Journal
FISHERIES RESEARCH
Volume 195, Issue -, Pages 19-27Publisher
ELSEVIER
DOI: 10.1016/j.fishres.2017.06.011
Keywords
Species distribution models; Snapper-grouper complex; Boosted regression trees; Fish habitat; Baited remote underwater video (BRUV)
Categories
Funding
- Colonel Willys E. Lord, DVM and Sandina L. Lord Endowed Scholarship
- Carol Ann and Myron K. Hayashida Scholarship
- MFS-Sea Grant Population Dynamics Fellowship
- State of Hawaii Division of Aquatic Resources
- Federal Aid in Sport Fish Restoration program [F17R35-study IX]
- NOAA Pacific Islands Regional Office
- Kahoolawe Island Reserve Commission
- NOAA Pacific Island Fisheries Science Center
- Joint Institute for Marine and Atmospheric Research at the University of Hawaii at Manoa
- NOAA Fisheries Office of Science and Technology
- NOAA Fisheries Advanced Science at Technology Working Group
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Deepwater snappers and groupers are valuable components of many subtropical and tropical fisheries globally and understanding the habitat associations of these species is important for spatial fisheries management. Habitat-based species distribution models were developed for the deepwater snapper-grouper complex in the main Hawaiian Islands (MHI). Six eteline snappers (Pristipomoides spp., Aphareus rutilans, and Etelis spp.) and one endemic grouper (Hyporthodus quernus) comprise the species complex known as the Hawaiian Deep Seven Bottomfishes. Species occurrence was recorded using baited remote underwater video stations deployed between 30 and 365 m (n = 2381) and was modeled with 12 geomorphological covariates using GLMs, GAMs, and BRTs. Depth was the most important predictor across species, along with ridge-like features, rugosity, and slope. In particular, ridge-like features were important habitat predictors for E. coruscans and P. filamentosus. Bottom hardness was an important predictor especially for the two Etelis species. Along with depth, rugosity and slope were the most important habitat predictors for A. rutilans and P. zonatus, respectively. Models built using GAMs and BRTs generally had the highest predictive performance. Finally, using the BRT model output, we created species-specific distribution maps and demonstrated that areas with high predicted probabilities of occurrence were positively related to fishery catch rates.
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