Brittle Stars--Santa Barbara Channel, California

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What does this data set describe?

Title: Brittle Stars--Santa Barbara Channel, California
Abstract:
This part of DS 781 presents data for the map showing the predicted distribution of brittle stars in the Santa Barbara Channel, California, region. The raster data file is included in "BrittleStars_SantaBarbaraChannel.zip," which is accessible from https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/data_catalog_SantaBarbaraChannel.html.
Presence-absence data of benthic macro-invertebrates and associated habitat (that is, sediment type and depth) were collected using a towed camera sled in selected areas along the coast off southern California during a ground-truth observation cruise conducted by the U.S. Geological Survey and NOAA National Marine Fisheries Service for the California Seafloor Mapping Program. Benthic community structure was determined from 35 video towed-camera transects within California's State Waters 3-nautical-mile limit in the Santa Barbara Channel. These transects produced a total of 923 10-second observations from the Offshore of Refugio Beach map area (34.5 degrees N., 120.1 degrees W.) to the Hueneme Canyon and vicinity map area (34.1 degrees N., 119.2 degrees W.).
Presence-absence data were collected for 29 benthic, structure-forming nonmobile taxa. Using this information, generalized linear models (GLMs) were developed to predict the probability of occurrence of five commonly observed taxa (cup corals, hydroids, short and tall sea pens, and brittle stars in the sediment) in five map areas within the Santa Barbara Channel (SBC). A sixth map area (Offshore of Carpinteria) was not modeled owing to insufficient data.
The analysis demonstrates that the community structure for the five map areas can be divided into three statistically distinct groups: (1) the Hueneme Canyon and vicinity and the Offshore of Ventura map areas; (2) the Offshore of Santa Barbara and the Offshore of Coal Oil Point map areas; and (3) the Offshore of Refugio Beach map area. These three distinct groups are the main reason that the probability for each taxa can be so dramatically different within one predictive-distribution map area. The five most frequently observed benthic macro-invertebrate taxa were selected for these predictive-distribution grids. Presence-absence data for each selected invertebrate were fit to specific generalized linear models using geographic location, depth, and seafloor character as covariates. Data for the covariates were informed by the bathymetry, seafloor character, and other ground-truth data from the different map areas of the Santa Barbara Channel region that are part of the California State Waters Map Series DS 781.
Observations based on depth were limited by the capability of the towed camera sled; as a result, no predictions were made below depths of 150 m (in other words, on the continental slope or in Hueneme Canyon). Cup corals and hydroids had high predicted probabilities of occurrence in areas of hard substrata, whereas short and tall sea pens were predicted to occur in parts of the SBC that had unconsolidated and mixed sediment. Our model predicted that brittle stars would occur throughout the entire SBC on various bottom types.
Supplemental_Information:
Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in Esri format, this metadata file may include some Esri-specific terminology.
  1. How might this data set be cited?
    Krigsman, Lisa M., Yoklavich, Mary M., Cochrane, Guy R., and Golden, Nadine E., 2013, Brittle Stars--Santa Barbara Channel, California: Data Series DS 781, U.S. Geological Survey, Reston, VA.

    Online Links:

    This is part of the following larger work.

