On Sustainability and Financial Return of Fishery Resources

ON SUSTAINABILITY AND FINANCIAL RETURN OF FISHERY RESOURCES

Abdulrahman Ben Hasan

A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

The Faculty of Graduate and Postdoctoral Studies

(Oceans and Fisheries)

THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)

July 2021

The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled:

On sustainability and financial return of fishery resources

submitted by Abdulrahman Ben Hasan in partial fulfillment of the requirements for

the degree of Doctor of Philosophy

in Oceans and Fisheries

Examining Committee:

Villy Christensen, Professor, Oceans and Fisheries, UBC Supervisor

Carl J. Walters, Professor Emeritus, Oceans and Fisheries, UBC Supervisory Committee Member

Charles R. Menzies, Professor, Anthropology, UBC University Examiner

Peter Arcese, Professor, Forest and Conservation Sciences, UBC University Examiner

Additional Supervisory Committee Members:

U. Rashid Sumaila, Professor, Oceans and Fisheries, UBC Supervisory Committee Member

Brett van Poorten, Assistant Professor, Resource and Environmental Management, SFU Supervisory Committee Member

Abstract

Overexploitation and resource rent dissipation are some of the fundamental issues in fisheries management. The first undermines food security while the second implies a minimal economic return to the owning society. Sustainable fisheries are predominantly attained in conjunction with high management intensity, which keeps exploitation rates in check. Yet controlling exploitation becomes a daunting task under many complex fisheries contexts. Further, although profitability of fishing industry has improved after introducing quota-based systems, it is perceived that society is not receiving a fair share of the resource rent. In this dissertation, I focus on the Arabian Gulf region as a microcosm to examine various complex fisheries problems and underline, globally, the society’s compensation from the fishing industry. I begin by discussing situations where open access conditions are irreversible due to inherently poor management institutions or high dependency on fishing for livelihood. I show that well-designed size restriction— an easily implementable approach—can help avert overexploitation, rebuild depleted fish stocks and enhance yields without controlling exploitation rates. Next, I examine internationally shared fish stocks, whose sustainability requires managing exploitation rates at the international level rather than merely locally. I develop an age-structured model to evaluate bioeconomic trade-offs under alternative fishing scenarios. Harvesting a shared fish stock under cooperation or local but sustainable management provides much higher bioeconomic gains than competition. I then discuss the impacts of escalated market demand for dried swim bladder on fish, people and management in source countries. I highlight that while management interventions are required, the extremely high value of swim bladder would complicate regulatory efforts by stimulating black-market systems. Finally, I examine whether resource rent charges are imposed on catch share fisheries, and systematically compare that with forestry, oil, gas, and mining in 18 countries. I show that fishing is the only industry that consistently lacks resource rent charges, implying a forgoing stream of income in most countries. My dissertation contributes toward alleviating overfishing when exploitation rates are difficult to manage and underscores the need for national policies to consider the enhanced profitability of the fishing industry under catch share systems.

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Lay Summary

Fishery resources provide food and livelihood for millions of people, and because they are public resources, they can generate a stream of income to coastal states. However, sustainability is difficult to achieve under many contexts, and society is perceived to be deprived of the income stream from exploiting its resources. I tackle the first problem by analyzing diverse fisheries contexts that complicate management, like fundamentally weak regulatory agencies, transboundary fish stocks, and escalated market demand. I deliver practical insights that help protect fish stocks and enhance fishery catches under these contexts. I address the second problem by investigating whether society is receiving compensation from fisheries. I find that out of 18 countries and among four other major extractive industries, fishing is the only industry that lacks resource rent charges in most countries. My research supports sustainability in challenging fisheries conditions and highlight a forgone economic return from fishery resources.

Preface

The Introduction in Chapter 1 includes edited parts of a published paper: Ben-Hasan, A. and V. Christensen. 2019. “Vulnerability of the marine ecosystem to climate change impacts in the Arabian Gulf—an urgent need for more research.” Global Ecology and Conservation 17:e00556. I was the lead author with responsibilities covering all major areas of concept formation, analyses, and manuscript structure and composition. V. Christensen contributed to concept formation as well as manuscript edits. A version of Chapter 2 has been published [Ben-Hasan, A., C. Walters, A. Hordyk, V. Christensen, M. Al-Husaini. 2021. Alleviating growth and recruitment overfishing through simple management changes: insights from an overexploited long-lived fish. Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 13 (2): 87–98]. I was the lead author with responsibilities covering all major areas of concept formation, analyses, and manuscript structure and composition. C. Walters was involved in concept formation, all analyses and manuscript structure and composition; A. Hordyk and V. Christensen contributed to concept formation as well as manuscript edits; and M. Al-Husaini contributed to data collection. A version of Chapter 3 has been published in Ocean & Coastal Management [Ghanbarzadeh, M., A. Ben-Hasan, A. Salarpouri, C. Walters, E. Kamrani, and M. S. Ranjbar. 2021. “Coping with Steep Exploitation Rates in an Open Access Fishery.” Ocean & Coastal Management 201 (February): 105499.]. I was the second author with responsibility covering concept formation, analyses, and manuscript structure and composition. C. Walters contributed to analyses and supervised my responsibilities particularly with respect to concept formation and analyses. A version of Chapter 4 has been published in ICES Journal of Marine Science [Ben- Hasan, A., C. Walters, V. Christensen, G. Munro, U. R. Sumaila, and A. Al-Baz. 2020. Age-structured bioeconomic model for strategic interaction: an application to pomfret stock in the Arabian/Persian Gulf. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsaa049]. I was the lead author with responsibility covering all major areas of concept formation, analyses, and manuscript structure and composition. C. Walters was involved in concept formation, all analyses and manuscript edits; V. Christensen, G. Munro, and U. R. Sumaila were involved in concept formation and manuscript edits; and A. Al-Baz contributed to data collection. A version of Chapter 5 is in press [A. Ben-Hasan, Y. Sadovy de Mitcheson, M. A. Cisneros-Mata, É. A. Jimenez, M. Daliri, A. M. Cisneros-Montemayor, R. J. Nair, S.A. Thankappan, C. J. Walters, V. Christensen. In press. China’s fish maw demand and its implications for fisheries in source countries. Marine Policy]. I was the lead author with responsibilities covering all major areas of concept formation, analyses, as well as

manuscript structure and composition. Y. Sadovy de Mitcheson was involved in the early stages of concept formation and contributed to manuscript edits; M. A. Cisneros- Mata, É. A. Jimenez, M. Daliri, A. M. Cisneros-Montemayor, R. J. Nair, S. A. Thankappan contributed to data collection and manuscript edits; and C. Walters and V. Christensen were involved in concept formation. A version of Chapter 6 is under review [Ben-Hasan, A., S. De La Puente, D. Flores, M. C. Melnychuk, E. Tivoli, V. Christensen, W. Cui, C. Walters. In review. Constrained public benefits from global catch share fisheries]. I was the lead author with responsibilities covering all major areas of concept formation as well as manuscript structure and composition, and, to a lesser degree, analyses. S. De La Puente contributed to concept formation, analyses and manuscript edits; D. Flores, M. C. Melnychuk, and E. Tivoli were involved in data collection, analyses and manuscript edits; V. Christensen, W. Cui, and C. Walters contributed to concept formation and manuscript edits.

Table of Contents

Abstract ...... iii

Lay Summary ...... iv

Preface ...... v

Table of Contents ...... vii

List of Tables ...... xii

List of Figures ...... xv

Acknowledgments ...... xix

Dedication ...... xx

1 Introduction ...... 1

1.1 Weak management institutions and limited alternatives to fishing ...... 4

1.2 Internationally shared fish stocks and strategic interaction ...... 5

1.3 Gold rush fisheries ...... 7

1.4 Fisheries as public, valuable resources ...... 8

1.5 The Arabian Gulf: An overview ...... 10

1.5.1 The marine environment: Extreme and polluted ...... 10

1.5.2 Fisheries and fisheries management ...... 11

2 Alleviating growth and recruitment overfishing through simple management changes: insights from an overexploited long-lived fish ...... 13

2.1 Summary ...... 13

2.2 Introduction ...... 14

2.3 Material and methods ...... 17

2.3.1 Study site ...... 17

2.3.2 Fishery data ...... 17

2.3.3 Structure of the model ...... 18

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2.3.4 Equilibrium biomass, yield and reference points ...... 19

2.3.5 Projections of biomass and catch under alternative management options ...... 20

2.4 Results ...... 22

2.4.1 Length and age frequency distributions ...... 22

2.4.2 Reconstructing biomass and recruitment trends ...... 25

2.4.3 Effects of age at 50% vulnerability to capture on equilibrium biomass and yield ...... 27

2.4.4 Alternative options for management ...... 29

2.5 Discussion ...... 32

3 Coping with steep exploitation rates in an open access fishery ...... 36

3.1 Summary ...... 36

3.2 Introduction ...... 37

3.3 Material and methods ...... 40

3.3.1 Data ...... 40

3.3.2 Age-structured model ...... 41

3.3.3 Reconstructing historical changes in the status of the Indian halibut ...... 43

3.4 Results ...... 45

3.4.1 Status of the Indian halibut ...... 45

3.4.2 Effects of various age at 50% vulnerability and exploitation rates on equilibrium biomass and yield ...... 46

3.4.3 Future projections under steep exploitation rates ...... 48

3.5 Discussion ...... 50

3.6 Conclusion ...... 52

4 Age-structured bioeconomic model for strategic interaction: an application to pomfret stock in the Arabian Gulf ...... 53

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4.1 Summary ...... 53

4.2 Introduction ...... 54

4.3 Age-structured bioeconomic model of a shared stock ...... 55

4.3.1 Stock dynamics ...... 55

4.3.2 Effort dynamics ...... 57

4.3.3 Estimation of MSY for the shared stock ...... 60

4.4 Competition, cooperation, and country-independent regimes ...... 60

4.4.1 Competition ...... 60

4.4.2 Cooperation ...... 61

4.4.3 National Fmsy management ...... 61

4.5 Empirical application to silver pomfret in the Gulf ...... 61

4.6 Results ...... 64

4.6.1 Model fitting and prediction ...... 64

4.6.2 Joint MSY obtained under a range of Kuwait-Iran fishing mortalities ...... 66

4.6.3 Trade-offs under competition, cooperation and national Fmsy management ...... 67

4.7 Discussion and conclusion ...... 70

5 China’s fish maw demand and its implications for fisheries in source countries .... 73

5.1 Summary ...... 73

5.2 Introduction ...... 74

5.3 Methods ...... 78

5.3.1 Sources of maw: Species and countries ...... 78

5.3.2 Price and fish body weight ...... 79

5.3.3 Datasets ...... 81

5.4 Results and discussion ...... 82

5.4.1 Relationship between price of flesh and maw ...... 82

5.4.2 Trends in country, species, and pricing ...... 86

5.4.3 Susceptibility of fish and fisheries to increased maw demand ...... 91

5.4.4 Socioeconomic implications ...... 96

5.5 Management considerations ...... 97

5.6 Conclusion ...... 98

6 Constrained public benefits from global catch share fisheries ...... 100

6.1 Summary ...... 100

6.2 Introduction ...... 101

6.3 Results ...... 103

6.3.1 Magnitude of catch share fisheries at national and global levels ...... 103

6.3.2 Allocation and duration of catch shares ...... 106

6.3.3 Resource rent charges in catch share fisheries vs. other extractive industries ...... 107

6.4 Discussion ...... 110

6.5 Materials and methods ...... 114

6.5.1 Data sources ...... 114

6.5.2 Data preparation ...... 116

6.5.3 Review of resource rent charges ...... 119

7 Conclusion ...... 124

Bibliography ...... 129

Appendices ...... 182

Appendix A: Supplementary Material for Chapter 2 ...... 182

Appendix B: Supplementary Material for Chapter 3 ...... 185

Appendix C: Supplementary Material for Chapter 4...... 191

Appendix D: Supplementary Material for Chapter 5...... 196

Appendix E: Supplementary Material for Chapter 6 ...... 203

List of Tables

Table 2.1. Parameter values used in the age-structured model to construct life-history schedules for Malabar blood snapper (Lutjanus malabaricus) in Kuwait. Values of von Bertalanffy growth parameters (퐿∞, 푘, 푡표) are obtained from pooled sexes (Al-Husaini et al. 2000). All parameters are pre-specified (sources listed in the table), except for the age at 50% vulnerability to capture (푎푣) and vulnerability spread parameters (푣푠), which are estimated in the fitting procedure...... 21

Table 2.2. Values of exploitation rates (U) and age at 50% vulnerability to capture (푎푣) used to project biomass and catch in the future (2030 and 2050) under alternative management options. UBAU is the exploitation rate equivalent to the average exploitation rates estimated by the age-structured model over the last twenty years (1997–2017);

Umsy is obtained by calculating the equilibrium incidence function; 푎푣 = 1 year is equivalent to the estimated 푎푣 from fitting the model to the age-frequency data (Table 2.1); and 푎푚 is the age at first maturity (Table 2.1)...... 31

Table 2.3. Short-term changes in catch levels relative to the present (2017) level under two scenarios: business as usual (BAU) and transitioning to a higher minimum size limit (Improved 푎푣; see Table 2.2 for details about scenarios)...... 31

Table 3.1. Parameter values used in the age-structured model to construct life-history schedules for Indian halibut (Psettodes erumei) in the southern Arabian Gulf. The mean age at maturity (푎푚) was converted from the length at 50% maturity (38.2 cm) estimated in (Ghanbarzadeh 2019) using von Bertalanffy growth function. Vulnerability at age and vulnerability spread parameter (푣푠) have been estimated outside the fitting procedure of the assessment model (Appendix B)...... 43

Table 3.2. Parameters and their equations used to calculate equilibrium biomass and yield (Detailed description of the derivation of incidence functions is provided in (Walters & Martell 2004), Box 3.1)...... 44

Table 4.1. Input parameters and estimated parameters from fitting the bioeconomic model to data. Input parameters 퐿∞ (cm), 푘 (year-1), 푀 (year-1) and 푣푎 are pre-specified

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(Al-Husaini et al. 2007). Estimated parameters are obtained from minimizing the objective function (Appendix C; Eq. (C.1))...... 59

Table 4.2. Kuwait maximum sustainable yield (MSY in 103t) and fishing mortality that -1 maintain MSY (Fmsy year ) subject to a range of fishing mortalities (F) by Iran’s fishing fleet...... 66

Table 4.3. Catch (103t) and relative profits obtained by Kuwait and Iran fisheries under national Fmsy management and the minimum shares of the overall Fmsy at which each -1 country would be willing to cooperate (Fmsy = 0.3 year ). Fmsy share = 50:50 is the equal cooperation scenario, and Fmsy share = 40:60 for Kuwait and Iran, respectively, reflect a share based on the estimated average proportion of the overall stock available in each exclusive economic zone...... 70

Table 5.1. Some fish species identified in the maw trade based on molecular and/or fishery information (Sadovy de Mitcheson et al. 2019; Tuuli et al. 2016; Wen et al. 2015)...... 82

Table 5.2. Mean ex-vessel prices of maw and flesh (USD/kg) across fish species. Prices of maw are received by the fishers at, for example, auctions, landing sites or from selling in the black market. Numbers in brackets give the range of maw prices. Information on timeframe and source of prices are provided in the Appendix D (Table D.1)...... 84

Table 5.3. Possible earliest reported year or period of maw trade between source countries and Hong Kong and China. Australia and Kuwait and Mexico’s Gulf corvina start years are based on the reported period at which the prices escalated to exceptional levels (but could have been earlier)...... 91

Table 5.4. Status of fish and fisheries relevant for maw production in source countries. NT and Qld are Northern Territory and Queensland, respectively. Further information on the common fishing gears used to target maw-supplying species are provided in the Appendix D (Table D.3)...... 95

Table 6.1. Mean fisheries yield and value of marine living resources by country, 2000– 2017. Individual countries listed are those with catch share programs in place. Countries

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in bold are among the world’s top 25 countries in terms of landed tonnage (FAO 2018b)...... 104

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List of Figures

Figure 1.1: Equilibrium stock size and yield subjected to a range of exploitation rates (dot represents the maximum sustainable yield). Calculations of stock size and yield are based on incidence functions (Walters & Martell 2004)...... 2

Figure 1.2: Map of the Arabian Gulf ...... 11

Figure 1.3: Annual catches categorized by country in the Arabian Gulf for the period 1950–2014 (source: (Al-Abdulrazzak et al. 2015))...... 12

Figure 2.1. Historical catch for Malabar blood snapper (Lutjanus malabaricus) in Kuwait...... 16

Figure 2.2. Single cohort biomass relative to the maximum value against the mean length (cm) (for the estimation of mean length, see Table 2.1)...... 23

Figure 2.3. Average frequency of length classes over two periods: (A) 1981–1989 (sample size = 45,516 fish) and (B) 1992–1998 (sample size = 3,067). Dark gray lines represent legal minimum size limit (40 cm) and highlighted length classes reflect average lengths at first maturity (61–64 cm)...... 24

Figure 2.4. Average frequency of age classes over 1985–1994 (except for 1991 and 1992; sample size = 3,243 fish)...... 25

Figure 2.5. (A) Reconstructed historical spawning stock biomass and recruitment trends (base case). Effect of uncertainty of the value of compensation ratio on historical (B) biomass and (C) recruitment trends using the base case (high compensation ratio = 48) versus refitting the model with low compensation ratio (10)...... 26

Figure 2.6. Effects of increasing the age at 50% vulnerability (푎푣) on (A) relative biomass (Bo is the biomass at which exploitation rate = 0); and (B) yield...... 28

Figure 2.7. Change in future (A) spawning stock biomass and (B) catch relative to present values (2017) across three alternative management options: (i) business as usual (exploitation rate U = 0.35 year-1 and age at 50% vulnerability 푎푣 = 1 year); (ii) Improved 푎푣 (increasing 푎푣 from 1 year to 푎푣= 5 years); and (iii) Improved 푎푣 and -1 Regulated U, where the regulated U = Umsy = 0.3 year ...... 30

Figure 3.1. Hormozgan province, South of the Arabian Gulf...... 39

Figure 3.2. Annual yield (line) and relative price (bar) of the Indian halibut (Psettodes erumei) in Hormozgan province (HFO 1960-2017; IFO 2000-2018)...... 40

Figure 3.3. Kobe plot with the biomass in year t (Bt) relative to the biomass that would produce the maximum sustainable yield (Bmsy) vs. exploitation rate in year t (Ut) relative to the exploitation rate that would maintain the maximum sustainable yield (Umsy). Numbers represent years, where the current status of the Indian halibut is colored red.

Quadrant “A” denotes a healthy stock (Bt > Bmsy) and an exploitation rate lower than

Umsy (Ut < Umsy); Quadrant “B” denotes an overfished stock (Bt < Bmsy) but no overfishing

(Ut < Umsy); Quadrant “C” denotes both an overfished stock and overfishing (Ut > Umsy); And quadrant “D” denotes overfishing but not an overfished stock...... 45

Figure 3.4. Effects of different age at 50% vulnerability (푎푣) and exploitation rates on: A. depletion, the relative spawning stock biomass (SSB) to unfished biomass (SSB0; the biomass at which exploitation rate = 0); and B. yield...... 47

Figure 3.5. Difference in future (2050) A. yield and B. depletion under different age at 50% vulnerability to capture (푎푣) relative to those obtained from 푎푣 = 4 years (equal to the estimated 푎푣 by the SRA model, see Table 3.1). For 푎푣 = 4, estimated depletion is 40, 30, and 20% under exploitation rates 0.5, 0.6 and 0.7, respectively. For all 푎푣 policies, future yields and depletion are subjected to exploitation rates U = 0.5, 0.6 and 0.7...... 49

Figure 4.1. Northern Arabian Gulf...... 63

Figure 4.2. Catch time-series for Iran and Kuwait over the period 1950-2017...... 64

Figure 4.3. (a) Predicted catch (line) fitted to Kuwait catch data (dots); (b) Predicted catch fitted to Iran catch data; (c) Predicted biomass fitted to the overall biomass estimates of the silver pomfret stock in the northern Arabian Gulf; and (d) Fishing mortality generated by Iran and Kuwait fisheries...... 65

Figure 4.4. Kuwait equilibrium yield subjected to a range of fishing mortalities by Kuwait and Iran fisheries...... 67

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Figure 4.5. Trade-offs in equilibrium (a) catch, (b) relative profit, and (c) biomass for each country under competition, cooperation and national Fmsy management. Competition reflects the average observed catch between 2007–2017, and the average biomass and relative profit estimated from the bioeconomic model over the same -1 period; Cooperation reflects biomass, catch, and relative profits when Fmsy = 0.3 year is either equally shared between countries (Cooperation 50:50) or partitioned based on the estimated average proportion of the overall stock available in each EEZ (40% and

60% for Kuwait and Iran, respectively); and national Fmsy management reflects biomass, catch, and relative profits when each country exerts fishing mortality equivalent to Fmsy...... 69

Figure 5.1. Fish maw for sale at the dried seafood market in Hong Kong. Photographs by Y. Sadovy de Mitcheson...... 78

Figure 5.2. A. The relationship between mean ex-vessel prices of maw (Table 5.1) and the common weight of different species, excluding totoaba. B. The relationship between mean ex-vessel prices of maw and the common weight of species, including totoaba. 85

Figure 5.3. A. Catch of black-spotted croaker off India’s west and east coasts (2007– 2018); B. Catch and ex-vessel prices for black-spotted croaker in Iran (1997–2017); C. Catch of totoaba (1950–2014, the fishing ban started in 1975) and Gulf corvina (1991– 2019) in the Gulf of California, Mexico; D. Uganda’s export volume and value of Nile perch’s maw (2011–2016). E. Catch of the black-spotted croaker in Northern Territory (NT; 2008–2017) and Queensland (Qld; 1993–2018), and the ex-vessel prices in Northern Territory (2008–2017). F. Catch (1979–2018) and ex-vessel prices (2000– 2018) for black-spotted croaker in Kuwait...... 90

Figure 6.1. Proportion of marine capture production under catch share (CS) programs. Data are means of annual proportions between 2000–2017, separated by: fishing country or territory (A), FAO major fishing area (B), and taxonomic group (C)...... 105

Figure 6.2. Occurrence of rent recovery mechanisms (RRM) by major extractive industries in selected countries. ‘Fisheries’ comprise firms operating under catch share programs (Appendix E, Table E.2). The categories “RRM common or ubiquitous” and “RRM limited or absent” indicate the extent to which RRM occurs at the national level or

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by the administrative entities and its prevalence within an industry (see Materials and Methods for details). Asterisks indicate natural resources that are insufficient or underexplored (Appendix E, Table E.3)...... 109

Figure 6.3. Decision tree for determining whether the mechanism corresponds to resource rent recovery (RRM) or cost-recovery (CR). RRMs that are not tied to production generally reflect auctions...... 123

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Acknowledgments

On many levels, I believe that I had an exceptional experience during my PhD program at the Institute for the Oceans and Fisheries, UBC. I was fortunate to have Villy Christensen and Carl Walters supervising, supporting and enlightening my research work. Far from imposing any research project, Villy and Carl encouraged me to grab ahold of pressing research problems that interest me, and then they provided a lens through which I could look at those problems in a novel way. They never turned down any research idea I proposed, but they revitalized it with scientific rigor. I think that this approach did not only deliver the essentials of critical thinking, but also made me wake up every day excited to start my research. I will always be in Villy’s and Carl’s debt.

I am truly grateful to committee members, Rashid Sumaila and Brett van Poorten, for the tremendous support and feedback. Rashid, as you always emphasize, I will “keep pushing” in future endeavors. Brett, thank you for the challenging questions and discussions during the committee meetings and comprehensive exam; they kept me prepared for the most difficult scenario.

I had the privilege of working with so many researchers from diverse fields who enriched my academic experience from inside and outside UBC; to name a few, Adrian Hordyk, Gordon Munro, Michael Melnychuk and Yvonne Sadovy de Mitcheson. None of my dissertation chapters would have been completed without the guidance of my academic supervisors and committee members and the help from collaborators.

The most deeply felt acknowledgment should go to my family: my mother, Masoumah, and my wife, Fatemah—I could not have done it without your love, prayers, and sacrifices. Here is to the beginning of a new journey.

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Dedication

Masoumah and Fatemah, I am happy for you

1 Introduction

Before the implementation of exclusive economic zones (EEZ), fish were considered common-pool resources: all nations had unrestricted access to the fishery resources in areas beyond the three-mile limit (Caddy & Cochrane 2001). The common-pool nature of these resources had largely led to overexploitation and economic waste (Bjørndal & Munro 2012; Ludwig et al. 1993). However, the coming of the EEZ regime in the 1980s—under which coastal states extended their jurisdictions to 200 nautical miles off their coasts—provided the opportunity for coastal states to establish effective property rights to their fishery resources (McRae & Munro 1989). This regime made it feasible for coastal states to effectively pursue two fundamental objectives: sustainable exploitation of fish stocks, which is vital to maintaining income and food security; and maximize resource rent for society—the resource owner (Clark & Munro 2017; Wilen 2000).

To address the first objective, the classic theory of fishing suggests that sustainable fishing is possible by maintaining exploitation rates near the stock size that produces the largest surplus production (Beverton & Holt 1957; Ricker 1958). For example, limiting the total catch near the maximum sustainable yield (MSY) can maximize the long-term average catch and avert overexploitation (Figure 1.1). Indeed, applying this theory has reduced exploitation rates and rebuilt stock sizes to MSY-based levels in fisheries providing around half of the world’s reported catch (Hilborn et al. 2020). A key feature of these outcomes is high intensity of management: conducting routine monitoring of fish abundance, running assessment models to determine appropriate exploitation rates, and setting and enforcing regulations (Hilborn et al. 2020). Under many contexts, however, such requirements are formidable—or even unattainable—rendering exploitation rates hard to control. While these contexts are diverse, some of them can be clustered into three categories: (i) poor management institutions (e.g., limited management capacity) and/or high reliance on fishing for livelihood (Hicks & McClanahan 2012; McClanahan & Mangi 2004; Sadovy 2005); (ii) internationally shared fish stocks, where exploitation rates need to be managed internationally because fishing activities in one EEZ impact the stock availability in other EEZs that share the stock (Bailey et al. 2010; Munro 1979); and (iii) high market

demand for seafood delicacies, where the extremely high prices provoke fisheries into hasty exploitation of marine resources and stimulate illegal fishing and black-market systems (Anderson et al. 2011; Sadovy de Mitcheson et al. 2019). These contexts, though non-exhaustive, pose distinct management challenges and directly affect marine ecosystems, food security and livelihoods of coastal communities.

The second fundamental objective is to manage fishery resources so as to maximize the surplus or resource rent for society (Clark 2006b; Wilen 2000). Though traditional fisheries approaches—e.g., total allowable catch (TAC) and license limitation—have been effective in sustainably managing fishery resources (Melnychuk et al. 2012), they fail to prevent the competitive race to fish, causing overcapacity and dissipation of resource rent (Hilborn 2012; Knapp 1996). That is, resource rent is mainly wasted when fishing enterprises excessively invest in fleet capacity to harvest more fish than their competitors (Clark & Munro 2017). The need to mitigate this social implication laid the foundation for designing and implementing programs that allocate shares of fish quotas to a limited number of fishing enterprises—commonly known as catch shares (Wilen 2000). The basic notion of catch shares is to dampen the competition among fishers by giving them shares of the TAC beforehand (Arnason 2013). Since their adoption in the late 20th century, catch shares have diminished the race to fish and increased the profitability and efficiency of the fishing industry. Consequently, they are currently applied in more than 20 countries to manage more than 600 fish stocks and are increasingly replacing traditional fisheries approaches (Arnason 2013). However, policymakers seem to have overlooked the underlying motivation from applying catch shares, leaving resource rent accumulating within the industry and hence depriving the society of a sustainable stream of revenues (Gunnlaugsson et al. 2018; Jensen 2008).

In this dissertation, I tackle harvesting conditions that complicate the application of the classic theory of fishing—and I underscore the contribution of catch share fisheries to coastal states. That is, I analyze various fisheries contexts whereby sustainability is difficult to achieve by merely controlling exploitation rates (or catch; chapters 2–5). The final chapter (chapter 6) serves to highlight the limited public benefits from catch share fisheries, calling for policy considerations that ensure adequate compensation for the public.

In the following sections, I introduce the individual chapters, which combined make several contributions to fisheries sustainability when exploitation rates are difficult to control and underscores that coastal states may be forgoing income streams arising from fishery resources.

1.1 Weak management institutions and limited alternatives to fishing

Inherent factors such as weak management institutions with limited power to assess, implement and enforce regulations, and high reliance on fishing for income in conjunction with the absence of effective community-based management could render fisheries operating under open access conditions (Cabral et al. 2019; Prince & Hordyk 2019). These conditions can bring about serious consequences; for example, nearshore fish stocks in the developing tropics are substantially overfished, pushing coastal communities with high dependence on marine-based protein into food insecurity (Cabral et al. 2019; Christensen 1998; Cinner 2014; Costello et al. 2012; Jones & Unsworth 2020). Primary fisheries management approaches are adequate in correcting the open access regime. However, they generally entail (i) large management capacity (routine monitoring, stock assessment, strict enforcement) and (ii) require restrictions on the number of fishers, time or fishing grounds (Hilborn & Ovando 2014). In low-income countries, for example, the former is limited, and the latter receives little support since alternatives to fishing are few (Halim et al. 2020; Hicks & McClanahan 2012). Therefore, open access conditions are unlikely to be remedied by primary management approaches, highlighting the need for easily implemented measures within the existent regime, and that consider the high dependency on the ocean for livelihood.

Well-designed size limitation can be an appealing fishery approach in these complex settings (Prince & Hordyk 2019). Given that fishing gears are selective to the size and age of exploited species, setting a minimum size limit near the mean size at maturity helps protect the stock from overexploitation. In addition, such a size limit design would maintain “pretty good yields”—or 80% of MSY—under high exploitation rates (Beverton & Holt 1957; Froese & Binohlan 2000; Hilborn 2010). Although size limitation is common in low-income countries as well as other regions, they are usually either crudely designed or implemented based on non-scientific criteria like market needs, undercutting their fishery and conservational benefits (Colloca et al. 2013; Mahon & Hunte 2001; Najmudeen & Sathiadhas 2008). For example, size restrictions are widespread in the Mediterranean region, yet because they are designed based on market preference for smaller fish, issues like growth and recruitment overfishing seem

prevalent and affecting the abundance and catch of many exploited fish stocks (Colloca et al. 2013). These issues would be effectively mitigated if size restrictions are consistent with the scientific advice, which is expected to result in considerably higher abundance, catch, and income for the Mediterranean fisheries (Colloca et al. 2013). In fact, even in commercial fisheries in developed countries, targeting fish at sizes that optimize the yield per recruit has the potential to increase the profitability of fishing fleets substantially. The cod fishery in the Barents Sea, a large-scale industrial fishery, is forgoing a two-fold increase in resource rent because of the current suboptimal mesh size of the trawlers (Diekert et al. 2010).

In light of the management potential of size limitations when open access conditions cannot be remedied, I show that simple adjustments of existing size limits or implementing them for the first time in an unmanaged fishery may deliver biological and economic benefits such as rebuilding an overfished stock, averting future overexploitation, and maximizing sustainable harvests over the long term (chapters 2 and 3).

1.2 Internationally shared fish stocks and strategic interaction

The EEZ regime encompasses about 90% of global fishery resources (Grønbæk et al. 2018). Yet because many capture fishery resources migrate, many fishery resources move between neighboring EEZs, and/or the nearby High Seas, and are subject to exploitation by different states. This dynamic gives rise to internationally shared fish stocks.

Fisheries harvesting internationally shared fish stocks are among the most challenging to manage (Bjørndal & Munro 2012). A central feature of those fisheries is the strategic interaction between/among countries harvesting the stock: the fishing activity of one coastal state will impact the fishing opportunities available to other coastal states (Grønbæk et al. 2018). The main difficulty in controlling exploitation rates to sustainable levels emerges from the need to establish effective management at an international level rather than only at the national level (Bailey et al. 2010). If international cooperation in managing a shared stock between coastal states could not be reached, there is little incentive for the development of sustainable fisheries, 5

because limiting exploitation rates in one coastal state would likely benefit the unregulated fishery in the neighboring state. For example, the Peruvian government is hesitant to strictly limit the total catch of the southern anchovy stock, which is shared with Chile, because of the expectation that such restriction would benefit the Chilean fishing industry (Schreiber & Halliday 2013). Even when a cooperative agreement exists among countries, time consistency or “resilience” is a fundamental condition. The agreed-on arrangement is likely to be hit by unpredictable shocks related to environmental, economic, or political conditions (Kaitala & Pohjola 1988; Miller & Munro 2004). If the arrangement lacks resilience to such conditions, a previously successful cooperative management would be undermined. For example, the northeast Atlantic mackerel—historically exploited by Norway, European Union and the Faroe Islands under a trilateral agreement—has recently extended its distribution to Iceland. Prior to 2009, the trilateral management was effective in keeping the catch under the biologically recommended reference points. However, management was jeopardized when the three countries and the new competitor, Iceland, could not reach an agreement about quota allocations, resulting in excessive harvests (40% more harvests than the recommended levels; (Jensen et al. 2015)).

Game-theoretic models are effective tools applied to examine the biological and economic consequences under different strategic interactions (e.g., cooperation vs. competition) and thus providing crucial insights in managing internationally shared fish stocks. Age-structured models are particularly useful in analyzing strategic interactions because they consider situations where different coastal states exploit a shared fish stock at different age structures (Gauteplass & Skonhoft 2018). These dynamics are critical to take into account: frequent migrations between nursery and spawning/feeding areas imply that the coastal state with the nursery area cannot sustainably maximize its yields, since MSY policy would typically cause growth overfishing. Although fish stocks commonly undergo seasonal migrations, like North Sea herring (Dickey-Collas et al. 2010); hake stock in southern Africa (Armstrong & Sumaila 2004); and hilsa shad in the northeastern part of the Indian Ocean (Salini et al. 2004), strategic interaction models typically do not take into account these crucial life history aspects (Skonhoft et al. 2012; Sumaila 1999).

In chapter 4, I examine international competition for harvesting a shared fishery resource. I develop and apply an age-structured bioeconomic model to address the competitive fishing for silver pomfret in the Arabian Gulf and evaluate the biological- economic trade-offs under competition, cooperation, and sustainable country- independent management.

1.3 Gold rush fisheries

Gold rush fisheries arise from escalated market demand for marine resources considered as seafood delicacies. In the absence of regulatory measures, fishers typically respond to this demand by engaging in hasty exploitation of these valuable marine resources. Market-driven exploitation triggers two types of harvesting dynamics that often render management responses too slow to start controlling exploitation rates: sequential depletion and serial exploitation (Anderson et al. 2011; Berkes et al. 2006). Sequential depletion describes an exploitation pattern that only expands spatially when target species in initial areas are depleted, whereas serial exploitation implies a geographical expansion of fisheries regardless of the condition of the resource (that is, depletion does not drive expansion) (Anderson et al. 2011; Berkes et al. 2006). Even in nations with developed regulatory measures, management agencies struggle in controlling exploitation rates due to the emergence of black-market systems (Hilborn et al. 2005; Penny et al. 2018; Tailby & Gant 2002). Indeed, the experience with gold rush fisheries is that they severely undermine the sustainability of marine resources; examples include the sturgeon of the Caspian Sea for its caviar (Gault et al. 2008); southern and Atlantic bluefin tuna stocks (Collette et al. 2011); global abalone and sea cucumber populations (Anderson et al. 2011; Courchamp et al. 2006; Hobday et al. 2000); croakers for their swim bladder (Juarez et al. 2016; Sadovy de Mitcheson & Cheung 2003); and sharks and rays for their fins (Dulvy et al. 2014).

As an example of gold rush fisheries, I focus in chapter 5 on fisheries that supply fish maw (or swim bladder) to China for several reasons. First, fish maw is considered one of the most expensive dried seafood delicacies in Hong Kong, the principal hub for the trade of dried seafood delicacies globally. The mean price of maw in Hong Kong (101.5 USD/kg) surpasses that obtained from major dried seafood delicacies like shark

fins (26.4 USD/kg) and all types of dried sea cucumber (73.5 USD/kg) (Sadovy de Mitcheson et al. 2019). Second, multiple signs indicate that maw demand has recently intensified: a 150-fold increase in maw imports compared with 1930s levels; an increase in the number of source countries (currently more than 100 countries supply maw); and an expansion to new maw species (Sadovy de Mitcheson et al. 2019). Third, because maw is being promoted as a beauty product due to its high collagen content and as a substitute for shark fin soup, the trade is expected to further grow in the future (Sadovy de Mitcheson et al. 2019). Finally, despite these trends, maw trade remains poorly studied compared with other major markets like shark fins and sea cucumbers. The unchecked high demand for maw was the primary driver of the demise of two of the largest croakers – Chinese bahaba and totoaba – with serious ecological and socioeconomic impacts (Juarez et al. 2016; Sadovy de Mitcheson & Cheung 2003). Throughout chapter 5, I review and synthesize peer-reviewed studies and local reports, expert opinion, media reports, and species-specific catch trends to examine: (i) the commercial importance of maw to fisheries; (ii) trends of maw demand in source countries, species and pricing; (iii) susceptibility of fish and fisheries to an escalated demand for maw; and (iv) socioeconomic impacts.

1.4 Fisheries as public, valuable resources

Fishery resources within EEZs are the property of the adjacent coastal state (McRae & Munro 1989). Globally, these resources provide most of the global marine capture production and can yield $80 billion in resource rent to the coastal states (Schiller et al. 2018; World Bank 2017). Catch share fisheries generally have exclusive access to the wealth of fishery resources and harvest some of the largest and most commercial fish stocks (Hale & Rude 2017; Tveteras et al. 2011). However, capturing resource rent for the public purse from catch share fisheries has received limited attention from policymakers, unlike other major renewable and non-renewable resources where industries pay a fee for exploiting public resources (Grafton et al. 1998; Smith 2019). This is a serious knowledge gap because of the fairness and distributional issues arising from the lack of a financial return for the society, and because of the high public expenditures on this fishery reform. For example, applying catch share programs entail

high management costs that are entirely or mostly paid by society (Beddington et al. 2007; Mangin et al. 2018), and most importantly, these programs involve giving away free shares of fish quotas, in many cases valued around $1 billion of public assets, to a selected number of fishing enterprises (Hale & Rude 2017). In chapter 6, I therefore ask: how prevalent are global catch share fisheries? Do governments capture the industry’s resource rent for the public purse? And how frequent are resource rent charges in fisheries compared with other major extractive industries including forestry, oil, gas and mining? Given that catch share fisheries are increasingly applied around the globe, addressing these questions have critical implications for national policies.

Combined, this dissertation expands our understanding of management responses to overexploitation under complex fisheries contexts. Hence, it contributes to addressing some of the factors driving the steady increase in the fraction of the world’s overfished stocks (FAO 2020c). Furthermore, this dissertation underscores the overlooked sustainable economic benefits to society from fishery resources, facilitating a re-evaluation of current policies so as to be consistent with the industry’s enhanced profitability as well as the coherent practice of garnering resource rent for the public purse.

Three chapters (chapters 2–4) discuss case studies that are primarily drawn from the Arabian Gulf (also called the Persian Gulf). This region is understudied and suffers numerous present and future challenges that directly affect one of its few renewable resources—marine fish and invertebrates (Ben-Hasan & Christensen 2019). Therefore, below I provide an overview of the oceanographic conditions and fisheries management regimes in the Arabian Gulf to highlight the unique features of this region and the main challenges that face its fishery resources. However, while these case studies occur in a specific region, recommendations resonate across a wide range of fisheries.

