This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals and sponges offshore of the continental U.S. West Coast to 1200 m depth. It includes raster datasets at 200 x 200 m spatial resolution depicting the mean of the predicted relative habitat suitability, the coefficient of variation of the predicted relative habitat suitability, the classified mean relative habitat suitability, and the ‘robust high’ habitat suitability prediction for each of 31 taxa of deep-sea corals and 15 taxa of sponges and raster datasets at 200 x 200 m spatial resolution depicting the number of taxa of deep-sea corals associated with hard substrate that have ‘high’ habitat suitability or ‘robust high’ habitat suitability at each grid cell. The data collection also includes raster datasets at 200 x 200 m spatial resolution depicting each of the 66 spatial environmental predictor variables considered for fitting the models.

This data collection contains geospatial data from models predicting the spatial distributions of deep-sea corals (DSCs) and hardbottom habitats offshore of the southeastern U.S. It includes a database (.csv text file) containing records of occurrence (presence-absence) for DSCs with associated measures of sampling effort and bottom type from 20 datasets comprised of data from visual field surveys conducted with underwater vehicles. It also includes raster datasets at 100 x 100 m spatial resolution depicting the median and coefficient variation of the predicted occurrence (occupancy probability) for 24 taxa of DSCs (23 genera, 1 family) and hardbottom habitats. Additional raster datasets depict the median and coefficient of variation of the predicted genus richness for the 23 genera of DSCs. The data collection also includes raster datasets at 100 x 100 m spatial resolution depicting each of the 62 spatial environmental predictors considered for fitting the models. For more information, see Poti et al. (2022). The project to compile this model took place between 2016 and 2021, however the model input data range from 2001-2018 and the model output covers the same timeframe.

This dataset is a compilation of modeled spatial distributions of Threatened corals, Orbicella annularis (lobed star coral) and Orbicella faveolata (mountainous star coral)/Orbicella franksi (boulder star coral). in the shallow and upper mesophotic waters (0 – 60 meters depth) on the eastern Puerto Rico shelf (encompassing St. Thomas and St. John, U.S. Virgin Islands and the partial waters around Culebra and Vieques, Puerto Rico). Models for O. faveolata/O. franksi were combined into one model. All three Orbicella spp. area listed as Threatened under the U.S. Endangered Species Act. The raster datasets contain predicted probability of occurrence and prediction uncertainty for O. annularis and O. faveolata/O. franksi at four different model extents: • 0 – 60 meter depth model: Orbicella spp. models encompassing the entire modeling region, both shallow and mesophotic depths from 0 to 60 meters (capped at 60 meters for Orbicella spp. known depth range in this region) • 0 – 60 meter depth south shore only model: Orbicella spp. models of the south shore only removal of the mesophotic depths of the north shore, north of St. Thomas and St. John, USVI • Shallow only model (0 – 30 meter depth): Orbicella spp. models of the shallow waters only, removal of the mesophotic depths • Mesophotic only model (30 – 60 meter depth: Models of O. faveolata/O. franksi at mesophotic depths only remove of the shallow depths Models were conducted using a presence/background sample model, which involves the use of presence-only data. Maximum entropy modeling was used specifically, initiated through the Java software program, MaxEnt.

This dataset is a compilation of modeled current and future density distributions of threatened elkhorn corals Acropora palmata in the shallow water (bottom depth ≥ -20 m) off St. Thomas, St. John and St. Croix, U.S. Virgin Islands. The raster data sets contain predicted distributions of species density and the prediction uncertainty in 2013, 2014, 2015, 2035 and 2055 estimated using process-based random forest (RF) and dynamic range models (DRM). These predictions were generated to inform Caribbean A. palmata restoration plans in the U.S. Virgin Islands.

Observations of coral species and size were documented by scuba divers along pre-determined transects and are presented here in comma-separated format.

This dataset includes predictive habitat models for deep-sea corals in Alaskan waters, including the U.S. Exclusive Economic Zone of several coral taxa at the order (Antipatharia and Scleractinia) and the suborder level (Alcyoniina, Calcaxonia, Filifera, Holaxonia, Scleraxonia, and Stolonifera). The fauna were modeled at a ~ 700 x 700 m spatial resolution with a variety physical, chemical, and environmnetal predictors under the MaxEnt framework with a spatial partioning cross-validation approach. For more details on the construction of the models, see Guinotte and Davies (2013). All models are present in GeoTIFF.

Coral Point Count with Excel extensions (CPCe; Kohler and Gill, 2006) was used to help calculate percent of coral cover or other benthic substrates from a randomly selected subset of seafloor photographs collected on the west Hawaii Island coast.