Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014

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Frequently anticipated questions:

What does this data set describe?

Title:
Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014
Abstract:
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites where plants are and are not observed in 2008, 2010, and 2014. The data are then used to develop probabilistic models that provide spatially explicit maps of habitat suitability that help to identify high-priority areas for amaranth protection. The modeling effort also helps to inform management decisions that are most likely to result in the protection of a long-term sustainable population.
Supplemental_Information: none
  1. How might this data set be cited?
    Gutierrez, Benjamin T., Heslin, Julia L., Sturdivant, Emily J., Henderson, Rachel E., and Sterne, Travis K., 20230426, Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014: data release DOI:10.5066/P9GKXN3H, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Gutierrez, Benjamin T., Heslin, Julia L., Sturdivant, Emily J., Henderson, Rachel E., and Sterne, Travis K., 2023, Seabeach amaranth presence-absence and barrier island geomorphology metrics as relates to shorebird habitat for Assateague Island National Seashore — 2008, 2010, and 2014: data release DOI:10.5066/P9GKXN3H, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Gutierrez, B.T., Heslin, J.L., Henderson, R.E., Sterne, T.K., and Sturdivant, E.J., 2023, Seabeach amaranth presence-absence and barrier island geomorphology metrics as relates to shorebird habitat for Assateague Island National Seashore — 2008, 2010, and 2014: U.S. Geological Survey data release, https://doi.org/10.5066/P9GKXN3H.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -75.3919
    East_Bounding_Coordinate: -75.0981
    North_Bounding_Coordinate: 38.3230
    South_Bounding_Coordinate: 37.8507
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/61d6543cd34ed79293ffa772?name=ASIS_Amaranth_2014.jpg (JPEG)
    Map extent of seabeach amaranth presence-absence data on Assateague Island, for the year 2014
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2014
    Currentness_Reference:
    Ground condition measured by source data for given attribute as specified in process steps.
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: tabular and shapefile
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Point data set. It contains the following vector data types (SDTS terminology):
      • Entity Point (156)
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.0197565634. Longitudes are given to the nearest 0.0249839887. Latitude and longitude values are specified in Decimal seconds. The horizontal datum used is North_American_Datum_1983.
      The ellipsoid used is GRS_1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222101.
  7. How does the data set describe geographic features?
    Presence_Absence_pts2014.shp
    Point dataset containing the presence-absence of seabeach amaranth, as well as site specific barrier island geomorphology. A csv file containing the same fields for all sample records is also included in the dataset; see the Entity and Attribute Overview for a description of the .csv file. Point object count: 156 (Source: U.S. Geological Survey)
    FID
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Shape
    Feature geometry. (Source: Esri) Type of shape feature
    OBJECTID
    Feature geometry. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    NewID
    A running tally of the total number for field collected data. Points added to the dataset (random points) have a value of -99999 (Source: NPS)
    ValueDefinition
    -99999No data
    Range of values
    Minimum:10221
    Maximum:10259
    Units:plant ID
    plant_pres
    Numerical value indicating the presence (1) or absence (0) of the plant species seabeach amaranth (Amaranthus pumilus). (Source: NPS)
    ValueDefinition
    0absence of amaranth
    1presnece of amaranth
    elev_mhw
    The distance (in meters) above the Mean High Water elevation (see process description for DEM raster extraction: local_MHW_DEM). Points that fall outside the DEM are given a value of -99999 (Source: U.S. Geological Survey)
    Range of values
    Minimum:-0.83
    Maximum:4.64
    Units:meters
    aspect
    The aspect (compass direction that a slope faces) of the land surface (see process description for DEM raster extraction: aspect). Points that fall outside the DEM are given a value of -99999 (Source: U.S. Geological Survey)
    Range of values
    Minimum:1.90
    Maximum:359.98
    Units:degrees
    slope
    The slope of the land surface (see process description for DEM raster extraction: slope). Points that fall outside the DEM are given a value of -99999 (Source: U.S. Geological Survey)
    Range of values
    Minimum:0.02
    Maximum:41.1
    Units:percent rise ([rise/run] * 100)
    distSL_ful
    Values extracted from a the "distance_to_full_shoreline" raster, (see process step for Morphology raster extraction) (Source: U.S. Geological Survey)
