Climate Change Impacts on Livelihood Vulnerability Assessment-Adaptation and Mitigation Options in Marine Hot Spots in Kerala, India

Abstract

Climate change, a global challenge facing mankind necessitates governments to develop mitigation and adaptation plans. The climate change has multidimensional impacts on environment, fishery, social, economic and development drivers. Climate change hot spots –can be defined as the ‘live labs’ where the manifestation of the climate change impacts is observed “first”. The South west India has been recognised as one among the twenty four hot spot regions identified globally. The present paper assessed the climate change vulnerability of over 800 fisher households in two major fishing villages of Kerala from the south west hotspot regions of India. Exposure (E), Sensitivity (S) and Adaptive Capacity (AC) are the pertinent factors that determine the vulnerability of households which were captured using a structured household questionnaire. One ninety eight indicators were identified in the construction of vulnerability indices of which 37 related to sensitivity, 36 related to exposure and the other 125 indicators dealt with adaptive capacity. The overall vulnerability of the regions was assessed and the analysis revealed that the Poonthura village of Kerala was more vulnerable when compared to Elamkunnapuzha. The coastal population on their vulnerability scores were categorised into low, moderate, high and very high based on score values and geo-spatial analysis was attempted. The results revealed that majority of fisher households in both villages were highly vulnerable to climate change, which is a major cause of concern. The study advocates the need for a bottom up approach with the proactive participation of the fishers in developing location specific adaptation and mitigation plans to ensure the livelihood of the fishers and the sustainable development of the fisheries sector in the climate change regime.

Full text

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*Corresponding author: E-mail: shyam.icar@gmail.com;
International Journal of Environment and Climate Change
8(3): 180-199, 2018; Article no.IJECC.2018.013
Previously known as
British Journal of Environment & Climate Change
ISSN: 2231–4784
Climate Change Impacts on Livelihood Vulnerability
Assessment-Adaptation and Mitigation Options in
Marine Hot Spots in Kerala, India
Shyam S. Salim
1*
, R. Narayanakumar
1
, R. Remya
1
, P. K. Safeena
1
,
M. Ramees Rahman
1
and Harsha Elizabeth James
1
1
Socio-Economic Evaluation and Technology Transfer Division (SEETTD),
ICAR,-Central Marine
Fisheries Research Institute (CMFRI), Post Box No: 1603, Ernakulam 682 018, India.
Authors’ contributions
This work was carried out in collaboration between all authors. All authors read and approved the final
manuscript.
Article Information
DOI: 10.9734/IJECC/2018/43280
Received 14 May 2018
Accepted 25 July 2018
Published 05 September 2018
ABSTRACT
Climate change, a global challenge facing mankind necessitates governments to develop
mitigation and adaptation plans. The climate change has multidimensional impacts on
environment, fishery, social, economic and development drivers. Climate change hot spots –can
be defined as the ‘live labs’ where the manifestation of the climate change impacts is observed
“first”. The South west India has been recognised as one among the twenty four hot spot regions
identified globally. The present paper assessed the climate change vulnerability of over 800 fisher
households in two major fishing villages of Kerala from the south west hotspot regions of India.
Exposure (E), Sensitivity (S) and Adaptive Capacity (AC) are the pertinent factors that determine
the vulnerability of households which were captured using a structured household questionnaire.
One ninety eight indicators were identified in the construction of vulnerability indices of which 37
related to sensitivity, 36 related to exposure and the other 125 indicators dealt with adaptive
capacity. The overall vulnerability of the regions was assessed and the analysis revealed that the
Poonthura village of Kerala was more vulnerable when compared to Elamkunnapuzha. The coastal
population on their vulnerability scores were categorised into low, moderate, high and very high
based on score values and geo-spatial analysis was attempted. The results revealed that majority
of fisher households in both villages were highly vulnerable to climate change, which is a major
cause of concern. The study advocates the need for a bottom up approach with the proactive
participation of the fishers in developing location specific adaptation and mitigation plans to ensure
the livelihood of the fishers and the sustainable development of the fisheries sector in the climate
change regime.
Original Research Article

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181
Keywords: Climate change; hot spot; vulnerability; geo-spatial; sustainable development.
1. INTRODUCTION
Climate change, one of the most debated topics
over the last few decades, is no more a myth but
a reality. Governments around the world are
looking for practical and time-bound plans to
cope with the changing environment Shyam et al.
[1]. The consequence of climate change is
experienced by both inland and coastal regions,
but coasts being the transition zone between the
lithosphere and hydrosphere are prone to more
changes than the other zones. Not only are
coastal regions geographically important, but
they are vital domains in terms of economy and
biology. Sixty percent of the world’s metropolises
with a population of over 5 million are located
within 100 km of the coast, including 12 of the
world’s 16 cities with populations greater than 10
million IPCC [2]; Shyam et al. [1].Among the
coastal sates of India, Kerala stands tall with
substantial contribution in the marine landings
and value realisation. Even though, Kerala
possess third position in terms of marine
landings this year (CMFRI 2014) [3], a
substantial reduction in the fish landings was
reported in the state than the previous year
owing to various reasons. In addition to the
inherent problems of this sector, the reduction in
landings also created food and livelihood security
concerns among the fisher folk. There exists an
irony that eventhough Kerala possess the
highest literacy rate in the country, the fishers are
marginalised and are way behind with
comparatively low level of literacy rate and
educational attainment which, limits them with
minimal alternative livelihood options.
Furthermore Shyam et al. [1] reported low level
of awareness on climate change among fisher
folk of Kerala owing to the fact that climate
change issues are entangled with other
developmental issues; thereby community could
not decipher climate change issues in particular.
Though there are many climate change studies
done in fisheries sector, it is guiding to the fact
that the scientific knowledge generated hasn’t
trickled down to the grass roots.
However, climate change is not impacting all
ocean regions equally with sea surface
temperature (SST) warming in some 20 regions
occurring at several times the average global
rate of warming. Identification of these marine
hotspots Hobday and Pecl [4], and the
associated biological impacts suggests that
coastal communities in these areas may be at
higher risk compared to other regions. These
hotspots represent live labs for observing change
mainly because impacts are already being
observed or will likely be observed early,
incentives to develop adaptive strategies will be
strong; models developed for prediction can be
validated earlier; and adaptation options can be
developed, implemented and tested Hobday and
Pecl [4].
Ocean warming ‘hotspots’ or Marine hotspots are
regions characterized by above-average
temperature increases over recent years, for
which there are significant consequences for
both living marine resources and the societies
that depend on them. As such, they represent
early warning systems for understanding the
impacts of marine climate change, and test-beds
for developing adaptation options for coping with
those impacts. These particular hotspots have
underpinned a large international partnership that
is working towards improving community
adaptation by characterizing, assessing and
projecting the likely future of coastal-marine food
resources through the provision and sharing of
knowledge Popova et al. [5]. On the basis of
historical observations of Sea Surface
Temperature (SST), Hobday & Pecl [4] identified
24 fast-warming marine areas –so-called
hotspots and suggested that these could serve
as ‘natural laboratories’ where the mechanistic
links between ocean warming and biological
responses could be studied in advance of wider
scale impacts predicted for later in the 21
st
century. Furthermore, climate adaptation options
in marine hotspots could be explored as human
dependence on marine resources is very high in
many of these areas. During the 21
st
century,
changes in ocean physical and biogeochemical
parameters are anticipated to greatly impact
ocean ecosystems. Coastal-marine food
resources will alter as a result of species-specific
direct responses to drivers of climate change,
such as distribution and abundance of species
changing in response to temperature, as already
reported from south-east Australia Frusher et al.
[6], or ocean acidification in the Arctic (e.g.
Mathis et al. [7]. Such impacts to living marine
resources will require individuals, communities,
industries and governments to understand and
adapt to the changing climate Barange et al. [8];
Frusher et al. [6]. However, adaptation options
within the context of climate change must build
on a solid understanding of the physical,
biological and human aspects of the given

