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Global fisheries economics in the face of change in climate Lam, Vicky Wing Yee 2013

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 GLOBAL FISHERIES ECONOMICS IN THE FACE OF CHANGE IN CLIMATE by Vicky Wing Yee Lam  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY in The Faculty of Graduate and Postdoctoral Studies  (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2013  ? Vicky Wing Yee Lam 2013   ii  Abstract  Climate change and changes in biogeochemical conditions of the ocean lead to changes in distribution of marine species and ocean productivity. These changes would affect fisheries, food security, livelihood of fishing communities and eventually the whole economy in different countries. This thesis uses simulation modelling to assess the direct impacts of change in physical and biogeochemical conditions of the ocean on marine fisheries and the socio-economic implications at both global and regional scales.  I develop a new global database of fishing cost, and provide an overview of current fishing cost patterns at national, regional, and global scales. The outcomes lay the foundation for the subsequent economic analysis in the thesis, and should also be useful for other future fisheries economic studies. Using these results and other data from the Sea Around Us Project, I estimate the change in landings of over 800 species of fish within the Exclusive Economic Zones (EEZs) under climate change scenarios based on dynamic bioclimate envelope model (DBEM), and an empirical model. About 75% of EEZs are projected to show declines in landings under the Special Report on Emission Scenario (SRES) A2. Most of them are in developing countries, which are socio-economically more vulnerable to climate change.  In West Africa, which is one of the most vulnerable regions to climate change, our model projects that there will be a reduction in landings in the 2050s, with some countries experiencing declines of more than 50% under the ?business-as-usual? scenario. This substantial decline not only affects the food supply and security in the region, but also has a negative impact on employment opportunities and the downstream economic impact on the  iii  whole society. I also analyze how change in climate and ocean acidity under scenarios of anthropogenic CO2 emission is expected to affect the economics of marine fisheries in the Arctic region. My model only projected a slight decrease in catch potential of marine fish and invertebrates under the impact of ocean acidification in the 2050s. Future studies accounting for the synergistic effects among climate change, ocean acidification and other factors on marine ecosystems are needed.     iv  Preface  This thesis was designed, conducted and written by me, with the guidance of Dr. Rashid Sumaila. Other than Chapters 1 and 6, all the Chapters in this dissertation have been published or prepared for publication. Chapters 2 and 4 have been published. Chapter 5 is currently under review while Chapter 3 is being prepared for submission. I am the first author on all of these papers, and I led their design, analysis and writing.  Chapter 2 is co-authored with Rashid Sumaila, Andrew Dyck, Daniel Pauly and Reg Watson. A version of this Chapter was published in 2011 in ICES Journal of Marine Science, Volume 68(9), pages 1996?2004. I designed and constructed the fishing cost database, collected and analyzed the data, and drafted the manuscript. Rashid Sumaila provided guidance throughout the process of paper preparation. Andrew Dyck provided technical assistance in the construction of the database. Daniel Pauly and Reg Watson helped and advised me on the design of the database and also provided inputs on the analysis.   Chapter 3 is co-authored with William Cheung and Rashid Sumaila, and is being prepared for submission. I formulated the concept and methods of the paper, created and run the models, prepared the maps and the manuscript. William Cheung contributed to the construction of the biological models and advised on the analysis. Rashid Sumaila advised on the economic analysis and provided guidance throughout the development of the paper.   Chapter 4 is co-authored with William Cheung, Wilf Swartz and Rashid Sumaila. A version of this Chapter was published in African Journal of Marine Science, Volume 34(1), pages 103?117, 2012. I designed the study, collected and analyzed the data, created and run  v  the models, and prepared the maps and manuscript. William Cheung contributed to the creation of the biological model. Wilf Swartz assisted with the use of the ex-vessel price database. Rashid Sumaila advised on the economic analysis and provided guidance throughout the whole study.   Chapter 5 is co-authored with William Cheung and Rashid Sumaila. A version of this Chapter has been submitted to a peer-reviewed journal. I was responsible for modelling the distribution of the marine species and performing part of the biological and all the economic analyses, and drafting the manuscript. William Cheung contributed to the creation of the biological model. Rashid Sumaila helped to conduct the economic analysis and provided guidance throughout.      vi  Table of Contents  Abstract .................................................................................................................................. ii Preface ................................................................................................................................... iv Table of Contents .................................................................................................................. vi List of Tables ......................................................................................................................... ix List of Figures ..................................................................................................................... xiii Acknowledgements ............................................................................................................ xvii Dedication .......................................................................................................................... xviii 1 Introduction ................................................................................................................... 1 1.1 Climate change impacts on marine resources ............................................................ 2 1.2 Impact on marine fisheries under climate change ...................................................... 6 1.3 Socio-economic impacts under climate change ......................................................... 9 1.4 Research gap ............................................................................................................ 17 1.5 Research questions ................................................................................................... 17 1.6 Theory and analytical framework ............................................................................ 18 1.7 Thesis outline ........................................................................................................... 25 2 Construction and first applications of a global cost of fishing database ................ 33 2.1 Synopsis ................................................................................................................... 33 2.2 Introduction .............................................................................................................. 33 2.3 Methods .................................................................................................................... 37 2.4 Results ...................................................................................................................... 45 2.5 Discussion ................................................................................................................ 56 3 Change in global fisheries economics with climate change ..................................... 58  vii  3.1 Synopsis ................................................................................................................... 58 3.2 Introduction .............................................................................................................. 59 3.3 Methods .................................................................................................................... 66 3.4 Results ...................................................................................................................... 76 3.5 Discussion ................................................................................................................ 88 4 Climate change impacts on fisheries in West Africa: implications for economic, food and nutritional security .................................................................... 96 4.1 Synopsis ................................................................................................................... 96 4.2 Introduction .............................................................................................................. 97 4.3 Background ............................................................................................................ 100 4.4 Methods .................................................................................................................. 105 4.5 Results .................................................................................................................... 120 4.6 Discussion .............................................................................................................. 131 5 Marine capture fisheries in the Arctic: winners or losers under climate change and ocean acidification? .............................................................................. 136 5.1 Synopsis ................................................................................................................. 136 5.2 Introduction ............................................................................................................ 137 5.3 Study area ............................................................................................................... 141 5.4 Methods .................................................................................................................. 143 5.5 Results .................................................................................................................... 153 5.6 Discussion .............................................................................................................. 165 6 Conclusions ................................................................................................................ 173 Bibliography ........................................................................................................................ 187 Appendices .......................................................................................................................... 227  viii  Appendix A: Construction and first applications of a global cost of fishing database....................................................................................................... 227 Appendix B: Change in global fisheries economics with climate change ........................ 230 Appendix C: Current and projected fish production and fish demand in West Africa .......................................................................................................... 237 Appendix D: Marine capture fisheries in the Arctic: winners or losers under climate change and ocean acidification? .................................................... 239      ix  List of Tables  Table 2.1 Quality score of fishing cost data. ....................................................................... 44 Table 2.2 Observed cost records for countries in FAO regions of the world, covering 46 of 144 maritime countries. ............................................................... 45 Table 2.3 Observed cost records by gear type. .................................................................... 48 Table 2.4 Summary statistics of all cost types in the cost of fishing database based on all data (both observed and interpolated; US$ per tonne of catch in 2005 real value). ..................................................................................... 49 Table 2.5 Summary statistics for variable total fishing costs and average ex-vessel price by gear type (US$ per tonne of catch). ............................................ 54 Table 3.1 Change in species composition of three EEZs, which are in the top 10 most important EEZs in term of their current total revenue, at different latitudinal regions under climate change (SRES A2 scenario) in the 2050s. ................................................................................................................... 80 Table 3.2  Change in fishing gear composition of three EEZs, which are in the top 10 countries with the highest total revenue in the 2000s, at different latitudinal regions under climate change (SRES A2 scenario) in the 2050s. ......................................................................................................... 84 Table 4.1 Annual per capita fish food consumption (kg capita-1 year-1) in WA countries in the 2000s.. ...................................................................................... 112    x  Table 4.2 Average dietary protein consumption (g/person/day) and annual protein consumption (billion g) for each West African country in 2003 ? 2005. ......................................................................................................................... 115 Table 4.3 Economic impact in West African countries using National multipliers based on fisheries output impact in 2003 (source: Dyck and Sumaila 2010). ................................................................................................................. 119 Table 4.4 Current landings, projected landings, percentage change in landings over current level and the prevalence of undernourishment in the population of each West African country under two different climate change scenarios. ............................................................................................... 121 Table 4.5 Percentage loss in marine protein relative to current protein consumption in each West African country under the two climate change scenarios. ............................................................................................... 125 Table 4.6 Landed values and total economic impact from the fisheries sector in the 2000s and under the two climate change scenarios (US$ millions per year). ............................................................................................................ 127 Table 5.1 Potential wages (income) earned by fishers by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA) .......................................................................... 157 Table 5.2 Annual total fishing cost by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA). .......................................................................................................... 159  xi  Table 5.3 Economic impact by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA)........... 163 Table 5.4 Household income impact by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA). .......................................................................................................... 164 Table C.1 Current and projected fish production and fish demand in West Africa. ............ 237 Table D.1 Catch by country under three scenarios ? Current; Climate Change (CC) and Climate  Change plus Ocean Acidification (CC+OA). ...................... 239 Table D.2 (a) The current (in the 2000s) and projected catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts, and income impacts from the four Earth System Models (ESMs) under climate change only (SRES A2) scenario in the 2050s. (b) The current (in the 2000s) and projected catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts, and income impacts from the four Earth System Models (ESMs) under climate change (SRES A2) and ocean acidification scenario in the 2050s.......................................................................................... 240 Table D.3 Landed value by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA)........... 242 Table D.4 Top 10 species with the highest landing harvested by Canada in the Arctic Region under the current status (the 2000s), under climate change scenario (SRES A2) and under both climate change and ocean acidification scenario in the 2050s. Species with * are temperate  xii  species and they become more important in the catch under climate change and ocean acidification scenarios. ......................................................... 243 Table D.5 Fisheries output impacts by country (Dyck and Sumaila 2010). ....................... 244 Table D.6 (a) Medians, lower and upper limits of catch, total revenues, fishers? incomes, total fishing cost, economic rents, economic impacts and income impacts under different scenarios in the 2050s using Monte Carlo method. (?Current = Current Status; ?CC? = Climate Change only scenario (SRES A2); ?CC+OA? = Climate Change (SRES A2) with Ocean Acidification scenario); (b) Percentage change (medians, lower and upper limits) of catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts and income impacts under different scenarios in the 2050s relative to the current status (2000s) using the Monte Carlo method. (?CC? = Climate Change only scenario (SRES A2); ?CC+OA? = Climate Change (SRES A2) with Ocean Acidification scenario); (c) Percentage change (medians, lower and upper limits) of catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts and income impacts from the climate change only scenario (SRES A2) when ocean acidification is taken into account using the Monte Carlo method.................... 245     xiii   List of Figures  Figure 2.1 Countries with fishing cost data in the database, and the percentage of catch contributed by these countries to regional and global landings. ................ 46 Figure 2.2 Percentage of landed values contributed by the countries with cost data to regional and global landed values.................................................................... 47 Figure 2.3 Comparison of the average variable and total fishing cost per tonne of catch across FAO regions. ................................................................................... 51 Figure 2.4 Percentage of different variable fishing cost types to the total variable fishing costs in the six FAO regions. ................................................................... 52 Figure 2.5 Total variable fishing cost (US$ thousand) in 2005. ........................................... 55 Figure 2.6 Average variable fishing cost per tonne of catch (US$ per tonne) in 2005. .................................................................................................................... 56 Figure 3.1 Percentage change in projected landings of each Exclusive Economic Zone (EEZ) in the 2050s relative to the levels in the 2000s under SRES A2 scenario (GFDL).................................................................................. 78 Figure 3.2 Percentage change in landings, total revenue and total fishing cost of the top ten most important EEZs in term of their total revenue in the 2050s under SRES A2 scenario relative to the values in the 2000s. ................... 79 Figure 3.3 Percentage changes in projected total resource rent of each Exclusive Economic Zone (EEZ) in the 2050s relative to the levels in the 2000s under SRES A2 scenario (GFDL). ...................................................................... 86  xiv  Figure 3.4 Percentage change in resource rent of the top ten most important EEZs in term of their total revenue in the 2050s under SRES A2 scenario relative to the values in the 2000s. ....................................................................... 87 Figure 3.5 Compare percentage changes in (a) landings; (b) total revenue; (c) total fishing cost; and (d) total resource rent  under SRESS A2 scenario in the 2050s relative to the values in the 2000s among the three different Earth System Models (ESM) including GFDL, IPSL and CSM 1.4. ....................................................................................................... 88 Figure 4.1 Map of fourteen West African countries included in this study. ....................... 101 Figure 4.2 Projection of population in each West African country in my study from 1950 to 2050.............................................................................................. 101 Figure 4.3 Percentage of fish catch to forecasted fish demand in WA countries by the 2050s under high range climate change scenario, SRES A1B (dark bars) and low range climate change scenario, constant 2000 level (grey bars). .................................................................................................................. 124 Figure 4.4 Percentage change in landed value of fishing countries in WA from 2000 to 2050 under two climate change scenarios. ........................................... 128 Figure 4.5 Projected annual average change in fisheries related jobs in the 2050s relative to number of jobs in the 2000s under high range climate change scenario, SRES A1B (dark bars) and the low range climate change scenario, constant 2000 level (grey bars). ............................................. 129 Figure 5.1 Anomaly of annual mean sea surface temperature (SST) and total phytoplankton biomass or primary productivity (PP) of four different  xv  Earth System Models (NOAA?s Geophyiscal Fluid Dynamic Laboratory (GFDL) Earth System Model 2.1.; IPSL-CM4-LOOP model from the Institute Pierre Simon Laplau (IPSL); and the two versions of the Community Climate System Model (CSM1.4-carbon and CCSM3-BEC) from the National Center for Atmospheric Research).). ................................................................................................................. 145 Figure 5.2 Landings by country under three scenarios ? Current; Climate Change (CC) and Climate Change with Ocean Acidification (CC+OA).. ..................... 154 Figure 5.3 Percentage change in total revenue (landed value) by country under the effect of ocean acidification i.e., the percentage change in total revenue from that projected under climate change only scenario (SRES A2) to that projected under both climate change and ocean acidification scenarios........................................................................................ 155 Figure 5.4 Percentage change in the economic rent of countries in the Arctic when comparing the economic rent projected under climate change and ocean acidification scenario with that projected under climate change only (A2) scenario. ................................................................................ 161 Figure B.1 Percentage changes in projected catch potential of each Exclusive Economic Zone (EEZ) from my model in the 2050s relative to the levels in the 2000s under SRES A2 scenario (GFDL). ..................................... 236 Figure D.1 Distribution of unit fishing cost (US$/tonne) (Figure D.1(a)) and unit labour cost (US$/tonne) (Figure D.1(b)) by the all gear/vessel types. Figure D.1(c)) shows the distribution of ex-vessel price (US$/tonne) of  xvi  all species in the Arctic countries. Note logarithmic scales on the x-axis. The fishing cost and labour cost data are based on data from Chapter 2 and Lam et al. (2011) and the ex-vessel price data are extracted from Swartz et al. (2012). .................................................................. 248    xvii  Acknowledgements  I would like to thank my supervisor, Rashid Sumaila, for his precious advice, assistance, enormous patience and great support throughout my PhD journey. I am also thankful to my supervising committee: Daniel Pauly, Brian Klinkenberg and Kerry Turner. Special thanks to Daniel Pauly for his constant advice and continuous support throughout my time at the Fisheries Centre. Thanks also to Reg Watson for his useful advice on the design of the global cost database. I also thank Liesbeth van der Meer for her great assistance in collecting and compiling the fishing cost data and information. I would like to extend my gratitude to Grace Pablico, who has helped me a lot in extracting data from the Sea Around Us database. Thanks to all my friends and colleagues at the Fisheries Economic Research Unit. I would also like to thank my friends and colleagues including Deng Palomares, Louise Teh, Lydia Teh, Grace Ong, Yajie Liu, Eny Buchary, Jonathan Anticamara, Marivic Pajaro, Yoshitaka Ota, Megan Bailey, Rhona Govender, Lingbo Li and Dyhia Belhabib for their invaluable support to me throughout my time at the University of British Columbia. This thesis would not have been completed without funding support from the Pew Charitable Trusts through the Sea Around Us Project and the Fisheries Research Economic Unit.   My deepest gratitude to my husband, William, for his endless love and support throughout the years. My greatest thanks to my parents, brother and sister-in-law for their unconditional love, understanding and huge support for me.    xviii  Dedication        To my husband, parents, brother and sister-in-law    1  Chapter 1: Introduction  The main objective of this thesis is to assess climate change impacts on catch, catch value, cost of fishing, profitability, resource rents, and thus on the socio-economics of marine fisheries globally and in two of the most climate change vulnerable regions in the world (i.e., West Africa and the Arctic region) (ACIA 2004, Allison et al. 2009). Climate change is suggested to be a factor that is increasingly affecting the ecosystems of terrestrial and marine biomes (Cramer et al. 2001, Parmesan and Yohe 2003, Hughes et al. 2003). Species may have various responses to climate change, including changes to their physiology, phenology, distribution and ecology (Hughes 2000). These may result in changes in productivity and shifts in distribution of commercially important marine species (Perry et al. 2005, Cheung et al. 2008 a, b). Such changes would have significant impact on fishing communities and people depending on fish for food and income, and thus the society as a whole. To begin with, it is also necessary to understand the current status of fisheries. I assessed the current economic status of fisheries by gathering information on landings, landed values and fishing cost, which was not readily available in most of the fisheries and countries. As such, the major research questions of this thesis are: (1) what are the costs of fishing in major fishing countries under the current climate regime? (2) how are landings, total revenue, fishing costs and resource rent of major commercial marine fisheries in Exclusive Economic Zones (EEZs) likely to change in the face of climate change? (3) what will be the influence on the economics and food security issues in West Africa through fisheries under climate change? (4) what are the impacts of climate change and ocean acidification on marine capture fisheries in the Arctic?   2   In this chapter, I aim to provide a background on the impacts of climate change on marine resources. First, I explain the impact of change in physical and biogeochemical conditions in the ocean on marine species. Second, I review the impact of climate change on fisheries and the subsequent economic impacts in the societies. Finally, I provide a summary of the objectives and issues addressed in each chapter.  1.1 Climate change impacts on marine resources  Climate change not only affects the physiology, phenology, distribution ranges and ecology of marine species (Hughes 2000; Parmesan 2006), but may also lead to population collapse, and even the extinction of certain species (Pounds 2001; Thomas et al. 2004; Drinkwater 2005; Carpenter et al. 2008). Global mean air temperature is predicted to increase at a rate of around 0.2 oC per decade in this century (IPCC 2007a). In the ocean, together with the change in sea surface temperature, which is projected by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report to increase by about 1 to 3 oC by 2100 (Nicholls et al. 2007), several other biophysical phenomena are expected to change under anthropogenic climate change. For example, sea-level is predicted to experience an accelerated rise by up to 0.6 m by 2100, tropical and extra-tropical cyclones will intensify, waves and storm surges will be more extreme, and precipitation will fluctuate more (Nicholls et al. 2007). Also, elevating human-induced greenhouse gas (GHG) emissions have been affecting other environmental conditions in the marine ecosystem such as acidity, ocean currents, salinity, expansion of oxygen minimum zones, extent of sea ice coverage and  3  productivity, and these changes are likely to continue under various scenarios of global climate change (Hobday et al. 2006; Hoegh-Guldberg et al. 2007). Other than change in water temperature, change in ocean acidity has recently gained more attention in the scientific world. The pH of surface ocean waters has already dropped by an average of about 0.1 units from preindustrial levels, particularly in high-latitude regions (Feely et al. 2009). The ocean pH level may decrease by an additional 0.3 to 0.4 units under the scenarios where atmospheric CO2 concentration increases to 800 ppm by the end of the century (Orr et al. 2005).   These changes may have impacts on marine species, which may respond to these changes through various means. The predicted impacts may also have large implications on the people, communities and countries that depend on marine species for food and income (Roessig 2004; Allison et al. 2009; Cooley and Doney 2009). To make sensible decisions on how human societies are going to prevent, mitigate or adapt to climate change, we need to understand the costs and benefits of change in marine resources to people under different climate change scenarios.   1.1.1 Shift in distribution range Climate change is likely to shape the pattern of biodiversity in the ocean. In response to the forecasted warming up of sea surface temperature, marine ectotherms either shift their latitudinal range (Perry et al. 2005; Hiddink and Hofstede 2008; Mueter and Litzow 2008; Cheung et al. 2009) or their depth range (Perry et al. 2005; Dulvy et al. 2008). Historically, many marine exploited species have already responded to the change in climate by  4  expanding or contracting their distribution ranges. For example, Atlantic herring (Clupea harengus) and cod (Gadus morhua) shifted their distribution ranges northwards in the North Atlantic when the water from Greenland to Norway warmed up significantly from 1920 to 1940 (Rose 2005). Blanchard et al. (2005) also showed a reduction in the spatial extent of optimal habitat of Atlantic cod with a gradual increase in temperature from 1977 to 2002 in the North Sea. In addition, surveys also showed that several fish and squid species, including Atlantic mackerel (Scomber scombrus), have extended their ranges poleward as the temperature increases in the northwest Atlantic since the 1960s (Murawski 1993). Although the rate of ocean warming varies in different locations, the responses of marine species have already been observed in various ocean basins with the magnitude of changes more prominent in the poleward regions. In the northern hemisphere, about two-third of the species that were investigated in a study by Perry et al. (2005) responded to the warming temperature by shifting their mean latitude or depth range. Similar patterns are also found in semi-enclosed basins, such as for sardinella (Sardinella aurita) in the western Mediterranean (Sabat?s et al. 2006), because of increase in sea water temperature.   Atlantic cod in the Barents Sea is expected to shift northward along the coasts of the northeastern United States, Greenland and Labrador and may even invade the continental shelves of the Arctic Ocean (Drinkwater 2005; Fogarty et al. 2007). Forecasted increase in temperature of the Barents Sea may also lead to an increase in the north-eastward distribution of capelin and cod (Stenevik and Sundby 2007). These responses may result in local extinction or invasion of species. Cheung et al. (2009) shows that higher invasion rates would occur in higher latitude regions, with numerous local extinctions at sub-polar, the  5  tropics and the semi-enclosed seas (Drinkwater 2005). Furthermore, since the rates of shift of different species are not synchronized and vary in extent, this may result in changes in spatial overlap and interactions of species, and may also complicate the decoupling effect of climate change on phenology (Perry et al. 2005).    1.1.2 Other impacts on marine ecosystem Warmer temperature is likely to stunt fish growth. Previous studies showed that fishes reach smaller maximum body sizes and smaller sizes at maturity when ocean temperature increases (Daufresne et al. 2009; Baudron et al. 2011; Sheridan and Bickford 2011). The level of oxygen in the ocean also affects the body growth of fish. A recent study suggests that reduced oxygen can cause the maximum body size of fish to shrink by 14?24% globally from 2000 to 2050 under a high-emission scenario when both changes in distribution and physiology are considered (Cheung et al. 2013). In the meantime, some studies reveal that species may response to the more acidic water in different ways; however, the mechanisms of biological effects of ocean acidification and their long-term impacts are not fully understood (Dupont and Thorndyke 2009; Melzner et al. 2009; Kroeker et al. 2010). The decrease in the saturation level of calcium carbonate minerals may induce an adverse impact on calcium carbonate-secreting organisms. Most of these studies focused on a few invertebrates, while the effect on fish is not well understood.      6  1.2 Impact on marine fisheries under climate change  Climate change may impact the distribution and productivity of marine fisheries as the warming-related changes in ocean condition affect primary productivity, species distribution, population dynamic of species, community and food web structure. These responses may cause some species to become more vulnerable to fishing. Impacts of climate change on the distribution of fisheries catches and benefits to different countries, regions and groups, will also have a significant impact on the economics and management of shared stocks (Miller and Munro 2004; Miller et al. 2013). Although it is not straightforward to detect climate change-related effects on fisheries, some attempts have been made in various regions on a few species to identify such impacts (e.g. Roessig et al. 2004; Drinkwater 2005; Brander 2007; Mueter and Litzow 2008). A recent study showed that the global mean temperature of the catch (MTC), which is the average inferred temperature preference of the exploited species weighted by their annual catch, increased at a rate of 0.19?C per decade in the past four decades (Cheung et al. 2013). Changes in MTC in most of the marine coastal and shelf areas are significantly and positively related to the change in sea surface temperature (Cheung et al. 2013). Thus, a signature of climate change effects on fisheries can be observed in the last four decades.   In the polar region, the vulnerability of Atlantic cod (Gadus morhua) to fishing may be increased by the shift in the spatial extent of its optimal habitat and reduction in the recruitment rates caused by gradual warming of the North Sea and the North Atlantic (Blanchard et al. 2005, Rose 2005). Steady warming trends in the North Sea in the next 50  7  years may lead to continuous declines in the recruitment success of cod in the North Sea, and hence further increase the vulnerability of this species (Clark et al. 2003). In addition, many other species also showed similar ecological responses to the change in climate, such as Atlantic salmon (Salmo salar), capelin, herring (Clupea harengus), sardines (Sardina Pilchardus) and haddock (Alheit and Hagen 1997; Ottersen et al. 2001; Rose 2005), and these responses may lead to increases in the variability of captured fisheries. The spatial redistribution of commercial fish stocks in the Barents Sea under projected global warming may have different impacts on different fishing countries targeting these stocks (Stenevik and Sundby 2007; Miller et al. 2013).  In the temperate region, where many profitable fisheries are located, the impact of climate change on fisheries may have huge implications for the economics of the fisheries. For example, the large increase in the catch of horse mackerel (Trachurus trachurus) in the northern North Sea reflected the northern shift of the stock and the distribution shift coincided with the increase in sea surface temperature (Reid et al. 2001). The long term change in the catches of many pelagic species, including, anchovies and sardines in the Adriatic Sea, was also coincident with climate oscillations (Grbec et al. 2002).   In the tropical regions, fisheries in coral reef areas are particularly crucial as a source of animal protein to impoverished societies (Teh et al. 2009). One-quarter of the fish catch in developing countries are provided by reef-associated fisheries (Bryant et al. 1998; Hoegh-Guldberg 1999). Therefore, climate change may further exacerbate the already overstressed resources of reef-associated fisheries in the tropics. Hence, in this region, studies are mainly  8  focused on the impact of climate change-related effects on coral reef ecosystems and their fish communities. More frequent bleaching events will inevitably put more stress on invertebrates and fishes, which forage on coral and/or use it for refuge. Atlantic reef fish communities changed as species shifted into new areas and changed in abundance when the bottom water temperature was getting warmer (Parker and Dixon 1998). In the non-reef tropical regions, e.g., along the coasts of West Africa, climate change may also exacerbate and interact with the existing stresses facing by the fisheries in these countries, such as overfishing, habitat degradation, discard of by-catch, illegal, unreported and unregulated fishing, etc. (Noone et al., 2013). Along the coast of Ghana, the landings of pelagic fish fluctuate with the inter-annual variability of the Guinea Current marine ecosystem (Perry and Sumaila 2007). Although artisanal fishers have developed strategies to adapt to the seasonal and inter-annual environmental variability over the past decades, impact of climate change on marine resources will induce uncertainty to the fishing communities. Long-term decline in zooplankton biomass in the upwelling region of the Gulf of Guinea was affected by global warming and the predation by Sardinella (Wiafe et al. 2008). This change has direct influence on the abundance of pelagic fish and their landings in this region.   Cheung et al. (2010) conducted the first attempt to project climate change impacts on marine fisheries on a global scale, and found that that climate change may lead to a large redistribution of global catch potential, with an increase in catch in high latitude regions but decline in the tropics. This implies that tropical countries, which are strongly dependent on fisheries resources for food and income, may suffer a negative impact on their food security.    