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Spatial prediction of coral reef habitats and coral reef community components was approached on the basis of the 'predict first, classify later' paradigm. Individual community compo- nents (biotic and geomorphologic bottom features) were first predicted and then classified into com- posite habitats. This approach differs from widely applied methods of direct classification based on remote sensing only. In situ coral reef community-condition assessment was first used to measure a response variable (percentage cover of habitat). Reef bottom features (topographic complexity, sand- sediment, rock-calcareous pavement and rubble) were then predicted using generalized additive models (GAMs) applied to continuous environmental maps, high-resolution Ikonos satellite images and a reef digital topographic model (DTM). Next, using GAMs on newly created bottom maps, mod- els were fitted to predict coral community components (hard coral, sea-grass, algae, octocorals). At this stage, high-resolution maps of the geomorphologic and biotic components of the coral reef com- munity at an experimental site (Akumal Reef in the Mexican Caribbean) were produced. Coral reef habitat maps were derived using GIS following a hierarchical classification procedure, and the result- ing merged map depicting 8 habitats was compared against thematic maps created by traditional supervised classification. This general approach sets a baseline for future studies involving more complex spatial and ecological predictions on coral reefs.

Spatial prediction of coral reef habitats: integrating ecology with spatial modeling and remote sensing