* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download 56 - University of the West Indies, Cave Hill Campus
Global warming hiatus wikipedia , lookup
Global warming controversy wikipedia , lookup
Heaven and Earth (book) wikipedia , lookup
Fred Singer wikipedia , lookup
Climatic Research Unit email controversy wikipedia , lookup
ExxonMobil climate change controversy wikipedia , lookup
Economics of climate change mitigation wikipedia , lookup
Climate change feedback wikipedia , lookup
Climate change denial wikipedia , lookup
Climate engineering wikipedia , lookup
Global warming wikipedia , lookup
2009 United Nations Climate Change Conference wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Climate resilience wikipedia , lookup
General circulation model wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Solar radiation management wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Climate change in the United States wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Economics of global warming wikipedia , lookup
Effects of global warming wikipedia , lookup
United Nations Framework Convention on Climate Change wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate governance wikipedia , lookup
Politics of global warming wikipedia , lookup
Climate sensitivity wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change adaptation wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate change, industry and society wikipedia , lookup
CERMES Technical Report No 56 Vulnerability of the fisheries sector to climate change impacts in Small Island Developing States and the Wider Caribbean: early findings I. MONNEREAU, R. MAHON, P. MCCONNEY, L. NURSE Centre for Resource Management and Environmental Studies (CERMES) University of the West Indies, Faculty of Science and Technology, Cave Hill Campus, Barbados 2013 ABSTRACT Small Island Developing States (SIDS) are expected to be disproportionally affected by climate change due to their social, economic and geographical characteristics – such as limited size, proneness to natural hazards, low-lying areas, and low adaptive capacity. In recent years various assessments of vulnerability of national fisheries to potential climate change impacts have been carried out. Understanding the impacts of climate change on fisheries is crucial as fisheries are important for food security, livelihood and employment and the generation of foreign exchange for national governments throughout the world. The impacts of climate change are expected to be different within and between regions and nations, and thus it is important to investigate where climate change impacts on fisheries have greatest social and economic significance. The vulnerability assessment carried out by Allison et al. (2009) was the first global analysis to provide an indicator-based analysis of the relative vulnerabilities of 132 countries to climate change impacts on fisheries.That study, however, scarcely includes SIDS and Wider Caribbean countries as they were considered by the authors to be „data-deficient countries‟ (Allison et al, 2009: 183).This study followed the framework of the vulnerability assessment by Allison et al. (2009) with the most recent data and has broadened the analysis to include nearly all coastal states in the world including all, except one, Small-Island Developing States and Wider Caribbean Region countries. It assesses the outcome and relevance of this vulnerability assessment for SIDS and Wider Caribbean countries and discusses possible new ways of integrating the advances in the field of national level vulnerability studies that have been made since the previous assessment. Key words: Vulnerability, fisheries, climate change, Small Island Developing States i ACKNOWLEDGEMENTS This research was carried out with support from the Caribbean Community Climate Change Centre (CCCCC), Belize, and the Centre for Resource Management and Environmental Studies (CERMES), University of the West-Indies, Cave Hill Campus, Barbados. We would like to thank Dr. E. Allison for his support in carrying out this study. Cover photograph: Utila Cays, Bay Islands, Honduras – a fisheries dependent community. Courtesy Michelle Fernandez andLight Hawk Foundation. ii CONTENTS 1 Introduction .........................................................................................................................1 2 3 4 Vulnerability assessments ....................................................................................................3 2.1 Vulnerability assessments .............................................................................................3 2.2 National level vulnerability assessments........................................................................5 2.3 Vulnerability of fisheries in the face of climate change .................................................5 2.4 Vulnerability of Small-Island Developing States ...........................................................7 Methodology...................................................................................................................... 10 3.1 Modifications to the Allison et al. (2009) model .......................................................... 10 3.2 Indicators .................................................................................................................... 13 3.2.1 Exposure .............................................................................................................. 16 3.2.2 Sensitivity ............................................................................................................ 17 3.2.3 Adaptive capacity ................................................................................................ 20 Results ............................................................................................................................... 22 4.1 Exposure ..................................................................................................................... 22 4.2 Sensitivity ................................................................................................................... 24 4.3 Adaptive capacity........................................................................................................ 28 4.4 Overall vulnerability ................................................................................................... 32 5 Discussion.......................................................................................................................... 37 6 Conclusion ......................................................................................................................... 39 7 References ......................................................................................................................... 41 Citation Monnereau, I., R. Mahon, P. McConney and L. Nurse. 2013. Vulnerability of the fisheries sector to climate change impacts in Small Island Developing States and the Wider Caribbean: early findings. CERMES Technical Report No. 56. 45pp. iii ABBREVIATIONS FAO GHG IPCC LDC SIDS SRES TAR UNFCCC Food andAgricultureOrganisation GreenhouseGases Intergovernmental Panel on Climate Change LeastDevelopedCountries Small Island DevelopingStates Special Report on Emissions Scenarios Third Assessment Report United Nations Framework Convention on Climate Change iv 1 INTRODUCTION There is increasing concern over the consequences of climate change and climate variability for fisheries production and the state of marine ecosystems (Brander 2010; Cheung et al. 2010; Mora et al. 2013). Both slow-onset changes (e.g. sea surface temperatures) and increased extremeweather events are expected to impact fisheries worldwide (Brander 2007). Small changes in environmental conditions, such as temperature, salinity, wind and ocean currents, can change the abundance, distribution and availability of fish populations (McIlgorm et al. 2010). This while global fisheries are already under pressure from stressors including overfishing, loss of habitat, pollution, disturbance of coral reefs, and introduced species (Allison et al. 2005, 2009; HoeghGuldberg et al. 2007; Brander 2010).Understanding the impacts of climate change on fisheries is crucial as fisheries are important for food security, livelihood and employment and the generation of foreign exchange for national governments throughout the world (Allison 2011; FAO 2012). The impacts of climate change are expected to be different within and between regions and nations, and thus it is important to investigate where climate change impacts on fisheries have greatest social and economic significance (Allison et al. 2009). Small Island Developing States (SIDS) are expected to be disproportionally affected by climate change due to their social, economic and geographical characteristics. In the United Nations system, three major SIDS regions are recognized: the Caribbean with 23 members, the Pacific with 20 members and the Africa, Indian Ocean, Mediterranean and South China Sea (AIMS region) which has 9 member states. Although SIDS are by no means homogenous, varying by geography, physical, climatic, social, political, cultural, and ethnic characters well as level of economic development, they also share several common characteristics (Nurse et al. 2001). Many Small Island Developing States face special disadvantages associated with small size, insularity, remoteness, economic vulnerability and proneness to natural disasters (Briguglio 1995; Mimura et al. 2007; Turvey 2007). This vulnerability of SIDS is in conjunction with high levels of exposure to climate change impacts such as sea-level rise, sea surface temperature change and being located in tropical and sub-tropical regions. Fisheries in SIDS are expected to be vulnerable due to high levels of exposure of local fisheries to physical climate effects, economic dependence on the fishing industry, and poor adaptive capacity (Guillotreau et al. 2012). This while SIDS are of special concern for coral reef fishery management and food security (Thorpe et al. 2005; Hughes et al. 2012) as many SIDS have an exceptionally high dependence on the fish and shellfish associated with coral reefs for food. In the Wider Caribbean1region ffisheries are of crucial importance for human well-being as it provides food and livelihood and employment for many thousands of coastal inhabitants as well as a source of foreign exchange (Chakalall et al. 2007). Fisheries in the Wider Caribbean region are expected to be severely impacted by climate change as threats to exposure and sensitivity are high while adaptive capacity is low (Nurse et al. 2011). One would therefore expect SIDS and countries of Wider Caribbean Region to come out as highly vulnerable in an assessment as carried out by Allison et al. (2009) on the vulnerability of nations‟ fisheries in the face of climate change. Allison et al. (2009) have undertaken a seminal and widely cited work where the authors map nations‟ fisheries vulnerability in the face of 1 We have followed the Wider Caribbean Region country list of UNEP, except we have excluded the United States. 1 climate change and analyze where climate change impacts on fisheries have greatest social and economic significance. The vulnerability assessment carried out by Allison et al. (2009) was the first global analysis to provide an indicator-based analysis of the relative vulnerabilities of 132 countries to climate change impacts on fisheries. The authors examine the vulnerability of nation‟s fisheries by considering the vulnerability of fisheries to climate change as a function of (a) the degree of exposure to the threat; (b) the sectors‟ sensitivity to the risk; and (c) the capacity of the sector to cope with or adapt to the threat (Nurse et al. 2001; IPCC 2007; Allison et al. 2009). The indicators used were at the national level as national level assessments provide a broad view of vulnerability patterns (Allison et al. 2009). They can also be used to identify particularly vulnerable regions and eventually facilitate comparison of vulnerability assessments across natural resource-dependent industries, potentially providing insight into processes that cause and exacerbate vulnerability (Adger et al. 2004; Brooks et al. 2005; Yohe et al. 2006; Allison et al. 2009 ). Different countries will reflect different combinations of climate exposure, sensitivity or fisheries dependence and adaptive capacity. It is argued that understanding how these various factors combine to influence vulnerability provides a useful starting point for directing future research and climate change adaptation and mitigation initiatives (Allison et al. 2009). The authors concluded that two-thirds of the most vulnerable countries are in tropical Africa. Many of these included fisheries in many of the world‟s Least Developed Countries (LDC) and 19 out of the 30 most vulnerable countries were LDCs. This is in line with the fact that LDCs, along with SIDS,are recognized as being the most vulnerable to the adverse effects of climate change by the United Nations Framework Convention on Climate Change (UNFCCC). 