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Transboundary fish stocks and their management under climate change Palacios Abrantes, Juliano Emmanuel 2021

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TRANSBOUNDARY FISH STOCKSAND THEIR MANAGEMENTUNDER CLIMATE CHANGEbyJuliano Emmanuel Palacios AbrantesB.Sc. Universidad Autonoma Metropolitana unidad Xochimilco, MexicoCity, Mexico, 2012M.Sc. University of California at Santa Barbara, Santa Barbara California,US, 2016A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THEREQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES(Zoology)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)March 2021© Juliano Emmanuel Palacios Abrantes, 2021The following individuals certify that they have read, and recommend to the Faculty of Graduate andPostdoctoral Studies for acceptance, the dissertation entitled:Transboundary fish stocks and their management under climate changesubmitted by Juliano Emmanuel Palacios Abrantes in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin ZoologyExamining Committee:William W.L. Cheung, Zoology, University of British ColumbiaResearch SupervisorU. Rashid Sumaila, Institute for Oceans and Fisheries, University of British ColumbiaSupervisory Committee MemberLes Lavkulich, Land and Food Systems, University of British ColumbiaUniversity ExaminerScott Hinch, Forestry, University of British ColumbiaUniversity ExaminerEllen K. Piktch, School of Marine and Atmospheric Sciences, Stony Brook UniversityExternal ExamineriiAbstractUnder the United Nations Law of the Seas and the delineation of Exclusive Economic Zones (EEZs), fishstocks that cross neighbouring EEZs are known as transboundary stocks. The sustainability of these stocksdepends on international cooperation. However, cooperation is faced with the challenges of the insufficientunderstanding of where and how much fisheries resources are transboundary and climate change is shifting thedistribution of marine species. My main objective is to understand the impacts of climate change-inducedshifts on transboundary fish stocks distributions and their management, thereby informing internationalfisheries governance to prepare and respond to climate change. I rely on multiple data sources and numericalmodelling to project species distributions under different scenarios of climate change.I found that 67% of the species analyzed are transboundary and that between 2005 and 2014,fisheries targeting these species within global‐EEZs caught on average 48 million tonnes per year, equivalent toUSD 77 billion in fishing revenue. As climate change alters ocean properties, the distribution of these species’transboundary stocks are projected to shift to higher latitude, deeper waters or follow local environmentalgradients. Specifically, 60% of the global transboundary stocks will have shifted beyond their historicaldistribution by 2020, and by 2075, all EEZs are projected to have a shifting transboundary stock. Moreover,the shared proportion of the catch of transboundary stocks between neighboring EEZs will change by 2030relative to the historic proportion. The changes in the distribution and share proportion of transboundarystocks can potentially impacts the management of the related fisheries. For example, Canada and the UnitedStates manage important transboundary stocks. However, by 2050, the proportion of the total catch of sometransboundary fish stocks shared between the two countries are expected to change relative to the present,even under a low greenhouse gas emissions scenario.My findings improve our understanding about the current status of transboundary stocks highlight-ing the challenges that fisheries management will face in a changing climate. Finally, I identify potentialadaptation options for transboundary fisheries management such as side payments, dynamic rules, and in-terchangeable quotas that can improve their sustainability under climate change.iiiLay SummaryThe ocean is getting warmer, less oxygenated, and more acidic. As a consequence, marine species are shiftingtheir distribution, challenging the management of fisheries that target stocks shared between ExclusiveEconomic Zones (transboundary stocks). Here, I aim to understand how climate change-driven shifts inspecies distribution will affect the management of transboundary stocks. I use a combination of numericalmodels and scenarios to project future oceanic conditions and species distributions. Two main findingsemerge from my dissertation. First, transboundary species are more common and contributes more topresent-day’s fisheries than previously known, highlighting the importance of ensuring effective managementof their fisheries under the changing climate. Second, the distributions of many transboundary stocks havealready shifted and many more are expected to shift in the coming decades. The incorporation of dynamicmanagement could help reduce the negative impacts of climate change on the sustainability of shared stocksand their fisheries.ivPrefaceI am the main author of all chapters and lead author of all papers resulting from this work. I took primaryresponsibility for the research contained in the chapters, including the design, data curation, analysis andwriting. This was done under the guidance of my supervisor Dr. William W.L. Cheung, and my committeemembers Dr. Daniel Pauly, Dr. U. Rashid Sumaila, and Dr. Villy Christensen. Moreover, each chapter hasdifferent co-authors that played a critical role in the development of the studies providing their expertiseand advice with ideas, methods and data interpretation. Some of the chapters of this dissertation have beenpublished or submitted for publication as stated below.A version of Chapter 2 has been published in a peer review journal. I did the conceptualization,developed the methodology, performed the formal analysis and wrote the original draft. Gabriel Reygondeausupported the methodology, did the data curation, and contributed to the writing, reviewing and editing ofthe final draft. Colette Wabnitz supported the methodology, and contributed to the writing, reviewing andediting of the final draft. William Cheung did the supervision, and contributed to the writing, reviewingand editing of the final draft. The publication reference is as follows:• Palacios-Abrantes, J., Reygondeau, G., Wabnitz, C. C. C., and Cheung, W. W. L., 2020. The trans-boundary nature of the world’s exploited marine species. Scientific Reports, 10 (1), 415–12.A version of Chapter 3 is under review at peer review journal. I did the conceptualization, co-developed the methodology, performed the formal analysis and wrote the original draft. Thomas Frölicherand William Cheung helped develop the methods, William Cheung supported the conceptualization, did thesupervision, and contributed to the writing, reviewing and editing of the final draft. Rashid Sumaila, GabrielReygondeau, and Colette Wabnitz contributed to the discussion analysis, writing, reviewing and editing ofthe final draft. The working title of this chapter is:• Juliano Palacios-Abrantes, Thomas Frölicher, U. Rashid Sumaila, Alessandro Tagliabue, Gabriel Rey-gondeau, Colette Wabnitz, and William W.L. Cheung. Imminent emergence of range shift-inducedchallenges in managing transboundary fish stocks under climate change.A version of Chapter 4 has been published in a peer review journal. I developed the methodology,performed the formal analysis, and wrote the original draft. Rashid Sumaila and William Cheung supportedthe conceptualization of the study and contributed to the writing, reviewing and editing of the final draft.• Palacios-Abrantes, J., Sumaila, U. R., and Cheung, W. W. L., 2020. Challenges to transboundaryfisheries management in North America under climate change. Ecology and Society, 25 (4), art41–17.vContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiGlossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii1 Chapter One: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Transboundary fisheries management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Climate change and its effect on marine species . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Transboundary fisheries management under climate change . . . . . . . . . . . . . . . . . . . 71.4 Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Chapter Two: The transboundary nature of the world’s exploited marine species . . . 112.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.2.1 Databases of species’ geographic distribution . . . . . . . . . . . . . . . . . . . . . . . 122.2.2 Determining transboundary species trait . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.3 Fisheries trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.2.4 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2.5 Key uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Chapter Three: Early emergence of range shift-induced challenges in managing trans-boundary fish stocks under climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.2.1 Databases and species selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303.2.2 Projecting species distributions under climate change . . . . . . . . . . . . . . . . . . . 303.2.3 Calculating an index of transboundary range shift . . . . . . . . . . . . . . . . . . . . 313.2.4 Estimating the threat point of transboundary stock share . . . . . . . . . . . . . . . . 333.2.5 Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.3.1 Time of emergence of transboundary stocks . . . . . . . . . . . . . . . . . . . . . . . . 353.3.2 Changes in the stock share ratio of transboundary stocks . . . . . . . . . . . . . . . . 403.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433.4.1 High present-day climate risk of on transboundary fisheries management . . . . . . . . 433.4.2 Hotspot of climate risk on transboundary fisheries management . . . . . . . . . . . . . 453.4.3 Changes in stock share ratio will be ubiquitous . . . . . . . . . . . . . . . . . . . . . . 463.5 Caveats and uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 473.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48vi4 Chapter Four: Challenges to transboundary fisheries management in North Americaunder climate change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494.2 Materials and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2.1 Study area and fisheries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 514.2.2 The International Pacific Halibut Commission . . . . . . . . . . . . . . . . . . . . . . . 524.2.3 The Gulf of Maine Arrangement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 524.2.4 Projecting future species distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 544.2.5 Estimation of Maximum Catch Potential change . . . . . . . . . . . . . . . . . . . . . 554.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564.3.1 Projected change to stocks managed by the IPHC . . . . . . . . . . . . . . . . . . . . 564.3.2 Projected change to stocks managed under the Gulf of Maine arrangement . . . . . . 584.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 645 Synthesis and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.1 Synthesis and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.1.1 Research contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 655.1.2 Strategies for transboundary fisheries management under climate change . . . . . . . . 675.1.3 Adaptation of joint management to shifting stocks . . . . . . . . . . . . . . . . . . . . 695.1.4 Limitations and uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 705.1.5 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 735.1.6 Final remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Appendix B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Appendix C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113viiList of Tables2.1 Summary of data sources to estimate species’ distributions. All data has a ressolution of 0.5degrees latitude x 0.5 degrees longitude. All data is publically available, see references fordetails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.2 Rules to determine the category of each transboundary species . . . . . . . . . . . . . . . . . 174.1 Historic quota allocation suggested for Atlantic cod, haddock and yellowtail flounder in theGulf of Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53A2.1 Size of all the EEZs in each sub region determined by the United Nations . . . . . . . . . . . 104A2.2 Average ± (standard deviation) number of shared and non-shared transboundary species perEEZ for each catch trend category . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104A3.1 Statistical results for multiple comparison test after Kruskal-Wallis on time of emergenceacross United Nations sub regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111A3.2 Statistical results for multiple comparison test after Kruskal-Wallis on time of emergenceacross United Nations sub regions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112viiiList of Figures1.1 Schematic representation of the different types of shared stocks as defined by the UnitedNations’ Food and Agriculture Organization (FAO) . . . . . . . . . . . . . . . . . . . . . . . . 31.2 Past and future changes in sea surface temperature, surface ocean pH and oxygen content. . . 41.3 Schematic representation of different pattern of distribution shifts in shared stocks. . . . . . . 81.4 Schematic diagram illustrating the structure of this dissertation. . . . . . . . . . . . . . . . . 102.1 Number of transboundary species and their contribution to global fisheries catch and revenue. 202.2 Weighted benefits of transboundary species by km2 and UN sub-region. . . . . . . . . . . . . 222.3 Number of transboundary species by catch trend and EEZ. . . . . . . . . . . . . . . . . . . . 232.4 Number of EEZs shared by transboundary species. . . . . . . . . . . . . . . . . . . . . . . . . 263.1 Time of emergence of the transboundary index by Exclusive Economic Zone (EEZ) and trans-boundary stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.2 Average time of emergence of stocks’ transboundary index. . . . . . . . . . . . . . . . . . . . 373.3 Average time of emergence of stocks’ transboundary index per fishing entity, aggregated bycolour and shape according to region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.4 Comparison of regional time of emergence of shared stocks’ range shifts by species’ habitatassociation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.5 Changes in stock share ratio below each country’s threat point by 2030 (2021-2040) relativeto 1951-2005. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.1 Map of Canada and the US with the regulatory areas of the International Pacific HalibutCommission and the NAFO sub-divisions containing the Gulf of Maine arrangement . . . . . 514.2 Percentage change of MCP for stocks managed by the IPHC . . . . . . . . . . . . . . . . . . . 574.3 Percentage change of stock-share ratio for IPHC . . . . . . . . . . . . . . . . . . . . . . . . . 584.4 Percentage change of MCP in the Gulf of Maine . . . . . . . . . . . . . . . . . . . . . . . . . 594.5 Changes in MCP stock-share ratio for Gulf of Maine . . . . . . . . . . . . . . . . . . . . . . . 60A2.1 Number of transboundary species per EEZ and their contributions to countries’ EEZ catch . 101A2.2 Percentage of transboundary species by catch trend and EEZ. . . . . . . . . . . . . . . . . . . 102A2.3 Histogram of number of transboundary species using different Area Index threshold values. . 103A3.1 World Exclusive Economic Zones used in this study as defined by the Sea Around Us andtheir centroids (points). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105A3.2 Graphical representation of the transboundary index (TI) to determine the time of emergenceof transboundary stocks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106A3.3 Sensitivity analysis of the number of grid cells with projected stock abundance within theneighbouring EEZs sharing the stock . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107A3.4 Average Time of Emergence and standard deviation for a 64% confidence threshold (purple)and a 98% confidence threshold (yellow) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108A3.5 Distribution of Stock Share Ratio percentage change by the early (2021-2040) and middle(2040-2060) 21st century relative to today (1951-2005) . . . . . . . . . . . . . . . . . . . . . . 109A3.6 Changes in Stock Share Ration by 2050 (2041-2060) relative to 1951-2005. . . . . . . . . . . . 110A4.1 Percentage change of MCP for stocks managed by the IPHC for end-century (2081–2100)relative to 2005–2014 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113A4.2 Percentage change of MCP in the Gulf of Maine for the end of the 21st century (2080–2100)relative to present (2005– 2014). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114A4.3 Projected environmental variables under climate change from 2010 to 2010 for Arctic regionsof the IPHC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115A4.4 Depth profile of the Gulf of Maine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116A4.5 Changes in maximum catch potential of yellowtail flounder (*Limanda ferruginea*) withinthe study area by mid-century relative to present time. . . . . . . . . . . . . . . . . . . . . . . 117A4.6 Projected MCP change relative to present (2005-2014) for each IPHC regulatory area. . . . . 118ixGlossaryCI Confidence IntervalEEZ Exclusive Economic ZoneEU European UnionFAO Food and Agricultural Organization of the United NationsGFDL Geophysical Fluid Dynamics LaboratoryGoMA Gulf of Maine ArrangementIOF Institute for the Oceans and FisheriesIPCC Intergovernmental Panel on Climate ChangeIPHC International Pacific Halibut CommissionIPSL Institute Pierre Simon LaplaceMCP Maximum Catch PotentialMPA Marine Protected AreaMPI Max Planck InstituteMSY Maximum Sustainable YieldPNA Parties to the Nauru AgreementRCP Representative Concentration PathwayRCP 2.6 Representative Concentration Pathway (Low Emission Scenario)RCP 8.5 Representative Concentration Pathway (High Emission Scenario)RFMO Regional Fisheries Management OrganizationSST Sea Surface TemperatureToE Time of EmergenceUBC University of British ColumbiaUN United NationsUNCLOS United Nations Convention on the Law of the SeaUS United States of AmericaUSD United States DollarsxAcknowledgementsI would like to start by acknowledging that this dissertation was mainly written in the traditional, ancestral,and unceded territory of the Musqueam People.Many thanks to my adviser, William, for your brilliant guidance in this adventure, these werefour years of smooth sailing! Thank you for your commitment and support to my professional developmentand for giving me so many opportunities. But also thank you for your kindness and humanity, you arecertainly an inspiring person and a role model for current and future generations. I am very grateful tomy committee members Daniel, Rashid and Villy for they support and advice. Special thanks to Rashidwho was fundamental to many aspects of this dissertation, for keep pushing me to grow and do better, fortrusting me, and for providing that spark of joy in our lives that we all need in the most troubled days.Rashid, I will keep pushing! Special thanks to Andrés Cisneros for all the insightful advice and guidance; toGabriel Reygondeau for your patience in the multiple statistic discussions and for the football matches; andto Laura Rodríguez for your advice and support during this period. You are all truly inspiring people! Bigshout-out to Colette Wabnitz, you are a wonderful human being and example to follow, thank you for allthe unconditional support, the long discussions, and the advice in both my professional and personal life.They say doing a PhD is a long and lonely path, I beg to differ. Colleagues and friends fromCORU and from the IOF, this would not have been able without your friendship and support. Special shout-out to Ravi Maharaj for serving as a Vancouver ambassador, for all the numerous laughs, philosophicaldiscussions and late nights shared over drinks decompressing from PhD anxieties; to Tanvi Vaidyanathanfor your friendship and care for Niema; to my fearless leader Lian Kwong for inspiring me to be a betterhuman and for all the help and support thorough the PhD process; to Samantha James for all those walkson campus that I so much miss; and Tim Cashion for making all those hard working days look easy. A misqueridos amigos del pescado Latino Americano, Rocío López, Santiago de la Puente, Luis Outeiro y TaylerClarke, no es fácil estar lejos de casa y ustedes sin duda alguna hicieron el viaje más ameno. Friends outsidethe IOF, Gabrielle Menard, Bruna Silvestroni, Jeff Scott, Mauro Sugawara, Nicole Margot, Maite Erauskin,Pierre Rogy, Phil Underwood, Gerardo y Sarahí, and Hasasne Allegue, you guys were fundamental in keepingmy sanity in these four years and I am truly grateful for all the amazing moments we shared. Bill French,Compa, thank you for making my Canadian experience more Mexican, for the laughs, and the camaraderie.Lastly, this dissertation is the result of a 10-year long and winding road and its completion wouldnot have been possible without the support of my family. Guillermo y Raquel, ustedes son mi pilar moralxiy modelo a seguir, gracias por apoyarme en todas mis decisiones, por más incoherentes que fueran; Martingracias por siempre estar ahí para aconsejarme en las dicisiones más críticas. Angela e Amara, obrigado pelaparceria e paciência no último ano do doutorado, vocês foram fundamentais na conclusão desste processo.Juliana Herrera, Renato Molina, Pablo Almuli, Mariano Palacios, Paula Bermudez, Luis Villegas, DiegoVillegas, Maria Fernanda Sánchez, Mariela Sánchez, Osvaldo Morales, Rodolfo Hernández, Allan Tamallo,Delia Cuturiel, Severino Campos, Isabel Studer, Jean François, Tere Rojas, Luis Kandel, gracias por siempreestar ahí para apoyarme.Juliano Palacios AbrantesNo solo no [hubiéra] logrado nada sin ustedessino con toda la gente que estuvo a [mi] alrededor desde el comienzo;algunos siguen hasta hoy. ¡Gracias totales!- Gustavo CeratixiiThe only problem with dogs is that they do not live long enough.xiii1 Chapter One: IntroductionDelineating spatial boundaries is one of the fundamental approaches in marine resource management andgovernance (Song et al. 2017a). Examples of such spatial delineation range from the designation of ExclusiveEconomic Zones (EEZs) to marine protected areas and fishing regulatory areas. The effectiveness of cur-rent ocean management will be affected as climate change re-shapes the distribution of marine biodiversityworldwide and living marine resources shift across the existing spatial boundaries (Fredston-Hermann et al.2018). This dissertation centers around the challenges that climate change will bring to the managementof marine fish stocks that straddle neighboring EEZs. Specifically, I look at how climate-induced shiftsin marine species distributions will affect shared marine resources and the consequences for the dependentfishing communities. My main motivation for this work is to improve fisheries management in the light ofthe changing climate, highlighting the need for adaptive and cooperative management strategies. In thisintroductory chapter, I provide a brief history of the management of ‘shared stocks’ (e.g., fish stocks thatcross international boundaries) in marine fisheries, followed by a summary of the impacts of climate changeon the physical and chemical properties of the ocean and the consequences of these changes on marine life.Finally, I will review the existing knowledge about the management of shared stocks under climate change.1.1 Transboundary fisheries managementFrom 1973 to 1982, members of the United Nations (UN) held a series of meetings to discuss regulationsregarding the high seas, a region of international common property, at that time, consisting of waters beyond12 nautical miles (22.22 km) from shore (Munro, Willmann, et al. 2004). Among the main outcomes of thesemeetings was the establishment of the UN Convention on the Law of the Sea (UNCLOS). Under UNCLOScoastal states were allowed to claim special rights over the exploration and exploitation of marine resourcesup to 200 nautical miles (370 km) from their coasts. Area within which such special rights were claimeddelineate today the states’ Exclusive Economic Zone (EEZ) (United Nations 2018).While intended to improve management of marine resources, including fisheries, by granting prop-erty rights of the resources to states through the designation of EEZs, the delineation of such resourcemanagement boundaries was not informed by biogeography of living marine resources. The natural distribu-tion of marine species in time and space is not shaped by human actions but by the species’ contemporarybiotic and abiotic factors and their evolutionary history (Hutchinson 1957, Nelson et al. 2016). In manycases, fish stocks distribute across EEZs boundaries; in view of this, UNCLOS created the concept of whatwe know today as shared fish stocks (Miller and Munro 2002).1Generally speaking, shared fish stocks are those populations that move between different jurisdic-tions in the course of their life. The Food and Agricultural Organization (FAO) recognizes four types ofshared stocks: (i) transboundary stocks are those that are shared between neighboring coastal nations e.g.,Pacific halibut (Hippoglossus stenolepis) shared between Canada and the United States; (ii) straddling stocksare those that occur in two or more adjacent national jurisdictions and the high seas e.g., Chilean jack mack-erel (Trachurus murphyi) off the coast of Chile and Peru but also the high seas; (iii) highly migratory stocksare those that are found in the EEZs of coastal nations that are not necessarily adjoining, and the high seas(e.g., Bluefin tuna Thunnus thynnus do circum-Atlantic migrations from the Mediterranean to the Gulf ofMexico); and (iv) discrete high seas stocks whose distribution is limited to the high seas (Figure 1.1). The in-ception of the concept of “shared stocks” in fisheries management called for the establishment of new fisheriesgovernance approaches and organizations. Many straddling and highly migratory stocks are managed byRegional Fisheries Management Organizations (RFMOs) (Cullis-Suzuki and Pauly 2010, Monllor-Hurtadoet al. 2017, Song et al. 2017b). At the EEZ level, transboundary stocks can be jointly managed by neigh-bouring countries under bi-lateral agreements or unilaterally without legal-binding agreements with othercountries.The management of shared stocks is often viewed through the lens of game theory as a mean toanalyze strategic interactions among decision-makers. Previous studies that apply game theory to studyshared stocks have demonstrated that cooperation between nations in managing these stocks will most likelyresult in the best overall ecological and economic outcome for nations sharing a common resource (Bailey etal. 2010, Eide et al. 2013). Lack of cooperation in managing shared stocks may result in over-exploitation(Clark 1980, Nguyen et al. 2018), reduction of the potential profitability of the fishery (Merino et al. 2007),and lead to conflict between coastal nations (Spijkers and Boonstra 2017). Only in some cases, when thetarget stock is transboundary but sessile (e.g., clams) non-cooperation could provide a better overall solution(Miller and Munro 2002).2Figure 1.1: Schematic representation of the different types of shared stocks as definedby the United Nations’ Food and Agriculture Organization (FAO). Adapted from FAO(http://www.fao.org/figis/servlet/IRS?iid=2665). Fish images from Dr. Seuss. 