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Integrating social–ecological vulnerability assessments with climate forecasts to improve local climate adaptation planning for coral reef fisheries in Papua New Guinea

A major gap exists in integrating climate projections and social–ecological vulnerability analyses at scales that matter, which has affected local-scale adaptation planning and actions to date. We address this gap by providing a novel methodology that integrates information on: (i) the expected future climate, including climate-related extreme events, at the village level; (ii) an ecological assessment of the impacts of these climate forecasts on coral reefs; and (iii) the social adaptive capacity of the artisanal fishers, to create an integrated vulnerability assessment on coastal communities in five villages in Papua New Guinea. We show that, despite relatively proximate geographies, there are substantial differences in both the predicted extreme rainfall and temperature events and the social adaptive capacity among the five fishing-dependent communities, meaning that they have likely different vulnerabilities to future climate change. Our methodology shows that it is possible to capture social information and integrate this with climate and ecological modeling in ways that are best suited to address the impacts of climate-mediated environmental changes currently underway across different scales.
- University of Queensland Australia
- University of Queensland Australia
- Wildlife Conservation Society United States
- James Cook University Australia
- School for Field Studies United States
550, Socioecological vulnerability, 2306 Global and Planetary Change, 551, 333, Adaptation planning, Climate change, Coral reef fisheries
550, Socioecological vulnerability, 2306 Global and Planetary Change, 551, 333, Adaptation planning, Climate change, Coral reef fisheries
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).30 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
