Science, Faculty of
Oceans and Fisheries, Institute for the
DSpace
UBCV
Cashion, Timothy Robert
Doctor of Philosophy - PhD
University of British Columbia
Fishing gears’ damaging effects on the ecosystems in which they operate are a major environmental challenge of capture fisheries. The full cost of fisheries is currently not internalized into the economy, and thus to properly evaluate the benefits received from fisheries globally we must undertake a quantitative evaluation of the costs and benefits of including the ecosystem services upon which it relies. This thesis fills this gap through a full cost-benefit analysis of the world’s fishing fleets. First, I quantify the catches, discards and values of marine capture fisheries by gear type. Different gears disproportionately contribute to landings and landed value in comparison to their costs of discards and the potential shadow-value of these discards. Second, I propose a method to evaluate the short-term trade-offs necessary for fishing firms to maximize their revenues and profits. I find that through optimal management and rebuilding that fishers could increase their catches and profits in the selected case studies. Third, I evaluate whether current consumer-based sustainability initiatives are achieving ‘on-the-water’ effects. Here, I find that there is little reward for best-performing actors but a strong effect causing worst-performers to decrease their catches. Finally, I use the data on capture fisheries by gear type from earlier in the thesis to evaluate areas of low-cost conservation for species threatened by fishing gear. My results highlight areas of high conservation concern for particular fishing gears, and areas of high overlap between multiple fishing gear threats and multiple species of conservation concern. Taken together, this thesis advances our knowledge of impacts and trade-offs associated with different fishing gear types and improves our ability to restrict these impacts through management and market-based initiatives.
GET IT IN GEAR: EVALUATING GEAR-BASED TRADE-OFFS IN FISHERIES by Timothy Robert Cashion B.A., Huron University College, 2013 M.E.S., Dalhousie University, 2015 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Oceans and Fisheries) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) MAY 2020 © Tim Cashion, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Get it in gear: evaluating gear-based trade-offs in fisheries submitted by Timothy Robert Cashion in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Oceans and Fisheries Examining Committee: Rashid Sumaila, Institute for the Oceans and Fisheries Supervisor Daniel Pauly, Institute for the Oceans and Fisheries Supervisory Committee Member Frederik Noack, Land and Food Systems Supervisory Committee Member Richard Barichello, Land and Food Systems University Examiner Gunilla Öberg, Institute for Resources, Environment and Sustainability University Examiner iii Abstract Fishing gears’ damaging effects on the ecosystems in which they operate are a major environmental challenge of capture fisheries. The full cost of fisheries is currently not internalized into the economy, and thus to properly evaluate the benefits received from fisheries globally we must undertake a quantitative evaluation of the costs and benefits of including the ecosystem services upon which it relies. This thesis fills this gap through a full cost-benefit analysis of the world’s fishing fleets. First, I quantify the catches, discards and values of marine capture fisheries by gear type. Different gears disproportionately contribute to landings and landed value in comparison to their costs of discards and the potential shadow-value of these discards. Second, I propose a method to evaluate the short-term trade-offs necessary for fishing firms to maximize their revenues and profits. I find that through optimal management and rebuilding that fishers could increase their catches and profits in the selected case studies. Third, I evaluate whether current consumer-based sustainability initiatives are achieving ‘on-the-water’ effects. Here, I find that there is little reward for best-performing actors but a strong effect causing worst-performers to decrease their catches. Finally, I use the data on capture fisheries by gear type from earlier in the thesis to evaluate areas of low-cost conservation for species threatened by fishing gear. My results highlight areas of high conservation concern for particular fishing gears, and areas of high overlap between multiple fishing gear threats and multiple species of conservation concern. Taken together, this thesis advances our knowledge of impacts and trade-offs associated with different fishing gear types and improves our ability to restrict these impacts through management and market-based initiatives. iv Lay Summary Many fisheries in their current form are unsustainable for a variety of reasons. Fisheries often have negative impacts on marine ecosystems, but these negative impacts often also result in negative economic outcomes for fisheries. This thesis contributes to our understanding of fisheries and puts forward analysis on current sustainable transitions. I do this by looking at the incentive of fishing companies to act in their own best interest, evaluate the effectiveness of a consumer-oriented eco-labeling program, and the spatial trade-offs it will require to conserve species of conservation concern. In this thesis, I find there are often benefits to protecting fishing resources for the companies involved, but the current consumer-based incentives are not enough for this to be realized. In addition, I find that conservation does not need to result in large scale losses to fisheries. v Preface All chapters except for chapters 1 and 6 were prepared as manuscripts for publication. Chapters 2 and 3 have been published in peer-reviewed journals. Chapters 4 and 5 are under review at peer-reviewed journals. I am the lead author on all manuscripts prepared. I led the design, implementation and writing of all manuscripts with appropriate assistance from co-authors. No part of this research required ethics approval. Chapter 2 has been published as: Cashion, T., Al-Abdulrazzak, D., Belhabib, D., Derrick, B., Divovich, E., Moutopoulos, D. K., Noel, S., Palomares, M.L.D., Teh, L.C.L., Zeller, D., & Pauly, D. (2018). Reconstructing global marine fishing gear use: Catches and landed values by gear type and sector. Fisheries Research, 206, 57-64. The original conception was designed by D. Zeller, D. Pauly, and me. B. Derrick and S. Noel aided with the small-scale sector data. The remaining authors contributed to original data sources and revised the manuscript. Chapter 3 has been published as: Cashion, T., de la Puente, S., Belhabib, D., Pauly, D., Zeller, D., & Sumaila, U. R. (2018). Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach. PloS one, 13(11), e0207768. I led the data collection and analysis. The model was constructed by S. de la Puente and me. I led the writing of the manuscript. All authors added comments to improve the original study design, and the final manuscript. Chapter 4 has been prepared for submission with co-authors F. Noack and U.R. Sumaila. F. Noack contributed substantially to the study design and analysis of results. I led the implementation of the study design, and original interpretation of results and writing of the article. All authors contributed to the analysis and improvement of the manuscript. Chapter 5 has been prepared for submission with co-authors T. Tai, V. Lam, D. Pauly, and U.R. Sumaila. I led the design and analysis and wrote the original version of the manuscript. T. Tai improved the original analysis and made constructive comments on an early for of this paper. V. Lam contributed data and expertise on the cost of fishing database, and all authors helped analyse results and edited the final manuscript to its improved form. vi Table of Contents Abstract ................................................................................................................................ iii Lay Summary ....................................................................................................................... iv Preface ................................................................................................................................... v Table of Contents ................................................................................................................. vi List of Tables ......................................................................................................................... x List of Figures ..................................................................................................................... xii List of Abbreviations ......................................................................................................... xiv Acknowledgements ............................................................................................................. xv Dedication ........................................................................................................................... xvi Chapter 1: Introduction ....................................................................................................... 1 1.1 Ecological impacts of fishing and fishing gear ...................................................... 2 1.2 Economic challenges of fisheries ........................................................................... 3 1.3 Solutions ................................................................................................................. 4 1.3.1 Fisheries management .................................................................................... 4 1.3.2 Marine conservation ....................................................................................... 5 1.3.3 Consumer-oriented solutions .......................................................................... 6 Chapter 2: Reconstructing global marine fishing gear use: Catches and landed values by gear type and sector ........................................................................................................ 8 2.1 Introduction ............................................................................................................ 8 2.2 Materials and methods ............................................................................................ 9 2.2.1 Industrial non-tuna gears .............................................................................. 10 2.2.2 Tuna and large pelagic gears ........................................................................ 13 2.2.3 Artisanal gears .............................................................................................. 13 vii 2.2.4 Landed values ............................................................................................... 15 2.3 Results .................................................................................................................. 16 2.4 Discussion ............................................................................................................. 19 2.5 Conclusion ............................................................................................................ 23 Chapter 3: Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach....................................................................................... 25 3.1 Introduction .......................................................................................................... 25 3.2 Methods ................................................................................................................ 27 3.2.1 Peruvian anchoveta case study ..................................................................... 32 3.2.2 Menhaden case study .................................................................................... 35 3.3 Results .................................................................................................................. 36 3.3.1 Peruvian anchoveta ....................................................................................... 36 3.3.2 Menhaden ..................................................................................................... 42 3.4 Discussion ............................................................................................................. 45 3.4.1 Overall .......................................................................................................... 45 3.4.2 Anchoveta ..................................................................................................... 47 3.4.3 Menhaden ..................................................................................................... 49 3.4.4 Limitations .................................................................................................... 50 3.5 Conclusion ............................................................................................................ 52 Chapter 4: Sustainability ratings and outcomes in US fisheries .................................... 53 4.1 Introduction .......................................................................................................... 53 4.2 Literature .............................................................................................................. 55 4.3 Framework ............................................................................................................ 57 4.4 Background ........................................................................................................... 58 viii 4.4.1 Data ............................................................................................................... 59 4.5 Estimation strategy ............................................................................................... 60 4.6 Summary statistics ................................................................................................ 62 4.7 Results .................................................................................................................. 65 4.8 Discussion ............................................................................................................. 68 4.9 Conclusion ............................................................................................................ 69 Chapter 5: Low cost conservation: Fisheries gear threats to marine species ............... 70 5.1 Introduction .......................................................................................................... 70 5.2 Methods ................................................................................................................ 71 5.2.1 IUCN data ..................................................................................................... 71 5.2.2 Fisheries data ................................................................................................ 73 5.2.3 Analysis ........................................................................................................ 76 5.3 Results .................................................................................................................. 79 5.3.1 Fishing threats to IUCN species ................................................................... 79 5.3.2 Low-cost solutions to gear threats and easy conservation wins ................... 80 5.3.3 Protecting the high seas ................................................................................ 83 5.4 Discussion ............................................................................................................. 83 5.5 Conclusion ............................................................................................................ 85 Chapter 6: Discussion ......................................................................................................... 87 6.1 Caveats and Limitations ....................................................................................... 90 6.2 Recommendations for action ................................................................................ 90 Bibliography ........................................................................................................................ 91 Appendices ........................................................................................................................ 115 Appendix A..................................................................................................................... 115 ix Appendix B ..................................................................................................................... 121 B.1 Robustness tests .............................................................................................. 127 B.2 Supplemental tables ........................................................................................ 128 B.3 Fixed-effects models....................................................................................... 129 B.4 Supplemental figures ...................................................................................... 130 Appendix C ..................................................................................................................... 133 C.1 Supplemental tables ........................................................................................ 134 x List of Tables Table 2.1 Gear categories and gear types with descriptions (adapted from Gabriel et al., 2005). .................................................................................................................................... 11 Table 2.2 Regionally representative fishing countries used for small-scale fishing gear assignment. ........................................................................................................................... 15 Table 3.1 Fishing quota for Peruvian anchoveta and fleet size by company in Peru during 2011-2015. ............................................................................................................................ 33 Table 3.2 Average biomass and landings outputs (103 t) of the scenario analysis for North-Central and Southern anchoveta stocks of Peru. .................................................................. 38 Table 3.3 Average attained landings for the top seven companies for 2011-2015 (103 t; accounting for 57% of landings in Peru), scenario landings (103 t) and their impact on revenue and profits (USD 106) for FMSY and PGY scenarios. .......................................... 41 Table 3.4 Peru and Chile's average actual and modeled landings (103 t) of the shared Southern Peru/Northern Chile stock over two time periods. ................................................ 42 Table 3.5 Realized landings (103 t), MSY (103 t), and catch-based indicators of stock status for Atlantic and Gulf menhaden. .......................................................................................... 42 Table 3.6 Major menhaden fishing companies in the United States of America. ................ 44 Table 3.7 Average landings (103 t), potential MSY (103 t), and associated unrealized revenues (USD 106) for menhaden by the major fishing companies in the USA. ............... 44 Table 4.1. Summary statistics by Seafood Watch rating ...................................................... 64 Table 4.2 Regression discontinuity results of catch models. All have ln(Tonnes) as the dependent variable. ............................................................................................................... 66 Table 4.3 Regression discontinuity results of price models. All have ln(Price) as the dependent variable. ............................................................................................................... 67 Table 5.1. Mean values of measures of fisheries conservation concern and profit by gear type at a 0.5° by 0.5° scale. .................................................................................................. 81 xi Table A.1 Cost of fishing and ex-vessel prices by fishery (USD·tonne-1). ........................ 116 Table A.2 Scenario analysis for north-central and southern anchoveta stocks of Peru ...... 117 Table A.3 Landings (103 t) by company by year for the Peruvian anchoveta fishery based on quota ownership. ............................................................................................................ 118 Table A.4 Average attained landings for the top seven companies for 2011-2015 (103 t), modeled higher landings (103 t) based on Optimal F, and their impact on revenue and profits (USD 106). ............................................................................................................... 119 Table A.5 Estimated landings (103 t) by company for the US menhaden fishery. ............. 120 Table B.1 Robustness test: Transfer between levels for all pairs of ratings (min n=100).. 122 Table B.2 Robustness test: Equation 1 with lagged dependent variable for landings. ....... 123 Table B.3 Robustness test: Equation 1 with lagged dependent variable for prices. ........... 125 Table B.4 Robustness test: Equation 2 with lagged log(Tonnes) as predictor of assessment. ............................................................................................................................................ 127 Table B.5 Biomass by Seafood Watch rating. .................................................................... 128 Table B.6 Fixed-effects results of catch models. All have ln(Tonnes) as the dependent variable. .............................................................................................................................. 129 Table B.7 Fixed-effects results of price models. All have ln(Price) as the dependent variable. .............................................................................................................................. 130 Table C.1 Weighting of IUCN Categories based on (O’Hara et al., 2019). ....................... 134 Table C.2 Search terms used for identifying gear types in IUCN narrative text. ............... 135 Table C.3 Gear types identified in the Sea Around Us database. ....................................... 136 Table C.4 Regression results for gear types used and their gear multipliers used. ............ 138 Table C.5 Area of high seas by categorization. .................................................................. 138 xii List of Figures Figure 2.1. Cumulative stacked reconstructed global marine fisheries catches by major fishing gear types, for (A) total catches (landings plus discards); (B) landings only; and (C) discards only. ........................................................................................................................ 17 Figure 2.2. Cumulative stacked landed value of reconstructed global marine fisheries catches by major fishing gear types, for (A) total catches (landings plus discards); (B) landings only; and (C) discards only. ................................................................................... 18 Figure 2.3. Cumulative stacked reconstructed global marine fisheries catches major fishing gear types for the small-scale sector, for (A) total catches; and (B) landed value. .............. 20 Figure. 3.1. Schematic representation of method for linking fish stocks to company level fisheries revenues and potential profits ................................................................................ 28 Figure 3.2. Fishing mortality (F/FMSY), biomass (B/BMSY), and fisheries revenues for the North-Central and Southern Peruvian anchoveta stocks under Baseline, Optimized F (Opt-F) and Pretty Good Yield (PGY) scenarios. ......................................................................... 37 Figure 4.1. Demand and supply of seafood before and after rating. In panel A) demand increases in response to the rating of a product as sustainable (green) and it decreases in response to the classification as unsustainable (red). Supply then responds to the changes in demand. In panel B) supply is regulated and completely inelastic but it responds to rating by increasing or reducing supply according to the outcome of the rating (red – unsustainable or green –sustainable). The supply outcomes of panel B) are similar to the outcomes in panel A) but in contrast to panel A) the price of sustainably produced goods decreases and the price of unsustainable goods increases in response to rating in panel B. 58 Figure 4.2 US capture fisheries by assessed status by weight .............................................. 63 Figure 4.3 US capture fisheries by assessed status by value ($) .......................................... 63 Figure 5.1. Reason for elimination of species from our final analysis ................................. 73 Figure 5.2. IUCN category for all marine species (A; n=14,871) and marine species included in this study (B, n=4,579). ..................................................................................... 74 xiii Figure 5.3. Theoretical basis for competition between fisheries and conservation. ............. 77 Figure 5.4. Conceptual methods figure. ............................................................................... 78 Figure 5.5. IUCN fishing gear threats: species categorized by fishing gear threat (A); fishing gear threats by gear types listed (B); and identified gear types by threat status (C). Note: DD: Data Deficient; LC: Least Concern; NT: Near Threatened; VU: Vulnerable; EN: Endangered; CR: Critically Endangered. ............................................................................. 80 Figure 5.6. Fisheries profits (log scaled) and weighted score by cell (0.5° by 0.5° grid) for major gear types. Grey dashed lines indicate the median values (excluding values of 0) across gear types for fisheries profits per km2 and weighted threat score. ........................... 82 Figure 5.7. Scatterplot of cells within the High Seas (areas beyond national jurisdiction) according to their categories from our conceptual diagram. Each point is the values for a specific gear type in a 0.5° by 0.5° cell. Dashed lines indicated mean values of all cells (High Seas and EEZs)........................................................................................................... 83 Figure B.1 Histograms of landings and ln(landings) for all data included in the study. Note: Both graphs have the same number of bins (100). ............................................................. 130 Figure B.2 Histograms of price and ln(price) for all data included in the study. Note: Both graphs have the same number of bins (100). ...................................................................... 131 Figure B.3 Violin plot of natural logarithm catch of fisheries by assessed status. Red dot indicates the median value for each assessment category. ................................................. 131 Figure B.4 Violin plot of natural logarithm price of fisheries by assessed status. Red dot indicates the median value for each assessment category. ................................................. 132 Figure B.5 Boxplot of biomass (B/Bmsy) by assessment status. ....................................... 132 xiv List of Abbreviations AIS - Automatic Information System BMSY – Biomass necessary for Maximum Sustainable Yield BSM - Bayesian Schaefer model CMSY – Catch-based Maximum Sustainable Yield CPUE – Catch Per Unit Effort EEZ – Exclusive Economic Zone FAO – Food and Agriculture Organization FMSY – Fishing mortality at which Maximum Sustainable Yield is realized IUCN – International Union for the Conservation of Nature MPA – Marine Protected Area MSC – Marine Stewardship Council MSY – Maximum Sustainable Yield NMFS – National Marine Fisheries Service PGY – Pretty Good Yield RFMO – Regional Fisheries Management Organization VMS - Vessel Monitoring System WTS – Weighted Threat Score xv Acknowledgements This research was supported financially by the Social Sciences and Humanities Research Council of Canada, and the University of British Columbia’s Four-Year Fellowship. I am very grateful to my supervisor, Dr. Rashid Sumaila, for his support of my vision throughout this thesis, and for his help in me pushing through the harder times of this thesis. Dr. Daniel Pauly took me on as a research assistant initially and has moved my knowledge of fisheries forward tremendously. I also appreciate the thought-provoking questions and working sessions with Dr. Frederik Noack to improve my knowledge in economics. Thank you to my examining committee members, Dr. Gunilla Öberg and Dr. Richard Barichello for their efforts and their improvements of this thesis. Thank you to my external examiner, Dr. Denis Bailly, for his review of my thesis, and for providing thoughtful questions for my oral exam. I could not have done this without the love and support of my wife Madeline Cashion. I am very grateful to my parents for their encouragement and support throughout this time. Finally, my close friends in Vancouver have been an amazing supportive community throughout this process. Thank you. xvi Dedication For the Halibut. 1 Chapter 1: Introduction Fisheries are an essential source of nutrition and livelihoods for billions and millions, respectively, around the globe (Teh and Sumaila, 2013; FAO, 2016). Unfortunately, these are built on shaky ground. While the number of fisheries that are sustainably fished at or near their maximum sustainable levels has increased to 60%, the percentage of fisheries that are overfished has continued to increase to over 30% (FAO, 2018). In addition, there are fewer unexploited or underexploited stocks that society can turn to for new fisheries resources to meet any short-term losses that may be necessary while the 30% of overfished stocks are rebuilt (FAO, 2018). The impacts of fisheries are not limited to the target stocks (Zeller, Cashion, Palomares, & Pauly, 2018), and often extend into other negative ecological repercussions (Pauly, Christensen, Dalsgaard, Froese, & Torres, 1998; Worm et al., 2006). For example, the loss of future fisheries production through habitat modification and unintentional capture of juveniles that do not yield full market value is one area where the ecological damage has future negative economic effects (Auster et al., 1996; Diekert, 2012; Hiddink, Johnson, Kingham, & Hinz, 2011). The negative economic and ecological outcomes of fisheries are often intertwined, with many externalities in fisheries leading to decreased net benefits. There is a push to fix the above mentioned fisheries challenges from a multitude of aims through fisheries management (Cabral et al., 2018; Costello et al., 2016), market-based incentives (Marine Stewardship Council, 2011; Seafood Watch, 2016), and greater marine conservation (Convention on Biological Diversity, 2018; IUCN – SSC Species Conservation Planning Sub-Committee, 2017). These ‘fixes’, however, are not without their own costs. Therefore, this dissertation aims to evaluate the trade-offs associated with fisheries as they relate to both ecological and economic realms. This introductory chapter will provide some background information to situate the dissertation on the ecological impacts of fishing and fisheries gear, economic challenges in fisheries, and current proposed solutions. Finally, I will present my research questions and the structure of the dissertation. 2 1.1 Ecological impacts of fishing and fishing gear Fisheries create physical and energetic disturbances in ecosystems through targeted and non-targeted removal of fishes (herein including marine finfishes and invertebrates) and physical impacts on habitat (Committee on Ecosystem Effects of Fishing, 2002; Pauly & Christensen, 1995). Fisheries are by their nature at least partially selective (von Brandt, 1984). Fisheries target species for their value to humans, but this can disturb the balance of ecosystems by removing certain species and groups and thus altering the predator-prey distribution. Different fishing gears interact with the marine environment in different ways, but often have negative effects that have been characterized by high-levels of incidental catch of non-target species (Alverson, Freeberg, Murawski, & Pope, 1994; Kelleher, 2005), and habitat perturbations including modifications or temporary suspension of sediments (Oberle, Storlazzi, & Hanebuth, 2016). Fisheries can compromise ecosystem services through overfishing, especially, when it involves unselective fishing practices. For example, growth overfishing occurs when fish are caught before they reach a size that would maximize their yield per recruit (Pauly, 1983). This practice can deplete the population reducing future fisheries catches, as in the case of the French Nephrops fishery (Macher, Guyader, Talidec, & Bertignac, 2008). In addition, growth overfishing reduces the average size of the fish that are caught and thus the landings receive a lower average price at the dock (Asche & Guillen, 2012; Lee, 2014; Mcconnell & Strand, 2000) leading to suboptimal resource rents. Unselective practices often lead to impacts on non-targeted populations that fulfill vital ecosystem roles (from prey to top predators (Hiddink et al., 2017; Jennings, Dinmore, Duplisea, Warr, & Lancaster, 2001)), or have other economic value attributed to them such as for tourism (e.g., marine mammals and marine turtles (Cisneros-Montemayor, Barnes-Mauthe, Al-Abdulrazzak, Navarro-Holm, & Sumaila, 2013; Cisneros-Montemayor, Sumaila, Kaschner, & Pauly, 2010; Gallagher & Hammerschlag, 2011)), in addition to their existence and intrinsic value (Beaumont et al., 2007). In addition, the potential value of fish and 3 invertebrates that are discarded could be substantial although these individuals never reach the market, as will be addressed in Chapter 2. The impact of fishing gear on habitat has been extensively researched and is particularly relevant to bottom towed gears. The degradation of habitat can change the amount and type of organisms that can inhabit that area (Hiddink et al., 2017; Jennings et al., 2001). This has clear implications for ecosystem services as it can alter, temporarily or near-permanently, the productivity of these ecosystems depending on the recovery rates of the organisms that inhabit them (Kaiser, Hormbrey, Booth, Hinz, & Hiddink, 2018). Other than the impact on the target population, and possibly their predators and prey, the impact of fisheries is often specific to the fishing gear and how it is used. This can be seen in a relative ranking of fishing gears for the US that demonstrates that different gears perform better or worse on relative indicators such as physical and biological habitat impacts, and by-catch for various groups of concern such as sharks, seabirds, and marine mammals (Chuenpagdee, Morgan, Maxwell, Norse, & Pauly, 2003b). Therefore, it is important in analyses of these impacts to differentiate between the different types of fishing gear and their relative merits and demerits, as I do in in Chapters 2 and 5 of this thesis. 