1.5 The Arabian Gulf: An overview1

1.5.1 The marine environment: Extreme and polluted

The Gulf lies in Southwest Asia (commonly known as the Arabian Peninsula; Figure 1.2), which is a major region of oil and gas production that contributes significantly to the global emissions of CO2 (Boden et al. 2010). Located in an arid region that is characterized by extremely dry and hot weather, a recent study projected that under a business-as-usual emission scenario (Representative Concentration Pathway 8.5; see

(Moss et al. 2010) for CO2 emission scenarios), people living in the Gulf region will encounter great difficulties in doing basic outdoor activities by the end of the century due to the extremely high temperature (Pal & Eltahir 2015). Likewise, the marine system exhibits severe oceanographic conditions; notably, the world’s highest sea temperature with seasonal maxima between 34–36 °C, along with abnormal seasonal fluctuations (about 20 °C) and hypersaline seawater (> 40 psu) (Hume et al. 2015). In addition to extreme evaporation, the northern part of the Gulf is experiencing a steady rise in salinity levels, owing to the significant water diversions and dam constructions along the Tigris-Euphrates river basins, which are drastically reducing the discharge of freshwater into the Gulf (Shatt-Al-Arab; Figure 1.2). The likely repercussion of such elevated salinity levels is declining trends in the phytoplankton community and fish recruitment (Al-Said et al. 2017; Ben-Hasan et al. 2018a). Other significant anthropogenic stressors include substantial coastal developments, sewage discharge and disposal of brine from desalination plants (Saeed et al. 2012; Sale et al. 2011). Given these extreme physical water properties coupled with various sources of marine pollution, ectotherms inhabiting the Gulf are already under tremendous pressure—and the conditions are expected to be amplified by climate change (Buchanan et al. 2016). (Shirvani et al. 2015) reported an increase of 0.57 °C over the period 1950–2010, raising concerns about the fate of biodiversity in the Gulf.

1 This section is part of a published paper: A. Ben-Hasan and V. Christensen. 2019. “Vulnerability of the marine ecosystem to climate change impacts in the Arabian Gulf—an urgent need for more research.” Global Ecology and Conservation 17:e00556. 10

Figure 1.2: Map of the Arabian Gulf

1.5.2 Fisheries and fisheries management

Marine fish resources in the Gulf are second to oil and natural gas productions in their economic importance, contribute to the national food security and support the livelihood of coastal communities (Van Lavieren et al. 2011). Despite their importance, fisheries are typically operating under an open-access regime with certain input controls: (i) management regulations are based on the life-history of the exploited fish stocks such as fishing gear restrictions, and spatial and temporal closures; (ii) lack output measures (e.g., catch limits); and (iii) absent license limitation programs. Importantly, management measures are weakly enforced, and fisheries are being subsidized both directly (e.g., fuel subsidies) and indirectly through low-cost labor (see, for example, the case of Kuwait shrimp fishery in (Ben-Hasan et al. 2018b)). The total catches from the Gulf fisheries have recently leveled off, after a period of sharp decline (a ~40% reduction of about 300,000 tons between 1997–2004; Figure 1.3). Also, many studies have reported that fish stocks and fisheries in the Gulf suffer from: (i) overexploitation (Al-Baz et al.

2018; Grandcourt et al. 2005; Jabado et al. 2018; Niamaimandi et al. 2015); (ii) overcapitalization (Al-Abdulrazzak et al. 2015); and (iii) extremely high discarded bycatch rates (Chen et al. 2012)—all presenting serious conservation problems. Further, most fish stocks in the Gulf are poorly monitored, hence hampering the ability to assess and design recovery plans. As an illustration, because of the lack of primary long-term fisheries data for many of the stocks, the majority of the published studies have applied simple assessment models, like per-recruit analysis, in evaluating the status of fish stocks (Al-Husaini et al. 2002; Grandcourt et al. 2010); consequently, squandering critical information that could be gained from applying models such as age- structured and statistical catch-at-age.

1000

800

United Arab Emirates t)

3 600 Saudi Arabia

10 Qatar Oman (Musandam only) 400

Kuwait Catch ( Catch Iraq 200 Bahrain Iran 0 1950 1957 1964 1971 1978 1985 1992 1999 2006 2013 Year

Figure 1.3: Annual catches categorized by country in the Arabian Gulf for the period 1950– 2014 (source: (Al-Abdulrazzak et al. 2015)).

2 Alleviating growth and recruitment overfishing through

simple management changes: insights from an

overexploited long-lived fish2

2.1 Summary

Growth and recruitment overfishing can co-occur when a fishery is subjecting small and immature fish in conjunction with adult fish to excessive exploitation rates such that it reduces the spawning biomass to the point where recruitment is significantly impaired. Such conditions are generally evident in open access fisheries and are especially detrimental to long-lived species as they reach maturity at older ages. Here, we investigate the conditions of a long-lived lutjanid, Malabar blood snapper (Lutjanus malabaricus) in Kuwait waters, for which catches declined by about 95% between 1995 and 2009 with negligible recovery afterward; yet exploitation rates are likely high and remain hardly regulated. Using an age-structured model and length and age distributions for over 47,000 snappers, we (i) underscore the impacts of recruitment and growth overfishing on fish biomass and catch; and (ii) demonstrate the efficacy of improving the size limit policy to address both issues. The proportion of small fish (length classes: <50 cm; age classes: 1–4 years) in the catch rose from 40–50% in 1981 to over 70% between 1992–1998 indicating growth overfishing. Due to the selection of immature fish at high exploitation rates, the age-structured model showed that recruitment dropped virtually linearly with decreasing biomass by mid-1990s, implying recruitment overfishing. Future scenarios show that by increasing the current mean age at vulnerability (1 year or 34 cm) to the age at first maturity (5 years or 61 cm), both biomass and catch would increase by at least 300% and 130% relative to status quo. Biomass would rebuild to higher levels if exploitation rates are regulated at

2 A version of this chapter is in press [Ben-Hasan, A., C. Walters, A. Hordyk, V. Christensen, M. Al- Husaini. Accepted. Alleviating growth and recruitment overfishing through simple management changes: insights from an overexploited long-lived fish. Marine and Coastal Fisheries] 13

sustainable levels. Our study highlights the importance of simple management changes in alleviating both types of overfishing, particularly when open access conditions cannot be rapidly remedied due to weak management institutions.

2.2 Introduction

All fish stocks are capable of regeneration; thus, they can be exploited on a sustainable basis. Left unchecked, however, overfishing can plummet fish abundance to extremely low levels, jeopardizing ecosystem processes (Baum & Worm 2009; Myers et al. 2007); bringing about suboptimal economic gains (World Bank 2017); and impacting livelihoods and food security (Cinner 2014; Srinivasan et al. 2010). Overfishing for a single stock is commonly categorized as recruitment overfishing or growth overfishing (Pauly 1994). Recruitment overfishing occurs when high exploitation rates reduce the abundance of mature individuals such that recruitment is impaired, ultimately causing fish collapse (Myers et al. 1994). The infamous collapse of northern cod has vividly demonstrated the consequences of recruitment overfishing (Walters & Maguire 1996), and most developed countries now design their management frameworks so as to guard against this type of overfishing. Notably, many countries now manage their fish stocks based on reference points related to the maximum sustainable yield (MSY) concept designed to minimize the risk of stock depletion (Worm et al. 2009). Growth overfishing, on the other hand, relates to economic losses for fisheries: when fish are harvested before they reach the size where yield per recruit is optimized, the weight and value of the catch may be reduced. The economic cost of growth overfishing can be substantial; for example, due to harvesting juvenile fish of various species, fisheries in India suffer a net economic loss of 18.6 billion US dollars every year (Najmudeen & Sathiadhas 2008).

Although recruitment and growth overfishing are often discussed separately in the literature, they can occur simultaneously. For example, growth overfishing can occur when a fishery is targeting small and often immature fish together with adult fish, which, if exploitation rates are high enough, will result in a decline in spawning biomass to the point where recruitment is significantly impaired (recruitment overfishing). These conditions might well be present in regions with poor fisheries management such as SE

Asia (Olaño et al. 2018; Teh & Sumaila 2007; Zhai & Pauly 2019); Middle East (Ben- Hasan & Christensen 2019; Grandcourt et al. 2005); and the Caribbean (Mahon & Hunte 2001; Newton et al. 2007), where a large proportion of coastal fisheries are unmanaged, or only managed by crude technical measures. For example, per-recruit analyses conducted on 21 exploited fish species in China showed that all these species are caught using extremely small mesh sizes and are suffering excessive fishing mortalities (Zhai & Pauly 2019). Additionally, the sharp increase in India’s catch over the past two decades involves severe growth overfishing but targeting juveniles of long- lived species like groupers also increases the risk of recruitment overfishing (Najmudeen & Sathiadhas 2008). While fast-growing and short-lived species to some degree can withstand juvenile fishing (Winemiller 2005), long-lived and late-maturing species—including groupers, snappers and skates—show dramatic reductions in catch and abundances under such fishing conditions (Grandcourt et al. 2005; Myers & Mertz 1998; Sadovy 2001). Given that open access conditions are unlikely to be quickly remedied in places with inherently poor management institutions, simple management changes that can be applied easily within the existent management system are essential to avoid depleting fish populations and catches.

Here, we underscore the efficacy of changing the age (size) selectivity as a simple management change in mitigating growth and recruitment overfishing of a long- lived fish. Because fishing gears are selective for the size/age of the exploited species, size limits have the potential of protecting recruitment and maximizing yields, while also being easily implementable in societies with weak institutional systems (Prince & Hordyk 2019). As a case study, we use the Malabar blood snapper (Lutjanus malabaricus) in Kuwait waters, for which catches have decreased precipitously (~95%) between 1995–2009 without signs of notable recovery after that period (Figure 2.1). Whereas L. malabaricus is one of the most long-lived in the Lutjanidae family (the reported maximum age is 45 years in Kuwait waters), young fish (age classes 1–4 years) have dominated the catch at least in the 1990s (Al-Husaini et al. 2000) Currently, L. malabaricus is perceived to be overfished (Al-Husaini et al. 2015; Dashti & Ali 2019). Despite these precarious conditions, fishing for this highly sought-after lutjanid continues, as usual, a business with minimal management intervention.

In response to these trends, our contribution is threefold. First, we examine length and age frequency distributions for more than 47,000 fish collected from 1981 to 1998. Second, we fit an age-structured model to the age frequency data so as to highlight the status quo of L. malabaricus. Third, relative to this status quo, we evaluate the degree to which increasing the minimum size limit—corresponding to the age at 50% vulnerability to capture—addresses both types of overfishing and hence improve fish biomass and fishery yields in the future. Because Kuwait fisheries management has been depending on Marine Protected Area (MPA) to protect its nearshore fish stocks for at least three decades without apparent success, we discuss the possibility that growth and recruitment overfishing are undermining its effectiveness.

300

200 Catch(t)

100

1983 1997 1979 1981 1985 1987 1989 1991 1993 1995 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 Year

Figure 2.1. Historical catch for Malabar blood snapper (Lutjanus malabaricus) in Kuwait.

2.3 Material and methods

2.3.1 Study site

While there are multiple commercially harvested lutjanids in Kuwait, L. malabaricus is the most sought-after (Al-Husaini et al. 2015; Dashti & Ali 2019). This species has one of the highest longevities in the Lutjanidae family (45 years maximum observed age; Table 2.1) and its age at first maturity is between 5 to 6 years (Al-Husaini et al. 2015).

L. malabaricus is mainly targeted by a trap fishery. Traps are the predominant fishing gears used in Kuwait, providing 50% and 70% of the total catch and value of landed fish throughout the 1980s (Al-Baz et al. 2018). The contribution of the trap fishery to total catch has declined after the mid-1990s owing to the reduction in the abundance of demersal fish, in particular, L. malabaricus and the orange-spotted grouper (Epinephelus coioides) (Al-Baz et al. 2018; Ben-Hasan et al. 2017). The management agency in Kuwait imposed a single output control: 40-cm minimum size limit, which has never been modified. MPAs have been the major management approach used by the management agency; most notably, closing the off the entire coastline of Kuwait out to three miles from the coast.

2.3.2 Fishery data

We obtained the annual catch statistics of L. malabaricus between 1979–2017 from official fisheries reports (Figure 2.1; (CSB 1979-2017)). In addition, fisheries-dependent length and age frequency data, as well as growth data, have been obtained from extensive sampling programs conducted as part of the Stock Assessment of Fin-Fish Resources project (Al-Husaini et al. 2000). The length frequency data were collected over the periods 1981–1989 and 1992–1998, and the age frequency data were collected between 1985–1994 (except for the years 1991 and 1992). However, important data such as long-term abundance indices or fishing effort are lacking for almost all fish stocks in Kuwait.

2.3.3 Structure of the model

To highlight the stock status of L. malabaricus, we estimate the average biomass, recruitment and exploitation rate using an age-structured model that is forced to fit exactly to the historical catch data (stock reduction analysis approach; (Walters et al. 2006)). The underlying idea of this approach is to run a population assessment model starting at unfished conditions, and remove the observed catch history from the fish stock while accounting for recruitment and natural mortality. The two leading parameters, recruitment compensation ratio and average unfished recruitment (Appendix A, Table A.1), are varied during the fitting procedure so as to provide a reconstruction of historical patterns of the population and fishery impacts that could have credibly given raise to the data (Walters et al. 2006).

In the absence of long-term abundance indices and fishing effort, we used the age frequency distribution in the model fitting procedure to estimate the biomass, recruitment and exploitation trends associated with the catch decline shown in Figure 2.1. To do that, we fitted the observed age frequencies—collected over the period 1985–1994—to the predicted age frequencies from the age-structured model using a binomial log-likelihood criterion, and a penalty for having the predicted exploitation rate in the end year (2017) be much lower than moderate levels (0.2 year-1) given the escalated value of L. malabaricus and the apparently low fishing costs in Kuwait (Ben- Hasan et al. 2018b). Such age composition fitting procedure is carried out by most statistical catch at age models (e.g., (Methot 2000)). We also used growth information provided in (Al-Husaini et al. 2000) as input parameters in the model and to estimate natural mortality (Table 2.1).

We used the following equation to predict numbers over age a and year t

푁푎+1,푡+1 = 푁푎,푡푆(1 − 푣푎푈푡) (1)

−푀 where 푆 is the natural survival rate from natural mortality 푀 (푆 = 푒 ); 푣푎 is the vulnerability at age; and Ut is the exploitation rate in year t. While the shape of the vulnerability function of the traps harvesting L. malabaricus is unknown, the logistic function has generally been found to be suitable to model the vulnerability at age (푣푎) of

traps (Stewart & Ferrell 2003; Xu & Millar 1993), and it is typically applied for the trap fishery targeting demersal species in the Arabian Gulf (Grandcourt et al. 2005, 2009).

Therefore, we apply a logistic function with the parameters age at 50% vulnerability (푎푣) and vulnerability spread (푣푠) estimated in the fitting procedure (Table 2.1).

We estimated spawning stock biomass 퐵푡 in year t as

퐵푡 = ∑ 푁푎,푡푤푎 (2) where 푤푎 refers to the average weight at age, which is modeled as a power function of the average length at a given age. In the age-structured model, exploitation rate U is varied so as to force the model to exactly predict the observed catches 퐶

퐶푡 Ut = (3) 푉퐵푡 where 퐶푡 is the catch in year t; and 푉퐵푡 is the vulnerable biomass in year t (푉퐵푡 =

∑ 푁푎,푡푣푎푤푎). We used the Beverton-Holt model to describe the stock-recruitment relationship

훼퐺푡 푅푡 = (4) 1+훽퐺푡

Where 푅푡 is recruitment in year t; 훼 and 훽 are stock-recruitment parameters obtained using equilibrium incidence functions (Appendix A, Table A.1); 퐺푡 is relative egg production in year t (퐺푡 = ∑ 푁푎,푡푓푎 where 푓푎 is relative fecundity at age). Similar to most fisheries models, our modeling framework assumes constant growth and natural mortality (Table 2.1).

2.3.4 Equilibrium biomass, yield and reference points

We demonstrate the effects of increasing the age at 50% vulnerability (푎푣) on the spawning stock biomass and yield over a range of exploitation rates (U) using the equilibrium incidence functions (Forrest et al. 2010; Walters & Martell 2004). Equilibrium biomass and yield can be obtained for any assumed exploitation rate, which in this study was ranged between 0–1 year-1 at 0.025 increments, by combining survivorship- to-age calculations with age schedules of survivorship, weight, and vulnerability along

with stock-recruitment relationship (that is, incidence functions consider the impact of fishing on recruitment; equations are presented in Appendix A, Table A.1). Reference points, including the maximum sustainable yield (MSY) and exploitation rate that maintain MSY (Umsy), corresponds to the calculated maximum equilibrium yield.

2.3.5 Projections of biomass and catch under alternative management options

Using the age-structured model, we project spawning stock biomass and catch in the future (2030 and 2050) under three alternative management options and compare them with present (2017) biomass and catch levels. First, business as usual (BAU), where future biomass experiences an exploitation rate equivalent to the average exploitation rates exerted over the last twenty years (UBAU; 1997–2017) with 푎푣 value equivalent to the estimated 푎푣 from fitting the model to the age-frequency data. The second management option provides a scenario where the management agency only considers increasing the minimum size limit to 61 cm so as to delay the current 푎푣 to the age at first maturity (푎푣 = 5 years; Table 2.1) while leaving exploitation rates unregulated (U =

UBAU). Finally, the third management option explores catch and biomass responses to an increase in minimum size limit combined with a reduction in UBAU to the sustainable rate that would maintain MSY (Umsy).

(푎푣) and vulnerability spread parameters (푣푠), which are estimated in the fitting procedure.

Parameter Symbol Value Source

Maximum age 푎푚푎푥 45 years (Al-Husaini et al. 2000)

Asymptotic length 퐿∞ 72 cm (Al-Husaini et al. 2000) von Bertalanffy metabolic rate 푘 0.3 year-1 (Al-Husaini et al. 2000)

Scale parameter of growth 푡표 -1.2 years (Al-Husaini et al. curve 2000)

Age at first maturity 푎푚 5–6 years (Al-Husaini et al. 2015)

Age at 50% vulnerability to 푎푣 1 year This study capture Natural mortality 푀 0.1 year-1 This study*

Vulnerability at age 푣푎 푣1= 0.4; 푣2= 0.9; 푣3−45= 1 This study

Vulnerability spread 푣푠 0.4 This study

*Natural mortality was estimated using One-parameter tmax equation from (Then et al. 2015) and specified before the fitting procedure.

2.4 Results

2.4.1 Length and age frequency distributions

The simulated cohort of L. malabaricus shows that the biomass is maximized between mean sizes 63.6–65.8 cm (6–7 years), given the growth and mortality parameters (Figure 2.2; Table 2.1). This basic simulation implies that catch weight (and generally value) would be maximized if the fishery targeted these sizes; alternatively, if the majority of individuals are caught below these sizes, the fishery would cause growth overfishing. The length-frequency distributions obtained from the fishery catches demonstrate that the fishery targeted a wide range of length classes, particularly during the 1981–1989 period (Figure 2.3 (A)); and a considerable proportion of L. malabaricus caught was below the legal minimum size limit, which is set at suboptimal size (as shown in Figure 2.2), and well below the length at first maturity (61–64 cm and between 5–6 years respectively; Table 2.1). The latter trend can be observed across all years, strongly suggesting growth overfishing (Figure 2.3 (A) and (B)). The catch increasingly consisted of smaller fish over the years 1992–1998 (Figure 2.3 (B)). For example, length distributions collected during 1981–1989 consisted of 45% of small and immature fish (length classes <50 cm; Figure 2.3 (A)); this percentage increased sharply to 70% during 1992–1998 (Figure 2.3 (B)). The large difference in sample sizes between the periods 1981–1989 (45,516 fish) and 1991–1998 (3,067 fish) was mainly due to the substantial decline in the amount of fish landed (Al-Husaini et al. 2000). Age-frequency distributions comprised age classes 0–30 years (Figure 2.4); however, frequency histograms show that, from the first year of data onwards, fish of age classes 1–4 years consisted of 72% of the catch samples.

100%

80%

60%

40%

20% Biomass/series Biomass/series maximum

71.7 34.2 44.0 51.3 56.6 60.6 63.6 65.8 67.4 68.6 69.5 70.1 70.6 71.0 71.2 71.4 71.6 71.8 Mean length (cm)

Figure 2.2. Single cohort biomass relative to the maximum value against the mean length (cm) (for the estimation of mean length, see Table 2.1).

40%

32%

24%

16% Frequency

0% 0-1 4-5 8-9 12-13 16-17 20-21 24-25 28-30 Age classes (year)

Figure 2.4. Average frequency of age classes over 1985–1994 (except for 1991 and 1992; sample size = 3,243 fish).

2.4.2 Reconstructing biomass and recruitment trends

By fitting the age frequency distributions (Appendix A, Figure A.1), our model indicated a sharp reduction in recruitment virtually linearly with the reduction in biomass by mid- 1990s to 2007 (Figure 2.5 (A)). This decline is likely driven by selecting immature fish observed in length/age histograms in conjunction with high exploitation rates (average U = 0.43 year-1 over the same period) on all size classes, which has resulted in recruitment overfishing from the reduced spawning potential of the adult stock. Although recruitment has roughly doubled since 2007 and stabilized afterward, biomass remains at a very low level, which might be attributed to the high retention of age classes 1–4 evident in Figure 2.4. Reconstructions of biomass and recruitment trends depend on the uncertainty around leading parameter recruitment compensation ratio: the relative improvement in juvenile survival rate when the stock is greatly reduced (Walters et al. 2006; Walters & Martell 2004). The estimated compensation ratio is quite uncertain, with the best fit value of 48 (the base case), implying a high improvement in juvenile survival rate under severe depletion of abundance. When the model is refit with a much

lower compensation ratio (10), similar estimates are obtained of historical biomass and recruitment trends (Figure 2.5 (B) and (C)). This implies that the finding that severe recruitment overfishing occurred by the mid-1990s is robust to the uncertainty around the compensation ratio. Further details about the implication of this uncertainty on future predictions of stock size and catch recovery under improved management are presented in Appendix A (Figure A.2) and examined in the discussion section.

2.4.3 Effects of age at 50% vulnerability to capture on equilibrium biomass and yield

The effects of different age at 50% vulnerability (푎푣) show that low 푎푣 requires low exploitation rates to sustain the biomass and yield, whereas high 푎푣 result in higher biomass and maximized yield, which are obtained under more intense exploitation rates

(Figure 2.6 (A) and (B)). For example, equilibrium results at 푎푣 = 1 year—the current 푎푣 of L. malabaricus (Table 2.1)—demonstrates that a moderate exploitation rate would cause a considerable decline in the biomass (Figure 2.6 (A)). Specifically, an exploitation rate of 0.2 year-1 removes ~82% of the biomass relative to the average unfished biomass level (the level at which U = 0). Higher exploitation rates (U > 0.35

-1 year ) would collapse the biomass under 푎푣 = 1 year. It is critical to note that recruitment overfishing is more likely to occur (occurs at a lower exploitation rate) when the age at maturity is high relative to the age at 50% vulnerability to capture; for L. malabaricus examined here, the age at maturity is around 5 or 6 years (Table 2.1) whereas the current 푎푣 = 1 year. In addition, targeting L. malabaricus with 푎푣 = 1 year is expected to cause growth overfishing since the overall equilibrium yield (or yield per recruit) is substantially reduced from the maximum shown in Figure 2.6 (B).

On the contrary, delaying 푎푣 to the age at maturity and maintaining an -1 exploitation rate of 0.3 year would leave five times the biomass level obtained under 푎푣

= 1 (Figure 2.6 (A)). Further, MSY for the improved 푎푣 policies (MSY = 85 and 87 tons for 푎푣 = 5 and 6 years, respectively) are much larger and occurs at higher exploitation -1 rates (Umsy = 0.3 and 0.4 year for 푎푣 = 5 and 6 years) compared with targeting fish when they are too young (i.e., the current 푎푣) (Figure 2.6 (B)).

Figure 2.6. Effects of increasing the age at 50% vulnerability (푎푣) on (A) relative biomass

(Bo is the biomass at which exploitation rate = 0); and (B) yield.

2.4.4 Alternative options for management

We compared present (2017) catch and biomass levels with those projected under business as usual (BAU), only increasing 푎푣 while leaving exploitation rates unregulated

(“Improved 푎푣”) and increasing 푎푣 combined with a reduction in U to the sustainable rate that would maintain MSY (Table 2.2; Figure 2.7 (A) and (B)). Long-term projection (2050) shows that in the unregulated scenario (BAU), catch and biomass would decline by 20–27% relative to present values. In contrast, by only avoiding the capture of fish younger than age 5 years (or <61 cm), substantial conservation and fishery benefits are -1 predicted even if future exploitation rates stay unregulated (UBAU = 0.35 year ). For example, biomass would increase by as much as 500%, and catch would improve by about 250%. Such substantial improvements are predicted because there has been a severe growth overfishing. Thus, by alleviating growth overfishing, much of the predicted improvements are biomass increase due to “refilling” the population age distribution even before recruitment increase occurs. When projecting biomass and catch with a regulated U and improved 푎푣 policy, biomass would rebuild to the highest levels but future catch levels would be similar to that obtained under “Improved 푎푣” policy (Figure 2.7 (A) and (B)).

On the short-term (2020–2023), BAU scenario ensures much higher harvests than the improved 푎푣 policy (Table 2.3). Relative to the present level, we estimated that there would be a drastic (up to 58%) decline in catch associated with implementing the higher size limit policy. This decline is expected because the fishery would forgo yields of capturing fish in age classes 1–4 years. However, if current fishing trends continue (i.e., BAU scenario), the catch would gradually decrease since exploitation rates are excessive and fish are captured far below the optimum size (Figure 2.2 and Figure 2.6A). On the other hand, if the management decided to move away from BAU to the improved 푎푣 policy, the short-term loss in catch would be more than offset by the long- term gains while also allowing the stock to recover to higher levels (Table 2.3; Figure 2.7 (A)).

Figure 2.7. Change in future (A) spawning stock biomass and (B) catch relative to present values (2017) across three alternative management options: (i) business as usual

-1 (exploitation rate U = 0.35 year and age at 50% vulnerability 푎푣 = 1 year); (ii) Improved

푎푣 (increasing 푎푣 from 1 year to 푎푣= 5 years); and (iii) Improved 푎푣 and Regulated U, -1 where the regulated U = Umsy = 0.3 year .

Table 2.2. Values of exploitation rates (U) and age at 50% vulnerability to capture (푎푣) used to project biomass and catch in the future (2030 and 2050) under alternative management options.

UBAU is the exploitation rate equivalent to the average exploitation rates estimated by the age- structured model over the last twenty years (1997–2017); Umsy is obtained by calculating the equilibrium incidence function; 푎푣 = 1 year is equivalent to the estimated 푎푣 from fitting the model to the age-frequency data (Table 2.1); and 푎푚 is the age at first maturity (Table 2.1).

Age at 50% vulnerability to Options for management Exploitation rate capture

-1 Business as usual (BAU) UBAU = 0.35 year 푎푣 = 1 year

Improved 풂풗 U = UBAU 푎푣 = 푎푚 = 5 years Improved 풂 and 풗 -1 U = Umsy = 0.3 year 푎푣 = 5 years regulated U

Table 2.3. Short-term changes in catch levels relative to the present (2017) level under two scenarios: business as usual (BAU) and transitioning to a higher minimum size limit (Improved

푎푣; see Table 2.2 for details about scenarios).

Year BAU Improved 풂풗 2020 10% -58% 2021 8% -47% 2022 6% -28% 2023 5% -5% 2024 3% 16% 2025 1% 31%

2.5 Discussion

Our study showed that the marked decline in catch and abundance of L. malabaricus is driven by growth and recruitment overfishing. L. malabaricus is considered to be the most threatened commercially exploited fish in Kuwait with recruitment overfishing suspected to be a major factor (Al-Husaini et al. 2000, 2015). The pattern of higher retention of small fish as fewer old fish left in the stock, as shown in the length- frequency (1992–1998) and age frequency distributions of L. malabaricus, has been observed in the collapse of several fish species: the North Pacific and Atlantic herrings, cod stocks in the North Atlantic, and Lake Erie’s walleye (Myers & Mertz 1998). Alarmingly, L. malabaricus is still one of the most commercial fish species in Kuwait: due to the high market demand and low catch, the average ex-vessel price for this species is escalating with the price more than tripled in 2017 compared with 2000 (CSB 1979-2017). Such high prices exacerbate the current condition of the stock since it is economical to generate higher exploitation rates to harvest L. malabaricus even at very low fish biomass (i.e., harvest removal becomes independent of the stock size). Thus, there is an urgent need to reverse the current condition of L. malabaricus.

Since the 1980s, the management regime in Kuwait has been heavily relying on MPAs as the primary approach to fisheries management and conservation by closing the entire nearshore area to fishing (3 miles off the coast of Kuwait, including islands), in addition to several other area closures. The basic notion for this management approach is that the MPA would increase the biomass of fish and enhance yields for nearby fisheries through density-dependent spillover, even if adjacent areas remain heavily fished (i.e., near open access conditions). Although this notion has an empirical basis (Goñi et al. 2010; Kerwath et al. 2013), the present study implies that growth and recruitment overfishing are possible symptoms for the apparent ineffectiveness of the current MPA in Kuwait. Specifically, the continued decline in L. malabaricus catch over the past two decades despite area protection is indicative of high exploitation rates on younger and immature fish, which are preventing the accumulation of older fish in the “protected” length/age range. However, this would only occur if the size of the MPA is too small relative to L. malabaricus movement, rendering part of the stock regularly

vulnerable to the fishery (Botsford et al. 2003; Hilborn 2018). Although movement patterns of L. malabaricus are poorly understood in Kuwait, this species, like other fish species, may undergo seasonal migrations implying frequent exposure to the fishery (Aschenbrenner & Ferreira 2015; Longhurst & Pauly 1987; Pauly 1985). In general, such seasonal migrations are rarely considered when designing MPAs (White 2015). In sum, questions remain regarding the effectiveness of these old MPAs in Kuwait given that no studies investigated their benefits. Nevertheless, we recommend that if Kuwait’s management agency decides to establish the proposed twenty MPAs (Van Lavieren & Klaus 2013), research effort should be initiated to determine factors potentially undermining the benefits of the designated ones.

Instead, our study suggests that increasing the minimum size/age limit from the current 40 cm to at least 61 cm (푎푣 = 5 years) would alleviate growth and recruitment overfishing. By carrying out this critical management change, our model predicted a substantial positive impact on the future of L. malabaricus abundance and its fishery. Similar considerable gains from merely improving the size limit policy have been estimated for relatively short-lived species like reef fishes in the Caribbean, and various species in China’s coastal waters (Bozec et al. 2016; Zhai & Pauly 2019). Indeed, there are two main effects of imposing a minimum size restriction at or larger than the age/size at maturity: (i) protecting recruitment (i.e., allowing fish to spawn before harvest); and (ii) maximizing yield per recruit, which typically is highest when fishing starts at sizes near the optimum length (that is, the length that maximizes the cohort biomass; (Froese & Binohlan 2000)). These two effects safeguard against both recruitment (effect (i)) and growth (effect (ii)) overfishing. While fishing at Umsy provided the highest long-term biomass levels, the high minimum size limit is a key buffer against uncertainty around exploitation rates (Figure 2.6 (A) and (B)), which are often unknown and difficult to control in data-poor fisheries (Hicks & McClanahan 2012).

However, there are two important considerations to account for when increasing the minimum size limits. First, illegal or high discard mortality of smaller fish, especially those caught by deeper water traps, would undercut recruitment protection and lower the fishing rate that maximizes yield, regardless of what the yield per recruit analyses

suggest (Coggins et al. 2007). Since size limits are difficult to enforce, we recommend that 푎푣 is managed by using a coarser trap mesh size for better “squeezability” or escape gaps, which can aid in reducing the likelihood of retaining small fish. For example, experiments conducted to evaluate the retention of small fish in trap fisheries reported that traps of larger mesh sizes have significantly reduced the retention of small and immature fish compared with conventional traps (Robichaud et al. 1999; Sary et al. 1997). In addition, the outcomes of other field studies demonstrated the benefits of installing escape gaps in curbing the retention of undersized fish and crustaceans, like Australia’s yellowfin bream by 54% and the blue swimmer crabs by 64%, without the need to increase the mesh size (Broadhurst et al. 2019). Second, like many other management measures aimed at rebuilding overexploited fish stocks (e.g., lowered catch limits), transitioning to higher minimum size limits means that over the short-term, fishers would forgo a high fraction of their yield relative to the present level. Still, while increasing the current size limits would have transient impacts on the fishery, long-term fishery and conservation gains would be considerably greater than continuing the current harvesting trends. In short, the management agency in Kuwait may need to consider communicating the considerable long-term benefits of transitioning to a higher minimum size limit, increase mesh size or install escape gaps and implement regulations through co-management.

It is important to note that although improving 푎푣 policy is expected to rebuild the biomass and improve the catch of L. malabaricus, it is difficult to accurately determine when these gains are realized in the future. Projections of recruitment, stock size and catch recovery times are contingent on the recruitment compensation ratio. Different assumptions about recruitment compensation ratio have resulted in a substantial uncertainty about how rapidly the stock will recover under improved management. The lower recruitment compensation ratio predicts much slower recruitment, stock size, and catch recovery in the future under an improved 푎푣 policy than the scenario in which recruitment compensation ratio is high (Appendix A, Figure A.2). Due to this limitation, future recovery times of biomass, recruitment and catch levels need to be viewed with caution.

Finally, robust fisheries management approaches in countries with limited resources or inherently weak management institutions are difficult to adopt; yet easily implemented changes to size limits could be critical in alleviating growth and recruitment overfishing. Our findings provide management insights for (i) protecting fish that are vulnerable to a fishery at young ages but mature later in their life-spans as these species are extremely sensitive to high exploitation rates; and (ii) countries where larger and long-lived species such as snappers and groupers are of high commercial value both locally and in international markets but managed as open access resources (Al- Husaini et al. 2015; Cawthorn & Mariani 2017; Grandcourt et al. 2005).

3 Coping with steep exploitation rates in an open access

fishery3

3.1 Summary

Fisheries in developing nations commonly operate in an open access regime, largely due to fundamental factors like poor fisheries institutions and the infeasibility of imposing regulations like limiting the number of fishers or harvest due to high dependency on income and food from fisheries. Such factors are evident in Hormozgan, the largest fishing province in Iran, rendering many fish stocks in precarious conditions. The catch of the Indian halibut in Hormozgan has increased sixfold over the past 17 years, along with increasing socioeconomic importance. Yet ever-growing exploitation rates and lack of regulations are threatening both the sustainability of the stock and the livelihoods of fishers that depend on it. In this paper, we examine the status of the Indian halibut using an age-structured model and demonstrate the effectiveness of managing the age selectivity (age at 50% vulnerability to capture (푎푣)) on future biomass and yields under excessive exploitation rates. Our results indicated that the annual exploitation rates had been steadily climbing since 2001, with the stock currently experiences overfishing. Future projections showed that, under intensified exploitation

-1 rates (e.g., 0.7 year ), increasing 푣푎 from the present 4 years (mean size 39.7 cm) to 5 years (43.3 cm) is expected to maintain biomass at a sustainable level (61% of an unfished level) and enhance yields by 84%. Higher 푎푣 policies (> 5 years) would result in the least depletion but forgo substantial yields. In addition to providing practical management insights for harvesting the Indian halibut, this study underscores the potential of size restrictions in sustaining open access fishery resources subjected to steep exploitation rates.

3 A version of this chapter appears as Ghanbarzadeh, M., A. Ben-Hasan, A. Salarpouri, C. Walters, E. Kamrani, and M. S. Ranjbar. 2021. “Coping with Steep Exploitation Rates in an Open Access Fishery.” Ocean & Coastal Management 201 (February): 105499. 36

3.2 Introduction

Marine fisheries deliver 17% of the global protein intake and support the livelihoods of hundreds of millions of people globally (Darling 2014; FAO 2018b). Because fish stocks are renewable resources, these benefits can be sustained over long periods of time. However, overfishing undermines sustainability by overly decreasing fish abundances, leading to reduced catches and amplified risk of fish collapse (Coleman & Williams 2002; Sadovy de Mitcheson et al. 2013; Scheffer et al. 2005). These consequences are particularly detrimental to the coastal communities in developing nations, where food security and employment hinge on the ocean and its services (Cinner 2014; Srinivasan et al. 2010). Implementing robust fisheries approaches—like total allowable catch (TAC) and license limitations—have shown to halt overfishing and increase fish biomass (Hilborn et al. 2020; Sissenwine et al. 2014). However, fundamental issues such as unstable political regime, strong reliance on fishing for food and income, and/or poor management institutions in many developing nations render fisheries operating under open access (Cabral et al. 2019). How to ensure the sustainability of fish stocks under such conditions is therefore a critical question, particularly for artisanal fisheries.

Output controls like size restrictions can be instrumental in protecting fish biomass and enhancing fishery yields. Since fishing gears are selective with respect to the size and age of the exploited species, setting a minimum size limit near the size at maturity have shown to safeguard against overexploitation and sustain fishery yields without controlling exploitation rates (Beverton & Holt 1957; Froese & Binohlan 2000; Prince & Hordyk 2019). In addition, applying size-based measures facilitate overcoming some of the fundamental management challenges faced in the developing nations. For example, size restrictions do not limit the number of fishers nor restrict the timing and area of fishing, which is crucial for artisanal fishers that lack alternative income options (Hicks & McClanahan 2012). Such measures can also be implemented easily, which is particularly needed in situations where the capacity for fisheries management is limited (Froese 2004).

Among the countries bordering the Arabian Gulf, the coastal provinces of Iran comprise, by far, the largest coastal communities with about 3.5 million people residing

within 100 km of the ocean (Bayani 2016). Collectively, these provinces over the past decade harvested an average of 250,000 tons of fish, or ⁓50% of the total catch taken by all countries surrounding the Arabian Gulf (Al-Abdulrazzak et al. 2015). Yet management intervention in Iran is minimal: coastal fisheries are predominantly unregulated—or regulated with only crude technical measures—and exert high exploitation rates (Ben-Hasan et al. 2020; Niamaimandi et al. 2015). This is evident in Hormozgan province, encompassing at least 22,000 artisanal fishers that harvest 60% of Iran’s total catch (Figure 3.1; (Daliri et al. 2016)). Because of several factors, notably the buildup of the size of the artisanal fishing fleet and the unregulated exploitation rates, many commercially important fish stocks in Hormozgan are in precarious shape (Daliri et al. 2016). The catch of the Indian halibut, Psettodes erumei, rose sixfold between 2000–2017 along with increasing socioeconomic importance (Figure 3.2). While several fisheries catch the Indian halibut (e.g., trawling and stake net fisheries), most of the production is obtained from the gillnet fishery (HFO 2001-2017). However, despite the growing importance of this species, the fishery remains open access in Hormozgan, threatening the stock’s sustainability.

Given that exploitation rates for harvesting the Indian halibut are expected to increase in the future and that open access conditions are unlikely to be corrected, we provide practical management insights that could help sustain stock biomass and fishery yields. We focus on managing the age selectivity of the fishery by examining the effects of different age at 50% vulnerability to capture on future (2050) biomass and yields under alternative steep exploitation rates.

Figure 3.1. Hormozgan province, South of the Arabian Gulf.

1000 5

800 4

600 3

Price/series mean Catch (t) 400 2

200 1

0 0

1962 1986 2010 1960 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2012 2014 2016 2018 Year

Figure 3.2. Annual yield (line) and relative price (bar) of the Indian halibut (Psettodes erumei) in Hormozgan province (HFO 1960-2017; IFO 2000-2018).

3.3 Material and methods

3.3.1 Data

Annual catch statistics of the Indian halibut in Hormozgan province (the southern part of the Arabian Gulf) for the 1960–2017 period were obtained from Hormozgan Fisheries Office (Figure 3.2). Annual catch per unit effort data (CPUE) were recorded over a much shorter period, from 2001–2016 (except 2006). Age data for the Indian halibut— collected mainly from the gillnet fishery from October 2016–November 2017 (Ghanbarzadeh 2019)—were used to calculate von Bertalanffy growth parameters and vulnerability at age parameters (Table 3.1; Appendix B).

3.3.2 Age-structured model

The average annual biomass and exploitation rate of the Indian halibut were reconstructed using the stock reduction analysis approach (SRA; (Walters et al. 2006)). The basic notion of SRA is to run a population assessment model starting at unfished conditions, remove the observed catch history from the fish stock while accounting for recruitment and natural mortality.

