Data Registry

Each coastal State may claim a territorial sea that extends seaward up to 12 nautical miles from its baselines. The coastal State exercises sovereignty over its territorial sea, the air space above it, and the seabed and subsoil beneath it.

Automatic Identification System (AIS) data are information collected by the U.S. Coast Guard to monitor real-time vessel information to improve navigation safety. Data such as ship name, purpose, course, and speed are acquired 24 hours per day primarily in coastal U.S. waters. However, the data sets featured on this website are the 2009 to 2017 archived AIS data sets intended to be used by the ocean planning community to better understand vessel traffic patterns. These data are provided for analysis in desktop GIS software. For more information, visit the Nationwide Automatic Identification System website.

Derived from 2009 Automatic Identification System (AIS) broadcast returns. Each count per aliquot represents the number of vessels traveling through the block during the year of 2009. An aliquot measures 1/16 of a full OCS leasing block or 1200 x 1200 meters. The original raw data for June, 2009 is missing 25 days of broadcast data. Only areas where BOEM publishes Official Protraction Diagrams will contain the aliquot AIS counts, therefore, large areas of inland state waters may be missing aliquot AIS counts. Vessel count and types can be viewed by downloading the data.

These data were generated to provide insight into traffic patterns on a macro scale so they could be analyzed across the coastal waters of the Continental United States. For this dataset, a transit is counted for every unique vessel intersecting a 1 kilometer square grid cell each day. This data represents the total number of vessel transits from October 2009 - October 2010. There were some grid cells which were unable to be processed, but it is not perceived that this interferes with the integrity of these data. Please note multiple connection errors occurred during the time frame of this study. In most cases, data gaps were filled by making subsequent requests to the Coast Guard or other groups receiving the same data feed. However, due to resource constraints, uninterrupted coverage was not obtained. Overall data outages were minimal, on the order of less than a day per month. Because outages were random and affect all areas uniformly, they do not have a significant effect on the integrity of the data. As stated on the USCG NAIS website, AIS data are not representative of all vessel traffic and USCG NAIS receivers do not fully cover the entire extent of this study area. Please take time to understand both of these limitations.

Each coastal State may claim an Exclusive Economic Zone (EEZ) beyond and adjacent to its territorial sea that extends seaward up to 200 nautical miles from its baselines (or out to a maritime boundary with another coastal State).

Derived from 2010 Automatic Identification System (AIS) broadcast returns. Each vessel count per aliquot represents the number of vessels traveling through the block during the year of 2010. An aliquot measures 1/16 of a full OCS leasing block or 1200 x 1200 meters. Only areas where BOEM publishes Official Protraction Diagrams will contain the aliquot AIS counts, therefore, large areas of inland state waters may be missing aliquot AIS counts. The data has also been clipped so that any aliquot that touches land has been deleted so that the user can discern the location of the coastline. Vessel count and types can be viewed by downloading the data.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of all vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of cargo vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of fishing vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of passenger vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of pleasure craft and sailing vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of tanker vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of tug and towing vessel traffic in 2011 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Derived from 2011 Automatic Identification System (AIS) broadcast returns. Each vessel count per aliquot represents the number of vessels traveling through the block during the year of 2011. An aliquot measures 1/16 of a full OCS leasing block or 1200 x 1200 meters. Only areas where BOEM publishes Official Protraction Diagrams will contain the aliquot AIS counts, therefore, large areas of inland state waters may be missing aliquot AIS counts. The data has also been clipped so that any aliquot that touches land has been deleted so that the user can discern the location of the coastline. Vessel count and types can be viewed by downloading the data.

Oil and gas resource management of the Outer Continental Shelf (OCS) is governed by the OCS Lands Act (OCSLA) which sets forth procedures for leasing, exploration, development and production. OCSLA sec. 18 requires the preparation of an oil and gas leasing program with a 5-year schedule of lease sales to best meet U.S. energy needs. The Department of the Interior’s Bureau of Ocean Energy Management (BOEM) is responsible for preparing the leasing program. BOEM has prepared a Five Year Program for 2012-2017. This data is from the Proposed Final Program (PFP), the third in a series of mandated leasing proposals developed for public review before the Secretary of the Interior approves the new Five Year Program for 2012-2017. This data will be replaced with the Final Five Year Program layers once it has been approved. The areas shown in this layer represents the largest areas that may be considered for leasing within the 5 year period.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of all vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of cargo vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of fishing vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of passenger vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of pleasure craft and sailing vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of tanker vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of tug and towing vessel traffic in 2013 for the US Atlantic and West Coast from vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. All vessel types have been included in this summary, including null and none reported types. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to cargo vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to fishing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to passenger vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to pleasure craft and sailing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to tanker vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to tug and towing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. All vessel types have been included in this summary, including null and none reported types. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to cargo vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to fishing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to passenger vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to pleasure craft and sailing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to tanker vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to tug and towing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. All vessel types have been included in this summary, including null and none reported types. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to cargo vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to fishing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to passenger vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to pleasure craft and sailing vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to tanker vessels.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines, and then summarized at a 100 m grid cell resolution. A single transit is counted each time a vessel track passes through, starts, or stops within a grid cell. This layer is depicting transits attributed to tug and towing vessels.

The Exclusion Option Areas for the 2019-2024 Draft Proposed Program are areas that the Secretary of the Interior has included for additional analysis and consideration in the 2019-2024 National Outer Continental Shelf Oil and Gas Leasing Proposed Program. The final decision on areas to be offered for oil and gas lease sales is scheduled to occur in late 2019. If an Exclusion Option Area is chosen as part of the final oil and gas lease sale decision, no lease sales will be offered in that area.

This layer represents the program areas of the Outer Continental Shelf (OCS) that could be offered for oil and gas lease sales during 2019-2024. The Secretary of the Interior included these areas for additional analysis and consideration in the 2019-2024 National OCS Oil and Gas Leasing Draft Proposed Program. The Draft Proposed Program is the first of three proposals for OCS oil and gas leasing, and includes 25 of 26 planning areas from the four OCS regions. The final decision on areas to be offered for oil and gas lease sales is scheduled to occur in late 2019.

Each coastal State may claim a contiguous zone adjacent to and beyond its territorial sea that extends seaward up to 24 nautical miles from its baselines. In its contiguous zone, a coastal State may exercise the control necessary to prevent the infringement of its customs, fiscal, immigration or sanitary laws and regulations within its territory or territorial sea, and punish infringement of those laws and regulations committed within its territory or territorial sea.

This data represents the extent of the Abandoned Shipwreck Act (ASA). The ASA allows states to manage a broad range of resources within submerged lands, including abandoned shipwrecks that have been deserted and to which the owner has relinquished ownership rights with no retention. With the exception of wrecks on certain federal public lands, the ASA asserts U.S. title to abandoned shipwrecks and automatically transfers the title to coastal states. Citing to the Submerged Lands Act, the ASA generally extends seaward a distance of three nautical miles from the coastline to the inner limit of the outer continental shelf. In the cases of Texas, the Gulf coast of Florida, and Puerto Rico, the ASA extends seaward three marine leagues—or nine nautical miles—from the coastline. When investigating geo-regulatory boundaries near the boundary edges, users should consult the most up-to-date applicable jurisdictional boundaries from all respective authoritative sources.

In 1973, the International Maritime Organization adopted the International Convention for the Prevention of Pollution by Ships and subsequently modified it by Protocol in 1978. The Convention is widely known as MARPOL 73/78. Its objective is to limit ship-borne pollution by restricting operational pollution and reducing the possibility of accidental pollution. MARPOL 73/78 consists of six separate Annexes – each sets out regulations covering the various sources of ship-generated pollution. Currently, the United States is signatory to Annexes I, II, III, V and VI. Annexes I, II, V and VI have been incorporated into U.S. law by the Act to Prevent Pollution from Ships (APPS) and implemented within 33 U.S.C. § 1901 and 33 C.F.R. Part 151. The AAPS was subsequently amended by the Marine Plastic Pollution Research and Control Act (1987) and the Marine Pollution Prevention Act (2008). When investigating geo-regulatory boundaries near the boundary edges, users should consult the most up to date applicable jurisdictional boundaries available on MarineCadastre.gov. These geo-regulatory boundaries have not been updated in more than 13 years.

Portion of Outer Continental Shelf Lease Blocks which are currently leased out to private entities for oil and/or gas mining rights. Active leases include those that are exploratory, non-producing, and producing. This is current and refreshed on a daily basis.

Blocks which have been leased by a company with intent to build a wind energy facility. No projects are currently in the development stage at this time; permits may be issued for development provided further site assessment for each leased area.

Structures intended to assist a navigator to determine position or safe course, or to warn of dangers or obstructions to navigation. This dataset includes lights, signals, buoys, day beacons, and other aids to navigation. Not for navigation.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. Information such as location, time, ship type, length, width, and draft have been extracted from the raw data and prepared as track lines for analyses in desktop GIS software.

Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. Information such as location, time, ship type, length, width, and draft have been extracted from the raw data and prepared as track lines for analyses in desktop GIS software.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines for each vessel. Vessels can have one or more tracks of any length, and can be separated by gaps due to intermittent loss of the AIS signal. Tracks will not necessarily start or stop at a well defined port, or when a vessel is not in motion. Vessel tracks are an efficient and spatially unbiased indicator of vessel traffic.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines for each vessel. Vessels can have one or more tracks of any length, and can be separated by gaps due to intermittent loss of the AIS signal. Tracks will not necessarily start or stop at a well defined port, or when a vessel is not in motion. Vessel tracks are an efficient and spatially unbiased indicator of vessel traffic.

Vessels traveling in U.S. coastal and inland waters frequently use Automatic Identification Systems (AIS) for navigation safety. The U.S. Coast Guard collects AIS records using shore-side antennas. These records have been filtered and converted from a series of points to a set of track lines for each vessel. Vessels can have one or more tracks of any length, and can be separated by gaps due to intermittent loss of the AIS signal. Tracks will not necessarily start or stop at a well defined port, or when a vessel is not in motion. Vessel tracks are an efficient and spatially unbiased indicator of vessel traffic.

A vessel track shows the location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area. The vessel track positions in this data set are collected and recorded from land-based antennas as part of a national network operated by the U.S. Coast Guard.

A vessel track shows the location and characteristics of commercial and recreational boats as a sequence of positions transmitted by an Automatic Identification System (AIS). AIS signals are susceptible to interference and this can result in a gap within a vessel track. The distribution, type, and frequency of vessel tracks are a useful aid to understanding the risk of conflicting uses within a certain geographic area. The vessel track positions in this data set are collected and recorded from land-based antennas as part of a national network operated by the U.S. Coast Guard.

