shing behaviour and does nothing to illustrate tradeos in policy decisions (allocations) with community well-being. This is of course an argument that can be made across many forms of sheries management and is not at all exclusive to the challenges of internationally- shared stocks, but it is worth mentioning here. Importantly, the incorporation of short- term 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 oversh their allocation. If countries cooperate in dening their objectives in participating in the joint shery (above and beyond catch), that could help in developing some sort of tradeo matrix. What mix of targets is optimal? What costs and amount of risk are communities and governments willing take to promote economically viable sheries? 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 sheries, developed a model linking catches to processing and community livelihoods in Canadian maritime regions, taking into account 
eet dynamics of Spanish and Portuguese sheries (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 benets above and beyond catches. Figure 4.2: Grand Banks shery model schematic (Lane, 2008). c
 Journal of Northwest Atlantic Fisheries Science, with permission through Creative Commons Attribution-Non Commercial 2.5 Canada. Rationality, 
exibility and reviews In order for members to agree on a cooperative management solution, they must be better o 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 
exibility built into the cooperative agreement, often called resilience (Miller and Munro, 2004; Munro, 2008), is of paramount importance to ensure the ratio- nality 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 shery 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 o 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 aorded 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 le 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 sh distributions. If the allocation scheme is xed and based on sh distributions, such changes can aect the viability of national sheries 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 Pacic 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 benets 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 Pacic 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 unfore- seen shifts in abundance. This retrospective analysis helps to illustrate why resiliency and 
exibility in a cooperative agreement is important for stability. This is becoming of increasing importance as climate forecasts coupled with models of sh stock distributions suggests there could be major shifts in terms of future access to shared resources (Cheung et al., 2009). Eciency and transferability Economic eciency does not seem to play into allocation decisions for any tuna RFMO (Cox, 2009). This is probably because most eciency gains from allocation programs are seen to derive from some loss in equity (Pinkerton and Edwards, 2009).8 Ex-vessel prices, shing costs, and 
eet capacity are rarely mentioned in stock assessment reports describing allocation. One argument that has been put forth in the literature is the possibility for 8A tradeo between eciency and equity does not have to occur. A lack of dialogue between economists and non-economists about eciency and equity has bred continued confusion about this apparent tradeo. 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 eciency. 58 4.3. The future of allocation schemes auctioning quota or allocation shares (Copes and Charles, 2004) to increase eciency. This has not been taken seriously to date. Given that cooperation must bring benets 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 inecient from an economic perspective, however, as transferability allows for the most ecient vessels or nations to harvest sh (Gibbs, 2009). Eciency 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 sheries 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 eciency and benet 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 eciency 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 sheries. 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 signicant harm to another state; 3. States are to consult with, and provide timely notication to, other states about any possible adverse eects resulting from new policies or a change in policy. A novel approach to negotiations between states sharing watercourse, called the \Mu- tual 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 o 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 benets and costs of non-cooperative and cooperative management lies with each individual country (Sander- son, 2009). In this way, each country calculates and communicates what it is likely to gain through cooperation, but these perceived benets, or utility functions, need not be com- parable 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 benets of cooperation, but in the event that one party would end up being worse o than through non-cooperation, a renegotiation of the sharing rules takes place (Sanderson, 2009). In a more applied assess- ment 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 allo- cation 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). Eciency is achieved through market-based trading allowances. The implications for sheries would be as follows: one proportion of the TAC is allocated to nations as xed, with the remaining quota classied as 
exible, 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 manag- ing internationally-shared sheries 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 sheries stock, and national eco- nomic wealth (Palma, 2010). Incorporating these may oer alternative allocation possi- bilities that could increase the scope for cooperation in internationally-shared sh 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 wa- tercourses, oers some insights into the future of sheries 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 sheries has invariably been based on a political process (Lodge et al., 2007), something that has not served sustainabil- ity 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 o 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 benets from the shery 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 xed percentage of the annual sealskins (Barrett, 2003). The need for side payments to factor more heavily in cooperative sheries 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 prob- lems associated with shared resources is the idea that self-interest almost always trumps collective interest,9 he also explored brie
y 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 dierent objectives with dierent measurements in a joint utility function to improve the management of common pool re- sources. 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 eciency has not routinely been a component of international allocation schemes. Socio-economics have been largely ignored in allocation formulations in part because, although RFMOmembers are required to report some biological and catch statistics, there is no requirement to report statistics related to shing 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 in- centive structure to promote sustainable sheries. Most RFMOs, especially those tasked with managing highly migratory sh like tunas, face problems of illegal, unregulated and unreported shing (IUU), TAC overages, competing sector interests, and challenges as- sociated with multi-species and multi-gear sheries, such as juvenile bycatch. Perhaps a de-politicized incentive structure whereby allocations are aorded based on more than just catch histories and abundance estimates is required to address these problems and improve RFMO management of shared sheries resources. 9It 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 Pacic 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 (Kat- suwonus pelamis), yellown (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 dierent 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 mem- bers, 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 shers 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 sheries in the CT range from small-scale subsistence and artisanal shing to large-scale commercial operations. In 2010, about a third of the reported tuna catch in the WCPO was taken by the combined 
eets 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 sheries and those to the east (Vera and Hipolito, 2006; Ingles et al., 2008). The most recent stock assessment for yellown reports that the domestic sheries 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 Pacic Island Countries (PICs) face in obtaining adequate rents from their sheries, 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 sheries will ll 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, yellown and bigeye are the three main tuna species targeted in the Coral Tri- angle, 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 sh aggregat- ing 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 sheries 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 shed. The biological diversity of the CT, along with the shelter of the archipelagic region, make this area prime nursery habitat for juvenile yellown and bigeye. These small juve- niles are often captured as bycatch in the skipjack shery, due to their association with skipjack stocks around FADs, and subsequently sent to canneries. Juvenile sh 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, yellown 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 shed in the Coral Triangle, along with the gears used, markets supplied and status of the stocks. Species Age Gears Markets Stock sta- tus Skipjack Adult Purse seine, pole and line Canned, domestic Underexploited Yellown Juvenile Purse seine (by- catch) Canned, domestic Fully ex- ploited Adult Purse seine, han- dline, longline, pole and line Sashimi, steaks, loins Bigeye Juvenile Purse seine (by- catch) Canned, domestic Overshing occurring Adult Handline, long- line, pole and line Sashimi ers, as well as by domestic sheries in Pacic Island Countries. Juvenile bycatch reduces the possible catch to these other shing groups due to growth overshing (see Chapter 6). This results in a con
ict of interest between purse seine sheries in the CT, who would prefer to exploit juveniles now, with longline sheries outside the CT, who would benet from reduced juvenile bycatch (Bailey et al., In press; Sumaila and Bailey, 2011; Hanich, 2012). Stock assessments report that yellown are fully exploited (Langley et al., 2009b), and that there has been signicant depletion of yellown in the WCPO due to shing \by the domestic sheries of the Philippines and Indonesia and the combined purse seine shery" (Hampton, 2002c). Yellown mature at about one and a half to two years of age, however, juvenile yellown are encountered in commercial sheries 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 misidentied as yellown in its juvenile years, catch estimates are signicantly underesti- mated (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 overshing is occurring on the bigeye population (Harley et al., 2010) (Table 5.1). 65 5.2. Coral Triangle tuna 1950 1958 1966 1974 1982 1990 1998 2006 Year Ca tc h (t) 0 20 00 0 60 00 0 10 00 00 domestic longline pole/line purse seine other 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 Pacic Fisheries Commission (WCPFC), a regional sheries 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 shing 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 dierent RFMOs against best-practices criteria (Cullis-Suzuki and Pauly, 2010). As sustainability issues with regional bigeye and yellown sheries 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 Pacic 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 sheries, and contributes substantially to the scientic program of the WCPFC. Of the three countries highlighted in this Chapter, only Papua New Guinea 66 5.2. Coral Triangle tuna  WCPFC SPC FFA Philippines Indonesia PNG Figure 5.2: Map of the statistical area of the Western and Central Pacic Fisheries Com- mission ( c
 WCPFC, used with permission), shown by solid lines, and regional coverage 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 Pacic tuna stocks. The FFA works to help facilitate eective 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 scientic assessments is compromised. This then results in the WCPFC having diculty setting informed management recommendations, let alone having those recommendations followed. That being said, it is argued here that aliation 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 sheries 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 shery exports totalled US $2.1 billion, 12% of which were tuna and tuna products, mostly fresh or frozen (Ministry of Marine Aairs and Fisheries, 2007). About 44% of all Indonesian tuna exports go to Japan, and about 27% go to the USA (Ministry of Marine Aairs and Fisheries, 2007). The current availability of information regarding tuna shing and sheries in Indonesia falls short of the information available for neighbouring countries. Catch and eort 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 
eet is 1989. At the time of writing, Indonesia is not a full member of the WCPFC. Tuna sheries Indonesian shers employ a variety of gears to harvest tuna. A pole and line skipjack shery has existed in Indonesia since at least the 1940s (Ishida et al., 1994). A major expansion began in 1977, with catches of yellown 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 shing 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 specically target large tunas (yellown and bigeye) in Indonesia (Ingles et al., 2008). All gears apparently sh only within Indonesian waters, as catch statistics available from the WCPFC suggest that there were no distant water sheries after 1990 (Lawson, 2008b). However, some Indonesian handliners catch and ooad 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 sh 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 exemplies what is likely to happen when well-intended policies are not broadly considered. Small- and medium-sized purse seine 
eets catch tuna seasonally, often targeting other small pelagic species throughout the year. There is also a 
eet of large purse seine vessels (> 100 gross registered tonnes, GRT) that works in tandem with several catcher, carrier, ski and light boats to operate. This 
eet 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 
eet (Ishida et al., 1994). Recently, the longline shery in Indonesia has decreased in terms of its importance in the sheries sector, which can be attributed to a decline in the availability of bait sh, as well as increasing fuel costs (Ingles et al., 2008). Eort has shifted to smaller-scale shing gears, such as troll and tuna handline, which can provide high quality sh 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 sh. The government has, however, initiated plans to increase and improve the processing sector in an eort 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 
eets shing 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 sh 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 sheries are underexploited, and are thus increasing their joint-venture relationships with foreign 
eet owners (Anon., 2007). Management measures and challenges In 2004, the Indonesian government enacted Fisheries Act No. 31, resulting in the man- agement of tuna sheries being segmented into 9 Fisheries Management Areas (FMA), overseen by the Ministry of Marine Aairs 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 shing area, and are thus based on ecological boundaries, not political ones. Although this is relevant from a sheries point of view, it can make management dicult. Often times several provincial and regency governments must coop- erate 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 dicult 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 conser- vation eorts. 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 aects tuna sheries in other countries. Indonesia does not have eective 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 shers are caught shing 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) shing allow Indonesia to meet its international obligations for sheries 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 
eet for export-oriented markets (Ishida et al., 1994). The country currently uses subsidies to promote several dierent parts of their shing sector. For example, trollers in the Ambon region (FMA-V Banda Sea) have received free boats and motors to enter the shery (Ingles et al., 2008). The government also provides shers 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 shing than is currently protable. The MMAF has stated that the country will strive to be the world's biggest producer of sh, with the goal of increasing its sheries sector by 300% by 2012 The Jakarta Post (2009). Government-driven sheries 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 sheries management in Indonesia arises from the grouping of landed sh into categories useful for trade or for sale, not according to biology. For example, the category for landed `tuna' includes both bigeye and yellown tuna, and could also include southern bluen, albacore and long tail tuna (Ingles et al., 2008). Similarly, the `skipjack' category probably includes juvenile yellown 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 identication seems to vary within FMA, often due to local language dierences. 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 sheries 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 In- donesia in 2004 (Lawson, 2008b), however, based on MMAF data, sher 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 ocially 52,042 and 31,160 tonnes of yellown 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 gures for 2009 were 210,590 t of skipjack, 94,141 t of yellown and 11,568 t of bigeye (SPC, 2009). The WCPFC is apparently working with grossly underestimated catches, leading to management diculty 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 sheries 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 shing 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 yellown and bigeye by the purse seine 
eet, 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 dierences 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 shers, 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 sheries resources (Barut and Garvilles, 2005). Fisheries contribute about 4% to the country's Gross Domestic Product (GDP), with tuna sheries comprising about 20% of marine sheries production (Barut and Garvilles, 2005). Commercial tuna 72 5.4. Philippines Table 5.2: Summary of Indonesia's tuna sheries and management. Fisheries Purse seine, longline, handline, gillnet, pole and line, small seines, troll Processing Below industry standards, but economically im- portant Challenges Unregulated FADs, juvenile bycatch (making up 18-90% of purse seine catch by weight), under- reporting, directed subsidies for FADs, inconsis- tent data collection Management measures No size limits, no FADs plan, no unied data collection program, some closed areas sheries 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 `kat- suobushi'). As catches started to decrease in the Philippine EEZ, and as American and Japanese demand for tuna increased, eort moved into the waters of Indonesia, Papua New Guinea and the high seas (Barut and Garvilles, 2005). Philippine sheries now supply to both domestic and foreign markets. Capture sheries are divided into two main sectors: municipal and commercial. Tuna vessels are usually classied as commercial because shing occurs outside of municipal waters, using vessels larger than 3 GRT (Vera and Hipolito, 2006). Census data from 2002 estimated that the sheries sector employed almost 1.8 million municipal shers and about 8,000 commercial shers10 (Vera and Hipolito, 2006). Tuna sheries The Philippines domestic 
