A predictive approach to benthic marine habitat mapping: Efficacy and management implications
- PMID: 29886940
- DOI: 10.1016/j.marpolbul.2018.04.016
A predictive approach to benthic marine habitat mapping: Efficacy and management implications
Abstract
The availability of marine habitats maps remains limited due to difficulty and cost of working at sea. Reduced light penetration in the water hampers the use of optical imagery, and acoustic methods require extensive sea-truth activities. Predictive spatial modelling may offer an alternative to produce benthic habitat maps based on complete acoustic coverage of the seafloor together with a comparatively low number of sea truths. This approach was applied to the coralligenous reefs of the Marine Protected Area of Tavolara - Punta Coda Cavallo (NE Sardinia, Italy). Fuzzy clustering, applied to a set of observations made by scuba diving and used as sea truth, allowed recognising five coralligenous habitats, all but one existing within EUNIS (European Nature Information System) types. Variable importance plots showed that the distribution of habitats was driven by distance from coast, depth, and lithotype, and allowed mapping their distribution over the MPA. Congruence between observed and predicted distributions and accuracy of the classification was high. Results allowed calculating the occurrence of the distinct coralligenous habitats in zones with different protection level. The five habitats are unequally protected since the protection regime was established when detailed marine habitat maps were not available. A SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis was performed to identify critical points and potentialities of the method. The method developed proved to be reliable and the results obtained will be useful when modulating on-going and future management actions in the studied area and in other Mediterranean MPAs to develop conservation efforts at basin scale.
Keywords: Coralligenous reefs; EUNIS; Fuzzy clustering; Marine Protected Area; Random forest; SWOT analysis.
Copyright © 2018. Published by Elsevier Ltd.
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