Equation 1 was used to predict numbers over age a and year t

푁푎+1,푡+1 = 푁푎,푡푆(1 − 푣푎푈푡) (1)

−푀 where S is the natural survival rate from natural mortality M (푆 = 푒 ); 푣푎 is the vulnerability at age; and Ut is the exploitation rate in year t. Vulnerability at age was modelled using a logistic equation, with the age at 50% vulnerability to capture (푎푣) and vulnerability spread (푣푠) parameters estimated by predicting and fitting the age composition data (Appendix B, Figure B.1). Alternatively, we fit the age composition data by assuming that 푣푎 is normally distributed by fish age—a two-parameter model with mean age at capture and standard deviation—which resulted in a strongly dome- shaped 푣푎 (Appendix B, Figs. S1 and S2). Because the logistic model for 푣푎 fitted the age composition data better than the normal distribution model, as determined by the Akaike criterion, the stock status and policy options presented here are based on the logistic 푣푎 (Table 3.1; Appendix B). However, it bears noting that stock status trajectories resulted from dome-shaped 푣푎 are consistent with the trajectories obtained from the logistic 푣푎 (Appendix B, Figure B.3).

The spawning stock biomass (SSB) in year t was reconstructed as

푆푆퐵푡 = ∑ 푁푎,푡푤푎 (2) where 푤푎 is the average weight at age, which is estimated using a power function. In the SRA model, exploitation rate (U) is varied so as to force the model to exactly predict the observed catches (C) given model predicted vulnerable biomass (B)

퐶푡 푈푡 = (3) 퐵푡

where B in year t is calculated as 퐵 = ∑ 푁푎,푡푣푎푤푎. SRA uses the Beverton-Holt model to describe the stock-recruitment relationship (Walters et al. 2006)

훼ℰ푡 푅1,푡 = (4) 1+훽ℰ푡 where 훼 and 훽 are stock-recruitment parameters; ℰ푡 is relative egg production in year t

(ℰ푡 = ∑ 푁푎,푡푓푎) where 푓푎 is relative fecundity at age. Like most other stock assessment models, our model assumes constant growth and natural mortality (Table 3.1).

SRA was fitted to CPUE using the Z statistic approach (Walters & Ludwig 1994). The CPUE observations are assumed to be proportional to vulnerable biomass (i.e., 퐶푃푈퐸 = 푞퐵; where 푞 is the catchability coefficient). The Z statistic in year t for each CPUE 퐶푃푈퐸 observation was calculated as 푍푡 = 푙푛( ). The arithmetic mean of “Z statistic” (푍̅) is a 퐵푡 conditional (on the biomass prediction) maximum likelihood estimate of 푙푛(푞). The CPUE observations were fitted to the model by minimizing the following objective function (Walters & Ludwig 1994) (Appendix B, Figure B.4)

2 푆푆 = 푙푛 ∑푡(푍푡 − 푍̅) (5)

Table 3.1. Parameter values used in the age-structured model to construct life-history schedules for Indian halibut (Psettodes erumei) in the southern Arabian Gulf. The mean age at maturity

(푎푚) was converted from the length at 50% maturity (38.2 cm) estimated in (Ghanbarzadeh 2019) using von Bertalanffy growth function. Vulnerability at age and vulnerability spread parameter (푣푠) have been estimated outside the fitting procedure of the assessment model (Appendix B).

Parameter Symbol Value (units)

Age at 50% maturity 푎푚 4 years

Asymptotic length 퐿∞ 51.4 cm

Maximum age 푎푚푎푥 10 years

Metabolic rate 푘 0.4 year-1

Natural mortality 푀 0.5* year-1

푣1= 0; 푣2= 0.02; 푣3= 0.1; 푣4 = 0.5**; 푣5 = Vulnerability at age 푣 푎 0.9; 푣6−10 = 1

Vulnerability spread 푣푠 0.5 year

*Using One-parameter tmax equation from (Then et al. 2015).

**푣4 corresponds to the age at 50% vulnerability to capture (푎푣).

3.3.3 Reconstructing historical changes in the status of the Indian halibut

A Kobe plot was used to assess the status of the Indian halibut stock by tracking the biomass and exploitation rates over time relative to Bmsy and Umsy. These reference points are obtained by applying the equilibrium incidence functions: calculating biomass and yield for any assumed exploitation rate by combining survivorship-to-age calculations with age schedules of survivorship, weight, and vulnerability along with stock-recruitment relationship (Table 3.2). These functions are also used to evaluate the

effects of different age at 50% vulnerability (푎푣) on the equilibrium biomass and yield over assumed exploitation rates ranging from 0–0.9 year-1 at 0.025 increments.

Parameter Description Equation 퐶푅 휶 Stock-recruitment parameter 훼 = 퐸푃푅0 Compensation ratio “leading 퐶푅 is estimated from the fitting 푪푹 parameter” procedure Average unfished egg per 퐸푃푅0 = ∑ 푓푎푙푥푎 푬푷푹ퟎ recruit

풇풂 Relative fecundity at age 푓푎 = 푤푎 − 푤푚* −푀 풍풙풂 Survivorship at age a 푙푥1=1, 푙푥푎 = 푙푥푎−1푒

Average fished egg per 푈 퐸푃푅퐹 = ∑ 푓푎푙푥푎 푬푷푹푼 recruit

푼 −푀−푈푒푞푣푎 풍풙풂 Fished survivorship at age a 푙푥1=1, 푙푥푎 = 푙푥푎−1푒 ** 퐶푅 − 1 휷 Stock-recruitment parameter 훽 = 푅0(퐸푃푅0)

Average unfished 푅0 is estimated from the fitting

푹ퟎ recruitment “leading procedure parameter”

푎퐸푃푅푈 − 1 푹풆풒 Equilibrium recruitment 푅푒푞 = 훽(퐸푃푅푈)

푈 푺푺푩풆풒 Equilibrium biomass 푆푆퐵푒푞 = 푅푒푞 ∑ 푙푥푎 푤푎

풀풆풒 Equilibrium yield 푌푒푞 = 푈푒푞푉퐵푒푞

*푤푚 is the weight at maturity.

-1 **푈푒푞 is the equilibrium exploitation rate (ranged from 0–0.9 year at an increment of

0.025); and 푣푎 is the vulnerability at age.

3.4 Results

3.4.1 Status of the Indian halibut

The Indian halibut remained lightly fished (i.e., Ut < Umsy) for 40 years: from the start of the fishery in 1960 to 2000 (Figure 3.3 quadrant “A”). However, the ever-increasing -1 -1 exploitation rates—from about half of the Umsy (Umsy = 0.4 year ) in 2002 to 0.5 year in 2017—has triggered overfishing, which is reducing the biomass sharply with the trajectory approaching quadrant C (that is, the stock would be considered overfished and experiences overfishing; Figure 3.3). In sum, the Kobe plot underscores a steady increase in the exploitation rates since 2000; left uncontrolled, such exploitation rates would potentially result in an overfished stock.

2017 C D

msy 1

t U

2010 1990 1980 B A 2000 1970 0 1960 0 1 2 3 Bt/Bmsy Figure 3.3. Kobe plot with the biomass in year t (Bt) relative to the biomass that would produce the maximum sustainable yield (Bmsy) vs. exploitation rate in year t (Ut) relative to the exploitation rate that would maintain the maximum sustainable yield (Umsy). Numbers represent years, where the current status of the Indian halibut is colored red. Quadrant “A” denotes a healthy stock (Bt > Bmsy) and an exploitation rate lower than Umsy (Ut < Umsy);

Quadrant “B” denotes an overfished stock (Bt < Bmsy) but no overfishing (Ut < Umsy); Quadrant

“C” denotes both an overfished stock and overfishing (Ut > Umsy); And quadrant “D” denotes overfishing but not an overfished stock.

3.4.2 Effects of various age at 50% vulnerability and exploitation rates on equilibrium biomass and yield

Increasing age at 50% vulnerability to capture (푎푣) demonstrate two important effects: (i) harvesting the Indian halibut at low 푎푣 (푎푣 = 0–3 years) generates the highest maximum sustainable yield (MSY) but requires low exploitation rates to sustain both yields and spawning stock biomass; and (ii) high 푎푣 results in less depletion (i.e., higher biomass) but lower MSY (Figure 3.4 A and B). Importantly, while MSY in the second trend is less than that obtained at low 푎푣, it corresponds to much higher exploitation rates. For instance, equilibrium results at 푎푣 = 2 years show that a modest exploitation rate would cause a large decline in the biomass: an exploitation rate of 0.2 year-1 removes ~60% of the biomass relative to the average unfished biomass level (SSB0, the level at which U = 0) (Figure 3.4 B). Higher exploitation rates (~0.3–0.5 year-1) would collapse the biomass if the Indian halibut is captured at 푎푣 = 0–3. However, delaying 푎푣 to at least the age at -1 maturity (푎푚 = 4 years; Table 3.1), while maintaining an exploitation rate of 0.25 year would only remove 26% of the biomass relative to the unfished level (Figure 3.4 A and

B). Additionally, harvesting the Indian halibut at 푎푣 = 4–7 would allow the fishery to obtain yields at a wide range of exploitation rates.

3.4.3 Future projections under steep exploitation rates

Given the open access conditions of the gillnet fishery and that exploitation rates are expected to increase in the future, we compare future (2050) yields and biomass depletion obtained from high age at 50% vulnerability to capture (푎푣 = 5, 6 and 7 years) with those obtained from the estimated 푎푣 (4 years; Table 3.1). These scenarios are subjected to the exploitation rate equal to that estimated by the model in the last year of -1 data (U2017 = 0.5 year ) and intensified exploitation rates (U = 0.6 and 0.7).

Relative to the future yields obtained under 푎푣 = 4, fishery gains are highest at 푎푣 = 5 (28% and 84% increase when U = 0.6 and 0.7, respectively), though there is no difference between the two 푎푣 policies when yields are subjected to exploitation rates equal to 0.5 (Figure 3.5 A). If exploitation rates are between 0.5–0.6, increasing 푎푣 from

4 to 6 and 7 would result in either no difference (푎푣 = 6 and U = 0.6) or loss in future yields. Under the most intense exploitation rate (0.7), however, positive yields are expected to be obtained across all values of 푎푣 (Figure 3.5 A).

At 푎푣 = 4, depletion is estimated to be 40, 30 and 20% under exploitation rates 0.5, 0.6 and 0.7, respectively. Compared to these depletion estimates, the highest difference (i.e., the least depletion) is expected to occur at 푎푣 = 7 and decreases at lower 푎푣 values (Figure 3.5 B). For example, if the fishery is generating U = 0.7 but catching fish at 푎푣 = 7, the biomass would remain at 90% of unfished levels by 2050, whereas generating the same exploitation rates under 푎푣 = 4 would deplete the stock to

20% of the unfished level (i.e., 350% difference in depletion between the two 푎푣 policies; Figure 3.5 B). This also explains why 푎푣 = 7 has a negative difference in yield

(and marginal gains under U = 0.7) compared with 푎푣 = 4: implementing a size restriction consistent with 푎푣 = 7 renders a significant fraction of the biomass invulnerable to the fishery, hence losing considerable potential yields (Figure 3.5 A and

B). Overall, the difference in future yields is highest at 푎푣 = 5 and while higher 푎푣 policies (6 and 7) results in much less depletion, 푎푣 = 5 would still leave a sustainable level of relative biomass—more than 50% of unfished biomass—under the steepest exploitation rate.

20% under exploitation rates 0.5, 0.6 and 0.7, respectively. For all 푎푣 policies, future yields and depletion are subjected to exploitation rates U = 0.5, 0.6 and 0.7.

3.5 Discussion

Owing to fundamental factors like weak management institutions and limited alternatives to fishing, the open access conditions in Iran are unlikely to be remedied by traditional fisheries approaches (e.g., TAC, limited-access programs), underscoring the need for other approaches that sustain fishery resources and fishers’ income. In this study, we showed that high age at 50% vulnerability to capture (푎푣) can effectively cope with the expected climb in future exploitation rates to harvest the Indian halibut stock; thus, substantially mitigating depletion and enhancing yields in the future. Importantly, these outcomes are attainable by applying a minimum size restriction—an easily implemented measure within the current management regime.

Our analysis indicated that, among multiple 푎푣 policies, 푎푣 = 5 years (or mean length 43 cm) generated the highest yields in the future compared with the estimated 푎푣 = 4, while also maintaining relative biomass at sustainable levels under steep exploitation rates. Given that this 푎푣 policy is slightly higher than the mean age/size at maturity (length at 50% maturity = 38.2 cm; (Ghanbarzadeh 2019)), it approximately corresponds to the “optimal” size at which a cohort biomass is maximized, as indicated by a correlative study examining 265 species, resulting in largest long term yields

(Froese & Binohlan 2000). Higher 푎푣 policies (> 5 years) would result in the least depletion but would forgo substantial yields, especially under 푎푣 = 7, impacting the local fishing community that rely on this resource for food and income.

More broadly, our findings are consistent with empirical and simulation-based research (Bozec et al. 2016; Froese & Binohlan 2000; Prince & Hordyk 2019; Zhai & Pauly 2019). For example, using size-based simulation models with analyses incorporating a wide range of life-history strategies, (Prince & Hordyk 2019) demonstrated that even under excessive exploitation rates, size restrictions that are designed to retain sizes above the size at maturity results in high levels of biomass and yields. Their simulations also suggested the opposite: if the targeted size is below the size at maturity and coupled with high exploitation rates, extinction risks of the exploited species would be amplified. By implementing higher minimum size restrictions, studies have estimated substantial conservation and fishery gains for various species and

fisheries; for example, the coastal fisheries in China (Zhai & Pauly 2019) and the coral reef fishery in Kenya (Hicks & McClanahan 2012).

We primarily focused on managing age/size selectivity to sustain fish biomass and yields under open access conditions. However, other approaches have been proposed for managing fisheries under similar conditions—notably, marine protected areas (Cabral et al. 2019). Such recommendation stems from the notion that safeguarding fish stocks inside an MPA would build up fish biomass and may ultimately enhance nearby fisheries’ yields through density-dependent spillover (Gell & Roberts 2003). Studies have reported conservation (i.e., increase in biomass) and fishery (i.e., increase in catches) benefits of implementing MPAs (Goñi et al. 2010; Kerwath et al. 2013); however, factors like enforcement and adequate knowledge about species movement are essential for an effective MPA (Hilborn 2018). For instance, strict enforcement of MPAs is crucial to deter illegal fishing that would undermine the potential benefits. Further, careful consideration of species movement in designing MPAs ensures that the species are not regularly exposed to the fishery (i.e., MPA size is too small) and that spillover effects are extended to the fished areas (unlikely to occur if MPA is set too large relative to the species movement) (Hilborn et al. 2004b). Generally, size restrictions are much more appealing as a management approach in Iran than MPAs because fisheries agencies are resource-limited—hence enforcement would be minimal—and information about species movement is lacking for the vast majority of the exploited species, including the Indian halibut.

However, there are critical considerations to bear in mind. The SRA reconstruction of biomass and exploitation rates is most influenced by the value of the leading parameter compensation ratio (퐶푅) (Walters et al. 2006). We could not determine a reliable value of 퐶푅 because the model would fit the data equally well whether the stock is unproductive and large (i.e., low 퐶푅) or productive and small (i.e., high 퐶푅). Nevertheless, when the effects of a comprehensive range of 퐶푅 values on the trajectory of Kobe plot are evaluated by forcing the model to fit alternative 퐶푅 values, they do not qualitatively change the basic conclusion of the pattern of the trajectory; namely, that exploitation rates are steadily increasing over time (Appendix B, Figure B.5). The

trajectory analyzed here is the most conservative (see “base case” in Appendix B, Figure B.5). Moreover, because important parameters like vulnerability, growth, and natural mortality are time-variant, our trajectories are a simplification of the stock dynamics. An additional consideration is that our analyses do not take into account the impacts of discarding of undersized or younger/smaller fish on biomass and yield. Assuming that the survival rate of the Indian halibut from discards is low, capturing young and immature individuals under steep exploitation rates can potentially cause large declines in fish biomass and yields (Coggins et al. 2007). In fact, even if the Indian halibut has a high survival rate per capture, repeated recapture of individuals, which is difficult to avoid when exploitation rates are high, would still undermine the benefits of high age/size at 50% vulnerability to capture. Yet this issue can be adequately addressed if managers considered gear regulations such as minimum mesh sizes that directly control the age/size at 50% vulnerability to capture (Prince & Hordyk 2019).

3.6 Conclusion

The Indian halibut stock is gaining socioeconomic importance in Hormozgan province; yet it is currently unmanaged and exploitation rates are expected to escalate in the future further. We showed that implementing a minimum size restriction equivalent to the age/size at 50% vulnerability to capture (푎푣 = 5 years) would buffer against overexploitation and improve fishery yields. Higher 푎푣 policies (> 5 years) would result in the least depletion of the biomass of the Indian halibut; however, this suggests that a large fraction of the biomass would be invulnerable to the fishery and hence future catch would be reduced. Because size restrictions do not limit the number of fishers, catch, or fishing areas, this study provides practical management insights for sustaining artisanal fisheries with a strong dependency on the ocean for food and income.

4 Age-structured bioeconomic model for strategic

interaction: an application to pomfret stock in the Arabian

Gulf4

4.1 Summary

When fish stocks migrate across multiple EEZs, they compel managers to examine management at both national and international levels. A strategic interaction emerges when the fishing activity of one country impacts fishing opportunities available for other countries sharing the stock. Left unaddressed, strategic interaction could lead to overexploitation and suboptimal payoffs. Here we develop and apply a bioeconomic model to address the competitive fishing for silver pomfret in the Arabian Gulf—a highly commercial fish stock shared between Kuwait and Iran—and evaluate biological– economic trade-offs under competition, cooperation, and country-independent management using maximum sustainable yield (MSY) and fishing rate that maintain

MSY (Fmsy) policies. When cooperation involves an equal share of the overall Fmsy or a share based on the proportion of the stock available in each EEZ, countries are expected to cooperate given the substantially higher catch, biomass, and relative profits compared to other management regimes. However, other than these two arrangements, countries would favor different regimes. Besides providing policy insights to improve the perilous status of silver pomfret, our approach could be useful in exploring alternative fishing arrangements to sustainably harvest a transboundary fish stock while maximizing average yields.

4 A version of this chapter appears as Ben-Hasan, A., C. Walters, V. Christensen, G. Munro, U. R. Sumaila, and A. Al-Baz. 2020. Age-Structured Bioeconomic Model for Strategic Interaction: An Application to Pomfret Stock in the Arabian Gulf. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsaa049 53

4.2 Introduction

The world’s Exclusive Economic Zones (EEZ), established under the UN Convention on the Law of the Sea, comprise about 90% of marine fishery resources worldwide (Grønbæk et al. 2018). However, because of their mobility, many fishery resources migrate from the EEZ of one country to the neighboring EEZs, where they are subject to exploitation by other countries—giving rise to internationally shared fish stocks (Bailey et al. 2010). A fundamental feature of those fisheries is the strategic interaction, which arises when the fishing activity of a given country impacts the fishing opportunity of other countries (Grønbæk et al. 2018). Under these circumstances, resource managers are forced to examine fisheries management not only at the national level but also at the international level. Failing to recognize strategic interactions might jeopardize the sustainability of shared fishery resources and reduce economic returns. In North America, for example, Pacific salmon is shared between Canada and U.S. with currently stable transboundary cooperation, where it is illegal to catch salmon in the high seas according to the Canada-U.S. Pacific Salmon Treaty (Miller & Munro 2004). This joint ownership, however, took place after the Canadian fishing fleet deliberately overexploited the stock when the U.S. Senate blocked the Treaty (Munro 2008). Because such competitive fishing had impacted salmon catches in Washington and Oregon States, policymakers in the U.S. were compelled to ratify the Treaty—that is, revert to cooperation.

One powerful approach to address strategic interaction in fisheries is game theory, which entails building a mathematical tool to evaluate compromises stemming from cooperative and non-cooperative regimes (Sumaila 1999). In this paper, we develop a bioeconomic model to evaluate the biological–economic trade-offs under alternative management regimes for a transboundary fish stock harvested by two coastal states. The biological component of this model is an age-structured approach with the same underlying mathematical structure as those routinely applied in fisheries stock assessment (e.g., stock reduction analysis in (Walters et al. 2006); and integrated analysis approaches like Stock Synthesis (Methot 2000)), but uncommonly used in strategic interaction situations (as pointed out by (Skonhoft et al. 2012; Sumaila 1999)).

Age-structured approaches have the advantage of considering the vulnerability of different age groups to exploitation (Gauteplass & Skonhoft 2018). This feature is critical to consider in strategic interaction situations: as fish stocks cross multiple EEZs migrating between nursery and spawning/feeding areas, fishing in any EEZ can impact the stocks’ age structure and overall recruitment rates. Such seasonal movements have been reported for numerous shared fish stocks—for example, North Sea herring (Dickey-Collas et al. 2010); Arcto-Norwegian cod (Sumaila 1995); hake stock in southern Africa (Armstrong & Sumaila 2004); and hilsa shad in the northeastern part of the Indian Ocean (Salini et al. 2004). Additionally, by utilising equilibrium incidence functions (Forrest et al. 2008, 2010; Walters & Martell 2004), age-structured models conveniently estimate important fisheries reference points; notably, maximum sustainable yield (MSY) reference points, which shape management frameworks in many countries globally and recommended for those countries that lack them (Code of Conduct for Responsible Fisheries; (FAO 1995); (Worm et al. 2009)).

Here, we apply the bioeconomic model outlined below to analyse the strategic interaction between Kuwait and Iran fisheries arising from harvesting the silver pomfret stock. To examine the bioeconomic returns of moving beyond the current competitive fishing, we compare the status quo with alternative management regimes constructed around MSY and the fishing rate that maintain MSY (Fmsy) using the incidence function approach. We start by elucidating the modeling framework and describe three regimes: competition, cooperation, and country-independent management. Then we provide an overview of Iran-Kuwait fisheries targeting silver pomfret. While the model described here is applied to the silver pomfret stock in the Gulf, it can be extended to other transboundary fisheries to evaluate the possibilities of sustainably harvesting a shared fish stock while maximizing average yields.

4.3 Age-structured bioeconomic model of a shared stock

4.3.1 Stock dynamics

We applied the stock reduction analysis approach (SRA) to simulate the abundance at age in each year for the pomfret stock (Walters et al. 2006). We used the following equation to predict numbers at age a and year t 55

(−푀−푣푎푥퐹푡푥−푣푎푦퐹푡푦) 푁푎+1,푡+1 = 푁푎,푡푒 (1)

where 푁푎,푡 is the numbers at age a in year t; 푀 is the natural mortality estimated using the one-parameter tmax model ((Then et al. 2015); Table 4.1); 푣푎푥 and 푣푎푦 are the vulnerabilities at age a in each country (x and y); and 퐹푡푥 and 퐹푡푦 are the fishing mortalities in year t. Given that both countries have nursery and spawning areas within their EEZs (Al-Husaini et al. 2007), 푣푎 values indicate that the fish stock spawns widely and mixes between the two nations at all fish ages (Table 4.1). We estimated the overall vulnerable biomass in year t as

퐵푡 = ∑ 푝푡푁푎,푡푣푎푥 푤푎 + ∑(1 − 푝푡)푁푎,푡푣푎푦푤푎 (2)

where 푝푡 is the proportion of the stock available in one country in year t, so that (1-푝푡) is what is available in the other country, and it is varied annually so as to fit the by-country catch data, i.e., given the country’s effort and catchability, 푝푡 is varied to give the biomass that predicts observed catch (Eq. 5, 6 and 7 below; Appendix C); and 푤푎 refers to the average weight at age a, which is modelled as a power function of the average length 퐿 at age a

푏 푤푎 = 푔퐿푎 (3) where 푔 is a scaling constant; and 푏 is the allometric growth parameter. We modelled the average length at age a (퐿푎) using the von Bertalanffy growth curve

(−푘(푎−푡0)) 퐿푎 = 퐿∞(1 − 푒 ) (4)

where 퐿∞ is the asymptotic length; 푘 is the rate at which the individual fish approaches its 퐿∞; and 푡0 is the scale parameter of the growth curve. We modelled the fishing mortality over time as

퐹푡 = 푝푡푞푡퐸푡 (5)

where 푞푡 is the catchability coefficient in year t; and 퐸푡 is the fishing effort in year t. To model 퐹푡 in the other country, 푝푡 in Eq. (5) is substituted with (1 − 푝푡).

To predict the annual catch in each country, we applied the Baranov equation (Hilborn & Walters 1992) because it accounts for the simultaneous removal of fish from each age by fishing mortalities and natural mortality M

퐵 퐹 푡 푡푥 (−퐹푡푥+퐹푡푦) 퐶푡푥 = (1 − 푒 ) (6) (퐹푡푥+퐹푡푦)

퐵푡퐹푡 푦 (−퐹푡푦+퐹푡푥) 퐶푡푦 = (1 − 푒 ) (7) (퐹푡푦+퐹푡푥)

To describe the stock-recruitment relationship, SRA assumes a Beverton-Holt relationship of the form (Walters et al. 2006)

훼퐺푡 푅1,푡 = (8) 1+훽퐺푡 where 훼 and 훽 are the stock-recruitment parameters; 퐺푡 is the relative egg production in year t 퐺푡 = ∑ 푁푎,푡푓푎 where 푓푎 is the relative fecundity at age (Appendix C, Table C.1).

4.3.2 Effort dynamics

Under heavily fished conditions, the dynamic catchability coefficient is given by (Walters & Martell 2004)

푞0 푞푡 = (9) (푟푐 + (1−푟푐) ∗ 퐵푡/퐵0) where 푞0 represents the initial catchability; 푟푐 is a range contraction parameter, which relates area occupied to stock depletion (퐵푡/퐵0), where 푟푐 << 1 implies severe range contraction; and 퐵0 is the average unfished biomass, calculated as 퐵0 = 푅0 ∑ 푙푥푎푣푎푤푎, where 푅0 is the average unfished recruitment estimated from fitting the model to data, and 푙푥푎 is the survivorship at age a (Appendix C, Table C.1). We modelled the realized fishing effort—the proportion of the relative fleet size used over time—using a logistic model, which assumes that effort will depend on each individual’s perception of their net benefit from fishing (van Poorten et al. 2016; Walters & Martell 2004)

푓푡 퐸푡 = 퐼 (10) (− 푡 ) 1+푒 휎퐸

where 푓푡 is the relative fleet size in year t; 퐼푡 is the income in year t ( 퐼푡 = 퐵푡푞푡푃푙 − 푐푒; where 푃푙 and 푐푒 are the price per amount landing and the cost per unit effort, respectively); and 휎퐸 is the effort response standard deviation parameter. Relative fleet size (푓) dynamics are modelled by assuming that fleet size will grow until costs balance revenues; this growth may result from the reinvestment of profit or new vessel entries

푝푖푛푣푆푡 푓푡+1 = 푓푡 − 푑푟푓푡 + ( ) (11) 푐푣 where 푑푟 is the vessel depreciation rate; 푝푖푛푣 is the probability of investing the profits in the vessels; 푐푣 is the cost per vessel; and 푆푡 is the relative total profit in year t

푆푡 = 푃푙퐶푡 − 푐푒퐸푡 (12) Since the units of price per amount landing are arbitrary, we scaled them so that price

(푃푙) = 1, and then estimated cost per effort relative to this base price so as to give relative profit (푆푡).

Table 4.1. Input parameters and estimated parameters from fitting the bioeconomic model to

-1 -1 data. Input parameters 퐿∞ (cm), 푘 (year ), 푀 (year ) and 푣푎 are pre-specified (Al-Husaini et al. 2007). Estimated parameters are obtained from minimizing the objective function (Appendix C; Eq. (C.1)).

Values for Values for Biological parameters Description Estimated? Kuwait Iran

푳∞ Asymptotic length 33 33 No 풌 Metabolic rate 1 1 No 푴 Natural mortality 0.5 0.5 No

Vulnerability at 푣1= 0.5; 푣2= 푣1= 0.5; 푣2= 풗풂 No age a 0.8; 푣3−10= 1 0.8; 푣3−10= 1

풓풄 Range contraction 0.1 0.1 Yes Economic/Technological parameters Effort response 흈푬 0.3 0.3 Yes standard deviation

풒ퟎ Initial catchability 0.16 0.17 Yes Cost per unit 풄풆 0.24 0.22 Yes effort Probability of

풑풊풏풗 investing profits in 0.19 0.23 Yes vessel

풅풓 Depreciation rate 0.05 0.05 Yes

풄풗 Cost per vessel 1.94 2.99 Yes

4.3.3 Estimation of MSY for the shared stock

For the SRA model used in this analysis, the equilibrium population size and yield can be easily obtained for any assumed fishing mortality rate(s) by calculating incidence functions: combining survivorship-to-age calculations with age schedules of survivorship, weight, and vulnerability along with stock-recruitment relationship ((Walters & Martell 2004); (Forrest et al. 2010); Appendix C). For any such equilibrium, the average yield obtained by each country can be calculated from that country’s contribution to the overall fishing mortality. Therefore, we obtained the maximum sustainable yield (MSY) and fishing rate that maintain MSY (Fmsy) by subjecting the overall stock to a range of a combination of fishing mortalities by the two countries (Appendix C, Tables C.2 and C.3). It should be noted that the population dynamics parameters only predict MSY (and Fmsy) for the fish stock as a whole.

4.4 Competition, cooperation, and country-independent regimes

We explore the trade-offs in equilibrium biomass, catch, and relative profits for each country under three fishing regimes: competition, cooperation and country-independent management. Given that the concept of MSY forms the core of many fisheries management frameworks around the world (Worm et al. 2009), and that other countries are encouraged to develop their MSY policies (Code of Conduct for Responsible Fisheries; (FAO 1995)), we develop the cooperative and country-independent (referred to as “national Fmsy management” below) regimes around this concept. The competition game, on the other hand, reflects the current status of the fisheries.

4.4.1 Competition

Absent any formal, legally binding joint management between the countries to control the fishing effort or catch of the harvested stock, we let the status quo represents competition. The competitive scenario therefore represents the average observed pomfret catch over the last 10 years of data (2007–2017) and the average estimated F, biomass and relative profits over the same period.

4.4.2 Cooperation

Under joint management, we assume that the objective of both countries is to harvest the stock at the level that maximises yields without compromising future yields (i.e.,

MSY), and reduces the current fishing mortality to Fmsy. The cooperative regime assumes that the overall Fmsy estimated for the stock is partitioned between the two nations. Specifically, we assume that both nations have agreed not to allow fishing mortality F to exceed the overall Fmsy. We simulate two scenarios under cooperation: (i) the two countries have agreed to set their by-nation F either equally; or (ii) on the basis of the estimated average proportion of the overall stock available in each EEZ over the past decade. We generate the cooperative regime by simulating the equilibrium biomass, catch and relative profits at overall F=Fmsy.

4.4.3 National Fmsy management

National Fmsy management describes a regime where each country attempts to harvest the stock as a whole at Fmsy; that is, each country regulates its fishery so as to harvests the shared stock at an independent Fmsy within its EEZ. Such a regime could occur when nations are doubtful about sharing scientific information that can be used against them by their contenders, or due to a charged political atmosphere that hinders communications (McKelvey et al. 2003; Munro et al. 2004). Under national Fmsy management, equilibrium biomass, catch and relative profits are simulated at overall F = 2Fmsy (i.e., each country would be exerting Fmsy).

4.5 Empirical application to silver pomfret in the Gulf

Even though countries bordering the Arabian Gulf (“Gulf” hereafter) share several valuable fish stocks—for example, Kuwait and Iran share at least three commercial fish stocks—strategic interaction remains unexplored in this region. Further, a number of these shared stocks are in a dire situation, including the silver pomfret examined here (Ben-Hasan & Christensen 2019). Silver pomfret (Pampus argenteus; “pomfret” hereafter) is perhaps the most valuable finfish species in the Gulf, with a total landed value of more than $6 million for an average production of 200 t ((CSB 2012-2017); (Al-Husaini et al. 2015)). In the

northern part of the Gulf, pomfret’s distribution is largely restricted to three countries (Figure 4.1); however, its main nursery and spawning areas occur in Kuwait and Iran (Al-Husaini et al. 2015). A joint scientific project conducted between Kuwait and Khuzestan province in Iran indicated that the highest densities of pomfret were found in Kuwait Bay (Al-Husaini et al. 2007), but the general migratory pattern of this stock remains poorly understood. After the Iran-Iraq war (i.e., from 1988 to 2000), the amount of pomfret caught in Iran soared as a result of a growing fishing fleet ((Al-Husaini 2003); Figure 4.2). This fleet expansion likely reduced the amount of catch in both countries by 75–90% between 1995–2017, without signs of notable recovery, particularly in Kuwait. To protect the pomfret stock, Kuwait’s fisheries agency implements several management measures: (i) permanent area closures of some of the main fishing grounds due to their overlap with pomfret’s nursery and spawning areas (e.g., Bubiyan Island and Kuwait Bay; Figure 4.1); (ii) limited-access program; and (iii) size restriction. In Iran, however, it is unclear what type of management measures are imposed to regulate the pomfret fishery, but it is likely that fishing effort is unregulated (Al-Husaini 2003). In the early 1990s, both nations have agreed to impose a 45-days fish ban to protect the stock during its spawning season; aside from this period, harvesting pomfret is allowed year-round without control over the amount of catch in both countries. Given that Kuwait is currently closing the main spawning and nursery areas to fishing, especially Kuwait Bay where the highest densities of pomfret were observed (Al- Husaini et al. 2007), it is suspected that these closed areas are supplying a large fraction of fish recruitments in the region. Absent long-term abundance indices, we fit the model to two data sets: (i) catch trends for Kuwait and Iran shown in Figure 4.2; and (ii) pomfret’s biomass estimates obtained from catch at age data collected for the combined stock in the northern Gulf (Al-Husaini et al. 2007). The pomfret catch data in Kuwait was obtained from (Al- Abdulrazzak 2013), and the most recent data (2015–2017) was obtained from the official catch statistics. The catch data in Iran was obtained from (Moniri et al. 2013) for the period before Iran’s official catch were available (1950–1996) but scaled down to match the official catch over the period for which the official catch is available (1997–

2017). Technological/economic parameters shown in Table 4.1 have been estimated by fitting the model to the by-country catch and the biomass estimates. After fitting the model, we projected the biomass, catch and relative profits under two regimes: cooperation and national (country-independent) management.

Figure 4.1. Northern Arabian Gulf.

Iran

4 Kuwait

t) 3 3

2 Catch (10

0 1950 1956 1962 1968 1974 1980 1986 1992 1998 2004 2010 2016 Year

Figure 4.2. Catch time-series for Iran and Kuwait over the period 1950-2017.

4.6 Results

4.6.1 Model fitting and prediction

The bioeconomic model provided good fits to by-country catch and biomass estimates for the combined stock (Figure 4.3a–c). While the model reasonably fitted Kuwait catch data by varying the proportion of stock vulnerable in Kuwait over time (푝푡) (Appendix C; Figure C.1), it could not explain the sudden pulse in Iran catch data between 1994– 1996, then sudden drop in 1999–2002 even though the estimation procedure placed a high proportion of the stock in Iran during that period—perhaps suggesting a misreporting issue (Figure 4.3a and b). In addition, the model predicted severe stock depletion from 1990, coinciding with the rapid expansion of Iran’s fishing fleet over the period 1989–2000 ((Al-Husaini 2003); Figure 4.3c). This, in turn, has drastically reduced the amount of catch in both countries. During the last five years, fishing mortalities in

Kuwait continued to fall, consistent with a declining fleet size due to increased effort controls and low pomfret catch (CSB 2012-2017). On the other hand, fishing mortalities in Iran are starting to increase again, keeping the stock size at low levels (Figure 4.3d).

4.6.2 Joint MSY obtained under a range of Kuwait-Iran fishing mortalities

Figure 4.4 and Table 4.2 demonstrate how a range of fishing mortalities F exerted by

Iran’s fishery affects F (and Fmsy) and equilibrium yields obtained by the fishery in Kuwait. The same results would occur from examining the opposite situation because we are using the same population dynamics parameters and vulnerabilities at age to calculate yields (i.e., pomfret stock is modelled as a single stock with fish mix between Kuwait and Iran at all ages; numerical results are presented in Appendix C, Tables C.2 -1 and C3). The largest yield is obtained at Fmsy = 0.3 year when only Kuwait’s fishery is harvesting the stock; however, as the fishery in Iran starts harvesting the stock, yields begin to fall (Figure 4.4 and Table 4.2). In short, these equilibrium calculations show that each country’s best F choice depends on the other country’s F choice.

Table 4.2. Kuwait maximum sustainable yield (MSY in 103t) and fishing mortality that maintain

-1 MSY (Fmsy year ) subject to a range of fishing mortalities (F) by Iran’s fishing fleet.

Iran F Kuwait MSY Kuwait Fmsy 0.0 2.3 0.3 0.1 1.7 0.3 0.2 1.1 0.3 0.3 0.8 0.2 0.4 0.5 0.2 0.5 0.3 0.2 0.6 0.1 0.1 0.7 0.03 0.1

Figure 4.4. Kuwait equilibrium yield subjected to a range of fishing mortalities by Kuwait and Iran fisheries.

4.6.3 Trade-offs under competition, cooperation and national Fmsy management

If both countries decide to cooperate in managing the stock with an equal share of Fmsy (i.e., Cooperation 50:50), biomass, catch and relative profits would be substantially improved as a result of reducing the overall competitive F to Fmsy (Figure 4.5a–c). In contrast, choosing to harvest the stock under country-independent Fmsy policy would reduce the bioeconomic gains relative to the cooperative regime under 50:50 Fmsy share; for example, the overall biomass would be lower by about 60%, while catch and profits are expected to drop by ⁓35–44%. In summary, the cooperative regime of equal

Fmsy shares outperforms the status quo and national Fmsy management in all measures and for both countries.

Harvesting the stock under a cooperative regime with an Fmsy share consistent with the estimated average proportion of the stock available in each EEZ (i.e., Cooperation 40:60 for Kuwait and Iran, respectively) would result in much higher catch

and profits for Iran, compared to the other scenarios. Kuwait would still generate higher catch and profits under this unequal cooperation than the competition and national Fmsy management (Figure 4.5a and b). When catch and relative profits are simulated over a wide range of Fmsy shares under cooperation (provided in Appendix C, Tables C.4 and C.5), we found that 50:50 and 40:60 (whether it is Kuwait:Iran or vice versa) are the minimum shares at which each country would choose to cooperate rather than independently harvest the stock at Fmsy (Table 4.3). In other words, compared with the national Fmsy management, this finding suggests that countries would only seek cooperation if the overall Fmsy is either shared equally (50:50) or set at 40:60.

-1 catch, and relative profits when Fmsy = 0.3 year is either equally shared between countries (Cooperation 50:50) or partitioned based on the estimated average proportion of the overall stock available in each EEZ (40% and 60% for Kuwait and Iran, respectively); and national Fmsy management reflects biomass, catch, and relative profits when each country exerts fishing mortality equivalent to Fmsy.

Table 4.3. Catch (103t) and relative profits obtained by Kuwait and Iran fisheries under national

Fmsy management and the minimum shares of the overall Fmsy at which each country would be

-1 willing to cooperate (Fmsy = 0.3 year ). Fmsy share = 50:50 is the equal cooperation scenario, and

Fmsy share = 40:60 for Kuwait and Iran, respectively, reflect a share based on the estimated average proportion of the overall stock available in each exclusive economic zone.

