AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y COVER PHOTO CREDIT Curioso / Shutterstock.com D ECEM B E R 2 01 9 Climate Change and Marine Fisheries in Africa Assessing Vulnerability and Strengthening Adaptation Capacity © 2019 The World Bank 1818 H Street NW, Washington DC 20433 Telephone: 202-473-1000; Internet: www.worldbank.org Some rights reserved This work is a product of the staff of the World Bank. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of the Executive Directors of the World Bank or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of the World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. 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Contents Foreword and Acknowledgements............................................................................................................ 1 Abbreviations and Acronyms.....................................................................................................................3 Executive Summary......................................................................................................................................5 1. Introduction...........................................................................................................................................7 2. Socioeconomic Importance of Marine Fisheries for African Coastal Countries and Contribution to the Global Agenda............................................................................................ 9 Looking beyond the mere value of catches...................................................................................................................... 9 Fish for jobs................................................................................................................................................................................ 9 Fish for food and health........................................................................................................................................................10 Best estimates of value of catches......................................................................................................................................11 Contribution to higher objectives: SDGs and Africa 2063.......................................................................................... 12 3. Methodology Overview — Ecological and Socioeconomic Approaches..................................... 15 Projected changes in catch potential under the impacts of climate change........................................................15 Socioecological risk of climate change.............................................................................................................................16 4. Projected Changes in Catch Potential Under the Impacts of Climate Change........................... 17 Approach................................................................................................................................................................................... 17 Results: Future projections of fish catch potential under climate change............................................................. 17 Catch Potential Maps: Understanding the Legend........................................................................................................ 21 5. Mapping Adaptation Through Uncertainty...................................................................................... 31 6. Socioecological Risk of Climate Change......................................................................................... 45 Approach..................................................................................................................................................................................45 Results........................................................................................................................................................................................48 How to interpret these results............................................................................................................................................ 53 7. Conclusion: A Game Changer for Marine Fisheries Management in Africa................................ 55 References...................................................................................................................................................57 Annex 1. Volume and Value Of Catches – Food and Agriculture Organization Data and Reconstructed Catches.............................................................................................................. 58 Annex 2. Description of Models and Methodologies............................................................................60 Annex 3. Definitions and Sources for Socioecological Indicators...................................................... 64 iv CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA PHOTO CREDIT Charlotte De Fontaubert / World Bank Foreword and Acknowledgements Rigorous assessments of the impacts of climate of the same impacts of climate change, and preliminary change, both observed and modeled, are increasingly estimates of the vulnerability of marine fisheries. demonstrating that the effects on marine ecosystems, A series of consultations with a selected network of fisheries, and the millions of fishers and processors targeted partners and contributors with expertise in who depend on them are likely to be more severe climate change and African fisheries was undertaken in than originally expected. This is an alarming finding, the preparation of this report. This work was financed especially in sub-Saharan Africa, where the intensity of by the Nordic Development Fund (NDF) and the Global climate impacts, combined with the limited adaptation Environmental Facility (GEF). The Nippon Foundation capacity of many in the fisheries sector, contributes to Nereus Program at the University of British Columbia the vulnerability of the affected communities. Despite and a World Bank team led by Bérengère Prince the growing body of evidence documenting the impacts prepared this report. The Fisheries Economic Research of climate change, much remains unknown, or at least Unit, the Sea Around Us, and the Changing Ocean unquantified, including their precise direction and Research Unit provided data and advice. Experts from effect. Policy makers, donors, and other stakeholders the Nordic Development Fund provided valuable advice urgently need additional analysis and evidence-based and guidance. Consultations were held with high-level information to guide investments and initiatives in representatives of institutions such as the African Union climate change mitigation and adaptation, with the Inter-African Bureau of Animal Resources; the African ultimate goal of maximizing prospects for development Union Development Agency–New Partnership for Africa’s and poverty reduction throughout Africa. Development; FAO; the South African Department To that end, the World Bank called on a network of of Agriculture, Forestry and Fisheries; the Fisheries expert partners and contributors to fill this knowledge Committee for the West Central Gulf of Guinea; the gap and deepen our understanding of the impacts of Sub-regional Fisheries Commission of West Africa; and climate change on marine fisheries in Africa. This process the German Agency for International Cooperation, who builds directly on the Impacts of Climate Change on all strongly supported the preparation of the report and Fisheries and Aquaculture report that the UN Food and provided useful guidance and inputs throughout the Agriculture Organization (FAO) published in 2018 and drafting process. Representatives from research institutes on the special report of the Intergovernmental Panel and academic institutions such as the University of on Climate Change (IPCC) Global Warming of 1.5°C. Ghana, University of Senegal, University of Cape Town, This report takes stock of available knowledge on the Western Indian Ocean Marine Science Association, and economic importance of marine fisheries in sub-Saharan Kenya Marine and Fisheries Research Institute were Africa and the populations that depend on them and also consulted. The report was written by Vicky Lam provides a biophysical analysis of the impacts of climate (UBC) and Charlotte de Fontaubert (World Bank), with change as they have already been measured and how contributions by Daniel Lyng and Carolina Giovannelli they are modeled to evolve, a socioeconomic analysis (World Bank) and benefited from review by and suggestions from the Chair of IPCC Working Group II AR6. 1 PHOTO CREDIT Pierre-Yves Babelon / Shutterstock.com Abbreviations and Acronyms DBEM Dynamic Bioclimate Envelope Model EEZ Exclusive Economic Zone ESM Earth system model FAO Food and Agriculture Organization of the United Nations GDP Gross domestic product IPCC Intergovernmental Panel on Climate Change MCP Maximum catch potential SDG Sustainable Development Goal 3 PHOTO CREDIT Curioso / Shutterstock.com Executive Summary This study used ecological and socioecological simulation modeling to forecast the impacts of climate change in Africa on fish stocks and the fisheries and fishing communities that depend on them, by 2050 and 2100. It also examined the subsequent impacts on African countries and communities, highlighting those most at risk. Climate change is likely to have a significant impact on and population exposure) from socioeconomic or Africa’s marine fisheries by as early as 2050. Countries indirect impacts (degree to which coastal populations are likely to be affected to varying degrees, but tropical are sensitive to climate change or have room for West African countries stand to be the most affected, adaptation). The study found that the ecological risk whereas higher-latitude countries are less likely to be is very high for a large proportion of Africa’s coastal affected and, in some limited instances, could see some countries, including in the Gulf of Guinea, from Gabon to benefits. The simulation models forecasting the impacts Guinea-Bissau, and along Africa’s east coast from Eritrea of climate change on marine fisheries show that the to Mozambique. The study also highlights margins of maximum catch potential (MCP) will decrease by 30 adaptation, where countries with high ecological risk percent or more as early as 2050 in many tropical West do not necessarily face equally high socioecological risk African countries, including the Democratic Republic of depending on their adaptation capacity (e.g., the extent Congo, Côte d’Ivoire, Equatorial Guinea, Gabon, Liberia, to which marine resources—including fisheries—are and São Tomé and Príncipe. At higher latitudes, by under effective management, or whether alternatives to contrast, catch potential is projected to decrease only affected fisheries are available). moderately or even increase (e.g., in the waters off For all African coastal countries, climate change impacts Senegal, The Gambia, and Cabo Verde). will require decision makers to rethink their approach to The impacts of climate change on marine fisheries will fisheries management. Even under best-case scenarios, make it difficult for many countries that depend on the models clearly show that the impact of climate these fisheries to achieve several of the Sustainable change on fisheries will be serious, although not evenly Development Goals (SDGs). This is particularly true with felt, and that stressed fisheries resources, for example regard to SDG 1 (No poverty), SDG 2 (Zero hunger), overfished stocks, are at additional risk from this and SDG 3 (Good health and well-being) in fishing additional impact. This is crucial given that fisheries are communities that are especially vulnerable to climate often exploited to the point at which uncontrolled levels change because of their economic dependence on of fishing prevail, causing certain stocks to collapse and fisheries for their livelihoods and for food and nutrition leading to moratoria or other measures designed to give security. these stocks the opportunity to recover. In the face of anticipated reductions in MCP, however, these corrective Building on projections of ecological impacts of measures may need to be more stringent, and moratoria climate change on fisheries, this study also considered will likely need to be longer—and thus economically social and economic repercussions by assessing more onerous—and in some cases could even fail to give socioecological risk scores. These risk scores can help affected stocks the chance to recover. In other words, disentangle ecological impacts (risk to marine species 5 the boom and bust overfishing cycle may no longer be most optimistic Intergovernmental Panel on Climate one from which fish stocks can recover