Factors linked with adaptation in the Indian marine fishing community

Ocean and Coastal Management 171 (2019) 37–46

Contents lists available at ScienceDirect

Ocean and Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman

Factors linked with adaptation in the Indian marine fishing community a,∗

Krishna Malakar , Trupti Mishra a b c

a,b

, Anand Patwardhan

c

T

Interdisciplinary Program (IDP) in Climate Studies, Indian Institute of Technology Bombay, Mumbai, Maharashtra, 400076, India Shailesh J. Mehta School of Management, Indian Institute of Technology Bombay, Mumbai, Maharashtra, 400076, India School of Public Policy, University of Maryland, MD, 20742, USA

ARTICLE INFO

ABSTRACT

Keywords: Adaptation Capital Driver India Marine fishing

Understanding the factors associated with adaptation can be crucial for building community adaptive-capacity. This study aims to provide a country-level assessment of some of the factors driving adaptation responses in marine fishing, and is based on data from 2564 villages and 66 districts along the coastline of India. Mechanization, usage of Global Positioning System (GPS) and diversification are important adaptation responses in the community. This study employs an analytical framework to understand three types of factors influencing adaptation: vulnerability/risk, difference in the macro environment and community-level factors (human, economic and social capitals, such as education, poverty and cooperative membership). The results indicate that the different factors are not uniformly associated with the adaptation responses. Regions which are vulnerable and face greater cyclone risk have low adaptation. The macro-environment of the state as well as support through cooperatives can play a role in adaptation. The results also highlight the importance of higher levels of education for adaptation responses, such as, using advanced navigation technology (GPS) and diversifying into other professions. The study provides useful insights regarding the factors linked with adaptation responses in the community and can be helpful for designing interventions. The paper can complement findings from regional studies and emphasizes the need for future rigorous research on adaptation in marine fishing.

1. Introduction Marine fishing, being a natural resource dependent livelihood, is vulnerable to multiple stressors. The livelihoods of small-scale marine fishermen are especially threatened by their impacts. Industrial fishing, pollution, destruction of breeding sites like mangroves and poor governance (Daw et al., 2009; Sievanen, 2014) are some of the stressors impacting marine fishermen. Apart from these stressors, change in climate is projected to impact marine life extensively (Barange et al., 2014; Daw et al., 2009). The fishing community of India is one of the most sensitive to climate change (Allison et al., 2005). Marine fisheries of India are threatened by increase in sea surface temperature, sea level rise, changes in pH, precipitation, extreme events like storms, cyclones and El Nino (Vivekanandan, 2011). These can subsequently affect spawning and fish breeding habitats; lead to reduction in fish population and biodiversity; and increase risk to life and property (Vivekanandan, 2011). At present, fish production in India contributes 6.3% to entire global production (National Fisheries Development Board, 2016). But about 69% of fish species found in the Indian seas are highly vulnerable to climate change (Zacharia et al., 2016). Out of 147 countries, marine fisheries of India ranks 29th in its vulnerability to ∗

projected climate change (Blasiak et al., 2017). Fish distributions are changing and there is already evidence of species, such as oil sardines, moving up towards northern latitudes with rise in sea surface temperature around India (Vivekanandan et al., 2009). These changes can result in a declining fish catch for the marine fishing community (Chassot et al., 2010; Golden et al., 2016). Further, oceanic storms and cyclones have increased in frequency and intensity around the seas bordering India (Anderson et al., 2002; Murakami et al., 2017; Singh, 2007; Young et al., 2011), thereby increasing risks to lives and property of fishermen. Thus, numerous changes in the ocean ecosystem can consequently affect livelihoods of the marine fishing community (Sumaila et al., 2011). And it is crucial for the community to adapt to current and formidable future changes. In this paper, adaptation is considered as responses to two main risks faced by fisheries: likelihood of species being affected by climate change and risk from cyclones to the Indian fishing community. Capacity to adapt is crucial for undertaking adaptation. Multiple social and economic factors can influence adaptive capacity and subsequently adaptation responses (Adger et al., 2005). Such factors, which enable sustaining and adapting livelihoods, are also described as capitals by the Sustainable Livelihoods Approach (SLA) (Allison and Ellis, 2001;

Corresponding author. E-mail addresses: [email protected], [email protected] (K. Malakar).

https://doi.org/10.1016/j.ocecoaman.2018.12.026 Received 29 March 2018; Received in revised form 1 October 2018; Accepted 24 December 2018 Available online 28 January 2019 0964-5691/ © 2019 Elsevier Ltd. All rights reserved.

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

Cleary et al., 2003). Adaptation can also be facilitated by the state's macro-economic environment and policies designed for sustaining livelihoods of communities (Adger et al., 2005). For example, policies regarding availability of credits and awareness about impacts of climate change might influence adaptation. It can also be driven by the vulnerability or risk experienced by the community (Patt and Schröter, 2008). The study of adaptation and its socio-economic indicators/facilitators in marine fishing communities is recently being discussed in the literature (Allison and Ellis, 2001; Allison and Horemans, 2006; Blythe et al., 2014; Cinner et al., 2015; Divakarannair, 2007; Himes-cornell and Kasperski, 2015; Islam et al., 2014a; Iwasaki et al., 2009; MorzariaLuna et al., 2014; Senapati and Gupta, 2017; Sievanen, 2014). In India, the studies have mostly been concentrated on small regional scales. Divakarannair (2007) and Iwasaki et al. (2009) qualitatively explored adaptation and livelihood assets in selected fishing communities of Kerala and Chilika lagoon (Odisha) respectively. Senapati and Gupta (2017) developed socio-economic vulnerability indices for the community in the city of Mumbai in India. There have also been primary studies, focusing on socio-economics and livelihoods of the community, in many regions of India revealing the community's struggle to sustain fishing because of declining fish catch and limited access to infrastructure, education, credit and subsidies (Basavakumar et al., 2011; Karnad and Karanth, 2016; Sarkar, 2012; Venkatesh, 2006). However, these studies have not touched upon the context of adaptation in the communities. Further, all these studies are geographically small-scaled, and are devoid of empirical evidence on association of adaptation responses with their socio-economic drivers in the community. This is mostly because of lack of large-scale data on adaptation and other demographics of traditional livelihood-based communities. Probable adaptation strategies for the marine fishing industry in India have been explored by Vivekanandan (2011), which include usage of improved gear and craft by fishermen to adapt to changing fish distributions. But studies that quantitatively assess the existing levels of adaptation responses and associated factors are limited for fishing communities in India as well as around the world. Such assessments can play a crucial role in grasping the attributes driving adaptation and planning appropriate management strategies. The current study is a first attempt to address this gap in the literature by assessing the some of the factors linked with adaptation in the Indian marine fishing community considering the entire coast line of mainland India. The objective of the study is to empirically examine whether difference in macro-environment of the state (with HDI-Human Development Index as a proxy), vulnerability/risk, or human, economic and social capitals, such as education, absence of poverty and cooperative membership, influence adaptation responses in the fishing villages/districts. Thus, the framework of the study combines macro (differences in economy and governance through state HDI as a proxy) as well as micro (community capitals) factors along with biophysical vulnerability/risk. The study is especially relevant in context of nations having limited data and studies on adaptation in such communities. Findings of the study can complement regional or household-level analyses which provide a more comprehensive examination of varied drivers of adaptation in communities.