This layer is part of the Bureau of Indian Affairs Land Area Representation (LAR) data set. The LARs illustrate land areas for Federally-recognized tribes. Alaska Native Regional Corporations (ANRCs) are corporate entities organized to conduct both for-profit and non-profit affairs of Alaska Natives pursuant to the Alaska Native Claims Settlement Act. ANRCs have legally defined boundaries that subdivide all of Alaska into twelve regions except for the area of the Metlakatla - Annette Island Indian Reservation which is the only Reservation in Alaska.

This layer is part of the Bureau of Indian Affairs Land Area Representation (LAR) data set. The LARs illustrate land areas for Federally-recognized tribes. The Alaska Native Villages data set was developed according to Alaska Native Claims 43 USCS § 1602, which defines "Native village" to mean any tribe, band, clan, group, village, community, or association in Alaska listed in sections 11 and 16 of the Act [43 USCS §§ 1610 and 1615], or which meets the requirements of this Act, and which the Secretary determines was, on the 1970 census enumeration date composed of twenty-five or more Natives. "Native" means a citizen of the United States who is a person of one-fourth degree or more Alaska Indian (including Tsimshian Indians not enrolled in the Metlaktla Indian Community) Eskimo, or Aleut blood, or combination thereof. The term includes any Native as so defined either or both of whose adoptive parents are not Natives. It also includes, in the absence of proof of a minimum blood quantum, any citizen of the United States who is regarded as an Alaska Native by the Native village or Native group of which he claims to be a member and whose father or mother is (or, if deceased, was) regarded as Native by any village or group.

An anchorage area is a place where boats and ships can safely drop anchor. These areas are created in navigable waterways when ships and vessels require them for safe and responsible navigation. A variety of designations refer to types of anchorage areas or restrictions, or even to alerts of potential dangers within an area. Every boater and captain should be aware of the various types of areas. These data are intended for coastal and ocean planning. Not for navigation.

Chlorophyll-a is a specific chlorophyll pigment used by photosynthetic organisms. Phytoplankton, the microscopic floating organisms that live in oceans, lakes, and rivers, use chlorophyll to convert solar energy into organic matter. Chlorophyll-a concentration is a common proxy for the amount of phytoplankton present in the ocean. Phytoplankton are at the base of the marine food web and serve as indicators for understanding the health and productivity of a marine ecosystem. Units are in milligrams per cubic meter.

Aragonite is one of two common forms of calcium carbonate, a mineral essential to the growth and survival of many marine organisms with calcium carbonate structures, such as corals, bivalves, and some forms of phytoplankton macroalgae. Aragonite is one of the more soluble forms of calcium carbonate, and aragonite saturation state may be useful for the planning of shellfish aquaculture as it is a measure of carbonate ion concentration, a commonly used indicator of ocean acidification vulnerability. Furthermore when aragonite saturation state falls below 3, calcifying organisms become stressed; below 1, shells and other aragonite-based structures begin to dissolve. Aragonite data were obtained from the National Centers for Environmental Information (NCEI) and represent a long-term composite of historic data spanning from 1989 to 2010. These data are only available as surface values.

Aquaculture, also known as aquafarming, is the farming of aquatic organisms such as fish, crustaceans, mollusks, and aquatic plants. The presence and location of aquaculture sites were derived from multiple state websites and include only those in coastal and marine saltwater areas. The following states are included in this layer: Alaska, California, Connecticut, Florida, Louisiana, Maine, New York, North Carolina, Oregon Rhode Island, Virginia and Washington. The MarineCadastre.gov data team is continuing to work with aquaculture coordinators in each coastal state to fill current data gaps and improve the accuracy of existing data sets. As such, these data should be considered a work in progress. The user is encouraged to read the metadata of each individual state’s data carefully, since geometry, attribute details, and timeliness are not necessarily consistent among data sets used to develop this layer. The naming conventions for the type of aquaculture site (license versus lease) were retained as they are defined and administered by the individual states. The layer is shown by the area type: finfish, shellfish, and other (algae, crustacean, or unknown farming). Details of each state’s data source are described in the data-processing section. The data are not a complete collection of aquaculture locations within the U.S., nor are the locations to be considered exact. These data are intended for coastal and ocean planning.

An artificial reef is a human-made underwater structure, typically built to promote marine life in areas with a generally featureless bottom. These reefs help control erosion, block ship passage, or improve surfing. Many reefs are built using objects intended for other purposes, including the sinking oil rigs, scuttling ships, or deploying rubble or construction debris. Other artificial reefs are purpose-built using materials such as PVC or concrete. Regardless of construction method or source material, artificial reefs generally provide hard surfaces where algae and invertebrates such as barnacles, corals, and oysters can attach. The accumulation of attached marine life in turn provides an intricate structure of food for fish assemblages. This data set is NOT a complete collection of artificial reefs on the seafloor, nor are the locations to be considered exact. The presence and location of the artificial reefs have been derived from multiple state websites. These data are intended for coastal and ocean planning.

Multibeam bathymetry (in meters) collected to support a potential claim of an extended continental shelf by the United States, under the United Nations Convention on the Law of the Sea, Article 76. These data include full-coverage multibeam sonar measurements in water depths of approximately 1000 to 5000 m in order to precisely define the location of the 2500-m isobath and the "foot of the slope". The Center for Coastal and Ocean Mapping – Joint Hydrographic Center is a cooperative partnership between the University of New Hampshire and the National Oceanic and Atmospheric Administration.

This is a single data set from a larger study. The full study is titled "Socio-Economic Impact of Outer Continental Shelf Wind Energy Development on Fishing in the U.S. Atlantic". Each quarter square km (500 m) cell has been summed for the mean correlated economic value over the six year period analyzed (2007-2012). This information was created for each state, gear type, Fishery Management Plan (FMP), top 30 exposed ports and top 30 exposed species. This was calculated using Vessel Trip Reports (VTR), Cumulative Distribution Function (CDF) which estimates radial distance within which fishing activity is likely to occur, and a 500 m raster cell output. The value is in US dollars for 2012 representing the sum of the mean values for all six years, and then classified into one of the 8 classes. The top 30 species included in this assessment are: Ocean Quahog, Surf Clam, Little Skate, Squid (Illex & Loligo), Menhaden, Winter Skate, Channeled Whelk, Red Grouper, Atlantic Herring, Vermillion Snapper, Atlantic Croaker, Jonah Crab, Red Hake, Atlantic Mackerel, Silver Hake, King Mackerel, Butterfish, Yellowtail Founder, Winter Flounder, Summer Flounder, Black Sea Bass, Monkfish, Bluefish, Lobster, Spiny Dogfish, Scup, Skates, Cod, Sea Scallop. Note: Local and regionally important fisheries such as shrimp are not included in this analysis so users should remember that the actual mean revenue if all species were included may be much greater in some areas.

These data depict annual average wind speed (meters per second) at 100 meters above sea level for the Atlantic offshore areas of the United States, for a seven year period from 2007 to 2013. The sampling resolution of 2km was generalized into polygons shown in the legend classes. Data include both point and polygon data sets intended to provide broad estimates of wind speed variation for the purposes of identifying potential offshore wind energy sites. They are not intended to provide specific estimates of energy production for the purpose of making offshore wind project investment or financing decisions in specific locations. The source data is National Renewable Energy Laboratory’s Wind Integration National Dataset (WIND) Toolkit. Please see metadata for more detailed process information. Monthly averages are available within the downloads menu, either as GIS data or Esri REST map services.

The Atlantic OCS Aliquots with Sand Resources layer is a planning layer to assist in the management of Outer Continental Shelf (OCS) sediment resources, reduce multiple use conflicts, minimize interference with existing leases (e.g., renewable energy) and rights-of-way (e.g., submerged infrastructure, shipping lanes, military operations, etc.), and help avoid sensitive areas (e.g., archaeological sites, protected habitat). The aliquots represent areas within the OCS protraction grid where sand resources have been identified through reconnaissance and/or design-level OCS studies. Additional OCS studies may be necessary to refine and quantify the extents of sand resources within these areas. The Marine Minerals Program (MMP) within the Bureau of Ocean Energy Management (BOEM) is responsible for managing non-energy minerals (primarily sand and gravel) on the OCS. Access to and identification of potential OCS sand resources is critical for the long-term success and cost-effectiveness of many shore protection, beach nourishment, and coastal habitat restoration projects along the Gulf of Mexico and Atlantic Ocean coasts.

The continental margin sediment distribution data from the USGS Continental Margin Mapping Program (CONMAP) is derived sediment distribution for the East Coast. The purpose of the data layer is to show the sediment grain size distributions along the U.S. East Coast. This data layer is supplied primarily as a gross overview to show general textural trends. It does not accurately depict small-scale sediment distributions.