eets caught about 266,600 t in 2009 (SPC, 2009). Gillnets were used in Philippine tuna sheries until 1997, and today, purse seines, ringnet, longline and handlines are all used. Lower-value sh, like skipjack or smaller yellown, are generally consumed domestically, or sent to the canneries, whereas higher-value sh, such as adult yellown 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 sh in Indonesia, Palau, Papua New Guinea and the high seas (Vera and Hipolito, 2006). The only vessels allowed to sh in Philippine waters are those 
agged to the country. However, in 1995 10To avoid double counting, any sher engaging in both municipal and commercial shing was counted as only a municipal sher, and thus commercial sher numbers are most likely underestimated. 73 5.4. Philippines as much as 10,000 t of tuna, 40% of which was yellown, were caught by longline vessels illegally shing in Philippine waters (Barut and Garvilles, 2005) Purse seine The domestic and distant water purse seine 
eets target mostly skipjack and some adult yellown, but also catch juvenile yellown 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% yellown, and 10% bigeye11. In 1995, as much as 90% of purse seine catch from commercial shers 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 sh 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 
eets in these waters has adversely aected 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 
eet 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 prot sharing. The boat owner nances the boat, while the master sher hires the crew. Fishers continue shing 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 shers 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 sh 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; 11Glennville 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 sh 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. Protability 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 shers are the primary Philippine producers of high-grade sashimi sh. They target adult skipjack, yellown and bigeye, as well as other species. There are two clas- sications of handlines: the palaran vessel, which is conned to municipal waters, and the pamariles, which can venture into deep Philippine and international waters (Vera and Hipolito, 2006), shing 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 shers, are active in the Philippines (Vera and Hipolito, 2006). Municipal handline shers are opportunistic, in that they catch a large variety of species, depending on what is abundant at the time of shing. On average, a palaran sher catches about four tuna per week (Vera and Hipolito, 2006). The quality of the sh is of primary importance, and as such, industry and government began discussing a possible subsidy that would help handline shers 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 yellown tuna as a more sustainable food choice for consumers12. Pamariles shers target only tuna. A mother-boat will carry auxiliary vessels and head out to sh on anchored FADs, known as payaos. Handline-caught tuna, although often shed with FADs, is usually adult-sized therefore the problems of juvenile bycatch 12http://wwf.panda.org/?199811/Small-scale-shers-in-the-Coral-Triangle-get-big-break-in-global- market 75 5.4. Philippines associated with shing on FADs are less relevant in the pamariles shery. Most FADs in Philippine waters are owned by purse seiners, but handliners are allowed to sh on these FADs given that the purse seine 
eet has shing priority. Furthermore, allowing handliners to sh 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 shing trip (Vera and Hipolito, 2006). Prot sharing is employed, with shers getting a percentage of the value of their catch, which amounts to about US $95 - $150 on average per month for a pamariles sher (Vera and Hipolito, 2006). Longline The Philippine distant water longline 
eet targets adult yellown 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 
eets and processing plants. Far East Seafood, Inc., shared information about the structure of their longline oper- ations. The following are their generalities:  Trips last about 20 days, with vessels shing about 200 miles from the shore;  Average vessel catches about 12-15 tonnes of tuna per trip, the majority of which is yellown;  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 sh (com- manding about US$7.50/kg). 76 5.4. Philippines Processing The catch value, itself substantial, is only part of the economic benet that tuna sheries 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 Cota- bato, 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 shers, this port is closer for them, based on where they sh, and therefore is a more economical landing site. Compared to Indonesia, the Philippine cannery sector is also more economically ecient. 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 sh, 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 yellown catch is smoked, dried, salted, or processed into sausages and ham (Barut and Garvilles, 2005). Larger yellown are often sold as fresh or frozen loins, or exported as lower-grade sashimi. Management measures and challenges Two national laws provide the sheries policy framework in the Philippines: the Fish- eries 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 sheries resources, which include measures to control commercial shing in 13Benjamin Tobias, BFAR, personal communication. 14Miguel Lamberte, FDA, personal communication. 77 5.4. Philippines municipal waters, managing sheries with regard to maximum sustainable yield (MSY), implementation of user fees, gear regulations, such as limiting the use of active shing gears in municipal waters, and policies toward decentralization of sheries management (Vera and Hipolito, 2006). Interestingly, the 1997 Act is focused on modernizing the sh- eries sector, and thus sometimes promotes development-based measures that are in direct con
ict with the more conservation-based measures promoted by the Fisheries Code of 1998 (Vera and Hipolito, 2006). There are several dierent organizations overseeing tuna management in the Philip- pines. The Bureau of Fisheries and Aquatic Resources (BFAR; www.bfar.gov.ph) is the highest federal entity in charge of sheries management. BFAR tuna management func- tions include: monitoring and review of international shing agreements; authorization of Philippine vessels shing in international waters; regulation of transhipped products; and enforcement of sheries laws and rules, except in municipal waters. Licenses are required to sh and are good for three years. The annual revenue from all sheries licenses is quite low, about 1-3 million Pesos (US $6,000 - $18,000) in 2006 and 2007 15. Licensing is given locally for shing in municipal waters, or federally for shing 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 sheries 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 sh and its scientists are responsible for conducting stock assessments for domes- tic sheries. 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 Philip- pines has its own system for management of domestic tuna sheries, 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 shing 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 15Augusto Natividad, BFAR, personal communication. 16Benjamin Tobias, BFAR, personal communication. 17Benjamin Tobias, BFAR, personal communication. 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 inter- views shers 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 sh. Histamine is a byproduct of bacterial action and can build up in the muscle tissue of sh if it is not kept at near-frozen temper- atures. 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 repre- sentatives serve as liaisons in an eort 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 sheries (Table 5.3), however, many vessels still use 1 inch meshes for three reasons. Firstly, many shers 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 shers there can no longer use their 1 inch meshes. And secondly, for Philippine companies who can aord 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 sh as a main source of protein, but most residents can only aord cheaper sh, 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 sh rulers to people frequenting sh markets to discourage them from buying juvenile sh. 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 yellown and bigeye, however, sh of this size are still juvenile. A proposed \net amnesty" program would allow shers 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 shers, but currently domestic shers pay the full cost of about $1/litre. Commercial distant water 
eets (shing 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 ramications. Firstly, shing eort 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, shers who were able to sh, used their gear closer to shore where more juvenile sh are found. The removal of fuel subsidies therefore contributed to an increase in the by-catch of juvenile sh. Any policy reform is likely to alter sher 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 sh, for example, is a form of subsidy. Juvenile catch The catching of juvenile yellown 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 sh, for example, bigeye and yellown 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 Trian- gle area, those caught in purse seines are larger, about 50+ cm in length, but still juvenile. This makes it dicult to enact sweeping management recommendations regarding juve- nile by-catch by the WCPFC, because the catch varies so much between countries, and management measures would adversely aect 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 yellown and bigeye captured by purse seines in Philippine archipelagic waters were juveniles (Ingles and Pet- Soede, 2010). In 2009, this resulted in a total of over 61,000 t of juvenile sh, of all three species combined, being removed from the ecosystem (Ingles et al., 2008). The use of 18Bayani 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 shing, and thus can lead to both overshing and an overcapitalized shery. 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. Eective enforcement of such a limit is obviously a substantial subsequent issue, however, making shers 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 sheries and management. Fisheries Purse seine, longline, handline, ringnet Processing Very important economically, undergoing improve- ments to secure EU accessibility, more ecient than Indonesia Challenges Unregulated FADs, juvenile bycatch (averaging 15-50 cm in length), subsidies, ineective controls Management measures Mesh size limits (3.5 inch, but ineective), no FADs plan, juvenile catch limits (10% by weight) 5.5 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 sheries in PNG include tuna, prawns, sea cucumber (or bêche-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 specically to facilitate multi-lateral cooperation in regional purse seining. In February of 2010, they undertook measures to have skipjack tuna eco-certied as sustainable by the Marine Stewardship Council (MSC). Their request species that only tuna caught by purse seines setting on free schools (that is, without the use of FADs or any 
oating object) in PNA country EEZs should be considered for certication (Marine Stewardship Council, 2010). After going through the MSC appeals process, the shery was ocially declared MSC-certied in December, 2011. . 19Benjamin Tobias, BFAR, personal communication. 81 5.5. Papua New Guinea Tuna sheries The tuna sheries of PNG are the shing 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 
eets, as well as a locally-based foreign purse seine 
eet, and a foreign access purse seine 
eet. Of 194 licensed vessels in 2008, 9 were PNG-
agged, 30 were locally-based foreign vessels, and the other 155 were foreign access distant water shing vessels. Over 80% of the landed catch is skipjack, with about 20% being yellown and less than 1% bigeye. Figure 5.3 shows the catch trends for Papua New Guinea's sheries 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. 1970 1980 1990 2000 0 60 12 0 18 0 Skipjack catch Year Ca tc h (10 00  t) PS PL 1974 1986 1998 0 20 00 0 40 00 0 Yellowfin catch Year Ca tc h (t) PSPL LL 1974 1986 1998 0 25 00 50 00 Bigeye catch Year Ca tc h (t) PS LL 1970 1980 1990 2000 0 50 10 0 20 0 Total catches (all gears) Year Ca tc h (10 00  t) Total SkJ BE YF 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 ocial 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 sheries sector is governed and regulated by two federal initiatives: the Fisheries Management Act of 1998 and the Fisheries Management Regulation of 2000. These initia- tives specically mandate that PNG sheries resources be managed in a sustainable and equitable way for current and future generations. Under the 1998 Act, the National Fish- eries Authority (NFA) is responsible for the management and development of the sheries sector, under the overall policy direction from the Minister for Fisheries. Tuna sheries 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 envi- ronment (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 sh- eries management. The national program is founded on the basic principle that as the national shing 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 
eet was fully domesticated in 1995, giving the government better manage- ment control over that sector. The NTMP has included control measures such as number of licenses; setting of the total allowable catch (TAC); control of shing eort (i.e., number of boats/day, shing days); season closures; species length/weight limits; gear type lim- its; and delineated shing 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 
eets; 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 shing. 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 initia- tives 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. Partici- pation in, and compliance with, regional agreements has greatly facilitated eective tuna management in PNG. Over the past decade, Papua New Guinea has seen improvements in its catch and eort 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 shing. PNG's management measures are summarized in Table 5.4 Pacic Marine Industrial Zone PNG is considering the development of a Pacic 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 shing grounds, thus making it easier for shing companies to ooad their catch at a competitive cost. PNG is also hoping the Zone will increase the level of shing 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 sh 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 shers 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 Pacic Purse Seine Fishery (the Palau Arrangement), to regulate purse seine shing days in the waters of PNA countries. VDS came into eect in December 2007, and was implemented as a way to provide for eective management in the face of declining sh stocks, and in an attempt to improve economic returns by creating a limit on the number of shing days. PNG allocates shing days to all bilateral shing 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 utiliza- tion of allocated shing 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 sher 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 rst 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 shing 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 certication 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 sheries in the Coral Triangle provide food and income security to Indonesia, the Philippines and Papau New Guinea. These sheries also substantially contribute to the world supply of tuna. As described above, both Indonesia and the Philippines face chal- lenges 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 sheries and management. Fisheries Purse seine (FADs-free shery MSC-certied), longline, pole and line Processing Important, plans to expand, opportunities for PICs to use facilities, designated Seafood Com- petent Authority Challenges Some juvenile bycatch, subsidies Management measures 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 ma- jor 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 yellown 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 coun- tries, 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 Pacic Island community. In paying membership dues to the WCPFC, the Philippines receives more in nancial assistance than they put in. Data collection and handling in Indonesia is unacceptable for such a major player in regional tuna sheries. If nancial 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 nancial 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 sheries 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 eectiveness of this in the Philippines has yet to be seen. Weakly enforced mesh limits, if any, and ineective size controls, result in juvenile yellown 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 manage- ment 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 Indonesia Philippines Papua New Guinea Regional memberships None WCPFC WCPFC, SPC, FAA Size of EEZ (million km2) 3.61 2.27 2.40 Skipjack catch (1,000 t) 211 179 169 Yellown catch (1,000 t) 94.1 81.5 45.6 Bigeye catch (1,000 t) 11.6 6.3 6.6 Percentage of total WCPFC catch 13.6 11.4 9.5 Management measures Catch limits A A A Eort limits A A P FADs plan A A P Closures A A P Mesh size limits A P P Length limits A A P Harmful subsidies (million USD) 790 610 427 Harmful subsidies (% of Landed value) 40 32 28 shery. 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 overshing whereby sh 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 sh are more valuable at port. The current recommendations do nothing to counter this, and set up a system that continues to rob tuna-shing 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 eort 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 overshing of yellown and bigeye stocks. The WCPFC has recommended a 30% decrease in shing mortality on bigeye tuna (from 2001-2004 levels), and limiting the shing mortality on yellown to its 2001-2004 level (Hampton and Harley, 2009). However, decreases in shing 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 shing with FADs, purse seining in general was found to have a lower carbon footprint than other forms of tuna shing (Tyedmers and Parker, 2012), and thus there may be increasing pressure to continue shing 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' - sh enhancing devices. These would be safe havens for the sh. 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 eort quota system, that is expected to eliminate some of the competitive nature of shared sheries. The entire Philippine industry expects that they will soon have to participate in this scheme (Barut and Garvilles, 2005). Philippine distant water 
eets operating in the waters of Papua New Guinea are already required to participate. Estimates of shing eort 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 shing days are being utilized. Limiting eort 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 re- gion. 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 beneting from the shery. Given that purse seine shers are experiencing increased prots 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 sheries, although it is not known at this 88 5.6. Regional options time, what proportion of those subsidies goes directly to tuna sheries. Tackling the subsidy problem could be a very good rst economic step to promoting more sustainable sheries (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 oshore shers, then secondary measures need to be in place to ensure that the 