2Yet, only nine of the 52 tropical island states that make up SIDS were included the analysis undertaken by Allison et al. (2009).3SIDS represented only 7% of the total number of countries (132) considered in the study. Those SIDS not included in the study were considered by the authors to be „data-deficient countries‟ (Allison et al. 2009:183).The inclusion of only four SIDS in the national vulnerability assessment of coral reef fisheries by Hughes et al. (2012) was similarly attributed to a lack of available data. The Wider Caribbean region was also underrepresented in the analysis by Allison et al. (2009) as only 14 out of the 28 Wider Caribbean states were included and none of the small island overseas territories of the region. A comprehensive vulnerability assessment of fisheries in SIDS and the Wider Caribbean is therefore important. Nevertheless, the lack of readily available data required to undertake such a comprehensive vulnerability assessment inhibits this. There is thus a need to address the data gaps in SIDS to better understand the vulnerability of Small Island Developing States (SIDS) fisheries to climate change (Hughes et al. 2012) and that of Caribbean SIDS in particular (Mahon 2002; ECLAC, 2011).This study aims to address this gap by extending the database used by Allison et al. (2009), with the aim of incorporating all SIDS and countries in the Wider Caribbean region. This analysis is therefore a first attempt to include all SIDS and the Wider Caribbean countries in a systematic comparison of the relative vulnerability of national economies to potential climate change impacts on fisheries. 2 under Articles 4.8 and 4.9 UN List of SIDS http://www.un.org/special-rep/ohrlls/ohrlls/allcountries-regions.pdf.The United Nations currently classifies 52 countries and territories as Small Island Developing States (SIDS), with a total population of over 50 million people. 3 2 For comparative purposes, we have used the Allison et al. (2009) vulnerability framework as a baseline, but have modified four of the indicators applied in that work and omitted one of the ten indicators originally used. In this study we have provided an indicator-based analysis of the relative vulnerabilities of 173 countries to climate change impacts on fisheries. We have been able to include all Wider Caribbean states and 50 out of 52 SIDS. In addition, our database includes seven small island overseas territories of which six are located in the Caribbean region. This paper addresses the vulnerability of coastal fisheries at the global level, with a specific focus on the three groups of small islands: Caribbean, Pacific, and Africa, Indian Ocean, Mediterranean and South China Sea (AIMS). It also seeks to compare the relative vulnerability of fisheries in the Wider Caribbean region in comparison to other regions in the world. This analysis is a first attempt to include all SIDS and the Wider Caribbean countries in a systematic comparison of the relative vulnerability of national economies to potential climate change impacts on fisheries. 2 2.1 VULNERABILITY ASSESSMENTS Vulnerability assessments Assessments of vulnerability to climate change are aimed at informing the development of policies that reduce the risks associated with climate change (Füssel and Klein 2006); raising awareness of climate change (Hinkel 2011); and monitoring adaptation processes (Hinkel 2011).Indicators synthesize complex state-of-affairs such as the vulnerability of regions, countries, households and individuals into a single number that can be easily used by policy makers and thus act as a bridge between academic work and political needs (Hinkel 2011). Such knowledge can help in the prioritization of management efforts that need to be undertaken to minimize risks or to mitigate possible consequences.Assessing which individuals, households, communities, regions, or nations are vulnerable, and in what ways, enables clear and effective responses to be formulated. The plurality of definitions of vulnerability and approaches to assessing vulnerability has led to intensive conceptual work that attempts to clarify concepts and methodologies (Hinkel 2011). A general discussion of definitions, methodologies and conceptual frameworks is beyond the scope of this paper but for summaries see Adger (2006), Eaking and Luers (2006). The way one defines vulnerability is crucial as it can influence the choice of the vulnerability „model‟ and possibly the results from the model (Park et al. 2012). As we are replicating an earlier vulnerability assessment carried out by Allison et al. (2009) we follow the definition of vulnerability used by the Intergovernmental Panel on Climate Change (IPCC). The Third Assessment Report (TAR) of the IPCC defines vulnerability as: “the degree to which a system is susceptible to, and unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude, and rate of climate change and variation to which a system is exposed, its sensitivity, and its adaptive capacity (McCarthy et al. 2001:995). The vulnerability of any sector to climate change is a function of (a) the degree of exposure to the threat and factors internal to a sector; (b) the sector‟s sensitivity to the risk; and (c) the 3 capacity of the sector to cope with or adapt to the threat (IPCC 2001; Smit et al. 2001; Fussel and Klein 2005; Smit and Wandel 2006;Parry et al. 2009; Barange et al. 2011;see Figure 1). Figure 2.1IPCC Vulnerability analysis framework (Source: Allison et al. (2009) adapted from IPCC (2001)) One difficulty that arises from this defintion of vulnerability is the fact that the defining concepts of exposure, sensitivity and adaptive capacity themselves are vague and difficult to make operational (Hinkel 2011). The way in which you operationalize the three concepts will determine the outcome of the model. A second difficulty lies in the lack of clarity on how the defining concepts are combined (Hinkel 2011; Park et al. 2012). Many assessments have focused on assessing the three elements of the function (i.e. exposure, sensitivity, and adaptive capacity) separately, paying less or no attention to how to combine these arguments. Yet, this combination is essential (Hinkel 2011). In this study we follow the way Allison et al. (2009) have combined the three components by weighting each component equally in the final vulnerability assessment. Another difficulty that arises relates to whether vulnerability is regarded as an „end‟ or „starting point' (O‟Brien et al. 2004). In the end point view, vulnerability is a residual of climate change impacts minus adaptation. Here, vulnerability represents the net impacts of climate change; it serves as a means of defining the extent of the climate problem and can guide policy decisions regarding the cost of climate change versus costs related to greenhouse gas mitigation efforts (Kelly and Adger 2000). In the starting point view, vulnerability is considered a characteristic or state generated by multiple environmental and social processes, but exacerbated by climate change (Kelly and Adger 2000). The second interpretation considers vulnerability as a starting point for analysis. Rather than being defined by future climate change scenarios and anticipated adaptations, vulnerability represents a present inability to cope with external pressures or changes, in this case changing climate conditions. Here, vulnerability is considered a characteristic of social and ecological systems that is generated by multiple factors and processes. The study by Allison et al.(2009) applies the starting point interpretation of vulnerability; although the exposure component examining the predicted climate change impacts 4 of air surface temperature change is based on future projections, but sensitivity and adaptive capacity indicators are based on current conditions. 2.2 National level vulnerability assessments Concerns have been raised within national level vulnerability assessments about the use of national-level indicators (Park et al. 2012). The strength of national scale assessments is that they allow us to compare the vulnerability and adaptive capacity of different countries and regions (Adger et al. 2004). Additionally, country-level indicators are more widely available and subnational vulnerability is influenced by processes operating at the national scale (Adger et al. 2004). According to Allison et al. (2009) the decision to focus on vulnerability at a national scale, was taken mainly because adaptation policies are generally formulated and implemented at this scale, and also because many global indicators are available only at the national scale. According to Adger et al. (2007) there is some evidence that national-level indicators of vulnerability and adaptive capacity are used by climate change negotiators, practitioners, and decision makers in determining policies and allocating priorities for funding (Eriksen and Kelly 2007). However, the literature lacks consensus on the usefulness of indicators of generic adaptive capacity and the robustness of the results (Adger et al. 2007). A comparison of results across five vulnerability assessments shows that the 20 countries ranked „most vulnerable‟ show little uniformity across studies (Eriksen and Kelly 2007). It has also been argued that national indicators fail to capture many of the processes and contextual factors that influence adaptive capacity, and thus provide little insight on adaptive capacity at the level where most adaptations will take place (Eriksen and Kelly 2007; Park et al. 2012).The capacity of the various actors to adapt is also of critical importance. Such capacity not only includes the preconditions necessary to enable adaptation, including social and physical elements, but also the ability to mobilize them (Nelson et al. 2007; Coulthard 2012).In recent years vulnerability assessments of fisheries at lower levels of analysis have also been carried out (see eg. Marshall et al. 2009; Cinner et al. 2012, 2013;Park et al. 2012 ).As we use the framework of Allison et al. (2009)our analysis is carried out at the national level. 2.3 Vulnerability of fisheries in the face of climate change The IPCC projects a global mean surface air temperature increase of approximately 0.2°C for the next two decades (IPCC 2007), while mean global sea surface temperature (SST) is expected to be approximately 1.0-2.0 °C higher than the 1990 mean before the end of the 21 st century (IPCC 2001; Nurse 2011). During the 20th century, global sea levels rose at a rate approximately ten times faster than the average rate for the previous 3,000 years (IPCC 2007). Sea level rise is expected to increase even if Greenhouse Gas (GHG) emissions were to be stabilized at year 2000 levels (IPCC 2000). In addition, an increase in the intensity of storms is expected (Webster et al. 2005)as well as an observed trend of increasing sea surface temperatures (Nurse 2011). In the specific case of tropical oceans, temperatures are projected to be 2°C higher by the 2050s and 3°C higher by the 2080s, relative to the same baseline (Nurse 2011). The link between ocean warming, El Niño occurrences and coral bleaching has now been well established (Nurse 2011). Climate change will impact the productivity of marine fisheries through alteration of water temperature, currents and upwelling, as well as through the indirect (ecological) and direct (biological) effects of ocean acidification affecting reef fisheries, declines in dissolved oxygen 5 and disruption of fish reproductive patterns and migratory routes (Allison et al. 2005; Cheung et al. 2010; Nurse 2011;Guillotreau 2012 ). There already exist a good generic understanding of the potential impacts of climate change and climate variability on key factors and processes that influence recruitment, abundance, migration, and the spatial and temporal distribution of many fish stocks (Nurse 2011). The changes in fisheries productivity and potential yield will have socio-economic consequences. This in turn will have adverse socio-economic impacts on global fisheries which are already under pressure from other stressors including overfishing, loss of habitat, pollution, disturbance of coral reefs, and introduced species (Allison et al. 2005, 2009; Hoegh-Guldberg et al. 2007; Brander 2010). In addition, increased frequency of extreme events can lead to more frequent loss of fishing days due to bad weather, increasing loss of nets, traps and longlines, damage to boats and shore facilities, increased loss of life among fishermen, and increased damage to coastal communities, by means of houses and farmland (Allison et al. 2005).Coasts are experiencing the adverse consequences of hazards to climate change and sea level rise and slow-onset changes (Nicholls et al. 2007). Marine fishers and their dependents mostly live in the coastal zone which is considered to be highly vulnerable to climate change impacts (Nicholls et al. 2007; Cochrane et al. 2009). Vulnerability assessments of national fisheries in the face of climate change are essential as fisheries are of great importance for food and nutrition worldwide. World‟s fisheries provide more than 2.6 billion people with at least 20% of their average annual per capita protein intake (Allison et al. 2009). We found that fisheries, excluding aquaculture, provide employment to 41.6 million fishers. 