    Range of values
    Minimum:0
    Maximum:890.5
    Units:meters
    distSL_oce
    Values extracted from a the "distance_to_oceanside_shoreline" raster, (see process step for Morphology raster extraction) (Source: U.S. Geological Survey)
    Range of values
    Minimum:5
    Maximum:1904
    Units:meters
    distDH_200
    Values extracted from a the "distance_to_DH " raster, (see process step for Morphology raster extraction). Any distance beyond 200 meters was given a -99999 value. (Source: U.S. Geological Survey)
    Range of values
    Minimum:5.0
    Maximum:197.2
    Units:meters
    distDL_200
    Values extracted from a the "distance_to_DL " raster, (see process step for Morphology raster extraction). Any distance beyond 200 meters was given a –9999 value. (Source: U.S. Geological Survey)
    Range of values
    Minimum:7.1
    Maximum:190.1
    Units:meters
    dist_OCinl
    Distance to Ocean City Inlet, MD. (see process description for DEM raster extraction: distance_to_OceanCity_inlet raster). (Source: U.S. Geological Survey)
    Range of values
    Minimum:489.3
    Maximum:29444.1
    Units:meters
    veg_type
    Vegetation type value extracted from "vegetation_raster" (see process description for Vegetation processing:vegetation_raster) No data are given a value of -99999 (Source: U.S. Geological Survey)
    ValueDefinition
    1Woody Vegetation
    2Sparse Vegetation
    3Herbaceous Vegetation
    4Water
    5Mud Flat
    -99999No data
    cnt30preYR
    Number of plants (seabeach amaranth) from the previous year (2013) within 30 m of the data point. (Source: U.S. Geological Survey)
    Range of values
    Minimum:0
    Maximum:0
    Units:number of plants
    ABdistpreYR
    Absolute value of the minimum distance to a plant (seabeach amaranth) from the previous year (2013). (see process step: Field data raster extraction, for details). (Source: U.S. Geological Survey)
    Range of values
    Minimum:68
    Maximum:30304.9
    Units:meters
    x_utm
    The x coordinate of the data point (NAD83 UTM Zone 18N) (Source: U.S. Geological Survey)
    Range of values
    Minimum:465744.9
    Maximum:491374.1
    Units:meters
    y_utm
    The y coordinate of the data point (NAD83 UTM Zone 18N) (Source: U.S. Geological Survey)
    Range of values
    Minimum:4189319.9
    Maximum:4241659.6
    Units:meters
    YEAR
    The year the survey data relates to. (Source: U.S. Geological Survey) The year the survey data relates to.
    AREA_cm2
    Total leaf/ plant area in centimeters squared occupied by the living portions of the plant when seen from above in a horizontal plane. Value of –99999 indicates no data/randomly generated point data. (Source: NPS)
    Range of values
    Minimum:2
    Maximum:63
    Units:cm2
    Caged
    Was the plant caged - Yes (1) or No (0). Value of –99999 indicates no data. (Source: NPS)
    ValueDefinition
    0no cage
    1cage
    -99999No data
    Ungulate_G
    Was the plant grazed by ungulates (horses or deer)? Yes (1) or No (0). Value of –99999 indicates no data or randomly generated point data. (Source: NPS)
    ValueDefinition
    0no graze
    1ungulate grazed
    -99999No data
    Insect_Gra
    Was the plant grazed by insects? Yes (1) or No (0). Value of –99999 indicates no data or randomly generated point data. (Source: NPS)
    ValueDefinition
    0no graze
    1insect grazed
    -99999No data
    Grazed
    Was the plant grazed by anything? If identification is possible, by what? Value of –99999 indicates no data or randomly generated point data. (Source: NPS)
    ValueDefinition
    0not grazed
    1grazed
    2Deer herbivory
    3Horse herbivory
    4Insect herbivory
    5Horse and insect herbivory
    6Deer and insect herbivory
    7ungrazed
    8grazed
    9Ungulate and insect grazed but not able to identify source of ungulate grazing - deer or horse.
    -99999No data
    lon_nad83
    The longitude of the data point (Source: U.S. Geological Survey)
    Range of values
    Minimum:-75.389504
    Maximum:-75.098683
    Units:meters
    lat_nad83
    The latitude of the data point. (Source: U.S. Geological Survey)
    Range of values
    Minimum:37.850696
    Maximum:38.323013
    Units:meters
    Entity_and_Attribute_Overview:
    This section describes the tabular data associated with the shapefile and the corresponding csv file Presence_Absence_pts2014 (.shp/.csv). These metadata list all possible values for enumerated domains (e.g. Grazed); however, some of the values described may not be present in this dataset. These files describe the same data, and share the same attribute information. The tabular .csv data does not have the attributes "FID" or "Shape" The shapefile is projected in GCS_North_American_1983, and the .csv file has both GCS_North_American_1983 (Point_X, Point_Y) and UTM NAD83 coordinates )(Northing, Easting) defined. Please review the individual attribute descriptions for detailed information.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey - ScienceBase

