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

• Published in: Journal of Biogeography
• Main Authors: Couce, Elena, Ridgwell, Andy, Hendy, Erica J.
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2012
• Subjects: Boosted regression trees; CART; classification trees; coral reef biogeography; environmental limits; Maxent; maximum entropy; SDM; species distribution modelling; SURFACE SOLAR IRRADIANCE; GREAT-BARRIER-REEF; CLIMATE-CHANGE; SPECIES DISTRIBUTIONS; HABITAT-SUITABILITY; OCEAN ACIDIFICATION; EASTERN AUSTRALIA; REGRESSION TREES; MODELING METHODS; PRESENCE-ABSENCE; Ocean acidification
• Online Access: https://hdl.handle.net/1983/e7188644-e4fd-4bdb-bc3a-0ec26e41bd80; https://research-information.bris.ac.uk/en/publications/e7188644-e4fd-4bdb-bc3a-0ec26e41bd80; https://doi.org/10.1111/j.1365-2699.2012.02706.x; https://research-information.bris.ac.uk/ws/files/32611457/Couce_Revised_Manuscript_JBI_11_0045.pdf; http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2699.2012.02706.x/full

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 (OArag), 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 degrees x 1 degrees 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 OArag. 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 ...