National Fmsy Cooperation 40:60 Cooperation 40:60 Cooperation

management (Kuwait:Iran) (Iran:Kuwait) 50:50 Catch Profit Catch Profit Catch Profit Catch Profit Kuwait 0.7 0.5 0.9 0.6 1.4 1.0 1.2 0.8 Iran 0.7 0.6 1.4 1.0 0.9 0.7 1.2 0.9 Total 1.4 1.1 2.3 1.6 2.3 1.7 2.4 1.7

4.7 Discussion and conclusion

Rather than examining fisheries management at only the regional level, the coming of the EEZ regime forced resource managers to evaluate the risks and gains in managing internationally shared fish stocks. In this paper, we developed a bioeconomic model to analyse strategic interaction between Kuwait and Iran fisheries—and examined biological and economic trade-offs under three management regimes, which, besides competition, revolved around MSY policy. It is important to note that, like the majority of models, the outcomes of this model may be linked to the bioeconomic framework; hence, alternative specifications may result in different outcomes.

The cooperative scenario with an equal share of the overall Fmsy offered the highest biological and economic benefits for both countries compared to the status quo

(i.e., competition) and optimal but independent management (i.e., national Fmsy management). While Kuwait fishery can attain that F target by effectively maintaining or minimally reducing its current fishing mortality, this cooperation entails radical fishery reform for Iran. The average fishing mortality generated by the fishery in Iran over the past decade is much higher than the required level of cooperation—and recent estimates are climbing. As such, reducing the current fishing mortality to Fmsy means

that Iran pomfret fishery needs to go through the painful process of fishing effort reduction (e.g., limited access programs, total allowable catch (TAC), etc.)—a process that directly impacts the livelihood of Iran’s numerous fishing communities (Esmaeili 2009). Still, programs aimed at reducing fishing mortality in a wide range of fisheries are conducted with rewarding results, even by countries with limited means (e.g., Mexico, Chile, and Peru; (Aranda 2009; Worm et al. 2009; Zimmermann & Werner 2019)). It is plausible that resource managers in Iran reject the proposition of an equal share of Fmsy outright and instead call for a share that is compatible with the proportion of the stock available in each EEZ. This call is plausible because the estimated proportion of the stock available in Iran’s EEZ has been consistently higher than in Kuwait’s EEZ. Although the gains obtained by Kuwait under this unequal cooperation are higher than the competition and national Fmsy management, we should draw attention to two critical points concerning Kuwait pomfret fishery. First, resource managers in Kuwait have been implementing a string of measures that potentially capped pomfret catch over the years. For example, in addition to limitation on the number of fishing licenses, managers started imposing permanent area closures since the 1980s (Al-Husaini et al. 2015), notably: (i) Kuwait Bay, which encompasses pomfret’s main spawning area; and (ii) territorial waters (three miles from the shore, including islands). These areas overlap with some of the main fishing grounds of the pomfret fishery. If these management measures have indeed been limiting pomfret catch, the annual proportion of stock available in Kuwait’s EEZ would be underestimated by the model since the proportion of the stock in a given EEZ is varied so as to fit the catch data (Eq. 2; Appendix C; Figure C.1). Second, the largest biological production is possibly occurring within Kuwait’s EEZ, according to a research project conducted jointly by Kuwait and Iran (Al-Husaini et al. 2007). Therefore, these two points indicate that Kuwait might not be a “small player.” Assuming the countries do not reach a certain agreement, the worst-case scenario would be one where Kuwait management considers expanding its fishing capacity—and hence fishing mortality—within its EEZ. An obvious consequence of this scenario is not only less catch for Iran pomfret fishery but also compromising the entire pomfret stock in the Gulf. The fishing sector in Kuwait has been demanding

decisionmakers to start recognising the competitive fishing in the region particularly that the catch of shared commercial stocks like pomfret and hilsa shad have not rebounded for more than two decades, despite area and seasonal closures. Additionally, the benefits of such management measures would only be accrued by the neighboring countries. Although the model described here was applied to the pomfret stock in the Gulf, its framework could be extended to other transboundary fisheries. In particular, fisheries targeting different fish age classes and aiming to maximise yields while sustainably harvesting the shared stock. As an illustration, by setting different age-vulnerability (푣푎) patterns in each EEZ, it is straightforward to evaluate alternative management regimes for a stock where its nursery area is in EEZ A—i.e., juveniles are vulnerable to the fishing fleet in EEZ A—with fish dispersing into EEZ B at older ages and caught there. Under such a scenario, each country would have a different vulnerability schedule and age-varying mortality pattern dependent on the other country’s harvest management regime. But for the stock as a whole, the MSY policy would typically involve no harvesting in EEZ A since fishing there would cause growth overfishing; thus, if a cooperative agreement is wanted, alternative options need to be considered, like transfer payments (Munro 1979). Such basic evaluations could provide useful insights to resource managers and stakeholders for designing harvest arrangements that avoid overfishing and improve economic gains.

5 China’s fish maw demand and its implications for fisheries

in source countries5

5.1 Summary

The demand for fish maw (i.e., dried swim bladder) has apparently intensified during the past decades in Hong Kong and mainland China; currently, maw has similar annual import volumes but far higher mean unit values than other important seafood delicacies like shark fins and sea cucumber. Escalated demand for seafood delicacies can significantly contribute to the depletion of marine resources; yet a comprehensive understanding of maw value and the fisheries that supply it is lacking. We review available information on eight important maw-supplying species in major and largely undocumented source countries to examine the susceptibility and exposure of fisheries to the maw trade, which primarily serves Chinese demand. Comparing ex-vessel price ratios of maw to flesh (USD/kg), the overall mean price of maw can be as much as 72 times higher (range between 12:1 and 8389:1). Catch, price and export trends demonstrate that demand for maw is likely intensifying in countries already supplying it, shifting or expanding to new species, and emerging in new regions. We find that most maw-supplying species are under high fishing pressure, poorly or not protected. Those that yield the highest maw prices exhibit spawning aggregations, making them exceptionally vulnerable to overexploitation. While management interventions are needed to sustain fishery resources and capture economic benefits, their effectiveness will be challenged by the high value of maw.

5 A version of this chapter is in press [A. Ben-Hasan, Y. Sadovy de Mitcheson, M. A. Cisneros-Mata, É. A. Jimenez, M. Daliri, A. M. Cisneros-Montemayor, R. J. Nair, S.A. Thankappan, C. J. Walters, V. Christensen. In press. China’s fish maw demand and its implications for fisheries in source countries. Marine Policy] 73

5.2 Introduction

China is the primary market for many types of dried seafood delicacies, including shark fin, sea cucumber, abalone and fish swim bladder (Anderson et al. 2011; Clarke et al. 2007; Fabinyi 2012; Jaquemet & Conand 1999; Sadovy de Mitcheson et al. 2019). In tandem with rising income and urbanization in many parts of the country, the demand for seafood delicacies, among other luxury goods, has increased dramatically over the past several decades (Barron et al. 2014; Fabinyi & Liu 2016). Much of these dried seafood products are sourced internationally rather than being locally produced, with Hong Kong representing a major importer and trade hub for handling dried seafood trades in general, including for mainland China. For example, Hong Kong manages 30– 50% of the global shark fin trade for mainland China, and accounts for 58% of global sea cucumber imports (Anderson et al. 2011; Fabinyi 2012).

Fish swim bladder – commonly known as maw – is among the most expensive dried seafood in China (Figure 5.1; (Sadovy de Mitcheson et al. 2019; Sadovy de Mitcheson & Cheung 2003)). Similar to other expensive seafood delicacies in the country, fish maw is linked to wealth, prestige and honor (Sadovy de Mitcheson et al. 2019). Traditionally, it is used in important family and business gatherings as well as being offered as valuable gifts while its use more recently has expanded to other areas such as beauty products and as a substitute for shark fin (Lin 1939; Sadovy de Mitcheson et al. 2019). Yet, relative to other major trades, fish maw has received limited attention in the scientific literature or from fishery or seafood product management agencies in source countries. In 2018, the declared import value of maw trade in Hong Kong exceeded that of sea cucumber or shark fin by at least 100 million USD (Sadovy de Mitcheson et al. 2019). The mean value of maw per kg is also much higher now than that of shark fins and all forms of dried sea cucumbers (Sadovy de Mitcheson et al. 2019).

Maw trade and consumption in Hong Kong and China can be traced back to at least the early 20th century (Lin 1939). Most commonly, maw is used as a nutritious source of food and in traditional Chinese medicine (Brierley 2018; Olden et al. 2020). For example, dried maw is usually softened and then used in soups to enrich the flavor and boost the nutritional value of the meal (Figure 5.1). In traditional Chinese medicine, maw

is recommended for postpartum recovery or to reduce post-surgery pain, although scientific evidence that supports such claims is lacking (Sadovy de Mitcheson et al. 2019). Other purposes, not restricted to China, are wine and beer clarification for which maw (or isinglass) is used to collect the insoluble yeast and other small particles; however, such uses have apparently declined with the advent of modern brewing methods (Olden et al. 2020). Because it is collagen-rich, maw is now being heavily promoted as a beauty product in Hong Kong and mainland China on websites and in major retail seafood stores (Sadovy de Mitcheson et al. 2019; Wen et al. 2016).

Since the early 20th century, maw trade has undergone a marked growth in both volume and range of source countries, with signs of intensification particularly over the past twenty years (Sadovy de Mitcheson et al. 2019). In the early 1900s, the supply of maw was limited to Chinese waters, adjacent countries in Asia and South America (Lin 1939). Additionally, only a few species were evidently targeted for their maw, including four species of croakers considered to provide the highest quality of maw, as well as sea catfish, pufferfish and eel (Lin 1939). However, as the Hong Kong census and statistic department started to keep track of the volume (and value) of imported maw in 2015, the mean import volume between 2015–2018 was recorded to be considerable, at 3,405 t, which is about 120 times the amount of maw reported in the 1930s (Lin 1939) and a significant proportion of the total global dried seafood trade (Sadovy de Mitcheson et al. 2019). Over 100 countries are now exporting maw to Hong Kong, and the range of species targeted for maw is also expanding, with maw supplied from the Nile perch (a non-traditional source) of Lake Victoria, in particular, gaining much importance in volume and value (Bagumire et al. 2018). Maw production and value are on the rise and are strongly expected to grow in the future, largely due to initiatives that are promoting maw as a beauty product and as a substitute for shark fins, driven by the growing wealth within China (Ho & Shea 2015).

Left unmanaged, increased or targeted fishing on species specifically in response to the high and growing demand for fish maw can lead to detrimental social and ecological consequences. While the maw component of most fisheries and trade has been poorly documented until recently and maw has probably been largely a by-product of fisheries

that focus on flesh for food, this situation is changing and, for certain species, maw has been a specific target with concerning conservation outcomes. This is best exemplified by two critically endangered croakers, the Chinese bahaba (Bahaba taipingensis) in mainland Chinese and Hong Kong waters, and totoaba (Totoaba macdonaldi) in the Gulf of California in Mexico (Findley 2010; Liu 2020). These two species both have very limited geographic distributions for marine fish species. They were heavily targeted specifically for their valuable maw (to a lesser extent, flesh in the case of totoaba) in the early 1900s. The sharp declines in the catches of both species, with overfishing being a major factor, further intensified exploitation rates because the prices of maw escalated as they became rarer. For example, by the time that the Chinese bahaba had almost vanished from catches in the late 1990s, its value per unit weight surpassed that of gold by up to seven times. The fish is now rarely caught, but occasionally one is hooked and often makes headlines. In 2012, a single ~80 kg fish was sold for £300,000 (Moore 2012; Sadovy de Mitcheson & Cheung 2003). The abundance of Chinese bahaba appears to be so low that it may be the first commercial marine species to become extinct in the wild (Sadovy de Mitcheson & Cheung 2003).

Demand for the totoaba’s maw, combined with poor enforcement or lack of effective regulations, has had impacts far beyond biological overexploitation of this species. The illegal fishery played a key role in impacting the endemic vaquita porpoise (Phocoena sinus) of the Gulf of California, which has declined by 92% since 1997 as a result of becoming entangled in totoaba gillnets (Taylor et al. 2017). This situation also contributed to the deprivation of the local coastal communities, which depended on selling totoaba fillets internationally (Juarez et al. 2016). For both the totoaba and the Chinese bahaba, the economic incentive to catch and transport them, including illegally, are extremely high, seriously undermining current protective measures and threatening both species.

Given that the maw trade is expected to expand in the future, our aim is to highlight the potential impacts of escalating and unmanaged maw demand on natural resources by examining some of the most valuable maw-supplying species and their fisheries. While we recognize that there are many and increasing numbers of species

entering the trade, our focus is constrained to those relatively few species that have sufficient economic and fishery or international trade information available to allow for analysis. In addition, we focus on specific source countries and on the most valuable maw-supplying species for two reasons (Table 5.1). First, although fish maw in Hong Kong and mainland China is sourced from more than 100 countries, around 58% and 70% of maw volume and value are provided by only five countries: Brazil, Uganda, Tanzania, Vietnam and India (Sadovy de Mitcheson et al. 2019). Additionally, these source countries encompass fisheries and species that are understudied with communities that directly depend on fishing for livelihoods and food (e.g., north and northeast Brazil, East Africa, Mexico, India and Arabian Gulf; (Ben-Hasan & Christensen 2019; Jimenez et al. 2020; Medard et al. 2019; Nóbrega & Lessa 2007)). Second, while a variety of species supply maw, only a few – like croakers and sea catfishes, and, more recently, the Nile perch – have maw that is particularly highly sought-after and associated with high prices (Bagumire et al. 2018; Sadovy de Mitcheson et al. 2019). We recognize that high economic value can be a major driver of fisheries. Therefore, under an escalated demand for maw, these taxa are particularly likely to be or to become the most targeted and possibly the most threatened by overexploitation. We address the aim of this paper by reviewing and synthesizing peer- reviewed studies and local reports, expert opinion, media reports, and species-specific catch, price and trade/export trends to examine: (i) the commercial importance of maw to fishers; (ii) trends in targeting maw-supplying species in source countries; and (iii) susceptibility of fish and fisheries to an escalated demand for maw in the absence of management.

Figure 5.1. Fish maw for sale at the dried seafood market in Hong Kong. Photographs by Y. Sadovy de Mitcheson.

5.3 Methods

5.3.1 Sources of maw: Species and countries

We examined eight species that have been identified in the maw trade based on molecular studies and fishery information (Table 5.1) and which have sufficient economic and fishery information for our study aims. The major source countries, as determined in terms of volumes (weights) of maw imported to Hong Kong since 2015, are Brazil, India, Uganda, Tanzania and Vietnam (Sadovy de Mitcheson et al. 2019). However, we focus on the first four countries because there is little information on species or fisheries in Vietnam that supply maw (Sadovy de Mitcheson et al. 2019). Croakers (family Sciaenidae), which comprise half of the species examined here (Table

5.1), are the most important taxon by volume and value in Chinese markets and trade (Sadovy de Mitcheson et al. 2019). Croakers are exported by all countries in Table 5.1 except for Uganda, Tanzania and Kenya, which mainly export the Nile perch’s maw. We consider the Nile perch of Lake Victoria because it is the most important maw-supplying species in East Africa (Uganda, Tanzania and Kenya) and due to its high volumes in the trade (Bagumire et al. 2018; Sadovy de Mitcheson et al. 2019). In addition to acoupa weakfish in Brazil, we further consider three sea catfishes that have been identified as important maw-supplying species (Jimenez et al. 2020; Sadovy de Mitcheson et al. 2019). Though Mexico, Australia, and some countries in the Arabian Gulf are not major source countries noted in Hong Kong import data, we include them in our study because they are experiencing escalated demand for maw – according to national media, local scientific reports and experts – but have received limited attention in the literature. Some of them may be important sources, but their export trade is likely obscured due to complex trade routings via other countries. Therefore, they may provide additional insights into the trends in, and potential impacts of, maw demand on natural resources and local communities. Given data constraints around the global maw trade overall, we recognize that our study is limited to a subset of important species traded. Nonetheless, we consider that there are sufficient data to provide an overall picture of current and possible future trends and to highlight areas for future studies.

5.3.2 Price and fish body weight

We used a variety of sources to compile the ex-vessel prices for maw and flesh; for example, peer-reviewed studies, published and unpublished country-specific reports, newspapers, and local expert knowledge (detailed information about ex-vessel prices are provided in Appendix D, Table D.1). The condition of maw – that is, whether it is fresh (unprocessed) or dried, large or small – is often not mentioned in source information, although these factors can substantially influence its value (e.g., (Dutta et al. 2014)). The prices of fresh maw (unprocessed) usually reflect the ex-vessel price, but in some cases fishers dry it before selling (e.g., (Jimenez et al. 2020)). We are interested in the price of maw received by the fisher (i.e., the ex-vessel price) because prices can influence the intensity of exploitation rates (Sethi et al. 2010). However, we

also recognize that heavy mark-ups along trade chains can incentivize traders to focus on certain maw types.

When there are different prices for different weights of maw of a single species (mainly in the case of black-spotted croaker and Nile perch), we take the geometric mean across all prices and weights, which provides more conservative values than the arithmetic mean. For a species being exploited for maw in several countries, we take the geometric mean ex-vessel price of flesh and maw across all countries to produce a single value for the species (Appendix D, Table D.1). If the price of maw is reported as a range, we take the geometric mean. When necessary, prices were converted to USD using the currency exchange rates published by the World Bank (World Bank 2021), which corresponded to the same year (or period) in which the ex-vessel price was reported (Appendix D, Table D.1).

We investigated the relationship between the mean maw price and mean fish body weight across species to understand whether larger species yielded a more valued maw. We used the common length of maw-supplying species reported in FishBase (FishBase 2021), which is defined as “the length at which most individuals of the population would be sampled”, to calculate the common weight of each maw- supplying species:

Wc = aLcb (1)

Where Wc is the common weight (g); a and b are constants (obtained from FishBase); and Lc is the common length (cm) (Appendix D, Table D.2). The common weight was then converted to kg. Because totoaba and Gulf corvina lack information about common length in FishBase, we used a common weight of 26 kg for poached totoaba, consistent with the mean weight of totoaba caught between 2016–2017 (Cisneros-Mata 2020). In addition, we used a common weight of 2.9 kg for the Gulf corvina, following the modal weight of the fish reported over the 2007–2017 period (Mascareñas et al. 2018). An examination of maw price and the maximum species weight is provided in Appendix D, Figure D.1.

5.3.3 Datasets

There are several fundamental considerations when examining international maw trade statistics. Maw exports are most often reported in combination with other fish by- products (e.g., fish heads and tails) and few export data are recorded at the species level. Hong Kong has only recently (in 2015) started tracking the volume and value of maw alone, yet without information on the species composition. The Hong Kong data include maw coming into and being re-exported from Hong Kong; although most import data indicate ‘country of origin’ in the dataset, this may not always reflect where the maw originated if maw is traded through an intermediate country first. The available limited trade data may mask potentially important trends in maw fisheries. On the other hand, since catch data are readily available and long-term, we explored trends in maw fisheries using species-specific catch and price data.

We obtained the catch data for totoaba from the Sea Around Us Project database (Sea Around Us 2021). We compiled catch data for the black-spotted croaker from India, Iran, Kuwait and Australia, and the Gulf corvina in Mexico from local official fisheries statistics as they are not available in global catch databases (ABARES 1993- 2018; CSB 1979-2017; dataMares 2021; IFO 2000-2017). The ex-vessel price data were obtained for black-spotted croaker in Iran, Kuwait, and Australia (ABARES 1993- 2018; CSB 1979-2017; IFO 2000-2017). For the Nile perch, we obtained volume and value data of maw exports from Uganda for the 2011–2016 period from Bugamire et al., (Bagumire et al. 2018); this is the only country in East Africa that has reported such data over several years. In Brazil, there is no reliable historical catch data for maw-supplying species. This scenario is even worse in the case of small-scale fisheries, in which catches are most often greatly underestimated in official statistics (e.g., (Jimenez et al. 2020)). Therefore, we do not consider Brazil’s maw-supplying species when examining catch trends in maw fisheries.

Table 5.1. Some fish species identified in the maw trade based on molecular and/or fishery information (Sadovy de Mitcheson et al. 2019; Tuuli et al. 2016; Wen et al. 2015).

Species Family Identification approach Source country

Acoupa weakfish (Cynoscion Molecular and fishery Sciaenidae Brazil acoupa) information Black spotted croaker Molecular and fishery India, Kuwait*, Iran* and Sciaenidae (Protonibea diacanthus) information Australia Couma sea catfish (Sciades Ariidae Fishery information Brazil couma) Crucifix sea catfish (Sciades Molecular and fishery Ariidae Brazil proops) information Gillbacker sea catfish Molecular and fishery Ariidae Brazil (Sciades parkeri) information Gulf corvina (Cynoscion Sciaenidae Fishery information Mexico othonopterus) Molecular and fishery Nile perch (Lates niloticus) Latidae Uganda, Tanzania, Kenya information Totoaba (Totoaba Sciaenidae Fishery information Mexico macdonaldi) *These countries have not been identified in the literature; further information is provided below.

5.4 Results and discussion

5.4.1 Relationship between price of flesh and maw

Maw is substantially more expensive than flesh per unit weight for all eight species examined: the overall mean maw:flesh ex-vessel price, using the most current data available for each species, is 72:1, and 33:1 when excluding the totoaba (the most expensive species) maw price (Table 5.2). The differences between the mean prices of maw and flesh for a species can be considerable; for example, the flesh price for the totoaba in the Gulf of California (where illegal fishing is still ongoing) is just 0.6 USD/kg. In contrast, the price of its maw is 5,033 (range: 3,000–8,500) USD/kg (Table 5.2). Even for species that are widely distributed and/or commonly caught, such as the black- spotted croaker or Nile perch, maw price is considerably higher than flesh per unit

weight (Table 5.2). To put the prices of maw in the broader context of other highly valuable dried seafood species: the mean price of black-spotted croaker is comparable to the price of shark fins derived from highly desirable species (400 USD/kg; (Clarke 2004)) and to that of the highly valued sea cucumber Apostichopus japonicus (over 400 USD/kg; (Chen 2005)). The price of totoaba is substantially higher than that of all other species examined. The high maw prices have implications for management and conservation of species providing the most highly valued maw, as their demand and prices are likely to increase when resources decline (e.g., (Anderson et al. 2011; Sadovy de Mitcheson et al. 2019; Sadovy de Mitcheson & Cheung 2003)).

The common weight of different species is not associated with the ex-vessel price of maw (excluding the highest price of totoaba; Figure 5.2), which is interesting considering that larger maws are usually more valuable. The same finding holds when we explore the relationship between the maximum body weight and the price per kg (Appendix D, Figure D.1). Except for the Gulf corvina, croakers’ maw fetches higher prices per kg than other much larger species like gillbacker sea catfish and the Nile perch (Figure 5.2; Table 5.2). The perceived high quality of croakers’ maw in Hong Kong accounts for this finding (Sadovy de Mitcheson et al. 2019). Some of the important qualities that distinguish croakers’ maw from that of other maw-supplying species include the size, form or shape, and the nutritional value (Lin 1939). Croakers’ maws are cylindrical with thicker walls and they are believed to be more nutritious than other maw-supplying species (e.g., Chinese bahaba’s maw; (Lin 1939)). Marketing the maw of other species, such as the maw of the Nile perch, as croakers’ maw highlights consumer preference for croakers’ maw (Tuuli et al. 2016; Wen et al. 2015). It is worth noting that, within a species, maw of the larger individuals is markedly more valuable than smaller individuals, as maw size is nearly proportional to body size (Bagumire et al. 2018; Cisneros-Mata 2020; Ghosh et al. 2009; Lin 1939). It is possible that the lack of relationship shown in Figure 5.2 is due to pricing arrangements between fishers and traders with massive mark-ups along the trade chain above the fisher level. Different countries may have been able to negotiate different levels of ex-vessel prices with local traders depending on how much bargaining power fishers have. This could explain the differences between countries in ex-vessel prices for particular species.

Species Maw Flesh Maw:flesh

Totoaba 5,033.1 (3,000–8,500) 0.6 8389 Black-spotted croaker 409.5 (139–2,299) 6.0 68 Acoupa weakfish 252.0 (230–276) 2.2 113 Nile perch* 92.8 (65–126) 2.5 37 Gillbacker sea catfish 30.0 (25–36) 1.8 16 Gulf corvina 17.1 (14–21) 0.8 21 Crucifix and couma sea catfishes 11.6 (9–15) 0.9 12 Overall geometric mean 72 Overall geometric mean excluding totoaba 33 *In East Africa, maw is largely sold by maw extractors and collectors (Bagumire et al. 2018).

Figure 5.2. A. The relationship between mean ex-vessel prices of maw (Table 5.1) and the common weight of different species, excluding totoaba. B. The relationship between mean ex- vessel prices of maw and the common weight of species, including totoaba.

5.4.2 Trends in country, species, and pricing

Several factors could account for the catch trends in fisheries that supply maw to the international market (Figure 5.3). First, maw demand in China is growing and this could increase fishing pressure in countries already supplying it. For example, India has been exporting maw, including the black-spotted croaker’s maw, to China at least since the early 1900s (Table 5.3). Little information was readily available on the maw aspect of the fishery of this species until about 2009 when the maw trade began to attract much attention from the national media and local researchers, who described it as “flourishing”, particularly due to the exceptional maw prices of the black-spotted croaker (Dutta et al. 2014; Ghosh et al. 2009; Mohamed et al. 2009). Over the available catch time-series (Figure 5.3A), harvests of black-spotted croakers on the west and east coasts of India reached their peak in 2008–2009, followed by an overall decline. Although there are other species that are targeted for maw in India, including several eels, maw extracted from the black-spotted croaker is the most valuable (Ghosh et al. 2009). The mean price of a fresh maw extracted from a male croaker was 192 USD/kg and the mean price of the dried form was 497 USD/kg in 2009 (Ghosh et al. 2009). Some fishers have made a windfall in recent years targeting the black-spotted croakers in India, with a large fish (around 30 kg) sold in auctions for ⁓7,000 USD as soon as it was landed (Thankappan and Nair, thesis in prep.). Currently, China holds 95% of the maw exported from India (Thankappan and Nair, thesis in prep.).

Despite the high prices gained by some fishers in India for large croakers, the overall value of the black-spotted croaker fishery is not documented, while in Iran little is known of the trade but value data are available for the fishery. Iranian fishers have extracted and sold black-spotted croaker’s maw since the late 1980s ((Behzadi 2020); Table 5.3). The demand for maw in Iran seems to have escalated relatively recently: the mean price of black-spotted croakers increased by 251% – from 4,882 USD/t in 1997 to 17,129 USD/t in 2012 (Figure 5.3B). Parallel to this, the catch has declined steeply (70%) over the last five years, with recent estimates of biomass showing a declining pattern (Behzadi 2020). The prices remained stable at around 9,235 USD/t between 2014–2017. The mean price of the black-spotted croaker’s maw in Iran is 2,299

USD/kg, much higher than its price elsewhere (Appendix D, Table D.1). Further insights into volumes of maw from the Middle East region can be gained by tracing trade routes. For example, Hong Kong import data (Hong Kong Census and Statistics Department 2015–2016) show imports from the United Arab Emirates (UAE) (7,829 and 17,652 kg in 2015 and 2016) but not from Kuwait or Iran, both of which perhaps first export the black-spotted croaker or only its maw to the UAE, which then sends the maw to Hong Kong.

The second factor that could account for catch trends observed is that demand for maw is shifting to new species (especially those with similar maw types), either as a response to declines in the abundance of similar, or preferred, species and/or due to absolute growth in maw demand. Relative to the other species included in this study, the maw of both the Gulf corvina and Nile perch have more recently entered the trade, according to available data (Table 5.3). The ban on harvesting totoaba in 1975 most likely resulted in the development of a fishery that targets Gulf corvina in the early 1990s (Figure 5.3C). The time gap between the totoaba moratorium and the emergence of the Gulf corvina fishery could be due to continued illegal fishing for totoaba, which eventually decimated stock size. At the beginning of the Gulf corvina fishery, the species was largely exploited for its flesh; however, ex-vessel value of maw in general experienced the greatest increase compared with other luxury seafood in Mexico between 2005–2012 (Barron et al. 2014). Now the fishery targets Gulf corvina for both flesh and maw, with the price of maw being 21 times higher than that of flesh (Table 5.2).

The demand for maw has expanded to other taxa, particularly the Nile perch in Lake Victoria, which is harvested and exported to Hong Kong by Uganda, Tanzania and, to a lesser degree, Kenya (Bagumire et al. 2018). The Nile perch maw was commercialized in the late 1980s but only with a few tons of maw exported from Uganda and Tanzania at the time (Bagumire et al. 2018; Reynolds et al. 1995). Owing to increased demand and high prices of the maw, the trade has developed into a major international market ((IOC 2015); Figure 5.3D). For example, Tanzania processes and exports at least 2 tons of maw daily, while Uganda’s export volume and value of maw

rose from 67 tons (value: 800,000 USD) between 1989–1990 to 422 tons (value: 29 million USD) between 2015–2016 ((Bagumire et al. 2018; Reynolds et al. 1995)). The recent decline in the maw volume exported from Uganda is most likely due to the formalization of the maw trade in Tanzania, which led to a decrease in maw entering Uganda and then being shipped abroad ((Bagumire et al. 2018); Figure 5.3D). However, the 2018 estimates indicate that maw export value surpassed 40 million USD (Bagumire et al. 2018). Because of such high volume and value, policymakers and resource managers have been urged to impose new measures to regulate both the Nile perch fishery and the maw trade (IOC 2015).

Finally, maw demand is apparently expanding to new countries, where the emergence of maw fisheries is likely recent. In Australia, the black-spotted croaker is mainly targeted in the Northern Territory (where the highest catch occurs), the Gulf of Carpentaria and the East coast of Queensland. Recently, however, all regions are experiencing signs of escalating demand for the black-spotted croaker’s maw, including: (i) development of illegal fishery that extracts maw and discards the flesh, a historically non-existent practice in the Northern Territory (Penny et al. 2018); (ii) a thirty-fold increase in the catch of black-spotted croaker, from 5 tons to 158 tons in Queensland’s East coast over just three years (Figure 5.3E), which is mostly attributed to the price of maw (ranging between 135–340 USD/kg with a mean 214 USD/kg; Appendix D, Table D.1); and (iii) a noticeable rise in the fishing pressure in the Gulf of Carpentaria, as evidenced by the recent increase in fishing licenses that is ascribed to the high prices of maw ((Penny et al. 2018); Figure 5.3E). In the Northern Territory, the mean price of black-spotted croaker has increased by more than six-fold over a 9-year period, from 1,927 USD/t to 12,009 USD/t (Figure 5.3E).

The demand for maw in countries of the western Arabian Gulf has not been previously documented in the literature. The abrupt rise in the price of the black-spotted croaker in at least Saudi Arabia and Kuwait has attracted the attention of the national media, given that these prices are unprecedented for this species in the region. For example, on some occasions, a single black-spotted croaker fetched 1,050–1,300 USD in Kuwait (Al-Sulaimani & Shaker 2013; Ramadan 2019). The price trend of black-

spotted croaker in Kuwait has escalated by 553% during 2006–2014, declining afterward, along with the quantity landed (Figure 5.3F). In 2013 and 2015, exceptionally high prices for the same species have also been reported in nearby countries (Saudi Arabia and Iraq) (Al-Abandi 2015); however, catch or price statistics for black-spotted croaker in these countries are lacking. Many media reports in Kuwait and Saudi Arabia clearly attributed the exceptional prices of the black-spotted croaker to its maw, rather than the flesh (Al-Abandi 2015; Al-Sulaimani & Shaker 2013; Ramadan 2019).

Other countries supplying significant quantities of maw to Hong Kong are Indonesia, Pakistan and Bangladesh (Hong Kong Census and Statistics Department 2015–2018; (Sadovy de Mitcheson et al. 2019)). Although there are no species-specific export data and it is not known how long this export trade has been operating, in Bangladesh longline fisheries are increasingly supplying the maw trade, targeting the high-value Protonibea diacanthus and Congresox talabonoides (Illius 2020). There is some concern that these fisheries pose risks to threatened cetaceans and elasmobranchs in Bangladeshi waters (Mansur 2020).

Figure 5.3. A. Catch of black-spotted croaker off India’s west and east coasts (2007–2018); B. Catch and ex-vessel prices for black-spotted croaker in Iran (1997–2017); C. Catch of totoaba (1950–2014, the fishing ban started in 1975) and Gulf corvina (1991–2019) in the Gulf of California, Mexico; D. Uganda’s export volume and value of Nile perch’s maw (2011– 2016). E. Catch of the black-spotted croaker in Northern Territory (NT; 2008–2017) and Queensland (Qld; 1993–2018), and the ex-vessel prices in Northern Territory (2008–2017). F. Catch (1979–2018) and ex-vessel prices (2000–2018) for black-spotted croaker in Kuwait.

Possible start year or Country Species Source period Acoupa weakfish; Brazil* Gillbacker, crucifix and Early 1900s (Lin 1939) couma sea catfishes India Black-spotted croaker Early 1900s (Lin 1939) Mexico Totoaba 1910 (Juarez et al. 2016) Iran Black-spotted croaker 1989–1990 (Behzadi 2020) Kenya, Uganda, Nile perch 1990–1994 (Reynolds et al. 1995) Tanzania Mexico Gulf corvina 2005–2012 (Barron et al. 2014) (Al-Sulaimani & Shaker Kuwait Black-spotted croaker 2012–2013 2013) Australia Black-spotted croaker 2017 (Maddocks 2017) *Brazil was not mentioned explicitly in Lin (Lin 1939); rather, Lin (Lin 1939) mentioned “America”. But we suspect that the term “America” included Brazil because of its long history (dating back to 1800s) in exporting isinglass to the USA and Europe (Furtado 1990). At present, Brazil is the largest exporter of maw to Hong Kong (Sadovy de Mitcheson et al. 2019).

5.4.3 Susceptibility of fish and fisheries to increased maw demand

The impacts of demand-driven exploitation can be severe when coupled with factors such as lack of resource management or effective enforcement of regulations (Berkes et al. 2006); high accessibility to natural resources (e.g., nearshore fishing grounds) (Christensen et al. 2003; Morato et al. 2006); and high susceptibility of targeted species to fishing activities due to certain life-history characteristics or reproductive behavior (Jennings et al. 1998; Sadovy & Domeier 2005). We examine these factors in fisheries supplying maw and present the conservation status of these natural resources, as

reported in the literature (Table 5.4). For example, even though fisheries in the northeastern coastal region of Brazil are intensively targeting croakers (acoupa and green weakfishes) and sea catfishes (gillbacker, crucifix and couma) for food and maw and some have undergone declines, they remain under open access conditions. Acoupa weakfish and gillbacker sea catfish were considered to be fully exploited in North Brazil 15 years ago (Lucena Frédou & Asano-Filho 2006), yet fishing effort kept climbing without restriction (Betancur et al. 2015). The most recent IUCN assessments listed both species as ‘Vulnerable’ (Betancur et al. 2015; Chao et al. 2021).

Fishing for the black-spotted croaker in India and fisheries in the Arabian Gulf are inadequately regulated, with evidence indicating unsustainable fishing practices. In India, there are signs of overfishing, with large individuals (> 119 cm) experiencing the highest exploitation rates (Ghosh et al. 2010). While the Arabian Gulf regional IUCN report indicated that the status of the black-spotted croaker stocks is of ‘Least Concern’ (Abdulqader et al. 2015), its conclusion does not seem to be consistent with the fishing intensity or the apparent high market demand for its maw in the region. Fisheries in the Arabian Gulf generate high exploitation rates that have resulted in substantial declines in many demersal (e.g., groupers and snappers) and pelagic (e.g., silver pomfret) commercial species (Ben-Hasan et al. 2020; Grandcourt et al. 2006). While marine protected areas are considered to be sufficient to address the high fishing pressure, positive outcomes have not been shown and weak enforcement is apparently common in the northern Arabian Gulf (Ben‐Hasan et al. 2021; Van Lavieren & Klaus 2013). These conditions may warrant further investigation into the status of black-spotted croaker in the region.

Management regulations are adopted for several maw-supplying species in Mexico, East Africa and Brazil (Table 5.4); however, weak enforcement of regulations in conjunction with the high value of maw is evidently undermining positive conservation outcomes. In East Africa, fisheries targeting the Nile perch are managed by slot size limit (50–85 cm). However, a recent length frequency analysis (sample size = 1,289,325 fish) showed that 97% of the fish belonged to length classes far below the minimum slot size, indicating growth and recruitment overfishing as well as illegal fishing (Aloo et al.

2017). In addition, the high demand for maw is perhaps the main driver of the severe harvesting of the larger Nile perch (> 85 cm) because larger individuals yield the most expensive maw (Bagumire et al. 2018; Brierley 2018; IOC 2015; Kayanda et al. 2017). Although management actions have been called for to alleviate overfishing (Kayanda et al. 2017; Njiru et al. 2018), issues like the use of banned gears and targeting both undersized fish and mega-spawners remain prevalent and pose some of the biggest threats to the sustainability of the Nile perch (Aloo et al. 2017). In the Gulf of California, a series of management actions have been implemented along with a complete ban on harvesting totoaba, such as restricting international trade of totoaba, designating the Upper Gulf of California as a biosphere reserve, and prohibiting the use of totoaba- specific gillnets (Juarez et al. 2016). Indications of modest recovery were reported, but the stock is still threatened by illegal fisheries that supply the black market with maw and the local market with flesh (mislabeled as a seabass flesh; (Juarez et al. 2016)). Likewise, Gulf corvina maw is often marketed illegally to the extent that the selling of poached maw is generating a comparable total revenue to that earned from selling it legally (Sadovy de Mitcheson et al. 2019).

In Australia, even when sound regulations and rigorous enforcement are combined, the emergence of black-market systems represents a significant management concern and challenge. Given that the maw value of the black-spotted croaker is escalating, Australia’s management agencies are implementing stricter and more cautious measures, like limiting the annual catch and applying high fines for illegal trafficking of fish or fish products. Nevertheless, illegal fishing activities, which are reported to be the top enforcement issue (Brann 2019), are complicating black-spotted croaker’s rebuilding plan in the Northern Territory and threatening its sustainability in Queensland.

5.4.3.1 Exposed fish aggregations

A critical reproductive behavior that we focus on is spawning aggregation because of its prevalence in fishes (SCRFA 2021), and that the effects of targeting these aggregations are detrimental to fish stocks (Erisman et al. 2011; Sadovy de Mitcheson 2016). High

exploitation rates exerted on fish spawning aggregations rapidly reduce the reproductive capacity of the stock as adults become easy targets (Sadovy & Domeier 2005); and they truncate the age/length structure with the consequence of leaving the stock less productive and more variable through harvesting the larger/older individuals (Eklund et al. 2000; Hsieh et al. 2006; Sala et al. 2001). If exploitation rates persisted, such effects could result in the loss of spawning aggregations or insufficient recruitment to replenish the stocks (Beamish et al. 2006; Sadovy De Mitcheson et al. 2008).

Croakers, Nile perch and crucifix sea catfish all exhibit aggregations (Table 5.4), and targeting these aggregations is likely to be one of the underlying causes of overexploitation and declines. Indeed, fish aggregations are heavily targeted because they represent valuable fishing opportunities. For instance, some of the turning points in the exploitation of totoaba were the use of gillnets and the discovery of the spawning aggregation locations almost a century ago in the northern Gulf of California. Spurred on by the high demand for maw, the intense exploitation of aggregating fish precluded the recovery of totoaba (Cisneros-Mata 2020). Similarly, the Gulf corvina forms a single massive spawning aggregation comprising several millions of adults every spring (Erisman et al. 2012). This spawning aggregation shrinks the population’s spatial distribution to less than 1% of its original distribution (Erisman & Rowell 2017). Using a single seine net, one small fiberglass fishing boat – known as a panga – can catch up to four tons of Gulf corvina within minutes. The local fleet, which includes around 500 pangas, catch 5,900 tons annually (⁓2 million fish) (Erisman et al. 2012, 2014). This fishing pattern is subjecting the stock to a substantial risk of collapse: the size of the adult population has drastically declined due to the sustained removal of the spawning aggregation. The Gulf corvina is now considered overexploited (Erisman & Rowell 2017; Mendivil-Mendoza et al. 2018). In East Africa, researchers have warned that because Nile perch exhibits dense aggregations, often up to 33 tons/km2, the stock is highly susceptible to overexploitation, but such aggregations remain unprotected (Kayanda et al. 2017).