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Benjamin T. Gutierrez
    • Julia L. Heslin
    • Emily J. Sturdivant
    • Rachel E. Henderson
    • Travis K. Sterne
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Benjamin T. Gutierrez
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    US

    508-548-8700 x2289 (voice)
    508-457-2310 (FAX)
    bgutierrez@usgs.gov

Why was the data set created?

These data (tabular and point shapefile) include observational data of seabeach amaranth as well as random points added for analysis of habitat. Data from existing lidar, vegetation, and substrate datasets were sampled to measure site specific barrier island characteristics where seabeach amaranth (Amaranthus pumilus) was observed on Assateague Island National Seashore. In addition to compiling data where seabeach amaranth are present, three times the number of randomly distributed points were sampled from sites where no seabeach amaranth were present for the purposes of analysis and the development of habitat suitability models.

How was the data set created?

  1. From what previous works were the data drawn?
    vegetation_raw (source 1 of 5)
    Assateague Island National Seashore, 20200511, ASISMDCoverMap19932014: Assateague Island National Seashore, Berlin, Maryland.

    Other_Citation_Details:
    This file contains the “over-sand vehicle” or OSV beach habitat (the zone where driving is allowed on the beach) for 2013, the North End habitat for 2014, the July 2014 shoreline, and the 1993 vegetation map for the rest of Assateague Island. The data were requested and sent directly to the USGS via email from the National Park Service. The data are projected in NAD UTM Zone 18N. The data were requested and sent directly to the USGS via email from the National Park Service.
    Type_of_Source_Media: digital vector data
    Source_Contribution:
    The map data were used to extract information about the vegetation type.
    DEM_raw (source 2 of 5)
    NOAA, 20151220, 2014 NOAA NGS Topobathy Lidar DEM: Post-Sandy (SC to NY): NOAA, Charleston, SC.

    Online Links:

    Other_Citation_Details: Website accessed 4/27/2022.
    Type_of_Source_Media: online
    Source_Contribution: Lidar data used to extract geomorphology metrics.
    morphology_raw (source 3 of 5)
    Doran, Kara J., Long, Joseph W., Birchler, Justin J, Brenner, Owen T., Hardy, Matthew W., Karen L. M. Morgan, Stockdon, Hilary F., and Torres, Miguel L., 2017, Lidar-derived Beach Morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. Sandy Coastlines: U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Affiliated datasets created using the same methods. A version of this geomorphology points dataset is available as 14CNT01_morphology.zip These data were made available to us prior to publication and as a result, processing steps may differ from what is described here. The first link is to the data release page, the second is to download a .zip file with the data used for this report. Website accessed 4/27/2022.
    Type_of_Source_Media: online
    Source_Contribution:
    Lidar-derived Beach Morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. Sandy Coastlines used to extract distance from morphology to sampling locations
    field_data_raw (source 4 of 5)
    Chase, Jonathan, Hulslander, Bill, Strum, Mark, Lee, Chris, Gutierrez, Benjamin, Henderson, Rachel E., and Sterne, Travis K., 2023, Assateague Island Seabeach Amaranth Survey Data — 2001 to 2018: U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Chase, J., Hulslander, B., Strum, M., Lea, C., Gutierrez, B., Henderson, R.E., and Sterne T.K., 2023, Assateague Island Seabeach Amaranth Survey Data — 2001 to 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IZMQ1B This dataset (P9IZMQ1B) contributed to this data release (P9GKXN3H) and was released simultaneously.
    Type_of_Source_Media: online
    Source_Contribution:
    Data collected by NPS from 2001 - 2018, used to identify the presence of seabeach amaranth at Assateague Island.
    morphology_raw (source 5 of 5)
    Sturdivant, Emily J., Zeigler, Sara L., Gutierrez, Benjamin T., and Weber, Kathryn M., 20191220, DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Assateague Island, MD & VA, 2014: data release DOI:10.5066/P9V7F6UX, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    Other_Citation_Details: Website accessed 4/27/2022.
    This is part of the following larger work.