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systems and recognition that marine systems
and human societies are really parts of a unified
marine socio-ecological system Perry et al. [9].
However, rising temperatures are not the only
climatic factor impacting ocean ecosystems.
‘Warming up, turning sour, losing breath’ Gruber
[10] has become a widely used summary of the
major climatic stressors of ocean ecosystems:
warming, acidification and deoxygenation, all
with implications for marine productivity Doney et
al. [11]; Bopp et al. [12]. Changing ocean
stratification and circulation may also provide
wide-ranging biological effects Doney et al. [10].
Changes in these climatic factors are driven by
different mechanisms and different aspects of
global ocean dynamics and biogeochemistry
Bopp et al. 2013[12], and consequently, patterns
of their fastest changes (or hotspots) do not
necessarily coincide in space. Although warming
of the ocean may not always be the strongest
climatic factor affecting marine ecosystems e.g.
Maranon et al. [13], the rise of the SST probably
remains the most unequivocal signature of the
climate change Vivekanandan, [14].
India also has a number of marine hotspots
regions. The southern region of India extending
from 8°N to 13°N in the Arabian Sea and Bay of
Bengal has wide differences in the
oceanographic parameters and fisheries. The
continental shelf of the Arabian Sea is vast with
intense upwelling during the southwest monsoon.
The region is rich in productivity and small
pelagic species. On the Bay of Bengal side, the
continental shelf is very narrow with less
productivity. However, both the adjacent regions
are subjected to intense fishing activity Rao and
Shyam [15]. Studies on the impact of climate
change on fisheries (fish species, stock
distribution etc) have been carried out by Central
Marine Fisheries Research Institute (CMFRI),
Kochi Shyam and Manjusha, [16]. Investigations
carried out by the CMFRI show that different
Indian marine species will respond to climate
change as follows:
(i) Changes in species composition of
phytoplankton may occur at higher
temperature.
(ii) Small pelagics may extend their
boundaries. This could be explained by
taking the case of oil sardine (Sardinella
longiceps) and Indian Mackerel
(Rastrelliger kanagurta). These small
pelagics, especially the oil sardine, have
been known for restricted distribution –
between latitude 8°N and 14°N and
longitude 75°E and 77°E (Malabar
upwelling zone along the southwest coast
of India) where the annual average SST
ranges from 27 to 29°C. Until 1985, almost
the entire catch was from the Malabar
upwelling zone, there was little or no catch
from latitudes north of 14°N. During the
last two decades, however, catches from
latitude 14°N - 20°N are increasing. The
higher the SST, the better the oil sardine
catch. The surface waters of the Indian
seas are warming by 0.04°C per decade.
Since the waters in latitudes north of 14°N
are warming, the oil sardine and Indian
mackerel are moving to northern latitudes.
It is seen that catches from the Malabar
upwelling zone have not gone down.
Inference: The sardines are
extending northward, not shifting
northward. The Indian mackerel is also
found to be extending northward in a
similar way.
(iii) Some species may be found in deeper
waters as well. For example, the Indian
mackerel R. kanagurta, besides exploring
northern waters, has been descending
deeper as well during the last two decades
CMFRI, [17]
(iv) Phenological changes such as shift in
spawning season of thread fin breams,
Nemipterus japonicus are now evident in
Indian seas. The timing of spawning, an
annually occurring event, is an important
indicator of climate change.
Moreover the earlier reports of Ridgway [18]; Cai
et al. [19]; Cai [20] have shown that Southern
India is situated in regions that are predicted to
warm substantially faster than the global
average. As such, the impacts of climate change
are expected to be observed and documented in
this region first, making it sentinels of climate
impacts for other regions in India as well as other
regions globally. Parallel with the line of selection
of Indian marine hotspots for macro analysis, few
definite locations has to be identified based on
appropriate indicators for micro analysis, and
action. The two districts from the south west
hotspot regions namely Thiruvananthapuram and
Ernakulam, the highly vulnerable and the
moderately vulnerable districts respectively
Shyam et al. [1] which are located at the farthest
end of the south west hotspot is selected to
embrace the maximum diversities for
comparison. Moreover Ernakulam, being the
commercial capital of Kerala, the options for
alternative avocation will be more when

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compared to Thiruvananthapuram which also
gives adequate scope for comparison.
2. REVIEW OF LITERATURE
Although, studies on the assessment of climate
change vulnerability on fisheries sector has been
done at the global level, very few works
focussing on the vulnerability of fisher folk to
climate change in the tropical regions have been
done at the local level. Some of the studies
relevant to the present work are detailed below in
review of literature.
The twin problems of unemployment and
malnourishment at the rural sphere in India can
be simultaneously addressed by proper and
planned utilization of available local resources
through involvement of local people Datta and
Kundu [21].There are several technical and
socio-economic constraints coming in the way of
increasing fish production. Several fish
production groups / co-operatives are best with
untoward socio-economic and socio-cultural
features Rahim and Padhy [22] and in many
cases there are illiterate / semiliterate, indigent
fisherman who lack the knowledge of latest
fishery technologies and proper attitude towards
fishery development Chakrabarthy et al.[23]. The
prospects of fishing enterprise depend in a
critical way on the attitude, capability and
expectation of the fisher folk associated with the
co-operatives Capistrano et al. [24]. Proper
management policy involves appropriate choice
of inputs that can have a major impact on
employment in fishery which inturn influences the
economy of the concerned locality Heady [25].
On February 22 2008, the United Nations
Environment Programme (UNEP) [26], issued a
report titled "In Dead Water: Merging of climate
change with pollution, overharvest, and
infestations in the world's fishing grounds”,
warning that three quarters of the world's key
fishing grounds are at risk of being seriously
impacted by rising temperatures. They reported
potential consequences as changes in oceanic
circulation patterns, currents that bring nutrients
and remove waste from fisheries, rising surface
temperatures that are expected to bleach and kill
as much as 80% of the world's coral reefs, a
major tourist attractions and nurseries for many
juvenile fish, and, the possible acidification of the
ocean's waters as warmer water absorbs more
atmospheric carbon emissions. Increased acidity
would impact organisms that utilize calcium for
shell-production. According to Intergovernmental
Panel on Climate Change [27], climate change
could have dramatic impacts on fish production,
which would affect the supply of fishmeal and
fish oils and that future aquaculture production
could be limited by the supply of fishmeal or fish
oils if stocks of species used in the production of
fishmeal are negatively affected by climate
change and live-fish production. According to
FAO [28], the world is likely to see significant
changes in fisheries production in the seas and
oceans. For communities who heavily rely on
fisheries, any decreases in the local availability
or quality of fish for food or increases in their
livelihoods’ instability will pose even more
serious problems. From relatively limited and
narrow uses two decades ago, the concept of
vulnerability has emerged as a key dimension of
the development debate, often discussed and
analysed along with its counterpart: resilience
Miller et al. [29]. Vulnerability is a complex and
subjective topic, and its etymology has evolved
over time. Many scholars from the natural and
social sciences have worked on what
vulnerability means in particular disciplinary
contexts, resulting in interpretations of
vulnerability focused on different components of
the social–ecological system under study,
different physical and time scales, and different
methodologies of investigations. Thus, the
disciplinary perspectives from which vulnerability
is considered shape the questions asked and the
methodologies used to answer these questions,
conditioning not only the focus of the analysis
and enquiry process, but also the interpretation
of the findings and subsequent adaptation
actions. For those wishing to implement a
Vulnerability Assessment, it is therefore
important to understand these disciplinary roots,
as this will ultimately influence their
understanding of the vulnerability of the system
at hand McLaughlin and Dietz [30]. Allison et al.
[31] in their work on the vulnerability of national
economies to the impacts of climate change on
fisheries have compared the vulnerability of 132
national economies to potential climate change
impacts on their capture fisheries using an
indicator-based approach. They found out that
countries in Central and Western Africa (e.g.
Malawi, Guinea, Senegal, and Uganda), Peru
and Colombia in north-western South America,
and four tropical Asian countries (Bangladesh,
Cambodia, Pakistan, and Yemen) were identified
as most vulnerable. FAO report outlines the
interpretation of vulnerability from the
risk/hazard, political economy or ecology, and
resilience schools of thought. These are three
dominant disciplinary traditions, that have a
strong influence on how research on vulnerability