when combined Change scenario, these risks are still alarmingly high), with the additional impacts of climate change. and the socioeconomic factors, over which they can, and should, have direct control. Exposure, sensitivity, and Each country has different pathways to adapt to the adaptation capacity can all be influenced through policy impacts of climate change on its marine fisheries. interventions and are the only elements over which An important distinction needs to be made between coastal states have any control. ecological risks, which, to a large extent, are beyond the control of African coastal states (and even under the Key Findings y The impacts of climate change on African fisheries will be serious, even under the most optimistic scenarios, and countries will be affected differently. y Ecological risks: African countries at low latitudes will be hardest hit. Tropical West African countries stand to be the most affected, whereas the impact on higher-latitude countries is likely to be milder. – By 2050: The models forecasting the impacts of climate change on marine fisheries show that MCP will decrease by 30 percent or more in many tropical West and Central African countries, including the Democratic Republic of Congo, Côte d’Ivoire, Equatorial Guinea, Gabon, Liberia, and São Tomé and Príncipe. – By 2100: It is likely that the largest decrease in MCP (40 percent or more) will occur in tropical West and Central African countries, including Ghana, São Tomé and Príncipe, Liberia, and Côte d’Ivoire. – In higher-latitude regions, it is projected that catch potential will decrease much less, for example in Senegal, The Gambia, and Cabo Verde. y Socio-ecological risks: A distinction needs to be made between ecological risks, which, to a large extent, are beyond the control of African coastal states, and socio-ecological risks, which can be miti- gated through a variety of management measures. – The Horn of Africa, parts of West Africa, and Nigeria are particularly at risk, with climate change posing great risk to the national economies of these countries through fisheries. y The impacts of climate change on fisheries and fishing communities are not a foregone conclusion; the extent of socio-ecological risk depends on a number of important variables, including the effective- ness of fisheries management measures. 6 1. Introduction Our understanding of the impacts of climate change on fisheries is constantly increasing and can be organized around several main factors—ocean acidification, sea-level rise, higher water temperatures, deoxygenation, changes in ocean currents— although these factors are unequally known and hard to model in terms of scope— where they will occur and where they will be felt the most—and severity. For instance, although the impacts of acidification are not as well understood as the effects of the other impacts, and are more difficult to measure, it is likely that they are more severe and widespread, particularly on shell-forming species, invertebrates, and coral- associated species and throughout any carbon-dependent ecological processes. The impacts of climate change are already being in marine goods and services. Fishing communities felt and can be measured. The special report of the and African academics are already reporting and Intergovernmental Panel on Climate Change (IPCC), documenting some of these changes (box 1). Global Warming of 1.5°C, showed moderate impact on Climate change is becoming a game changer for small-scale low-latitude fisheries from 2006 to 2015 fisheries management for two reasons: one, it has and forecasted—with high confidence—large impact strengthened the case for a comprehensive approach, on fisheries productivity, especially at low latitudes. including the status of fish stocks and ecosystems that These impacts will be felt at three fundamental levels: are at the forefront of the impacts of climate change. on the fish stocks themselves, on the critical marine and And two, it adds a sense of urgency to necessary coastal ecosystems on which they depend, and on fishing management reforms, because these relatively new communities exposed to more-frequent extreme weather and growing impacts interact with those of overfishing events. Climate change has already begun to alter ocean and mismanagement, further increasing the level conditions, particularly water temperature and various of uncertainty and removing the safety mechanism aspects of ocean biogeochemistry. Marine biodiversity that allowed depleted stocks to recover after responds to shifting temperatures and other ocean overexploitation. These impacts, which are inexorably conditions through changes in organismal physiology becoming more severe, are and will continue to be felt in and phenology1 and in population dynamics and fisheries that are globally being fully used, and in some distribution. It has been projected that these responses to cases overused, and are often in need of comprehensive ocean–atmospheric changes will lead to altered patterns governance reform. of species richness, changes in community structure and ecosystem functions, and consequential changes 1 Phenology is the study of the timing of recurring biological events, the causes of their timing with regard to biotic and abiotic forces, and the interrelation of phases of the same or different species (Lieth 1974) AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 7 Africa is considered particularly vulnerable,2 given the a brief overview of the socioeconomic importance of unique characteristics of its marine ecosystems and the sector for sub-Saharan Africa, simulation modeling the socioeconomic reliance of communities on this approaches are described that assess the impacts, sector for food, jobs, livelihoods, and revenues. Marine vulnerability, and risk to their marine biodiversity species are reaching their environmental limits because and fisheries from climate change. It focuses on how of a combination of extreme environmental conditions, the observed and anticipated ecological impacts of the array of human disturbances to which African climate change are likely to affect fish stocks and the fisheries are exposed, and the sensitivity of the biota to fisheries that depend on them and highlights the coastal environmental fluctuations. In addition, African fleets countries and regions in Africa that are most vulnerable tend to be small, not very mobile, and vulnerable to to climate change. Based on these projections, the extreme weather events. report further assesses subsequent socioeconomic impacts on coastal countries and communities. The This report aims to assess, to the extent possible, the report concludes with a discussion of lessons learned potential impact of climate change on fisheries and the from the modeling results. related well-being of coastal African countries. After BOX 1. Climate change as witnessed and monitored All along the African coast, fishing communities report changes in fishing pattern and species caught. In 2013, the World Bank surveyed 463 fishermen in Morocco, who reported fewer fishing days because of weather events, changes in species caught, increased sea temperatures, and shifts in current patterns (figure B1.1) (World Bank 2013a). In Liberia, the number of fishing days has decreased because of longer rainy seasons.* In Mauritania, the National Fisheries and Oceanographic Research Institute reports an increase in sea surface temperature of 0.34°C over 20 years (22.69°C in 1989–1998 to 23.03°C in 2009– 2018) and a decrease in upwelling strength trends from 1980 to 2018 (Institut Mauritanien de Recherches Océanographiques et de Pêches 2019). Survey results of fisher climate observations (% of fishers observing increased phenomena) Extreme events Temperature rise Change in currents Sea level rise Ocean acidification 0 10 20 30 40 50 60 70 Source: Morocco Climate Change Mitigation and Adaptation Strategy, The World Bank, 2013 * See video at: West Africa Regional Fisheries Program in Liberia https://www.youtube.com/watch?v=6m06e6s8RZo. 2 In its present iteration, this report focuses on sub-Saharan Africa and therefore does not include the Mediterranean area, the coast of North Africa, or the Red Sea, although many of its conclusions are likely to apply to the latter two, there are important differences in oceanography and other biophysical aspects. 8 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA 2. Socioeconomic Importance of Marine Fisheries for African Coastal Countries and Contribution to the Global Agenda Although policy makers often undervalue the importance of their fisheries sector, the contribution of fisheries to national economies is considerable. African fisheries are vital drivers of pro-poor economic growth, principally because the many small-scale fisheries are a significant source of employment and livelihoods for people in coastal communities. LOOKING BEYOND THE MERE contribution to food security, including animal protein, is VALUE OF CATCHES of crucial importance. Catch data capture only a fraction To better capture the accurate value of African fisheries, of the actual value that the sector generates along the we must consider not only the value generated from value chain. catch landings, but also the value added through FISH FOR JOBS postharvest activities and multiplier effects. This in turn requires understanding the fisheries value chain From a socioeconomic standpoint, the Food and on the continent. A typical value chain is shown in Agriculture Organization of the United Nations (FAO) figure 1, although it does not consider the spectrum of estimates that approximately 10 percent of the consumers and the diversity of standards required under global population derives its livelihood from fisheries. national regulations. African catch data are often underestimated because gatherers and gleaners are usually omitted. Despite this Although the valuation of a sector is typically measured shortcoming, official sources confirm the importance of according to its contribution to gross domestic product the sector for employment. (GDP), other metrics allow for a more comprehensive and accurate assessment of the sector’s importance. Estimates of jobs that the marine fisheries sector First, the gross value of a sector should include the generates, including the postharvest segment, vary value added by activities up and down the value chain, from 6.4 million (de Graaf and Garibaldi 2014) to 25.5 with particular focus on the contribution of postharvest million (World Bank 2012). Lack of data and inconsistent processing. The employment that the sector generates or unreliable reporting can explain this wide range. and livelihoods it supports should also be considered, Employment figures, especially employment of women, with special focus on the significance of the sector to may be undercounted in the first estimate because it is vulnerable groups, including those living in poverty based on survey responses from government officials and extreme poverty and women. Finally, the sector’s who are often confronted with data scarcity and who AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 9 FIGURE 1. Typical value chain for fish products 1 Home consumption 2 Sold to consumers Fresh fish processing 3 Sold to fishmongers Sold to consumers and transport to markets LANDED Sold to industrial Industrial fish processing FISH 4 processors and transport markets Sold to consumers Sold to artisanal Artisanal fish processing 5 Sold to consumers processors and transport markets Sold to non-food 6 processing Source: de Graaf and Garibaldi 2014. may have “underestimated [the number of] women sector, the number of women employed as processors working part-time as processors” (de Graaf and is approximately equal to the number of women Garibaldi 2014). employed in the sector.) Studies converge on the prominence of the In some coastal countries, up to 20 percent of the postharvest segment. De Graff and Garibaldi (2014) labor force is employed in fisheries. Although total estimate that 56.5 percent of these jobs are filled in employment is a small fraction of the total labor force the processing subsector, and the World Bank (2012) of coastal regions in Africa, in some least developed report concludes that the majority of employment in countries, small-scale marine fisheries provide the fisheries sector is not in the catching of fish but employment for up to 20 percent of the labor force in postharvest activities such as processing. This is (Belhabib, Sumaila, and Pauly 2015). When the number particularly significant because assessments of the of dependents is incorporated, 4.8 million people, or 16 postharvest workforce reveal high levels of female percent of the coastal population, depend on small- employment, whereas women are typically not as scale marine fisheries in West Africa alone (Belhabib, involved in catching fish. Sumaila, and Pauly 2015). Although similar data are unavailable for other regions of Africa, these findings Marine fisheries are an important source of illustrate the importance of the marine fisheries sector employment for women in Africa. De Graaf and in providing employment and livelihoods for coastal Garibaldi (2014) estimate that women make up communities. 