This layer provides access to an avian group relative abundance map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. This layer demonstrates relative vulnerability to collision with offshore wind energy projects (Robinson Willmott et al. 2013). For all avian species together, total relative abundance maps are calculated in a GIS by stacking each individual species’ predicted annual long-term average relative abundance layers and summing the values of the cells. The result is the total predicted long-term average relative abundance of all individuals (of the included species in the group) in that cell. It is important to note these products represent and reflect relative abundance, not predicted absolute abundance. These species are included: Arctic tern; Atlantic puffin; Audubon's shearwater; Black guillemot; Black scoter; Black-legged kittiwake; Bridled tern; Common eider; Common loon; Common murre; Common tern; Cory's shearwater; Double-crested cormorant; Great black-backed gull; Great shearwater; Great skua; Herring gull; Horned grebe; Laughing gull; Leach's storm petrel; Long-tailed duck; Manx shearwater; Northern fulmar; Northern gannet; Parasitic jaeger; Pomarine jaeger; Razorbill; Red phalarope; Red-breasted merganser; Red-necked phalarope; Red-throated loon; Roseate tern; Sooty shearwater; Sooty tern; South polar skua; Surf scoter; Thick-billed murre; White-winged scoter; Wilson's storm petrel. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to an avian group species richness map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. This layer demonstrates relative vulnerability to collision with offshore wind energy projects (Robinson Willmott et al. 2013). For all avian species together, total species richness maps are calculated in a GIS by stacking each individual species’ predicted presence and counting the total number of species present in each cell. A species is considered present in a cell if the model predicts density above a certain very low density threshold. That threshold is determined by defining the smallest area holding 95% of the total predicted abundance for the species. These species are included: Arctic tern; Atlantic puffin; Audubon's shearwater; Black guillemot; Black scoter; Black-legged kittiwake; Bridled tern; Common eider; Common loon; Common murre; Common tern; Cory's shearwater; Double-crested cormorant; Great black-backed gull; Great shearwater; Great Skua; Herring gull; Horned grebe; Laughing gull; Leach's storm petrel; Long-tailed duck; Manx shearwater; Northern fulmar; Northern gannet; Parasitic jaeger; Pomarine jaeger; Razorbill; Red phalarope; Red-breasted merganser; Red-necked phalarope; Red-throated loon; Roseate tern; Sooty shearwater; Sooty tern; South polar skua; Surf scoter; Thick-billed murre; White-winged scoter; Wilson's storm petrel. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to an avian group abundance map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. This layer demonstrates relative vulnerability to displacement with offshore wind energy projects (Robinson Willmott et al. 2013). For all avian species together, total relative abundance maps are calculated in a GIS by stacking each individual species’ predicted annual long-term average relative abundance layers and summing the values of the cells. The result is the total predicted long-term average relative abundance of all individuals (of the included species in the group) in that cell. It is important to note these products represent and reflect relative abundance, not predicted absolute abundance. These species are included: Arctic tern; Atlantic puffin; Black guillemot; Black scoter; Bridled tern; Common eider; Common loon; Common murre; Common tern; Great black-backed gull; Long-tailed duck; Manx shearwater; Northern gannet; Razorbill; Red-throated loon; Roseate tern; Sooty tern; Surf scoter; Thick-billed murre;White-winged scoter. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to an avian group species richness map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. This layer demonstrates relative vulnerability to displacement with offshore wind energy projects (Robinson Willmott et al. 2013). For all avian species together, total species richness maps are calculated in a GIS by stacking each individual species’ predicted presence and counting the total number of species present in each cell. A species is considered present in a cell if the model predicts density above a certain very low density threshold. That threshold is determined by defining the smallest area holding 95% of the total predicted abundance for the species. These species are included: Arctic tern; Atlantic puffin; Black guillemot; Black scoter; Bridled tern; Common eider; Common loon; Common murre; Common tern; Great black-backed gull; Long-tailed duck; Manx shearwater; Northern gannet; Razorbill; Red-throated loon; Roseate tern; Sooty tern;Surf scoter; Thick-billed murre; White-winged scoter. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to an avian species richness map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. For all avian species together, total species richness maps are calculated in a GIS by stacking each individual species’ predicted presence and counting the total number of species present in each cell. A species is considered present in a cell if the model predicts relative abundance above a certain very low threshold. That threshold is determined by defining the smallest area holding 95% of the total predicted abundance for the species. The summary map products provide a means to distill multiple layers and time periods into simplified maps and build a general understanding of the ecological richness or abundance in a particular area. Development of these maps required decisions and thresholds that are explained in the Metadata and Publication links. These decisions were made after conducting research, testing of several options, and incorporating feedback from expert work group members. Understanding these decisions and thresholds is crucial to proper interpretation of these map products. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to an avian total relative abundance data map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. For all avian species together, total relative abundance maps are calculated in a GIS by stacking each individual species’ predicted annual long-term average relative abundance layers and summing the values of the cells. The result is the total predicted long-term average relative abundance of all individuals (of the included species in the group) in that cell. It is important to note these products represent and reflect relative abundance, not predicted absolute abundance. The summary map products provide a means to distill multiple layers and time periods into simplified maps and build a general understanding of the ecological richness or abundance in a particular area. Development of these maps required decisions and thresholds that are explained in the Metadata and Publication links. These decisions were made after conducting research, testing of several options, and incorporating feedback from expert work group members. Understanding these decisions and thresholds is crucial to proper interpretation of these map products. Note: this raster layer is not compatible with ArcGIS Online.

This layer contains bathymetric contours which provide the size, shape and distribution of underwater features. The DEM utilized was the Global Multi-Resolution Topography (GMRT) Synthesis which is a multi-resolution gridded global Digital Elevation Model (DEM) that includes cleaned processed ship-based multibeam sonar data at their full spatial resolution (~100m in the deep sea). The contours are depicted from zero to -100 meters (m) with a contour every 10 meters, a -101m to -500m with contours every 25 meters, and -501m+ with contours every 100m.

Beach nourishment projects occur throughout coastal states in the United States. These projects can be privately, federally or state funded. Material used in nourishment comes from a variety of sources (inland sources, dredged navigational channels or offshore sites). These data combine historical data compiled in the Western Carolina University Beach Nourishment Viewer database, as well as the National Beach Nourishment Database generated by the American Shore and Beach Preservation Association. The data contain attribute information on the general location of sand placement, primary funding source and funding type, volume of sediment emplacement (in cubic yards), length of beach nourished (in feet) and cost and inflated cost for over 2,000 beach nourishment episodes dating back to 1920.

Benthic cover (habitat) maps are derived from aerial imagery, underwater photos, acoustic surveys, and data gathered from sediment samples. Shallow to moderate-depth benthic habitat information assists ecosystem-based marine resource management. Many habitats, including hard and soft corals, are home to a diversity of marine organisms, which provide many important ecosystem services, including fishing, tourism, water quality enhancement, and shoreline protection. Coral reef ecosystems and associated bottom types are under increasing pressure from environmental and anthropogenic stressors. Mitigating these threats requires analyzing their spatial distribution, making benthic habitat mapping a key component to the conservation and management activities of state and federal agencies. This data currently contains geographic coverage for the following locations: American Samoa, Florida Keys, Guam, Hawaii, Majuro, Northern Marianas Islands, North West Hawaiian Islands, Palmyra Atoll, Palua, Puerto Rico, Puerto Rico Northeast Ecological Reserve, and the US Virgin Islands.

Feeding Biologically Important Areas (BIAs) for Cetaceans include areas and months within which a particular species or population selectively feeds. These may either be found consistently in space and time, or may be associated with ephemeral features that are less predictable but can be delineated and are generally located within a larger identifiable area. BIAs were defined by compiling the best available information from scientific literature (including books, peer-reviewed articles, and government or contract reports), unpublished data (sighting, acoustic, tagging, genetic, photo identification), and expert knowledge. This information was then used to create written summaries and maps highlighting areas shoreward of the U.S. Exclusive Economic Zone that are biologically important to cetacean species (or populations), either seasonally or year-round. Other BIA types include migratory corridor, reproduction, and small and resident population.

Migratory Corridor Biologically Important Areas (BIAs) for Cetaceans include areas and months within which a substantial portion of a species or population is known to migrate. BIAs were defined by compiling the best available information from scientific literature (including books, peer-reviewed articles, and government or contract reports), unpublished data (sighting, acoustic, tagging, genetic, photo identification), and expert knowledge. This information was then used to create written summaries and maps highlighting areas shoreward of the U.S. Exclusive Economic Zone that are biologically important to cetacean species (or populations), either seasonally or year-round. Other BIA types include feeding, reproduction, and small and resident population.

Reproduction Biologically Important Areas (BIAs) for Cetaceans include areas and months within which a particular species or population selectively mates, gives birth, or is found with neonates or other sensitive age classes. BIAs were defined by compiling the best available information from scientific literature (including books, peer-reviewed articles, and government or contract reports), unpublished data (sighting, acoustic, tagging, genetic, photo identification), and expert knowledge. This information was then used to create written summaries and maps highlighting areas shoreward of the U.S. Exclusive Economic Zone that are biologically important to cetacean species (or populations), either seasonally or year-round. Other BIA types include feeding, migratory corridor, and small and resident population.

Small and Resident Population Biologically Important Areas (BIAs) for Cetaceans include areas and months within which small and resident populations occupying a limited geographic extent. BIAs were defined by compiling the best available information from scientific literature (including books, peer-reviewed articles, and government or contract reports), unpublished data (sighting, acoustic, tagging, genetic, photo identification), and expert knowledge. This information was then used to create written summaries and maps highlighting areas shoreward of the U.S. Exclusive Economic Zone that are biologically important to cetacean species (or populations), either seasonally or year-round. Other BIA types include migratory corridor, reproduction, and feeding.

This layer represents the location of the submarine cables for the Block Island Wind Farm (BIWF) and Block Island Transmission System (BITS). The cables that connect directly to the wind farm will consist of the Inter Array cable, which will interconnect each turbine, and the Export cable, which connects the northernmost turbine with Block Island. The BITS will be a submarine cable that connects Block Island with the Rhode Island mainland which will deliver power to and from the mainland.

The Block Island Wind Farm (BIWF) is a 30-megawatt offshore wind farm located approximately three miles southeast of Block Island. The BIWF will generate over 125,000 megawatt hours annually. Deepwater Wind began offshore construction in 2015. This dataset shows the locations of the BIWF wind turbine generators. For additional information, consult the project overview at: http://dwwind.com/project/block-island-wind-farm/.

The Navy and other military users of the marine environment are required to assess the impact of their activities on marine mammals to comply with the Marine Mammal Protection Act, the Endangered Species Act, and the National Environmental Policy Act. The number of marine mammals that might be impacted by Navy activities must be estimated in an Environmental Assessment or Environmental Impact Statement. A key element of this estimation is knowledge of cetacean densities in specific areas where those activities will occur. Data collected by NOAA’s Southwest Fisheries Science Center (SWFSC) from 1986-2006 using accepted, peer-reviewed survey methods were used to develop models to forecast cetacean densities. This work represents an important step towards understanding marine mammal habitat use with respect to regions utilized by the U. S. Navy. MarineCadastre.gov has included Blue, Fin, Humpback, and Sperm whale models for the summer months as examples of these models. Please view the publication for specific details on the development and use of these models. NOTE: Updated Pacific models should be available in mid-2017.

Aliquots are generated from full OCS blocks by sub-dividing each block into 16ths and allow for more detailed boundary delineation in offshore energy leasing. The aliquots use a letter designation in addition to their parent protraction number and OCS block number (i.e. NK-1802, 6822F). A full OCS block is 4800 x 4800 meters, while an aliquot measures 1200 x 1200 meters. Smaller, clipped aliquots are found along the Fed/State OCS boundary and along UTM zone borders. This dataset includes aliquots for 60 protractions out of the available 80 protractions in the Atlantic and 36 of 71 off the US West Coast . The remaining 56 protractions are located on the seaward edge of the OCS . Aliquots for these protractions will be produced at a later date as needed. Not to be used for official BOEM lease documents. Planning uses only.

This data set represents the most recent changes for the Marine Hydrokinetic (MHK) Development Planning Areas in the Outer Continental Shelf. MHK Planning Areas in this dataset represent up to six different types of announcements within the US Federal Register that can be used to show the current status of an area that is being considered for wave, current, or tidal power development.