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184
is carried out, Adger [32]; Eakin and Luers [33];
Füssel [34]; McLaughlin and Dietz [30]. Popova
et al. [5] have examined five hotspots off the
coasts of eastern Australia, South Africa,
Madagascar, India and Brazil. These particular
hotspots have underpinned a large international
partnership that is working towards improving
community adaptation by characterizing,
assessing and projecting the likely future of
coastal-marine food resources through the
provision and sharing of knowledge. Our
simulation finds that the temperature-defined
hotspots studied here will continue to experience
warming but, with the exception of eastern
Australia, may not remain the fastest warming
ocean areas over the next century as the
strongest warming is projected to occur in the
sub polar and polar areas of the Northern
Hemisphere. Additionally, we find that recent
rapid change in SST is not necessarily an
indicator that these areas are also hotspots of
the other climatic stressors examined. Hobday et
al. in their work on ‘Planning adaptation to
climate change in fast-warming marine regions
with seafood-dependent coastal communities’
have described physical, biological, social and
governance tools to allow hotspot comparisons,
and several methods to evaluate and enhance
interactions within a multi-nation research team.
Strong partnerships within and between the focal
regions are critical to scientific and political
support for development of effective approaches
to reduce future vulnerability. Comparing these
hotspot regions will enhance local adaptation
responses and generate outcomes applicable to
other regions. Shyam et al. [1] have assessed
the socio economic profile and awareness level
of the fisher households in the context of climate
change in coastal Kerala. The study advocates
the need for a bottom up approach in developing
location specific plans to ensure the livelihood of
the fishers and the sustainable development of
the fisheries sector in the climate change regime.
Shyam et al. [16] have determined the scope of
developing village level adaptation and mitigation
plan for the community through a comprehensive
analysis of the community perception on climate
change impacts, vulnerability and existing
adaptation mitigation strategies using Climate
Resilient Village Adaptation and Mitigation Plan
(CReVAMP). The study revealed that the actual
science and consequences of climate change
impacts in a long run are not perceived well. The
work suggests that concerted efforts in bringing
about resilient community can be achieved
through global understanding of the issue and
area specific solutions with the inclusion of the
much forgotten social factor- the stakeholders.
Even with the importance of fishing industry in
Indian economy, traditional fishing communities,
are lagging behind many other communities in
terms of socio economic development.The life of
the fisher folk is centered on the fishing seasons,
the fish they catch and the technology they use.
It was estimated that roughly 12.5 lakh people
are involved in active fishing while 15 lakh
involve in secondary and about 2 lakh in tertiary
sectors. The active fishers in the mechanized
segment increased from 24 percent in 1980-81 to
35 percent in the recent study and motorized
sector from 17 percent to 25 percent. The share
of active fishers in the non-mechanized sector
decreased from 75 percent to 34 percent. Thus,
the objective of the study is to assess the overall
vulnerability of fishery based livelihood due to the
impact of climate variation using composite
livelihood vulnerability index. The study was
done by comparing two major fishing villages of
Kerala namely Elamkunnapuzha of Ernakulam
district and Poonthura of Thiruvananthapuram
district. The study also determined the scope of
developing village level adaptation and mitigation
plan for the community through a comprehensive
analysis of the community perception on climate
change impacts, vulnerability and existing
adaptation mitigation strategies.
3. METHODOLOGY
3.1 Spatial Scale and Data Source
The coastal state of Kerala is situated on the
southwest coast of the Indian sub-continent, with
an area of about 38,863 square kilometres and
has a coastline of 590kms, which apparently
forms less than 6 per cent of India’s total
coastline. The fisheries are a source of livelihood
for the fishermen in Kerala, with fishing being an
imperative part of the economy of the state
(Kurien 2001).
Study sites: The present study was conducted in
two major fishing villages, viz., Poonthura and
Elamkunnapuzha, of Kerala from the south west
hotspot regions of India lying between 8°29’N
and 76°59 E and 10°00' N and 76°15 E
respectively.
The Poonthura village, located in the suburbs of
Thiruvananthapuram district, the capital of
Kerala, is a coastal village predominantly
comprising of fisher-folk settlement. The village

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with an area of 0.8 sqkm is a part of the south
west coast experiencing tropical climate with a
comparatively more active monsoon. The
Elamkunnapuzha village is one of the major
fishing villages situated in the Vypin taluk of
Ernakulam district. It is one of the predominant
fishing villages of the commercial capital of
Kerala, the Ernakulam.
Coastal fisheries are of immense importance as
they provide livelihood opportunities for a
large share of the population. The vulnerability
assessment using Exposure (E), sensitivity
(S) and Adaptive Capacity (AC) as the key
factors were collected from 800
fisher households. 198 indicators were
used in the construction of vulnerability
indices, 37 related to sensitivity, 36 related to
exposure and the other 125 indicators deal with
adaptive capacity.
Although, Kerala possess the highest quality of
life in the country substantiated with human
development indicators, the state's fishing
communities general development lagged behind
other sectors. The level of awareness is minimal
which directly indicates the fishers’ inability to
correlate environmental changes consequent to
climate change to their livelihood (Shyam et al.
2013). The fact that the literacy level and
educational attainment of fishers is much lower
than that of the general population illustrates
clearly the plight of fishers when compared to the
mainstream population (Panfish Book, 2011).
The villages were ranked in terms of their socio
economic performance and the different
parameters used in the assessment include
number of families below poverty line, adult- child
ratio, average family size, gender ratio, literacy
rate, dependency on fishing activities, craft and
gear inventories, participation in cooperatives
and ancillary activities using Patnaik and Narain
method(Shyam et al. 2014). Based on the
Vulnerability index table, the highest vulnerable
villages of Thiruvananthapuram and Ernakulam
District, viz., Elamkunnapuzha and Poonthura
villages was selected as the units of study.
3.1.1 Climate variation in the study region-
temperature and rainfall pattern
Poonthura: Being a part of the south west coast,
Poonthura receives active monsoon. The rainfall
is received both from the Southwest (June-
August) and Northeast (October - December)
monsoons with an annual average of 183.5 cm.
Like all other southern coastal areas of Kerala,
the soil of Poonthura is loose and sandy. The
average temperature in the vicinities was found
to be ranging from 16.4°C to 38.0°C, with 85%
as the highest range of humidity recorded during
the month of June.
Elamkunnapuzha: Receives rainfall from both the
Southwest (June-August) and Northeast
(October - December) monsoons with an annual
average of 309cms. The south-west monsoon
provides maximum rain in this region, which
brings down the high salinity of the soil and
makes it suitable for various seasonal agriculture
practices. The weather is of moderate type with a
maximum temperature of 30.2°C and a minimum
of 20.6°C with very high relative humidity of 85-
95%.
3.1.2 Occurrence of extreme events-floods
and cyclones
Poonthura: It was reported that Poonthura is
exposed to natural calamities like sea
intrusion, storms, and shoreline changes. The
houses are constructed very near to the sea
often resulting in displacement and relocation
during heavy monsoon. Even though the tsunami
of 2004 did not result in much causalities, it
caused huge damage to fishing inventories like
boats, nets, houses, and other fishing
equipments. The shoreline changes and sea
water intrusion were the visible impacts of
climate change in this area. According to the
respondents the monsoons are a bit ferocious in
the area and hence relocations are necessary
during monsoons.
Elamkunnapuzha: There were no significant
reports on the incidence of storms and droughts
in the past five years. The particular observation
on climate change related phenomena, which
majority of the respondent households could
associate with, was the occurrence of the
tsunami in 2004.The visible impacts related to
climate change that most of the respondent
households could observe was a relative
increase in sea level and subsequent shoreline
changes caused by accretion and erosion. Since
majority of the respondent households were
located in close proximity to the sea shore such
houses were found to be affected by shoreline
changes. It was observed that the basements of
most of the houses had sunken a few feet deep
in to the earth and hence water stagnation was
observed in the surrounding areas even inside
the compounds of houses. In order to rectify this

