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)
Southeast Fisheries Science Center
Data Set
(DS)
| ID: 67830
| Published / External
Created: 2022-09-06
|
Last Modified: 2025-03-06
Project (PRJ) | ID: 70317
ID: 67830
Data Set (DS)
* Discovery• First Pass
» Metadata Rubric
Item Identification
* » Title | 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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Short Name | |
* Status | Completed |
Creation Date | |
Revision Date | |
• Publication Date | 2022-07-29 |
* » Abstract |
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. |
* Purpose |
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.. |
Notes |
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. |
Other Citation Details |
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] |
• Supplemental Information |
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. |
DOI (Digital Object Identifier) | 10.25921/efv4-9z56 |
DOI Registration Authority | NOAA |
DOI Issue Date | 2022-07-29 |
Keywords
Theme Keywords
Thesaurus | Keyword |
---|---|
Global Change Master Directory (GCMD) Data Center Keywords | DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce |
Global Change Master Directory (GCMD) Data Center Keywords | DOC/NOAA/NESDIS/NODC > National Oceanographic Data Center, NESDIS, NOAA, U.S. Department of Commerce |
Global Change Master Directory (GCMD) Data Center Keywords | DOC/NOAA/NMFS/SEFSC > Southeast Fisheries Science Center, National Marine Fisheries Service, NOAA, U.S. Department of Commerce |
Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > MAMMALS > CETACEANS |
Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > MARINE MAMMALS |
Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > MARINE MAMMALS > DOLPHINS |
Global Change Master Directory (GCMD) Science Keywords | EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > ANIMALS/VERTEBRATES > REPTILES > TURTLES > SEA TURTLES |
World Register of Marine Species (WoRMS) | Balaenoptera ricei |
World Register of Marine Species (WoRMS) | Caretta caretta |
World Register of Marine Species (WoRMS) | Chelonia mydas |
World Register of Marine Species (WoRMS) | Dermochelys coriacea |
World Register of Marine Species (WoRMS) | Feresa attenuata |
World Register of Marine Species (WoRMS) | Globicephala |
World Register of Marine Species (WoRMS) | Grampus griseus |
World Register of Marine Species (WoRMS) | Kogia |
World Register of Marine Species (WoRMS) | Lepidochelys kempii |
World Register of Marine Species (WoRMS) | Mesoplodon |
World Register of Marine Species (WoRMS) | Orcinus orca |
World Register of Marine Species (WoRMS) | Peponocephala electra |
World Register of Marine Species (WoRMS) | Physeter macrocephalus |
World Register of Marine Species (WoRMS) | Pseudorca crassidens |
World Register of Marine Species (WoRMS) | Stenella attenuata |
World Register of Marine Species (WoRMS) | Stenella clymene |
World Register of Marine Species (WoRMS) | Stenella coeruleoalba |
World Register of Marine Species (WoRMS) | Stenella frontalis |
World Register of Marine Species (WoRMS) | Stenella longirostris |
World Register of Marine Species (WoRMS) | Tursiops truncatus |
World Register of Marine Species (WoRMS) | Ziphius |
ESA/MMPA Permit Number | 14450 |
ESA/MMPA Permit Number | 779-1633 |
NCEI ACCESSION NUMBER | 0256800 |
NCEI ACCESSION NUMBER | 256800 |
NODC DATA TYPES THESAURUS | cetacean |
NODC DATA TYPES THESAURUS | DOLPHINS |
NODC DATA TYPES THESAURUS | MARINE MAMMALS |
NODC DATA TYPES THESAURUS | SEA TURTLES |
NODC DATA TYPES THESAURUS | species abundance |
NODC PROJECT NAMES THESAURUS | Gulf of Mexico Marine Assessment Program for Protected Species (GOMMAPPS) |
Temporal Keywords
Thesaurus | Keyword |
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None | 2003-2019 |
* Spatial Keywords
Thesaurus | Keyword |
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Global Change Master Directory (GCMD) Location Keywords | OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > CARIBBEAN SEA |
Global Change Master Directory (GCMD) Location Keywords | OCEAN > ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > GULF OF MEXICO |
NODC SEA AREA NAMES THESAURUS | Caribbean Sea |
NODC SEA AREA NAMES THESAURUS | Gulf of Mexico |
Stratum Keywords
Thesaurus | Keyword |
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Instrument Keywords
Thesaurus | Keyword |
---|---|
Global Change Master Directory (GCMD) Instrument Keywords | BINOCULAR > BINOCULAR |
Global Change Master Directory (GCMD) Instrument Keywords | INCLINOMETERS > INCLINOMETERS |
Global Change Master Directory (GCMD) Instrument Keywords | VISUAL OBSERVATIONS > VISUAL OBSERVATIONS |
NODC INSTRUMENT TYPES THESAURUS | BIG EYE BINOCULARS |
NODC INSTRUMENT TYPES THESAURUS | Distance Sampling |
NODC INSTRUMENT TYPES THESAURUS | inclinometer |
NODC INSTRUMENT TYPES THESAURUS | Line Transect Sampling |
NODC INSTRUMENT TYPES THESAURUS | visual estimate |
