Categorizing global MPAs: A cluster analysis approach

Last modified: 
September 10, 2019 - 10:30am
Type: Journal Article
Year of publication: 2019
Date published: 10/2019
Authors: John Bohorquez, Anthony Dvarskas, Ellen Pikitch
Journal title: Marine Policy
Volume: 108
Pages: 103663
ISSN: 0308597X

Marine Protected Areas (MPAs) are a widely used and flexible policy tool to help preserve marine biodiversity. They range in size and governance complexity from small communally managed MPAs, to massive MPAs on the High Seas managed by multinational organizations. As of August 2018, the Atlas of Marine Protection (MPAtlas.org) had catalogued information on over 12,000 Marine Protected Areas. We analyzed this global database to determine groups of MPAs whose characteristics best distinguished the diversity of MPA attributes globally, based upon our comprehensive sample. Groups were identified by pairing a Principal Components Analysis (PCA) with a k-means cluster analysis using five variables; age of MPA, area of MPA, no-take area within MPA, latitude of the MPA's center, and Human Development Index (HDI) of the host country. Seven statistically distinct groups of MPAs emerged from this analysis and we describe and discuss the potential implications of their respective characteristics for MPA management. The analysis yields important insights into patterns and characteristics of MPAs around the world, including clusters of especially old MPAs (greater than 25 and 66 years of age), clusters distributed across nations with higher (HDI ≥ 0.827) or lower (HDI ≤ 0.827) levels of development, and majority no-take MPAs. Our findings also include statistical verification of Large Scale Marine Protected Areas (LSMPAs, approximately >180,000km2) and a sub-class of LSMPA's we call “Giant MPAs” (GMPAs, approximately >1,000,000km2). As a secondary outcome, future research may use the clusters identified in this paper to track variability in MPA performance indicators across clusters (e.g., biodiversity preservation/restoration, fish biomass) and thereby identify relationships between cluster and performance outcomes. MPA management can also be improved by creating communication networks that connect similarly clustered MPAs for sharing common challenges and best practices.

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