    Sturdivant, Emily J., Zeigler, Sara L., Gutierrez, Benjamin T., and Weber, Kathryn M., 2019, Barrier island geomorphology and shorebird habitat metrics: Sixteen sites on the U.S. Atlantic Coast, 2013–2014: data release DOI:10.5066/P9V7F6UX, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Sturdivant, E.J., Zeigler, S.L., Gutierrez, B.T., and Weber, K.M., 2019, Barrier island geomorphology and shorebird habitat metrics—Sixteen sites on the U.S. Atlantic Coast, 2013–2014: U.S. Geological Survey data release, https://doi.org/10.5066/P9V7F6UX.
    Type_of_Source_Media: online
    Source_Contribution:
    Lidar-derived Beach Morphology (Dune Crest, Dune Toe, and Shoreline) for U.S. Sandy Coastlines used to extract distance from morphology to sampling locations
  2. How were the data generated, processed, and modified?
    Date: 2021 (process 1 of 12)
    This and subsequent process steps were performed in ArcGIS Pro’s Python window. The original code was written by Emily J. Sturdivant, and the code was translated into process steps by Julia L. Heslin. Person who carried out this activity:
    Julia L. Heslin
    U.S. Geological Survey
    Geographer
    384 Woods Hole Road
    Woods Hole, MA

    508-457-2262 (voice)
    jheslin@usgs.gov
    Date: 01-Jun-2021 (process 2 of 12)
    1) DEM Processing: A few steps were required to process the DEM from raw data (DEM_raw). It is important to note that all source data were projected to UTM zone 18N NAD83 prior to processing. The DEM tile(s) were copied from their native GeoTIFF format to a file geodatabase (Data Management Tools >> Raster >> Raster Dataset >> Copy Raster. The study area may be comprised of multiple DEM tiles which were mosaicked into a single mosaic dataset (Data Management Tools >> Raster >> Raster Dataset >> Mosaic) (DEM_preprocessed) . Any NoData values that were present in the DEM were set to null (Spatial Analyst Tools >> Conditional >> Set Null). Unless otherwise noted, the following steps were performed by one person, Julia L. Heslin. Person who carried out this activity:
    Julia L. Heslin
    U.S. Geological Survey
    Geographer
    384 Woods Hole Road
    Woods Hole, MA