27 percent of the workforce in the African marine fisheries sector, but this figure is low because of the FISH FOR FOOD AND HEALTH aforementioned undercounting of women engaged in Fish products are also an important source of nutrition, fish processing. Given that 54.4 percent of processors particularly protein, for Africans and are therefore a are women, the World Bank (2012) estimate of the vital contributor to food security. WorldFish (2009) number of processors of 17.6 million puts women’s estimates that 400 million Africans rely on fish as an employment at 9.6 million. (Because fewer than 1 essential component of their diets. FAO data suggest percent of women are fishers in the marine fisheries that fish provides 22 percent of animal protein intake 10 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA in Africa but more than half of animal protein intake Graff and Garibaldi 2014). Per capita fish consumption in some poor coastal countries (FAO 2018). Figure 2 in sub-Saharan Africa is projected to decline at an illustrates the dependence of several African nations annual rate of 1 percent to 5.6 kg from 2010 to 2030 on fish for protein intake. Fish also provides up to (World Bank 2013b), which is the result of demand for 9 percent (180 calories) of daily calorie intake for fish growing faster than production. Fish imports in individuals in coastal areas (FAO 2018). Although 2030 are projected to be 11 times as high as in 2000. reliable data disaggregating the contribution of marine Fish can provide essential amino acids, fats, and fisheries from inland fishing and aquaculture are micronutrients such as iron, iodine, vitamin D, and unavailable, small-scale fisheries of all kinds together calcium. Experts from the FAO and World Health account for “the bulk of [African] fish supply” (AUC Organization emphasize that fish consumption reduces and NEPAD 2014). Given that 45.2 percent by weight mortality due to coronary heart disease in adults and of fresh and processed fish is landed from marine improves the neurodevelopment of fetuses and infants. fisheries, it is reasonable to conclude that marine It is thus an important part of the diets of pregnant fisheries are responsible for a large amount of the women and nursing mothers (FAO and WHO 2011). African fish-provided protein and calorie supply (de FIGURE 2. Consumption of Protein from Fish as Percentage of Total Consumption of Animal Proteins IBRD 44792 | DECEMBER 2019 Consumption of proteins from fish in % of total consumption Less than 10 20 to 30 10 to 20 More than 30 Source: Earthtrend database, World Resource Institute (WRI), FaoStat, Food and Agriculture Organization of the United Nations (FAO) Source: Earthtrend database, World Resources Institute (WRI), Washington; FAOSTAT, FAO. Adapted from Philippe Rekacewicz, February 2006, available at: http://www.grida.no/resources/5620 BEST ESTIMATES OF VALUE OF CATCHES contribute 45.2 percent by weight of fresh and Based on reported catch levels, the total value of processed fish to Africa. Postharvest activities are the marine fisheries sector in Africa is estimated to usually divided into three main categories: marketing be slightly less than USD15 billion (de Graaf 2014), of fresh fish, artisanal fish processing, and industrial accounting for approximately 0.78 percent of the fish processing. Across the total fisheries sector continent’s GDP. Although most fresh and processed (including inland fisheries), the sale of fresh fish creates fish come from inland fishing, marine fisheries the majority of postharvest value (USD1,230,750 (70 percent)), followed by artisanal fish processing AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 11 (USD356,074 (20 percent)), and finally by industrial fish for women and families in areas where they might processing (USD171,045 (10 percent)). otherwise be unavailable. In turn, this sector contributes to several Sustainable Development Goals (SDGs). If reconstructed catch data are used (Annex 1), the direct contribution of marine fisheries excluding postharvest CONTRIBUTION TO HIGHER OBJECTIVES: activities is more than USD16.7 billion (if total landed SDGS AND AFRICA 2063 value is considered to represent the value of capture).3 The impacts of climate change on marine fisheries will Based on de Graaf’s estimate that postharvest activities make it difficult for many countries that depend on account for 34 percent of gross value added, the total these fisheries to achieve several of the SDGs. With value of the marine fisheries sector, when adjusted regard to SDG 1 (End poverty), SDG 2 (Zero hunger), under data reconstruction, accounts for more than and SDG 3 (Ensure healthy lives and promote well-being USD25 billion, or 1.3 percent of African GDP, but de for all at all ages), fishing communities are especially Graaf considers only downstream components of the vulnerable because of their dependence on fisheries value chain and ignores the potential contribution of for their livelihoods, food security, and nutrition. This upstream industries, such as those selling fishing gear, report aims to support achievement of these SDGs building boats, and fixing engines (figure 1). Although by emphasizing the need for adaptation measures to data estimating the value of upstream activities are increase resilience and consequently reduce poverty, not available, considering upstream industries would increase food security, and improve health by improving significantly increase the sector’s gross value added. nutrition. By fostering identification of cost-effective adaptation measures for African communities that These metrics help quantify the overall value of marine depend on fisheries and considering the potential fisheries in Africa and are particularly important for contribution of fisheries to job creation and economic Africa’s poorest people. Marine fisheries generate growth, the report also supports achievement of SDG a significant amount of employment for Africans in 8 (Decent work and economic growth). In addition, the the poorest coastal states; provide calories, protein, report fits squarely within the framework of activities and other essential nutrients; and create livelihoods 3 Fisheries data are not consistently reported, and even in cases in which they are available, they are often not collated in comparable and compatible formats. Reconstructed catch data are based on official catch estimates and corrected to add estimated catches from illegal, unregulated, and unreported fishing and discards at sea, usually of bycatch. Although reconstructed catch data are available to compensate for data scarcity and shortcomings, the methodology supporting these data is debatable. 12 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA that support achievement of SDG 13 (Climate action) capacity and, in parallel, the ability of vulnerable people, and SDG 14 (Life below water). such as African coastal communities, to increase their resilience. The core objective of this report is to analyze The report also contributes to achievement of the the biophysical and socioeconomic impacts of climate African Union Agenda 2063, the strategic framework change, current and modeled, to estimate risk to marine for the socioeconomic transformation of the continent fisheries from climate change and ultimately guide through 2063 because, in the face of climate change, decision makers in making prioritized, cost-effective actions aimed at reducing poverty and inequality as investments in the fisheries sector to respond to set in Agenda 2063 hinge on the ability of countries uncertainty due to climate change. to design and apply effective measures to reduce the impacts of climate change and enhance adaptation PHOTO CREDIT Pierre-Yves Babelon / Shutterstock.com AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 13 PHOTO CREDIT Charlotte De Fontaubert / World Bank 3. Methodology Overview–Ecological and Socioeconomic Approaches PROJECTED CHANGES IN CATCH POTENTIAL turn affect the abundance, physiology, phenology, and UNDER THE IMPACTS OF CLIMATE CHANGE spatial distribution of targeted species, which contribute The impact of climate change on marine biodiversity to changes in food webs and, particularly when and fisheries in Africa can be projected by estimating combined with ongoing fishing efforts,4 to maximum changes in catch potential caused by a variety of catch potential (MCP) (figure 3). MCP should not be ecological impacts and factors (e.g., increases in sea considered as a proxy for real catches, as outlined in box temperatures, oxygen concentration, ocean acidification, 2. Variations in MCP as a result of climate change are changes in frequency and intensity of extreme events, highlighted in section IV. changes in biochemical structures). These changes in FIGURE 3. Impact of climate change on marine resources Source: Gabriel Reygondeau and Vicky Lam, University of British Columbia. 4 The fishing effort is a measure of the level of fishing. Frequently, some surrogate is used related to a given combination of inputs into the fishing activity, such as the number of hours or days spent fishing, number of hooks used (in longline fishing), or kilometers of nets used. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 15 BOX 2. Effects of climate change as measured in change in maximum catch potential (MCP) MCP is the maximum theoretical catch of a species in an ecosystem. The projections developed from the two models used in this report do not reflect potential changes from current catch levels but rather esti- mate changes from their current capacity in the future capacity of oceans to produce fish. This capacity is different from actual catches because the latter depend on two important factors: the productive capac- ity of the oceans (as measured in MCP) and management decisions made in response to this productive capacity. CATCHES = CAPACITY + EFFECTS OF FISHERIES MANAGEMENT This, in turn, means that changes in MCP may not correlate exactly with catch variations. For example, future catches in an area where the productive capacity is expected to decline may actually increase if management measures can restore stocks that are overexploited. Conversely, in areas where MCP increas- es, catches could fail to increase if adequate management measures are not implemented. SOCIOECOLOGICAL RISK OF varies depending on the regions where the impacts of CLIMATE CHANGE climate change are felt, also depends on the economies Although ecological studies exploring climate change and fisheries management of the countries involved. impacts on shift in species distribution and MCP To identify coastal countries in Africa that are most measure the hazard and exposure level, the degree of vulnerable to the impacts of climate change, a exposure of a species does not reflect its sensitivity socioecological risk assessment framework, based on and adaptation capacity, and a relationship therefore the IPCC approach, was applied. An ecological risk cannot be inferred. Thus, this study used an ecological assessment (species-specific estimates of exposure and risk assessment combining the biological and ecological ecological and biological traits) was conducted that was characteristics of marine species to identify and assess then integrated into the socioecological risk assessment countries with high ecological risk to climate change and (figure 14) to assess the risk on African fisheries and species that are particularly vulnerable. The impact of their dependent communities from climate change. these changes on marine biodiversity and MCP, which PHOTO CREDIT Charlotte De Fontaubert / World Bank 16 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA 4. Projected Changes in Catch Potential Under the Impacts of Climate Change APPROACH RESULTS: FUTURE PROJECTIONS OF FISH CATCH POTENTIAL UNDER CLIMATE CHANGE This study used the approach outlined in the FAO report on the impact of climate change on fisheries and For each model, GHG emission scenario, and timeline, the aquaculture published in 2018. It assesses the ecological projected changes in MCP vary greatly geographically, impacts of climate change to project future changes in with substantial differences between African countries. MCP for the main species within the exclusive economic Averaged across the two models, the projections show zones (EEZs) of