Per-class and model-wide variable importance.

Kelps form ecologically important habitats around the globe but are threatened by anthropogenic stressors in much of their range. Within the Gulf of Maine, these stressors include rising ocean temperatures and species invasions. Monitoring these habitats is important, but our ability to do so varies regionally based on kelp species. Modelling techniques based on optical satellite imagery are useful for floating kelps but can only identify the subsurface kelps found in the Gulf of Maine within a small upper portion of their depth range. We developed an integrative approach to kelp habitat classification using two existing data sources: sea surface temperature data from Landsat 8 and high-resolution acoustic bathymetry data in a 10-by-13 km area around the Isles of Shoals. Ground truth data were collected by lowering and raising cameras from the seabed; observations were divided into bare substrate, kelp habitat, red turf macroalgae habitat, and intermediate “mixed” macroalgae habitat classes, and used to train a Random Forest model. The model classified benthic habitats with 71% accuracy. Depth, median summer sea surface temperature, vector ruggedness measure, and slope were among the most important variables in classifying kelp habitat. This approach improves upon previous modelling and monitoring methods by expanding the depth range and total amount of area that can be assessed, while also addressing the importance of temperature in mediating substrate competition between kelps and other macroalgae. It may be generalizable to the Gulf of Maine and to other regions where kelp habitats face similar stressors and may aid in identifying healthy habitats for conservation.