This study examined the level of management complexity (simultaneously occurring licensed human activities) within the currently designated Marine Protected Areas (MPAs) in Scottish waters and through modelling and statistical analysis determined which variables play an important role in defining the level of management effort required for each MPA. This study utilised surveyed distribution data for Priority Marine Features and modelled distribution data showing potential "Most Suitable" Priority Marine Feature habitat through the species distribution model, Maxent. Prediction indicators were developed through Spearman[U+05F3]s Rank coefficients and a linear regression model. Results showed that, (1) there was a significant negative correlation between the management complexity score within 5. km of a MPA and the number of casework events (defined as any work or statutory consultation associated with an MPA, such as planning applications, discharges or new fisheries); and (2) a significant positive correlation between the number of casework events and location, number of features, and the type of features and most suitable scores. Calculations showed that Lochs Duich, Long and Alsh, Loch Sunart, Loch Sween and Loch Creran may potentially require most effort to manage once they are designated as MPAs. This study showed that it is possible to evaluate options within an MPA network to achieve cost effective options for the biodiversity and socio-economic objectives of MPA networks and that some MPAs are likely to be more efficient than others in terms of management time.
- Management complexity
- Management effort
- Marine protected areas
- Priority marine features
ASJC Scopus subject areas
- Aquatic Science
- Economics and Econometrics
- Environmental Science(all)
- Management, Monitoring, Policy and Law
FingerprintDive into the research topics of 'Can management effort be predicted for Marine Protected Areas? New considerations for network design'. Together they form a unique fingerprint.
- School of Energy, Geoscience, Infrastructure and Society - Professor
- School of Energy, Geoscience, Infrastructure and Society, Institute for Life and Earth Sciences - Professor
Person: Academic (Research & Teaching)