    508-457-2262 (voice)
    jheslin@usgs.gov
    Data sources used in this process:
    • DEM_raw
    Data sources produced in this process:
    • DEM_preprocessed
    Date: 01-Jun-2021 (process 3 of 12)
    2) Morphology processing: A dataset with morphological characteristics, was available for the extraction of multiple variables, specifically dune toe, dune crest, and shoreline point location information for Assateague Island. For 2014, these points were already converted and available from this data release (https://www.sciencebase.gov/catalog/item/5daa377fe4b09fd3b0c9ce84). Data sources used in this process:
    • morphology_raw
    Data sources produced in this process:
    • DL_pts
    • DH_pts
    • SL_pts
    Date: 01-Jun-2021 (process 4 of 12)
    3) Barrier island boundary processing: A polygon delineating the barrier island boundary was created using the DEM (DEM_preprocessed) and morphology source data (SL_pts) (see process steps 1 and 2).
    Several tools were needed to convert the previously created boundary data to the barrier island boundary used in this project. Using the Con tool (Geoprocessing Tools >> Spatial Analyst >> Conditional >> Con), the DEM was reclassified where anything above the local MHW value was assigned as 1. The raster was converted to a polygon feature class (Geoprocessing Tools >> Conversion >> From Raster >> Raster to Polygon) and aggregated (Geoprocessing Tools >> Cartography >> Generalization >> Aggregate Polygons) to merge any features at least 300 square meters in size within a 10 meter distance of each other. These same steps were performed to create polygon features at the mean tide level (MTL), which is MHW (0.34) plus the local mean low water (MLW) (-0.13) divided by two. The purpose of this is to capture the extent of the bay side shoreline of the island.
    The MHW and MTL polygons were then combined into one bayside polygon. Before this, inlet lines were manually drawn to capture the edges of the barrier island using the basemap imagery from Esri. These inlet lines were saved as a feature class (inlet_lines). Then, the Symmetrical Difference tool (Geoprocessing Tools >> Analysis >> Overlay >> Symmetrical Difference) was used to find where the MHW and MTL polygons intersected and keep features where they do not overlap (symdiff). Using the Feature to Polygon tool (Geoprocessing Tools >> Data Management >> Features >> Feature to Polygon), any extraneous shoreline polygon features were split using the symdiff and inlet_lines feature classes (split). This split feature class was then spatially joined with the SL_pts features (extracted from process step 2). Any of the split polygon features that completely contained shoreline points were kept (split_join). Then, any features from the symdiff and split_join feature classes that overlapped were removed using the Erase tool (Geoprocessing Tools >> Analysis >> Overlay >> Erase).
    The bayside polygon was combined with the MHW polygon using the Union tool (Geoprocessing Tools >> Analysis >> Overlay >> Union). All features from this output were dissolved to make a single part polygon (Geoprocessing Tools >> Data Management >> Generalization >> Dissolve). Any extraneous features that did not reflect the barrier island boundary (barrier_boundary) were manually deleted.
    The last step in creating the barrier island boundary polygon was to snap the polygon to the shoreline points within a 25 meter distance where they don’t already match. The Densify tool (Geoprocessing Tools >> Editing >> Densify) was used to add new vertices to the barrier island boundary at 25 meter increments. These vertices were then snapped to the shoreline points as long as they were within 25 meters of each other. After this step, any holes that existed in the barrier island boundary polygon were deleted using the Eliminate Polygon Part tool (Geoprocessing Tools >> Data Management >> Generalization >> Eliminate Polygon Part). Any part less than 99% of the total polygon area that was contained in the barrier island boundary was deleted. This is to ensure that all holes in the polygon were eliminated. Finally, the Dissolve tool was used to aggregate the individual polygons into a single multi-part feature. Data sources used in this process:
    • DEM_preprocessed
    • SL_pts
    Data sources produced in this process:
    • barrier_boundary
    • inlet_lines
    Date: 01-Jun-2021 (process 5 of 12)
    4) DEM raster extraction: Using the barrier island boundary as the extent (see step 3), the DEM was then resampled to a five meter pixel size using a bilinear resampling technique (Data Management Tools >> Raster >> Raster Processing >> Resample). It was then adjusted to the local mean high water (MHW) for the study area (0.34m) by subtracting 0.34 from the DEM (3D Analyst Tools >> Raster Math >> Minus)(local_MHW_DEM).
    Additional rasters were calculated using the DEM, including aspect (3D Analyst Tools >> Raster Surface >> Aspect) and slope (with the output measurement set as percent rise) (3D Analyst Tools >> Raster Surface >> Slope). Data sources used in this process:
    • DEM_preprocessed
    Data sources produced in this process:
    • slope
    • aspect
    • local_MHW_DEM
    Date: 01-Jun-2021 (process 6 of 12)
    5) Morphology raster extraction: Using the barrier island boundary as the extent (see step 3), distance rasters using the Euclidean Distance tool (Geoprocessing Tools >> Spatial Analyst >> Distance >> Euclidean Distance) were created using the preprocessed morphology points.
    Two rasters were created related to the shoreline morphology: distance to the full island shoreline; and distance to the oceanside shoreline. In order to retrieve the distance to the full island shoreline, the barrier_boundary was converted to a line using the Polygon to Line tool (Geoprocessing Tools >> Data Management >> Features >> Polygon to Line). This line feature was used to calculate the distance to the full boundary shoreline. To calculate the distance to the oceanside shoreline, first the barrier_boundary and inlet_lines were converted using the Feature to Line tool (Geoprocessing Tools >> Data Management >> Features >> Feature to Line) indicating where the inlets split the shoreline. Then, the SL_pts file was spatially joined to the split shoreline feature class. Any line features that completely contained the SL_pts were kept. The shoreline was then aggregated to one oceanside shoreline feature using the Dissolve tool (Geoprocessing Tools >> Data Management >> Generalization >> Dissolve) and used to generate the oceanside shoreline distance raster (distance_to_oceanside_shoreline).
    Additional distance rasters using the dune toe, dune crest, and inlet features were created. The DL_pts and DH_pts were used to derive the distance_to_DL and distance_to_DH rasters (with 200 meters being the maximum threshold value). The inlet at Ocean City (which was manually selected and saved as a separate feature class) was used to create the distance_to_OceanCity_inlet raster. Data sources used in this process:
    • DL_pts
    • DH_pts
    • SL_line
    • barrier_boundary
    • inlet_lines
    Data sources produced in this process:
    • distance_to_full_shoreline
    • distance_to_oceanside_shoreline
    • distance_to_DH
    • distance_to_DL
    • distance_to_OceanCity_inlet
    Date: 01-Jun-2021 (process 7 of 12)
    6) Field data processing: Field data documenting locations of seabeach amaranth plants (field_data_raw) were used in the creation of this dataset. The field dataset has records of seabeach amaranth locations, grazed status, and other attributes from 2001-2018.
    The seabeach amaranth field data, originally formatted as an Excel spreadsheet, were imported as a Python Pandas data frame (amaranth_data_frame). Since the field data contains data from multiple years, rows from the year in question were selected.
    The data frame was converted to a feature class by first keeping all columns from the data frame and converting them to a NumPypy array. The NumPyArrayToFeatureClass function (Python >> Data Access Module >> Functions >> NumPyArrayToFeatureClass) was used to convert the array to a feature class.
    A training dataset with random points and plant observations (training_pts) was created first by generating random points using the Create Random Points tool (Geoprocessing Tools >> Data Management >> Sampling >> Create Random Points). The random points distributed within the barrier island boundary polygon (created in step 3) no less than 5 meters apart. The random points were merged with the plant observations into one feature class. A flag field called plant_presence was created to distinguish plants from random points (a value of 1 indicates plant presence and 0 indicates a random point).
    A feature class of plants from the previous year (lastyr_pts) was created by selecting the rows in the field data data frame from the previous year, which for this dataset was 2011 The selected rows in the data frame were converted to a NumPy array then to feature class using the NumPyArrayToFeatureClass function (Python >> Data Access Module >> Functions >> NumPyArrayToFeatureClass). A tally field (called cnt) was added to the lastyear_pts feature class. Data sources used in this process:
    • field_data_raw
    Data sources produced in this process:
    • amaranth_data_frame
    • training_pts
    • lastyr_pts
    Date: 01-Jun-2021 (process 8 of 12)
    7)Field data raster extraction:
    A raster dataset indicating the number of last year’s (2013) points within a 30 meter radius of a grid cell was created. The NbrCircle tool was used to specify this radius value (Python >> Spatial Analyst module >> Classes >> Neighborhood classes >> NbrCircle). The Point Statistics tool (Geoprocessing Tools >> Spatial Analyst >> Neighborhood >> Point Statistics) was used to create a raster of the number of last year’s points in the 30 meter radius.
    Distance and direction rasters to the nearest plant from the previous year were also created. The Euclidean Distance tool was used to calculate: a distance raster with 200 meters being the maximum threshold distance the raster values cannot exceed (distance_to_plant); and a direction raster (direction_to_plant). Another direction raster relative to the adjacent mean high water shoreline was generated as well (direction_to_shore).
    The difference between direction_to_plant and direction_to_shore was calculated with the Minus tool (3D Analyst Tools >> Raster Math >> Minus) (direction_to_plant_alongshore). The values from this raster indicate updrift (0 to 180) and downdrift (less than 0 or greater than 180). The dir2plant_alongshore raster was reclassified into these two categories (1 for updrift and –1 for downdrift) (Geoprocessing Tools >> Spatial Analyst >> Reclass >> Reclassify). The reclassified raster was multiplied by the distance_to_plant raster to get the final distance raster (distance_to_plant_final), where positive values indicate updrift and negative indicate downdrift. Data sources used in this process:
    • training_pts
    • lastyr_pts
    Data sources produced in this process:
    • direction_to_plant
    • distance_to_plant_final
    Date: 01-Jun-2021 (process 9 of 12)
    8)Vegetation processing: The original vegetation data (vegetation_raw) were in a shapefile format and were first imported into a file geodatabase for processing. The vegetation feature class was then converted to a raster (Conversion Tools >> To Raster >> Polygon to Raster) (vegetation_raster). Data sources used in this process:
    • vegetation_raw
    Data sources produced in this process:
    • vegetation_raster
    Date: 01-Jun-2021 (process 10 of 12)
    9)Raster to spreadsheet: From the raster datasets created from the process steps above(vegetaion, elevation, and distance rasters), the pixel values were extracted to the corresponding training points feature class (training_pts) (Spatial Analyst Tools >> Extraction >> Extract Multi Values to Points). Extracted values can be found in the field attributes of the final csv.
    The training points feature class (training_pts) was then converted to a data frame by first converting the points to a NumPy array (Python >> Data Access Module >> Functions >> FeatureClassToNumPyArray), then to a Python dictionary, then a data frame using the Pandas library.
    The plants data frame (amaranth_data_frame) described in process step 6 was then joined with this data frame described in the previous paragraph (data frame from training_pts). Fields for lat and lon were added as attributes to this data frame and calculated using the PyProj library (https://pyproj4.github.io/pyproj/v2.2.0rel/api/transformer.html#pyproj-transform). The data frame was then written to an Excel spreadsheet using the Python Pandas library (https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html). Data sources used in this process:
    • slope
    • aspect
    • local_MHW_DEM
    • distance_to_full_shoreline
    • distance_to_oceanside_shoreline
    • distance_to_DH
    • distance_to_DL
    • direction_to_plant
    • distance_to_plant_final
    • vegetation_raster
    • amaranth_data_frame
    • training_pts
    Data sources produced in this process:
    • Presence_Absence_pts2014.csv
    Date: 05-Dec-2021 (process 11 of 12)
    10) Conversion of csv to shapefile. The .csv file was converted to a shapefile by right clicking the dataset in ArcMap, and selecting "Display XY Data" where x_utm and y_utm were chosen for the x and y fields respectively, and the coordinate system NAD_1983_UTM_Zone_18N was selected for the dataset. Person who carried out this activity:
    Rachel E. Henderson
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 (voice)
    rehenderson@contractor.usgs.gov
    Data sources used in this process:
    • Presence_Absence_pts2014.csv
    Data sources produced in this process:
    • Presence_Absence_pts2014.shp
    Date: 05-Jan-2022 (process 12 of 12)
    11) Coordinate system updated from projected coordinates in "NAD_1983_UTM_Zone_18N" to geographic "GCS_North_American_1983" for publication using ArcToolbox > Data Management Tools > Projections and Transformations > Project. Person who carried out this activity:
    Rachel E. Henderson
    U.S. Geological Survey
    384 Woods Hole Road
    Woods Hole, MA
    USA