4 Including aquaculture, the FAO estimates the number of fishers worldwide at 54 million people in the primary sector (FAO 2012). Apart from the primary production sector, fisheries and aquaculture also provide numerous jobs in secondary activities such as processing, packaging, marketing and distribution, gear making, and boat construction. In conjunction with the dependents, fisheries are thus estimated to support the livelihoods of 660– 820 million people, or about 10–12 percent of the world‟s population (FAO 2012). Fish is also a generator for foreign exchange as fish trade reached a value of US$109 million in 2010 (FAO 2012). This is particularly important for developing countries, as for many, fish trade represents a significant source of foreign exchange earnings (Kurien 2005; FAO 2012),but also additional revenues from taxation, license fees, and from fees paid by foreign ships for access to resources (Allison 2011). In 2010, developing countries confirmed their fundamental importance as suppliers to world markets with more than 50 percent of all fishery exports in value terms and more than 60 percent in quantity (live weight) (FAO 2012). Fisheries are of crucial importance for human well-being in the Wider Caribbean region5 as it provides food and livelihood and employment for many thousands of coastal inhabitants as well as a source of foreign exchange (Chakalall et al. 2007).Research on the impacts of climate change and climate variability on Caribbean fisheries is lagging behind in comparison to other regions (Nurse 2011). Yet, certain observations about the potential impacts of climate change on fisheries can be made. Cheung et al. (2010), for example, predict the major poleward redistribution of many species by more than 40 km per decade, and that developing countries in the tropics will suffer the largest losses. Fisheries in the Wider Caribbean Region are expected to 4 5 Based on data collection on number of fishers of 212 countries. See methodology section for further details We have followed the Wider Caribbean region country list by UNEP except we have excluded the United States. 6 be severely impacted by climate change as threats to exposure and sensitivity is high while adaptive capacity is low (Nurse et al. 2011).This while fisheries in the region already face pressures of weak fisheries governance, overfishing, and habitat loss (McConney et al. 2009; Nurse 2011). The majority of Wider Caribbean states are also SIDS and thus share the vulnerability characteristics of SIDS which we will describe below. 2.4 Vulnerability of Small Island Developing States Caribbean SIDS form a major component of all SIDS worldwide. There are 52 territories categorized as SIDS, across the Atlantic (both in Africa and the Caribbean Sea), Indian and Pacific oceans (see Table 2.1). SIDS are distinguished as a group of small sovereign archipelagic or island states having a population ofl.5 million or below (Campling and Rosalie 2006; Mimura et al. 2007). Table 2.1 A list of SIDS by region 1 2 3 Caribbean (23 states) Anguilla Antigua and Barbuda Aruba Pacific(20 states) American Samoa Cook islands Federated States of Micronesia Fiji French Polynesia Guam Kiribati Marshall Islands Nauru New Caledonia Niue Northern Mariana Islands Palua Papua New Guinea Samoa Solomon Islands Timor-Leste Tonga Tuvalu Vanuatu AIMS (9 states) Bahrain Cape Verde Comoros Bahamas Guinea-Bissau 4 Barbados Maldives 5 Belize Mauritius 6 British Virgin Islands São Tomé and Príncipe 7 Cuba Seychelles 8 Dominica Singapore 9 Dominican Republic 10 Grenada 11 Guyana 12 Haiti 13 Jamaica 14 Montserrat 15 Netherlands Antilles6 16 Puerto Rico 17 St. Kitts and Nevis 18 St. Lucia 19 St. Vincent and the Grenadines 20 Suriname 21 Trinidad and Tobago 22 US Virgin Islands 23 Source: United Nations Office of the High Representative for the Least Developed Countries, Landlocked developing Countries and Small Island Developing States7 6 Aruba seceded from the Netherlands Antilles in 1986 to become a constituent country of the Kingdom of the Netherlands. This left only five island territories in The Netherlands Antilles.The Netherlands Antilles were dissolved as a unified political entity on 10 October 2010. The remaining five constituent islands each attained a new constitutional status within the Kingdom of the Netherlands. Bonaire, Saba and Sint Eustatius all became special municipalities of the Netherlands, Curaçao and Sint Maarten both became constituent countries within the Kingdom of the Netherlands. For this analysis the division signified that separate data were available for Aruba but the five other islands were all grouped under the „Netherlands Antilles‟. 7 http://www.un.org/special-rep/ohrlls/sid/list.htm 7 The Caribbean SIDS form the largest group with 23 members, followed by the Pacific Ocean region with 20 members and the African and Indian Ocean SIDS with nine members. Figure 2.2 shows the location of the different SIDS in the world. Figure 2.2 Location of the 52 SIDS worldwide Source: Wikepedia SIDS8 Although SIDS produce only 0.6%9 of global greenhouse gases, they are expected to be disproportionally affected by climate change (Mimura et al. 2007). SIDS are by no means homogenous, varying by geography, physical, climatic, social, political, cultural, and ethnic character as well as level of economic development, they also share several common characteristics (Nurse et al. 2001). Many SIDS face special disadvantages associated with small size, insularity, remoteness, economic vulnerability and proneness to natural disasters (Briguglio 1995; Mimura et al. 2007; Turvey 2007). They share certain commonalities which make them more prone to climate change impacts (Mimura et al. 2007). SIDS are small island masses surrounded by ocean and thus prone to climate change affects such as sea-level rise, increased intensity and frequency of ENSO events, sea surface temperature rise, and ocean acidification. They are located in tropical and subtropical regions and thus frequently prone to natural disasters. SIDS have general characteristics such as smallness of islands; limited resource base; vulnerable coastlines; and insularity and remoteness make them particularly prone. They are mostly low-lying islands with vulnerable coastlines with a high percentage of population and vital infrastructure located in the coastal zone. Economies are reliant on a limited resource base, subject to external forces, changing terms of trade, economic liberalisation, and migration flows. Their small size, limited resources, geographic dispersion and isolation from markets, place them 8 http://en.wikipedia.org/wiki/File:SIDS_map_en.svg. Accessed 25 July 2013 This percentage is based on our calculations of SIDS‟ carbon production in 2009 from the Carbon Dioxide Information Analysis Center. See http://cdiac.ornl.gov/trends/emis/meth_reg.html (accessed July 23rd 2013). All SIDS are included in this analysis except for: American Samoa; Guam; Puerto Rico; Tuvalu; and the US Virgin Islands. 9 8 at a disadvantage economically as it prevent economies of scale and makes them volatile to global market developments. The adaptive capacity of SIDS is considered to be low as they have small populations with a limited human and technical resource base, small economies with high per capita costs for infrastructure and services, single-town centred populations, limited hazard forecasting ability and low level of insurance coverage (Table 2.2). Table 2.2 Vulnerability indicators of SIDS Sensitivity of SIDS Smallness Vulnerability characteristics Limited natural resource base High competition between land use, spatial concentration of population, infrastructure and other productive assets in coastal zone Insularity and remoteness High external transport costs time delays and high costs in accessing external goods delays and reduced quality in information flows Geopolitically weakened Lack of available resources Heavily reliance on limited number of natural resources (high reliance on coastal and marine resources) Environmental factors High reliance on marine resources to meet their protein needs High coral reef and mangrove dependence High dependence on fish for food security; livelihood and employment and exports Economic factors Small economies (small internal market) Limited resource base Dependence on external finance Heavily dependence on natural resources Demographic factors Small population (limited human resource base) Small economies (high per capita costs for infrastructure and services) single-town centred populations Disaster mitigation capacity Limited hazard forecasting ability, complacency, little insurance cover Source: Pelling and Uitto (2001); Mimura et al. (2007); UN-OHRLLS (2009) SIDS fisheries are considered to be highly sensitive to climate change impacts (Nurse 2011; Guillotreau et al. 2012). Climate change impacts such as sea surface temperature increases; ocean acidification; increased intensity of storms; and sea level rise are expected to trigger a series of biophysical impacts on nation‟s fisheries (Mahon 2002; Brander et al. 2007; Allison et al. 2009; Cheung et al. 2010;, Nurse 2011; Mora et al. 2013). Changes include the abundance, production, recruitment, and spatial and temporal distribution of fish stocks (Mahon, 2002; Cheung 2008; Nurse 2011; Guillotreau et al. 2012). These effects are, however, not uniform around the globe and give rise to regional differences. Cheung et al. (2008) project redistributions of global catch potential with benefits mainly accrued to high latitude regions and catch potential declining in the tropics. This implies economic benefits for fisheries of high latitude islands (Guillotreau et al. 2012) but detriments in economic benefits for fisheries dependent small island economies, many of which are located in the tropical and subtropical regions. SIDS express a high dependence on fish for food security, livelihood and employment and foreign exchange earnings for national governments in comparison to other nations. These factors relate to SIDS to being surrounded by oceans; having extensive Exclusive Economic Zones; presence of coral reefs; high production of fisheries in tropical and sub-tropical regions; limited resource base for other sources of protein; and limited array of export products due to 9 remoteness and smallness of economies. The vulnerability indicators presented in Table 2.2 add to the vulnerability of fisheries in SIDS. 3 METHODOLOGY 3.1 Modifications to the Allison et al. (2009) model In this assessment we have followed the model developed by Allison et al. 2009. We have updated the data for all countries and altered four out of the 10 indicators they used, omitting one (see Figure 3.1). In line with the definition used by the IPCC10, vulnerability is defined by Allison et al. 2009 as a function of exposure, sensitivity and adaptive capacity. Vulnerability to climate change depends upon three key elements: Exposure (E) to physical effects of climate change, the degree of intrinsic sensitivity of the natural resource system or dependence of the national economy upon social and economic returns from that sector (S), and the extent to which adaptive capacity (AC) enables these potential impacts to be offset (Allison et al. 2009). Combining risk exposure, sensitivity and adaptive capacity gives a composite index of vulnerability (Allison et al. 2005).For each component of vulnerability (E, S and AC) indicators were assigned. Allison et al. 2009 chose measures of exposure, sensitivity, and adaptive capacity that, they argued, best capture the properties of interest based on previous vulnerability assessments (e.g. Brooks et al. 2005; Metzger et al. 2005; O‟Brien et al. 2005). The aim of our vulnerability assessment was to capture a snapshot of present-day vulnerability of fisheries production systems to future climate changes. Data for the sensitivity and adaptive capacity components thus represent current socio-economic conditions of the system studied, while exposure indicators are based on projected temperature changes. Allison et al. (2009) chose their indicators based on the latest available data, the number of countries for which data was available, and the degree of direct relevance to the phenomenon that the indicators are intended to represent. Their focus is on measuring vulnerability at a national level, as they argue that appropriate policies are generally formulated and implemented at this scale, and the fact that many global indicators are available only at the national level (Allison et al., 2009: 175). Allison et al. (2009) gathered data on 132 countries. Due to lack of available data and consistent country-level data only nine countries in the analysis were SIDS. The SIDS included in the Allison et al. 2009 model are: Belize, Fiji, Guinea-Bissau, Guyana, Haiti, Jamaica, Mauritius, Suriname and Trinidad and Tobago. Of the nine SIDS six were located in the Wider Caribbean region. We have been able to include 50 of the 52 SIDS (missing is American Samoa and Northern Mariana Islands due to lack of available data). In addition, we also have included 7 non-autonomous small islands in tropical and sub-tropical regions as we believe that despite their non-autonomous status they share many characteristics with SIDS. Six of the seven small islands are in the Caribbean region, while the other one, Réunion, is located in the Indian Ocean. The final list of 57 small island states included in our analysis is shown in Table 3.1. 10 See IPCC Working Group II, Fourth Assessment Report, 2007, Appendix 1: Glossary 10 Vulnerability ALLISON ET AL. 2009 THIS STUDY 2013 Exposure Projected mean surface temp change • B2 Projected mean surface temp change • B2 Sensitivity • Fisherfolk • Fisherfolk/EAP • Fishexport as % total export • Fishcatch (mt) • Fish as % animal protein • Fisherfolk (marine)/EAP • Fishexport as % total export • Fishcatch (capture) (mt)/population • Fish as % animal protein • Health (HALE) • Education (literacy rate and GER) • Governance Index • GDP • • • • Adap. cap. Health (HALE) Education (literacy rate) Governance Index GDP per capita Figure 3.1 Vulnerability indicators by Allison et al. 2009 and modifications of this study Table 3.1 Small island states included in this study (missing only American Samoa and Northern Mariana I.) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Caribbean Anguilla Antigua and Barbuda Aruba Bahamas Barbados Belize* Bermuda British Virgin Islands Cayman Islands Cuba Dominica Dominican Republic French Guyana Grenada Guadeloupe Guyana* Haiti* Jamaica* Martinique Montserrat Netherlands Antilles Puerto Rico St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Pacific Cook islands Federated States of Micronesia Fiji* French Polynesia Guam Kiribati Marshall Islands Nauru New Caledonia Niue Palua Papua New Guinea Samoa Solomon Islands Timor-Leste Tonga Tuvalu Vanuatu 11 AIMS Bahrain Cape Verde Comoros Guinea-Bissau* Maldives Mauritius* Réunion São Tomé and Príncipe Seychelles Singapore Caribbean Pacific Suriname* 26 Trinidad and Tobago* 27 Turks and Caicos 28 US Virgin Islands 29 Islands in italics are non-autonomous small islands * also included in the Allison et al. 2009 model AIMS Table 3.2 below shows the differences between the categories of countries used by Allison et al. (2009) model („original)and the current modified model („revised‟). We have included 173 countries in comparison to the 132 countries in the original model. We were able to gather data on 209 countries worldwide but choose to exclude landlocked states as our focus is exclusively on coastal states, therefore decreasing the number of countries present in our final analysis to 173. In the original model by Allison et al. (2009) 24 landlocked states were included (equivalent of 18% of total number of countries) and nine SIDS. This means that in original model, 17% of the total number of countries were SIDS in comparison to 33% in the revised model. Table 3.2 Countries included in the original Allison et al. (2009) model and revised Monnereau et al. (2013) model Total no.of countries Original model 132 Revised model 173* 9 51 SIDS (total 52) 0 6 Small Island territories 33 Small islands as % total 7 countries 14 35 Wider Caribbean 24 22** LDCs 24 (18%) 0 Landlocked states 99 94 Other *data available for #209 countries **an additional 12 overlapping SIDS and LDCs are grouped as SIDS in the analysis The number of LDCs in the revised model is higher (24 in Allison et al. 2009 model and 34 in our updated model). In our analysis we have grouped 12 overlapping LDCs and SIDS under SIDS. We have also examined the vulnerability by geographic region. For the analysis we grouped the countries in six regions: 1) Europe and North-America, 2) Wider Caribbean, 3) Latin America, 4) Africa, 5) Asia, and 6) Oceania. In the Allison et al. (2009) model, 14 out of the 28 countries in the Wider Caribbean were included.11We have included all 24 countries in the Wider Caribbean12and 11 small overseas island territories (from France, Great Britain and the Netherlands) to the regional grouping. In the analysis the total number of countries in the Wider Caribbean is therefore 35. 11 Following the UNEP list of countries in the Wider Caribbean http://www.unep.org/regionalseas/programmes/unpro/Caribbean/ 12 Excluding the United States 12 3.2 Indicators Allison et al. (2009) used ten indicators; one for exposure (E), five for sensitivity (S) and four for adaptive capacity (AC) (see Table 3.3). We used nine indicators: one for exposure, four for sensitivity and four for adaptive capacity. The indicators used were averaged per component. Each of the indicators in the component was weighted equally in computing the index. The final vulnerability score was the average of the three averages of each component. Variables were normalized using the indexing method following the general formula: Index value = (actual value – minimum value) / (maximum value –minimum value) Indicators are indexed on a scale of 0 to 1, where 0 represents the minimum value (i.e. low sensitivity) in the data set and 1 represents the maximum value (i.e. high sensitivity). For the adaptive capacity we reversed the index values by using the 1-index value to ensure that high index values indicate high vulnerability (i.e. Norway who had the highest adaptive capacity are at the bottom of our table, high index values corresponding with low level of adaptive capacity). 13 Table 3.3 Comparison of indicators per component between the original and revised models In bold the indicators adapted. Categories Original model Air surface temperature change B2 scenario Exposure Fisherfolk Sensitivity Fisherfolk/EAP Sensitivity Fishexport as % of total export Sensitivity Fishcatch (mt) Sensitivity Fish as % animal protein Sensitivity Health (HALE) Adaptive capacity Education (Literacy rate and GER) Adaptive capacity Governance Index Adaptive capacity GDP Adaptive capacity Revised model Air surface temperature change B2 scenario Fisherfolk (marine)/EAP Fishexport as % of total export Fishcatch (capture)(mt)/population Fish as % animal protein Health (HALE) Education (Literacy rate) Governance Index GDP per capita Table 3.4 provides an overview of the indicators, their units of measurement and relevance. These aspects will be discussed in the texts below regarding each indicator. The years of coverage and sources will be discussed per component in paragraphs 3.2.1, 3.2.2. and 3.2.3. 13 When examining the raw data of model 1 we found that two indicators were not renormalized after the maximum score was taken out of the analysis (1) food nutrition (fish protein as percentage of total animal protein) and 2) final adaptive capacity scoring scores. Four countries starting with a C were mixed up between different data sets. 13 Table 3.4 Overview of the indicators, their units of measurement and relevance Component Indicator Exposure (E) Air surface temperature change projections Projected temperature change by 2050 -B2 scenario Sensitivity (S) Livelihood and employment Number of fisherfolk/Economic Active Population Percentage of fisherfolk as % of total Economic Active Population Index of Economic Dependency on the Fisheries sector Fisheries exports as % of total exports Percentage of fisheries exports as % of total exports Countries with higher contributions of fisheries to export income, and thus deliver foreign exchange to a nation, are more likely to be impacted (positively or negatively) by warming-related changes in the whole fishery productions systems of that nation. Total catch in tons/Population Fish caught by one person in MT Fish catches contribute to employment and food security. Countries with higher fish catches are more likely to be impacted (positively or negatively) by warming-related changes in the whole fishery productions systems of that nation. Fish protein as % of all animal protein /capita/day in g Percentage of animal protein is fish Nutritional dependency identifies countries reliant on fish as a primary source of animal protein. This is expressed by fish protein as the percentage of all animal protein per capita per day in grams. This assumes that countries with higher dietary protein of fish are more likely to be impacted (positively or negatively) by warming-related changes. Food security Variable Unit of measurement Average of the degrees Centigrade (at 1.5m above the surface) change by 2050 of one scenario 14 Relevance Projected temperature change is a direct measure of future climatic change. Warmingrelated impacts (both positive and negative) upon physical and biological variables affecting fisheries production and fishery operations will be greater in areas where projected air temperature changes are greater Countries with higher contributions of fisheries to employment are more likely to be impacted (positively or negatively) by warming-related changes in the whole fishery productions systems of that nation. Component Adaptive Capacity (AC) Indicator Variable Unit of measurement Years Relevance Health Healthy life expectancy (HALE) Education Literacy rates % of people ages 15 and above who are literate Governance Political stability Ranking between – 2.5 and 2.5 All dimensions of governance are relevant to the adaptive capacity of a country. Determinants 3 (institutions and decision-making authority), 6 (social capital), and 7 (information) are particularly important to develop and carry out adaptation measures. Political instability or inability to exercise regulatory control over domestic entities might be barriers to the adoption and implementation of new technological options (determinant 1) and policies (determinant 2). Government Ranking between – Government effectiveness, Regulatory quality and rule of 15 Life expectancy provides a useful indicator of the overall health effects of environmental and other risk factors in a given population according to the World Health Organization. The link between health and climate protection is one of opportunity cost. Countries with significant public health problems (and related societal consequences like those mentioned above) are likely to find it socially and politically difficult to allocate resources to climate protection. Countries with higher levels of education are likely to have higher adaptive capacity. Low levels of literacy, and education in general, can impede the economic development of a country in the current rapidly changing technology-driven world. Higher education signifies more skilled staff to undertake important functions related to climate protection, including skills for implementing adaptation programs, information management systems, and an array of other activities. Component Indicator Variable Unit of measurement 2.5 and 2.5 effectiveness Size of Economy Relevance law are important for adequate fisheries governance Regulatory quality Ranking between – 2.5 and 2.5 Government effectiveness, Regulatory quality and rule of law are important for adequate fisheries governance Rule of law Ranking between – 2.5 and 2.5 Government effectiveness, Regulatory quality and rule of law are important for adequate fisheries governance Voice and accountability Ranking between – 2.5 and 2.5 Higher levels of “voice and accountability” might open up political space for NGOs and other interest groups to demand government actions on climate change and fisheries management. Corruption Ranking between – 2.5 and 2.5 Low levels of corruption are important for adequate fisheries management GPD per capita GDP per capita in US$ (ppp) Higher levels of economic power by residents and the country as a whole will enforce the adaptive capacity of the nation in the face of impacts of climate change 3.2.1 Exposure For the exposure component Allison et al. (2009) used the average of two projections of the average mean surface temperature change by 2050. They used mean projected air temperature change data at 1.5 m above surface for 2050. This data was derived by rescaling the 2080 values from the TYN model. This model provides country-specific projections based on gridded values from the UK Hadley Centre climate model (HadCM3)outputs (Allison et al. 2009).Changes in annual mean temperature for 2050 were estimated by applying scalers from Mitchell et al. (2004) to temperature anomalies for 2080 (2071-2100), as compared to 1961-90. The framework includes projections based on two different Special Report on Emissions Scenarios (SRES) climate change scenarios, A1FI and B2. These scenarios were selected by Allison et al. 2009 because they describe two contrasting potential futures; the A1FI world is characterized by a high dependency on fossil fuels, reflected in higher temperatures than in the B2 world, in which economic development is more moderate. 14 In the analysis, however, we found Allison et al. 2009 only analyzed the results of the B2 scenario. The results of the A1FI scenario was shown in 14 Country-specific values were derived by Mitchell et al. (2003), based on gridded values from HadCM3 climate model outputs. Where possible, values for countries missing from the TYN CY 3.0 dataset were interpolated based on the nearest available regions (typically in the case of small islands). 16 Table 3 (2009:180) but no systematic analysis was carried out for this scenario. 15 We therefore followed their line of analysis and only included the B2 scenario. We were unable to collect more up-to-date data at the national level and used the same database. The exposure index aims to represent the degree to which fisheries production systems are exposed to climate change. Climatic change influences fisheries production directly (e.g. through effects on species abundance and distribution), and less directly (e.g. through impacts on aquatic habitats, food supply, competitors, and predators), through various pathways. These include changes in temperature, precipitation, salinity, ocean circulation and mixing, river flow, nutrient levels, sea and lake levels, ice cover, storm frequency and intensity, and flooding. The known direct effects of climate change include changes in the abundance and distribution of exploited species and assemblages (Cheung et al. 2010; Nurse 2011) and increases in the frequency and severity of extreme events, such as floods and storms, which affect fishing operations and infrastructure (Adger et al. 2005). Allison et al. (2009) chose only air surface temperature change projections as an indicator of exposure due to lack of other downscaled parameters e.g. sea surface temperature or sea-level rise at the national level. The exposure component therefore uses projected air surface temperature change to 2050 as a general proxy variable of climate change exposure. Projected temperature change is the most direct measure of future climatic change. They made the simplifying assumption that warming-related impacts (both positive and negative) upon physical and biological variables affecting fisheries production and fishery operations will be greater in areas where projected air temperature changes are greater (Allison et al. 2009:176). We have applied all values of each country as used in Allison et al. (2009) 16. The unit of measurement is degrees Centigrade (at 1.5m above the surface) (see Table 3.4 for indicators, units of measurements and relevance). Caveats regarding the use of the TYN CY 3.0 data are described by Mitchell (2003). Source(s): TYN CY 3.0 dataset compiled by Mitchell et al. (2003)17 3.2.2 Sensitivity The sensitivity index aims to represent the degree to which fisheries production systems are affected by climate change, taking into account their inherent characteristics. Allison et al. (2009) regard sensitivity as the fisheries dependence of national economies. This category shows how “sensitive” countries would be to climate change by understanding their dependency on fisheries production systems in terms of number of fisherfolk involved in the activity, economic dependency and food security. It thus reflects the sensitivity of the economy (and not a single fishery per se) to potential climate change impacts on the fisheries sector as a whole (Allison et al. 2009). This assumes that countries with higher landings and higher contributions of fisheries 15 Allison et al. 2009 indicate that as both scenario‟s are highly correlated no further analysis of the A1FI model is necessary. 