A framework for vulnerability assessment of coastal fisheries ecosystems to climate change―Tool for understanding resilience of fisheries (VA―TURF)

Abstract Vulnerability assessment (VA) is increasingly developed and utilized in various sectors and fields of society. VA provides a better understanding of the interactions among system, pressures, and threats, which serves as a basis for targeted adaptation strategies. The framework or tool named tool for understanding resilience of fisheries (VA–TURF) was developed to assess the vulnerability of the coastal fisheries ecosystems in the tropics to climate change. VA–TURF has three major components, namely, fisheries, reef ecosystem, and socio-economics. Although each component has intrinsic properties, the three components are strongly interrelated. Indicators associated to sensitivity, exposure, and adaptive capacity were developed for each component of TURF. The exposure variable used was wave. VA–TURF uses information obtained through rapid assessments except for the reef ecosystem component. The analytical approach for integrating scores is straightforward and devoid of highly sophisticated mathematical methods. The utility of VA–TURF primarily considers the fishers of a coastal community (barangay) as the major stakeholder, thereby facilitating familiarization and community ownership of the tool. VA–TURF was demonstrated in all the coastal barangays of two island municipalities (Lubang and Looc, Occidental Mindoro, Philippines) located along the Verde Island Passage, which has the world's highest marine shore fish biodiversity. Local stakeholders such as fishers, barangay leaders, residents, and local executive staff of the two municipalities participated in the process of scoring and determining the vulnerability of the sites during a series of workshops. The local-level fisheries vulnerability assessment framework developed encourages community-level actions and provides opportunities for strategic actions and scaling-up of efforts at various governance levels.