source of livelihood. In marine fishing, using improved gear and technology that can help sustain fish catch are intensification strategies (Blythe et al., 2014; McCay, 1978). Thus, using mechanized boats to fish in a larger area; using GPS (Global Positioning System) to locate fishing zones and for navigation; investing in special gear and spending more time in the sea are some of the intensification strategies in marine fishing. These strategies can help in increasing fish catch and consequently income. This is especially true for the Indian fishing community as it is still technologically naive. On the other hand, diversification refers to being involved in multiple sources of income, for example, having a part-time job other than fishing (Blythe et al., 2014). This study examines two important intensification strategies in the traditional marine fishing community, which are, using mechanized boat, and GPS; and diversification (into other professions). Mechanized boats have motors for propulsion in the water and are equipped with advanced fishing gear, enabling greater fish catch and mobility (compared to traditional non-motorized boats) with lesser effort. 2.2. Assessment of factors linked with adaptation 2.2.1. Hypothesis development The literature on adaptation overwhelmingly suggests that the macro-economic and social environment, exposure/experience of risks and capitals/assets drive adaptation responses of communities (Adger et al., 2005; Deressa et al., 2009; Islam et al., 2014b). Thus, this study assesses evidence of association of adaptation with differences in states' HDI (used as a proxy for the macro-economic and social environment), vulnerability/risk and community-level socio-economic factors (described as capitals) in Indian marine fishing (Fig. 1). The driving factors examined in the paper are detailed in Table 1 along with their basis of selection, as noted from previous studies. It is hypothesized, from the literature, that the variables are associated positively or negatively to intensification and diversification. The detailed hypotheses are also listed in Table 1. 2.2.2. Empirical analysis Regression has been commonly used as a statistical technique to understand determinants of adaptation strategies in agricultural communities (Below et al., 2012; Deressa et al., 2009; Jain et al., 2015; Yila and Resurreccion, 2013). In this study, fractional logit regression is utilized to investigate the significant factors associated with adaptation in the community. Fractional regression is preferred over ordinary regression as the dependent variables in the models are in proportions (that is, percentage of adoption of the strategies in the villages/districts as described in section 3), and hence would not adhere to the assumptions of ordinary least square regression. Adaptation levels are the dependent variables and probable driving factors of adaptation, listed in Table 1, are the independent variables in this regression analysis. Human Development Index-HDI (as a proxy for macroenvironment) Vulnerability of marine fish to climate change Or Cyclone risk

2. Research design and method 2.1. Adaptation responses in marine fishing Adaptation in livelihoods can take place in two ways: intensification and diversification (Batterbury and Forsyth, 1999; Bryan et al., 2013; Niehof, 2004; Paavola, 2008). There have been studies on identifying and analyzing intensification strategies and diversification in marine fishing in the past (Albert et al., 2014, 2015; Blythe et al., 2014; McCay, 1978; Perry et al., 2011; Roeger et al., 2016). Intensification refers to investing more time and effort to optimize yields from one's primary

Adaptation responses

Capitals (Human, economic and social) Fig. 1. Framework showing macro-environment, vulnerability/risk and capitals as factors linked to adaptation. 38

39

Continuous

Primary education Education till higher secondary Education above higher secondary

Ordinal

Vulnerability rank

Ordinal

Continuous

Human Development Index (HDI)

Cyclone risk rank

Type

Independent variable

Proportion of fisherfolk population

Ranges from 1(lowest) to 4 (highest)

Ranges from 1(lowest) to 4 (highest)

–

Range/Unit

Table 1 Independent variables in the regression models.

Human capital

Risk of damage to life and property of fishermen from cyclones

Vulnerability of marine fishes to climate change

Difference in the macro-economic and social environment, which is assumed to extend to differences in institutional facilities and policies among states

Indicator for Previous literature has proposed that the macro-economic environment, institutional policies and governance can affect adaptation responses of communities (Adger, 2001; Brooks et al., 2005; Eakin and Lemos, 2006). The states in India can have varied levels of economic well-being as indicated by their respective per capita NSDP (Net State Domestic Product) (Government of India, 2017). Further, fishing in the territorial water of India is under the jurisdiction of respective states and subject to state-specific management measures (ICSF, 2017). Thus, the state in which a district/village is located can influence levels of adaptation (Adger et al., 2005). However, there is lack of state-level data on the institutional benefits and other indicators on governance pertaining specifically to the community. Hence, HDI which is a composite indicator of income, education and health of the population (United Nations Development Programme, 2016) is used as a proxy for the macro-economic and social status of the state. It is assumed that a state with higher HDI will represent a better macro-environment and governance supporting livelihoods and socio-economic wellbeing of marine fishing communities as well. The vulnerability rank is based on the percentage of fish species vulnerable to climate change along the coast line of the districts/ villages, as calculated by Zacharia et al. (2016). The Indian coast line was divided into four zones and the estimations were made considering 17 variables (including climatic variables such as sea surface temperature and rainfall) affecting fish population in Indian seas (Details in Appendix). The higher the vulnerability rank, the higher is the potential for a declining fish catch. Vulnerability to climate change can be diminished through adequate adaptation (Smit and Wandel, 2006; Yohe, 2000). This study aims to check whether levels of adaptation through mechanization and diversification are associated with vulnerability. A positive association between the variables will help to understand if vulnerable villages have adapted. Adaptation to cyclone risks is also important for fishing communities (Holbrook and Johnson, 2014). Mechanized boats, which are heavier and larger than non-motorized or motorized boats, may provide more stability during storms and cyclones. Communities in area with greater cyclone risk may also diversify into less-risky professions. Further, GPS can be a useful tool for navigation in the sea and can be immensely helpful during strong winds and cyclones (Geospatial World, 2004). Thus, the study also examines whether adoption of GPS is associated with the rank of cyclone risk zone (according to the location of the district). The categorization of districts has been done according to the wind and cyclone hazard map of India by BMTPC (2010). A positive association between GPS and the strategies will help to understand if high-risk districts have adapted. Education can influence access to information and hence can affect adaptation decisions of individuals (Islam et al., 2014a; Pauly, 2005). Also, education improves opportunities for diversification into other jobs (Khatun and Roy, 2012; Rahut et al., 2014). Hence, influence of different levels of education on adaptation (Blythe et al., 2014) has been assessed in the study. Segregating education levels will, further, enable examining if being highly educated affects adoption of strategies in fishing.