1960. One fish, twofish, red fish, blue fish. Penguin Random House LLC.Forty years after the implementation of UNCLOS, the scale and status of all transboundary speciesand their fisheries in the world ocean is still not accurately characterized. Caddy (1997) estimated that therecould be up to 1,500 transboundary fish stocks in the world. However, such estimation was not explicitlyinformed by the biogeography of marine species (Caddy 1997). More recently, Teh and Sumaila (2010)estimated that 206 exploited species could be considered shared stocks, accounting for a total catch of 34.2million t and an estimated landed value of USD 30.7 trillion (in 2006 value). While the estimates by Tehand Sumaila (2010) were based on a literature review of shared species, the estimates likely representedthe lower limit of the number of shared species and the scale of their fisheries because many shared speciesmay not be reported in the literature. Particularly, recent research highlighted the ecological and spatialinterconnection of marine species (Ramesh et al. 2019), suggesting that the number of shared species maybe more than previous estimates. A bottom-up approach that estimates the number of shared stocks basedon the biogeography of exploited marine species may thus be accurate.In Chapter 2 of this dissertation, using information from species’ biogeography, I aim to estimatethe number of exploited marine species shared by neighboring countries and determine their contributionto global and regional catch and fisheries revenue. However, as climate change is causing a shift in the bio-3geography of marine species (Chapter 3), it adds complexity in understanding and managing transboundaryfisheries (Chapter 4).1.2 Climate change and its effect on marine speciesBy 2017, greenhouse gas emissions from human activities are estimated to have warmed the Earth’s surfaceby 1 ± 0.2𝑜𝐶 above pre-industrial levels causing changes in ocean conditions (IPCC 2018) (Figure 1.2).Figure 1.2: Past and future changes in sea surface tem-perature, surface ocean pH and oxygen content (100-600 m depth). Adapted from IPCC (2019)The ocean has absorbed 93% of heat pro-duced from greenhouse gas emissions since 1970(Rheim et al. 2013, IPCC 2019). As a consequence,global sea surface temperature (SST) has increasedby 0.63𝑜C relative to 1850-1900 (IPCC 2019). Also,an assessment by the Intergovernmental Panel onClimate Change (IPCC) has concluded that marineheatwaves have become more frequent (Frölicher etal. 2018), and ice sheets and glaciers have lost massat an average rate of 220 ± 30 Gt 𝑦𝑟–1 between2006 and 2015 (IPCC 2019). The ocean has alsosequestered about 30% of the carbon dioxide (𝐶𝑂2)emissions from the atmosphere since the late 1980’s,causing a pH decrease of 0.1 unit that correspondsto a 26% increase in acidity (Ross et al. 2011, IPCC2019). Moreover, increasing stratification of oceanicwaters, changes in ventilation and biogeochemistryhave led to a 0.5 to 3.3% loss of oxygen content inthe upper layer (1000 m) of the open ocean between1970 and 2010. During the same period, oxygen minimum zones have expanded by 3 to 8% in volume (IPCC2019).There are substantial regional variations in the 𝐶𝑂2-induced changes in ocean properties. Forexample, the Southern Ocean accounted for 35 to 43% of the total heat gain in the upper 2000 m layer of theglobal ocean between 1970 and 2017, and 25% of the global oxygen decline between 1970 and 1992 (IPCC2019). Large reductions in oxygen have also been recorded for the north Pacific (IPCC 2019). In terms of4pH, the largest declines between 1991 and 2011 were in the Indian Ocean (–0.027 units decade–1), easternEquatorial Pacific (–0.026 units decade–1) and the South Pacific subtropical oceans (–0.022 units decade–1)(IPCC 2019).Ocean properties are expected to continue to change in the 21𝑠𝑡 century, although the intensityof these changes depends on the decisions’ society will take in the future in relation to carbon mitigation(Gattuso et al. 2015, IPCC 2019). The IPCC assessed the future climate using scenarios of greenhousegas concentration in the atmosphere. These scenarios, known as Representative Concentration Pathways(RCPs), range from a “strong climate mitigation” (RCP 2.6) scenario to a “no mitigation” scenario wheresociety has no effective greenhouse gases mitigation policies (RCP 8.5). Under RCP 2.6, radiative forcingincreases 2.6 𝑊/𝑚2 by 2100 relative to pre-industrial conditions while RCP 8.5 leads to a radiative forcingof 8.5 𝑊/𝑚2 (Vuuren, Stehfest, et al. 2011). Under RCP 2.6, SST is projected to increase by 1.6𝑜C (CI1: 1.1- 2.0𝑜C) by 2031 - 2050 relative to 1850-1900, maintaining this trend (1.6𝑜C; CI: 0.9 - 2.4𝑜C) towards the endof the 21𝑠𝑡 century. In contrast, following RCP 8.5 would result in an increase of 2𝑜C (CI: 1.5 - 2.4𝑜C) by2031 - 2050 reaching 4.3𝑜𝐶 (CI: 3.2 - 5.4𝑜C) by the end of the 21𝑠𝑡 century relative to the same time period(Riahi et al. 2011, IPCC 2019). In addition, the IPCC considers two intermediate scenarios, RCPs 4.5 and6.0. While RCP 4.5 leads to a radiative forcing of 4.5 𝑊/𝑚2 by 2100 relative to pre-industrial conditionsand RCP 6.0 leads to 6.0 𝑊/𝑚2 (Masui et al. 2011, Thomson et al. 2011). Here, I focus on RCP 2.6 andRCP 8.5 to capture the lower and upper range of climate change impacts on shared fish stocks and fisheries.The ocean is projected to become warmer, less oxygenated and with lower pH in the 21𝑠𝑡 centuryrelative to the present day (e.g., 1986-2005) under all RCPs (IPCC 2019). Specifically, by 2050, global meanSST is expected to increase by 0.65𝑜C (CI: 0.33 to 0.96𝑜C) under RCP 2.6 and 0.95𝑜C (CI: 0.60 to 1.29𝑜C)under RCP 8.5. The largest SST warming will be in the North Pacific, the tropical East Pacific, and inparts of the Arctic (Gattuso et al. 2015). At the same time, surface pH will be reduced by -0.072 (CI:–0.072 to –0.072) units under RCP 2.6 and 0.108 (CI: –0.106 to –0.110) units under RCP 8.5, with the Arcticexperiencing the largest decrease (Gattuso et al. 2015, IPCC 2019). Dissolved oxygen within the first 600 mwill also be reduced by -0.9% (CI: -0,3 to -1.5) under RCP 2.6 and -1.4% (CI: –1.0 to –1.8) under RCP 8.5(IPCC 2019). The subsurface layer of mid-latitude regions is prone to see the largest changes in dissolvedoxygen (Gattuso et al. 2015). By the end of the 21𝑠𝑡 century under RCP 8.5, ocean surface will be 3.2𝑜Cwarmer, –0.4 pH units lower, and have 3.9% less dissolved oxygen relative to the pre-industrial level. Incontrast, under RCP 2.6, ocean surface will be 1.2𝑜C warmer, pH will be –0.14 units lower and oxygen willbe about 1% less than the pre-industrial level (Gattuso et al. 2015, IPCC 2019).1In this case, the confidence interval used by the IPCC lies between the 17–83% probability range.5In responses to the change in ocean properties, marine species are altering their productivity, distri-bution, and phenology (Scheffers et al. 2016). The type and magnitude of the responses will vary dependingon the oceanic region, taxonomic group and life history and ecological characteristics of the organisms (Gat-tuso et al. 2015, Poloczanska et al. 2016, Scheffers et al. 2016). Changes in maximum sustainable yield(MSY) of 235 populations of 162 species have been related to increasing water temperature. Between 1930and 2010, MSY decreased by 4.1% (1.4 million tons) with some ecoregions (e.g., East Asia) experiencinglosses of up to 35% (Free et al. 2019). Future projections suggest that by the end of the 21𝑠𝑡 century, 41and 91% of global fish stocks will see a decline in MSY relative to 2012 under RCP 2.5 and 8.5 respectively(Gaines et al. 2018). Moreover, shifting distributions has been the most widely documented response ofmarine species to ocean warming (Gattuso et al. 2015, Poloczanska et al. 2016). Marine species, from phy-toplankton to top predators have shifted their distribution ranges, mostly poleward, by an average of 72.0± 13.5 km per decade (Poloczanska et al. 2016). These shifts are consistent with projections from speciesdistribution models and are expected to continue in the 21𝑠𝑡 century, even under a low emission scenario(RCP 2.6) (Cheung et al. 2010, Barange et al. 2014, Garcı’a Molinos et al. 2015). Warming and lessoxygenated waters have also been related to changes in size of marine fishes (Forster et al. 2012, Audzijonyteet al. 2020, Oke et al. 2020). Overall, fish body size is expected to decrease under a warming ocean (Cheunget al. 2012, Pauly and Cheung 2017, 2018) with variations between regions and species (Audzijonyte et al.2020).Shifts in exploited marine species abundance, distribution and phenology will consequently impactthe economics of fisheries (Lam et al. 2016, Sumaila 2019, Sumaila et al. 2019) and food security (Goldenet al. 2016, Bell et al. 2017) of communities that depend on them. It was estimated that in 2010, global fishlandings were about 100 million tonnes (most of it destined to direct human consumption) generating aboutUSD 150 billion (Tai et al. 2017). Estimates under RCP 8.5 suggest that global catches and revenue fromfisheries could drop by 8 and 10%, respectively by 2050 with large regional differences (Lam et al. 2016).For example, under RCP 8.5, the Exclusive Economic Zone (EEZ) of many tropical countries are expectedto see >50% reductions in both catch and revenue (Lam et al. 2016). This is the case of many PacificIslands nations like the Philippines where fish represents a high proportion of animal-sourced food (Bell etal. 2017, Asch et al. 2018, FAO 2018a). On the contrary, high latitude EEZs such as the Russian in thenorth and the Chilean in the south could see increases of up to 30% (Lam et al. 2016). Similarly, societalvulnerability to climate change impacts on fisheries will not be the same across coastal communities. Forexample, indigenous fishing communities that rely on marine resources for food security and wellbeing areparticularly vulnerable to climate change due to reduced access to resources (Cisneros-Montemayor et al.62016, Weatherdon et al. 2016). Reducing greenhouse gas emissions to limit warming to a 1.5𝑜C increaseby the end of the 21𝑠𝑡 century (RCP 2.6) could increase fisheries catch potential by up to 30% (Cheung,Reygondeau, et al. 2016). This would result in a USD 4.6 billion annual revenue increase benefiting 75% ofcoastal nations from which ~90% represent developing countries (Sumaila et al. 2019).1.3 Transboundary fisheries management under climate changeCurrent transboundary fisheries management might not be adapted to cope with shifts in species distributionsunder climate change (Pinsky et al. 2018, Chapter 4). When a fish stock shifts from one EEZ to anotherdue to climate change, fisheries in the states that lose the resource may opt to maximize the exploitationof the fish stock. In contrast, the states that benefits from an increasing share of the fish stock may havemore incentive to conserve it because of its increasing stake on the state’s fisheries resources (Diekert andNieminen 2017). As climate change continues to drive shifts in biogeography of exploited species, fish stocksare expected to expand into EEZs they did not occupy before. In some cases, abundance will decrease inthe lower latitudinal range of the species potentially exacerbating conflicts over stock’s ownership and quotaallocation (Pinsky et al. 2018, Oremus et al. 2020). Despite historical and projected changes in speciesdistributions, today, many treaties concerning on the management of shared stocks are not well equipped torespond to stock shifts (Cullis-Suzuki and Pauly 2010, Pinsky et al. 2018, Sumby et al. 2021, Chapter 4).Specifically, some basic fisheries management strategies, such as quota allocations, are based on historicalcatch proportions, or stock’s historical distribution, and could become outdated as stocks shift to copewith a changing ocean (Beaugrand et al. 2011, Chapter 4, Pinsky and Mantua 2014). Lack of or insufficientadaptation of management of shared stocks to changing ocean conditions and stocks distributions will impacttheir effectiveness in ensuring the sustainability and benefits from the fisheries (Sumaila et al. 2020).Both the magnitude and pattern of range shifts are important to characterize the risk and impactsof shifting shared stocks on transboundary fisheries management (Link et al. 2010, Pinsky and Fogarty2012). The distribution of a fish stock can change in multiple ways. For example, the stock range canshift from one EEZ to another while approximately maintaining its total range size. Also, the distributionof the stock can contract from or expand into a neighboring EEZ, resulting in a decrease or increase inrange size, respectively (Figure 1.3). The magnitude and characteristics of range shifts of shared stocks willhave different consequences to their management (Link et al. 2010). Historically, such shifts have causedconflict between neighboring nations and compromised the sustainability of diverse shared stocks. Prominentexamples include the 1980’s conflict over Pacific salmon between the United States (US) and Canada (Miller7and Munro 2004, Song et al. 2017b).Figure 1.3: Schematic representation of different pattern of distribution shifts in shared stocks. Adaptedfrom Link et al. (2010)In addition to the magnitude and pattern of range shifts, it is important to understand the timeframe at when the shifts and their impacts on fisheries will become apparent (Hawkins and Sutton 2012,Pinsky and Mantua 2014). The IPCC Special Report on the Ocean and Cryosphere in a Changing Climateused the concept of “time of emergence” (ToE) to discuss the challenge of changing ocean conditions, marineecosystems and their challenges to ocean governance (IPCC 2019). As defined by the IPCC (adopted fromHawkins and Sutton, 2012), ToE is the moment in time when an anthropogenic change signal (e.g., futuretrend) rises above the background noise of natural variability (e.g., historical variation) (IPCC 2019). Thepremise behind ToE is that we can only be confident that a significant change has been detected when thesignal of anthropogenic climate change is larger than the background of natural climate variability (Hawkinsand Sutton 2012). The concept of ToE has been extensively used to determine the time by which diverseoceanic variables (e.g., temperature, oxygen, 𝐶𝑂2) have raised above natural variability (e.g., emerged) inthe global ocean (Mahlstein et al. 2011, Hawkins and Sutton 2012, Keller et al. 2014, Rodgers et al. 2015,Frölicher et al. 2016). However, this concept has not been widely used on studies related to climate changeand fisheries, until a recent study investigating the impacts of marine heatwaves in selected fisheries of thenortheast Pacific (Cheung and Frölicher 2020). The concept ToE is useful to elucidate the impacts of climatechange on transboundary fisheries and the consequences for their governance. For example, in the US, thearrival of the jumbo squid Dosidicus gigas to Washington state in 2009 created a new fishery under a defacto open access regime due to a lack of existing regulation. The time it took policy to be implemented was8too long, and the fishery was over-exploited (Pinsky and Mantua 2014). Moreover, since 2007 when Atlanticmackerel (Scomber scombrus) extended its distribution to Iceland’s EEZ, the European Union (EU) andNorway, and Iceland have been in dispute over fishing rights and quota allocation (Spijkers and Boonstra2017).1.4 Research objectivesThe goal of this dissertation is to understand the impacts of climate change on transboundary fish stocks andfisheries and their management, thereby informing international fisheries governance to prepare and respondto climate change. Specifically, my dissertation is guided by the following questions:• What are the existing transboundary species, and what is their importance to global fisheries in termsof catch and revenue?• How will climate change affect the distribution of shared stocks between neighbouring nations?• What are the consequences of climate change-driven distribution shifts to the management of trans-boundary stocks?The dissertation is divided in three main data chapters and a conclusion chapter (Figure 1.4).In Chapter 2, I present a global estimation of the current transboundary species and their importance tofisheries catches and revenue. Chapter 3 looks at the consequences that climate change will have to themanagement of shared stocks, more specifically, how are changes in species’ distributions going to affect theproportion of shared stocks between neighboring countries. In Chapter 4, I use two case studies of sharedmanagement between Canada and the United States to look at the policy implications of such shifts. Finally,in the conclusion chapter (Chapter 5), I provide an overall synthesis of the impacts of climate change tothe management of shared stocks, not only highlighting the identified challenges informed by the previouschapters but also suggesting solutions to cope with a changing world. The methods of my dissertation arebroadly based on numerical modelling and combination and synthesis of multiple datasets. In terms ofmodelling the methods relay on coupled species distribution models and Earth system models’ simulationsto project future changes in transboundary species distribution in the 21𝑠𝑡 century under two climate changescenarios.9Figure 1.4: Schematic diagram illustrating the structure of this dissertation. It starts with a global identi-fication of transboundary species. It then proceeds to assess the climate change impacts on transboundarystocks of the identified transboundary species. Finally, it uses two case studies to explore the managementimplications of climate change to the joint management of transboundary stocks102 Chapter Two: The transboundary nature of the world’s ex-ploited marine species2.1 IntroductionDistributions of marine species around the world are not constrained by human-made boundaries; rather theyare shaped by biotic and abiotic factors as well as species’ evolutionary history (Hutchinson 1957, Nelson etal. 2016). A species can be widely distributed (cosmopolitan) or endemic (Nelson et al. 2016). Fisheriesmanagement is predicated on the definition of “stocks”, delineated, in most cases by human-made spatialboundaries that often do not correspond to biologically-meaningful population units (Song et al. 2017a,Fredston-Hermann et al. 2018). The development of Exclusive Economic Zones (EEZs) under Part V ofthe United Nations Convention on the Law of the Seas (UNCLOS) in the early 80s (United Nations 1986),for instance, extended political boundaries from 12 to 200 nautical miles to give coastal nations propertyrights over marine resources (Østhagen 2020). However, EEZ boundaries cut across the distribution of manyspecies, creating shared stocks between nations (United Nations 1986). Shared stocks can be classified intothree non-exclusive categories; “transboundary” - stocks that cross the EEZs of two or more bordering coastalstates; “straddling” - stocks that cross neighboring EEZs and the adjacent high seas; and “highly migratory”- stocks that cross non-neighboring EEZs and the high seas (mainly tunas) (Munro, Van Houtte, et al. 2004).My study focuses on the “transboundary” nature of shared species exploited by fisheries operating withinEEZs.Theory and empirical evidence have shown that fisheries targeting resources that straddle politicalboundaries complicate fisheries management and potentially reduce the effectiveness of policies to achievetheir stated objectives (Miller and Munro 2002, Englander 2019). For instance, climate-driven changes inspecies distributions have led to conflicts between nations, driven, at least partly, by changes in the proportionof captures (Miller and Munro 2002), quota allocation, and fishery newcomers (Spijkers and Boonstra 2017).Moreover, climate change is likely to exacerbate such conflicts and presents new challenges for politicalrelations between neighboring countries (Pinsky et al. 2018) and fisheries management (Miller et al. 2013).Therefore, having an accurate understanding of the distribution and scale of transboundary and straddlingfish stocks as well as associated fisheries is important to inform their sustainable management, particularlyunder climate change.Forty years after the formal adoption of UNCLOS (United Nations 1986) and the subsequent 1995United Nations Fish Stock Agreement for the cooperation on the management and conservation of straddling11and highly migratory resources (United Nations 1995), accurate estimates of the number of exploited marinespecies shared by neighboring nations are still unavailable. An informed guess, based on limited biogeo-graphical data, suggested that there are approximately 500 to 1500 exploited transboundary stocks (Caddy1997). A recent literature review included 344 shared taxa and their historical contributions to fisheries (Tehand Sumaila 2015). However, these studies did not consider species’ biogeography in the quantification oftransboundary stocks. Here, I aim to estimate the number of exploited marine species shared by neighboringcountries and determine their contribution to global and regional catches as well as fisheries revenue. More-over, I categorize species according to their catch trends while identifying differences among species basedon habitat preference. I hypothesize that the methodological constraints of previous studies resulted in anunderestimation of the number of transboundary species and their contribution to global catch and revenue.I overlaid the known distribution of 938 commercially valuable marine species responsible for anaverage of 96.5% of global EEZ catches between 2005 and 2014, and 280 EEZs of 198 coastal countries (SeeMethods). I define a ‘stock’ unit as a species in an EEZ, instead of a genetically or morphological distinctunit (Nelson et al. 2016), due to the lack of such biological information being available for almost all thespecies included in this study (Teh and Sumaila 2015). While I acknowledge that species could have multiplestocks within an EEZ, many fisheries within a country or EEZ are managed at the species instead of stocklevel (or even as groups containing multiple species). For example, shrimp (e.g., Litopenaeus stylirostrisor Farfantepenaeus californiensis) along the Pacific coast of Mexico (Diario Oficial de la Federación (DOF)2018) and hammerhead shark (Sphyrna zygaena) in Peru (MAP 2017) are managed at the species level,yet include multiple populations exploited by each country’s fisheries. Moreover, recent research showsconnectivity across fish stocks through larval dispersal (Ramesh et al. 2019) and adult migration (Levin etal. 2018, Dunn et al. 2019, Popova et al. 2019), although considerable level of uncertainty exists at differentlife stages (Archambault et al. 2016, Kaplan et al. 2016). For this analysis, I only considered shared speciesbetween neighboring EEZs, rather than the species’ extended distribution (e.g., I did not include the highseas). I rely on multiple data sources including occurrence, distribution models and catch data, and onlyconsider a species to be present in a grid cell if all data sources showed positive occurrence (see Methods).2.2 Materials and methods2.2.1 Databases of species’ geographic distributionThe Sea Around Us has reconstructed global fisheries catches from 1950 to 2014, identifying commercialmarine fish and invertebrates as well as fishing regions (Zeller et al. 2016). I used the Sea Around Us12Table 2.1: Summary of data sources to estimate species’ distributions. All data has a ressolution of 0.5degrees latitude x 0.5 degrees longitude. All data is publically available, see references for detailsSource Abreviation Main Method SourcesOccurrence data Occurrence Occurrence data from multiplesourcesReygondeau, 2019Ecological niche model ENM-Nereus Environmental niche modelbased on environmentalvariables and different modelalgorithmsAsch et al., 2017 &Reygondeau, 2019Species distributionmodelSDM-SAU Species distribution modelbased on species traitsPalomares et al., 2016Catch data Catch-SAU Spatial catch allocation basedon country-by-countryreconstructionsZeller et al., 2016reconstructed species list to determine the number of transboundary marine species exploited by fisherieswithin each of the world’s EEZs. The total number of species analyzed was 938, representing 67% ofidentified species by the Sea Around Us and accounting for 96.52% of the catch identified at the species’level. To determine the current distribution of exploitable marine species, I used four data sources ofspecies-distributions: (i) occurrence data, (ii) an Ensemble Environmental Niche Model (ENMs), (iii) alife-history-based distribution model, and (iv) fisheries catch data (Table 2.1). Each source represents adifferent method of estimating the distribution of a given species, thus providing a more robust result thanan analysis focused on a single data source. Only commercially fished species with data from all four sourceswere included in my analysis.2.2.1.1 Occurrence dataOccurrence data was collected by Reygondeau (2019) from five publicly available repositories: Fish-Base (http://fishbase.org), the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/), theOcean Biogeographic Information System (OBIS; https://obis.org/), the Intergovernmental