NODC INSTRUMENT TYPES THESAURUS | visual observation |
Platform Keywords
Thesaurus | Keyword |
---|---|
Global Change Master Directory (GCMD) Platform Keywords | DHC-6 > DeHavilland Twin Otter |
Global Change Master Directory (GCMD) Platform Keywords | Ships |
NODC PLATFORM NAMES THESAURUS | NOAA Ship Gordon Gunter |
NODC PLATFORM NAMES THESAURUS | NOAA Ship Pisces |
Physical Location
• » Organization | National Centers for Environmental Information - Silver Spring, Maryland |
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• » City | Silver Spring |
• » State/Province | MD |
• Country | |
• » Location Description |
NCEI Archive |
Data Set Information
* Data Set Scope Code | Data Set |
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• Data Set Type | Mixed |
• Maintenance Frequency | As Needed |
Maintenance Note | |
» Data Presentation Form | |
• Entity Attribute Overview | |
Entity Attribute Detail Citation | |
Entity Attribute Detail URL | |
Distribution Liability | |
Data Set Credit | Litz, Jenny; Aichinger Dias, Laura; Rappucci, Gina; Martinez, Anthony; Soldevilla, Melissa; Garrison, Lance; Mullin, Keith; Barry, Kevin; Foster, Marjorie (2022) |
Support Roles
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Rappucci, Gina |
Address |
75 Virginia Beach Drive Miami, FL 33149 |
Email Address | gina.rappucci@noaa.gov |
Phone | (305) 361-4283 |
Fax | |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Mullin, Keith D |
Address |
3209 Frederic St. Pascagoula, MS 39568 |
Email Address | keith.d.mullin@noaa.gov |
Phone | 228-549-1632 |
Fax | 228-769-9200 |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Soldevilla, Melissa |
Address |
75 Virginia Beach Drive Miami, FL 33149 USA |
Email Address | melissa.soldevilla@noaa.gov |
Phone | 305-361-4238 |
Fax | |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Garrison, Lance |
Address |
75 Virginia Beach Dr Miami, FL 33149 |
Email Address | Lance.Garrison@noaa.gov |
Phone | 305-361-4488 |
Fax | 305-361-4478 |
Mobile | |
URL | |
Business Hours | 8:00 AM - 4:30 PM EST/EDT |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Litz, Jenny |
Address |
75 Virginia Beach Drive Miami, FL 33139 |
Email Address | Jenny.Litz@noaa.gov |
Phone | 305-361-4224 |
Fax | 305-365-4102 |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | 2023-07 |
Person | Barry, Kevin |
Address |
3209 Frederic St. Pascagoula, MS 39568 |
Email Address | kevin.barry@noaa.gov |
Phone | 228-549-1608 |
Fax | 228-769-9200 |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Dias, Laura A |
Address |
75 Virginia Beach Drive Miami, FL 33149 |
Email Address | laura.dias@noaa.gov |
Phone | (305) 361-4269 |
Fax | |
Mobile | |
URL | https://orcid.org/0000-0001-9378-4577 |
Business Hours | |
Contact Instructions |
* » Support Role | Author |
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* » Date Effective From | 2022 |
Date Effective To | 2025-02 |
Person | Martinez, Anthony |
Address |
75 Virginia Beach Drive Miami, FL 33149 USA |
Email Address | anthony.martinez@noaa.gov |
Phone | 305-361-4486 |
Fax | 305-361-4478 |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Data Steward |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Litz, Jenny |
Address |
75 Virginia Beach Drive Miami, FL 33139 |
Email Address | Jenny.Litz@noaa.gov |
Phone | 305-361-4224 |
Fax | 305-365-4102 |
Mobile | |
URL | |
Business Hours | |
Contact Instructions |
* » Support Role | Distributor |
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* » Date Effective From | 2022 |
Date Effective To | |
Organization | NOAA National Centers for Environmental Information (NCEI) |
Email Address | ncei.info@noaa.gov |
Phone | |
Fax | |
Mobile | |
URL | https://www.ncei.noaa.gov/contact |
Business Hours | |
Contact Instructions |
* » Support Role | Metadata Contact |
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* » Date Effective From | 2022 |
Date Effective To | |
Person | Dias, Laura A |
Address |
75 Virginia Beach Drive Miami, FL 33149 |
Email Address | laura.dias@noaa.gov |
Phone | (305) 361-4269 |
Fax | |
Mobile | |
URL | https://orcid.org/0000-0001-9378-4577 |
Business Hours | |
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* Contact Instructions |
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Extents
Currentness Reference | Ground Condition |
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Extent Group 1
Extent Description |
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Extent Group 1 / Geographic Area 1
* » W° Bound | -97.65 |
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* » E° Bound | -80.366 |
* » N° Bound | 30.983 |
* » S° Bound | 17.766 |
* » Description |
Extent Group 1 / Vertical Extent
EPSG Code | |
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Vertical Minimum | |
Vertical Maximum |
Extent Group 1 / Time Frame 1
* » Time Frame Type | Range |
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* » Start | 2003 |
End | 2019 |
