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 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 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 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.

Deep-sea coral and sponge presence and abundance (measured as catch per unit effort (CPUE) were generated based off data from bottom trawl surveys between 1993 to 2013. Sponge models were left at the class level due to taxonomic uncertainty. Models of coral were the combination of seven families (Acanthogorgidae, Paragorgidae, Isididae, Plexauridae, Primnoidae and Stylasteridae). One coral family, due to its dominance in the region, was modeled on its own. Another coral group, sea whips (order Pennatuloidea), were also modeled independently due to their dominance in soft substrate environments. All models are presented in the GeoTIFF format. Environmental layers used in the modeling of the taxa are also part of this data package. They include the parameters GeoTIFF rasters of latitude, longitude, bathymetry, mean bottom temperature for the region, current speed and direction for the deepest depth bin at each sampling point, ocean color, seafloor slope, mean current speed (exclusive of tides), and max tidal current predicted over a year. For details on the measurement of each environmental layer, see the journal article Rooper et al. (2017).

These data are a set of raster maps of community-level predictions of deep-sea coral and sponge taxa distributions off the continental U.S. west coast, spanning depths from 50 to 1200 m. The raster files come in two versions: one where predicted distribution suitability range from 0 - 1 and one where the predicted suitability is classified into five classes; very low (0–0.2), low (0.21–0.40), moderate (0.41–0.60), high (0.61–0.80) and very high (0.81–1.00). These raster maps were derived from 40 habitat suitability models (HSMs) conducted at the genus- and species-level maps done by Poti et al. (2020). A cluster analysis of the original individually-modeled taxa identified 10 groups whose member HSMs were stacked and averaged to produce a stacked species distribution model (S-SDM). Further details about the generation of the S-SDMs and their interpretation can be found in Shantharam et al. (2025).

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 consists of raster geotiff outputs of relative environmental favorability for coral growth and survival in the United States territories of Guam and American Samoa across 3 climate scenarios: Present, Intermediate Emissions (Representative Concentration Pathway 4.5), and Worst Case Emissions (Representative Concentration Pathway 4.5). These datasets were generated from a synthesis of spatial variability in many environmental conditions, including thermal stress, wave power, irradiance, chlorophyll concentrations, macroalgal cover, calcite concentrations, turbidity, and erosion. Input conditions were classified as “Managed” or “Non-managed” based on whether the condition could be managed at the island scale. Environmental favorability scores range from 0 to 1, with 0 representing the worst observed conditions for coral and 1 representing the best observed conditions within each territory and scenario. The full methods and results are described in detail in the parent manuscript, “Where favorable environmental conditions and resilient corals coincide: Guam and American Samoa” (2022).

This dataset consists of physics-based XBeach Non-hydrostatic hydrodynamic models input files used to study how coral reef restoration affects waves and wave-driven water levels over coral reefs, and the resulting wave-driven runup on the adjacent shoreline. Coral reefs are effective natural coastal flood barriers that protect adjacent communities. Coral degradation compromises the coastal protection value of reefs while also reducing their other ecosystem services, making them a target for restoration. Here we provide a physics-based evaluation of how coral restoration can reduce coastal flooding for various types of reefs. These input files accompany the modeling conducted for the following publication: Roelvink, F.E., Storlazzi, C.D., van Dongeren, A.R., and Pearson, S.G., 2021, Coral reef restorations can be optimized to reduce coastal flooding hazards: Frontiers in Marine Science, https://doi.org/10.3389/fmars.2021.653945.