9  1.3 Socio-economic impacts under climate change  Research on the impacts of climate change on fisheries has been moving from biological, ecological and physical studies to those on economics, food security and vulnerability assessment. Various approaches have been adopted to predict the economic impact of climate change on fisheries, such as using stochastic simulation and bioeconomic models. For non-marketable aquatic species, a few studies attempt to estimate the potential economic impact of climate change by using double-bound contingent valuation methods (CVMs) (Tseng and Chen 2008) and hedonic travel cost methods (Pendleton and Mendelsohn 1998). However, research on warming-related impacts on fisheries from a socioeconomic context is still inadequate, especially regarding the consequence of re-distribution of fish stocks on small-scale fishers in developing countries, which are more vulnerable to climate change than small-scale fishers in other regions (McClanahan et al. 2008a; Allison et al. 2009). In addition, the existing literature on the impact of climate change on fisheries economics has been focused on regional and local studies (e.g., Aaheim and Sygna 2000; Hannesson et al. 2006; Arnason 2007a, Eide 2007), and there is still no in-depth and quantitative study in this area at the global scale. Climate change not only affects marine capture fisheries but also affects inland fisheries by altering water temperature, dissolved oxygen levels, toxicity of pollutants, and the rate of water discharge of rivers (Ficke et al. 2007). Although the effect of climate change on inland fisheries is beyond the scope of this study, these changes in freshwater ecosystems would also result in an increase in variability for both estuary and marine catches. For example, it has been shown that there is correlation between the  10  commercial catch of school prawns (Metapenaeus macleayi) with the rate of river discharge in northern New South Wales, Australia (Ives et al. 2009).   1.3.1 Landed values and ex-vessel prices Landed values from the world?s fisheries may be affected by both changes in the volume and composition of landings under climate change, and the fluctuation of the ex-vessel price of targeted species. The annual total global landed value is estimated to be US$ 80 billion in 2000 dollars (Sumaila et al. 2007a). Since global fish catch patterns are projected to shift under climate change scenarios, the landed value obtained from each EEZ and country may also change from current levels. Countries at higher latitudes, such as Norway, Greenland, the United States (Alaska) and Russia, may economically benefit from climate change in terms of increase in catch potential. Arnason (2007a) predicted that global warming may have positive effect on the fisheries in Iceland and Greenland and thus contribute positively to their gross domestic product (GDP). In contrast, countries such as Indonesia, the United States (excluding, Alaska and Hawaii), Chile and China may face negative economic impacts, as the maximum catch potential in their EEZs is predicted to decline under climate change (Cheung et al. 2010). As countries at different geographical locations are affected by different degrees of climate change-related impacts on their marine resources, the degree of economic impact on their fisheries may also vary, with developing countries at lower latitudes being affected the most (Allison et al. 2009; Cheung et al.2010).  However, increase in catch does not necessarily assure positive change in economic benefits, because less valuable fish may be moving in to replace more valuables ones. The  11  composition of catch affects the landed values of fish. Projected shifts in the distribution of marine species will affect the species composition and relative abundance of species with different maximum sizes or trophic levels (Cheung et al. 2012). For example, both long-term intensive exploitation and climate variability caused the decline in the composition of the more valuable high trophic level species in the catch from the Celtic Sea. This can result in negative economic consequences even though there is an increase in the catch of relatively lower value, lower trophic-level species (Pinnegar et al. 2002). Also, Link and Tol (2009) show that the projected increase in capelin landings in the Barents Sea is not adequate to compensate for the decline in profitability caused by declines in the Barents Sea cod fishery under climate change. Hence, a higher projected fishing yield under climate change scenarios may not necessarily lead to an increase in the total landed values. It depends on the composition of exploited species and the quantity of different types of fish delivered to the market.  Instead of solely being determined by local markets, fish prices are also affected by the global supply of fish with increasing globalization (Asche et al. 1999; Hannesson 1999). Fish prices not only reflect the preference of consumers and consumer income but also fluctuate with the abundance and scarcity of targeted species. For example, everything else being equal, fish prices will increase with less supply (Murawski and Serchuk 1989; OECD 1997; Pinnegar et al. 2006). Thus, climate change-induced shifts in the fish price of a particular species in a particular EEZ may also influence the prices of this species and landed values in other regions. Although the historical price data can be obtained from Sumaila et al. (2007) and Swartz et al. (2012), future ex-vessel prices are difficult to predict.  12   1.3.2 Cost of fishing Changes in maximum catch potential of different fish species under climate change leads to changes in the composition of exploited species, the gear types employed and hence the fishing cost for each country. There is a wide range of differences in the magnitude of variable fishing cost associated with different gear types; for example, the average variable fishing cost of dredge in Italy is up to US$ 2,250 per tonne of catch (European Commission 2006) whereas that of bagnets in India is only about US$ 120 per tonne (Sehara and Karbharl 1987) in 2005 dollars. Since specific gear usually target certain species (McClanahan et al. 2008b), one of the strategies for fishers to adapt to the change in species abundance and/or composition under climate change is by switching their gear types (Grafton 2010). Another strategy adopted by some mobile fishers to maintain profitability under climate change is to move to other fishing grounds with higher abundance of targeted species. Thus, the distance from the fishing ports to the fishing grounds may increase, along with traveling cost. For example, the spatial distribution of purse-seine fleets targeting skipjack tuna in the central western Pacific shifts according to the influence of El Ni?o Southern Oscillation (ENSO) events (Lehodey et al. 1997). In a study devoted to understanding the potential responses of artisanal fishers to climate change, high proportions of respondents chose to adapt to the decline in marine resources under climate change scenarios in Tanzania either by changing their fishing gear types or fishing grounds (Cinner et al. 2010). Both responses may result in change in fishing cost and economic rents. However, not all fishers have the capacity and the willingness to switch their gear types or fishing grounds (Coulthard 2008; Cinner et al. 2009). Some fishers may even choose to reduce fishing effort or quit fishing, provided that  13  they have alternative livelihoods. However, fishers usually have limited options in most of fishing communities.   The impacts of climate change on fisheries economics is further complicated by the heavy dependence of modern fisheries on fossil fuels (Rawitscher and Mayer 1977; Watanabe and Okubo 1989). In 2000, global fisheries were estimated to have burned almost 50 billion L of fuel in fishing per year, an amount equivalent to about 1.2 % of global oil consumption (Tyedmers et al. 2005). Recent predictions on global oil production show a declining trend after 2010 (Heinberg 2003). Thus, fuel prices may continue to rise because of the limited supply and increasing demand for crude oil. High energy prices imply increases in the cost of fishing (Beare and McKenzie 2006; Arnason 2007b; Stouten et al. 2007). More flexible fishers may adapt to the change in fuel price by shifting their fishing behaviour or practices, including: changing fishing strategy or landing ports, replacing older inefficient engines, changing fishing methods, modifying gears (Le Floc?h 2007) and improvement in the maintenance of gears and engines (Rossiter 2006). However, these adaptation strategies may be difficult to adopt (in legal and administrative terms) as fishing activities are usually limited by current institutions and management measures, which do not incorporate climate change. All these adaptation strategies will add additional uncertainties to the change in fishing costs under climate change. Although the change in fuel price also affects the fishing cost, the impact of this factor on the fishing cost is not going to be addressed in this thesis.     14  1.3.3 Resource rent Resource rent is the return to the resource owner after deducting fishing costs and subsidies from total revenue. It is used as an indicator of fisheries performance (Clark 1990). World exports of marine fish and invertebrates and their products was as high as US$ 86 billion a year  in 2006 real dollars (FAO 2009), which is an important source of revenue for many developing countries. Due to the substantial amount of subsidies to the fishing sector, which are estimated to be US$ 25 ? 29 billion a year in 2003 real dollars (Sumaila et al. 2010), the resource rent, that is, the return to the resource owner, from the world?s fisheries is currently negative (i.e., US$ 13 billion per year) (World Bank and FAO 2008; Sumaila et al. 2012). However, the resource rent varies among countries. Some fisheries are well-managed and they are having positive resource rent, whereas fisheries in some other countries are poorly managed and are heavily subsidized and hence their resource rents are negative. The predicted change in catch potential may result in further declines in global resource rent. In fact, the national and regional economic impact may be either positive or negative as different countries may respond differently to climate change. Also, an increase in the total revenue of a country or EEZ under climate change does not guarantee an increase in economic rent as the cost of fishing may also be impacted by climate change.   1.3.4 Income to fishers The whole fishing industry, including aquaculture, provides more than 170 million jobs globally (FAO 2009; Teh et al. 2012). The negative impact on the profitability of fishing enterprises will directly influence the income of fishers. Furthermore, the fisheries sector, particularly the artisanal sector, is a major source of employment and income for unskilled  15  young men and women of coastal communities in developing countries through direct and ancillary activities (Teh et al. 2012). Thus, the economic and social impacts on artisanal fishers is more detrimental in developing countries, particularly, those in the tropics (Allison et al. 2009).   1.3.5 Economic impact on other sectors The global fisheries sector generates about US$ 80-85 billion of annual gross revenues (Sumaila et al. 2007a; World Bank and FAO 2008; Swartz et al. 2013). Consequently, the marine fisheries sector is estimated to contribute about US $225 ? 240 billion to the world economy through direct, indirect, and induced impacts annually (Dyck and Sumaila 2010). The added value or impact through the fish value chain is the indirect economic effects of fisheries due to their impact on activities such as boat building/maintenance, equipment supply, international transport and the restaurant sector (Pontecorvo et al. 1980). As such, climate change may have great implication on the whole economy through fisheries. Cooley and Doney (2009) estimated that there may be a substantial decline in revenues, jobs and indirect impacts on the whole economy in the United States as commercial fisheries are impacted by climate change-related ocean acidifications.   1.3.6 Food security impacts Food security depends on the supply of food and also the economic ability to purchase sufficient quantities of food of appropriate quality. The importance of fish to the dietary needs of people is more significant in terms of animal proteins than the energy it provides (FAO 2009). Due to the decline in the performance of agriculture and other natural resource  16  sectors, the main source of cheap protein for many developing countries is coastal and offshore fisheries, particularly in areas with high population growth (Swartz and Pauly 2008). Globally, fish supplies more than 20% of the per capita animal protein intake, and at least 50% of total animal protein and minerals in coastal Low-Income-Food-Deficient countries. (FAO 2012). The contribution of fish protein to total world animal protein supplies rose from 13.7 percent in 1961 to 15.3 percent in 2005, with a peak of 16.0 percent in 1996 (FAO 2009). Fish not only acts as a major source of protein, but also acts as an important source of essential micronutrients, minerals and fatty acids that are not found in other staples such as rice, maize and cassava (Valdimarsson and James 2001; FAO 2009). Global per capita fish consumption has increased considerably, from 9.0 kg in 1961 to 16.7 kg in 2005 (FAO 2007a; FAO 2009). In the same period of time, per capita consumption of all other animal-source foods also increase by more or less the same margin when per capita incomes grows in some countries (FAO, WFP and IFAD 2012; WHO 2013). The overexploitation of fish stocks poses a threat to food security in the world (Pauly et al. 2005). Increasing aquaculture production is believed by many people to be a way for relieving the food security issue; however, it is not an assured way as some farmed species require large inputs of wild fish for feed (Naylor et al. 2000). About 23 percent of total world fish production was assigned to fishmeal and fish oil production in 2006 and the majority of these products were used by the aquaculture sector (FAO 2009). Reduction in the catches projected under climate change scenarios may further exacerbate the food security problem in a direct way through depleting the nutritional quality, particularly in small island states and many developing tropical countries (Cheung et al. 2010).   17  Many maritime countries also rely on fish and fisheries as source of income. Most people can get the income to purchase low-value high-calorie staples through selling captured fish. As a result, climate change-related effects on marine resources may intensify the food security issue indirectly by lowering the income of fishers and hence their ability to meet their nutritional requirement.   1.4 Research gap  Attempts to assess the potential global economic impact of climate change on society and economics have been made (Stern 2007). Since climate change is a global issue, development of mitigation and adaptation policies requires understanding its impacts at a comparable scale. However, existing research in this area, in the context of fisheries, has centered on regional and local studies (e.g., Aaheim and Syna 2000; Hannesson et al. 2006; Arnason 2007a; Allison et al. 2009). There is still no in-depth quantitative study on the impact of climate change on fisheries economics on the global scale. Thus, the goal of this research is to fill this gap.   1.5 Research questions  The main hypothesis of my study is that global climate change and ocean acidification will have an overall adverse effect on both cost and/or benefit of the global fisheries sector. The overall objective of my research is to assess the potential direct impacts of global climate  18  change on the economics of global marine fisheries in terms of landed values, fishing costs and economic rent by addressing the following research questions:  1. What is the global cost of fishing and its pattern under the current climate regime? 2. What is the extent of economic impact on global fisheries under different climate change scenarios? Do these impacts affect developing countries more than developed ones?  3. What are the socio-economic implications of the impacts of projected climate change on marine resources of West Africa, one of the regions of the world identified as having the highest vulnerability to climate change, with emphasis on food security and local economy impacts? 4. What are the influences of climate change and ocean acidification on the marine capture fisheries of the Arctic, and its subsequent economic impacts on this region? The Arctic is another most vulnerable region to climate change as it has experienced the most rapid rate of warming in recent years. Other studies also showed that projected climate change leads to pH in the Arctic Ocean to decrease in an accelerated rate because of freshening and increase in carbon uptake as a result of sea ice retreat (Steinacher et al. 2009).   1.6 Theory and analytical framework  1.6.1 Catches To assess the impact of climate change on global fisheries economics, I must have global scale information of warming-related changes in biological responses of commercial marine  19  species, the potential catch and all the economic parameters such as cost of fishing, ex-vessel prices, incomes and wages earned by fishers, profits made by fishing enterprises and economic rent. In this study, I used the percentage change in maximum catch potential of different species projected from the Dynamic Bioclimate Envelop Model (DBEM) as a proxy for determining the potential change in landings of different species in different countries (Cheung et al. 2010). As fish productivity is a function of abundance, species? geographic ranges, life histories, and ecology, the future global fisheries production was projected based on the empirical model developed by Cheung et al. (2008a).   This model allows change in fisheries productivity to be predicted by the change in primary productivity (Sarmiento et al. 2004) from the exploited range and species distribution (Cheung et al. 2009). I assumed that the current landings are the result of a strategy that maximizes resource use.   1.6.2 Climate change and socio-economic scenarios The Intergovernmental Panel on Climate Change (IPCC) developed a set of new long term greenhouse gas GHG emission scenarios in the IPCCs Special Report on Emission Scenarios (SRES) (IPCC 2000). The scenarios are based on an extensive assessment of a wide range of drivers, including, demographic, economic, and technological drivers of GHG and sulfur emissions in the scenario literature. Each scenario represents a ?storyline? describing the way the world?s population, economies, political structure and lifestyles may develop over the next few decades. There are altogether four scenario families (i.e., A1, A2, B1 and B2) and they eventually led to the buildup of six scenario groups (i.e., A1F1, A1T, A1B, A2, B1 and  20  B2). There are three groups in the A1 family representing alternative developments of energy technologies: A1F1 (fossil fuel intensive), A1B (balanced) and A1T (predominately non-fossil fuel). The four SRES families of scenarios represent different world futures in two dimensions, i.e., economic versus environmental concerns and global versus regional development (Figure 1.1).   Figure 1.1  SRES storyline           Recently, climate change researchers of different disciplines have created a new parallel process for developing a new set of scenarios called Representative Concentration Pathways (RCPs) in IPCC?s fifth Assessment Report (AR5) (Moss et al. 2010). The parallel approach allows better integration, consistency, and consideration of feedbacks and more time to assess and impacts than the previous sequential approach. There are four emissions trajectories that are based on how much heating they would produce at the end of the century          Global Local Economic Environmental Development Governance A1  A2 B1 B2 (Low population growth) (Low population growth) (Rapid population growth) (Medium population growth)  21  ? 8.5, 6, 4.5 and 2.6 watts per square metre (W?m?2). There are three main reasons for developing a new set of scenarios (Moss et al. 2010). First, the SRES scenarios only focused on no-climate policy worlds only. In this new set of scenarios, the lowest scenario is consistent with the targets of limiting the increase in global mean temperature to less than 2?C. Second, with the advancement in scientific community, there is a need for more detailed scenario information than was provided by SRES; for example, gridded information on land use. Finally, there is a need for closer cross-disciplinary collaboration in climate scenario formulation and use. This new process shortens development time and facilitates additional scientific advances. However, I use the SRE scenarios developed in the IPCC Fourth Assessment Report as the RCPs scenarios were still at their development stage at the time my study was carried out. Both SRES A1B and A2 scenarios are consistent with the RCP 8.5 scenario for projections to 2050.   In my study, I used climate change scenarios SRES A2 and A1B to represent high-range greenhouse gas (GHG) emissions with climate projections generated by the Geophysical Fluid Dynamics Laboratory of the United States National Oceanic and Atmospheric Administration (NOAA) (Delworth et al. 2006; Dunne et al. 2010). The high-range climate change scenario is represented by SRES A1B (?business-as-usual"(atmospheric carbon dioxide (CO2) stabilization at 850 ppm by year 2100) in the West African study (Chapter 4). SRES A1B scenario was chosen as it represents a mid-range scenario. However, recent studies showed that the increase of fossil fuel CO2 emissions have accelerated at a greater rate than previous decades (Canadell et al. 2007; Raupach et al. 2007). Given this, I replaced the SRES A1B scenario with a more extreme SRES A2  22  (atmospheric CO2 stabilization at 1,250 ppm by year 2100) in the global (Chapter 3) and the Arctic studies (Chapter5). Indeed, there is little divergence in the projected temperature between these two scenarios until 2050. The A1B scenario describes a world with very rapid economic growth with increasing globalization, low population growth, rapid technological changes and moderate use of resources using balanced application of technologies. The A2 scenario describes a very heterogeneous world with continuously increasing population. Under this scenario, per capita economic growth and technological change are more fragmented and slower than other storylines. In the West African study (Chapter 4), the constant 2000 level scenario is also included. Under this scenario, the greenhouse gas concentration was stabilized at the end of 20th century levels, i.e. 360 ppm and was run to the year 2100.   1.6.3 Ex-vessel price and land values Fish market prices and ex-vessel prices may be affected by both fish demand and fish supply, which may depend on the intensity of climate change and also the effectiveness of fisheries management. Fish price may increase when fish supply declines and fish demand increases. Other than being determined by price elasticity of supply and demand, ex-vessel price is also determined by cross price elasticity and substitution effects. Cross price elasticity measured the rate of response of quantity demanded of one fish commodity due to the price change of another fish commodity. Substitution effects measure consumer?s preferences for relatively less costly alternatives. Although price elasticity, cross price effects and substitution effects of some of the fish commodities can be measured, they are not readily available. Also, estimating the change in ex-vessel prices was beyond the scope of this study. As such, the  23  unit ex-vessel price was assumed to be constant throughout the study period in this thesis. Assuming the ex-vessel price to be constant is one of the shortfalls of this research and it is also an area of our future research.   1.6.4 Fishing Cost As aforementioned, change in fishing cost is an important piece of information for predicting both direct and indirect economic impacts of climate change on fisheries. It is thus crucial to determine fishing cost under the current situation. However, most of these data are neither well documented nor readily available. One of the objectives of this thesis is to develop a global fishing cost database of commercial fishing for understanding the current extent and pattern of fishing cost. Observed cost data were collected from different sources and they were used to fill in the data gaps by using a progressive refinement process (see below). Two types of costs, variable (operating) and fixed costs, were covered in the database. The variable cost includes fuel cost, salaries for crew members on board, repair and maintenance cost and costs dependent on vessel activities in general, whereas fixed cost includes interest (i.e., opportunity cost of capital) and depreciation costs. This global database is designed in a way that fishing cost data can be linked to other fisheries-related spatial data such as those for catch and ex-vessel prices.   Fishers may redistribute their fishing effort across different fisheries, targeted species and fishing locations in order to maximize their economic rents (Gordon 1954). Therefore, when the distribution and catch potential of various commercially exploited species is changed under different climate change scenarios, fishers will potentially shift to other  24  species and/or relocate to other fishing grounds. Hence, the total cost of fishing for each country would change to various extents and degrees. Meanwhile, the potentially increasing trend of energy prices in the future will likely put more pressures on reducing fuel cost incurred by fishing vessels (Beare and McKenzie 2006; Arnason 2007b; Stouten et al. 2007); however, change in fuel price is not addressed in this study.   As the distribution range of the exploited species and ultimately their catch potential may change under climate change, the targeted species composition of each EEZ and each country may also shift. Given the increased awareness of climate change in this decade, I assume better communication of potential impacts and adaptation strategies to stakeholders. So, in this research, I assume fishers will have the capacity to change to different gear types in order to adapt to the change in the species composition in each country?s EEZ.   1.6.5 Economic rent The change in fishing cost and landed value under different climate change and socio-economic scenarios is computed. By comparing the change in the net profit of each country under different climate change scenarios, I can identify the ?losers? and ?winners? in terms of economic benefit in the fisheries sector. However, the economic rent is defined as the return to the resource owner, and it will be computed by taking off the total non-fuel subsidies, which are the transfer payments from the rest of the economy to the fishing industry, from the net profit. Fuel subsidies are not included in the model because they may have already been applied to the energy price in order to reduce the cost for the consumers.     25  1.7 Thesis outline  This thesis consists of six chapters, including four main chapters, an introductory and a conclusion chapter. It is structured in the manuscript style format.   Chapter 1 (Introduction) The objective of this chapter is to highlight the importance of predicting the impact of climate change on the economics of fisheries on a global scale and review the current literature in this area.   Chapter 2 (Construction and application of fishing cost database) My goal in this chapter is to create a global database on fishing cost, which provides fundamental information for scientists, researchers and fisheries managers to assess the economic status of fisheries at local, regional and global scales. The cost of fishing database was created in three major steps. Firstly, I categorized the cost of fishing and designed the structure of the database. Two types of cost, variable (operating) and fixed costs are distinguished. Each record in the database represented a country and gear type combination. The gear types, which I included in the database, were based on the gear categorization system of the Sea Around Us Project (Watson et al. 2004, www.seaaroundus.org)    Secondly, I collected fishing cost data from secondary sources in major fishing nations in the six FAO regions of the world. In order to include as many values of observed cost as possible, I sought to access all available sources, irrespective of publication year, thus  26  extending my efforts in collecting cost data from 1950 to the most recent year for which data was available. The data were then converted to 2005 real values using the consumer price index (CPI) for each country obtained from the World Bank (2007). To enable comparison of fishing cost among different regions and countries, I converted all the fishing cost from local currencies to US dollars by using currency exchange rates provided by the World Bank (2007). To allow comparisons of fishing costs among regions and countries, I standardized the original cost to annual cost per tonne of catch (US$ per tonne).   Finally, I adopted a process of progressive refinement (Watson et al. 2006; Tyedmers et al. 2005; Sumaila et al. 2007) to estimate the cost of all gear types in each fishing country from the observed cost data I collected. Here, I ensured that all gear types in each maritime country of the world were assigned a cost, either the observed value where available, or an appropriate average cost. After interpolating the data of the whole database, fishing costs were compared across countries, FAO regions and gear types.  The database covers variable and fixed cost of 144 maritime countries, representing approximately 98% of global landings in 2005. The global average variable cost per tonne of catch and the total annual global fishing cost were also estimated.   Chapter 3 (Change in global fisheries economics with climate change)  In this chapter, I assessed the impacts of climate change on the economics of fisheries of all major Exclusive Economic Zone (EEZ) regions in the world in terms of landed values, profits made by fishing enterprises, fishing costs and resource rent.  27    Based on simulations from ocean-atmosphere coupled climate models, I estimated the landings in the 2050s under various climate change scenarios. The current ex-vessel price per tonne of catch of each species caught by each fishing country was obtained from the ex-vessel price database of the Fisheries Economics Research Unit (FERU) and Sea Around Us Project. The future landed values in each EEZ were projected by linking the unit ex-vessel price data to the projected catch.    With the projected future total fishing cost and landed values, the resource rent obtained by fishing in each EEZ can be calculated. Change in species composition and gear types in each EEZ under climate change was also analyzed. In this study, the sensitivity of the assessment was explored and the uncertainty of the climate model was addressed by using multi-model ensembles.    Chapter 4 (Climate change impacts, marine resources, food security and local economy vulnerability of maritime West African countries) Here, I set out to analyze the socio-economic implications of the impacts of projected climate change on marine resources of West Africa, with an emphasis on food security and local economies.   West African (WA) countries were chosen as my case study because Africa is one of the continents with the highest vulnerability to climate change (Boko et al. 2007; Allison et al. 2009) and West Africa, in particular, is characterized by some of the most variable  28  climates in the world (Brown and Crawford 2008). The situation is further exacerbated by the interaction of multiple stresses such as existing economic, population and environmental stresses, and low adaptive capacity. I assessed the implication of climate change for food security through its impacts on fisheries in the region. The countries that will face higher vulnerability to food security due to climate change effects on marine resources were identified. Then, I assessed how climate change may affect employment in the fisheries sector and its impact on local economies by using an indicator-based approach. With this approach, countries that may be more affected by climate change because of higher exposure, higher dependence on fisheries and less capacity to adapt to the change were identified.  Implications for food security Food security is defined as the physical, social and economic access to sufficient, safe and nutritious food to meet the dietary needs and food preferences for an active and healthy life (World Food Summit 1996). This definition is made up of four key dimensions: availability, access, stability and utilization (FAO 2006a). In this chapter, I assessed food availability through changes in marine fish catch and also the per capita fish supply/consumption under climate change. The risk of undernourishment and protein deficiencies under climate change was also studied.    I projected the future change in distribution of species along Western African maritime countries were simulated by using a dynamic bioclimate envelope model (Cheung et al. 2008b; 2009), which linked species? environmental preference to population dynamic.  29  Then, I estimated species? maximum catch potential (MSY) in the EEZs of WA countries from Equation (9) in Appendix B (Cheung et al. 2010) by using the projected primary production from Sarmiento et al. (2004). The change in maximum catch potential from 2000 to 2050 was projected under two climate change scenarios (i.e., low range greenhouse gas (GHG) emission (Stable 2000) and high range GHG emission (SRES A1B) scenarios). Then, I estimated the change in the landings of each exploited marine species captured in WA EEZs by using the ratio of projected change in the maximum catch potential under climate change scenarios in each of the 14 West African EEZ.    The projected population in WA countries (United Nations 2009) can be used to forecast the need for fish for food security in 2050. In order to maintain nutrition at its current level, it is reasonable to forecast the demand for fish in 2050 based on current consumption per capita. The prevailing per capita fish supply, which was calculated from catch, import and export data from the Sea Around Us database (http://www.seaaroundus.org) for each WA country, is used to estimate the future fish demand by combining this figure with the forecasted increase in population in the region. The results of the forecasted fish demand are compared with the projected landings under various climate change and overfishing scenarios.    To estimate the loss in marine protein, I assumed marine fish has protein content of about 15 to 20% by weight (FAO 2005). Then, the potential protein losses due to overfishing and climate change are compared to the dietary protein consumption (g/person/day) in 2003 ? 2005, as reported by the FAO for each WA country.  30   Implications on local economies The impact of climate change on marine resources for local economies is assessed through changes in landed values and number of jobs associated with fisheries. The landed values of each WA country under two climate change scenarios by 2050 are estimated using ex-vessel prices and the projected fisheries landings. The real ex-vessel price (after adjusting for inflation) is assumed to be constant throughout the study period.   I estimated the number of people employed only in marine capture fisheries in the 2000s by combining the proportion of fishers involved in marine fisheries in the 1990s (FAO 1999) with the total number of fishers reported by the World Resources Institute (WRI) (www.wri.org). These employment data are converted to number of fisheries related jobs per unit tonne caught in the 2000s. I then estimated the number of job losses in marine fisheries by using the projected catch loss under different scenarios by the 2050s.  Chapter 5 (Impact of climate change and ocean acidification on marine capture fisheries in the Arctic) This chapter aims to develop improved quantitative scenarios of the impacts of climate change and ocean acidification on maximum fisheries catch potential and their economic implications for fisheries in the Arctic. Fisheries play an important social, cultural and economic role in the Arctic, which is the region with the most uncertain change in primary productivity and the most rapid decrease in pH saturation of carbonate minerals in the ocean (Steinacher et al. 2009). Future scenarios of oceanographic changes were developed under  31  the Special Report for Emission Scenarios (SRES) A2 scenario. Using the Dynamic Bioclimate Envelope Model (DBEM) and outputs from four different Earth System Models (ESM), I firstly projected the changes in distribution and relative biomass of exploited marine fishes and invertebrates by 2050 relative to 2000 (20-year average). Then, using these results, I applied an empirical equation to project future changes in maximum catch potential. Finally, I computed the economic effect of ocean acidification (OA) in terms of changes using a number of economic indicators of commercial fisheries of the Arctic, including: (i) landed value (or total revenue) of fish; (ii) fishers' incomes; (iii) economic and income impacts throughout the economy; (iv) fishing cost; and (v) resource rents based on the projected catch change. I compared the scenarios with and without consideration of OA to assess the potential impacts of OA on fisheries. I also performed a sensitivity analysis using the Monte Carlo method (Buckland 1984) to determine the level of uncertainty associated with the projected estimates of my analysis. Results of this study would be useful for understanding the potential impacts of climate change and OA on Arctic fisheries, and designing effective adaptation strategies and measures to mitigate such impacts.   Chapter 6 (General conclusion) The objective of this chapter is to pull all the findings from Chapter 2 to 5 together and discuss how the results can help policy-makers and stakeholders in the fisheries sector to adapt and mitigate the economic impacts of climate change on the fisheries sector.    My study provides important quantitative information on the impact of climate change on the economics of fisheries at global scale. The global fishing cost database is useful for providing data for future economic analysis on fisheries including further  32  researches on the impact of climate change. Results will also be useful for the policy-makers to identify the adaptation and management measures that are necessary to reduce the threats of climate change on fisheries.      