BOEM Planning areas are used to divide the Outer Continental Shelf into smaller more manageable regions for the purpose of planning and defining areas of potential lease sales within the Oil and Gas Leasing 5 year Program Plan. There are currently 26 planning areas within the OCS. Fifteen of these areas are in Alaska. Lease sales will usually occur for only one planning area at a time. The 5 year program identifies which planning areas may have sales within the next 5 years. If areas are not identified for potential lease sales in the 5 year program, new areas cannot be added for lease within that 5 year period.

This data layer shows the extent of the probable oil or gas plays identified within the U.S. Outer Continental Shelf (OCS). Plays are groups of hydrocarbon pools that share a common history of generation, migration, reservoir development, and entrapment. Geologic classifications of each play and its potential are described in the associated reports for each region (see "More Info"). The analysis was based on seismic surveys analyzed by BOEM geologists. Other plays may exist in OCS waters that could be identified in future seismic survey results. The listed values in the attribute table represent the mean expected resource within an entire play. Bbo – billions of barrels of oil; Tcf – trillion cubic feet; BOE – barrel of oil equivalent – the amount of all potentially recoverable types of hydrocarbons in relation to the energy produced by a barrel of oil.

This data set represents the most recent changes for the Wind Development Planning Areas in federally managed offshore waters. Wind Planning Areas in this dataset represent up to seven different types of announcements within the US Federal Register that can be used to show the current status of an area that is being considered for Wind Power Development. For the areas represented here, once an area has changed from one type of area to another, it will be replaced entirely by the new area. In many cases the area may be more than one type of area at the same time. The Category or Info attribute will show the reader which type of category the area is. The types of areas and their descriptions can be found in the attributes section of the metadata record.

Multibeam bathymetry (in meters) collected to support a potential claim of an extended continental shelf by the United States, under the United Nations Convention on the Law of the Sea, Article 76. These data include full-coverage multibeam sonar measurements in water depths of approximately 1000 to 5000 m in order to precisely define the location of the 2500-m isobath and the "foot of the slope". The Center for Coastal and Ocean Mapping – Joint Hydrographic Center is a cooperative partnership between the University of New Hampshire and the National Oceanic and Atmospheric Administration.

The California Seafloor Mapping Program (CSMP) has divided coastal California into 110 map blocks, each to be published individually as USGS Open-File Reports or Scientific Investigations Maps at a scale of 1:24,000. For each map block, CSMP has created data layers for bathymetry, bathymetric contours, acoustic backscatter, seafloor character, potential benthic habitat, and offshore geology. In addition, CSMP has compiled regional-scale data layers for sediment thickness, depth to transition, transgressive contours, isopachs, and predicted distributions of benthic macro-invertebrates, as well as visual observations of benthic habitat from video cruises over the entire state. Note, however, that not all data layers are available for all map blocks, owing to insufficient data. Also, some map blocks may include additional data layers that record certain unique characteristics or features not present in other areas. Please note: Only the index layer is available in MarineCadastre.gov, not the seafloor data. This layer is currently only available to add to ArcGIS.com.

The Clean Water Act (CWA) establishes the basic structure for regulating discharges of pollutants into U.S. waters and regulating quality standards for surface waters. The basis of the CWA was enacted in 1948 and was called the Federal Water Pollution Control Act, but the Act was significantly reorganized and expanded in 1972. "Clean Water Act" became the Act's common name with amendments in 1977. The CWA’s goal is to restore and maintain the chemical, physical, and biological integrity of the Nation’s waters. The Act regulates both the direct and indirect discharge of pollutants into navigable waters and waters of the contiguous zone, as well as onto adjoining shorelines, that may be harmful to the public or to natural resources. The term "navigable waters" means the waters of the United States, including the territorial seas. The term "territorial seas" means the belt of the seas measured from the line of ordinary low water along that portion of the coast which is in direct contact with the open sea and the line marking the seaward limit of inland waters, and extending seaward a distance of three miles. The term "contiguous zone" means the entire zone established or to be established by the United States under article 24 of the Convention of the Territorial Sea and the Contiguous Zone. (Note: the article 24 boundaries differ from the contemporary extent of the territorial seas and contiguous zone.) Federal permits may be required by the EPA for routine discharges of wastes within the Outer Continental Shelf, out to the Exclusive Economic Zone (EEZ).

These data represent those waters of the United States that are within the jurisdiction of the United States Coast Guard. The jurisdiction of the U.S. Coast Guard is defined within 33 U.S. Code Chapter 3 (Navigation and Navigable Waters), which specifies all navigable waters of the United States landward from the Exclusive Economic Zone.

To remove the federal incentive to develop coastal barriers, the Coastal Barrier Resources Act (CBRA) designated relatively undeveloped coastal barriers along the Atlantic and Gulf coasts as part of the John H. Chafee Coastal Barrier Resources System (CBRS). This Act made these areas ineligible for most new federal expenditures and financial assistance. CBRA encourages the conservation of hurricane prone, biologically rich coastal barriers by restricting federal expenditures that encourage development, such as federal flood insurance. The CBRS data provided on MarineCadastre.gov are only representations of the official boundaries and are NOT to be considered authoritative. They can be used to help stakeholders determine whether or not properties or project sites may be affected by CBRA, but not to make official determinations. Please view the Source Info for specific restrictions and use constraints. Please note there are multiple layers within this service. Please zoom in to view these.

This dataset represents US counties and independent cities which have at least one coastal border and select non-coastal counties and independent cities based on proximity to estuaries and other coastal counties.

The Endangered Species Act (ESA) requires the Federal government to designate critical habitat, areas of habitat essential to the species’ conservation, for ESA listed species. This dataset is a compilation of the NOAA National Marine Fisheries Service and the U.S. Fish & Wildlife Service designated critical habitat in coastal areas. Critical habitat is defined as: Specific areas within the geographical area occupied by the species at the time of listing that contain physical or biological features essential to conservation, which may require special management considerations or protection; and specific areas outside the geographical area occupied by the species if the agency determines that the area itself is essential for conservation. It is important to keep in mind that maps published before May 31, 2012 were only for illustrative purposes and the Federal Register text descriptions should be used for authoritative purposes. For designations published after May 31, 2012, the maps (and any clarifying textual descriptions) are the definitive source for critical habitat boundaries. See metadata for online linkages to reference full listings of proposed and final critical habitat areas.

These data depict the location of facilities that generate electricity. The locations are created from the Environmental Protection Agency Emissions & Generation Resource Integrated Database (eGRID). Only facilities in coastal states are shown. Contained within the database are records that define the fuel source and other characteristics of the facility that may benefit ocean planners. In some cases, the presence of a facility may indicate that certain power transmission infrastructure exists nearby. Absence of a facility or lack of sufficient capacity at a facility in a given area may also be an important characteristic in future energy planning activities.

This layer shows coastal channels and waterways that are maintained and surveyed by the U.S. Army Corps of Engineers (USACE). These channels are necessary transportation systems that serve economic and national security interests. The possibility of silting is always present. Local authorities should be consulted for the controlling depth. NOS Charts frequently show controlling depths in a table, which is kept current by the US Coast Guard Local Notice to Mariners. These data are intended for coastal and ocean use planning. Not for navigation.

This dataset represents US states and equivalent territorial units which have at least one coastal border.

This data represents the extent of the nation's coastal zone, as defined by the individual states and territories under the Coastal Zone Management Act of 1972 (CZMA). The CZMA was established to preserve, protect, develop, and where possible, to restore or enhance the resources of the nation's coastal zone. The zone generally extends seaward to the boundary of the Submerged Lands Act. The zone extends inland from the shorelines only to the extent necessary to control shorelands that have a direct and significant impact on coastal waters. Lands held in trust by the Federal Government have been included in this boundary unless otherwise noted, as accurately representing these could be erroneous. State jurisdiction extends to 3nm, except for Texas, Puerto Rico and Florida’s Gulf coast extends to 9nm. Great Lakes states have jurisdiction to the international boundary with Canada. The CZMA applies within the EEZ through the federal consistency provision (Sec. 307). Areas of state-defined federal consistency review locations can be found within the "Federal Consistency Geographic Location Descriptions" layer. This boundary is unofficial. For precise, regulatory boundaries, please contact the state or territorial coastal program office.

U.S. collision regulation boundaries are lines of demarcation delineating those waters upon which mariners shall comply with the International Regulations for Preventing Collisions at Sea, 1972 (72 COLREGS) and those waters upon which mariners shall comply with the Inland Navigation Rules. The waters inland of these lines are subject to the Inland Navigation Rules Act of 1980. The waters outside these lines are subject to the International Navigation Rules of the International Regulations for Preventing Collisions at Sea, 1972 (COLREGS). The Coast Guard has the legal authority to effect regulatory changes to COLREGS. Creation of features was interpreted from descriptions published in the Code of Federal Regulations Title 33, Part 80.

This data represents geographic terms used within the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). CERCLA, commonly known as Superfund, provides a federal "superfund" to clean up uncontrolled or abandoned hazardous waste sites as well as accidents, spills, and other emergency releases of pollutants and contaminants into the environment. The CERCLA gave the U.S. Environmental Protection Agency (EPA) power to seek out those parties responsible for any release of pollutants and contaminants, and assure their cooperation in the cleanup. If interested in knowing the location of Superfund sites, please refer to the layer called "Superfund Sites". When investigating geo-regulatory boundaries near the boundary edges, users should consult the most up-to-date applicable jurisdictional boundaries from all respective authoritative sources.

This layer depicts the 116th Congressional Districts for the United States. Found within this layer is the listing of the 116th House of Representatives. Elected to a two-year term, each representative serves the people of a specific congressional district by introducing bills and serving on committees, among other duties. The districts are symbolized by the political party of the current representative.

These data represent the location of Danger Zones and Restricted Areas within coastal and marine waters, as outlined by the Code of Federal Regulations (CFR) and the Raster Navigational Charts (RNC). The CFR defines a Danger Zone as, "A defined water area (or areas) used for target practice, bombing, rocket firing or other especially hazardous operations, normally for the armed forces. The danger zones may be closed to the public on a full-time or intermittent basis, as stated in the regulations." The CFR defines a Restricted Area as, "A defined water area for the purpose of prohibiting or limiting public access to the area. Restricted areas generally provide security for Government property and/or protection to the public from the risks of damage or injury arising from the Government's use of that area." Other features in this dataset include: Danger Area, Missile Testing Area, Naval Operations Area, Prohibited Area, Restricted Airspace, Test Area, and Torpedo Testing Area. Authoritative information relating to these data may be found in Title 33, Chapter II of the CFR (Part 334).

