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STUDY TITLE: Cross-Shelf Benthic Habitat Suitability Modeling on the Pacific Outer 

Continental Shelf (OCS)

REPORT TITLE: Cross-Shelf Habitat Suitability Modeling: Characterizing Potential Distributions 

of Deep-Sea Corals, Sponges, and Macrofauna Offshore of the US West Coast

OCS STUDY NUMBER: BOEM 2020-021

NUMBER OF PAGES: 267

CONTRACT NUMBER: M16PG00014

SPONSORING OCS REGION: Pacific

APPLICABLE PLANNING AREA: Continental US West Coast

FISCAL YEAR(S) OF PROJECT FUNDING: FY2016: $112,000; FY2017: $117,000; FY2018: 

$121,000

COSTS BY FISCAL YEAR: FY2016: $0; FY2017: $193,510.71; FY2018: $155,498.26; FY2019: 

$991.03; FY2020: $0; FY2021: $0

CUMULATIVE PROJECT COST: $350,000

COMPLETION DATE OF REPORT: October 2020

PROJECT MANAGER: John Christensen

AFFILIATION OF PROJECT MANAGER: U.S. Department of Commerce, National Oceanic and 

Atmospheric Administration, National Ocean Service, National Centers for Coastal 

Ocean Science, Marine Spatial Ecology Division, Biogeography Branch, NCCOS IAA 

MOA-2016-055/10325

ADDRESS: 1305 East-West Hwy, SSMC-4, N/SCI-1, Silver Spring, MD 20910

PRINCIPAL INVESTIGATORS*: Matthew Poti and John Christensen

KEY WORDS: benthic, benthic infauna, boosted regression trees, cold-water corals, continental 

shelf, continental slope, deep-sea corals, deep-sea sponges, eastern Pacific continental 

margin, habitat suitability modeling, invertebrates, macrofauna, MaxEnt, Pacific Outer 

Continental Shelf, spatial predictive modeling, US West Coast

BRIEF ABSTRACT: This study developed maps of the modeled spatial distributions of deepsea corals, sponges, and benthic macrofauna offshore of the continental US West Coast to 

1,200 m depth. Occurrence data for deep-sea corals, sponges, and macrofauna and spatial 

environmental predictors depicting seafloor topography, substrate, oceanography, and 

geography were used in statistical models to predict and map suitable habitat for 31 deep-sea 

coral taxa, 15 sponge taxa, and 43 macrofauna taxa. Maps identify areas where these 

organisms are more likely and less likely to occur, and preliminary results were used to guide 

future data collection efforts. This information can contribute to regional ocean planning efforts 

and assessments for offshore energy, ground fishing, conservation, and other activities 

potentially impacting benthic biota in deeper waters offshore of the US West Coast.
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BACKGROUND: Many deep-sea corals and sponges add structural complexity to benthic 

habitats, provide refuge and substrate, and increase the number and availability of 

microhabitats for other organisms, thereby creating hotspots of biological diversity. These 

organisms are generally long-lived, slow-growing, and fragile, making them vulnerable to human 

impacts. Benthic macrofauna are sensitive to changes in sediment conditions and can be 

indicators of habitat quality. These organisms may be impacted by human activities in the 

marine environment that physically disturb the seafloor, including offshore energy development. 

Information on the spatial distributions of these organisms is critical for assessing potential 

impacts from these activities.

OBJECTIVES: Produce maps of the modeled spatial distributions of deep-sea corals, sponges, 

and benthic macrofauna offshore of the continental US West Coast.

METHODS: Records of deep-sea coral and sponge occurrence (presences) were obtained from 

the NOAA National Database for Deep-Sea Corals and Sponges. Dr. Sarah Henkel at Oregon 

State University provided records of macrofauna occurrence (presences and absences) at 

sampling stations. Spatial predictive models were generated to estimate relationships between 

occurrence and spatial environmental predictors depicting seafloor topography, substrate, 

oceanography, and geography. Maximum entropy (MaxEnt) models were used to predict and 

map habitat suitability for each deep-sea coral and sponge taxon, while boosted regression tree 

models were used to predict and map the probability of occurrence for each macrofauna taxon 

across the entire study area. Variability in model predictions was also mapped for each taxon to 

provide a measure of the level of confidence in model predictions.

RESULTS: Maps of the predicted habitat suitability are presented for 31 deep-sea coral taxa 

and 15 sponge taxa and maps of the predicted probability of occurrence are presented for 43 

macrofauna taxa. Corresponding maps of the coefficient of variation of model predictions 

provide a measure of the variability, or uncertainty, associated with the model predictions for 

each taxon. For macrofauna, the relative importance of each environmental predictor to model 

fitting is also presented and may provide useful hypotheses for future studies aimed more at 

inference about the ecological drivers and mechanisms behind the spatial distributions of 

macrofauna in the study area. Deep-sea coral taxa associated with hard substrate were 

predicted to have robust high habitat suitability across all latitudes and out to the 1,200 m limit of 

the study area. Multiple taxa were predicted to occur in the Monterey Canyon and surrounding 

the Channel Islands in southern California.

