Coral and sponge presence, abundance (measured as catch per unit effort (CPUE)), and diversity (number of coral families) models were generated based off of data from bottom trawl surveys in the Aleutian Islands, Alaska from 1991-2011 using the generalized additive modeling method at a 100 x 100 m spatial resolution. Sponge models were left at the phylum level due to taxonomic uncertainty. Greater certainty in coral taxonomy allowed for modeling of individual Stylasteridae and Primnoidae coral families. A further overall coral group modeling was conducted to include all hydrocoral and gorgonian corals identified. All models are presented in the GeoTIFF raster 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, aspect relative to mean current, mean bottom temperature for the region, current speed and direction for the deepest depth bin at each sampling point, ocean color, seafloor rugosity, sediment type, seafloor slope, mean current speed (exclusive of tides), and max tidal current measured over a year. For details on the measurement of each environmental layer, see Rooper et al. (2014).

Predictive models of density, presence, and individual height were generated from deep-sea coral and sponge specimen collected during bottom trawl surveys conducted along the East Bering Sea continental slope (<1200 m). The taxonomic resolution was left at coarse levels (corals, sea whips, and sponges) due to uncertainty in identification and the uncommon encounter rate of individuals caught in the trawls. Structure-forming corals were modeled as an individual group while sea whip and sea pens were modeled together collectively as a group called sea whips. All sponges were modeled as a singular group. Environmental layers employed in the modeling process are also available as part of this package except for the sediment parameters which have been previously archived (Smith and McConnaughey 1999). They include the parameters latitude, longitude, bathymetry, max bottom temperature for the region, current speed for the deepest depth bin at each sampling point, ocean color, and seafloor slope. For details on the measurement or computation of each environmental layer, see the journal article Sigler et al. (2015). All models and environmental layers are available as GeoTIFF rasters.

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.

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 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.

Generalized linear models (GLM) generated for predicting the distribution of macroinvertebrate-sized hydroids, brittle stars, and short and tall sea pens that live in the Santa Barbara Channel (SBC) off California. Models contain probability of occurrence in the SBC from Refugio State Beach (34.58 N, 120.18 W) to Hueneme Canyon (34.18 N, 119.28W) along 5 survey blocks: Refugio, Coal Oil Point, Santa Barbara, Ventura, and Hueneme Canyon. Presence-absence data from geo-referenced video data and still images were fit to binomial GLMs with logit link functions (multiple logistic regression). For more details on the modeling process, see the journal article Krigsman et al. (2012). All models are presented in the GeoTIFF format.