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problem, many households had resorted to sand
filling, or even relocated from the area to safer
areas.
3.1.3 Fisheries and aquaculture parameters
Poonthura: Fishing is the major economic activity
of the Poonthura village, with 1584 active
fishermen in the community along with the allied
activities like marketing of fish,
curing/processing, peeling, net repairing, etc. Not
many mechanized crafts are working in the
village. The fishing activity is mainly done with
motorized/non motorized canoes, plywood
canoes, catamarans, etc.
Elamkunnapuzha: It represents the typical and
unique coastal topography of West Coast of
India, which is congenial for the techno-
interventions of capture and culture fisheries,
agri-horticulture and animal husbandry.
Fisheries play a predominant role as a
major occupation of the vast majority of
people in Elamkunnapuzha village. The
farmers here adopt diversified aquaculture
practices like the monoculture of crabs,
Mugil cephalus and Chanos chanos, and
polyculture of different types of finfish Sathiadas
et al.
3.1.4 Demographic parameters
Poonthura: According to CMFRI Marine fisheries
census (2010), the total fishermen population of
Poonthura fishing village is 8871 within a total
number of 1290 fishermen families. Out of
which, 968 families (75%) come under the BPL
category. The average family size is 4.30 with a
sex ratio (females per 1000 males) of 982 and
with a dependency ratio of 3. Around 20 per cent
of the population are having primary education,
3.4 per cent are having higher secondary
education, and a mere one per cent was
possessing education above higher secondary
level. Poonthura village, inhabited by fish
workers, has a majority of Latin Catholic
Mukkuva (LCM) community, along with a few
families from ‘Dheevara’ community living at the
northern part of the village. LCMs are Christians
and Dheevaras are Hindus. W hile the Christian
community remains backward in many ways
including aspects of sustainable development, in
spite of several interventions by NGOs and the
Church, the Dheevara community has
succeeded to climb up in the development and
economic ladder (Nayak 2006).Majority of the
population belong to the fisherfolk community
with males outnumbering the females. About
39% of the population possess education above
primary level. About 65% of the population are
active fishermen and the rest of the population
are engaged in other allied activities like
marketing of fish, making/repairing net,
curing/processing, peeling, Labourer etc
CMFRI.
Elamkunnapuzha: Majority of the population
belong to the fisher folk community with females
outnumbering the males. According to the marine
fisheries census 2010, there are about 390
fishermen families out of which 185 families
(47%) are under the BPL category. The average
family size is 4.46 with a sex ratio (females per
1000 males) of 1006 and with a dependency
ratio of 2.99. Out of the marine fisher folk around
21 per cent are registered active marine fisher
men, and 50 per cent of them are full time fishers
Panfish Book, [35]. About 47% of the population
possess education above primary level. CMFRI
[36].The religious orientation revealed that,
Elamkunnapuzha village has a majority of
Dheevara community (57.6%) followed by Latin
Catholics (30.5%) and others (11.8%) Panfish
Book, [35].
3.2 Selection of the Indicators of Major
Components of Vulnerability
Exposure, sensitivity, and adaptive
capacity are the key factors that determine
the vulnerability of households and communities
to the impacts of climate variability and
change (IPCC 2001). Indicators for each of
these factors are therefore essential elements of
a comprehensive vulnerability assessment.
We assume that exposure to climate variations
will affect the current sensitivity, either
positively or negatively, and that fishers will
respond to these changes in a climate sensitive
manner if they have sufficient adaptive capacity.
There can also be means and measures by
which the adaptive capacity could be improved
(Fig. 2).
3.2.1 Exposure
Exposure relates to the degree of climate stress
upon a particular sector of analysis. It may be
represented either as long-term changes in the
conditions of climate, or by changes in climate
variability, including the magnitude and
frequency of extreme events Das et al. [37].
Many climate variables influence fisheries
through a range of direct and indirect pathways.

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Exposure in the context of this study is the
nature and degree to which a fishery-based
livelihood system is exposed to
significant climatic variations (modified
from IPCC 2001). Exposure components
selected for the study regions includes
attitude and perception to climate change,
environmental changes, personal exposure, and
occurrence of storms, floods, drought and
shoreline.
3.2.2 Sensitivity
Sensitivity in this context is the degree to which a
fishery-based livelihood system is affected by or
responsive to climate stimuli. Sensitivity
indicators characterise the first-order effects of
stresses IPCC [2]. At the local level, exposure
and sensitivity are almost inseparable, and it is
challenging to characterise them. The sensitivity
parameters included for the present study
include livelihood characteristics such as social
dependence, economic dependence on fishing,
economic dependence on other resources and
historical and cultural dependence on fishing.
Components reflecting the dependence of the
region’s economy on fisheries were selected to
assess the sensitivity of the sector to potential
climate variations Das et al. [37].
3.2.3 Adaptive capacity
Adaptive capacity in the context of this study is
the ability or capacity of the fishery-based
livelihood systems to adjust to climate change
(including variability and extremes), to take
advantage of opportunities, or to cope with the
consequences (modified from IPCC 2001).
However, there is little consensus about the
characteristics and determinants of adaptive
capacity at household, community, and national
levels, because the exploration of adaptive
capacity has only just begun. The adaptive
capacity parameters selected for the present
study include flexibility options, social
capital, human capital, financial capital, physical
capital, natural capital and adaptation options.
3.3 Survey Design and Development
Inorder to ensure clarity of the figure, only a few
components for each category are depicted and
only some of the sub-components and indicators
of each higher level are shown in this figure,
generalised survey instrument was developed
with questions corresponding directly to each
indicator and modified with local community
involvement in preliminary field testing.
Fig. 1. Study area – Kerala, India

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Fig. 2. Category of the integrated framework with exposure, sensitivity and adaptive capacity
with different components and its indicators (Make it small case)
3.4 Data Collection
Within both communities fishery-dependent
households was targeted, which constituted 500
and 300 households from Elamkunnapuzha and
Poonthura respectively. The data was collected
during 2014 using a multi-method approach.
Sensitivity, adaptive capacity and exposure data
were collected using household questionnaires.
A stratified random sampling technique was
followed to select response households.
Participants were mostly head of households or

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an adult member. The method of data collection
was unique with initially, developing relationships
and rapport with the local self-government
officials (Panchayath), line departments and
women self-help groups within the communities
by regular visits and focussed group discussions.
Secondly, local self-governments of each district
involved in the study educated local people for
further training, prior to the implementation of
survey. Thirdly, these selected people were
trained in topics covering climate change,
vulnerability, sensitivity, exposure, adaptive
capacity and resource management. They were
also specifically trained in conducting household
surveys among fishers. Face to face interviews
was conducted at household level which almost
consumed an hour. Periodic monitoring and
evaluation was done followed by a sensitisation
workshop for the two study regions. In order to
assess vulnerability at household level, the ward
details of each study area was collected. The
Elamkunnapuzha and Poonthura villages
consisted of five and two coastal wards
respectively from where data was collected. The
data collection aimed at identifying the extent of
vulnerability as well as the component structure
of vulnerability category measured by items with
a Likert-type response scale and to summarize
the data contained in numerous items into one or
more subscales of vulnerability category that can
be used in further models.
3.5 Design of a Composite Livelihood
Vulnerability Index and Data Analysis
A composite vulnerability index approach was
used in this study to evaluate relative exposure,
sensitivity, and adaptive capacity (Islam et al.
2014). A composite index approach calculates
vulnerability indices using aggregate data for a
set of indicators. An indicator represents a
characteristic or a parameter of a system Cutter
et al. and it is a pragmatic, observable measure
of a concept Siniscalco and Auriat. Using the set
of indicators described in tables, we
quantitatively assessed the vulnerability of
fishery based livelihood systems using the
combination of individual indicators. Since each
indicator was measured on a different scale, they
were normalised (rescaled from 0 to 1) by using
the following equations
; if increases
with vulnerability
; if
decreases with vulnerability
Where, x
ij
and y
ij
are the variables representing
effects on the vulnerability indices.
The values after normalisation were transformed
into a four point Likert scale with assigned score
values of, 1 (low), 2 (moderate), 3 (high) and 4
(very high) respectively. The mean values of the
three sub-indices of Exposure (E), Sensitivity (S),
and Adaptive Capacity (AC) were combined to
develop a composite vulnerability index by using
the following additive (averaging) equation Islam
et al.
Vulnerability (V) = Exposure (E) + Sensitivity
(S) - Adaptive Capacity (AC)
Thus, the overall vulnerability index was
calculated each for Elamkunnapuzha and
Poonthura regions and the computation was
attempted to arrive at vulnerability indices at
household level. The spatial distribution of
households vulnerable under each category for
the two study regions were also mapped in a GIS
platform using Open domain Quantum GIS
(QGIS).
Respondents households were asked to opine
responses referring to their knowledge on degree
of vulnerability related to various aspects of
climate change, livelihood and adaptation and
mitigation options etc. Different components
were identified under various categories like
Exposure, Sensitivity and Adaptive capacity
which were found to influence the overall
vulnerability of the coastal population of both the
study areas (Fig. 2). Data reduction technique
using Categorical Principal Component Analysis
(CATPCA) was employed to reduce the category
components to a number of uncorrelated
principal components. The variables employed in
the study include nominal and ordinal
characterised with non linearity. Therefore linear
or standard PCA (Principal Component Analysis)
is often not the most appropriate analysis
method, although it is commonly used.
Consequently, the variables resulting from the
questionnaire were ordered categorical (i.e.,
ordinal) variables. The standard PCA could be
appropriately used in the presence of categorical
variables only after verified the existence of
 