33  Chapter 2: Construction and first applications of a global cost of fishing database   2.1 Synopsis  The development of a new global database of fishing cost is described, and an overview of fishing cost patterns at national, regional, and global scales is provided. This fishing cost database provides economic information required for assessing the economics of fisheries at various scales. It covers variable and fixed costs of maritime countries, representing ~98% of global landings in 2005. Linked to country and gear-type combinations, cost estimates can be mapped to a database of spatially allocated fisheries catches for future analysis in both spatial and temporal dimensions. The global average variable cost per tonne of catch in 2005 is estimated to range between US$ 639 and US$ 1,217, and the total cost per tonne from US$763 to US$ 1,477, with mean values of US$ 928 and US$ 1,120, respectively. The total global variable fishing cost is estimated to be in the range US$ 50 ? 96 billion per year, with a mean of US$ 73 billion per annum in 2005 dollar equivalents.   2.2 Introduction  Socio-economic indicators of fisheries such as fishing cost and gross revenue play an important role in economic analysis and ecosystem modelling, and so are useful information  34  for sustainable fisheries management, planning, and policy-making (Sainsbury and Sumaila 2001; Le Gallic 2002; Christensen et al. 2009). These indicators are used in monitoring and assessing the economic and social performance of fisheries and the impact of fisheries in a broader context. However, most of them are neither well documented nor readily available, which can lead to inaccurate estimates of management options.   At a global scale, researchers and intergovernmental agencies have recently put effort into collecting, compiling, analysing, and making available key economic data such as ex-vessel prices and subsidies (Sumaila and Pauly 2006; FAO 2009). However, the cost of fishing is still poorly documented and studied in most regions, particularly at a global scale. Fishing cost and cost structure vary depending on the type of fishery, and the gear and vessel employed. When fishing costs are known, various types of social and economic analysis on global fisheries are possible; notably, fishery managers can utilize the data to assess the current economic status of the sector. Also, socio-economic analyses that identify the most appropriate management measures by comparing the economic efficiency of fisheries under different options become feasible (Clark 1979). Additionally, cost data are important for evaluating trends in fleet effort and the distribution of fishing fleets around ports, and researchers can use fishing cost information to study the impact of climate change on the economics of fisheries and its ripple effects on society. Therefore, to understand the economic viability of the fisheries sector, it is crucial to have consistent and reliable information on the cost of fishing. For example, commercial fishers are likely only to fish when it is profitable to do so, so there will be no fishing or investment when stocks decline  35  below bionomic equilibrium, unless they benefit from government subsidies (Schrank 2003; OECD 2005; Sumaila and Pauly 2006).   Information on fishing cost in most countries and regions of the world is scarce, widely scattered, and incomplete. The major reason for this is the significant effort required to obtain the data in the first place, but also because private fishing enterprises are understandably reluctant to disclose information that may be exploited by their competitors (Obeng 2003). In addition, there is rarely a requirement for government agencies to record and disseminate this information systematically (Bonzon 2000; Whitmarsh et al. 2000; Gasalla et al. 2010). Moreover, the mutual trust between fishers and government agencies that would enable such information to be collected routinely is generally lacking.  Despite these challenges, at the regional scale, the 2010 Annual Economic Report on the European Fishing Fleet (European Commission, 2010) provides comprehensive cost and landing data for several different vessel types of 22 European countries. The geographic coverage of the report has increased in the past few years because of the introduction of the economic components of the European Union (EU) fisheries data collection framework (DCF) in 2008. Under this framework, there are legislative requirements for exhaustive cost-data collection on all EU Member States? fishing fleets. For countries in other regions, fishing cost data, if any, are usually collected by governments and non-governmental bodies, and are available through websites or Annual Reports, for example, Japan (Statistics Department, Ministry of Agriculture, Forestry and Fisheries, 2006) and the UK (Sea Fish  36  Industry Authority, SeaFish, accessed 2009; http://www.seafish.orgStatistics). In the US, the National Oceanic and Atmospheric Administration (NOAA) provides cost and earnings data for commercial fishing vessels in several regions, for example, the Northeast US (Gautam and Kitts 1996; NOAA 2009). However, for most countries, fishing costs are not collected or not made available to the public, and only scattered information on fishing cost for particular fisheries and/or countries can be found, mainly in the grey literature, for example, in technical papers issued by the FAO (see, for example, Lery et al. 1999). The World Bank and FAO (2008) attempted to estimate global fishing cost in the ?Sunken Billions? project, which sought to evaluate the loss of economic rent attributable to the mismanagement of world fisheries. They estimated the global total fishing operating cost (including fuel and labour) to be about $73.3 billion in 2004. However, this figure was based only on cost data of the European fleets and India?s fisheries, which jointly contributed just 9.3% of global marine fish landings in 2004 (World Bank and FAO 2008). The global database presented here was developed to improve upon this estimate.  First, I describe the procedure for creating the global cost of fishing database and its structure, and then discuss the preliminary results obtained with the help of this database, providing an overview of fishing cost patterns among countries and gear types.    37  2.3 Methods  2.3.1 Developing a global fishing cost database The database was created through three major steps: first, I categorized the different types of fishing costs and designed the structure of the database. Second, I collected cost data (?observed cost?) from different sources. Finally, I filled in gaps in the database by interpolation.  Data categorization and database design The economic costs of fishing (rather than accounting cost) are the focus of this study. Economic costs represent the value of inputs at the next alternative best use (Reid et al. 2003). My categorization of cost is based on the ?Economic performance of selected European fishing fleets ? Annual Report? (European Commission 2006) because it provides the most comprehensive definitions of different fishing cost categories found in the literature. Two types of cost, variable (operating) and fixed, were distinguished. Costs associated with operating fishing vessels were categorized as variable costs because they vary with the level of fishing activity. The major items under variable costs include fuel, salaries for crew, repair and maintenance costs of vessels and gear, and the cost of selling fish via auction, of fish handling and processing (e.g. the purchase of ice). Fixed costs do not vary with the level of fishing activity and are usually regarded as ?sunk?, and consist mainly of the amount invested in vessels (i.e. their capital value). However, investment in a vessel does not  38  necessarily represent a ?sunk? cost if the vessel can be used in other fisheries or economic pursuits. Interest and depreciation costs fall into this category. Under the DCF, depreciation and interest (whether actual or opportunity costs) are referred to as ?capital costs?. Interest cost reflects the opportunity cost of capital, and depreciation cost1 is the replacement cost for normal wear and tear of the fishing vessels. Cost components such as tax payment, and license fees are not included in this study as they can be considered to be transfers and not real economic costs. Other than the cost of fishing, I also compiled data (by country) on gear and vessel types used, vessel length category, the weight of fish caught, and the reported value of landings, if they were available.  Each record in the database represented each country and gear-type combination. Gear types included in the database were based on the gear categorization system of the Sea Around Us project (http://www.seaaroundus.org/).  Data collection  Following Sumaila et al. (2007), I focused on collecting secondary data for vessels operating in major fisheries and in major fishing nations in each of the six FAO regions of the world: (1) Africa; (2) Asia; (3) Europe; (4) North America; (5) Oceania; and (6) South and Central America, including the Caribbean. The first step was to identify sources of fishing cost data, mainly secondary sources (i.e. websites and grey literature, such as government, FAO, and                                                           1 In some cases, the depreciation cost may be heavily influenced by taxation  policy, so account s based values may not truly reflect economic depreciation.   39  consultant reports). Lastly, I contacted partners around the world to help us locate additional data sources.   Data-collection effort was targeted on major fishing countries in each of the six FAO regions (i.e. continents, with North and South America counted separately), with a combined total catch of >98% of global landings in 2005. Using this approach, the cost data for most fisheries in each region were captured, thereby ensuring a representative sample of the world?s marine fisheries.   In order to include as many values of observed cost as possible, access was sought to all available sources, irrespective of publication year, thereby extending my efforts in collecting cost data from 1950 to the most recent year for which data were available. The data were then converted to 2005 real values using the consumer price index (CPI) for each country, obtained from the World Bank (2007). To facilitate the comparison of fishing cost among different regions and countries, all fishing costs were converted from local currencies to US dollars (US$) using currency exchange rates in 2005 provided by the World Bank (2007), and the original cost was standardized to the annual average weighted cost per tonne of catch (US$ per tonne), i.e., total cost/total landings. Expressing fishing costs in $?t?1 facilitates the combination of this database with other data sources such as the Sea Around Us project catch database which is used in the analysis.    40  Progressive refinement process for filling data gaps To estimate the cost of all gear types in each fishing country from the observed cost I collected, I adopted a process of progressive refinement (Tyedmers et al. 2005; Watson et al. 2006; Sumaila et al. 2007), in which more-specific estimates for a given region and gear type are computed to replace the average cost values computed in the previous step. Therefore, all gear types in each maritime country of the world were assigned a cost, either the observed value where available, or an appropriate estimate.  Before interpolation, the observed data were examined for outliers, because estimated cost can be influenced heavily by extreme values. Rather than removing outliers, I applied the method known as Winsorization, an approach that replaces extreme data points with values from within a predetermined acceptable range before estimating the population mean (Gwet and Rivest 1992). In this study, all outliers were set to a specified percentile of the data. An 80% Winsorization was used, i.e. all values below the 10th percentile were set equal to the value corresponding to the 10th percentile, and correspondingly for values above the 90th percentile. The mean squared error of the Winsorized sample mean was smaller than that of the full sample mean (Searls 1966; Ernst 1980; Fuller 1991).  After filling the database with observed costs, global fishing costs were estimated using a three-step progressive refinement process (see Appendix A). First, the overall weighted-by-catch average cost based on all the observed data disregarding gear type and country was calculated. Second, I assumed that vessels using the same gear type had similar  41  fishing costs regardless of the FAO region in which they operated. Under this assumption, if a type of fishing gear in a specific FAO region did not have an observed fishing cost, then it was assigned the weighted average fishing cost of the same gear type for all other FAO regions combined. These estimates were refined further using the overall average fishing cost ratio for each cost component between FAO regions, and were then used to replace the more general estimate from the previous step. Finally, it was assumed that vessels with the same gear type incurred similar fishing costs within the same region, so a more specific estimate was assigned to a particular gear type in a country without observed cost. This last step was followed to obtain the weighted-by-catch average costs of each gear type from all observed cost values for the same gear type in each FAO region. This value was then assigned to the same gear type of all countries in the same FAO region where observed cost data were not available. At this point, every gear type in each fishing country was assigned a more-specific cost estimate if an observed cost was not available.   A scoring system was used to indicate the quality of the cost estimates. In such a system, a score was assigned to the cost in each of the above steps to indicate whether the cost data were observed or estimated. This scoring system reveals the quality of the cost data reported and areas where future data collection efforts need to be concentrated. Table 2.1 provides a summary of the quality of the cost data in the current version of the database, by showing the match between country and gear type at each level of uncertainty for each type of fishing cost. A score of 1 was assigned to records with data from secondary sources (i.e., an exact match of country and gear type). A score of 2 means that the data were assigned from records with the same gear type in the same region (i.e. matching region and gear type).  42  A score of 3 means that the cost data were the weighted average of the cost from all other FAO regions using the same gear type (i.e., matching gear type only). The records with the highest score (i.e., 4) are those with the lowest quality in the database. The cost data of these records are the weighted average from all countries and gear types. Some 98% of the global catch was assigned fishing cost data for each cost type based on a match either for country or gear type (or both). Among different cost types, fuel cost data had the best quality (i.e., ~10% of records had exact matches in country and gear type). Although only some 7?10% of the country and gear type combinations had observed values for at least one cost type in this version of the database (i.e. an exact match in country and gear type), they took 33?47% of the global catch.   2.3.2 Fishing costs analysis Although both variable and fixed costs were collected in the database, my focus was mainly on variable costs in the analyses that follow. Fixed costs were not given as much weight as variable costs in the analyses because the former are only incurred once by vessel owners and fishers, so can be considered as ?sunk? costs if they are non-malleable2. Therefore, once the investment on vessels and gears has been made, variable cost is the only cost that fishers and vessel owners need to consider when they decide whether or not to continue fishing (Clark, 2006).                                                            2 This assumption is held only for the short term analysis. Fixed cost is also considered as variable for the long term analysis as all costs would become variable in the long run.   43  Using interpolated cost data, fishing costs were compared across countries and FAO regions; the spatial distribution of variable fishing cost was plotted to assess the pattern of fishing costs on a global scale. I also compared the difference in fishing cost across different gear types, allowing assessment of the cost effectiveness of different gear types. Finally, a global weight-by-catch average variable fishing cost in 2005 was estimated using all cost data regardless of gear type, country, and year. Combining the average cost data with the total global landings, it was possible to compute the total variable fishing cost of 144 maritime countries.    44  Table 2.1 Quality score of fishing cost data.  Data quality score Description Matching spatial scale? Matching gear types? Number of records Percentage of records Catch     (million t) Percentage of global catch Fuel cost 1 Exact match of country and gear type    Country Yes 157 10.0 44.2 46.5 2 Match region and gear type Region Yes 804 51.4 44.3 46.6 3 Match gear type only and from all other regions  Yes 395 25.2 5.1 5.4 4 Average from all countries and gear types  No 209 13.4 1.4 1.4 Running cost 1 Exact match of country and gear type    Country Yes 140 9.0 40.0 42.1 2 Match region and gear type Region Yes 795 50.8 48.3 50.8 3 Match gear type only and from all other regions  Yes 421 26.9 5.4 5.6 4 Average from all countries and gear types  No 209 13.4 1.4 1.4 Repair cost 1 Exact match of country and gear type    Country Yes 148 9.5 42.2 44.4 2 Match region and gear type Region Yes 810 51.8 44.6 46.9 3 Match gear type only and from all other regions  Yes 398 25.4 6.9 7.2 4 Average from all countries and gear types  No 209 13.4 1.4 1.4 Labour cost 1 Exact match of country and gear type    Country Yes 155 9.9 42.2 44.4 2 Match region and gear type Region Yes 803 51.3 44.6 46.9 3 Match gear type only and from all other regions  Yes 398 25.4 6.9 7.2 4 Average from all countries and gear types  No 209 13.4 1.4 1.4 Depreciation cost 1 Exact match of country and gear type    Country Yes 138 8.8 41.4 43.6 2 Match region and gear type Region Yes 764 48.8 44.9 47.4 3 Match gear type only and from all other regions  Yes 436 27.9 7.2 7.6 4 Average from all countries and gear types  No 227 14.5 1.5 1.6 Interest 1 Exact match of country and gear type    Country Yes 108 6.9 31.2 32.8 2 Match region and gear type Region Yes 803 51.3 50.5 53.1 3 Match gear type only and from all other regions  Yes 548 35.0 11.8 12.4 4 Average from all countries and gear types  No 227 14.5 1.5 1.6   45  2.4 Results   2.4.1 Observed fishing cost in the database The number of observations collected from each country in the cost of fishing database is summarized in Table 2.2.  Table 2.2 Observed cost records for countries in FAO regions of the world, covering 46 of 144 maritime countries.  Region Country Number of records Region Country Number of records Europe Belgium 18 South, Central America and Caribbean Antigua and Barbuda 5  Denmark 52 Argentina 6  Estonia 23 Barbados 2  Finland 25 Brazil  9  France 41 Chile 9  Germany 22  Dominica 4  Greece 12  Peru 12  Iceland 24  Trinidad and Tobago 3  Ireland 8 Africa Ghana 13  Italy 41  Namibia 8  Latvia 12 Senegal 37  Lithuania 16 South Africa 4  Netherlands 25     Norway 37 Asia Bangladesh 1  Poland 12  China Main 4  Portugal 27 India 28  Spain 31  Indonesia 13  Sweden 51  Japan 18  UK 36  Korea Rep 18     Malaysia 10 North America Canada 5  Sri Lanka 5  USA 250  Taiwan 11     Thailand 9 Oceania Australia 7  Vietnam 1    46  Observed cost is available for 46 of 144 maritime countries, covering the years 1985?2009. These 46 countries in the database contributed nearly 80% of the global landings in 2005. Each FAO region of the world is represented in the observed data (Table 2.2), although most of the observed data records were derived from Europe. Countries with data are highlighted in Figure 2.1 and the percentage of catch contributed by those countries to regional and global landings is also provided. Those countries jointly contributed 76% of the global landed value in 2005 (Figure 2.2); among them, 32 were categorized as developed and 14 as developing countries, based on the UN?s Human Development Indicator (UNDP, 2008).  Figure 2.1 Countries with fishing cost data in the database, and the percentage of catch contributed by these countries to regional and global landings.            83% 7% 90% 17% 37% 2% 19% 0.3% 84% 41% 75% 12% Percentage of catch contributed by countries with cost data to regional landings. Percentage of catch contributed by countries with cost data to global landings.   47  Figure 2.2 Percentage of landed values contributed by the countries with cost data to regional and global landed values.              The database includes observed fishing cost for 14 of 18 gear types, which were identified according to the gear categorization system of the Sea Around Us project (Von Brandt, 1984; see Table 2.3). Bottom trawls, which contributed some 11% of the total catch in 2005, represented 40% of the observations, and seines and gillnets contributed some 30% of observed cost data and altogether contribute 50% of the world catch.      8% 70% 3% 15% 1% 86% 53% 77% 10% Percentage of landed values contributed by countries with cost data to regional landed values. Percentage of landed values contributed by countries with cost data to global landed values. 27% 1% 88%  48  Table 2.3 Observed cost records by gear type.  Gear type Number of records Percentage of global catch Seine 100 29 Gilllnet 193 21 Midwater trawl 85 16 Bottom trawl 400 11 Hook and line 107 8 Longline tuna 7 3 Shrimp trawl 7 3 Trap 48 2 Net 8 2 Dredge 37 2 Pole and line tuna 4 1 Purse-seine tuna 8 1 Hand 0 1 Spear 0 <0.5 Castnet 0 <0.5 Liftnet 1 <0.5 Trammelnet 1 <0.5 Bomb/chemical 0 <0.5  2.4.2 Fishing cost analysis using estimated data Developed vs. developing country fishing cost The combination of observed and interpolated cost data yields an estimate of global average variable cost per tonne of catch in 2005 of US$ 928, and US$ 192 for fixed cost. The weighted mean of all cost types in the database, lower and upper bounds, computed based on a 90% confidence interval, are listed in Table 2.4. Among the 62 developed countries, which have an HDI score ? 0.8 (UNDP, 2008), the weighted average real (2005) variable cost per tonne of catch was estimated to be $1,181, and the weighted average real (2005) fixed cost per tonne of catch was $198. In comparison, of 82 developing countries, with an HDI score  49  <0.8 (UNDP, 2008), the weighted average real (2005) variable cost per tonne of catch was estimated to be $724 and the weighted average real (2005) fixed cost per tonne of catch $187.  Table 2.4 Summary statistics of all cost types in the cost of fishing database based on all data (both observed and interpolated; US$ per tonne of catch in 2005 real value). Lower and upper bounds are calculated based on the 90% confidence interval.  Cost type Weighted mean Lower bound Upper bound Variable cost       Fuel  210 152 268    Runninga 194 130 258    Repair 131 92 171    Labour 393 265 521 Fixed cost       Depreciation 114 68 161    Interest 77 56 99 Total fishing cost 1 120 763 1 477  a The running cost includes costs dependent on vessel activities, excluding fuel, for example, the cost of selling fish via auction, cost of treatment of fish (e.g. ice), and food.   Fishing costs across FAO regions A comparison of the average variable cost per tonne of catch across all FAO regions (Figure 2.3) shows that the FAO region with the highest average cost per tonne of catch is Oceania (US$ 2,508 per tonne). Landings there were taken mainly with bottom trawls (some 30% of  50  the total catch in Oceania), which have a relatively high variable cost (US$ 1,969 per tonne) in the region. The fishing costs of other gear types such as trammelnets, tuna longlines, shrimp trawls, and hook and line were also extremely high there. These exceptionally high cost values (>US$ 4,000 per tonne) elevate the weighted average cost in Oceania. However, the cost of fishing in the waters of (mostly developing) Pacific Island states were estimated using the data in developed countries in the same region because observed costs from the small states were not available. Therefore, there is a real possibility that fishing costs in Oceania were overestimated. From Figure 2.3, it is clear that South and Central America, including the Caribbean, had the lowest average variable cost per tonne of catch (i.e. US$344 per tonne). This finding can be explained by the large proportion (95%) of the world?s small-scale fishers being based in developing countries (FAO 2010). The low variable fishing cost in that region may put further stress on fisheries by encouraging more people to enter fisheries and reinforcing the likelihood of fisheries acting as ?the occupation of last resort?, a known characteristic of small-scale fisheries.     51  Figure 2.3 Comparison of the average variable and total fishing cost per tonne of catch across FAO regions.    When comparing the cost structure across FAO region (Figure 2.4), labour costs constitute the largest proportion of operating costs in all regions except Africa and South, Central America and the Caribbean, where together labour and fuel costs constitute the largest shares of fishing cost. Labour costs relative to total variable cost is highest in North America (57% of total variable cost) compared with other regions (Figure 2.4), which is not surprising given the high gross national product (GNP) per capita of two (US and Canada) of the three countries in the region (World Bank, http://siteresources.worldbank.org/DATASTATISTICS/Resources/GNIPC.pdf; accessed 2009).   52  Figure 2.4 Percentage of different variable fishing cost types to the total variable fishing costs in the six FAO regions.    Comparing the cost of fishing across different fishing gears The weighted average variable cost of the 18 gear types included in the fishing costs database are summarized in Table 2.5. Variable costs of several gear types exceed the value of landings (Table 2.5), and subsidies, misreporting, or generally poor-quality data are likely the reason for this. It is also possible that the outcome of the analysis would differ if a time-series of cost data were to be used rather than the single-year analysis undertaken here. Among gear types with observed cost, tuna longliners have the highest estimate of average  53  variable cost (US$ 2,604 per tonne). The high cost of that gear type can be justified by the high ex-vessel price of tuna, the target species of the gear. For example, ex-vessel prices of bigeye tuna (Thunnus obesus) for Japanese longline fleets can be as high as US$ 10,636 per tonne in 2001 real dollars (Reid et al. 2003). Trammelnets and traps follow tuna longliners in terms of highest average variable cost, with estimates of US$ 2,164 and US$ 2,040 per tonne of catch, respectively. The fourth highest average variable fishing cost gear type, dredge, is estimated to be US$ 1,879 per tonne. This category includes dragged gear, sweepnets, runnernets, and hand-, boat-, and mechanical dredges. Labour costs contributed the largest proportion of variable cost (59% of the total variable cost) for dredge gears, because this fishing method usually requires more labour for at-sea processing of meat from shellfish such as scallops. For example, dredge vessels of the mid-Atlantic sea scallop (Placopecten magellanicus) fishery usually operate for 24 h per day and 10 ? 20 days per trip, so more crew are needed (Kirkley et al. 1995). The average variable costs of vessels using net, seine, and gillnet were the lowest of the other gear types. Vessels using static gears such as surrounding nets, beach-seines, and fixed and set gillnets generally consume less fuel, so have lower operating costs. Midwater trawls also have relatively low variable cost (US$ 885 per tonne of catch) and they are generally towed at different depths above the bottom in the water column and towed by one (otter trawls) or two boats (pair trawls). Vessels using otter trawls consume 50% less fuel than those using other bottom trawl methods, such as beam trawls (Polet et al. 2006).    54  Table 2.5 Summary statistics for variable total fishing costs and average ex-vessel price by gear type (US$ per tonne of catch).  Gear type Variable cost Total cost Ex-vessel priceb Longline tuna 2 604 2 903 3 042 Trammelnet 2 164 2 292 2 644 Trap 2 040 2 378 3 333 Dredge 1 879 2 099 878 Hook and line 1 636 1 886 1 304 Shrimp trawl 1 582 1 858 3 833 Pole and line tuna 1 255 1 429 3 602 Bottom trawl 1 204 1 428 1 218 Liftnet 1 087 1 261 773 Purse-seine tuna 977 1 119 3 002 Midwater trawl 885 1 062 583 Castneta 851 1 025 1 013 Speara 851 1 025 582 Handa 851 1 025 548 Bomb/chemicala 851 1 025 330 Gillnet 747 1 015 848 Seine 472 573 385 Net 180 241 2 655 a No raw data for these gear types. The weighted mean is based on the overall average of the raw data from other gear types.  b Average ex-vessel prices are extracted from the ex-vessel price database of the Sea Around Us project (Sumaila et al., 2007).  The global cost of fishing The total inflation-adjusted variable cost of fishing (real 2005 US$) for 144 maritime countries is estimated to have been some US$ 73.2 billion. This estimate is consistent with the estimate of fishing costs, US$ 73.3 billion in 2004, calculated by the ?Sunken Billions? study (World Bank and FAO 2008).   55  2.4.3 Global pattern of fishing cost The spatial distribution of total variable fishing cost in the world in 2005 is shown in Figure 2.5. The database suggests that countries in the coastal areas of Asia, North America, Europe, and West Africa have higher total variable cost of fishing than other areas. Comparing the average variable fishing cost per tonne of catch among different locations (Figure 2.6), the highest unit fishing cost areas are the coastal regions of Australia, the South Pacific, and Antarctica. This can be explained by the use of fishing gears with high variable fishing costs in those areas. The lowest unit fishing costs were in areas along the coasts of Peru, Chile, and India, whose catch mainly consists of small pelagic fish taken nearshore in huge schools that are relatively inexpensive to catch.  Figure 2.5 Total variable fishing cost (US$ thousand) in 2005.       56  Figure 2.6 Average variable fishing cost per tonne of catch (US$ per tonne) in 2005.      2.5 Discussion  I have presented the procedures for developing a global cost of fishing database and highlighted some of its potential applications. The database is the first version of what should be considered a ?living? database, meaning that effort will be devoted to updating and improving it in the future. In particular, my opinion is that focus should be on collecting cost data from countries in regions with low representation of observed data in the current version. Ideally, database users should state the quality of data from the database they are using and analysing, perhaps using the scoring system provided here (Table 2.1). The current version is already aiding researchers, fishery managers and other parties, allowing them to assess the economic status of fisheries and the impact of different management policy  57  scenarios at different spatial scales. When combined with landed values (see Sumaila et al. 2007), this information will allow estimation of the economic rent from fisheries around the globe, as well as the profitability of fishing operations. As total costs exceed landed values for most gear types (Table 2.5), it seems clear that some fisheries would not be viable without a subsidy. The database also allows the mapping of port-based fishing effort by fleets, so can help estimate the distance travelled by fleets, and it can be used too to explore research areas such as assessment of the cost structure and efficiency of different gear types in different regions of the world, and aid in developing fishing cost functions. Most importantly, the fishing cost data provides useful information in projecting the impact of climate change on fisheries economic at both regional and global scales in the subsequent chapters.     58  Chapter 3: Change in global fisheries economics with climate change   3.1 Synopsis  Change in climate leads to a shift in the distribution and catch potential of marine species. These shifts not only cause changes in landings, but can also lead to changes in exploited species composition and the fishing gear type composition in different Exclusive Economic Zone (EEZ) regions. All of these impacts can have great implications for the economics, food security and livelihood of fishing communities and eventually other sectors of the whole economy. I apply the Dynamic Bioclimate Envelope Model (DBEM) and outputs from Earth System Models (ESM) to project future changes in distribution and maximum catch potential of exploited marine fishes and invertebrates under the SRES A2 scenario. I estimate that climate change may lead to reduction in global landings by 18% (11 ? 24%) and total revenue by 14% in the 2050s, assuming that prices do not increase in response to decreased catch. Moreover, global fishing cost is also projected to decrease by 17%. About 75% of Exclusive Economic Zones (EEZs) show a decline in landings under climate change, and most of them are developing countries, which are more vulnerable to temperature change. Although global resource rent is projected to increase by 17% under climate change, its amount is still negative, which means that fisheries will still be underperforming in the 2050s under climate change. The results highlight the importance of studying climate change  59  impacts on marine fisheries to support planning and designing of effective management measures.   3.2 Introduction  Along with other non-climatic factors such as changes in markets, demographics, overexploitation, management and governance regimes, climate change is considered to be a major issue that will shape global fisheries in the future. Although several studies suggested these non-climatic stresses and changes in management regime may have a greater impact on fisheries than climate change in the short term (Eide 2007; Daw et al. 2008), increasing uncertainty in climate still poses a major threat to world fisheries in the long run. Rising water temperatures, as well as related changes in ice cover, salinity, carbon dioxide levels, oxygen levels and circulation may lead to shifts in distribution range of many marine species (Polovina 1996; Clark et al. 2003; Drinkwater 2005; Rose 2005; Cheung et al. 2008a), negative impacts on corals (Graham et al. 2007; Hoegh-Guldberg 2007), decline in net primary productivity in some areas (Polovina et al. 2011; Steinacher et al. 2010), and shifts in timing of biological events (IPCC 2007b). Change in ocean temperatures and primary productivity have caused the poleward shift of distribution boundaries of commercially important marine fishes and shellfish in the world?s ocean and such change is expected to continue in the future (Cheung et al. 2009). The combined effects of the predicted distribution shift and changes in ocean primary productivity under climate change are expected to lead to global redistribution of maximum potential catch (Cheung et al. 2010). A recent study also showed that warmer temperature may lead to decrease in maximum body  60  sizes of marine fishes (Cheung et al. 2012). These changes have large implications for people who depend on fish for food and income, and thus economics of society as a whole (Sumaila et al. 2011).  3.2.1 Current fisheries Currently, global marine fisheries landings were estimated at an annual average of 80 million tonne with corresponding mean annual gross revenues of US$ 80 ? 85 billion annually (Sumaila et al. 2007, World Bank and FAO 2008, FAO 2012). The global variable and total cost of fishing were estimated at US$ 73 (US$ 50 ? 96) billion per year and US$ 88 (US$ 60 ? 116) billion per year, respectively (Chapter 2; Lam et al. 2011). Fisheries is a primary industry (Roy et al. 2009) and it supports the well-being of nations through direct employment in fishing (Teh and Sumaila 2011), processing, and ancillary services, as well as through subsistence-based activities at the community level contributing to US$ 220 to 235 billion in 2003 (Dyck and Sumaila 2010). When employment in ancillary sectors and their dependents are considered, the fisheries sector supports livelihoods of 660 ? 820 million people directly or indirectly, i.e., about 10 ? 12% of the world?s population (FAO 2012). Globally, fish also provides nearly 4.3 billion people with 20 percent of their animal protein needs (FAO 2012). Fish do not only act as protein and nutritional source to people who inhabit in the 144 maritime countries of the world because international fish trade has made fisheries truly global (Kurien 2005). In most low-and middle-income maritime countries, such as those in tropical regions, fisheries employment is crucial as it provides a safety net to some of the world?s poorest, providing them cash income and nutrition, especially during times of economic hardship (B?n? et al. 2010).  61   3.2.2 Impact of climate change on fisheries Climate-induced global ocean-atmospheric changes may affect the marine ecosystems at different levels, including individual organism, populations, communities and ecosystems (Sumaila et al. 2011). Change in the quantity, distribution and composition of marine fishes under climate change may have large implications for the economics of the community and fishing nations. Climate change affects marine fisheries through three different mechanisms: (1) productivity; (2) fishing operations; and (3) the sharing of fish stocks among different countries.   3.2.3 Impact on productivity of marine fisheries Shift in species distribution and reduction in maximum body size of fishes cause changes in maximum catch potential and hence the volume of catch by fishing countries (Cheung et al. 2010, 2012). Large scale re-distribution of global catch potential may result from climate change, with an average 30 ? 70% increase in high-latitude regions and a decrease of up to 40% in the tropics (Cheung et al. 2010). The magnitude and direction of change in total catch will be different across different countries based on the location of the fishing sites, targeted species and species composition. These changes may increase the variability of capture fisheries. There have been already some studies showing that the change in distribution and catch potential coincided with the warming of sea temperature; for example, the large increase in the catch of horse mackerel (Trachurus trachurus) in the North Sea (Reid et al. 2001), the northward shift of Atlantic cod (Gadus morhua) in North Atlantic and Barent Seas (Rose 2005, Brander et al. 2006), and the northward shift of landings of 4 species (lobster  62  (Homarus americanus), yellowtail flounder (Limanda ferruginea), summer flounder (Paralichthys dentatus) and red hake (Urophycis chuss) in the northeastern United States (Pinsky and Fogarty 2012). Changes in mean temperature of the catch (MTC), which is a measure of taxonomic composition that is sensitive to the temperature affinities of the component species, in most of the marine coastal and shelf areas are significantly and positively related to the change in sea surface temperature (Cheung et al. 2013).  