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Modelling susceptibility of coral reefs to environmental stress using remote sensing data and GIS models

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  • Maina, Joseph
  • Venus, Valentijn
  • McClanahan, Timothy R.
  • Ateweberhan, Mebrahtu

Abstract

There is a need to develop methods and a decision support system to establish marine protected areas that harbour coral reefs that are resilient to climate change. This requires understanding patterns of coral's susceptibility to thermal stress and how coral communities will change with the environmental variables associated with climate change. This study combined surface currents, wind velocity, sea surface temperature (SST), UV radiation, photosynthetically active radiation (PAR), and chlorophyll-a concentration for the western Indian Ocean and known relationships with coral bleaching and mortality to derive predictor variables that correlate with thermal stress. SST variability, UV, maximum temperature and wind speed had the highest influence on susceptibility estimates. Two susceptibility models were developed using Spatial Principal Component Analysis (SPCA) and cosine amplitude-AHP methods and a fuzzy logic technique for integrating environmental variables into a measure of susceptibility. Susceptibility models identified regional gradients in environmental stress, with the north-western Indian Ocean and some central Indian Ocean Islands as high and the southern African coastline and islands east of Madagascar as low vulnerability regions. Half of the strictly no take zones in the region are situated in locations with medium to high susceptibility. Future studies using high-resolution data can better estimate the vulnerability of specific locations and the establishment of marine protected areas where coral reef diversity is more likely to persist through climate change.

Suggested Citation

  • Maina, Joseph & Venus, Valentijn & McClanahan, Timothy R. & Ateweberhan, Mebrahtu, 2008. "Modelling susceptibility of coral reefs to environmental stress using remote sensing data and GIS models," Ecological Modelling, Elsevier, vol. 212(3), pages 180-199.
  • Handle: RePEc:eee:ecomod:v:212:y:2008:i:3:p:180-199
    DOI: 10.1016/j.ecolmodel.2007.10.033
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    References listed on IDEAS

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    Cited by:

    1. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    2. Kamal Jyoti Maji & Anil Kumar Dikshit & Ashok Deshpande, 2017. "Can fuzzy set theory bring complex issues in sizing air quality monitoring network into focus?," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(4), pages 2118-2128, December.
    3. Yee, Susan Harrell & Santavy, Deborah L. & Barron, Mace G., 2008. "Comparing environmental influences on coral bleaching across and within species using clustered binomial regression," Ecological Modelling, Elsevier, vol. 218(1), pages 162-174.
    4. Timothy McClanahan & Joseph Maina & Mebrahtu Ateweberhan, 2015. "Regional coral responses to climate disturbances and warming is predicted by multivariate stress model and not temperature threshold metrics," Climatic Change, Springer, vol. 131(4), pages 607-620, August.

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