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Dataset Title:  CCS Species Distribution Models for coastal pelagic species Subscribe RSS
Institution:  NOAA/SWFSC/FRD,NOAA/SWFSC/ERD,UCSC,NOAA/NWFSC,PSMFC   (Dataset ID: FRD_CPS_SDMs)
Information:  Summary ? | License ? | FGDC | ISO 19115 | Metadata | Background (external link) | Files | Make a graph
 
Dimensions ? Start ? Stride ? Stop ?  Size ?    Spacing ?
 time (UTC) ?      8488    1 day 0h 0m 51s (uneven)
  < slider >
 latitude (degrees_north) ?      180    0.1 (even)
  < slider >
 longitude (degrees_east) ?      185    0.1 (even)
  < slider >
 
Grid Variables (which always also download all of the dimension variables) 
 anchovy_BRT (Predicted probability of northern anchovy occurrence from BRT, /1) ?
 anchovy_GAM (Predicted probability of northern anchovy occurrence from GAM, /1) ?
 PacificMackerel_BRT (Predicted probability of Pacific mackerel occurrence from BRT, /1) ?
 PacificMackerel_GAM (Predicted probability of Pacific mackerel occurrence from GAM, /1) ?
 herring_BRT (Predicted probability of Pacific herring occurrence from BRT, /1) ?
 herring_GAM (Predicted probability of Pacific herring occurrence from GAM, /1) ?
 jackMackerel_BRT (Predicted probability of Jack mackerel occurrence from BRT, /1) ?
 jackMackerel_GAM (Predicted probability of Jack mackerel occurrence from GAM, /1) ?
 sardine_BRT (Predicted probability of Pacific sardine occurrence from BRT, /1) ?
 sardine_GAM (Predicted probability of Pacific sardine occurrence from GAM, /1) ?
 marketSquid_BRT (Predicted probability of market squid occurrence from BRT, /1) ?
 marketSquid_GAM (Predicted probability of market squid occurrence from GAM, /1) ?

File type: (more information)

(Documentation / Bypass this form) ?
 
(Please be patient. It may take a while to get the data.)


 

The Dataset Attribute Structure (.das) for this Dataset

Attributes {
  time {
    String _CoordinateAxisType "Time";
    Float64 actual_range 8.75664e+8, 1.6093728e+9;
    String axis "T";
    String ioos_category "Time";
    String long_name "Time";
    String standard_name "time";
    String time_origin "01-JAN-1970 00:00:00";
    String units "seconds since 1970-01-01T00:00:00Z";
  }
  latitude {
    String _CoordinateAxisType "Lat";
    Float64 actual_range 30.1, 48.0;
    String axis "Y";
    String ioos_category "Location";
    String long_name "Latitude";
    String standard_name "latitude";
    String units "degrees_north";
  }
  longitude {
    String _CoordinateAxisType "Lon";
    Float64 actual_range -133.9, -115.5;
    String axis "X";
    String ioos_category "Location";
    String long_name "Longitude";
    String standard_name "longitude";
    String units "degrees_east";
  }
  anchovy_BRT {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of northern anchovy occurrence from BRT";
    String units "/1";
  }
  anchovy_GAM {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of northern anchovy occurrence from GAM";
    String units "/1";
  }
  PacificMackerel_BRT {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Pacific mackerel occurrence from BRT";
    String units "/1";
  }
  PacificMackerel_GAM {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Pacific mackerel occurrence from GAM";
    String units "/1";
  }
  herring_BRT {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Pacific herring occurrence from BRT";
    String units "/1";
  }
  herring_GAM {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Pacific herring occurrence from GAM";
    String units "/1";
  }
  jackMackerel_BRT {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Jack mackerel occurrence from BRT";
    String units "/1";
  }
  jackMackerel_GAM {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Jack mackerel occurrence from GAM";
    String units "/1";
  }
  sardine_BRT {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Pacific sardine occurrence from BRT";
    String units "/1";
  }
  sardine_GAM {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of Pacific sardine occurrence from GAM";
    String units "/1";
  }
  marketSquid_BRT {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of market squid occurrence from BRT";
    String units "/1";
  }
  marketSquid_GAM {
    Float32 _FillValue 1.0e+32;
    String ioos_category "Other";
    String long_name "Predicted probability of market squid occurrence from GAM";
    String units "/1";
  }
  NC_GLOBAL {
    String _NCProperties "version=1|netcdflibversion=4.4.1.1|hdf5libversion=1.8.16";
    String cdm_data_type "Grid";
    String Conventions "CF-1.6, ACDD-1.3, COARDS";
    String defaultGraphQuery "sardine_GAM[(2013-04-30)][(30.1):(48.0)][(-133.9):(-115.5)]&.draw=surface&.vars=longitude%7Clatitude%7Csardine_GAM";
    Float64 Easternmost_Easting -115.5;
    Float64 geospatial_lat_max 48.0;
    Float64 geospatial_lat_min 30.1;
    Float64 geospatial_lat_resolution 0.09999999999999999;
    String geospatial_lat_units "degrees_north";
    Float64 geospatial_lon_max -115.5;
    Float64 geospatial_lon_min -133.9;
    Float64 geospatial_lon_resolution 0.10000000000000003;
    String geospatial_lon_units "degrees_east";
    String history 
"2022-07-01T13:57:10Z (local files)
2022-07-01T13:57:10Z http://coastwatch.pfeg.noaa.gov/erddap/griddap/FRD_CPS_SDMs.das";
    String infoUrl "https://repository.library.noaa.gov/view/noaa/37600";
    String institution "NOAA/SWFSC/FRD,NOAA/SWFSC/ERD,UCSC,NOAA/NWFSC,PSMFC";
    String license 
"The data may be used and redistributed for free but is not intended
for legal use, since it may contain inaccuracies. Neither the data
Contributor, ERD, NOAA, nor the United States Government, nor any
of their employees or contractors, makes any warranty, express or
implied, including warranties of merchantability and fitness for a
particular purpose, or assumes any legal liability for the accuracy,
completeness, or usefulness, of this information.";
    Float64 Northernmost_Northing 48.0;
    String sourceUrl "(local files)";
    Float64 Southernmost_Northing 30.1;
    String standard_name_vocabulary "CF Standard Name Table v70";
    String summary 
"Forage species such as Pacific sardine, northern anchovy, and market squid are critical ecological links between the planktonic food web and higher trophic levels in the California Current System, as well as supporting valuable fisheries. Environmental variability drives large fluctuations in their abundance and distribution. This dataset includes the outputs of Species Distribution Models (SDMs) for 6 key forage species, combining multiple survey datasets with environmental fields from a high-resolution Regional Ocean Modeling System (ROMS) developed at the University of California - Santa Cruz, and the Copernicus-Globcolour interpolated surface chlorophyll product (https://doi.org/10.48670/moi-00100). The sardine and anchovy SDMs also included predictors indexing stock biomass (MacCall et al. 2016; Kuriyama et al. 2020). As temporally continuous salinity fields are not available from the UCSC ROMS, we included a measure of the distance to the nearest major river (mean > 50,000 CFS discharge) in the herring SDM, to capture to association of this species with estuaries.

