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| Title | Sum- mary | FGDC, ISO, Metadata | Back- ground Info | RSS | E | Institution | Dataset ID | ||
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| data | graph | M | files | public | Common Minke Whale Habitat-based Marine Mammal Density Models for the U.S. Atlantic: Latest Versions |
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| ECMM_Common_minke_whale |
| Row Type | Variable Name | Attribute Name | Data Type | Value |
|---|---|---|---|---|
| attribute | NC_GLOBAL | acknowledgement | String | This project would not be possible without the contributions of many individuals and organizations. Above all, we acknowledge the work of those who collected, processed, and shared marine mammal and covariate data with us, and to those who funded the production of those data. In particular, we thank the observers, pilots, ship captains, and crew who collected the marine mammal observations that form the core of this analysis. Tables 4, 5, and 6 (See referenced citation Roberts et al. 2023) list the collaborating institutions, survey programs, and citations for marine mammal surveys used for the EC models. Tables 10 and 11 (See referenced citation Roberts et al. 2023) list those that were additionally used for the AFTT models. Table 2 (See referenced citation Roberts et al. 2023) lists the data sources and citations for covariates used in both models. Thank you all for the opportunity to analyze the data you produced; we hope you find this project a satisfactory outcome of your efforts. Many thanks to colleagues who shared additional data, reviewed portions of our work, provided valuable advice, or answered technical questions, including: Susan Barco, Suzanne Bates, Elizabeth Becker, Olly Boisseau, Gary Buchanan, Steve Buckland, Sam Chavez-Rosales, Danielle Cholewiak, Tim Cole, Peter Corkeron, Mark Cotter, Erin Cummings, Genevieve Davis, Rob DiGiovanni, Megan Ferguson, Karin Forney, Lance Garrison, Caroline Good, Tim Gowan, Phil Hammond, Jolie Harrison, Katie Jackson, Beth Josephson, Bob Kenney, Christin Khan, Scott Kraus, Erin LaBrecque, Claire Lacey, Sophie Laran, Ben Laws, Patrick Lehodey, Gwen Lockhart, Kate Lomac-MacNair, Tiago Marques, Ryan McAlarney, Bill McLellan, David L. Miller, Keith Mullin, Doug Nowacek, Orla O'Brien, Ann Pabst, Richard Pace, Debi Palka, Eric Patterson, Ester Quintana-Rizzo, Jessica Redfern, Vincent Ridoux, Doug Sigourney, Len Thomas, Sofie Van Parijs, Melanie White, Amy Whitt, and Ann Zoidis. We gratefully acknowledge Genevieve Davis and coauthors (Davis et al. 2017, 2020) for making passive acoustic monitoring data available for the purpose of evaluating baleen whale model predictions. These data appear in the accompanying taxon-specific reports for the baleen whales. Thanks also to our colleagues at MGEL who also assisted with data processing, analysis, model review, and project management, including Ana Ca¤adas, Jesse Cleary, Corrie Curtice, Ei Fujioka, and Rob Schick. Funding for this project was provided by United States Fleet Forces Command and was managed on their behalf by Naval Facilities Engineering Systems Command Atlantic. Development of the model for North Atlantic right whale was co-funded by NOAA under a cooperative research agreement. Certain data contributors requested that their programs or products be acknowledged in a specific way. We include these acknowledgements below. Virginia Aquarium & Marine Science Center?s Virginia CZM Wind Energy Area Surveys were funded by the Virginia Coastal Zone Management Program at the Department of Environmental Quality through Task 1 of Grant NA12NOS4190027 and Task 95.02 of Grant NA13NOS4190135 of the U.S. Department of Commerce, National Oceanic and Atmospheric Administration, under the Coastal Zone Management Act of 1972, as amended. The University of North