Location of fish aggregation device (FAD) buoys within the Main Hawaiian Islands.

The State of Hawaii Department of Land and Natural Resources (DLNR) Division of Aquatic Resources (DAR) is the primary agency responsible for coordinating Hawaii's reef management efforts in the main Hawaiian Islands. The Coral Reef Working Group (CRWG), made up of key state and federal partners involved in coral reef management, was established to help provide guidance for the State of Hawaii's coral program.

The State of Hawaii Department of Land and Natural Resources (DLNR) Division of Aquatic Resources (DAR) is the primary agency responsible for coordinating Hawaii's reef management efforts in the main Hawaiian Islands. The DAR marine monitoring program employs numerous methodologies developed by DAR scientists in collaboration with NOAA, USGS and the University of Hawaii (UH).

Output from a model to predict the total benthic cover of six coral species (Montipora capitata, Montipora flabellata, Montipora patulla, Porites lobata, Porites compressa, Porites meandrina) in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict coral cover for the entire geographic study domain.

Output from a model to predict the benthic cover of Montipora capitata in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict M. capitata cover for the entire geographic study domain.

Output from a model to predict the benthic cover of Montipora flabellata in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict M. flabellata cover for the entire geographic study domain.

Output from a model to predict the benthic cover of Montipora patula in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict M. patula cover for the entire geographic study domain.

Output from a model to predict the benthic cover of Porites compressa in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict P. compressa cover for the entire geographic study domain.

Output from a model to predict the benthic cover of Porites lobata in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict P. lobata cover for the entire geographic study domain.

Output from a model to predict the benthic cover of Pocillopora meandrina in the Hawaiian Islands as a proportion (0-1.0). Coral cover was modeled with boosted regression trees (BRT) in R software using data from coral cover surveys and environmental covariates derived from models and/or observations including wave height, benthic geomorphology, and downwelled irradiance. The best performing BRT model was used to predict P. meandrina cover for the entire geographic study domain.