gov.noaa.nmfs.inport:67830
eng
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dataset
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Southeast Fisheries Science Center
(305)361-5761
75 Virginia Beach Drive
Miami
FL
33149
USA
www.sefsc.noaa.gov
WWW:LINK-1.0-http--link
Website
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8:00 a.m. - 4:30 p.m. EST
resourceProvider
Dias, Laura A
(305) 361-4269
75 Virginia Beach Drive
Miami
FL
33149
laura.dias@noaa.gov
https://orcid.org/0000-0001-9378-4577
WWW:LINK-1.0-http--link
ORCID Page Laura Dias
Website listed for Dias, Laura A
information
pointOfContact
2024-02-29T00:00:00
ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data
ISO 19115-2:2009(E)
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)
2022-07-29
publication
NOAA/NMFS/EDM
67830
NOAA
2022-07-29
inForce
10.25921/efv4-9z56
https://www.fisheries.noaa.gov/inport/item/67830
WWW:LINK-1.0-http--link
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View the complete metadata record on InPort for more information about this dataset.
information
Rappucci, Gina
(305) 361-4283
75 Virginia Beach Drive
Miami
FL
33149
gina.rappucci@noaa.gov
author
Mullin, Keith D
228-549-1632
228-769-9200
3209 Frederic St.
Pascagoula
MS
39568
keith.d.mullin@noaa.gov
author
Soldevilla, Melissa
305-361-4238
75 Virginia Beach Drive
Miami
FL
33149
melissa.soldevilla@noaa.gov
author
Garrison, Lance
305-361-4488
305-361-4478
75 Virginia Beach Dr
Miami
FL
33149
Lance.Garrison@noaa.gov
8:00 AM - 4:30 PM EST/EDT
author
Litz, Jenny
305-361-4224
305-365-4102
75 Virginia Beach Drive
Miami
FL
33139
Jenny.Litz@noaa.gov
author
Dias, Laura A
(305) 361-4269
75 Virginia Beach Drive
Miami
FL
33149
laura.dias@noaa.gov
https://orcid.org/0000-0001-9378-4577
WWW:LINK-1.0-http--link
ORCID Page Laura Dias
Website listed for Dias, Laura A
information
author
Martinez, Anthony
305-361-4486
305-361-4478
75 Virginia Beach Drive
Miami
FL
33149
anthony.martinez@noaa.gov
author
https://www.doi.org/10.25921/efv4-9z56
WWW:LINK-1.0-http--link
Landing Page NCEI Accession 0256800
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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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]
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.
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..
Litz, Jenny; Aichinger Dias, Laura; Rappucci, Gina; Martinez, Anthony; Soldevilla, Melissa; Garrison, Lance; Mullin, Keith; Barry, Kevin; Foster, Marjorie (2022)
completed
Litz, Jenny
305-361-4224
305-365-4102
75 Virginia Beach Drive
Miami
FL
33139
Jenny.Litz@noaa.gov
custodian
asNeeded
DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce
DOC/NOAA/NESDIS/NODC > National Oceanographic Data Center, NESDIS, NOAA, U.S. Department of Commerce
DOC/NOAA/NMFS/SEFSC > Southeast Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce
dataCentre
Global Change Master Directory (GCMD) Data Center Keywords
17.0
EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > MAMMALS > CETACEANS
EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > MARINE MAMMALS
EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > MARINE MAMMALS > DOLPHINS
EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > REPTILES > TURTLES > SEA TURTLES
theme
Global Change Master Directory (GCMD) Science Keywords
17.0
Balaenoptera ricei
Caretta caretta
Chelonia mydas
Dermochelys coriacea
Feresa attenuata
Globicephala
Grampus griseus
Kogia
Lepidochelys kempii
Mesoplodon
Orcinus orca
Peponocephala electra
Physeter macrocephalus
Pseudorca crassidens
Stenella attenuata
Stenella clymene
Stenella coeruleoalba
Stenella frontalis
Stenella longirostris
Tursiops truncatus
Ziphius
theme
World Register of Marine Species (WoRMS)
OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA
OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF MEXICO
place
Global Change Master Directory (GCMD) Location Keywords
17.0
BINOCULAR > BINOCULAR
INCLINOMETERS > INCLINOMETERS
VISUAL OBSERVATIONS > VISUAL OBSERVATIONS
instrument
Global Change Master Directory (GCMD) Instrument Keywords
17.2
DHC-6 > DeHavilland Twin Otter
Ships
platform
Global Change Master Directory (GCMD) Platform Keywords
17.2
14450
779-1633
theme
ESA/MMPA Permit Number
0256800
256800
theme
