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The ongoing invasion of coral reefs in the greater Caribbean region by Indo-Pacific lionfish (Pterois spp.) poses challenges for managers. Al though the dynamics and ecological effects of lionfish invasions have been examined in detail at the scale of individual reefs, relatively little information is available on the patterns of the invasion over larger areas. This study combined species counts and microhabitat observations from SCUBA surveys with satellite-derived bathymetry and habitat data to create predictive species distribution maps of lionfish in a 58 km2 region along the southern edge of the island of Eleuthera in the Bahamas. Models predicted lionfish presence, ab sence, and density, and were created iteratively using various resolutions and types of data to mimic the datasets that may be available to management in the re gion. The best-fit model for presence and absence, which combined physical variables derived from remotely sensed satellite data and diver-collected microhabitat data, predicted 89% of lionfish presence and absence. The best-fit model for lionfish density used 2 resolutions of physical habitat data as well as biological data on densities of large na tive groupers and accurately predicted 67% lionfish density. These results suggest that physical habitat may be more important for initial lionfish presence and that biotic inter actions influence lionfish density on the reefs. Under standing the limitations and value of each type of data when creating models may be advantageous for planning targeted lionfish re movals and prioritizing sites for conservation efforts.

Integrating remote sensing and diver observations to predict the distribution of invasive lionfish on Bahamian coral reefs