African nations. These projections that, by the end of the century, the largest decrease are drawn from two models that model ecological (40 percent or more) will likely occur in tropical African processes in two different ways: the Dynamic Bioclimate countries, including Ghana, São Tomé and Príncipe, Liberia, Envelope Model and the Multi-species Size-based and Côte d’Ivoire, but over the longer term, potential Ecological Model. Both models draw on the same catches are also projected to decrease substantially outputs from collections of Earth system models (Phase (20 percent or more) in the temperate northeast and 5 of the Coupled Model Intercomparison Project) and southeast Atlantic. In higher-latitude regions, by contrast, are thus comparable. The models are run under two 5 catch potential is projected to increase or at least decrease greenhouse gas (GHG) emission scenarios, the lowest much less, as expected in temperate regions (e.g., Senegal, (Representative Concentration Pathway 2.6) and the The Gambia, Cabo Verde). highest (Representative Concentration Pathway 8.5), to Tables 1 and 2 lay out the percentage by which potential account for the uncertainty that still prevails in climate catches are expected to change—mostly decrease, but change modeling, with two different time horizons, also, in a few cases, increase—by 2050 and 2100. The 2050 and 2100. data in these tables are then shown on a series of maps, The results are presented in two sets, one for the which indicate that the impacts of climate change on Dynamic Bioclimate Envelope Model and the other MCP will vary greatly, from countries that will experience for the Multispecies Size Spectrum Ecological Model. the greatest changes to others that will remain relatively For each model, the results are divided between unscathed. Purely from an ecological standpoint, and changes under two different climate change scenarios without regard for fisheries management, the economic (Representative Concentration Pathways 2.6 and 8.5), importance of the sector, or the vulnerability of affected and for each scenario, the results are mapped for 2050 populations, climate change will have different impacts and 2100. on fisheries resources of different countries. 5 The Geophysical Fluid Dynamic Laboratory Earth system model 2G, the Institut Pierre Simon Laplace Climate Model, and the Max Planck Institute Earth system model. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 17 TABLE 1. Percentage Changes in Maximum Catch Potential (MCP) Under Low and High Greenhouse Gas (GHG) Emission Scenarios, by 2050 and 2100 (Dynamic Bioclimate Envelope Model) Low GHG emission scenario High GHG emission scenario (Representative Concentration (Representative Concentration Exclusive economic zone Pathway 2.6) Pathway 8.5) 2050 2100 2050 2100 Angola −23.70 −19.97 −43.65 −63.95 Benin −20.91 −15.34 −24.68 −65.97 Cameroon −18.28 −19.45 −34.01 −55.42 Cabo Verde 17.52 20.93 24.03 26.92 Comoros 0.51 −0.82 −9.82 −46.51 Congo, Dem. Rep. −29.09 −33.82 −42.65 −60.57 Congo, Rep. −46.29 −48.51 −53.86 −63.79 Côte d’Ivoire −31.14 −31.82 −37.73 −72.25 Equatorial Guinea −34.11 −34.24 −47.48 −67.72 Gabon −48.15 −47.39 −63.73 −69.85 Gambia, The 4.63 7.76 6.19 −28.31 Ghana −25.76 −25.19 −35.02 −76.15 Guinea −14.29 −14.62 −30.32 −65.00 Guinea-Bissau −14.32 −10.86 −20.95 −65.03 Kenya 1.88 3.42 2.02 −48.43 Liberia −41.32 −38.81 −44.32 −76.26 Madagascar −1.84 −4.59 −12.20 −39.90 Mauritania −5.26 −4.42 −6.13 −17.36 Mauritius −3.32 −6.54 0.23 −4.34 Mayotte (France) 3.58 1.81 −10.96 −48.84 Morocco −2.38 −7.32 −6.59 −14.46 Mozambique −8.51 −13.70 −14.25 −34.90 Namibia −12.47 −6.10 −16.60 −34.46 Nigeria −17.12 −15.38 −33.82 −52.75 Réunion (France) −6.05 −12.52 −11.17 −15.73 São Tomé and Príncipe −32.15 −33.05 −53.14 −82.68 Senegal 1.98 5.17 4.72 −28.38 Seychelles −8.39 −8.66 −15.58 −68.45 Sierra Leone −17.21 −22.69 −35.13 −57.41 Somalia −10.30 −9.52 −22.39 −60.89 South Africa −8.25 −9.54 −15.26 −21.19 Tanzania 0.80 2.14 −1.60 −52.40 Togo −22.60 −16.78 −30.63 −71.47 Djibouti and Eritrea are missing because MCP projections were available from only one model. 18 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA TABLE 2. Percentage Changes in Maximum Catch Potential (MCP) Under Low and High Greenhouse Gas (GHG) Emission Scenarios, by 2050 and 2100 (Multispecies Size Spectrum Ecological Modeling) Low GHG emission scenario High GHG emission scenario (Representative Concentration (Representative Concentration Exclusive economic zone Pathway 2.6) Pathway 8.5) 2050 2100 2050 2100 Angola −5.10 −3.40 −11.12 −34.43 Benin −17.57 −15.54 −16.93 −33.02 Cameroon −8.64 −4.76 −12.29 −22.87 Cabo Verde −10.73 −5.93 −19.33 −36.15 Comoros −12.38 −10.90 −14.31 −26.05 Congo, Dem. Rep. −5.83 −4.30 −9.61 −19.73 Congo, Rep. −7.51 −6.82 −11.41 −23.92 Côte d’Ivoire −22.73 −18.00 −20.46 −35.31 Equatorial Guinea −10.63 −6.65 −12.38 −28.37 Gabon −6.28 −4.56 −7.86 −18.72 Gambia, The −18.43 −10.39 −17.64 −35.14 Ghana −22.66 −15.15 −20.34 −38.36 Guinea −20.07 −15.88 −15.66 −29.72 Guinea-Bissau −24.69 −18.29 −17.37 −32.30 Kenya −18.78 −11.76 −19.93 −34.92 Liberia −20.96 −20.14 −19.71 −32.04 Madagascar −6.16 −4.88 −10.57 −18.86 Mauritania −2.52 −4.79 −8.57 −16.87 Mauritius −11.59 −12.37 −13.12 −23.09 Mayotte (France) −9.49 −8.43 −11.71 −21.86 Morocco 2.64 −2.68 5.05 −8.32 Mozambique −7.14 −4.84 −10.74 −20.37 Namibia −2.17 −2.33 −3.64 −11.22 Nigeria −10.81 −9.42 −11.14 −24.20 Réunion (France) −7.62 −9.14 −12.38 −21.45 São Tomé and Príncipe −11.24 −10.59 −13.45 −29.05 Senegal −16.76 −9.04 −18.98 −36.15 Seychelles −19.92 −14.86 −21.29 −33.51 Sierra Leone −22.44 −19.40 −18.70 −31.45 Somalia −15.46 −11.01 −19.06 −36.53 South Africa −2.13 −1.84 −2.29 −3.83 Tanzania −17.44 −12.40 −18.22 −32.24 Togo −17.97 −15.57 −17.13 −34.72 Djibouti and Eritrea are missing because MCP projections were available from only one model. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 19 PHOTO CREDIT Charlotte De Fontaubert / World Bank 20 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA CATCH POTENTIAL MAPS: LEGEND Change in maximum catch potential UNDERSTANDING THE LEGEND (MCP) (%) The following legend is used in Figures 4a through 7b, with differences throughout the maps easily recognized. -83 to -45 In some instances, the upper or lower bounds of the >-45 to -30 underlaying data will be less than others, but the band ranges never change, and the colors are consistent >-30 to -15 between the maps. This same logic is used for all other maps in the report. >-15 to -0 >0 to 27 PHOTO CREDIT Charlotte De Fontaubert / World Bank AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 21 FIGURE 4. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios by 2050 Using the Dynamic Bioclimate Envelope Model a. 22 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 4. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios by 2050 Using the Dynamic Bioclimate Envelope Model b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 23 FIGURE 5. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios by 2100 Using the Dynamic Bioclimate Envelope Model a. 24 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 5. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios by 2100 Using the Dynamic Bioclimate Envelope Model b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 25 FIGURE 6. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios in 2050 Using Multispecies Size Spectrum Ecological Modeling a. 26 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 6. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios in 2050 Using Multispecies Size Spectrum Ecological Modeling b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 27 FIGURE 7. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios in 2100 Using Multispecies Size Spectrum Ecological Modeling a. 28 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 7. Change in MCP (%) Under (a) Low and (b) High Greenhouse Gas Emission Scenarios in 2100 Using Multispecies Size Spectrum Ecological Modeling b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 29 30 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA PHOTO CREDIT Charlotte De Fontaubert / World Bank 5. Mapping Adaptation Through Uncertainty As highlighted above, the ecological modeling that the two scenarios, and between the two periods (2050, allows the ecological impact of climate change on 2100). The EEZs with the diagonal lines represent the fisheries to be assessed is based on two models run change in direction when these two models predict under two different climate change scenarios at two change in different directions. different times. This combination of variables leads to a These maps show that the models indicated different situation of high uncertainty, under which the difference levels of variation in MCP, although for the vast majority of between the models used could lead to markedly coastal African countries, the models converged around different results. a decrease in MCP by the middle and end of the century. To assess the differences that might arise, the six sets This finding is consistent with the IPCC’s special report, of maps below illustrate the differences in projected Global Warming of 1.5°C, which shows moderate impact change in MCP for each possible variation between the on small-scale low-latitude fisheries from 2006 to 2015 two models used (Dynamic Bioclimate Envelope Model, and forecasts—with high confidence—a large impact on Multispecies Size Spectrum Ecological Model), between fisheries productivity, especially at low latitudes. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 31 FIGURE 8. Change in MCP (%) Between Dynamic Bioclimate Envelope Model and Multispecies Size Spectrum Ecological Model Under Representative Concentration Pathway 2.6 by (a) 2050 and (b) 2100 a. 32 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 8. Change in MCP (%) Between Dynamic Bioclimate Envelope Model and Multispecies Size Spectrum Ecological Model Under Representative Concentration Pathway 2.6 by (a) 2050 and (b) 2100 b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 33 FIGURE 9. Change in MCP (%) Between Dynamic Bioclimate Envelope Model and Multispecies Size Spectrum Ecological Model Under Representative Concentration Pathway 8.5 in (a) 2050 and (b) 2100 a. 34 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 9. Change in MCP (%) Between Dynamic Bioclimate Envelope Model and Multispecies Size Spectrum Ecological Model Under Representative Concentration Pathway 8.5 in (a) 2050 and (b) 2100 b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 35 FIGURE 10. Change in MCP (%) Between Representative Concentration Pathways 2.6 and 8.5 Using the Dynamic Bioclimate Envelope Model in (a) 2050 and (b) 2100 a. 36 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 10. Change in MCP (%) Between Representative Concentration Pathways 2.6 and 8.5 Using the Dynamic Bioclimate Envelope Model in (a) 2050 and (b) 2100 b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 37 FIGURE 11. Change in MCP (%) Between Representative Concentration Pathways 2.6 and 8.5 Using Multispecies Size Spectrum Ecological Modeling in (a) 2050 and (b) 2100 a. 38 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 11. Change in MCP (%) Between Representative Concentration Pathways 2.6 and 8.5 Using Multispecies Size Spectrum Ecological Modeling in (a) 2050 and (b) 2100 b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 39 FIGURE 12. Change in MCP (%) Between 2050 and 2100 Using Dynamic Bioclimate Envelope Model Under Representative Concentration Pathways (a) 2.6 and (b) 8.5 a. 40 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 12. Change in MCP (%) Between 2050 and 2100 Using Dynamic Bioclimate Envelope Model Under Representative Concentration Pathways (a) 2.6 and (b) 8.5 b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 41 FIGURE 13. Change in MCP (%) Between 2050 and 2100 Using Multispecies Size Spectrum Ecological Modeling Under Representative Concentration Pathways (a) 2.6 and (b) 8.5 a. 42 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 13. Change in MCP (%) Between 2050 and 2100 Using Multispecies Size Spectrum Ecological Modeling Under Representative Concentration Pathways (a) 2.6 and (b) 8.5 b. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 43 44 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA PHOTO CREDIT Nicole Macheroux-Denault / Shutterstock.com 6. Socioecological Risk of Climate Change APPROACH change and assesses the risk level for each species Building on the latest IPCC approach to risk analysis, based on ecological and biological traits. As with the socioecological risk scores were estimated based on IPCC approach, ecological risk is measured as a function ecological and socioeconomic risk assessments. of hazard, exposure, and vulnerability (which itself is a function of sensitivity minus adaptation capacity). The socioecological risk indicator is composed of This approach was then used to project the future ecological hazard, exposure, sensitivity, and adaptation impact of climate change on marine living resources capacity components of the national and social aspects and fisheries and the resulting ecological hazard to the of the economy of each African country. coastal communities under climate change. The values of these indicators were estimated based on changes Ecological risk assessment in environmental variables, as measured in section IV. In The approach used in this report synthesizes data on each African country, the exploited marine species were species-specific exposure and hazard from climate identified, the average ecological risk values to climate FIGURE 14. Linked socioecological risk framework Hazard Exposure Vulnerability ECOLOGICAL Sensitivity Policy feedback Adaptation capacity Risk of marine species under climate Vulnerability impacts Sensitivity Hazard Exposure Adaptation capacity SOCIO-ECONOMIC Social-Ecological risks of Policy relevant climate change solutions AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 45 change for all marine species in each EEZ were then y Adaptation capacity is the ability of a social system calculated, and the resulting values were used as the in the current context to anticipate, respond to, hazard values in this analysis. and adjust to the impacts of climate change and to minimize, cope with, and recover from the Socioecological risk assessment consequences of climate change. Socioecological risk assessments have been used y Vulnerability is a function of sensitivity as modified in various disciplines to assess the susceptibility of by adaptation capacity. natural or human systems to human activities or natural pressures. According to the IPCC Working Group II Fifth y Risk measures the potential impacts of climate Assessment Report, the socioecological risk of climate change on the national and social aspects and change is a function of hazard, exposure, and adaptation economies as a function of hazard, exposure, and capacity (sensitivity minus adaptation capacity). Under vulnerability. this framework, the definitions of the indicators are For each of the four components of risk—hazard, modified for the context of fisheries and climate change. exposure, sensitivity, adaptation capacity—a number of y Hazard is the climate-related impact on the marine indicators were selected in consultation with experts on ecosystem. The risk from climate change for each living marine resources in Africa (table 3). The relative marine species estimated in the ecological risk risk score, and the scores of each component, range assessment is used as a proxy for estimating the from 0 to 100. In countries with higher risk scores, hazard to the socioeconomic system. climate change poses a greater threat to the national economies of these countries through fisheries, but it y Exposure is the presence of people and exploited is important to remember that these scores are relative marine resources that could be adversely affected. and merely compare countries with one another. In other y Sensitivity indicates the intrinsic degree to which words, if one country has a score of 20 and another a the national economies and food security depend score of 40, it does not mean that the risk is twice as on fisheries. high in the second as in the first but merely that one is more at risk than the other. 46 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA TABLE 3. Examples of Indicators for Each of the Risk Components and Risk Assessment INDICATOR VARIABLE Hazard Climate-related impacts on Ecological risks of climate change for all marine species and their marine ecosystem related ecosystem in each exclusive economic zone in small-scale and industrial fisheries sectors Exposure Relative human presence in Percentage of coastal to total population for each African country coastal areas People involved in fisheries sector Number of male and female fishers in small-scale and industrial fisheries sectors People involved in fisheries- Number of employees in upstream and downstream activities, including related sector marketing, processing, exports, boat building Sensitivity Employment Proportion of economically active population in fisheries sector Proportion of economically active population employed in upstream and downstream activities such as marketing, processing, exports, boat building Nutritional dependence Fish protein as proportion of all animal protein Child malnutrition Economic dependence Country’s dependence on fisheries sector for revenue; fisheries’ contribution to gross domestic product Fisheries export value as proportion of total exports Total fisheries landings Poverty rate (number of people and percentage of population below national poverty line) Coastal protection dependence Population density in low-elevation zone Land area below 5 m elevation Adaptation capacity Health Life expectancy at birth Education Literacy rates (number and percentage of people over age 15 who can read and write, both sexes) School enrollment ratios (number and percentage of tertiary-age people enrolled in tertiary education, both sexes) Governance (sector specific) Political stability and absence of violence Government effectiveness Regulatory quality Rule of law Voice and accountability Corruption Fisheries management Proportion of territorial sea protected Area coverage of effectively managed marine protected areas established in support of fisheries Size of economy Gross domestic product Access to scientific knowledge Proportion of “good”* fisheries subsidies to total fisheries subsidies Employment alternatives Economic diversity Political action Climate adaptation planning AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 47 * ”Good” subsidies is used as a proxy for quantifying access to scientific knowledge, because part of “good” subsidies is often used for scientific research and management purposes. Integrating the two approaches fisheries). The results can be used to inform policy- The risks to ecological and socioeconomic systems are relevant solutions for mitigating and adapting to their considered by integrating the two risk assessments impacts. Meanwhile, a policy feedback path loops back (figure 14). In this overall framework that IPCC has to the ecological and socioeconomic assessments. provided, the risks to marine species in each country A detailed description of this methodology is available under climate change affect the subsequent ecosystem in Annex 2. Source data are also provided in Annex 3. goods and services that the ocean provides (e.g., RESULTS Ecological risk indicator TABLE 4. Ecological Risk Score for Each African Country Ecological Risk Ecological Risk Country Country Indicator Indicator Angola 66.307 Madagascar 75.843 Benin 80.216 Mauritania 64.471 Cameroon 78.947 Mauritius 78.376 Cape Verde 80.220 Mayotte (France) 77.853 Comoros 69.996 Morocco 53.896 Congo, Dem. Rep. 67.747 Mozambique 76.737 Congo, Rep. 66.703 Namibia 58.916 Côte d’Ivoire 78.224 Nigeria 75.270 Djibouti 84.273 Réunion (France) 79.255 Equatorial Guinea 79.959 São Tomé and Príncipe 76.409 Eritrea 87.304 Senegal 70.766 Gabon 77.648 Seychelles 77.320 Gambia, The 80.427 Sierra Leone 83.969 Ghana 76.872 Somalia 81.796 Guinea 79.226 South Africa 65.469 Guinea-Bissau 83.411 Tanzania 83.232 Kenya 75.339 Togo 76.582 Liberia 75.849 Socioecological risk indicator produces, which aim to incorporate unreported catches, The results reflect the differences in catch value based including catches from subsistence and recreational on how these catches are measured (tables 5 and 6): fishing sectors; discards; and illegal, unregulated, and the FAO data that are collected based on information unreported fishing, which, by definition, are not part of that the government of each country provides and official national data reported to the FAO. the reconstructed catches that the Sea Around Us 48 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA FIGURE 15. Ecological Risk Score for Each Coastal African Coastal Country AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 49 TABLE 5. Individual Component Risk Scores, According to Country (Food and Agriculture Organization–Reported Catches) Adaptation Country Hazard Exposure Sensitivity Vulnerability Risk capacity Score Angola 37 20 11 28 41 41 Benin 79 28 6 29 56 55 Cameroon 75 21 6 19 52 52 Cape Verde 79 44 1 32 47 46 Comoros 48 18 2 18 38 38 Congo, Dem. Rep. 41 18 35 30 57 57 Congo, Rep. 38 38 1 18 22 21 Côte d’Ivoire 73 13 6 25 58 58 Djibouti 91 21 0 29 63 62 Equatorial Guinea 78 15 1 13 51 51 Eritrea 100 18 2 34 72 72 Gabon 71 31 1 19 43 42 Gambia, The 79 19 2 52 70 70 Ghana 69 37 14 23 47 47 Guinea 76 17 11 30 63 63 Guinea-Bissau 88 24 10 46 73 73 Kenya 64 32 9 9 38 37 Liberia 66 20 3 26 50 50 Madagascar 66 38 13 31 49 48 Mauritania 32 36 3 38 31 31 Mauritius 73 58 0 20 30 30 Mayotte (France) 72 12 0 1 43 43 Morocco 0 54 15 23 4 3 Mozambique 68 25 18 27 57 57 Namibia 15 63 1 25 1 0 Nigeria 64 29 100 38 100 100 Réunion (France) 76 n.a. 0 0 0 51 São Tomé and Príncipe 67 32 1 35 48 48 Senegal 50 35 12 36 44 44 Seychelles 70 41 0 33 44 44 Sierra Leone 90 10 5 32 71 71 Somalia 84 5 9 21 68 68 South Africa 35 56 12 16 15 15 Tanzania 88 32 12 14 54 54 Togo 68 20 2 25 50 50 50 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA TABLE 6. Individual Component Risk Scores, According to Country (Sea Around Us, Reconstructed Catch Data) Adaptation Country Hazard Exposure Sensitivity Vulnerability Risk capacity Score Angola 37 20 11 28 41 41 Benin 79 28 6 29 56 56 Cameroon 75 21 6 19 52 52 Cape Verde 79 44 1 32 47 47 Comoros 48 18 2 18 38 38 Congo, Dem. Rep. 41 18 35 30 57 57 Congo, Rep. 38 38 1 18 22 22 Côte d’Ivoire 73 13 6 25 58 58 Djibouti 91 21 0 29 63 63 Equatorial Guinea 78 15 1 13 51 51 Eritrea 100 18 2 34 72 72 Gabon 71 31 1 19 43 43 Gambia, The 79 19 2 52 71 71 Ghana 69 37 14 23 48 47 Guinea 76 17 11 31 64 64 Guinea-Bissau 88 24 10 48 74 74 Kenya 64 32 9 9 38 38 Liberia 66 20 3 26 50 50 Madagascar 66 38 13 31 49 49 Mauritania 32 36 3 40 32 32 Mauritius 73 58 0 20 30 30 Mayotte (France) 72 12 0 0 43 42 Morocco 0 54 15 23 4 3 Mozambique 68 25 18 26 57 57 Namibia 15 63 1 24 0 0 Nigeria 64 29 100 37 100 100 Réunion (France) 76 n.a. 0 0 0 51 São Tomé and Príncipe 67 32 1 35 48 48 Senegal 50 35 12 35 44 44 Seychelles 70 41 0 33 44 44 Sierra Leone 90 10 5 32 72 71 Somalia 84 5 9 21 68 68 South Africa 35 56 12 14 15 15 Tanzania 88 32 12 14 54 54 Togo 68 20 2 25 50 50 AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 51 FIGURE 16. Socioecological Risk Indicator (Normalized Score) for Each Coastal African Country 52 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA HOW TO INTERPRET THESE RESULTS Second, when studying individual countries, a country’s high ecological risk from the impacts of Although the map of socioecological risk identifies climate change does not necessarily translate into pockets of high risk, it represents risks as they are high socioecological risk, either because the national currently assessed assuming ecological impacts occur economy is not particularly dependent on fisheries or as modeled in section IV and the level of management because adaptation capacity is high (e.g., if there are remains the same. In that sense, the map is rather static, alternatives available) or even because better fisheries in that it does not show what could be if, for example, management measures are in place. This last point fisheries management were to improve, coastal habitat is well illustrated in the case of Namibia, where the protection was increased, or destructive activities were ecological risk is relatively low, and the sector benefits curtailed. Perhaps the most striking observation that can from particularly sustainable fisheries practices, which be made from the comparison of the maps in figures 15 together contribute to the lowest socioecological and 16 is that some of the countries that are the most risk for the continent. There are many points of entry at risk ecologically are not the most vulnerable from where governments and stakeholders can take action a socioeconomic standpoint, illustrating that, even if and change what would otherwise have resulted from the impacts of climate change are as dire as might be intense ecological change. It is the ultimate purpose expected, governments can increase their adaptation of this report to identify the pathways through which capacity and reduce the overall vulnerability of their governments can identify action and investment fisheries sectors. In addition, lessons from the results of projects through which they can mitigate these this assessment