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Marine Ecology Progress Series

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MEPS 623:1-11 (2019)  -  DOI: https://doi.org/10.3354/meps13067

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

Alexandra C. D. Davis*

Oregon State University, Department of Integrative Biology, 3029 Cordley Hall, 2701 SW Campus Way, Corvallis, OR 97331, USA
Present address: Department of Biological Sciences, University of Alberta, CW 405, Biological Sciences Building, Edmonton, AB T6E 4R4, Canada
*Corresponding author:

ABSTRACT: The ongoing invasion of coral reefs in the greater Caribbean region by Indo-Pacific lionfish (Pterois spp.) poses challenges for managers. Although 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, absence, 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 region. 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 native groupers and accurately predicted 67% lionfish density. These results suggest that physical habitat may be more important for initial lionfish presence and that biotic interactions influence lionfish density on the reefs. Understanding the limitations and value of each type of data when creating models may be advantageous for planning targeted lionfish removals and prioritizing sites for conservation efforts.


KEY WORDS: Lionfish · Pterois · Species distribution models · Remote sensing · Invasive species · Habitat mapping


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Cite this article as: Davis ACD (2019) Integrating remote sensing and diver observations to predict the distribution of invasive lionfish on Bahamian coral reefs. Mar Ecol Prog Ser 623:1-11. https://doi.org/10.3354/meps13067

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