Remark

(continued on next page)

H4: Education is positively related to both intensification and diversification.

H3: Cyclone risk zone (of the district) is positively associated with mechanization, usage of GPS and diversification,

H2: Vulnerability rank is positively related to intensification through mechanization and diversification among fishermen

H1: HDI is positively linked to the adoption of intensification strategies and diversification among fishermen.

Hypothesis of influence on intensification/ diversification

K. Malakar et al.

Ocean and Coastal Management 171 (2019) 37–46

Ocean and Coastal Management 171 (2019) 37–46

H5: Poverty is negatively related to intensification and diversification.

Economic capital can facilitate adaptation among communities (Deressa et al., 2009; Islam et al., 2014a). The percentage of fishermen families above the poverty line can denote the economic well-being of the community in the area (Blythe et al., 2014). Hence, existence of poverty is taken as a proxy of not having access to economic capital, and assessed for its association with adaptation. Fishing cooperatives are forms of economic capital as they act as nodal points of dissemination of government credit schemes. They also act as a source of information regarding institutional facilities and developmental schemes. Cooperatives can also help improve social networks. Such memberships can enable adoption of intensification strategies in fishing (Blythe et al., 2014; Divakarannair, 2007; Sievanen, 2014). However, fishermen who are not members of cooperatives may not have access to intensification opportunities and may choose to diversify. Economic capital

3. Study site and data India has a coastline of about 7500 kms. It has 13 coastal states and union territories (Fig. 2), which again constitutes 73 coastal districts (Centre for Coastal Zone Management and Coastal Shelter Belt, 2011; CMFRI, 2010). These districts are strewn with 3432 fishing villages. This study is based on observations from marine fishing villages and districts in India. However, Andaman and Nicobar islands are excluded from the sample because of absence of village level data on poverty. Thus, Lakshadweep islands, constituting 10 villages, are also excluded to make the study pertain exclusively to the Indian mainland. After exclusion of villages due to inconsistent and missing data, a final sample size of 2564 villages is obtained. The analysis regarding usage of GPS is based on district-level data, resulting in a sample size of 66, as such information is not available at the village-level. Data for calculation of variables indicating capitals (listed in Table 1) as well as on levels of adaptation, i.e., proportion of mechanized boats (of all types of boats), proportion of fishermen having GPS and doing part-time fishing (diversification), are collected from the respective Marine Fisheries Census 2010 (CMFRI, 2010) of the states and union territories. HDI values used in the study are sourced from India Human Development Report 2011 (Planning Commission Government of India, 2011) Information regarding the categorization and ranking of districts/villages according to vulnerability of marine fishes and cyclone risk are adopted from Zacharia et al. (2016) and BMTPC (2010) respectively (List of districts and their categorization is presented in Table A of Appendix).

Proportion of fisherfolk population Continuous

Economic and social capital

Proportion of fishing families Continuous

4. Results 4.1. Descriptive statistics Table 2 lists the descriptive statistics of the continuous variables considered in the study. The mean values show that the highest adopted strategy is mechanization (22.85%). Variability is also greater in case of mechanization as indicated by higher standard deviation. Adoption of GPS is the least (2.77%). The average HDI is 0.51. The mean population with education declines with higher levels, with the average population having education above higher secondary being the least (4.17%). The percentage of families below poverty line is very high (62.29%) and membership in cooperative is low (15.24%). 4.2. Regression results: factors linked with adaptation responses 4.2.1. Mechanization In the regression considering vulnerability rank (of fish specie to climate change), mechanization is significantly related to HDI (Table 3). The statistical significance of HDI indicates that the macroenvironment is an important driver for adopting mechanization. However, HDI is not a significant driver when cyclone risk rank is considered in the regression. Villages in the vulnerable zone are not linked

Membership in fishing cooperative

Families below poverty line

Prior to running regression, the variables are checked for correlation among them. The correlation between vulnerability rank and cyclone risk is high (61.1%). Thus, both the variables have not been used simultaneously in a model. Both vulnerability of fish species to climate change and cyclone risk rank are hypothesized to be a related to mechanization and diversification. On the other hand, GPS, which is a useful device for navigation, might be linked only to cyclone risk. The rest of the correlations are less than 35% and the Variance Inflation Factors (VIF) are less than 2. Hence, none of the other variables are dropped from the regression models. The coefficients obtained from fractional logit regression cannot be directly interpreted as they simply denote the change in log of odds ratio. Thus, the marginal effect of each independent variable, or driving factor, on adaptation is also estimated. The marginal effect depicts the change in probability in adoption of a strategy given a unit change in the independent variable.

H6: Cooperative membership is positively related to intensification and negatively related to diversification.

Hypothesis of influence on intensification/ diversification Remark Indicator for Range/Unit Type Independent variable

Table 1 (continued)

K. Malakar et al.

40

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

Fig. 2. Map showing the coastal states (highlighted) of India.

to mechanization indicated by a non-significant coefficient for the same. The significant negative coefficient for cyclone risk rank also shows that villages facing risks from cyclones are not adopting mechanized boats. In both the regressions considering vulnerability and cyclone risk rank, education till higher secondary and poverty are negatively linked to mechanization. Education is negatively related indicating that lack of education influences mechanization. The educated population appears to be related to other adaptation responses such as diversification (Table 3). Villages having mechanized boats also have lower poverty, suggesting higher economic capital is linked to mechanization. Thus, the results support hypotheses H1 and H5 for mechanization.

coefficient/marginal effect for HDI. Usage of GPS is also strongly associated with higher levels of education (that is, variable ‘above higher secondary’) and low levels of poverty in the districts (Table 3), confirming the hypotheses (Table 1). However, the marginal effect of primary education on GPS usage shows a negative impact, probably indicating that villages with lower level of education are unable to utilize GPS. Hence, the hypothesis H4 is accepted only for ‘education above higher secondary’ in case of having a GPS. Using a GPS can be a challenging task for fishermen and require training and information (MSSRF, 2014). Thus, higher education should facilitate use of GPS among fishermen. However, use of GPS is not associated with cyclone risk. This shows that the community in high cyclone risk zones has not adopted GPS. Awareness and training programs can help increase the usage of GPS. Further, local factors, such as fishermen's experience of cyclones and decision to not go out in the sea, should also be assessed for their influence on usage of GPS in order to make definitive policy suggestions.

4.2.2. Global Positioning System Use of GPS is linked to HDI, primary education, education above higher secondary and poverty. The macro-environment influences usage of GPS (supporting H1) as indicated by a significant and positive Table 2 Descriptive statistics of continuous variables.

Dependent Independent

S.No.

