ERDDAP > info > ECMM_Atlantic_spotted_dolphin

Grid DAP Data	Sub- set	Table DAP Data	Make A Graph	W M S	Source Data Files	Acces- sible	Title	Sum- mary	FGDC, ISO, Metadata	Back- ground Info	RSS	E mail	Institution	Dataset ID
data			graph	M	files	public	Atlantic Spotted Dolphin Habitat-based Marine Mammal Density Models for the U.S. Atlantic: Latest Versions		F   I   M	background			Duke University M...	ECMM_Atlantic_spotted_dolphin

The Dataset's Variables and Attributes

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 (2014-05-14): Initial version. Version 2 (2014-09-02): 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()). Version 3 (2014-10-17): Adjusted g(0) estimates based on feedback from September 2014 review. Adjusted proxy species used in certain detection functions to be consistent with other dolphin species. Updated distance to eddy predictors using Chelton et al.'s 2014 database. Removed distance to eddy and wind speed predictors from on shelf model. Fixed missing pixels in several climatological predictors, which led to not all segments being utilized. Version 4 (2014-11-13): Reconfigured detection hierarchy and adjusted NARWSS detection functions based on additional information from Tim Cole. Updated documentation. Version 5 (2014-11-19): Removed CumVGPM180 predictor and refitted models. Updated documentation. Version 6 (2014-12-05): Fixed bug that applied the wrong detection function to segments NE_narwss_1999_widgeon_hapo dataset. Refitted model. Updated documentation. Version 7 (2015-01-24): Forced abundance to zero in the vicinity of New York-New Jersey Harbor. We found no documentation that Atlantic spotted dolphins occur here, but our model predicts some abundance. We believe this prediction is in error and are manually correcting it. Version 7.1 (2015-03-06): Updated the documentation. No changes to the model. Version 7.2 (2015-05-14): Updated calculation of CVs. Switched density rasters to logarithmic breaks. No changes to the model. Version 7.3 (2015-09-03): Updated the documentation. No changes to the model. Model files released as supplementary information to Roberts et al. (2016). Version 8 (2018-04-14): Began update to Roberts et al. (2015) model. Introduced new surveys from AMAPPS, NARWSS, UNCW, VAMSC, and the SEUS NARW teams. 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 9 (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 9.1 (2023-05-27): Completed the supplementary report documenting the details of this model. The model itself was not 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_Atlanticspotteddolphin
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	Stenella frontalis,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	9.1
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	Atlantic spotted dolphin
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 Atlantic spotted dolphin.
attribute	NC_GLOBAL	taxon_comment	String	observer reported identifications included Atlantic spotted dolphin, Atlantic spotted or bottlenose dolphin, and atlantic or pantropical spotted dolphin.
attribute	NC_GLOBAL	taxon_lsid	String	urn:lsid:marinespecies.org:taxname:137108
attribute	NC_GLOBAL	taxon_name	String	Stenella frontalis
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 00:00:00,2020-12-31 00:00:00
attribute	NC_GLOBAL	time_coverage_end	String	2009-12-16T00:00:00Z
attribute	NC_GLOBAL	time_coverage_start	String	2009-01-16T00:00:00Z
attribute	NC_GLOBAL	title	String	Atlantic Spotted Dolphin 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 8h 43m 38s