NCEI ACCESSION NUMBER
DOLPHINS
MARINE MAMMALS
SEA TURTLES
cetacean
species abundance
theme
NODC DATA TYPES THESAURUS
Gulf of Mexico Marine Assessment Program for Protected Species (GOMMAPPS)
theme
NODC PROJECT NAMES THESAURUS
Caribbean Sea
Gulf of Mexico
place
NODC SEA AREA NAMES THESAURUS
BIG EYE BINOCULARS
Distance Sampling
Line Transect Sampling
inclinometer
visual estimate
visual observation
instrument
NODC INSTRUMENT TYPES THESAURUS
NOAA Ship Gordon Gunter
NOAA Ship Pisces
platform
NODC PLATFORM NAMES THESAURUS
2003-2019
temporal
DOC/NOAA/NMFS/SEFSC > Southeast Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce
dataCentre
Global Change Master Directory (GCMD) Data Center Keywords
2017-04-24
publication
8.5
Density of Cetaceans and Turtles in the Gulf of Mexico
project
InPort
otherRestrictions
Cite As: Southeast Fisheries Science Center, [Date of Access]: 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) [Data Date Range], https://www.fisheries.noaa.gov/inport/item/67830.
NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose.
otherRestrictions
Access Constraints: Open To Everyone
unclassified
NOAA Data Management Plan (DMP)
NOAA/NMFS/EDM
67830
https://www.fisheries.noaa.gov/inportserve/waf/noaa/nmfs/sefsc/dmp/pdf/67830.pdf
WWW:LINK-1.0-http--link
NOAA Data Management Plan (DMP)
NOAA Data Management Plan for this record on InPort.
information
crossReference
eng; US
oceans
-97.65
-80.366
17.766
30.983
| Currentness: Ground Condition
2003
2019
Related Funding Agencies: U.S. Department of Interior (DOI), Bureau of Ocean Energy Management (BOEM), the U.S. Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Marine Fisheries Service (NMFS), Southeast Fisheries Science Center (SEFSC), NOAA Office of Response and Restoration (ORR), United States Navy and the NOAA RESTORE Science Program.
false
eng
false
Other
NCEI Accession 0256800 Zipped Data Files
2023-07-23
publication
NCEI Archived Data Accession
National Centers for Environmental Information - Silver Spring, Maryland
(301) 713-3277
NOAA/NESDIS E/OC SSMC3, 4th Floor, 1351 East-West Highway
Silver Spring
MD
20910-3282
distributor
https://www.ncei.noaa.gov/archive/archive-management-system/OAS/bin/prd/jquery/accession/download/256800
WWW:LINK-1.0-http--link
256800.2.2.tar.gz
download
dataset
Accuracy
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
Completeness Measure
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
Conceptual Consistency
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 taxagroups (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
Big Eye Binocular
binoculars
25x150 mm
NOAA Ship Gordon Gunter
The Gordon Gunter primarily serves the NMFS Pascagoula Laboratory in Mississippi. The Gordon Gunter is a 224-ft. multi-use platform. It is equipped with a thermosalinograph, CTD, fluorometer, and other oceanographic instruments that monitor atmospheric and oceanic conditions while traveling. A variety of research gears are deployed from the vessel including stern trawling, longlining, plankton tows, and dredging. The Gordon Gunter operates in all three SEFSC research areas.
Big Eye Binocular
binoculars
25x150 mm
NOAA Twin Otter Aircraft
NOAA Twin Otter Aircraft
Big Eye Binocular
binoculars
25x150 mm
Pisces (PC)
The Pisces supports NOAA's mission to protect, restore, and manage the use of living marine, coastal, and ocean resources through ecosystem-based management. Her primary objective is to study, monitor, and collect data on a wide range of sea life and ocean conditions, primarily in U.S. waters from the Gulf of Mexico, Caribbean, and South Atlantic to North Carolina. The region includes important commercial and recreational fisheries, and is one of the world's best known and most productive marine areas. The data collected by the ship is used by scientists who study variation in ocean conditions and sea life to better inform the nation's decision makers about such issues as sustainable fisheries, ecosystem structure and function, fish habitats and habitat restoration, coral reefs, and protected species status. Pisces also observes weather, sea state, and other environmental conditions, conducts habitat assessments, and survey marine mammal and marine bird populations.
Big Eye Binocular
binoculars
25x150 mm