Carolina Wilmington's Navy surveys were funded by U.S. Navy Fleet Forces Command with Joel Bell as the COTR. University of North Carolina Wilmington?s right whale surveys were funded by NOAA. New England Aquarium's surveys of the Massachusetts and Rhode Island Wind Energy Areas, known in this report as the NLPSC and MMS-WEA programs, were funded by Massachusetts Clean Energy Center and the Bureau of Ocean Energy Management. Odd Aksel Bergstad, Thomas de Lange Wenneck, Leif N?ttestad, and Gordon Waring contributed the MAR-ECO survey under the Norwegian License for Open Government data (NLOD). The REMMOA and SAMM surveys were contributed by Observatoire PELAGIS at the University of La Rochelle, France. Funding for the development of HYCOM has been provided by the National Ocean Partnership Program and the Office of Naval Research. Data assimilative products using HYCOM are funded by the U.S. Navy. The 1/12 degree global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org. The Ssalto/Duacs altimeter products were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (https://marine.copernicus.eu). The altimetry and the Mesoscale Eddy Trajectory Atlas products were produced by Ssalto/Duacs and distributed by AVISO+ (https://www.aviso.altimetry.fr) with support from CNES, in collaboration with Oregon State University with support from NASA. CCMP vector wind analyses are produced by Remote Sensing Systems. Data are available at https://www.remss.com. This study has been conducted using E.U. Copernicus Marine Service Information, including SEAPODYM (doi: 10.48670/moi-00020) and Copernicus GlobColour (CMEMS product code OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_082). |
| attribute | NC_GLOBAL | cdm_data_type | String | Grid |
| attribute | NC_GLOBAL | contributor_name | String | Jason J. Roberts, Tina M. Yack, Patrick N. Halpin, Benjamin D. Best, Laura Mannocci, Ei Fujioka, Debra L. Palka, Lance P. Garrison, Keith D. Mullin, Timothy V. N. Cole, Christin B. Khan, William A. McLellan, D. Ann Pabst & Gwen G. Lockhart |
| attribute | NC_GLOBAL | Conventions | String | ACDD-1.3, CF-1.11, COARDS |
| attribute | NC_GLOBAL | creator_email | String | If you have any questions about this model or its files, please contact Jason Roberts (jason.roberts@duke.edu) and Tina Yack (tina.yack@duke.edu). |
| attribute | NC_GLOBAL | creator_institution | String | Marine Geospatial Ecology Laboratory, Duke University, Durham, NC 27708, USA |
| attribute | NC_GLOBAL | creator_name | String | Jason J. Roberts |
| attribute | NC_GLOBAL | creator_type | String | person |
| attribute | NC_GLOBAL | creator_url | String | https://mgel.env.duke.edu/ |
| attribute | NC_GLOBAL | date_created | String | 2023-05-27 |
| attribute | NC_GLOBAL | defaultGraphQuery | String | &.draw=surface&.vars=longitude|latitude|density&.colorBar=KT_dense|||0|5|&.bgColor=0xffccccff |
| attribute | NC_GLOBAL | Easternmost_Easting | double | -56.23721928665664 |
| attribute | NC_GLOBAL | geospatial_bounds_crs | String | EPSG:4326 |
| attribute | NC_GLOBAL | geospatial_lat_max | double | 47.701744867003754 |
| attribute | NC_GLOBAL | geospatial_lat_min | double | 23.156215948215642 |
| attribute | NC_GLOBAL | geospatial_lat_resolution | double | 0.04889547593384086 |
| attribute | NC_GLOBAL | geospatial_lat_units | String | degrees_north |
| attribute | NC_GLOBAL | geospatial_lon_max | double | -56.23721928665664 |
| attribute | NC_GLOBAL | geospatial_lon_min | double | -82.34740343532766 |
| attribute | NC_GLOBAL | geospatial_lon_resolution | double | 0.048895475933840866 |
| attribute | NC_GLOBAL | geospatial_lon_units | String | degrees_east |
