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 data collection contains geospatial data from models predicting the spatial distributions of benthic macrofauna offshore of the continental U.S. West Coast to 1200 m depth. It includes raster datasets at 25 x 25 m spatial resolution depicting the mean of the predicted probability of occurrence and the coefficient of variation of the predicted probability of occurrence for 43 taxa of benthic macrofauna. The data collection also includes raster datasets at 25 x 25 m spatial resolution depicting each of the 66 spatial environmental predictor variables considered for fitting the models.

This dataset contains geospatial data from spatial predictive models that were developed for 22 deep-sea coral and sponge (DSCS) taxa within the Papahānaumokuākea Marine National Monument (PMNM) from depths of 100-3,500 m. It includes raster datasets at 360 x 360 m spatial resolution depicting the predicted probability of occurrence for each of these taxa and a raster dataset at 360 x 360 m spatial resolution depicting the predicted taxonomic richness. These predictions provide a baseline for the potential distribution of these vulnerable and ecologically significant communities in the northwestern Hawaiian Islands (NWHI), and will support management planning, permitting, exploration and sanctuary designation efforts by the Monument. The data collection also includes raster datasets at 360 x 360 m spatial resolution depicting each of the 44 spatial environmental predictor variables considered for fitting the models.

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.

Shapefiles of the Aquaculture Opportunity Area (AOA) study developed during 2021 for the Gulf of Mexico. Included in this dataset are: (1) Study areas in the Gulf of Mexico developed based on depth, jurisdictional boundaries, and Level III biogeographical breaks. (2) Compiled observations of Harmful Algal Blooms (Karina brevis) from 2000 to 2018 in the Gulf of Mexico and eastern Florida. (3) Suitability modeling results for the West, Central, East, and Southeast Gulf of Mexico study areas are presented as categories (“Unsuitable,” “Low,” “Moderate,” “High”) based on ocean use and conservation concerns, including: national security, natural and cultural resources, industry, navigation, transportation, aquaculture, and fishing. (4) High-High clusters (HH) identified as the most suitable areas from LISA (Local Index of Spatial Association) analysis. (5) Refined HH clusters that could accommodate at least one 500-acre AOA option. (6) Highest ranking options for each of the refined HH clusters representing a 500- to 2000-acre area between 50 to 150 meters depth that has relatively high suitability for generalized marine aquaculture based on a within cluster model evaluating logistics, vessel traffic, commercial fishing, and oceanography data. (7) Location and areal extent of options identified for each study region meeting a dispersion rule (greater than 30 nautical miles distance between locations).

The sand shoals (modeled) polygons represent the hypothesized distribution of sand shoals of the Gulf of Mexico and US Atlantic Coast based on seafloor characteristics and distance to shoreline variables. Defined by Rutecki et al. (2014), a sand shoal is "a natural, underwater ridge, bank, or bar consisting of, or covered by, sand or other unconsolidated material, resulting in shallower water depths than surrounding areas." In the dataset, attributes characterize shoals with a classification scheme developed with a basis in the Coastal and Marine Ecological Classification Standard (CMECS).