attribute	time	_CoordinateAxisType	String	Time
attribute	time	actual_range	double	1.232064E9, 1.2609216E9
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	17749|4336|1718|2036|1716|1879|1838|1332|1097|1111|1071|1095|936|783|755|675|618|628|534|536|495|562|416|351|252|256|233|210|221|230|256|246|266|248|257|349|340|352|278|221|185|72|39|31|17|19|22|24|30|20|16|18|17|24|18|18|13|14|15|19|18|21|22|21|15|18|15|16|10|14|20|19|12|16|20|19|17|18|16|18|16|17|12|14|15|15|15|15|13|9|12|19|16|9|14|12|14|15|6|17|16|8|22|4|10|6|16|15|9|12|14|15|18|11|7|8|13|17|14|11|15|16|12|14|17|18|6|13|11|15|6|11|13|17|12|9|15|9|8|13|11|16|12|15|9|6|8|12|14|4|9|20|10|13|11|10|4|8|9|12|6|8|8|14|8|6|10|10|12|6|6|8|8|7|7|4|9|10|5|8|9|4|3|7|13|6|7|9|6|10|5|8|8|12|10|6|7|8|4|6|5|4|6|8|13|5|5|2|2|2|5|8|5|2|3|2|4|8|1|3|3|4|7|3|5|4|4|2|2|3|4|3|2|2|2|1|4|2|3|1|1|0|2|1|0|3|0|0|0|0|1|3|0|0|0|2|
attribute	a_5_percent	STATISTICS_HISTOMAX	double	17.467198352561
attribute	a_5_percent	STATISTICS_HISTOMIN	double	7.217282327332E-13
attribute	a_5_percent	STATISTICS_HISTONUMBINS	int	256
attribute	a_5_percent	STATISTICS_MAXIMUM	double	17.467198352561
attribute	a_5_percent	STATISTICS_MEAN	double	0.81747998148978
attribute	a_5_percent	STATISTICS_MEDIAN	int	0
attribute	a_5_percent	STATISTICS_MINIMUM	double	7.2172848872337E-13
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	1.7916470817773
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	16912|963|967|771|827|568|527|614|835|812|610|469|538|474|432|417|364|294|197|170|126|136|118|110|129|128|142|137|144|195|203|263|325|316|327|393|418|495|487|588|595|598|521|492|411|430|331|331|275|259|258|257|282|306|368|396|362|302|285|259|214|199|216|192|158|155|192|157|158|152|165|158|152|156|187|163|147|166|148|148|150|125|124|128|99|117|98|98|103|130|130|112|141|126|162|138|129|120|120|114|118|109|100|106|112|114|109|135|122|130|134|169|147|143|160|168|133|141|168|126|138|122|88|91|91|84|65|58|52|42|42|36|38|50|34|36|35|20|28|40|34|34|31|36|30|26|28|26|31|32|27|33|35|22|27|33|21|23|14|16|23|20|19|14|9|18|11|20|14|20|19|15|8|17|17|16|10|13|8|10|13|7|11|8|8|4|5|5|2|3|0|2|0|0|3|2|0|0|0|0|4|1|2|0|2|1|3|0|1|1|0|3|0|2|2|0|1|1|1|2|1|1|1|0|0|2|0|0|1|1|0|0|0|1|1|0|0|0|1|0|0|0|1|2|0|0|0|1|1|0|1|0|0|0|0|1|
attribute	a_95_percent	STATISTICS_HISTOMAX	double	77.674584522221
attribute	a_95_percent	STATISTICS_HISTOMIN	double	1.1128903354418E-10
attribute	a_95_percent	STATISTICS_HISTONUMBINS	int	256
attribute	a_95_percent	STATISTICS_MAXIMUM	double	77.674584522221
attribute	a_95_percent	STATISTICS_MEAN	double	9.9422472066515
attribute	a_95_percent	STATISTICS_MEDIAN	int	0
attribute	a_95_percent	STATISTICS_MINIMUM	double	1.1128903558325E-10
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	12.271448940719
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	37|125|334|293|314|296|306|247|236|227|208|194|176|152|185|240|230|188|184|200|252|235|227|183|200|173|190|186|170|172|177|167|168|162|170|149|146|140|142|141|114|119|117|88|107|92|102|96|116|114|111|138|160|206|268|320|405|356|485|495|600|626|668|703|634|687|639|567|596|565|583|605|677|651|733|805|737|744|739|649|670|596|570|562|609|585|622|615|592|584|642|649|709|743|714|730|803|657|617|552|540|563|484|476|448|431|425|375|304|309|281|245|298|305|344|295|265|224|190|167|158|116|130|128|114|114|114|108|94|91|91|106|101|100|105|147|139|142|108|101|123|130|110|92|101|107|112|119|108|103|81|76|71|71|67|59|54|34|52|47|44|38|35|34|33|44|25|32|22|36|28|24|25|22|20|27|24|18|21|20|21|21|14|23|22|22|29|25|30|20|12|16|18|24|13|18|16|26|23|30|26|24|19|21|26|22|17|18|17|16|26|32|30|17|17|23|16|16|40|16|4|2|17|59|15|4|7|5|3|4|2|0|3|4|2|2|3|3|1|9|5|3|4|1|2|2|3|1|3|8|62|126|110|33|14|4|
