Influence of Climate Variability on Coastal Small-Scale Fishing Communities in Kenya

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dc.contributor.authorAkunga, Beatrice Ghettuba
dc.date.accessioned2015-08-03T08:31:54Z
dc.date.available2015-08-03T08:31:54Z
dc.date.issued2015
dc.descriptionDepartment of Environmental Science, 260p. 2015en_US
dc.description.abstractGlobally, fisheries support the livelihoods of over half a billion people. Around 90% of the 38 million people recorded globally as fishers are classified as small-scale. Small-scale fishers are considered vulnerable to the negative impacts of climate variability and change. There is limited understanding of how climate variability currently affects the livelihoods of small-scale fishing communities in Kenya. This study investigated the extent of climate variability in Mombasa and Malindi in Kilifi County and its influence on fish catches and the livelihoods of fishing communities. The study also examined the coping mechanisms developed by small-scale fishing communities; factors influencing their coping strategies; institutional capacities to deal with current and future climate extremes; and the perceptions of small-scale fishers on fish abundance and influencing factors. The study adopted a descriptive study design. Stratified random sampling was used to select a sample of 218 fishers from a total population of 240. Primary data were collected by use of semi-structured questionnaires, interview schedules, direct observation and Focus Group Discussions. Data were analyzed statistically (p<0.05). Pearson Correlation was used to analysis the correlation between sea surface temperature, rainfall and fish catches. The analysis yielded an inverse correlation between sea surface temperature and fish catches (parrot fish r= -0.565; cavilla jacks r= -0.431; shark r= -0.481); and a positive correlation between fish catches and rainfall (parrot fish r=0.159; cavilla jacks r=0.237 and shark r=0.220). In terms of livelihood assets, results of the study showed no significant association (χ² =36.27, df=36, p=0.456) between gear type andeducation level. Spearman correlation analysis between gear type and income level showed a strong interaction (r=1.00, p<0.05). Results of binary logistic model of selected variables established that education level (Wald =0.013, df=1 p=0.909) and period in fishing (Wald=0.017, df=1, p=0.895) were not significant determinants influencing migration as a coping strategy to climate variability. However, age, (Wald=6.614, df=1, p=0.01), and vessel ownership (Wald=5.003, df=1 p=0.025) were. Education level (χ²= 8.346, df =6, p=0.214; age χ²= 1.323, df=2, p=0.516) and period in fishing (χ²=1.210, df=6, p=0.976) had no significant association with using the same gear but fishing inshore as a coping strategy. Ordinal logistic regression model indicated that level of education (Wald= (0.960, 0.004, 0.593) with associated p-values of 0.327, 0.948, 0.441 and experience in fishing (Wald= (0.002, 1.690, 0.092) with associated p-values of 0.965, 0.194 and 0.761 were not significant determinants that influenced the perception of fishers on temperature variability, being a key factor that influences fish abundance. However, age (Wald= 12.150, p= 0.000) was. The study recommends increasing fishers access to educational, physical, financial and livelihood opportunities to help reduce their vulnerability to the adverse effects of climate variability.en_US
dc.description.sponsorshipKenyatta Universityen_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/13278
dc.language.isoenen_US
dc.publisherKenyatta Universityen_US
dc.titleInfluence of Climate Variability on Coastal Small-Scale Fishing Communities in Kenyaen_US
dc.typeThesisen_US
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