Fish Fisheries

Form Target Country or IUCN Fishing Main management Species aggregations Stock status aggregations region assessment grounds approach ? ? Acoupa Brazil Yes Undefined Vulnerable Accessible Yes Unmanaged weakfish Black-spotted Experience Near Accessible and India Yes Yes Unmanaged croaker overfishing threatened offshore Black-spotted Arabian Least Yes Undefined Accessible Yes Unmanaged croaker Gulf Concern Catch limits; gear restrictions; limited Black-spotted NT: recovering; Qld: Near entry; size limits; Australia Yes Accessible Yes croaker Undefined threatened spatial and temporal closures; vessel restrictions Crucifix sea Brazil Yes Undefined Not evaluated Accessible Yes Unmanaged catfish Couma sea Least Brazil Unknown Undefined Accessible Unknown Unmanaged catfish Concern Gillbacker sea Temporal closure; Brazil Unknown Undefined Vulnerable Accessible Unknown catfish Size limits Catch share Gulf corvina Mexico Yes Overexploited Vulnerable Accessible Yes program Experience Least Accessible and Size limits; Gear Nile perch East Africa Yes Yes overfishing Concern offshore restrictions Critically Totoaba Mexico Yes Overexploited Accessible Yes Fishing ban endangered

5.4.4 Socioeconomic implications

For some communities in the north and northeast of Brazil, a great diversity of fish species is used for food, as fish represents an essential source of protein. However, while small-scale fisheries in the Amazon coast target around 100 species, a few (5–10) species account for 70% of the total catch; maw-supplying species comprise some of these main food species (Isaac et al. 2008; PROZEE 2006). Maw-supplying species – like acoupa weakfish and gillbacker sea catfish – are vital to these fisheries: they are considered primary, popular, and traditional sources of food and they significantly contribute to fishers’ income because of their moderate to high market prices (Isaac et al. 2008; Jimenez et al. 2020; PROZEE 2006). Yet the elevated demand for maw is likely fueling overexploitation, illegal fishing practices, and fish discards and could negatively impact food security. For instance, fishers often relate the increase in fishing pressure and the subsequent decline in catches to the high maw prices, where most fishers aim to catch about one kg of maw from weakfishes and sea catfishes (Jimenez et al. 2019). Further, the lucrative nature of trading in fish maw, or “sea gold” as known by local fishers, is contributing to illegal fishing (Azzaro 2019; Jimenez et al. 2019). Such illegal activities include fishing during the seasonal closure of gillbacker sea catfish or fishing in closed areas like the Cabo Orange National Park and the territory of French Guiana (Azzaro 2019; Jimenez et al. 2019). In addition, recent anecdotal reports indicate that maw prices are causing some fishing fleets to discard the flesh of low valued species and only retain the maw. However, discarding of the flesh is likely uncommon and might be only occurring when catches are extremely high. It worth noting that this wasteful practice has also been observed in Australia, East Africa, and French Guiana, although, similar to the Brazil’s fisheries case, it is unclear how prevalent is discarding of flesh (Azzaro 2019; Bagumire et al. 2018; Maddocks 2017). Given these threats, the elevated maw demand combined with the lack of regulations and enforcement could impact the supply of high-quality food species as well as the revenues generated from selling them in the coastal communities of northern Brazil.

In places where fisheries regulations are implemented, the livelihood of legal fishers may be impacted by the emergence or expansion of illegal fishing activities,

which supply the black market with maw. Because of the recent escalated demand for the black-spotted croaker’s maw in Australia, for example, the management agency has capped the total allowable catch at 20 tons for the commercial fishery. In the 2020 fishing season, the annual fleet-wide quota cap was reached in the first two months of the season, resulting in a complete fishery closure for the rest of the year (The State of Queensland 2020). This early closure has been financially devastating to some fishers who had invested a sizable amount of money to enter the fishery, and possibly to those who cannot participate in other commercial fisheries due to the cost of purchasing new fishing permits or lack of required expertise (Sibson 2019).

In East Africa, the growing demand for the Nile perch maw has resulted in substantial bankruptcy of local businesses and increased unemployment. Licensed fish- processing factories are required to buy whole rather than gutted fish from fishers. However, because maw is far more valuable than flesh, maw traders have shifted the catch destination (i.e., fishers to fish-processing factories) through purchasing maw directly from fishers, who then sell the gutted fish in the local market. While some fishers sell the gutted Nile perch, others discard the fish onboard and sell the maw illegally (Bagumire et al. 2018; Medard et al. 2019). The consequence of this shift in fish supply is dramatic: over the last ten years, about 50% of the licensed fish-processing factories have been forced to shut down due to the severely limited supply of fish (i.e., with maw) – and those currently in business are operating at around 30% of the factory’s operating capacity. This has increased unemployment and decreased the quantity of fish sold for export, affecting revenues from the international market (Bagumire et al. 2018; Brierley 2018).

5.5 Management considerations

Effective local management, which applies primary fisheries management approaches, is the first line of defense against overexploitation (Hilborn et al. 2020). Nevertheless, most of the maw fisheries examined here, which comprise some of the top source countries, occur in regions where regulations are lacking or cannot be strictly enforced (Table 5.4). Also, many of the fisheries supplying maw have few or no alternatives to diversify income. Hence, primary fisheries approaches like imposing license limitations 97

and/or restricting harvests may be impractical. Under these complex conditions, however, easily implementable approaches like well-designed size limitations and seasonal spawning area closures can help protect fish resources without overly burdening the livelihoods of fishers (Cabral et al. 2019; Grüss et al. 2014; Kerwath et al. 2013; Prince & Hordyk 2019). For example, given that fishing gears are selective for the size of the exploited species, setting a minimum size limit adequately above the mean size at maturity can help to buffer against overexploitation (Froese & Binohlan 2000; Prince & Hordyk 2019). This measure safeguards the stock from depletion without the need to control the intensity of harvest rates (e.g., without limiting the number of fishers) and requires simple data that are readily available or obtained (notably size or age at maturity) (Prince & Hordyk 2019). Spawning area closures can be important for protecting fish aggregations (Grüss et al. 2014). Most important, if such measures are enforced through co-management, global analyses suggest that compliance can be enhanced (Cinner et al. 2012; Gutiérrez et al. 2011). Sustainable maw fisheries can generate substantial biological-economic benefits both in the short and long terms. However, it bears noting that high maw prices and the development of black-market systems for maw will continue to pose distinct challenges to management, even for nations with highly developed fisheries regulatory systems.

5.6 Conclusion

Fish maw is one of the most luxurious dried seafood sold in Hong Kong and China, with almost all of the supply imported from other countries. By reviewing maw fish and fisheries in some of the major source countries, our study provides insights into the value of maw, and some of the trends and impacts of escalated demand for maw on vulnerable fishery resources and local communities. Source countries examined here provided three key insights about trends in fisheries that largely or heavily exploit fish for their maw : (i) escalation in demand in countries with a long history in the maw trade; (ii) emerging demand for new species, including the Nile perch and Gulf corvina; and (iii) expansion to new and previously undocumented regions like Australia and some countries in the Arabian Gulf.

Maw is extremely lucrative: the mean value of maw per kg exceeds that of the flesh by >30 times (70 when including the extremely high price of totoaba’s maw). This is particularly alarming because maw species are largely unprotected by any fishery regulations. In regions imposing some form of regulations, like Australia, the high value of maw can still drive illegal selling or trafficking maw into black markets, undercutting conservation measures and affecting legal fisheries. As the maw market is expected to expand in the future, effective local governance in source countries is needed to avert overexploitation and sustain the economic benefits of maw trade for the local fishers and fishing communities.

6 Constrained public benefits from global catch share fisheries6

6.1 Summary

Across publicly-owned natural resources, the practice of recovering financial compensation, commonly as resource rent, from extractive industries influences wealth distribution and general welfare of society. Fishery resources within exclusive economic zones provide 96% of annual marine capture production and have the potential of generating more than $80 billion in resource rent for coastal states. Catch shares are the primary approach adopted to diminish the economically-wasteful race to fish by allocating shares of fish quotas—public assets—to selected fishing firms. It is perceived that resource rent is concentrated within catch share fisheries, but there has been no systematic comparison of rent-charging practices with other extractive industries. Here we estimate the global prevalence of catch share fisheries and compare rent recovery mechanisms in the fishing industry with other extractive industries. We show that while catch share fisheries harvest 17.4 million tons (19% of global capture fisheries landings), with a value of 17.7 billion USD (17% of global capture fisheries landed value), only five of 18 countries impose rent charges with shares of fish quotas primarily allocated free of charge. When compared with the other extractive industries, fishing is the only industry that consistently lacks rent recovery mechanisms. These results imply that most coastal states are likely forgoing potential revenues from fishery resources. Our study could be a starting point for coastal states to consider adapting policies to the enhanced economic condition of the fishing industry under catch shares.

6 A version of this chapter is under review [Ben-Hasan, A., S. De La Puente, D. Flores, M. C. Melnychuk, E. Tivoli, V. Christensen, W. Cui, C. Walters. In review. Constrained public benefits from global catch share fisheries.] 100

6.2 Introduction

A fundamental practice in the exploitation of public natural resources is the recovery of a financial return for the resource owner—the society—from extractive industries. This practice can influence wealth distribution, the general welfare of the society and the sustainability of natural resources (Ascher 2000). If national agencies fail to recover a financial return, they deprive the society of a potential stream of economic benefits while at the same time leaving these benefits accumulating within the industry (Ascher 2000). Charging industries for the use of public natural resources is widespread, with charges generally reflecting resource rent—the surplus or above-normal profit related to the natural resource itself rather than to the actions of private enterprises (Robinson 1969). For example, in North America, stumpage fees are imposed on the forestry industry for harvesting timber on public lands (Grafton et al. 1998), and the oil and gas industry is charged royalties for extracting sub-soil minerals (Mintz & Chen 2012). In contrast, recovering rent from global fisheries has received limited attention from policymakers, despite the widespread harvest of marine living resources, with fish being one of the top traded food commodities in the world (FAO 2018b).

Fishery resources within Exclusive Economic Zones (EEZ) have the potential of generating more than $80 billion in resource rent annually for coastal states (World Bank 2017). Worldwide, around 96% of the annual marine capture production (102 million tons) is caught within EEZs (Schiller et al. 2018). Contrary to open-access and other competitive fisheries, allocating portions of the total quota of a fish stock to a restricted number of fishing firms (individual fishers, fishing vessels, or producers), i.e., ‘catch shares’, effectively mitigates an inherent economic problem in fisheries management: the competitive race to fish (Birkenbach et al. 2017). After their inception in the 1980s, mounting evidence highlighted the improved profitability of fishing firms under catch share programs in North America (Agar et al. 2014; Dupont 2014), South America (Gómez-Lobo et al. 2011; Tveteras et al. 2011), Europe (Andersen et al. 2010; Hannesson 2013) and Australasia (Kompas & Che 2005; Mace et al. 2014). Currently, fisheries under catch share programs capture some of the largest and most economically-valuable fish stocks in the world; yet, it is perceived that society is not

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compensated for private firms exploiting these public resources (Bromley 2009; Gunnlaugsson et al. 2018).

Absent a rent recovery mechanism, catch share programs could lead to fairness and distributional issues. For example, these programs entail high management costs related to administration, research, surveillance and enforcement that are entirely or partly covered by society (Beddington et al. 2007; Mangin et al. 2018). It has been reported that countries intensively adopting catch shares have some of the highest management costs per fishing boat in the world (OECD 2003). Additionally, for most catch share programs, national agencies have allocated fishing quotas to firms free of charge on the basis of historical participation (i.e., grandfathering) (Lynham 2014). In the United Kingdom, for example, the estimated total value of the grandfathered fishing quota is around $1 billion (Appleby et al. 2016). Under such conditions, society fails to offset expenditures attributable to the industry and forgoes a potential stream of revenues that could be maintained indefinitely. The extent to which governments collect resource rent from catch share fisheries therefore has critical ramifications for national policies and the general public alike, particularly as catch share programs are increasingly adopted worldwide (Costello et al. 2008).

Our goal in this study is threefold. First, we evaluate the prevalence of catch share fisheries at the national and global levels through combining catch statistics from the Food and Agriculture Organization of the United Nations (FAO) with three datasets of catch share fisheries (Materials and Methods). Second, to determine society’s compensation from the fishing industry, we examine whether rent recovery mechanisms—like auctions, production-based charges, or rent-based charges—occur in catch share programs. Our review focuses on 56 programs harvesting 174 fish stocks in 18 countries, comprising some of the world’s largest and most valuable fisheries. Finally, we demonstrate how common rent capture schemes are in catch share fisheries compared with four major extractive industries—forestry, oil, gas, and mining—in the same countries.

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6.3 Results

6.3.1 Magnitude of catch share fisheries at national and global levels

Marine capture production under catch share fisheries accounted for 17.4 million tons with a value of 17.7 billion USD, amounting to 19% and 17% of the weight and value of global landings declared to FAO between 2000–2017, respectively (Table 6.1). Catch share fisheries were identified in 29 countries, 14 of which are among the top 25 largest fish-producing countries of the world (FAO 2018b). Among these 29 countries, the fraction of harvest obtained by catch share fisheries ranged from 0.04% to 84% of the national harvest, with greatest proportions (>50%) in Peru, South Africa, Iceland, New Zealand, Canada, Russia, Chile and Norway (Figure 6.1A). The harvest values are roughly proportional to the weight of fish landed in all countries (Table 6.1; Appendix E, Figures E.1 and E.2). More than half of the resources captured in the Arctic Sea, as well as the Northeastern, Southeastern and Southwestern Pacific Ocean, were caught by catch share fisheries (Figure 6.1B). Catch share fisheries target some of the most abundant and valuable target species in global fisheries (Appendix E, Table E.1). For example, 61% of gadids (e.g., Alaska pollock, Atlantic cod, blue whiting, North Pacific hake) and 41% of forage fishes (e.g., Peruvian anchoveta, Atlantic herring and capelin) landed between 2000–2017 were caught by catch share fisheries (Figure 6.1C; Appendix E, Table E.1).

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Table 6.1. Mean fisheries yield and value of marine living resources by country, 2000–2017. Individual countries listed are those with catch share programs in place. Countries in bold are among the world’s top 25 countries in terms of landed tonnage (FAO 2018b).

Mean yield under Mean value of yield Mean yield (103 Mean value of Country catch share under catch share t) yield (106 USD) programs (103 t) programs (106 USD) Argentina 894 1,099 314 288 Australia 191 669 31 141 Belgium 26 71 2 7 Canada 972 1,944 601 1,028 Chile 3,205 2,771 1,940 1,831 Denmark 912 480 384 182 Estonia 88 41 27 6 France 525 853 40 72 Greenland 230 438 111 201 Iceland 1,456 1,220 1,062 876 Ireland 262 254 28 6 Italy 305 477 23 22 Japan 4,115 5,383 75 121 Mauritania 328 287 43 11 Mexico 1,619 1,842 1 13 Morocco 1,131 995 121 90 Namibia 504 379 2 11 Netherlands 451 365 105 90 New Zealand 486 560 350 433 Norway 2,411 1,796 1,277 1,179 Peru 6,752 7,070 5,700 5,805 Poland 198 109 22 2 Portugal 211 264 10 14 Russian 3,891 3,986 2,369 2,446 Federation South Africa 653 659 515 509 Spain 977 1,307 4 23 Sweden 237 99 104 46 United 671 926 157 288 Kingdom United States 4,853 6,195 1,971 1,956 Other 54,512 64,650 0 0 Countries‡ World¤ 93,066 107,189 17,388 17,697 ‡Other countries include data from 205 countries and territories.

¤World includes data from all countries and territories (N=234) that reported marine catches to FAO between 2000–2017.

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6.3.2 Allocation and duration of catch shares

In the vast majority of catch share programs surveyed (48 out of 56 programs; Appendix E, Tables E.2 and E.3), catch quota allocations were based on grandfathering (i.e., proportional to historical catch of individual quota holders) or were equal among fishing firms (6 programs). These allocation schemes typically involve the (free) granting of access to fish, considered to be either a property right or a limited privilege, depending on the system. The allocations of two catch share programs in Chile are based on a combination of grandfathering and auction: a fraction of the total quota is allotted to the highest bidders in a public sale, while the remainder is allotted based on historical catch (Appendix E, Tables E.2 and E.3). Over the first ten years of the quota management system (QMS) implementation in New Zealand, the majority (61%) of the total quota was allocated to firms based on catch history (Hale & Rude 2017). The rest of the total quota was retained by the Crown (39% of the total quota) and had either been sold (e.g., selling around half the quota of the hoki stock) or allocated to the Māori under the Treaty of Waitangi (Hale & Rude 2017). The duration of catch share allocations vary across programs: (i) 65% of programs have medium (6–12 years) and indeterminate (unspecified) duration terms (36% and 29%, respectively); (ii) 14% long term (16–25 years); 11% short term (1–5 years); and 11% have permanent allocations (Appendix E, Table E.2). While medium and short-term durations may be viewed as less secure fishing opportunities, historical allocations have most often been consistently allocated to the same fishing firms. For example, although the duration of quota allocations in the UK catch share fisheries are officially short-term, quota allocations have experienced minimal changes since 1999; the French, Polish and Peruvian catch share programs undergo similar allocation processes (Appleby et al. 2016; Carpenter & Kleinjans 2017). While permanent fishing opportunities are less common, they have been granted for valuable fishery resources: all New Zealand and Iceland catch share fisheries, and Australia’s rock lobster and abalone fisheries (Appendix E, Table E.2).

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6.3.3 Resource rent charges in catch share fisheries vs. other extractive industries

Rent recovery mechanisms (which we distinguish from a cost-recovery scheme because the latter is imposed on extractive industries to recover management costs paid by the government; (Sinner & Scherzer 2007)) were characterized for 56 catch share programs (174 stocks) in 18 countries (Figure 6.2; Appendix E, Tables E.2 and E.3). These mechanisms were levied on catch share programs in five countries: they are fully imposed on catch share programs in Argentina, Iceland, Peru and Russia, and are partially imposed on Australia’s catch share programs (Figure 6.2; Appendix E, Table E.2). In these countries, rent recovery mechanisms levied as a fee proportional to the amount of landings or landed value (Appendix E, Table E.4). Of the six catch share programs examined in Chile, the state only receives a financial return from auctioning a portion of the total allowable catch set for two catch share programs (Appendix E, Tables E.2 and E.3). In New Zealand, currently the Crown auctions off fish quota if (i) there is a remaining quota after allocating quotas of a new fish stock entering the QMS to Māori (20% of the total quota) and commercial fishers; and if (ii) fishers surrender their quota or the quota is forfeited by the Crown (Ministry for Primary Industries 2020). In total, of the 174 stocks (covered by 56 catch share programs), rent recovery mechanisms are imposed on harvesting 36 of them (15 programs); half of these 36 stocks are located in Russia (Appendix E, Table E.2). Rent recovery mechanisms are standard practice in other extractive industries (Figure 6.2). Overall, among 18 countries, rent recovery mechanisms for oil and gas resources occur in 17 (94%) and 16 (89%) countries, respectively, for mineral resources occur in 13 (72%) countries and for forest resources occur in 8 (44%) countries (Figure 6.2, Appendix E, Tables E.3 and E.4). For some natural resources that are insufficient or underexplored, policies that specify rent recovery mechanisms are in place; this is the case for the oil and/or gas resources in Chile, France, Iceland, Portugal and South Africa (Figure 6.2, Appendix E, Tables E.3 and E.4). Rent recovery mechanisms linked to production (e.g., $ per amount extracted) or value (e.g., % of value extracted) occur in all resource industries while mechanisms sensitive to the profitability (e.g., resource rent tax) of firms occur in fossil fuel and mining industries (Appendix E, Table E.4).

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Auctions are commonly applied to the forestry industry in the United States, France, Russia and, to a lesser extent, Canada (Brown et al. 2012; Elyakime & Cabanettes 2009; Karvinen & Mutanen 2019; Luckert et al. 2011) (Appendix E, Tables E.3 and E.4). In some circumstances, a rent recovery regime can combine both auctions and rent charges; for instance, leases for the federal onshore oil and gas resources in the United States are awarded to the highest bidders, who also pay charges as a proportion of the production value during operation (Appendix E, Tables E.3 and E.4). While reasons are rarely explicitly stated in the reviewed documents, generally the scarcity or absence of resource rent mechanisms in the mining and forestry industries in some countries could be ascribed to the state of the natural resource or ownership. Mineral resources in some countries are generally inadequate, economically-nonviable to extract or have been depleted. For example, France generally has scarce non-fuel minerals with metallic minerals are no longer commercially-viable for mining (Figure 6.2, Appendix E, Table E.3). Iceland has few proven mineral resources and currently lacks metallic mines. Metallic mineral resources in the United Kingdom have either been exhausted or substituted by cheaper imported minerals, though the country is a major producer of other types of minerals. Most mining rights in the United Kingdom are generally privately held with entitlement to all mineral deposits in the subsoil, excluding those owned by the Crown like silver and gold (Appendix E, Table E.3). In Sweden, the ambiguity of ownership of minerals covered by the Mineral Act is likely complicating the implementation of an explicit royalty on the mining industry for exploiting minerals (Johnson & Ericsson 2015). With respect to forestry, public ownership of forestland and/or the size of forest resources are apparently insignificant in some countries. In Portugal, Norway and Denmark, for example, the percentage of forest area under public ownership is approximately 3%, 12% and 24%, respectively—and the total area allocated for production is much smaller than the forest area (Appendix E, Table E.3). Forest resources and location are insufficient in Iceland and so they do not sustain extensive forestry industry (the share of the forest area to land area is 0.5%, Appendix E, Table E.3).

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Country Fisheries Forestry Oil Gas Mining Argentina Australia Canada Chile * * Denmark France * * * Iceland * * * * RRM common or ubiquitous Netherlands New Zealand RRM limited or absent

Norway Peru Information deficient Poland Portugal * * Russian Federation South Africa * Sweden * * United Kingdom United States

Figure 6.2. Occurrence of rent recovery mechanisms (RRM) by major extractive industries in selected countries.

‘Fisheries’ comprise firms operating under catch share programs (Appendix E, Table E.2). The categories “RRM common or ubiquitous” and “RRM limited or absent” indicate the extent to which RRM occurs at the national level or by the administrative entities and its prevalence within an industry (see Materials and Methods for details). Asterisks indicate natural resources that are insufficient or underexplored (Appendix E, Table E.3). 109

6.4 Discussion

Catch share fisheries often have exclusive access to fishery resources in coastal waters of national EEZs. Yet the vast majority of catch share programs are not charged resource rent for the capture of public resources; this contrasts with other extractive industries, where rent recovery mechanisms are commonplace.

Our findings at the global level are consistent with studies that indicated that mechanisms to recover rent—e.g., auctions, production-based charges, or rent-based charges—are uncommon in catch share fisheries (Clark 2006b; Gunnlaugsson et al. 2018; Lynham 2014). Our findings are also consistent with work that highlighted, though anecdotally, fairness issues stemming from the absence of economic compensation from catch share fisheries to society, especially considering that quota shares are largely allocated free of charge. For example, Smith (Smith 2019) pointed out that even though the fishing industries in countries like the United States, New Zealand and Australia have been allocated quota shares for free, and that they generally have the flexibility to use, sell, or lease them to new entrants, society receives no compensation. This situation is compounded when society bears the costs of fisheries management, which may include administration, research, surveillance and enforcement (Mangin et al. 2018; Sinner & Scherzer 2007; Smith 2019). While various forms of cost-recovery, usually as license fees, exist in most catch share programs around the world, they often only partly cover management costs paid by society (Gunnlaugsson et al. 2018; Martell et al. 2009).

The absence of resource rent mechanisms in some catch share fisheries may be ascribed to multiple reasons. Upon implementing many catch share programs, selling quotas to fishers or imposing charges on the industry was not considered feasible as some fisheries were already in financial distress due to declining fish stocks and overcapacity. Consequently, the gratis allocation of quotas for initial holders and the absence of rent charges were considered necessary costs for the government to create an efficient management system (Hannesson 2013, 2014). In addition, many countries with strong fishing lobbies are generally sensitive to the industry’s interest because of the central role that the industry plays in the success of new management regimes 110

(Kahui et al. 2016; Lynham 2014). Governments in New Zealand and Denmark, for example, sought to secure the industry’s endorsement in catch share systems by assigning quotas on the basis of participation in years leading up to implementing the program (Kahui et al. 2016). However, it is generally accepted in the literature that, at least in fisheries where efficiency has been realized, the continued lack of rent-recovery mechanisms in catch share fisheries is unwarranted (Bromley 2009, 2015; Clark 2006a; Grafton et al. 2009; Kahui et al. 2016). Another possible reason could be the unique history of exploiting fishery resources relative to other extractive industries and the associated impact on the generation of resource rent. Prior to the establishment of EEZs in the late 1970s and early 1980s, fishery resources were considered “common pool” resources largely open for exploitation to all (Caddy & Cochrane 2001). Even after EEZ establishment, resource rent was largely dissipated under management systems that did not address the race to fish (Anderson et al. 2018). Catch share programs began to be adopted after this period by a few countries aiming to maximize resource rent (Melnychuk et al. 2021; Wilen 2000). While rent recovery mechanisms remain relatively scarce in fisheries, they are currently under consideration by some countries with well-established catch share programs (e.g., Norway (OECD 2018)).

Although the recovery of resource rent is possible, determining and imposing a rent recovery mechanism on extractive industries is not straightforward. In fisheries, the size of resource rent depends on available fish biomass and on fish prices (Campbell & Haynes 1990). While catch share fisheries are expected to generate resource rent, this outcome may not be immediate (Asche et al. 2009). Setting excessive rent charges could affect landed tonnage and may result in bankruptcies or job losses. Nevertheless, for catch share fisheries with quotas already allocated, rent-based charges like a tax on resource rent or net cash flow can have minimal distortive effects because they depend on the profitability of the industry: resource rent is generally recovered after deducting all significant capital and operating costs from the revenue (Land 2010; Mintz & Chen 2012; Sinner & Scherzer 2007). During years when firms’ expenditures exceed income, rent-based charges can be designed such that losses are carried forward at an interest rate—that is, a loss in one year decreases the charges paid on profits in future years. While in principle fishing firms subjected to rent-based charges would still be able to

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earn normal profits even if governments recover all the rent, a partial recovery can be important in minimizing the impact on firms’ incentive for innovative investment (Campbell & Haynes 1990; Land 2010; Mintz & Chen 2012; Sinner & Scherzer 2007). Sharing rents between the government and firms is common practice in several petroleum and mining industries, where resource rent tax rates generally range between 10%–40% (Land 2010; Mintz & Chen 2012). Rent-based charges have been recommended or implemented in oil and gas (Land 2010; Mintz & Chen 2012), mining (Chen & Mintz 2013), forestry (Grafton et al. 1998), developed and developing catch share fisheries (Campbell & Haynes 1990), and more recently in growing resource industries like aquaculture (Ministry of Finance 2020). However, a rent-based charge regime is but one of many possibilities to capture resource rent from extractive industries. For underdeveloped catch share fisheries in which quotas have not been fully allocated, special models of auctions—possibly in combination with a production- based charge or rent-based charge—have been proposed instead of grandfathering to allocate quotas and recover revenue while considering the economic condition of firms entering the fishery (Bromley 2009; Campbell & Haynes 1990). Because auctions entail recurrent sales of publicly held quotas, which necessitates that quotas be retained and reallocated by the government, their application might be politically contentious in developed catch share fisheries whereby quotas have already been assigned based on historical participation. Ultimately, the choice of a rent recovery mechanism heavily depends on critical factors such as the circumstances or historical context of the fishery, governance capacity of the country, political acceptability, potential revenue, and administrative costs.

The scope of our study does not cover all considerations relevant to resource rent and catch share fisheries. We did not consider whether the resource rent charges paid by catch share fisheries and other extractive industries are adequate, excessive or insufficient. Economic assessments that use fishery-specific data are essential to determine the magnitude of resource rent and set an appropriate charge that: (i) ensures adequate compensation for the public; (ii) avoids affecting the deployment of capital and labor to harvest fish; and (iii) has minimal impacts on the innovation incentive of firms. Nor did we consider all possible financial transfers between industries

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and governments. In all extractive industries examined, the presence of resource rent charges does not necessarily lead to net revenue for the government. In the United States, for example, the forestry industry is charged for cutting timber on public lands, but policies related to who bears the cost of building roads are diverse. These may require that the state pays all construction costs, that industry is responsible for the costs, or that costs are shared between the state and industry (Brown et al. 2012). The lack of rent recovery mechanisms in catch share fisheries may have serious consequences within the fishing industry. Allowing fishing firms to fully retain the resource rent would lead to high quota values because the value of the uncollected or inadequately-collected resource rent becomes capitalized into the quota value. Consequently, the cost of purchasing quota could form a major hurdle for potential participants, limit income diversification for fishers, and in conjunction with the concentration of ownership, force fishers to lease fishing quotas that in some cases may cost 70–80% of their total revenue in high-value fisheries (Edwards & Pinkerton 2020); though in most cases lease rates are much lower. Some studies have suggested that the absence of rent recovery mechanisms may lead to such consequences (Campbell & Haynes 1990; Clark 2006a); case studies addressing this issue are limited and represent a fruitful direction for future research.

By highlighting that resource rent capture is a consistent practice across most extractive industries, our findings bolster calls for national agencies to consider collecting rent from profitable catch share fisheries (Clark 2006a; Holm et al. 2015; Macinko & Bromley 2002). As many catch share fisheries are now well-established, prosperous and expanding (Carpenter & Kleinjans 2017; Hale & Rude 2017; Hannesson 2013), depriving society of a potential income stream contradicts the coherent practice of redistributing a share of private gains to the public purse.

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6.5 Materials and methods

6.5.1 Data sources

Marine fisheries landings data as reported by countries to the Food and Agriculture Organization of the United Nations (FAO) were accessed from the FAO Global Capture Production dataset through FishStatJ (FAO 2020a). Landed quantities of live weight (in tons) were separated by country, species, FAO area, and year. The following taxa were excluded as they are not typically fished in wild capture fisheries managed under quota systems: mammals, reptiles, amphibians, sponges, corals, pearls, shells, and plants. Listed species are occasionally at higher levels of taxonomic aggregation such as Genus, Family, or Order, and are occasionally pooled into ‘nei’ (not elsewhere included) miscellaneous species groups such as ‘Marine fishes nei’. Mean landings over years 2000-2017 were calculated for unique entities of country, FAO marine area, and species, here termed ‘stockC,F,S’.

Mean landed tonnages at the stock level, 퐶퐶,퐹,푆, were multiplied by mean ex- vessel prices of corresponding species, 푃푆, to estimate mean landed values at the stock level, 푉퐶,퐹,푆. Predicted time series of nominal ex-vessel prices, back-calculated from export prices of fish commodities based on FAO databases and predicted for all taxa in the FAO landings database (Melnychuk et al. 2017), were extracted for years 2005- 2012. The mean ex-vessel price for each species over this period was paired with the mean catches of corresponding stocks. Although the ranges of years for 퐶퐶,퐹,푆 (2000-

2017) and for 푃푆 (2005-2012) only partly overlap, the interannual variability within stocks or species is generally much less than the variability among stocks or species, for both catches and prices. For Antarctic krill and Norwegian krill, 푃푆 was instead based on years 1990-1994 as those were the most recent available predicted prices. Of the

21,271 unique stockC,F,S entities, 124 (0.6%) did not have paired prices available, but were relatively small, all with mean landings of <2,200 t.

Information about the exclusivity of catch share (CS) sectors fishing unique stocks was assembled from several datasets. CS fisheries are those in which a proportion of a total allowable catch (TAC) is allocated to individuals. These individuals

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may be fishers, vessels, companies, communities, or in some cases fishing co- operatives. The CS units vary in their degree of transferability among individuals. For each stock assessment unit, the mean proportion of the stock’s total catch fished by CS fleets (%CCS) was calculated. For some stocks, %CCS was based on allocation of TACs among sectors instead of on catches by sector. Values were drawn from the following three datasets in preferential order. (1) Data collected under the Science for Nature and People Partnership program’s working group “Fisheries Measures”, hosted by the National Center for Ecological Analysis and Synthesis, included 297 assessed stocks from around the world, primarily reflecting the period 2011-2015 (Melnychuk et al. 2021). (2) “Fisheries Status and Attributes” expert surveys included 258 stocks from the United States, western Canada, and the United Kingdom, primarily reflecting the period 2005-2011 (Melnychuk et al. 2013). (3) “Catch share programs” expert surveys included 439 stocks from around the world, primarily reflecting the period 2000-2010 (Melnychuk et al. 2016). Some stocks overlapped among these three datasets; a total of 533 stocks had an available estimate of %CCS. Although the focal periods differed across these three datasets, the proportional allocation of catches or TACs among fleets, including

%CCS, generally remains similar over time in the absence of major changes to the management system.

Detailed information about characteristics of CS fisheries were compiled in the “Catch Share database” by the University of California-Santa Barbara (UCSB) and Environmental Defense Fund (EDF; (Flores et al. 2018)), which used the EDF database as a core source (http://catchshares.edf.org/database). Values were drawn from two versions of the database in preferential order: (4) a more detailed dataset of 251 CS fisheries from 20 countries; and (5) a dataset of 377 CS fisheries from 24 countries. Some fisheries overlapped between these two datasets; in total, 412 CS fisheries were contained in either dataset. These fisheries were each linked to one of 316 unique combinations of country, FAO area, and species; i.e., more than one CS fishery was often linked to a given stockC,F,S.

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6.5.2 Data preparation

For later pairing with FAO landings data and landed values, the biological stock-level data for %CCS were re-structured to acquire the same definition as ‘FAO stocks’: unique entities of country, FAO area, and species (stockC,F,S). Datasets 4 and 5 above were already defined at this level so did not require re-structuring. For datasets 1-3, unit stocks as defined in stock assessments, stockb, were assigned a primary country of capture and a primary FAO area of capture, here termed 푠푡표푐푘푏퐶,퐹. This allowed each biological stock to be assigned to a single stockC,F,S of the same species (or other taxonomic level). For most analyses, this structure for stockC,F,S was later pooled across

FAO areas, to define stocks at the country and species level (stockC,S).

Because the spatial distribution of biological unit stocks is often smaller than the spatial extent of FAO statistical areas, there was often more than one stockb linked to a single stockC,F,S. Weighted means of %CCS were calculated for each stockC,F,S or stockC,S, weighted by the catch of biological stocks comprising the stockC,F,S or stockC,S.

First, the mean catch of the last 10 years of available data for each stockb was calculated (퐶푏퐶,퐹), extracted from the RAM Legacy Stock Assessment Database (RAM

CS 2019). This was multiplied by the estimate of %C for the biological stock, %퐶퐶푆푏, to represent the recent catch tonnage under CSs. Biological stocks without estimates of either %CCS or 퐶푏퐶,퐹 were omitted from this calculation. Second, the sums of total catch and catch tonnage under CSs were calculated across biological stocks for each stockC,F,S and for each stockC,S. Third, a ratio of the summed catch tonnage under CSs to the summed total catch was calculated to represent a weighted mean %CCS, either for each stockC,F,S:

∑ (%퐶 )(퐶 ) 푏 퐶푆푏 푏퐶,퐹 %퐶퐶푆 = (1), 퐶,퐹,푆 ∑ (퐶 ) 푏 푏퐶,퐹

or for each stockC,S:

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∑ ∑ (%퐶 )(퐶 ) 퐹 푏 퐶푆푏 푏퐶,퐹 %퐶퐶푆 = (2). 퐶,푆 ∑ ∑ (퐶 ) 퐹 푏 푏퐶,퐹

This resulted in 311 entities at the stockC,F,S level with estimates of %퐶퐶푆퐶,퐹,푆, 31% of which comprised two or more biological stocks. At the stockC,S level, this resulted in

295 entities with estimates of %퐶퐶푆퐶,푆, 36% of which comprised two or more biological stocks.

Datasets 1-3 aggregated to the stockC,F,S level and to the stockC,S level were merged with datasets 4-5 at the same level of aggregation, avoiding duplication of stocks. Datasets 1-3 provided weighted mean %CCS, but datasets 4-5 did not contain information about the proportional allocation of total catch into CS fleets. Because datasets 4-5 were focused exclusively on CS fisheries, we made the simplifying assumption that for stocks in these datasets, %CCS=100%. For stocks that occurred in

(at least one of) datasets 1-3 as well as (at least one of) datasets 4-5, the value of %CCS from datasets 1-3 was preferred because of its higher resolution of %CCS estimates between 0-100%. After combining datasets, 455 stockC,F,S entities (and 423 stockC,S entities) with estimates of %CCS were available. Of these, 166 (and 154) were derived from datasets 1-3 only, 147 (and 129) were derived from datasets 4-5 only, and 142 (and 140) were available from both, with the values from datasets 1-3 selected for use.

Weighted mean estimates of %퐶퐶푆퐶,퐹,푆 and %퐶퐶푆퐶,푆 from databases 1-5 were paired with FAO landings data and estimated landed values at the stockC,F,S level. If there was no corresponding value of %퐶퐶푆 available to pair with an entity in the FAO landings dataset, we assumed %퐶퐶푆=0% for that entity. For each stockC,F,S in the FAO database, mean landed tonnage (퐶퐶,퐹,푆) or mean landed value (푉퐶,퐹,푆) was multiplied by

%퐶퐶푆퐶,퐹,푆 as well as by %퐶퐶푆퐶,푆, providing estimates of landed tonnage and landed value under CSs for each stockC,F,S and at both aggregation levels. Landed tonnages or landed values were then summed across species, and for one of the two levels of aggregation, also summed across FAO areas. A ratio of these sums provided an overall

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weighted mean estimate of the proportion of total landings caught by CS fleets, and a similar estimate for landed value. This metric was calculated at the country:FAO area level:

∑ (%퐶 )(퐶 ) 푆 퐶푆퐶,퐹,푆 퐶,퐹,푆 %퐶퐶푆퐶,퐹 = (3), ∑푆 퐶퐶,퐹,푆

as well as at the country level:

∑ ∑ (%퐶 )(퐶 ) 퐹 푆 퐶푆퐶,푆 퐶,퐹,푆 %퐶퐶푆퐶 = (4). ∑퐹 ∑푆 퐶퐶,퐹,푆

Similar calculations were performed using 푉퐶,퐹,푆 instead of 퐶퐶,퐹,푆, resulting in metrics %푉퐶푆퐶,퐹 and %푉퐶푆퐶. Summarised values in Table 6.1 were based on Eq. 4.

These calculated proportions %퐶퐶푆퐶,퐹 and %퐶퐶푆퐶 (or %푉퐶푆퐶,퐹 and %푉퐶푆퐶) are assumed to be indices of CS exclusivity for each country:FAO area entity or for each country, respectively. They may underestimate true proportions if fisheries under CS management were not accounted for in datasets 1-5. Second, because of the assignment of biological stocks to a single primary country and single primary FAO area, %CCS for countries and areas other than the primary ones do not include the other-than-primary contributions from those stocks. Finally, while the assumption of

%CCS=100% for stocks in datasets 4-5 is generally reasonable because stocks in this dataset are under CS fleets, this may overestimate %CCS for some stocks. Although there is uncertainty in overall magnitudes of %퐶퐶푆퐶,퐹 and %퐶퐶푆퐶 for the above reasons

(and even greater uncertainty in magnitudes of %푉퐶푆퐶,퐹 and %푉퐶푆퐶, because of the additional uncertainties associated with ex-vessel price predictions), the indices are expected to be reliable as a means for comparison across countries.

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6.5.3 Review of resource rent charges

Some of the main differences among the extractive industries examined in this study are pertinent to ease of monitoring and the nature of property rights. As an illustration, inventory estimation is relatively straightforward for forestry compared with oil, gas, mineral and fishery resources. Additionally, property rights in forestry, oil, gas and mining are tied to pieces of land. With the exception of cases like Territorial Use Rights Fisheries (TURFs), this is not the case for fisheries, in which the property rights are not spatially-explicit. In general, the investigated extractive industries require permits or privileges that are awarded by the government to access the resource. While some sort of property security is necessary for private firms to pursue the extraction of natural resources, such security can be provided through a variety of institutional forms. For example, a government can offer oil drilling and extraction sale; lease lumber rights on public land; or issue tradable fishing permits for a limited number of firms. The heterogeneity of these legal structures contrasts with a critical purpose underlying each of them: to provide security of resource rents to firms.