1.2 Economic challenges of fisheries There are many behaviours by fishers that are often not viewed as morally or socially desired, but are economically profitable given the current state of fisheries. In areas where fisheries are still an open access resource, they are expected to be overfished due to a lack of incentive to conserve the resource (Gordon, 1954; Hardin, 1968). For example, discarding in a mixed-species fishery is often economically more profitable than landing a lower priced species (M. A. Turner, 1997). This economic incentive has been partially restricted, when properly enforced, by placing limitations on discarding or designing quotas that include discards (i.e., total catches) or are value rather than quantity based (M. A. Turner, 1997). These restrictions and quotas can apply to habitat impacts such as in the case of the British Columbian bottom trawl fishery that had a quota placed on the amount of 4 sponge it could capture, which incentivized the fishers to communicate areas with high amount of sponges to help them meet their quota together (Wallace et al., 2015). While many of these costs are not included, the situation is worsened by governments decreasing the cost of fishing through extensive subsidies to enhance fisheries capacity and reduce fuel costs (Sumaila et al., 2010; Sumaila, Lam, Le Manach, Swartz, & Pauly, 2016). Excess capacity in fisheries allows fisheries to overfish the resource faster and reduced fuel costs ‘open up’ new fishing grounds that were formerly too cost-prohibitive (Sumaila, Teh, Watson, Tyedmers, & Pauly, 2008). 1.3 Solutions 1.3.1 Fisheries management There have been many attempts to internalize these negative ecological and economic outcomes into fisheries cost structures for fishers to make economic decisions with the full suite of costs in mind, and to shift fisheries towards more economically optimal outcomes. The lack of ownership over common pool resources has led for many to call for privatization of fisheries resources, often in the form of individual transferable quotas, that is expected to reverse the current trend of declining fisheries stocks (Arnason, 1990; Costello et al., 2016). But even ITQs are no panaceas to address the complex nature of fisheries sustainability and the many benefits of fisheries (see Copes, 1986; Sumaila, 2010). Fisheries management also uses spatial and temporal management, such as open and closed seasons in certain areas (Hilborn & Walters, 1992), and gear-based modifications such as turtle-excluder devices on nets or circle-hooks to reduce mortality to turtles and elasmobranchs (Cox et al., 2007; Favaro & Côté, 2015; Fuller et al., 2008). These gear-based modifications aim to reduce the negative effects of fisheries at certain life stages, or to remove them entirely for species that are not of value to fisheries but are of conservation concern. Many of the concerns raised in marine conservation concern fisheries’ impact on incidentally captured or affected organisms, such as bottom trawling’s impact on sessile invertebrates (Clark et al., 2016; Jennings et al., 2001) or the incidental capture of turtles 5 and elasmobranchs in longline fisheries (Burgess et al., 2018; Chuenpagdee, Morgan, Maxwell, Norse, & Pauly, 2003a; Fuller et al., 2008). These gear-specific impacts require gear-specific solutions. In this way, we can limit the impact on species of conservation concern while not overly restricting fishing activity as I propose in Chapter 5. 1.3.2 Marine conservation There currently exists a suite of strategies to enhance the conservation of marine ecosystems. Many of these conservation strategies have sought to work with fisheries through altering where, when, and how fishers fish. While increased spatial management of fisheries can limit impacts on individuals during certain life stages by protecting spawning grounds or other essential habitat (Roberts & Sargant, 2002), others have called for complete protective refuges in the form of no-take marine protected areas (MPAs; Sala & Giakoumi, 2018). Some advocate for large MPAs to mimic the formerly unfished distant waters of the past, before fisheries expanded into most areas of the oceans (Swartz, Sala, Tracey, Watson, & Pauly, 2010). This expansion of full-scale protection of marine ecosystems is enshrined in international agreements like the Convention on Biological Diversity’s Aichi Targets (Convention on Biological Diversity, 2018). However, the implementation of these can be costly in terms of the lost opportunity cost for fishers and other users of these areas. There are also a number of measures being taken to protect individual species or groups of species. The International Union for the Conservation of Nature (IUCN) Red List works to assess the status of all species globally and advocates for their protection (IUCN – SSC Species Conservation Planning Sub-Committee, 2017), whether they are of commercial importance or not. This helps species receive improved consideration by national governments and fisheries management organizations to enhance their protection from fisheries and other threats. These organizations efforts are partially restricted by limited budgets for conservation from both civil society and governments. To address the limited resources for conservation and pressing threats on these species, I investigate potential low-cost conservation areas in Chapter 5. To do this, I estimate the lost profits from gear- 6 specific spatial fisheries closures versus the protection it would offer to species threatened by specific fishing gear types. 1.3.3 Consumer-oriented solutions To combat current unsustainable and undesirable practices in fisheries, a suite of certification and labeling programs have arisen. The programs highlight different practices and have differing extent in their coverage. One of the first is the dolphin-safe tuna label in response to the high levels of by-catch of dolphins in tuna purse seiners, and that can still be found on canned tuna (Brown, 2005). Much broader programs exist that hope to encompass all aspects of a fishery from the negative impacts on habitat and by-catch, to the state of the underlying biological resource. The Marine Stewardship Council (MSC) oversees the MSC certification program that takes this broader approach to the fishery (Marine Stewardship Council, 2011). Another program is Seafood Watch that was started by the Monterey Bay Aquarium in the late 1990s to encourage consumers to buy the ‘right’ kind of seafood that gives consumers a color-coded label for common seafood items (Seafood Watch, 2016). One of the main contrasts between the MSC and the Seafood Watch programs is that the MSC certifies fisheries that apply to their program whereas Seafood Watch evaluates fisheries they deem important. This financial connection between the fisheries (and aquaculture farmers) and the certifying body has been called into question by several researchers and NGOs that question the legitimacy of the program under financial conflict of interest between certifiers and the to-be certified fisheries (Jacquet et al., 2010). This becomes especially important as many more fisheries are being certified that are far from sustainable (Christian et al., 2013; Froese & Proelss, 2012). The underlying theory of these programs is that they incentivize fishers to conform to a higher sustainable standard by creating a financial incentive (Marine Stewardship Council, 2018). This theory has two challenges of whether these sustainably labeled or certified prices do indeed receive a higher price, and second whether this (hypothesized) price premium is passed along to the producers. Without these two elements, the future of these programs and their ability to motivate change is in question. While there is some evidence that these programs have an effect on consumer prices (Roheim, Asche, & Santos, 2011; 7 Sogn-Grundvag, Larsen, & Young, 2013), there is limited evidence the same effect is present for the producers (Stemle, Uchida, & Roheim, 2016). I evaluate whether these programs are indeed having changes ‘on the water’ in Chapter 4, through estimating the effect of the Seafood Watch Program on landings and prices in US marine fisheries. 1.4 Research Questions The research will aim to answer the fundamental questions (linked to the chapter structure for my dissertation as presented below): • What fishing gear types are used globally and what are their economic and ecological implications? • How can we align incentives for fishing companies towards sustainable fisheries practices? • What effects do consumer-based seafood sustainability programs have on fishers? • Where are fisheries having the least benefit for their relative threat to IUCN Red List Threatened species? The outline for this dissertation is four main chapters prepared as manuscripts for publication that address these questions. Chapter 2 reconstructs historical fishing gear use for the Sea Around Us catch data to better analyze the trends in global fisheries and their relative contribution to landings, discards, and landed values. Chapter 3 develops a method to link large seafood companies to their fisheries and demonstrate the potential benefits of rebuilding and optimizing fisheries exploitation at a company level. Chapter 4 tests the efficacy of a fisheries eco-labelling program to alter fisheries behaviour and outcomes for US domestic fisheries. Finally, Chapter 5 uses spatialized gear data (from Chapter 2) to estimate low-cost trade-offs between fisheries profits and high-impact conservation to marine species threatened by fisheries gears. Throughout this dissertation, there is a focus on trade-offs associated with fisheries based on the gears used (Chapters 2 and 5), and economic implications tied to sustainability outcomes (Chapters 3 and 4). While the chapters shift between global and regional scales, they focus on different outcomes of fisheries that must be balanced for future fisheries to be sustainable. 8 Chapter 2: Reconstructing global marine fishing gear use: Catches and landed values by gear type and sector1 2.1 Introduction Fishing gears enable fishers to interact with finfishes and marine invertebrates (hereafter ‘fish’) in the marine environment, this interaction between gears and fish is the fundamental definition of ‘fisheries’. There is a wide diversity of fishing gears that have been employed by fishers around the world over the last 60+ years, from simple small-scale gears operated with one’s hands like spears, traps, handlines or a variety of beach seines and gillnets, to industrial-scale bottom- and midwater-trawls, and mechanically powered seine nets (Gabriel, Lange, Dahm, & Wendt, 2005). There are major concerns regarding the use of fishing gears including by-catch of non-target organisms (Alverson et al., 1994), often leading to substantial discarding (Zeller et al., 2018), habitat alterations and destruction (S. J. Turner, Thrush, Hewitt, Cummings, & Funnell, 1999; Watling & Norse, 1998), and associated high fuel use (Parker & Tyedmers, 2014). While many detailed, local studies exist on fishing gears used in fisheries, little research exists on a global scale on the total patterns and trends in their use. An exception is the earlier work of the Sea Around Us research initiative (Pauly & Zeller, 2015), which produced an assignment of fishing gears associated with officially reported landings data, largely as assembled and reported by the Food and Agriculture Organization of the United Nations (FAO) on behalf of it member countries (Watson, Revenga, & Kura, 2006a, 2006b). This earlier approach did not account for unreported catches, nor could it allocate catches to different fishing sectors such as small-scale commercial (i.e., artisanal) as compared to large-scale commercial (i.e., industrial). However, recently the Sea Around Us completed their global reconstructions of marine catch data for all countries in the world (Pauly & Zeller, 2016a, 2016b), which augmented 1 A version of this chapter is published as Cashion, T., Al-Abdulrazzak, D., Belhabib, D., Derrick, B., Divovich, E., Moutopoulos, D. K., ... & Pauly, D. (2018). Reconstructing global marine fishing gear use: Catches and landed values by gear type and sector. Fisheries Research, 206, 57-64. 9 the officially reported landings data with complete time-series estimates of unreported catches over the last 60+ years, including commonly un- or under-reported small-scale sectors (Zeller, Harper, Zylich, & Pauly, 2015), recreational fisheries (Smith & Zeller, 2016), as well as major discards (Zeller et al., 2018). These country-level reconstructions were further complemented with a global reconstruction and harmonization of catches by the industrial large pelagic fisheries in each ocean basin that are administered by Regional Fisheries Management Organizations (RFMOs), mainly for tuna, billfishes and pelagic sharks (Manach et al., 2016). In addition to comprehensively reconstructing total reported and unreported catch data, all catches are spatially allocated to a global half degree ocean grid system across the world’s marine waters, taking into account biological probability distributions for each taxon in the data (Palomares & Cheung, 2016) as well as known and derived foreign fishing access information to national EEZ waters in each country (Zeller et al., 2016). The species-gear associations derived earlier by the Sea Around Us (Watson et al., 2006a, 2006b) can now be replaced with a new, detailed and improved catch-by-fishing gear dataset that accounts for country, species and annual changes in gear use by fishing sector. I synthesized the global findings of this new and comprehensive global fishing gear database that harmonizes with and links directly to the globally reconstructed catch data of the Sea Around Us. I am now able to examine all catches (whether reported or unreported) by gear types, by fishing sector, and by country, in space and time back to 1950. These freely available data will allow all users to evaluate and analyze a variety of policy questions around marine fisheries and gear use over the last 65+ years, and will hopefully assist in better understanding trends in global fisheries and gear use. 2.2 Materials and methods I used the reconstructed Sea Around Us catch data (Pauly & Zeller, 2016b, 2016a), available at www.seaaroundus.org, by fishing sector, taxon and fishing country to assign fishing gear types to the catch data. All gear types and categories assigned here are listed in Table 2.1. The catch reconstructions for each country or territory used country- or region-specific definitions to assign catches to either large-scale (i.e., industrial) or small-scale sectors (i.e., artisanal, subsistence, recreational). However, all fishing gears that are moved 10 through the water or across the seafloor using engine power were defined as industrial (Martín, 2012), irrespective of vessel size. Thus, some trawl or purse seine operations defined as ‘small-scale’ by a given country due to vessel length were re-assigned as industrial sector fisheries in the Sea Around Us database (Zeller et al., 2016). The Sea Around Us reconstructed catch data are composed of millions of catch records, each consisting of a catch tonnage for a particular taxon, particular fishing country and year, caught in a particular area (e.g., a certain EEZ, FAO or RFMO area), by a particular fishing sector, and which is either landed or discarded catch, and either deemed reported or unreported. The Sea Around Us catch data are structured into three distinct data layers: domestic fisheries, foreign fisheries, and industrial tuna and other large pelagic species fisheries. The distinction between these layers, and thus generally where fisheries operate (i.e., in a country’s home EEZ, or other countries’ EEZs, or High Seas areas), was used in part to inform potential gear types for different countries. All gear assignments were made while considering temporal and geographical changes for the respective fishing country and taxon. I addressed the gear use in three different segments: industrial non-tuna gears (i.e., excluding industrial tuna and large pelagic fisheries), industrial tuna and large pelagic gears, and artisanal (i.e., small-scale) gears. 2.2.1 Industrial non-tuna gears For all industrial catch data records (excluding catches for tuna and other large pelagics, see below), I first determined the likely gear types or gear categories that an industrial catch record could be assigned to, based on the definitions of fishing gears in Gabriel et al. (2005). Second, I assigned all data rows that have gear information already included (e.g., from catch data reconstructions) to the appropriate gear categories, while confirming and validating this choice based on additional information in the reconstruction and its source material. Examples where this was done include the domestic fisheries in the EEZs of countries around the Red Sea (Tesfamichael & Rossing, 2016) where gears were assigned that readily allowed standardized gear categories to be associated. Third, I assigned all taxa that are caught by a single gear by a fishing 11 Table 2.1 Gear categories and gear types with descriptions (adapted from Gabriel et al., 2005). Sector Gear category Gear type Description Large-scale Bottom trawl Bottom trawl Nets dragged by vessels in contact with the seabed Bottom trawl Shrimp trawl Bottom trawl Beam trawl Bottom trawl Otter trawl Pelagic trawl Pelagic trawl Nets dragged by vessels not in seabed contact and targeting pelagic or semi-pelagic taxa Longline Lines Includes all gears where lines of any kind are the primary fishing gear Longline Pole and line Longline Longline Longline Hand lines Purse seine Encircling nets All net-based gears that encircle their catch rather than entangle it Purse seine Purse seine Purse seine Small encircling nets Gillnets Gillnet All net-based gears that entangle their catch rather than encircle it Gillnets Trammel net Other Dredge Other gears which are not major contributors to global industrial catches and do not fit into the major gear categories Other Other Other Pots or traps Other Other nets Other Other lines Other Dragged gear Other Mixed gear Unknown Unknown class Small-scale Bag nets Bag nets Nets used by individuals to lift or scoop fish from the water Cast nets Cast net Nets thrown atop the intended catch from shore or boats. 12 Table 2.1 Gear categories and gear types with descriptions (adapted from Gabriel et al., 2005). Cast nets Lampara nets Small-scale Hand or tools Hand collection Fishing by hand, skin diving, harpoons, or other methods not involving nets or lines Hand or tools Diving Hand or tools Harpoons Small-scale encircling nets Encircling nets Nets in small-scale fisheries that surround fish from the sides and beneath Small-scale encircling nets Purse seine Small-scale gillnets Set net Nets that entangle catch, deployed from a boat and/or suspended in the water column Small-scale gillnets Driftnet Small-scale gillnets Trammel net Small-scale lines Hand line Gear composed of fishing lines with baited or unbaited hooks Small-scale lines Trolling line Small-scale lines Longline Small-scale lines Pole line Small-scale lines Squid jigging Small-scale pots or traps Fyke net Manufactured structures that trap fish, including net weirs and pots Small-scale pots or traps Traps Small-scale seines Beach seine Weighted, surrounding nets that encircle fish Small-scale seines Boat seine Small-scale other nets Small-scale other nets Nets not belonging to other net categories Artisanal fishing gear Dynamite, chemical Other fishing gear not belonging to any of the above categories, or used in a mixed gear fishery or is unknown Artisanal fishing gear Mixed gear Artisanal fishing gear Unknown 13 country in a geographic region in a given year. Examples of this include taxa that are only caught by one industrial gear type by a country, such as the use of purse seines for anchoveta (Engraulis ringens, Engraulidae) in Peru (Mendo & Wosnitza-Mendo, 2014a). Fourth, I assigned gear types to all taxa that are caught by multiple gears by the same fishing country in the same geographic area. This required assigning proportions of the different gear types to the mixed gear category for each unique fishing country, year, taxon and geographic region. In this way, catches of Atlantic cod (Gadus morhua, Gadidae) by the United Kingdom could be separated into Danish seines, bottom trawls, gillnets, and line gear. These identified major gear types were assigned to one of the major gear categories defined here (Table 2.1). The details of the gear-to-catch matching results for each country and region (e.g., national EEZ, territory, high seas area) can be found in Cashion et al. (2018). 2.2.2 Tuna and large pelagic gears The industrial tuna, billfish and other large pelagic fisheries that are administered and managed by various RFMOs are addressed separately in the Sea Around Us data (Manach et al., 2016; Zeller et al., 2016). They are spatially assigned with their gears based on reported data from each tuna RFMO, and these gears were ascribed to the gear types and categories described in Table 2.1. The industrial gears make up the greatest proportion of reported catches by RFMOs and largely belong to purse seines and longlines, with smaller amounts of catch from industrial pole and line and gillnet fisheries. Many of the gear types reported by RFMOs are originally more descriptive of the various types and even sub-types of longlines, pole and line or net gears used, but these details were re-assigned here into their more general major gear types. The reconstructed discards of these fisheries correspond to the gears used in the respective fishery. 2.2.3 Artisanal gears To derive catch-to-gear assignments for the highly diverse small-scale fisheries of the world (Chuenpagdee, 2011), I developed a country-to-region-substitution approach. The three countries with the largest artisanal catches in each region were identified (Table 2.2) 14 and their catches assigned to small-scale gear types (Table 2.1) for each taxon for each year (for additional information on how this assignment was done, see the supplementary methods). Annual breakdowns of artisanal catch of each taxon by gear were pooled for regions (Africa, Asia, Europe, South America) and these breakdowns were used to assign artisanal gear use for all other countries within each region. Taxa that did not occur in these regional gear breakdowns based on the sample countries, but were present elsewhere within the region were assigned a gear breakdown based on a similar taxon on the basis of similar taxonomic lineage, habitat, or functional group. Overall breakdowns for broader taxonomic categories (families, orders, classes, etc.) were pooled with data for all taxa that fell within the category in order to best reflect the various gears used for catching broad categories of taxa. For regions with particularly stark differences in economic development or climate and thus gears used, the top three sample countries (Table 2.2) were not pooled, but instead a breakdown was assigned based on the countries or areas most similar in fisheries development and taxa caught. For example, for the North American region artisanal gear breakdowns from Mexico were assumed to be most similar for countries in Central America, while Cuba was assumed to best represent small-scale gear use in the wider Caribbean island countries. Gear information from the Alaskan Arctic EEZ and the Canadian Arctic were used for Greenland and data from the Canadian Atlantic EEZ was used for St Pierre and Miquelon. For the Oceania region, New Zealand data were assumed to most closely resemble the situation in Australia2, while all other EEZs within the region were assumed to be represented most closely by a pooled breakdown from Papua New Guinea and Tonga. For further details on the small-scale catch-by-gear assignments within these regions, see Cashion et al. (2018). 2 Artisanal fishing gear use information was not available for Australia. 15 Table 2.2 Regionally representative fishing countries used for small-scale fishing gear assignment. Region Countries Artisanal catch in region (%) Key references1 Africa Ghana, Nigeria, Senegal 38.5 (Camara, 2008; FAO, 2007; Heddon, 2006; Nunoo et al., 2014) Asia Japan, South Korea, Thailand 28.4 (Anon., 2014a; Ministry of Agriculture Forestry and Fisheries, 2016; SEAFDEC, 2018) Europe Denmark, Norway, UK 38.2 (Anon., 2004, 2006, 2014b; Anon, 2005) North America – North/Central Canada*, USA* 96.3 (Fisheries and Oceans Canada, 2018; NOAA, 2016; Pooley, 1993) North America - South Mexico 90.4 (Cisneros-Montemayor, Cisneros-Mata, Harper, & Pauly, 2015) Caribbean Cuba 29.4 (Joyce, 1996) South America Brazil, Chile, Venezuela 45.1 (Cárdenas, Achury, & Guaiquirián, 2009; Freire et al., 2015; Subsecretaría de Pesca y Acuicultura, 2016) Oceania - Australia New Zealand 66.4 (King, 1986) Oceania – Others Papua New Guinea, Tonga 24.2 (Dalzell, Adams, & Polunin, 1996; Tu’avao, Kava, & Udagawa, 1996) *Data for North American arctic and subarctic EEZs and the Atlantic coast of Canada were used to assign artisanal gear breakdowns to Greenland and St. Pierre and Miquelon, respectively. 1. Full references for artisanal gears can be found in the supplemental methods. 2.2.4 Landed values While the catch data used for fishing gear assignment in the present study were based on the global reconstructed catch database of the Sea Around Us (Pauly & Zeller, 2016a), I expanded our synthesis of gear type use to incorporate the landed value of these catches by gear type. To derive landed values, I relied on the global dataset of ex-vessel prices first developed by (Sumaila, Marsden, Watson, & Pauly, 2007) and updated by (Swartz, Sumaila, & Watson, 2013) and (Tai, Cashion, Lam, Swartz, & Sumaila, 2017). This dataset includes prices for each country, year and taxon present in the Sea Around Us catch database that have been normalized to 2010 real USD through market exchange rates and purchasing power parity between countries. These prices represent the average ex-vessel price for a taxon in a country in a year, and were assumed to be representative of the value of discarded catches in the absence of other ‘shadow prices’ for these ‘goods’ without a 16 market (Cisneros-Montemayor, Harper, & Tai, 2018; Drèze & Stern, 1990). Thus my estimate of discards is the potential value of these catches if they had been landed. 2.3 Results Based on the assignment of globally reconstructed fisheries catches to the gear types considered here, industrial bottom trawl and purse seine gears, jointly with small-scale gears accounted for over 75% of total global catch from 1950-2014 (Figure 2.1A). Most general gear categories maintained their relative share of total fisheries catches over time when the 1950s were compared to the last 5 years (2010-2014). However, the relative distribution of gear types differed when considering landed catches only (i.e., excluding discards), as landings were dominated by purse seine gears (28%), followed by small-scale gears (25%), with bottom trawling accounting for less, with 23% (Figure 2.1B). While accounting for only around 23% of fisheries landings, bottom trawls (including shrimp trawls) accounted for nearly 60% of fisheries discards (Figure 2.1C). Thus, bottom trawling disproportionately contributes to discarding in comparison to its landings. Purse seines, on the other hand, displayed the opposite pattern, being responsible for nearly 29% of landings but only 8% of discards (Figure 2.1B and Figure 2.1C). Changes in observed gear use over time are driven by the expansion of fisheries and fluctuating landings. For instance, single species fisheries that dominate with high tonnages, such as the Peruvian anchoveta fisheries, may drive the global pattern in single gear use (here purse seine, Figure 2.1B), due to their high inter-annual variation in catches. Discrepancies between gear types in terms of landings versus discards become even more distinct when one considers the landed value of the catch (Error! Reference source not found.). While purse seines account for 29% of global landings (Figure 2.1B), they only account for 15% of global landed value Error! Reference source not found.A, B). Bottom trawls on the other hand dominate total catch in terms of landed value (Error! Reference source not found.A), but small-scale gears clearly provide higher landed value of the actual landed catch (Error! Reference source not found.B). Thus, I demonstrate the large potential value of discarded catch by bottom trawls (Error! Reference source not 17 found.C), if these catches had been landed, by quantifying the losses associated with this gear type. Pelagic trawls, which often catch lower-value species, now account for nearly 12% of global catches (Figure 2.1A), but less than 6% of total value of catches in 2014 (Error! Reference source not found.A). 18 Figure 2.1. Cumulative stacked reconstructed global marine fisheries catches by major fishing gear types, for (A) total catches (landings plus discards); (B) landings only; and (C) discards only. Figure 2.2. Cumulative stacked landed value of reconstructed global marine fisheries catches by major fishing gear types, for (A) total catches (landings plus discards); (B) landings only; and (C) discards only. 19 The small-scale fisheries sector, which accounts for nearly 23% of total catch (landings plus discards, Figure 2.1A) and 27% of total landed value (Error! Reference source not found.A) over the 1950-2014 period, displayed a more diverse spread of major gear types being used. Gillnets, line gear, encircling nets, and pots and traps make up the majority of catches, accounting for over 70% of global total small-scale catches in 2014 (Figure 2.3A). Of interest is a noticeable change in gear use over time in small-scale fisheries, with encircling nets having been a major contributor in the 1980s and early 1990s, but declined in importance thereafter, while more recently various pots and traps accounted for a larger share of small-scale catches (Figure 2.3A). With regards to the landed value of small-scale catches, four gear types account for the majority (75%) of landed value from small-scale fisheries (Figure 2.3B), i.e., gillnets, various line gears and pots and traps. Interestingly, ‘hand and tools’ also account for a substantial portion of landed value (11% in 2014). Likely the importance of pots/traps and ‘hand and tools’ in landed value is due to high-value invertebrates (especially crustaceans) being targeted using such gears. 2.4 Discussion Previous studies have addressed the different discard rates of different gear types (e.g., Kelleher, 2005). In addition, many studies have pointed to the various indirect impacts associated with different fishing gears, ranging from the effects of bottom trawling on the seafloor (Jones, 1992; Watling & Norse, 1998), to the incidental catch (i.e., by-catch) in longlines and gillnets of non-target fish, seabirds, and marine mammals (Hall, Alverson, & Metuzals, 2000), and to the general considerations of discarding (Zeller et al., 2018). With the completion of the harmonized catch-by-gear type assignment undertaken here for the global reconstructed catch data of the Sea Around Us (Pauly & Zeller, 2016a, 2016b), I can now provide a global overview and synthesis of the use of different fishing gears over time and by fisheries sectors to examine the extent of such impacts. I have clearly illustrated the differing contributions of various gear types in terms of landings, discards, and landed value. While bottom trawling contributes most to total catches, this is partially attributed to the substantial amounts of discards they account for, 20 which is also reflected in the high landed value of discarded bottom trawl catches, a wasteful practice that represent a massive unrealized value (see also Zeller et al., 2018) and Figure 2.3. Cumulative stacked reconstructed global marine fisheries catches major fishing gear types for the small-scale sector, for (A) total catches; and (B) landed value. production forgone for both fish stocks and fishing fleets (Jensen, Reider, & Kovalak, 1988). Purse seine fisheries on the other hand, while also contributing massively to global landings, have much less discards. Pelagic trawls and purse seines often catch lower value species, including substantial catches for non-human consumption (Cashion, Le Manach, Zeller, & Pauly, 2017), and this is reflected here in the higher proportions of catches as measured in tonnage compared to their contribution to the value of catches. Inversely, certain gear types contribute disproportionately more to landed value than they do to 21 catches. For example, in the small-scale sector, high-value species (mainly crustaceans and other invertebrates) are often targeted by pots/traps and hand/tools. The catches that was discarded by these fisheries were likely less economically valuable than the landed catch due to species, size, or quality of the fish. Part of a fisher’s catch is discarded, as it is economically beneficial to the individual fisher in the short term to discard: i) less valuable catch, or ii) catch for which they do not have a quota (M. A. Turner, 1997). As fishers must make the decision of what fish to fill their hulls with, they will prioritize the most economically valuable portion of their catch or choose to discard less valuable catches now in hopes of future higher value catches to land. These discards provide energy for opportunistic feeders in pelagic and demersal ecosystems passing this energy to higher trophic levels. However, such artificial ecosystem feed supplements are likely to benefit only a few groups of species, such as gulls (family Laridae), which are one of the few seabird families not showing declining population patterns (Paleczny, Hammill, Karpouzi, & Pauly, 2015; Washburn, Elbin, & Davis, 2016). In general, the costs and benefits of discarding are borne by different actors (i.e., the discarded species versus their consumers and fishers. Fishing gear is often used to differentiate and manage fisheries at the national level. Industrial fisheries, and more specifically, those gears believed to be most harmful are often banned in protected areas (Chuenpagdee et al., 2003b), such as bottom trawls being banned in Belize’s waters (Ramos et al., 2011). While these gear bans can occur at a very fine resolution spatial scale, the catches and value produced at a national level can be used to analyze and inform policies that affect the fisheries sector. The data presented herein is an improvement over previously available data and can be used evaluate the costs and benefits of fisheries at national and global scales. To a casual reader, the results presented here do not seem to greatly differ from the earlier catch-by-gear data derived by the Sea Around Us (Watson et al., 2006b). However, the data presented here differ fundamentally from the earlier work in two substantial ways: it now accounts in great detail for (1) country-by-country specific unreported landings and discards (Pauly & Zeller, 2016a, 2016b), i.e., it accounts for total removals from marine 22 ecosystems by gear over time; and (2) the four major fishing sectors (industrial, artisanal, subsistence, and recreational; D. Zeller et al., 2016) that differentiate global fisheries. This allows us, for the first time and without having to make generalized assumptions (as required in the earlier attempt; Watson et al., 2006a) to describe and examine gear-use by the various fisheries sectors across time, taxon, fishing country, and space. Using complementary work of the Sea Around Us on ex-vessel prices (Sumaila, Marsden, et al., 2007; Swartz et al., 2013; Tai et al., 2017), I can also estimate the unrealized shadow value (Cisneros-Montemayor et al., 2018; Drèze & Stern, 1990) of discarding for global fisheries by gear categories and fishing sector. Through this, I am able to not only document the amount of fish discarded by different gear types, but the potential lost economic value of these discards by gear type. Notably, the unrealized economic value of discards from bottom trawling from 1950 to 2014 exceeds the value of all catches from longline fisheries ($432 billion to $560 billion, respectively). Such global-level analyses of fisheries catches and landed values by gear-type improves the resolution of lost value from fisheries discards, and enhances our understanding of the contribution of different gear types. The small-scale fisheries sectors, although highly diverse, wide-spread and of crucial food security importance (Chuenpagdee, 2011; Golden et al., 2016), are generally more poorly understood in terms of catch-by-gear and gear-type use over time and space than the industrial sector, despite contributing to nearly a quarter of total fisheries catches worldwide (Pauly & Zeller, 2016a). Particularly, it is challenging to create a comprehensive quantitative picture of the use of gears by these fisheries, especially in developing countries where such information may be sparse or nonexistent. I was able to assign all globally reconstructed small-scale catches to a large (although not complete or exhaustive) list of small-scale gears used around the world, by country and taxon over time. I achieved this through a country-to-region-substitution approach in which I derived small-scale gear information and catch-by-gear breakdown for representative countries (selected for their high contribution to total small-scale catches in each region) within each of nine geographic regions around the world. While there are obvious limitations in using such a substitution 23 approach, as patterns of gear use in small-scale fisheries can vary between neighboring countries, it permitted a first-order differentiation of catch-by-gear for all small-scale fisheries around the world for the first time based on the comprehensive, reconstructed small-scale fisheries catches (Pauly & Zeller, 2016a). I hope and anticipate that further research and studies will deepen and expand the coverage of catch-by-gear for small-scale fisheries in various countries, which will allow us to improve these gear assignments in the future. The large-scale and small-scale data presented herein were assembled from a large diversity of sources and attempts to best reflect the gears used in different fisheries over time. However, the results are limited by the available information initially recorded which means there is still a great diversity of data quality between countries. These data can and will continue to be improved over time through periodic reviews, and through further collaborations from these countries. Finally, improving the Sea Around Us gear data presented here can lead to improvements in the spatial allocation of catch data (Palomares & Cheung, 2016; Zeller et al., 2016), as some fishing gears are only used in certain areas based on depth, area and gear restrictions. This is already being put into practice for the Sea Around Us large pelagic data (Manach et al., 2016) by restricting countries fishing areas to where the gear is used by any other country using the same gear. Through progressive refinement this can be integrated into the spatial allocation process of the Sea Around Us to refine the spatial representation of global fisheries catches. 