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

Abstract

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.