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Dataset Overview | National Centers for Environmental Information (NCEI)

Cetacean and sea turtle spatial density model outputs from visual observations using line-transect survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 to 2019-07-31 (NCEI Accession 0256800)

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Based on ship-based and aerial line-transect surveys conducted in the U.S. waters of the Gulf of Mexico between 2003 and 2019, the NOAA Southeast Fisheries Science Center (SEFSC) developed spatial density models (SDMs) for cetacean and sea turtle species for the entire Gulf of Mexico. SDMs were developed using a generalized additive modeling (GAM) framework to determine the relationship between species abundance and environmental variables (monthly averaged oceanographic conditions during 2015 - 2019). Models were extrapolated beyond the U.S. Gulf of Mexico to provide insight into potential high density areas throughout the Gulf of Mexico. However, extrapolations of this type should be interpreted with caution. This dataset includes 19 shapefiles for the SDMs for each cetacean and sea turtle species or species group.
  • Cite as: Litz, Jenny; Aichinger Dias, Laura; Rappucci, Gina; Martinez, Anthony; Soldevilla, Melissa; Garrison, Lance; Mullin, Keith; Barry, Kevin; Foster, Marjorie (2022). Cetacean and sea turtle spatial density model outputs from visual observations using line-transect survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 to 2019-07-31 (NCEI Accession 0256800). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/efv4-9z56. Accessed [date].
gov.noaa.nodc:0256800
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Distribution Formats
  • Shapefile
  • Word
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Distributor NOAA National Centers for Environmental Information
+1-301-713-3277
NCEI.Info@noaa.gov
Dataset Point of Contact NOAA National Centers for Environmental Information
ncei.info@noaa.gov
Time Period 2003-06-12 to 2019-07-31
Spatial Bounding Box Coordinates
West: -97.65
East: -80.366
South: 17.766
North: 30.983
Spatial Coverage Map
General Documentation
Associated Resources
  • Cetacean, sea turtle, and seabird visual observations using line-transect survey methods from ships and aircraft during the Gulf of Mexico Marine Assessment Program for Protected Species (GOMMAPPS) surveys from 2017 to 2020
    • NCEI Collection
      Navigate directly to the URL for data access and direct download.
  • Project Information
  • Parent ID (indicates this dataset is related to other data):
    • gov.noaa.nodc:GOMMAPPS
Publication Dates
  • publication: 2022-07-29
  • revision: 2022-08-01
Data Presentation Form Digital table - digital representation of facts or figures systematically displayed, especially in columns
Dataset Progress Status Complete - production of the data has been completed
Historical archive - data has been stored in an offline storage facility
Data Update Frequency As needed
Supplemental Information
Submission Package ID: EU7CXU
Purpose The primary goal was to create spatial density models for cetaceans and sea turtles in the Gulf of Mexico.
Use Limitations
  • accessLevel: Public
  • Distribution liability: NOAA and NCEI make no warranty, expressed or implied, regarding these data, nor does the fact of distribution constitute such a warranty. NOAA and NCEI cannot assume liability for any damages caused by any errors or omissions in these data. If appropriate, NCEI can only certify that the data it distributes are an authentic copy of the records that were accepted for inclusion in the NCEI archives.
  • All cetacean data were collected under the National Marine Fisheries Service (NMFS), the Marine Mammal Protection Act (MMPA) and Endangered Species Act (ESA) permit number: NMFS ESA/MMPA Permit No. 779-1633 and No. 14450.
Dataset Citation
  • Cite as: Litz, Jenny; Aichinger Dias, Laura; Rappucci, Gina; Martinez, Anthony; Soldevilla, Melissa; Garrison, Lance; Mullin, Keith; Barry, Kevin; Foster, Marjorie (2022). Cetacean and sea turtle spatial density model outputs from visual observations using line-transect survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 to 2019-07-31 (NCEI Accession 0256800). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/efv4-9z56. Accessed [date].