can be learned at different levels. ecological impacts and ultimately increase the resilience First, and although this study focused on 178 of the of their fisheries sectors. exploited marine species in the region, the findings Third, an important distinction needs to be made regarding the general pattern of climate change between ecological risks, which to a large extent are impacts on marine biodiversity are likely to be beyond the control of African coastal states (and applicable to many fishes and invertebrates in Africa. even under the most optimistic IPCC scenario, these Because many species are highly adapted or already at risks are still alarmingly high), and the socioeconomic the edge of their environmental ranges, their sensitivity factors over which they can, and should, have direct to any environmental or habitat perturbation is likely control. Socioeconomic exposure, sensitivity, and to be high. The likelihood that some local species could adaptation capacity can all be influenced through policy be driven to extinction is thus also high, particularly interventions and are the only elements over which if other contributing factors such as rampant habitat coastal states have much control. destruction or other anthropogenic impacts are allowed to continue unabated. AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 53 54 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA PHOTO CREDIT EcoPrint / Shutterstock.com Conclusion: A Game 7. Changer for Marine Fisheries Management in Africa Despite the differences between scenarios, between need to reduce overcapacity or address problems models, and over the different timelines, the findings of that stem from open access and to the need to this modeling exercise are sufficiently dire to raise the adapt to climate change. The importance lies alarm for decision makers, who now know enough to not so much in knowing in which category these take preventive and adaptive measures to address the conservation and management measures fall as in risks, both ecological and socioecological, facing their knowing that they will be undertaken. fisheries in the face of climate change. The major lessons c. Likewise, when examining the different components from these findings can be summed up as follows. of risk — hazard, exposure, sensitivity, and a. Even under the best-case scenarios, the models adaptation capacity — it is apparent that some clearly show that the impact of climate change of their constituent parts may fall beyond the on fisheries will be serious, although not evenly narrow scope of fisheries management. For felt, and that stressed fisheries resources such instance, strengthening the adaptation capacity of as overfished stocks are at added risk from a community may require improvements in areas this additional impact. This is crucial because such as education, health, and alternative livelihood fisheries are often mismanaged to the point where development, which would not usually fall within uncontrolled levels of fishing prevail and certain the purview of a ministry in charge of fisheries. stocks collapse, which then calls for moratoria or Preparing fisheries to withstand the impacts of other measures designed to allow stocks to recover. climate change will thus likely require a multisectoral In the face of anticipated reductions in MCP, these and coordinated approach. Besides, this corrective measures may need to be more severe, multisectoral approach to fisheries management and moratoria will likely need to be longer—and is a no-regret investment because it has positive thus economically more onerous—and in some outcomes with or without climate change. cases might not be adequate to allow affected d. The African countries on which this report focuses stocks to recover. In other words, the boom and are likely to suffer disproportionately more from bust overfishing cycle may no longer be one from the impacts of climate change than other countries which fish stocks can recover when combined with that may have contributed to a much higher degree the additional impacts of climate change. to the causes of climate change (e.g., in the case b. There are thus clear parallels between measures of species that will migrate away from the equator designed to strengthen the adaptation capacity of toward the poles in response to increases in sea fishing communities to climate change and broader temperatures), although this does not mean that measures targeted at fostering fisheries governance African countries can focus solely on climate reform. The same measures may be justified by the change adaptation. Development of national AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 55 fishing fleets in Africa is expected to add to climate (e.g., coastal development, sand mining, destruction change, making the situation even more dire. The of coral reefs, deforestation of mangroves). response to climate change should thus incorporate Again, it is likely that these adaptation measures mitigation as well as adaptation. will fall outside the traditional scope of fisheries management and require a broader multisectoral e. As illustrated in the brief overview of the ecological approach. patterns of climate change on marine fisheries, impacts will be felt directly on the species that Beyond these general observations about what the next fisheries traditionally target and on the marine and steps should be, each country will need to determine its coastal ecosystems on which these species depend. path to adaptation, its blueprint for preparing national This in turn calls for adaptation measures targeted fisheries for the impacts of climate change, at the not only at the stocks, including through reductions national level. This will require a detailed review of the in the level of fishing and capacity, but also at the individual components of each contributing factor. protection of these ecosystems, which too often are already subject to excessive anthropogenic impacts PHOTO CREDIT Roberto Binetti / Shutterstock.com 56 References Allison, E. H., A. L. Perry, M. C. Badjeck, W. Neil Adger, K. Brown, D. Conway, A. S. Halls, G. M. Pilling, J. D. Reynolds, N. L. Andrew, and N. K. Dulvy. 2009. “Vulnerability of National Economies to the Impacts of Climate Change on Fisheries.” Fish and Fisheries 10 (2): 173–196. AUC (African Union Commission) and NEPAD (New Partnership for Africa’s Development). 2014. The Policy Framework and Reform Strategy for Fisheries and Aquaculture in Africa. Addis Ababa, Ethiopia: AUC and NEPAD. Belhabib, Dyhia, U. Rashid Sumaila, and Daniel Pauly. 2015. “Feeding the Poor: Contribution of West African Fisheries to Employment and Food Security.” Ocean and Coastal Management 111 (2015): 72–81. doi:10.1016/j.ocecoaman.2015.04.010. Cinner, J. E., T. R. McClanahan, N. A. J. Graham, T. M. Daw, J. Maina, S. M. Stead, A. Wamukota, K. Brown, and Ö. Bodin. 2012. “Vulnerability of Coastal Communities to Key Impacts of Climate Change on Coral Reef Fisheries.” Global Environmental Change 22 (1): 12–20. de Graaf, G., and L. Garibaldi. 2014. The Value of African Fisheries. FAO Fisheries and Aquaculture Circular. No. 1093. Rome: FAO. Dufresne, JL., Foujols, MA., Denvil, S. et al. 2013. “Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5.” Climate Dynamics 40 (9–10): 2123–2165. https://doi.org/10.1007/s00382-012-1636-1. Dunne, J. P., J. G. John, E. Shevliakova, R. J. Stouffer, J. P. Krasting, S. L. Malyshev, P. C. D. Milly, L. T. Sentman, A. J. Adcroft, W. Cooke, and K. A. Dunne. 2013. “GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part II: Carbon System Formulation and Baseline Simulation Characteristics.” Journal of Climate 26 (7): 2247–2267. FAO (Food and Agriculture Organization of the United Nations). 2018. Impacts of Climate Change on Fisheries and Aquaculture, Synthesis of Current Knowledge, Adaptation and Mitigation Measures. FAO Fisheries and Aquaculture Technical Paper 627. Rome: FAO FAO. 2018. FAOSTAT Database. Rome: FAO. http://faostat3.fao.org/home/E. FAO and WHO (World Health Organization). 2011. Report of the Joint FAO/WHO Expert Consultation on the Risks and Benefits of Fish Consumption, Rome, 25–29 January 2010. FAO Fisheries and Aquaculture Report No. 978. Rome: FAO and WHO. Giorgetta, M. A., J. Jungclaus, C. H. Reick, S. Legutke, J. Bader, M. Böttinger, V. Brovkin, T. Crueger, M. Esch, K. Fieg, and K. Glushak. 2013. “Climate and Carbon Cycle Changes from 1850 to 2100 in MPI-ESM Simulations for the Coupled Model Intercomparison Project Phase 5.” Journal of Advances in Modeling Earth Systems 5 (3): 572–597. Institut Mauritanien de Recherches Océanographiques et de Pêches, Ninth Working Group Meeting, February 2019 Jones, M. C., and W. W. Cheung. 2018. “Using Fuzzy Logic to Determine the Vulnerability of Marine Species to Climate Change.” Global Change Biology 24 (2): e719–e731. IPCC, 2018: Global Warming of 1.5°C.An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press. Lieth, Helmut, ed. 1974. Phenology and Seasonality Modeling. New York: Springer-Verlag. World Bank. 2012. Hidden Harvest, The Global Contribution of Capture Fisheries. Washington, DC: World Bank. World Bank. 2013a. “Royaume du Maroc—Changement Climatique et Secteur Halieutique: Impacts et Recommandations.” in Morocco Climate Change Mitigation and Adaptation Strategy, 2013. http://documents.banquemondiale.org/curated/ fr/638931468275132100/Royaume-du-Maroc-Changement-climatique-et-secteur-halieutique-impacts-et-recommandations World Bank. 2013b. Fish to 2030, Prospects for Fisheries and Aquaculture. Washington, DC: World Bank. WorldFish. 2009. Fish Supply and Food Security for Africa. Penang, Malaysia: WorldFish. 57 Annex 1. Volume and Value Of Catches – Food and Agriculture Organization Data and Reconstructed Catches Reconstructed catch data are based on official catch estimates and corrected to add estimated catches from illegal, unregulated, and unreported fishing and discards at sea, usually of bycatch. Landed value in Landings (tons) Landings (tons) 2010 real USD value Country according to Food and according to Sea according to Sea Agriculture Organization Around Us Around Us Angola 401,057 679,098 1,373,871,366 Benin 18,599 75,837 97,486,370 Cameroon 167,365 156,150 154,722,458 Cape Verde 28,887 23,377 56,965,480 Comoros 26,091 23,185 35,388,362 Congo, Dem. Rep. 4,528 25,472 48,959,732 Congo, Rep. 39,031 101,370 150,675,689 Côte d’Ivoire 61,747 172,319 213,400,383 Djibouti 2,011 3,618 7,601,240 Equatorial Guinea 7,316 37,975 97,381,769 Eritrea 4,098 9,437 14,664,823 Gabon 23,902 184,107 259,373,132 Gambia, The 43,716 210,696 250,113,495 Ghana 238,993 420,725 496,843,307 Guinea 109,282 867,539 1,123,880,584 Guinea-Bissau 6,548 710,894 1,117,672,872 Kenya 10,179 18,310 48,357,359 Liberia 12,000 93,980 115,998,627 Madagascar 89,740 163,117 338,985,723 Mauritania 416,570 1,754,744 1,826,249,782 Mauritius 11,674 20,712 62,469,965 Mayotte (France) 15,254 5,739 17,913,435 Morocco 1,244,835 2,989,906 4,020,365,748 Mozambique 160,809 160,413 218,804,921 Namibia 468,405 649,241 654,710,602 Nigeria 367,954 476,755 1,176,551,504 58 Landed value in Landings (tons) Landings (tons) 2010 real USD value Country according to Food and according to Sea according to Sea Agriculture Organization Around Us Around Us Réunion (France) 2,785 3,572 17,093,491 São Tomé and Príncipe 9,233 14,098 27,151,556 Senegal 420,300 677,822 1,044,893,930 Seychelles 87,594 65,189 116,703,162 Sierra Leone 200,476 325,388 260,003,279 Somalia 29,800 149,624 258,776,435 South Africa 570,550 636,853 692,175,636 Tanzania 93,704 130,967 237,429,721 Togo 17,609 61,901 70,289,137 Total 5,412,642 12,100,128 16,703,925,076 59 Annex 2. Description of Models And Methodologies DESCRIPTION OF MODELS USED The DBEM estimated the temperature-preference FOR PROJECTED ECOLOGICAL profile of each species by overlaying the estimated IMPACTS OF CLIMATE CHANGE species distribution with annual seawater temperature Dynamic Bioclimate Envelope Model (DBEM) and calculated the area-corrected distribution of description relative abundance across temperature for each year The DBEM is a dynamic process-based species from 1971 to 2000, subsequently averaging annual distribution model that simulates changes in distribution, temperature-preference profiles. The estimated abundance, and catches of 178 exploited marine fishes temperature-preference profile was used to predict the and invertebrates in African exclusive economic zones thermal physiological performance of a species (aerobic (EEZs) under climate-change scenarios. These species scope) in each area. Population carrying capacity in account for 25 percent of total fisheries catches from the each spatial cell is a function of the unfished biomass EEZs in Africa in the 2000s. Ocean variables projected of the