South Atlantic Hardbottom & Deep-Sea Coral

This indicator measures known and predicted suitable locations of hardbottom habitat and deep-sea corals. It combines multiple datasets from the National Oceanic and Atmospheric Administration and The Nature Conservancy.

Reason for Selection

Hardbottom provides an anchor for important seafloor habitats such as deep-sea corals, plants, and sponges. Many deep-sea corals form tree-like shapes and complex reefs that provide valuable habitat structure. Hardbottom and associated deep-sea coral communities support a wide range of invertebrate and fish species (NOAA 2018).

Input Data

• South Atlantic Blueprint 2021 extent
• National Oceanic and Atmospheric Administration (NOAA) U.S. Northeast/Mid-Atlantic Deep-Sea Coral Habitat Suitability Models and U.S. Southeast Deep-Sea Coral Habitat Suitability Models, accessed 1-19-2021
• NOAA National Database for Deep-Sea Corals and Sponges (version 20201021-0), accessed 1-27-2021, NOAA Deep-Sea Coral Research & Technology Program• The Nature Conservancy’s (TNC) South Atlantic Bight Marine Assessment (chapter 3 of the final report provides more detail on the seafloor habitats analysis)
• Predictive Modeling and Mapping of Hardbottom Seafloor Habitats off the Southeast U.S. – unpublished NOAA data and draft final report entitled Assessment of Benthic Habitats for Fisheries Management provided by Dr. Matt Poti with the NOAA National Centers for Coastal Ocean Science (matthew.poti@noaa.gov)

Mapping Steps

Preparing the NOAA Coral Habitat Suitability Data

From the NOAA coral habitat suitability data, use the following thresholded logistic output models:

• Northeast and Southeast models for Scleractinia (SE SCLER-FRAME, SE SCLER-NONFRAME, NE SCLER). Combine the Southeast frame-forming and non-frame-forming models for consistency with the Northeast data.
• Northeast and Southeast models for Gorgonian Alcyonacea (SE ALCY-GORG, NE ALCY-GORG)
• Southeast model for Antipatharia (SC ANTI), since no corresponding Northeast model was available

Combine the above models using the ArcPy Spatial Analyst Cell Statistics-Maximum Function. This assigns each pixel the highest score it received in any of the above models, which ranges from:

• 10 – Very High
• 5 – High
• 2 – Medium
• 1 – Low

Preparing the NOAA Hardbottom Data

Reclassify the NOAA hardbottom data to match the above NOAA coral habitat suitability data:

• Give FPR:FNR>10:1 a value of 10 - Very High
• Give FPR:FNR>5:1 a value of 5 - High
• Give FPR:FNR>2:1 a value of 2 - Medium
• Give FPR:FNR>1:1 a value of 1 - Low

These data contain additional classes to further discriminate between levels of lower predicted hardbottom suitability. Change all of the remaining values to NoData to match the NOAA coral habitat suitability data.

Preparing the TNC Data

Convert the TNC data to raster and classify using values from the “TEXT_DESC” field.

Assign a value of 11 to TNC data with the following values in the TEXT_DESC field:

• mapped hard bottom area

Assign a value of 10 to TNC data with the following values in the TEXT_DESC field:

• 1. high confidence hard bottom slope
• 1. shelf upper slope: high confidence hard bottom
• 1. hardbottom slope: high confidence hard bottom
• 1. high confidence hard bottom pavement

Assign a value of 5 to TNC data with the following values in the TEXT_DESC field:

• 1. probable hard bottom slope
• 1. shelf upper slope: probable hard bottom
• 1. hardbottom slope: probable hard bottom
• 1. probable hard bottom pavement

Assign a value of 2 to TNC data with the following values in the TEXT_DESC field:

• 1. potential hard bottom slope
• 1. shelf upper slope: potential hard bottom
• 1. hardbottom slope: potential hard bottom

Assign a value of 1 to TNC data with the following values in the TEXT_DESC field:

• 1. possible hard bottom slope

Preparing the NOAA Coral and Sponge Observation Data

• From the NOAA National Database for Deep-Sea Corals and Sponges (version 20201021-0), select records with “VernacularNameCategories” fields of: stony coral (cup coral), stony coral (branching), black coral, gorgonian coral, or stony coral (unspecified). These vernacular name categories best correspond to the taxa included in the suitability models used above.
• Buffer the point data by 185 m to create a footprint similar to the 370 m pixel resolution of the NOAA suitability models.
• Give these locations a value of 11.