    508-548-8700 (voice)
    rehenderson@contractor.usgs.gov
  3. What similar or related data should the user be aware of?
    Gutierrez, Benjamin T., and Lentz, Erika E., 2023, Developing a habitat model to support management of threatened seabeach amaranth (Amaranthus pumilus) at Assateague Island National Seashore, Maryland and Virginia: Scientific Investigations Report 2023–5034, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Scientific Investigations Report associated with data releases DOI:10.5066/P9IZMQ1B and DOI:10.5066/P9GKXN3H. Suggested citation: Gutierrez, B.T., and Lentz, E.E., 2023, Developing a habitat model to support management of threatened seabeach amaranth (Amaranthus pumilus) at Assateague Island National Seashore, Maryland and Virginia: U.S. Geological Survey Scientific Investigations Report 2023–5034, https://doi.org/10.3133/sir20235034.
    Weber, Kathryn M., List, Jeffrey H., and Karen L. M. Morgan, 20050101, An Operational Mean High Water Datum for Determination of Shoreline Position from Topographic Lidar Data: Open-File Report 2005-1027, U.S. Geological Survey, Reston, VA.

    Online Links:

    Gutierrez, Benjamin T., Sturdivant, Emily J., and Zeigler, Sara L., 20190101, Evaluating barrier island characteristics and piping plover (Charadrius melodus) habitat availability along the U.S. Atlantic coast—Geospatial approaches and methodology: Open-File Report 2019–1071, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Details the methods used to process these data for use in barrier island and piping plover habitat modeling.