   
min
max min
ij i ij
ij
i ij i ij
X X
x
X X


ij
x
 
   
max
max min
i ij ij
ij
i ij i ij
X X
y
X X


ij
y

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linearity in the variables and in the relationships
with variables. To avoid the limitations of
standard PCA, CATPCA has been introduced
and developed during the last 40 years Gifi 1990;
Linting et al.
4. RESULTS AND DISCUSSION
The vulnerability of fishery-based livelihoods to
the impacts of climate variability and change was
assessed using locally relevant indicators of
exposure, sensitivity, and adaptive capacity. An
understanding of how these components and
indicators influence the vulnerability of livelihoods
provides an important base for directing future
research and climate change coping and
adaptation strategies in developing countries,
particularly those with fishery systems that are
similar to those of India. Fishery-based
livelihoods in households of Elamkunnapuzha
and Poonthura have high exposure to climate-
related shocks and stresses, because the
communities are located near the coastline which
could be corroborated from the CATPCA results.
One of the salient findings of the study is that
majority of the respondents were unaware about
climate change. The respondents admitted that
they are experiencing most of the aftermaths of
climate change, however ignorant about the
causal factors and likely impacts. According to
them the depletion of fish resources, occurrence
of extreme events, loss in fishing days,
over/targeted fishing are impending factors
consequent to climate change.
4.1 Vulnerability
The overall vulnerability values indicate that
Poonthura village is slightly more vulnerable than
Elamkunnapuzha (Table 1). The proximity of
Poonthura village to the sea can be attributed as
the major factor contributing to the increased
vulnerability .In addition higher exposure on
account of environmental changes, occurrence of
drought and shoreline changes is also attributed
to higher vulnerability in Poonthura. However,
the sensitivity values are high in
Elamkunnapuzha when compared to Poonthura
due to high socio-economic dependence on
other resources as well as historical and cultural
dependence on fishing which are discussed in
the below section (Table 2). The adaptive
capacity of the selected villages were low when
compared to exposure and sensitivity values,
indicating the urgent need for developing
appropriate adaptive interventions.
The Fig. 3 represent the overall vulnerability of
the household population of Elamkunnapuzha
and Poonthura. The colour gradation represents
the extent of vulnerability with vulnerability
increasing along with increasing colour
gradation. In addition, the individual household
vulnerability indices were calculated and based
on the frequency distribution (Likerts scale) they
were classified into low, moderate, high and very
high. It was found that about 35% of the
population in Elamkunnapuzha are coming under
moderate category, 64% of them under high and
2% of them under very high category. On
comparison in Poonthura, 18% of the population
are represented under moderate category, 63%
under high and 18% under very high category. A
GIS plot was mapped to see the spatial
distribution of households near the coastal area.
The plot indicates that the population adjacent to
coastal areas are more vulnerable when
compared to those residing farther thus,
indicating a prominent shift in the spatial
distribution. Thus, the coastal side of Poonthura
region is inhabited by the very high vulnerable
group which is indicated by the dark red portion
compared to Elamkunnapuzha where there is
less percentage of very high category. However
both the regions have almost equal percentages
of high vulnerable population contributing to the
increasing vulnerability index.
4.2 Exposure
Poonthura had a high exposure value when
compared to Elamkunnapuzha (Table 1). This
could be substantiated by the CATPCA analysis
and also from the figure discussed below.
CATPCA was done to delineate the factors
contributing to the high values. Under the
exposure category, the ordinal variable
components/sub components like attitude and
perception to climate change, environmental
changes, personal exposure, and occurrence of
storms, floods and drought and shoreline
changes were employed for the analysis. In this
analysis, the-two dimensional CATPCA on the
exposure data ensures the largest eigen value of
2.876 at Elamkunnapuzha, providing that 41.08%
of the variance in the transformed variables is
explained by the first component. The eigen
value of the second component is 1.275,
providing that its percentage of variance
accounted for is 18.21%. Thus, all of the
components account for a substantial percentage
of 59.299% and 43.230% of the total variance in
the transformed variables at Elamkunnapuzha
and t Poonthura respectively.

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Table 1. Vulnerability of Elamkunnapuzha and Poonthura
Locations
Exposure
Sensitivity
Adaptive capacity
Vulnerability
Elamkunnapuzha 2.67 2.70 2.57 2.80
Poonthura 2.80 2.57 2.52 2.85
Fig. 3. Spatial distribution of Vulnerability along Elamkunnapuzha and Poonthura
While delineating the different factors which
contribute to the loadings under exposure
category, it was found that the first principal
component is strongly correlated with increasing
personal exposure (0.754), and with increasing
occurrence of drought (0.603) and floods (0.591)
(Table 2). It has also strong correlation with
decreasing shoreline changes (-.741) and
occurrence of storms (-.719) along
Elamkunnapuzha. The lowest value was found
for attitude and perception under the first
principal component (-.228). On comparison
along Poonthura, first principal component is
strongly correlated with increasing environmental
change (0.711), occurrence of drought (0.532)
and shoreline change (0.540). The lowest value
was obtained for personal exposure (0.421)
along Poonthura under the first principal
component. The second principal component
along Elamkunnapuzha is strongly correlated
with occurrence of flood (0.622) and drought
(0.596) whereas in Poonthura, it is strongly
correlated with attitude and perception (0.708),
occurrence of floods (0.670) and personal
exposure (0.517).
Table 2. Component loadings under exposure category
Components
Dimension Elamkunnapuzha
Dimension Poonthura
1
2
1
2
Attitude and perception -.228 -.414 -.084
.
708*
Environmental change -.689* .391 .711* -.186
Personal exposure .754* -.343 .421 .517
*
Storms -.719* .125 .495 .107
Floods .591* .622* -.424 .670
*
Drought .603* .596* .532* .189
Shoreline change -.741* .275 .540* .192
Note: The strongest correlation of a variable to a component appears in asterisks