population, habitat suitability, and net primary from the Earth system models (ESMs) that drive the production. It was assumed that the average of the top- simulations in the DBEM include seawater temperature 10 annual catches was roughly equal to the maximum (surface and bottom), oxygen concentration (surface sustainable yield of the species. and bottom), hydrogen ion concentration (surface and The model simulated changes in relative abundance and bottom), net primary production (depth integrated), biomass of a species based on changes in population salinity (surface and bottom), and surface advection. carrying capacity, intrinsic population growth, and the All model data have been re-gridded onto a 0.5° latitude advection-diffusion of the adults and larvae of the x 0.5° longitude grid using a bilinear interpolation population driven by ocean conditions projected from method. The current distributions of the species, the ESMs. The DBEM calculates a characteristic weight representing the average pattern of relative abundance representing the average mass of the population in a in recent decades (1970–2000), were produced using cell. The model simulated how changes in temperature an algorithm that predicts the relative abundance of and oxygen content would affect growth and body size a species on a 0.5° latitude x 0.5° longitude grid. The of the individuals using a sub-model derived from a distributions were further refined by assigning habitat generalized von Bertalanffy growth function. Movement preferences to each species, such as affinity to shelf and dispersal of adults and larvae were modeled (inner and outer), estuaries, and coral reef habitats. through advection-diffusion-reaction equation for larvae An index of habitat suitability for each species in each and adult stages. Its predicted pelagic larval duration spatial cell is derived from temperature (bottom and partly determines Larval movement. Population growth surface temperature for demersal and pelagic species, was represented using a logistic function. respectively), bathymetry, specific habitats, salinity, and Maximum catch potential (MCP) from each population sea ice, with 30-year averages of outputs from 1971 to was predicted by applying a fishing mortality rate at 2000 from ESMs. the level required to achieve maximum sustainable 60 yield. For each simulation, changes in total annual MCP The plankton community is represented with 10 size by 2050 (2046–2055) and 2095 (2091–2000) from classes per tenfold increase in weight, which requires 2000 (1996–2005) under Representative Concentration a total of 70 (Institut Pierre Simon Laplace) to 120 Pathways 2.6 and 8.5 in each EEZ of the world’s oceans (Geophysical Fluid Dynamic Laboratory) size classes. were calculated. The ensemble average across MCP For each plankton functional type in the ESM, we projections from the three ESMs is presented. distribute the biomass of the plankton type over its specific weight range such that each interval of the Size-based model description same width (in log space) contains equal biomass. The The size-based model uses the same parametrization authors of the ESMs (Institut Pierre Simon Laplace) unless stated otherwise below. It distinguishes fish provided weight ranges for each plankton type, or the according to their size but does not consider individual weight ranges were based on the nominal size ranges species. The ESM outputs that drive the size spectrum used in other models for the same plankton functional model are monthly sea surface temperatures and type (Geophysical Fluid Dynamic Laboratory). The final surface concentrations of different plankton functional plankton size spectrum is constructed by combining the types. In addition, the annual mean vertical distribution size spectrum contributions of all plankton functional of total plankton biomass is used to infer the depth types. The fish model is driven with monthly mean distribution of fish. All inputs were spatially averaged surface concentrations of each plankton functional type, over each EEZ, and the fish model was subsequently which are converted to concentrations of each plankton run per EEZ. For the historical simulation (1850–2005), size class. The fish model subsequently simulates size the model was spun up for 50 years from low fish spectra of the fish community in the surface ocean. biomass using climatological temperatures and plankton The predicted fish concentrations at the surface are concentrations. For the projections (2006–2100), the converted to depth-integrated biomass by taking the model was initialized with the final state of the historical vertical distribution of fish proportional to the annual simulation. mean vertical distribution of plankton. Trophic interactions are represented explicitly; ECOLOGICAL AND SOCIOECONOMIC fish feed according to size-based rules, preferring RISK ASSESSMENTS prey a hundredth of their weight. Accordingly, the Description of ecological risk assessment framework smallest fish feed on large plankton (e.g., diatoms A fuzzy logic expert system was used to assess the and mesozooplankton), and larger fish feed on small level of exposure to hazard, sensitivity, adaptation fish. In the latter case, predation moves biomass from capacity, and the resulting overall risk of marine smaller weight classes (prey) to larger weight classes fish and invertebrates to climate change and ocean (predator). A search rate that depends on predator size acidification in African waters (Jones and Cheung mediates prey discovery. Ingestion and assimilation 2018). Such a system allows a subject to belong to more of food leads to growth of individual fish, which is than one set simultaneously, with a fuzzy membership represented by moving part of the fish in a size class to function defining the extent of membership in each the next class. Mortality is due to predation and intrinsic instead of a subject being allocated to only one size-dependent mortality, as well as fishing, represented category. Therefore, fuzzy logic allows the uncertainty by mortality of 0.2 per year for all fish larger than surrounding our knowledge of fish biological and 1.25 g. Recruitment is not modeled explicitly. Instead, ecological characteristics, as well as their contribution to the abundance of the first size class of fish (1 mg) is vulnerability, to be taken into consideration. Because the derived by extending the plankton size spectrum; this spatial distribution of each species is taken into account, assumes a continuous size spectrum from plankton the vulnerability of the related ecosystem is also to fish. Physiological rates increase with temperature assessed. In the ecological context, exposure is defined according to an Arrhenius relationship with activation as the extent to which given species will be subject to energy of 0.63 eV. This also affects trophic interactions. climate hazards, as measured in projected change in AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 61 physical environment. Exposure is estimated based on regular grid of 0.5° using the nearest neighbor method, current species distribution ranges obtained from the and values in some coastal cells were extrapolated using Sea Around Us (www.seaaroundus.org). bilinear extrapolation. Projected changes in environmental parameters were The sensitivity of marine species to climate change is used to represent climate hazard. Annual average based on a series of ecological and biological traits, values of surface and bottom sea water temperature which are identified based on published literature (table (°C), oxygen concentration (mL/L), salinity, net primary 2.1). The sensitivity and adaptation capacity indexes are production (mgC/km2 per year), surface advection estimated using an expert system based on heuristic (zonal and meridional vectors, m/s), and percentage rules. The sensitivity and adaptation capacity combine of sea ice coverage were determined from the outputs to indicate the vulnerability of each species. Finally, the of the Geophysical Fluid Dynamics Laboratory Earth risk index of the impacts of climate on each marine fish System Model (Dunne et al. 2013), the Institut Pierre and invertebrate species is calculated for each 0.5° x Simon Laplace Model (Dufresne et al. 2013), and the Max 0.5° spatial grid cell based on the combination of hazard Planck Institute for Meteorology Model (Giorgetta et al. exposure in each cell where each species may occur and 2013). Each environmental output was re-gridded onto a the vulnerability index of that species in each cell. TABLE 2.1. Variables and Data Used in Ecological Assessment Indicator Variable or data Unit Hazard Mean change in environmental variable between baseline and 2050 — divided by standard deviation over baseline period Exposure Current species distribution range — Sensitivity Temperature tolerance °C Maximum body length cm Maximum body length and high coral reef association — Taxonomic group (ocean acidification) — Adaptation Latitudinal breadth ° capacity Depth range m Fecundity Eggs or pups per year Habitat specificity — Description of socioecological risk assessment the socioecological analysis. A number of indicators framework of living marine resources in Africa were selected In this study, the risk from climate change to each in consultation with experts for each of the four marine species estimated in the ecological risk dimensions: hazard, exposure, sensitivity, and adaptation assessment was used as a proxy to estimate hazard to capacity (table 3). National data on most of these the socioeconomic system. In each country, exploited indicators are available from various global databases marine species are identified based on the Sea Around and the national statistical departments of individual Us catch database, distinguishing industrial from governments. Regional and local data were obtained small-scale fisheries (or shares of a given species from communications with local research institutions that industrial and small-scale fishers harvest). Then, and experts. average ecological risk values to climate change for Exposure was measured as the presence of people and all marine species in each EEZ are calculated, and exploited marine resources that the ecological hazard the resulting values are used as the hazard values in could adversely affect. Sensitivity usually refers to the 62 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA intrinsic degree to which national economies depend on A country with a high risk score is assumed to have high fisheries and are therefore sensitive to changes in the hazard to climate change, significant exposure to climate sector. Adaptation capacity is the ability of the social change, a significant level of fisheries’ contributions to system to anticipate, respond to, and adjust to changes its national economy and food security (sensitivity), or from climate stresses and to minimize, cope with, and limited ability to respond and adapt to the risks that recover from the consequences of climate change. climate change poses. Adaptation capacity thus includes elements of social Notes on data used to derive socioeconomic risk capital, human capital, and the appropriateness and Number of people involved in fisheries and fisheries- effectiveness of governance structures. related sectors is missing for Réunion (France), Djibouti, Using the same framework, a number of recent studies and Mayotte (France). have highlighted the vulnerability of national economies Number of people living in areas of elevation less than 5 to changes in their fisheries from climate change. m and land area of elevation less than 5 m is missing for Knowing their risk scores will enable societies and their Cabo Verde. national economies to manage immediate changes and trade-offs imposed by climate change. They will also be Number of fishers, proportion of economically active able to develop and institute appropriate climate change population in fishery sector, fish protein as proportion adaptation measures and seize opportunities that may of all animal protein consumed, proportion of children arise from climate change. under five years who are malnourished (underweight), number of people living in areas of elevation less than 5 Calculation of risk scores m, and land area of elevation less than 5 m are missing The risk of each country to impacts on its fisheries due for Mayotte (France) and Réunion (France). to climate change is calculated by taking the average of the standardized indexes for each dimension of Proportion of territorial sea protected, cost of climate risk. Although there are many ways of combining the change adaptation, and governance indicator are components, we made no a priori assumptions about missing for Cabo Verde. the importance of each dimension (or indicator within each