Using the global fisheries database compiled by UCSB (Flores et al. 2018), we identified 56 transferable and non-transferable catch share programs in 18 countries. We examined these types of catch share programs because theory and evidence indicate that they diminish the race to fish (Birkenbach et al. 2017). For each program, we obtained detailed information on allocation, permanence, transferability and rent recovery mechanisms through conducting extensive review of literature, covering a range of documents such as peer-reviewed publications; grey literature (i.e., government reports); legal documents (e.g., laws, regulations, tax codes); and government data repositories. Similarly, we reviewed the occurrence of resource rent schemes in the other extractive industries—namely forestry, oil, gas, and mining—in the same 18 countries. The sources used to determine whether resource rent schemes exist in these industries consisted mostly of (i) actual legislation/regulation and (ii) summaries of industry or national regulatory framework. The former type of source consisted of government documents, websites, and guides, while the latter consisted of private sector industry overviews, whether from legal firms (e.g., Thomson Reuters

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Practical Law), industry organizations, or multinational organizations (e.g., OECD, UN, and FAO).

The resulting resource rent dataset consisted of 90 country-industries, each of which may or may not have a rent recovery mechanisms (RRM). These observations take on a binary value: the RRM exist (‘1’) or do not exist (‘0’). Due to data limitations, there are also observations where: (i) there was little or no data on the country-industry taxation policy; or (ii) where there was some data on the country-industry, but either the sources were, for example, contradictory, unclear, limited, or questionable, such that it is unlikely an RRM exists but the imperfect evidence leaves much room for error. Such observations were assigned ‘0?’.

Classifying the collected information involved two main steps: (i) distinguishing an RRM from a cost-recovery (CR) scheme; and (ii) determining whether there is enough information about the occurrence of RRM in an industry. The purpose of (i) is to exclude payment schemes that are aiming to recover management costs paid by the government. If there is a clear and sufficient basis for deciding that some tax policy concerning resources exists, then the question becomes which category the particular policy belongs in, but in more complicated cases, the two questions may go hand in hand.

At the start of the classification process, the first distinction is made on the basis of the mechanism (Figure 6.3). A RRM is considered an explicit payment by an extractive industry to the government for the access and extraction of publicly-owned natural resources. Generally, RRMs comprise two broad forms. First, resource rent charges that are linked to the production, value or profitability of firms (not solely applied to cover regulatory costs) are one form (Chen & Mintz 2013; Sinner & Scherzer 2007) (Figure 6.3). Common examples are royalties set as a share of production output, and rent-based charges intended to capture resource rent after deducting current and capital costs from revenues (Mintz & Chen 2012; Sinner & Scherzer 2007). The second form, auctions, generally serve two roles: (i) create a revenue ex ante for the government through selling access rights (i.e., not tied to production); and (ii) allocate natural resources to the most efficient firms (Boadway & Keen 2010). A relatively

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uncommon mechanism to recover resource rent is privatization, in which through selling natural resources, governments recover resource rent as the sale value in “one strike” (Sinner & Scherzer 2007). The purpose of a CR, on the other hand, is to recover costs incurred by the government that largely benefit the private sector (Sinner & Scherzer 2007). In fisheries, CR mechanisms include costs such as annual license fees, limited entry fees, or landings taxes collected for specific management costs. When differentiating between an RRM and CR, one major ambiguity lies in the link between the charges paid by companies and the benefits they gain from government activities; the link may be weak or not visible. While some charges are extremely clear—for example, Portugal’s mining industry pays a CR based on an estimate of the cost to the government agency of writing up the mining contract—others are less so. The general rule we followed is that payment must be linked to government activity somehow, even if it is rather weak. Fees that are charged per inspection instead of being linked to production, or any regular operation that involves government participation, are not considered an RRM (i.e., it is a CR).

The second classification concerns the sufficiency of the evidence and the distinction between ‘0?’ and ‘0’. The main difficulty arises from the epistemic issue that lack of evidence for is not evidence against. In other words, it is easy to find a document stating that a charge exists, but there are no documents stating which charges do not exist—the fact that proof of a charge was not found does not provide proof that the charge does not exist. Even within the uncertainty this epistemic fact creates, there is variation in the evidence found and the way we categorized it. If clear evidence of an RRM policy (as classified by the decision tree in Figure 6.3) was found for a country- industry, then a ‘1’ was assigned. If a source that seemed to exhaustively describe the regulatory framework of the country-industry was found, and it made no mention of a policy resembling an RRM, then a ‘0’ was assigned as there was a reasonable basis for claiming that no RRM exists within that country-industry. However, there were also cases where sources describing a country-industry were conflicting, clearly non- exhaustive, limited, or otherwise insufficient. In such cases, a ‘0?’ was used to indicate that some information on the country-industry was found, but was not as conclusive as those assigned a (cautiously) confident ‘0’.

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For each of the 18 countries evaluated, and each extractive industry type, we summarized ‘1’ as “RRM common or ubiquitous”; ‘0’ as “RRM limited or absent”; and ‘0?’ as “Information deficient” (Figure 6.2). Given the wide range of possible RRM policies within each country-industry, the categories “RRM common or ubiquitous” and “RRM limited or absent” indicate the extent to which RRMs: (i) occur at the national level or by the administrative entities (largely relevant to Canada and the United States); and (ii) are prevalent within an industry. For (i), we assigned ‘1’ if we found a RRM at the federal level and/or if RRMs occur in the majority of states or provinces (Appendix E, Table E.3). To illustrate (ii), we assigned ‘1’ for the oil industry in Portugal because RRM is prevalent in the oil industry in general (RRM imposed on offshore and onshore operations), despite that offshore production in fields that are deeper than 200 meters is completely exempted from RRM (Appendix E, Table E.3). These discrepancies in RRM implementations are sometimes observed in the oil and gas industries where RRM regimes usually distinguish between, for example, offshore and onshore operations. While these categories can be viewed as coarse in some countries-industries, they communicate the wide variations in RRM implementation within countries-industries.

Several caveats exist in the resource rent assignments. First, proof of the existence of a charge gives far more certainty to a ‘1’ than the lack of proof gives to a ‘0’. This tendency implies that, in general, the proportion of ‘1s’ relative to ‘0s’ is biased upwards. In other words, we are more likely to have overlooked a ‘0’ than a ‘1’, since ‘1s’ are easier to find. Second, certain industries, like oil and gas, have particularly good data. Combined with the first caveat, we may suspect that country-industries with more accessible and detailed documents relevant to RRM are more likely to have ‘1s’ than ‘0s’, suggesting that the proportion of rent charges for these country-industries might be biased upwards. Finally, a single charge tied to output resembles an RRM rather than a CR, but we have little to no information about how the funds from RRM are used. If we find evidence of an RRM, but no evidence of a separate CR or of the funds from an RRM going to cost-recovery purposes, we consider it as an RRM despite the possibility that some or most of the RRM revenues might be paid for cost-recovery.

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Figure 6.3. Decision tree for determining whether the mechanism corresponds to resource rent recovery (RRM) or cost-recovery (CR). RRMs that are not tied to production generally reflect auctions.

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7 Conclusion

Ocean fisheries maintain the livelihoods of hundreds of millions of people globally; in some coastal nations, they deliver more than 60% of the per capita intake of animal protein (HLPE 2014); and along with aquaculture, they generate annual gross revenue of more than 100 billion dollars (Golden et al. 2017). This degree of dependence on fishery resources needs profound attention to their sustainability. However, maintaining exploitation rates near sustainable levels often requires high management intensity and thus can be hardly met in many complex fisheries contexts. Further, even though the profitability of the fishing industry has improved under catch share programs, there is limited knowledge on whether these fisheries contribute to the general welfare of society.

Studies in this dissertation examine some fisheries situations where exploitation rates are difficult to control, and highlight whether catch share fisheries is charged for harvesting fish, a practice seen in the exploitation of other publicly-owned natural resources. My second and third chapters explore how we can rebuild fish stocks under weak management institutions and avoid overexploitation in places where fisheries represent one of the few sources of protein and income. These two chapters highlight how size restrictions, modified so as to be consistent with retaining fish near the size/age at maturity, can alleviate pervasive issues like growth and recruitment overfishing or avert overexploitation. The basic effects of this simple rule—i.e., limiting the harvest of fish to sizes near the size at maturity—is to protect recruitment by allowing fish to spawn before harvest, and maximize yield per recruit, which typically is highest when fishing starts near the size at maturity. For example, I show that if implemented properly, this fundamental change in minimum size limits can help rebuild the Malabar blood snapper stock in Kuwait and enhance catches even if exploitation remains unregulated. Insights derived from chapters 2 and 3 can be useful to help sustain fish biomass and catches in fisheries subjected to minimal management interventions due to inherent factors (e.g., limited capacity to fisheries management, unstable political regime), inhibiting the use of traditional fisheries approaches.

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My fourth chapter assesses transboundary fisheries using an age-structured model to inform decisions about biological and economic compromises under alternative international and local management scenarios. This model can be used to ask questions about the bioeconomic trade-offs arising from competition, cooperation and country-independent management. For example, one of the main findings of this chapter indicated that while cooperation between Kuwait and Iran in harvesting the silver pomfret stock results in the highest bioeconomic gains, country-independent but sustainable management yields much higher gains than the current competitive situation. In other words, if each country decides to sustainably harvest the resource at the maximum sustainable yield level—a widely-adopted fisheries objective—without international agreement, the model predicted higher overall fish biomass as well as catch and profits for both countries than competition (i.e., status quo). The modeling framework in chapter 4 also considers the age-structure of the internationally-shared stock. This consideration is important when analyzing strategic interaction: fish stocks often cross multiple exclusive economic zones (EEZ) when migrating between nursery and spawning/feeding areas. In this case, fishing in any EEZ can impact the stocks’ age structure and overall recruitment rates. The developed model can be applied to understand how different patterns of the vulnerability at age of the shared stock in each EEZ influence catch, biomass and profits.

The fifth chapter underscores the impacts of the escalated demand for fish maw (or dried swim bladder)—one of the major dried seafood delicacies in China—on fishery resources and fisheries management in source countries. By reviewing and synthesizing available information on eight important maw-supplying species in major and mostly undocumented source countries, this chapter contributes to our understanding of fish maw demand and its potential threats to fishery resources in source countries. For example, trends in species-specific catch, price and export demonstrate that demand is likely intensifying in countries already supplying fish maw, shifting or expanding to new species, and emerging in new regions. I show that most of the maw species are inadequately managed, largely overexploited, and exhibit life- history strategies that make them extremely vulnerable to high fishing pressure. Management interventions are urgently required, which can aid in sustaining fish stocks

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and capturing the economic benefits derived from the maw market. However, I emphasize that maw’s extremely high value would complicate efforts to regulate these lucrative fisheries by stimulating smuggling and black-market systems.

My final chapter estimates the global volume and value of catch harvested by catch share fisheries. Chapter 6 also focuses on how frequent resource rent recovery mechanisms are in the fishing industry compared with other major extractive industries. The practice of imposing rent recovery mechanisms on extractive industries springs from the ownership of the resource and from the status of rent as a surplus related to the natural resource itself rather than from the actions of private enterprises. It is the first global study, to my knowledge, that demonstrates that global catch share fisheries largely lack rent recovery mechanisms, as opposed to mining, forestry, oil and gas, where capturing resource rent is the practice. This indicates that in most countries examined in chapter 6, the public is likely forgoing potential income streams from its fishery resources.

There are two important challenges to sustainable fisheries that were not considered in this dissertation. First, the multispecies nature of many fisheries, whereby fishing fleets target—or inadvertently take—a variety of species, can be a major cause of overfishing, particularly to slow life-history species (Hilborn & Walters 1992; Quetglas et al. 2016). Typically, there is a wide variation among stocks in sustainable exploitation rates, with productive species generally withstanding high exploitation rates. As such, overfishing in multispecies fisheries primarily occurs where more productive stocks drive bionomic dynamics to generate higher fishing rates than optimum for less productive (usually long-lived) species. In nations with developed regulatory systems, the impacts of multispecies fisheries can be mitigated, notably by placing constraints on allowable exploitation rates or catches of less productive species—or restricting fishing in areas with high abundance of those species (Hilborn et al. 2004a). However, the former approach often forces fisheries to underutilize productive species, leading to lowered food production and reduced profitability (Hilborn et al. 2004a). In places where there are scarce resources for management, for example in many parts of Asia, the Caribbean and Africa, the complexity of managing multispecies fisheries systems

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impose hurdles to the sustainable harvest of low resilient species (Hicks & McClanahan 2012; Zhai & Pauly 2019).

Second, managing fish stocks through controlling exploitation rates would fail if fishing is not the primary source of the decline in fish productivity. This is evident in well- managed fisheries that have not recovered despite reducing exploitation rates to levels that should allow rebuilding, indicating that other factors have contributed to reductions in stock productivity (Brown et al. 2019; Neubauer et al. 2013; Szuwalski & Thorson 2017). Indeed, extrinsic factors, especially changes in fish habitat and predator-prey interaction, have shown to impact fish biomasses and hinder the rebuilding of depleted ones under reduced fishing mortalities (Brown et al. 2019; Walters et al. 2020).

As an example, the sharp declines in the catch of several major fish stocks in the northern Arabian Gulf have been partly attributed to the decrease in estuarine rearing habitat—hence recruitment carrying capacity—through damming Tigris-Euphrates rivers (Al-Husaini et al. 2015; Ben-Hasan et al. 2018a). If recruitment changes are unrelated to stock size but to changes in Tigris-Euphrates rivers’ flow, future projections show that reducing exploitation rates would not increase catches of at least the grouper fishery (Ben-Hasan et al. 2017). Likewise, the growing abundance of marine mammals worldwide (notably seals and sea lions) brings about elevated predation levels on their prey, which can be highly commercial fish species. For example, even though exploitation rates are controlled rigorously and kept low, high predation levels are contributing to large declines in the productivity and body size of salmon stocks in the northeast Pacific Ocean (Nelson et al. 2019; Ohlberger et al. 2019; Walters et al. 2020). Given that marine habitats are facing large-scale degradation (Hamilton & Casey 2016; Waycott et al. 2009), and predation rates are intensifying in some regions, national agencies need to consider such extrinsic factors, possibly through adaptive management, to correctly characterize declines of fished species (Walters 1986).

There is no doubt that effective fisheries management and sound science have made important strides toward sustainable fishery resources and a profitable fishing industry (Birkenbach et al. 2017; Hilborn et al. 2020). However, the fraction of overfished stocks has steadily grown from 10% in 1974 to 34% in 2017 (FAO 2020c),

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mainly in regions with weak management institutions. In addition, concerns have been heightened that society is forgoing a stream of income from its quota-managed fishery resources. My second and third chapters provide insights into the potential of size restriction as an easily implementable policy to sustain fish stocks from depletion and enhance fishery yields. My sixth chapter confirms the limited economic contributions of catch share fisheries to coastal states, supporting calls for imposing resource rent charges on the fishing industry. By tackling some of the complex fisheries problems and highlighting the lack of resource rent charges in the fishing industry, I hope that my dissertation can constructively contribute to discussions and actions in fisheries sustainability, policy, and management.

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Appendices

Appendix A: Supplementary Material for Chapter 2

Table A.1 – Parameters and their equations used to calculate equilibrium biomass and yield.

풇풂 Relative fecundity at age 푓푎 = 푤푎 − 푤푚* −푀 풍풙풂 Survivorship at age a 푙푥1=1, 푙푥푎 = 푙푥푎−1푒 **

Average fished egg per 푈 퐸푃푅퐹 = ∑ 푓푎푙푥푎 푬푷푹푼 recruit

푼 −푀−푈푒푞푣푎 풍풙풂 Fished survivorship at age a 푙푥1=1, 푙푥푎 = 푙푥푎−1푒 *** 퐶푅 − 1 휷 Stock-recruitment parameter 훽 = 푅0(퐸푃푅0) Average unfished 푅0 is estimated from the fitting 푹ퟎ recruitment “leading procedure parameter”

푎퐸푃푅푈 − 1 푹풆풒 Equilibrium recruitment 푅푒푞 = 훽(퐸푃푅푈)

Equilibrium spawning stock 퐵 = 푅 ∑ 푙푥푈푤 푩풆풒 푒푞 푒푞 푎 푎 biomass

풀풆풒 Equilibrium yield 푌푒푞 = 푈푒푞푉퐵푒푞****

*푤푎 is the average weight at age and 푤푚 is the weight at maturity.

**M is natural mortality.

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-1 ***푈푒푞 is the equilibrium exploitation rate (range from 0–1 year at an increment of

0.025); 푣푎 is the vulnerability from length at age.

푈 ****푉퐵푒푞 is the vulnerable biomass calculated as 푉퐵푒푞= 푅푒푞 ∑ 푙푥푎 푤푎푣푎.

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Figure A.1 – Fitted observed to predicted age frequencies from the age structured model. Numbers reflect years, and (n) is sample size.

Figure A.2 – Effect of uncertainty about compensation ratio (High compensation ratio =

48 (base case); and compensation ratio = 10) on projections of biomass, recruits and catch. (a) biomass; (b) recruitment; and (c) catch. Future projections are carried out

-1 using 푎푣 = 5 years and exploitation rate = 0.3 year over 2020–2050 period.

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Appendix B: Supplementary Material for Chapter 3

To fit the catch at age data, we modelled the vulnerability at age (푣푎) using two approaches: (i) logistic function and (ii) assuming that gill net vulnerability is normally distributed by fish age with two parameters, mean age at capture and standard deviation. The logistic model is calculated by:

−(푎−푎푣)/푣푠 푣푎 = 1/(1 + 푒 ) (B.1)

Where 푎 is age; 푎푣 is the age at 50% vulnerability to capture; and 푣푠 is the vulnerability spread parameter. The normal distribution for modelling vulnerability at age was calculated as

1 푎−휇 1 − ( )2 푣 = 푒 2 휎 (B.2) 푎 휎√2휋

Where 휎 is standard deviation; and 휇 is the mean age at capture.

Parameters in equations B.1 (푎푣 and 푣푠) and B.2 (휇 and 휎), along with the fishing mortality (Fest, see below), were estimated by maximizing the Poisson log-likelihood objective function between the observed catch at age and predicted catch at age (see below). The observed catch at age data was obtained from (Ghanbarzadeh 2019), where age composition data for the Indian halibut were collected from October 2016 to November 2017 mainly from the gillnet fishery in Hormozgan Province, Iran (sample size = 343 fish).

We calculated the numbers at age (푁푎) using the following equation:

(−푀−푣푎−1퐹푒푠푡) 푁푎 = 푁푎−1푒 (B.3)

Where M is the natural mortality estimated to be 0.5 (see Table 3.1 in the main text).

Note that the initial numbers at age (푁0) is set to 1. We calculated catch at age (퐶푎) by:

퐶푎 = 퐹푒푠푡푣푎/(푀 + 퐹푒푠푡푣푎)( 푁푎 − 푁푎+1) (B.4)

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The Poisson log-likelihood for observed catch at age data was maximized at best fit under each approach by varying 퐹푒푠푡 and the parameters in equation B.1 for the logistic function, and parameters in equation B.2:

퐿퐿 = ∑퐶 ∑퐶 (−퐶푎 + 퐶푎 ln (퐶푎)) (B.5) 푎 푎표푏푠 표푏푠

Where 퐶푎표푏푠 is the observed catch at age.

We computed the Akaike criterion (AIC) for 퐿퐿 obtained from each approach using the maximum log-likelihood (퐿퐿푚푎푥) results and the number of estimated parameters (3 parameters for each approach) (Walters & Martell 2004):

퐴퐼퐶 = −2퐿퐿푚푎푥 + 2푝 (B.6)

Where 푝 is the number of parameters treated as variables during the fitting procedure to maximize 퐿퐿.

Fitting to data

The AIC of 퐿퐿 obtained when modelling vulnerability at age using a logistic function was -2122.123, while fitting data based on assuming that gill net vulnerability is normally distributed resulted in AIC = -2103.005. This indicates that catch at age data are fitted better when vulnerability at age is assumed asymptotic than when assuming that vulnerability at age is normally distributed by fish age (i.e., dome-shaped; see Figure B.2).

Estimates of parameters under each approach are presented in Table 3.1.

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120

100

Catch (numbers) 40

0 0 2 4 6 8 10 12 Age (year)

Figure B.1. Fitting catch at age data by modelling the vulnerability at age using a logistic function (solid line; equation B.1) and by assuming that gillnet vulnerability at age is normally distributed by fish age (dashed line; equation B.2). Dots represent the observed catch at age (sample size = 343).

Figure B.2. The estimated vulnerability at age from fitting to catch at age data. A. modelling

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vulnerability using a logistic function; B. modelling vulnerability using normal distribution.

Table B.1. Estimated parameters from fitting the catch at age data (parameters are described in equations B.1 and B.2).

Parameters Logistic Dome-shaped

푎푣 4.1 years -

푣푠 0.48 year - 휇 - 6.4 years 휎 - 1.5 years

-1 -1 퐹푒푠푡 0.36 year 1.8 year

Effects of alternative vulnerabilities at age on stock status

Figure B.3. The effect of alternative logistic and dome-shaped vulnerabilities at age on Bt/Bmsy and Ut/Umsy. The current status of the Indian halibut is colored red (for the year 2017). Quadrant “A” denotes a healthy stock (Bt > Bmsy) and an exploitation rate lower than Umsy (Ut < Umsy); Quadrant “B” denotes an overfished stock (Bt < Bmsy) but no overfishing

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(Ut < Umsy); Quadrant “C” denotes both an overfished stock and overfishing (Ut > Umsy); And quadrant “D” denotes overfishing but not an overfished stock.

180 160 CPUE 140 120 Predicted CPUE

100 ), ), Predicted CPUE

2 80 60 40

CPUE (kg/nm CPUE 20 0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Year

Figure B.4. Predicted catch per unit effort (CPUE) fitted to the observed CPUE time series for the halibut gillnet fishery in Hormozgan Province, Iran. For further description on the model and fitting procedure, see Material and Methods in the main text.

Effects of different compensation ratio values on stock status

Figure B.5. The effect of alternative compensation ratio (CR) values on Bt/Bmsy and Ut/Umsy. The year that reflects the current status of the Indian halibut for the base case is colored red.

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Quadrant “A” denotes a healthy stock (Bt > Bmsy) and an exploitation rate lower than Umsy (Ut < Umsy); Quadrant “B” denotes an overfished stock (Bt < Bmsy) but no overfishing (Ut < Umsy); Quadrant “C” denotes both an overfished stock and overfishing (Ut > Umsy); And quadrant “D” denotes overfishing but not an overfished stock.

190

Appendix C: Supplementary Material for Chapter 4

The overall sum of squared deviations is given by

푆푆 = ∑ (퐶 − 퐶푝푟푑)2 + ∑ (퐶 − 퐶푝푟푑)2 + ∑ {ln (퐵 /퐵푝푟푑)2} (C.1) 푡 푡푥 푡푥 푡 푡푦 푡푦 푡 푡 푡 푝푟푑 where 퐶푡 and 퐶푡 are observed and predicted catch for taken by the fishing fleet of 푝푟푑 each country (x and y); 퐵푡 and 퐵푡 are the biomass estimates from catch at age data (Al-Husaini et al. 2007) and predicted overall biomass from Eq. (2) under section 3.1 푝푟푑 “Stock dynamics”, respectively. 퐵푡 and 퐵푡 in Eq. (C.1) are compared using log-normal deviations, following (Walters & Ludwig 1994) advice. In addition to estimating parameters shown in Table 4.1, the modeling approach has varied the proportion of stock available 푝 within each EEZ annually so as to minimize 푆푆 (Figure C.1).

0.8

0.6

0.4

0.2 Proportion of thestock available

0 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 Year

Figure C.1. Proportion of the stock available within each EEZ estimated from fitting the bioeconomic model to data. The grey line represents proportions of pomfret stock within

Kuwait’s EEZ (pt) while the black line represents the proportions within Iran’s EEZ (pt − 1).

191

We calculated equilibrium recruitment as follows:

훼(퐸푃푅퐹−1) 푅푒푞 = (C.2) 훽(퐸푃푅퐹)

Parameters are described in Table C.1.

The equilibrium yield (푌푒푞) is then predicted as equilibrium recruitment 푅푒푞 multiplied by 퐹 equilibrium yield per recruit (푌푃푅푒푞 = 퐹푒푞 ∑ 푣푎푙푥푎 푤푎); that is, the calculation accounts for recruitment change as well as yield per recruit. Tables C.2 and C.3 present the equilibrium numerical results when the combined stock is subjected to different combinations of fishing mortalities by the two countries. MSY value used in cooperative and national Fmsy management regimes corresponds to the maximum equilibrium yield in Tables C.2 and C.3 (and, by definition, Fmsy would be F which has resulted in that maximum equilibrium yield).

Table C.1. Parameters and their equations used to calculate the equilibrium yield. Incidence functions in the third column are also described in (Forrest et al. 2010) and (Walters & Martell 2004).

풇풂 Relative fecundity at age 푓푎 = 푤푎 − 푤푚* −푀 풍풙풂 Survivorship at age a 푙푥1=1, 푙푥푎 = 푙푥푎−1푒 **

Average fished egg per 퐹 퐸푃푅퐹 = ∑ 푓푎푙푥푎 푬푷푹푭 recruit

푭 −푀−퐹푒푞푣푎 풍풙풂 Fished survivorship at age a 푙푥1=1, 푙푥푎 = 푙푥푎−1푒 *** 퐶푅 − 1 휷 Stock-recruitment parameter 훽 = 푅0(퐸푃푅0)

192

Average unfished 푅0 is estimated from the fitting

푹ퟎ recruitment “leading procedure parameter”

퐹 풀푷푹풆풒 Equilibrium yield per recruit 푌푃푅푒푞 = 퐹푒푞 ∑ 푣푎푙푥푎 푤푎

*푤푎 is the average weight at age a (Eq. 3) and 푤푚 is the weight at maturity (푤푚 = 0.28 kg for the pomfret; (Al-Husaini et al. 2007)).

** 푀 is the natural mortality, estimated using the one-parameter tmax model (Then et al. 2015).

-1 ***퐹푒푞 is the equilibrium fishing mortality (maximum range from 0–0.9 year at an increment of 0.1, see Table C.2 and C.3); and 푣푎 is the vulnerability at age a.

Table C.2. Predicted Kuwait equilibrium yields subject to a range of a combination of fishing mortalities F. Maximum sustainable yield (MSY; and Fmsy) used in cooperative and national Fmsy managements corresponds to the maximum equilibrium yield in the Table (MSY = 2,400 tons).

-1

Fishing mortalities F (year ) within Kuwait EEZ

Kuwait Kuwait F 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 MSY Fmsy (103t)

0 0 1.5 2.1 2.3 2.2 1.9 1.4 0.9 0.2 0 2.3 0.3

)within Iran 1

- 0.1 0 1.1 1.5 1.7 1.5 1.2 0.8 0.2 0 0 1.7 0.3

(year 0.2 0 0.8 1.1 1.1 1.0 0.6 0.2 0 0 0 1.1 0.3

EEZ 0.3 0 0.6 0.8 0.7 0.5 0.2 0 0 0 0 0.8 0.2

0.4 0 0.4 0.5 0.4 0.1 0 0 0 0 0 0.5 0.2

0.5 0 0.2 0.3 0.1 0 0 0 0 0 0 0.3 0.2

0.6 0 0.1 0.1 0 0 0 0 0 0 0 0.1 0.1

Fishingmortalities 0.7 0 0.03 0 0 0 0 0 0 0 0 0.03 0.1

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Table C.3. Predicted Iran equilibrium yields subject to a range of a combination of fishing mortalities F. Maximum sustainable yield (MSY; and Fmsy) used in cooperative and national

Fmsy managements corresponds to the maximum equilibrium yield in the Table (MSY = 2,400 tons).

-1 Fishing mortalities F (year ) within Kuwait EEZ

F 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

0 0 0 0 0 0 0 0 0

0.1 1.5 1.1 0.8 0.6 0.4 0.2 0.1 0.03

0.2 2.1 1.5 1.1 0.8 0.5 0.3 0.1 0

)within Iran EEZ 1

- 0.3 2.3 1.7 1.1 0.7 0.4 0.1 0 0

0.4 2.2 1.5 1.0 0.5 0.1 0 0 0

(year

F 0.5 1.9 1.2 0.6 0.2 0 0 0 0

0.6 1.4 0.8 0.2 0 0 0 0 0

0.7 0.9 0.2 0 0 0 0 0 0

0.8 0.2 0 0 0 0 0 0 0

Fishingmortalities 0.9 0 0 0 0 0 0 0 0

Iran MSY (103t) 2.3 1.7 1.1 0.8 0.5 0.3 0.1 0.03

Iran Fmsy 0.3 0.3 0.3 0.2 0.2 0.2 0.1 0.1

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Table C.4. Catch (103t) and relative profits obtained by Kuwait and Iran fisheries under national Fmsy management and cooperation with different shares of the overall Fmsy

-1 (Kuwait:Iran; overall Fmsy = 0.3 year ).

National Cooperation Cooperation Cooperation Cooperation Cooperation

Fmsy 10:90 20:80 30:70 40:60 50:50 Catc Profi Catch Profits Catch Profits Catch Profits Catch Profits Catch Profits h t Kuwai 0.7 0.5 0.2 0.2 0.5 0.3 0.7 0.5 0.9 0.6 1.2 0.8 t Iran 0.7 0.6 2.1 1.6 1.9 1.4 1.6 1.2 1.4 1.0 1.2 0.9 Total 1.4 1.1 2.3 1.8 2.4 1.7 2.3 1.7 2.3 1.6 2.4 1.7

Table C.5. Catch (103t) and relative profits obtained by Kuwait and Iran fisheries under national Fmsy management and cooperation with different shares of the overall Fmsy

-1 (Iran:Kuwait; overall Fmsy = 0.3 year ).

National Cooperation Cooperation Cooperation Cooperation Cooperation

Fmsy 10:90 20:80 30:70 40:60 50:50

Catc Profi Catch Profits Catch Profits Catch Profits Catch Profits Catch Profits h t Kuwai 0.7 0.5 2.1 1.4 1.9 1.3 1.6 1.1 1.4 1.0 1.2 0.8 t Iran 0.7 0.6 0.2 0.2 0.5 0.4 0.7 0.5 0.9 0.7 1.2 0.9 Total 1.4 1.1 2.3 1.6 2.4 1.7 2.3 1.6 2.3 1.7 2.4 1.7

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Appendix D: Supplementary Material for Chapter 5

Table D.1. Mean ex-vessel prices of maw and flesh (USD/kg). Prices of maw are received by the fishers at the auctions, at landing sites or from selling in the black market (with the exception of Nile perch in East Africa, where maw are largely sold by maw extractors and collectors; (Bagumire et al. 2018)).

Timeframe Timeframe Price of Price of Species Country of flesh of maw Source flesh maw price price Totoaba (EIA 2016; (Totoaba Mexico 0.6 2018-2019 5,033 2016 Juarez et al. macdonaldi) 2016) Gulf corvina (Mascareña (Cynoscion Mexico 0.8 2010-2018 17 2015-2018 s et al. othonopterus) 2018) Black-spotted croaker (Ghosh et India - - 139 2009 (Protonibea al. 2009) diacanthus) Black-spotted croaker (CSB 1979- Kuwait 6.9 2000-2018 - - (Protonibea 2017) diacanthus) Black-spotted (Behzadi croaker Iran 6.4 2008-2017 2,299 2019 2020; IFO (Protonibea 2000-2017) diacanthus) Black-spotted (ABARES croaker 1993-2018; Australia 4.9 2008-2017 214 2019 (Protonibea Sadovy de diacanthus) Mitcheson

196

et al. 2019)

Acoupa weakfish (Jimenez et Brazil 2.2 2014-2016 252 2014-2016 (Cynoscion al. 2020) acoupa) Gillbacker sea catfish (Jimenez et Brazil 1.8 2014-2016 30 2014-2016 (Sciades al. 2020) parkeri) Crucifix (Sciades proops) and (Jimenez et couma Brazil 0.9 2014-2016 12 2014-2016 al. 2020) (Sciades couma) sea catfishes Nile perch (Bagumire (Lates Uganda 2.5* 2015 98 2018 et al. 2018; niloticus) IOC 2015) Nile perch (Bagumire (Lates Tanzania 2.5* 2015 65 2018 et al. 2018; niloticus) IOC 2015) Nile perch (Bagumire (Lates Kenya 2.5* 2015 126 2018 et al. 2018; niloticus) IOC 2015) *The price represents the average price for the Nile perch flesh in East Africa rather than being a country-specific price.

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Table D.2. The maximum and common length (cm) and a and b parameter values used to calculate the maximum and common weight of each species (Data obtained from (FishBase 2021)). The common length is defined in FishBase as “Refers to the peak of the population size histogram; that is, the length (in centimeters) at which most individuals of the population would be sampled.” (FishBase 2021). The value of common weight for totoaba and Gulf corvina are provided in the Methods section.

Species Maximum length Common length a b

Totoaba 200 - 0.00891 3.07

Gulf corvina - - - -

Black-spotted croaker 150 100 0.013 2.94

Acoupa weakfish 110 45 0.0072 3.07

Gillbacker sea catfish 190 90 0.0061 3.25

Crucifix sea catfish 100 50 0.006 3.19

Couma sea catfish 97 50 0.013 3.04

Nile perch 200 100 0.0089 3.08

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Table D.3. Common fishing gears used to target (or remove as a bycatch) maw-supplying species.

Source Species country or Fishing gears Description References region Gillnets with mesh sizes (Almeida et al. ranging from 30 mm to 200 2011; Bentes et mm, with 70 m to 9 km in al. 2012; Acoupa Gillnets and Brazil length and 2 to 9 m in Jimenez et al. weakfish longlines height. Longlines with 2019, 2020; 1600−2000 m in length and Mourão et al. 1400−3000 hooks. 2009) The fishing in this estuarine region is carried out in the Gillnet with 12 cm Black- shallow waters of the Bay (Dutta et al. mesh size; trawls; spotted India of Bengal up to 70 km from 2014; Ghosh et and dol croaker the coast. The majority of al. 2010) nets the catch is obtained by trawls. In Iran, gillnets with 8 inch mesh size 1600 inch length and 280 inch height are (Abdulqader et Black- used. Bottom trawls with al. 2015; Arabian Gillnets; trawls; and spotted 7.5 cm mesh size (full Behzadi 2006; Gulf* traps croaker meshj) in the cod-end and Moradinasab 72 m head rope are used. 2021) Traps are used in Kuwait to target this species. Beach seine; Black-spotted croaker in Black- demersal longline; Australia is mainly found in (Penny et al. spotted Australia** fish trap; gillnet; Norther Territory, 2018) croaker hand line, hand or Queensland and Western powered reels; Australia. In Norther

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hook and line; Territories and midwater trawl; net; Queensland, it seems that otter trawl; pelagic net and line fisheries, gillnet; trawl; respectively, are the main trotline; and fisheries targeting the unspecified fishing species. Overall, out of 13 gear commercial fishing methods, the highest number occur in Northern Territory (9), Western Australia (5) and then Queensland (3). Longlines with 1600−2000 (Almeida et al. m in length and 1400−3000 2011; Bentes et Couma hooks. Gillnets with mesh al. 2012; Longlines and sea Brazil sizes ranging from 30 mm Jimenez et al. gillnets catfish to 200 mm, with 70 m to 9 2019, 2020; km in length and 2 to 9 m in Mourão et al. height. 2009) Longlines with 1600−2000 (Almeida et al. m in length and 1400−3000 2011; Bentes et Crucifix hooks. Gillnets with mesh al. 2012; Longlines and sea Brazil sizes ranging from 30 mm Jimenez et al. gillnets catfish to 200 mm, with 70 m to 9 2019, 2020; km in length and 2 to 9 m in Mourão et al. height. 2009) Longlines with 1600−2000 (Almeida et al. m in length and 1400−3000 2011; Bentes et Gillbacker hooks. Gillnets with mesh al. 2012; Longlines and sea Brazil sizes ranging from 30 mm Jimenez et al. gillnets catfish to 200 mm, with 70 m to 9 2019, 2020; km in length and 2 to 9 m in Mourão et al. height. 2009) Gulf Mexico Gillnets Gillnets of 293 m length, 5 (Solana-

200

corvina 1/3 inch mesh size are Sansores et al. used. 2012) The legal mesh size of gillnets is ≥5 inch but illegal (Kayanda et al. East Gillnets and mesh sizes (< 5 inch) are 2017; Mkumbo Nile perch Africa*** longlines also applied. Hook sizes & Marshall used for longlines vary 2015) widely. Gillnet of 500 m long (average) with 10 inch Gillnets and mesh size. Longlines are (Cisneros-Mata Totoaba Mexico longlines 300 m (average) with 200 2020) #3 size hooks. The main fishing gear used is gillnet. *Mainly Iran and Kuwait

**Fishing gears used in the Northern Territory, Queensland and Western Australia and for commercial fisheries only

***Uganda, Tanzania, Kenya

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Figure D.1. A. The relationship between mean ex-vessel prices of maw (Table 5.2) and the maximum weight of different species, excluding totoaba. B. The relationship between mean ex-vessel prices of maw the maximum weight of different species, including totoaba. The maximum recorded weight for the Gulf corvina is 12 kg (dataMares 2021).

202

Appendix E: Supplementary Material for Chapter 6

Figure E.1. Proportion of marine capture value under catch share (CS) programs. Data are means of annual proportions of landed value between 2000–2017, separated by: fishing country or territory (A) and FAO major fishing area (B).

203

Figure E.2. Relationships between the proportion of landed value and the proportion of catch weight under catch share (CS) programs. Data are means of annual proportions of global marine capture production (value or weight) between 2000–2017, separated by: fishing country, FAO major fishing area and major taxonomic aggregation.

Table E.1. Country, species, taxonomic group, and FAO major fishing area of catch share fisheries included in this study. Values listed by fishery are mean catch under catch shares (CS; 103 t), total catch across all sectors (103 t), the ratio of these catch values, and the value (106 US$) of yield caught under catch shares. Mean values are estimated for the period 2000-2017 using global species-level ex-vessel price estimates.