eet does not start shing 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-certication 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 Pacic 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 Pacic, a group of highly migra- tory species, we rst need an understanding of life history parameters, distribution and migratory patterns, and the ecological relationship between tuna and other organisms ag- gregating around FADs. Research is currently being conducted to meet these needs. That being said, there are some simple rst 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 sheries 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 eort limits (including VDS) may not be completely adequate to correct the biological and economic problems that mis-managed sheries can create (Joseph et al., 2010), these measures are simple rst steps that In- donesia and the Philippines, who have valuable sheries, 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 sheries potential for other nations in the region. If these sheries 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 benets sharing system, a `tuna trust fund' of sorts (Bailey and Sumaila, 2008a), so that all sheries could share in the possible economic gains from decreasing the bycatch of juvenile sh (see Chapter 6 for a general discussion). This possibility of cooperation has been theorized (Kaitala and Munro, 1993, 1997), quantied (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 eective tuna management in region. 90 Chapter 6 Can cooperative management of tuna sheries in the western Pacic solve the growth overshing problem? 6.1 Introduction The western and central Pacic Ocean (WCPO) is home to many species of commercially targeted sh, the most protable 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), yellown (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 
oating objects known as sh aggregating devices (FADs). This association leads to the bycatch of juvenile yellown and bigeye tuna in the purse seine shery primarily targeting skipjack and adult yellown. The term bycatch has been dened several dierent 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 shery. The catching of juvenile sh of a target species can lead to growth overshing, 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 sheries 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 yellown 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 Pacic Island States, and in the high seas (i.e., tuna sheries in this area catch larger sh). It has been shown through tagging studies that there is a high degree of interaction between tuna sheries in the western part of the WCPO with sheries 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 shery has declined in recent years, with the majority of harvested sh 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 shery (Williams and Reid, 2007). Growth overshing of bigeye, and probably yellown, is occurring, and stock depletion of these species has been linked, in part, to juvenile bycatch. Other types of shing mortality are also thought to contribute to depletion. If left in the ocean, those yellown 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 shers, whose catch commands a much higher price than that paid for juvenile sh. There is thus a con
ict of interest between purse seine shers in the Coral Triangle and longline and handline shers targeting adult yellown and bigeye. It is important to ask then, could cooperative management of tuna sheries in this region reduce the economic losses due to growth overshing? 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 sheries in the WCPO resulting from three alternative management scenar- ios: (1) the status quo, (2) a regulated FAD plan, and (3) the total elimination of FAD shing 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 shing 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 shing, our third scenario examines the equilibrium solution to the game where there is no shing on FADs, and thus we assume no juvenile bycatch. We are interested in how the nal outcomes could create the necessary incentives to encourage change. 20The 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 sheries Various gears are used to sh 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 shery in the region, but it accounts for only 3%, 6% and 3% of bigeye, skipjack and yellown catch, respectively (SPC, 2009). As such, in this paper we are concerned with the three main commercial sheries. Table 6.1 reviews the stock status and main sheries for each of the three species of interest in this study. Purse seine The purse seine shery developed rapidly in the 1970s and 1980s. This was due to improved technology, as well as expanded foreign 
eets from Korea, Japan and Taiwan. Furthermore, declining market demand for tuna caught in the eastern Pacic Ocean, where dolphin bycatch can be high, along with changing access due to extended jurisdiction, resulted in 
eets moving to the western Pacic. Purse seine vessels from both domestic and distant water 
eets target both skipjack and adult yellown, and they sh with or without FADs. The term FAD is a catch-all word ranging from simple 
oating objects, such as a log or a coconut, to high-tech devices capable of transmitting sonar information via satellite. Recent research suggests that the shery is moving in that direction - increasing their capacity through increased technological innovation (Guillotreau et al., 2011). Tuna and other pelagic sh naturally aggregate around 
oating objects in the open ocean and the use of FADs greatly increases eciency of purse seine shing. Smaller pelagic feed sh gather at the FAD (or are released), which attracts skipjack schools, as well as juvenile yellown and bigeye. FADs reduce the fuel costs of shing, 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 xed 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 shery (Williams and Terawasi, 2009). About 220 of these were distant water vessels from Japan, Korea, Chinese-Taipei, the US, and from the domestic sheries of the Pacic Island Countries, while over 1,000 vessels reportedly shed from the Japanese coastal shery, and from Indonesia and the Philippines (Williams and Terawasi, 2009). In 2008, an overall eort 21Dexter Teng, TSP Industries, personal communication. 93 6.1. Introduction of about 58,000 shing days was reported (Williams and Terawasi, 2009), but this is aggregating all days searching and shing 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 
eet operating in its waters (WCPFC, 2009). A study of FAD use by the Korean purse seine 
eet reported fork lengths for bigeye and yellown 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 
eet shes in deep water, targeting both adult yellown 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 shery in 2007 (Lawson, 2008b), however, countries report this dierently, so there is uncertainty in this estimate. These vessels represent two categories of the 
eet. The rst 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 yellown catch in the 1970s and early 1980s, to a majority bigeye catch in recent years (Williams and Reid, 2007) Longline- caught yellown tuna command ex-vessel prices between about $5,000-$7,000 (Williams and Reid, 2007). Handline Handlining 
eets vary in scale from very small vessels, able to sh only in municipal waters, to large operations that include a mother-boat carrying auxiliary vessels that heads out to sh on anchored FADs in deeper waters. Handliners in Indonesia and the Philippines often sh on FADs owned by purse seine companies. Handliners are allowed to sh on 22Benjamin 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 sh 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 
eet is especially poor, with catches often being lumped under \other". Table 6.1: Summary of sheries and markets for WCPO tuna species used in the model. Species Stock sta- tus Target sheries Total 2009 catch* Markets Skipjack Sustainable Purse seine, artisanal 1,783,986 Cannery, some do- mestic Yellown Fully- exploited Purse seine, longline, pole and line, artisanal 433,275 Cannery, sashimi, fresh/frozen loin Bigeye Overshing occurring Longline, pole and line, artisanal 118,023 Sashimi *Source: SPC (2009). Skipjack The skipjack stock in the western and central Pacic 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 shed 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 shing 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 23J. 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 sh 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). Yellown The WCPO yellown 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 exploita- tion could negatively impact the stock (Langley et al., 2009b) (Table 6.1). Yellown in the western and Central Pacic 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). Yellown is targeted by purse seines and longlines, and in addition to adult sh being caught, there is also a large amount of bycatch of juvenile yellown in the skipjack purse seine shery, where juveniles are found associating with skipjack schools around FADs. Although large yellown receive a price premium at the cannery, recent research from Indian Ocean tuna sheries suggests that this economic incentive does not really in
uence sher behaviour to avoid juvenile catch (Guillotreau et al., 2011). Yellown biomass declined in the 1990s, primarily due to lower average recruitment in those years, as well as high shing mortality (Hampton, 2002c). The estimated juvenile shing 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 signicant depletion in some areas of the WCPO due to shing \by the domestic sheries of the Philippines and Indonesia and the combined purse seine shery". Yellown 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 yellown are encountered in commercial sheries 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 yellown tuna, whereas longliners tend to take mostly adult sh (Langley et al., 2007). The longline yellown catch in 2009 was estimated at about 69,000 t, while purse seine catch was about 264,000 t (SPC, 2009). The longline-caught yellown shery 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 
ow over a wide area (Harley et al., 2010). Overshing is occurring on the stock, (Langley et al., 2009a), meaning that more sh 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 shing began), and has declined an additional 20% in the last decade (Langley et al., 2009a). A reduction in longline shing 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 shing states (DWFS) as well as Pacic 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 signicantly underestimated (Lawson, 2008a) as bigeye is often mistakenly classied as yellown 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 misidentication (Williams and Reid, 2007). However, data were not available for the domestic 
eets 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 Pacic 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 Pacic 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 sheries in the WCPO are managed by the Western and Central Pacic Fisheries Commission (WCPFC), which is the regional sheries 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 
eets shing in the Pacic), 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 sh 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 Pacic Ocean in 2000, is currently faced with the challenge of managing declining tuna stocks in the area, namely, yellown and bigeye. Reduction in juvenile and adult shing mortalities on these stocks would likely result in decreased economic benets to both purse seine and longline sheries, at least in the short-term, especially those operating in the Coral Triangle countries, where it appears that the smallest bigeye and yellown are caught. It is estimated that over 150 million people live in the Coral Triangle, and that about 2.25 million shers depend on marine resources for their livelihood (The Nature Conservancy, 2004). It is therefore important to create sustainable sheries management regimes in an eort to provide the population with continued benets from regional sheries, which include the valuable tuna sheries. The issue of juvenile mortality in the WCPO was explored by Bertignac et al. (2000), who concluded that shifting the sheries from younger to older sh would improve e- ciency. This work, however, notes its limitations in modeling bigeye bycatch in the purse seine shery due to data deciencies (Bertignac et al., 2000). Their study estimated that a reduction in eort 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 nding, eort 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 eort is required to maximize the combined longline and purse seine prot because of the high level of juvenile bycatch. A more re- cent bioeconomic modeling paper found similar results: a major reduction in purse seine shing eort is needed to fully realize economic benets in the region (Campbell et al., 2010). Here, we tackle the issue specically from a FADs management perspective through a game-theoretic model, asking whether or not management of FADs shing, through a decrease in juvenile bycatch, could yield higher joint benets in the region. 98 6.1. Introduction Some preliminaries on `sheries 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 sheries management, as many of the world's sheries 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 aected 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 par- ties cannot communicate (Nash, 1951). Game theory has been applied to sheries for over 30 years (Munro, 1979; Bailey et al., 2010). Much attention has been paid to analyzing the management of transboundary and high seas sheries 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 sheries 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 sheries 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 bluen 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 aected optimal exploitation. All studies concluded that the sheries were currently over- capitalized, and that economic benets could be increased through cooperation. However, some authors also went on to nd that cooperation is not a stable outcome, and that play- ers in the tuna sheries 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 soli- tary 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 re- spective industry organizations. The game is partitioned by gear type because dynamic externality exists at the gear level: in these sheries all three gear types catch yellown and bigeye tuna. Players are assumed to be individually rational, that is, they want to maximize their equilibrium prot, and will choose the strategy that does this. Further- more, a player will only agree to cooperate if the payo they receive through cooperation is at least equal to the payo they would expect from non-cooperation. Players are asym- metric in several ways. The costs of shing dier, as do the prices the players command for their products. The gears impart dierent shing mortalities on the stocks, through diering selectivity. Side payments The term side payments has been used in sheries economics to describe the transfer of benets from one player to another. They are a type of cooperation facilitator (see Chapter 3), in that they would allow a player who benets from cooperation to transfer some of their payo 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 payo through cooperation is at least what they would receive by not cooperating. If the cooperative payo is lower than the non-cooperative payo, 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 tradeos in shing eort and economic benets between purse seine, longline and handline shers. Given WCPFC recommendations for regional nations to adopt a FAD management plan (WCPFC, 2009), we are interested in knowing the optimal shing eort each player (gear) will choose in order to maximize individual and joint net benets from the resource under dierent management options: status quo, reduced FADs and no FADs. We model the status quo as a non-cooperative outcome, whereby each gear chooses their shing eort 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 payo, that is the sum of payos 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 eects. Recruitment of the three sh stocks is assumed to be of the Beverton and Holt (Bev- erton and Holt, 1957) form, (Langley et al., 2007, 2009a; Langley and Hampton, 2008). Lengths and weights are assumed to follow von Bertalany growth, although it has been suggested that growth of yellown and bigeye may divert from this pattern for part of their life histories24 (Langley et al., 2009b; Harley et al., 2010). Age-specic survivorship is a function of age-specic natural and shing mortality, where natural mortality decreases with increases in length (see Lorenzen, 1996, for more details). Selectivity-at-age is as- sumed to be dome-shaped for the purse seine shery, and asymptotic for the longline and handline sheries, and is based on the age at which 50% of the population is fully vulner- able 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-specic. The reader is referred to Table 6.2 for a review of the variable denitions 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, yellown 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: lia = L i 1(1 eK iai) (6.1) wia = (al i a) b (6.2) mia =M i  Li1 lia  (6.3) f ia = w i a  wim; f ia  0 (6.4) 24This deviation would not have a signicant 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 denitions g gear type i sh species la length at age L1 mean asymptotic length wa weight at age wm weight at maturity va vulnerability at age lh length at 50% vulnerability sd standard deviation in vulnerability ma mortality at age za total mortality (natural plus shing) fa fecundity at age lxa unshed survivorship at age lza shed survivorship at age R recruits a, b recruitment parameters K von Bertalany metabolic coecient  Goodyear compensation ratio V B per recruit vulnerable biomass B per recruit biomass e per recruit egg production (unshed) h per recruit egg production (shed) heq per recruit yield q catchability coecient y total yield p ex-vessel price c unit cost of eort TR total revenue TC total cost F shing eort  prot 102 6.2. Model where Li1 andW i1 are the mean asymptotic lengths and weights, respectively, for each species, i, and ki is the von Bertalany metabolic coecient. Fecundity is the dierence between the weight at age and the weight at maturity, wimat, and is assumed to be 0 if wa < wm. Survivorship to age in an unshed population, lxia, is the probability of an individual sh surviving to age a given natural mortality at age: lxia = lx i a1e mia1 ; given lx0 = 1; 0 < a  A lxiA = lx i A=(1 em i a); a = A (6.5) We next calculate the equilibrium eggs per recruit in the unshed population ie: ie = AX a=0 lxiaf i a (6.6) 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 yellown and bigeye individuals being more vulnerable to the gear than older individuals. Longlines and hand- lines are assumed to exert asymptotic selectivity, where sh 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): vi;ga =  1 1 + esd 1 1 (l i;g a lhi;g1 )   1 1 + esd 1 2 (l i;g a lhi;f2 )  ; g = 1 (6.7) vi;ga =  1 1 + esd 1 1 (l i;g a âi;g)  ; g = 2; 3 (6.8) Here, lhi;g1 and lh i;g 2 dene the length at which sh are 50% vulnerable to the shery, 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, zia, in the shed population is then calculated as the sum of natural mortality at age, mia and the sum of the gear-specic mortalities imparted by the three sheries: zia = m i a + X g vi;ga F g (6.9) 103 6.2. Model 0 1 2 3 4 5 0. 0 0. 4 0. 8 skipjack Age Vu ln er a bi lity purse seine longline handline 0 1 2 3 4 5 6 0. 0 0. 4 0. 8 yellowfin Age Vu ln er a bi lity purse seine longline handline 0 1 2 3 4 5 6 7 0. 0 0. 4 0. 8 bigeye Age Vu ln er a bi lity purse seine longline handline Figure 6.1: Status quo vulnerability to gears at age for three tuna species. where F i;g is the shing mortality, which is the product of the gear- and species-specic catchabilities, qi;g, and the shing eort, fg. Survivorship to age, lzia, in a shed population is calculated in a similar manner to the unshed survivorship, only that it is a function of the total mortality, not just natural mortality: lzia = lz i a1e zia1 ; given lz0 = 1; 0 < a  A lziA = lz i A=(1 ez i a); given a = A (6.10) Equilibrium incidence functions are then calculated for each species in the shed popu- lations, including, eggs per recruit, if , per recruit gear-specic yield for one unit of shing eort, i;gV B, recruits, R i e, spawning biomass, B i e, and gear-specic yield, Y i e : 104 6.2. Model if = AX a lziaf i a (6.11) i;gV B = AX a qi;gvi;ga lziaw i a(1 ez i a) zia (6.12) Rie = R i o i   ioe if ! i  1 ; R i e  0 (6.13) Bie = R i elz f a AX a wia (6.14) where i is the Goodyear compensation ratio for a given sh stock25. The unshed recruits parameter, Rio, is used here as a global scalar. Finally, the equilibrium yield of species i for a specic gear g is given by26: Y i;ge = R i e i;g V BF g (6.15) Economics Total revenue for a given gear type is calculated as the sum of the product of the equilib- rium yield and the ex-vessel price for each species targeted by the gear. Costs are expressed on a per unit eort basis. For the purse seine and handline 