The National Oceanic and Atmospheric Administration (NOAA) Deep Sea Coral Research and Technology Program (DSCRTP) have developed a National Database for Deep-Sea Corals and Sponges (database). The database is designed to compile and disseminate existing biological observations on deep-sea corals and sponges and their locations, and serve as a primary data outlet for new spatial data records from the DSCRTP’s funded research. These data are made available in standard formats that are useful to scientists, natural resource managers, and the public through an online map portal at https://deepseacoraldata.noaa.gov. Please note this layer may be slow to draw due to its geographic extent and breadth of data.

Deep-sea corals, also known as cold water corals, create complex communities that provide habitat for a variety of invertebrate and fish species, such as grouper, snapper, and sea bass. The map depicts the relative likelihood of finding suitable habitat for soft corals at a given location and is a prediction based on a statistical model relating several environmental characteristics to the presence of soft corals using observations of soft coral. Soft corals, unlike stony corals, do not form calcium-based skeletons. A common example of a soft coral is a sea fan. Please also reference the “Deep-Sea Stony Coral Habitat Suitability” layer. Predictions from these habitat suitability models can be used to support conservation and management of deep-sea corals and to assist with targeting areas for mapping and exploration.

Deep-sea corals, also known as cold water corals, create complex communities that provide habitat for a variety of invertebrate and fish species, such as grouper, snapper, and sea bass. The map depicts the relative likelihood of finding suitable habitat for stony corals at a given location and is a prediction based on a statistical model relating several environmental characteristics to the presence of stony corals using observations of stony corals. Stony coral are the primary reef-building corals and produce hard skeletons made of aragonite, a crystal form of calcium carbonate. Please also reference the “Deep-Sea Soft Coral Habitat Suitability” layer. Predictions from these habitat suitability models can be used to support conservation and management of deep-sea corals and to assist with targeting areas for mapping and exploration.

The Deepwater Port Licensing Program is the application process designed to promote the construction of Liquefied Natural Gas (LNG) and oil deepwater ports. This license system was established by the Deepwater Port Act of 1974, as amended by the Maritime Transportation Security Act of 2002. MARAD DOT assesses the financial capability of potential licenses, prepares the project Record of Decision, and issues or denies the deepwater port license. If the license was denied, surrendered, or withdrawn they are not displayed in this layer. The licensed deepwater ports are: Delfin LNG (Louisiana), Louisiana Offshore Oil Port (Louisiana), Neptune LNG (Massachusetts), and Northeast Gateway (Massachusetts).

This data set shows the results of a Department of Defense assessment of the compatibility of offshore wind development with military assets and activities. These data should not be used to infer compatibility or conflict between military assets or activities with any use other than offshore wind. The data set contains 4 categories of OCS lease blocks: No Restrictions - No significant conflicts were identified. Site Specific Stipulations - Potential conflicts exist and may require site specific mitigation measures. Recommended Wind Exclusion - Significant conflicts were identified. Not Assessed - DoD only assesses specific lease blocks as requested by BOEM and the appropriate coastal state task force. Data shown here are not available for download at the request of the data provider.

Structures used to drill into the seabed for mineral exploration or to bring resources to the surface, particularly oil and gas. This is current and refreshed on a daily basis.

NOAA’s National Marine Fisheries Service works with regional fishery management councils to identify Essential Fish Habitat (EFH) for every life stage of federally managed species using the best available scientific information. EFH Areas Protected from Fishing visually represent spatial information for areas in which the use of fishing gear has been restricted or modified to minimize the adverse effects of fishing on EFH, as required by Section 303(a)(7) of the Magnuson-Stevens Act. This data set is a comprehensive list of fishery management measures implemented for the explicit purpose of protecting EFH but does not represent an exhaustive list of areas where fishing is restricted or prohibited by other state or federal regulations. A comprehensive data layer depicting all areas where fishing is restricted or prohibited is not known to be available, but the user is encouraged to consult the Marine Protected Area (MPA) Inventory’s MPAs with Fishing Restrictions data set, available on MarineCadastre.gov, for additional information. Users should use the ID tool to investigate each individual polygon that is of interest, since each area may contain multiple fishing gear restrictions.

This data represents geographic terms used within the Endangered Species Act (ESA). The purpose of the ESA is to protect and recover imperiled species and the ecosystems upon which they depend. The U.S. Fish and Wildlife Service (FWS) and the National Oceanic and Atmospheric Administration’s National Marine Fisheries Service (NMFS) administer the ESA. Under the ESA, species may be listed as either endangered or threatened. “Endangered” means a species is in danger of extinction throughout all or a significant portion of its range. “Threatened” means a species is likely to become endangered within the foreseeable future. All species of plants and animals, except pest insects, are eligible for listing as endangered or threatened. For the purposes of the ESA, Congress defined species to include subspecies, varieties, and, for vertebrates, distinct population segments. When investigating geo-regulatory boundaries near the boundary edges, users should consult the most up-to-date applicable jurisdictional boundaries from all respective authoritative sources.

This data represents geographic terms used within the Energy Policy Act (EPA). The EPA addresses energy production in the United States, including: (1) energy efficiency; (2) renewable energy; (3) oil and gas; (4) coal; (5) Tribal energy; (6) nuclear matters and security; (7) vehicles and motor fuels, including ethanol; (8) hydrogen; (9) electricity; (10) energy tax incentives; (11) hydropower and geothermal energy; and (12) climate change technology. When investigating geo-regulatory boundaries near the boundary edges, users should consult the most up to date applicable jurisdictional boundaries from all respective authoritative sources.

This dataset delineates EPA Region boundaries extended to the 200nm exclusive economic zone and was created by U.S. EPA using 2011 TIGER/Line state boundaries from the U.S. Census Bureau and U.S. Maritime Boundaries made available by NOAA Office of Coast Survey.

Environmental Sensitivity Index (ESI) maps provide a summary of coastal resources that are at risk if an oil spill occurs. They can be used when a spill occurs to inform responders, and can also be used by planners to identify vulnerable locations, establish protection priorities, and identify cleanup strategies. This data contains the aggregated ESI shoreline, with the following shoreline categories: armored; rocky and steep shorelines; beaches; flats; and vegetated. The symbolization is based on the highest numeric general ESI type present in the segment. The value in generalized shoreline field ranges from 1 to 5, with each value representing a broad Environmental Sensitivity Index (ESI) category. All ESI values have been mapped to the appropriate “general” field for this presentation.

NOAA’s National Marine Fisheries Service works with regional fishery management councils to identify Essential Fish Habitat (EFH) for every life stage of federally managed species using the best available scientific information. The data in MarineCadastre.gov are developed by NOAA Fisheries using methods that reflect regional differences in both source data and management needs. Because of the variability in the quality and intended usage of these data, each should be considered individually when interpreting the accuracy and utility of the information that they provide. Please be sure to view the EFH Mapper (http://www.habitat.noaa.gov/protection/efh/efhmapper/) and read the information under the Data Quality tab in the Help menu to fully understand the usage constraints for each data layer and the completeness and accuracy of the information.

This layer is representative of US State offshore waters derived from best scale nautical charts coastline out to the extent of the Submerged Lands Act (SLA) Boundary or closest approximation using US former 3 mile (old Territorial Sea) line. It also represents the area of waters normally attributed to US Federal Government oversight which is shown from the SLA boundary out to either the Exclusive Economic Zone or closest international boundary, whichever is closer. Some boundary delineations based on the SLA were approximated in this data set, including areas in Hawaii, Alaska, and Washington State. Although some state borders have not been legally defined offshore or are disputed, it was necessary to approximate these borders to produce this data set. The boundaries depicted in this data set are for visual purposes only. Note: The SLA boundary is generally 3 nautical miles seaward from the best NOAA charted coastline with exceptions offshore of Texas and the West Coast of Florida which are 3 marine leagues (approx. 9 nautical miles). Most SLA boundaries ambulate with shoreline change except those fixed by Supreme Court Decree.

These data represent state geographic location descriptions (GLDs). Section 307 of the "Coastal Zone Management Act of 1972" (CZMA), called the “federal consistency” provision, gives states a strong voice in federal agency decision making, which they otherwise would not have, for activities that may affect a state’s coastal uses or resources. Federal agency, federal license or permit, and federal financial assistance activities are subject to federal consistency review if the activity occurs outside of the state’s coastal zone but within a GLD. This location description encompasses an area outside the coastal zone in which an activity would have reasonably foreseeable coastal effects. For activities outside of a state’s coastal zone to be automatically subject to federal consistency review, the state federal consistency list must identify the activity and its associated geographic location description. These data represents the GLDs for all states that have defined them.

This layer represents the regions of the Federal Emergency Management Agency (FEMA). Regional Offices manage, operate and maintain all delegated programs, functions and activities not managed, operated or maintained by headquarters organizational units. The FEMA Regional Offices serve as the primary organizational unit for liaison to states and local governments within each region, and non-governmental and private sector entities within each Regional Office's geographical area.

These data represent federal and traditional lightering zones and prohibited lightering zones within the Gulf of Mexico. Lightering or lightering operation means the transfer of a cargo of oil in bulk from one oil tanker less than 150 gross tons to another oil tanker less than 150 gross tons, or a cargo of hazardous material in bulk from one vessel to another. Lightering includes all phases of the operation, from the beginning of the mooring operation to the departure of the service vessel from the vessel to be lightered, except when that cargo is intended only for use as fuel or lubricant aboard the receiving vessel. The U.S. Coast Guard has designated areas in which tankers may lighter cargoes of oil bound for coastal onshore terminals. Transfer of oil between vessels is to occur within 'Lightering Rendezvous Areas' and is prohibited in 'Prohibited Areas'. Not all ships choose to utilize the Lightering Rendezvous Areas depicted in this dataset, but all are required to avoid lightering in the Prohibited Areas.

Federal Outer Continental Shelf (OCS) Sand and Gravel Borrow Areas (Lease Areas) are polygons which are maintained by the Bureau of Ocean Energy Management (BOEM), part of the U.S. Department of the Interior. The polygons define areas where entities that have entered into or requested a Negotiated Non-Competitive Lease or Memorandum of Agreement with BOEM can dredge sand, gravel, or shell resources from the OCS. Section 8 (k) of the OCS Lands Act (OCSLA), as amended by Public Law 103-426 (enacted in 1994), provides BOEM the authority to negotiate an agreement for the use of OCS sand, gravel, and shell resources for use in: (1) a project for shore protection, beach restoration, or coastal restoration undertaken by a Federal, State, or local government agency; or (2) for use in a construction project funded in whole or in part by, or authorized by, the Federal government. This dataset is a collection of proposed, previous and current authorized lease areas under BOEM's purview. The intent is to update the dataset when leases are proposed, added or renewed. Attribution consists of state, sand volume, and dates and identification properties associated with the lease. Revisions to the data, which would most often be to attribution, will occur regularly. BOEM is responsible for managing leasing within federal offshore waters only. Sand leases and resources within State waters are not available through this data layer.