2.5 Conclusion I assigned fishing gear categories to all reconstructed catches (by fishing sector) of the Sea Around Us, thereby revising and correcting earlier catch-by-gear data approaches of the Sea Around Us. Bottom trawl gears account for a large portion of discard catch and value over these 65 years, whereas purse seine gears deliver a large portion of global landings with relatively little discards. Small-scale gears as a group account for a large portion of landings and generate substantial value. This catch-by-gear database explicitly matches 24 reconstructed catches to gears by fishing sector, and improves our understanding of which particular fishing gears are responsible for the benefits and costs associated with global fisheries. This improved database can be used for future studies of fishing gear impacts at the global level. 25 Chapter 3: Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach3 3.1 Introduction The marine fishing industry is currently failing to realize economic benefits estimated to be between USD 51 and 83 billion annually (Costello, Kinlan, Lester, & Gaines, 2012; Sumaila et al., 2012; World Bank, 2017). These unrealized benefits are largely due to stocks whose biomass has been reduced below levels that generate maximum sustainable yields (MSY), which leads to their continued exploitation generating higher costs. While some regions are experiencing a growth in fish catches due to rebuilding fish stocks (Costello et al., 2008), global catches are declining at a rate of 1.2 million tonnes annually (Pauly & Zeller, 2016a). These current trends illustrate the global picture of the state of the oceans, and point to the lost economic potential of capture fisheries. However, it also presents a major opportunity to establish appropriate incentives to rebuilding stocks globally. Through strong rebuilding, the benefits of increased catches and lower costs of fishing could outweigh short-term costs of temporarily reduced catches within a decade or so (Sumaila et al., 2012). A major challenge to addressing these potentially unrealized profits at the global scale is the opacity of seafood supply chains. Seafood is among the most highly traded food commodities (Asche, Bellemare, Roheim, Smith, & Tveteras, 2015), but its supply chains often include illegal and unreported fish or fish that is mis-labelled (Agnew et al., 2009; Jaquet & Pauly, 2008). Thus, the stock-origin of much of the globally traded seafood supply is not readily ascertainable. Unreported and especially illegal fisheries production can be implicated in unsustainable fisheries practices, and creates challenges for the proper management of resources. Therefore, a remaining major obstacle to separating sustainable and unsustainable seafood is the lack of transparency and traceability in seafood supply chains. In response to concerns about overfishing and the lack of transparency, as well as the general state of marine fisheries, many certification, ecolabeling and traceability schemes have been 3 Cashion, T., de la Puente, S., Belhabib, D., Pauly, D., Zeller, D., & Sumaila, U. R. (2018). Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach. PloS one, 13(11), e0207768. 26 proposed, each with their own criteria of what makes a fishery or aquaculture system ‘sustainable’. For fisheries, the largest ecocertification initiative is the Marine Stewardship Council, which now covers 14% of reported global landings, and is aiming for 33% by 2030 (Marine Stewardship Council, 2017). However, this will require voluntary buy-in from the fishing companies involved, which limits the scope of these types of programs to those that self-select into them. In addition, the adequacy and enforcement of ecocertification criteria have come under scrutiny (Jacquet et al., 2010; SeafoodSource, 2017). For example, the Star Shrimper XXV is certified as a sustainable prawn fishing vessel under the ‘Friend of the Sea’ criteria because it uses nets that reduce turtle bycatch; however, the vessel was detained for fishing in the Exclusive Economic Zone (EEZ) of Liberia without an access agreement and while not using their selective fishing gear (MAREX, 2017). One alternative to ecocertification and labeling schemes is an external review that links fishing practices of specific fish stocks to individual fishing companies, to better inform owners and potential investors. To gain a better coverage of the fishery practices of the seafood industry, I take this latter approach with regards to large seafood companies some of which are already involved in other seafood sustainability initiatives outside ecocertification programs (Österblom et al., 2015a). Previous external reviews of fishing companies have demonstrated company links to illegal fishing, in addition to human trafficking and other illicit practices (Marcus Aurelius Value, 2017). Clearly there are reputational and legal risks to these companies engaged in these activities, but here I focus on the biological sustainability of the fisheries they are engaged in and its implications for their economic returns. My objective is to link fisheries stocks with declining catches to the companies that are the key actors in those particular fisheries. Thereby, I aim to demonstrate the potential economic losses, or unrealized revenues and profits, at the specific company level from fisheries targeting overfished stocks, or not realizing the full potential benefits of currently lightly fished stocks. By demonstrating these unrealized revenues and profits, I aim to demonstrate the benefits for the companies involved of healthy fish populations to motivate private action towards fisheries sustainability. Through this, I aim to move beyond ‘name and shame’ strategies to focus on improving buy-in from private actors for sustainable resource use. 27 I have developed an approach that attempts to help clarify the opaque environment that characterizes many seafood supply chains. Through a diversity of methods, I characterize current fishing practices of companies and relate a company’s products to the fishing grounds and the fish stocks targeted. I then evaluate the changing revenue streams from these fisheries due to changing fish populations and evaluate the fishery stock-status using established catch-based indicators of stock health applicable to every fishery in the world. I do this with an estimation of revenues and profits for these companies to give an approximation of their potential benefits from sustainable fisheries. It is worth noting that in current application, I explore linkages at the origins of the seafood supply chains (i.e., fish in the ocean with the companies that catch them). However, this method could be easily extended to processors and sellers downstream; hence encompasing complete seafood supply chains. I acknowledge the uncertainties linked to the latter approach, although similar uncertainties exist in other stock assessment approaches. In addition, the data I use includes a collection of industry, national, reconstructed, and peer-reviewed data that together creates a holistic view of these fisheries. 3.2 Methods I developed a seven-step method to link any fishing company (including its revenues and profits) with the fish resources that it exploits and their biological status (Figure. 3.1). While I outline this here as a step-by-step process for clarity, in reality it can be iteratively reevaluated as more information becomes available from different sources. In addition, there is often uncertainty with regard to the actual resources companies exploit (i.e., which specific stock of which specific species in which specific area), largely due to non-clarity in company reporting. The steps I provide can be adapted, whether a specific set of resources or a specific company is the focus of the study. Companies that are publicly traded on stock exchanges are more amenable to this method than private companies, as public companies have legal requirements to disclose information for shareholders, which makes assigning their landings generally more straightforward and less uncertain. 28 Figure. 3.1. Schematic representation of method for linking fish stocks to company level fisheries revenues and potential profits Step 1 is to choose a fishery of interest and to identify the companies that operate in the fishery in question. This can be done through a broad search of company websites, annual reports, securities filings, or other publicly available information. Alternatively, if the study is focused on a specific fish wholesale company (or companies), then the first step is to identify the fishery or fisheries from which they source their fish. 29 Step 2 is to identify the company’s fleet involved in this fishery. This step is needed if the geographic area of the actual fishing activity is unknown or spread over a large area. For example, tuna fishing companies often target tuna in different oceans or ocean areas, but their fleet can be traced to the tuna RFMOs they participate in, or through vessel monitoring systems (see Step 3 below). A company’s fleet information may be available on their company website, through third-party verification and certification programs, or through available vessel registries. Step 3 is to identify or confirm the fishing area. Some companies report with greater specificity the areas they fish, while others do not. This can be overcome with vessel information if these vessels can be linked to available Automatic Information System or Vessel Monitoring System data (commonly referred to as AIS and VMS, respectively). If this is not possible, the species bio-geographic extent (i.e., species range) would limit the fishing activity or whether the vessels’ flag state has access to or has been observed fishing in certain areas (e.g., other countries’ EEZs). Step 4 is to confirm the fish species and stock being targeted by the company. If a company targets many stocks of the same species, these are treated individually. Step 5 is to gather relevant fisheries information on the stock in question, including biomass, effort and landings data, ex-vessel prices and cost of fishing data. The sources of these data may vary by the stock under study, but include national and regional level data wherever available, and can be supplemented by reconstructed landings (Pauly & Zeller, 2016a) and global coverage of the ex-vessel value of fish and cost of fishing (Lam, Sumaila, Dyck, Pauly, & Watson, 2011; Sumaila, Marsden, et al., 2007; Tai et al., 2017). Step 6 uses published abundance, relative abundance (e.g., catch per unit effort (CPUE)), and catch data as inputs into the Catch-MSY (CMSY) stock assessment method developed to allow evaluation of any fish stock using a Bayesian Schaefer model (BSM) with priors and available catch and biomass information (Froese, Demirel, Coro, Kleisner, & Winker, 2017; Martell & Froese, 2013). The BSM model uses catch and abundance data to predict values of the rate of growth (r) and carrying capacity (k) based on a priori known ranges of the current biomass. This step can also be undertaken with any other stock assessment method, depending on the data-poor or data-rich nature of the stock. However, I chose the highly versatile CMSY method as the default, as it is a method that can easily be applied to all fish stocks around the world, whether 30 previously evaluated or not, and whether the stock and fishery in question is data-rich or data-poor. The version used here has been updated (Froese et al., 2017) to address earlier challenges including biases in the estimation of stock size and productivity. In addition, the CMSY method in general performed well compared to other stock assessment methods (Rosenberg et al., 2014). The results of the CMSY method can be used to model past or future stock biomass scenarios based on different catch strategies to demonstrate the benefits of alternative fishing approaches on future biomass, landings, and revenues. Modeling future scenarios incorporates additional uncertainty, while modeling alternative past scenarios can demonstrate costs and benefits if alternative strategies had been taken and can be used to inform present decisions. This study uses the CMSY stock assessment model outputs of MSY as a theoretical benchmark to what these fisheries are measured against. I do not suggest that catches at MSY levels should be constant, as fish stocks are subject to natural variability. In addition, MSY cannot be achieved for all stocks in an ecosystem simultaneously due to predator-prey population dynamics in ecosystems where fishing is occurring (Walters, Christensen, Martell, & Kitchell, 2005). Also, MSY needs to be viewed as a ‘limit reference point’ rather than a ‘target reference point’ for fisheries management (Caddy & Mahon, 1995; Hilborn & Walters, 1992). Management strategies must take this into account, primarily by aiming for levels of catch below MSY and biomass levels above levels theoretically required to yield MSY (i.e., BMSY); or by applying fishing mortality rates (F, estimated by dividing the catch by biomass in a given time) lower than that which would yield MSY (i.e., F < FMSY ). This ensures that healthy biomass levels can be maintained within functioning marine ecosystems while potentially yielding higher catches at sustainable levels (i.e., provided that current biomass levels are above BMSY and/or that the current yield is below MSY). I explore different management strategies through the scenario analysis component of this method, so that we can achieve different fisheries management goals such as consistently high biomass (at, or preferably above, BMSY) while providing higher fisheries yields. However, fisheries are often required to be managed for MSY including in the United Nations Convention on the Law of the Sea and the European Union’s Common Fisheries Policy (Regulation (EU) No 1380/2013 of the European Parliament and of the Council of 11 December 2013 on the Common Fisheries Policy, amending Council Regulations (EC) No 1954/2003 and (EC) No 1224/2009 and repealing Council Regulations (EC) No 2371/2002 and 31 (EC) No 639/2004 and Council Decision 2004/585/EC, n.d.; United Nations, 1982). Therefore, it is important to test this method for MSY as a target as well as for more precautionary limits including harvest control rules that use a fraction of MSY, commonly termed ‘pretty good yield’ (Hilborn, 2010). Step 7 uses the above information in conjunction with other company information to evaluate the company’s revenues, and if possible, profits. I estimated a company’s average landings for the most recent five-year period for which information was accessible, based on a company’s ownership of quota, or reported average landings in recent years. I determined a simple estimation of potentially maximum unrealized revenues as determined by the difference between the average current landings and landings at MSY or maximum economic yield (e.g., the biomass and catches that maximize economic rents of the fishery) levels, assuming the proportion for each company would remain the same. I expressed unrealized revenues at the company level as related to unrealized ex-vessel value only (i.e., first point of sale, without post-landing processing or value-adding). Finally, I converted unrealized revenues to unrealized profits based on the average costs and increased revenues from higher catches. This serves as an approximation of the benefits that could be achieved at the company level, and can be estimated without the comprehensive data requirements of a company by company segregated bioeconomic model. Here, I demonstrate the applicability of this method with two case studies: the Peruvian anchoveta (Engraulis ringens; Engraulidae), and the USA’s menhaden fisheries (Atlantic and Gulf menhaden; Brevoortia tyrannus; Clupeidae and B. patronus; Clupeidae, respectively). I also demonstrate a retrospective approach to scenario analysis from Step 6 for the more complex scenario of the anchoveta fisheries. This involved modeling the potential landings, revenues, and profits given alternative fishing mortality rates based on known previous changes. I opted for a retrospective scenario analysis approach here due to the high environmental and biological variability of the anchoveta stock which makes forecasts highly uncertain (Brochier et al., 2013; Espinoza-Morriberón et al., 2017). This scenario analysis can be specified for different outcomes (e.g., higher biomass, more consistent landings, higher revenues), and details on the alternative scenario for the anchoveta fisheries can be found in section 3.2.1. The ex-vessel prices used were 32 adjusted to account for elasticity of demand under higher or lower landings (see Supplemental Methods). 3.2.1 Peruvian anchoveta case study The Peruvian anchoveta (Engraulis ringens) has two distinct stocks: the North-Central, and the smaller Southern stock, which is shared with Chile (Mendo, 2018). I focused here on the North-Central stock and Peru’s catches from the Southern stock, as Peru contributes most of the world’s anchoveta landings (FAO, 2017a). These fisheries are undertaken solely with purse seines (PRODUCE, 2017), with industrial and semi-industrial vessels off Peru’s coast. In general, these fisheries have low by-catch of other species and no interaction with the seabed. The major challenge for these fisheries is the by-catch of juvenile anchoveta, which negatively affects population growth and hence subsequent landings, and which leads to temporary closures, e.g., of the second 2016 fishing season (FAO, 2017b). These fisheries are reduction fisheries, where virtually all the fisheries landings are ‘reduced’ to fishmeal and fish oil. Peruvian anchoveta was for most of the late 20th century the largest single species fishery in the world with catches exceeding 16 million tonnes for a few years (Castillo & Mendo, 1987; Mendo & Wosnitza-Mendo, 2016). However, the returns of this fishery are highly variable and are influenced strongly by the El Niño and La Niña oceanographic oscillation (S. Bertrand, Dewitte, Tam, Díaz, & Bertrand, 2008; Espinoza-Morriberón et al., 2017; Ñiquen & Bouchon, 2004). I limited our analysis to the seven largest anchoveta fishing companies in Peru over the 2011-2015 period. I determined vessel ownership through Peru’s Ministerio de la Producción (PRODUCE) (PRODUCE, 2017). As the Peruvian fishery occurs solely within Peru’s EEZ, it was not necessary to confirm vessel-level spatial fishing activity through external sources. I determined company level landings through current fishing quotas allocated to vessels (Table 3.1), and I assumed all companies captured their quota share of the actual landings in each year. I tested the validity of this assumption against actual proportions of landings over the study period and found no difference in results (see below). I evaluated the catch, abundance data, and biological priors using the CMSY method (Froese et al., 2017). Catch and abundance data by season were accessed from Peru’s fisheries management organizations PRODUCE and Instituto del Mar de Perú (IMARPE) (PRODUCE, 2016). The Southern stock, which is shared with Chile, was modeled using reconstructed fisheries catches from Peru and Chile (Mendo & 33 Wosnitza-Mendo, 2014; Van Der Meer, Arancibia, Zylich, & Zeller, 2015), and biomass data from FishSource (FishSource, 2017) as a relative indicator of abundance. The catches were annually distributed between Peru and Chile based on each country’s known proportional contribution to the total anchoveta catch reported within the stock’s geographical extent. The intrinsic rate of growth parameter (r) for use in the CMSY calculations was obtained from FishBase (Froese & Pauly, 2012) for Peruvian anchoveta using two standard deviations from the mean as the lower and upper bound estimates (1.36-3.17 year-1). I evaluated the actual average landings by company from 2011-2015 against the estimated company landings when landings are modeled under the scenario analysis (see below). I used the most recent ex-vessel price available for Peru’s anchoveta fishery of $134 USD·tonne-1 based on the landed value of the anchoveta fishery (Christensen, De la Puente, Sueiro, Steenbeek, & Majluf, 2014). The cost of fishing was aggregated based on earlier surveys of the two main Peruvian pelagic fleets (steel and wooden purse seiners), which land almost all of their landings for reduction (Table A.1) (Christensen et al., 2014). Table 3.1 Fishing quota for Peruvian anchoveta and fleet size by company in Peru during 2011-2015. Company North-Central stock quota(%)1 Southern stock quota (%)1 Estimated 2015 landings (103 t) Estimated 2015 revenue (USD 106) Tecnologica De Alimentos S.A. 14 17 524 70 Corporacion Pesquera Inca S.A.C.2 11 3 368 49 Pesquera Diamante S.A. 9 8 309 41 Austral Group S.A.A 7 4 241 32 CFG Investment S.A.C.2 6 11 237 32 Pesquera Exalmar S.A.A. 7 5 236 32 Pesquera Hayduk S.A. 6 3 225 30 1. (PRODUCE, 2017) 2. I present Corporacion Pesquera Inca S.A.C. and CFG Investment S.A.C. separately here, as they were not under the same ownership for the entire study period of 2011-2015. 34 I used the biological parameters estimated by the BSM and CMSY method (biomass, intrinsic rate of growth (r), carrying capacity (k), and FMSY) to model an alternative scenario over the most recent 16-year period (2000-2015). I then modified the r parameter to account for recruitment anomalies, which are common in the Humboldt Current ecosystem (A. Bertrand et al., 2011; Cahuin, Cubillos, Ñiquen, & Escribano, 2009). This modification was done by minimizing the sum of squared deviations between the CMSY estimated output and the scenario-predicted biomass given the surplus-production function (Hilborn & Walters, 1992; Schaefer, 1957) by modifying the r value each year. This allowed us to estimate biomass and landings under alternative fishing scenarios. I estimated the target biomass of fish to be excluded from the fishing mortality as is done for the management of the anchoveta stock, hereafter referred to as the biomass reference point (Majluf, De la Puente, & Christensen, 2017). This was done using the FMSY output of the model as a constant. The baseline scenario was defined as the observed biomass and catches from 2000-2015. The first alternative scenario (hereafter referred to as the Optimized F scenario) was to optimize fishing mortality (F; restricted to be below FMSY model output) to achieve a higher or equal biomass one year after the time series than present, higher average biomass than under the baseline scenario, and to maximize the difference between the landings over the time period. FMSY was set as an upper limit as this ensures a more conservative approach where FMSY can only be achieved in years with biomass equal to or above BMSY. All optimization scenarios were estimated using Microsoft Excel’s Solver function using the generalized reduced gradient nonlinear solution method. Once the alternative scenario was complete, I re-modeled with the current policy to apply fishing mortality obeying a biological reference point, and without this restriction. The second alternative scenario (hereafter defined as (‘pretty good yield’ [PGY]) applied an alternative precautionary harvest control rule of catching MSY·0.91 when B>BMSY and linearly adjusting catches downwards until B=0.5 BMSY where catches are reduced to zero (Froese et al., 2011). 35 3.2.2 Menhaden case study Another major, although much smaller, reduction fishery is for Atlantic (Brevoortia tyrannus) and Gulf menhaden (B. patronus) in the Southeastern USA. There, menhaden play an important role in ecosystems as a forage fish species, i.e., a major source of food for higher-trophic level organisms (SEDAR, 2015; VanderKooy & Smith, 2015). The fisheries for these two species are managed at a regional level by the Atlantic and Gulf States Marine Fisheries Commissions (ASMFC and GSMFC, respectively). Omega Protein (owned by Cooke Aquaculture) takes over 75% of all Atlantic menhaden landings (Omega Protein Corporation, 2015) and jointly, Omega Protein Corporation and Daybrook Fisheries (owned by the Oceana Group) are the major fishing companies for Gulf menhaden. These companies operate in federal- and state-controlled waters of the USA and are thus subject to the regulations of the ASFMC and GSMFC. These two species are fished exclusively with purse seines when caught by reduction fisheries (NMFS, 2017a), and have very low rates of by-catch and discards (Harrington, Myers, & Rosenberg, 2005). If considered together, the menhaden fishery is the second largest fishery in US waters by weight (NMFS, 2017a), even though they are much smaller than in the past (Henry, 1971). I determined company vessel ownership through company reports (Daybrook fisheries, 2016) and third-party sources (Friend of the Sea, 2016b, 2016a). Landings data were obtained from the National Marine Fisheries Service using taxon identifier 'Menhaden' separated by Atlantic and Gulf regions (NMFS, 2017a). Minor landings of other menhaden species (i.e., B. gunteri and B. smithi) may be included in the data from the National Marine Fisheries Service, but are unlikely to influence the general pattern observed. Catch per unit effort data were used as an indicator of relative abundance. CPUE data were obtained from each stock’s most recent stock assessment (SEDAR, 2015; VanderKooy & Smith, 2015). The intrinsic rate of growth parameter (r) was obtained from FishBase (Froese & Pauly, 2012) using two standard deviations from the mean as the lower and upper bound estimates for Atlantic and Gulf menhaden (0.43-1.16 and 0.32-1.38 year-1, respectively). The landings, CPUE, and biological priors were used in the BSM and CMSY method (Froese et al., 2017). The most recent ex-vessel prices were used from the National Marine Fisheries Service (NMFS, 2017a). The cost of fishing was based on Atlantic menhaden, taking total costs of their operations minus processing plant labor costs (Table A.1, 36 Kirkley, 2011). While this may slightly overestimate the cost per tonne of catch, it kept our estimates of potential profits conservative and thus this estimation was used for both Atlantic and Gulf menhaden. As there are only two companies that fish Gulf menhaden, their actual individual landings are obscured by privacy laws (NMFS, 2017c). However, separating their catch by known catch proportions reported by each company was possible based on landings by area and company reports (Daybrook Fisheries;, 2016; Daybrook fisheries, 2016; Omega Protein Corporation, 2015). 3.3 Results 3.3.1 Peruvian anchoveta I estimated that the current biomass levels of Peruvian anchoveta -of both stocks- were below that which would potentially yield MSY (Figure 3.2), suggesting that the stocks are in a depleted state (B < BMSY). However, recent levels of fishing mortality rates are below the fishing mortality at MSY (F < FMSY), signaling that overfishing was not occurring (Figure 3.2). While the anchoveta stocks are naturally volatile in their biomass (due to sensitivity to highly variable oceanographic conditions), ensuring the stocks remain at higher biomass levels (i.e., biomass levels that could theoretically provide MSY) could have increased yields for the most recent period (Figure 3.2 and Table 3.2 and Table A.2). Average annual landings over the 2011-2015 period modelled here under the hypothetical, optimized-F scenario could have increased from 3.88 million tonnes to 4.16 million tonnes ( 940,000 tonnes) for the North-Central stock, and from 509,000 tonnes to 563,000 tonnes ( 58,000 tonnes) for the Southern stock. Overall, the average landings over the 2011-2015 period could have been increased by about 330,000 t·year-1. In this scenario, the North-Central stock would be responsible for 60% of the total potential increase in landings, although its productivity would only increase by 7%. The application of the optimized-F fishing strategy on the shared stock could increase total landings by 47% in 2015. 37 Figure 3.2. Fishing mortality (F/FMSY), biomass (B/BMSY), and fisheries revenues for the North-Central and Southern Peruvian anchoveta stocks under Baseline, Optimized F (Opt-F) and Pretty Good Yield (PGY) scenarios. 38 Table 3.2 Average biomass and landings outputs (103 t) of the scenario analysis for North-Central and Southern anchoveta stocks of Peru. North-Central North-Central North-Central South1 South1 South1 Scenario (Biomass)2 Baseline Optimized-F (807)2 PGY (807)2 Baseline Optimized-F (0)2 PGY Years Biomass Landings Biomass Landings Biomass Landings Biomass Landings Biomass Landings Biomass Landings 2000-2005 10,319 7,093 10,654 6,380 11,597 5,589 3,133 940 3,009 817 3,351 749 2006-2010 8,742 4,744 10,444 5,602 11,883 5,713 2,281 939 3,050 981 3,800 916 2011-2015 8,711 3,880 9,781 4,158 8,709 4,330 1,873 509 2,926 563 3,440 532 Mean (103 t) 9,324 5,355 10,315 5,443 10,784 5,234 2,473 805 2,996 789 3,519 733 Coefficient of Variation (%) 10.5 36.0 13.9 26.3 19.0 16.9 24.8 57.0 11.0 33.3 11.7 31.2 95% CI (103 t) 482 946 705 702 1,003 433 301 225 162 129 202 112 1. The Southern stock biomass refers to the whole stock, which is shared with Chile, but the landings reflect only Peru’s landings of the Southern stock as this is the focus of this study. 