Prediction of habitat complexity using a trait-based approach on coral reefs in Guam

Scleractinian corals are primary contributors to the structural complexity of coral reef ecosystems. The structure derived from their carbonate skeletons underpins the biodiversity and myriad of ecosystem services provided by coral reefs. This study used a trait-based approach to provide new insights into the relationships between habitat complexity and coral morphology. Three-Dimensional (3D) photogrammetry techniques were used to survey 208 study plots on the island of Guam, from which structural complexity metrics were derived and physical traits of corals were quantified. Three traits at the individual colony level (e.g., morphology, size, and genera) and two site-level environmental characteristics (e.g., wave exposure and substratum-habitat type) were examined. Standard taxonomy-based metrics were also included at the reef-plot level (e.g., coral abundance, richness, and diversity). Different traits disproportionately contributed to 3D metrics of habitat complexity. Larger colonies with a columnar morphology have the highest contribution to surface complexity, slope, and vector ruggedness measure, whereas branching and encrusting columnar colonies have the highest contribution to planform and profile curvature. These results highlight the importance of considering colony morphology and size in addition to conventional taxonomic metrics for the understanding and monitoring reef structural complexity. The approach presented here provides a framework for studies in other locations to predict the trajectory of reefs under changing environmental conditions.

Automated summary

This study used a trait-based approach to examine the relationships between habitat complexity and coral morphology. The results showed that different coral traits contribute disproportionately to different metrics of habitat complexity. This highlights the importance of considering colony morphology and size for understanding and monitoring reef structural complexity.