These boundaries were created for BOEM administrative purposes only, such as delineating BOEM planning areas or determining shared state revenue sharing within the 3 nautical mile zone seaward of the SLA boundary known as the Revenue Sharing Boundary. They were created using the equidistant principle used to divide offshore areas between countries as defined within the UNCLOS. They are not meant to depict areas offshore as pertaining to or controlled by any particular state.

The Navy and other military users of the marine environment are required to assess the impact of their activities on marine mammals to comply with the Marine Mammal Protection Act, the Endangered Species Act, and the National Environmental Policy Act. The number of marine mammals that might be impacted by Navy activities must be estimated in an Environmental Assessment or Environmental Impact Statement. A key element of this estimation is knowledge of cetacean densities in specific areas where those activities will occur. Data collected by NOAA’s Southwest Fisheries Science Center (SWFSC) from 1986-2006 using accepted, peer-reviewed survey methods were used to develop models to forecast cetacean densities. This work represents an important step towards understanding marine mammal habitat use with respect to regions utilized by the U. S. Navy. MarineCadastre.gov has included Blue, Fin, Humpback, and Sperm whale models for the summer months as examples of these models. Please view the publication for specific details on the development and use of these models. NOTE: Updated Pacific models should be available in mid-2017.

This layer provides access to a fish species richness map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, Loyola University Chicago, and The Nature Conservancy. For all sampled fish species together, total richness maps are calculated in a GIS by stacking each individual species’ sampled presence (created from the Inverse Distance Weighted interpolation layers) and counting the total number of species present in each cell. A species is considered present in a cell if the interpolated biomass is above a certain very low threshold. That threshold is determined by defining the smallest area holding 95% of the total interpolated biomass for the species. The legend for this map shows the estimated number of species from average annual interpolations (2km x 2km). Fish summary products were developed using the NEFSC fall survey data source. Fish group species richness represent the expected richness of a survey trawl done in that area, and are not representative of the true fish species richness in that location. This is the expected richness for the gear type used in NEFSC fall trawls, and does not account for each species’ catch-ability. These data are a fishery descriptor, not an ecosystem descriptor and are not meant to be used to determine absolute fish biomass hotspots. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to a fish species richness map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, Loyola University Chicago, and The Nature Conservancy. For all sampled fish species together, total richness maps are calculated in a GIS by stacking each individual species’ sampled presence (created from the Inverse Distance Weighted interpolation layers) and counting the total number of species present in each cell. A species is considered present in a cell if the interpolated biomass is above a certain very low threshold. That threshold is determined by defining the smallest area holding 95% of the total interpolated biomass for the species. The legend for this map shows the estimated number of species from average annual interpolations (2km x 2km). Fish summary products were developed using the NEFSC spring survey data source. Fish group species richness represent the expected richness of a survey trawl done in that area, and are not representative of the true fish species richness in that location. This is the expected richness for the gear type used in NEFSC spring trawls, and does not account for each species’ catch-ability. These data are a fishery descriptor, not an ecosystem descriptor and are not meant to be used to determine absolute fish biomass hotspots. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to a fish biomass map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, Loyola University Chicago, and The Nature Conservancy. For all sampled fish species together, total biomass maps are calculated in a GIS by stacking each individual species’ Inverse Distance Weighted (IDW) interpolation layers and summing the values of the cells. The result is the total interpolated biomass of all individuals of the sampled species in that cell, for example forage fish. Note that individual fish species IDW maps and these aggregate maps have been cubic root transformed. The legend for this map shows the sum of the average annual interpolated biomass for all species and for all tows (2km x 2km). Fish summary products were developed using the NEFSC fall survey data source. The summary map products provide a means to distill multiple layers and time periods into simplified maps and build a general understanding of the ecological richness or abundance in a particular area. Development of these maps required decisions and thresholds that are explained in the Metadata and Publication links. These decisions were made after conducting research, testing of several options, and incorporating feedback from expert work group members. Understanding these decisions and thresholds is crucial to proper interpretation of these map products. Note: this raster layer is not compatible with ArcGIS Online.

This layer provides access to a fish biomass map made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, Loyola University Chicago, and The Nature Conservancy. For all sampled fish species together, total biomass maps are calculated in a GIS by stacking each individual species’ Inverse Distance Weighted (IDW) interpolation layers and summing the values of the cells. The result is the total interpolated biomass of all individuals of the sampled species in that cell, for example forage fish. Note that individual fish species IDW maps and these aggregate maps have been cubic root transformed. The legend for this map shows the sum of the average annual interpolated biomass for all species and for all tows (2km x 2km). Fish summary products were developed using the NEFSC spring survey data source. The summary map products provide a means to distill multiple layers and time periods into simplified maps and build a general understanding of the ecological richness or abundance in a particular area. Development of these maps required decisions and thresholds that are explained in the Metadata and Publication links. These decisions were made after conducting research, testing of several options, and incorporating feedback from expert work group members. Understanding these decisions and thresholds is crucial to proper interpretation of these map products. Note: this raster layer is not compatible with ArcGIS Online.

These Formerly Used Defense Sites (FUDS) are the locations from which military activity occurred and the various trajectory and ranges from these areas where weapons were deployed or tested. Therefore, this dataset acts as a possible proxy for Unexploded Ordinances (UXOs) or hazardous material locations. UXOs are explosive weapons (bombs, bullets, shells, grenades, mines, etc.) that did not explode when they were employed and still pose a risk of detonation. Two related datasets that should be viewed in tandem are Unexploded Ordnance Areas displays known or possible former explosive dumping areas and Unexploded Ordnance Locations displays known or possible individual or tightly grouped unexploded ordnances on the ocean floor.

Multibeam bathymetry (in meters) collected to support a potential claim of an extended continental shelf by the United States, under the United Nations Convention on the Law of the Sea, Article 76. These data include full-coverage multibeam sonar measurements in water depths of approximately 1000 to 5000 m in order to precisely define the location of the 2500-m isobath and the "foot of the slope". The Center for Coastal and Ocean Mapping – Joint Hydrographic Center is a cooperative partnership between the University of New Hampshire and the National Oceanic and Atmospheric Administration.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are BOEM contours shown at 100 ft intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are BOEM contours shown at 100 m intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are BOEM contours shown at 1000 ft intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are BOEM contours shown at 500 ft intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are BOEM contours shown at 500 m intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are NOAA contours shown at 100 m intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are NOAA contours shown at 1000 ft intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are NOAA contours shown at 500 ft intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

Gulf of Mexico Depth Contours derived from NOAA's NGDC bathymetric grids and from BOEM's seismic grid compilation. Both NOAA and BOEM contours are shown in meters or feet depending on the user's preference. The contours depicted in this layer are NOAA contours shown at 500 m intervals. Contours were created to compare and contrast the older NOAA NGDC grid to the newer BOEM gridded bathymetry described below: BOEM's deepwater Gulf of Mexico bathymetry grid- Created by mosaicing over 100 3D seismic surveys. XY grid size is 40 ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200 ft) and maximum (-11,000 ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

BOEM has accumulated a large database of X-Y-Z data from 3-D seismic data, as well as amplitude, which was gridded together to create one contiguous map across the deep water GOM. The resultant map is the highest and most consistent resolution regional map available to date. Created by mosaicking over 100 3D seismic surveys. XY grid size is 40ft and depth is in feet. Depth accurate to 0.1% (one-tenth of one-percent) of water depth in most places. Depth accuracy decreases slightly when approaching minimum (-200ft) and maximum (-11,000ft) depth extents due to the nature of the depth transformation method used. BOEM thanks the following companies for allowing BOEM use of their data to create this new map: CGG Services (U.S.) Inc., Houston, TX; Exxon; Geophysical Pursuit; PGS; Seitel; TGS; and WesternGeco, LLC.

These data depict annual average wind speed (meters per second) at 100 meters above sea level for the Gulf of Mexico offshore areas of the United States, for a seven year period from 2007 to 2013. The sampling resolution of 2km was generalized into polygons shown in the legend classes. Data include both point and polygon data sets intended to provide broad estimates of wind speed variation for the purposes of identifying potential offshore wind energy sites. They are not intended to provide specific estimates of energy production for the purpose of making offshore wind project investment or financing decisions in specific locations. The source data is National Renewable Energy Laboratory’s Wind Integration National Dataset (WIND) Toolkit. Please see metadata for more detailed process information. Monthly averages are available within the downloads menu, either as GIS data or Esri REST map services.

The Gulf of Mexico OCS Blocks with Significant Sediment Resources is a planning tool to assist in the management of Outer Continental Shelf (OCS) sediment resources, reduce multiple use conflicts, minimize interference with existing leases (e.g., oil and gas) and rights-of-way (e.g., submerged infrastructure, shipping lanes, military operations, etc.), and help avoid sensitive areas (e.g., archaeological sites, protected habitat). These OCS blocks represent areas within the OCS protraction grid where sand resources have been identified through reconnaissance and/or design-level OCS studies. Additional OCS studies may be necessary in order to refine and quantify the extents of sand resources within these areas. The Marine Minerals Program (MMP) within the Bureau of Ocean Energy Management (BOEM) is responsible for managing non-energy minerals (primarily sand and gravel) on the OCS. Access to and identification of potential OCS sand resources is critical for the long-term success and cost-effectiveness of many shore protection, beach nourishment, and coastal habitat restoration projects along the Gulf of Mexico coast.

Habitat Areas of Particular Concern (HAPC) are discrete subsets of Essential Fish Habitat (EFH) that provide extremely important ecological functions or are especially vulnerable to degradation.

Shallow-water (<30 m) benthic habitats digitized from satellite imagery. Areas were categorized by Type (coral, coraline algae, emergent vegetation, macroalgae, seagrass, turf) and percent cover. The minimum mapping unit was an acre. For the full dataset description go to linked Publication under the Additional Information tab and see: Bauer, L., et al. 2016. Chap 3 Benthic Habitats and Corals. Pp 57-136. In: Costa, B.M., and M. S. Kendall (eds). 2016. Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. More detailed information can be found in: Battista, T.A., B.M. Costa, and S.M. Anderson. 2007. Benthic Habitats of the Main Hawaiian Islands. NOAA Technical Memorandum NOS NCCOS CCMA 152. Silver Spring, MD. 48 pp The download is for the entire Benthics geodatabase which includes over 100 feature classes. This layer is: Shallow_CompiledHabitatRecords_Benthic_Habitat_Map.

