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Remote Sens. 2013, 5(3), 1311-1334; doi:10.3390/rs5031311

Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data

Department of Geography, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada
Wildlife Conservation Society, 11 Ma'afu St, Fiji Country Program, Suva, Fiji
School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia
Author to whom correspondence should be addressed.
Received: 31 December 2012 / Revised: 4 March 2013 / Accepted: 5 March 2013 / Published: 14 March 2013
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In the face of increasing climate-related impacts on coral reefs, the integration of ecosystem resilience into marine conservation planning has become a priority. One strategy, including resilient areas in marine protected area (MPA) networks, relies on information on the spatial distribution of resilience. We assess the ability to model and map six indicators of coral reef resilience—stress-tolerant coral taxa, coral generic diversity, fish herbivore biomass, fish herbivore functional group richness, density of juvenile corals and the cover of live coral and crustose coralline algae. We use high spatial resolution satellite data to derive environmental predictors and use these in random forest models, with field observations, to predict resilience indicator values at unsampled locations. Predictions are compared with those obtained from universal kriging and from a baseline model. Prediction errors are estimated using cross-validation, and the ability to map each resilience indicator is quantified as the percentage reduction in prediction error compared to the baseline model. Results are most promising (percentage reduction = 18.3%) for mapping the cover of live coral and crustose coralline algae and least promising (percentage reduction = 0%) for coral diversity. Our study has demonstrated one approach to map indicators of coral reef resilience. In the context of MPA network planning, the potential to consider reef resilience in addition to habitat and feature representation in decision-support software now exists, allowing planners to integrate aspects of reef resilience in MPA network development. View Full-Text
Keywords: coral reefs; resilience; spatial prediction; mapping; random forest; universal kriging; Fiji coral reefs; resilience; spatial prediction; mapping; random forest; universal kriging; Fiji

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Knudby, A.; Jupiter, S.; Roelfsema, C.; Lyons, M.; Phinn, S. Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data. Remote Sens. 2013, 5, 1311-1334.

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