| attribute | NC_GLOBAL | history | String | Version 1 (2013-05-07): Initial version. Version 2 (2013-05-08): Text edited to correct minor errors. Version 3 (2014-03-01): Switched from four seasonal models to two. Reformulated density model using a Horvitz-Thompson estimator. Eliminated GAM for group size (consequence of above). Added group size as a candidate covariate in detection functions (benefit of above). Added survey ID as a candidate covariate in NOAA NARWSS detection functions. Took more care in selecting right-truncation distances. Fitted models with contemporaneous predictors, for comparison to climatological. Switched SST and SST fronts predictors from NOAA Pathfinder to GHRSST CMC0.2deg L4. Changed SST fronts algorithm to use Canny operator instead of Cayula-Cornillon. Switched winds predictors from SCOW to CCMP (SCOW only gives climatol. estimates.) Added DistToEddy predictors, based on Chelton et al. (2011) eddy database. Added cumulative VGPM predictors, summing productivity for 45, 90, and 180 days. Added North Atlantic Oscillation (NAO) predictor; included 3 and 6 month lags. Transformed predictors more carefully, to better minimize leverage of outliers. Implemented hybrid hierarchical- forward / exhaustive model selection procedure. Model selection procedure better avoids concurvity between predictors. Allowed GAMs to select between multiple formulations of dynamic predictors. Adjusted land mask to eliminate additional estuaries and hard-to-predict cells. Version 4 (2014-05-14): Added discussion of acoustic monitoring studies to text. Density models unchanged. Version 5 (2014-05-20): Fixed bug in temporal variability plots. Density models unchanged. Version 6 (2014-10-18): Added surveys: NJ-DEP, Virginia Aquarium, NARWSS 2013, UNCW 2013. Extended study area up Scotian Shelf. Added SEAPODYM predictors. Switched to mgcv estimation of Tweedie p parameter (family=tw()). Added Palka (2006) survey- specific g(0) estimates. Removed distance to eddy predictors and wind speed predictor from all models; they were not ecologically justified. Fixed missing pixels in several climatological predictors, which led to not all segments being utilized. Adjusted subregion extents. Eliminated Cape Cod Bay subregion. Version 7 (2014-11-13): Reconfigured detection hierarchy and adjusted NARWSS detection functions based on additional information from Tim Cole. Switched to uniform distribution of density for southeast slope and abyss in winter. Removed CumVGPM180 predictor. Updated documentation. Version 8 (2014-12-03): Fixed bug that applied the wrong detection function to segments NE_narwss_1999_widgeon_hapo dataset. Refitted model. Updated documentation. Version 8.1 (2015-02-02): Updated the documentation. No changes to the model. Version 8.2 (2015-05-14): Updated calculation of CVs. Switched density rasters to logarithmic breaks. No changes to the model. Version 8.3 (2015-09-26): Updated the documentation. No changes to the model. Version 8.4 (2016-04-21): Switched calculation of monthly 5% and 95% confidence interval rasters to the method used to produce the year-round rasters. (We intended this to happen in version 8.2 but I did not implement it properly.) Updated the monthly CV rasters to have value 0 where we assumed the species was absent, consistent with the year-round CV raster. No changes to the other (non-zero) CV values, the mean abundance rasters, or the model itself. Model files released as supplementary information to Roberts et al. (2016). Version 9 (2017-06-01): Began update to Roberts et al. (2015) model. Introduced new surveys from AMAPPS, NARWSS, UNCW, and VAMSC. Updated modeling methodology. Refitted detection functions and spatial models from scratch using new and reprocessed covariates. Model released as part of a scheduled update to the U.S. Navy Marine Species Density Database (NMSDD). Version 10 (2022-06-20): This model is a major update over the prior version, with substantial additional data, improved statistical methods, and an increased spatial resolution. It was released as part of the final