    Golden, Nadine E., 2013, California State Waters Map Series Data Catalog: Data Series DS 781, U.S. Geological Survey, Reston, VA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -119.643628
    East_Bounding_Coordinate: -119.471725
    North_Bounding_Coordinate: 34.390598
    South_Bounding_Coordinate: 34.320467
  3. What does it look like?
    https://pubs.usgs.gov/ds/781/SantaBarbaraChannel/images/BrittleStars_SantaBarbaraChannel.jpg (JPEG)
    Map of predicted distribution of brittle stars in the Santa Barbara Channel region.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 2005
    Ending_Date: 2009
    Currentness_Reference:
    ground condition
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: GeoTIFF
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 23905 x 47950 x 1, type Pixel
    2. What coordinate system is used to represent geographic features?
      The map projection used is WGS 1984 UTM Zone 11N.
      Projection parameters:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -117.0
      Latitude_of_Projection_Origin: 0.0
      False_Easting: 500000.0
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.000000002220024164500956
      Ordinates (y-coordinates) are specified to the nearest 0.000000002220024164500956
      Planar coordinates are specified in Meter
      The horizontal datum used is D WGS 1984.
      The ellipsoid used is WGS 1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    brittlestar
    The raster attributes include VALUE - code for the Predicted-Distribution of Brittle Stars for Santa Barbara Channel Region and COUNT - number of pixels. (Source: ESRI)
    Rowid
    Internal feature number. (Source: ESRI) Sequential unique whole numbers that are automatically generated.
    VALUE
    VALUE - code for the Predicted-Distribution of Brittle Stars for Santa Barbara Channel Region (Source: USGS)
    Range of values
    Minimum:2.22045e-016
    Maximum:.705603
    COUNT
    The number of pixels (2 m x 2 m size grid cell) represented in each probability raster (see Attribute: VALUE). (Source: ESRI) Sequential unique whole numbers that are automatically generated.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Lisa M. Krigsman
    • Mary M. Yoklavich
    • Guy R. Cochrane
    • Nadine E. Golden
  2. Who also contributed to the data set?
    The authors would like to thank E.J. Dick (Fisheries Ecology Division, Southwest Fisheries Science Center, Santa Cruz, CA). He was a critical partner in the development of the predictive models used in this study.
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA
    US

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

These data are intended for science researchers, students, policy makers, and the general public. The data can be used with geographic information systems (GIS) software to display oceanographic information.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 2010 (process 1 of 2)
    The seafloor-character map, GLMs, and the Marine Geospatial Ecology Tool (MGET; http://mgel.env.duke.edu) in ArcGIS were used to develop predictive-probability maps of occurrence per 12 sq m of seafloor for selected invertebrate taxa. The 12 sq m area was calculated by multiplying the average width of the video images by the average distance covered during the 10-second samples. The seafloor-character map provided the habitat-class data.
    
      Taxa Model Formulas for GLM:
      Brittle Star
      taxa~Depth+I(Depth^2)+ Class+Block+Depth:Block, # Depth+ Depth^2+Class+Block+Depth:Block tryCatch(taxa33.glm <- glm(taxa~Depth+I(Depth^2)+
      Class+Block+Depth:Block, family=binomial(link="logit"), data=taxa.df),
      warning = function(x) { output.df[33,"flag"] <<- 1 }, finally = taxa33.glm <- glm(taxa~Depth+I(Depth^2)+ Class+Block+Depth:Block, family=binomial(link="logit"), data=taxa.df)
            )
      summary(taxa33.glm)
    
      MODEL SUMMARY:
      ==============
    
      Call:
      glm(formula = brit_in ~ Depth + I(Depth^2) + factor(Class) + factor(Block) + Depth:factor(Block), family = binomial(link="logit"), data = na.omit(d))
    
      Deviance Residuals:
      Min       1Q   Median       3Q      Max
      -1.5551  -0.6983  -0.4912  -0.1107   2.7408
    
      Coefficients:
                             Estimate Std. Error z value Pr(>|z|)
      (Intercept)          -1.6441219  0.6640992  -2.476  0.01330 *
      Depth                 0.1144813  0.0234621   4.879 1.06e-06 ***
      I(Depth^2)           -0.0013578  0.0002068  -6.567 5.13e-11 ***
      factor(Class)2       -0.9254904  0.3303862  -2.801  0.00509 **
      factor(Class)3       -0.8756778  0.5539213  -1.581  0.11391
      factor(Block)2       -1.5455724  0.5615242  -2.752  0.00591 **
      factor(Block)3       -4.4970380  0.7311817  -6.150 7.73e-10 ***
      Depth:factor(Block)2  0.0061871  0.0092539   0.669  0.50376
      Depth:factor(Block)3  0.0807144  0.0147237   5.482 4.21e-08 ***
      ---
      Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    
      (Dispersion parameter for binomial family taken to be 1)
    