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The component environmental change indicates
an increase or decrease in the following
characteristics at sea during the past five years
viz. sea level, rain, wind, air temperature, wave
height, current strength, rough seas, sea
temperature and bottom temperature. Both
Elamkunnapuzha and Poonthura have high
values pertaining to environmental change which
imply that these regions are heavily exposed to
the above mentioned environmental parameters.
The Indian Ocean tsunami which took place on
December 2004 was the only massive recent
environmental disaster which occurred along the
coasts of Indian Ocean. Eventhough the tsunami
did not take any lives; massive destruction was
caused to fishing equipments and boats along
Kerala coast. In Elamkunnapuzha, an increase in
environmental change will lead to an increase in
personal exposure, occurrence of storms,
drought and shore line change as can be inferred
from the results of CATPCA (Table 2). Therefore
measures to combat drought, flood and other
environmental changes should be taken to bring
down the exposure level with regard to climate
change along both the locations.Personal
exposure include indicators related to whether
the house is located in an area that is prone to
flooding and also the safety level of the
fishermen with respect to their main occupation
of fishing in the context of climate exposure. This
entails that both the locations are prone to
flooding during an event of climate change. The
fishers with fishing as their main source of
income were highly vulnerable to climate change
necessitating the need for alternative avocation
in both the study locations. An increase and a
decrease in shoreline change will also make the
communities vulnerable to high level of
exposure. Both the communities are highly
exposed to shoreline changes; therefore
stringent measures should be adopted to
maintain the shoreline level which includes the
proper maintenance of bioshields in places
where they are present. In those areas where
there are no bioshields, steps could be taken to
plant the same. Construction of sea walls could
also be implemented after studying the
aftereffects. The component attitude and
perception has low values in both
Elamkunnapuzha and Poonthura regions under
the first principal component. Attitude and
perception includes indicators related to attitude
to changes in environment, perceptions of
change, and interest in the environment. Attitude
to change measures the risk that an
environmental change poses to the community,
income and livelihood. Perceptions of change
involve the role played by weather conditions
while fishing, the extent of difficulty to fish in the
areas which the fishers have been fishing and
the number of fishing areas which are getting
depleted. An interest in the environment relates
to ideas to ensure the sustainability of the main
species which the fishers catch. In both the
study regions the communities’ attitude and
perception on climate change are low which can
be improved by creating awareness on climate
change issues and on the need to protect the
fishing areas and thereby increasing the
biodiversity. To ensure the sustainability of the
targeted species, illegal fishing and capture of
juveniles should be regulated and adherence to
‘Minimum Legal Size’ for each species should be
complied with. Also, it could be noted that
personal exposure which have high value in
Elamkunnapuzha is found to be very low in
Poonthura which means that the component has
less impact on the population of Poonthura.
The figure given below (Fig. 4) depicts the plot of
households under exposure category. It could be
seen that as discussed above, the population in
Poonthura are heavily exposed to climate
change compared to those in Elamkunnapuzha.
About 52% of the population in Elamkunnapuzha
falls under moderate exposure category and
about 48% under high exposure category. In
Poonthura about 21% of the population is
classified under moderate category, 71% under
high and about 8% of the population under very
high exposure category, which is a matter of
great concern. As seen in the earlier figure, the
coastal population in the outskirts is highly
exposed when compared to others, demarcated
by the variation in the colour pattern.
4.3 Sensitivity
The sensitivity values were high for
Elamkunnapuzha when compared with
Poonthura (Table 1). This has been further
clarified through CATPCA analysis (Table 3) and
also through GIS plots of the two study areas
(Fig. 4). To find out the factors contributing to
high sensitivity, the following sub components
such as socio-economic dependence on fishing,
economic dependence on other resources and
historical and cultural dependence on fishing
were selected for doing CATPCA. In this
analysis, the-two dimensional CATPCA on the
sensitivity data ensures the largest eigen value of
1.287at Poonthura providing that 32.181% of the
variance in the transformed variables is
explained by the first component. The eigen
value of the second component is 1.078,

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providing that its percentage of variance
accounted for is 26.956%. Thus, all of the
components account for a substantial percentage
of 59.137% and 56.374% of the total variance in
the transformed variables at Elamkunnapuzha
and Poonthura respectively.
While delineating the different factors which
contribute to the loadings under exposure
category, the first principal component is strongly
correlated with increasing social dependence
and economic dependence on other resources
and with decreasing historical and cultural
dependence on fishing along Elamkunnapuzha.
Along Poonthura it is strongly correlated with
increasing historical and cultural dependence on
fishing and with decreasing social dependence
(Table 3). Least correlation was obtained for
economic dependence on fishing in both
Elamkunnapuzha and Poonthura under the first
principal component. In Elamkunnapuzha, the
second principal component is strongly
correlated with social dependence on fishing and
economic dependence on fishing, and in
Poonthura, the second principal component is
strongly correlated with economic dependence
on fishing and economic dependence on other
resources (Table 3).
At Elamkunnapuzha, under the sensitivity
category, the components social dependence
(0.516) (which include the social group like
family, fellow workers, scientists, safety
authorities, other fishers, marine reserve
managers etc. which the fishermen interact with
while going fishing), economic dependence on
other resources (0.581) and historical and
cultural dependence on fishing (-.695) were the
factors which have high correlation values under
the first dimension. Economic dependence on
other resources indicates the most important
food source for the households, whether the
households possess a garden, and livestock
details. Historical and cultural dependence on
fishing involve indicators like ‘how long have you
been a fisher’, ‘When did the fishery you are
involved with begin in this area’, and ‘Were
previous generations of your family/ancestors
fishers’ etc. This indicator has high negative
values in Elamkunnapuzha and high positive
value in Poonthura (0.816).This denotes that the
fishermen community have not been engaged in
this sector since many past generations in
Elamkunnapuzha whereas in Poonthura the
fishers have been strongly involved in the
fisheries sector since many past generations
which make them highly vulnerable to climate
change. The high value of sensitivity in
Elamkunnapuzha might be due to the high
negative magnitude of the indicator, historical
and cultural dependence on fishing. In
Elamkunnapuzha, the component social
dependence and economic dependence on other
resources influence each other. In Poonthura
also, the component social dependence is found
to play a major role. To bring down the high
sensitivity in Elamkunnapuzha, the component
social dependence and economic dependence
on other resources could be reduced and
historical and cultural dependence on fishing
could be increased. Whereas in Poonthura, the
component historical and cultural dependence on
fishing should be decreased and social
dependence could be increased to lower the
sensitivity levels. Social dependence is to be
instilled among the fishermen by means of
awareness campaigns by deciphering the
importance of social values and their role in
society, especially among the fishermen
community while they go for fishing. The
component economic dependence on fishing,
which include indicators like ‘How many days a
week do you and your household consume fresh
marine food?’, ‘Is the fish you eat from your own
catch of fish or do you buy fish’?, ‘Which type of
fish do you consume most often?’, was found to
have a low contribution to the first dimension in
both the locations which implies that the indicator
has low influence to the overall sensitivity.
Table 3. Component loadings under sensitivity category
Dimension-Elamkunnapuzha
Dimension-Poonthura
1
2
1
2
Social dependence .516* .644* -.765* -.108
Economic dependence on
fishing
-.293 .780* .140 .654*
Economic dependence on
other resources
.581* -.238 -.133 .795*
Historical and cultural
dependence on fishing
-.695* -.050 .816* -.084
Note: The strongest correlation of a variable to a component appears in asterisks

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Fig. 4. Spatial distribution of exposure along Elamkunnapuzha and Poonthura
The below given figure (Fig. 5) is a
representation of households belonging to
sensitive category. As given in Table 1, it can be
observed from the figure that more number of
households in Elamkunnapuzha belongs to high
sensitive category when compared to Poonthura.
It has been estimated that about 90% of the
population belong to high sensitive category in
Elamkunnapuzha with about 10% under
moderate category. In Poonthura around 70% of
the population belong to high sensitive category
and 30% belong to moderate category. Almost
the entire population in Elamkunnapuzha are
highly sensitive; the factors contributing to the
same have been discussed in the earlier section.
4.4 Adaptive Capacity
The Adaptive Capacity values were almost
similar for both the study locations (Table 1).
Under the AC category, the following sub
components like flexibility options, social capital,
human capital, financial capital, physical capital,
natural capital and adaptation options were
selected for the analysis. In this analysis, the-
two dimensional CATPCA on the exposure data
ensures the largest eigen value of 1.452 at
Elamkunnapuzha providing that 20.747% of the
variance in the transformed variables is
explained by the first component. The eigen
value of the second component is 1.253,
providing that its percentage of variance
accounted for is 17.902%. Thus, all of the
components account for a substantial percentage
of 38.649% of the total variance in the
transformed variables at Elamkunnapuzha
whereas 39.63% at Poonthura.
While delineating the different factors which
contribute to the loadings under exposure
category (Table 4), the first principal component
is strongly correlated with increasing natural
capital (0.517) and adaptation options (0.539)and
with decreasing flexibility options (-.665)along
Elamkunnapuzha, whereas along Poonthura, it is
strongly correlated with increasing social capital
(0.726) and physical capital(0.570). In
Elamkunnapuzha, the second principal
component is strongly correlated with human
capital (0.619) and inversely proportional with
social capital (-0.595).In Poonthura, the second
principal component is strongly correlated with
increasing natural capital (0.757) and with
decreasing flexibility options (-0.515) and human
capital (-.518). Low positive values were found
for human capital for both Elamkunnapuza
(0.228) and Poonthura (.104) under the first
principal component.
At Elamkunnapuzha, adaptation options which
include the various alternatives that the
fishermen would likely adopt adverse situations
related with extreme climate change, market
fluctuations, fish unavailability etc. was found to
go hand in hand with natural capital. The natural
capital indicators include changing marine
resource base, factors that cause decline in fish
numbers, factors that could help to increase the
number of fish in the sea in the fishermen area
and changes that have occurred to their