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Spatial Information
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* » Security Class | Unclassified |
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• Data Access Policy |
Open to everyone |
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• » Data Access Constraints |
Open To Everyone |
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Distribution Information
Start Date | 2022 |
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End Date | Present |
» Download URL | https://www.ncei.noaa.gov/archive/archive-management-system/OAS/bin/prd/jquery/accession/download/256800 |
Distributor | NOAA National Centers for Environmental Information (NCEI) (2022 - Present) |
File Name | 256800.2.2.tar.gz |
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File Type (Deprecated) | NCEI Archived Data Accession |
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URLs
URL | https://www.doi.org/10.25921/efv4-9z56 |
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Name | Landing Page NCEI Accession 0256800 |
URL Type | Online Resource |
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Description |
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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Data Quality
Representativeness | |
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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 |
Analytical Accuracy | |
Horizontal Positional Accuracy | |
Vertical Positional Accuracy | |
Quantitation Limits | |
Bias | |
Comparability | |
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 |
Precision | |
Analytical Precision | |
Field Precision | |
Sensitivity | |
Detection Limit | |
Completeness Report | |
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 |
» Quality Control Procedures Employed |
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 |
Data Management
» Have Resources for Management of these Data Been Identified? | No |
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» Approximate Percentage of Budget for these Data Devoted to Data Management | Unknown |
» Do these Data Comply with the Data Access Directive? | Yes |
» Is Access to the Data Limited Based on an Approved Waiver? | No |
» If Distributor (Data Hosting Service) is Needed, Please Indicate | YES |
» Approximate Delay Between Data Collection and Dissemination | |
» If Delay is Longer than Latency of Automated Processing, Indicate Under What Authority Data Access is Delayed | |
» Actual or Planned Long-Term Data Archive Location | NCEI-MD |
» Approximate Delay Between Data Collection and Archiving | |
» How Will the Data Be Protected from Accidental or Malicious Modification or Deletion Prior to Receipt by the Archive? |
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Acquisition Information
Instruments
Identifier | Big Eye Binocular |
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Instrument / Gear | Instrument |
Instrument Type | binoculars |
Description |
25x150 mm |
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Identifier | NOAA Ship Gordon Gunter | ||
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Description |
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. |
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Identifier | NOAA Twin Otter Aircraft | ||
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Description |
NOAA Twin Otter Aircraft |
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Identifier | Pisces (PC) | ||
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Docucomp UUID | 8ce0b2c0-19fa-11e1-bddb-0800200c9a66 | ||
Description |
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. |
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Child Items
Rubric scores updated every 15m
Score | Type | Title |
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Document (DOC) | 0256800_lonlat.txt | |
Document (DOC) | 0256800_map.jpg | |
Document (DOC) | EU7CXU-confirmation_email.txt | |
Entity (ENT) | NCEI Accession 0256800 Zipped Data Files | |
Document (DOC) | NOAA SEFSC Cetacean and Sea Turtle Spatial Density Models for the Gulf of Mexico.docx | |
Document (DOC) | journal.txt | |
Related Items
Item Type | Relationship Type | Title |
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Catalog Details
Catalog Item ID | 67830 |
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Metadata Record Created By | Lee M Weinberger |
Metadata Record Created | 2022-09-06 09:29+0000 |
Metadata Record Last Modified By | Lee M Weinberger |
» Metadata Record Last Modified | 2025-03-06 17:53+0000 |
Metadata Record Published | 2023-07-23 |
Owner Org | SEFSC |
Metadata Publication Status | Published Externally |
Do Not Publish? | N |
Metadata Workflow State | Published / External |
Metadata Last Review Date | 2022-09-16 |
Metadata Review Frequency | 1 Year |
Metadata Next Review Date | 2023-09-16 |
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