Variable

Mean

Minimum

Maximum

Standard deviation

1. 2. 3. 4. 5. 6. 7. 8. 9.

Mechanized (in %) GPS (in %) (based on district-level data) Diversified (in %) Human Development Index Primary education (in %) Education till higher secondary (in %) Education above higher secondary (in %) Families below poverty line (in %) Membership in fishing cooperative (in %)

22.85 2.77 16.30 0.51 25.44 21.85 4.17 62.29 15.24

0.00 0.00 0.00 0.36 0.00 0.00 0.00 0.00 0.00

100.00 33.46 100.00 0.79 87.83 81.35 46.43 100.00 82.94

33.28 5.25 24.12 0.12 14.63 15.68 4.78 35.29 16.20

41

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

Table 3 Parameter estimates of the regression models. Mechanized

HDI Vulnerability rank Cyclone risk rank Primary education Education till higher secondary Education above higher secondary Families below poverty line Membership in fishing cooperative Pseudo R2 Observations

GPS

Diversified

Coefficient

Marginal effect

Coefficient

Marginal effect

Coefficient

Marginal effect

Coefficient

Marginal effect

Coefficient

Marginal effect

0.274* −0.083 – −0.596 −1.028***

0.045* −0.013 – −0.097 −0.168***

−1.599 – −0.332*** −0.774 −1.429***

−0.258 – −0.054*** −0.125 −0.231***

3.482* – 0.151 −2.813* −0.265

0.090* – 0.004 −0.073* −0.007

−4.259*** −0.270*** – 0.159 1.757***

−0.554*** −0.035*** – 0.021 0.229***

−5.413*** – −0.312*** 0.102 1.566***

−0.703*** – −0.040*** 0.013 0.203***

−1.922

−0.314

−2.819

−0.456

18.430***

0.476***

1.573**

0.205**

1.477**

0.192**

−1.761***

−0.287***

−1.686***

−0.272***

−2.709***

−0.070***

−0.009

−0.001

−0.074

−0.010

0.284

0.046

−0.093

−0.015

−1.089

−0.028

−1.584***

−0.206***

−2.236***

−0.290***

0.0664 2564

–

0.0763

–

0.1122 66

–

0.0526 2564

–

0.0552

–

*Significant at 90% confidence level. **Significant at 95% confidence level. ***Significant at 99% confidence level.

4.2.3. Diversification Diversification in the villages is associated with HDI, vulnerability to climate change, cyclone risk rank, education till and above higher secondary and cooperative membership (Table 3). However, a negative coefficient for HDI indicates that the macro-environment is not supporting greater diversification. Diversification is also negatively associated with vulnerability to climate change, cyclone risk and cooperative membership. Thus, it appears that fisherfolk in the vulnerable and high cyclone risk regions have low diversification. Again, low membership in fishing cooperatives can also influence the population to diversify. This is because fisherfolk lacking cooperative membership would not be able to access the various economic benefits provided by the government through the cooperatives to sustain their livelihood. On the other hand, higher education, till and above higher secondary, is significantly connected to diversification. Preceded by HDI, education and low cooperative membership have high marginal effects. Overall, hypotheses H4 (education till and above higher secondary) and H6 are supported for diversification.

behavioral studies can help design effective adaptation management plans for the community. For example, if communities are not aware about the projected impacts of climate change on their livelihood, appropriate awareness generation programmes need to be implemented. If the communities are aware and have not been able to adopt any strategy, appropriate economic and institutional interventions need to be designed to support the community. Some studies based on primary surveys indicate that communities (in Maharashtra, Tamil Nadu and Odisha) have been experiencing a declining catch (Karnad and Karanth, 2016; Venkatesh, 2006) and perceive climate change to be one of its contributors (in Maharashtra, Kerala, Andhra Pradesh and West Bengal) (Salagrama, 2012; Salim et al., 2015). However, it is difficult to understand the level of awareness of climate change or other stresses affecting fish catch in the entire coastline of India with the available data. Future studies can examine levels of awareness about climatic and environmental changes, and understand linkage between impacts from these stressors and fish catch, through primary surveys among the community.

5. Discussion

5.2. Role of capitals in adaptation responses

5.1. Low adaptation in vulnerable and cyclone risk regions

While low percentage of cooperative membership is linked to diversification, it is not significantly associated with mechanization. Adoption of mechanization, which requires greater financial resources, might not be influenced by cooperative membership but through individual assets and ability to access credits from other sources, for example, from nationalized banks. This is also signified by a negative association between mechanization and poverty, suggesting economic capital to be very important for mechanization. Also, lower poverty is related to diversification of livelihood. Again, due to data limitations, the study cannot be conclusive about the unidirectional relation between poverty and the adaptation responses. Greater economic capital might be leading to adaptation or better adaptation in livelihoods might result in lower poverty. Future research which considers simultaneous relations among variables can help examine this inter-linkage between these variables. It is found that having primary levels of education does not positively influence any of the adaptation strategies. Higher education being significantly and positively associated with GPS and

The regression coefficients (Table 3) indicate that intensification through mechanization is low in cyclone risk regions, and low proportion of diversification is associated with vulnerable regions. Thus, mechanized boats which are bigger and more stable (compared to nonmotorized and motorized boats), and hence are prone to lesser damage during cyclones are not being adopted in higher risk zones. Further, communities in vulnerable regions have not been able to diversify into other forms of livelihood to supplement their income. Thus, adaptation in more vulnerable and greater cyclone risk zones is low. This shows that the Indian marine fishing community is in urgent need of programmes and policies which can help them adopt advanced technologies or diversify in order to sustain their livelihood. Nevertheless, the results of the current study needs to be complemented with regional findings based on primary surveys, which can capture the perceptions of the community-on vulnerability of fish catch and cyclone risk-and their influence on adaptation decisions. Such

42

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

diversification show that it might help in using advanced technology as well as to move into other professions. However, there is scope to examine other local-level factors, such as decision to not fish during cyclones and availability of other livelihood opportunities, which can drive the use of GPS and diversification respectively. Nevertheless, the findings of the present study suggest that improving the outreach of educational facilities to these communities can substantially benefit them. Further, usage of GPS in the districts not being linked to its cyclone risk zone/rank suggests that interventions that provide subsidies, awareness about the benefits of using modern navigation systems and its training are required. It is to be noted that communities have identified cyclone as a major hazard to fishing livelihoods in high risk states of Odisha and Andhra Pradesh (Menon et al., 2016; Venkatesh, 2006). Thus, examining fishermen's perception/experiences of cyclones and its influence on the usage of GPS can also help to be more conclusive about its drivers and policy implications. On the other hand, the findings reflect that higher education does not drive usage of mechanized boats. This is a financially intensive form of adaptation and hence, economic capital (indicated by families below poverty line) and the state's macro-economic/governance status (which can provide formal credits and infrastructure) can play a more significant role in its adoption.