Cited Authors
Principal Investigators
Contributors
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Publishers
Acknowledgments
  • Related Funding Agency: U.S. Department of Interior (DOI), Bureau of Ocean Energy Management (BOEM)
  • Related Funding Agency: U.S. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS), Southeast Fisheries Science Center (SEFSC)
  • Related Funding Agency: NOAA Office of Response and Restoration (ORR)
  • Related Funding Agency: United States Navy
  • Related Funding Agency: NOAA RESTORE Science Program
  • This study was funded by the U.S. Department of the Interior, Bureau of Ocean Energy Management through Interagency Agreement M17PG00013 with the U.S. Department of Commerce, National Oceanic and Atmospheric Administration (NOAA).
  • This research was carried out [in part] under the auspices of the Cooperative Institute for Marine and Atmospheric Studies (CIMAS), a Cooperative Institute of the University of Miami and the National Oceanic and Atmospheric Administration, cooperative agreements NA15OAR4320064 and NA20OAR4320472.
  • Thank you to the marine mammal observers and crew who participated in the SEFSC surveys.
Theme keywords NODC DATA TYPES THESAURUS NODC OBSERVATION TYPES THESAURUS WMO_CategoryCode
  • oceanography
Global Change Master Directory (GCMD) Science Keywords Provider Keywords
  • Beaked whales (Ziphius and Mesoplodon spp.)
  • Blackfish (Orcinus orca, Peponocephala electra, Feresa attenuata and Pseudorca crassidens combined)
  • Clymene dolphin (Stenella clymene)
  • Green sea turtle (Chelonia mydas)
  • Kemp's Ridley sea turtle (Lepidochelys kempii)
  • Leatherback sea turtle (Dermochelys coriacea)
  • Loggerhead sea turtle (Caretta caretta)
  • Oceanic Atlantic spotted dolphin (Stenella frontalis)
  • Oceanic Common bottlenose dolphin (Tursiops truncatus)
  • Pantropical spotted dolphin (Stenella attenuata)
  • Pilot whales (Globicephala sp.)
  • Pygmy or Dwarf sperm whales (Kogia spp.)
  • Rice’s whale (Balaenoptera ricei)
  • Risso’s dolphin (Grampus griseus)
  • Shelf Atlantic spotted dolphin (Stenella frontalis)
  • Shelf Common bottlenose dolphin (Tursiops truncatus)
  • Sperm whale (Physeter macrocephalus)
  • Spinner dolphin (Stenella longirostris)
  • Striped dolphin (Stenella coeruleoalba)
Provider Observation Categories
  • POPULATION ABUNDANCE
  • SPECIES DISTRIBUTION MODELS
Data Center keywords NODC COLLECTING INSTITUTION NAMES THESAURUS NODC SUBMITTING INSTITUTION NAMES THESAURUS Global Change Master Directory (GCMD) Data Center Keywords
Platform keywords NODC PLATFORM NAMES THESAURUS Global Change Master Directory (GCMD) Platform Keywords ICES/SeaDataNet Ship Codes Provider Platform Names
  • DeHavilland DHC-6 Twin Otter aircraft
Instrument keywords NODC INSTRUMENT TYPES THESAURUS Global Change Master Directory (GCMD) Instrument Keywords Provider Instruments
  • AIRCRAFT
Place keywords NODC SEA AREA NAMES THESAURUS Global Change Master Directory (GCMD) Location Keywords
Project keywords NODC PROJECT NAMES THESAURUS Global Change Master Directory (GCMD) Project Keywords Provider Project Names
  • University of Miami’s Cooperative Institute for Marine and Atmospheric Studies (CIMAS)
Keywords NCEI ACCESSION NUMBER
Use Constraints
  • Cite as: Litz, Jenny; Aichinger Dias, Laura; Rappucci, Gina; Martinez, Anthony; Soldevilla, Melissa; Garrison, Lance; Mullin, Keith; Barry, Kevin; Foster, Marjorie (2022). Cetacean and sea turtle spatial density model outputs from visual observations using line-transect survey methods aboard NOAA vessel and aircraft platforms in the Gulf of Mexico from 2003-06-12 to 2019-07-31 (NCEI Accession 0256800). [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/efv4-9z56. Accessed [date].