OceanographicCommission (IOC; http://ioc-unesco.org), and the International Union for Conservation of Nature (IUCN;https://www.iucn.org/technical-documents/spatial-data) (Reygondeau 2019).2.2.1.2 Distribution modelsIn addition to occurrence data, I used two different methods to estimate species distributions,hereafter referred to as Ecological Niche Model-Nereus (ENM-Nereus) and Species Distribution Model-SAU(SDM-SAU). Although they use the same occurrence and environmental data, the models are structurallydifferent complementing each other and providing robustness to the results.13The ENM-Nereus consists of a multimodel approach based on a Bioclim and a Boosted RegressionTree model (Thuiller et al. 2009), a Maxent model (Phillips et al. 2006), and a Non-Parametric ProbabilisticEcological Niche Model (Beaugrand et al. 2011). Environmental variables utilized in the models includesea surface temperature, surface pH, surface oxygen concentration, and vertically integrated (0–100 m)net primary production (NPP) (Asch et al. 2018). Global environmental conditions were average for the30-year climate normal period of 1970-2000 and averaged for three Earth System Models developed bythe Geophysical Fluid Dynamics Laboratory (GFDL- https://www.gfdl.noaa.gov/earth-system-model/), theInstitute Pierre Simon Laplace (IPSL- www.icmc.ipsl.fr/), and the Max Planck Institute for Meteorology(MPI- www.mpimet.mpg.de/en/science/models/). See Asch et al. (2018) and Reygondeau et al. (2019) formodel details.The SDM-SAU model follows a five-step process based on species-specific life history data, ratherthan environmental variables (Close et al. 2006, Pauly and Zeller 2016). For each commercial marine species,the model first uses the FAO major fishing areas and countries’ EEZs to determine a broad distribution. Itthen uses life history information to delimit its range within the FAO fishing area (e.g., thermal preference,depth limit). The range is delimited even further by expert-review polygons and compared with distributionsfrom AquaMaps (Kaschner et al. 2016), as well as OBIS and GBIF occurrence data. The model thendetermines a species’ habitat-preference based on the assumption that the relative abundance of a speciesis determined by the number of habitats in a grid cell and the species’ distance to each habitat, as well asthe importance of the habitat to the total size of the species distribution. Finally, the species equatorialsubmergence (e.g., the latitudinal region where a species is not seen in between poles) is estimated for eachspecies. See Close et al. (2006) and Pauly et al. (2016) for model details.2.2.1.3 Catch dataThe previous models combine observational data with a series of biotic and abiotic data to determinethe probability that a species will be found in a given space at a given time. However, this does not mean thatthe species in question will actually be there. While the models do use approaches to double-check speciesoccurrences (e.g., ENM-Nereus uses four different species distribution algorithms and SDM-SAU undertakesvalidation by means of other models), I used a fourth data set to corroborate the models’ outputs. TheSea Around Us estimates total reconstructed catches - catches based on all publicly available informationsources and including discards, as well as unreported and illegal catches that are not included in availableFAO data - for each country. Catches are also spatially allocated on a 0.5∘ x 0.5∘ latitude longitude grid(Zeller et al. 2016). Roughly, the Sea Around Us method consist of the following steps. First, it takes each14country’s officially reported catch data (e.g., National, FAO or RFMO). Secondly, it uses literature (e.g.,peer review, grey literature) to identify missing components (e.g., species, gears) and sources of alternativeinformation for missing components. It then derives country estimates for missing data and creates timeseries interpolation. Finally, the estimated and official data are aggregated, making up the total reconstructedcatch data (see Pauly et al (2019) and Zeller et al. (2016) for catch reconstruction and spatial allocationdetails). I used the Sea Around Us catch reconstruction database from 2005 to 2014 as the fourth dataset toestimate transboundary species and to estimate their catch contribution within EEZs. I selected this timeframe to investigate the recent (my time frame includes the last decade of available data) contribution oftransboundary fisheries to catches and revenue from fisheries and to reduce the uncertainty embedded in thereconstruction process (see Key Uncertainties). Note that in call cases, I report the average catch from 2005to 2014.2.2.2 Determining transboundary species traitI developed a three-criteria methodology to determine whether or not a species can be considered trans-boundary. Only species that met all criteria at least once were considered as “transboundary”, while speciesthat did not meet the criteria for any EEZ analyzed were considered as “discrete”. Note that in cases wherea species met all criteria for some EEZs, but not for other EEZs, these species were still considered as“transboundary”.2.2.2.1 Criteria 1; Neighboring EEZsAs mentioned above, I define transboundary species as those marine species that occur within theEEZs of two or more neighboring countries. Hence, according to this criteria a species was only consideredas transboundary if it was shared between two neighboring countries, regardless of the species extendeddistribution. The analysis was undertaken only within the boundaries of the EEZs of coastal states using theSea Around Us shapefile (updated 1 July 2015, available from http://www.seaaroundus.org) - noting that itsubdivides the EEZs of 198 coastal states into 280 regions (e.g., Mexico’s EEZ is divided in Mexico Pacificand Mexico Atlantic), including islands territories - and determined the intersections between polygonsusing the R package sf (Pebesma et al. 2018). When estimating transboundary species, I filtered out thoseshared by EEZs sub-regions (e.g., USA Gulf of Mexico and USA Atlantic), and when aggregating results bycountry, species that occurred in more than one sub-region were only accounted for once. Species that werepresent in EEZs that were non-continental territories neighboring other countries were kept as separate (e.g.,Argentina and Falkland Islands), but removed in cases where the non-continental territory belonged to the15same nation (e.g., Brazil and Fernando de Noronha). Associated states like Puerto Rico and New Caledoniawere not considered separately (e.g., Puerto Rico was grouped with the United States and New Caledoniawith France).2.2.2.2 Criteria 2; Data agreementI used the occurrence database, the ENM-Nereus model, and SDM-SAU model to determine thepresence of each species within each of the world’s 0.5∘ x 0.5∘ marine grid cells. All analyses only consideredcases with agreement across all databases to obtain a more conservative estimate of transboundary species.Moreover, I assumed that a species was only present in a given grid-cell if it was reported in the SeaAround Us database. Therefore, all species that were not reported as caught in any single year between thereference years (2005 to 2014) in a given grid-cell were dropped. This rule assumes that if a commercialspecies is projected within the EEZ of any fishing country, such a species would have been fished, and thuslikely reported at some point over the last decade of data (2005 - 2014), thereby validating the models andselecting “currently” shared species. I acknowledge that this criteria might limit the global distribution ofspecies therefore resulting in a conservative estimate of transboundary species.2.2.2.3 Criteria 3; Spatial distributionFinally, to have a more robust result and not categorize a species as transboundary based on itspresence in a single 0.5∘ x 0.5∘ grid cell within an EEZ, I computed an Area Index. The Area Index representsthe proportion of a given species’ overarching shared distribution between neighboring EEZs accounted forby each individual EEZ. I classified a species as transboundary if both neighboring EEZs enclosed over25% of the species joint shared distribution. While a species that has less than the selected threshold isnot considered transboundary in this chapter, this threshold can be lowered for a more relaxed result orincreased for a more conservative estimate (Figure A2.2).2.2.3 Fisheries trendsI estimated the economic contribution in 2019 real USD of transboundary species for each country using globalex-vessel price data (Tai et al. 2017). The database I draw from includes ex-vessel price derived from multiplesources and a structured interpolation method (e.g., similar countries, species) to fill in data gaps (Sumaila etal. 2015). The dataset is harmonized with the Sea Around Us catch data to estimate yearly fishing revenue (asex-vessel price) for all species and EEZs considered in this study (http://www.seaaroundus.org/data/#/feru).16Table 2.2: Rules to determine the category of each transboundary speciesCategories RulesA - Increasingcatch trend(Year of Catch > Year Post Max. Min. & Post Max Min Catch < (MaxCatch*0.10)) & (Catch > (Max Catch*0.10) & Catch < (Max Catch*0.50)) or Yearof Catch < Year of Max. Catch & Catch <= (Max Catch*0.50) or Year of MaxCatch = Last Year of data)B - Constant catchtrendCatch > (Max Catch*0.50)C - Decreasingcatch trend FisheryYear of Catch > Year of Max. Catch & (Catch > (Max. Catch*0.10) & Catch <(Max. Catch*0.50) or Catch < Max. Catch*0.10No Status None of the above rules appliedI report average fishing revenue derived from fishing activity within global EEZs between 2005 to 2014. I didnot include revenue from areas beyond national jurisdiction. I used the monthly average US Consumer PriceIndex (CPI) according to the U.S. Bureau of Labor Statistic (https://www.bls.gov/cpi/) to standardize theoriginal 2010 real USD value to 2019 real USD.I used catch data as described above to determine the catch trend of each species within an EEZ.Although this method has previously been used to estimate stock status (Grainger and Garcia 1996), thecategories presented here are intended to represent catch trends, and not fishing status for each species asmany environmental and social-economic factors (e.g., temperature, markets, fishing policies, and fishingeffort) affect catches (Branch 2008, Pauly et al. 2013). I only assessed species within each EEZ for which atleast ten years of data were available between 1951 and 2014 and with at least five consecutive years of data.Three final categories were drawn up for each species depending on catch volume within each EEZ (e.g.,present, maximum, and minimum EEZ’s historical catch) and the year (e.g., year of maximum historicalcatch of the species within that EEZ) (Table 2.2) (Kleisner and Pauly 2011). Accordingly, Category Arepresents fisheries that are registering increases in catch (“increasing”); Category B, species that have aconstant catch rate (“constant”); and Category C, species that have registered declines in catch over the last10 years (“decreasing”). Finally, I report the predominant category over the time period considered.2.2.4 Statistical analysisAll analyses were run using the statistical software R version 3.5.2 (2018-12-20) with the packages data.table(Dowle et al. 2019), janitor (Firke et al. 2018), wesanderson (Ram et al. 2018), rfishbase (Boettigeret al. 2019), R.matlab (Bengtsson et al. 2018), sf (Pebesma et al. 2018), sp (Pebesma et al. 2019),tidiverse (Wickham 2017), tidytext (De Queiroz et al. 2019), and zoo (Zeileis et al. 2019). All code isavailable at https://github.com/jepa/FishForVisa. All maps were made with Natural Earth data available at17https://www.naturalearthdata.com/. I performed a series of one-way analysis of variance (one-way ANOVA)and Multivariate analysis of variance (MANOVA) to determine statistically significant differences betweenthe means of different groups (e.g., geographical regions, species, catch trends) of transboundary speciesand their contribution to catch and fishing revenue (Krzanowski 1990, Chambers et al. 1992). I used thestandard R functions for the ANOVA and MANOVA after testing for assumptions. In cases where theANOVA assumptions were not clearly met, I ran the non-parametric version Kruskal-Wallis Rank Sum Testto confirm results (Hollander and Wolfe 2013).2.2.5 Key uncertaintiesI have identified four key uncertainties in the method utilized that may affect the estimation of transboundaryspecies. Firstly, as I ran the analysis at the species level due to limited spatial-specific data on species subpopulations (stocks), I am not able to identify transboundary stocks within EEZs. While I acknowledge thata species could have multiple stocks within an EEZ, many fisheries within a country or EEZ are managedat the species instead of stock level. Also, recent research suggests strong connectivity between some stocks,even when separated by thousands of kilometers (Ramesh et al. 2019) providing additional ecological groundfor my analysis (Dunn et al. 2019, Popova et al. 2019). However, it is important to acknowledge that thereis considerable uncertainty associated in determining levels of connectivity across time and space for marinepopulations at different life stages, from larvae (Kaplan et al. 2016) to adults (Archambault et al. 2016). Thisis of special concern, but-not-limited-to (Archambault et al. 2016), highly migratory species whose rangesspan multiple jurisdictions and the high seas, such as tunas (Moore et al. 2020), billfishes (Sepulveda et al.2019) and sharks (Vandeperre et al. 2014) challenging management decisions based on meta-populations(Moore et al. 2020). Here, I limited the definition of “transboundary” to include species spanning onlyadjacent countries (e.g., the US and Canada), excluding countries that were separated by another nation(e.g., Canada and Mexico) and/or the high seas (e.g., Canada and Spain). Consequently, my results likelyprovide a conservative estimate of transboundary species, as I did not cover all marine taxa in the world(Reygondeau 2019) and only analyzed species present in all four data sources. Nevertheless, my resultsare representative of a substantial proportion of the world’s marine catches and revenue from economicallyimportant marine fisheries. Secondly, the predicted species’ distribution is affected by the uncertainties ofthe environmental data and models used for such predictions. Structural differences within Earth SystemModels result in variations of oceanic conditions, which undoubtedly affect the ENM-Nereus. I averagedresults from the three models in an effort to capture the structural variation across models. Natural climatevariability is a major driver of marine species’ distributions, potentially removing a species from an EEZ for18long periods (e.g., anchovies and sardines “regimes” in the Eastern Pacific are strongly influenced by watertemperature decal oscillations (Chavez et al. 2003). Thus, a species’ distribution can potentially be reducedor shifted in such a way that it only covers one EEZ until oceanic conditions change again and the associatedspecies’ distribution expands. To account for such climate variability, I derived the ENM-Nereus results asan average of oceanic conditions between 1970 and 2000. This is not an issue for the SDM-SAU as it does notdirectly require environmental variables (Pauly and Zeller 2016). Thirdly, I assumed that if the Sea AroundUs reconstructed data recorded a species as caught in any given grid cell, then the species was present withinthat grid cell. While catch data are not exempt of uncertainty, in most cases, differences between the SeaAround Us and the FAO self-reported data are smaller towards the end of the time series (Pauly and Zeller2019). Thus, I limited the catch data reference period in my analyses to between 2005 and 2014, the lastten years of data available. Likewise, the spatial allocation of the catch is subjected to imprecision, mainlyproduced by differences in the spatial scale of the original data and the method employed by the Sea AroundUs. Finally, this study’s results only considered species for which all datasets agreed on presence and had anArea Index of at least 25% (e.g., the species shared distribution was at least 25% in each EEZ). Therefore,again, my approach presents a relatively conservative estimate of the number of transboundary species inthe world. Using a smaller Area Index will result in more transboundary species (Figure A2.2).2.3 Results and DiscussionI identified 633 exploited transboundary species worldwide (67.5% of the 938 species analyzed), almostdouble previous estimates (Teh and Sumaila 2015). Between 2005 and 2014, national fleets targeting thesetransboundary species within EEZs caught an annual average of 48.5 million tonnes, representing 82.3% ofEEZ-derived catches reconstructed by the Sea Around Us at the species level (Figure 2.1a). These catchesgenerated a yearly average of USD 77,591 million in fishing revenue (78.5% of global fishing revenue) overthe same time period. My findings are considerably higher than the previous estimates of 34.2 milliontonnes in catches and USD 40,187 million (in 2019 value) in fishing revenue from shared stocks (Teh andSumaila 2015). When I re-estimated transboundary species’ catches using data consistent with those usedin previous studies (FAO global reported data in year 2006), the 633 transboundary species identified hereaccounted for 40.4 million tonnes of annual catches (i.e., 18% higher than previously estimated). My resultssuggest that the contribution of transboundary species to global catch and fishing revenue might previouslyhave been underestimated due to an incomplete understanding of the transboundary nature of marine fishedspecies. The 305 non-transboundary species (termed as ‘discrete’ species here, see Methods - Determiningtransboundary species trait) accounted for a much smaller proportion of total catch and revenue; 2.8 million19tonnes and USD 4,282 million annually respectively, on average, between 2005 and 2014. These resultsunderscore the importance of transboundary species at the global level.Figure 2.1: Number of transboundary species and their contribution to global fisheries catch and revenue. a)The number of species and amount of revenue are represented by color coding of EEZs and land polygons,respectively. b) Contribution of transboundary species to regional revenue (left) and catch (right). Regionsclassified according to the United Nations sub-regions. Points = mean ± sd. Revenue in 2010 real USD20In many cases, according to my categorization criteria, a transboundary species can be distributedin multiple EEZs but only counted as transboundary in a subset of EEZs (see Methods). For example,the distribution of Peruvian anchoveta (Engraulis ringens) spans the EEZs of Peru, Chile, and Ecuador.However, my study only considered the stocks in Peru and Chile as transboundary (Cashion et al. 2018).Anchoveta in Ecuadorian waters only include a small proportion of the shared distribution range (spatialthreshold between Ecuador and Peru < 10%; see Methods - Criteria 3) and thus do not meet my criteriafor consideration as a transboundary stock. A situation similar to the example of Peruvian anchoveta inEcuadorian waters is common amongst the transboundary species identified in this study. Overall, 590of the 633 transboundary species have stocks in EEZs that do not meet my criteria for consideration astransboundary stock. The annual average contribution from all excluded stocks of transboundary speciesto fisheries (e.g., Ecuador’s anchoveta catch) between 2005 and 2014 was 10.8 million tonnes, representingUSD 19,243 million in fishing revenue over the same time period.At a regional level, I found that transboundary species are particularly economically important forNorthern America (average country revenue = USD 4,680 ± 6,000 million) and Eastern Asia (USD 3,779 ±3,093 million) (Figure 2.1b). The estimated per country revenues from transboundary species in these tworegions is significantly different from other regions (one-way ANOVA; DF(16,165) = 5.081, p < 0.001, 𝛼 =0.05). China (USD 7,284 million) and the USA (USD 11,604 million) contribute 55% and 82% to the annualaverage revenue from 2005 to 2014 in Eastern Asia and Northern America, respectively. In addition to Chinaand the USA, Russia (USD 7,379 million), Peru (USD 6,044 million) and Japan (USD 3,907 million) wereamong the top five countries with the most fishing revenue generated from transboundary species between2005 and 2014 (Figure 2.1a). These five nations were responsible for 41% of the yearly global fisheries revenuefrom transboundary species.Peru and Russia, having the two largest fisheries by total catch in the world (FAO 2018a), wereeach responsible for over 5.8 million tonnes of transboundary species catch annually, on average, between2005 and 2014 (Figure A2.1). Peru’s catches consisted mainly of Peruvian anchoveta (Engraulis ringens)and accounted for 79% of the national transboundary species production. Peru and Chile recently signedan agreement to work towards standardized stock assessments through coordinated management of thesouthern anchoveta stock (UNDP 2016). A similar management agreement was signed by Russia, Japan andthe USA over shared Alaskan pollock (Theragra chalcogramma) in the Bering Sea in 1988 (NOAA FIsheries2019). Transboundary species also make large contributions to fisheries in Eastern Asia (one-way ANOVA;DF(16,158) = 2.265, p = 0.005, 𝛼 = 0.05). China, the world’s top fish producer (FAO 2018a), obtains onethird (5.1 million tonnes) of its total catches from transboundary species, followed by Japan (1.8 million21tonnes) and South Korea (1.06 million tonnes). Differences in the regional importance of transboundaryfisheries can also be illustrated in terms of catch-revenue over the area (𝑘𝑚2) of the EEZ (Figure 2.2). Asan example, the aggregated EEZ area for all Northern European countries where transboundary species arepresent is 3.3 million 𝑘𝑚2, the 6𝑡ℎ smallest of the 17 groups analyzed (Table A2.1. However, it had thesecond highest average revenue (USD 26.1 thousand per 𝑘𝑚2) and the highest average catch (19.9 tonnesper 𝑘𝑚2) of transboundary species per EEZ area between 2005 and 2014. At the country level, countriesfrom Western Europe accrued significantly more revenue from transboundary fisheries per 𝑘𝑚2 than anyother country (one-way ANOVA, DF(16,165) = 3.267, p < 0.001, 𝛼 = 0.05; Tukey’s post hoc test p ≤ 0.05;Figure 2.2).Figure 2.2: Weighted benefits of transboundary species by km2 and UN sub-region. a) Revenue in thousand2019 USD. b) Catch in tonnes. Points = sub-region mean ± s.d. by country.I determined the catch trend of each species within each EEZ, classifying them as increasing (Cate-gory A), constant (Category B) or decreasing (Category C) (Figures 2.3; A2.2). While previous studies havedemonstrated that catch trends may be used to infer whether a stock is healthy, re-building, over-exploitedor collapsed (Kleisner and Pauly 2011), several factors can influence stock status. My intention here is toexamine where the catch trends of transboundary species differ from non-transboundary species between2005 and 2014 relative to historic catch since 1951 (see Methods). I found significant differences for allcatch trend categories for transboundary species (one-way ANOVA, DF(2,459) = 47.94, p < 0.001, 𝛼 =220.05; Tukey’s post hoc test p ≤ 0.001), and no significant differences in catch trend categories for discretespecies (one-way ANOVA, DF(2,106) = 1.885, p = 0.157; 𝛼 = 0.05). I also found significant differences incatch trends when directly comparing transboundary to discrete species categories (MANOVA, DF(2,459)= 19.001, p < 0.001). Overall, transboundary species only targeted by one country are generally less likelyto have a decreasing catch trend compared to instances where the shared species is fished by neighboringcountries (Table A2.2).Figure 2.3: Number of transboundary species by catch trend and EEZ. Category A, Increasing; CategoryB, Constant; Category C, Decreasing. “No Category” reflects species with less than 10 years of catch dataand/ or less than 5 consecutive years of catch data. Only showing first 100 species.23Empirical analysis suggests that in most cases, management of transboundary species will yieldbetter outcomes in terms of fish catches when nations cooperate (Miller and Munro 2002). Yet, cooperationcan be a complex process (Jensen et al. 2015), and in specific cases joint management might not be thebest strategy (Munro 2015). Examples of successful joint management include agreements between Norwayand Russia over Atlantic cod (Gadus morhua) (Eide et al. 2013) and Namibia and South Africa over hake(Merluccius spp) (Sumaila et al. 2003). Lack of collaboration over shared stocks may threaten stock sus-tainability, reduce the future profitability potential of the fishery, and result in conflict between neighboringnations (Clark 1980, Spijkers and Boonstra 2017).Transboundary fisheries are important to a number of countries with notorious fisheries-relatedconflicts, including Canada, the USA, the European Union (EU) and Russia (Spijkers et al. 2019). Forexample, since 2007, the EU, Norway, Iceland, and the Faroe Islands (Denmark) have been at odds overthe size and relative allocation of the total allowable catch for Atlantic mackerel (Scomber scombrus) dueto the species’ climate-driven shift in distribution (Spijkers and Boonstra 2017). Atlantic Mackerel is atransboundary species that straddles into the high seas. Among the countries involved in the 2007 fisheriesdispute, Atlantic mackerel contributed an annual average (between 2005 and 2014) of 598 thousand tonnes(8%) in total catch and USD 850 million (7%) in total fishing revenue.Climate change is expected to continue changing the distribution and shared proportion of fishstocks among countries, resulting in the emergence of new transboundary species (Pinsky et al. 2018,Chapter 4), and disappearance of some species from EEZs (Oremus et al. 2020). Exploring the detailedeffects of climate change on the distribution of shared species is key to the development of local adaptationmethods that can anticipate negative impacts to sustainability. For example, understanding how climatechange will modify the proportion of transboundary species shared by neighboring EEZs, the time-frame overwhich such changes will happen (Chapter 4), and the economic