16 The calculation of the projection of the A1F1 scenario for French Polynesia was 0.16 by Allison et al. 2009 but should have been 1.06. We used the latter when comparing the B2 and A1F1 results. 17 http://www.cru.uea.ac.uk/~timm/climate/ateam/TYN_CY_3_0.html 17 to employment, export income and dietary protein are more likely to be impacted (positively or negatively) by warming-related changes in the whole fishery productions systems of that nation. The original model used five indicators in the sensitivity component: (i) fisherfolk, (ii)fisherfolk/economic active population,(iii) fishexports as a percentage of total exports, (iv) fishcatch and (v) fish consumption as a percentage of total animal protein consumption We have altered the indicators slightly as can be seen in Figure 3.1 and Table 3.3. First, we dropped indicator number 1,absolute number of fisherfolk, as we argue that this absolute number undervalues the importance of fisherfolk in smaller nations. We also consider that the second indicator already adequately captures the importance of fisheries employment. Fisherfolk in Allison et al. (2009)include fishers involved in aquaculture as well as fish capture. In the revised model we focus only on coastal states in the world and have thus only include number of persons involved in capture fisheries. Allison et al. 2009 used absolute numbers (in metric tonnes) of fish caught per country. This downplays the importance of fishcatches for smaller countries and we have therefore divided fishcatch (in metric tonnes) by the population of the country. As stated above each indicator was normalized and scaled to range from 0 to 1, with higher values reflecting greater sensitivity. In addition, however, each sensitivity indicator was logtransformed in line with Allison et al. (2009) who logtransformed the four sensitivity indicators. Although the logtransformation of each of the sensitivity indicators was not explicitly stated in the methodology section of the paper by Allison et al. (2009),our recalculations showed that this was the procedure used. The final composite index of sensitivity is calculated as an unweighted average of the four indicators. Fisherfolk/Economic Active Population Fisherfolk Year(s) of coverage: The number of fishers was derived from a variety of sources and based on the latest available data. The years used were thus not consistent across the different countries. Although every attempt was made to find the most current data, for a few countries the latest available data date as far back as 1990. The years of coverage thus run from 1990-2011 with the majority of countries having data spanning the period 2000-2011. We have therefore sought to update the information used by Allison et al. (2009), which is extracted from FAO (1996), with data covering the years 1990 to 1996. A major limitation of large scale vulnerability analyses is the lack of detailed social and economic statistics on the fishing industries and fisheries of nations (Allison et al. 2009). The problem is particularly acute for artisanal and subsistence fishers, who tend to be overlooked in national censuses or are aggregated into and hidden within the agricultural sector (Sadovy 2005; Allison et al. 2009). Thus we are aware in the FAO data that small-scale and subsistence fisheries are most likely underrepresented (see e.g. Chuenpagdee 2006). In our analysis we have separated aquaculture fishers from all other fishers. We collected the number of fishers for a total 212 countries (in the final analysis we omitted all landlocked states and a few others due to lack of data) which resulted in a total number of 41.6 million fishers worldwide, excluding aquaculture fishers. 18 Sources: o Data used were based on latest available data from FAOSTAT. 18 o Fishery and Aquaculture Country Profiles of individual countries by FAO o Unpublished FAO data on latest employment provided as a courtesy of FAO Statistical component o Alternative sources of information (NGOs, documents by governments, project publications, as well as email communications with government officials) Economic Active Population Year(s) of coverage: 2012.19Sources: Data used were based on 2012 data from FAOSTAT.20 Fish export as % of total exports Year(s) of coverage: 2009 Source(s): FAO trade balance sheets: The relative importance of trade in fishery products in 2009.21For 16 countries we had to use alternative sources due to lack of coverage of these countries by FAO. 22Statistics include species from which the commodity is produced, the commodity form (whole, filleted, shucked, etc.) and form of preservation (fresh, frozen, canned, cured, meal, etc.). They do not include turtles, frogs and crocodiles (products such as costume jewellery and fish leather are also excluded). Products from imported raw materials are included (re-exports).23 Fisheries landings/total population of country Fisheries landings Year(s) of coverage: 2010.Source(s): FAO 2010 24. World fisheries production, by capture and aquaculture, by country (2010) in MT. 25 18 http://faostat.fao.org/ Except for China Hong Kong SAR and Taiwan .These countries were not listed in FAO list so we had to use an alternative source. 20 http://faostat.fao.org/ 21 ftp://ftp.fao.org/fi/stat/summary/a7ybc.pdf. Accessed 23 February 2013. 22 We had to use alternative sources of data for the following countries: Angola; Anguilla; Brunei Darussalam; Cook Islands; French Guiana; Guadeloupe; Guam; Marshall Islands; Martinique; Micronesia; Nauru; Puerto Rico; Réunion; Solomon Islands; Cook Islands; Trinidad and Tobago. 23 http://www.fao.org/fishery/topic/16110/en 24 The Falkland Islands catch a large volume of fish by foreign vessels of which little flows back to the country, yet the value was so high it was distorting our calculations. Therefore, we have only taken 20% of their total catch 99560 MT. This percentage is based on the article „Reforming management of commercial fisheries in a small island territory‟ by Harte and Barton, Marine Policy 2007 31(4): 371-378. For Taiwain we used an alternative source as no data in FAO was available. 25 ftp://ftp.fao.org/fi/stat/summary/a-0a.pdf 19 19 Population Year(s) of coverage: 2013.Source(s): CIA Factbook 2013. 26 Fish protein as % of animal protein Year(s) of coverage:2009 Source(s): FAO Fish and Fishery Products: Food Balance Sheets and Fish Contribution to Protein Supply 2009. 27 3.2.3 Adaptive capacity We have retained the four indicators of adaptive capacity used by Allison et al. 2009. Any reductions in fisheries production will likely require fishers to further diversify their activities and exploit resources optimally, as they become available. Fishers‟ vulnerability to change will largely be a function of their capacity to adapt. Their ability to adapt is, however, largely constrained by their lack of financial and human capital. Allison et al. (2005) argue their typically poor health and inadequate health care systems further increases their vulnerability to extreme events and outbreaks of disease. At the national level, adaptive capacity has been shown to be strongly related to factors such as health, literacy and governance (Brooks et al. 2004). The education indicator of the original model included both the literacy rate as well as the Gross Enrollment Rate (GER) of a country. We have only used the literacy rate as the GER ratio data were missing for many countries. It should also be noted that even though this index had been previously used in the computation of the Human Development Index to calculate the level of education of a country along with literacy, the GER ratio has not been used since 2010. Governance is considered important for adaptive capacity. All dimensions of governance are relevant in assessing the adaptive capacity of a country. Average values were calculated for several island groups where governance data were unavailable (see below for further information). Gross Domestic Product (GDP) was used by Allison et al (2009) as an indicator to reflect the fact that the size of the economy can play a role in adaptive capacity. However, in this study we have substituted GDP for GDP per capita so not to disadvantage the smaller nations. Health Index Measurement unit: years. Healthy life expectancy (HALE) is based on life expectancy, but includes an adjustment for time spent in poor health. This indicator measures the equivalent number of years in full health that a newborn child can expect to live based on the current mortality rates and prevalence distribution of health states in the population. Healthy life expectancy is influenced by a wide range of factors, including air quality, access to clean water and sanitation, shelter, the prevalence of disease (e.g., AIDS, malaria, and tuberculosis), and occupational health risks among others. Overall, in adjusting life expectancy, HALE estimates account for 135 disease and injury causes. Year(s) of coverage: 2007. Source(s): United Nations Healthy Life Expectancy 2007. For the 22 countries28for which these data were unavailable, we used the CIA life expectancy data from 26 https://www.cia.gov/library/publications/the-world-factbook/rankorder/2119rank.html. Accessed May 13th 2013. We had to use alternative sources for French Guiana; Guadeloupe; Martinique; Netherlands Antilles; and Réunion. 27 ftp://ftp.fao.org/fi/CDrom/CD_yearbook_2010/root/food_balance/introduction.pdf 20 2012.29Since the correlation between the two data sets was high (R=0.94), we used the formula y= 0.9735x -8.0396 to derive values for the years with missing data. Education Index Description: Education levels are measured by adult literacy rates. Adult literacy is the percentage of people aged 15 30 and above who, with understanding, can read and write a short, simple statement on their everyday life. Measurement Units: (1) Literacy: % of people ages 15 and above. Year(s) of coverage: Latest available year, mostly between 2000 and 2010.Source(s): CIA Factbook31 Governance Index Description: This indicator attempts to capture the complex and multifaceted aspects of governance as a composite index based on six dimensions of governance: (1) political stability (e.g., perceptions of the likelihood of armed conflict); (2) government effectiveness (e.g., bureaucratic quality); (3) regulatory quality (e.g., regulatory burden, market-friendliness); (4) rule of law (e.g., black markets, enforceability of contracts); (5) voice and accountability (e.g., free and fair elections, political rights); and (6) corruption (e.g., prevalence among public officials). Each of these dimensions is weighted equally in this indicator. This governance indicator, devised by the World Bank, draws on 17 separate sources of subjective data on perceptions of governance constructed by 15 organizations. We have used the 2011 update of the Worldwide Governance Indicators (WGI) research project, covering 212 countries and territories and measuring the six dimensions of governance between 1996 and 2008. 32 These aggregate indicators are based on hundreds of specific and disaggregated individual variables taken from 30 data sources provided by 33 different organizations. Each governance indicator was ranked from approximately -2.5 (weak) to 2.5 (strong). We took the average of the six scores of the six indicators as the final score. This number was subsequently normalized and reversed. Measurement Units: governance ranking between -2.5 and 2.5.Year(s) of coverage: 2011. Source(s):http://info.worldbank.org/governance/wgi/index.asp 28 Missing: Anguilla; Aruba; Bermuda; British Virgin Islands; Cayman Islands; China Hong Kong SAR Faeroe Is.; French Guyana; French Polynesia; Greenland; Guadeloupe; Guam; Martinique; Montserrat; Netherlands Antilles; New Caledonia; Puerto Rico; Réunion; Taiwan; Tokelau; Turks and Caicos Is.; US Virgin Islands. 29 French Guyana and Guadeloupe were not listed in CIA life expectancy. For these countries we used another source for life expectancy but used the same formula to calculate HALE. 30 Albania calculated its literacy rate from 9 years. 31 The following countries were not listed in the CIA factbook and we have used alternative sources: Falkland Is.; French Guiana; Guadeloupe; Kiribati; Martinique; Nauru; Netherlands Antilles; Réunion; Solomon Islands; Tokelau; Tuvalu; US Virgin Islands. https://www.cia.gov/library/publications/the-world-factbook/fields/2103.html 32 For some smaller islands no data was available and we had to use an average score of similar islands in the same region for which data was available. For British Virgin Islands, Montserrat and Turks and Caicos we used the average of Anguilla; Bermuda and Cayman Islands. For the Cook Islands; Niue and Tokelau we used the average score of Nauru; Palau and Tuvalu. 