eets, one unit of eort is a shing day. For the longline 
eet, one unit of eort is dened as 1 hook. Total cost is therefore the product of the unit cost and the equilibrium eort. Equilibrium resource rent for a given gear type is simply the dierence 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: TRg = X i Y i;ge p i;g (6.16) where pi;g is the ex-vessel price of sh species i caught by gear type g, and Y i;ge is the yield. 25The Goodyear compensation ratio is calculated from reported steepness estimates in the stock assess- ments, using a conversion equation derived in Appendix B of Martell et al. (2008). 26Here, our catch equation assumes constant return in catch to changes in shing mortality. 105 6.2. Model The total cost of a given shing gear is the product of the unit cost of shing, cg and the shing eort,fg: TCg = cgfg (6.17) Total rent to the gear is the dierence between the total revenue and cost: g = TRg  TCg (6.18) We assume that in the non-cooperative game, each player (gear) is trying to maxi- mize this rent without explicitly taking into account implications of their actions on the potential benets of the other players: maxg; 8g (6.19) From a modeling perspective, we assume that each individual player calculates the optimal eort they should employ to maximize this rent. This is done by calculating the entire space of all possible rent estimates at all possible eort 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 overshing of juvenile sh. The competition between 2 players (purse seine and longline) is shown in Figure 6.2. It is clear that major reductions in potential longline prots result at increasing levels of purse seine eort. For the cooperative game, we assume that players seek to maximize the overall, or joint prot: max = X g g (6.20) Here, we assume that each player takes into account the actions of the other players, and chooses the eort 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 prots to the longline 
eet at varying levels of relative purse seine eort (x axis). 1.0 refers to the status quo, 0.5 refers to 50% of the status quo eort, and 1.5 refers to 150% of the status quo eort. Varying levels of longline eort are represented by the coloured lines. 107 6.2. Model in ex-vessel prices for tuna. Estimates for costs of shing were taken from Reid et al. (2003), where shing costs are meant to exclude costs representing a division of prot (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 dierent 
eets (for example, both domestic and foreign purse seine 
eets). Due to these data uncertainties, although the direction of simulation outcomes would most likely not change as a result of price 
uctuations and disaggregation of costs, the magnitude may dier. 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. 27I 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 6 .2. M o d el Table 6.3: Biological and shing parameter inputs for skipjack tuna. Biological Value Source L1 Mean asymptotic length (cm) 106 Molony (2008) (average) Lm Length at maturity (cm) 43 Langley et al. (2005) Wm Weight at maturity (kg) 1.56 Langley et al. (2005) a Length-weight relationship 8.6388E-06 Langley and Hampton (2008) b Length-weight relationship 3.2174 Langley and Hampton (2008) K Growth coecient 0.3105 Molony (2008) (average)  Recruitment compentation 36 Calculated from Langley and Hampton (2008) M Adult mortality (per year) 2 Molony (2008) Fishing Value Source qg (PS, LL, HL) Catchabilities 3.35e-06, 0, 0 Derived from Williams and Reid (2007); Lawson (2008b) lh1 (PS) Start length of capture (cm) 20 Molony (2008) lh2 (PS) End length of capture (cm) 80 Molony (2008) sd1; sd2 Standard deviation on length of cap- ture 5, 1 c (PS) Unit cost of eort (per day) (USD) 22,000 Reid et al. (2003) p (PS) Ex-vessel price (USD/t) 1,500 Williams and Reid (2007) PS = purse seine, LL = longline, HL = handline. 10 9 6.2. Model 0 1 2 3 4 5 6 0. 0 0. 4 0. 8 yellowfin Age Vu ln er a bi lity status quo less FADs no FADs 0 1 2 3 4 5 6 7 0. 0 0. 4 0. 8 bigeye Age Vu ln er a bi lity status quo less FADs no FADs Figure 6.3: Adjusted vulnerability at age to purse seine gear for yellown and bigeye tuna. In running simulations, we assume three possible scenarios. The rst scenario is in- tended to represent the status quo where shing on FADs is permitted and we model the non-cooperative equilibrium. Here, shing for juvenile sh is current practice, but adult yellown are also harvested. This, in eect, means that purse seine shers must take into account the fact that their removal of juvenile yellown sh does in fact aect their ability to harvest adult yellown. In the second scenario we consider the cooperative equilibrium with reduced shing on FADs, perhaps through spatial or temporal closures. The third scenario also assumes a cooperative game where shing on FADs is not allowed. To implement scenarios two and three, we modify the vulnerability of yellown 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 shing eort combinations. The non-cooperative simulations are done in two steps. In the rst step, each player chooses the eort, given all possible combinations of eort by the three gears, that will maximize its rent from the resource. This level of eort 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 eort choices. In the cooperative game, eorts 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 shing, I change the length at which 50% of the population is vulnerable to the purse seine gear (lhi1). By changing 110 6 .2. M o d el Table 6.4: Biological and shing parameter inputs for yellown tuna. Biological Value Source L1 Mean asymptotic length (cm) 175 Molony (2008) Lm Length at maturity (cm) 100 Molony (2008) Wm Weight at maturity (kg) 19 Molony (2008) a Length-weight relationship 2.512E-05 Langley et al. (2009b) b Length-weight relationship 2.9396 Langley et al. (2009b) K Growth coecient 0.392 Molony (2008) (average)  Recruitment compentation 12 Calculated from Langley et al. (2009b) M Adult mortality (per year) 1 Molony (2008) (average) Fishing Value Source qg (PS, LL, HL) Catchabilities 1.34e-06, 1.09e-9, 2.84e-7 Derived from Williams and Reid (2007); Lawson (2008b) lh1 (PS) Start length of capture (cm) 20 Molony (2008) lh2 (PS) End length of capture (cm) 100 Molony (2008) sd1; sd2 Standard deviation on length of cap- ture 15, 15 âg Age at 50% vulernability (LL,HL) (years) 2, 3 Molony (2008) c (PS, LL, HL) Unit cost of eort (USD) 0, 1, 50 Reid et al. (2003),J. Ingles, pers. com. p (PS, LL, HL) Ex-vessel price (USD/t) 1,500, 5,000, 4,000 Williams and Reid (2007) PS = purse seine, LL = longline, HL = handline. 11 1 6 .2. M o d el Table 6.5: Biological and shing parameter inputs for bigeye tuna. Biological Value Source L1 Mean asymptotic length (cm) 180 Hampton (2002a) Lm Length at maturity (cm) 102 Molony (2008) Wm Weight at maturity (kg) 23 Molony (2008) a Length-weight relationship 1.973E-05 Harley et al. (2010) b Length-weight relationship 3.0247 Harley et al. (2010) K Growth coecient 0.188 Harley et al. (2010) (average)  Recruitment compentation 12 Calculated from Harley et al. (2010) (average) M Adult mortality (per year) 0.361 Molony (2008) (average) Fishing Value Source qg (PS, LL, HL) Catchabilities 2.26e-06, 1.36e-8, 1.57e-6 Derived from Williams and Reid (2007); Lawson (2008b) lh1 (PS) Start length of capture (cm) 25 Molony (2008) lh2 (PS) End length of capture (cm) 80 Molony (2008) sd1; sd2 Standard deviation on length of cap- ture 15, 2 â Age at 50% vulernability (LL,HL) (years) 2, 3 Molony (2008) c (PS, LL, HL) Unit cost of eort (USD) 0, 1, 50 Reid et al. (2003), J. Ingles, pers. com. p (PS,LL,HL) Ex-vessel price (USD/t) 1,500, 7,000, 6,000 Williams and Reid (2007) PS = purse seine, LL = longline, HL = handline. 11 2 6.2. Model these lengths to larger sizes, I force the model to decrease shing pressure on juvenile sh, which is what we would probably observe if shing on FADs was not allowed. In the second scenario, I allow a reduced amount of yellown 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 yellown and bigeye, respectively. In the no FADs scenario, I change the parameters so that adult yellown 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 yellown, and innity for bigeye. The end length of capture, lhi2 is also increased to 120 cm for yellown, from 100 cm in the status quo simulations. This is done because older, and thus larger, yellown are captured when setting on unassociated schools, that is, schools not associated with 
oating 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 yellown would now be all adult- sized, 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, aect 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 yellown 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 modied simulations, pe, is calculated by the following equation (Reid et al., 2003): pei;g = pi;g  pi;g  yei;g  qi;g qi;g  1  (6.21) where pi;g is the gear- and species-specic ex-vessel price, as earlier dened, yei;g is the yield, as earlier dened, and  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 
eet, 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, qi;g, is taken from the catch quantities estimated in the non- cooperative status quo scenario. In this way, the non-cooperative outcome is a reference or 28Reid et al. (2003) explain that there is a size premium paid for larger sh; with sh 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 yellown ex-vessel price as stated above. 6.3 Results Status quo: Non-cooperative game The optimum rent for each gear type is reached at eort levels of about 98,000 purse seine shing days, 591 million longline hooks, and 1.6 million handline days29 (Table 6.6). At equilibrium, skipjack, yellown 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 shery of almost USD $1.4 billion (Table 6.6). Interestingly, in the non-cooperative status quo simulation, longline is not a protable 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 yellown 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 sh are being harvested (Figure 6.4). Reduction in FADs shing: 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 yellown 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 shing 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 eorts 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 eort, 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 29The estimated number of handline shing days for small and large Philippine vessels averaged about one million per year over the years 2005-2009 (J. Ingles, pers. comm.) 114 6.3. Results Status quo Less FADs No FADs Skipjack Yellowfin Bigeye R at io  o f v ul ne ra bl e bi om as s to  s pa w n e r bi om as s 0. 0 0. 5 1. 0 1. 5 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, yellown 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 shing: Cooperative game 2 The third scenario assumes that shing on FADs no longer occurs, and thus, there is no juvenile bycatch of yellown 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 sheries. Again, a major reduction in purse seine eort is needed to maximize joint rent in this scenario. Similar to the reduced FADs situation, eorts 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 shing 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, yellown 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 yellown, due to the price premium for large sh. Here, we allow the price to respond to changes in the quantity of sh supplied to the market from the WCPO (i.e., the catch). This results in much higher rent possibilities to the purse seine 
eet in both the reduced and no FADs scenarios. The optimal equilibrium eort, estimated at just over 21,000 purse seine shing 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 yellown 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 yellown, 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 eort catches fewer sh, 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 non- cooperative and cooperative games assuming fuel costs were 10% and 25% higher for all 
eets 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 
eet as well. Results stated above are robust to these changes: the optimal solution is still the less FAD option, although total rent and eort for all 
eets is reduced (Figure 6.5). 30Dexter Teng, TSP Industries and Mark Filipe, Far East Seafood, Inc., personal communication. 116 6 .3. R esu lts Table 6.6: Scenario results Status quo Low FAD No FAD PS LL HL PS LL HL PS LL HL Eort* 97,829 591 1.624 20,742 830 2.074 22,062 882 2.206 Skipjack catch (t) 2,138,396 0 0 575,622 0 0 515,674 0 0 Yellown catch (t) 210,543 173,184 28,360 28,025 282,972 40,831 14,395 302,626 43,581 Bigeye (t) 44,194 37,571 24,392 9,138 139,498 79,495 0 155,796 87,840 Revenue (m. USD) 3,590 1,129 260 921 2,391 640 797 2,604 701 Cost (m. USD) 2,152 1,183 162 456 1,659 207 485 1,765 221 Rent (m. USD) 1,438 0 98 465 732 433 312 839 480 Total rent (m. USD) 1,536 1,630 1,630 Increase in skipjack spawning biomass (%) - 200 203 Increase in yellown spawning biomass (%) - 120 121 Increase in bigeye spawning biomass (%) - 274 281 *PS=purse seine (eort = num days), LL=longline (eort = num hooks), HL=handline (eort = num days) 11 7 6.3. Results nc nc10 nc25 c1 c1_10 c1_25 c2 c2_10 c2_25 SCENARIO R en t, m illi on  U SD 0 50 0 10 00 15 00 20 00 purse seine longline handline 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 noncoop- erative 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 sheries in the WCPO have the potential to be protable, but evidence suggests that at least two of the targeted species, namely, yellown and bigeye, may be fully exploited or overshed (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 sheries in the area to continue providing benets to the region. The con
ict between purse seine shers catching juvenile yellown and bigeye tuna, and longline shers 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 
eets (WCPFC, 2009). Assuming constant prices, both reduction and total elimination of FADs yield almost equivalent payos. Losses to the purse seine sector are evident when making this type of policy change. When we allow for prices to re
ect 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 benets, an improvement of US $458 million per year. Purse seine eort was estimated at about 58,000 vessel days in 2008 (Williams and Terawasi, 2009). The equilibrium eort 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 signicantly. In the status quo, the rent generated for each purse seine shing 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 eort, but the same amount of catch (if there was a way to actually enforce that), and just allow the protability of the shery 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 shing on FADs to shing on unassoci- ated 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 relation- ship between the supply of sh produced by a multispecies shery and the market price, which, although highly in
uenced 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 esti- mates 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 benecial to think of them in terms of sharing the catches in these sheries, instead of sharing the rent. For example, purse seine 
eets could be given a share of the longline catch, as compensation for not shing with FADs. If they choose not to enter the longline shery, their shares could be leased out to other longline 
eets, enabling them to derive rent from the shery. An alternative form of a side payment could be realized through longline shers leasing catch shares for access to the purse seine shery, which they would choose to not sh. They would therefore be contributing to osetting the loss of that shing ground to purse seiners who are active in the shery. These types of arrangements could probably be easily achieved for countries that have both purse seine and longline 
eets, such as Taiwan. However, as international sheries quota markets are still in their infancy, trading among countries may prove dicult in the near future (Bailey et al., 2010). The potential of the longline shery to bring regional benets may rest on an eective decrease in juvenile shing by purse seiners. Both the reduction or removal of shing on FADs yields benets to the region. In this study, however, we did not address the costs of management. The overall benet 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 specic reference to the Eastern Pacic Ocean (EPO), the authors suggest that assigning property rights to set on 
oating 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 shing season, and require that they be returned upon closures, with nes 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 Inter- American 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 sh, if, for example, a type of quota on by- catch is implemented. Additionally, demand-side measures, such as consumers demanding FAD-free tuna in Britain, may help to force canneries to rethink their purchasing deci- sions (Pala, 2011). There are several measures currently underway, or in the foreseeable future, that could tackle the sustainability issues associated with growth overshing and juvenile byctach. Obvious challenges to implementing management measures in WCPO tuna sheries exist. These challenges, however, are not an excuse to allow the continued growth overshing of yellown 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 sheries are important for employment and food security, and the tuna stocks themselves provide ecosystem functions throughout the world's oceans. Unfortunately, both sheries 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 auent, 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 benets to shers, countries, and consumers throughout the past sixty years. Continuing the status quo of tuna sheries management, however, will lead to a future of increasingly competitive sheries, 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 deciencies in the way tuna sheries are currently managed, and provide ways forward to improve global and regional management. Paths to improve- ment include better incorporation of economic information in policy-making and stronger national accountability with regards to shing subsidies (Chapter 2), increases in cooper- ation (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 sheries are an important revenue source for many sh- 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 shing for dierent tuna species using dierent shing methods (Chapter 2). Manage- ment formulated with these asymmetries in mind may have a greater likelihood of being eectively implemented. Fishers employing longline gear are faced with the lowest rent per tonne of all the major tuna shing gears, whereas gillnet and purse seine shers 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 bluen (Atlantic, Pacic and southern) still brings in positive private rents, even though two of these stocks are overshed. Skipjack sheries, considered underexploited today, provide the majority of the global tuna supply, and are protable to sh before and after subsidies are accounted for. As economic theory suggests, eort will continue moving into sheries that are prof- itable, and thus shing with purse seines, and for bluen species and skipjack, may increase in the short term. Depending on the population status of a given species, this increase in eort could be more or less worrisome. A management regime that is proactive and takes into account where resource rent is generated, and where eort 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 articially-higher prots (Chapter 2). In the case of global tuna sheries, 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 sheries management can bring benets 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 shing of tuna and non-tuna stocks has con- tinued largely unabated. Tuna RFMOs are often composed of multiple members, which can make cooperation more dicult, and further to this, face the problem of free riders and new members. The evidence that cooperation will facilitate improvements in sustain- ability 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 specic 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 allo- cation 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 sheries oer many benets to nations above and beyond catch quotas. The Western and Central Pacic 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 dened by national interests could help us shift the allocation focus from a strictly catch-based perspective to a more benets-based sheries management paradigm. Indonesia, the Philippines and Papua New Guinea are found within the WCPFC con- vention 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 Pacic Ocean comes from this sub-region where eective management capacity is limited. Indonesia and the Philippines have poor data collection and management programs, non-existent or ineective shing restrictions, lack a plan for how to manage sh aggregating devices (FADs) and have limited membership with regional scientic 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 sheries in the Coral Triangle region (Chapter 5). Cooperation in management of strad- dling stocks should extend beyond just sitting in on annual meetings. A future in which tuna shing nations help raise the standards of shing 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 yellown 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 sh 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 benet to the entire region is considered, would increase the spawning biomass of these species, and oer long-term economic benets to longliners who target adult sh. Further to this, although the amount of purse seine eort would decrease under a cooperative scenario where juvenile bycatch is limited, the potential protability per purse seine shing day will increase. The analysis conducted in Chapter 6 is a multi- species 3-player game, that seeks to analyze a highly complex interaction between these dierent shing gears. More work is needed here to identify solutions to the con