delivery of the U.S. Navy Marine Species Density Database (NMSDD) for the Atlantic Fleet Testing and Training (AFTT) Phase IV Environmental Impact Statement. Several new collaborators joined and contributed survey data: New York State Department of Environmental Conservation, TetraTech, HDR, and Marine Conservation Research. We incorporated additional surveys from all continuing and new collaborators through the end of 2020. (Because some environmental covariates were only available through 2019, certain models only extend through 2019.) We increased the spatial resolution to 5 km and, at NOAA's request, we extended the model further inshore from New York through Maine. We reformulated and refitted all detection functions and spatial models. We updated all enviromental covariates to newer products, when available, and added several covariates to the set of candidates. For models that incorporated dynamic covariates, we estimated model uncertainty using a new method that accounts for both model parameter error and temporal variability. Version 10.1 (2023-05-27): Completed the supplementary report documenting the details of this model. Corrected the 5 and 95 percent rasters so that they contain the value 0 where the taxon was asssumed absent, rather than NoData. Nothing else was changed. NETCDF conversion:Raw data provided at https://seamap.env.duke.edu/models/Duke/EC/ were converted from raster images having an Anderson equal area projection into python xarray objects and reprojected to WGS-1984. See crs attribute for more details regarding the geographic project. Multiple raster files for monthly density estimates where merged into a single .nc file having time dimension and month coordinate. For datasets with multiple eras, only the most recent era is represented here. Data conversion to CF-NetCDF from raster files was performed by james.caplinger@NOAA.gov |
| attribute | NC_GLOBAL | id | String | ECMM_densityModel_Commonminkewhale |
| attribute | NC_GLOBAL | infoUrl | String | https://seamap.env.duke.edu/models/Duke/EC/ |
| attribute | NC_GLOBAL | institution | String | Duke University Marine Geospatial Ecology Laboratory |
| attribute | NC_GLOBAL | key_words | String | Balaenoptera acutorostrata,Density models, Line-transect surveys, Passive acoustic monitoring, Abundance estimation, Generalized additive models |
| attribute | NC_GLOBAL | keywords | String | 5_percent, 95_percent, atlantic, based, cell, coefficient, confidence, data, density, duke, ecology, error, estimated, estimates, geospatial, gom, grid, gulf, habitat, habitat-based, interval, laboratory, latest, lower, mammal, marine, mean, mexico, models, oceanography, per, percent, physical, physical oceanography, standard, standard_error, statistics, time, university, upper, US, variation, versions |
| attribute | NC_GLOBAL | license | String | This document and the accompanying files are Copyright (C) 2022 by the Duke University Marine Geospatial Ecology Laboratory and are licensed under a Creative Commons Attribution 4.0 International License(https://creativecommons.org/licenses/by/4.0/). |
| attribute | NC_GLOBAL | metadata_link | String | https://seamap.env.duke.edu/models/Duke/EC/ |
| attribute | NC_GLOBAL | naming_authority | String | edu.duke.env.seamap |
| attribute | NC_GLOBAL | Northernmost_Northing | double | 47.701744867003754 |
| attribute | NC_GLOBAL | product_version | String | 10.1 |
| attribute | NC_GLOBAL | publisher_email | String | erd.data at noaa.gov |
| attribute | NC_GLOBAL | references | String | (1) Roberts, J., Best, B., Mannocci, L. et al. Habitat-based cetacean density models for the U.S. Atlantic and Gulf of Mexico. Sci Rep 6, 22615 (2016). https://doi.org/10.1038/srep22615; (2) Roberts, Jason J., Tina M. Yack, and Patrick N. Halpin. "Marine mammal density models for the US Navy Atlantic Fleet Training and Testing (AFTT) study area for the Phase IV Navy Marine Species Density Database (NMSDD)." Document version 1 (2023). |