consequence of such effects can inform thedevelopment of more anticipatory and climate-resilient international treaties improving fisheries management(Sumaila et al. 2020).Most marine fish species occur in tropical and subtropical waters around the world (Nelson etal. 2016, Reygondeau 2019), from highly migratory species associated with pelagic-oceanic ecosystems liketunas (Thunnus sp.), to less mobile reef-associated species like greater amberjack (Seriola dumerili), andspecies found in demersal ecosystems like gilthead seabream (Sparus aurata). Species associated with pelagic-oceanic ecosystems are the only group whose EEZ range (i.e., the number of EEZs where the species occuras transboundary) is significantly different from other groups (one-way ANOVA; DF(5,597) = 53.82, p <0.001, 𝛼 = 0.05; Tukey’s post hoc test p < 0.05), with a median of 40 EEZs per species. The median for24species of all other ecosystem preferences is close to, or less than, 20, as many of these species have a narrowerdistribution or are less mobile (Figure 2.4a). This result is likely an effect of the broad distribution that manyof the species with preference for pelagic-oceanic ecosystems have. Many pelagic-oceanic species are highlymigratory and straddle across EEZs while crossing the high seas. Thus, the number of non-neighboring EEZssharing a highly migratory species can be over 100, as in the case of bigeye tuna (Thunnus obesus) (Figure2.4a). Due to their vast migration patterns and presence in areas beyond national jurisdiction, many highlymigratory species are managed by Regional Fisheries Management Organizations (RFMOs).25Figure 2.4: Number of EEZs shared by transboundary species. a) Number of EEZs by species organizedaccording to ecosystem preference as defined by FishBase. Showing only species that share > 20 EEZs. b)Average catch between 2005 and 2014 for top five countries capturing the top five shared species for eachecosystem preference category (color coding is as shown in legend in a). Note that there could be > 5 speciesdue to similar sharing values. The category “Other” consists of species that have no ’ecosystem preference’classification in FishBase26Many transboundary species are not considered highly migratory but are still shared by numerousneighboring EEZs (e.g., garfish Belone belone) (Figure 2.4a). In addition, many fish stocks have meta-populations that are connected through larval dispersal with ‘source’ populations potentially supporting ‘sink’populations thousands of kilometers away (Ramesh et al. 2019). For example, while coral reef-associatedspecies were found to share fewer neighboring EEZs than other species (Figure 2.4a), coral regions oftenshare multiple species through larval connectivity (Schill et al. 2015) and adult movement. However, it isimportant to acknowledge the uncertainty in the connectivity of marine populations at different life stagesfrom larvae (Kaplan et al. 2016) to adults (Archambault et al. 2016). In some areas like the Caribbean andthe Western Indian Ocean, among other regions, transboundary marine protected areas have been identifiedas potential tools to support fisheries and conservation goals (Levin et al. 2018, Perez et al. 2019). Theeffective management of coral reef species is critical to many coastal communities, as they tend to be highlydependent on subsistence fishing for food and nutrition security as well as livelihoods (Cisneros-Montemayoret al. 2016, Hanich et al. 2018). For instance, a number of countries with the largest catches of transboundaryreef and pelagic-oceanic associated species (Figure 2.4b) are also associated with some of the highest ratesof fish consumption (FAO 2018a). In the Philippines, both pelagic and reef fishes contribute substantiallyto both local food and nutrition security, as well as livelihoods (Cabral and Geronimo 2018).2.4 ConclusionsIn this chapter, I identified species currently shared by neighboring coastal nations and highlights the impor-tance of these species’ contribution to global catch and revenue derived from wild fisheries. My results showthat captures and revenues from shared species are substantially higher than previously estimated (Teh andSumaila 2015) and also much greater than catches and revenues obtained from discrete species. This resulthighlights the importance of transboundary fisheries and their potential contribution to food and nutritionsecurity, as well as livelihoods. Moreover, I show significant differences in the catch trends of transboundaryand discrete species, suggesting a need to improve the management of transboundary fisheries. Previouswork has highlighted that collaboration is key to better outcomes for shared marine resources (Miller andMunro 2002). Identifying existing transboundary species is the first step towards cooperative joint manage-ment frameworks that are precautionary, strive for sustainability, and can be flexible to accommodate theuncertain future driven by climate change.273 Chapter Three: Early emergence of range shift-induced chal-lenges in managing transboundary fish stocks under climatechange3.1 IntroductionOver the last century, human activities have altered the physical and biogeochemical conditions of theocean, including warming, acidification and reducing oxygen content (IPCC 2019). Distributions of marinespecies are closely related to the environment and the species’ preferences to environmental conditions (e.g.,temperature, oxygen, salinity) (Hutchinson 1957, Nelson et al. 2016). As a result of climate change, manymarine species have changed their distributions towards higher latitude, deeper water or have followed localtemperature gradients (Poloczanska et al. 2016). Biogeography of marine species is projected to continueto shift as ocean conditions are changing in the 21𝑠𝑡 century under climate change (Cheung et al. 2010),impacting fisheries production and compromising our capacity to reach international sustainability goalssuch as Sustainable Development Goal 14 - life below water (Barange et al. 2014, Singh et al. 2017, UnitedNations 2018). The projected risks of impacts can be reduced through improving the effectiveness of currentfisheries management (Cheung et al. 2018, Gaines et al. 2018), including the fisheries management of speciesthat cross international borders, i.e., shared stocks (Gaines et al. 2018, Pinsky et al. 2018).The concept of shared stocks was developed following the ratification of the United Nations Con-vention on the Law of the Sea (UNCLOS) and the claiming of Exclusive Economic Zones (EEZs) by States(United Nations 1986). As defined by the United Nations’ Food and Agriculture Organization (FAO), sharedstocks can be classified into four non-exclusive categories: (i) transboundary stocks, those that cross neigh-boring EEZs; (ii) straddling stocks, that, in addition to neighboring EEZs, also visit the adjacent high seas;(iii) highly migratory stocks, mainly tunas and bill-fishes, that migrate across vast oceanic regions includingboth the high seas and EEZs; and finally (iv) discrete stocks that are only present in the high seas (Munro,Van Houtte, et al. 2004). This chapter focuses on transboundary stocks exploited by fisheries operatingwithin EEZs. While countries are responsible for the management of stocks within their EEZs, under UN-CLOS, States are encouraged to cooperate when stocks are shared (United Nations 1986). In the previouschapter, I estimated that there are 633 transboundary fish species globally, representing 67% or the identifiedfished taxa, yielding an annual average of 48.5 million tonnes of catch and USD 78 billion in fishing revenue(between 2005 and 2010).28The effectiveness of fisheries management for transboundary stocks is challenged by the shifts inmarine stocks distribution under climate change (Pinsky and Mantua 2014, Pinsky et al. 2018). In manycases, catch or fishing effort quotas for transboundary stocks are based on historical records (Baudron etal. 2020) and do not necessarily consider the biogeography of the stocks (Fredston-Hermann et al. 2018),nor the effects of climate change on the fish stocks and fisheries (Sumby et al. 2021, Chapter 2). Mis-alignment between fisheries resources allocation and stocks’ distributional shifts have previously resultedin unsustainable harvest and international disputes (Miller et al. 2013, Song et al. 2017b, Spijkers andBoonstra 2017), patterns that are expected to be exacerbated by intensifying climate change (Pinsky et al.2018). With future shifts in species distributions under climate change, the challenges for the managementof fisheries targeting transboundary stocks will continue to increase during the 21𝑠𝑡 century. It is importantto constrain when climate change will affect the dynamics of transboundary stocks and the intensity of theresulting impacts in order to prepare ocean governance to meet the resource management challenges arisingfrom shifting transboundary stocks (Link et al. 2010, Chapter 2, Pinsky et al. 2018).The timing and intensity of climate impacts on managing transboundary fish stocks can be quan-titatively examined through the concepts of ‘time of emergence’ (ToE) and ‘threat point’ (TP). Time ofemergence is defined as the moment in time when a signal (e.g., future anthropogenic trend) emerges fromthe background noise of natural variability (e.g., historical natural variation) (Hawkins and Sutton 2012)and has been applied to multiple oceanic physical and biogeochemical variables (Keller et al. 2014, Rodgerset al. 2015, Frölicher et al. 2016, Henson et al. 2017, Schlunegger et al. 2019, 2020, Cheung and Frölicher2020). The premise behind the time of emergence is that we can only be confident that a significant changehas been detected when the signal of anthropogenic climate change is larger than the background noise ofnatural climate variability (Hawkins and Sutton 2012). The concept of threat point comes from game theoryand is defined as the minimum payoff a player is willing to receive to cooperate in a game theoretic model(Nash 1953). Game theory has been widely used to investigate and manage transboundary stocks (Sumaila2013), including the impacts of climate change on the economics of transboundary stocks between Canadaand the United States (Sumaila et al. 2020).Here, I estimate the time of emergence and threat point of the share distribution of transboundarystocks worldwide. I employ a species distribution model driven by outputs from a comprehensive EarthSystem Model with ten ensemble members to project the distribution of 663 transboundary species (Chapter2) across 280 EEZs of 198 coastal countries under a high greenhouse gas emissions scenario RCP 8.5 (seeMethods). I treated each species in an EEZ as a single stock due to the lack of more spatially resolveddata to delineate the boundary of a population (Chapter 2) and only considered shared stocks between29neighboring EEZs (i.e., transboundary stocks that exclude the high seas). I find that the distributionof many transboundary stocks has already shifted from historical natural variability and exceeded theirshare threat point in most neighboring EEZs, posing a threat to their sustainability and the resilience ofinternational treaties.3.2 Materials and methods3.2.1 Databases and species selectionThis analysis includes 633 exploited marine transboundary species previously identified to account for 80% ofthe catch taken from the world’s EEZs between 2005 and 2014 (Chapter 2). Each time a species was sharedby a pair of neighbouring EEZs it was considered a transboundary stock (Teh and Sumaila 2015, Chapter 2),resulting in a total of 9,132 transboundary stocks in this analysis. Using the transboundary species Atlanticcod (Gadus morhua) as an example, and based on the definition of transboundary stock in this chapter,the United States and Canada share a stock of Atlantic cod, and Canada and Greenland share anotherstock of Atlantic cod, but the United States and Greenland do not share a stock of this transboundaryspecies. I defined the boundaries of the world’s EEZs using the Sea Around Us spatial division (updated 1July 2015, available from http://www.seaaroundus.org), noting that it subdivides the EEZs of 198 coastalstates into 280 regions (Figure A3.1) including island territories. I determined the intersections betweenpolygons using the R statistical software package sf (Pebesma et al. 2018). Each EEZ was categorized bygeopolitical region according to the United Nations (https://population.un.org/wpp/DefinitionOfRegions/)and biome location (Reygondeau 2019). The habitat association of each species was determined followingthe classification in FishBase for fish species (http://www.fishbase.org) and SeaLifeBase for invertebrates(http://www.fishbase.org). For each stock and EEZ, I used the Sea Around Us data to estimate catch andfishing revenue from fishing activities within global EEZs. I report both average catch and revenue for thelast decade (2005-2014) (Sumaila et al. 2015, Zeller et al. 2016, Tai et al. 2017). The monthly average USConsumer Price Index (CPI) according to the U.S. Bureau of Labor Statistic (https://www.bls.gov/cpi/)was used to standardize all monetary values to 2019 real USD.3.2.2 Projecting species distributions under climate changeI projected the distribution of marine species using a dynamic bioclimatic envelope model (hereafter calledDBEM) (Cheung et al. 2010, Cheung, Jones, Reygondeau, et al. 2016). The DBEM represents species’physiology, habitat suitability, depth and latitudinal ranges, and spatial population dynamics as well as30preferences for sea temperature, salinity, oxygen content, sea ice extent (for polar species) and bathymetry.For pelagic species, the model uses environmental variables at the surface whereas demersal species distribu-tion is driven by ocean bottom variables. The DBEM then estimates species abundance and maximum catchpotential (a proxy of maximum sustainable yield) over a 0.5∘ x 0.5∘ grid (see Cheung et al. (2010, 2016) formodel details). Importantly, DBEM is able to project catches for the world’s large marine ecosystems andchanges in catches by EEZs that are consistent with observational-based estimates of catch from 1950 to2016 (Cheung, Jones, Reygondeau, et al. 2016, Cheung et al. in revision).The DBEM was forced with simulated ocean conditions from a ten-ensemble member simulation ofthe Geophysical Fluid Dynamics Laboratory Earth system model (GFDL-ESM2M) to project the distribu-tions of the 633 species from 1951 to 2100 (John et al. 2012, 2013, Rodgers et al. 2015). The GFDL-ESM2Mwas run under historical forcing until 2005 and followed the high greenhouse gas emissions scenario, theRepresentative Concentration Pathway 8.5 (RCP 8.5) over the 2006-2100 period (Riahi et al. 2011). Be-cause the main approach of this chapter relies on understanding the spatial and temporal variation of astocks’ distribution, I have to understand distribution variability during both the historical and the futureperiods, to infer differences between time frames. I do this by following an ensemble member approachwhere each member constitutes a different realization of the Earth system variability condition (Frölicher etal. 2009, Rodgers et al. 2015). Thus, for my experiment, each of the ten GFDL-ESM2M ensembles werestarted from infinitesimally small differences in Earth system initial conditions in year 1950 resulting in aunique atmosphere and ocean state at each point in time after about three years for surface waters and eightyears for subsurface waters (Frölicher et al. 2020). By design, variations among ensemble members are thensolely due to natural variability. This approach allows us to estimate the natural internal variability (e.g.,background noise) and isolate the forced anthropogenic climate signal of a stock’s distribution by averagingthe secular trend over all ten ensemble members.3.2.3 Calculating an index of transboundary range shiftI developed an index to evaluate range shifts in transboundary stocks under climate change (TI). Thisindex was based on the shifts in the distribution centroid of a transboundary stock relative to the centroidof the neighboring EEZs that share this stock (Figure A3.2). The centroid of a transboundary stock wasdetermined by the average (µ) latitude (𝑙𝑎𝑡𝑡𝑠) and longitude (𝑙𝑜𝑛𝑡𝑠) across the grid cells with the highestabundance within the neighboring EEZs sharing the stock e.g., Pacific halibut (Hippoglossus stenolepis) thatis transboundary between the United States and Canada. Therefore,31𝑙𝑎𝑡𝑡𝑠 = 𝜇(𝑙𝑎𝑡𝑝𝑒𝑟)𝑙𝑜𝑛𝑡𝑠 = 𝜇(𝑙𝑜𝑛𝑝𝑒𝑟)(3.1)where 𝑙𝑎𝑡𝑝𝑒𝑟 and 𝑙𝑜𝑛𝑝𝑒𝑟 are the latitudes and longitudes of the grid cells holding the 𝑝𝑒𝑟𝑡ℎ percentileof the projected transboundary stock abundance. To focus on areas where transboundary stocks are moreabundant and fishing activities are more likely to take place, I included grid cells where the projected stockabundance within the neighbouring EEZs sharing the stock was above the top 95th percentile (e.g., per= 95%). A sensitivity analysis was ran using a subset of species (n = 34) for all EEZs under differentabundance levels (per = 20𝑡ℎ, 50𝑡ℎ and 90𝑡ℎ percentile) and examined the effects of different thresholds onthe calculated index value (Figure A3.3). The centroid of each EEZ was estimated using the st package inR (Figure A3.1). For each ensemble member, neighboring EEZs and transboundary stock, I computed thedistance between centroids assuming the earth is a perfect sphere and ignoring geographic barriers using thegeosphere package in R;𝐷𝑒𝑛𝑠 = 𝑎𝑐𝑜𝑠(𝑠𝑖𝑛(𝑙𝑎𝑡𝑒𝑒𝑧) ∗ 𝑠𝑖𝑛(𝑙𝑎𝑡𝑡𝑠) + 𝑐𝑜𝑠(𝑙𝑎𝑡𝑒𝑒𝑧) ∗ 𝑐𝑜𝑠(𝑙𝑎𝑡𝑡𝑠) ∗ 𝑐𝑜𝑠(𝑙𝑜𝑛𝑒𝑒𝑧 − 𝑙𝑜𝑛𝑡𝑠)) (3.2)where 𝑙𝑎𝑡𝑒𝑒𝑧 and 𝑙𝑎𝑡𝑡𝑠 are the latitudes of the EEZ and transboundary stock centroids, respectivelyand 𝑙𝑜𝑛𝑒𝑒𝑧 and 𝑙𝑜𝑛𝑡𝑠 are the respective longitudes. Then, for each year I calculated the transboundary indexas follows:𝑇𝐼 = ( 𝐷𝐴,𝑡𝑠𝑑(𝐷𝐴,𝑡ℎ)− 𝐷𝐵,𝑡𝑠𝑑(𝐷𝐵,𝑡ℎ))2 (3.3)where 𝐷𝐴 and 𝐷𝐵 represent the distance between the distribution centroids of a stock and thecentroid of the pair of neighbouring EEZs (A and B) sharing the stock for each time step from 2006 to 2100(t); and sd is the standard deviation of the historical (th, 1951 - 2005) centroid distribution for 𝐷𝐴 and 𝐷𝐵.Thus, a higher TI means that a shared stock becomes more or less abundant relative to its neighbouringEEZs that is beyond what the fisheries experienced historically. From the perspective of a State’s fisheries,higher TI indicates an increase in the challenges to effective management and sustainability of the sharedstock’ fisheries. In contrast, a smaller TI suggests that the sharing of the stocks between neighbouring EEZsis becoming relatively more stable; a condition that favors effective management and sustainability of the32shared stocks’ fisheries. The index was smoothed to a 10-year average to reduce interannual variability.3.2.3.1 Calculating the time of emergence of the transboundary indexKnowing the point in time (e.g., year) at which the distribution of a shared stock will diverge fromits natural internal variability is important for informing the lead-time to which climate adaptation needsto be implemented (Link et al. 2010). Here, I defined time of emergence as the time when the stock sharedbetween neighbouring EEZs exceeded the historical natural internal variability as follows:𝐸𝑚𝑒𝑟𝑔𝑒𝑛𝑐𝑒𝑇 𝐼𝑛𝑑𝑒𝑥 =𝜇𝑇𝐼[𝑡]𝑆𝐷𝑇𝐼𝑡ℎ𝑇𝑜𝐸 = 𝑇𝑜𝐸𝑡 > 1𝑠.𝑑.(3.4)where 𝜇𝑇𝐼 is the yearly mean TI (𝜇) across all ten ensemble members in year t and 𝑆𝐷𝑇𝐼 isthe standard deviation of the smoothed (10-year moving average) TI across the ensemble members for thehistorical (th) reference period (1951 - 2005). I set a threshold of TI above which I considered a sharedstock to have emerged from the historical internal variability of the TI. The threshold was set as onestandard deviation of the variability (𝑆𝐷𝑇𝐼) (i.e., Equation (3.4)), representing a probability of 68% thatthe transboundary index has emerged from historical variability). I tested the sensitivity of the calculatedemergence index to a higher emergence threshold of two times the standard deviation (i.e., representing aprobability of 95% that the index has emerged).3.2.4 Estimating the threat point of transboundary stock shareIn game theory, cooperation over a shared resource will more likely result in a better overall outcome for thesharing parties than those operating on a non-cooperative basis (Munro 1979). However, the benefits thateach player gets from a cooperative strategy must be above a minimum payoff i.e., “threat point” (Sumaila2013). Here, I defined the threat point as the minimum required proportion of a shared stock present withinan EEZ for a country to engage in cooperative management with their sharing neighbor (Chapter 2, Sumailaet al. 2020). Any proportion below the defined threat point of a country would result in a unilateralmanagement of the shared stock.First, I estimated the stock share ratio (SSR) of each transboundary stock shared by neighbouringnations between 1951 and 2100. I did this by aggregating the number of 0.5∘ x 0.5∘ grid cells in which the33stock is present across neighboring EEZs, and then calculated the proportion of the stock held within eachEEZ (Chapter 2). Second, I averaged the calculated proportion of stock within an EEZ held into threetime periods to reduce the effects of variability. The first time period (𝑆𝑆𝑅𝑡ℎ) spans 1951 to 2005 and waschosen to match the historical period in the GFDL ESM2M simulations, over which the model was forcedwith observation-based greenhouse gas, aerosol and natural external forcing (John et al. 2012, 2013). It wasassumed to represent the historic baseline of shared distributions for each stock. I then selected two futureperiods; the early 21𝑠𝑡 century as the average of 2021 to 2040 (𝑆𝑆𝑅𝑡𝑒), and the mid 21𝑠𝑡 century as theaverage of 2041 to 2060 (𝑆𝑆𝑅𝑡𝑚). I chose these time periods to match the challenges of achieving fisheries-related UN-SDGs such as SDG 14.4 (end overfishing), SDG 2.4 (ensure sustainable food production systems)or SDG 1.2 (poverty reduction), to be achieved by 2030 (Singh et al. 2017). The analysis was replicatedfor projected stocks distributions from each of the ten ensemble members and results were averaged acrossensemble members. Third, I defined a threat point for each EEZ’s stock as 𝑆𝑆𝑅𝑡ℎ±𝜎, where 𝜎 is the standarddeviation of 𝑆𝑆𝑅𝑡ℎ. This way, a change of SSR beyond an EEZ’s threat point happened when the futureSSR exceeded one standard deviation of the historical variations of the SSR i.e when 𝑆𝑆𝑅𝑡𝑒 ≧ (𝑆𝑆𝑅𝑡ℎ +𝜎)or 𝑆𝑆𝑅[𝑡]𝑒,𝑚 ≦ (𝑆𝑆𝑅𝑡ℎ −𝜎). Finally, I estimated the percentage change in the SSR (Δ𝑆𝑆𝑅) of each futuretime period (𝑆𝑆𝑅𝑓) relative to the historic time period (𝑆𝑆𝑅𝑡ℎ) for each stock whose share ratio overpassedthe threat point following (See Chapter 4 - Methods),Δ𝑆𝑆𝑅𝑒,𝑠 =(𝑆𝑆𝑅𝑓 − 𝑆𝑆𝑅𝑡ℎ)𝑆𝑆𝑅𝑡ℎ∗ 100 (3.5)3.2.5 Statistical analysisThe time of emergence results were tested for normality (e.g., skewness, kurtosis) and performed two non-parametric Kruskal–Wallis test by ranks (Hollander and Wolfe 2013) to investigate geopolitical and eco-logical differences in the ToE of transboundary stocks. Specifically, I tested if the habitat association oftransboundary species and the geographic location of EEZs would have any effect on the time of emergenceof transboundary stocks. For both cases, my null hypothesis was that there were no significant differencesin the time of emergence across habitat association nor EEZs. All analyses were run using the statisticalsoftware R version 3.5.2 (2018-12-20; Eggshell Igloo) with the packages data.table (Dowle et al. 2019),janitor (Firke et al. 2018), rfishbase (Boettiger et al. 2019), sf (Pebesma et al. 2018), sp (Pebesma et al.2019), tidiverse (Wickham 2017), tidytext (De Queiroz et al. 2019), gmt (Magnusson 2017) and zoo (Zeileiset al. 2019), ggrepel (Slowikowski 2020), zeallot (Teetor 2018), viridis (Garnier 2018), cowplot (Wilke2019), moments (Komsta and Novomestky 2015) and pgirmess (Giraudoux 2018). All code is available at34https://github.com/jepa/TransEmergence3.3 Results3.3.1 Time of emergence of transboundary stocksThe average time of emergence of the transboundary index across the emerging stocks in all EEZs analyzed isprojected to be 2029 ± 26 years (Figure 3.1). This means that on average, the distribution of transboundarystocks will diverge from their natural internal variability by 2029. In total, the projected transboundaryindex of 5,745 stocks (63% of the studied stocks) will emerge from their historical variability between 2006and 2100. Fish stocks with transboundary index that first emerged from their historical variability in eachEEZ is projected to be as early as 2006 and the last one in 2100. About 55% (n = 3,154) of those emergingstocks are projected to have their transboundary index emerged between 2006 and 2020 (e.g., Peruveananchoveta - Engraulis ringens - shared by Chile and Peru emerged in 2010). The number of EEZs withemerging stocks is projected to increase steeply from 87% in 2006 to 96% in 2020. After the mid-2020s,the rate of increase in the number EEZs slows from a 1% to an average of 0.7% per year. By 2081, all ofthe EEZs analyzed here will have at least one emerging stock (Figure 3.1). Moreover, 96% of the world’sEEZs saw at least one stock having their distribution shifts between 2006 and 2020 and around one fifth(19%) of the EEZs have an average time of emergence of the transboundary index across stocks prior to2020. Shifts in stocks’ distributions will continue to exceed historical limits steadily towards the end of the21𝑠𝑡 century (Figure 3.1). I based the transboundary index on a transboundary stock’s centroid definedas the average grid cells that present the higher stock’s abundance (see Methods - Calculating an index oftransboundary range shift). Results from the sensitivity analysis suggest that this method of estimating thetime of emergence of the transboundary index is robust to the abundance level (e.g., the top 95%) of thestock (Figure A3.3).35Figure 3.1: Time of emergence of the transboundary index by Exclusive Economic Zone (EEZ) and trans-boundary stockThe median time of emergence of the transboundary index of stocks varies significantly accordingto the geographic region of the neighboring EEZs (Kruskal-Wallis, 𝑋2 = 287.23, DF = 93, p < 0.001; Figure3.2A). Overall, most tropical EEZs will see an earlier time of emergence with the EEZs of Latin Americaand the Caribbean and Polynesia having significantly earlier times of emergence (p < 0.05; see Table A3.1for test statistics) than almost all other regions e.g., Ecuador (ToE = 2013) and Papa New Guinea (ToE= 