21 Size of Economy Index Description: Gross domestic product (GDP) is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. We used GDP per capita measured in purchasing power parity (PPP). Measurement Units: GDP-PPP is gross domestic product converted to international dollars using purchasing power parity rates. An international dollar has the same purchasing power in the domestic currency as a U.S. dollar has in the United States. Year(s) of coverage: 2012. Source(s):CIA Factbook. We have used CIA 2012 Factbook data unless unavailable. To fill in some of the gaps we have used the World Bank data for the missing countries and used a formula to calculate the final number from World Bank data to CIA data set of GDP per capita. The values for missing countries33were calculated from the formula y=1.07x+46.60 (R=0.973). For a number of countries no information on GDP per capita existed in either CIA or WorldBank data sets. In these cases, we have used alternative sources.34 4 4.1 RESULTS Exposure Under the B2 scenario of the four main scenarios in the Special Report on Emissions Scenarios (SRES; HAdCM 3 model) warming will be greater in northern parts of Europe, North America and Latin America, Asia and Africa35. Relatively smaller temperature increases are expected in the tropical and sub-tropical regions. Figure 4.1shows the expected air surface temperature changes across six different regions in the world. It shows the regions of highest vulnerability in terms of exposure are expected in Europe and North-America, followed by Latin-America, Asia, Africa, Wider Caribbean region and Oceania. When the mean for the Wider Caribbean (0.31)is compared with that of all other countries (0.47),it is noted that the values for the Wider Caribbean are significantly lower than for the other countries(difference is significant at ≤ .000). The boxplots do not give the mean but the median. 33 Egypt; Fiji; Latvia; Lithuania; Tonga; Venezuela. French Guiana; Guadeloupe; Martinique; Myanmar; Myanmar; Netherlands Antilles; Réunion; Somalia; Tokelau. 35 In the Allison et al. 2009 paper the sequence was Northern parts of Asia, Europe, North America and South America. As we are using the same data this discrepancy is most likely due to the classifying by Allison et al. 2009 of the „northern countries‟ of Asia separately from the other Asian countries. Comparing the B2 and the A1F1 scenario we find the sequence from high to lower scores for A1F1 to be identical to B2. Highest vulnerability with regards to Exposure is in Europe and North America, Latin-America, Asia, Africa, Wider Caribbean and Oceania. We followed the UN Composition of macro geographical (continental) regions, geographical sub-regions as well as their list on Least Developed Countries. We have thus grouped all Asian countries. http://unstats.un.org/unsd/methods/m49/m49regin.htm(Accessed June 13th 2013) 34 22 Figure 4.1 Scoring of different regions in the world on exposure component When we compare SIDS to Least Developed Countries (LDCs) and the remaining group of countries we find SIDS to be the least vulnerable to exposure in this analysis (see Figure. 4.2). „Other‟ countries are most vulnerable with a mean of 0.55, followed by LDCs with an average of 0.52. Of the three SIDS groups, Caribbean SIDS are most vulnerable with a mean of 0.26, followed by SIDS in Africa and the Indian ocean (0.24), with the Pacific SIDS (0.17) appearing to be least vulnerable . The composite mean scores for the exposure variable for SIDS, LDCs and Others are 0.23, 0.52 and 0.54, respectively (difference is significant ≤ .000). 23 Figure 4.2 SIDS and LDCs scoring in the exposure component 4.2 Sensitivity Scores for the sensitivity component of the vulnerability index suggest that Oceania (see Figure 4.3)is the region that depends most on fisheries and has the highest sensitivity. A comparison of the means of the six different groups shows that the average for Oceania (0.72) is significantly higher than that of any other region. Europe and North America have the lowest score with 0.51. All other regions are around0.54. The countries most reliant on fisheries in terms of employment are found in Oceania, followed by the Wider Caribbean and Latin America (see Figure 4.4). Export income dependency on fisheries was highest in nations in Oceania, Latin America and Europe and North America. In the latter region Greenland, Faeroe Is., Iceland, and Falkland Is. are particularly reliant on fisheries export income, and appear in the top thirty countries most dependent on export income from this source (see Figure 4.4). The largest reported landings were associated with countries located in Oceania, followed by Africa and Asia. Nutritional reliance on fish as a source of animal protein was greatest in Oceania, followed by Latin America, Europe and North America. Figure 4.4shows clearly the high sensitivity of Oceania relative to other regions. 24 Figure 4.3 Scoring of different regions of the world in sensitivity component 25 Figure 4.4 Scoring of regions in the world per indicator in sensitivity component When the sensitivity of SIDS is compared to that of the LDCs and other countries, Pacific SIDS appear to be most vulnerable (see Figure 4.5). These are followed in descending order of sensitivity by SIDS in the African and Indian Ocean, LDCs, Caribbean SIDS and ‟Others.‟A further comparison of SIDS to non-SIDS groups shows that the mean for the former is significantly higher with a value of 0.61, compared to the latter with a value of 0.52 (≤.000). Figure 4.5 Scoring of the different SIDS groups and LDCs on sensitivity component SIDS in the pacific region are most sensitive across all four indicators; in terms of fisherfolk, dependence on fish exports, fish catches and fish as a source of animal protein (see Figure. 4.6). 26 Figure 4.6 Scoring per sensitivity indicator across three SIDS groups and LDCs Table 4.1(below) shows clearly that SIDS are heavily represented in the top 30 countries across the four different indicators. Of the top 30 countries SIDS account for 23 in terms of importance of fisheries to employment, 19 with respect to dependence on fish exports, 14 in terms of importance of fish landings, and 13 out of the 30 when fish as a source of animal protein is considered. Pacific SIDS are most represented among SIDS. Nevertheless, the Caribbean SIDS also stand out as an important group as they account for 4 out of the 23 SIDS that rank high in importance in relation to employment, five out of the 19 most dependent on fish exports, five out of the 14 in terms of importance of fish landings, and four out of the top 13 which are highly dependent on fish as a source of animal protein. Greenland, Faeroe Is., Iceland and Falkland Islands are among the northern countries most dependent on fisheries in terms of fish exports and fish landings. Table 4.1 Thirty most dependent countries across the four sensitivity indicators Light coloured countries are SIDS; darker coloured countries are Caribbean SIDS Top 30 Fisherfolk/EAP Fishexport Fishcatch/population Fish consumption 1 Tuvalu Maldives Faeroe Islands Sierra Leone 2 Nauru Greenland Falkland Is.(Malvinas) Maldives 3 Kiribati Falkland Is.(Malvinas) Vanuatu Greenland Comoros Faeroe Islands Iceland Solomon Islands Micronesia, Fed.States of Congo, Dem Rep British Virgin Islands 6 Marshall Islands Micronesia, Fed.States of Tuvalu Tuvalu Cambodia 7 Palau Kiribati Cook Islands French Guiana 8 Tonga Samoa Seychelles Martinique 9 Solomon Islands Seychelles Marshall Islands Niue 4 5 27 10 Tokelau French Guiana Saint Vincent/Grenadines Gambia 11 Niue Tonga Norway Bangladesh 12 Samoa Cook Islands Kiribati Guadeloupe 13 Lithuania Cape Verde Saint Kitts and Nevis Sri Lanka 14 Vanuatu Iceland Vanuatu Kiribati 15 Myanmar Panama Belize Ghana 16 Montserrat Gambia Micronesia, Fed.States of Equatorial Guinea 17 French Polynesia Turks and Caicos Is. Maldives Indonesia 18 Estonia Marshall Islands Sweden Sao Tome and Principe 19 Maldives Namibia Namibia Nauru 20 Thailand Grenada Chile Cameroon 21 Falkland Is.(Malvinas) Denmark Seychelles 22 Cambodia Sao Tome Principe Mauritius Peru Côte d'Ivoire 23 Faeroe Islands Fiji Turks and Caicos Is. Myanmar 24 Grenada Senegal New Zealand Philippines 25 Bahamas Bahamas Niue Senegal 26 VietNam Morocco Korea, Dem People's Rep Congo, Dem Rep 27 Seychelles Madagascar Netherlands Antilles Malaysia 28 Belize Mauritania Nigeria 29 Cook Islands Saint Vincent/Grenadines Solomon Islands Latvia Palau 30 Mauritius Mauritania Estonia Korea, Rep 4.3 and Adaptive capacity The countries with the lowest adaptive capacity are concentrated in Africa (see Figure 4.7), with 22 of the top 30 states in this category belonging to this region. Somalia, Sierra Leone and Guinea have the lowest adaptive capacity of all countries in our analysis. The mean value for adaptive capacity of Africa is highest among all regions (0.67), followed by Asia (0.45), Oceania (0.42), Latin America (0.40), Wider Caribbean region (0.39) and Europe and North America (0.25). Asian countries in the top 30 are Pakistan, Yemen, Bangladesh, Timor-Leste and Myanmar. Haiti is the only Caribbean country on the list of top 30 nations with the lowest adaptive capacity (and thus the highest vulnerability). 28 Figure 4.7 Scoring on adaptive capacity component across different regions in the world Africa has the lowest adaptive capacity (and thus the highest scores) for all four indicators: health, education, governance and GDP per capita. For the health component Africa is followed by Oceania and Asia, while for the education and governance elements, Asia and Oceania rank respectively after Africa. The wider Caribbean region has lower adaptive capacity than Asia in terms of GDP per capita, but is shown to have the highest adaptive capacity in all other categories (Figure 4.8). 29 Figure 4.8 Scoring on different adaptive capacity indicators by geographical region. LDCs clearly have a lower adaptive capacity than the three SIDS groups and „Others‟ category. The Pacific Ocean SIDS have the lowest adaptive capacity in comparison with the other two SIDS groups, with Papua New Guinea and Timor-Leste ranked lowest in the group. The adaptive capacity of the Caribbean SIDS is even higher than of the ‟Others‟ group, although Haiti stands out as an example in the region with a very low level of adaptive capacity. Contrastingly, Bermuda has a very high level of adaptive capacity. When the mean scores of the SIDS group (0.42) are compared to the average values for non-SIDS countries (0.45),the difference does not appear to be noteworthy. However, a comparison of the scores of SIDS with those of „Others‟ and LDCs, shows that the mean for the „Others‟ group is lowest (0.38), closely followed by SIDS (0.42) and the LDCs with a significantly higher score (0 .73; Anova; significance ≤.000). 30 Figure 4.9 Scoring on adaptive capacity of the different SIDS groups, LDCs and others In all four components the LDCs exhibit the lowest adaptive capacity. Pacific Ocean SIDS rank second with respect to health, governance and GDP per capita, while the AIMS SIDS rank lower than Pacific SIDS when the education component is considered (see Figures. 4.8 and 4.10). The Caribbean SIDS‟ adaptive capacity is very high (low scores) across all four categories. 31 Figure 4.10 Scoring on adaptive capacity indicators per SIDS group, LDCs and others 4.4 Overall vulnerability The region most vulnerable to climate-induced changes in fisheries is Africa, particularly in the Sahel and Central-Africa region. Figure 4.11 shows that of the six regions examined, Africa is the region with the highest level of vulnerability. The mean (not depicted in the boxplots) of Africa is 0.57, followed by Asia (0.5), Latin-America (0.49), Oceania (0.44), Europe and North America (0.44). The Wider Caribbean region ranks as the least vulnerable region with a mean score of 0.41. 32 Figure 4.11 Scoring on overall vulnerability of different geographical regions Table 4.2shows that 19 out of 30 countries are located in Africa, five are located in Asia and only two are located in Wider Caribbean region. The most vulnerable countries are also amongst the poorest nations of the world with 18 out the 30 countries listed as Least Developed Countries. Only one country, Djibouti, was found to rank highest in two of the three components of vulnerability. Table 4.2 The 30 most vulnerable countries Countries highlighted in colour are from Wider Caribbean and SIDS Top 30 Most vulnerable countries Exposure Sensitivity Adap. Cap. Vulnerability 1 Mauritania 0.68 0.67 0.72 0.69 2 Sierra Leone 0.44 0.73 0.90 0.69 3 Senegal 0.57 0.69 0.77 0.68 4 Gambia 0.54 0.72 0.73 0.67 5 Guinea 0.53 0.61 0.83 0.66 6 Korea, Dem People's Rep 0.76 0.63 0.56 0.65 7 Mozambique 0.60 0.61 0.75 0.65 8 Congo, Dem Rep 0.60 0.62 0.74 0.65 9 Morocco 0.66 0.64 0.62 0.64 10 Russian Federation 0.92 0.55 0.45 0.64 33 Top 30 Most vulnerable countries Exposure Sensitivity Adap. Cap. Vulnerability 11 Djibouti 0.68 0.52 0.68 0.63 12 Nigeria 0.47 0.63 0.78 0.63 13 Angola 0.66 0.51 0.71 0.62 14 Myanmar 0.46 0.75 0.66 0.62 15 Bangladesh 0.48 0.68 0.70 0.62 16 Cambodia 0.50 0.70 0.64 0.62 17 Peru 0.76 0.67 0.42 0.62 18 Côte d'Ivoire 0.50 0.56 0.77 0.61 19 Yemen 0.60 0.53 0.71 0.61 20 Guyana 0.62 0.67 0.53 0.61 21 Namibia 0.61 0.69 0.51 0.60 22 Guinea-Bissau 0.50 0.48 0.80 0.60 23 Somalia 0.44 0.40 0.93 0.59 24 Belize 0.57 0.67 0.53 0.59 25 Ghana 0.47 0.66 0.63 0.58 26 Tanzania, United Rep 0.47 0.60 0.69 0.58 27 Greenland 0.63 0.85 0.25 0.58 28 Ukraine 0.83 0.43 0.47 0.58 29 Benin 0.48 0.50 0.75 0.58 30 Togo 0.47 0.54 0.72 0.58 The top 30 of most vulnerable countries do not show consistency in terms of ranking highly on all three categories: exposure, sensitivity, and adaptive capacity. Our results show exposure and sensitivity were negatively correlated (r=-0.35, N=173, p< 0.001). Those countries scoring high in Exposure are thus likely to score low in the Sensitivity component. exposure and adaptive capacity were positively correlated (r=0.15, N=173, p=0.049).Sensitivity and adaptive capacity are not correlated. Our findings are strikingly different to the results of Allison et al. (2009). Their analysis showed a positive correlation between Exposure and Sensitivity (r=0.37,N=132, p< 0.001), and a very high correlation (r=0.75, N=132, p<0.001) between exposure and adaptive capacity. Sensitivity and adaptive capacity were also not correlated in their results. The difference in results between our findings and Allison et al. (2009) are most likely the result of the different countries included in the analysis. The countries used in our analysis included 33% SIDS which score low in exposure but very high in sensitivity. The absence of land-locked states in our analysis might also partly explain the large discrepancy in results. Figure 4.12 shows that in this assessment SIDS are considered to be less vulnerable than LDCs and other groups. The LDCs are the most vulnerable group with a mean score of 0.61. Pacific Island SIDS are the most vulnerable SIDS group with a mean score of 0.45, followed by SIDS in the African and Indian Ocean with 0.43 and Caribbean SIDS with a mean of 0.39. Caribbean SIDS in this analysis are thus the least vulnerable group of the three SIDS groups, and in fact, the least vulnerable group of all groups. The two most vulnerable countries in the Wider Caribbean 34 are Belize and Guyana, and based on the results of this analysis are among the 30 most vulnerable countries in the world. Figure 4.12 Scoring of overall vulnerability of different SIDS groups, LDCs and others Caribbean scores considerably lower than the other two groups on exposure, and is relatively similar in ranking for sensitivity and adaptive capacity, while the LDCs score highest in all components. It is therefore evident that the low score in the exposure component contributes significantly to the overall low vulnerability score for the Wider Caribbean. 