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Environmental controls on the global distribution of shallow-water coral reefs

Authors

  • Elena Couce,

    1. School of Earth Sciences, Queens Road, University of Bristol, Bristol BS8 1RJ, UK
    2. School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
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  • Andy Ridgwell,

    1. School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK
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  • Erica J. Hendy

    Corresponding author
    1. School of Earth Sciences, Queens Road, University of Bristol, Bristol BS8 1RJ, UK
    2. School of Biological Sciences, Woodland Road, University of Bristol, Bristol BS8 1UG, UK
      Erica J. Hendy, School of Earth Sciences, Queens Road, University of Bristol, Bristol BS8 1RJ, UK.
      E-mail: e.hendy@bristol.ac.uk
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Erica J. Hendy, School of Earth Sciences, Queens Road, University of Bristol, Bristol BS8 1RJ, UK.
E-mail: e.hendy@bristol.ac.uk

Abstract

Aim  Elucidating the environmental limits of coral reefs is central to projecting future impacts of climate change on these ecosystems and their global distribution. Recent developments in species distribution modelling (SDM) and the availability of comprehensive global environmental datasets have provided an opportunity to reassess the environmental factors that control the distribution of coral reefs at the global scale as well as to compare the performance of different SDM techniques.

Location  Shallow waters world-wide.

Methods  The SDM methods used were maximum entropy (Maxent) and two presence/absence methods: classification and regression trees (CART) and boosted regression trees (BRT). The predictive variables considered included sea surface temperature (SST), salinity, aragonite saturation state (ΩArag), nutrients, irradiance, water transparency, dust, current speed and intensity of cyclone activity. For many variables both mean and SD were considered, and at weekly, monthly and annually averaged time-scales. All were transformed to a global 1° × 1° grid to generate coral reef probability maps for comparison with known locations. Model performance was compared in terms of receiver operating characteristic (ROC) curves and area under the curve (AUC) scores. Potential geographical bias was explored via misclassification maps of false positive and negative errors on test data.

Results  Boosted regression trees consistently outperformed other methods, although Maxent also performed acceptably. The dominant environmental predictors were the temperature variables (annual mean SST, and monthly and weekly minimum SST), followed by, and with their relative importance differing between regions, nutrients, light availability and ΩArag. No systematic bias in SDM performance was found between major coral provinces, but false negatives were more likely for cells containing ‘marginal’ non-reef-forming coral communities, e.g. Bermuda.

Main conclusions  Agreement between BRT and Maxent models gives predictive confidence for exploring the environmental limits of coral reef ecosystems at a spatial scale relevant to global climate models (c. 1° × 1°). Although SST-related variables dominate the coral reef distribution models, contributions from nutrients, ΩArag and light availability were critical in developing models of reef presence in regions such as the Bahamas, South Pacific and Coral Triangle. The steep response in SST-driven probabilities at low temperatures indicates that latitudinal expansion of coral reef habitat is very sensitive to global warming.

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