2018) (Figure 3.2B). In contrast, EEZs located in temperate regions like northern Europe and easternAsia have significantly later times of emergence (p < 0.05; see Table A3.1 for test statistics) than the restof the world, e.g., Ireland (ToE = 2067) and North Korea (ToE = 2058). However, notable exceptions tothese broad patterns exist, with tropical EEZs displaying a later average time of emergence across stocksincluding the Pacific EEZ of Honduras (ToE = 2071) and Panama (ToE = 2070) and despite being outsidethe tropics, Belgium (ToE = 2015) and Norway (ToE = 2019) display a relatively early average time ofemergence (Figure 3.2A). There are only a few cases where the transboundary index of a stock does notemerge between 2006 and 2100. In the Arctic EEZs of Canada and the US, the present chapter includedthree stocks: capelin (Mallotus villosus), saffron cod (Eleginus gracilis), and Pacific herring (Clupea pallasii).In the case of Brazil, I included four transboundary stocks, all shared with Uruguay (Chapter 2): Argentinemenhaden (Brevoortia pectinata), Argentine shortfin squid (Illex argentinus), Argentine shrimp (Pleoticusmuelleri), and pink shrimp (Penaeus paulensis). However, on all cases, I find that the transboundary indexof these stocks did not emerge before 2100 relative to the historic (1951-2005) distribution (Figure 3.2A).36Figure 3.2: Average time of emergence of stocks’ transboundary index. A) Land polygon shows the partic-ipation of the emerging stocks in the country’s total fishing revenue from transboundary stocks. ExclusiveEconomic Zone polygon represents the average time of emergence of the transboundary index across allstocks. Aqua color represents EEZs with no time of emergence between 2006 and 2100. B) Time of emer-gence of the transboundary index according to the United Nations sub-regions. Whiskers represent 1.5*interquartile range. Box represents interquartile range as distance between first and third quartiles. Linerepresents median, and black points represent outliers with values exceeding of 1.5 times the interquartilerange.37My analysis suggests that most (n = 47) countries responsible for 75% of the yearly revenue gener-ated from transboundary stocks between 2005 and 2010 will see the distribution range of their stocks shiftbeyond historical variabilities between 2025 and 2040 (Figure 3.3). In some cases, like Indonesia (ranked 8𝑡ℎin terms of revenue from transboundary stocks), Thailand (ranked 7𝑡ℎ in terms of revenue from transbound-ary stocks) and the United States (ranked 4𝑡ℎ in terms of fishing revenue from transboundary stocks), therange shift of transboundary stocks will emerge by around 2030. The 47 countries that make up the bulk ofthe global fisheries revenues also have more transboundary stocks with range shifts emerging from historicalvariability, such as Spain (n = 122), France (n = 85), China and Portugal (both n = 73) and Senegal (n= 66), which are the top 5 leading countries in terms of number of transboundary stocks emerging fromhistorical variability.On average, stocks that see their transboundary index emerge by 2100 represent 27% ± 23% ofthe revenue generated by fisheries that target transboundary stocks within their EEZs between 2004 and2010. However, large variation exists in the contribution of stocks with emerging transboundary index to thecurrent revenue participation across EEZs. In some cases, like El Salvador and New Zealand, stocks withemerging transboundary index represent less than 1% of the revenue generated from transboundary fisheriesin their respective EEZs between 2004 and 2014. On the contrary, stocks with emerging transboundaryindex from the Marshall Islands and Tokelau represent over 90% of the revenue generated from fisheriestargeting transboundary stocks within their respective EEZs during the same time period (Figure 3.2A).In addition, some EEZs are projected to have a few emerging stocks that represent a large proportion ofrevenue from transboundary stocks like Peru and Finland (with 10 and 5 emerging stocks representing 86%and 78% of average fishing revenue between 2004 to 2010, respectively). Overall, Morocco and Malaysiaare the only two countries amongst the top 10 countries with the larger number of stocks with emergingtransboundary index (n = 56 and 53, respectively) where the contribution of such stocks to fishing revenueis less than 20%. This indicates that, despite having a large proportion of transboundary stocks emerging,the economic contribution of these stocks are relatively small to the overall revenue generated by fisheriestargeting transboundary stocks in these countries.38Figure 3.3: Average time of emergence of stocks’ transboundary index per fishing entity, aggregated by colourand shape according to region. Fishing revenue on a logarithmic scale. Showing country names for the top90th percentile of annual fishing revenue.I set an arbitrary threshold of 1 s.d. (67% confidence) to determine the time of emergence of thetransboundary index and the change in stock share ratio below the threat point (see Methods). I testedthe sensitivity to a larger threshold for the time of emergence (e.g., 2 s.d for a 95% confidence) and theglobal average time of emergence of the transboundary index considering a 2 s.d threshold was 2036 ± 28,a difference of about 7 years relative to the 1 s.d. threshold (Figure A3.2). In total, 4,119 transboundarystocks would emerge between 2006 and 2100 considering a more conservative metric.I compared stocks time of emergence within EEZs by the species’ habitat association (Figure 3.4).Each species is assigned to be associated to one of the seven habitat types based on information availablefrom FishBase and SeaLifeBase for fish and invertebrates, respectively (see Methods). I found significantstatistical differences in the time of emergence of stocks range shifts, indicated by the transboundary index,by their habitat association (Kruskal-Wallis, 𝑋2 = 286.48, DF = 93, p < 0.001). Specifically, the time of39emergence of the transboundary index of species that are associated with pelagic-oceanic and reef habitatswere significantly different than other habitat preferences (See Table A3.2). On average, species associatedwith pelagic oceanic habitats e,g, tunas (Thunnus sp.), bathy-pelagic habitats e.g., blue whiting (Microme-sistius poutassou) and corals (e.g., Bigeye snapper Lutjanus lutjanus) are projected to have an earliest time ofemergence (Figure 3.4). Species associated with bathydemersal habitats such as Alaska plaice (Pleuronectesquadrituberculatus) generally have an average time of emergence of range shifts that is later than 2025 (Figure3.4).Figure 3.4: Comparison of regional time of emergence of shared stocks’ range shifts by species’ habitat associ-ation. Classification is based on the habitat preference information obtained from FishBase and SeaLifeBase(see Methods). The number of species included in this analysis for each habitat type is noted in parenthesis.Whiskers represent 1.5* interquartile range. Box represents interquartile range as distance between first andthird quartiles. Line represents median, and black points represent outliers with values exceeding 1.5 timesthe interquartile range.3.3.2 Changes in the stock share ratio of transboundary stocksI have identified the stocks that are projected to have changes in stock share ratio beyond an EEZ’s threatpoint and estimated the changes in the stock share ratio by the early (2020-2040) and the mid 21𝑠𝑡 century(2040-2060), relative to the recent past (1951-2005) (see methods – Equation (3.5)). By the early 21𝑠𝑡 century,40the global average proportion of transboundary stocks that are projected to change their stock share ratiobeyond the EEZ’s threat point is 18% ± 15% (Figure 3.5A). The changes in stock share ratio are expectedto increase slightly towards the mid 21𝑠𝑡 century (Figures A3.4 and A3.5). However, changes in stock shareratio for specific stocks and in some EEZs are projected to be exceptionally high. For example, Guatemalashares Panulirus gracilis stock with Mexico, and Guatemala’s share is projected to increase by >50%, thatis from 12% to 26% by the early 21𝑠𝑡 century. Towards the mid 21𝑠𝑡 century, changes in stock share ratio perEEZ will not be substantial (Figure A3.5) relative to the early 21𝑠𝑡 century but are expected to increase inintensity (Figure A3.4). My results suggest that 87% (n = 238) of the world EEZs will experience changesin stock share ratio beyond a country’s threat point by the early 21𝑠𝑡 century (Figure 3.5A). These EEZsare projected to have changes in the stock share ratio beyond the threat point in one third (33% ± 21%) oftheir transboundary stocks (Figure 3.5A). Particularly, Brazil, French Guiana, Guam, Kergelen and Pitcairinare projected to have changes beyond the threat point for all of the transboundary stocks analyzed, whileVenezuela (6%), Bonaire (9%), China (10%) and Vietnam (10%) are the only countries with less than 10%of stocks changing beyond the threat point.41Figure 3.5: Changes in stock share ratio below each country’s threat point by 2030 (2021-2040) relative to1951-2005. Lines represent the average gain of transboundary stock share ratio with arrows going from EEZwith decreasing stock share (point) to those that are gaining shares (arrowhead). Land polygons representthe percentage of stocks that are projected to change their stock share ratio beyond the identified threatpoint. Panel B zooms in to specific areas shown within the grey dashed line boundaries in A.Changes of stock share ratio are largely related to regional changes in biogeography and the geom-etry of the EEZs (Figure 3.5). In some cases, like the Atlantic and Pacific coasts of Northern and SouthernAmerica and the Atlantic coast of Southern Africa, the stock share ratio is expected to follow the projectedpoleward shift of transboundary species under climate change. However, central America Pacific and WestAfrican coasts follow an equatorial direction as range shifts of transboundary stocks follow local oceano-42graphic gradients, with relatively lower temperature waters in lower latitude (Clarke et al. 2020). Moreover,regions where EEZs are relatively small and have multiple borders are expected to have a particularly com-plex exchange of stock share ratio with no established pattern, as in the Caribbean, Mediterranean andnorthwestern Europe (Figure 3.5B).3.4 DiscussionIn this chapter, I investigate the timing and intensity of changes in the sharing of transboundary stocksbetween countries. My findings highlight the early emergence of transboundary distribution shifts fromhistorical variations for most of the shared stocks in the world. Such early emergence of range shifts indicatesthe urgency of addressing the challenges that climate change is posing on international fisheries management.Moreover, I identify regional patterns of high climate risk to important fish stocks and associated fisheries atthe present-day that will require immediate responses from transboundary fisheries management. Finally, Ishow that in many instances around the world, countries’ fisheries will experience changes in the sharing ofmany transboundary stocks to levels below of that needed to encourage cooperative resource managementbetween countries. Adaptation measures are therefore needed to reduce the impacts of species range shiftson current international transboundary fisheries treaties.3.4.1 High present-day climate risk of on transboundary fisheries managementMy projections for the emergence of distribution shifts of transboundary stocks in the early 21𝑠𝑡 centuryconcur with previous studies that have detected and attributed changes in marine catch composition (Cheunget al. 2013). For example, in the early 2000s, Humboldt squid (Dosidicus gigas) substantially expanded itsgeographic range poleward, reaching the coast of Washington state (US). A new fishery targeting Humboldtsquid was developed quickly following its range expansion (Zeidberg and Robison 2007, Pinsky and Mantua2014). In the northeast Atlantic, the Atlantic mackerel (Scomber scombrus) fisheries are multi-laterallymanaged by the EU, Norway, Iceland, Russia, and Denmark (on behalf of the Faroe Islands and Greenland)through the North-East Atlantic Fisheries Commission (NEAFC). However, a range expansion of Atlanticmackerel into Icelandic waters in 2007 resulted in Iceland capturing 6% of the total fishery catch and afurther 18% capture in 2008, without consultation with NEAFC and threatening the sustainability of thestock (Spijkers and Boonstra 2017). Such changes resulted in disputes between Iceland and the FaroesIslands, as well as the NEAFC member states (Spijkers and Boonstra 2017). As climate change impactson transboundary stocks continue to emerge from historical variability in the coming years, mal-adapted43fisheries management and international disputes like that of Atlantic mackerel are likely to increase infrequency (Pinsky et al. 2018).The projected range shifts of large number of transboundary stocks emerging from historical vari-ability since the early 2000s can be partially attributed to the parallel emergence of several environmentalvariables (e.g., temperature and oxygen) that influence their distribution (Mahlstein et al. 2011, Keller etal. 2014, Rodgers et al. 2015, Frölicher et al. 2016). Marine organisms have a thermal tolerance that limitstheir habitable temperature range (IPCC 2019), which then explains the sensitivity of the animal to warm-ing and is a main driver of any distributional change (IPCC 2019). Oxygen plays a similar role to that oftemperature (Clarke et al. 2020), especially for tuna species that require large amounts of dissolved oxygenfor their metabolism (IPCC 2014). It has been estimated that since the early 2000s, increased sea surfacetemperature (SST) and decrease in subsurface oxygen have emerged from historical variability for 79% and23% of the global ocean, respectively, and that by 2080, SST is projected to emerge from historical variabilityin 90% of the global ocean while the area affected by lower 𝑂2 will have doubled under a high greenhousegas emissions scenario (RCP 8.5) (Rodgers et al. 2015). Moreover, before 2100, surface acidification, nitratecontent and net primary production, also important variables for species distributions, will have emergedfrom the historical variability of 1861-1900 in 30% and 60% of the ocean under a low (RCP 2.6) and strong(RCP 8.5) climate change scenario, respectively (IPCC 2019). These oceanographic changes are importantdrivers of change in species distributions as represented in the DBEM (Cheung, Jones, Reygondeau, et al.2016, Lefevre et al. 2017).Regional differences in the time of emergence of many transboundary stocks are attributed to thevariations in environmental conditions, species’ climate vulnerability, and the characteristics of politicalboundaries. Specifically, my findings are consistent with results from previous studies that show tropicalspecies are already shifting their distributions since the last few decades (Poloczanska et al. 2016). Thetropics are among the most species-rich biomes (Reygondeau 2019) and have potentially experienced someof the highest levels of warming relative to natural internal variability (Roemmich et al. 2015, IPCC 2019).In the tropics, marine species also live close to their thermal tolerance, making them highly vulnerable towarming (IPCC 2014). Consequently, species have to move towards deeper or sub-tropical waters as a result ofclimate change (Poloczanska et al. 2016), although in some regions species follow local temperature gradientstowards the equator or shallow waters (Clarke et al. 2020). Moreover, tropical species are expanding theirrange into sub-tropical biomes (Fogarty et al. 2017) and catch-data analysis shows a reduction in sub-tropicalspecies catches in tropical waters between 1970 and 2006 (Cheung et al. 2013). In addition to species naturaltraits and the environmental pressure from climate change, the tropics comprise areas with relatively small44EEZs that border with multiple countries (e.g., the Caribbean). These tropical areas are projected to havethe highest number of transboundary stocks’ distribution shifts emerging in the incoming years.3.4.2 Hotspot of climate risk on transboundary fisheries managementMy results underscore regional ‘hotspots’ of climate risk for transboundary fisheries management that willrequire the prompt adaptation of management plans (Figure 3.2). For example, Polynesia and Micronesiaare the only regions where the average time of emergence of transboundary stocks’ distribution across allEEZs emerged before 2022 (Figure 3.5). Moreover, countries in these hotspot regions are highly dependenton pelagic-oceanic (e.g., tunas and bill fishes; (Johnson et al. 2017)) and reef-associated transboundarystocks (Cabral and Geronimo 2018) for both food security and profits (Cisneros-Montemayor et al. 2016,Hanich et al. 2018). On the other hand, “coldspots” of climate risk exist for transboundary fisheries, likethe Northeast Atlantic and Northwest Pacific, where stocks are expected to emerge later in the 21𝑠𝑡 century.Assuming that these countries already have adequate transboundary management to deal with historicaland current variations in fish stocks’ distribution e.g., the Pacific Salmon Treaties (Miller and Munro 2004),management agencies are likely to have more time to prepare for emerging stock shifts.In cases where climate driven changes in stocks are emerging, or have already emerged, fromhistorical variability adaptation policies are urgently needed to address the potential impact on importantfisheries (Burden and Fujita 2019). Currently, fisheries targeting shared tuna stocks in the South PacificIslands region operate under the Vessel Day Scheme, which because it takes into account shifts in stocksand catches makes it a good example of a management system that is somewhat anticipatory (Aqorau et al.2018). However, even strategies that take into consideration the shift of the stock within the party’s area willlikely also have to deal with the ‘newcomer issue’ that NEAFC is currently facing regarding Atlantic mackerel(Spijkers and Boonstra 2017, Pinsky et al. 2018). As the climate trend in stocks distribution emerges fromtheir historical variability sch since 2006, regional management bodies must revise their management actionsas most are not currently equipped with strategies to cope with shifting species distributions (Oremus et al.2020, Sumby et al. 2021). This is of specific concern for those management bodies located in the emerging“hotspots” here identified.Some EEZs lack stocks with emerging transboundary index because of a combination of their shape,climate projections, and a reduced number of transboundary stocks. For example, there are 59 commercialfish stocks fished in the Arctic with the vast majority on the Barents, Bering and Norwegian seas, with limitedknowledge about the species biology (Christiansen et al. 2014). However, the Arctic has been warming faster45than many other regions, resulting in a sea-ice reduction (Douglas 2010) consequently increasing the potentialfor occupation by species not previously present (IPCC 2019). As a result, the biomass of Arctic marineanimals (Bryndum-Buchholz et al. 2019) and catch and revenue of local fisheries (Lam et al. 2014, Taiet al. 2019) are expected to increase in the incoming years, although substantial uncertainty exists on themagnitude of change (Bryndum-Buchholz et al. 2019). Moreover, projections suggest a larger incrementon transboundary stocks in the coming years in the Arctic (Pinsky et al. 2018) as climate change pushessub-polar species further north (Frainer et al. 2017, Morley et al. 2018). In the specific case of Brazil,in addition to having few transboundary stocks (only four shared with Uruguay in this chapter), the largedifference in the EEZs lengths could also be influencing the results.3.4.3 Changes in stock share ratio will be ubiquitousChanges in stock share ratio beyond a country’s threat point will be seen across the world, suggesting thatclimate change impacts on transboundary stock management is a global problem (Figure 3.5). Polewardshifts in stock share ratio are expected in regions of the world with relatively long EEZs and few boundaries,like the northeast Pacific and the southeast Atlantic. However, despite observations and modelling workindicating that climate change is generally shifting species distributions poleward (Cheung et al. 2010,Poloczanska et al. 2016, Fogarty et al. 2017, IPCC 2019), some regions like the eastern tropical Pacific andeastern tropical Atlantic Oceans will experience a shift in distribution towards the equator. Specifically, inthe eastern tropical Pacific, the direction of shift is expected to be from Mexico to Ecuador with northernEEZs losing stock share ratio to their southern neighbors. Such range shifts towards the equator reflectmovements to cooler habitats characteristic of equatorial upwelling systems (Clarke et al. 2020) and areconsistent with regional modeling studies suggesting that by the mid 21𝑠𝑡 century, marine species will movebetween 50 and 100 km southward of Mexico’s EEZ towards Peru under a high emission scenario (Clarkeet al. 2020). Moreover, regions characterized by multiple political boundaries, like the Caribbean and thenortheast Atlantic, will see a complex network of changes in stock share ratio with no clear pattern (Figure3.5B). These regions are known for being highly connected by both fish adult migration (Levin et al. 2018)and dispersal of larvae (Ramesh et al. 2019) supporting the implementation of joint management plans(Burden and Fujita 2019) and the call for new implemented shared policies (Levin et al. 2018). However,game theory predicts that the greater the number of negotiating parties, the harder it is for parties toreach an agreement (Sumaila 2013), thus, making it particularly challenging for countries in these regions tocoordinate the management of shifting shared stocks.Changes in stock share ratio beyond the threat point of countries sharing a common resource46could instigate international conflict and hinder bi-lateral agreements (Miller et al. 2013, Sumaila et al.2020). Lessons learned from historic distributional shifts of shared stocks suggest that treaties that are notprepared to respond to such shifts will be less resilient to changes in transboundary fish stocks share ratiodue to climate change (Miller et al. 2013, Sumaila et al. 2020). For example, disputes over Pacific salmonbetween Canada and the US arose in the 1990s when a climate-related shift in stock abundance favouredAlaska’s salmon fisheries over Canada’s (Miller and Munro 2002, Miller et al. 2013, Song et al. 2017b). Theconflict lasted over ten years until Canada and the US agreed on a mutual conservation fund where the USwould contribute the larger proportion to support scientific research, habitat restoration and enhancementof wild stock production in transboundary rivers (Miller and Munro 2002). Identified strategies to cope withchanges in the share proportion of transboundary stocks include the incorporation of side payments (Tunca2019), increased international cooperation (Miller et al. 2013) and dynamic ocean management (Pinsky etal. 2018). Side payments are mechanisms that can provide a solution to the inequality produced by shiftsin the stock’s distribution (Miller and Munro 2004, Tunca 2019). These can be monetary (e.g., the stock-“winning” state compensates the stock-“losing” state for the proportional shift) or other methods like thePacific salmon case (e.g., a conservation fund paid mainly by one party) (Miller and Munro 2004, Miller etal. 2013). Strengthening current international cooperation will be required as stock distribution will moveto neighboring jurisdictions, requiring combined efforts to generate information and set proper managementrules (Miller et al. 2013). Finally, flexible management rules might be more appropriate to capturing tdistributional shifts. For example, a quota allocation based on the stock’s current distribution like that forPacific halibut (Hippoglossus stenolepis) managed by Canada and the US (Sumaila et al. 2020, Chapter 2)would be more agile than one based on a fixed-historical proportion like the case of the EU (Baudron et al.2020).3.5 Caveats and uncertaintiesThere are two main sources of uncertainty in my analysis. First, I rely on the combination of a single Earthsystem model (GFDL-ESM2M), fish and fisheries model, and climate change scenario (RCP 8.5). The use ofmultiple ESMs can capture the structural uncertainty of the models (Frölicher et al. 2016, Lotze et al. 2019),however, exercises looking at changes in fish biomass with multiple models show an overall agreement betweenmodels in terms of direction of change but variable in magnitude. Further research that includes multipleESMs and species distribution models could elucidate a broader range structural uncertainty of the climate,fish and fisheries models (Lotze et al. 2019). Secondly, due to limited biological and spatially-specific dataon stocks sub-population structure of transboundary species, my analysis uses political boundaries (EEZs)47to delineate a stock that does not necessarily align with biologically-defined sub-populations within an EEZ.On the other hand, in many EEZs, fisheries are often managed at the species level (Chapter 2) and sub-populations are potentially interconnected (Ramesh et al. 2019), thus, providing additional ecological groundfor my analysis (Dunn et al. 2019, Popova et al. 2019). However, reproducing my analysis locally, wherespatially explicit stock data is available would allow to produce more certain results in changes in both timeof emergence and stock share ratio of transboundary stocks, and potentially identity different types of sharedstock shifts within meta-populations level (Link et al. 2010, Archambault et al. 2016, Kaplan et al. 2016).Addressing these uncertainties systematically can serve as a roadmap for future studies to provide additionalinformation to inform policy towards sustainable and equitable international fisheries management underclimate change.3.6 ConclusionThe global community has set the ambitious goal to manage all fisheries sustainably (SDG 14 – Life belowwater) by 2030; achieving this goal would have clear effects on multiple others (Singh et al. 2017, UnitedNations 2018). Preparing anticipatory policy to species on the move is key to achieve these sustainabledevelopment goals and