35 . Figure 4.13 Scoring of the Wider Caribbean region across the three components and final vulnerability in comparison to LDCs and others Figure 4.14 Scoring of SIDS across the three components and final vulnerability in comparison to LDCs and others The situation for SIDS is similar in some respects. For the exposure variable, SIDS score less than half the value assigned to that of LDCs and Others. SIDS are more sensitive than either of the other groups but only marginally so. The scores for adaptive capacity of SIDS are fairly similar to those of other groups, except the LDCs which score highest. The final vulnerability score of the SIDS thus ends up even lower than the mean for the other two groups. Again, itis 36 largely the scores in the exposure component that lead to the overall low vulnerability ranking of the SIDS. 5 DISCUSSION On a global scale it is not easy to identify the main „losers‟ and „winners‟ from changes in fisheries as a result of climate change. The analysis by Allison et al. (2009) provides a framework for assessing fisheries vulnerability to climate change. In their analysis, however, SIDS and Wider Caribbean countries were largely absent. One of the aims of this study was to fill this gap by updating the data used in the Allison et al. (2009) model in an attempt to incorporate all SIDS and countries in the Wider Caribbean Region. In line with the findings by Allison et al. (2009) this analysis also found Africa to be the most vulnerable region and LDCs to be the world‟s most vulnerable group of countries. Only two SIDS, Guyana and Belize, both also belonging to the Wider Caribbean region, are included in the world‟s most vulnerable 30nations with respect to the impacts of climate change on fisheries. Although fisheries in SIDS are considered to be highly vulnerable to climate change impacts (Mimura et al. 2007; Guillotreau et al. 2012), our results suggest that SIDS in general, and Caribbean SIDS in particular, have very low levels of vulnerability. In addition, although the literature suggests that the fisheries in the Wider Caribbean region are highly vulnerable to the impacts of climate change (Nurse 2011) our results suggest that the Wider Caribbean is the least vulnerable region in the world. We argue that the discrepancy between the low vulnerability outcome of SIDS and the Wider Caribbean region is, however, largely due to the choice of indicators and the weighting of the three components and not due the actual low level of vulnerability of either SIDS or the Wider Caribbean region. We believe that the use of projected surface air temperature as the single exposure variable contributes significantly to this outcome, as it skews the results towards the middle and high latitude regions of the North where projected temperatures are highest. Therefore, these results should not be interpreted to imply that the impacts in the Wider Caribbean and other SIDS will be benign, it may simply be that the surface air temperature indicator might not adequately represent the true level of „risk exposure‟ of nations in the tropical and subtropical regions. We suggest that a „national-level‟ indicator approach that incorporates other exposure indicators such as sea level rise, sea surface temperature change, ocean acidification and changing ENSO events may be more appropriate. The sensitivity scores of the Wider Caribbean are only marginally higher than those calculated for Europe, North America and Latin America. Oceania stands out as the region of highest vulnerability in the sensitivity component. Although SIDS rank highest in the sensitivity score, they score only marginally higher. To better reflect the sensitivity of the fisheries sector in SIDS to climate change, factors such as elevation above sea level, rate of sea-level rise and changes in the characteristics of storms should be regarded as vital model inputs. In most SIDS, the majority of people live on the narrow coastal plains. The related infrastructure, social services, tourism facilities, airports, seaports, roads and vital utilities are mostly located in the low-lying area (UNFCCC 2005: 21). Marine fishers and their dependents mostly live in the coastal zone which is considered to be highly vulnerable to climate change impacts (Nicholls et al. 2007). The indicators for sensitivity therefore need to be broadened to include e.g. data on the number of people living within 10 km from the coastline; percentage of land within 5 meters above sea level and presence (or absence) of natural coastal protection (e.g. coral reefs and mangroves). 37 Adaptive capacity is lowest in Africa, followed by Asia, Oceania and The Wider Caribbean. In comparison with the other regions, the Wider Caribbean has a high level of adaptive capacity and scores high across all four indicators: health; education; governance and GDP per capita. Europe and North America are ranked after the Wider Caribbean in terms of adaptive capacity. Our results demonstrate LDCs have a lower adaptive capacity than the three SIDS groups and countries in the „Others‟ category. Among the SIDS, Pacific Ocean countries have the lowest adaptive capacity relative to that of the two other SIDS regions. The choice of indicators for the adaptive capacity component in this study is, however, very general and does not cover sector specific indicators such as the level of fisheries governance or disaster risk management and response capability. Change in the frequency and/or intensity of storms may adversely impact the livelihood and employment of fishers living on the coast as well as cause damage to landing sites and boats, thus placing the livelihoods of fishers at further risk. The ability to adequately deal with disaster is therefore of crucial importance. Including disaster risk management capacity can help to provide a more accurate picture of a country‟s adaptive capacity. Theoretical and field studies show that fish populations and fisheries systems become more sensitive to climate impacts when they are heavily exploited (Brander 2007). Adequate fisheries governance is thus of great importance in adaptation planning. The use of other indicators such as economic volatility and remoteness may also provide guidance to policy makers faced with the challenge of making adaptation choices. The results from this study show that although SIDS of the Wider Caribbean Region and other regions are very dependent on fisheries and score high in the sensitivity component, this does not translate into a final high vulnerability score. This is due to the fact these countries score very low on the exposure and high on adaptive capacity. Allison et al. (2009) argue that the order of ranking of the most vulnerable countries in their analysis was due to the combined effect of predicted warming, the relative importance of fisheries to national economies and diets, and limited societal capacity to adapt to potential impacts and opportunities. This is only partly reflected in their results, however. For example, none of the countries ranked as the most vulnerable fall within the upper quartile for all three components of vulnerability. However, their results do show a positive correlation between exposure and sensitivity, as well as between exposure and adaptive capacity. Contrary to their results our results do not show a combined effect of exposure, sensitivity and adaptive capacity at all. The inclusion of SIDS and all Wider Caribbean countries and the exclusion of land-locked states has resulted in a negative correlation between Exposure and Sensitivity and only a marginal positive correlation between Exposure and Adaptive capacity. Our analysis shows that only 11 countries out of the 33 in the highest quartile of total vulnerability were even within the top quartile for two components. Across the top quartile in the three separate categories (exposure, sensitivity and adaptive capacity) only 14 countries were listed in two categories. This implies that of the 99 countries, 84 were individual countries. The fact that countries scored so differently across the different components supports the view that there may be opportunities for refining the Allison et al (2009) model. This might be accomplished by developing a more inclusive list of exposure, sensitivity and adaptive capacity indicators, combined with more detailed region-specific projections of climate change impacts on fisheries (e.g. see Cheung et al 2010). 38 6 CONCLUSION In this study we have assessed the vulnerability of fisheries to climate change in SIDS and countries in the Wider Caribbean Region within the framework provided by Allison et al. (2009). We focussed exclusively on coastal states and were able to include 50 out of 52 SIDS and all Wider Caribbean countries. In comparison to the Allison et al. (2009)study this analysis thus covered nearly all coastal nations and SIDS in the world. The study used the most recent data available and has highlighted the differences in vulnerability across the three components of vulnerability in all regions, and between the three SIDS groups and LDCs. The results show that vulnerability of nations‟ fisheries is far from uniform across the globe and each region or sets of countries are likely to face different challenges. As a result of the differences of vulnerability and potential impacts, the policy and other responses necessary to adapt to changes in climate will be different across regions and countries. Our results also demonstrate that there are opportunities for refining the Allison et al (2009) model. We have found that the main caveats of the Allison et al. (2009) model are the limited number of countries and near complete absence of SIDS and countries in the Wider Caribbean; the use of only one exposure indicator (air surface temperature change); lack of incorporation of coastal vulnerability of low-lying nations; and lack of refinement of adaptive capacity indicators towards sector specific indicators. Our results indicate that, in line with the findings by Allison et al. (2009), the most vulnerable nations to climate change impacts on their fisheries are also the poorest; 19 out of the top 30 most vulnerable countries are LDCs and are all located in Africa. The Wider Caribbean Region and SIDS are ranked much less vulnerable. This is the result of their extreme low scores on the exposure component in comparison to other regions and LDCs, marginally higher scores on sensitivity while scoring low on adaptive capacity (see Table 6.1). We argue however that the low vulnerability scores of SIDS and the Wider Caribbean may partly be due to configuration of the model, as suggested earlier. It is possible that by using a more comprehensive set of exposure indicators and incorporating indicators of other climate change impacts such as sealevel rise, sea surface temperature change and ocean acidification, a more representative picture of fisheries vulnerability in tropical and subtropical regions might be achieved. For the sensitivity component (see Table 6.1), our analysis showed that Oceania was the most vulnerable region, followed by Latin America and Africa. Among the SIDS and LDCs groups, Pacific Ocean SIDS ranked highest in sensitivity, followed by SIDS from the African and Indian Ocean, LDCs and Caribbean SIDS. The African region clearly has the lowest adaptive capacity. Of the top 30 countries with lowest adaptive capacity, 22 are from the African region. Latin America and Asia are ranked second and third respectively, after Africa. SIDS have relatively high levels of adaptive capacity in comparison to LDCs and Others (Table 6.1). Caribbean SIDS rank highest in adaptive capacity, as a group. Building adaptive capacity is a necessary response, both for countries where climate change may bring improved fishing opportunities and for those where detrimental impacts are foreseen. Countries with weak economies and poor governance are less able to translate improved fishery productivity into reduced poverty. 