This also emphasizes that the broader context regarding availability of capitals can be explained by considering the macro-environment as a component of adaptive capacity and as a major driver of adaptation. The results suggest that implementation of policies supporting fishing livelihoods might be quite different among states and fishermen might be having varying levels of facilitators of such adaptation strategies. This calls for need of policy dialogues and learning among states. Further, as vulnerability of fishermen located in different states may vary, state-specific policies on community adaptation management are also required. 6. Conclusion and limitation Marine fisherfolk are vulnerable to multiple stressors. Hence, it is imperative for them to adapt their livelihoods. Assessments of determinants of adaptation are quite rare for the marine fishing sector, which is one of the most vulnerable and is in dire need for adaptation. Thus, this study provides one of the first country-level empirical assessments to identify some of the important factors facilitating adaptation among marine fishermen in India. It can be concluded that drivers/factors have distinctive influence on capacity to adopt various strategies. Hence, one of the insights from the study is that future studies should carefully consider such differential contribution of indicators, while measuring adaptive capacity of communities having varied responses. The findings suggest that usage of GPS is not influenced by cyclone risk. Adaptation through mechanization and diversification is low in cyclone risk zones. Also, regions facing high vulnerability (of fish species to climate change) have not diversified their livelihood. The HDI of the state in which the villages are located, education and cooperative membership also significantly influence the various adaptation strategies. Policy-focus in vulnerable states, awareness about environmental and climatic risks, and initiatives supporting education, cooperative membership and access to credits are required for the community. The study contributes to the literature by providing evidence of the relationship between adaptation responses and some of their driving factors at a national-scale specifically in marine fishing, by judiciously using limited data. It provides a macro view of adaptation in the community, which can complement future regional/micro-level studies (Malakar et al., 2018; Malakar and Mishra, 2019). This discussion can consequently help in identifying the barriers to adaptation, and facilitate adaptation management and planning in the community. Unavailability of data is one of the major challenges faced during the study. The study extensively uses the data available in the Marine Fisheries Census 2010 and highlights it as a significant source of information about the community in India. But again, since the study has solely used secondary data, it is constrained and limited as it is not inclusive of: (i) various other adaptation strategies (such as adopting multiple types of nets and fishing for longer hours), (ii) variables pertaining to wider definitions of the capitals and (iii) other institutional and cognitive factors. For example, variables on assets/income, perceptions about current vulnerability/future changes and indicating institutional facilities, which are also important determinants of adaptation, could not be captured in the study. Thus, future work may examine greater number of variables/drivers (Malakar et al., 2018) subject to availability of data. This might also help to improve the explanatory power of the regression models. Further, the study has not considered the simultaneous relationship that might exist between the independent and dependent variables. Poverty is hypothesized to negatively influence adaptation, but adoption of strategies might also lead communities to improve their livelihood and come above the poverty line. Therefore, there is scope for applying statistical models which can include such interactions. Availability of data at various time points can also help model and understand such interactions. Data over time can also help track changes in the adoption of the strategies in response to changes in risk from climate and cyclones. Lastly, the study

5.3. Macro-environment affects adaptation There is absence of comprehensive regional analysis of the level of institutional support to marine fishing communities in each of the states of the country. District and village-level data on variables, such as access to subsidies, credits, insurance, public infrastructure and proxies for efficiency in policy implementation, is not available. In the absence of such specific information on benefits available for the community, it is assumed that the HDI of the states can be a representative of its macro-economic/social status and institutional supportavailable to its overall population which is inclusive of the marine fishing community. Thus, the present study uses HDI of the states as a proxy of varied macro-level facilitators of adaptation in the community. Results show that the HDI is significantly and positively related to intensification adaptation strategies, that is, mechanization and adoption of GPS. However, HDI is negatively linked to diversification. This suggests that a good macro-environment indicated by a greater HDI helps communities to sustain their primary sources of livelihood comfortably, without making these communities diversify into other professions. Various factors, such as the states' economic standing and their status of implementation of employment generation programmes (which is not represented in HDI), might have led to such findings. For example, the per capita NSDP (which is also a component of HDI) indicates that some states in the western coast are relatively better than the east economically (Government of India, 2017). This might facilitate better levels of economic support to the community to sustain their livelihood in states with greater HDI, leading to a positive link between HDI and intensification strategies. Similarly, the success of Mahatma Gandhi National Rural Employment Guarantee Act 2005 (MGNREGA, a programme by the Government of India to provide job opportunities and livelihood security) is variable among the states (Mathur and Bolia, 2016). HDI is not inclusive of such factors indicating success of livelihood diversification programmes, which may be one of the reasons behind its negative link with diversification. There can also be other factors specific to policy implementation in marine fishing which may have resulted in the link of adaptation responses with HDI of the states. Identification of these factors and their data availability can help to have comprehensive empirical evidence of their influence on adaptation. Thus, intensification strategies varying with HDI shows that the macroeconomic environment/governance is extremely important for adoption of advanced crafts, which require economic investments and infrastructure (such as fish landing centers). 43

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

acknowledges that mechanization in traditional marine fishing might not be a long-term adaptation strategy and its impact on ocean sustainability needs to be assessed. However, the objective of the study is only limited to understanding some of the possible drivers of various adaptation strategies.

Acknowledgements This work is funded by the Department of Science and Technology, Government of India, under the grant 11DST078.

Appendix 1. Vulnerability rank The villages have been ranked according to the vulnerability of marine fishes to climate change along their coast line. These rankings are based on findings of Zacharia et al. (2016). The study had divided the Indian coast line into four zones: north west, south west, south east and north east. A vulnerability index considering 17 variables was developed for fish species found in the Indian seas. These variables include: sea surface temperature, rainfall, ocean current (speed and direction), coastal upwelling, chlorophyll concentration, fecundity, complexity in early development, growth coefficient, trophic level, life span, length ratio, anomaly in catch effort, exploitation, price, distribution (horizontal and vertical), duration of spawning and prey specificity. Many of the variables measuring exposure to climate change such as sea surface temperature, rainfall and coastal upwelling considers data spanning over 40 years (1975–2014). The four zones are ranked according to their abundance of vulnerable fish species. The south west, north west, north east and south east zones have 30%, 33%, 72% and 77% vulnerable species respectively, hence they have been ranked from 1 to 4 respectively. The ranks do not denote the magnitude of differences in vulnerability. It is also acknowledged that fishermen from villages in one zone can also travel to their other nearest zone. But the strict segregation and ranking of villages into four categories was inevitable for the regression analysis. Here, 1 = low, 2 = moderate, 3 = high and 4 = very high vulnerability. 2. Cyclone risk rank The districts have been categorized into low, moderate, high and very high damage risk zone according to the vulnerability atlas of India (BMTPC, 2010). They have been ranked from 1 to 4 where 1 = low, 2 = moderate, 3 = high and 4 = very high risk. BMTPC (2010) has ranked these zones based on the probability of experiencing different velocities of wind and cyclones, which are calculated from the wind speeds and cyclones experienced by the regions in previous years. Table A