effective governance of the world’s fisheries (Pinsky et al. 2018). Here, I haveaddressed two fundamental steps to achieve sustainable international fisheries in a changing world (Linket al. 2010). I have identified the transboundary stocks that will see a significant shift compared to theirhistorical average in shared distribution, the year in which their distribution will undergo such a shift, as wellas the degree of change. My results have direct implications for ocean governance as the data provided caninform and be used by regional management bodies and countries to anticipate the potential complicationsbrought about by climate change to international fisheries management. While future studies, specificallyat more localized scale, will provide valuable nuance in designing effective policies, my results provide animportant baseline on which to build when preparing ocean governance for species on the move (Gaines etal. 2018, Chapter 2, Pinsky et al. 2018). To address the issues raised in this chapter, international fisheriesmanagement will require coordination between nations to generate adaptive, collaborative and equitablemanagement strategies to address the uncertainties of a changing world.484 Chapter Four: Challenges to transboundary fisheries manage-ment in North America under climate change4.1 IntroductionIn 1982, the United Nations Law of the Sea Convention (UNCLOS) formalized the concept of exclusiveeconomic zones (EEZs) creating the concept that we know today as shared stocks (United Nations 1986),i.e., stocks that migrate between countries EEZs (known as transboundary stocks) or between EEZs and thehigh seas (also called straddling stocks) (Song et al. 2017a). Today, an estimated 347 (Teh and Sumaila 2015)to 633 (Chapter 2) fish stocks have distributions that cross national borders, some of them jointly managedby two or more countries. These species are responsible for more than 70% of these countries total fish catches(Chapter 2). Under Article 63, UNCLOS incentives actions to cooperate on the management of shared stocks(United Nations 1986) as management success often depends on effective cooperation between parties (Millerand Munro 2002, Sumaila 2013). Since the definition of shared stocks, game theory has been one of themost common approaches used to analyze the management of these type of stocks. However, shared stocks’management can be convoluted due to the participation of several fishing “players”, different countries andsometimes jurisdictions within a country, the migration patterns of the stock, and their abundance fluctuationwithin space and time (Miller and Munro 2002, Engler 2020). In addition, international treaties might notbe prepared to address the effects that climate change will bring to shared fish stocks (Engler 2020, Koubrakand VanderZwaag 2020)The ocean is getting warmer (Rheim et al. 2013), less oxygenated (Schmidtko et al. 2017), andincreasing in acidity (Ross et al. 2011, IPCC 2019). To cope with these changes in ocean biophysicalproperties, marine species, including shared fish stocks, have been shifting their distribution towards thepoles and/or deeper waters (Poloczanska et al. 2016). As climate change reshapes the ocean’s environmentworldwide (Gattuso et al. 2015), shared fisheries’ governance is threatened as new migration patterns mayarise (Miller et al. 2013, Pinsky et al. 2018), historic distribution and abundances might shift (Cheung etal. 2010), and species basic natural traits may modify (Pauly and Cheung 2017). Catches of shared stockslike tunas, have significantly increased in some regions such as the subtropical Atlantic and western PacificOceans and are projected to continue (Monllor-Hurtado et al. 2017, Erauskin-Extramiana et al. 2019).Multiple shared species in North America have been observed to shift in distribution following, in part,changes in optimal conditions such as sockeye salmon (Oncorhynchus nerka) (McDaniels et al. 2010, Milleret al. 2013, Song et al. 2017b), Atlantic cod (Pershing et al. 2015), and flounders (Pinsky and Fogarty492012). Moreover, these shifts are projected to continue towards the end of the 21𝑠𝑡 century (Cheung 2018).As a result, some countries or management jurisdictions may see more shared fisheries and their catchesshifting into their waters while others will stand to lose (Pinsky et al. 2018, Chapter 3, Oremus et al. 2020).Nevertheless, management rules for shared stocks (e.g., quota or spatial delimitation) are often determinedbased on current and/or historic knowledge of the stock’s distribution and do not consider future shifts indistributions (Fredston-Hermann et al. 2018).The shifts in distribution of shared fish stocks will impact the economics of their fisheries (Pinskyand Fogarty 2012, Sumaila 2019, Sumaila et al. 2020), and create international disputes between countries(Miller and Munro 2002, Spijkers and Boonstra 2017, Pinsky et al. 2018). Canada and the US shareimportant transboundary stocks of salmon (Oncorhynchus spp.), Pacific halibut (Hippoglossus stenolepis),and Atlantic cod (Gadus morhua) offering a unique lens to understand the extent to which climate-induceddistributional shifts will challenge the future sustainability of transboundary fisheries. These countries havea long history of fisheries cooperation participating in diverse, jointly managed, commercial transboundarystocks through various fisheries management organizations (NOAA 2018). Furthermore, climate-relatedfluctuations in stocks’ distribution have historically created disputes between Canada and the US, increasinginternational conflict and threatening the health of diverse transboundary stocks (Miller and Munro 2002,CIA 2017).It is expected that climate induced shifts in stocks’ distribution will affect the rules in place that keepinternational treaties alive. Therefore, the main objective of this article is to assess the level of exposure thatbi-lateral transboundary fisheries treaties between Canada and the US have to climate change through shiftsin stock distributions. Specifically, I rely on a species distribution model and scenario planning to projectthe changes in the distribution of selected fish stocks jointly managed by Canada and the US focusing on twocase studies (the International Pacific Halibut Commission and a fisheries arrangement for the Gulf of Maine)Finally, I explore similar situations around the world and identify opportunities to improve the adaptabilityof transboundary stocks management to climate change in North America. Despite an overall expectationof species following a poleward shift, important geographic constraints (e.g., Gulf of Alaska representing alatitudinal block) (Kleisner et al. 2016), geo-political features (e.g., the localization of Alaska in referenceto Canada and the contiguous states), and management rules (e.g., quota allocations, spatial managementrules) may play an important role in the redistribution of benefits. Understanding these stocks shifts willshed a light on future conditions and inform decision-makers on the paths to follow under a changing climate.504.2 Materials and methods4.2.1 Study area and fisheriesIn this chapter, I used the International Pacific Halibut Commission (IPHC) and the Gulf of Maine arrange-ment (hereafter referred as GoMa) as case studies to discuss the implications that climate change could havein the management of transboundary stocks. For the IPHC, I used the most updated spatial regulatory dataalong its 12 regulatory areas (IPHC and Gustafson 2017, IPHC 2019). For this specific case, I consideredAlaska as a separate entity, the US contiguous states as a second one (Washington, Oregon and California),and lastly British Columbia (Canada). For the GoMa, I used the Northwest Atlantic Fisheries Organization’s(NAFO)2 divisions 5Y, 5Ze, and 4X within latitudes 46.2°N and 41.5°S, and longitudes -72°W and -64°E(Figure 4.1). It is worth mention that, while NAFO’s divisional zones were used in this study for domesticmanagement, NAFO does not manage fisheries within the EEZs of Canada and the United States. Fisheriesdata for the projections was gathered from the Sea Around Us from 1951 to 2014 (Zeller et al. 2016).Figure 4.1: Map of Canada and the US with the regulatory areas of the International Pacific HalibutCommission and the NAFO sub-divisions containing the Gulf of Maine arrangement2Northwest Atlantic Fisheries Organization, Available at https://www.nafo.int/Science/514.2.2 The International Pacific Halibut CommissionThe IPHC was established in 1923 by Canada and the US to oversee the management of Pacific halibut(Hippoglossus stenolepis) (IPHC 2014). There are 12 regulatory areas from which 3AB holds 51.2% of thestock, followed by regions 2ABC and 4ACDE with 23.1% and 20.4%, respectively, and lastly region 4Bwith only 5.2% of the stock distribution (IPHC and Gustafson 2018). In terms of management, the IPHCimplements a total allowable catch (TAC) based on a yearly sampling of the Convention area in additionto a series of regulations to control fishing effort (IPHC and Gustafson 2018). The TAC is divided betweenrecreational, subsistence and commercial fishery, with a portion set aside for bycatch of other fisheries (IPHC2019). The commercial fishing season starts in March ending around November with restrictions allowingonly set line gear with J-type hooks targeting individuals over 81.3 cm of total length (IPHC 2019).4.2.3 The Gulf of Maine ArrangementSince 1998 Canada and the US have used a “Resource Sharing Understanding” to inform the management ofEastern George Bank’s Atlantic cod (Gadus morhua), haddock (Melanogrammus aeglefinus) and yellowtailflounder (Limanda ferruginea) (Pudden and VanderZwaag 2007, TRAC 2016, Song et al. 2017b). From2010 onward, the GoMa suggests catch-limits based on a weighted method where 10% represents the stocks’historical distribution (from 1967 to 1994) and 90% current distributions as determined by quarterly surveysand catch (TRAC 2016). Since its introduction, the average quota allocation for each species proposed by theGoMa has been (Table 4.1; Atlantic cod 77% Canada and 23% US, haddock 55% Canada and 45% US, andyellowtail flounder 34% Canada and 66% US (TRAC 2015a, 2015b, 2015c, 2018a, 2018b, 2018c, Lake 2019).However, because this is an unofficial agreement, Canada and the US ultimately take single managementdecisions (Soboil and Sutinen 2006). In terms of management, the US has a multi species harvest controlrule with area and season closures, mesh sizes, effort control, and mobile gear vessels that use bottom ottertrawl gear (Soboil and Sutinen 2006). In contrast, Canada has a quota system in addition to limited-entrylicensing, fleet allocations, and mesh and fish size regulation, among other input controls. Canada inshorevessels fish cod with longline and gillnet while haddock is mainly caught with bottom otter trawl gear (Soboiland Sutinen 2006).52Table 4.1: Historic quota allocation suggested for Atlantic cod, haddock and yellowtail flounder in the Gulfof MaineAtlantic cod Haddock Yellowtail flounderYear Canada US Canada US Canada USHistoric Average* 72 28 65 35 27 732010^ 77 23 59 41 40 602011^ 82 18 57 43 44 562012† 71 29 57 43 51 492013† 83 17 62 38 57 432014† 71 29 61 39 18 822015† 83 17 52 48 30 702016† 83 17 59 41 24 762017† 86 14 41 59 31 692018† 70 30 61 39 29 712019‡ 71 29 50 50 24 762020‡ 71 29 46 54 26 74Weighted method Average’ 77 23 55 45 34 66Weighted method St. Dev.” 6 6 7 7 12 12Note:* Average from 2006 to 2009, before the implementation of the weighted method’ Since the implementation of the weighted method” Standard deviation of the wiighted average^ TRAC (2015abc)† TRAC (2018abc)‡ Lake (2019)534.2.4 Projecting future species distributionsI used a Dynamic Bioclimatic Envelope Model (DBEM) to project the distribution of species from 2015 to2100 under two scenarios of climate change (Cheung et al. 2010, Cheung, Jones, Reygondeau, et al. 2016).The DBEM algorithm integrated ecophysiology and habitat suitability with spatial population dynamicsof exploited fishes and invertebrates to project shifts in abundance and potential fisheries catches underclimate change. The algorithm predicted species distribution based on depth and latitudinal range, habitatpreferences and an index of species association with major habitat types to estimate changes in abundancedistribution over a 0.5∘ x 0.5∘ grid of the world ocean. For each grid cell and time step, the model thencalculated species carrying capacity according to sea surface temperature, salinity, oxygen content, sea iceextent (for polar species) and bathymetry, as well as the species preferences to these conditions. It thenincorporated the intrinsic population growth, settled larvae, and net migration of adults from surroundingcells using an advection-diffusion-reaction equation. Finally, the model also simulated the effects of changes intemperature and oxygen content on growth of individuals (Cheung et al. 2013, Cheung, Jones, Reygondeau,et al. 2016). Ultimately, the model simulated spatial and temporal population dynamics, and estimated aproxy of maximum sustainable yield (MSY) by applying fishing at MSY level for each grid cell, hereafterreferred as maximum catch potential (MCP).I projected the DBEM using three Earth system models (ESM), the Geophysical Fluid Dynam-ics Laboratory Earth System Models 2M (GFDL)3, the Institute Pierre Simon Laplace Climate Model 5(IPSL-CM5)4, and the Max Planck Institute for Meteorology Earth System Model (MPI)5. Each model wasdownscaled to match the DBEM 0.5∘ x 0.5∘ grid using the nearest neighbor method, and in some cases,bilinear interpolation (Cheung, Jones, Lam, et al. 2016). Finally, I used the model outputs for two scenariosof the Intergovernmental Panel on Climate Change (IPCC)-Representative Concentration Pathways (RCP)2.6 and 8.5 representing a low greenhouse gas emission (strong mitigation) and a high greenhouse gas emis-sion (week mitigation) scenario, respectively (IPCC 2014). To estimate model robustness and capture thestructural uncertainty build within ESM models, I averaged the DBEM results for all three models (𝜇 ± 𝜎)and marked regions where at least one ESM disagree in direction with the rest.3Geophysical Fluid Dynamics Laboratory Earth System Models 2M [online] https://www.gfdl.noaa.gov4Institute Pierre Simon Laplace Climate Model 5 [online] https://www.icmc.ipsl.fr5Max Planck Institute for Meteorology Earth System Model [online] https://www.mpimet.mpg.de/en/science/models544.2.5 Estimation of Maximum Catch Potential changeFor estimating the percentage change of MCP at the regional scale, I first aggregated the yearly mean MCPof all grid cells per region (𝑋𝑦𝑟) and period:𝑋𝑦𝑟 =𝑛∑𝑠=1̂𝑀𝐶𝑃𝑠 (4.1)where y is year, r is region, s is grid, n is total number of grids in the region, and ̂𝑀𝐶𝑃 is the MCPof each stock. In the case of the GoMa, region was defined as the 0.5∘ x 0.5∘ grid-cell within the specificNAFO divisional zones. For the IPHC analysis, region was defined as the Commission’s regulatory areas(Figure 4.1). I then averaged the values in three time periods (t) to reduce temporal model sensitivity. Thus,I computed the regional percentage change in MCP (Δ𝑀𝐶𝑃 ) as follows:Δ𝑀𝐶𝑃𝑟 = −(1 −𝑋𝑡𝑋𝑡0) ∗ 100 (4.2)where 𝑋𝑡 is the future average MCP for each of the two time periods analyzed in this study and 𝑡0is the present averaged MCP (𝜇 2005-2014). Note that in cases where 𝑋0 = 0 and 𝑋𝑡 > 0, then (Δ𝑀𝐶𝑃 )= 100%, consequently, the opposite case would give a -100% result. This way, equation (4.2) shows thepercentage change in MCP by mid 21𝑠𝑡 century when 𝑋𝑡 = 𝜇 2041-2060, and end of 21𝑠𝑡 century when 𝑋𝑡= 𝜇 2080-2099, relative to today (𝑡0). The rationale between choosing these time periods was to provide arelative short-term projection (mid-century) that would be more policy-relevant but also show the long-termtrend (end of the century).In addition, I borrowed the concept of “threat point” from game theory defined as the minimumpayoff that a player is willing to receive in order to cooperate with other players (Sumaila et al. 2020). Thus,I estimated the change in the (Δ𝑀𝐶𝑃 ) (threat point) that each country (players) would have for each stock(hereafter referred as stock-share ratio), for both the IPHC and the GoMa. The stock-share ratio can beseen as the proportion of the stock’s distribution within the study area that each country has. For this, Ifirst modified equation (4.1), to estimate the aggregated yearly mean MCP of each stock per region. I thenaveraged the results by the same previously motioned periods (present, mid and end of the 21𝑠𝑡 century).Next, for each stock I estimated the stock-share ratio (𝛼𝑠) that each region had during each time period:𝛼𝑠 =𝜃𝑟𝑡𝛿𝑟𝑡(4.3)55Where 𝜃𝑟𝑡* is the stock’s aggregated ̂𝑀𝐶𝑃 of each region at time period t, and 𝛿𝑡𝑠 is the stock’saggregated ̂𝑀𝐶𝑃 of the whole stock’s distribution within the study area at the same time period. Finally,I estimated the percentage change in stock-share ratio substituting 𝑋𝑡0 and 𝑋𝑡 by 𝛼𝑡0 and 𝛼𝑡, respectivelyin equation (4.2). The process was carried out for each ESM and results presented as average ± standarddeviation (sd). All of the analysis was done in the statistical software R version 3.5.2 (2018-12-20) with theassociated packages, data.table (Dowle et al. 2019), ggrepel (Slowikowski et al. 2019), gridExtra (Auguie2017), knirt (Xie 2020), RColorBrewer (Neuwirth 2014), sf (Pebesma et al. 2018), and tidyverse (Wickham2017). All code is available at http://www.github.com/jepa/OC_Transboundary.4.3 Results4.3.1 Projected change to stocks managed by the IPHCAt least one third of the IPHC regulatory areas will see a reduction in MCP of Pacific halibut by 2050relative to current MCP, regardless of the climate change scenario (Figure 4.2). It is likely that the stockshift from the US. contiguous states towards Canada will offset the shift of the later towards northern regions,resulting in undetectable changes in Canadian area 2B and Alaskan 2C under both climate change scenarios.The potential movement of halibut westward will increase the MCP of regulatory areas 3B (under a lowemission scenario) and 4ABCE along the Aleutian Islands and Bering Sea. Regions 4DE, the most polewardregulatory areas of the IPHC, are expected to gain MCP by mid (Figure 4.2) and end of the 21𝑠𝑡 century(Figure A4.1) under a high emission scenario due to the expansion of halibut suitable habitat as sea iceretreats (Figure A4.3). In contrast, under a low emission scenario, sea ice is expected to stabilize towardsmid 21st century, thus providing less “new” suitable habitat for Pacific halibut and resulting in undetectablechanges in MCP for the region (Figure 4.2B) and decreasing even more towards 2100 (Figure A4.1).56Figure 4.2: Percentage change of MCP for stocks managed by the IPHC for mid 21st century (2041-2060)relative to present 2005-2014 under a A) high emission scenario and B) low emission scenario. Labels markedwith * represent regions where models do not agree in direction of change.The same poleward trend is expected in the change of Pacific halibut stock-share ratio with theaverage proportion increasing up to 25% in some northern regions and decreasing by 10% in southern regions,relative to the present proportion (Figure 4.3). Maintaining emissions to lower levels through 2050 wouldpotentially leave unchanged the stock-share ratio of three regulatory areas (3AC, and 4D) and negativelychange regulatory area 2A. On the other hand, failing to achieve such target will decrease the stock-shareratio in the most productive regulatory areas (2AC, 3AB).57Figure 4.3: Percentage change of stock-share ratio for IPHC under A) high emission scenario and B) lowemission scenario for mid 21st century (2041-2060) relative to present 2005-2014. Values represent the meanof 3 ESM; error bars represent ± sd.4.3.2 Projected change to stocks managed under the Gulf of Maine arrangementWhile some regulatory areas of the IPHC will see an incremental increase in Pacific halibut MCP, the resultsfor the Gulf of Maine show an overall decrease in MCP by 2050, regardless of the climate change scenarioor ESM (Figure 4.4), intensifying by the end of the 21𝑠𝑡 century (Figure A4.2). For cod and haddock,MCP will decrease within the whole Gulf with no apparent win for any country in reference to the currentperiod (Figure 4.4). For yellowtail flounder, despite an overall reduction, some discrete areas are expectedto increase with no particular pattern and high uncertainty, as ESMs in these regions do not agree in thedirection of change (Figure A4.5). Despite the overall reduction in MCP for all three stocks in comparisonto current values, there is a benefit of achieving a low emission scenario as reductions intensify under thehigh emission scenario.58Figure 4.4: Percentage change of MCP in the Gulf of Maine under, A) high emission scenario; and B) lowemission for the mid 21st century (2041-2060) relative to present (2005-2014). Points represent regions wheremodels do not agree in direction of change.Despite the expected decrease in MCP for the region, changes in the stock-share ratio of stockswithin the Gulf of Maine show different outcomes dependent on the climate change scenario and stock inquestion. Following a high emission path will negatively affect mostly Canada’s share of yellowtail flounderand in less degree haddock, with an increase of cod share. Under the low emission scenario, haddock and codshare patterns intensify, while yellowtail flounder’s share approaches almost no change (Figure 4.5). Suchpattern is likely due to the combination of the bathymetry or the Gulf, the warming gradient, and the stock’sdistribution (see Discussion).59Figure 4.5: Changes in MCP stock-share ratio for Gulf of Maine under (A) high emission; and (B) lowemission scenarios for mid 21st century (2041-2060) relative to present (2005-2014). Values represent themean of 3 ESM; error bars represent ± sd.4.4 DiscussionThe results of the present chapter suggest that climate change will alter the MCP of jointly managedtransboundary fish stocks in North America consequently altering Canada’s and the US’s stock’s stock-shareratio, regardless of the climate change scenario. These results are aligned with regional (Morley et al. 2018)projections suggesting that climate change will push marine species towards the poles and deeper water(Pinsky et al. 2013) in search of their ecological niche (Poloczanska et al. 2016). Moreover, IPHC data6suggest that some of these shifts are already happening. For example, since 2010, the distribution proportionof Pacific halibut has increased from 9% to 11% in region 2B, from 7.5% to 13% in region 2C, and from12.3% to 13.5% in region 4CDE. On the other hand, regions 3A and 3B have seen the largest decreases inthe IPHC regulatory areas since 2010, from 35.3% to 30.6% and 20.6% to 15.9%, respectively. Similarly, inthe Gulf of Maine, the projected stock-share gain of yellowtail flounder and haddock by the US (Figure 4.5)follows a historical trend where in 2019, Canada’s stock-share decreased from 35% to 32% and 60% to 40%relative to 2010, respectively (Lake 2019).6See IPHC Time Series Datasets, Modelled FISS Stock Distribution Estimates [internet] https://www.iphc.int/data/time-series-datasets60Geographic barriers (Cheung et al. 2015, Kleisner et al. 2016), local temperature gradients (Pinskyet al. 2013), species interactions and human activities (Serpetti et al. 2017) might change the rate anddirection of species shifts. For the IPHC, geographic barriers might induce a westward increase of stock-share in IPHC regions where the stock can only migrate northward into the Arctic Ocean through the BeringSea and Bering Strait (Cheung et al. 2015) (Figure 4.3). In the Gulf of Maine, future projections couldbe a response to a temperature gradient shift combined with geographic barriers as southern waters aredeeper and warming slower than northern waters according to the ESMs (Figure A4.4). Moreover, Mainehas seen its landings of yellowtail flounder increase at the expenses of southern states (Pinsky and Fogarty2012). This could be influencing the US gain in MCP in the GoMa in relation to Canada as stocks shift theirdistribution from lower latitudes naturally reaching the US (lower) region first. As the effects of climatechange endure, even with high mitigation, joint plans should prepare to face changes in the stock-share ratioof transboundary stocks along both coast of North America.The shifts in the distribution of transboundary stocks can jeopardize management objectives suchas conservation measures and gear operation. Fish moving out of fishing grounds and into protected areascould result in a pressure increase to open such area to fishing. Moreover, overlapping shifting stocks couldinterfere in gear-limitation management rules of multiple fisheries generating conflicts between fleets (VanDer Voo 2016). The effectiveness of the IPHC-Closed Area (“CA” in Figure 4.2) in terms of protectingjuveniles has been historically questioned as trawling for other species is still allowed in the area (Karimet al. 2010, IPHC 2017). In 2015, for example, 97% of the trawl by-catch in areas 4CDE and the ClosedArea were juveniles (IPHC 2017). Consequently, the Alaskan trawl fisheries has been closed before reachingannual quota due to the attainment of Pacific halibut bycatch quota limits (Karim et al. 2010). Thus, thecommission has been asked to open the closed area for Pacific halibut fishing, under the premise that theexpansion of the trawl fishery is likely reducing any conservation goal for juvenile Pacific halibut (IPHC 2017).Although not assessed in this study, some trawling target species like Pacific cod (Gadus macrocephalus),flathead sole (Hippoglossoides elassodon), and Alaskan plaice (Pleuronectes quadrituberculatus) have alreadyshifted their distributions due to warming waters (Stram and Evans 2009) and some are expected to continueshifting in similar direction as Pacific halibut (Pinsky et al. 2013). The overlap of target species could beaddressed by applying ecosystem-based management and dynamic management tools (Hazen et al. 2018) tomanage these fisheries and reduce potential loss of sustainable harvest for both the halibut and the trawlfisheries.Quota allocation ruled by historic distributions will most likely be outdated incentivizing maladap-tation (Miller et al. 2013, FAO 2018b, Gaines et al. 2018). In Europe, for example, the EU Common61Fisheries Policy quota allocation is based on historic reference period of the 70’s (Harte et al. 