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 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 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 was created for potential use as an environmental predictor in spatial predictive models of deep-sea coral habitat suitability. Deep-sea corals are of particular conservation concern due to their slow growth rates and vulnerability to disturbance. This is a derived product. Modeling can lend insights into the environmental factors driving the distribution of deep-sea corals, helping to build understanding of how these unique ecosystems function. This dataset depicts predicted likelihood of suitable habitat for the deep-sea corals: Alcyonacea (order), the suborders Calcaxonia, Holaxonia, Scleraxonia, Alcyoniina, Stolonifera: of order Alcyonacea; Pennatulacea (order), the suborders Sessiliflorae, Subsessiliflorae: of order Pennatulacea; Scleractinia (order), genera Dasmosmilia and Desmophyllum: of order Scleractinia, family Caryophylliidae; and the family Flabellidae: of order Scleractinia. The dataset also depicts categorical seafloor aspect (slope direction) in the U.S. Northeast Atlantic and Mid-Atlantic derived from a bathymetry dataset. The likelihood of suitable habitat for deep-sea corals of the above order, suborders, genera, and families are depicted by threshold levels created for the dataset.

Predictive habitat models for deep-sea corals within the U.S. West Coast Exclusive Economic Zone were developed to aid future research and spatial mapping. Models were built at a 500 x 500 m spatial resolution with a range of physical, chemical, and environmental variables thought to influence the distribution of deep-sea corals. Models were generated using records, from a variety of sources, reliably identified at the order and suborder levels including Alcyoniina, Antipatharia, Calcaxonia, Holaxonia, Scleraxonia, and Scleracaxonia under the MaxEnt framework with a spatial spatial partitioning cross-validation approach. Further, models were generated for all taxa records with thresholded predicted outputs at the 0.5 and 0.75 cutoff presence/absence value. For more details on the construction of the models, see Guinotte and Davies (2014). All models are present in GeoTIFF format.

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.

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.

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).

This part of DS 781 presents data for the map showing the predicted distribution of cup corals in the Santa Barbara Channel, California, region. The raster data file is included in "CupCorals_SantaBarbaraChannel.zip," which is accessible from https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/data_catalog_SantaBarbaraChannel.html. Presence-absence data of benthic macro-invertebrates and associated habitat (that is, sediment type and depth) were collected using a towed camera sled in selected areas along the coast off southern California during a ground-truth observation cruise conducted by the U.S. Geological Survey and NOAA National Marine Fisheries Service for the California Seafloor Mapping Program. Benthic community structure was determined from 35 video towed-camera transects within California's State Waters 3-nautical-mile limit in the Santa Barbara Channel. These transects produced a total of 923 10-second observations from the Offshore of Refugio Beach map area (34.5 degrees N., 120.1 degrees W.) to the Hueneme Canyon and vicinity map area (34.1 degrees N., 119.2 degrees W.). Presence-absence data were collected for 29 benthic, structure-forming nonmobile taxa. Using this information, generalized linear models (GLMs) were developed to predict the probability of occurrence of five commonly observed taxa (cup corals, hydroids, short and tall sea pens, and brittle stars in the sediment) in five map areas within the Santa Barbara Channel (SBC). A sixth map area (Offshore of Carpinteria) was not modeled owing to insufficient data. The analysis demonstrates that the community structure for the five map areas can be divided into three statistically distinct groups: (1) the Hueneme Canyon and vicinity and the Offshore of Ventura map areas; (2) the Offshore of Santa Barbara and the Offshore of Coal Oil Point map areas; and (3) the Offshore of Refugio Beach map area. These three distinct groups are the main reason that the probability for each taxa can be so dramatically different within one predictive-distribution map area. The five most frequently observed benthic macro-invertebrate taxa were selected for these predictive-distribution grids. Presence-absence data for each selected invertebrate were fit to specific generalized linear models using geographic location, depth, and seafloor character as covariates. Data for the covariates were informed by the bathymetry, seafloor character, and other ground-truth data from the different map areas of the Santa Barbara Channel region that are part of the California State Waters Map Series DS 781. Observations based on depth were limited by the capability of the towed camera sled; as a result, no predictions were made below depths of 150 m (in other words, on the continental slope or in Hueneme Canyon). Cup corals and hydroids had high predicted probabilities of occurrence in areas of hard substrata, whereas short and tall sea pens were predicted to occur in parts of the SBC that had unconsolidated and mixed sediment. Our model predicted that brittle stars would occur throughout the entire SBC on various bottom types.