Access Constraints
  • Use liability: NOAA and NCEI cannot provide any warranty as to the accuracy, reliability, or completeness of furnished data. Users assume responsibility to determine the usability of these data. The user is responsible for the results of any application of this data for other than its intended purpose.
Fees
  • In most cases, electronic downloads of the data are free. However, fees may apply for custom orders, data certifications, copies of analog materials, and data distribution on physical media.
Lineage information for: dataset
Processing Steps
  • 2022-07-29T16:19:07Z - NCEI Accession 0256800 v1.1 was published.
  • 2022-08-01T02:33:36Z - NCEI Accession 0256800 was revised and v2.2 was published.
    Rationale: Updates were received for this dataset. These updates were copied into the data/0-data/ directory of this accession. These updates may provide additional files or replace obsolete files. This version contains the most complete and up-to-date representation of this archival information package. All of the files received prior to this update are available in the preceding version of this accession.
Output Datasets
Lineage information for: dataset
Processing Steps
  • Parameter or Variable: CETACEANS, SEA TURTLES, VISUAL OBSERVATIONS, DISTANCE SAMPLING, LINE-TRANSECT SURVEY (measured); Units: NA; Observation Category: In Situ/Laboratory Instruments, SPECIES DISTRIBUTION MODELS, POPULATION ABUNDANCE; Sampling Instrument: AIRCRAFT, LINE-TRANSECT SURVEY, DISTANCE SAMPLING, INCLINOMETERS, BIG EYE BINOCULARS, SHIPS; Sampling and Analyzing Method: The goal of this research was to develop Gulf-wide cetacean and sea turtle spatial density models (SDMs) based on line-transect surveys conducted in the U.S. waters of the Gulf of Mexico. Surveys used to develop the SDMs for species occupying continental shelf and oceanic waters of the Gulf of Mexico were conducted during the GoMMAPPS project and comparable-prior-year surveys. Aerial survey data from seasonal surveys conducted during 2011/2012 and 2017/2018 (GoMMAPPS Surveys) were used to develop SDMs for cetacean and sea turtle species over the continental shelf. Data collected from vessel surveys, including the two-team surveys conducted during summer 2017, winter 2018, and summer/fall 2018 (GoMMAPPS Surveys) and 2003, 2004, and 2009 (that included only one survey team), were used to develop SDMs for cetaceans in oceanic waters. In addition, for Rice’s whales, surveys conducted in 2018 and 2019 were also used in developing the SDMs specific for this species. Habitat-based species distribution models were developed using a generalized additive modeling (GAM) framework to determine the relationship between cetacean and sea turtle abundance and environmental variables. Samples for modeling were created by summarizing survey effort and environmental variables with a hexagon grid developed by the Environmental Protection Agency expanded to fit the entire Gulf of Mexico. The grid was created in a Lambert azimuthal equal area projection and the area of each hexagon is 40 km2. For all hexagons that contained survey effort segments, cetacean and sea turtle density was calculated using total number of animals observed, segment effort length and average sighting condition covariates in the hexagon, and the parameters estimated in distance sampling abundance models. Oceanographic variables were used as dynamic covariates in SDMs and were obtained from multiple sources that included both remotely sensed data and hydrographic model output. Data products were obtained from their respective sources at varying temporal and spatial resolutions. To develop the explanatory variables for the SDMs, we summarized each data source spatially by overlaying the hexagon grid and calculating the average variable for each cell at the highest temporal resolution available. These data were then matched to the survey effort data so that each trackline segment in each grid cell. The survey effort segments were the sampling unit in the spatial density models (SDMs). For prediction maps, we developed monthly averages of the gridded data for all survey years from 2003-2018. Species were visually identified to the lowest taxonomic level possible. For sea turtles, oceanic dolphins and small whale species, sightings that could not be identified to the species level were apportioned among the identified species based upon spatial density models (SDMs) for these taxa groups (Hardshell sea turtle, Unidentified Stenellid Dolphins, Unidentified Dolphins, and Unidentified Small Whales). In addition, for beaked whales species, genera Ziphius and Mesoplodon, very few sightings could be identified to species, and therefore all species were combined into a common "beaked whale" category for this analysis. Likewise, killer whales, false killer