3.2.4 Impact on fisheries operations Change in species distribution and catch potential not only affect the stock size and volume of landings, but also affect the composition of catch. Both fishing cost and landed values will change under climate change scenarios. Fishers may redistribute their fishing effort across different fisheries, targeted species and fishing locations in order to maximize their economic revenues (Gordon 1954). Therefore, when the distribution and catch potential of various commercially exploited species is changed under different climate change scenarios, fishers may potentially shift to other fishing gear types to target other species and/or relocate to other fishing grounds and landed fish in other ports. The total cost of fishing for each country would, therefore, change to various degrees. The national and regional economic impact may be either positive or negative as fisheries in different countries may respond differently to climate change.  3.2.5 Impact on the shared fish stocks among different countries Climate change impacts on the distribution of fisheries catches and benefits to different countries, regions and groups, will also have a significant impact on the economics of shared fish stocks (Miller and Munro 2004; Miller et al. 2013). For example, the northward shift of  63  Atlantic mackerel (Scomber scombrus) into Icelandic and Faeroese waters is causing disputes over the right to take a share of the > 0.5 million tonnes annual catch. The salmon treaty between Canada and the United States will also need to be re-negotiated when salmon distribution shifts with climate change (McIlgorm et al. 2010). Also, transboundary management of Pacific sardine (Sardinops sagax) targeted by Mexico, Canada and United States will need to be carefully designed in a cooperative way to prevent biological and economic waste following climate change (Ishimura et al. 2010; Miller et al. 2013).    3.2.6 Impact on the economics  All of these changes would then impact on the economics of fisheries of the fishing nations, global food security, energy supply and food prices (Sumaila et al. 2011). Cheung et al. (2010) projected that many tropical regions are expected to have a large reduction in their maximum catch potential by the 2050s, based on the Special Report on Emissions Scenario (SRES) A1B, while high latitude regions may gain. The national and regional economic impact of climate change may be either positive or negative, depending on fishery and country. For example, Arnason (2007a) predicted that global warming may have positive effects on the fisheries in Iceland and Greenland and thus contribute positively to their gross domestic product (GDP), while earnings to the European sardine fishery are estimated to decrease by up to 1.4% on average per year with rising temperatures (Garza-Gil et al. 2010). In the southern hemisphere, the reduction in landings of pelagic fisheries in Peru because of change in sea surface temperature during the 1997/98 El Ni?o caused more than US$ 26 million of revenue loss (Badjeck et al. 2010). Chapter 4 and Lam et al. (2012) projected that climate change may lead to 21% decline in landed value, 50% decline in fisheries-related  64  jobs and a total annual loss of US$ 311 million in the whole economy in West Africa by the 2050s under climate change scenario.  Other than the landed values or total revenue, climate change also impacts fishing cost, wages of fishers or payment to labor, resource rent and economic impact throughout the wider economies of all nations from fisheries. Total revenue is the top line or gross revenue from fishing, which may be affected by both changes in the volume and composition of landings under climate change and also fluctuations in ex-vessel prices of targeted species. Fishing cost may also be affected under climate change in the way as described under section 3.2.4. The primary sector of capture fisheries provides jobs for 38 million people in 2010 (FAO 2012), and fisheries are the major source of employment for the artisanal sector. Impacts on the profitability of fishing enterprises directly influence the job security and the income of fishers. Wages or payments to labor are the amounts earned by people who expend their labor, skills and expertise in the sector. Resource rent is the return to the resource owner after deducting the fishing costs and the subsidies from the total revenue. It is used as an indicator of fisheries performance (Clark 1990). Thus, a projected increase in total revenue under climate change does not necessarily mean an increase in economic performance.  The added value or impact through the fish value chain is the indirect economic effects of fisheries due to their impact on activities such as boat building/maintenance, equipment supply, international transport and the restaurant sector (Pontecorvo et al. 1980). As such, climate change may have great implication on the whole economy through fisheries.   Countries in the tropics are projected to be more vulnerable to climate change, and these countries are mostly poor and developing countries (Allison et al. 2009). To assist each  65  fishing country to design climate mitigation, adaptation policies and fisheries management plans, it is important to understand the magnitude and direction of change in landed values, fishing costs and resource rent under climate change. Since climate change is a global issue, development of mitigation and adaptation policies requires understanding of its impacts at a comparable scale. However, existing research in this area, in the context of fisheries, has centered on regional and local studies (Aaheim and Syna 2000, Hannesson et al. 2006, Arnason 2007a, Allison et al. 2009). There is still no in-depth quantitative study on the impact of climate change on fisheries economics on a global scale. Thus, this research will attempt to add knowledge to this area.   In this study, I aimed to investigate the potential direct impacts of global climate change on the economics of fisheries of all major EEZs (178) and regions in the world in terms of total revenue, fishing costs and resource rent. By examining the change in these economic parameters of each country, I identify those countries which are more economically vulnerable to climate change. To achieve these objectives, I first estimate the differences in catch potential and change in catch of over 800 species by each fishing country under climate change scenarios based on a published bioclimate envelope model (BEM) and an empirical model (Cheung et al. 2008a, 2008b, 2009, 2010, 2011). Economic impacts are then analyzed through the change in total revenue, fishing costs and economic rent (Sumaila et al. 2007, 2010; Chapter 2; Lam et al. 2011) at the Exclusive Economic Zone (EEZ) levels. I also examine the change in species composition and fishing gear type composition within each EEZ under climate change.    66  3.3 Methods  Distributional shifts in exploited marine species were investigated using a bioclimate envelope model (Cheung et al. 2008b, 2009). Change in the catch was estimated based on differences in the maximum catch potential by the 2050s projected using an empirical model (Cheung et al. 2008a, 2010). The current spatial catch data from the Sea Around Us Project database (Watson et al. 2004) were used for projecting the catch of different species by each fishing country in the 2050s. Then, I combined both projected catch data with the economic parameters in my global fisheries economic databases, including ex-vessel fish prices (Sumaila et al. 2007, Swartz et al. 2012), fishing costs (Chapter 2; Lam et al. 2011) and subsidies (Sumaila et al. 2010), in order to compute both current and potential landed values, fishing costs, wages and resource rents in each country under climate change. Details of the climate change scenarios, bioclimate envelope model, model for estimating the maximum catch potential and the economic model are provided below.    3.3.1 Climate scenarios In this study, I used the SRES A2 scenario (Nakicenovic and Swart 2000), which assumes carbon dioxide concentration at 720 ppm by year 2060, representing high-range of greenhouse gas (GHG) emissions. The A2 scenario was selected because it is consistent with the current level of emissions (Rahmstorf et al. 2007), and is considered to be conservative regarding the level of global economic growth (Van Vuuren and O?Neil 2006). The A2 scenario describes a very heterogeneous world with regionally-orientated economic  67  development, high population growth, and slow technological changes (IPCC 2000, 2007). Under this scenario, the average temperature is projected to increase by 3.4?C by the 2100s relative to the current temperature.   The climate projections were extracted from the outputs of Earth System Model (ESM2.1), which is a comprehensive ice-land-ocean-atmosphere coupled general circulation model including both physical climate and ocean biogeochemical dynamics, developed by the Geophysical Fluid Dynamics Laboratory (GFDL) of the United States National Oceanic and Atmospheric Administration (NOAA) (Dunne et al. 2010). I extracted ocean current, bottom temperature, sea surface temperature, sea ice extent, sea surface oxygen concentration, bottom oxygen concentration, salinity and primary productivity data from the GFDL ESM2.1. The outputs from the coupled model has variable resolution with grid cells being 1? at latitudes higher than 30?N and 30?S. The resolution becomes finer towards the equator (i.e., 1/3 of a degree along the equator). In order to match with the resolution of the other data sets being used in this study, for example the catch data from the Sea Around Us project database (www.seaaroundus.org) and economic data, I used a nearest neighbour method to interpolate the physical variables from the ESM2.1 to a resolution of 30? x 30? in latitude and longitude. The nearest neighbour interpolation method assigns the value to the cell at the centre of the output raster dataset using the value of the nearest point of the input raster dataset (i.e., datasets from ESM2.1). This method does not change any of the values of the cells from the input raster dataset. Therefore, this interpolation method allows me to avoid making complicated assumptions about the relationship between the coarser-resolution model outputs and their downscaled values.   68   3.3.2 Biological model Projecting future species distribution under climate change Distributions of 700 demersal and 109 pelagic marine fish and invertebrate species in the recent few decades on a 30? latitude x 30? longitude grid of the world?s ocean were predicted using an algorithm that was based on the species? depth range, latitudinal range, habitat preferences and broad known occurrence regions (Close et al. 2006, Cheung et al. 2008b). The parameter values of each species were obtained from online databases such as FishBase (www.fishbase.org) and SealifeBase (www.sealifebase.org).   I simulated future changes in species distribution by using a dynamic bioclimate envelope model (DBEM) (Cheung et al. 2008b, 2009). Firstly, the model identified the current species? preference profiles with the environmental conditions by overlaying environmental data, for example, sea surface temperature, salinity, oxygen content, etc. with maps of relative abundance of species. Preference profiles are defined as the suitability represented by the relative density of the species in each environmental condition and habitat type of each of the environmental conditions preferred by each species (Cheung et al. 2010).   Species? environmental preference profiles were linked to the expected carrying capacity in a population dynamic model in which growth, mortality and spatial dynamics of adult movement and larval dispersal along ocean currents were explicitly represented (Cheung et al. 2008b, 2009). The model algorithm was derived from the von Bertalanffy  69  growth function (VBGF; von Bertalanffy 1951). The model simulated changes in relative abundance of a species in each spatial cell by incorporating the intrinsic population growth and settled larvae and net migration of adults from surrounding cells. Since animals are assumed to migrate along calculated gradients of habitat suitability, my model assumes that carrying capacity varies positively with the degree to which the habitat is suitable in each spatial cell, which is dependent on species? preference profiles to the environmental conditions in a given cell. The final carrying capacity value of a cell was calculated from the product of the habitat suitability of all the environmental conditions considered in the model. The details of the algorithm of this model, which were adapted from Cheung et al. (2008a, 2009), are provided in the Appendix B. With the projected changes in ocean conditions from the NOAA?s GFDL ESM2.1, annual changes in relative abundance of exploited fishes and invertebrates from 1970 to 2060 were simulated by the model (Cheung et al. 2010).   Projecting maximum catch potential and landings Based on the projections of the future distribution of the selected marine fishes and invertebrates, I calculated the potential change in maximum catch potential by the 2050s (i.e., average of 2041 to 2060) relative to the 2000s (i.e., average of 1987 to 2006) in each spatial cell. Firstly, I calculated the average landings from 1987 to 2006 by species and EEZ, as estimated by the Sea Around Us global catch database (www.seaaroundus.org). The Sea Around Us developed an algorithm that disaggregated reported catch data from 1950 to 2006 into a 30? latitude x 30? longitude grid of the world?s ocean (Watson et al. 200). The main source of catch data was the fisheries statistics from the Food and Agriculture Organization  70  of the United Nations (FAO), which was modified where more appropriate data was available.  I estimated the annual maximum catch potential based on the projected primary production from the output of the ESM2.1 using the published empirical model of Cheung et al. (2008a). This empirical model estimates the species? annual maximum catch potential for each of the spatial cells (30?x30?) based on total primary production within its exploitable range, the area of its geographic range, its trophic level, and includes terms correcting for the biases from the observed catch potential. The above empirical model was described in detail in Cheung et al. (2008a, 2010) and in Appendix B. To minimize the effect of interannual variability of the climate projections, I applied a 10-year running average to the estimated catch potential. In each cell, the projected catch of the species was allocated to each fishing gear type. Then, catch of EEZ was calculated from the sum of catch in cells belonging to that EEZ.   I then estimated the percentage change in catch potential of each species in each EEZ exploited by a particular gear type between the 2000s and the 2050s. The percentage change in maximum catch potential was then used as a proxy for estimating the potential landings by each fishing country in the 2050s. The current fisheries landings in each EEZ were estimated by aggregating the catch of the selected 876 marine fish and invertebrate species, which were extracted from the Sea Around Us Project catch database (Watson et al. 2004) from 1987 to 2006. It should be noted that tuna species and highly migratory species such as Atlantic blue  71  marlin (Makaira nigricans) were excluded from the study as the current distribution ranges of tuna species could not be accurately estimated using my algorithm (Close et al. 2006, Cheung et al. 2008b). As these are fairly major exploited species worldwide, they will be included in the next round of analysis using the improved DBEM. The projected annual landing in each EEZ in the 2050s was estimated using the projected percentage change in total maximum catch potential of all species caught in a given EEZ and its current landings.   3.3.3 Exploring uncertainties of the Earth System Model (ESM) I used a multi-model ensemble to explore sensitivity of the assessment and address the uncertainties of the ESMs. Other than GFDL, I also acquired the projected changes in physical parameters from the outputs of two other global coupled carbon cycle-climate models under the IPCC SRES A2 scenario. These two models were IPSL-CM4-LOOP model from the Institute Pierre Simon Laplau (IPSL) and Community Climate System Model (CSM1.4-carbon) from the National Center for Atmospheric Research (Steinacher et al. 2010). I then compared the direction of the predicted changes in landings in each EEZ estimated from the three models. For the EEZs with the directions of change disagreed among the three models, they represented the regions with high uncertainties of the impact of climate change.   3.3.4 Estimating economic parameters Total revenue Total revenue (TR) is the product of ex-vessel price (P) and landing (L) in the case of commercial fisheries. Global total revenue (TR) can be expressed as:    72                            (1) where Pij is ex-vessel price and Lij is the current landing of species i caught by each country j or within each EEZ j. The total revenue in each EEZ j was first computed by summing up landed values of n species caught from EEZ j. When I computed the landed value derived from each EEZ, I used the average ex-vessel price of each species caught by all fishing countries in that particular EEZ. The current total revenue was estimated using a 20-year (from 1987 ? 2006) average ex-vessel prices of each species in 2005 real dollars (Sumaila et al. 2007, Swartz et al. 2012) of each EEZ and the 20-year average catch data from the Sea Around Us Project catch database (Watson et al. 2004). Then, the global total revenue was obtained by summing up the total revenue of all EEZs, where the total number of EEZs is m (m=178).   Projected landing (L?ij) of each species i by country j or EEZ j under climate change in the 2050s was computed using the current landings (     and the proportional change in the modeled catch potential between the 2050s and the current status. This can be expressed by the following equation:                           (2) where ?Xij  is the proportion change in projected catch potential under climate change (?Xcc) to modeled catch potential in the 2000s (Xij(2000s)). Change in catch potential under climate change (?Xcc) equals the difference in the projected catch potential from the empirical model  73  in the 2050s (Xij(A2)2050s) under SRES A2 scenario and the modeled catch in the 2000s (Xij(2000s)). ?Xij can be represented by:                                                           (3) Projected total revenue (TR?) is the product of ex-vessel price (P?) and projected landing (L?) and can be expressed as:                               (4) The ex-vessel price of each species i in each country j or within each EEZ j (P?ij) was assumed to be constant through time, although fish prices could be influenced by local markets, the global supply of fish, preference of consumers, prices of alternative products on the market and also the abundance of targeted species (Murawski and Serchuk 1989; OECD 1997; Asche et al. 1999; Hannesson 1999; Pinnegar et al. 2006). The projected imbalance between fish supply and demand might also lead to increases in fish price (Alexandratos 1995; Sverdrup-Jensen 1997). This study assumes that the real ex-vessel price (after adjusting for inflation) to be constant throughout the study period because the projection of future price is limited by data availability and model complexity. Also, real ex-vessel fish prices have remained relatively stable since 1970 (Delgado and Courbois 1997; Swartz et al. 2012). Although real fish prices are likely to rise in the future, for example, fish prices were projected to increase by about 6 ? 15% over the 1997 level by 2020 (Delgado et al. 2003), it allows me to get quick results of the impacts of climate change based on what data I have (Delgado and Courbois 1997).   74   Total fishing cost Costs associated with operating fishing vessels or variable costs include fuel, salaries for crew, repair and maintenance costs of vessels and gear, and the cost of selling fish via auction, of fish handling and processing, for example, the purchase of ice (Chapter 2; Lam et al. 2011). As the distribution range of the exploited species and ultimately their catch potential were expected to change under climate change, the targeted species composition of each country may also shuffle. In this study, I assume that fishers may have the capacity to change to different gear types in order to adapt to the change in the species composition caught by each country. Therefore, the total fishing cost (TC) is the product of the unit fishing cost and the landing. TC can be expressed as:                                    (5)  where Ciaj is the total fishing cost per tonne of catch, which is obtained from the global cost of fishing database (Chapter 2 in this volume; Lam et al. 2011), and Liaj is the landing of species i caught by fishing gear type a and fishing country j. Although equation (5) may not be able to capture the complicated mechanism of the change in fishing cost, it is the best approach for projecting change in fishing cost using readily available information. If fish stocks decline under climate change, the catch per unit of effort will decrease and hence the cost per unit tonne of catch would increase proportionally. However, if fishers are well-informed and capable to adjust to the change in fish stocks, they would lower their fishing  75  efforts accordingly and the cost per unit tonne would be more or less the same as the current status. Therefore, it is reasonable to assume the unit cost of fishing to be constant. Although the unit fishing cost is assumed to be constant through time, the total variable cost of each country under climate change (TC?) may still change as the catch potential of each species, the composition of target species and eventually the composition of gear types will change when the ocean warms up.   Resource rent When the change in fishing cost and landed value under climate change scenario were computed, the net profit (?j) for the fisheries sector in each country j or each EEZ j was computed by:             (6)   where TRj is the total revenue and TCj is the total fishing cost. By comparing the change in the net profit of each country under climate change scenario, I identified the loser and winner in terms of economic benefit in the fisheries sector. However, the economic rent (Rj) of country j or EEZ j is defined as the return to the resources? owner and it was computed using the following equation:            (7)   76  where TSj is the total non-fuel subsidies, i.e., the transfer payments from tax payers to the fishing industry (Sumaila et al. 2010). Fuel subsidies were not included in the model because they might have already been applied to energy price to reduce the cost for the consumers. As the subsidy data was reported by fishing country (Sumaila et al. 2010), the total non-fuel subsidies in each EEZ were estimated by using the proportion of landings from the EEZ to the total landings of each fishing country.    3.4 Results  Change in landings under climate change The current total landings of all the species in this analysis was estimated at 66 million tonnes, which represents 82% of total global average (annual) landings  in the 2000s, and the projected landings was 54 million tonnes in the 2050s under GFDL SRES A2 scenario. Thus, global landings were projected to decrease by 18% from the current level in the 2050s when climate change was considered. The percentage change in the landings in the 2050s of each EEZ under climate change was shown in Figure 3.1. For EEZs with ?5% change in landings, they were assigned as no change. From my analysis, 133 EEZs (75%) were projected to have a decline in their landings and only 25 EEZs (14%) would see higher landings under climate change. However, from the results of multi-model analysis, a high proportion of EEZs (42% of the total EEZs) had high uncertainties or disagreement on the projections among the results that were driven by the three ESMs.    77  Among the 20 most important fishing Exclusive Economic Zone (EEZ) regions in terms of their current total revenue, EEZs with the largest decline in landings by the 2050s include Malaysia, Peru, Vietnam, South Korea and Spain (Figure 3.1). On the other hand, the EEZs of Chile and Taiwan, for example, were projected to show increases in landings.     78  Figure 3.1 Percentage change in projected landings of each Exclusive Economic Zone (EEZ) in the 2050s relative to the levels in the 2000s under SRES A2 scenario (GFDL). Countries in red represent decrease from the current level whereas countries in yellow have no change in landings (i.e. ?5% relative to the current levels). Countries in blue correspond to countries with an increase in landings over the current level. Countries highlighted in diagonal line pattern are areas in which the direction of changes in projected landings estimated from the 3 Earth System Models (IPSL, GFDL and CSM1.4) disagreed with each other.    Change in total revenues under climate change Change in landings in EEZs also leads to change in the total revenue within the EEZs. The current total revenue of all species in this study was estimated to be US$ 73 billion in 2005 real dollars respresenting 70% of the average annual global revenue in the 2000s. Under the SRES A2 scenario, the projected total revenue in the 2050s is US$ 63 billion in 2005 real dollars. Warming of the ocean caused the total revenue to decrease by 14% from the current status in the 2050s. The global pattern of change in total revenue was similar to the pattern of change in landings (Figure 3.1). The difference in the decrease in landings (18%) versus total  79  revenues (14%) was caused by the landings of less valuable fish having decreased more than those of more valuable ones.   The impact of climate change on total revenue was projected to be higher than that on landings in some countries such as Chile and India (Figure 3.2). For example, in Chile, the percentage increase in total revenue (43%) was almost double the percentage increase in landings. The substantial increase in total revenue in Chile was due to the increase in projected catch of high ex-vessel price species such as Patagonian grenadier (Macruronus magellanicus) and Taca clam (Protothaca thaca). In contrast, the extent of change in total revenue in some EEZs was less than that of catch when low-valued species were affected more under climate change, e.g., China and Japan.  Figure 3.2 Percentage change in landings, total revenue and total fishing cost of the top ten most important EEZs in term of their total revenue in the 2050s under SRES A2 scenario relative to the values in the 2000s.     80  Species composition of catch in EEZs was also projected to change under climate change. I listed the top five most important species in term of their landings in each of the three EEZs in Table 3.1. Each EEZ represents different latitudinal regions. Some dominant species (i.e., those with higher proportion of total catch in the EEZ) become less important in terms of their catch in the 2050s under climate change scenario, such as Goldstripe sardinella (Sardinella gibbosa) in Indonesia and Inca scad (Trachurus murphyi) in Chile (Table 3.1). If the change in the distribution of high value species is large, climate change may have a large impact on the economy of the fishing communities even though the adverse impact on landings is relatively small, e.g., India.   Table 3.1 Change in species composition of three EEZs, which are in the top 10 most important EEZs in term of their current total revenue, at different latitudinal regions under climate change (SRES A2 scenario) in the 2050s.  Region EEZ Species Name % of current catch in the EEZ % of projected catch in the EEZ % of current total revenue in the EEZ % of projected total revenue in the EEZ % catch / total revenue change Price (US$ in 2005 real dollar) High latitude  region (North) Denmark European sprat (Sprattus sprattus) 26 40 5 8 10 249 Atlantic herring (Clupea harengus) 25 17 9 6 -50 415 Atlantic cod (Gadus morhua) 20 14 30 22 -49 1,862 Blue mussel (Mytilus edulis) 12 10 12 11 -34 1,265 European plaice (Pleuronectes platessa) 8 8 22 23 -25 3,181  81  Table 3.1 Change in species composition of three EEZs, which are in the top 10 most important EEZs in term of their current total revenue, at different latitudinal regions under climate change (SRES A2 scenario) in the 2050s. (cont?d)  Region EEZ Species Name % of current catch in the EEZ % of projected catch in the EEZ % of current total revenue in the EEZ % of projected total revenue in the EEZ % catch / total revenue change Price (US$ in 2005 real dollar) Tropical Indonesia Short mackerel (Rastrelliger brachysoma) 18 26 10 13 3 593 Goldstripe sardinella (Sardinella gibbosa) 11 5 3 2 -64 330 Bali sardinella (Sardinella lemuru) 10 9 3 3 -32 330 Yellowstripe scad (Selaroides leptolepis) 9 6 6 4 -51 694 Barramundi (Lates calcarifer) 9 8 11 10 -35 1,499 High latitude region (South) Chile Anchoveta (Engraulis ringens) 49 72 56 73 86 752 Inca scad (Trachurus murphyi) 26 10 14 5 -51 352 Araucanian herring (Strangomera bentincki) 16 10 6 3 -22 245     82  Table 3.1 Change in species composition of three EEZs, which are in the top 10 most important EEZs in term of their current total revenue, at different latitudinal regions under climate change (SRES A2 scenario) in the 2050s. (cont?d)  Region EEZ Species Name % of current catch in the EEZ % of projected catch in the EEZ % of current total revenue in the EEZ % of projected total revenue in the EEZ % catch / total revenue change Price (US$ in 2005 real dollar) High latitude region (South) Chile Patagonian grenadier (Macruronus magellanicus) 3 2 12 9 6 3,014 South Pacific hake (Merluccius gayi gayi) 2 1 3 2 8 1,151  Change in fishing cost under climate change Changes in the distribution and the maximum catch potential of different fish species under climate change were projected to alter the composition of exploited species, the gear types employed, its catch per unit effort (CPUE) and hence the fishing cost for each country. Total fishing cost of all the studied fisheries in the 2000s was estimated to be US$ 64 billion in 2005 real dollars. Climate change was projected to lead the decrease in the total fishing cost to US$ 53 billion in 2005 real dollar. Thus, climate change would lead to 17% decrease in the fishing cost in the 2050s.   As species composition is closely related to the fishing gear deployed, the projected change in species composition is expected to affect fishing gear composition in each EEZ (Table 3.2). For example, both gillnet and midwater trawl were the dominant gear types in  83  terms of their catch in Denmark in the 2000s, but midwater trawl is projected to become the most dominant gear type (40% of all the landings in the EEZs) in the 2050s. However, gillnet was associated to high unit cost whereas midwater trawl was a relatively low cost gear type. Projected increase in the use of midwater trawl, which has a low unit fishing cost, was estimated to lead to a substantial decrease in the total fishing cost in the EEZ of Denmark.     84  Table 3.2  Change in fishing gear composition of three EEZs, which are in the top 10 countries with the highest total revenue in the 2000s, at different latitudinal regions under climate change (SRES A2 scenario) in the 2050s.  Region EEZ Gear type % of current catch in the EEZ % of projected catch in the EEZ % of current total fishing cost in the EEZ % of projected total fishing cost in the EEZ % catch / total fishing cost change Unit total fishing cost (US$/tonne in 2005 real dollar)  High latitude  region (North) Denmark gillnet 27 21 56 49 -45 3,547 midwater trawl 27 40 6 10 11 385 seine 24 17 14 12 -47 1,015 hand 7 6 4 4 -31 1,025 hook and line 5 5 9 10 -24 3,109 Tropical Indonesia gillnet 37 40 38 41 -21 1,208 seine 30 30 26 26 -26 1,012 hook and line 7 9 6 8 -10 1,032 bottom trawl 7 5 6 4 -47 997 shrimp trawl 7 6 11 9 -37 1,882 High latitude region (South) Chile seine 86 91 66 79 34 320 gillnet 5 2 20 9 -51 1,649 midwater trawl 4 3 7 5 -17 641 bottom trawl 2 1 2 2 19 551 hand 1 1 2 2 9 1,025  Change in resource rent under climate change Total resource rent of all the EEZs in the 2000s was estimated to be negative US$ 5 billion in 2005 real dollar. In the 2000s, about a quarter of EEZs (78 EEZs) had negative resource rent with subsidies taken into account. Under the climate change scenario, the total resource rent was projected to increase by 17% or US$ 0.9 million (Figure 3.3). However,  85  the total resource rent was projected to remain negative under climate change in the 2050s (i.e., loss US$ 4 billion). Also, the number of EEZs with negative resource rent was projected to increase to 85 in the 2050s. Although the projected total revenue in some EEZs decreases under the climate change scenario, their total fishing cost was also projected to decrease at the same time. When more marine species caught using high cost fishing gears decreased, the percentage decrease in fishing cost over-compensated for the decrease in landed values. Thus, the resource rents were projected to be higher than the current status even though the landed values decreased, e.g., in, China, Japan, Indonesia, Denmark and Korea (Figure 3.4).    86  Figure 3.3 Percentage changes in projected total resource rent of each Exclusive Economic Zone (EEZ) in the 2050s relative to the levels in the 2000s under SRES A2 scenario (GFDL). Countries in red represent decrease from the current level whereas countries in yellow have no change in total resource rent (i.e. ?5% relative to the current level). Countries in blue correspond to countries with an increase in total resource rent over the current level. Countries highlighted in diagonal line pattern are areas in which the direction of changes in projected landings estimated from the 3 Earth System Models (IPSL, GFDL and CSM1.4) disagreed with each other.      87  Figure 3.4 Percentage change in resource rent of the top ten most important EEZs in term of their total revenue in the 2050s under SRES A2 scenario relative to the values in the 2000s.      Uncertainties of the Earth System Model (ESM) Global landings were projected to decrease by an average of 18% (11 ? 24%) from the three ESMs. This value is very close to the percentage of change in global landings projected using the GFDL model (i.e., 18%). Under the IPSL model, the projected decrease in global landings was 24% whereas the CSM 1.4 produced a more conservative projection with only 11% decrease in landings in the 2050s under the SRES A2 scenario. The percentage change in landings, total revenues, fishing cost and resource rent projected from the three ESMs are compared using boxplots (Figure 3.5). Since the medians from the three models were similar  88  (Figure 3.5), the uncertainties in projecting the global change from the models were within reasonable limits (i.e., mean deviation from the mean = ?4.5%).    Figure 3.5 Comparison of percentage changes in (a) landings; (b) total revenue; (c) total fishing cost; and (d) total resource rent  under SRESS A2 scenario in the 2050s relative to the values in the 2000s among the three different Earth System Models (ESM) including GFDL, IPSL and CSM 1.4.      3.5 Discussion  The projected changes in landings in the EEZs do not show a zonal pattern following latitude as did the previous study (Cheung et al. 2010). The study from Cheung et al. (2010) showed  89  that the maximum catch potential may increase in some high-latitude countries/regions while many tropical and subtropical regions may suffer a decline. Here, an advancement over Cheung et al. (2010) is that the changes in ocean biogeochemistry and plankton community structure were taken into consideration (Cheung et al. 2011). The Earth System Models used in this study showed that the primary productivity of fish species were projected to decrease in most regions, including North Atlantic, the tropics and the permanently stratified regions in at the mid-latitude. This resulted in the decrease in the global mean net primary productivity (NPP) whereas an increase in global mean NPP was projected using empirical models and algorithms Cheung et al. (2010). By considering the change in phytoplankton community, the catch potential may be further reduced by 10% (Cheung et al. 2011). For example, the ESM projected the primary productivity to have a decrease in North Atlantic (Steinacher et al. 2010), so the landings in some Nordic regions such as Greenland also showed a decrease in catch potential. However, most of the regions in the tropical and subtropical regions, where many socioeconomically vulnerable countries are located, still have tremendous loss in their landings.   In this analysis, fisheries were assumed not to utilize species that they did not catch historically. This assumption was partly due to the extremely high uncertainty in predicting fisheries? adaptability and catches of novel species. As a result, the projected future increase in landings and revenues may be considered conservative.  Climate change not only affects the total landings and revenues in each EEZ but also the species composition, which may have large implications for the socioeconomics of  90  fishing communities. Total revenues from fisheries are not only dependent on the amount of catch, but also on catch composition. Therefore, an increase in landings may not necessarily increase the total revenue if the impact is mainly on the low ex-vessel price species or vice versa.  For example, the total landed value in the Celtic Sea decreased even though there was an increase in fishing yields under historical change in climate because of the reduction in catch of more valuable higher trophic level species (Pinnegar et al. 2002).  In this study, the total landings in Mexico?s EEZ was projected to increase slightly (8%) under climate change. However, most of the high value species were projected to decrease in landings, such as Pacific calico scallop (Argopecten circularis), Caribbean spiny lobster (Panulirus argus) and Northern red snapper (Lutjanus campechanus). The total revenues in this EEZ was projected to decrease slightly from the current level (9%).    The change in total revenues may have large implications on the whole economy through the indirect economic effects of fisheries on activities such as boat building/maintenance, equipment supply and the restaurant sector (Pontecorvo et al. 1980). In some of the countries, fisheries constitute a base industry to the whole national economy, for example in, Iceland and Greenland (Arnason 2007a). Fish trade also plays an important role in the economies of many countries such as China and Thailand (FAO 2012). Many developing countries are net exporters of fish and fish products. Fish trade in these countries not only provides a significant source of foreign currency earnings, but also acts as a source for generating income, employment and food security and nutrition. Thus, change in total revenues in the EEZs under climate change may impact their fishing communities, their whole economies and also the economies in other countries through global trade.  