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livelihood as a result of changes to the marine
habitat. Therefore more adaptation options like
better policy framework, proper planning
measures, and effective disaster management
techniques should be implemented to increase
the adaptive capacity of the fishermen
community to climate change. Steps to improve
natural capital like curbing marine pollution,
maintaining prey-predator relationship in the
oceans, promoting the culture of species in
marine habitats (Cage culture), regulation of
fishing rights across the Indian seas, extending
the period of trawl ban etc, maybe looked into as
major elements while framing adaptation options
in Elamkunnapuzha.
Flexibility options are grouped under Personal
flexibility, Occupational flexibility and Institutional
flexibility. Personal flexibility includes indicators
to test one’s flexibility to change from his present
job of fishing to some other job in case of an
emergency. Occupational flexibility relates to
occupation based indicators like history of
employment change, preference of new
occupation over previous one, interest in doing a
job other than fishing with same amount of profit
earning as that of fishing and listing of alternative
employment sectors. Institutional flexibility
relates to the number of markets where one can
buy fish, relationship with middlemen in the
community, stability of fish prices, pertinent factor
determining the price realisation of fish presence
of marine resource management institutions,
whether rules or practices have changed in the
past in response to environmental changes, the
authority to whom the catches have to be
reported and list of community organisations. In
Elamkunnapuzha as the component ‘flexibility
options’ have a negative impact on adaptive
capacity, all efforts should be directed towards
improving the flexibility of the community.
Providing alternative avocations, imparting
counselling to fishers to take up new jobs and to
have a positive outlook towards life during
hardships, improving the relationship with
middlemen during fish auction, to have a better
valuation of fishes and strengthening of marine
resource management institutions would
definitely help in improving the flexibility. At
Poonthura, social capital (family, friends,
neighbours, local organisations etc ) which the
fishers could assist in times of a financial crisis
was found to have a high correlation with
physical capital (house details), house hold
assets, freshwater supply, source of energy,
cooking fuels and waste management options).
From this it is convincing that social capital and
physical capital are comparatively stronger in
Poonthura when compared to other components
like human capital and natural capital. Hence
measures to improve the health, education,
skills, knowledge etc of the fishers (human
capital) along with an uplift of the natural capital
could be done to augment the adaptive capacity
of Poonthura region.
From the figure (Fig. 6) it could be discerned that
Adaptive Capacity values are less for the
household population in the vicinity of sea at both
the locations. The values are found to be
increasing for households residing away from the
coastal areas as mentioned in the text earlier.
Majority of the households (74%) in
Elamkunnapuzha belong to high adaptive
category and 26 per cent belong to moderate
category. In Poonthura, 58 per cent belong to
moderate adaptive capacity category and 42% to
high adaptive capacity category. In the below
figures, a shift in colour gradation from light red
to dark red indicates change in adaptive capacity
from moderate to high. In both the locations,
coastal population residing near the sea were
found to be highly vulnerable with moderate
adaptive capacity (represented in dark red)
compared to those residing inland (light red) with
high adaptive capacity.
Table 4. Component loadings under Adaptive Capacity category
Dimension-Elamkunnapuzha Dimension-Poonthura
1 2 1 2
Flexibility options -.665* -.193 -.355 -.515*
Social capital -.180 -.595* .726* .003
Human capital .228 .619* .104 -.518*
Financial capital .481 .293 .499 -.367
Physical capital -.368 .346 .570* -.072
Natural capital .517* -.385 .193 .757*
Adaptation options .539* -.354 .490 -.108
Note: The strongest correlation of a variable to a component appears in bold.

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196
Note: The strongest correlation of a variable to a component appears in asterisks
Fig. 5. Spatial distribution of sensitivity along Elamkunnapuzha and Poonthura
Fig. 6. Spatial distribution of adaptive capacity along Elamkunnapuzha and Poonthura
Exposure, sensitivity, and adaptive capacity
influence the vulnerability of fishery-based
livelihoods in varied ways. Vulnerability to climate
change varies between places, communities, and
social classes Adger; Smit and Wandel. The
contextual nature of livelihood vulnerability and
considerations of spatial and temporal scale
make it challenging to develop robust indicators.

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197
The selection of indicators often involves a trade-
off between specificity, transferability, accuracy,
and certainty Vincent. In the coming decades,
vulnerability of the fishery-based livelihoods may
markedly increase because of climate change. In
the absence of adaptation, it would result in
increased frequency and intensity of storms and
floods, greater loss of life at sea and in the
coastal zone, damage to fishing materials and
household assets, and loss of fishery related
income. The future livelihood vulnerability is
closely linked with technological, demographic,
and socioeconomic trends and how these factors
influence the ability of fishery dependent
households and communities to adapt.
5. CONCLUSION
We analysed vulnerability of fishery-based
livelihoods to climate variability and change using
a combination of composite indices and
qualitative methods. Our findings suggest that
different components of vulnerability affect
livelihoods in varied ways. The most important
climate-related elements of exposure are
‘personal exposure’and ‘shore line change’ in
Elamkunnapuzha whereas in Poonthura
‘environmental changes’ contribute to greater
exposure value. In both the locations, the key
factors determining sensitivity of an individual
household are the indicators related to ‘social
dependence’ and ‘historical and cultural
dependence on fishing’. The factors influencing
adaptive capacity were identified as ‘Flexibility
options’ and ‘Adaptation options’ in
Elamkunapuzha and ‘Social capital’ in
Poonthura. Thus, it could be inferred from the
study that since exposure and sensitivity are
governed by the environment, the overall
vulnerability could be reduced only by increasing
the adaptive capacity of the population. The
study has visibly brought out the major areas
where thrust is required, to reduce exposure and
sensitivity and increase adaptive capacity of the
study areas. The results of the study can be
utilised by policy makers to frame and enact
better policies and management measures. It
also provides an important base for directing
future research into the vulnerability of fishery
based livelihood systems to climate change.
Further work is needed in order to move towards
an enhanced characterisation of vulnerability and
to identify most suitable means for households
and communities to cope with and adapt to the
impacts of climate change. Thus, findings of this
research, it draw that efforts to reduce livelihood
vulnerability in coastal fishing communities
should be multidimensional in nature so as to
simultaneously tackle exposure, sensitivity, and
adaptive capacity.The study advocates the need
for a bottom up approach in developing location
specific plans to ensure the livelihood of the
fishers and the sustainable development of the
fisheries sector in the climate change regime with
the proactive participation of the fishers.
ACKNOWLEDGEMENTS
I wish to express my sincere gratitude to the
Director of CMFRI Dr. A. Gopalakrishnan for
providing me the opportunity to embark on this
research work under the GULLS project funded
by Belmont Forum. The funding agency had no
role in the study design, collection, analysis and
interpretation of data; in the writing of the
manuscript. I also wish to sincerely thank all my
team members who rendered their help during
the period of work.
COMPETING INTERESTS
Authors have declared that no competing
interests exist.
REFERENCES
1. Shyam S Salim, Kripa V, Zachariah PU,
NiveditaShridhar, Ambrose TV. Climate
change awareness, preparedness,
adaptation and mitigation strategies, fisher
folks perception in coastal Kerala. Journal
of Aquatic Biology and Fisheries. 2014;2:
670-681.
2. IPCC. Climate change: Impacts,
adaptation and vulnerability: Contribution
of working group II to the third assessment
report of the intergovernmental panel on
climate change. In: McCarthy JJ, Canziani
OF, Leary NA, Dokken DJ, White KS (eds)
Cambridge University Press, Cambridge;
2001.
3. CMFRI. Annual report. New Delhi, India:
Indian Council of Agricultural Research;
2014.
4. Hobday AJ, Pecl GT. Identification of
global marine hotspots: Sentinels for
change and vanguards for adaptation
action. Rev Fish Biol Fisheries. 2014;24:
415.
DOI: 10.1007/s11160-013-9326-6
5. Popova EE, Yool A, Byfield V, Cochrane K,
Coward A, Icar S, Gasalla M, Henson S,
Hobday A, Pecl G, Sauer W, Roberts M.
From global to regional and back again:

