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 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 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 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.

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 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.

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.

This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the data were reduced by clustering cross-shore profiles based on morphology and hydrodynamic response to typical wind and swell wave conditions. By representing a large variety of coral reef morphologies with a reduced number of RCPs, a computationally feasible number of numerical model simulations can be done to obtain wave-runup estimates. The RCPs identified here can be combined with probabilistic tools that can provide an enhanced prediction given a multivariate wave and water level climate and reef ecology state. These data accompany the following publication: Scott, F., Antolinez, J.A., McCall, R.T., Storlazzi, C.D., Reniers, A., and Pearson, S., 2020, Hydro-morphological characterization of coral reefs for wave runup prediction: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2020.000361.