The 6 species represented are Pacific sardine (Sardinops sagax), northern anchovy (Engraulis mordax), market squid (Doryteuthis opalescens), Pacific herring (Clupea pallasii), Pacific (chub) mackerel (Scomber japonicus), and Jack mackerel (Trachurus symmetricus). Results from two different SDMs are shown: Generalized Additive Models and Boosted Regression Trees, and both models predict the probability of occurrence of each species. We used data from the NOAA Southwest Fisheries Science Center Coastal Pelagic Species and Columbia River Predator (Emmett et al. 2006) trawl surveys to train all SDMs, with the exception of market squid, where juvenile salmon survey data were used instead (see Chasco et al. 2022 for description of these data). More details on preliminary versions of the sardine and anchovy SDMs are available in Muhling et al. (2019). An additional manuscript in preparation will include description of up-to-date data, methods, and model structure. Until this is published, we strongly recommend contacting Barbara Muhling (Barbara.Muhling@noaa.gov) before working with this dataset, to ensure complete understanding of the details and caveats. Funding for this work was provided by NOAA Office of Sustainable Fisheries, and the NOAA Climate and Fisheries Adaptation program.
References

Chasco, B. E., Hunsicker, M. E., Jacobson, K. C., Welch, O. T., Morgan, C. A., Muhling, B. A., & Harding, J. A. (2022). Evidence of Temperature-Driven Shifts in Market Squid Doryteuthis opalescens Densities and Distribution in the California Current Ecosystem. Marine and Coastal Fisheries, 14(1), e10190.

Emmett, R. L., Krutzikowsky, G. K., & Bentley, P. (2006). Abundance and distribution of pelagic piscivorous fishes in the Columbia River plume during spring/early summer 1998-2003: relationship to oceanographic conditions, forage fishes, and juvenile salmonids. Progress in Oceanography, 68(1), 1-26.

Kuriyama, P. T., Zwolinski, J. P., Hill, K. T., & Crone, P. R. (2020). Assessment of the Pacific sardine resource in 2020 for US management in 2020-2021. NOAA Technical Memorandum NOAA-TM-NMFS-SWFSC-628

MacCall, A. D., Sydeman, W. J., Davison, P. C., & Thayer, J. A. (2016). Recent collapse of northern anchovy biomass off California. Fisheries Research, 175, 87-94.   

Muhling, B., Brodie, S., Snodgrass, O., Tommasi, D., Dewar, H., Childers, J., Jacox, M. Edwards, C. A., Xu, Y. & Snyder, S. (2019). Dynamic habitat use of albacore and their primary prey species in the California Current System. CalCOFI Reports 60: 1-15.";
    String time_coverage_end "2020-12-31T00:00:00Z";
    String time_coverage_start "1997-10-01T00:00:00Z";
    String title "CCS Species Distribution Models for coastal pelagic species";
    Float64 Westernmost_Easting -133.9;
  }
}

 

Using griddap to Request Data and Graphs from Gridded Datasets

griddap lets you request a data subset, graph, or map from a gridded dataset (for example, sea surface temperature data from a satellite), via a specially formed URL. griddap uses the OPeNDAP (external link) Data Access Protocol (DAP) (external link) and its projection constraints (external link).

The URL specifies what you want: the dataset, a description of the graph or the subset of the data, and the file type for the response.

griddap request URLs must be in the form
https://coastwatch.pfeg.noaa.gov/erddap/griddap/datasetID.fileType{?query}
For example,
https://coastwatch.pfeg.noaa.gov/erddap/griddap/jplMURSST41.htmlTable?analysed_sst[(2002-06-01T09:00:00Z)][(-89.99):1000:(89.99)][(-179.99):1000:(180.0)]
Thus, the query is often a data variable name (e.g., analysed_sst), followed by [(start):stride:(stop)] (or a shorter variation of that) for each of the variable's dimensions (for example, [time][latitude][longitude]).

For details, see the griddap Documentation.


 
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