whales, pygmy killer whales, and melon-headed whales were combined into a “Blackfish” category, given the relatively infrequent encounters with these species and difficulty to identify them to species level. The final resulting SDMs therefore account for both identified and unidentified sightings. Prediction maps were developed for each species or species group based upon the monthly averaged oceanographic conditions during 2015 - 2019. The appropriate SDM was used to predict animal density in each 40 km2 spatial cell for either shelf or oceanic waters for each month. The coefficient of variation (CV) of the density estimate (based upon uncertainty in the GAM model fit) is used to display the level of precision of the model and identify regions of high density and high uncertainty where model extrapolation is less reliable. Abundance estimates for each month are the sum of predicted abundance in each spatial cell. These estimates vary in response to dynamic oceanographic variables.; Data Quality Method: SDMs include a combination of two modeling approaches to address potential sources of bias and develop species-habitat relationships that are used to develop spatially and temporally explicit predictions of animal density. For aerial surveys, two survey teams were used in all surveys, and a combined MRDS model was developed to estimate detection probability in the survey strip. In the case of vessel surveys, a detection probability function was estimated using data from the flying bridge survey team for all surveys (2003-2018) using multiple covariate distance (MCDS) function models. While the probability of detection on the trackline was developed using MRDS methods from the 2017-2018 surveys. For each species or species group, the best multiple covariate distance sampling (MCDS) model was selected by first examining the distribution of perpendicular sighting distances (PSD) and selecting an appropriate right truncation distance and key function. Then, all combinations of detection covariates were considered, and the model with the lowest AIC was selected. For the MCDS model, the relationship between group size and detection distance was examined, and the log of group size was included as a covariate where there was a statistically significant correlation. Following selection of the MCDS portion of the model, detection probability covariates were considered for inclusion in the MRDS model along with distance from the trackline and observer platform (flying bridge or bridge wings). Following the selection of the best MRDS model, the second component of the SDM was implemented to develop species-habitat relationships. The sampling units for the SDM model were the segments of “on-effort” trackline within each grid cell for each survey. For each segment, the searched area was calculated as the product of the segment length, the surveyed strip width (based on the truncation distance from the MRDS model) and the estimated detection probability within the segment predicted from the MRDS model and the appropriate detection probability covariates on the survey strip. This searched area was included as an offset term in the SDM. The response variable was the total number of a particular species (or species group) observed on a given segment. A GAM was used to quantify the effect of habitat variables on animal density using a log count model assuming a Tweedie error distribution to account for overdispersed (i.e., zero-inflated) count data. An initial GAM model was fit using all available oceanographic and physiographic variables. A reduced model was then selected including only model terms with p-value < 0.2. This reduced model was compared to the full model using AIC to ensure selection of the best fitting, most parsimonious model. Model fit was assessed through the examination of randomized quantile residuals and the associated Q-Q plot for deviance residuals. While the two-team approach accounts for the likelihood of detection on the trackline of groups that are available at the surface, it does not account for those that are underwater while in the viewing area of the vessel (beaked and sperm whales). For these two taxa, we applied an additional correction for availability. Tag data that recorded sperm whale or beaked whale dive-surface behavior were reviewed to obtain estimated dive and surface durations. The resulting correction factor was included in the SDM to obtain an unbiased estimate of sperm whale and beaked whale density and abundance..
Acquisition Information (collection)
Instrument
  • BIG EYE BINOCULARS
  • Distance Sampling
  • inclinometer
  • Line Transect Sampling
  • visual estimate
  • visual observation
Platform
  • NOAA Ship Gordon Gunter
  • NOAA Ship Pisces
Last Modified: 2023-01-26T14:18:56Z
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