Districts categorized by region, vulnerability and cyclone risk. State

District

Region

Current vulnerability rank

Current cyclone risk rank

Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Gujarat Daman & Diu Daman & Diu Maharashtra Maharashtra Maharashtra Maharashtra Maharashtra Goa Goa Karnataka Karnataka Karnataka Kerala Kerala Kerala Kerala Kerala Kerala Kerala Kerala Kerala Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu

Valsad Navsari Surat Bharuch Anand Bhavnagar Amreli Junagadh Porbander Jamnagar Rajkot Kutch Daman Diu Thane Greater Mumbai Raigad Ratnagiri Sindhudurg SouthGoa NorthGoa Dakshina Kannada Udupi Uttara Kannada Thiruvananthapuram Kollam Alappuzha Ernakulam Thrissur Malappuram Kozhikode Kannur Kasaragod Kanyakumari Thiruvallur Chennai Kanchipuram

North North North North North North North North North North North North North North North North North North South South South South South South South South South South South South South South South South South South South

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4

2 2 2 2 2 4 4 4 4 4 4 4 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 4 4

West West West West West West West West West West West West West West West West West West West West West West West West West West West West West West West West West West East East East

44

(continued on next page)

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

Table A (continued) State

District

Region

Current vulnerability rank

Current cyclone risk rank

Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu Tamil Nadu Puducherry Puducherry Puducherry Andhra Pradesh Andhra Pradesh Andhra Pradesh Andhra Pradesh Andhra Pradesh Andhra Pradesh Andhra Pradesh Andhra Pradesh Andhra Pradesh Odisha Odisha Odisha Odisha Odisha Odisha West Bengal West Bengal

Villupuram Cuddalore Nagapattinam Thiruvarur Thanjavur Pudukkottai Ramanathapuram Tuticorin Tirunelveli Puducherry Karaikal Mahe West Godavari Krishna Guntur Prakasam Nellore Srikakulam Vizianagaram Visakhapatnam East Godavari Balasore Bhadrak Kendrapara Jagatsinghpur Puri Ganjam South 24 Parganas Purba Medinipur

South South South South South South South South South South South South South South South South South North North North North North North North North North North North North

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3

4 4 3 3 3 3 1 1 1 4 3 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

East East East East East East East East East East East East East East East East East East East East East East East East East East East East East

Blythe, J.L., Murray, G., Flaherty, M., 2014. Strengthening threatened communities through adaptation: insights from coastal Mozambique. Ecol. Soc. 19. https://doi. org/10.5751/ES-06408-190206. BMTPC, 2010. Wind and Cyclone Hazard Map of India, second ed. [WWW Document]. Vulnerability Atlas. http://www.bmtpc.org/DataFiles/CMS/file/map of india/windindia.pdf, Accessed date: 31 October 2015. Brooks, N., Adger, W.N., Kelly, P.M., 2005. The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Global Environ. Change 15, 151–163. https://doi.org/10.1016/j.gloenvcha.2004.12.006. Bryan, E., Ringler, C., Okoba, B., Roncoli, C., Silvestri, S., Herrero, M., 2013. Adapting agriculture to climate change in Kenya: household strategies and determinants. J. Environ. Manag. 114, 26–35. https://doi.org/10.1016/j.jenvman.2012.10.036. Centre for Coastal Zone Management and Coastal Shelter Belt, 2011. Coastal States of India. [WWW Document]. http://iomenvis.nic.in/index2.aspx?slid=758& sublinkid=119&langid=1&mid=1, Accessed date: 8 March 2015. Chassot, E., Bonhommeau, S., Dulvy, N., Mélin, F., Watson, R., Gascuel, D., Pape, O. Le, 2010. Global marine primary production constrains fisheries catches. Ecol. Lett. 13, 495–505. https://doi.org/10.1111/j.1461-0248.2010.01443.x. Cinner, J.E., Huchery, C., Hicks, C.C., Daw, T.M., Marshall, N., Wamukota, A., Allison, E.H., 2015. Changes in adaptive capacity of Kenyan fishing communities. Nat. Clim. Change 5, 872–876. https://doi.org/10.1038/nclimate2690. Cleary, D., Baumann, P., Bruno, Ma, Flores, X., Warren, P., 2003. People-centred Approaches: a Brief Literature Review and Comparison of Types. LSP Working Paper 5. CMFRI, 2010. State Marine Fisheries Census (For Gujarat, Daman & Diu, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Puducherry, Andhra Pradesh, Odisha and West Bengal). (Kochi, India). Daw, T., Adger, W.N., Brown, K., 2009. Climate change and capture fisheries: potential impacts, adaptation and mitigation. In: Cochrane, K., Dee Yong, C., Soto, D., Bahri, T. (Eds.), Climate Change Implications for Fisheries and Aquaculture: Overview of Current Scientific Knowledge, FAO Fisheries and Aquaculture Technical Paper. No. 530. FAO, Rome, pp. 107–153. Deressa, T.T., Hassan, R.M., Ringler, C., Alemu, T., Yesuf, M., 2009. Determinants of farmers' choice of adaptation methods to climate change in the Nile Basin of Ethiopia. Global Environ. Change 19, 248–255. https://doi.org/10.1016/j.gloenvcha.2009.01. 002. Divakarannair, N., 2007. Livelihood Assets and Survival Strategies in Coastal Communities in Kerala. University of Victoria, India. Eakin, H., Lemos, M.C., 2006. Adaptation and the state: Latin America and the challenge of capacity-building under globalization. Global Environ. Change 16, 7–18. https:// doi.org/10.1016/j.gloenvcha.2005.10.004. Geospatial World, 2004. GPS Navigators Help Indian Fishermen Steer Clear of Cyclone Geospatial World. [WWW Document]. Geospatial World. https://www. geospatialworld.net/news-posts/gps-navigators-help-indian-fishermen-steer-clear-ofcyclone/, Accessed date: 23 February 2017. Golden, C., Allison, E.H., Cheung, W.W.L., Dey, M.M., Halpern, B.S., McCauley, D.J., Smith, M., Vaitla, B., Zeller, D., Myers, S.S., 2016. Fall in fish catch threatens human