91   Also, fishing communities in many low-income food deficit countries (LIFDCs) heavily depend on fish catches for their food sources. Despite the relative low level of per capita fish consumption in these countries (10kg/year), the contribution of fish to animal protein intake in the LIFDCs is relatively large (24%) (FAO 2012). Fish also acts as an important source of essential micronutrients such as iron, iodine, zinc, calcium, vitamin A and vitamin B that are not found in other staples such as rice, maize and cassava (Roos et al. 2007; Kawarazuka 2010). LIFDCs also rely on fish and fisheries as a source of income and job opportunities. The value-added from fisheries allows people to purchase high calorie staples such as rice and wheat, and other nutritious food such as vegetables and meat. Negative impact on the landings and total revenue of LIFDCs and developing countries may have greater implications on food security than the impact on developed countries (Srinivasan et al. 2010, 2013; Costello et al. 2013).  Furthermore, in many of these developing countries, the marine resources within their own EEZs are also exploited and threatened by distant water fishing fleets from other fishing countries (Alder and Sumaila 2004); for example, countries in West Africa. Although the change in catch under climate change may also have impacts on these foreign fishing countries, they can either stop fishing in these EEZs or shift to other fishing grounds. However, small-scale fisheries in developing countries are those who would suffer the impact the most as they have relatively lower capacity to adapt to the change.   Change in distribution and maximum catch potential of different fish species under climate change leads to change in composition of exploited species, the gear types employed  92  and hence the fishing cost for each country. Since exploited species are usually gear specific (McClanahan et al. 2008), one of the strategies for fishers to adapt to the change in species abundance and/or composition under climate change is by switching their gear types (Grafton 2010). Another strategy adopted by some mobile fishers to maintain profitability under climate change is to move to other fishing grounds with higher abundances of targeted species. As such, the distance from the fishing ports may change and the traveling cost will ultimately increase.   However, the extent of changes may vary among different fishing fleets and their adaptive capacity, which is defined as the feasibility of the fishers to find ways to maintain their livelihoods under unfavorable climate condition. Meanwhile, the potentially increasing trend of fuel price in the future will likely put more pressure on the fuel cost of fishing vessels (Beare and McKenzie 2006; Arnason 2007b; Stouten et al. 2007). The proportion of fuel cost is expected to vary positively with energy prices (i.e., increase in energy price will lead to an increase in the proportion of fuel cost in total cost). The higher fuel cost may also add more stress to fishers and reduce their ability to adapt to changes in species composition and distribution under climate change.   Total global resource rent was still projected to be negative in the 2050s. That means that fisheries will still be underperforming under climate change. Other than the influence of an increase in temperature, overfishing is the major factor that leads to such a consequence (Sumaila et al. 2012). Although I did not include the synergistic effect of fishing in this  93  study, climate change may further exacerbate the stress on marine resources due to fishing and other threats. Also, overexploitation increases the vulnerability of marine resources to the impact of climate change. It is therefore necessary to rebuild world fisheries as soon as possible to increase their resilience to climate change (Brown et al. 2012; Sumaila et al. 2012). Fisheries can be rebuilt by management measures such as reducing the fishing effort to appropriate level, zoning of areas to limit the level of fishing effort (for example., marine protected areas (MPAs)), and having fisheries regulations with strong precautionary principle (Pauly et al. 2002). If all fisheries were well-managed, there would still be loss in landings and total revenues under climate change if its impact was severe but the loss would definitely be less than the loss under poor management. Delaying in the implementation of sustainable fisheries management may increase the risk of collapse and reduce long-term harvest under ecosystem and climate change (Brown et al. 2012).   Impact on effectiveness of fisheries management measures It would be beneficial to consider climate change impacts in the process of planning and designing of effective fisheries management measures; for example, when planning marine protected area (MPA) networks. A good example of this can be found in recent studies that highlight the importance of identifying corals that are resilient to climate disturbance, and key research priorities for coral reef management in the face of climate change (Darling 2012; McClanahan et al. 2012). If climate change impacts were not included when designing MPA networks, their effectiveness in conserving biodiversity and fisheries may decline in the next several decades (McLeod et al. 2009). Climate-induced changes will also alter spatial aspects of fisheries management, especially for multispecies, transboundary and  94  highly migratory stocks (Miller et al. 2013). For example, the potential of the west-east movement of the South Western Pacific purse-seine tuna fishery, following ENSO events and ocean temperature rise, may impact on both the exploitation and management of this fishery, which is a complex oceanic fishery comprising both domestic and foreign purse-seine vessels (McIlgorm 2010). Another example is the dispute in catch quota for Atlantic mackerel (Scomber scombrus), a warmer-water species, which is believed to be shifting northward, from Scotland towards Iceland and Faeroes Islands (Davies 2010). Therefore, with the influence of climate change, the existing process of management and resource use agreement may not be effective or applicable. For example, the attractiveness of paying accessing fees to fish in the Exclusive Economic Zone (EEZ) of Papua New Guinea could change for Distance Water Fishing Nations (DWFNs) (McIlgorm 2010).    It would also be beneficial to incorporate more flexibility into the political and management systems, for example, the harvest quota system. Moreover, a higher degree of local community involvement in resources management has to be initiated. More stakeholders such as subsistence fishers, scientists and policy-makers have to work together in order to find solutions, mitigation measures and adaptive strategies to the changes in marine resources caused by climate change.  Subsequent research on regional analyses  This chapter provides a global perspective on the impact of climate change on fisheries economics. From the results, regional variation is evident. Given that, it would be useful to examine some sub-areas in more detail. In particular, research effort should be given to areas that are already particularly vulnerable to climate change such as West Africa and the Arctic.  95  Therefore, I assess the extent of climate change impact on fisheries and how this change affects the food security and nutritional quality, directly and indirectly, of the people of West Africa in Chapter 4. Also, the impact of climate change on fisheries-related employment and economic impact on other sectors in West Africa will be studied in Chapter 4. I will also assess how changes in climate and ocean pH are likely to affect the economics of marine fisheries in the Arctic region in Chapter 5.    96  Chapter 4: Climate change impacts on fisheries in West Africa: implications for economic, food and nutritional security  4.1 Synopsis  West Africa was identified as one of the most vulnerable regions to climate change in previous global analyses. Adverse changes in marine resources under climate change may pose significant threats to the livelihoods and well-being of the communities and countries that depend on fisheries for food and income. However, quantitative studies on the potential impact of climate change on fisheries and its subsequent impact on human well-being in West Africa are still scarce. In this chapter, I aim to assess the potential impacts of climate change on fisheries and their effects on the economics, food and nutritional security in West Africa. I use a dynamic bioclimatic envelope model to project future distribution and maximum fisheries catch potential of fish and invertebrates in West African waters. My projections show that climate change may lead to substantial reduction in marine fish production and decline in fish protein supply in this region by the 2050s under the Special Report on Emission Scenarios (SRES) A1B. Integrating economic parameters, I project a 21% drop in annual landed value, a 50% decline in fisheries-related jobs and a total annual loss of US$ 311 million in the whole economy of West Africa. These changes are expected to increase the vulnerability of the region?s economics and food security to climate change.   97  4.2 Introduction  Climate change is affecting marine ecosystems and is expected to affect fisheries and other ecosystem services (e.g., Hughes et al. 2003; Cheung et al. 2010, 2011; Sumaila et al. 2011). Marine species may show various responses to climate change, including changes to physiology, phenology, distribution ranges and ecology (e.g., Perry et al. 2005; Cheung et al. 2009). These biological responses affect the distribution and productivity of marine fisheries. Using simulation models that account for the major biological responses to climate and ocean changes, Cheung et al. (2010) projected that many tropical regions are expected to have a large reduction in their maximum catch potential by the 2050s under the Special Report on Emissions Scenario (SRES) A1B, while high latitude regions may gain. As the economics of the fishing sector is tightly linked to the status of fisheries resources, the projected changes in catch potential due to climate change will result in changes in economic rent that can be derived from fisheries. The national and regional economic impact of climate change may be either positive or negative depending on fishery and country. For example, Arnason (2007) predicted that global warming may have positive effects on the fisheries in Iceland and Greenland and thus contribute positively to their gross domestic product (GDP), while earnings to the European sardine fishery are estimated to decrease by up to 1.4% on average per year with rising temperatures (Garza-Gil et al. 2010). Fisheries in low latitudinal regions such as West Africa (WA) may be affected the most (Cheung et al. 2010) and this sector is particularly crucial as a source of protein and income to impoverished societies in these regions.   98   Climate change may further exacerbate the existing stresses on food security and economic activities in West Africa. According to Boko et al. (2007) and Allison et al. (2009), Africa is one of the continents with the highest vulnerability to climate change. Even without taking into account the impact of climate change, it is predicted that about 6% of the population in sub-Saharan Africa will suffer from chronic hunger or undernourishment by 2050 (FAO 2006b). With climate change, agricultural productivity would be negatively affected in many WA countries (World Bank 2007; Shah et al. 2008). Simultaneously, Cheung et al. (2010) showed that climate change would largely reduce the potential fisheries catch in the Exclusive Economic Zones (EEZs) of West African nations.   The predicted decrease in productivity of marine resources under climate change may have large impacts on the livelihoods of the WA communities. In general, fishers have different strategies and responses they can use to adapt to resource fluctuations and uncertainties (Allison and Ellis 2001), including temporarily switching to alternative occupations, permanently leaving the fishery, increasing fishing effort, changing fishing grounds and shifting to alternative fishing gears that are usually more efficient and often more destructive (Pauly 1990; Cinner et al. 2008). However, the adaptive ability of fishers depends on several factors including mobility of fishing vessels and availability of alternative livelihoods. In WA, the dominance of domestic fleets may limit their ability to adapt to changes in resource and environmental conditions. Also, opportunities for coastal  99  communities in WA are usually limited by the lack of alternative occupations, low education levels and high levels of poverty.   Climate change may also affect demographics such as population growth and migration patterns, and various other factors that influence food security in WA. Rapid population growth in West Africa from 2000 to 2050 is expected to exert tremendous stress on the food security in this region (United Nations 2009). Since 40% of the population of West Africa live in coastal cities, the combination of rising sea levels and extreme weather events may cause a large group of people to move inland (Boko et al. 2007). In contrast, prolonged drought in the inland regions of West Africa may cause more human migration to the coastal regions (Perry and Sumaila 2007). The inland-to-coastal migration is expected to impact on employment opportunities and the exploitation of natural resources in coastal regions.   Despite the large implications of climate change for the communities and economies in the WA region, a comprehensive study of the potential impacts on fisheries catch under climate change on the food and nutritional security and the economy in this region is still absent. My contribution aims to analyze the socio-economic implications of the potential catch projections under climate change (Cheung et al. 2010) by the 2050s, with an emphasis on food and nutritional security, and local economies of the region. Food security is defined as the physical, social and economic access to sufficient, safe and nutritious food to meet the dietary needs and food preferences for an active and healthy life (World Food Summit 1996;  100  FAO 1999a). Although this definition is made up of four key dimensions: availability, access, stability and utilization (FAO 2006c), I only address the first two dimensions here.   In this chapter I begin with an assessment of the direct impact of climate change on the food security and nutritional quality for West Africans through the change in fish supply and the total amount of protein from marine captured fish (i.e., food availability). Then, the indirect impact of climate change on food security is assessed through change in food access, which can be evaluated by projecting changes in landed value and fisheries-related employment. Both of these factors affect people?s purchasing power with respect to buying cheap sources of calorie-rich staples such as rice, millet, yam, maize, cassava etc., and other nutritious food. The change in landed values under climate change also has indirect and induced effects on other sectors of the economy in WA. The direct, indirect and induced economic impact from the fisheries sector was estimated using economic output multipliers (Dyck and Sumaila 2010). The countries that will face higher vulnerability to food security and local economy due to climate change effects on marine resources are identified.   4.3 Background  I included 14 West African countries in this study, including Western Sahara, which is a ?non self-governing territory? according to the United Nations. The countries included are, from Northwest to Southeast: Western Sahara, Mauritania, Cape Verde, Senegal, Gambia,  101  Guinea-Bissau, Guinea, Sierra Leone, Liberia, Cote d?Ivoire, Ghana, Togo, Benin and Nigeria (Figure 4.1).  Figure 4.1 Map of fourteen West African countries included in this study.     4.3.1 Importance of fish and fisheries to West Africa West Africa is highly dependent on fish and fisheries as a source of food and income. The average annual per capita food fish consumption of WA is 14.6 kg per capita, with Senegal having the highest consumption (27.8 kg per capita) in the region in the early 2000s  102  (averages from 1999?2003) (FAO 2011). Although the annual per capita consumption of fish in WA is not as high as that in other regions and also lower than the global average annual per capita consumption (15.9 kg per capita from 1999 to 2003, FAO 2011), the proportion of dietary protein of West Africans that comes from fish is extremely high; for example, Ghanaians and Sierra Leoneans are reported to obtain 63% of their animal fish protein from fish (B?n? and Heck 2005). Thus, comparing fish dependence in WA with other regions is more instructive than comparing the absolute figures of fish consumption per capita. Fish also acts as an important source of essential micronutrients such as iron, iodine, zinc, calcium, vitamin A and vitamin B that are not found in other staples such as rice, maize and cassava (Roos et al. 2007; Kawarazuka 2010). Due to the decline in the performance of agriculture and other natural resource sectors, the main source of cheap animal protein in many West African countries is fish caught in coastal and offshore fisheries. Fish from capture fisheries and aquaculture contributes as much as 50% of animal protein consumed in these countries (FAO 2009; Smith et al. 2010). Countries in WA also rely on fish and fisheries as a source of income, providing jobs for 7 million West and Central Africans (FAO 2006c). The value-added from fisheries allows people to purchase high calorie staples such as rice and wheat, and other nutritious food such as vegetables and meat.   Although marine fish and invertebrates exported from West Africa are worth only US$ 600 million annually (FAO 2007b), and contribute only about 2% to the total export value from WA countries, the fisheries sector in the region plays an important role in the local economy of certain WA countries, for example, Mauritania and Senegal, which are net exporters of fish. However, Smith et al. (2010) revealed that the low level of exports from  103  West Africa relative to other regions reflects access agreements between West African countries and countries in Europe and Asia. The landings under these access agreements are not considered as African exports, but the value of license agreement fees comes in some other category. Furthermore, the fisheries sector, particularly the artisanal, is a major source of employment and income for unskilled young men and women of coastal communities through direct and ancillary activities (FAO 2006c).  4.3.2 Current status and problems of fisheries in West Africa Fisheries resources are highly productive along the continental shelf of West Africa. The high productivity is supported by the upwelling resulting from the Canary Current and Guinea Current along the coast of Western Africa. Currently, fish stocks in WA waters are already overexploited, driven to a large extent by the dominance of foreign distant water fleets in the EEZs of the West African countries (Alder and Sumaila 2004; Atta-Mills et al. 2004). Before the enactment of the United Nations Convention on the Law of the Seas (UNCLOS) in the 1980s, fishing vessels from the European Union (EU) fished freely in African waters. Later with UNCLOS, the European Union officially negotiated and signed bilateral fishing agreements with Western African countries (Alder and Sumaila 2004). The main EU countries that fished in West Africa were France, Spain and Portugal; in addition, the former Soviet Union and China were also active. Moreover, some EU countries found an indirect way through joint ventures with local businesses to fish in West African waters. The total number of years foreign countries signed agreements with Western African countries for fishing access added together for each decade have increased significantly since they first  104  started in the 1960s (Alder and Sumaila 2004). The negotiations and agreements are usually made at political levels with almost no involvement of local scientific or community inputs from West Africa countries. Simultaneously, there was a strong demand for fisheries resources as a source of food, income and livelihoods for coastal Western African communities. As a result, fisheries resources in West African waters have been heavily exploited both by local fleets, which are mainly small scale artisanal, and foreign vessels, since the 1960s.  The pressure on the fisheries resources of West Africa has caused the decline of fish stocks; however, the demand for fish keeps increasing. As a result, fishers are using more and more sophisticated, sometimes destructive methods and illegal means, to fish (Pauly 1990, McClanahan et al. 2005). High-technology fishing techniques with the potential of finding the last remaining fish, which do not leave part of the stock to reproduce, are being used (Ovetz 2007). Some fishing gears are simply destructive to the ecosystem, such as a bottom trawl used by the industrial fishery, which sweeps the ocean floor and clears everything in its way, or dynamite fishing by small scale fisheries such as those near the coast of Dakar (Campredon and Cuq 2001) and in Moree, Ghana, before it was banned through co-management (Over? 2001). In addition, artisanal fishers (e.g., in Ghana) use very small mesh sizes, which catch even very small fish before they become sexually mature. Trawlers sometimes operate close to the shore, destroying coastal habitats and the gear of artisanal fishers (Over? 2001). A global assessment of illegal fishing found West Africa to be an area of high risk with an estimated illegal catch of 40% above the reported catch (Agnew et al. 2009). Together with other problems such as discards of by-catch (Kelleher 2005) and  105  trade in ?trash fish? (Nunoo et al. 2009), all these stresses in the region?s fisheries increase the number of people at risk of facing hunger (Brown and Crawford 2008, Shah et al. 2008).  4.4 Methods  4.4.1 Projecting fisheries landings under climate change scenarios I employed a combination of models in order to project future fisheries catch potential and landings in each country. Basically, there are two major steps in projecting future maximum catch potential of species: (1). projecting future species distribution ranges under different climate change scenarios using a simulation model approach (Cheung et al, 2009); and (2). calculating maximum catch potential using an empirical model (Cheung et al. 2008a, 2010).   4.4.2 Projecting future species distribution under climate change The calculation included 128 marine fish and invertebrate species exploited by West African countries in their EEZs. The current (i.e., 1980?2000) distributions of these species were produced using an algorithm, which predicts the occurrence of a species on a half degree latitude x half degree longitude grid of the world ocean based on the species? depth range, latitudinal range, habitat preferences and broad known occurrence regions (Close et al. 2006, Cheung et al. 2008b). The parameter values of each species were obtained from online databases, notably FishBase (www.fishbase.org) and SealifeBase (www.sealifebase.org).   106   I then simulated future changes in species distribution by using a dynamic bioclimate envelope model (Cheung et al. 2008b; 2009). First, the model identified the current species? preference profiles with the environmental conditions by overlaying environmental data, i.e., sea surface temperature, salinity, etc., with distribution maps of relative abundance of species. Preference profiles are defined as the suitability, which is represented by the relative density of the species in each environmental condition and habitat type, of each of the environmental conditions to each species (Cheung et al. 2010). This is the same method that I used to estimate the current distribution and project the distribution shift in section 3.3.2 of Chapter 3. The details of the bioclimate envelope model are in Appendix B.  Then, species? environmental preference profiles were linked to the expected carrying capacity in a population dynamic model in which growth, mortality and spatial dynamics of adult movement and larval dispersal along ocean currents were explicitly represented (Cheung et al. 2008b; 2009). The model simulated changes in relative abundance of a species in each spatial cell by incorporating the intrinsic population growth and settled larvae and net migration of adults from surrounding cells. Since animals are assumed to migrate along the calculated gradient of habitat suitability, the model assumes that carrying capacity varies positively with habitat suitability of each spatial cell, which is dependent on species? preference profiles to the environmental conditions in each cell. The final carrying capacity value of a cell is calculated from the product of the habitat suitability of all the environmental conditions considered in the model. The details of the algorithm of this model are provided in  107  Cheung et al. (2008a, 2009). With the projected changes in the physical data from the NOAA/Geophysical Fluid Dynamic Laboratory (GFDL) ocean-atmosphere-coupled global circulation model (CM 2.1), annual changes in relative abundance of exploited fishes and invertebrates from West African waters from 2001 to 2060 were simulated by the model (Cheung et al. 2010).   I examined two emission scenarios, the SRES A1B (?business-as-usual?) scenario (Nakicenovic and Swart 2000) and the ?constant 2000 level? scenario, representing high- and low-range greenhouse gas emissions, respectively. The projected changes in primary productivity of these scenarios are available in Sarmiento et al. (2004). The SRES A1B scenario assumes that the greenhouse gas concentration was stabilized at 720 ppm by the year 2100. It describes a world of very rapid economic growth, low population growth, rapid introduction of new and more efficient technologies, and moderate use of resources with a balanced use of technologies. Large displacements and migrations of populations to resource-rich regions are expected from this scenario. The SRES A1B scenario is considered to be a conservative scenario when compared with other scenarios with higher future greenhouse gas emissions in the IPCC assessment (e.g., SRES A1F; IPCC 2007a), but they are not included here. Thus, the projected impacts on economic, food and nutritional security in this study are also conservative. In contrast, the ?constant 2000 level? scenario assumes that greenhouse gas concentration stabilized at 360 ppm, which is the same as the level in 2000. Changes in environmental conditions in the ocean, including sea temperature, sea ice coverage, salinity and advection from 2001 to 2059 under climate change scenarios were based on projection outputs from the NOAA/ GFDL ocean-atmosphere-coupled global  108  circulation model (CM 2.1) (Delworth et al. 2006). I re-gridded original data in the spatial cells onto the 30? latitude/longitude spatial grid used in this study in order to match the species distribution data.   4.4.3 Projecting maximum catch potential and landings Based on the projections of the future distribution of fish stocks, I calculated the potential change in maximum catch potential by the 2050s (i.e., average of 2050 to 2059) relative to the 2000s (i.e., average of 2001 to 2010) in these 14 coastal countries in WA. The details of the model for estimating the maximum catch potential can be found in Appendix B. Firstly, I calculated the average catch from 1999 to 2003 by species and country, as estimated by the Sea Around Us global catch database (www.seaaroundus.org). The Sea Around Us developed an algorithm that disaggregated reported catch data from 1950 to 2006 into a half degree latitude x half degree longitude grid of the world?s ocean (Watson et al. 2004). The main source of catch data is the fisheries statistics from the Food and Agriculture Organization of the United Nations (FAO), which is modified where more appropriate data is available.  Secondly, I obtained the projected primary production from West African waters in the future from Sarmiento et al. (2004), in which the primary production was estimated using various empirical equations. With these models, Sarmiento et al. (2004) first predicted surface chlorophyll content in the ocean from ocean-atmosphere-coupled global circulation model (GCM) outputs. In the next step, they used three published algorithms described in Behrenfeld and Falkowski (1997), Carr (2002) and Marra et al. (2003), to calculate the  109  annual phytoplankton primary productivity by using modelled surface chlorophyll content and its distribution, light supply and vertical attenuation, and sea surface temperature as a function (Sarmiento et al. 2004). The annual primary productivity in West African waters was obtained from 2001 to 2060 for the two climate change scenarios.   Finally, I calculated the annual maximum catch potential using the published empirical model of Cheung et al. (2008a). This empirical model estimates the species? annual maximum catch potential for each of the spatial cells (30?x30?) based on total primary production within its exploitable range, the area of its geographic range, its trophic level, and includes terms correcting for the biases from the observed catch potential. The above empirical model was described in detail in Cheung et al. (2008a, 2010). To minimize the effect of interannual variability of the climate projections, I applied a 10-year running average to the estimated catch potential. The annual maximum catch potential for each species in each EEZ was calculated by summing up all the projected values in the cells within that particular EEZ. I then estimated the percentage change in catch potential of each species in each EEZ between the 2000s and the 2050s, with the mean of catch potential estimated from the three primary production algorithms.  The percentage change in catch potential was then used as a proxy for estimating the potential landings by each of the WA countries by the 2050s. The current fisheries landings of each country was estimated by aggregating the catch of all species landed in a given country from 1999?2003. The projected annual landing for each country in the 2050s was  110  estimated using the projected percentage change in total maximum catch potential of all species caught by a given country and its current landings.   The projected change in maximum catch potential from Cheung et al. (2010) was extracted for the two Large Marine Ecosystems (LMEs) (Sherman and Hempel 2008), i.e., Canary Current and Guinea Current, within which these 14 countries are located. To better represent the resolution of the projected changes in catch potential, I calculated the relative changes in potential catch in each country EEZs based on the projected changes at the LME level. Also, the overall changes of the fisheries resources in each country are represented by the results obtained from projections from animal groups that are reported at the species level.   4.4.4 Estimating forecasted fish demand in the 2050s I used the current per capita fish consumption and the forecasted population in each WA country to predict the future total fish demand in the region. As per capita fish consumption depends on several factors including income, prices of fish and their substitutes, prices of complements, tastes and non-price factors (i.e., health education, urbanization, distribution, storage capabilities, etc.) (Kinnucan and Nelson 1993; Delgado and McKenna 1997), there is high uncertainty in projecting future per capita fish consumption. Despite the continuing rise in global per capita consumption of fish projected by the FAO to 2030 (FAO 2002), fish consumption per person in Sub-Saharan Africa is expected to stagnate or even decline (FAO 2002, Delgado et al. 2003). Also, the per capita consumption in WA has remained stagnant  111  historically (1969 to 1992) (Delgado and McKenna 1997). Therefore, I assume per capita fish consumption to be constant over my study period (i.e., to be constant till the 2050s). The per capita fish supply in the 2000s in each WA country was estimated using catch, import and export data from the Sea Around Us database (www.seaaroundus.org) and national population data from the United Nations (2009). Although FAO (2009) also provides data on fish food consumption, their estimates include fish supplies from aquaculture, inland and marine fisheries, which cannot be easily disaggregated. I therefore computed the per capita fish consumption by including only the capture marine catches of each WA country (Table 4.1).   The high projected rate of population growth in WA countries implies an increase in projected overall fish demand; however, when the per capita consumption is assumed to remain constant, I used the projected population in WA countries reported in United Nations (2009) to forecast the need for fish to meet WA?s food security needs in 2050 (Figure 4.2). To maintain the current nutritional level, it is reasonable to forecast the need for fish in 2050 based on current consumption per capita data. I do this here using the following equation:   ccc dPD 20502050??  (1) where c denotes country, Dc is the total fish demand in each WA country in the 2050s, cP2050  represents the projected population in each WA country in 2050 and cd2050  is the estimated per capita fish demand in the 2050s, which is assumed to be equal to the current per capita  112  fish demand,          . I also realize that people in WA may seek protein from other sources when there is a reduction in marine fish production. However, I assume here that there is no substitute for fish and thus the threat to food security posed by the loss in fish supply under climate change can be clearly demonstrated.   Table 4.1 Annual per capita fish food consumption (kg capita-1 year-1) in WA countries in the 2000s.  Country Name  Fish food consumption (kg/capita/year) b Senegal 25.9 Gambia 23.2 Ghana 17.5 Nigeria 14.5 Sierra Leone 14.2 C?te d'Ivoire 12.0 Cape Verde 9.9 Guinea 7.2 Mauritania 5.7 Togo 4.5 Benin 3.9 Liberia 3.1 Guinea-Bissau 1.2 Western Sahara a -  Note: a As the estimated catch for Western Sahara in its own EEZ is very low (< 1 tonnes), its per capita fish supply is considered to be negligible. b The values are calculated using the national populations (United Nations 2009).    113  Figure 4.2 Projection of population in each West African country in my study from 1950 to 2050. Data source: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2008 Revision, http://esa.un.org/unpp.    I then compared this projected total fish demand in each country with the projected fish production from captured marine fisheries under the two climate change scenarios by the 2050s, in order to compute the percentage of projected fish production to the fish demand for each WA country. If this percentage is equal to or greater than 100%, it implies that the fish production would be enough to meet the total fish demand in a given country.   114   4.4.5 Estimating potential loss in fish protein under climate change I analyzed the impact of climate change on the protein consumed by West Africans from marine resources under the scenario that fishing efforts were maintained at the current level. To estimate the loss in marine protein, marine fish is assumed to have protein content of about 15 to 20% by weight (FAO 2005). Then, the potential protein losses due to climate change are compared to the current annual protein consumption, which is estimated from the average population and dietary protein consumption (g/person/day) in 2003?2005, as reported by the FAO for each WA country (Table 4.2). Here, I make the strong assumption that there is no diet switching to other sources of protein and micronutrients when the fish supply declines.    115  Table 4.2  Average dietary protein consumption (g/person/day) and annual protein consumption (billion g) for each West African country in 2003 ? 2005.    Country Dietary Protein Consumption (g/person/day) Annual protein consumption (billion g) Benin 55 134 Cape Verde 63   10 Cote d'Ivoire 50   315 Gambia 50 24 Ghana 55 392 Guinea 54   165 Guinea-Bissau 40   19 Liberia 33   34 Mauritania 83 79 Nigeria 59   2,688 Senegal 55 199 Sierra Leone 47   73 Togo 46 88 Western Sahara * -   Note: * Western Sahara has no data on the dietary protein consumption (g/person/day).  4.4.6 Estimating landed values under climate change The annual total landed values of fish in WA countries is estimated at US$ 0.7 billion in the 2000s. This value was estimated using ex-vessel prices of each species in 2000 real dollars (Sumaila et al. 2007) and the average catch data from the Sea Around Us Project catch  116  database (www.seaaroundus.org). The landed values of each WA country under two climate change scenarios by 2050 are also estimated using ex-vessel prices (in 2000 real dollars) and the projected fisheries landings. Fish prices are greatly influenced by local markets, global supply of fish, preference of consumers, prices of alternative products on the market and also abundance of targeted species (Murawski and Serchuk 1989, OECD 1997, Asche et al. 1999, Hannesson 1999, Pinnegar et al. 2006). The projected imbalance between fish supply and demand may lead to increases in fish price (Alexandratos 1995; Sverdrup-Jensen 1997). As stated previously, in this study, I assume that the real ex-vessel price (after adjusting for inflation) to be constant throughout the study period because the projection of future price is limited by data availability and model complexity. Also, the real ex-vessel fish prices have remained relatively stable since 1970 (Delgado and Courbois 1997). Although real fish prices are likely to rise in the future, for example, fish prices were projected to increase by about 6?15% over the 1997 level by 2020 (Delgado et al. 2003), it allows us to get quick results of all critical issues, including, impacts of climate change based on what I have (Delgado and Courbois 1997).   