| attribute | NC_GLOBAL | sourceUrl | String | (local files) |
| attribute | NC_GLOBAL | Southernmost_Northing | double | 23.156215948215642 |
| attribute | NC_GLOBAL | species | String | Common minke whale |
| attribute | NC_GLOBAL | standard_name_vocabulary | String | CF Standard Name Table v70 |
| attribute | NC_GLOBAL | summary | String | In 2016, together with our collaborators, we published density models for 26 cetacean species and 3 guilds inhabiting U.S. waters of the western North Atlantic and northern Gulf of Mexico (Roberts et al. 2016). After publication, we began an update to the Atlantic (a.k.a. "East Coast") models that utilized the same methodology but incorporated additional survey data not available for use in models published in 2016. During 2017-2021, we released updated models for many of the East Coast taxa as additional data accumulated and new collaborators joined the project. Finally, in 2022, we released a comprehensive set of updated models covering all taxa. This model contains the 2022 update for common minke whale. |
| attribute | NC_GLOBAL | taxon_lsid | String | urn:lsid:marinespecies.org:taxname:137087 |
| attribute | NC_GLOBAL | taxon_name | String | Balaenoptera acutorostrata |
| attribute | NC_GLOBAL | time_coverage_comment | String | Days, months, and time associated with start, end, and duration are approximate. For datasets with multiple eras, only the most recent era is represented here. |
| attribute | NC_GLOBAL | time_coverage_duration | String | 1998-01-01 00:00:00,2019-12-31 00:00:00 |
| attribute | NC_GLOBAL | time_coverage_end | String | 2008-12-16T00:00:00Z |
| attribute | NC_GLOBAL | time_coverage_start | String | 2008-01-16T00:00:00Z |
| attribute | NC_GLOBAL | title | String | Common Minke Whale Habitat-based Marine Mammal Density Models for the U.S. Atlantic: Latest Versions |
| attribute | NC_GLOBAL | Westernmost_Easting | double | -82.34740343532766 |
| dimension | time | double | nValues=12, evenlySpaced=false, averageSpacing=30 days 10h 54m 33s | |
| attribute | time | _CoordinateAxisType | String | Time |
| attribute | time | actual_range | double | 1.2004416E9, 1.2293856E9 |
| attribute | time | axis | String | T |
| attribute | time | climatology | String | climatology_bounds |
| attribute | time | ioos_category | String | Time |
| attribute | time | long_name | String | Time |
| attribute | time | standard_name | String | time |
| attribute | time | time_origin | String | 01-JAN-1970 00:00:00 |
| attribute | time | units | String | seconds since 1970-01-01T00:00:00Z |
| dimension | latitude | double | nValues=503, evenlySpaced=true, averageSpacing=-0.04889547593384086 | |
| attribute | latitude | _CoordinateAxisType | String | Lat |
| attribute | latitude | actual_range | double | 23.156215948215642, 47.701744867003754 |
| attribute | latitude | axis | String | Y |
| attribute | latitude | ioos_category | String | Location |
| attribute | latitude | long_name | String | Latitude |
| attribute | latitude | standard_name | String | latitude |
| attribute | latitude | units | String | degrees_north |
| dimension | longitude | double | nValues=535, evenlySpaced=true, averageSpacing=0.048895475933840866 | |
| attribute | longitude | _CoordinateAxisType | String | Lon |
| attribute | longitude | actual_range | double | -82.34740343532766, -56.23721928665664 |
| attribute | longitude | axis | String | X |
| attribute | longitude | ioos_category | String | Location |
| attribute | longitude | long_name | String | Longitude |
| attribute | longitude | standard_name | String | longitude |
| attribute | longitude | units | String | degrees_east |
| variable | a_5_percent | float | time, latitude, longitude | |
| attribute | a_5_percent | _FillValue | float | -3.4E38 |