References Adger, W.N., 2001. Scales of governance and environmental justice for adaptation and mitigation of climate change. J. Int. Dev. 13, 921–931. https://doi.org/10.1002/jid. 833. Adger, W.N., Arnell, N.W., Tompkins, E.L., 2005. Successful adaptation to climate change across scales. Global Environ. Change 15, 77–86. https://doi.org/10.1016/j. gloenvcha.2004.12.005. Albert, J.A., Beare, D., Schwarz, A.M., Albert, S., Warren, R., Teri, J., Siota, F., Andrew, N.L., 2014. The contribution of nearshore fish aggregating devices (FADs) to food security and livelihoods in Solomon Islands. PLoS One 9, 1–19. https://doi.org/10. 1371/journal.pone.0115386. Albert, S., Aswani, S., Fisher, P.L., Albert, J., 2015. Keeping food on the table: human responses and changing coastal fisheries in Solomon Islands. PLoS One 10, 1–13. https://doi.org/10.1371/journal.pone.0130800. Allison, E.H., Adger, W., Badjeck, M.-C., Brown, K., Conway, D., Dulvy, N.K., Halls, A., Perry, A., Reynolds, J.D., 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, Fisheries Management Science Programme MRAG/DFID. Project no. R4778J. London, UK. http://r4d.dfid. gov.uk/pdf/outputs/R4778Ja.pdf. Allison, E.H., Ellis, F., 2001. The livelihoods approach and management of small scale fisheries. Mar. Policy 25, 377–388. https://doi.org/10.1016/S0308-597X(01) 00023-9. Allison, E.H., Horemans, B., 2006. Putting the principles of the Sustainable Livelihoods Approach into fisheries development policy and practice. Mar. Policy 30, 757–766. https://doi.org/10.1016/j.marpol.2006.02.001. Anderson, D.M., Overpeck, J.T., Gupta, A.K., 2002. Increase in the Asain southwest monsoon during the past four centuries. Science (80-. ) 297, 596–599. https://doi. org/10.1126/science.1072881. Barange, M., Merino, G., Blanchard, J.L., Scholtens, J., Harle, J., Allison, E.H., Allen, J.I., Holt, J., Jennings, S., 2014. Impacts of climate change on marine ecosystem production in societies dependent on fisheries. Nat. Clim. Change 4, 211–216. https:// doi.org/10.1038/NCLIMATE2119. Basavakumar, K.V., Devendrappa, S., Srenivas, S.T., 2011. A study on profile of fishing community of a village in Karnataka. Karnataka J. Agric. Sci. 24, 684–687. Batterbury, S., Forsyth, T., 1999. Fighting back: human adaptations in marginal environments. Environ. Sci. Policy Sustain. Dev. 41, 6–9. https://doi.org/10.1080/ 00139159909604639. Below, T.B., Mutabazi, K.D., Kirschke, D., Franke, C., Sieber, S., Siebert, R., Tscherning, K., 2012. Can farmers' adaptation to climate change be explained by socio-economic household-level variables? Global Environ. Change 22, 223–235. https://doi.org/10. 1016/j.gloenvcha.2011.11.012. Blasiak, R., Spijkers, J., Tokunaga, K., Pittman, J., Yagi, N., Österblom, H., 2017. Climate change and marine fisheries: least developed countries top global index of vulnerability. PLoS One 12, 1–15. https://doi.org/10.1371/journal.pone.0179632.

45

Ocean and Coastal Management 171 (2019) 37–46

K. Malakar et al.

envsci.2008.06.002. Patt, A.G., Schröter, D., 2008. Perceptions of climate risk in Mozambique: implications for the success of adaptation strategies. Global Environ. Change 18, 458–467. https:// doi.org/10.1016/j.gloenvcha.2008.04.002. Pauly, D., 2005. Rebuilding fisheries will add to Asia's problems. Nature 433, 457. https://doi.org/10.1038/433457a. Perry, R.I., Ommer, R.E., Barange, M., Jentoft, S., Neis, B., Sumaila, U.R., 2011. Marine social-ecological responses to environmental change and the impacts of globalization. Fish Fish. 12, 427–450. https://doi.org/10.1111/j.1467-2979.2010.00402.x. Planning Commission Government of India, 2011. India Human Development Report 2011: Towards Social Inclusion. New Delhi. . Rahut, D.B., Ali, A., Kassie, M., Marenya, P.P., Basnet, C., 2014. Rural livelihood diversification strategies in Nepal. Poverty & Public Policy 6, 259–281. https://doi.org/ 10.1002/pop4.75. Roeger, J., Foale, S., Sheaves, M., 2016. When “fishing down the food chain” results in improved food security: evidence from a small pelagic fishery in Solomon Islands. Fish. Res. 174, 250–259. https://doi.org/10.1016/j.fishres.2015.10.016. Salagrama, V., 2012. Climate Change and Fisheries: Perspectives from Small-scale Fishing Communities in India on Measures to Protect Life and Livelihood. (Chennai). Salim, S., Sathianandan, T.V., Lekshmi, P.S.S., Narayanakumar, R., Zacharia, P.U., Rohit, P., Manjusha, U., Antony, B., Safeena, P.K., Shridhar, N., Ramees, M., Jayakumar, R., Kumar, N.S., Nimisha, C.P., 2015. Assessment of fishers perception in developing climate change adaptation and mitigation plans. J. Mar. Biol. Assoc. India 57, 21–30. https://doi.org/10.6024/jmbai.2014.56.1.01826-0x. Sarkar, P., 2012. Verifying the statement of Gordon Scott 1954 , “ the Fishermen remains poor ”, in the context of coastal fishery in India. Int. J. Sci. Eng. Res. 3, 1–10. Senapati, S., Gupta, V., 2017. Socio-economic vulnerability due to climate change: deriving indicators for fishing communities in Mumbai. Mar. Policy 76, 90–97. https:// doi.org/10.1016/j.marpol.2016.11.023. Sievanen, L., 2014. How do small-scale Fishers adapt to environmental variability? Lessons from Baja California, Sur, Mexico. Marit. Stud. 13, 9. https://doi.org/10. 1186/s40152-014-0009-2. Singh, O.P., 2007. Long-term trends in the frequency of severe cyclones of Bay of Bengal : observations and simulations. Mausam 58, 59–66. Smit, B., Wandel, J., 2006. Adaptation, adaptive capacity and vulnerability. Global Environ. Change 16, 282–292. https://doi.org/10.1016/j.gloenvcha.2006.03.008. Sumaila, U.R., Cheung, W.W.L., Lam, V.W.Y., Pauly, D., Herrick, S., 2011. Climate change impacts on the biophysics and economics of world fisheries. Nat. Clim. Change 1, 449–456. https://doi.org/10.1038/nclimate1301. United Nations Development Programme, 2016. Human Development Report 2016: Human Development for Everyone. UNDP, New York. Venkatesh, S., 2006. Trends in Poverty and Livelihoods in Coastal Fishing Communities of Orissa State, India. FAO, Rome, Italy Fisheries Technical Paper No. 490. Vivekanandan, E., 2011. Climate Change and Indian Marine Fisheries, CMFRI Special Publication No. 105, Marine Fisheries Policy Brief-3. (Kochi, India). Vivekanandan, E., Rajagopalan, M., Pillai, N.G.K., 2009. Recent trends in sea surface temperature and its impact on oil sardine. In: Aggarwal, P.K. (Ed.), Global Climate Change and Indian Agriculture. ICAR, New Delhi, pp. 89–92. Yila, J.O., Resurreccion, B.P., 2013. Determinants of smallholder farmers' adaptation strategies to climate change in the semi arid Nguru Local Government Area, Northeastern Nigeria. Manag. Environ. Qual. 24, 341–364. https://doi.org/10.1108/ 14777831311322659. Yohe, G., 2000. Assessing the role of adaptation in evaluating vulnerablity to climate change. Clim. Change 46, 371–390. Young, I.R., Zieger, S., Babanin, A.V., 2011. Global trends in wind speed and wave height. Science (80-. ) 332, 451–455. https://doi.org/10.1126/science.1197219. Zacharia, P.U., Dineshbabu, A.P., Thomas, S., Kizhakudan, S.J., Vivekanandan, E., Pillai, S.L., Sivadas, M., Ghosh, S., Ganga, U., Rajesh, K.M., Nair, R.J., Najmudeen, T.M., Koya, M., Chellappan, A., Dash, G., Divipala, I., Akhilesh, K.V., Muktha, M., Dash, S. Sen, 2016. Relative Vulnerability Assessment of Indian Marine Fishes to Climate Change Using Impact and Adaptation Attribute. CMFRI, Kochi, India Special Publication No. 125, (CMFRI-NICRA Publication No. 5).

