Download 56 - University of the West Indies, Cave Hill Campus

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