Salim et al.; IJECC, 8(3): 180-199, 2018; Article no.IJECC.2018.013
198
Unifying mechanisms of climate change
relevant for adaptation across five ocean
warming hotspots. Glob Change Biol;
2016.
DOI: 10.1111/gcb.13247
6. Frusher SD, Hobday AJ, Jennings SM,
et al. The short history of research in a
marine climate change hotspot: From
anecdote to adaptation in south-east
Australia. Reviews in Fish Biology and
Fisheries. 2014;24:593–611.
7. Mathis JT, Cross JN, Evans W , Doney
SC. Ocean acidification in the surface
waters of the Pacific-Arctic boundary
regions. Oceanography. 2015;28(2):122–
135.
DOI: 10.5670/oceanog.2015.36
8. Barange M, Merino G, Blanchard JL,
Scholtens J, Harle J, Allison EH, Allen JI,
et al. Impacts of climate change on marine
ecosystem production in societies
dependent on fisheries. Nature Climate
Change. 2014;4:211–216.
9. Perry RI, Ommer RE, Barange M, Werner
F. Sustaining marine social-ecological
systems under the additional stress of
climate change. Current Opinion in
Environmental Sustainability. 2010;2:353–
363.
10. Gruber. Warming up, turning sour, losing
breath: Ocean biogeochemistry under
global change, Philosophical Transactions
of the Royal Society A: Mathematical,
Physical and Engineering Sciences.
2011;369:1980-1996.
11. Doney SC, Ruckelshaus M, Duffy JE, et al.
Climate change impacts on marine
ecosystems. In: Annual Review of Marine
Science. Carlson CA, Giovannoni SJ (eds).
Annual Reviews. Palo Alto, CA. 2012;4:
11–37.
12. Bopp L, Resplandy L, Orr JC, et al.
Multiple stressors of ocean ecosystems in
the 21
st
century: Projections with CMIP5
models. Bio geosciences. 2013;10:6225–
6245.
13. Marañón E, Cermeño P, Huete-Ortega M,
López-Sandoval DC, Mouriño-Carballido
B, Rodríguez-Ramos T. Resource supply
overrides temperature as a controlling
factor of marine phytoplankton growth.
PLoS ONE. 2014;9:e99312.
DOI: 10.1371/journal.pone.0099312
14. Vivekanandan E. Marine fisheries policy
brief-3; climate change and indian marine
fisheries. CMFRI Special Publication.
2011;105:1-97.
15. Rao G Syda, Shyam S Salim. Preparing
for climate change in marine system of
Australia and India.Project Report. Central
Marine Fisheries Research Institute, Kochi;
2013.
16. Shyam S Salim, Manjusha U. Climate
change impacts: Implications on marine
resources and resource users. In
Summer School on Recent Advances in
Marine Biodiversity Conservation and
Management; 2015.
17. CMFRI. Annual Report. New Delhi, India:
Indian Council of Agricultural Research;
2008.
18. Ridgway KR. Long-term trend and decadal
variability of the southward penetration of
the East Australian Current.Geophys. Res.
Lett. 2007a;34:L13613.
DOI: 10.1029/2007GL0303
19. Cai W, Shi G, Cowan T, Bi D, Ribbe J. The
response of the Southern annular mode,
the East Australian Current and the
southern mid-latitude ocean circulation to
global warming; 2005.
20. Cai W. Antarctic ozone depletion causes
an intensification of the Southern Ocean
super-gyre circulation. Geophys. Res. Lett.
2006;33:L03712.
DOI: 10.1029/2005GL02491
21. Datta SK, Kundu R. Socio-economic
appraisal of culture based fisherman: A
case study in West Bengal. J. Soc. Sci.
2007;15(3):255-262.
22. Rahim KMB, Padhy M. Scope and
constraints of inland pisciculture in West
Bengal: A case study of Birbhum district,
Ed: Giriyappa S. Daya publishing house,
New Delhi. 1994;141-158.
23. Chakraborthy C, Dutta S, Katiha P.
Fishery co-operatives in West Bengal: A
socio economic appraisal. Environ. Ecol.
2005;23:50-57.
24. Capistrano D, HossainAM, Ahmed M.
Poverty alleviation, empowerment and
Sustainable resource use: Experiments in
inland fisheries management in
Bangladesh Paper presented at the 3
rd
Int.
Conf. of the Int. Soc. for Ecological
Economics in San Jose, Costa rica; 1997.
25. Heady C. Natural resource sustainability
and poverty reduction Environ. Dev. Econ.
2000;5:241-258.
26. United Nations Environment Programme,
UNEP. In Dead Water: Merging of climate
change with pollution, over-harvest, and
infestations in the world’s fishing grounds
GRID-Arendal, Norway; 2008.

Salim et al.; IJECC, 8(3): 180-199, 2018; Article no.IJECC.2018.013
199
Available:www.grida.no
Available:http://www.unep.org/pdf/InDead
Water_LR.pdf Climate Change Network
Available:http://climatechange.unep.net
27. Intergovernmental Panel on Climate
Change. An assessment of the
intergovernmental panel on climate change
synthesis report. Summary for policy
makers; 2001.
Available:www.ipcc.ch/pdf/climate-
changes2001/...spm/synthesis-spm-en.pdf
28. Food and Agriculture Organization of the
United Nations. Report of the FAO Expert
Workshop on Climate Change Implications
for Fisheries and Aquaculture: Rome, Italy;
2008. FAO Fisheries Report no. 870 (Food
and Agriculture Organization of the United
Nations; 2008.
Available:http://tiny.cc/fisheries
29. Miller F, Osbahr H, Boyd E, Thomalla F,
Bharwani S, Ziervogel G, Walker B,
Birkmann J, Van der Leeuw S,
Rockström J, Hinkel J, Downing T, Folke
C, Nelson D Resilience and vulnerability:
Complementary or conflicting concepts?
Ecology and Society. 2010;15(3):11–
36.
30. McLaughlin P, Dietz T. Structure, agency
and environment: Toward an integrated
perspective on vulnerability. Global
Environmental Change. 2008;39(4):99-
111.
31. Allison Edward, Perry Allison, Badjeck
Marie-Caroline, Adger W, Brown Katrina,
Conway Declan, Halls Ashley, Pilling
Graham, Reynolds John, L. Andrew Neil,
Dulvy Nicholas. Vulnerability of national
economies to the impacts of climate
change on fisheries. Fish and Fisheries;
2009.
DOI: 10. 173 - 196. 10.1111/j.1467-2979.
2008.00310.x
32. Adger WN. Vulnerability. Global Environ-
mental Change. 2006;16(3):268–281.
33. Eakin H, Luers AL. Assessing the
vulnerability of social–environmental
systems. Annual Review of Environment
and Resources. 2006;31:365–394.
34. Füssel HM. Vulnerability: A generally
applicable conceptual framework for
climate change research. Global Environ-
mental Change. 2007;17(2).
35. Panfish Book. District fisheries resource
data book. Department of Fisheries,
Kerala; 2011.
36. CMFRI. Marine fisheries census. New
Delhi, India: Indian Council of Agricultural
Research; 2010.
37. Das MK, Srivastava PK, Rej A, Mandal L
Md, Sharma AP. Mitig. Adapt. Strateg.
Glob. Change. 2014;21:279.
DOI: 10.1007/s11027-014-9599-7
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