2. The number in brackets refers to the biomass (103 t) reference point, i.e. the biomass not subject to fishing mortality each year for the scenarios as established based on the scenario analysis methods 39 In reality, landings were lower due to the stocks being in depleted states, which even prompted the shortening of several fishing seasons in recent years (Table A.2). The landings under the Optimized-F scenario oscillate between being higher and lower than the realized landings in each year, but on average are higher for the North-Central stock, and could have been achieved with a higher and more stable biomass than observed in reality for both stocks (Table 3.2). The benefits highlighted above in the most recent period are driven by reducing fishing mortality to at or below FMSY over the period of 2000-2010 leading to higher and more stable biomass. This can be achieved while keeping average landings over these 11 years nearly identical, although in most years the catches would have to be lower than they were under the baseline scenario (Table A.2). With a more conservative harvest control rule (PGY), the average landings in the first 6 years are much lower than the baseline (5,589 compared to 7,093 thousand tonnes), but are also higher for both of the following 5 year periods in the North-Central stock (Table 3.2). These lower catches for the first 10 years would translate into lower fisheries revenues for the companies targeting these stocks, although the result under either of the alternative strategies (FMSY or PGY) leads to higher and more consistent biomass and landings. The seven largest companies involved in the anchoveta fishery in Peru control about 60% of the quota for the North-Central stock, and about 50% of the Peruvian share of the Southern stock (Table 3.1). The average unrealized potential revenues per company (depending on their quota share) ranged between USD 3.0 million and USD 9.1 million (Table 3.3). When accounting for the additional cost of fishing if catches could have been taken to MSY, the total unrealized potential profit would have been approximately USD 8 million (a 10% increase from estimated current levels). I compared the results whether I use current quota percentage or historical landings proportions to assign landings to companies, and the difference in results were minor (Table A.3). Another outcome of the scenario analysis was the potential decrease in inter-annual variability under the FMSY strategy. Under the Optimized-F scenario (i.e., fishing at FMSY when B  BMSY), the coefficient of variation (a measure of the relative variability, calculated as the standard deviation divided by the mean times 100) for potential landings decreases for both stocks, suggesting reduced inter-annual variability. While the potential increase in landings was modest, 40 41 Table 3.3 Average attained landings for the top seven companies for 2011-2015 (103 t; accounting for 57% of landings in Peru), scenario landings (103 t) and their impact on revenue and profits (USD 106) for FMSY and PGY scenarios. Scenario Company Landings1 Scenario landings2 Unrealized revenue Cost of landings shortfall Unrealized profits FMSY Scenario Tecnologica de Alimentos S.A. 616 685 9.14 6.93 2.20 Corporacion Pesquera Inca S.A.C. 429 464 4.64 3.51 1.13 Pesquera Diamante S.A. 363 401 5.07 3.84 1.22 Austral Group S.A.A. 282 307 3.41 2.58 0.83 Pesquera Exalmar S.A.A. 277 303 3.49 2.64 0.84 Pesquera Hayduk S.A. 262 285 3.01 2.28 0.73 CFG Investment S.A.C. 280 317 4.87 3.70 1.17 PGY Scenario Tecnologica de Alimentos S.A. 616 704 11.64 8.85 2.78 Corporacion Pesquera Inca S.A.C. 429 481 6.94 5.27 1.67 Pesquera Diamante S.A. 363 413 6.64 5.05 1.59 Austral Group S.A.A. 282 318 4.80 3.65 1.15 Pesquera Exalmar S.A.A. 277 313 4.81 3.66 1.15 Pesquera Hayduk S.A. 262 295 4.36 3.32 1.05 CFG Investment S.A.C. 280 324 5.78 4.40 1.38 1. See Table A.3 for estimated company landings. 2. Optimized-F landings are generated by optimizing the biomass reference point, and fishing at FMSY when B  BMSY . 42 the biomass of both stocks would be at higher average levels and the variability in landings would likely be lower or similar for the Northern stock, thus implying more consistent landings and revenue for fishing companies. The costs and benefits of rebuilding the Southern stock are not equally shared by Peru and Chile. Optimized-F Chilean anchoveta landings corresponding to the shared stock increased in both periods (2000-2010, and 2011-2015) and at much higher rates than they did in Peru (Table 3.4). The losses from rebuilding during the first ten years are not compensated for over the whole period although the most recent landings are higher on average. Table 3.4 Peru and Chile's average actual and modeled landings (103 t) of the shared Southern Peru/Northern Chile stock over two time periods. Actual Optimized-F PGY Years Peru Chile Peru Chile Peru Chile 2000-2010 940 790 892 825 825 772 2011-2015 509 1,006 563 1,177 532 1,079 3.3.2 Menhaden The most recent landings included in this study (2012-2016) for Atlantic and Gulf menhaden were at levels below the estimated MSY (Table 3.5 and Table A.5). In addition, the biomass of each stock was estimated as being above BMSY (B > BMSY) and the fishing mortality was below FMSY (F < FMSY) according to our model results (Table 3.5). Therefore, the stocks appear neither overfished nor being overfished. Table 3.5 Realized landings (103 t), MSY (103 t), and catch-based indicators of stock status for Atlantic and Gulf menhaden. 43 Menhaden stock 2015 Landings1 MSY2 B/BMSY2 F/FMSY2 Atlantic 201 340 1.38 0.278 Gulf 539 698 1.18 0.500 1. (NMFS, 2017a) 2. CMSY model output Omega Protein and Daybrook Fisheries account for the bulk of all menhaden landings in the USA (Table 3.6). The total revenue of these two fisheries was approximately USD 140 million annually. However, landings were below MSY, and if increased to the theoretical MSY level could potentially increase economic returns from the fishery (Table 3.7). The cumulative unrealized potential revenue (USD 72 million) for these two companies (Table 3.7) was around 50% of the current total fisheries revenues (USD 140 million; Table 3.6). In addition, when accounting for potential fishing costs for these unrealized landings as well as the lower ex-vessel price given the elasticity (see Appendix A), there were unrealized potential profits of around USD 15 million (a 40% increase from current estimated levels). The findings were similar for the PGY scenario although somewhat lower due to the lower yields when the stock is in a healthy state as it is at present. Even with these precautionary limits, the fishery could have additional revenues of over 50 million USD annually and profits of around 12 million USD annually. 44 Table 3.6 Major menhaden fishing companies in the United States of America. Company Menhaden stock Landings (%) Estimated 2016 landings (103 t) Estimated 2016 revenue ($ 106) Source Daybrook Fisheries Gulf ~40.01,2 194 50 (Daybrook fisheries, 2016), (Daybrook Fisheries;, 2016) Omega Protein Gulf 48.53 235 60 (Omega Protein Corporation, 2015) Omega Protein Atlantic 76.83 144 29 (Omega Protein Corporation, 2015) Table 3.7 Average landings (103 t), potential MSY (103 t), and associated unrealized revenues (USD 106) for menhaden by the major fishing companies in the USA. Scenario Company Menhaden Mean landings1 (2012-2016) Potential landings Unrealized revenue Fishing cost at MSY Unrealized profits MSY Daybrook Fisheries Gulf 188 279 22.58 16.20 6.38 Omega Protein Gulf 228 338 27.37 19.64 7.74 Omega Protein Atlantic 147 262 22.09 20.39 1.70 PGY Daybrook Fisheries Gulf 188 254 16.36 11.74 4.62 Omega Protein Gulf 228 308 19.83 14.23 5.61 Omega Protein Atlantic 147 238 17.56 16.20 1.36 1. See Table A.5 for estimated company landings. 45 3.4 Discussion 3.4.1 Overall Here, I was able to apply this method to two large case studies and the nine major companies involved in their exploitation. The total potentially unrealized annual profits for the two case studies presented here are USD 8 million for the Peruvian anchoveta (10% increase) and nearly USD 16 million for the US menhaden fisheries (40% increase). These unrealized profits are based on the assumption that these fisheries can be managed to maintain biomass levels equal to or larger than required to produce MSY (BMSY) on an ongoing basis, and fisheries yield can be at MSY or FMSY when biomass is below BMSY. For clarity and simplicity, I demonstrated these potential unrealized benefits for two low-value fisheries, with high landings, and heavily concentrated fishing company actors. The biomass of the anchoveta stocks are below levels that optimize or maximize potential catches (i.e., BMSY levels), and thus would benefit from stock rebuilding. In contrast, the menhaden stocks are at healthy biomass levels with regards to estimated MSY, and there is thus flexibility for carefully increased catch, revenue and profits. These unrealized revenues and profits can motivate stakeholders at different levels in the fisheries sector, including investors in these companies, due to the unrealized potential benefits originating from suboptimal levels of fisheries stocks. In addition, fisheries with healthy biomass levels are more likely to qualify for eco-certification programs (Gutiérrez et al., 2012) that may receive a price premium (Sogn-Grundvag et al., 2013) giving an extra incentive for these companies. While the present study has focused our approach on fisheries examples where the stock biomass is lower (anchoveta) or higher than BMSY (menhadens), there is also the extreme case of overfishing leading to collapsed stocks. While not fitting the strict economic definition of ‘stranded assets’, where assets must be subject to regulatory or legislative changes rather than biological changes (Wilen, 2009), fish stocks that collapse are indeed a form of ‘lost’ or unusable assets. For companies invested in such heavily overfished or even collapsed stocks that are then likely subject to more stringent limitations of fishing pressure, these fish stocks represent a form of stranded assets whose values to the companies are reduced (Wilen, 2009). When quotas for stocks cannot be realized due to low biomass of the fish stock, the owner’s resource rents are reduced to zero. However, even without a complete closure of fishing, continued fishing of 46 stocks in a suboptimal state will reduce the long-term revenues of these fisheries. Thus, the concept of stranded assets can be extended to where overexploitation reduces the value of fisheries assets marginally or completely. The scenario analysis of the anchoveta fisheries conducted here, comparing the Optimized-F scenario and the PGY scenario to the baseline scenario of actual fisheries and stock conditions illustrated differences in landings and revenues under different management scenarios. While I was able to demonstrate potentially more optimal fishing mortality rates to maximize these differences in the Optimize-F scenario, it is somewhat unrealistic for fisheries managers as they operate in an environment of imperfect information. However, the PGY scenario often had similar results with clear rules that can be operated on given knowledge about the status of the stock. The catch and revenue difference can be substantial over the time period analysed, but on an annual basis are quite comparable for the Northern stock. Thus, it demonstrates the current high performance of the managers to maximize anchoveta yields and revenues of this fishery even though they could be higher if B > BMSY as in the case of the menhaden fisheries. I compared current and past landings to landings under modeled scenarios that rebuild the stocks with either optimal fishing mortality rates or a precautionary harvest control rule (0.91·MSY). It is important to recognize that multiple fisheries on a variety of species in the same area cannot achieve MSY simultaneously for all species due to ecosystem interactions (Walters et al., 2005). It is thus important to retain a precautionary approach and not maximize landings (in terms of MSY) and ignore the ecological function that species fill in their ecosystem (Pikitch et al., 2014). The PGY scenario models this more closely to reality as there is a precautionary limit placed on MSY with reductions for when the stock is below BMSY. Our example for anchoveta would eventually lead to increased landings and revenues, but only after having increased biomass and decreased annual variability in biomass. Our case study for Gulf menhaden was informed by the GSFMC that incorporates menhaden’s role as a forage fish into their fishing limits (VanderKooy & Smith, 2015). Both case studies demonstrated the benefits of rebuilding fisheries stocks at the scale of individual companies acting in these fisheries. In the case of the anchoveta, currently unrealized benefits could only be attained after rebuilding the stock biomass to levels above BMSY. However, the menhaden fishery with high biomass levels has the potential for increased landings 47 and profits at present, should the appropriate economic drivers emerge such as increased prices for fishmeal or lower costs of fishing. It may be helpful to quantify potentially unrealized benefits at different scales and to express them in units relevant to the different stakeholders in the fisheries sector and beyond. Not only could it be important to those actors directly involved, such as company owners, shareholders and investors, but also to other important stakeholders such as governments that face foregone tax revenue or licensing fees due to overfished stocks or by not optimizing potential benefits of their natural resources. Unfortunately, governments far too often support fisheries well past the point of being economically profitable within ecologically sustainable limits through extensive harmful subsidies (Sumaila et al., 2010, 2016). 3.4.2 Anchoveta The anchoveta stocks are still in a depleted state. This is partially due to overfishing, particularly in the Southern stock, but also due to fluctuations driven by El Niño and La Niña events, and increasing variability due to climate change (Merino, Barange, Mullon, & Rodwell, 2010; Ñiquen & Bouchon, 2004). Our findings generally concur with the latest numbers of the national stock assessments conducted by PRODUCE and Chile’s Instituto de Fomento Pesquero (FishSource, 2017; PRODUCE, 2016). The large differences in the modeled scenarios’ landings for the Southern stock are due to the current low biomass of leading to lower catches over the earlier years, whereas, the North-Central stock is fished close to the level in the Optimized-F and PGY scenarios proposed here. The North-Central stock’s biomass declined recently in the observed and modeled scenarios due to strong recruitment anomalies caused by recent El Niño and La Niña events (Cubillos & Arcos, 2002; FishSource, 2017). Both anchoveta stocks could have delivered higher landings in the past if biomass levels would have been maintained at higher levels by pro-active management action. Increasing the anchoveta biomass by reducing fishing mortality rates to levels at or below FMSY or instituting a precautionary limit on MSY (see methods for further details) could benefit these fishing companies in the future. However, it is important to highlight that rebuilding fish stocks has a cost. The proposed theoretical MSY strategy generated losses in the earlier years of its implementation, particularly in the Southern stock (Figure 2). This shows that rebuilding the shared stock has an initial cost for Peruvian companies, but they benefit from the increased (and likely more stable) landings that result from a stock with higher average biomass in the later period (i.e., in the medium-term: 48 after 10 years). Reported landings in Peru were higher than the scenarios’ landings for those initial years because the model restricted the higher levels of fishing to prevent overfishing the stock (F > FMSY). Thus, the current low landings reported by Peru in the Southern stock are likely due to continued overfishing by both countries pressuring the stock into a depleted state (Figure 2). Recent landings of anchoveta have been volatile (Table A.2 and Table A.3), although they were much more variable historically, particularly in the 1970s and 1980s. The current management regime based on individual transferrable quotas (ITQ) has the potential to improve the state of the anchoveta resource and fisheries over time (Aranda, 2009), as long as the high data and assessment requirements for ITQ systems are maintained and management action is swift and proactive with regards to setting and adjusting annual allowable catch limits (Walters & Pearse, 1996). However, environmental fluctuations and the large effect of El Niño and La Niña events may continue to have negative repercussions for this industry (Ñiquen & Bouchon, 2004). Based on current fishing exploitation (FIS, 2017; PRODUCE, 2016), there could be greater benefits realized in terms of higher landings and higher economic returns if biomass levels were to be increased and maintained at higher levels based on our estimates of BMSY. A constant MSY does not optimize yields of this fishery due to the high variation in stock sizes (Larkin, 1977), but policies that optimize and adjust fishing mortality rates annually based on overall higher biomass levels have the potential to achieve higher landings and thus revenues. It is particularly important to highlight that the unrealized potential benefits of the industrial anchoveta fishery could be much higher if alternative uses for the landings were considered, namely direct human consumption rather than reduction (Christensen et al., 2014; Majluf et al., 2017). Based on local value-chain multipliers (Christensen et al., 2014) and the reported landings of anchoveta in 2013, a transition from the current use scheme (reduction) to a scenario where all landings were used by the canning industry for direct human consumption would result in up to 20% increases in net private profits, a near doubling of employment, and an additional 700,000 tonnes a year of seafood for the human consumption market, while decreasing fishmeal production by half (Majluf et al., 2017). However, this is not done at present due to a limited market for canned anchoveta. In addition, this reduction of fishmeal production from one of the 49 largest supplies would obviously have negative effects for fishmeal intensive forms of aquaculture. The Southern stock of anchoveta is expected to shift its distribution further south due to climate change where the catches will no longer be available in Peru’s EEZ (Merino et al., 2012). This means the share of this stock available to Peruvian companies will likely decline in the long-term, but our results show that these companies can benefit in the short to medium term (5-15+ years) from reducing fishing pressure and rebuilding the stock. The current overfished state of the stock is having negative repercussions for fishing companies in Peru and Chile, and the stocks being rebuilt could deliver higher landings and revenues in the future. While Chile is expected to have larger fisheries landings and revenues under the modeled scenario and into the future, this does not preclude Peru from cooperating in an agreement to rebuild the Southern stock. The short-term costs borne by Peru combined with the majority of benefits being derived by Chile could explain why Peruvian companies have been reluctant to support joint management of the shared stock. Finally, although not considered here, the bulk (~90%) of Chile’s landings of the shared stock is caught by a single company that could benefit considerably from the rebuilding strategies modeled here. 3.4.3 Menhaden The CMSY stock assessment method used here was in agreement with other published assessments on these stocks that biomass levels are above those potentially supporting MSY level catches and fishing mortalities are below MSY levels (SEDAR, 2015; VanderKooy & Smith, 2015). These results of potentially higher landings for MSY must be qualified as they are based on a single stock and do not consider the important ecosystem implications of menhaden (SEDAR, 2015; VanderKooy & Smith, 2015). Thus, current fisheries pressures on these stocks should be considered more precautionary and in-line with ecosystem considerations in fisheries than any attempts to maximize landings and economic returns via fishing at MSY. For Gulf menhaden, the responsible fisheries management agency (Gulf States Marine Fisheries Commission, GSMFC) increased the catch target reference point by over 70% to 829,737 t·year-1 (Schueller, 2016), even after accounting for the important ecosystem role of menhaden under ecosystem-based fisheries management (VanderKooy & Smith, 2015). However, only around 50 436,000 t are expected to be caught (NOAA, 2017). There are four likely explanations for the continuing lower actual fishing levels: i) the demand for menhaden fishmeal is not high enough to encourage heavier fishing; ii) the increases in fishing costs past a certain level of catch makes it less profitable to continue fishing, iii) the price negatively responds to an increased supply, which makes it more profitable to stop fishing early, or iv) a conservative rate of exploitation given broader ecosystem concerns. I demonstrated that increased supply does cause the ex-vessel price to decrease, but this price decrease was minor (see Supplemental Methods). If the total of the catch reference point proposed by the management agency were to be realized, Omega Protein’s theoretical unrealized maximized proportion could reach over 400,000 t·year-1, and Daybrook Fisheries’ over 330,000 t·year-1. These increased catches are above our estimates generated by the CMSY method (Table 3.7), and the difference in catches is attributed to a lower estimate of MSY from our model that could be driven by differences in input parameters. While our results suggest that landings could be increased to achieve higher yields (i.e., to theoretical MSY levels), this may negatively affect profitability because of increased fishing costs. In addition, the importance of precautionary ecosystem-based management should limit future catches of menhaden in both ecosystems to ensure enough biomass remains for higher level predators. This is especially important if the current baseline data being used is not truly reflective of long-term historical baselines, which have largely been forgotten (Pauly, 1995). In addition, there are other important fisheries in the ecosystem, and there is evidence that previous fishing of menhaden has negatively impacted other species and their fisheries, including the striped bass recreational fishery (Uphoff, 2003). 3.4.4 Limitations A limitation of the research presented here is that it does not consider the trade-offs these companies may be making with higher levels of fishing. Although the three species and four stocks included in this study are all forage fish and thus represent an important part of their respective ecosystems as major food item for higher predators, these predators can also increase or decrease the populations of their prey due to their own changes in abundance. Alternatively, higher levels of fishing on these forage fish may reduce populations of other species, which may negatively influence fisheries or the populations of non-targeted species. Such broader ecosystem 51 effects need to be considered carefully and precautionary in ecosystem-based management of fisheries. Some of the data needed for this study were not readily available or are subject to continually changing conditions. This study represents a snapshot in time and extrapolated the values at present (such as percentage of quota held by a company’s vessels) to try and understand how changing conditions could affect these companies. I sought to minimize parameter and structural uncertainties in our method by relying on the best available data from a diversity of sources. However, some assumptions had to be made to distribute landings (such as company reported landings for menhaden in the Gulf and Atlantic fisheries) and thus revenues to companies. I used a conservative price-elasticity estimated for the menhaden fisheries to qualify future revenues subject to potential increases in landings. Regardless, the examples presented here are a first application of a method that can continue to be improved upon with additional estimates and data. Future research should focus on applying this method to a wider range of fisheries and companies to test the ability of this approach to motivate changes by fishing companies. The ability to perform this analysis in data limited cases will vary, but the several public and global databases described in the methods should allow at least a coarse idea of the benefits and costs of rebuilding fisheries. In addition, it would be valuable to include additional dimensions to improve the financial analysis, including price-effects of increased supply and relevant costs of fishing at different fishing effort levels (Gordon, 1954). Also, it is important to consider if there are other costs associated with reductions in fisheries effort and catches (Sumaila et al., 2012) and how these may affect individual fisheries. Finally, alternative uses, from canning for direct human consumption to Omega-3 production for the pharmaceutical industry, as well as the importance of the ecological role played by the target species (e.g., forage fish, predator), should also be considered when assessing the potential unrealized benefits of a fishery at company level, as companies might have interests in multiple end-uses and target various species within the same ecosystem. 52 3.5 Conclusion I present a method for relating a company’s fisheries quotas and landings from the stocks they exploit to potential unrealized revenues and profits due to sub-optimal stock biomass and landings. I believe this approach can be used both at an aggregate level and at a micro level to analyze the state of fisheries stocks and the effects on balance sheets of fishing companies. This method imposes traceability on companies by piecing together these disparate parts, rather than wait for voluntary measures to be adopted. This is an important step to be taken to advance transparency in seafood supply chains. 53 Chapter 4: Sustainability ratings and outcomes in US fisheries 4.1 Introduction There are concerted efforts to change behaviour in favour of sustainable outcomes. Many of these efforts focus on consumers in hopes of informing and shifting consumer behaviour towards more sustainable choices (Ranganathan et al., 2016). There are at least a dozen such initiatives for fisheries and aquaculture alone. These have mainly been implemented through the form of eco-labeling, rating, and certification programs (hereafter referred to as sustainability systems) to incentivize sustainable fisheries practices through consumers. However, these initiatives often also influence policies either directly or through changing the public opinion about the performance of regulation (Seafood Watch, 2017). The channel through which a sustainability system affects sustainability outcomes is important as it determines the impact on producers. The impact of certification on consumers behavior is well documented (Hallstein & Villas-boas, 2013; Hilger, Hallstein, Stevens, & Villas-Boas, 2018), but their impact on sustainability outcomes for fisheries is largely unknown. One of the oldest and largest seafood ratings programs in North America is the Seafood Watch program, run by the Monterey Bay Aquarium. It provides independent ratings for seafood products from fisheries and aquaculture. To date, it has distributed over 60 million seafood guides and 1.8 million people have downloaded their seafood app (Seafood Watch, 2019). The result of their assessments is a ‘stop-light’ coloring system where the product is recommended as a ‘Best Choice’ (green), ‘Good Alternative’ (yellow), or ‘Avoid’ (red; hereafter I use the colors as a shorthand for the named ratings) (Seafood Watch, 2016). An aim of this program is to inform consumers to make more sustainable seafood choices. However, another aim of the Seafood Watch is to influence marine policy directly through collaborating with policy makers. Seafood Watch states that “Over the past 18 years, our standards have set high thresholds for the environmental performance of fisheries and aquaculture and have encouraged producers to improve practices as well as governments to improve regulations and management…” (Seafood Watch, 2017). To our knowledge, this claim has not been tested empirically. Most eco-labels, eco-certification and ratings systems assume the same theory of change for their program or initiative (Marine Stewardship Council, 2018; Seafood Watch, 2017). They hope to 54 capitalize on an existing segment of the population that values their ‘sustainable’ product form in contrast to other unsustainable or unrated alternatives. Through greater awareness of the issues surrounding their product, they hope to increase the number of consumers willing to pay the price premium associated with this sustainable product. The subsequent increase in demand for this sustainable product should then influence the producers to respond by increasing supply of sustainable products through either producing more or increasing the number of products considered sustainable. The increased demand should (at least in the beginning) increase the prices paid to sustainable producers in contrast to unsustainable producers and thereby incentivizing other producers to shift their production to sustainable means (if the price premium is high enough). Resource use in general and fisheries specifically are highly regulated in North America. For fisheries there is often a total allowable catch limit set by the regulator (the National Oceanic and Atmospheric Administration in the case of the US) which determines the total amount of a particular seafood that can be produced. Therefore, producers have limited opportunity to respond to increasing prices as a result of increased demand. However, the regulator may respond to the information of the certification program either directly or in response to the public opinion. A regulator that is perceived by the public to manage the resource stocks unsustainably may be pressured by the public and non-governmental organizations to reassess their regulatory policy. Therefore, changes in consumer’s behavior may not be the main way these programs affect sustainability outcomes. To quantify the impact of seafood sustainability systems on sustainability outcomes I created a novel large dataset combining US capture fisheries and Seafood Watch ratings. I do this to determine the effect of a seafood sustainability system on prices received by the producers and changes to their landings. I focus on the Seafood Watch program’s potential effect on US fisheries to increase (decrease) landings or receive higher (lower) prices due to increased (decreased) demand for a sustainable (unsustainable) product. The main concern that I address in our analysis is that seafood ratings are endogenous and respond to trends in catches. For example, an overfished fishery may become classified as unsustainable in response to declining catches. I therefore exploit the discrete change of the classification in a regression discontinuity design to parse apart the impact of the classification from the underlying trends that may have caused the classification in the first place. By 55 controlling for the trend in catches before and after classification, I separate out the effects of the rating from underlying trends in these fisheries. Using bandwidths of 3 and 5 years and controlling for fisheries identity and common shocks I find that becoming classified as unsustainable (“Avoid” or “red”) reduces catches by about 24%. A classification of “Good Alternative” or “yellow” does not effect catches, but has a significant effect on prices of an increase of 6%. Lastly, a classification as sustainable (“Best Choice” or “green”) has no statistically significant implication for catches, but may lead to a small increase in prices 8%. The results therefore suggest that the Seafood Watch program affects the sustainability of fisheries by reducing production of unsustainable fisheries and incentivizing the production of more sustainable options. I interpret the results on prices as an indication that the impact comes from a change in consumer behavior and a price premium for sustainable seafood products. However, as no negative price effect is seen for red-rated fisheries I interpret the change in red-rated catches to be driven by regulation. The increased scarcity of unsustainable products due to regulation should lead to higher prices, but I do not observe these here. Therefore, I estimate there to be a strong effect on decreasing unsustainable production from regulation after the Seafood Watch rating, and unsustainable product prices do not change because of a mix of increased demand from scarcity and decreased demand for unsustainable products. 4.2 Literature Certification plays a prominent role in ‘green’ markets where joint products are purchased often involving a consumer good and an environmental public good (Kotchen, 2006). Eco-certification helps to differentiate products for consumers that wish to participate in green markets (Kotchen, 2006). The effect of eco-certification programs in general has been questioned with mixed results for their efficacy to both signal environmentally superior products and to translate these products into benefits (Hamilton & Zilberman, 2006). Certification can also be important in markets where the certification conveys additional information related to product quality or the production system. There are two major questions that are addressed in the eco-certification and eco-labeling literature: 1) how much are consumers willing to pay for the ‘green characteristic’ of these goods, and 2) does it translate into achieving its desired outcomes? The first question has mainly 56 focused on the presence of price premiums for goods in these green markets such as green office space (Eichholtz, Kok, & Quigley, 2010), rainforest coffee (Schollenberg, 2012), and certified seafood (Blomquist, Bartolino, & Waldo, 2015; Stemle et al., 2016; Wakamatsu, 2014). One of the most popular and well-known seafood certifications is the Marine Stewardship Council (MSC). Stemle et al. (2016) found that some fisheries received higher ex-vessel prices post-MSC certification, but this effect was not present for all fisheries in their study. Wakamatsu (2014) found no evidence of a price premium for flathead flounder (Hippoglossoides dubious), but did find evidence that its price is no longer influenced by the larger uncertified flathead flounder markets signifying it is perceived as its own product. Finally, Blomquist et al. (2015) find a non-significant difference in price before and after MSC certification for Atlantic cod (Gadus morhua). Therefore, there is little conclusive evidence of MSC certification translating into higher prices for fishers. This lack of benefits may undermine the incentive for seafood producers to enter or continue to participate in these programs (Kaiser & Edwards-Jones, 2006). The second question has focused on whether these programs translate into their desired outcomes such as decreased deforestation or sustainable fisheries practices (Blackman, Goff, & Rivera, 2018; Carlson, Heilmayr, Gibbs, Noojipady, & Burns, 2018). Palm oil plantations certified by the Roundtable on Sustainable Palm Oil have lower rates of deforestation post-certification, but higher rates of deforestation pre-certification indicating an increase in unsustainable behaviour while it is still ‘allowed’ (Carlson et al., 2018). However, certification of forestland in Mexico by the Forest Stewardship Council, one of the major market-based programs aimed at the forestry sector, has not been shown to be effective at reducing deforestation (Blackman et al., 2018). These are just two examples where certification leads to successful or unsuccessful outcomes depending on mediating factors. For seafood, very little research has been done on eco-labeling, ratings, and certification programs translating into ‘on the water’ changes. Gutierrez et al.’s (2012) study also finds a positive effect on biomass of certified fisheries, although the sample of this is quite small due to the data requirements and relatively recent implementation of MSC when this study was completed. However, other studies have pointed to its failure to improve stocks in Europe (Froese & Proelss, 2012). A review of the claims raised against MSC finds that reviewers are 57 often lenient in the certification of fisheries (Christian et al., 2013), indicating this might not lead to changes in sustainability outcomes. Here, I focus on whether the Seafood Watch eco-labeling program: i) causes changes in incentives for producers, or ii) has an effect on seafood production through encouraging sustainable production systems and discouraging unsustainable ones. For Seafood Watch, previous evaluations have found a self-reported change in consumer behaviour (Kemmerly & Macfarlane, 2009), but no decrease in pressure on fishing stocks from the publishing of their ratings (Quadra Planning Consultants Ltd, 2004). However, those evaluations were 10 to 15 years ago when the program was still young. In the next section, I develop a simple graphical framework to illustrate the potential impacts of eco-labeling on sustainability outcomes in a highly regulated environment. 