CONCLUSIONS: This effort represents the highest resolution to date for modeling habitat 

suitability of deep-sea corals, sponges, and macrofauna for the continental US West Coast and 

can be used to inform planning for ocean activities such as offshore energy development. While 

the models developed in this study did not directly predict distributions of communities or 

provide direct, spatial information about taxonomic richness, the maps presented here can be 

used to infer patterns in predicted habitat suitability across taxa. The maps identify areas where 

deep-sea corals, sponges, and macrofauna are more likely and less likely to occur. This 

information can contribute to marine planning and assessments of potential impacts of activities 

on these sensitive benthic biota.
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STUDY PRODUCTS:

1. BOEM study report: Poti, M., Henkel, S.K., Bizzarro, J.J., Hourigan, T.F., Clarke, M.E., 

Whitmire, C.E., Powell, A., Yoklavich, M.M., Bauer, L., Winship, A.J., Coyne, M., Gillett, 

D.J., Gilbane, L., Christensen, J., and Jeffrey, C.F.G. 2020. Cross-Shelf Habitat Suitability 

Modeling: Characterizing Potential Distributions of Deep-Sea Corals, Sponges, and 

Macrofauna Offshore of the US West Coast. Camarillo (CA): US Department of the 

Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2020-021. 267 p.

2. Digital data package including four separate ESRI file geodatabases (.gdb), explained

here and in the document M16PG000014 External Hard Drive Inventory 20201101.doc:

Table 1. Contents of ESRI file geodatabases in data deliverable.

File Geodatabase

File 

Size 

(GB)

Contents

NCEI 

Accession 

Number

Websites Web/Map Services

USWestCoast_MultibeamBat

hymetry.gdb 8.36

455 Raster 

Datasets, 455 

Feature Classes 

N/A TBD None

USWestCoast_DeepSeaCora

lsSponges.gdb 1.88 1 Feature Class, 186 Raster Datasets TBD TBD TBD

USWestCoast_Macrofauna.g

db 78.6 1 Feature Class, 86 Raster Datasets TBD TBD TBD

USWestCoast_Environmental

Predictors.gdb 51.4 132 Raster Datasets TBD TBD TBD

a. USWestCoast_MultibeamBathymetry.gdb (NOT SUBMITTED TO NCEI; DERIVED 

PRODUCTS CAN BE FOUND IN USWestCoast_EnvironmentalPredictors.gdb)

i. containing a spatial file geodatabase raster dataset for each of the 455 

multibeam bathymetry datasets (listed in Appendix B of the study report) used to 

create the bathymetry synthesis;

- file name: [Name of Bathymetry Dataset]

ii. containing a spatial file geodatabase feature class of the survey footprint for each 

of the 455 multibeam bathymetry datasets (listed in Appendix B of the study 

report) used to create the bathymetry synthesis;

- file name: [Name of Bathymetry Dataset]_Footprint

b. USWestCoast_DeepSeaCoralsSponges.gdb

i. containing a spatial file geodatabase feature class of the deep-sea coral and 

sponge occurrence records (i.e., presences) extracted from the NOAA National 

Database;

- file name: DSCRTP_NatDB_version20190208_West_Coast_to_1200m

ii. containing spatial file geodatabase raster datasets (200 x 200 m) depicting the 

model predicted habitat suitability (mean, classified mean, robust high habitat 

suitability, and coefficient of variation) for each of the 46 taxa of deep-sea corals 

and sponges (listed in Tables 3.2 and 3.3 of the study report); the classified 

mean prediction and robust high habitat suitability should be used to make 

comparisons among taxa, while the coefficient of variation can be used to identify 

locations where there is more variability (high CV) or less variability (low CV) in 

model predictions

- file names:

[Name of Taxon]_Mean_Prediction
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[Name of Taxon]_Classified_Mean_Prediction

[Name of Taxon]_Robust_High_Suitability_Prediction

[Name of Taxon]_CV_Prediction

iii. containing spatial file geodatabase raster datasets (200 x 200 m) depicting the 

number of taxa of deep-sea corals associated with hard substrate that have high 

habitat suitability and robust high habitat suitability

- file names:

Num_Taxa_Corals_HardSubstrate_with_High_Suitability

Num_Taxa_Corals_HardSubstrate_with_Robust_High_Suitability

c. USWestCoast_Macrofauna.gdb

i. containing a spatial file geodatabase feature class of macrofauna abundance for 

each taxon at 2,905 sampling stations

- file name: Macrofauna_Occurrence_Data_by_Station

ii. containing spatial file geodatabase raster datasets (25 x 25 m) depicting the 

model predicted probability of occurrence (mean, coefficient of variation) for 43 

taxa of macrofauna (listed in Table 4.2 of the study report); the mean prediction 

represents the probability of a taxon being observed in sample taken in each grid 

cell, while the coefficient of variation can be used to identify locations where 

there is more variability (high CV) or less variability (low CV) in model 

predictions; because individual raster datasets are large files (~8 GB), these 

datasets may be slow to draw/render in a GIS 

- file names:

[Name of Taxon]_Mean_Prediction

[Name of Taxon]_CV_Prediction

d. USWestCoast_EnvironmentalPredictors.gdb

i. containing spatial file geodatabase raster datasets at 200 x 200 m grid resolution 

for the 66 environmental predictor variables (listed in Tables 2.1, 2.2, and 2.4 of 

the study report) considered for inclusion in the models of deep-sea coral and 

sponge occurrence;

- file name: [Name of Predictor]_200x200m

ii. containing spatial file geodatabase raster datasets at 25 x 25 m grid resolution 

for the 66 environmental predictor variables (listed in Tables 2.1, 2.2, and 2.4 of 

the study report) considered for inclusion in the models of macrofauna 

occurrence; because individual raster datasets are large files (~8 GB for 25 x 25 

m grids), these datasets may be slow to draw/render in a GIS.

- file name: [Name of Predictor]_25x25m

3. Study footprint: OCS_Study_BOEM_2020_021_StudyFootprint.gdb

4. Study metadata: OCS_Study_BOEM_2020_021_Study_Footprint_Data_Dictionary.xls 

5. Websites hosting study information or data products

a. NCCOS project page

b. BOEM ESP study profile

c. NOAA’s Deep-Sea Coral Data Portal

d. Marine Cadastre

e. FRAM Portal

f. West Coast Ocean Data Portal

6. Publications
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7. Presentations

a. Creation of a U.S. West Coast substratum map and its use in estimating deep-water 

coral habitat suitability. Presented at the Marine Geological & Biological Habitat 

Mapping (GeoHab) Meeting, Santa Barbara, CA, May 2018.

b. Characterizing potential distributions of deep-sea corals and sponges offshore the US 

West Coast through spatial predictive modeling. Presented at the 7th International 

Symposium on Deep-Sea Corals, Cartagena, Colombia, August 2019.

c. Integrating predictions of deep-sea coral and sponge habitat with ocean mapping and 

exploration offshore the US West Coast – an example from the EXPRESS campaign. 

Presented at the Ocean Sciences Meeting, San Diego, CA, February 2020.

d. Characterizing potential distributions of deep-sea corals and sponges offshore the US 

West Coast through spatial predictive modeling. Presented as part of NOAA Deep-Sea 

Coral Research and Technology Program (DSCRTP) webinar series, March 2020.

8. Data requests

a. Potential dive tracks were provided at 21 sites for AUV and ROV surveys conducted 

during a 2018 expedition by the NOAA Ship Bell M. Shimada as part of the EXPRESS 

campaign. Data from AUV surveys were used in the example model validation analysis 

presented in the study report. Contacts: Liz Clarke (elizabeth.clarke@noaa.gov), Curt 

Whitmire (curt.whitmire@noaa.gov), Tom Laidig (tom.laidig@noaa.gov)

b. Potential dive tracks were provided at 7 potential areas of interest within the CalDIG

study area. Contact: Lisa Gilbane (lisa.gilbane@boem.gov)

c. Provided a summary of the predicted habitat suitability for deep-sea corals and 

sponges at 20 sites surveyed during a 2019 expedition by the NOAA Ship Ruben 

Lasker as part of the EXPRESS campaign. Contact: Curt Whitmire 

(curt.whitmire@noaa.gov)

d. Data package was provided to Oregon Coastal Management Program to support 

analysis of effects of offshore seafood processing. Contacts: Tanya Haddad 

(tanya.haddad@state.or.us), Nick Tealer (nick.tealer@state.or.us)

e. Data package was provided to CINMS for use in an ONMS education portal in 

SeaSketch (‘Exploring Deep-Sea Coral and Sponge Communities’). Contact: Lizzie 

Duncan (lizzie.duncan@noaa.gov)

f. Data package and advice were provided to CINMS for planning ROV surveys 

conducted by the E/V Nautilus during expedition NA123 in October 2020.

9. Citations

i) Abi Powell (in prep)

ii) Di Watters (in prep)

10. Media

* The affiliation of the Principal Investigator(s) may be different than that listed for Project 

Manager(s).
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MAP OF STUDY AREA (Figure 1.1 of report OCS Study BOEM 2020-021):

Figure 1. Map of the study area offshore of the continental US West Coast from 0 to 1,200 m 

depth.