Mean Proportio Mean Taxono FAO major Mean yield n of yield value of Country Species mic fishing total under under yield under group area catch CS CS CS Argentine Atlantic, Argentina gadids 222.61 294.82 0.76 200.28 hake Southwest Patagonia Atlantic, Argentina n gadids 68.60 85.75 0.80 58.48 Southwest grenadier Patagonia other Atlantic, Argentina 3.73 3.73 1.00 21.21 n toothfish marine Southwest

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fish Southern Atlantic, Argentina blue gadids 18.85 25.82 0.73 8.10 Southwest whiting bivalves- Indian Blacklip Australia gastropo Ocean, 0.36 0.36 1.00 3.35 abalone ds Eastern bivalves- Blacklip Pacific, Australia gastropo 0.11 0.11 1.00 1.04 abalone Southwest ds Indian Blue Australia gadids Ocean, 4.86 4.86 1.00 4.14 grenadier Eastern Blue Pacific, Australia gadids 0.40 0.40 1.00 0.34 grenadier Southwest Blue Indian crabs- Australia swimming Ocean, 0.88 0.88 1.00 2.93 lobsters crab Eastern other Bluenose Pacific, Australia marine 0.01 0.01 1.00 0.01 warehou Southwest fish other Indian Common Australia marine Ocean, 0.15 0.15 1.00 0.18 warehou fish Eastern other Common Pacific, Australia marine 0.01 0.01 1.00 0.01 warehou Southwest fish other Indian Flatheads Australia scorpae Ocean, 3.61 3.61 1.00 9.43 nei nids Eastern other Flatheads Pacific, Australia scorpae 0.80 0.80 1.00 2.10 nei Southwest nids Indian Ghost elasmob Australia Ocean, 0.12 0.12 1.00 0.06 shark ranchs Eastern Green crabs- Pacific, Australia rock 0.11 0.11 1.00 2.21 lobsters Southwest lobster other Indian Australia John dory marine Ocean, 0.06 0.06 1.00 0.19 fish Eastern other Pacific, Australia John dory marine 0.08 0.08 1.00 0.24 Southwest fish other Indian Mackerel Australia marine Ocean, 0.58 0.58 1.00 0.71 icefish fish Antarctic other Indian Australia Mirror dory marine Ocean, 0.29 0.29 1.00 0.88 fish Eastern other Pacific, Australia Mirror dory marine 0.23 0.23 1.00 0.70 Southwest fish

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other Indian marine Australia Morwongs Ocean, 0.58 0.58 1.00 0.71 percoidi Eastern ds other marine Pacific, Australia Morwongs 0.26 0.26 0.98 0.31 percoidi Southwest ds other Indian Orange Australia marine Ocean, 1.75 1.75 1.00 2.02 roughy fish Eastern other Orange Pacific, Australia marine 0.02 0.02 1.00 0.02 roughy Southwest fish other Patagonia Pacific, Australia marine 0.19 0.19 1.00 1.06 n toothfish Southwest fish other Indian Pink cusk- Australia marine Ocean, 1.04 1.05 0.99 1.96 eel fish Eastern other Pink cusk- Pacific, Australia marine 0.29 0.29 1.00 0.55 eel Southwest fish other Indian Australia Redfish marine Ocean, 0.56 0.56 1.00 0.92 fish Eastern other Pacific, Australia Redfish marine 0.37 0.37 1.00 0.60 Southwest fish other Indian Sillago- marine Australia Ocean, 0.45 1.40 0.32 1.17 whitings percoidi Eastern ds other Sillago- marine Pacific, Australia 1.30 1.30 1.00 3.39 whitings percoidi Southwest ds other Indian Silver Australia marine Ocean, 0.30 0.30 1.00 0.14 gemfish fish Eastern other Silver Pacific, Australia marine 0.04 0.06 0.72 0.02 gemfish Southwest fish other Indian Silver Australia marine Ocean, 1.47 1.47 1.00 1.79 warehou fish Eastern other Silver Pacific, Australia marine 0.10 0.10 1.00 0.12 warehou Southwest fish other Smooth Pacific, Australia marine 0.01 0.01 1.00 0.01 oreo dory Southwest fish Southern Indian crabs- Australia rock Ocean, 3.57 3.73 0.96 73.16 lobsters lobster Eastern

206

other Pacific, Australia Spiky oreo marine 0.01 0.01 1.00 0.01 Southwest fish carangid Indian White s- Australia Ocean, 0.10 0.10 1.00 0.08 trevally mackere Eastern ls Common pleurone Atlantic, Belgium 1.01 3.85 0.26 5.74 sole ctids Northeast European pleurone Atlantic, Belgium 0.83 6.88 0.12 1.72 plaice ctids Northeast Alaska pollock(= Pacific, Canada gadids 3.42 3.42 1.00 2.28 Walleye Northeast poll.) Amer. plaice(=Lo pleurone Atlantic, Canada 1.46 2.02 0.72 2.25 ng rough ctids Northwest dab) American crabs- Atlantic, Canada 2.21 64.36 0.03 19.59 lobster lobsters Northwest American bivalves- Atlantic, Canada sea gastropo 64.66 68.11 0.95 109.28 Northwest scallop ds Arrowtooth pleurone Pacific, Canada 3.17 3.17 1.00 6.87 flounder ctids Northeast Atlantic Atlantic, Canada gadids 16.50 23.01 0.72 33.37 cod Northwest Atlantic pleurone Atlantic, Canada 2.54 2.54 1.00 16.31 halibut ctids Northwest Atlantic forage Atlantic, Canada 73.04 152.03 0.48 16.35 herring fish Northwest Atlantic sebastid Atlantic, Canada redfishes 11.68 13.18 0.89 16.02 s Northwest nei forage Atlantic, Canada Capelin 13.70 30.44 0.45 5.35 fish Northwest Flatfishes pleurone Pacific, Canada 5.40 5.40 1.00 10.14 nei ctids Northeast Greenland pleurone Atlantic, Canada 6.54 13.83 0.47 20.03 halibut ctids Northwest Atlantic, Canada Haddock gadids 11.79 16.66 0.71 15.09 Northwest other Pacific, Canada Lingcod scorpae 2.03 2.11 0.96 5.30 Northeast nids North Pacific, Canada Pacific gadids 65.43 65.43 1.00 62.04 Northeast hake Northern Atlantic, Canada shrimps 126.57 136.40 0.93 230.10 prawn Northwest Pacific, Canada Pacific cod gadids 1.06 1.06 1.00 2.09 Northeast Pacific bivalves- Pacific, Canada 1.47 1.47 1.00 1.40 geoduck gastropo Northeast

207

ds Pacific pleurone Pacific, Canada 4.35 5.06 0.86 34.27 halibut ctids Northeast Pacific forage Pacific, Canada 19.85 20.39 0.97 6.89 herring fish Northeast Pacific sebastid Pacific, Canada ocean 4.89 4.89 1.00 4.24 s Northeast perch Pandalus Atlantic, Canada shrimps shrimps 7.83 8.90 0.88 14.24 Northwest nei Picked elasmob Pacific, Canada 2.69 2.69 1.00 2.04 dogfish ranchs Northeast Queen crabs- Atlantic, Canada 93.73 93.73 1.00 312.38 crab lobsters Northwest Rays, elasmob Pacific, Canada stingrays, 1.20 1.20 1.00 0.66 ranchs Northeast mantas nei other Pacific, Canada Sablefish scorpae 3.08 3.11 0.99 12.35 Northeast nids Saithe(=P Atlantic, Canada gadids 5.82 5.82 1.00 6.75 ollock) Northwest Scorpionfi shes, sebastid Pacific, Canada 11.54 13.80 0.84 14.07 redfishes s Northeast nei bivalves- Stimpson's Atlantic, Canada gastropo 24.01 24.01 1.00 22.74 surf clam Northwest ds White Atlantic, Canada gadids 1.80 2.71 0.67 2.74 hake Northwest Winter pleurone Atlantic, Canada 1.68 1.68 1.00 3.65 flounder ctids Northwest Witch pleurone Atlantic, Canada 0.27 1.59 0.17 0.60 flounder ctids Northwest Yellowtail pleurone Atlantic, Canada 8.21 8.42 0.98 17.79 flounder ctids Northwest Anchoveta forage Pacific, 1041.5 1041.5 Chile (=Peruvian 1.00 1060.64 fish Southeast 5 5 anchovy) Carrot crabs- Pacific, Chile squat 4.10 4.10 1.00 27.65 lobsters Southeast lobster carangid Chilean s- Pacific, Chile jack 560.82 862.79 0.65 202.35 mackere Southeast mackerel ls bivalves- Chilean Pacific, Chile gastropo 1.91 1.91 1.00 0.59 mussel Southeast ds Chilean Pacific, Chile nylon shrimps 4.46 4.46 1.00 8.11 Southeast shrimp

208

Chilean echinod Pacific, Chile 38.16 38.16 1.00 115.82 sea urchin erms Southeast bivalves- Chilean Pacific, Chile gastropo 1.61 1.61 1.00 1.52 semele Southeast ds bivalves- Cholga Pacific, Chile gastropo 3.99 3.99 1.00 2.01 mussel Southeast ds bivalves- Choro Pacific, Chile gastropo 0.50 0.50 1.00 0.25 mussel Southeast ds Falkland forage Pacific, Chile 21.03 21.03 1.00 1.60 sprat fish Southeast bivalves- False Pacific, Chile gastropo 2.59 2.59 1.00 4.72 abalone Southeast ds other Giant Pacific, Chile invertebr 0.25 0.25 1.00 0.68 barnacle Southeast ates Giant crabs- Pacific, Chile 0.18 0.18 1.00 0.61 stone crab lobsters Southeast bivalves- Macha Pacific, Chile gastropo 2.14 2.14 1.00 2.02 clam Southeast ds other Mote Pacific, Chile scorpae 70.05 70.05 1.00 183.02 sculpin Southeast nids Patagonia Pacific, Chile n gadids 71.44 71.44 1.00 60.90 Southeast grenadier Peruvian bivalves- Pacific, Chile calico gastropo 0.04 0.04 1.00 0.07 Southeast scallop ds other Pink cusk- Pacific, Chile marine 1.80 3.59 0.50 3.37 eel Southeast fish Softshell crabs- Pacific, Chile 3.35 3.35 1.00 21.53 red crab lobsters Southeast South forage Pacific, Chile American 7.53 7.53 1.00 1.80 fish Southeast pilchard South Pacific, Chile Pacific gadids 34.13 56.89 0.60 37.32 Southeast hake Southern Pacific, Chile blue gadids 19.64 19.64 1.00 8.44 Southeast whiting Southern Pacific, Chile gadids 11.99 23.99 0.50 20.95 hake Southeast Southern crabs- Pacific, Chile 4.19 4.19 1.00 26.88 king crab lobsters Southeast other Southern marine Pacific, Chile rays 15.50 15.50 1.00 23.15 percoidi Southeast bream ds

209

bivalves- Pacific, Chile Taca clam gastropo 14.19 14.19 1.00 13.44 Southeast ds Atlantic forage Atlantic, Denmark 127.44 127.44 1.00 28.53 herring fish Northeast Atlantic salmoni Atlantic, Denmark 0.18 0.18 1.00 0.48 salmon ds Northeast European forage Atlantic, Denmark 18.71 204.66 0.09 1.43 sprat fish Northeast Northern Atlantic, Denmark shrimps 3.68 3.68 1.00 6.69 prawn Northeast Norway crabs- Atlantic, Denmark 4.33 4.33 1.00 27.24 lobster lobsters Northeast Norway Atlantic, Denmark gadids 24.77 35.38 0.70 30.75 pout Northeast Sandeels( forage Atlantic, Denmark =Sandlanc 201.21 287.44 0.70 75.97 fish Northeast es) nei pleurone Atlantic, Denmark Turbot 0.72 0.72 1.00 5.35 ctids Northeast Atlantic forage Atlantic, Estonia 27.26 29.63 0.92 6.10 herring fish Northeast Atlantic Atlantic, France gadids 9.50 9.50 1.00 19.20 cod Northeast Common pleurone Atlantic, France 0.24 7.51 0.03 1.34 sole ctids Northeast European Atlantic, France gadids 3.01 21.52 0.14 4.09 hake Northeast European pleurone Atlantic, France 3.19 3.19 1.00 6.63 plaice ctids Northeast Great bivalves- Atlantic, France Atlantic gastropo 23.72 23.72 1.00 40.08 Northeast scallop ds Greenlan Northern Atlantic, shrimps 2.49 2.49 1.00 4.52 d prawn Northeast Amer. plaice(=Lo pleurone Atlantic, Iceland 1.05 1.05 1.00 1.61 ng rough ctids Northeast dab) Atlantic Atlantic, Iceland gadids 209.57 213.85 0.98 423.74 cod Northeast Atlantic forage Atlantic, Iceland 221.19 221.19 1.00 49.51 herring fish Northeast carangid Atlantic s- Atlantic, Iceland 84.86 84.86 1.00 57.80 mackerel mackere Northeast ls forage Atlantic, Iceland Capelin 420.47 420.47 1.00 164.23 fish Northeast Common pleurone Atlantic, Iceland 1.63 1.63 1.00 1.18 dab ctids Northeast European pleurone Atlantic, Iceland 5.94 5.94 1.00 12.35 plaice ctids Northeast Atlantic, Iceland Haddock gadids 57.74 62.43 0.92 73.94 Northeast

210

bivalves- Iceland Atlantic, Iceland gastropo 1.44 1.44 1.00 2.43 scallop Northeast ds Lemon pleurone Atlantic, Iceland 1.87 1.87 1.00 5.29 sole ctids Northeast Northern Atlantic, Iceland shrimps 10.98 10.98 1.00 19.96 prawn Northeast Norway crabs- Atlantic, Iceland 1.79 1.79 1.00 11.25 lobster lobsters Northeast Saithe(=P Atlantic, Iceland gadids 39.83 53.69 0.74 46.23 ollock) Northeast Witch pleurone Atlantic, Iceland 1.47 1.47 1.00 3.18 flounder ctids Northeast Atlantic forage Atlantic, Ireland 27.55 27.55 1.00 6.17 herring fish Northeast bivalves- Mediterran Striped Italy gastropo ean and 22.82 22.82 1.00 21.62 venus ds Black Sea Pacific forage Pacific, Japan 51.58 51.58 1.00 19.48 sandlance fish Northwest Red snow crabs- Pacific, Japan 18.97 18.97 1.00 63.21 crab lobsters Northwest Southern Indian tuna- Japan bluefin Ocean, 1.23 1.27 0.97 10.91 billfish tuna Eastern Atlantic, Mauritani European forage Eastern 0.00 13.73 0.00 0.00 a anchovy fish Central Jack and carangid Atlantic, Mauritani horse s- Eastern 5.23 32.64 0.16 1.89 a mackerels mackere Central nei ls Atlantic, Mauritani Sardinella forage Eastern 37.92 115.72 0.33 9.05 a s nei fish Central Caribbean Atlantic, crabs- Mexico spiny Western 0.71 0.71 1.00 13.29 lobsters lobster Central carangid Atlantic Atlantic, s- Morocco chub Eastern 37.95 99.87 0.38 47.37 mackere mackerel Central ls European Atlantic, forage Morocco pilchard(= Eastern 77.68 690.16 0.11 39.96 fish Sardine) Central Jack and carangid Atlantic, horse s- Morocco Eastern 2.57 19.75 0.13 0.93 mackerels mackere Central nei ls Cape rock crabs- Atlantic, Namibia 0.20 0.20 1.00 4.14 lobster lobsters Southeast West crabs- Atlantic, Namibia African 2.12 2.12 1.00 7.08 lobsters Southeast geryon Netherlan Atlantic forage Atlantic, 85.17 85.17 1.00 19.07

211

ds herring fish Northeast Netherlan Common pleurone Atlantic, 9.22 10.48 0.88 52.57 ds sole ctids Northeast Netherlan European pleurone Atlantic, 6.94 28.75 0.24 14.42 ds plaice ctids Northeast Netherlan Greater forage Atlantic, 3.20 3.20 1.00 3.91 ds argentine fish Northeast bivalves- New Abalones Pacific, gastropo 0.98 0.99 0.99 9.20 Zealand nei Southwest ds other New Australian marine Pacific, 2.47 2.47 1.00 6.45 Zealand salmon percoidi Southwest ds other Black New marine Pacific, cardinal 1.58 1.58 1.00 1.92 Zealand percoidi Southwest fish ds other New Pacific, Black oreo marine 3.17 3.17 1.00 2.31 Zealand Southwest fish New Blue Pacific, gadids 142.52 142.52 1.00 121.50 Zealand grenadier Southwest New elasmob Pacific, Blue shark 0.70 0.70 1.00 0.60 Zealand ranchs Southwest other New Bluefin Pacific, scorpae 3.63 3.63 1.00 3.87 Zealand gurnard Southwest nids other New Bluenose Pacific, marine 2.11 2.11 1.00 2.57 Zealand warehou Southwest fish New Common Pacific, gadids 1.14 1.14 1.00 1.41 Zealand mora Southwest New Dark ghost elasmob Pacific, 1.87 1.87 1.00 1.02 Zealand shark ranchs Southwest bivalves- New Delicate Pacific, gastropo 0.05 0.05 1.00 0.08 Zealand scallop Southwest ds New Ghost elasmob Pacific, 1.30 1.30 1.00 0.71 Zealand shark ranchs Southwest other New Giant Pacific, marine 3.24 3.24 1.00 3.95 Zealand stargazer Southwest fish Green New crabs- Pacific, rock 0.03 0.03 1.00 0.60 Zealand lobsters Southwest lobster other New Pacific, John dory marine 0.95 0.95 1.00 2.86 Zealand Southwest fish other New Pacific, King dory marine 0.50 0.50 1.00 1.52 Zealand Southwest fish New other New Pacific, Zealand marine 2.29 2.29 1.00 5.98 Zealand Southwest blue cod fish

212

New bivalves- New Zealand Pacific, gastropo 0.66 0.66 1.00 0.57 Zealand dredge Southwest ds oyster New New crabs- Pacific, Zealand 0.83 0.83 1.00 8.05 Zealand lobsters Southwest lobster New bivalves- New Pacific, Zealand gastropo 1.84 1.84 1.00 3.11 Zealand Southwest scallop ds New New Zealand elasmob Pacific, 0.66 0.66 1.00 0.36 Zealand smooth ranchs Southwest skate other New Pacific, Opah marine 0.13 0.13 1.00 0.10 Zealand Southwest fish other New Orange Pacific, marine 12.55 12.55 1.00 14.45 Zealand roughy Southwest fish other New marine Pacific, Parore 0.07 0.07 1.00 0.19 Zealand percoidi Southwest ds other New Pink cusk- Pacific, marine 15.88 16.03 0.99 29.78 Zealand eel Southwest fish New elasmob Pacific, Porbeagle 0.08 0.08 1.00 0.06 Zealand ranchs Southwest New Red rock crabs- Pacific, 2.65 2.65 1.00 54.43 Zealand lobster lobsters Southwest other New Pacific, Redfish marine 0.08 0.08 1.00 0.12 Zealand Southwest fish other New marine Pacific, Rubyfish 0.50 0.50 1.00 0.61 Zealand percoidi Southwest ds New Shortfin elasmob Pacific, 0.12 0.12 1.00 0.10 Zealand mako ranchs Southwest other New Silver Pacific, marine 0.76 0.80 0.95 0.36 Zealand gemfish Southwest fish other New Silver marine Pacific, 6.41 6.41 1.00 19.91 Zealand seabream percoidi Southwest ds other New Silver Pacific, marine 9.32 9.32 1.00 11.37 Zealand warehou Southwest fish other New Smooth Pacific, marine 7.14 7.14 1.00 5.20 Zealand oreo dory Southwest fish New other Pacific, Snoek 25.68 25.68 1.00 12.01 Zealand marine Southwest

213

fish Southern New Pacific, blue gadids 29.31 29.31 1.00 12.60 Zealand Southwest whiting New Southern Pacific, gadids 8.95 8.95 1.00 15.64 Zealand hake Southwest New Southern pleurone Pacific, 0.37 0.37 1.00 0.42 Zealand lemon sole ctids Southwest Spotted New estuary elasmob Pacific, 1.39 1.39 1.00 1.20 Zealand smooth- ranchs Southwest hound bivalves- New Stutchbury Pacific, gastropo 1.34 1.34 1.00 1.27 Zealand 's venus Southwest ds other New marine Pacific, Tarakihi 5.21 5.79 0.90 6.36 Zealand percoidi Southwest ds Velvet other New Pacific, leatherjack marine 0.60 0.60 1.00 1.16 Zealand Southwest et fish Wellington New cephalo Pacific, flying 43.70 43.70 1.00 62.49 Zealand pods Southwest squid carangid New White s- Pacific, 3.18 3.35 0.95 2.51 Zealand trevally mackere Southwest ls other New White Pacific, marine 1.95 1.95 1.00 2.38 Zealand warehou Southwest fish Atlantic Atlantic, Norway gadids 299.42 299.42 1.00 605.41 cod Northeast Atlantic forage Atlantic, Norway 249.57 658.67 0.38 55.86 herring fish Northeast carangid Atlantic s- Atlantic, Norway 146.80 178.39 0.82 99.98 mackerel mackere Northeast ls Blue Atlantic, Norway whiting(=P gadids 300.22 454.88 0.66 70.89 Northeast outassou) Greater forage Atlantic, Norway 0.62 0.62 1.00 0.76 argentine fish Northeast Atlantic, Norway Haddock gadids 84.09 90.42 0.93 107.69 Northeast Red king crabs- Atlantic, Norway 1.80 1.80 1.00 11.56 crab lobsters Northeast Saithe(=P Atlantic, Norway gadids 193.76 193.76 1.00 224.90 ollock) Northeast White Atlantic, Norway gadids 0.09 0.09 1.00 0.14 hake Northeast Anchoveta forage Pacific, 5700.3 5700.3 Peru 1.00 5804.81 (=Peruvian fish Southeast 1 1

214

anchovy) Atlantic salmoni Atlantic, Poland 0.08 0.08 1.00 0.20 salmon ds Northeast European forage Atlantic, Poland 21.93 70.74 0.31 1.67 sprat fish Northeast Atlantic sebastid Atlantic, Portugal redfishes 1.67 1.67 1.00 2.29 s Northeast nei Blue and Atlantic, Portugal shrimps 0.07 0.07 1.00 0.19 red shrimp Northeast Alaska Russian pollock(= Pacific, 1326.8 1326.8 Federatio gadids 1.00 883.93 Walleye Northwest 0 0 n poll.) Russian Atlantic Atlantic, Federatio gadids 236.26 274.72 0.86 477.71 cod Northeast n Russian Atlantic sebastid Atlantic, Federatio redfishes 0.11 5.64 0.02 0.15 s Northwest n nei Russian Blue king crabs- Pacific, Federatio 4.84 4.84 1.00 31.08 crab lobsters Northwest n Chinook(= Russian Spring=Ki salmoni Pacific, Federatio 0.40 0.40 1.00 0.68 ng) ds Northwest n salmon Russian Chum(=Ke salmoni Pacific, Federatio ta=Dog) 40.59 40.59 1.00 62.53 ds Northwest n salmon Russian Coho(=Sil salmoni Pacific, Federatio ver) 3.48 3.48 1.00 9.23 ds Northwest n salmon Russian Coonstripe Pacific, Federatio shrimps 0.64 0.64 1.00 1.16 shrimp Northwest n Russian European forage Atlantic, Federatio 0.57 0.57 1.00 0.71 smelt fish Northeast n Russian Mediterran Freshwate other fw Federatio ean and 0.03 0.03 1.00 0.03 r bream fish n Black Sea Russian Golden crabs- Pacific, Federatio 2.75 2.75 1.00 17.65 king crab lobsters Northwest n Russian Atlantic, Federatio Haddock gadids 78.43 78.43 1.00 100.44 Northeast n Russian Humpy Pacific, Federatio shrimps 0.74 0.74 1.00 1.35 shrimp Northwest n Russian Japanese cephalo Pacific, Federatio flying 1.75 1.75 1.00 2.51 pods Northwest n squid

215

Russian Kamchatk pleurone Pacific, Federatio 8.86 8.86 1.00 19.21 a flounder ctids Northwest n Russian Northern Pacific, Federatio shrimps 8.63 8.63 1.00 15.69 prawn Northwest n Russian Pacific forage Pacific, Federatio 278.02 278.02 1.00 96.52 herring fish Northwest n Russian Pink(=Hu salmoni Pacific, Federatio mpback) 194.61 194.61 1.00 360.04 ds Northwest n salmon Russian Red king crabs- Atlantic, Federatio 5.39 5.39 1.00 34.58 crab lobsters Northeast n Schoolma Russian ster cephalo Pacific, Federatio 42.12 42.12 1.00 60.23 gonate pods Northwest n squid Russian Mediterran other fw Federatio Sichel ean and 0.04 0.04 1.00 0.04 fish n Black Sea Russian Sockeye(= salmoni Pacific, Federatio Red) 21.57 21.57 1.00 56.58 ds Northwest n salmon South Cape Atlantic, gadids 120.41 135.40 0.89 145.21 Africa hakes Southeast Indian South Cape gadids Ocean, 0.01 0.01 1.00 0.01 Africa hakes Western carangid Cape South s- Atlantic, horse 15.74 26.23 0.60 5.68 Africa mackere Southeast mackerel ls South Cape rock crabs- Atlantic, 1.68 2.09 0.80 34.36 Africa lobster lobsters Southeast other South Patagonia Atlantic, marine 0.01 0.01 1.00 0.03 Africa n toothfish Southeast fish other Indian South Patagonia marine Ocean, 0.24 0.24 1.00 1.36 Africa n toothfish fish Antarctic bivalves- South Perlemoen Atlantic, gastropo 0.02 0.21 0.10 0.20 Africa abalone Southeast ds Southern South forage Atlantic, African 222.48 222.48 1.00 226.56 Africa fish Southeast anchovy Southern South forage Atlantic, African 153.98 153.98 1.00 88.42 Africa fish Southeast pilchard Southern South crabs- Atlantic, spiny 0.58 0.58 1.00 5.99 Africa lobsters Southeast lobster Spain Albacore tuna- Atlantic, 0.12 12.43 0.01 0.18

216

billfish Northeast Atlantic tuna- Atlantic, Spain bluefin 0.66 2.34 0.28 6.04 billfish Northeast tuna other Atlantic, Spain Barnacle invertebr 0.17 0.17 1.00 0.46 Northeast ates tuna- Atlantic, Spain Swordfish 0.22 1.72 0.13 0.82 billfish Northeast Atlantic Atlantic, Sweden gadids 13.20 13.20 1.00 26.69 cod Northeast Atlantic forage Atlantic, Sweden 10.94 95.94 0.11 2.45 herring fish Northeast European forage Atlantic, Sweden 76.63 76.63 1.00 5.83 sprat fish Northeast Northern Atlantic, Sweden shrimps 1.92 1.92 1.00 3.49 prawn Northeast Norway crabs- Atlantic, Sweden 1.20 1.20 1.00 7.55 lobster lobsters Northeast United Atlantic Atlantic, gadids 25.82 26.65 0.97 52.21 Kingdom cod Northeast United Atlantic forage Atlantic, 62.37 87.19 0.72 13.96 Kingdom herring fish Northeast United Common pleurone Atlantic, 1.31 2.36 0.55 7.44 Kingdom sole ctids Northeast United European pleurone Atlantic, 4.69 17.64 0.27 9.76 Kingdom plaice ctids Northeast United Atlantic, Haddock gadids 31.48 38.03 0.83 40.32 Kingdom Northeast United Norway crabs- Atlantic, 25.10 33.11 0.76 157.97 Kingdom lobster lobsters Northeast United Atlantic, Whiting gadids 6.18 11.52 0.54 5.78 Kingdom Northeast Alaska United pollock(= Pacific, 1139.9 1351.9 States of gadids 0.84 759.42 Walleye Northeast 1 5 America poll.) Amer. United plaice(=Lo pleurone Atlantic, States of 1.77 1.81 0.98 2.72 ng rough ctids Northwest America dab) United American bivalves- Atlantic, States of sea gastropo 101.56 191.62 0.53 171.64 Northwest America scallop ds United Arrowtooth pleurone Pacific, States of 1.71 29.59 0.06 3.71 flounder ctids Northeast America United other Atka Pacific, States of scorpae 17.87 51.68 0.35 19.64 mackerel Northeast America nids United Atlantic tuna- Atlantic, States of bluefin 0.31 0.63 0.49 2.84 billfish Northwest America tuna United Atlantic Atlantic, gadids 6.68 6.89 0.97 13.51 States of cod Northwest

217

America United Atlantic sebastid Atlantic, States of redfishes 1.98 1.99 1.00 2.72 s Northwest America nei United bivalves- Atlantic Atlantic, States of gastropo 114.87 120.92 0.95 108.82 surf clam Northwest America ds United Aurora sebastid Pacific, States of 0.00 0.00 0.63 N/A rockfish s Northeast America United Pacific, Bank sebastid States of Eastern 0.03 0.05 0.63 N/A rockfish s America Central United Black sebastid Pacific, States of 0.00 0.18 0.00 0.00 rockfish s Northeast America other United Black marine Atlantic, States of 0.16 1.23 0.13 0.68 seabass percoidi Northwest America ds United Canary sebastid Pacific, States of 0.03 0.04 0.72 0.02 rockfish s Northeast America United Pacific, Chilipeppe sebastid States of Eastern 0.14 0.19 0.75 0.12 r rockfish s America Central United Darkblotch sebastid Pacific, States of 0.11 0.11 0.95 0.09 ed rockfish s Northeast America United English pleurone Pacific, States of 0.64 0.68 0.95 0.72 sole ctids Northeast America United Flatfishes pleurone Pacific, States of 0.02 15.61 0.00 0.04 nei ctids Northeast America United Flathead pleurone Pacific, States of 8.76 15.66 0.56 9.88 sole ctids Northeast America other United Atlantic, marine States of Gag Western 0.20 0.84 0.23 0.36 percoidi America Central ds other United Great marine Atlantic, States of Northern 0.74 0.78 0.95 0.90 percoidi Northwest America tilefish ds other United Atlantic, Groupers marine States of Western 0.04 0.12 0.31 0.07 nei percoidi America Central ds United Atlantic, States of Haddock gadids 5.40 5.48 0.99 6.92 Northwest America United crabs- Pacific, King crabs 7.76 8.78 0.88 49.80 States of lobsters Northeast

218

America United other Pacific, States of Lingcod scorpae 0.19 0.43 0.45 0.50 Northeast America nids United Longspine sebastid Pacific, States of thornyhea 0.06 0.07 0.95 #N/A s Northeast America d United North Pacific, States of Pacific gadids 206.94 206.94 1.00 196.22 Northeast America hake other United Northern Atlantic, marine States of red Western 1.02 2.10 0.48 3.25 percoidi America snapper Central ds United bivalves- Ocean Atlantic, States of gastropo 83.50 85.21 0.98 79.10 quahog Northwest America ds United Pacific, States of Pacific cod gadids 26.53 267.12 0.10 52.55 Northeast America United bivalves- Pacific Pacific, States of gastropo 2.21 4.41 0.50 2.09 geoduck Northeast America ds United Pacific pleurone Pacific, States of 20.90 25.15 0.83 164.56 halibut ctids Northeast America United Pacific sebastid Pacific, States of ocean 26.93 31.81 0.85 23.36 s Northeast America perch United Petrale pleurone Pacific, States of 1.94 2.04 0.95 2.19 sole ctids Northeast America United Queen crabs- Pacific, States of 18.36 20.40 0.90 61.18 crab lobsters Northeast America United Rays, elasmob Pacific, States of stingrays, 8.04 8.93 0.90 4.41 ranchs Northeast America mantas nei other United Atlantic, Red marine States of Western 1.87 2.61 0.71 3.42 grouper percoidi America Central ds United pleurone Pacific, States of Rock sole 33.39 42.38 0.79 37.67 ctids Northeast America United other Pacific, States of Sablefish scorpae 14.76 19.35 0.76 59.28 Northeast America nids United Saithe(=P Atlantic, States of gadids 5.38 5.42 0.99 6.25 ollock) Northwest America other United Atlantic, marine States of Scamp Western 0.20 0.20 1.00 0.37 percoidi America Central ds

219

Scorpionfi United other shes, Pacific, States of scorpae 6.75 13.58 0.50 8.23 redfishes Northeast America nids nei Scorpionfi United Pacific, shes, sebastid States of Eastern 0.00 0.00 0.06 0.00 redfishes s America Central nei United Shortspine sebastid Pacific, States of thornyhea 0.36 0.89 0.40 0.44 s Northeast America d United Splitnose sebastid Pacific, States of 0.04 0.04 0.95 0.03 rockfish s Northeast America United Tanner crabs- Pacific, States of 1.98 2.20 0.90 7.37 crab lobsters Northeast America other United Atlantic, Warsaw marine States of Western 0.05 0.05 1.00 0.10 grouper percoidi America Central ds United White Atlantic, States of gadids 2.38 2.40 0.99 3.62 hake Northwest America United Widow sebastid Pacific, States of 0.87 0.95 0.91 0.75 rockfish s Northeast America United Winter pleurone Atlantic, States of 2.97 3.21 0.92 6.43 flounder ctids Northwest America United Witch pleurone Atlantic, States of 1.49 1.53 0.98 3.24 flounder ctids Northwest America other United Atlantic, Yellowedg marine States of Western 0.42 0.42 1.00 0.76 e grouper percoidi America Central ds United Yellowfin pleurone Pacific, States of 82.52 107.92 0.76 58.91 sole ctids Northeast America United Yellowtail pleurone Atlantic, States of 2.79 2.98 0.94 6.04 flounder ctids Northwest America United Yellowtail sebastid Pacific, States of 1.09 1.24 0.88 0.95 rockfish s Northeast America

220

Table E.2. Catch share programs by country, highlighting type of program, method used for allocating individual shares, permanence of share tenure, and whether or not resource rent is charged.

Program Allocation Permanen Country Name of catch share program type method ce of share Argentine Individual Transferable Quota Grandfatheri Medium- Argentina IVQ Program ng term Australian New South Wales Abalone Equal Australia ITQ Perpetuity Fishery shares Grandfatheri Australia Australian Tasmanian Rock Lobster Fishery ITQ Perpetuity ng Equal Australia Australian Victorian Abalone Fishery ITQ Short-term shares Grandfatheri Australia Australian Victorian Rock Lobster Fishery ITQ Short-term ng Equal Indetermin Canada Canadian Atlantic Snow Crab Fishery IQ, IVQ shares ate Canadian Gulf of St. Lawrence Herring Large Grandfatheri Medium- Canada ITQ Purse Seine Fishery ng term Grandfatheri Indetermin Canada Canadian Gulf Shrimp Fishery ITQ ng ate Equal Indetermin Canada Canadian Northern Shrimp Fishery ITQ shares ate Equal Indetermin Canada Canadian Nova Scotia Shrimp Fishery ITQ shares ate Canadian Scotia-Fundy Atlantic Herring Purse Grandfatheri Medium- Canada ITQ Seine Fishery ng term Canadian Scotian Shelf and Southern Grand Grandfatheri Indetermin Canada ITQ Banks Atlantic Halibut Fishery ng ate Grandfatheri Chile Chilean Anchoveta Fishery ITQ Long-term ng Auction, Chile Chilean Jurel (Jack Mackerel) Fishery ITQ Grandfatheri Long-term ng Auction, Chilean Langostino Colorado (Red Chile ITQ Grandfatheri Long-term Prawn/Squat Lobster) Fishery ng Chilean Merluza Comun (Common Hake) Grandfatheri Chile ITQ Long-term Fishery ng Chilean Merluza de Cola (Patagonia Grandfatheri Chile ITQ Long-term Grenadier) Fishery ng Chilean Merluza del Sur o Austral (Southern Grandfatheri Chile ITQ Long-term Hake) Fishery ng

Rent recovery mechanisms occur in catch share programs with boldfaced names. Program types include: Individual quota (IQ), Individual transferable quota (ITQ), and Individual vessel quota (IVQ). Short-term shares are allocated for 0-5 years, medium-term shares for 6-15 years, long-term shares for 16-25 years, while ‘perpetuity’ represents indefinite tenure. 221

Danish Demersal Transferable Fishing Grandfatheri Indetermin Denmark IVQ Concession ng ate Danish Pelagic Transferable Fishing Grandfatheri Indetermin Denmark ITQ Concession ng ate Grandfatheri France French Great Atlantic Scallop Fishery IQ Short-term ng Icelandic Individual Transferable Quota Grandfatheri Iceland ITQ Perpetuity System ng Dutch Cutter (Beam Trawl) Individual Grandfatheri Netherlands ITQ Perpetuity Transferable Quota ng Grandfatheri New Zealand New Zealand Quota Management System ITQ Perpetuity ng Grandfatheri Norway Norwegian Coastal Fleet IVQ Long-term ng Grandfatheri Norway Norwegian Ocean-Going Fleet IVQ Long-term ng Peruvian Anchoveta Northern-Central Stock Grandfatheri Indetermin Peru IVQ Individual Vessel Quota Program ng ate Grandfatheri Poland Polish Cod Fishery IQ Short-term ng Grandfatheri Portugal Portuguese NAFO Individual Vessel Quotas IVQ Perpetuity ng Grandfatheri Medium- Russia Russian Baltic Sea Fishery IQ ng term Grandfatheri Medium- Russia Russian Far Eastern Basin Coastal Fishery IQ ng term Russian Far Eastern Basin Offshore Grandfatheri Medium- Russia IQ Fishery ng term Grandfatheri Medium- Russia Russian Faroe Islands Pelagics Fishery IQ ng term Grandfatheri Medium- Russia Russian Fishery in the Norwegian EEZ IQ ng term Russian NEAFC Managed Area Demersal Grandfatheri Medium- Russia IQ Fishery ng term Russian Northern Barents Sea Crab and Grandfatheri Medium- Russia IQ Scallop Fishery ng term Russian Northern Basin International Areas Grandfatheri Medium- Russia Fishery Joint Russian-Norwegian Fisheries IQ ng term Commission Russian Northern Basin Territorial Fishery Grandfatheri Medium- Russia Joint Russian-Norwegian Fisheries IQ ng term Commission Grandfatheri Medium- Russia Russian Pacific Salmon Fishery IQ ng term Grandfatheri Medium- South Africa South African Hake Deep Sea Trawl Fishery ITQ ng term Grandfatheri Medium- South Africa South African Hake Handline Fishery ITQ ng term Grandfatheri Medium- South Africa South African Hake Inshore Trawl Fishery ITQ ng term Grandfatheri Medium- South Africa South African Hake Longline Fishery ITQ ng term South African West Coast Rock Lobster Near Grandfatheri Medium- South Africa IQ Shore Fishery ng term

222

South African West Coast Rock Lobster Grandfatheri Medium- South Africa IQ Offshore Fishery ng term Grandfatheri Indetermin Sweden Swedish Demersal Fishery ITQ ng ate Grandfatheri Medium- Sweden Swedish Pelagic ITQ ITQ ng term United Kingdom English Catch Quota Pilot Grandfatheri UK IQ Short-term Program ng United Kingdom Scottish Catch Quota Pilot Grandfatheri UK IQ Short-term Program ng US Alaska Fixed-gear Commercial Halibut and Grandfatheri Indetermin USA ITQ Sablefish Individual Fishing Quota Program ng ate US Atlantic Sea Scallop Individual Fishing Grandfatheri Indetermin USA Quota Program - Limited Access General ITQ ng ate Category US Atlantic Surfclam and Ocean Quahog Grandfatheri Indetermin USA ITQ Individual Transferable Quota Program ng ate US Bering Sea and Aleutian Islands Crab Grandfatheri Indetermin USA ITQ Rationalization Program ng ate US Maryland Summer Flounder Individual Equal Indetermin USA ITQ Fishing Quota Program shares ate US Pacific Coast Groundfish Limited Entry Grandfatheri Indetermin USA ITQ Trawl Individual Fishing Quota Program ng ate Grandfatheri Indetermin USA US Pacific Sablefish Permit Stacking Program ITQ ng ate

223

Table E.3. Overview of each country-industry. The fossil fuel industry comprises information on the oil industry and the gas industry. In fossil fuel exploitation, a concession entails “an oil and gas company is granted exclusive rights to exploration and production of the concession area and owns all oil and gas production. Under concession oil and gas companies typically pay royalties and corporate income tax. Other payments to the government may be applicable, such as bonuses, rentals, resource taxes, special petroleum or windfall profit taxes, export duties, state participation and others.” (EY 2019). In forestry, public ownership of forestland is the share of the sum of areas under all ownerships (public, private and unknown ownerships) for the most recent year reported in (FAO 2018a); the sum of areas under all ownerships gives similar estimates of total forest area in all countries except for Argentina, where the sum is much lower than the total forest area.

Country Industry Description Source

In 1998, the government enacted its Federal Fisheries Act to achieve sustainable development of its marine resources. Part of this legislation established an Individual Vessel Quota (IVQ) system. Shares were allocated to vessels, and eligibility to participate in the program was (Bertolotti et restricted to vessels owned by individuals or companies al. 2016; holding fishing licenses for one or more of the four Fisheries Consejo species included in the IVQ. The allocation of the quotas Federal through grandfathering considered the legal catch, Pesquero n.d.) employed labor force, investments and production of fishery resources by individual fishing companies or Argentina vessel owners between 1997-2009. This system has a permanence/validity of 15 years (the system was implemented in 2010).