4.3 Framework The typical argument for sustainability systems is that they increases the demand for sustainable products while reducing the demand for the unsustainable products. The mechanism is depicted in panel A) of Figure 4.1. The baseline demand before labeling is shown in blue. Labeling the product as sustainable (green) increases the demand and in consequence also its price. The supply responds to the increased price and supplies more of the sustainable good. The reverse is true for the unsustainable good (red). In contrast to many other goods, the supply of fish of a certain species is often highly regulated and the total production is limited by the total allowable catch. The supply is inelastic as a result. However, the regulator may respond to the certification and increase the total allowable catch of the sustainably harvested species or reduce the total allowable catch of the unsustainably harvested species. A scenario in which the consumer does not respond to the certification, but the regulator does is depicted in panel B) of Figure 4.1. The catch of the sustainably harvested species increases as in the previous scenario but in contrast to the previous scenario the price drops of the sustainably harvested species. Both scenarios lead to similar sustainability outcomes but the outcomes for fishers differ. Fishers benefit from eco-labeling when demand responds to the additional information. However, the impact of eco-labeling on the fishers is less clear when only the regulator responds. These are the two extreme 58 scenarios but intermediate scenarios are more likely in reality. How catches and prices respond to certification remains, however, mainly an empirical question. Figure 4.1. Demand and supply of seafood before and after rating. In panel A) demand increases in response to the rating of a product as sustainable (green) and it decreases in response to the classification as unsustainable (red). Supply then responds to the changes in demand. In panel B) supply is regulated and completely inelastic but it responds to rating by increasing or reducing supply according to the outcome of the rating (red – unsustainable or green –sustainable). The supply outcomes of panel B) are similar to the outcomes in panel A) but in contrast to panel A) the price of sustainably produced goods decreases and the price of unsustainable goods increases in response to rating in panel B. 4.4 Background The Seafood Watch program began in 1999, and ratings for US marine capture fisheries began in 2003. Each rating is specific to a species or stock using particular fishing methods (i.e., fishing gear) in a defined area. The rating is provided with a score (for fisheries assessed after 2013) and an overall rating of ‘Best Choice’ (i.e., green), ‘Good Alternative’ (i.e., yellow), or ‘Avoid’ (i.e., red). The overall rating is based on the score as well as meeting certain criteria. Overall, the fishery is rated based on: i) impact on target stock, ii) impact on other species, iii) management effectiveness, and iv) impacts on habitat and ecosystem (Seafood Watch, 2016). Seafood Watch staff determine the fisheries’ ratings using peer-reviewed and grey literature about the fisheries. 59 The final rating is determined through an average score of the different criteria with additional cut-off criteria so that overfished stocks cannot receive an overly positive rating if they score well on the other criteria (Seafood Watch, 2016). The Seafood Watch program reassesses fisheries throughout time to ensure their ratings stay accurate and up to date with knowledge of fisheries impacts and sustainability. For complete information on the current standard used by Seafood Watch, see https://www.seafoodwatch.org/seafood-recommendations/our-standards (Seafood Watch, 2016). 4.4.1 Data Our data was composed of three sources: Seafood Watch fisheries ratings, National Marine Fisheries Service fisheries (NMFS) landings, and stock status and biomass data from RAM Legacy Database. I obtained all ratings directly from the Seafood Watch program database. I obtained annual fisheries landings and values by species, gear type, and state from the NMFS of the USA (NOAA, 2016). This data is updated annually by the NMFS and is available in various formats. As the Seafood Watch program is gear-specific in its ratings, I used the dataset including fishing gear which is aggregated by gear, species and state annually. Each record in this data thus contains the annual landings and value for each gear, species and state combination. Some data are not available due to privacy concerns where there are three or fewer vessels or firms reporting in a given year. In addition, some states provide higher resolution of their fisheries landings by gear than others, with a general trend in the Pacific towards aggregated reporting into the category of ‘Unspecified Gear’. Where all species from a particular state had the same overall rating from Seafood Watch and the NMFS provided no detailed information on fishing gear used, I assumed the overall rating to apply to all landings from that state for that species in a given year. As the majority of Seafood Watch ratings are from 2003 and onwards, I only used NMFS fisheries data from 1998 onwards. There are 132 fishing gears reported by the NMFS and these were aggregated into 14 broader gear categories to correspond to the gear descriptions used by Seafood Watch. In total, there were 87,479 fisheries observations and 561 unique fisheries assessed. I accessed the latest version of the RAM legacy database (RAM Legacy Stock Assessment Database, 2018; Ricard, Minto, Jensen, & Baum, 2012) and used the indexes of biomass over 60 biomass at maximum sustainable yield (i.e., B/BMSY) to measure relative stock health. I matched the stocks to their respective US states based on information in the database, and compiled several stocks where they overlapped between states. I matched the NMFS fisheries data to the fisheries that have been assessed based on their criteria in the Seafood Watch database and reports that include information on the location or state, fishing gear, and species. For this analysis, I focused only on marine capture fisheries in the US due to rich data availability on fisheries landings and value by gear and state available (NOAA, 2016). When a rating’s location is generalized above the state level, matching was done using the definitions provided by NMFS for state groups (i.e., Gulf of Maine states) or based on likely matches. Fisheries with unspecified gear (e.g., ‘Uncoded’, or ‘Unclassified’) were excluded from this analysis, as gear used is a key part of the Seafood Watch ratings. Ratings were excluded for fisheries using the same gear for the same species had different ratings depending on the fishing area that was not specific to a state. For example, Atlantic cod (Gadus morhua) caught by handlines in the Gulf of Maine is rated as ‘Avoid’ while that caught by handlines on Georges Banks is rated as ‘Good Alternative’. I assumed both of these would be landed in New England states thus giving conflicting ratings for Atlantic cod landed in these states, and thus not helpful to isolate a landings or price effect here. 4.5 Estimation strategy The focus of our empirical strategy is to separate the causal impact of the assessment on the catches and prices from the underlying changes that may cause the assessment in the first place. For example, an overfished and declining fishery may be classified as unsustainable. Declining fish catches may therefore be correlated with a red assessment, but the causality could be in either direction. I address this challenge through a regression discontinuity design allowing unsustainable and sustainable species to follow different trends in the years before and after the assessment event. However, this strategy depends on the assumption that the assessment does not respond immediately to major events affecting fisheries production. The Seafood Watch’s guidelines for assessment support this assumption by stating: “To create a rating, the Seafood Watch Science team compiles all relevant scientific information from journal articles, government reports, white papers, and industry data and evaluates this information against the standard. Prior to publication, each assessment is peer-reviewed by a minimum of three external 61 experts from the academic, government, industry, and NGO communities” (Seafood Watch, 2017). This rigorous assessment process makes an immediate response to fisheries events unlikely. However, even if Seafood Watch does not follow these guidelines closely, our analysis is unbiased as long as the underlying changes are continuous. In addition, as the rating is not made solely on target stock status but includes other criteria (see section 4), there should not be a perfect relationship between rating level and stock status that could be linked to catches. Our main empirical approach uses regression discontinuity design in which I restrict the study period (i.e., bandwidth) to 3 and 5 years before and after the rating. To allow the assessment to respond to trends in catches and prices I include a running variable (years before and after the assessment). Our basic econometric model is therefore specified as: 𝑙𝑜𝑔(𝑦𝑖𝑡) = 𝛼𝐴𝑠𝑠𝑒𝑠𝑠𝑚𝑒𝑛𝑡𝑇𝑖𝑚𝑒𝑖𝑡 + 𝛽𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 𝛾𝑖 + 𝛿𝑡 + 𝜖𝑖𝑡 (4.1) Where 𝑦𝑖𝑡 is the outcome (landings in tonnes or price in nominal USD) of a fishery in year 𝑡. The landings and price are log-transformed due to their distribution following a log-normal distribution (Figure B.1 and Figure B.2). A fishery here is defined as a unique species, state, and gear combination. 𝐴𝑠𝑠𝑒𝑠𝑠𝑚𝑒𝑛𝑡𝑇𝑖𝑚𝑒𝑖𝑡 is a running variable that measures the years before and after the rating with zero being the year of the assessment. The variable varies by fisheries as a result of their different assessment years. In a second specification, I further allow the trend to vary by rating outcome i.e. I allow a different trend for those species that are later classified red, yellow and green. 𝑅𝑒𝑐𝑜𝑚𝑚𝑒𝑛𝑑𝑎𝑡𝑖𝑜𝑛 is a vector of dummy variables for rating (green, yellow, or red) that are zero before the assessment and become one after the assessment. The parameter 𝛾𝑖 and 𝛿𝑡 are fishery and year fixed effects, respectively. The coefficient 𝛽 depends not only on the outcome of the assessment but also on the initial assessment status i.e. a change from ‘red’ to ‘green’ may have a different impact on sustainability outcomes than the change from ‘yellow’ to ‘green’. In our main specification, I therefore restrict our sample to the initial assessment (from ‘not assessed’ to either ‘red’, ‘yellow’ or ‘green’) and include a specification with complete pairs of assessment changes (e.g. ‘not assessed – red’, ‘yellow –red’, ‘green – yellow’, etc.) as a robustness test (Table B.1). I use this specification only as a robustness test since there are 12 pairs several of which are rare while the most common pairs are from ‘not assessed’ to ‘red’, ‘yellow’, and ‘green’. 62 Our main concern is that the assessment and the preceding ratings respond to declining catches. I test this in two different ways. In a first test, I estimate equation (4.1) but with lagged catches and prices as the dependent variables. The parameter estimate for 𝛽 should approach zero the larger the lag for 𝑙𝑜𝑔(𝑦𝑖𝑡) if the causal effect is of the rating on catches (Table B.2 and Table B.3). In a second robustness test, I estimate if lagged catches (by 1 and 2 years) affect the assessment of the fishery i.e. I estimate 𝐴𝑠𝑠𝑒𝑠𝑠𝑚𝑒𝑛𝑡𝑖𝑡 = ∑ 𝛿𝑠𝑚𝑠=1 𝐶𝑎𝑡𝑐ℎ𝑖𝑡−𝑠 + 𝜂𝑖 + 𝜃𝑖𝑡 + 𝜖𝑖𝑡 (4.2) for each assessment level separately. Here, 𝐴𝑠𝑠𝑒𝑠𝑠𝑚𝑒𝑛𝑡𝑖𝑡 is a variable that equals one in the year in which the rating is made, and it is zero otherwise. If ratings respond to deviations of catches from the trend, the 𝛿𝑠 should be different from zero (Table B.4). All analyses were undertaken in R (R Core Team, 2017) using the tidyverse, rMarkdown, and lfe packages (Baumer & Udwin, 2015; Gaure, 2019; Wickham, 2016). All scripts used for cleaning data, analysis, and the manuscript are provided in the supplementary materials. 4.6 Summary statistics Overall, the US catches ~4 million tonnes of seafood annually with a first-sale (i.e., ex-vessel) value of between 3.5 and 6 billion nominal USD over the study period. The portion that has been assessed by Seafood Watch is a small but growing portion of this (Figure 4.2). The vast majority of assessed US fisheries landings are labeled as either green or yellow, with only a small portion labeled as red (Figure 4.2). However, when viewing the results by value, the increase has been much larger in recent years (Figure 4.3). This logically leads to the conclusion that seafood that has been assessed by Seafood Watch accounts for on average a higher priced product (Figure B.4). 63 Figure 4.2 US capture fisheries by assessed status by weight Figure 4.3 US capture fisheries by assessed status by value ($) Fisheries that are assessed by Seafood Watch have higher average landings and prices than unassessed fisheries (Table 4.1 and Figure B.3). While not matching all unassessed fisheries in terms of diversity, SFW has assessed over 561 unique fisheries for over 100 species, and has coverage over the majority of the states and years included in our sample. There is also a general pattern present for the different ratings with worse performing fisheries being from lower- 64 volume and higher-priced fisheries, and more sustainable fisheries having higher average landings and lower average prices (Table 4.1 and Figure B.4). In addition, fisheries with more negative ratings (‘Good Alternative and ‘Avoid’) generally have lower landings and higher prices. Finally, I present the summary statistics by Seafood Watch rating for which I have biomass data (as measured by B/BMSY, Table B.5). A value of one indicates that a fish stock is at the population level for maximum sustainable yield. A value of 0.5 implies that the fish stock is half the size that would yield the maximum sustainable long-term harvest. More green and yellow-labeled fisheries do correspond to having higher mean biomass levels (in reference to their BMSY) than red-labeled fisheries. It is important to note the large standard deviations in this biomass data across all rating levels. The results indicate that fisheries rated as red have on average 0.4 lower biomass than the average fishery (1.9 B/BMSY) while fisheries that are rated as green have a much healthier stock size than the average fish stock (2.8 B/BMSY). The Seafood Watch rating is therefore closely related to stock status and overfishing, although this is not the only determinant. Table 4.1. Summary statistics by Seafood Watch rating Variable None Avoid Good Alternative Best Choice Mean Landings (sd) 842 (21,878) 247 (1,135) 2,766 (15,855) 6,328 (28,390) Mean Price (sd) 5,555 (67,116) 9,026 (151,318) 5,166 (5,536) 4,538 (4,401) Number of Gears 14 11 11 10 Number of Species 571 37 74 42 Number of States 27 21 23 20 Number of Years 19 12 13 13 Observations 83967 1356 1365 791 The models are presented in several sets separating out the main dependent variables of landings (A) and prices (B). I present the regression discontinuity for two different specifications with the running variable as an additive variable (Equation 4.1, models 1 & 3) or as an interaction between the running variable and the time-invariant Seafood Watch rating (models 2 & 4). I test the regression discontinuity with a bandwidth (i.e., years before and after the Seafood Watch rating) of 5 (models 1 & 2) and 3 years (models 3 & 4). 65 4.7 Results The results of the regression models are presented in Table 4.2 and 66 Table 4.3. All models incorporate fishery and year fixed effects and I cluster standard errors on species and year levels to take correlation of errors across fisheries of the same species (i.e., stock) and errors across species within one year into account. The baseline estimates for the impact of Seafood Watch assessment on fish catches in column A1 of Table 4.2 shows that the assessment by Seafood Watch is associated with a general reduction in catches although this effect is not statistically significant for the fisheries that are assessed as green and yellow. In contrast, there is a statistically significant decline in catches for those fisheries that are assessed as red. The second column (A2) allows the running variable to vary between those fisheries that will be assessed as green, yellow or red. The parameter estimate for red-labeled fisheries implies that catches decline by about -24%. The specifications reported in column A3 and A4 are similar to those reported in A1 and A2 but reduce the bandwidth to 3 years before and after the assessment. However, the results are similar to the results reported in A1 and A2 especially with respect to the relative changes. This is also true for robustness checks using a fixed effects specification reported in Appendix B (Table B.6 and Table B.7), which seem to suffer from misspecified trends but show similar relative impacts of the Seafood Watch assessment. The results with prices as dependent variable are reported in 67 Table 4.3. There is a positive effect of assessment on fish prices of fisheries that were assessed as ‘green’ and ‘yellow’, although this effect is not significant in most specifications. The results suggest that assessment as green increases the price by 8% while assessment as yellow raises the price by 6%. In contrast to these two assessment outcomes I find no statistically significant evidence for an impact of the assessment as ‘red’ on prices. While the significance levels are below our threshold for most model results, there is a fairly consistent pattern of green rated fisheries being the most positive (3% to 8%), yellow rated fisheries having a moderate positive effect (4% to 6%), and red-rated fisheries with effect sizes close to zero (1% to 2%). I interpret this result as evidence of consumer response to the rating. However, the lack of response of prices to a red rating may result from a response of consumers to rating changes and to the increased scarcity due to 68 Table 4.2 Regression discontinuity results of catch models. All have ln(Tonnes) as the dependent variable. Term A1 A2 A3 A4 Green -0.122 (0.147) -0.167 (0.161) -0.067 (0.169) -0.098 (0.159) Yellow -0.109 (0.125) -0.146 (0.146) -0.1 (0.128) -0.128 (0.165) Red -0.333** (0.155) -0.273* (0.141) -0.325** (0.14) -0.281 (0.206) Running Variable 0.06*** (0.017) 0.001 (0.023) Green:Running Variable 0.068** (0.028) 0.01 (0.047) Yellow:Running Variable 0.066** (0.028) 0.009 (0.041) Red:Running Variable 0.047 (0.041) -0.013 (0.064) Observations 4930 4930 3369 3369 Adjusted R^2 0.892 0.892 0.909 0.909 F-Statistic 61.657 61.456 51.555 51.369 Data +/- 5 Years From Rating +/- 5 Years From Rating +/- 3 Years From Rating +/- 3 Years From Rating Note: Models A1 and A3 have a running variable and estimate the effect of rating. Models A2 and A4 have an interaction term of running variable and rating level and estimate the effect of rating on catches. All models have year and fishery fixed effects, and cluster standard errors on species and year. ***, **, * = significant at 1, 5, 10% level. 69 Table 4.3 Regression discontinuity results of price models. All have ln(Price) as the dependent variable. Term B1 B2 B3 B4 Green 0.055 (0.041) 0.066 (0.051) 0.074** (0.037) 0.032 (0.035) Yellow 0.055* (0.032) 0.041 (0.029) 0.034 (0.031) 0.056 (0.037) Red 0.01 (0.043) 0.018 (0.045) 0.009 (0.046) 0.009 (0.055) Running Variable 0.022*** (0.006) 0.016* (0.008) Green:Running Variable 0.019 (0.014) 0.03** (0.012) Yellow:Running Variable 0.024*** (0.009) 0.01 (0.011) Red:Running Variable 0.02*** (0.007) 0.017 (0.013) Observations 4930 4930 3369 3369 Adjusted R^2 0.927 0.927 0.947 0.947 F-Statistic 94.097 93.787 91.198 90.956 Data +/- 5 Years From Rating +/- 5 Years From Rating +/- 3 Years From Rating +/- 3 Years From Rating Note: Models B1 and B3 have a running variable and estimate the effect of rating. Models B2 and B4 have an interaction term of running variable and rating level and estimate the effect of rating on catches. All models have year and fishery fixed effects, and cluster standard errors on species and year. ***, **, * = significant at 1, 5, 10% level. 70 reduced catches. This outcome is consistent with a mix of consumers and regulators responses to seafood ratings. Overall, our findings suggest that a red or unsustainable rating reduces catches by about -24% while it has no impact on prices. In contrast, a green or yellow i.e. as green or yellow rating has no impact on catches but a positive impact on price. Being rated as green or yellow increases prices by 6 to 8 percent. These patterns are also present in the fixed effects model reported in Appendix B.3 although these models suffer from misspecified trends due to the strong fluctuations and trends in fisheries. I interpret our results and especially the mismatch between responses in prices and responses in catches as evidence for a response of both consumers and regulators to a Seafood Watch rating. 4.8 Discussion This analysis demonstrates a strong effect of Seafood Watch ratings on discouraging catches of worst performing fisheries (e.g., ‘red-labeled’ fisheries). I also find some evidence supporting an increase in prices for sustainable or yellow and green-labeled fisheries. Our results do not fully support either Figure 4.1A or Figure 4.1B on their own, but conform to a hybrid mechanism where the rating influences both regulators and consumers. For the effect on red-labeled fisheries, the lack of change in price could be due to a decreased demand from ‘green consumers’ combined with higher prices for a scarcer good. However, this is only one explanation for the potential lack of changes in price observed here. Therefore, my results do not necessarily support the common assumptions of a ‘punishment’ for poor performers in the market, and only a minor ‘reward’ for those sustainable actors. While not considered here, there may be other mechanisms that effect fishers' incentives from the Seafood Watch program. There is anecdotal evidence of greater access to markets for green and yellow-labeled fisheries. The author is aware of only two other studies on seafood eco-labeling involving a graded rating system like Seafood Watch (Hallstein & Villas-boas, 2013; Hilger et al., 2018). It may be that the amount of information is confusing to consumers as suggested by some, and that a simple ‘yes-or-no’ system communicates the information more effectively. Based on the results of our study, it may be more important to differentiate between sustainable and unsustainable fisheries, than a graded rating. Here, I have only focused on marine capture fisheries caught in the USA. While this is a limited segment, it can be used to illustrate the effect of seafood eco-labels on ex-vessel prices and 71 landings. Similar effects have not been able to be analysed due to granularity needed in the data for this analysis. For example, wide-scale data on aquaculture by species and production method is yet to be available which would be necessary to similarly evaluate the impacts of the Seafood Watch program on aquaculture. Other studies could follow up on previous case studies of particular products for the aquaculture sector and the effect of eco-labels like Global Aquaculture Alliance and Aquaculture Stewardship Council (MSC’s counterpart). A drawback to our focus is the nature of the US fisheries sector. A large portion of the seafood caught in the US is destined for export, with approximately 35% of seafood consumed in the US of domestic origin (Gephart, Froehlich, & Branch, 2019). This may limit the influence a sustainability program like Seafood Watch can have on the ex-vessel prices fishers receive domestically. However, the effect of this program may occur at other stages of the supply chain including access to markets and consumer preferences. In addition, it is likely these effects are consistent across all species/species groups in the US marine fisheries sector, and that the effect is stronger for some groups than for others. 4.9 Conclusion Our results show reason to be optimistic for the sustainable seafood movement. Although there may be more minor effects on best-performing fisheries, this program does effectively discourage against the worst-performers. Therefore, it would be appropriate to further consider the use of ‘carrots’ and ‘sticks’ in eco-labeling. 72 Chapter 5: Low cost conservation: Fisheries gear threats to marine species4 5.1 Introduction Fishing is a major threat to many marine species globally (Jackson et al., 2001). However, fishing gears vary in their spatial extent and their impact on different species. Therefore, treating different fisheries homogenously with regard to their spatial management likely causes unnecessary conflict between fisheries and conservation priorities, and may impose too high costs without necessarily achieving their intended conservation goals. Here, I aim to understand the threats from specific fishing gears and their spatial overlap with species of conservation concern. Recent studies have highlighted the spatial extent of fisheries (Amoroso, Parma, Pitcher, Mcconnaughey, & Jennings, 2018; Kroodsma et al., 2018), as well as the potential hazards that fisheries pose to species of conservation concern (Queiroz et al., 2019). While fisheries pose obvious risks to both targeted and bycatch species (Lewison, Crowder, Read, & Freeman, 2004), they also contribute to livelihoods of millions of people and food and nutritional security of billions of people (FAO, 2018). Thus, the goals of fisheries and conservation ought to be balanced to minimize the social, environmental, and economic costs and maximize the benefits where possible. Previous analyses have demonstrated trade-offs between fisheries and ecosystem health (Cheung & Sumaila, 2008). However, win-win situations are often shown to occur in overly simplified models that do not account for all variables such as employment for fishers (Cheung & Sumaila, 2008) or spillover impacts to other areas of importance (McShane et al., 2011). It is therefore important to consider the different scales at which these conservation plans act and the implicit trade-offs between social and ecological outcomes in many fisheries management plans (McShane et al., 2011). In addition, taking the heterogeneity of fisheries into account can lead to more positive fishery outcomes without compromising conservation goals (Klein, Steinback, Watts, Scholz, & Possingham, 2010). 4 Cashion, T., Tai, T.C., Lam, V.W.Y., Pauly, D., Sumaila, U.R. (Under Review). Low cost conservation: Fisheries gear threats to marine species. 73 Here, I establish the first estimate of large-scale conservation trade-offs when protecting species from their major fishing gear threats. This analysis is based on 4,579 marine species included in the International Union for the Conservation of Nature (IUCN) Red List with specific fishing gears listed as threats. I use species’ distribution range maps with their threat status (IUCN, 2019) (for the 2,226 out of 4,579 with distribution maps and gear threats) and combine them with a spatialized fisheries catch by gear database (Pauly & Zeller, 2015). I use gear threats by species described by the IUCN, and weight IUCN conservation status on a linear scale (in line with (O’Hara, Carlos Villaseñor-Derbez, Ralph, & Halpern, 2019)) to derive a weighted threat score for all marine areas of the world (Table C.1). I then combine this with fisheries catch and profit data by gear type to highlight areas with low-cost trade-offs between fisheries and conservation, and areas of high conservation concern that are also highly important to fisheries where there is likely to be competition between fisheries and conservation objectives. 5.2 Methods 5.2.1 IUCN data The International Union for Conservation of Nature (IUCN) maintains the IUCN Red List of Threatened Species (hereafter, ‘Red List’) that documents the population status and threats of species globally. Species (and sub-populations of species) that are assessed by the IUCN are categorised into one of the following in order of increasing conservation threat: Least Concern, Near Threatened, Vulnerable, Endangered, Critically Endangered, and Extinct. A final category exists of ‘Data Deficient’ that indicates there is not enough information to properly assess the population status of a species. Together, Vulnerable, Endangered, and Critically Endangered are often grouped together as ‘Threatened’. In addition to the categorisation of the threat status, the IUCN provides species range maps, detailed description of threats, and other important information on the species included in the Red List. Red List Categories, spatial habitat maps, identified threats by species, and a description of threats were gathered from the IUCN API version 2019-2 (IUCN, 2019). The known threats to each marine species were extracted from the IUCN API focusing on threats identified in Category 5: Biological Resource Use. The Red List identifies four different types of fisheries impacts from either intentional or unintentional capture/harvest from the large- or small-scale fisheries. These threats are categorised by the IUCN as unknown, past, no, low, medium, or high 74 impact. In addition, the text from the ‘Detailed Threats’ and ‘Use and Trade’ sections for each marine species was extracted from the IUCN API. After extracting the description of the species threats and use and trade information, I tokenized (i.e., separated the text into one word strings) the text extracting standard stop words and searched for single words and bigrams of fishing gears (e.g., ‘bottom trawl’ or ‘longline’, see Table C.2). Bigrams were used as they can include more specific gear types than single words alone (e.g., ‘bottom trawl’ versus ‘trawl’ and ‘purse seine’ versus ‘seine’). The label for different fishing gears analysed are included in Table C.2. These were devised to match the IUCN narrative text most closely to relevant Sea Around Us gears (Table C.3) in line with Chapter 2. I accessed spatial species distribution shape files from the Red List for comprehensively assessed groups that include marine organisms (IUCN, 2019). I supplemented this with Bird Life International species distribution files for birds that occur in marine areas as the IUCN Red List does not provide species distribution data for birds (BirdLife International & Handbook of the Birds of the World, 2018). Marine molluscs have not been comprehensively assessed (i.e., less than 80% of the species in this group have been assessed) by the IUCN except for Cone Snails (Conus spp.), and this is a gap in our data coverage. I converted the species distribution maps to raster format at various spatial scales to correspond to existing datasets in fisheries research, but used a 0.5° latitude by 0.5° longitude for our analysis as it matches the Sea Around Us fisheries catch database. I rasterised the species distribution files following the same process of O’Hara (2019). I used the same prioritisation of species IUCN Categories based on regional assessments where possible as in O’Hara (2019). Here, regional assessments were matched to associated (i.e., overlapping) marine ecoregions (Spalding et al., 2007) that were then associated with their corresponding cells of the spatial grid used. Regional assessments were used in preference of global assessments where possible. 75 Figure 5.1. Reason for elimination of species from our final analysis The Red List contains detailed information on the species system and habitat types. The system assigns species to terrestrial, freshwater, and marine ecosystems (with species able to occur in multiple ecosystems) and this was used to restrict our analysis to marine species. In addition, I restricted those within the marine ecosystem to specific habitats to eliminate species that are not dependent on the marine environment nor likely to be affected by fisheries, following O’Hara et al. (2019). After restricting our analysis to marine dependent species that are threatened by fishing gears and have species range maps available from the IUCN or BirdLife International, our analysis focused on 2,226 species (Figure 5.1). As our study was restricted to Red List marine species with spatial information, I attempted to ensure this was representative of broader patterns in Red List species. Generally, the breakdown between IUCN categories appears to be quite similar between all marine species and those included in this study (Figure 5.2). 