ict of interest. Although my analysis provides evidence that cooperation brings benets 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 o 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-shing nations, side payments have to be employed more eectively. 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 sh on FADs, and thus reduce the catch of juvenile yellown and bigeye, in exchange for the right to catch adult tuna in the waters of Pacic Island Countries or the high seas. If open discussions surrounding the present day issues in tuna management are en- couraged, hopefully tuna RFMOs can begin the process of solidifying their mandates and promoting more sustainable sheries. 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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) Opportunity Cost (USD) Unit Rent (USD/t) Algeria Atlantic bf pole/line -142,531 -147,706 5,175 -1,453 Algeria Atlantic bf trap -421,624 -436,167 14,542 -1,530 Algeria Atlantic bf hook/line -3,449 -3,563 115 -1,587 Algeria Atlantic bf purse seine -281,534 -297,607 16,073 -924 Algeria Atlantic bf longline -717,705 -731,923 14,218 -2,663 Am Samoa* yellown longline 871,897 386,236 485,661 7,355 Am Samoa skipjack hook/line 288,722 140,292 148,430 7,969 Am Samoa bigeye longline 250,162 110,818 139,344 7,355 Am Samoa skipjack longline 178,939 79,267 99,672 7,355 Am Samoa albacore longline 8,220,031 3,641,340 4,578,691 7,355 Am Samoa bigeye hook/line 379,356 184,332 195,024 7,969 Am Samoa skipjack gillnet 165,260 93,035 72,224 9,374 Am Samoa skipjack pole/line 239,850 134,130 105,720 9,295 Am Samoa skipjack purse seine 388,605 224,693 163,912 9,713 Am Samoa albacore hook/line 3,578,798 1,738,963 1,839,835 7,969 Am Samoa yellown hook/line 700,538 340,396 360,141 7,969 Am Samoa albacore mw trawl 6,301,060 3,776,072 2,524,988 10,224 Am Samoa albacore pole/line 5,000,623 2,796,466 2,204,157 9,295 Am Samoa yellown pole/line 636,881 356,159 280,722 9,295 Am Samoa bigeye pole/line 149,533 83,623 65,911 9,295 Am Samoa albacore purse seine 1,971,953 1,140,190 831,762 9,713 Am Samoa yellown purse seine 1,708,835 988,055 720,780 9,713 Am Samoa yellown gillnet 535,281 301,344 233,937 9,374 Am Samoa bigeye purse seine 303,417 175,437 127,980 9,713 Am Samoa bigeye gillnet 560 315 245 9,374 Angola yellown pole/line -52,570 -59,831 7,260 -1,453 Angola yellown purse seine -14,816 -18,033 3,217 -924 Angola yellown longline -156,576 -168,374 11,798 -2,663 Angola bigeye pole/line -40,653 -46,267 5,615 -1,453 Angola bigeye purse seine -7,671 -9,336 1,666 -924 Angola bigeye longline -103,131 -110,902 7,771 -2,663 Australia albacore hook/line 44,839 6,361 38,478 8,310 Australia southern bf longline 33,552,653 -4,668,900 38,221,553 6,260 Australia yellown longline 12,255,680 -1,705,396 13,961,076 6,260 Australia skipjack gillnet 960 372 589 11,637 Australia bigeye longline 5,563,754 -774,204 6,337,959 6,260 Table continued on next page 147 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Australia yellown hook/line 41,199 5,845 35,354 8,310 Australia skipjack longline 8,873 -1,235 10,107 6,260 Australia albacore longline 3,417,754 -475,585 3,893,340 6,260 Australia bigeye pole/line 46,965 14,429 32,536 10,294 Australia southern bf pole/line 1,816,157 557,978 1,258,178 10,294 Australia yellown gillnet 10,339 4,003 6,335 11,637 Australia albacore pole/line 916,447 281,560 634,886 10,294 Australia yellown pole/line 416,568 127,982 288,585 10,294 Australia bigeye gillnet 1,135 440 696 11,637 Australia albacore gillnet 18,233 7,060 11,173 11,637 Australia skipjack pole/line 175,942 54,055 121,888 10,294 Barbados bigeye purse seine 7,178 4,858 2,319 1,786 Barbados albacore longline 14,577 8,469 6,108 1,377 Barbados albacore purse seine 3,070 2,078 992 1,786 Barbados bigeye longline 29,158 16,940 12,218 1,377 Barbados bigeye pole/line 3,199 2,153 1,046 1,765 Barbados albacore pole/line 1,219 820 399 1,765 Barbados skipjack pole/line 385 259 126 1,765 Barbados skipjack purse seine 1,992 1,348 643 1,786 Barbados yellown purse seine 212,078 143,552 68,525 1,786 Barbados skipjack longline 918 534 385 1,377 Barbados yellown longline 214,101 124,388 89,713 1,377 Barbados yellown pole/line 31,468 21,177 10,291 1,765 Bermuda* skipjack pole/line 38 -49 88 352 Bermuda albacore longline -1,292 -1,946 654 -1,587 Bermuda bigeye longline -1,245 -1,875 630 -1,587 Bermuda albacore hook/line -1 -1 0 -973 Bermuda Atlantic bf hook/line -56 -103 47 -973 Bermuda skipjack purse seine 430 -18 448 771 Bermuda yellown longline -47,753 -71,927 24,175 -1,587 Bermuda Atlantic bf purse seine 69 -3 72 771 Bermuda Atlantic bf pole/line 0 0 0 352 Bermuda bigeye pole/line 24 -30 54 352 Bermuda skipjack longline -529 -797 268 -1,587 Bermuda Atlantic bf longline -145 -219 74 -1,587 Bermuda albacore purse seine 102 -4 106 771 Bermuda bigeye purse seine 115 -5 120 771 Bermuda Atlantic bf trap -10 -19 8 -971 Bermuda yellown pole/line 1,215 -1,557 2,772 352 Bermuda yellown purse seine 21,165 -908 22,073 771 Bermuda albacore pole/line 19 -24 43 352 Brazil bigeye longline 663,977 153,650 510,327 598 Brazil bigeye pole/line 41,619 22,201 19,418 986 Brazil yellown longline 14,668,088 8,286,138 6,381,950 3,409 Brazil skipjack pole/line 10,721,005 4,274,992 6,446,013 426 Brazil bigeye purse seine 26,657 14,485 12,172 1,007 Brazil skipjack purse seine 510,939 218,476 292,463 447 Brazil yellown purse seine 969,271 592,709 376,563 3,818 Table continued on next page 148 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Brazil albacore pole/line 122,279 70,429 51,849 1,686 Brazil yellown pole/line 10,499,122 6,397,139 4,101,984 3,797 Brazil albacore hook/line 198 -420 618 229 Brazil skipjack longline 6,995 -39,405 46,400 39 Brazil albacore purse seine 1,733 1,008 726 1,707 Brazil albacore longline 754,762 339,224 415,538 1,298 Belize bigeye longline 183,491 -31,998 215,488 1,377 Belize yellown longline 224,825 -39,206 264,031 1,377 Belize skipjack longline 163 -28 191 1,377 Belize albacore longline 811,368 -141,489 952,857 1,377 Belize bigeye gillnet 172 -515 687 405 Belize yellown gillnet 450 -1,348 1,798 405 Belize albacore gillnet 35,675 -106,836 142,511 405 Belize yellown pole/line 35,819 2,987 32,832 1,765 Belize bigeye pole/line 2,398 200 2,198 1,765 Belize skipjack pole/line 9,044 754 8,290 1,765 Belize yellown hook/line 793 -3,033 3,826 335 Belize albacore pole/line 72,065 6,010 66,055 1,765 Solomon Is.* bigeye longline 11,291 -1,245 12,536 3,893 Solomon Is. skipjack longline 7,078,882 -780,850 7,859,732 3,893 Solomon Is. yellown hook/line 6,983,769 2,128,808 4,854,960 6,218 Solomon Is. skipjack hook/line 16,836,843 5,132,245 11,704,598 6,218 Solomon Is. bigeye pole/line 10,874 4,944 5,930 7,927 Solomon Is. bigeye hook/line 25,238 7,693 17,545 6,218 Solomon Is. bigeye gillnet 47 25 22 9,270 Solomon Is. Pacic bf hook/line 37,306 11,372 25,934 6,218 Solomon Is. yellown longline 5,896,612 -650,437 6,547,049 3,893 Solomon Is. bigeye purse seine 21,998 10,484 11,514 8,258 Solomon Is. skipjack purse seine 24,695,154 11,769,668 12,925,486 8,258 Solomon Is. skipjack pole/line 15,288,202 6,951,516 8,336,686 7,927 Solomon Is. yellown gillnet 6,763,616 3,609,980 3,153,637 9,270 Solomon Is. yellown purse seine 18,564,381 8,847,752 9,716,629 8,258 Solomon Is. skipjack gillnet 12,214,827 6,519,482 5,695,345 9,270 Solomon Is. yellown pole/line 6,939,883 3,155,552 3,784,332 7,927 Virgin Is.* yellown pole/line 43 -55 98 352 Virgin Is. yellown purse seine 627 -27 653 771 Virgin Is. yellown longline -1,690 -2,546 856 -1,587 Canada albacore purse seine -6,581 -8,007 1,426 -1,455 Canada albacore pole/line -212 -331 119 -563 Canada albacore longline -9,886,093 -11,079,440 1,193,347 -2,613 Cape Verde yellown purse seine -715,721 -1,361,168 645,447 -924 Cape Verde bigeye purse seine -258 -492 233 -924 Cape Verde bigeye longline -997 -1,308 312 -2,663 Cape Verde skipjack pole/line -250,416 -394,043 143,628 -1,453 Cape Verde yellown longline -1,817,750 -2,386,575 568,825 -2,663 Cape Verde skipjack purse seine -172,465 -327,997 155,532 -924 Cape Verde bigeye pole/line -503 -791 288 -1,453 Cape Verde yellown pole/line -584,186 -919,249 335,064 -1,453 Table continued on next page 149 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Cape Verde skipjack longline -13,444 -17,652 4,207 -2,663 Sri Lanka bigeye gillnet 2,727 1,965 762 3,400 Sri Lanka yellown hook/line 331,750 174,768 156,982 2,008 Sri Lanka skipjack pole/line 36,522,592 20,544,546 15,978,046 2,172 Sri Lanka skipjack gillnet 3,146,610 2,267,472 879,139 3,400 Sri Lanka skipjack hook/line 2,532 1,334 1,198 2,008 Sri Lanka yellown gillnet 1,595,416 1,149,669 445,747 3,400 Sri Lanka yellown longline 27,842,917 11,930,367 15,912,550 1,662 Sri Lanka bigeye longline 418,075 179,140 238,935 1,662 Sri Lanka bigeye pole/line 5,570 3,133 2,437 2,172 Sri Lanka yellown pole/line 4,683,845 2,634,738 2,049,107 2,172 Sri Lanka skipjack longline 26,417,153 11,319,443 15,097,710 1,662 Chile bigeye purse seine -594 -741 147 -78 Chile skipjack longline -863 -877 13 -582 Chile skipjack purse seine -751 -790 39 -173 Chile yellown longline -12,216 -12,304 88 -623 Chile bigeye longline -3,100 -3,223 123 -487 Chile skipjack pole/line -33 -35 2 -194 Chile yellown pole/line -114 -117 2 -236 Chile albacore longline -388 -418 30 -388 Chile yellown purse seine -7,066 -7,213 148 -215 China Main bigeye pole/line 1,722,191 502,646 1,219,545 2,172 China Main Atlantic bf pole/line 8,372 2,444 5,929 2,172 China Main albacore hook/line 102,733 24,046 78,687 2,008 China Main yellown hook/line 143,142 33,504 109,637 2,008 China Main bigeye purse seine 1,937,604 482,109 1,455,495 2,047 China Main albacore gillnet 24,211 12,245 11,967 3,111 China Main albacore longline 743,715 -9,000,351 9,744,065 117 China Main Atlantic bf trap 10,492 -8,988 19,481 828 China Main yellown gillnet 60,845 30,772 30,073 3,111 China Main bigeye longline 3,297,799 -39,909,593 43,207,392 117 China Main albacore purse seine 1,758 438 1,321 2,047 China Main yellown longline 2,550,863 -30,870,260 33,421,123 117 China Main yellown purse seine 559,055 139,102 419,953 2,047 China Main Atlantic bf purse seine 12,461 3,100 9,360 2,047 China Main skipjack longline 5,613,180 -67,930,088 73,543,268 117 China Main Atlantic bf longline 163 -1,974 2,137 117 China Main bigeye gillnet 110,167 55,716 54,451 3,111 China Main albacore pole/line 242,716 70,840 171,876 2,172 China Main yellown pole/line 1,468,147 428,500 1,039,647 2,172 Taiwan skipjack purse seine 11,874 9,621 2,253 2,573 Taiwan albacore gillnet 2,852,480 1,898,738 953,742 1,460 Taiwan Pacic bf longline 460,606 239,645 220,961 1,018 Taiwan bigeye pole/line 9,103,434 7,056,697 2,046,737 2,172 Taiwan bigeye gillnet 251,138 167,168 83,969 1,460 Taiwan bigeye purse seine 6,109,794 4,950,619 1,159,175 2,573 Taiwan yellown pole/line 17,551,474 13,605,354 3,946,121 2,172 Taiwan skipjack longline 157,797,869 82,099,303 75,698,566 1,018 Table continued on next page 150 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Taiwan skipjack pole/line 181,894 140,998 40,895 2,172 Taiwan yellown hook/line -22,730 -425,567 402,837 -28 Taiwan southern bf longline 934,085 485,987 448,098 1,018 Taiwan albacore longline 34,989,508 18,204,392 16,785,116 1,018 Taiwan yellown purse seine 2,800,080 2,268,837 531,243 2,573 Taiwan Pacic bf seine 7,082 5,187 1,895 1,825 Taiwan southern bf gillnet 32,440 21,594 10,847 1,460 Taiwan albacore pole/line 5,068,786 3,929,164 1,139,622 2,172 Taiwan yellown longline 113,440,811 59,021,149 54,419,662 1,018 Taiwan Pacic bf mw trawl 261,574 220,996 40,578 3,147 Taiwan Pacic bf hook/line -20,406 -382,050 361,644 -28 Taiwan Pacic bf gillnet 128,104 85,272 42,832 1,460 Taiwan skipjack gillnet 22,177,193 14,762,127 7,415,065 1,460 Taiwan yellown gillnet 794,720 529,001 265,719 1,460 Taiwan bigeye longline 68,173,408 35,469,359 32,704,049 1,018 Taiwan albacore purse seine 555,954 450,476 105,478 2,573 Taiwan albacore hook/line -19,683 -368,511 348,829 -28 Colombia bigeye longline -346,240 -523,384 177,144 -254 Colombia bigeye purse seine 280,095 45,790 234,305 155 Colombia skipjack purse seine 1,457,143 238,213 1,218,930 155 Colombia skipjack longline -695,781 -1,051,757 355,976 -254 Colombia bigeye pole/line 130 4 126 134 Colombia skipjack pole/line 46,267 1,348 44,918 134 Colombia yellown longline -2,074,921 -3,136,496 1,061,575 -254 Colombia yellown purse seine 2,382,234 389,447 1,992,787 155 Colombia yellown pole/line 29,082 848 28,234 134 Comoros bigeye pole/line -512 -528 16 -1,453 Comoros yellown longline -13,753,869 -13,985,738 231,869 -2,663 Comoros skipjack pole/line -4,649,861 -4,793,539 143,678 -1,453 Comoros yellown gillnet -19,943 -20,779 837 -1,070 Comoros yellown hook/line -124,482 -128,005 3,522 -1,587 Comoros bigeye gillnet -118 -123 5 -1,070 Comoros yellown pole/line -928,102 -956,780 28,678 -1,453 Comoros bigeye longline -91,984 -93,535 1,551 -2,663 Mayotte* bigeye pole/line -325 -350 24 -1,405 Mayotte bigeye gillnet -96 -104 8 -1,326 Mayotte albacore longline -34,239 -35,317 1,078 -3,345 Mayotte yellown longline -884,178 -912,025 27,847 -3,345 Mayotte albacore pole/line -1,739 -1,869 130 -1,405 Mayotte skipjack pole/line -663,262 -712,986 49,724 -1,405 Mayotte albacore gillnet -2,023 -2,184 161 -1,326 Mayotte bigeye longline -75,916 -78,307 2,391 -3,345 Mayotte yellown hook/line -10,966 -11,389 423 -2,731 Mayotte yellown pole/line -45,941 -49,386 3,444 -1,405 Mayotte yellown gillnet -1,264 -1,365 100 -1,326 Cook Is* bigeye gillnet 954 900 55 9,374 Cook Is yellown hook/line 569,532 531,148 38,384 7,969 Cook Is yellown purse seine 1,389,270 1,312,449 76,821 9,713 Table continued on next page 151 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Cook Is bigeye purse seine 517,456 488,843 28,613 9,713 Cook Is yellown longline 708,845 657,084 51,762 7,355 Cook Is bigeye hook/line 646,963 603,361 43,602 7,969 Cook Is albacore hook/line 2,901,959 2,706,381 195,578 7,969 Cook Is skipjack longline 38,522 35,709 2,813 7,355 Cook Is albacore longline 6,665,420 6,178,695 486,725 7,355 Cook Is albacore purse seine 1,599,008 1,510,590 88,418 9,713 Cook Is yellown gillnet 435,179 410,246 24,933 9,374 Cook Is bigeye longline 426,633 395,480 31,154 7,355 Cook Is skipjack hook/line 62,155 57,966 4,189 7,969 Cook Is skipjack pole/line 51,634 48,651 2,984 9,295 Cook Is skipjack gillnet 35,577 33,538 2,038 9,374 Cook Is albacore mw trawl 5,109,376 4,840,964 268,412 10,224 Cook Is albacore pole/line 4,054,883 3,820,577 234,307 9,295 Cook Is bigeye pole/line 255,018 240,282 14,736 9,295 Cook Is skipjack purse seine 83,658 79,032 4,626 9,713 Cook Is yellown pole/line 517,779 487,860 29,919 9,295 Croatia Atlantic bf pole/line -73,701 -99,776 26,075 -699 Croatia Atlantic bf trap -523,153 -596,423 73,270 -1,766 Croatia Atlantic bf purse seine 323,792 242,810 80,983 989 Croatia Atlantic bf longline -1,059,655 -1,131,292 71,637 -3,659 Croatia Atlantic bf hook/line -1,780 -2,358 578 -762 Cuba skipjack pole/line 99,193 82,365 16,828 1,765 Cuba yellown purse seine 13,800 11,487 2,313 1,786 Cuba yellown pole/line 2,048 1,700 347 1,765 Cuba skipjack longline 236,488 185,084 51,404 1,377 Cuba yellown longline 13,931 10,903 3,028 1,377 Cuba skipjack purse seine 512,827 426,871 85,956 1,786 Cyprus albacore pole/line 190,936 -174,361 365,297 1,602 Cyprus Atlantic bf longline -57,375 -186,932 129,557 -1,357 Cyprus Atlantic bf purse seine 157,209 10,749 146,460 3,290 Cyprus albacore longline -169,567 -552,464 382,897 -1,357 Cyprus albacore hook/line 206,551 -204,797 411,348 1,539 Cyprus Atlantic bf hook/line 525 -520 1,045 1,539 Cyprus Atlantic bf pole/line 24,649 -22,509 47,158 1,602 Cyprus albacore purse seine 153,725 10,511 143,214 3,290 Cyprus Atlantic bf trap 23,138 -109,373 132,511 535 Benin bigeye longline -3,986 -4,474 487 -2,663 Benin bigeye purse seine -1,034 -1,398 364 -924 Benin bigeye pole/line -2,012 -2,462 451 -1,453 Dominica skipjack pole/line 7,319 -14,082 21,401 1,765 Dominica yellown longline 86,520 -237,610 324,131 1,377 Dominica yellown pole/line 12,717 -24,466 37,183 1,765 Dominica skipjack purse seine 37,840 -71,472 109,312 1,786 Dominica skipjack longline 17,450 -47,922 65,371 1,377 Dominica yellown purse seine 85,703 -161,876 247,579 1,786 Dominican Rp yellown purse seine 79,892 43,950 35,942 1,786 Dominican Rp yellown pole/line 11,854 6,456 5,398 1,765 Table continued on next page 152 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Dominican Rp skipjack pole/line 13,675 7,448 6,227 1,765 Dominican Rp skipjack longline 32,603 13,582 19,022 1,377 Dominican Rp yellown longline 80,654 33,598 47,056 1,377 Dominican Rp skipjack purse seine 70,700 38,893 31,807 1,786 Ecuador bigeye pole/line 1,220 367 853 134 Ecuador skipjack purse seine 16,617,799 6,605,565 10,012,234 155 Ecuador yellown pole/line 58,858 17,701 41,157 134 Ecuador skipjack pole/line 639,511 192,330 447,182 134 Ecuador yellown longline -3,770,951 -5,160,522 1,389,571 -254 Ecuador bigeye longline -2,086,924 -2,855,942 769,018 -254 Ecuador yellown purse seine 4,386,087 1,743,467 2,642,620 155 Ecuador bigeye purse seine 2,589,078 1,029,157 1,559,921 155 Ecuador skipjack longline -7,852,651 -10,746,301 2,893,651 -254 Ecuador yellown gillnet -26 -28 2 -1,226 El Salvador yellown purse seine 11,919,554 10,418,320 1,501,235 1,786 El Salvador bigeye purse seine 1,350,395 1,180,316 170,078 1,786 El Salvador skipjack purse seine 8,416,745 7,356,680 1,060,066 1,786 Faroe* Is Atlantic bf pole/line -27 -80 54 -83 Faroe Is Atlantic bf purse seine 170 85 85 335 Faroe Is Atlantic bf longline -234 -254 19 -2,023 Faroe Is Atlantic bf trap -1,485 -1,661 177 -1,406 