Non-confidential fishery dependent catch data describing the numbers of pounds (lbs) of Deep 7 bottomfish caught per fishing trip. Landings are summarized by location based on the Hawaiian Commercial Fisheries Statistical Carts. For the full dataset description go to linked Publication under the Additional Information tab and see: Stamoulis, K.A., et. al. 2016. Chapter 4: Fishes - Reef Fish. pp. 156-196. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. Dataset citation: DAR. 2014. Fishery-Dependent Data. State of Hawaii, Department of Land and Natural Resources, Division of Aquatic Resources. Data Provided 28 October 2015. The download is for the entire Fisheries geodatabase which includes over 100 feature classes depicting species locations and benthic characteristics of the Main Hawaiian Island. This layer is: BottomFishCommunities_CompiledDARFisheryRecords_Deep7_Catch_Summary.

Predicted relative abundance for Brown Boobys (Sula leucogaster) from August to October. These regional models were developed using a boosted zero-inflated count framework. Their purpose was to help inform future data collection efforts around the Main Hawaiian Islands. Models are based on data collected by NOAA Southwest Fisheries Science Center shipboard surveys. Data was from August to October 1998 – 2012, with the majority of data collected on two surveys in 2002 and 2010. For the full dataset description go to linked Publication under the Additional Information tab and see: Winship A. J., et al. 2016. Chapter 7 Seabirds. Pp283-320 In: Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Seabirds geodatabase which includes 78 feature classes. This layer is: ModelPredictions_BrownBooby_RelativeDensity. Note: this raster layer is not compatible with ArcGIS Online.

Presences of Brown Booby (Sula leucogaster) detected on transects. Data collected by NOAA Southwest Fisheries Science Center shipboard surveys. Data was from August to October 1998 – 2012, with the majority of data collected on two surveys in 2002 and 2010. For the full dataset description go to linked Publication under the Additional Information tab and see: Winship A. J., et al. 2016. Chapter 7 Seabirds. Pp283-320 In: Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Seabirds geodatabase which includes 78 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: SightingLocations_BrownBooby_Summer.

Presences of Humpback Whales detected on transects in the winter. Data comes from multiple sources including, NOAA Southwest Fisheries Science Center, NOAA Pacific Islands Fisheries Science Center, Cascadia Research Collective, and the University of Hawaii. For the full dataset description go to linked Publication under the Additional Information tab and see: Pittman, S.J., et. al. 2016. Chapter 6: Marine Mammals - Cetaceans. pp. 227-265. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Marine Mammals geodatabase which includes 70 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: CetaceanSightingLocations_HumpbackWhale_Winter.

Predicted relative abundance for Humpback Whales in the winter. These long-term, regional models are based on data from 1993 – 2014 collected by multiple sources. The purpose is to help inform future data collection efforts around the Main Hawaiian Islands. For the full dataset description go to linked Publication under the Additional Information tab and see: Pittman, S.J., et. al. 2016. Chapter 6: Marine Mammals - Cetaceans. pp. 227-265. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Marine Mammals geodatabase which includes 70 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: CetaceanModelPredictions_HumpbackWhale_winter_RelativeAbundance. Note: this raster layer is not compatible with ArcGIS Online.

These data depict annual average wind speed (meters per second) at 100 meters above sea level for the Hawaii offshore areas of the United States, for a seven year period from 2007 to 2013. The sampling resolution of 2km was generalized into polygons shown in the legend classes. Data include both point and polygon data sets intended to provide broad estimates of wind speed variation for the purposes of identifying potential offshore wind energy sites. They are not intended to provide specific estimates of energy production for the purpose of making offshore wind project investment or financing decisions in specific locations. The source data is National Renewable Energy Laboratory’s Wind Integration National Dataset (WIND) Toolkit. Please see metadata for more detailed process information. Monthly averages are available within the downloads menu, either as GIS data or Esri REST map services.

Probability that eddy rings will form off the Main Hawaiian Islands in the summer. Source data for this model comes from NASA Maps of Absolute Dynamic Topography (MADT) 1993 – 2014. For the full dataset description go to linked Publication under the Additional Information tab and see: Costa, B. M., et al. 2016. Chapter 2 Environmental Setting. Pp 13-56 In: Costa, B.M., and M. S. Kendall (eds). 2016. Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Environmental_Settings geodatabase which includes over 100 feature classes depicting environmental characteristics of the Main Hawaiian Island. This layer is PhysicalOceanography_WaterMovement_Probability_of_Cyclonic_Eddy_Rings_Summer_Mean. Note: this raster layer is not compatible with ArcGIS Online.

Probability that eddy rings will form off the Main Hawaiian Islands in the winter. Source data for this model comes from NASA Maps of Absolute Dynamic Topography (MADT) 1993 – 2014. For the full dataset description go to linked Publication under the Additional Information tab and see: Costa, B. M., et al. 2016. Chapter 2 Environmental Setting. Pp 13-56 In: Costa, B.M., and M. S. Kendall (eds). 2016. Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Environmental_Settings geodatabase which includes over 100 feature classes depicting environmental characteristics of the Main Hawaiian Island. This layer is PhysicalOceanography_WaterMovement_Probability_of_Cyclonic_Eddy_Rings_Winter_Mean. Note: this raster layer is not compatible with ArcGIS Online.

Predicted reef fish biomass (grams/square meter) values for Hawaii from boosted regression tree models. Models were based on 29 environmental datasets from 4 categories (topographic, benthic habitat composition, geographic, and oceanographic). For the full dataset description go to linked Publication under the Additional Information tab and see: Stamoulis, K.A., et. al. 2016. Chapter 4: Fishes - Reef Fish. pp. 156-196. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Fisheries geodatabase which includes over 100 feature classes depicting species locations and benthic characteristics of the Main Hawaiian Island. This layer is a compilation of 4 layers: ReefFishCommunities_ModelPredictions_[IslandName]_Total_Biomass_Mean. Note: this raster layer is not compatible with ArcGIS Online.

Standardized total biomass values from underwater visual surveys for reef fishes in Hawaii. Data came from a variety of sources from 1992 - 2012 and were compiled by the University of Hawaii Fisheries Ecology Research Lab. For the full dataset description go to linked Publication under the Additional Information tab and see: Stamoulis, K.A., et. al. 2016. Chapter 4: Fishes - Reef Fish. pp. 156-196. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. Dataset citation: https://fisheriesecologyresearchlab.wordpress. com/ The download is for the entire Fisheries geodatabase which includes over 100 feature classes depicting species locations and benthic characteristics of the Main Hawaiian Island. This layer is: ReefFishCommunities_Compiled_Survey_Records.

Speed of sea surface currents around the Main Hawaiian Islands in the summer. Source data for this model comes from HYCOM +NCODA global 1/12° Reanalysis. For the full dataset description go to linked Publication under the Additional Information tab and see: Costa, B. M., et al. 2016. Chapter 2 Environmental Setting. Pp 13-56 In: Costa, B.M., and M. S. Kendall (eds). 2016. Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Environmental_Settings geodatabase which includes over 100 feature classes depicting environmental characteristics of the Main Hawaiian Island. This layer is PhysicalOceanography_WaterMovement_SurfaceCurrent_Speed_Summer_Mean. Note: this raster layer is not compatible with ArcGIS Online.

Speed of sea surface currents around the Main Hawaiian Islands in the winter. Source data for this model comes from HYCOM +NCODA global 1/12° Reanalysis. For the full dataset description go to linked Publication under the Additional Information tab and see: Costa, B. M., et al. 2016. Chapter 2 Environmental Setting. Pp 13-56 In: Costa, B.M., and M. S. Kendall (eds). 2016. Marine Biogeographic Assessment of the Main Hawaiian Islands. Bureau of Ocean Energy Management and National Oceanic and Atmospheric Administration. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Environmental_Settings geodatabase which includes over 100 feature classes depicting environmental characteristics of the Main Hawaiian Island. This layer is PhysicalOceanography_WaterMovement_SurfaceCurrent_Speed_Winter_Mean. Note: this raster layer is not compatible with ArcGIS Online.

Predicted relative abundance for Spinner Dolphins in the summer. These long-term, regional models are based on data from 1993 – 2014 collected by multiple sources. The purpose is to help inform future data collection efforts around the Main Hawaiian Islands. For the full dataset description go to linked Publication under the Additional Information tab and see: Pittman, S.J., et. al. 2016. Chapter 6: Marine Mammals - Cetaceans. pp. 227-265. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Marine Mammals geodatabase which includes 70 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: CetaceanModelPredictions_SpinnerDolphin_summer_RelativeAbundance. Note: this raster layer is not compatible with ArcGIS Online.

Predicted relative abundance for Spinner Dolphins in the winter. These long-term, regional models are based on data from 1993 – 2014 collected by multiple sources. The purpose is to help inform future data collection efforts around the Main Hawaiian Islands. For the full dataset description go to linked Publication under the Additional Information tab and see: Pittman, S.J., et. al. 2016. Chapter 6: Marine Mammals - Cetaceans. pp. 227-265. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Marine Mammals geodatabase which includes 70 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: CetaceanModelPredictions_SpinnerDolphin_winter_RelativeAbundance. Note: this raster layer is not compatible with ArcGIS Online.

Presences of Spinner Dolphins detected on transects in the summer. Data comes from multiple sources including, NOAA Southwest Fisheries Science Center, NOAA Pacific Islands Fisheries Science Center, Cascadia Research Collective, and the University of Hawaii. For the full dataset description go to linked Publication under the Additional Information tab and see: Pittman, S.J., et. al. 2016. Chapter 6: Marine Mammals - Cetaceans. pp. 227-265. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Marine Mammals geodatabase which includes 70 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: CetaceanSightingLocations_SpinnerDolphin_Summer.

Presences of Spinner Dolphins detected on transects in the winters of 1993 – 2014. Data comes from multiple sources including, NOAA Southwest Fisheries Science Center, NOAA Pacific Islands Fisheries Science Center, Cascadia Research Collective, and the University of Hawaii. For the full dataset description go to linked Publication under the Additional Information tab and see: Pittman, S.J., et. al. 2016. Chapter 6: Marine Mammals - Cetaceans. pp. 227-265. In: B.M. Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Marine Mammals geodatabase which includes 70 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: CetaceanSightingLocations_SpinnerDolphin_Winter.