4.4.7 Estimating the effect of climate change on fisheries-related jobs FAO (1999) provided data on the number of people involved in capture marine fisheries from 1990 to 1997 for the 14 WA countries. More recent employment data (2000) can be obtained from the World Resources Institute (WRI) (http://www.wri.org). However, this data source provided the total number of people employed in both fishing and aquaculture in 2000. Hence, the number of people employed only in marine capture fisheries in the 2000s  117  was estimated by combining the proportion of fishers involved in marine fisheries in the 1990s (FAO 1999b) with the total number of fishers reported by the World Resources Institute. These employment data were converted to number of fisheries-related jobs per tonne caught in 2000 by dividing it with the average landings in the 2000s in each country. Here, I assumed that a decrease in employment is related to lower landings and lower fish stock levels. Then, the upper limit of number of job losses in marine fisheries was estimated by using the projected catch loss (as described above) under the climate change scenario by the 2050s.   4.4.8 Estimating the indirect and induced economic impacts of the fisheries sector under climate change  The fisheries sector is a primary or an economic base industry and supports a large number of secondary economic activities, including, boat building, fish processing, international transport, etc. I estimate the indirect and induced economic impacts of climate change impacts on the fisheries sector in WA countries by applying the national fishing output multipliers3 reported in Dyck and Sumaila (2010) (Table 4.3), in which the total current impact of the fisheries sector was calculated by applying the Leontief technological coefficients at current production. The total economic impact of the fisheries sector (      ) using net multipliers is calculated by:                                                            3 If we use the re-estimated multipliers under climate change (Norman-Lopez et al. 2011), the impact of reduced revenues on the whole economy would be less.   118                          (2)  where c denotes the country and,        is landed values for the fisheries sector in each country from 1999 to 2003. The Leontief multiplier (      ) for each country minus one represents the net multiplier (Dyck and Sumaila 2010). The contribution of fisheries production to other sectors of the economy was assumed to be unchanged over time under climate change scenarios. Therefore, the impact of climate change to the whole economy in WA countries was estimated by applying the fishing output multipliers to the projected landed values in the 2050s.      119  Table 4.3 Economic impact in West African countries using national multipliers based on fisheries output impact in 2003 (source: Dyck and Sumaila 2010).  Country Name  Landed Value (US$ millions) Economic impact (US$ millions) Benin 5.76 8.78 Cape Verde 3.83 5.84 Cote d'Ivoire 34.57 52.67 Gambia 30.27 46.12 Ghana 67.94 103.51 Guinea 99.43 151.48 Guinea-Bissau 4.80 7.31 Liberia 3.20 4.87 Mauritania 6.18 10.00 Nigeria 195.50 55.52 Senegal 151.77 335.36 Sierra Leone 58.15 88.60 Togo 3.78 5.75 Western Sahara* - -  Note: *As the estimated catch for Western Sahara in its own EEZ is very low (< 1 tonnes), its landed value is considered to be negligible.    120  4.5 Results  4.5.1 Fisheries landings under climate change scenarios According to the FAO (2009), the contribution of fish to total animal protein supply is around 19% of the total in Africa. In West African coastal countries, the percentage of dietary protein from fish relative to other animal proteins can be very high, with some percentages at more than 50%, e.g., 62% in Gambia and 63% in Ghana and Sierra Leone (B?n? and Heck 2005). As such, variation in fisheries landings under climate change would have a direct impact on the protein intake and hence food security in West Africa. Under the ?constant 2000 level? scenario, the annual landings of almost all of the countries in WA, except Gambia, Western Sahara, Mauritania and Senegal, are predicted to decline by the 2050s (Table 4.4). The total annual landings of the 14 WA countries combined is projected to be reduced by 20,000 tonnes (i.e., a reduction of about 8% over current levels) in the 2050s under this scenario.  121  Table 4.4 Current landings, projected landings, percentage change in landings over current level and the prevalence of undernourishment in the population of each West African country under two different climate change scenarios.    Low range GHG emission scenario  (constant 2000) High range GHG emission scenario (SRES A1B)  EEZ Name Current landings in the 2000s (tonnes)a Projected landings in the 2050s (tonnes)b Potential percentage change in catch (%) over current level (2000s) Projected landings in the 2050s (tonnes)b Potential percentage change in catch (%) over current level (2000s) Prevalence of undernourishment in total population (%)  (2000-2002) Ghana 264,796 154,806 -41.5 119,243 -55.0 12 Cote d'Ivoire 58,268 35,752 -38.6 25,434 -56.4 15 Liberia 22,848 14,599 -36.1 11,318 -50.5 43 Togo 14,907 10,520 -29.4 5,959 -60.0 41 Nigeria 288,140 220,682 -23.4 136,456 -52.6 10 Sierra Leone 59,307 51,000 -14.0 27,723 -53.3 51 Guinea 107,380 97,331 -9.4 79,924 -25.6 18 Benin 8,148 7,456 -8.5 6,172 -24.2 22 Cape Verde 17,007 15,996 -5.9 13,328 -21.6 19 Guinea-Bissau 13,351 12,940 -3.1 10,331 -22.6 29 Gambia 32,147 34,471 7.2 29,637 -7.8 29 Western Sahara 821,642 890,892 8.4 691,230 -15.9 - Mauritania 293,861 327,211 11.3 251,541 -14.4 7 Senegal 608,982 717,029 17.7 527,598 -13.4 32 West Africa Region (14 countries) 2,610,786 2,590,686 -8 1,935,895 -25.9 15 Note: aAverage annual landing data from 1999 to 2003 obtained from the Sea Around Us Project catch database (www.seaaroundus.com).  b Annual landings in the 2050s projected by using the model described in this chapter.  122   Under the high-range greenhouse gas emission scenario (SRES A1B), the sum of annual landings in the early 2000s in WA EEZs was 2.6 million tonnes (Sea Around Us Project database). By using the percentage change in the maximum catch potential of each species estimated in Cheung et al. (2010), the potential loss in total annual landings from these regions is estimated to be 670 000 tonnes (i.e., a reduction of 26% over current levels) by the 2050s under the SRES A1B scenario. The EEZs of six countries (Ghana, C?te d?Ivoire, Liberia, Togo, Nigeria and Sierra Leone) are projected to suffer substantial reductions in landings, of up to and over 50% of their current production under the high-range greenhouse gas emission scenario (Table 4.4). These countries with large reductions in landings are located near the Equator. Also, a few of these countries, such as Sierra Leone, Liberia and Togo, have already had high proportions of their populations in a condition of undernourishment (> 40%) (Table 4.4), so reductions in their landings would have great implications in terms of food security. In addition, some WA countries, for example, Mauritania and Senegal, are currently net exporters of fish. Thus, a drop in landings under the SRES A1B scenario will not only affect the food security in these countries but also greatly impact their economies through reductions in fish exports.  4.5.2 Forecasted fish demand in the 2050s To maintain the per capita supply of fish at the current level in WA to the 2050s, the marine capture fish production in this region needs to increase by five times over the current level. As projected by the FAO (2002), the global fish food demand will only be met by continuing  123  the expansion of fish cultivation by 2030. The share of capture fisheries to the world fish production will continue to diminish even without the consideration of climate change (FAO 2002). My results show that climate change will further exacerbate this situation (Appendix C Table C.1). The percentages of projected fisheries landings to forecasted fish demand in WA countries, under climate change by the 2050s, are shown in Figure 4.3. Under both climate change scenarios, the projected fish catch in most of the WA countries cannot meet the forecasted fish demand in the 2050s except for Guinea-Bissau and Mauritania. In these two countries, a large proportion of their marine catches are currently exported. In Guinea-Bissau and Mauritania, 76% and 89% of their exports were fish, respectively, in the 2000s. Although the projected landings in these two countries under climate change scenarios is predicted to meet the domestic fish demand, the future export strategies may need to be carefully revised to avoid threatening food security in this region. Among the 14 WA countries in this study, Benin, C?te d'Ivoire, Nigeria and Western Sahara show the largest gap between the forecasted demand for fish and the projected fish catch under both climate change scenarios, with the projected catch providing less than 10% of the projected demand in some cases. Although these countries (Benin, C?te d?Ivoire and Nigeria) mainly rely on imported fish for food consumption in the 2000s, the predicted decline in catch in the 2050s, under climate change scenarios, implies that these countries will have to increase the amount of imported fish and/or develop sustainable aquacultural potential to meet fish demand in the future.     124  Figure 4.3 Percentage of fish catch to forecasted fish demand in WA countries by the 2050s under high range climate change scenario, SRES A1B (dark bars) and low range climate change scenario, constant 2000 level (grey bars).  (Note: * The forecasted fish demand is estimated using the current per capita fish food consumption and projected national populations (United Nations 2009)).   4.5.3 Potential loss in fish protein under climate change Sierra Leone and Ghana are projected to lose an average of 7.6% and 7% of the protein that they consumed in the 2000s, respectively, and they show the largest loss in marine protein among other WA countries under the SRES A1B scenario by the 2050s (Table 4.5). According to B?n? and Heck (2005), these two countries are both highly dependent on fish protein relative to other animal proteins (i.e., with percentage of fish/animal protein at about  125  63%). Thus, a decline in protein from fish will have large implications for the nutritional quality of people?s diets in these countries if it is hard for them to access other protein sources. .   Table 4.5 Percentage loss in marine protein relative to current protein consumption in each West African country under the two climate change scenarios.    Percentage loss in marine protein (%) Country Low range GHG emission scenario (Stable 2000) High range GHG emission scenario (SRES A1B) Sierra Leone 1.7 ? 2.3  6.5 ? 8.7 Ghana 4.5 ? 6.0 6.0 ? 8.0 Senegal ** 3.9 ? 5.2 Mauritania ** 3.6 ? 4.8 Guinea 0.8 ? 1.1 2.3 ? 3.0 Cape Verde 0.5 ? 0.7 1.9 ? 2.5 Togo 0.8 ? 1.0 1.5 ? 2.0 Gambia ** 1.6 ? 2.1 Cote d'Ivoire 1.0 ? 1.3 1.4 ? 1.9 Liberia 1.0 ? 1.3 1.4 ? 1.8 Guinea-Bissau 0.15 ? 0.2 1.1 ? 1.5 Nigeria 0.4? 0.5 0.9 ? 1.1 Benin 0.08 ? 0.1 0.2 ? 0.3 Western Sahara * - -  Note: * Western Sahara has no data on the dietary protein consumption (g/person/day);  ** no marine protein loss because of increase in catch under this scenario.   126   4.5.4 Landed values under climate change The annual total landed value of WA countries is estimated to drop by 21% (i.e., from the current US$ 732 to US$ 577 million in constant 2000 dollars) under SRES A1B scenario from 2000 to 2050 (Table 4.6). Under the ?constant 2000 level? scenario, decline in the annual total landed value is also predicted but with a lower magnitude of change (8%). Almost all of the WA countries in my study show reductions in their landed values from fish caught in their EEZs under the high-range greenhouse gas emission scenario (SRES A1B) except Gambia (Figure 4.4). Cote d'Ivoire, Ghana and Togo are the countries that will suffer the greatest impact on their landed values, with up to 40% declines under the SRES A1B scenario by the 2050s. All the countries in my study suffer declines in landed values to a lesser extent under the low range GHG emission scenario (constant 2000 level) except Western Sahara, Mauritania, Senegal and Gambia, which are projected to enjoy an  increase in their landed values under this scenario.    127   Table 4.6 Landed values and total economic impact from the fisheries sector in the 2000s and under the two climate change scenarios (US$ millions per year).   Current (2000s) Constant 2000 level scenario SRES A1B scenario Country Name  Landed value ($M/year) Economic impact  Landed value Economic impact Landed value Economic impact Benin 3.49 5.32 3.20 4.89 2.95 4.50 Cape Verde 3.34 5.09 3.10 4.73 2.90 4.43 Cote d'Ivoire 34.93 53.22 26.81 40.85 20.92 31.88 Gambia 28.57 43.53 30.66 46.72 29.29 44.62 Ghana 84.66 128.98 61.85 94.23 49.51 75.43 Guinea 77.10 117.46 70.29 107.09 63.96 97.44 Guinea-Bissau 5.07 7.72 4.87 7.42 4.54 6.91 Liberia 3.10 4.72 2.50 3.80 1.99 3.03 Mauritania 107.64 174.17 114.52 185.31 102.37 165.65 Nigeria 188.07 53.41 150.79 42.82 120.00 34.08 Senegal 150.46 332.47 167.22 369.50 145.44 321.38 Sierra Leone 42.43 64.65 37.42 57.01 31.30 47.69 Togo 3.02 4.59 2.23 3.39 1.79 2.72 Western Sahara* - - - - - - West Africa Region 731.88 995.35 675.46 967.74 576.96 839.75  Note: *As the estimated catch for Western Sahara in its own EEZ is very low (< 1 tonnes), its landed value is considered to be negligible.    128  Figure 4.4 Percentage change in landed value of fishing countries in WA from 2000 to 2050 under two climate change scenarios.    4.5.6 The effect of climate change on fisheries-related jobs The projected change in fisheries-related jobs in the 2050s relative to the number of jobs in the 2000s under the two climate change scenarios is shown in Figure 4.5. The total number of jobs provided by the fisheries sector in WA countries is about 760 000 in 2000. Under the high-range GHG emission scenario (SRES A1B), the total number of fisheries-related jobs is projected to be 390 000 (a job loss of almost 50%), which may lead to serious socio-political problems. In particular, C?te d'Ivoire, Ghana, Liberia, Nigeria, Sierra Leone and Togo will  129  also face severe impacts on the number of jobs supported by the fisheries sector with more than 50% of job losses under the high range GHG emission (SRES A1B) scenario (Figure 4.5). By contrast, the total number of jobs associated with fisheries under the low range GHG emission scenario (constant 2000 level) is predicted to be 580 000 with a job loss of only about 23%.    Figure 4.5 Projected annual average change in fisheries related jobs in the 2050s relative to number of jobs in the 2000s under high range climate change scenario, SRES A1B (dark bars) and the low range climate change scenario, constant 2000 level (grey bars).  (Note: Western Sahara is not included in this analysis because no employment data is available.)   b.  a.   130   4.5.7 The indirect and induced economic impacts of the fisheries sector under climate change Total indirect and induced economic impacts of fisheries in all WA countries are presented in Table 4.6. The last row shows the landed values and the economic impact of the fisheries sector in the West Africa region under the current status and the climate change scenarios. Together with a US$ 155 million loss of annual total landed values (SRES A1B), I estimated that another US$ 156 million may be lost per year in other economic sectors by the 2050s under high-range greenhouse gas emission scenario (Table 4.6). This adds up to US$ 311 million annual loss to the whole economy in WA.   At the country level, climate change has the biggest impact on the direct, indirect or induced economic impact from the fisheries sector in Senegal. Under SRES A1B scenario, there will be a reduction of about US$ 5 million of landed value but the impact on the secondary activities is double (i.e., a decrease of US$ 11 million). Ghana and Nigeria are predicted to suffer the greatest loss in the economic output of fisheries in terms of direct, indirect or induced impacts under SRES A1B scenario of US$ 89 million and US$ 87 million of their total economic impact from fisheries in the 2050s under climate change, respectively.     131  4.6 Discussion   The decline in marine fish landings and the huge discrepancy between the projected supply and demand imply that climate change may worsen the food security issue in coastal WA countries by the 2050s, especially if the predicted decline in the agricultural sector under climate change materializes. Forecasted fish demand for WA countries should alert governments to the amounts of fish that will be required by the 2050s in order to meet the needs of the populations of WA countries. The difference between the forecasted demand for fish and the projected fish catch makes the risk of insufficient fish supply under climate change and unsustainable fishing practices apparent. This should provide decision makers and fisheries managers with useful information to help derive appropriate policy to meet the challenges ahead. The projected high demand and low supply of fish may likely cause fish prices to increase by the 2050s, further hindering the ability of poor WA communities to access the limited fish supply and thereby aggravating the already difficult food security situation under climate change. In the meantime, people will increase their reliance on imported food to compensate for the depleted local food production. This has broader balance of payment issues that will also affect the rest of the economy in terms of exchange rates, which will most likely decrease and hence imports will become more expensive but exports will get higher price. In order to maintain and improve the current nutritional level, reduce the risk of hunger and minimize the impact on economic, decision makers and fisheries managers in WA have to find ways of minimizing the gap between the forecasted fish demand and the catch by exploring some adaptive strategies.    132  The reduction in landed values and fisheries-related jobs under climate change may have indirect impact on food security by reducing the purchasing power of people to buy both fish and other food with higher calories. It is known that the consumption of non-staple food such as fish increases rapidly with income on a percentage basis (Bouis 2000). The linkage of employment to food security is based on the assumption that the earning of the household from fisheries-related jobs is related to the purchasing power of the household members for staple and non-staple food. With limited alternative employment opportunities in WA, a 50% drop in the fisheries-related jobs in WA region under high range climate change scenario implies that the standard of living and food purchasing power in this region will be greatly degraded. Therefore, the impact of climate change on landed values and employment would not only affect the livelihoods of small-scale fishers, but would also affect the food security in WA indirectly.   Aquaculture is considered as one of the possible solutions to reduce the risks and uncertainties of capture fisheries, and fill the gap between the fish supply and demand. Globally, aquaculture provides about 19% of total fish production (excluding China) in 2006 with an increase from 14% since 2002 (FAO 2009). The annual average growth rate of aquacultural production is 8.4 % between 1970 and 2008 (Hall et al. 2011). However, the total output from the aquaculture sector is very low relative to the capture sector in WA, contributing only about 2% of total fish supply in WA. In Nigeria, the aquaculture sector only contributed about 0.01% of the national fish supply in the year 2000 (Anetekhai et al. 2004). Although aquaculture is growing rapidly in some countries such as China over the last decade (Ahmed and Lorica 2002), the growth rate of aquaculture in West Africa lags far  133  behind. A GIS model developed by FAO estimated that 37% of sub-Saharan Africa is suitable for small-scale artisanal fish farming (Kapetsky 1994; Aguilar-Manjarrez and Nath 1998). However, the development of aquaculture in WA is limited by many factors, including a very low production base, inefficient and poorly developed value chains, substantial resources demand (Hall et al. 2011), poor infrastructure, political instability and poor market development (Brummett et al. 2008). Although aquaculture production in Africa is growing fast, it will not be able to fill the gap between fish supply and demand over the next decade (Hall et al. 2011). As such, aquaculture is not seen as a viable alternative source of fish.  The broader impact of climate change on marine resources is evaluated by applying the national net multiplier. The full impact of the fisheries sector in West Africa is much greater than the value of the initial activity. In the 2000s, the total economic impact of the fisheries sector contributes about 2% of the total GDP in West Africa. My results suggest that climate change (high range GHG emission scenario) not only affects the economics of the fisheries sector, but also has a similar impact on the whole economy of WA by the 2050s.   Many of the WA countries are already facing high levels of poverty, and their marine resources are also threatened by overfishing, both by local and distant water fishing fleets (Alder and Sumaila 2004). These countries should focus more on developing adaptation strategies to reduce climate change impacts (Dulvy and Allison 2009). The results of this analysis have great implications for the planning processes of both national and international institutions with respect to the policies they put in place to combat climate change. My study  134  also underlines the importance of building the adaptive capacity of WA countries in coping with climate change impacts on fisheries.   To begin to tackle current problems and prepare for the impending challenges from climate change, WA countries need to (i) know the state of their fish stocks and ecosystems; (ii) know the value (in a broad sense) of their fishery resources; and (iii) strengthen fisheries management, especially monitoring, control and surveillance. Without these three foundations, WA countries should not engage in global fish trade, sign access agreements and/or provide subsidies that are ecologically sustainable, and economically and socially beneficial to their coastal communities (Sumaila 2007).   Currently, the status of fisheries in West Africa is sub-optimal in terms of helping the region achieve its food security (with respect to catches, incomes and profits) and ecological sustainability objectives. There is ample room for WA countries to improve the conservation status of their marine fish stocks without compromising the overall long-term economic benefits or food security from the fisheries (Cheung and Sumaila 2007). Such improvements could be achieved by reducing fishing effort now and rebuilding depleted stocks, which will also help to make the marine ecosystems of WA more resilient and capable of absorbing impacts of climate change. Fisheries that have been successfully managed to achieve resource sustainability will probably have a higher capacity and be a better positioned to respond to the unpredictability of climate change. On the contrary, fisheries with current catch above Maximum Sustainable Yield may be more sensitive to shifts in climatic, oceanographic and biological conditions. These fisheries would also need to respond much  135  more proactively to disruptive changes resulting from climate change (Sumaila et al. 2011).  To increase the robustness of fish stocks to climate change, there may be short-term costs. However, the medium and long term negative implications for food security in the region would likely be minimized.    In the next chapter, I assess the impact of climate change and ocean acidification on the fisheries economics of another most vulnerable region, the Arctic.      136  Chapter 5: Marine capture fisheries in the Arctic: winners or losers under climate change and ocean acidification?  5.1 Synopsis  Climate change and ocean acidification, and the subsequent changes in marine productivity, may affect fisheries and eventually the whole economy in the Arctic. I analyzed how change in climate and ocean pH under scenarios of anthropogenic CO2 emission is likely to affect the economics of marine fisheries in the Arctic. I applied the Dynamic Bioclimate Envelope Model (DBEM) and outputs from four different Earth System Models (ESMs) to project future changes in the distribution and maximum catch potential of exploited marine fishes and invertebrates. Using these potential catch changes, I estimated that total revenues from fisheries in the Arctic region increased by 39% (14?59%) from the 2000s to the 2050s under the Special Reports on Emission A2 Scenario (SRES). Simultaneously, total fishing costs, fishers? incomes, household incomes and economy-wide impacts in the Arctic are also projected to increase. The effect of ocean acidification reduces the catch and these economic indicators slightly compared to the climate change only scenario. These impacts are relatively small because my results include only potential direct impacts, and only 0.4% of the catches are from shelled molluscs and these species are expected to have the greatest impact through undersaturation of aragonite. The predicted impacts are likely to be conservative as I did not include potential indirect effects such as trophic interactions and  137  synergistic effects with other factors. Results of this study would be useful for designing effective adaptation strategies to climate change, and measures to mitigate the potential negative impacts of ocean acidification in the Arctic.   5.2 Introduction  In the past few decades, annual average temperature anomalies in the Arctic region continued to be higher than the average temperature between 1961 and 1990 (Overland et al. 2011). According to different climate models that are used by the Intergovernmental Panel on Climate Change (IPCC), the average increase in temperature in the Arctic is projected to range from about 0.5?C to 16?C by the year 2080 relative to the present day period (Anisimov et al. 2007). Besides warming and the resulting decrease in the sea ice extent in the Arctic, the pH of surface ocean waters is also expected to decrease with an increased concentration of atmospheric CO2. The absorption of carbon dioxide into the ocean leads to reductions in carbonate ion concentrations, increases the acidity of the ocean, and hence reduces the level of calcium carbonate mineral saturation. Studies have shown that the pH of surface ocean waters has dropped by an average of about 0.1 units from preindustrial levels, and more in high latitude regions (Feely et al. 2009), and that the ocean pH level may decrease by an additional 0.3 to 0.4 units under the scenario where atmospheric CO2 concentration increases to 800 ppm by the end of the century (Orr et al. 2005). Globally, pH saturation of carbonate minerals in the Arctic Ocean is predicted to decrease the most rapidly (Steinacher et al. 2009).   138   Both changes in the physical (e.g., temperature, ocean current patterns) and biogeochemical (e.g., acidity, oxygen content, primary productivity, plankton community structure) conditions in the ocean may lead to changes in physiology, species distribution, phenology, and species assemblages (e.g., Edwards and Richardson 2004; Richardson and Schoeman 2004; Perry et al. 2005; Hiddink and Hofstede 2007; Rosa and Seibel 2008, P?rtner 2010). Recent studies show that there has been a 20% overall increase in net primary production by phytoplankton in the Arctic Ocean from 1998 to 2009 because of the increase in open water extent and the duration of the open water season (Arrigo and van Dijken 2011). The projected continuing warming of the Arctic Ocean may further increase primary productivity in the future, although there are large variations between the projections among the four global coupled carbon cycle-climate models (Steinacher et al. 2010). Global studies on climate change impacts on ocean biodiversity suggest that species invasion is projected to be most intense in the Arctic (Perry et al. 2005; Cheung et al. 2009). Furthermore, the decrease in the saturation level of calcium carbonate minerals may induce an adverse impact on calcium carbonate-secreting organisms in high latitude regions (Feely et al. 2009; Kroeker et al. 2010). Impacts on calcifying organisms may eventually affect higher trophic level species and hence the structure and biodiversity of the polar ecosystem. However, the degrees of response of marine organisms to more acidic water vary considerably among different species. Also, the biological effects of ocean acidification and its long-term impacts are not yet fully understood (Dupont and Thorndyke 2009; Melzner et al. 2009; Cheung et al. 2011).     139  The economic impact of climate change and ocean acidification on fisheries, however, has not been fully studied. Although global simulation modeling (e.g., Cheung et al. 2010) suggests that climate change may lead to increases in the potential fisheries catch in the Arctic, follow-up studies with a model that accounts for hypothesized physiological effects of ocean acidification suggest that there may be a substantial reduction in potential fisheries catch in more acidic waters in the North Atlantic (Cheung et al. 2011). These potential changes are expected to have direct implications for the economics of fisheries as a result of fluctuations in the quantity, quality and predictability of catches (Sumaila et al. 2011). Previous studies show that climate change may lead to increases in the Gross Domestic Product (GDP) of Iceland and Greenland stemming from fisheries (Arnason 2007); however, this may be countered by the effect of ocean acidification. Also, ocean acidification (OA) will impact the amount of seafood available to humans, and this reduction in seafood is mainly due to the negative impact of OA on molluscs (Branch et al. 2013). The impact of OA on fish consumption is still uncertain but it might be reduced if Arctic fishes respond to OA in the same way as tropical reef fishes (Branch et al. 2013). Although some studies on the impact of ocean acidification on fisheries with an emphasis on economics do exist, the majority of these studies are species-specific and are only focused on shell forming species or species in a particular region (e.g., Cooley and Doney 2009; Narita et al. 2011; Cooley et al. 2012). A comprehensive, broad-based approach for understanding the impact of ocean acidification on the marine ecosystem, fisheries, and their socio-economic dimensions, is still not available (Hilmi et al. 2012).    140  In this study, I aim to assess the impact of projected changes in the distribution and fisheries catch potential on the economics of marine fisheries in the Arctic under scenarios of climate change and ocean acidification. I focused on assessing the direct impacts of climate change and ocean acidification on marine resources and fisheries. Direct impacts refer to the impact of the biophysical and biogeochemical changes on biological productivity of fish stocks and hence the economic yield. However, the impact of climate change and ocean acidification on the rest of the economy (i.e., the indirect impact) is also examined. I applied the Dynamic Bioclimate Envelope Model (DBEM) (Cheung et al. 2009; 2011) to project future changes in the distribution and relative abundance of exploited marine fishes and invertebrates in the Arctic based on outputs from a global coupled carbon cycle climate model. I then determined the potential catch losses that could be attributed to ocean acidification (OA) in the countries bordering the Arctic. This empirical model estimated the species? annual maximum catch potential for each of the spatial cells (30?x30?) based on total primary production within its exploitable range, the area of its geographic range, its trophic level and terms correcting the biases from the observed catch potential. Using these catch changes, I compute the economic effect of OA in terms of changes using a number of economic indicators of commercial fisheries of the Arctic, including: (i) price and value (or total revenue) of fish and fisheries products; (ii) fishers' incomes; (iii) fishing costs; (v) economic rents; and (iv) economic and income impacts throughout the economy.     141  5.3 Study area  The Arctic Circle, which is bounded to the north by the North Pole and to the south by the latitude 66?N, consists of the Arctic Ocean, which is surrounded by Canada, Greenland, Faeroe Islands, Iceland, Norway, Russia, the United States and several small islands. The Arctic Ocean is partly covered by sea ice throughout the year but the rapid reduction of Arctic sea ice cover since 2007 has led to global concerns (Comiso et al. 2008). The Arctic Ocean?s temperature and salinity vary seasonally as the ice cover melts and freezes. The low surface salinity in the Arctic region is due to low evaporation, heavy freshwater inflows from rivers and streams, and the limited linkage with, and outflow to the surrounding oceans with high salinity.   The Arctic Circle encompasses the United Nations Food and Agriculture Organization?s (FAO) Statistical Area 18, part of FAO Area 27 (i.e., Northeast Atlantic Ocean), and the northern part of FAO Area 21 (i.e., Northwest Atlantic), which includes Baffin Bay and Hudson Bay. In the area that falls within FAO 18, the major fishing countries are Russia, the State of Alaska and Canada. The top five major commercially exploited species in this region are sardine cisco (Coregonus sardinella), muksun (Coregonus muksun), broad whitefish (Coregonus nasus), Arctic cisco (Coregonus autumnalis) and chum salmon (Oncorhynchus keta). The most important species targeted is sardine cisco (Coregonus sardinella), which accounts, on average, for 27% of the total annual catch (Zeller et al. 2011). In the Northeast Atlantic Ocean (i.e., FAO Area 27), the five major  142  fishing nations are Norway, Iceland, Russia, Faroe Island and Greenland. The top five major commercially exploited species in this region are capelin (Mallotus villosus), Atlantic herring (Clupea harengus), Atlantic cod (Gadus morhua), blue whiting (Micromesistius poutassou) and Northern prawn (Pandalus borealis). The catches of capelin and Atlantic herring altogether contribute to 50% of the total annual catch in this region. In Northwest Atlantic Ocean (i.e., FAO Area 21), the top five major fishing countries are Greenland, the United States, Canada, Iceland and Norway. These countries mainly fished for Northern prawn (Pandalus borealis), Greenland halibut (Reinhardtius hippoglossoides), Atlantic cod (Gadus morhua), queen crab (Chionoecetes opilio) and deepwater redfish (Sebastes mentella). Northern prawn is the most important exploited species in this region and it contributes to almost 50% of the total annual catch. Fishery resources in the Northwest Atlantic are under stress from past and current exploitation (about 35% of stocks were estimated to be depleted in 2008) (FAO 2010). Although some of these stocks have recently shown signs of recovery because of improved management recently, such as the successful northeast Atlantic cod (Eide et al. 2013), the collapsed northwest Atlantic cod has not yet recovered.  According to catch data extracted from the Sea Around Us Project catch database (Watson et al. 2004; Zeller et al., 2011), the annual landings of all the marine species from the Arctic region (i.e., catches only from FAO 18, part of FAO 27 and part of FAO 21) was 2.5 million tonnes in the 2000s. The total revenue or landed value in the same period and region was US$ 2.2 billion in 2005 real dollars (Sumaila et al. 2007). Norway has the largest share of total revenue amongst the other fishing countries in the Arctic region (i.e., US$ 953 million). Iceland and Greenland are the second and the third countries with the highest total  143  revenues in the Arctic region of US$ 516 million and US$ 251 million, respectively. Although the total revenue from the fisheries sector only contributes to a small proportion of the Gross Domestic Product (GDP) of each country (for example, total revenue from fishing in Norway is only 0.3% of its nation?s GDP (United Nations 2011)), the revenue from fishing is very important to the subsistence fishers in these countries (Booth and Zeller 2008). For example, subsistence catches of 15 coastal communities account for approximately 54% of the estimated total catch in the Arctic coast of Alaska (Booth and Zeller 2008).   5.4 Methods  5.4.1 Modeling biophysical impacts The Dynamic Bioclimate Envelope Model (DBEM) simulates how changes in temperature, oxygen content (represented by O2 concentration) and pH (represented by H+ concentration), as well as other variables such as ocean current pattern, salinity, and sea ice extent would affect growth and distribution of marine fishes and invertebrates (Cheung et al. 2011, 2012). In this analysis, I used a multi-model ensemble to explore sensitivity of the assessment and address uncertainties. The projected changes in physical parameters were the outputs from four different global coupled carbon cycle-climate models under the IPCC SRES A2 scenario. These models included NOAA?s Geophyiscal Fluid Dynamic Laboratory (GFDL) Earth System Model 2.1., IPSL-CM4-LOOP model from the Institute Pierre Simon Laplau (IPSL), and the two versions of the Community Climate System Model (CSM1.4-carbon and  144  CCSM3-BEC) from the National Center for Atmospheric Research (Steinacher et al. 2010). Changes in the average sea surface temperature (SST) and the average total phytoplankton biomass or primary productivity (PP) from the mean over the time series (1950 ? 2069) among these four ESMs are shown in Figure 5.1. Specifically, changes in these ocean conditions were transformed by the model into changes in life history, growth, carrying capacity, population growth, net migration, habitat suitability, and thus, the relative abundance of a species in each cell it occupies. Given the projected changes in ocean conditions under climate change scenarios, DBEM simulated the annual changes in the distribution of the relative abundance of each species on the global 30? x 30? grid (see the Appendix B for details). Comparison of simulated historical changes in the distribution of marine invertebrates and fishes in the Bering Sea and the Northeast Atlantic with observed distribution shifts from the 1970s to 2000s showed the ability of DBEM in correctly projecting distribution shifts (Cheung et al. 2013).    145  Figure 5.1 Anomaly of annual mean sea surface temperature (SST) and total phytoplankton biomass or primary productivity (PP) of four different Earth System Models (NOAA?s Geophyiscal Fluid Dynamic Laboratory (GFDL) Earth System Model 2.1.; IPSL-CM4-LOOP model from the Institute Pierre Simon Laplau (IPSL); and the two versions of the Community Climate System Model (CSM1.4-carbon and CCSM3-BEC) from the National Center for Atmospheric Research). Climate change scenario is from the Intergovernmental Panel on Climate Change?s (IPCC?s) Special Report on Emissions Scenarios (SRES): pathways A2. (a) anomaly of sea surface temperature (SST) (?C); (b) anomaly of total phytoplankton biomass or primary productivity (mg m-3).   (a)     146  (b)   I explored two hypotheses of potential climate change and ocean acidification effects on 62 species of exploited Arctic marine fishes and invertebrate, which contribute up to 93% of the total revenue of these countries in the Arctic region in the 2000s. In the first scenario, I assumed that these marine species are not sensitive to ocean acidification. Thus, in the model, their biology is affected by changes in ocean biogeochemistry (e.g., temperature, salinity, advection and O2) except for ocean acidity. In the second scenario, I incorporated the possible sensitivity of marine fishes and invertebrates to ocean acidification. Specifically, based on the meta-analysis by Kroeker et al. (2010), I assumed that, for fishes and invertebrates, the basal metabolic rate increases by 15% and larval mortality increases by 25%. For invertebrates only, I also assume that mortality of adults would increase by 25%  147  with the doubling of [H+] because of the negative impacts of ocean acidification on calcification. The sensitivity of these species on ocean acidification may change progressively as acidity increases. Here, I assume that species may have some tolerance to the acidity level until a critical level was reached. Other initial parameterization for the DBEM was based on Cheung et al. (2011). The sensitivity of the results to uncertainty associated with these parameters was explored later.  