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.
Predicted probability of occurrence of Etelis coruscans in the main Hawaiian Islands
The “Etelis_coruscans.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Etelis coruscans as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Etelis_coruscans.txt”) and an associated projection file (Etelis_coruscans.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Etelis coruscans probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Etelis_coruscans.zip
Predicted probability of occurrence of Etelis carbunculus in the main Hawaiian Islands
The “Etelis_carbunculus.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Etelis carbunculus as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Etelis_carbunculus.txt”) and an associated projection file (Etelis_carbunculus.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Etelis carbunculus probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Etelis_carbunculus.zip
Predicted probability of occurrence of Aphareus rutilans in the main Hawaiian Islands
The “Aphareus_rutilans.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Aphareus rutilans as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Aphareus_rutilans.txt”) and an associated projection file (Aphareus_rutilans.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Aphareus rutilans probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Aphareus_rutilans.zip
Predicted probability of occurrence of Hyporthodus quernus in the main Hawaiian Islands
The “Hyporthodus_quernus.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Hyporthodus quernus as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Hyporthodus_quernus.txt”) and an associated projection file (Hyporthodus_quernus.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Hyporthodus quernus probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Hyporthodus_quernus.zip
Predicted probability of occurrence of Pristipomoides filamentosus in the main Hawaiian Islands
The “Pristipomoides_filamentosus.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Pristipomoides filamentosus as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Pristipomoides_filamentosus.txt”) and an associated projection file (Pristipomoides_filamentosus.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Pristipomoides filamentosus probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Pristipomoides_filamentosus.zip
Predicted probability of occurrence of Pristipomoides sieboldii in the main Hawaiian Islands
The “Pristipomoides_sieboldii.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Pristipomoides sieboldii as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Pristipomoides_sieboldii.txt”) and an associated projection file (Pristipomoides_sieboldii.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Pristipomoides sieboldii probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Pristipomoides_sieboldii.zip
Predicted probability of occurrence of Pristipomoides zonatus in the main Hawaiian Islands
The “Pristipomoides_zonatus.zip” file contains two files: an asci raster grid file of predicted probability of occurrence of Pristipomoides zonatus as a proportion (0.0-1.0) across the main Hawaiian Islands between 50 and 400 m depth (“Pristipomoides_zonatus.txt”) and an associated projection file (Pristipomoides_zonatus.prj). The asci file includes a six-line header section with data in 10315 columns (“ncols”) and 6394 rows (“nrows”) georegistered to 326589.18933012 and 2083866.5021671 at the lower left corner of the grid (“xllcorner”, “yllcorner”). Grid cell size is 60 m and no data values are -9999. Fish presence/absence data were collected from BRUV (BotCam) surveys collected between 2007-2015 and was modelled using boosted regression trees with various benthic habitat variables including depth, slope, rugosity, and bathymetric position index. The optimal model was used to predict Pristipomoides zonatus probability of occurrence across the entire spatial domain. Further details on methodology and results are contained in Oyafuso et al. (2017) Habitat-based species distribution modelling of the Hawaiian deepwater snapper-grouper complex. Fisheries Research, 195:19-27. https://doi.org/10.1016/j.fishres.2017.06.011
Pristipomoides_zonatus.zip