| attribute | a_5_percent | cell_methods | String | time: mean within years time time: mean over years |
| attribute | a_5_percent | colorBarMaximum | double | 100.0 |
| attribute | a_5_percent | colorBarMinimum | double | 0.0 |
| attribute | a_5_percent | description | String | The lower 90% confidence interval around the density estimates. These have the same unit as the density values. |
| attribute | a_5_percent | ioos_category | String | Statistics |
| attribute | a_5_percent | LAYER_TYPE | String | athematic |
| attribute | a_5_percent | long_name | String | lower 90 percent confidence interval of density estimates |
| attribute | a_5_percent | standard_name | String | 5_percent |
| attribute | a_5_percent | STATISTICS_HISTOBINVALUES | String | 6268|6548|1300|757|773|535|449|460|418|441|455|516|532|597|602|538|473|472|415|351|283|290|247|236|195|190|141|13353|143|131|91|94|72|77|61|50|51|50|44|43|23|25|21|13|24|22|22|16|24|13|23|18|16|24|17|15|10|14|15|13|6|7|9|8|10|12|9|5|5|7|3|6|2|5|3|2|6|3|5|3|3|5|2|2|4|5|1|5|2|0|0|2|3|4|1|1|2|2|3|0|1|0|4|3|1|2|1|2|3|1|2|3|0|4|0|1|0|1|1|3|1|1|3|2|2|1|0|0|2|1|0|0|0|0|0|2|0|2|0|1|0|0|1|2|3|2|0|0|2|1|0|0|0|0|0|0|1|1|0|1|0|2|1|0|0|1|2|0|0|0|0|1|0|0|0|0|0|0|0|1|0|2|0|0|0|0|2|1|0|1|0|0|0|0|0|0|1|1|1|0|1|0|1|1|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1| |
| attribute | a_5_percent | STATISTICS_HISTOMAX | double | 1.3028804720994 |
| attribute | a_5_percent | STATISTICS_HISTOMIN | int | 0 |
| attribute | a_5_percent | STATISTICS_HISTONUMBINS | int | 256 |
| attribute | a_5_percent | STATISTICS_MAXIMUM | double | 1.3028804720994 |
| attribute | a_5_percent | STATISTICS_MEAN | double | 0.07308475864993 |
| attribute | a_5_percent | STATISTICS_MEDIAN | int | 0 |
| attribute | a_5_percent | STATISTICS_MINIMUM | int | 0 |
| attribute | a_5_percent | STATISTICS_MODE | int | 0 |
| attribute | a_5_percent | STATISTICS_SKIPFACTORX | int | 1 |
| attribute | a_5_percent | STATISTICS_SKIPFACTORY | int | 1 |
| attribute | a_5_percent | STATISTICS_STDDEV | double | 0.072906537418065 |
| attribute | a_5_percent | units | String | number of individual animals per 100 square km |
| variable | a_95_percent | float | time, latitude, longitude | |
| attribute | a_95_percent | _FillValue | float | -3.4E38 |
| attribute | a_95_percent | cell_methods | String | time: mean within years time time: mean over years |
| attribute | a_95_percent | colorBarMaximum | double | 100.0 |
| attribute | a_95_percent | colorBarMinimum | double | 0.0 |
| attribute | a_95_percent | description | String | The upper 90% confidence interval around the density estimates. These have the same unit as the density values. |
| attribute | a_95_percent | ioos_category | String | Statistics |
| attribute | a_95_percent | LAYER_TYPE | String | athematic |
| attribute | a_95_percent | long_name | String | upper 90 percent confidence interval of density estimates |
| attribute | a_95_percent | standard_name | String | 95_percent |
| attribute | a_95_percent | STATISTICS_HISTOBINVALUES | String | 4381|6367|2381|1692|1186|1159|1207|1392|1317|956|663|463|421|346|13535|255|182|118|115|92|69|61|70|63|60|45|56|38|39|50|33|28|35|27|26|27|19|22|31|16|25|14|13|15|18|8|9|8|5|9|6|4|5|11|5|8|4|4|3|6|4|3|4|5|3|6|4|2|3|5|2|9|5|5|4|4|1|4|1|2|2|0|6|3|2|2|0|2|3|1|2|2|1|1|2|0|1|3|2|1|0|0|1|0|3|1|0|0|0|2|0|1|2|1|0|1|4|2|0|0|0|0|1|0|0|0|0|0|1|1|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|1|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1| |
| attribute | a_95_percent | STATISTICS_HISTOMAX | double | 5.7855715510711 |
| attribute | a_95_percent | STATISTICS_HISTOMIN | int | 0 |
| attribute | a_95_percent | STATISTICS_HISTONUMBINS | int | 256 |
| attribute | a_95_percent | STATISTICS_MAXIMUM | double | 5.7855715510711 |
| attribute | a_95_percent | STATISTICS_MEAN | double | 0.18650864789026 |