Predicted relative abundance for Wedge-tailed Shearwaters (Puffinus pacificus) from August to October. These long-term, regional models were developed using a boosted zero-inflated count framework. Their purpose was to help inform future data collection efforts around the Main Hawaiian Islands. Models are based on data collected by NOAA Southwest Fisheries Science Center shipboard surveys. Data was from August to October 1998 – 2012, with the majority of data collected on two surveys in 2002 and 2010. For the full dataset description go to linked Publication under the Additional Information tab and see: Winship A. J., et al. 2016. Chapter 7 Seabirds. Pp283-320 In: Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Seabirds geodatabase which includes 78 feature classes. This layer is: ModelPredictions_WedgetailedShearwater_RelativeDensit. Note: this raster layer is not compatible with ArcGIS Online.

Presences of Wedge-tailed Shearwater (Puffinus pacificus) detected on transects. Data collected by NOAA Southwest Fisheries Science Center shipboard surveys. Data was from August to October 1998 – 2012, with the majority of data collected on two surveys in 2002 and 2010. For the full dataset description go to linked Publication under the Additional Information tab and see: Winship A. J., et al. 2016. Chapter 7 Seabirds. Pp283-320 In: Costa and M.S. Kendall (eds.). Marine Biogeographic Assessment of the Main Hawaiian Islands. BOEM and NOAA. OCS Study BOEM 2016-035 and NOAA Technical Memorandum NOS NCCOS 214. 359 pp. The download is for the entire Seabirds geodatabase which includes 78 feature classes depicting species locations and predictive models for the Main Hawaiian Island. This layer is: SightingLocations_WedgetailedShearwater_Summer.

These data show the point locations of High Frequency (HF) radar systems across the United States. HF radars measure the speed and direction of ocean surface currents in near real time. These radars, which can operate under any weather conditions, measure currents over a large region of the coastal ocean, as far as 200 km offshore. They are located near the water’s edge, and do not need to be situated atop a high point of land. Dozens of institutions own and operate HF radars within the United States, and many are coordinated through the US Integrated Ocean Observing System. Ocean surface current data from these radars are shared on national servers by the National Data Buoy Center and Scripps Institution of Oceanography.

The Navy and other military users of the marine environment are required to assess the impact of their activities on marine mammals to comply with the Marine Mammal Protection Act, the Endangered Species Act, and the National Environmental Policy Act. The number of marine mammals that might be impacted by Navy activities must be estimated in an Environmental Assessment or Environmental Impact Statement. A key element of this estimation is knowledge of cetacean densities in specific areas where those activities will occur. Data collected by NOAA’s Southwest Fisheries Science Center (SWFSC) from 1986-2006 using accepted, peer-reviewed survey methods were used to develop models to forecast cetacean densities. This work represents an important step towards understanding marine mammal habitat use with respect to regions utilized by the U. S. Navy. MarineCadastre.gov has included Blue, Fin, Humpback, and Sperm whale models for the summer months as examples of these models. Please view the publication for specific details on the development and use of these models. NOTE: Updated Pacific models should be available in mid-2017.

This layer is part of the Bureau of Indian Affairs Land Area Representation (LAR) data set. The LARs illustrate land areas for Federally-recognized tribes. The purpose of the American Indian and Alaska Native LAR data set is to depict the external extent of Federal Indian reservations and the external extent of associated land "held in trust” by the United States, “restricted fee” or “mixed ownership” status for Federally recognized tribes and individual Native Americans. This data set includes other land area types such as Public Domain Allotments, Dependent Indian Communities and Homesteads. This GIS data set is prepared strictly for illustrative and reference purposes only and should not be used, and is not intended for legal, survey, engineering or navigation purposes.

This entry provides access to avian relative abundance maps made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. The individual Avian species maps represent the results of predictive modeling applied to data from the Northwest Atlantic Seabird Catalog (US Fish and Wildlife Service) and the Eastern Canada Seabirds at Sea database (Canadian Wildlife Service, Environment and Climate Change Canada). Relative density model results are the long-term average relative abundance of individuals per unit area. It is not possible to infer absolute abundance because of how the survey data were collected and compiled, and how the models were generated. Individual species models are found in the Map Service link under MDAT/Avian_Abundance. There are also folders in MDAT for 90% Confidence Interval and the Coefficient of Variation. View the Source Info link to download individual species models with supporting statistical measures of model uncertainty and documentation. Note: viewing in the National Viewer is not enabled for this layer.

These data predict the likelihood of suitable deep-sea coral habitat maps made available by the NOAA Deep-Sea Coral Research and Technology Program (DSCRTP). The data represent predicted habitat suitability for several taxa of deep-sea corals. Predictions were modeled using a statistical machine-learning algorithm called maximum entropy (MaxEnt). Data provided by DSCRTP and other contributors with environmental and oceanographic data were used to generate the predictive models of deep-sea coral distribution. These models are used to produce regional maps of deep-sea coral habitat. In these regions, deep-sea coral occurs on the continental shelves and slopes, at ocean depths of approximately 50 to greater than 2,000 meters. Model predictions are organized into five hierarchical categories that permit comparison of the data across coral taxa and across regions. The categories correspond to the predicted likelihood of suitable deep-sea coral habitat occurring. Note: viewing in the National Viewer is not enabled for this layer.

This entry provides access to the individual fish data layers made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center (NEFSC), Loyola University Chicago, and The Nature Conservancy (TNC). In 2019, MDAT member TNC produced fish biomass and distribution products in partnership with OceanAdapt (a collaboration between the Pinksy Lab at Rutgers University and the National Marine Fisheries Service) and the NOAA Northeast Fisheries Science Center for 81 individual fish species in spring and fall. These products are also bubble plots of raw observations and IDW surfaces at a 2km x 2km resolution for bottom trawl data from NEFSC during 2010-2017 (fall) and 2010-2016 (spring). All units are kilograms per tow. Survey samples for fall trawls were collected primarily in September and October, with some in November and a small number in December. Spring survey samples were collected from February to April. Note: viewing in the National Viewer is not enabled for this layer.

This entry provides access to individual marine mammal species maps made available by the Marine-life Data and Analysis Team (MDAT) led by the Duke University Marine Geospatial Ecology Lab, and including NOAA National Centers for Coastal Ocean Science, NOAA Northeast Fisheries Science Center, and Loyola University Chicago. Individual marine mammal species maps are available that represent the results of distance sampling modeling methodology applied to over 20 years of aerial and shipboard cetacean surveys, linked with remote sensing and ocean model environmental covariates. Mammal abundance products are available monthly (or annually for different species) and show predicted abundances of animals for the given time period. Individual species models are found in the Map Service link. There are also map service folders under MDAT for 95% and 5% Confidence Interval, the Coefficient of Variation, and Standard Error. View the Source Info link to download individual species models with supporting statistical measures of model uncertainty and documentation. Note: viewing in the National Viewer is not enabled for this layer.

The Navy is committed to demonstrating environmental stewardship while executing its national defense mission and is also responsible for compliance with a suite of federal environmental and natural resources laws and regulations, including the National Environmental Policy Act, the Marine Mammal Protection Act, and the Endangered Species Act. In order to comply with these mandates, up-to-date, area-specific marine mammal and sea turtle density estimates for the Operating Areas (OPAREAs) and adjacent Navy training regions are required. MarineCadastre.gov has included Kemp’s Ridley, Leatherback, and Loggerhead turtle models for all seasons as examples of these models. Please view the publication for specific details on the development and use of these models. Data shown here are not available for download at the request of the data provider.

The Navy is committed to demonstrating environmental stewardship while executing its national defense mission and is also responsible for compliance with a suite of federal environmental and natural resources laws and regulations, including the National Environmental Policy Act, the Marine Mammal Protection Act, and the Endangered Species Act. In order to comply with these mandates, up-to-date, area-specific marine mammal and sea turtle density estimates for the Operating Areas (OPAREAs) and adjacent Navy training regions are required. MarineCadastre.gov has included Kemp’s Ridley, Leatherback, and Loggerhead turtle models for all seasons as examples of these models. Please view the publication for specific details on the development and use of these models. Data shown here are not available for download at the request of the data provider.

The Navy is committed to demonstrating environmental stewardship while executing its national defense mission and is also responsible for compliance with a suite of federal environmental and natural resources laws and regulations, including the National Environmental Policy Act, the Marine Mammal Protection Act, and the Endangered Species Act. In order to comply with these mandates, up-to-date, area-specific marine mammal and sea turtle density estimates for the Operating Areas (OPAREAs) and adjacent Navy training regions are required. MarineCadastre.gov has included Kemp’s Ridley, Leatherback, and Loggerhead turtle models for all seasons as examples of these models. Please view the publication for specific details on the development and use of these models. Data shown here are not available for download at the request of the data provider.

The Navy is committed to demonstrating environmental stewardship while executing its national defense mission and is also responsible for compliance with a suite of federal environmental and natural resources laws and regulations, including the National Environmental Policy Act, the Marine Mammal Protection Act, and the Endangered Species Act. In order to comply with these mandates, up-to-date, area-specific marine mammal and sea turtle density estimates for the Operating Areas (OPAREAs) and adjacent Navy training regions are required. MarineCadastre.gov has included Kemp’s Ridley, Leatherback, and Loggerhead turtle models for all seasons as examples of these models. Please view the publication for specific details on the development and use of these models. Data shown here are not available for download at the request of the data provider.

The Navy is committed to demonstrating environmental stewardship while executing its national defense mission and is also responsible for compliance with a suite of federal environmental and natural resources laws and regulations, including the National Environmental Policy Act, the Marine Mammal Protection Act, and the Endangered Species Act. In order to comply with these mandates, up-to-date, area-specific marine mammal and sea turtle density estimates for the Operating Areas (OPAREAs) and adjacent Navy training regions are required. MarineCadastre.gov has included Kemp’s Ridley, Leatherback, and Loggerhead turtle models for all seasons as examples of these models. Please view the publication for specific details on the development and use of these models. Data shown here are not available for download at the request of the data provider.

The Navy is committed to demonstrating environmental stewardship while executing its national defense mission and is also responsible for compliance with a suite of federal environmental and natural resources laws and regulations, including the National Environmental Policy Act, the Marine Mammal Protection Act, and the Endangered Species Act. In order to comply with these mandates, up-to-date, area-specific marine mammal and sea turtle density estimates for the Operating Areas (OPAREAs) and adjacent Navy training regions are required. MarineCadastre.gov has included Kemp’s Ridley, Leatherback, and Loggerhead turtle models for all seasons as examples of these models. Please view the publication for specific details on the development and use of these models. Data shown here are not available for download at the request of the data provider.