I simulated changes in maximum catch potential based on the methods described in Cheung et al. (2010). For each studied species, changes in maximum catch potential were calculated based on changes in species distributions (projected from the DBEM) and primary productivity from four global coupled carbon cycle-climate models under the SRES A2 scenario. I then applied the empirical method described in Cheung et al. (2010) to calculate the projected changes in catch potential in each 30? x 30? cell by 2041-2060 relative to 1991-2010 (see Appendix B).  5.4.2 Modeling economic impacts on commercial fishing An increase in ocean acidity can add more stress to the marine ecosystem and human communities that depend on them. I developed a quantitative approach for assessing the potential economic consequences of climate change and ocean acidification through fisheries in terms of changes in (i) value of fish and fisheries products (or total revenue); (ii) fishers' incomes (or payments to labor); (iii) fishing costs; (iv) economic rents; (v) economic impacts; and (vi) income impacts throughout the wider economies of the Arctic nations.  148   Total revenue or landed value is the product of ex-vessel price and catch in the case of commercial fisheries. This is the top line or gross revenue from fishing. The current gross revenue was estimated using ex-vessel prices of each species in 2005 real dollars (Sumaila et al. 2007; Swartz et al. 2012) and the average catch data from 1996 to 2005 from the Sea Around Us Project catch database (Watson et al. 2004, www.seaaroundus.org) (equation 13). Wages or payments to labor are the amounts earned by people who expend their labor, skills, and expertise in the sector (Dyck and Sumaila 2010). Two types of fishing costs, variable (operating) and fixed, were included. Costs associated with operating fishing vessels were categorized as variable costs because they vary with the level of fishing activity. The major items under variable costs include fuel, salaries for crew, repair and maintenance costs of vessels and gear, and the costs of selling fish via auction, fish handling and processing (e.g., the purchase of ice). Fixed costs do not vary with the level of fishing activity and are usually regarded as ?sunk? and consist mainly of the amount invested in vessels, i.e., their capital value. Interest and depreciation costs fall into this category. Interest cost reflects the opportunity cost of capital, and depreciation cost is the replacement cost for normal wear and tear of the fishing vessels. Both wages and unit fishing costs were obtained from the global cost of fishing database (Chapter 2; Lam et al. 2010).   Resource rent, which is what remains after deducting fishing costs and subsidies from revenue, is one of the most important indicators for reflecting fisheries performance. To capture important contributions of fish populations to the whole economy, the value created  149  through the production chain was also captured in my study. The added value or impact through the fish value chain is the indirect economic effects of fisheries due to their impact on activities such as boat building/maintenance, equipment supply and the restaurant sector (Pontecorvo et al. 1980). In much of the Arctic, fisheries constitute a base industry to the whole economy, for example, in Iceland and in Greenland (Arnason 2007). The estimated indirect and induced economic impacts of climate change and ocean acidification on the fisheries sector in the Arctic region were estimated by applying the national fishing output multipliers4 reported in Dyck and Sumaila (2010) (Appendix D Table D.1). Also, the effect of climate change on Arctic fisheries may already have changed the sector?s ability to generate income (Eide and Heen 2002). I estimated the total household income throughout the economy supported by the output of the fishing sector through indirect and induced effects using income multipliers (Dyck and Sumaila 2010, Appendix D Table D.1).  I assume that each economic indicator is related to landings (h) in the following manner:                    (1)               (2)                                                              4 If we use the re-estimated multipliers under climate change (Norman-Lopez et al. 2011), the impact of reduced revenues on the whole economy would be less. Similarly, cost per tonne is dependent on the underlying stock, therefore an increase in stock my result in a lower fishing cost per tonne because of the the stock effect. By linking the total cost to the fish stock, it is likely that total cost will be over-estimated and hence multiplier effects will also be over-estimated as input use will not increase proportionally with revenue.    150             (3)                             (4)                           (5)                      (6)  where        represent unit price and wages, respectively. The parameter, c, is the fishing cost per tonne of catch, while S denotes the subsidies provided by governments (Sumaila et al. 2010). The parameters M and Z represent the economic impact multiplier and income multiplier for fisheries, for countries bordering on the Arctic, as estimated by Dyck and Sumaila (2010), respectively. The contribution of fisheries production to other sectors of the economy was assumed to be unchanged over time under climate change.   As mentioned previously in Chapter 3 and 4, the inflation adjusted ex-vessel price (p), wages per tonne of catch (w), the unit fishing cost (c) and the subsidies (S) were assumed to be equal to the current level under both projected scenarios. This study assumes that the real ex-vessel price (after adjusting for inflation) is constant throughout the study period because the projection of future prices is limited by data availability and model complexity. Also, real ex-vessel fish prices have, in general, remained relatively stable since 1970  151  (Delgado and Courbois 1999; Swartz et al. 2012). Although real fish prices are likely to increase in the future, for example, fish prices were projected to increase by about 6?15% over the 1997 level by 2020 (Delgado et al. 2003), using constant price allows us to shed initial light on critical questions, including those pertaining to the impacts of climate change.   Fishing costs may also change throughout the study time period depending on fuel prices and the adaptive capacity of the fishers. The adaptive capacity of fishers to climate change is the feasibility of the fishers to find tactics to maintain their livelihood under unfavorable climate conditions. However, I did not include a model for projecting the change in fishing costs. The unit fishing costs and unit wages were assumed to be unchanged with time in this study. Since the subsidies data was country-based (Sumaila et al. 2010), I estimated the subsidies provided by each country in the Arctic region using the proportion of their total revenue from this region.   5.4.3 Uncertainty analysis I performed an uncertainty analysis using the Monte Carlo method (Buckland 1984) to determine the level of uncertainty associated with the projected estimates of my analysis. With this method, the projected estimates were calculated using randomly generated parameters drawn from the probability distribution specified for each, and the calculation was repeated 10,000 times. Then, probability distributions of the projected total catch, total revenues, fishers? incomes, fishing costs, economic rents, economic and household income impacts were calculated, from which I determined the medians and associated uncertainty of the final results. This method allowed me to estimate the range of values for my projected  152  variables and addressed the uncertainties of the impact of OA on the marine species and the economic estimates.   All input parameters, including projected catches, unit fishing costs and ex-vessel prices had some uncertainty associated with them. The uncertainty had been already addressed for the projected catches by using four different ESMs as described above. Furthermore, the uncertainty regarding how sensitive marine species are to OA was addressed by assuming a range of percentage impacts of OA on these species. I assumed that basal metabolic rate increases by 10 ? 20% and larval mortality increases by 20 ? 30% for fishes and invertebrates. I also assumed that the mortality of adults would increase by 20 ? 30% with the doubling of [H+] because of the negative impacts of ocean acidification on calcification for invertebrates only. Then, I ran the DBEM using the two extremes and the median of these ranges, respectively. The projected changes in physical parameters are the outputs from GFDL only as the projected catches under this model lie in the middle range among the results from the other 3 ESMs. The maximum potential catch under OA was then projected by using the empirical method described above. For the projected catch by country and gear type under OA, I assumed that the possible values follow a triangular distribution that was defined by a mid-point and the upper (i.e., lower sensitivity to OA) and lower limits (i.e., higher sensitivity to OA).  The Monte Carlo method was used to deal with the uncertainty associated with economic parameters in the model. The unit fishing cost of each gear type by each fishing country was obtained from the global cost of fishing database (Chapter 2; Lam et al. 2011).  153  The ex-vessel prices of all species for each country in the Arctic region were extracted from the global price database (Sumaila et al. 2007; Swartz et al. 2012).    Following the standard procedures of the Monte Carlo method, the economic analysis (equations 1 ? 6) was repeated 10,000 times, each with a different set of inputs drawn randomly from the distributions associated with the different parameters. For the unit fishing cost of each fishing gear type and the ex-vessel price in each Arctic country, I assumed that the possible values follow a triangular distribution that is defined by a mid-point and the upper and lower limits (Appendix D Figure D.1). The distribution of the outputs was then used to calculate the medians of all economic outputs. I also reported the 2.5th and 97.5th percentile values.   5.5 Results  The overall landing by all Arctic countries combined, currently 2.5 million tonnes?year-1 in the 2000s, is projected to increase by 39% (12 to 61%) or 0.9 million tonne?year-1 under climate change scenarios in the 2050s relative to the 2000s. The mean values and the ranges reported here are from the results of the four ESMs (Appendix D Table D.2). All countries showed an increase in fish landings in the 2050s under the climate change only scenario (Figure 5.2). The gain in catches in the Arctic countries is mainly due to the redistribution of fish towards the Arctic as a result of warming (Cheung et al. 2010; 2013). With the projected increase in catch, the total increase in total revenue in constant real dollars of Arctic fisheries  154  is 39% (14 to 59%) or US$ 751 million?year-1. Total revenue increases by approximately the same proportion as catch. Under the climate change only scenario, all the countries showed an increase in total revenue in the 2050s (Figure 5.3).   Figure 5.2 Landings by country under three scenarios ? Current; Climate Change (CC) and Climate Change with Ocean Acidification (CC+OA).        155   Figure 5.3 Percentage change in total revenue (landed value) by country under the effect of ocean acidification i.e., the percentage change in total revenue from that projected under climate change only scenario (SRES A2) to that projected under both climate change and ocean acidification scenarios.   Part of this gain, however, is lost when OA is taken into account. The overall landing increases by only 35% (11 to 70%) or 0.8 million t?year-1 under the climate change with OA scenario in the 2050s. Under the same scenario, the total revenues increase by only 33% (13 to 62%) or US$ 639 million?year-1 in the Arctic region in the 2050s relative to the 2000s. The landings and total revenues change by -2.9% (-16 to 6%) or less 95,000 tonne?year-1 and -6.5% (-17 to 2%) or less US$ 112 million?year-1 from the climate change only scenario and when ocean acidification is taken into account, respectively. Although the impact of OA on molluscs is expected to be larger than that on other species (Kroeker et al. 2010), the effect of OA on both the landings and the total revenue solely on molluscs change by only -4.2% (-18 to 1%), respectively.    156  The impact of OA on the total revenues is higher than that on the landings because OA has a higher impact on those species with higher ex-vessel prices such as Atlantic halibut (Hippoglossus hippoglossus) and lemon sole (Microstomus kitt) (Appendix D Table D.3). Under the impact of ocean acidification, the total revenues of all countries decrease slightly compared to the total revenue generated under the climate change only scenario (Figure 5.2). As the proportion of invertebrates was very low (i.e., only 5% of the total catch) in this study, the impact of ocean acidification on the landings and total revenue was also relatively low.    Projected change in catches under the two scenarios also leads to subsequent impacts on fishers? income and the whole economy in the Arctic. The current income of fishers is US$ 1.20 billion?year-1. When the total annual landing was projected to increase, fishers? income was also projected to increase to US$ 1.7 billion?year-1 (US$1.4 ? 1.9 billion?year-1) and US$1.6 billion?year-1 (US$ 1.3 ? 2.0 billion/?year-1) in the 2050s under climate change and climate change with OA scenarios, respectively (Table 5.1). As such, the impact of OA on the fishers? incomes is very small in the 2050s, i.e., with a loss of US$ 56 million?year-1or change by -5.8% (-16 to 5%) relative to the climate change only scenario.     157  Table 5.1 Potential wages (income) earned by fishers by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA)  Country Current wages (US$ millions) CC wages (US$ millions) CC+OA wages (US$ millions) Canada 7.03 9.02  (7.58 ? 10.56) 8.56 (7.47 ? 10.53) Faeroe Is 58.98 81.74 (61.21 ? 93.62) 78.64 (61.00 ? 96.40) Finland 0.015 0.018 (0.016 ? 0.021) 0.017 (0.013 ? 0.021) Greenland 87.78 116.00 (452.50 ? 618.87) 110.99 (96.71 ? 137.55) Iceland 374.49 527.99 (387.30 ? 553.96) 520.62 (448.45 ? 675.33) Norway 352.36 494.03 (387.30 ? 553.96) 470.09 (383.73 ? 564.97) Russian Fed 299.87 408.10 (325.82 ? 470.32) 393.21 (324.20 ? 485.44) Sweden 3.78 4.86 (3.92 ? 5.65) 4.64 (3.86 ? 5.70) USA 13.10 22.31 (18.89 ? 25.65) 21.23 (19.04 ? 24.76) USA (Alaska) 0.24 0.55 (0.32 ? 0.84) 0.54 (0.32 ? 0.79) Total 1197.64 1,664.62 (1,358.28 ? 1,913.43) 1608.55  (1,347.42 ? 2,001.02)     158  Change in the distribution and maximum catch potential of different fish species under climate change leads to change in the composition of exploited species, and hence the landed value of each fishing country as fishers may have a larger/smaller share of high/low value species in their catch than before. Some of the temperate species, which contributed only to a minor proportion or did not occur in the previous catches, may become an important species in the catch of some countries under climate change, for example, Atlantic cod and Atlantic mackerel in Canada (Appendix D Table D.4). The composition of species in the harvest is not only important for the total revenue earned, but also crucial in determining the gear types employed, the catch per unit effort (CPUE) and hence the fishing costs of each country. A potential increase in catch under climate change scenarios also explains the increase in total fishing costs in the Arctic region (Table 5.2). The total fishing cost increase by 39% (14 to 60%) or US$ 1.2 billion?year-1 under climate change scenarios in the 2050s relative to the 2000s. The overall total fishing cost increases less (35% (i.e., 11 to 70%) or US$ 1.1 billion?year-1) under the climate change with OA scenario in the 2050s. That means the total fishing costs change by only -5.6% (-16 to 5%) when ocean acidification is considered in the 2050s.     159  Table 5.2 Annual total fishing cost by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA). Country Current Total Fishing Cost (US$ millions) CC Total Fishing Cost (US$ millions) CC+OA Total Fishing Cost (US$ millions) Canada 13.99 17.94 (15.09 ? 21.01) 17.03 (14.87 ? 20.96) Faeroe Is 124.71 172.43 (130.12 ? 197.82) 165.98 (129.63 ? 203.96) Finland 0.04 0.047 (0.04 ? 0.055) 0.04 (0.03 ? 0.05) Greenland 174.12 230.34 (191.96 ? 269.38) 220.71 (191.83 ? 274.29) Iceland 1,144.23 1,612.31 (1,371.35 ? 1.891.33) 1,590.56 (1,359.22 ? 2,065.57) Norway 793.25 1,116.70 (886.01 ? 1,256.48) 1,061.69 (876.54 ? 1,275.21) Russian Fed 943.59 1,287.67 (1,022.45 ? 1,484.51) 1,241.01 (1,017.77 ? 1,531.52) Sweden 11.64 15.23 (12.10 ? 17.76) 14.63 (12.04 ? 18.19) USA 25.08 42.16 (35.50 ? 48.28) 40.13 (35.74 ? 46.72) US (Alaska) 0.50 1.15 (0.67 ? 1.76) 1.14 (0.68 ? 1.66) Total 3,231.16 4,495.98 (3,674.17 ? 5,176.82) 4,352.92 (3,644.62 ? 5,437.16)    160  Under the current status, almost all of the Arctic countries, except Greenland, Norway, United States and Alaska (U.S.), get negative resource rent if subsidies are taken into account. The total resource rent in the Arctic region in the 2000s was negative US$1,282 million?year-1. Under the climate change only scenarios, the total resource rent was projected to decrease by 40% (13 ? 63%) or US$ 514 million?year-1. A similar pattern was projected for the economic rents when OA was included in the scenario. The total economic rent in the Arctic region was projected to decrease by 38% (12 ? 77%) or US$ 483 million?year-1 under the climate change with ocean acidification scenarios. Although the total revenues in all countries increased under the climate change scenarios, their total fishing costs also increased at the same time. When more marine species are caught using high cost fishing gear increases, the percentage increase in fishing costs is more than the percentage increase in landed values. Thus, the economic rents are lower than the current status even though the landed values increase. When OA is taken into account, Greenland, Iceland, Norway, United States and Alaska (US) show a decrease in economic rents (Figure 5.4) when compared to the climate change only (A2) scenario; however, OA has a positive impact on economic rent in other countries. Although catches and the total revenue increase in these five countries when OA is incorporated, the total fishing cost also increase to a larger extent. Hence, the economic rents in these three countries decline further under the OA scenario.  The weighted average economic multiplier for impact on the Arctic economy in the Arctic Region is estimated at 3.02 (Appendix D Table D.5). Therefore, a fair amount of economic activity is expected to benefit from the increased fisheries output because of the poleward shift of marine species under climate change scenarios. This multiplier suggests  161  that one dollar of the fisheries sector output supports approximately three dollars of output in other economic sectors. Under the climate change scenarios studied, the contribution of fisheries to the economy in this region is projected to increase by 37.5% (12 ? 58%) or US$2.5 billion?year-1 (Table 5.3). The projected effect of OA is to change the economic impact by -6.6% (-16.9 to 1.9%) or US$ 390 million?year-1 less when compared to that generated under the climate change only scenario in the 2050s.  Figure 5.4 Percentage change in the resource rent of countries in the Arctic when comparing the resource rent projected under climate change and ocean acidification scenario with that projected under climate change only (A2) scenario.    162  The amount of household income supported by the fisheries sector in the Arctic was also estimated. Among the 10 countries, Norway has the highest total income supported by capture fisheries sector. The total income impact in Norway, in fact, amounts to 53% of the household income in the whole Arctic region (Table 5.4). Under the climate change scenario, the total income impact in the Arctic region increases by 38% (13 ? 59%) or US$ 583 million?year-1 (Table 5.4). When assuming the per capita income is constant, the increase in total household income impact is equivalent to an increase of approximately 20,000 jobs from the current 212,000?70,000 fisheries-related jobs in these 10 Arctic countries (Teh and Sumaila 2013). The change in the number of jobs is calculated by dividing the projected change in household income impact under climate change scenario by the annual personal average gross income in the Arctic Region (i.e., ~US$ 29,000), which was the average calculated per capita personal income from Alaska, Canada, Russia, Greenland, Iceland, and Norway in 2005 (http://www.arcticstat.org). When ocean acidification is taken into account, the total household income impact decreases by US$ 90 million?year-1 (i.e., change by -6.6% (-17.0 to 2.0%)) compared to that generated under the climate change only scenario in the 2050s. This can be equivalent to the loss of approximately 3,100 jobs (i.e., ~2% of job loss from the current situation) when the amount of income loss due to OA (i.e. US$ 90 million) is divided by the annual personal average gross income in the Arctic Region (i.e., ~US$29,000).       163  Table 5.3 Economic impact by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA).  Country Current Economic Impacts (US$ millions) CC Economic Impacts (US$ millions) CC+OA Economic Impacts (US$ millions) Canada 30.03 37.93 (32.27 ? 44.75) 36.14 (32.23 ? 44.74) Faeroe Is 153.07 209.19 (169.24 ? 240.77) 200.72 (168.75 ? 245.42) Finland 0.011 0.014 (0.012 ? 0.016) 0.013 (0.01 ? 0.016) Greenland 1,515.47 1,980.75 (1,652.18 ? 2,332.18) 1,891.51 (1,650.88 ? 2,345.26) Iceland 1,254.81 1,756.50 (1,497.02 ? 2,016.14) 1,701.78 (1,492.31 ? 2,109.36) Norway 3,117.60 4,341.58 (3,479.41 ? 4,935.73) 4,143.80 (3,458.61 ? 5,019.58) Russian Fed 478.85 648.80 (525.35 ? 746.35) 620.21 (523.98 ? 754.95) Sweden 16.03 20.95 (17.28 ? 24.55) 20.19 (17.18 ? 25.32) USA 102.26 171.19 (145.52 ? 196.31) 162.59 (145.26 ? 193.91) US (Alaska) 2.57 6.00 (3.38 ? 9.33) 5.92 (3.54 ? 8.85) Total 6,670.70 9,172.91 (7,521.67 ? 10,546.13) 8,782.87 (7,553.45 ? 10,742.09)     164  Table 5.4 Household income impact by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA).  Country Current Income Impacts (US$ millions) CC Income Impacts (US$ millions) CC+OA Income Impacts (US$ millions) Canada 9.76 12.33 (10.48 ? 14.54) 11.74 (10.47 ? 14.54) Faeroe Is 40.83 55.80 (45.14 ? 64.22) 53.54 (45.01 ? 65.46) Finland 0.0030 0.0038 (0.0032 ? 0.0044) 0.0036 (0.0027 ? 0.0044) Greenland 272.02 355.54 (296.56 ? 418.62) 339.52 (296.33 ? 420.97) Iceland 254.79 356.66 (303.98 ? 409.38) 345.55 (303.02 ? 428.31) Norway 803.59 1,119.08 (896.85 ? 1,272.22) 1,068.10 (891.48  ? 1,293.84) Russian Fed 95.22 129.01 (104.47 ? 148.41) 123.33 (104.19 ? 150.12) Sweden 3.48 4.54 (3.75 ? 5.32) 4.38 (3.73 ? 5.49) USA 42.71 71.50 (60.78 ? 81.99) 67.91 (60.67 ? 80.99) US (Alaska) 1.07 2.51 (1.41 ? 3.90) 2.47 (1.48 ? 3.70) Total 1,523.47 2,106.97 (1,723.42 ? 2,418.62) 2,016.54 (1,728.64 ? 2,461.20)     165  5.5.1 Uncertainty analysis The median and associated range of uncertainty for the final results of the projected catch, total revenue, fishers? incomes, total fishing costs, economic rents, economic and income impacts are shown in the Appendix D (Table D.6 (a ? c)). Sensitivity analysis shows that the uncertainties of the sensitivity of marine species to OA do not greatly affect the results (affect the catch by only ?1%). My results are also robust as to the uncertainty associated with input variables of the economic models, including ex-vessel price and fishing costs (Table D.6 (b)). The small range of the distribution of the economic outputs from the sensitivity analysis indicates that the uncertainty from the economic parameters is smaller than the uncertainty among the different ESMs.   5.6 Discussion  5.6.1 Impact on landings The preliminary projections highlight the potential economic impacts of climate change and ocean acidification on Arctic fisheries despite the quantitative projections being uncertain. Although trophic interactions and potential evolutionary and adaptive responses were not considered, the key assumptions in the modeling analysis include the level and mechanism of biological responses of organisms to ocean acidification and other marine climate changes. Also, projected changes in ocean biogeochemistry, particularly sea ice and primary production, vary considerably between Earth System Models (Steinacher et al. 2010). As there is disagreement in the projected primary productivity among these four models in the  166  Arctic (Steinacher et al. 2010), the projected ranges of the parameters in my analysis are large. However, projections from the four earth system models can help assess the level of uncertainty in my estimations.   From our results (as stated in section 5.5), landings of all fish and invertebrates are projected to decrease by only 2.9% (-15.7 to 5.7%) from the climate change only scenario under the impact of OA in the 2050s. Although the impact of OA on molluscs was found to be more severe in other studies, i.e., 10?25% decrease in the harvest of US molluscs from the 2007 level (Gazeau et al. 2007; Cooley and Doney 2009) and a 10?40% decrease in calcifying organism with a drop of 0.4 unit of pH in coastal lagoon environments (Wootton et al. 2008), my results showed that molluscs in the Arctic are only slightly impacted by OA with a mean of 4.2% decrease in landings. The uncertainty of the projected change in landings, which ranges from -18% to 1%, over the current status is large. The projected positive impact of OA on the landings (i.e., the upper range) is within the random variability of the model due to stochastic dispersal of species in the two models (i.e., the one with OA and that without OA).   The divergence of my results from other studies on the impact of OA on marine resources can be illustrated by the different assumptions among these analyses and the large variation of OA impact in different regions. Instead of equalizing the rate of harvest loss of shellfish to the decrease in the calcification rate due to OA (Cooley and Doney 2009), I assumed the change in basal metabolic rate and larval and adult mortality under the effect of  167  OA based on the meta-analysis by Kroeker et al. (2010) and used these assumptions for the input of the catch projection model. Also, the indirect impact of OA on the food web through species interaction was not addressed in my study. I addressed the uncertainty of the sensitivity of different marine species to OA using the Monte Carlo method. Uncertainty from the sensitivity of different marine species is minor when compared to the uncertainty from the ESM. The wide range of uncertainty from four different ESMs indicated that my model cannot fully explain the complexity of the impact of OA on fish and invertebrates. As the proportion of catches of invertebrates to the total marine catch in the Arctic is small (5%), and only 0.4% of the catches is from shelled molluscs, which are expected to have the greatest impact through undersaturation of aragonite, the impact of OA on the overall landings is not expected to be as significant as the impact of climate change in the Arctic. Although ocean acidification only causes the catch and the economics parameters to decrease slightly from the climate change only scenario, the uncertainty of the projected impact from different ESMs is very large. Thus, detailed studies on the impact of ocean acidification and its synergistic effects with other factors on the marine ecosystem in the Arctic still needs to be performed.    5.6.2 Economic impact The projected increase in catch under the A2 scenario in the Arctic leads to a substantial increase in the total revenues in all countries in the Arctic. It should be noted, however, that ths projected positive impacts are highly uncertain as the melting of ice may open up the marine resources for other fishing nations such as Japan and China. Therefore, climate  168  change may not be beneficial to Arctic countries unless an Arctic fishing accord, which bans commercial fishing in this area until more precise scientific studies are conducted on the environmental conditions and fish populations, is reached. Five Arctic nations, including Norway, Denmark, Canada, the United States and Russia are currently discussing such an agreement (Kramer 2013). These positive impacts are also weakened when OA is taken into account; however, OA?s projected effects on landings and total revenues are relatively minor. Although the economic impact of OA is estimated to be small in this analysis, it is still difficult to draw a conclusion to the exact degree of impact as I assumed the future demand and consumption rate of molluscs to be constant. If the demand for molluscs increases because of an expected income growth or shift in consumers? preferences, the impact of OA on the economics in the Arctic will be more extensive. Thus, my estimation of the economic impact of OA in the Arctic is conservative.   A change in the distribution and maximum catch potential of different fish species under climate change leads to a change in not only the composition of exploited species, but also the gear types employed and hence the fishing costs for each country. Since exploited species are usually gear specific (McClanahan et al. 2008b), one of the strategies for fishers to adapt to the change in species abundance and/or composition under climate change is to switch their gear types (Grafton 2010). Another strategy adopted by some mobile fishers to maintain profitability under climate change is to move to other fishing grounds with a higher abundance of the targeted species. As such, the distance from the fishing ports may alter and thus ultimately affect the traveling costs. However, the extent of changes may vary among different fishing fleets.  169   Under different management regimes, the variable fishing costs may also vary. Under the state of no restriction on the fishing efforts or open access fishery, the variable fishing costs may increase when the maximum catch potential of marine species increase. For example, the international waters beyond the Exclusive Economic Zone (EEZs) of the Arctic countries are not currently governed by any international fisheries agreements or management measures. A rise in fishing costs results because more effort is allocated to the fishing ground when the maximum catch potential increases. However, for those managed fisheries, e.g., with access limited to vessels, catch quota or limited fishing days, the variable fishing costs may decrease when the targeted species become more abundant as fishers may spend less time and effort searching for and catching the same amount of fish.   Also, if the fishers have a greater capacity to adapt to the change in relative abundance, for example with better technologies and higher mobility, their fishing costs may remain more or less the same even if the distribution of marine species shifts. If the fishers have a lower adaptive capacity, the fishing cost may increase when the species distribution and composition shift. Subsistence fishers are usually considered to be the ones with a lower capacity to adapt to change. There is a high percentage of subsistence fisheries, which usually operate with small boats, in the Arctic region. Subsistence catches contributed about 54% of the total catch (i.e. 48,200 tonnes) for 15 coastal and near-coastal communities in the Arctic Alaska (FAO Statistical Area 18) from 1950 to 2006 (Booth and Zeller 2008). Although there is a projected increase in the maximum catch potential in the Arctic region,  170  the mobility of these subsistence vessels may not be high enough to move to new fishing grounds. Also, these subsistence fishers most likely will not have sufficient capital and technology to switch their gear types for the newly invaded exploited species under climate change. The above two reasons may lead to an increase in the unit fishing costs. Although the total fishing cost may be lower when the maximum potential catch of exploited species increase, other factors such as fuel prices may also affect fishing costs. In the worst case scenario, these subsistence fishers may have to relocate if the resource moves beyond their reach. Therefore, the impact of climate change on subsistence fisheries should be treated differently from that on commercial fisheries. This is an area that needs further study and consideration.   Although the unit fish price is kept constant in my study, I understand that fish prices may fluctuate across the study period. Fish prices are generally influenced by local markets, the global supply of fish, preferences of consumers, prices of alternative products on the market, and an abundance of targeted species (Murawski and Serchuk 1989; OECD 1997; Asche et al. 1999; Hannesson 1999; Pinnegar et al. 2006). The projected imbalance between fish supply and demand may thus lead to increases in fish prices (Alexandratos 1995; Sverdrup-Jensen 1997). Some Arctic countries including Norway, the United States and Canada were in the top ten exporters of fish and fishery products in 2008, while the United States was also the second largest importer of fish that year (FAO 2012). Therefore, fish prices in the Arctic may be affected by the change in abundance, catch potential, seafood demand by consumers, import amounts, and prices of fishes in other countries. Thus, climate  171  change induced shifts in the fish price of a particular species in a particular EEZ may also influence the prices of this species and landed values in the Arctic region.   Arctic marine ecosystem and fisheries are not only important to national economies of the Arctic countries, but also act as an even more important role to the livelihoods and culture of indigenous peoples. Thus, the change in the availability of marine resources may have negative impacts on the food security and nutritional health to the communities in the Arctic region. In fact, the indigenous peoples in many parts of the Arctic regions have already been experiencing the impacts caused by climate change. Under current environmental change, there is a general trend of decline in the harvest level of fish species including whitefish, herring, cisco and char in several communities in the Inuit region in the Canadian Arctic (Wesche and Chan 2010). Furthermore, the availability of migratory fish also becomes less (Andrachuk and Pearce 2010). Fish is one of the important traditional foods for the Arctic indigenous peoples, so the decline in catch and quality of these species not only poses a threat to the food security but also affects the nutritional security in this region, as fish provides important nutrients such as omega-3 fatty acids which are important to the health of Inuvialuit people (Weshe and Chan 2010). In contrast, the newly ice-free waters in the centre of the Arctic Ocean resulting from climate change may also open new opportunities for human exploitation. Although my results projected a substantial increase in fisheries catch and potential economic benefits to the Arctic communities when the Arctic ocean warms up, these opportunities may lead to unsustainable exploitation under poor or no management and become a threat to the marine resources. Hence, effective management plans are needed right away to help ensure sustainable use. Preventive action has already  172  been taken by five Arctic countries, including Norway, Denmark, Canada, the United States and Russia. Recently (April 2013), these countries reached an agreement with the intention to manage the potential commercial exploitation of stocks in this new ice-free zone (The New York Times 2013).   173  Chapter 6: Conclusions   Anthropogenic climate change is causing changes in ocean conditions that will affect marine organisms at individual, population, community and ecosystems scales (Barange and Perry 2009; Cheung et al. 2009; Daufresne et al. 2009; P?rtner and Knust 2007; Rijnsdorp et al. 2009; Hoegh-Guldberg and Bruno 2010; P?rtner 2010). With a substantial increase in the heat content of the ocean, it has warmed on average (about 0.2?C increase) between the mid 1950s and the mid 1990s (Domingues et al. 2009; Levitus et al. 2005) with regional variations (Belkin 2009), and this trend is expected to continue in the next century under the climate change scenarios considered by IPCC (2007). The extent of sea ice has been declining since 1980 (Schofield et al. 2010), with both Arctic and Antarctic sea ice projected to shrink substantially under all emission scenarios (Meehl et al. 2007; Stroeve et al. 2007). Climate change may also lead to changes in salinity, ocean current and mixing patterns, expansion of hypoxic or oxygen minimum zones, sea-level rises and increases in extreme events and short and long term variability of these conditions. Increased concentration of CO2 in the atmosphere also leads to ocean acidification (Doney et al. 2009). These changes alter the physiology, productivity, distribution, phenology, and composition of marine flora and fauna, leading to changes in ecosystem structure and eventually affecting the fisheries sector (Barange and Perry 2009; Brander 2010; Cheung et al. 2010; Cheung et al. 2011).    Previous studies have already projected that many exploited marine fishes and invertebrates will continue to shift latitudinally and into deeper waters by 2050 relative to the current status, under most of the IPCC emission scenarios (Cheung et al. 2009; Hare et al.  174  2010; Hobday 2010; Cheung et al. 2011). In the meantime, other synergistic factors such as high uncertainties in the projected primary productivity under climate change (Sarmiento et al. 2004; Steinachner et al. 2010), increase in oxygen minimum zones and increase in ocean acidity also affect the recruitment, growth, and distribution of fishes and invertebrates (Brander 2010; Cheung et al. 2012). Studies and data in different regions have already shown that shifts in species? geographic range under climate change has been affecting fisheries resources and fishing operations (for example, Chavez et al. 2003; Miller and Munro 2004; Caputi et al. 2010). Global modeling studies also linked climate-induced changes in physical conditions of the ocean, primary productivity, physiology, population dynamics, geographic distributions, ocean pH and hypoxia with impacts on potential fisheries catch (Cheung et al. 2010, 2011; Hobday 2010; Hare et al. 2010). The change in fisheries yield may have great implications on people who depend on fisheries for food and income and eventually the economics of the whole society. To develop effective adaptive strategies and measures for maintaining the sustainability of fisheries, it is crucial for us to consider the effect of climate change.   