Fiji skipjack pole/line 130,681 71,303 59,378 7,927 Fiji bigeye longline 434,172 32,492 401,681 3,893 Fiji yellown gillnet 2,072,536 1,267,323 805,212 9,270 Fiji bigeye pole/line 418,153 228,155 189,997 7,927 Fiji bigeye purse seine 845,909 476,987 368,922 8,258 Fiji skipjack longline 60,509 4,528 55,981 3,893 Fiji albacore pole/line 12,981,698 7,083,162 5,898,536 7,927 Fiji albacore longline 13,244,159 991,139 12,253,020 3,893 Fiji albacore mw trawl 19,190,300 12,433,186 6,757,114 10,229 Fiji yellown longline 1,806,865 135,218 1,671,647 3,893 Fiji bigeye gillnet 1,815 1,110 705 9,270 Fiji albacore hook/line 10,707,017 5,783,439 4,923,578 7,832 Fiji skipjack hook/line 181,290 97,925 83,366 7,832 Fiji skipjack gillnet 104,410 63,845 40,565 9,270 Fiji yellown pole/line 2,126,549 1,160,302 966,247 7,927 Fiji albacore purse seine 5,103,759 2,877,883 2,225,876 8,258 Fiji skipjack purse seine 211,089 119,028 92,061 8,258 Fiji yellown purse seine 5,688,577 3,207,647 2,480,930 8,258 Fiji yellown hook/line 2,695,704 1,456,095 1,239,608 7,832 Fiji bigeye hook/line 1,222,551 660,366 562,185 7,832 France Atlantic bf hook/line -34,380 -44,160 9,780 -1,740 France bigeye pole/line -111,160 -866,822 755,662 -119 France skipjack longline -207,131 -394,984 187,853 -1,526 France bigeye longline -22,581,026 -28,515,146 5,934,120 -3,079 France yellown hook/line -1,026,209 -1,220,503 194,294 -2,036 France albacore gillnet -10,421 -20,553 10,132 -1,032 France yellown purse seine 11,898,120 1,081,833 10,816,287 424 Table continued on next page 153 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) France albacore purse seine 446,443 232,557 213,886 2,094 France skipjack purse seine 73,454,314 40,901,520 32,552,794 3,122 France yellown longline -165,473,435 -180,571,778 15,098,343 -4,224 France Atlantic bf longline -9,957,218 -11,211,725 1,254,507 -3,928 France Atlantic bf purse seine 2,261,171 706,705 1,554,466 720 France yellown pole/line -15,143,715 -19,760,526 4,616,811 -1,264 France skipjack gillnet -3 -832 829 -4 France albacore pole/line 215,049 -315,964 531,013 406 France Atlantic bf pole/line -1,091,180 -1,648,898 557,718 -968 France bigeye purse seine 1,101,467 533,521 567,946 1,569 France Atlantic bf trap -2,793,544 -4,416,061 1,622,517 -852 France yellown gillnet -445,375 -508,886 63,511 -2,703 France skipjack pole/line 49,273,348 1,732,005 47,541,342 1,434 France albacore hook/line -828,340 -3,105,317 2,276,977 -365 France albacore mw trawl 2,832,589 329,698 2,502,891 1,136 France bigeye gillnet -31,761 -48,260 16,499 -1,557 France albacore longline -1,262,371 -1,758,473 496,102 -2,553 French Polyne- sia* albacore longline 18,564,374 8,306,821 10,257,553 7,355 French Polynesia bigeye longline 4,457,215 1,994,427 2,462,788 7,355 French Polynesia yellown longline 10,201,580 4,564,802 5,636,777 7,355 French Polynesia skipjack longline 8,259,823 3,695,943 4,563,880 7,355 Gabon yellown longline -1,956 -2,048 93 -2,663 Gabon yellown purse seine -770 -875 105 -924 Gabon yellown pole/line -628 -683 55 -1,453 Ghana skipjack longline -1,649,934 -1,735,222 85,288 -2,663 Ghana yellown purse seine -6,734,863 -7,738,149 1,003,286 -924 Ghana skipjack purse seine -21,165,398 -24,318,387 3,152,989 -924 Ghana bigeye purse seine -3,597,077 -4,132,930 535,853 -924 Ghana bigeye pole/line -6,999,551 -7,662,722 663,171 -1,453 Ghana skipjack pole/line -30,731,673 -33,643,340 2,911,667 -1,453 Ghana yellown longline -17,104,838 -17,989,023 884,185 -2,663 Ghana yellown pole/line -5,497,130 -6,017,954 520,825 -1,453 Ghana bigeye longline -13,869,752 -14,586,708 716,956 -2,663 Kiribati skipjack hook/line 8,049,328 3,607,400 4,441,929 6,218 Kiribati bigeye hook/line 767,601 344,009 423,592 6,218 Kiribati bigeye longline 343,392 40,736 302,656 3,893 Kiribati bigeye gillnet 1,435 904 531 9,270 Kiribati yellown hook/line 2,250,337 1,008,515 1,241,823 6,218 Kiribati skipjack longline 3,384,259 401,469 2,982,790 3,893 Kiribati yellown pole/line 2,236,196 1,268,224 967,973 7,927 Kiribati skipjack gillnet 5,839,641 3,678,242 2,161,399 9,270 Kiribati bigeye purse seine 669,039 391,066 277,973 8,258 Kiribati skipjack pole/line 7,308,955 4,145,160 3,163,795 7,927 Kiribati yellown gillnet 2,179,399 1,372,748 806,651 9,270 Kiribati yellown longline 1,900,030 225,397 1,674,633 3,893 Kiribati skipjack purse seine 11,806,211 6,900,954 4,905,257 8,258 Kiribati yellown purse seine 5,981,888 3,496,527 2,485,361 8,258 Table continued on next page 154 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Kiribati bigeye pole/line 330,722 187,564 143,158 7,927 Greece albacore hook/line 242,384 -267,021 509,406 771 Greece Atlantic bf trap -102,408 -306,151 203,743 -695 Greece Atlantic bf purse seine 335,570 110,380 225,190 2,060 Greece albacore purse seine 275,954 98,600 177,354 2,522 Greece Atlantic bf longline -372,840 -572,042 199,201 -2,588 Greece albacore longline -621,615 -1,095,788 474,173 -2,125 Greece Atlantic bf hook/line 359 -1,247 1,607 309 Greece albacore pole/line 232,830 -219,547 452,377 834 Greece Atlantic bf pole/line 19,514 -52,994 72,508 372 Grenada bigeye pole/line 237 -116 353 1,765 Grenada yellown pole/line 53,130 -25,991 79,120 1,765 Grenada albacore hook/line 5 -37 42 335 Grenada yellown longline 361,478 -328,229 689,708 1,377 Grenada skipjack longline 10,102 -9,173 19,276 1,377 Grenada albacore longline 32,518 -29,527 62,045 1,377 Grenada albacore pole/line 2,720 -1,330 4,050 1,765 Grenada bigeye longline 2,160 -1,961 4,121 1,377 Grenada albacore purse seine 6,849 -3,228 10,077 1,786 Grenada bigeye purse seine 532 -251 782 1,786 Grenada skipjack purse seine 21,907 -10,325 32,232 1,786 Grenada yellown purse seine 358,062 -168,753 526,816 1,786 Grenada skipjack pole/line 4,237 -2,073 6,310 1,765 Guam* skipjack longline 18,639 8,257 10,382 7,355 Guam yellown hook/line 17,927 8,711 9,216 7,969 Guam yellown purse seine 43,730 25,285 18,445 9,713 Guam yellown pole/line 16,298 9,114 7,184 9,295 Guam yellown gillnet 13,698 7,712 5,987 9,374 Guam skipjack gillnet 17,215 9,691 7,523 9,374 Guam yellown longline 22,312 9,884 12,428 7,355 Guam skipjack pole/line 24,984 13,972 11,013 9,295 Guam skipjack hook/line 30,075 14,614 15,461 7,969 Guam skipjack purse seine 40,480 23,406 17,074 9,713 Guatemala yellown longline 1,956,191 1,284,433 671,758 1,377 Guatemala bigeye longline 516,962 339,437 177,525 1,377 Guatemala yellown purse seine 5,525,920 4,062,654 1,463,266 1,786 Guatemala bigeye pole/line 612,665 448,457 164,207 1,765 Guatemala skipjack purse seine 7,531,211 5,536,944 1,994,267 1,786 Guatemala yellown pole/line 1,124,457 823,078 301,379 1,765 Guatemala bigeye purse seine 501,065 368,383 132,682 1,786 Guatemala skipjack pole/line 5,359,644 3,923,143 1,436,501 1,765 Guatemala skipjack longline 268,215 176,109 92,105 1,377 Guinea yellown gillnet -44 -47 3 -1,070 Guinea yellown hook/line -274 -289 14 -1,587 Guinea yellown pole/line -2,045 -2,161 117 -1,453 Guinea yellown longline -30,305 -31,247 942 -2,663 Honduras bigeye purse seine 2,343,858 1,626,272 717,586 1,786 Honduras yellown purse seine 3,069,674 2,129,875 939,799 1,786 Table continued on next page 155 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Honduras yellown pole/line 44,535 30,734 13,800 1,765 Honduras skipjack purse seine 6,633,604 4,602,687 2,030,917 1,786 Honduras bigeye pole/line 1,367 943 424 1,765 Honduras skipjack longline 1,732,609 1,044,708 687,901 1,377 Honduras bigeye longline 1,500,042 904,477 595,565 1,377 Honduras yellown longline 1,409,408 849,828 559,580 1,377 Honduras skipjack pole/line 254,780 175,829 78,951 1,765 India yellown longline -25,768,758 -26,816,385 1,047,626 -3,445 India yellown gillnet -21,370 -34,227 12,857 -233 India skipjack longline -1,412,848 -1,470,287 57,439 -3,445 India skipjack gillnet -5,559 -8,904 3,345 -233 India yellown hook/line -140,977 -154,911 13,934 -1,417 India skipjack hook/line -46 -51 5 -1,417 India yellown pole/line -1,305,705 -1,437,170 131,465 -1,391 India skipjack pole/line -2,033,457 -2,238,197 204,739 -1,391 Indonesia southern bf gillnet -296,140 -384,296 88,156 -761 Indonesia albacore longline -8,700,998 -9,206,590 505,593 -3,897 Indonesia southern bf seine -97,422 -136,428 39,006 -566 Indonesia skipjack gillnet -34,718,360 -43,483,011 8,764,651 -806 Indonesia skipjack pole/line -34,409,032 -39,653,726 5,244,694 -1,335 Indonesia yellown hook/line -24,628,110 -34,155,735 9,527,626 -585 Indonesia yellown pole/line -2,062,088 -2,424,164 362,076 -1,290 Indonesia albacore hook/line -13,397,433 -18,580,362 5,182,929 -585 Indonesia skipjack hook/line -101,999,059 -134,908,359 32,909,300 -631 Indonesia yellown gillnet -4,618,210 -5,992,968 1,374,758 -761 Indonesia southern bf hook/line -597,944 -829,265 231,321 -585 Indonesia bigeye gillnet -21,087 -27,365 6,277 -761 Indonesia yellown longline -48,388,633 -51,200,372 2,811,739 -3,897 Indonesia skipjack longline -95,388,182 -100,311,512 4,923,331 -3,942 Indonesia bigeye hook/line -17,082,196 -23,690,611 6,608,415 -585 Indonesia bigeye longline -33,879,424 -35,848,070 1,968,646 -3,897 Indonesia southern bf mw trawl -44,246 -76,160 31,914 -314 Indonesia albacore gillnet -255,052 -330,977 75,924 -761 Indonesia bigeye pole/line -114,350 -134,429 20,078 -1,290 Iran yellown longline 4,476,275 -49,278,667 53,754,942 117 Iran yellown hook/line 1,163,212 346,594 816,618 2,008 Iran yellown pole/line 10,243,223 3,594,768 6,648,455 2,172 Iran yellown gillnet 428,079 234,148 193,931 3,111 Iran skipjack pole/line 175,139,908 61,463,795 113,676,113 2,172 Ireland skipjack pole/line 18 -2,956 2,973 4 Ireland albacore hook/line -1,385 -16,061 14,675 -59 Ireland albacore mw trawl 455,949 293,514 162,435 1,763 Ireland Atlantic bf trap -483 -814 332 -914 Ireland Atlantic bf longline -171 -207 36 -2,956 Ireland Atlantic bf purse seine 429 270 159 1,692 Ireland skipjack longline -410 -497 87 -2,956 Ireland skipjack purse seine 8,674 5,454 3,220 1,692 Italy albacore gillnet 1,854 -1,878 3,732 1,128 Table continued on next page 156 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Italy albacore pole/line 2,053,337 619,259 1,434,078 3,251 Italy Atlantic bf purse seine 7,666,626 4,142,360 3,524,266 4,939 Italy Atlantic bf pole/line 1,624,778 490,012 1,134,766 3,251 Italy yellown pole/line 1,745,064 526,288 1,218,776 3,251 Italy bigeye gillnet 1,089 -1,104 2,193 1,128 Italy albacore longline 195,425 -1,329,602 1,525,027 291 Italy albacore purse seine 1,220,483 659,440 561,043 4,939 Italy yellown gillnet 17,659 -17,892 35,551 1,128 Italy albacore hook/line 2,262,589 651,133 1,611,456 3,188 Italy yellown hook/line 210,189 60,489 149,700 3,188 Italy yellown longline 1,262,769 -8,591,433 9,854,202 291 Italy Atlantic bf longline 399,497 -2,718,040 3,117,537 291 Italy Atlantic bf hook/line 35,305 10,160 25,145 3,188 Italy skipjack longline 4,946 -33,649 38,594 291 Italy skipjack pole/line 13,151,928 3,966,448 9,185,480 3,251 Italy Atlantic bf trap 1,011,288 -2,177,336 3,188,624 720 Italy bigeye pole/line 10,044 3,029 7,015 3,251 Italy bigeye longline 88,134 -599,629 687,763 291 Cote d'Ivoire* yellown pole/line -54,993 -59,234 4,240 -1,453 Cote d'Ivoire yellown longline -171,117 -178,316 7,199 -2,663 Cote d'Ivoire skipjack purse seine -596,522 -668,844 72,322 -924 Cote d'Ivoire yellown purse seine -67,376 -75,544 8,169 -924 Cote d'Ivoire skipjack longline -46,501 -48,458 1,956 -2,663 Cote d'Ivoire skipjack pole/line -866,136 -932,923 66,787 -1,453 Japan Atlantic bf pole/line 9,029,156 5,679,904 3,349,252 21,662 Japan skipjack hook/line 551 176 374 3,169 Japan Atlantic bf hook/line 536,794 324,271 212,523 20,295 Japan southern bf gillnet 9,236,433 5,953,415 3,283,018 22,739 Japan bigeye pole/line 51,172,425 29,120,078 22,052,347 6,812 Japan yellown pole/line 24,834,824 10,906,037 13,928,787 2,742 Japan Pacic bf gillnet 10,869,936 6,784,709 4,085,227 8,440 Japan southern bf longline 62,380,996 35,835,061 26,545,935 18,993 Japan skipjack longline 9,677,299 -2,378,351 12,055,650 1,729 Japan Atlantic bf trap 27,518,100 16,635,757 10,882,343 20,319 Japan yellown hook/line 674,807 -79,530 754,337 1,376 Japan albacore gillnet 1,593,085 849,179 743,905 2,215 Japan albacore pole/line 1,723,684 325,585 1,398,098 1,275 Japan yellown longline -6,201,145 -155,349,819 149,148,674 -64 Japan skipjack pole/line 1,624,459,449 853,007,414 771,452,035 4,535 Japan albacore longline -110,455,479 -185,051,639 74,596,160 -1,531 Japan Atlantic bf longline 10,805,338 6,200,703 4,604,635 18,856 Japan bigeye longline 434,654,185 116,134,530 318,519,655 4,006 Japan albacore purse seine 169,945 14,091 155,854 1,128 Japan southern bf mw trawl 7,123,261 4,595,358 2,527,903 22,775 Japan bigeye purse seine 56,228,992 31,461,932 24,767,060 6,665 Japan albacore hook/line -30,450 -374,814 344,364 -91 Japan Atlantic bf purse seine 16,379,809 10,262,317 6,117,493 21,515 Japan bigeye gillnet 525,590 326,546 199,044 7,752 Table continued on next page 157 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Japan yellown purse seine 21,680,918 8,830,650 12,850,268 2,595 Japan skipjack purse seine 413,215,387 210,391,298 202,824,089 4,388 Japan Pacic bf mw trawl 1,519,189 950,661 568,527 8,476 Japan yellown gillnet 580,244 337,863 242,381 3,682 Japan southern bf purse seine 1,164,088 729,550 434,539 21,652 Japan southern bf hook/line 52,006,834 31,435,100 20,571,734 20,433 Japan southern bf pole/line 864,271 543,831 320,440 21,799 Japan skipjack gillnet 697,933 423,375 274,558 5,475 Japan Pacic bf longline 3,934,976 1,275,965 2,659,011 4,694 Japan Pacic bf hook/line 4,063,559 1,962,205 2,101,354 6,134 Kenya skipjack pole/line -781,798 -911,849 130,051 -1,453 Korea Rep Atlantic bf hook/line 2,786 -364 3,150 1,234 Korea Rep yellown hook/line -13,579 -66,527 52,948 -212 Korea Rep yellown longline -192,997,756 -258,228,919 65,231,164 -2,444 Korea Rep albacore purse seine 936 340 596 1,519 Korea Rep Atlantic bf pole/line 184,271 42,130 142,141 1,808 Korea Rep Atlantic bf trap -184,215 -583,622 399,407 -643 Korea Rep albacore hook/line 4,828 -26,868 31,696 147 Korea Rep yellown pole/line 322,971 -412,756 735,728 363 Korea Rep yellown purse seine 176,462 50,845 125,617 1,160 Korea Rep albacore pole/line 65,155 -22,167 87,323 722 Korea Rep bigeye pole/line 2,203,040 1,040,601 1,162,439 5,093 Korea Rep southern bf longline -32,937 -78,965 46,028 -998 Korea Rep Atlantic bf purse seine 824,761 383,311 441,450 2,606 Korea Rep albacore gillnet 42,589 8,047 34,542 1,193 Korea Rep Atlantic bf longline -279,444 -669,946 390,503 -998 Korea Rep albacore longline -9,135,415 -13,374,382 4,238,966 -2,085 Korea Rep bigeye purse seine 1,972,289 1,072,532 899,757 5,891 Korea Rep bigeye longline 85,137,810 -14,918,574 100,056,384 2,287 Korea Rep yellown gillnet 37,827 341 37,486 833 Korea Rep bigeye gillnet 70,128 36,257 33,872 5,564 Korea Rep skipjack longline -815,885,692 -905,507,308 89,621,617 -3,551 Latvia yellown pole/line -97,973 -170,788 72,815 -699 Latvia yellown longline -870,455 -994,070 123,615 -3,659 Latvia yellown purse seine 266,977 126,710 140,267 989 Liberia yellown pole/line -37,395 -38,798 1,403 -1,453 Liberia yellown longline -116,359 -118,740 2,381 -2,663 Liberia yellown purse seine -45,815 -48,517 2,702 -924 Liberia bigeye longline -31,891 -32,544 653 -2,663 Liberia bigeye pole/line -16,094 -16,698 604 -1,453 Liberia bigeye purse seine -8,271 -8,759 488 -924 Libya yellown pole/line -22,940 -23,860 920 -1,453 Libya yellown longline -71,380 -72,943 1,563 -2,663 Libya yellown purse seine -28,105 -29,878 1,773 -924 Lithuania skipjack longline -14,055 -14,961 906 -3,659 Lithuania skipjack purse seine 140,448 106,949 33,498 989 Lithuania skipjack pole/line -91,685 -122,620 30,934 -699 Malaysia bigeye pole/line -4,494 -5,833 1,338 -682 Table continued on next page 158 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Malaysia yellown longline -5,320,537 -5,715,389 394,852 -2,736 Malaysia yellown gillnet -21,490 -35,689 14,199 -307 Malaysia bigeye gillnet -633 -1,052 418 -307 Malaysia yellown purse seine -40,764 -51,030 10,267 -806 Malaysia yellown hook/line -32,393 -41,350 8,957 -734 Malaysia albacore gillnet -401 -666 265 -307 Malaysia bigeye longline -1,768,381 -1,899,618 131,237 -2,736 Malaysia albacore longline -23,787 -25,553 1,765 -2,736 Malaysia yellown pole/line -181,160 -235,114 53,954 -682 Maldives yellown longline 17,230,265 8,896,544 8,333,721 960 Maldives skipjack pole/line 284,493,835 223,626,776 60,867,059 2,170 Maldives skipjack longline 880,512 454,637 425,875 960 Maldives skipjack hook/line 148 114 34 2,037 Maldives yellown pole/line 4,820,468 3,789,135 1,031,332 2,170 Maldives bigeye pole/line 22,868 17,976 4,893 2,170 Maldives bigeye longline 991,827 512,112 479,714 960 Maldives bigeye gillnet 8,410 6,881 1,530 2,553 Maldives yellown gillnet 181,407 148,413 32,994 2,553 Maldives yellown hook/line 552,493 426,531 125,962 2,037 Maldives skipjack gillnet 136,347 111,548 24,799 2,553 Malta albacore longline -16,131 -28,732 12,601 -3,659 Malta albacore hook/line -3,610 -17,147 13,538 -762 Malta Atlantic bf trap -177,288 -464,219 286,931 -1,766 Malta albacore purse seine 1,631 -3,082 4,713 989 Malta Atlantic bf longline -359,099 -639,634 280,534 -3,659 Malta Atlantic bf pole/line -24,976 -127,089 102,113 -699 Malta Atlantic bf hook/line -603 -2,866 2,263 -762 Malta albacore pole/line -2,941 -14,963 12,022 -699 Malta Atlantic bf purse seine 109,728 -207,406 317,134 989 Mauritius yellown gillnet -539 -590 52 -1,070 Mauritius yellown longline -371,587 -385,901 14,314 -2,663 Mauritius bigeye longline -153,482 -159,395 5,912 -2,663 Mauritius skipjack pole/line -22,377 -23,957 1,580 -1,453 Mauritius yellown hook/line -3,363 -3,581 217 -1,587 Mauritius albacore longline -101,499 -105,409 3,910 -2,663 Mauritius albacore pole/line -6,694 -7,167 473 -1,453 Mauritius bigeye gillnet -197 -216 19 -1,070 Mauritius albacore gillnet -6,083 -6,666 583 -1,070 Mauritius bigeye pole/line -854 -914 60 -1,453 Mauritius yellown pole/line -25,074 -26,845 1,770 -1,453 Mexico Pacic bf hook/line -954,621 -1,021,493 66,872 -2,115 Mexico bigeye purse seine -138,531 -151,338 12,807 -1,603 Mexico Pacic bf gillnet -2,661 -4,149 1,488 -265 Mexico Atlantic bf purse seine -12,686 -13,859 1,173 -1,603 Mexico Atlantic bf longline -22,238 -23,432 1,194 -2,760 Mexico skipjack pole/line -908,404 -1,049,205 140,802 -828 Mexico albacore longline -13,305 -14,019 714 -2,760 Mexico yellown longline -15,889,345 -16,742,452 853,107 -2,760 Table continued on next page 159 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Mexico Pacic bf purse seine -651,000 -711,183 60,183 -1,603 Mexico yellown purse seine -216,555,343 -236,575,359 20,020,016 -1,603 Mexico albacore pole/line -13,976 -16,889 2,914 -711 Mexico skipjack purse seine -22,240,516 -23,900,339 1,659,823 -1,721 Mexico bigeye longline -304,936 -321,308 16,372 -2,760 Mexico skipjack longline -1,210,073 -1,264,070 53,997 -2,878 Mexico yellown gillnet -26 -41 15 -265 Mexico Pacic bf longline -268,880 -283,317 14,436 -2,760 Mexico Atlantic bf trap -2,440 -2,577 137 -2,644 Mexico Atlantic bf pole/line 0 0 0 -711 Mexico Pacic bf pole/line -22,143 -26,760 4,617 -711 Mexico bigeye pole/line -6,475 -7,825 1,350 -711 Mexico yellown pole/line -1,910,151 -2,308,393 398,242 -711 Mexico Atlantic bf hook/line -10,799 -11,556 756 -2,115 Morocco Atlantic bf longline -88,615 -92,944 4,329 -2,663 Morocco bigeye purse seine -134,144 -153,028 18,883 -924 Morocco yellown purse seine -70,456 -80,374 9,918 -924 Morocco yellown longline -178,940 -187,680 8,741 -2,663 Morocco bigeye pole/line -261,031 -284,401 23,370 -1,453 Morocco bigeye longline -517,239 -542,504 25,265 -2,663 Morocco skipjack longline -70,084 -73,507 3,423 -2,663 Morocco Atlantic bf trap -2,442,258 -2,649,958 207,700 -1,530 Morocco Atlantic bf purse seine -792,445 -903,996 111,551 -924 Morocco yellown pole/line -57,507 -62,656 5,149 -1,453 Morocco Atlantic bf pole/line -13,803 -15,039 1,236 -1,453 Morocco albacore pole/line -99,759 -108,690 8,931 -1,453 Morocco skipjack purse seine -897,608 -1,023,963 126,355 -924 Morocco albacore hook/line -59,223 -64,078 4,856 -1,587 Morocco Atlantic bf hook/line -334 -361 27 -1,587 Morocco skipjack pole/line -1,302,573 -1,419,192 116,618 -1,453 Morocco albacore purse seine -295 -337 42 -924 Morocco albacore longline -190,968 -200,296 9,328 -2,663 Oman yellown gillnet 162,295 86,480 75,815 3,111 Oman yellown longline 1,697,066 -19,317,758 21,014,825 117 Oman yellown pole/line 3,883,458 1,284,328 2,599,131 2,172 Oman yellown hook/line 441,002 121,756 319,247 2,008 Oman skipjack pole/line 1,557,656 515,144 1,042,512 2,172 Namibia yellown purse seine -16,896 -18,669 1,772 -924 Namibia albacore hook/line -61,368 -104,748 43,380 -137 Namibia southern bf pole/line -611 -652 41 -1,453 Namibia yellown longline -192,363 -199,365 7,002 -2,663 Namibia bigeye longline -423,193 -438,598 15,405 -2,663 Namibia southern bf longline -1,543 -1,599 56 -2,663 Namibia albacore purse seine -13,121 -14,498 1,376 -924 Namibia yellown pole/line -64,648 -68,962 4,313 -1,453 Namibia bigeye purse seine -30,968 -34,217 3,249 -924 Namibia albacore pole/line -1,611,343 -1,718,851 107,508 -1,453 Namibia bigeye pole/line -166,514 -177,623 11,110 -1,453 Table continued on next page 160 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Namibia albacore longline -2,059,890 -2,134,874 74,984 -2,663 Nauru bigeye gillnet 13 6 7 9,270 Nauru skipjack hook/line 20,336 3,099 17,237 6,218 Nauru bigeye hook/line 6,883 1,049 5,834 6,218 Nauru bigeye purse seine 5,999 2,171 3,829 8,258 Nauru yellown purse seine 34,320 12,418 21,902 8,258 Nauru skipjack longline 8,550 -3,025 11,575 3,893 Nauru bigeye longline 3,079 -1,089 4,169 3,893 Nauru yellown gillnet 12,504 5,395 7,109 9,270 Nauru bigeye pole/line 2,966 994 1,972 7,927 Nauru yellown hook/line 12,911 1,967 10,944 6,218 Nauru skipjack purse seine 29,828 10,792 19,035 8,258 Nauru skipjack pole/line 18,466 6,188 12,277 7,927 Nauru yellown pole/line 12,830 4,300 8,530 7,927 Nauru yellown longline 10,901 -3,857 14,758 3,893 Nauru skipjack gillnet 14,754 6,366 8,388 9,270 Netherlands skipjack purse seine -4,216 -4,356 140 -411 Netherlands skipjack longline -1,403 -1,407 4 -5,059 Netherlands skipjack pole/line -19,878 -20,007 130 -2,099 New Caledonia* yellown hook/line 617,797 302,744 315,052 7,969 New Caledonia yellown pole/line 561,659 316,082 245,576 9,295 New Caledonia albacore hook/line 1,946,063 953,646 992,417 7,969 New Caledonia bigeye longline 147,382 65,948 81,435 7,355 New Caledonia bigeye pole/line 88,097 49,578 38,519 9,295 New Caledonia albacore pole/line 2,719,217 1,530,283 1,188,934 9,295 New Caledonia albacore purse seine 1,072,300 623,643 448,657 9,713 New Caledonia bigeye hook/line 223,496 109,522 113,975 7,969 New Caledonia albacore longline 4,469,852 2,000,081 2,469,770 7,355 New Caledonia bigeye purse seine 178,758 103,964 74,793 9,713 New Caledonia yellown purse seine 1,507,005 876,464 630,540 9,713 New Caledonia yellown longline 768,917 344,060 424,857 7,355 New Caledonia skipjack longline 311 139 172 7,355 New Caledonia yellown gillnet 472,059 267,410 204,649 9,374 New Caledonia bigeye gillnet 330 187 143 9,374 New Caledonia albacore mw trawl 3,426,363 2,064,371 1,361,993 10,224 New Caledonia skipjack gillnet 287 163 124 9,374 New Caledonia skipjack purse seine 675 392 282 9,713 New Caledonia skipjack hook/line 501 246 256 7,969 New Caledonia skipjack pole/line 416 234 182 9,295 Vanuatu yellown longline 3,554,934 720,646 2,834,287 3,893 Vanuatu bigeye purse seine 24,597,987 15,353,048 9,244,940 8,258 Vanuatu albacore hook/line 838,251 419,805 418,446 6,218 Vanuatu albacore purse seine 528,843 330,082 198,761 8,258 Vanuatu bigeye longline 3,319,770 672,975 2,646,796 3,893 Vanuatu bigeye pole/line 84,385 51,343 33,043 7,927 Vanuatu albacore pole/line 2,162,156 1,315,526 846,629 7,927 Vanuatu yellown purse seine 105,895,138 66,095,371 39,799,767 8,258 Vanuatu albacore longline 39,156,884 7,937,773 31,219,110 3,893 Table continued on next page 161 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Vanuatu skipjack purse seine 555,333,696 346,616,355 208,717,341 8,258 Vanuatu yellown pole/line 435,117 264,740 170,378 7,927 Vanuatu skipjack longline 3,331,983 675,450 2,656,533 3,893 Vanuatu skipjack pole/line 778,650 473,756 304,894 7,927 New Zealand Pacic bf seine 47,349 44,808 2,541 10,008 New Zealand southern bf hook/line 711,890 650,396 61,494 6,218 New Zealand skipjack gillnet 24,196,998 22,795,097 1,401,902 9,270 New Zealand bigeye longline 543,998 468,946 75,052 3,893 New Zealand albacore longline 4,995,017 4,305,883 689,133 3,893 New Zealand bigeye pole/line 523,927 488,426 35,500 7,927 New Zealand bigeye purse seine 1,059,885 990,954 68,931 8,258 New Zealand yellown pole/line 2,700,685 2,517,692 182,993 7,927 New Zealand southern bf gillnet 684,203 644,563 39,641 9,270 New Zealand southern bf mw trawl 348,435 330,140 18,296 10,229 New Zealand bigeye gillnet 2,274 2,142 132 9,270 New Zealand skipjack pole/line 30,285,212 28,233,148 2,052,064 7,927 New Zealand southern bf seine 416,675 394,314 22,361 10,008 New Zealand yellown hook/line 2,717,763 2,483,000 234,764 6,218 New Zealand albacore mw trawl 7,237,595 6,857,562 380,033 10,229 New Zealand albacore hook/line 3,205,691 2,928,780 276,911 6,218 New Zealand Pacic bf gillnet 77,750 73,246 4,505 9,270 New Zealand albacore pole/line 4,896,030 4,564,285 331,745 7,927 New Zealand Pacic bf hook/line 80,897 73,909 6,988 6,218 New Zealand skipjack longline 14,022,932 12,088,269 1,934,662 3,893 New Zealand yellown purse seine 7,224,408 6,754,556 469,852 8,258 New Zealand albacore purse seine 1,924,876 1,799,688 125,188 8,258 New Zealand yellown gillnet 2,632,089 2,479,594 152,495 9,270 New Zealand skipjack purse seine 48,919,942 45,738,351 3,181,592 8,258 New Zealand Pacic bf mw trawl 39,595 37,516 2,079 10,229 New Zealand skipjack hook/line 33,353,003 30,471,931 2,881,072 6,218 New Zealand bigeye hook/line 1,216,027 1,110,985 105,042 6,218 New Zealand yellown longline 2,294,692 1,978,107 316,585 3,893 Nicaragua bigeye purse seine 27,246 19,926 7,319 1,786 Nicaragua skipjack pole/line 41,286 30,060 11,226 1,765 Nicaragua bigeye hook/line 1,114 -480 1,594 335 Nicaragua yellown pole/line 6,201 4,515 1,686 1,765 Nicaragua skipjack purse seine 4,356,218 3,185,976 1,170,242 1,786 Nicaragua bigeye pole/line 1,995 1,452 542 1,765 Nicaragua bigeye longline 18,307 11,930 6,378 1,377 Nicaragua skipjack longline 280,761 182,950 97,811 1,377 Nicaragua yellown longline 236,160 153,887 82,272 1,377 Nicaragua yellown purse seine 12,746,637 9,322,417 3,424,220 1,786 Niue* albacore longline 154,617 143,327 11,291 7,355 Niue yellown purse seine 114,371 108,047 6,324 9,713 Niue bigeye gillnet 43 41 2 9,374 Niue skipjack purse seine 10,795 10,198 597 9,713 Niue skipjack gillnet 4,591 4,328 263 9,374 Niue yellown gillnet 35,826 33,773 2,053 9,374 Table continued on next page 162 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Niue bigeye longline 19,392 17,976 1,416 7,355 Niue skipjack longline 4,971 4,608 363 7,355 Niue albacore hook/line 67,317 62,780 4,537 7,969 Niue albacore mw trawl 118,522 112,296 6,226 10,224 Niue bigeye purse seine 23,521 22,220 1,301 9,713 Niue albacore purse seine 37,092 35,041 2,051 9,713 Niue skipjack hook/line 8,020 7,480 541 7,969 Niue yellown pole/line 42,626 40,163 2,463 9,295 Niue albacore pole/line 94,061 88,626 5,435 9,295 Niue yellown hook/line 46,886 43,726 3,160 7,969 Niue bigeye pole/line 11,592 10,922 670 9,295 Niue bigeye hook/line 29,407 27,426 1,982 7,969 Niue yellown longline 58,355 54,094 4,261 7,355 Niue skipjack pole/line 6,662 6,278 385 9,295 North Mari- anus* yellown hook/line 26,201 12,731 13,470 7,969 North Marianas yellown pole/line 23,820 13,321 10,499 9,295 North Marianus skipjack hook/line 186,466 90,605 95,861 7,969 North Marianas skipjack purse seine 250,974 145,114 105,860 9,713 North Marianas yellown purse seine 63,913 36,955 26,958 9,713 North Marianus yellown gillnet 20,020 11,271 8,750 9,374 North Marianas yellown longline 32,610 14,446 18,164 7,355 North Marianus skipjack gillnet 106,730 60,085 46,645 9,374 North Marianas skipjack longline 115,565 51,193 64,371 7,355 North Marianas skipjack pole/line 154,903 86,625 68,278 9,295 Micronesia skipjack purse seine 53,141,729 11,792,879 41,348,850 8,258 Micronesia skipjack gillnet 26,285,193 8,065,692 18,219,501 9,270 Micronesia bigeye purse seine 927,899 205,914 721,986 8,258 Micronesia bigeye gillnet 1,991 611 1,380 9,270 Micronesia albacore hook/line 239 -8 247 6,218 Micronesia albacore pole/line 365 69 296 7,927 Micronesia yellown hook/line 4,476,885 -149,741 4,626,626 6,218 Micronesia skipjack pole/line 32,898,816 6,229,622 26,669,194 7,927 Micronesia albacore mw trawl 539 200 339 10,229 Micronesia skipjack longline 15,233,110 -9,910,303 25,143,414 3,893 Micronesia yellown longline 3,779,973 -2,459,162 6,239,135 3,893 Micronesia albacore purse seine 143 32 112 8,258 Micronesia skipjack hook/line 36,231,362 -1,211,851 37,443,213 6,218 Micronesia bigeye hook/line 1,064,598 -35,608 1,100,206 6,218 Micronesia bigeye pole/line 458,683 86,855 371,828 7,927 Micronesia albacore longline 372 -242 614 3,893 Micronesia yellown purse seine 11,900,537 2,640,892 9,259,644 8,258 Micronesia bigeye longline 476,255 -309,840 786,096 3,893 Micronesia yellown gillnet 4,335,758 1,330,441 3,005,317 9,270 Micronesia yellown pole/line 4,448,753 842,403 3,606,351 7,927 Marshall Is yellown pole/line 8,155,513 3,708,296 4,447,217 7,927 Marshall Is yellown hook/line 8,207,083 2,501,702 5,705,381 6,218 Marshall Is skipjack hook/line 74,407,177 22,680,965 51,726,211 6,218 Table continued on next page 163 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Marshall Is bigeye longline 996,646 -109,937 1,106,583 3,893 Marshall Is skipjack longline 31,283,747 -3,450,814 34,734,561 3,893 Marshall Is bigeye hook/line 2,227,854 679,100 1,548,755 6,218 Marshall Is yellown longline 6,929,497 -764,372 7,693,869 3,893 Marshall Is bigeye pole/line 959,873 436,452 523,421 7,927 Marshall Is skipjack pole/line 67,563,248 30,720,880 36,842,368 7,927 Marshall Is skipjack gillnet 53,981,057 28,811,586 25,169,471 9,270 Marshall Is skipjack purse seine 109,135,430 52,013,757 57,121,673 8,258 Marshall Is yellown gillnet 7,948,368 4,242,323 3,706,045 9,270 Marshall Is yellown purse seine 21,816,226 10,397,576 11,418,651 8,258 Marshall Is bigeye purse seine 1,941,790 925,454 1,016,336 8,258 Marshall Is bigeye gillnet 4,166 2,223 1,942 9,270 Palau yellown purse seine 715 -94 809 8,258 Palau yellown hook/line 269 -135 404 6,218 Palau yellown gillnet 261 -2 263 9,270 Palau yellown pole/line 267 -48 315 7,927 Palau yellown longline 227 -318 545 3,893 Pakistan yellown pole/line 1,140,878 495,974 644,905 2,172 Pakistan yellown hook/line 129,557 50,345 79,213 2,008 Pakistan yellown longline 498,562 -4,715,705 5,214,268 117 Pakistan yellown gillnet 47,679 28,867 18,811 3,111 Pakistan skipjack pole/line 7,772,076 3,378,752 4,393,325 2,172 Panama albacore hook/line 1,196 -341 1,536 335 Panama bigeye longline 7,115,945 4,889,174 2,226,770 1,377 Panama yellown longline 13,242,029 9,098,242 4,143,787 1,377 Panama yellown purse seine 44,167,459 33,509,789 10,657,670 1,786 Panama yellown pole/line 3,156,410 2,385,498 770,912 1,765 Panama albacore longline 9,432 6,481 2,952 1,377 Panama albacore purse seine 54 41 13 1,786 Panama bigeye purse seine 9,676,058 7,341,211 2,334,846 1,786 Panama skipjack purse seine 49,378,232 37,463,195 11,915,037 1,786 Panama albacore pole/line 11,571 8,745 2,826 1,765 Panama bigeye pole/line 1,488,980 1,125,316 363,664 1,765 Panama skipjack pole/line 11,493,016 8,685,997 2,807,019 1,765 Panama skipjack longline 6,987,193 4,800,713 2,186,481 1,377 Papua N Guin yellown hook/line 54,884,611 16,730,052 38,154,558 6,218 Papua N Guin albacore pole/line 3,045,253 1,384,671 1,660,583 7,927 Papua N Guin skipjack longline 109,403,338 -12,067,946 121,471,283 3,893 Papua N Guin skipjack pole/line 236,277,461 107,434,910 128,842,551 7,927 Papua N Guin albacore hook/line 1,993,889 607,782 1,386,107 6,218 Papua N Guin bigeye purse seine 7,437,197 3,544,555 3,892,642 8,258 Papua N Guin albacore purse seine 1,197,243 570,604 626,639 8,258 Papua N Guin yellown purse seine 145,895,246 69,533,422 76,361,824 8,258 Papua N Guin bigeye hook/line 8,532,845 2,601,001 5,931,844 6,218 Papua N Guin bigeye longline 3,817,227 -421,067 4,238,294 3,893 Papua N Guin albacore longline 3,106,821 -342,704 3,449,524 3,893 Papua N Guin skipjack hook/line 260,211,634 79,318,304 180,893,330 6,218 Papua N Guin albacore mw trawl 4,501,668 2,599,376 1,902,293 10,229 Table continued on next page 164 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Papua N Guin yellown longline 46,340,767 -5,111,707 51,452,474 3,893 Papua N Guin bigeye pole/line 3,676,384 1,671,645 2,004,739 7,927 Papua N Guin bigeye gillnet 15,955 8,516 7,439 9,270 Papua N Guin yellown gillnet 53,154,458 28,370,401 24,784,056 9,270 Papua N Guin yellown pole/line 54,539,722 24,799,107 29,740,614 7,927 Papua N Guin skipjack purse seine 381,660,894 181,898,921 199,761,972 8,258 Papua N Guin skipjack gillnet 188,778,748 100,757,849 88,020,899 9,270 Peru yellown pole/line 14,777 2,413 12,364 134 Peru yellown longline -1,132,935 -1,632,470 499,535 -254 Peru skipjack pole/line 596 97 499 134 Peru skipjack longline -9,853 -14,197 4,344 -254 Peru yellown purse seine 1,164,258 324,918 839,340 155 Peru skipjack purse seine 17,642 4,924 12,719 155 Philippines albacore longline -20,926 -26,622 5,696 -2,416 Philippines skipjack hook/line -19,023,580 -42,696,561 23,672,981 -528 Philippines skipjack longline -58,534,856 -74,431,449 15,896,593 -2,418 Philippines albacore pole/line -379 -1,067 689 -362 Philippines yellown longline -231,579,141 -293,156,435 61,577,294 -2,436 Philippines yellown gillnet 5,942 -964 6,906 557 Philippines albacore gillnet 746 -103 849 578 Philippines bigeye gillnet 2,488 -343 2,832 578 Philippines bigeye pole/line -4,983 -14,039 9,057 -362 Philippines skipjack purse seine -19,438,220 -45,580,483 26,142,264 -488 Philippines skipjack gillnet 10,101,163 -1,417,873 11,519,036 576 Philippines yellown pole/line -112,668 -303,619 190,951 -382 Philippines yellown hook/line -19,495 -42,621 23,126 -546 Philippines skipjack pole/line -9,341,951 -26,203,198 16,861,248 -364 Philippines bigeye longline -56,258,056 -71,571,853 15,313,797 -2,416 Portugal yellown longline -24,988 -141,180 116,192 -527 Portugal albacore hook/line -16,110 -41,949 25,839 -743 Portugal Atlantic bf pole/line -10,677 -16,467 5,790 -1,125 Portugal albacore longline -126,148 -176,926 50,779 -2,960 Portugal Atlantic bf longline -35,743 -41,082 5,338 -4,085 Portugal bigeye pole/line 1,062,473 -1,286,764 2,349,238 703 Portugal bigeye purse seine 711,197 248,837 462,360 2,391 Portugal yellown hook/line 427 -192 619 1,690 Portugal skipjack longline -86,556 -93,933 7,377 -4,520 Portugal yellown purse seine 278,178 112,917 165,260 4,121 Portugal albacore pole/line 16 -211,484 211,500 0 Portugal southern bf longline -32,677 -37,558 4,881 -4,085 Portugal skipjack purse seine 75,869 -152,692 228,560 128 Portugal Atlantic bf purse seine 6,026 -503 6,529 563 Portugal albacore purse seine 685 202 483 1,688 Portugal yellown gillnet 19 -128 147 308 Portugal yellown pole/line 176,965 -1,106 178,071 2,433 Portugal Atlantic bf trap -111,915 -143,064 31,148 -2,192 Portugal bigeye longline -898,890 -1,518,106 619,215 -2,257 Portugal skipjack pole/line -3,946,223 -4,920,557 974,334 -1,560 Table continued on next page 165 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Timor Leste* yellown longline 716 -3,238 3,954 278 Timor Leste yellown pole/line 734 225 509 2,218 Timor Leste yellown hook/line 23 -16 39 893 Timor Leste yellown gillnet 166 55 111 2,298 Puerto Rico skipjack pole/line 884 -1,028 1,912 352 Puerto Rico skipjack purse seine 9,880 114 9,766 771 Puerto Rico yellown purse seine 5,953 68 5,884 771 Puerto Rico skipjack longline -12,172 -18,012 5,841 -1,587 Puerto Rico yellown pole/line 409 -475 884 352 Puerto Rico yellown longline -16,055 -23,759 7,704 -1,587 Reunion* albacore pole/line -102,727 -110,429 7,701 -1,405 Reunion yellown pole/line -142,236 -152,899 10,663 -1,405 Reunion bigeye gillnet -2,553 -2,756 203 -1,326 Reunion bigeye longline -2,020,026 -2,083,647 63,621 -3,345 Reunion albacore longline -2,022,737 -2,086,443 63,706 -3,345 Reunion skipjack pole/line -113,822 -122,356 8,533 -1,405 Reunion yellown hook/line -33,951 -35,261 1,310 -2,731 Reunion bigeye pole/line -8,655 -9,304 649 -1,405 Reunion yellown gillnet -3,914 -4,225 311 -1,326 Reunion yellown longline -2,737,438 -2,823,654 86,216 -3,345 Reunion albacore gillnet -119,536 -129,035 9,499 -1,326 Russian Fed bigeye pole/line -242 -461 219 -699 Russian Fed yellown pole/line -454 -864 410 -699 Russian Fed yellown purse seine 1,236 446 790 989 Russian Fed bigeye longline -1,369 -1,606 237 -3,659 Russian Fed yellown longline -4,030 -4,726 696 -3,659 Russian Fed bigeye purse seine 277 100 177 989 St Helena* albacore pole/line -23,268 -25,123 1,855 -1,405 St Helena skipjack pole/line -451,074 -487,041 35,967 -1,405 St Helena albacore hook/line -18,245 -18,994 749 -2,731 St Helena albacore longline -38,629 -39,923 1,294 -3,345 St Helena yellown pole/line -118,091 -127,507 9,416 -1,405 St Helena bigeye pole/line -9,441 -10,194 753 -1,405 St Helena yellown purse seine -34,084 -37,953 3,869 -987 St Helena albacore purse seine -209 -233 24 -987 St Helena bigeye purse seine -1,939 -2,159 220 -987 St Helena yellown longline -456,338 -471,625 15,286 -3,345 St Helena bigeye longline -31,163 -32,206 1,044 -3,345 St Lucia yellown purse seine 124,922 68,721 56,201 1,786 St Lucia albacore pole/line 188 102 85 1,765 St Lucia albacore longline 2,243 934 1,308 1,377 St Lucia bigeye purse seine 532 292 239 1,786 St Lucia yellown pole/line 18,536 10,095 8,441 1,765 St Lucia bigeye longline 2,160 900 1,260 1,377 St Lucia yellown longline 126,114 52,536 73,578 1,377 St Lucia skipjack purse seine 158,329 87,099 71,230 1,786 St Lucia bigeye pole/line 237 129 108 1,765 St Lucia skipjack longline 73,013 30,415 42,598 1,377 Table continued on next page 166 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) St Lucia skipjack pole/line 30,625 16,679 13,945 1,765 St Lucia albacore purse seine 472 260 212 1,786 St Vincent bigeye longline 56,156 23,393 32,763 1,377 St Vincent albacore hook/line 12 -16 28 335 St Vincent bigeye purse seine 13,824 7,605 6,219 1,786 St Vincent albacore pole/line 5,908 3,218 2,690 1,765 St Vincent skipjack pole/line 55,086 30,002 25,084 1,765 St Vincent bigeye pole/line 6,162 3,356 2,806 1,765 St Vincent skipjack longline 131,331 54,709 76,622 1,377 St Vincent yellown longline 1,497,973 624,015 873,958 1,377 St Vincent albacore purse seine 14,878 8,185 6,694 1,786 St Vincent albacore longline 70,643 29,428 41,215 1,377 St Vincent skipjack purse seine 284,793 156,668 128,125 1,786 St Vincent yellown purse seine 1,483,817 816,267 667,550 1,786 St Vincent yellown pole/line 220,170 119,913 100,257 1,765 Sao Tome Prn yellown longline -141,783 -147,747 5,965 -2,663 Sao Tome Prn skipjack purse seine -78,652 -88,188 9,536 -924 Sao Tome Prn yellown purse seine -55,825 -62,594 6,768 -924 Sao Tome Prn bigeye pole/line -3,018 -3,250 233 -1,453 Sao Tome Prn skipjack longline -6,131 -6,389 258 -2,663 Sao Tome Prn skipjack pole/line -114,201 -123,007 8,806 -1,453 Sao Tome Prn yellown pole/line -45,566 -49,079 3,514 -1,453 Sao Tome Prn bigeye longline -5,980 -6,231 252 -2,663 Sao Tome Prn bigeye purse seine -1,551 -1,739 188 -924 Senegal bigeye purse seine -186,354 -213,038 26,684 -924 Senegal yellown longline -1,274,414 -1,337,735 63,321 -2,663 Senegal skipjack longline -180,022 -188,967 8,945 -2,663 Senegal yellown pole/line -408,996 -446,243 37,246 -1,453 Senegal skipjack pole/line -3,353,096 -3,658,456 305,360 -1,453 Senegal bigeye longline -718,552 -754,254 35,702 -2,663 Senegal skipjack purse seine -2,309,332 -2,640,000 330,668 -924 Senegal yellown purse seine -500,889 -572,611 71,721 -924 Senegal yellown gillnet -24 -27 3 -1,003 Senegal bigeye pole/line -362,627 -395,650 33,024 -1,453 Seychelles albacore gillnet -19,260 -25,718 6,458 -1,070 Seychelles bigeye gillnet -34,445 -45,996 11,551 -1,070 Seychelles albacore longline -321,360 -364,671 43,311 -2,663 Seychelles albacore pole/line -21,043 -26,241 5,198 -1,453 