dimension) in the overall function R = f (H, E, S, Life expectancy, adult literacy rates, school enrollment AC) and took the average because of the lack of a rate, governance indicator, proportion of ‘good’ subsidies, clear understanding of the interaction among these proportion of territorial sea protected, and cost of climate constituent components. In this way, each of the change adaptation are missing for Mayotte (France). indicators is viewed as making an equal contribution Employment opportunity in other sectors, life expectancy, (balanced weight) to a country’s overall vulnerability. adult literacy rates, school enrollment rate, governance A previous study showed that vulnerability is resistant indicator, proportion of ‘good’ subsidies, proportion of to change in the weightings of its components and territorial sea protected, and cost of climate change different methods of calculations (averaging or adaptation are missing for Réunion (France). multiplying) (Allison et al. 2009; Cinner et al. 2012)). AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 63 Annex 3. Definitions and Sources for Socioecological Indicators DEFINITION OF SOCIOECOLOGICAL INDICATORS Indicator Definition Composite index Variable Relative human presence in Coastal population; total   Percentage of coastal to total coastal areas population of each country population for each sub- Saharan African country People involved in fisheries   Number of male and female Number of people sector fishers in small-scale and industrial fisheries sectors People involved in fisheries-   Number of employees in Number of people related sector upstream and downstream activities, including marketing, processing, exports, boat building Employment Importance of marine Number of fishers in marine Number of fishers fisheries sector to local fisheries sector livelihoods Number of fishers relative to Proportion of economically other sectors active population in fisheries sector Nutritional dependence Importance of fish as source Country’s dependence on fish Fish protein as proportion of of nutrition and whether as source of protein all animal protein consumed nutrition that fisheries Child malnutrition Proportion of children provide is sufficient to under five years who are support the health of the malnourished (underweight) population Economic dependence Dependence of country’s Country’s dependence on its Landed values as proportion economy on its fisheries fisheries sector for revenue of total gross domestic sector product Fisheries export value Value of fisheries exports as proportion of total exports Total fisheries landings Catch (tons) Poverty rate Number and percentage of people below national poverty line Coastal protection Importance of marine Country’s current and Number and percentage ecosystem services to future dependence on of people living in areas of minimize risks of climate marine systems for coastal elevation <5 m change protection Health Average number of years that Life expectancy Life expectancy at birth a person can expect to live Indicator Definition Composite index Variable Education Education level Adult literacy rates Number and percentage of people over age 15 that can read and write, both sexes School enrollment ratios Number and percentage of tertiary-age people enrolled in tertiary education, both sexes Governance Public institutions’ ability Political stability and absence Perceptions of likelihood to conduct public affairs, of violence of political instability and manage public resources, politically motivated violence implement decisions, ensure (−2.5 to +2.5) rule of law, be accountable, Government effectiveness Perceptions of quality of and address corruption, public services, civil service which are generally seen and its independence as essential elements of a from political pressures, framework within which and policy formulation economies can prosper and implementation and credibility of government’s commitment to such policies (−2.5 to +2.5) Regulatory quality Perceptions of ability of government to formulate and implement sound policies that permit private sector development (−2.5 to +2.5) Rule of law Perceptions of extent to which agents have confidence in and abide by rules of society, quality of contract enforcement, property rights, police, and courts (−2.5 to +2.5) Voice and accountability Extent to which country’s citizens can participate in selecting their government, freedom of expression, freedom of association, and free media (−2.5 to +2.5) Control of corruption Perception of extent to which public power is exercised for private gain, including petty and grand corruption and ‘capture’ of the state by elites and private interests (−2.5 to +2.5) Fisheries management Resources allocated by Marine protected areas Proportion of territorial sea government to manage its protected fisheries sustainably Access to scientific   Proportion of ‘good’ Value paid in USD knowledge subsidies Political action Climate adaptation planning   Cost of adaptation Employment alternatives Employment opportunities in other sectors AS S E S S IN G VUL N ER A B I L I T Y A N D ST R EN GT HEN I N G A DA PTAT I O N CA PAC I T Y 65 SOURCES OF VARIABLES 7. ANIMAL PROTEIN CONSUMPTION PER CAPITA DATE: Average of last five years 1. EMPLOYMENT IN INDUSTRY, MALE NUMBER OF ENTITIES: 26 DATE: Average of last five years UNIT: grams of fish protein per day per capita NUMBER OF ENTITIES: 32 SOURCE: FAOSTAT (eggs, freshwater fish, demersal UNIT: Percentage fish, pelagic fish, marine fish, other, crustaceans, SOURCE: World Bank (2018) cephalopods, molluscs, other, meat, aquatic https://data.worldbank.org/indicator/SL.IND.EMPL. mammals, aquatic animals, others) MA.ZS?view=chart http://www.fao.org/faostat/en/ 2. EMPLOYMENT IN INDUSTRY, FEMALE 8. NUMBER OF PEOPLE IN ECONOMICALLY ACTIVE POPULATION DATE: Average of last five years DATE: Average of last five years NUMBER OF ENTITIES: 32 NUMBER OF ENTITIES: 25 UNIT: Percentage UNIT: Thousands of individuals SOURCE: World Bank (2018) SOURCE: International Labour Organization data https://data.worldbank.org/indicator/SL.IND.EMPL. provided by countries - FE.ZS?view=chart https://bit.ly/2KMqim6 3. EMPLOYMENT IN SERVICES, MALE 9. TOTAL POPULATION OF SUB-SAHARAN DATE: Average of last five years AFRICAN COUNTRIES NUMBER OF ENTITIES: 32 DATE: Average of last five years UNIT: Percentage NUMBER OF ENTITIES: 36 SOURCE: World Bank (2018) UNIT: Thousands of individuals https://data.worldbank.org/indicator/SL.SRV.EMPL. SOURCE: UNDP (2018) MA.ZS?view=chart https://esa.un.org/unpd/wpp/DataQuery 4. EMPLOYMENT IN SERVICES, FEMALE 10. NUMBER OF PEOPLE IN INDIRECT DATE: Average of last five years EMPLOYMENT OF FISHERIES NUMBER OF ENTITIES: 32 DATE: 2013 UNIT: Percentage NUMBER OF ENTITIES: 33 SOURCE: World Bank (2018) UNIT: Thousands of individuals https://data.worldbank.org/indicator/SL.SRV.EMPL. SOURCE: Teh and Sumaila (2013) FE.ZS?view=chart 11. NUMBER OF FISHERS 5. TOTAL AVERAGE RISK INDEX DATE: 2013 DATE: Average of last five years NUMBER OF ENTITIES: 33 NUMBER OF ENTITIES: 35 UNIT: Thousands of individuals UNIT: Percentage SOURCE: Teh and Sumaila (2013) SOURCE: 12. QUANTITIES OF FISHERIES EXPORT 6. FISH PROTEIN CONSUMPTION PER CAPITA DATE: Average of last five years DATE: Average of last five years NUMBER OF ENTITIES: 33 NUMBER OF ENTITIES: 26 UNIT: Tons UNIT: grams of fish protein per day per capita SOURCE: FAO FishStatJ (2017) SOURCE: FAOSTAT (Demersal Fish, Pelagic Fish, http://www.fao.org/fishery/statistics/global- Marine Fish, Other, Crustaceans, Cephalopods, commodities-production/en Molluscs, Other) - http://www.fao.org/faostat/en/ 66 13. VALUE OF FISHERIES AND AQUACULTURE 19. PROPORTION OF CHILDREN UNDER FIVE EXPORTS YEARS OLD WHO ARE MALNOURISHED (UNDERWEIGHT) DATE: Average of last five years NUMBER OF ENTITIES: 33 DATE: Average of last five years UNIT: USD NUMBER OF ENTITIES: 32 SOURCE: FAO FishStatJ (2017) - UNIT: Percentage http://www.fao.org/fishery/statistics/global- SOURCE: WHO 2018 commodities-production/en https://data.worldbank.org/indicator/SH.STA. UN Trade Statistics (2015) MALN.ZS 14. VALUE OF FISHERIES LANDED 20. LAND AREA OF ELEVATION <5 M (% OF POPULATION) DATE: Average of last five years DATE: Average of last five years NUMBER OF ENTITIES: 35 NUMBER OF ENTITIES: 32 UNIT: 2010 USD, millions UNIT: Percentage SOURCE: Sea Around Us http://www.seaaroundus.org/ SOURCE: World Bank Group (2018) https://data.worldbank.org/indicator/EN.POP.DNST 15. TOTAL FISHERIES LANDINGS 21. PERCENTAGE OF POPULATION BELOW DATE: Average of last five years NATIONAL POVERTY LINES NUMBER OF ENTITIES: 35 DATE: Average of last five years UNIT: Tons NUMBER OF ENTITIES: 32 SOURCE: FAO UNIT: Percentage http://www.fao.org/fishery/statistics/global- SOURCE: World Bank Group (2016) production/en 22. NUMBER OF PEOPLE AND PERCENTAGE OF 16. TOTAL FISHERIES LANDINGS POPULATION LIVING IN AREAS OF ELEVATION DATE: Average of last five years <5 M NUMBER OF ENTITIES: 35 DATE: Average of last five years UNIT: Tons NUMBER OF ENTITIES: 32 SOURCE: Sea Around Us UNIT: Percentage http://www.seaaroundus.org/ SOURCE: World Bank Group (2018) https://data.worldbank.org/indicator/EN.POP.DNST 17. FISH PROTEIN AS PROPORTION OF ANIMAL PROTEIN CONSUMED 23. POLITICAL STABILITY AND ABSENCE OF DATE: Average of last five years VIOLENCE NUMBER OF ENTITIES: 26 DATE: Average of last five years UNIT: Percentage NUMBER OF ENTITIES: 32 SOURCE: FAOSTAT (2017) UNIT: −2.5 to +2.5 (worst to best) http://www.fao.org/fishery/statistics/global- SOURCE: World Bank Group (2018), World consumption/en Governance Indicators http://info.worldbank.org/governance/wgi/#home 18. PROPORTION OF ECONOMICALLY ACTIVE POPULATION IN FISHERY SECTOR 24. GOVERNMENT EFFECTIVENESS DATE: Average of last five years DATE: Average of last five years NUMBER OF ENTITIES: 24 NUMBER OF ENTITIES: 32 UNIT: Percentage UNIT: −2.5 to +2.5 (worst to best) SOURCE: Teh and Sumaila (2013); SOURCE: World Bank Group (2018), World Governance Indicators http://info.worldbank.org/governance/wgi/#home 67 25. REGULATORY QUALITY (-2.5 TO +2.5) 30. GOOD SUBSIDY DATE: Average of last five years DATE: NUMBER OF ENTITIES: 32 NUMBER OF ENTITIES: UNIT: −2.5 to +2.5 (worst to best) UNIT: 2009 USD, thousands SOURCE: World Bank Group (2018), World SOURCE: Governance Indicators http://info.worldbank.org/governance/wgi/#home 31. LIFE EXPECTANCY AT BIRTH DATE: 2016 26. RULE OF LAW (-2.5 TO +2.5) NUMBER OF ENTITIES: 32 DATE: Average of last five years UNIT: Year NUMBER OF ENTITIES: 32 SOURCE: World Bank Group (2018) UNIT: −2.5 to +2.5 (worst to best) https://data.worldbank.org/indicator/SP.DYN.LE00. SOURCE: World Bank Group (2018), World IN Governance Indicators http://info.worldbank.org/governance/wgi/#home 32. GROSS DOMESTIC PRODUCT DATE: Average of last five years 27. VOICE AND ACCOUNTABILITY (-2.5 TO +2.5) NUMBER OF ENTITIES: 33 DATE: Average of last five years UNIT: USD NUMBER OF ENTITIES: 32 SOURCE: World Bank Group (2018) UNIT: −2.5 to +2.5 (worst to best) https://data.worldbank.org/indicator/NY.GDP.MKTP. SOURCE: World Bank Group (2018), World CD Governance Indicators http://info.worldbank.org/governance/wgi/#home 33. PROPORTION OF TERRITORIAL SEA PROTECTED 28. CONTROL OF CORRUPTION (-2.5 TO +2.5) DATE: Average of last five years DATE: Average of last five years NUMBER OF ENTITIES: 32 NUMBER OF ENTITIES: 32 UNIT: Percentage UNIT: −2.5 to +2.5 (worst to best) SOURCE: IUCN and UNEP-WCMC (2014), World Bank SOURCE: World Bank Group (2018), World (2018) Governance Indicators http://info.worldbank.org/governance/wgi/#home https://data.worldbank.org/indicator/ER.MRN. PTMR.ZS 34. NUMBER OF TERTIARY-AGE PEOPLE 29. CLIMATE ADAPTATION PLANNING (COST OF ADAPTATION) ENROLLED IN TERTIARY EDUCATION, BOTH SEXES (% OF POPULATION) DATE: Average of last five years DATE: Average of last five years NUMBER OF ENTITIES: 21 NUMBER OF ENTITIES: 32 UNIT: USD, billions UNIT: Percentage SOURCE: Pauw, W. P, D. Cassanmagnano, K. Mbeva, J. SOURCE:World Bank Group (2018), https://data. Hein, A. Guarin, C. Brandi, A. Dzebo, N. Canales, K. M. worldbank.org/indicator/SE.ADT.LITR.ZS Adams, A. Atteridge, T. Bock, J. Helms, A. Zalewski, E. Frommé, A. Lindener, and D. Muhammad. 2016. 35. PERCENTAGE OF POPULATION OVER AGE 15 “NDC Explorer.” German Development Institute / THAT CAN READ AND WRITE, BOTH SEXES Deutsches Institut für Entwicklungspolitik (DIE), DATE: Average of last five years African Centre for Technology Studies (ACTS), NUMBER OF ENTITIES: 32 Stockholm Environment Institute (SEI). DOI: 10.23661/ndc_explorer_2017_2.0 UNIT: Percentage SOURCE:World Bank Group (2018), https://data. worldbank.org/indicator/SE.ADT.LITR.ZS 68 BACKCOVER PHOTO CREDIT Charlotte De Fontaubert / World Bank www.worldbank.org/en/topic/environment 70 CLIM AT E C H AN GE AN D MAR IN E F IS HER I ES I N A F R I CA