| attribute | a_95_percent | STATISTICS_MEDIAN | int | 0 |
| attribute | a_95_percent | STATISTICS_MINIMUM | int | 0 |
| attribute | a_95_percent | STATISTICS_MODE | int | 0 |
| attribute | a_95_percent | STATISTICS_SKIPFACTORX | int | 1 |
| attribute | a_95_percent | STATISTICS_SKIPFACTORY | int | 1 |
| attribute | a_95_percent | STATISTICS_STDDEV | double | 0.19070776086127 |
| attribute | a_95_percent | units | String | number of individual animals per 100 square km |
| variable | cv | float | time, latitude, longitude | |
| attribute | cv | _FillValue | float | -3.4E38 |
| attribute | cv | cell_methods | String | time: mean within years time time: mean over years |
| attribute | cv | description | String | The estimated coefficient of variation of the density estimates. These values are unitless and were computed as standard error divided by density. |
| attribute | cv | ioos_category | String | Physical Oceanography |
| attribute | cv | LAYER_TYPE | String | athematic |
| attribute | cv | long_name | String | estimated coefficient of variation of density estimates |
| attribute | cv | standard_name | String | cv |
| attribute | cv | STATISTICS_HISTOBINVALUES | String | 4165|0|0|0|0|0|0|161|1022|1871|15552|1831|1211|771|481|409|363|349|301|273|269|276|269|300|406|4798|2440|297|108|76|60|46|53|44|33|46|37|33|26|24|20|19|25|25|26|16|26|24|11|15|18|14|13|12|7|7|7|8|11|9|6|5|7|9|11|8|4|10|8|5|8|11|9|5|8|0|5|5|5|7|5|8|7|3|5|6|6|2|4|4|4|6|7|5|7|2|3|8|1|4|4|2|4|5|0|4|1|4|0|4|2|5|2|3|5|4|3|4|1|3|5|2|4|2|3|3|6|1|7|4|5|1|2|2|5|2|2|2|2|3|1|3|1|1|4|5|3|3|3|2|3|5|2|0|5|2|1|1|1|5|3|3|1|6|6|1|3|5|3|1|1|2|4|3|3|2|2|1|1|2|3|2|4|5|4|1|1|3|1|3|3|1|2|3|1|2|2|4|2|4|0|4|5|4|0|5|2|3|2|2|2|5|4|4|3|4|1|3|1|1|0|3|1|1|4|4|1|5|5|2|2|2|3|4|1|3|7|2|2|3|2|2|0|2|3|1|3|1|3|2|2|3|1|1|0|1| |
| attribute | cv | STATISTICS_HISTOMAX | double | 6.7138991869764 |
| attribute | cv | STATISTICS_HISTOMIN | int | 0 |
| attribute | cv | STATISTICS_HISTONUMBINS | int | 256 |
| attribute | cv | STATISTICS_MAXIMUM | double | 6.7138991869764 |
| attribute | cv | STATISTICS_MEAN | double | 0.40446899615364 |
| attribute | cv | STATISTICS_MEDIAN | int | 0 |
| attribute | cv | STATISTICS_MINIMUM | int | 0 |
| attribute | cv | STATISTICS_MODE | int | 0 |
| attribute | cv | STATISTICS_SKIPFACTORX | int | 1 |
| attribute | cv | STATISTICS_SKIPFACTORY | int | 1 |
| attribute | cv | STATISTICS_STDDEV | double | 0.51478237728122 |
| attribute | cv | units | String | 1 |
| variable | density | float | time, latitude, longitude | |
| attribute | density | _FillValue | float | -3.4E38 |
| attribute | density | cell_methods | String | time: mean within years time time: mean over years |
| attribute | density | colorBarMaximum | double | 100.0 |
| attribute | density | colorBarMinimum | double | 0.0 |
| attribute | density | description | String | The estimated mean density per grid cell for the given month, averaged over all years the model was predicted. Density is expressed as the number of individual animals per 100 square km. To convert to individuals per 1 square km, divide the cell values by 100. To convert to individuals per grid cell, divide by 100 and multiply by 25, the cell area in km. |
| attribute | density | ioos_category | String | Statistics |
| attribute | density | LAYER_TYPE | String | athematic |
| attribute | density | long_name | String | estimated mean density per grid cell |
| attribute | density | standard_name | String | density |
| attribute | density | STATISTICS_HISTOBINVALUES | String | 4732|7419|1621|1146|884|745|665|701|767|877|797|827|821|588|443|322|300|310|271|13407|225|155|141|89|92|75|76|57|47|42|44|28|31|29|23|28|31|32|31|19|26|16|24|16|18|22|16|13|21|12|15|11|4|9|5|9|6|2|8|7|3|6|4|5|2|6|6|3|2|4|3|2|2|4|2|3|5|5|2|3|3|3|0|5|2|1|0|1|3|3|3|3|2|1|2|5|4|2|2|4|1|0|1|1|2|3|1|1|0|0|0|2|1|1|1|0|0|0|1|0|0|4|0|1|1|0|1|1|0|0|0|1|1|1|0|0|2|0|0|2|0|0|0|0|0|0|1|0|1|0|1|1|1|0|1|1|0|0|1|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|2|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1| |