Seychelles yellown hook/line -926,488 -1,136,076 209,588 -1,587 Seychelles bigeye pole/line -149,561 -186,507 36,945 -1,453 Seychelles yellown longline -102,391,884 -116,191,732 13,799,848 -2,663 Seychelles skipjack pole/line -66,917,008 -83,447,158 16,530,150 -1,453 Seychelles bigeye longline -26,877,524 -30,499,938 3,622,414 -2,663 Seychelles yellown pole/line -6,909,540 -8,616,366 1,706,827 -1,453 Seychelles yellown gillnet -149,183 -199,209 50,026 -1,070 Singapore skipjack longline 79 -195 275 117 Singapore skipjack gillnet 1,524 1,324 199 3,111 Singapore skipjack pole/line 1,557 1,265 291 2,172 Table continued on next page 167 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Singapore skipjack hook/line 2,021 1,611 409 2,008 Singapore skipjack purse seine 2,275 1,823 452 2,047 South Africa bigeye gillnet -253 -274 22 -1,070 South Africa yellown gillnet -220 -239 19 -1,070 South Africa yellown hook/line -917 -996 79 -1,061 South Africa yellown longline -1,798,720 -1,860,584 61,864 -2,663 South Africa albacore hook/line -647,844 -703,764 55,920 -1,061 South Africa albacore longline -2,820,865 -2,917,884 97,019 -2,663 South Africa bigeye pole/line -120,965 -128,590 7,625 -1,453 South Africa bigeye purse seine -22,293 -24,502 2,210 -924 South Africa albacore gillnet -628 -682 54 -1,070 South Africa skipjack longline -253 -262 9 -2,663 South Africa yellown pole/line -563,805 -599,347 35,541 -1,453 South Africa skipjack purse seine -149 -163 15 -924 South Africa yellown purse seine -144,681 -159,021 14,340 -924 South Africa bigeye longline -501,753 -519,010 17,257 -2,663 South Africa albacore purse seine -17,902 -19,676 1,774 -924 South Africa albacore pole/line -2,199,102 -2,337,731 138,628 -1,453 South Africa southern bf longline -10,653 -11,020 366 -2,663 South Africa skipjack pole/line -2,535 -2,694 160 -1,453 Spain yellown longline -311,666,952 -351,580,046 39,913,095 -4,294 Spain albacore longline -1,070,563 -2,258,415 1,187,853 -1,778 Spain albacore purse seine 411,800 128,599 283,201 2,869 Spain skipjack purse seine 7,790,008 -6,787,961 14,577,969 268 Spain Atlantic bf trap -4,501,736 -6,993,835 2,492,099 -1,705 Spain Atlantic bf longline -2,416,735 -3,050,754 634,019 -3,598 Spain albacore pole/line 13,689,124 -9,178,707 22,867,831 1,181 Spain yellown gillnet -467,616 -602,222 134,605 -1,910 Spain albacore hook/line 115,726 -25,101,944 25,217,670 9 Spain bigeye purse seine 4,591,794 -398,271 4,990,064 671 Spain Atlantic bf hook/line -6,291 -9,571 3,280 -1,810 Spain yellown purse seine 2,725,372 -1,508,489 4,233,861 354 Spain skipjack longline -7,315,492 -8,152,446 836,955 -4,380 Spain bigeye longline -55,722,119 -65,942,520 10,220,401 -3,977 Spain bigeye pole/line -5,089,532 -8,739,943 3,650,411 -1,017 Spain skipjack gillnet -55 -69 14 -1,996 Spain skipjack pole/line -155,052,827 -209,757,888 54,705,061 -1,420 Spain yellown hook/line -2,583,455 -3,150,258 566,803 -2,506 Spain yellown pole/line -14,731,221 -20,802,898 6,071,677 -1,334 Spain Atlantic bf pole/line -530,839 -1,316,179 785,340 -638 Spain southern bf longline -10,793 -13,624 2,831 -3,598 Spain Atlantic bf purse seine 1,615,855 163,567 1,452,288 1,050 Spain albacore gillnet 6,482 -14,655 21,137 605 Spain bigeye gillnet -53,249 -77,629 24,380 -1,593 Syria Atlantic bf purse seine 64,332 16,007 48,325 2,047 Syria albacore longline 8,555 -103,534 112,089 117 Syria Atlantic bf hook/line 450 105 345 2,008 Syria albacore hook/line 157,216 36,799 120,417 2,008 Table continued on next page 168 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Syria Atlantic bf pole/line 21,973 6,413 15,560 2,172 Syria albacore purse seine 55,811 13,887 41,924 2,047 Syria Atlantic bf trap 23,550 -20,173 43,723 828 Syria albacore pole/line 151,011 44,075 106,937 2,172 Syria Atlantic bf longline 3,263 -39,486 42,748 117 Thailand albacore pole/line -9,989 -12,692 2,703 -860 Thailand albacore gillnet 1,357 -2,616 3,973 80 Thailand bigeye gillnet 436 -840 1,275 80 Thailand skipjack longline -344 -372 27 -2,914 Thailand albacore longline -333,174 -359,788 26,614 -2,914 Thailand bigeye longline -5,006,886 -5,406,833 399,946 -2,914 Thailand skipjack gillnet 1 -1 2 80 Thailand yellown longline -5,990,171 -6,468,662 478,490 -2,914 Thailand yellown pole/line -218,665 -277,845 59,180 -860 Thailand skipjack pole/line -6,856,457 -8,712,116 1,855,659 -860 Thailand bigeye pole/line -15,072 -19,151 4,079 -860 Thailand yellown gillnet 590 -1,137 1,727 80 Thailand yellown hook/line -31,974 -39,243 7,269 -1,024 Togo bigeye pole/line -8,047 -8,689 642 -1,453 Togo bigeye longline -15,946 -16,640 694 -2,663 Togo bigeye purse seine -4,135 -4,654 519 -924 Tonga albacore longline 421,087 -192,927 614,014 3,893 Tonga skipjack pole/line 2,841 806 2,034 7,927 Tonga yellown hook/line 235,626 20,505 215,120 6,218 Tonga yellown purse seine 626,344 195,806 430,538 8,258 Tonga albacore pole/line 412,743 117,159 295,583 7,927 Tonga yellown pole/line 234,145 66,463 167,681 7,927 Tonga bigeye pole/line 123,568 35,075 88,492 7,927 Tonga yellown gillnet 228,198 88,462 139,736 9,270 Tonga skipjack purse seine 4,589 1,435 3,154 8,258 Tonga albacore hook/line 270,244 23,518 246,727 6,218 Tonga albacore purse seine 162,270 50,728 111,541 8,258 Tonga bigeye purse seine 249,973 78,146 171,827 8,258 Tonga albacore mw trawl 610,140 271,532 338,607 10,229 Tonga bigeye longline 128,302 -58,783 187,085 3,893 Tonga skipjack gillnet 2,270 880 1,390 9,270 Tonga skipjack hook/line 3,129 272 2,856 6,218 Tonga bigeye hook/line 286,799 24,958 261,840 6,218 Tonga skipjack longline 1,315 -603 1,918 3,893 Tonga yellown longline 198,946 -91,150 290,096 3,893 Tonga bigeye gillnet 536 208 328 9,270 Trinidad Tob albacore hook/line 110 41 69 1,286 Trinidad Tob yellown pole/line 77,369 42,138 35,231 1,765 Trinidad Tob albacore pole/line 14,452 7,871 6,581 1,765 Trinidad Tob bigeye longline 149,503 62,279 87,224 1,377 Trinidad Tob bigeye purse seine 36,803 20,246 16,557 1,786 Trinidad Tob bigeye pole/line 16,404 8,934 7,470 1,765 Trinidad Tob albacore longline 172,809 71,988 100,822 1,377 Table continued on next page 169 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Trinidad Tob yellown purse seine 521,424 286,842 234,582 1,786 Trinidad Tob yellown longline 526,399 219,284 307,115 1,377 Trinidad Tob albacore purse seine 36,396 20,022 16,374 1,786 Tunisia Atlantic bf longline -2,454,550 -2,613,300 158,751 -2,663 Tunisia Atlantic bf trap -1,441,955 -1,604,325 162,370 -1,530 Tunisia Atlantic bf pole/line -487,456 -545,240 57,784 -1,453 Tunisia Atlantic bf purse seine -962,846 -1,142,308 179,462 -924 Tunisia Atlantic bf hook/line -11,795 -13,075 1,280 -1,587 Turkey albacore longline -18,573 -22,380 3,808 -2,106 Turkey Atlantic bf hook/line -489 -1,467 978 -216 Turkey albacore purse seine -582 -2,007 1,424 -177 Turkey Atlantic bf longline -591,526 -712,793 121,267 -2,106 Turkey Atlantic bf purse seine -56,071 -193,159 137,088 -177 Turkey albacore pole/line -439 -4,071 3,633 -52 Turkey Atlantic bf trap -400,827 -524,859 124,032 -1,395 Turkey Atlantic bf pole/line -5,334 -49,474 44,141 -52 Turkey albacore hook/line -2,047 -6,137 4,090 -216 Tuvalu* skipjack hook/line 2,406,014 2,406,014 0 7,969 Tuvalu yellown purse seine 3,363,850 3,363,850 0 9,713 Tuvalu yellown gillnet 1,053,702 1,053,702 0 9,374 Tuvalu skipjack purse seine 3,238,376 3,238,376 0 9,713 Tuvalu skipjack gillnet 1,377,165 1,377,165 0 9,374 Tuvalu skipjack longline 1,491,155 1,491,155 0 7,355 Tuvalu yellown longline 1,716,333 1,716,333 0 7,355 Tuvalu skipjack pole/line 1,998,749 1,998,749 0 9,295 Tuvalu yellown hook/line 1,379,011 1,379,011 0 7,969 Tuvalu yellown pole/line 1,253,703 1,253,703 0 9,295 UK albacore longline -135 -140 5 -4,832 UK albacore mw trawl -110 -276 166 -113 Tanzania yellown longline -1,541,446 -1,620,449 79,004 -2,663 Tanzania yellown hook/line -13,951 -15,151 1,200 -1,587 Tanzania yellown gillnet -2,235 -2,520 285 -1,070 Tanzania yellown pole/line -104,016 -113,787 9,771 -1,453 USA bigeye gillnet 16,483 9,342 7,141 7,097 USA skipjack gillnet 3,847 1,396 2,451 1,620 USA Pacic bf gillnet 189,154 99,892 89,262 4,323 USA bigeye longline 7,524,426 2,497,287 5,027,138 4,602 USA Atlantic bf pole/line 20 12 8 12,516 USA albacore pole/line 815,825 -317,781 1,133,606 584 USA albacore purse seine -40,519 -147,392 106,873 -308 USA bigeye pole/line 1,143,552 614,923 528,629 6,651 USA yellown gillnet 859 448 410 4,079 USA albacore mw trawl 14,275 7,702 6,573 1,763 USA albacore longline -11,496,496 -17,868,467 6,371,971 -1,465 USA Pacic bf longline 67,606 -7,836 75,441 1,828 USA skipjack longline -288,773 -629,102 340,329 -875 USA albacore hook/line -970 -1,930 960 -820 USA Pacic bf hook/line 200,388 35,062 165,326 2,473 Table continued on next page 170 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) USA Atlantic bf purse seine 539,440 295,241 244,199 11,624 USA yellown purse seine 32,168,111 9,294,099 22,874,013 2,741 USA skipjack purse seine 11,031,485 -29,385,437 40,416,922 282 USA skipjack pole/line 4,310,042 521,397 3,788,645 1,174 USA yellown longline 2,099,004 -483,945 2,582,949 1,584 USA yellown pole/line 2,120,081 982,737 1,137,344 3,633 USA Atlantic bf hook/line 332,610 175,096 157,514 11,112 USA bigeye purse seine 58,477,642 27,257,642 31,219,999 5,759 USA Atlantic bf trap 57,287 28,804 28,483 10,583 USA Atlantic bf longline 494,516 245,907 248,610 10,467 USA Pacic bf purse seine 135,042 42,755 92,287 2,985 Uruguay Atlantic bf purse seine 12 9 3 155 Uruguay albacore longline -8,118 -9,284 1,166 -254 Uruguay Atlantic bf pole/line 3 2 1 134 Uruguay bigeye longline -15,729 -17,988 2,259 -254 Uruguay Atlantic bf longline -15 -17 2 -254 Uruguay Atlantic bf hook/line -50 -51 1 -1,296 Uruguay yellown longline -164,645 -188,291 23,645 -254 Uruguay Atlantic bf trap -20 -22 2 -363 Venezuela skipjack purse seine 2,113,427 907,288 1,206,139 155 Venezuela skipjack pole/line 136,819 46,197 90,622 134 Venezuela bigeye purse seine 17,974 7,716 10,258 155 Venezuela albacore purse seine 5,381 2,310 3,071 155 Venezuela albacore longline -54,171 -73,079 18,908 -254 Venezuela yellown pole/line 264,020 89,146 174,874 134 Venezuela bigeye longline -78,247 -105,559 27,312 -254 Venezuela albacore pole/line 1,863 629 1,234 134 Venezuela albacore hook/line -189 -202 13 -1,296 Venezuela yellown gillnet -7 -8 1 -1,226 Venezuela yellown purse seine 6,015,594 2,582,477 3,433,117 155 Venezuela yellown longline -2,046,778 -2,761,197 714,419 -254 Venezuela bigeye pole/line 2,886 974 1,911 134 Venezuela skipjack longline -532,366 -718,187 185,820 -254 Samoa bigeye longline 65,690 20,554 45,137 3,893 Samoa albacore pole/line 1,842,028 1,220,417 621,610 7,927 Samoa yellown hook/line 214,107 121,996 92,111 6,218 Samoa skipjack pole/line 21,307 14,116 7,190 7,927 Samoa albacore hook/line 1,206,073 687,208 518,865 6,218 Samoa yellown purse seine 569,144 384,794 184,350 8,258 Samoa yellown pole/line 212,762 140,963 71,799 7,927 Samoa albacore purse seine 724,194 489,622 234,571 8,258 Samoa skipjack purse seine 34,417 23,269 11,148 8,258 Samoa skipjack longline 9,866 3,087 6,779 3,893 Samoa bigeye gillnet 275 195 79 9,270 Samoa albacore longline 1,879,269 587,999 1,291,270 3,893 Samoa bigeye purse seine 127,986 86,531 41,456 8,258 Samoa albacore mw trawl 2,722,991 2,010,901 712,090 10,229 Samoa bigeye pole/line 63,267 41,917 21,350 7,927 Table continued on next page 171 Appendix A. Rent Analysis Country Species Gear Private Rent (USD) Social Rent (USD) Opportunity Cost (USD) Unit Rent (USD/t) Samoa yellown longline 180,777 56,563 124,214 3,893 Samoa skipjack hook/line 23,465 13,370 10,095 6,218 Samoa bigeye hook/line 146,841 83,669 63,173 6,218 Samoa skipjack gillnet 17,023 12,111 4,912 9,270 Samoa yellown gillnet 207,358 147,525 59,833 9,270 Yemen skipjack pole/line 20,249,577 10,942,381 9,307,196 2,172 Yemen yellown gillnet 345,361 234,567 110,794 3,111 Yemen yellown longline 3,611,318 -27,099,131 30,710,448 117 Yemen yellown pole/line 8,263,908 4,465,616 3,798,293 2,172 Yemen yellown hook/line 938,443 471,905 466,538 2,008 Montenegro* Atlantic bf longline -4,017 -5,272 1,255 -2,023 Montenegro Atlantic bf hook/line -23 -33 10 -1,409 Montenegro Atlantic bf pole/line -60 -517 457 -83 Montenegro Atlantic bf trap -2,856 -4,140 1,284 -1,406 Montenegro Atlantic bf purse seine 752 -667 1,419 335 High seas* albacore purse seine -16 -55 39 -179 High seas yellown hook/line -541,563 -662,593 121,030 -1,923 High seas albacore hook/line -5,507 -6,738 1,231 -1,923 High seas skipjack longline -480,493 -561,885 81,392 -2,537 High seas bigeye purse seine -68,744 -233,427 164,683 -179 High seas albacore longline -1,830,414 -2,140,474 310,060 -2,537 High seas southern bf seine -14 -50 36 -170 High seas yellown purse seine -528,969 -1,796,176 1,267,207 -179 High seas southern bf hook/line -1,726 -2,112 386 -1,923 High seas yellown gillnet -44,251 -80,960 36,709 -518 High seas skipjack gillnet -4 -7 3 -518 High seas bigeye gillnet -10,197 -18,656 8,459 -518 High seas skipjack pole/line -16,465,509 -28,306,576 11,841,067 -598 High seas southern bf mw trawl 38 -11 49 331 High seas southern bf gillnet -81 -147 67 -518 High seas yellown pole/line -2,242,462 -3,855,115 1,612,653 -598 High seas albacore gillnet -55,519 -101,575 46,056 -518 High seas skipjack purse seine -924,372 -3,138,811 2,214,440 -179 High seas yellown longline -53,090,316 -62,083,454 8,993,138 -2,537 High seas bigeye pole/line -321,033 -551,902 230,869 -598 High seas bigeye longline -16,962,130 -19,835,399 2,873,269 -2,537 High seas albacore pole/line -33,767 -58,051 24,284 -598 * Denotes that weighted means were used in cost calculations for these countries 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 Pacic Salmon Pacic salmon are a transboundary resource, shared by the United States and Canada. In 1985, the Pacic Salmon Treaty (PST) was signed by both parties, after 25 years of negotiations. Prior to the Treaty, both countries engaged in \sh 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 Pacic Salmon Fisheries Com- mission (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 Pacic Salmon Commission is the regulatory body put in place to implement the Treaty. There are ve species of Pacic salmon managed jointly under the treaty: sock- eye (Oncorhynchus nerka), chinook (O. tshawytscha), coho (O. kisutch), chum (O. keta), and pink (O. gorbuscha). Pacic 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 shery. These chapters are renegotiated separately every 4 to 12 years. Article III 1(b) requires each country to manage its sheries and enhancement programs so as to ensure that each country receives \benets 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. Pacic hake The Commission has long dealt with the issue of \interceptions": those sh 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 sh, resulting in a loss of about $500 million (CAD) to Canada (Emery, 1997). Notably, Pacic salmon cannot be shed in the high seas, as per the North Pacic 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 Pacic Northwest Tribes, instead of just one fed- eral 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 dicult negotiations, agreement was nally reached amongst the ve U.S. jurisdictions and Canada on renewed shing arrangements for An- nex IV. For Fraser River sockeye, an annual international TAC is calculated as follows Cohen Commission (2010b): TAC = returnsockeye harvested (test)escapement targetMAAFE (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 xed percentage of the international TAC, currently 16.5% Cohen Commission (2010b). It is unclear how this xed percentage was formulated. B.2 Pacic hake North Pacic hake (Merluccius productus), also known as Pacic 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 ground- sh species in the California current system. The catch is primarily processed into H&G blocks, llets or surimi. Prior to 2002, the U.S. was claiming an 80% share of the hake sh- ery, while Canada was claiming 30%, leading to non-cooperation and overshing (United States Senate, 2004). This was perhaps due to dierences in stock assessments performed by scientists within each country. Thus, in 2003, both countries signed the U.S.-Canada Pacic Hake/Whiting Agreement. While the Agreement was ratied in 2003, it was not formally implemented until 2012 (Fisheries and Oceans Canada, 2011). However, from 174 B.3. Pacic 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 rst time, agreed percentage shares of the trans- boundary stock of Pacic hake, also known as Pacic whiting. It also creates a process through which U.S. and Canadian scientists and sheries managers will recommend the total catch of Pacic hake each year, to be divided by a set percentage formula. (United States Senate, 2004)" A TAC is decided upon jointly, with input from scientic 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 xed allotment, determined through bilateral negotiation, is in eect for nine years, and will remain xed unless both Parties agree to change it. B.3 Pacic halibut Pacic halibut (Hippoglossus stenolepis) are found along the continental shelf in the North Pacic as well as the Bering Sea, and have been commercially harvested by Canada and the United States since the late 1880s. Since 1923, the Pacic halibut shery 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 dierent countries sharing access to a commercially valuable sh stock. It was initially called the International Fisheries Commission, but today is known as the Inter- national Pacic Halibut Commission (IPHC). Prior to 2006, halibut was managed under the assumption that there were several sep- arate stocks along the Pacic coast with negligible migrations between regulatory areas. Due to an easterly migration of halibut that was originally not accounted for, a dispro- portionate share of catches were being taken from the eastern areas, notably the waters of Canada and Washington State (Hare, 2010). Modied 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 31Bruce Turris, Pacic Fisheries Management Inc., personal communication. 175 B.4. Northwest Atlantic: NAFO data, and a xed 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 sh 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 dierent 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 histor- ical 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 shers 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 dierent regional stocks (Anderson, 1998). The Commission recognized the need for 
exibility in allocation schemes, especially because overshing was already occurring on some stocks, and TACs needed to be adjusted downward in subsequent years. ICNAF 32It 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 
eets turned their attention to heavy shing just outside of the EEZ lim- its, 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 coun- tries (Anderson, 1998) and the area has been plagued by overshing for decades (Lane, 2008). NAFO was also challenged by non-member shing 
eets, for example those from Panama, Chili and Mexico (Anderson, 1998) who shed the resource without being party to the group, essentially free-riders. Today, the NAFO allocation system is based on xed 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 diculty 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 shing opportunities will be limited, for example to sheries 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 redsh (Bjorndal, 2009). De- spite 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, specically 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 recommen- dations, 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 shing 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 shermen's organizations (Bjorndal, 2009). Currently, NEAFC operates their allocation program based on xed proportions of the TAC (Cox, 2009). A promising sign of improved sheries management in the North Atlantic is com- munication 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 un- reported (IUU) shing (Bjorndal, 2009). IUU vessels 
agged on the waters of one RFMO would be reported to the other group. 178


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