The forest area is 27112 (1000 ha; around 9.9% of land area). About 62% of the area is owned by the public. The forest area allocated for production is 1202 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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Fossil fuel resources include conventional onshore and offshore resources in addition to unconventional shale. While oil production decreased by about 30% over the 2005-2017 period, a large potential exists for oil production due to high prices internationally and favorable Fossil (EY 2019; local policies that encourage investment. Production of fuel OECD 2021) both oil and gas has increased around the period 2015- 2019, largely caused by the increased activity in some of the largest shale oil and gas reservoirs in the world. Oil and gas operations are conducted under a concession between the industry and the government.

The country has various and significant mineral resources with the potential of expanding the mining sector in the future. All mineral deposits are owned by the state. The production of minerals like aluminum, gold, iron and steel, copper and lithium dominate the mineral sector in the (Baker & country. The country is one of the largest producers of McKenzie lithium globally. Generally, the industry consists of local Mining 2020; and foreign private firms as well as public firms. Mining Inestroza activities are conducted by issuing an exploration permit 2021) or under a concession, and mining rights are given in perpetuity if the industry pays the yearly charge and invest in every stage of the mining activity. Activities like exploration and exploitation of minerals are granted to firms on a first-come, first-served basis.

Individual Transferable Quota (ITQ) systems were implemented for the fisheries detailed in the database (New South Wales abalone, Victorian abalone, Tasmanian rock lobster and Victorian rock lobster fisheries). However, there are some differences regarding quota allocation methods. In the Victorian Abalone and the Australian New South Wales Abalone fisheries quotas were equally distributed between license holders. In the Tasmanian Rock Lobster and Victorian Rock Lobster (McIlgorm & fisheries allocation was based on grandfathering. For Goulstone instance, in the Tasmanian rock lobster the catch history 2001; SMP Australia Fisheries period was used for the allocation (i.e., the sum of best 2000; TAC three years from the period November 1988 to October Committee 1997). 2004) The duration of the ITQ systems varies among fisheries. For the New South Wales abalone and the Tasmanian rock lobster fisheries, shares were allocated in perpetuity (i.e., with guaranteed renewal every 10-year period). Meanwhile, in the Victorian rock lobster and abalone fisheries licenses are issued annually and must be renewed prior to expiry. If not renewed on time, no quota will be allocated to the license and it will be removed from

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the fishery.

One of the top ten countries for forest area (ranked sixth). About 73% of the area is owned by the public. The forest area is 124751 (1000 ha; around 16.2% of land area). (FAO 2018a, Forestry The forest area allocated for production is 2017 (1000 2020b) ha). National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots is conducted.

Mining for coal dominates the country’s energy production. Recently the proven reserves of gas have expanded significantly with the commercialization of substantial volumes of unconventional gas. The industry (Buteyn 2021; Fossil is based on free enterprise whereby private firms are EY 2019; Tse fuel involved in for example exploration, production, 2012) processing and marketing. Oil and gas operations are conducted under a concession between the industry and the government.

The country is one of the world’s leaders in the production of minerals like iron ore, copper, and gold. The government owns mineral resources whether they are located on freehold land or Crown land. Mining activities Mining including explorations and other mining operations are (Buteyn 2021) conducted by private entities. National agencies grant rights for mining activities and identify mineral resources but industry conducts exploration or development of minerals.

Fisheries in the database are managed under an Individual Transferable Quota (ITQs) with the exemption of the Atlantic Snow Crab Fishery in which transferability of shares is restricted (IQs). Regarding allocation, most fisheries assigned quotas through grandfathering, based Canada Fisheries (OECD n.d.) on catch history. For example, in the Canadian Gulf Shrimp Fishery each participant received a shrimp quota based on catch history from 1987 to 1989. However, three fisheries (Atlantic snow crab, northern shrimp and Nova Scotia shrimp fisheries) used an equal shares method to initially assign quotas. Lastly, there are some

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fisheries in which share duration is undefined. Others, such as the Gulf of St. Lawrence Herring Large Purse Seine Fishery, have a 10-year plan duration.

One of the top ten countries for forest area (ranked third). The forest area is 347069 (1000 ha; around 38.2% of land area). About 91% of the area is owned by the public. The (FAO 2018a, Forestry forest area allocated for production is 18310 (1000 ha). 2020b) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

One of the leading countries for the production of oil and gas. The country comes third in the largest proven oil reserves globally. Its production of oil sand has increased rapidly, and proven natural gas reserves have grown in Fossil (EY 2019; recent years. The industry for the exploration and fuel OECD 2021) production of petroleum is highly competitive and consists of hundreds of companies across the country. Oil and gas operations are conducted under a concession between the industry and the government.

One of the major mining countries in the world in the production of minerals like gold, potash, cobalt, and aluminium with many exploration, development and mining projects underway. In general, the government of (Barry 2019) the province or territory owns and manages the mineral Mining resources. The industry consists of private firms that are involved in different mining activities (e.g., exploration, production, processing and marketing). New mines and some developments of existing mines need Federal review and approval as well as satisfying other permitting requirements.

The anchoveta, common hake, Patagonia grenadier, southern hake, jack mackerel and squat lobster fisheries are managed under fully transferable individual quotas (ITQs). Although quotas were grandfathered in these fisheries, (Bernal et al. there are some exceptions. The jack mackerel and squat 1999; Cerda- lobster fisheries used a combination of auctions and Amico & Chile Fisheries grandfathering. In 1992, the initial auction allocated 90% Urbina-Véliz of the total catch in the squat lobster fishery. The 2001; Kroetz remaining shares were assigned based on the historical et al. 2017) catch reported over the previous three years (grandfathering). Alternatively, in the jack mackerel fishery up to 15% of the annual total allowable catch is auctioned since 2013.

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In 2013, the government amended the Fisheries Law. One of the many changes was related to the duration of the quota system. It was extended for another 20 years, with the possibility of further renewals. Regarding quotas associated with auctions, they would have a permanence of 20 years. After this, these quotas have to be auctioned again without the possibility of renewal. Nonetheless, in November 2020, this law was repealed by Congress. The Government announced that it would appeal this decision to the country’s Constitutional Court.

The forest area is 17735 (1000 ha; around 23.9% of land area). About 25% of the area is owned by the public. The forest area allocated for production is 6835 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots is conducted.

The country’s production of local fossil fuels is insignificant and import almost all of its oil and gas. Despite having the third-largest shale gas reservoirs in (Eldridge & South America, Chile’s legal frameworks does not Brown 2018; Fossil promote the exploration and production of this kind of Espejo 2020; fuel fossil fuel. Oil and gas deposits in maritime waters under EY 2019; national jurisdictions can only be extracted by state- OECD 2021) owned companies. Oil and gas operations are conducted under a concession between the industry and the government.

In Latin America, the country is a leading mineral producer. It is one of the major global producers of minerals like copper, iodine, rhenium, and lithium. All (Bambach & mineral deposits are owned by the state. Lithium deposits Pulgar 2020; in maritime waters under national jurisdiction and other Eldridge & Mining substances located in areas that have been categorized Brown 2018; as essential to national security can only be exploited by Inestroza state-owned companies. Generally, the industry consists 2019) of local and foreign private firms as well public firms with copper being one of the most economically-important mineral.

The government introduced an Individual Transferable Quota (ITQ) Program for the Danish herring fishery in (Bonzon et al. 2003. In 2007, the system was extended to cover Denmark Fisheries 2010; additional pelagic species. In the same year, this Carpenter & Individual Vessel Quota Program was established for the Kleinjans Danish demersal fisheries.

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In both cases, quotas were allocated to individual 2017) registered fishers for exclusive use on a registered fishing vessel. Allocations were based on grandfathering according to their weighted catch history from 2003, 2004, and 2005, with weights of 20%, 30%, and 50%, respectively.

Rights in both fisheries are held by active fishers for an indefinite period. However, since 2017, the ministry holds the right to reallocate quotas with a required 16 years’ minimum notice.

The forest area is 612 (1000 ha; around 14.4% of land area). About 24% of the area is owned by the public. The forest area allocated for production is 484 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

Oil and gas resources are substantial in the North Sea, and have been extracted since the 1970s. Denmark only second to the United Kingdom in the scale of oil Fossil (EY 2019; production. However, oil production has been decreasing fuel OECD 2021) since the beginning of the 2000s as well as natural gas in recent years. Oil and gas operations are conducted under a concession between the industry and the government.

The country’s metallic mineral resources are limited. The industry mainly extracts non-metallic minerals like salt, granite gravel, lime, sand and stone. Under Danish law, private ownership, exploration, development and production of minerals are permitted. Generally, exploration and exploitation permits for resources found in (Hojem 2015; Mining the seabed and continental shelf are owned by the state Plaza-Toledo and are awarded using auctions. Fees or taxes are 2019) absent for exploitation on land and for licenses to explore the seabed and continental shelf. The Raw Materials Act sets the level of resource rent charges mainly for seabed mineral resources. The Act on the Use of the Danish Subsoil of 2011 regulates the exploitation of salt.

Quotas for the Great Atlantic scallop fishery are not (Carpenter & transferable, although track records do get transferred France Fisheries Kleinjans with the vessel only when the vessel changes owner 2017) (IVQs). Quotas were allocated by grandfathering, based on three national criteria: historical track records, socio-

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economic equilibrium, and market orientation, taking as reference period the years 2001 to 2003. The duration of quotas is linked to licenses, which are annually issued with a strong likelihood of renewal.

The forest area is 16989 (1000 ha; around 31% of land area). About 25% of the area is owned by the public. The forest area allocated for production is 6193 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

The country has scarce fossil fuel resources, the production of gas and oil is marginal and the country is dependent on gas and oil imports. It is prohibited to explore for or produce unconventional oil by Law No. 2017-1839. Since the permanent closure of Lacq natural (Guénaire et Fossil gas field, there was no production of natural gas in the al. 2020; fuel country. As part of decarbonizing its economy, new OECD 2021) authorizations will not be granted to extract fossil fuels and the current licenses will not be extended after 2040. Oil and gas operations are conducted under a concession between the industry and the government.

The country has only a few non-energy minerals and no longer exploits metallic minerals: deposits are not economically viable for mining. Explorations are (Clément et al. underway for some mineral resources like lead, silver, Mining 2020; Renaud copper and lithium. Some of the minerals that had major 2020) increases in production were crude gypsum, kaolin clay, and crude granite. The country will likely continue to import a large amount of its ores and industrial minerals.

Individual quotas were introduced in the herring fishery in 1975. In 1979, these quotas were made transferable, creating a fully-fledged Individual Transferable Quota (ITQs) system in the fishery. Shares were allocated by (Runolfsson & grandfathering. In the demersal, lobster, and deep-sea Iceland Fisheries Arnason 2001; shrimp fisheries, shares were based on historical catches Xinshan 2000) during base years. In demersal fisheries, quotas were allocated on the basis of catch history over 1981-1983. ITQs were allocated in perpetuity and are freely transferable.

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The forest area is 49 (1000 ha; around 0.5% of land area). About 35% of the area is owned by the public. The (FAO 2018a; forest area allocated for production is 7 (1000 ha). The Food and country has insufficient forest resources or the location for Agriculture Forestry large-scale forestry industry. National policy is lacking but Organization legislation and regulations exist to support sustainable of the United forest management. Forest inventory like ground plots Nations 2016) and aerial/remote sensing is conducted.

Iceland transitioned from oil to geothermal sources for generating heating between 1940-1975, and the country does not produce fossil fuels. Renewable energy sources (EY 2019; Fossil represent the primary energy supply (89%). Exploration of OECD 2021; fuel oil and gas resources on the Icelandic continental shelf is Orkustofnun in the initial phase. There are still no proven mineral fuel 2021) reserves. A license is required for prospecting, exploration and production of oil and gas in the country.

The country has a relatively minor mineral industry compared to the Fennoscandian countries and has few verified mineral resources. Currently, it does not have metallic mines. Explorations are underway mainly for (Goclawska Mining gold. Some extractions are conducted for pumice and 2020; Hojem some exploration exists for gold. For granting an 2015) exploration or exploitation of all resources underground and under the seabed, an opinion needs to be collected from national agencies.

Individual transferable quotas were implemented in the major pelagic and demersal stocks. With ITQs being first introduced for sole and plaice in 1976, the government granted quota management to producer organizations in (Carpenter & early 1993. Now they are responsible for managing most Kleinjans Fisheries major pelagic, and demersal species are under this 2017; OECD system. Quotas for ITQ stocks were initially allocated 2006) according to historical catches and engine power (catch & fishing capacity). Quotas were also given in perpetuity. Netherlands The shares are regarded as permanent entitlements by fisheries stakeholders.

The forest area is 376 (1000 ha; around 11.1% of land area). About 49% of the area is owned by the public. The Forestry (FAO 2018a) forest area allocated for production is 3 (1000 ha). National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like

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ground plots is conducted.

Oil production has been decreasing (declined by 50% between 2002-2012) in part because of technical Fossil difficulties in extracting the remaining reserves. The (EY 2019; fuel upstream oil and gas industries are private and OECD 2021) liberalized. Oil and gas operations are conducted under a concession between the industry and the government.

The country processes metallic minerals and exploit industrial minerals like salt. In the non-energy mineral sector, the country is mainly involved in downstream activities rather than production, primarily using imported (Hastorun Mining ores and industrial minerals. Mining is restricted to non- 2019; Perez metallic minerals like sand and gravel, limestone, and 2017) salt, but the definition of “minerals” in the Mining Act of The Netherlands does not comprise them. The mineral industry is predominantly privately-owned.

Individual Transferable Quotas (ITQs) for 26 of the most economically important fishery species were implemented in 1986. Quotas issued for the various species were determined on the basis of catch histories. For example, (Clark et al. in the inshore allocations, the primary criteria were the Fisheries 1988; Connor catch history over the years 1982-1984. Furthermore, 2001) quotas were allocated as a perpetual right to a share of the fish harvest, designated for a particular species or species group taken from a specified quota management area every year.

New Zealand The forest area is 10152 (1000 ha; around 38.6% of land area). About 60% of the area is owned by the public. The government owns most of the native forests with most areas being protected in conservation areas. Most of the (FAO 2018a; exotic plantation forests, on which the forestry industry is Ministry for Forestry largely based, are privately owned (96% of plantation Primary forests are privately owned). The forest area allocated for Industries production is 2065 (1000 ha). National policy and 2020) regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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The country is well-endowed by oil and gas resources. It imports most of its oil but not gas; the production of gas has increased over the recent years. Currently, there are (EY 2019; Fossil 20 oil and gas fields in operation. Oil and gas operations OECD 2021; fuel are conducted under a concession between the industry PEPANZ n.d.) and the government, though in 2018 a legislation was passed that bans the issuance of new offshore oil exploration permits.

Compared with adjacent countries, like Australia, the mining industry in New Zealand has a limited share in the international market and contributes in a minor way to the Mining country’s economy. The only metallic minerals mined in (Buteyn 2018) the country are gold, iron sand and silver. Other minerals such as aluminum, cement, clay, lime sand and gravel are also produced.

An Individual Vessel Quota (IVQ) system was established for the Norwegian Coastal Fleet, in 1991, and for the Norwegian Ocean-Going Fleet in 2005. In this system, quotas are not the property of individuals, since they are non-transferable. The link between an individual and the permit to access the resource goes through the ownership of the vessel. (Hannesson Fisheries Initial allocations were determined through a formula 2013; von linking a ‘‘base quota’’ to the length of the vessel. A Moltke 2011) vessel is placed into a length group based on the length of the vessel on a historic date. Usually, the historic date chosen was the vessel’s length at the closing of the Norway fishery. Quotas can be retained for 20 years. Vessels being stripped of their licenses must be permanently withdrawn from the fishery.

The forest area is 12112 (1000 ha; around 39.8% of land area). About 12% of the area is owned by the public. The forest area allocated for production is 6633 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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One of the major countries for oil and gas production. The production of oil increased dramatically over the 1980 and 1997 period and though it has been declining, the (Deloitte production of gas is offsetting the decline in oil production. Taxation and Fossil Firms mainly invest in the upstream activities in the Investment fuel Norwegian continental shelf since there are no onshore Guides 2014; activities in Norway. Oil and gas operations are EY 2019) conducted under a concession between the industry and the government.

Extractions of mineral resources like gravel, hard-rock aggregates and clay exist. Exploration and extraction of minerals as well as obtaining mining rights are regulated by the 2010 Norwegian Minerals Act. The country owns metals with a density of 5 g/cm3 and above as well as the (Hojem 2015; ores of these metals; all other minerals are owned by the Mining Plaza-Toledo landowner. However, all resources underwater are owned 2020) by the state. The industry consists of both state-owned and privately-owned mining operations. In 2013, there were three active mines for metallic minerals in the country. Norway has expressed its ambitions in promoting mining in conjunction with sustainable development.

In 2009, the government implemented an Individual Vessel Quota (IVQ) for the industrial purse seine fleet (comprised of steel and wooden hull vessels) of the northern-central stock of Peruvian anchoveta. Quotas were allocated through grandfathering by using the (Aranda 2009; highest catch of vessels in the period between January Kroetz et al. Fisheries 2004 and June 2008. However, different formulas were 2016; Young & applied to the steel and wooden hull fleets. For the steel Lankester hull vessels, 60% of the allocation was based on catch 2013) history and 40% on holding capacity. For the wooden hull vessels, the only criteria considered was their historical Peru catch. The quotas were originally granted for a 10-year period and in 2019 were renewed for ten more years.

One of the top ten countries for forest area (ranked ninth). The forest area is 73973 (1000 ha; around 57.8% of land area). About 82% of the area is owned by the public. The (FAO 2018a, Forestry forest area allocated for production is 17881 (1000 ha). 2020b) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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Fossil fuel reservoirs are found both inland and within Peru’s EEZ. The largest reservoirs are found in public land within the Peruvian Amazon. National agencies map Fossil potential reservoirs, allowing private companies access to (EY 2019) fuel oil and gas exploration, extraction and refining operations under concession schemes. Oil production has been declining since the mid-1980s, yet natural gas production and exportation have increased substantially since 2003.

The country is one of the world’s leading producers of minerals like copper, silver, zinc and gold and the mining (Gurmendi sector is expected to further grow in the future. The Mining 2012; Soto- industry consists of both domestic private and foreign Viruet 2019) firms that are involved in the prospecting, exploration, production, and trade of minerals.

Since joining the European Union in 2004, this country’s resource management policies have been harmonized with the Common Fisheries Policy (CFP) in which management and quota allocations are established. However, as of 2017 the system for Poland does not (Carpenter & allow the transferability of quotas, only between vessels Kleinjans of the same owner. 2017; Fisheries European Most quotas were allocated through grandfathering to Parliament vessel-length groups, which is largely based on the 2011) historical track records of the length segment and their technical capacity. Quotas are only viable for the duration Poland of the calendar year. Fishers do not have a sustained right to a share of the fish stock.

The forest area is 9435 (1000 ha; around 30.8% of land area). About 82% of the area is owned by the public. The forest area allocated for production is 3714 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots is conducted.

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Almost half of the energy supply in Poland is provided by coal rather than oil and gas. In 2018, imports of oil and gas accounted for about 64% of total imports pertinent to Fossil energy. The Polish Oil and Gas Company produces (EY 2019; fuel around 81% of the local oil mainly from onshore oil wells, OECD 2021) and the company is largely owned by the state. Oil and gas operations are conducted under a concession between the industry and the government.

The country is Europe’s leading producer of minerals like copper and cement, and one of the world’s leading producers of silver. Generally, the deposits of certain (Abdale 2021; minerals are owned by the state irrespective of their Mining Eldridge & location. In the case of multiple parties interested in Brown 2018) conducting mining activities, the granting of a concession may be preceded by tendering. Most of the mining firms in the country are privately owned.

In 1992, Individual Vessel Quotas were allocated for Portuguese vessels fishing in the waters of the North Atlantic Fisheries Organization (NAFO). Quotas were initially allocated via grandfathering, using historical catch (MRAG et al. Fisheries records. Transfer of rights is allowed between Portuguese 2009) vessels, and the transfer with other Member States’ vessels operating in the NAFO regulatory areas is also allowed but only after permission from the Portuguese government. The rights have been permanently allocated. Portugal

The forest area is 3182 (1000 ha; around 35.3% of land area). About 3% of the area is owned by the public. The forest area allocated for production is 1587 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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Portugal is heavily reliant on imported fossil fuels, with all of its gas is imported. Substantial deposits of oil and gas have not yet been discovered, but explorations continue on the continental shelf. The royalty rates vary with (OECD 2012a; Fossil production volume and field’s depth. For example, Oil Pacheco & fuel production from offshore fields deeper than 200 meters Neves 2020) are completely exempt from the production charge. To conduct a survey, explore, develop or produce oil and gas, a concession is required which is awarded by either a tender procedure or direct negotiation.

The country comprises a variety of metallic and non- metallic mineral resources such as copper, limestone, silver, tin and zinc. It is a globally important producer of lithium. Production and processing of mineral resources (Abdale 2019; are conducted by privately-owned firms but all resources Eldridge & Mining are owned by the state. The royalty charge and other Brown 2018; compensations are usually specified in a concession on a Vítor 2019) case-by-case basis rather than being specified in the mining law and the rate is usually negotiated between the state and industry.

The absence of quota transfer regulations for Russian fisheries hinders and restricts access to new operators. Russian fish quotas were allocated through auctions between 2001-2003. In 2003, the auction system was abandoned due to strong pressure from the industry and the overall negative economic performance of the sector. Since then, quota allocation has been based on grandfathering. In 2008, the government approved rules (Anferova et for the allocation of quotas in areas under international al. 2005; Fisheries fishery agreements. In this specific case, the initial Eurofish 2005; allocation is based on the catch history of the four Fishnet 2008) preceding years.

Russian Regarding the permanence of shares, in 2016 the Federation principle of long-term assignment of resources was introduced, which means that quota shares were assigned for 15 years.

One of the top ten countries for forest area (ranked first). The forest area is 814931 (1000 ha; around 49.8% of land area). About 99% of the area is owned by the public. The (FAO 2018a, Forestry forest area allocated for production is 415074 (1000 ha). 2020b) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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One of the significant countries for the production of oil and gas, ranking third globally in the production of fossil fuels. The country holds 6.4% of the world’s oil reserves and the second-largest proven reserves of gas. The oil industry consists of domestic firms, many of which are Fossil vertically integrated. The gas production is much more (EY 2019; fuel concentrated within the state-owned Gazprom but the OECD 2021) concentration of production has declined sharply from 90% to 68% between the early 2000s to 2018. Gazprom accounted for 90% of gas production in the early 2000s. Oil and gas operations are conducted under a concession between the industry and the government.

The country is one of the world’s leading producers of various mineral resources. The industry largely consists of firms owned by the private sector, a few owned by the government and some owned by foreign or/and domestic (Adachi 2009; Mining entities. The Law on Subsoil, which regulates the Safirova 2019) allocation and exercise of mining rights, requires that rights are granted on a competitive basis (e.g., auctions or tenders). However, it has been pointed out that this law has to be revised with some critical amendments.

The South African hake deep-sea trawl, hake handline, hake-inshore trawl, and hake longline fisheries are part of an ITQ system. For the West Coast Rock Lobster Near Shore and Offshore fisheries, there are restrictions on the transferability of rights. For example, rights granted in these fisheries cannot be transferred within the first two (DEAT 2005; Fisheries years of being granted. Shares were allocated given Isaacs 2011) priority to fishers and fishing communities can demonstrate their historical involvement in the sector, and the use comprises traditional fishing practices. Overall South rights in South African fisheries were allocated for a Africa period between 10 to 15 years.

The forest area is 9241 (1000 ha; around 7.6% of land area). About 60% of the area is owned by the public. The forest area allocated for production is 1763 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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Unlike coal, for which South Africa is considered one of (Department: the biggest producers in the world, crude oil resources Mineral are relatively marginal. The state-owned company, Resources Petroleum Oil and Gas Corporation of South Africa and Energy (PetroSA) is responsible for the majority of the oil Fossil Republic of production and almost all of the natural gas. However, in fuel South Africa 2012 PetroSA only comprised 6% of refining, distribution 2021; and sale of petroleum products. Given that gas reserves Oberholzer are available in adjacent countries and the discovery of 2020; OECD offshore gas reserves in South Africa, the gas industry is 2021) experiencing fast expansion.

The country is one of the world’s leading producers of minerals as well as in processing minerals with some of Mining the most important minerals being platinum, gold and (Yager 2019) other metal ores. The industry largely consists of firms owned by the private sector.

A system of transferable fishing quotas for pelagic fisheries was proposed in 2005. Then, in August 2009, a new Act on transferable fishing rights came into force that included pelagic fisheries. From 2017 onwards, a new IQ system was introduced to cover demersal stocks as well. Quota allocation for major pelagic and demersal species was based on historical catch records (i.e., catch records (Carpenter & between 2004 and 2006 were considered for pelagic Kleinjans Fisheries fisheries, while the 2011-2014 period was considered for 2017; Winder demersal fisheries). 2018)

The shares in the pelagic fishery system were allocated for ten years (since 2009), after which they could be renewed. For other quotas, the validity/duration of the shares was not specified in the regulations.

Sweden

The forest area is 28073 (1000 ha; around 68.4% of land area). About 25% of the area is owned by the public. The forest area allocated for production is 19699 (1000 ha). Forestry (FAO 2018a) National policy and regulatory frameworks exist to support sustainable forest management. Forest inventory like ground plots is conducted.

Fossil Fossil fuel resources are negligible, and the country (OECD 2021) fuel depends on imported oil and gas.

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Of the Nordic countries, Sweden has the largest mining industry with 15 metallic mineral mines. Generally, an exploration permit may be obtained by submitting an application and a work plan to the Mining Inspectorate. An (Baker & exploitation permit is also largely governed by the Mining McKenzie Mining Inspectorate, which depends on several factors like the 2020; Johnson economic feasibility of extraction and land use (e.g., used & Ericsson for mining vs other uses). The state receives a minor 2015) portion of the value of minerals, and the revenue is earmarked for research and development of mineral resources.

This country has a system of individual quotas. For the Scottish Catch Quota Pilot Program, the system was established in 2008 and for the English Catch Quota Pilot Program in 2011. Shares can only be transferred when selling the vessel. Inshore and non-sector quotas cannot be leased or transferred. The quota system in the UK is differentiated between fishers that are members of Producers Organizations (sector) and ones that do not have a membership of a producer organization (non-sector). The quotas were initially allocated through grandfathering on the basis of (Carpenter & historical catch records for sector vessels. Monthly Kleinjans Fisheries individual catch limits are rationed equally to the non- 2017; MRAG sector, based on total landings as a group during the et al. 2009) same reference period. Besides, sector fishers have their quota managed by their producer organizations, while non-sector fishers’ quotas are managed by the fisheries administration. United Kingdom The permanence for sector quotas applies for the whole quota year, whereas non-sector quotas are allocated on a monthly basis. However, quota allocations are considered secure as the system has existed for many years with minimal changes.

The forest area is 3144 (1000 ha; around 13% of land area). About 28% of the area is owned by the public. Privatization of publicly-owned forestland is apparent in (FAO 2018a; specific regions of the United Kingdom, though it has Hodge & Forestry been criticized by the public and further proposals to sell Adams 2013; publicly-owned land have been retracted. National policy Wong et al. and regulatory frameworks exist to support sustainable 2015) forest management. Forest inventory like ground plots and aerial/remote sensing is conducted.

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The country has been a significant producer of both oil and gas since the 1980s but due to reserves being depleted from the continental shelf in the North Sea, the (Abdale 2020; Fossil long-term pattern of production is decreasing. The gas EY 2019; fuel industry is entirely privatized. Oil and gas operations are OECD 2021) conducted under a concession between the industry and the government.

Metallic mineral resources have been depleted or replaced by less costly imported metallic minerals. Since the 1980s, the production of metal ores was largely ceased, and the domestic production is restricted to small amounts of lead and tungsten. The country is a significant producer of barite, chalk, clays, lime, salt, sand and (Abdale 2020; Mining gravel. The industry consists of globally major firms, Matzko 2019) which are domestic and foreign. In general, most mineral rights are privately held except for gold and silver and fuel minerals; the rights for these minerals are held by the Crown. Except for minerals reserved by the Crown, there are no specific licenses for exploration and exploitation.

The first program in the database was established in 1990 (Atlantic Surfclam and Ocean Quahog), then the Alaska Halibut and Sablefish Fixed Gear program in 1995. Programs such as the Sablefish Permit Stacking Program were implemented in 2001, whereas the Bering Sea and Aleutian Islands Crab Rationalization and the Maryland Summer Flounder programs started in 2005. In 2010 the (Brinson & ITQ program was implemented for the Atlantic Sea Thunberg Scallop Program, and in 2011 ITQ program was applied 2013; to the Pacific Coast Groundfish Limited Entry Trawl Maryland Fisheries fishery. Department of Natural Initial quota allocation was grandfathered for most of Resources United these fisheries based on stakeholders’ historical n.d.) States participation in the fishery in terms of landings. The only exemption is with the Maryland Summer Flounder in which the share allocation equitably distributes the quota among harvesters. Regarding the duration of the US quotas, the shareholding privilege is indefinite.

One of the top ten countries for forest area (ranked fourth). The forest area is 28073 (1000 ha; around 68.4% (FAO 2018a, Forestry of land area). About 42% of the area is owned by the 2020b) public. The forest area allocated for production is 91339 (1000 ha). National policy and regulatory frameworks exist to support sustainable forest management. Forest

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inventory like ground plots and aerial/remote sensing is conducted.

The country is one of the world’s major producers of fossil fuels. The new hydrocarbon discoveries in the Gulf of Mexico and the improved technology to extract shale gas (Department of and tight oil in several states have boosted the declining the Interior reserves. The oil market is entirely deregulated and open Natural Fossil to competition. Oil and gas operations are conducted Resources fuel under a concession between the industry and the 2021; EY government. About 30% of the recoverable oil resources 2019; OECD are situated in federal lands or offshore waters. Federal 2021) onshore and offshore leases are awarded to the highest bidder.

The country is one of the top producers of minerals and one of the world’s leading in the production of minerals like copper, gold, silver, zinc and iron ore. Applying for licenses and permits generally requires approval from (Baker & several different levels (e.g., federal, state and local McKenzie agencies) and the number of permits varies from one 2020; Osborne Mining state to another and relies on several factors such as 2020; U.S. project details and land and mineral ownership. US Geological citizens have a statutory right to explore locatable Survey 2020) minerals, which are located within a mining claim on public lands and include metallic and non-metallic minerals. In general, the industry largely consists of private firms.

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Table E.4. Occurrence of rent recovery mechanisms (RRM) by major extractive industries in selected countries. ‘Fisheries’ comprise firms operating under catch share programs examined in this study (see Table E.2 for programs included). Minerals comprise non-fuel minerals, which vary drastically from one country to another. The “mechanism or rate of RRM” is not exhaustive and RRM imposition can vary widely within each industry and country. For each of the 18 countries and five extractive industry types evaluated, we categorized “RRM common or ubiquitous” as ‘1’; “RRM limited or absent” as ‘0’; and “Information deficient” as ‘0?’ (see Methods and Materials). For oil and/or gas resources in Chile, France, Iceland, Portugal and South Africa, policies that specify rent recovery mechanisms are in place, but these resources are generally inadequate or underexplored (see Table E.3).

Occurrence Mechanism or Name or origin of Country Industry Source of RRM rate of RRM RRM

$ per unit Fisheries 1 N/A (Flores et al. 2018) extracted

% of value Forestry 1 N/A (FAO 2005) extracted

Argentina

% of value (Deloitte 2016; EY Gas 1 Royalty extracted 2019)

% of value (Deloitte 2016; EY Oil 1 Royalty extracted 2019)

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Provincial mining Mining 1 Mine head value (pwc 2012) royalties

(Rodgers & Fisheries 1 % of landed value Charge or royalty Webster 2007)

Forestry 0? Unknown N/A N/A

Creditable against (Deloitte 2016; Petroleum Resource Gas 1 the petroleum Mintz & Chen Rent Tax resource rent 2012) Australia

Creditable against (Deloitte 2016; Petroleum Resource Oil 1 the petroleum Mintz & Chen Rent Tax resource rent 2012)

i. % value or i. State Royalties; ii. (Hogan 2007; pwc Mining 1 volume extracted; Minerals Resource 2012) ii. Mining profit Rent Tax

Fisheries 0 N/A N/A N/A

Canada* (Government of Forestry 1 Varies by province N/A Canada 2013; Luckert et al. 2011)

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Federal and province (EY 2019; Mintz & Gas 1 Varies by province royalties Chen 2012)

Federal and province (EY 2019; Mintz & Oil 1 Varies by province royalties Chen 2012)

(Chen & Mintz 2013; Eldridge & Mining 1 Varies by province Varies by province Brown 2018; Toms & McIlveen 2013)

Fisheries 0 N/A N/A N/A

Forestry 0 N/A N/A N/A

Special taxation Gas 1 % of revenue (Espejo 2020) regime Chile

Special taxation Oil 1 % of revenue (Espejo 2020) regime

(Bambach & Pulgar Mining 1 % of profit Specific Mining Tax 2020; pwc 2012)

Denmark Fisheries 0 N/A N/A N/A

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Forestry 0? Unknown N/A N/A

Special Hydrocarbon Gas 1 % of profit (EY 2019) Tax

Special Hydrocarbon Oil 1 % of profit (EY 2019) Tax

i. Auction; ii. $ per cubic metre of Mining 1 N/A (Hojem 2015) material or per area

Fisheries 0 N/A N/A N/A

i. Auctions; ii. (Elyakime & Forestry 1 Direct sale; iii. N/A Cabanettes 2009) Supply contract

France $ per unit (Guénaire et al. Gas 1 Royalty extracted 2020)

$ per unit (Guénaire et al. Oil 1 Royalty extracted 2020)

Mining 0 N/A N/A N/A

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(Cox et al. 2011; % net landed Fisheries 1 Fee Gunnlaugsson et value al. 2018)

Forestry 0 N/A N/A N/A

Iceland Special Hydrocarbon Gas 1 % of profit (EY 2019) Tax

Special Hydrocarbon Oil 1 % of profit (EY 2019) Tax

Mining 0 N/A N/A N/A

Fisheries 0 N/A N/A N/A

Forestry 0? Unknown N/A N/A

% of value Royalty; Mining Act (Deloitte 2016; EY Gas 1 extracted of the Netherlands 2019; NLOG n.d.) Netherlands

% of value Royalty; Mining Act (Deloitte 2016; EY Oil 1 extracted of the Netherlands 2019; NLOG n.d.)

Mining 0 N/A N/A N/A

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Fisheries 0 N/A N/A N/A

Forestry 1 Privatization N/A (Roche 2008)

% of revenues or Crown Minerals Act (Deloitte 2016; EY Gas 1 profits (1991) 2019)

New

Zealand Crown Minerals % of revenues or (Deloitte 2016; EY Oil 1 Royalties for profits 2019) Petroleum (2013)

Crown Minerals (Royalties for (Parliamentary % of revenues or Mining 1 Minerals Other than Council Office profits Petroleum) 2014) Regulations (2013)

Fisheries 0 N/A N/A N/A

Forestry 0 N/A N/A N/A

Norway (Mintz & Chen Gas 1 Rent-based tax Special tax 2012)

(Mintz & Chen Oil 1 Rent-based tax Special tax 2012)

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$ per area per Mining 1 N/A (Hojem 2015) year

$ per unit Legislative Decree Fisheries 1 (Flores et al. 2018) extracted Nº 1084 (2008)

$ per volume (de la REPÚBLICA Forestry 1 N/A extracted del PERÚ n.d.)

Several Peru Gas 1 Royalty (EY 2019) mechanisms

Several Oil 1 Royalty (EY 2019) mechanisms

% of value Mining 1 Mining Royalty (pwc 2012) extracted

Fisheries 0 N/A N/A N/A

$ per acre (progressive, Forestry 1 Forestry Tax Act (OECD 2015) Poland varying with soil and productivity)

i. % of profits; ii. % Gas 1 i. Special (EY 2019) of value extracted Hydrocarbon Tax; ii.

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Royalty

i. Special i. % of profits; ii. % Oil 1 Hydrocarbon Tax; ii. (EY 2019) of value extracted Royalty

Product of rate (Eldridge & Brown Mining 1 and amount of N/A 2018) minerals extracted

Fisheries 0 N/A N/A N/A

Forestry 0 N/A N/A N/A

Gas 0 N/A N/A N/A

Portugal i. $ per area-unit of concession; ii. i. Surface rent; ii. Oil Oil 1 % per unit production tax (OECD 2012a; extracted (royalty) Pacheco & Neves (progressive rate) 2020)

Royalty specified in a % of mine head Mining 1 concession on a (Vítor 2019) value of the ore case-by-case basis

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$ per unit (Eurofish 2005; Fisheries 1 N/A extracted Flores et al. 2018)

(Eismont et al. i. Auctions; ii. Forestry 1 N/A 2002; Karvinen & Stumpage fees Mutanen 2019)

Russian Federation $ per unit Mineral Resource Gas 1 (OECD 2012b) extracted Extraction Tax

$ per unit Mineral Resource Oil 1 (OECD 2012b) extracted Extraction Tax

% of value or Mineral Resource Mining 1 (pwc 2012) quantity extracted Extraction Tax

Fisheries 0 N/A N/A N/A

Forestry 0 N/A N/A N/A

South Africa

Mineral and Petroleum (Cawood 2010; EY Gas 1 % of revenue Resources Royalty 2019) Act

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Mineral and Petroleum (Cawood 2010; EY Oil 1 % of revenue Resources Royalty 2019) Act

Mining and Mining 1 % of revenue Petroleum Resource (pwc 2012) Royalty

Fisheries 0 N/A N/A N/A

Forestry 0? Unknown N/A N/A

Sweden Gas 0? Unknown N/A N/A

Oil 0? Unknown N/A N/A

Mining 0 N/A N/A N/A

Fisheries 0 N/A N/A N/A

Forestry 0 N/A N/A N/A United Kingdom

Rent-based tax Supplementary (Mintz & Chen Gas 1 (special income Charge 2012) tax)

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Rent-based tax Supplementary (Mintz & Chen Oil 1 (special income Charge 2012) tax)

Mining 0 N/A N/A N/A

Fisheries 0 N/A N/A N/A

Forestry 1 Varies by state N/A (Brown et al. 2012)

(EY 2019; i. Federal Royalty; ii. Kolesnikoff & Gas 1 Varies by state State severance tax Brown 2018; Mintz United & Chen 2012) States**

(EY 2019; i. Federal royalty; ii. Kolesnikoff & Oil 1 Varies by state State severance tax Brown 2018; Mintz & Chen 2012)

Mining 1 Varies by state State severance tax (pwc 2012)

*In Canada, federal RRM is imposed on oil and gas extractions as well as an RRM in many provinces (EY 2019; Mintz & Chen 2012). For the mining industry, we did not find a federal RRM but the majority of the provinces impose RRM (e.g., royalties) (Chen & Mintz 2013; Toms & McIlveen 2013). Canada’s provinces generally charge the timber industry stumpage fees to harvest timber (Government of Canada 2013; Luckert et al. 2011).

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**In the United States, RRM generally occurs in oil and gas industries at the federal level and in the majority of the states, with the royalties (or severance taxes) varying extensively among states (Department of the Interior Natural Resources 2021; EY 2019; Kolesnikoff & Brown 2018; Mintz & Chen 2012; pwc 2012). The federal government generally does not impose royalties on extracting locatable minerals (also known as hardrock minerals, which include metallic and non-metallic minerals) occurring on public domain lands (i.e., usually outside states and privately-owned lands. For example, most of the mining activities on public domain lands are not subjected to royalties (U.S. Government Accountability Office 2020). However, out of the 19 states where mining “locations” may be made (Baker & McKenzie 2020), RRM occurs in the majority of them (U.S. Government Accountability Office 2019). For publicly- owned forestlands in the United States, much of the state-administered forestlands were obtained by federal land grants (Brown et al. 2012). The sale of the right to harvest timber on state-administered forestlands is carried out in 43 states (Brown et al. 2012). Stumpage prices are predominantly determined by auctions, and the general mechanisms for payment are lump sum ($ for the entire tract) or log scale ($ per unit extracted (Brown et al. 2012; Sedjo 2006)).

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