In this thesis, I assess climate change impacts on catch, profitability, cost of fishing, resource rents, and thus the socio-economics of marine fisheries globally and in two of the most climate change vulnerable regions i.e., West Africa and the Arctic region (ACIA 2004; Allison et al. 2009). To understand the economic impact of climate change through fisheries, I first estimated the cost of fishing in major fishing countries under the current climate regime (Chapter 2), then I projected the change in landings, total revenue, fishing costs and resource rent of major commercial marine fisheries in EEZs  in the face of climate change  175  (Chapter 3),  and finally I projected the impact of climate change on the fisheries and its subsequent economic impact at regional scales, i.e., West Africa (Chapter 4) and the Arctic (Chapter 5).     In Chapter 2, I describe the development of a new global database of fishing cost, and provide an overview of fishing cost patterns at national, regional, and global scales. Information on fishing cost in most countries and regions of the world is scarce, widely scattered, and incomplete. Thus, the construction of a global cost of fishing database is a pioneer approach in this area. This study is important not only because it estimates the variable and fixed costs of fishing in major maritime countries under the current status, but also allows us to project the cost of fishing and resource rent under climate change in the subsequent chapters in this thesis. The outcomes are also useful for other future fisheries economic studies.    In this chapter, I present the procedures for developing a global cost of fishing database and highlight some of its potential applications. The total global variable fishing cost is estimated to be in the range US$ 50?96 billion per year, with a mean of $73 billion per annum in 2005 dollar equivalents. The database is the first version of what should be considered a ?living? database, meaning that effort will be devoted to updating and improving it in the future. The current version is already aiding researchers, fishery managers and other parties assess the economic status of fisheries and the impact of different management policy scenarios at different spatial scales (for example, Srinivasan et al. 2012;  176  Suamila et al. 2012,  Walden 2013). When combined with landed values (Sumaila et al., 2007, Swartz et al. 2012), this information allows estimation of the economic rent from fisheries around the globe, as well as the profitability of fishing operations (Sumaila et al. 2012). The database also allows the mapping of port-based fishing effort by fleets, so can help estimate the distance travelled by fleets, and it can be used to explore research areas such as assessment of the cost structure and efficiency of different gear types in different regions of the world, and aid in developing fishing cost functions.   In Chapter 3, I investigate the potential direct impacts of global climate change on the economics of fisheries in major EEZs (178) and regions in the world in terms of total revenue, fishing costs and resource rent by using the current information on ex-vessel prices (Sumaila et al. 2007; Swartz et al. 2012) and fishing cost (from Chapter 2). By examining the change in these economic parameters of each country, I identify those countries which are more economically vulnerable to climate change. Since climate change is a global issue, development of mitigation and adaptation policies requires an understanding of its impacts at a comparable scale. This study is a pioneer project for predicting the impact of climate change on the economics of fisheries globally. The results of this study would act as a complement to other economic analyses on the impact of climate change and will help policy-makers in deciding climate-change policies for avoiding, adapting or mitigating this impact.   In this chapter, I first estimate the differences in catch potential and change in catch of over 800 species by each fishing country under climate change scenarios based on a  177  published bioclimate envelope model (BEM) and an empirical model (Cheung et al. 2008a, 2008b, 2009 and 2010). Economic impacts were then analyzed through the change in different economic parameters, including total revenue, fishing costs and economic rent (Sumaila et al. 2007, 2010, Chapter 2) at the Exclusive Economic Zone (EEZ) levels. I finally examine the change in species composition and fishing gear composition within each EEZ under climate change. I also use a multi-model ensemble to explore sensitivity of the assessment and address the uncertainties of the ESMs. Although no zonal pattern of catch change along the latitude is identified in this study, landings and economic parameters are projected to decrease in most of the EEZs (72%) under climate change scenario in the 2050s. Decline in the landings under climate change also causes substantial decrease in total revenue and fishing cost. The global resource rent is projected to increase by 17% under climate change, driven largely by the more rapid decrease in fishing cost relative to total revenue as a result of expected responses of fishing efforts to climate change. However, global resource rent amount is still negative, which means fisheries is still underperforming in the 2050s under change. Thus, it is necessary to include climate change impacts in the process of planning and designing of effective fisheries management measures.  The impacts of climate change on fisheries have different direction and magnitude in different regions, and countries have different vulnerability to these impacts based on their exposure, sensitivity and adaptive capacity (Allison et al. 2009). West Africa was identified as one of the most vulnerable regions to climate change in previous global analyses (e.g., Allison et al. 2009). Adverse changes in marine resources under climate change may pose significant threats to the livelihoods and well-being of the communities and countries which  178  depend on fisheries for food and income. In Chapter 4, I describe the model for assessing the potential impacts of climate change on fisheries and their effects on the economics, food and nutritional security in West Africa. A dynamic bioclimatic envelope model was used to project future distribution and maximum fisheries catch potential of fish and invertebrates in West African waters.  Under the high-range greenhouse gas emission scenario (SRES A1B) there is a noticeable projected reduction in landings in the 2050s with some countries suffering more than 50% decline from their current production. A few of these countries had high proportions of their populations in a condition of undernourishment, so reductions in their landings would have great implications in terms of food security. The effect of climate change will further exacerbate the large gap between fish demand and fish supply. Climate change impacts not just on food security and the nutritional quality of people?s diet, but also has great impact on the economies in West Africa in terms of landed values, employment opportunities and the economy of other sectors. Ghana and Nigeria are predicted to suffer their greatest loss in the economic output fisheries in terms of direct, indirect and induced impacts under climate change. Thus, the impact of climate change not only affects the livelihoods of small-scale fishers, but also affects food security in West Africa indirectly.    By understanding the potential future states of the fisheries in West Africa, appropriate policies related to climate challenges can be designed and implemented. The feasibility of using aquaculture as a solution for reducing the risks and uncertainties of  179  capture fisheries was also been discussed in this chapter.  While preparing for the adverse impacts from climate change, measures to tackle current fisheries problems are crucial for providing foundations for the future challenges.    Another region with high vulnerability to climate change is the Arctic. Different climate models showed that Arctic has the greatest temperature increase and the most rapid rate of decrease in pH of the surface ocean waters under climate projections (Anisimov et al. 2007; Steinacher et al. 2010). Also, there are large uncertainties in the projection of future primary productivity in the Arctic Ocean (Steinacher et al. 2010). Projected temperature increase may lead to high rate of species invasion in the Arctic; however, the decrease in the saturation level of calcium carbonate minerals may induce an adverse impact on calcium carbonate-secreting organisms and other species in higher trophic levels (Feely et al. 2009; Kroeker et al. 2010). In Chapter 5, I focus on assessing the impact of projected changes in distribution and fisheries catch potential on the economics of marine fisheries in the Arctic under scenarios of climate change and ocean acidification.    My model projected an increase in landings and total revenue in the Arctic region in the 2050s under SRES A2 scenario when only climate change (not ocean acidification) is not taken into account. Although my model only projected a slight decrease in catch potential of marine fish and invertebrates under the impact of OA in the 2050s, it is still difficult for me to draw a conclusion as to the exact degree of impact as the uncertainty of the projected impact from different Earth System Models (ESMs) is large. Moreover, I assumed that the  180  demand and consumption rate of molluscs, which are expected to have the greatest response through undersaturation of aragonite, are constant in the future. As such, detailed studies on the impact of ocean acidification and its synergistic effects with other factors on marine ecosystems in the Arctic are still needed.     In my research, landings in the Arctic region are projected to increase (Chapter 5), whereas landings in the EEZs at the high latitudinal regions are projected to decrease (Chapter 3) in the 2050s under the same climate change scenario. Chapter 5 focused on the catches in the Arctic Circle, which is bounded to the north by the North Pole and to the south by the latitude 66?N. However, I focused on EEZs in the global study chapter (Chapter 3). Upon closer examination of the results, the projected increase in the catch of marine fish and invertebrates only takes place at very high latitudes, where the catch only contributes a relatively small proportion to the total catch of those fishing countries that operate fisheries in the Arctic, i.e., 18% of the total catch by these fishing countries.    It is important to increase the robustness of fish stocks to climate change by strengthening fisheries management and rebuilding depleted stocks. Although there may be short-term costs, the medium and long term negative implications for food security in these vulnerable regions would likely be minimized. To reduce the impact of climate change on fisheries and ensure the sustainability of fisheries, it is also crucial to reduce the emission of greenhouse gases (GHG). As climate change is a global issue, it requires the cooperation and the commitment of different countries on the international treaties for the reduction GHG  181  such as the Kyoto Protocol. However, several developed countries, who contributed up to 77% of emissions between 1750 and 2004 (Raupach et al. 2007), have no binding target in the second commitment period between 2013 and 2020.  As such, it seems that we cannot be too optimistic on the mitigation measures and should develop adaptation strategies to tackle the impact of climate change on fisheries wherever possible  Adaptation Adaptation strategies should be applied at both the fisher?s level, and at the management and governance levels (Sumaila and Cheung 2010). Fishers can adapt to the change by changing their fishing grounds, targeted species and fishing gear types. At the fisher?s level, the adaptive capacity depends on the accessibility to capital resources, which is greatly determined by the employment security, knowledge on the changing environment, and flexibility in harvesting practices and institutions. For example, Arctic?s indigenous peoples have already developed the capacity and flexibility to utilize their local resources with variable historical seasonal and ecological changes (Nuttall 2007). They have adopted different strategies to adapt to the changing conditions, including using the substitution of store foods for traditional foods, changing the routes to the harvesting grounds, and switching to harvest the more abundant species (Andrachuk and Pearce 2010; Wesche and Chan 2010). In these Arctic communities, these adaptation tactics are only reactive and without long-term adaptation planning to deal with the projected future climate changes. This adaptive capacity varies among fishers and different regions. Commercial fishers are expected to have higher adaptive capacity than the small-scale fishers, as the formers have  182  higher capital for adapting to the change. Also, developing countries with lower capital and technology, and countries with higher exposure to temperature changes may have lower adaptive capacity to these changes.   To adapt to climate change, artisanal fishers need to have sufficient capital in order to afford the new strategies; for example, the purchase of mechanized vessels, new gear types and additional fuel cost when using alternative fishing routes. To enhance the capital resources of the artisanal fishers, some programs for aiding fishers to secure their incomes can be implemented; for example, harvester assistant programs, job focused skills training, capacity building program and economic diversification (Andrachuk and Pearce 2010). Meanwhile, traditional environmental knowledge passed from generations to generations also plays an important role in adapting to climate change. Therefore, educational programs are needed to develop in order to guarantee the transfer to the indigenous knowledge to younger generations.   At the management and governance level, it is necessary to include climate change impacts in the process of planning and designing of effective fisheries management measures. Also, it is crucial to incorporate more flexibility into the political and management systems, for example, the harvest quota system. Also, a higher degree of involvement of local communities in resources management has to be initiated, as subsistence fishers are usually constrained by institutional frameworks and management structures. More stakeholders such as subsistence fishers, scientists and policy-makers have to work together  183  in order to find the solutions, mitigation measures and adaptive strategies to tackle the change in the marine resources caused by climate change and ocean acidification. Before designing adaptation strategies, it is important for countries, especially those that are highly vulnerable, to better manage their current fisheries in order to increase their resilience and capability of absorbing impacts of climate change. To begin to tackle current problems, these countries need to (i) know the state of their fish stocks and ecosystems; (ii) know the value (in a broad sense) of their fishery resources; (iii) strengthen fisheries management, especially monitoring, control and surveillance; and (iv) rebuild depleted stocks. To increase the robustness of fish stocks to climate change, there may be short-term costs. However, the medium and long term negative implications for food security in these countries would likely be minimized.   Limitations of the analysis  Although this is the first attempt for conducting a holistic quantitative study on the economic impact of climate change on marine exploited species at global scale, my model still has several limitations. Firstly, this model only includes the potential direct impacts of climate change on marine exploited species but it does not include the indirect impacts such as interspecies interactions. A newer version of DBEM has been developed recently (Fernandes et al. 2013). This new approach has combined DBEM with the size-based trophic model (Blanchard et al. 2011; 2012) and hence the impact of climate change on trophic interaction has been considered. The results from Fernandes et al. (2013) showed that species interaction only caused the average latitudinal distribution range shifts to reduce by 20% in  184  North Atlantic. Secondly, our economic model also does not consider the adaptive capacity of the fishers and the fishing fleets. The capacity to adapt to climate change varies among fishers, communities and countries. Some fishers may be able to change their gear types to target those invasive species, which shift their distribution range under climate change, while some other fishers may not be able to change their target species because they do not have sufficient capital to change and/or modify thegear. Thirdly, as aforementioned, this model does not consider the change in fishing price and cost of exploited marine species through time. In reality, ex-vessel fish prices would change with the landing volume, consumer preferences, cross price and substitution effects, etc. Also, fishing cost may also change with fishing effort, adaptive capacity of fishers and also energy prices. However, all of these factors have not been addressed in this model and are considering in my futures research effort.   Future Research Direction On the biological side, it is necessary to study the potential of marine species to adapt to the changing temperature and biogeochemical conditions of the ocean. The understanding of how climate change and ocean acidification may affect marine trophic interaction and the consequence of these impacts at ecosystem level is necessary. Knowledge on the effect of ocean acidification on non-calcifying marine species is still inadequate. Therefore, more studies on the impact of ocean acidification on the phenology, growth rate, reproduction and mortality rate of a wider range of marine species are needed. Also, it is crucial to study the  185  synergistic effect of different stresses on marine species and fisheries including climate change, ocean acidification, increase in hypoxic zones, overfishing, pollution etc.   More efforts on studying the impacts of climate change on the economic behavior of fishers through fisheries are recommended. It is important to study the economic costs of all externalities, for example, economic emissions from fishing vessels. As price elasticity may add complicity to the projection of climate change, it is necessary to model the change in ex-vessel price of marine species by including global supply of fish, preferences of consumers, price of alternative products on the market and also the abundance of targeted species. Cost of fishing is an important factor affecting the adaptive capacity of fishers and countries to climate change. Thus, it is important to have study on projecting the change in cost of fishing with the change of oil prices, composition of fishing gear types, distance travelled by the vessels, fishing locations and landing ports. I would also recommend that study on how climate change affects the effectiveness of the current cooperative resources management arrangement is necessary. This can be done by assessing their stability and vulnerability to the change in climate through an indicator based approach.   The research presented in this thesis is a first attempt to quantitatively predict the impact of climate change on the economics of fisheries globally. 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To provide all combinations of gear types and countries with an initial estimate, I first started by calculating the average cost values based on all the raw data collected disregarding gear type and country,  (1) where c  is the average cost data (US$ per tonne), ci is the observed fishing cost (US$ per tonne) for each country and gear combination, hi is the catch (tonne) of each country and gear combination.   ????? niiniiihhcc11   228  Secondly, I assume that vessels with the same gear type have similar relative fishing costs regardless of their FAO regions. With this assumption, if a fishing gear type in an FAO region did not have any cost data from any source, then it can get a more specific estimate from the average costs of the same gear type of all other FAO regions combined. To get this estimate, I computed the average cost of each gear type in each region and then calculated the average cost ratio between two regions,ikR , which is given by:  (2) where ic  is the ratio of average cost in one region and kc  is the average cost of another region. The weighted average cost for gear type j in region i can be calculated from other regions, jic , and is given by:  (3) where cjk is the average cost of gear type j in region k, hjk is the catch of gear type j in region k, Rik is the cost ratio of region i to region k and n is the total number of regions with cost for gear type j.   Finally, I assume that vessels with the same gear type have similar fishing costs within the same region, so a more specific estimate can be assigned to a particular gear type in a ?????nkjknkikjkjkhRhccji11  kiik ccR ?  229  country without any raw data. The last step is to obtain the weighted average costs of each gear type in each FAO region,  (4) where cji is the weighted average cost of gear type j in region i, cjm is the average cost of gear type j in country m, hjm is the catch of gear type j in country m, n is the total number of countries with cost for gear type j in region i. Then, I assigned this value to the same gear type of all the countries in the same region where raw data was not available. At this point, every gear type in each fishing country should have been assigned a more specific cost data if raw data was not available.   ?????nmjmnmjmjmhhccji11   230  Appendix B: Change in global fisheries economics with climate change   Details on the biological model for projecting species shift distribution and maximum catch potential (adapted from Cheung et al. 2008a; 2008b; 2010)  The Dynamic Bioclimate Envelop Model (DBEM) simulates how changes in temperature, oxygen content (represented by O2 concentration) and pH (represented by H+ concentration) would affect growth of marine fishes and invertebrates. The model algorithm is derived from a growth function:                       (1) where H and k are coefficients for anabolism and catabolism, respectively. Anabolism scales with body weight (W) with an exponent a<1, while catabolism scales linearly with W.  Solving for dB/dt = 0, I obtained       where W? is the asymptotic weight. Equation (1) can be integrated to a generalized von Bertalanffy Growth Function (VBGF; Pauly 1981):                                (2) where K is the von Bertalanffy growth parameter.    231  For simplification, I assume that a = 0.7, although empirical studies show that a generally varies from 0.50 to 0.95 between fish species (Pauly 1981, 2010), with 2/3 corresponding to the special or standard VBGF. Moreover, metabolism is temperature dependent and aerobic scope is dependent on oxygen availability in water and maintenance metabolism is affected by physiological stress (e.g., increased acidity). Thus:                        (3a)                         (3b) where j = Ea/R with Ea and R are the activation energy and Boltzmann constant, respectively, while T is temperature in Kelvin. In addition, the aerobic scope of marine fishes and invertebrates decreases as temperature approaches their upper and lower temperature limits (P?rtner 2010).  The coefficients g and h were derived from the average W?, K and environmental temperature (To) of the species reported in literature:                               (4a)                               (4b) where      and k = K / (1-a) (eq. 1 and 2).   232  The model predicts changes in VGBF parameters according to changes in temperature, oxygen and pH in the ocean relative to the initial conditions, as:                         (5a)                   (5b)  Based on the computed VGBF parameters and environmental conditions, the model determined change in carrying capacity in each 30? latitude x 30? longitude cell. The model identifies the ?environmental preference profiles? of the studied species, defined by sea water temperature (bottom and surface), depth, salinity, distance from sea-ice and habitat types. Preference profiles are defined as the suitability of each of these environmental conditions to each species, with suitability calculated by overlaying environmental data (1970-2000) with maps of relative abundance of the species (Cheung et al. 2009). For example, for each species, the model calculated a temperature preference profile for the adult and pre-recruit phases based on the relative abundance and the computed recruitment strength of the species. Sea surface temperature is then used for temperature preference profiles for pre-recruit phase while bottom temperature is applied to preference profiles for adult demersal species. Moreover, carrying capacity is expressed as a function of expected biomass per recruit and recruitment. Expected biomass per recruit was determined by a size-based population model. Thus, change in species? carrying capacity in each spatial cell is dependent on its calculated theoretical relative abundance and environmental preferences. Natural mortality rate (M) and length at maturity are determined from published empirical equations (see Cheung et al. 2011 for details). Initial relative recruitment strength (R) is calculated from the initial relative  233  abundance (A, normalized across the 30? x 30? degree resolution grid) and calculated biomass per recruit in each cell, as BPR = c?A/R. where c is a constant that scales from relative abundance to absolute abundance. Thus, R= c?A/BPR and A=BPR?R/c.  The model simulates changes in relative abundance of a species by:                         (6) where Ai is the relative abundance of a 30? x 30? cell i, G is the intrinsic population growth and Lji and Iji are settled larvae and net migrated adults from surrounding cells (j), respectively.  Intrinsic growth is modeled by a logistic equation:                      (7) where r is the intrinsic rate of population increase. The model explicitly represents larval dispersal through ocean current with an advection-diffusion-reaction model (see Cheung et al. 2008a, 2009 for details).  ? ? ? ? LavNvyLavuxyLavDyxLavDxtLav ?????????????????????????????????? ?              (8)  234    where change in relative larvae abundance over time (?Lav/?t) is determined by diffusion (i.e., the first two terms on the right-hand side of eq. 13) and current-driven movements (i.e., the third and fourth terms of eq. 13). Diffusion is characterized by a diffusion parameter D, while advection is characterized by the two current velocity parameters (u, v) which describe the east-west and north-south current movement.   Estimation of maximum catch potential Using a published empirical model described in (Cheung et al., 2008), I calculated the annual maximum catch potential for each of the spatial cells. The empirical model estimates a species maximum catch potential (MSY) based on the total primary production within its exploitable range (P), the area of its geographic range (A), its trophic level (?) and includes terms correcting the biases from the observed catch potential (CT: number of years of exploitation and HTC: catch reported as higher taxonomic level aggregations):                                                                                              (9)  I assume that the proportion of exploitable range relative to the geographic range of a species (?), tropic level (?) of each species and CT remain constant in the future. Thus, P was calculated from the sum of primary production available for fisheries weighted by the  235  relative abundance in each spatial cell for the specific year t. Range area was the sum of area of all spatial cells that contribute to 95% of the total abundance at year t from which distributions of relative abundance were simulated from the dynamic bioclimate envelope model described above. The spatial distribution of the calculated maximum catch potential was assumed to be proportional to the relative abundance of each species in each cell.      236   Figure B.1  Percentage changes in projected catch potential of each Exclusive Economic Zone (EEZ) from my model in the 2050s relative to the levels in the 2000s under SRES A2 scenario (GFDL). Countries in red represent decrease from the current status whereas countries in yellow have no change in these parameters (i.e. ?10% relative to the current levels). Countries in blue correspond to countries with an increase in these parameters over the current status.       237  Appendix C: Current and projected fish production and fish demand in West Africa C  Table C.1 Current and projected fish production and fish demand in West Africa.  Annual captured marine fish production (thousand tonnes)  Population (thousand)  Country Currenta Projected in the 2050s (under constant 2000 level scenario)b Projected in the 2050s (under SRES A1B scenario)b Per capita fish food consumption in the 2000s (kg/person/year)c Current (2000)d Projected (2050)d Projected fish demand in the 2050s (thousand tonnes)e Benin 8.1 7.4 6.1 3.85 6,659 21,982 84.5 Cape Verde 5.8 5.5 4.6 9.92 439 703 7.0 Cote d'Ivoire 53.6 32.9 23.4 12.01 17,281 43,373 520.8 Gambia 32.1 34.4 29.6 23.23 1,302 3,763 87.4 Ghana 284.4 166.3 128.1 17.54 19,529 45,213 793.0 Guinea 97.0 87.9 72.2 7.21 8,384 23,975 173.0 Guinea Bissau 6.2 6.0 4.8 1.20 1,304 3,555 4.3 Liberia 6.1 3.9 3.0 3.10 2,824 8,841 27.4 Mauritania 130.0 144.7 111.2 5.70 2,604 6,061 34.6 Nigeria 287.9 220.5 136.3 14.51 124,842 289,083 4,195.0 Senegal 382.8 450.7 331.6 25.93 9,902 26,102 676.8 Sierra Leone 59.0 50.7 27.6 14.21 4,228 12,446 176.9 Togo 14.9 10.5 5.9 4.50 5,247 13,196 59.4 Western Sahara <0.001 <0.001 <0.001 0.02 315 938 0.017  West Africa Region 1,367.8 1,221.4 884.5 13.9 204,860.0 499,231.0 6,840.0  Note: aAverage annual landing data from 1999 to 2003 obtained from the Sea Around Us Project catch database (www.seaaroundus.com). b Annual landings in the 2050s projected by using the model described in this paper. c The per capita food consumption is calculated by dividing the  238  total fish food consumption (i.e. sum of landing values and the imported fish) by the population in 2000. d Population data obtained from Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects: The 2008 Revision, http://esa.un.org/unpp. e The projected fish demand is calculated by using the prevailing per capita fish consumption and the forecasted population in each WA country.    239  Appendix D: Marine capture fisheries in the Arctic: winners or losers under climate change and ocean acidification? D  Table D.1 Catch by country under three scenarios ? Current; Climate Change (CC) and Climate  Change plus Ocean Acidification (CC+OA).  Country Current Catch (thousand tonnes) CC Catch (thousand tonnes) CC+OA Catch (thousand tonnes) Canada 6.11 7.73 (6.58 ? 9.09) 7.36 (6.51 ? 9.08) Faeroe Is 77.29 105.80 (82,81 ? 122.68) 102.38 (82.29 ? 128.24) Finland 0.04 0.048 (0.041 ? 0.056) 0.05 (0.03 ? 0.06) Greenland 91.79 122.26 (100.06 ? 144.85) 118.24 (100.11 ? 149.96) Iceland 692.60 974.41 (838.45 ? 1,146.77) 962.69 (830.53 ? 1,257.11) Norway 917.57 1,283.87 (979.61 ? 1,463.80) 1,235.17 (970.04 ? 1,527.75) Russian Fed 530.78 719.01 (580.64 ? 831.70) 693.44 (577.50 ? 860.81) Sweden 15.18 20.22 (15.97 ? 23.69)  19.56 (15.86 ? 24.75) USA 12.79 20.45 (16.81 ? 22.85) 19.47 (16.89 ? 22.63) US (Alaska) 0.62 1.42 (0.8 ? 2.22) 1.40 (0.80 ? 2.10) Total 2,344.78 3,255.21 (2,631.07 ? 3,766.00) 3,159.76 (2,600.57 ? 3,982.49)    240  Table D.2 (a) The current (in the 2000s) and projected catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts, and income impacts from the four Earth System Models (ESMs) under climate change only (SRES A2) scenario in the 2050s. (b) The current (in the 2000s) and projected catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts, and income impacts from the four Earth System Models (ESMs) under climate change (SRES A2) and ocean acidification scenario in the 2050s. Table D.2 (a)    Projected outputs from the Earth System Models  (ESMs) under A2 scenario in the 2050s Output variables Current status (2000s) GFDL ISPL CCSM3 CSM14 Catch (thousand  t/ year) 2,345 3,543 2,631 3,080 3,766 Total Revenues  (US$ billion year-1) 1.95 2.91 2.23 2.57 3.09 Fishers? incomes  (US$ billion year-1) 1.20 1.81 1.36 1.57 1.91 Total fishing cost  (US$ billion year-1) 3.23 4.89 3.67 4.24 5.18 Economic rents  (US$ billion year-1) -1.28 -1.98 -1.45 -1.67 -2.08 Economic impacts  (US$ billion year-1) 6.67 9.94 7.60 8.61 10.54 Income impacts  (US$ billion year-1) 1.52 2.28 1.74 1.99 2.41     241  Table D.2 (b)    Projected outputs from the Earth System Models  (ESMs) under CC and OA scenario in the 2050s Output variables Current status (2000s) GFDL ISPL CCSM3 CSM14 Catch (thousand  t/ year) 2,345 2,986 2,609 3,063 3,981 Total Revenues  (US$ billion year-1) 1.95 2.42 2.21 2.56 3.16 Fishers? incomes  (US$ billion year-1) 1.20 1.52 1.35 1.56 2.00 Total fishing cost  (US$ billion year-1) 3.23 4.11 3.64 4.22 5.44 Economic rents  (US$ billion year-1) -1.28 -1.70 -1.43 -1.66 -2.27 Economic impacts  (US$ billion year-1) 6.67 8.26 7.55 8.58 10.74 Income impacts  (US$ billion year-1) 1.52 1.89 1.73 1.98 2.46    242  Table D.3 Landed value by country under three scenarios ? Current; Climate Change (CC) and Climate Change plus Ocean Acidification (CC+OA)  Country Current Land Value (US$ millions) CC Land Values (US$ millions) CC+OA Land Values (US$ millions) Canada 9.10 11.50 (9.78 ? 13.56) 10.95 (9.77 ? 13.56) Faeroe Island 72.89 99.62 (80.59 ? 114.65) 95.58 (80.36 ? 116.87) Finland 0.007 0.009 (0.008 ? 0.01) 0.008 (0.006 ? 0.01) Greenland 205.35 268.39 (223.87 ? 316.01) 256.30 (223.70 ? 317.79) Iceland 503.94 705.42 (601.21 ? 809.70) 683.45 (599.32 ? 847.13) Norway 927.86 1,292.14 (1,035.54 ? 1,468.97) 1,233.27 (1,029.35 ? 1,493,92) Russian Fed 191.54 259.52 (210.14 ? 298.54) 248.08 (209.59 ? 301.98) Sweden 5.12 6.69 (5.52 ? 7.84) 6.45 (5.49 ? 8.09) USA  32.99 55.22 (46.94 ? 55.22) 52.45 (46.86 ? 62.55) US (Alaska) 0.83 1.94 (1.09 ? 3.01) 1.91 (1.14 ? 2.86) Total 1,949.62 2,700.44 (2,226.35 ? 3.092.73) 2,588.46 (2,205.57 ? 3,164.75)   243  Table D.4 Top 10 species with the highest landing harvested by Canada in the Arctic Region under the current status (the 2000s), under climate change scenario (SRES A2) and under both climate change and ocean acidification scenario in the 2050s. Species with * are temperate species and they become more important in the catch under climate change and ocean acidification scenarios.    Current (2000s) Climate change (2050s) Climate change and OA (2050s) Rank Common Name % Landed Value Common Name % Landed Value Common Name % Landed Value 1 Northern prawn 39 Northern prawn 52 Northern prawn 53 2 Greenland halibut 21 Greenland halibut 38 Greenland halibut 37 3 Pandalus shrimps 18 Charr 9 Charr 9 4 Crustaceans 12 Atlantic salmon 0.67 Atlantic salmon 0.66 5 Charr 8 Atlantic cod* 0.07 Atlantic cod* 0.06 6 Aesop shrimp 1.26 Atlantic mackerel* 0.04 Atlantic mackerel* 0.04 7 Queen crab 0.53 Blue mussel* 0.03 Blue mussel* 0.03 8 Atlantic salmon 0.35 Pacific herring* 0.03 Pacific herring* 0.03 9 Marine crabs 0.10 Capelin* 0.02 Capelin* 0.02 10 Scallops 0.05 Atlantic halibut* 0.01 Atlantic rainbow smelt* 0.01    244  Table D.5 Fisheries output impacts by country (Dyck and Sumaila 2010).  Countries Average economic multiplier Average income multiplier Canada 3.30 1.07 Faeroe Is 2.10 0.56 Finland 1.56 0.43 Greenland 7.38 1.32 Iceland 2.49 0.51 Norway 3.36 0.87 Russian Fed 2.50 0.50 Sweden 3.13 0.68 USA 3.10 1.29 US (Alaska) 3.10 1.29     245  Table D.6 (a) Medians, lower and upper limits of catch, total revenues, fishers? incomes, total fishing cost, economic rents, economic impacts and income impacts under different scenarios in the 2050s using Monte Carlo method. (?Current = Current Status; ?CC? = Climate Change only scenario (SRES A2); ?CC+OA? = Climate Change (SRES A2) with Ocean Acidification scenario); (b) Percentage change (medians, lower and upper limits) of catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts and income impacts under different scenarios in the 2050s relative to the current status (2000s) using the Monte Carlo method. (?CC? = Climate Change only scenario (SRES A2); ?CC+OA? = Climate Change (SRES A2) with Ocean Acidification scenario); (c) Percentage change (medians, lower and upper limits) of catch, total revenues, fishers? incomes, total fishing costs, economic rents, economic impacts and income impacts from the climate change only scenario (SRES A2) when ocean acidification is taken into account using the Monte Carlo method. Table D.6 (a)  Output variables Scenarios Median 2.5th percentile 97.5th percentile Catch (thousand t/ year) Current 2,344 - - CC 3,255 - - CC + OA 2,992 2,959 3,026 Total Revenues (US$ billion/year) Current 2.51 2.23 2.78 CC 3.52 3.13 3.91 CC + OA 3.26 2.89 3.63 Fishers? incomes (US$ billion/year) Current 1.49 1.18 1.81 CC 2.11 1.68 2.56 CC + OA 1.92 1.52 2.35 Total fishing cost (US$ billion/year) Current 4.21 3.44 5.08 CC 5.94 4.87 7.16 CC + OA 5.43 4.43 6.60 Economic rents (US$ billion/year) Current -1.71 -2.62 -0.90 CC -2.42 -3.72 -1.29 CC + OA -2.18 -3.39 -1.10 Economic impacts (US$ billion/year) Current 8.08 7.20 8.96 CC 11.26 10.03 12.49 CC + OA 10.44 9.27 11.61 Income impacts (US$ billion/year) Current 1.87 1.65 2.09 CC 2.63 2.32 2.93 CC + OA 2.43 2.15 2.73   246   Table D.6 (b)   Output variables Scenarios Median 2.5th percentile 97.5th percentile Catch  CC 40.14 - - CC + OA 28.79 27.39 30.29 Total Revenues  CC 40.41 39.08 41.68 CC + OA 29.98 27.65 32.51 Fishers? incomes  CC 41.28 39.51 43.79 CC + OA 28.60 24.90 32.87 Total fishing costs  CC 41.02 39.49 42.58 CC + OA 29.03 25.83 32.27 Economic rents  CC -41.82 -47.43 -37.55 CC + OA -27.59 -35.26 -17.94 Economic impacts  CC 39.35 37.89 40.72 CC + OA 29.18 26.88 31.77 Income impacts  CC 40.48 39.02 41.89 CC + OA 32.00 30.80 29.80     247  Table D.6 (c)  Output variables Median 2.5th percentile 97.5th percentile (%) Catch -8.10 -9.10 -7.03 Total Revenues  -7.43 -8.77 -5.93 Fishers? incomes  -8.97 -11.04 -6.88 Total fishing costs  -8.50 -10.33 -6.64 Economic rents  10.11 6.28 15.86 Economic impacts  -7.28 -8.59 -5.77 Income impacts  -7.31 -8.71 -5.71     248  Figure D.1 Distribution of unit fishing cost (US$/tonne) (Figure D.1(a)) and unit labour cost (US$/tonne) (Figure D.1(b)) by the all gear/vessel types. Figure D.1(c)) shows the distribution of ex-vessel price (US$/tonne) of all species in the Arctic countries. Note logarithmic scales on the x-axis. The fishing cost and labour cost data are based on data from Chapter 2 and Lam et al. (2011) and the ex-vessel price data are extracted from Swartz et al. (2012).   (a)      249  (b)   250  (c)  

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