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

  1. How well have the observations been checked?
    Attribute values at each point represent a 5 x 5 m square centered at the point. The values are compiled from multiple sources. The following method was used to validate attribute accuracy: symbolized display of point attribute values overlaid for each raster dataset followed by overlaying point data over each raster to confirm spot checks for data and topological accuracy using ArcGIS Pro version 2.0. These checks were performed by at least one of the authors. Mean high-water (MHW) shoreline position and dune metric (foredune crest and dune toe position and elevation) accuracy depend on the accuracy of the source data (Doran and others, 2017). Distances to foredune crest and dune toe positions were only considered when they occurred within 200 m of a point. Where these metrics exceeded this distance NoData values (-99999) were used.
  2. How accurate are the geographic locations?
    Depending on the attribute, the accuracy is assumed to be between 5 and 25 m. The horizontal positional accuracy is dependent on the accuracy of the source data and error incorporated during processing. Refer to the process steps for details.
  3. How accurate are the heights or depths?
    The vertical accuracy of those attributes that incorporate vertical position is dependent on the digital elevation model and its source data (see Source Information in Process Steps) as well as the MHW datum produced by Weber and others (2005).
  4. Where are the gaps in the data? What is missing?
    Dataset completeness is dependent on the completeness of the source data. Points exist where seabeach amaranth presence for the given year (2014) are observed. Additional randomly distrubuted points are added to the dataset following previous species distribution model research (see Gutierrez and Lentz [2023] for citations), allowing detailed geomorphic characteristics to be sampled where plants occur as well as at random point locations across the island.
  5. How consistent are the relationships among the observations, including topology?
    These datasets consist of data compiled from multiple sources and are aggregated spatially. The data were reviewed by the authors using standard USGS review procedures. No checks for topological consistency beyond those described in the Attribute Accuracy Report were performed on these data. The primary vertical datum used is NAVD88, the same as the source datasets, lidar and geomorphology points (e.g. shoreline, dune crest, and dune toe). Some fields (those with a 'mhw' suffix and explained in the Entity and Attribute section) include the elevation adjusted to the mean high water (MHW) datum calculated by Weber and others (2005) for the area. Where a given attribute value could not be calculated due to lack of input data in the source dataset, a NoData value of -99999 was recorded for the attribute.

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 Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO
    US

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? The dataset contains the point shapefile (Presence_Absence_pts2014.shp with all associated components) and the tabular data (Presence_Absence_pts2014.csv) for presence-absence of seabeach amaranth and site-specific barrier island geomorphology metrics, and the FGDC CSDGM metadata in HTML and XML format (Presence_Absence_pts2014_metadata.html, Presence_Absence_pts2014_metadata.xml)
  3. What legal disclaimers am I supposed to read?
    Neither the U.S. Government, the Department of the Interior, nor the USGS, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by the USGS in the use of these data or related materials. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), and have been processed successfully on a computer system at the USGS, no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    To utilize these data, the user must have software capable of reading a comma-delimited data file. These data are also available in a point shapefile format. The user must have software to read and process the data components of a shapefile.

Who wrote the metadata?

Dates:
Last modified: 26-Apr-2023
Metadata author:
U.S. Geological Survey
Attn: Rachel E. Henderson
384 Woods Hole Road
Woods Hole, MA
USA

508-548-8700 (voice)
whsc_data_contact@usgs.gov
Contact_Instructions:
The metadata contact email address is a generic address in the event the person is no longer with USGS.
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P9GKXN3H/Presence_Absence_pts2014_metadata.faq.html>
Generated by mp version 2.9.51 on Wed Apr 26 14:01:49 2023