Creating the Combined Indicator

Combine the resulting rasters using the cell statistics tool with the overlay statistic Maximum. For better consistency with the numbering of other indicators, reclassify the values as follows:

• Change 11 to 5
• Change 10 to 4
• Change 5 to 3
• Keep 2 as 2
• Keep 1 as 1

As a final step, clip to the spatial extent of South Atlantic Blueprint 2021.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint 2022 Data Download under BlueprintInputs > BaseBlueprint2022 > 6_Code.

Final Indicator Values

Indicator values are assigned as follows:

• 5 = Observed coral or hardbottom
• 4 = Very high suitability for coral or hardbottom
• 3 = High suitability for coral or hardbottom
• 2 = Medium suitability for coral or hardbottom
• 1 = Low suitability for coral or hardbottom

Known Issues

• This indicator likely underpredicts deep-sea coral in areas just above the South Atlantic Bight. A number of deep-sea coral species that do occur there were not modeled in the NOAA deep-sea coral suitability data due to a lack of observation data at the time. Updated models are in development for that area and will hopefully be incorporated into this indicator in the near future.
• It likely underpredicts hardbottom suitability overall. While this indicator does include newer known coral locations, the underlying TNC and NOAA data was developed in 2017 and 2013, respectively. Rerunning those models with new information would likely expand suitability in some areas. Updated NOAA deep-sea coral suitability models are in development and will hopefully be incorporated into this indicator in the near future.
• While this layer has a 30 m resolution, the NOAA deep-sea coral suitability data was coarser than that. We downsampled 370 m pixels to 30 m.
• In the Northeast NOAA Coral Habitat Suitability Data, the layers contain an artifact/edge effect on the southern boundary of the ALCY-GORG and SCLER models. As a result, this indicator overpredicts suitable coral habitat at the edge of the modeling extent for the Northeast data.

Disclaimer: Comparing with Older Indicator Versions

There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov).

Literature Cited

Conley, M.F., M.G. Anderson, N. Steinberg, and A. Barnett, eds. 2017. The South Atlantic Bight Marine Assessment: Species, Habitats and Ecosystems. The Nature Conservancy, Eastern Conservation Science. [http://easterndivision.s3.amazonaws.com/Marine/SABMA/SABMA_Report_11April2018.pdf].

Kinlan, Brian P., Matthew Poti, Amy F. Drohan, David B. Packer, Dan S. Dorfman, and Martha S. Nizinski. Predictive modeling of suitable habitat for deep-sea corals offshore the Northeast United States, Deep Sea Research Part I: Oceanographic Research Papers, Volume 158, 2020, 103229. ISSN 0967-0637. [https://doi.org/10.1016/j.dsr.2020.103229].

National Oceanographic and Atmospheric Administration. Unpublished Draft: Assessment of Benthic Habitats for Fisheries Management. May 2015. [http://secassoutheast.org/pdf/DRAFT_Benthic%20Habitats%20for%20Fisheries%20Management%20Final%20Report%20April%202015%20with%20MP%20additions.pdf].

National Oceanographic and Atmospheric Administration. Deep Coral Predictive Habitat Modeling in the U.S. Atlantic and Gulf of Mexico: Focusing on Uncharted Deep-Sea Corals. U.S. Northeast/Mid-Atlantic Deep-Sea Coral Habitat Suitability Models – Digital Data Package and U.S. Southeast Deep-Sea Coral Habitat Suitability Models - Digital Data Package. [https://coastalscience.noaa.gov/project/deep-coral-habitat-modeling-atlantic-gulf-mexico/]. Accessed 19 January 2021.

National Oceanographic and Atmospheric Administration. Deep Sea Coral Research and Technology Program 2018 Report to Congress. December 2018. [https://www.ncei.noaa.gov/data/oceans/coris/library/NOAA/DSCRTP/Other/Reports_To_Congress/2018/DSCRTP2018_Report_to_Congress.pdf].

NOAA National Database for Deep-Sea Corals and Sponges (version 20201021-0). NOAA Deep-Sea Coral Research & Technology Program. [https://deepseacoraldata.noaa.gov/]. Accessed 27 January 2021.