M. Yanoff Ophthalmic Clinical Advisor, Diagnosis and Treatment, Second Edition 2007 Butterworth Heinemann St. Louis, M.O Softcover, 384 pages, 210 illustrations, $66.95.

Abstract

Marine protected areas (MPAs) are increasingly regarded as socio-ecological systems. In addition to their reported ecological effects, MPAs may have important social, economic and cultural effects on local communities and marine and coastal stakeholders. Those effects should be considered within an ecosystem approach to MPA planning, designation and management. Here we present a new framework to monitor and assess the socioeconomic effects of MPAs saliently and soundly: the Integrated MPA Socio-Economic Assessment (IMPASEA). The IMPASEA considers and analyses those factors deemed most important for marine and coastal stakeholders in a spatially referenced, sound and cost-effective manner. The development of the IMPASEA followed a mixed-methods research design in 3 phases: literature review, stakeholder survey and geo-statistical analysis using a Multiple-Paired-Before-After-Control-Impact design (MPBACI). The framework was tested on a set of 6 multiple-use MPAs on the French side of the English Channel. Of the eight socioeconomic variables analysed at the scale of ‘commune’ in the geo-statistical phase, only one variable (‘number of hotel rooms’) might have been affected by the designation of MPAs. Factors such as MPA designation category and management status are likely to have contributed to the non-significant differences shown at the scale of commune for the selected MPAs. In contrast, most of the six variables related to fishing showed differences between ports inside and outside MPAs, although these results need further ground truthing to discriminate attribution of effects. The characteristics of the IMPASEA make it a sound monitoring and assessment framework that could be applied in different contexts and to different types of sustainability assessments involving protected areas or other spatially-defined entities under certain conditions: existence of consistent time series of fine-scale socioeconomic data and avoidance of overlap of designation categories over single spaces.