39 Table 6.1 Summary of findings Categories Overall vulnerability Exposure Sensitivity Adaptive capacity Results Wider Caribbean is the least vulnerable SIDS region in the world according to our results using this framework SIDS are the least vulnerable group in comparison to LDCs and others Most vulnerable: Europe and North America,Latin America, Asia and Africa Oceania, followed by the Wider Caribbean are the least vulnerable regions SIDS are the least vulnerable in comparison to LDCs and others SIDS are the most vulnerable, particularly the Pacific SIDS Oceania is the region most vulnerable, followed by the Wider Caribbean Adaptive capacity is lowest among LDCs in comparison to SIDS and others Adaptive capacity is lowest in Africa, followed by Asia, Oceania, Latin America and the Wider Caribbean Applying the method adopted by Allison et al (2009), this analysis provides a ranking of countries and regions with respect to relative exposure, sensitivity and adaptive capacity in the fisheries sector. We suggest, however, that a broader analysis of the components of vulnerability at the sectoral level is needed to further enhance our understanding of countries‟ and regions‟ ability to respond to the impacts of climate change in the fisheries sector. This will require consideration of additional indicators, including governance arrangements that better reflect exposure, sensitivity and adaptive capacity. The fisheries sector already faces challenges from multiple stressors (e.g. overfishing, habitat loss, pollution, invasive species, water abstraction), and climate change is likely to exacerbate these challenges. Adaptation measures should be properly aligned with attempts to build resilience and strengthen governance and livelihoods, and thus reduce poverty in the sector at the community, national and global level. 40 7 REFERENCES Adger, N., N. Brooks, G. Bentham, M. Agnew and S. Eriksen. 2004 “New Indicators of Vulnerability and Adaptive Capacity.” Technical Report. Tyndall Centre for Climate Change Research. No. 7 Tyndall Centre for Climate Change Research, Norwich. Adger, N., Arnell, N. and E. Tompkins. 2005 “Successful adaptation to climate change across scales.”Global Environmental Change 15: 77-86. Adger, N. 2006. “Vulnerability.” Global Environmental Change, 16 (3): 268–281 Adger, W.N., S. Agrawala, M.M.Q. Mirza, C. Conde, K. O‟Brien, J. Pulhin, R. Pulwarty, B. Smit and K. Takahashi. 2007 “Assessment of adaptation practices, options, constraints and capacity.”In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 717-743. Allison, E., N. Adger, M. Badjeck, K. Brown, D. Conway, N.Dulvy, A. Halls, A. Perry and J. Reynolds. 2005. “Effects of climate change on the sustainability of capture and enhancement fisheries important to the poor: analysis of the vulnerability and adaptability of fisherfolk living in poverty.” Technical report. Project R4778J. DFID. Allison, E.H., Perry, A.L., Badjeck, M.C., Adger, W.N., Brown, K., Conway, D., Halls, A.S., Pilling, G.M., Reynolds, J.D., Andrew, N.L., and N. Dulvy. 2009. “Vulnerability of national economies to the impacts of climate change on fisheries.”Fish and Fisheries 10: 173–196. Allison, E. 2011. Aquaculture, fisheries, poverty and food security.Working Paper 2011-65. The WorldFishCenter.Penang, Malaysia. Barange M, Allen I, Allison E, Badjeck M-C, Blanchard J, Drakeford B, Dulvy NK, Harle J, Holmes R, Holt J, et al. 2011. Predicting the impacts and socio-economic consequences of climate change on global marine ecosystems and fisheries: the QUEST_Fish framework. In Coping with Climate Change in Marine Socio-Ecological Systems, Ommer R, et al. (eds). Blackwell FAR Series:Oxford; 31–59. Brander, K. 2007. “Global fish production and climate change.”PNAS 104 (50): 19709-19714. Brander, K. 2010.“Impacts of climate change on fisheries.”Journal of Marine Systems 79: 389402 Briguglio, L. 1995. “Small island developing states and their economic vulnerabilities.”World Development 23 (9): 1615-1632. Brooks, N., N. Adger, and M.P. Kelly. 2005 „The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation‟. Global Environmental Change 15 (2): 151-163. 41 Campling, L and M. Rosalie. 2006.“Sustaining Social Development in a Small Island Developing State? The Case of Seychelles.”Sustainable Development 14: 115–125 Chakalall, B, R. Mahon, P. McConney, L. Nurse, and D. Oderson. 2007.” Governance of fisheries and other living marine resources in the wider Caribbean.”Fisheries Research 87: 9299. Cheung, W., V. Lam, J. Sarmiento, K. Kearny, R. Watson., D. Zeller and D. Pauly. 2010. “Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change.” Global Change Biology 16: 24-35. Chuenpagdee, R. L. Liguori, M. Palomares and D. Pauly 2006. “Bottom-Up, Global Estimates of Small-Scale Marine Fisheries Catches.” Fisheries Centre Research Reports 14 (8). Cinner, J., T. T. McClanahan, N. Graham, T. Daw, J. Maina, S. Stead, A. Wamukota., K. Brown and O Bodin. 2012.“Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries.”Global Environmental Change 22: 12-20. Cinner J, C. Huchery, E. Darling, A. Humphries, N. Graham, C. Hicks, N. Marshall, T. McClanahan. 2013.“Evaluating Social and Ecological Vulnerability of Coral Reef Fisheries to Climate Change.”PLoS ONE 8(9): e74321. doi:10.1371/journal.pone.0074321 Cochrane, K., De Young, C., Soto, D.,and T. Bahri. (eds). 2009. Climate change implications for fisheries and aquaculture: overview of current scientific knowledge. FAO Fisheries and Aquaculture Technical Paper. No. 530. Rome, FAO. 212p Coulthard, S. 2012. “Can we be both resilient and well, and what choices do people have? Incorporating agency into the resilience debate from a fisheries perspective.”Ecology and Society 17 (1): 4. Eakin,H. and A. Luers. 2006. “Assessing the vulnerability of Social-environmental systems.”Annu. Rev. Environ. Resour.31: 365–94 ECLAC, 2011. “The economics of climate change in the Caribbean.” Summary report 2011. United Nations Economic Commission For Latin America And The Caribbean. Port-of-Spain, Trinidad and Tobago. Eriksen, S. and M.P. Kelly. 2007. “Developing credible vulnerability indicators for climate adaptation policy assessment.”Mitigation and Adaptation Strategies for Global Change 12: 495524. FAO 2012. The state of the world fisheries and aquaculture (SOFIA) 2012. Rome: FAO. Fussel, H. and Klein, R. 2006. “Climate change vulnerability assessments: an evolution of conceptual thinking.”Climatic Change 75:301–329. 42 Gordon, C., Cooper, C., Senior, C.A. et al. (2000) The simulation of SST, sea ice extents and ocean heattransports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16: 147–168. Guillotreau, P., L. Campling, and J. Robinson. 2012. “Vulnerability of small island fishery economies to climate and institutional changes.” Current Opinion in Environmental Sustainability 4(3): 287-291. Hinkel, J. 2011. “Indicators of vulnerability and adaptive capacity: towards a clarification of the science-policy interface.”Glob. Environmental Change 21: 198-208. Hoegh-Guldberg, O., P. Mumby, A. Hooten, R. Steneck, P. Greenfield, E. Gomez, C. Harvell, P. Sale, A. Edwards, K. Caldeira, N. Knowlton, C. Eakin, R. Iglesias-Prieto, N. Muthiga, R. Bradbury, A. Dubi, M. Hatziolos. 2007. “Coral reefs under rapid climate change and ocean acidification.”Science 318: 1737-1742. Hughes, S., A. Yau, L. Max, N. Petrovic, F. Davenport, M. Marshall, T. McClanahan, E. Allison and J. Cinner. 2012. “A framework to assess national level vulnerability from the perspective of food security: The case of coral reef fisheries.”Environmental Science and Policy 23: 95-108. Intergovernmental panel on climate change (IPCC) “Summary for policy- makers.” 2007. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL, editors. Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge, UK and New York, NY: Cambridge University Press; 2007. Kelly, M. P. and N. Adger. 2000. “Theory and practice in assessing vulnerability to climate change and facilitating adaptation.”Climate Change 47: 325-352. Kurien, J. 2005. Responsible fish trade and food security. FAO Fisheries Technical Paper 456. FAO, Rome. Mahon, R. 2002. Adaptation of Fisheries and Fishing Communities to the Impacts of Climate Change in the CARICOM Region. Mainstreaming Adaptation to Climate Change (MACC) of the Caribbean Center for Climate Change (CCCC), Organization of American States, Washington, D. C., 33pp. Marshall N., P. Marshall, J. Tamelander, D. Obura, D. Malleret-King and J. Cinner. 2009. A Framework for Social Adaptation to Climate Change; Sustaining Tropical Coastal Communities and Industries. Gland, Switzerland, IUCN. v + 36 pp McCarthy, J., Canziani, O., Leary, N., Dokken, D. and White, K. 2001. Climate change 2001: Impacts, Adaptation and vulnerability, Working Group II, Third Assessment Report, Intergovernmental Panel on Climate Change, University Press, Cambridge. McConney, P., Nurse, L., James, P., 2009. Impacts of Climate Change on Small-scale Fisheries in the Eastern Caribbean, Final Report to IUCN, CERMES Technical Report No. 18, 2009. 43 McIlgorm, A., S. Hanna, G. Knapp, P Le Floc‟H, F. Millerd and M. Pan. 2010. “How will climate change alter fishery governance? Insights from seven international case studies.” Marine Policy 34 (1): 170-177. Metzger, M., Leemans, R. and Schröter, D. 2005. “A multidisciplinary multi-scale framework for assessing vulnerability to global change.”International Journal of Applied Earth Observation and Geoinformation7: 253– 267. Mitchell, T.D., Carter, T.R., Jones, P.D., Hulme, M., New, M., 2004. A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: the observed record (19012000) and 16 scenarios (2001-2100).Tyndall Centre for Climate Change Research: Working Paper 55. Mimura, N., Nurse, L., McLean, R., Agard, J., Briguglio, L., Lefale, P., Payet, R. and Sem G. 2007 „Small islands‟, in Parry, M.L.,Canziani, O.F.,Palutikof, J.P., van der Linden, P.J. and Henson, C.E.(Eds.) Climate Change 2007: Impacts, Adaptations and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge Press, Cambridge, pp. 687-716. Mora, C., A. Frazier,, R. Longman, R. Dacks, M. Walton, E. Tong, J. Sanchez, L. Kaiser, Y. Stender, J. Anderson, C. Ambrosino, I. Fernandez-Silva, L. Giuseffi, T. Giambelluca. 2013. “The projected timing of climate departure from recent variability.”Nature 502: 183-187. Nelson, D., N. Adger, and K. Brown. 2007. “Adaptation to Environmental Change: Contributions of a Resilience framework.”Annu. Rev. Environ. Resour 32 pp 395-419. Nicholls, RJ, Wong, PP, Burkett, VR, Codignotto, J, Hay, J, McLean, R, Ragoonaden, S, and Woodroffe, CD, 2007 Coastal systems and low-lying areas, in Parry, ML, Canziani, OF, Palutikof, JP, van der Linden, PJ, and Hanson, CE (ed) Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change, Cambridge, UK, Cambridge University Press, pp. 315-356 Nurse, L., G. Sem, J.E. Hay, A.G. Suarez, P.P. Wong, L. Briguglio and S. Ragoonaden, 2001 “Small island states.” In: Climate Change 2001: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, J.J. McCarthy, O.F. Canziani, N.A. Leary, D.J. Dokken and K.S. White, Eds., Cambridge University Press, Cambridge, 842-975. Nurse, L. 2011 “The implications of global climate change for fisheries management in the Caribbean.”Climate and Development 3 (3): 228-241. O‟Brien, K. S. Eriksen, A. Schjolden and L. Nygaard. 2004. What‟s in a word? Conflicting interpretations of vulnerability in climate change research. CICERO Working Paper 2004: 04 O‟Brien, K., Leichenko, R., Kelkar, U. et al. 2005. “Mapping vulnerability to multiple stressors: climate change and globalization in India.”Global Environment Change 14: 303–313. 44 Park, S., M. Howden, and S. Crimp 2012. “Informing regional level policy development and actions for increased adaptive capacity in rural livelihoods. Environmental science and policy 15: 23-37. Parry, M., Lowe, J., Hanson, C., 2009.”Overshoot, adapt and recover.”Nature 458: 1102–1103. Pelling, M., and J.I. Uitto. 2001 “Small island developing states: Natural disaster vulnerability and global change.”Environmental Hazards 3 2 pp, 49–62. Sadovy, Y. 2005.“Trouble on the reef: the imperative for managing vulnerable and valuable fisheries.”Fish and Fisheries 6, 167–185. Smit, B. et al. 2001. Adaptation to climate change in the context of sustainable development and equity. In McCarthy, J.J., Canziani, O., Leary, N.A., Dokken, D.J. and White, K.S., editors, Climate change 2001: impacts, adaptation and vulnerability. IPCC Working Group II. Cambridge: Cambridge University Press, 877–912. Smit, B. and Wandel, J. 2006. “Adaptation, adaptive capacity and vulnerability.” Global Environmental Change 16: 282-92. Turvey, R. 2007. “Vulnerability assessment of Developing Countries: The case of Small-Island Developing States.”Development Policy Review 25 (2): 243-264. UNFCCC 2005 Climate Change, Small Island Developing States. Issued by the CLIMATE CHANGE SECRETARIAT (UNFCCC), Bonn, Germany. UN-OHRLLS. 2009. The impact of climate change on the development prospects of the least developed countries and Small Island Developing States. Office of the High Representative for the Least Developed Countries, Landlocked Developing Countries and Small Island Developing States. New York, USA. Yohe, G., Malone, E. and Brenkert, A., et al. 2006 A Synthetic Assessment of the Global Distribution of Vulnerability to Climate Change from the IPCC Perspective That Reflects Exposure and Adaptive Capacity. CIESIN (Center for International Earth Science Information Network), Columbia University, Palisades. 45