| attribute | density | STATISTICS_HISTOMAX | double | 2.7455251216888 |
| attribute | density | STATISTICS_HISTOMIN | int | 0 |
| attribute | density | STATISTICS_HISTONUMBINS | int | 256 |
| attribute | density | STATISTICS_MAXIMUM | double | 2.7455251216888 |
| attribute | density | STATISTICS_MEAN | double | 0.11481609713854 |
| attribute | density | STATISTICS_MEDIAN | int | 0 |
| attribute | density | STATISTICS_MINIMUM | int | 0 |
| attribute | density | STATISTICS_MODE | int | 0 |
| attribute | density | STATISTICS_SKIPFACTORX | int | 1 |
| attribute | density | STATISTICS_SKIPFACTORY | int | 1 |
| attribute | density | STATISTICS_STDDEV | double | 0.1140818060713 |
| attribute | density | units | String | number of individual animals per 100 square km |
| variable | standard_error | float | time, latitude, longitude | |
| attribute | standard_error | _FillValue | float | -3.4E38 |
| attribute | standard_error | cell_methods | String | time: mean within years time time: mean over years |
| attribute | standard_error | colorBarMaximum | double | 100.0 |
| attribute | standard_error | colorBarMinimum | double | 0.0 |
| attribute | standard_error | description | String | estimated standard error of the density estimates. These have the same unit as the density rasters. This and the other uncertainty statistics below incorporated variance resulting from uncertainty in the models parameter estimates as well as variance resulting from temporal variations in dynamic covariates. The temporal variability incorporated depended on the covariates used. For models that utilized contemporaneous dynamic covariates, the variability included both seasonal and interannual variability. For models that utilized climatological dynamic covariates, it included seasonal variability only. For technical information on the method used to estimate uncertainty, please see Roberts et al. (2021). |
| attribute | standard_error | ioos_category | String | Statistics |
| attribute | standard_error | LAYER_TYPE | String | athematic |
| attribute | standard_error | long_name | String | estimated standard error of density estimates |
| attribute | standard_error | standard_name | String | standard_error |
| attribute | standard_error | STATISTICS_HISTOBINVALUES | String | 4366|5761|3140|2022|1804|2146|1685|1017|810|585|13648|424|298|195|142|86|64|65|64|77|59|40|57|58|43|51|39|31|30|40|53|35|30|31|14|22|24|18|13|16|13|12|14|8|9|5|7|6|6|7|9|7|6|2|7|6|8|4|3|9|4|2|3|2|4|6|7|5|4|2|4|4|5|2|7|2|2|2|1|1|0|1|3|0|0|1|0|0|2|2|2|1|1|0|2|1|0|1|1|2|1|2|1|0|2|0|0|0|0|1|1|0|0|1|2|0|0|0|0|0|0|0|1|1|0|0|0|2|1|0|1|0|0|0|1|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|0|1| |
| attribute | standard_error | STATISTICS_HISTOMAX | double | 1.3108794689178 |
| attribute | standard_error | STATISTICS_HISTOMIN | int | 0 |
| attribute | standard_error | STATISTICS_HISTONUMBINS | int | 256 |
| attribute | standard_error | STATISTICS_MAXIMUM | double | 1.3108794689178 |
| attribute | standard_error | STATISTICS_MEAN | double | 0.03361368058057 |
| attribute | standard_error | STATISTICS_MEDIAN | int | 0 |
| attribute | standard_error | STATISTICS_MINIMUM | int | 0 |
| attribute | standard_error | STATISTICS_MODE | int | 0 |
| attribute | standard_error | STATISTICS_SKIPFACTORX | int | 1 |
| attribute | standard_error | STATISTICS_SKIPFACTORY | int | 1 |
| attribute | standard_error | STATISTICS_STDDEV | double | 0.037244010167706 |
| attribute | standard_error | units | String | number of individual animals per 100 square km |
The information in the table above is also available in other file formats (.csv, .htmlTable, .itx, .json, .jsonlCSV1, .jsonlCSV, .jsonlKVP, .mat, .nc, .nccsv, .tsv, .xhtml) via a RESTful web service.