2019). How-ever, climate change has shifted the distribution of multiple European commercial stocks (Baudron et al.2020), outdating the fixed quotas and thus compromising the sustainability of European fisheries (FAO 2018b,Baudron et al. 2020). Management regimes that include a dynamic harvest control (e.g., adjusting the quotabased on the stocks distribution) have the potential of increasing fish biomass, harvest and profits underclimate change (Gaines et al. 2018). In North America, poleward shifts of Pacific halibut along the coastof Oregon, Washington and British Columbia have been previously addressed by the IPHC resulting in theadoption of a dynamic quota allocation method (McCaughran and Hoag 1992). By allocating quotas basedon yearly surveys along the Convention area, the IPHC should be able to capture shifts in Pacific halibutdistribution due to climate change, reducing the chances of over exploitation of the stock due to these shifts(Miller et al. 2013). Similarly, for the Gulf of Maine, since the GoMa’s method to estimate quota allocationis weighted based on stocks distribution (90%) and historical catch (10%) (TRAC 2016). This process isespecially important for cod and haddock due to their distribution variation within the Gulf (Soboil andSutinen 2006, TRAC 2016). However, since 2010, when the weighted method was implemented, the quotaallocation has favored the US over Canada, especially in terms of haddock and yellowtail flounder (Lake2019). A perpetuation of this trend with no mitigation policy could jeopardize the arrangement as Canada’squota reduction could disincentive cooperation (Sumaila et al. 2020). Previous international action suggeststhat shifts in transboundary fish stocks distributions for fisheries with relatively low catch or economic valuecould lead to international disputes in quota allocation. For example, Atlantic mackerel (Scomber scombrus)represents a small proportion of the total catch of the EU, Norway, and the Faroe Islands (Denmark) whoshare the stock. Yet, they have all been in dispute with Iceland over quota allocation and rights since thestock shifted in 2017 reaching Icelandic waters (Spijkers and Boonstra 2017, Chapter 2). Similarly, Peruhas recently signed an agreement with Chile to manage the southern stock of Anchoveta (Engraulis ringens)which is substantially smaller than the northern Peruvian stock (UNDP 2016, Cashion et al. 2018). Theseexamples highlight the need for adaptation of transboundary fisheries management to climate change forall a broad range of species in order to reduce the risk of international conflict and unsustainable fisheriesdevelopment. Naturally, no strategy to mitigate climate change impacts will be beneficial if a fishery isexploited to extinction, however, reformulation of fisheries management could actually offset many of thenegative effects of climate change (Gaines et al. 2018). Thus, the impacts and solutions presented here needto be align with sustainable fisheries management.Side payments have been previously used to address changes in stock’s distribution, including casescaused by environmental forcing. In game theory, a side payment is received by a player as a compensation62from the other player in a shared resource agreement, with the premise that cooperation will result in abetter overall outcome (Bjørndal and Munro 2012, Sumaila 2013). Side payments do not have to be inmonetary form and are widely used in transboundary stocks around the world. For example, Norway andRussia have implemented a quota swap strategy for jointly managed stocks of cod, haddock and capelin(Mallotus villosus) in the Barents Sea (FAO 2020). Similarly, species’ quota swaps are allowed, up to adegree, within regulatory areas of the European union (Baudron et al. 2020). Specifically, for northernEuropean spring spawning herring (Clupea harengus), Norway, Iceland, Faroe Islands, Russia and the EUreached an agreement to manage the stock after its collapsed, partially due to climate variations (Miller etal. 2013). Among the implemented rules, the agreement established a dynamic quota allocation, allowingmembers to fish part of their quota within Norway’s EEZ, and land the catch in Norwegian ports. In NorthAmerica, Canada and the US have previous history with the utilization of side payments when in the 70’sPacific salmon shifted its distribution resulting in large interceptions of Canada’s salmon by Alaskan fisheries(Miller et al. 2013, Song et al. 2017b). The conflict was resolved by the implementation of a conservationfund that worked as a side payment for both Canada and the state of Alaska (Miller et al. 2013, Song et al.2017b). The potential adaptation of side payments in terms of quota swaps or allocating EEZ-fishing rightsacross the Gulf of Maine EEZs could be a potential solution as stocks shift due to climate change.Transboundary fisheries management have to be prepared for the uncertainty that comes with achanging world. Future climate change will depend on the path society as a whole will take, and thus I relyon scenario planning to account for the uncertainty build in future decision making (Vuuren, Edmonds, et al.2011). In these results, the “winners and losers” of climate change, and the intensity of the change, will bescenario dependent. For instance, stock-share of yellowtail flounder under a high emission scenario will belarger for the US while Canada’s gain of cod stock-share will be larger under a low emission scenario (Figure4.5). Applying previously described strategies (e.g., quota swaps or EEZ-fishing rights) could increase theresilience of treaties by preventing members from leaving the agreement due to a shift in threat points, ashappened in the Pacific salmon case (Miller et al. 2013).Models are attempts to represent reality (in this case a future reality) based on observationaldata, previously established theory, and future scenarios, and are thus, subjected to different degrees ofuncertainty (Payne et al. 2016). An ensemble of models is a way to present a more robust result thataccounts for differences in the structural composition of each model (Cheung, Frölicher, et al. 2016). In here,I used three ESMs to project future changes in species maximum catch potential. The levels of uncertaintyrelated to the ESMs differ among case studies. Overall, results for the Gulf of Maine agree with a reductionin MCP of all three stocks. However, some discrete areas show a positive change for yellowtail flounder by63mid 21𝑠𝑡 century (Figure 4.3), mainly driven by the GFDL model (Figure A4.5). Potential model artifactscould also be contributing to the results, especially in the northern part of the study area (Bay of Fundy) asmost disagreeing grids are covered by land, which could be influencing the results. In contrast, considerableuncertainty exists in the change of MCP along the IPHC Convention area shown by a disagreement betweenESMs (Figure A4.6). Off the coast of British Columbia, increasing temperature trends are consistent amongESMs, however, other processes such as acidification and deoxygenation are still not well understood fromBritish Columbia to the Gulf of Alaska (Talloni-Álvarez et al. 2019). Moreover, considerable uncertaintyexists along the Bering Sea (Douglas 2010) and Antarctic Pacific regarding the extent and intensity of futuresea-ice reduction under climate change (Steiner et al. 2015, IPCC 2019). Regarding the DBEM, its structuraluncertainty has been previously tested for agreement against commonly used species distribution algorithmssuch as Maxent (Phillips et al. 2006) and AquaMaps (Ready et al. 2010, Kaschner et al. 2011) resulting inno qualitative differences in trends between algorithms (Cheung, Jones, Reygondeau, et al. 2016). Finally,is worth mentioning that future changes to species distributions could be influenced by factors not capturedby my model such as interactions between species (Pecl et al. 2017), adaptation of species to environmentalchanges, and anthropogenic factors (Serpetti et al. 2017). However, these factors are expected to increasethe rate of range-shifting of the species making my results conservative (Cheung et al. 2010, Serpetti et al.2017).4.5 ConclusionsShifts in stocks distribution due to climate change have the potential of creating local extinction of economi-cally important stocks while enhancing fisheries in areas where they were not present before. In this chapter,I have explored the potential impacts of climate change in the joint management of selected transbound-ary stocks managed by Canada and the US. I found that, transboundary stocks are likely to shift in theupcoming years changing the proportion of the catch of jointly managed fisheries of Canada and the US.Lessons from other countries can provide solutions to such challenges. More specific, side payments, dynamicmanagement, and interchangeable quotas were identified as potential solutions for North American region.While not directly addressed in this chapter, socio-economic impacts of shifting transboundary stocks couldadd an extra layer of complexity to the problem. Addressing shifts in stocks distribution sooner rather thanlatter could avert the so called “fish wars”, improve sustainability of jointly managed stocks, and secure thelivelihood of thousands of families that depend on stocks that move freely between national jurisdictions.Finally, preparing for an uncertain future is key to achieve sustainable fisheries.645 Synthesis and Conclusion5.1 Synthesis and ConclusionIn this dissertation, I endeavored to understand the impacts of climate change on transboundary fish stocksand their fisheries, with the objective of informing international fisheries management to prepare and respondto species on the move. Specifically, my dissertation focused on fish stocks that are shared between neigh-bouring Exclusive Economic Zones (EEZs). Managing such fish stocks often present a unique set of challengesfor international fisheries management compared to other types of transboundary fisheries (Chapter 1). Inthis chapter, I synthesize the main findings from Chapters 2, 3, and 4, and provide a critical reflection on theimportance of these findings and their uncertainties. Based on these findings, I identify a set of strategiesfor international fisheries management to cope with the changing climate. Finally, I propose some futureresearch directions regarding climate change impacts on the management of transboundary marine stocks.5.1.1 Research contributionsSubstantial uncertainty exists in the number of transboundary species since the delineation of EEZs byUNCLOS in the early 1980s (United Nations 1986, Miller and Munro 2002). Previous estimates range from1500 taxa based on an informed guess (Caddy 1997), to 344 taxa based on a literature review (Teh andSumaila 2015). Thus, to understand the impacts of climate change on transboundary fish stocks, I firstdetermined what exploited marine fished species are actually transboundary, and more specifically, thosethat are shared by neighboring EEZs. In Chapter 2, I developed a methodology and collated and analyzedglobal ecological and fisheries data sets to provide a more robust estimate of the number of transboundaryspecies that is based on available information on the biogeography and fisheries of exploited marine species.Such analysis identified a substantially higher number of global transboundary species compared to previousstudies and suggested that we may have previously underestimated the importance of transboundary speciesto global fisheries catch and revenue. The updated estimates of the number of transboundary species andtheir importance in global and regional fisheries will contribute to the foundational knowledge for discussingfisheries management policies to achieve ocean sustainability under climate change.While substantial attention has been placed over the impacts of shifting stocks distributions to themanagement of marine resources (Pinsky et al. 2018, Oremus et al. 2020), many questions remain unan-swered regarding the risks of impacts of shifting transboundary stocks on their fisheries and the effectivenessof their management under climate change. In Chapter 3, I explored two specific aspects of the risks of65impacts of shifting transboundary stocks: (1) the changes in the sharing of transboundary stocks betweencountries and (2) the time frame and intensity in which such changes will occur. These aspects of shiftingtransboundary stocks can help understand the challenges that climate change will bring to the managementof transboundary fisheries (Link et al. 2010, Gaines et al. 2018). I applied integrated climate-ocean-fishstock models and analyzed the model outputs through the lens of game theory in economics. I found thatvirtually all EEZs would experience at least one transboundary stock emerging by 2006 relative to the histor-ical distribution (1951 - 2005). Consequently, the sharing proportion of several stocks have already shiftedin at least one EEZ border. Moreover, the results highlight the high risk of impacts of climate change ontransboundary management of the highly productive and economically important fisheries in small tropicalEEZs. The results from this chapter can support international fisheries management anticipate the social-ecological consequences of shifting transboundary stocks and better prepare ocean governance in the face ofclimate change.The risk of impacts of climate change on the management of transboundary fisheries will depend onthe existing policies and specific international treaties (Pinsky and Mantua 2014, Chapter 3). Policies andtreaties on transboundary fish stocks that lack a proper framework to address the consequences of shiftingdistributions of the fisheries resources can lead to international dispute and conflict, and drive unsustainablefishing activities (Miller et al. 2013, Song et al. 2017b, Spijkers and Boonstra 2017, Sumaila et al. 2020). InChapter 4, I studied specifically two bi-lateral fisheries agreements between Canada and the United States:The International Pacific Halibut Commission that manages Pacific halibut (Hippoglossus stenolepis) anda resource sharing arrangement in the Gulf of Maine for cod (Gadus morhua), haddock (Melanogrammusaeglefinus) and yellowtail flounder (Limanda ferruginea). First, I examined the projected changes in relativesharing of these fish stocks under climate change (through the calculated stock share ratio described inChapter 4). Second, I assessed the potential management consequences of the shifts in stock sharing. Finally,I explored examples of international fisheries governance that could potentially work in each of these twocase study fisheries agreements. I showed that by 2050, the proportions of the catch of some of the jointlymanaged fisheries between Canada and the US are likely to change. Specifically, by 2050 Pacific halibut isexpected to have moved poleward along the northeast Pacific coast and westward from the Gulf of Alaskato the Aleutian Islands and up to the Bering Sea under RCP 8.5. In the Gulf of Maine, climate change isexpected to overall reduce the catch potential of cod, haddock and yellowtail flounder by 2050, regardless ofthe RCP. Here, changes in the stock share ratio will have different outcomes dependent on the climate changescenario and stock in question. For example, RCP 8.5 will affect Canada’s share of yellowtail flounder andhaddock and increase cod share. From an ecological perspective, some frameworks seem to be more resilient66to changes in fish stocks than others (i.e., quota is allocated following yearly samples and areas are closed tofishing). I identified that dynamic management that compensates fisheries in States with decreasing shareof fish stocks through direct payment and/or interchangeable quotas are amongst the potential solutions forthe adaptation of transboundary fisheries management under climate change in the North American region.5.1.2 Strategies for transboundary fisheries management under climate changeBased on the findings from this dissertation, I have identified a set of potential solutions to address theimpacts of shifts in transboundary stocks distributions on their management under climate change. Overall,all of the identified solution options for international fisheries management will require cooperation betweenneighboring nations to generate adaptive, collaborative and equitable management strategies to address theuncertainties of a changing world (Link et al. 2010, Miller et al. 2013, Pinsky et al. 2018, Oremus et al.2020). As I show below, dealing with shifting transboundary stocks to adjacent areas can be summarizedin three main steps: (1) determine if distribution shifts of transboundary stock have occurred, (2) gathersufficient data on the stock’s distribution and abundance, and (3) implement fisheries management that isadaptive to changing stocks, such as dynamic quota allocation and harvest controls (Link et al. 2010, Milleret al. 2013, Twiname et al. 2020).5.1.2.1 Determining distributional shiftsDetecting distribution shifts of transboundary stocks requires monitoring of the populations, theenvironmental conditions of their habitats, and the fisheries that are exploiting these stocks (Link et al.2010, Twiname et al. 2020). A multi-disciplinary and multi-institutional monitoring program is neededto gather data information on the biophysical, social-economic and governance aspects of transboundaryfisheries (Bonebrake et al. 2017).Approaches to identify shifts in distributions of transboundary stocks include fisheries- independentand dependent surveys, citizen science, and monitoring of fishing activities. Fisheries-independent scientificsurveys of fish stocks such as those undertaken by the IPHC are standardized and allow researchers togather specific biological and oceanic information (IPHC 2019, Chapter 4). Moreover, scientific monitoringprograms can help detect shifts in stocks biogeography, such as identifying range expansion of stocks to newareas (Fogarty et al. 2017). However, scientific surveys are often expensive (e.g., the IPHC survey costsabout USD $4 million annually), limiting the temporal and spatial coverage of such surveys. Many regionsor countries also do not have the financial and technical capacity to undertake regular fisheries-independent67scientific monitoring programs. In contrast, catch data analyses have been used to analyze shifting stocksamong global EEZs as well as European regulatory areas (Cheung et al. 2013, Baudron et al. 2020). Catch-data analyses are cheaper and provide longer time series and broader geographic range (e.g., FAO officialcatch statistics start in 1951 and include most coastal nations). However, the quality of catch data is affectedby biases from the reporting methods, lack of specific geographic information and are dependent on fishingactivities (Pauly et al. 2013, Zeller et al. 2016).Other methods of monitoring through interviews and “citizen science” can also provide informationon shifting transboundary stocks. For example, in the East coast of the US, fishers have manifested theneed to travel further into other States waters to “chase the fish” providing valuable information on shifts instocks distribution by collecting information about fishing activities (Pinsky and Fogarty 2012). Moreover,in Australia, data collected by citizen science has been used in species distribution models to quantify rangeshifts (Champion et al. 2018). Engaging in community-based monitoring such as citizen science, partneringup with fishing industries, and considering other disciplines as well as incorporating local ecological knowledgecan provide valuable information to validate models and identify distributional shifts in transboundary fishstocks.5.1.2.2 Sharing data for resilient treatiesHaving reliable, standardized, stock-level data is crucial to achieve sustainable fisheries manage-ment (Hilborn and Ovando 2014). Particularly, transboundary fish stocks management requires countriesto monitor and share data about the fish stocks (Miller et al. 2013, Pinsky et al. 2018). The effectiveness ofsuch data sharing will be facilitated by protocols to standardize data collection and processing across EEZs.However, gaps in knowledge and capacity in monitoring fish stocks and fisheries between countries may posebarriers for the harmonization of data collection across countries. Such barriers are particularly apparentin the case of transboundary fish stocks shared between countries with different capacity e.g., Pacific sar-dine between Mexico and the US (Cisneros-Montemayor et al. 2020). Fostering more equitable internationaltreaties that include support for capacity building and knowledge transfers between countries can help reducesuch barriers in data sharing. Examples of international cooperation on knowledge and data transfer includeregular international meetings such as the Mexico - US “Mexus” meetings (NOAA 2018) and collaborationin the collection and sharing of data between neighboring countries such as the case between Canada andthe US in the Gulf of Maine (Miller et al. 2013, Chapter 4)685.1.3 Adaptation of joint management to shifting stocksCooperation in the management of transboundary fish stocks is most likely to improve its effectivenessin achieving sustainable outcomes (Bailey et al. 2010, Sumaila 2013). However, the management of sharedstocks requires countries to coordinate data collection, management actions, align conservation and extractionpolicies, and effectively implement the agreed policies (Bailey et al. 2010, Sumaila 2013). However, arecent analysis looking at total biomass over biomass required at maximum sustainable yield suggests thattransboundary stocks are in worse shape than stocks that are only exploited in one EEZ (Liu and Molinan.d.). Climate change-induced stock distribution shifts will add additional challenge to the management ofshared marine stocks (Chapters 2 and 3). Currently, most international treaties on transboundary fisheriesmanagement lack the mechanism to account for the shifts in stocks’ distributions and the associated responsesby the fisheries (Oremus et al. 2020, Sumby et al. 2021). Creating alternative mechanisms that compensateone country by the other when shared stocks shift can motivate collaboration and improve the adaptabilityand resilience of current treaties (Miller et al. 2013, Gaines et al. 2018, Pinsky et al. 2018, Oremus et al.2020).Fisheries management need to be more dynamic in nature to accommodate the increased uncertain-ties associated with the futures of fish stocks and fisheries under climate change (Frölicher et al. 2016) and beflexible enough to reduce the chance of causing international disputes caused by disagreement in managingfish stocks that are shifting their distributions (Miller et al. 2013, Song et al. 2017b, Spijkers and Boonstra2017). Dynamic management is defined by Maxwell et al. (2015) as “management that changes rapidly inspace and time in response to the shifting nature of the ocean and its users based on the integration ofnew biological, oceanographic, social and/or economic data in near real-time.” Some examples of dynamicmanagement identified along this dissertation include yearly estimations of quota allocation, exchangeablequotas, MPAs with moving boundaries and changes in fishing areas (Chapters 3 and 4).Quota allocation of shared stocks based on a fix-historical proportion like the case of the EU willbe outdated as species shift their distribution (Baudron et al. 2020). On the contrary, allocation strategiesbased on periodical estimations of stock distribution such as that of the IPHC for Pacific halibut (IPHC2018) or a mix of historical and current distributions like in the Gulf of Maine (Chapter 4), should be ableto adapt to shifts in stocks distributions. Similarly, cases like that of Norway and Russia in the BarentsSea where a portfolio of stocks (and quota) can be exchangeable provide a way to deal with shifts in thedistribution of shared stocks (FAO 2020). Marine Protected Areas are among the most popular tools inmarine spatial management (Song et al. 2017a). However, their design mostly consists in fixed borders that69ignore the stock’s biogeography nor shifts (Fredston-Hermann et al. 2018, Cashion et al. 2020). Solutions tothis problem include the incorporation of dynamic protected areas like the fishing refuges in Mexico (RefugioPesquero) where specific areas are temporary banned to fishing (e.g., 1 to 5 years) with the possibility offurther expansion, re-location, and changes in protection level (Pescando Datos 2020). Different designs ofMPAs like mobile, networks (Cashion et al. 2020), and transboundary (Costello and Molina 2021) have alsobeen identified as potential solutions for shifting stocks, and consequently, transboundary stocks management.However, more empirical evidence is needed to understand the potential of these tools (Fredston-Hermannet al. 2018, Cashion et al. 2020). Other strategies of dynamic ocean management potentially applicable totransboundary stocks include programs that use real time (i.e., satellite) data tracking and align them withmanagement, stock movement, and fisheries to aid in bycatch reduction (Howell et al. 2008, Hobday et al.2010, Maxwell et al. 2015, Hazen et al. 2018).In addition to dynamic management, current agreements will need to become (more) resilient to theconsequence of climate change to avoid discontinuity (Miller et al. 2013). Setting the framework to includecompensation methods (e.g., side payments) for when stocks shift between EEZs is one way to prepare forthe uncertainty of the effects of climate change to stock’s distributions (Miller et al. 2013, Sumaila et al.2020). Moreover, including criteria for newcomers to the fishery when the stock expands to new areas canalso reduce the potential of conflict and undesired harvest levels (Pinsky et al. 2018). While new and existingagreements will need to be adaptable, so that fisheries management can anticipate potential issues causedby shifting stocks or be more effective in dealing with disagreement between resource users.5.1.4 Limitations and uncertaintiesOverall, this dissertation relies on global databases, climate change and species distributions projections,and climate change scenarios. These aspects contribute to the main sources of uncertainties associated withthe analysis. In this section I describe the uncertainties related to these aspects while providing the generalapproach taken to address them.5.1.4.1 Data and scale uncertaintyThis dissertation