attribute	cv	STATISTICS_HISTOMAX	double	3.559523202231
attribute	cv	STATISTICS_HISTOMIN	double	0.11210403490651
attribute	cv	STATISTICS_HISTONUMBINS	int	256
attribute	cv	STATISTICS_MAXIMUM	double	3.559523202231
attribute	cv	STATISTICS_MEAN	double	1.2078568569806
attribute	cv	STATISTICS_MEDIAN	int	0
attribute	cv	STATISTICS_MINIMUM	double	0.11210403490651
attribute	cv	STATISTICS_MODE	int	0
attribute	cv	STATISTICS_SKIPFACTORX	int	1
attribute	cv	STATISTICS_SKIPFACTORY	int	1
attribute	cv	STATISTICS_STDDEV	double	0.56368484472271
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	16522|1429|908|959|1524|1121|668|517|442|502|623|517|377|309|210|289|307|300|263|376|413|470|587|557|577|488|530|463|566|504|398|316|291|237|309|284|312|326|322|307|317|330|320|314|281|269|259|267|304|321|316|284|309|264|270|267|327|277|232|242|221|165|177|171|158|120|116|139|110|122|114|121|109|112|117|123|118|123|135|140|136|130|132|143|145|123|86|120|119|103|111|114|113|140|144|121|135|119|111|131|121|130|104|112|126|135|134|111|119|121|115|98|119|85|66|76|62|59|54|54|53|53|46|43|40|34|57|35|38|31|37|44|28|25|40|49|31|29|31|29|22|17|19|17|16|15|15|15|12|14|8|6|15|9|7|13|9|6|9|9|13|9|9|8|10|12|12|9|5|5|5|6|12|7|7|15|7|5|8|8|15|11|10|6|7|11|6|9|9|5|8|11|7|6|7|6|9|4|6|8|5|5|9|4|11|10|5|4|6|9|3|4|3|3|5|2|3|4|3|7|5|2|3|2|4|4|5|4|6|5|5|2|0|5|1|3|2|1|0|3|2|0|2|0|1|2|0|0|1|1|0|2|0|0|0|2|
attribute	density	STATISTICS_HISTOMAX	double	22.125289916992
attribute	density	STATISTICS_HISTOMIN	double	8.9621782217719E-12
attribute	density	STATISTICS_HISTONUMBINS	int	256
attribute	density	STATISTICS_MAXIMUM	double	22.125289916992
attribute	density	STATISTICS_MEAN	double	2.4885071185411
attribute	density	STATISTICS_MEDIAN	int	0
attribute	density	STATISTICS_MINIMUM	double	8.9621686807928E-12
attribute	density	STATISTICS_MODE	int	0
attribute	density	STATISTICS_SKIPFACTORX	int	1
attribute	density	STATISTICS_SKIPFACTORY	int	1
attribute	density	STATISTICS_STDDEV	double	3.3045766003855
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	17014|1009|973|799|877|620|510|609|837|847|652|449|524|531|445|500|438|332|300|243|208|183|187|169|133|166|139|161|155|203|180|209|267|324|291|337|401|412|444|490|548|556|580|487|404|398|377|310|270|240|213|210|215|221|260|295|375|327|291|252|236|225|170|191|181|145|127|136|147|140|124|136|142|126|145|137|158|138|140|132|123|144|118|114|117|100|107|93|95|91|96|109|123|111|118|145|134|138|133|126|119|111|114|123|87|101|109|102|115|101|125|119|136|133|151|150|149|147|168|124|149|158|134|129|124|95|92|91|90|64|58|46|49|33|40|35|40|48|32|30|33|26|27|37|30|32|29|36|30|23|33|30|21|36|27|37|32|12|30|28|28|16|17|16|24|23|15|16|10|7|21|10|21|12|18|18|13|10|19|17|11|11|13|12|7|14|8|8|8|7|5|6|5|2|3|1|1|0|1|0|1|2|1|2|1|0|2|1|1|2|2|0|3|1|2|2|1|0|0|3|2|0|1|1|0|0|2|0|1|1|0|0|0|1|0|1|0|0|1|0|0|0|1|2|0|0|0|1|1|0|0|1|0|0|0|1|
attribute	standard_error	STATISTICS_HISTOMAX	double	19.177665944959
attribute	standard_error	STATISTICS_HISTOMIN	double	2.7534280411246E-11
attribute	standard_error	STATISTICS_HISTONUMBINS	int	256
attribute	standard_error	STATISTICS_MAXIMUM	double	19.177665944959
attribute	standard_error	STATISTICS_MEAN	double	2.4353248333257
attribute	standard_error	STATISTICS_MEDIAN	int	0
attribute	standard_error	STATISTICS_MINIMUM	double	2.7534283880692E-11
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	3.0981024583497
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.