health. Nature 534, 317–320. https://doi.org/10.1038/534317a. Government of India, 2017. Per Capita NSDP at Current Prices (2004-05 to 2014-15). [WWW Document]. NITI Aayog. http://niti.gov.in/content/capita-nsdp-currentprices-2004-05-2014-15, Accessed date: 25 October 2017. Himes-cornell, A., Kasperski, S., 2015. Assessing Climate Change Vulnerability in Alaska's Fishing Communities 162. pp. 1–11. Holbrook, N.J., Johnson, J.E., 2014. Climate change impacts and adaptation of commercial marine fisheries in Australia: a review of the science. Clim. Change 124, 703–715. https://doi.org/10.1007/s10584-014-1110-7. ICSF, 2017. Fisheries Development and Management. WWW Document]. https:// indianfisheries.icsf.net/en/page/614-Fisheries Development and Management.html, Accessed date: 21 February 2017. Islam, M.M., Sallu, S., Hubacek, K., Paavola, J., 2014a. Vulnerability of fishery-based livelihoods to the impacts of climate variability and change: insights from coastal Bangladesh. Reg. Environ. Change 14, 281–294. https://doi.org/10.1007/s10113013-0487-6. Islam, M.M., Sallu, S., Hubacek, K., Paavola, J., 2014b. Limits and barriers to adaptation to climate variability and change in Bangladeshi coastal fishing communities. Mar. Policy 43, 208–216. https://doi.org/10.1016/j.marpol.2013.06.007. Iwasaki, S., Razafindrabe, B.H.N., Shaw, R., 2009. Fishery livelihoods and adaptation to climate change: a case study of Chilika lagoon, India. Mitig. Adapt. Strateg. Glob. Change 14, 339–355. https://doi.org/10.1007/s11027-009-9167-8. Jain, M., Naeem, S., Orlove, B., Modi, V., DeFries, R.S., 2015. Understanding the causes and consequences of differential decision-making in adaptation research: adapting to a delayed monsoon onset in Gujarat, India. Global Environ. Change 31, 98–109. https://doi.org/10.1016/j.gloenvcha.2014.12.008. Karnad, D., Karanth, K.K., 2016. Declining Indian Fisheries: Perceptions of Fisher Folk from Maharashtra and Tamil Nadu. [WWW Document]. Conserv. India. http:// www.conservationindia.org/articles/declining-indian-fisheries-perceptions-of-fisherfolk-from-maharashtra-and-tamil-nadu, Accessed date: 21 December 2016. Khatun, D., Roy, B.C., 2012. Rural livelihood diversification in West Bengal: determinants and constraints. Agric. Econ. Res. Rev. 25, 115–124. Malakar, K., Mishra, T., Patwardhan, A., 2018. A framework to investigate drivers of adaptation decisions in marine fishing: Evidence from urban, semi-urban and rural communities. Sci. Total Environ. 637–638, 758–770. https://doi.org/10.1016/j. scitotenv.2018.04.429. Malakar, K., Mishra, T., 2019. Adaptation and its socioeconomic facilitators in the marine fishing community of Maharashtra, India. In: Venkataraman, C., Mishra, T., Ghosh, S., Karmakar, S. (Eds.), Climate Change Signals and Response. Springer, Singapore, pp. 231–245. https://doi.org/10.1007/978-981-13-0280-0_14. Mathur, S., Bolia, N., 2016. The MGNREGA Index. The Hindu. McCay, B.J., 1978. Systems ecology, people ecology, and the anthropology of fishing communities. Hum. Ecol. 6, 397–422. https://doi.org/10.1007/BF00889417. Menon, M., Ghosh, S., Kumar, M.S., Rao, M.V.H., Mahesh, V.U., Zacharia, P.U., 2016. Fishermen's perception of climate change - a study from Andhra Pradesh. Indian J. Fish. 63, 110–119. https://doi.org/10.21077/ijf.2016.63.3.51566-16. Morzaria-Luna, H.N., Turk-Boyer, P., Moreno-Baez, M., 2014. Social indicators of vulnerability for fishing communities in the Northern Gulf of California, Mexico: implications for climate change. Mar. Policy 45, 182–193. https://doi.org/10.1016/j. marpol.2013.10.013. MSSRF, 2014. Improving Livelihoods of Fishermen- Case Studies from Andhra Pradesh and Union Territory of Puducherry. M.S. Swaminathan Research Foundation, Chennai. Murakami, H., Vecchi, G.A., Underwood, S., 2017. Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nat. Clim. Change 1–5. https://doi.org/ 10.1038/s41558-017-0008-6. National Fisheries Development Board, 2016. About Indian Fisheries. [WWW Document]. http://nfdb.gov.in/about-indian-fisheries.htm, Accessed date: 1 January 2017. Niehof, A., 2004. The significance of diversification for rural livelihood systems. Food Pol. 29, 321–338. https://doi.org/10.1016/j.foodpol.2004.07.009. Paavola, J., 2008. Livelihoods, vulnerability and adaptation to climate change in Morogoro, Tanzania. Environ. Sci. Policy 11, 642–654. https://doi.org/10.1016/j.

46