uses data from diverse sources that have different inherent uncertainties and biases.Particularly, fisheries reconstructed catch data from the Sea Around Us and the ex-vessel price data formTai et al. (2017) relies on several assumptions creating uncertainties in the estimations (Zeller et al. 2016,Tai et al. 2017, Pauly and Zeller 2019). Regarding the Sea Around Us Catch database, while reconstructions70address negative bias in reported catch, the estimation of such non-reported catch implies some level ofuncertainty (Pauly and Zeller 2019). For example, when data is not available for a year or a whole EEZ,the catch reconstruction method will extrapolate from previous years or estimates from parts of the EEZ.While this is not ideal, the method parts from the principle that “a best estimate is better than zero” whena fishery is known to exist (Pauly and Zeller 2019). Similarly, price estimates are assigned by species withina country and when not available, similar species or neighbouring countries. Thus, it is not ideal to use thisdatabase for specific stocks, but rather large-scale regional and global analyses as I do in Chapters 2 and 3(Tai et al. 2017).In total, I analyzed 938 marine species with different habitat preference and life history. Despiteall of these species being marine, some of them are considered anadromus, that is, species like salmons(Oncorhynchus sp.) that spawn in fresh water (e.g., rivers) but spend most of their adult life in the ocean. Inthese cases, the models I used captured the oceanic life stage of the species but not the freshwater component.A recent study looking at climate change impacts on Chinook salmon (Oncorhynchus tshawytscha) at alllife stages found that the freshwater stages of Chinook salmon is relatively resilient to climate change whilelarge climate impacts are found in its marine stage (Crozier et al. 2021). Thus, for some of the anadromousspecies, while my study did not consider the freshwater stage of these species, my results have captured themarine life stage where large climate impacts are expected to happen and where these impacts challengetransboundary management (Miller et al. 2013, Song et al. 2017b, Crozier et al. 2021).In this dissertation I define a “fish stock” as a species within an EEZ (e.g., meta-population). Thus,I am not able to identify characteristics of transboundary stocks at finer resolutions (e.g., within an EEZ aspecies could have multiple stocks with only a subset of them transboundary; Chapter 2). This is importantsince management decisions based on conclusions from meta-populations could be erroneous as they couldbe ignoring key characteristics of sub-populations (Archambault et al. 2016, Kaplan et al. 2016). Thisresolution affects mainly Chapters 2 and 3. Results from these chapters should be taken as a guidance tothe potential impacts of climate change to transboundary fisheries management. More detailed research(e.g., at sub-stock level) will allow for less uncertain results better suited for management plans (See Futuredirections).5.1.4.2 Modeling uncertaintyThis dissertation integrates different modeling techniques to project species distributions under cli-mate change. Models are representations of the reality that are based on available information and knowledgeabout the subject that the models represent. Therefore, models are subjected to uncertainties associated with71assumptions, structure and data. My dissertation employed species distribution models (SDMs) as a mainanalytic tool. SDMs are numerical ways to determine associations between species geographic distributionsand the environment, as well as the relationship of such association (Peterson et al. 2012). Uncertaintiesassociated with marine SDMs have been previously studied (Araújo and Guisan 2006, Beale and Lennon2012) with some of them specific to their applications to study the impacts of climate change on species’biogeography (Araújo and Luoto 2007, Wiens et al. 2009, Goberville et al. 2015, Heikkinen et al. 2016).The uncertainties associated with SDMs that are particularly relevant to this dissertation include: the lackof or insufficient representation of the effects of species interactions (Pecl et al. 2017), evolutionary and/orepigenetic adaptation to environmental change, and other non-climatic anthropocentric factors (Serpetti etal. 2017).My dissertation’s findings are also affected by uncertainties associated with the climate changeprojections from Earth system models. These uncertainties can be categorized into three main sources: i)model structure, ii) internal climate variability, and iii) different carbon emission scenarios (Hawkins andSutton 2012, Frölicher et al. 2016).(i) Model uncertainty is produced by the different ways models address fundamental environmentalprocesses, in this case, regardless of the radiative forcing (Bopp et al. 2013, Frölicher et al. 2016). As aresult, different models can have divergent projections for a determined environmental variable (Chapter 4).Model uncertainty is examined in Chapters 2 and 4 by using projections from three ESMs.(ii) Internal climate variability refers to the natural climate variation embedded in the Earth climatesystem (e.g., without considering radiative forcing). Such variation is mainly produced by large-scale eventslike El Niño-Southern Oscillation or the Atlantic Multidecal and Pacific Decadal Oscillations, although small-scale processes are also included (Frölicher et al. 2016). In ESMs, climate variability is addressed by runningmultiple ensemble members, each one representing an initial condition of the Earth system (Frölicher et al.2009, Rodgers et al. 2015). Thus, the ESMs used in Chapters 2 and 4 represent the average of each ESMensemble members. However, for Chapter 3 I used ten ensemble members of the GFDL ESM (i.e., ten runs ofthe same ESM). This allowed me to identify the climate change signal within the internal climate variability(See Chapter 3 Methods - Projecting species distributions under climate change).(iii) Carbon emission scenarios represent the future pathways society can take in mitigating green-house gas emissions considering unknown technological and social factors (Frölicher et al. 2016). These arecommonly known as RCPs used by the IPCC to represent distinct potential futures of radiative forcing (Gat-tuso et al. 2015, IPCC 2019). The RCPs considered by the IPCC range from a “strong climate mitigation”72(RCP 2.6) scenario to a “no mitigation” (RCP 8.5) scenario where society mainly relaxes greenhouse gasesmitigation policies (See Introduction). In Chapter 4, I base my results on RCP 2.6 and RCP 8.5 capturingthe main spectrum of future pathways currently considered by the IPCC. In Chapter 3, I only use outputsfor RCP 8.5 scenario meaning that my results represent a “worst case” future scenario under climate changeand that, any mitigation effort could reduce the intensity of the results.5.1.5 Future directionsI have identified neighbouring countries managing shared stocks (e.g., Norway and Iceland) with some casesof addressing environmental-driven shifts in species distributions (e.g., Canada and the U.S.). However,there is no complete database of management plans for shared stocks. Oremus et. al. (2020) developeda database of international treaties capturing 127 agreements in tropical waters. Building on the effort byOremus et al. (2020), expanding the database to non-tropical regions while including key information suchas managed species, the countries included in the treaty as well as the management rules placed could helpanswer key questions regarding the management of transboundary fisheries. Some of these key questionsinclude the status of the stocks and its relationship to differences in level of cooperation in managing sharedstocks between States, and the extent to which existing management plans are resilient to climate changeresilience. This will allow us to have a better understanding of the current state of transboundary stocksand what management tools are potentially effective to address changes in stock distributions under climatechange.More work is needed to understand the effectiveness of dynamic management of transboundarystocks under climate change. Dynamic tools have been proved to be useful in the management of mobilemarine resources (Maxwell et al. 2015) and have been identified as a potential strategy for shifting dis-tributions of shared stocks (Gaines et al. 2018, Pinsky et al. 2018, Oremus et al. 2020). However, mostdynamic tools are yet to be tested under a climate change and transboundary fisheries framework, remainingas hypothetical solutions. Further development of models testing the functionality of different dynamic toolsunder climate change (Cashion et al. 2020) while also considering the nuisances of managing shared marineresources (Costello and Molina 2021) could provide substantial advance to international ocean governance.Such research must be conducted under a multi-disciplinary framework to identify important trade-offsbetween the diverse set of societal values that social-ecological systems have.Further analyses should try to consider, when possible, spatially explicit biologically meaningfulpopulation units to inform local management. This is of specific concern for regions of the world where73fisheries data is lacking like most fisheries in developing countries and many in developed countries. Thisdissertation considers a species within an EEZ as a stock, ignoring that discrete populations within a meta-population can have fundamental differences to management frameworks (See Limitations and uncertainties- Data and scale uncertainty). Reproducing the experiments that I conducted here considering biologicallymeaningful population units within neighbouring EEZs will likely provide managers with more confidentresults to base decisions. Such analysis, however, will require a set of local-scale baseline information, frompopulation biology, to climate modeling, to economics of fishing, which are often missing. The furtherdevelopment of baseline scientific information at the local level can be supported by model-downscaling (e.g.,the reduction of models’ spatial resolution), and by including other sources of information such as citizenscience (Aceves-Bueno et al. 2015), or multiple-source frameworks (Zeller et al. 2016).5.1.6 Final remarksThe last eight months of this dissertation (and its formal defense) were written under a strict social-distancingand sometimes quarantine regime because of the 2020 COVID-19 pandemic. In many instances, the wayto combat the COVID-19 pandemic is similar to that of climate change. To address both issues, we needglobal unification, science-based decision making, reduction of world inequality, and a global solidarization,specially of those more privilege with the most in need. The United Nations proclaimed that the next decade(2021-2030) will be focused on improving Ocean Science for Sustainable Development. Thus, we, as a globalsociety, have the opportunity (and responsibility) to take action and improve fisheries management in theface of a changing climate. While the future is full of uncertainties, it is certain that the sustainability oftransboundary fisheries in a changing world will only be achieved by following a multidisciplinary approachwhere concepts of ecology, conservation, economics, and social science are integrated and multiple societalvalues for nature are taken into consideration. Only then we will be able to achieve more equitable agreements,that benefit society as whole, rather than specific groups.74BibliographyAceves-Bueno, E., Adeleye, A. S., Bradley, D., Brandt, W. T., Callery, P., Feraud, M., Garner, K. L.,Gentry, R., Huang, Y., Mccullough, I., Pearlman, I., Sutherland, S. A., Wilkinson, W., Yang, Y.,Zink, T., Anderson, S. E., and Tague, C., 2015. 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Category A, Increasing; CategoryB, Constant; Category C, Decreasing. “No Category” reflects species with less than 10 years of catch dataand/ or less than 5 consecutive years of catch data.102The number of grid cells in which a species is present (Area Index) is expected to directlyinfluence whether or not a species is considered transboundary. Therefore, we tested the sensitivity ofour results to the Area Index by estimating the number of transboundary species along a gradient ofvalues (Figure A2.3). As expected, using a more relaxed value (e.g. the species is present within 10% ormore of the EEZ) will result in a greater number of transboundary species (median of 40 EEZs sharedper species), while a more restrictive value (e.g., 50%) results in a median estimate of 4. The currentanalysis utilized a 25% Area Index threshold (Figure A2.3 - darker histogram).Figure A2.3: Histogram of number of transboundary species using different Area Index threshold values.103Table A2.1: Size of all the EEZs in each sub region determined by the United NationsUN sub region Area of EEZs (Km^2)Northern Africa 1423774Western Asia 1744784Western Europe 2018481Southern Europe 2484760Southern Asia 4206953Northern Europe 6401144Eastern Asia 7329899Melanesia 7525195Eastern Europe 7777970Micronesia 8313054Polynesia 9577373South-eastern Asia 11098246Australia and New Zealand 12282183Sub-Saharan Africa 12349817Latin America and the Caribbean 20723420Northern America 22081778Table A2.2: Average ± (standard deviation) number of shared and non-shared transboundary species perEEZ for each catch trend categoryCategory Mean shared Mean not-sharedCategory A 8.19 ± (7.94) 3.95 ± (3.77)Category B 14.16 ± (13.61) 6.86 ± (7.76)Category C 25.12 ± (20.4) 12.45 ± (14.61)104Appendix B - Supplementary information for ”Early emergenceof range shift-induced challenges in managing transboundary fishstocks under climate change”Figure A3.1: World Exclusive Economic Zones used in this study as defined by the Sea Around Us and theircentroids (points).105Figure A3.2: Graphical representation of the transboundary index (TI) to determine the time of emergenceof transboundary stocks. The index is based on the distance between the distributional centroid of thetransboundary stock and the geographic centroid of the neighbouring Exclusive Economic Zones sharing thestock (Top panel). Time of emergence is defined as the first year when the TI overpasses historical values(Bottom panel).106Figure A3.3: Sensitivity analysis of the number of grid cells with projected stock abundance within theneighbouring EEZs sharing the stock. The level used in this article was the top 95th percentile (e.g., farright column).107Figure A3.4: Average Time of Emergence and standard deviation for a 64% confidence threshold (purple)and a 98% confidence threshold (yellow)108Figure A3.5: Distribution of Stock Share Ratio percentage change by the early (2021-2040) and middle(2040-2060) 21st century relative to today (1951-2005)109Figure A3.6: Changes in Stock Share Ration by 2050 (2041-2060) relative to 1951-2005. Lines represent theaverage gain of transboundary stock share ratio with arrows going from the losing EEZ (point) to the winner(arrowhead). Polygons represent the percentage of initial species that will see a shift. Panel B zooms in tospecific areas shown in grey boxes of A.110Table A3.1: Statistical results for multiple comparison test after Kruskal-Wallis on time of emergence acrossUnited Nations sub regionsHabitat preferencecomparrisonSignifican level ObservationdifferenceCritical difference K-W DifferenceAustralia and NewZealand-EasternAsia0.05 715.011934 1465.9298 FALSEAustralia and NewZealand-EasternEurope0.05 564.958758 1605.2391 FALSEAustralia and NewZealand-LatinAmerica and theCaribbean0.05 1569.939379 1381.8702 TRUEAustralia and NewZealand-Melanesia0.05 1127.076002 1558.6091 FALSEAustralia and NewZealand-Micronesia0.05 1284.742986 1954.7306 FALSEAustralia and NewZealand-NorthernAfrica0.05 367.027459 1426.8945 FALSEAustralia and NewZealand-NorthernAmerica0.05 319.846054 1634.3439 FALSEAustralia and NewZealand-NorthernEurope0.05 597.982863 1487.1176 FALSEAustralia and NewZealand-Polynesia0.05 1711.663455 1611.5762 TRUEAustralia and NewZealand-South-easternAsia0.05 248.225363 1397.9867 FALSEAustralia and NewZealand-SouthernAsia0.05 522.496243 1526.7065 FALSEAustralia and NewZealand-SouthernEurope0.05 20.481074 1397.5455 FALSEAustralia and NewZealand-Sub-SaharanAfrica0.05 561.927280 1398.5285 FALSEAustralia and NewZealand-WesternAsia0.05 202.306373 1440.4445 FALSEAustralia and NewZealand-WesternEurope0.05 10.591741 1495.5026 FALSEEasternAsia-EasternEurope0.05 1279.970692 995.4690 TRUEEasternAsia-LatinAmerica and theCaribbean0.05 2284.951313 568.9736 TRUEEasternAsia-Melanesia0.05 1842.087936 918.3835 TRUEEasternAsia-Micronesia0.05 1999.754921 1495.0377 TRUEEasternAsia-NorthernAfrica0.05 1082.039393 670.9648 TRUEEasternAsia-NorthernAmerica0.05 1034.857988 1041.7513 FALSEEasternAsia-NorthernEurope0.05 117.029071 791.0022 FALSEEasternAsia-Polynesia0.05 2426.675389 1005.6558 TRUEEastern Asia-South-easternAsia0.05 963.237297 607.0687 TRUEEasternAsia-SouthernAsia0.05 1237.508177 863.1329 TRUEEasternAsia-SouthernEurope0.05 735.493008 606.0521 TRUEEasternAsia-Sub-SaharanAfrica0.05 1276.939214 608.3155 TRUEEasternAsia-Western Asia0.05 917.318307 699.3183 TRUEEasternAsia-WesternEurope0.05 704.420193 806.6559 FALSEEasternEurope-LatinAmerica and theCaribbean0.05 1004.980621 866.9333 TRUEEasternEurope-Melanesia0.05 562.117244 1127.5064 FALSEEasternEurope-Micronesia0.05 719.784229 1631.8640 FALSEEasternEurope-NorthernAfrica0.05 197.931299 937.0358 FALSEEasternEurope-NorthernAmerica0.05 245.112704 1230.0765 FALSEEasternEurope-NorthernEurope0.05 1162.941621 1026.4146 TRUEEasternEurope-Polynesia0.05 1146.704697 1199.6607 FALSEEastern Europe-South-easternAsia0.05 316.733395 892.3984 FALSEEasternEurope-SouthernAsia0.05 42.462515 1082.9778 FALSEEasternEurope-SouthernEurope0.05 544.477684 891.7071 FALSEEastern Europe-Sub-SaharanAfrica0.05 3.031478 893.2469 FALSEEasternEurope-WesternAsia0.05 362.652385 957.5429 FALSEEasternEurope-WesternEurope0.05 575.550499 1038.5259 FALSELatin America andthe Caribbean-Melanesia0.05 442.863377 777.2021 FALSELatin America andthe Caribbean-Micronesia0.05 285.196393 1412.7110 FALSELatin America andthe Caribbean-NorthernAfrica0.05 1202.911920 459.1390 TRUELatin America andthe Caribbean-NorthernAmerica0.05 1250.093325 919.7069 TRUELatin America andthe Caribbean-NorthernEurope0.05 2167.922242 621.5299 TRUELatin America andthe Caribbean-Polynesia0.05 141.724076 878.6116 FALSELatin America andthe Caribbean-South-easternAsia0.05 1321.714017 359.3708 TRUELatin America andthe Caribbean-SouthernAsia0.05 1047.443136 711.0649 TRUELatin America andthe Caribbean-SouthernEurope0.05 1549.458305 357.6508 TRUELatin America andthe Caribbean-Sub-SaharanAfrica0.05 1008.012099 361.4728 TRUELatin America andthe Caribbean-WesternAsia0.05 1367.633007 499.6608 TRUELatin America andthe Caribbean-WesternEurope0.05 1580.531120 641.3335 TRUEMelanesia-Micronesia0.05 157.666984 1586.0170 FALSEMelanesia-NorthernAfrica0.05 760.048543 854.6964 FALSEMelanesia-NorthernAmerica0.05 807.229948 1168.5709 FALSEMelanesia-NorthernEurope0.05 1725.058865 951.8386 TRUEMelanesia-Polynesia0.05 584.587453 1136.5103 FALSEMelanesia-South-easternAsia0.05 878.850640 805.5089 TRUEMelanesia-SouthernAsia0.05 604.579759 1012.5762 FALSEMelanesia-SouthernEurope0.05 1106.594928 804.7431 TRUEMelanesia-Sub-SaharanAfrica0.05 565.148722 806.4489 FALSEMelanesia-WesternAsia0.05 924.769629 877.1307 TRUEMelanesia-WesternEurope0.05 1137.667743 964.8865 TRUEMicronesia-NorthernAfrica0.05 917.715528 1456.7825 FALSEMicronesia-NorthernAmerica0.05 964.896933 1660.5021 FALSEMicronesia-NorthernEurope0.05 1882.725850 1515.8186 TRUEMicronesia-Polynesia0.05 426.920469 1638.0980 FALSEMicronesia-South-easternAsia0.05 1036.517624 1428.4796 FALSEMicronesia-SouthernAsia0.05 762.246743 1554.6768 FALSEMicronesia-SouthernEurope0.05 1264.261912 1428.0479 FALSEMicronesia-Sub-SaharanAfrica0.05 722.815706 1429.0099 FALSEMicronesia-WesternAsia0.05 1082.436614 1470.0570 FALSEMicronesia-WesternEurope0.05 1295.334727 1524.0457 FALSENorthernAfrica-NorthernAmerica0.05 47.181405 986.0646 FALSENorthernAfrica-NorthernEurope0.05 965.010322 716.0741 TRUENorthernAfrica-Polynesia0.05 1344.635996 947.8508 TRUENorthern Africa-South-easternAsia0.05 118.802096 505.5790 FALSENorthernAfrica-SouthernAsia0.05 155.468784 795.0321 FALSENorthernAfrica-SouthernEurope0.05 346.546385 504.3579 FALSENorthern Africa-Sub-SaharanAfrica0.05 194.899821 507.0753 FALSENorthernAfrica-WesternAsia0.05 164.721086 613.2893 FALSENorthernAfrica-WesternEurope0.05 377.619200 733.3290 FALSENorthernAmerica-NorthernEurope0.05 917.828917 1071.3609 FALSENorthernAmerica-Polynesia0.05 1391.817401 1238.3349 TRUENorthern America-South-easternAsia0.05 71.620691 943.7490 FALSENorthernAmerica-SouthernAsia0.05 202.650189 1125.6679 FALSENorthernAmerica-SouthernEurope0.05 299.364980 943.0954 FALSENorthern America-Sub-SaharanAfrica0.05 242.081227 944.5515 FALSENorthernAmerica-WesternAsia0.05 117.539681 1005.5724 FALSENorthernAmerica-WesternEurope0.05 330.437794 1082.9697 FALSENorthernEurope-Polynesia0.05 2309.646318 1036.2972 TRUENorthern Europe-South-easternAsia0.05 846.208226 656.5828 TRUENorthernEurope-SouthernAsia0.05 1120.479106 898.6473 TRUENorthernEurope-SouthernEurope0.05 618.463937 655.6429 FALSENorthern Europe-Sub-SaharanAfrica0.05 1159.910143 657.7356 TRUENorthernEurope-WesternAsia0.05 800.289236 742.7076 TRUENorthernEurope-WesternEurope0.05 587.391122 844.5485 FALSEPolynesia-South-easternAsia0.05 1463.438092 903.7477 TRUEPolynesia-SouthernAsia0.05 1189.167212 1092.3488 TRUEPolynesia-SouthernEurope0.05 1691.182381 903.0651 TRUEPolynesia-Sub-SaharanAfrica0.05 1149.736175 904.5856 TRUEPolynesia-WesternAsia0.05 1509.357082 968.1289 TRUEPolynesia-WesternEurope0.05 1722.255196 1048.2944 TRUESouth-easternAsia-SouthernAsia0.05 274.270880 741.8995 FALSESouth-easternAsia-SouthernEurope0.05 227.744289 415.5907 FALSESouth-easternAsia-Sub-SaharanAfrica0.05 313.701918 418.8844 FALSESouth-easternAsia-Western Asia0.05 45.918990 542.6440 FALSESouth-easternAsia-WesternEurope0.05 258.817103 675.3592 FALSESouthernAsia-SouthernEurope0.05 502.015169 741.0679 FALSESouthernAsia-Sub-SaharanAfrica0.05 39.431037 742.9200 FALSESouthernAsia-Western Asia0.05 320.189870 819.1022 FALSESouthernAsia-WesternEurope0.05 533.087984 912.4561 FALSESouthern Europe-Sub-SaharanAfrica0.05 541.446206 417.4097 TRUESouthernEurope-WesternAsia0.05 181.825299 541.5064 FALSESouthernEurope-WesternEurope0.05 31.072815 674.4456 FALSESub-SaharanAfrica-WesternAsia0.05 359.620908 544.0383 FALSESub-SaharanAfrica-WesternEurope0.05 572.519021 676.4801 FALSEWesternAsia-WesternEurope0.05 212.898113 759.3574 FALSE111Table A3.2: Statistical results for multiple comparison test after Kruskal-Wallis on time of emergence acrossUnited Nations sub regionsSub region comparrison Significan level Observation difference Critical difference K-W Differencebathydemersal-bathypelagic 0.05 1618.20177 1347.2420 TRUEbathydemersal-benthopelagic 0.05 769.28313 841.6236 FALSEbathydemersal-demersal 0.05 664.41978 832.8192 FALSEbathydemersal-pelagic 0.05 784.99848 1851.4997 FALSEbathydemersal-pelagic-neritic 0.05 1040.43584 823.9146 TRUEbathydemersal-pelagic-oceanic 0.05 1633.56909 810.1675 TRUEbathydemersal-reef-associated 0.05 1353.67920 829.8040 TRUEbathypelagic-benthopelagic 0.05 848.91864 1119.7065 FALSEbathypelagic-demersal 0.05 953.78199 1113.1039 FALSEbathypelagic-pelagic 0.05 833.20329 1993.3548 FALSEbathypelagic-pelagic-neritic 0.05 577.76593 1106.4572 FALSEbathypelagic-pelagic-oceanic 0.05 15.36731 1096.2590 FALSEbathypelagic-reef-associated 0.05 264.52257 1110.8497 FALSEbenthopelagic-demersal 0.05 104.86335 363.6887 FALSEbenthopelagic-pelagic 0.05 15.71535 1693.1429 FALSEbenthopelagic-pelagic-neritic 0.05 271.15271 342.8074 FALSEbenthopelagic-pelagic-oceanic 0.05 864.28595 308.3068 TRUEbenthopelagic-reef-associated 0.05 584.39607 356.7300 TRUEdemersal-pelagic 0.05 120.57870 1688.7837 FALSEdemersal-pelagic-neritic 0.05 376.01606 320.5846 TRUEdemersal-pelagic-oceanic 0.05 969.14931 283.3914 TRUEdemersal-reef-associated 0.05 689.25942 335.4309 TRUEpelagic-pelagic-neritic 0.05 255.43736 1684.4102 FALSEpelagic-pelagic-oceanic 0.05 848.57060 1677.7288 FALSEpelagic-reef-associated 0.05 568.68071 1687.2988 FALSEpelagic-neritic-pelagic-oceanic 0.05 593.13324 256.0431 TRUEpelagic-neritic-reef-associated 0.05 313.24335 312.6680 TRUEpelagic-oceanic-reef-associated 0.05 279.88989 274.4039 TRUE112Appendix C - Supplementary information for ”Challenges totransboundary fisheries management in North America underclimate change”Figure A4.1: Percentage change of MCP for stocks managed by the IPHC for end-century (2081–2100)relative to 2005–2014 under a A) high emission scenario and B) low emission scenario. Labels marked with* represent regions where models do not agree in direction of change.113Figure A4.2: Percentage change of MCP in the Gulf of Maine under (RCP 8.5) high emission scenario and(RCP 2.6) low emission for the end of the 21stcentury (2080–2100) relative to present (2005– 2014). Valuesrepresent the mean of 3 ESM. Points represent regions where ESMs do not agree in direction of change114Figure A4.3: Projected environmental variables under climate change from 2010 to 2010 for Arctic regionsof the IPHC (Top:4D, Bottom, 4E). The solid line represents the average of all three ESMs and the shadedarea represents the model’s uncertainty (s.d.).115Figure A4.4: Depth profile (A) and bottom water warming of the Gulf of Maine. Everything deeper than1000 meters is colored in green. B) Percentage change of bottom temperature relative to the present showingmore intense warming in northern regions, especially under a high emission climate change scenario.116Figure A4.5: Changes in maximum catch potential of yellowtail flounder (*Limanda ferruginea*) within thestudy area by mid-century relative to present time. Results for the three global circulation models (GFDL,IPSL, MPIS) used in the current study and two climate change scenarios (Top: High emission – RCP 8.5,Bottom: Low Emission – RCP 2.6). Grid-cells marked in yellow represent discrete areas where average MCPis projected to increase by mid-century.117Figure A4.6: Projected MCP change relative to present (2005-2014) for each IPHC regulatory area. Colorsrepresents the different ESM used in the study. Solid line represents a high emission scenario (RCP 8.5) anddashed line represents a low emission scenario (RCP 2.6)118

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