5.2.2 Fisheries data The Sea Around Us has reconstructed marine fisheries catches for all marine countries and territories from 1950 to present. The process of reconstruction supplements and corrects reported fisheries catches with estimates of known to be overwhelmingly excluded fishing sectors (e.g., artisanal and recreational fishing) and practices (e.g., discarding) (Zeller et al., 2016). These catches are spatially allocated according to a rule-based assignment and based on known 76 Figure 5.2. IUCN category for all marine species (A; n=14,871) and marine species included in this study (B, n=4,579). information on where the fishery was operating (within domestic EEZs, foreign EEZs, the high seas, etc.). The spatial scale used is 0.5° latitude by 0.5° longitude. The reconstructed catches were then assigned to their respective fishing gear types for industrial and small-scale sectors. This process relied on similar methods as the catch reconstructions relying on official catch statistics by gear type, as well as fisheries reports, catch surveys, newspaper articles, and other grey literature. The process by which each countries fishing gear was assigned is documented in Cashion et al. (2018). The result is a catch database with gear information included for all catches. I used the Sea Around Us database (v.47) for catches by gear type (Chapter 2 of this thesis; Cashion, Al-abdulrazzak, et al., 2018; Pauly & Zeller, 2015) and were accessed from the Sea Around Us database by the first author. This database includes spatially allocated reconstructed fisheries catches by gear type and taxon for all fishing countries and territories of the world. The spatial scale of this dataset is 0.5° latitude by 0.5° longitude and has been used in many global fisheries studies (Grémillet et al., 2018; Schiller, Bailey, Jacquet, & Sala, 2018). In addition, I use the corollary Fisheries Economics Research Unit (FERU) ex-vessel price database that includes reported and estimated first-sale prices for all taxa for each fishing country by year (Tai et al., 2017) to incorporate the potential lost revenues for fisheries closures in areas 77 of high biological importance. Revenue is calculated as the ex-vessel price (real 2010 USD per metric tonne) of a specific taxon caught by a country in a given year multiplied by its landings amount in metric tonnes. Due to the importance of gear to our analysis, and its importance as a determinant on the ex-vessel price of fish (Asche & Guillen, 2012; Lee, 2014; Mcconnell & Strand, 2000), I modify the ex-vessel prices by a gear multiplier. I determine this gear multiplier through a hedonic pricing model where gear type is an explanatory variable of the ex-vessel fish price. First, I combined two datasets that report fisheries ex-vessel revenues by gear type and species. The first is the U.S. National Marine Fisheries Service annual commercial landings by gear type (NOAA, 2016). The second is the monthly landings by gear type, port and species of the UK’s fisheries (Marine Management Organisation, 2017). I harmonized the gear types listed in each to match our existing dataset gear types. While these two datasets are not representative of global fisheries nor ex-vessel prices for all species, they give adequate coverage to derive the effect of different gear types used for catching different species and how it modifies ex-vessel prices. I then used a fixed-effects model with linear regression to derive the effect of changing gear type on the ex-vessel price 𝑃𝑟𝑖𝑐𝑒𝑥𝑦𝑧𝑡 for species 𝑥, gear 𝑦, in country 𝑧 in year 𝑡. I used the natural logarithm of landings and prices as these variables are closer to a normal distribution when log-transformed, and thus reduces potential heteroscedasticity in our residuals. I also use the country, species, and year as explanatory variables to account for other changes in the price both over time and between these different markets. Our regression equation is: 𝑙𝑛(𝑃𝑟𝑖𝑐𝑒𝑥𝑦𝑧𝑡) = 𝛽0 +𝑀 𝐺𝑒𝑎𝑟𝑦 + 𝛽2𝑙𝑛(𝐿𝑥𝑦𝑧𝑡) + 𝛽3𝑆𝑝𝑒𝑐𝑖𝑒𝑠𝑥 + 𝛽4𝑌𝑒𝑎𝑟𝑡 + 𝛽5𝐶𝑜𝑢𝑛𝑡𝑟𝑦𝑧 (5.1) I used the estimated gear-type coefficients as gear specific multipliers 𝑀𝑦 that will modify the ex-vessel price of fish caught (Table C.4). Finally, I used an updated version of FERU’s cost of fishing database (Lam et al., 2011) to account for the cost of fishing that varies by country and gear type. Cost of fishing is broken down into its component parts in the database (e.g., fuel, labour, capital, maintenance, etc.), and here I use the total cost by gear type and country per tonne of fish landed (𝐶𝑦𝑧). Where the cost 78 of fishing was not available for a particular gear and country, I used the regional average for that gear type, and where this was not possible, I used the average cost across gear types for the region. I used the FAO socioeconomic regions for this stage of the analysis (FAO, 2017a). I then derive the profit by gear type and country for each cell i, based on the catch by gear type multiplied by the ex-vessel price (Vx) multiplied by the gear type multiplier (𝑀𝑦) minus the cost of fishing for that amount of landings (Lxyi) in tonnes. Therefore, profit (P) in cell i is equal to: 𝑃𝑖 = ∑ (𝑥𝑥=1 𝐿𝑥𝑦𝑖 ∗ 𝑉𝑥 ∗ 𝑀𝑦) − (𝐿𝑥𝑦𝑖 ∗ 𝐶𝑦) (5.2) Both catch and profits are expressed throughout in tonnes/km2 and $/km2 calculated based on the water area in each 0.5° by 0.5° cell. I would expect profit to be relatively equal across gear types, as fishers would switch production systems if one was seen as more profitable. However, profit is expected to vary by gear type in practice in regulated fisheries due to restrictions on switching gear types or target species in regulated limited-entry fisheries (i.e., limited license or quota availability). Further, it is likely that it varies in fisheries not regulated by limited entry through high transition costs between switching gear types, which can be further restricted by credit constraints (Noack & Costello, 2019), and distortions from subsidies (Schuhbauer, Chuenpagdee, Cheung, Greer, & Sumaila, 2017). Our analysis focuses on the trade-offs among different fisheries with specific gear types. As such, I did not use the Global Fishing Watch data where gear types are often aggregated (bottom and pelagic trawlers grouped together as trawlers), or only represent a narrow subset of the fleet (e.g., drifting longlines instead of all longlines) (Kroodsma et al., 2018). While the Global Fishing Watch data covers a large part of the global fishing effort in non-coastal areas (between 50% and 70% in areas greater than 100 nautical miles from land (Kroodsma et al., 2018)), it does not have the same coverage of vessels. The dataset is biased against small-scale vessels which are not harm-free. 5.2.3 Analysis First, I analysed the major fishing gear threats based on their appearance in the narrative text of the Red List species profiles. I associated these descriptors to their gear types and examined the number of species by Red List Category and weighted threat status by gear type. I used a linearised weighted scoring method to weight the presence of a species by its IUCN Category 79 (e.g., ‘Least Concern’ = 0, ‘Endangered’=0.6, etc., Table C.1) (2019). Older and outdated categories were updated to their current descriptors such as ‘Lower Risk/near threatened’ to ‘Near Threatened’. I mapped areas of high fisheries interest and high conservation concern. The areas with high overlap between conservation concern but low fisheries catches and effort are areas that could be fully protected from these threats for the long term (termed here, ‘easy wins’). However, the areas with high overlap of fisheries interest and high conservation concern are where the impacts on species may be greatest both in terms of fisheries threats and the potential benefits for conservation from fisheries closures. Figure 5.3. Theoretical basis for competition between fisheries and conservation. I use three main metrics frequently in our analysis. First, I use the biodiversity risk score developed by O’Hara et al. (2019). This indicator is a measure of the average conservation threat status of an area of the marine environment (Equation 5.3). It is expressed as: 𝐵𝑖𝑜𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦𝑅𝑖𝑠𝑘𝑆𝑐𝑜𝑟𝑒𝑐𝑒𝑙𝑙 =1𝑁𝑐𝑒𝑙𝑙∑ 𝑠𝑖𝑁𝑐𝑒𝑙𝑙𝑖=1 (5.3) 80 Where 𝑁 is number of assessed species in the cell, 𝑠𝑖 is the conservation status (𝑠) of species (𝑖). All biodiversity risk scores are thus values of 0-1 that roughly correspond to the linear IUCN Category scale described above (Table C.1). Second, I adapt this metric so that it is not normalised to the mean threat in the cell but representative of scale (number of species) and severity (IUCN Category) of threat. This metric is named the weighted threat score defined as the sum of species present multiplied by their threat category numeric score, but is not divided by the number of species in a given cell. I do not normalise this to highlight areas of conservation concern based on the number of species in that cell in addition to their threat status. 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑𝑇ℎ𝑟𝑒𝑎𝑡𝑆𝑐𝑜𝑟𝑒𝑖,𝑔 = ∑ 𝑠𝑖𝑁𝑐𝑒𝑙𝑙𝑖=1 (5.4) The methods our summarized in Figure 5.4. This analysis was conducted in R (R Core Team, 2017), and used the tidyverse (Wickham, 2016), rMarkdown (Baumer & Udwin, 2015), tidytext (Fay, 2018); gridExtra (Auguie, 2017); sf (Pebesma, 2019), rgdal (Keitt & Bivand, 2011), cowplot (Huber et al., 2017), and wesanderson (Ram & Wickham, 2018) packages. All code and outputs are available at www.github.com/timcashion/iucnfishingthreats. Figure 5.4. Conceptual methods figure. 81 5.3 Results 5.3.1 Fishing threats to IUCN species For the 14,126 marine species included on the IUCN Red List, fishing is identified by the IUCN as a threat to 4,455 of them (31.5%) (Threat category 5.4: fishing and harvesting aquatic resources). This threat is from both large- and small-scale sectors, and from intentional and unintentional capture (i.e., by-catch, Figure 5.5A). According to the identified threats, the small-scale sector is a threat to a greater number of species than intentional or unintentional capture by the large-scale sector. Interestingly, the impacts of fishing are identified to be low or unknown on most of the species, whereas medium or high fishing impacts are identified for very few species. Within the Red List, trawls are identified as a gear threat for more than 2,500 marine species (Figure 5.5B). While many of the species caught by this gear type have an elevated risk of extinction (Near Threatened and higher), most are either Least Concern or Data Deficient within these gear types. The ‘gear types’ that appear in most species threat description are generally more vague gear terms (e.g., ‘trawls’ and ‘nets’) and could be attributable to several types of fishing gear (e.g., trawl nets, gillnets, seine nets all fall under ‘nets’). The more specific gear categories identified are used in the remaining parts of the analysis (Table C.2). From the gear types I have previously identified (Figure 5.5C) I see again that bottom trawl fisheries are associated with the largest number of Red List species. However, I see these species are mainly in the Data Deficient, Least Concern, and Near Threatened categories while gillnet gears have the most number of species in the Threatened categories (Vulnerable, Endangered, and Critically Endangered). 82 Figure 5.5. IUCN fishing gear threats: species categorized by fishing gear threat (A); fishing gear threats by gear types listed (B); and identified gear types by threat status (C). Note: DD: Data Deficient; LC: Least Concern; NT: Near Threatened; VU: Vulnerable; EN: Endangered; CR: Critically Endangered. 5.3.2 Low-cost solutions to gear threats and easy conservation wins Globally, annual fisheries profits by bottom trawl fisheries are estimated to be at an average of $57 real USD per km2 of area fished (Table 5.1). However, an average of 11 species are targeted by bottom trawls in each of the half degree by half degree grids cells with this gear, with an average biodiversity risk score of Near Threatened (0.19). Taken together, the generally high value of their catches make them perform relatively well overall. In contrast, longline fisheries have low profits per area occupied ($8) as they fish over a large spatial area. While they operate in areas with a similar biodiversity risk score as bottom trawl fisheries (0.19), they do so with much lower returns meaning they produce less fisheries profits for their relative conservations risk (weighted threat score) than bottom trawl fisheries. I divided the global ocean into half degree by half degree grid cells. If I consider each spatial cell an independently managed grid, I can examine the trade-offs required in each cell based on their 83 fisheries profits and conservation value (Figure 5.6). Although bottom trawl fisheries operate in many spatial cells of conservation concern (high ‘weighted threat score’ values), they also generate substantial profits from these fisheries. Gillnets, alternatively, have a large number of cells that are below the median value for fisheries profits and have high weighted threat scores. Pelagic trawls have low weighted threat scores overall and thus the reduction in their use may not lead to large conservation gains. However, pelagic trawls are shown to be non-profitable ($-144) and it means this gear type is inefficient. Hence, persistent use of this gear type may not be economically beneficial to societal well-being. Table 5.1. Mean values of measures of fisheries conservation concern and profit by gear type at a 0.5° by 0.5° scale. Gear type Profit ($/km2) Number of species Weighted Threat Score Biodiversity Risk Score Bottom trawl 48.09 10.9 1.98 0.19 Gillnet 22.55 35.4 5.95 0.16 Longline 8.11 25.5 5.01 0.19 Pelagic trawl -133.66 1.8 0.20 0.06 Purse seine 8.14 22.0 3.90 0.16 Small scale 530.67 23.0 2.81 0.13 The six major gear types included here (Table 5.1) together account for 92% of global fisheries catches (landings and discards) (Cashion, Al-abdulrazzak, et al., 2018). Bottom trawls, gillnets, and longlines are used over a large extent (i.e., have many more cells) where the weighted threat score is higher than the median, especially in contrast to pelagic trawls, purse seines and small-scale gears. Among these higher impact gears, the fisheries profit gained in each grid cell varies substantially. Interestingly, gillnet fisheries are operating in many areas of high conservation 84 concern with fisheries profits below the median value (across gear types). This demonstrates that the social costs of this gear are higher than previously thought (Chuenpagdee et al., 2003b), and the social benefit may not be net positive given the relatively low profits achieved (mainly below the median). In contrast, bottom trawl fisheries while overlapping on the weighted threat score dimension with gillnet fisheries, achieve higher profits thus representing a conflict between conservation goals and fisheries goals. Small-scale gears and purse seine have mixed results with weighted threat scores not nearly as high as gillnet or bottom trawls, and a mix of high and low profit areas. Pelagic trawls are often used solely for relatively low-value species (from krill to Alaska pollock), but have very low weighted threat scores throughout their range of fisheries profits. Using these results can be used to restricting fisheries by gear type in areas where their benefits are not worth their risk to species of conservation concern. Figure 5.6. Fisheries profits (log scaled) and weighted score by cell (0.5° by 0.5° grid) for major gear types. Grey dashed lines indicate the median values (excluding values of 0) across gear types for fisheries profits per km2 and weighted threat score. 85 5.3.3 Protecting the high seas Areas beyond national jurisdiction (i.e., the high seas) have recently received increased attention for their protection for biodiversity and fishery gains (Sumaila et al., 2015; White & Costello, 2014), while leading to little losses in terms of food security (Schiller et al., 2018; Sumaila, Zeller, Watson, Alder, & Pauly, 2007). According to our framework, the high seas have cells in all four quadrants of our conceptual figure (Figure 5.3), but the majority are “areas of low concern” and “easy wins” (Figure 5.7, Table C.5). This confirms earlier analyses of the lack of importance of high seas fisheries (Schiller et al., 2018; Sumaila et al., 2015; Sumaila, Zeller, et al., 2007; White & Costello, 2014), and although the high seas is dominated by areas of low concern, it has vast amounts that fall into ‘easy wins’ with very few cells in fishery prioritization or fishery competition. Therefore, a relevant question may be reframed from which parts of the high seas should we protect, to which parts of the high seas should remain as fishing areas (Walters, 1998), if any? Figure 5.7. Scatterplot of cells within the High Seas (areas beyond national jurisdiction) according to their categories from our conceptual diagram. Each point is the values for a specific gear type in a 0.5° by 0.5° cell. Dashed lines indicated mean values of all cells (High Seas and EEZs). 5.4 Discussion The United Nations has called for countries to work towards a wide range of Sustainable Development Goals (SDGs). Balancing these SDGs, in particular SDG 14 is necessary for the 86 success of fisheries management. Previous research has focused on potential trade-offs between fisheries and conservation and where we can search for win-win situations in these two objectives. While it has been shown that often there are benefits to reducing fisheries capacity and fishing to both fisheries (Sumaila et al., 2012) and conservation (Burgess et al., 2018), these benefits are not shared evenly. Reductions in fishing capacity often mean reductions in employment (SDG 8) and seafood supply (SDG 2) (Cheung & Sumaila, 2008) in the short term (Sumaila, 2004). In addition, closing areas to fisheries can force some fishers out with an often unequal distribution of benefits and costs between different sectors (including eco-tourism) and between different fisheries (SDG 10) (Cinner et al., 2014; Gill et al., 2019). While O’Hara et al.(2019) highlighted areas of conservation need, I identify the areas that would also meet with more or less fisheries overlap and thus competition. In these areas of competition, I assume the placement of marine protected areas (MPAs) would be more contentious due to the reliance on these natural resources. I also highlight areas with a low trade-off between fisheries profits for the most conservation protection where conservation should be prioritized, and that represent potentially ‘easy wins’ for conservation. The majority of grid cells within the high seas are rated as being of low conservation concern and low fisheries profitability. While there are likely benefits to coastal fisheries from closing the high seas (Sumaila et al., 2015; White & Costello, 2014), the conservation benefit of this measure for IUCN Red List species is currently low. Most of the high seas is currently categorized as ‘areas of low concern’ for fisheries and conservation. Only a small area of the high seas (4%) is currently categorized as ‘fishery prioritization’, and another 16% are areas where there are conservation concerns but also valuable fisheries. The number of cells that are categorized to be ‘conservation prioritization’ or ‘easy wins’ dwarfs the number of cells that are important to fisheries (both ‘fishery prioritization’ and ‘competition’). This may change in the future as fisheries continue to expand offshore. Therefore, if treated as a whole, the benefits to conservation outweigh the benefits to fisheries in the high seas. This analysis highlights at a broad scale where fisheries or conservation can be prioritized, and where there are competing aims between these areas. Coastal areas are of large importance to fisheries, especially to small-scale fisheries, but coastal areas are also the most biodiverse regions of the marine realm (Gagné et al., 2020). These areas are generally categorized as 87 ‘competition’ areas. However, this analysis adds to existing MPA discussions that may lead to less contentious implementation of MPAs where certain fisheries can co-exist depending on the MPA goals (species conservation versus resource conservation) and current fisheries threats. A drawback of this approach is that it must be done in concert with approaches to reduce overall pressure on these species of conservation concern. Our study is static and focuses on data reflective of the present situation. I therefore do not account for the future marine spatial planning challenges associated with changing species ranges (Cheung, Lam, Kearney, & Pauly, 2009), and the response of fisheries to climate change and changing environmental parameters (Crespo et al., 2018; Young et al., 2019) and their economic and human consequences (Sumaila et al., 2019). The concepts of this study could be incorporated into models that allow for gear substitution to model fisheries adaptation to climate change along a path that reduces impacts on Threatened species. Our study may underestimate the impact of some fishing gears based on the descriptions of threats and use for each IUCN species. The study is inherently limited to those species included in the IUCN Red List, as well as to those species that have enough information to be included in the analysis (see supplemental materials). For the species that have gear threat information, it is unlikely these threats are biased towards particular fishing methods as the threats generally highlight all known (fisheries) threats. However, there is known to be a systemic bias taxonomically and geographically for conservation research and species assessed by the Red List (Donaldson et al., 2016). One area where this is not fully accounted for is the impact of bottom trawls, dredges, and other bottom-impacting gear on seafloor habitat (Committee on Ecosystem Effects of Fishing, 2002), which is not fully captured in the IUCN assessments (supplemental materials). In addition, as Data Deficient species are given a risk score of 0, I likely underestimate the risk to these species. For example, a quarter of Chondrichthyes (sharks, rays, and chimaeras) species currently categorized as data deficient were predicted to be Threatened (Dulvy et al., 2014). 5.5 Conclusion These results highlight areas of high conservation concern for particular fishing gears, and areas of high overlap between multiple fishing gear threats and multiple species of conservation concern. I also highlight areas with the potential for low-cost fishing closures leading to 88 maximum protection of species negatively affected by these fishing gears. Interestingly, the study suggests that gillnet fisheries represent a greater overall threat than the often criticised bottom trawl fisheries due to the high fisheries profits derived from many bottom trawl catches. This analysis can help inform future conservation planning with areas of low-cost trade-offs in comparison to areas with much higher costs for equal conservation benefits. 89 Chapter 6: Discussion The current state of the oceans is one that has been profoundly changed by human activity over the past century. The oceans are warming and acidifying (IPCC, 2014), many fish populations have been drastically reduced (Costello et al., 2008; FAO, 2018; Tremblay-Boyer, Gascuel, Watson, Christensen, & Pauly, 2011), and the seas formerly filled with life are increasingly filled by pollution and plastic (Abbott & Sumaila, 2019; Cózar et al., 2014). While all of these challenges appear to be worsening, humanity does not have to continue on this negative trajectory. This thesis has drawn attention to some of these challenges, but it also has aimed to point to solutions that can reverse these trends and improve the overall health of our ocean. Within this thesis, I present a more coherent view of the value of global fisheries today segmented by gear type; provide an enhanced vision of the trade-offs we are making at present; and test whether some of our current consumer-oriented solutions are having their desired effect. Many of the negative impacts of fisheries are due to the types of fishing gear being deployed. In Chapter 2, I use the previously established catch reconstruction methods (Zeller et al., 2016) to establish the trends in fishing gear use from 1950 to 2014. This highlights the large catches, and potentially significant fisheries revenues, lost from bottom trawling gear over this period. Not only does this chapter make a contribution to our understanding of global fisheries, it opens up future areas of research given the high-resolution data generated and made available to the public. While the high-level view of global fisheries statistics generated by this study is strategically and scientifically useful to understand broad trends in global fisheries, it is also important to focus at the micro-scale of the individual actors in fisheries. More so than ever before, fisheries are driven by large companies that act as keystone species in global fisheries with disproportionate influence over the ecosystems they operate in (Österblom et al., 2015b). Therefore, I developed a method to link large seafood companies to their fisheries and demonstrate the potential benefits of rebuilding and operating fisheries optimally at a company level (Chapter 3). The objective of this chapter is to develop a methodology to carry out such an analysis and provide a first example of its application in order to motivate individual companies to act in their best interests that often are in line with the health of the fisheries resources. The findings of the chapter 90 demonstrates the current suboptimal fishing patterns, and the potential gains that could be achieved at the company level if fisheries were conducted optimally. Chapter 3 originated out of a project to better capture the effects of stranded assets for fisheries companies (McCarron, 2017). Many initiatives continue to work with large private actors towards improving overall fisheries sustainability. They are in effect taking a multi-pronged approach through engaging directly with the largest seafood companies, as in the SeaBOS initiative (Österblom, Jouffray, Folke, & Rockström, 2017), engaging with marine insurance companies that insure fishing vessels (Miller et al., 2016), and the continuation of the Fish Tracker Initiative working with Japanese seafood companies (McLuckie & Thoumi, 2019). In Chapter 4, I evaluated the effect of the Seafood Watch Program of the Monterey Bay Aquarium, on US domestic fisheries. This program is consumer-oriented, which aims to direct consumers towards sustainable seafood choices (Kemmerly & Macfarlane, 2009). However, this kind of market-oriented program is only effective if it translates into incentives for fishers to modify their practices (Marine Stewardship Council, 2011). Here, I find an effect of the Seafood Watch Program to discourage the worst performing fisheries (‘Avoid’ or ‘red’) leading to lower catches and likely lower prices. I find a small, but significant, effect for the medium (‘Good Alternative’ or ‘yellow’) and best (‘Best Choice’ or ‘green’) performing fisheries from this program. In addition, our results conform more closely to results not solely driven by market initiatives, but where the rating does appear to have a strong non-market effect on the regulator of the resource leading to lower catches. In addition to the findings in this dissertation, a similar research program introduced into grocery stores suggest little ‘positive’ effects on prices and consumption patterns of the consumers from a graded seafood rating system (Hilger et al., 2018). In contrast to this study, there is some evidence that fisheries certified under the Marine Stewardship Council are receiving higher prices at the ex-vessel and retail level (Roheim et al., 2011; Stemle et al., 2016). However, as discussed in Chapter 4, the legitimacy and efficacy of this program has been called into question several times (Christian et al., 2013; Froese & Proelss, 2012) although this is still debated (Agnew et al., 2013; Gutiérrez et al., 2012). Finally, I used the catches by fishing gear from Chapter 2 to examine the trade-offs between fisheries and conservation that are present globally. Importantly, I analyze species-gear specific 91 interactions based on the IUCN Red List of Threatened Species to highlight areas where fisheries (separated by gear type) are of most conservation concern. Major efforts have been made to protect marine fishes and invertebrates from the impacts of fisheries through temporary or permanent spatial fishing closures. While those protected areas that are closed to specific gears are often criticized as not going far enough, here I show that there are disproportionate effects by certain gear types on species of conservation concern and perhaps even more importantly that there are low-hanging fruits where the conservation benefit for these Threatened species is substantial and the loss to fisheries is meager. By explicitly accounting for the value of fisheries spatially and by fishing method, we can move forward the discussion of how to spatially manage fisheries while highlighting areas where the aims of resource user and conservation actors are in competition. This analysis demonstrates the trade-offs present with different gears that can hopefully aid in more nuanced marine spatial planning. Chapter 5 focused on the intersection of fisheries and species of conservation concern. When looking at low cost trade-offs, we must make do with the data available to us. This study could be improved once we have fishing effort activity with increased VMS and AIS coverage (to cover smaller vessels) as well as with greater gear specificity in Global Fishing Watch data (Kroodsma et al., 2018). In addition, this chapter will continue to benefit from updates to the IUCN Red List as more species are assessed and more information is gathered on their threats. While these will make improvements to the analysis and confidence in the conclusions, it does not mean that we do not have enough to start applying these findings. The goal I set for this thesis has been to move our understanding of fisheries and fishing gear related impacts and economic implications forward. Here, I applied advanced data to help characterize trends in global fisheries gear use (Chapter 2), developed a method for estimating benefits to fishing companies from alternative management regimes (Chapter 3), estimated the effect of market-based incentives for fisheries sustainability (Chapter 4), and developed a gear-based prioritization for fisheries conservation (Chapter 5). The underlying vision of the thesis is that the world could be otherwise, and that our analyses should be conducted to understand the implications of our current practices in relation to broader sustainability goals. 92 6.1 Caveats and Limitations The limitations of each part of my research has been addressed in each chapter separately, but there is a unifying source of uncertainty throughout the thesis. This thesis relies primarily on a synthesis of reports and documents, or available and existing datasets. While this allows research to be conducted that otherwise would be impossible, it has its own drawbacks where the results are only as valid as the underlying datasets. In addition, this can lead to false conclusions where the data is used for a purpose it was not originally intended when there are issues with the data collection or processing that are unknown to the secondary researcher (Hox & Boeije, 2004). This challenge appears in each chapter differently. In Chapter 2, the synthesis required drawing on grey literature, peer reviewed papers, and government statistics to fill in the holes of our knowledge of fishing gear use over the last 65 years. In Chapter 3, this issue mainly draws on getting accurate information on fisheries quotas, prices, and cost of fishing for public and private companies. Chapter 4 suffers from changing quality of publicly available fisheries landings data from the USA’s National Marine Fisheries Service. In Chapter 5, the data are subject to the bias inherent in global biodiversity and conservation research that underpins the IUCN Red List (Donaldson et al., 2016). The solutions to this overarching problem include the publishing of currently private data (including solving the ‘filing cabinet problem’ of academics), and additional research into data-poor areas and topics. 6.2 Recommendations for action It is common practice to end a dissertation with recommendations for future research and a final conclusion. However, I have come to the conclusion that we know enough to act. We may not be able to reverse all of the various harms we have inflicted on ocean ecosystems, but it is possible to slow or stop our current path by reducing greenhouse gas emissions and reducing fishing capacity and overfishing since these are the major threats to our oceans at present. The evidence for the efficacy of consumer-oriented solutions to fisheries challenges is mixed. It is no longer reasonable to leave the ethical and environmental implications of fisheries to those who are willing to pay for it. Governments should step in where private industries threaten our fisheries sustainability. Environmental and ecological problems are felt collectively, and must be addressed as such rather than through limited private action. 93 Bibliography Abbott, J. K., & Sumaila, U. R. (2019). Reducing Marine Plastic Pollution: Policy Insights from Economics. Review of Environmental Economics and Policy, 13(2), 327–336. https://doi.org/10.1093/reep/rez007 Agnew, D. J., Gutiérrez, N. L., Stern-Pirlot, A., Smith, A. D. M., Zimmermann, C., & Sainsbury, K. (2013). Rebuttal to Froese and Proelss “Evaluation and legal assessment of certified seafood.” Marine Policy, 38, 551–553. https://doi.org/10.1016/j.marpol.2012.07.002 Agnew, D. J., Pearce, J., Pramod, G., Peatman, T., Watson, R., Beddington, J. R., & Pitcher, T. J. (2009). Estimating the Worldwide Extent of Illegal Fishing. PLoS ONE, 4(2), e4570. https://doi.org/10.1371/journal.pone.0004570 Alverson, D. L., Freeberg, M. H., Murawski, S. A., & Pope, J. G. (1994). A global assessment of fisheries bycatch and discards. Retrieved from http://www.fao.org/3/T4890E/T4890E00.htm Amoroso, R. O., Parma, A. M., Pitcher, C. R., Mcconnaughey, R. A., & Jennings, S. (2018). Comment on “Tracking the global footprint of fisheries.” Science, 361(6404), eaat6713. https://doi.org/10.1126/science.aat6713 Anon. (2004). Danish National Programme for Collection of Fisheries Data for 2005. Copenhagen. Anon. (2006). Danish National Programme for Collection of Fisheries Data for 2007. Copenhagen. Anon. (2014a). 2014 수산연감 [Korean fisheries yearbook 2014]. In Korean Fisheries Yearbook. Seoul: Korea Fisheries Association. Anon. (2014b). Danish National Programme for Collection of Fisheries Data for 2011–2013. Copenhagen. Anon. (2005). Danish National Programme for Collection of Fisheries Data for 2006. Copenhagen. Aranda, M. (2009). Developments on fisheries management in Peru: The new individual vessel quota system for the anchoveta fishery. Fisheries Research, 96(2), 308–312. https://doi.org/10.1016/J.FISHRES.2008.11.004 Arnason, R. (1990). Minimum Information Management in Fisheries. The Canadian Journal of 94 Economics, 23(3), 630–653. Asche, F., Bellemare, M. F., Roheim, C., Smith, M. D., & Tveteras, S. (2015). Fair Enough? Food Security and the International Trade of Seafood. World Development, 67, 151–160. https://doi.org/10.1016/j.worlddev.2014.10.013 Asche, F., & Guillen, J. (2012). The importance of fishing method , gear and origin: The Spanish hake market. Marine Policy, 36(2), 365–369. https://doi.org/10.1016/j.marpol.2011.07.005 Auguie, B. (2017). Package ‘ gridExtra .’ R CRAN Project. Auster, P. J., Malatesta, R. J., Langton, R. W., Watting, L., Valentine, P. C., Donaldson, C. L. S., … Babb, W. G. (1996). The impacts of mobile fishing gear on seafloor habitats in the gulf of Maine (Northwest Atlantic): Implications for conservation of fish populations. Reviews in Fisheries Science, 4(2), 185–202. https://doi.org/10.1080/10641269609388584 Baumer, B., & Udwin, D. (2015). R Markdown. Wiley Interdisciplinary Reviews: Computational Statistics. https://doi.org/10.1002/wics.1348 Beaumont, N. J., Austen, M. C., Atkins, J. P., Burdon, D., Degraer, S., Dentinho, T. P., … Zarzycki, T. (2007). Identification, definition and quantification of goods and services provided by marine biodiversity: implications for the ecosystem approach. Marine Pollution Bulletin, 54(3), 253–265. https://doi.org/10.1016/j.marpolbul.2006.12.003 Bertrand, A., Chaigneau, A., Peraltilla, S., Ledesma, J., Graco, M., Monetti, F., & Chavez, F. P. (2011). Oxygen: A fundamental property regulating pelagic ecosystem structure in the coastal southeastern tropical pacific. PLoS ONE, 6(12), 2–9. https://doi.org/10.1371/journal.pone.0029558 Bertrand, S., Dewitte, B., Tam, J., Díaz, E., & Bertrand, A. (2008). Impacts of Kelvin wave forcing in the Peru Humboldt Current system: Scenarios of spatial reorganizations from physics to fishers. Progress in Oceanography, 79(2–4), 278–289. https://doi.org/10.1016/j.pocean.2008.10.017 BirdLife International, & Handbook of the Birds of the World. (2018). No Title. Retrieved July 5, 2019, from Bird species distribution maps of the world. Version 2018.1. website: http://datazone.birdlife.org/species/requestdis Blackman, A., Goff, L., & Rivera, M. (2018). Does eco-certification stem tropical deforestation? Forest Stewardship Council certification in Mexico. Journal of Environmental Economics and Management, 89, 306–333. https://doi.org/10.1016/j.jeem.2018.04.005 95 Blomquist, J., Bartolino, V., & Waldo, S. (2015). Price Premiums for Providing Eco-labelled Seafood: Evidence from MSC-certified Cod in Sweden. Journal of Agricultural Economics, 66(3), 690–704. https://doi.org/10.1111/1477-9552.12106 Brochier, E., Echevin, V., Tam, J., Chaigneau, A., Brochier, T., Echevin, V., … Bertrand, A. (2013). Climate change scenarios experiments predict a future reduction in small pelagic fish recruitment in the Humboldt Current system. Global Change Biology, 19(6), 1841–1853. https://doi.org/10.1111/gcb.12184 Brown, J. (2005). An account of the dolphin-safe tuna issue in the UK. Marine Policy, 29(1), 39–46. https://doi.org/10.1016/j.marpol.2004.03.001 Burgess, M. G., McDermott, G. R., Owashi, B., Peavey Reeves, L. E., Clavelle, T., Ovando, D., … Costello, C. (2018). Protecting marine mammals, turtles, and birds by rebuilding global fisheries. Science (New York, N.Y.), 359(6381), 1255–1258. https://doi.org/10.1126/science.aao4248 Cabral, R. B., Mayorga, J., Clemence, M., Lynham, J., Koeshendrajana, S., Muawanah, U., … Costello, C. (2018). Rapid and lasting gains from solving illegal fishing. Nature Ecology & Evolution, 2(4), 650. https://doi.org/10.1038/s41559-018-0499-1 Caddy, J. F., & Mahon, R. (1995). Reference points for fisheries management. FAO Fisheries Technical Paper, 341, 1–83. Cahuin, S. M., Cubillos, L. A., Ñiquen, M., & Escribano, R. (2009). Climatic regimes and the recruitment rate of anchoveta, Engraulis ringens, off Peru. Estuarine, Coastal and Shelf Science, 84(4), 591–597. https://doi.org/10.1016/j.ecss.2009.07.027 Camara, M. B. (2008). Quelle gestion des pêches artisanales en Afrique de l’Ouest? Etude de la complexité de l’espace halieutique en zone littorale sénégalaise. Cárdenas, J. J., Achury, A., & Guaiquirián, Y. J. (2009). La Pesca Marítima Artesanal en Oriente de Venezuela, Evolución y Comportamiento en los Últimos 20 Años. Proceedings of the 62nd Gulf and Caribbean Fisheries Institute. Cumana, Venezuela: Gulf and Caribbean Fisheries Institute. Carlson, K. M., Heilmayr, R., Gibbs, H. K., Noojipady, P., & Burns, D. N. (2018). Effect of oil palm sustainability certification on deforestation and fire in Indonesia. 115(1), 121–126. https://doi.org/10.1073/pnas.1704728114 Cashion, T., Al-abdulrazzak, D., Belhabib, D., Derrick, B., Divovich, E., Moutopoulos, D. K., … 96 Pauly, D. (2018). Reconstructing global marine fishing gear use: Catches and landed values by gear type and sector. Fisheries Research, 206(April), 57–64. https://doi.org/10.1016/j.fishres.2018.04.010 Cashion, T., Al-Abdulrazzak, D., Belhabib, D., Derrick, B., Divovich, E., Moutopoulos, D., … Pauly, D. (2018). A global fishing gear dataset for integration into the Sea Around Us global fisheries databases. In Fisheries Centre Research Reports (Vol. 28). Vancouver: Institute of the Oceans and Fisheries, University of British Columbia. Cashion, T., Le Manach, F., Zeller, D., & Pauly, D. (2017). Most fish destined for fishmeal production are food-grade fish. Fish and Fisheries, (January), 837–844. https://doi.org/10.1111/faf.12209 Castillo, S., & Mendo, J. (1987). Estimation of unregistered Peruvian anchoveta (Engraulis ringens) in official catch statistics, 1951 to 1982. In D. Pauly & I. Tsukayama (Eds.), The Peruvian Anchoveta and Its Upwelling Ecosystem: Three Decades of Change (pp. 109–116). Manilla, Philippines: ICLARM studies and reviews. Cheung, W. W. L., Lam, V. W. Y., Kearney, K., & Pauly, D. (2009). Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10(3), 235–251. Cheung, W. W. L., & Sumaila, U. R. (2008). Trade-offs between conservation and socio-economic objectives in managing a tropical marine ecosystem. Ecological Economics, 66(1), 193–210. https://doi.org/10.1016/j.ecolecon.2007.09.001 Christensen, V., De la Puente, S., Sueiro, J. C., Steenbeek, J., & Majluf, P. (2014). Valuing seafood: The Peruvian fisheries sector. Marine Policy, 44, 302–311. https://doi.org/10.1016/j.marpol.2013.09.022 Christian, C., Ainley, D., Bailey, M., Dayton, P., Hocevar, J., LeVine, M., … Jacquet, J. (2013). A review of formal objections to Marine Stewardship Council fisheries certifications. Biological Conservation, 161, 10–17. https://doi.org/10.1016/j.biocon.2013.01.002 Chuenpagdee, R. (Ed.). (2011). World Small-scale Fisheries: Contemporary Visions. Retrieved from http://books.google.ca/books?id=drVoXsTC8RgC Chuenpagdee, R., Morgan, L. E., Maxwell, S. M., Norse, E. A., & Pauly, D. (2003a). Shifting gears: assessing collateral impacts of fishing methods in US waters. Frontiers in Ecology, 1(10), 517–524. Chuenpagdee, R., Morgan, L. E., Maxwell, S. M., Norse, E. A., & Pauly, D. (2003b). Shifting 97 gears: Assessing collateral impacts of fishing methods in US waters. Frontiers in Ecology and the Environment, 1(10), 517–524. https://doi.org/10.1890/1540-9295(2003)001[0517:SGACIO]2.0.CO;2 Cinner, J. E., Daw, T., Huchery, C., Thoya, P., Wamukota, A., Cedras, M., & Abunge, C. (2014). Winners and Losers in Marine Conservation: Fishers’ Displacement and Livelihood Benefits from Marine Reserves. Society and Natural Resources, 27(9), 994–1005. https://doi.org/10.1080/08941920.2014.918229 Cisneros-Montemayor, A. M., Barnes-Mauthe, M., Al-Abdulrazzak, D., Navarro-Holm, E., & Sumaila, U. R. (2013). Global economic value of shark ecotourism: Implications for conservation. Oryx, 47(3), 381–388. https://doi.org/10.1017/S0030605312001718 Cisneros-Montemayor, A. M., Cisneros-Mata, M. A., Harper, S., & Pauly, D. (2015). Unreported marine fisheries catch in Mexico, 1950-2010. Cisneros-Montemayor, A. M., Harper, S., & Tai, T. C. (2018). The market and shadow value of informal fish catch: a framework and application to Panama. Natural Resources Forum. https://doi.org/10.1111/1477-8947.12143 Cisneros-Montemayor, A. M., Sumaila, U. R., Kaschner, K., & Pauly, D. (2010). The global potential for whale watching. Marine Policy, 34, 1273–1278. https://doi.org/10.1016/j.marpol.2010.05.005 Clark, M. R., Althaus, F., Schlacher, T. A., Williams, A., Bowden, D. A., & Rowden, A. A. (2016). The impacts of deep-sea fisheries on benthic communities: A review. ICES Journal of Marine Science, 73, i51–i69. https://doi.org/10.1093/icesjms/fsv123 Committee on Ecosystem Effects of Fishing. (2002). Effects of Trawling and Dredging on Seafloor Habitat. https://doi.org/10.17226/10323 Convention on Biological Diversity. Aichi Biodiversity Targets. , (2018). Copes, P. (1986). A critical review of the individual quota as a device in Fisheries Management. Land Economics, 62(3), 278–291. Costello, C., Kinlan, B. P., Lester, S. E., & Gaines, S. D. (2012). The Economic Value of Rebuilding Fisheries. OECD Food, Agriculture and Fisheries Papers, (55), 1–68. https://doi.org/http://dx.doi.org/10.1787/5k9bfqnmptd2-en OECD Costello, C., Ovando, D., Clavelle, T., Strauss, C. K., Hilborn, R., Melnychuk, M. C., … Leland, A. (2016). Global fishery prospects under contrasting management regimes. Proceedings of 98 the National Academy of Sciences of the United States of America, 113(18), 5125–5129. https://doi.org/10.1073/pnas.1520420113 Costello, C., Ovando, D., Hilborn, R., Gaines, S. D., Deschenes, O., & Lester, S. E. (2008). Status and Solutions for the World’s Unassessed Fisheries. Science, 338(6106), 517–520. https://doi.org/10.1126/science.1172133 Cox, T. M., Lewison, R. L., Žydelis, R., Crowder, L. B., Safina, C., & Read, A. J. (2007). Comparing Effectiveness of Experimental and Implemented Bycatch Reduction Measures: the Ideal and the Real. Conservation Biology, 21(5), 1155–1164. https://doi.org/10.1111/j.1523-1739.2007.00772.x Cózar, A., Echevarría, F., González-Gordillo, J. I., Irigoien, X., Úbeda, B., Hernández-León, S., … Duarte, C. M. (2014). Plastic debris in the open ocean. Proceedings of the National Academy of Sciences of the United States of America, 111(28), 10239–10244. https://doi.org/10.1073/pnas.1314705111 Crespo, G. O., Dunn, D. C., Reygondeau, G., Boerder, K., Worm, B., Cheung, W. W. L., … Halpin, P. N. (2018). The environmental niche of the global high seas pelagic longline fleet. Science Advances, 4(8), 1–14. https://doi.org/10.1126/sciadv.aat3681 Cubillos, L. A., & Arcos, D. F. (2002). Recruitment of common sardine (Strangomera bentincki) and anchovy (Engraulis ringens) off central-south Chile in the 1990s and the impact of the 1997-1998 El Niño. Aquatic Living Resources, 15(2), 87–94. https://doi.org/10.1016/S0990-7440(02)01158-0 Dalzell, P., Adams, T. J. H., & Polunin, N. V. C. (1996). Coastal fisheries in the Pacific Islands. In A. Ansell, R. Gibson, & M. Barnes (Eds.), Oceanography and Marine Biology: an annual review. Vol. 34. London: UCL Press. Daybrook Fisheries; (2016). Gulf Menhaden Fish Oil and Fishmeal. Retrieved from http://oceana.co.za/wp-content/uploads/2016/09/BookletFinal_2014.pdf Daybrook fisheries. (2016). Welcome to Daybrook Fisheries, INC. Retrieved from http://oceana.co.za/wp-content/uploads/2016/09/DaybrookPosters.pdf Diekert, F. K. (2012). Growth Overfishing: The Race to Fish Extends to the Dimension of Size. Environ Resource Econ, 52, 549–572. https://doi.org/10.1007/s10640-012-9542-x Donaldson, M. R., Burnett, N. J., Braun, D. C., Suski, C. D., Hinch, S. G., Cooke, S. J., & Kerr, J. T. (2016). Taxonomic bias and international biodiversity conservation research. Facets, 99 1(1), 105–113. https://doi.org/10.1139/facets-2016-0011 Drèze, J., & Stern, N. (1990). Policy reform, shadow prices, and market prices. Journal of Public Economics. https://doi.org/10.1016/0047-2727(90)90042-G Dulvy, N. K., Fowler, S. L., Musick, J. A., Cavanagh, R. D., Kyne, P. M., Harrison, L. R., … White, W. T. (2014). Extinction risk and conservation of the world’s sharks and rays. ELife, 3. https://doi.org/10.7554/eLife.00590 Eichholtz, P., Kok, N., & Quigley, J. M. (2010). Doing Well by Doing Good? Green Office Buildings. American Economic Review, 100(5), 2492–2509. Espinoza-Morriberón, D., Echevin, V., Colas, F., Tam, J., Ledesma, J., Vásquez, L., & Graco, M. (2017). Impacts of El Niño events on the Peruvian upwelling system productivity. Journal of Geophysical Research: Oceans, 122(7), 5423–5444. https://doi.org/10.1002/2016JC012439 FAO. (2007). General Information on Ghana Related to Fisheries. FAO. (2016). The State of World Fisheries and Aquaculture 2016: Contributing to Food Security and Nutrition for All. Rome, Italy. FAO. (2017a). Fishery Statistical Collections: Global capture production. (1950-2015). Accessed through FishStatJ software. Rome. FAO. (2017b). High juvenile presence in Peru ends an already tough year for fishmeal production. Retrieved October 13, 2017, from GLOBEFISH website: http://www.fao.org/in-action/globefish/market-reports/resource-detail/en/c/896543/ FAO. (2018). The State of World Fisheries and Aquaculture 2018 - Meeting the sustainable development goals. https://doi.org/978-92-5-130562-1 Favaro, B., & Côté, I. M. (2015). Do by-catch reduction devices in longline fisheries reduce capture of sharks and rays? A global meta-analysis. Fish and Fisheries, 16(2), 300–309. https://doi.org/10.1111/faf.12055 Fay, C. (2018). Text Mining with R : A Tidy Approach . Journal of Statistical Software. https://doi.org/10.18637/jss.v083.b01 FIS. (2017). SNP estimates capture of 5 million tonnes of anchovy this year. Retrieved October 12, 2017, from FIS - World News website: http://www.fis.com/fis/worldnews/worldnews.asp?l=e&id=91308&ndb=1 Fisheries and Oceans Canada. (2018). Commercial Catch Statistics. Retrieved from 100 http://www.pac.dfo-mpo.gc.ca/stats/comm/index-eng.html FishSource. (2017). Anchoveta, Southern Peru/Northern Chile. Freire, K., Aragão, J. A. N., Araújo, A. R. R., Ávila-da-Silva, A. O., Bispo, M. C. S., Canziani, G. V, … Divovich, E. (2015). Reconstruction of catch statistics for Brazilian marine waters (1950-2010). In K. Freire & D. Pauly (Eds.), Fisheries catch reconstructions for Brazil’s mainland and oceanic islands. (pp. 3–30). Vancouver: University of British Columbia. Friend of the Sea. (2016a). Omega Protein Inc. FAO Area 21, Purse seine, Brevoortia tyrannus. Retrieved from http://www.friendofthesea.org/public/catalogo/2017 Omega Protein Wild report.pdf Friend of the Sea. (2016b). Omega Protein Inc. FAO Area 31, Purse seine, Brevoortia petronus. Retrieved from http://www.friendofthesea.org/public/catalogo/2017 Omega Protein Wild report.pdf Froese, R., Branch, T. A., Proelß, A., Quaas, M., Sainsbury, K., & Zimmermann, C. (2011). Generic harvest control rules for European fisheries. Fish and Fisheries, 12(3), 340–351. https://doi.org/10.1111/j.1467-2979.2010.00387.x Froese, R., Demirel, N., Coro, G., Kleisner, K. M., & Winker, H. (2017). Estimating fisheries reference points from catch and resilience. Fish and Fisheries, 18(3), 506–526. https://doi.org/10.1111/faf.12190 Froese, R., & Pauly, D. (2012). FishBase. Retrieved from World Wide Web electronic publication. version (04/2012). website: www.fishbase.org Froese, R., & Proelss, A. (2012). Evaluation and legal assessment of certified seafood. Marine Policy, 36(6), 1284–1289. https://doi.org/10.1016/j.marpol.2012.03.017 Fuller, S. D. S. D., Picco, C., Ford, J., Tsao, C., Morgan, L. E., Hangaard, D., & Chuenpagdee, R. (2008). How we fish matters: Addressing the ecological impacts of Canadian fishing gear. Retrieved from http://www.mun.ca/geog/courses/eedinger/HowWeFish_4650.pdf Gabriel, O., Lange, K., Dahm, E., & Wendt, T. (Eds.). (2005). Von Brandt’s Fish Catching Methods of the World (4th Editio). Retrieved from https://books.google.ca/books?hl=en&lr=&id=wkd3JHwDnKgC&oi=fnd&pg=PR3&dq=Von+Brandt%27s+Fish+Catching+Methods+of+the+World&ots=13QQ_dPqxP&sig=U2kJrRfGlRALqKOe5Orojv68iAg&redir_esc=y#v=onepage&q=Von Brandt’s Fish Catching Methods of the World&f=false 101 Gagné, T. O., Reygondeau, G., Jenkins, C. N., Sexton, J. O., Bograd, S. J., Hazen, E. L., & van Houtan, K. S. (2020). Towards a global understanding of the drivers of marine and terrestrial biodiversity. PLoS ONE, 15(2), 1–17. https://doi.org/10.1371/journal.pone.0228065 Gallagher, A. J., & Hammerschlag, N. (2011). Global shark currency: The distribution frequency and economic value of shark ecotourism. Current Issues in Tourism, 14(8), 797–812. https://doi.org/10.1080/13683500.2011.585227 Gaure, S. (2019). lfe: Linear Group Fixed Effects. The R Journal. https://doi.org/10.32614/rj-2013-031 Gephart, J. A., Froehlich, H. E., & Branch, T. A. (2019). To create sustainable seafood industries, the United States needs a better accounting of imports and exports. Proceedings of the National Academy of Science, 116(19), 9142–9146. https://doi.org/10.1073/pnas.1905650116 Gill, D. A., Cheng, S. H., Glew, L., Aigner, E., Bennett, N. J., & Mascia, M. B. (2019). Social Synergies, Tradeoffs, and Equity in Marine Conservation Impacts. Annual Review of Environment and Resources, 44(1), annurev-environ-110718-032344. https://doi.org/10.1146/annurev-environ-110718-032344 Golden, C. D., Allison, E. H., Cheung, W. W. L., Dey, M. M., Halpern, B. S., McCauley, D. J., … Myers, S. S. (2016). Fall in fish catch threatens human health. Nature, 534, 317–320. Retrieved from https://www.nature.com/polopoly_fs/1.20074!/menu/main/topColumns/topLeftColumn/pdf/534317a.pdf Gordon, H. . S. (1954). The Economic Theory of a Common-Property Resource : The Fishery. Journal of Political Economy, 62(2), 124–142. Grémillet, D., Ponchon, A., Paleczny, M., Palomares, M.-L., Karpouzi, V., & Pauly, D. (2018). Persisting Worldwide Seabird-Fishery Competition Despite Seabird Community Decline. Current Biology, 28(24), 4009-4013.e2. https://doi.org/10.1016/j.cub.2018.10.051 Gutiérrez, N. L., Valencia, S. R., Branch, T. A., Agnew, D. J., Baum, J. K., Bianchi, P. L., … Williams, N. E. (2012). Eco-label conveys reliable information on fish stock health to seafood consumers. PLoS ONE, 7(8), 1–8. https://doi.org/10.1371/journal.pone.0043765 Hall, M. A., Alverson, D. L., & Metuzals, K. I. (2000). By-Catch: Problems and Solutions. 102 Marine Pollution Bulletin, 41(1–6), 204–219. https://doi.org/10.1016/S0025-326X(00)00111-9 Hallstein, E., & Villas-boas, S. B. (2013). Can household consumers save the wild fish? Lessons from a sustainable seafood advisory. Journal of Environmental Economics and Management, 66(1), 52–71. https://doi.org/10.1016/j.jeem.2013.01.003 Hamilton, S. F., & Zilberman, D. (2006). Green markets, eco-certification, and equilibrium fraud. Journal of Environmental Economics and Management, 52(3), 627–644. https://doi.org/10.1016/j.jeem.2006.05.002 Hardin, G. (1968). The tragedy of the commons. Sciemce, 162, 1243–1248. https://doi.org/10.1201/9781351071765 Harrington, J. M., Myers, R. A., & Rosenberg, A. A. (2005). Wasted Resources: Bycatch and discards in U.S. Fisheries. Heddon, S. (2006). The Constraints of the Artisanal Fishing Industry in Lamu District. Retrieved from School of International Training and Tawasal Institute website: http://www.oceandocs.org/bitstream/handle/1834/7347/ktf0290.pdf?sequence=1&isAllowed=y Henry, K. A. (1971). Atlantic Menhaden (Brevoortia tyrannus). Resource and Fishery-Analysis of Decline. Seattle. Hiddink, J. G., Jennings, S., Sciberras, M., Szostek, C. L., Hughes, K. M., Ellis, N., … Kaiser, M. J. (2017). Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance. Proceedings of the National Academy of Sciences, 114(31), 8301–8306. https://doi.org/10.1073/pnas.1618858114 Hiddink, J. G., Johnson, A. F., Kingham, R., & Hinz, H. (2011). Could our fisheries be more productive? Indirect negative effects of bottom trawl fisheries on fish condition. Journal of Applied Ecology, 48(6), 1441–1449. https://doi.org/10.1111/j.1365-2664.2011.02036.x Hilborn, R. (2010). Pretty Good Yield and exploited fishes. Marine Policy, 34(1), 193–196. https://doi.org/10.1016/J.MARPOL.2009.04.013 Hilborn, R., & Walters, C. (1992). Quantitative fisheries stock assessment : choice, dynamics and uncertainty. Retrieved from https://books.google.ca/books?id=Y0EGCAAAQBAJ&dq=Walters+Hilborn&lr=&source=gbs_navlinks_s 103 Hilger, J., Hallstein, E., Stevens, A. W., & Villas-Boas, S. B. (2018). Measuring Willingness to Pay for Environmental Attributes in Seafood. Environmental and Resource Economics, 1–26. https://doi.org/10.1007/s10640-018-0264-6 Hill, R. C., Griffiths, W. E., & Lim, G. C. (2011). Principles of Econometrics (4th Editio). Hoboken: John Wiley & Sons. Hox, J. J., & Boeije, H. R. (2004). Data Collection, Primary vs. Secondary. Encyclopedia of Social Measurement, pp. 593–599. https://doi.org/10.1016/B0-12-369398-5/00041-4 Huber, W., Carey, J., V., Gentleman, R., … R Core Team. (2017). cowplot: Streamlined Plot Theme and Plot Annotations for “ggplot2.” Nature Methods. IPCC. (2014). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In Journal of Crystal Growth (Vol. 218). https://doi.org/10.1016/S0022-0248(00)00575-3 IUCN. (2019). IUCN Red List of Threatened Species. Version 2019-2. Retrieved June 27, 2019, from www.iucnredlist.org IUCN – SSC Species Conservation Planning Sub-Committee. (2017). Guidelines for Species Conservation Planning - version 1.0. https://doi.org/10.2305/iucn.ch.2017.18.en Jackson, J. B. C., Kirby, M. X., Berger, W. H., Bjorndal, K. a, Botsford, L. W., Bourque, B. J., … Warner, R. R. (2001). Historical overfishing and the recent collapse of coastal ecosystems. Science (New York, N.Y.), 293(5530), 629–637. https://doi.org/10.1126/science.1059199 Jacquet, J., Pauly, D., Ainley, D., Holt, S., Dayton, P., & Jackson, J. (2010). Seafood Stewardship in Crisis. Nature, 467, 28–29. Jaquet, J., & Pauly, D. (2008). Trade secrets: Renaming and mislabeling of seafood. Marine Policy, 32(3), 309–318. https://doi.org/10.1016/J.MARPOL.2007.06.007 Jennings, S., Dinmore, T. A., Duplisea, D. E., Warr, K. J., & Lancaster, J. E. (2001). Trawling disturbance can modify benthic production processes. Journal of Animal Ecology, 70(3), 459–475. https://doi.org/10.1046/j.1365-2656.2001.00504.x Jensen, A. L., Reider, R. H., & Kovalak, W. P. (1988). Estimation of Production Forgone. North American Journal of Fisheries Management, 8(2), 191–198. https://doi.org/10.1577/1548-8675(1988)008<0191:eopf>2.3.co;2 Jones, J. B. (1992). Environmental impact of trawling on the seabed : a review. 26(July 1991), 104 59–67. Joyce, I. T. (1996). The fisheries of the Cuban insular shelf: culture, history, and revolutionary performance. Simon Fraser Univeristy, Burnaby. Kaiser, M. J., & Edwards-Jones, G. (2006). The role of ecolabeling in fisheries management and conservation. Conservation Biology, 20(2), 392–398. https://doi.org/10.1111/j.1523-1739.2006.00319.x Kaiser, M. J., Hormbrey, S., Booth, J. R., Hinz, H., & Hiddink, J. G. (2018). Recovery linked to life history of sessile epifauna following exclusion of towed mobile fishing gear. Journal of Applied Ecology, 55(3), 1060–1070. https://doi.org/10.1111/1365-2664.13087 Keitt, T., & Bivand, R. (2011). rgdal: bindings for the Geospatial Data Abstraction Library. … . Org/Package= Rgdal. Kelleher, K. (2005). Discards in the world’s marine fisheries: An update. In FAO Technical Paper (Vol. 470). https://doi.org/10.1136/ebmed-2011-0012 Kemmerly, J. D., & Macfarlane, V. (2009). The elements of a consumer-based initiative in contributing to positive environmental change: Monterey bay Aquarium’s seafood watch program. Zoo Biology, 28(5), 398–411. https://doi.org/10.1002/zoo.20193 King, M. R. (1986). Catch statistics for foreign and domestic commercial fishing in New Zealand waters, January-December 1983. In Fisheries Research Division Occasional Publication Data Series No. 21. Wellington, New Zealand: New Zealand Ministry of Agriculture and Fisheries. Kirkley, J. E. (2011). An Assessment of the Social and Economic Importance Of Menhaden (Brevoortia tyrannus) (Latrobe, 1802) in Chesapeake Bay Region. In VIMS Marine Resource Report No. 2011-14. Gloucester Point, VA. Gloucester Point. Klein, C. J., Steinback, C., Watts, M., Scholz, A. J., & Possingham, H. P. (2010). Spatial marine zoning for fisheries and conservation. Frontiers in Ecology and the Environment, 8(7), 349–353. https://doi.org/10.1890/090047 Kotchen, M. J. (2006). Green markets and private provision of public goods. Journal of Political Economy, 114(4), 816–834. https://doi.org/10.1086/506337 Kroodsma, D. A., Mayorga, J., Hochberg, T., Miller, N. A., Boerder, K., Ferretti, F., … Worm, B. (2018). Tracking the global footprint of fisheries. Science (New York, N.Y.), 359(6378), 904–908. https://doi.org/10.1126/science.aao5646 105 Lam, V. W. Y., Sumaila, U. R., Dyck, A., Pauly, D., & Watson, R. (2011). Construction and first applications of a global cost of fishing database. ICES Journal of Marine Science, 68(9), 1996–2004. https://doi.org/10.1093/icesjms/fsr121 Larkin, P. A. (1977). An Epitaph for the Concept of Maximum Sustained Yield. Transactions of the American Fisheries Society, 106(1), 1–11. https://doi.org/10.1577/1548-8659(1977)106<1:AEFTCO>2.0.CO;2 Lee, M. (2014). Hedonic Pricing of Atlantic Cod: Effects of Size, Freshness, and Gear. Marine Resource Economics, 29(3). Lewison, R. L., Crowder, L. B., Read, A. J., & Freeman, S. A. (2004). Understanding impacts of fisheries bycatch on marine megafauna. Trends in Ecology & Evolution, 19(11), 598–604. https://doi.org/10.1016/J.TREE.2004.09.004 Macher, C., Guyader, O., Talidec, C., & Bertignac, M. (2008). A cost-benefit analysis of improving trawl selectivity in the case of discards: The Nephrops norvegicus fishery in the Bay of Biscay. Fisheries Research, 92(1), 76–89. https://doi.org/10.1016/j.fishres.2007.12.021 Majluf, P., De la Puente, S., & Christensen, V. (2017). The little fish that can feed the world. Fish and Fisheries, 18(4), 772–777. https://doi.org/10.1111/faf.12206 Manach, F. Le, Chavance, P., Cisneros-Montemayor, A. M., Lindop, A., Padilla, A., Schiller, L., … Pauly, D. (2016). Global catches of large pelagic fishes, with emphasis on the High Seas (pp. 34–45). pp. 34–45. Island Press. Marcus Aurelius Value. (2017). Pingtan Marine: A Fraud That Finances Human Trafficking and Poaching - Aurelius Value. Retrieved June 5, 2018, from http://www.aureliusvalue.com/research/pingtan-marine-fraud-finances-human-trafficking-poaching/ MAREX. (2017). “Sustainable” Shrimp Trawler Arrested in Liberia. Retrieved June 3, 2018, from Maritime Executive website: https://www.maritime-executive.com/article/sustainable-shrimp-trawler-arrested-in-liberia Marine Management Organisation. (2017). UK fleet landings and foreign fleet landings into the UK by port. Retrieved August 13, 2019, from https://www.gov.uk/government/statistical-data-sets/uk-fleet-landings-and-foreign-fleet-landings-into-the-uk-by-port Marine Stewardship Council. (2011). Market Forces for Environmental Change: Standards and 106 Scoring System. 1–5. Retrieved from www.msc.org Marine Stewardship Council. (2017). Teeming with life: A summary of the Marine Stewardship Council’s Strategic Plan, 2017-2020. Marine Stewardship Council. (2018). Why get your fishery MSC certified. Retrieved November 16, 2018, from https://www.msc.org/en-us/for-business/fisheries/why-get-certified Martell, S., & Froese, R. (2013). A simple method for estimating MSY from catch and resilience. Fish and Fisheries, 504–514. https://doi.org/10.1111/j.1467-2979.2012.00485.x Martín, J. I. (2012). The small-scale coastal fleet in the reform of the common fisheries policy. McCarron, B. (2017). Empty nets: How overfishing risks leaving investors stranded. London, UK. Mcconnell, K. E., & Strand, I. E. (2000). Hedonic Prices for Fish: Tuna Prices in Hawaii. American Journal of Agricultural Economics, 82(1), 133–144. https://doi.org/10.1093/ajae/aap040 McLuckie, M., & Thoumi, G. (2019). Perfect storm: Profits at risk in the Japanese Seafood Industry. London, UK. McShane, T. O., Hirsch, P. D., Trung, T. C., Songorwa, A. N., Kinzig, A., Monteferri, B., … O’Connor, S. (2011). Hard choices: Making trade-offs between biodiversity conservation and human well-being. Biological Conservation, 144(3), 966–972. https://doi.org/10.1016/j.biocon.2010.04.038 Mendo, J. (2018). Electrophoretic Studies of Peruvian Anchoveta Engraulis ringens Confirm the Existence of Distinct North/Central and Southern Stocks. In D. Pauly & V. Ruiz-Leotaud (Eds.), Marine and Freshwater Miscellanea. Fisheries Centre Research Reports (Vol. 26, pp. 16–26). Vancouver: Institute for the Oceans and Fisheries, University of British Columbia. Mendo, J., & Wosnitza-Mendo, C. (2014). Reconstruction of total marine fisheries catches for Peru: 1950-2010 (No. 21). Vancouver, BC. Mendo, J., & Wosnitza-Mendo, C. (2016). Peru. In D. Pauly & D. Zeller (Eds.), Global atlas of marine fisheries: a critical appraisal of catches and ecosystem impacts (p. 366). Washington, DC: Island Press. Merino, G., Barange, M., Blanchard, J. L., Harle, J., Holmes, R., Allen, I., … Rodwell, L. D. (2012). Can marine fisheries and aquaculture meet fish demand from a growing human 107 population in a changing climate? Global Environmental Change, 22(4), 795–806. https://doi.org/10.1016/j.gloenvcha.2012.03.003 Merino, G., Barange, M., Mullon, C., & Rodwell, L. (2010). Impacts of global environmental change and aquaculture expansion on marine ecosystems. Global Environmental Change, 20(4), 586–596. https://doi.org/10.1016/j.gloenvcha.2010.07.008 Miller, D. D., Sumaila, U. R., Copeland, D., Zeller, D., Soyer, B., Nikaki, T., … Pauly, D. (2016). Cutting a lifeline to maritime crime: marine insurance and IUU fishing. Frontiers in Ecology and the Environment, 14(7), 357–362. https://doi.org/10.1002/fee.1293 Ministry of Agriculture Forestry and Fisheries. (2016). Survey on Marine Fishery Production (Statistics Bureau of Japan, Ed.). Japan Statistical Yearbook. Tokyo. Ñiquen, M., & Bouchon, M. (2004). Impact of El Niño events on pelagic fisheries in Peruvian waters. Deep-Sea Research Part II: Topical Studies in Oceanography, 51(6), 563–574. https://doi.org/10.1016/j.dsr2.2004.03.001 NMFS. (2017a). Commercial Fisheries - Annual Landings. Retrieved from http://www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/annual-landings/index NMFS. (2017b). Commercial Fisheries - Monthly Landings. Retrieved April 5, 2017, from http://www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/annual-landings/index NMFS. (2017c). Commercial Landings - Data Caveats. Retrieved from http://www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/data-caveats/index NOAA. (2016). Commercial Fisheries Statistics: Annual Commercial Landings by Gear Type. Retrieved from https://www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/landings-by-gear/index NOAA. (2017). Forecast for the 2017 Gulf and Atlantic Menhaden Purse-Seine Fisheries and Review of the 2016 Fishing Season. Beaufort, NC. Noack, F., & Costello, C. (2019). Credit Market Development, Property Rights and Resource Extraction: Evidence from Global Fisheries. Retrieved from https://pdfs.semanticscholar.org/66f2/fa265c98e9fc3596523f57a18b7bef505e2d.pdf Nunoo, F. K. E., Asiedu, B., Amador, K., Belhabib, D., Lam, V. W. Y., & Pauly, D. (2014). Marine Fisheries Catches in Ghana : Historic Reconstruction for 1950 to 2010 and Current 108 Economic Impacts Marine Fisheries Catches in Ghana : Historic Reconstruction for 1950 to 2010 and Current Economic Impacts. 8249(March 2016). https://doi.org/10.1080/23308249.2014.962687 O’Hara, C. C., Carlos Villaseñor-Derbez, J., Ralph, G. M., & Halpern, B. S. (2019). Mapping status and conservation of global at-risk marine biodiversity. Conservation Letters. https://doi.org/10.1111/conl.12651 Oberle, F. K. J. J., Storlazzi, C. D., & Hanebuth, T. J. J. J. (2016). What a drag: Quantifying the global impact of chronic bottom trawling on continental shelf sediment. Journal of Marine Systems, 159, 109–119. https://doi.org/10.1016/j.jmarsys.2015.12.007 Omega Protein Corporation. (2015). Form 10-K. Österblom, H., Jouffray, J.-B., Folke, C., Crona, B., Troell, M., Merrie, A., & Rockström, J. (2015a). Transnational Corporations as ‘Keystone Actors’ in Marine Ecosystems. Plos One, 10(5), e0127533. https://doi.org/10.1371/journal.pone.0127533 Österblom, H., Jouffray, J.-B., Folke, C., Crona, B., Troell, M., Merrie, A., & Rockström, J. (2015b). Transnational Corporations as ‘Keystone Actors’ in Marine Ecosystems. PLOS ONE, 10(5), e0127533. https://doi.org/10.1371/journal.pone.0127533 Österblom, H., Jouffray, J. B., Folke, C., & Rockström, J. (2017). Emergence of a global science–business initiative for ocean stewardship. Proceedings of the National Academy of Sciences of the United States of America, 114(34), 9038–9043. https://doi.org/10.1073/pnas.1704453114 Paleczny, M., Hammill, E., Karpouzi, V., & Pauly, D. (2015). Population trend of the world’s monitored seabirds, 1950-2010. PLoS ONE, 10(6). https://doi.org/10.1371/journal.pone.0129342 Palomares, M.-L., & Cheung, W. W. L. (2016). The distribution of exploited marine biodiversity. In D. Pauly & D. Zeller (Eds.), Global Atlas of Marine Fisheries: A Critical Appraisal of Catches and Ecosystem Impacts. Retrieved from https://books.google.com/books?hl=en&lr=&id=55V_CwAAQBAJ&oi=fnd&pg=PA46&ots=9UHtx4nQT7&sig=H0PuirTEoLOqudhB1Pj4MVEWNi8 Parker, R., & Tyedmers, P. H. (2014). Fuel consumption of global fishing fleets: current understanding and knowledge gaps. Fish and Fisheries, In Press. https://doi.org/10.1111/faf.12087 109 Pauly, D. (1983). Some simple methods for the assessment of tropical fish stocks. In Fish. Tec. Pap. (Vol. 234). https://doi.org/10.1103/PhysRevB.71.041403 Pauly, D. (1995). Anecdotes and the shifting baseline syndrome of fisheries. Trends in Ecology & Evolution, 10(10), 430. https://doi.org/10.1016/S0169-5347(00)89171-5 Pauly, D., & Christensen, V. (1995). Primary production required to sustain global fisheries. Nature, 374(6519), 255–257. Pauly, D., Christensen, V., Dalsgaard, J., Froese, R., & Torres, F. (1998). Fishing down marine food webs. Science (New York, N.Y.), 279(5352), 860–863. Pauly, D., & Zeller, D. (Eds.). (2015). Sea Around Us: Concepts, Design and Data. Retrieved from http://www.seaaroundus.org Pauly, D., & Zeller, D. (2016a). Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nature Communications, 7(10244), 1–9. https://doi.org/10.1038/ncomms10244 Pauly, D., & Zeller, D. (Eds.). (2016b). Global Atlas of Marine Fisheries: A Critical Appraisal of Catches and Ecosystem Impacts. Retrieved from https://books.google.ca/books?id=55V_CwAAQBAJ Pebesma, E. (2019). Simple Features for R: Standardized Support for Spatial Vector Data. The R Journal. https://doi.org/10.32614/rj-2018-009 Pikitch, E. K., Rountos, K. J., Essington, T. E., Santora, C., Pauly, D., Watson, R., … Munch, S. B. (2014). The global contribution of forage fish to marine fisheries and ecosystems. Fish and Fisheries, 15(1), 43–64. https://doi.org/10.1111/faf.12004 Pooley, S. G. (1993). Hawaii’s marine fisheries: Some history, long-term trends, and recent developments. Marine Fisheries Review, 55(2), 7–19. PRODUCE. (2016). Evaluación de las cuotas individuales transferibles en la pesquería de anchoveta peruana (Engraulis ringens) Stock Norte-Centro. Retrieved from http://www.produce.gob.pe/index.php/dgchi/publicaciones PRODUCE. (2017). Embarcaciones Pesqueras. Retrieved July 27, 2017, from http://www.produce.gob.pe/index.php/shortcode/servicios-pesca/embarcaciones-pesqueras Quadra Planning Consultants Ltd. (2004). Seafood Watch Evaluation: Summary Report. Saltspring Island, Canada. Queiroz, N., Humphries, N. E., Couto, A., Vedor, M., da Costa, I., Sequeira, A. M. M., … Sims, 110 D. W. (2019). Global spatial risk assessment of sharks under the footprint of fisheries. Nature, 4(October 2018). https://doi.org/10.1038/s41586-019-1444-4 R Core Team. (2017). R Development Core Team. R: A Language and Environment for Statistical Computing, Vol. 55, pp. 275–286. https://doi.org/http://www.R-project.org Ram, K., & Wickham, H. (2018). wesanderson: A Wes Anderson Palette Generator. Retrieved from https://cran.r-project.org/package=wesanderson RAM Legacy Stock Assessment Database. (2018). Version 4.44-assessment-only. https://doi.org/10.5281/zenodo.2542919 Ramos, S., Vázquez-Rowe, I., Artetxe, I., Moreira, M. T., Feijoo, G., & Zufía, J. (2011). Environmental assessment of the Atlantic mackerel (Scomber scombrus) season in the Basque Country. Increasing the timeline delimitation in fishery LCA studies. The International Journal of Life Cycle Assessment, 16(7), 599–610. https://doi.org/10.1007/s11367-011-0304-8 Ranganathan, J., Vennard, D., Waite, R., Dumas, P., Lipinski, B., & Searchinger, T. (2016). Shifting Diets for a Sustainable Future. Creating a Sustainable Food Future, 11(April), 90. https://doi.org/10.2499/9780896295827_08 Regulation (EU) No 1380/2013 of the European Parliament and of the Council of 11 December 2013 on the Common Fisheries Policy, amending Council Regulations (EC) No 1954/2003 and (EC) No 1224/2009 and repealing Council Regulations (EC) No 2371/2002 and (EC) No 639/2004 and Council Decision 2004/585/EC. (n.d.). Retrieved from https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2013:354:0022:0061:EN:PDF Ricard, D., Minto, C., Jensen, O. P., & Baum, J. K. (2012). Examining the knowledge base and status of commercially exploited marine species with the RAM Legacy Stock Assessment Database. Fish and Fisheries, 13(4), 380–398. https://doi.org/10.1111/j.1467-2979.2011.00435.x Roberts, C. M., & Sargant, H. (2002). Fishery benefits of fully protected marine reserves: why habitat and behavior are important. Natural Resource Modeling, 15(4), 487–507. Roheim, C. A., Asche, F., & Santos, J. I. (2011). The elusive price premium for ecolabelled products: Evidence from seafood in the UK market. Journal of Agricultural Economics, 62(3), 655–668. https://doi.org/10.1111/j.1477-9552.2011.00299.x Rosenberg, A. A., Fogarty, M. J., Cooper, A. B., Dickey-Collas, M., Fulton, E. A., Gutierrez, N. 111 L., … Ye, Y. (2014). Developing new approaches to global stock status assessment and fishery production potential of the seas. In FAO Fisheries and Aquaculture Circular (Vol. 1086). Rome. Sala, E., & Giakoumi, S. (2018). No-take marine reserves are the most effective protected areas in the ocean. ICES Journal of Marine Science, 75(3), 1166–1168. https://doi.org/10.1093/icesjms/fsx059 Schaefer, M. B. (1957). Some considerations of population dynamics and economics in relation to the management of the commercial marine fisheries. Journal of the Fisheries Research Board of Canada, 14(5), 669–681. Schiller, L., Bailey, M., Jacquet, J., & Sala, E. (2018). High seas fisheries play a negligible role in addressing global food security. In Sci. Adv (Vol. 4). Retrieved from http://advances.sciencemag.org/ Schollenberg, L. (2012). Estimating the hedonic price for Fair Trade coffee in Sweden. British Food Journal, 114(3), 428–446. https://doi.org/10.1108/00070701211213519 Schueller, A. (2016). GDAR 02 - Gulf Menhaden Stock Assessment. Oceans Springs, Mississippi. Schuhbauer, A., Chuenpagdee, R., Cheung, W. W. L., Greer, K., & Sumaila, U. R. (2017). How subsidies affect the economic viability of small-scale fisheries. Marine Policy, 82, 114–121. https://doi.org/10.1016/j.marpol.2017.05.013 SEAFDEC. (2018). Marine Capture Production by Type of Fishing Gear and by species. Retrieved from http://map.seafdec.org/fisherybulletin/Statistics/fishing_gear.php Seafood Watch. (2016). Seafood Watch Standard for Fisheries. Retrieved from http://www.seafoodwatch.org/-/m/sfw/pdf/criteria/fisheries/mba_seafood watch_fisheries standard_version f3.2.pdf?la=en Seafood Watch. (2017). Monitoring and Evaluation System. Retrieved from https://www.seafoodwatch.org/-/m/sfw/pdf/monitoring and evaluation/seafood watch monitoring evaluation system report.pdf?la=en Seafood Watch. (2019). Seafood Watch Facts & Figures. Retrieved December 5, 2019, from https://newsroom.montereybayaquarium.org/press/seafood-watch-facts-figures SeafoodSource. (2017). 50 NGOs critique MSC over bycatch. Retrieved April 11, 2018, from SeafoodSource website: https://www.seafoodsource.com/news/environment-sustainability/50-ngos-critique-msc-over-bycatch 112 SEDAR. (2015). SEDAR 40 – Atlantic Menhaden Stock Assessment Report. Charleston, South Carolina. Smith, N., & Zeller, D. (2016). Unreported catch and tourist demand on local fisheries of small island states: the case of The Bahamas, 1950-2010. Fishery Bulletin, 114(1), 117–131. Retrieved from http://eds.b.ebscohost.com/abstract?site=eds&scope=site&jrnl=00900656&AN=112319280&h=Un3fsAlK95vIxVtNKIokW9vArB8ktCdiAhQ4CFeJgRbXaaicyOSDUOgv9fzmQU7daXnaXgDdDMHi07p5j6MSDw%3D%3D&crl=c&resultLocal=ErrCrlNoResults&resultNs=Ehost&crlhashurl=login.aspx%3Fdire Sogn-Grundvag, G., Larsen, T. A., & Young, J. A. (2013). The value of line-caught and other attributes: An exploration of price premiums for chilled fish in UK supermarkets. Marine Policy, 38, 41–44. https://doi.org/10.1016/j.marpol.2012.05.017 Spalding, M. D., Fox, H. E., Allen, G. R., Davidson, N., Ferdaña, Z. A., Finlayson, M., … Robertson, J. (2007). Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. BioScience. https://doi.org/10.1641/b570707 Stemle, A., Uchida, H., & Roheim, C. A. (2016). Have dockside prices improved after MSC certification? analysis of multiple fisheries. Fisheries Research, 182, 116–123. https://doi.org/10.1016/j.fishres.2015.07.022 Subsecretaría de Pesca y Acuicultura. (2016). Estado de situación de las principales pesquerías chilenas, año 2015. Santiago. Sumaila, U. R. (2004). Intergenerational cost – benefit analysis and marine ecosystem restoration. Fish and Fisheries, 5(May), 329–343. https://doi.org/10.1111/j.1467-2679.2004.00166.x Sumaila, U. R. (2010). A cautionary note on individual transferable quotas. Ecology and Society, 15(3). https://doi.org/10.5751/ES-03391-150336 Sumaila, U. R., Cheung, W. W. L., Dyck, A., Gueye, K., Huang, L., Lam, V. W. Y., … Zeller, D. (2012). Benefits of rebuilding global marine fisheries outweigh costs. PLoS ONE, 7(7). https://doi.org/10.1371/journal.pone.0040542 Sumaila, U. R., Khan, A. S., Dyck, A. J., Watson, R., Munro, G., Tydemers, P., & Pauly, D. (2010). A bottom-up re-estimation of global fisheries subsidies. Journal of Bioeconomics, 12(3), 201–225. https://doi.org/10.1007/s10818-010-9091-8 113 Sumaila, U. R., Lam, V. W. Y., Le Manach, F., Swartz, W., & Pauly, D. (2016). Global fisheries subsidies: An updated estimate. Marine Policy, 69, 189–193. https://doi.org/10.1016/j.marpol.2015.12.026 Sumaila, U. R., Lam, V. W. Y., Miller, D. D., Teh, L., Watson, R. A., Zeller, D., … Pauly, D. (2015). Winners and losers in a world where the high seas is closed to fishing. Scientific Reports, 5(1), 8481. https://doi.org/10.1038/srep08481 Sumaila, U. R., Marsden, A. D., Watson, R., & Pauly, D. (2007). A global ex-vessel fish price database: Construction and applications. Journal of Bioeconomics, 9(1), 39–51. https://doi.org/10.1007/s10818-007-9015-4 Sumaila, U. R., Tai, T. C., Lam, V. W. Y., Cheung, W. W. L., Bailey, M., Cisneros-Montemayor, A. M., … Gulati, S. S. (2019). Benefits of the Paris Agreement to ocean life, economies, and people. Science Advances, 5. Retrieved from http://advances.sciencemag.org/ Sumaila, U. R., Teh, L. S. L., Watson, R., Tyedmers, P., & Pauly, D. (2008). Fuel price increase , subsidies , overcapacity , and resource sustainability. 832–840. Sumaila, U. R., Zeller, D., Watson, R., Alder, J., & Pauly, D. (2007). Potential costs and benefits of marine reserves in the high seas. Marine Ecology Progress Series, 345, 305–310. https://doi.org/10.3354/meps07065 Swartz, W., Sala, E., Tracey, S., Watson, R., & Pauly, D. (2010). The spatial expansion and ecological footprint of fisheries (1950 to present). PloS One, 5(12), e15143. https://doi.org/10.1371/journal.pone.0015143 Swartz, W., Sumaila, U. R., & Watson, R. (2013). Global Ex-vessel Fish Price Database Revisited: A New Approach for Estimating “Missing” Prices. Environmental and Resource Economics, 56(4), 467–480. https://doi.org/10.1007/s10640-012-9611-1 Tai, T. C., Cashion, T., Lam, V. W. Y., Swartz, W., & Sumaila, U. R. (2017). Ex-vessel fish price database: disaggregating prices for low-priced species from reduction fisheries. Frontiers in Marine Science, 4(363), 1–10. https://doi.org/doi.org/10.3389/fmars.2017.00363 Tesfamichael, D., & Rossing, P. (2016). Saudi Arabia. In D. Tesfamichael & D. Pauly (Eds.), The Red Sea ecosystem and fisheries (pp. 79–92). https://doi.org/10.1007/978-94-017-7435-2 114 Tremblay-Boyer, L., Gascuel, D., Watson, R., Christensen, V., & Pauly, D. (2011). Modelling the effects of fishing on the biomass of the world’s oceans from 1950 to 2006. Marine Ecology Progress Series, 442, 169–185. https://doi.org/10.3354/meps09375 Tu’avao, T., Kava, V., & Udagawa, K. (1996). Fisheries in the Tongatapu Island Group. In Integrated Fisheries Survey Report. Tokyo: Ministry of Fisheries Tonga, Japan International Cooperation Volunteers, and Japan International Cooperation Agency. Turner, M. A. (1997). Quota-Induced Discarding in Heterogeneous Fisheries. Journal of Environmental Economics and Management, 33, 186–195. Turner, S. J., Thrush, S. F., Hewitt, J. E., Cummings, V. J., & Funnell, G. (1999). Fishing impacts and the degradation or loss of habitat structure. Fisheries Management and Ecology, 6(5), 401–420. https://doi.org/10.1046/j.1365-2400.1999.00167.x United Nations. United Nations convention on the law of the sea. , UN § (1982). Uphoff, J. H. (2003). Predator-prey analysis of striped bass and Atlantic menhaden in upper Chesapeake Bay. Fisheries Management and Ecology, 10(5), 313–322. https://doi.org/10.1046/j.1365-2400.2003.00366.x Van Der Meer, L., Arancibia, H., Zylich, K., & Zeller, D. (2015). Reconstruction of total marine fisheries catches for mainland Chile (1950-2010). Retrieved from https://sau-technical-reports.s3.amazonaws.com/152_van der meer et al_2015_Chile Mainland_WP.pdf?Signature=%2FuRpSXDaVFs%2F2drLDzZv%2BSMk8Qo%3D&Expires=1524846210&AWSAccessKeyId=ASIAIAD6BN6URY2NWTFQ&x-amz-security-token=FQoDYXdzEJH//////////wEaD VanderKooy, S. J., & Smith, J. . (2015). The menhaden fishery of the Gulf of Mexico, United States: A regional management plan – 2015 revision. Oceans Springs, Mississippi. von Brandt, A. (1984). Classification of catching methods. In Fish catching methods of the world (3rd ed., pp. 274–328). Surrey: Fishing News Books. Wakamatsu, H. (2014). The Impact of MSC Certification on a Japanese Certified Fishery. Marine Resource Economics, 29(1), 2–12. Wallace, S., Turris, B., Driscoll, J., Bodtker, K., Mose, B., & Munro, G. (2015). Canada’s pacific groundfish trawl habitat agreement: A global first in an ecosystem approach to bottom trawl impacts. Marine Policy, 60, 240–248. https://doi.org/10.1016/j.marpol.2015.06.028 Walters, C. (1998). Designing fisheries management systems that do not depend upon accurate 115 stock assessment. In T. J. Pitcher, D. Pauly, & P. Hart (Eds.), Reinventing Fisheries Management (pp. 279–288). Dordrecht. Walters, C., Christensen, V., Martell, S. J., & Kitchell, J. F. (2005). Possible ecosystem impacts of applying MSY policies from single-species assessment. ICES Journal of Marine Science, 62(3), 558–568. https://doi.org/10.1016/j.icesjms.2004.12.005 Walters, C., & Pearse, P. H. (1996). Stock information requirements for quota management systems in commercial fisheries. Reviews in Fish Biology and Fisheries, 6, 21–42. https://doi.org/10.1007/BF00058518 Washburn, B. E., Elbin, S. B., & Davis, C. (2016). Historical and Current Population Trends of Herring Gulls ( Larus argentatus ) and Great Black-Backed Gulls ( Larus marinus ) in the New York Bight, USA . Waterbirds, 39(sp1), 74–86. https://doi.org/10.1675/063.039.sp114 Watling, L., & Norse, E. A. (1998). Disturbance of the Seabed by Mobile Fishing Gear: A Comparison to Forest Clearcutting. Conservation Biology, 12(6), 1180–1197. Watson, R., Revenga, C., & Kura, Y. (2006a). Fishing gear associated with global marine catches. I. Database development. Fisheries Research, 79, 97–102. https://doi.org/10.1016/j.fishres.2006.01.010 Watson, R., Revenga, C., & Kura, Y. (2006b). Fishing gear associated with global marine catches. II. Trends in trawling and dredging. Fisheries Research, 79(1–2), 103–111. https://doi.org/10.1016/j.fishres.2006.01.013 White, C., & Costello, C. (2014). Close the High Seas to Fishing? PLoS Biology, 12(3), 1–5. https://doi.org/10.1371/journal.pbio.1001826 Wickham, H. (2016). tidyverse: Easily Install and Load “Tidyverse” Packages. In R package version 1.0.0. Retrieved from https://cran.r-project.org/package=tidyverse Wilen, J. E. (2009). Stranded Capital in Fisheries: The Pacific Coast Groundfish / Whiting Case. Marine Resource Economics, 24(1), 1–18. https://doi.org/10.5950/0738-1360-24.1.1 World Bank. (n.d.). GEM Commodities. Retrieved from https://datacatalog.worldbank.org/dataset/gem-commodities World Bank. (2017). The Sunken Billions Revisited: Progress and Challenges in Global Marine Fisheries. Washington, DC. Worm, B., Barbier, E. B., Beaumont, N., Duffy, J. E., Folke, C., Halpern, B. S., … Watson, R. (2006). Impacts of biodiversity loss on ocean ecosystem services. Science, 314(5800), 787– 116 790. https://doi.org/10.1126/science.1132294 Young, T., Fuller, E. C., Provost, M. M., Coleman, K. E., Martin, K. S., McCay, B. J., & Pinsky, M. L. (2019). Adaptation strategies of coastal fishing communities as species shift poleward. ICES Journal of Marine Science, 76(1), 93–103. https://doi.org/10.1093/icesjms/fsy140 Zeller, D., Cashion, T., Palomares, M.-L., & Pauly, D. (2018). Global marine fisheries discards: A synthesis of reconstructed data. Fish and Fisheries, 19(1), 30–39. https://doi.org/10.1111/faf.12233 Zeller, D., Harper, S., Zylich, K., & Pauly, D. (2015). Synthesis of underreported small-scale fisheries catch in Pacific island waters. Coral Reefs, 34(1), 25–39. https://doi.org/10.1007/s00338-014-1219-1 Zeller, D., Palomares, M.-L., Tavakolie, A., Ang, M., Belhabib, D., Cheung, W. W. L., … Pauly, D. (2016). Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Marine Policy, 70, 145–152. https://doi.org/10.1016/J.MARPOL.2016.04.046 117 Appendices Appendix A Appendix A contains the supporting information to Chapter 3. The final step of estimating fisheries revenues and profits is subject to additional uncertainty, as prices change over time. To account for this, I created a multivariate log-transformed linear regression model to measure the ex-vessel price elasticity of menhaden to increased landings. I compiled monthly data on menhaden landings, menhaden ex-vessel prices and fishmeal world prices for 1990-2016 (NMFS, 2017b; World Bank, n.d.). I log transformed all variables to properly account for their distribution. In addition, when the model is log transformed on both sides (i.e., a log-log model), the estimated coefficients are equivalent to the elasticity of the independent variable, and this elasticity is constant (Hill, Griffiths, & Lim, 2011). The equation estimated was ln(Pt) = 0 + 1ln(Lt) + 2ln(FMt) + t (A.1) Where P is the ex-vessel price of menhaden, L is the amount of landings, FM is the price of fishmeal, and t is the time period (month and year), and  is the error term. As I only had monthly price and landings data for menhaden, I assumed the price elasticity to be the same for the anchoveta fishery. The multivariate linear regression model of menhaden ex-vessel prices had an adjusted R2 of 0.685, suggesting a reasonable fit to the data. The coefficient I were concerned with in this study, i.e., log(Landings), was -0.075 and was statistically significant (p < 2e-16). This coefficient was equal to the elasticity effect on price and can therefore be interpreted as a 1% increase in landings will result in a 0.075% decrease in ex-vessel prices. This price-elasticity estimate was applied to both anchoveta and menhaden ex-vessel prices when landings increased in our modeled scenarios. 118 Table A.1 Cost of fishing and ex-vessel prices by fishery (USD·tonne-1). Species Cost of fishing Ex-vessel price Anchoveta 100.481 133.501 Atlantic menhaden 177.572 204.313 Gulf menhaden 177.572 256.833 1. (Christensen et al., 2014) 2. (Kirkley, 2011) Assumed same cost of fishing for Gulf menhaden as Atlantic menhaden 3. (NMFS, 2017a) 4. Note: This does not include the elasticity adjusted prices based on increases in landings. 119 Table A.2 Scenario analysis for north-central and southern anchoveta stocks of Peru North-Central North-Central North-Central South1 South1 South1 Scenario (Biomass)2 Baseline Modeled (807)2 PGY (807)2 Baseline Modeled (0)2 PGY (0)2 Years Biomass Landings Biomass Landings Biomass Landings Biomass Landings Biomass Landings Biomass Landings 2000 9,086 9,110 9,086 5,365 9,086 4,970 2,777 606 2,777 636 2,777 551 2001 10,951 5,960 9,975 5,940 10,370 5,713 3,108 511 2,871 787 3,094 726 2002 10,628 6,720 15,042 9,224 13,214 5,713 3,347 1,844 3,054 1,093 3,405 1,046 2003 9,938 5,130 9,802 5,828 12,476 5,713 3,192 261 2,746 501 3,132 483 2004 10,704 8,070 10,060 5,995 12,347 5,713 3,327 991 3,816 879 4,254 728 2005 10,606 7,570 9,956 5,928 12,090 5,713 3,044 1,428 2,788 1,009 3,442 959 2006 9,681 5,000 12,083 7,306 13,943 5,713 2,926 1,161 3,038 1,205 3,715 1,051 2007 8,737 5,140 11,072 5,357 13,643 5,713 2,532 1,259 3,473 1,153 4,216 980 2008 8,654 5,300 9,689 5,106 11,311 5,713 2,161 1,134 2,912 1,115 3,713 1,039 2009 8,445 5,260 9,689 5,149 10,561 5,713 1,983 740 2,913 928 3,699 883 2010 8,195 3,020 9,690 5,093 9,957 5,713 1,805 403 2,913 507 3,658 625 2011 8,996 6,310 9,690 4,037 9,334 5,256 1,915 881 2,912 649 3,266 663 2012 9,031 3,330 9,568 5,676 7,988 3,656 1,944 483 2,678 529 2,978 524 2013 9,289 4,500 10,267 4,170 10,512 5,713 1,947 332 3,495 496 3,826 376 2014 7,907 1,920 9,689 3,122 8,378 4,113 1,768 451 2,979 559 3,726 498 2015 8,332 3,340 9,690 3,785 7,332 2,913 1,793 396 2,564 582 3,403 602 Mean (103 t) 9,324 5,355 10,315 5,443 10,784 5,234 2,473 805 2,996 789 3,519 733 Coefficient of Variation (%) 10.5 36.0 13.9 26.3 19.0 16.9 24.8 57.0 11.0 33.3 11.7 31.2 95% CI (103 t) 481.5 945.9 704.8 702.4 1002.9 433.1 300.8 224.8 162.0 128.7 202.4 112.0 1. Note: Catches can exceed biomass in certain years in the baseline scenario due to a high recruitment anomaly, and a lack of intra-year measurement. 2. The number in brackets refers to the biomass (103 t) reference point, i.e. the biomass not subject to fishing mortality each year for the modeled scenario as established based on the scenario analysis methods. 120 Table A.3 Landings (103 t) by company by year for the Peruvian anchoveta fishery based on quota ownership. Company 2011 2012 2013 2014 2015 Average Tecnologica De Alimentos S.A. 1011 534 681 331 524 616 Corporacion Pesquera Inca S.A.C. 699 369 491 217 368 429 Pesquera Diamante S.A. 595 314 404 192 309 363 Austral Group S.A.A 460 243 319 145 241 282 Pesquera Exalmar S.A.A. 452 239 312 144 236 277 Pesquera Hayduk S.A. 428 226 299 134 225 262 CFG Investment S.A.C. 462 244 303 156 237 280 1. I present Corporacion Pesquera Inca S.A.C. and CFG Investment S.A.C. separately here, as they were not under the same ownership for the entire period. 121 Table A.4 Average attained landings for the top seven companies for 2011-2015 (103 t), modeled higher landings (103 t) based on Optimal F, and their impact on revenue and profits (USD 106). Company Landings1 Optimal F landings1,2 Revenue shortfall Cost of landings shortfall Unrealized profits Tecnologica de Alimentos S.A. 617 700 10.99 8.36 2.64 Corporacion Pesquera Inca S.A.C. 398 435 4.82 3.65 1.17 Pesquera Diamante S.A. 368 414 6.04 4.59 1.45 Austral Group S.A.A. 287 319 4.27 3.24 1.03 Pesquera Exalmar S.A.A. 221 243 2.93 2.22 0.71 Pesquera Hayduk S.A. 273 302 3.82 2.90 0.92 CFG Investment S.A.C. 252 291 5.09 3.87 1.21 1. Company level landings are from vessel level landings provided by (PRODUCE, 2017). Optimal F landings here are not based on company quota but on average share of landings from 2011-2015. 2. Optimal-F landings are generated by optimizing the biomass reference point, and fishing at FMSY when B _BMSY. 122 Table A.5 Estimated landings (103 t) by company for the US menhaden fishery. Company Menhaden 2012 2013 2014 2015 2016 Average Omega Protein Gulf 242 214 187 262 235 225 Omega Protein Atlantic 173 129 134 155 144 148 Daybrook Fisheries Gulf 200 176 154 216 194 186 123 Appendix B Appendix B contains the supporting information to Chapter 4. Supplemental models Our primary models estimate the effect of the change from no rating to one rating level. Here I include a test including all paired changes in rating levels that have at least 100 observations (Table B.1). Perhaps unsurprisingly, the most stark switches are those that are not included because of this restriction (green to red, and red to green). All models have year and fishery fixed effects, and additionally account for whether it is already assessed and a running variable of years before and after assessment. Our results under this alternative specification agree with the results of our primary specifications. The additional results demonstrate some agreement with our conclusions of higher landings for more sustainable sources (increase for yellow to green, although red to yellow does not show this trend). These additional results should be interpreted with caution due to the limited sample size from which they are drawn. I estimate equation (1) but with lagged dependent variables. I did this with our two primary specifications, one with a time-constant running variable centered on the assessment year (year of rating is equal to 0), and one with a time constant running variable for each rating level. I test with lagged dependent variables of 1, 2, 3, and 4 years. The parameter estimate for 𝛽 should approach zero the larger the lag for 𝑙𝑜𝑔(𝑦𝑖𝑡) if the causal effect is of the rating on catches or prices. The results improve our confidence in our results as the rating never significantly predicts the change in catches in years immediately preceding (1 and 2) the rating (Table B.2 and Table B.4). The results are significantly different from 0 for red-labeled fisheries (A7 and A8), but this is accounted for by a general trend of declining catches in red-labeled fisheries in specifications A11 and A12. There is a significant increase in yellow-labeled fisheries prices for 2 and 3 years before the rating, but this effect is not present when the running variable is interacted with the time-invariant rating. This is further evidence for the effectiveness of our primary specification that includes the time constant running variable for each rating level. 124 Table B.1 Robustness test: Transfer between levels for all pairs of ratings (min n=100). Term A13 A14 B13 B14 None-Green -0.186 (0.16) -0.132 (0.149) 0.06 (0.055) 0.043 (0.036) None-Yellow -0.14 (0.138) -0.151 (0.144) 0.044 (0.029) 0.066* (0.038) None-Red -0.263* (0.137) -0.321 (0.201) 0.04 (0.047) 0.031 (0.054) Red-Yellow 0.031 (0.358) 0.005 (0.312) -0.072 (0.098) -0.076 (0.09) Yellow-Green 0.627* (0.352) 0.299*** (0.032) 0.109* (0.056) 0.067 (0.05) Green-Yellow 0.013 (0.116) 0.268** (0.116) 0.169 (0.152) 0.028 (0.04) Yellow-Red -0.231*** (0.034) 0.078 (0.056) 0.042*** (0.01) 0.004 (0.013) Observations 6208 4200 6208 4200 Adjusted R^2 0.889 0.904 0.92 0.931 F-Statistic 73.731 58.601 105.006 83.422 Data +/- 5 Years From Rating +/- 3 Years From Rating +/- 5 Years From Rating +/- 3 Years From Rating Note: Models A13 and A14 have catch as a dependent variable, and models B13 and B14 have price as a dependent variable. Models estimate the effect of the switch from one rating (including ‘none’) to another. Models A13 and B13 have a bandwidth of 5 years, and A14 and B14 have a bandwidth of 3 years. All models have year and fishery fixed effects, and additionally account for whether it is already assessed and a running variable of years before and after assessment. Standard errors are clustered on species and year. ***, **, * = significant at 1, 5, 10% level. 125 Table B.2 Robustness test: Equation 1 with lagged dependent variable for landings. Term A5 A6 A7 A8 A9 A10 A11 A12 Green -0.005 (0.134) 0.111 (0.13) 0.119 (0.133) 0.129 (0.149) -0.086 (0.156) 0.071 (0.136) 0.052 (0.138) -0.09 (0.136) Yellow 0.1 (0.133) 0.063 (0.13) -0.049 (0.114) -0.049 (0.128) 0.118 (0.17) 0.08 (0.139) -0.194 (0.129) -0.129 (0.132) Red -0.131 (0.155) -0.137 (0.124) -0.238* (0.122) -0.337*** (0.124) -0.105 (0.186) -0.131 (0.196) -0.061 (0.137) -0.137 (0.162) Running Variable -0.043** (0.018) -0.04* (0.022) -0.043** (0.02) -0.032 (0.022) Green:Running Variable -0.027 (0.029) -0.032 (0.027) -0.03 (0.031) 0.014 (0.036) Yellow:Running Variable -0.046 (0.032) -0.043 (0.031) -0.013 (0.029) -0.014 (0.027) Red:Running Variable -0.047 (0.049) -0.041 (0.047) -0.08** (0.037) -0.073* (0.04) Observations 4893 4809 4648 4431 4893 4809 4648 4431 Adjusted R^2 0.887 0.893 0.899 0.898 0.887 0.893 0.899 0.898 F-Statistic 58.729 63.124 66.573 63.679 58.535 62.902 66.472 63.63 Dependent variable Tonnes_t-1 Tonnes_t-2 Tonnes_t-3 Tonnes_t-4 Tonnes_t-1 Tonnes_t-2 Tonnes_t-3 Tonnes_t-4 Data +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating Note: Models A5-A8 have a running variable and estimate the effect of rating. Models A9-A12 have an interaction term of running variable and rating level and estimate the effect of rating on catches. Each specification is tested with a lagged dependent variable of 1, 126 2, 3, and 4 years. All models have year and fishery fixed effects, and cluster standard errors on species and year. ***, **, * = significant at 1, 5, 10% level. 127 Table B.3 Robustness test: Equation 1 with lagged dependent variable for prices. Term B5 B6 B7 B8 B9 B10 B11 B12 Green 0.029 (0.038) 0.023 (0.037) 0.027 (0.029) 0.014 (0.038) -0.006 (0.043) 0.042 (0.037) 0.032 (0.039) 0.052 (0.048) Yellow 0.047 (0.029) 0.071** (0.03) 0.067** (0.033) 0.06 (0.04) 0.015 (0.039) 0.048 (0.038) 0.056 (0.042) 0.021 (0.052) Red -0.037 (0.029) -0.023 (0.029) -0.014 (0.03) 0.002 (0.034) 0.014 (0.036) -0.01 (0.035) -0.006 (0.027) 0.02 (0.054) Running Variable 0.032*** (0.005) 0.041*** (0.005) 0.035*** (0.004) 0.031*** (0.007) Green:Running Variable 0.037*** (0.009) 0.037*** (0.008) 0.034*** (0.008) 0.023** (0.01) Yellow:Running Variable 0.037*** (0.01) 0.046*** (0.01) 0.037*** (0.009) 0.039*** (0.01) Red:Running Variable 0.02** (0.009) 0.038*** (0.008) 0.033*** (0.005) 0.027*** (0.01) Observations 4893 4809 4648 4431 4893 4809 4648 4431 Adjusted R^2 0.946 0.948 0.95 0.952 0.947 0.948 0.95 0.952 F-Statistic 130.224 137.302 140.393 142.485 130.178 136.904 139.902 142.27 Dependent variable Price_t-1 Price_t-2 Price_t-3 Price_t-4 Price_t-1 Price_t-2 Price_t-3 Price_t-4 Data +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating Note: Models B5-B8 have a running variable and estimate the effect of rating. Models B9-B12 have an interaction term of running variable and rating level and estimate the effect of rating on catches. Each specification is tested with a lagged dependent variable of 1, 128 2, 3, and 4 years. All models have year and fishery fixed effects, and cluster standard errors on species and year. ***, **, * = significant at 1, 5, 10% level. 129 B.1 Robustness tests I estimate equation 2 for each assessment level separately to determine if lagged catches (by 1 and 2 years) affect the assessment of the fishery. In the year immediately preceding, I find no evidence that changes in catches predict the assessment level. For green-labeled fisheries, I do find a lag of 2 years does predict the assessment level, but the effect is minor (increased probability of assessment of 2.7%). Therefore, I conclude that changes in catches before the assessment is not likely a source of endogeneity. Table B.4 Robustness test: Equation 2 with lagged log(Tonnes) as predictor of assessment. Term Green Yellow Red Ln(Tonnes) t-1 0 (0.024) 0.007 (0.013) -0.015 (0.012) Ln(Tonnes) t-2 0.026* (0.015) -0.009 (0.01) 0.001 (0.019) Observations 1019 1807 1983 Adjusted R^2 -0.088 -0.03 -0.04 F-Statistic 0.465 0.827 0.755 Data +/- 5 Years From Rating +/- 5 Years From Rating +/- 5 Years From Rating Note: All models have year and fishery fixed effects, and cluster standard errors on species and year. ***, **, * = significant at 1, 5, 10% level. 130 B.2 Supplemental tables Table B.5 Biomass by Seafood Watch rating. Variable None Avoid Good Alternative Best Choice Mean B/BMSY (sd) 1.87 (1.92) 1.62 (2.18) 2.28 (2.38) 2.78 (2.23) Observations 13238 457 335 146 131 B.3 Fixed-effects models I estimated our data with ordinary least squares in a fixed-effects model. However, I do not believe this model accounts for pre-existing characteristic differences and trends in the fisheries assessed by Seafood Watch. Specification A15 and B15 estimates the effect of the different rating levels. Specification A16 and B16 estimates the effect of being assessed by Seafood Watch in addition to the effect of the different rating levels. Specification A17 and B17 uses a fixed-effect for all fisheries that are assessed at some point in our data, and interacts this fixed effect with the Year fixed effect to account for varying interest over time in the ratings. There is a consistent effect of a positive increase in catches for assessed fisheries in A15 and A16, but only for yellow-labeled fisheries in A17. For price, the only significant result is an increase for red-labeled fisheries in specification B17. Table B.6 Fixed-effects results of catch models. All have ln(Tonnes) as the dependent variable. Term A15 A16 A17 Green 1.831*** (0.312) 0.62 (0.393) 0.052 (0.344) Yellow 2.2*** (0.267) 0.989*** (0.338) 0.505** (0.227) Red 1.212*** (0.243) -0.269 (0.251) Assessed 1.212*** (0.243) Observations 87458 87458 87458 Adjusted R^2 0.306 0.306 0.341 F-Statistic 62.084 62.084 70.846 Will be Assessed fixed-effect Y I(Will be Assessed:Year) fixed-effect Y Data All All All Green 1.831*** (0.312) 0.62 (0.393) 0.052 (0.344) Note: All models estimate effect of rating without running variables. All models have year, species, gear, and state fixed effects, and cluster standard errors on species. ***, **, * = significant at 1, 5, 10% level. 132 Table B.7 Fixed-effects results of price models. All have ln(Price) as the dependent variable. Term B15 B16 B17 Green -0.021 (0.047) -0.067 (0.057) -0.007 (0.05) Yellow -0.003 (0.036) -0.049 (0.047) 0.02 (0.036) Red 0.046 (0.033) 0.06* (0.035) Assessed 0.046 (0.033) Observations 87458 87458 87458 Adjusted R^2 0.662 0.662 0.662 F-Statistic 273.326 273.326 265.388 Will be Assessed fixed-effect Y I(Will be Assessed:Year) fixed-effect Y Data All All All Green -0.021 (0.047) -0.067 (0.057) -0.007 (0.05) Note: All models estimate effect of rating without running variables. All models have year, species, gear, and state fixed effects, and cluster standard errors on species. ***, **, * = significant at 1, 5, 10% level. B.4 Supplemental figures Figure B.1 Histograms of landings and ln(landings) for all data included in the study. Note: Both graphs have the same number of bins (100). 133 Figure B.2 Histograms of price and ln(price) for all data included in the study. Note: Both graphs have the same number of bins (100). Figure B.3 Violin plot of natural logarithm catch of fisheries by assessed status. Red dot indicates the median value for each assessment category. 134 Figure B.4 Violin plot of natural logarithm price of fisheries by assessed status. Red dot indicates the median value for each assessment category. Figure B.5 Boxplot of biomass (B/Bmsy) by assessment status. 135 Appendix C Appendix C contains the supporting information to Chapter 5. Habitat modification Of the 2,603 marine species that have trawling (‘trawls unspecified’) identified as a threat, only 70 have habitat modification also identified as a threat. When reviewing the threat description for these species, many of them are identified as two separate threats. Only 9 of these have ‘destructive fishing practices’ identified as a threat. 136 C.1 Supplemental tables Table C.1 Weighting of IUCN Categories based on (O’Hara et al., 2019). Former code Category Current Code Category Score VU Vulnerable VU 0.4 V Vulnerable VU 0.4 CR Critically Endangered CR 0.8 EN Endangered EN 0.6 E Endangered EN 0.6 NT Near Threatened NT 0.2 LC Least Concern LC 0.0 LR/nt Lower Risk/near threatened NT 0.2 LR/lc Lower Risk/least concern LC 0.0 DD Data Deficient DD NA R Rare EN 0.6 O Out of Danger LC 0.0 NA “Very rare but believed to be stable or increasing” CR 0.8 NA “Status inadequately known-survey required or data sought” DD NA LR/cd Lower Risk/conservation dependent VU 0.4 K Insufficiently Known DD NA NA “Very rare and believed to be decreasing in numbers” CR 0.8 NA “Less rare but believed to be threatened-requires watching” EN 0.6 I Indeterminate DD NA EW Extinct in the Wild EX 1.0 EX Extinct EX 1.0 Ex Extinct EX 1.0 NE Not Evaluated NE NA N.E. Not Evaluated NE NA T Threatened EN 0.6 NR Not Recognized DD NA 137 Table C.2 Search terms used for identifying gear types in IUCN narrative text. Gear type Search terms Bottom trawl bottom trawl; deepwater trawl; demersal trawl; prawn trawl; benthic trawl; otter trawl; beam trawl; shrimp trawl Pelagic trawl pelagic trawl; shallow trawl; midwater trawl; surface trawl Trawl trawl; trawling Longlines longline; hooks Lines hook and line; hook and; and line; hook; line; pole and; jig; hand line Nets net Seines seine; purse; encircling Gillnets gill net; gillnet; entangl; entangle Dredges dredge; drag Traps pot; trap; fyke Drifting longlines drifting longlines; drifting lines Squid jiggers jig; squid jiggers; jiggers; squid jigging; jigging Small scale small scale; artisanal; subsistence; small-scale Others harpoon; bagnets; bag; spear 138 Table C.3 Gear types identified in the Sea Around Us database. Gear type Super code Bottom trawl Bottom trawl Shrimp trawl Bottom trawl Beam trawl Bottom trawl Otter trawl Bottom trawl Pelagic trawl Pelagic trawl Lines Longline Pole and line Longline Longline Longline Hand lines Longline Encircling nets Purse seine Purse seine Purse seine Small encircling nets Purse seine Gillnet Gillnet Trammel nets Gillnet Other Other Pots or traps Other Other nets Other Dredge Other Hand or tools Other Dragged gear Other Mixed gear Other Artisanal fishing gear Small scale Long distance small scale Small scale Recreational fishing gear Small scale Subsistence fishing gear Small scale Cast nets Small scale Bagnets Small scale Harpoon Small scale Hand or tools Small scale Small scale seine nets Small scale Small scale encircling nets Small scale Small scale trammel net Small scale Small scale gillnets Small scale 139 Small scale other nets Small scale Small scale pots or traps Small scale Small scale lines Small scale Small scale hand lines Small scale Small scale pole lines Small scale Small scale longline Small scale Small scale purse seine Small scale Unknown class Unknown Unknown by source Unknown Unknown by author Unknown 140 Table C.4 Regression results for gear types used and their gear multipliers used. Gear type Estimate Standard error T-Statistic p-value Multiplier (mean) Multiplier (lower bound) Multiplier (upper bound) Gillnet 0.155 0.002 72.594 *** 1.168 1.164 1.172 Longline 0.202 0.003 57.857 *** 1.224 1.217 1.230 Other 0.039 0.004 10.007 *** 1.039 1.032 1.047 Pelagic trawl -0.856 0.059 -14.606 *** 0.425 0.310 0.540 Pots and traps 0.227 0.003 78.490 *** 1.255 1.250 1.261 Purse seine -0.209 0.015 -14.064 *** 0.812 0.783 0.841 Small scale 0.409 0.014 29.904 *** 1.505 1.478 1.532 Bottom trawl 0.000 0.000 0.000 Not Estimated 1.000 1.000 1.000 Unknown 0.000 0.000 0.000 Not Estimated 1.000 1.000 1.000 Table C.5 Area of high seas by categorization. Category Water Area (million km2) Proportion of High Seas (%) Fishery competition 453 16.0 Fishery prioritization 112 3.9 Area of low concern 1467 51.8 Easy wins 802 28.3
Thesis/Dissertation
eng
Oceans and Fisheries
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Get it in gear : evaluating gear-based trade-offs in fisheries
Text