          Null deviance: 1009.10  on 922  degrees of freedom
      Residual deviance:  841.58  on 914  degrees of freedom
      AIC: 859.58
    
      Number of Fisher Scoring iterations: 5
    
      Analysis of Deviance Table
    
      Model: binomial, link: logit
    
      Response: brit_in
    
      Terms added sequentially (first to last)
    
    
                           Df Deviance Resid. Df Resid. Dev
      NULL                                   922    1009.10
      Depth                 1     2.14       921    1006.96
      I(Depth^2)            1    85.43       920     921.53
      factor(Class)         2     8.31       918     913.22
      factor(Block)         2    43.91       916     869.30
      Depth:factor(Block)   2    27.72       914     841.58
    resampled to 10 meters
    
    Krigsman, L.M., M.M. Yoklavich, E.J. Dick, and G.R. Cochrane (2012) Models and maps: predicting the distribution of corals and other benthic macro-invertebrates in shelf habitats. Ecosphere 3:1-16.
    Date: 01-May-2023 (process 2 of 2)
    Metadata was modified to bring up to current USGS PCMSC standards. USGS Thesaurus and keywords were added, and added keywords section with USGS persistent identifier as theme keyword. Information about the data available for download and different Network Resource Name links and details were given in the Standard Order Process section. Minor typos were corrected. No data information was changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata dates to determine which metadata file is most recent. Person who carried out this activity:
    Susan A Cochran
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Geologist
    2885 Mission Street
    Santa Cruz, CA
    USA

    (831) 460-7545 (voice)
    scochran@usgs.gov
  3. What similar or related data should the user be aware of?
    Johnson, Samuel Y., Dartnell, Peter, Cochrane, Guy R., Golden, Nadine E., Phillips, Eleyne L., Ritchie, Andrew C., Kvitek, Rikk G., Greeene, H. Gary, Krigsman, Lisa M., Endris, Charles A., Clahan, Kevin B., Sliter, Ray W., Wong, Florence L., Yoklavich, Mary M., and Normark, William R., 2012, California State Waters Map Series--Hueneme Canyon and Vicinity, California: Scientific Investigations Map 3225, U.S. Geological Survey, Reston, VA.

    Online Links:


How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    Pixel resolution 2 m.
  2. How accurate are the geographic locations?
    On the order of 10 meters.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    Please see USGS Scientific Investigations Map 3225 pamphlet, chapter 5, for information about omissions, selection criteria, generalization, definitions used, and other rules used to derive the data set.
  5. How consistent are the relationships among the observations, including topology?
    Logical Consistency untested.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None
Use_Constraints USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey and NOAA National Marine Fisheries as the originator(s) of the dataset and in products derived from these data. This information is not intended for navigation purposes.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey
    345 Middlefield Rd
    Menlo Park, CA
    USA

    (650) 329-4309 (voice)
  2. What's the catalog number I need to order this data set? The .zip file includes the .lyr, .tfw, .tif, .tif.aux.xml, and .tif.ovr files, as well as FGDC-compliant metadata.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    The downloadable data file has been compressed with the "zip" command and can be unzipped with Winzip (or other tool) on Windows systems. To utilize these data, the user must have software capable of uncompressing the WinZip file and importing and viewing an Esri ArcMap TIFF. Users should download the ArcGIS Project File, SantaBarbaraChannelGIS.mxd.zip, a compressed (with the "zip" command) version of the ArcMap document (.mxd) that has all the data layers loaded in the table of contents for the Santa Barbara Channel region, and has all the data symbolized as on the map sheets. Download and save this ArcGIS project file, including all data layers, to the directory the user has created for this GIS.

Who wrote the metadata?

Dates:
Last modified: 01-May-2023
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA
US

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

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